Unsupervised Annotation of Phenotypic Abnormalities via Semantic Latent Representations on Electronic Health Records

The extraction of phenotype information which is naturally contained in electronic health records (EHRs) has been found to be useful in various clinical informatics applications such as disease diagnosis. However, due to imprecise descriptions, lack of gold standards and the demand for efficiency, annotating phenotypic abnormalities on millions of EHR narratives is still challenging. In this work, we propose a novel unsupervised deep learning framework to annotate the phenotypic abnormalities from EHRs via semantic latent representations. The proposed framework takes the advantage of Human Phenotype Ontology (HPO), which is a knowledge base of phenotypic abnormalities, to standardize the annotation results. Experiments have been conducted on 52,722 EHRs from MIMIC-III dataset. Quantitative and qualitative analysis have shown the proposed framework achieves state-of-the-art annotation performance and computational efficiency compared with other methods.Comment: Accepted by BIBM 2019 (Regular

Integrated Multi-omics Analysis Using Variational Autoencoders: Application to Pan-cancer Classification

Different aspects of a clinical sample can be revealed by multiple types of omics data. Integrated analysis of multi-omics data provides a comprehensive view of patients, which has the potential to facilitate more accurate clinical decision making. However, omics data are normally high dimensional with large number of molecular features and relatively small number of available samples with clinical labels. The "dimensionality curse" makes it challenging to train a machine learning model using high dimensional omics data like DNA methylation and gene expression profiles. Here we propose an end-to-end deep learning model called OmiVAE to extract low dimensional features and classify samples from multi-omics data. OmiVAE combines the basic structure of variational autoencoders with a classification network to achieve task-oriented feature extraction and multi-class classification. The training procedure of OmiVAE is comprised of an unsupervised phase without the classifier and a supervised phase with the classifier. During the unsupervised phase, a hierarchical cluster structure of samples can be automatically formed without the need for labels. And in the supervised phase, OmiVAE achieved an average classification accuracy of 97.49% after 10-fold cross-validation among 33 tumour types and normal samples, which shows better performance than other existing methods. The OmiVAE model learned from multi-omics data outperformed that using only one type of omics data, which indicates that the complementary information from different omics datatypes provides useful insights for biomedical tasks like cancer classification.Comment: 7 pages, 4 figure

Reboost Large Language Model-based Text-to-SQL, Text-to-Python, and Text-to-Function -- with Real Applications in Traffic Domain

The previous state-of-the-art (SOTA) method achieved a remarkable execution accuracy on the Spider dataset, which is one of the largest and most diverse datasets in the Text-to-SQL domain. However, during our reproduction of the business dataset, we observed a significant drop in performance. We examined the differences in dataset complexity, as well as the clarity of questions' intentions, and assessed how those differences could impact the performance of prompting methods. Subsequently, We develop a more adaptable and more general prompting method, involving mainly query rewriting and SQL boosting, which respectively transform vague information into exact and precise information and enhance the SQL itself by incorporating execution feedback and the query results from the database content. In order to prevent information gaps, we include the comments, value types, and value samples for columns as part of the database description in the prompt. Our experiments with Large Language Models (LLMs) illustrate the significant performance improvement on the business dataset and prove the substantial potential of our method. In terms of execution accuracy on the business dataset, the SOTA method scored 21.05, while our approach scored 65.79. As a result, our approach achieved a notable performance improvement even when using a less capable pre-trained language model. Last but not least, we also explore the Text-to-Python and Text-to-Function options, and we deeply analyze the pros and cons among them, offering valuable insights to the community

Inefficient Translocation of Preproinsulin Contributes to Pancreatic β Cell Failure and Late-onset Diabetes

Among the defects in the early events of insulin biosynthesis, proinsulin misfolding and endoplasmic reticulum (ER) stress have drawn increasing attention as causes of β cell failure. However, no studies have yet addressed potential defects at the cytosolic entry point of preproinsulin into the secretory pathway. Here, we provide the first evidence that inefficient translocation of preproinsulin (caused by loss of a positive charge in the n region of its signal sequence) contributes to β cell failure and diabetes. Specifically, we find that, after targeting to the ER membrane, preproinsulin signal peptide (SP) mutants associated with autosomal dominant late-onset diabetes fail to be fully translocated across the ER membrane. The newly synthesized, untranslocated preproinsulin remains strongly associated with the ER membrane, exposing its proinsulin moiety to the cytosol. Rather than accumulating in the ER and inducing ER stress, untranslocated preproinsulin accumulates in a juxtanuclear compartment distinct from the Golgi complex, induces the expression of heat shock protein 70 (HSP70), and promotes β cell death. Restoring an N-terminal positive charge to the mutant preproinsulin SP significantly improves the translocation defect. These findings not only reveal a novel molecular pathogenesis of β cell failure and diabetes but also provide the first evidence of the physiological and pathological significance of the SP n region positive charge of secretory proteins

3D printing technology for titanium alloy and its defect

The development of 3D printing technology, basic principles and technical features are introduced. Several major 3D printing technologies of titanium alloy both at home and abroad are reviewed: selective laser sintering technology (SLS), selective laser melting technology (SLM), laser solid forming technology (LSF), electron beam selective melting technology (EBSM) and electron beam fuse deposition forming technology (EBF3). By comparison, EBSM technology is the most promising 3D printing technology for titanium alloy in the future because of its high efficiency, high accuracy, low cost and no pollution. The cause and detection of defects in the process of forming is an important research focus in the field of 3D printing, and it is also the basis of the realization of the application of 3D printing parts. The classification, harm and cause of the main defects (including spheroidization, cracks, porosity and warpage) in the process of 3D printing for titanium alloy, as well as the nondestructive testing technology commonly used in 3D printing are introduced. And combined with domestic and foreign research situation, the methods to restrain or improve the above defects are discussed. Finally, the prospect of the future development of 3D printing technology for titanium alloy is prospected from the aspects of materials, equipment and testing technology

Anti-hypoxia effects of ginseng (Panax Ginseng C A Meyer) oligopeptides in mice

Purpose: To study the anti-hypoxia effects of ginseng oligopeptides (GOPs) in mice. Methods: Mice were randomly assigned to six groups: vehicle control group, whey protein-fed group (0.30 g/kg body weight, BW), and four groups given GOP at doses of 0.075, 0.150, 0.300, 0.600 g/kg BW. All treatments were administered via gavage once a day for a total of 30 days. Results: GOPs significantly extended survival times under normobaric hypoxia, sodium nitrite toxicosis and acute cerebral ischemia. Moreover, GOPs enhanced the levels of RBC, Hb and Hct; decreased brain malonaldehyde (MDA) and lactate contents, enhanced brain lactate dehydrogenase (LDH) activity, and upregulated the mRNA expression levels of hypoxia-inducible factor 1alpha (HIF1α) and vascular endothelial growth factor (VEGF). Conclusion: GOPs exert anti-hypoxia effects via mechanisms which may involve improvement of oxygen-carrying capacity of blood and oxygen utilization, reduction of lipid peroxidation-associated lesions, enhancement of brain capacity to buffer against lactic acidosis, promotion of angiogenesis, and regulation of response to hypoxia

Improved biogas production from rice straw by co-digestion with kitchen waste and pig manure

In order to investigate the effect of feedstock ratios in biogas production, anaerobic co-digestions of rice straw with kitchen waste and pig manure were carried out. A series of single-stage batch mesophilic (37 +/- 1 degrees C) anaerobic digestions were performed at a substrate concentration of 54 g/L based on volatile solids (VS). The results showed that the optimal ratio of kitchen waste, pig manure, and rice straw was 0.4:1.6:1, for which the C/N ratio was 21.7. The methane content was 45.9-70.0% and rate of VS reduction was 55.8%. The biogas yield of 674.4 L/kg VS was higher than that of the digestion of rice straw or pig manure alone by 71.67% and 10.41%, respectively. Inhibition of biogas production by volatile fatty acids (VFA) occurred when the addition of kitchen waste was greater than 26%. The VFA analysis showed that, in the reactors that successfully produced biogas, the dominant intermediate metabolites were propionate and acetate, while they were lactic acid, acetate, and propionate in the others. (C) 2013 Elsevier Ltd. All rights reserved