Mccurdyibrahim3891

The results showed that the increasing rPP contamination leads to deteriorating elongation at break and tensile impact strength. Furthermore, the addition of OBC as a compatibilizer into worse-case contamination scenario (15 wt% rPP in rPE-HD) significantly improved elongation at break and tensile impact strength. Scanning electron microscopy (SEM) confirms the improvement in adhesion between rPP and rPE-HD from recycled bottle waste with the addition of OBC as a compatibilizer. Rheological measurements reveal the interfacial interaction among rPP, rPE-HD and OBC. The low temperature tensile test demonstrated that the addition of OBC as a compatibilizer improved low temperature tensile elongation at break.The World Health Organization (WHO) recommends periodic assessment of the therapeutic efficacy of praziquantel (PZQ) to detect reduced efficacy that may arise from drug resistance in schistosomes. In this multi-country study (2014), we assessed the therapeutic efficacy of a single oral dose of PZQ (40 mg/kg) against Schistosoma mansoni (Brazil, Cameroon, Ethiopia, Mali, Madagascar and Tanzania), S. haematobium (Cameroon, Ethiopia, Mali, Tanzania and Zanzibar) and S. japonicum (the Philippines) infections in school-aged children, across a total of 12 different trials. Each trial was performed according to the standardized methodology for evaluating PZQ efficacy as described by the WHO. Overall, therapeutic efficacy, measured as the reduction in arithmetic mean of schistosome egg counts following drug administration (egg reduction rate; ERR), was high for all three schistosome species (S. mansoni 93.4% (95%CI 88.8-96.8); S. haematobium 97.7% (95%CI 96.5-98.7) and S. japonicum 90.0% (95%CI 68.4-99.3). At the triic efficacy of PZQ.In research focused on the intestine of parasitic nematodes, we recently identified small molecule inhibitors toxic to intestinal cells of larval Ascaris suum (nematode intestinal toxins/toxicants; "NITs"). Some NITs had anthelmintic activity across the phylogenetic diversity of the Nematoda. The whole-worm motility inhibition assay quantified anthelmintic activity, but worm responses to NITs in relation to pathology or affected molecular pathways was not acquired. In this study we extended this research to more comprehensively determine in whole larval A. suum the cells, organ systems, molecular targets, and potential cellular pathways involved in mechanisms of toxicity leading to cell death. The experimental system utilized fluorescent nuclear probes (bisbenzimide, propidium iodide), NITs, an A. suum larval parasite culture system and transcriptional responses (RNA-seq) to NITs. The approach provides for rapid resolution of NIT-induced cell death among organ systems (e.g. intestine, excretory, esophagus, hyrd systematic development of new anthelmintics.This study introduces a method that allows the generation and safety evaluation of a scenario catalog derived from potential car-pedestrian conflict situations. It is based on open-source software components and uses the road layout standard OpenDRIVE to derive participants' motion profiles with the support of available accident data. The method was implemented upon the open-source framework openPASS and can simulate results for different active safety system setups and facilitates the prediction of system capabilities to decrease the relative impact velocities and collision configurations such as the point of impact. A demonstration case was performed where the scenario catalog was derived and used to evaluate pedestrian collisions with and without a generic autonomous emergency braking (AEB) system. The AEB system aims to intervene in the event of an impending collision and might affect the outcome of a baseline scenario. The study indicated a change in the collision configuration and identified conflict situations less affected by the system. A particularly interesting finding was that some scenarios even led to a higher number of collisions (at lower impact) for the AEB intervention in comparison to the baseline cases.

Car-driving performance is negatively affected by the intake of alcohol, tranquillizers, sedatives and sleep deprivation. Although several studies have shown that the standard deviation of the lateral position on the road (SDLP) is sensitive to drug-induced changes in simulated and real driving performance tests, this parameter alone might not fully assess and quantify deviant or unsafe driving.

Using machine learning we investigated if including multiple simulator-derived parameters, rather than the SDLP alone would provide a more accurate assessment of the effect of substances affecting driving performance. We specifically analysed the effects of alcohol and alprazolam.

The data used in the present study were collected during a previous study on driving effects of alcohol and alprazolam in 24 healthy subjects (12 M, 12 F, mean age 26 years, range 20-43 years). Various driving features, such as speed and steering variations, were quantified and the influence of administration of alcohol or alprazolam wssment of drug-induced abnormal driving behaviour. The created models may facilitate quantitative description of abnormal driving behaviour in the development and application of psychopharmacological medicines. Our models require further validation using similar and unknown interventions.Selecting an appropriate exposure measure and functional form for Safety Performance Functions (SPFs) is critical in precisely predicting crash counts by different crash types for intersections. This study proposes a new approach, namely Generalized Negative Binomial-P (GNB-P) model, to model the complex relationship between crashes and different exposure measures by crash type for intersections, which helps not only identify the most reliable exposure measure for intersection SPFs, but also explore the most appropriate functional form of the NB models. To this end, three types of SPF functional forms, namely Power function, Hoerl function 1 and Hoerl function 2 with different exposure measures including major road AADT, minor road AADT and total AADT were estimated by crash type for stop-controlled and two types of signalized intersections. The over-dispersion of the SPF models was estimated using the exposure measures to account for crash data variation across different intersections. The SPF estimation results highlighted that the mean-variance structure of NB models is not consistent and varies by crash data. The over-dispersion of SPFs by crash type is not constant and varies across different intersections. The minor road AADT is shown to be positively correlated with the over-dispersion of SPFs in estimating crash counts for Same-Direction Crashes (SDC), Intersecting-Direction Crashes (IDC) and Single-Vehicle Crashes (SVC). Estimating the over-dispersion using exposure measures results in more reliable SPF results. Furthermore, it is found that the Power function with major road and minor road AADT as the exposure measure performs the best in estimating SPFs for Opposite-Direction Crashes (ODC). The Hoerl function 2 with total AADT and the proportion of minor road AADT over the total as the exposure measure performs the best in estimating SVC SPFs for intersections. The Hoerl function 1 with major road and minor road AADT as the exposure measure is more accurate in estimating SPFs for both SDC and IDC.Pedestrian safety plays an important role in the transportation system. find more Intersections are dangerous locations for pedestrians with mixed traffic. This paper aims to predict the near-accident events between pedestrians and vehicles at signalized intersections using PET (Post Encroachment Time) and TTC (Time to Collision). With automated computer vision techniques, mobility features of pedestrians and vehicles are generated. Extreme Value Theory (EVT) is used to model PET and minimum TTC values to select the most appropriate threshold values to label pedestrians' near-accident events. A Gated Recurrent Unit (GRU) neural network is further used to predict these events. The established model reaches an AUC (Area Under the Curve) value of 0.865 on the test data set. Moreover, the proposed model can also be applied to develop collision warning systems under the Connected Vehicle environment.

Albuminuria is not an effective marker for early diagnosis of diabetic renal complication with several subjects progressing to chronic kidney disease without any albuminuria. A biomarker that can predict early changes of the diabetic kidney will be useful in effective management of type 2 diabetes. Mass spectrometry based metabolomics approach offers tremendous promise for the identification of novel metabolite biomarkers.

A case-control approach was carried out to identify renal biomarkers among Asian Indian subjects in a hospital setting. A total of 29 subjects were included in the study that included groups of diabetic controls, diabetic subjects with eGFR >90ml/min/1.72m

and diabetic subjects with eGFR between 60 and 89ml/min/1.72m

and eGFR between 15 and 30ml/min/1.72m

. We employed an un-targeted mass spectrometry method for the identification of plasma metabolites.

A total of 1414 and 975 metabolites were identified in the positive and negative ion mode respectively. 19 metabolites were up regulated and 18 metabolites were down regulated in CKD2 and CKD4 groups when compared to control. Correlation analysis of the differential metabolites revealed Pregnenolone sulfate, creatinine and ganglioside GA1 to be negatively correlated and hexyl glucoside, all-trans-carophyll yellow and PG to be positively correlated with eGFR.

We have identified Pregnenolone sulfate, GA1, PG and all-trans-Carophyll yellow as biomarkers for early identification of diabetic nephropathy. These markers could aid in better management of diabetic nephropathy that may result delaying the progression of the disease.

We have identified Pregnenolone sulfate, GA1, PG and all-trans-Carophyll yellow as biomarkers for early identification of diabetic nephropathy. These markers could aid in better management of diabetic nephropathy that may result delaying the progression of the disease.The data imbalance problem in classification is a frequent but challenging task. In real-world datasets, numerous class distributions are imbalanced and the classification result under such condition reveals extreme bias in the majority data class. Recently, the potential of GAN as a data augmentation method on minority data has been studied. In this paper, we propose a classification enhancement generative adversarial networks (CEGAN) to enhance the quality of generated synthetic minority data and more importantly, to improve the prediction accuracy in data imbalanced condition. In addition, we propose an ambiguity reduction method using the generated synthetic minority data for the case of multiple similar classes that are degenerating the classification accuracy. The proposed method is demonstrated with five benchmark datasets. The results indicate that approximating the real data distribution using CEGAN improves the classification performance significantly in data imbalanced conditions compared with various standard data augmentation methods.