Detection of Massive Forming Galaxies at Redshifts Greater than One

The complex problem of when and how galaxies formed has not until recently been susceptible of direct attack. It has been known for some time that the excessive number of blue galaxies counted at faint magnitudes implies that a considerable fraction of the massive star formation in the universe occurred at z < 3, but, surprisingly, spectroscopic studies of galaxies down to a B magnitude of 24 found little sign of the expected high-z progenitors of current massive galaxies, but rather, in large part, small blue galaxies at modest redshifts z \sim 0.3. This unexpected population has diverted attention from the possibility that early massive star-forming galaxies might also be found in the faint blue excess. From KECK spectroscopic observations deep enough to encompass a large population of z > 1 field galaxies, we can now show directly that in fact these forming galaxies are present in substantial numbers at B \sim 24, and that the era from redshifts 1 to 2 was clearly a major period of galaxy formation. These z > 1 galaxies have very unusual morphologies as seen in deep HST WFPC2 images.Comment: 10 pages LaTeX + 5 PostScript figures in uuencoded gzipped tar file; aasms4.sty, flushrt.sty, overcite.sty (the two aastex4.0 and overcite.sty macros are available from xxx.lanl.gov) Also available (along with style files) via anonymous ftp to ftp://hubble.ifa.hawaii.edu/pub/preprints . E-print version of paper adds citation cross-references to other archived e-prints, where available. To appear in Nature October 19, 199

Near-Native Protein Loop Sampling Using Nonparametric Density Estimation Accommodating Sparcity

Unlike the core structural elements of a protein like regular secondary structure, template based modeling (TBM) has difficulty with loop regions due to their variability in sequence and structure as well as the sparse sampling from a limited number of homologous templates. We present a novel, knowledge-based method for loop sampling that leverages homologous torsion angle information to estimate a continuous joint backbone dihedral angle density at each loop position. The φ,ψ distributions are estimated via a Dirichlet process mixture of hidden Markov models (DPM-HMM). Models are quickly generated based on samples from these distributions and were enriched using an end-to-end distance filter. The performance of the DPM-HMM method was evaluated against a diverse test set in a leave-one-out approach. Candidates as low as 0.45 Å RMSD and with a worst case of 3.66 Å were produced. For the canonical loops like the immunoglobulin complementarity-determining regions (mean RMSD <2.0 Å), the DPM-HMM method performs as well or better than the best templates, demonstrating that our automated method recaptures these canonical loops without inclusion of any IgG specific terms or manual intervention. In cases with poor or few good templates (mean RMSD >7.0 Å), this sampling method produces a population of loop structures to around 3.66 Å for loops up to 17 residues. In a direct test of sampling to the Loopy algorithm, our method demonstrates the ability to sample nearer native structures for both the canonical CDRH1 and non-canonical CDRH3 loops. Lastly, in the realistic test conditions of the CASP9 experiment, successful application of DPM-HMM for 90 loops from 45 TBM targets shows the general applicability of our sampling method in loop modeling problem. These results demonstrate that our DPM-HMM produces an advantage by consistently sampling near native loop structure. The software used in this analysis is available for download at http://www.stat.tamu.edu/~dahl/software/cortorgles/

Extending basic principles of measurement models to the design and validation of Patient Reported Outcomes

A recently published article by the Scientific Advisory Committee of the Medical Outcomes Trust presents guidelines for selecting and evaluating health status and health-related quality of life measures used in health outcomes research. In their article, they propose a number of validation and performance criteria with which to evaluate such self-report measures. We provide an alternate, yet complementary, perspective by extending the types of measurement models which are available to the instrument designer. During psychometric development or selection of a Patient Reported Outcome measure it is necessary to determine which, of the five types of measurement models, the measure is based on; 1) a Multiple Effect Indicator model, 2) a Multiple Cause Indicator model, 3) a Single Item Effect Indicator model, 4) a Single Item Cause Indicator model, or 5) a Mixed Multiple Indicator model. Specification of the measurement model has a major influence on decisions about item and scale design, the appropriate application of statistical validation methods, and the suitability of the resulting measure for a particular use in clinical and population-based outcomes research activities

Integrated terrestrial-freshwater planning doubles conservation of tropical aquatic species

Conservation initiatives overwhelmingly focus on terrestrial biodiversity, and little is known about the freshwater cobenefits of terrestrial conservation actions. We sampled more than 1500 terrestrial and freshwater species in the Amazon and simulated conservation for species from both realms. Prioritizations based on terrestrial species yielded on average just 22% of the freshwater benefits achieved through freshwater-focused conservation. However, by using integrated cross-realm planning, freshwater benefits could be increased by up to 600% for a 1% reduction in terrestrial benefits. Where freshwater biodiversity data are unavailable but aquatic connectivity is accounted for, freshwater benefits could still be doubled for negligible losses of terrestrial coverage. Conservation actions are urgently needed to improve the status of freshwater species globally. Our results suggest that such gains can be achieved without compromising terrestrial conservation goals

Evidence-based Kernels: Fundamental Units of Behavioral Influence

This paper describes evidence-based kernels, fundamental units of behavioral influence that appear to underlie effective prevention and treatment for children, adults, and families. A kernel is a behavior–influence procedure shown through experimental analysis to affect a specific behavior and that is indivisible in the sense that removing any of its components would render it inert. Existing evidence shows that a variety of kernels can influence behavior in context, and some evidence suggests that frequent use or sufficient use of some kernels may produce longer lasting behavioral shifts. The analysis of kernels could contribute to an empirically based theory of behavioral influence, augment existing prevention or treatment efforts, facilitate the dissemination of effective prevention and treatment practices, clarify the active ingredients in existing interventions, and contribute to efficiently developing interventions that are more effective. Kernels involve one or more of the following mechanisms of behavior influence: reinforcement, altering antecedents, changing verbal relational responding, or changing physiological states directly. The paper describes 52 of these kernels, and details practical, theoretical, and research implications, including calling for a national database of kernels that influence human behavior

Evaluation of appendicitis risk prediction models in adults with suspected appendicitis

Background Appendicitis is the most common general surgical emergency worldwide, but its diagnosis remains challenging. The aim of this study was to determine whether existing risk prediction models can reliably identify patients presenting to hospital in the UK with acute right iliac fossa (RIF) pain who are at low risk of appendicitis. Methods A systematic search was completed to identify all existing appendicitis risk prediction models. Models were validated using UK data from an international prospective cohort study that captured consecutive patients aged 16–45 years presenting to hospital with acute RIF in March to June 2017. The main outcome was best achievable model specificity (proportion of patients who did not have appendicitis correctly classified as low risk) whilst maintaining a failure rate below 5 per cent (proportion of patients identified as low risk who actually had appendicitis). Results Some 5345 patients across 154 UK hospitals were identified, of which two‐thirds (3613 of 5345, 67·6 per cent) were women. Women were more than twice as likely to undergo surgery with removal of a histologically normal appendix (272 of 964, 28·2 per cent) than men (120 of 993, 12·1 per cent) (relative risk 2·33, 95 per cent c.i. 1·92 to 2·84; P < 0·001). Of 15 validated risk prediction models, the Adult Appendicitis Score performed best (cut‐off score 8 or less, specificity 63·1 per cent, failure rate 3·7 per cent). The Appendicitis Inflammatory Response Score performed best for men (cut‐off score 2 or less, specificity 24·7 per cent, failure rate 2·4 per cent). Conclusion Women in the UK had a disproportionate risk of admission without surgical intervention and had high rates of normal appendicectomy. Risk prediction models to support shared decision‐making by identifying adults in the UK at low risk of appendicitis were identified