Population structure and evolutionary history of the greater cane rat (Thryonomys swinderianus) from the Guinean Forests of West Africa

Grasscutter (Thryonomys swinderianus) is a large-body old world rodent found in sub-Saharan Africa. The body size and the unique taste of the meat of this major crop pest have made it a target of intense hunting and a potential consideration as a micro-livestock. However, there is insufficient knowledge on the genetic diversity of its populations across African Guinean forests. Herein, we investigated the genetic diversity, population structures and evolutionary history of seven Nigerian wild grasscutter populations together with individuals from Cameroon, Republic of Benin, and Ghana, using five mitochondrial fragments, including D-loop and cytochrome b (CYTB). D-loop haplotype diversity ranged from 0.571 (± 0.149) in Republic of Benin to 0.921 (± 0.013) in Ghana. Within Nigeria, the haplotype diversity ranged from 0.659 (± 0.059) in Cross River to 0.837 (± 0.075) in Ondo subpopulation. The fixation index (FST), haplotype frequency distribution and analysis of molecular variance revealed varying levels of population structures across populations. No significant signature of population contraction was detected in the grasscutter populations. Evolutionary analyses of CYTB suggests that South African population might have diverged from other populations about 6.1 (2.6–10.18, 95% CI) MYA. Taken together, this study reveals the population status and evolutionary history of grasscutter populations in the region

MasakhaNEWS:News Topic Classification for African languages

African languages are severely under-represented in NLP research due to lack of datasets covering several NLP tasks. While there are individual language specific datasets that are being expanded to different tasks, only a handful of NLP tasks (e.g. named entity recognition and machine translation) have standardized benchmark datasets covering several geographical and typologically-diverse African languages. In this paper, we develop MasakhaNEWS -- a new benchmark dataset for news topic classification covering 16 languages widely spoken in Africa. We provide an evaluation of baseline models by training classical machine learning models and fine-tuning several language models. Furthermore, we explore several alternatives to full fine-tuning of language models that are better suited for zero-shot and few-shot learning such as cross-lingual parameter-efficient fine-tuning (like MAD-X), pattern exploiting training (PET), prompting language models (like ChatGPT), and prompt-free sentence transformer fine-tuning (SetFit and Cohere Embedding API). Our evaluation in zero-shot setting shows the potential of prompting ChatGPT for news topic classification in low-resource African languages, achieving an average performance of 70 F1 points without leveraging additional supervision like MAD-X. In few-shot setting, we show that with as little as 10 examples per label, we achieved more than 90\% (i.e. 86.0 F1 points) of the performance of full supervised training (92.6 F1 points) leveraging the PET approach

Soil seed bank dynamics in Tithonia diversifolia dominated fallowland at Ile- Ife, South-western Nigeria

The soil seed bank of Tithonia diversifolia, an invasive species which dominates open waste fallowland vegetation was studied. Two different roadside sites which vary in extent of open waste land were selected. The species composition of the established vegetation was assessed in the two different sites. Twenty top soil samples were collected at 0-15 cm five different distances (15 m, 30 m, 45 m, 60 m, and 75 m) inwards away from each main road in dry and rainy seasons and the seed bank composition was determined by greenhouse germination over a 6 month period. The similarity between the composition of the seed bank flora and that of the established vegetation was low. The least and the highest emerged seedlings density were recorded in the 15 m and 75 m respectively inwards away from the main road in both seasons. The results of the seedlings emergence are a reflection of the extent of open waste land dominated by the invasive species due to human disturbance (road construction) on both sites. Overall results suggest that the emergence of the species from the soil seed bank may be due to the impact of the invasive species – Tithonia diversifolia on other plant species in the study environment.© 2009 International Formulae Group. All rights reserved.Keywords: Fallowland, invasive species, seedling emergence, seed bank

Biogenic synthesis of silver nanoparticles using a pod extract of Cola nitida: Antibacterial and antioxidant activities and application as a paint additive

This work reports the biogenic synthesis of silver nanoparticles (AgNPs) using the pod extract of Cola nitida, the evaluation of their antibacterial and antioxidant activities, and their application as an antimicrobial additive in paint. The AgNPs were characterized with UV–Vis spectroscopy, Fourier-transform infrared (FTIR) spectroscopy, and transmission electron microscopy (TEM). The AgNP solution was dark brown with a maximum absorbance occurring at 431.5 nm. The FTIR spectrum showed strong peaks at 3336.85, 2073.48, and 1639.49 cm−1, indicating that proteins acted as the capping and stabilization agents in the synthesis of the AgNPs. The AgNPs were spherical, with sizes ranging from 12 to 80 nm. Energy dispersive X-ray (EDX) analysis showed that silver was the prominent metal present, while the selected area electron diffraction pattern conformed to the face-centred cubic phase and crystalline nature of AgNPs. At various concentrations between 50 and 150 μg/ml, the AgNPs showed strong inhibition of the growth of multidrug resistant strains of Klebsiella granulomatis, Pseudomonas aeruginosa, and Escherichia coli. In addition, at 5 μg/ml, the AgNPs completely inhibited the growth of Staphylococcus aureus, Escherichia coli, Pseudomonas aeruginosa, Aspergillus niger, A. flavus and A. fumigatus in a paint-AgNP admixture. The AgNPs exhibited a potent antioxidant activity with an IC50 of 43.98 μg/ml against 2,2-diphenyl-1-picrylhydrazyl and a ferric ion reduction of 13.62–49.96% at concentrations of 20–100 μg/ml. This study has demonstrated the biogenic synthesis of AgNPs that have potent antimicrobial and antioxidant activities and potential biomedical and industrial applications. To the best of our knowledge, this work is the first to use the pod extract of C. nitida for the green synthesis of nanoparticles

MasakhaNEWS:News Topic Classification for African languages

African languages are severely under-represented in NLP research due to lack of datasets covering several NLP tasks. While there are individual language specific datasets that are being expanded to different tasks, only a handful of NLP tasks (e.g. named entity recognition and machine translation) have standardized benchmark datasets covering several geographical and typologically-diverse African languages. In this paper, we develop MasakhaNEWS -- a new benchmark dataset for news topic classification covering 16 languages widely spoken in Africa. We provide an evaluation of baseline models by training classical machine learning models and fine-tuning several language models. Furthermore, we explore several alternatives to full fine-tuning of language models that are better suited for zero-shot and few-shot learning such as cross-lingual parameter-efficient fine-tuning (like MAD-X), pattern exploiting training (PET), prompting language models (like ChatGPT), and prompt-free sentence transformer fine-tuning (SetFit and Cohere Embedding API). Our evaluation in zero-shot setting shows the potential of prompting ChatGPT for news topic classification in low-resource African languages, achieving an average performance of 70 F1 points without leveraging additional supervision like MAD-X. In few-shot setting, we show that with as little as 10 examples per label, we achieved more than 90\% (i.e. 86.0 F1 points) of the performance of full supervised training (92.6 F1 points) leveraging the PET approach