Antimicrobial resistance (AMR), especially antibiotic resistance (ABR), could cause up to 10 million deaths per year by 2050. While ABR is a natural phenomenon, it is exacerbated by antibiotic misuse, poor sanitation, insufficient health infrastructure, weak biosecurity, and environmental contamination. The cross-transmission of resistant bacteria between humans, animal and the environment requires a multi-sectoral response inspired by the One Health concept. With the support of international partners and in line with global policies, Senegal has established a national One Health platform and legal measures to combat ABR. Our study proposes to examine the One Health governance capacities for managing ABR in Senegal, and to identify ways of improvement from a One Health perspective. A qualitative approach was adopted, combining the review of 64 legal instruments and 26 semi-structured interviews conducted between January and August 2023 with actors from governmental, private and non-governmental institutions. The analysis focused on governance structure, legal instrument development and implementation, and ABR surveillance programmes. The thematic analysis of the interview contents highlighted factors influencing the One Health governance capacities in Senegal. One Health governance has been institutionalized in Senegal since 2017 with the creation of the national One Health platform housed at the Prime Minister's Office. However, its effectiveness is limited by weak formalization, inadequate resources, and reliance on external funding. Outdated and poorly enforced legal frameworks hinder ABR management, while rural areas lack sufficient laboratory infrastructure, leading to inappropriate antibiotic use and weak surveillance systems. Power imbalances among stakeholders impede collective action and data-sharing. Despite progress toward international standards, systemic barriers persist. Strengthening the legal framework, improving healthcare infrastructure, and enhancing the One Health platform's status and funding are critical. Institutionalization must connect global orientations with local realities to avoid inequities. Sustainable ABR management requires inclusive, context-sensitive policies, robust domestic investment, and equitable collaboration among stakeholders across the sectors of human, animal, and environmental health.
Eco-friendly solutions using natural, biocompatible, biodegradable materials with low-cost fabrication techniques, such as solution processes, have been utilised in organic field-effect transistors (OFETs). This paper evaluated the integration of mango and cactus saps as natural high-k dielectric materials in OFETs. It was observed that the thinner (170 nm) mango sap dielectric layers showed a 30
Despite their linguistic diversity and global significance, African languages remain underrepresented in research and resources to support NLP. We aim to bridge this gap by introducing AfriSUD , the first multilingual collection of syntactically annotated treebanks for nine diverse African languages spanning major language families and regions across Sub-Saharan Africa. Using the Surface-Syntactic Universal Dependencies (SUD) framework, our community-led effort provides high-quality, native-speaker-verified data that capture typological key features such as agglutination and tone. We evaluate a range of models on AfriSUD for part-of-speech tagging and dependency parsing, including non-transformer baselines, multilingual pretrained encoders, and LLMs. Our results reveal a significant syntax gap, where models still show clear limitations across the nine languages, suggesting that existing architectures may not fully capture the structural diversity of African-language syntax.
Many nascent breeding programs aim to achieve genetic gain by crossing locally-elite germplasm, but a lack of systematic approaches to develop elite gene pools from locally adapted varieties hinders their progress. Motivated by the observation of undesirable transgressive segregation in presumed elite crosses in Senegalese cereal breeding programs, we designed approaches for de novo development of elite gene pools from locally adapted landrace-derived germplasm. We first define two types of “elite” germplasm: iso-elite, phenotypically similar and genetically homogeneous for locally adapted traits (“attained traits”); versus allo-elite, phenotypically similar, but genetically heterogeneous for attained traits. Next, we defined two genomic approaches for de novo inference of elite gene pools: population-based genotypic inference (PGI) and QTL-based genotypic inference (QGI), and compared to a family-based phenotypic inference (FPI) approach. Using simulations that trace the evolution from locally adapted landraces to elite breeding lines, we evaluate the effectiveness of these strategies in nascent forward breeding programs. QGI accurately and cost-effectively identifies both iso- and allo-elite pairs, regardless of the underlying trait architecture, while PGI is less sensitive when trait architecture is oligogenic. Over ten cycles of phenotypic recurrent selection, programs based on iso-elite crosses consistently outperformed those based on allo-elite crosses for genetic gain. The findings highlight the value of trait genetic architecture knowledge for elite gene pool development and provide a practical roadmap for elite germplasm development in modernizing breeding programs.
Wireless Sensor Networks (WSNs) are widely used in applications such as environmental monitoring, smart infrastructure, and Internet of Things (IoT) systems. However, the limited energy resources of sensor nodes make efficient topology control a critical challenge for extending network lifetime. Connected Dominating Set (CDS)-based approaches have been widely adopted to construct virtual backbones that reduce redundant transmissions and improve routing efficiency. Nevertheless, most existing distributed CDS algorithms rely on static decision rules and often assume synchronous network operation, which limits their adaptability in dynamic and asynchronous environments. This paper proposes an artificial intelligence-assisted distributed CDS construction algorithm for energy-efficient asynchronous wireless sensor networks. The proposed approach integrates a lightweight reinforcement learning mechanism into the classical Single Phase Multiple Initiator (SPMI) algorithm, enabling each node to autonomously decide whether to participate in the CDS based on local observations such as residual energy, neighborhood density, and connectivity contribution. Theoretical analysis demonstrates that the proposed algorithm guarantees CDS connectivity with linear message complexity. Simulation results show that the proposed method improves energy balancing, reduces CDS size, and significantly extends network lifetime compared to classical CDS-based approaches. (Abstract)
