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The leafcutter ants (LCA) are considered plague in a great part of the American continent, causing great damage in production fields. Knowing the locomotion and foraging rhythm in LCA on a continuous basis would imply a significant advance for ecological studies, fundamentally of animal behavior. However, studying the forage rhythm of LCA in the field involves a significant human effort. This also adds a risk of subjective results due to the operator fatigue. In this work a new development named 'AntVideoRecord' is proposed to address this issue. This device is a low-cost autonomous system that records videos of the LCA path in a fixed position. The device can be easily reproduced using the freely accessible source code provided. The evaluation of this novel device was successful because it has exceeded all the basic requirements in the field: record continuously for at least seven days, withstand high and low temperatures, capture acceptable videos during the day and night, and have a simple configuration protocol by mobile devices and laptops. It was possible to confirm the correct operation of the device, being able to record more than 1900 h in the field at different climate conditions and times of the day., Competing Interests: The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (© 2022 The Author(s).)

The Severe Acute Respiratory Syndrome-Coronavirus 2 (SARS-CoV-2) has been recently found responsible for the pandemic outbreak of a novel coronavirus disease (COVID-19). In this work, a novel approach based on deep learning is proposed for identifying precursors of small active RNA molecules named microRNA (miRNA) in the genome of the novel coronavirus. Viral miRNA-like molecules have shown to modulate the host transcriptome during the infection progression, thus their identification is crucial for helping the diagnosis or medical treatment of the disease. The existence of the mature miRNAs derived from computationally predicted miRNA precursors (pre-miRNAs) in the novel coronavirus was validated with small RNA-seq data from SARS-CoV-2-infected human cells. The results demonstrate that computational models can provide accurate and useful predictions of pre-miRNAs in the SARS-CoV-2 genome, underscoring the relevance of machine learning in the response to a global sanitary emergency. Moreover, the interpretability of our model shed light on the molecular mechanisms underlying the viral infection, thus contributing to the fight against the COVID-19 pandemic and the fast development of new treatments. Our study shows how recent advances in machine learning can be used, effectively, in response to public health emergencies. The approach developed in this work could be of great help in future similar emergencies to accelerate the understanding of the singularities of any viral agent and for the development of novel therapies. Data and source code available at : https://sourceforge.net/projects/sourcesinc/files/aicovid/., Competing Interests: The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (© 2021 The Author(s).)

MicroRNAs (miRNAs) are small non-coding RNAs that have a key role in the regulation of gene expression. The importance of miRNAs is widely acknowledged by the community nowadays and computational methods are needed for the precise prediction of novel candidates to miRNA. This task can be done by searching homologous with sequence alignment tools, but results are restricted to sequences that are very similar to the known miRNA precursors (pre-miRNAs). Besides, a very important property of pre-miRNAs, their secondary structure, is not taken into account by these methods. To fill this gap, many machine learning approaches were proposed in the last years. However, the methods are generally tested in very controlled conditions. If these methods were used under real conditions, the false positives increase and the precisions fall quite below those published. This work provides a novel approach for dealing with the computational prediction of pre-miRNAs: a convolutional deep residual neural network (mirDNN). This model was tested with several genomes of animals and plants, the full-genomes, achieving a precision up to 5 times larger than other approaches at the same recall rates. Furthermore, a novel validation methodology was used to ensure that the performance reported in this study can be effectively achieved when using mirDNN in novel species. To provide fast an easy access to mirDNN, a web demo is available at http://sinc.unl.edu.ar/web-demo/mirdnn/. The demo can process FASTA files with multiple sequences to calculate the prediction scores and generates the nucleotide importance plots. FULL SOURCE CODE: http://sourceforge.net/projects/sourcesinc/files/mirdnn and https://github.com/cyones/mirDNN. CONTACT: gstegmayer@sinc.unl.edu.ar., (Copyright © 2021 Elsevier Ltd. All rights reserved.)

This article makes available several genome-wide datasets, which can be used for training microRNA (miRNA) classifiers. The hairpin sequences available are from the genomes of: Homo sapiens, Arabidopsis thaliana, Anopheles gambiae, Caenorhabditis elegans and Drosophila melanogaster . Each dataset provides the genome data divided into sequences and a set of computed features for predictions. Each sequence has one label: i) "positive": meaning that it is a well-known pre-miRNA, according to miRBase v21; or ii) "unlabeled": indicating that the sequence has not (yet) a known function and could be a possible candidate to novel pre-miRNA. Due to the fact that selecting an informative feature set is very important for a good pre-miRNA classifier, a representative feature set with large discriminative power has been calculated and it is provided, as well, for each genome. This feature set contains typical information about sequence, topology and structure. Dataset was publically shared in https://sourceforge.net/projects/sourcesinc/files/mirdata/.

Tapeworms (cestodes) of the genus Hymenolepis are the causative agents of hymenolepiasis, a neglected zoonotic disease. Hymenolepis nana is the most prevalent human tapeworm, especially affecting children. The genomes of Hymenolepis microstoma and H. nana have been recently sequenced and assembled. MicroRNAs (miRNAs), a class of small non-coding RNAs, are principle regulators of gene expression at the post-transcriptional level and are involved in many different biological processes. In previous work, we experimentally identified miRNA genes in the cestodes Echinococcus, Taenia and Mesocestoides. However, current knowledge about miRNAs in Hymenolepis is limited. In this work we described for the first known time the expression profile of the miRNA complement in H. microstoma, and discovered miRNAs in H. nana. We found a reduced complement of 37 evolutionarily conserved miRNAs, putatively reflecting their low morphological complexity and parasitic lifestyle. We found high expression of a few miRNAs in the larval stage of H. microstoma that are conserved in other cestodes, suggesting that these miRNAs may have important roles in development, survival and for host-parasite interplay. We performed a comparative analysis of the identified miRNAs across the Cestoda and showed that most of the miRNAs in Hymenolepis are located in intergenic regions, implying that they are independently transcribed. We found a Hymenolepis-specific cluster composed of three members of the mir-36 family. Also, we found that one of the neighboring genes of mir-10 was a Hox gene as in most bilaterial species. This study provides a valuable resource for further experimental research in cestode biology that might lead to improved detection and control of these neglected parasites. The comprehensive identification and expression analysis of Hymenolepis miRNAs can help to identify novel biomarkers for diagnosis and/or novel therapeutic targets for the control of hymenolepiasis., (Copyright © 2019 Australian Society for Parasitology. Published by Elsevier Ltd. All rights reserved.)

Addition of the lithium salt of N-hydroxy-2,2,6,6-tetramethylpiperidine 10 to carbon disulfide and subsequent methylation affords the rearranged dithiocarbonic acid S-methyl-S'-(2,2,6,6-tetramethylpiperidin-1-yl)-ester 9 rather than the expected xanthate ester S-methyl-O'-(2,2,6,6-tetramethylpiperidin-1 -yl)-ester 8. n-Butylacrylate could be polymerized with the novel compound 9 even though the polymerization was not controlled.