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Drought stress during wheat (Triticum aestivum L.) grain filling can affect the grain yield and its quality; effects that in some regions can be increased by the event La Niña, the cold phase of the climatic phenomenon El Niño Southern Oscillation. The aim was to evaluate if the evolution of relative water content (RWC) and stomatal conductance (gs) can be related to grain yield and its quality. Four spring wheat cultivars grown in the southern cone of South America were subjected to two levels of irrigation applied after anthesis, a well-watered treatment (Control), and another with 50 % of the Control (Stress). RWC and gs were determined weekly during the stress period and agronomic traits at harvest. With a mean grain yield decrease of 14.8 % (P

Aim of the study: To analyze the course of leaf water status, water-use efficiency and growth in Eucalyptus grandis and hybrids throughout seasons with different rainfall. Area of study: The study was conducted in northern Uruguay. Methods: A randomized block trial was established containing E. grandis (ABH17), E. grandis × Eucalyptus camaldulensis (GC172), E. grandis × Eucalyptus tereticornis (GT529), and E. grandis × Eucalyptus urophylla (GU08). Predawn leaf water potential (Ψpd) and midday leaf water potential (Ψmd) were measured every six weeks from the age of 16 months, throughout two growing seasons. Stomatal conductance (gs), net photosynthetic rate (A), and leaf-level transpiration (E) were measured once in each growing season, along with leaf carbon isotope discrimination (∆13C) and tree growth. Stomatal density and distribution were studied. Results: ABH17 and GU08 had the lowest daily fluctuation of leaf water potential and showed stronger stomatal regulation; they were hypostomatic, and stomata on the adaxial leaf surfaces remained immature. GC172 and GT529 (Red-Gum hybrids) were amphistomatic and transpired more intensively; they were less efficient in instantaneous and intrinsic water use and grew faster under high soil moisture (inferred from rainfall). Under such conditions, GC172 reached the highest gas-exchange rate due to an increase in tree hydraulic conductance. ABH17 and GU08 were hypostomatic and used water more efficiently because of stronger stomatal regulation. Research highlights: Red-Gum hybrids evidenced less water use efficiency due to lower stomatal regulation, different stomatal features, and distinct growth patterns as a function of soil moisture (inferred from rainfall). Keywords: Eucalypt hybrids; stomatal conductance; water-use efficiency; transpiration. Abbreviations used: Ψpd:predawn leaf water potential;Ψmd: midday leaf water potential; ΔΨ: daily fluctuation of leaf water potential ( ; A: net photosynthetic rate, E: leaf transpiration rate, gs: stomatal conductance, WUE: instantaneous water-use efficiency; WUEi: integrated water-use efficiency; A/E: leaf photosynthesis-to-leaf transpiration ratio; ∆13C: leaf carbon isotope discrimination; K: tree hydraulic conductance; E/∆Ψ: ratio between leaf transpiration and daily fluctuation of leaf water potential; δ13C: natural abundance of 13C.

Abstract: Paspalum dilatatum Poir. is a promising perennial summer grass forage for temperate regions, but among other factors, slow stand establishment has hindered its adoption. One of the reasons may be seed dormancy. Intraspecific variability in seed dormancy has been reported in P. dilatatum, but the mechanisms underlying this variability remain unclear. In this paper, we focus on the role of seed external covering structures on germination, particularly the lemma. Seeds of apomictic and sexual biotypes of P. dilatatum were subjected to acid scarification and removal of the lemma to study their germination, imbibition rate and sensitivity to ABA. Seeds without lemma showed higher germination than other treatments in dormant genotypes. We found that the lemma delayed but did not prevent water uptake in any genotype. Comparing sexual biotypes, P. dilatatum subsp. flavescens showed higher lemma-imposed dormancy than P. dilatatum “Virasoro”, and also higher seed dormancy at the caryopsis level. The thickness of the lemma was not different between these biotypes, so this trait did not explain differences in dormancy. Intraspecific variability of seed dormancy in this species may be attributable to the lemma incidence, however differences in the caryopsis component of seed dormancy should be considered.

O capitalismo informacional e a ubiquidade tecnológica conduzem ao aumento de fontes de informação que criam diversidade de escolhas para o indivíduo para a satisfação das suas necessidades e desejos. Este é um estudo de caso da plataforma digital Comunidade Cultura e Arte, autodefinida como uma plataforma de distribuição e/ou de acesso de conteúdos de cariz informativo. Neste sentido, efetua-se a observação direta e a análise de conteúdo a todas as publicações da plataforma no mês de janeiro de 2020, e aplica-se um inquérito por questionário aos seus leitores (n=1525), procurando compreender o que motiva os leitores a utilizar esta plataforma como fonte de informação. Os resultados destacam elementos do processo de produção e difusão de conteúdos, que contribuem para a experiência dos leitores, entre outros: a unicidade do projeto editorial, com uma diversidade de conteúdos culturais e artísticos, a presença em plataformas de redes sociais online e a linguagem utilizada.

Mammography is the primary medical imaging method used for routine screening and early detection of breast cancer in women. However, the process of manually inspecting, detecting, and delimiting the tumoral massess in 2D images is a very time-consuming task, subject to human errors due to fatigue. Therefore, integrated computer-aided detection systems have been proposed, based on modern computer vision and machine learning methods. In the present work, mammogram images from the publicly available Inbreast dataset are first converted to pseudo-color and then used to train and test a Mask R-CNN deep neural network. The most common approach is to start with a dataset and split the images into train and test set randomly. However, since there are often two or more images of the same case in the dataset, the way the dataset is split may have an impact on the results. Our experiments show that random partition of the data can produce unreliable training, so the dataset must be split using case-wise partition for more stable results. In experimental results, the method achieves an average true positive rate of 0.936 with 0.063 standard deviation using random partition and 0.908 with 0.002 standard deviation using case-wise partition, showing that case-wise partition must be used for more reliable results.