Literacy, Gender and Social Agency: Adventures in Empowerment : A Research Report for ActionAid UK

Labor and Human Capital,

Association between malaria exposure and Kaposi's sarcoma-associated herpes virus seropositivity in Uganda.

OBJECTIVE: Unlike other herpes viruses, Kaposi's sarcoma-associated herpes virus (KSHV) is not ubiquitous worldwide and is most prevalent in sub-Saharan Africa. The reasons for this are unclear. As part of a wider investigation of factors that facilitate transmission in Uganda, a high prevalence country, we examined the association between antimalaria antibodies and seropositivity against KSHV. METHODS: Antibodies against P. falciparum merozoite surface protein (PfMSP)-1, P. falciparum apical membrane antigen (PfAMA)-1 and KSHV antigens (ORF73 and K8.1) were measured in samples from 1164 mothers and 1227 children. RESULTS: Kaposi's sarcoma-associated herpes virus seroprevalence was 69% among mothers and 15% children. Among mothers, KSHV seroprevalence increased with malaria antibody titres: from 60% to 82% and from 54% to 77%, comparing those with the lowest and highest titres for PfMSP-1 and PfAMA-1, respectively (P < 0.0001). Among children, only antibodies to PfAMA-1 were significantly associated with KSHV seropositivity, (P < 0.0001). In both mothers and children, anti-ORF73 antibodies were more strongly associated with malaria antibodies than anti-K8.1 antibodies. CONCLUSION: The association between malaria exposure and KSHV seropositivity suggests that malaria is a cofactor for KSHV infection or reactivation

Determinants of Kaposi's sarcoma-associated herpesvirus seropositivity, viral DNA detection and cellular immune responses in Uganda

Kaposi's Sarcoma-associated Herpesvirus (KSHV), is a necessary cause of Kaposi's sarcoma (KS), the risk of which increases among people with immune suppression, such as that caused by infection with HIV. KS incidence varies, being highest in places with a high prevalence of KSHV. Controlling KSHV transmission is key in reducing KS incidence. Documented KSHV prevalence is reported to be higher in rural Uganda than has been found elsewhere. This PhD research focused on investigating environmental and immunological factors associated with KSHV antibody responses and viral detection/shedding in blood and in saliva as well as KSHV specific cell-mediated immune responses in Uganda. ELISA and Luminex were used for antibody measurements, real-time PCR for viral detection and quantification and an ELISPOT assay for cell-mediated IFN-γ response measurement. Infections such as malaria and helminths were the main environmental risk factors analysed. The factors associated with higher KSHV antibody responses included early age of infection, malaria parasitaemia, low haemoglobin levels and Schistosoma mansoni infection. Malaria infection was also associated with higher levels of KSHV DNA in blood while male sex was associated with increased viral shedding in saliva. Children had the highest proportion of individuals with detectable KSHV DNA in blood and in saliva. In relation to IFN- production, individuals responded to a wide variety of KSHV peptides without any immune dominance. In conclusion, KSHV transmission in endemic areas occurs mainly in childhood; this may play a role in the failure to control the virus, leading to increased viral shedding and increased viral transmission. Parasite infections such as malaria and worms may play a significant role in rendering children susceptible to KSHV infection as well as in enhancing reactivation of the virus, increasing lytic replication and, thereby increasing transmission and 3 pathogenesis.The cell-mediated immune response to KSHV is complex due to the lack of immunodominance

Metastatic Breast Cancer and Hormonal Receptor Status among a Group of Women in Sub Saharan Africa

Background: Breast cancer is the third commonest cancer in women in Uganda. The majority of breast cancer patients in Uganda present with advanced disease. Many studies show that metastatic lesions frequently lodge in bones, lung and liver. Tumour hormone receptor status correlates with site of metastatic lesions and survival among breast cancer patients.Objective: To determine the sites of metastatic breast lesions and how they relate to the hormonal receptor status.Methods: In this cross sectional descriptive study, 71 women with histologically confirmed incident breast cancer with metastases were analysed, their hormonal receptor status was determined. All patients underwent a chest X-ray, an abdominopelvic ultrasound scan and a bone scan. The χ² and t tests were used to compare variables for statistical differences.Results: The mean age of participants was 45 years. Most metastases were to bone 56% (40/71), of these 45% (32/71 ) tumours were exclusive to bone and 94% of these (30/32) were ER+ . Of the 13 (18% of all patients) who had metastases to the liver, 7 were exclusive to the liver, and 1 (14.%) was ER positive. Of the 30 (42 %) patients with lung metastases, 23 patients were exclusive to lungs and 9/30 (39%) were ER+. In all 68% (48/71) were ER+, and bone metastases were associated with ER positivity and low grade tumors.Conclusion: Breast metastases had a preponderance to bone in this largely premenopausal group of women and these tumors were mostly ER positive. In the absence of tests to determine ER status, empirical antihormonal therapy may be used.Key words: Metastatic Breast Cancer, Hormonal Receptor Statu

Trends in Kaposi's sarcoma-associated Herpesvirus antibodies prior to the development of HIV-associated Kaposi's sarcoma: a nested case-control study

HIV-associated Kaposi's sarcoma (KS) is a public health challenge in sub-Saharan Africa since both the causative agent, Kaposi's sarcoma associated-herpesvirus (KSHV), and the major risk factor, HIV, are prevalent. In a nested case-control study within a long-standing clinical cohort in rural Uganda, we used stored sera to examine the evolution of antibody titres against the KSHV antigens K8.1 and latency-associated nuclear antigen (LANA) among 30 HIV-infected subjects who subsequently developed HIV-related KS (cases) and among 108 matched HIV/KSHV coinfected controls who did not develop KS. Throughout the 6 years prior to diagnosis, antibody titres to K8.1 and LANA were significantly higher among cases than controls (p &#60; 0.0001), and titres increased prior to diagnosis in the cases. K8.1 titres differed more between KS cases and controls, compared to LANA titres. These differences in titre between cases and controls suggest a role for lytic viral replication in the pathogenesis of HIV-related KS in this setting

Dataset selection for aggregate model implementation in predictive data mining

Data mining has become a commonly used method for the analysis of organisational data, for purposes of summarizing data in useful ways and identifying non-trivial patterns and relationships in the data. Given the large volumes of data that are collected by business, government, non-government and scientific research organizations, a major challenge for data mining researchers and practitioners is how to select relevant data for analysis in sufficient quantities, in order to meet the objectives of a data mining task. This thesis addresses the problem of dataset selection for predictive data mining. Dataset selection was studied in the context of aggregate modeling for classification. The central argument of this thesis is that, for predictive data mining, it is possible to systematically select many dataset samples and employ different approaches (different from current practice) to feature selection, training dataset selection, and model construction. When a large amount of information in a large dataset is utilised in the modeling process, the resulting models will have a high level of predictive performance and should be more reliable. Aggregate classification models, also known as ensemble classifiers, have been shown to provide a high level of predictive accuracy on small datasets. Such models are known to achieve a reduction in the bias and variance components of the prediction error of a model. The research for this thesis was aimed at the design of aggregate models and the selection of training datasets from large amounts of available data. The objectives for the model design and dataset selection were to reduce the bias and variance components of the prediction error for the aggregate models. Design science research was adopted as the paradigm for the research. Large datasets obtained from the UCI KDD Archive were used in the experiments. Two classification algorithms: See5 for classification tree modeling and K-Nearest Neighbour, were used in the experiments. The two methods of aggregate modeling that were studied are One-Vs-All (OVA) and positive-Vs-negative (pVn) modeling. While OVA is an existing method that has been used for small datasets, pVn is a new method of aggregate modeling, proposed in this thesis. Methods for feature selection from large datasets, and methods for training dataset selection from large datasets, for OVA and pVn aggregate modeling, were studied. The experiments of feature selection revealed that the use of many samples, robust measures of correlation, and validation procedures result in the reliable selection of relevant features for classification. A new algorithm for feature subset search, based on the decision rule-based approach to heuristic search, was designed and the performance of this algorithm was compared to two existing algorithms for feature subset search. The experimental results revealed that the new algorithm makes better decisions for feature subset search. The information provided by a confusion matrix was used as a basis for the design of OVA and pVn base models which aren combined into one aggregate model. A new construct called a confusion graph was used in conjunction with new algorithms for the design of pVn base models. A new algorithm for combining base model predictions and resolving conflicting predictions was designed and implemented. Experiments to study the performance of the OVA and pVn aggregate models revealed the aggregate models provide a high level of predictive accuracy compared to single models. Finally, theoretical models to depict the relationships between the factors that influence feature selection and training dataset selection for aggregate models are proposed, based on the experimental results.Thesis (PhD)--University of Pretoria, 2010.Computer Scienceunrestricte

Methods for speeding up recommender system computations using a graph database

Recommender systems are commonly used for Internet-based activities to assist users in making decisions on what items to select. One very common use of recommender systems is in electronic commerce purchases. The need for recommender systems in electronic commerce is due to the vast amounts of items to choose from. Due to this vast amount of items, generation of recommendations for recommender systems is a computationally intensive activity. This paper reports on studies that were conducted to investigate methods for speeding up the computations for generating recommendations when the data that is used to generate recommendations is stored in a graph database. The proposed methods involve the pre-computation and storage of values that are used in the generation of recommendations. This leads to a speed-up of the computations for generating recommendations.Proceedings of the World Congress on Engineering 2021 WCE 2021, July 7-9, 2021, London, U.K.http://www.iaeng.org/LNECSam2022Computer Scienc

In Search of Local Knowledge on ICTs in Africa

By reviewing and comparing literature on the role of ICTs in statebuilding and peacebuilding in Africa, with a particular focus on neighboring Somalia, Kenya, and Ethiopia, this paper examines whether the claims of the transformative power of ICTs are backed by evidence and whether local knowledge – e.g., traditional mechanisms for conflict resolution – is taken into consideration by ICT-based initiatives. Several key findings emerged, including: 1) empirical evidence on the successful use of ICTs to promote peacebuilding and statebuilding is thin; 2) few differences exist between scholarship emanating from the Global North and from Africa; and 3) the literature exhibits a simplistic assumption that ICTs will drive democratic development without sufficient consideration of how ICTs are actually used by the public

Human cytomegalovirus epidemiology and relationship to tuberculosis and cardiovascular disease risk factors in a rural Ugandan cohort.

Human cytomegalovirus (HCMV) infection has been associated with increased mortality, specifically cardiovascular disease (CVD), in high-income countries (HICs). There is a paucity of data in low- and middle-income countries (LMICs) where HCMV seropositivity is higher. Serum samples from 2,174 Ugandan individuals were investigated for HCMV antibodies and data linked to demographic information, co-infections and a variety of CVD measurements. HCMV seropositivity was 83% by one year of age, increasing to 95% by five years. Female sex, HIV positivity and active pulmonary tuberculosis (TB) were associated with an increase in HCMV IgG levels in adjusted analyses. There was no evidence of any associations with risk factors for CVD after adjusting for age and sex. HCMV infection is ubiquitous in this rural Ugandan cohort from a young age. The association between TB disease and high HCMV IgG levels merits further research. Known CVD risk factors do not appear to be associated with higher HCMV antibody levels in this Ugandan cohort

Improving data quality in the banking supervisory data of Southern Africa central banks

The importance of effective supervision of banking institutions in developing countries cannot be over-emphasised. Central banks are responsible for the supervision of the banking institutions of a country. In order to effectively perform the supervisory role of banking institutions, central banks need to collect and analyse data about the operations of the banking institutions they supervise. Data analysis is conducted in order to establish the health of these institutions. Central banks in the SADC region experience many problems with the quality of the data that they receive from the banking institutions they supervise. Needless to say, decisions made on the basis of poor quality data increase the risk of allowing unsound institutions to continue in operation. This creates the real risk of loss of money by the individuals and organisations that form the clientele of these institutions, and will adversely affect the economy of the country. Despite the commendable efforts made by the regional central banks to implement a banking supervision application to expedite the supervisory activities through instituting a one-stop-shop for all the banking supervisory information, and the introduction of an electronic platform for collecting and analysing periodic banking supervision returns, there are still many imperfections in the information. A survey was conducted to investigate data quality problems at three SADC central banks. Recommendations are provided in this paper on measures that can be taken to improve the quality of the banking supervisory data for the SADC central banks.http://www.ejisdc.orgdm201