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The most common, severe cases of fetal and neonatal alloimmune thrombocytopenia among whites are caused by antibodies against human platelet antigen 1a (HPA-1a). The aims of this systematic review and meta-analysis are to determine the association between maternal HLA-DRB3*01:01 and: (1) HPA-1a-alloimmunization and (2) neonatal outcome in children born of HPA-1a-immunized women. A systematic literature search identified 4 prospective and 8 retrospective studies. Data were combined across studies to estimate pooled odds ratios (ORs) and the associated 95% confidence intervals (CIs). The population represented by the prospective studies was more than 150 000. In the prospective studies, there were 64 severely thrombocytopenic newborns (platelet count <50 × 109/L) of whom 3 had intracranial hemorrhage. The mothers of all 64 children were HLA-DRB3*01:01+. The number of severely thrombocytopenic children born of HPA-1a-alloimmunized women in the retrospective studies was 214; 205 of whom were born of HLA-DRB3*01:01+ women. For HLA-DRB3*01:01- women, the OR (95% CI) for alloimmunization was 0.05 (0.00-0.60), and for severe neonatal thrombocytopenia 0.08 (0.02-0.37). This meta-analysis demonstrates that the risk of alloimmunization and of having a child with severe thrombocytopenia are both very low for HPA-1a- women who are HLA-DRB3*01:01-.

BACKGROUND AND OBJECTIVES: In Caucasians, fetal/neonatal alloimmune thrombocytopenia (FNAIT) is most commonly due to maternal HPA-1a antibodies. HPA-1a typing followed by screening for anti-HPA-1a antibodies in HPA-1bb women may identify first pregnancies at risk. Our goal was to review results from previous published studies to examine whether the maternal antibody level to HPA-1a could be used to identify high-risk pregnancies. MATERIALS AND METHODS: The studies included were categorized by recruitment strategies: screening of unselected pregnancies or samples analyzed from known or suspected FNAIT patients. RESULTS: Three prospective studies reported results from screening programmes, and 10 retrospective studies focused on suspected cases of FNAIT. In 8 studies samples for antibody measurement, performed by the monoclonal antibody immobilization of platelet antigen (MAIPA) assay, and samples for determining fetal/neonatal platelet count were collected simultaneously. In these 8 studies, the maternal antibody level correlated with the risk of severe thrombocytopenia. The prospective studies reported high negative predictive values (88-95%), which would allow for the use of maternal anti-HPA-1a antibody level as a predictive tool in a screening setting, in order to identify cases at low risk for FNAIT. However, due to low positive predictive values reported in prospective as well as retrospective studies (54-97%), the maternal antibody level is less suited for the final diagnosis and for guiding antenatal treatment. CONCLUSION: HPA-1a antibody level has the potential to predict the severity of FNAIT.

Aerodynamic canopy height (ha) is the effective height of vegetation canopy for its influence on atmospheric fluxes and is a key parameter of surface-atmosphere coupling. However, methods to estimate ha from data are limited. This synthesis evaluates the applicability and robustness of the calculation of ha from eddy covariance momentum-flux data. At 69 forest sites, annual ha robustly predicted site-to-site and year-to-year differences in canopy heights (R2 = 0.88, 111 site-years). At 23 cropland/grassland sites, weekly ha successfully captured the dynamics of vegetation canopies over growing seasons (R2 > 0.70 in 74 site-years). Our results demonstrate the potential of flux-derived ha determination for tracking the seasonal, interannual, and/or decadal dynamics of vegetation canopies including growth, harvest, land use change, and disturbance. The large-scale and time-varying ha derived from flux networks worldwide provides a new benchmark for regional and global Earth system models and satellite remote sensing of canopy structure.

Aerodynamic canopy height (ha) is the effective height of vegetation canopy for its influence on atmospheric fluxes and is a key parameter of surface-atmosphere coupling. However, methods to estimate ha from data are limited. This synthesis evaluates the applicability and robustness of the calculation of ha from eddy covariance momentum-flux data. At 69 forest sites, annual ha robustly predicted site-to-site and year-to-year differences in canopy heights (R2 = 0.88, 111 site-years). At 23 cropland/grassland sites, weekly ha successfully captured the dynamics of vegetation canopies over growing seasons (R2 > 0.70 in 74 site-years). Our results demonstrate the potential of flux-derived ha determination for tracking the seasonal, interannual, and/or decadal dynamics of vegetation canopies including growth, harvest, land use change, and disturbance. The large-scale and time-varying ha derived from flux networks worldwide provides a new benchmark for regional and global Earth system models and satellite remote sensing of canopy structure.

Photosynthesis is the process by which plants harvest sunlight to produce sugars from carbon dioxide and water. It is the primary source of energy for all life on Earth; hence it is important to understand how this process responds to climate change and human impact. However, model-based estimates of gross primary production (GPP, output from photosynthesis) are highly uncertain, in particular over heavily managed agricultural areas. Recent advances in spectroscopy enable the space-based monitoring of sun-induced chlorophyll fluorescence (SIF) from terrestrial plants. Here we demonstrate that spaceborne SIF retrievals provide a direct measure of the GPP of cropland and grassland ecosystems. Such a strong link with crop photosynthesis is not evident for traditional remotely sensed vegetation indices, nor for more complex carbon cycle models. We use SIF observations to provide a global perspective on agricultural productivity. Our SIF-based crop GPP estimates are 50-75% higher than results from state-ofthe- art carbon cycle models over, for example, the US Corn Belt and the Indo-Gangetic Plain, implying that current models severely underestimate the role of management. Our results indicate that SIF data can help us improve our global models for more accurate projections of agricultural productivity and climate impact on crop yields. Extension of our approach to other ecosystems, along with increased observational capabilities for SIF in the near future, holds the prospect of reducing uncertainties in the modeling of the current and future carbon cycle.

Photosynthesis is the process by which plants harvest sunlight to produce sugars from carbon dioxide and water. It is the primary source of energy for all life on Earth; hence it is important to understand how this process responds to climate change and human impact. However, model-based estimates of gross primary production (GPP, output from photosynthesis) are highly uncertain, in particular over heavily managed agricultural areas. Recent advances in spectroscopy enable the space-based monitoring of sun-induced chlorophyll fluorescence (SIF) from terrestrial plants. Here we demonstrate that spaceborne SIF retrievals provide a direct measure of the GPP of cropland and grassland ecosystems. Such a strong link with crop photosynthesis is not evident for traditional remotely sensed vegetation indices, nor for more complex carbon cycle models. We use SIF observations to provide a global perspective on agricultural productivity. Our SIF-based crop GPP estimates are 50-75% higher than results from state-ofthe- art carbon cycle models over, for example, the US Corn Belt and the Indo-Gangetic Plain, implying that current models severely underestimate the role of management. Our results indicate that SIF data can help us improve our global models for more accurate projections of agricultural productivity and climate impact on crop yields. Extension of our approach to other ecosystems, along with increased observational capabilities for SIF in the near future, holds the prospect of reducing uncertainties in the modeling of the current and future carbon cycle.