A Flux-Limited Sample of z~1 Ly-alpha Emitting Galaxies in the CDFS

We describe a method for obtaining a flux-limited sample of Ly-alpha emitters from GALEX grism data. We show that the multiple GALEX grism images can be converted into a three-dimensional (two spatial axes and one wavelength axis) data cube. The wavelength slices may then be treated as narrowband images and searched for emission-line galaxies. For the GALEX NUV grism data, the method provides a Ly-alpha flux-limited sample over the redshift range z=0.67-1.16. We test the method on the Chandra Deep Field South field, where we find 28 Ly-alpha emitters with faint continuum magnitudes (NUV>22) that are not present in the GALEX pipeline sample. We measure the completeness by adding artificial emitters and measuring the fraction recovered. We find that we have an 80% completeness above a Ly-alpha flux of 10^-15 erg/cm^2/s. We use the UV spectra and the available X-ray data and optical spectra to estimate the fraction of active galactic nuclei in the selection. We report the first detection of a giant Ly-alpha blob at z<1, though we find that these objects are much less common at z=1 than at z=3. Finally, we compute limits on the z~1 Ly-alpha luminosity function and confirm that there is a dramatic evolution in the luminosity function over the redshift range z=0-1.Comment: 18 pages, in press at The Astrophysical Journa

Measurement of the local Jahn-Teller distortion in LaMnO_3.006

The atomic pair distribution function (PDF) of stoichiometric LaMnO_3 has been measured. This has been fit with a structural model to extract the local Jahn-Teller distortion for an ideal Mn(3+)O_6 octahedron. These results are compared to Rietveld refinements of the same data which give the average structure. Since the local structure is being measured in the PDF there is no assumption of long-range orbital order and the real, local, Jahn-Teller distortion is measured directly. We find good agreement both with published crystallographic results and our own Rietveld refinements suggesting that in an accurately stoichiometric material there is long range orbital order as expected. The local Jahn-Teller distortion has 2 short, 2 medium and 2 long bonds.Comment: 5 pages, 3 postscript figures, minor change

Does a short-term intervention promote mental and general health among young adults? – An evaluation of counselling

<p>Abstract</p> <p>Background</p> <p>Since 1988, self-reported mental health problems in Sweden have increased more among young people than in any other age group. Young adults aged 18 – 29 with minor mental health problems were welcomed to four (at most) counselling sessions led by psychotherapists. The present study aimed to evaluate the method's appropriateness and usefulness.</p> <p>Methods</p> <p>The study population was recruited consecutively during six months (N = 74) and consisted of 59 women and 15 men. Fifty-one, 46 women and five men, met the criterion for a <it>personal semi-structured interview </it>three months post intervention. Self-assessed health data were collected on three occasions using the General Health Questionnaire (GHQ-12), Pearlin's Personal Mastery Scale and two items from the Swedish Living Conditions Surveys. Thirteen women and six men were not statistically assessed due to incomplete data, but were <it>interviewed by telephone</it>. Four men refused to be interviewed and became <it>dropouts</it>.</p> <p>Results</p> <p>The largest group of the study population had long been troubled by their problem(s): 43 percent for over three years and 28 percent for over one year. Among those <it>personally interviewed</it>, 76 percent reported psychological distress (> 3 GHQ points) before the counselling. After the counselling, GHQ-12 distress decreased by 50 percent while mastery and perceived health status increased significantly. A majority experienced an improved life situation, found out something new about themselves and could make use of the sessions afterwards. Personal participant session contentment was about 70 percent and all counsellees would recommend the intervention to a friend. Those <it>interviewed by telephone </it>were not statistically assessed due to incomplete health data. Their personal contentment was just under 50 percent, though all except one would recommend the counselling to a friend. Their expectations of the intervention were more result-orientated compared to the more process-directed personally-interviewed group.</p> <p>Conclusion</p> <p>This evaluation shows a clear improvement in self-rated mental and general health, mastery and control in the group completing the study agreement. The intervention seems to be effective for young adults with minor mental health problems, but due to the skewed gender-distribution it is unclear if the method is appropriate for men. After the proposed internal quality improvements, this short-term counselling could enhance mental and general health among young people.</p

Kinome-wide interaction modelling using alignment-based and alignment-independent approaches for kinase description and linear and non-linear data analysis techniques

<p>Abstract</p> <p>Background</p> <p>Protein kinases play crucial roles in cell growth, differentiation, and apoptosis. Abnormal function of protein kinases can lead to many serious diseases, such as cancer. Kinase inhibitors have potential for treatment of these diseases. However, current inhibitors interact with a broad variety of kinases and interfere with multiple vital cellular processes, which causes toxic effects. Bioinformatics approaches that can predict inhibitor-kinase interactions from the chemical properties of the inhibitors and the kinase macromolecules might aid in design of more selective therapeutic agents, that show better efficacy and lower toxicity.</p> <p>Results</p> <p>We applied proteochemometric modelling to correlate the properties of 317 wild-type and mutated kinases and 38 inhibitors (12,046 inhibitor-kinase combinations) to the respective combination's interaction dissociation constant (K_d). We compared six approaches for description of protein kinases and several linear and non-linear correlation methods. The best performing models encoded kinase sequences with amino acid physico-chemical z-scale descriptors and used support vector machines or partial least- squares projections to latent structures for the correlations. Modelling performance was estimated by double cross-validation. The best models showed high predictive ability; the squared correlation coefficient for new kinase-inhibitor pairs ranging P²= 0.67-0.73; for new kinases it ranged P²_kin= 0.65-0.70. Models could also separate interacting from non-interacting inhibitor-kinase pairs with high sensitivity and specificity; the areas under the ROC curves ranging AUC = 0.92-0.93. We also investigated the relationship between the number of protein kinases in the dataset and the modelling results. Using only 10% of all data still a valid model was obtained with P²= 0.47, P²_kin= 0.42 and AUC = 0.83.</p> <p>Conclusions</p> <p>Our results strongly support the applicability of proteochemometrics for kinome-wide interaction modelling. Proteochemometrics might be used to speed-up identification and optimization of protein kinase targeted and multi-targeted inhibitors.</p

Food selection associated with sense of coherence in adults

BACKGROUND: Favorable dietary habits promote health, whereas unfavorable habits link to various chronic diseases. An individual's "sense of coherence" (SOC) is reported to correlate with prevalence of some diseases to which dietary habits are linked. However, understanding what determines an individual's dietary preferences and how to change his/her behavior remains limited. The aim of the present study was to evaluate associations between dietary intake and SOC in adults. METHODS: Diet intake was recorded by an 84-item semi-quantitative food frequency questionnaire and SOC was measured by the 13-item Antonovsky questionnaire in 2,446 men and 2,545 women (25–74 years old) from the population based northern Sweden MONICA screening in 1999. RESULTS: Intakes of energy, total and saturated fat, ascorbic acid, sucrose, and servings of fruits, vegetables, cereals, and sweets correlated with SOC among women, whereas intakes of total and saturated fat, ascorbic acid, fiber, and alcohol, and servings of fruits, vegetables, bread, bread and cereals, fish, and potatoes correlated with SOC among men. With a few exceptions, intakes of these nutrients/foods were significantly explained by SOC quartile scores in linear GLM models. Both women and men classified into the highest SOC quartile had significantly higher age-BMI-education standardized mean intakes of vegetables than those in the lowest quartiles. Women in the highest SOC quartile also had higher intake of fruits but lower intakes of energy, total and saturated fat, sucrose, and sweets. Projection to latent structures (PLS) multivariate modeling of intakes of the 84 food items and food aggregates simultaneously on SOC scores supported low SOC to coincide with a presumably less health promoting dietary preference, e.g. intake of pizza, soft drinks, candies, sausages for main course, hamburgers, mashed potato, chips and other snacks, potato salad, French fries, whereas men and women with high SOC scores were characterized by e.g. high intake of rye crisp whole meal bread, boiled potato, vegetables, berries, and fruits. CONCLUSION: Both men and women in the highest, as compared with the lowest, SOC score quartile reported more "healthy" food choices. Dietary habits for individuals in the lowest SOC quartile therefore may render a higher risk for various endemic diseases

Anthocyanin management in fruits by fertilization

Anthocyanins are water-soluble vacuolar plant pigments that are mainly synthesized in epidermal layers and the flesh of fruits such as apples, cherries, grapes, and other berries. Because of their attractive red to purple coloration and their health-promoting potential, anthocyanins are significant determinants for the quality and market value of fruits and fruit-derived products. In crops, anthocyanin accumulation in leaves can be caused by nutrient deficiency which is usually ascribed to insufficient nitrogen or phosphorus fertilization. However, it is a little-known fact that the plant’s nutrient status also impacts anthocyanin synthesis in fruits. Hence, strategic nutrient supply can be a powerful tool to modify the anthocyanin content and consequently the quality and market value of important agricultural commodities. Here we summarize the current knowledge of the influence of plant nutrients on anthocyanin synthesis in fruits of major global market value and discuss the underlying cellular processes that integrate nutrient signaling with fruit anthocyanin formation. It is highlighted that fertilization that is finely tuned in amount and timing has the potential to positively influence the fruit quality by regulating anthocyanin levels. We outline new approaches to enrich plant based foods with health-promoting anthocyanins

Hierarchical Anatomical Brain Networks for MCI Prediction: Revisiting Volumetric Measures

Owning to its clinical accessibility, T1-weighted MRI (Magnetic Resonance Imaging) has been extensively studied in the past decades for prediction of Alzheimer's disease (AD) and mild cognitive impairment (MCI). The volumes of gray matter (GM), white matter (WM) and cerebrospinal fluid (CSF) are the most commonly used measurements, resulting in many successful applications. It has been widely observed that disease-induced structural changes may not occur at isolated spots, but in several inter-related regions. Therefore, for better characterization of brain pathology, we propose in this paper a means to extract inter-regional correlation based features from local volumetric measurements. Specifically, our approach involves constructing an anatomical brain network for each subject, with each node representing a Region of Interest (ROI) and each edge representing Pearson correlation of tissue volumetric measurements between ROI pairs. As second order volumetric measurements, network features are more descriptive but also more sensitive to noise. To overcome this limitation, a hierarchy of ROIs is used to suppress noise at different scales. Pairwise interactions are considered not only for ROIs with the same scale in the same layer of the hierarchy, but also for ROIs across different scales in different layers. To address the high dimensionality problem resulting from the large number of network features, a supervised dimensionality reduction method is further employed to embed a selected subset of features into a low dimensional feature space, while at the same time preserving discriminative information. We demonstrate with experimental results the efficacy of this embedding strategy in comparison with some other commonly used approaches. In addition, although the proposed method can be easily generalized to incorporate other metrics of regional similarities, the benefits of using Pearson correlation in our application are reinforced by the experimental results. Without requiring new sources of information, our proposed approach improves the accuracy of MCI prediction from (of conventional volumetric features) to (of hierarchical network features), evaluated using data sets randomly drawn from the ADNI (Alzheimer's Disease Neuroimaging Initiative) dataset

Defining functional DNA elements in the human genome

With the completion of the human genome sequence, attention turned to identifying and annotating its functional DNA elements. As a complement to genetic and comparative genomics approaches, the Encyclopedia of DNA Elements Project was launched to contribute maps of RNA transcripts, transcriptional regulator binding sites, and chromatin states in many cell types. The resulting genome-wide data reveal sites of biochemical activity with high positional resolution and cell type specificity that facilitate studies of gene regulation and interpretation of noncoding variants associated with human disease. However, the biochemically active regions cover a much larger fraction of the genome than do evolutionarily conserved regions, raising the question of whether nonconserved but biochemically active regions are truly functional. Here, we review the strengths and limitations of biochemical, evolutionary, and genetic approaches for defining functional DNA segments, potential sources for the observed differences in estimated genomic coverage, and the biological implications of these discrepancies. We also analyze the relationship between signal intensity, genomic coverage, and evolutionary conservation. Our results reinforce the principle that each approach provides complementary information and that we need to use combinations of all three to elucidate genome function in human biology and disease

Sparse PLS discriminant analysis: biologically relevant feature selection and graphical displays for multiclass problems

Background: Variable selection on high throughput biological data, such as gene expression or single nucleotide polymorphisms (SNPs), becomes inevitable to select relevant information and, therefore, to better characterize diseases or assess genetic structure. There are different ways to perform variable selection in large data sets. Statistical tests are commonly used to identify differentially expressed features for explanatory purposes, whereas Machine Learning wrapper approaches can be used for predictive purposes. In the case of multiple highly correlated variables, another option is to use multivariate exploratory approaches to give more insight into cell biology, biological pathways or complex traits.Results: A simple extension of a sparse PLS exploratory approach is proposed to perform variable selection in a multiclass classification framework.Conclusions: sPLS-DA has a classification performance similar to other wrapper or sparse discriminant analysis approaches on public microarray and SNP data sets. More importantly, sPLS-DA is clearly competitive in terms of computational efficiency and superior in terms of interpretability of the results via valuable graphical outputs. sPLS-DA is available in the R package mixOmics, which is dedicated to the analysis of large biological data sets