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In land plants, the pentatricopeptide repeat (PPR) proteins form a large family accounting for post-transcriptional process in mitochondria and chloroplasts. Although studies showed a number of PPR proteins generally influence the editing of organellar genes acting as a member of editing complex, few of them were characterized detailed in rice. Here, we reported a PLS-E subclass PPR protein in rice, PPR756, loss function of which lead to the abolishment of RNA editing events among three sites in mitochondrial genes including atp6 (ATP synthase F0 subunit 6), ccmC (cytochrome c biogenesis C) and nad7 (NADH dehydrogenase subunit 7). The defective C-to-U transformation of atp6-368, ccmC-236 and nad7-83 resulted in the retention of proline, proline and serine codon respectively to the right leucine in rice. Furthermore, PPR756 could bind to the three target genes directly and interacted with three OsMORFs (multiple organellar RNA editing factors), OsMORF1, OsMORF8-1 and OsMORF8-2. The knock-out plants of PPR756 exhibited retarded growth and greener leaves during the early stages, accompanied with sterile pollens at reproductive stage. These results established a role for PPR756 in the RNA editing of three various transcripts, cooperating with MORFs via editosome manner in rice. One sentence summary PPR756 takes part in the RNA editing of three various mitochondrial transcripts, cooperating with MORFs via editosome manner in rice.

Background : The majority of the rural populace of West and Central Africa participate actively in bushmeat commodity trade. The peak of this trade usually occurs during the migration of massive flocks of fruit bats. Unfortunately, this commercial period happens to coincides with the most conducive time for the spread of Ebola Virus Disease (EVD) which is transmitted by these fruit bats. We, therefore, conducted this study to predict EVD infestation trend among individuals who might be vulnerable and/or infected (hunters, vendors, and consumers of bushmeat) as a result of this seasonal bats migration. Methods: A secondary data collected in Ghana and semi-structured questionnaires were administered to international students from 7 countries (Democratic Republic of Congo (DRC), Ghana, Liberia, Zambia, Gabon, Guinea, and Ivory-Coast) and responses collated were used in this study. Bivariate correlation, probabilistic transmission rate of EVD, and probabilistic model (P-SIR) were employed to predict susceptible and infected individuals. Results : In all affected countries, it was observed that; there was a yearly bats’ migration pattern. 80 % of all interviewees confirmed that hunting, trading and consuming of bats influenced the rapid spread of EVD. The bivariate correlation between fruit bats and people involved in the bushmeat commodity chain was significant (p < 0.05) and the probability of infected bats were equally significant across the sampled countries. The estimated probabilistic rate was between 0.0099 and 0.01120, while individuals susceptible to EVD were predicted to be approximately between 5 and 45 (hunters), 0-18 f(vendors), and 25-90 (consumers). Conclusion : The migratory season of fruit bats contributes to EVD transmission in the most affected countries. People should avoid bushmeat consumption or eradicate all activities leading to the interaction with fruit bats to reduce the spread of EVD (Ebola) especially during the migratory seasons. Policy-makers and government agencies should also put in mechanisms that will protect their citizens during the fruit bats season. Keywords : bushmeat commodity chain, Ebola, migration season, EVD, fruit bats

Background: Infectious diseases are predominantly within poor population living in low-income countries, while are either treatable or preventable with existing medicines in the first occurring. The highlighted cause is some government choose to spend national budget on several projects do not coincide the basic needs and demands of the population. The objectives of this study were to 1) compare the performance between new cases and deaths caused by diseases; 2) show the effect of gross national income (GNI) in the mortalities reduction, and 3) assess potential evolution in eradicating mortalities in East African countries. Method: WHO database contains data on several responses (new cases of Malaria, Neonates protected at birth against neonatal tetanus, mortalities from tuberculosis among HIV-negative people and new cases of leprosy) recorded from 2004 to 2015. IMB SPSS modeler and Origin 8 were used especially, One-way ANOVA and Pearson’s correlation to achieve the objectives of the study. Results: The p-values for either Levene’ and Brown-Forsythe compared with 0.05 significant level for testing the performance between countries, correlation between GNI with leprosy is -0.5 to -1.0, in five countries, with TB is closer t0 -1.0 in four countries, with deaths from Malaria, is -0.5 to -1.0 in three countries, and new cases from Malaria and protected neonates is 0.5 to 1.0. Conclusion: The relationship between GNI and new cases and deaths indicate the weak effect of GNI in the process of eradicating mortalities, therefore, the government should prioritize the healthcare and use a national budget to monitoring the all complications related to infectious diseases. Key wards: infectious diseases, eradicating mortalities, gross national income

This study focused on exploiting machine learning algorithms for classifying and predicting injury severity of vehicle crashes in Yemen. The primary objective is to assess the contribution of the leading causes of injury severity. The selected machine learning algorithms compared with traditional statistical methods. The filtrated second data collected within two months (August-October 2015) from the two main hospitals included 156 injured patients of vehicle crashes reported from 128 locations. The data classified into three categories of injury severity: Severe, Serious, and Minor. It balanced using a synthetic minority oversampling technique (SMOTE). Multinomial logit model (MNL) compared with five machine learning classifiers: Naive Bayes (NB), J48 Decision Tree, Random Forest (RF), Support Vector Machine (SVM), and Multilayer Perceptron (MLP). The results showed that most of machine learning-based algorithms performed well in predicting and classifying the severity of the traffic injury. Out of five classifiers, RF is the best classifier with 94.84% of accuracy. The characteristics of road type, total injured person, crash type, road user, transport way to the emergency department (ED), and accident action were the most critical factors in the severity of the traffic injury. Enhancing strategies for using roadway facilities may improve the safety of road users and regulations.

The standard incubator used to monitor the development of preterm infants, with much attention for random optimization can interrupt the three main parameters (oxygen, environmental temperature, and humidity) responsible for preterm growth. The artificial neural network (ANN) has been recently proposed as a novel technique to control those parameters to provide a better and stabilized environment in an incubator. Unfortunately, this novel technique cannot continuously provide and indicate the update challenge of preterm growth. The objective of this paper is to apply a Markov multi-state growth process incorporates with multilayer feed-forward artificial neural network as an improved methodology to continuously control and provide an update of preterm growth in an incubator. The exchangeable Markov growth process, transition graph, and artificial neural network discussed on and applied in the designed incubator as methodology in paper and then make a joint density function of Markov multi-states growth process through multi-steps designed Algorithm to get the theoretical results. The updated measurements (weight, height, and head-perimeter) associated with controlled parameters used as input to the threshold logic unit (TLU) of ANN and then distinguish whether the growth process is abnormal or normal at each state. The summarized algorithm and multilayer feed-forward ANN utilized the panel data collected at Murunda hospital in Rwanda as input to show the application of improved methodology proposed in this paper, specifically, multi-state growth process of preterm infants across gender. As results, the continuous exchangeability of the growth process at each state has updated and may show abnormal or normal of growth process, and then sensors may notify these change through the joint density function of Markov multi-states growth process. Thus, improved methodology can increase the security and minimize time consumption in continuous monitoring growth process in an advanced way in time this idea has been implemented.