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Background: The impact of climate on zoonotic infectious diseases (or can be referred to as climate-sensitive zoonotic diseases) is confirmed. Yet, research on the association between brucellosis and climate is limited. We aim to understand the impact of meteorological factors on the risk of brucellosis, especially in northeastern China. Methods: Monthly incidence data for brucellosis from 2005 to 2019 in Jilin province was obtained from the China Information System for Disease Control and Prevention (CDC). Monthly meteorological data (average temperature (°C), wind velocity (m/s), relative humidity (%), sunshine hours (h), air pressure (hPa), and rainfall (mm)) in Jilin province, China, from 2005 to 2019 were collected from the China Meteorological Information Center (http://data.cma.cn/). The Spearman's correlation was used to choose among the several meteorological variables. A distributed lag non-linear model (DLNM) was used to estimate the lag and non-linearity effect of meteorological factors on the risk of brucellosis. Results: A total of 24,921 cases of human brucellosis were reported in Jilin province from 2005 to 2019, with the peak epidemic period from April to June. Low temperature and low sunshine hours were protective factors for the brucellosis, where the minimum RR values were 0.50 (95 % CI = 0.31–0.82) for −13.7 °C with 1 month lag and 0.61 (95 % CI = 0.41–0.91) for 110.5h with 2 months lag, respectively. High temperature, high sunshine hours, and low wind velocity were risk factors for brucellosis. The maximum RR values were 2.91 (95 % CI = 1.43–5.92, lag = 1, 25.7 °C), 1.85 (95 % CI = 1.23–2.80, lag = 2, 332.6h), and 1.68 (95 % CI = 1.25–2.26, lag = 2, 1.4 m/s). The trends in the impact of extreme temperature and extreme sunshine hours on the transmission of brucellosis were generally consistent. Conclusion: High temperature, high sunshine hours, and low wind velocity are more conducive to the transmission of brucellosis with an obvious lag effect. The results will deepen the understanding of the relationship between climate and brucellosis and provide a reference for formulating relevant public health policies.

Autonomous vehicle (AV) technologies are expected to disrupt the existing urban transportation systems. AVs’ multi-sensor system can generate large amount of data, often used for localization and safety purposes. This study proposes and demonstrates a practical framework for real-time measurement of local traffic states using LiDAR data from AVs. Fundamental traffic flow variables including volume, density, and speed are computed along with the traffic time-space diagrams. The framework is tested using the Waymo Open dataset. Results provide insights into the possibility of real-time traffic state estimation using AVs’ data for traffic operations and management applications.

Managing severe bleeding, particularly in soft tissues and visceral injuries, remains a significant challenge in trauma and surgical care. Traditional hemostatic methods often fall short in wet and dynamic environments. This study addresses the critical issue of severe bleeding in soft tissues, proposing an innovative solution using a polyethylene glycol (PEG)-based hydrogel combined with zinc oxide (ZnO). The developed hydrogel forms a dual-network structure through amide bonds and metal ion chelation, resulting in enhanced mechanical properties and adhesion strength. The hydrogel, exhibiting excellent biocompatibility, is designed to release zinc ions, promoting coagulation and accelerating hemostasis. Comprehensive characterization, including gelation time, rheological properties, microstructure analysis, and swelling behavior, demonstrates the superior performance of the PEG/ZnO hydrogel compared to traditional PEG hydrogels. Mechanical tests confirm increased compression strength and adhesive properties, which are crucial for withstanding tissue dynamics. In vitro assessments reveal excellent biocompatibility and enhanced procoagulant ability attributed to ZnO. Moreover, in vivo experiments using rat liver and tail bleeding models demonstrate the remarkable hemostatic performance of the PEG/ZnO hydrogel, showcasing its potential for acute bleeding treatment in both visceral and peripheral scenarios.

The chemical composition of the river indicates chemical weathering, which is the main source of ions and the most important avenue for carbon sink in the river. The hydrochemical characteristics of alpine catchment can provide the basis for clarifying the chemical weathering process and CO2 consumption, which is meaningful for the chemical weathering evolution law of the alpine catchment and even the Third Pole. The Yarlung Tsangpo River is the most typical river in the alpine catchment and the biggest river of the Third Pole. In this study, the river water samples were collected in March and September 2019 from the main stream at the upper reaches of Yarlung Tsangpo River above Lhatse County, as well as main tributaries Dogxung Tsangpo and Damqog Tsangpo. Based on the comparative analysis of the water samples in different seasons, the following conclusions are reached: precipitation contributes little to the ion content of rivers, and ions mainly come from chemical weathering, the main type of water body is HCO3−-SO42−-Ca2+; carbonate rock weathering in the main stream of Yarlung Tsangpo River contributes more to the ions of river than that in other tributaries; the CO2 consumption is mainly caused by silicate rock weathering and carbonate rock weathering, and the carbon weathering rate increases faster than that of silicate weathering rate from March to September 2019; the consumption rate of CO2 in the wet season is higher than that in the dry season. HIGHLIGHTS The seasonal differences of chemical differentiation between dry season and wet season in the alpine catchment are analyzed.; Study on the water chemical characteristics and ion source analysis of the upper reaches of Yarlung Tsangpo River.; The absorption rate of CO2 by weathering of silicate rock and carbonate rock are studied according to weathering rate.;

BackgroundChickenpox is an old but easily neglected infectious disease. Although chickenpox is preventable by vaccines, vaccine breakthroughs often occur, and the chickenpox epidemic is on the rise. Chickenpox is not included in the list of regulated communicable diseases that must be reported and controlled by public and health departments; therefore, it is crucial to rapidly identify and report varicella outbreaks during the early stages. The Baidu index (BDI) can supplement the traditional surveillance system for infectious diseases, such as brucellosis and dengue, in China. The number of reported chickenpox cases and internet search data also showed a similar trend. BDI can be a useful tool to display the outbreak of infectious diseases. ObjectiveThis study aimed to develop an efficient disease surveillance method that uses BDI to assist in traditional surveillance. MethodsChickenpox incidence data (weekly from January 2017 to June 2021) reported by the Yunnan Province Center for Disease Control and Prevention were obtained to evaluate the relationship between the incidence of chickenpox and BDI. We applied a support vector machine regression (SVR) model and a multiple regression prediction model with BDI to predict the incidence of chickenpox. In addition, we used the SVR model to predict the number of chickenpox cases from June 2021 to the first week of April 2022. ResultsThe analysis showed that there was a close correlation between the weekly number of newly diagnosed cases and the BDI. In the search terms we collected, the highest Spearman correlation coefficient was 0.747. Most BDI search terms, such as “chickenpox,” “chickenpox treatment,” “treatment of chickenpox,” “chickenpox symptoms,” and “chickenpox virus,” trend consistently. Some BDI search terms, such as “chickenpox pictures,” “symptoms of chickenpox,” “chickenpox vaccine,” and “is chickenpox vaccine necessary,” appeared earlier than the trend of “chickenpox virus.” The 2 models were compared, the SVR model performed better in all the applied measurements: fitting effect, R2=0.9108, root mean square error (RMSE)=96.2995, and mean absolute error (MAE)=73.3988; and prediction effect, R2=0.548, RMSE=189.1807, and MAE=147.5412. In addition, we applied the SVR model to predict the number of reported cases weekly in Yunnan from June 2021 to April 2022 using the same period of the BDI. The results showed that the fluctuation of the time series from July 2021 to April 2022 was similar to that of the last year and a half with no change in the level of prevention and control. ConclusionsThese findings indicated that the BDI in Yunnan Province can predict the incidence of chickenpox in the same period. Thus, the BDI is a useful tool for monitoring the chickenpox epidemic and for complementing traditional monitoring systems.