No abstract

The prevalence and nature of reported behaviour impairments in rural Bangladesh have implications for public health planning and delivery of health services.

We assessed zoonotic tuberculosis (zTB) knowledge and prevention and control practices of 404 cattle handlers via a survey in three dairy-intensive districts of Bangladesh. Most respondents were aged 30–49 (52%) and male (95%). Almost all (99%) recognized the important public health burden of tuberculosis in Bangladesh, however, most (58%) had inadequate knowledge about zTB transmission to humans. Inappropriate practices such as: not using protective equipment (98%); smoking, drinking or eating food whilst working with cattle (69%); and sharing the same premises with animals (83%) were identified. Cattle handlers educated at secondary or higher levels were 2.82- (95% CI: 1.59–5.10) and 5.15 times (95% CI: 1.74–15.20) more likely to have adequate knowledge of control and prevention activities compared to those with no formal education. Those who had reared animals for 1–5 years were 2.67 times (95% CI: 1.44–4.91) more likely to have adequate knowledge, compared to those who reared animals for >15 years. Cattle handlers with a monthly incomes of 10,000–20,000 taka were significantly (Odds Ratio = 0.36, 95% CI: 0.14–0.92) less likely to have adequate knowledge compared to those with monthly incomes <10,000 taka. Cattle handlers with high school or higher education were 6.98 times (95% CI: 2.47–19.71) more likely to use appropriate zTB control and prevention practices compared to those without formal education. Those who had reared animals for 1–5 years, 6–10 years and 11–15 years were 2.72- (95% CI: 1.42–5.24), 2.49- (95% CI: 1.29–4.77) and 2.86 times (95% CI: 1.13–7.23) more likely to apply appropriate practices compared to those who reared animals for >15 years. Overall, education, duration of cattle rearing and monthly income predicted zTB knowledge and practices. There is an urgent need to educate those at high-risk of zTB transmission on issues including the handling of infected animals, and general hygiene. A One Health approach, to support the Sustainable Development Goals and the End TB strategy, appears to be the way forward.

No abstract

The aim of this work is to find a good mathematical model for the classification of brain states during visual perception with a focus on the interpretability of the results. To achieve it, we use the deep learning models with different activation functions and optimization methods for their comparison and find the best model for the considered dataset of 31 EEG channels trials. To estimate the influence of different features on the classification process and make the method more interpretable, we use the SHAP library technique. We find that the best optimization method is Adagrad and the worst one is FTRL. In addition, we find that only Adagrad works well for both linear and tangent models. The results could be useful for EEG-based brain–computer interfaces (BCIs) in part for choosing the appropriate machine learning methods and features for the correct training of the BCI intelligent system.