Improving handgun detectors with human pose classification

[Abstract] Unfortunately, attacks with firearms such as handguns have become too common. CCTV surveillance systems can potentially help to prevent this kind of incidents, but require continuous human supervision, which is not feasible in practice. Image-based handgun detectors allow the automatic location of these weapons to send alerts to the security staff. Deep learning has been recently used for this purpose. However, the precision and sensitivity of these systems are not generally satisfactory, causing in most cases both false alarms and undetected handguns, particularly when the firearm is far from the camera. This paper proposes the use of information related to the pose of the subject to improve the performance of current handgun detectors. More concretely, a human full-body pose classifier has been developed which is capable of separating between shooting poses and other non-dangerous poses. The classified pose is then used to reduce both the number of false positives (FP) and false negatives (FN). The proposed method has been tested with several datasets and handgun detectors, showing an improvement under various metrics.This work was partially funded by projects PDC2021-121197-C22 (funded by MCIN/AEI/ 10.13039/501100011033 and by the European Union NextGenerationEU/PRTR) and SBPLY/21/180501/000025 (funded by the Autonomous Government of Castilla-La Mancha and the European Regional Development Fund -ERDF-). The first author is supported by Postgraduate Grant PRE2018-083772 from the Spanish Ministry of Science, Innovation, and Universities.Junta de Comunidades de Castilla-La Mancha; SBPLY/21/180501/00002

MicroRNAs en Insuficiencia Cardiaca: Papel Etiológico

Objetivos: El objetivo de la revisión es recoger la evidencia actual disponible sobre la relación fisiopatológica, diagnóstica y terapéutica de los microRNAs con la insuficiencia cardiaca. Metodología: Se han utilizado 94 artículos obtenidos de bases de datos científicas como PubMed y ScienceDirect, recopilando la bibliografía mediante la plataforma Mendeley. Se ha organizado la información obtenida en diferentes tablas que contienen la interacción de los microRNAs con las diferentes causas de insuficiencia cardiaca aquí tratadas. Contenido: Los microRNAs (miRNAs o miRs) son moléculas de RNA no codificante de cadena simple, formados por entre 17-25 nucleótidos. Éstos regulan la actividad de proteínas, actuando sobre la traducción o síntesis proteica. La insuficiencia cardiaca es una enfermedad con una elevada incidencia y una importante mortalidad, así como con causas potencialmente corregibles. Se han seleccionado cinco causas de insuficiencia cardiaca (hipertrofia miocárdica, hipertensión arterial, isquemia miocárdica, fibrilación auricular y miocardiopatía diabética), explicándose su fisiopatología y como se relaciona con los respectivos microRNAs. Se exploran las diferentes perspectivas futuras diagnósticas y terapéuticas de los microRNAs. Existen diferentes técnicas de detección de microRNAs y de RNA mensajero diana basadas en nuevas técnicas de secuenciación (-ómicas), gracias a las cuales se han conseguido crear bibliotecas de microRNAs de libre acceso: miRBase, Rfam o miRGen. Cabe destacar una nueva línea terapéutica consistente en la regeneración miocárdica. Conclusiones: Se han estimado los microRNAs más sensibles para diagnosticar insuficiencia cardiaca, así como los más específicos.<br /

Handgun detection using combined human pose and weapon appearance

Closed-circuit television (CCTV) systems are essential nowadays to prevent security threats or dangerous situations, in which early detection is crucial. Novel deep learning-based methods have allowed to develop automatic weapon detectors with promising results. However, these approaches are mainly based on visual weapon appearance only. For handguns, body pose may be a useful cue, especially in cases where the gun is barely visible. In this work, a novel method is proposed to combine, in a single architecture, both weapon appearance and human pose information. First, pose keypoints are estimated to extract hand regions and generate binary pose images, which are the model inputs. Then, each input is processed in different subnetworks and combined to produce the handgun bounding box. Results obtained show that the combined model improves the handgun detection state of the art, achieving from 4.23 to 18.9 AP points more than the best previous approach.Comment: 17 pages, 18 figure

A low-cost automated digital microscopy platform for automatic identification of diatoms

This article belongs to the Special Issue Advanced Intelligent Imaging Technology Ⅱ[EN] Currently, microalgae (i.e., diatoms) constitute a generally accepted bioindicator of water quality and therefore provide an index of the status of biological ecosystems. Diatom detection for specimen counting and sample classification are two difficult time-consuming tasks for the few existing expert diatomists. To mitigate this challenge, in this work, we propose a fully operative low-cost automated microscope, integrating algorithms for: (1) stage and focus control, (2) image acquisition (slide scanning, stitching, contrast enhancement), and (3) diatom detection and a prospective specimen classification (among 80 taxa). Deep learning algorithms have been applied to overcome the difficult selection of image descriptors imposed by classical machine learning strategies. With respect to the mentioned strategies, the best results were obtained by deep neural networks with a maximum precision of 86% (with the YOLO network) for detection and 99.51% for classification, among 80 different species (with the AlexNet network). All the developed operational modules are integrated and controlled by the user from the developed graphical user interface running in the main controller. With the developed operative platform, it is noteworthy that this work provides a quite useful toolbox for phycologists in their daily challenging tasks to identify and classify diatomsSIThis research was funded by the Spanish Government under the AQUALITAS-RETOS project with Ref. CTM2014-51907-C2-2-R-MINEC

Pyrrolopyrimidine vs imidazole-phenyl-thiazole scaffolds in nonpeptidic dimerization inhibitors of leishmania infantum trypanothione reductase

Disruption of protein-protein interactions of essential oligomeric enzymes by small molecules represents a significant challenge. We recently reported some linear and cyclic peptides derived from an α-helical region present in the homodimeric interface of Leishmania infantum trypanothione reductase (Li-TryR) that showed potent effects on both dimerization and redox activity of this essential enzyme. Here, we describe our first steps toward the design of nonpeptidic small-molecule Li-TryR dimerization disruptors using a proteomimetic approach. The pyrrolopyrimidine and the 5-6-5 imidazole-phenyl-thiazole α-helix-mimetic scaffolds were suitably decorated with substituents that could mimic three key residues (K, Q, and I) of the linear peptide prototype (PKIIQSVGIS-Nle-K-Nle). Extensive optimization of previously described synthetic methodologies was required. A library of 15 compounds bearing different hydrophobic alkyl and aromatic substituents was synthesized. The imidazole-phenyl-thiazole-based analogues outperformed the pyrrolopyrimidine-based derivatives in both inhibiting the enzyme and killing extracellular and intracellular parasites in cell culture. The most active imidazole-phenyl-thiazole compounds 3e and 3f inhibit Li-TryR and prevent growth of the parasites at low micromolar concentrations similar to those required by the peptide prototype. The intrinsic fluorescence of these compounds inside the parasites visually demonstrates their good permeability in comparison with previous peptide-based Li-TryR dimerization disruptors.We thank the Spanish Government (MINECO/FEDER Projects SAF2015-64629-C2, BFU2017-90030-P), the Comunidad de Madrid (BIPEDD-2-CM ref S-2010/BMD-2457), and the Consejo Superior de Investigaciones Cientıfí cas (CSIC Project 201980E028) for financial support. We thank staff from ALBA Synchrotron (Barcelona, Spain) for support during data collection.Peer Reviewe

Low-cost oblique illumination: an image quality assessment

P. 1-14We study the effectiveness of several low-cost oblique illumination filters to improve overall image quality, in comparison with standard bright field imaging. For this purpose, a dataset composed of 3360 diatom images belonging to 21 taxa was acquired. Subjective and objective image quality assessments were done. The subjective evaluation was performed by a group of diatom experts by psychophysical test where resolution, focus, and contrast were assessed. Moreover, some objective nonreference image quality metrics were applied to the same image dataset to complete the study, together with the calculation of several texture features to analyze the effect of these filters in terms of textural properties. Both image quality evaluation methods, subjective and objective, showed better results for images acquired using these illumination filters in compari-son with the no filtered image. These promising results confirm that this kind of illumination filters can be a practical way to improve the image quality, thanks to the simple and low cost of the design and manufacturing process.S

Lights and pitfalls of convolutional neural networks for diatom identification

P. 1-10Diatom detection has been a challenging task for computer scientist and biologist during past years. In this work, the new state of art techniques based on the deep learning framework have been tested, in order to check whether they are suitable for this purpose. On the one hand, RCNNs (Region based Convolutional Neural Networks), which select candidate regions and applies a convolutional neural network and, on the other hand, YOLO (You Only Look Once), which applies a single neural network over the whole image, have been tested. The first one is able to reach poor results in out experimentation, with an average of 0.68 recall and some tricky aspects, as for example it is needed to apply a bounding box merging algorithm to get stable detections; but the second one gets remarkable results, with an average of 0.84 recall in the evaluation that have been carried out, and less aspects to take into account after the detection has been performed. Future work related to parameter tuning and processing are needed to increase the performance of deep learning in the detection task. However, as for classification it has been probed to provide succesfully performance.S

Gamificación como complemento para el aprendizaje en Química Orgánica

En este trabajo se recogen los resultados más relevantes de la aplicación de la técnica de gamificación como un complemento en el aprendizaje de la asignatura de Química Inorgánica y Orgánica que se imparte en el cuarto semestre (segundo curso) del grado en Ingeniería Química en la Escuela Técnica Superior de Ingeniería y Diseño Industrial (E.T.S.I.D.I.). En concreto, la metodología de aprendizaje basada en gamificación que se ha empleado consiste en la realización de cuestionarios con el fin de que los estudiantes adquieran los conocimientos necesarios de Química Orgánica. Los cuestionarios fueron colgados en la plataforma Moodle y servían de apoyo a los estudiantes. La experiencia se repitió en dos ocasiones: el primer cuestionario se realizó antes del primer examen y el segundo cuestionario se realizó antes del tercer y último examen de la evaluación continua. Gracias a la utilización de esta herramienta, muchos de los estudiantes han superado el curso con éxito y la valoración que ellos han hecho sobre la misma ha resultado altamente satisfactoria

Gamificación en la asignatura de Química

En este trabajo se recogen los resultados más relevantes de la aplicación de la técnica de gamificación como un complemento en el aprendizaje de la asignatura de Química que se imparte en el primer semestre (primer curso) de todos los grados impartidos en la Escuela Técnica Superior de Ingeniería y Diseño Industrial (ETSIDI). Esta metodología se ha implementado durante la realización de las Acciones Cooperativas, que todos los estudiantes realizan durante el curso, en el caso de que decidan cursar la asignatura mediante evaluación continua. La metodología ha estado basada principalmente en la realización de diferentes tipos de cuestionarios resueltos en grupos de 5-6 estudiantes, con el fin de comprobar si habían entendido los conceptos tratados hasta la fecha. Las herramientas empleadas para llevar a cabo esta metodología se han desarrollado con un alto grado de participación y han sido altamente valoradas por los estudiantes. Hay que destacar, que los estudiantes que participaron en esta experiencia superaron el curso de manera satisfactoria

Automated Diatom Classification (Part A): Handcrafted Feature Approaches

This article belongs to the Special Issue Automated Analysis and Identification of Phytoplankton Images[EN] This paper deals with automatic taxa identification based on machine learning methods. The aim is therefore to automatically classify diatoms, in terms of pattern recognition terminology. Diatoms are a kind of algae microorganism with high biodiversity at the species level, which are useful for water quality assessment. The most relevant features for diatom description and classification have been selected using an extensive dataset of 80 taxa with a minimum of 100 samples/taxon augmented to 300 samples/taxon. In addition to published morphological, statistical and textural descriptors, a new textural descriptor, Local Binary Patterns (LBP), to characterize the diatom’s valves, and a log Gabor implementation not tested before for this purpose are introduced in this paper. Results show an overall accuracy of 98.11% using bagging decision trees and combinations of descriptors. Finally, some phycological features of diatoms that are still difficult to integrate in computer systems are discussed for future workSIThe authors acknowledge financial support of the Spanish Government under the Aqualitas-retos project (Ref. CTM2014-51907-C2-2-R-MINECO), http://aqualitas-retos.es/en