SummaryLand snails are sources of protein to man and are hosts to a number of parasites. It is imperative that the roles of the snail hosts and parasites are clearly defi ned. Before then however, the parasites of the different land snails collected in any locality should be identifi ed. Land snails were collected in the wild in both dry and wet seasons. The internal organs and the faeces were examined for the presence of parasite. In the rainy season of 2015, a total of 272 snails were collected across four major towns (Benin, Uromi, Ekpoma and Auchi) in Edo State, Nigeria, while in the dry season, fewer snails (n=91) were handpicked. The snail species seen are: Achatina achatina (Linnaeus, 1758), Achatina fulica (Férussac, 1821), Acharchatina marginata (Swainson, 1982), Limicolaria aurora (Jay, 1839), L. fl ammea (Müller, 1774) and Limicolariopsis spp. The larvae of Strongyloides stercoralis were isolated from the various snail species with overall prevalence of 54.04 %. Snails positive with Alaria mesocercariae were L. aurora, L. fl ammea and Limicolariopsis spp. Additionally, few L. fl ammea were positive of the cercariae of Drocoelium dedriticum. Meanwhile, some samples of A. fulica harboured larvae of Angiostrongylus cantonesis, sporocysts of Fasciola gigantica and Schistosoma mansoni. Therefore, these edible snails could pose serious health hazard to man and animals by serving as a possible alternative parasite transmission route.

Autism spectrum disorder (ASD) is associated with significant social, communication, and behavioral challenges. The insufficient number of trained clinicians coupled with limited accessibility to quick and accurate diagnostic tools resulted in overlooking early symptoms of ASD in children around the world. Several studies have utilized behavioral data in developing and evaluating the performance of machine learning (ML) models toward quick and intelligent ASD assessment systems. However, despite the good evaluation metrics achieved by the ML models, there is not enough evidence on the readiness of the models for clinical use. Specifically, none of the existing studies reported the real-life application of the ML-based models. This might be related to numerous challenges associated with the data-centric techniques utilized and their misalignment with the conceptual basis upon which professionals diagnose ASD. The present work systematically reviewed recent articles on the application of ML in the behavioral assessment of ASD, and highlighted common challenges in the studies, and proposed vital considerations for real-life implementation of ML-based ASD screening and diagnostic systems. This review will serve as a guide for researchers, neuropsychiatrists, psychologists, and relevant stakeholders on the advances in ASD screening and diagnosis using ML.

Failure in a cloud system is defined as an even that occurs when the delivered service deviates from the correct intended behavior. As the cloud computing systems continue to grow in scale and complexity, there is an urgent need for cloud service providers (CSP) to guarantee a reliable on-demand resource to their customers in the presence of faults thereby fulfilling their service level agreement (SLA). Component failures in cloud systems are very familiar phenomena. However, large cloud service providers' data centers should be designed to provide a certain level of availability to the business system. Infrastructure-as-a-service (Iaas) cloud delivery model presents computational resources (CPU and memory), storage resources and networking capacity that ensures high availability in the presence of such failures. The data in-production-faults recorded within a 2 years period has been studied and analyzed from the National Energy Research Scientific computing center (NERSC). Using the real-time data collected from the Computer Failure Data Repository (CFDR), this paper presents the performance of two machine learning (ML) algorithms, Linear Regression (LR) Model and Support Vector Machine (SVM) with a Linear Gaussian kernel for predicting hardware failures in a real-time cloud environment to improve system availability. The performance of the two algorithms have been rigorously evaluated using K-folds cross-validation technique. Furthermore, steps and procedure for future studies has been presented. This research will aid computer hardware companies and cloud service providers (CSP) in designing a reliable fault-tolerant system by providing a better device selection, thereby improving system availability and minimizing unscheduled system downtime.

SummarySchoolchildren in primary schools are mostly at risk of acquiring soil-transmitted helminths (STHs) infections due to their habits (geophagy, onychophagy and playing with barefoot). Profiling soil parasites on school playgrounds is expected to provide an insight to an array of parasites schoolchildren are constantly at risk of acquiring; and this information could guide on intervention programmes. Soil samples from sixteen primary school playgrounds in Edo State (South-South, Nigeria) were collected over a six-month period both in the dry (January, February and March) and wet (May, June and July) seasons in 2018 and early 2019. Samples were processed and analysed following standard parasitological procedures. Of the 576 soil samples collected, 318(55.2 %) were positive with one or more soil parasites. Generally, the predominant parasites recovered from the total number of soil samples collected were: Ascaris 127(22 %), Strongyloides 111(19.27 %) and hookworm 50(8.68 %). Ascaris was most preponderant in the dry season, while Strongyloides was the most occurring in the wet season. The mean differences in the parasite load for Ascaris and hookworm between dry and wet seasons were not significant; while for Strongyloides it was higher in the wet than dry season. These results could be a consequence of observed poor state of toilet/sanitary facilities as well as the lack or poor state of basic infrastructure like proper drainage and waste disposal systems in the host communities. There is therefore urgent need to interrupt the STHs transmission cycles in the environment and possibly in schoolchildren by instituting sustainable intervention programmes within schools located in STHs endemic regions like southern Nigeria.