Background: Effective global antiretroviral vaccines and therapeutic strategies depend on the diversity, evolution, and epidemiology of their various strains as well as their transmission and pathogenesis. Most viral disease-causing particles are clustered into a taxonomy of subtypes to suggest pointers toward nucleotide-specific vaccines or therapeutic applications of clinical significance sufficient for sequence-specific diagnosis and homologous viral studies. These are very useful to formulate predictors to induce cross-resistance to some retroviral control drugs being used across study areas. Objective: This research proposed a collaborative framework of hybridized (Machine Learning and Natural Language Processing) techniques to discover hidden genome patterns and feature predictors for HIV-1 genome sequences mining. Methods: 630 human HIV-1 genome sequences above 8500 bps were excavated from the National Center for Biotechnology Information (NCBI) database (https://www.ncbi.nlm.nih.gov) for 21 countries across different continents, except for Antarctica. These sequences were transformed and learned using a self-organizing map (SOM). To discriminate emerging/new sub-strain(s), the HIV-1 reference genome was included as part of the input isolates/samples during the training. After training the SOM, component planes defining pattern clusters of the input datasets were generated for cognitive knowledge mining and subsequent labeling of the datasets. Additional genome features, including dinucleotide transmission recurrences, codon recurrences, and mutation recurrences, were finally extracted from the raw genomes to construct output classification targets for supervised learning. Results: SOM training explains the inherent pattern diversity of HIV-1 genomes as well as inter-and intra-country transmissions in which mobility might play an active role, as corroborated by the literature. Nine sub-strains were discovered after disassembling the SOM correlation hunting matrix space attributed to disparate clusters. Cognitive knowledge mining separated similar pattern clusters bounded by a certain degree of correlation range, as discovered by the SOM. Kruskal-Wallis rank-sum test and Wilcoxon rank-sum test showed statistically significant variations in dinucleotide, codon, and mutation patterns. Conclusion: Results of the discovered sub-strains and response clusters visualizations corroborate the existing literature, with significant haplotype variations. The proposed framework would assist in the development of decision support systems for easy contact tracing, infectious disease surveillance, and studying the progressive evolution of the reference HIV-1 genome.

Semi-Latin rectangles are generalizations of Latin squares and semi-Latin squares. Although they are called rectangles, the number of rows and the number of columns are not necessarily distinct. There are k treatments in each cell (row-column intersection): these constitute a block. Each treatment of the design appears a definite number of times in each row and also a definite number of times in each column (these parameters also being not necessarily distinct). When k = 2, the design is said to have block size two. Regular-graph semi-Latin rectangles have the additional property that the treatment concurrences between any two pairs of distinct treatments differ by at most one. Constructions for semi-Latin rectangles of this class with k = 2 which have v treatments, v/2 rows and v columns, where v is even, are given in Bailey and Monod (2001). These give the smallest designs when v is even. Here we give constructions for smallest designs with k = 2 when v is odd. These are regular-graph semi-Latin rectangles where the numbers of rows, columns and treatments are identical. Then we extend the smallest designs in each case to obtain larger designs.

African Perspective 2 3 Moses Ekpenyong, Faith-Michael Uzoka, Mercy Edoho, Udoinyang Inyang, Ifiok Udo, Nseobong Uto, 4 Itemobong Ekaidem, Anietie Moses, Enoabasi Anwana, Youchou Tatfeng, Geoffery Joseph, Emmanuel 5 Dan, Juliana Ndunagu 6 7 8 Department of Computer Science, University of Uyo, Nigeria 9 Department of Mathematics and Computing, Mount Royal University, Canada 10 School of Mathematics and Statistics, University of Saint Andrews, United Kingdom 11 College of Health Sciences, University of Uyo, Nigeria 12 Department of Botany and Ecological Studies, University of Uyo, Nigeria 13 College of Health Sciences, Niger Delta University, Nigeria 14 Department of Computer Science, National Open University, Nigeria 15 16 * – Corresponding Author 17 18 Abstract 19

As we advance towards individualized therapy, the ‘one-size-fits-all’ regimen is gradually paving the way for adaptive techniques that address the complexities of failed treatments. Treatment failure is associated with factors such as poor drug adherence, adverse side effect/reaction, co-infection, lack of follow-up, drug-drug interaction and more. This paper implements a transfer learning approach that classifies patients' response to failed treatments due to adverse drug reactions. The research is motivated by the need for early detection of patients' response to treatments and the generation of domain-specific datasets to balance under-represented classification data, typical of low-income countries located in Sub-Saharan Africa. A soft computing model was pre-trained to cluster CD4+ counts and viral loads of treatment change episodes (TCEs) processed from two disparate sources: the Stanford HIV drug resistant database (https://hivdb.stanford.edu), or control dataset, and locally sourced patients' records from selected health centers in Akwa Ibom State, Nigeria, or mixed dataset. Both datasets were experimented on a traditional 2-layer neural network (NN) and a 5-layer deep neural network (DNN), with odd dropout neurons distribution resulting in the following configurations: NN (Parienti et al., 2004) [32], NN (Deniz et al., 2018) [53] and DNN [9 7 5 3 1]. To discern knowledge of failed treatment, DNN1 [9 7 5 3 1] and DNN2 [9 7 5 3 1] were introduced to model both datasets and only TCEs of patients at risk of drug resistance, respectively. Classification results revealed fewer misclassifications, with the DNN architecture yielding best performance measures. However, the transfer learning approach with DNN2 [9 7 3 1] configuration produced superior classification results when compared to other variants/configurations, with classification accuracy of 99.40%, and RMSE values of 0.0056, 0.0510, and 0.0362, for test, train, and overall datasets, respectively. The proposed system therefore indicates good generalization and is vital as decision-making support to clinicians/physicians for predicting patients at risk of adverse drug reactions. Although imbalanced features classification is typical of disease problems and diminishes dependence on classification accuracy, the proposed system still compared favorably with the literature and can be hybridized to improve its precision and recall rates.

Background: The increased number of accessible genomes has prompted large-scale comparative studies for decerning evolutionary knowledge of infectious diseases, but challenges such as non-availability of close reference sequence(s), incompletely assembled or large number of genomes, preclude real time multiple sequence alignment and sub-strain(s) discovery. This paper introduces a cooperatively inspired open-source framework, for intelligent mining of severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) genomes. We situate this study within the African context, to drive advancement on state-of-the-art, towards intelligent infectious disease characterization and prediction. The outcome is an enriched Knowledge Base, sufficient to provide deep understanding of the viral sub-strains’ identification problem. We also open investigation by gender, which to the best of our knowledge has been ignored in related research. Data for the study came from the Global Initiative on Sharing All Influenza Data database (https://gisaid.org) and processed for precise discovery of viral sub-strains transmission between and within African countries. To localize the transmission route(s) of each isolate excavated and provide appropriate links to similar isolate strain(s), a cognitive solution was imposed on the genome expression patterns discovered by unsupervised self-organizing map (SOM) component planes visualization. The Freidman-Nemenyi’s test was finally performed to validate our claim. Results: Evidence of inter- and intra-genome diversity was noticed. While some isolates (or genomes) clustered differently, implying different evolutionary source (or high-diversity), others clustered closely together, indicating similar evolutionary source (or less-diversity). SOM component planes analysis revealed multiple sub-strains patterns, strongly suggesting local- or intra-community and country to country transmissions. Cognitive maps of both male and female isolates revealed multiple transmission routes. Freidman’s test results showed highly significant difference (p<0.01) among the various isolate groups. Nemenyi’s test revealed groups that differed in their isolates. Conclusion: The proposed framework offers explanations to SARS-CoV-2 diversity and provides real time identification to disease transmission routes, as well as rapid decision support for facilitating inter- and intra-country contact tracing of infected case(s). Intermediate data produced in this paper are helpful to enrich the genome datasets for intelligent characterization and prediction of COVID-19 and related pandemics, as well as the construction of intelligent device for accurate infectious disease monitoring.

There exists a set of designs which form a subclass of semi-Latin rectangles.These designs, besides being semi-Latin rectangles, exhibit an additional property of balance, where no two distinct pairs of symbols (treatments) differ in their concurrences, that is, each pair of distinct treatments concurs a constant number of times in the design. Such a design exists for a limited set of parameter combinations. We designate it a balanced semi-Latin rectangle and give some properties and necessary conditions for its existence. Furthermore, algorithms for constructing the design for experimental situations where there are two treatments in each row–column intersection (block) are also given.

In this paper, we investigate the effect of speaker variability on emotions and languages, and propose a classification system. To achieve these, speech features such as the fundamental frequency (F0) and intensity of two languages (Ibibio, New Benue Congo and Yoruba, Niger Congo) were exploited. A total of 20 speakers (10 males and 10 females) were recorded and speech features extracted for analysis. A methodological framework consisting of 4 main components: speech recording, knowledge base, preprocessing and analytic. ANOVA was used to test the intra- and inter-variability among speakers of various languages on emotions and languages, while the predictive analysis was carried out using support vector machine (SVM). We observed that language and speech features are dependent on speakers' characteristics. Furthermore, there exists a highly significant variability in the effect of emotions and languages on speech features. Results of SVM classification yielded 66.04% accuracy for emotions classification and 79.40% accuracy for language classification. Hence, classification performance favored the language classifier compared to emotion classifier, as the former produced low root mean squared error (RMSE) when compared with the later.

In this work, we investigate the contrasts properties of the optimal semiLatin squares with side six and block size two with a view to discriminating amongst them. The Variances, as well as the average Variance of the estimate of elementary contrasts of treatments was computed for each square with the aid of MATLAB and the results compared with each other. From the results, the average variance of elementary treatment contrasts for the three squares 1, 2 and 3 is approximately the same. Again, the squares 2 and 3 have the same and minimal number of distinct variances of elementary contrasts compared to 1, hence preferable to 1. Furthermore, 3 has a minimal value of the maximum variances of contrasts amongst these squares. Thus, 3 becomes the most preferable square amongst all of them; while 2 is preferable to 1