Optoelectronics with electrically tunable PN diodes in a monolayer dichalcogenide

One of the most fundamental devices for electronics and optoelectronics is the PN junction, which provides the functional element of diodes, bipolar transistors, photodetectors, LEDs, and solar cells, among many other devices. In conventional PN junctions, the adjacent p- and n-type regions of a semiconductor are formed by chemical doping. Materials with ambipolar conductance, however, allow for PN junctions to be configured and modified by electrostatic gating. This electrical control enables a single device to have multiple functionalities. Here we report ambipolar monolayer WSe2 devices in which two local gates are used to define a PN junction exclusively within the sheet of WSe2. With these electrically tunable PN junctions, we demonstrate both PN and NP diodes with ideality factors better than 2. Under excitation with light, the diodes show photodetection responsivity of 210 mA/W and photovoltaic power generation with a peak external quantum efficiency of 0.2%, promising numbers for a nearly transparent monolayer sheet in a lateral device geometry. Finally, we demonstrate a light-emitting diode based on monolayer WSe2. These devices provide a fundamental building block for ubiquitous, ultra-thin, flexible, and nearly transparent optoelectronic and electronic applications based on ambipolar dichalcogenide materials.Comment: 14 pages, 4 figure

A Cognitive Architecture Based on a Learning Classifier System with Spiking Classifiers

© 2015, Springer Science+Business Media New York. Learning classifier systems (LCS) are population-based reinforcement learners that were originally designed to model various cognitive phenomena. This paper presents an explicitly cognitive LCS by using spiking neural networks as classifiers, providing each classifier with a measure of temporal dynamism. We employ a constructivist model of growth of both neurons and synaptic connections, which permits a genetic algorithm to automatically evolve sufficiently-complex neural structures. The spiking classifiers are coupled with a temporally-sensitive reinforcement learning algorithm, which allows the system to perform temporal state decomposition by appropriately rewarding “macro-actions”, created by chaining together multiple atomic actions. The combination of temporal reinforcement learning and neural information processing is shown to outperform benchmark neural classifier systems, and successfully solve a robotic navigation task

Cadaveric and three-dimensional computed tomography study of the morphology of the scapula with reference to reversed shoulder prosthesis

<p>Abstract</p> <p>Purpose</p> <p>The purpose of this study is to analyze the morphology of the scapula with reference to the glenoid component implantation in reversed shoulder prosthesis, in order to improve primary fixation of the component.</p> <p>Methods</p> <p>Seventy-three 3-dimensional computed tomography of the scapula and 108 scapular dry specimens were analyzed to determine the anterior and posterior length of the glenoid neck, the angle between the glenoid surface and the upper posterior column of the scapula and the angle between the major craneo-caudal glenoid axis and the base of the coracoid process and the upper posterior column.</p> <p>Results</p> <p>The anterior and posterior length of glenoid neck was classified into two groups named "short-neck" and "long-neck" with significant differences between them. The angle between the glenoid surface and the upper posterior column of the scapula was also classified into two different types: type I (mean 50°–52°) and type II (mean 62,50°–64°), with significant differences between them (p < 0,001). The angle between the major craneo-caudal glenoid axis and the base of the coracoid process averaged 18,25° while the angle with the upper posterior column of the scapula averaged 8°.</p> <p>Conclusion</p> <p>Scapular morphological variability advices for individual adjustments of glenoid component implantation in reversed total shoulder prosthesis. Three-dimensional computed tomography of the scapula constitutes an important tool when planning reversed prostheses implantation.</p

Optimizing Preprocessing and Analysis Pipelines for Single-Subject fMRI: 2. Interactions with ICA, PCA, Task Contrast and Inter-Subject Heterogeneity

A variety of preprocessing techniques are available to correct subject-dependant artifacts in fMRI, caused by head motion and physiological noise. Although it has been established that the chosen preprocessing steps (or “pipeline”) may significantly affect fMRI results, it is not well understood how preprocessing choices interact with other parts of the fMRI experimental design. In this study, we examine how two experimental factors interact with preprocessing: between-subject heterogeneity, and strength of task contrast. Two levels of cognitive contrast were examined in an fMRI adaptation of the Trail-Making Test, with data from young, healthy adults. The importance of standard preprocessing with motion correction, physiological noise correction, motion parameter regression and temporal detrending were examined for the two task contrasts. We also tested subspace estimation using Principal Component Analysis (PCA), and Independent Component Analysis (ICA). Results were obtained for Penalized Discriminant Analysis, and model performance quantified with reproducibility (R) and prediction metrics (P). Simulation methods were also used to test for potential biases from individual-subject optimization. Our results demonstrate that (1) individual pipeline optimization is not significantly more biased than fixed preprocessing. In addition, (2) when applying a fixed pipeline across all subjects, the task contrast significantly affects pipeline performance; in particular, the effects of PCA and ICA models vary with contrast, and are not by themselves optimal preprocessing steps. Also, (3) selecting the optimal pipeline for each subject improves within-subject (P,R) and between-subject overlap, with the weaker cognitive contrast being more sensitive to pipeline optimization. These results demonstrate that sensitivity of fMRI results is influenced not only by preprocessing choices, but also by interactions with other experimental design factors. This paper outlines a quantitative procedure to denoise data that would otherwise be discarded due to artifact; this is particularly relevant for weak signal contrasts in single-subject, small-sample and clinical datasets

What Can Causal Networks Tell Us about Metabolic Pathways?

Graphical models describe the linear correlation structure of data and have been used to establish causal relationships among phenotypes in genetic mapping populations. Data are typically collected at a single point in time. Biological processes on the other hand are often non-linear and display time varying dynamics. The extent to which graphical models can recapitulate the architecture of an underlying biological processes is not well understood. We consider metabolic networks with known stoichiometry to address the fundamental question: “What can causal networks tell us about metabolic pathways?”. Using data from an Arabidopsis BaySha population and simulated data from dynamic models of pathway motifs, we assess our ability to reconstruct metabolic pathways using graphical models. Our results highlight the necessity of non-genetic residual biological variation for reliable inference. Recovery of the ordering within a pathway is possible, but should not be expected. Causal inference is sensitive to subtle patterns in the correlation structure that may be driven by a variety of factors, which may not emphasize the substrate-product relationship. We illustrate the effects of metabolic pathway architecture, epistasis and stochastic variation on correlation structure and graphical model-derived networks. We conclude that graphical models should be interpreted cautiously, especially if the implied causal relationships are to be used in the design of intervention strategies

The Conselice Study of Brain Ageing

Among the age-related diseases, the development of cognitive impairments, in particular dementia, is the most devastating for the individual and has great social and healthcare costs. Accurate information is needed about the prevalence and incidence of cognitive disorders and the physiology of the ageing brain. In particular, only scant data are available about the relationship between ageing, cognitive status and nutritional factors. In order to address these issues we planned the Conselice Study of Brain Ageing, a longitudinal study of physiologic and pathologic brain ageing. The center involved in the study was the municipality of Conselice, Ravenna province, in the Northern Italian region Emilia-Romagna. A total of 1016 subjects aged 65 and over was enrolled at baseline. Information about cognitive status at 4-years of follow-up was collected for 940 of them. These data have been used to estimate prevalence and incidence of dementia in the elderly Italian population and to investigate the possible role of baseline blood homocysteine as risk factors for dementia

Identification of genes and pathways associated with cytotoxic T lymphocyte infiltration of serous ovarian cancer

BACKGROUND: Tumour-infiltrating lymphocytes (TILs) are predictors of disease-specific survival (DSS) in ovarian cancer. It is largely unknown what factors contribute to lymphocyte recruitment. Our aim was to evaluate genes and pathways contributing to infiltration of cytotoxic T lymphocytes (CTLs) in advanced-stage serous ovarian cancer. METHODS: For this study global gene expression was compared between low TIL (n=25) and high TIL tumours (n=24). The differences in gene expression were evaluated using parametric T-testing. Selectively enriched biological pathways were identified with gene set enrichment analysis. Prognostic influence was validated in 157 late-stage serous ovarian cancer patients. Using immunohistochemistry, association of selected genes from identified pathways with CTL was validated. RESULTS: The presence of CTL was associated with 320 genes and 23 pathways (P<0.05). In addition, 54 genes and 8 pathways were also associated with DSS in our validation cohort. Immunohistochemical evaluation showed strong correlations between MHC class I and II membrane expression, parts of the antigen processing and presentation pathway, and CTL recruitment. CONCLUSION: Gene expression profiling and pathway analyses are valuable tools to obtain more understanding of tumour characteristics influencing lymphocyte recruitment in advanced-stage serous ovarian cancer. Identified genes and pathways need to be further investigated for suitability as therapeutic targets

Stochastic variation of transcript abundance in C57BL/6J mice

<p>Abstract</p> <p>Background</p> <p>Transcripts can exhibit significant variation in tissue samples from inbred laboratory mice. We have designed and carried out a microarray experiment to examine transcript variation across samples from adipose, heart, kidney, and liver tissues of C57BL/6J mice and to partition variation into within-mouse and between-mouse components. Within-mouse variance captures variation due to heterogeneity of gene expression within tissues, RNA-extraction, and array processing. Between-mouse variance reflects differences in transcript abundance between genetically identical mice.</p> <p>Results</p> <p>The nature and extent of transcript variation differs across tissues. Adipose has the largest total variance and the largest within-mouse variance. Liver has the smallest total variance, but it has the most between-mouse variance. Genes with high variability can be classified into groups with correlated patterns of expression that are enriched for specific biological functions. Variation between mice is associated with circadian rhythm, growth hormone signaling, immune response, androgen regulation, lipid metabolism, and the extracellular matrix. Genes showing correlated patterns of within-mouse variation are also associated with biological functions that largely reflect heterogeneity of cell types within tissues.</p> <p>Conclusions</p> <p>Genetically identical mice can experience different individual outcomes for medically important traits. Variation in gene expression observed between genetically identical mice can identify functional classes of genes that are likely to vary in the absence of experimental perturbations, can inform experimental design decisions, and provides a baseline for the interpretation of gene expression data in interventional studies. The extent of transcript variation among genetically identical mice underscores the importance of stochastic and micro-environmental factors and their phenotypic consequences.</p

Molecular Characterization of a Novel Intracellular ADP-Ribosyl Cyclase

Background. ADP-ribosyl cyclases are remarkable enzymes capable of catalyzing multiple reactions including the synthesis of the novel and potent intracellular calcium mobilizing messengers, cyclic ADP-ribose and NAADP. Not all ADP-ribosyl cyclases however have been characterized at the molecular level. Moreover, those that have are located predominately at the outer cell surface and thus away from their cytosolic substrates. Methodology/Principal Findings. Here we report the molecular cloning of a novel expanded family of ADP-ribosyl cyclases from the sea urchin, an extensively used model organism for the study of inositol trisphosphate-independent calcium mobilization. We provide evidence that one of the isoforms (SpARC1) is a soluble protein that is targeted exclusively to the endoplasmic reticulum lumen when heterologously expressed. Catalytic activity of the recombinant protein was readily demonstrable in crude cell homogenates, even under conditions where luminal continuity was maintained. Conclusions/Significance. Our data reveal a new intracellular location for ADP-ribosyl cyclases and suggest that production of calcium mobilizing messengers may be compartmentalized