Projections for measuring the size of the solar core with neutrino-electron scattering

We quantify the amount of data needed in order to measure the size and position of the $^8$B neutrino production region within the solar core, for experiments looking at elastic scattering between electrons and solar neutrinos. The directions of the electrons immediately after scattering are strongly correlated with the incident directions of the neutrinos, however this is degraded significantly by the subsequent scattering of these electrons in the detector medium. We generate distributions of such electrons for different neutrino production profiles, and use a maximum likelihood analysis to make projections for future experimental sensitivity. We find that with approximately 20 years worth of data the Super Kamiokande experiment could constrain the central radius of the shell in which $^8$B neutrinos are produced to be less than 0.22 of the total solar radius at 95% confidence.Comment: 5 pages, 2 figures. Matches version accepted to PRL. Improved 2D analysis and results discussio

A fuzzy classification technique for predicting species' distributions: applications using invasive alien plants and indigenous insects

A new predictive modelling technique called the fuzzy envelope model (FEM) is introduced. The technique can be used to predict potential distributions of organisms using presence-only locality records and a set of environmental predictor variables. FEM uses fuzzy logic to classify a set of predictor variable maps based on the values associated with presence records and combines the results to produce a potential distribution map for a target species. This technique represents several refinements of the envelope approach used in the BIOCLIM modelling package. These refinements are related to the way in which FEMs deal with uncertainty, the way in which this uncertainty is represented in the resultant potential distribution maps, and the way that these maps can be interpreted and applied. To illustrate its potential use in biogeographical studies, FEM was applied to predicting the potential distribution of three invasive alien plant species (Lantana camara L., Ricinus communis L. and Solanum mauritianum Scop.), and three native cicada species (Capicada decora Germar, Platypleura deusta Thun. and P. capensis L.) in South Africa, Lesotho and Swaziland. These models were quantitatively compared with models produced by means of the algorithm used in the BIOCLIM modelling package, which is referred to as a crisp envelope model (the CEM design). The average performance of models of the FEM design was consistently higher than those of the CEM design. There were significant differences in model performance among species but there was no significant interaction between model design and species. The average maximum kappa value ranged from 0.70 to 0.90 for FEM design and from 0.57 to 0.89 for the CEM design, which can be described as 'good' to 'excellent' using published ranges of agreement for the kappa statistic. This technique can be used to predict species' potential distributions that could be used for identifying regions at risk from invasion by alien species. These predictions could also be used in conservation planning in the case of native species

Metabolome-Informed Microbiome Analysis Refines Metadata Classifications and Reveals Unexpected Medication Transfer in Captive Cheetahs.

Even high-quality collection and reporting of study metadata in microbiome studies can lead to various forms of inadvertently missing or mischaracterized information that can alter the interpretation or outcome of the studies, especially with nonmodel organisms. Metabolomic profiling of fecal microbiome samples can provide empirical insight into unanticipated confounding factors that are not possible to obtain even from detailed care records. We illustrate this point using data from cheetahs from the San Diego Zoo Safari Park. The metabolomic characterization indicated that one cheetah had to be moved from the non-antibiotic-exposed group to the antibiotic-exposed group. The detection of the antibiotic in this second cheetah was likely due to grooming interactions with the cheetah that was administered antibiotics. Similarly, because transit time for stool is variable, fecal samples within the first few days of antibiotic prescription do not all contain detected antibiotics, and the microbiome is not yet affected. These insights significantly altered the way the samples were grouped for analysis (antibiotic versus no antibiotic) and the subsequent understanding of the effect of the antibiotics on the cheetah microbiome. Metabolomics also revealed information about numerous other medications and provided unexpected dietary insights that in turn improved our understanding of the molecular patterns on the impact on the community microbial structure. These results suggest that untargeted metabolomic data provide empirical evidence to correct records and aid in the monitoring of the health of nonmodel organisms in captivity, although we also expect that these methods may be appropriate for other social animals, such as cats.IMPORTANCE Metabolome-informed analyses can enhance omics studies by enabling the correct partitioning of samples by identifying hidden confounders inadvertently misrepresented or omitted from carefully curated metadata. We demonstrate here the utility of metabolomics in a study characterizing the microbiome associated with liver disease in cheetahs. Metabolome-informed reinterpretation of metagenome and metabolome profiles factored in an unexpected transfer of antibiotics, preventing misinterpretation of the data. Our work suggests that untargeted metabolomics can be used to verify, augment, and correct sample metadata to support improved grouping of sample data for microbiome analyses, here for nonmodel organisms in captivity. However, the techniques also suggest a path forward for correcting clinical information in microbiome studies more broadly to enable higher-precision analyses

Predicting Outcomes Among Adolescents With Disruptive Disorders Being Treated in a System of Care Program

BACKGROUND: “Systems of care” are strengths-based approaches to treating adolescents and others with disruptive disorders. However, little is known about why some adolescents improve and others do not. OBJECTIVE: To examine changes in personal strengths and family functioning as predictors of behavioral and social functioning among adolescents with disruptive disorders who participated in a system of care program. DESIGN: Secondary analyses of data from 114 adolescents (12-17 years of age) with disruptive disorders and their caregivers who participated in the Dawn Project Evaluation Study. Caregivers completed in-depth interviews conducted by trained data collectors using standardized questionnaires. Baseline and 12- month data are reported here. RESULTS: Improvement in personal strengths was a significant predictor of improvement in adolescent behavioral and social functioning, controlling for demographics (p < .001). CONCLUSION: In adolescents with disruptive disorders, psychiatric nurses should focus on enhancing adolescents’ personal strengths to improve behavioral and social functioning

Biomass distribution among tropical tree species grown under\ud differing regional climates

In the Neotropics, there is a growing interest in establishing plantations of native tree species for commerce, local consumption, and to replant on abandoned agricultural lands. Although numerous trial plantations have been established, comparative information on the performance of native trees under different regional environments is generally lacking. In this study, we evaluated the accumulation and partitioning of above-ground biomass in 16 native and two exotic tree species growing in replicated species selection trials in Panama under humid and dry regional environments. Seven of the 18 species accumulated greater total biomass at the humid site than at the dry site over a two-year period. Species specific biomass partitioning among leaves, branches and trunks was observed. However, awide range of total biomass found among species (from 1.06 kg for Dipteryx panamensis to 29.84 kg for Acacia mangium at Soberania) justified the used of an Aitchison log ratio transformation to adjust for size. When biomass partitioning was adjusted for size, a majority of these differences proved to be a result of the ability of the tree to support biomass components rather than the result of differences in the regional environments at the two sites. These findings were confirmed by comparative ANCOVAs on Aitchison-transformed and non-Aitchison-transformed variables. In these comparisons, basal diameter, height and diameter at breast height were robust predictors of biomass for the pooled data from both sites, but Aitchison-transformed\ud variables had little predictive power

Multilevel Modelling of Country Effects: A Cautionary Tale

Country effects on outcomes for individuals are often analysed using multilevel (hierarchical) models applied to harmonized multi-country data sets such as ESS, EU-SILC, EVS, ISSP, and SHARE. We point out problems with the assessment of country effects that appear not to be widely appreciated, and develop our arguments using Monte Carlo simulation analysis of multilevel linear and logit models. With large sample sizes of individuals within each country but only a small number of countries, analysts can reliably estimate individual-level effects but estimates of parameters summarizing country effects are likely to be unreliable. Multilevel modelling methods are no panacea

Evidence for Shape Co-existence at medium spin in 76Rb

Four previously known rotational bands in 76Rb have been extended to moderate spins using the Gammasphere and Microball gamma ray and charged particle detector arrays and the 40Ca(40Ca,3pn) reaction at a beam energy of 165 MeV. The properties of two of the negative-parity bands can only readily be interpreted in terms of the highly successful Cranked Nilsson-Strutinsky model calculations if they have the same configuration in terms of the number of g9/2 particles, but they result from different nuclear shapes (one near-oblate and the other near-prolate). These data appear to constitute a unique example of shape co-existing structures at medium spins.Comment: Accepted for publication in Physics Letters

The tobacco industry’s past role in weight control related to smoking

Background: Smoking is thought to produce an appetite-suppressing effect by many smokers. Thus, the fear of body weight gain often outweighs the perception of health benefits associated with smoking cessation, particularly in adolescents. We examined whether the tobacco industry played a role in appetite and body weight control related to smoking and smoking cessation. Methods: We performed a systematic search within the archives of six major US and UK tobacco companies (American Tobacco, Philip Morris, RJ Reynolds, Lorillard, Brown & Williamson and British American Tobacco) that were Defendants in tobacco litigation settled in 1998. Findings are dated from 1949 to 1999. Results: The documents revealed the strategies planned and used by the industry to enhance effects of smoking on weight and appetite, mostly by chemical modifications of cigarettes contents. Appetite-suppressant molecules, such as tartaric acid and 2-acetylpyridine were added to some cigarettes. Conclusion: These tobacco companies played an active and not disclaimed role in the anti-appetite effects of smoking, at least in the past, by adding appetite-suppressant molecules into their cigarettes

Development and validation of a questionnaire to measure moral distress in community pharmacists

The Author(s) 2016. . This article is published with open access at Springerlink.com This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. Jayne L. Astbury, and Cathal T. Gallagher, 'Development and validation of a questionnaire to measure moral distress in community pharmacists', International Journal of Clinical Pharmacy (2017) Vol 39(1): 156-164, first published online on 22 December 2016, the version of record is available on line via doi: 10.1007/s11096-016-0413-3 Funding for this work was provided by Pharmacy Research UK (PRUK).Background Pharmacists work within a highly-regulated occupational sphere, and are bound by strict legal frameworks and codes of professional conduct. This regulatory environment creates the potential for moral distress to occur due to the limitations it places on acting in congruence with moral judgements. Very little research regarding this phenomenon has been undertaken in pharmacy: thus, prominent research gaps have arisen for the development of a robust tool to measure and quantify moral distress experienced in the profession. Objective The aim of this study was to develop an instrument to measure moral distress in community pharmacists. Setting Community pharmacies in the United Kingdom. Method This study adopted a three-phase exploratory sequential mixed-method design. Three semi-structured focus groups were then conducted to allow pharmacists to identify and explore scenarios that cause moral distress. Each of the identified scenarios were developed into a statement, which was paired with twin seven-point Likert scales to measure the frequency and intensity of the distress, respectively. Content validity, reliability, and construct validity were all tested, and the questionnaire was refined. Main outcome measure The successful development of the valid instrument for use in the United Kingdom. Results This research has led to the development of a valid and reliable instrument to measure moral distress in community pharmacists in the UK. The questionnaire has already been distributed to a large sample of community pharmacists. Conclusion Results from this distribution will be used to inform the formulation of coping strategies for dealing with moral distress.Peer reviewedFinal Published versio

Mapping natural capital: optimising the use of national scale datasets

Understanding the spatial distribution of specific environmental variables and the interdependencies of these variables is crucial for managing the environment in a sustainable way. Here we discuss two methods of mapping – a Geographical Information System classification-based approach and a statistical model-based approach. If detailed, spatially comprehensive covariate datasets exist to complement the ecological-response data, then using a statistical model-based analysis provides the potential for greater understanding of underlying relationships, as well as the uncertainty in the spatial predictions. Further, the model-based approach facilitates scenario testing. Although similar methods are already adopted in species distribution modeling, the flexibility of the model framework used is rarely exploited to go beyond modeling occupancy or suitability for a single species, into modeling complex derived metrics such as community composition and indicators of natural capital. As an example, we assess the potential benefits of the statistical model-based approach to mapping natural capital through the use of two national survey datasets; The Centre for Ecology and Hydrology (CEH) Land Cover Map (LCM) and the British Geological Survey’s (BGS) Parent Material Model (PMM), to predict national soil microbial community distributions based on data from a sample of > 1000 soils covering Great Britain. The results are mapped and compared against a more traditional, land classification-based approach. The comparison shows that, although the maps look broadly similar, the model-based approach provides better overall spatial prediction, and the contribution of individual model terms (along with their uncertainty) are far easier to understand and interpret, whilst also facilitating any scenario testing. We therefore both recommend the use of spatial statistical modelling techniques to map natural capital and anticipate that they will become more prominent over the forthcoming years