Research Datasets

Permanent URI for this communityhttp://dspace.nm-aist.ac.tz/handle/123456789/3178

Browse

Now showing 1 - 9 of 9

Dataset: Evolution of toll-like receptors in the context of terrestrial ungulates and cetaceans diversification
(Zenodo, 2017-02-06) Ishengoma, Edson; Agaba, Morris
Background: Toll-like receptors (TLRs) are the frontline actors in the innate immune response to various pathogens and are expected to be targets of natural selection in species adapted to habitats with contrasting pathogen burdens. The recent publication of genome sequences of giraffe and okapi together afforded the opportunity to examine the evolution of selected TLRs in broad range of terrestrial ungulates and cetaceans during their complex habitat diversification. Through direct sequence comparisons and standard evolutionary approaches, the extent of nucleotide and protein sequence diversity in seven Toll-like receptors (TLR2, TLR3, TLR4, TLR5, TLR7, TLR9 and TLR10) between giraffe and closely related species was determined. In addition, comparison of the patterning of key TLR motifs and domains between giraffe and related species was performed. The quantification of selection pressure and divergence on TLRs among terrestrial ungulates and cetaceans was also performed. Results: Sequence analysis shows that giraffe has 94–99% nucleotide identity with okapi and cattle for all TLRs analyzed. Variations in the number of Leucine-rich repeats were observed in some of TLRs between giraffe, okapi and cattle. Patterning of key TLR domains did not reveal any significant differences in the domain architecture among giraffe, okapi and cattle. Molecular evolutionary analysis for selection pressure identifies positive selection on key sites for all TLRs examined suggesting that pervasive evolutionary pressure has taken place during the evolution of terrestrial ungulates and cetaceans. Analysis of positively selected sites showed some site to be part of Leucine-rich motifs suggesting functional relevance in species-specific recognition of pathogen associated molecular patterns. Notably, clade analysis reveals significant selection divergence between terrestrial ungulates and cetaceans in viral sensing TLR3. Mapping of giraffe TLR3 key substitutions to the structure of the receptor indicates that at least one of giraffe altered sites coincides with TLR3 residue known to play a critical role in receptor signaling activity. Conclusion: There is overall structural conservation in TLRs among giraffe, okapi and cattle indicating that the mechanism for innate immune response utilizing TLR pathways may not have changed very much during the evolution of these species. However, a broader phylogenetic analysis revealed signatures of adaptive evolution among terrestrial ungulates and cetaceans, including the observed selection divergence in TLR3. This suggests that long term ecological dynamics has led to species-specific innovation and functional variation in the mechanisms mediating innate immunity in terrestrial ungulates and cetaceans.
Integrating stakeholders' perspectives and spatial modelling to develop scenarios of future land use and land cover change in northern Tanzania
(Zenodo, 2021-01-13) Kariuki, Rebecca; Munishi, Linus; Courtney-Mustaphi, Colin; Capitani, Claudia; Shoemaker, Anna; Lane, Paul; Marchant, Rob
Rapid rates of land use and land cover change (LULCC) in eastern Africa and limited instances of genuinely equal partnerships involving scientists, communities and decision makers challenge the development of robust pathways toward future environmental and socioeconomic sustainability. We use a participatory modelling tool, Kesho, to assess the biophysical, socioeconomic, cultural and governance factors that influenced past (1959-1999) and present (2000-2018) LULCC in northern Tanzania and to simulate four scenarios of land cover change to the year 2030. Simulations of the scenarios used spatial modelling to integrate stakeholders' perceptions of future environmental change with social and environmental data on recent trends in LULCC. From stakeholders' perspectives, between 1959 and 2018, LULCC was influenced by climate variability, availability of natural resources, agriculture expansion, urbanization, tourism growth, and legislation governing land access and natural resource management. Among other socio-environmental-political LULCC drivers, the stakeholders envisioned that from 2018 to 2030 LULCC will largely be influenced by land health, natural and economic capital, and political will in implementing land use plans and policies. The projected scenarios suggest that by 2030 agricultural land will have expanded by 8-20% under different scenarios and herbaceous vegetation and forest land cover will be reduced by 2.5-5% and 10-19% respectively. Stakeholder discussions further identified desirable futures in 2030 as those with improved infrastructure, restored degraded landscapes, effective wildlife conservation, and better farming techniques. The undesirable futures in 2030 were those characterized by land degradation, poverty, and cultural loss. Insights from our work identify the implications of future LULCC scenarios on wildlife and cultural conservation and in meeting the Sustainable Development Goals (SDGs) and targets by 2030. The Kesho approach capitalizes on knowledge exchanges among diverse stakeholders, and in the process promotes social learning, provides a sense of ownership of outputs generated, democratizes scientific understanding, and improves the quality and relevance of the outputs.
Dataset: Availability and functionality of neontal care units in health facilities
(2022-07-21) Kamala, Serveus
Background Evidence shows that delivery of prompt and appropriate in-patient newborn care (IPNC) through health facility (HF)-based neonatal care and stabilization units (NCU/NSUs) reduce preventable newborn mortalities (NMs). This study investigated the HFs for the availability and performance of NCU/NSUs in providing quality IPNC, as well as an investigation of influencing factors in Mtwara region, Tanzania. Results About 70.6% (12/17) of surveyed HFs had at least one NCU/NSU room dedicated for delivery of IPNC, and 74.7% (3,600/4,819) of needy newborns were admitted/transferred in for management. Essential medicines such as tetracycline eye ointment was unavailable in 75% (3/4) of the district hospitals (DHs). A disparity existed between the availability and functioning of equipment including infant radiant warmers (92% vs 73%). Governance, support from implementing patterns (IPs), and access to healthcare commodities were identified from qualitative inquiries as factors influencing the establishment and running of NCUs/NSUs at the HFs in Mtwara region, Tanzania. Conclusion Despite the positive progress, the establishment and performance of NCUs/NSUs in providing quality IPNC in HFs in Mtwara has lagged behind the Tanzania neonatal care guideline standards, particularly after the IPs of newborn health interventions completed their terms in 2016. This study suggests additional improvement plans for Mtwara region and other comparable settings to optimize the provision of quality IPNC and lower avoidable NMs. Notes
Dataset: Banana Imagery Dataset - Tanzania
(Zenodo, 2023-02-23) Mduma, Neema; Elinisa, Christian
The banana images dataset was created to contribute to the study of banana diseases diagnostics. The images target the diagnostics of Black Sigatoka and Fusarium Wilt Race 1 diseases. We are motivated in developing end to end tools to help farmers diagnose diseases and improve banana productivity. The dataset was created to facilitate image classification and object detection tasks.
Dataset: Machine Learning Dataset for Poultry Diseases Diagnostics - PCR annotated
(2023-12-23) Machuve, Dina; Nwankwo, Ezinne; Lyimo, Emmanuel; Maguo, Evarist; Munisi, Charles
The dataset of poultry disease diagnostics was annotated using Polymerase Chain Reaction (PCR). Polymerase Chain Reaction (PCR) is a molecular biology technique for rapid diagnostics. We gathered both the fecal images and fecal samples from layers, cross and indigenous breeds of chicken from poultry farms in Arusha and Kilimanjaro regions in Tanzania between September 2020 and February 2021. Each fecal sample collected was coded to its corresponding image during data collection. PCR method is used for detection and identification of pathogens through amplification of DNA sequences unique to the pathogen. We used existing primers from literature to amplify the target DNA/RNA on the poultry fecal samples for PCR. The targets were Coccidiosis, Newcastle disease and Salmonella. We used the primers for PCR diagnostics at the molecular laboratory of the Nelson Mandela African Institution of Science and Technology (NM-AIST). The fecal samples were stored at -80 degrees celsius. The PCR diagnostics were conducted using reagents and kits from Zymo Research and the protocol is summarized in these five stages: 1. DNA sample loading 2. DNA extraction 3. Amplification; 4. Quantification and 5. Detection.
Dataset: SNP genotyping of indigenous goats of Uganda based on the Goat_IGGC_65K_v2 illumina chip
(2024-05-23) Nantongo, Ziwena; Birungi, Josephine; Obol Opiyo, Stephen; Shirima, Gabriel; Mugerwa, Swidiq; Mutai, Collins; Kyalo, Martina; Munishi, Linus; Agaba, Morris; Mrode, Raphael
Uganda's indigenous goats are characterised based on ethnic communities that raise them, average mature weight, and hair coat characteristics. Uganda's indigenous goats have been genotyped based on the Goat_IGGC_65K_v2 illumina chip to study their population structure and genetic characteristics. Information generated from this data is vital for the sustainable utilisation, development, and conservation of Uganda's goat genetic resources.
Dataset: Optimizing LoRaWAN Throughput in Maritime Environments Through Adaptive Coding and Modulation in Rayleigh Fading Channels
(Zenodo, 2025) Lyimo, Martine; Mgawe, Bonny; Leo, Judith; Dida, Mussa; Michael, Kisangiri
This dataset supports the article “Optimizing LoRaWAN Throughput in Maritime Environments through Adaptive Coding and Modulation under Rayleigh Fading”. It includes simulation outputs and MATLAB source code for reproducing all figures and results in the study. Files include throughput, PER, energy efficiency, spectral efficiency, and an ACM algorithm function.
Dataset: A Labeled Dataset of Healthy and Diseased Common Beans from Tanzania
(Zenodo, 2025-07-01)
The common bean images dataset was developed to advance research in diagnosing key bean diseases and boosting crop productivity. Sponsored by GrowFurther and carried out by researchers at the Nelson Mandela African Institution of Science and Technology (NM-AIST) in collaboration with the Tanzania Agriculture Research Institute (TARI), the project produced a labeled dataset featuring leaf images of healthy beans, Bean Anthracnose and Bean Rust. The dataset supports both image classification and object detection tasks.
Dataset: A Labeled Dataset of Healthy and Diseased Maize from Tanzania
(Zenodo, 2025-07-04) Mduma, Neema; Laizer, Hudson; Kiriba, Deodatus
The maize images dataset was developed to support research in diagnosing major maize diseases and improving crop yields. Sponsored by GrowFurther and conducted by researchers at the Nelson Mandela African Institution of Science and Technology (NM-AIST) in collaboration with the Tanzania Agriculture Research Institute (TARI), the project produced a labeled dataset featuring healthy maize, Maize Lethal Necrosis and Maize Streak Virus. The dataset is designed for image classification and object detection tasks.

Browse

Browsing Research Datasets by Issue Date