Agaricus Blazei In The Diet Of Broiler Chickens On Immunity, Serum Parameters And Antioxidant Activity

The effect of adding Agaricus blazei to the diet of broilers on immunity, serum parameters, and antioxidant activity was evaluated. A total of 840 1-day-old chicks were used, distributed among five levels of a completely randomized design (0.0, 0.05, 0.10, 0.15, and 0.20% A. blazei), with six replications and 28 birds per experimental unit. The weights of the thymus, spleen and cloacal bursa were not influenced (P > 0.05). Leukocytes, macrophages and nitric oxide were unaffected (P > 0.05), but at each supplementation level compared with the control, differences appeared in the percentages of eosinophils and macrophages (P 0.05) at 42 days. Hypocholesterolemic effect was demonstrated (P 0.05). The antioxidant activity of mushroom showed a positive linear effect (P < 0.05) on DPPH capture on day zero of meat cooling. The inclusion of A. blazei in the diet of broilers provided an immunostimulatory activity and hypocholesterolemic effect. Residual compounds with antioxidant activity were present in the meat, which may promote tissue protection of the animal in vivo, making possible the use of A. blazei as a natural additive.3742235224

Region proposals for saliency map refinement for weakly-supervised disease localisation and classification

First Online: 29 September 2020The deployment of automated systems to diagnose diseases from medical images is challenged by the requirement to localise the diagnosed diseases to justify or explain the classification decision. This requirement is hard to fulfil because most of the training sets available to develop these systems only contain global annotations, making the localisation of diseases a weakly supervised approach. The main methods designed for weakly supervised disease classification and localisation rely on saliency or attention maps that are not specifically trained for localisation, or on region proposals that can not be refined to produce accurate detections. In this paper, we introduce a new model that combines region proposal and saliency detection to overcome both limitations for weakly supervised disease classification and localisation. Using the ChestX-ray14 data set, we show that our proposed model establishes the new state-of-the-art for weakly-supervised disease diagnosis and localisation. We make our code available at https://github.com/renato145/RpSalWeaklyDet.Renato Hermoza, Gabriel Maicas, Jacinto C. Nascimento, Gustavo Carneir

PA-Tree: A Parametric Indexing Scheme for Spatio-temporal Trajectories

Abstract. Many new applications involving moving objects require the collec-tion and querying of trajectory data, so efficient indexing methods are needed to support complex spatio-temporal queries on such data. Current work in this domain has used MBRs to approximate trajectories, which fail to capture some basic properties of trajectories, including smoothness and lack of internal area. This mismatch leads to poor pruning when such indices are used. In this work, we revisit the issue of using parametric space indexing for historical trajectory data. We approximate a sequence of movement functions with single continuous polynomial. Since trajectories tend to be smooth, our approximations work well and yield much finer approximation quality than MBRs. We present the PA-tree, a parametric index that uses this new approximation method. Experiments show that PA-tree construction costs are orders of magnitude lower than that of com-peting methods. Further, for spatio-temporal range queries, MBR-based methods require 20%–60 % more I/O than PA-trees with clustered indicies, and 300%– 400 % more I/O than PA-trees with non-clustered indicies.

Reliability and validity of body weight and body image perception in children and adolescents from the South American Youth/Child Cardiovascular and Environmental (SAYCARE) Study

Objective: To assess the reliability and validity of body weight (BW) and body image (BI) perception reported by parents (in children) and by adolescents in a South American population. Design: Cross-sectional study. BW perception was evaluated by the question, "Do you think you/your child are/is: severely wasted, wasted, normal weight, overweight, obese?" BI perception was evaluated using the Gardner scale. To evaluate reliability, BW and BI perceptions were reported twice, two weeks apart. To evaluate validity, the BW and BI perceptions were compared with WHO BMI Z-scores. Kappa and Kendall's tau-c coefficients were obtained. Setting: Public and private schools and high schools from six countries of South America (Argentina, Peru, Colombia, Uruguay, Chile, Brazil). Participants: Children aged 3-10 years (n 635) and adolescents aged 11-17 years (n 400). Results: Reliability of BW perception was fair in children's parents (k=0·337) and substantial in adolescents (k=0·709). Validity of BW perception was slight in children's parents (k=0·176) and fair in adolescents (k=0·268). When evaluating BI, most children were perceived by parents as having lower weight. Reliability of BI perception was slight in children's parents (k=0·124) and moderate in adolescents (k=0·599). Validity of BI perception was poor in children's parents (k=-0·018) and slight in adolescents (k=0·023). Conclusions: Reliability of BW and BI perceptions was higher in adolescents than in children's parents. Validity of BW perception was good among the parents of the children and adolescents with underweight and normal weight

A novel infrared video surveillance system using deep learning based techniques

This is the author accepted manuscript. The final version is available from Springer via the DOI in this record.This paper presents a new, practical infrared video based surveillance system, consisting of a resolution-enhanced, automatic target detection/recognition (ATD/R) system that is widely applicable in civilian and military applications. To deal with the issue of small numbers of pixel on target in the developed ATD/R system, as are encountered in long range imagery, a super-resolution method is employed to increase target signature resolution and optimise the baseline quality of inputs for object recognition. To tackle the challenge of detecting extremely low-resolution targets, we train a sophisticated and powerful convolutional neural network (CNN) based faster-RCNN using long wave infrared imagery datasets that were prepared and marked in-house. The system was tested under different weather conditions, using two datasets featuring target types comprising pedestrians and 6 different types of ground vehicles. The developed ATD/R system can detect extremely low-resolution targets with superior performance by effectively addressing the low small number of pixels on target, encountered in long range applications. A comparison with traditional methods confirms this superiority both qualitatively and quantitativelyThis work was funded by Thales UK, the Centre of Excellence for Sensor and Imaging System (CENSIS), and the Scottish Funding Council under the project “AALART. Thales-Challenge Low-pixel Automatic Target Detection and Recognition (ATD/ATR)”, ref. CAF-0036. Thanks are also given to the Digital Health and Care Institute (DHI, project Smartcough-MacMasters), which partially supported Mr. Monge-Alvarez’s contribution, and to the Royal Society of Edinburgh and National Science Foundation of China for the funding associated to the project “Flood Detection and Monitoring using Hyperspectral Remote Sensing from Unmanned Aerial Vehicles”, which partially covered Dr. Casaseca-de-la-Higuera’s, Dr. Luo’s, and Prof. Wang’s contribution. Dr. Casaseca-de-la-Higuera would also like to acknowledge the Royal Society of Edinburgh for the funding associated to project “HIVE”

Qualidade sanitária e fisiológica de sementes de abóbora variedade menina Brasileira.

O trabalho teve como objetivos avaliar e correlacionar a qualidade sanitária e fisiológica de sementes de abóbora, variedade Menina Brasileira (Cucurbita moschata.). Foram avaliados dois lotes de sementes de abóbora produzidas no sistema agroecológico e quatro no sistema convencional, com e sem tratamento químico. Os lotes foram submetidos aos testes de sanidade, seguindo a metodologia do “Blotter test”, com congelamento, germinação e vigor (primeira contagem, índice de velocidade de germinação, envelhecimento acelerado e emergência de plântulas). Os resultados indicaram a separação dos lotes de diferentes origens a partir da qualidade sanitária e fisiológica, onde as maiores incidências de fungos foram observadas nos lotes agroecológicos e o maior potencial fisiológico foi observado nos lotes de origem convencional não tratados. Foram encontrados os fungos Fusarium oxysporum, Alternaria alternata, Cladosporium cucumerinum, Aspergillus niger, Penicillium digitatum, Rhizopus stolonifer e Phoma terrestris. A qualidade sanitária não interferiu na qualidade fisiológica das sementes de abóbora, variedade Menina Brasileira