ghanaopendata/ghanaian-research-english huggingface dataset:
Predicting the effect of sulfadoxine-pyrimethamine uptake by pregnant women on birth weight using a generalized ordered partial proportional odds model

Background: Low birth weight is a public health problem in Africa with the cause attributable to malaria in pregnancy. World Health Organization recommends the use of intermittent preventive treatment in pregnancy (IPTp) with sulfadoxine-pyrimethamine to prevent malaria during pregnancy. The objective of this study was to evaluate the prevalence and trajectories of birth weight and the direct impact and relationship between sulfadoxine-pyrimethamine and birth weight in Ghana since 2003. Method: This study used secondary data obtained from the Demographic and Health Survey conducted in Ghana since 2003. Low birth weight was defined as weight4000 g. In all the analysis, we adjusted for clustering, stratification and weighting to reduce bias and improve precision of the estimates. Analysis was performed on each survey year as well as the pooled dataset. The generalized ordered partial proportional odds model was used due to violations of the parallel regression model assumptions. Efforts were made to identify all confounding variables and these were adjusted for. Predictive analysis was also executed. Results: The overall prevalence of low birth weight was 9% while that of macrosomia was 13%. The low birth weight for 2003 was 12% while in 2008 it was 21% and then 68% in 2014. The mean birth weight of the children in 2014 was 3.16 (3.14, 3.19), 2008 was 3.37 (3.28, 3.45) and 2003 was 3.59 (3.49, 3.69) while that of the pooled data was 3.28 (3.25, 3.30). The adjusted model (taking into consideration all confounding variables) showed that non-uptake of SP could result in 51% odds of giving birth to a low-birth-weight compared with normal birth weight child. An insignificant result was observed between macrosomia and low birth weight. Conclusion: There is higher probability that low birth weight could increase over the next couple of years if measures are not taking to reverse the current trajectories. The uptake of sulfadoxine-pyrimethamine should continue to be encouraged and recommended because it has a direct beneficial effect on the weight of the child.

Source: UG
Predicting the energy and protein requirements of the pregnant grasscutter (Thryonomys swinderianus, Temminck) using the changes in weight and composition of the foetus and associated tissues of pregnancy

Eighteen (18) gravid uteri collected from hunters' kills during February and March 2008 were used to investigate growth and accretion of energy and protein in the gravid uterus during pregnancy in the grasscutter. Each gravid uterus was separated into foetal, placental, empty uterus and foetal fluids (amniotic and allantoic fluids combined) components. Individual tissues were weighed and analyzed for dry matter (DM), ash, crude protein (CP) and crude fat. Regression equations were fitted to the weights of each component to describe the effects of gestational age. The DM, protein and energy contents of individual uterine tissues, with the exception of the DM of the foetal fluids, increased significantly with advancing gestation age so that the DM of the gravid uterus increased ~20-fold from days 45 to 152 (3.85 v. 76.24 g). However, the average elemental N in the foetus was 8.3 g N/100 g. Percentage DM did not change with advancing gestation age. Fat content of the foetal grasscutter was 2.79 %. Changes in foetal protein and energy fitted a multi-phasic regression that consisted of two linear equations. Similarly, changes in foetal fat and water contents fitted a multi-phasic regression that consisted of two linear equations, indicating that protein and energy growth accelerated after days 95 and 97 of gestation, respectively, with increases in fat and water accelerating after days 127 and 96 of gestation, respectively. Foetal protein and energy accretions were 0.04 g day-1 and 0.001 MJ day-1 before days 95 and 97 of gestation, respectively, and increased to 0.31 g day-1 and 0.009 MJ day-1 after days 95 and 97 of gestation, respectively. Fat and water accretions were also 0.012 g day-1 and 0.15 ml day-1 before days 127 and 96 of gestation, respectively, and increased to 0.104 g day-1 and 1.29 ml day-1 after the respective days of gestation. Protein needs for tissue protein gain increased ~8-fold after day 95 of gestation. Results of this study indicate that the growth of the foetus and foetal tissues occur at different rates during gestation and points to a two-phase feeding strategy before and after day 95 of gestation for pregnant grasscutters. © 2013 Springer Science+Business Media Dordrecht.

Source: UG
Predicting The Environmental Suitability For Anopheles Stephensi Under The Current Conditions In Ghana

The Department of Parasitology conducts research into parasitic diseases of public health importance with the overall goal of reducing their transmission and the heavy disease burden that they impose on affected populations. The Department maintains focus on parasitic diseases in general. These include major diseases such as malaria, and others listed under the Neglected Tropical Diseases (NTD) control initiative such as, lymphatic filariasis, onchocerciasis, schistosomiasis, soil-transmitted helminthiasis, trypanosomiasis and leishmaniasis.

Source: UG
Predicting the mean liquid film thickness and profile along the annular length of a uniformly heated channel at dryout

The objective of this study was to predict the mean liquid film thickness and profile at high shear stress using a mechanistic approach. Knowledge of the liquid film thickness and its variation with two-phase flow parameters is critical for the estimation of safety parameters in the annular flow regime. The mean liquid film thickness and profile were predicted by the PLIFT code designed in Fortran 95 programming language using the PLATO FTN95 compiler. The film thickness was predicted within the annular flow regime for a flow boiling quality ranging from 40 to 80 % at high interfacial shear stress. Results obtained for a laminar liquid film flow were dumped into an excel file when the ratio of the actual predicted film thickness to the critical liquid film thickness lied within the range of 0.9 to unity. The film thickness was observed to decrease towards the exit of the annular regime at high flow boiling qualities and void fractions. The observation confirmed the effect of evaporation in decreasing the film thickness as quality is increased towards the exit of the annular regime. © Maxwell Scientific Organization, 2011.

Source: UG
Predicting the timing of second praziquantel treatment and its effect on reduction of egg counts in southern Ghana. Nsowah-

The overall goal of the department is to conduct research and provide solutions to nutritional problems in Ghana. In pursuance of this goal, the Nutrition Department since its inception has focused its research in the areas of maternal, infant and young child nutrition, food consumption and food security and micronutrient deficiency and interventions. These are priority food and nutrition problems in Ghana.

Source: UG
Prediction of acid mine drainage generation potential in selected mines in the Ashanti Metallogenic Belt using static geochemical methods

Acid mine/rock drainage (AMD/ARD) is the biggest environmental threat facing the mining industry. This study investigates AMD/ARD possibilities in three mines in the Ashanti Belt, using acid base accounting (ABA) and net acid generation pH (NAGpH) tests. Twenty-eight samples of rock units and mine spoil from these mines were collected for ABA and NAGpH tests. Two tailing dumps at Prestea and Nsuta were confirmed by both methods as acid generating with NAGpH of 4.5 and 4.6 and neutralization potential ratio values of 4.38 and 4.60, respectively. Six other samples are classified as potentially acid generating using a variety of established classification criteria. The rest of the samples either exhibited very low sulphur and carbonate content or had excess carbonate over sulphur. Consistency between results from ABA and NAGpH tests validates these tests as adequate tools for preliminary evaluation of AMD/ARD possibilities in any mining project in the Ashanti Belt. © 2006 Springer-Verlag.

Source: UG
Prediction of antischistosomal small molecules using machine learning in the era of big data

Schistosomiasis is a neglected tropical disease caused by helminths of the Schistosoma genus. Despite its high morbidity and socio-economic burden, therapeutics are just a handful with praziquantel being the main drug. Praziquantel is an old drug registered for human use in 1982 and has since been administered en masse for chemotherapy, risking the development of resistance, thus the need for new drugs with diferent mechanisms of action. This review examines the use of machine learning (ML) in this era of big data to aid in the prediction of novel antischistosomal molecules. It frst discusses the chal lenges of drug discovery in schistosomiasis. Explanations are then ofered for big data, its characteristics and then, some open databases where large biochemical data on schistosomiasis can be obtained for ML model development are examined. The concepts of artifcial intelligence, ML, and deep learning and their drug applications are explored in schistosomiasis. The use of binary classifcation in predicting antischistosomal compounds and some algorithms that have been applied including random forest and naive Bayesian are discussed. For this review, some deep learning algorithms (deep neural networks) are proposed as novel algorithms for predicting antischistosomal molecules via binary classifcation. Databases specifcally designed for housing bioactivity data on antischistosomal molecules enriched with functional genomic datasets and ontologies are thus urgently needed for developing predictive ML models.

Source: UG
Prediction of gold mineralization zones using spatial techniques and geophysical data: A case study of the Josephine prospecting licence, NW Ghana

In this study, predictive models that characterize gold potential zones within the Josephine Prospecting Licence (PL) Area of Northwestern Ghana have been created by data-driven methods comprising frequency ratio and information value. These predictive models were evaluated using known locations of gold (Au) occurrence datasets and compared to each other. The mineral prospectivity models (MPMs) of gold occurrence areas within the Josephine PL Area were con structed by determining the spatial correlation between known locations of Au occurrences and eight mineralization related factors. The locations of these known Au occurrences, which char acterize regions of anomalously high Au geochemical concentration and regions of previous or ongoing artisanal mining operations were identified by using geographic positioning systems (GPS). Eight mineralization related factors (geoscientific thematic layers) over the entire study area composed of analytic signal, lineament density, uranium-thorium ratio, uranium, potassium thorium ratio, potassium, reduction-to-equator and geology were used to generate the MPMs. The predictive capacity of each of the MPMs generated was determined by employing the area under the receiver operating characteristics curve (AUC). The AUC score obtained for the predictive models produced based on the information value and the frequency ratio approaches were respectively 0.794 and 0.815. The AUC scores generated indicate that the MPMs produced are good predictive models (with an AUC greater than 0.7) and can therefore assist in narrowing down the highly prospective zones of mineral occurrences within the study area. However, the overall predictive potential of the frequency ratio approach was better than the model produced by the information value approach.

Source: UG
Prediction of surface area of garden egg (Solanum Aethiopicum L) from weight measurements

Estimation of surface area of various agricultural produce can have a wide range of applications for both producers and processors. The methods available are tedious and in certain cases destructives Studies have been conducted with respect to predicting the surface area of some fruits and vegetables such as apples and tomatoes. Very few, if any, similar attempts have been made to estimate the surface area of indigenous African crops. The objective of this study was to develop a model that will predict the surface area of the garden egg (Solanum aethiopicum L) from weight measurements. It is easier to measure the weight of the garden egg than to measure its surface area. Weight and surface area measurements of each garden egg were used to develop equations to predict fruit surface area (cm2) from the raw weight. The linearregression model developed showed strong correlation between surface area and weight measurement and can be used to estimate the surface area of garden egg. For a garden egg weighting 47.4 gm, the model predicts its surface area between 96.3 and 118.5 mm2 with 95 % confidence.

Source: UG
Prediction of volume, weight and surface area of banana (Musa acuminata) using picture image analysis

The design of processing equipments, materials and processes for the handling of crops require data of the physical properties of the crops such as weight, volume and surface area. Obtaining these data can be very tedious and destructive to the samples being used. This research aims at a non- destructive, fast, but accurate and easy way of finding volume, weight, and surface area of a crop from its picture image. Banana (Musa acuminata) was used for the study and the mean weight, volume, surface area, plan area, and plan perimeter were 142.07±23.48 g, 158.90±33.14 cm3, 171.25±22.32 cm2, 79.34±10.45 cm2 and 47.41±3.72 cm, respectively. The values obtained were correlated, using plan area and perimeter as the independent variables and weight, volume and surface area as the dependent variables. The prediction equations obtained using plan area measurements are Weight (g) = 1.866×plan area (cm2) - 5.9649; Volume (cm3) = 1.8518×plan area (cm2) - 11.978; Surface area (cm2) = 1.5568×plan area (cm2) - 47.737. These prediction equations offer better prediction values than perimeter, but of these equations only the prediction equation for weight can be used with an appreciable r-squared value of 68.89%. Therefore, this equation can be used by designers after obtaining a picture image of the banana.

Source: UG
Predictive mapping of soil copper for site-specific micronutrient management using GIS-based sequential Gaussian simulation

Given that soil properties including micronutrient contents vary in space and time, geospatial mapping is necessary for robust site-specific management planning. Kriging interpolation is often used in GIS environments for mapping, but it has inherent limitation of having a smooth effects. In this study, sequential Gaussian simulation (SGS) was used to map the spatial distribution of Cu concentration in and model the spatial uncertainties for an arable dryland in central Botswana. The field was divided into 30 parallel lines in an NE–SW orientation. Soil samples were collected at intervals of 25 m on each line; and 35 sampling points were obtained. A total of 1050 soils sampled at a depth of about 20 cm were air dried and analyzed using an Olympus Delta Sigma® portable X-ray fluorescence analyser. The average concentration of Cu (146 mg kg−1) in the Maibele Airstrip of Botswana is higher than the global average copper concentration (30 mg kg−1). Low copper contents were found dotted around the northern and southern edges of the study area, while high content zone is found in the interior. In comparison to kriging interpolation, SGS technique shows better performance. Kriging generally overestimated where lower values are probable and underestimated where higher values are probable. A large portion of the area has Cu content above the critical threshold of 125 mg kg−1. Since total and not plant available Cu were the measured parameter, we recommend that further study in this location should focus on confirming the plant available Cu in the high-risk areas.

Source: UG
Predictive model and determinants of under-five child mortality: evidence from the 2014 Ghana demographic and health survey

Background Globally, millions of children aged below 5 years die every year and some of these deaths could have been prevented. Though a global problem, under-five mortality is also a major public health problem in Ghana with a rate of 60 deaths per 1000 live births. Identification of drivers of mortality among children aged below 5 years is an important problem that needs to be addressed because it could help inform health policy and intervention strategies aimed at achieving the United Nations SDG Goal 3 target 2. The aim of this study is to develop a predictive model and to identify determinants of under-five mortality. Method The 2014 Ghana Demographic and Health Survey data was used in this study. Analyses were conducted on 5884 children. The outcome variable is child survival status (alive or dead). Single level binary logistic and multilevel logistic regression models were employed to investigate determinants of under-five mortality. The fit of the model was checked using Variance Inflation Factor and Likelihood Ratio tests. The Receiver Operating Characteristic curve was used to assess the predictive ability of the models. A p-value< 0.05 was used to declare statistical significance. Results The study observed 289 (4.91%) deaths among children aged below 5 years. The study produced a good predictive model and identified increase in number of total children ever born, number of births in last 5 years, and mothers who did not intend to use contraceptive as critical risk factors that increase the odds of under-five mortality. Also, children who were born multiple and residing in certain geographical regions of Ghana is associated with increased odds of under-five mortality. Maternal education and being a female child decreased the odds of under-five mortality. No significant unobserved household-level variations in under-five mortality were found. The spatial map revealed regional differences in crude under-five mortality rate in the country. Conclusion This study identified critical risk factors for under-five mortality and strongly highlights the need for family planning, improvement in maternal education and addressing regional disparities in child health which could help inform health policy and intervention strategies aimed at improving child survival. Electronic supplementary material The online version of this article (10.1186/s12889-019-6390-4) contains supplementary material, which is available to authorized users.

Source: UG
A predictive model and socioeconomic and demographic determinants of under-five mortality in Sierra Leone

Sierra Leone is among the countries that recorded high under-five child mortality rate in the world. To design and implement policies that can address this public health challenge, the present study developed a predictive model of factors that explained under-five mortality in Sierra Leone using the 2008 and 2013 Sierra Leone Demographic and Health Survey (SDHS) datasets. LASSO regression technique was used to select the predictors to build the under-five predictive single-level logit and multilevel logit models. Statistical analyses were performed in the R freeware version 3.6.1. About 588 (10.4%) and 1320 (11.1%) children under five were reported dead in 2008 and 2013, respectively. The significant predictors of under-five mortality in Sierra Leone were the total number of children ever born, number of children under five in the household, mother's birth in the last five years, mother's number of living children, and number of household members, household wealth, maternal contraceptive use and intention, number of eligible women in the household, type of toilet facility, sex of the child, and weight of the child at birth. The study identified certain predictors that deserve policy attention and interventions to strengthen the efforts of creating child welfare and survival atmosphere in Sierra Leone.

Source: UG
A predictive model, and predictors of under-five child malaria prevalence in Ghana: How do LASSO, Ridge and Elastic net regression approaches compare?

Malaria is among the leading causes of mortality and morbidity among children in Ghana. Therefore, identifying the predictors of malaria prevalence in children under-five is among the priorities of the global health agenda. In Ghana, the paradigm shifts from using traditional statistics to machine learning techniques to identifying predictors of malaria prevalence are scarce. Thus, the present study used machine learning techniques to identify variables to build the best fitting predictive model of malaria prevalence in Ghana. We analysed the data on 2867 under-five children with malaria RDT results from the 2019 Ghana Malaria Indicator Survey. LASSO, Ridge, and Elastic Net regression methods were used to select variables to build predictive models. The R freeware version 4.0.2 was used. One out of four children tested positive for malaria (25.04%). The logit models based on selected features by LASSO, Ridge, and Elastic Net contained eleven, fifteen, and thirteen features, respectively. The LASSO regression model is preferred because it contains the smallest number of predictors and the smallest prediction error. The significant predictors of malaria among children were being older than 24 months, residing in the poorest household, being severely anaemic, residing in households without electricity, and residing in a rural area. The predictors identified in our study deserve policy attention and interventions to strengthen malaria control efforts in Ghana. The machine learning techniques employed in our study, especially the LASSO regression technique could be beneficial for identifying predictors of malaria prevalence in this group of children

Source: UG
A predictive study of heat wave characteristics and their spatiotemporal trends in climatic zones of Nigeria

Heat waves (HWs) have always been the silent natural disaster but the most impactful, especially concerning health and agriculture. A crucial question is being asked after the evidence has shown increases in the climate extreme events especially the temperature: how will the future climate conditions be? The present investigation examines and analyzes the future occurrence and trend of HWs in the five climatic zones of Nigeria. WRF model output extracted from CORDEX-Africa for the period 2018–2100 was compiled using maximum and minimum temperatures under RCP4.5 and RCP8.5. Different HW characteristics were studied: the heat wave number, the duration, the frequency, the amplitude and the magnitude exploiting four different definitions, the temperature based 90th percentile thresholds (TN90 and TX90), the Excess Heat Factor (EHF) and the Heat Wave Magnitude Index daily (HWMId). The prediction under the two scenarios RCP4.5 and RCP8.5 has shown a spatial increase in the frequency and magnitude of HWs during different periods. In the 2050s, there will be a spatial increase and also an increase in the duration of HWs in the study area. The HWMId revealed Ultra extreme HWs when the Coastal zone will be having Super extreme HWs. The RCP8.5 revealed more dramatic and dreadful HWs from 2073. The trend showed significant increasing trends in the major parts of the country

Source: UG