Effects of Some Heavy Metals on Chlorophyll Accumulation in \u3cem\u3eBarbula lambarenensis\u3c/em\u3e

Samples of moss (Barbula lambarenensis) with their substrates collected from Belewu Drive, Oke Odo, Ilorin, were taken to the screen house of the University of Ilorin biological garden to monitor the effects and tolerance of this plant to different heavy metals. The moss samples were divided into eleven regimes, widely separated from one another to avoid contaminations. Ten regimes were differently irrigated with 1000 ppm and 2000 ppm of lead, copper, cadmium, iron and vanadium thrice a week. The eleventh treatment served as the control, and was irrigated with distilled water. It was found that these heavy metals caused some damage to the chloroplasts of this plant as the bright green colours changed light green, yellowish green or brown. The concentrations of the used heavy metals are phytotoxic. In light of this, Barbula lambarenensis can serve as a bio-indicator of heavy metals

Freshwater Fish Diversity of a Tropical Rainforest River in Southeast Nigeria

Fish samples were collected at three stations twice per month from January to December 2013 with the help of local fishers using hook and line, gill net, cast net, bagnet and local traps for ecological studies of important fish species and resource management issues of Oramiri-Ukwa River, southeast Nigeria. An estimate of 25 fish species, 15 genera, 21 families and six orders were obtained. Ecological indices indicate a polydiverse community and no single species exhibited true dominance (? 50%). Paired group cluster analysis establishes Tilapia zilli and Hemichromis fasciatus as the focal species and identifies the associated species combinations that characterize spatial variability and account for the biodiversity resources and structure of the artisanal fishery. Other important species include Tilapia mariae>Synodontis nigrita while Polypterus senegalus>Parachanna africana and Shilbe mystus were the least in number. Monospecific and rare fish species of ecological and conservation significance identified include Ctenopoma kingsleyae, Clarias gariepinus as well as Erpetoichthys calabaricus and Pantodon buchholzi derived from interconnections with other African rivers. This study presents lower fish diversity compared to earlier reports. This difference may be linked to increased human activities and fluctuating biotic and abiotic factors of the ecosystem, among others. Keywords: Abundance, biodiversity, conservation, rare specie

Economic Analysis of Cassava Production: Prospects and Challenges in Irepodun Local Government Area, Kwara State, Nigeria

This study was carried out to analyse cassava production, prospects and challenges in Irepodun local government area, Kwara State, Nigeria. It aimed to examine the determinant variables and determine the profitability level of cassava enterprises. The study was based on primary data obtained with the aid of structured questionnaire from 100 cassava farmers drawn through multi-stage sampling techniques from the study area. Data were analysed using descriptive statistics, ordinary least square (OLS) regression model and gross margin analysis. The result of the OLS regression estimates showed that farm size, cost of fertilizer application, farm size, herbicides, family and hired labour were significant variable affecting cassava production in the study area. Fertilizer, farm size and hired labour are significant at 1% while herbicide and family labour are significant at 10%. The study found that the average gross margin per hectare for cassava production in the study area was ₦24,749.28 ($65.30) with a gross benefit ratio of 1.38. This shows that for every ₦1 invested in the business of cassava production, there is a corresponding profit of ₦1.38. The major challenges identified in cassava enterprise are huge transportation cost, high cost of production, lack of improved cassava cultivars, and lack of market linkages. The study concluded despite the problem encountered in the study area, cassava production is profitable and can serve as a panacea for economic improvement of households. Therefore, the study recommends that basic inputs such as improved cassava varieties, herbicides and fertilizer should be made available at affordable price. Also, infrastructural facilities such as good road network and improve marketing channels should be provided in order to sustain current cassava production rate and economic gains from the production

Seismic Stratigraphy and Reservoir Characterization of ‘E’ Field Sediments: Inferences from South-Eastern Late Miocene - Pliocene Records, Offshore Niger Delta

Studies of Late Miocene – Pliocene continental shelf and slopes sediments on the south-eastern continental margin, Niger Delta (a broad region from the shelf – slope break extending to the ultra-deep waters: > 1500m), have revealed markedly different responses to sea level fluctuations. Significant features of the stratigraphy include  siliciclastic-dominated facies consisting principally of one or more of the following genetic types: deltaic distributary mouth bars, channel and shoreface sands, barrier beach, shelf and offshore turbidites. These sands are Late Miocene – Early Pliocene in age and were deposited in deep water settings on the slope of the ‘Y’ field by a range of depositional processes that include slumps, debris flows and turbidity currents. Most of these sands could be interpreted to relate to periods of base level fall, if not Global Eustatic lowstands. Working within a sequence stratigraphic framework, eight (8) sequences have been delineated on the basis of reflection termination patterns. The major sequences were related to sea level fall during which the shelf was exposed to erosion. A cross section of the stratigraphic correlation drawn showed that the horizons are laterally continuous. However, pinch-out channel sands and lenticular sandbodies are evident. The recognition of depositional surfaces on the stratigraphic cross-sections allows subdivision of the stratigraphy into systems tracts: HST, FSST, TST and LST. On the seismic package, three (3) main seismic surfaces with distinct chronostratigraphic expressions are evident. They include non-marine, marine and fault plane surfaces. In addition, clinoform strata in the basin-margin setting of this field have relatively flat topsets and sloping clinoforms. On the shelf settings, a composite surface exists consisting of the merged sequence boundary, otherwise marked and interpreted as 4.2 Ma sequence boundary, transgressive surface (TS) and maximum flooding surface (MFS), unless separated by an incised valley fill (IVF). In the ‘Y’ field, failure, slumping and re-sedimentation processes that cause base-of-slope thickening in response to gravity and geotropic flows modify the slope. Furthermore, within the same basinal setting, affected by the same sea level rise, the facies boundaries are diachronous. Keywords: Seismic stratigraphy, Petrophysics, Sea level change, South-eastern, Miocene – Pliocene Sedimentation, Offshore Niger Delta DOI: 10.7176/JNSR/11-14-04 Publication date:July 31st 202

Assessing Medical Doctors’ Knowledge and their Confidence in Spot Diagnosing Monkeypox in South-South Nigeria

Human monkeypox is an emerging viral zoonotic infectious disease caused by a DNA virus that belongs to the Orthopoxvirus genus. Knowledge of monkeypox, high index and sound clinical judgement particularly amongst medical doctors is critical to responding to monkeypox effectively. Previous studies have shown poor knowledge of monkeypox infection amongst doctors. This study aims to assess doctors’ knowledge of monkeypox and their confidence in diagnosing monkeypox prior to laboratory confirmation. A cross-sectional online survey containing 28-item scale and explanatory variables were used to assess respondents’ knowledge, confidence and risk perception on monkeypox. The participants were reached with online Google form posted on the Nigerian Medical Association group WhatsApp, Cross River State. The questionnaires were structured closed-ended and were self-administered to collect quantitative data. A total of 164 medical doctors working in Cross River State participated. Only 38 (23.2%) of them had good knowledge of monkeypox, using a > 60% cutoff point for good knowledge. Seventy-two percent (72%) displayed confidence to clinically diagnose monkeypox in their daily clinic runs. There was statistically significant relationship between knowledge category and medical sub-specialties (X2 =6.98; p=0.03). We conclude that knowledge of monkeypox amongst medical doctors practicing in Cross River State, Nigeria is currently low, though confidence to diagnose it is high, this confidence should be backed with sound medical knowledge to improve doctors’ capacity to respond to the emerging monkeypox infection

Comparative Analysis of Neural Network Models for Petroleum Products Pipeline Monitoring

In recent years, Neural Network (NN) has gained popularity in proffering solution to complex nonlinear problems. Monitoring of variations in Petroleum Products Pipeline (PPP) attributes (flow rate, pressure, temperature, viscosity, density, inlet and outlet volume) which changes with time is complex due to existence of non linear interaction amongst the attributes. The existing works on PPP monitoring are limited by lack of capabilities for pattern recognition and learning from previous data. In this paper, NN models with pattern recognition and learning capabilities are compared with a view of selecting the best model for monitoring PPP. Data was collected from Pipelines and Products Marketing Company (PPMC), Port Harcourt, Nigeria. The data was used for NN training, validation and testing with different NN models such as Multilayer Perceptron (MLP), Radial Basis Function (RBF), Generalized Feed Forward (GFF), Support Vector Machine (SVM), Time Delay Network (TDN) and Recurrent Neural Network (RNN). Neuro Solutions 6.0 was used as the front-end-engine for NN training, validation and testing while My Structured Query Language (MySQL) database served as the back-end-engine. Performance of NN models was measured using Mean Squared Error (MSE), Mean Absolute Error (MAE), Correlation Coefficient (r), Akaike Information Criteria (AIC) and Minimum Descriptive Length (MDL). MLP with one hidden layer and three processing elements performed better than other NN models in terms of MSE, MAE, AIC, MDL and r values between the computed and the desired output

Relationship between the plasma testosterone level and pain reaction times in male rats

Male prepubertal (about 4 weeks old) Wistar rats were used to estimate the pain reaction times using the tail-flick and hot-plate models; the testosterone concentration in all the animals before the tests in the blood plasma was measured. The same sets of animals were kept for the next 4 weeks under standard conditions; the experiment was repeated, and pain reaction times were also evaluated in the 8-week-old rats with blood samples collected to determine the plasma testosterone level. The results showed significant (P < 0.01) increases in the pain reaction times in both pain models in pubertal animals observed in a parallel manner with a corresponding significant (P < 0.01) increase in the plasma testosterone level. Therefore, age and sex are important factors in the choice of animals in pain experiments.У щурів-самців препубертатного віку (чотири тижні) вимірювали латентні періоди больових реакцій в умовах тестів «відсмикування хвоста» та «гарячої пластинки»; у всіх тварин перед тестами вимірювалася концентрація тестостерону в плазмі крові. Ці ж самі групи тварин утримувалися протягом чотирьох тижнів у стандартних умовах, після чого експеримент повторювали на восьмитижневих щурах (вимірювали час больових реакцій та рівень тестостерону). Для тварин, що досягли віку статевої зрілості, було характерне істотне (P < 0.01) збільшення латентних періодів больових реакцій в обох використаних моделях, що відбувалося паралельно з відповідним вірогідним (P < 0.01) збільшенням рівня тестостерону в плазмі. Отже, вік і стать є найважливішими факторами при відборі тварин для проведення експериментів з больовою стимуляцією

Évaluation de la performance des modèles d’apprentissage Ensemble-Tree sur l’ensemble de données sur le cancer du sein

Advancements of feature extraction enable the collection of prognostic data values which can be used to distinguish between benign and malignant tumours. While single learning models are capable of making predictions, combining weak learners to form an ensemble can improve predictive performance. This study evaluates and compares the performance of a few selected ensemble-tree machine learning models as applied to a Wisconsin Diagnostic breast cancer (WDBC) dataset. The dataset is split, producing a 60% training and 40% test division set. Random Forest classifier, Extremely Randomized Trees classifier, Gradient Boosting machine classifier and Extreme Gradient Boosting classifier were initialized with 3 weak learners and fit to the training set, with subsequent predictions made on the test set. Evaluation metrics used include Accuracy, Area under Receiver Operating Characteristic curves (AUROC), Precision-Recall curves and F2 scores followed by a Stratified 5-fold cross-validation procedure. Taking Precision and Recall into higher consideration, Extreme Gradient Boosting classifier and Extremely Randomized Trees classifier produced better performances with an average accuracy of 0.9386 and 0.9460 respectively. Overall, the Extremely Randomized Trees classifier outperforms the rest of the models with an average F2 score of 0.4232. Keywords: Breast cancer; Classification models; Tree-based Ensemble; Supervised learningLes progrès réalisés dans l’extraction des caractéristiques permettent de recueillir des valeurs de données pronostiques qui peuvent être utilisées pour distinguer les tumeurs bénignes des tumeurs malignes. Alors que les modèles d’apprentissage uniques sont capables de faire des prédictions, la combinaison d’apprenants faibles pour former un ensemble peut améliorer les performances prédictives. Cette étude évalue et compare les performances de quelques modèles d’apprentissage arborescent sélectionnés appliqués à un ensemble de données sur le cancer du sein du Wisconsin (WDBC). L’ensemble de données est divisé, produisant un ensemble de 60 % de divisions d’entraînement et un ensemble de 40 % de divisions de test. Le classificateur Random Forest, le classificateur Extremely Randomized Trees, le classifieur machine Gradient Boosting et le classifieur Extreme Gradient Boosting ont été initialisés avec 3 apprenants faibles et ajustés à l’ensemble d’apprentissage, avec des prédictions ultérieures effectuées sur l’ensemble de test. Les mesures d’évaluation utilisées comprennent la précision, les courbes de la zone sous Caractéristique de Opérationnel de le Récepteur (AUROC), les courbes de précision-de rappel et les scores F2, suivies d’une procédure de validation croisée stratifiée à 5 plier. Considérant la précision et le rappel plus, le classificateur Extreme Gradient Boosting et le classificateur Extremely Randomized Trees ont produit de meilleures performances avec une précision moyenne de 0,9386 et 0,9460 respectivement. Dans l’ensemble, le classificateur Extremely Randomized Trees surpasse le reste des modèles avec un score F2 moyen de 0,4232. Mots clés : Cancer du sein ; Modèles de classification ; Ensemble arborescent ; Apprentissage supérvisé