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Electrophysiological evidence suggested primarily the involvement of area MT in depth cue integration in macaques, as opposed to human imaging data pinpointing area V3B/KO. To clarify this conundrum, we decoded monkey fMRI responses evoked by stimuli signaling near or far depths defined by binocular disparity, relative motion and their combination, and we compared results with those from an identical experiment previously performed in humans.Responses in macaque area MT are more discriminable when two cues concurrently signal depth, and information provided by one cue is diagnostic of depth indicated by the other. This suggests that monkey area MT computes fusion of disparity and motion depth signals, exactly as shown for human area V3B/KO. Hence, these data reconcile previously reported discrepancies between depth processing in human and monkey by showing the involvement of the dorsal stream in depth cue integration using the same technique, despite the engagement of different regions. data describing fig 1-8 and sfig 1-12data.zip

Perception adapts to mismatching multisensory information, both when different cues appear simultaneously and when they appear sequentially. While both multisensory integration and adaptive trial-by-trial recalibration are central for behavior, it remains unknown whether they are mechanistically linked and arise from a common neural substrate. To relate the neural underpinnings of sensory integration and recalibration, we measured whole-brain magnetoencephalography while human participants performed an audio-visual ventriloquist task. Using single-trial multivariate analysis, we localized the perceptually-relevant encoding of multisensory information within and between trials. While we found neural signatures of multisensory integration within temporal and parietal regions, only medial superior parietal activity encoded past and current sensory information and mediated the perceptual recalibration within and between trials. These results highlight a common neural substrate of sensory integration and perceptual recalibration, and reveal a role of medial parietal regions in linking present and previous multisensory evidence to guide adaptive behavior. PARK_KAYSER_RecalMEG_2019Please see README file.

Dogs were present in the Americas prior to the arrival of European colonists, but the origin and fate of these pre-contact dogs are largely unknown. We sequenced 71 mitochondrial and seven nuclear genomes from ancient North American and Siberian dogs spanning ~9,000 years. Our analysis indicates that American dogs were not domesticated from North American wolves. Instead, American dogs form a monophyletic lineage that likely originated in Siberia and dispersed into the Americas alongside people. After the arrival of Europeans, native American dogs almost completely disappeared, leaving a minimal genetic legacy in modern dog populations. Remarkably, the closest detectable extant lineage to pre-contact American dogs is the canine transmissible venereal tumor, a contagious cancer clone derived from an individual dog that lived up to 8,000 years ago. Mitochondrial DNA FASTA fileFASTA file containing 1166 dog mtDNA genomes used in this studyfull_mtDNA_alignment.fastaNEXUS treeMaximum likelihood tree (RAxML) of 1166 dogs mtDNA genomes used in this studyfull_mtDNA_alignment.treExcel sheetPublication source of the 1166 mtDNA genomes used in this studyfull_mtDNA_alignment.xlsxPlink (bed) fileContains genotype for dogs 54 dogsfull_data.bedPlink file (bim)Contains genotype for 54 dogsfull_data.bimPlink file (fam)Contains genotype for 54 dogsfull_data.famNJ tree in Figure 2bNJ tree in Figure 2b (see Table S2 for more info)Figure_b.treNexus fileNexus file used for producing Figure S12 (MKV model in MrBayes)Binary_char_MKV.nexNEXUS treeBayesian tree in Figure S12 (see Table S2 for more info)Figure_S12.tre

In previous papers, we introduced a normative scheme for scene construction and epistemic (visual) searches based upon active inference. This scheme provides a principled account of how people decide where to look, when categorising a visual scene based on its contents. In this paper, we use active inference to explain the visual searches of normal human subjects; enabling us to answer some key questions about visual foraging and salience attribution. First, we asked whether there is any evidence for 'epistemic foraging'; i.e. exploration that resolves uncertainty about a scene. In brief, we used Bayesian model comparison to compare Markov decision process (MDP) models of scan-paths that did - and did not - contain the epistemic, uncertainty-resolving imperatives for action selection. In the course of this model comparison, we discovered that it was necessary to include non-epistemic (heuristic) policies to explain observed behaviour (e.g., a reading-like strategy that involved scanning from left to right). Despite this use of heuristic policies, model comparison showed that there is substantial evidence for epistemic foraging in the visual exploration of even simple scenes. Second, we compared MDP models that did - and did not - allow for changes in prior expectations over successive blocks of the visual search paradigm. We found that implicit prior beliefs about the speed and accuracy of visual searches changed systematically with experience. Finally, we characterised intersubject variability in terms of subject-specific prior beliefs. Specifically, we used canonical correlation analysis to see if there were any mixtures of prior expectations that could predict between-subject differences in performance; thereby establishing a quantitative link between different behavioural phenotypes and Bayesian belief updating. We demonstrated that better scene categorisation performance is consistently associated with lower reliance on heuristics; i.e., a greater use of a generative model of the scene to direct its exploration. Scan-path dataThis file contains the scan-paths of the subjects that performed the "Scene construction task" described in the paper "Human visual exploration reduces uncertainty about the sensed world". There are some additional files that can be used to regenerate the figures and results in this paper.

chicken.all_samples.galGal3.tpm.refgene.oscData for the analysis of the chicken chromosome Z. FANTOM5 chicken libraries consisted of 25 CAGE libraries including: chicken aortic smooth muscles, hepatocytes, mesenchymal stem cells, leg buds, wing buds, embryo extra-embryonic tissue (day 7 and day 15), and whole body developmental time course (from 5 hours 30 minutes to 20 days). The number of available datapoints to which TPM was normalized was limited by the number of annotated chicken RefSeq transcripts (which was approximately six times smaller than human, N = 4,426 on autosomes, and N = 241 on chromosome Z). Consequently, the cutoff for a gene to be classified as “on” was adjusted six times higher to 60 TPM.human.primary_cell.hCAGE.hg19.tpm.refgene.oscThe FANTOM5 dataset for human primary cells.human.cell_line.hCAGE.hg19.tpm.refgene.oscThe FANTOM5 dataset for human cancer cell-lines.human.tissue.hCAGE.hg19.tpm.refgene.oscThe FANTOM5 dataset for human tissue. CAGE tags were mapped to RefSeq transcripts +/-500 base pairs (bps) from their TSSes and normalized to tags per million (TPM), as previously described [37,45]. The signal of ten TPM was chosen as the cutoff for a gene to be classified as “on” (this cutoff was accepted as the standard for human data throughout the consortium). FANTOM5 is the most comprehensive expression dataset ever generated, including 952 human and 396 mouse tissues, primary cells and cancer cell-lines. FANTOM5 is based on cap analysis of gene expression (CAGE) a unique technology that characterizes TSSes across the entire genome in an unbiased fashion and at a single-base resolution level [21]. CAGE automatically sums expression levels of all transcripts beginning at a given transcription start site.raw_Z_Exp_Anc_LData for Fig 2 "The comparison of change in gene expression (Z) since the human-Chimpanzee common ancestor for five somatic tissues."SUPPLEMENTARY TABLESData in Table S3 underlies Figure 4. Data in Table S7 partially underlies Fig 1. Data in Tables S4 underlies Fig 3. Data in Tables S10-12 underlies Fig S1.data for Fig1R environment containing data underlying Fig1. The environment contains the following variables sorted identically as the gene list in refSeqs: chromosome (chromosomal location), chromosome_short (location on autosomes,chrX, or chrY?), data_matrix (F5 data matrix in TPM for human tissues)‚ MAX (maximal expression for each RefSeq)‚ max (maximal expression for each tissue)‚ strata_classification (strata classification for genes on chromosome X)‚ refSeqs_2entrezIDs (entrez ids mapped to refseqs)‚ boe (the breadth of expression)env_fig1GC-contents data for for Fig S6 and S7This R environment contains GC-contents data for either proximal promoters or isochore around the TSS (marked as big). The data is calculated for either masked or unmasked genome seqeuence.env_gc_contentsdata for Fig S3numbers of ENCODE transcription factor binding sites mapped to TSSes of RefSeq genes in symmetrical windows of different sizes (from 250 to 20000 bps) and depending on ENCODE quality cut-off (strict or all).FigS3_data.txtdata underlying Fig S8Breadth of expression and maximal expression is compared in three groups of observations: (1) autosomal paralogs of X-linked genes, (2) other autosomal paralogs matched by age, (3) X-linked paralogs. Newly formed paralogs are defined as those mapped by phylogenetic timing to taxa Theria or younger. Pre-existing duplications are defined as those descending from duplication notes mapped by phylogenetic timing to taxa Amniota or older.FigS8_data.txtdata underlying Fig7Fig7_data.txtTreeFam data for timing of gene duplications in R environmentsThese files are R environments. Use load() to load them into your R session! You ls() to view contents. You may use attach() syntax to load the namespace or access data members of the environment using the "$" reference operator. There is no warranty for this softwareenv_duplicator_baseAdditional TreeFam gene duplication data with duplication timingenv_duplicator_vectors X chromosomes are unusual in many regards, not least of which is their nonrandom gene content. The causes of this bias are commonly discussed in the context of sexual antagonism and the avoidance of activity in the male germline. Here, we examine the notion that, at least in some taxa, functionally biased gene content may more profoundly be shaped by limits imposed on gene expression owing to haploid expression of the X chromosome. Notably, if the X, as in primates, is transcribed at rates comparable to the ancestral rate (per promoter) prior to the X chromosome formation, then the X is not a tolerable environment for genes with very high maximal net levels of expression, owing to transcriptional traffic jams. We test this hypothesis using The Encyclopedia of DNA Elements (ENCODE) and data from the Functional Annotation of the Mammalian Genome (FANTOM5) project. As predicted, the maximal expression of human X-linked genes is much lower than that of genes on autosomes: on average, maximal expression is three times lower on the X chromosome than on autosomes. Similarly, autosome-to-X retroposition events are associated with lower maximal expression of retrogenes on the X than seen for X-to-autosome retrogenes on autosomes. Also as expected, X-linked genes have a lesser degree of increase in gene expression than autosomal ones (compared to the human/Chimpanzee common ancestor) if highly expressed, but not if lowly expressed. The traffic jam model also explains the known lower breadth of expression for genes on the X (and the Z of birds), as genes with broad expression are, on average, those with high maximal expression. As then further predicted, highly expressed tissue-specific genes are also rare on the X and broadly expressed genes on the X tend to be lowly expressed, both indicating that the trend is shaped by the maximal expression level not the breadth of expression per se. Importantly, a limit to the maximal expression level explains biased tissue of expression profiles of X-linked genes. Tissues whose tissue-specific genes are very highly expressed (e.g., secretory tissues, tissues abundant in structural proteins) are also tissues in which gene expression is relatively rare on the X chromosome. These trends cannot be fully accounted for in terms of alternative models of biased expression. In conclusion, the notion that it is hard for genes on the Therian X to be highly expressed, owing to transcriptional traffic jams, provides a simple yet robustly supported rationale of many peculiar features of X’s gene content, gene expression, and evolution.

During meiosis, crossover recombination is essential to link homologous chromosomes and drive 22 faithful chromosome segregation. Crossover recombination is non-random across the genome, 23 and centromere-proximal crossovers are associated with an increased risk of aneuploidy, 24 including Trisomy 21 in humans. Here, we identify the conserved Ctf19/CCAN kinetochore sub- 25 complex as a major factor that minimizes potentially deleterious centromere-proximal crossovers 26 in budding yeast. We uncover multi-layered suppression of pericentromeric recombination by the 27 Ctf19 complex, operating across distinct chromosomal distances. The Ctf19 complex prevents 28 meiotic DNA break formation, the initiating event of recombination, proximal to the centromere. 29 The Ctf19 complex independently drives the enrichment of cohesin throughout the broader 30 pericentromere to suppress crossovers, but not DNA breaks. This non-canonical role of the 31 kinetochore in defining a chromosome domain that is refractory to crossovers adds a new layer 32 of functionality by which the kinetochore prevents the incidence of chromosome segregation 33 errors that generate aneuploid gametes. eLife_Dryad_Recombine_dataanalysis files from the Recombine software for clb2-iml3 tetrads

Spatio-temporal patterns of the spread of infectious diseases are commonly driven by environmental and ecological factors. This is particularly true for vector-borne diseases because vector populations can be strongly affected by host distribution as well as by climatic and landscape variables. Here, we aim to identify environmental drivers for bluetongue virus (BTV), the causative agent of a major vector-borne disease of ruminants that has emerged multiple times in Europe in recent decades. In order to determine the importance of climatic, landscape and host-related factors affecting BTV diffusion across Europe, we fitted different phylogeographic models to a dataset of 113 time-stamped and geo-referenced BTV genomes, representing multiple strains and serotypes. Diffusion models using continuous space revealed that terrestrial habitat below 300 m altitude, wind direction and higher livestock densities were associated with faster BTV movement. Results of discrete phylogeographic analysis involving generalized linear models broadly supported these findings, but varied considerably with the level of spatial partitioning. Contrary to common perception, we found no evidence for average temperature having a positive effect on BTV diffusion, though both methodological and biological reasons could be responsible for this result. Our study provides important insights into the drivers of BTV transmission at the landscape scale that could inform predictive models of viral spread and have implications for designing control strategies.

During online speech processing, our brain tracks the acoustic fluctuations in speech at different timescales. Previous research has focused on generic timescales (for example, delta or theta bands) that are assumed to map onto linguistic features such as prosody or syllables. However, given the high intersubject variability in speaking patterns, such a generic association between the timescales of brain activity and speech properties can be ambiguous. Here, we analyse speech tracking in source-localised magnetoencephalographic data by directly focusing on timescales extracted from statistical regularities in our speech material. This revealed widespread significant tracking at the timescales of phrases (0.6–1.3 Hz), words (1.8–3 Hz), syllables (2.8–4.8 Hz), and phonemes (8–12.4 Hz). Importantly, when examining its perceptual relevance, we found stronger tracking for correctly comprehended trials in the left premotor (PM) cortex at the phrasal scale as well as in left middle temporal cortex at the word scale. Control analyses using generic bands confirmed that these effects were specific to the speech regularities in our stimuli. Furthermore, we found that the phase at the phrasal timescale coupled to power at beta frequency (13–30 Hz) in motor areas. This cross-frequency coupling presumably reflects top-down temporal prediction in ongoing speech perception. Together, our results reveal specific functional and perceptually relevant roles of distinct tracking and cross-frequency processes along the auditory–motor pathway. AK_CK_speech_tracking_2018