Leveraging Pretrained Representations with Task-related Keywords for Alzheimer's Disease Detection

With the global population aging rapidly, Alzheimer's disease (AD) is particularly prominent in older adults, which has an insidious onset and leads to a gradual, irreversible deterioration in cognitive domains (memory, communication, etc.). Speech-based AD detection opens up the possibility of widespread screening and timely disease intervention. Recent advances in pre-trained models motivate AD detection modeling to shift from low-level features to high-level representations. This paper presents several efficient methods to extract better AD-related cues from high-level acoustic and linguistic features. Based on these features, the paper also proposes a novel task-oriented approach by modeling the relationship between the participants' description and the cognitive task. Experiments are carried out on the ADReSS dataset in a binary classification setup, and models are evaluated on the unseen test set. Results and comparison with recent literature demonstrate the efficiency and superior performance of proposed acoustic, linguistic and task-oriented methods. The findings also show the importance of semantic and syntactic information, and feasibility of automation and generalization with the promising audio-only and task-oriented methods for the AD detection task.Comment: 5 pages, 3 figures, 3 table

Social Determinants of Community Health Services Utilization among the Users in China: A 4-Year Cross-Sectional Study

Background To identify social factors determining the frequency of community health service (CHS) utilization among CHS users in China. Methods Nationwide cross-sectional surveys were conducted in 2008, 2009, 2010, and 2011. A total of 86,116 CHS visitors selected from 35 cities were interviewed. Descriptive analysis and multinomial logistic regression analysis were employed to analyze characteristics of CHS users, frequency of CHS utilization, and the socio-demographic and socio-economic factors influencing frequency of CHS utilization. Results Female and senior CHS clients were more likely to make 3–5 and ≥6 CHS visits (as opposed to 1–2 visits) than male and young clients, respectively. CHS clients with higher education were less frequent users than individuals with primary education or less in 2008 and 2009; in later surveys, CHS clients with higher education were the more frequent users. The association between frequent CHS visits and family income has changed significantly between 2008 and 2011. In 2011, income status did not have a discernible effect on the likelihood of making ≥6 CHS visits, and it only had a slight effect on making 3–5 CHS visits. Conclusion CHS may play an important role in providing primary health care to meet the demands of vulnerable populations in China. Over time, individuals with higher education are increasingly likely to make frequent CHS visits than individuals with primary school education or below. The gap in frequency of CHS utilization among different economic income groups decreased from 2008 to 2011

The rates and the determinants of hypertension according to the 2017 definition of hypertension by ACC/AHA and 2014 evidence-based guidelines among population aged ≥40 years old

Background : In November 2017, the American College of Cardiology/American Heart Association (ACC/AHA) updated their definition of hypertension from 140/90 mm Hg to 130/80 mm Hg. Objectives : We sought to assess the situation of hypertension and the impact of applying the new threshold to a geographically and ethnically diverse population. Methods : We analyzed selected data on 237,142 participants aged ≥40 who had blood pressure taken for the 2014 China National Stroke Screening and Prevention Project. Choropleth maps and logistic regression analyses were performed to estimate the prevalence, geographical distribution and risk factors of hypertension using both 2017 ACC/AHA guidelines and 2014 evidence-based guidelines. Results : The present cross-sectional study showed the age- and sex-standardized prevalence of hypertension was 37.08% and 58.52%, respectively, according to 2014 evidence-based guidelines and 2017 ACC/AHA guidelines. The distribution of hypertension and risk factors changed little between guidelines, with data showing a high prevalence of hypertension around Bohai Gulf and in south central coastal areas using either definition. The age- and sex-standardized prevalence of newly labeled as hypertensive was 21.44%. Interestingly, the high prevalence region of newly labeled as hypertensive was found in the north China. Conclusion : The prevalence of hypertension increased significantly on 2017 ACC/AHA guidelines compared to the prevalence when using 2014 evidence-based guidelines, with high prevalence areas of newly labeled as hypertensive now seen mainly in north China. There need to be correspondingly robust efforts to improve health education, health management, and behavioral and lifestyle interventions in the north

Dissection of CDK4-binding and Transactivation Activities of p34SEI-1 and Comparison between Functions of p34SEI-1 and p16INK4

[[sponsorship]]生物化學研究所,基因體研究中心[[note]]已出版;[SCI];有審查制度;不具代表性[[note]]http://gateway.isiknowledge.com/gateway/Gateway.cgi?GWVersion=2&SrcAuth=Drexel&SrcApp=hagerty_opac&KeyRecord=0006-2960&DestApp=JCR&RQ=IF_CAT_BOXPLOT[[note]]http://gateway.isiknowledge.com/gateway/Gateway.cgi?GWVersion=2&SrcAuth=RID&SrcApp=RID&DestLinkType=FullRecord&DestApp=ALL_WOS&KeyUT=00023246310000

Regulatory mechanisms of tumor suppressor P16(INK4A) and their relevance to cancer

[[sponsorship]]生物化學研究所,基因體研究中心[[note]]已出版;[SCI];有審查制度;不具代表性[[note]]http://gateway.isiknowledge.com/gateway/Gateway.cgi?GWVersion=2&SrcAuth=Drexel&SrcApp=hagerty_opac&KeyRecord=0006-2960&DestApp=JCR&RQ=IF_CAT_BOXPLOT[[note]]http://gateway.isiknowledge.com/gateway/Gateway.cgi?GWVersion=2&SrcAuth=RID&SrcApp=RID&DestLinkType=FullRecord&DestApp=ALL_WOS&KeyUT=00029189700000

Aggregate fingerprints identification based on its compositions and machine learning algorithm

The type and properties of an aggregate affect the properties of their mixtures with either Portland cements or asphalt binders. How to quickly identify the information on an aggregate, providing a reliable basis for the quality assurance and quality control of aggregates, i.e., guarantee the source of aggregates is vital important. The purpose of this study is to explore a new and rapid detective technology for aggregate fingerprint identification using Fourier Transform Infrared Spectroscopy (FTIR). Machine learning algorithm of statistical analysis software (SPSS) was performed for principal component analysis, cluster analysis and linear discriminant analysis on collected information of the aggregates. The results showed that the aggregates of the same origin can be aggregated well by principal component analysis, cluster analysis and linear discriminant analysis as well. The cross-validation accuracy is very high

Relational prompt-based single-module single-step model for relational triple extraction

The relational triple extraction is a fundamental and essential information extraction task. The existing approaches of relation triple extraction achieve considerable performance but still suffer from 1) treating the relation between entities as a meaningless label while ignoring the relational semantic information of the relation itself and 2) ignoring the interdependence and inseparability of three elements of the triple. To address these problems, this paper proposes a Relational Prompt approach, based on which constructs a Single-module Single-step relational triple extraction model (RPSS). In particular, the proposed relational prompt approach consist of a relational hard-prompt and a relational soft-prompt, while provide take into account different level of relational semantic information, covering both the token-level and the feature-level relational prompt information. Then, we jointly encode entities and relational prompts to obtain a unified global representation. We mine deep correlations between different embeddings through attention mechanism and then construct a triple interaction matrix. Then, all triples could be directly extracted from a single module in a single step. Experiments demonstrate the effectiveness of the relational prompt approach, as well as relational semantics and triple integrity are essential for relation extraction. Experimental results on two benchmark datasets demonstrate our model outperforms current state-of-the-art models