The Historic Square Foot Dataset – Outstanding small-scale richness in Swiss grasslands around the year 1900

Grasslands host a significant share of Europe's species diversity but are among the most threatened vegetation types of the continent. Resurvey studies can help to understand patterns and drivers of changes in grassland diversity and species composition. However, most resurveys are based on local or regional data, and hardly reach back more than eight decades. Here, we publish and describe the Historic Square Foot Dataset, comprising 580 0.09-m² and 43 1-m² vegetation plots carefully sampled between 1884 and 1931, covering a wide range of grassland types across Switzerland. We provide the plots as an open-access data set with coordinates, relocation accuracy and fractional aboveground biomass per vascular plant species. We assigned EUNIS habitat types to most plots. Mean vascular plant species richness in 0.09 m² was 19.7, with a maximum of 47. This is considerably more than the present-day world record of 43 species for this plot size. Historically, species richness did not vary with elevation, differing from the unimodal relationship found today. The data set provides unique insight into how grasslands in Central Europe looked more than 100 years ago, thus offering manifold options for studies on the development of grassland biodiversity and productivity.     

Regression to the Mean and Predictors of MRI Disease Activity in RRMS Placebo Cohorts - Is There a Place for Baseline-to-Treatment Studies in MS?

BackgroundGadolinium-enhancing (GD+) lesions and T2 lesions are MRI outcomes for phase-2 treatment trials in relapsing-remitting Multiple Sclerosis (RRMS). Little is known about predictors of lesion development and regression-to-the-mean, which is an important aspect in early baseline-to-treatment trials.ObjectivesTo quantify regression-to-the-mean and identify predictors of MRI lesion development in placebo cohorts.Methods21 Phase-2 and Phase-3 trials were identified by a systematic literature research. Random-effects meta-analyses were performed to estimate development of T2 and GD+ after 6 months (phase-2) or 2 years (phase-3). Predictors of lesion development were evaluated with mixed-effect meta-regression.ResultsThe mean number of GD+-lesions per scan was similar after 6 months (1.19, 95%CI: 0.87-1.51) and 2 years (1.19, 95%CI: 1.00-1.39). 39% of the patients were without new T2-lesion after 6 month and 19% after 2 years (95%CI: 12-25%). Mean number of baseline GD+-lesions was the best predictor for new lesions after 6 months.ConclusionBaseline GD-enhancing lesions predict evolution of Gd- and T2 lesions after 6 months and might be used to control for regression to the mean effects. Overall, proof-of-concept studies with a baseline to treatment design have to face a regression to 1.2 GD+lesions per scan within 6 months.     

Cardiac Autonomic Nervous System Activation and Metabolic Profile in Young Children: The ABCD Study

Background

In adults, increased sympathetic and decreased parasympathetic nervous system activity are associated with a less favorable metabolic profile. Whether this is already determined at early age is unknown. Therefore, we aimed to assess the association between autonomic nervous system activation and metabolic profile and its components in children at age of 5–6 years.

Methods

Cross-sectional data from an apparently healthy population (within the ABCD study) were collected at age 5–6 years in 1540 children. Heart rate (HR), respiratory sinus arrhythmia (RSA; parasympathetic activity) and pre-ejection period (PEP; sympathetic activity) were assessed during rest. Metabolic components were waist-height ratio (WHtR), systolic blood pressure (SBP), fasting triglycerides, glucose and HDL-cholesterol. Individual components, as well as a cumulative metabolic score, were analyzed.

Results

In analysis adjusted for child’s physical activity, sleep, anxiety score and other potential confounders, increased HR and decreased RSA were associated with higher WHtR (P< 0.01), higher SBP (p<0.001) and a higher cumulative metabolic score (HR: p < 0.001; RSA: p < 0.01). Lower PEP was only associated with higher SBP (p <0.05). Of all children, 5.6% had 3 or more (out of 5) adverse metabolic components; only higher HR was associated with this risk (per 10 bpm increase: OR = 1.56; p < 0.001).

Conclusions

This study shows that decreased parasympathetic activity is associated with central adiposity and higher SBP, indicative of increased metabolic risk, already at age 5–6 years.     

Cks1 is required for tumor cell proliferation but not sufficient to induce hematopoietic malignancies

The Cks1 component of the SCF(Skp2) complex is necessary for p27(Kip1) ubiquitylation and degradation. Cks1 expression is elevated in various B cell malignancies including Burkitt lymphoma and multiple myeloma. We have previously shown that loss of Cks1 results in elevated p27(Kip1) levels and delayed tumor development in a mouse model of Myc-induced B cell lymphoma. Surprisingly, loss of Skp2 in the same mouse model also resulted in elevated p27(Kip1) levels but exhibited no impact on tumor onset. This raises the possibility that Cks1 could have other oncogenic activities than suppressing p27(Kip1). To challenge this notion we have targeted overexpression of Cks1 to B cells using a conditional retroviral bone marrow transduction-transplantation system. Despite potent ectopic overexpression, Cks1 was unable to promote B cell hyperproliferation or B cell malignancies, indicating that Cks1 is not oncogenic when overexpressed in B cells. Since Skp2 overexpression can drive T-cell tumorigenesis or other cancers we also widened the quest for oncogenic activity of Cks1 by ubiquitously expressing Cks1 in hematopoetic progenitors. At variance with c-Myc overexpression, which caused acute myeloid leukemia, Cks1 overexpression did not induce myeloproliferation or leukemia. Therefore, despite being associated with a poor prognosis in various malignancies, sole Cks1 expression is insufficient to induce lymphoma or a myeloproliferative disease in vivo.     

The Environmental Foodprint of Obesity

Emissions of greenhouse gases (GHG) are linked to global warming and adverse climate changes. Meeting the needs of the increasing number of people on the planet presents a challenge for reducing total GHG burden. A further challenge may be the size of the average person on the planet and the increasing number of people with excess body weight. We used data on GHG emissions from various sources and estimated that obesity is associated with ~20% greater GHG emissions compared with the normal‐weight state. On a global scale, obesity contributes to an extra GHG emissions of ~49 megatons per year of CO₂ equivalent (CO₂eq) from oxidative metabolism due to greater metabolic demands, ~361 megatons per year of CO₂eq from food production processes due to increased food intake, and ~290 megatons per year of CO₂eq from automobile and air transportation due to greater body weight. Therefore, the total impact of obesity may be extra emissions of ~700 megatons per year of CO₂eq, which is about 1.6% of worldwide GHG emissions. Inasmuch as obesity is an important contributor to global GHG burden, strategies to reduce its prevalence should prioritize efforts to reduce GHG emissions. Accordingly, reducing obesity may have considerable benefits for both public health and the environment.     

Skp2B stimulates mammary gland development by inhibiting REA, the repressor of the estrogen receptor

Skp2B, an F-box protein of unknown function, is frequently overexpressed in breast cancer. In order to determine the function of Skp2B and whether it has a role in breast cancer, we performed a two-hybrid screen and established transgenic mice expressing Skp2B in the mammary glands. We found that Skp2B interacts with the repressor of estrogen receptor activity (REA) and that overexpression of Skp2B leads to a reduction in REA levels. In the mammary glands of MMTV-Skp2B mice, REA levels are also low. Our results show that in virgin transgenic females, Skp2B induces lobuloalveolar development and differentiation of the mammary glands normally observed during pregnancy. As this phenotype is identical to what was observed for REA heterozygote mice, our observations suggest that the Skp2B-REA interaction is physiologically relevant. However, in contrast to REA(+/-) mice, MMTV-Skp2B mice develop mammary tumors, suggesting that Skp2B affects additional proteins. These results indicate that the observed expression of Skp2B in breast cancer does contribute to tumorigenesis at least in part by modulating the activity of the estrogen receptor.     

Prediction of Genetic Values of Quantitative Traits in Plant Breeding Using Pedigree and Molecular Markers

The availability of dense molecular markers has made possible the use of genomic selection (GS) for plant breeding. However, the evaluation of models for GS in real plant populations is very limited. This article evaluates the performance of parametric and semiparametric models for GS using wheat (Triticum aestivum L.) and maize (Zea mays) data in which different traits were measured in several environmental conditions. The findings, based on extensive cross-validations, indicate that models including marker information had higher predictive ability than pedigree-based models. In the wheat data set, and relative to a pedigree model, gains in predictive ability due to inclusion of markers ranged from 7.7 to 35.7%. Correlation between observed and predictive values in the maize data set achieved values up to 0.79. Estimates of marker effects were different across environmental conditions, indicating that genotype × environment interaction is an important component of genetic variability. These results indicate that GS in plant breeding can be an effective strategy for selecting among lines whose phenotypes have yet to be observed.PEDIGREE-BASED prediction of genetic values based on the additive infinitesimal model (Fisher 1918) has played a central role in genetic improvement of complex traits in plants and animals. Animal breeders have used this model for predicting breeding values either in a mixed model (best linear unbiased prediction, BLUP) (Henderson 1984) or in a Bayesian framework (Gianola and Fernando 1986). More recently, plant breeders have incorporated pedigree information into linear mixed models for predicting breeding values (Crossa et al. 2006, 2007; Oakey et al. 2006; Burgueño et al. 2007; Piepho et al. 2007).The availability of thousands of genome-wide molecular markers has made possible the use of genomic selection (GS) for prediction of genetic values (Meuwissen et al. 2001) in plants (e.g., Bernardo and Yu 2007; Piepho 2009; Jannink et al. 2010) and animals (Gonzalez-Recio et al. 2008; VanRaden et al. 2008; Hayes et al. 2009; de los Campos et al. 2009a). Implementing GS poses several statistical and computational challenges, such as how models can cope with the curse of dimensionality, colinearity between markers, or the complexity of quantitative traits. Parametric (e.g., Meuwissen et al. 2001) and semiparametric (e.g., Gianola et al. 2006; Gianola and van Kaam 2008) methods address these problems differently.In standard genetic models, phenotypic outcomes, , are viewed as the sum of a genetic value, , and a model residual, ; that is, . In parametric models for GS, is described as a regression on marker covariates (j = 1,  …  , p molecular markers) of the form , such that(or , in matrix notation), where is the regression of on the jth marker covariate .Estimation of via multiple regression by ordinary least squares (OLS) is not feasible when p > n. A commonly used alternative is to estimate marker effects jointly using penalized methods such as ridge regression (Hoerl and Kennard 1970) or the Least Absolute Shrinkage and Selection Operator (LASSO) (Tibshirani 1996) or their Bayesian counterpart. This approach yields greater accuracy of estimated genetic values and can be coupled with geostatistical techniques commonly used in plant breeding to model multienvironments trials (Piepho 2009).In ridge regression (or its Bayesian counterpart) the extent of shrinkage is homogeneous across markers, which may not be appropriate if some markers are located in regions that are not associated with genetic variance, while markers in other regions may be linked to QTL (Goddard and Hayes 2007). To overcome this limitation, many authors have proposed methods that use marker-specific shrinkage. In a Bayesian setting, this can be implemented using priors of marker effects that are mixtures of scaled-normal densities. Examples of this are methods Bayes A and Bayes B of Meuwissen et al. (2001) and the Bayesian LASSO of Park and Casella (2008).An alternative to parametric regressions is to use semiparametric methods such as reproducing kernel Hilbert spaces (RKHS) regression (Gianola and van Kaam 2008). The Bayesian RKHS regression regards genetic values as random variables coming from a Gaussian process centered at zero and with a (co)variance structure that is proportional to a kernel matrix K (de los Campos et al. 2009b); that is, , where , are vectors of marker genotypes for the ith and jth individuals, respectively, and is a positive definite function evaluated in marker genotypes. In a finite-dimensional setting this amounts to modeling the vector of genetic values, , as multivariate normal; that is, where is a variance parameter. One of the most attractive features of RKHS regression is that the methodology can be used with almost any information set (e.g., covariates, strings, images, graphs). A second advantage is that with RKHS the model is represented in terms of n unknowns, which gives RKHS a great computational advantage relative to some parametric methods, especially when p ≫ n.This study presents an evaluation of several methods for GS, using two extensive data sets. One contains phenotypic records of a series of wheat trials and recently generated genomic data. The other data set pertains to international maize trials in which different traits were measured in maize lines evaluated under severe drought and well-watered conditions.     

Novel Techniques for Weak Alignment of Proteins in Solution Using Chemical Tags Coordinating Lanthanide Ions

A molecule with an anisotropic magnetic susceptibility is spontaneously aligned in a static magnetic field. Alignment of such a molecule yields residual dipolar couplings and pseudocontact shifts. Lanthanide ions have recently been successfully used to provide an anisotropic magnetic susceptibility in target molecules either by replacing a calcium ion with a lanthanide ion in calcium-binding proteins or by attaching an EDTA derivative to a cysteine residue via a disulfide bond. Here we describe a novel enantiomerically pure EDTA derived tag that aligns stronger due to its shorter linker and does not suffer from stereochemical diversity upon lanthanide complexation. We observed residual (15)N,(1)H-dipolar couplings of up to 8 Hz at 800 MHz induced by a single alignment tensor from this tag.     

The Drosophila ARF6-GEF Schizo controls commissure formation by regulating Slit

The CNS of bilateral symmetric organisms is characterized by intensive contralateral axonal connections. Genetic screens in Drosophila have identified only a few genes required for guiding commissural growth cones toward and across the midline. Two evolutionarily conserved signaling molecules, Netrin and Slit, are expressed in the CNS midline cells. Netrin acts primarily as an attractive signaling cue, whereas Slit mediates repulsive functions. Here, we describe a detailed analysis of the Drosophila gene schizo, which is required for commissure formation. schizo leads to a commissural phenotype reminiscent of netrin mutant embryos. Double-mutant analyses indicate that Netrin and Schizo act independently. The schizo mutant phenotype can be suppressed by either expressing netrin in the CNS midline cells or by a reduction of the slit gene dose, indicating that the balance of attractive and repulsive signaling is impaired in schizo mutants. Overexpression of the schizo RNA in the CNS midline using the GAL4/UAS system leads to a slit phenocopy, suggesting that schizo primarily antagonizes Slit signaling. This is further supported by cell type-specific rescue experiments. The schizo gene generates at least two proteins containing a conserved Sec7 and a pleckstrin homology domain (PH) characteristic for guanine nucleotide exchange factors (GEF) acting on ARF GTPases, which are known to regulate endocytosis. In support of the notion that schizo regulates Slit expression via endocytosis, we found that block of endocytosis leads to a schizo-like phenotype. We thus propose that the balance of the two signaling cues Netrin and Slit can be regulated, controlling membrane dynamics.