DOES REVERSE CAUSALITY EXPLAIN THE RELATIONSHIP BETWEEN DIET AND DEPRESSION？—POSSIBLY RELATED TO DIET'S SUBJECTIVE BEHAVIOR

Depression can result in changes in eating behavior and decrease the quality of eating. It has been shown that maternal depression during pregnancy can result in malnutrition, which can have adverse effects on the pregnancy and the offspring. There is currently no clear association between depression and diet; (2) Methods: Five hundred and forty-nine pregnant women recruited from Danyang Maternal and Child Health Hospital in Jiangsu Province participated in this study and were administered the Intuitive Eating Scale-2(IES-2), Edinburgh Post-natal Depression Scale (EPDS), Pregnancy Stress Scale (PPS), Self-rating Anxiety Scale (SAS), and Dietary Guidelines Adherence Index for Pregnant Women during Pregnancy (CDGCI-PW). The nutritional software collected dietary records for three consecutive days in mid-pregnancy to calculate dietary intake and nutrients that support energy production. The mediation analyses were conducted using SPSS 24.0 macro PROCESS; (3) Results: The relationship between depressive symptoms during pregnancy and diet quality was moderated primarily by two aspects of eating behavior, “Reliance on Hunger and Satiety Cues” (RHS) and “Body-Food Choice Congruence” (BFC). Depressive symptoms (EPDS scores) showed a negative correlation with RHS, BFC, and RHS, and BFC showed a positive correlation with diet quality, yielding a significant specific indirect effect. the multiple mediation model explained 14.7% of the variance in the diet quality; (4) Conclusions: Individual awareness of depression may influence the causal association between nutrition and depression. This study highlights the important role of eating behaviors during pregnancy in the relationship between depressive symptoms (EPDS scores) and diet quality and provides preliminary evidence for feasible ways pregnant women with depressive symptoms can improve diet quality, promote maternal and child health, and reduce depression

Examining Temporalities on Stance Detection Towards COVID-19 Vaccination

Previous studies have highlighted the importance of vaccination as an effective strategy to control the transmission of the COVID-19 virus. It is crucial for policymakers to have a comprehensive understanding of the public's stance towards vaccination on a large scale. However, attitudes towards COVID-19 vaccination, such as pro-vaccine or vaccine hesitancy, have evolved over time on social media. Thus, it is necessary to account for possible temporal shifts when analysing these stances. This study aims to examine the impact of temporal concept drift on stance detection towards COVID-19 vaccination on Twitter. To this end, we evaluate a range of transformer-based models using chronological and random splits of social media data. Our findings demonstrate significant discrepancies in model performance when comparing random and chronological splits across all monolingual and multilingual datasets. Chronological splits significantly reduce the accuracy of stance classification. Therefore, real-world stance detection approaches need to be further refined to incorporate temporal factors as a key consideration

A competitive mechanism based multi-objective particle swarm optimizer with fast convergence

In the past two decades, multi-objective optimization has attracted increasing interests in the evolutionary computation community, and a variety of multi-objective optimization algorithms have been proposed on the basis of different population based meta-heuristics, where the family of multi-objective particle swarm optimization is among the most representative ones. While the performance of most existing multi-objective particle swarm optimization algorithms largely depends on the global or personal best particles stored in an external archive, in this paper, we propose a competitive mechanism based multi-objective particle swarm optimizer, where the particles are updated on the basis of the pairwise competitions performed in the current swarm at each generation. The performance of the proposed competitive multi-objective particle swarm optimizer is verified by benchmark comparisons with several state-of-the-art multiobjective optimizers, including three multi-objective particle swarm optimization algorithms and three multi-objective evolutionary algorithms. Experimental results demonstrate the promising performance of the proposed algorithm in terms of both optimization quality and convergence speed

VaxxHesitancy: A Dataset for Studying Hesitancy Towards COVID-19 Vaccination on Twitter

Vaccine hesitancy has been a common concern, probably since vaccines were created and, with the popularisation of social media, people started to express their concerns about vaccines online alongside those posting pro- and anti-vaccine content. Predictably, since the first mentions of a COVID-19 vaccine, social media users posted about their fears and concerns or about their support and belief into the effectiveness of these rapidly developing vaccines. Identifying and understanding the reasons behind public hesitancy towards COVID-19 vaccines is important for policy markers that need to develop actions to better inform the population with the aim of increasing vaccine take-up. In the case of COVID-19, where the fast development of the vaccines was mirrored closely by growth in anti-vaxx disinformation, automatic means of detecting citizen attitudes towards vaccination became necessary. This is an important computational social sciences task that requires data analysis in order to gain in-depth understanding of the phenomena at hand. Annotated data is also necessary for training data-driven models for more nuanced analysis of attitudes towards vaccination. To this end, we created a new collection of over 3,101 tweets annotated with users' attitudes towards COVID-19 vaccination (stance). Besides, we also develop a domain-specific language model (VaxxBERT) that achieves the best predictive performance (73.0 accuracy and 69.3 F1-score) as compared to a robust set of baselines. To the best of our knowledge, these are the first dataset and model that model vaccine hesitancy as a category distinct from pro- and anti-vaccine stance.Comment: Accepted at ICWSM 202

Designing Novel Cognitive Diagnosis Models via Evolutionary Multi-Objective Neural Architecture Search

Cognitive diagnosis plays a vital role in modern intelligent education platforms to reveal students' proficiency in knowledge concepts for subsequent adaptive tasks. However, due to the requirement of high model interpretability, existing manually designed cognitive diagnosis models hold too simple architectures to meet the demand of current intelligent education systems, where the bias of human design also limits the emergence of effective cognitive diagnosis models. In this paper, we propose to automatically design novel cognitive diagnosis models by evolutionary multi-objective neural architecture search (NAS). Specifically, we observe existing models can be represented by a general model handling three given types of inputs and thus first design an expressive search space for the NAS task in cognitive diagnosis. Then, we propose multi-objective genetic programming (MOGP) to explore the NAS task's search space by maximizing model performance and interpretability. In the MOGP design, each architecture is transformed into a tree architecture and encoded by a tree for easy optimization, and a tailored genetic operation based on four sub-genetic operations is devised to generate offspring effectively. Besides, an initialization strategy is also suggested to accelerate the convergence by evolving half of the population from existing models' variants. Experiments on two real-world datasets demonstrate that the cognitive diagnosis models searched by the proposed approach exhibit significantly better performance than existing models and also hold as good interpretability as human-designed models.Comment: 15 pages, 12 figures, 5 table

MicroRNA-374a Inhibits Aggressive Tumor Biological Behavior in Bladder Carcinoma by Suppressing Wnt/β-Catenin Signaling

Background/Aims: microRNA (miR)-374a plays a crucial role in cancer progression by promoting the metastasis and proliferation of various types of malignant tumors. Because its role in bladder cancer is unknown, we investigated whether miR-374a affects the progression of bladder cancer and studied the underlying mechanism. Methods: The Cancer Genome Atlas was used to analyze the clinical relevance of miR-374a. Quantitative PCR, western blotting, and luciferase and immunofluorescence assays were used to detect the expression patterns, downstream targets, and function of miR-374a in bladder cancer cells. Apoptosis was evaluated by flow cytometry after cisplatin treatment. Results: Via in silico analysis, low levels of miR-374a were associated with poor prognosis in bladder cancer patients with distant metastasis. WNT5A was a direct target of miR-374a in two bladder cancer cell lines. miR-374a mimic abrogated the metastatic potential and invasiveness of bladder cancer cells via WNT5A downregulation in both T24 and TCCSUP human bladder cancer cells; the opposite was observed with miR-374a inhibitor. In addition, miR-374a treatment reduced the phosphorylation and nuclear translocation of β-catenin. Cisplatin treatment significantly increased the apoptosis rate. Expression levels of cancer stemness-related proteins were reduced in miR-374a mimic-pretreated cells. Conclusion: Lower expression of miR-374a is associated with poor prognosis and miR-374a improves tumor biological behavior in bladder cancer cells, suggesting that miR-374a might be a novel small-molecule therapeutic target

A benchmark test suite for evolutionary many-objective optimization

The file attached to this record is the author's final peer reviewed version. The Publisher's final version can be found by following the DOI link. Open Access journalIn the real world, it is not uncommon to face an optimization problem with more than three objectives. Such problems, called many-objective optimization problems (MaOPs), pose great challenges to the area of evolutionary computation. The failure of conventional Pareto-based multi-objective evolutionary algorithms in dealing with MaOPs motivates various new approaches. However, in contrast to the rapid development of algorithm design, performance investigation and comparison of algorithms have received little attention. Several test problem suites which were designed for multi-objective optimization have still been dominantly used in many-objective optimization. In this paper, we carefully select (or modify) 15 test problems with diverse properties to construct a benchmark test suite, aiming to promote the research of evolutionary many-objective optimization (EMaO) via suggesting a set of test problems with a good representation of various real-world scenarios. Also, an open-source software platform with a user-friendly GUI is provided to facilitate the experimental execution and data observation

Transcriptome-wide characterization and functional analysis of MATE transporters in response to aluminum toxicity in Medicago sativa L.

Multidrug and toxic compound extrusion (MATE) transporters contribute to multidrug resistance and play major determinants of aluminum (Al) tolerance in plants. Alfalfa (Medicago sativa L.) is the most extensively cultivated forage crop in the world, yet most alfalfa cultivars are not Al tolerant. The basic knowledge of the MATE transcripts family and the characterisation of specific MATE members involved in alfalfa Al stress remain unclear. In this study, 88 alfalfa MATE (MsMATE) transporters were identified at the whole transcriptome level. Phylogenetic analysis classified them into four subfamilies comprising 11 subgroups. Generally, five kinds of motifs were found in group G1, and most were located at the N-terminus, which might confer these genes with Al detoxification functions. Furthermore, 10 putative Al detoxification-related MsMATE genes were identified and the expression of five genes was significantly increased after Al treatment, indicating that these genes might play important roles in conferring Al tolerance to alfalfa. Considering the limited functional understanding of MATE transcripts in alfalfa, our findings will be valuable for the functional investigation and application of this family in alfalfa