Tibetan Multi-Dialect Speech Recognition Using Latent Regression Bayesian Network and End-To-End Mode

We proposed a method using latent regression Bayesian network (LRBN) toextract the shared speech feature for the input of end-to-end speech recognition model.The structure of LRBN is compact and its parameter learning is fast. Compared withConvolutional Neural Network, it has a simpler and understood structure and lessparameters to learn. Experimental results show that the advantage of hybridLRBN/Bidirectional Long Short-Term Memory-Connectionist Temporal Classificationarchitecture for Tibetan multi-dialect speech recognition, and demonstrate the LRBN ishelpful to differentiate among multiple language speech sets.

Emerging markers for antimicrobial resistance monitoring

The appearance and spread of antibiotic-resistant pathogens known as antimicrobial resistance(AMR)is one of the major worldwide health crises that humanity have to deal with over the next decades.One of the main methods for addressing AMR is the effective screening for antimicrobial insensitivity in clinical and environmental monitoring.Current clinical laboratory procedures use traditional culturebased antibiotic susceptibility testing(AST)methods,which can take up to 24 h to identify which drug is suitable for the infection inhibition.Therefore,it is vital to develop novel strategies that offer quick,simple,affordable,reliable,sensitive and accurate AMR monitoring.Sensors for AMR markers detection could possess the essential qualities for quickly identifying resistant microorganisms and could give vital data for the selection of antibacterial drugs administration.This review offers a summary of the innovative application of these AMR markers detection strategies focusing on healthcare and environmental surveillance for the AMR genotypic or phenotypic assessment.

Research on typical operating conditions of hydrogen production system with off-grid wind power considering the characteristics of proton exchange membrane electrolysis cell

Hydrogen energy,with its abundant reserves,green and low-carbon characteristic,high energy density,diverse sources,and wide applications,is gradually becoming an important carrier in the global energy transformation and development.In this paper,the off-grid wind power hydrogen production system is considered as the research object,and the operating characteristics of a proton exchange membrane(PEM)electrolysis cell,including underload,overload,variable load,and start-stop are analyzed.On this basis,the characteristic extraction of wind power output data after noise reduction is carried out,and then the self-organizing mapping neural network algorithm is used for clustering to extract typical wind power output scenarios and perform weight distribution based on the statistical probability.The trend and fluctuation components are superimposed to generate the typical operating conditions of an off-grid PEM electrolytic hydrogen production system.The historical output data of an actual wind farm are used for the case study,and the results confirm the feasibility of the method proposed in this study for obtaining the typical conditions of off-grid wind power hydrogen production.The results provide a basis for studying the dynamic operation characteristics of PEM electrolytic hydrogen production systems,and the performance degradation mechanism of PEM electrolysis cells under fluctuating inputs.

Analysis of the relationship between bile duct and duodenal microbiota reveals that potential dysbacteriosis is the main cause of primary common bile duct stones

Bacteria play an important role in the formation of primary Common Bile Duct(CBD)stones.However,the composition and function of the microbiota of bile duct in patients with primary CBD stones remained to be explored.We utilized the 16S rRNA gene high-throughput sequencing technology to analyze the microbial diversity and community composition of biliary and duodenal microbiota in 15 patients with primary CBD stones and 4 patients without biliary tract diseases.Alpha diversity analysis showed that the microbiota richness was similar in bile and intestinal fluid;Beta diversity analysis showed that there were differences in the composition between biliary microbiota and the duodenal microbiota,but the abundance of the main groups showed similarities.The composition of the biliary microbiota from gallstone patients was more complex,as was the duodenal microbiota.Proteobacteria and Firmicutes were the dominant bacteria at phylum level,accounting for at least 75%of the total reads in each subgroup.Pseudomonas and EscherichiaShigella were the major genus among subgroups,but Escherichia-Shigella had increased abundance in duodenal microbiota with primary choledocholithiasis,which may play an important role in stone formation.It is noteworthy that Clostridiumsensu_stricto,Lachnospiraceae_UCG-008,Butyrivibrio and Roseburia which could produce short chain fatty acids(SCFAs),were significantly decreased in biliary microbiota with primary CBD stones(p<0.05).Our study provided new insights into the compositional of normal biliary microbiota.The micro-ecology of biliary and duodenal in patients with stones is complex and closely related,and there is a potential for dysbacteriosis.The decrease in abundance of certain major acid-producing bacteria affects the health of the biliary tract and thus leads to the formation of stones.

Pharmacokinetic study of salvianolic acid D after oral and intravenous administration in rats

A sensitive, specific and rapid LC-MS method was developed and validated for the determination of salvianolic acid D (SAlD) in rat plasma. This method used a single quadrupole mass spectrometer with an electrospray ionization (ESI) source. A single ion monitoring scanning (SIM) mode was employed. it showed good linearity over the concentration range from 3.3 to 666.7 ngfint. for the determination of Sala The R.S.D.% of intra-day and inter-day precision values were no more than 7.69%, and the accuracy was within 91%-104% at all quality control Levels. This LC MS method was applied to the pharmacokinetic study of SaID in rats. A two-compartmental model analysis was employed. The plasma concentrations at 2 mm (C-2min) were 5756.06 +/- 719.61, 11,073.01 +/- 1783.46 and 21,077.58 +/- 5581.97 nit, for 0.25, 0.5 and I mg/kg intravenous injection, respectively. The peak plastna concentration (C-max) was 333.08 +/- 61.21 pg/L for 4 mg/kg oral administration. The area under curve (AUC(0-t)) was 14,384.379 +/- 8443.t84. 22,813.369 +/- 11,860.823, 46,406.122 +/- 27,592.645 and 8201.740+4711.961 mu g/L.h for intravenous injection (0.25, 0.5 and 1 mg/kg) and oral administration (4 mg/kg), respectively. The bioavailability of SalD) was calculated to be 4.159% +/- 0.517%. (C) 2015 Chinese Pharmaceutical Association and Institute of Materia IMedica, Chinese Academy of Medical Sciences. Production and hosting by Elsevier B.V.