Pervasive gaps in Amazonian ecological research

Biodiversity loss is one of the main challenges of our time,1,2 and attempts to address it require a clear un derstanding of how ecological communities respond to environmental change across time and space.3,4 While the increasing availability of global databases on ecological communities has advanced our knowledge of biodiversity sensitivity to environmental changes,5–7 vast areas of the tropics remain understudied.8–11 In the American tropics, Amazonia stands out as the world’s most diverse rainforest and the primary source of Neotropical biodiversity,12 but it remains among the least known forests in America and is often underrepre sented in biodiversity databases.13–15 To worsen this situation, human-induced modifications16,17 may elim inate pieces of the Amazon’s biodiversity puzzle before we can use them to understand how ecological com munities are responding. To increase generalization and applicability of biodiversity knowledge,18,19 it is thus crucial to reduce biases in ecological research, particularly in regions projected to face the most pronounced environmental changes. We integrate ecological community metadata of 7,694 sampling sites for multiple or ganism groups in a machine learning model framework to map the research probability across the Brazilian Amazonia, while identifying the region’s vulnerability to environmental change. 15%–18% of the most ne glected areas in ecological research are expected to experience severe climate or land use changes by 2050. This means that unless we take immediate action, we will not be able to establish their current status, much less monitor how it is changing and what is being lostinfo:eu-repo/semantics/publishedVersio

Reconstruction of interactions in the ProtoDUNE-SP detector with Pandora

The Pandora Software Development Kit and algorithm libraries provide pattern-recognition logic essential to the reconstruction of particle interactions in liquid argon time projection chamber detectors. Pandora is the primary event reconstruction software used at ProtoDUNE-SP, a prototype for the Deep Underground Neutrino Experiment far detector. ProtoDUNE-SP, located at CERN, is exposed to a charged-particle test beam. This paper gives an overview of the Pandora reconstruction algorithms and how they have been tailored for use at ProtoDUNE-SP. In complex events with numerous cosmic-ray and beam background particles, the simulated reconstruction and identification efficiency for triggered test-beam particles is above 80% for the majority of particle type and beam momentum combinations. Specifically, simulated 1 GeV/$c$ charged pions and protons are correctly reconstructed and identified with efficiencies of 86.1$\pm0.6$% and 84.1$\pm0.6$%, respectively. The efficiencies measured for test-beam data are shown to be within 5% of those predicted by the simulation.Comment: 39 pages, 19 figure

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

Pervasive gaps in Amazonian ecological research

Biodiversity loss is one of the main challenges of our time,1,2 and attempts to address it require a clear understanding of how ecological communities respond to environmental change across time and space.3,4 While the increasing availability of global databases on ecological communities has advanced our knowledge of biodiversity sensitivity to environmental changes,5,6,7 vast areas of the tropics remain understudied.8,9,10,11 In the American tropics, Amazonia stands out as the world's most diverse rainforest and the primary source of Neotropical biodiversity,12 but it remains among the least known forests in America and is often underrepresented in biodiversity databases.13,14,15 To worsen this situation, human-induced modifications16,17 may eliminate pieces of the Amazon's biodiversity puzzle before we can use them to understand how ecological communities are responding. To increase generalization and applicability of biodiversity knowledge,18,19 it is thus crucial to reduce biases in ecological research, particularly in regions projected to face the most pronounced environmental changes. We integrate ecological community metadata of 7,694 sampling sites for multiple organism groups in a machine learning model framework to map the research probability across the Brazilian Amazonia, while identifying the region's vulnerability to environmental change. 15%–18% of the most neglected areas in ecological research are expected to experience severe climate or land use changes by 2050. This means that unless we take immediate action, we will not be able to establish their current status, much less monitor how it is changing and what is being lost

Magnetic resonance imaging of cavernous sinus invasion by pituitary adenoma diagnostic criteria and surgical findings Ressonância magnética da invasão do seio cavernoso por adenomas hipofisários, critérios diagnósticos e achados cirúrgicos

This study used MRI to define preoperative imaging criteria for cavernous sinus invasion (CSI) by pituitary adenoma (PA). MR images of 103 patients with PA submitted to surgery (48 with CSI) were retrospectively reviewed. The following MR signs were studied and compared to intraoperative findings (the latter were considered the gold standard for CSI detection): presence of normal pituitary gland between the adenoma and CS, status of the CS venous compartments, CS size, CS lateral wall bulging, displacement of the intracavernous internal carotid artery (ICA) by adenoma, grade of parasellar extension (Knosp-Steiner classification) and percentage of intracavernous ICA encased by the tumor. Statistical analysis was performed using qui-square testing and sensitivity, specificity, positive predictive value (PPV) and negative predictive value (NPV) were obtained for each MR finding. The following signs have been found to represent accurate criteria for non-invasion of the CS: 1- normal pituitary gland interposed between the adenoma and the CS (PPV, 100%); 2- intact medial venous compartment (PPV, 100%); 3- percentage of encasement of the intracavernous ICA lower than 25% (NPV, 100%) and 4- medial intercarotid line not crossed by the tumor (NPV, 100%). Criteria for CSI were: 1- percentage of encasement of the intracavernous ICA higher than 45%; 2- occlusion of three or more CS venous compartments and 3- occlusion of the CS lateral venous compartment. The CS was very likely to be invaded if the inferior venous compartment was not detected (PPV. 92,8%), if the lateral intercarotid line was crossed (PPV. 96,1%) or if a bulging lateral dural wall of the CS was seen (PPV, 92,3%). The preoperative diagnosis of CSI by PA is extremely important since endocrinological remission is rarely obtained after microsurgery alone in patients with invasive tumors. The above mentioned MR imaging criteria may be useful in advising most of the patients preoperatively on the potential need for complimentary therapy after surgery.Este estudo utilizou exames de RM para definir critérios pré-operatórios de imagem para invasão do seio cavernoso (ISC) em adenomas hipofisários (AH). As imagens de RM de 103 pacientes com AH tratados cirurgicamente (48 com ISC) foram revisadas retrospectivamente. Os seguintes sinais de RM foram estudados e comparados aos achados intraoperatórios (considerados o padrão-ouro para invasão do seio cavernoso): presença de glândula hipofisária normal interposta entre o adenoma e o SC, situação dos compartimentos venosos do SC, tamanho do SC, abaulamento da parede lateral do SC, deslocamento da artéria carótida interna (ACI) intracavernosa pelo adenoma, grau de extensão paraselar (classificação de Knosp-Steiner) e porcentagem de envolvimento da ACI intracavernosa pelo tumor. A análise estatística foi realizada utilizando o teste de qui-quadrado e a sensibilidade, especificidade, valor preditivo positivo (VPP) e valor preditivo negativo (VPN) foram obtidos para cada critério de imagem. Os seguintes sinais representaram critérios precisos de ausência de invasão do SC: 1- presença de glândula hipofisária normal interposta (VPP de 100%); 2-compartimento venoso medial visível (VPP de 100%); 3-porcentagem de envolvimento da ACI intracavernosa inferior a 25% (VPN de 100%); 4-não cruzamento da linha intercarotídea medial pelo tumor (VPN de 100%). Os critérios definidos para invasão do SC foram: 1-porcentagem de envolvimento da ACI intracavernosa maior que 45%; 2-não visualização de 3 ou mais compartimentos venosos do SC; 3-não visualização do compartimento venoso lateral do SC. A presença de invasão do SC era muito sugestiva quando o compartimento venoso inferior não era visível (VPP de 92,8%), a linha intercarotídea lateral era cruzada (VPP de 96,1%) ou quando a parede lateral do seio cavernoso estava abaulada (VPP de 92,3%). O diagnóstico pré-operatório de ISC por adenomas hipofisários é extremamente importante, pois a remissão endócrina é raramente obtida em pacientes com tumores invasivos tratados apenas por microcirurgia. Os critérios de imagem acima mencionados podem ser úteis para alertar a maioria dos pacientes no pré-operatório da necessidade potencial de tratamento complementar adjuvante após a cirurgia