Leveraging Knowledge-Based Approaches to Promote Antiretroviral Toxicity Monitoring in Underserved Settings

As access and use of antiretroviral therapy continue to increase, the need to improve antiretroviral toxicity monitoring becomes more critical. This is particularly so in underserved settings, where patterns of antiretroviral toxicities possibly alter the need for and frequency of antiretroviral toxicity monitoring. However, barriers such as few skilled healthcare providers and poor infrastructure make antiretroviral toxicity monitoring in underserved settings difficult. The purpose of this dissertation was to investigate how standard clinical guidelines, knowledge-based clinical decision support, and task delegation could be leveraged to overcome barriers to antiretroviral toxicity monitoring in underserved settings. The strategy adopted in this dissertation was guided by the Design Science Research Methodology that emphasizes the generation of scientific knowledge through building novel artifacts. Two qualitative descriptive studies were conducted to characterize the contextual factors associated with antiretroviral toxicity monitoring in underserved settings. Supported by the findings from these studies, a knowledge-based software application prototype that implements clinical practice guidelines for antiretroviral toxicity monitoring was developed. Next, a quantitative validation study was used to evaluate the structure and behavior of the prototype’s knowledge base. Lastly, a quantitative usability study was conducted to assess lay health worker perceptions of the satisfaction and mental effort associated with the use of checklists generated by the prototype. This dissertation research produced empirical evidence about the broad motives and strategies for promoting medication adherence, safety, and effectiveness in underserved settings. It also identified barriers and facilitators of antiretroviral toxicity monitoring within ambulatory HIV care workflows in underserved settings. Additionally, it provided evidence about the extent to which antiretroviral toxicity domain knowledge could be implemented in a knowledge-based application for supporting point-of-care antiretroviral toxicity monitoring. Lastly, the research provided previously unavailable empirical evidence about the perceptions of lay peer health workers on the use of checklists for the documentation of antiretroviral toxicities

Bisphenol A Detection on a Carbon Graphite Electrode Modified with a Polyaniline- 2,2’-Azinobis-(3-Ethyl Benzothiazolin-6-Sulfonic Acid) Composite using Horseradish Peroxidase Enzyme as a Bio-recognition Unit, Preliminary Results

Bisphenol A is an endocrine disruptor which is almost ubiquitous. It is found in plastics, supermarket thermal receipts, CDs, food containers, water bottles, building materials amongst others. Bisphenol A has being associated with serious health issues ranging from obesity to cancer. This study aimed at developing an electrochemical method for the detection of bisphenol A in the environment. Horse radish peroxidase enzyme encapsulated on a modified polyaniline graphite carbon paste electrode was the recognition system. All electrochemical experiments were done on the cyclic voltammetric mode. The conducting properties of the polyaniline- Polyaniline 2, 2’-Azinobis [3-ethylbenzothiazoline-6-sulfonic acid] polymer composites were carried out using UV-Vis spetrophotometry. The presence of polaron and bipolaron bands in the UV-Vis spectra at around 446 nm and or greater than 650 nm indicated improved polymer conductivity. Preliminary results for the HRP/H2O2 system displayed a 1.54 x10-4 peak current signal de-attenuation on addition of cumulative aliquots of 0 – 2500 nM. The greater than 90% signal de-attenuation was due to presence of BPA provided- a basis for creating a BPA detection methodology based on inhibition in future studies

Decadal rainfall variability modes in observed rainfall records over East Africa and their relations to historical sea surface temperature changes.

Detailed knowledge about the long-term interface of climate and rainfall variability is essential for managing agricultural activities in Eastern African countries. To this end, the space-time patterns of decadal rainfall variability modes over East Africa and their predictability potentials using Sea Surface Temperature (SST) are investigated. The analysis includes observed rainfall data from 1920-2004 and global SSTs for the period 1950-2004. Simple correlation, trend and cyclical analyses, Principal Component Analysis (PCA) with VARIMAX rotation and Canonical Correlation Analysis (CCA) are employed. The results show decadal signals in filtered observed rainfall record with 10 years period during March - May (MAM) and October – December (OND) seasons. During June - August (JJA), however, cycles with 20 years period are common. Too much / little rainfall received in one or two years determines the general trend of the decadal mean rainfall. CCA results for MAM showed significant positive correlations between the VARIMAX-PCA of SST and the canonical component time series over the central equatorial Indian Ocean. Positive loadings were spread over the coastal and Lake Victoria regions while negative loading over the rest of the region with significant canonical correlation skills. For the JJA seasons, Atlantic SSTs had negative loadings centred on the tropical western Atlantic Ocean associated with the wet / dry regimes over western / eastern sectors. The highest canonical correlation skill between OND rainfall and the Pacific SSTs showed that El Niño-Southern Oscillation (ENSO)/La Niña phases are associated with wet/dry decades over the region

The influence of low frequency sea surface temperature modes on delineated decadal rainfall zones in Eastern Africa region

Influence of low frequency global Sea Surface Temperatures (SSTs) modes on decadal rainfall modes over Eastern Africa region is investigated. Fore-knowledge of rainfall distribution at decadal time scale in specific zones is critical for planning purposes. Both rainfall and SST data that covers a period of 1950–2008 were subjected to a ‘low-pass filter’ in order to suppress the high frequency oscillations. VARIMAX-Rotated Principal Component Analysis (RPCA) was employed to delineate the region into decadal rainfall zones while Singular Value Decomposition (SVD) techniques was used to examine potential linkages of these zones to various areas of the tropical global oceans. Ten-year distinct decadal signals, significant at 95% confidence level, are dominant when observed in-situ rainfall time series are subjected to spectral analysis. The presence of variability at El Niño Southern Oscillation (ENSO)-related timescales, combined with influences in the 10–12 year and 16–20 year bands were also prevalent. Nine and seven homogeneous decadal rainfall zones for long rainfall season i.e. March-May (MAM) and the short rainfall season i.e. October-December (OND), respectively, are delineated. The third season of June–August (JJA), which is mainly experienced in western and Coastal sub-regions had eight homogenous zones delineated. The forcing of decadal rainfall in the region is linked to the equatorial central Pacific Ocean, the tropical and South Atlantic Oceans, and the Southwest Indian Ocean. The high variability of these modes highlighted the significant roles of all the global oceans in forcing decadal rainfall variability over the region

Trends in the start of the wet season over Africa

A quarter of a century of daily rainfall data from the Global Telecommunications System are used to define the temporal and spatial variability of the start of the wet season over Africa and surrounding extreme south of Europe and parts of the Middle East. From 1978 to 2002, the start of the wet season arrived later in the year for the majority of the region, as time progressed. In some parts of the continent, there was an annual increase in the start date of up to 4 days per year. On average, the start of the wet season arrived 9–21 days later from 1978 to 2002, depending on the threshold used to define the start of the rains (varying from 10–30 mm over 2 days, with no dry period in the following 10 days). It is noted that the inter-annual variability of the start of the wet season is high with the range of start dates varying on average from 116 to 142 days dependent on the threshold used to determine the start date. These results may have important implications for agriculturists on all levels (from the individual farmer to those responsible for regional food supply), as knowledge of potential future climate changes starts to play an increasingly important role in the agricultural decision-making process, such as sowing and harvesting times

Domain-agnostic and Multi-level Evaluation of Generative Models

While the capabilities of generative models heavily improved in different domains (images, text, graphs, molecules, etc.), their evaluation metrics largely remain based on simplified quantities or manual inspection with limited practicality. To this end, we propose a framework for Multi-level Performance Evaluation of Generative mOdels (MPEGO), which could be employed across different domains. MPEGO aims to quantify generation performance hierarchically, starting from a sub-feature-based low-level evaluation to a global features-based high-level evaluation. MPEGO offers great customizability as the employed features are entirely user-driven and can thus be highly domain/problem-specific while being arbitrarily complex (e.g., outcomes of experimental procedures). We validate MPEGO using multiple generative models across several datasets from the material discovery domain. An ablation study is conducted to study the plausibility of intermediate steps in MPEGO. Results demonstrate that MPEGO provides a flexible, user-driven, and multi-level evaluation framework, with practical insights on the generation quality. The framework, source code, and experiments will be available at https://github.com/GT4SD/mpego

West African monsoon 2012

Living up to its reputation as a highly variable climate system, the West African Monsoon (WAM) 2012 contrasted strikingly with the previous year. In 2011, the West African rainy season was delayed, patchy, and irregular. In 2012, whilst it was anomalously wet in many area, the Guinea coastal countries and some crucial agricultural regions remained very dry, persisting from the previous year. As a result, 2012 generated the third big food crisis to hit the region in the last seven years. The 2012 WAM forecast, observed climate conditions and the ongoing socio-economic implications for the region are reviewed here

Development and validation of climate and ecosystem-based early malaria epidemic prediction models in East Africa

Process-based models were constructed for computing the risk of malaria epidemic using temperature and rainfall data. The model has a lead-time of two to four months between detection of the epidemic signal and evolution of the epidemic. Malaria data was collected from eight sites in Kenya, Tanzania and Uganda with temperature and rainfall data from meteorological stations closest to the source of the malaria data. The sensitivity, specificity and positive predictive power were used to validate the models. Results validate the additive and multiplicative models, which were shown to be robust and with high climate-based, early epidemic predictive power