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It should now be possible to correctly predict the RhD phenotype in Chinese subjects. D(el) can also be designated as a particular weak D type.

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This prospective clinical trial revealed that routine determination of the fetal D status from maternal plasma is feasible. Noninvasive fetal RHD genotyping can be considered as sensitive as the traditional postnatal serologic assay.

The complexity of forest structures plays a crucial role in regulating forest ecosystem functions and strongly influences biodiversity. Yet, knowledge of the global patterns and determinants of forest structural complexity remains scarce. Using a stand structural complexity index based on terrestrial laser scanning, we quantify the structural complexity of boreal, temperate, subtropical and tropical primary forests. We find that the global variation of forest structural complexity is largely explained by annual precipitation and precipitation seasonality (R² = 0.89). Using the structural complexity of primary forests as benchmark, we model the potential structural complexity across biomes and present a global map of the potential structural complexity of the earth´s forest ecoregions. Our analyses reveal distinct latitudinal patterns of forest structure and show that hotspots of high structural complexity coincide with hotspots of plant diversity. Considering the mechanistic underpinnings of forest structural complexity, our results suggest spatially contrasting changes of forest structure with climate change within and across biomes.

The use of leukocyte-depleted blood components has become the standard therapy for multiply transfused patients during the past few years, as a measure to reduce the frequency of alloimmunization and refractoriness. We assessed frequency and causes of refractoriness, defined as a repeated 24-h post-transfusion platelet count below 20,000/microliters, in 145 consecutive patients who received three or more single-donor platelet concentrates during a 1-year period. Flow-cytometric detection of anti-platelet antibodies and a glycoprotein-specific ELISA were applied for the diagnosis of alloimmunization. Forty patients (27.6%) had at least one episode of refractoriness. In 25 of these 40 patients (62.5%), nonimmune factors (fever, sepsis, coagulopathy, splenomegaly) alone were the cause. In 15 refractory patients alloantibodies were detected. In seven patients (17.5%), alloimmunization alone caused an inadequate transfusion response, while in eight refractory patients (20.0%) alloimmunization and fever or sepsis were present. HLA antibodies were detected in 17 patients (11.7%); three patients (2%) had platelet-specific antibodies in addition to HLA antibodies; in two patients panreactive platelet antibodies were detectable. All 17 patients had a history of previous transfusions or pregnancy. We did not observe primary immunization in patients transfused exclusively with filtered (leukodepleted) blood products. Our data suggest that alloimmunization in patients with a negative risk history can be prevented by the exclusive use of leukodepleted blood components.

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Probability estimation for binary and multicategory outcome using logistic and multinomial logistic regression has a long-standing tradition in biostatistics. However, biases may occur if the model is misspecified. In contrast, outcome probabilities for individuals can be estimated consistently with machine learning approaches, including k-nearest neighbors (k-NN), bagged nearest neighbors (b-NN), random forests (RF), and support vector machines (SVM). Because machine learning methods are rarely used by applied biostatisticians, the primary goal of this paper is to explain the concept of probability estimation with these methods and to summarize recent theoretical findings. Probability estimation in k-NN, b-NN, and RF can be embedded into the class of nonparametric regression learning machines; therefore, we start with the construction of nonparametric regression estimates and review results on consistency and rates of convergence. In SVMs, outcome probabilities for individuals are estimated consistently by repeatedly solving classification problems. For SVMs we review classification problem and then dichotomous probability estimation. Next we extend the algorithms for estimating probabilities using k-NN, b-NN, and RF to multicategory outcomes and discuss approaches for the multicategory probability estimation problem using SVM. In simulation studies for dichotomous and multicategory dependent variables we demonstrate the general validity of the machine learning methods and compare it with logistic regression. However, each method fails in at least one simulation scenario. We conclude with a discussion of the failures and give recommendations for selecting and tuning the methods. Applications to real data and example code are provided in a companion article (doi:10.1002/bimj.201300077).