Oficio de tinieblas por Galileo Galilei: una composición paradigmática del compositor Patricio Wang para Quilapayún

Los aspectos de interés para este artículo son tres. Primero se aborda la figura de Patricio Wang como compositor, su formación académica y su relación con Quilapayún. En la segunda parte, se realiza un análisis músico-poético, de Oficio de tinieblas por Galileo Galilei, teniendo como base, la creación en su forma y contenido, con énfasis en los recursos técnicos utilizados, que no corresponden al lenguaje ni a la estética de la canción popular. Para el análisis, se observó la obra como una escena única dividida en cuatro planos. Por último, se realiza una visión panorámica a la historia del grupo Quilapayún, describiendo y analizando los factores que posibilitaron que fueran capaces de asimilar una propuesta creativa de estas características. Este estudio ha sido articulado, en base a la información proporcionada directamente por Patricio Wang al autor, y que ha permitido vislumbrar: su historia como compositor, sus influencias directas, su visión particular de la obra; y su relación profesional con Quilapayún. Esto junto a la utilización de bibliografía descriptiva relacionada a la Nueva Canción Chilena y sus exponentes, han permitido sostener la idea, de que esta obra, y la inclusión de su compositor al conjunto, generan un nuevo paradigma creativo, materializado en el giro estético que estos artistas darán en sus producciones discográficas de los años 80

Iris Reconstruction: A Surgeon's Guide.

Objectives: The aim of this review paper is to summarise surgical options available for repairing iris defects at the iris-lens plane, focusing on suturing techniques, iridodialysis repair, and prosthetic iris devices. Methods: A thorough literature search was conducted using multiple databases, including Medline, PubMed, Web of Science Core Collection, and the Cochrane Library, from inception to February 2024. Relevant studies were screened based on predefined criteria, and primary references cited in selected articles were also reviewed. Results: Various surgical techniques were identified for iris defect repair. Suturing methods such as interrupted full-thickness sutures and the McCannel technique offer solutions for smaller defects, while iridodialysis repair techniques address detachment of the iris from the ciliary body. Prosthetic iris devices, including iris-lens diaphragm devices, endocapsular capsular tension ring-based devices, and customizable artificial iris implants, provide options for larger defects, each with its own advantages and limitations. Conclusions: Successful iris reconstruction requires a personalised approach considering factors like defect size, ocular comorbidities, and patient preference. Surgeons must possess a thorough understanding of available techniques and prosthetic devices to achieve optimal outcomes in terms of both visual function and, nonetheless, cosmetic appearance

Proliferative vitreoretinopathy: an update on the current and emerging treatment options.

Proliferative vitreoretinopathy (PVR) remains the main cause of failure in retinal detachment (RD) surgery and a demanding challenge for vitreoretinal surgeons. Despite the large improvements in surgical techniques and a better understanding of PVR pathogenesis in the last years, satisfactory anatomical and visual outcomes have not been provided yet. For this reason, several different adjunctive pharmacological agents have been investigated in combination with surgery. In this review, we analyze the current and emerging adjunctive treatment options for the management of PVR and we discuss their possible clinical application and beneficial role in this subgroup of patients

Resiliencia del profesorado de Música chileno en el contexto de pandemia de COVID-19

El presente artículo pretende dar a conocer cómo ha afectado el contexto de pandemia por COVID-19 las condiciones laborales y personales del profesorado de Música en Chile. Durante el primer año de esta pandemia, se consultó a 154 docentes de Música sobre su situación personal y profesional con el objetivo de conocer cómo estaban enfrentando el contexto educativo a distancia. Los datos recopilados y contrastados con la bibliografía consultada, permiten observar no solo una modificación y reinvención de sus prácticas educativas, sino además una importante capacidad de reinvención, de automotivación y de salir adelante. Estas capacidades se engloban en el concepto de resiliencia. Todo lo observado permite sentar las bases de una educación musical contextualizada y con la capacidad de adaptarse a la realidad y los continuos cambios que esta presenta, y donde el rol de la educación musical dentro del currículum se torna a su vez mas importante y significativo, tanto para el estudiantado, así como también para el propio desarrollo curricular del sistema educativ

Optical Flow in a Smart Sensor Based on Hybrid Analog-Digital Architecture

The purpose of this study is to develop a motion sensor (delivering optical flow estimations) using a platform that includes the sensor itself, focal plane processing resources, and co-processing resources on a general purpose embedded processor. All this is implemented on a single device as a SoC (System-on-a-Chip). Optical flow is the 2-D projection into the camera plane of the 3-D motion information presented at the world scenario. This motion representation is widespread well-known and applied in the science community to solve a wide variety of problems. Most applications based on motion estimation require work in real-time; hence, this restriction must be taken into account. In this paper, we show an efficient approach to estimate the motion velocity vectors with an architecture based on a focal plane processor combined on-chip with a 32 bits NIOS II processor. Our approach relies on the simplification of the original optical flow model and its efficient implementation in a platform that combines an analog (focal-plane) and digital (NIOS II) processor. The system is fully functional and is organized in different stages where the early processing (focal plane) stage is mainly focus to pre-process the input image stream to reduce the computational cost in the post-processing (NIOS II) stage. We present the employed co-design techniques and analyze this novel architecture. We evaluate the system’s performance and accuracy with respect to the different proposed approaches described in the literature. We also discuss the advantages of the proposed approach as well as the degree of efficiency which can be obtained from the focal plane processing capabilities of the system. The final outcome is a low cost smart sensor for optical flow computation with real-time performance and reduced power consumption that can be used for very diverse application domains

Ischemic postconditioning fails to reduce infarct size in pig models of intermediate and prolonged ischemia

This study was funded by the Spanish Ministry of Science and Innovation (“RETOS 2019” grant No PID2019-107332RB-I00 to B.I). B.I is funded by the European Commission (ERC-CoG grant No 819775, and H2020-HEALTH grant No 945118). J.N. is recipient of a predoctoral grant (Jordi Soler Soler) through CIBERCV. The CNIC is supported by the Instituto de Salud Carlos III (ISCIII), the Ministry of Science and Innovation and the Pro CNIC Foundation, and is a Severo Ochoa Center of Excellence (CEX2020-001041-S).S

Event reconstruction for KM3NeT/ORCA using convolutional neural networks

The KM3NeT research infrastructure is currently under construction at two locations in the Mediterranean Sea. The KM3NeT/ORCA water-Cherenkov neutrino detector off the French coast will instrument several megatons of seawater with photosensors. Its main objective is the determination of the neutrino mass ordering. This work aims at demonstrating the general applicability of deep convolutional neural networks to neutrino telescopes, using simulated datasets for the KM3NeT/ORCA detector as an example. To this end, the networks are employed to achieve reconstruction and classification tasks that constitute an alternative to the analysis pipeline presented for KM3NeT/ORCA in the KM3NeT Letter of Intent. They are used to infer event reconstruction estimates for the energy, the direction, and the interaction point of incident neutrinos. The spatial distribution of Cherenkov light generated by charged particles induced in neutrino interactions is classified as shower- or track-like, and the main background processes associated with the detection of atmospheric neutrinos are recognized. Performance comparisons to machine-learning classification and maximum-likelihood reconstruction algorithms previously developed for KM3NeT/ORCA are provided. It is shown that this application of deep convolutional neural networks to simulated datasets for a large-volume neutrino telescope yields competitive reconstruction results and performance improvements with respect to classical approaches

Event reconstruction for KM3NeT/ORCA using convolutional neural networks

The KM3NeT research infrastructure is currently under construction at two locations in the Mediterranean Sea. The KM3NeT/ORCA water-Cherenkov neutrino de tector off the French coast will instrument several megatons of seawater with photosensors. Its main objective is the determination of the neutrino mass ordering. This work aims at demonstrating the general applicability of deep convolutional neural networks to neutrino telescopes, using simulated datasets for the KM3NeT/ORCA detector as an example. To this end, the networks are employed to achieve reconstruction and classification tasks that constitute an alternative to the analysis pipeline presented for KM3NeT/ORCA in the KM3NeT Letter of Intent. They are used to infer event reconstruction estimates for the energy, the direction, and the interaction point of incident neutrinos. The spatial distribution of Cherenkov light generated by charged particles induced in neutrino interactions is classified as shower-or track-like, and the main background processes associated with the detection of atmospheric neutrinos are recognized. Performance comparisons to machine-learning classification and maximum-likelihood reconstruction algorithms previously developed for KM3NeT/ORCA are provided. It is shown that this application of deep convolutional neural networks to simulated datasets for a large-volume neutrino telescope yields competitive reconstruction results and performance improvements with respect to classical approaches