El efecto de la poda de Acropora palmata como estrategia para la obtención de tejido vivo en acciones de restauración arrecifal

Introducción: Los arrecifes de coral son ecosistemas altamente degradados, por lo que ha sido necesario implementar acciones de restauración activa para recuperar su estructura y funcionamiento. Se ha implementado la propagación clonal para obtener fragmentos pequeños (~ 10 cm) de las ramas distales de colonias donadoras de corales de la especie Acropora palmata, para posteriormente fijarlos en el sustrato arrecifal, simulando el efecto de dispersión que ocurre de manera natural en esta especie, a lo que en este trabajo se denomina ''dispersión asistida". Sin embargo, es necesario evaluar los efectos de esta técnica como son: la cantidad de fragmentos que se puede obtener de cada colonia, el periodo de recuperación de tejido de las colonias donadoras y los fragmentos sembrados. Objetivo: Evaluar el efecto de poda en las colonias donadoras estimando el porcentaje de tejido podado de colonias donadoras de A. palmata y su tasa de recuperación 30 meses después. Métodos: Se realizaron cuatro monitoreos: antes, inmediatamente después de la poda, un mes después de la siembra, y 30 meses después, en cuatro colonias de A. palmata localizadas en el Parque Nacional Costa Occidental de Isla Mujeres, Punta Cancún y Punta Nizuc en el Caribe mexicano. La modelación 3D basada en fotogrametría se realizó con el software Agisoft Metashape Pro, mientras que las métricas de área de superficie de tejido, extensión radial y apical se obtuvieron mediante el software CloudCompare. Resultados: Posterior a la colecta de fragmentos de las colonias, se observó que el material utilizado en la dispersión asistida representa menos del 12% del tejido vivo. Después de un mes, las colonias donadoras presentaban una recuperación del 5% con tejido nuevo recubriendo las áreas de corte. Las colonias donadoras perdieron, en promedio, 65% de tejido vivo tras el impacto de cuatro huracanes, y en un caso la colonia fue totalmente eliminada, pero con los fragmentos sembrados se pudo conservar el genotipo. Conclusiones: La dispersión asistida podría incrementar el tejido vivo de corales ramificados en intervalos de tiempo relativamente cortos, sin comprometer la integridad de la colonia donadora, si se poda menos del 12%.

Introduction: Coral reefs are highly degraded ecosystems, for which it has been necessary to implement active restoration actions to recover their structure and functioning. Asexual propagation has been implemented to obtain small fragments (~10 cm) from the distal branches of donor colonies of corals of the species Acropora palmata, to subsequently relocate them in the reef substrate, simulating the dispersion effect that occurs naturally in the species, which in this work is called assisted propagation. However, it is necessary to evaluate the effects of this technique, such as the number of fragments that can be obtained from each colony, the tissue recovery period of the donor colonies and fragments. Objective: To address the effect of pruning on donor colonies by estimating the percentage of live tissue removed from donor colonies of A. palmata and their recovery rate after 30-months. Methods: Four surveys were carried out: before, immediately after pruning, one month after outplanting, and 30 months after pruning on four colonies of A. palmata located in the Parque Nacional Costa Occidental de Isla Mujeres, Punta Cancún and Punta Nizuc in the Mexican Caribbean. Photogrammetry-based 3D modeling was performed using Agisoft Metashape Pro software, while tissue surface area, radial and apical growth were obtained using CloudCompare software. Results: After fragment collection, the material used in the assisted propagation represents less than 12% of the living tissue. After one month, the donor colonies showed a recovery of 5%, with new tissue covering the cut areas. The donor colonies lost on average 65 % of living tissue after four hurricanes, and in one case the colony was lost all together, but with the outplanted fragments the genotype could be preserved. Conclusions: Assisted propagation could increase living tissue of branching corals in relatively short intervals of time, without serious damage to the donor colony if less than 12 % is removed.

Binary and multi-class automated detection of age-related macular degeneration using convolutional- and transformer-based architectures.

BACKGROUND AND OBJECTIVE: Age-related macular degeneration (AMD) is an eye disease that happens when ageing causes damage to the macula, and it is the leading cause of blindness in developed countries. Screening retinal fundus images allows ophthalmologists to early detect, diagnose and treat this disease; however, the manual interpretation of images is a time-consuming task. In this paper, we aim to study different deep learning methods to diagnose AMD. METHODS: We have conducted a thorough study of two families of deep learning models based on convolutional neural networks (CNN) and transformer architectures to automatically diagnose referable/non-referable AMD, and grade AMD severity scales (no AMD, early AMD, intermediate AMD, and advanced AMD). In addition, we have analysed several progressive resizing strategies and ensemble methods for convolutional-based architectures to further improve the performance of the models. RESULTS: As a first result, we have shown that transformer-based architectures obtain considerably worse results than convolutional-based architectures for diagnosing AMD. Moreover, we have built a model for diagnosing referable AMD that yielded a mean F1-score (SD) of 92.60% (0.47), a mean AUROC (SD) of 97.53% (0.40), and a mean weighted kappa coefficient (SD) of 85.28% (0.91); and an ensemble of models for grading AMD severity scales with a mean accuracy (SD) of 82.55% (2.92), and a mean weighted kappa coefficient (SD) of 84.76% (2.45). CONCLUSIONS: This work shows that working with convolutional based architectures is more suitable than using transformer based models for classifying and grading AMD from retinal fundus images. Furthermore, convolutional models can be improved by means of progressive resizing strategies and ensemble methods.

MotilityJ: An open-source tool for the classification and segmentation of bacteria on motility images.

BACKGROUND AND OBJECTIVES: Infectious diseases produced by antimicrobial resistant microorganisms are a major threat to human, and animal health worldwide. This problem is increased by the virulence and spread of these bacteria. Surface motility has been regarded as a pathogenicity element because it is essential for many biological functions, but also for disease spreading; hence, investigations on the motility behaviour of bacteria are crucial to understand chemotaxis, biofilm formation and virulence in general. To identify a motile strain in the laboratory, the bacterial spread area is observed on media solidified with agar. Up to now, the task of measuring bacteria spread was a manual, and, therefore, tedious and time-consuming task. The aim of this work is the development of a set of tools for bacteria segmentation in motility images. METHODS: In this work, we address the problem of measuring bacteria spread on motility images by creating an automatic pipeline based on deep learning models. Such a pipeline consists of a classification model to determine whether the bacteria has spread to cover completely the Petri dish, and a segmentation model to determine the spread of those bacteria that do not fully cover the Petri dishes. In order to annotate enough images to train our deep learning models, a semi-automatic annotation procedure is presented. RESULTS: The classification model of our pipeline achieved a F1-score of 99.85%, and the segmentation model achieved a Dice coefficient of 95.66%. In addition, the segmentation model produces results that are indistinguishable, and in many cases preferred, from those produced manually by experts. Finally, we facilitate the dissemination of our pipeline with the development of MotilityJ, an open-source and user-friendly application for measuring bacteria spread on motility images. CONCLUSIONS: In this work, we have developed an algorithm and trained several models for measuring bacteria spread on motility images. Thanks to this work, the analysis of motility images will be faster and more reliable. The developed tools will help to advance our understanding of the behaviour and virulence of bacteria.

SpheroidJ: An Open-Source Set of Tools for Spheroid Segmentation.

BACKGROUND AND OBJECTIVES: Spheroids are the most widely used 3D models for studying the effects of different micro-environmental characteristics on tumour behaviour, and for testing different preclinical and clinical treatments. In order to speed up the study of spheroids, imaging methods that automatically segment and measure spheroids are instrumental; and, several approaches for automatic segmentation of spheroid images exist in the literature. However, those methods fail to generalise to a diversity of experimental conditions. The aim of this work is the development of a set of tools for spheroid segmentation that works in a diversity of settings. METHODS: In this work, we have tackled the spheroid segmentation task by first developing a generic segmentation algorithm that can be easily adapted to different scenarios. This generic algorithm has been employed to reduce the burden of annotating a dataset of images that, in turn, has been employed to train several deep learning architectures for semantic segmentation. Both our generic algorithm and the constructed deep learning models have been tested with several datasets of spheroid images where the spheroids were grown under several experimental conditions, and the images acquired using different equipment. RESULTS: The developed generic algorithm can be particularised to different scenarios; however, those particular algorithms fail to generalise to different conditions. By contrast, the best deep learning model, constructed using the HRNet-Seg architecture, generalises properly to a diversity of scenarios. In order to facilitate the dissemination and use of our algorithms and models, we present SpheroidJ, a set of open-source tools for spheroid segmentation. CONCLUSIONS: In this work, we have developed an algorithm and trained several models for spheroid segmentation that can be employed with images acquired under different conditions. Thanks to this work, the analysis of spheroids acquired under different conditions will be more reliable and comparable; and, the developed tools will help to advance our understanding of tumour behaviour.

CLoDSA: a tool for augmentation in classification, localization, detection, semantic segmentation and instance segmentation tasks.

BACKGROUND: Deep learning techniques have been successfully applied to bioimaging problems; however, these methods are highly data demanding. An approach to deal with the lack of data and avoid overfitting is the application of data augmentation, a technique that generates new training samples from the original dataset by applying different kinds of transformations. Several tools exist to apply data augmentation in the context of image classification, but it does not exist a similar tool for the problems of localization, detection, semantic segmentation or instance segmentation that works not only with 2 dimensional images but also with multi-dimensional images (such as stacks or videos). RESULTS: In this paper, we present a generic strategy that can be applied to automatically augment a dataset of images, or multi-dimensional images, devoted to classification, localization, detection, semantic segmentation or instance segmentation. The augmentation method presented in this paper has been implemented in the open-source package CLoDSA. To prove the benefits of using CLoDSA, we have employed this library to improve the accuracy of models for Malaria parasite classification, stomata detection, and automatic segmentation of neural structures. CONCLUSIONS: CLoDSA is the first, at least up to the best of our knowledge, image augmentation library for object classification, localization, detection, semantic segmentation, and instance segmentation that works not only with 2 dimensional images but also with multi-dimensional images.

DecoFungi: a web application for automatic characterisation of dye decolorisation in fungal strains.

BACKGROUND: Fungi have diverse biotechnological applications in, among others, agriculture, bioenergy generation, or remediation of polluted soil and water. In this context, culture media based on color change in response to degradation of dyes are particularly relevant; but measuring dye decolorisation of fungal strains mainly relies on a visual and semiquantitative classification of color intensity changes. Such a classification is a subjective, time-consuming and difficult to reproduce process. RESULTS: DecoFungi is the first, at least up to the best of our knowledge, application to automatically characterise dye decolorisation level of fungal strains from images of inoculated plates. In order to deal with this task, DecoFungi employs a deep-learning model, accessible through a user-friendly web interface, with an accuracy of 96.5%. CONCLUSIONS: DecoFungi is an easy to use system for characterising dye decolorisation level of fungal strains from images of inoculated plates.

Extending GelJ for interoperability: Filling the gap in the bioinformatics resources for population genetics analysis with dominant markers.

BACKGROUND AND OBJECTIVE: The manual transformation of DNA fingerprints of dominant markers into the input of tools for population genetics analysis is a time-consuming and error-prone task; especially when the researcher deals with a large number of samples. In addition, when the researcher needs to use several tools for population genetics analysis, the situation worsens due to the incompatibility of data-formats across tools. The goal of this work consists in automating, from banding patterns of gel images, the input-generation for the great diversity of tools devoted to population genetics analysis. METHODS: After a thorough analysis of tools for population genetics analysis with dominant markers, and tools for working with phylogenetic trees; we have detected the input requirements of those systems. In the case of programs devoted to phylogenetic trees, the Newick and Nexus formats are widely employed; whereas, each population genetics analysis tool uses its own specific format. In order to handle such a diversity of formats in the latter case, we have developed a new XML format, called PopXML, that takes into account the variety of information required by each population genetics analysis tool. Moreover, the acquired knowledge has been incorporated into the pipeline of the GelJ system - a tool for analysing DNA fingerprint gel images - to reach our automatisation goal. RESULTS: We have implemented, in the GelJ system, a pipeline that automatically generates, from gel banding patterns, the input of tools for population genetics analysis and phylogenetic trees. Such a pipeline has been employed to successfully generate, from thousands of banding patterns, the input of 29 population genetics analysis tools and 32 tools for managing phylogenetic trees. CONCLUSIONS: GelJ has become the first tool that fills the gap between gel image processing software and population genetics analysis with dominant markers, phylogenetic reconstruction, and tree editing software. This has been achieved by automating the process of generating the input for the latter software from gel banding patterns processed by GelJ.

Surveying and benchmarking techniques to analyse DNA gel fingerprint images.

DNA fingerprinting is a genetic typing technique that allows the analysis of the genomic relatedness between samples, and the comparison of DNA patterns. The analysis of DNA gel fingerprint images usually consists of five consecutive steps: image pre-processing, lane segmentation, band detection, normalization and fingerprint comparison. In this article, we firstly survey the main methods that have been applied in the literature in each of these stages. Secondly, we focus on lane-segmentation and band-detection algorithms-as they are the steps that usually require user-intervention-and detect the seven core algorithms used for both tasks. Subsequently, we present a benchmark that includes a data set of images, the gold standards associated with those images and the tools to measure the performance of lane-segmentation and band-detection algorithms. Finally, we implement the core algorithms used both for lane segmentation and band detection, and evaluate their performance using our benchmark. As a conclusion of that study, we obtain that the average profile algorithm is the best starting point for lane segmentation and band detection.

A survey of tools for analysing DNA fingerprints.

DNA fingerprinting is a genetic typing technique that allows the analysis of the genomic relatedness between samples, and the comparison of DNA patterns. This technique has multiple applications in different fields (medical diagnosis, forensic science, parentage testing, food industry, agriculture and many others). An important task in molecular epidemiology of infectious diseases is the analysis and comparison of pulsed-field gel electrophoresis (PFGE) patterns. This is applied to determine the clonal diversity of bacteria in the follow-up of outbreaks or for tracking specific clones of special relevance. The resulting images produced by DNA fingerprinting are sometimes difficult to interpret, and multiple tools have been developed to simplify this task. In this article, we present a survey of tools for analysing DNA fingerprints. In particular, we compare 33 tools using a set of predefined criteria. The comparison was carried out by hands-on experiences-whenever possible-and inspecting the documentation of the tools. As no system is preferred in all the possible scenarios, we have created a spreadsheet that can be customized by researchers to determine the best system for their needs.

GelJ--a tool for analyzing DNA fingerprint gel images.

BACKGROUND: DNA fingerprinting is a technique for comparing DNA patterns that has applications in a wide variety of contexts. Several commercial and freely-available tools can be used to analyze DNA fingerprint gel images; however, commercial tools are expensive and usually difficult to use; and, free tools support the basic functionality for DNA fingerprint analysis, but lack some instrumental features to obtain accurate results. RESULTS: In this paper, we present GelJ, a feather-weight, user-friendly, platform-independent, open-source and free tool for analyzing DNA fingerprint gel images. Some of the outstanding features of GelJ are mechanisms for accurate lane- and band-detection, several options for computing migration models, a number of band- and curve-based similarity methods, different techniques for generating dendrograms, comparison of banding patterns from different experiments, and database support. CONCLUSIONS: GelJ is an easy to use tool for analyzing DNA fingerprint gel images. It combines the best characteristics of both free and commercial tools: GelJ is light and simple to use (as free programs), but it also includes the necessary features to obtain precise results (as commercial programs). In addition, GelJ incorporates new functionality that is not supported by any other tool.