Титульная страница и содержание

Une étude épidémiologique sur les handicaps chroniques à la marche a été effectuée de Novembre 1988 à Janvier 1989 en zone rurale dans trois provinces du Burkina Faso. Le recrutement réalisé au porte à porte montre que le taux de prévalence des handicaps chroniques à la marche dépasse 9 pour mille habitants. La poliomyélite occupe le premier rang des étiologies avec un tiers des cas, suivie de l'ensemble des autres maladies neurologiques, les affections rhumatologiques et orthopédiques, et surtout les séquelles de dracunculose. Contraitement à d'autres études réalisées en milieu urbain, le rôle des sciatites par injection médicamenteuse intrafessière est négligeable dans les régions rurales faiblement médicalisées. Le rôle de certaines affections neurologiques telles les paraparésies spastiques tropicales reste à déterminer. (Résumé d'auteur

CodeGrid: A Grid Representation of Code

peer reviewedCode representation is a key step in the application of AI in software engineering. Generic NLP representations are effective but do not exploit all the rich structure inherent to code. Recent work has focused on extracting abstract syntax trees (AST) and integrating their structural information into code representations.These AST-enhanced representations advanced the state of the art and accelerated new applications of AI to software engineering. ASTs, however, neglect important aspects of code structure, notably control and data flow, leaving some potentially relevant code signal unexploited. For example, purely image-based representations perform nearly as well as AST-based representations, despite the fact that they must learn to even recognize tokens, let alone their semantics. This result, from prior work, is strong evidence that these new code representations can still be improved; it also raises the question of just what signal image-based approaches are exploiting. We answer this question. We show that code is spatial and exploit this fact to propose , a new representation that embeds tokens into a grid that preserves code layout. Unlike some of the existing state of the art, is agnostic to the downstream task: whether that task is generation or classification, can complement the learning algorithm with spatial signal. For example, we show that CNNs, which are inherently spatially-aware models, can exploit outputs to effectively tackle fundamental software engineering tasks, such as code classification, code clone detection and vulnerability detection. PixelCNN leverages 's grid representations to achieve code completion. Through extensive experiments, we validate our spatial code hypothesis, quantifying model performance as we vary the degree to which the representation preserves the grid. To demonstrate its generality, we show that augments models, improving their performance on a range of tasks, On clone detection, improves ASTNN's performance by 3.3% F1 score

Candidate gene polymorphisms study between human African trypanosomiasis clinical phenotypes in Guinea

Human African trypanosomiasis (HAT), a lethal disease induced by Trypanosoma brucei gambiense, has a range of clinical outcomes in its human host in West Africa: an acute form progressing rapidly to second stage, spontaneous self-cure and individuals able to regulate parasitaemia at very low levels, have all been reported from endemic foci. In order to test if this clinical diversity is influenced by host genetic determinants, the association between candidate gene polymorphisms and HAT outcome was investigated in populations from HAT active foci in Guinea.Samples were collected from 425 individuals; comprising of 232 HAT cases, 79 subjects with long lasting positive and specific serology but negative parasitology and 114 endemic controls. Genotypes of 28 SNPs in eight genes passed quality control and were used for an association analysis. IL6 rs1818879 allele A (p = 0.0001, OR = 0.39, CI95 = [0.24-0.63], BONF = 0.0034) was associated with a lower risk of progressing from latent infection to active disease. MIF rs36086171 allele G seemed to be associated with an increased risk (p = 0.0239, OR = 1.65, CI95 = [1.07-2.53], BONF = 0.6697) but did not remain significant after Bonferroni correction. Similarly MIF rs12483859 C allele seems be associated with latent infections (p = 0.0077, OR = 1.86, CI95 = [1.18-2.95], BONF = 0.2157). We confirmed earlier observations that APOL1 G2 allele (DEL) (p = 0.0011, OR = 2.70, CI95 = [1.49-4.91], BONF = 0.0301) is associated with a higher risk and APOL1 G1 polymorphism (p = 0.0005, OR = 0.45, CI95 = [0.29-0.70], BONF = 0.0129) with a lower risk of developing HAT. No associations were found with other candidate genes.Our data show that host genes are involved in modulating Trypanosoma brucei gambiense infection outcome in infected individuals from Guinea with IL6 rs1818879 being associated with a lower risk of progressing to active HAT. These results enhance our understanding of host-parasite interactions and, ultimately, may lead to the development of new control tools

Learning to Represent Patches

Patch representation is crucial in automating various software engineering tasks, like determining patch accuracy or summarizing code changes. While recent research has employed deep learning for patch representation, focusing on token sequences or Abstract Syntax Trees (ASTs), they often miss the change's semantic intent and the context of modified lines. To bridge this gap, we introduce a novel method, Patcherizer. It delves into the intentions of context and structure, merging the surrounding code context with two innovative representations. These capture the intention in code changes and the intention in AST structural modifications pre and post-patch. This holistic representation aptly captures a patch's underlying intentions. Patcherizer employs graph convolutional neural networks for structural intention graph representation and transformers for intention sequence representation. We evaluated Patcherizer's embeddings' versatility in three areas: (1) Patch description generation, (2) Patch accuracy prediction, and (3) Patch intention identification. Our experiments demonstrate the representation's efficacy across all tasks, outperforming state-of-the-art methods. For example, in patch description generation, Patcherizer excels, showing an average boost of 19.39% in BLEU, 8.71% in ROUGE-L, and 34.03% in METEOR scores

Macrophage migrating inhibitory factor expression is associated with Trypanosoma brucei gambiense infection and is controlled by trans-acting expression quantitative trait loci in the Guinean population

Infection by Trypanosoma brucei gambiense is characterized by a wide array of clinical outcomes, ranging from asymptomatic to acute disease and even spontaneous cure. In this study, we investigated the association between macrophage migrating inhibitory factor (MIF), an important pro-inflammatory cytokine that plays a central role in both innate and acquired immunity, and disease outcome during T. b. gambiense infection. A comparative expression analysis of patients, individuals with latent infection and controls found that MIF had significantly higher expression in patients (n=141; 1.25 +/- 0.07; p<.0001) and latent infections (n=25; 1.23 +/- 0.13; p=.0005) relative to controls (n=46; 0.94 +/- 0.11). Furthermore, expression decreased significantly after treatment (patients before treatment n=33; 1.40 +/- 0.18 versus patients after treatment n=33; 0.99 +/- 0.10, p=.0001). We conducted a genome wide eQTL analysis on 29 controls, 128 cases and 15 latently infected individuals for whom expression and genotype data were both available. Four loci, including one containing the chemokine CXCL13, were found to associate with MIF expression. Genes at these loci are candidate regulators of increased expression of MIF after infection. Our study is the first data demonstrating that MIF expression is elevated in T. b. gambiense-infected human hosts but does not appear to contribute to pathology

Negative Results of Fusing Code and Documentation for Learning to Accurately Identify Sensitive Source and Sink Methods An Application to the Android Framework for Data Leak Detection

Almost two-thirds of the population owns a mobile phone. Given that there is a profusion of mobile applications that manipulate all sorts of data, privacy-related concerns arise more and more. New regulations such as the General Data Protection Regulation (GDPR) provide rules for which developers must comply when their apps process sensitive and/or private data. Ensuring that no such data is leaked without the consent of the user is a primary objective in each GDPR compliance check. Researchers have proposed sophisticated approaches to track sensitive data within mobile apps, all of which rely on specific lists of sensitive source and sink methods. The data flow analysis results greatly depend on these lists' quality. Previous approaches either used incomplete hand-written lists and quickly became outdated or relied on machine learning. The latter, however, leads to numerous false positives, as we show. This paper introduces CoDoC that aims to revive the machine-learning approach to precisely identify the privacy-related source and sink API methods. In contrast to previous approaches, CoDoC uses deep learning techniques and combines the source code with the documentation of API methods. Firstly, we propose novel definitions that clarify the concepts of taint analysis, source, and sink methods. Secondly, based on these definitions, we build a new ground truth of Android methods representing sensitive source, sink, and neither methods that will be used to train our classifier. We evaluate CoDoC and show that, on our validation dataset, it achieves a precision, recall, and F1 score of 91%, outperforming the state-of-the-art SuSi. However, similarly to existing tools, we show that in the wild, i.e., with unseen data, CoDoC performs poorly and generates many false-positive results. Our findings suggest that machine-learning models for abstract concepts such as privacy fail in practice despite good lab results. To encourage future research, we release all our artifacts to the community

Evaluating Representation Learning of Code Changes for Predicting Patch Correctness in Program Repair

A large body of the literature of automated program repair develops approaches where patches are generated to be validated against an oracle (e.g., a test suite). Because such an oracle can be imperfect, the generated patches, although validated by the oracle, may actually be incorrect. While the state of the art explore research directions that require dynamic information or rely on manually-crafted heuristics, we study the benefit of learning code representations to learn deep features that may encode the properties of patch correctness. Our work mainly investigates different representation learning approaches for code changes to derive embeddings that are amenable to similarity computations. We report on findings based on embeddings produced by pre-trained and re-trained neural networks. Experimental results demonstrate the potential of embeddings to empower learning algorithms in reasoning about patch correctness: a machine learning predictor with BERT transformer-based embeddings..

Learning to Represent Patches

Patch representation is crucial in automating various software engineering tasks, like determining patch accuracy or summarizing code changes. While recent research has employed deep learning for patch representation, focusing on token sequences or Abstract Syntax Trees (ASTs), they often miss the change's semantic intent and the context of modified lines. To bridge this gap, we introduce a novel method, Patcherizer. It delves into the intentions of context and structure, merging the surrounding code context with two innovative representations. These capture the intention in code changes and the intention in AST structural modifications pre and post-patch. This holistic representation aptly captures a patch's underlying intentions. Patcherizer employs graph convolutional neural networks for structural intention graph representation and transformers for intention sequence representation. We evaluated Patcherizer's embeddings' versatility in three areas: (1) Patch description generation, (2) Patch accuracy prediction, and (3) Patch intention identification. Our experiments demonstrate the representation's efficacy across all tasks, outperforming state-of-the-art methods. For example, in patch description generation, Patcherizer excels, showing an average boost of 19.39% in BLEU, 8.71% in ROUGE-L, and 34.03% in METEOR scores

Prevalence of Trachoma in Northern Benin: Results from 11 Population-Based Prevalence Surveys Covering 26 Districts.

AIMS: We sought to evaluate trachoma prevalence in all suspected-endemic areas of Benin. METHODS: We conducted population-based surveys covering 26 districts grouped into 11 evaluation units (EUs), using a two-stage, systematic and random, cluster sampling design powered at EU level. In each EU, 23 villages were systematically selected with population proportional to size; 30 households were selected from each village using compact segment sampling. In selected households, we examined all consenting residents aged one year or above for trichiasis, trachomatous inflammation - follicular (TF), and trachomatous inflammation - intense. We calculated the EU-level backlog of trichiasis and delineated the ophthalmic workforce in each EU using local interviews and telephone surveys. RESULTS: At EU-level, the TF prevalence in 1-9-year-olds ranged from 1.9 to 24.0%, with four EUs (incorporating eight districts) demonstrating prevalences ≥5%. The prevalence of trichiasis in adults aged 15+ years ranged from 0.1 to 1.9%. In nine EUs (incorporating 19 districts), the trichiasis prevalence in adults was ≥0.2%. An estimated 11,457 people have trichiasis in an area served by eight ophthalmic clinical officers. CONCLUSION: In northern Benin, over 8000 people need surgery or other interventions for trichiasis to reach the trichiasis elimination threshold prevalence in each EU, and just over one million people need a combination of antibiotics, facial cleanliness and environmental improvement for the purposes of trachoma's elimination as a public health problem. The current distribution of ophthalmic clinical officers does not match surgical needs