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epulsive forces the obvious question is where s all this negative mass and repulsive force well first let s consider that the universe is quite old and as a result if we posit such a force has always existed then this would eventually divide the universe into at least two regions one filled with mostly positive mass and one filled with mostly negative mass if this process is less than complete then we should see some inexplicable acceleration due to interactions between positive and negative masses which is consistent with dark energy now the slac 144 experiment demonstrates that light on light collisions can produce matter antimatter pairs further we know that matter antimatter collisions can create light let s assume again that we re in a positive matter region of the universe and as such if we allow to represent a photon we could write that to represent a positron and electron colliding to form a photon similarly we could write that to represent a light on light collision producing an electron positron pair with that notation now let s posit the negative mass versions of the electron and positron which we will denote as and the question is what do we get when we combine the negative mass versions of an electron and positron expressed symbolically where is some unknown particle if it s an ordinary photon then we arguably have a problem because photon collisions have been shown to produce positive mass electron positron pairs that is under this hypothetical in the negative mass regions of the universe when you do light on light collisions you still get positive mass pairs which should eventually be kicked out of that region of space in contrast in the positive mass regions of the universe when you do light on light collisions you also get positive mass pairs but they stick around because it s ordinary mass we could hypothesize that this is just the way things are but we get an elegant potential solution to the source of dark energy if we instead posit the existence of negative mass light as a particle distinct from ordinary light specifically if we further posit that negative mass light does not interact with positive mass and that positive mass light our ordinary photons do not interact with negative mass then that negative mass would not be detectable using ordinary photons ironically this is a kind of dark matter but it s not the nonsense hypothesized hitherto african denisovans and potential misclassification january 21 2026 january 22 2026 erdosfan leave a comment introduction my work on mtdna has led to a thesis that human life begins in africa spreads to asia and then spreads 1 back west to europe and africa and 2 further east into east asia and the pacific i call this the migration back hypothesis and you can read all about it here 1 and here and on my blog generally where you ll find a ton of material on topic one of the most interesting observations in my work is that the living modern day people of cameroon test as having the most ancient genomes in the dataset of complete human mtdna genomes i ve assembled which contains 19 archaic mtdna genomes that are heidelbergensis 1 genome neanderthal 10 genomes and denisovan 8 genomes this is not too shocking considering that 53 01 of the 664 genomes in the dataset are at least a 60 match to at least one archaic genome this comparison to the archaic genomes is done using the only sensible global alignment for mtdna so you can t argue that it s chance or cherry picking there are a lot of living people that have archaic mtdna the reason i m writing this note is because i think two of the neanderthal genomes were misclassified by the scientists that sequenced the genomes i ve written previously that the neanderthals are decidedly heterogenous on the maternal line in that there are 10 neanderthal genomes that can be broken into 6 completely distinct clusters i e groups of similar genomes i m using a global alignment for all of this work except where noted below and as noted above there s only 1 sensible global alignment for mtdna so these distinctions are objective specifically i genomes 1 2 and 10 are at least a 99 5 mutual match to each other ii genomes 5 and 6 are a 63 4 match to each other iii genomes 8 and 9 are a 99 9 match to each other and iv genomes 3 4 and 7 are unique and have no meaningful match to each other or the rest of the neanderthal genomes this note focuses on genomes 5 and 6 which appear to be misclassified as neanderthals and instead seem to be denisovans based upon their mtdna all of the provenance files for the relevant genomes are linked to below at the bottom of the article and each provenance file includes a fasta file that contains the applicable full genome the full dataset i ve assembled which includes all of these archaic genomes is available in 1 above neanderthal genome 5 the provenance file for neanderthal genome 5 row 389 of my dataset lists the organism field as homo sapiens neanderthalensis and the sub_species field as neanderthalensis however the genome title includes the phrase denisova 17 and the isolate field is listed as denisovan 17 further the article associated with the genome suggests that the genome is actually from the denisova cave in siberia yet they classified it as neanderthal which doesn t look right the relevant quote is on page 30 page 3 of the pdf we estimated the molecular age of the mtdna of the newly identified neanderthal denisova 17 to 134 ka 95 height posterior density hpd 94 177 ka using bayesian dating note that denisovan 17 is a label used by the authors of the quoted article i m using indexes and row numbers keyed to my dataset i e denisovan 17 is neanderthal genome 5 in my dataset however as noted above neanderthal genome 5 is a 63 4 match to neanderthal genome 6 only and is not a significant match to any other neanderthal genome this suggests that these two genomes are as noted above a distinct maternal line that lived among other maternal lines that have all been archeologically classified as neanderthals however neanderthal genome 5 was found in the denisovan cave in siberia per the article quoted above which is already evidence for the claim that it is actually a denisovan at least with respect to its maternal line further neanderthal genome 5 has 8 915 bases i e 53 77 of the full genome in common with denisovan genome 1 row 377 of my dataset using the whole genome global alignment which is well beyond chance i e 25 00 of the full genome in contrast neanderthal genome 5 has 5 300 bases i e 31 96 of the full genome in common with its closest match among the other neanderthal genomes save for neanderthal genome 6 which also seems to be denisovan and is discussed below finally neanderthal genome 5 has 16 328 bases i e 98 48 of the full genome in common with a cameroon genome row 591 of my dataset that cameroon genome in turn has 8 898 bases i e 53 47 of the full genome in common with the same denisovan genome 1 row 377 of my dataset the plain conclusion is that neanderthal genome 5 is an archaic siberian denisovan individual with a close maternal connection to living west africans as noted above the cameroon test as the most ancient people across my dataset suggesting a migration from cameroon to siberia which is consistent with the out of africa hypothesis but does not contradict my migration back hypothesis since it s entirely possible that later denisovans migrated back to europe or africa from siberia or further into east asia and the pacific however that is not the point of this note which is limited to the misclassification of two neanderthal genomes neanderthal genome 6 similarly neanderthal genome 6 has 5 289 bases i e 31 90 of the full genome in common with its closest match among the other neanderthal genomes save for neanderthal genome 6 which also seems to be denisovan as discussed above in contrast neanderthal genome 6 has 8 588 bases i e 51 80 of the full genome in common with denisovan genome 1 row 377 of my dataset further neanderthal genome 6 has 10 461 bases i e 63 09 of the full genome in common with the same cameroon genome discussed above however unlike neanderthal genome 5 the provenance file for neanderthal genome 6 and the related article make it clear the genome was discovered in scladina which is an archeological site in belgium even using a local alignment the resultant number of matching bases between neanderthal genome 6 and the cameroon genome is 16 183 which is lower than the number of matching bases between neanderthal genome 6 and that same genome i e 16 328 using a global alignment note that local alignments maximize the number of matching bases the sensible conclusion being that neanderthal genome 6 is actually denisovan though it is not as close to the cameroon genome as neanderthal genome 5 though it is close enough to infer african ancestry this is again consistent with the out of africa hypothesis though it s not clear whether this genome has any connection to asia at least limited to this discussion alone and as such it adds no further credibility to my migration back hypothesis though it does not contradict the migration back hypothesis in any way since it s entirely possible at least some people left africa directly for europe or other places in contrast the migration back hypothesis is about the overall migration patterns of some of the most modern mtdna genomes in the dataset linking otherwise disparate modern humans across enormous distances genome provenance links neanderthal genomes 1 https www ncbi nlm nih gov nuccore om062614 1 2 https www ncbi nlm nih gov nuccore mt677921 1 3 https www ncbi nlm nih gov nuccore mt795654 1 4 https www ncbi nlm nih gov nuccore mt921957 1 5 https www ncbi nlm nih gov nuccore mt576650 1 6 https www ncbi nlm nih gov nuccore mk123269 1 7 https www ncbi nlm nih gov nuccore ky751400 2 8 https www ncbi nlm nih gov nuccore mk033602 1 9 https www ncbi nlm nih gov nuccore mk033602 1 10 https www ncbi nlm nih gov nuccore ku131206 2 denisovan genomes 1 https www ncbi nlm nih gov nuccore kx663333 1 2 https www ncbi nlm nih gov nuccore kt780370 1 3 https www ncbi nlm nih gov nuccore mt576653 1 4 https www ncbi nlm nih gov nuccore mt576652 1 5 https www ncbi nlm nih gov nuccore mt576651 1 6 https www ncbi nlm nih gov nuccore nc_013993 1 7 https www ncbi nlm nih gov nuccore fr695060 1 8 https www ncbi nlm nih gov nuccore fn673705 1 cameroon genome 1 https www ncbi nlm nih gov nucleotide kf358472 1 human migration and mtdna january 7 2026 january 7 2026 erdosfan leave a comment genetic alignment because of relatively recent advances in genetic sequencing we can now read entire mtdna genomes however because mtdna is circular it s not clear where you should start reading the genome as a consequence when comparing two genomes you have no common starting point and the selection of that starting point will impact the number of matching bases as a simple example consider the two fictitious genomes and if we count matching bases using the first index of each genome then the number of matching bases is zero if instead we start at the first index of and the second index of and loop back around to the first g of the match count will be four or 100 of the bases as such determining the starting indexes for comparison i e the genome alignment is determinative of the match count it turns out that mtdna is unique in that it is inherited directly from the mother generally without any mutations at all as such the intuition for combinations of sequences typically associated with genetics is inapplicable to mtdna since there is no combination of traits or sequences inherited from the mother and the father and instead a basically perfect copy of the mother s genome is inherited as a result it makes perfect sense to use a global alignment which we did above where we compared one entire genome to another entire genome in contrast we could instead make use of a local alignment where we compare segments of two genomes for example consider genomes and first you ll note these genomes are not the same length unlike in the example above which is another factor to be considered when developing an alignment for comparison if we simply use the first three bases of each genome for comparison then the match count will be one since the first two initial a s match if instead we use index two of and index one of then the entire sequence matches and the resultant match count will be three note that the number of possible global alignments is simply the length of the genome that is when using a global alignment you fix one genome and rotate the other one base at a time and that will cover all possible global alignments between the two genomes in contrast the number of local alignments is much larger since you have to consider all local alignments of each possible length as a result it is much easier to consider all possible global alignments between two genomes than local alignments in fact it turns out there is exactly one plausible global alignment for mtdna making global alignments extremely attractive in terms of efficiency specifically it takes 0 02 seconds to compare a given genome to my entire dataset of roughly 650 genomes using a global alignment performing the same task using a local alignment takes one hour and the algorithm i ve been using considers only a small subset of all possible local alignments that said local alignments allow you to take a closer look at two genomes and find common segments which could indicate a common evolutionary history this note discusses global alignments i ll write something soon that discusses local alignments as a second look to support my work on mtdna generally nearest neighbor the nearest neighbor algorithm can provably generate perfect accuracy for certain euclidean datasets that said dna is obviously not euclidean and as such the results i proved do not hold for dna datasets however common sense suggests we might as well try it and it turns out you get really good results that are significantly better than chance to apply the nearest neighbor algorithm to an mtdna genome we simply find the genome that has the most bases in common with i e its best match in the dataset and hence its nearest neighbor symbolically you could write as for accuracy using nearest neighbor to predict the ethnicity of each individual in my dataset produces an accuracy of 30 87 and because there are 75 global ethnicities chance implies an accuracy of as such we can conclude that the nearest neighbor algorithm is not producing random results and more generally produces results that provide meaningful information about the ethnicities of individuals based solely upon their mtdna which is remarkable since ethnicity is a complex trait that clearly should depend upon paternal ancestry as well the global distribution of mtdna it turns out the distribution of mtdna is truly global and a result we should not be surprised that the accuracy of the nearest ...
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