Exploring cognitive trajectories and their association with physical performance: evidence from the China Health and Retirement Longitudinal Study

Jingdong Suo,Xianlei Shen,Jinyu He,Haoran Sun,Yu Shi,Rongxin He,Xiao Zhang,Xijie Wang,Yuandi Xi,Wannian Liang 한국역학회 2023 Epidemiology and Health Vol.45 No.-

OBJECTIVES: The long-term trends of cognitive function and its associations with physical performance remain unclear, particularly in Asian populations. The study objectives were to determine cognitive trajectories in middle-aged and elderly Chinese individuals, as well as to examine differences in physical performance across cognitive trajectory groups. METHODS: Data were extracted from the China Health and Retirement Longitudinal Study. A total of 5,701 participants (47.7% male) with a mean age of 57.8 (standard deviation, 8.4) years at enrollment were included. A group-based trajectory model was used to identify cognitive trajectory groups for each sex. Grip strength, repeated chair stand, and standing balance tests were used to evaluate physical performance. An ordered logistic regression model was employed to analyze differences in physical performance across cognitive trajectory groups. RESULTS: Three cognitive trajectory groups were identified for each sex: low, middle, and high. For both sexes, higher cognitive trajectory groups exhibited smaller declines with age. In the fully adjusted model, relative to the low trajectory group, the odds ratios (ORs) of better physical performance in the middle cognitive group were 1.37 (95% confidence interval [CI], 1.17 to 1.59; p<0.001) during follow-up and 1.40 (95% CI, 1.20 to 1.64; p<0.001) at the endpoint. The ORs in the high trajectory group were 1.94 (95% CI, 1.61 to 2.32; p<0.001) during follow-up and 2.04 (95% CI, 1.69 to 2.45; p<0.001) at the endpoint. CONCLUSIONS: Cognitive function was better preserved in male participants and individuals with higher baseline cognitive function. A higher cognitive trajectory was associated with better physical performance over time.

Dexamethasone enhances glucose uptake by SGLT1 and GLUT1 and boosts ATP generation through the PPP-TCA cycle in bovine neutrophils

Xinbo Wang,Mingyu Tang,Yuming Zhang,Yansong Li,Jingdong Mao,Qinghua Deng,Shusen Li,Zhenwei Jia,Liyin Du 대한수의학회 2022 Journal of Veterinary Science Vol.23 No.5

Background: Clinical dexamethasone (DEX) treatment or stress in bovines results in extensive physiological changes with prominent hyperglycemia and neutrophils dysfunction. Objectives: To elucidate the effects of DEX treatment in vivo on cellular energy status and the underlying mechanism in circulating neutrophils. Methods: We selected eight-month-old male bovines and injected DEX for 3 consecutive days (1 time/d). The levels of glucose, total protein (TP), total cholesterol (TC), and the proinflammatory cytokines interleukin (IL)-1β, IL-6 and tumor necrosis factor (TNF)-α in blood were examined, and we then detected glycogen and adenosine triphosphate (ATP) content, phosphofructosekinase-1 (PFK1) and glucose-6-phosphate dehydrogenase (G6PDH) activity, glucose transporter (GLUT)1, GLUT4, sodium/glucose cotransporter (SGLT)1 and citrate synthase (CS) protein expression and autophagy levels in circulating neutrophils. Results: DEX injection markedly increased blood glucose, TP and TC levels, the Ca2+/P5+ ratio and the neutrophil/lymphocyte ratio and significantly decreased blood IL-1β, IL-6 and TNF-α levels. Particularly in neutrophils, DEX injection inhibited p65-NFκB activation and elevated glycogen and ATP contents and SGLT1, GLUT1 and GR expression while inhibiting PFK1 activity, enhancing G6PDH activity and CS expression and lowering cell autophagy levels. Conclusions: DEX induced neutrophils glucose uptake by enhancing SGLT1 and GLUT1 expression and the transformation of energy metabolism from glycolysis to pentose phosphate pathway (PPP)-tricarboxylic acid (TCA) cycle. This finding gives us a new perspective on deeper understanding of clinical anti-inflammatory effects of DEX on bovine.

Semi-supervised Software Defect Prediction Model Based on Tri-training

( Fanqi Meng ),( Wenying Cheng ),( Jingdong Wang ) 한국인터넷정보학회 2021 KSII Transactions on Internet and Information Syst Vol.15 No.11

Aiming at the problem of software defect prediction difficulty caused by insufficient software defect marker samples and unbalanced classification, a semi-supervised software defect prediction model based on a tri-training algorithm was proposed by combining feature normalization, over-sampling technology, and a Tri-training algorithm. First, the feature normalization method is used to smooth the feature data to eliminate the influence of too large or too small feature values on the model's classification performance. Secondly, the oversampling method is used to expand and sample the data, which solves the unbalanced classification of labelled samples. Finally, the Tri-training algorithm performs machine learning on the training samples and establishes a defect prediction model. The novelty of this model is that it can effectively combine feature normalization, oversampling techniques, and the Tri-training algorithm to solve both the under-labelled sample and class imbalance problems. Simulation experiments using the NASA software defect prediction dataset show that the proposed method outperforms four existing supervised and semi-supervised learning in terms of Precision, Recall, and F-Measure values.

Creating Knowledge Graph of Electric Power Equipment Faults Based on BERT–BiLSTM–CRF Model

Meng Fanqi,Yang Shuaisong,Wang Jingdong,Xia Lei,Liu Han 대한전기학회 2022 Journal of Electrical Engineering & Technology Vol.17 No.4

Creating a large-scale knowledge graph of electric power equipment faults will facilitate the development of automatic fault diagnosis and intelligent question answering (QA) in the electric power industry. However, most existing methods have lower accuracy in Chinese entity recognition, thus it is hard to build such a high-quality knowledge graph by extracting knowledge from Chinese technical literature. To solve the problem, a novel model called BERT–BiLSTM–CRF is proposed. It blends Bi-directional Encoder Representation from Transformers (BERT), Bi-directional Long Short-Term Memory (BiLSTM), and Conditional Random Field (CRF). The model fi rstly identifi es and extracts electric power equipment entities from preprocessed Chinese technical literature. Then, the semantic relations between the entities are extracted based on the relation classifi cation method based on dependency parsing. Finally, the extracted knowledge is stored in the Neo4j database in the form of the triplet and visualized in the form of a graph. Through the above steps, a Chinese knowledge graph of electric power equipment faults can be built. The novelty of the model just lies in its subtle blend: the BERT module can not only learn phrase-level information representation, but also learn rich semantic information features; the CRF module realizes the constraint on the label prediction value and reduces the irregular recognition rate, so the accuracy rate of entity recognition is improved. Taking the Chinese technological literature, which is about fault diagnosis of electric power equipment as the experimental object, the experimental results show that the model identifi es and extracts Chinese entities more accurately than traditional methods. Thus, a comprehensive and accurate Chinese knowledge graph of electric power equipment faults could be constructed more easily.

Strength and Mechanism of Carbonated Solidified Clay with Steel Slag Curing Agent

Man Li,Qiang Wang,Jingdong Yang,Xiaoliang Guo,Wenjun Zhou 대한토목학회 2021 KSCE JOURNAL OF CIVIL ENGINEERING Vol.25 No.3

Industrial wastes, such as steel slag and desulfurized gypsum, are piled up in large quantities and only a very small portion is recycled, adversely impacting natural ecosystems. Meanwhile, environmental problems caused by CO2 have increasingly received attention. Hence, this study introduces a novel environmentally friendly composite, formed by sodium hydroxide (used as an activator), desulfurized gypsum and steel slag (S-GS). The main objective of this study is to evaluate the potential use of S-GS for solidifying clay under the condition of carbonation curing. Besides, the samples’ characteristics are investigated according to the tests of unconfined compressive strength (UCS), pH, carbonation depth, quality loss and scanning electron microscope (SEM). It is found that the UCS of solidified clay decreases with the increasing water content while it increases first and then decreases with the increasing desulfurized gypsum content, reaching the maximum when the water content is 0.5 times the liquid limit and the content of desulfurized gypsum is 8%. Moreover, compared with the standard curing, carbonation curing can stimulate the activity of S-GS to improve the UCS of samples more effectively. With the increasing carbonation curing time, the mass loss rate and carbonation depth of samples increase while the pH value decreases. Additionally, based on the normalized analysis, the carbonation time has the most significant effect on the UCS. Furthermore, the SEM results indicate that formation of Calcium carbonate and Magnesium carbonate are primary reasons for improving the UCS of the stabilised clay during carbonization. This research promotes steel slag and desulfurized gypsum as green stabilisers for soil stabilization, and the method of carbonation curing contributes to the higher UCS, which also greatly shortens the curing time.

Preliminary Report on the Selection of Cold and Drought Resistant Dwarfing Apple Rootstocks

Tingzhen Yang,Qian Wang,Jingdong Gao,Kunpeng Li,Feng Han,Jianbao Tian 한국원예학회 2008 한국원예학회 기타간행물 Vol.- No.-