AWTE-BERT:Attending to Wordpiece Tokenization Explicitly on BERT for Joint Intent Classification and SlotFilling

Intent classification and slot filling are two core tasks in natural language understanding (NLU). The interaction nature of the two tasks makes the joint models often outperform the single designs. One of the promising solutions, called BERT (Bidirectional Encoder Representations from Transformers), achieves the joint optimization of the two tasks. BERT adopts the wordpiece to tokenize each input token into multiple sub-tokens, which causes a mismatch between the tokens and the labels lengths. Previous methods utilize the hidden states corresponding to the first sub-token as input to the classifier, which limits performance improvement since some hidden semantic informations is discarded in the fine-tune process. To address this issue, we propose a novel joint model based on BERT, which explicitly models the multiple sub-tokens features after wordpiece tokenization, thereby generating the context features that contribute to slot filling. Specifically, we encode the hidden states corresponding to multiple sub-tokens into a context vector via the attention mechanism. Then, we feed each context vector into the slot filling encoder, which preserves the integrity of the sentence. Experimental results demonstrate that our proposed model achieves significant improvement on intent classification accuracy, slot filling F1, and sentence-level semantic frame accuracy on two public benchmark datasets. The F1 score of the slot filling in particular has been improved from 96.1 to 98.2 (2.1% absolute) on the ATIS dataset

Population Size, Genetic Diversity and Molecular Evidence of a Recent Population Bottleneck in Hynobius chinensis, an Endangered Salamander Species

Severe population declines can reduce species to small populations, offering permissive conditions for deleterious processes. For example, following such events, species can become prone to inbreeding and genetic drift which can lead to a loss of genetic diversity and evolutionary potentials. Hynobius chinensis is a poorly studied very rare and declining endangered amphibian species endemic to China in Changyang County. We investigated adult census population size by monitoring breeding populations from 2015 to 2018, developed microsatellite markers from the transcriptome and used them to investigate genetic diversity, and a population bottleneck in this species. We found H. chinensis in 4 different localities in a total area of 2.18 km2 and estimated the overall adult census population size at 386–404 individuals. The adult census size (mean ± SE) per breeding pond ranged from 44 ± 6 to 141 ± 8 individuals and appeared smaller than that reported in closely related species in undisturbed habitats. We developed and characterized 13 microsatellite markers in total. Analysis of data at 7 loci (N = 118) in Hardy-Weinberg equilibrium gathered from the largest population showed that genetic diversity level was low. The average number of alleles per locus was 2.14. The observed and expected heterozygosities averaged 0.38 and 0.40, respectively. The inbreeding coefficient was –0.06. All tests performed to investigate a population bottleneck, i.e. The Garza-Williamson test, Heterozygosity excess test, Mode shift test of allele frequency, and effective population size estimates detected a population bottleneck. The contemporary and the historical effective population sizes were estimated at 36 and 234 individuals, respectively. We argue that as bottleneck effects, the studied population may have become prone to genetic drift and inbreeding, losing microsatellite alleles and heterozygosity. Our results suggest that populations of H. chinensis may have been extirpated in the study area

Discovery of potential biomarkers for osteoporosis using LC/GC−MS metabolomic methods

PurposeFor early diagnosis of osteoporosis (OP), plasma metabolomics of OP was studied by untargeted LC/GC−MS in a Chinese elderly population to find possible diagnostic biomarkers.MethodsA total of 379 Chinese community-dwelling older adults aged ≥65 years were recruited for this study. The BMD of the calcaneus was measured using quantitative ultrasound (QUS), and a T value ≤-2.5 was defined as OP. Twenty-nine men and 47 women with OP were screened, and 29 men and 36 women were matched according to age and BMI as normal controls using propensity matching. Plasma from these participants was first analyzed by untargeted LC/GC−MS, followed by FC and P values to screen for differential metabolites and heatmaps and box plots to differentiate metabolites between groups. Finally, metabolic pathway enrichment analysis of differential metabolites was performed based on KEGG, and pathways with P ≤ 0.05 were selected as enrichment pathways.ResultsWe screened metabolites with FC>1.2 or FC<1/1.2 and P<0.05 and found 33 differential metabolites in elderly men and 30 differential metabolites in elderly women that could be potential biomarkers for OP. 2-Aminomuconic acid semialdehyde (AUC=0.72, 95% CI 0.582-0.857, P=0.004) is highly likely to be a biomarker for screening OP in older men. Tetradecanedioic acid (AUC=0.70, 95% CI 0.575-0.818, P=0.004) is highly likely to be a biomarker for screening OP in older women.ConclusionThese findings can be applied to clinical work through further validation studies. This study also shows that metabolomic analysis has great potential for application in the early diagnosis and recurrence monitoring of OP in elderly individuals

Tetra(p-tolyl)borate-Functionalized Solvent Polymeric Membrane A Facile and Sensitive Sensing Platform for Peroxidase and Peroxidase Mimetics.pdf

Arsenic (As) contamination is a severe problem in the intertidal zones of the Bohai Sea (China) with wide salinity variation. In the present study, we combined proteomics and metabolomics to characterize the differential responses of arsenic in clam Ruditapes philippinarum under different salinities (31.1, 23.3 and 15.6 psu). Both proteomic and metabolomic responses indicated that varying salinities could significantly affect the toxicological responses of clams to As. Metabolic biomarkers revealed that the environmentally relevant arsenic (20 mu g L-1) exposure induced disturbance in energy metabolism and/or osmotic regulation under different salinities, whereas protein biomarkers indicated oxidative stress, cellular injury and apoptosis and disturbance in energy metabolism. In addition, the up-regulated proteins including ATP synthase, succinyl-CoA synthetase and nucleoside diphosphate kinase were validated by related metabolites, succinate and ATP, which confirmed the disturbance in energy metabolism in clam gills at low salinity (15.6 psu). These findings provide important insights into toxicological effects of environmental contaminant at molecular levels using combined proteomics and metabolomics. (C) 2013 Elsevier B.V. All rights reserved.Arsenic (As) contamination is a severe problem in the intertidal zones of the Bohai Sea (China) with wide salinity variation. In the present study, we combined proteomics and metabolomics to characterize the differential responses of arsenic in clam Ruditapes philippinarum under different salinities (31.1, 23.3 and 15.6 psu). Both proteomic and metabolomic responses indicated that varying salinities could significantly affect the toxicological responses of clams to As. Metabolic biomarkers revealed that the environmentally relevant arsenic (20 mu g L-1) exposure induced disturbance in energy metabolism and/or osmotic regulation under different salinities, whereas protein biomarkers indicated oxidative stress, cellular injury and apoptosis and disturbance in energy metabolism. In addition, the up-regulated proteins including ATP synthase, succinyl-CoA synthetase and nucleoside diphosphate kinase were validated by related metabolites, succinate and ATP, which confirmed the disturbance in energy metabolism in clam gills at low salinity (15.6 psu). These findings provide important insights into toxicological effects of environmental contaminant at molecular levels using combined proteomics and metabolomics. (C) 2013 Elsevier B.V. All rights reserved

Carbonized Cellulose Nanofibril with Individualized Fibrous Morphology: toward Multifunctional Applications in Polycaprolactone Conductive Composites

Drying cellulose nanofibril (CNF) from aqueous suspensions often leads to aggregated fibril morphology, negatively affecting its performance in ensuing applications. In this work, we introduced a new solvent drying approach to acquire dry CNF from aqueous suspensions and subsequently pyrolyzed the CNF precursor to obtain carbonized CNF (CCNF) without loss of its fibrous morphology. The fibrous CCNF was dispersed homogeneously in polycaprolactone (PCL) thermoplastic resin, greatly enhancing PCL composite tensile performance. After being further mixed with carbon black (CB), the CCNF helped to minimize CB aggregation due to formation of interconnected three-dimensional (3D) structures. The CCNF/CB/PCL composite exhibited superior electrical conductivity ascribed to electrons transporting more efficiently among CB aggregates. The composite is also suitable for applications such as 3D printed electromagnetic interference (EMI) shielding and deformation sensing. Specifically, the 3D printed EMI shielding composite efficiently absorbed EM radiation in the frequency range of 4-26 GHz, and the 3D printed deformation sensor exhibited excellent sensitivity, durability, and flexibility in monitoring mechanical distortions. Herein, this study sheds light on the development of multifunctional conductive composites embedded with fibrous CCNF from sustainable resources