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Objective: Predicting who will not benefit enough from Internet-Based Cognitive Behavioral (ICBT) Therapy early on can assist in better allocation of limited mental health care resources. Repeated measures of symptoms during treatment is the strongest predictor of outcome, and we want to investigate if methods that explicitly account for time-dependency are superior to methods that do not, with data from (a) only two pre-treatment timepoints and (b) the pre-treatment timepoints and three timepoints during initial treatment. Methods: We use 1) commonly used time-independent methods (i.e., Linear Regression and Random Forest models) and 2) time-dependent methods (i.e., multilevel model regression, mixed-effects random forest, and a Long Short-Term Memory model) to predict symptoms during treatment, including the final outcome. This is done with symptom scores from 6436 ICBT patients from regular care, using robust multiple imputation and nested cross-validation methods. Results: The models had a 14 %-12 % root mean squared error (RMSE) in predicting the post-treatment outcome, corresponding to a balanced accuracy of 67-74 %. Time-dependent models did not have higher accuracies. Using data for the initial treatment period (b) instead of only from before treatment (a) increased prediction results by 1.3 % percentage points (12 % to 10.7 %) RMSE and 6 % percentage points BACC (69 % to 75 %). Conclusion: Training prediction models on only symptom scores of the first few weeks is a promising avenue for symptom predictions in treatment, regardless of which model is used. Further research is necessary to better understand the interaction between model complexity, dataset length and width, and the prediction tasks at hand.

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BACKGROUND: Public health and social measures (PHSMs) are considered the most effective approaches for controlling epidemic diseases. This study aimed to explore variations in the time-dependent characteristics of and public preferences for cosmetic treatments during and after the implementation of PHSMs during the COVID-19 pandemic. METHOD: Medical records from six medical institutions were extracted retrospectively. Time-series analyses were conducted to reveal the variations in characteristics in volume and proportion of cosmetic treatments according to PHSMs. A cross-sectional study was conducted with online questionnaire designed for the general population during and after the implementation of PHSMs. RESULT: A total of 141 033 records were included in this retrospective study. The implementation of PHSMs led to extremely low treatment volumes; compared with the increases in private hospitals, the treatment volumes in public hospitals exhibited earlier and more significant increases, even higher than pre-PHSM levels (p < 0.05), which mainly contributed to the increase in plastic surgery volumes during and after the implementation of PHSMs. The differences in the anxiety state, self-perceived appearance, and cosmetic treatment intentions of the participants were illustrated during and after PHSMs. We further demonstrated the participants' decisions on cosmetic treatments after the implementation of PHSMs during the COVID-19 pandemic. CONCLUSION: The immediate effects and aftereffects of PHSMs on cosmetic treatments were different in public and private hospitals. Furthermore, as PHSMs guided the adjustment of cosmetic treatments in the post-COVID-19 era, the intention to undergo cosmetic treatment during PHSMs was associated with the anxiety states and preferences of the population.

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The association of short-term ambient air pollution exposure with osteoarthritis (OA) outpatient visits has been unclear and no study has assessed the modifying roles of district-level characteristics in the association between ambient air pollution exposure and OA outpatient visits. We investigated the cumulative associations of ambient air pollution exposure with daily OA outpatient visits and vulnerable factors influencing the associations using data from 16 districts of Beijing, China during 2013-2019. A total of 18,351,795 OA outpatient visits were included in the analyses. An increase of 10 µg/m3 in fine particulate matter (PM2.5), inhalable particulate matter (PM10), nitrogen dioxide (NO2), sulfur dioxide (SO2), maximum 8-hour moving-average ozone (8 h-O3), and 0.1 mg/m3 in carbon monoxide (CO) at representative lag days were associated with significant increases of 0.31 %, 0.06 %, 0.77 %, 0.87 %, 0.30 %, and 0.48 % in daily OA outpatient visits, respectively. Considerable OA outpatient visits were attributable to short-term ambient air pollution exposure. In addition, low temperature and high humidity aggravated ambient air pollution associated OA outpatient visits. District-level characteristics, such as population density, green coverage rate, and urbanization rate modified the risk of OA outpatient visits associated with air pollution exposure. These findings highlight the significance of controlling ambient air pollution during the urbanization process, which is useful in policy formation and implementation.

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Hand, foot, and mouth disease (HFMD) is an acute infectious illness primarily caused by enteroviruses. The present study aimed to describe the epidemiological characteristics of hospitalized HFMD patients in a hospital in Henan Province (Zhengzhou, China), and to predict the future epidemiological parameters. In this study, we conducted a retrospective analysis of general demographic and clinical data on hospitalized children who were diagnosed with HFMD from 2014 to 2023. We used wavelet analysis to determine the periodicity of the disease. We also conducted an analysis of the impact of the COVID-19 epidemic on the detection ratio of severe illness. Additionally, we employed a Seasonal Difference Autoregressive Moving Average (SARIMA) model to forecast characteristics of future newly hospitalized HFMD children. A total of 19 487 HFMD cases were included in the dataset. Among these cases, 1515 (7.8%) were classified as severe. The peak incidence of HFMD typically fell between May and July, exhibiting pronounced seasonality. The emergence of COVID-19 pandemic changed the ratio of severe illness. In addition, the best-fitted seasonal ARIMA model was identified as (2,0,2)(1,0,1)12. The incidence of severe cases decreased significantly following the introduction of the vaccine to the market (χ2 = 109.9, p < 0.05). The number of hospitalized HFMD cases in Henan Province exhibited a seasonal and declining trend from 2014 to 2023. Non-pharmacological interventions implemented during the COVID-19 pandemic have led to a reduction in the incidence of severe illness.

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AIM: To investigate the impact of public health emergencies on the prevalence of suicidal ideation among healthcare workers (HCWs) and medical students. METHODS: The prevalence of suicidal ideation among HCWs and medical students was searched for analysis. The platforms included PubMed, medRVix, bioRvix, Cumulative Index to Nursing and Allied Health Literature (CINAHL), and Web of Science. Interrupted time-series analysis was employed to determine whether the COVID-19 pandemic influenced the prevalence and trends of suicidal ideation. To account for autocorrelation and heteroskedasticity, Newey-West standard errors were utilized with a lag of order one. RESULTS: Seventy studies with 145,641 HCWs and medical students from 30 countries were included in the final analysis, with 30 studies before COVID-19 and 40 studies during the pandemic. Before the pandemic outbreak (April 2020), the monthly increasing rate was 0.063 % (95 % CI: -0.009 %, 0.135 %, z = 1.73, P = 0.084). The tendency of suicidal ideation prevalence increased by 1.116 % (95%CI: 0.888 %, 1.344 %, z = 9.60, P < 0.001). In other words, the calculated monthly growth rate of suicidal ideation after the pandemic outbreak is 1.179 % (95%CI: 0.968 %, 1.391 %, z = 10.93, P < 0.001) per month. The overall growing trend of prevalence of suicidal ideation during the pandemic is 1.896 % per month in America; 1.590 % in Europe; 0.443 % (95%CI: 0.213 %, 0.673 %, z = 3.77, P < 0.001) in Asia; 1.055 % in HCWs; and 0.645 % in medical students. CONCLUSION: This study highlights that the COVID-19 pandemic can significantly impact the prevalence of suicidal ideation among HCWs and medical students, and the prevalence showed an upward trend.

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Wildland fire activity is provided in a geospatial database of polygons over the conterminous United States for the 2004 through 2017 time period. The location, timing, and size of the fires are derived from a fusion of wildland fire activity from a consistent set of national ground reports, satellite, and geospatial fire data. A combination of information from the underlying data sources and a regional climatological approach is used to differentiate prescribed fire from unplanned wildfire. The data were developed as part of the United States Environmental Protection Agency (US EPA) Air Quality Time Series project (EQUATES). This dataset can be useful for the evaluation of wildland fire activity over time and across regions.

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Tissue hysteresivity is an important marker for determining the onset and progression of respiratory diseases, calculated from forced oscillation lung function test data. This study aims to reduce the number and duration of required measurements by combining multivariate data from various sensing devices. We propose using the Forced Oscillation Technique (FOT) lung function test in both a low-frequency prototype and the commercial RESMON device, combined with continuous monitoring from the Equivital (EQV) LifeMonitor and processed by artificial intelligence (AI) algorithms. While AI and deep learning have been employed in various aspects of respiratory system analysis, such as predicting lung tissue displacement and respiratory failure, the prediction or forecasting of tissue hysteresivity remains largely unexplored in the literature. In this work, the Long Short-Term Memory (LSTM) model is used in two ways: (1) to estimate the hysteresivity coefficient η using heart rate (HR) data collected continuously by the EQV sensor, and (2) to forecast η values by first predicting the heart rate from electrocardiogram (ECG) data. Our methodology involves a rigorous two-hour measurement protocol, with synchronized data collection from the EQV, FOT, and RESMON devices. Our results demonstrate that LSTM networks can accurately estimate the tissue hysteresivity parameter η, achieving an R2 of 0.851 and a mean squared error (MSE) of 0.296 for estimation, and forecast η with an R2 of 0.883 and an MSE of 0.528, while significantly reducing the number of required measurements by a factor of three (i.e., from ten to three) for the patient. We conclude that our novel approach minimizes patient effort by reducing the measurement time and the overall ambulatory time and costs while highlighting the potential of artificial intelligence methods in respiratory monitoring.

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Given the frequent association between freshwater plankton and water quality degradation, several predictive models have been devised to understand and estimate their dynamics. However, the significance of biotic and abiotic interactions has been overlooked. In this study, we aimed to address the importance of the interaction term in predicting plankton community dynamics by applying graph convolution embedded long short-term memory networks (GC-LSTM) models, which can incorporate interaction terms as graph signals. Temporal graph series comprising plankton genera or environmental drivers as node features and their relationships for edge features from two distinct water bodies, a reservoir and a river, were utilized to develop these models. To assess the predictability, the performances of the GC-LSTM models on community dynamics were compared those of LSTM and GCN models at various lead times. Moreover, GNNExplainer was used to examine the global and local importance of the nodes and edges for all predictions and specific predictions, respectively. The GC-LSTM models outperformed the LSTM models, consistently showing higher prediction accuracy. Although all the models exhibited performance degradation at longer lead times, the GC-LSTM models consistently demonstrated better performance regarding each graph signal and plankton genus. GNNExplainer yielded interpretable explanations for important genera and interaction pairs among communities, revealing consistent importance patterns across different lead times at both global and local scales. These findings underscore the potential of the proposed modeling approach for forecasting community dynamics and emphasize the critical role of graph signals with interaction terms in plankton communities.

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BACKGROUND: Malaria remains an important public health problem, particularly in sub-Saharan Africa. In Rwanda, where malaria ranks among the leading causes of mortality and morbidity, disease transmission is influenced by climatic factors. However, there is a paucity of studies investigating the link between climate change and malaria dynamics, which hinders the development of effective national malaria response strategies. Addressing this critical gap, this study analyses how climatic factors influence malaria transmission across Rwanda, thereby informing tailored interventions and enhancing disease management frameworks. METHODS: The study analysed the potential impact of temperature and cumulative rainfall on malaria incidence in Rwanda from 2012 to 2021 using meteorological data from the Rwanda Meteorological Agency and malaria case records from the Rwanda Health Management and Information System. The analysis was performed in two stages. First, district-specific generalized linear models with a quasi-Poisson distribution were applied, which were enhanced by distributed lag non-linear models to explore non-linear and lagged effects. Second, random effects multivariate meta-analysis was employed to pool the estimates and to refine them through best linear unbiased predictions. RESULTS: A 1-month lag with specific temperature and rainfall thresholds influenced malaria incidence across Rwanda. Average temperature of 18.5 °C was associated with higher malaria risk, while temperature above 23.9 °C reduced the risk. Rainfall demonstrated a dual effect on malaria risk: conditions of low (below 73 mm per month) and high (above 223 mm per month) precipitation correlated with lower risk, while moderate rainfall (87 to 223 mm per month) correlated with higher risk. Seasonal patterns showed increased malaria risk during the major rainy season, while the short dry season presented lower risk. CONCLUSION: The study underscores the influence of temperature and rainfall on malaria transmission in Rwanda and calls for tailored interventions that are specific to location and season. The findings are crucial for informing policy that enhance preparedness and contribute to malaria elimination efforts. Future research should explore additional ecological and socioeconomic factors and their differential contribution to malaria transmission.

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Traumatic Brain Injury (TBI) presents a broad spectrum of clinical presentations and outcomes due to its inherent heterogeneity, leading to diverse recovery trajectories and varied therapeutic responses. While many studies have delved into TBI phenotyping for distinct patient populations, identifying TBI phenotypes that consistently generalize across various settings and populations remains a critical research gap. Our research addresses this by employing multivariate time-series clustering to unveil TBI's dynamic intricates. Utilizing a self-supervised learning-based approach to clustering multivariate time-Series data with missing values (SLAC-Time), we analyzed both the research-centric TRACK-TBI and the real-world MIMIC-IV datasets. Remarkably, the optimal hyperparameters of SLAC-Time and the ideal number of clusters remained consistent across these datasets, underscoring SLAC-Time's stability across heterogeneous datasets. Our analysis revealed three generalizable TBI phenotypes (α, ß, and γ), each exhibiting distinct non-temporal features during emergency department visits, and temporal feature profiles throughout ICU stays. Specifically, phenotype α represents mild TBI with a remarkably consistent clinical presentation. In contrast, phenotype ß signifies severe TBI with diverse clinical manifestations, and phenotype γ represents a moderate TBI profile in terms of severity and clinical diversity. Age is a significant determinant of TBI outcomes, with older cohorts recording higher mortality rates. Importantly, while certain features varied by age, the core characteristics of TBI manifestations tied to each phenotype remain consistent across diverse populations.

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Type 2 diabetes (T2D) and prediabetes are classically defined by the level of fasting glucose or surrogates such as hemoglobin HbA1c. This classification does not take into account the heterogeneity in the pathophysiology of glucose dysregulation, the identification of which could inform targeted approaches to diabetes treatment and prevention and/or predict clinical outcomes. We performed gold-standard metabolic tests in a cohort of individuals with early glucose dysregulation and quantified four distinct metabolic subphenotypes known to contribute to glucose dysregulation and T2D: muscle insulin resistance, ß-cell dysfunction, impaired incretin action, and hepatic insulin resistance. We revealed substantial inter-individual heterogeneity, with 34% of individuals exhibiting dominance or co-dominance in muscle and/or liver IR, and 40% exhibiting dominance or co-dominance in ß-cell and/or incretin deficiency. Further, with a frequently-sampled oral glucose tolerance test (OGTT), we developed a novel machine learning framework to predict metabolic subphenotypes using features from the dynamic patterns of the glucose time-series ("shape of the glucose curve"). The glucose time-series features identified insulin resistance, ß-cell deficiency, and incretin defect with auROCs of 95%, 89%, and 88%, respectively. These figures are superior to currently-used estimates. The prediction of muscle insulin resistance and ß-cell deficiency were validated using an independent cohort. We then tested the ability of glucose curves generated by a continuous glucose monitor (CGM) worn during at-home OGTTs to predict insulin resistance and ß-cell deficiency, yielding auROC of 88% and 84%, respectively. We thus demonstrate that the prediabetic state is characterized by metabolic heterogeneity, which can be defined by the shape of the glucose curve during standardized OGTT, performed in a clinical research unit or at-home setting using CGM. The use of at-home CGM to identify muscle insulin resistance and ß-cell deficiency constitutes a practical and scalable method by which to risk stratify individuals with early glucose dysregulation and inform targeted treatment to prevent T2D.

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When predicting physical phenomena through simulation, quantification of the total uncertainty due to multiple sources is as crucial as making sure the underlying numerical model is accurate. Possible sources include irreducible aleatoric uncertainty due to noise in the data, epistemic uncertainty induced by insufficient data or inadequate parameterization, and model-form uncertainty related to the use of misspecified model equations. In addition, recently proposed approaches provide flexible ways to combine information from data with full or partial satisfaction of equations that typically encode physical principles. Physics-based regularization interacts in nontrivial ways with aleatoric, epistemic and model-form uncertainty and their combination, and a better understanding of this interaction is needed to improve the predictive performance of physics-informed digital twins that operate under real conditions. To better understand this interaction, with a specific focus on biological and physiological models, this study investigates the decomposition of total uncertainty in the estimation of states and parameters of a differential system simulated with MC X-TFC, a new physics-informed approach for uncertainty quantification based on random projections and Monte-Carlo sampling. After an introductory comparison between approaches for physics-informed estimation, MC X-TFC is applied to a six-compartment stiff ODE system, the CVSim-6 model, developed in the context of human physiology. The system is first analyzed by progressively removing data while estimating an increasing number of parameters, and subsequently by investigating total uncertainty under model-form misspecification of non-linear resistance in the pulmonary compartment. In particular, we focus on the interaction between the formulation of the discrepancy term and quantification of model-form uncertainty, and show how additional physics can help in the estimation process. The method demonstrates robustness and efficiency in estimating unknown states and parameters, even with limited, sparse, and noisy data. It also offers great flexibility in integrating data with physics for improved estimation, even in cases of model misspecification.

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BACKGROUND: The relationship between human gut microbiota and high-altitude hypoxia acclimatization remains highly controversial. This stems primarily from uncertainties regarding both the potential temporal changes in the microbiota under such conditions and the existence of any dominant or core bacteria that may assist in host acclimatization. RESULTS: To address these issues, and to control for variables commonly present in previous studies which significantly impact the results obtained, namely genetic background, ethnicity, lifestyle, and diet, we conducted a 108-day longitudinal study on the same cohort comprising 45 healthy Han adults who traveled from lowland Chongqing, 243 masl, to high-altitude plateau Lhasa, Xizang, 3658 masl, and back. Using shotgun metagenomic profiling, we study temporal changes in gut microbiota composition at different timepoints. The results show a significant reduction in the species and functional diversity of the gut microbiota, along with a marked increase in functional redundancy. These changes are primarily driven by the overgrowth of Blautia A, a genus that is also abundant in six independent Han cohorts with long-term duration in lower hypoxia environment in Shigatse, Xizang, at 4700 masl. Further animal experiments indicate that Blautia A-fed mice exhibit enhanced intestinal health and a better acclimatization phenotype to sustained hypoxic stress. CONCLUSIONS: Our study underscores the importance of Blautia A species in the gut microbiota's rapid response to high-altitude hypoxia and its potential role in maintaining intestinal health and aiding host adaptation to extreme environments, likely via anti-inflammation and intestinal barrier protection.

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Electronic Health Records (EHRs) are a significant source of big data used to track health variables over time. The analysis of EHR data can uncover medical markers or risk factors, aiding in the diagnosis and monitoring of diseases. We introduce a novel method for identifying markers with various temporal trend patterns, including monotonic and fluctuating trends, using machine learning models such as Long Short-Term Memory (LSTM). By applying our method to pneumonia patients in the intensive care unit using the MIMIC-III dataset, we identified markers exhibiting both monotonic and fluctuating trends. Specifically, monotonic markers such as red cell distribution width, urea nitrogen, creatinine, calcium, morphine sulfate, bicarbonate, sodium, troponin T, albumin, and prothrombin time were more frequently observed in the mortality group compared to the recovery group throughout the 10-day period before discharge. Conversely, fluctuating trend markers such as dextrose in sterile water, polystyrene sulfonate, free calcium, and glucose were more frequently observed in the mortality group as the discharge date approached. Our study presents a method for detecting time-series pattern markers in EHR data that respond differently according to disease progression. These markers can contribute to monitoring disease progression and enable stage-specific treatment, thereby advancing precision medicine.

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Background and Objectives: Minimally invasive trauma management, including interventional radiology and non-operative approaches, has proven effective. Consequently, our hospital established a trauma IVR protocol called "Ohta Nishinouchi Hospital trauma protocol (ONH trauma protocol) in 2013, mainly for trunk trauma. However, the efficacy of the ONH trauma protocol has remained unverified. We aimed to assess the protocol's impact using interrupted time-series analysis (ITSA). Materials and Methods: This retrospective cohort study was conducted at Ohta Nishinouchi hospital, a tertiary emergency hospital, from January 2004 to December 2019. We included patients aged ≥ 18 years who presented to our institution due to severe trauma characterized by an Abbreviated Injury Scale of ≥3 in any region. The primary outcome was the incidence of in-hospital deaths per 100 transported patients with trauma. Multivariable logistic regression analysis was conducted with in-hospital mortality as the outcome, with no exposure before protocol implementation and with exposure after protocol implementation. Results: Overall, 4558 patients were included in the analysis. The ITSA showed no significant change in in-hospital deaths after protocol induction (level change -1.49, 95% confidence interval (CI) -4.82 to 1.84, p = 0.39; trend change -0.044, 95% CI -0.22 to 0.14, p = 0.63). However, the logistic regression analysis revealed a reduced mortality effect following protocol induction (odds ratio: 0.50, 95% CI: 0.37 to 0.66, p < 0.01, average marginal effects: -3.2%, 95% CI: -4.5 to -2.0, p < 0.01). Conclusions: The ITSA showed no association between the protocol and mortality. However, before-and-after testing revealed a positive impact on mortality. A comprehensive analysis, including ITSA, is recommended over before-and-after comparisons to assess the impact of the protocol.

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Passive sinking flux of particulate organic matter in the ocean plays a central role in the biological carbon pump and carbon export to the ocean's interior. Particle-associated microbes colonize particulate organic matter, producing "hotspots" of microbial activity. We evaluated variation in particle-associated microbial communities to 500 m depth across four different particle size fractions (0.2-1.2, 1.2-5, 5-20, >20 µm) collected using in situ pumps at the Bermuda Atlantic Time-series Study site. In situ pump collections capture both sinking and suspended particles, complementing previous studies using sediment or gel traps, which capture only sinking particles. Additionally, the diagenetic state of size-fractionated particles was examined using isotopic signatures alongside microbial analysis. Our findings emphasize that different particle sizes contain distinctive microbial communities, and each size category experiences a similar degree of change in communities over depth, contradicting previous findings. The robust patterns observed in this study suggest that particle residence times may be long relative to microbial succession rates, indicating that many of the particles collected in this study may be slow sinking or neutrally buoyant. Alternatively, rapid community succession on sinking particles could explain the change between depths. Complementary isotopic analysis of particles revealed significant differences in composition between particles of different sizes and depths, indicative of organic particle transformation by microbial hydrolysis and metazoan grazing. Our results couple observed patterns in microbial communities with the diagenetic state of associated organic matter and highlight unique successional patterns in varying particle sizes across depth.

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Parcel-scale crop classification utilizing time-series satellite observations is of significant importance in precision agriculture. The prior knowledge that crop types can be organized in a hierarchical tree structure is beneficial for improving crop classification. Moreover, the crop hierarchy aligns with the coarse-to-fine cognitive process of geographic scenes. Based on the crop hierarchy, this study developed a general hierarchical classification framework for enhancing crop mapping using time-series Sentinel-1 data. Central to this method is a deep-learning-based hierarchical classification model that explores and makes use of crop hierarchical knowledge. First, preprocessed Sentinel-1 data were geometrically overlaid onto farmland parcel maps to derive parcel-scale time-series features. Second, we constructed a hierarchical crop type system for study areas based on the crop phenology of labeled crop-type samples. Third, we developed a deep-learning-based hierarchical classification model to identify crop types for each parcel, to generate final crop-type classification maps. The proposed approach was further discussed and verified through the implementation of parcel-scale time-series crop hierarchical classifications in a study area in France with farmland parcel maps and time-series Sentinel-1 data. The classification results, indicating significant improvements greater than 4.0% in overall accuracy and 5.0% in F1 score over comparative methods, demonstrated the effectiveness of the proposed method in learning multi-scale time-series features for hierarchical crop classification utilizing Sentinel-1 data sequences.

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Published studies on outdoor air pollution and tuberculosis risk have shown heterogeneous results. Discrepancies in prior studies may be partially explained by the limited geographic scope, diverse exposure times, and heterogeneous statistical methods. Thus, we conducted a multi-province, multi-city time-series study to comprehensively investigate this issue. We selected 67 districts or counties from all geographic regions of China as study sites. We extracted data on newly diagnosed pulmonary tuberculosis (PTB) cases, outdoor air pollutant concentrations, and meteorological factors in 67 sites from January 1, 2014 to December 31, 2019. We utilized a generalized additive model to evaluate the relationship between ambient air pollutants and PTB risk. Between 2014 and 2019, there were 172,160 newly diagnosed PTB cases reported in 67 sites. With every 10-µg/m3 increase in SO2, NO2, PM10, PM2.5, and 1-mg/m3 in CO, the PTB risk increased by 1.97% [lag 0 week, 95% confidence interval (CI): 1.26, 2.68], 1.30% (lag 0 week, 95% CI: 0.43, 2.19), 0.55% (lag 8 weeks, 95% CI: 0.24, 0.85), 0.59% (lag 10 weeks, 95% CI: 0.16, 1.03), and 5.80% (lag 15 weeks, 95% CI: 2.96, 8.72), respectively. Our results indicated that ambient air pollutants were positively correlated with PTB risk, suggesting that decreasing outdoor air pollutant concentrations may help to reduce the burden of tuberculosis in countries with a high burden of tuberculosis and air pollution.

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BACKGROUND: Federal policies targeting antipsychotic use among nursing home (NH) residents may have increased reporting of diagnoses for approved uses, including schizophrenia, Tourette's syndrome, and Huntington's Disease (called "exclusionary diagnoses" because they exclude residents from the antipsychotic quality metric). We assessed changes in new exclusionary diagnoses among long-stay NH admissions specifically with dementia following federal policies. METHODS: Retrospective, quarterly, interrupted time-series analysis (2009-2018) of new long-stay NH residents with dementia and no exclusionary diagnoses reported before NH admission. The National Partnership and the addition of facility level antipsychotic use to the Five Star Quality Rating system were key time exposures. Outcome was quarterly facility level predicted percentage of exclusionary diagnoses within 2 years of admission stratified by NH characteristics. RESULTS: For 264,095 long-stay admissions, mean percentage of new exclusionary diagnoses was 2.2% before the Partnership. After the Partnership, there was an unadjusted increase in the percentage over time (slope change, 0.044, p = 0.018), but the percentage never exceeded 2.9%. The Partnership contributed to a one-time decrease in diagnoses in NHs with an intermediate percentage of Black residents (-1.29%, p = 0.004). Before the Partnership, diagnoses were increasing among not-for-profit relative to for-profit NHs (0.044; p = 0.012), but after the Partnership, the pattern reversed. For-profit NHs saw an increase (+0.034, p = 0.002); not-for-profit NHs experienced a decrease (-0.014, p = 0.039). Quality Rating modifications had no significant effect. CONCLUSIONS: Exclusionary diagnosis reporting among long-stay NH residents with dementia, the group most at risk from antipsychotics, did not increase in response to federal policies. Evaluation of reasons for the observed increase in exclusionary diagnoses among non-dementia NH residents is warranted along with continued attention to how to incentivize the appropriate use of medications in residents with dementia that is crucial for high-quality NH care.

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Astronomy is entering an unprecedented era of big-data science, driven by missions like the ESA's Gaia telescope, which aims to map the Milky Way in three dimensions. Gaia's vast dataset presents a monumental challenge for traditional analysis methods. The sheer scale of this data exceeds the capabilities of manual exploration, necessitating the utilization of advanced computational techniques. In response to this challenge, we developed a novel approach leveraging deep learning to estimate the metallicity of fundamental mode (ab-type) RR Lyrae stars from their light curves in the Gaia optical G-band. Our study explores applying deep-learning techniques, particularly advanced neural-network architectures, in predicting photometric metallicity from time-series data. Our deep-learning models demonstrated notable predictive performance, with a low mean absolute error (MAE) of 0.0565, the root mean square error (RMSE) of 0.0765, and a high R2 regression performance of 0.9401, measured by cross-validation. The weighted mean absolute error (wMAE) is 0.0563, while the weighted root mean square error (wRMSE) is 0.0763. These results showcase the effectiveness of our approach in accurately estimating metallicity values. Our work underscores the importance of deep learning in astronomical research, particularly with large datasets from missions like Gaia. By harnessing the power of deep-learning methods, we can provide precision in analyzing vast datasets, contributing to more precise and comprehensive insights into complex astronomical phenomena.