Assessment of faecal contamination in selected concrete and earthen ponds stocked with African catfish, Clarias gariepinus

Background: Microorganisms constitute significant fraction of the aquatic ecosystem and have been reported to be the cause of emerging novel infectious diseases in aquacultural practices. The prevalence of infectious diseases has been observed to depend on the interaction between fish pathogens and the aquatic environment. This study was conducted to assess the levels of faecal pollution markers in catfish (Clarias gariepinus) and their growing waters in selected earthen and concrete ponds in the teaching and research fish farm of the Federal University of Technology, Akure (FUTA), Nigeria in the dry (February-April) and wet seasons (May-July) of the year. Methodology: Two earthen and 2 concrete ponds were randomly selected as sampling sites due to their frequent usage. A total of 120 grabs of catfishes from the earthen (n=60) and concrete (n=60) ponds, and 84 pond water samples from earthen (n=42) and concrete (n=42) ponds, were randomly collected over a 6-month period of study. Enteric bacteria count in the water and catfish samples were determined using membrane filtration and pour plate methods respectively. The physiochemical characteristics of the water samples were determined using standard methods. The rate of bioaccumulation of faecal indicator bacteria was obtained by dividing the log count of each organism in the catfish by the corresponding log count in the growing waters. Results: Faecal coliforms count (log10 CFU/100ml) in the catfish from concrete and earthen ponds ranged from 1.41 to 2.28 and 1.3 to 2.47, and in the growing waters; 1.43 to 2.41 and 1.50 to 2.80 respectively. There was positive correlation of faecal coliforms with alkalinity of water samples from the earthen (r=0.61) and concrete ponds (r=0.62). Salmonella and faecal coliforms had the highest and least bioaccumulation in catfish raised in earthen pond while Salmonella and enterococci had the highest and least bioaccumulation in catfish raised in concrete pond respectively. Faecal coliforms and Escherichia coli had the highest and least counts in water samples from the earthen pond during the dry and wet months while Salmonella and E. coli had the highest and least counts in water samples from the concrete pond during the dry and wet months. Conclusion: High levels of bacterial faecal pollution markers in water samples and catfishes from the earthen and concrete ponds are reported in this study. Physicochemical characteristics and seasonality played major roles in the rate of bioaccumulation of the faecal pollution markers in catfishes raised in both earthen and concrete ponds

Integration of Random Forest Classifiers and Deep Convolutional Neural Networks for Classification and Biomolecular Modeling of Cancer Driver Mutations

Development of machine learning solutions for prediction of functional and clinical significance of cancer driver genes and mutations are paramount in modern biomedical research and have gained a significant momentum in a recent decade. In this work, we integrate different machine learning approaches, including tree based methods, random forest and gradient boosted tree (GBT) classifiers along with deep convolutional neural networks (CNN) for prediction of cancer driver mutations in the genomic datasets. The feasibility of CNN in using raw nucleotide sequences for classification of cancer driver mutations was initially explored by employing label encoding, one hot encoding, and embedding to preprocess the DNA information. These classifiers were benchmarked against their tree-based alternatives in order to evaluate the performance on a relative scale. We then integrated DNA-based scores generated by CNN with various categories of conservational, evolutionary and functional features into a generalized random forest classifier. The results of this study have demonstrated that CNN can learn high level features from genomic information that are complementary to the ensemble-based predictors often employed for classification of cancer mutations. By combining deep learning-generated score with only two main ensemble-based functional features, we can achieve a superior performance of various machine learning classifiers. Our findings have also suggested that synergy of nucleotide-based deep learning scores and integrated metrics derived from protein sequence conservation scores can allow for robust classification of cancer driver mutations with a limited number of highly informative features. Machine learning predictions are leveraged in molecular simulations, protein stability, and network-based analysis of cancer mutations in the protein kinase genes to obtain insights about molecular signatures of driver mutations and enhance the interpretability of cancer-specific classification models

Artificial lift selection methods in conventional and unconventional wells: a summary and review from old techniques to machine learning applications.

Artificial lift (AL) selection is an important process in enhancing oil and gas production from reservoirs. This article explores the old and current states of AL selection in conventional and unconventional wells, identifying the challenges faced in the process. The role of various factors such as production and reservoir data and economic and environmental considerations is highlighted. The article also examines the use of machine learning (ML) techniques in the AL selection process, emphasising their potential to increase the accuracy of selection and reduce data analysis time. The findings of this article provide valuable insights for researchers and practitioners in the oil and gas industry, as well as for those interested in the development of AL selection methods

Effect of Basic Education Policy on Rural Development in Ekiti State, Nigeria

The study examined the effect of basic education policy on rural development in the study area; and analysed the challenges confronting basic education policy implementation for rural development in the State. These were with a view to providing information on the effect of basic education policy on rural development of Ekiti State. The study adopted survey research design. Primary and secondary data were utilised for the study. Primary data were collected through the administration of questionnaire and interview. The distribution of the sample size is as follow: 93 teachers, 34 members of Local Government Departments of Education and 89 members of Community Development Association. In addition, interview was conducted on six purposively selected respondents, which comprised Local Inspectors of Education in the selected local governments. Secondary data was obtained from books, journal articles, internet, annual reports, workshops, conferences and seminar papers, newspapers and policy documents relating to the subject matter. Data collected was analysed using appropriate descriptive statistics. The descriptive statistics included the use of frequency distribution, percentages and measure of central tendency (mean). The study revealed that reduction in poverty level, economic growth and development, and equal accessibility to basic education were some of the effect of basic education policy on rural development in Ekiti State. The study reported that basic education policy has significant effect on rural development in Ekiti State (r = +0.685, p < 0.05)

A summary of artificial lift failure, remedies and run life improvements in conventional and unconventional wells.

Artificial lift (AL) systems are crucial for enhancing oil and gas production from reservoirs. However, the failure of these systems can lead to significant losses in production and revenue. This paper explores the different types of AL failures and the causes behind them. The article discusses the traditional methods of identifying and mitigating these failures and highlights the need for new designs and technologies to improve the run life of AL systems. Advances in AL system design and materials, as well as new methods for monitoring and predicting failures using data analytics and machine learning techniques, have been discussed. The findings of this work provide valuable insights for researchers and practitioners in the development of more reliable and efficient AL systems

Fabrication of forced air cool austempered ductile iron and exploring its corrosion behaviour in a simulated mine water

Abstract: The production of austempered ductile iron (ADI) with uniform microstructure and properties is constrained by the austempering process vis-à-vis the quenching medium. This is as a result of the stringent operating parameters with costly facilities. This limitation has restricted the application of ADI, despite its inherent mechanical and chemical properties. An emerging technology of overcoming this limitation is by austempering with force air cooling equipment, which is accessible, available and cost-efficient. This work characterizes the behaviour of the forced air cool ADI in simulated mine water due to the strategic importance of the mining industry in the global economy. The study establishes the influence of sample section thickness on the corrosion performance. The sample’s thickness were 5, 15, and 20 mm. Electrochemical experiments were performed on the forced air cool ADI at atmospheric pressure and room temperature with method such as open circuit potential (OCP). The post-corrosion analyses were performed using X-ray diffractometry (XRD) and field emission scanning electron microscopy (FESEM). The research highlighted that small section thickness has a more favourable performance compared with larger section. Consideration is also accorded to the capability of the ADI in the studied environment

Effect of metal poisoning and the implications of gender and age on the elemental composition in patients with mental behavioural disorders

The objective of this work was to investigate the possible correlation between the exposure to selected toxic metals and the behavioural disorder of mentally ill patients. The study also sought to establish if gender and age of the patient had an effect on the pattern of the elemental distribution in their head hair and blood samples. To achieve this, the concentrations of a number of selected toxic metal elements were determined in 60 mentally ill patients and 43 healthy individuals (control) in Ile-Ife area, in Nigeria, using inductively coupled plasma spectrophotometer-optical emission spectrometer (ICP-OES). The behavioural disorder cases investigated were 8 bipolar, 2 post partum psychosis, 43 schizophrenia and 7 non-specific cases. The concentration ranges of Cu, Zn, Ca, Li, V, Be (for both males and females), Cd and Sr (for females only) as analyzed from the patients’ head hair with behavioural disorders, were found to be similar with those of the controls. However, the concentration ranges of Al, Ba, Mg, Cr and Cd, Sr (for males only) were higher in patients than in the controls, while those for K and Fe were found to be higher in the controls than in the patients for both males and females. Blood samples analysis showed that, nearly all the elements were higher in the female (patients and control) than in the males; a possible indication that women may be at greater risk than men. It was also shown that, age may have an influence on the accumulation of some specific elements. The accuracy of the analytical results was experimentally demonstrated by NCS DC 73347 certified reference material that was analyzed along the standards while the significance of the data obtained was tested statistically at both p = 0.01 and 0.05