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PloS One 2024Agricultural non-point source pollution control (ANSPC) is a complex, long-term and dynamic environmental protection process. In order to motivate multiple subjects to...
Agricultural non-point source pollution control (ANSPC) is a complex, long-term and dynamic environmental protection process. In order to motivate multiple subjects to participate in ANSPC, this paper constructs a tripartite evolutionary game model of local government, village collectives and farmers, which explores the strategic choices and influencing factors of different subjects through simulation analysis. The results indicate that: There are five stable strategy points in the ANSPC game system, which can be divided into four stages based on subject interactions. Village collectives should play an intermediary role in ANSPC and try to coordinate the behaviour of different subjects. The ideal and stable evolution state is "weak supervise, positive response, and active participate", but it cannot be realized at present. The strategy selection of subjects is determined by relative net income. Providing penalties requires considering the heterogeneity of subjects, but incentives are beneficial for achieving tripartite governance. This study provides new evidence for understanding the role of multi-agency participation in agricultural non-point source pollution control, and provides theoretical guidance for the government to formulate differentiated intervention mechanisms, which is an important reference for achieving sustainable development goals.
Topics: Agriculture; Humans; Game Theory; Environmental Pollution; Computer Simulation; Farmers; Models, Theoretical
PubMed: 38941318
DOI: 10.1371/journal.pone.0305191 -
PloS One 2024Application essays are a commonly used admission instrument for students entering higher education. The quality of the essay is usually scored, but this score is often...
Application essays are a commonly used admission instrument for students entering higher education. The quality of the essay is usually scored, but this score is often subjective and has poor interrater reliability due to the unstructured format of the essays. This results in mixed findings on the validity of application essays as an admission instrument. We propose a more objective method of using application essays, using Latent Dirichlet Allocation (LDA), a text mining method, to distinguish seven moves occurring in application essays written by students who apply to a master degree program. We use the probability that these moves occur in the essay to predict study success in the master. Thereby we answer the following research question: What is the effect of discussing different moves in students' application essays on the student grades in a master program? From the seven different moves (functional unit of text) we distinguished, five of which have a significant effect on student grades. The moves we labeled as 'master specific' and 'interest to learn' have a positive effect on student grades, and the moves we labeled as 'research skills', 'societal impact' and 'city and university' have a negative effect. Our interpretation of this finding is that topics related to intrinsic motivation and specific knowledge, as opposed to generic knowledge, are positively related with study success. We thereby demonstrate that application essays can be a valid predictor of study success. This contributes to justifying their use as admission instruments.
Topics: Humans; Students; School Admission Criteria; Universities; Educational Measurement
PubMed: 38941298
DOI: 10.1371/journal.pone.0304394 -
PloS One 2024During the Omicron pandemic, clinical first-line nurses played a crucial role in healthcare. Their innovative behavior enhanced the quality of nursing and served as a...
Innovative behavior and organizational innovation climate among the Chinese clinical first-line nurses during the Omicron pandemic: The mediating roles of self-transcendence.
BACKGROUND
During the Omicron pandemic, clinical first-line nurses played a crucial role in healthcare. Their innovative behavior enhanced the quality of nursing and served as a vital factor in driving the sustainable development of the nursing discipline and healthcare industry. Many previous studies have confirmed the significance of nurses' innovative behavior worldwide. However, the correlations among innovative behaviors, organizational innovation climate, self-transcendence, and their mediating roles in Chinese clinical first-line nurses need further research.
METHODS
A cross-sectional study was conducted, and the quality reporting conformed to the STROBE Checklist. From March 2022 to February 2023, a convenience sample of 1,058 Chinese clinical first-line nurses was recruited from seven tertiary grade-A hospitals of Tianjin city in Northern China. The Demographic Characteristics Questionnaire, Nurse Innovative Behavior Scale (NIBS), Nurse Organizational Innovation Climate Scale, and the Self-Transcendence Scale were used. The data was analyzed using descriptive statistics, correlation, and process plug-in mediation effect analyses.
RESULTS
The total scores of innovative behavior, organizational innovation climate, and self-transcendence were 33.19 ± 6.71, 68.88 ± 12.76, and 41.25 ± 7.83, respectively. Innovative behavior was positively correlated with the organizational innovation climate (r = 0.583, p < 0.01) and self-transcendence (r = 0.635, p < 0.01). Self-transcendence partially mediated mediating role between innovative behavior and organizational innovation climate, accounting for 41.7%.
CONCLUSION
The innovative behavior, organizational innovation climate, and self-transcendence among the first-line nurses during the Omicron pandemic were relatively moderate, which needs improving. Organizational innovation climate can directly affect the innovative behavior among Chinese clinical first-line nurses and indirectly through the mediating role of self-transcendence. It is recommended that nursing managers adjust their management strategies and techniques based on the unique characteristics of nurses during the pandemic. This includes fostering a positive and inclusive environment for organizational innovation, nurturing nurses' motivation and awareness for innovation, enhancing their ability to gather information effectively, overcoming negative emotions resulting from the pandemic, and promoting personal growth. These efforts will ultimately enhance nursing quality and satisfaction during the Omicron pandemic.
Topics: Humans; COVID-19; China; Cross-Sectional Studies; Adult; Female; Male; Organizational Innovation; Surveys and Questionnaires; Pandemics; Nurses; SARS-CoV-2; Organizational Culture; Middle Aged; East Asian People
PubMed: 38941294
DOI: 10.1371/journal.pone.0306109 -
Nursing Open Jul 2024To examine changes in advanced nurse practitioner (ANP) well-being, satisfaction and motivation over a four-year period. (Review)
Review
AIMS
To examine changes in advanced nurse practitioner (ANP) well-being, satisfaction and motivation over a four-year period.
DESIGN
Longitudinal Cohort study.
METHODS
Surveys were carried out each year from 2019 to 2022 with the same cohort of ANPs in the United Kingdom (UK). The survey consisted of demographics, questions on contemporary issues in advanced practice, National Health Service (NHS) staff survey questions and validated questionnaires. A core set of questions were asked every year with some changes in response to the COVID-19 pandemic.
RESULTS
Response rate ranged from 40% to 59% and appeared to be affected by COVID-19. Staff satisfaction with pay and the well-being score were stable throughout. Other questions on well-being, job satisfaction and motivation saw statistically significant reductions after 4 years. Open-ended questions about ongoing well-being concerns show participants are concerned about exhaustion levels caused by workload, staffing issues, abuse from patients and colleagues' mental health.
CONCLUSION
The findings highlight a decline in ANP well-being, job satisfaction and motivation post-COVID-19. Reasons for this, explored in the qualitative data, show that ANPs have faced extremely difficult working conditions. Urgent action is required to prevent a workforce retention crisis as many nursing staff are close to retirement and may not be motivated to remain in post.
IMPACT
This study has followed ANPs through the most challenging years the NHS has ever seen. Job satisfaction, motivation and enjoyment of the job all significantly reduced over time. In many areas, the ANP role has been used to fill medical workforce gaps, and this will become harder to do if ANPs are dissatisfied, disaffected and struggling with stress and burnout. Addressing these issues should be a priority for policymakers and managers.
PATIENT OR PUBLIC CONTRIBUTION
None as this study focussed on staff. Staff stakeholders involved in the design and conduct of the study.
Topics: Humans; Job Satisfaction; COVID-19; United Kingdom; Nurse Practitioners; Female; Male; Longitudinal Studies; Surveys and Questionnaires; Adult; Middle Aged; SARS-CoV-2; Motivation; Cohort Studies; State Medicine; Pandemics; Workload; Burnout, Professional
PubMed: 38940475
DOI: 10.1002/nop2.2218 -
Cancer Medicine Jul 2024The Cancer Health Awareness through screeNinG and Education (CHANGE) initiative delivers cancer awareness education with an emphasis on modifiable risk factors and...
BACKGROUND
The Cancer Health Awareness through screeNinG and Education (CHANGE) initiative delivers cancer awareness education with an emphasis on modifiable risk factors and navigation to screening for prostate, breast, and colorectal cancers to residents of public housing communities who experience significant negative social determinants of health.
METHODS
Residents of five communities participated. Community advisory board members were recruited and provided feedback to local environmental change projects, recruitment, and community engagement at each site. At each site, four education sessions were provided by trained facilitators on cancer risk factors and etiology, racial disparities, eligibility for cancer screening, and participation in clinical trials. Attendance, knowledge, attitudes and beliefs about cancer, and height, weight, and waist circumference were measured at baseline and 1-week post-CHANGE sessions.
RESULTS
90 residents (60% 65 and older years old, 33% male, 60% High School education, 93% AA) participated in the program. 95% completed post-intervention evaluation. Participants were eligible for breast (n = 12), prostate (n = 15), and colorectal screening (n = 25) based on American Cancer Society guidelines, and 22 for tobacco cessation; 21 participants accepted navigation assistance for these services. At post-test, participants significantly increased in knowledge and behaviors around obesity/overweight risk for cancer, nutrition, and physical activity. Colorectal, prostate, and breast cancer knowledge scores also increased, but were not significant.
CONCLUSIONS
CHANGE participants demonstrated improved health knowledge and intentions to improve their modifiable health behaviors. Participants reported being motivated and confident in seeking preventive care and satisfaction with community engagement efforts. Replication of this project in similar communities may improve knowledge and health equity among underserved populations.
Topics: Humans; Male; Female; Early Detection of Cancer; Aged; Health Knowledge, Attitudes, Practice; Middle Aged; Health Equity; Prostatic Neoplasms; Health Education; Neoplasms; Breast Neoplasms; Colorectal Neoplasms; Adult; Risk Factors
PubMed: 38940418
DOI: 10.1002/cam4.7357 -
Bioinformatics (Oxford, England) Jun 2024Electronic health records (EHRs) represent a comprehensive resource of a patient's medical history. EHRs are essential for utilizing advanced technologies such as deep...
MOTIVATION
Electronic health records (EHRs) represent a comprehensive resource of a patient's medical history. EHRs are essential for utilizing advanced technologies such as deep learning (DL), enabling healthcare providers to analyze extensive data, extract valuable insights, and make precise and data-driven clinical decisions. DL methods such as recurrent neural networks (RNN) have been utilized to analyze EHR to model disease progression and predict diagnosis. However, these methods do not address some inherent irregularities in EHR data such as irregular time intervals between clinical visits. Furthermore, most DL models are not interpretable. In this study, we propose two interpretable DL architectures based on RNN, namely time-aware RNN (TA-RNN) and TA-RNN-autoencoder (TA-RNN-AE) to predict patient's clinical outcome in EHR at the next visit and multiple visits ahead, respectively. To mitigate the impact of irregular time intervals, we propose incorporating time embedding of the elapsed times between visits. For interpretability, we propose employing a dual-level attention mechanism that operates between visits and features within each visit.
RESULTS
The results of the experiments conducted on Alzheimer's Disease Neuroimaging Initiative (ADNI) and National Alzheimer's Coordinating Center (NACC) datasets indicated the superior performance of proposed models for predicting Alzheimer's Disease (AD) compared to state-of-the-art and baseline approaches based on F2 and sensitivity. Additionally, TA-RNN showed superior performance on the Medical Information Mart for Intensive Care (MIMIC-III) dataset for mortality prediction. In our ablation study, we observed enhanced predictive performance by incorporating time embedding and attention mechanisms. Finally, investigating attention weights helped identify influential visits and features in predictions.
AVAILABILITY AND IMPLEMENTATION
https://github.com/bozdaglab/TA-RNN.
Topics: Electronic Health Records; Neural Networks, Computer; Humans; Deep Learning; Alzheimer Disease
PubMed: 38940180
DOI: 10.1093/bioinformatics/btae264 -
Bioinformatics (Oxford, England) Jun 2024In drug discovery, it is crucial to assess the drug-target binding affinity (DTA). Although molecular docking is widely used, computational efficiency limits its...
MOTIVATION
In drug discovery, it is crucial to assess the drug-target binding affinity (DTA). Although molecular docking is widely used, computational efficiency limits its application in large-scale virtual screening. Deep learning-based methods learn virtual scoring functions from labeled datasets and can quickly predict affinity. However, there are three limitations. First, existing methods only consider the atom-bond graph or one-dimensional sequence representations of compounds, ignoring the information about functional groups (pharmacophores) with specific biological activities. Second, relying on limited labeled datasets fails to learn comprehensive embedding representations of compounds and proteins, resulting in poor generalization performance in complex scenarios. Third, existing feature fusion methods cannot adequately capture contextual interaction information.
RESULTS
Therefore, we propose a novel DTA prediction method named HeteroDTA. Specifically, a multi-view compound feature extraction module is constructed to model the atom-bond graph and pharmacophore graph. The residue concat graph and protein sequence are also utilized to model protein structure and function. Moreover, to enhance the generalization capability and reduce the dependence on task-specific labeled data, pre-trained models are utilized to initialize the atomic features of the compounds and the embedding representations of the protein sequence. A context-aware nonlinear feature fusion method is also proposed to learn interaction patterns between compounds and proteins. Experimental results on public benchmark datasets show that HeteroDTA significantly outperforms existing methods. In addition, HeteroDTA shows excellent generalization performance in cold-start experiments and superiority in the representation learning ability of drug-target pairs. Finally, the effectiveness of HeteroDTA is demonstrated in a real-world drug discovery study.
AVAILABILITY AND IMPLEMENTATION
The source code and data are available at https://github.com/daydayupzzl/HeteroDTA.
Topics: Drug Discovery; Molecular Docking Simulation; Proteins; Deep Learning; Pharmacophore
PubMed: 38940179
DOI: 10.1093/bioinformatics/btae240 -
Bioinformatics (Oxford, England) Jun 2024RNA design shows growing applications in synthetic biology and therapeutics, driven by the crucial role of RNA in various biological processes. A fundamental challenge...
MOTIVATION
RNA design shows growing applications in synthetic biology and therapeutics, driven by the crucial role of RNA in various biological processes. A fundamental challenge is to find functional RNA sequences that satisfy given structural constraints, known as the inverse folding problem. Computational approaches have emerged to address this problem based on secondary structures. However, designing RNA sequences directly from 3D structures is still challenging, due to the scarcity of data, the nonunique structure-sequence mapping, and the flexibility of RNA conformation.
RESULTS
In this study, we propose RiboDiffusion, a generative diffusion model for RNA inverse folding that can learn the conditional distribution of RNA sequences given 3D backbone structures. Our model consists of a graph neural network-based structure module and a Transformer-based sequence module, which iteratively transforms random sequences into desired sequences. By tuning the sampling weight, our model allows for a trade-off between sequence recovery and diversity to explore more candidates. We split test sets based on RNA clustering with different cut-offs for sequence or structure similarity. Our model outperforms baselines in sequence recovery, with an average relative improvement of 11% for sequence similarity splits and 16% for structure similarity splits. Moreover, RiboDiffusion performs consistently well across various RNA length categories and RNA types. We also apply in silico folding to validate whether the generated sequences can fold into the given 3D RNA backbones. Our method could be a powerful tool for RNA design that explores the vast sequence space and finds novel solutions to 3D structural constraints.
AVAILABILITY AND IMPLEMENTATION
The source code is available at https://github.com/ml4bio/RiboDiffusion.
Topics: RNA; Nucleic Acid Conformation; RNA Folding; Computational Biology; Algorithms; Software; Neural Networks, Computer; Sequence Analysis, RNA
PubMed: 38940178
DOI: 10.1093/bioinformatics/btae259 -
Bioinformatics (Oxford, England) Jun 2024World Health Organization estimates that there were over 10 million cases of tuberculosis (TB) worldwide in 2019, resulting in over 1.4 million deaths, with a worrisome...
MOTIVATION
World Health Organization estimates that there were over 10 million cases of tuberculosis (TB) worldwide in 2019, resulting in over 1.4 million deaths, with a worrisome increasing trend yearly. The disease is caused by Mycobacterium tuberculosis (MTB) through airborne transmission. Treatment of TB is estimated to be 85% successful, however, this drops to 57% if MTB exhibits multiple antimicrobial resistance (AMR), for which fewer treatment options are available.
RESULTS
We develop a robust machine-learning classifier using both linear and nonlinear models (i.e. LASSO logistic regression (LR) and random forests (RF)) to predict the phenotypic resistance of Mycobacterium tuberculosis (MTB) for a broad range of antibiotic drugs. We use data from the CRyPTIC consortium to train our classifier, which consists of whole genome sequencing and antibiotic susceptibility testing (AST) phenotypic data for 13 different antibiotics. To train our model, we assemble the sequence data into genomic contigs, identify all unique 31-mers in the set of contigs, and build a feature matrix M, where M[i, j] is equal to the number of times the ith 31-mer occurs in the jth genome. Due to the size of this feature matrix (over 350 million unique 31-mers), we build and use a sparse matrix representation. Our method, which we refer to as MTB++, leverages compact data structures and iterative methods to allow for the screening of all the 31-mers in the development of both LASSO LR and RF. MTB++ is able to achieve high discrimination (F-1 >80%) for the first-line antibiotics. Moreover, MTB++ had the highest F-1 score in all but three classes and was the most comprehensive since it had an F-1 score >75% in all but four (rare) antibiotic drugs. We use our feature selection to contextualize the 31-mers that are used for the prediction of phenotypic resistance, leading to some insights about sequence similarity to genes in MEGARes. Lastly, we give an estimate of the amount of data that is needed in order to provide accurate predictions.
AVAILABILITY
The models and source code are publicly available on Github at https://github.com/M-Serajian/MTB-Pipeline.
Topics: Mycobacterium tuberculosis; Machine Learning; Drug Resistance, Bacterial; Microbial Sensitivity Tests; Anti-Bacterial Agents; Whole Genome Sequencing; Genome, Bacterial; Humans
PubMed: 38940175
DOI: 10.1093/bioinformatics/btae243 -
Bioinformatics (Oxford, England) Jun 2024Genetic perturbations (e.g. knockouts, variants) have laid the foundation for our understanding of many diseases, implicating pathogenic mechanisms and indicating...
MOTIVATION
Genetic perturbations (e.g. knockouts, variants) have laid the foundation for our understanding of many diseases, implicating pathogenic mechanisms and indicating therapeutic targets. However, experimental assays are fundamentally limited by the number of measurable perturbations. Computational methods can fill this gap by predicting perturbation effects under novel conditions, but accurately predicting the transcriptional responses of cells to unseen perturbations remains a significant challenge.
RESULTS
We address this by developing a novel attention-based neural network, AttentionPert, which accurately predicts gene expression under multiplexed perturbations and generalizes to unseen conditions. AttentionPert integrates global and local effects in a multi-scale model, representing both the nonuniform system-wide impact of the genetic perturbation and the localized disturbance in a network of gene-gene similarities, enhancing its ability to predict nuanced transcriptional responses to both single and multi-gene perturbations. In comprehensive experiments, AttentionPert demonstrates superior performance across multiple datasets outperforming the state-of-the-art method in predicting differential gene expressions and revealing novel gene regulations. AttentionPert marks a significant improvement over current methods, particularly in handling the diversity of gene perturbations and in predicting out-of-distribution scenarios.
AVAILABILITY AND IMPLEMENTATION
Code is available at https://github.com/BaiDing1234/AttentionPert.
Topics: Computational Biology; Humans; Gene Regulatory Networks; Neural Networks, Computer; Gene Expression Profiling
PubMed: 38940174
DOI: 10.1093/bioinformatics/btae244