TRY plant trait database - enhanced coverage and open access

Plant traits-the morphological, anatomical, physiological, biochemical and phenological characteristics of plants-determine how plants respond to environmental factors, affect other trophic levels, and influence ecosystem properties and their benefits and detriments to people. Plant trait data thus represent the basis for a vast area of research spanning from evolutionary biology, community and functional ecology, to biodiversity conservation, ecosystem and landscape management, restoration, biogeography and earth system modelling. Since its foundation in 2007, the TRY database of plant traits has grown continuously. It now provides unprecedented data coverage under an open access data policy and is the main plant trait database used by the research community worldwide. Increasingly, the TRY database also supports new frontiers of trait-based plant research, including the identification of data gaps and the subsequent mobilization or measurement of new data. To support this development, in this article we evaluate the extent of the trait data compiled in TRY and analyse emerging patterns of data coverage and representativeness. Best species coverage is achieved for categorical traits-almost complete coverage for 'plant growth form'. However, most traits relevant for ecology and vegetation modelling are characterized by continuous intraspecific variation and trait-environmental relationships. These traits have to be measured on individual plants in their respective environment. Despite unprecedented data coverage, we observe a humbling lack of completeness and representativeness of these continuous traits in many aspects. We, therefore, conclude that reducing data gaps and biases in the TRY database remains a key challenge and requires a coordinated approach to data mobilization and trait measurements. This can only be achieved in collaboration with other initiatives

TRY plant trait database - enhanced coverage and open access

Plant traits—the morphological, anatomical, physiological, biochemical and phenological characteristics of plants—determine how plants respond to environmental factors, affect other trophic levels, and influence ecosystem properties and their benefits and detriments to people. Plant trait data thus represent the basis for a vast area of research spanning from evolutionary biology, community and functional ecology, to biodiversity conservation, ecosystem and landscape management, restoration, biogeography and earth system modelling. Since its foundation in 2007, the TRY database of plant traits has grown continuously. It now provides unprecedented data coverage under an open access data policy and is the main plant trait database used by the research community worldwide. Increasingly, the TRY database also supports new frontiers of trait‐based plant research, including the identification of data gaps and the subsequent mobilization or measurement of new data. To support this development, in this article we evaluate the extent of the trait data compiled in TRY and analyse emerging patterns of data coverage and representativeness. Best species coverage is achieved for categorical traits—almost complete coverage for ‘plant growth form’. However, most traits relevant for ecology and vegetation modelling are characterized by continuous intraspecific variation and trait–environmental relationships. These traits have to be measured on individual plants in their respective environment. Despite unprecedented data coverage, we observe a humbling lack of completeness and representativeness of these continuous traits in many aspects. We, therefore, conclude that reducing data gaps and biases in the TRY database remains a key challenge and requires a coordinated approach to data mobilization and trait measurements. This can only be achieved in collaboration with other initiatives

Scintillation light detection in the 6-m drift-length ProtoDUNE Dual Phase liquid argon TPC

DUNE is a dual-site experiment for long-baseline neutrino oscillation studies, neutrino astrophysics and nucleon decay searches. ProtoDUNE Dual Phase (DP) is a 6  ×  6  ×  6 m 3 liquid argon time-projection-chamber (LArTPC) that recorded cosmic-muon data at the CERN Neutrino Platform in 2019-2020 as a prototype of the DUNE Far Detector. Charged particles propagating through the LArTPC produce ionization and scintillation light. The scintillation light signal in these detectors can provide the trigger for non-beam events. In addition, it adds precise timing capabilities and improves the calorimetry measurements. In ProtoDUNE-DP, scintillation and electroluminescence light produced by cosmic muons in the LArTPC is collected by photomultiplier tubes placed up to 7 m away from the ionizing track. In this paper, the ProtoDUNE-DP photon detection system performance is evaluated with a particular focus on the different wavelength shifters, such as PEN and TPB, and the use of Xe-doped LAr, considering its future use in giant LArTPCs. The scintillation light production and propagation processes are analyzed and a comparison of simulation to data is performed, improving understanding of the liquid argon properties

Separation of track- and shower-like energy deposits in ProtoDUNE-SP using a convolutional neural network

Liquid argon time projection chamber detector technology provides high spatial and calorimetric resolutions on the charged particles traversing liquid argon. As a result, the technology has been used in a number of recent neutrino experiments, and is the technology of choice for the Deep Underground Neutrino Experiment (DUNE). In order to perform high precision measurements of neutrinos in the detector, final state particles need to be effectively identified, and their energy accurately reconstructed. This article proposes an algorithm based on a convolutional neural network to perform the classification of energy deposits and reconstructed particles as track-like or arising from electromagnetic cascades. Results from testing the algorithm on data from ProtoDUNE-SP, a prototype of the DUNE far detector, are presented. The network identifies track- and shower-like particles, as well as Michel electrons, with high efficiency. The performance of the algorithm is consistent between data and simulation

Separation of track- and shower-like energy deposits in ProtoDUNE-SP using a convolutional neural network

Liquid argon time projection chamber detector technology provides high spatial and calorimetric resolutions on the charged particles traversing liquid argon. As a result, the technology has been used in a number of recent neutrino experiments, and is the technology of choice for the Deep Underground Neutrino Experiment (DUNE). In order to perform high precision measurements of neutrinos in the detector, final state particles need to be effectively identified, and their energy accurately reconstructed. This article proposes an algorithm based on a convolutional neural network to perform the classification of energy deposits and reconstructed particles as track-like or arising from electromagnetic cascades. Results from testing the algorithm on experimental data from ProtoDUNE-SP, a prototype of the DUNE far detector, are presented. The network identifies track- and shower-like particles, as well as Michel electrons, with high efficiency. The performance of the algorithm is consistent between experimental data and simulation

Conservação pós-colheita de melão Charentais tratado com 1-MCP e armazenado sob refrigeração e atmosfera modificada Postharvest conservation of charentais melons treated with 1-MCP and stored under refrigeration and modified atmosphere

Objetivando avaliar a vida útil pós-colheita de melão tipo Charentais (Cucumis melo L.) sob refrigeração, tratados com 1-MCP e associado ou não a atmosfera modificada (AM), foram conduzidos dois experimentos em laboratório da Embrapa Agroindústria Tropical, Fortaleza-CE. Os frutos foram provenientes da Agroindústria Nolem Comercial Importadora e Exportadora Ltda, localizada no Agropólo Mossoró Açu-RN. Os frutos foram tratados com 300 e 600 ppb de 1-MCP, em seguida, metade desses frutos foram embalados em filmes plásticos, mantendo-se frutos embalados sem aplicação de 1-MCP nas mesmas condições de armazenamento dos demais. Os melões foram armazenados por 21 dias sendo 14 dias (9±1ºC e 87±5% UR) + 7 dias (22±2ºC e 70±5% UR). Em função da aparência externa, a vida útil pós-colheita dos frutos armazenados sob atmosfera modificada, com ou sem tratamento inicial de 1-MCP foi de 21 dias, enquanto que dos frutos tratados inicialmente apenas com 1-MCP foi de 19 dias. A aplicação do 1-MCP proporcionou redução na atividade respiratória e na produção de etileno, e maior retenção da firmeza da polpa, menor perda de massa e melhor aparência externa quando associado a atmosfera modificada. A atmosfera modificada, isoladamente, foi eficiente em reduzir a perda de massa e manter melhor aparência externa.<br>Aiming to evaluate the postharvest shelf life of Charentais melon (Cucumis melo L.) stored under refrigeration, the fruits were treated with 1-MCP, associated or not with modified atmosphere (MAP). Two experiments were carried out at the laboratory of Embrapa Agroindústria Tropical in Fortaleza, Brazil, analyzing the chemical and physic quality characteristics. The fruits were obtained at the Agroindústria Nolem Comercial Importadora e Exportadora Ltda, located in Mossoró Açu, Brazil. The fruits were treated with 300 and 600 nL L-1 of 1-MCP, half of those were wrapped in plastic films, which were wrapped without the use of 1-MCP in the same conditions of storage. The melons were stored during 21 days, being 14 days (9±1ºC and 87±5% UR) + 7 days (22±2ºC and 70±5% UR). Based on external appearance, postharvest shelf life of the fruits stored under modified atmosphere, with or without initial treatment of 1-MCP was 21 days, while in fruits treated initially only with 1- MCP, it was 19 days. The application of the 1-MCP provided reduction in the respiratory activity and ethylene production, and higher flesh firmness retention, smaller weight loss and better external appearance, when associated the modified atmosphere. The modified atmosphere, separately, was efficient to reduce the weight loss and to maintain better external appearance