Spatial disparities and associated factors of Under-five diarrhea disease in Ilubabor Zone, Oromia Regional State, Ethiopia

Diarrhea is commonly a sign of an infection in the intestinal tract that is caused by different bacteria, virus and parasitic entities. It is one of the leading causes of child mortality worldwide, especially in sub-Saharan Africa countries including Ethiopia. The main objective of this study was to identify spatial disparities and associated factors of under- five diarrhea disease in Ilubabor zone, Oromia regional state, Ethiopia. The study has been conducted in Ilu Aba Bor zone of entire districts and the data is basically both primary and secondary which were obtained from each woreda health office of Ilu Aba Bor zone and corresponding mother or care givers of sampled child. Spatial disparities of under-five diarrhea were identified using global and local measures of spatial autocorrelation. Geo-additive regression model was used to identify the spatial disparities and associated factors of under-five diarrheal disease. The value of global and local measures of spatial autocorrelation shows that under-five diarrheal disease varies according to geographical location and shows significant positive spatial autocorrelation. The results of Geo-additive regression model showed that statistically significant relationship between under-five diarrhea disease and independent variables .There is evidence of significant under-five diarrheal disease clustering in Ilu Aba Bor zone, southwest Ethiopia. Model based data analysis showed that there is significant relationship between Under-five diarrhea and covariates (mother’s age, mother’s education, source of drinking water, quality of toilet facility, DPT 3 vaccination, Polio 3 vaccination and household wealth index.).

Bayesian optimization with adaptive surrogate models for automated experimental design

Bayesian optimization (BO) is an indispensable tool to optimize objective functions that either do not have known functional forms or are expensive to evaluate. Currently, optimal experimental design is always conducted within the workflow of BO leading to more efficient exploration of the design space compared to traditional strategies. This can have a significant impact on modern scientific discovery, in particular autonomous materials discovery, which can be viewed as an optimization problem aimed at looking for the maximum (or minimum) point for the desired materials properties. The performance of BO-based experimental design depends not only on the adopted acquisition function but also on the surrogate models that help to approximate underlying objective functions. In this paper, we propose a fully autonomous experimental design framework that uses more adaptive and flexible Bayesian surrogate models in a BO procedure, namely Bayesian multivariate adaptive regression splines and Bayesian additive regression trees. They can overcome the weaknesses of widely used Gaussian process-based methods when faced with relatively high-dimensional design space or non-smooth patterns of objective functions. Both simulation studies and real-world materials science case studies demonstrate their enhanced search efficiency and robustness.

E-Mail Spam Filtering

E-mail is that the most typical method of communication because of its ability to get, the rapid modification of messages and low cost of distribution. E-mail is one among the foremost secure medium for online communication and transferring data or messages through the net. An overgrowing increase in popularity, the quantity of unsolicited data has also increased rapidly. Spam causes traffic issues and bottlenecks that limit the quantity of memory and bandwidth, power and computing speed. To filtering data, different approaches exist which automatically detect and take away these untenable messages. There are several numbers of email spam filtering technique like Knowledge-based technique, Clustering techniques, Learning-based technique, Heuristic processes so on. For data filtering, various approaches exist that automatically detect and suppress these indefensible messages. This paper illustrates a survey of various existing email spam filtering system regarding Machine Learning Technique (MLT) like Naive Bayes, SVM, K-Nearest Neighbor, Bayes Additive Regression, KNN Tree, and rules. Henceforth here we give the classification, evaluation and comparison of some email spam filtering system and summarize the scenario regarding accuracy rate of various existing approaches. Keywords: e-mail spam, unsolicited bulk email, spam filtering methods.

Genome-wide association study of periodontal pocketing in Finnish adults

Background A genome‐wide association study is an analytical approach that investigates whether genetic variants across the whole genome contribute to disease progression. The aim of this study was to investigate genome-wide associations of periodontal condition measured as deepened periodontal pockets (≥ 4 mm) in Finnish adults. Methods This study was based on the data of the national Health 2000 Survey (BRIF8901) in Finland and the Northern Finland Birth Cohort 1966 Study totalling 3,245 individuals. The genotype data were analyzed using the SNPTEST v.2.4.1. The number of teeth with deepened periodontal pockets (≥ 4 mm deep) was employed as a continuous response variable in additive regression analyses performed separately for the two studies and the results were combined in a meta-analysis applying a fixed effects model. Results Genome-wide significant associations with the number of teeth with ≥ 4 mm deep pockets were not found at the p-level of < 5 × 10−8, while in total 17 loci reached the p-level of 5 × 10−6. Of the top hits, SNP rs4444613 in chromosome 20 showed the strongest association (p = 1.35 × 10−7). Conclusion No statistically significant genome-wide associations with deepened periodontal pockets were found in this study.

Prediction of Inpatient Pressure Ulcers Based on Routine Healthcare Data Using Machine Learning Methodology

Despite the relevance of pressure ulcers (PU) in inpatient care, the predictive power and role of care-related risk factors (e.g. surgical anesthesia) remain unclear. We investigated the predictability of PU incidence and its association with multiple care variables. We included all somatic cases between 2014 and 2018 with length of stay ≥2 days in a German university hospital. For regression analyses and prediction we used Bayesian Additive Regression Trees (BART) as nonparametric modeling approach. To assess predictive accuracy, we compared BART and logistic regression (LR) using area under the curve (AUC) and confusion matrices. The analysis of 149,006 cases revealed high predictive variable importance and associations between incident PU and intensive care with ventilation, age, surgical anesthesia (≥1 hour) and number of care-involved wards. Despite high AUCs (LR: 0.89; BART: 0.9), the confusion matrices showed a higher number of false negative (LR: 816; BART: 826) than true positive (LR: 138; BART: 68) predictions. In summary, particularly intensive care with ventilation, age, anesthesia and number of care-involved wards were associated with incident PU. Using surgical anesthesia as a proxy for immobility, our results suggest hourly repositioning. High rates of false negative predictions indicate a general challenge in the predictability of PU.