Time-Contrastive Learning Based Deep Bottleneck Features for Text-Dependent Speaker Verification

There are a number of studies about extraction of bottleneck (BN) features from deep neural networks (DNNs)trained to discriminate speakers, pass-phrases and triphone states for improving the performance of text-dependent speaker verification (TD-SV). However, a moderate success has been achieved. A recent study [1] presented a time contrastive learning (TCL) concept to explore the non-stationarity of brain signals for classification of brain states. Speech signals have similar non-stationarity property, and TCL further has the advantage of having no need for labeled data. We therefore present a TCL based BN feature extraction method. The method uniformly partitions each speech utterance in a training dataset into a predefined number of multi-frame segments. Each segment in an utterance corresponds to one class, and class labels are shared across utterances. DNNs are then trained to discriminate all speech frames among the classes to exploit the temporal structure of speech. In addition, we propose a segment-based unsupervised clustering algorithm to re-assign class labels to the segments. TD-SV experiments were conducted on the RedDots challenge database. The TCL-DNNs were trained using speech data of fixed pass-phrases that were excluded from the TD-SV evaluation set, so the learned features can be considered phrase-independent. We compare the performance of the proposed TCL bottleneck (BN) feature with those of short-time cepstral features and BN features extracted from DNNs discriminating speakers, pass-phrases, speaker+pass-phrase, as well as monophones whose labels and boundaries are generated by three different automatic speech recognition (ASR) systems. Experimental results show that the proposed TCL-BN outperforms cepstral features and speaker+pass-phrase discriminant BN features, and its performance is on par with those of ASR derived BN features. Moreover,....Comment: Copyright (c) 2019 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other work

Entangling two distant nanocavities via a waveguide

In this paper, we investigate the generation of continuous variable entanglement between two spatially-separate nanocavities mediated by a coupled resonator optical waveguide in photonic crystals. By solving the exact dynamics of the cavity system coupled to the waveguide, the entanglement and purity of the two-mode cavity state are discussed in detail for the initially separated squeezing inputs. It is found that the stable and pure entangled state of the two distant nanocavities can be achieved with the requirement of only a weak cavity-waveguide coupling when the cavities are resonant with the band center of the waveguide. The strong couplings between the cavities and the waveguide lead to the entanglement sudden death and sudden birth. When the frequencies of the cavities lie outside the band of the waveguide, the waveguide-induced cross frequency shift between the cavities can optimize the achievable entanglement. It is also shown that the entanglement can be easily manipulated through the changes of the cavity frequencies within the waveguide band.Comment: 8 pages, 8 figure

A Fractal Model for the Maximum Droplet Diameter in Gas-Liquid Mist Flow

Distribution characteristics of liquid droplet size are described using the fractal theory for liquid droplet size distribution in gas-liquid mist flow. Thereby, the fractal expression of the maximum droplet diameter is derived. The fractal model for maximum droplet diameter is obtained based on the internal relationship between maximum droplet diameter and the droplet fractal dimension, which is obtained by analyzing the balance between total droplet surface energy and total gas turbulent kinetic energy. Fractal model predictions of maximum droplet diameter agree with the experimental data. Maximum droplet diameter and droplet fractal dimension are both found to be related to the superficial velocity of gas and liquid. Maximum droplet diameter decreases with an increase in gas superficial velocity but increases with an increase in liquid superficial velocity. Droplet fractal dimension increases with an increase in gas superficial velocity but decreases with an increase in liquid superficial velocity. These are all consistent with the physical facts

Pressure Transient Analysis of Dual Fractal Reservoir

A dual fractal reservoir transient flow model was created by embedding a fracture system simulated by a tree-shaped fractal network into a matrix system simulated by fractal porous media. The dimensionless bottom hole pressure model was created using the Laplace transform and Stehfest numerical inversion methods. According to the model's solution, the bilogarithmic type curves of the dual fractal reservoirs are illustrated, and the influence of different fractal factors on pressure transient responses is discussed. This semianalytical model provides a practical and reliable method for empirical applications

Neddylation inhibitor MLN4924 suppresses cilia formation by modulating AKT1

Abstract The primary cilium is a microtubule-based sensory organelle. The molecular mechanism that regulates ciliary dynamics remains elusive. Here, we report an unexpected finding that MLN4924, a small molecule inhibitor of NEDD8-activating enzyme (NAE), blocks primary ciliary formation by inhibiting synthesis/assembly and promoting disassembly. This is mainly mediated by MLN4924-induced phosphorylation of AKT1 at Ser473 under serum-starved, ciliary-promoting conditions. Indeed, pharmaceutical inhibition (by MK2206) or genetic depletion (via siRNA) of AKT1 rescues MLN4924 effect, indicating its causal role. Interestingly, pAKT1-Ser473 activity regulates both ciliary synthesis/assembly and disassembly in a MLN4924 dependent manner, whereas pAKT-Thr308 determines the ciliary length in MLN4924-independent but VHL-dependent manner. Finally, MLN4924 inhibits mouse hair regrowth, a process requires ciliogenesis. Collectively, our study demonstrates an unexpected role of a neddylation inhibitor in regulation of ciliogenesis via AKT1, and provides a proof-of-concept for potential utility of MLN4924 in the treatment of human diseases associated with abnormal ciliogenesis.https://deepblue.lib.umich.edu/bitstream/2027.42/148214/1/13238_2019_Article_614.pd

Propofol Inhibits Lipopolysaccharide-Induced Tumor Necrosis Factor-Alpha Expression and Myocardial Depression through Decreasing the Generation of Superoxide Anion in Cardiomyocytes

TNF-α has been shown to be a major factor responsible for myocardial depression in sepsis. The aim of this study was to investigate the effect of an anesthetic, propofol, on TNF-α expression in cardiomyocytes treated with LPS both in vivo and in vitro. In cultured cardiomyocytes, compared with control group, propofol significantly reduced protein expression of gp91phox and phosphorylation of extracellular regulated protein kinases 1/2 (ERK1/2) and p38 MAPK, which associates with reduced TNF-α production. In in vivo mice studies, propofol significantly improved myocardial depression and increased survival rate of mice after LPS treatment or during endotoxemia, which associates with reduced myocardial TNF-α production, gp91phox, ERK1/2, and p38 MAPK. It is concluded that propofol abrogates LPS-induced TNF-α production and alleviates cardiac depression through gp91phox/ERK1/2 or p38 MAPK signal pathway. These findings have great clinical importance in the application of propofol for patients enduring sepsis

Astragalus mongholicus Bunge and Curcuma aromatica Salisb. modulate gut microbiome and bile acid metabolism to inhibit colon cancer progression

IntroductionAlterations in the gut microbiome and bile acid metabolism are known to play a role in the development and progression of colon cancer. Medicinal plants like Astragalus mongholicus Bunge and Curcuma aromatica Salisb. (AC) have shown preferable therapeutic effect on cancer therapy, especially digestive tract tumors like colon cancer. However, the precise mechanisms of AC inhibiting colon cancer, particularly in relation to the gut microbiome and bile acid dynamics, are not fully understood.MethodsOur research aimed to investigate the anti-tumor properties of AC in mice with CT26 colon cancer and further investigate its underlying mechanism via intestinal microbiota. The size and pathological changes of solid tumors in colon cancer are used to evaluate the inhibitory effect of AC on colon cancer. Metagenomics and 16s rRNA gene sequencing were employed to clarify the dysbiosis in the gut microbiome of colon cancer and its impact on colon cancer. The levels of bile acids (BAs) in the feces of mice from each group were measured using UPLC-Qtrap-MS/MS.ResultsAC effectively suppressed the growth of colon cancer and reduced histological damage. Notably, AC treatment led to changes in the gut microbiome composition, with a decrease in pathogenic species like Citrobacter and Candidatus_Arthromitus, and an increase in beneficial microbial populations including Adlercreutzia, Lachnospiraceae_UCG-001, and Parvibacter. Additionally, AC altered bile acid profiles, resulting in a significant decrease in pro-carcinogenic bile acids such as deoxycholic acid (DCA) and lithocholic acid (LCA), while increasing the concentration of the cancer-inhibitory bile acid, ursodeoxycholic acid (UDCA). Tracking and analyzing the data, AC may mainly upregulate FabG and baiA genes by increasing the relative abundance of Adlercreutzia and Parvibacter bacteria, which promoting the metabolism of pro-carcinogenic LCA.DiscussionThese findings provide strong evidence supporting the role of AC in regulating gut microbiome-mediated bile acid metabolism, which is crucial in impeding the progression of colon cancer