Bar-Ilan University

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.

Evidence for a Neuroendocrinological Foundation of Human Affiliation: Plasma Oxytocin Levels Across Pregnancy and the Postpartum Period Predict Mother-Infant Bonding

Abstract: Although research on the neurobiological foundation of social affiliation has implicated the neuropeptide oxytocin in processes of maternal bonding in mammals, there is little evidence to support such links in humans. Plasma oxytocin and cortisol of 62 pregnant women were sampled during the first trimester, last trimester, and first postpartum month. Oxytocin was assayed using enzyme immunoassay, and free cortisol was calculated. After the infants were born, their interactions with their mothers were observed, and the mothers were interviewed regarding their infant-related thoughts and behaviors. Oxytocin was stable across time, and oxytocin levels at early pregnancy and the postpartum period were related to a clearly defined set of maternal bonding behaviors, including gaze, vocalizations, positive affect, and affectionate touch; to attachment-related thoughts; and to frequent checking of the infant. Across pregnancy and the postpartum period, oxytocin may play a role in the emergence of behaviors and mental representations typical of bonding in the human mother.

Effects of Age and Gender on Blogging

Abstract: Analysis of a corpus of tens of thousands of blogs – incorporating close to 300 million words – indicates significant differences in writing style and content between male and female bloggers as well as among authors of different ages. Such differences can be exploited to determine an unknown author’s age and gender on the basis of a blog’s vocabulary.

Assessing the Ability of LSTMs to Learn Syntax-Sensitive Dependencies

Abstract: The success of long short-term memory (LSTM) neural networks in language processing is typically attributed to their ability to capture long-distance statistical regularities. Linguistic regularities are often sensitive to syntactic structure; can such dependencies be captured by LSTMs, which do not have explicit structural representations? We begin addressing this question using number agreement in English subject-verb dependencies. We probe the architecture's grammatical competence both using training objectives with an explicit grammatical target (number prediction, grammaticality judgments) and using language models. In the strongly supervised settings, the LSTM achieved very high overall accuracy (less than 1% errors), but errors increased when sequential and structural information conflicted. The frequency of such errors rose sharply in the language-modeling setting. We conclude that LSTMs can capture a non-trivial amount of grammatical structure given targeted supervision, but stronger architectures may be required to further reduce errors; furthermore, the language modeling signal is insufficient for capturing syntax-sensitive dependencies, and should be supplemented with more direct supervision if such dependencies need to be captured.

Electrostatic focusing of unlabelled DNA into nanoscale pores using a salt gradient

Abstract: Solid-state nanopores are sensors capable of analysing individual unlabelled DNA molecules in solution. Although the critical information obtained from nanopores (for example, DNA sequence) comes from the signal collected during DNA translocation, the throughput of the method is determined by the rate at which molecules arrive and thread into the pores. Here, we study the process of DNA capture into nanofabricated SiN pores of molecular dimensions. For fixed analyte concentrations we find an increase in capture rate as the DNA length increases from 800 to 8,000 base pairs, a length-independent capture rate for longer molecules, and increasing capture rates when ionic gradients are established across the pore. Furthermore, we show that application of a 20-fold salt gradient allows the detection of picomolar DNA concentrations at high throughput. The salt gradients enhance the electric field, focusing more molecules into the pore, thereby advancing the possibility of analysing unamplified DNA samples using nanopores.