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Enhanced and effective hydrological monitoring plays a crucial role in understanding water-related processes in a rapidly changing world. Within this context, image-based river monitoring has shown to significantly enhance data collection, improve analysis and accuracy, and support effective and timely decision-making. The integration of remote and proximal sensing technologies, with citizen science, and artificial intelligence may revolutionize monitoring practices. Therefore, it is crucial to quantify the quality of current research and ongoing initiatives to envision the potential trajectories for research activities within this specific field. The evolution of monitoring strategies is progressing in multiple directions that should converge to build critical mass around relevant challenges to meet the need for innovative solutions to overcome limitations of traditional approaches. The present study reviews showcases and good practices of enhanced hydrological monitoring in different applications, reflecting the strengths and limitations of new approaches.

Habitat-forming cold-water corals (CWCs) represent a key component of deep-sea benthic communities and a priority target for conservation. Although research efforts have been mounting to try and identify the drivers of CWC distributions, progress has been limited by the scarcity of ecological data. The present work employs habitat suitability models (HSMs) to estimate spatial distributions, environmental drivers and co-existence patterns of 14 habitat-forming CWCs in the Azores, an area considered a hotspot of coral diversity in the Atlantic. The modelled CWCs showed a strong bathymetric zonation, which seems to be determined by the vertical stratification of water masses in the region. In particular, the modelled CWCs can be clustered in four groups named after the isopycnal (vertical) layers in which Atlantic water masses are organized: species restricted to upper water masses, species extending down from upper water masses, species restricted to intermediate water masses and species extending up from deep water masses. Horizontal patterns further indicate that the Azores Current and different production regimes north and south of the archipelago likely influence the distribution of CWCs in sub-surface waters. Such results have important implications for the regional management of deep-sea benthic communities and, in particular, for the design of representative networks of protected areas. The combined habitat of all modelled species covered only 11%. Given that they all possess the characteristics of benthic foundation organisms and represent indicator taxa of vulnerable marine ecosystems all the modelled species should be viewed as important targets for conservation. The lace coral Errina dabneyi deserves particular attention since this species appears to be endemic to the Azores and has a very limited estimated distribution.

Digital agriculture has evolved significantly over the last few years due to the technological developments in automation and computational intelligence applied to the agricultural sector, including vineyards which are a relevant crop in the Mediterranean region. In this work, a study is presented of semantic segmentation for vine detection in real-world vineyards by exploring state-of-the-art deep segmentation networks and conventional unsupervised methods. Camera data have been collected on vineyards using an Unmanned Aerial System (UAS) equipped with a dual imaging sensor payload, namely a high-definition RGB camera and a five-band multispectral and thermal camera. Extensive experiments using deep-segmentation networks and unsupervised methods have been performed on multimodal datasets representing four distinct vineyards located in the central region of Portugal. The reported results indicate that SegNet, U-Net, and ModSegNet have equivalent overall performance in vine segmentation. The results also show that multimodality slightly improves the performance of vine segmentation, but the NIR spectrum alone generally is sufficient on most of the datasets. Furthermore, results suggest that high-definition RGB images produce equivalent or higher performance than any lower resolution multispectral band combination. Lastly, Deep Learning (DL) networks have higher overall performance than classical methods. The code and dataset are publicly available on https://github.com/Cybonic/DL_vineyard_segmentation_study.git.

Aims: To investigate the effects of the lectin from Punica granatum sarcotesta (PgTeL) on growth, viability, cell structure, biofilm formation and chitinase activity of Listeria monocytogenes. In addition, the effect of PgTeL on the adhesion and invasion of human cells (HeLa) was determined. Methods and Results: PgTeL showed bacteriostatic and bactericidal effects on the strains L. monocytogenes N53-1 and EGD-e, causing morphometric alterations, cell aggregation, strong deformation and cell disruption. PgTeL inhibited biofilm formation by EGD-e and N53-1 and also interfered with the adhesion and invasion processes of EGD-e and N53-1 in HeLa cells. Finally, the chitinase activity of L. monocytogenes EGD-e was reduced in the presence of PgTeL, which can be involved in the inhibition of adhesion process. Conclusion: PgTeL is an antibacterial agent against L. monocytogenes, inhibiting growth and promoting cell death, as well as impairing biofilm formation and bacterial adhesion and invasion into human cells. Significance and Impact of the Study: The results stimulate future investigations on the potential of PgTeL for protection of contamination in food products.

Digital agriculture has evolved significantly over the last few years due to the technological developments in automation and computational intelligence applied to the agricultural sector, including vineyards which are a relevant crop in the Mediterranean region. In this work, a study is presented of semantic segmentation for vine detection in real-world vineyards by exploring state-of-the-art deep segmentation networks and conventional unsupervised methods. Camera data have been collected on vineyards using an Unmanned Aerial System (UAS) equipped with a dual imaging sensor payload, namely a high-definition RGB camera and a five-band multispectral and thermal camera. Extensive experiments using deep-segmentation networks and unsupervised methods have been performed on multimodal datasets representing four distinct vineyards located in the central region of Portugal. The reported results indicate that SegNet, U-Net, and ModSegNet have equivalent overall performance in vine segmentation. The results also show that multimodality slightly improves the performance of vine segmentation, but the NIR spectrum alone generally is sufficient on most of the datasets. Furthermore, results suggest that high-definition RGB images produce equivalent or higher performance than any lower resolution multispectral band combination. Lastly, Deep Learning (DL) networks have higher overall performance than classical methods. The code and dataset are publicly available at https://github.com/Cybonic/DL_vineyard_segmentation_study.git, Comment: Accepted in Computer and Electronics in Agriculture journal

With the increasing role that unmanned aerial systems (UAS) are playing in data collection for environmental studies, two key challenges relate to harmonizing and providing standardized guidance for data collection, and also establishing protocols that are applicable across a broad range of environments and conditions. In this context, a network of scientists are cooperating within the framework of the Harmonious Project to develop and promote harmonized mapping strategies and disseminate operational guidance to ensure best practice for data collection and interpretation. The culmination of these efforts is summarized in the present manuscript. Through this synthesis study, we identify the many interdependencies of each step in the collection and processing chain, and outline approaches to formalize and ensure a successful workflow and product development. Given the number of environmental conditions, constraints, and variables that could possibly be explored from UAS platforms, it is impractical to provide protocols that can be applied universally under all scenarios. However, it is possible to collate and systematically order the fragmented knowledge on UAS collection and analysis to identify the best practices that can best ensure the streamlined and rigorous development of scientific products. © 2020 by the authors. Licensee MDPI, Basel, Switzerland.

With the increasing role that unmanned aerial systems (UAS) are playing in data collection for environmental studies, two key challenges relate to harmonizing and providing standardized guidance for data collection, and also establishing protocols that are applicable across a broad range of environments and conditions. In this context, a network of scientists are cooperating within the framework of the Harmonious Project to develop and promote harmonized mapping strategies and disseminate operational guidance to ensure best practice for data collection and interpretation. The culmination of these efforts is summarized in the present manuscript. Through this synthesis study, we identify the many interdependencies of each step in the collection and processing chain, and outline approaches to formalize and ensure a successful workflow and product development. Given the number of environmental conditions, constraints, and variables that could possibly be explored from UAS platforms, it is impractical to provide protocols that can be applied universally under all scenarios. However, it is possible to collate and systematically order the fragmented knowledge on UAS collection and analysis to identify the best practices that can best ensure the streamlined and rigorous development of scientific products. © 2020 by the authors. Licensee MDPI, Basel, Switzerland.

The recent paper by AlShebli et al. (2020) investigates the impact of mentorship in young scientists. Among their conclusions, they state that female prot\'eg\'es benefit more from male than female mentorship. We herein expose a critical flaw in their methodological design that is a common issue in Astronomy, namely "selection biases". An effect that if not treated properly may lead to unwarranted causality claims. In their analysis, selection biases seem to be present in the response rate of their survey (8.35%), the choice of database, success criterion, and the overlook of the numerous drawbacks female researchers face in academia. We discuss these issues and their implications -- one of them being the potential increase in obstacles for women in academia. Finally, we reinforce the dangers of not considering selection bias effects in studies aimed at retrieving causal relations., Comment: Submitted to Nature Communications on Nov. 27 -- comments are welcome

The aim of this study is to test the unidimensional 9-items factorial model, about psychological stress, with a non-clinical sample of Canadian students, evaluated by the metric principals of factorial analysis and internal consistency. A sample of 546 university students (Women = 79.6%, Men = 20.4%, Mean age = 23.2, Standard Deviation = 7.3) were used. The results of the exploratory factorial analysis (explaining about 55.3% of the total variance of the construct) and confirmatory (GFI = 0.994, AGFI = 0.991, CMIN / DF = 3.77, RMSEA = 0.071, CFI = 0.985) satisfactorily confirmed its unidimensionality. The results of the internal consistency study, obtained by Cronbach's Alpha, McDonald's Omega, Greatest Lower Bound coefficient, and also the EAP scores, ensure the accuracy of the tested model. New studies should explore and test other important metric qualities of this instrument (content validity and test-retest reliability, among others).

The aim of this study is to test the unidimensional 9-items factorial model, about psychological stress, with a non-clinical sample of Canadian students, evaluated by the metric principals of factorial analysis and internal consistency. A sample of 546 university students (Women = 79.6%, Men = 20.4%, Mean age = 23.2, Standard Deviation = 7.3) were used. The results of the exploratory factorial analysis (explaining about 55.3% of the total variance of the construct) and confirmatory (GFI = 0.994, AGFI = 0.991, CMIN / DF = 3.77, RMSEA = 0.071, CFI = 0.985) satisfactorily confirmed its unidimensionality. The results of the internal consistency study, obtained by Cronbach's Alpha, McDonald's Omega, Greatest Lower Bound coefficient, and also the EAP scores, ensure the accuracy of the tested model. New studies should explore and test other important metric qualities of this instrument (content validity and test-retest reliability, among others).

The beef production chain has evolved in the last few years towards the differentiation of the quality of its products. Production systems started to generate differentiated products, forcing the packing plants to pay premiums for quality. However, the quality of the finished products may be impaired by logistic and management issues. The main quality problem causing losses in the packing plant is carcass bruising, as such carcasses need to be removed from the processing line, resulting in carcass yield and quality losses. Bruising is caused when the animal is still alive by poor pre-slaughter management, poor facility design, and by individual animal characteristics. However, such lesions are identified only after the hide is removed, and it is difficult to determine when they were caused. In this study, a literature review was carried out to identify the main causes of carcass bruising in beef cattle during the pre-slaughter period focusing on critical points related to farm management, transportation, lairage, pre-slaughter fasting, and animal characteristics., A cadeia produtiva da carne bovina tem evoluído nos últimos anos em direção à diferenciação de produtos de qualidade. Os sistemas de produção começaram a gerar produtos diferenciados, que forçaram os frigoríficos a buscar e remunerar pela qualidade dos animais. Entretanto, problemas logísticos e de manejo têm prejudicado a cadeia produtiva, no sentido de gerarem perdas nos produtos acabados. As contusões nas carcaças representam as principais perdas para o frigorifico, pois, a medida que removidas na linha de abate, ocasionam quedas de rendimento de carcaça, danos à cortes e consequentemente diminuição da qualidade da carcaça. Ocasionadas no animal ainda vivo, as contusões são reflexo de problemas durante o manejo pré-abate, problemas nas instalações e/ou das características dos animais, no entanto sua verificação é possível apenas após a retirada do couro, tornando difícil avaliar o momento em que ocorrem. Objetivou-se no presente estudo, através de revisão bibliográfica, identificar as principais causas de contusões em carcaças bovinas durante a etapa de pré-abate enfatizando pontos críticos relacionados ao manejo na propriedade, transporte, descanso ou jejum pré-abate e características relacionadas aos animais.

A cadeia produtiva da carne bovina tem evoluído nos últimos anos em direção à diferenciação de produtos de qualidade. Os sistemas de produção começaram a gerar produtos diferenciados, que forçaram os frigoríficos a buscar e remunerar pela qualidade dos animais. Entretanto, problemas logísticos e de manejo têm prejudicado a cadeia produtiva, no sentido de gerarem perdas nos produtos acabados. As contusões nas carcaças representam as principais perdas para o frigorifico, pois, a medida que removidas na linha de abate, ocasionam quedas de rendimento de carcaça, danos à cortes e consequentemente diminuição da qualidade da carcaça. Ocasionadas no animal ainda vivo, as contusões são reflexo de problemas durante o manejo pré-abate, problemas nas instalações e/ou das características dos animais, no entanto sua verificação é possível apenas após a retirada do couro, tornando difícil avaliar o momento em que ocorrem. Objetivou-se no presente estudo, através de revisão bibliográfica, identificar as principais causas de contusões em carcaças bovinas durante a etapa de pré-abate enfatizando pontos críticos relacionados ao manejo na propriedade, transporte, descanso ou jejum pré-abate e características relacionadas aos animais., The beef production chain has evolved in the last few years towards the differentiation of the quality of its products. Production systems started to generate differentiated products, forcing the packing plants to pay premiums for quality. However, the quality of the finished products may be impaired by logistic and management issues. The main quality problem causing losses in the packing plant is carcass bruising, as such carcasses need to be removed from the processing line, resulting in carcass yield and quality losses. Bruising is caused when the animal is still alive by poor pre-slaughter management, poor facility design, and by individual animal characteristics. However, such lesions are identified only after the hide is removed, and it is difficult to determine when they were caused. In this study, a literature review was carried out to identify the main causes of carcass bruising in beef cattle during the pre-slaughter period focusing on critical points related to farm management, transportation, lairage, pre-slaughter fasting, and animal characteristics.

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Volume: cap. 41, Pages: 267-272

Pages: 267-272

Pages: 267-272

Volume: cap. 41, Pages: 267-272

Volume: cap. 41, Pages: 267-272

Volume: cap. 41, Pages: 267-272

Volume: cap. 41, Pages: 267-272