Identifying control ensembles for information processing within the cortico-basal ganglia-thalamic circuit

In situations featuring uncertainty about action-reward contingencies, mammals can flexibly adopt strategies for decision-making that are tuned in response to environmental changes. Although the cortico-basal ganglia thalamic (CBGT) network has been identified as contributing to the decision-making process, it features a complex synaptic architecture, comprised of multiple feed-forward, reciprocal, and feedback pathways, that complicate efforts to elucidate the roles of specific CBGT populations in the process by which evidence is accumulated and influences behavior. In this paper we apply a strategic sampling approach, based on Latin hypercube sampling, to explore how variations in CBGT network properties, including subpopulation firing rates and synaptic weights, map to variability of parameters in a normative drift diffusion model (DDM), representing algorithmic aspects of information processing during decision-making. Through the application of canonical correlation analysis, we find that this relationship can be characterized in terms of three low-dimensional control ensembles within the CBGT network that impact specific qualities of the emergent decision policy: responsiveness (a measure of how quickly evidence evaluation gets underway, associated with overall activity in corticothalamic and direct pathways), pliancy (a measure of the standard of evidence needed to commit to a decision, associated largely with overall activity in components of the indirect pathway of the basal ganglia), and choice (a measure of commitment toward one available option, associated with differences in direct and indirect pathways across action channels). These analyses provide mechanistic predictions about the roles of specific CBGT network elements in tuning the way that information is accumulated and translated into decision-related behavior.     

Using all Gene Families Vastly Expands Data Available for Phylogenomic Inference

Traditionally, single-copy orthologs have been the gold standard in phylogenomics. Most phylogenomic studies identify putative single-copy orthologs using clustering approaches and retain families with a single sequence per species. This limits the amount of data available by excluding larger families. Recent advances have suggested several ways to include data from larger families. For instance, tree-based decomposition methods facilitate the extraction of orthologs from large families. Additionally, several methods for species tree inference are robust to the inclusion of paralogs and could use all of the data from larger families. Here, we explore the effects of using all families for phylogenetic inference by examining relationships among 26 primate species in detail and by analyzing five additional data sets. We compare single-copy families, orthologs extracted using tree-based decomposition approaches, and all families with all data. We explore several species tree inference methods, finding that identical trees are returned across nearly all subsets of the data and methods for primates. The relationships among Platyrrhini remain contentious; however, the species tree inference method matters more than the subset of data used. Using data from larger gene families drastically increases the number of genes available and leads to consistent estimates of branch lengths, nodal certainty and concordance, and inferences of introgression in primates. For the other data sets, topological inferences are consistent whether single-copy families or orthologs extracted using decomposition approaches are analyzed. Using larger gene families is a promising approach to include more data in phylogenomics without sacrificing accuracy, at least when high-quality genomes are available.     

Diversity of anaerobic gut fungal populations analysed using ribosomal ITS1 sequences in faeces of wild and domesticated herbivores

Gut fungal-specific PCR primers have been used to selectively amplify the ITS1 region of gut fungal rDNA recovered from faeces of domestic and wild animals to investigate population diversity. Two different gel-based methods are described for separating populations of gut fungal rDNA amplicons, namely (1) denaturing gradient gel electrophoresis (DGGE) and (2) separation according to small size differences using Spreadex, a proprietary matrix for electrophoresis. Gut fungal populations were characterised by analysis of rDNA in faeces of seventeen domesticated and ten wild herbivores. Sequences derived from these gel-based characterisations were analysed and classified using a hidden Markov model-based fingerprint matching algorithm. Faecal samples contained a broad spectrum of fungi and sequences from five of the six recognised genera were identified, including Cyllamyces, the most recently described gut fungal genus, which was found to be widely distributed in the samples. Furthermore, four other novel groupings of gut fungal sequences were identified that did not cluster with sequences from any of the previously described genera. Both gel- and sequence- based profiles for gut fungal populations suggested a lack of geographical restriction on occurrence of any individual fungal type.     

Virulence of 32 Salmonella strains in mice

Virulence and persistence in the BALB/c mouse gut was tested for 32 strains of Salmonella enterica for which genome sequencing is complete or underway, including 17 serovars within subspecies I (enterica), and two representatives of each of the other five subspecies. Only serovar Paratyphi C strain BAA1715 and serovar Typhimurium strain 14028 were fully virulent in mice. Three divergent atypical Enteritidis strains were not virulent in BALB/c, but two efficiently persisted. Most of the other strains in all six subspecies persisted in the mouse intestinal tract for several weeks in multiple repeat experiments although the frequency and level of persistence varied considerably. Strains with heavily degraded genomes persisted very poorly, if at all. None of the strains tested provided immunity to Typhimurium infection. These data greatly expand on the known significant strain-to-strain variation in mouse virulence and highlight the need for comparative genomic and phenotypic studies.     

Beyond ecosystem modeling: A roadmap to community cyberinfrastructure for ecological data‐model integration

In an era of rapid global change, our ability to understand and predict Earth's natural systems is lagging behind our ability to monitor and measure changes in the biosphere. Bottlenecks to informing models with observations have reduced our capacity to fully exploit the growing volume and variety of available data. Here, we take a critical look at the information infrastructure that connects ecosystem modeling and measurement efforts, and propose a roadmap to community cyberinfrastructure development that can reduce the divisions between empirical research and modeling and accelerate the pace of discovery. A new era of data‐model integration requires investment in accessible, scalable, and transparent tools that integrate the expertise of the whole community, including both modelers and empiricists. This roadmap focuses on five key opportunities for community tools: the underlying foundations of community cyberinfrastructure; data ingest; calibration of models to data; model‐data benchmarking; and data assimilation and ecological forecasting. This community‐driven approach is a key to meeting the pressing needs of science and society in the 21st century.