Sensor-Based Classification of Primary and Secondary Car Driver Activities Using Convolutional Neural Networks.

To drive safely, the driver must be aware of the surroundings, pay attention to the road traffic, and be ready to adapt to new circumstances. Most studies on driving safety focus on detecting anomalies in driver behavior and monitoring cognitive capabilities in drivers. In our study, we proposed a classifier for basic activities in driving a car, based on a similar approach that could be applied to the recognition of basic activities in daily life, that is, using electrooculographic (EOG) signals and a one-dimensional convolutional neural network (1D CNN). Our classifier achieved an accuracy of 80% for the 16 primary and secondary activities. The accuracy related to activities in driving, including crossroad, parking, roundabout, and secondary activities, was 97.9%, 96.8%, 97.4%, and 99.5%, respectively. The F1 score for secondary driving actions (0.99) was higher than for primary driving activities (0.93-0.94). Furthermore, using the same algorithm, it was possible to distinguish four activities related to activities of daily life that were secondary activities when driving a car.

DeeplyEssential: a deep neural network for predicting essential genes in microbes.

BACKGROUND: Essential genes are those genes that are critical for the survival of an organism. The prediction of essential genes in bacteria can provide targets for the design of novel antibiotic compounds or antimicrobial strategies. RESULTS: We propose a deep neural network for predicting essential genes in microbes. Our architecture called DEEPLYESSENTIAL makes minimal assumptions about the input data (i.e., it only uses gene primary sequence and the corresponding protein sequence) to carry out the prediction thus maximizing its practical application compared to existing predictors that require structural or topological features which might not be readily available. We also expose and study a hidden performance bias that effected previous classifiers. Extensive results show that DEEPLYESSENTIAL outperform existing classifiers that either employ down-sampling to balance the training set or use clustering to exclude multiple copies of orthologous genes. CONCLUSION: Deep neural network architectures can efficiently predict whether a microbial gene is essential (or not) using only its sequence information.

Association between the salinity level with miscarriage and unintended pregnancy in Bangladesh: Impact of salinity level on miscarriage and unintended pregnancy in Bangladesh.

Miscarriage is a significant public health concern worldwide, particularly in developing nations like Bangladesh. Moreover, people in coastal areas are more affected by miscarriage as compared to other areas. Increasing sea levels and salinity is the main reason for this discrepancy. This study aimed to investigate the association between different salinity levels (S1, S2, S3, S4, and S5) and miscarriage and unintended pregnancy. The outcome variables are pregnancy-related outcomes (miscarriage, unintended pregnancy), and the independent variables are different salinity levels. A frequency table and correlation analysis were done to find the descriptive scenarios of miscarriage, unintended pregnancy, and salinity levels. We found 621 miscarriage patients and 2271 unintended pregnant patients in our study. Furthermore, the Poisson regression model was used to observe the incidence of miscarriage and unintended pregnancy for different salinity levels. A higher amount of miscarriage and unintended pregnancy rate was found in Dhaka and Khulna, while these rates were lower in Barisal and Chittagong. However, the salinity levels were highest in Barisal and Khulna. Both miscarriage and unintended pregnancy are highly and negatively correlated with salinity levels. The Poisson regression model shows that the salinity levels s1-s5 are strongly associated with miscarriage. Lower and moderate levels of salinity are strongly associated with miscarriage than higher levels of salinity. Again, the average number of miscarriages decreases with the salinity levels. Likewise, unintended pregnancy was also negatively associated with salinity levels. However, it only reported a significant association with lower and moderate salinity levels, and higher salinity levels did not affect unintended pregnancy. Taking initiatives for raising awareness from government and non-government organizations, setting up deep tube water pumps extensively, and properly treating coastal areas women during pregnancy would be the ideal next step to reduce the miscarriage and unintended pregnancy rate in coastal zones in Bangladesh.

Association of climate factors with dengue incidence in Bangladesh, Dhaka City: A count regression approach.

Background: In Bangladesh, particularly in Dhaka city, dengue fever is a major factor in serious sickness and hospitalization. The weather influences the temporal and geographical spread of the vector-borne disease dengue in Dhaka. As a result, rainfall and ambient temperature are considered macro factors influencing dengue since they have a direct impact on Aedes aegypti population density, which changes seasonally dependent on these critical variables. This study aimed to clarify the relationship between climatic variables and the incidence of dengue disease. Methods: A total of 2253 dengue and climate data were used for this study. Maximum and minimum temperature (°C), humidity (grams of water vapor per kilogram of air g.kg-1), rainfall (mm), sunshine hour (in (average) hours per day), and wind speed (knots (kt)) in Dhaka were considered as the independent variables for this study which trigger the dengue incidence in Dhaka city, Bangladesh. Missing values were imputed using multiple imputation techniques. Descriptive and correlation analyses were performed for each variable and stationary tests were observed using Dicky Fuller test. However, initially, the Poisson model, zero-inflated regression model, and negative binomial model were fitted for this problem. Finally, the negative binomial model is considered the final model for this study based on minimum AIC values. Results: The mean of maximum and minimum temperature, wind speed, sunshine hour, and rainfall showed some fluctuations over the years. However, a mean number of dengue cases reported a higher incidence in recent years. Maximum and minimum temperature, humidity, and wind speed were positively correlated with dengue cases. However, rainfall and sunshine hours were negatively associated with dengue cases. The findings showed that factors such as maximum temperature, minimum temperature, humidity, and windspeed are crucial in the transmission cycles of dengue disease. On the other hand, dengue cases decreased with higher levels of rainfall. Conclusion: The findings of this study will be helpful for policymakers to develop a climate-based warning system in Bangladesh.