There is growing interest in and provision of cadaveric simulation courses for surgical trainees. This is being driven by the need to modernize and improve the efficiency of surgical training within the current challenging training climate. The objective of this systematic review is to describe and evaluate the evidence for cadaveric simulation in postgraduate surgical training.A PRISMA‐compliant systematic literature review of studies that prospectively evaluated a cadaveric simulation training intervention for surgical trainees was undertaken. All relevant databases and trial registries were searched to January 2019. Methodological rigour was assessed using the widely validated Medical Education Research Quality Index (MERSQI) tool.A total of 51 studies were included, involving 2002 surgical trainees across 69 cadaveric training interventions. Of these, 22 assessed the impact of the cadaveric training intervention using only subjective measures, five measured impact by change in learner knowledge, and 23 used objective tools to assess change in learner behaviour after training. Only one study assessed patient outcome and demonstrated transfer of skill from the simulated environment to the workplace. Of the included studies, 67 per cent had weak methodology (MERSQI score less than 10·7).There is an abundance of relatively low‐quality evidence showing that cadaveric simulation induces short‐term skill acquisition as measured by objective means. There is currently a lack of evidence of skill retention, and of transfer of skills following training into the live operating theatre. This systematic review of cadaveric simulation for surgical training showed that there is low‐quality evidence of short‐term skill acquisition after training, as measured by objective means. There is a lack of evidence of skill retention longitudinally.Making a comeback? Medical Subject Headings (MeSH) terms and text words from the MEDLINE search strategy ; cadaveric training ); assessment of educational impact , post‐test assessment and evidence of skill transfer (if applicable)).The data were extracted from the full‐text articles using piloted data extraction forms, by two reviewers working independently. Data items collected included: participant characteristics ; study characteristics (single‐centre Included studies were assigned a level of evidence score using a modified version of the Oxford Centre for Evidence‐Based Medicine (OCEBM) classification, which has been adapted by the European Association of Endoscopic Surgery and is used widely in educational systematic reviewsFig. A qualitative, narrative synthesis of evidence was undertaken, structured around an adapted Kirkpatrick's hierarchy for assessing the educational impact of a teaching intervention Fig. .Fig. Tables –The initial search generated 5726 results, of which 5073 were clearly ineligible and rejected at title review  and the UK (8), with the remainder from Canada (4), Australia (2) and one each from Germany and Japan. All studies, except oneThe number of participants in the included studies ranged from three to 390, totalling 2002 individual participants across 69 cadaveric training interventions, representing the breadth of surgical training grades.Tables –In total, 12 surgical specialties and subspecialties were included Tables –S5. MThe mean MERSQI score was 9·4 (range 5–14). In terms of level of evidence, only two of 51 studies were OCEBM level 1b , six studies were OCEBM 2a , six were OCEBM 2b , and 37 were OCEBM level 3 .A linear relationship was observed between Kirkpatrick level and mean MERSQI score, suggesting that quality of evidence is linked with robust methodology.An assessment of educational impact of the training intervention was made using objective measures in 28 of the 51 included studies, and using subjective measures only in the other 23 studies. Sixteen of the 28 studies that used objective outcome measures attempted to measure skill transfer after training.Twenty‐two of the 51 studies measured the educational impact of a cadaveric training intervention using subjective measures of learner reaction/opinion. Oneviva voce and oral checklist examinations.Five studies assessed the educational impact of the cadaveric training intervention by measuring change in learner knowledge. OneCadaveric simulation training made no difference to the postintervention test scores in the study of AlJamal and colleaguesTwenty‐three studies assessed the educational impact of a cadaveric training intervention by attempting to measure a change in learner behaviour. Objective assessment methods of learner behaviour were highly variable, and included operational metrics  and final product analysis. Various score‐based methods were also used, including procedure scores, global rating scale (GRS), OSATS  and the GOALS  scale. Sevenet al.Compared with no simulation training, cadaveric simulation‐trained learners showed significant improvement in most of the tested skill domainset al.Conversely, Anastakis and co‐workersversus experienced performance, oneversus low‐fidelity simulation‐trained cohorts, and oneOf the 16 cohort studies measuring change in learner behaviour, five were comparative in design. Three studieset al.The primary objective of the three studies comparing inexperienced and experienced performance was construct validation of the simulator and/or assessment tools used in the studies. Zirkle and colleagueset al.When comparing cadaveric simulation‐trained and low‐fidelity (VR) simulation‐trained cohorts performing laparoscopic sigmoid colectomy, LeBlanc Of the 11 non‐comparative descriptive studies, all reported improvement in trainee performance after cadaveric simulation training, using a variety of outcome measures such as FPA, GRS and operational metrics.P < 0·02). Initial trauma resuscitation time after training decreased from approximately 25 to 10 min in 80 patients treated by the participantsOnly oneA wide variety of cadaveric models were used in the included studies. Three studiesFresh cadavers were used in 12Fresh‐frozen cadavers, used in 12in vivo.Soft‐fix Thiel embalming techniques were used in two studieset al.Only one studyAlmost half of the included studies  assessed the impact of the cadaveric training using subjective measures only, representing the lowest level of impact in educational research (Kirkpatrick level 1). The second most prevalent category was studies measuring the impact of the training intervention by learner behaviour change  (Kirkpatrick level 3). Of these, seven were RCTs and 16 were cohort studies. Of the 23 studies assessing behaviour change following cadaveric training, only oneThe objective of this systematic review was to describe and evaluate evidence for the current use of cadaveric simulation in postgraduate surgical training. Fifty‐one studies involving 2002 surgical trainees across 69 cadaveric training interventions were included. Although there was research activity encompassing the breadth of surgical specialties, most studies were within general surgery (14), trauma and orthopaedic surgery (9) and neurosurgery (7). The majority were conducted in the USA (35) and UK (8). A wide range of methodology was used. Eight of 51 studies were RCTs (OCEBM level 1b and 2a), six were parallel cohort studies (OCEBM level 2b) and 37 were non‐comparative descriptive studies (OCEBM level 3).There is evidence from three RCTsThe mean overall MERSQI score correlated well with the Kirkpatrick level: the higher the level of impact measured, the better the study methodology. Previous predictive studiesSeveral studies reported mixed‐modality training interventions, making assessment of the cadaveric component of the training in isolation impossible. There was also inadequate or absent description of the cadaveric model used in more than one‐third of the studies, which renders results pertaining to the face and content validity of training impossible to assess.Half of the studies were published in the last 3 years, reflecting the recent explosion in popularity of cadaveric simulation training. Despite the clear attraction of cadaveric simulation training as measured by subjective means, there remains a dearth of evidence that there is retention and translation of skills learnt in the cadaveric laboratory into the operating theatre. The major ongoing challenge within educational research is demonstrating effective, sustained changes in learner behaviour, and improved patient outcome following a training interventionThis review has shown that there is an abundance of relatively low‐quality evidence indicating that cadaveric simulation may induce short‐term skill acquisition as measured by objective means. Adequately powered studies are needed to show whether skills are retained and transferable into the operating theatre before major investment in cadaveric simulation for surgical training can be recommended.The authors declare no conflict of interest.Table S1 MEDLINE search strategyTable S2 Kirkpatrick Level 1: Studies that subjectively measure the impact of the training intervention by learner opinionTable S3 Kirkpatrick Level 2: Studies that objectively measure the impact of the training intervention by learner knowledgeTable S4 Kirkpatrick Level 3: Studies that objectively measure the impact of the training intervention by change in learner behaviourTable S5 Kirkpatrick Level 4: Studies that objectively measure the impact of the training intervention by change in patient outcomeClick here for additional data file.


Landscape genetics has provided many insights into how heterogeneous landscape features drive processes influencing spatial genetic variation in free‐living organisms. This rapidly developing field has focused heavily on vertebrates, and expansion of this scope to the study of infectious diseases holds great potential for landscape geneticists and disease ecologists alike. The potential application of landscape genetics to infectious agents has garnered attention at formative stages in the development of landscape genetics, but systematic examination is lacking. We comprehensively review how landscape genetics is being used to better understand pathogen dynamics. We characterize the field and evaluate the types of questions addressed, approaches used and systems studied. We also review the now established landscape genetic methods and their realized and potential applications to disease ecology. Lastly, we identify emerging frontiers in the landscape genetic study of infectious agents, including recent phylogeographic approaches and frameworks for studying complex multihost and host‐vector systems. Our review emphasizes the expanding utility of landscape genetic methods available for elucidating key pathogen dynamics (particularly transmission and spread) and also how landscape genetic studies of pathogens can provide insight into host population dynamics. Through this review, we convey how increasing awareness of the complementarity of landscape genetics and disease ecology among practitioners of each field promises to drive important cross‐disciplinary advances. AND (disease* OR pathogen* OR parasit* OR virus* OR virol* OR epidem* OR infect* OR transmi*))We conducted a literature search in February 2018 using the ISI Web of Science database with the following terms:n = 15), meeting abstracts (n = 1), purely methods‐based papers (n = 6) and articles that identified as or mentioned landscape genetics but did not sufficiently incorporate landscape factors or genetic data into the study (n = 32), studies that referred to any of our pathogen‐related search terms without it being a primary motivation for the study (n = 21), and studies that used words like “transmit” or “parasite” outside of the context of infectious agents (such as the transmission of behaviours) (n = 6). One paper was excluded due to a lack of access at our institutions. Studies that qualitatively discussed landscape with respect to genetic variation were kept, although one might argue that landscape genetics requires quantitative testing of landscape effects. We classified each paper according to the type of host system studied , the type of pathogen studied  and the source of genetic data , and we estimated the severity of disease that each studied pathogen causes in its sampled host or vector. We also categorized each article according to its general conceptual approach. Most examples described in this study were found in our literature search, while several other examples were cited by papers from our search and subsequently also discussed here.The search returned 133 results. We read each article and retained the 51 empirical papers that used landscape genetic methods to address questions related to pathogens  used genetic data from the host for comparison with landscape features  is facilitated by movement of free‐living hosts or vectors, the risk of spread of the agent with respect to heterogeneous landscape features can be estimated by relating those features to host/vector gene flow. This approach represents a direct application of the conventional landscape genetic paradigm to the study of disease transmission, where a typical animal landscape genetic study is interpreted in the context of the pathogenic organism. This can provide useful indications of the potential for individuals carrying pathogens to disperse across particular landscape features, which can be used to inform management efforts. For example, DeYoung et al.  identifiUsing host or vector genetic data to predict pathogen spread is attractive as it avoids sampling of the agent itself, which may be substantially more difficult, especially in wildlife populations. Identification of infected hosts often requires laboratory testing and may require specific, potentially invasive sampling approaches  for accurate diagnosis. In addition, extensive sampling may be required to obtain adequate sample sizes when prevalence is low and must be conducted strategically to capture spatial heterogeneity. Direct study of pathogens may not be possible when predicting the risk of spread in as‐yet uninfected populations, or where identification of infected individuals is unreliable. However, a direct association of host or vector gene flow with spread of the microparasite should not be assumed given the potential influence of other factors such as other host and/or vector species, environmental persistence, pathogen reproductive mode or simply transmission via movement of nonreproducing hosts  are typically of utmost interest due to their potential implications for wildlife conservation, agricultural production and human health. Among the studies identified in our literature search, agents that form chronic infections and have moderate or high pathogenic effects on their sampled host/vector organisms were more frequently investigated  on functional connectivity using techniques such as least‐cost paths  as a predictor of elletier  identifielletier  used reselletier  tested l3.4Graph theoretical approaches, which describe connections (edges) between discrete objects (nodes) Newman, , are a fNetwork topology may be used to identify populations or habitat patches that form important “stepping stones” for maintaining genetic connectivity across an entire system. Such an approach enables experimental simulation whereby nodes may be selectively removed and the overall effect on the system's topology  assessed. Metrics pertaining to the importance of individual nodes to network topology can be correlated with variables such as landscape to identify important drivers of network processes. Despite their unique applications, graph theory and network approaches are relatively underutilized in landscape genetics compared to methods specifically derived from population genetics and landscape ecology. However, among studies of infectious agents, network approaches in wildlife are becoming increasingly popular Craft, . Epidemi3.5While landscape genetics initially was used to investigate spatial genetic patterns using relatively few neutral markers, the more modern advent of landscape genomics allows the study of variation across the entire genome and effectively expands the scope of landscape genetics to include the study of functional, adaptive genetic variation. Next‐generation sequencing (NGS) techniques such as restriction‐site‐associated DNA sequencing (RADseq) require minimal prior knowledge of the genome under study and can genotype thousands of SNPs randomly distributed across the genome. Some of these SNPs will by chance be located within or near (and thus linked to) genes or regulatory regions that are under selection. Genomewide association studies (GWAS) can make use of this information to identify loci linked to phenotypic variation such as disease susceptibility. Genotyping of candidate loci identified using quantitative trait locus mapping and GWAS can be expanded across a large number of individuals using methods such as targeted sequence capture  tests  using GWAS, as well as Fst outlier loci, to investigate parasite‐driven genetic structure across a landscape. Garroway et al. (Parus major) to conduct a GEA with respect to avian malaria infection risk and contrasted this with neutral gene flow. Another targeted approach employed exon capture to genotype SNPs that were then subjected to outlier and environmental association tests to investigate variation in pathogen exposure with respect to environment  have yet to see widespread usage. Current examples focus on established candidate loci from the host known to be relevant to immune function from prior research, such as in commercially important salmon  that are yet to be reflected in host genetic structure (Landguth et al., The application of microparasite molecular markers to the study of host movements should be considered with some caveats in mind. As we have discussed previously, host movements and parasite transmission are not necessarily coupled (Mazé‐Guilmo et al., 4.3Phylogenetic approaches can reconstruct very recent epidemic histories, providing insights into particular transmission events and pathways that may be contextualized temporally and spatially (Corman et al., Several approaches may be used for relating phylogenetic information with landscape variables. Neighbour joining trees can identify clusters for quantifying population‐level landscape genetic relationships (Joannon et al., 5Overall, landscape genetics has been relatively underutilized in disease ecology research. We believe this is partly due to a lack of cross‐disciplinary awareness between the two fields, but also a lack of a clear landscape genetic framework specifically designed for tackling pathogen systems, which are often complex and do not facilitate easy translation of existing landscape genetic tools. However, we note there has been a recent effort to develop new frameworks for such research, expanding the utility of the landscape genetic toolset. These tools will increase our capacity to study complex multihost and host‐vector systems, improving the integration of multiple genetic datasets and accounting for interspecific interactions. Improved understanding of host–parasite associations will facilitate the use of microparasite genetic markers to provide insights into host processes that may be difficult to detect using conventional host landscape genetics. Identification of idealized systems that are designed to target specific ecological questions will also facilitate progress in this field. Recent methods that enable the incorporation of quantitative landscape data into spatio‐temporal phylogenetic reconstructions of recent transmission events, coupled with advances in high‐throughput sequencing, hold great promise for studying how the landscape shapes transmission processes. We believe that these recent developments represent a renewed interest in advancing landscape genetic research in pathogen systems, which we expect will translate to continued growth of research in this area.None declared. Click here for additional data file.


Drug‐induced aseptic meningitis (DIAM) is an inflammation of the membranes of the central nervous system caused by certain medications. It is a rare clinical entity whose risk factors are not yet fully elucidated. A local pattern of disproportionality within a global database of suspected adverse drug reactions (ADRs) revealed an increased reporting of aseptic meningitis and amoxicillin‐clavulanic acid (AC) in males. The aim of this report is to explore the clinical probability of a higher risk in males to support the use of statistical methods to identify subgroups at risk for adverse drug reactions.Disproportionality analysis was performed for all drug‐adverse event (AE) pairs in the entire database and for the subsets of males and females. AC‐aseptic meningitis was highlighted for an increased disproportionality in the male subgroup in the absence of an elevated disproportionality measure for the database overall. A clinical review was undertaken.Clinical review revealed a similar statistical pattern of gender difference observed for amoxicillin, evidence to suggest a delayed type 4 hypersensitivity reaction with Th1 cells as a mechanism for amoxicillin‐aseptic meningitis, the existence of sex differences in immune responses (Th1/Th2), and an analogous increased risk of drug‐induced liver injury by AC in males.Subgroup disproportionality analysis has revealed a larger than expected number of reports of aseptic meningitis after amoxicillin and AC in males. Evidence synthesis supports the statistical finding. Further exploration of spontaneous databases with more extensive analyses could usher in a new era of “precision pharmacovigilance.” A higher than expected number of reports of aseptic meningitis after amoxicillin and amoxicillin‐clavulanate have been received from males into a global database of suspected adverse drug reactions.Sex differences in immune responses, such as delayed type hypersensitivity reactions, which can be a mechanism for drug induced aseptic meningitis, have been described.More extensive analyses of databases of spontaneous adverse drug reaction (ADR) reports could be used to assist in the identification of potential risk factors for ADRs.1Drug‐induced aseptic meningitis (DIAM) is an inflammation of the membranes of the central nervous system caused by the administration of certain medications. Clinically, patients experience symptoms of fever, headache, and changes in mental status. Diagnostically, analysis of cerebrospinal fluid (CSF) reveals the presence of an increased number of white blood cells, elevated protein level, but a normal glucose level. All microbiological cultures are negative. The diagnosis of DIAM is often one of exclusion of more common infectious causes of meningitis.Given the rarity of its occurrence, the incidence of DIAM is unknown and risk factors for it have not been fully elucidated. However, cases of DIAM caused by amoxicillin and amoxicillin‐clavulanic acid (AC) have been extensively reported in the literature, and a series of reviews have summarised a number of aspects of DIAM from the published case reports. The most commonly reported agents to cause DIAM are non‐steroidal anti‐inflammatory agents, antimicrobials, intravenous immunoglobulins, OKT3 antibodies, and anticonvulsants. The time‐to‐onset report can vary between several minutes to several months. Systemic lupus erythematous is the most common underlying condition reported with DIAM. Additionally, recurrent episodes of DIAM have been described in multiple case reports; such patients are predominantly females and a large proportion have underlying inflammatory diseases.20005 is the lower endpoint of a 99.9% credibility interval for the information component (IC), and as such was used to support analysis of subgroup specific associations between substances and AE. Initial manual assessment expanded the review to include amoxicillin, which included both an increased disproportionality in males as well as in the entire database. A thorough clinical review of the drug‐ADR‐risk group triplet was undertaken, which included further review of spontaneously reported ADRs, the reported case series, as well as the medical literature for evidence of biological plausibility of an increased risk in males.Disproportionality analysis was performed in VigiBase, the global database for individual case safety reports, for all drug‐adverse event (AE) pairs in the entire database and for the subsets of males and females using methodology for local pattern discovery.3As of September 9, 2018, there was a total of 2695 individual case safety reports in VigiBase, which included the preferred term (PT) “meningitis aseptic”; 56.7% of the reports described females, 37.3% males, and in 6.0% of the reports, gender was not stated. The most common drugs reported in association with aseptic meningitis were human immunoglobulins (35.3%) and ibuprofen (6.6%). There was a total of 47 reports with amoxicillin  and 24 for AC .0005 0.80), and 21 cases were observed and nine expected in females (IC0005–0.14). Further investigation revealed a similar pattern of gender difference for AC; 18 cases were observed compared with seven cases expected in males (IC0005 0.04); six cases were observed and nine expected in females (IC0005–3.14).For the combination of amoxicillin and the MedDRA PT, meningitis aseptic, 26 cases were observed compared with seven expected in males  direct chemical irritation of the meninges by drugs directly administered into the CSF, or (b) immunological hypersensitivity reactions. All types of immunological hypersensitivity reactions types 1‐4) have been implicated in various case reports of DIAM, suggesting that mechanisms may be different for the various types of drugs. have beeIn the case of amoxicillin and AC, there is growing evidence that it is a delayed type 4 hypersensitivity or T‐cell mediated hypersensitivity that underlies its associated cases of aseptic meningitis. Kastenbauer et al described a case of a 62‐year‐old man with aseptic meningitis after AC, in which additional investigation did not reveal evidence of an underlying type 1 or type 3 hypersensitivity mechanism.There is growing recognition of the existence of sex differences in immune responses. Contributing factors to the differential development and function of the immune system between males and females include sex chromosome genes and sex hormones as well as nutritional status and composition of the microbiome in the gastrointestinal tract. The results of these differences in immune responses is evidence of the differential susceptibility of males and females to autoimmune diseases, malignancies and infectious diseases, and of responses to vaccination. More specifically, there is evidence that females have higher CD4+ T cells and greater T cell activation and proliferation while males have higher CD8+ T cell frequencies. Females tend to be polarised towards Th2‐type responses, while males are more Th1‐biased and have more regulatory T cells.An analogy can be found with drug‐induced liver injury (AC‐DILI) and AC. deLemos et al examined a large cohort of AC‐DILI cases prospectively enrolled from the United States DILI Network. A total of 117 cases of AC‐DILI were identified and compared with the remaining 921 cases in the registry. 62% of DILI cases ascribed to AC occurred in males, compared with 39% of DILI ascribed to all other antimicrobials. Of 31 liver specimens available for pathologic examination, almost all revealed immune‐allergic features (21) with eosinophils, 28 with granulomas, thought to be caused by infiltration of portal triads within the liver by cytotoxic CD8+ cells.4Disproportionality analyses within spontaneous reporting systems are typically employed to identify potential causal relationships between drugs and adverse drug reactions. Previous work has shown that disproportionality analyses within subsets may reveal local patterns not detected in routine statistical screening of large databases. Such an analysis within a global database of suspected adverse drug reactions has revealed a larger than expected number of reports of aseptic meningitis after AC and amoxicillin in males. Biological plausibility for the statistically detected signal is supported by data suggesting a T‐cell mediated mechanism for amoxicillin‐induced aseptic meningitis as well as evidence of sex differences in immune responses, specifically increased frequencies in males of type 4 hypersensitivity reactions of a Th1‐type and/or involving a predominance of CD8+ cells. Further elucidation of the interindividual variation in immune responses may lead to better understanding of drug hypersensitivity reactions.Risk characterisation is typically performed at the time of clinical signal assessment, looking for any obvious patterns between the patients described within the case series. This exercise has revealed the potential for risk identification at the level of statistical screening within large databases. Further exploration of spontaneous databases with more extensive analyses could usher in a new era of “precision pharmacovigilance.”The authors state that no ethical approval was needed.The opinions expressed in this piece are not necessarily those of the national pharmacovigilance centres of the WHO Programme for International Drug Monitoring or of the WHO.The author has no conflicts of interest that are directly relevant to the content of this piece.


ValIdated Systematic IntegratiON of epigenomic data in mouse and human hematopoiesis. The epigenomic data included nuclease accessibility in chromatin, CTCF occupancy, and histone H3 modifications for 20 cell types covering hematopoietic stem cells, multilineage progenitor cells, and mature cells across the blood cell lineages of mouse. The analysis used the Integrative and Discriminative Epigenome Annotation System (IDEAS), which learns all common combinations of features (epigenetic states) simultaneously in two dimensions—along chromosomes and across cell types. The result is a segmentation that effectively paints the regulatory landscape in readily interpretable views, revealing constitutively active or silent loci as well as the loci specifically induced or repressed in each stage and lineage. Nuclease accessible DNA segments in active chromatin states were designated candidate cis‐regulatory elements in each cell type, providing one of the most comprehensive registries of candidate hematopoietic regulatory elements to date. Applications of VISION resources are illustrated for the regulation of genes encoding GATA1, GATA2, GATA3, and Ikaros. VISION resources are freely available from our website http://usevision.org.Members of the GATA family of transcription factors play key roles in the differentiation of specific cell lineages by regulating the expression of target genes. Three GATA factors play distinct roles in hematopoietic differentiation. In order to better understand how these GATA factors function to regulate genes throughout the genome, we are studying the epigenomic and transcriptional landscapes of hematopoietic cells in a model‐driven, integrative fashion. We have formed the collaborative multi‐lab VISION project to conduct  ATAC‐seqAssay for Transposase‐Accessible Chromatin using sequencingBlymphoid B cellscCREcandidate cis‐regulatory elementCFUEcolony‐forming units erythroidCFUMkcolony‐forming units megakaryocyticChIP‐seqChromatin ImmunoPrecipitation assayed using sequencingCLPcommon lymphoid progenitor cell populationCMPcommon myeloid progenitor cell populationDNase‐seqDNase accessible chromatin assayed using sequencingENCODEEncyclopedia of DNA ElementsERYerythroblastsG1E and ER4cell lines that serve as a model for GATA1‐dependent erythroid maturationGMPgranulocyte monocyte progenitor cell populationGTExGenotype and Tissue Expression projectGWASGenome Wide Association StudyH3K4me1Histone H3 monomethylated on lysine 4, associated with enhancersH3K4me3Histone H3 trimethylated on lysine 4, associated with promotersH3K9me3Histone H3 trimethylated on lysine 9, associated with heterochromatinH3K27acHistone H3 acetylated on lysine 27, associated with active regulatory elementsH3K27me3Histone H3 trimethylated on lysine 27, associated with transcriptional repressionH3K36me3Histone H3 trimethylated on lysine 36, associated with transcriptional elongationHi‐Cgenome‐wide assay for chromosome conformation captureHPC7cell line model of a multipotent myeloid progenitor cellIDEASIntegrative and Discriminative Epigenome Annotation SystemIHECInternational Human Epigenome ConsortiumiMkimmature megakaryocytic cell populationLSKlineage minus, Sca1+, Kit+ cell population (includes hematopoietic stem cells and early multilineage progenitor cells)MEPmegakaryocytic erythroid progenitor cell populationMkmegakaryocytesMONmonocytesNEUneutrophilsNKnatural killer cellsPLTplateletsRBCred blood cellsT CD4CD4+ T‐cellsT CD8CD8+ T‐cellsVISIONValIdated Systematic IntegratiON of epigenomic data1A person's genetic profile can have a significant impact on complex traits such as disease susceptibility and response to specific treatments. Genome‐wide association studies (GWASs) have mapped loci at which a common genetic variation is associated with complex traits, but the mechanistic connection between genotype and phenotype is rarely understood. This is because most trait‐associated genetic variants are not in the 1–2% of the genome that encodes mRNA, but rather in a much larger noncoding genome.The problem we address is how to utilize the enormous amounts of emerging epigenetic data effectively both for basic research and precision medicine. Powered by advances in sequencing technologies, biochemical reagents, and bioinformatic analyses, many laboratories and large consortia, such as ENCODE,cis‐regulatory elements (cCREs), including enhancers, silencers, or insulators. While progress continues to be made in the predicting cCREs, issues of completeness (how sensitive are the cCREs for discovering true regulatory elements?) and specificity (how likely is it that the cCREs are true regulatory elements?) are actively debated. Developing more useful collections or registries of high‐quality cCREs is a major current need in functional genomics.Consider any genetic locus implicated in development, differentiation, behavior, or disease. Investigators may want to study the regulation of expression of gene(s) in that locus, for example, to understand how genetic variants could affect its expression. This investigation could be greatly facilitated by abundant genome‐wide data sets on multiple epigenetic features. Currently, to utilize such information, an investigator would examine epigenetic data around this locus in web‐based genome browsers and databases. These resources are useful, but they do not cover all the relevant aspects of chromatin structure, dynamics, and expression. After finding the available data, the investigator will need to analyze the results to predict candidate ValIdated Systematic IntegratiON of epigenomic data (VISION) to analyze and interpret molecular mechanisms regulating hematopoiesis in mouse and human. We are consolidating hundreds of epigenomic data sets and applying integrative approaches to generate robust candidate functional assignments to DNA segments. These assignments, coupled with gene target predictions and results of genome editing experiments, are the input to machine‐learning approaches that generate quantitative models for how each candidate CRE contributes to the regulation of its target gene. Importantly, these models will be rigorously tested and validated by targeted genome editing in reference loci and then applied genome wide. Furthermore, we are developing resources to enable more accurate translation of regulatory insights between mouse and human. The results from our project will inform investigators about candidate CREs and their predicted roles in regulating their loci of interest, thus enabling them to design model‐driven experiments to deepen their understanding of the investigated process.We have formed an interdisciplinary collaborative team to address these needs via Ikzf1 gene encoding the Ikaros transcription factor illustrates the power of our integrative approaches to deduce data‐driven hypotheses about differential regulation of gene expression in hematopoiesis.In this concise review, we focus on our efforts to integrate the large amount of genome‐wide information on epigenetic features and transcriptomes in a systematic manner to assign chromatin states across hematopoietic cells and predict cCREs. These resources are illustrated with respect to the GATA factors, both the genes encoding them and the binding patterns of the proteins in erythroid and lymphoid T cells. A further examination of the 2Over the past decade, the amount of information about gene expression levels and epigenetic regulatory landscapes in mammalian hematopoietic cells has increased exponentially, both through the work of individual laboratoriesCODEX resource, developed by the Gottgens group, maintains a compendium of next‐generation sequencing data sets pertaining to transcriptional programs of mouse and human blood development.SBR‐Blood resource, developed by the Bodine lab, has compiled expression data, ChIP‐seq, and Methyl‐seq data for mouse and human hematopoietic cells (990 data sets), including normalizations across disparate data sets.VISION project, which provides raw and normalized data sets selected to cover specific groups of features in mouse and human hematopoiesis, segmentations by integrative modeling (see below), and catalogs of cCREs, among other resources, on the website http://usevision.org. This website includes a link to a genome browser with epigenetic and expression data sets during hematopoiesis as well as the 3D Genome Browser developed by the Yue lab.Building on resources independently developed in laboratories within the VISION project, we have established a distributed data network to enhance accessibility and develop a unified interface to the users. The Our initial efforts were in mouse hematopoiesis because of the large number of epigenomic and transcriptomic data sets that were available in both primary maturing cells (exemplary references at the beginning of this section) and in the multilineage progenitors to blood cells.3Integrative and Discriminative Epigenome Annotation System) for two‐dimensional segmentation along chromosomes and across cell types because of its improved accuracy and consistency in assigning epigenetic states.The large numbers of interrelated epigenetic data sets described above present immense opportunities for understanding differential gene regulation if these data can be integrated into robust annotation of likely functional DNA. A key challenge is to build quantitative models explaining how the dynamics of epigenomes across many cell types lead to gene expression changes and phenotypic diversity.Importantly, the statistical framework for IDEAS allows it to assign likely epigenetic states to cell types based on the data distributions for signals across cell types. Thus, even if particular features have not been determined in a cell type, the system can still assign a likely state based on the known signals in other locally related cell types. This model‐based inference of states has better performance than current data imputation procedures.Zfpm1 and a mix of K4 monomethylation and K36 trimethylation of H3 through the body of the gene. However, the mixing can also blend too many colors together to distinguish clear states, such as around the transcription start site (TSS) and the region around the 3′ end of Zfpm1. The systematic integration by segmentation can be thought of as a principled, objective way to find well‐defined, discrete combinations of features that occur frequently throughout the epigenomes examined (the epigenetic states). Each genomic segment is then assigned to the one state that best matches the known (or inferred) epigenetic signals in each cell type. Thus, the IDEAS track below “merge tracks” gives a principled resolution of the mixtures of the epigenetic features.The IDEAS segmentation method is analogous to integration by mixing. One can consider the signal track for each epigenetic feature as a signal with a distinctive color, as illustrated in Figure For the eight epigenetic features across 20 mouse hematopoietic cell types, IDEAS generated a 27‐state model Figure . Each stThe epigenomic data were determined in many different laboratories at different times, with systematic differences in protocols, sequencing depths, and other factors that could impact the integrative analysis. Thus, consistency in data processing and appropriate normalization of the data were also key components of the IDEAS segmentation pipeline. More complete descriptions of the approaches developed and utilized are given elsewhere.Zfpm1 gene in erythroblasts to be bound by GATA1. Indeed, the ChIP‐seq pattern for GATA1 coincided well with those states , the IDEAS method also brings out differences between cell types. The segmentation of s Figure . Many ofs Figure . Thus, e4cis‐regulatory elements or cCREs.The integrative segmentation from IDEAS allowed us to take a straightforward approach to predicting candidate The initial registry of cCREs consisted of 205,019 DNA intervals in 18 hematopoietic cell types in mouse . The absence of full knowledge of functional elements and neutral elements across genome precludes a rigorous determination of the sensitivity and specificity of this initial cCRE registry. However, this collection does look promising in several respects. The registry captured virtually all the known erythroid cCREs, and it included a large majority (two‐thirds) of DNA segments bound by the transcription co‐activator EP300 in murine erythroleukemia cells, CH12 cells (a model for B cells), and fetal liver.Zfpm1 gene are shown on the bottom line of Figure The cCREs in and around the 5Gata1 was most prevalent in erythroid cells and the multilineage progenitor cell populations CMP and MEP, with more modest expression in megakaryocytic cells. The Gata2 gene was expressed more highly in the multilineage progenitor cell populations with some persistence into megakaryocytic cells. High levels of Gata3 expression were found primarily in a subset of the lymphoid cells, namely, NK, CD4+, and CD8+ T‐cells. In contrast, expression of Ikzf1 was expressed at higher levels and in a broader pattern, with expression in most hematopoietic cell types albeit lower in maturing erythroid cells.As discussed above, the GATA family of transcription factors is well known for regulation of gene expression in specific cell types and lineages. Examination of the genes encoding these factors and another key regulator of gene expression in hematopoietic cells, Ikaros (IKZF1), illustrates the types of insights that investigators can glean from the integrative analyses in the VISION project. Levels of expression of the genes were estimated from the RNA‐seq data from recent publicationsGata1 showed active epigenetic states in MEP, erythroid, and megakaryocytic cells  involved in cell type‐specific control of expression. The IDEAS tracks around s Figure b, which Gata2 locus, both proximal and internal to the gene as well as distal, close to the Rpn1 gene.Gata1 locus, the Gata2 locus was in a polycomb‐repressed state (H3K27me3) in many of the nonexpressing cells. These distinct mechanisms inferred for repression (quiescent vs. polycomb) were deduced simply by examining the IDEAS tracks, and they illustrate insights that follow easily from integrative analysis and modeling.Several regulatory elements have been mapped in the Gata3 gene was in an active promoter‐like epigenetic state and was bound by GATA3 in lymphoid cells, consistent with the expression pattern  were in active states and were inferred to be cCREs. Thus, the regulation of Gata3 may involve multiple CREs. As with the Gata2 locus, the Gata3 locus tended to be in a polycomb‐repressed state in many nonexpressing cell types. Surprisingly, the cCREs around Gata3 were in active epigenetic states in multilineage progenitor cells such as LSK, despite the very low levels of expression. This apparently precocious activation of the epigenetic landscape may serve as a type of lineage priming, or it could reflect some lineage commitment in this cell population.The DNA interval around the TSS of the n Figure d. HoweveIkzf1 revealed patterns indicative of lineage‐specific regulatory mechanisms  for further inferences about potential functions of the cCREs.Building from these initial resources, we have now compiled a large number of epigenomic data sets on human blood cells from the IHEC Blueprint ConsortiumA continuing challenge for utilizing cCRE predictions is the ambiguity in inferring a target gene. Regulatory elements can be far away from their target gene, and it is not uncommon for a CRE to be separated from its target gene by multiple nontarget genes. Substantial efforts within the VISION project and elsewhere are tackling this enduring challenge. Measurements of chromatin interaction frequencies in an all‐against‐all mode such as Hi‐CThe integrative maps of the regulatory landscape and the cCRE predictions were designed to provide accessible views and resources to enable a wide spectrum of users to benefit from the numerous and deep epigenomic data sets available. For the most part, the epigenetic states learned by IDEAS match those expected from decades of work on the impact of chromatin structure on gene regulation. However, there is still the potential for discoveries of novel relationships. We are currently using the epigenetic states and cCREs as input into additional analytical approaches to try to uncover novel insights and global models. For example, we are using multivariate regressionshttp://usevision.org. We hope that this review will encourage use of these resources.All resources from the VISION project are publicly available via our website


Many computational theories have been developed to improve artificial phonetic classification performance from linguistic auditory streams. However, less attention has been given to psycholinguistic data and neurophysiological features recently found in cortical tissue. We focus on a context in which basic linguistic units–such as phonemes–are extracted and robustly classified by humans and other animals from complex acoustic streams in speech data. We are especially motivated by the fact that 8-month-old human infants can accomplish segmentation of words from fluent audio streams based exclusively on the statistical relationships between neighboring speech sounds without any kind of supervision. In this paper, we introduce a biologically inspired and fully unsupervised neurocomputational approach that incorporates key neurophysiological and anatomical cortical properties, including columnar organization, spontaneous micro-columnar formation, adaptation to contextual activations and Sparse Distributed Representations (SDRs) produced by means of partial N-Methyl-D-aspartic acid (NMDA) depolarization. Its feature abstraction capabilities show promising phonetic invariance and generalization attributes. Our model improves the performance of a Support Vector Machine (SVM) classifier for monosyllabic, disyllabic and trisyllabic word classification tasks in the presence of environmental disturbances such as white noise, reverberation, and pitch and voice variations. Furthermore, our approach emphasizes potential self-organizing cortical principles achieving improvement without any kind of optimization guidance which could minimize hypothetical loss functions by means of–for example–backpropagation. Thus, our computational model outperforms multiresolution spectro-temporal auditory feature representations using only the statistical sequential structure immerse in the phonotactic rules of the input stream. It is well known that human beings can reliably discriminate phonemes as well as other linguistic units by categorizing them, despite considerable variability across different speakers with different pitches and prosody. Furthermore, this ability extends to noisy and reverberant environments.Although such proficiency could in part be attributed to top-down information  originatIt is even more remarkable that an extremely complex task of early language acquisition as is the segmentation of words from fluent speech, is fulfilled by 8-month-old infants based simply on the statistical relationships between neighboring speech sounds . With onThis incidentally acquired invariance in phonetic perception found in mammals must be grounded necessarily in anatomical and neurophysiological characterisitcs of the mammalian cortex. The features we foresee as potentially relevant are brought together in order to pose our computational hypotheses.Linden and Schreiner  highlighCompelling physiological studies have shown that Primary Auditory Cortex (A1) shares common structural characteristics with other sensory cortices. Furthermore, when retinal inputs are routed into the auditory thalamus, auditory cortical cells develop visual response properties such as direction selectivity, orientation preference and complex and simple receptive fields –17. RetiThe above data suggest the existence of neuronal circuitry with similar processing capabilities for different modalities. Consequently, we gather physiological and anatomical characteristics found in cortical tissue in general which we foresee as relevant for phonetic perception invariance and generalization.One of the main neuroanatomical features of brain cortex in mammals is that cortical cells are spacially arranged into domains defined by common receptive field locations. These alignments are called Cortical Columns (CCs) –23. WithOne of the functional properties found in many of these networks is adaptation to contextual stimuli , 26. ThiFinally, recent findings in neuroscience show that mammalian cortex processes information by means of SDRs . This meIn the present work the above mentioned anatomical and neurophysiological features of the mammalian cortex are gathered as potentially relevant in order to attain phonetic invariance in the mammalian auditory cortex. Our pyramidal neuron model dissociates proximal from distal dendritic branches . ProximaDistal dendrites receive only lateral and apical information acting as independent detectors. Distal dendritic information pre-activates neural units putting them in a predictive state in order to receive future afferent information.We test those principles paying special attention to temporal dynamics of speech, which play the most important role in linguistic contrasts . We use We generate corpora of 500 words with mono, di and trisyllabic randomly chosen English words from 10 different vocabularies of five words for each syllabic condition using Festival Text to Speech Synthesis .We generate cross synthesizer mark-up-language files with SABLE . In suchThe organization of the corpora has certain rules and restrictions in order to avoid biases in the training processes. The voices are sequentially chosen (pseudo-randomly) with the restriction that no voice could utter a second time until all the voices had uttered in their turns. Every voice utters two words per turn–in pseudo-random order–and no word is repeated until all the words are used by such voice.cmu_us_fem_cg, cmu_us_gka_cg, cmu_us_ksp_cg, cmu_us_rxr_cg, cmu_us_jmk_cg, cmu_us_rms_cg, cmu_us_slt_cg, cmu_us_jmk_arctic_clunits, cmu_us_rms_arctic_clunits, cmu_us_slt_arctic_clunits. Set two had 5 male and 5 female voices: cmu_us_ahw_cg, cmu_us_aup_cg, cmu_us_axb_cg, cmu_us_eey_cg, cmu_us_awb_cg, cmu_us_bdl_cg, cmu_us_clb_cg, cmu_us_ljm_cg, cmu_us_bdl_arctic_clunits, cmu_us_clb_arctic_clunits.We use two sets of 10 different English speaking voices, each provided by Festival. Set one consisted of 8 male and 2 female voices: cat, uttered by the same voice used for the last word. We use the text2wave program provided by Festival in order to generate a wav file from the SABLE file.Every word in the audio file is followed by a silence gap whose time is equivalent to the uttering time of the monosyllabic word We generated all the datasets (audio file corpora) employed in the present research to train the EL and the SVMs and to test the complete Cortical Spectro-Temporal Model (CSTM). This folder includes a set of 840 corpora which are distributed in 2 corpora for each configuration organized by 2 sets of synthesized voices, 3 syllabic conditions and 10 vocabularies all distributed in 6 acoustic variants, beyond the original version of the corpora. The 6 acoustic variants corresponds to: two levels of white noise  average Root Mean Square (RMS) power rate), two levels of reverberation (Reveberation-Time 60 dB (RT-60) value of 0.61 seconds and 1.78 seconds) and variations of pitch on both directions (from E to G and from E to C).We propose a computational approach called CSTM, which simulates a patch of cortical tissue and incorporates columnar organization, spontaneous micro-columnar formation, partial NMDA depolarization and adaptation to contextual activations. We simulate pyramidal cells with proximal connections from afferent dendritic branches and distal connections from lateral dendritic branches. Similar afferent stimuli activate clusters of neurons with proximal physical locations in a CC in the same way that afferent information activates the mini columns found in cortical tissue.Afferent information activates different clusters of units in a CC establishing a first and raw approximation of the phonetic features abstracted from the input auditory stream. Our model fine-tunes such raw features by means of previous contextual activations produced in the same and/or in neighboring CCs. Such contextual information is sent to each CC by means of lateral distal dendritic branches which work as independent processing elements in a cell. Current activation in such dendritic elements will affect the way in which cells receives future afferent information.Novel computational theories have posited a feasible explanation about the role of distal synapses related to NMDA phenomenon  by combiIn addition, we simulate the growth of distal dendritic branch synapses by means of Spike-timing dependent plasticity (STDP) mechanisms together with homeostatic regulations. In this way, distal synapses will be established only among pyramidal cells with sequential patterns of activation. Afferently excited clusters which do not have partially depolarized cells, will fire together producing a Massive Firing Event (MFE) (lack of inhibition) as a response to a prediction fault ; otherwise they will respond with normal firing events (inhibition and therefore SDRs) when the sequential stimulus is correctly predicted.SDRs exhibit interesting mathematical properties which give them high noise rejection and fault tolerance . These aHence, the evolution of our network does not predetermine a neuron to fire but biases its probability of doing so during training. Additionally and under specific conditions, afferent dendritic arborizations activate themselves at random with levels whose boundary values are established by learning.It has been shown that overfitting–a phenomenon in which a statistical model describes random error or noise instead of the underlying relationship–is greatly reduced by stochastic properties in training procedures applied to neural networks (dropout) .In order to produce the inputs from auditory streams we base on MRSTSA . In our The CSTM consists of two parts: The Multiresolution Spectro-Temporal Sound Analysis (MRSTSA) layer and the Encoder Layer (EL).The algorithm MRSTSA, which processes the sound waves to feed inputs to the EL, is a technique inspired by Chi T. et al. . In theiThe EL converts a multidimensional array of real numbers into a multidimensional Sparse Distributed Representation (SDR). This stage is composed by a set of Self Organizing Maps (SOMs) , 47 and As mentioned above, Chi T. et al.  developeThe original algorithm has a subcortical and a cortical stage. For the subcortical stage, first an affine wavelet transform of the acoustic signal represents the spectral analysis performed by the cochlear filter bank. Second, the cochlear filter outputs are transduced into auditory-nerve patterns by a hair cell stage consisting of a high-pass filter, a nonlinear compression and a membrane leakage low-pass filter. Third, a first-order derivative with respect to the tonotopic axis followed by a half-wave rectifier simulates the action of a lateral inhibitory network postulated to exist in the cochlear nucleus, which effectively enhances the frequency selectivity of the cochlear filter bank. The final output of this stage is obtained by integrating over a short window, with time constant of 8 ms, mimicking the further loss of phase locking observed in the midbrain.The cortical stage mimics aspects of the responses of higher central auditory stages, especially A1. Functionally, this stage estimates the spectral and temporal modulation content of the auditory spectrogram. It does so computationally via a bank of filters that are selective to different spectrotemporal modulation parameters that range from slow to fast rates temporally, and from narrow to broad scales spectrally. The Spectro-Temporal Receptive Fields (STRFs) of these filters are also centered at different frequencies along the tonotopic axis.In the present work, since we aim to integrate neurophysiological properties–mainly centered in cortical features–we followed the main guidelines in the implementation of the cortical section of such model. As shown in We obtained the magnitude of each convolution and applied normalization to each time window as a mean of automatic gain control in order to prioritize the information delivered by the spectral configuration and not the absolute values delivered by the filters.By means of this constraint we account for the mechanical and chemical properties of hair cells in the mammalian inner ear which constitute a transduction mechanism that appears to adapt to recent stimulus history in a way that can affect its gain –50. We dThe EL is responsible for generating SDRs from the inputs delivered by the MRSTSA stage described in the previous section and from the activation history in its own CCs.The EL simulates a patch of cortical tissue called Cortical Layer (CL)using an n-dimensional array of complex structures called Complex Self-Organizing Maps (CSOMs) that simulate CCs in the brain.Each CC in the EL is connected to the MRSTSA below by means of afferent connections. It is also connected to neighboring CCs–including possibly itself–in the EL by means of lateral connections and to CCs from other CLs above by means of apical connections. Such connection scheme is shown in Both lateral and apical are feedback connections that constitute contextual information channels. These channels put the current afferent excitation under the context of previous activations. Such connections damp the activity of some units allowing only the precise activations of specific neural units in an afferently excited CC. Such precise activations match the sequential paradigms learned by the network.Recent findings in neuroscience  support In the present work we only implement lateral connections since there are no upper layers from which to bring apical information in the present implementation.Each cell unit in a CC has two types of dendritic branches; proximal and distal. Proximal and distal dendritic branches lead to proximal and distal connections in a cell unit respectively. Proximal and distal connections produce different effects on a neural unit’s plasticity and activation. Neural units in the EL simulates pyramidal cells in cortical tissue in the brain. In reference to proximal dendritic connections in the EL, each neural unit in a CC has the same set of proximal connections to the MRSTSA . Such coAlgorithm 1Plasticity in Proximal Synapses. Self Organizing Map (SOM) algorithm.input vector, find the nearest unit to such input vector in the input space1: given an unit towards the input vector in the input space (the magnitude of such movement depends on the learning rate)2: move such units to the nearest one towards the input vector (the magnitude of such movement depends on the learning rate and on a neighborhood measure over the topology of the network of units)3: also move neighbor m dimensions in which each unit represents a set of vectors from the continuous distribution in an input space of n dimensions. Generally, m < n in order to reduce the dimensionality in the discrete representation. We added such restriction in our columnar algorithm.A SOM is an unsupervised clustering algorithm which distributes a continuous multidimensional distribution in a discrete multidimensional distribution of units , 47. In In the SOM algorithm, each input vector has to be completely determined. In our case, several elements in the inputs from the MRSTSA could be null, and considering such null inputs could inpair learning. Hence, each input vector could not have the information of each of its components available. We incorporated a stochastic mechanism in the EL in order to deal with such situation. We made each afferent connection to learn statistical boundaries from its corresponding input. We establish a minimum-maximum margin in each proximal connection in the EL. Such margin is consistent with the statistical distribution in the history of its corresponding input. When an afferent input is undetermined, in a context in which some afferent inputs have available information, the EL chooses the value in the undetermined input randomly between the boundaries learned for such input.We call our implementation of the SOM algorithm, Static Self-Organizing Map (SSOM). The SSOM algorithm accounts for proximal lateral intra-column interaction, LTP and LTD. It also dissociates proximal dendritic inputs from distal dendrites, since it modifies proximal connections following the statistical distribution from the MRSTSA independently of the units that fire in such CC. This independence in the plasticity of the proximal dendritic inputs is supported by the property found in cortical tissue by means of which there is dendritic plasticity in the context of partial depolarization of the soma –that is,static comes from the fact that the patterns learned from proximal afferent dendrites do not account for the contextual history in the dynamic evolution of the algorithm.The term In terms of distal dendritic branches, each CC in the EL is connected to other CCs–in green in Such links in potential connection is used, because it describes a pair of neural units linked by its physical location and dendritic and axonal disposition in cortical tissue  (Alg. 2). The learning mechanisms implemented on such algorithm simulate neurophysiological phenomena such as STDP, and homeostatic regulation plasticity in the synaptic strength regulation in distal dendritic branches.Algorithm 2Plasticity in Distal Synapses. This algorithm accounts for Spiketiming dependent plasticity (STDP) and homeostatic regulation phenomenon in distal dendritic synapses.for every active unit in this cortical columndo1: for every dendrite in this active unitdo2:  synapses–in this dendrite–potentially connected to units which were active in the last time step3:   increment all the end for4:  end for5: for every active unit in this cortical columndo6: for every dendrite in this active unitdo7:  synapses–in this dendrite–potentially connected to units which are active in this time step8:   decrement all the end for9:  end for10: if updated step reaches certain value then11: for every unit in this cortical columndo12:  for every dendrite in this unitdo13:   if the sum of the synapses in this dendrite is greater than one then14:    synapses in this dendrite15:     normalize all end if16:    end for17:   end for18:  step = 019:  updated end if20: step++21: updated Basically, Alg. 2 updates all distal axonal-dendritic synapses, incrementing them always that pre and post-synaptic neural units become active in consecutive time steps, and decrementing them when they become active at the same time step. Occasionally, all the synaptic weights belonging to the same dendrite are normalized every certain number of time steps. This normalization is repeated for all neural units and for all dendritic branches in each neural unit whose sum of synaptic weights is beyond unity.t = 0 in the EL, will partially depolarize a set of neural units in time step t = 1 in such CC, by means of distal–lateral and apical–dendritic branch synapses established by learning in the DSOM algorithm.Finally, in reference to the activation rules of neural units inside a CC in the EL, first a group of cell units in a CC is partially depolarized by distal connections among such neural units and cell units activated in the previous time step in the EL–t = 1–Second, afferent proximal connections from MRSTSA will tend to depolarize certain clusters of units in such CC in time step If a sufficient number of partially depolarized units are in the set of afferently excited units, such partially depolarized units will fire previously in the group–Partial depolarization states put cell units in a predictive state generated by the activations produced in the EL in previous time steps. That is, lateral and apical activation in previous time steps constitute a context in which current afferent inputs are received.From the group of units that tend to be depolarized by current afferent inputs from the MRSTSA, only a reduced sub-set of those units are likely to fire in the previous contextual firing history in the EL–In case there is no context, that is, if not enough units normally depolarized by afferent inputs are partially depolarized by previous–lateral and apical–activations–number of afferently excited units refers to the maximum number of units that can be activated by the afferent input in a CC and minimum number of active units refers to the number of units that will be active in a CC if a SDR is achieved as a result of optimal prediction (lines 3 and 4 respectively).Such activation mechanism is depicted in Alg. 3. In Alg. 3 (Part 1) a ranking is established among neural units–inside a CC–in terms of its afferent excitability, given the afferent inputs (lines 1 and 2). The Algorithm 3Units activation (Part 1). This algorithm establishes the activation rules in a CSOM object.distances = given an input vector find the euclidean distance each unit has to such input in the input space from proximal afferent synapses1: ranking = sort indexes from the smallest to the largest distances2: 3: number of afferently excited units = proximal activation percentage*number of units4: minimum number of active units = (1-sparsity)*number of unitsif randomness is disabled then5: units = gets the first number of afferently excited units elements from ranking6:  excited else7: units = gets number of afferently excited units random indexes from distances with probabilities determined by the relative reciprocal of the distances element values8:  excited end if9: forunit = 0 tounit = number of units do10: 11:  auxiliary = 0fordendrite = 0 todendrite = number of distal dendrites do12:  dendrite accumulator = 013:   foractive unit = 0 toactive unit = number of linked active units do14:   index = find the first coincident index in potential connections[dendrite][unit] with linking units[dendrite][active unit]15:    potential if there exist coincidence then16:    dendrite accumulator += dynamic synapses[dendrite][unit][potential index]17:     end if18:    end for19:   ifdendrite accumulator > 100*DISTAL_SYNAPTIC_THRESHOLD then20:   21:    auxiliary++end if22:   end for23:  responses[unit] += auxiliary24:  total end for25: distances = element wise quotient between distances and total responses26: updated ranking = sort indexes from the smallest to the largest updated distances27: updated number of afferently excited units units is chosen at random by means of a discrete distribution whose probabilities are the afferent excitation of each unit. If randomness is disabled, number of afferently excited units first units are chosen from the ranking of afferently excited units (lines 5 to 9).If randomness is enabled, From line 10 to 25 each neural unit accumulates distal–lateral and apical–excitation in order to determine its partial depolarization from units which were active in the previous time step. For each neural unit in a CC, for each distal dendrite in such unit and for each active unit in such distal dendrite the algorithm looks for coincidences between some potential connection in such distal dendrite in the neural unit and the active unit in such distal dendrite. That is, in line 15, the algorithm asks if there is coincidence between some potential connection in this distal dendrite inside the unit and the neural unit activated in the previous time step in the CC linked by such distal dendrite. If there is coincidence, the value of the synaptic weight in such potential connection is accumulated in a dendrite accumulator. After all active units are examined for this dendrite, if the dendrite accumulator is greater than certain threshold, such dendrite is considered active and the total response of the unit is incremented in one.Each neural unit ends up with an excitation value due to its distal dendrites. The unit distances vector is element- wise divided by distal dendritic excitations vector to get the updated distances and an updated ranking of the units (lines 26 and 27). In this way, units with more distal excitation will decrease its distance more and will be put in a more favorable place in the ranking in order to be activated.minimum number of active units, the next minimum updated distance is found in the group of afferently excited units and a new set of units–inside the group of afferently excited units–is identified which have such next minimum updated distance. This new set is added to the previous one until the number of units in this accumulative set is greater than or equal to the minimum number of active units.In Alg. 3 (Part 2) the minimum updated distance is found in the group of afferently excited units. Then, a set of units–inside the group of afferently excited units–is identified which have such minimum updated distance. While the number of identified units is less than Algorithm 3Units activation (Part 2). This algorithm establishes the activation rules in a CSOM object.distances = get the updated distances elements whose indexes are in excited units1: new distance = get the minimum element from new distances2: new minimum indexes = get indexes from updated distances vector whose values are equal to new minimum distance3: minimum active = get the coincident indexes between excited units and minimum indexes4: apt to be distances vector, all the elements whose value is equal to new minimum distance5: erase from new while number of elements in apt to be active vector < minimum number of active units and new distances has at least one element do6: distance = get the minimum element from new distances7:  new minimum indexes = get indexes from updated distances vector whose values are equal to new minimum distances8:  minimum active = get the coincident indexes between excited units and minimum indexes9:  partial apt to be active elements into apt to be active vector10:  incorporate partial apt to be distances vector, all the elements whose value is equal to new minimum distance11:  erase from new end while12: if ENABLE_RANDOM_BEHAVIOUR then13: active vector14:  shuffle apt to be end if15: fornumber = 0 tonumber = number of apt to be active elements do16: output the excited units[apt to be active[number]]17:  incorporate to end for18: returnoutput19: minimum number of active units will be active. In the case of the occurrence of a MFE, the synaptic plasticity is modulated in order to form stronger synapses of those neural units activated during such event.The functional result of Alg. 3 is that there must be a sufficient amount of–partially and previously depolarized–neural units inside the afferently activated cluster of units in order to get a SDR pattern of activation. Otherwise, the CC will end up with a massive activation pattern, a Massive Firing Event (MFE) in which more than a Each neural unit in a CC establishes its state of partial depolarization based on the contribution from distal dendritic branches from lateral and apical connections. A dendritic branch will contribute to the partial depolarization of the soma in such cell only if such dendritic branch exceeds an activation threshold by means of the contribution from its individual synapses in the context of the patterns of activation in the previous time step.This mechanism has compelling sequential properties , which hRegarding MRSTSA, we apply FFT to the audio files with a sample period of 8 milliseconds. We use the FFTW package , 55 withThe function coefficients are 10 for the 8 ms time window, 8 for the 16 ms time window, 6 for the 32 ms time window, 4 for the 64 ms time window and 2 for the 128 ms time window. We then compute the magnitude from the convolution and normalize in each time step. By means of this procedure we obtain from the audio file a multiresolution spectro-temporal response composed by an array of 128 columns–one column per filter–and 5 rows–one row per resolution–with real numbers which range from 0 to 1, for each time step.In reference to the EL, we implement an EL with 225 CSOMs arranged in a two-dimensional array of 15 by 15 CCs. Each CC is automatically distributed using individual locations along its afferent inputs in a uniform way. Each CC receives afferent information by means of two-dimensional afferent receptive fields of 5 by 227 filters centered at individual locations over the MRSTSA. We enable the wraparound property in order to make each receptive field span the entire MRSTSA array. We also instruct each column to receive only 31 inputs, which is a minor percentage of such receptive field. Individual afferent inputs for each CC are chosen randomly in the EL initialization.For this model instance we implement only distal lateral dendritic branches since there are no more CLs from which to bring information through apical dendritic branches. We configure each CC to have a lateral receptive field with 9 by 9 neighboring CCs and to receive information from 72 of the 81 CCs in the receptive field–a 90% of the receptive field.Each CC is composed of a two-dimensional array with 15 by 15 (225) neural units and each unit in a column could be potentially connected with only 6 neural units from each linked neighboring column. That is, each neural unit in a CC ends up with 72 lateral dendritic branches with 6 potential connections each . Such potential synapses are randomly chosen for each neural cell and for each dendritic branch in the cell during the Encoder initialization procedure. The EL consists of 50625 cellular units with 1569375 proximal synapses and 21870000 distal synapses. Such specifications state the number of free parameters of the model, but it is important to highlight that distal synapses represent potential connections from which only a small percentage has a significant synaptic weight as to be considered as an established connection. Weak synapses are periodically pruned by means of homeostatic processes in the network leaving distal dendrites with a sparse connectivity in the receptive fields. Typical sparseness in such connectivity matrices could exceed the 90%.We train the EL using a 500 word corpora generated by the procedure described in section Corpora generation. The training procedure consists of 4 stages and for each stage the EL receives the same corpus 4 times.During each learning stage, certain parameters–such as the learning rates in proximal and distal synapses and the lateral intra-column interaction–are exponentially and progressively decreased from an initial value, which also decreases for each successive stage. An additional stage is executed with the learning parameters fixed.The sparsity in the activation for each CC is 99% . On the other hand, the afferent excitation affects 10% of the units inside the clusters in each CC .C which we swept to find the best trained model for each classifier.Afterwards, we scale the vectors–as the Library for Support Vector Machine (LIBSVM) documentation suggests–so as to improve the classification performance. We train and test the SVM classifiers using 5-fold cross-validation and configure them to use a linear kernel with one parameter In the present work, we studied the level of invariance in the phonetic features abstracted by the EL, by means of comparing such representations with the multiresolution spectro-temporal auditory features returned by the MRSTSA algorithm. To this end, we evaluated the features returned by each algorithm in different word classification tasks. In order to asses word classification performance in each algorithm, we used the SVM technique–section Computational model–with the experimental setup depicted in In the experimental procedure we first trained 10 different ELs–section Computational model–for each syllabic condition. Such ELs were trained using the voices in set one and the corpora were generated by the method described in section Corpora generation. Afterwards, we processed the same corpora with the corresponding ELs in inference mode. In such mode, the ELs processed the information with their learning properties disabled. In this manner, during inference, the ELs did not modify its synapses and just returned patterns of activation in response to the stimuli they received. We then used the outputs from the MRSTSA and the ELs in inference mode to train the SVM classifiers with the procedure depicted in section Implementation. The average cross validation training performances are shown in In a second stage, we ran the ELs in inference mode again, but this time we used different corpora generated using the same voices and manipulated with several types of acoustic variants , generated using the Audacity software. We also ran the ELs in inference mode using corpora generated with different voices (set two voices in section Corpora generation). We tested the performances of the–already trained–classifiers in the presence of the features returned by the algorithms in response to the corpora affected by the acoustic variants which we introduced to the new corpora by means of Audacity . The acoThe classification performances are shown in Regarding white noise, we introduced additive white noise to the corpora signals with signal-noise average RMS power rate of 19.9 dB (White Noise 1) and 13.8 dB (White Noise 2). In terms of reverberation, we modified the corpora signals by means of RT-60 values of 0.61 seconds (Reverberation 30%) and 1.78 seconds (Reverberation 60%). RT-60 Is the time that a signal takes to decrease its amplitude to 60 dBs under its initial value. As regards pitch variations, we modified the corpora signals pitch in +20% (from E to G) (Pitch Up) and in–20% (from E to C) (Pitch Down). We also used corpora generated with different voices from the ones used to train the ELs and the SVMs.p < 0.22)–there was Statistical Significance for all conditions considering (p < 0.05).We performed two-tailed paired t-tests for 10 different corpora generated from 10 different vocabularies–section Corpora generation. As can be seen in Given that we conducted 7 t-tests for each independent word classification task , we performed Holm–Bonferroni corrections with a correction factor of 7 in order to reduce the probability of type I and type II errors in the context of the different experimental conditions . By meanResults obtained in the present work support the computational hypotheses posed in our modeling approach in order to mimic incidental phonetic invariance and generalization. Some of these hypotheses have already been explained in terms of their properties , but morComputational models have been previously developed to understand how phonetic categories are acquired . The goaIn a more recent work, the capacity of Deep Maxout Networks (DMNs)–a modification of Deep Neural Networks (DNNs) feed-forward architecture that uses a max-out activation function–to handle environmental noise was investigated into different broad phonetic classes and for different noise conditions . In suchIn the present work, we reported classification performances of–for example–90.8% on the EL vs. 78.58% on the MRSTSA for trisyllabic words against Changed Voices  will be needed to analyze the capacities of our approach more deeply. Nevertheless, our main objective in the present work was to assess the sequential phonetic invariance exhibited by the EL under strictly controlled experimental conditions in which we precisely knew the levels of noise, reverberation and pitch variations with which the stimulus was affected. The EL training material included only the original corpora with 500 words, but more importantly, the EL was never exposed–during learning–to the disturbances used to test its classification performance. The experimental profile applied in this work  makes itIn future researches, emergent dynamic properties could arise from the addition of subsequent cortical layers–beyond the Encoder Layer (EL)–in the processing pipeline of this model. In this way we will be able to implement backward distal apical dendrites, which will bring context through an unsupervised hierarchical implementation. Even though such feedback could be beneficial in an incidental phonetic acquisition context, modeling adult phonetic competence could possibly require the implementation of more complex hypotheses in our model. The incorporation of biologically accurate cost functions in order to feed back any kind of activation error would require precise biological hypotheses. Should those errors be scalar signals (reinforcement mechanisms) or should they be vectors (supervised mechanisms)? Should they come from the same modality or should they be gathered from a different one? Should they vary across different cortical patches or should they vary during temporal development? . A supertop500.org), and assuming one cortical column per compute node with 64 cores, we could be running 527 neural units per thread in a CPU. Furthermore, configuring a three-dimensional array of 1000 cortical columns per cortical layer, a model of 4 layers could be running on approximately 256,000 threads. Such simulations could allow us to leverage phonotactic acquisition as well as phonetic generalization capacities beyond the levels reported in this paper.Future versions of the model will increase biological plausibility by increasing the number of cells per CC with massively scaling High Performance Computing (HPC) simulations and using a Growing Self Organizing Map (GSOM) per CC in order to incorporate neural resource recruitment specialization in each CC depending on the statistical dispersion of its stimuli . For insWe show via a computational simulation that our cortical model leverages the performance in word classification tasks under specific environmental conditions  and for certain acoustic variants applied to the auditory stimuli . The model acquires the phonotactic rules in the input, without any kind of supervised or reinforced optimization procedure, taking only advantage of auto-organized algorithmic properties. We also show effectiveness in classifying multisyllabic words, which suggests that our implementation of neurophysiological predictive dynamics plus stochastic sparse patterns of activation outperforms the MRSTSA algorithm in terms of phonotactic sequential invariance for disturbances applied to the audio signal. Most importantly, the present model–based on current neurophysiological and neuroanatomical data of the human auditory pathway–is able to mimic incidental phonetic acquisition observed in human infants, which is a key mechanism involved during early language learning. Increasing the models complexity , could allow the model to replicate further mechanisms involved during human language acquisition, such as inferential learning or prediction generation. In addition, neurophysiological and anatomical properties in our model could be considered potentially relevant to the design of artificial intelligence systems and may achieve higher levels of phonetic invariance and generalization than the ones achieved by current deep learning architectures.