Association of neuroticism with incident dementia, neuroimaging outcomes, and cognitive function

INTRODUCTION: Higher neuroticism might be associated with dementia risk. Here we investigated modification by genetic predisposition to dementia, mediation by mental health and vascular conditions, neuroimaging outcomes, and cognitive function. METHODS: Cox proportional‐hazards models were used to assess the association between neuroticism score and incident dementia over up to 15 years in 1,74,164 participants. Cross‐sectional analyses on dementia‐related neuroimaging outcomes and cognitive function were conducted in 39,459 dementia‐free participants. RESULTS: Higher neuroticism was associated with an 11% higher risk of incident dementia, especially vascular dementia (15% higher risk), regardless of genetic predisposition to dementia. Mental and vascular conditions mediated the association of neuroticism with all‐cause dementia and vascular dementia. Neuroticism was associated with higher cerebrovascular pathology, lower gray matter volume, and worse function across multiple cognitive domains. DISCUSSION: Neuroticism could represent a risk factor for dementia, and vascular and mental health might drive these associations. Highlights: Neuroticism was associated with an increased risk of incident all‐cause dementia, particularly vascular dementia. Associations were not modified by genetic predisposition to dementia. Associations were largely mediated by mental and vascular conditions. Neuroticism was associated with increased cerebrovascular pathology and lower gray matter volume. Neuroticism was associated with worse function across multiple cognitive domains

Efficient and Joint Hyperparameter and Architecture Search for Collaborative Filtering

Automated Machine Learning (AutoML) techniques have recently been introduced to design Collaborative Filtering (CF) models in a data-specific manner. However, existing works either search architectures or hyperparameters while ignoring the fact they are intrinsically related and should be considered together. This motivates us to consider a joint hyperparameter and architecture search method to design CF models. However, this is not easy because of the large search space and high evaluation cost. To solve these challenges, we reduce the space by screening out usefulness yperparameter choices through a comprehensive understanding of individual hyperparameters. Next, we propose a two-stage search algorithm to find proper configurations from the reduced space. In the first stage, we leverage knowledge from subsampled datasets to reduce evaluation costs; in the second stage, we efficiently fine-tune top candidate models on the whole dataset. Extensive experiments on real-world datasets show better performance can be achieved compared with both hand-designed and previous searched models. Besides, ablation and case studies demonstrate the effectiveness of our search framework.Comment: Accepted by KDD 202

Weak Supervision for Fake News Detection via Reinforcement Learning

Today social media has become the primary source for news. Via social media platforms, fake news travel at unprecedented speeds, reach global audiences and put users and communities at great risk. Therefore, it is extremely important to detect fake news as early as possible. Recently, deep learning based approaches have shown improved performance in fake news detection. However, the training of such models requires a large amount of labeled data, but manual annotation is time-consuming and expensive. Moreover, due to the dynamic nature of news, annotated samples may become outdated quickly and cannot represent the news articles on newly emerged events. Therefore, how to obtain fresh and high-quality labeled samples is the major challenge in employing deep learning models for fake news detection. In order to tackle this challenge, we propose a reinforced weakly-supervised fake news detection framework, i.e., WeFEND, which can leverage users' reports as weak supervision to enlarge the amount of training data for fake news detection. The proposed framework consists of three main components: the annotator, the reinforced selector and the fake news detector. The annotator can automatically assign weak labels for unlabeled news based on users' reports. The reinforced selector using reinforcement learning techniques chooses high-quality samples from the weakly labeled data and filters out those low-quality ones that may degrade the detector's prediction performance. The fake news detector aims to identify fake news based on the news content. We tested the proposed framework on a large collection of news articles published via WeChat official accounts and associated user reports. Extensive experiments on this dataset show that the proposed WeFEND model achieves the best performance compared with the state-of-the-art methods.Comment: AAAI 202

Effects of Comparative Metabolism on Tomato Fruit Quality under Different Levels of Root Restriction

In a soilless culture (perlite substrate), root restriction cannot only reduce production costs but also improve fruit quality. Therefore, this study used different levels of root restriction [T1: 0.5 L, T2: 4 L, nonrestriction treatment (CK): 35 L] on tomatoes to explore their impact on quality. Results showed that total soluble solids (TSS), glucose, fructose, and sucrose contents were increased, whereas L-tryptophan, L-tyrosine, and titratable acidity were decreased under two restriction treatments. Meanwhile, root restriction also promoted the accumulation of phenylalanine and proline. For lycopene and flavonoid biosynthesis (prunin, naringin, naringenin), the restriction groups were significantly higher than those in the control group. Overall, T1 and T2 treatment had a better effect than CK treatment. This study provided an idea for improving substrate use efficiency and tomato quality

MiR-543 Promotes Migration, Invasion and Epithelial-Mesenchymal Transition of Esophageal Cancer Cells by Targeting Phospholipase A2 Group IVA

Background/Aims: The aim of this study was to investigate the roles of miR-543 and phospholipase A2 group IVA (PLA2G4A) in cell mobility and the invasiveness cascade in esophageal squamous cell carcinoma (ESCC) and to validate the interactive relationship between miR-543 and PLA2G4A. Methods: Microarray analysis showed the different expression levels of PLA2G4A in two ESCC cell lines (KYSE30 and KYSE180). The expression levels of miR-543 and PLA2G4A in ESCC tissues were confirmed by qRT-PCR and Western blotting. The targeted relationship between miR-543 and PLA2G4A was studied and verified by a luciferase activity assay. Then, the invasion and metastasis ability of ESCC cell lines transfected with miR-543 mimics, miR-543 inhibitor, or PLA2G4A and miR-543 mimics were analyzed separately by Transwell migration and invasion assays. In addition, the roles of miR-543 and PLA2G4A in the expression of E-cadherin and vimentin were also investigated. Results: PLA2G4A up-regulated the level of E-cadherin and down-regulated the level of vimentin, which curbed ESCC cell mobility and invasion. In ESCC cells, the expression of miR-543 was significantly higher, whereas the expression of PLA2G4A was markedly lower. MiR-543 facilitated ESCC cell mobility and invasion by repressing PLA2G4A. Conclusions: MiR-543 enhanced the cell mobility and the invasiveness cascade in ESCC cells via the down-regulation of PLA2G4A expression