Suppression of hippocampal neurogenesis is associated with developmental stage, number of perinatal seizure episodes, and glucocorticosteroid level.

Seizures increase dentate granule cell proliferation in adult rats but decrease proliferation in young pups. The particular period and number of perinatal seizures required to cause newborn granule cell suppression in development are unknown. Therefore, we examined cell proliferation with bromodeoxyuridine (BrdU) immunohistochemistry during the peak of neurogenesis (e.g., P6 and P9) and at later postnatal ages (e.g., P13, P20, or P30) following single and multiple episodes of perinatal status epilepticus induced by kainate (KA). Because an inverse relationship exists between glucocorticosteroids (CORT) levels and granule cell proliferation, plasma CORT levels and electroencephalographic (EEG) activity were simultaneously monitored to elucidate underlying mechanisms that inhibit cell proliferation. In control animals, the number of BrdU-labeled cells increased then declined with maturation. After 1x KA or 2x KA administered on P6 and P9, the numbers of BrdU-labeled cells were not different from age-matched controls. However, rat pups with 3x KA (on P6, P9, and P13) had marked suppression of BrdU-labeled cells 48-72 h after the last seizure (43 +/- 6.5% of control). Cell proliferation was also significantly inhibited on P20 after 2x KA (to 56 +/- 6.9%) or 3x KA (to 54 +/- 7.9%) and on P30 with 3x KA (to 74.5 +/- 8.2% of age-matched controls). Cell death was not apparent as chromatin stains showed increased basophilia of only inner cells lining the granule cell layers, in the absence of eosinophilia, argyrophilia, or terminal deoxynucleotidyl dUTP nick endlabeling (TUNEL) labeling at times examined. In P13 pups with 3x KA, electron microscopy revealed an increased number of immature granule cells and putative stem cells with irregular shape, condensed cytoplasm, and electron dense nuclei, and they were also BrdU positive. The EEG showed no relationship between neurogenesis and duration of high-synchronous ictal activity. However, endocrine studies showed a correlation with BrdU number and age, sustained increases in circulating CORT levels following 1x KA on P6 (0.7 +/- 0.1 to 2.40 +/- 0.86 microg/dl), and cumulative increases that exceeded 10 microg/dl at 4-8 h after 3x KA on P13 or P20. In conclusion, a history of only one or two perinatal seizure(s) can suppress neurogenesis if a second or third seizure recurs after a critical developmental period associated with a marked surge in CORT. During the first 2 weeks of postnatal life sustained increases in postictal circulating CORT levels but not duration or intensity of ictal activity has long-term consequences on neurogenesis. The occurrence of an increased proportion of immature granule cells and putative stem cells with irregular morphology in the absence of neurodegeneration suggests that progenitors may not differentiate properly and remain in an immature state

Improving Performance Estimation for FPGA-based Accelerators for Convolutional Neural Networks

Field-programmable gate array (FPGA) based accelerators are being widely used for acceleration of convolutional neural networks (CNNs) due to their potential in improving the performance and reconfigurability for specific application instances. To determine the optimal configuration of an FPGA-based accelerator, it is necessary to explore the design space and an accurate performance prediction plays an important role during the exploration. This work introduces a novel method for fast and accurate estimation of latency based on a Gaussian process parametrised by an analytic approximation and coupled with runtime data. The experiments conducted on three different CNNs on an FPGA-based accelerator on Intel Arria 10 GX 1150 demonstrated a 30.7% improvement in accuracy with respect to the mean absolute error in comparison to a standard analytic method in leave-one-out cross-validation.Comment: This article is accepted for publication at ARC'202

Dramatic age-related changes in nuclear and genome copy number in the nematode Caenorhabditis elegans

The nematode Caenorhabditis elegans has become one of the most widely used model systems for the study of aging, yet very little is known about how C. elegans age. The development of the worm, from egg to young adult has been completely mapped at the cellular level, but such detailed studies have not been extended throughout the adult lifespan. Numerous single gene mutations, drug treatments and environmental manipulations have been found to extend worm lifespan. To interpret the mechanism of action of such aging interventions, studies to characterize normal worm aging, similar to those used to study worm development are necessary. We have used 4′,6′-diamidino-2-phenylindole hydrochloride staining and quantitative polymerase chain reaction to investigate the integrity of nuclei and quantify the nuclear genome copy number of C. elegans with age. We report both systematic loss of nuclei or nuclear DNA, as well as dramatic age-related changes in nuclear genome copy number. These changes are delayed or attenuated in long-lived daf-2 mutants. We propose that these changes are important pathobiological characteristics of aging nematodes

Validating module network learning algorithms using simulated data

In recent years, several authors have used probabilistic graphical models to learn expression modules and their regulatory programs from gene expression data. Here, we demonstrate the use of the synthetic data generator SynTReN for the purpose of testing and comparing module network learning algorithms. We introduce a software package for learning module networks, called LeMoNe, which incorporates a novel strategy for learning regulatory programs. Novelties include the use of a bottom-up Bayesian hierarchical clustering to construct the regulatory programs, and the use of a conditional entropy measure to assign regulators to the regulation program nodes. Using SynTReN data, we test the performance of LeMoNe in a completely controlled situation and assess the effect of the methodological changes we made with respect to an existing software package, namely Genomica. Additionally, we assess the effect of various parameters, such as the size of the data set and the amount of noise, on the inference performance. Overall, application of Genomica and LeMoNe to simulated data sets gave comparable results. However, LeMoNe offers some advantages, one of them being that the learning process is considerably faster for larger data sets. Additionally, we show that the location of the regulators in the LeMoNe regulation programs and their conditional entropy may be used to prioritize regulators for functional validation, and that the combination of the bottom-up clustering strategy with the conditional entropy-based assignment of regulators improves the handling of missing or hidden regulators.Comment: 13 pages, 6 figures + 2 pages, 2 figures supplementary informatio

NMR of liquid 3He in clay pores at 1.5 K

In the present work a new method for studying porous media by nuclear magnetic resonance of liquid 3He has been proposed. This method has been demonstrated in an example of a clay mineral sample. For the first time the integral porosity of clay sample has been measured. For investigated samples the value of integral porosity is in the range of 10-30%. Inverse Laplace transform of 3He longitudinal magnetization recovery curve has been carried out, thus distribution of relaxation times T1 has been obtained.Comment: 9 pages, 5 figure

Evolving Gaussian Process Kernels for Translation Editing Effort Estimation

In many Natural Language Processing problems the combination of machine learning and optimization techniques is essential. One of these problems is estimating the effort required to improve, under direct human supervision, a text that has been translated using a machine translation method. Recent developments in this area have shown that Gaussian Processes can be accurate for post-editing effort prediction. However, the Gaussian Process kernel has to be chosen in advance, and this choice in- fluences the quality of the prediction. In this paper, we propose a Genetic Programming algorithm to evolve kernels for Gaussian Processes. We show that the combination of evolutionary optimization and Gaussian Processes removes the need for a-priori specification of the kernel choice, and achieves predictions that, in many cases, outperform those obtained with fixed kernels.TIN2016-78365-

Feature selection for chemical sensor arrays using mutual information

We address the problem of feature selection for classifying a diverse set of chemicals using an array of metal oxide sensors. Our aim is to evaluate a filter approach to feature selection with reference to previous work, which used a wrapper approach on the same data set, and established best features and upper bounds on classification performance. We selected feature sets that exhibit the maximal mutual information with the identity of the chemicals. The selected features closely match those found to perform well in the previous study using a wrapper approach to conduct an exhaustive search of all permitted feature combinations. By comparing the classification performance of support vector machines (using features selected by mutual information) with the performance observed in the previous study, we found that while our approach does not always give the maximum possible classification performance, it always selects features that achieve classification performance approaching the optimum obtained by exhaustive search. We performed further classification using the selected feature set with some common classifiers and found that, for the selected features, Bayesian Networks gave the best performance. Finally, we compared the observed classification performances with the performance of classifiers using randomly selected features. We found that the selected features consistently outperformed randomly selected features for all tested classifiers. The mutual information filter approach is therefore a computationally efficient method for selecting near optimal features for chemical sensor arrays

Negotiating agency: Amish and ultra-Orthodox women’s responses to the Internet

This study explores how women in two devout religious communities cope with the Internet and its apparent incompatibility with their communities’ values and practices. Questionnaires containing both closed and open-ended questions were completed by 82 participants, approximately half from each community. While their discourses included similar framings of danger and threat, the two groups manifested different patterns of Internet use (and nonuse). Rigorous adherence to religious dictates is greatly admired in these communities, and the women take pride in manipulating their status in them. Their agency is reflected in how they negotiate the tension inherent in their roles as both gatekeepers and agents-of-change, which are analyzed as valuable currencies in their cultural and religious markets

Mutant induced pluripotent stem cell lines recapitulate aspects of TDP-43 proteinopathies and reveal cell-specific vulnerability

Transactive response DNA-binding (TDP-43) protein is the dominant disease protein in amyotrophic lateral sclerosis (ALS) and a subgroup of frontotemporal lobar degeneration (FTLD-TDP). Identification of mutations in the gene encoding TDP-43 (TARDBP) in familial ALS confirms a mechanistic link between misaccumulation of TDP-43 and neurodegeneration and provides an opportunity to study TDP-43 proteinopathies in human neurons generated from patient fibroblasts by using induced pluripotent stem cells (iPSCs). Here, we report the generation of iPSCs that carry the TDP-43 M337V mutation and their differentiation into neurons and functional motor neurons. Mutant neurons had elevated levels of soluble and detergent-resistant TDP-43 protein, decreased survival in longitudinal studies, and increased vulnerability to antagonism of the PI3K pathway. We conclude that expression of physiological levels of TDP-43 in human neurons is sufficient to reveal a mutation-specific cell-autonomous phenotype and strongly supports this approach for the study of disease mechanisms and for drug screening