LSTM based Similarity Measurement with Spectral Clustering for Speaker Diarization

More and more neural network approaches have achieved considerable improvement upon submodules of speaker diarization system, including speaker change detection and segment-wise speaker embedding extraction. Still, in the clustering stage, traditional algorithms like probabilistic linear discriminant analysis (PLDA) are widely used for scoring the similarity between two speech segments. In this paper, we propose a supervised method to measure the similarity matrix between all segments of an audio recording with sequential bidirectional long short-term memory networks (Bi-LSTM). Spectral clustering is applied on top of the similarity matrix to further improve the performance. Experimental results show that our system significantly outperforms the state-of-the-art methods and achieves a diarization error rate of 6.63% on the NIST SRE 2000 CALLHOME database.Comment: Accepted for INTERSPEECH 201

CellBRF: A Feature Selection Method for Single-Cell Clustering Using Cell Balance and Random Forest

Motivation Single-cell RNA sequencing (scRNA-seq) offers a powerful tool to dissect the complexity of biological tissues through cell sub-population identification in combination with clustering approaches. Feature selection is a critical step for improving the accuracy and interpretability of single-cell clustering. Existing feature selection methods underutilize the discriminatory potential of genes across distinct cell types. We hypothesize that incorporating such information could further boost the performance of single cell clustering. Results We develop CellBRF, a feature selection method that considers genes’ relevance to cell types for single-cell clustering. The key idea is to identify genes that are most important for discriminating cell types through random forests guided by predicted cell labels. Moreover, it proposes a class balancing strategy to mitigate the impact of unbalanced cell type distributions on feature importance evaluation. We benchmark CellBRF on 33 scRNA-seq datasets representing diverse biological scenarios and demonstrate that it substantially outperforms state-of-the-art feature selection methods in terms of clustering accuracy and cell neighborhood consistency. Furthermore, we demonstrate the outstanding performance of our selected features through three case studies on cell differentiation stage identification, non-malignant cell subtype identification, and rare cell identification. CellBRF provides a new and effective tool to boost single-cell clustering accuracy. Availability and implementation All source codes of CellBRF are freely available at https://github.com/xuyp-csu/CellBRF

Myricetin ameliorates cognitive impairment in 3×Tg Alzheimer’s disease mice by regulating oxidative stress and tau hyperphosphorylation

Background: Alzheimer's disease is characterized by abnormal β-amyloid (Aβ) plaque accumulation, tau hyperphosphorylation, reactive oxidative stress, mitochondrial dysfunction and synaptic loss. Myricetin, a dietary flavonoid, has been shown to exert neuroprotective effects in vitro and in vivo. Here, we aimed to elucidate the mechanism and pathways involved in the protective effect of myricetin. Methods: The effect of myricetin was assessed on Aβ42 oligomer-treated neuronal SH-SY5Y cells and in 3×Tg mice. Behavioral tests were performed to assess the cognitive effects of myricetin (14 days, ip) in 3×Tg mice. The levels of beta-amyloid precursor protein (APP), synaptic and mitochondrial proteins, glycogen synthase kinase3β (GSK3β) and extracellular regulated kinase (ERK) 2 were assessed via Western blotting. Flow cytometry assays, immunofluorescence staining, and transmission electron microscopy were used to assess mitochondrial dysfunction and reactive oxidative stress. Results: We found that, compared with control treatment, myricetin treatment improved spatial cognition and learning and memory in 3×Tg mice. Myricetin ameliorated tau phosphorylation and the reduction in pre- and postsynaptic proteins in Aβ42 oligomer-treated neuronal SH-SY5Y cells and in 3×Tg mice. In addition, myricetin reduced reactive oxygen species generation, lipid peroxidation, and DNA oxidation, and rescued mitochondrial dysfunction via the associated GSK3β and ERK 2 signalling pathways. Conclusions: This study provides new insight into the neuroprotective mechanism of myricetin in vitro in cell culture and in vivo in a mouse model of Alzheimer’s disease

Zinc-Tiered Synthesis of 3D Graphene for Monolithic Electrodes

A high-surface-area conductive cellular carbon monolith is highly desired as the optimal electrode for achieving high energy, power, and lifetime in electrochemical energy storage. 3D graphene can be regarded as a first-ranking member of cellular carbons with the pore-wall thickness down to mono/few-atomic layers. Current 3D graphenes, derived from either gelation or pyrolysis routes, still suffer from low surface area, conductivity, stability, and/or yield, being subjected to methodological inadequacies including patchy assembly, wet processing, and weak controllability. Herein, a strategy of zinc-assisted solid-state pyrolysis to produce a superior 3D graphene is established. Zinc unprecedentedly impregnates and delaminates a solid ( nonhollow ) char into multiple membranes, which eliminates the morphological impurities ever-present in the previous pyrolyses using solid-state carbon precursors. Zinc also catalyzes the carbonization and graphitization, and its in situ thermal extraction and recycling enables the scaled-up production. The created 3D graphene network consists integrally of morphologically and chemically pure graphene membranes. It possesses unrivaled surface area, outstanding stability, and conductivity both in air and electrolyte, exceeding preexisting 3D graphenes. The advanced 3D graphene thus equips a porous monolithic electrode with unparalleled energy density, power density, and lifetime in electric-double-layer capacitive devices

Cost-minimization predictive energy management of a postal-delivery fuel cell electric vehicle with intelligent battery State-of-Charge Planner

Fuel cell electric vehicles have earned substantial attentions in recent decades due to their high-efficiency and zero-emission features, while the high operating costs remain the major barrier towards their large-scale commercialization. In such context, this paper aims to devise an energy management strategy for an urban postal-delivery fuel cell electric vehicle for operating cost mitigation. First, a data-driven dual-loop spatial-domain battery state-of-charge reference estimator is designed to guide battery energy depletion, which is trained by real-world driving data collected in postal delivery missions. Then, a fuzzy C-means clustering enhanced Markov speed predictor is constructed to project the upcoming velocity. Lastly, combining the state-of-charge reference and the forecasted speed, a model predictive control-based cost-optimization energy management strategy is established to mitigate vehicle operating costs imposed by energy consumption and power-source degradations. Validation results have shown that 1) the proposed strategy could mitigate the operating cost by 4.43% and 7.30% in average versus benchmark strategies, denoting its superiority in term of cost-reduction and 2) the computation burden per step of the proposed strategy is averaged at 0.123ms, less than the sampling time interval 1s, proving its potential of real-time applications

Investigating the causal mediating effect of type 2 diabetes on the relationship between traits and systolic blood pressure: A two-step Mendelian randomization study

BackgroundType 2 diabetes mellitus (T2DM) and hypertension commonly coexist, and we presumed that T2DM might mediate the relationship between some shared risk factors and systolic blood pressure (SBP).MethodsThe causal association between T2DM and SBP was first confirmed using Mendelian randomization (MR) analyses, and a two-step MR design was then used to test the causal mediating effect of T2DM on the relationship between 107 traits and SBP using summary statistics from genome-wide association studies.ResultsT2DM was causally associated with SBP. The univariable MR of the two-step causal mediation analyses suggested that 44 and 45 of the 107 traits had causal associations with T2DM and SBP, respectively. Five of the 27 traits that were significantly associated with both T2DM and SBP could not be reversely altered by T2DM and were included in the second step of the causal mediation analyses. The results indicated that most of the investigated traits causally altered SBP independent of T2DM, but the partial causal mediating effect of T2DM on the association between fasting insulin and SBP was successfully identified with a mediation proportion of 33.6%.ConclusionsOur study provides novel insights into the role of risk factors in the comorbidity of T2DM and high blood pressure, which is important for long-term disease prevention and management

THE BALANCE AMONG QUALITY, SCALE AND EFFECTIVENESS AN EMPIRICAL STUDY ON SCALE EFFECT IN TEACHING VENUES AT PREFECTURE AND CITY LEVEL OF RADIO & TV UNIVERSITIES IN CHINA

Teaching venues are playing an important role in the filed of open and distance education (ODL). In China's ODL system over 70% enrolled students are from Radio and TV Universities (RTVUs). Many off-campus teaching venues of e-colleges in 67 conventional universities have been established in RTVUs at prefecture and city level. In this article, the authors have done a direct survey on the data of distance education cost over the past consecutive 4 years in 21 RTVUs at prefecture and city level from 3 provinces (Xinjiang, Hunan and Fujian) in China's western, central and eastern areas, and empirically studied the scale effect in teaching venues at prefecture and city level under the hypothesis condition-teaching effect of ODL and conventional education is the same. The study result illustrates that the average student cost of RTVUs at prefecture and city level demonstrates a scale economy feature, that is, with the expansion of enrolled students scale, there is a fall in the average student cost. The study indicates large differences between average student variable cost and average student cost, which shows a possible average cost reduction in RTVUs at prefecture and city level. In terms of geographical and economic differences, some imbalances of development have been found among RTVUs at prefecture and city level. A quadratic form regression model has been established for doing this research in the perspective of education economics. The results of the regression show that the optimal enrolled student number of RTVUs nationwide at prefecture and city level is around 7,800. For provinces, the most optimal enrolled student numbers are as follows: 9,100 for Fujian, 3,800 for Hunan, and 2,200 for Xianjiang, which can serve as the reference for expanding scale economy in ODL. The research finding also indicate the changes in student average cost resulting from the changes in scale, the problem of determining investment direction. It is suggested that more input which benefit the quality improvement of distance education should be made