Biology subjects

Mohammed, A.

Publications and source records attributed to Mohammed, A..

4 recordsLinked to original sources

bioRxiv2018

Assessing the blood-host plasticity and dispersal rate of the malaria vector Anopheles coluzzii

Difficulties with observing the dispersal of insect vectors in the field have hampered understanding of several aspects of their behaviour linked to disease transmission. Here, a novel method based on detection of blood-meal sources is introduced to inform two critical and understudied mosquito behaviours: plasticity in the malaria vectors blood-host choice and vector dispersal. Strategically located collections of Anopheles coluzzii from a malaria-endemic village of southern Ghana showed statistically significant variation in host species composition of mosquito blood-meals. Trialling a new sampling approach gave the first estimates for the remarkably local spatial scale across which host choice is plastic. Using quantitative PCR, the blood-meal digestion was then quantified for field-caught mosquitoes and calibrated according to timed blood digestion in colony mosquitoes. We demonstrate how this new molecular Sella score approach can be used to estimate the dispersal rate of blood-feeding vectors caught in the field.

ecology↗

bioRxiv2018

A systematic review and meta-analysis of the human blood index of the major African malaria vectors

BACKGROUNDThe proportion of mosquito blood-meals that are of human origin, referred to as the human blood index or HBI, is a key determinant of malaria transmission. We conducted a systematic review of the HBI for the major African malaria vectors.\n\nRESULTSEvidence is presented for higher HBI among Anopheles gambiae (M/S forms and An. coluzzii/An. gambiae s.s. are not distinguished for most studies and therefore combined) as well as An. funestus when compared with An. arabiensis (prevalence odds ratio adjusted for collection location [i.e. indoor or outdoor]: 1.62; 95%CI 1.09-2.42; 1.84; 95%CI 1.35-2.52, respectively). This finding is keeping with the entomological literature which describes An. arabiensis to be more zoophagic than the other major African vectors. However, analysis also revealed that HBI was more associated with location of mosquito captures (R2=0.29) than with mosquito (sibling) species (R2=0.11).\n\nCONCLUSIONSOur findings call into question the appropriateness of current methods of assessing host preferences among disease vectors and have important implications for strategizing vector control.

ecology↗

bioRxiv2018

The neuronal ceroid lipofuscinosis protein, Cln7, regulates neural development from the post-synaptic cell.

The neuronal ceroid lipofuscinoses (NCLs) are a group of fatal, monogenic neurodegenerative disorders with an early onset in infancy or childhood. Despite identification of the genes disrupted in each form of the disease, their normal cellular role and how their deficits lead to disease pathology is not fully understood. Cln7, a major facilitator superfamily domain-containing protein, is affected in a late infantile-onset form of NCL. Using the Drosophila larval neuromuscular junction as a model to study neural development, we demonstrate that Cln7 is required for the normal growth of synapses. In a Cln7 mutant, synapses fail to develop fully leading to reduced function and behavioral changes with dysregulation of TOR activity. Cln7 expression is restricted to the post-synaptic cell and the protein localizes to vesicles immediately adjacent to the post-synaptic membrane. Our data suggest an involvement for Cln7 in regulating trans-synaptic communication.

developmental biology↗

bioRxiv2017

CancerDiscover: A configurable pipeline for cancer prediction and biomarker identification using machine learning framework

MotivationUse of various high-throughput screening techniques has resulted in an abundance of data, whose complete utility is limited by the tools available for processing and analysis. Machine learning holds great potential for deciphering these data in the context of cancer classification and biomarker identification. However, current machine learning tools require manual processing of raw data from various sequencing platforms, which is both tedious and time-consuming. The current classification tools lack flexibility in choosing the best feature selection algorithms from a range of algorithms and most importantly inability to compare various learning algorithms.\n\nResultsWe developed CancerDiscover, an open-source software pipeline that allows users to efficiently and automatically integrate large high-throughput datasets, preprocess, normalize, and selects best performing features from multiple feature selection algorithms. The pipeline lets users apply various learning algorithms and generates multiple classification models and evaluation reports that distinguish cancer from normal samples, as well as different types and subtypes of cancer.\n\nAvailability and ImplementationThe open source pipeline is freely available for download at https://github.com/HelikarLab/CancerDiscover.\n\nContactelikar2@unl.edu\n\nSupplementary InformationPlease refer to the CancerDiscover README (Supplementary File 1) for detailed instructions on installation and operation of the pipeline. For a list of available feature selection methods, see Supplementary File 2.

bioinformatics↗