Suchergebnisse

Laboratory toxicity test and field control effect test are important experiments to evaluate the effect of pesticides on disease control. The safety of pesticide application depends on whether the pesticide residue on the fruit complies with the limits related to the good agricultural practices which were assessed by the legislative framework. In addition, many factors may affect the control effect of fungicides in the field experiment, among which one of the most important factors is the precipitation during the application process in the growing season. In this study, raw data of the laboratory toxicity of different fungicides to Colletotrichum fructicola, disease severity after treatment with different agents, the pesticide residue on the fruit after preharvest application, and the precipitation in the growing season are provided. In addition, this study also introduced the method and calculation process of raw data processing. We hope that these raw data will serve as a reference for other researchers who are studying the prevention and control of Glomerella leaf spot (GLS).

LAY SUMMARY Combat Veterans are vulnerable to suicidal thoughts and behaviour. Many who die by suicide deny having suicidal ideation (SI). Typically, researchers try to find variables indicating the presence of SI using traditional statistical approaches. These approaches do not possess the capacity to detect highly complex multivariable interactions. In contrast, machine learning (ML) is designed to detect such patterns and can consequently yield much higher predictive accuracy. In this study, the authors trained ML algorithms using 192 variables extracted from questionnaires administered to 738 Veterans and serving personnel to detect the presence of self-harm and SI (SHSI). Using the 10 most predictive non-suicide-related items, the ML algorithms could detect SHSI with 75.3% accuracy. Most of these items reflect psychological phenomena that can change quickly over time, allowing repeated risk reassessment from day to day. The study's findings suggest that ML methods may play an important role in the discovery, within a large data set, of predictive patterns that might be useful in suicide risk assessment.

Background
Cardiovascular disease is the leading cause of death in the United States, and Black populations are disproportionately affected. Black populations also have high rates of religiosity, which may be an important health motivator, but mechanisms are unclear.


Objective
We examined the relationship between perceived religious influence on health and cardiovascular health behaviors, risk factors, and confidence participating in medical care in Black church congregants.


Methods
We surveyed 302 members of 13 churches with predominantly Black congregations in New Orleans, Louisiana. Participants reported if religious beliefs had an influence on their health and if they avoided harmful behaviors because of religion. Fruit and vegetable intake, physical activity, smoking status, confidence asking questions to health care providers, understanding treatment plans and self-reported hypertension, hypercholesterolemia, and diabetes were assessed. Logistic regression was used adjusting for age, sex, and education.


Results
Survey respondents were 77% female with a median age of 66 years, and 72%, 56%, and 37% reported hypertension, hypercholesterolemia, and diabetes, respectively. Perceived religious influence on health was positively associated with fruit and vegetable intake, physical activity, and confidence asking questions to health care providers. Avoiding harmful behaviors because of religion was positively associated with physical activity. There was no association between perceived religious influence on health and smoking, hypertension, hypercholesterolemia, or diabetes.


Conclusion
Perceived religious influence on health was associated with beneficial cardiovascular health behaviors and confidence participating in medical care. These findings can inform the design and delivery of interventions to reduce cardiovascular disease among Black religious communities.

BACKGROUND: We sought to identify students and their sexual partners in a molecular transmission network. METHODS: We obtained 5996 HIV protease and reverse transcriptase gene sequences in Guangxi (165 from students and 5831 from the general populations) and the relevant demographic data. We constructed a molecular transmission network and introduced a permutation test to assess the robust genetic linkages. We calculated the centrality measures to describe the transmission patterns in clusters. RESULTS: At the network level, 68 (41.2%) students fell within the network across 43 (8.1%) clusters. Of 141 genetic linkages between students and their partners, only 25 (17.7%) occurred within students. Students were more likely than random permutations to link to other students (odds ratio [OR], 7.2; P < .001), private company employees aged 16–24 years (OR, 3.3; P = .01), private company or government employees aged 25–49 years (OR, 1.7; P = .03), and freelancers or unemployed individuals aged 16–24 years (OR, 5.0; P < .001). At the cluster level, the median age of nonstudents directly linked to students (interquartile range) was 25 (22–30) years, and 80.3% of them had a high school or higher education background. Compared with students, they showed a significantly higher median degree (4.0 vs 2.0; P < .001) but an equivalent median Eigenvector Centrality (0.83 vs 0.81; P = .60). CONCLUSIONS: The tendency of genetic linkage between students and nonstudent young men and their important position in the HIV transmission network emphasizes the urgent need for 2-pronged public health interventions based on both school and society.