The performance of the EU-Rotate_N model in predicting the growth and nitrogen uptake of rotations of field vegetable crops in a Mediterranean environment

The EU-Rotate_N model was developed as a tool to estimate the growth and nitrogen (N) uptake of vegetable crop rotations across a wide range of European climatic conditions and to assess the economic and environmental consequences of alternative management strategies. The model has been evaluated under field conditions in Germany and Norway and under greenhouse conditions in China. The present work evaluated the model using Italian data to evaluate its performance in a warm and dry environment. Data were collected from four 2-year field rotations, which included lettuce (Lactuca sativa L.), fennel (Foeniculum vulgare Mill.), spinach (Spinacia oleracea L.), broccoli (Brassica oleracea L. var. italica Plenck) and white cabbage (B. oleracea convar. capitata var. alba L.); each rotation used three different rates of N fertilizer (average recommended N1, assumed farmer's practice N2=N1+0·3×N1 and a zero control N0). Although the model was not calibrated prior to running the simulations, results for above-ground dry matter biomass, crop residue biomass, crop N concentration and crop N uptake were promising. However, soil mineral N predictions to 0·6 m depth were poor. The main problem with the prediction of the test variables was the poor ability to capture N mineralization in some autumn periods and an inappropriate parameterization of fennel. In conclusion, the model performed well, giving results comparable with other bio-physical process simulation models, but for more complex crop rotations. The model has the potential for application in Mediterranean environments for field vegetable production

New distal marker closely linked to the fragile X locus

We have isolated II-10, a new X-chromosomal probe that identifies a highly informative two-allele TaqI restriction fragment length polymorphism at locus DXS466. Using somatic cell hybrids containing distinct portions of the long arm of the X chromosome, we could localize DXS466 between DXS296 and DXS304, both of which are closely linked distal markers for fragile X. This regional localization was supported by the analysis, in fragile X families, of recombination events between these three loci, the fragile X locus and locus DXS52, the latter being located at a more distal position. DXS466 is closely linked to the fragile X locus with a peak lod score of 7.79 at a recombination fraction of 0.02. Heterozygosity of DXS466 is approximately 50%. Its close proximity and relatively high informativity make DXS466 a valuable new diagnostic DNA marker for fragile X

Genome-Wide Identification of Expression Quantitative Trait Loci (eQTLs) in Human Heart.

In recent years genome-wide association studies (GWAS) have uncovered numerous chromosomal loci associated with various electrocardiographic traits and cardiac arrhythmia predisposition. A considerable fraction of these loci lie within inter-genic regions. The underlying trait-associated variants likely reside in regulatory regions and exert their effect by modulating gene expression. Hence, the key to unraveling the molecular mechanisms underlying these cardiac traits is to interrogate variants for association with differential transcript abundance by expression quantitative trait locus (eQTL) analysis. In this study we conducted an eQTL analysis of human heart. For a total of 129 left ventricular samples that were collected from non-diseased human donor hearts, genome-wide transcript abundance and genotyping was determined using microarrays. Each of the 18,402 transcripts and 897,683 SNP genotypes that remained after pre-processing and stringent quality control were tested for eQTL effects. We identified 771 eQTLs, regulating 429 unique transcripts. Overlaying these eQTLs with cardiac GWAS loci identified novel candidates for studies aimed at elucidating the functional and transcriptional impact of these loci. Thus, this work provides for the first time a comprehensive eQTL map of human heart: a powerful and unique resource that enables systems genetics approaches for the study of cardiac traits

Not All Scale-Free Networks Are Born Equal: The Role of the Seed Graph in PPI Network Evolution

The (asymptotic) degree distributions of the best-known “scale-free” network models are all similar and are independent of the seed graph used; hence, it has been tempting to assume that networks generated by these models are generally similar. In this paper, we observe that several key topological features of such networks depend heavily on the specific model and the seed graph used. Furthermore, we show that starting with the “right” seed graph (typically a dense subgraph of the protein–protein interaction network analyzed), the duplication model captures many topological features of publicly available protein–protein interaction networks very well

Thoracic and Lumbar Vertebral Bone Mineral Density Changes in a Natural Occurring Dog Model of Diffuse Idiopathic Skeletal Hyperostosis

Ankylosing spinal disorders can be associated with alterations in vertebral bone mineral density (BMD). There is however controversy about vertebral BMD in patients wuse idiopathic skeletal hyperostosis (DISH). DISH in Boxer dogs has been considered a natural occurring disease model for DISH in people. The purpose of this study was to compare vertebral BMD between Boxers with and without DISH. Fifty-nine Boxers with (n=30) or without (n=29) DISH that underwent computed tomography were included. Vertebral BMD was calculated for each thoracic and lumbar vertebra by using an earlier reported and validated protocol. For each vertebral body, a region of interest was drawn on the axial computed tomographic images at three separate locations: immediately inferior to the superior end plate, in the middle of the vertebral body, and superior to the inferior end plate. Values from the three axial slices were averaged to give a mean Hounsfield Unit value for each vertebral body. Univariate statistical analysis was performed to identify factors to be included in a multivariate model. The multivariate model including all dogs demonstrated that vertebral DISH status (Coefficient 24.63; 95% CI 16.07 to 33.19; p <0.001), lumbar vertebrae (Coefficient -17.25; 95% CI -23.42 to -11.09; p < 0.01), and to a lesser extent higher age (Coefficient -0.56; 95% CI -1.07 to -0.05; p = 0.03) were significant predictors for vertebral BMD. When the multivariate model was repeated using only dogs with DISH, vertebral DISH status (Coefficient 20.67; 95% CI, 10.98 to 30.37; p < 0.001) and lumbar anatomical region (Coefficient -38.24; 95% CI, -47.75 to -28.73; p < 0.001) were again predictors for vertebral BMD but age was not. The results of this study indicate that DISH can be associated with decreased vertebral BMD. Further studies are necessary to evaluate the clinical importance and pathophysiology of this finding

Dynamic Collective Entity Representations for Entity Ranking

Entity ranking, i.e., successfully positioning a relevant entity at the top of the ranking for a given query, is inherently difficult due to the potential mismatch between the entity's description in a knowledge base, and the way people refer to the entity when searching for it. To counter this issue we propose a method for constructing dynamic collective entity representations. We collect entity descriptions from a variety of sources and combine them into a single entity representation by learning to weight the content from different sources that are associated with an entity for optimal retrieval effectiveness. Our method is able to add new descriptions in real time and learn the best representation as time evolves so as to capture the dynamics of how people search entities. Incorporating dynamic description sources into dynamic collective entity representations improves retrieval effectiveness by 7% over a state-of-the-art learning to rank baseline. Periodic retraining of the ranker enables higher ranking effectiveness for dynamic collective entity representations

Clinically acceptable segmentation of Organs at Risk in cervical cancer radiation treatment from clinically available annotations

Deep learning models benefit from training with a large dataset (labeled or unlabeled). Following this motivation, we present an approach to learn a deep learning model for the automatic segmentation of Organs at Risk (OARs) in cervical cancer radiation treatment from a large clinically available dataset of Computed Tomography (CT) scans containing data inhomogeneity, label noise, and missing annotations. We employ simple heuristics for automatic data cleaning to minimize data inhomogeneity and label noise. Further, we develop a semi-supervised learning approach utilizing a teacher-student setup, annotation imputation, and uncertainty-guided training to learn in presence of missing annotations. Our experimental results show that learning from a large dataset with our approach yields a significant improvement in the test performance despite missing annotations in the data. Further, the contours generated from the segmentation masks predicted by our model are found to be equally clinically acceptable as manually generated contours

An information-theoretic framework for semantic-multimedia retrieval

This article is set in the context of searching text and image repositories by keyword. We develop a unified probabilistic framework for text, image, and combined text and image retrieval that is based on the detection of keywords (concepts) using automated image annotation technology. Our framework is deeply rooted in information theory and lends itself to use with other media types. We estimate a statistical model in a multimodal feature space for each possible query keyword. The key element of our framework is to identify feature space transformations that make them comparable in complexity and density. We select the optimal multimodal feature space with a minimum description length criterion from a set of candidate feature spaces that are computed with the average-mutual-information criterion for the text part and hierarchical expectation maximization for the visual part of the data. We evaluate our approach in three retrieval experiments (only text retrieval, only image retrieval, and text combined with image retrieval), verify the framework’s low computational complexity, and compare with existing state-of-the-art ad-hoc models