Block clustering of Binary Data with Gaussian Co-variables

The simultaneous grouping of rows and columns is an important technique that is increasingly used in large-scale data analysis. In this paper, we present a novel co-clustering method using co-variables in its construction. It is based on a latent block model taking into account the problem of grouping variables and clustering individuals by integrating information given by sets of co-variables. Numerical experiments on simulated data sets and an application on real genetic data highlight the interest of this approach

Multinomial logistic model for coinfection diagnosis between arbovirus and malaria in Kedougou

In tropical regions, populations continue to suffer morbidity and mortality from malaria and arboviral diseases. In Kedougou (Senegal), these illnesses are all endemic due to the climate and its geographical position. The co-circulation of malaria parasites and arboviruses can explain the observation of coinfected cases. Indeed there is strong resemblance in symptoms between these diseases making problematic targeted medical care of coinfected cases. This is due to the fact that the origin of illness is not obviously known. Some cases could be immunized against one or the other of the pathogens, immunity typically acquired with factors like age and exposure as usual for endemic area. Then, coinfection needs to be better diagnosed. Using data collected from patients in Kedougou region, from 2009 to 2013, we adjusted a multinomial logistic model and selected relevant variables in explaining coinfection status. We observed specific sets of variables explaining each of the diseases exclusively and the coinfection. We tested the independence between arboviral and malaria infections and derived coinfection probabilities from the model fitting. In case of a coinfection probability greater than a threshold value to be calibrated on the data, duration of illness above 3 days and age above 10 years-old are mostly indicative of arboviral disease while body temperature higher than 40{\textdegree}C and presence of nausea or vomiting symptoms during the rainy season are mostly indicative of malaria disease

Developing open science in Africa : barriers, solutions and opportunities

African science systems largely operate independently of each other, creating siloes of incompatible policies, practices and data sets that are not mutually consistent or inter-operable. The paper argues for the development of open science in Africa as a means of energizing national science systems. It focuses on the complexity of social and economic challenges created by climate change, the demographic explosion, and the difficulty of confronting these conditions in the absence of an adequate digital infrastructure. The paper draws on questionnaire data from 15 African Science Granting Councils and the state-of-the-art report “Open Science in Research and Innovation for Development in Africa.”Swedish International Development Cooperation AgencyDepartment for International Development (UK)National Research Foundation (South Africa

Open science in research and innovation for development in Africa

The paper argues that Science Granting Councils (SGC) in their intermediary role between governments and the science community, can act as a collective to achieve efficiencies of scale, stimulating virtual critical masses and intra-African collaboration while creating an African space for open science. It details some tools and processes necessary for supporting open science, as well as the rationale for sharing scientific data, which permits re-use by others as open data. It describes open science platforms or “commons” to provide support to the research process, from information technology infrastructure to high-level analytics and artificial intelligence (AI) procedures.United Kingdom’s Department for International Development (DFID)South Africa’s National Research Foundation (NRF)Swedish International Development Cooperation Agency (Sida

Molecular Diagnostics of Ebola Patient Samples by Institut Pasteur de Dakar Mobile Laboratory in Guinea 2014–2016

As part of the laboratory response to the Ebola virus outbreak in Guinea, the Institut Pasteur de Dakar mobile laboratory (IPD-ML) was set up in Donka hospital from 2014 to 2016. EBOV suspected samples collected at Ebola Treatment Centers (ETC) and from community deaths were sent daily to IPD-ML. Analysis was performed using dried oligonucleotide mixes for real-time RT-PCR designed for field diagnostic. From March 2014 to May 2015, a total of 6055 patient samples suspected for EBOV collected from seven regions of Guinea were tested by real-time RT-PCR. These patients’ clinical included serum samples (n = 2537 samples) and swabs (n = 3518 samples) with positivity rates of 36.74 and 6.88% respectively. Females were significantly more affected than males with positivity rates of 22.39 and 17.22% respectively (p-value = 5.721e-7). All age groups were exposed to the virus with significant difference (p-value <= 2.2e-16). The IPD-ML contributed significantly to the surveillance and patient management during the EBOV outbreak in Guinea. Furthermore, dried reagents adapted for field diagnostic of EVD suspect cases could be useful for future outbreak preparedness and response

Digital revolution, open science and innovation for development in Sub-Saharan Africa

This policy brief recommends that Science Granting Councils (SGC) through the SGC Initiative (SGCI) intensify the level, scope and ambition of collective action by developing an African open science area, and by seeking support and acquiescence of respective country governments in doing so. It provides background to an open science movement, which includes a digitized ‘commons’ or platform, through collective action by member states in sub-Sharan Africa. The African Technology Policy Studies Network (ATPS) provides platforms for regional and international research and knowledge sharing in order to build Africa’s capabilities in STI policy research, policymaking and implementation for sustainable development.United Kingdom’s Department for International Development (DFID)South Africa’s National Research Foundation (NRF)Swedish International Development Cooperation Agency (Sida

Development and validation of sensitive real-time RT-PCR assay for broad detection of rabies virus

Rabies virus (RABV) remains one of the most important global zoonotic pathogens. RABV causes rabies, an acute encephalomyelitis associated with a high rate of mortality in humans and animals and affecting different parts of the world, particularly in Asia and Africa. Confirmation of rabies diagnosis relies on laboratory diagnosis, in which molecular techniques such as detection of viral RNA by reverse transcription polymerase chain reaction (RT-PCR) are increasingly being used.&nbsp; In this study, two real-time quantitative RT-PCR assays were developed for large-spectrum detection of RABV, with a focus on African isolates. The primer and probe sets were targeted highly conserved regions of the nucleoprotein (N) and polymerase (L) genes.&nbsp; The results indicated the absence of non-specific amplification and cross-reaction with a range of other viruses belonging to the same taxonomic family, i.e Rhabdoviridae, as well as negative brain tissues from various host species. Analytical sensitivity ranged between 100 to 10 standard RNA copies detected per reaction for N-gene and L-gene assays, respectively. Effective detection and high sensitivity of these assays on African isolates showed that they can be successfully applied in general research and used in diagnostic process and epizootic surveillance in Africa using a double-check strategy

An Exhaustive, Non-Euclidean, Non-Parametric Data Mining Tool for Unraveling the Complexity of Biological Systems – Novel Insights into Malaria

Complex, high-dimensional data sets pose significant analytical challenges in the post-genomic era. Such data sets are not exclusive to genetic analyses and are also pertinent to epidemiology. There has been considerable effort to develop hypothesis-free data mining and machine learning methodologies. However, current methodologies lack exhaustivity and general applicability. Here we use a novel non-parametric, non-euclidean data mining tool, HyperCube®, to explore exhaustively a complex epidemiological malaria data set by searching for over density of events in m-dimensional space. Hotspots of over density correspond to strings of variables, rules, that determine, in this case, the occurrence of Plasmodium falciparum clinical malaria episodes. The data set contained 46,837 outcome events from 1,653 individuals and 34 explanatory variables. The best predictive rule contained 1,689 events from 148 individuals and was defined as: individuals present during 1992–2003, aged 1–5 years old, having hemoglobin AA, and having had previous Plasmodium malariae malaria parasite infection ≤10 times. These individuals had 3.71 times more P. falciparum clinical malaria episodes than the general population. We validated the rule in two different cohorts. We compared and contrasted the HyperCube® rule with the rules using variables identified by both traditional statistical methods and non-parametric regression tree methods. In addition, we tried all possible sub-stratified quantitative variables. No other model with equal or greater representativity gave a higher Relative Risk. Although three of the four variables in the rule were intuitive, the effect of number of P. malariae episodes was not. HyperCube® efficiently sub-stratified quantitative variables to optimize the rule and was able to identify interactions among the variables, tasks not easy to perform using standard data mining methods. Search of local over density in m-dimensional space, explained by easily interpretable rules, is thus seemingly ideal for generating hypotheses for large datasets to unravel the complexity inherent in biological systems