Robust, automated sleep scoring by a compact neural network with distributional shift correction.

Studying the biology of sleep requires the accurate assessment of the state of experimental subjects, and manual analysis of relevant data is a major bottleneck. Recently, deep learning applied to electroencephalogram and electromyogram data has shown great promise as a sleep scoring method, approaching the limits of inter-rater reliability. As with any machine learning algorithm, the inputs to a sleep scoring classifier are typically standardized in order to remove distributional shift caused by variability in the signal collection process. However, in scientific data, experimental manipulations introduce variability that should not be removed. For example, in sleep scoring, the fraction of time spent in each arousal state can vary between control and experimental subjects. We introduce a standardization method, mixture z-scoring, that preserves this crucial form of distributional shift. Using both a simulated experiment and mouse in vivo data, we demonstrate that a common standardization method used by state-of-the-art sleep scoring algorithms introduces systematic bias, but that mixture z-scoring does not. We present a free, open-source user interface that uses a compact neural network and mixture z-scoring to allow for rapid sleep scoring with accuracy that compares well to contemporary methods. This work provides a set of computational tools for the robust automation of sleep scoring

Critical Point-Finding Methods Reveal Gradient-Flat Regions of Deep Network Losses

Despite the fact that the loss functions of deep neural networks are highly non-convex, gradient-based optimization algorithms converge to approximately the same performance from many random initial points. One thread of work has focused on explaining this phenomenon by characterizing the local curvature near critical points of the loss function, where the gradients are near zero, and demonstrating that neural network losses enjoy a no-bad-local-minima property and an abundance of saddle points. We report here that the methods used to find these putative critical points suffer from a bad local minima problem of their own: they often converge to or pass through regions where the gradient norm has a stationary point. We call these gradient-flat regions, since they arise when the gradient is approximately in the kernel of the Hessian, such that the loss is locally approximately linear, or flat, in the direction of the gradient. We describe how the presence of these regions necessitates care in both interpreting past results that claimed to find critical points of neural network losses and in designing second-order methods for optimizing neural networks.Comment: 18 pages, 5 figure

Modeling Transport in Gas Chromatography Columns for the Micro-ChemLab

The gas chromatography (GC) column is a critical component in the microsystem for chemical detection ({mu}ChemLab{trademark}) being developed at Sandia. The goal is to etch a meter-long GC column onto a 1-cm{sup 2} silicon chip while maintaining good chromatographic performance. Our design strategy is to use a modeling and simulation approach. We have developed an analytical tool that models the transport and surface interaction process to achieve an optimized design of the GC column. This analytical tool has a flow module and a separation module. The flow module considers both the compressibility and slip flow effects that may significantly influence the gas transport in a long and narrow column. The separation module models analyte transport and physico-chemical interaction with the coated surface in the GC column. It predicts the column efficiency and performance. Results of our analysis will be presented in this paper. In addition to the analytical tool, we have also developed a time-dependent adsorption/desorption model and incorporated this model into a computational fluid dynamics (CFD) code to simulate analyte transport and separation process in GC columns. CFD simulations can capture the complex three-dimensional flow and transport dynamics, whereas the analytical tool cannot. Different column geometries have been studied, and results will be presented in this paper. Overall we have demonstrated that the modeling and simulation approach can guide the design of the GC column and will reduce the number of iterations in the device development

A Semi-Physiologically Based Pharmacokinetic Model Describing the Altered Metabolism of Midazolam Due to Inflammation in Mice

This is the author's accepted manuscript.Purpose To investigate influence of inflammation on metabolism and pharmacokinetics (PK) of midazolam (MDZ) and construct a semi-physiologically based pharmacokinetic (PBPK) model to predict PK in mice with inflammatory disease. Methods Glucose-6-phosphate isomerase (GPI)-mediated inflammation was used as a preclinical model of arthritis in DBA/1 mice. CYP3A substrate MDZ was selected to study changes in metabolism and PK during the inflammation. The semi-PBPK model was constructed using mouse physiological parameters, liver microsome metabolism, and healthy animal PK data. In addition, serum cytokine, and liver-CYP (cytochrome P450 enzymes) mRNA levels were examined. Results The in vitro metabolite formation rate was suppressed in liver microsomes prepared from the GPI-treated mice as compared to the healthy mice. Further, clearance of MDZ was reduced during inflammation as compared to the healthy group. Finally, the semi-PBPK model was used to predict PK of MDZ after GPI-mediated inflammation. IL-6 and TNF-α levels were elevated and liver-cyp3a11 mRNA was reduced after GPI treatment. Conclusion The semi-PBPK model successfully predicted PK parameters of MDZ in the disease state. The model may be applied to predict PK of other drugs under disease conditions using healthy animal PK and liver microsomal data as inputs

Widening of the genetic and clinical spectrum of Lamb-Shaffer syndrome, a neurodevelopmental disorder due to SOX5 haploinsufficiency

Purpose Lamb-Shaffer syndrome (LAMSHF) is a neurodevelopmental disorder described in just over two dozen patients with heterozygous genetic alterations involving SOX5, a gene encoding a transcription factor regulating cell fate and differentiation in neurogenesis and other discrete developmental processes. The genetic alterations described so far are mainly microdeletions. The present study was aimed at increasing our understanding of LAMSHF, its clinical and genetic spectrum, and the pathophysiological mechanisms involved. Methods Clinical and genetic data were collected through GeneMatcher and clinical or genetic networks for 41 novel patients harboring various types ofSOX5 alterations. Functional consequences of selected substitutions were investigated. Results Microdeletions and truncating variants occurred throughout SOX5. In contrast, most missense variants clustered in the pivotal SOX-specific high-mobility-group domain. The latter variants prevented SOX5 from binding DNA and promoting transactivation in vitro, whereas missense variants located outside the high-mobility-group domain did not. Clinical manifestations and severity varied among patients. No clear genotype-phenotype correlations were found, except that missense variants outside the high-mobility-group domain were generally better tolerated. Conclusions This study extends the clinical and genetic spectrum associated with LAMSHF and consolidates evidence that SOX5 haploinsufficiency leads to variable degrees of intellectual disability, language delay, and other clinical features

β1 Integrin Maintains Integrity of the Embryonic Neocortical Stem Cell Niche

IInteractions between laminins and integrin receptors hold neural stem cells in place at the ventricular surface of embryonic brain. Transient disruption leads to abnormal stem cell divisions and permanent cortical malformation

The promise and peril of chemical probes

Chemical probes are powerful reagents with increasing impacts on biomedical research. However, probes of poor quality or that are used incorrectly generate misleading results. To help address these shortcomings, we will create a community-driven wiki resource to improve quality and convey current best practice

A taxonomic backbone for the global synthesis of species diversity in the angiosperm order Caryophyllales

The Caryophyllales constitute a major lineage of flowering plants with approximately 12500 species in 39 families. A taxonomic backbone at the genus level is provided that reflects the current state of knowledge and accepts 749 genera for the order. A detailed review of the literature of the past two decades shows that enormous progress has been made in understanding overall phylogenetic relationships in Caryophyllales. The process of re-circumscribing families in order to be monophyletic appears to be largely complete and has led to the recognition of eight new families (Anacampserotaceae, Kewaceae, Limeaceae, Lophiocarpaceae, Macarthuriaceae, Microteaceae, Montiaceae and Talinaceae), while the phylogenetic evaluation of generic concepts is still well underway. As a result of this, the number of genera has increased by more than ten percent in comparison to the last complete treatments in the Families and genera of vascular plants” series. A checklist with all currently accepted genus names in Caryophyllales, as well as nomenclatural references, type names and synonymy is presented. Notes indicate how extensively the respective genera have been studied in a phylogenetic context. The most diverse families at the generic level are Cactaceae and Aizoaceae, but 28 families comprise only one to six genera. This synopsis represents a first step towards the aim of creating a global synthesis of the species diversity in the angiosperm order Caryophyllales integrating the work of numerous specialists around the world