Dissociating memory networks in early Alzheimer's disease and frontotemporal lobar degeneration - a combined study of hypometabolism and atrophy

Introduction: We aimed at dissociating the neural correlates of memory disorders in Alzheimer’s disease (AD) and frontotemporal lobar degeneration (FTLD). Methods: We included patients with AD (n = 19, 11 female, mean age 61 years) and FTLD (n = 11, 5 female, mean age 61 years) in early stages of their diseases. Memory performance was assessed by means of verbal and visual memory subtests from the Wechsler Memory Scale (WMS-R), including forgetting rates. Brain glucose utilization was measured by [18F]fluorodeoxyglucose positron emission tomography (FDG-PET) and brain atrophy by voxel-based morphometry (VBM) of T1-weighted magnetic resonance imaging (MRI) scans. Using a whole brain approach, correlations between test performance and imaging data were computed separately in each dementia group, including a group of control subjects (n = 13, 6 female, mean age 54 years) in both analyses. The three groups did not differ with respect to education and gender. Results: Patients in both dementia groups generally performed worse than controls, but AD and FTLD patients did not differ from each other in any of the test parameters. However, memory performance was associated with different brain regions in the patient groups, with respect to both hypometabolism and atrophy: Whereas in AD patients test performance was mainly correlated with changes in the parieto-mesial cortex, performance in FTLD patients was correlated with changes in frontal cortical as well as subcortical regions. There were practically no overlapping regions associated with memory disorders in AD and FTLD as revealed by a conjunction analysis. Conclusion: Memory test performance may not distinguish between both dementia syndromes. In clinical practice, this may lead to misdiagnosis of FTLD patients with poor memory performance. Nevertheless, memory problems are associated with almost completely different neural correlates in both dementia syndromes. Obviously, memory functions are carried out by distributed networks which break down in brain degeneration

Space Charge Transfer in Hybrid Inorganic/Organic Systems

We discuss density functional theory calculations of hybrid inorganic/organic systems (HIOS) that explicitly include the global effects of doping (i.e. position of the Fermi level) and the formation of a space-charge layer. For the example of tetrafluoro-tetracyanoquinodimethane (F4TCNQ) on the ZnO(000$\bar{1}$) surface we show that the adsorption energy and electron transfer depend strongly on the ZnO doping. The associated work function changes are large, for which the formation of space-charge layers is the main driving force. The prominent doping effects are expected to be quite general for charge-transfer interfaces in HIOS and important for device design

Wüchsigkeit und physiologische Aktivität der Rebe in Abhängigkeit von verschiedenen weinbaulichen Bewirtschaftungssystemen

Based on a field trial, the impact of three different viticultural management strategies on vigour and grapevine physiology of Vitis vinifera cv. Riesling was compared. The vines were planted in 1991 at Geisenheim (Rheingau, Germany) and three different management strategies i.e. integrated (code of good practice), organic (European Union Regulation 834/07 and ECOVIN standard) and biodynamic (European Union Regulation 834/07 and DEMETER standard) were established in 2006. Even though all treatments received the same level of nutrients and water a decline in vigour, expressed as lateral growth, was observed for the organic and biodynamic treatment during three seasons (2010 to 2012). During dryer conditions (2011) a reduction of physiological activity expressed as stomatal conductance gs, assimilation rate A and transpiration E two weeks after full-bloom and a reduction in pre-dawn water potential at veraison were assessed for the biological treatments. In 2012 under wetter growing conditions neither differences in physiological activity nor in pre-dawn water potential were observed. Therefore changes in physiological activity and pre-dawn water potential are just partially responsible for the reduced vigour in the biological treatments

Dissecting grain yield pathways and their interactions with grain dry matter content by a two-step correlation approach with maize seedling transcriptome

<p>Abstract</p> <p>Background</p> <p>The importance of maize for human and animal nutrition, but also as a source for bio-energy is rapidly increasing. Maize yield is a quantitative trait controlled by many genes with small effects, spread throughout the genome. The precise location of the genes and the identity of the gene networks underlying maize grain yield is unknown. The objective of our study was to contribute to the knowledge of these genes and gene networks by transcription profiling with microarrays.</p> <p>Results</p> <p>We assessed the grain yield and grain dry matter content (an indicator for early maturity) of 98 maize hybrids in multi-environment field trials. The gene expression in seedlings of the parental inbred lines, which have four different genetic backgrounds, was assessed with genome-scale oligonucleotide arrays. We identified genes associated with grain yield and grain dry matter content using a newly developed two-step correlation approach and found overlapping gene networks for both traits. The underlying metabolic pathways and biological processes were elucidated. Genes involved in sucrose degradation and glycolysis, as well as genes involved in cell expansion and endocycle were found to be associated with grain yield.</p> <p>Conclusions</p> <p>Our results indicate that the capability of providing energy and substrates, as well as expanding the cell at the seedling stage, highly influences the grain yield of hybrids. Knowledge of these genes underlying grain yield in maize can contribute to the development of new high yielding varieties.</p

Chemical composition of field grown radish (Raphanus sativus L. var. sativus) as influenced by season and moderately reduced water supply

Seasonal variations in water availability as increasingly provoked by climate change pose severe challenges for vegetable production, particularly for crops requiring reliable and high water supply for achieving satisfactory quality. In contrast to most previous studies applying severe water deficits, we examined the effects of moderate water deficits on the chemical composition of red radish roots during three consecutive years with variable climatic conditions. Radish were cultivated in open field, applying two different water supply treatments and following a randomized block design comprising four sets of six plots each. The resulting water reductions of 3-20&nbsp;% led to a significant increase of dry matter-based myo-inositol levels, whereas those of selected minerals and trace elements, phenolics and glucosinolates decreased. Anthocyanin levels remained unchanged. Fresh-matter related levels of most analytes increased upon reduced water treatments due to higher dry matter contents. While pigment levels in radish remained unchanged, mild water deficit affected other quality-related parameters such as pungency-related glucosinolates

Scattering of hydrogen molecules from a reactive surface: Strong off-specular and rotationally inelastic diffraction

Six-dimensional quantum dynamical calculations of the scattering of H_2 from a Pd(100) surface using a potential energy surface derived from density-functional theory calculations are presented. Due to the corrugation and anisotropy of the PES strong off-specular and rotationally inelastic diffraction is found. The dependence of the diffraction intensitities on the incident kinetic energy is closely examined. In particular we focus on the quantum oscillations for normal and off-normal incidence.Comment: RevTeX, 5 pages, 5 figures, to appear in Chem. Phys. Let

LitInspector: literature and signal transduction pathway mining in PubMed abstracts

LitInspector is a literature search tool providing gene and signal transduction pathway mining within NCBI's PubMed database. The automatic gene recognition and color coding increases the readability of abstracts and significantly speeds up literature research. A main challenge in gene recognition is the resolution of homonyms and rejection of identical abbreviations used in a ‘non-gene’ context. LitInspector uses automatically generated and manually refined filtering lists for this purpose. The quality of the LitInspector results was assessed with a published dataset of 181 PubMed sentences. LitInspector achieved a precision of 96.8%, a recall of 86.6% and an F-measure of 91.4%. To further demonstrate the homonym resolution qualities, LitInspector was compared to three other literature search tools using some challenging examples. The homonym MIZ-1 (gene IDs 7709 and 9063) was correctly resolved in 87% of the abstracts by LitInspector, whereas the other tools achieved recognition rates between 35% and 67%. The LitInspector signal transduction pathway mining is based on a manually curated database of pathway names (e.g. wingless type), pathway components (e.g. WNT1, FZD1), and general pathway keywords (e.g. signaling cascade). The performance was checked for 10 randomly selected genes. Eighty-two per cent of the 38 predicted pathway associations were correct. LitInspector is freely available at http://www.litinspector.org/

Stacked ensembles on basis of parentage information can predict hybrid performance with an accuracy comparable to marker-based GBLUP

Testcross factorials in newly established hybrid breeding programs are often highly unbalanced, incomplete, and characterized by predominance of special combining ability (SCA) over general combining ability (GCA). This results in a low efficiency of GCA-based selection. Machine learning algorithms might improve prediction of hybrid performance in such testcross factorials, as they have been successfully applied to find complex underlying patterns in sparse data. Our objective was to compare the prediction accuracy of machine learning algorithms to that of GCA-based prediction and genomic best linear unbiased prediction (GBLUP) in six unbalanced incomplete factorials from hybrid breeding programs of rapeseed, wheat, and corn. We investigated a range of machine learning algorithms with three different types of predictor variables: (a) information on parentage of hybrids, (b) in addition hybrid performance of crosses of the parental lines with other crossing partners, and (c) genotypic marker data. In two highly incomplete and unbalanced factorials from rapeseed, in which the SCA variance contributed considerably to the genetic variance, stacked ensembles of gradient boosting machines based on parentage information outperformed GCA prediction. The stacked ensembles increased prediction accuracy from 0.39 to 0.45, and from 0.48 to 0.54 compared to GCA prediction. The prediction accuracy reached by stacked ensembles without marker data reached values comparable to those of GBLUP that requires marker data. We conclude that hybrid prediction with stacked ensembles of gradient boosting machines based on parentage information is a promising approach that is worth further investigations with other data sets in which SCA variance is high

Haplotype blocks for genomic prediction: a comparative evaluation in multiple crop datasets

In modern plant breeding, genomic selection is becoming the gold standard for selection of superior genotypes. The basis for genomic prediction models is a set of phenotyped lines along with their genotypic profile. With high marker density and linkage disequilibrium (LD) between markers, genotype data in breeding populations tends to exhibit considerable redundancy. Therefore, interest is growing in the use of haplotype blocks to overcome redundancy by summarizing co-inherited features. Moreover, haplotype blocks can help to capture local epistasis caused by interacting loci. Here, we compared genomic prediction methods that either used single SNPs or haplotype blocks with regards to their prediction accuracy for important traits in crop datasets. We used four published datasets from canola, maize, wheat and soybean. Different approaches to construct haplotype blocks were compared, including blocks based on LD, physical distance, number of adjacent markers and the algorithms implemented in the software “Haploview” and “HaploBlocker”. The tested prediction methods included Genomic Best Linear Unbiased Prediction (GBLUP), Extended GBLUP to account for additive by additive epistasis (EGBLUP), Bayesian LASSO and Reproducing Kernel Hilbert Space (RKHS) regression. We found improved prediction accuracy in some traits when using haplotype blocks compared to SNP-based predictions, however the magnitude of improvement was very trait- and model-specific. Especially in settings with low marker density, haplotype blocks can improve genomic prediction accuracy. In most cases, physically large haplotype blocks yielded a strong decrease in prediction accuracy. Especially when prediction accuracy varies greatly across different prediction models, prediction based on haplotype blocks can improve prediction accuracy of underperforming models. However, there is no “best” method to build haplotype blocks, since prediction accuracy varied considerably across methods and traits. Hence, criteria used to define haplotype blocks should not be viewed as fixed biological parameters, but rather as hyperparameters that need to be adjusted for every dataset

Short- and long-term evaluation of disease-specific symptoms and quality of life following uterine artery embolization of fibroids

Background: The purpose of this study is to evaluate uterine artery embolization (UAE) for the management of symptomatic uterine leiomyomas regarding changes in quality of life after treatment in a large patient collective. This study retrospectively analyzed prospectively acquired standardized questionnaires of patients treated with UAE. Clinical success was evaluated before and after embolization. Patients were stratified into short- (7 months) follow-up groups depending on the time of completion of the post-interventional questionnaire. Uterine leiomyomas were furthermore divided into small (= 10 cm) tumors based on the diameter of the dominant fibroid. Results: A total of 245 patients were included into the final data analysis. The Kaplan-Meier analysis showed a cumulative clinical success rate of 75.8% after 70 months until the end of follow-up (9.9 years). All questionnaire subscales showed a highly significant clinical improvement from baseline to short- and long-term follow-up (p = 10 cm who had a twofold higher probability of re-intervention in the Cox-regression model. Conclusions: UAE is an effective treatment method for symptomatic fibroids that leads to quick relief of fibroid-related symptoms with marked improvement of quality of life and is associated with a low risk for re-interventions. Patients with small fibroids tend to show a better response to UAE compared to patients with large fibroids