Effect of hypoosmotic environment on canine semen sperm viability

La prueba hipoosmótica ha sido utilizada ampliamente en la valoración de la calidad seminal en varias especies animales. El presente estudio tuvo la finalidad de comparar el efecto de la incubación de espermatozoides caninos en dos soluciones hipoosmóticas (0 mOsm/l - HOST-s y 150 mOsm/l - HOST) sobre la vitalidad espermática (VE). Se obtuvieron 15 eyaculados (2ª fracción) mediante manipulación digital en 10 perros, cuyos espermiogramas fueron considerados normales. De cada eyaculado se tomaron alícuotas de 5 µl para diluirlas en 45 µl de cada solución hipoosmótica, e incubarlas a 37 ºC por 5 y 45 min, respectivamente. La VE se evaluó mediante tinción eosina-nigrosina. En la prueba hipoosmótica se obtuvo 92.1 y 90.1% de espermatozoides dilatados para HOST-s y HOST, respectivamente (p<0.05) y en la prueba de VE se obtuvo 66.5 y 78.3% de espermatozoides vivos para HOST-s y HOST, respectivamente (p<0.01). Se concluye que la menor osmolaridad genera menor VE posincubación.The hypoosmotic test is widely used for determining sperm quality in several animal species. The present study aimed to compare the effect of incubation of canine sperm into two hypoosmotic solutions (0 mOsm/l - HOST-s and 150 mOsm/l - HOST) on sperm viability. Fifteen ejaculates (2nd fraction) were obtained by digital manipulation from 10dogs and the spermiograms were considered as normals. A 5 μl aliquots per ejaculate were diluted in 45 μl of each of the two hypoosmotic solutions and incubated at 37 °C for 5 and 45 min respectively. Sperm viability was assessed using eosin-nigrosin staining. Results of the hypoosmotic test were 92.1 and 90.1% of dilated spermatozoa for HOST-s and HOST respectively (p<0.05) and the sperm viability was 66.5 and 78.3% of live spermatozoa for HOST-s and HOST respectively (p<0.01). It was concluded that thelower osmolality generates lower sperm viability post-incubation

Deep Neural Network for damage detection in Infante Dom Henrique bridge using multi-sensor data

This paper proposes a data-driven approach to detect damage using monitoring data from the Infante Dom Henrique bridge in Porto. The main contribution of this work lies in exploiting the combination of raw measurements from local (inclinations and stresses) and global (eigenfrequencies) variables in a full-scale SHM application. We exhaustively analyze and compare the advantages and drawbacks of employing each variable type and explore the potential of combining them. An autoencoder-based Deep Neural Network is employed to properly reconstruct measurements under healthy conditions of the structure, which are influenced by environmental and operational variability. The damage-sensitive feature for outlier detection is the reconstruction error that measures the discrepancy between current and estimated measurements. Three autoencoder architectures are designed according to the input: local variables, global variables, and their combination. To test the performance of the methodology in detecting the presence of damage, we employ a Finite Element model to calculate the relative change in the structural response induced by damage at four locations. These relative variations between the healthy and damaged responses are employed to affect the experimental testing data, thus producing realistic time-domain damaged measurements. We analyze the Receiver Operating Curves and investigate the latent feature representation of the data provided by the autoencoder in the presence of damage. Results reveal the existence of synergies between the different variable types, producing almost perfect classifiers throughout the performed tests when combining the two available data sources. When damage occurs far from the instrumented sections, the area under the curve in the combined approach increases $50\%$ compared to using local variables only. The classificatoin metrics also demonstrate the enhancement of combining both sources of data in the damage detection task, reaching close to $97\%$ precission values for the four considered test damage scenarios. Finally, we also investigate the capability of local variables to localize the damage, demonstrating the potential of including these variables in the damage detection task.HAZITEK programme (ERROTAID project) and TCRINI project (KK-2023-0029) European Horizon (HE) with LIASON project (GA 101103698), and FUTURAL project (101083958

Deep learning enhanced principal component analysis for structural health monitoring

This paper proposes a Deep Learning enhanced Principal Component Analysis (PCA) approach for outlier detection to assess the structural condition of bridges. We employ a partially explainable autoencoder architecture to replicate and enhance the data compression and reconstruction ability of PCA. The particularity of the method lies in the addition of residual connections to account for nonlinearities. We apply the proposed method to monitoring data obtained from two bridges under real operation conditions and compare the results before and after adding the residual connections. Results show that the addition of residual connections enhances the outlier detection ability of the network, allowing to detect lighter damages.This work has received funding from: the European Union's Horizon 2020 research and innovation program under the grant agreement No 769373 (FORESEE project) and the Marie Sklodowska-Curie grant agreement No 777778 (MATHROCKS); the Base Funding - UIDB/04708/2020 of the CONSTRUCT - Instituto de I\&D em Estruturas e Construções - funded by national funds through the FCT/MCTES (PIDDAC); the European Regional Development Fund (ERDF) through the Interreg V-A Spain-France-Andorra program POCTEFA 2014-2020 Project PIXIL (EFA362/19); the Spanish Ministry of Science and Innovation with references PID2019-108111RB-I00 MCIN/AEI/10.13039/501100011033 (FEDER/AEI) and the “BCAM Severo Ochoa” accreditation of excellence (SEV-2017-0718); and the Basque Government through the BERC 2018-2021 program, the two Elkartek projects 3KIA (KK-2020/00049) and MATHEO (KK-2019-00085), the grant "Artificial Intelligence in BCAM number EXP. 2019/00432", and the Consolidated Research Group MATHMODE (IT1294-19) given by the Department of Education

Bridge damage identification under varying environmental and operational conditions combining Deep Learning and numerical simulations

This work proposes a novel supervised learning approach to identify damage in operating bridge structures. We propose a method to introduce the effect of environmental and operational conditions into the synthetic damage scenarios employed for training a Deep Neural Network, which is applicable to large-scale complex structures. We apply a clustering technique based on Gaussian Mixtures to effectively select Q representative measurements from a long-term monitoring dataset. We employ these measurements as the target response to solve various Finite Element Model Updating problems before generating different damage scenarios. The synthetic and experimental measurements feed two Deep Neural Networks that assess the structural health condition in terms of damage severity and location. We demonstrate the applicability of the proposed method with a real full-scale case study: the Infante Dom Henrique bridge in Porto. A comparative study reveals that neglecting different environmental and operational conditions during training detracts the damage identification task. By contrast, our method provides successful results during a synthetic validation

Deep learning enhanced principal component analysis for structural health monitoring

This paper proposes a Deep Learning Enhanced Principal Component Analysis (PCA) approach for outlier detection to assess the structural condition of bridges. We employ partially explainable autoencoder architecture to replicate and enhance the data compression and reconstruction ability of PCA. The particularity of the method lies in the addition of residual connections to account for nonlinearities. We apply the proposed method to monitoring data obtained from two bridges under real operation conditions and compare the results before and after adding the residual connections. Results show that the addition of residual connections enhances the outlier detection ability of the network, allowing to detect lighter damages

Hábitos alimentares do aruanã (Osteoglossum bicirrhosum Vandelli, 1829) (Pisces: Osteoglossidae) no alto rio Putumayo, área do Parque Nacional de Paya, Putumayo, Colombia

Hábitos alimentarios de la arawana (Osteoglossum bicirrhosum Vandelli, 1829) (Pisces: Osteoglossidae) en el alto río Putumayo, área del Parque Nacional Natural La Paya, Putumayo, ColombiaHábitos alimentares do aruanã (Osteoglossum bicirrhosum Vandelli, 1829) (Pisces: Osteoglossidae) no alto rio Putumayo, área do Parque Nacional de Paya, Putumayo, Colombia  

Treatments for alopecia areata: a network meta-analysis

Acknowledgement: “This Protocol of a Cochrane Review was published in the Cochrane Database of Systematic Reviews 2020, Issue 9. Cochrane Protocols and Reviews are regularly updated as new evidence emerges and in response to feedback, and the Cochrane Database of Systematic Reviews should be consulted for the most recent version of the Protocol.'Copyright © 2020 The Cochrane Collaboration. Published by John Wiley & Sons, Ltd. Objectives: This is a protocol for a Cochrane Review (intervention). The objectives are as follows:. To assess the comparative effectiveness and safety of interventions used in the management of alopecia areata (AA), including patchy alopecia (PA), alopecia totalis (AT) and alopecia universalis (AU). To establish rankings of the available treatments for AA, based on their effectiveness and safety (primary outcomes), through a network meta-analysis