NLAPOST2021 1st Shared Task on Part-of-Speech Tagging for Nguni Languages

Part-of-speech tagging (POS tagging) is a process of assigning labels to each word in text, to indicate its lexical category based on the context it appears in. The POS tagging problem is considered a mostly solved problem in languages with a lot of NLP resources such as English. However, this problem is still an open problem for languages with fewer NLP resources such as the Nguni languages. This is owing to unavailability of large amounts of labelled data to train POS tagging models. The rich morphological structure and the agglutinative nature of these languages make the POS tagging problem more challenging when compared to a language like English. With this in mind, we have organised a challenge for training POS tagging models on a limited amount of data for four Nguni languages: isiZulu, Siswati, isiNdebele, and isiXhosa

Case report of a child with nephronophthisis from South Africa

Abstract Background Nephronophthisis (NPHP) is an autosomal recessive disorder with a subset of patients presenting with extrarenal manifestations such as retinal degeneration, cerebella ataxia, liver fibrosis, skeletal abnormalities, cardiac malformations, and lung bronchiectasis. However, the involvement of other organ systems has also been documented. Extrarenal manifestations occur in approximately 10–20% of patients. In developed countries, it has been reported as one of the most common causes of monogenic chronic kidney failure (CKF) during the first three decades of life, with more than 25 genes associated with this condition. The current treatment options for managing NPHP include supportive care, management of complications, and kidney replacement therapy when necessary. The index patient is a 10-year-old Caucasian female who presented with recurrent attacks of abdominal pain. Her elder sister, TN, who was 17 years old, was diagnosed with CKF and noted to have persistently elevated liver enzymes (gamma-glutamyl transferase, alanine, and aspartate transaminases). Following genetic testing, her elder sister was shown to have Nephronophthisis Type 3, and a liver biopsy showed early fibrotic changes. Subsequent genetic testing confirmed the index patient as having NPHP Type 3. A kidney biopsy showed focal sclerosed glomeruli with patchy areas of tubular atrophy and related tubulointerstitial changes in keeping with NPHP. We present the first confirmatory case of NPHP from South Africa based on histopathology and genetic testing in a 10-year-old Caucasian female who presented with recurrent attacks of abdominal pain, whose elder sister also presented with CKF and early liver fibrosis, confirmed on biopsy and genetic testing. Conclusion In low-middle-income countries, genetic testing should be undertaken whenever possible to confirm the diagnosis of NPHP, especially in those with a suggestive biopsy or if there is CKF of unknown aetiology with or without extra-renal manifestations

Performance Modeling of Proposed GUISET Middleware for Mobile Healthcare Services in E-Marketplaces

GUISET is a proposed middleware engine currently under study in South Africa. The goal is to provide utility services for small, medium, and macroenterprises in the context of mobile e-services. Three things are important to make this engine effective and efficient: the implementation, performance, and the pricing strategy. The literature has delved richly into implementation issue of similar projects. Both the performance and the pricing strategy issues have not been fully discussed especially in the context of mobile healthcare services. Some literature has addressed the performance issue using the exogenous nonpriority and the preemptive model. However, with providers offering different services using that approach may prove to be difficult to implement. This work extends existing and widely adopted theories to non-preemptive model by using the queuing theory and the simulation model in the context of mobile healthcare services. Our evaluation is based on non-preemptive priority and nonpriority discipline. Our results reveal that the unconditional average waiting time remains the same with reduction in waiting time over the non-preemptive priority model in four out of the five classes observed. This is envisaged to be beneficial in mobile healthcare services where events are prioritized and urgent attention is needed to be given to urgent events