Vibrational and optical properties of MoS2_2: from monolayer to bulk

Molybdenum disulfide, MoS2, has recently gained considerable attention as a layered material where neighboring layers are only weakly interacting and can easily slide against each other. Therefore, mechanical exfoliation allows the fabrication of single and multi-layers and opens the possibility to generate atomically thin crystals with outstanding properties. In contrast to graphene, it has an optical gap of 1.9 eV. This makes it a prominent candidate for transistor and opto-electronic applications. Single-layer MoS$_2$ exhibits remarkably different physical properties compared to bulk MoS$_2$ due to the absence of interlayer hybridization. For instance, while the band gap of bulk and multi-layer MoS$_2$ is indirect, it becomes direct with decreasing number of layers. In this review, we analyze from a theoretical point of view the electronic, optical, and vibrational properties of single-layer, few-layer and bulk MoS$_2$. In particular, we focus on the effects of spin-orbit interaction, number of layers, and applied tensile strain on the vibrational and optical properties. We examine the results obtained by different methodologies, mainly ab initio approaches. We also discuss which approximations are suitable for MoS$_2$ and layered materials. The effect of external strain on the band gap of single-layer MoS$_2$ and the crossover from indirect to direct band gap is investigated. We analyze the excitonic effects on the absorption spectra. The main features, such as the double peak at the absorption threshold and the high-energy exciton are presented. Furthermore, we report on the phonon dispersion relations of single-layer, few-layer and bulk MoS$_2$. Based on the latter, we explain the behavior of the Raman-active $A_{1g}$ and $E^1_{2g}$ modes as a function of the number of layers

Hippocampal structural plasticity accompanies the resulting contextual fear memory following stress and fear conditioning

The present research investigated the resulting contextual fear memory and structural plasticity changes in the dorsal hippocampus (DH) following stress and fear conditioning. This combination enhanced fear retention and increased the number of total and mature dendritic spines in DH. Intra-basolateral amygdala (BLA) infusion of midazolam prior to stress prevented both the enhancement of fear retention and an increase in the density of total and mature dendritic spines in DH. These findings emphasize the role of the stress-induced attenuation of GABAergic neurotransmission in BLA in the promoting influence of stress on fear memory and on synaptic remodeling in DH. In conclusion, the structural remodeling in DH accompanied the facilitated fear memory following a combination of fear conditioning and stressful stimulation.Fil: Giachero, Marcelo. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Córdoba. Instituto de Farmacología Experimental de Córdoba. Universidad Nacional de Córdoba. Facultad de Ciencias Químicas. Instituto de Farmacología Experimental de Córdoba; ArgentinaFil: Calfa, Gaston Diego. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Córdoba. Instituto de Farmacología Experimental de Córdoba. Universidad Nacional de Córdoba. Facultad de Ciencias Químicas. Instituto de Farmacología Experimental de Córdoba; ArgentinaFil: Molina, Víctor Alejandro. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Córdoba. Instituto de Farmacología Experimental de Córdoba. Universidad Nacional de Córdoba. Facultad de Ciencias Químicas. Instituto de Farmacología Experimental de Córdoba; Argentin

Active learning in annotating micro-blogs dealing with e-reputation

Elections unleash strong political views on Twitter, but what do people really think about politics? Opinion and trend mining on micro blogs dealing with politics has recently attracted researchers in several fields including Information Retrieval and Machine Learning (ML). Since the performance of ML and Natural Language Processing (NLP) approaches are limited by the amount and quality of data available, one promising alternative for some tasks is the automatic propagation of expert annotations. This paper intends to develop a so-called active learning process for automatically annotating French language tweets that deal with the image (i.e., representation, web reputation) of politicians. Our main focus is on the methodology followed to build an original annotated dataset expressing opinion from two French politicians over time. We therefore review state of the art NLP-based ML algorithms to automatically annotate tweets using a manual initiation step as bootstrap. This paper focuses on key issues about active learning while building a large annotated data set from noise. This will be introduced by human annotators, abundance of data and the label distribution across data and entities. In turn, we show that Twitter characteristics such as the author's name or hashtags can be considered as the bearing point to not only improve automatic systems for Opinion Mining (OM) and Topic Classification but also to reduce noise in human annotations. However, a later thorough analysis shows that reducing noise might induce the loss of crucial information.Comment: Journal of Interdisciplinary Methodologies and Issues in Science - Vol 3 - Contextualisation digitale - 201