DNetDB: The human disease network database based on dysfunctional regulation mechanism

Additional analysis and concepts explanation. This file contains 1) comparison of DNetDB and the results of differential expression analysis (DEA-) based method ; 2) comparison of DNetDB and traditional disease classification; 3) negative disease relationships and 4) DCp and DCe. (DOCX 6926 kb

Prevalence of bovine viral diarrhea virus in cattle between 2010 and 2021: A global systematic review and meta-analysis

BackgroundBovine viral diarrhea is one of the diseases that cause huge economic losses in animal husbandry. Many countries or regions have successively introduced eradication plans, but BVDV still has a high prevalence in the world. This meta-analysis aims to investigate the prevalence and risk factors of BVDV in the world in recent 10 years, and is expected to provide some reference and theoretical basis for BVDV control plans in different regions.MethodRelevant articles published from 2010 to 2021 were mainly retrieved from NCBI, ScienceDirect, Chongqing VIP, Chinese web of knowledge (CNKI), web of science and Wanfang databases.Results128 data were used to analyze the prevalence of BVDV from 2010 to 2021. BVDV antigen prevalence rate is 15.74% (95% CI: 11.35–20.68), antibody prevalence rate is 42.77% (95% CI: 37.01–48.63). In the two databases of antigen and antibody, regions, sampling time, samples, detection methods, species, health status, age, sex, breeding mode, and seasonal subgroups were discussed and analyzed, respectively. In the antigen database, the prevalence of dairy cows in the breed subgroup, ELISA in the detection method subgroup, ear tissue in the sample subgroup, and extensive breeding in the breeding mode were the lowest, with significant differences. In the antibody database, the prevalence rate of dairy cows in the breed subgroup and intensive farming was the highest, with a significant difference. The subgroups in the remaining two databases were not significantly different.ConclusionThis meta-analysis determined the prevalence of BVDV in global cattle herds from 2010 to 2021. The prevalence of BVDV varies from region to region, and the situation is still not optimistic. In daily feeding, we should pay attention to the rigorous and comprehensive management to minimize the spread of virus. The government should enforce BVDV prevention and control, implement control or eradication policies according to local conditions, and adjust the policies in time

The role of learners' field dependence and gender on the effects of conversational versus non- Conversational narrations in multimedia environment

The main objective of this study was to ascertain if the effectiveness of conversational narrations and non-conversational narrations in multimedia environment will be mediated by learners’ field dependence and gender. 53 participants (25 field dependent and 28 field independent subjects) were randomly divided to interact with either one of the two multimedia lessons on C-Programming: conversational narrations or non-conversational narrations. Learning achievements of participants were then assessed in three different measures: drawing, terminology and comprehension. A 2 (narration types) x 2 (field dependence) MANOVA showed a significant interaction effect between field dependence and narrations types on comprehension scores; field independent learners in conversational group significantly outperformed field independent learners in non-conversational group on comprehension scores. Field dependence was also shown to be a factor in learning achievement; field independent learners outperformed field dependent learners on drawing and retention measures. A 2 (narration types) x 2 (gender) MANOVA showed no interaction effect between gender and types of narrations; however, male participants significantly outperformed female participants on drawing and comprehension measures

Quantification and Driving Factors of Cultivated Land Fragmentation in Rapidly Urbanizing Area: A Case Study in Guangdong Province

Cultivated land resources are crucial for food security and economic and social development. However, with the acceleration of urbanization and shifts in land use, cultivated land fragmentation (CLF) has emerged as a significant factor constraining the sustainable development of agriculture in China. As the most urbanized region, optimizing cultivated land resources and coordinating urban and rural development has become an urgent issue for rural sustainable development in Guangdong Province. This study analyzes the spatiotemporal characteristics of CLF in Guangdong Province from 2000 to 2020 using landscape pattern indices, CRITIC empowerment, and a multiscale geographically weighted regression (MGWR) model. The cultivated land fragmentation index (CLFI) for Guangdong Province reveals a fluctuating trend from 2000 to 2012, increasing from 0.453 in 2012 to 0.641 in 2020. The CLFI is notably high in the Pearl River Delta region, as well as in Meizhou and Maoming. The results show the dynamic changes of the driving factors of CLF at the county scale in 2000, 2010, and 2020. Slope and grain output consistently emerge as key driving factors of CLF. Furthermore, agricultural benefits played a significant role in 2000 and 2020, whereas the coefficient for social economic development was more pronounced in 2010. By identifying the heterogeneity of the driving factors, this study suggests that strategies to address CLF should comprehensively consider aspects such as the optimization of cultivated land resources, farmers’ interests, industrial restructuring, and the multifunctional development of farmland. The study findings can assist government policy-making for rural sustainable development, addressing CLF and food insecurity, and alleviating the regional development imbalance and urban–rural income gap, with the ultimate aim of achieving common prosperity

Analysis of Key Commuting Routes Based on Spatiotemporal Trip Chain

Commuting pattern is one of the most important travel patterns on the road network; the analysis of commuting pattern can provide support for public transport system optimization, policy formulation, and urban planning. In this study, a framework of the key commuting route mining algorithm based on license plate recognition (LPR) data is proposed. And the proposed algorithm framework can be migrated to any similar spatiotemporal data, such as GPS trajectory data. Commuting pattern vehicles are first extracted, and then, the spatiotemporal trip chains of all commuting pattern vehicles are mined. Based on the spatiotemporal trip chains, the spatiotemporal similarity matrix is constructed by dynamic time warping (DTW) algorithm. Finally, the characteristics of commuting pattern are analysed by the density-based spatial clustering of applications with noise (DBSCAN) algorithm. Different from other researches that analyse the commuting pattern using machine learning algorithms based on all data, this study first extracts commuting pattern vehicles and then designs a key commuting route mining algorithm framework for commuting pattern vehicles. Taking Hangzhou as an example, through the framework of mining algorithm proposed in this study, the commuting pattern characteristics and key commuting routes in Hangzhou have been successfully excavated, and policy suggestions based on the analysis results have also been put forward

<i>Cunninghamia lanceolata</i> Canopy Relative Chlorophyll Content Estimation Based on Unmanned Aerial Vehicle Multispectral Imagery and Terrain Suitability Analysis

This study aimed to streamline the determination of chlorophyll content in Cunninghamia lanceolate while achieving precise measurements of canopy chlorophyll content. Relative chlorophyll content (SPAD) in the Cunninghamia lanceolate canopy were assessed in the study area using the SPAD-502 portable chlorophyll meter, alongside spectral data collected via onboard multispectral imaging. And based on the unmanned aerial vehicle (UAV) multispectral collection of spectral values in the study area, 21 vegetation indices with significant correlation with Cunninghamia lanceolata canopy SPAD (CCS) were constructed as independent variables of the model’s various regression techniques, including partial least squares regression (PLSR), random forests (RF), and backpropagation neural networks (BPNN), which were employed to develop a SPAD inversion model. The BPNN-based model emerged as the best choice, exhibiting test dataset coefficients of determination (R2) at 0.812, root mean square error (RSME) at 2.607, and relative percent difference (RPD) at 1.942. While the model demonstrated consistent accuracy across different slope locations, generalization was lower for varying slope directions. By creating separate models for different slope directions, R2 went up to about 0.8, showcasing favorable terrain applicability. Therefore, constructing inverse models with different slope directions samples separately can estimate CCS more accurately

How social media expression can reveal personality

Background: Personality psychology studies personality and its variation among&nbsp;individuals and is an essential branch of psychology. In recent years, machine&nbsp;learning research related to personality assessment has started to focus on&nbsp;the online environment and showed outstanding performance in personality&nbsp;assessment. However, the aspects of the personality of these prediction models&nbsp;measure remain unclear because few studies focus on the interpretability of&nbsp;personality prediction models. The objective of this study is to develop and&nbsp;validate a machine learning model with domain knowledge introduced to&nbsp;enhance accuracy and improve interpretability. Methods: Study participants were recruited via an online experiment platform.&nbsp;After excluding unqualified participants and downloading the Weibo posts of&nbsp;eligible participants, we used six psycholinguistic and mental health-related&nbsp;lexicons to extract textual features. Then the predictive personality model&nbsp;was developed using the multi-objective extra trees method based on 3,411&nbsp;pairs of social media expression and personality trait scores. Subsequently, the&nbsp;prediction model&rsquo;s validity and reliability were evaluated, and each lexicon&rsquo;s&nbsp;feature importance was calculated. Finally, the interpretability of the machine&nbsp;learning model was discussed. Results: The features from Culture Value Dictionary were found to be the most important predictors. The fivefold cross-validation results regarding the&nbsp;prediction model for personality traits ranged between 0.44 and 0.48 (p &lt; 0.001).&nbsp;The correlation coefficients of five personality traits between the two &ldquo;splithalf&rdquo;datasets data ranged from 0.84 to 0.88 (p &lt; 0.001). Moreover, the model&nbsp;performed well in terms of contractual validity. Conclusion: By introducing domain knowledge to the development of a machine&nbsp;learning model, this study not only ensures the reliability and validity of the&nbsp;prediction model but also improves the interpretability of the machine learning&nbsp;method. The study helps explain aspects of personality measured by such&nbsp;prediction models and finds a link between personality and mental health. Our&nbsp;research also has positive implications regarding the combination of machine&nbsp;learning approaches and domain knowledge in the field of psychiatry and its&nbsp;applications to mental health.</p

Is involvement in school bullying associated with increased risk of murderous ideation and behaviours among adolescent students in China?

Abstract Background School bullying is a destructive behaviour common among adolescents that can sometimes escalate to criminal activity. This study aimed to examine the association between four types of school bullying (i.e., physical, verbal, relational, and cyber) and murderous ideation and behaviours (i.e., ideation, plans, preparation, and attempts) among adolescent students. Methods Data were collected from 5726 middle and high school students using self-administered questionnaires in December 2013. The participants were selected using a 3-stage random cluster-sampling strategy. The participants were asked about the frequency of their bullying experiences in the past two months and the frequencies of their murderous ideation and behaviours in the past six months. Multivariate logistic regressions were performed to explore the association between school bullying and murderous ideation and behaviours. Results Each type of school bullying perpetration was associated with murderous ideation and behaviours, as was each type of bullying victimization. Students who experienced more types of school bullying perpetration and victimization were more likely to report murderous ideation and behaviours. Moreover, the number of types of bullying perpetration and victimization had a dose-response association with murderous ideation and behaviours (aOR min = 1.45, aOR max = 2.72), as did the frequency of involvement in bullying perpetration and victimization (aOR min = 1.33, aOR max = 2.00). Being a bully-victim was a risk factor for murderous ideation and behaviours (aOR min = 3.88, aOR max = 7.24). Conclusions Each type of school bullying was associated with an increased risk for murderous ideation and behaviours among adolescents. Dose-response relationships between the frequency of bullying and number of bullying types experienced and murderous ideation and behaviours were found in this study. Future studies are warranted to confirm our findings and explore the mechanisms underlying the relationship between school bullying and murderous ideation and behaviours