Rossby wave dynamics of the North Pacific extra-tropical response to El Niño: importance of the basic state in coupled GCMs

The extra-tropical response to El Nino in a "low" horizontal resolution coupled climate model, typical of the Intergovernmental Panel on Climate Change fourth assessment report simulations, is shown to have serious systematic errors. A high resolution configuration of the same model has a much improved response that is similar to observations. The errors in the low resolution model are traced to an incorrect representation of the atmospheric teleconnection mechanism that controls the extra-tropical sea surface temperatures (SSTs) during El Nino. This is due to an unrealistic atmospheric mean state, which changes the propagation characteristics of Rossby waves. These erroneous upper tropospheric circulation anomalies then induce erroneous surface circulation features over the North Pacific. The associated surface wind speed and direction errors create erroneous surface flux and upwelling anomalies which finally lead to the incorrect extra-tropical SST response to El Nino in the low resolution model. This highlights the sensitivity of the climate response to a single link in a chain of complex climatic processes. The correct representation of these processes in the high resolution model indicates the importance of horizontal resolution in resolving such processes

Flowering Time Diversification and Dispersal in Central Eurasian Wild Wheat Aegilops tauschii Coss.: Genealogical and Ecological Framework

Timing of flowering is a reproductive trait that has significant impact on fitness in plants. In contrast to recent advances in understanding the molecular basis of floral transition, few empirical studies have addressed questions concerning population processes of flowering time diversification within species. We analyzed chloroplast DNA genealogical structure of flowering time variation in central Eurasian wild wheat Aegilops tauschii Coss. using 200 accessions that represent the entire species range. Flowering time measured as days from germination to flowering varied from 144.0 to 190.0 days (average 161.3 days) among accessions in a common garden/greenhouse experiment. Subsequent genealogical and statistical analyses showed that (1) there exist significant longitudinal and latitudinal clines in flowering time at the species level, (2) the early-flowering phenotype evolved in two intraspecific lineages, (3) in Asia, winter temperature was an environmental factor that affected the longitudinal clinal pattern of flowering time variation, and (4) in Transcaucasus-Middle East, some latitudinal factors affected the geographic pattern of flowering time variation. On the basis of palaeoclimatic, biogeographic, and genetic evidence, the northern part of current species' range [which was within the temperate desert vegetation (TDV) zone at the Last Glacial Maximum] is hypothesized to have harbored species refugia. Postglacial southward dispersal from the TDV zone seems to have been driven by lineages that evolved short-flowering-time phenotypes through different genetic mechanisms in Transcaucasus-Middle East and Asia

TURBOMOLE: Modular program suite for ab initio quantum-chemical and condensed-matter simulations

TURBOMOLE is a collaborative, multi-national software development project aiming to provide highly efficient and stable computational tools for quantum chemical simulations of molecules, clusters, periodic systems, and solutions. The TURBOMOLE software suite is optimized for widely available, inexpensive, and resource-efficient hardware such as multi-core workstations and small computer clusters. TURBOMOLE specializes in electronic structure methods with outstanding accuracy–cost ratio, such as density functional theory including local hybrids and the random phase approximation (RPA), GW-Bethe–Salpeter methods, second-order Møller–Plesset theory, and explicitly correlated coupled-cluster methods. TURBOMOLE is based on Gaussian basis sets and has been pivotal for the development of many fast and low-scaling algorithms in the past three decades, such as integral-direct methods, fast multipole methods, the resolution-of-the-identity approximation, imaginary frequency integration, Laplace transform, and pair natural orbital methods. This review focuses on recent additions to TURBOMOLE’s functionality, including excited-state methods, RPA and Green’s function methods, relativistic approaches, high-order molecular properties, solvation effects, and periodic systems. A variety of illustrative applications along with accuracy and timing data are discussed. Moreover, available interfaces to users as well as other software are summarized. TURBOMOLE’s current licensing, distribution, and support model are discussed, and an overview of TURBOMOLE’s development workflow is provided. Challenges such as communication and outreach, software infrastructure, and funding are highlighted

TURBOMOLE: Modular program suite for ab initio quantum-chemical and condensed-matter simulations

TURBOMOLE is a collaborative, multi-national software development project aiming to provide highly efficient and stable computational tools for quantum chemical simulations of molecules, clusters, periodic systems, and solutions. The TURBOMOLE software suite is optimized for widely available, inexpensive, and resource-efficient hardware such as multi-core workstations and small computer clusters. TURBOMOLE specializes in electronic structure methods with outstanding accuracy–cost ratio, such as density functional theory including local hybrids and the random phase approximation (RPA), GW-Bethe–Salpeter methods, second-order Møller–Plesset theory, and explicitly correlated coupled-cluster methods. TURBOMOLE is based on Gaussian basis sets and has been pivotal for the development of many fast and low-scaling algorithms in the past three decades, such as integral-direct methods, fast multipole methods, the resolution-of-the-identity approximation, imaginary frequency integration, Laplace transform, and pair natural orbital methods. This review focuses on recent additions to TURBOMOLE’s functionality, including excited-state methods, RPA and Green’s function methods, relativistic approaches, high-order molecular properties, solvation effects, and periodic systems. A variety of illustrative applications along with accuracy and timing data are discussed. Moreover, available interfaces to users as well as other software are summarized. TURBOMOLE’s current licensing, distribution, and support model are discussed, and an overview of TURBOMOLE’s development workflow is provided. Challenges such as communication and outreach, software infrastructure, and funding are highlighted

In epithelial cancers, aberrant COL17A1 promoter methylation predicts its misexpression and increased invasion

Background: Metastasis is a leading cause of death among cancer patients. In the tumor microenvironment, altered levels of extracellular matrix proteins, such as collagens, can facilitate the first steps of cancer cell metastasis, including invasion into surrounding tissue and intravasation into the blood stream. However, the degree of misexpression of collagen genes in tumors remains understudied, even though this knowledge could greatly facilitate the development of cancer treatment options aimed at preventing metastasis. Methods: We systematically evaluate the expression of all 44 collagen genes in breast cancer and assess whether their misexpression provides clinical prognostic significance. We use immunohistochemistry on 150 ductal breast cancers and 361 cervical cancers and study DNA methylation in various epithelial cancers. Results: In breast cancer, various tests show that COL4A1 and COL4A2 overexpression and COL17A1 (BP180, BPAG2) underexpression provide independent prognostic strength (HR = 1.25, 95% CI = 1.17–1.34, p = 3.03 × 10; HR = 1.18, 95% CI = 1.11–1.25, p = 8.11 × 10; HR = 0.86, 95% CI = 0.81–0.92, p = 4.57 × 10; respectively). Immunohistochemistry on ductal breast cancers confirmed that the COL17A1 protein product, collagen XVII, is underexpressed. This strongly correlates with advanced stage, increased invasion, and postmenopausal status. In contrast, immunohistochemistry on cervical tumors showed that collagen XVII is overexpressed in cervical cancer and this is associated with increased local dissemination. Interestingly, consistent with the opposed direction of misexpression in these cancers, the COL17A1 promoter is hypermethylated in breast cancer and hypomethylated in cervical cancer. We also find that the COL17A1 promoter is hypomethylated in head and neck squamous cell carcinoma, lung squamous cell carcinoma, and lung adenocarcinoma, in all of which collagen XVII overexpression has previously been shown. Conclusions: Paradoxically, collagen XVII is underexpressed in breast cancer and overexpressed in cervical and other epithelial cancers. However, the COL17A1 promoter methylation status accurately predicts both the direction of misexpression and the increased invasive nature for five out of five epithelial cancers. This implies that aberrant epigenetic control is a key driver of COL17A1 gene misexpression and tumor cell invasion. These findings have significant clinical implications, suggesting that the COL17A1 promoter methylation status can be used to predict patient outcome. Moreover, epigenetic targeting of COL17A1 could represent a novel strategy to prevent metastasis in patients

Novel approach to analysing large data sets of personal sun exposure measurements

Personal sun exposure measurements provide important information to guide the development of sun awareness and disease prevention campaigns. We assess the scaling properties of personal ultraviolet radiation (pUVR) sun exposure measurements using the wavelet transform (WT) spectral analysis to process long-range, high-frequency personal recordings collected by electronic UVR dosimeters designed to measure erythemal UVR exposure. We analysed the sun exposure recordings of school children, farmers, marathon runners and outdoor workers in South Africa, and construction workers and work site supervisors in New Zealand. We found scaling behaviour in all the analysed pUVR data sets. We found that the observed scaling changes from uncorrelated to long-range correlated with increasing duration of sun exposure. Peaks in the WT spectra that we found suggest the existence of characteristic times in sun exposure behaviour that were to some extent universal across our data set. Our study also showed that WT measures enable group classification, as well as distinction between individual UVR exposures, otherwise unattainable by conventional statistical methods