1
00:00:00,000 --> 00:00:29,760
Ladies and gentlemen, welcome back once again to the Rounding the Earth podcast.

2
00:00:29,760 --> 00:00:34,560
Rounding the Earth is a multimedia education project based on the popular newsletter series

3
00:00:34,560 --> 00:00:40,600
published on Substack written by applied statistician and educator Matthew Crawford.

4
00:00:40,600 --> 00:00:46,960
Topics of discussion range from critical analysis of conventional wisdom to Bitcoin and everything

5
00:00:46,960 --> 00:00:52,640
in between, in particular the ongoing plandemonium, so called.

6
00:00:52,640 --> 00:00:57,560
Our goal is a careful examination of important topics and perspectives shaping the world

7
00:00:57,560 --> 00:00:59,840
that too few people talk about.

8
00:00:59,840 --> 00:01:05,420
Subscribe to Rounding the Earth on Locals, Substack and Rumble to join a burgeoning research

9
00:01:05,420 --> 00:01:09,200
community and to help us unflatten the earth.

10
00:01:09,200 --> 00:01:11,640
Shout out to all of our BitChute viewers.

11
00:01:11,640 --> 00:01:13,280
My name is Liam Sturgis.

12
00:01:13,280 --> 00:01:19,320
I am a musician, music producer and writer slash editor coming at you live from the beautiful

13
00:01:19,320 --> 00:01:24,120
Vancouver, British Columbia, Canada, and I will be your host for today.

14
00:01:24,120 --> 00:01:29,280
And now please allow me to introduce the author of Rounding the Earth and my cohost for the

15
00:01:29,280 --> 00:01:31,720
podcast, Matthew Crawford.

16
00:01:31,720 --> 00:01:32,720
Good afternoon, Matthew.

17
00:01:32,720 --> 00:01:33,720
Good afternoon.

18
00:01:33,720 --> 00:01:37,960
Have you, you've never been to Vancouver?

19
00:01:37,960 --> 00:01:38,960
I have.

20
00:01:38,960 --> 00:01:40,960
Well, no, no, no.

21
00:01:40,960 --> 00:01:41,960
Um, okay.

22
00:01:41,960 --> 00:01:45,480
So I'm actually technically not sure.

23
00:01:45,480 --> 00:01:46,480
It was a while.

24
00:01:46,480 --> 00:01:47,480
I have not.

25
00:01:47,480 --> 00:01:48,480
I have not.

26
00:01:48,480 --> 00:01:54,080
Um, I went to, um, uh, Tillicum Village is the closest I've been.

27
00:01:54,080 --> 00:01:56,240
Do you know where that is?

28
00:01:56,240 --> 00:01:57,760
I think I'm saying that right.

29
00:01:57,760 --> 00:02:00,200
That this is like, this is way back in high school.

30
00:02:00,200 --> 00:02:02,440
Uh, I was in Seattle.

31
00:02:02,440 --> 00:02:03,440
Okay.

32
00:02:03,440 --> 00:02:10,440
And I know that, um, we went to this like island, um, that I guess is still, you know,

33
00:02:10,440 --> 00:02:16,160
Washington, but, uh, technically I've only actually crossed into Canada once.

34
00:02:16,160 --> 00:02:18,800
And that was, that was actually way back in middle school.

35
00:02:18,800 --> 00:02:24,400
Um, like just, uh, Windsor, like across from Detroit.

36
00:02:24,400 --> 00:02:25,400
Yeah.

37
00:02:25,400 --> 00:02:26,400
Yeah.

38
00:02:26,400 --> 00:02:27,400
The other side of Canada.

39
00:02:27,400 --> 00:02:32,680
So that's the only time you've ever actually been in Canada.

40
00:02:32,680 --> 00:02:33,680
Let's see.

41
00:02:33,680 --> 00:02:34,680
Unfortunately, unfortunately.

42
00:02:34,680 --> 00:02:37,080
And so I've been close several times.

43
00:02:37,080 --> 00:02:41,760
I've been in upstate New York near the Niagara area and I've been in, uh, in the state of

44
00:02:41,760 --> 00:02:42,760
Washington.

45
00:02:42,760 --> 00:02:46,440
Um, uh, so no, I have not been to Vancouver.

46
00:02:46,440 --> 00:02:51,720
Well you're welcome anytime and this came up because, uh, we have, uh, uh, one of our

47
00:02:51,720 --> 00:02:55,880
lovely guests for today who, well, I don't want to give away too many details, but let's

48
00:02:55,880 --> 00:03:02,000
just say we were talking about Vancouver and let's get her thoughts on my beautiful hometown

49
00:03:02,000 --> 00:03:05,320
and others, uh, allow us to introduce our guest, Jessica Rose.

50
00:03:05,320 --> 00:03:06,320
How are you Jess?

51
00:03:06,320 --> 00:03:08,320
Yes, I am beautiful.

52
00:03:08,320 --> 00:03:10,360
Yes, you are.

53
00:03:10,360 --> 00:03:16,680
Um, now I don't want to give away details of your recent trip, but, uh, what are your

54
00:03:16,680 --> 00:03:17,680
thoughts on that?

55
00:03:17,680 --> 00:03:19,880
So we can talk about it.

56
00:03:19,880 --> 00:03:23,000
Well, so you were in Vancouver.

57
00:03:23,000 --> 00:03:26,160
Give me all of your best compliments about my lovely city.

58
00:03:26,160 --> 00:03:33,000
See, the thing is I've been there many times and it never made the impression that it made

59
00:03:33,000 --> 00:03:34,240
on me this time.

60
00:03:34,240 --> 00:03:41,720
So it's probably more that I've changed since last being there, but I found myself really

61
00:03:41,720 --> 00:03:45,960
aligned with it, um, which is nice.

62
00:03:45,960 --> 00:03:50,160
I mean, it's always nice to find alignment when you travel.

63
00:03:50,160 --> 00:03:57,800
Um, I was, I was just stricken mostly by how much you could walk and, and where you could

64
00:03:57,800 --> 00:04:04,200
walk anywhere, which, uh, like I can go to the dangerous areas, but, um, like East Hastings

65
00:04:04,200 --> 00:04:10,520
or whatever, but like I, I didn't feel restricted in my wandering, which I really appreciate.

66
00:04:10,520 --> 00:04:20,560
Um, lots of green spaces, um, huge trees like ancient trees, which I really love, um, in

67
00:04:20,560 --> 00:04:21,560
Stanley Park.

68
00:04:21,560 --> 00:04:23,680
And I didn't see any mountain stuff.

69
00:04:23,680 --> 00:04:29,280
Well, I mean, I did see, but I didn't experience the mountains this time.

70
00:04:29,280 --> 00:04:40,080
Um, nice people, uh, lots of variety, um, in what you can, um, I suppose experience

71
00:04:40,080 --> 00:04:41,080
in food.

72
00:04:41,080 --> 00:04:42,080
Oh yeah.

73
00:04:42,080 --> 00:04:47,880
Well, well, yeah, but it was very expensive.

74
00:04:47,880 --> 00:04:55,920
I was shocked by how much food has risen in cost, um, in BC at least, cause that's, that's

75
00:04:55,920 --> 00:04:57,520
where I was.

76
00:04:57,520 --> 00:05:00,960
Um, eight bucks for a loaf of bread, for example.

77
00:05:00,960 --> 00:05:06,600
I mean, I just, I was literally blown away by that every time I asked, uh, you know,

78
00:05:06,600 --> 00:05:11,480
for my, my price or whatever, for the thing I wanted to buy, I was literally just like

79
00:05:11,480 --> 00:05:12,760
in goth mode.

80
00:05:12,760 --> 00:05:19,120
Like what, what, you know, how, how are people living, you know, day to day who have families

81
00:05:19,120 --> 00:05:20,120
and stuff.

82
00:05:20,120 --> 00:05:24,280
But, um, anyway, the good stuff, uh, it's beautiful.

83
00:05:24,280 --> 00:05:25,320
It's spring.

84
00:05:25,320 --> 00:05:29,040
So the weather was, it was just, I don't know if it was because I was there, but it was

85
00:05:29,040 --> 00:05:31,760
just spectacular the whole time I was there.

86
00:05:31,760 --> 00:05:39,120
Like it was, it was warmer in, on the mainland than it was on the island.

87
00:05:39,120 --> 00:05:45,960
Cause I was also on the island on Vancouver Island, but, um, yeah, it was, I was surprised

88
00:05:45,960 --> 00:05:47,200
it was only three days.

89
00:05:47,200 --> 00:05:50,200
So I probably just had some kind of honeymoon experience.

90
00:05:50,200 --> 00:05:51,200
You know what I mean?

91
00:05:51,200 --> 00:05:54,480
It's like, but I mean, it was, I liked it.

92
00:05:54,480 --> 00:05:56,200
It was, it was a nice time.

93
00:05:56,200 --> 00:05:58,920
Well, I'm really happy you got to come back.

94
00:05:58,920 --> 00:06:03,640
Cause for those who don't know, Jessica is Canadian, been living in Israel for, uh, for

95
00:06:03,640 --> 00:06:04,640
how many years now?

96
00:06:04,640 --> 00:06:09,400
Uh, I don't know forever.

97
00:06:09,400 --> 00:06:11,040
Long enough that one forgets.

98
00:06:11,040 --> 00:06:15,560
Um, and you tell it like, just, just broadly, you, you went to other parts of Canada as

99
00:06:15,560 --> 00:06:16,560
well.

100
00:06:16,560 --> 00:06:19,400
Give us just the Coles notes of the rest of your trip.

101
00:06:19,400 --> 00:06:22,960
Um, Victoria was nice.

102
00:06:22,960 --> 00:06:29,200
Uh, it was changed though, from the last time I've seen it because of the COVID shit.

103
00:06:29,200 --> 00:06:40,600
Um, just kind of more rep oppressed, more homelessness, uh, more, I don't know how to

104
00:06:40,600 --> 00:06:41,600
describe it.

105
00:06:41,600 --> 00:06:44,960
It was something in the people that you felt when you walked by them.

106
00:06:44,960 --> 00:06:49,880
And it's always kind of been there in Victoria, like Victoria is kind of a weird population

107
00:06:49,880 --> 00:06:55,960
of like people who go there because they want to be homeless because it's warm and old people.

108
00:06:55,960 --> 00:07:02,520
So there's kind of like a non communication there anyway, amongst, you know, the community,

109
00:07:02,520 --> 00:07:05,000
but it seems enhanced somehow.

110
00:07:05,000 --> 00:07:09,720
It might've just been me because I'm, you know, I'm like a tourist or whatever, but,

111
00:07:09,720 --> 00:07:12,800
um, it's also the capital of the province.

112
00:07:12,800 --> 00:07:16,080
It's like our Washington DC or our Ottawa, you know?

113
00:07:16,080 --> 00:07:17,800
So I think there's a lot of stuck up types.

114
00:07:17,800 --> 00:07:20,880
There's a lot of government people who live there.

115
00:07:20,880 --> 00:07:21,880
That might be it.

116
00:07:21,880 --> 00:07:22,880
No, you're absolutely right.

117
00:07:22,880 --> 00:07:23,880
Yeah.

118
00:07:23,880 --> 00:07:24,880
Yeah.

119
00:07:24,880 --> 00:07:26,960
No, there was a lot of stick up or I noticed that too.

120
00:07:26,960 --> 00:07:30,000
Um, but yeah, it was beautiful as well.

121
00:07:30,000 --> 00:07:38,560
I mean, it's, uh, lots of flowers, natural beauty, um, enormous amounts of wealth, which

122
00:07:38,560 --> 00:07:41,120
was, um, crazy.

123
00:07:41,120 --> 00:07:43,680
I mean, there's no two homes that look anything alike.

124
00:07:43,680 --> 00:07:49,880
I mean, this, this is a more to do with the architecture, but it was, yeah, it was, it

125
00:07:49,880 --> 00:07:54,000
struck me how everybody had a unique home with land.

126
00:07:54,000 --> 00:07:57,520
It's not something that you experience in many parts of the world anymore.

127
00:07:57,520 --> 00:08:02,680
Um, and I went to Pender Island, which is one of the smaller islands that you were probably

128
00:08:02,680 --> 00:08:04,320
close to Matthew.

129
00:08:04,320 --> 00:08:07,320
Um, and that was lovely.

130
00:08:07,320 --> 00:08:14,280
Um, you know, small community, everybody knows each other type thing.

131
00:08:14,280 --> 00:08:20,480
Um, but yeah, I just, I tried to experience as much of the nature as I could.

132
00:08:20,480 --> 00:08:23,320
Um, and, and I found plenty.

133
00:08:23,320 --> 00:08:26,880
So yeah, I was, it was a hectic time.

134
00:08:26,880 --> 00:08:29,800
It was quick, but it was, it was nice.

135
00:08:29,800 --> 00:08:35,720
Um, I was pleasantly surprised because I thought there was going to be a lot more, um, stuff

136
00:08:35,720 --> 00:08:38,120
that would make me want to throw things.

137
00:08:38,120 --> 00:08:39,120
Yeah.

138
00:08:39,120 --> 00:08:46,920
Come at the tail end of that and hopefully not the calm, you know, in between storms.

139
00:08:46,920 --> 00:08:49,640
Uh, well, I'm glad you had a good time.

140
00:08:49,640 --> 00:08:53,200
And as you suggest, next time you come, we're going to get our drink on drink referring

141
00:08:53,200 --> 00:08:58,200
to tea, of course we're going to, in fact, we should, we should do high tea.

142
00:08:58,200 --> 00:09:03,240
There's a bunch of high places here where we can just pretend we're royalty.

143
00:09:03,240 --> 00:09:06,040
Now let's talk that.

144
00:09:06,040 --> 00:09:07,800
And this looks nice.

145
00:09:07,800 --> 00:09:08,800
What is this?

146
00:09:08,800 --> 00:09:11,320
Oh, I just looked up Vancouver trees.

147
00:09:11,320 --> 00:09:15,680
Just to see if I can see some pictures.

148
00:09:15,680 --> 00:09:16,960
What's that?

149
00:09:16,960 --> 00:09:19,280
It looks like Stanley park.

150
00:09:19,280 --> 00:09:22,160
There are some massive cedars there.

151
00:09:22,160 --> 00:09:26,560
I mean, you could make an entire ship out of one of these trees.

152
00:09:26,560 --> 00:09:29,000
Not, not that I want to cut any trees down.

153
00:09:29,000 --> 00:09:30,000
No way.

154
00:09:30,000 --> 00:09:31,000
Wow.

155
00:09:31,000 --> 00:09:32,000
Look at that.

156
00:09:32,000 --> 00:09:33,000
Where's that?

157
00:09:33,000 --> 00:09:34,000
Oh, let's see.

158
00:09:34,000 --> 00:09:35,240
Oh, Capilano suspension bridge.

159
00:09:35,240 --> 00:09:37,120
So that is on the North shore.

160
00:09:37,120 --> 00:09:41,280
That is, I wouldn't walk there, but it's so close to where I live.

161
00:09:41,280 --> 00:09:49,640
Um, and yeah, you can go over a big, uh, uh, a big, well, a very tall bridge that freaks

162
00:09:49,640 --> 00:09:53,040
people out, um, over a waterfall that you don't want to fall into.

163
00:09:53,040 --> 00:09:55,040
Uh, but it's just so beautiful.

164
00:09:55,040 --> 00:09:56,040
Okay.

165
00:09:56,040 --> 00:09:59,480
Well, if the world goes mad max, let's just take that area over and build a tree house

166
00:09:59,480 --> 00:10:02,080
and well, they're already built.

167
00:10:02,080 --> 00:10:04,480
That's the best part.

168
00:10:04,480 --> 00:10:07,680
Um, well, this is awesome.

169
00:10:07,680 --> 00:10:12,600
Um, now today, so we've got Jessica with us now and it looks as though we've got Jumi

170
00:10:12,600 --> 00:10:17,800
coming in a little bit later and I think JJ is still coming in a bit later as well.

171
00:10:17,800 --> 00:10:24,720
So, um, do you guys want to officially get started with our second monthly, awfully interesting

172
00:10:24,720 --> 00:10:26,720
science journal club?

173
00:10:26,720 --> 00:10:28,800
Yeah, might as well.

174
00:10:28,800 --> 00:10:35,600
I was actually, I thought I was, um, uh, throwing a paper in that would be kind of like, um,

175
00:10:35,600 --> 00:10:39,440
you know, one that we would tack on or, or if we had extra time or something like that,

176
00:10:39,440 --> 00:10:43,040
because, um, you know, here we are with so many scientists who, I mean, there's so many

177
00:10:43,040 --> 00:10:45,840
papers that have to be read constantly.

178
00:10:45,840 --> 00:10:46,840
Right.

179
00:10:46,840 --> 00:10:50,480
Um, so, you know, I figured there was going to be something that was going to be very

180
00:10:50,480 --> 00:10:52,320
sort of timely and upfront.

181
00:10:52,320 --> 00:10:58,760
I was going to throw this one in as an example of, uh, of the science that looks sort of

182
00:10:58,760 --> 00:11:00,760
Orwellian upfront.

183
00:11:00,760 --> 00:11:05,600
Um, and we do have Jumi now.

184
00:11:05,600 --> 00:11:07,400
Hey, okay.

185
00:11:07,400 --> 00:11:09,400
Jumi, welcome.

186
00:11:09,400 --> 00:11:12,920
Hey, how are you, Jumi?

187
00:11:12,920 --> 00:11:13,920
Good.

188
00:11:13,920 --> 00:11:18,200
Well, we're doing a little bit slower introduction than usual.

189
00:11:18,200 --> 00:11:22,040
We haven't even gotten started with the first paper because, uh, Jessica has been telling

190
00:11:22,040 --> 00:11:26,440
us about all the awesomeness of Vancouver and, and of course Liam's, uh, Liam's eating

191
00:11:26,440 --> 00:11:27,440
that up.

192
00:11:27,440 --> 00:11:32,200
So I am, um, Jumi, uh, uh, how are you doing?

193
00:11:32,200 --> 00:11:34,640
How have you been since we last spoke?

194
00:11:34,640 --> 00:11:35,640
I'm doing well.

195
00:11:35,640 --> 00:11:36,640
Very well.

196
00:11:36,640 --> 00:11:37,640
Excellent.

197
00:11:37,640 --> 00:11:43,200
Um, what, what's new in your world of science and other exciting things and orthodontist

198
00:11:43,200 --> 00:11:44,200
appointments?

199
00:11:44,200 --> 00:11:47,920
Yeah, I came from an orthodontist appointment.

200
00:11:47,920 --> 00:11:50,920
I had invisalign and straightened out my teeth and everything.

201
00:11:50,920 --> 00:11:56,200
Um, now yeah, same old, just, uh, write my sub stack, you know?

202
00:11:56,200 --> 00:12:02,840
Um, yeah, it's kind of funny how some people, um, are interested in certain types of articles

203
00:12:02,840 --> 00:12:06,600
and sometimes I don't want to write those articles, but I can see what people are interested

204
00:12:06,600 --> 00:12:10,440
in is a little different sometimes than what I want to write, you know, interesting.

205
00:12:10,440 --> 00:12:12,440
Like what?

206
00:12:12,440 --> 00:12:17,400
Um, I think sometimes I want to like write about things having to do with like general

207
00:12:17,400 --> 00:12:22,960
science and like, you know, like, um, sometimes like what, like there was this paper, it was

208
00:12:22,960 --> 00:12:27,800
like a sociology paper and it seemed like from what they found, like large fields of

209
00:12:27,800 --> 00:12:32,560
science, um, sometimes people will just hone in on like the top few papers and they'll

210
00:12:32,560 --> 00:12:35,640
ignore the rest, you know, which is kind of interesting, right?

211
00:12:35,640 --> 00:12:38,120
Um, but maybe it was the way I wrote it.

212
00:12:38,120 --> 00:12:41,920
I don't, I don't know if anyone read that article, but anyway, it's fine.

213
00:12:41,920 --> 00:12:43,960
And people want to read about like vaccine injuries too.

214
00:12:43,960 --> 00:12:44,960
I get it.

215
00:12:44,960 --> 00:12:46,960
So that's my audience, I think.

216
00:12:46,960 --> 00:12:52,080
So you're finding yourself, uh, aimed in the direction of what your audience wants to hear.

217
00:12:52,080 --> 00:12:56,720
I think, and we've had other, like JJ has been, um, making some really good points about

218
00:12:56,720 --> 00:12:57,720
that lately.

219
00:12:57,720 --> 00:13:02,840
That maybe that phenomenon of if you have something that I suppose in your instance,

220
00:13:02,840 --> 00:13:05,200
we're talking about the audience seems to want to hear something.

221
00:13:05,200 --> 00:13:07,360
So there's pressure to then write about that.

222
00:13:07,360 --> 00:13:13,960
Um, do you, Jessica and Matthew as prolific substackers yourself, do you relate to this?

223
00:13:13,960 --> 00:13:15,440
You guys were nodding emphatically.

224
00:13:15,440 --> 00:13:23,560
Well, I mean, I, I, ultimately I got into substack because I had all this writing that

225
00:13:23,560 --> 00:13:29,040
I was already doing on like, you know, the chloroquine wars, the early treatment medicines

226
00:13:29,040 --> 00:13:34,600
initially and then, and then also, um, uh, when Monica Hughes called me and said, um,

227
00:13:34,600 --> 00:13:40,200
yeah, you need to start taking a look at the vaccine statistics in early 2021 after I'd

228
00:13:40,200 --> 00:13:44,200
been already writing for a year about the early treatment medicines.

229
00:13:44,200 --> 00:13:50,720
Um, so, so in a sense I kind of ran toward that, but I, you know, if I hadn't, I could

230
00:13:50,720 --> 00:13:54,880
certainly see that, uh, that's what people, you know, want to be talking about and hearing

231
00:13:54,880 --> 00:13:58,800
about because they're trying to make sense of it because there isn't a lot of sense in

232
00:13:58,800 --> 00:14:00,160
a lot of what's gone on.

233
00:14:00,160 --> 00:14:06,660
So, um, and, and, and I think people are, people are, uh, terrified and rightfully so

234
00:14:06,660 --> 00:14:10,680
of the amount of change that's going on in the world.

235
00:14:10,680 --> 00:14:21,680
And so if people can find a way to shuffle the deck, uh, I don't know, in a natural way,

236
00:14:21,680 --> 00:14:25,440
then maybe they can create an orderly amount of disorder in the world again or something

237
00:14:25,440 --> 00:14:26,440
like that.

238
00:14:26,440 --> 00:14:27,440
Right.

239
00:14:27,440 --> 00:14:32,480
Like, like people, people want, um, people want to push the clock back to, um, when it

240
00:14:32,480 --> 00:14:36,680
was that it didn't seem like controlled horror.

241
00:14:36,680 --> 00:14:38,480
Yes.

242
00:14:38,480 --> 00:14:43,440
People want, uh, those, those who can be honest and understand science to be writing about

243
00:14:43,440 --> 00:14:45,280
it, uh, right now.

244
00:14:45,280 --> 00:14:51,080
So I, yeah, uh, it doesn't shock me at all that, that Jimmy would get pressure or we,

245
00:14:51,080 --> 00:14:56,720
not pressure, but like, you know, um, interest encouragement from an audience to, to be,

246
00:14:56,720 --> 00:14:58,400
you know, doing those things.

247
00:14:58,400 --> 00:15:03,400
And she writes, uh, unique articles, uh, you know, in so far as, you know, pandemic science

248
00:15:03,400 --> 00:15:04,400
is concerned.

249
00:15:04,400 --> 00:15:11,560
So, um, you know, Jimmy being sort of outside of the pressure of, uh, the mainstream academia,

250
00:15:11,560 --> 00:15:18,920
um, means that, uh, she is, she has been able to be, you know, a more free voice and has

251
00:15:18,920 --> 00:15:22,880
put enough care into, you know, writing articles that people didn't want to see.

252
00:15:22,880 --> 00:15:25,760
So yeah, I can see why people appreciate it.

253
00:15:25,760 --> 00:15:31,000
Jessica, do you find the same, uh, the same phenomenon in your work?

254
00:15:31,000 --> 00:15:33,840
Um, not as much.

255
00:15:33,840 --> 00:15:34,840
Absolutely.

256
00:15:34,840 --> 00:15:38,240
I, I read a lot.

257
00:15:38,240 --> 00:15:44,680
If I had the time, I'd read all of the comments, but sometimes the hundreds and, um, my readers,

258
00:15:44,680 --> 00:15:53,680
I swear, like, um, Substack is so wonderful for its ability to, to bring together really,

259
00:15:53,680 --> 00:15:55,480
really interesting and smart people.

260
00:15:55,480 --> 00:15:57,960
Like my, my readers are so cool.

261
00:15:57,960 --> 00:16:04,560
Um, they give me feedback on, on, on things that I write about that make me feel like

262
00:16:04,560 --> 00:16:08,880
I didn't even understand what I was saying when I wrote it, but they do, which is really

263
00:16:08,880 --> 00:16:13,160
interesting because it was through the process of reading what I wrote that they understood

264
00:16:13,160 --> 00:16:14,160
something.

265
00:16:14,160 --> 00:16:15,160
You know what I mean?

266
00:16:15,160 --> 00:16:18,240
So it's like this interesting feedback and I wrote one lately.

267
00:16:18,240 --> 00:16:22,360
Um, it's the first one I've written in like two and a half weeks.

268
00:16:22,360 --> 00:16:28,880
Um, it was my, my, uh, my answer to I can define a woman, you know, cause there's a

269
00:16:28,880 --> 00:16:34,680
lot of these people out there these days who can't seem to define, uh, what a woman is

270
00:16:34,680 --> 00:16:35,680
when asked.

271
00:16:35,680 --> 00:16:36,680
Um, so I thought enough.

272
00:16:36,680 --> 00:16:37,680
So I wrote a set.

273
00:16:37,680 --> 00:16:38,680
So apparently that's funny.

274
00:16:38,680 --> 00:16:50,040
You say that because I said I can define a woman and it's not because I'm a biologist.

275
00:16:50,040 --> 00:16:51,040
That's actually the title.

276
00:16:51,040 --> 00:16:58,560
So yeah, I got so much positive feedback from this and a lot of people said, this is my

277
00:16:58,560 --> 00:17:02,080
favorite piece of writing that you've churned out.

278
00:17:02,080 --> 00:17:08,880
Like I mean, okay, maybe they like it more because it's not some jargon about immunology,

279
00:17:08,880 --> 00:17:16,040
but uh, yeah, I find, um, a lot of the articles that I write on the fly that I don't think

280
00:17:16,040 --> 00:17:19,520
about, it's just like something I need to get out of my head are the ones that people

281
00:17:19,520 --> 00:17:21,600
respond to the most.

282
00:17:21,600 --> 00:17:26,160
And so if anything is requested, it's, it's more of those personal stories.

283
00:17:26,160 --> 00:17:31,680
Cause I agree with what everybody, you know, you guys are saying, I think that people are

284
00:17:31,680 --> 00:17:39,480
really looking to have some, someone to relate to because if everything's crazy, it's like,

285
00:17:39,480 --> 00:17:43,800
what the hell did I just see on Twitter or what did I just hear in the news or what did

286
00:17:43,800 --> 00:17:45,360
just happen in my kid's school?

287
00:17:45,360 --> 00:17:47,960
Like, I feel like I'm in loopy land.

288
00:17:47,960 --> 00:17:50,240
So it's, it's, it's very grounding.

289
00:17:50,240 --> 00:17:55,360
Um, reading a lot of the, the sub stackers is his work.

290
00:17:55,360 --> 00:18:01,800
So I'm happy that, uh, that I can be one of those, um, grounds for people, uh, cause it

291
00:18:01,800 --> 00:18:03,160
really helps me too.

292
00:18:03,160 --> 00:18:08,800
Cause it's like, you know, I, I learn a lot when I write these things, like what a woman

293
00:18:08,800 --> 00:18:09,800
is anyway.

294
00:18:09,800 --> 00:18:10,800
Yeah.

295
00:18:10,800 --> 00:18:18,240
I just came back and we're defining what a woman is, which is fascinating.

296
00:18:18,240 --> 00:18:21,800
Well, there's a new paper out on it.

297
00:18:21,800 --> 00:18:22,800
Yeah.

298
00:18:22,800 --> 00:18:24,840
Not a paper.

299
00:18:24,840 --> 00:18:29,680
Well, I'm curious what Matthew's definition of a woman is.

300
00:18:29,680 --> 00:18:31,640
I hate to put you on the spot like this.

301
00:18:31,640 --> 00:18:32,640
I know it's controversial.

302
00:18:32,640 --> 00:18:33,640
Come on.

303
00:18:33,640 --> 00:18:51,360
It's someone who identifies.

304
00:18:51,360 --> 00:18:56,320
Now for the, for what is potentially the actual answer to that question, um, reminder in the

305
00:18:56,320 --> 00:19:00,240
description, no matter where you're watching, I have put these sub stack links for do me

306
00:19:00,240 --> 00:19:02,240
for Jessica and for JJ.

307
00:19:02,240 --> 00:19:05,600
So don't hesitate to go and get caught up if you haven't yet, if somehow you haven't

308
00:19:05,600 --> 00:19:09,040
yet subscribed, um, you guys should do that.

309
00:19:09,040 --> 00:19:16,920
Um, so let's use this opportunity to, um, jump in.

310
00:19:16,920 --> 00:19:18,720
Is there, Oh, very nice.

311
00:19:18,720 --> 00:19:23,240
So yes, I think all four of us have cats somewhere in our background.

312
00:19:23,240 --> 00:19:25,600
Mine are currently hiding behind my green screen.

313
00:19:25,600 --> 00:19:26,600
They are.

314
00:19:26,600 --> 00:19:36,880
Oh, there's a, um, so let's use this opportunity.

315
00:19:36,880 --> 00:19:38,840
Let's jump in now, Matthew, you've got a paper pull up.

316
00:19:38,840 --> 00:19:40,680
Do you want to, what, what, what is this?

317
00:19:40,680 --> 00:19:42,240
What are we going to look at here?

318
00:19:42,240 --> 00:19:43,240
Okay.

319
00:19:43,240 --> 00:19:44,680
Um, this is a paper.

320
00:19:44,680 --> 00:19:45,680
This was interesting.

321
00:19:45,680 --> 00:19:53,080
Uh, it's one that I came across, uh, during the pandemic in 2021, um, because Eric Topol

322
00:19:53,080 --> 00:20:02,720
had tweeted it out and it was, um, it was about whether, um, well, I guess, I guess,

323
00:20:02,720 --> 00:20:11,360
uh, we could, uh, take a look at the abstract here, but it's, it's about selection pressure.

324
00:20:11,360 --> 00:20:16,480
And um, and the interesting thing about the paper to me, well, it's about selection pressure

325
00:20:16,480 --> 00:20:18,880
on SARS-CoV-2, right.

326
00:20:18,880 --> 00:20:21,360
And like emerging variants.

327
00:20:21,360 --> 00:20:27,440
Um, and well, you know, let's take a look at the paper.

328
00:20:27,440 --> 00:20:33,440
Um, it's going to involve sort of a unique statistic, um, called Tajima's D. So before

329
00:20:33,440 --> 00:20:38,960
we actually, um, get into reading the paper, I was going to ask, um, if, uh, either Jessica

330
00:20:38,960 --> 00:20:46,040
or Jumi has like a common sense explanation for Tajima's D because, um, it was something

331
00:20:46,040 --> 00:20:52,120
that, uh, like for me, my familiarity was actually only what my wife had mentioned in

332
00:20:52,120 --> 00:20:55,440
passing conversations before.

333
00:20:55,440 --> 00:20:59,160
So I actually had to look up Tajima's D and learn a little bit about it in order to read

334
00:20:59,160 --> 00:21:00,160
the paper.

335
00:21:00,160 --> 00:21:06,120
Um, and, and that's the way, you know, uh, statistics goes sometimes, right?

336
00:21:06,120 --> 00:21:09,360
Like you have this, this metric that gets used in a field.

337
00:21:09,360 --> 00:21:12,520
Um, and if you're not familiar with it, you just look it up, you'll learn a little bit

338
00:21:12,520 --> 00:21:14,840
about it.

339
00:21:14,840 --> 00:21:20,200
And, uh, and so, yeah, what, what is there, is there Jessica or Jumi, are you, are either

340
00:21:20,200 --> 00:21:25,600
of you familiar enough with Tajima's D to, uh, to give it some sort of like a, a common

341
00:21:25,600 --> 00:21:28,480
sense explanation?

342
00:21:28,480 --> 00:21:29,480
I am not.

343
00:21:29,480 --> 00:21:30,480
Sorry.

344
00:21:30,480 --> 00:21:31,480
Okay.

345
00:21:31,480 --> 00:21:34,800
Well, I'm going to do my best having looked it up in fact, and maybe I'll just, um, I'll

346
00:21:34,800 --> 00:21:38,320
pull up a Wikipedia article.

347
00:21:38,320 --> 00:21:43,920
Now I do before you answer, JJ is in the background and he might have, he might have an answer

348
00:21:43,920 --> 00:21:44,920
to that question.

349
00:21:44,920 --> 00:21:49,240
I don't want to bring him in while he's in mid he's, he's eating something.

350
00:21:49,240 --> 00:21:54,280
So I don't want to be that guy who, who jumps him in without permission, but this, if he

351
00:21:54,280 --> 00:21:56,740
has an, okay, I'm going to bring him in.

352
00:21:56,740 --> 00:21:57,740
I'm going to bring him in.

353
00:21:57,740 --> 00:21:58,740
Sorry.

354
00:21:58,740 --> 00:22:03,440
I was eating, I was eating my oatmeal.

355
00:22:03,440 --> 00:22:06,520
Was it Tajima's oatmeal?

356
00:22:06,520 --> 00:22:07,520
No.

357
00:22:07,520 --> 00:22:14,960
Why don't you take a shot at explaining it first and then if, if I can't augment it,

358
00:22:14,960 --> 00:22:16,960
then I won't.

359
00:22:16,960 --> 00:22:17,960
Okay.

360
00:22:17,960 --> 00:22:27,720
Um, well, so they're, they're it within micro revolution, you've got like a genetic strand

361
00:22:27,720 --> 00:22:31,760
and you've got two different things going on with mutation.

362
00:22:31,760 --> 00:22:38,320
Statistically, one of them would be, um, mutation rate and one of them would be mutation frequency.

363
00:22:38,320 --> 00:22:45,440
And mutation frequency is just how often one of these, um, you know, like 30,000 for, for

364
00:22:45,440 --> 00:22:51,240
Sarge Cove two, you know, we'll just say at 30,000 nucleotide sequence of any sort.

365
00:22:51,240 --> 00:22:56,520
And these individual nucleotides, uh, have these single nucleotide polymorphisms.

366
00:22:56,520 --> 00:22:57,760
That's how most of it occurs.

367
00:22:57,760 --> 00:22:58,760
Right.

368
00:22:58,760 --> 00:23:04,720
So like, you know, a little change here, change there, you know, an A, a T, a C, a G, whatever,

369
00:23:04,720 --> 00:23:10,440
um, and they happen all over the strand at random intervals.

370
00:23:10,440 --> 00:23:14,840
And uh, and so those are single nucleotide polymorphisms snips.

371
00:23:14,840 --> 00:23:19,760
Your mutation frequency would be how often one of those blips occurs.

372
00:23:19,760 --> 00:23:22,640
One of those snips snips occurs.

373
00:23:22,640 --> 00:23:29,080
So there's mutation frequency and then there's, and you know, you could describe that as,

374
00:23:29,080 --> 00:23:34,440
as some amount, you know, some number of snips over some period of time.

375
00:23:34,440 --> 00:23:37,760
Um, and it doesn't necessarily have to be snips.

376
00:23:37,760 --> 00:23:41,760
You can have insertions and deletions, but most of it's snips.

377
00:23:41,760 --> 00:23:49,240
Um, but then there's, there's mutation rate, which is actually how far, um, that sequence

378
00:23:49,240 --> 00:23:53,080
drifts from its original position, right?

379
00:23:53,080 --> 00:23:57,640
You have this original state and it's sort of hard to imagine what drift is because in

380
00:23:57,640 --> 00:24:03,840
essence you've got this like 30,000 dimension, yeah, 30,000 Taurus dimension, you know,

381
00:24:03,840 --> 00:24:08,240
um, model of, of what that nucleotide sequence is.

382
00:24:08,240 --> 00:24:13,320
But um, you know, if, if you've got like a cluster of genomes and then it, it sort of

383
00:24:13,320 --> 00:24:18,120
moves in it, you know, outward in a direction, I kind of, I kind of try to imagine it as

384
00:24:18,120 --> 00:24:25,240
Conway's game of life and, uh, and thinking about, oh gosh, maybe I should, uh, I should

385
00:24:25,240 --> 00:24:27,320
restart this.

386
00:24:27,320 --> 00:24:38,440
Um, thinking about what would happen if you started sort of, you know, evolutionary progression,

387
00:24:38,440 --> 00:24:43,300
but then let's see, what am I able to do?

388
00:24:43,300 --> 00:24:53,200
But then you have like some sort of, um, something in the mix that pushes the, what would be

389
00:24:53,200 --> 00:24:56,480
the natural evolution in one direction, right?

390
00:24:56,480 --> 00:25:01,440
Um, so the changes are pushed.

391
00:25:01,440 --> 00:25:05,000
So this line that I'm drawing is, is, is perhaps an evolutionary pressure.

392
00:25:05,000 --> 00:25:11,040
And you can see that the original guy, it's so small, the original, you know, thing was

393
00:25:11,040 --> 00:25:15,880
like right in here, right, but all the changes are happening, you know, further to the right

394
00:25:15,880 --> 00:25:20,680
in the screen mostly, because that's where I'm, I don't know, putting pressure on the

395
00:25:20,680 --> 00:25:21,680
screen.

396
00:25:21,680 --> 00:25:25,240
I don't know if I, if I said that in a way that's sort of understandable or not, but

397
00:25:25,240 --> 00:25:33,280
you wind up with not just the mutation frequency, but the mutation rate would be, um, a measure

398
00:25:33,280 --> 00:25:39,600
of how fast ultimately those two things happen in combination.

399
00:25:39,600 --> 00:25:45,520
And Taj Mahdi specifically, let's see, um, and I'll have to go back and read exactly

400
00:25:45,520 --> 00:25:48,160
how it's computed again.

401
00:25:48,160 --> 00:25:55,720
Um, but specifically it is an interplay between mutation frequency and mutation rate, uh,

402
00:25:55,720 --> 00:25:59,400
like a ratio between these things.

403
00:25:59,400 --> 00:26:02,400
So how, how close am I, JJ?

404
00:26:02,400 --> 00:26:06,720
Um, well, as I understand it, you're not too far off.

405
00:26:06,720 --> 00:26:12,640
It gets more, I was going to see if I could find a paper that would help us do this.

406
00:26:12,640 --> 00:26:18,480
Cause it's, I'm still, I'm still getting a grasp on, on how this stuff all manifests.

407
00:26:18,480 --> 00:26:23,880
But the, the gist of it is all the, another thing that you should have in the, in your

408
00:26:23,880 --> 00:26:31,940
model in your head is the idea that when changes are made that are silent nucleotide polymorphisms,

409
00:26:31,940 --> 00:26:33,960
that doesn't mean that nothing has changed.

410
00:26:33,960 --> 00:26:40,520
So, um, there was a science paper in 2011 that had a cover on it where the zipper on

411
00:26:40,520 --> 00:26:45,120
its mouth of a lady and the cartoon was a zipper on the mouth that was being opened

412
00:26:45,120 --> 00:26:50,800
because this science article was trying to talk about something that geneticists didn't

413
00:26:50,800 --> 00:26:57,160
want to talk about, which is that a lot of the genetic disorders that we have in humans

414
00:26:57,160 --> 00:27:02,600
are actually defined by polymorphisms, which don't change the amino acid.

415
00:27:02,600 --> 00:27:07,120
They're silent mutations and these silent mutations for whatever reason, change the

416
00:27:07,120 --> 00:27:12,040
tertiary structure of the protein or how it's regulated or how it gets us for related or

417
00:27:12,040 --> 00:27:13,040
how it gets guy class.

418
00:27:13,040 --> 00:27:21,960
We don't know, but the end result is, is that the redundancy of this code, which they are

419
00:27:21,960 --> 00:27:29,520
using to describe whether or not the, the virus is changing in response to vaccination,

420
00:27:29,520 --> 00:27:36,200
um, is something that they, they understand how it occurs and they understand the mechanism

421
00:27:36,200 --> 00:27:38,720
by which it occurs.

422
00:27:38,720 --> 00:27:44,600
And the basis for which it occurs is something that they're not acknowledging.

423
00:27:44,600 --> 00:27:50,660
I'm kind of, because of the fact that, that the silent mutations are not silent.

424
00:27:50,660 --> 00:27:54,880
So they label this as, as synonymous or non-synonymous mutations.

425
00:27:54,880 --> 00:27:59,320
And they're trying to give you the idea that synonymous mutations don't do anything.

426
00:27:59,320 --> 00:28:05,000
And non-synonymous stations do, but in reality there's evolutionary pressure on both of those

427
00:28:05,000 --> 00:28:11,080
changes because it's likely that the synonymous change is not synonymous except for that.

428
00:28:11,080 --> 00:28:17,480
It doesn't change the amino acid, but it likely has some, some impact on the tertiary structure

429
00:28:17,480 --> 00:28:23,880
of the protein or how it is post translationally modulate modulated or how the mRNA is pre

430
00:28:23,880 --> 00:28:28,400
translationally modulated or post translationally regulated.

431
00:28:28,400 --> 00:28:32,960
And that's how all of these silent mutations result in genetic diseases.

432
00:28:32,960 --> 00:28:38,880
And it's also how they are oversimplifying the signal to try and describe whether the

433
00:28:38,880 --> 00:28:47,020
viruses is changing in response to the, the vaccination or not, because in reality we

434
00:28:47,020 --> 00:28:50,900
expect to see these changes in a certain ratio.

435
00:28:50,900 --> 00:28:56,800
And that certain ratio seems to be independent of the preservation of the protein in the

436
00:28:56,800 --> 00:29:02,200
spike, but with other proteins, and this is getting too deep, maybe, but other proteins

437
00:29:02,200 --> 00:29:08,400
are more functionally constrained so that even single nucleotide polymorphisms result

438
00:29:08,400 --> 00:29:10,300
in loss of function.

439
00:29:10,300 --> 00:29:17,320
So you can imagine a perfectly constructed enzyme is also dependent on the exact sequence

440
00:29:17,320 --> 00:29:23,200
that's there because if you change a silent mutation will result in a slight folding,

441
00:29:23,200 --> 00:29:26,340
which will result in a loss of efficiency of that enzyme.

442
00:29:26,340 --> 00:29:31,240
It's very likely that the RNA dependent RNA polymerase and its associated proteins are

443
00:29:31,240 --> 00:29:33,840
all highly constrained.

444
00:29:33,840 --> 00:29:38,160
That's why they're very homologous across all these coronaviruses, whereas the spike

445
00:29:38,160 --> 00:29:39,720
protein isn't.

446
00:29:39,720 --> 00:29:44,480
And what they find in this paper is that there are very many parts of the spike protein that

447
00:29:44,480 --> 00:29:47,640
are almost doesn't matter what they change to.

448
00:29:47,640 --> 00:29:54,120
And so if you vaccinate people to that protein, there's lots of room for it to change and

449
00:29:54,120 --> 00:30:00,360
avoid this memory, whereas all these other proteins don't have that space within which

450
00:30:00,360 --> 00:30:04,240
to maneuver because they're already so functionally constrained.

451
00:30:04,240 --> 00:30:10,640
And so they're trying to tell a story that this is a signal of evolution when in reality

452
00:30:10,640 --> 00:30:15,120
they're not being very precise about what these changes mean and don't mean and make

453
00:30:15,120 --> 00:30:20,040
very simple about how synonymous means one thing and non-synonymous means another.

454
00:30:20,040 --> 00:30:22,080
It's much worse than that.

455
00:30:22,080 --> 00:30:25,840
There are a bunch of questions I would want to ask, but I feel like we would wind up going

456
00:30:25,840 --> 00:30:32,960
down the virus-like particle rabbit hole.

457
00:30:32,960 --> 00:30:38,560
And I want to steer us toward the actual purpose of today, which is to take a look at a scientific

458
00:30:38,560 --> 00:30:40,800
paper and see what it is that we can do.

459
00:30:40,800 --> 00:30:46,160
And so the audience can watch what happens when several people together pick apart a

460
00:30:46,160 --> 00:30:47,160
paper.

461
00:30:47,160 --> 00:30:52,360
And I may even go out and find Eric Topol's tweet because it's actually, it's kind of

462
00:30:52,360 --> 00:30:53,360
funny.

463
00:30:53,360 --> 00:30:55,920
I'd like to hear what he said.

464
00:30:55,920 --> 00:31:02,120
Well, I think he had no idea what the paper said, which reads even though abstracted or

465
00:31:02,120 --> 00:31:04,520
even reads contradictory to me, right?

466
00:31:04,520 --> 00:31:09,560
I mean, I contradict themselves in the abstract as far as I can tell.

467
00:31:09,560 --> 00:31:10,560
Okay.

468
00:31:10,560 --> 00:31:13,040
So let's take a look at the paper.

469
00:31:13,040 --> 00:31:20,320
And that was my interpretation was that the authors stated something that was very, very

470
00:31:20,320 --> 00:31:23,920
different from what their actual data showed.

471
00:31:23,920 --> 00:31:25,240
Yeah.

472
00:31:25,240 --> 00:31:31,320
They say in the abstract that full vaccination against COVID-19 with other mitigation strategies

473
00:31:31,320 --> 00:31:37,400
is critical to suppress emergent mutations, but then their data shows that the fully vaccinated

474
00:31:37,400 --> 00:31:40,360
people are producing the mutations.

475
00:31:40,360 --> 00:31:41,360
It's extraordinary.

476
00:31:41,360 --> 00:31:42,360
Right.

477
00:31:42,360 --> 00:31:43,360
Okay.

478
00:31:43,360 --> 00:31:44,360
So yeah.

479
00:31:44,360 --> 00:31:48,320
So let's see, what is their statement taken together?

480
00:31:48,320 --> 00:31:53,880
Our data suggests that vaccination plays an important role in the purifying selection

481
00:31:53,880 --> 00:32:01,760
force of SARS-CoV-2 Delta variants.

482
00:32:01,760 --> 00:32:08,600
Purifying selection force is with you, young Skywalker.

483
00:32:08,600 --> 00:32:15,440
So any other thoughts on this?

484
00:32:15,440 --> 00:32:18,200
Has everybody read the paper?

485
00:32:18,200 --> 00:32:20,120
I skimmed it.

486
00:32:20,120 --> 00:32:22,120
I have not, sorry.

487
00:32:22,120 --> 00:32:23,120
Okay.

488
00:32:23,120 --> 00:32:25,600
But it's very confusing.

489
00:32:25,600 --> 00:32:29,520
I have the same impression from having skimmed it.

490
00:32:29,520 --> 00:32:33,200
Jumi has to clean the chalkboards when we're done then.

491
00:32:33,200 --> 00:32:43,440
Okay, because this is going to be a little harder than usual because there's more probably

492
00:32:43,440 --> 00:32:47,560
math that's new math to the average reader.

493
00:32:47,560 --> 00:32:55,520
This is not as easy to read as a lot of biology papers, but you know, so here's the abstract

494
00:32:55,520 --> 00:33:00,280
to say our data suggests that vaccination plays an important role in the purifying

495
00:33:00,280 --> 00:33:01,280
selection force.

496
00:33:01,280 --> 00:33:02,280
Oh my gosh.

497
00:33:02,280 --> 00:33:03,280
I forgot that part.

498
00:33:03,280 --> 00:33:04,280
I don't know.

499
00:33:04,280 --> 00:33:05,280
I skimmed it very much.

500
00:33:05,280 --> 00:33:06,280
You all skipped it.

501
00:33:06,280 --> 00:33:09,520
If you all skimmed it, if you didn't see the first sentence in the introduction and start

502
00:33:09,520 --> 00:33:16,920
laughing COVID-19 vaccination resistance has become a major challenge to prevent global

503
00:33:16,920 --> 00:33:19,920
SARS-CoV-2 transmission.

504
00:33:19,920 --> 00:33:22,480
Can I write off the back?

505
00:33:22,480 --> 00:33:27,320
And then the introduction says legally required vaccination against various infectious diseases

506
00:33:27,320 --> 00:33:31,760
and essential to public health policy in many countries.

507
00:33:31,760 --> 00:33:32,760
Damn.

508
00:33:32,760 --> 00:33:33,760
Yeah.

509
00:33:33,760 --> 00:33:34,760
Okay.

510
00:33:34,760 --> 00:33:36,840
See, I was going to point out the word resistance has multiple meanings, neither of which is

511
00:33:36,840 --> 00:33:38,520
it's not clear which one it is.

512
00:33:38,520 --> 00:33:39,520
Right.

513
00:33:39,520 --> 00:33:43,500
Talking about supposed emerging variants because of the immune pressure.

514
00:33:43,500 --> 00:33:47,400
So there's perhaps a resistance there, but no, they quite clearly because of later in

515
00:33:47,400 --> 00:33:53,040
the introduction mean I don't want your shot that qualifies as resistance vaccine resistance.

516
00:33:53,040 --> 00:33:54,040
Yikes.

517
00:33:54,040 --> 00:33:55,040
Yeah.

518
00:33:55,040 --> 00:34:00,620
I've actually wondered multiple times or many times during the pandemic if there is an attempt

519
00:34:00,620 --> 00:34:09,520
at lying without lying going on in many places, signaling information anyway.

520
00:34:09,520 --> 00:34:15,060
So introduction legally required vaccination against infectious disease is essential to

521
00:34:15,060 --> 00:34:18,840
public health policy in many countries.

522
00:34:18,840 --> 00:34:22,600
Okay.

523
00:34:22,600 --> 00:34:24,560
So let's see, where do we get to?

524
00:34:24,560 --> 00:34:29,800
Where we get to the point here?

525
00:34:29,800 --> 00:34:31,840
Get blathering till we get down here.

526
00:34:31,840 --> 00:34:36,200
Is there a correlation between mutation frequency and vaccination?

527
00:34:36,200 --> 00:34:37,200
Okay.

528
00:34:37,200 --> 00:34:40,600
And so as I said, there's mutation frequency and there's mutation rate.

529
00:34:40,600 --> 00:34:45,080
Frequency is just, you know, that's how often things are changing along that strand.

530
00:34:45,080 --> 00:34:48,640
That's how often you have the snips.

531
00:34:48,640 --> 00:34:52,680
So let's see, to explore this question, we analyze the correlation between the rates

532
00:34:52,680 --> 00:34:56,760
of full vaccination and the point mutation frequency.

533
00:34:56,760 --> 00:35:04,480
So they look at data, you know, collected from 20 different countries from these genomes.

534
00:35:04,480 --> 00:35:09,400
And this assumes that all of this, this assumes that all this data is collected with high

535
00:35:09,400 --> 00:35:11,820
fidelity.

536
00:35:11,820 --> 00:35:14,680
But you know, that's a question left for another time.

537
00:35:14,680 --> 00:35:17,840
They're taking in data.

538
00:35:17,840 --> 00:35:25,780
And so they find that mutation frequency is logarithmically reduced as the full vaccination

539
00:35:25,780 --> 00:35:32,880
rate increased in most countries.

540
00:35:32,880 --> 00:35:35,080
And does that make sense?

541
00:35:35,080 --> 00:35:38,960
You know, intuitively, does that make sense?

542
00:35:38,960 --> 00:35:41,920
Yeah, the purifying selection.

543
00:35:41,920 --> 00:35:44,560
Yeah, it makes sense to me.

544
00:35:44,560 --> 00:35:51,440
If you're sort of cutting off, you know, if you think of the potential genomes as this,

545
00:35:51,440 --> 00:35:55,080
you know, 30,000 dimension space, right?

546
00:35:55,080 --> 00:36:00,640
A selection pressure is something that takes away some of the domain of that space.

547
00:36:00,640 --> 00:36:09,480
So you know, you wouldn't really have snips into that space that there's pressure to avoid.

548
00:36:09,480 --> 00:36:13,040
So you would think that you would have reduced mutation frequency.

549
00:36:13,040 --> 00:36:16,680
So that part makes sense to me intuitively.

550
00:36:16,680 --> 00:36:20,400
And I would have said so if I were writing this paper, I would say I would expect this

551
00:36:20,400 --> 00:36:26,800
to occur, you know, just on a pure mathematical basis, if all the space were open, not that

552
00:36:26,800 --> 00:36:33,080
it is, but if or, you know, even if a subset of the space is open, and you cut off some

553
00:36:33,080 --> 00:36:42,320
other subset within that, then you have, you know, less domain for this thing to get into,

554
00:36:42,320 --> 00:36:52,680
you know, for genetic sequences to be, to emerge into, right?

555
00:36:52,680 --> 00:36:57,320
So okay, so that makes sense to me.

556
00:36:57,320 --> 00:37:01,320
So to our knowledge, this is the first evidence suggesting that vaccinations could successfully

557
00:37:01,320 --> 00:37:04,080
suppress viral mutations.

558
00:37:04,080 --> 00:37:06,600
Okay.

559
00:37:06,600 --> 00:37:12,920
So I mean, we have an expectation, and we're seeing the expectation happen in real time

560
00:37:12,920 --> 00:37:16,320
since the spike protein is the target of the vaccination program.

561
00:37:16,320 --> 00:37:19,960
We further examined mutations of Delta variant spike gene.

562
00:37:19,960 --> 00:37:23,880
Likewise, we found that the mutation frequency of the spike gene is also logarithmically

563
00:37:23,880 --> 00:37:28,900
reduced as the full vaccination rate increased.

564
00:37:28,900 --> 00:37:37,100
So they're, they are looking at, at, you know, percent fully vaccinated versus the mutation

565
00:37:37,100 --> 00:37:42,800
frequencies that are found.

566
00:37:42,800 --> 00:37:50,440
And take a look at the picture that they get pictures.

567
00:37:50,440 --> 00:38:01,760
So you can see the more vaccinated people are, the less mutation frequency there is.

568
00:38:01,760 --> 00:38:06,840
Okay.

569
00:38:06,840 --> 00:38:12,080
So but then Tajem is D brings in mutation rate.

570
00:38:12,080 --> 00:38:16,200
And you know, what do you guys think the mutation, you know, what should the pressure do to mutation

571
00:38:16,200 --> 00:38:22,040
rate, not just the frequency?

572
00:38:22,040 --> 00:38:24,480
Should the rate be also suppressed?

573
00:38:24,480 --> 00:38:29,120
No, I mean, it's going to go up.

574
00:38:29,120 --> 00:38:32,760
It should go up.

575
00:38:32,760 --> 00:38:33,760
Should it?

576
00:38:33,760 --> 00:38:40,480
You're cutting out a subset of space for this thing to move into.

577
00:38:40,480 --> 00:38:48,080
If they're looking at mutation rate in the spike, and then you, you make antibodies to

578
00:38:48,080 --> 00:38:54,520
the spike, it's, I, I don't know, it's, it's tricky because I don't think that that mechanism

579
00:38:54,520 --> 00:38:57,180
is really influencing too much.

580
00:38:57,180 --> 00:38:59,840
But I think you're right.

581
00:38:59,840 --> 00:39:04,200
I think frequency goes down and rate goes up.

582
00:39:04,200 --> 00:39:09,200
That's my intuition.

583
00:39:09,200 --> 00:39:11,240
Why does the frequency go up?

584
00:39:11,240 --> 00:39:15,360
The frequency goes down because it is constrained.

585
00:39:15,360 --> 00:39:19,840
You have a space that you can't move into.

586
00:39:19,840 --> 00:39:24,000
You have sort of immediate rejections of certain single nucleotide polymorphisms.

587
00:39:24,000 --> 00:39:25,000
Okay.

588
00:39:25,000 --> 00:39:27,800
Yeah, that was the earlier thing.

589
00:39:27,800 --> 00:39:28,800
Sorry.

590
00:39:28,800 --> 00:39:29,800
Yeah.

591
00:39:29,800 --> 00:39:34,800
And then rate would go up because you would, you would see the viral swarm being sort of

592
00:39:34,800 --> 00:39:40,580
pushed out of, you know, where you've got territory on one side for, you know, perhaps

593
00:39:40,580 --> 00:39:42,180
that is being cut off.

594
00:39:42,180 --> 00:39:49,640
And so it has to, it has to mutate in, in, in a direction that, that, you know, gradually

595
00:39:49,640 --> 00:39:53,720
pushes it away from the territory that's cut off.

596
00:39:53,720 --> 00:40:00,140
I think we're oversimplifying the fact that making immunity to the spike is going to necessarily

597
00:40:00,140 --> 00:40:05,240
benefit and reduce the space that they can move into because there's lots of ways that

598
00:40:05,240 --> 00:40:08,100
off-target antibodies can assist infection.

599
00:40:08,100 --> 00:40:13,000
So then you would, you would not have that selective pressure.

600
00:40:13,000 --> 00:40:16,960
So we have to take that into account as well.

601
00:40:16,960 --> 00:40:22,240
That if the antibodies are not helping you fight the infection, but are enabling it through

602
00:40:22,240 --> 00:40:30,440
FC receptor infection of immune cells, like a lot of people think, then a mutation frequency

603
00:40:30,440 --> 00:40:35,600
would go down also because the, because the vaccine is actually not putting pressure on

604
00:40:35,600 --> 00:40:40,240
the spike, but making it more likely that you're going to make antibodies that help,

605
00:40:40,240 --> 00:40:41,840
help the spike.

606
00:40:41,840 --> 00:40:48,040
And so if in that scenario, if the, if the majority of the antibodies that you make are

607
00:40:48,040 --> 00:40:54,040
not specific for a neutralizing site, but are, are essentially neutral or, or bad for

608
00:40:54,040 --> 00:40:58,600
you, then you would also find the mutation frequency would decrease in the vaccinated

609
00:40:58,600 --> 00:41:02,680
people because they're not putting any pressure on the virus by, by making these off-target

610
00:41:02,680 --> 00:41:04,600
antibodies to it.

611
00:41:04,600 --> 00:41:06,200
You can explain it two ways.

612
00:41:06,200 --> 00:41:07,200
Yeah.

613
00:41:07,200 --> 00:41:17,120
So I guess, um, the, the vaccine is only specific to like 13 ish percent of the sequence and

614
00:41:17,120 --> 00:41:21,080
there could be other interactions to the rest of the sequence.

615
00:41:21,080 --> 00:41:28,360
That even, yeah, even, even the, yeah, that's the whole basis of, of, of antibody dependent

616
00:41:28,360 --> 00:41:32,720
enhancement that the antibodies bind to the protein, but the protein can still every do

617
00:41:32,720 --> 00:41:35,000
everything that it normally does.

618
00:41:35,000 --> 00:41:39,480
But now with an antibody bound to it, it has a receptor for macrophages.

619
00:41:39,480 --> 00:41:42,120
So the, the virus is still infectious.

620
00:41:42,120 --> 00:41:47,040
It might even be stabilized in a highly infectious form because the antibodies stuck to it.

621
00:41:47,040 --> 00:41:50,880
But then now you also have an immune receptor present.

622
00:41:50,880 --> 00:41:57,000
And so it makes it, it increases its tissue specific or increases its infectiousness with

623
00:41:57,000 --> 00:41:59,200
the presence of the antibody.

624
00:41:59,200 --> 00:42:02,560
Um, that's, that's how Dengue fever works.

625
00:42:02,560 --> 00:42:07,600
And a lot of other, these, the antibody dependent enhancement is because the antibodies bind

626
00:42:07,600 --> 00:42:11,320
to the glycoprotein, but do not neutralize it.

627
00:42:11,320 --> 00:42:18,920
And so then you just have added infectiousness for the macrophages that take it in.

628
00:42:18,920 --> 00:42:20,640
That would not normally take it in.

629
00:42:20,640 --> 00:42:25,520
You see, that's the tricky part.

630
00:42:25,520 --> 00:42:30,640
So I can see why the mutation frequency would go down in those, but I think it's, it's,

631
00:42:30,640 --> 00:42:32,680
to me it's 50 50.

632
00:42:32,680 --> 00:42:37,560
You could say that the vaccine, if it makes good enough spike protein, which you and I

633
00:42:37,560 --> 00:42:41,720
both know it doesn't, but if it did make good enough spike protein, you might imagine that

634
00:42:41,720 --> 00:42:48,000
it produces specific pressure on the spike protein, but it's much more likely that it

635
00:42:48,000 --> 00:42:52,200
doesn't put pressure on the spike because it makes crappy antibodies, which are neutral

636
00:42:52,200 --> 00:42:54,000
or bad for you.

637
00:42:54,000 --> 00:42:58,120
And then that's the reason why the mutation frequency goes down.

638
00:42:58,120 --> 00:43:04,520
That's my guess is I've, I've really become convinced that this idea of enriching the

639
00:43:04,520 --> 00:43:08,800
virus is a way of, of pretending that the transfection worked because I don't think

640
00:43:08,800 --> 00:43:10,800
it did.

641
00:43:10,800 --> 00:43:12,960
Yeah.

642
00:43:12,960 --> 00:43:20,120
And as you guys know, I have, I have believed for two full years that, that the vaccines

643
00:43:20,120 --> 00:43:22,400
did not work.

644
00:43:22,400 --> 00:43:24,760
That's been my belief for two full years.

645
00:43:24,760 --> 00:43:30,680
When I looked at the national data going back, I was looking at the our world and data set.

646
00:43:30,680 --> 00:43:37,160
There's a positive correlation between vaccination rates and COVID-19 rates and mortality among

647
00:43:37,160 --> 00:43:38,960
nations around the world.

648
00:43:38,960 --> 00:43:44,520
And that's what I found all the way back in March of 2021 and kept up with that for like

649
00:43:44,520 --> 00:43:46,600
eight months.

650
00:43:46,600 --> 00:43:49,480
So anyhow, moving forward with this paper.

651
00:43:49,480 --> 00:43:50,900
So we have some intuition.

652
00:43:50,900 --> 00:43:55,120
We also have the possibility that the data isn't even real.

653
00:43:55,120 --> 00:44:02,800
You know, it's very possible that, that SARS-CoV-2 sequences were sort of, you know, modeled

654
00:44:02,800 --> 00:44:04,640
and put into the database.

655
00:44:04,640 --> 00:44:10,880
I'm bringing that out as, as, as a hypothesis that I thought is more and more realistic

656
00:44:10,880 --> 00:44:13,400
the more that I've learned.

657
00:44:13,400 --> 00:44:17,580
But let's just, let's move on with the paper and sort of see what it is that they come

658
00:44:17,580 --> 00:44:18,580
up with.

659
00:44:18,580 --> 00:44:23,680
I don't know how much of the technical detail we need to discuss here, but you know, I'll

660
00:44:23,680 --> 00:44:29,600
let you guys tell me, you know, when to stop here as we kind of scroll down and, and, and,

661
00:44:29,600 --> 00:44:33,600
you know, look at what's going on in the paper here.

662
00:44:33,600 --> 00:44:39,480
And we see there's some discussion of synonymous mutations or silent mutations.

663
00:44:39,480 --> 00:44:45,080
And JJ, I do want to have a conversation with you separate at some point about, about synonymous

664
00:44:45,080 --> 00:44:49,960
and non-synonymous mutations and virus-like particles, because I've actually wondered

665
00:44:49,960 --> 00:44:58,160
if, if, you know, if Omicron was engineered, like I suspect it was, then the reason for

666
00:44:58,160 --> 00:45:04,720
the high percentage of synonymous mutations might be to make it continue to fit a specific

667
00:45:04,720 --> 00:45:09,120
virus-like particle.

668
00:45:09,120 --> 00:45:10,120
If that makes sense.

669
00:45:10,120 --> 00:45:11,120
Do you hear what I'm saying?

670
00:45:11,120 --> 00:45:14,120
Oh, yeah, I'm just writing it down.

671
00:45:14,120 --> 00:45:15,120
Interesting.

672
00:45:15,120 --> 00:45:20,200
Have to think about that one for a little while.

673
00:45:20,200 --> 00:45:28,080
Yeah, I don't know how well a virus-like particle fits, you know, any particular viral strand.

674
00:45:28,080 --> 00:45:35,920
So I wasn't sure if, if there was some sort of like attempt to make it continue to fit

675
00:45:35,920 --> 00:45:36,920
by specific.

676
00:45:36,920 --> 00:45:39,280
Why, why are you just curious?

677
00:45:39,280 --> 00:45:44,900
Why do you, I thought you were just as convinced that maybe Omicron was always there.

678
00:45:44,900 --> 00:45:46,880
What is tipping the balance for you right now?

679
00:45:46,880 --> 00:45:50,800
That was one of my hypotheses, which is that Omicron was in the background.

680
00:45:50,800 --> 00:45:55,720
But the more that I've thought about viral swarms and the more that I've read, I like

681
00:45:55,720 --> 00:46:02,360
the survival of the flattest principle, which is that I don't think that the viral swarm

682
00:46:02,360 --> 00:46:09,840
is like, you know, these very far apart clusters so much as I think that it's, that it looks

683
00:46:09,840 --> 00:46:16,320
more like the Milky Way galaxy, you know, with a dense core and, and, you know, less

684
00:46:16,320 --> 00:46:24,240
and less material sort of in a related way around that core.

685
00:46:24,240 --> 00:46:30,800
So there is the possibility, like, I still think about the possibility that, that.

686
00:46:30,800 --> 00:46:32,280
What do you have in that core?

687
00:46:32,280 --> 00:46:36,160
I know it's in my core, but I wonder what you have in that core.

688
00:46:36,160 --> 00:46:40,400
Things that are closer to each other, you know.

689
00:46:40,400 --> 00:46:44,120
I think if you want to model it correctly in your imagination, you have to think about

690
00:46:44,120 --> 00:46:50,620
genes that are closest to each other, because that's the swarm illusion is that RNA viruses

691
00:46:50,620 --> 00:46:56,760
have this beautiful assortment of proteins that they use to attack us when we're in reality,

692
00:46:56,760 --> 00:47:01,240
they have this set of proteins and almost all of them are incredibly conserved.

693
00:47:01,240 --> 00:47:05,640
That's why they've always used pan coronavirus primers to find them because these proteins

694
00:47:05,640 --> 00:47:06,680
are all homologous.

695
00:47:06,680 --> 00:47:14,000
It's the spike protein that defines individual variants because it's the most varied protein.

696
00:47:14,000 --> 00:47:17,800
But I think they're, they're pulling the wool over our eyes on that one.

697
00:47:17,800 --> 00:47:21,920
And every time you find a spike protein, you get to claim a new strain.

698
00:47:21,920 --> 00:47:24,880
Every time you find a spike protein, you get to claim a new virus.

699
00:47:24,880 --> 00:47:27,960
And they've been doing that for way too long.

700
00:47:27,960 --> 00:47:31,640
When in reality, our immune system focuses on that center of the Milky Way, the genes

701
00:47:31,640 --> 00:47:32,640
that don't change.

702
00:47:32,640 --> 00:47:38,720
You know, it's funny to have a paper like this that claims to be looking at all of this

703
00:47:38,720 --> 00:47:45,880
genetic variants when the testing that was going on after each variant emergence, they

704
00:47:45,880 --> 00:47:52,120
were determining a variant not by specific sequence, but by S gene target failure.

705
00:47:52,120 --> 00:47:53,120
Yep.

706
00:47:53,120 --> 00:47:56,880
Meaning, meaning that we're going to call it whatever the new variant is simply because

707
00:47:56,880 --> 00:48:00,760
it doesn't match the last spike protein.

708
00:48:00,760 --> 00:48:06,720
And here you have this paper that seems to be taking granular detail from 20 different

709
00:48:06,720 --> 00:48:12,560
countries, which contradicts the idea that we would need to use S gene target failure

710
00:48:12,560 --> 00:48:13,720
to identify.

711
00:48:13,720 --> 00:48:17,140
But maybe, maybe somebody could say, well, that's just a matter of slowness of production

712
00:48:17,140 --> 00:48:18,560
of the tests.

713
00:48:18,560 --> 00:48:22,040
So I'll take that on good faith for the moment.

714
00:48:22,040 --> 00:48:26,040
I'll, you know, I don't, I don't know if that's a cartoon version or not, right?

715
00:48:26,040 --> 00:48:30,800
I don't know if that's, if that's a Scooby Doo as you put it.

716
00:48:30,800 --> 00:48:32,560
So you know, continuing with the paper.

717
00:48:32,560 --> 00:48:38,880
So you know, we've got, they did, they did do it, give them credit though.

718
00:48:38,880 --> 00:48:46,360
They did do it for the full genome and for the viral spike gene alone.

719
00:48:46,360 --> 00:48:50,720
I don't really see how they're displaying it yet, but table one seems to be both full

720
00:48:50,720 --> 00:48:56,920
genome and spiral spike gene, but I don't see how they divide that.

721
00:48:56,920 --> 00:49:04,400
So now we have this non-synonymous to synonymous ratio.

722
00:49:04,400 --> 00:49:06,440
Yeah.

723
00:49:06,440 --> 00:49:16,240
And the more fully vaccinated, the more we have non-synonymous mutations as opposed to

724
00:49:16,240 --> 00:49:23,160
synonymous mutations, which, which fits the idea of the vaccination providing selection

725
00:49:23,160 --> 00:49:30,660
pressure, because now you have mutations that are, you know, that lead, that make the amino

726
00:49:30,660 --> 00:49:33,040
acids functionally different.

727
00:49:33,040 --> 00:49:37,080
If you know, it forces them to be functionally different.

728
00:49:37,080 --> 00:49:42,760
They look more different in order to avoid the, the antibodies.

729
00:49:42,760 --> 00:49:51,040
And if you go back to, um, Trevor Bedford's talk at the, um, Fred Hutch Institute in 2021

730
00:49:51,040 --> 00:49:59,000
or two, where he's talking about this non-synonymous synonymous ratio in the spike protein or rather

731
00:49:59,000 --> 00:50:06,280
in any viral gene, he states very clearly that it is extremely rare to find this ratio

732
00:50:06,280 --> 00:50:10,240
higher than one in any flu gene.

733
00:50:10,240 --> 00:50:15,960
And the ratio at the spike, um, and his papers was at some point over one and a half, like

734
00:50:15,960 --> 00:50:17,280
they show it here for Israel.

735
00:50:17,280 --> 00:50:23,680
So, um, it's extraordinary because the virologists have decades of data that show that none of

736
00:50:23,680 --> 00:50:26,320
these genes change at this pace.

737
00:50:26,320 --> 00:50:28,760
So something is going on.

738
00:50:28,760 --> 00:50:34,120
The question is, are they just revealing a background and, and pretending it's changing

739
00:50:34,120 --> 00:50:37,040
or is the background really changing?

740
00:50:37,040 --> 00:50:39,560
And they're just emphasizing a change that doesn't matter.

741
00:50:39,560 --> 00:50:42,760
I think that's probably what it is.

742
00:50:42,760 --> 00:50:45,720
And was this curve sculpted, right?

743
00:50:45,720 --> 00:50:52,000
If there was a, if these are bio warfare agents that are being released, then you would expect

744
00:50:52,000 --> 00:50:58,160
someone to have modeled information like this sort of along the way and decide where to

745
00:50:58,160 --> 00:51:03,360
release it according to where they were going to sort of force vaccination rates.

746
00:51:03,360 --> 00:51:07,280
So, but you know, that's the conspiracy theory conversation.

747
00:51:07,280 --> 00:51:11,600
It's a real, it's a realistic conversation, but let's go ahead and, um, and just sort

748
00:51:11,600 --> 00:51:17,160
of take, take this on good faith for the moment.

749
00:51:17,160 --> 00:51:24,520
So, um, you know, we can see more synonymous mutations, um, as a proportion, the more people

750
00:51:24,520 --> 00:51:29,560
are fully vaccinated and that makes intuitive sense.

751
00:51:29,560 --> 00:51:31,440
Okay.

752
00:51:31,440 --> 00:51:36,400
So what is their conclusion from this?

753
00:51:36,400 --> 00:51:39,640
Well, let's see, let's, let's read this here.

754
00:51:39,640 --> 00:51:43,960
It's a neutrality test, I guess.

755
00:51:43,960 --> 00:51:44,960
Yeah.

756
00:51:44,960 --> 00:51:47,600
Um, synonymous versus non-synonymous.

757
00:51:47,600 --> 00:51:53,640
Non-synonymous means you're going to be changing the, the, uh, physical structure of the protein

758
00:51:53,640 --> 00:51:58,560
formed by a greater amount.

759
00:51:58,560 --> 00:52:04,200
Yeah, that's correct.

760
00:52:04,200 --> 00:52:07,520
I mean, I just, you have to remember that the spike protein has a lot of freedom to

761
00:52:07,520 --> 00:52:10,480
do that in some, some regions of it.

762
00:52:10,480 --> 00:52:15,920
There's a functionally constrained portions are the portion that's that hinge out and

763
00:52:15,920 --> 00:52:21,120
that are hydrophobic for, for membrane fusion.

764
00:52:21,120 --> 00:52:25,320
Um, sorry.

765
00:52:25,320 --> 00:52:40,080
Oh, my cats just, I didn't know if you were making a comment on the paper or not.

766
00:52:40,080 --> 00:52:42,400
No cat cat comment.

767
00:52:42,400 --> 00:52:51,400
I mean, sometimes I don't report.

768
00:52:51,400 --> 00:52:55,320
Yeah, sometimes I'm just like, you know, you, you are such a good statistic.

769
00:52:55,320 --> 00:53:01,120
You're such a good statistic to the cat.

770
00:53:01,120 --> 00:53:02,120
Nevermind.

771
00:53:02,120 --> 00:53:03,120
Nevermind.

772
00:53:03,120 --> 00:53:09,440
I, I, I, I, I find it interesting that they don't give the non-synonymous to synonymous

773
00:53:09,440 --> 00:53:11,920
ratio, um, for the full genome.

774
00:53:11,920 --> 00:53:14,040
They only give it for the spike gene.

775
00:53:14,040 --> 00:53:18,200
Um, and they only give it during the time that vaccination is being rolled out.

776
00:53:18,200 --> 00:53:19,200
Right.

777
00:53:19,200 --> 00:53:24,280
I'm not sure if I should have seen it like before, you know, is it, is it like stable

778
00:53:24,280 --> 00:53:28,360
and then it started to change after the vaccination rate?

779
00:53:28,360 --> 00:53:29,360
Yep.

780
00:53:29,360 --> 00:53:31,320
I really think it's interesting.

781
00:53:31,320 --> 00:53:35,960
It's an interesting paper because they don't get what they want and they spin it as though

782
00:53:35,960 --> 00:53:36,960
they did.

783
00:53:36,960 --> 00:53:37,960
Um, okay.

784
00:53:37,960 --> 00:53:38,960
So, okay.

785
00:53:38,960 --> 00:53:41,960
I, I agree with you.

786
00:53:41,960 --> 00:53:45,920
Tell it, tell, tell us, tell us what they want and what they got.

787
00:53:45,920 --> 00:53:52,920
Well, what they wanted to find was evidence that the vaccination limited the changes that

788
00:53:52,920 --> 00:53:55,160
they would observe.

789
00:53:55,160 --> 00:54:00,080
And the, the two ways that they show it are one, they look at a mutation frequency, which

790
00:54:00,080 --> 00:54:07,280
is calculated by a very interesting computation, which is somewhat dependent on the very dependent

791
00:54:07,280 --> 00:54:12,840
on the fidelity of the sequencing that they did, knowing that they're just telling you

792
00:54:12,840 --> 00:54:18,760
up here that they got sequences from all these people that had full coverage of the genome

793
00:54:18,760 --> 00:54:23,720
without really explaining adequately what that really means to them and how much, you

794
00:54:23,720 --> 00:54:28,600
know, how much faith they have in the consensus genome that was reported because each of these

795
00:54:28,600 --> 00:54:33,560
sequences that they're going to use as a basis for whether or not there was a mutation is

796
00:54:33,560 --> 00:54:34,920
a consensus sequence.

797
00:54:34,920 --> 00:54:40,720
So you know, there you're assuming that their average sequence and the polymorphisms that

798
00:54:40,720 --> 00:54:45,240
they claim to find are not errors, which they definitely could be depending on the depth

799
00:54:45,240 --> 00:54:46,420
of coverage.

800
00:54:46,420 --> 00:54:51,640
And then they're looking at full genomes from Australia, France, Germany, Indonesia, India,

801
00:54:51,640 --> 00:54:52,640
Ireland.

802
00:54:52,640 --> 00:54:58,840
They were all collected from the global initiative on sharing all influenza data, the just said

803
00:54:58,840 --> 00:55:07,000
database and the mutation frequency is calculated by the total number of instances of polymorphisms

804
00:55:07,000 --> 00:55:17,720
detected within the genome. And then they divide by a factor of the nucleotide length

805
00:55:17,720 --> 00:55:23,840
in the genome and the sequence, the number of sequences that they find this in. So that's

806
00:55:23,840 --> 00:55:28,680
also difficult for most people to understand. But when they do the sequencing, they get

807
00:55:28,680 --> 00:55:36,520
repeated copies of a amplicon. And then when they line them up, the number of times that

808
00:55:36,520 --> 00:55:43,000
the amplicons agree at a particular nucleotide gives them some idea of how likely it is that

809
00:55:43,000 --> 00:55:49,120
that nucleotide is actually conserved across most of those particles. And depending on

810
00:55:49,120 --> 00:55:53,680
the nucleotide they look at, they could get a very high probability or a very low probability

811
00:55:53,680 --> 00:56:00,240
of that nucleotide being right. And they don't seem to make any effort to distinguish between

812
00:56:00,240 --> 00:56:04,920
single nucleotide polymorphisms that are real and ones that might not be. And they just

813
00:56:04,920 --> 00:56:12,600
classify them as synonymous or non-synonymous, again, ignoring the fact that protein folding

814
00:56:12,600 --> 00:56:19,740
is dependent on these silent mutations where the amino acid isn't changed, but still results

815
00:56:19,740 --> 00:56:23,840
in a different form of the protein. And then if you do change the amino acid, of course,

816
00:56:23,840 --> 00:56:30,340
you do change the protein even more. And then what they do is they show you that the mutation

817
00:56:30,340 --> 00:56:37,720
frequency calculated in this manner is lower in the places where the vaccination occurred.

818
00:56:37,720 --> 00:56:42,340
And then they parade that as evidence that the vaccine is doing something useful, which

819
00:56:42,340 --> 00:56:50,040
is lowering the mutation frequency. However, we would point out, of course, that bad mutations

820
00:56:50,040 --> 00:56:56,720
in very few number could still ultimately be disastrous. And so the number of mutations

821
00:56:56,720 --> 00:57:03,760
that occur is not necessarily the best readout for whether or not the vaccine is stopping

822
00:57:03,760 --> 00:57:09,780
the mutations or limiting the evolution of the virus. And looking at non-synonymous to

823
00:57:09,780 --> 00:57:18,820
synonymous changes, we find that the vaccinated people appear to be propagating amino acid

824
00:57:18,820 --> 00:57:25,560
differences in the spike protein that non-vaccinated populations do not propagate. So where the

825
00:57:25,560 --> 00:57:32,440
mutation rate may have gone down, the possibility of dangerous mutations appears not to have

826
00:57:32,440 --> 00:57:37,480
gone down and maybe even is higher in those people that are vaccinated because they have

827
00:57:37,480 --> 00:57:43,800
the more consequential non-synonymous changes being present. Wow, that was too much words,

828
00:57:43,800 --> 00:57:44,800
too many words.

829
00:57:44,800 --> 00:57:57,400
They say in the final paragraph that we need to suppress the generation of deadly mutations.

830
00:57:57,400 --> 00:58:04,120
So yeah, it seems again, there's a bit of a contradiction in there. I just wonder like

831
00:58:04,120 --> 00:58:11,200
how much of this would have happened in the absence of the injectable products. Like I'd

832
00:58:11,200 --> 00:58:13,400
like to see that side by each plot.

833
00:58:13,400 --> 00:58:21,120
Right. It's why I wanted to see something like all of these statistics presented prior

834
00:58:21,120 --> 00:58:23,280
to vaccination.

835
00:58:23,280 --> 00:58:27,360
You need that because otherwise it's like, oh yeah, look what we found because of the

836
00:58:27,360 --> 00:58:28,360
injections.

837
00:58:28,360 --> 00:58:31,320
Right. Yeah. Did you know we're-

838
00:58:31,320 --> 00:58:34,420
Because of the injections. Yay.

839
00:58:34,420 --> 00:58:41,280
And so by hiding that, could they be hiding something like the fact that these variants

840
00:58:41,280 --> 00:58:46,480
were actually pre-engineered in the lab?

841
00:58:46,480 --> 00:58:52,040
Or just already just they found all the variants in the background and arranged them in such

842
00:58:52,040 --> 00:58:58,080
a way so that they knew what they needed to release to tell the story.

843
00:58:58,080 --> 00:59:04,640
That's possible. I just don't believe that all the variants were together in one cloud.

844
00:59:04,640 --> 00:59:11,400
That was something that I considered and I guess I'll still consider it, but I'm less

845
00:59:11,400 --> 00:59:18,640
likely to think that they're all there in the swarm than I was before, particularly

846
00:59:18,640 --> 00:59:27,440
Omicron. Maybe the regular variants were. Maybe I was explaining Milky Way with a dense

847
00:59:27,440 --> 00:59:33,840
core. Maybe it is that you have Wuhan strain here and Delta here and Alpha and Beta and

848
00:59:33,840 --> 00:59:40,240
whatever. Maybe some of those early strains were near enough each other to be part of

849
00:59:40,240 --> 00:59:47,400
the same core. But Omicron is so far away evolutionarily. It's like a 10 year evolutionary

850
00:59:47,400 --> 00:59:55,320
distance according to these mutation rates, sorry, mutation frequencies.

851
00:59:55,320 --> 01:00:00,840
In this fight. Frequencies or rates. I mean, the rate is

852
01:00:00,840 --> 01:00:05,360
contained in the frequency, right? You can't have a rate higher than a certain amount.

853
01:00:05,360 --> 01:00:10,880
You can't have a rate higher than the frequency. I still think we've got a we can't just give

854
01:00:10,880 --> 01:00:19,240
them the spike protein as their basis and their their solid handled in the sense of

855
01:00:19,240 --> 01:00:24,240
you can't let these evolutionary people sell the spike protein is the thing we should watch

856
01:00:24,240 --> 01:00:30,320
if all the other proteins are not changing, then our immune system is not being challenged.

857
01:00:30,320 --> 01:00:36,280
I can't stress enough how Omicron is different in the spike. It's not different in the XON

858
01:00:36,280 --> 01:00:41,080
gene. It's not different in any of the non structural proteins 1 through 14. It's different

859
01:00:41,080 --> 01:00:47,020
in the spike and that means very little to our immune system. It really does. Unless

860
01:00:47,020 --> 01:00:51,880
we're focused only on the antibodies that bind to that protein, then it then it matters,

861
01:00:51,880 --> 01:00:56,560
I guess. I want to I want to hear what thoughts this

862
01:00:56,560 --> 01:01:01,800
is bringing up in Jumi and Jessica because I know part of where JJ is coming from because

863
01:01:01,800 --> 01:01:07,280
I've been watching I watched your last video where this or two videos ago where this came

864
01:01:07,280 --> 01:01:13,440
up specifically and I know you emphasized how Matthew was writing about the Omicron

865
01:01:13,440 --> 01:01:18,480
hypothesis at a certain time and how compared to how in particular Brett Weinstein has been

866
01:01:18,480 --> 01:01:24,080
talking about it. And so I understand a bit of the recent context why this is sort of

867
01:01:24,080 --> 01:01:31,280
an important reconciliation of ideas. But I want to hear what Jumi what is coming to

868
01:01:31,280 --> 01:01:38,040
your mind for myself and the audience who are following along mostly but need perhaps

869
01:01:38,040 --> 01:01:42,680
a little bit of a bridge of of what we're talking about here. What is this talk about

870
01:01:42,680 --> 01:01:50,640
Omicron as it relates to the paper or more generally what are your thoughts right now?

871
01:01:50,640 --> 01:01:54,960
I don't know what to believe when it comes to Omicron. But I haven't looked that deeply

872
01:01:54,960 --> 01:02:01,160
especially at comparing sequences. But I'll say that there was a paper that Matthew I

873
01:02:01,160 --> 01:02:09,320
think we chatted about this a little bit on signal that paper with the prison the prison

874
01:02:09,320 --> 01:02:14,920
inmates and it just seemed like Omicron wasn't as infectious as we thought. You know and

875
01:02:14,920 --> 01:02:20,480
this was like a you know like in prison cells like people should be like you know giving

876
01:02:20,480 --> 01:02:26,240
each other infections up to right. So that was one thing. But another is just related

877
01:02:26,240 --> 01:02:32,080
to what this paper is talking about is I don't have so much to say about the paper but more

878
01:02:32,080 --> 01:02:39,360
like what could be the mechanism behind how the vaccine might possibly be influencing

879
01:02:39,360 --> 01:02:47,560
like the virus like this. Like there's animal studies right where it seems like sometimes

880
01:02:47,560 --> 01:02:54,080
occasionally it looks like it lowers the amount of virus in the upper lungs but usually not

881
01:02:54,080 --> 01:03:01,720
the lower lungs you know. So like let's say that we make antibodies in the and then there's

882
01:03:01,720 --> 01:03:06,600
antibodies in the blood right from the vaccine there has to be some way for it to get into

883
01:03:06,600 --> 01:03:13,400
the lungs. And I know of a mechanism in the lower lungs where like when there's inflammation

884
01:03:13,400 --> 01:03:19,080
sometimes like when you get an infection you can get some of the stuff in the plasma going

885
01:03:19,080 --> 01:03:22,560
like crossing into the lungs you know. But I don't know if that happens in the upper

886
01:03:22,560 --> 01:03:27,440
lungs. So like what's going on. I mostly just have questions like I'm very confused about

887
01:03:27,440 --> 01:03:34,960
all of this honestly. It's very good point that you mentioned about the prison study.

888
01:03:34,960 --> 01:03:44,000
I have I have not reconciled the claim that Omicron was discovered in you know South Africa

889
01:03:44,000 --> 01:03:52,120
or Botswana and like within like 17 days had visited like every discotheque in Europe.

890
01:03:52,120 --> 01:03:58,680
I just I have not I have not found that very believable at all. If that were the case then

891
01:03:58,680 --> 01:04:03,080
I think that the original Wuhan strain would have you know covered the earth in much more

892
01:04:03,080 --> 01:04:10,120
rapid time than it did probably even before we would have known it was out there. Right.

893
01:04:10,120 --> 01:04:16,220
You know our art is something that changes by context. You know you go to a slum in India

894
01:04:16,220 --> 01:04:22,440
and the are for a coronavirus can be 200. It can just sweep through the population just

895
01:04:22,440 --> 01:04:29,640
you know boom like wildfire. The idea that Omicron you know was several times more infectious

896
01:04:29,640 --> 01:04:37,400
or something like that. It just doesn't jive with with a lot of things that we've seen.

897
01:04:37,400 --> 01:04:45,040
It does jive with how quickly that wave occurred in early 2022. But but I think that there's

898
01:04:45,040 --> 01:04:50,080
some sort of shenanigans there. But I'm going to this is something I didn't know where this

899
01:04:50,080 --> 01:04:57,300
was in the paper when I originally read the paper. There's another graph that that was

900
01:04:57,300 --> 01:05:05,720
really important to me. And and maybe maybe it was in the appendix or something. But anyway

901
01:05:05,720 --> 01:05:13,440
one way or another I put it in an article that I wrote a year and a half ago ish. And

902
01:05:13,440 --> 01:05:23,400
this is it right here. And so we've got you know these Taj Mahdi you know computations

903
01:05:23,400 --> 01:05:33,120
down here. And you know we can see you know UK and India we have Taj Mahdi being pushed

904
01:05:33,120 --> 01:05:41,880
very negative after vaccine rollout. And then later we have Australia being pushed very

905
01:05:41,880 --> 01:05:46,840
negative though. That's I don't know. Australia is sort of a weird thing because everything

906
01:05:46,840 --> 01:05:54,640
happened differently there. But here's the thing. When Taj Mahdi goes more negative that's

907
01:05:54,640 --> 01:06:03,040
when you have a that's when you have a recent selective sweep as it's called. That you have

908
01:06:03,040 --> 01:06:10,120
a bottleneck that is constraining you know where the virus can be in that sort of Milky

909
01:06:10,120 --> 01:06:15,480
Way galaxy formation. You're cutting out some territory and therefore shifts away from from

910
01:06:15,480 --> 01:06:21,640
where it is. You've got the blackout or whatever. And so you have you know immediately higher

911
01:06:21,640 --> 01:06:28,020
rate of evolution. But you should have some sort of re equilibrium some sort of new equal

912
01:06:28,020 --> 01:06:34,480
equilibrium that it reaches for stability. So you should see it go negative but then

913
01:06:34,480 --> 01:06:43,080
come back up. And and we you know right here we just see the selective bottleneck pressure

914
01:06:43,080 --> 01:06:52,680
and it's sustained. You know it should come back up to positive but we don't have that

915
01:06:52,680 --> 01:06:59,960
in this paper or they cut us off to not see any further progression. Whereas I think the

916
01:06:59,960 --> 01:07:07,000
more interesting thing would be you know both the time before and the time after we're seeing

917
01:07:07,000 --> 01:07:16,080
this. And you know given given what this I would I would have thought I would have thought

918
01:07:16,080 --> 01:07:25,280
that like a follow up paper to this would actually be the more interesting paper. And

919
01:07:25,280 --> 01:07:30,120
there was no follow up to it was there. This wasn't even this didn't get past preprint

920
01:07:30,120 --> 01:07:36,880
am I right. Is it still not past preprint. They have data from flu that they could they

921
01:07:36,880 --> 01:07:43,560
could use to show us what a baseline looks like here. It's it's all very it's very all

922
01:07:43,560 --> 01:07:50,440
very sketchy just from the language that they choose to open their abstract in their introduction.

923
01:07:50,440 --> 01:07:58,840
It's clearly a biased paper. Now I don't want to jump to the end if there's more to go through

924
01:07:58,840 --> 01:08:05,200
with the paper itself. But I do think there's some interesting questions then if we explore

925
01:08:05,200 --> 01:08:09,880
the premise that this is biased presumably there's a reason why. So what we did last

926
01:08:09,880 --> 01:08:15,300
time was we went and did what everyone should do and looked at the funding and the conflict

927
01:08:15,300 --> 01:08:19,900
of interest disclosures. Is now a good time to move into that realm or is there anything

928
01:08:19,900 --> 01:08:24,840
else we want to cover before we go a little more meta. Oh it's not very interesting paper.

929
01:08:24,840 --> 01:08:32,480
There's no there's no fun and no conflicts apparently. Now I want to challenge us to

930
01:08:32,480 --> 01:08:40,840
think one step further. Is this where we stop or is there still useful information to glean

931
01:08:40,840 --> 01:08:45,320
that you would think would appear in those funding and conflict of interest areas. Spoiler

932
01:08:45,320 --> 01:08:54,680
alert the answer is yes. OK. What did you find. So let me see where it is. So if you

933
01:08:54,680 --> 01:09:04,320
look at yeah the the author affiliations tell me what you're seeing there. Agriculture biotechnology

934
01:09:04,320 --> 01:09:19,800
laboratory so these are biotech. So there's a specific company mentioned. Oxygen oxygen.

935
01:09:19,800 --> 01:09:25,360
I can cut it a bit of time. Do you mind if I share my screen. Go ahead. So I thought

936
01:09:25,360 --> 01:09:31,240
that was interesting. You know just finding that company. It doesn't mean that it is a

937
01:09:31,240 --> 01:09:35,560
funding source that should have been included. It doesn't mean that it's a conflict of interest

938
01:09:35,560 --> 01:09:44,960
per se. But I do think it is still probably good information to have because as of now

939
01:09:44,960 --> 01:09:50,320
it is the only information we have about who these people are and maybe why they would

940
01:09:50,320 --> 01:09:55,520
have put together such a paper. So this is oxygen. It is an agricultural biotechnology

941
01:09:55,520 --> 01:10:01,900
laboratory. They apply novel biocontrol solutions to human agricultural and environmental disease

942
01:10:01,900 --> 01:10:09,200
to reduce the chemical loading of our soils and waterways. And I. So here's here's a

943
01:10:09,200 --> 01:10:15,280
brief overview of their thing and just top right here I believe I guess this may refer

944
01:10:15,280 --> 01:10:22,160
to maybe there were more studies they published on covid but I think this is in reference

945
01:10:22,160 --> 01:10:28,840
in part to the preprint we just looked at. And I'm I'm not entirely sure how I guess

946
01:10:28,840 --> 01:10:33,880
I guess the rest of their pipeline has involved work with viruses so it's not totally out

947
01:10:33,880 --> 01:10:38,600
of the blue but they're developing stuff. So this is a commercial company. They do have

948
01:10:38,600 --> 01:10:44,920
products that it seems to me they would benefit from somehow publishing the research the way

949
01:10:44,920 --> 01:10:49,960
they did in order to advance their the development of whatever it is they're doing. They're an

950
01:10:49,960 --> 01:11:03,120
agricultural biotech company and the the co-founder and and chief scientific officer yay spent

951
01:11:03,120 --> 01:11:10,440
sixteen years at Johns Hopkins. Yes he did. Yes he did. And you know where he was before

952
01:11:10,440 --> 01:11:19,800
that well Cornell but that before that he was a medical commander in the Taiwanese army.

953
01:11:19,800 --> 01:11:25,560
So just interesting. So I I'm not totally sure. I don't I don't know what product this

954
01:11:25,560 --> 01:11:30,800
would be in reference to. It sounds like they've got private investors so it's nowhere near

955
01:11:30,800 --> 01:11:35,240
as obvious as last time where you've got all conflict of interest with Pfizer or they're

956
01:11:35,240 --> 01:11:42,640
developing a nasal vaccine or whatever. But I think I think that the minor takeaway lesson

957
01:11:42,640 --> 01:11:47,240
I would put in here is the information on funding conflict of interest who these people

958
01:11:47,240 --> 01:11:53,600
are isn't necessarily limited. You don't want to stop at the blank disclosures or the blank

959
01:11:53,600 --> 01:11:59,060
you know conflict of interest. Absolutely. Absolutely. I didn't get any feeling though

960
01:11:59,060 --> 01:12:10,040
that we were you know I don't think the paper itself is necessarily where there's misleading

961
01:12:10,040 --> 01:12:17,520
going on. But you know talking about Baltimore biotech what would you assume Baltimore biotech

962
01:12:17,520 --> 01:12:25,200
would be associated with. I mean yeah they say agriculture right. But you know what what's

963
01:12:25,200 --> 01:12:34,520
in Baltimore military. Well yeah you're real close to Annapolis right. That's the US Navy

964
01:12:34,520 --> 01:12:41,040
and of course we know that the US Navy has been the branch of the military that has been

965
01:12:41,040 --> 01:12:49,840
used as a major arm of you know like bioterrorism defense type stuff. Operation Sea Spray and

966
01:12:49,840 --> 01:12:57,480
subsequent operations that that very likely took place according to congressional testimony.

967
01:12:57,480 --> 01:13:03,600
So you know this is somebody who may have been brought to the US from the Taiwanese

968
01:13:03,600 --> 01:13:11,700
army who had a proclivity toward toward this type of stuff and stationed at you know first

969
01:13:11,700 --> 01:13:18,520
at at Johns Hopkins and later you know Bill helping build this company. So that that is

970
01:13:18,520 --> 01:13:25,360
interesting. That is interesting. So we should be viewing this from a lens of possible you

971
01:13:25,360 --> 01:13:36,040
know bio warfare operations. So just as an anecdote you know the guy that I was working

972
01:13:36,040 --> 01:13:44,080
for at the University of Pittsburgh was an active nuclear submarine captain semi-retired

973
01:13:44,080 --> 01:13:49,280
and you know with full clearance and everything he graduated top of his class in his engineering

974
01:13:49,280 --> 01:13:55,880
class. He was not an idiot but he was definitely in the Navy and definitely still had all the

975
01:13:55,880 --> 01:14:01,640
clearances that you would expect from somebody who was a captain. And he threw me out with

976
01:14:01,640 --> 01:14:11,080
with great prejudice while he was wearing two masks. Whether or not his Navy had anything

977
01:14:11,080 --> 01:14:15,640
to do with that but he was definitely a Navy guy and he didn't do anything for me in respecting

978
01:14:15,640 --> 01:14:23,040
other Navy guys. That's for sure. I hope he's in a novelty. So guys there was a suggestion

979
01:14:23,040 --> 01:14:31,600
in the chat on another paper that was being held up. What was the exact quote? Let's see.

980
01:14:31,600 --> 01:14:36,880
Held up in pro-vax camp as C it creates T cell memory too. Now I'm under no illusions

981
01:14:36,880 --> 01:14:41,360
we have time to do a full critique but I wanted to bring it up and see if this is something

982
01:14:41,360 --> 01:14:47,640
you guys have seen and maybe there are some initial perspectives that we can offer. Let

983
01:14:47,640 --> 01:14:54,720
me just pull this up here. You guys have time for it? I mean we you know this was a long

984
01:14:54,720 --> 01:14:58,920
discussion on one paper so we could call it a day here but you know I'm happy to keep

985
01:14:58,920 --> 01:15:04,840
going if you guys are. I don't know how much time you guys carved out for this. I'm all

986
01:15:04,840 --> 01:15:09,560
right let's do it. Right. Yes and to be clear I'm not asking us to do the same amount of

987
01:15:09,560 --> 01:15:13,720
time we just did on this but I figured it'd be good to give just a brief a brief take

988
01:15:13,720 --> 01:15:17,960
a look at. So are you guys familiar with this paper? It looks like it just came out just

989
01:15:17,960 --> 01:15:24,240
under a month ago. Theoromers reveal robust T cell responses to the Pfizer BioNTech vaccine

990
01:15:24,240 --> 01:15:31,040
and attenuated peripheral CD8. How do you say that? CD8 plus? Yeah or positive probably.

991
01:15:31,040 --> 01:15:37,640
Okay CD8 positive T cell responses post SARS-CoV-2 infection. That's a lot. Are you guys familiar

992
01:15:37,640 --> 01:15:45,800
with this already or is this brand new to you as well? I haven't seen the paper but

993
01:15:45,800 --> 01:15:50,920
it it makes sense in the title. Yeah new to me I have not been reading as many. What is

994
01:15:50,920 --> 01:15:55,240
a spheromer? That's the only thing I don't know I assume it's like. I don't know what

995
01:15:55,240 --> 01:16:02,520
it means. I can guess but never heard of it. Some technique that they used oh yeah it says

996
01:16:02,520 --> 01:16:10,080
it's in it. That's what I kind of thought some kind of technique to isolate the T cells.

997
01:16:10,080 --> 01:16:18,320
Very interesting okay. Yeah I also have not read it so just to do what I always do I'm

998
01:16:18,320 --> 01:16:24,600
going to start at the the disclosures part because that usually well what do you recommend

999
01:16:24,600 --> 01:16:29,200
in terms of order of how people would look at a paper like this? How do you guys approach

1000
01:16:29,200 --> 01:16:33,760
it? Do you do what I just did and you jump right to the bottom? Do you because I think

1001
01:16:33,760 --> 01:16:40,000
the interesting part here is showing people each everybody's process right? So if someone

1002
01:16:40,000 --> 01:16:44,000
like me is going through and starting to read something like this something we're not familiar

1003
01:16:44,000 --> 01:16:51,600
with what's the first thing you guys recommend people do? Read the abstract. Read the abstract?

1004
01:16:51,600 --> 01:16:58,280
Yeah and use the abstract to parse the title because the title is one of the most carefully

1005
01:16:58,280 --> 01:17:04,200
you know of language in the whole paper. If they chose the word may they really meant

1006
01:17:04,200 --> 01:17:09,120
the word may and if they chose required they really meant the word required and then you

1007
01:17:09,120 --> 01:17:14,000
got to decide whether or not they're allowed to use that word or not. That's really that's

1008
01:17:14,000 --> 01:17:18,520
the strategy I have. So what are they saying they're certain about? What are they speculating

1009
01:17:18,520 --> 01:17:25,680
about in the title? Says reveals robust T cell response. So their technique whatever

1010
01:17:25,680 --> 01:17:32,800
spheromers is is revealing a robust T cell response to the vaccine and an attenuated

1011
01:17:32,800 --> 01:17:41,760
peripheral CD8 T cell response post COVID infection. So there's two things we should expect to

1012
01:17:41,760 --> 01:17:48,400
see in their data robust T cell responses after the vaccine and an attenuated response

1013
01:17:48,400 --> 01:17:55,080
in the CD8 positive T cells post infection and so now the trick is are they really going

1014
01:17:55,080 --> 01:18:00,280
to prove that to you or not? I think if I remember correctly and this is just with a

1015
01:18:00,280 --> 01:18:04,440
disclaimer because I it's been a while I know when you glance at this but it was it was

1016
01:18:04,440 --> 01:18:09,780
post infection people who had been infected and then they looked at the response after

1017
01:18:09,780 --> 01:18:16,240
vaccination so I think they're comparing people but all were vaccinated but comparing people

1018
01:18:16,240 --> 01:18:22,000
who had been infected before and people who hadn't. Right. Yeah. And they're looking at

1019
01:18:22,000 --> 01:18:30,600
CD8 only or T cell broad and then they hone in on CD8 for the peripheral pool because

1020
01:18:30,600 --> 01:18:36,360
they found something. They put MHC molecules on little spheromers and then they suck up

1021
01:18:36,360 --> 01:18:43,200
the T cells with them. That's funny. Okay so help me out here. So the way that this

1022
01:18:43,200 --> 01:18:47,400
outlining I was expecting something that says abstract and I was going to go there but I

1023
01:18:47,400 --> 01:18:51,800
see we got a graphical abstract which is not what I was expecting and then we have a summary

1024
01:18:51,800 --> 01:18:55,920
which is also not what I was expecting. So when when you're saying let's look at the

1025
01:18:55,920 --> 01:19:01,480
abstract what would that refer to how do we translate that to this. Well graphical abstracts

1026
01:19:01,480 --> 01:19:08,200
are awesome. I find them when they're done right they're really descriptive of all the

1027
01:19:08,200 --> 01:19:13,680
important things that happened in the paper. So that's that's always a good if it's good.

1028
01:19:13,680 --> 01:19:19,400
I mean it's not always good but and the summaries you know it's it's basically the abstract.

1029
01:19:19,400 --> 01:19:26,000
It's the same thing. So I'd go for both of these. The only thing about the graphical

1030
01:19:26,000 --> 01:19:31,040
abstract is that you know you you have to have a background in everything that they're

1031
01:19:31,040 --> 01:19:37,360
talking about to put everything into context. Right. It can be difficult to interpret.

1032
01:19:37,360 --> 01:19:45,520
It definitely looks pretty. Yeah. They have they have fake people there. That's nice.

1033
01:19:45,520 --> 01:19:51,680
Are we are we to guess from this that the COVID-19 patients are also vaccinated because

1034
01:19:51,680 --> 01:20:00,960
it looks like a continuum. Yeah that's what we said. Oh wait right. Where is that in the

1035
01:20:00,960 --> 01:20:07,720
graphical abstract it looks like the COVID-19 patients are taken from the unexposed subjects

1036
01:20:07,720 --> 01:20:15,720
that received the Pfizer vaccine. Is that not the case. And then they also have recovered

1037
01:20:15,720 --> 01:20:24,000
patients receiving the Pfizer. So these people are cheating all the time. Oh wait wasn't

1038
01:20:24,000 --> 01:20:31,600
this also the one where the the some of the COVID-19 patients were like in some trial

1039
01:20:31,600 --> 01:20:38,120
or something. Didn't Amanda point that out. You know I'm talking about. Yes I think so.

1040
01:20:38,120 --> 01:20:41,800
Right. They were in some trial for some like treatment and COVID treatment. Right. And

1041
01:20:41,800 --> 01:20:47,920
then they use the same people for this. Yes. Yeah. Some of them some of the COVID patients.

1042
01:20:47,920 --> 01:20:52,880
It was so weird. It was like here's something on a left field. If you made that sphere and

1043
01:20:52,880 --> 01:20:59,160
then sprayed it on people it seems to me that would be really nasty because it would activate

1044
01:20:59,160 --> 01:21:05,880
it would bind to all T cells. Right. It's like a MHC multimer. It's like a universal

1045
01:21:05,880 --> 01:21:14,480
display or of antigen presenting cell. And I don't know which MHC if it's if it's really

1046
01:21:14,480 --> 01:21:19,360
the HLA subtypes then they're really that could be an incredibly dangerous thing to

1047
01:21:19,360 --> 01:21:27,520
have a powder of. Yikes. Maybe I'm completely crazy but I think that would that would make

1048
01:21:27,520 --> 01:21:32,200
your if you if you had that in your body your T cells would go bananas. That's why they

1049
01:21:32,200 --> 01:21:40,400
can pull T cells out with it. And furthermore it looks like I don't think that using that

1050
01:21:40,400 --> 01:21:46,240
spherom or you're going to pull out SARS specific T cells but it does say that they're doing

1051
01:21:46,240 --> 01:21:52,520
that. I wonder if the the peptide is the spike that could be the peptide MHC multiple. I

1052
01:21:52,520 --> 01:21:56,360
wonder what that is. I got to read that. OK. Well let me read this link. I'd like this

1053
01:21:56,360 --> 01:22:00,720
link. Yeah. Yeah. I put it in the private. OK. I got it. OK. Yeah. I'm going to switch

1054
01:22:00,720 --> 01:22:09,560
over to the summary now and maybe this is and we can we can sort of wrap up after this

1055
01:22:09,560 --> 01:22:16,000
but maybe this answers some questions that were not answered in that graphical summary

1056
01:22:16,000 --> 01:22:20,400
T cells are a critical component of the response to SARS covid 2 but their kinetics after infection

1057
01:22:20,400 --> 01:22:28,120
and vaccination are insufficiently understood using spherom peptide MHC multimer reagents.

1058
01:22:28,120 --> 01:22:35,200
We analyzed healthy subjects receiving two doses of the Pfizer BioNTech BNT 162 B2 vaccine.

1059
01:22:35,200 --> 01:22:41,320
Vaccination resulted in robust spike specific T cell responses for the dominant CD4 positive

1060
01:22:41,320 --> 01:22:49,200
HLA dash DRB1. This is crazy. I don't know how to read this thing. What does that refer

1061
01:22:49,200 --> 01:22:56,840
to. So human leukocyte antigen is the same thing as an MHC molecule usually say MHC molecule

1062
01:22:56,840 --> 01:23:04,400
or major histocompatibility complex in animals say HLA in humans but they're the same thing.

1063
01:23:04,400 --> 01:23:13,240
OK cool. And CD8 plus T cell epitopes antigen specific CD4 plus and CD8 plus T cell responses

1064
01:23:13,240 --> 01:23:18,600
were asynchronous with the peak CD4 plus T cell responses occurring one week post the

1065
01:23:18,600 --> 01:23:24,700
second vaccination boost. Now that's confusing because I thought that was the primary series

1066
01:23:24,700 --> 01:23:30,120
and not a booster anyway whereas CD8 plus T cells peak two weeks later these peripheral

1067
01:23:30,120 --> 01:23:35,800
T cell responses were elevated compared with COVID 19 patients. We also found that previous

1068
01:23:35,800 --> 01:23:41,360
SARS COVID 2 infection resulted in decreased CD8 plus T cell activation and expansion suggesting

1069
01:23:41,360 --> 01:23:45,680
that previous infection can influence the T cell response to vaccination. So isn't that

1070
01:23:45,680 --> 01:23:53,640
saying don't get the shot if you've had COVID. I think that's a pretty good short through

1071
01:23:53,640 --> 01:23:58,640
the corner way to say it indeed which I think a lot of people have already kind of known

1072
01:23:58,640 --> 01:24:07,400
it. It does seem to suggest that. Yeah this part of the discussion has been happening

1073
01:24:07,400 --> 01:24:18,600
for for 20 months now. Yeah the T cell immunity from post infection seems to be better than

1074
01:24:18,600 --> 01:24:25,080
than from post vaccination. And so the premise of this coming into the discussion was that

1075
01:24:25,080 --> 01:24:30,600
apparently it's being held up in the Provax camp as see it creates T cell memory too.

1076
01:24:30,600 --> 01:24:36,560
So I'd like to hear from you know from each of you does is that what this seems to show

1077
01:24:36,560 --> 01:24:41,960
without on a first glance keeping in mind we we have not done a full deep dive. What's

1078
01:24:41,960 --> 01:24:51,280
your impression just based on the little bit we've looked at it. What of the paper. Yeah.

1079
01:24:51,280 --> 01:24:59,360
Well it's funded by Gates so I don't care what it says. Seriously that's I went to the

1080
01:24:59,360 --> 01:25:04,880
you know that's the next step for me like look at the funders and then if it's not conflicted

1081
01:25:04,880 --> 01:25:09,840
then I'm more interested in reading about the methods to find out like exactly how these

1082
01:25:09,840 --> 01:25:19,520
spheromers work for example. But yeah I don't know it's like. I would piggyback on that.

1083
01:25:19,520 --> 01:25:25,520
I would piggyback on what she said that you can look at this methodology and what I found

1084
01:25:25,520 --> 01:25:30,520
with my brief select brief reading is that they can't really differentiate what kind

1085
01:25:30,520 --> 01:25:35,600
of CD4 positive cells they are so they don't know if they're pulling down a what ratio

1086
01:25:35,600 --> 01:25:39,480
of regulatory T cells to non regulatory T cells they're pulling down and whether or

1087
01:25:39,480 --> 01:25:47,080
not there there's a couple different kinds of CD4 positive activated T cells and not

1088
01:25:47,080 --> 01:25:51,480
all of them do the same thing and they're just pulling them all out and the same to

1089
01:25:51,480 --> 01:25:58,440
a lesser extent there are fewer differences between the the the pool of CD8 positive cells

1090
01:25:58,440 --> 01:26:05,560
that they would find and so it to me the trick is to know what proportion of these helper

1091
01:26:05,560 --> 01:26:11,400
cells are regulatory T cells because that'll determine how how the crescendo and decrescendo

1092
01:26:11,400 --> 01:26:19,360
of the immune system play out and so if they're not differentiating between those those populations

1093
01:26:19,360 --> 01:26:24,960
then they can't really be sure if there's a parallel and so you can say yeah they're

1094
01:26:24,960 --> 01:26:31,080
activating T cells but if they're activating only active T cells and not regulatory T cells

1095
01:26:31,080 --> 01:26:35,480
then there won't be a transition to memory cells in anybody that's vaccinated whereas

1096
01:26:35,480 --> 01:26:41,160
after infection you still have the whole complement and so you get a regulatory change that sends

1097
01:26:41,160 --> 01:26:46,760
some T cells into apoptosis and others into a memory state but you need that ratio to

1098
01:26:46,760 --> 01:26:51,560
be correct and I don't think they can measure that with these spheroids no they didn't measure

1099
01:26:51,560 --> 01:26:56,200
as far as I can see in the methods there's also something interesting in the limitations

1100
01:26:56,200 --> 01:27:05,320
like if you go down limitations in this study those are also really important to read yeah

1101
01:27:05,320 --> 01:27:11,160
our study has limitations in that we measure peripheral T cell responses and differential

1102
01:27:11,160 --> 01:27:16,240
tissue location of immune cells after mRNA vaccination starts between infection can contribute

1103
01:27:16,240 --> 01:27:21,240
to the differences observed between the cohorts so what does that mean peripheral T cell so

1104
01:27:21,240 --> 01:27:26,280
if they I think and I have to look at the study a little bit more carefully but I think

1105
01:27:26,280 --> 01:27:33,160
they only looked at blood samples right but what you find in the blood is different from

1106
01:27:33,160 --> 01:27:37,680
what might be going to your lungs like homing like T cells homing to the lungs which is

1107
01:27:37,680 --> 01:27:43,480
what you would probably want if you got a lung infection you know absolutely right in

1108
01:27:43,480 --> 01:27:49,800
your lungs you'd have less in your blood yeah this has been a common theme of a lot of the

1109
01:27:49,800 --> 01:27:57,320
vaccination studies is that is that they look in the blood and not at the not at this you

1110
01:27:57,320 --> 01:28:05,840
know surface layer between us and where respiratory viruses are are infecting us right it's this

1111
01:28:05,840 --> 01:28:14,200
is part of the illusion that they seem to be intent on and it may be that that that

1112
01:28:14,200 --> 01:28:19,280
there is that there is something going on that you might measure you know in the blood

1113
01:28:19,280 --> 01:28:23,680
and think oh well that's good that shows that it's working but if it's not working at at

1114
01:28:23,680 --> 01:28:30,640
that surface you know level then you're not actually preventing infection so it's like

1115
01:28:30,640 --> 01:28:36,000
okay so maybe you don't prevent infection but then there's some fight happening in a

1116
01:28:36,000 --> 01:28:44,040
part of the body where it doesn't even matter and yeah is that the fight that you want are

1117
01:28:44,040 --> 01:28:50,640
are you changing the grounds of the fight to the cardiovascular system and could this

1118
01:28:50,640 --> 01:29:00,800
be part of why it is we see so many cardiovascular problems and various for instance well that's

1119
01:29:00,800 --> 01:29:05,400
nuts Jessica thank you for pointing out the funding section because not only is it gates

1120
01:29:05,400 --> 01:29:11,440
it's also my my nemesis is the wrong word my point of interest over the last couple

1121
01:29:11,440 --> 01:29:20,120
months open philanthropy that's of course a effective altruism giant the dustin moskowitz

1122
01:29:20,120 --> 01:29:28,400
facebook mafia yeah that's that's ftx taking money from the crypto space and pulling into

1123
01:29:28,400 --> 01:29:35,680
the pandemic space which is um that true well open philanthropy yeah open philanthropy is

1124
01:29:35,680 --> 01:29:42,240
is sort of one of the the big like the biggest actually probably leaders in the effective

1125
01:29:42,240 --> 01:29:49,600
altruism space and they're of course behind uh funding virtually everything related to

1126
01:29:49,600 --> 01:29:56,600
the covert response including the tabletop exercises and all the sci-fi stuff yeah and

1127
01:29:56,600 --> 01:30:02,680
there is something very very weird that needs further investigation and and i wish i could

1128
01:30:02,680 --> 01:30:08,040
have spent even more time on it than i did but when i had my dinner in austin one of

1129
01:30:08,040 --> 01:30:14,320
the people who came to that dinner admitted to me having funded one of the mrna vaccines

1130
01:30:14,320 --> 01:30:20,920
secretly using bitcoin and you know then seeing all of this space yeah the guy's name is brian

1131
01:30:20,920 --> 01:30:28,960
bishop and he is um uh you know he's in the transhumanist community he's in the diy transhumanist

1132
01:30:28,960 --> 01:30:34,280
community meaning like you know let's do experiments on ourselves and see what we can accomplish

1133
01:30:34,280 --> 01:30:39,800
and figure out and he was uh you know putting together a company for designer babies at

1134
01:30:39,800 --> 01:30:48,040
some point and kind of got pushed out of certain circles as a result but um you know then and

1135
01:30:48,040 --> 01:30:52,720
i have no idea whether or not he has further connections to the ftx people but interestingly

1136
01:30:52,720 --> 01:30:57,880
uh he does have connections or at least told me he did know some of the people from like

1137
01:30:57,880 --> 01:31:05,800
the paypal mafia and uh also some of the people from the um the media group that created the

1138
01:31:05,800 --> 01:31:11,640
global covid summit yeah round table media right yeah he's friends with um with multiple

1139
01:31:11,640 --> 01:31:17,320
of the principal or the the uh the board members of round table media so like that that was

1140
01:31:17,320 --> 01:31:22,960
uh that was a little bit you know weird to find out and i have no idea you know necessarily

1141
01:31:22,960 --> 01:31:28,240
what all of these connections are but you know we have connections literally between

1142
01:31:28,240 --> 01:31:34,680
you know we have connections on both sides with cryptocurrency funding both sides of

1143
01:31:34,680 --> 01:31:40,320
the pandemic debates liam put that link up would you that i just sent to you and scroll

1144
01:31:40,320 --> 01:31:48,760
through it this is the reporter nih.gov site and the one niad grant that was reported there

1145
01:31:48,760 --> 01:31:57,800
the niad grant that was reported in that paper stems from 2006 to 2022 it is continuous but

1146
01:31:57,800 --> 01:32:05,200
it's shared between several different pis and it starts in 2006 at stanford or three

1147
01:32:05,200 --> 01:32:12,520
with ann arvin with protective mechanisms against pandemic respiratory virus and it's

1148
01:32:12,520 --> 01:32:22,240
continuous funded grant by the niad for literally 20 years it's exactly actually 20 years and

1149
01:32:22,240 --> 01:32:29,200
you can see how the the pi changes as the the grant moves through stanford but it stays

1150
01:32:29,200 --> 01:32:35,240
with the same cardinal driving number i don't know for sure what that means but i do think

1151
01:32:35,240 --> 01:32:40,640
it means a different kind of priority funding in the sense that they didn't have to reapply

1152
01:32:40,640 --> 01:32:45,840
from scratch every year even though it's a wide variety of pis that get listed as the

1153
01:32:45,840 --> 01:32:52,280
project leader it's extraordinary there is no one there's no one in neurobiology that

1154
01:32:52,280 --> 01:32:59,880
has a track record with a single grant application that has been renewed so many times in a row

1155
01:32:59,880 --> 01:33:04,600
nobody just understanding the the timeline here yeah it starts in 2003 and it gets renewed

1156
01:33:04,600 --> 01:33:12,320
annually but 2008 and 2009 is when it kicks way into high gear and it never stops as you

1157
01:33:12,320 --> 01:33:15,960
say oh yeah those are all collaborators now i see why those other names are there now

1158
01:33:15,960 --> 01:33:25,200
i get it i see wow that's really extraordinary well again just to be clear stanford was one

1159
01:33:25,200 --> 01:33:32,080
of the two competing arms of the human genome project right you've got the washington university

1160
01:33:32,080 --> 01:33:37,880
side which sort of became more famous just because they they won the race by like three

1161
01:33:37,880 --> 01:33:42,360
weeks or something like that right i mean it was it was literally like a neck and neck

1162
01:33:42,360 --> 01:33:47,800
race to map the human genome but the the other side of that was the stanford side where was

1163
01:33:47,800 --> 01:33:56,440
kevin in that mccurnin um so you know mit had stuff going on um within i mean mit was

1164
01:33:56,440 --> 01:33:59,960
you know obviously one of the centers where a lot of genetic research has been done but

1165
01:33:59,960 --> 01:34:05,160
like the primary sort of competition to map the human genome once the very first time

1166
01:34:05,160 --> 01:34:14,800
was washu stanford okay i see well this has been uh fascinating i think we should aim

1167
01:34:14,800 --> 01:34:20,760
to wrap up now um i really appreciate how much insight you guys have been able to bring

1168
01:34:20,760 --> 01:34:28,880
i think the thing that i like about doing these shows is it is about showing the process

1169
01:34:28,880 --> 01:34:34,360
and um i think there are a lot of people um who are you know scientists of their own caliber

1170
01:34:34,360 --> 01:34:38,360
who are tuning in and a few people have said you know i'm a i'm a stem student and this

1171
01:34:38,360 --> 01:34:44,160
fascinating but then there are a lot of people like me who are sort of passed more the conspiracy

1172
01:34:44,160 --> 01:34:48,720
theory type videos you know it's very interesting but at a certain point you really want to

1173
01:34:48,720 --> 01:34:55,160
get into the nitty gritty of what how how the world actually works and this is it this

1174
01:34:55,160 --> 01:35:00,540
is going in and trying to piece those pieces together um and it's not that any given paper

1175
01:35:00,540 --> 01:35:05,680
we're going to pick will answer all the questions but the act of going through the process the

1176
01:35:05,680 --> 01:35:13,240
practice of it i think will help refine skills that can be applied then to just critical

1177
01:35:13,240 --> 01:35:19,160
thought in the future but also how to read other papers um so i appreciate it very much

1178
01:35:19,160 --> 01:35:24,360
uh thank you and and another piece of that is uh the fact of the matter is as a group

1179
01:35:24,360 --> 01:35:28,840
we are better reading these papers than we are as individuals um and and it's going to

1180
01:35:28,840 --> 01:35:32,000
be different from paper to paper uh you know different people are going to have different

1181
01:35:32,000 --> 01:35:37,800
strengths and be able to pull different pieces out uh jumi mentioned something um which i

1182
01:35:37,800 --> 01:35:42,480
i didn't actually read this paper prior but my wife had read it and made some comments

1183
01:35:42,480 --> 01:35:49,280
on how the cohorts were sort of you know selected in an interesting way uh and and may not be

1184
01:35:49,280 --> 01:35:54,000
sort of true cohorts for the for the kind of comparison that the paper claims to be

1185
01:35:54,000 --> 01:35:59,320
making right and and different people um you know with their different levels of experience

1186
01:35:59,320 --> 01:36:03,640
and different things that they look at coming together and having a conversation are more

1187
01:36:03,640 --> 01:36:11,680
likely to um you know look at all the the proper corners of what's been done better

1188
01:36:11,680 --> 01:36:16,780
than any one person reading a paper so this is this is how a lot happens you know with

1189
01:36:16,780 --> 01:36:20,640
scientific discussion especially when when several people come together outside of the

1190
01:36:20,640 --> 01:36:25,480
lab where it happened we are not specialists at what what goes on in that lab only the

1191
01:36:25,480 --> 01:36:30,680
people in that lab are specialists in what goes on in that lab and so it takes time and

1192
01:36:30,680 --> 01:36:38,080
it takes you know um a lot of points of view jumi are you doing any cooking shows you really

1193
01:36:38,080 --> 01:36:42,200
look like you have a kitchen that's ready for a cooking show so if you want to like

1194
01:36:42,200 --> 01:36:46,240
you know do a mise en place while we're doing this next time

1195
01:36:46,240 --> 01:36:56,760
like a multitasking journal club it's very it looks like a very cozy kitchen jumi and

1196
01:36:56,760 --> 01:37:07,000
emu emu is the cat by the way for anybody who doesn't know i um okay guys well i want

1197
01:37:07,000 --> 01:37:12,720
to remind everybody that the sub stacks and or websites of all three of our fantastic

1198
01:37:12,720 --> 01:37:16,760
guests and friends here today are in the description of all of the platforms wherever you might

1199
01:37:16,760 --> 01:37:22,360
be watching this so please if you haven't yet go and visit and subscribe to all three

1200
01:37:22,360 --> 01:37:26,640
of them um i want to go around and and make sure we have a chance to plug anything else

1201
01:37:26,640 --> 01:37:30,360
you guys might be working on or anything i might have forgotten jessica what do you have

1202
01:37:30,360 --> 01:37:39,080
going on in the next couple of weeks oh my god um well catching up um but i'm doing something

1203
01:37:39,080 --> 01:37:45,720
really important for my my our country um i'm going to be providing testimony for the

1204
01:37:45,720 --> 01:37:54,080
national citizens inquiry i believe it's called um yeah dr laura brayden presented i think

1205
01:37:54,080 --> 01:38:01,120
it was today or recently and i after i finished listening to her testimony i was like well

1206
01:38:01,120 --> 01:38:08,720
i don't need to speak anymore she was she was she was incredibly good yeah she yeah

1207
01:38:08,720 --> 01:38:13,600
um she's a good friend uh through all of this i mean as good as you can be with someone

1208
01:38:13,600 --> 01:38:19,840
without meeting them in person yet but she's she's she's hardcore like canadian beauty

1209
01:38:19,840 --> 01:38:26,360
and uh yeah she did a fantastic job so it's not a plug for me it's a plug for the inquiry

1210
01:38:26,360 --> 01:38:33,200
and for her testimony like listen to um hers mine's gonna be coming up sometime between

1211
01:38:33,200 --> 01:38:41,160
the 13th and the 15th it's gonna be uh live streamed some at some canadian hour in winnipeg

1212
01:38:41,160 --> 01:38:46,800
oh yeah there's my name yeah expert on the fair data for some reason they wrote expert

1213
01:38:46,800 --> 01:38:55,800
and big letters so and by the way oh bears or something so i watched uh dr laura brayden

1214
01:38:55,800 --> 01:39:00,920
who's who's the immunologist near the top of this list um yeah that's one of the uh

1215
01:39:00,920 --> 01:39:07,760
she is excellent um she really was she did a bang up job man yeah i i put i think i posted

1216
01:39:07,760 --> 01:39:12,760
the video in um in our locals group um but i you know i would encourage like people who

1217
01:39:12,760 --> 01:39:19,480
haven't seen it like google that uh she she's excellent yep yeah she really are all these

1218
01:39:19,480 --> 01:39:24,800
here is going to be recorded yeah yeah so they're all live streamed and they're all

1219
01:39:24,800 --> 01:39:30,160
uh as far as i know they're made into clips uh after the fact and so the website is national

1220
01:39:30,160 --> 01:39:37,280
citizens inquiry dot ca i'll put that in the respective chats right now um this is a uh

1221
01:39:37,280 --> 01:39:44,480
a um this is a next step in a series of events that have happened one of which i was very

1222
01:39:44,480 --> 01:39:50,480
happy to have been a part of called a citizens hearing um and it's going on across canada

1223
01:39:50,480 --> 01:39:56,840
both in person and online and i'm hoping i might be myself testifying briefly in front

1224
01:39:56,840 --> 01:40:02,280
of the vancouver one um no confirmation there yet that's a cool symbol it's got a person

1225
01:40:02,280 --> 01:40:07,080
embedded in a maple leaf that's dope but it's also doesn't it look kind of like the classic

1226
01:40:07,080 --> 01:40:15,960
like niaid antibody yeah it does it does got all the things you need isn't that fun i just

1227
01:40:15,960 --> 01:40:21,480
want to see if i can pull this up um but yeah so we'll make sure the link uh to that is

1228
01:40:21,480 --> 01:40:32,120
uh available to everybody oh oh did everyone catch that i think there's you oh yeah this

1229
01:40:32,120 --> 01:40:35,880
was a anyway that's my little claim to fame but i actually have nothing to do with the

1230
01:40:35,880 --> 01:40:40,640
nci full disclosure okay so that's wonderful um jumi what do you got going on in uh in the

1231
01:40:40,640 --> 01:40:45,240
next couple weeks and is there anything else that i have failed to plug no i think you

1232
01:40:45,240 --> 01:40:51,640
already mentioned it my sub stack wonderful okay that's it that's all you're gonna say

1233
01:40:51,640 --> 01:40:58,160
okay um the weather's getting better i'm gonna get on a motorcycle soon and teach immunology

1234
01:40:58,160 --> 01:41:02,440
from a motorcycle for fun but other than that i'm just doing the same thing i always do

1235
01:41:02,440 --> 01:41:10,780
um i'm waiting for jessica to do some um from the surfboard clips where we hear about immunology

1236
01:41:10,780 --> 01:41:15,560
and see the wave or something like that but you know they have gopro you know you can

1237
01:41:15,560 --> 01:41:22,200
do that you could oh i do do that that's my profile picture for my twitter account i took

1238
01:41:22,200 --> 01:41:28,440
that photo myself of me nose writing it's really hard um but we haven't had waves in

1239
01:41:28,440 --> 01:41:35,920
ages we have something coming up but the the weather modifiers modifiers here are really

1240
01:41:35,920 --> 01:41:44,320
like so they only make waves on the weekends so my theory is that the weather weather modifiers

1241
01:41:44,320 --> 01:41:49,760
are surfers because they have to work during the week and so they make the waves happen

1242
01:41:49,760 --> 01:41:57,200
when they can surf i'm not kidding it's only on the weekend i swear to god and then i don't

1243
01:41:57,200 --> 01:42:03,600
want to surf because there's too many people oh you should get a login for harp because

1244
01:42:03,600 --> 01:42:11,400
then you could change that if you wanted okay you were just nearby it's just north of here

1245
01:42:11,400 --> 01:42:18,600
it's just just due north into alaska yeah um anyway um jj how about you my friend you've

1246
01:42:18,600 --> 01:42:24,200
had a you you did two weekends in a row presenting to uh medical doctors for covet ethics fantastic

1247
01:42:24,200 --> 01:42:30,520
presentations um anything else you got going on uh apart from the motorcycle yeah sure

1248
01:42:30,520 --> 01:42:34,960
i'll tell you i'm gonna do a star trek um series on youtube and i'm probably gonna premiere

1249
01:42:34,960 --> 01:42:39,800
it in the next couple days what does that mean a star trek series i've heard you say

1250
01:42:39,800 --> 01:42:47,240
it a few times um i think it's best if i just leave it but i have uh i have a plan to be

1251
01:42:47,240 --> 01:42:54,080
um on youtube regularly as a as a star trek i don't know what else to tell you and then

1252
01:42:54,080 --> 01:43:00,480
that's what i'm gonna do i'm excited about ears what's that he's got ears i got ears

1253
01:43:00,480 --> 01:43:05,360
and everything i'm ready for it it's gonna be funny and also biological that's the whole

1254
01:43:05,360 --> 01:43:09,800
point i want to get on youtube again without worrying about getting bounced and so i think

1255
01:43:09,800 --> 01:43:16,400
i've got an idea well uh i think i said it to you uh last time you're on say hi to youtube

1256
01:43:16,400 --> 01:43:22,120
for us i will i will um all right guys well last but not least make sure you join us over

1257
01:43:22,120 --> 01:43:27,480
at roundingtheearth.locals.com where we have been having a fantastic discussion uh this

1258
01:43:27,480 --> 01:43:34,040
entire time and there we are um uh and yeah it's a great place to come um either as a

1259
01:43:34,040 --> 01:43:37,560
free member where you can just stay up to date on everything we're doing in this wonderful

1260
01:43:37,560 --> 01:43:43,440
feed here or you can sign up to support the show um for as little as five dollars a month

1261
01:43:43,440 --> 01:43:49,120
and get access to our weekly locals exclusive supporters only live streams where we had

1262
01:43:49,120 --> 01:43:53,320
a listening party for uh an album that i had worked on at the beginning of the pandemic

1263
01:43:53,320 --> 01:43:58,120
in march 2020 and it just wound up being a music sharing session and it was a lot of

1264
01:43:58,120 --> 01:44:03,480
fun uh so if you want to hang out with us and do that kind of thing and uh talk about

1265
01:44:03,480 --> 01:44:11,200
stuff that is is better among a closer circle of compadres roundingtheearth.locals.com um

1266
01:44:11,200 --> 01:44:16,160
and we're gonna have a new show soon for subscribers uh we've got we've got our regular wednesday

1267
01:44:16,160 --> 01:44:20,880
night discussions but um we're gonna start doing one that's gonna be more general topics

1268
01:44:20,880 --> 01:44:25,040
uh like the universe education and everything well there's gonna be a lot of educational

1269
01:44:25,040 --> 01:44:31,120
content um uh you know discussions of education philosophically and everything else but i'll

1270
01:44:31,120 --> 01:44:36,160
just go ahead and leave it at that for now okay well thank you guys maybe i can throw

1271
01:44:36,160 --> 01:44:39,680
out one more thing next week maybe we can think about our next time we can think about

1272
01:44:39,680 --> 01:44:45,840
doing there's like three papers now that have shown these ig4 antibodies and what they're

1273
01:44:45,840 --> 01:44:50,040
doing maybe we can find like two or three of those and kind of cover them all together

1274
01:44:50,040 --> 01:44:54,600
at once that's one idea just to throw out there it's on the dub land yeah if you guys

1275
01:44:54,600 --> 01:45:02,600
want to send those out in our email chain we can do that okay sorry okay ladies and

1276
01:45:02,600 --> 01:45:08,320
gentlemen thank you so much for watching and we will see you uh well actually i'll be back

1277
01:45:08,320 --> 01:45:11,560
i mentioned this to matthew it's all announced here for the first time i'm going to be back

1278
01:45:11,560 --> 01:45:17,200
tomorrow with our friend christin elizabeth to talk about her uh fantastic article that's

1279
01:45:17,200 --> 01:45:21,800
just come out which a whole lot has happened since she posted it someone got arrested related

1280
01:45:21,800 --> 01:45:27,440
to the the rise above movement and the azof battalion it's getting nuts out here yeah

1281
01:45:27,440 --> 01:45:32,760
i'm really curious about this um i i have felt like um like things that were going on

1282
01:45:32,760 --> 01:45:39,100
in this supposed like white nationalist movement uh were were you know planned or seeded or

1283
01:45:39,100 --> 01:45:44,040
something like that because uh you look at all the statistics the survey statistics and

1284
01:45:44,040 --> 01:45:50,640
um you know all the measures of racism in the u.s were going down down down down down

1285
01:45:50,640 --> 01:45:55,280
for decades and then all of a sudden were beset with new stories and imageries about

1286
01:45:55,280 --> 01:46:00,720
these new groups popping up and it feels pretty artificial and so uh i'm curious as to hear

1287
01:46:00,720 --> 01:46:05,280
more you know she and i have talked a little bit but uh i want to hear some more well there's

1288
01:46:05,280 --> 01:46:11,120
your teaser for tomorrow uh lots to catch up on and um i hope everybody comes and joins

1289
01:46:11,120 --> 01:46:16,360
us for that thank you again so much any final words matthew before i let you go no good

1290
01:46:16,360 --> 01:46:23,040
show thank you good show okay guys thank you again and um i uh i appreciate every single

1291
01:46:23,040 --> 01:46:26,880
one of you who's been participating on everywhere you've been participating on rumble there's

1292
01:46:26,880 --> 01:46:31,520
been robust discussion on locals um and uh we had more viewers on rockfin i think than

1293
01:46:31,520 --> 01:46:45,040
we've ever had um so thank you all so much and we will see you tomorrow

1294
01:47:01,520 --> 01:47:28,000
you