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Institution

Bar-Ilan University

EducationRamat Gan, Israel
About: Bar-Ilan University is a education organization based out in Ramat Gan, Israel. It is known for research contribution in the topics: Population & Poison control. The organization has 12835 authors who have published 34964 publications receiving 995648 citations. The organization is also known as: Bar Ilan University & BIU.


Papers
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Journal ArticleDOI
TL;DR: This paper explored multiple prenatal antecedents and postnatal correlates of change in marital adjustment and satisfaction in men and women across the transition to parenthood, and 102 couples from diverse sociocultural backgrounds were studied longitudinally from pregnancy to the 9th postpartum month.
Abstract: To explore multiple prenatal antecedents and postnatal correlates of change in marital adjustment and satisfaction in men and women across the transition to parenthood, 102 couples from diverse sociocultural backgrounds were studied longitudinally from pregnancy to the 9th postpartum month. Guided by an ecological model, the pre- and postnatal assessments included questionnaires of marital adjustment, personality traits, attitudes toward parenthood, work role centrality, social support, as well as observations and ratings of infant behaviors, maternal and paternal behaviors, and marital communication

190 citations

Journal ArticleDOI
TL;DR: In this study, a new approach for the evaluation of brain energy metabolism in awake animals became possible as UV transmitting optical fibers became available and the brain was exposed to various physiological and pathological conditions which resulted in the increase or decrease in the level of NADH.

190 citations

Proceedings ArticleDOI
01 Sep 2018
TL;DR: An analysis into the inner workings of Convolutional Neural Networks for processing text shows that filters may capture several different semantic classes of ngrams by using different activation patterns, and that global max-pooling induces behavior which separates important n grams from the rest.
Abstract: We present an analysis into the inner workings of Convolutional Neural Networks (CNNs) for processing text. CNNs used for computer vision can be interpreted by projecting filters into image space, but for discrete sequence inputs CNNs remain a mystery. We aim to understand the method by which the networks process and classify text. We examine common hypotheses to this problem: that filters, accompanied by global max-pooling, serve as ngram detectors. We show that filters may capture several different semantic classes of ngrams by using different activation patterns, and that global max-pooling induces behavior which separates important ngrams from the rest. Finally, we show practical use cases derived from our findings in the form of model interpretability (explaining a trained model by deriving a concrete identity for each filter, bridging the gap between visualization tools in vision tasks and NLP) and prediction interpretability (explaining predictions).

190 citations

Journal ArticleDOI
TL;DR: In this article, the synthesis of "layered-layered" integrated xLi 2 Mno 3 ·(1 ― x)LiMn 1/3 Ni1/3 Co 1/ 3 O 2 materials (x = 0.3, 0.5, and 0.7) using the self-combustion reaction in solutions containing metal nitrates and sucrose was reported.
Abstract: We report herein on the synthesis of "layered-layered" integrated xLi 2 Mno 3 ·(1 ― x)LiMn 1/3 Ni 1/3 Co 1/3 O 2 materials (x = 0.3, 0.5, and 0.7) using the self-combustion reaction in solutions containing metal nitrates and sucrose. The nanoparticles of these materials were obtained by further annealing of the as-prepared product in air at 700°C for 1 h and submicrometric particles were obtained by further annealing at 900°C for 22 h. The effect of composition on the electrochemical performance was explored in this work. By a rigorous study with high resolution transmission electron microscopy (HRTEM), it became clear that the syntheses with the above stoichiometries produce two-phase materials comprising nanodomains of both rhombohedral LiNiO 2 -like and monoclinic Li 2 MnO 3 structures, which are closely integrated and interconnected with one another at the atomic level. Stable reversible capacities ∼220 mAh/g were obtained with composite electrodes containing submicrometer particles of 0.5Li 2 MnO 3 ·0.5LiMn 1/3 Ni 1/3 Co 1/3 O 2 . Structural aspects, activation of the monoclinic component, and stabilization mechanisms are thoroughly discussed using Raman spectroscopy, solid-state NMR, HRTEM, and X-ray diffraction (including Rietveld analysis) in conjunction with electrochemical measurements. This work provides a further indication that this family of integrated compounds contains the most promising cathode materials for high energy density Li-ion batteries.

189 citations

Journal ArticleDOI
TL;DR: A microfluidics-based approach for de novo discovery and quantitative biophysical characterization of DNA target sequences, validated by measuring sequence preferences for 28 Saccharomyces cerevisiae transcription factors with a variety of DNA-binding domains, including several that have proven difficult to study by other techniques.
Abstract: Gene expression is regulated in part by protein transcription factors that bind target regulatory DNA sequences. Predicting DNA binding sites and affinities from transcription factor sequence or structure is difficult; therefore, experimental data are required to link transcription factors to target sequences. We present a microfluidics-based approach for de novo discovery and quantitative biophysical characterization of DNA target sequences. We validated our technique by measuring sequence preferences for 28 Saccharomyces cerevisiae transcription factors with a variety of DNA-binding domains, including several that have proven difficult to study by other techniques. For each transcription factor, we measured relative binding affinities to oligonucleotides covering all possible 8-bp DNA sequences to create a comprehensive map of sequence preferences; for four transcription factors, we also determined absolute affinities. We expect that these data and future use of this technique will provide information essential for understanding transcription factor specificity, improving identification of regulatory sites and reconstructing regulatory interactions.

189 citations


Authors

Showing all 13037 results

NameH-indexPapersCitations
H. Eugene Stanley1541190122321
Albert-László Barabási152438200119
Shlomo Havlin131101383347
Stuart A. Aaronson12965769633
Britton Chance128111276591
Mark A. Ratner12796868132
Doron Aurbach12679769313
Jun Yu121117481186
Richard J. Wurtman11493353290
Amir Lerman11187751969
Zhu Han109140748725
Moussa B.H. Youdim10757442538
Juan Bisquert10745046267
Rachel Yehuda10646136726
Michael F. Green10648545707
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Performance
Metrics
No. of papers from the Institution in previous years
YearPapers
2023117
2022330
20212,286
20202,157
20191,920
20181,768