694 research outputs found
Growth and Yield of Pearl Millet (\u3cem\u3ePennisetum glaucum\u3c/em\u3e L.R. Br.) As Influenced by Downey Mildew and Smut Diseases in Kabuga Area of Kano State, Nigeria
The performance of four Pearl millet varieties comprising three improved varieties and one local variety was investigated in terms of their yield and response to natural infestation of Downy mildew (DM) and smut diseases. Data on yield parameters, disease score and weather were collected over the two year period of the investigation. The investigation revealed the existence of inherent differences among the four varieties tested and the influence of biotic and abiotic factors on some of the yield parameters. The variety ICMN IS 88224 was found to be the most susceptible to DM and smut disease while two improved varieties (ICMn IS series) were found to be tolerant. The local variety, INMV 55 was found to be the most tolerant and as such, ICMN IS 88212 is recommended for this location
STATISTICAL ANALYSIS OF TEMPORAL VARIATIONS IN INDOOR RADON DATA USING AN ADAPTED RESPONSE SURFACE METHOD
Temporary variations in indoor radon data (IRD), comprising radon concentration (RC), air temperature, relative humidity and barometric pressure were monitored hourly over a period of two months in a bungalow house in Abeokuta, Nigeria. A total of 1510 data was assembled and analyzed statistically using Shapiro-Wilk for normality test, response surface method (RSM) and adapted response surface method (ARSM) to investigate and model the influence of the meteorological parameters on the variations of RC in indoor air. The overall results showed that RC varies widely over time and correlates positively with relative humidity and temperature, but negatively with barometric pressure. Specific results of the two response surface methods were compared and contrasted and the multiple linear regression model of the ARSM was highlighted and established as the appropriate method for analyzing IRD. ARSM was presented in an easily reusable form that can easily be adopted by researchers and data analysts.ª¤
Solitary colonic neurofibroma in an African child
Neurofibromas are a group of heterogeneous neurocutaneous disorders. They are benign neoplasms consisting of neural and connective tissue components affecting any organ system. Gastrointestinal involvement in neurofibromatosis type 1 (NF1) are rare and are late manifestations of the disease, but in exceptional cases they can be the initial sign of neurofibromatosis in patients who have no external stigmata that arouse suspicion. Neurofibromatosis of the colon as a part of the NF1 is rare. Isolated colonic neurofibromatosis without other features suggestive of NF1 is rarely encountered in clinical practice. We report the case of a 12-year-old boy with an isolated colonic neurofibromatosis presenting with a right hypochondrial mass with no external features of NF1. We report this case as its presentation in children may give a diagnostic dilemma and the probability of malignant digestive disease associated with NF1 should be kept in mind, regardless of the age of the patient.Keywords: children, colonic, neurofibroma, rare, solitar
Characterization Studies and Yield Attributes of Some Varieties of Cowpea (\u3cem\u3eVigna unguiculata\u3c/em\u3e L.)
Investigations were carried out on sixteen improved varieties of cowpea (Vigna unguiculata L walp) obtained from International Institute for Tropical Agriculture, Ibadan. These varieties were grown in the Department of Biological Sciences Garden, of the University of Ilorin. Qualitative and quantitative characters were observed for the study. Positive correlations were obtained between leaf number and stem diameter, leaf number and number of seeds per pod, number of branches and plant height. Negative correlation existed between number of pods per plant and number of seed per pod. Analysis of variance (ANOVA) showed significant difference in all the quantitative characters analyzed among the varieties. Each character was also analyzed using Duncan test and F-LSD. The result showed that IT99K-529-1, IT99K – 429 – 2, IT97K – 461 – 4, IT98K – 506 – 1 and IT99K- 1122 were outstanding in term of vegetative and yield performances in Ilorin, Kwara State of Nigeria
IN VITRO EFFECT OF ACETYLSALICYLIC ACID ON CALCIUM OXALATE CRYSTALLIZATION: AN APPROACH TO ANTILITHIASIS
Objective: In recent years, significant progress has been made in identifying and counting physico-chemical processes involved in urinary stone formation. The ability of urine to inhibit calcium oxalate crystallization is considered an important mechanism against stone formation. Several natural substances were tested to inhibit calcium oxalate crystallization. In the present study, we evaluate the effects of acetylsalicylic acid, also known as Aspirin, as an inhibitor of calcium oxalate crystallization in vitro.Methods: The nucleation and aggregation of calcium oxalate crystals were studied using turbidimetric 400-sec time course measurements of optic density at 620 nm after mixing solutions containing calcium chloride and sodium oxalate at room temperature, pH 5.7. The formation of crystals is induced by the addition of the oxalate to calcium solution. The effects on calcium oxalate crystal growth of acetylsalicylic acid with various concentrations were examined. The maximum increase of optic density in the course of time reflects maximum rate of formation of new particles. After reaching equilibrium, a progressive decrease of optic density with time is observed. Rate of aggregation is derived from the maximum decrease in optic density.Results: The results showed that if a concentration of acetylsalicylic acid is more than 1,66 mM both rate of formations of new particles and Rate of aggregation decreased (P<0.05).Conclusion: acetylsalicylic acid has a significant effect on nucleation as well as on crystal growth stage; consequently, it inhibits the crystal formation of calcium oxalate urinary lithiasis. Â
Meiotic Chromosome Count for Chlorophytum sabiense Omok
Chlorophytum sabiense Omok (the newly identified taxon in the family Asparagaceae from the Western Tropical Africa) exhibits some exceptional characteristics having; three ovaries, each with a distinct style and stigmas with few lobes. These features separate Chlorophytum sabiense from most species reported in West Africa. Significance of floral characters in taxonomy cannot be overemphasis; as the most reliable morphological feature. In view of this, evidences are required to ascertain true taxonomic statue of the species. In the present contribution, meiotic study was employed as a veritable tool; to investigate pairing pattern of the haploid chromosomes of the species and also to determine the haploid chromosome number for the species. Flower buds at the right age were collected, treated using the conventional method and studied from pachytene to full metaphase. At diakinesis seven bivalents (7 II) were obtained, thus indicate that the species belong to n=7 series of the genus. Seven bivalents (7 II) were however less prevalent (31.9%) compared with multivalents, which indicates high incidence of chromosome stickiness at metaphase I of the species
HIERARCHICAL MODELLING OF INDOOR AND OUTDOOR (RESIDENTIAL) RADON DATA (RRD)
This work, proposes a Hierarchical Modelling (HM) for the indoor and outdoor Residential Radon Data (RRD). Indoor RRD and outdoor RRD are seen as distinct «¤??hierarchies«¤?? of carcinogenic radioactive radon and both hierarchies constitute the least exposure that can be experienced by an individual. Works on this issue have always been based on complicated models, even for single instances of both indoor and outdoor residential radon. Our proposed method can be used to analyse effectively the many-to-many (it, however, becomes numerically clumsy if more than 5-to-5 instances are considered) instances of residential radon, although we have illustrated, here, using a three-to-three situation. Our preference of this method is based on its simplicity, and probable higher precision, as compared with the complexity involved in other methods on the same issue. The data used for the illustration of our models were taken from the indoors (i.e. living-room, bedroom and the kitchen) and the rest outdoors (i.e. verandah, car-park and the well-water shed) of a residential building in a lightly populated estate (i.e. Asero housing estate). Observations were taken on a daily basis throughout the dry season covering ninety days (i.e. January, February and March), this constitutes our season I (i.e. dry). The same was repeated in the season II (i.e. wet) which was taken at the beginning of June through July and August.ª¤?ª¤?ª¤?ª¤?ª¤?ª¤?ª¤?ª¤
OAERP: a better measure than accuracy in discriminating a better solution for stochastic classification training
The use of accuracy metric for stochastic classification training could lead the solution selecting towards the sub-optimal solution due to its less distinctive value and also unable to perform optimally when confronted with imbalanced class problem. In this study, a new evaluation metric that combines accuracy metric with the extended precision and recall metrics to negate these detrimental effects was proposed. This new evaluation metric is known as Optimized Accuracy with Extended Recall-precision (OAERP). By using two examples, the results has shown that the OAERP metric has produced more distinctive and discriminating values as compared to accuracy metric. This paper also empirically demonstrates that Monte Carlo Sampling (MCS) algorithm that is trained by OAERP metric was able to obtain better predictive results than the one trained by the accuracy metric alone, using nine medical data sets. In addition, the OAERP metric also performed effectively when dealing with imbalanced class problems. Moreover, the t-test analysis also shows a clear advantage of the MCS model trained by the OAERP metric against its previous metric over five out of nine medical data sets. From the abovementioned results, it is clearly indicates that the OAERP metric is more likely to choose a better solution during classification training and lead towards a better trained classification model
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