End-to-End Quantum-like Language Models with Application to Question Answering

Language Modeling (LM) is a fundamental research topic ina range of areas. Recently, inspired by quantum theory, a novel Quantum Language Model (QLM) has been proposed for Information Retrieval (IR). In this paper, we aim to broaden the theoretical and practical basis of QLM. We develop a Neural Network based Quantum-like Language Model (NNQLM) and apply it to Question Answering. Specifically, based on word embeddings, we design a new density matrix, which represents a sentence (e.g., a question or an answer) and encodes a mixture of semantic subspaces. Such a density matrix, together with a joint representation of the question and the answer, can be integrated into neural network architectures (e.g., 2-dimensional convolutional neural networks). Experiments on the TREC-QA and WIKIQA datasets have verified the effectiveness of our proposed models

Production of Glycopeptide Derivatives for Exploring Substrate Specificity of Human OGA Toward Sugar Moiety.

O-GlcNAcase (OGA) is the only enzyme responsible for removing N-acetyl glucosamine (GlcNAc) attached to serine and threonine residues on proteins. This enzyme plays a key role in O-GlcNAc metabolism. However, the structural features of the sugar moiety recognized by human OGA (hOGA) remain unclear. In this study, a set of glycopeptides with modifications on the GlcNAc residue, were prepared in a recombinant full-length human OGT-catalyzed reaction, using chemoenzymatically synthesized UDP-GlcNAc derivatives. The resulting glycopeptides were used to evaluate the substrate specificity of hOGA toward the sugar moiety. This study will provide insights into the exploration of probes for O-GlcNAc modification, as well as a better understanding of the roles of O-GlcNAc in cellular physiology

Satellite-based precipitation datasets evaluation using gauge observation and hydrological modeling in a typical arid land watershed of Central Asia

Hydrological modeling has always been a challenge in the data-scarce watershed, especially in the areas with complex terrain conditions like the inland river basin in Central Asia. Taking Bosten Lake Basin in Northwest China as an example, the accuracy and the hydrological applicability of satellite-based precipitation datasets were evaluated. The gauge-adjusted version of six widely used datasets was adopted; namely, Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks-Climate Data Record (CDR), Climate Hazards Group Infrared Precipitation with Stations (CHIRPS), Global Precipitation Measurement Ground Validation National Oceanic and Atmospheric Administration Climate Prediction Center (NOAA CPC) Morphing Technique (CMORPH), Integrated Multi-Satellite Retrievals for GPM (GPM), Global Satellite Mapping of Precipitation (GSMaP), the Tropical Rainfall Measuring Mission (TRMM) and Multi-satellite Precipitation Analysis (TMPA). Seven evaluation indexes were used to compare the station data and satellite datasets, the soil and water assessment tool (SWAT) model, and four indexes were used to evaluate the hydrological performance. The main results were as follows: 1) The GPM and CDR were the best datasets for the daily scale and monthly scale rainfall accuracy evaluations, respectively. 2) The performance of CDR and GPM was more stable than others at different locations in a watershed, and all datasets tended to perform better in the humid regions. 3) All datasets tended to perform better in the summer of a year, while the CDR and CHIRPS performed well in winter compare to other datasets. 4) The raw data of CDR and CMORPH performed better than others in monthly runoff simulations, especially CDR. 5) Integrating the hydrological performance of the uncorrected and corrected data, all datasets have the potential to provide valuable input data in hydrological modeling. This study is expected to provide a reference for the hydrological and meteorological application of satellite precipitation datasets in Central Asia or even the whole temperate zone

N‑Linked Glycosylation Prevents Deamidation of Glycopeptide and Glycoprotein

Deamidation has been recognized as a common spontaneous pathway of protein degradation and a prevalent concern in the pharmaceutical industry; deamidation caused the reduction of protein/peptide drug efficacy and shelf life in several cases. More importantly, deamidation of physiological proteins is related to several human diseases and considered a timer for the diseases. N-linked glycosylation has a variety of significant biological functions, and it interestingly occurs right on the deamidation site-asparagine. It has been perceived that N-glycosylation could prevent deamidation, but experimental support is still lacking for clearly understanding the role of N-glycosylation on deamidation. Our results presented that deamidation is prevented by naturally occurring N-linked glycosylation. Glycopeptides and corresponding nonglycosylated peptides were used to compare their deamidation rates. All the nonglycosylated peptides have different half-lives ranging from one to 20 days, for the corresponding glycosylated peptides; all the results showed that the deamidation reaction was significantly reduced by the introduction of N-linked glycosylation. A glycoprotein, RNase B, also showed a significantly elongated deamidation half-life compared to nonglycosylated protein RNase A. At last, N-linked glycosylation on INGAP-P, a therapeutic peptide, increased the deamidation half-life of INGAP-P as well as its therapeutic potency

Circulating microRNAs as novel biomarkers for dilated cardiomyopathy

Background: Circulating microRNAs (miRNAs) potentially carry disease-specific information. In the current study, we aim to characterize the miRNA signature in plasma from dilated cardiomyopathy (DCM) patients and assess the possible correlation between expression levels of circulating miRNAs and symptom severity in DCM patients. Methods: Using microarray-based miRNA expression profiling, we compared the miRNA expression levels in plasma samples from 4 DCM patients and 3 healthy controls. The expression levels of selected differentially expressed, upregulated miRNAs (miR-3135b, miR-3908 and miR-5571-5p) were validated independently in plasma samples from 19 DCM patients and 20 controls. Results: We observed that plasma miR-3135b (p < 0.001), miR-3908 (p < 0.001) and miR-5571-5p (p < 0.001) were significantly upregulated in DCM patients. The area under receiver operating characteristic (ROC) curves for the 3 miRNAs ranged from 0.83 to 1.00. Moreover, miR-5571-5p levels in plasma were significantly upregulated with severe New York Heart Association (NYHA) classification (p < 0.05). Conclusions: The circulating miRNAs (miR-3135b, miR-3908 and miR-5571-5p) have potential as diagnostic biomarkers for DCM. Additionally, miR-5571-5p correlated with NYHA classification.

Chemical Synthesis of Homogeneous Human E-Cadherin N-Linked Glycopeptides: Stereoselective Convergent Glycosylation and Chemoselective Solid-Phase Aspartylation

We report herein an efficient chemical synthesis of homogeneous human E-cadherin N-linked glycopeptides consisting of a heptapeptide sequence adjacent to the Asn-633 N-glycosylation site with representative N-glycan structures, including a conserved trisaccharide, a core-fucosylated tetrasaccharide, and a complex-type biantennary octasaccharide. The key steps are a chemoselective on-resin aspartylation using a pseudoproline-containing peptide and stereoselective glycosylation using glycosyl fluororide as a donor. This synthetic strategy demonstrates potential utility in accessing a wide range of homogeneous N-linked glycopeptides for the examination of their biological function