Interior of Aenny Catzenstein's apartment; Onslo Court (?), London Architecture Residences Interior

Aenny Catzenstein is seen sitting on a sofa in the left corner of the picture

Interior of Aenny Catzenstein's apartment; Onslo Court (?), London Architecture Residences Interior

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Interior of Aenny Catzenstein's apartment; Onslo Court (?), London Architecture Residences Interior

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Interior of Aenny Catzenstein nee Gottschalk's cottage during WW II; Ascona, Switzerland Architecture Residences Interiors

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"Klo-KU-BA", Hans and Elizabeth Krakauer's first marital apartment in the basement of the Jewish Hospital; Hannover, Germany Architecture; Residences; Interior

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Eliciting and encoding expert knowledge on variable selection into classical or Bayesian species distribution models

The quality of species distribution models (SDMs) relies to a large degree on the quality of the input data, from bioclimatic indices to environmental and habitat descriptors (Austin, 2002). Recent reviews of SDM techniques, have sought to optimize predictive performance e.g. Elith et al., 2006. In general SDMs employ one of three approaches to variable selection. The simplest approach relies on the expert to select the variables, as in environmental niche models Nix, 1986 or a generalized linear model without variable selection (Miller and Franklin, 2002). A second approach explicitly incorporates variable selection into model fitting, which allows examination of particular combinations of variables. Examples include generalized linear or additive models with variable selection (Hastie et al. 2002); or classification trees with complexity or model based pruning (Breiman et al., 1984, Zeileis, 2008). A third approach uses model averaging, to summarize the overall contribution of a variable, without considering particular combinations. Examples include neural networks, boosted or bagged regression trees and Maximum Entropy as compared in Elith et al. 2006. Typically, users of SDMs will either consider a small number of variable sets, via the first approach, or else supply all of the candidate variables (often numbering more than a hundred) to the second or third approaches. Bayesian SDMs exist, with several methods for eliciting and encoding priors on model parameters (see review in Low Choy et al. 2010). However few methods have been published for informative variable selection; one example is Bayesian trees (O’Leary 2008). Here we report an elicitation protocol that helps makes explicit a priori expert judgements on the quality of candidate variables. This protocol can be flexibly applied to any of the three approaches to variable selection, described above, Bayesian or otherwise. We demonstrate how this information can be obtained then used to guide variable selection in classical or machine learning SDMs, or to define priors within Bayesian SDMs.

Defining the role of common variation in the genomic and biological architecture of adult human height

Using genome-wide data from 253,288 individuals, we identified 697 variants at genome-wide significance that together explained one-fifth of the heritability for adult height. By testing different numbers of variants in independent studies, we show that the most strongly associated approximately 2,000, approximately 3,700 and approximately 9,500 SNPs explained approximately 21%, approximately 24% and approximately 29% of phenotypic variance. Furthermore, all common variants together captured 60% of heritability. The 697 variants clustered in 423 loci were enriched for genes, pathways and tissue types known to be involved in growth and together implicated genes and pathways not highlighted in earlier efforts, such as signaling by fibroblast growth factors, WNT/beta-catenin and chondroitin sulfate-related genes. We identified several genes and pathways not previously connected with human skeletal growth, including mTOR, osteoglycin and binding of hyaluronic acid. Our results indicate a genetic architecture for human height that is characterized by a very large but finite number (thousands) of causal variants.

Metal-organic framework structures - how closely are they related to classical inorganic structures?

Metal-organic frameworks (MOFs) have emerged as an important family of compounds for which new properties are increasingly being found. The potential for such compounds appears to be immense, especially in catalysis, sorption and separation processes. In order to appreciate the properties and to design newer frameworks it is necessary to understand the structures from a fundamental perspective. The use of node, net and vertex symbols has helped in simplifying some of the complex MOF structures. Many MOF structures are beginning to be described as derived from inorganic structures. In this tutorial review, we have provided the basics of the node, the net and the vertex symbols and have explained some of the MOF structures. In addition, we have also attempted to provide some leads towards designing newer structures/topologies.

Inference of the genetic architecture underlying BMI and height with the use of 20,240 sibling pairs

Evidence that complex traits are highly polygenic has been presented by population-based genome-wide association studies (GWASs) through the identification of many significant variants, as well as by family-based de novo sequencing studies indicating that several traits have a large mutational target size. Here, using a third study design, we show results consistent with extreme polygenicity for body mass index (BMI) and height. On a sample of 20,240 siblings (from 9,570 nuclear families), we used a within-family method to obtain narrow-sense heritability estimates of 0.42 (SE = 0.17, p = 0.01) and 0.69 (SE = 0.14, p = 6 x 10(-)(7)) for BMI and height, respectively, after adjusting for covariates. The genomic inflation factors from locus-specific linkage analysis were 1.69 (SE = 0.21, p = 0.04) for BMI and 2.18 (SE = 0.21, p = 2 x 10(-10)) for height. This inflation is free of confounding and congruent with polygenicity, consistent with observations of ever-increasing genomic-inflation factors from GWASs with large sample sizes, implying that those signals are due to true genetic signals across the genome rather than population stratification. We also demonstrate that the distribution of the observed test statistics is consistent with both rare and common variants underlying a polygenic architecture and that previous reports of linkage signals in complex traits are probably a consequence of polygenic architecture rather than the segregation of variants with large effects. The convergent empirical evidence from GWASs, de novo studies, and within-family segregation implies that family-based sequencing studies for complex traits require very large sample sizes because the effects of causal variants are small on average.

Marker development for genes controlling mango tree architecture

Identification of candidate genes controlling, mango tree architecture.

Exterior view of expressionist painter Emil Luethy's home; Gotthardstr. 41 (Basel?) Architecture Residences Exterior

Handwritten on verso: zu obest mein Atelier (My studio on the top floor)

Rudolf and Antoinette von Kahler in living room; Vienna-Doebling Architecture; Residences

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Albert Einstein's Weekend Home; Caputh, near Berlin Architecture; Residences; Exterior

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Deck; Albert Einstein's Weekend Home; Caputh, near Berlin Architecture; Residences; Exterior

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