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Although recent advances in sequencing and computational analyses have facilitated use of endogenous retroviruses (ERVs) for deciphering coevolution among retroviruses and their hosts, sampling effects from different host populations present major challenges. Here we utilize available whole-genome data from wild and domesticated European rabbit (Oryctolagus cuniculus sp.) populations, sequenced as DNA pools by paired-end Illumina technology, for identifying segregating reference as well as nonreference ERV loci, to reveal their variation along the host phylogeny and domestication history. To produce new viruses, retroviruses must insert a proviral DNA copy into the host nuclear DNA. Occasional proviral insertions into the host germline have been passed down through generations as inherited ERVs during millions of years. These ERVs represent retroviruses that were active at the time of infection and thus present a remarkable record of historical virus–host associations. To examine segregating ERVs in host populations, we apply a reference library search strategy for anchoring ERV-associated short-sequence read pairs from pooled whole-genome sequences to reference genome assembly positions. We show that most ERVs segregate along host phylogeny but also uncover radiation of some ERVs, identified as segregating loci among wild and domestic rabbits. The study targets pertinent issues regarding genome sampling when examining virus–host evolution from the genomic ERV record and offers improved scope regarding common strategies for single-nucleotide variant analyses in host population comparative genomics.

Background: Electroencephalogram (EEG) interpretation plays a critical role in the clinical assessment of neurological conditions, including epilepsy. Manual analysis requires highly specialized and heavily trained personnel. Moreover, the rate of capturing abnormal events makes interpretation time-consuming, resource-hungry, and, overall, an expensive process.

Automatic detection offers the potential to improve the quality of patient care by shortening the time to diagnosis, managing big data, and optimizing the allocation of human resources.

Findings: We present MindReader, an unsupervised method for EEG signals. First, MindReader processes the signal through an autoencoder in order to detect EEG abnormalities. Next, patterns are hypothesized by a Hidden Markov Model. Our algorithm automatically generates labels for non-pathological phases, thus reducing the search space for trained personnel.

Conclusions: MindReader is effective in detecting EEG abnormalities in focal and generalized epilepsy.

Syncytin is a fossil protein exapted from retroviruses that fulfills a pivotal role during trophoblast implantation and placental metabolite exchange. However, little is yet known about the distribution of syncytin across vertebrates. Here, we searched a library of more than 150 high-quality assemblies across 17 taxonomical orders for syncytin homologs. We identified and syntenically aligned over 300 loci insertions, including not previously known insertions. Additionally, we predicted the tridimensional structures of the recover sequences using AlphaFold2. Sequence conservation and phylogenomics analyses suggest a complex dynamic of multiple retroviral insertions at different time points with sequence conservation specific to clades that share a similar histo-physiological placental type. This research has widened our knowledge about the physiology of placentation through a better understanding of the evolutionary role of syncytin.