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Frontiers in Artificial Intelligence 2024Time series is a typical data type in numerous domains; however, labeling large amounts of time series data can be costly and time-consuming. Learning effective...
Time series is a typical data type in numerous domains; however, labeling large amounts of time series data can be costly and time-consuming. Learning effective representation from unlabeled time series data is a challenging task. Contrastive learning stands out as a promising method to acquire representations of unlabeled time series data. Therefore, we propose a self-supervised time-series representation learning framework via Time-Frequency Fusion Contrasting (TF-FC) to learn time-series representation from unlabeled data. Specifically, TF-FC combines time-domain augmentation with frequency-domain augmentation to generate the diverse samples. For time-domain augmentation, the raw time series data pass through the time-domain augmentation bank (such as jitter, scaling, permutation, and masking) and get time-domain augmentation data. For frequency-domain augmentation, first, the raw time series undergoes conversion into frequency domain data following Fast Fourier Transform (FFT) analysis. Then, the frequency data passes through the frequency-domain augmentation bank (such as low pass filter, remove frequency, add frequency, and phase shift) and gets frequency-domain augmentation data. The fusion method of time-domain augmentation data and frequency-domain augmentation data is kernel PCA, which is useful for extracting nonlinear features in high-dimensional spaces. By capturing both the time and frequency domains of the time series, the proposed approach is able to extract more informative features from the data, enhancing the model's capacity to distinguish between different time series. To verify the effectiveness of the TF-FC method, we conducted experiments on four time series domain datasets (i.e., SleepEEG, HAR, Gesture, and Epilepsy). Experimental results show that TF-FC significantly improves in recognition accuracy compared with other SOTA methods.
PubMed: 38933470
DOI: 10.3389/frai.2024.1414352 -
Plants (Basel, Switzerland) Jun 2024"Omics" typically involves exploration of the structure and function of the entire composition of a biological system at a specific level using high-throughput... (Review)
Review
"Omics" typically involves exploration of the structure and function of the entire composition of a biological system at a specific level using high-throughput analytical methods to probe and analyze large amounts of data, including genomics, transcriptomics, proteomics, and metabolomics, among other types. Genomics characterizes and quantifies all genes of an organism collectively, studying their interrelationships and their impacts on the organism. However, conventional transcriptomic sequencing techniques target population cells, and their results only reflect the average expression levels of genes in population cells, as they are unable to reveal the gene expression heterogeneity and spatial heterogeneity among individual cells, thus masking the expression specificity between different cells. Single-cell transcriptomic sequencing and spatial transcriptomic sequencing techniques analyze the transcriptome of individual cells in plant or animal tissues, enabling the understanding of each cell's metabolites and expressed genes. Consequently, statistical analysis of the corresponding tissues can be performed, with the purpose of achieving cell classification, evolutionary growth, and physiological and pathological analyses. This article provides an overview of the research progress in plant single-cell and spatial transcriptomics, as well as their applications and challenges in plants. Furthermore, prospects for the development of single-cell and spatial transcriptomics are proposed.
PubMed: 38931111
DOI: 10.3390/plants13121679 -
International Journal of Environmental... Jun 2024Air pollution has emerged as a global public health concern. Specifically, in Medellín, Colombia, episodes of elevated air pollution have been documented. Medical...
Air pollution has emerged as a global public health concern. Specifically, in Medellín, Colombia, episodes of elevated air pollution have been documented. Medical students' knowledge of air pollution is paramount for implementing future interventions directed toward patients. The aim of this research was to delineate the knowledge, attitudes, and practices regarding air pollution among medical students at a private university in Medellín. A cross-sectional study involving 352 medical students was conducted. A questionnaire was administered, generating scores ranging from 0 to 100, where a higher score signified better knowledge, attitudes, and practices. Data were analyzed using frequencies, summary measures, non-parametric tests, and linear regression. : In total, 31% rated the education received at the university on the relationship between health and air quality as fair to poor, and 81% perceived the air quality in the city as poor. The knowledge score was 77.8 (IQR 71.1-85.6), with 90% acknowledging that exposure to air pollution increases the risk of various diseases. The attitudes score was 82.1 (IQR 71.8-87.2), and 25.9% believed that air pollution is a multifactorial problem, rendering their actions ineffective. In terms of practices, the score was 50 (IQR 42.9-57.1), indicating that students either did not employ protective measures against pollution or used inappropriate practices such as masks or air purifiers. Regression analysis revealed no association between knowledge and practices. The findings of this study underscore that medical students possess commendable knowledge regarding the health effects of air pollution. However, their adoption of inappropriate practices for self-protection is evident. The lack of correlation between knowledge and practices highlights the necessity of educational initiatives to be complemented by regulatory and cultural interventions.
Topics: Humans; Students, Medical; Health Knowledge, Attitudes, Practice; Female; Male; Air Pollution; Cross-Sectional Studies; Colombia; Surveys and Questionnaires; Young Adult; Adult
PubMed: 38929035
DOI: 10.3390/ijerph21060789 -
International Journal of Environmental... May 2024The aircraft-acquired transmission of SARS-CoV-2 poses a public health risk. Following PRISMA guidelines, we conducted a systematic review and analysis of articles,... (Review)
Review
The aircraft-acquired transmission of SARS-CoV-2 poses a public health risk. Following PRISMA guidelines, we conducted a systematic review and analysis of articles, published prior to vaccines being available, from 24 January 2020 to 20 April 2021 to identify factors important for transmission. Articles were included if they mentioned index cases and identifiable flight duration, and excluded if they discussed non-commercial aircraft, airflow or transmission models, cases without flight data, or that were unable to determine in-flight transmission. From the 15 articles selected for in-depth review, 50 total flights were analyzed by flight duration both as a categorical variable-short (<3 h), medium (3-6 h), or long flights (>6 h)-and as a continuous variable with case counts modeled by negative binomial regression. Compared to short flights without masking, medium and long flights without masking were associated with 4.66-fold increase (95% CI: [1.01, 21.52]; < 0.0001) and 25.93-fold increase in incidence rates (95% CI: [4.1, 164]; < 0.0001), respectively; long flights with enforced masking had no transmission reported. A 1 h increase in flight duration was associated with 1.53-fold (95% CI: [1.19, 1.66]; < 0.001) increase in the incidence rate ratio (IRR) of cases. Masking should be considered for long flights.
Topics: COVID-19; Humans; Aircraft; SARS-CoV-2
PubMed: 38928901
DOI: 10.3390/ijerph21060654 -
Cancers Jun 2024Despite treatment having improved through intensive surgical procedures and chemotherapy-and more recently, targeted therapies-ovarian cancer is the most fatal female...
BACKGROUND
Despite treatment having improved through intensive surgical procedures and chemotherapy-and more recently, targeted therapies-ovarian cancer is the most fatal female cancer. As such, we wanted to analyze age-specific survival trends for ovarian cancer in Denmark, Finland, Norway and Sweden over the past 50 years, with a special aim of comparing survival development between the age groups.
METHODS
We modelled survival data from the NORDCAN database for 1-, 5- and conditional 5/1-year relative (between years 1 and 5) survival for ovarian cancer from 1972 to 2021.
RESULTS
Young patients had a 70% 5-year survival while the survival was only 30% for the oldest patients. Conditional survival showed that survival between years 1 and 5 did not improve for patients older than 60 years throughout the 50-year period, during which time the gaps between the youngest and the oldest patients widened.
CONCLUSIONS
Improvement in 1-year survival was so large that it masked the modest development between years 1 and 5, resulting in a widening age disparity in 5-year survival. The current treatment practices, which appear increasingly effective for younger patients, have not helped remedy the large age differences in ovarian cancer survival. Early detection methods and therapeutic innovations are urgently needed, and aged patients need a special focus.
PubMed: 38927904
DOI: 10.3390/cancers16122198 -
Scientific Reports Jun 2024COVID-19 surveillance in Ukraine ceased after the Russian invasion of the country in 2022, on a background of low vaccination rates of 34.5% for two doses at this time....
COVID-19 surveillance in Ukraine ceased after the Russian invasion of the country in 2022, on a background of low vaccination rates of 34.5% for two doses at this time. We conducted a modelling study to estimate the epidemic trajectory of SARS-COV-2 in Ukraine after the start of the war. We use a COVID-19 deterministic Susceptible-Exposed-Infected-Recovered (SEIR) model for Ukraine to estimate the impact of increased vaccination coverage and masking as public health interventions. We fit the model output to case notification data between 6 January and 25 February 2022, then we forecast the COVID-19 epidemic trajectory in different scenarios of mask use and vaccine coverage. In the best-case scenario, 69% of the Ukrainian population would have been infected in the first half of 2022. Increasing mask use from 50 to 80% reduces cases and deaths by 17% and 30% respectively, while increasing vaccination rates to 60% and 9.6% for two and three doses respectively results in a 3% reduction in cases and 28% in deaths. However, if vaccination is increased to a higher coverage of 80% with two doses and 12.8% with three, or mask effectiveness is reduced to 40%, increasing vaccination coverage is more effective. The loss of health services, displacement, and destruction of infrastructure will amplify the risk of COVID-19 in Ukraine and make vaccine programs less feasible. Masks do not need the health infrastructure or cold-chain logistics required for vaccines and are more feasible for rapid epidemic control during war. However, increasing vaccine coverage will save more lives. Vaccination of refugees who have fled to other countries can be more feasibly achieved.
Topics: Ukraine; Humans; COVID-19; COVID-19 Vaccines; Vaccination Coverage; SARS-CoV-2; Masks; Vaccination
PubMed: 38926448
DOI: 10.1038/s41598-024-57447-7 -
BMJ Open Jun 2024Evidence-based psychological treatments for people with personality disorder usually involve attending group-based sessions over many months. Low-intensity psychological...
Clinical effectiveness and cost-effectiveness of Structured Psychological Support for people with probable personality disorder in mental health services in England: study protocol for a randomised controlled trial.
INTRODUCTION
Evidence-based psychological treatments for people with personality disorder usually involve attending group-based sessions over many months. Low-intensity psychological interventions of less than 6 months duration have been developed, but their clinical effectiveness and cost-effectiveness are unclear.
METHODS AND ANALYSIS
This is a multicentre, randomised, parallel-group, researcher-masked, superiority trial. Study participants will be aged 18 and over, have probable personality disorder and be treated by mental health staff in seven centres in England. We will exclude people who are: unwilling or unable to provide written informed consent, have a coexisting organic or psychotic mental disorder, or are already receiving psychological treatment for personality disorder or on a waiting list for such treatment. In the intervention group, participants will be offered up to 10 individual sessions of Structured Psychological Support. In the control group, participants will be offered treatment as usual plus a single session of personalised crisis planning. The primary outcome is social functioning measured over 12 months using total score on the Work and Social Adjustment Scale (WSAS). Secondary outcomes include mental health, suicidal behaviour, health-related quality of life, patient-rated global improvement and satisfaction, and resource use and costs. The primary analysis will compare WSAS scores across the 12-month period using a general linear mixed model adjusting for baseline scores, allocation group and study centre on an intention-to-treat basis. In a parallel process evaluation, we will analyse qualitative data from interviews with study participants, clinical staff and researchers to examine mechanisms of impact and contextual factors.
ETHICS AND DISSEMINATION
The study complies with the Helsinki Declaration II and is approved by the London-Bromley Research Ethics Committee (IRAS ID 315951). Study findings will be published in an open access peer-reviewed journal; and disseminated at national and international conferences.
TRIAL REGISTRATION NUMBER
ISRCTN13918289.
Topics: Humans; Cost-Benefit Analysis; England; Mental Health Services; Personality Disorders; Quality of Life; Treatment Outcome; Multicenter Studies as Topic; Adult; Psychosocial Intervention
PubMed: 38925701
DOI: 10.1136/bmjopen-2024-086593 -
Ibrain 2024This review comprehensively assesses the epidemiology, interaction, and impact on patient outcomes of perioperative sleep disorders (SD) and perioperative neurocognitive... (Review)
Review
This review comprehensively assesses the epidemiology, interaction, and impact on patient outcomes of perioperative sleep disorders (SD) and perioperative neurocognitive disorders (PND) in the elderly. The incidence of SD and PND during the perioperative period in older adults is alarmingly high, with SD significantly contributing to the occurrence of postoperative delirium. However, the clinical evidence linking SD to PND remains insufficient, despite substantial preclinical data. Therefore, this study focuses on the underlying mechanisms between SD and PND, underscoring that potential mechanisms driving SD-induced PND include uncontrolled central nervous inflammation, blood-brain barrier disruption, circadian rhythm disturbances, glial cell dysfunction, neuronal and synaptic abnormalities, impaired central metabolic waste clearance, gut microbiome dysbiosis, hippocampal oxidative stress, and altered brain network connectivity. Additionally, the review also evaluates the effectiveness of various sleep interventions, both pharmacological and nonpharmacological, in mitigating PND. Strategies such as earplugs, eye masks, restoring circadian rhythms, physical exercise, noninvasive brain stimulation, dexmedetomidine, and melatonin receptor agonists have shown efficacy in reducing PND incidence. The impact of other sleep-improvement drugs (e.g., orexin receptor antagonists) and methods (e.g., cognitive-behavioral therapy for insomnia) on PND is still unclear. However, certain drugs used for treating SD (e.g., antidepressants and first-generation antihistamines) may potentially aggravate PND. By providing valuable insights and references, this review aimed to enhance the understanding and management of PND in older adults based on SD.
PubMed: 38915944
DOI: 10.1002/ibra.12167 -
Implementation Science Communications Jun 2024Implementation research generally assumes established evidence-based practices and prior piloting of implementation strategies, which may not be feasible during a public...
BACKGROUND
Implementation research generally assumes established evidence-based practices and prior piloting of implementation strategies, which may not be feasible during a public health emergency. We describe the use of a simulation model of the effectiveness of COVID-19 mitigation strategies to inform a stakeholder-engaged process of rapidly designing a tailored intervention and implementation strategy for individuals with serious mental illness (SMI) and intellectual/developmental disabilities (ID/DD) in group homes in a hybrid effectiveness-implementation randomized trial.
METHODS
We used a validated dynamic microsimulation model of COVID-19 transmission and disease in late 2020/early 2021 to determine the most effective strategies to mitigate infections among Massachusetts group home staff and residents. Model inputs were informed by data from stakeholders, public records, and published literature. We assessed different prevention strategies, iterated over time with input from multidisciplinary stakeholders and pandemic evolution, including varying symptom screening, testing frequency, isolation, contact-time, use of personal protective equipment, and vaccination. Model outcomes included new infections in group home residents, new infections in group home staff, and resident hospital days. Sensitivity analyses were performed to account for parameter uncertainty. Results of the simulations informed a stakeholder-engaged process to select components of a tailored best practice intervention and implementation strategy.
RESULTS
The largest projected decrease in infections was with initial vaccination, with minimal benefit for additional routine testing. The initial level of actual vaccination in the group homes was estimated to reduce resident infections by 72.4% and staff infections by 55.9% over the 90-day time horizon. Increasing resident and staff vaccination uptake to a target goal of 90% further decreased resident infections by 45.2% and staff infections by 51.3%. Subsequent simulated removal of masking led to a 6.5% increase in infections among residents and 3.2% among staff. The simulation model results were presented to multidisciplinary stakeholders and policymakers to inform the "Tailored Best Practice" package for the hybrid effectiveness-implementation trial.
CONCLUSIONS
Vaccination and decreasing vaccine hesitancy among staff were predicted to have the greatest impact in mitigating COVID-19 risk in vulnerable populations of group home residents and staff. Simulation modeling was effective in rapidly informing the selection of the prevention and implementation strategy in a hybrid effectiveness-implementation trial. Future implementation may benefit from this approach when rapid deployment is necessary in the absence of data on tailored interventions.
TRIAL REGISTRATION
ClinicalTrials.gov NCT04726371.
PubMed: 38915130
DOI: 10.1186/s43058-024-00593-w -
Proceedings of the National Academy of... Jul 2024Predicting which proteins interact together from amino acid sequences is an important task. We develop a method to pair interacting protein sequences which leverages the...
Predicting which proteins interact together from amino acid sequences is an important task. We develop a method to pair interacting protein sequences which leverages the power of protein language models trained on multiple sequence alignments (MSAs), such as MSA Transformer and the EvoFormer module of AlphaFold. We formulate the problem of pairing interacting partners among the paralogs of two protein families in a differentiable way. We introduce a method called Differentiable Pairing using Alignment-based Language Models (DiffPALM) that solves it by exploiting the ability of MSA Transformer to fill in masked amino acids in multiple sequence alignments using the surrounding context. MSA Transformer encodes coevolution between functionally or structurally coupled amino acids within protein chains. It also captures inter-chain coevolution, despite being trained on single-chain data. Relying on MSA Transformer without fine-tuning, DiffPALM outperforms existing coevolution-based pairing methods on difficult benchmarks of shallow multiple sequence alignments extracted from ubiquitous prokaryotic protein datasets. It also outperforms an alternative method based on a state-of-the-art protein language model trained on single sequences. Paired alignments of interacting protein sequences are a crucial ingredient of supervised deep learning methods to predict the three-dimensional structure of protein complexes. Starting from sequences paired by DiffPALM substantially improves the structure prediction of some eukaryotic protein complexes by AlphaFold-Multimer. It also achieves competitive performance with using orthology-based pairing.
Topics: Sequence Alignment; Proteins; Amino Acid Sequence; Algorithms; Sequence Analysis, Protein; Computational Biology; Databases, Protein
PubMed: 38913900
DOI: 10.1073/pnas.2311887121