Repression of the Glucocorticoid Receptor Aggravates Acute Ischemic Brain Injuries in Adult Mice.

Strokes are one of the leading causes of mortality and chronic morbidity in the world, yet with only limited successful interventions available at present. Our previous studies revealed the potential role of the glucocorticoid receptor (GR) in the pathogenesis of neonatal hypoxic-ischemic encephalopathy (HIE). In the present study, we investigate the effect of GR knockdown on acute ischemic brain injuries in a model of focal cerebral ischemia induced by middle cerebral artery occlusion (MCAO) in adult male CD1 mice. GR siRNAs and the negative control were administered via intracerebroventricular (i.c.v.) injection 48 h prior to MCAO. The cerebral infarction volume and neurobehavioral deficits were determined 48 h after MCAO. RT-qPCR was employed to assess the inflammation-related gene expression profiles in the brain before and after MCAO. Western Blotting was used to evaluate the expression levels of GR, the mineralocorticoid receptor (MR) and the brain-derived neurotrophic factor/tropomyosin receptor kinase B (BDNF/TrkB) signaling. The siRNAs treatment decreased GR, but not MR, protein expression, and significantly enhanced expression levels of pro-inflammatory cytokines (IL-6, IL-1β, and TNF-α) in the brain. Of interest, GR knockdown suppressed BDNF/TrkB signaling in adult mice brains. Importantly, GR siRNA pretreatment significantly increased the infarction size and exacerbated the neurobehavioral deficits induced by MCAO in comparison to the control group. Thus, the present study demonstrates the important role of GR in the regulation of the inflammatory responses and neurotrophic BDNF/TrkB signaling pathway in acute ischemic brain injuries in adult mice, revealing a new insight into the pathogenesis and therapeutic potential in acute ischemic strokes

Singing Voice Synthesis with Vibrato Modeling and Latent Energy Representation

This paper proposes an expressive singing voice synthesis system by introducing explicit vibrato modeling and latent energy representation. Vibrato is essential to the naturalness of synthesized sound, due to the inherent characteristics of human singing. Hence, a deep learning-based vibrato model is introduced in this paper to control the vibrato's likeliness, rate, depth and phase in singing, where the vibrato likeliness represents the existence probability of vibrato and it would help improve the singing voice's naturalness. Actually, there is no annotated label about vibrato likeliness in existing singing corpus. We adopt a novel vibrato likeliness labeling method to label the vibrato likeliness automatically. Meanwhile, the power spectrogram of audio contains rich information that can improve the expressiveness of singing. An autoencoder-based latent energy bottleneck feature is proposed for expressive singing voice synthesis. Experimental results on the open dataset NUS48E show that both the vibrato modeling and the latent energy representation could significantly improve the expressiveness of singing voice. The audio samples are shown in the demo website

Zero-Bias Deep Neural Network for Quickest RF Signal Surveillance

The Internet of Things (IoT) is reshaping modern society by allowing a decent number of RF devices to connect and share information through RF channels. However, such an open nature also brings obstacles to surveillance. For alleviation, a surveillance oracle, or a cognitive communication entity needs to identify and confirm the appearance of known or unknown signal sources in real-time. In this paper, we provide a deep learning framework for RF signal surveillance. Specifically, we jointly integrate the Deep Neural Networks (DNNs) and Quickest Detection (QD) to form a sequential signal surveillance scheme. We first analyze the latent space characteristic of neural network classification models, and then we leverage the response characteristics of DNN classifiers and propose a novel method to transform existing DNN classifiers into performance-assured binary abnormality detectors. In this way, we seamlessly integrate the DNNs with the parametric quickest detection. Finally, we propose an enhanced Elastic Weight Consolidation (EWC) algorithm with better numerical stability for DNNs in signal surveillance systems to evolve incrementally, we demonstrate that the zero-bias DNN is superior to regular DNN models considering incremental learning and decision fairness. We evaluated the proposed framework using real signal datasets and we believe this framework is helpful in developing a trustworthy IoT ecosystem

Mechanism of Guilu Erxian ointment based on targeted metabolomics in intervening in vitro fertilization and embryo transfer outcome in older patients with poor ovarian response of kidney-qi deficiency type

ObjectiveTo study the effect of Guilu Erxian ointment on the outcome of IVF-ET in older patients with poor ovarian response infertility of kidney-qi deficiency type, and to verify and analyze the mechanism of action of traditional Chinese medicine on improving older patients with poor ovarian response infertility of kidney-qi deficiency type from the perspective of metabolomics using targeted metabolomics technology, identify the related metabolic pathways, and provide metabolic biomarker basis and clinical treatment ideas for improving older patients with poor ovarian response infertility.MethodsThis study was a double-blind, randomized, placebo-controlled trial, and a total of 119 infertile patients who underwent IVF-ET at Shandong Center for Reproduction and Genetics of Integrated Traditional Chinese and Western Medicine were selected. Eighty older patients with infertility undergoing IVF were randomly divided into older treatment group and older placebo group, and another 39 young healthy women who underwent IVF-ET or ICSI due to male factors were selected as the normal control group. Flexible GnRH antagonist protocol was used for ovulation induction in all three groups, and Guilu Erxian ointment and placebo groups started taking Guilu Erxian ointment and placebo from the third day of menstruation until IVF surgery. And ultra-high performance liquid chromatography-triple quadrupole mass spectrometer (UHPLC-QTRAP MS) was used to detect metabolites in the three groups of samples.ResultsCompared with the placebo group, the number of oocytes retrieved, 2PN fertilization, high-quality embryos, total number of available embryos and estrogen on HCG day were increased in the treatment group, and the differences were statistically significant (P > 0.05), but the clinical pregnancy rate of fresh embryos and frozen embryos were not statistically significant (P > 0.05). The results of targeted metabolomics analysis showed that follicular fluid in the treatment group clustered with the normal young group and deviated from the placebo group. A total of 55 significant differential metabolites were found in the follicular fluid of older patients with poor ovarian response of kidney-qi deficiency type and patients in the normal young group, after Guilu Erxian ointment intervention, Metabolites such as L-Aspartic acid, Glycine, L-Serine, Palmitoleic Acid, Palmitelaidic acid, L-Alanine, Gamma-Linolenic acid, Alpha-Linolenic Acid, and N-acetyltryptophan were down-regulated, mainly involving amino acid metabolism and fatty acid metabolism.ConclusionGuilu Erxian ointment can effectively improve the clinical symptoms and IVF outcomes of older patients with poor ovarian response of kidney-qi deficiency type. There were differences in follicular fluid metabolites between older patients with poor ovarian response of kidney-qi deficiency type and normal women. L-Aspartic acid, L-Alanine, Aminoadipic acid, L-Asparagine, L-Arginine, L-Serine, Gamma- Linolenic acid, Pentadecanoic acid and Alpha-Linolenic Acid are closely related to older patients with poor ovarian response due to deficiency of kidney-qi and may be inferred as biomarkers. The mechanism of Guilu Erxian ointment intervention may be mainly through amino acid metabolism and fatty acid metabolism regulation

Message-passing selection: Towards interpretable GNNs for graph classification

In this paper, we strive to develop an interpretable GNNs' inference paradigm, termed MSInterpreter, which can serve as a plug-and-play scheme readily applicable to various GNNs' baselines. Unlike the most existing explanation methods, MSInterpreter provides a Message-passing Selection scheme(MSScheme) to select the critical paths for GNNs' message aggregations, which aims at reaching the self-explaination instead of post-hoc explanations. In detail, the elaborate MSScheme is designed to calculate weight factors of message aggregation paths by considering the vanilla structure and node embedding components, where the structure base aims at weight factors among node-induced substructures; on the other hand, the node embedding base focuses on weight factors via node embeddings obtained by one-layer GNN.Finally, we demonstrate the effectiveness of our approach on graph classification benchmarks.Comment: 6 pages, 1 figure

Points of Interest (POI): a commentary on the state of the art, challenges, and prospects for the future

In this commentary, we describe the current state of the art of points of interest (POIs) as digital, spatial datasets, both in terms of their quality and affordings, and how they are used across research domains. We argue that good spatial coverage and high-quality POI features — especially POI category and temporality information — are key for creating reliable data. We list challenges in POI geolocation and spatial representation, data fidelity, and POI attributes, and address how these challenges may affect the results of geospatial analyses of the built environment for applications in public health, urban planning, sustainable development, mobility, community studies, and sociology. This commentary is intended to shed more light on the importance of POIs both as standalone spatial datasets and as input to geospatial analyses