Maasai Mara University
Recent publications
Microbial fuel cells, MFCs are finding populace in small-electrical appliances for electricity generation and wastewater purification. However, the dynamics of impedance applied and its consequent Ohmic behavior has not been fully investigated on performance of MFCs. This study purposed to investigate how increasing impedance affects key cell parameters of MFCs at ambient conditions using sewage water as the bio-anode. A H-type mediator-less MFC of capacity 4,556.25cm3 using porous graphite electrodes (surface area 5,127mm2 each) was used. The cathode was 1M NaCl solution while the anode was raw-sewage. 30% agar solution spiked with 2% NaCl solution was used as the salt bridge. The average operating temperature was 23.5±1.5°C for 10 retention days. Open current voltage (OCV), current and power densities were determined from a multimeter while discharge time and energy balance were calculated from these findings. Different resistor values (1,000Ω, 2,500Ω, 5,000Ω and 16,000Ω) were applied to study Ohmic behavior of the cell. The results indicated an exponential increase in OCV up to the 6th day followed by its gradual reduction. Ohmic behavior was observed in the current and power densities with the highest power density being 0.173mW/cm3 for the 1,000Ω resistor. The charge/discharge times ranged between 11.15 to 11.40 days with daily discharge rates of 8.77-8.96%. Ohmic behavior was also observed as the energy balance, energy capacity and energy density of the MFCs decreased with applied impedance. The highest values were obtained in the 1,000Ω MFC (energy balance, 336.1J/s; energy capacity, 4.227Wh/cm3 and energy density, 17.237Wh/kg respectively). Key words: Microbial fuel cell, Ohmic behavior, power density
Freezing stress is a major limiting factor in crop production. To increase frost‐hardiness of crops via breeding, deciphering the genes conferring freezing‐tolerance is vital. Potato cultivars (Solanum tuberosum) are generally freezing‐sensitive, but some potato wild species are freezing‐tolerant, including S. commersonii, S. malmeanum, and S. acaule. However, the underlying molecular mechanisms conferring the freezing‐tolerance to the wild species remain to be deciphered. In the present study, five representative genotypes of the above‐mentioned species with distinct freezing‐tolerance were investigated. Comparative transcriptomics analysis showed that SaCBL1–like (calcineurin B‐like protein) was up‐regulated substantially in all of the freezing‐tolerant genotypes. Transgenic over‐expression and known‐down lines of SaCBL1–like were examined. SaCBL1–like was shown to confer freezing‐tolerance without significantly impacting main agricultural traits. A functional mechanism analysis showed that SaCBL1–like increases the expression of the CBF‐regulon (C‐repeat binding factor) as well as causes a prolonged higher expression of CBF1 after exposure to cold conditions. Furthermore, SaCBL1–like was found to only interact with SaCIPK3–1 (CBL‐interacting protein kinase) among all apparent cold‐responsive SaCIPKs. Our study identifies SaCBL1–like to play a vital role in conferring freezing tolerance in potato, which may provide a basis for a targeted potato breeding for frost‐hardiness. This article is protected by copyright. All rights reserved.
Analyzing sample data is a difficult task made more difficult whenever the data contains extreme values that impair the precision of variance estimation under traditional moments because such moments assign equal weights to all observations, including extreme observations. Calibration is a method of adjusting sample weights to improve estimation. In this article, under a stratified adaptive cluster sampling, we propose new variance estimators with the appearance of extreme values through the use of calibration constraints along with linear and trimmed linear moments based on variance and coefficient of variation of the auxiliary variable. The percentage relative efficiency of the proposed estimators in comparison with the traditional ones is calculated. The proposed estimators' performance is assessed using real-life and artificial data. Based on numerical comparisons, the proposed estimators outperform the traditional variance estimator. Thus, the proposed esti-mators can be considered very resistant estimators and they surely boost the chances of obtaining more accurate estimates of population variance with the presence of extreme values.
e population parameters are estimated using sample surveys. However, the current investigation's goal is to estimate sub-populations parameters such as total through a calibration approach. e proposed estimator's properties have been given under simple random sampling. In addition, the class of estimators for subpopulation total has been discussed. e proposed estimator has been evaluated theoretically and empirically in a comparative study. e results demonstrated that a high-level estimate outperforms a low-level estimate for subpopulation total using a calibration approach.
Organizations and companies are starving to improve their business processes to stay in competition. As we know that process mining is a young and emerging study that lasts among data mining and machine learning. e main goal of process mining is to obtain accurate information from the data; therefore, in recent years, it attracts the attention of many researchers, practitioners, and vendors. However, the purpose of enhancement is to extend or develop an existing process model by taking information from the actual process recorded in an event log. One type of enhancement of a process mining model is repair. It is common practice that due to logging errors in information systems or the presence of a special behavior process, they have the actual event logs with the noise. Hence, the event logs are traditionally thought to be de ned as situation. Actually, when the logging is based on manual logging i.e., entering data in hospitals when patients are admitted for treatment while recording manually, events and timestamps are missing or recorded incorrectly. Our paper is based on theoretical and practical research work. e main purpose of our study is to use the knowledge gather from the process model, and give a technique to repair the missing events in a log. However, this technique gives us the analysis of incomplete logs. Our work is based on time and data perspectives. As our proposed approach allows us to repair the event log by using stochastic Petri net, alignment, and converting them into Bayesian analysis, which improves the performance of the process mining model. In the end, we evaluate our results by using the algorithms described in the alignment and generate synthetic/arti cial data that are applied as a plug-in in a process mining framework ProM.
Substitution behavior of the labile aqua ligand in four mononuclear ruthenium(II) terpyridyl complexes with different auxiliary N (pyridine) (Ru1), N^N (2,2′-bipyri-dyl (Ru2), 2′-(2-pyridyl)quinoline (Ru3), 2,2′-biqunoline (Ru4) ligands was investigated using three nucleophiles; thiourea, 1,1-dimethylthiourea and 1,1,3,3-tetra-methylthiourea. The effect of concentration and temperature on the substitution behavior of the complexes were studied under pseudo-first order conditions using UV-Vis spectrophotometer. The second order rate constants (k 2) of the aqua complexes decreased in the order: Ru4 > Ru3 > Ru1 > Ru2. The results showed that the rate of substitution of the aqua ligand increased with an increase in the π-surface area of the N^N bidentate auxiliary ligands. This is attributable to an upsurge in π-back-donation and electrophilicity of the complexes as the π-extension of the auxiliary ligands increases. Ru2 is less reactive than Ru1 due to the increased steric hin-derance introduced by the 2,2′-bipyridyl bidentate auxiliary ligand in Ru2 compared to Ru1 which has two independent trans pyridines. From computational results, it was observed that as the aromatic surface area of the auxiliary ligand increased from Ru1-Ru4, the HOMO-LUMO gap decreased accordingly. Consequently, the chemical softness and electrophilicity of the complexes increased accordingly. This is corroborated by the decrease in pKa values of the complexes as one moves from Ru1 to Ru4. As a result, the nucleophilic attack becomes facile from Ru1 to Ru4. All the reactions follow an associative interchange mechanism as indicated by the positive activation enthalpy and negative activation entropy. The crystal structure of bipyridylterpyridylthiourearuthenium(II) perchlorate show that the substitution product obtained is stable.
Both plant size and distribution of plants and resources across landscapes are known to influence pollinator behavior and resulting plant reproductive success. However, the relative influence of these intrinsic and extrinsic factors is unknown. We evaluated the relative contribution of individual plant size and landscape variables to reproductive success in bat-pollinated baobabs (Adansonia digitata) and determined if the interaction is scale-dependent. We recorded fruit number per baobab of 741 baobab in south-central Kenya and measured size metrics of individuals. We georeferenced baobabs and relevant resources across 10 km2 to generate landscape variables. Conditional inference forests determined scale-specific responses over 20 buffer distances (50-m to 1000-m) around baobabs and identified relative variable importance. We modeled presence of fruit, as not all trees produce fruit. For fruiting baobabs, we modeled whether there were few or many fruits. Conditional inference forests were significant at 50-m to 600-m buffer distances. Individual characteristics of baobabs were the primary drivers of fruit presence, with larger trees more likely to fruit. Fruit presence was modified by baobab height and landscape variables. Land use primarily drove baobab fruit production category, which was modified by baobab size and other landscape variables. The importance of distance to and density of alternate food resources changed with scale. Conclusions Individual characteristics and landscape variables both influence reproductive success in the bat-pollinated baobab system, and relative variable importance was scale-dependent. The pollinator landscape is complex and scale-dependent, encompassing not only the distribution of the baobab population but also attractants (pawpaws) and distractants (figs) that further influence reproductive success.
Traditional combustion devices and fuels such as charcoal, wood and biomass, are widely utilised in rural and urban households in Africa. Incomplete combustion can generate air pollutants which are of human toxicological importance, including polycyclic aromatic hydrocarbons (PAHs). In this study, portable multi-channel polydimethylsiloxane rubber traps were used to sample gas phase emissions from cooking devices used in urban and rural households in Bomet and Narok counties of Kenya. A wide range of total PAH concentrations was found in samples collected (0.82 – 173.69 µg/m3), which could be attributed to the differences in fuel type, combustion device, climate, and nature of households. Wood combustion using the 3-stone device had the highest average total PAH concentration of ~71 µg/m3. Narok had higher indoor total gas phase PAH concentrations averaging 35.88 µg/m3 in urban and 70.84 µg/m3 in rural households, compared to Bomet county (2.91 µg/m3 in urban and 9.09 µg/m3 in rural households). Ambient total gas phase PAH concentrations were more similar (Narok: 1.26 – 6.28 µg/m3 and Bomet: 2.44 – 6.30 µg/m3). Although the 3-stone device and burning of wood accounted for higher PAH emissions, the charcoal burning jiko stove produced the highest toxic equivalence quotient. Monitoring of PAHs emitted by these cooking devices and fuels is critical to public health and sustainable pollution mitigation.
For forty years, there has been growing uncertainty about whether Hill's horseshoe bat ( Rhinolophus hilli ) still persists in Nyungwe National Park, Rwanda. Only known from one small area within the National Park, R. hilli is listed as Critically Endangered by the International Union for the Conservation of Nature (IUCN), based on its extremely small geographic range and presumed low number of mature individuals. Here, we present and describe bat species occurrence data contributed to the Global Biodiversity Information Facility (GBIF) that we collected as part of a long-term collaborative project to rediscover this lost species. This data paper describes the survey methods and findings resulting from cave roost surveys, capture surveys, and acoustic sampling of bat echolocation activity in Nyungwe National Park and surrounding areas in south-western Rwanda from 2013-2020 and their conservation relevance. We report the discovery of an extant population of Hill's horseshoe bat ( Rhinolophus hilli ) in Nyungwe National Park, Rwanda, 40 years since the last reported observation of the species in 1981. We also report the first record of Lander's horseshoe bat ( Rhinolophus landeri ) in Nyungwe National Park and the first record of the Damara woolly bat ( Kerivoula argentata ) in Rwanda. The dataset contributed to GBIF and described in this paper includes 278 occurrence records from 10 bat species of five families detected at 71 locations in or near Nyungwe National Park, Rwanda. We include a description of the morphological descriptions of R. hilli and present the first acoustic echolocation signatures and phylogenetic information for this species.
Migrating grazers and carnivores respond to seasonal changes in the environment and often match peaks in resource abundance. However, it is unclear if and how frugivorous animals use phenological events to time migration, especially in the tropics. The straw‐colored fruit bat (Eidolon helvum), Africa’s most gregarious fruit bat, forms large seasonal colonies throughout much of sub‐Saharan Africa. We hypothesized that aggregations of E. helvum match the timing of their migration with phenologies of plant growth or precipitation. Using monthly colony counts from across much of the species’ range, we matched peak colony size to landscape phenologies and explored the variation among colonies matching the overall closest phenological event. Peak colony size was closest to the peak instantaneous rate of green‐up, and sites with closer temporal matching were associated with higher maximum greenness, short growing season, and larger peak colony size. Eidolon helvum seem to time their migrations to move into highly seasonal landscapes to exploit short‐lived explosions of food and may benefit from collective sensing to time migrations. The link between rapid changes in colony size and phenological match may also imply potential collective sensing of the environment. Overall decreasing bat numbers along with various threats might cause this property of large colonies to be lost. Remote sensing data, although, indirectly linked to fruiting events, can potentially be used to globally describe and predict the migration of frugivorous species in a changing world.
Understanding biodiversity patterns as well as drivers of population declines, and range losses provides crucial baselines for monitoring and conservation. However, the information needed to evaluate such trends remains unstandardised and sparsely available for many taxonomic groups and habitats, including the cave-dwelling bats and cave ecosystems. We developed the DarkCideS 1.0 (, a global database of bat caves and species synthesised from publicly available information and datasets. The DarkCideS 1.0 is by far the largest database for cave-dwelling bats, which contains information for geographical location, ecological status, species traits, and parasites and hyperparasites for 679 bat species are known to occur in caves or use caves in part of their life histories. The database currently contains 6746 georeferenced occurrences for 402 cave-dwelling bat species from 2002 cave sites in 46 countries and 12 terrestrial biomes. The database has been developed to be collaborative and open-access, allowing continuous data-sharing among the community of bat researchers and conservation biologists to advance bat research and comparative monitoring and prioritisation for conservation.
Irregular data or anomalies may occur due to human error, miscalculation, or malicious system behavior. The detection of anomalies is a difficult task that requires the use of multiple strategic methods and models. The ideal detection model should assess the strengths and optimize the results of its base models before making final decisions. This task of optimizing the results of the base models contributes to the generation of more accurate results overall. This work presents an optimized adaptive anomaly detection ensemble using heterogeneous algorithms (OAAE). In the first stage, it adaptively boosts the outcomes of preceding models by weighting their decisions and finding high‐performance ones, and in the second stage, it optimizes the base models by score margin maximization, which enlarges the contrast between the scores of the anomalies and other data prior to fusion to improve detection accuracy. To validate the model, four baselines (namely ALOI, BASE, SELECT and ADAHO) and test results from 10 benchmark datasets are compared. To assess its effectiveness in terms of distinguishing anomalies, the proposed model is tested and evaluated. The analyzed data are presented as cross‐tabulations, with detailed explanations and interpretations. The experiments show an improvement in results even when the least of anomalies (single cases up to 10%) are used. image
Water insecurity is a major concern both in the global and local contexts. The study estimated the sub-catchment water poverty index and the household water security index, on cross-sectional farm household data collected from 652 households randomly selected from eight sub-catchments of the Upper Ewaso Ng’iro North Catchment Area (ENNCA). The impact of water security on household income per adult equivalent and prevalence of waterborne diseases was assessed using ordinary least squares regression and Poisson regression models respectively. Water Poverty Index (WPI) results revealed that Sirimon and Ewaso Narok sub-catchments are faced with acute water stress, while the rest of the sub-catchments are faced with moderate water stress despite being in the sub-catchment area. The results showed that improved water security can offer welfare benefits to households through increments in household income and reduced water-borne disease prevalence. From the findings, therefore, improved water security can offer both economic and health solutions to some of the country's problems including poverty alleviation and reduce the government's budget spending on communicable and non-communicable water-related diseases.
Infection with sexually transmitted diseases and unwanted pregnancies are risks associated with adolescents’ risky sexual behavior. Mental models of attachment relationships influence behavior and may influence adolescents’ involvement in sexual activities that are risky. This study explored whether attachment styles predicted adolescents’ involvement in risky sexual behavior. A correlational study design was adopted with 367 students from public secondary schools in Nairobi County taking part in the study. The Attachment Styles Questionnaire and Risky Sexual Behavior Scale were used to collect data. Multiple regression analysis was used to determine whether secure, preoccupied, dismissing or fearful attachment styles predicted students’ risky sexual behavior. The study found that preoccupied attachment style and secure attachment style had predictive effects on risky sexual behavior. Preoccupied attachment style was predictive of greater involvement in risky sexual behavior whereas secure attachment style was predictive of less risky sexual behavior. It was evident based on the findings that secure attachment style was protective against risky sexual behavior whereas preoccupied attachment style created vulnerability to risky sexual behavior. It was recommended that special attention should be paid to adolescents with insecure attachment styles in guidance and counseling programs aimed at discouraging adolescents’ risky sexual behavior and parents/guardians should be encouraged to be more nurturing towards their children in order to encourage formation of secure attachment which was protective against risky sexual behavior. Keywords: attachment styles, attachment avoidance, attachment anxiety, secure attachment, risky sexual behavior
The resistance of microorganisms towards antibiotics remains a big challenge in medicine. Silver nanoparticles (AgNPs) received attention recently for their characteristic nanosized features and their ability to display antimicrobial activities. This work reports the synthesis of AgNPs using the Citrus sinensis peels extract in their aqueous, mild, and less hazardous conditions. The effect of concentration variation (1%, 2%, and 3%) of the plant extracts on the size and shape of the AgNPs was investigated. The antimicrobial activities were tested against gram-positive Staphylococcus aureus and gram-negative Klebsiella pneumoniae. Absorption spectra confirmed the synthesis by the surface Plasmon resonance peaks in the range 400–450 nm for all the AgNPs. FTIR spectra confirmed that Citrus sinensis peels extract acted as both reducing and surface passivating agent for the synthesized AgNPs. TEM revealed spherical AgNPs with average size of 12 nm for 3% concentration as compared to the agglomeration at 1% and 2%. All the AgNPs synthesized using Citrus sinensis peels extracts (1%, 2%, and 3%) exhibited antimicrobial activity against both gram-positive and negative bacteria. These results indicated a simple, fast, and inexpensive synthesis of silver nanoparticles using the Citrus sinensis peels extract that has promising antibacterial activity.
Understanding biodiversity patterns as well as drivers of population declines, and range losses provides crucial baselines for monitoring and conservation. However, the information needed to evaluate such trends remains unstandardised and sparsely available for many taxonomic groups and habitats, including the cave-dwelling bats and cave ecosystems. Here, we present the DarkCideS 1.0 , a global database of bat caves and bat species based on curated data from the literature, personal collections, and existing datasets. The database contains information for geographical distribution, ecological status, species traits, and parasites and hyperparasites for 679 bat species known to occur in caves or use caves in their life-histories. The database contains 6746 georeferenced occurrences for 402 cave-dwelling bat species from 2002 cave sites in 46 countries and 12 terrestrial biomes. The database has been developed to be a collaborative, open-access, and user-friendly platform, allowing continuous data-sharing among the community of bat researchers and conservation biologists. The database has a range of potential applications in bat research and enables comparative monitoring and prioritisation for conservation.
The genus Babesia has more than 100 species that are transmitted by ticks with some being zoonotic. They can infect humans, livestock, and wildlife. Although canine babesiosis occurs locally, published studies on the species involved are limited. Babesia parasites cause severe disease in dogs which can be fatal. Drawbacks of the current control methods necessitate vaccine development. The study objective was to identify the Babesia species infecting dogs from three Kenyan counties; Nairobi, Mombasa, Nakuru and determine their phylogenetic relationship. This will enable improved control and rule out zoonotic potential. The study period was October 2018 to November 2019.The study design was descriptive and sampling opportunistic. One hundred and forty-three dogs were sampled. From whole blood, total DNA was extracted using the TanBead extractor followed by PCR amplification targeting Babesia 18S rRNA. Positive samples were purified and sequenced using the Sanger Dideoxy method.CLC Genomics Workbench, GenBank™ and BLASTn™ on NCBI were used for sequence processing and analysis. Geneious prime™ was used for multiple sequence alignment and phylogenetic analysis. The overall prevalence of Babesia canis was 9.0% (95% CI: 4.37-13.81). Two out of 13 positive samples (2/13) were identified as Babesia canis vogeli, with a prevalence of 1.4% (95% CI: 1.38 - 14.2, n=143) while 11/13 were identified as Babesia canis rossi, with a prevalence of 7.69% (95% CI: 3.3 -12, n=143). The Babesia rossi sequences identified were closely related to sequences from black-backed jackals, while the Babesia vogeli ones were related to sequences from a pet cat in China. Babesia rossi which causes severe canine babesiosis was identified in 84.6% of the positive samples, immediate and aggressive clinical intervention is necessary. The possible sylvatic cycle of Babesia rossi and low levels of infections by Babesia vogeli should inform pertinent control measures.
In the light of the urgency raised by the COVID-19 pandemic, global investment in wildlife virology is likely to increase, and new surveillance programmes will identify hundreds of novel viruses that might someday pose a threat to humans. To support the extensive task of laboratory characterization, scientists may increasingly rely on data-driven rubrics or machine learning models that learn from known zoonoses to identify which animal pathogens could someday pose a threat to global health. We synthesize the findings of an interdisciplinary workshop on zoonotic risk technologies to answer the following questions. What are the prerequisites, in terms of open data, equity and interdisciplinary collaboration, to the development and application of those tools? What effect could the technology have on global health? Who would control that technology, who would have access to it and who would benefit from it? Would it improve pandemic prevention? Could it create new challenges? This article is part of the theme issue ‘Infectious disease macroecology: parasite diversity and dynamics across the globe’.
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883 members
Paul Webala
  • Forestry and Wildlife Management
Wycliffe Wanzala
  • The Institute of Ethnobiology and Ethnomedicines
Benard Kodak
  • Languages, Literature and Culture
Aloys Osano
  • Department of Physical Sciences
Cyril Sang
  • Computer Science
Narok, Kenya