A symptom-related monitoring program following pulmonary embolism for the early detection of CTEPH: a prospective observational registry study

Background Chronic thromboembolic pulmonary hypertension (CTEPH) is a long-term complication following an acute pulmonary embolism (PE). It is frequently diagnosed at advanced stages which is concerning as delayed treatment has important implications for favourable clinical outcome. Performing a follow-up examination of patients diagnosed with acute PE regardless of persisting symptoms and using all available technical procedures would be both cost-intensive and possibly ineffective. Focusing diagnostic procedures therefore on only symptomatic patients may be a practical approach for detecting relevant CTEPH. This study aimed to evaluate if a follow-up program for patients with acute PE based on telephone monitoring of symptoms and further examination of only symptomatic patients could detect CTEPH. In addition, we investigated the role of cardiopulmonary exercise testing (CPET) as a diagnostic tool. Methods In a prospective cohort study all consecutive patients with newly diagnosed PE (n=170, 76 males, 94 females within 26 months) were recruited according to the inclusion and exclusion criteria. Patients were contacted via telephone and asked to answer standardized questions relating to symptoms. At the time of the final analysis 130 patients had been contacted. Symptomatic patients underwent a structured evaluation with echocardiography, CPET and complete work-up for CTEPH. Results 37.7%, 25.5% and 29.3% of the patients reported symptoms after three, six, and twelve months respectively. Subsequent clinical evaluation of these symptomatic patients saw 20.4%, 11.5% and 18.8% of patients at the respective three, six and twelve months time points having an echocardiography suggesting pulmonary hypertension (PH). CTEPH with pathological imaging and a mean pulmonary artery pressure (mPAP) ≥ 25 mm Hg at rest was confirmed in eight subjects. Three subjects with mismatch perfusion defects showed an exercise induced increase of PAP without increasing pulmonary artery occlusion pressure (PAOP). Two subjects with pulmonary hypertension at rest and one with an exercise induced increase of mPAP with normal PAOP showed perfusion defects without echocardiographic signs of PH but a suspicious CPET. Conclusion A follow-up program based on telephone monitoring of symptoms and further structured evaluation of symptomatic subjects can detect patients with CTEPH. CPET may serve as a complementary diagnostic tool

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

Predação de sementes de andiroba [Carapa guianensis Aubl. e Carapa procera DC. (Meliaceae)] por insetos na Amazônias

Os objetivos deste trabalho foram identificar os insetos associados à predação de sementes de Carapa guianensis e Carapa procera e avaliar o potencial de dano nas sementes por insetos e a ocorrência de estratificação vertical na predação de sementes de andiroba. O estudo foi realiado em plantios de C. guianensis e C. procera na Reserva Florestal Ducke, Manaus, Estado do Amazonas, Brasil. As coletas foram realizadas semanalmente no chão da floresta e mensalmente em três diferentes alturas (terços) da copa das árvores. Para avaliar o efeito da predação na germinação, 30 sementes não predadas e 30 sementes predadas coletadas mensalmente do chão da floresta foram colocadas para germinar por um período de um mês. Os resultados indicaram que Hypsipyla grandella e H. ferrealis (Lepidoptera, Pyralidae) foram as principais espécies de insetos associadas à predação das sementes de C. procera e C. guianensis, resultando em taxas de predação média de 39% a 61,96%, respectivamente. Observou-se estratificação vertical na predação dos frutos e sementes na copa das árvores de ambas as espécies de Carapa. A predação das sementes de C. procera e C. guianensis por Hypsipyla spp. reduziu o processo de germinação. Este estudo produziu informações sobre a associação entre C. procera e C. guianensis e as espécies de Hypsipyla

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

TRY plant trait database - enhanced coverage and open access

This article has 730 authors, of which I have only listed the lead author and myself as a representative of University of HelsinkiPlant 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.Peer reviewe

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

Common, low-frequency, rare, and ultra-rare coding variants contribute to COVID-19 severity

The combined impact of common and rare exonic variants in COVID-19 host genetics is currently insufficiently understood. Here, common and rare variants from whole-exome sequencing data of about 4000 SARS-CoV-2-positive individuals were used to define an interpretable machine-learning model for predicting COVID-19 severity. First, variants were converted into separate sets of Boolean features, depending on the absence or the presence of variants in each gene. An ensemble of LASSO logistic regression models was used to identify the most informative Boolean features with respect to the genetic bases of severity. The Boolean features selected by these logistic models were combined into an Integrated PolyGenic Score that offers a synthetic and interpretable index for describing the contribution of host genetics in COVID-19 severity, as demonstrated through testing in several independent cohorts. Selected features belong to ultra-rare, rare, low-frequency, and common variants, including those in linkage disequilibrium with known GWAS loci. Noteworthily, around one quarter of the selected genes are sex-specific. Pathway analysis of the selected genes associated with COVID-19 severity reflected the multi-organ nature of the disease. The proposed model might provide useful information for developing diagnostics and therapeutics, while also being able to guide bedside disease management. © 2021, The Author(s)