In this article we introduce a robusta coffee leaf images dataset called RoCoLe. The dataset contains 1560 leaf images with visible red mites and spots (denoting coffee leaf rust presence) for infection cases and images without such structures for healthy cases. In addition, the data set includes annotations regarding objects (leaves), state (healthy and unhealthy) and the severity of disease (leaf area with spots). Images were all obtained in real-world conditions in the same coffee plants field using a smartphone camera. RoCoLe data set facilitates the evaluation of the performance of machine learning algorithms used in image segmentation and classification problems related to plant diseases recognition. The current dataset is freely and publicly available at https://doi.org/10.17632/c5yvn32dzg.2.

Aphids are small insects that feed on plant sap, and they belong to a superfamily called Aphoidea. They are among the major pests causing damage to citrus crops in most parts of the world. Precise and automatic identification of aphids is needed to understand citrus pest dynamics and management. This article presents a dataset that contains 665 healthy and unhealthy lemon leaf images. The latter are leaves with the presence of aphids, and visible white spots characterize them. Moreover, each image includes a set of annotations that identify the leaf, its health state, and the infestation severity according to the percentage of the affected area on it. Images were collected manually in real-world conditions in a lemon plant field in Junín, Manabí, Ecuador, during the winter, by using a smartphone camera. The dataset is called LeLePhid: lemon (Le) leaf (Le) image dataset for aphid (Phid) detection and infestation severity. The data can facilitate evaluating models for image segmentation, detection, and classification problems related to plant disease recognition.

Biologists aim to understand the genetic background of diseases, metabolic disorders or any other genetic condition. Microarrays are one of the main high-throughput technologies for collecting information about the behaviour of genetic information on different conditions. In order to analyse this data, clustering arises as one of the main techniques used, and it aims at finding groups of genes that have some criterion in common, like similar expression profile. However, the problem of finding groups is normally multi dimensional, making necessary to approach the clustering as a multi-objective problem where various cluster validity indexes are simultaneously optimised. They are usually based on criteria like compactness and separation, which may not be sufficient since they can not guarantee the generation of clusters that have both similar expression patterns and biological coherence. We propose a Multi-Objective Clustering algorithm Guided by a-Priori Biological Knowledge (MOC-GaPBK) to find clusters of genes with high levels of co-expression, biological coherence, and also good compactness and separation. Cluster quality indexes are used to optimise simultaneously gene relationships at expression level and biological functionality. Our proposal also includes intensification and diversification strategies to improve the search process. The effectiveness of the proposed algorithm is demonstrated on four publicly available datasets. Comparative studies of the use of different objective functions and other widely used microarray clustering techniques are reported. Statistical, visual and biological significance tests are carried out to show the superiority of the proposed algorithm. Integrating a-priori biological knowledge into a multi-objective approach and using intensification and diversification strategies allow the proposed algorithm to find solutions with higher quality than other microarray clustering techniques available in the literature in terms of co-expression, biological coherence, compactness and separation.

There is no consensus as to how a precursor lesion, germ cell neoplasia in situ (GCNIS), develops into the histologic types of testicular germ cell tumor type II (TGCT). The present meta-analysis examined RNA expressions of 24 candidate genes in three datasets. They included 203 samples of normal testis (NT) and histologic types of TGCT. The Fisher’s test for combined p values was used for meta-analysis of the RNA expressions in the three datasets. The histologic types differed in RNA expression of PRAME, KIT, SOX17, NANOG, KLF4, POU5F1, RB1, DNMT3B, and LIN28A (p < 0.01). The histologic types had concordant differences in RNA expression of the genes in the three datasets. Eight genes had overlap with a high RNA expression in at least two histologic types. In contrast, only seminoma (SE) had a high RNA expression of KLF4 and only embryonal carcinoma (EC) had a high RNA expression of DNMT3B. In conclusion, the meta-analysis showed that the development of the histologic types of TGCT was driven by changes in RNA expression of candidate genes. According to the RNA expressions of the ten genes, TGCT develops from NT over GCNIS, SE, EC, to the differentiated types of TGCT.

College dropout represents a significant challenge for universities, and despite advances in machine learning technologies, predicting dropout remains a complex task. This literature review focuses on investigating the factors that influence college dropout, examining the models used to predict it, and highlighting the most significant advances in explainability and simulation over the period 2012 to 2024 using the PRISMA methodology. They identified 520 factors in five categories (demographic, socioeconomic, institutional, personal, and academic), with the most studied factors in each category being, respectively, gender, scholarships, infrastructure, student identification, and grades. They also identified 83 machine learning models, with the most studied being the decision tree, logistic regression, and random forest. In addition, eight explanatory models were identified, with SHAP and LIME being the most widely used. Finally, no simulation models related to university dropout were identified. This study groups factors related to university dropout into key models for prediction and analyzes the methods used to explain the causal factors that influence university student dropout.