Activity Catalog Tool (ACT) user manual, version 2.0

This report comprises the user manual for version 2.0 of the Activity Catalog Tool (ACT) software program, developed by Leon D. Segal and Anthony D. Andre in cooperation with NASA Ames Aerospace Human Factors Research Division, FLR branch. ACT is a software tool for recording and analyzing sequences of activity over time that runs on the Macintosh platform. It was designed as an aid for professionals who are interested in observing and understanding human behavior in field settings, or from video or audio recordings of the same. Specifically, the program is aimed at two primary areas of interest: human-machine interactions and interactions between humans. The program provides a means by which an observer can record an observed sequence of events, logging such parameters as frequency and duration of particular events. The program goes further by providing the user with a quantified description of the observed sequence, through application of a basic set of statistical routines, and enables merging and appending of several files and more extensive analysis of the resultant data

Eigenvalue spectral properties of sparse random matrices obeying Dale's law

Understanding the dynamics of large networks of neurons with heterogeneous connectivity architectures is a complex physics problem that demands novel mathematical techniques. Biological neural networks are inherently spatially heterogeneous, making them difficult to mathematically model. Random recurrent neural networks capture complex network connectivity structures and enable mathematically tractability. Our paper generalises previous classical results to sparse connectivity matrices which have distinct excitatory (E) or inhibitory (I) neural populations. By investigating sparse networks we construct our analysis to examine the impacts of all levels of network sparseness, and discover a novel nonlinear interaction between the connectivity matrix and resulting network dynamics, in both the balanced and unbalanced cases. Specifically, we deduce new mathematical dependencies describing the influence of sparsity and distinct E/I distributions on the distribution of eigenvalues (eigenspectrum) of the networked Jacobian. Furthermore, we illustrate that the previous classical results are special cases of the more general results we have described here. Understanding the impacts of sparse connectivities on network dynamics is of particular importance for both theoretical neuroscience and mathematical physics as it pertains to the structure-function relationship of networked systems and their dynamics. Our results are an important step towards developing analysis techniques that are essential to studying the impacts of larger scale network connectivity on network function, and furthering our understanding of brain function and dysfunction.Comment: 18 pages, 6 figure

Microarray analysis of Shigella flexneri-infected epithelial cells identifies host factors important for apoptosis inhibition

<p>Abstract</p> <p>Background</p> <p><it>Shigella flexneri </it>inhibits apoptosis in infected epithelial cells. In order to understand the pro-survival effects induced by the bacteria, we utilized apoptosis-specific microarrays to analyze the changes in eukaryotic gene expression in both infected and uninfected cells in the presence and absence of staurosporine, a chemical inducer of the intrinsic pathway of apoptosis. The goal of this research was to identify host factors that contribute to apoptosis inhibition in infected cells.</p> <p>Results</p> <p>The microarray analysis revealed distinct expression profiles in uninfected and infected cells, and these changes were altered in the presence of staurosporine. These profiles allowed us to make comparisons between the treatment groups. Compared to uninfected cells, <it>Shigella-</it>infected epithelial cells, both in the presence and absence of staurosporine, showed significant induced expression of <it>JUN</it>, several members of the inhibitor of apoptosis gene family, nuclear factor κB and related genes, genes involving tumor protein 53 and the retinoblastoma protein, and surprisingly, genes important for the inhibition of the extrinsic pathway of apoptosis. We confirmed the microarray results for a selection of genes using <it>in situ </it>hybridization analysis.</p> <p>Conclusion</p> <p>Infection of epithelial cells with <it>S. flexneri </it>induces a pro-survival state in the cell that results in apoptosis inhibition in the presence and absence of staurosporine. The bacteria may target these host factors directly while some induced genes may represent downstream effects due to the presence of the bacteria. Our results indicate that the bacteria block apoptosis at multiple checkpoints along both pathways so that even if a cell fails to prevent apoptosis at an early step, <it>Shigella </it>will block apoptosis at the level of caspase-3. Apoptosis inhibition is most likely vital to the survival of the bacteria <it>in vivo</it>. Future characterization of these host factors is required to fully understand how <it>S. flexneri </it>inhibits apoptosis in epithelial cells.</p

Autoregressive models for biomedical signal processing

Autoregressive models are ubiquitous tools for the analysis of time series in many domains such as computational neuroscience and biomedical engineering. In these domains, data is, for example, collected from measurements of brain activity. Crucially, this data is subject to measurement errors as well as uncertainties in the underlying system model. As a result, standard signal processing using autoregressive model estimators may be biased. We present a framework for autoregressive modelling that incorporates these uncertainties explicitly via an overparameterised loss function. To optimise this loss, we derive an algorithm that alternates between state and parameter estimation. Our work shows that the procedure is able to successfully denoise time series and successfully reconstruct system parameters. This new paradigm can be used in a multitude of applications in neuroscience such as brain-computer interface data analysis and better understanding of brain dynamics in diseases such as epilepsy

Relationships between Irritable Bowel Syndrome Pain, Skin Temperature Indices of Autonomic Dysregulation, and Sensitivity to Thermal Cutaneous Stimulation

This study evaluated relationships between irritable bowel syndrome (IBS) pain, sympathetic dysregulation, and thermal pain sensitivity. Eight female patients with diarrhea-predominant IBS and ten healthy female controls were tested for sensitivity to thermal stimulation of the left palm. A new method of response-dependent thermal stimulation was used to maintain pain intensity at a predetermined level (35%) by adjusting thermal stimulus intensity as a function of pain ratings. Clinical pain levels were assessed prior to each testing session. Skin temperatures were recorded before and after pain sensitivity testing. The temperature of palmar skin dropped (1.5°C) when the corresponding location on the opposite hand of control subjects was subjected to prolonged thermal stimulation, but this response was absent for IBS pain patients. The patients also required significantly lower stimulus temperatures than controls to maintain a 35% pain rating. Baseline skin temperatures of patients were significantly correlated with thermode temperatures required to maintain 35% pain ratings. IBS pain intensity was not significantly correlated with skin temperature or pain sensitivity. The method of response-dependent stimulation revealed thermal hyperalgesia and increased sympathetic tone for chronic pain patients, relative to controls. Similarly, a significant correlation between resting skin temperatures and thermal pain sensitivity for IBS but not control subjects indicates that tonic sympathetic activation and a thermal hyperalgesia were generated by the chronic presence of visceral pain. However, lack of a significant relationship between sympathetic tone and ratings of IBS pain casts doubt on propositions that the magnitude of IBS pain is determined by psychological stress

Vapor-liquid-solid growth of highly-mismatched semiconductor nanowires with high-fidelity van der Waals layer stacking

Nanobelts, nanoribbons and other quasi-one-dimensional nanostructures formed from layered, so-called, van der Waals semiconductors have garnered much attention due to their high-performance, tunable optoelectronic properties. For layered alloys made from the gallium monochalcogenides GaS, GaSe, and GaTe, near-continuous tuning of the energy bandgap across the full composition range has been achieved in GaSe1-xSx and GaSe1-xTex alloys. Gold-catalyzed vapor-liquid-solid (VLS) growth of these alloys yields predominantly nanobelts, nanoribbons and other nanostructures for which the fast crystal growth front consists of layer edges in contact with the catalyst. We demonstrate that in the S-rich, GaS1-xTex system, unlike GaSe1-xSx and GaSe1-xTex, the Au-catalyzed VLS process yields van der Waals nanowires for which the fast growth direction is normal to the layers. The high mismatch between S and Te leads to extraordinary bowing of the GaS1-xTex alloy's energy bandgap, decreasing by at least 0.6 eV for x as small as 0.03. Calculations using density functional theory confirm the significant decrease in bandgap in S-rich GaS1-xTex. The nanowires can exceed fifty micrometers in length, consisting of tens of thousands of van der Waals-bonded layers with triangular or hexagonal cross-sections of uniform dimensions along the length of the nanowire. We propose that the low solubility of Te in GaS results in an enhancement in Te coverage around the Au catalyst-nanowire interface, confining the catalyst to the chalcogen-terminated basal plane (rather than the edges) and thereby enabling layer-by-layer, c-axis growth

Path Signatures for Seizure Forecasting

Forecasting the state of a system from an observed time series is the subject of research in many domains, such as computational neuroscience. Here, the prediction of epileptic seizures from brain measurements is an unresolved problem. There are neither complete models describing underlying brain dynamics, nor do individual patients exhibit a single seizure onset pattern, which complicates the development of a `one-size-fits-all' solution. Based on a longitudinal patient data set, we address the automated discovery and quantification of statistical features (biomarkers) that can be used to forecast seizures in a patient-specific way. We use existing and novel feature extraction algorithms, in particular the path signature, a recent development in time series analysis. Of particular interest is how this set of complex, nonlinear features performs compared to simpler, linear features on this task. Our inference is based on statistical classification algorithms with in-built subset selection to discern time series with and without an impending seizure while selecting only a small number of relevant features. This study may be seen as a step towards a generalisable pattern recognition pipeline for time series in a broader context

A Contraction Stress Model of Hypertrophic Cardiomyopathy due to Sarcomere Mutations.

Thick-filament sarcomere mutations are a common cause of hypertrophic cardiomyopathy (HCM), a disorder of heart muscle thickening associated with sudden cardiac death and heart failure, with unclear mechanisms. We engineered four isogenic induced pluripotent stem cell (iPSC) models of β-myosin heavy chain and myosin-binding protein C3 mutations, and studied iPSC-derived cardiomyocytes in cardiac microtissue assays that resemble cardiac architecture and biomechanics. All HCM mutations resulted in hypercontractility with prolonged relaxation kinetics in proportion to mutation pathogenicity, but not changes in calcium handling. RNA sequencing and expression studies of HCM models identified p53 activation, oxidative stress, and cytotoxicity induced by metabolic stress that can be reversed by p53 genetic ablation. Our findings implicate hypercontractility as a direct consequence of thick-filament mutations, irrespective of mutation localization, and the p53 pathway as a molecular marker of contraction stress and candidate therapeutic target for HCM patients