GenoVi, an open-source automated circular genome visualizer for bacteria and archaea.

The increase in microbial sequenced genomes from pure cultures and metagenomic samples reflects the current attainability of whole-genome and shotgun sequencing methods. However, software for genome visualization still lacks automation, integration of different analyses, and customizable options for non-experienced users. In this study, we introduce GenoVi, a Python command-line tool able to create custom circular genome representations for the analysis and visualization of microbial genomes and sequence elements. It is designed to work with complete or draft genomes, featuring customizable options including 25 different built-in color palettes (including 5 color-blind safe palettes), text formatting options, and automatic scaling for complete genomes or sequence elements with more than one replicon/sequence. Using a Genbank format file as the input file or multiple files within a directory, GenoVi (i) visualizes genomic features from the GenBank annotation file, (ii) integrates a Cluster of Orthologs Group (COG) categories analysis using DeepNOG, (iii) automatically scales the visualization of each replicon of complete genomes or multiple sequence elements, (iv) and generates COG histograms, COG frequency heatmaps and output tables including general stats of each replicon or contig processed. GenoVi's potential was assessed by analyzing single and multiple genomes of Bacteria and Archaea. Paraburkholderia genomes were analyzed to obtain a fast classification of replicons in large multipartite genomes. GenoVi works as an easy-to-use command-line tool and provides customizable options to automatically generate genomic maps for scientific publications, educational resources, and outreach activities. GenoVi is freely available and can be downloaded from https://github.com/robotoD/GenoVi.

LoRa Based IoT Platform for Remote Monitoring of Large-Scale Agriculture Farms in Chile.

Nowadays, conventional agriculture farms lack high-level automated management due to the limited number of installed sensor nodes and measuring devices. Recent progress of the Internet of Things (IoT) technologies will play an essential role in future smart farming by enabling automated operations with minimum human intervention. The main objective of this work is to design and implement a flexible IoT-based platform for remote monitoring of agriculture farms of different scales, enabling continuous data collection from various IoT devices (sensors, actuators, meteorological masts, and drones). Such data will be available for end-users to improve decision-making and for training and validating advanced prediction algorithms. Unlike related works that concentrate on specific applications or evaluate technical aspects of specific layers of the IoT stack, this work considers a versatile approach and technical aspects at four layers: farm perception layer, sensors and actuators layer, communication layer, and application layer. The proposed solutions have been designed, implemented, and assessed for remote monitoring of plants, soil, and environmental conditions based on LoRaWAN technology. Results collected through both simulation and experimental validation show that the platform can be used to obtain valuable analytics of real-time monitoring that enable decisions and actions such as, for example, controlling the irrigation system or generating alarms. The contribution of this article relies on proposing a flexible hardware and software platform oriented on monitoring agriculture farms of different scales, based on LoRaWAN technology. Even though previous work can be found using similar technologies, they focus on specific applications or evaluate technical aspects of specific layers of the IoT stack.

PromoterLCNN: A Light CNN-Based Promoter Prediction and Classification Model.

Promoter identification is a fundamental step in understanding bacterial gene regulation mechanisms. However, accurate and fast classification of bacterial promoters continues to be challenging. New methods based on deep convolutional networks have been applied to identify and classify bacterial promoters recognized by sigma (σ) factors and RNA polymerase subunits which increase affinity to specific DNA sequences to modulate transcription and respond to nutritional or environmental changes. This work presents a new multiclass promoter prediction model by using convolutional neural networks (CNNs), denoted as PromoterLCNN, which classifies Escherichia coli promoters into subclasses σ70, σ24, σ32, σ38, σ28, and σ54. We present a light, fast, and simple two-stage multiclass CNN architecture for promoter identification and classification. Training and testing were performed on a benchmark dataset, part of RegulonDB. Comparative performance of PromoterLCNN against other CNN-based classifiers using four parameters (Acc, Sn, Sp, MCC) resulted in similar or better performance than those that commonly use cascade architecture, reducing time by approximately 30-90% for training, prediction, and hyperparameter optimization without compromising classification quality.

A Pilot Study of Improving Self-Regulation and Social Interaction with Peers: An "Exciting School".

Social interaction skills are related to successful academic performance and mental health. One of the key elements of socio-emotional competence is self-regulation. The main aim of this study was to analyze the effect of a self-regulation program at a primary school on the social interactions of neurotypical children and children with special educational needs, from the teachers' and parents' perspectives. A pre-post study was conducted. The children (n = 107) followed 10 sessions, each one of 50 min, for ten weeks, between January and April 2021. To assess the changes in children's social interaction, the Peer Social Maturity Scale was administered to the teachers. After the intervention, parents completed a questionnaire designed ad hoc to understand the effectiveness of children's emotional self-regulation. The results showed a statistically significant improvement in peer interaction skills. The families were satisfied with the program, due to the improvement in their children's knowledge about their own emotions and those of the other people, and the learning strategies to regulate their emotions. Likewise, parents indicated that it would be necessary to complement the program with teaching and emotional regulation strategies for them. The "Exciting School" program could help improve the social skills of school-aged children.

Valor Predictivo del Volúmen Corpuscular Medio en la Ferropenia

Se revisaron 90 pacientes en el laboratorio del hospital Metropolitano, 36,67 por ciento hombres y 63.33 por ciento mujeres, con una edad media de 35.88+-7.10 años. Se relacionó el volumen corpuscular medio (VCM) con otros índices hemáticos como: hematocrito (Hcto), hemoglobina (Hb) y plaquetas y con los índices de hierro corporal: índice de saturación de transferrina, ferritina y hierro sérico. El VCM demostró una sensibilidad del 73.3 por ciento y una especificidad del 81.95 por ciento, lo que nos indica que el diagnóstico de anémia ferropénica, no puede basarse en el volumen corpuscular medio, incluso ni en el índice de saturación de transferrina aislada, dichas mediciones deben ser parte de un grupo de pruebas de laboratorio que complementadas con otras como la medición de ferritina sérica, puede servir para determinar con propiedad la existencia de anemia ferropénica.