Characterizing the non-linear growth of large-scale structure in the Universe

The local Universe displays a rich hierarchical pattern of galaxy clusters and superclusters. The early Universe, however, was almost smooth, with only slight 'ripples' seen in the cosmic microwave background radiation. Models of the evolution of structure link these observations through the effect of gravity, because the small initially overdense fluctuations attract additional mass as the Universe expands. During the early stages, the ripples evolve independently, like linear waves on the surface of deep water. As the structures grow in mass, they interact with other in non-linear ways, more like waves breaking in shallow water. We have recently shown how cosmic structure can be characterized by phase correlations associated with these non-linear interactions, but hitherto there was no way to use that information to reach quantitative insights into the growth of structures. Here we report a method of revealing phase information, and quantify how this relates to the formation of a filaments, sheets and clusters of galaxies by non-linear collapse. We use a new statistic based on information entropy to separate linear from non-linear effects and thereby are able to disentangle those aspects of galaxy clustering that arise from initial conditions (the ripples) from the subsequent dynamical evolution.Comment: Accepted for publication in Nature. For high-resolution Figure 3, please see http://www.nottingham.ac.uk/~ppzpc/phases/n0colorphase.html, For the animations and the idea of this paper please see http://www.nottingham.ac.uk/~ppzpc/phases/index.htm

KIDMAP, a web based system for gathering patients' feedback on their doctors

<p>Abstract</p> <p>Background</p> <p>The gathering of feedback on doctors from patients after consultations is an important part of patient involvement and participation. This study first assesses the 23-item Patient Feedback Questionnaire (PFQ) designed by the Picker Institute, Europe, to determine whether these items form a single latent trait. Then, an Internet module with visual representation is developed to gather patient views about their doctors; this program then distributes the individualized results by email.</p> <p>Methods</p> <p>A total of 450 patients were randomly recruited from a 1300-bed-size medical center in Taiwan. The Rasch rating scale model was used to examine the data-fit. Differential item functioning (DIF) analysis was conducted to verify construct equivalence across the groups. An Internet module with visual representation was developed to provide doctors with the patient's online feedback.</p> <p>Results</p> <p>Twenty-one of the 23 items met the model's expectation, namely that they constitute a single construct. The test reliability was 0.94. DIF was found between ages and different kinds of disease, but not between genders and education levels. The visual approach of the KIDMAP module on the WWW seemed to be an effective approach to the assessment of patient feedback in a clinical setting.</p> <p>Conclusion</p> <p>The revised 21-item PFQ measures a single construct. Our work supports the hypothesis that the revised PFQ online version is both valid and reliable, and that the KIDMAP module is good at its designated task. Further research is needed to confirm data congruence for patients with chronic diseases.</p

Orthokeratinized Odontogenic Cyst of the Mandible with Heterotopic Cartilage

Cartilaginous metaplasia is a rare but well-documented phenomenon occurring in the wall of odontogenic keratocyst. The mural cartilage not associated with odontogenic keratocyst has been reported only once in a maxillary teratoid cyst of congenital origin to our knowledge. A case presented is a 38-year-old man with intraosseous keratinizing epidermoid cyst in the mandible, the wall of which contained a nodule of mature hyaline cartilage. The present lesion likely represents a previously undescribed, histologic hybrid consisting of orthokeratinized odontogenic cyst and cartilaginous heterotopia

Crystal Structure of the Heteromolecular Chaperone, AscE-AscG, from the Type III Secretion System in Aeromonas hydrophila

10.1371/journal.pone.0019208PLoS ONE64

Reduced Health-Related Quality of Life in Elders with Frailty: A Cross-Sectional Study of Community-Dwelling Elders in Taiwan

PURPOSE: Exploring the domains and degrees of health-related quality of life (HRQOL) that are affected by the frailty of elders will help clinicians understand the impact of frailty. This association has not been investigated in community-dwelling elders. Therefore, we examined the domains and degree of HRQOL of elders with frailty in the community in Taiwan. METHODS: A total of 933 subjects aged 65 years and over were recruited in 2009 from a metropolitan city in Taiwan. Using an adoption of the Fried criteria, frailty was defined by five components: shrinking, weakness, poor endurance and energy, slowness, and low physical activity level. HRQOL was assessed by the short form 36 (SF-36). The multiple linear regression model was used to test the independent effects of frailty on HRQOL. RESULTS: After multivariate adjustment, elders without frailty reported significantly better health than did the pre-frail and frail elders on all scales, and the pre-frail elders reported better health than did the frail elders for all scales except the scales of role limitation due to physical and emotional problems and the Mental Component Summary (MCS). The significantly negative differences between frail and robust elders ranged from 3.58 points for the MCS to 22.92 points for the physical functioning scale. The magnitude of the effects of frail components was largest for poor endurance and energy, and next was for slowness. The percentages of the variations of these 10 scales explained by all factors in the models ranged from 11.1% (scale of role limitation due to emotional problems) to 49.1% (scale of bodily pain). CONCLUSIONS: Our study demonstrates that the disabilities in physical health inherent in frailty are linked to a reduction in HRQOL. Such an association between clinical measures and a generic measure of the HRQOL may offer clinicians new information to understand frailty and to conceptualize it within the broader context of disability

Nerve Growth Factor Stimulates Interaction of Cayman Ataxia Protein BNIP-H/Caytaxin with Peptidyl-Prolyl Isomerase Pin1 in Differentiating Neurons

Mutations in ATCAY that encodes the brain-specific protein BNIP-H (or Caytaxin) lead to Cayman cerebellar ataxia. BNIP-H binds to glutaminase, a neurotransmitter-producing enzyme, and affects its activity and intracellular localization. Here we describe the identification and characterization of the binding between BNIP-H and Pin1, a peptidyl-prolyl cis/trans isomerase. BNIP-H interacted with Pin1 after nerve growth factor-stimulation and they co-localized in the neurites and cytosol of differentiating pheochromocytoma PC12 cells and the embryonic carcinoma P19 cells. Deletional mutagenesis revealed two cryptic binding sites within the C-terminus of BNIP-H such that single point mutants affecting the WW domain of Pin1 completely abolished their binding. Although these two sites do not contain any of the canonical Pin1-binding motifs they showed differential binding profiles to Pin1 WW domain mutants S16E, S16A and W34A, and the catalytically inert C113A of its isomerase domain. Furthermore, their direct interaction would occur only upon disrupting the ability of BNIP-H to form an intramolecular interaction by two similar regions. Furthermore, expression of Pin1 disrupted the BNIP-H/glutaminase complex formation in PC12 cells under nerve growth factor-stimulation. These results indicate that nerve growth factor may stimulate the interaction of BNIP-H with Pin1 by releasing its intramolecular inhibition. Such a mechanism could provide a post-translational regulation on the cellular activity of BNIP-H during neuronal differentiation. (213 words

Combining Free Text and Structured Electronic Medical Record Entries to Detect Acute Respiratory Infections

The electronic medical record (EMR) contains a rich source of information that could be harnessed for epidemic surveillance. We asked if structured EMR data could be coupled with computerized processing of free-text clinical entries to enhance detection of acute respiratory infections (ARI).A manual review of EMR records related to 15,377 outpatient visits uncovered 280 reference cases of ARI. We used logistic regression with backward elimination to determine which among candidate structured EMR parameters (diagnostic codes, vital signs and orders for tests, imaging and medications) contributed to the detection of those reference cases. We also developed a computerized free-text search to identify clinical notes documenting at least two non-negated ARI symptoms. We then used heuristics to build case-detection algorithms that best combined the retained structured EMR parameters with the results of the text analysis.An adjusted grouping of diagnostic codes identified reference ARI patients with a sensitivity of 79%, a specificity of 96% and a positive predictive value (PPV) of 32%. Of the 21 additional structured clinical parameters considered, two contributed significantly to ARI detection: new prescriptions for cough remedies and elevations in body temperature to at least 38°C. Together with the diagnostic codes, these parameters increased detection sensitivity to 87%, but specificity and PPV declined to 95% and 25%, respectively. Adding text analysis increased sensitivity to 99%, but PPV dropped further to 14%. Algorithms that required satisfying both a query of structured EMR parameters as well as text analysis disclosed PPVs of 52-68% and retained sensitivities of 69-73%.Structured EMR parameters and free-text analyses can be combined into algorithms that can detect ARI cases with new levels of sensitivity or precision. These results highlight potential paths by which repurposed EMR information could facilitate the discovery of epidemics before they cause mass casualties

Protein-Protein Interaction Site Predictions with Three-Dimensional Probability Distributions of Interacting Atoms on Protein Surfaces

Protein-protein interactions are key to many biological processes. Computational methodologies devised to predict protein-protein interaction (PPI) sites on protein surfaces are important tools in providing insights into the biological functions of proteins and in developing therapeutics targeting the protein-protein interaction sites. One of the general features of PPI sites is that the core regions from the two interacting protein surfaces are complementary to each other, similar to the interior of proteins in packing density and in the physicochemical nature of the amino acid composition. In this work, we simulated the physicochemical complementarities by constructing three-dimensional probability density maps of non-covalent interacting atoms on the protein surfaces. The interacting probabilities were derived from the interior of known structures. Machine learning algorithms were applied to learn the characteristic patterns of the probability density maps specific to the PPI sites. The trained predictors for PPI sites were cross-validated with the training cases (consisting of 432 proteins) and were tested on an independent dataset (consisting of 142 proteins). The residue-based Matthews correlation coefficient for the independent test set was 0.423; the accuracy, precision, sensitivity, specificity were 0.753, 0.519, 0.677, and 0.779 respectively. The benchmark results indicate that the optimized machine learning models are among the best predictors in identifying PPI sites on protein surfaces. In particular, the PPI site prediction accuracy increases with increasing size of the PPI site and with increasing hydrophobicity in amino acid composition of the PPI interface; the core interface regions are more likely to be recognized with high prediction confidence. The results indicate that the physicochemical complementarity patterns on protein surfaces are important determinants in PPIs, and a substantial portion of the PPI sites can be predicted correctly with the physicochemical complementarity features based on the non-covalent interaction data derived from protein interiors