Ethyl (2E,4Z)-5-diethyl­amino-2-(phenyl­sulfon­yl)penta-2,4-dienoate

In the title compound, C17H23NO4S, the penta­diene group adopts a planar conformation, with an r.m.s. deviation of 0.0410 (14) Å. The phenyl ring makes a dihedral angle of 85.73 (11)° with the penta­diene group, while the penta­diene group makes dihedral angles of 11.38 (11) and 14.08 (10)°, respectively, with the amino and ester groups. In the crystal, molecules are linked via pairs of C—H⋯O inter­actions, forming inversion dimers

Environmental, social and governance transparency and firm value

We investigate whether ESG transparency, the extent of ESG disclosure, has an impact on firm value. Reducing investors’ information symmetry and agency costs is the mechanism by which better ESG transparency potentially impacts firm value. Using the Bloomberg ESG disclosure scores to assess a firm’s ESG transparency, we look at a sample of 1996 large cap companies across 47 developed and emerging countries and territories. Our empirical analyses suggest that the benefits from ESG disclosure outweigh their costs for the average listed firm. We find supporting evidence for greater disclosure of ESG issues boosting firm valuation measures, such as Tobin’s Q. Furthermore, our results suggest that firms with greater asset size, better liquidity, higher R&D intensity, fewer insider holdings, good past financial performance will be more transparent in ESG issues

Thermal-Mechanical Properties of Polyurethane-Clay Shape Memory Polymer Nanocomposites

Shape memory nanocomposites of polyurethane (PU)-clay were fabricated by melt mixing of PU and nano-clay. Based on nano-indentation and microhardness tests, the strength of the nanocomposites increased dramatically as a function of clay content, which is attributed to the enhanced nanoclay–polymer interactions. Thermal mechanical experiments demonstrated good mechanical and shape memory effects of the nanocomposites. Full shape memory recovery was displayed by both the pure PU and PU-clay nanocomposites.

Effects of epidural compression on stellate neurons and thalamocortical afferent fibers in the rat primary somatosensory cortex

A number of neurological disorders such as epidural hematoma can cause compression of cerebral cortex. We here tested the hypothesis that sustained compression of primary somatosensory cortex may affect stellate neurons and thalamocortical afferent (TCA) fibers. A rat model with barrel cortex subjected to bead epidural compression was used. Golgi‑Cox staining analyses showed the shrinkage of dendritic arbors and the stripping of dendritic spines of stellate neurons for at least 3 months post‑lesion. Anterograde tracing analyses exhibited a progressive decline of TCA fiber density in barrel field for 6 months post‑lesion. Due to the abrupt decrease of TCA fiber density at 3 days after compression, we further used electron microscopy to investigate the ultrastructure of TCA fibers at this time. Some TCA fiber terminal profiles with dissolved or darkened mitochondria and fewer synaptic vesicles were distorted and broken. Furthermore, the disruption of mitochondria and myelin sheath was observed in some myelinated TCA fibers. In addition, expressions of oxidative markers 3‑nitrotyrosine and 4‑hydroxynonenal were elevated in barrel field post‑lesion. Treatment of antioxidant ascorbic acid or apocynin was able to reverse the increase of oxidative stress and the decline of TCA fiber density, rather than the shrinkage of dendrites and the stripping of dendritic spines of stellate neurons post‑lesion. Together, these results indicate that sustained epidural compression of primary somatosensory cortex affects the TCA fibers and the dendrites of stellate neurons for a prolonged period. In addition, oxidative stress is responsible for the reduction of TCA fiber density in barrels rather than the shrinkage of dendrites and the stripping of dendritic spines of stellate neurons

Graph Neural Networks for Natural Language Processing: A Survey

Deep learning has become the dominant approach in coping with various tasks in Natural LanguageProcessing (NLP). Although text inputs are typically represented as a sequence of tokens, there isa rich variety of NLP problems that can be best expressed with a graph structure. As a result, thereis a surge of interests in developing new deep learning techniques on graphs for a large numberof NLP tasks. In this survey, we present a comprehensive overview onGraph Neural Networks(GNNs) for Natural Language Processing. We propose a new taxonomy of GNNs for NLP, whichsystematically organizes existing research of GNNs for NLP along three axes: graph construction,graph representation learning, and graph based encoder-decoder models. We further introducea large number of NLP applications that are exploiting the power of GNNs and summarize thecorresponding benchmark datasets, evaluation metrics, and open-source codes. Finally, we discussvarious outstanding challenges for making the full use of GNNs for NLP as well as future researchdirections. To the best of our knowledge, this is the first comprehensive overview of Graph NeuralNetworks for Natural Language Processing.Comment: 127 page

PLA2G6-Associated Neurodegeneration (PLAN): Review of Clinical Phenotypes and Genotypes

Phospholipase A2 group VI (PLA2G6)-associated neurodegeneration (PLAN) includes a series of neurodegenerative diseases that result from the mutations in PLA2G6. PLAN has genetic and clinical heterogeneity, with different mutation sites, mutation types and ethnicities and its clinical phenotype is different. The clinical phenotypes and genotypes of PLAN are closely intertwined and vary widely. PLA2G6 encodes a group of VIA calcium-independent phospholipase A2 proteins (iPLA2β), an enzyme involved in lipid metabolism. According to the age of onset and progressive clinical features, PLAN can be classified into the following subtypes: infantile neuroaxonal dystrophy (INAD), atypical neuroaxonal dystrophy (ANAD) and parkinsonian syndrome which contains adult onset dystonia parkinsonism (DP) and autosomal recessive early-onset parkinsonism (AREP). In this review, we present an overview of PLA2G6-associated neurodegeneration in the context of current research