Predicting morphotropic phase boundary locations and transition temperatures in Pb- and Bi-based perovskite solid solutions from crystal chemical data and first-principles calculations

Using data obtained from first-principles calculations, we show that the position of the morphotropic phase boundary (MPB) and transition temperature at MPB in ferroelectric perovskite solutions can be predicted with quantitative accuracy from the properties of the constituent cations. We find that the mole fraction of PbTiO$_3$ at MPB in Pb(B$'$B$''$)O$_3$-PbTiO$_3$, BiBO$_3$-PbTiO$_3$ and Bi(B$'$B$''$)O$_3$-PbTiO$_3$ exhibits a linear dependence on the ionic size (tolerance factor) and the ionic displacements of the B-cations as found by density functional theory calculations. This dependence is due to competition between the local repulsion and A-cation displacement alignment interactions. Inclusion of first-principles displacement data also allows accurate prediction of transiton temperatures at the MPB. The obtained structure-property correlations are used to predict morphotropic phase boundaries and transition temperatures in as yet unsynthesized solid solutions.Comment: Accepted for publication in J. Appl. Phy

Quality of life in restorative versus non-restorative resections for rectal cancer:systematic review

BACKGROUND: Low rectal cancers could be treated using restorative (anterior resection, AR) or non-restorative procedures with an end/permanent stoma (Hartmann’s, HE; or abdominoperineal excision, APE). Although the surgical choice is determined by tumour and patient factors, quality of life (QoL) will also influence the patient's future beyond cancer. This systematic review of the literature compared postoperative QoL between the restorative and non-restorative techniques using validated measurement tools. METHODS: The review was registered on PROSPERO (CRD42020131492). Embase and MEDLINE, along with grey literature and trials websites, were searched comprehensively for papers published since 2012. Inclusion criteria were original research in an adult population with rectal cancer that reported QoL using a validated tool, including the European Organization for Research and Treatment of Cancer QLQ-CR30, QLQ-CR29, and QLQ-CR38. Studies were included if they compared AR with APE (or HE), independent of study design. Risk of bias was assessed using the Risk Of Bias In Non-Randomized Studies of Interventions (ROBINS-I) tool. Outcomes of interest were: QoL, pain, gastrointestinal (GI) symptoms (stool frequency, flatulence, diarrhoea and constipation), and body image. RESULTS: Nineteen studies met the inclusion criteria with a total of 6453 patients; all papers were observational and just four included preoperative evaluations. There was no identifiable difference in global QoL and pain between the two surgical techniques. Reported results regarding GI symptoms and body image documented similar findings. The ROBINS-I tool highlighted a significant risk of bias across the studies. CONCLUSION: Currently, it is not possible to draw a firm conclusion on postoperative QoL, pain, GI symptoms, and body image following restorative or non-restorative surgery. The included studies were generally of poor quality, lacked preoperative evaluations, and showed considerable bias in the data

Tourism‑supported working lands sustain a growing jaguar population in the Colombian Llanos

Understanding large carnivore demography on human-dominated lands is a priority to inform conservation strategies, yet few studies examine long-term trends. Jaguars (Panthera onca) are one such species whose population trends and survival rates remain unknown across working lands. We integrated nine years of camera trap data and tourist photos to estimate jaguar density, survival, abundance, and probability of tourist sightings on a working ranch and tourism destination in Colombia. We found that abundance increased from five individuals in 2014 to 28 in 2022, and density increased from 1.88 ± 0.87 per 100 km2 in 2014 to 3.80 ± 1.08 jaguars per 100 km2 in 2022. The probability of a tourist viewing a jaguar increased from 0% in 2014 to 40% in 2020 before the Covid-19 pandemic. Our results are the first robust estimates of jaguar survival and abundance on working lands. Our findings highlight the importance of productive lands for jaguar conservation and suggest that a tourism destination and working ranch can host an abundant population of jaguars when accompanied by conservation agreements and conflict interventions. Our analytical model that combines conventional data collection with tourist sightings can be applied to other species that are observed during tourism activities. Entender los patrones demográficos de los grandes carnívoros al interior de paisajes antrópicos es fundamental para el diseño de estrategias de conservación efectivas. En el Neotrópico, el jaguar (Panthera onca) es una de estas especies cuyas tendencias poblacionales y tasas de supervivencia en paisajes productivos son desconocidas. Para entender mejor estas dinámicas, integramos nueve años de fototrampeo junto a fotos de turistas para estimar la densidad, supervivencia, abundancia y probabilidad de avistamiento de esta especie en una finca ganadera y destino turístico en Colombia. Entre 2014 y 2022 encontramos que la abundancia incrementó de cinco a 28 individuos y la densidad de 1.88 ± 0.87 jaguares/ 100 km2 a 3.80 ± 1.08 jaguares/ 100 km2. La probabilidad de avistamiento por turistas aumentó de 0% en 2014 a 40% en 2020 antes de la pandemia del Covid-19. Nuestros resultados presentan las primeras estimaciones robustas de abundancia y supervivencia de este felino en paisajes antrópicos dónde el manejo de sistemas productivos combinados con turismo e intervenciones para la mitigación del conflicto puede albergar poblaciones abundantes de jaguares, demostrando su importancia para la conservación de esta especie. Nuestro modelo, al combinar datos convencionales con avistamientos, podría ser aplicado a otras especies observadas durante actividades turísticas. Supplemental files attached below

Automating the packing heuristic design process with genetic programming

The literature shows that one-, two-, and three-dimensional bin packing and knapsack packing are difficult problems in operational research. Many techniques, including exact, heuristic, and metaheuristic approaches, have been investigated to solve these problems and it is often not clear which method to use when presented with a new instance. This paper presents an approach which is motivated by the goal of building computer systems which can design heuristic methods. The overall aim is to explore the possibilities for automating the heuristic design process. We present a genetic programming system to automatically generate a good quality heuristic for each instance. It is not necessary to change the methodology depending on the problem type (one-, two-, or three-dimensional knapsack and bin packing problems), and it therefore has a level of generality unmatched by other systems in the literature. We carry out an extensive suite of experiments and compare with the best human designed heuristics in the literature. Note that our heuristic design methodology uses the same parameters for all the experiments. The contribution of this paper is to present a more general packing methodology than those currently available, and to show that, by using this methodology, it is possible for a computer system to design heuristics which are competitive with the human designed heuristics from the literature. This represents the first packing algorithm in the literature able to claim human competitive results in such a wide variety of packing domains

County and Demographic Differences in Drug Arrests and Controlled Substance Use in Maine

. Introduction: The Diversion Alert Program (DAP) was established to curb misuse of drugs and help identify people who may need treatment for substance use disorder (SUD). Law enforcement compiled arrest data into a database accessible by health care providers. Our objectives were to identify regional and demographic differences in drug use and misuse in Maine. Methods: All arrests (N = 11 234) reported to the DAP from 2013 to 2018 were examined by county and arrestee demographics, and classified into families (opioids, stimulants, sedatives). The Drug Enforcement Administration’s Automation of Reports and Consolidated Orders System (ARCOS) tracks the distribution of controlled pharmaceuticals (Schedule II-III). Opioids were converted to oral morphine milligram equivalents (MMEs). County and zip-code maps were constructed. Results: The most arrests per capita occurred in Androscoggin, Knox, and Cumberland Counties. Opioids were the most common drug class in arrests in all counties except Aroostook County, where stimulants were most common. Medical distribution of opioids varied. Although buprenorphine doubled, many prescription opioids (eg, hydrocodone, fentanyl, oxymorphone) exhibited large (\u3e 50%) reductions in distribution. Methadone was the predominant opioid statewide (56.4% of total MMEs), although there were sizable differences between regions (Presque Isle = 8.6%, Bangor = 78.9%). Amphetamine distribution increased by 67.9%. Discussion: The DAP, a unique pharmacoepidemiological resource, revealed a 6-fold difference in drug arrests by county. Regional differences in methadone may be due to heterogeneities in methadone clinic distribution. Conclusions: The decrease in most prescription opioids, but increase in prescription stimulants, may warrant continued monitoring to improve public health

Changes in body composition and performance with supplemental HMB-FA+ATP (Manuscript Clarification)

Additional co-authors: Matthew D. Vukovich, Colin Wilborn, and Darryn S. Willoughb

American Gut: An Open Platform For Citizen Science Microbiome Research

Copyright © 2018 McDonald et al. Although much work has linked the human microbiome to specific phenotypes and lifestyle variables, data from different projects have been challenging to integrate and the extent of microbial and molecular diversity in human stool remains unknown. Using standardized protocols from the Earth Microbiome Project and sample contributions from over 10,000 citizen-scientists, together with an open research network, we compare human microbiome specimens primarily from the United States, United Kingdom, and Australia to one another and to environmental samples. Our results show an unexpected range of beta-diversity in human stool microbiomes compared to environmental samples; demonstrate the utility of procedures for removing the effects of overgrowth during room-temperature shipping for revealing phenotype correlations; uncover new molecules and kinds of molecular communities in the human stool metabolome; and examine emergent associations among the microbiome, metabolome, and the diversity of plants that are consumed (rather than relying on reductive categorical variables such as veganism, which have little or no explanatory power). We also demonstrate the utility of the living data resource and cross-cohort comparison to confirm existing associations between the microbiome and psychiatric illness and to reveal the extent of microbiome change within one individual during surgery, providing a paradigm for open microbiome research and education. IMPORTANCE We show that a citizen science, self-selected cohort shipping samples through the mail at room temperature recaptures many known microbiome results from clinically collected cohorts and reveals new ones. Of particular interest is integrating n = 1 study data with the population data, showing that the extent of microbiome change after events such as surgery can exceed differences between distinct environmental biomes, and the effect of diverse plants in the diet, which we confirm with untargeted metabolomics on hundreds of samples

Empathy among undergraduate medical students: A multi-centre cross-sectional comparison of students beginning and approaching the end of their course

BACKGROUND: Although a core element in patient care the trajectory of empathy during undergraduate medical education remains unclear. Empathy is generally regarded as comprising an affective capacity: the ability to be sensitive to and concerned for, another and a cognitive capacity: the ability to understand and appreciate the other person's perspective. The authors investigated whether final year undergraduate students recorded lower levels of empathy than their first year counterparts, and whether male and female students differed in this respect. METHODS: Between September 2013 and June 2014 an online questionnaire survey was administered to 15 UK, and 2 international medical schools. Participating schools provided both 5-6 year standard courses and 4 year accelerated graduate entry courses. The survey incorporated the Jefferson Scale of Empathy-Student Version (JSE-S) and Davis's Interpersonal Reactivity Index (IRI), both widely used to measure medical student empathy. Participation was voluntary. Chi squared tests were used to test for differences in biographical characteristics of student groups. Multiple linear regression analyses, in which predictor variables were year of course (first/final); sex; type of course and broad socio-economic group were used to compare empathy scores. RESULTS: Five medical schools (4 in the UK, 1 in New Zealand) achieved average response rates of 55 % (n = 652) among students starting their course and 48 % (n = 487) among final year students. These schools formed the High Response Rate Group. The remaining 12 medical schools recorded lower response rates of 24.0 % and 15.2 % among first and final year students respectively. These schools formed the Lower Response Rate Group. For both male and female students in both groups of schools no significant differences in any empathy scores were found between students starting and approaching the end of their course. Gender was found to significantly predict empathy scores, with females scoring higher than males. CONCLUSIONS: Participant male and female medical students approaching the end of their undergraduate education, did not record lower levels of empathy, compared to those at the beginning of their course. Questions remain concerning the trajectory of empathy after qualification and how best to support it through the pressures of starting out in medical practice

Mapping Migratory Bird Prevalence Using Remote Sensing Data Fusion

This is the publisher’s final pdf. The published article is copyrighted by the Public Library of Science and can be found at: http://www.plosone.org/home.action.Background: Improved maps of species distributions are important for effective management of wildlife under increasing anthropogenic pressures. Recent advances in lidar and radar remote sensing have shown considerable potential for mapping forest structure and habitat characteristics across landscapes. However, their relative efficacies and integrated use in habitat mapping remain largely unexplored. We evaluated the use of lidar, radar and multispectral remote sensing data in predicting multi-year bird detections or prevalence for 8 migratory songbird species in the unfragmented temperate deciduous forests of New Hampshire, USA. \ud \ud Methodology and Principal Findings: A set of 104 predictor variables describing vegetation vertical structure and variability from lidar, phenology from multispectral data and backscatter properties from radar data were derived. We tested the accuracies of these variables in predicting prevalence using Random Forests regression models. All data sets showed more than 30% predictive power with radar models having the lowest and multi-sensor synergy ("fusion") models having highest accuracies. Fusion explained between 54% and 75% variance in prevalence for all the birds considered. Stem density from discrete return lidar and phenology from multispectral data were among the best predictors. Further analysis revealed different relationships between the remote sensing metrics and bird prevalence. Spatial maps of prevalence were consistent with known habitat preferences for the bird species. \ud \ud Conclusion and Significance: Our results highlight the potential of integrating multiple remote sensing data sets using machine-learning methods to improve habitat mapping. Multi-dimensional habitat structure maps such as those generated from this study can significantly advance forest management and ecological research by facilitating fine-scale studies at both stand and landscape level