http://erepository.uonbi.ac.ke/handle/11295/765 2026-05-18T20:36:16Z 2026-05-18T20:36:16Z Kibegwa, Felix M Bett, Rawlynce C Gachuiri, Charles K Machuka, Eunice Stomeo, Francesca Mujibi, Fidalis D http://erepository.uonbi.ac.ke/handle/11295/163980 2023-11-16T05:35:52Z 2023-01-01T00:00:00Z

Diversity and functional analysis of rumen and fecal microbial communities associated with dietary changes in crossbreed dairy cattle Kibegwa, Felix M; Bett, Rawlynce C; Gachuiri, Charles K; Machuka, Eunice; Stomeo, Francesca; Mujibi, Fidalis D The objective of this study was to investigate the effect of varying roughage and concentrate proportions, in diet of crossbreed dairy cattle, on the composition and associated functional genes of rumen and fecal microbiota. We also explored fecal samples as a proxy for rumen liquor samples. Six crossbred dairy cattle were reared on three diets with an increasing concentrate and reducing roughage amount in three consecutive 10-day periods. After each period, individual rumen liquor and fecal samples were collected and analyzed through shotgun metagenomic sequencing. Average relative abundance of identified Operational Taxonomic Units (OTU) and microbial functional roles from all animals were compared between diets and sample types (fecal and rumen liquor). Results indicated that dietary modifications significantly affected several rumen and fecal microbial OTUs. In the rumen, an increase in dietary concentrate resulted in an upsurge in the abundance of Proteobacteria, while reducing the proportions of Bacteroidetes and Firmicutes. Conversely, changes in microbial composition in fecal samples were not consistent with dietary modification patterns. Microbial functional pathway classification identified that carbohydrate metabolism and protein metabolism pathways dominated microbial roles. Assessment of dietary effects on the predicted functional roles of these microbiota revealed that a high amount of dietary concentrate resulted in an increase in central carbohydrate metabolism and a corresponding reduction in protein synthesis. Moreover, we identified several microbial stress-related responses linked to dietary changes. Bacteroides and Clostridium genera were the principal hosts of these microbial functions. Therefore, the roughage to concentrate proportion has more influence on the microbial composition and microbial functional genes in rumen samples than fecal samples. As such, we did not establish a significant relationship between the rumen and fecal metagenome profiles, and the rumen and fecal microbiota from one animal did not correlate more than those from different animals.

2023-01-01T00:00:00Z Lelenguyah, Geoffrey L Nyangito, Moses M Wasonga, Oliver V Bett, Rawlynce C http://erepository.uonbi.ac.ke/handle/11295/163910 2023-11-08T10:19:45Z 2023-01-01T00:00:00Z

Spatio-temporal epidemiology of livestock diseases in the variable semi-arid rangelands of northern Kenya Lelenguyah, Geoffrey L; Nyangito, Moses M; Wasonga, Oliver V; Bett, Rawlynce C Diseases affecting livestock can have significant impacts on the animal, humans and the economy. Participatory epidemiology and spatial analysis were conducted to assess livestock disease problems in Samburu County, northern Kenya. Key informants were selected purposively with the help of local leaders. Among the livestock, goats were identified to have the most economic importance. On the other hand Pestes des Petits Ruminants (PPR), Foot and Mouth Disease (FMD) and Camel Trypanosomiasis diseases were identified to have the highest impact on pastoral livelihood. Spatial analysis indicated that all the disease hotspots were closely related to the distribution of herds during different seasons of the year. Correlations between the mean annual rainfall and selected livestock diseases were significant for East Coast Fever (ECF) (r = - 0.767, p = 0.001, N = 15), Cattle Helminthiasis (r = 0.639, p = 0.010, N = 15), Cattle Anaplasmosis (r = 0.631, p = 0.012, N = 15) and Camel Pox (r = - 0.646, p = 0.044, N = 10). There was a strong relationship between seasonality and livestock disease epidemiology. Disease control efforts should be focused towards the hotspots in the wet season and dry season grazing areas.

2023-01-01T00:00:00Z Kiriinya, Lindah K Kwakernaak, Markus C Akker, Simone Van den Verbist, Guy L M M Picken, Stephen J Kelder, Erik M http://erepository.uonbi.ac.ke/handle/11295/163902 2023-11-07T11:52:10Z 2023-01-01T00:00:00Z

Iron and Manganese Alginate for Rechargeable Battery Electrodes Kiriinya, Lindah K; Kwakernaak, Markus C; Akker, Simone Van den; Verbist, Guy L M M; Picken, Stephen J; Kelder, Erik M We present a sustainable, inherently safe battery chemistry that is based on widely available and cheap materials, that is, iron and manganese hosted in alginate bio-material known from the food and medical industry. The resulting battery can be recycled to allow circularity. The electrodes were synthesised by the alginate caging the multi-valent metals to form a hydrogel in an aqueous environment. Characterisation includes FTIR, XPS and Mössbauer spectroscopy. The electrochemical performance of the electrodes was investigated by performing cyclic voltammetry (CV) and (dis)charge experiments. Mn and Fe ions show good co-ordination with the alginic acid with higher oxidation states demonstrating complex bonding behaviour. The non-optimised iron and manganese alginate electrodes already exhibit a cycling efficiency of 98% and 69%, respectively. This work shows that Fe and Mn atomically disperse in a bio-based host material and can act as electrodes in an aqueous battery chemistry. While demonstrated at cell level, it is furthermore explained how these materials can form the basis for a (semi-solid) flow cell.

2023-01-01T00:00:00Z Waweru, Salome W http://erepository.uonbi.ac.ke/handle/11295/161606 2022-11-02T10:48:40Z 2022-01-01T00:00:00Z

Improvement of Existing Intrusion Detection Systems Through Neural Networks Waweru, Salome W Recently, there is an increased rate of improved and unknown intrusions in the Intrusion detection system (IDS) field. In this study, artificial neural networks (ANN) model is proposed and integrated to develop the IDS execution on classification. Continual semi-supervised learning is the primary method conducted on artificial neural networks to create the model. Neural networks undergo pre-training with extensive benchmark datasets used for intrusion detection depending on how the user profiles have been created that have exact simulation of events and behaviors that are checked on the network. The evaluation domain is summarized from a unique set of features from the data set. The dataset are different due to the different user profiles. The ANN are trained for diverse attacks like Network infiltration, DDOS, Web attacks and many other arising attacks. The continual learning allows the model to learn latest advancements in deep learning and updates on recent anomalies detected. This improves the IDS to detect anomalies accurately. The main contribution of the proposed model to mitigate the gap identified is to assist in better classification of data, through the continual learning of neural networks, so that false positives that pass as normal data can be reduced in the system. It is demonstrated with experimental findings that the technique proposed can maintain a real time feedback to the anomaly.

2022-01-01T00:00:00Z