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AIMS: To examine the incidence of cardiovascular disease (CVD) and mortality in China and in key subpopulations, and to estimate the population-level risks attributable to 12 common modifiable risk factors for each outcome. METHODS AND RESULTS: In this prospective cohort of 47 262 middle-aged participants from 115 urban and rural communities in 12 provinces of China, it was examined how CVD incidence and mortality rates varied by sex, by urban-rural area, and by region. In participants without prior CVD, population-attributable fractions (PAFs) for CVD and for death related to 12 common modifiable risk factors were assessed: four metabolic risk factors (hypertension, diabetes, abdominal obesity, and lipids), four behavioural risk factors (tobacco, alcohol, diet quality, and physical activity), education, depression, grip strength, and household air pollution. The mean age of the cohort was 51.1 years. 58.2% were female, 49.2% were from urban areas, and 59.6% were from the eastern region of China. The median follow-up duration was 11.9 years. The CVD was the leading cause of death in China (36%). The rates of CVD and death were 8.35 and 5.33 per 1000 person-years, respectively, with higher rates in men compared with women and in rural compared with urban areas. Death rates were higher in the central and western regions of China compared with the eastern region. The modifiable risk factors studied collectively contributed to 59% of the PAF for CVD and 56% of the PAF for death in China. Metabolic risk factors accounted for the largest proportion of CVD (PAF of 41.7%), and hypertension was the most important risk factor (25.0%), followed by low education (10.2%), high non-high-density lipoprotein cholesterol (7.8%), and abdominal obesity (6.9%). The largest risk factors for death were hypertension (10.8%), low education (10.5%), poor diet (8.3%), tobacco use (7.5%), and household air pollution (6.1%). CONCLUSION: Both CVD and mortality are higher in men compared with women, and in rural compared with urban areas. Large reductions in CVD could potentially be achieved by controlling metabolic risk factors and improving education. Lowering mortality rates will require strategies addressing a broader range of risk factors.
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Doenças Cardiovasculares , Hipertensão , China/epidemiologia , Feminino , Humanos , Hipertensão/epidemiologia , Masculino , Pessoa de Meia-Idade , Obesidade/complicações , Obesidade Abdominal/complicações , Obesidade Abdominal/epidemiologia , Estudos Prospectivos , Fatores de RiscoRESUMO
We propose and experimentally demonstrate the generation of dual-channels chaos with time delay signature (TDS) concealment by introducing a phase-modulated Sagnac loop in mutually coupled semiconductor lasers (MCSL). Furthermore, we demonstrate the utilization of the dual-channels chaos to solve multi-armed bandit (MAB) problem in reinforcement learning. The experimental results agree well with the numerical simulations. For the purpose of comparison, the MCSL with a conventional Sagnac loop is also considered. It is found that the TDS of dual-channels chaotic signals can be better concealed in our proposed system. Besides, the proposed system allows for a better decision making performance in MAB problem. Moreover, compared with the one-channel chaotic system, the proposed dual-channels chaotic system achieves ultrafast decision making in parallel, and thus, is highly valuable for further improving the security of communication systems and the performance of photonic intelligence.
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Coastal marine areas are frequently affected by human activities and face ecological and environmental threats, such as algal blooms and climate change. The community structure of phytoplankton-primary producers in marine ecosystems-is highly sensitive to environmental factors, such as temperature, salinity, and nutrients. However, traditional methods for exploring the relationship between phytoplankton communities and environmental factors in eutrophic marine areas are limited by various factors. Therefore, this study employed interpretable machine learning models, integrating high-dimensional data analysis and complex system modeling, to quantitatively and thoroughly analyze the dynamic relationship between phytoplankton communities and environmental variables in high-frequency samples collected over 53â¯weeks from eutrophic marine areas. The cell abundance of phytoplankton exhibited a distinct "two-peak pattern" variation. Interpretable machine learning model analysis revealed the dynamic contributions of different environmental factors during changes in the phytoplankton community structure. The results showed that temperature was a key environmental factor that affected phytoplankton growth during peak periods. In addition, the contribution of salinity increased during the second peak in phytoplankton abundance, highlighting its central role in the ecological dynamics of this phase. During green tide outbreaks, particularly in Area 01, the contributions of factors such as temperature and salinity increased, whereas those of phosphates and silicates decreased, indicating that green tide outbreaks substantially altered the nutritional dynamics of the ecosystem. Furthermore, different phytoplankton species, such as Skeletonema costatum, Thalassiosira spp., and Nitzschia spp., exhibit varying responses to environmental factors. Hence, the predictions made using random forest and generalized additive models for phytoplankton cell abundance in two marine areas revealed complex nonlinear relationships between environmental factors, such as temperature, salinity, and phytoplankton abundance.
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The forthcoming massive genome data generated by the Earth BioGenome Project will open up a new era of comparative genomics, for which genome synteny analysis provides an important framework. Profiling genome synteny represents an essential step in elucidating genome architecture, regulatory blocks/elements and their evolutionary history. Here we describe PanSyn, ( https://github.com/yhw320/PanSyn ), the most comprehensive and up-to-date genome synteny pipeline, providing step-by-step instructions and application examples to demonstrate its usage. PanSyn inherits both basic and advanced functions from existing popular tools, offering a user-friendly, highly customized approach for genome macrosynteny analysis and integrated pan-evolutionary and regulatory analysis of genome architecture, which are not yet available in public synteny software or tools. The advantages of PanSyn include: (i) advanced microsynteny analysis by functional profiling of microsynteny genes and associated regulatory elements; (ii) comprehensive macrosynteny analysis, including the inference of karyotype evolution from ancestors to extant species; and (iii) functional integration of microsynteny and macrosynteny for pan-evolutionary profiling of genome architecture and regulatory blocks, as well as integration with external functional genomics datasets from three- or four-dimensional genome and ENCODE projects. PanSyn requires basic knowledge of the Linux environment and Perl programming language and the ability to access a computer cluster, especially for large-scale genomic comparisons. Our protocol can be easily implemented by a competent graduate student or postdoc and takes several days to weeks to execute for dozens to hundreds of genomes. PanSyn provides yet the most comprehensive and powerful tool for integrated evolutionary and functional genomics.
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Evolução Molecular , Genoma , Genômica , Software , Sintenia , Genômica/métodos , Genoma/genéticaRESUMO
BACKGROUND: Despite increased literature focusing on the role of the built environment (BE) in health, few cohort studies have quantitatively analyzed neighborhood walkability environment in relation to the risk of death and cardiovascular disease (CVD). This longitudinal study aimed at evaluating the association between perceived BE attributeswith mortality and major CVD based on the Prospective Urban Rural Epidemiology study in China (PURE-China). METHODS: The PURE-China study recruited 47,931 participants aged 35-70 years from 12 provinces of China between 2005 and 2009. The perceived BE information, including land use, street, aesthetics, and safety, was collected using the neighborhood environment walkability scale (NEWS) questionnaire, with higher scores indicating a more favorable rating. Two primary outcomes are all-cause mortality and major CVD event. The Cox frailty model with random intercepts was used to assess the association between the perceived total BE/subscales score and outcomes. RESULTS: Of 32,163 participants included in this study, 19,253 (59.9 %) were women, and the mean (SD) age was 51.0 (9.5) years. After a median follow-up period of 11.7 years (IQR 9.4 - 12.2), we observed that one standard deviation higher of combined BE scores was related to a lower risk of all-cause mortality (HR = 0.85; 95 %CI, 0.80-0.90), and major CVD events (HR = 0.95; 95 %CI, 0.90-0.99). The subscales of perceived BE were related to a lower risk, although a few were not significant. Land use mix-diversity and safety from crime were the two most significant subscales. Stronger risks were observed among urban and female participants. CONCLUSION: Favorable perceived BE characteristics were linked with a lower risk of all-cause mortality and major CVD events in Chinese population, especially in urban areas and females. Our findings can be used by policymakers to take action to mitigate the adverse effect of poor community conditions on health, such as improving local amenities and transport connectivity, providing building paths for walking, running and cycling.
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Ambiente Construído , Doenças Cardiovasculares , Humanos , Doenças Cardiovasculares/mortalidade , Doenças Cardiovasculares/epidemiologia , Pessoa de Meia-Idade , Feminino , China/epidemiologia , Masculino , Adulto , Estudos Prospectivos , Idoso , Ambiente Construído/estatística & dados numéricos , Inquéritos e Questionários , População Rural/estatística & dados numéricos , Estudos Longitudinais , Características de Residência/estatística & dados numéricos , CaminhadaRESUMO
The imbalanced data makes the machine learning model seriously biased, which leads to false positive in screening of therapeutic drugs for breast cancer. In order to deal with this problem, a multi-model ensemble framework based on tree-model, linear model and deep-learning model is proposed. Based on the methodology constructed in this study, we screened the 20 most critical molecular descriptors from 729 molecular descriptors of 1974 anti-breast cancer drug candidates and, in order to measure the pharmacokinetic properties and safety of the drug candidates, the screened molecular descriptors were used in this study for subsequent bioactivity, absorption, distribution metabolism, excretion, toxicity, and other prediction tasks. The results show that the method constructed in this study is superior and more stable than the individual models used in the ensemble approach.
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Neoplasias da Mama , Detecção Precoce de Câncer , Humanos , Feminino , Neoplasias da Mama/tratamento farmacológico , Aprendizado de Máquina , Modelos LinearesRESUMO
In this paper, a novel early warning system that has usually been applied to predict the financial stress events is established to predict the likelihood of housing price crises in China. To achieve this goal, a signal extraction approach is used to monitor the evolution of a number of economic indicators that tend to exhibit the abnormal behaviors. 13 economic variables were selected as the individual indicators, and constructed as the four comprehensive indicators. Our empirical work shows that the early warning system for urban housing price crises is suitable for China's four province-level municipalities. The in-sample forecasting results indicate the reliability of the early warning system for urban housing price crises. By studying the out-of-sample forecasting results, the likelihood of housing price crises for the four cities can be effectively predicted. We construct a novel weighted average comprehensive indicator, which performs better than the three others in terms of overall performance across all of the criteria considered in. It is shown that the extended system is more flexible in decision making than the traditional early warning system.
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Habitação , China , Cidades , Previsões , Reprodutibilidade dos TestesRESUMO
Objectives: To investigate whether lower hypertension prevalence or blood pressure was associated with cleaner household fuel usage for cooking and heating among Chinese adults. Methods: We enrolled 44,862 Chinese adults at the baseline of the prospective urban and rural epidemiology (PURE) study in China during 2005-2009, as a subset of the PURE-global China site. Multilevel logistic regression and generalized linear mixed models were conducted to estimate the adjusted odds ratio (AOR) and regression coefficient for hypertension and blood pressure respectively, while subgroup analysis by ambient PM2.5 concentration and location was also examined. Results: Compared with the least clean household solid fuel group, gas (AOR = 0.91, 95% CI: 0.83, 0.99) or electricity (AOR = 0.72, 95% CI: 0.60, 0.87) was associated with significantly lower levels of hypertension prevalence and blood pressure, and a similar pattern of the association was consistently observed among participants with high ambient PM2.5 exposure and those living in urban areas. Conclusion: Household utility of cleaner fuel type was associated with lower hypertension prevalence and blood pressure in Chinese adults. Our study urges the utilization of cleaner household energy to mitigate the burden of hypertension.