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BackgroundChangeful seasonal influenza activity in subtropical areas such as Taiwan causes problems in epidemic preparedness. The Taiwan Centers for Disease Control has maintained real-time national influenza surveillance systems since 2004. Except for timely monitoring, epidemic forecasting using the national influenza surveillance data can provide pivotal information for public health response. ObjectiveWe aimed to develop predictive models using machine learning to provide real-time influenza-like illness forecasts. MethodsUsing surveillance data of influenza-like illness visits from emergency departments (from the Real-Time Outbreak and Disease Surveillance System), outpatient departments (from the National Health Insurance database), and the records of patients with severe influenza with complications (from the National Notifiable Disease Surveillance System), we developed 4 machine learning models (autoregressive integrated moving average, random forest, support vector regression, and extreme gradient boosting) to produce weekly influenza-like illness predictions for a given week and 3 subsequent weeks. We established a framework of the machine learning models and used an ensemble approach called stacking to integrate these predictions. We trained the models using historical data from 2008-2014. We evaluated their predictive ability during 2015-2017 for each of the 4-week time periods using Pearson correlation, mean absolute percentage error (MAPE), and hit rate of trend prediction. A dashboard website was built to visualize the forecasts, and the results of real-world implementation of this forecasting framework in 2018 were evaluated using the same metrics. ResultsAll models could accurately predict the timing and magnitudes of the seasonal peaks in the then-current week (nowcast) (ρ=0.802-0.965; MAPE: 5.2%-9.2%; hit rate: 0.577-0.756), 1-week (ρ=0.803-0.918; MAPE: 8.3%-11.8%; hit rate: 0.643-0.747), 2-week (ρ=0.783-0.867; MAPE: 10.1%-15.3%; hit rate: 0.669-0.734), and 3-week forecasts (ρ=0.676-0.801; MAPE: 12.0%-18.9%; hit rate: 0.643-0.786), especially the ensemble model. In real-world implementation in 2018, the forecasting performance was still accurate in nowcasts (ρ=0.875-0.969; MAPE: 5.3%-8.0%; hit rate: 0.582-0.782) and remained satisfactory in 3-week forecasts (ρ=0.721-0.908; MAPE: 7.6%-13.5%; hit rate: 0.596-0.904). ConclusionsThis machine learning and ensemble approach can make accurate, real-time influenza-like illness forecasts for a 4-week period, and thus, facilitate decision making.

Mpox, caused by monkeypox virus (MPXV), is a viral illness characterized by rash, influenza-like symptoms, and fever. A global outbreak of mpox attracted increased public attention in 2022 and became [...]

Abstract Background Calcium-dependent protein kinase (CDPK) plays a key role in cotton tolerance to abiotic stress. However, its role in cotton heat stress tolerance is not well understood. Here, we characterize the GhCDPK gene family and their expression profiles with the aim of identifying CDPK genes associated with heat stress tolerance. Results This study revealed 48 GhCDPK members in the cotton genome, distributed on 18 chromosomes. Tree phylogenetic analysis showed three main clustering groups of the GhCDPKs. Cis-elements revealed many abiotic stress and phytohormone pathways conserved promoter regions. Similarly, analysis of the transcription factor binding sites (TFBDS) in the GhCDPK genes showed many stress and hormone related sites. The expression analysis based on qRT-PCR showed that GhCDPK16 was highly responsive to high-temperature stress. Subsequent protein-protein interactions of GhCDPK16 revealed predictable interaction with ROS generating, calcium binding, and ABA signaling proteins. Overexpression of GhCDPK16 in cotton and Arabidopsis improved thermotolerance by lowering ROS compound buildup. Under heat stress, GhCDPK16 transgenic lines upregulated heat-inducible genes GhHSP70, GHSP17.3, and GhGR1, as demonstrated by qRT-PCR analysis. Contrarily, GhCDPK16 knockout lines in cotton exhibited an increase in ROS accumulation. Furthermore, antioxidant enzyme activity was dramatically boosted in the GhCDPK16-ox transgenic lines. Conclusions The collective findings demonstrated that GhCDPK16 could be a viable gene to enhance thermotolerance in cotton and, therefore, a potential candidate gene for improving heat tolerance in cotton.

Abstract Background Migraine, a widespread neurological condition, substantially affects the quality of life, particularly for adolescents and young adults. While its impact is significant, there remains a paucity of comprehensive global research on the burden of migraine in younger demographics. Our study sought to elucidate the global prevalence, incidence, and disability-adjusted life-years (DALYs) associated with migraine in the 15–39 age group from 1990 to 2021, utilizing data from the Global Burden of Disease (GBD) 2021 study. Methods Our comprehensive study analyzed migraine data from the GBD 2021 report, examining the prevalence, incidence, and DALYs across 204 countries and territories over a 32-year span. We stratified the information by age, sex, year, geographical region, and Socio-demographic Index (SDI). To evaluate temporal trends in these metrics, we employed the estimated annual percentage change (EAPC) calculation. Results Between 1990 and 2021, the worldwide prevalence of migraine among 15–39 year-olds increased substantially. By 2021, an estimated 593.8 million cases were reported, representing a 39.52% rise from 425.6 million cases in 1990. Global trends showed increases in age-standardized prevalence rate, incidence rate, and DALY rate for migraine during this period. The EAPC were positive for all three metrics: 0.09 for ASPR, 0.03 for ASIR, and 0.09 for DALY rate. Regions with medium SDI reported the highest absolute numbers of prevalent cases, incident cases, and DALYs in 2021. However, high SDI regions demonstrated the most elevated rates overall. Across the globe, migraine prevalence peaked in the 35–39 age group. Notably, female rates consistently exceeded male rates across all age categories. Conclusion The global impact of migraine on youths and young adults has grown considerably from 1990 to 2021, revealing notable variations across SDI regions, countries, age groups, and sexes. This escalating burden necessitates targeted interventions and public health initiatives, especially in areas and populations disproportionately affected by migraine.

Abstract Background In recent years, the escalating concern for neglected tropical diseases (NTDs) has been recognized as a pressing global health issue. This concern is acutely manifested in low- and middle-income countries, where there is an escalating prevalence among adolescents and young adults. The burgeoning of these conditions threatens to impair patients' occupational capabilities and overall life quality. Despite the considerable global impact of NTDs, comprehensive studies focusing on their impact in younger populations remain scarce. Our study aims to describe the global prevalence of neglected tropical diseases among people aged 15 to 39 years over the 30-year period from 1990 to 2019, and to project the disease burden of the disease up to 2040. Methods Annual data on incident cases, mortality, and disability-adjusted life years (DALYs) for NTDs were procured from the Global Burden of Disease Study 2019 (GBD 2019). These data were stratified by global and regional distribution, country, social development index (SDI), age, and sex. We computed age-standardized rates (ASRs) and the numbers of incident cases, mortalities, and DALYs from 1990 to 2019. The estimated annual percentage change (EAPC) in the ASRs was calculated to evaluate evolving trends. Results In 2019, it was estimated that there were approximately 552 million NTD cases globally (95% Uncertainty Interval [UI]: 519.9 million to 586.3 million), a 29% decrease since 1990. South Asia reported the highest NTD prevalence, with an estimated 171.7 million cases (95% UI: 150.4 million to 198.6 million). Among the five SDI categories, the prevalence of NTDs was highest in the moderate and low SDI regions in 1990 (approximately 270.5 million cases) and 2019 (approximately 176.5 million cases). Sub-Saharan Africa recorded the most significant decline in NTD cases over the past three decades. Overall, there was a significant inverse correlation between the disease burden of NTDs and SDI. Conclusion NTDs imposed over half a billion incident cases and 10.8 million DALYs lost globally in 2019—exerting an immense toll rivaling major infectious and non-communicable diseases. Encouraging declines in prevalence and disability burdens over the past three decades spotlight the potential to accelerate progress through evidence-based allocation of resources. Such strategic integration could substantially enhance public awareness about risk factors and available treatment options.

[Objective] To solve the problem of poor manual detection accuracy of traditional grain and chaff separator and improve production efficiency. [Methods] An image detection method based on machine vision was proposed, which realized the feature recognition and separation of grain rough through multi-stage progressive fusion of different image algorithms. The acquired images were selected in the ROI region and enhanced by Retinex algorithm. The Otsu algorithm was used to segment the image, and then the median filtering wwas combined with morphology to remove the image noise. The improved Canny algorithm was used to detect edge features of binary images, and the position information of the contour of the valley rough image was extracted by combining the Hough transform. Finally, the state estimation of the position information was performed by using the Kalman filter, and the best predicted value of the separated position was obtained, while the position offset error was reduced. [Results] The average detection error of the system was 3.14 mm, a decrease of 1.82 mm compared to before filtering, and the average standard deviation of filtering error was 0.8 mm. [Conclusion] This method can effectively detect the grain rough feature information and improve the separation accuracy.

Abstract Background The growing prevalence of non-alcoholic fatty liver disease (NAFLD) in younger populations, particularly those of working age (15–64 years), has become a public health concern. Being diagnosed at a younger age implies a greater likelihood of accruing disability-adjusted life years (DALYs) later in life due to potential progression to conditions such as cirrhosis or hepatocellular carcinoma. This study aims to analyze NAFLD prevalence trends over three decades globally, regionally, and nationally, with a focus on age, period, and birth cohort associations. Methods Global, regional, and country time trends in the prevalence of NAFLD among working-age people from 1990 to 2019: Age-period-cohort analysis based on Global Burden of Disease Study 2019 estimates and 95% uncertainty interval (UI) of NAFLD prevalence in the working age population was extracted from the Global Burden of Diseases, Injuries and Risk Factors Study 2019. Age-period-cohort models were used to estimate the prevalence within each age group from 1990 to 2019 (local drift, % per year), fitted longitudinal age-specific rates adjusted for period bias (age effect), and period/cohort relative risk (period/cohort effect). Results The global age-standardized prevalence (ASPR) of NAFLD increased significantly from 1990 (14,477.6 per 100 000) to 2019 (19,837.6 per 100 000). In the Western Pacific, there were 42,903.8 NAFLD cases in 2019, 54.15% higher than in 1990. The ASPR also increased significantly in the region over the past three decades. At the national level, Palau had the highest ASPR while Brunei Darussalam had the lowest. Age-period-cohort analysis showed that in the Western Pacific, unlike globally, the risk of NAFLD declined after age 60–64 years. Relative to 1980–1989, incidence and DALY risks decreased but prevalence increased in subsequent birth cohorts. Future predictions indicate an upward trend in NAFLD burden, especially among women and medium (SDI) regions like China. Conclusion Non-alcoholic fatty liver disease imparts an immense health burden that continues to grow globally and in the Asia Pacific region. Our work highlights working age adults as an at-risk group and calls attention to socioeconomic gradients within Western Pacific countries. Upward future projections demonstrate that NAFLD prevention is an urgent priority.