¿Qué nos dejó la pandemia?. Retos y aprendizajes para la educación superior

Experiencias, vivencias y reflexiones sobre la realidad actual de la educación universitaria forman parte de esta publicación. La transición de una educación mayoritariamente presencial a una educación de emergencia a distancia con la proyección de un futuro de un sistema híbrido, exige nuevos retos. Los cambios no pueden ser solo de forma: la crisis provocada por la pandemia demanda transformaciones en varios aspectos: la modalidad, el ritmo, los roles de estudiantes y docentes, la metodología, las formas de evaluar, las características del aprendizaje síncrono u asíncrono, el uso de la tecnología. Todo esto implica la construcción colectiva de una nueva realidad educativa orientada a la formación de una ciudadanía activa, con herramientas personales y profesionales para mejorar la realidad social actual

Selection of Conditions for Cellulase and Xylanase Extraction from Switchgrass Colonized by Acidothermus cellulolyticus

Solid-state fermentation has been widely used for enzyme production. However, secreted enzymes often bind to the solid substrate preventing their detection and recovery. A series of screening studies was performed to examine the role of extraction buffer composition including NaCl, ethylene glycol, sodium acetate buffer, and Tween 80, on xylanase and cellulase recovery from switchgrass. Our results indicated that the selection of an extraction buffer is highly dependent on the nature and source of the enzyme being extracted. While a buffer containing 50 mM sodium acetate at pH 5 was found to have a positive effect on the recovery of commercial fungal-derived cellulase and xylanase amended to switchgrass, the same buffer had a significant negative effect on enzyme extraction from solid fermentation samples colonized by the bacterium Acidothermus cellulolyticus. Xylanase activity was more affected by components in the extraction buffers compared to cellulase. This study demonstrated that extraction followed by diafiltration is important for assessing enzyme recovery from solid fermentation samples. Reduction in activity due to compounds present in the switchgrass extracts is reversible when the compounds are removed via diafiltration

Multi-messenger observations of a binary neutron star merger

On 2017 August 17 a binary neutron star coalescence candidate (later designated GW170817) with merger time 12:41:04 UTC was observed through gravitational waves by the Advanced LIGO and Advanced Virgo detectors. The Fermi Gamma-ray Burst Monitor independently detected a gamma-ray burst (GRB 170817A) with a time delay of ~1.7 s with respect to the merger time. From the gravitational-wave signal, the source was initially localized to a sky region of 31 deg2 at a luminosity distance of 40+8-8 Mpc and with component masses consistent with neutron stars. The component masses were later measured to be in the range 0.86 to 2.26 Mo. An extensive observing campaign was launched across the electromagnetic spectrum leading to the discovery of a bright optical transient (SSS17a, now with the IAU identification of AT 2017gfo) in NGC 4993 (at ~40 Mpc) less than 11 hours after the merger by the One- Meter, Two Hemisphere (1M2H) team using the 1 m Swope Telescope. The optical transient was independently detected by multiple teams within an hour. Subsequent observations targeted the object and its environment. Early ultraviolet observations revealed a blue transient that faded within 48 hours. Optical and infrared observations showed a redward evolution over ~10 days. Following early non-detections, X-ray and radio emission were discovered at the transient’s position ~9 and ~16 days, respectively, after the merger. Both the X-ray and radio emission likely arise from a physical process that is distinct from the one that generates the UV/optical/near-infrared emission. No ultra-high-energy gamma-rays and no neutrino candidates consistent with the source were found in follow-up searches. These observations support the hypothesis that GW170817 was produced by the merger of two neutron stars in NGC4993 followed by a short gamma-ray burst (GRB 170817A) and a kilonova/macronova powered by the radioactive decay of r-process nuclei synthesized in the ejecta

A Sparsity-Based Approach to 3D Binaural Sound Synthesis Using Time-Frequency Array Processing

Localization of sounds in physical space plays a very important role in multiple audio-related disciplines, such as music, telecommunications, and audiovisual productions. Binaural recording is the most commonly used method to provide an immersive sound experience by means of headphone reproduction. However, it requires a very specific recording setup using high-fidelity microphones mounted in a dummy head. In this paper, we present a novel processing framework for binaural sound recording and reproduction that avoids the use of dummy heads, which is specially suitable for immersive teleconferencing applications. The method is based on a time-frequency analysis of the spatial properties of the sound picked up by a simple tetrahedral microphone array, assuming source sparseness. The experiments carried out using simulations and a real-time prototype confirm the validity of the proposed approach

Nonnegative signal factorization with learnt instrument models for sound source separation in close-microphone recordings

Close-microphone techniques are extensively employed in many live music recordings, allowing for interference rejection and reducing the amount of reverberation in the resulting instrument tracks. However, despite the use of directional microphones, the recorded tracks are not completely free from source interference, a problem which is commonly known as microphone leakage. While source separation methods are potentially a solution to this problem, few approaches take into account the huge amount of prior information available in this scenario. In fact, besides the special properties of close-microphone tracks, the knowledge on the number and type of instruments making up the mixture can also be successfully exploited for improved separation performance. In this paper, a nonnegative matrix factorization (NMF) method making use of all the above information is proposed. To this end, a set of instrument models are learnt from a training database and incorporated into a multichannel extension of the NMF algorithm. Several options to initialize the algorithm are suggested, exploring their performance in multiple music tracks and comparing the results to other state-of-the-art approaches.This work was supported by the Andalusian Business, Science and Innovation Council under project P2010- TIC-6762, (FEDER) the Spanish Ministry of Economy and Competitiveness under the projects TEC2012-38142-C04-03 and TEC2012-37945-C02-0