I have had deep concerns about PCR since the start. Unfortunately, when I have presented the available evidence, in the form of the Instand report, which showed that positives for SARS-CoV-2 were being triggered from common cold samples, few would listen. Now we are in 2023 and not much has changed: they still deny the results from this report and defend PCR. Same when I show that much of the UK covid PCR testing was testing for one gene - I get barely a shrug. The whole edifice rests on sand.
If PCR tests produce such a huge number of false positives, then why has the percentage of PCR positive in different countries stayed below 1% for several months?
For example according to OWID's data for the UK, the percentage of positive PCR tests peaked at around 30% in April 2020, but it remained below 1% from July until early September 2020. And there was a second peak of about 13% on January 4th 2021, but after that the positivity rate again fell below 1% in March 2021: https://i.ibb.co/NmSsBSm/owid-uk-pcr-positivity-rate-vs-excess-mortality.png. The spikes in PCR positivity rate coincided with spikes in excess mortality in the UK. The spike in excess mortality in January 2021 also coincided with a spike in the number of new vaccines given, but both excess mortality and PCR positivity rate had already started to climb up before the first jab was rolled out: https://metatron.substack.com/p/how-to-create-a-deadly-pandemic-in/comment/17901542.
In Hong Kong, Taiwan, and Australia, the percentage of positive PCR tests remained close to 0% until 2022, but when the first big increase in PCR positivity rate came in 2022, it coincided with the first big increase in excess mortality: https://denisrancourt.substack.com/p/there-was-no-pandemic/comment/18215216. And in December 2022 when 83% of all COVID cases worldwide were in China according to WHO's data, the percentage of positive PCR tests in China rose from less than 1% at the beginning of December 2022 to about 30% on December 25th 2022 (ibid.).
Also for example in Connecticut, the excess ASMR went from about 127% in April 2020 to -3% in July 2020 to 49% in December 2020 to -7% in April 2021, but at the same time the percentage of positive PCR tests went from about 43% in April 2020 to 1% in July 2020 to 12% in December 2020 to 2% in April 2021: https://denisrancourt.substack.com/p/there-was-no-pandemic/comment/18087459.
For a start, I could think of several factors that could allow this to happen:
- The definition of a positive test could have been very strict, e.g. 3 genes, CT<25, etc.
- The amount of testing, pretesting with rapid tests, and the selection of people tested also influences the outcome.
We have no true stat. significant COVID-19 testing surveys that would show us the true population wide numbers.
Also, you are implying causation, by simply looking at correlation, which is problematic IMO, because there's no clear animal model which shows such pathogenic effects. In-fact one of the first outbreaks, the Diamond princess, showed that mortality was within expected ranges, and 90% of people didn't get sick!
Yeah but often in the same country, the PCR positivity rate has risen from less than 1% to more than 20% or 30% within a month or two, and then it has fallen back down soon afterwards. So if at first they got under 1% positives because of strict testing criteria, then why did the percentage of positives next jump up to over 20% or 30%?
> Infection of African green monkeys (AGM) with a low passage human isolate of SARS-CoV-2 by aerosol or mucosal exposure resulted in mild clinical infection with a transient decrease in lung tidal volume. Imaging with human clinical-grade 18F-fluoro-2-deoxy-D-glucose positron emission tomography (18F-FDG PET) co-registered with computed tomography (CT) revealed pulmonary lesions at 4 days post-infection (dpi) that resolved over time. Infectious virus was shed from both respiratory and gastrointestinal (GI) tracts in all animals in a biphasic manner, first between 2-7 dpi followed by a recrudescence at 14-21 dpi. Viral RNA (vRNA) was found throughout both respiratory and gastrointestinal systems at necropsy with higher levels of vRNA found within the GI tract tissues. All animals seroconverted simultaneously for IgM and IgG, which has also been documented in human COVID-19 cases.
> To establish a novel SARS-CoV-2 human challenge model that enables controlled investigation of pathogenesis, correlates of protection and efficacy testing of forthcoming interventions, 36 volunteers aged 18-29 years without evidence of previous infection or vaccination were inoculated with 10 TCID50 of a wild-type virus (SARS-CoV-2/human/GBR/484861/2020) intranasally in an open-label, non-randomized study (ClinicalTrials.gov identifier NCT04865237; funder, UK Vaccine Taskforce). After inoculation, participants were housed in a high-containment quarantine unit, with 24-hour close medical monitoring and full access to higher-level clinical care. The study's primary objective was to identify an inoculum dose that induced well-tolerated infection in more than 50% of participants, with secondary objectives to assess virus and symptom kinetics during infection. All pre-specified primary and secondary objectives were met. Two participants were excluded from the per-protocol analysis owing to seroconversion between screening and inoculation, identified post hoc. Eighteen (~53%) participants became infected, with viral load (VL) rising steeply and peaking at ~5 days after inoculation. Virus was first detected in the throat but rose to significantly higher levels in the nose, peaking at ~8.87 log10 copies per milliliter (median, 95% confidence interval (8.41, 9.53)). Viable virus was recoverable from the nose up to ~10 days after inoculation, on average. There were no serious adverse events. Mild-to-moderate symptoms were reported by 16 (89%) infected participants, beginning 2-4 days after inoculation, whereas two (11%) participants remained asymptomatic (no reportable symptoms). Anosmia or dysosmia developed more slowly in 15 (83%) participants. No quantitative correlation was noted between VL and symptoms, with high VLs present even in asymptomatic infection. All infected individuals developed serum spike-specific IgG and neutralizing antibodies. Results from lateral flow tests were strongly associated with viable virus, and modeling showed that twice-weekly rapid antigen tests could diagnose infection before 70-80% of viable virus had been generated.
And if the deaths that were attributed to COVID were not caused by COVID, then why have autopsy studies found that the lungs of people whose death was attributed to COVID were double the normal weight? A paper from July 2020 said: "We examined 7 lungs obtained during autopsy from patients who died from Covid-19 and compared them with 7 lungs obtained during autopsy from patients who died from acute respiratory distress syndrome (ARDS) secondary to influenza A(H1N1) infection and 10 age-matched, uninfected control lungs. [...] The mean (±SE) weight of the lungs from patients with proven influenza pneumonia was significantly higher than that from patients with proven Covid-19 (2404 ±560 g vs. 1681 ±49 g; P=0.04). The mean weight of the uninfected control lungs (1045 ±91 g) was significantly lower than those in the influenza group (P=0.003) and the Covid-19 group (P<0.001)." (https://www.nejm.org/doi/full/10.1056/NEJMoa2015432) I don't know if ventilation might result in an increase in lung weight, but the paper said that none of the seven people whose death was attributed to COVID had been treated with a ventilator. A German study from May 2020 also said: "In all 12 cases, the cause of death was found within the lungs or the pulmonary vascular system. However, macroscopically differentiating viral pneumonia with subsequent diffuse alveolar damage (a histologic diagnosis) from bacterial pneumonia was not always possible. Typically, the lungs were congested and heavy, with a maximum combined lung weight of 3420 g in case 11. The mean combined lung weight was 1988 g (median, 2088 g). Standard lung weights for men and women are 840 g and 639 g, respectively (13, 14). Only cases 6 and 9 presented with a relatively low lung weight: 550 g and 890 g, respectively (Appendix Table 1, available at Annals.org)." (https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7240772/) I guess you might argue that the increase in lung weight was caused by bacterial pneumonia. But then why was there a huge increase in deaths associated with bacterial pneumonia worldwide if it was not caused by a viral coinfection?
I can see you've collected a lot of data and I appreciate the time & effort it would have taken you to gather these sources.
However the issue is with the quality & the correlation - which is no fault at all on your behalf.
To analyse this properly - it requires taking a step back to consider what we know to be true.
Firstly the human race has been on earth for hundreds of thousands of years. It has continuously prospered. The only time there has ever been excess deaths (excess meaning above the natural baseline) is from events of genocide.
Nothing in nature suddenly started killing human beings 'in excess' in the year 2020. Homo-sapiens didn't suddenly start dying from a brand new word, a guy literally first coined on Twitter as "Covid" three years ago. Humans have died like they always have.
As for the PCR tests - think of it like a genetic profile evaluation.
I'm sure you already know that the PCR test wasn't invented for the purpose it was used for.
However the context it was used for - was surveillance. It allowed us to surveil the outbreak. Individual people (the population) were used to provide a reading.
In no way at all, did the test provide any information that related to a person's health. It simply gave us a profile that allowed us to track the spread of the virus (outbreak)
It was a binary test - if it was detected - it was positive.
For all it's worth - and to give an analogy - the data may as well be a world wide survey on how many individuals tested positive for an alcoholic beverage that day.
The main point being, is the data does not in anyway collate to people's deaths.
I haven't followed you or conversed with you before - so I don't know your views on the covid agenda at large.
However even if people have different theories or they disagree on reasons and intent - it is a fact that information is censored and therefore ambiguous.
So as for the autopsies you mentioned - they don't tell us anything because of the information that's omitted. For them to be considered - we'd want to know each person's medical history and who they were. A family member to come forward - with their life story so we know we can correlate the findings.
This table shows the percentage change in crude mortality rate in each country compared to the previous year: https://i.ibb.co/68Ym2xZ/un-crude-mortality-rate-pct-change-1951-to-2022.png. R code: https://pastebin.com/raw/qkqfN6kX. Even most African countries had a clear increase in CMR from 2019 to 2020, even though I don't know if the data is partially based on projected data, since some of the past population data in developing countries is based on projected estimates.
In the dataset published by the UN, the biggest increase in CMR from 2019 to 2020 was 66% in Azerbaijan. The biggest increase from 2020 to 2021 was 43% in Cuba. And the biggest increase from 2021 to 2022 was 38% increase in Ukraine. In some countries that got hit by COVID the hardest in the spring of 2020, like Ecuador, Belgium, Spain, and Italy, there was a large decrease in the CMR from 2020 to 2021. In all data since 1950, the biggest increase in CMR over the previous year was 523% in Rwanda from 1993 to 1994, followed by 296% in Cambodia from 1974 to 1975.
Or this plot shows yearly deaths per continent from the same dataset by the UN: https://i.ibb.co/vm7FTxt/unmortalitymulticontinent.png. Before 2020, the only major worldwide spike in the yearly number of deaths was caused by the Great Chinese Famine of 1959-1961, and the second biggest spike was caused by the 1971 Bangladesh war.
You wrote: "I'm sure you already know that the PCR test wasn't invented for the purpose it was used for." However in the patent for PCR that was issued in 1986 to Kary Mullis et al., they wrote that one application for PCR would be to diagnose the presence of pathogenic micro-organisms including viruses (https://patents.google.com/patent/US4683195).
If a group of several people who died of similar symptoms died with double the normal lung weight, then you can infer that some lung-related pathology probably contributed to their death even if you don't know their whole life story. But in table 7 of the supplementary PDF of the first autopsy study, there's some information about the health status of the patiens they examined: https://www.nejm.org/doi/suppl/10.1056/NEJMoa2015432/suppl_file/nejmoa2015432_appendix.pdf. The cause of death is listed as respiratory failure for 6 patients and cardiorespiratory failure for the 7th patient.
An individual first needs to present with symptoms - and a doctor might choose to conduct a PCR to confirm or eliminate a root cause of their sickness.
We do not - and have never before - run a PCR test on the living population.
What's the point? It proves nothing.
The point - it could be argued - was to engineer the fake rubric to get away with mass murder.
We can debate the reason/intention why - eg: whether it's incompetence or malice -- but it's certifiably false.
Our death and birth rates - trend.
Over generations it may trend up or down - but they do not spike or plummet.
A sudden change in the death or birth rate; can only be caused by man i.e genocide (Government).
It is the antithesis of an 'Act of God' or 'nature' or 'natural law' (choose your metaphor)
It is the literal definition of 'evil' and only 'man' can commit evil.
Excess deaths are 'unnatural' - because the baseline is the 'natural attenuation'
Based on any given population -- ⒳ amount of people die every day and ⒳ amount of babies are born everyday. It's human nature.
For example - Australia has a population of 25 million.
450 people die everyday & 850 babies are born everyday.
(We'll leave the birthrate for a separate conversation)
The average deaths per year is 165,000
or
Per month 13,750
Per day 450
That 'all cause mortality average' has held true for the last 20 years.
So 450 Australians die everyday for all different reasons - then in April 2021 the living population starts being injected with a man-made substance. That substance can not stop death. Nothing stops death. We are mortal. It can not make us immortal. Therefore - it can only cause death.
To illustrate my point - it's impossible for that substance to change/prevent/reduce the deaths per day - to say 300 a day.
It can only kill us - and that's exactly what it's doing.
This work seems very important to me... I hope to have time to study it soon. Bravo for going in this direction! Genomic technology and its limitations must be unpacked. It has captured too much territory.
Why are the ends of the genome important Ben? There are a lot of really great biological answers to this question. They are anchor points for viral proteins. The length of the end of the genome determines the stability of the genome. There are tertiary structures that interact with the rest of the genome.
But you never answered this because you have no basis or understanding of biology.
I showed you a paper that clearly mapped out the 5' and 3' regions. Of course it's going to take quite a bit more bioinformatics to tackle that paper then you are capable of, so you move the goal posts to "everything has to be sequenced in a single read"
When I show you a paper with a single read spanning 95% of the genome, that's not good enough, because virus denier reasons. Never mind that with in the single read the alignment to the assembled reads are exactly the same (invaliding what? three posts of yours casting doubt on assembly algorithms)
Your next substack should do the same analysis as in the entire paper:
Neil Ferguson can show you the value of modeling with fabricated data.
Your article lacks citation to work that is already done in this field. It leaves your readers with the impression that you haven’t read it and are likely unversed in the stew you are brewing.
The manufacturers are likely referring to the fact that heat and Magnesium are more random than RNAseIII based methods but they are not purely random.
Also the fragmentation isn’t the only step in that long process that can introduce bias.
Had you read the vast literature on RNA-seq instead of asking a leading question of a manufacturer of a kit (who will of course tell you it’s random) you would have gotten the answers shown in this paper, which is that there are many sources or bias outside the fragmentation step.
This doesn’t address the fact that you claimed those end sequences didn’t exist and yet they were readily found by just using BWA-mem.
This is an elaborate smoke screen to the fact that you didn’t know how to map reads and made bold claims from a Substack with no supporting citations and a failure to double check your own work.
In the Typescript code you used to simulate the reads, the reverse read is always the exact reverse complement of the forward read, so the reverse read always starts at the exact same position of the reference as the forward read. However that's not usually the case in the real reads by Wu et al., and when I used Bowtie2 to align Wu et al.'s reads against Wuhan-Hu-1, the average absolute difference in the starting positions of the paired reads was about 50 bases:
In the SAM format, the 4th field shows the starting position of the main read and the 8th field is the starting position of the paired mate. The 9th field shows the template length, which is the length between the start of the main read and the end of the paired mate. The Samtools manual uses the term template length as synonymous with the absolute value of the insert size, even though sometimes a differentiation is made where the term insert size is considered to include the adapters and the fragment size is not, and it's also possible for the aligned reads in a SAM file to include the adapters: https://www.biostars.org/p/95803/.
When I aligned Wu et al.'s reads against Wuhan-Hu-1, if template lengths of zero are excluded, the mode value of the template length was -186 for the forward reads but 186 for the reverse reads: https://media.discordapp.net/attachments/1093243194231246934/1127505846037921832/1.png. The forward reads usually had a negative template length, because in the sequencing protocol used by Wu et al., the forward reads are sense for the cDNA and therefore antisense for RNA, so the reverse reads are actually what people would intuitively consider the forward reads. However in your simulated reads, the template length is always the same as the read length or negative read length. And in Wu et al.'s reads, the reads which match the beginning of the genome of SARS2 all come from the reverse reads, and the reads which match the longest part of the poly(A) tail come from the forward reads, which is another aspect of the real reads which is not simulated accurately by your Typescript code.
Your simulated reads also don't reproduce these sequencing artifacts that McKernan mentioned: "Reads don't map to the margin of a reference because the given insert size you have selected in library construction won't exist. If you have 200bp inserts, you won't get the 1st ~50bases of the reference. This is known by all in space except Ben who thinks it's a discovery. [...] Secondly, there are biological reasons why those pieces of DNA aren't captured. The 5' end has a 5'cap which can't be cloned unless it's decapped. The 3' end is a variable poly A tail which won't sequence or amplify without polymerase slippage. reduced end seq is expected." (https://twitter.com/Kevin_McKernan/status/1674041259931979777)
Yes, that is because I have only considered fragments that fall within the range of 50-150bp length. Hence, Illumina should be able to read the entire fragment here (at 150cycles).
Are you suggesting, to include fragments with a true length of >150?
Read length is not the same as fragment length. The fragment is the piece of DNA that is sequenced from one end by the forward read and the other end by the reverse read, and often the fragment length is more than double the read length so that the forward and reverse reads don't even overlap and there's a gap between them.
In the manual for the Takara SMARTer® Stranded Total RNA-Seq Kit v2 which was used by Wu et al., it says: "Successful cDNA synthesis and amplification should produce a distinct curve spanning 200–1,000 bp, with a local maximum at ~300–400 bp, in the positive control RNA sample (see Figure 4A) and no product or very minimal background over the corresponding range in the negative control (see Figure 4B). A small amount of products ~150–200 bp in size, such as those found in the example in Figure 4A, will not interfere with sequencing. However, consider repeating the final cleanup (Section V.F) if an excessive amount of products <200 bp in size is present." (https://www.takarabio.com/documents/User%20Manual/SMARTer%20Stranded%20Total%20RNA/SMARTer%20Stranded%20Total%20RNA-Seq%20Kit%20v2%20-%20Pico%20Input%20Mammalian%20User%20Manual_050619.pdf)
When I aligned Wu et al.'s reads against Wuhan-Hu-1, the average absolute template length was only about 236 though and not around 300-400: `samtools view -f2 sorted26.bam|awk '{if($9<0)$9=-$9;a+=$9}END{print a/NR}'`. `samtools view -f2` filters out unpaired reads, which you can also do with `samtools view file.bam|awk \$9`.
I have had deep concerns about PCR since the start. Unfortunately, when I have presented the available evidence, in the form of the Instand report, which showed that positives for SARS-CoV-2 were being triggered from common cold samples, few would listen. Now we are in 2023 and not much has changed: they still deny the results from this report and defend PCR. Same when I show that much of the UK covid PCR testing was testing for one gene - I get barely a shrug. The whole edifice rests on sand.
If PCR tests produce such a huge number of false positives, then why has the percentage of PCR positive in different countries stayed below 1% for several months?
For example according to OWID's data for the UK, the percentage of positive PCR tests peaked at around 30% in April 2020, but it remained below 1% from July until early September 2020. And there was a second peak of about 13% on January 4th 2021, but after that the positivity rate again fell below 1% in March 2021: https://i.ibb.co/NmSsBSm/owid-uk-pcr-positivity-rate-vs-excess-mortality.png. The spikes in PCR positivity rate coincided with spikes in excess mortality in the UK. The spike in excess mortality in January 2021 also coincided with a spike in the number of new vaccines given, but both excess mortality and PCR positivity rate had already started to climb up before the first jab was rolled out: https://metatron.substack.com/p/how-to-create-a-deadly-pandemic-in/comment/17901542.
In Hong Kong, Taiwan, and Australia, the percentage of positive PCR tests remained close to 0% until 2022, but when the first big increase in PCR positivity rate came in 2022, it coincided with the first big increase in excess mortality: https://denisrancourt.substack.com/p/there-was-no-pandemic/comment/18215216. And in December 2022 when 83% of all COVID cases worldwide were in China according to WHO's data, the percentage of positive PCR tests in China rose from less than 1% at the beginning of December 2022 to about 30% on December 25th 2022 (ibid.).
Also for example in Connecticut, the excess ASMR went from about 127% in April 2020 to -3% in July 2020 to 49% in December 2020 to -7% in April 2021, but at the same time the percentage of positive PCR tests went from about 43% in April 2020 to 1% in July 2020 to 12% in December 2020 to 2% in April 2021: https://denisrancourt.substack.com/p/there-was-no-pandemic/comment/18087459.
For a start, I could think of several factors that could allow this to happen:
- The definition of a positive test could have been very strict, e.g. 3 genes, CT<25, etc.
- The amount of testing, pretesting with rapid tests, and the selection of people tested also influences the outcome.
We have no true stat. significant COVID-19 testing surveys that would show us the true population wide numbers.
Also, you are implying causation, by simply looking at correlation, which is problematic IMO, because there's no clear animal model which shows such pathogenic effects. In-fact one of the first outbreaks, the Diamond princess, showed that mortality was within expected ranges, and 90% of people didn't get sick!
Yeah but often in the same country, the PCR positivity rate has risen from less than 1% to more than 20% or 30% within a month or two, and then it has fallen back down soon afterwards. So if at first they got under 1% positives because of strict testing criteria, then why did the percentage of positives next jump up to over 20% or 30%?
In one paper where they infected monkeys with SARS2, the abstract says (https://pubmed.ncbi.nlm.nih.gov/32946524/):
> Infection of African green monkeys (AGM) with a low passage human isolate of SARS-CoV-2 by aerosol or mucosal exposure resulted in mild clinical infection with a transient decrease in lung tidal volume. Imaging with human clinical-grade 18F-fluoro-2-deoxy-D-glucose positron emission tomography (18F-FDG PET) co-registered with computed tomography (CT) revealed pulmonary lesions at 4 days post-infection (dpi) that resolved over time. Infectious virus was shed from both respiratory and gastrointestinal (GI) tracts in all animals in a biphasic manner, first between 2-7 dpi followed by a recrudescence at 14-21 dpi. Viral RNA (vRNA) was found throughout both respiratory and gastrointestinal systems at necropsy with higher levels of vRNA found within the GI tract tissues. All animals seroconverted simultaneously for IgM and IgG, which has also been documented in human COVID-19 cases.
There's even a paper from 2022 where human volunteers were infected with SARS2 (https://www.nature.com/articles/s41591-022-01780-9):
> To establish a novel SARS-CoV-2 human challenge model that enables controlled investigation of pathogenesis, correlates of protection and efficacy testing of forthcoming interventions, 36 volunteers aged 18-29 years without evidence of previous infection or vaccination were inoculated with 10 TCID50 of a wild-type virus (SARS-CoV-2/human/GBR/484861/2020) intranasally in an open-label, non-randomized study (ClinicalTrials.gov identifier NCT04865237; funder, UK Vaccine Taskforce). After inoculation, participants were housed in a high-containment quarantine unit, with 24-hour close medical monitoring and full access to higher-level clinical care. The study's primary objective was to identify an inoculum dose that induced well-tolerated infection in more than 50% of participants, with secondary objectives to assess virus and symptom kinetics during infection. All pre-specified primary and secondary objectives were met. Two participants were excluded from the per-protocol analysis owing to seroconversion between screening and inoculation, identified post hoc. Eighteen (~53%) participants became infected, with viral load (VL) rising steeply and peaking at ~5 days after inoculation. Virus was first detected in the throat but rose to significantly higher levels in the nose, peaking at ~8.87 log10 copies per milliliter (median, 95% confidence interval (8.41, 9.53)). Viable virus was recoverable from the nose up to ~10 days after inoculation, on average. There were no serious adverse events. Mild-to-moderate symptoms were reported by 16 (89%) infected participants, beginning 2-4 days after inoculation, whereas two (11%) participants remained asymptomatic (no reportable symptoms). Anosmia or dysosmia developed more slowly in 15 (83%) participants. No quantitative correlation was noted between VL and symptoms, with high VLs present even in asymptomatic infection. All infected individuals developed serum spike-specific IgG and neutralizing antibodies. Results from lateral flow tests were strongly associated with viable virus, and modeling showed that twice-weekly rapid antigen tests could diagnose infection before 70-80% of viable virus had been generated.
There were no excess mortalities until the vaccine started.
You can rest assured that a coronavirus (upper respiratory tract infection) does not kill human beings.
Yeah there were: https://i.ibb.co/M6K53W6/owid-heatmap-excess-mortality-new-vax-pcr-positivity.png. R code: https://pastebin.com/raw/8yrKriwM. Data: https://covid.ourworldindata.org/data/owid-covid-data.csv. And from my plot you can see that in countries and jurisdictions that had no very little excess deaths in 2020, the PC positivity rate generally remained near 0%, like in South Korea, Hong Kong, Taiwan, Thailand, Australia, Finland, and Uruguay.
And if the deaths that were attributed to COVID were not caused by COVID, then why have autopsy studies found that the lungs of people whose death was attributed to COVID were double the normal weight? A paper from July 2020 said: "We examined 7 lungs obtained during autopsy from patients who died from Covid-19 and compared them with 7 lungs obtained during autopsy from patients who died from acute respiratory distress syndrome (ARDS) secondary to influenza A(H1N1) infection and 10 age-matched, uninfected control lungs. [...] The mean (±SE) weight of the lungs from patients with proven influenza pneumonia was significantly higher than that from patients with proven Covid-19 (2404 ±560 g vs. 1681 ±49 g; P=0.04). The mean weight of the uninfected control lungs (1045 ±91 g) was significantly lower than those in the influenza group (P=0.003) and the Covid-19 group (P<0.001)." (https://www.nejm.org/doi/full/10.1056/NEJMoa2015432) I don't know if ventilation might result in an increase in lung weight, but the paper said that none of the seven people whose death was attributed to COVID had been treated with a ventilator. A German study from May 2020 also said: "In all 12 cases, the cause of death was found within the lungs or the pulmonary vascular system. However, macroscopically differentiating viral pneumonia with subsequent diffuse alveolar damage (a histologic diagnosis) from bacterial pneumonia was not always possible. Typically, the lungs were congested and heavy, with a maximum combined lung weight of 3420 g in case 11. The mean combined lung weight was 1988 g (median, 2088 g). Standard lung weights for men and women are 840 g and 639 g, respectively (13, 14). Only cases 6 and 9 presented with a relatively low lung weight: 550 g and 890 g, respectively (Appendix Table 1, available at Annals.org)." (https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7240772/) I guess you might argue that the increase in lung weight was caused by bacterial pneumonia. But then why was there a huge increase in deaths associated with bacterial pneumonia worldwide if it was not caused by a viral coinfection?
I can see you've collected a lot of data and I appreciate the time & effort it would have taken you to gather these sources.
However the issue is with the quality & the correlation - which is no fault at all on your behalf.
To analyse this properly - it requires taking a step back to consider what we know to be true.
Firstly the human race has been on earth for hundreds of thousands of years. It has continuously prospered. The only time there has ever been excess deaths (excess meaning above the natural baseline) is from events of genocide.
Nothing in nature suddenly started killing human beings 'in excess' in the year 2020. Homo-sapiens didn't suddenly start dying from a brand new word, a guy literally first coined on Twitter as "Covid" three years ago. Humans have died like they always have.
As for the PCR tests - think of it like a genetic profile evaluation.
I'm sure you already know that the PCR test wasn't invented for the purpose it was used for.
However the context it was used for - was surveillance. It allowed us to surveil the outbreak. Individual people (the population) were used to provide a reading.
In no way at all, did the test provide any information that related to a person's health. It simply gave us a profile that allowed us to track the spread of the virus (outbreak)
It was a binary test - if it was detected - it was positive.
For all it's worth - and to give an analogy - the data may as well be a world wide survey on how many individuals tested positive for an alcoholic beverage that day.
The main point being, is the data does not in anyway collate to people's deaths.
I haven't followed you or conversed with you before - so I don't know your views on the covid agenda at large.
However even if people have different theories or they disagree on reasons and intent - it is a fact that information is censored and therefore ambiguous.
So as for the autopsies you mentioned - they don't tell us anything because of the information that's omitted. For them to be considered - we'd want to know each person's medical history and who they were. A family member to come forward - with their life story so we know we can correlate the findings.
This table shows the percentage change in crude mortality rate in each country compared to the previous year: https://i.ibb.co/68Ym2xZ/un-crude-mortality-rate-pct-change-1951-to-2022.png. R code: https://pastebin.com/raw/qkqfN6kX. Even most African countries had a clear increase in CMR from 2019 to 2020, even though I don't know if the data is partially based on projected data, since some of the past population data in developing countries is based on projected estimates.
In the dataset published by the UN, the biggest increase in CMR from 2019 to 2020 was 66% in Azerbaijan. The biggest increase from 2020 to 2021 was 43% in Cuba. And the biggest increase from 2021 to 2022 was 38% increase in Ukraine. In some countries that got hit by COVID the hardest in the spring of 2020, like Ecuador, Belgium, Spain, and Italy, there was a large decrease in the CMR from 2020 to 2021. In all data since 1950, the biggest increase in CMR over the previous year was 523% in Rwanda from 1993 to 1994, followed by 296% in Cambodia from 1974 to 1975.
Or this plot shows yearly deaths per continent from the same dataset by the UN: https://i.ibb.co/vm7FTxt/unmortalitymulticontinent.png. Before 2020, the only major worldwide spike in the yearly number of deaths was caused by the Great Chinese Famine of 1959-1961, and the second biggest spike was caused by the 1971 Bangladesh war.
You wrote: "I'm sure you already know that the PCR test wasn't invented for the purpose it was used for." However in the patent for PCR that was issued in 1986 to Kary Mullis et al., they wrote that one application for PCR would be to diagnose the presence of pathogenic micro-organisms including viruses (https://patents.google.com/patent/US4683195).
If a group of several people who died of similar symptoms died with double the normal lung weight, then you can infer that some lung-related pathology probably contributed to their death even if you don't know their whole life story. But in table 7 of the supplementary PDF of the first autopsy study, there's some information about the health status of the patiens they examined: https://www.nejm.org/doi/suppl/10.1056/NEJMoa2015432/suppl_file/nejmoa2015432_appendix.pdf. The cause of death is listed as respiratory failure for 6 patients and cardiorespiratory failure for the 7th patient.
As for the PCR - yes it's a diagnostic test.
An individual first needs to present with symptoms - and a doctor might choose to conduct a PCR to confirm or eliminate a root cause of their sickness.
We do not - and have never before - run a PCR test on the living population.
What's the point? It proves nothing.
The point - it could be argued - was to engineer the fake rubric to get away with mass murder.
The UN data is false.
We can debate the reason/intention why - eg: whether it's incompetence or malice -- but it's certifiably false.
Our death and birth rates - trend.
Over generations it may trend up or down - but they do not spike or plummet.
A sudden change in the death or birth rate; can only be caused by man i.e genocide (Government).
It is the antithesis of an 'Act of God' or 'nature' or 'natural law' (choose your metaphor)
It is the literal definition of 'evil' and only 'man' can commit evil.
Excess deaths are 'unnatural' - because the baseline is the 'natural attenuation'
Based on any given population -- ⒳ amount of people die every day and ⒳ amount of babies are born everyday. It's human nature.
For example - Australia has a population of 25 million.
450 people die everyday & 850 babies are born everyday.
(We'll leave the birthrate for a separate conversation)
The average deaths per year is 165,000
or
Per month 13,750
Per day 450
That 'all cause mortality average' has held true for the last 20 years.
So 450 Australians die everyday for all different reasons - then in April 2021 the living population starts being injected with a man-made substance. That substance can not stop death. Nothing stops death. We are mortal. It can not make us immortal. Therefore - it can only cause death.
To illustrate my point - it's impossible for that substance to change/prevent/reduce the deaths per day - to say 300 a day.
It can only kill us - and that's exactly what it's doing.
This work seems very important to me... I hope to have time to study it soon. Bravo for going in this direction! Genomic technology and its limitations must be unpacked. It has captured too much territory.
Why are the ends of the genome important Ben? There are a lot of really great biological answers to this question. They are anchor points for viral proteins. The length of the end of the genome determines the stability of the genome. There are tertiary structures that interact with the rest of the genome.
But you never answered this because you have no basis or understanding of biology.
I showed you a paper that clearly mapped out the 5' and 3' regions. Of course it's going to take quite a bit more bioinformatics to tackle that paper then you are capable of, so you move the goal posts to "everything has to be sequenced in a single read"
When I show you a paper with a single read spanning 95% of the genome, that's not good enough, because virus denier reasons. Never mind that with in the single read the alignment to the assembled reads are exactly the same (invaliding what? three posts of yours casting doubt on assembly algorithms)
Your next substack should do the same analysis as in the entire paper:
https://www.sciencedirect.com/science/article/pii/S0092867420304062#fig2
Then at the end apologize for being so wrong.
Neil Ferguson can show you the value of modeling with fabricated data.
Your article lacks citation to work that is already done in this field. It leaves your readers with the impression that you haven’t read it and are likely unversed in the stew you are brewing.
The manufacturers are likely referring to the fact that heat and Magnesium are more random than RNAseIII based methods but they are not purely random.
Also the fragmentation isn’t the only step in that long process that can introduce bias.
Had you read the vast literature on RNA-seq instead of asking a leading question of a manufacturer of a kit (who will of course tell you it’s random) you would have gotten the answers shown in this paper, which is that there are many sources or bias outside the fragmentation step.
This doesn’t address the fact that you claimed those end sequences didn’t exist and yet they were readily found by just using BWA-mem.
This is an elaborate smoke screen to the fact that you didn’t know how to map reads and made bold claims from a Substack with no supporting citations and a failure to double check your own work.
https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8079181/#B31
I think you'll find the tail will always mutate to ensure transmission & to find it's spillover host.
In the Typescript code you used to simulate the reads, the reverse read is always the exact reverse complement of the forward read, so the reverse read always starts at the exact same position of the reference as the forward read. However that's not usually the case in the real reads by Wu et al., and when I used Bowtie2 to align Wu et al.'s reads against Wuhan-Hu-1, the average absolute difference in the starting positions of the paired reads was about 50 bases:
brew install bowtie2 samtools
wget ftp://ftp.sra.ebi.ac.uk/vol1/fastq/SRR109/081/SRR10971381/SRR10971381_{1,2}.fastq.gz
curl 'https://eutils.ncbi.nlm.nih.gov/entrez/eutils/efetch.fcgi?db=nuccore&rettype=fasta&id=MN908947.3'>sars2.fa
bowtie2-build sars2.fa{,2}
bowtie2 -p4 --no-unal -x sars2.fa -1 SRR10971381_1.fastq.gz -2 SRR10971381_2.fastq.gz|samtools sort ->sorted26.bam
samtools view sorted26.bam|awk '{x=$4-$8;if(x<0)x=-1;s+=x}END{print s/NR}'
In the SAM format, the 4th field shows the starting position of the main read and the 8th field is the starting position of the paired mate. The 9th field shows the template length, which is the length between the start of the main read and the end of the paired mate. The Samtools manual uses the term template length as synonymous with the absolute value of the insert size, even though sometimes a differentiation is made where the term insert size is considered to include the adapters and the fragment size is not, and it's also possible for the aligned reads in a SAM file to include the adapters: https://www.biostars.org/p/95803/.
When I aligned Wu et al.'s reads against Wuhan-Hu-1, if template lengths of zero are excluded, the mode value of the template length was -186 for the forward reads but 186 for the reverse reads: https://media.discordapp.net/attachments/1093243194231246934/1127505846037921832/1.png. The forward reads usually had a negative template length, because in the sequencing protocol used by Wu et al., the forward reads are sense for the cDNA and therefore antisense for RNA, so the reverse reads are actually what people would intuitively consider the forward reads. However in your simulated reads, the template length is always the same as the read length or negative read length. And in Wu et al.'s reads, the reads which match the beginning of the genome of SARS2 all come from the reverse reads, and the reads which match the longest part of the poly(A) tail come from the forward reads, which is another aspect of the real reads which is not simulated accurately by your Typescript code.
Your simulated reads also don't reproduce these sequencing artifacts that McKernan mentioned: "Reads don't map to the margin of a reference because the given insert size you have selected in library construction won't exist. If you have 200bp inserts, you won't get the 1st ~50bases of the reference. This is known by all in space except Ben who thinks it's a discovery. [...] Secondly, there are biological reasons why those pieces of DNA aren't captured. The 5' end has a 5'cap which can't be cloned unless it's decapped. The 3' end is a variable poly A tail which won't sequence or amplify without polymerase slippage. reduced end seq is expected." (https://twitter.com/Kevin_McKernan/status/1674041259931979777)
Yes, that is because I have only considered fragments that fall within the range of 50-150bp length. Hence, Illumina should be able to read the entire fragment here (at 150cycles).
Are you suggesting, to include fragments with a true length of >150?
Read length is not the same as fragment length. The fragment is the piece of DNA that is sequenced from one end by the forward read and the other end by the reverse read, and often the fragment length is more than double the read length so that the forward and reverse reads don't even overlap and there's a gap between them.
In the manual for the Takara SMARTer® Stranded Total RNA-Seq Kit v2 which was used by Wu et al., it says: "Successful cDNA synthesis and amplification should produce a distinct curve spanning 200–1,000 bp, with a local maximum at ~300–400 bp, in the positive control RNA sample (see Figure 4A) and no product or very minimal background over the corresponding range in the negative control (see Figure 4B). A small amount of products ~150–200 bp in size, such as those found in the example in Figure 4A, will not interfere with sequencing. However, consider repeating the final cleanup (Section V.F) if an excessive amount of products <200 bp in size is present." (https://www.takarabio.com/documents/User%20Manual/SMARTer%20Stranded%20Total%20RNA/SMARTer%20Stranded%20Total%20RNA-Seq%20Kit%20v2%20-%20Pico%20Input%20Mammalian%20User%20Manual_050619.pdf)
When I aligned Wu et al.'s reads against Wuhan-Hu-1, the average absolute template length was only about 236 though and not around 300-400: `samtools view -f2 sorted26.bam|awk '{if($9<0)$9=-$9;a+=$9}END{print a/NR}'`. `samtools view -f2` filters out unpaired reads, which you can also do with `samtools view file.bam|awk \$9`.