Estudos sobre a nutrição mineral do arroz: XXV. Exigências nutricionais da variedade Dourado Precoce

Rice plants, Dourado Precoce, were grown in nutrient solution in order to study its nutritional requirement as well as the accumulation of dry matter and macro and micronutrients (excet Mo) during the life cycle. Demand for mineral elements followed the following decreasing order: macronutrients -K, N, Ca, Mg and S; micronutrients - Fe, Mn, Cu, Zn and B. Dry matter yield reached a maximun 100 days after germination (DAG), whereas accumulation of elements showed the geghest values at largest (140 DAG).Em condições de solução nutritiva foi cultivado o arroz Dourado Precoce com a finalidade de se estudar a marcha de absorção de nutrientes e as exigências minerais. Verificou-se que as exigências minerais obedecem a seguinte ordem decrescente: macronutrientes -K, N, Ca, Mg e S; micronutrientes -Fe, Mn, Cu, Zn e B. A produção de matéria seca atingiu o pico aos 100 dias após a germinação (DAG). A acumulação de macro e micronutrientes na planta foi, entretanto, máxima no fim do ciclo (140 DAG)

Efeito de fertilizantes nitrogenados na produtividade de melão

Em Petrolina, PE, foi realizado um estudo com a cultura do melão (Cucumis melo L.), Valenciano Amarelo, num Latossolo, para avaliar o efeito de fontes de fertilizantes nitrogenados e de suas combinações. O delineamento experimental foi em blocos casualizados com arranjo em faixa, com quatro repetições e nove tratamentos englobando a testemunha e os fertilizantes nitrogenados na dose de 80 kg/ha de N, aplicados no solo e, ou, via água de irrigação, por um período de 42 dias após a germinação. Esses tratamentos foram: Uréia e Sulfato de Amônio isolados; Uréia (15 dias) + Nitrato de Potássio (16-42 dias); Uréia (15 dias) + Sulfato de Amônio (16-42 dias); Uréia (30 dias) + Nitrato de Potássio (31-42 dias); Uréia (15 dias) + Sulfato de Amônio (16-30 dias) + Nitrato de Potássio (31-42 dias). A uréia aplicada via fertirrigação até 42 dias proporcionou maior rendimento (31,14 t/ha), embora não estatisticamente diferente dos demais tratamentos. A testemunha e o sulfato de amônio mostraram-se menos produtivos, com rendimentos de 25,06 e 24,65 t/ha, respectivamente. O peso médio do fruto variou de 1,63 a 1,84 kg/fruto, e o teor de sólidos solúveis totais, de 12,1 a 13,1 ºBrix; não se verificaram diferenças estatísticas

Autoantibodies against type I IFNs in patients with life-threatening COVID-19

Interindividual clinical variability in the course of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection is vast. We report that at least 101 of 987 patients with life-threatening coronavirus disease 2019 (COVID-19) pneumonia had neutralizing immunoglobulin G (IgG) autoantibodies (auto-Abs) against interferon-w (IFN-w) (13 patients), against the 13 types of IFN-a (36), or against both (52) at the onset of critical disease; a few also had auto-Abs against the other three type I IFNs. The auto-Abs neutralize the ability of the corresponding type I IFNs to block SARS-CoV-2 infection in vitro. These auto-Abs were not found in 663 individuals with asymptomatic or mild SARS-CoV-2 infection and were present in only 4 of 1227 healthy individuals. Patients with auto-Abs were aged 25 to 87 years and 95 of the 101 were men. A B cell autoimmune phenocopy of inborn errors of type I IFN immunity accounts for life-threatening COVID-19 pneumonia in at least 2.6% of women and 12.5% of men

Scenario-Based Trajectory Optimization in Uncertain Dynamic Environments

We present an optimization-based method to plan the motion of an autonomous robot under the uncertainties associated with dynamic obstacles, such as humans. Our method bounds the marginal risk of collisions at each point in time by incorporating chance constraints into the planning problem. This problem is not suitable for online optimization outright for arbitrary probability distributions. Hence, we sample from these chance constraints to generate scenarios, which translate the probabilistic constraints into deterministic ones. In practice, each scenario represents the collision constraint for a dynamic obstacle at the location of the sample. The number of theoretically required scenarios can be very large. Nevertheless, by exploiting the geometry of the workspace, we show how to prune most scenarios before optimization and we demonstrate how the reduced scenarios can still provide probabilistic guarantees on the safety of the motion plan. Since our approach is scenario based, we are able to handle arbitrary uncertainty distributions. We apply our method in a Model Predictive Contouring Control framework and demonstrate its benefits in simulations and experiments with a moving robot platform navigating among pedestrians, running in real-time.</p

Recría y área del ojo de bife: efecto en la canal y cortes de valor*

Referencias nacionales e internacionales muestran que existe variación genética para las características de canal y carne, determinando que sea posible seleccionar reproductores para estas características y que su superioridad sea transmisible a sus descendientes. Asimismo, la etapa de recría se caracteriza por que el animal utiliza al máximo su recurso nutricional para alcanzar un tamaño que le permita producir con éxito. Este artículo presenta resultados que surgen de combinar el manejo de la alimentación post-destete de terneros Hereford, con diferencias en mérito genético para características carniceras y su efecto en la canal y los cortes de alto valor económico

Visually-guided motion planning for autonomous driving from interactive demonstrations

The successful integration of autonomous robots in real-world environments strongly depends on their ability to reason from context and take socially acceptable actions. Current autonomous navigation systems mainly rely on geometric information and hard-coded rules to induce safe and socially compliant behaviors. Yet, in unstructured urban scenarios these approaches can become costly and suboptimal. In this paper, we introduce a motion planning framework consisting of two components: a data-driven policy that uses visual inputs and human feedback to generate socially compliant driving behaviors (encoded by high-level decision variables), and a local trajectory optimization method that executes these behaviors (ensuring safety). In particular, we employ Interactive Imitation Learning to jointly train the policy with the local planner, a Model Predictive Controller (MPC), which results in safe and human-like driving behaviors. Our approach is validated in realistic simulated urban scenarios. Qualitative results show the similarity of the learned behaviors with human driving. Furthermore, navigation performance is substantially improved in terms of safety, i.e., number of collisions, as compared to prior trajectory optimization frameworks, and in terms of data-efficiency as compared to prior learning-based frameworks, broadening the operational domain of MPC to more realistic autonomous driving scenarios.Learning & Autonomous Contro