The effect of metapopulation processes on the spatial scale of adaptation across an environmental gradient

We show that the butterfly Aricia agestis (Lycaenidae) is adapted to its thermal environment in via integer changes in the numbers of generations per year (voltinism): it has two generations per year in warm habitats and one generation per year in cool habitats in north Wales (UK). Voltinism is an “adaptive peak” since individuals having an intermediate number of generations per year would fail to survive the winter, and indeed no populations showed both voltinism types in nature. In spite of this general pattern, 11% of populations apparently possess the “wrong” voltinism for their local environment, and population densities were lower in thermally intermediate habitat patches. Population dynamic data and patterns of genetic differentiation suggest that adaptation occurs at the metapopulation level, with local populations possessing the voltinism type appropriate for the commonest habitat type within each population network. When populations and groups of populations go extinct, they tend to be replaced by colonists from the commonest thermal environment nearby, even if this is the locally incorrect adaptation. Our results illustrate how stochastic population turnover can impose a limit on local adaptation over distances many times larger than predicted on the basis of normal dispersal movements

Real time monitoring of screw insertion using acoustic emission can predict screw stripping in human cancellous bone

Background To develop experience, orthopaedic surgeons train their own proprioception to detect torque during screw insertion. This experience is acquired over time and when implanting conventional/non-locked screws in osteopenic cancellous bone the experienced surgeon still strips between 38 and 45%. Technology needs to be investigated to reduce stripping rates. Acoustic-Emission technology has the ability to detect stress wave energy transmitted through a screw during insertion into synthetic bone. Our hypothesis is Acoustic-Emission waves can be detected through standard orthopaedic screwdrivers while advancing screws through purchase and overtightening in cancellous human bone with different bone mineral densities replicating the clinical state. Methods 77 non-locking 4 mm and 6.5 mm diameter cancellous bone screws were inserted through to stripping into the lateral condylar area of 6 pairs of embalmed distal femurs. Specimens had varying degrees of bone mineral density determined by quantitative CT. Acoustic-Emission energy and axial force were detected for each test. Results The tests showed a significant high correlation between bone mineral density and Acoustic-Emission energy with R = 0.74. A linear regression model with the mean stripping load as the dependent variable and mean Acoustic-Emission energy, bone mineral densities and screw size as the independent variables resulted in r 2 = 0.94. Interpretation This experiment succeeded in testing real time Acoustic-Emission monitoring of screw purchase and overtightening in human bone. Acoustic-Emission energy and axial compressive force have positive high correlation to bone mineral density. The purpose is to develop a known technology and apply it to improve the bone-metal construct strength by reducing human error of screw overtightening

Human well-being impacts of terrestrial protected areas

© 2013 Pullin et al.; licensee BioMed Central Ltd. Background: Establishing Protected Areas (PAs) is among the most common conservation interventions. Protecting areas from the threats posed by human activity will by definition inhibit some human actions. However, adverse impacts could be balanced by maintaining ecosystem services or introducing new livelihood options. Consequently there is an ongoing debate on whether the net impact of PAs on human well-being at local or regional scales is positive or negative. We report here on a systematic review of evidence for impacts on human well-being arising from the establishment and maintenance of terrestrial PAs. Methods: Following an a priori protocol, systematic searches were conducted for evidence of impacts of PAs post 1992. After article title screening, the review was divided into two separate processes; a qualitative synthesis of explanations and meaning of impact and a review of quantitative evidence of impact. Abstracts and full texts were assessed using inclusion criteria and conceptual models of potential impacts. Relevant studies were critically appraised and data extracted and sorted according to type of impact reported. No quantitative synthesis was possible with the evidence available. Two narrative syntheses were produced and their outputs compared in a metasynthesis. Results: The qualitative evidence review mapped 306 articles and synthesised 34 that were scored as high quality. The quantitative evidence review critically appraised 79 studies and included 14 of low/medium susceptibility to bias. The meta-synthesis reveals that a range of factors can lead to reports of positive and negative impacts of PA establishment, and therefore might enable hypothesis generation regarding cause and effect relationships, but resulting hypotheses cannot be tested with the current available evidence. Conclusions: The evidence base provides a range of possible pathways of impact, both positive and negative, of PAs on human well-being but provides very little support for decision making on how to maximise positive impacts. The nature of the research reported to date forms a diverse and fragmented body of evidence unsuitable for the purpose of informing policy formation on how to achieve win-win outcomes for biodiversity and human well-being. To better assess the impacts of PAs on human well-being we make recommendations for improving research study design and reporting

Better evidence, better decisions, better environment: emergent themes from the first environmental evidence conference

The first international Collaboration for Environmental Evidence (CEE) conference took place in August 2016 at the Swedish Museum of Natural History in Stockholm with nearly 100 participants from 14 countries. This conference reflected and contributed to th

Open data from the third observing run of LIGO, Virgo, KAGRA, and GEO

The global network of gravitational-wave observatories now includes five detectors, namely LIGO Hanford, LIGO Livingston, Virgo, KAGRA, and GEO 600. These detectors collected data during their third observing run, O3, composed of three phases: O3a starting in 2019 April and lasting six months, O3b starting in 2019 November and lasting five months, and O3GK starting in 2020 April and lasting two weeks. In this paper we describe these data and various other science products that can be freely accessed through the Gravitational Wave Open Science Center at https://gwosc.org. The main data set, consisting of the gravitational-wave strain time series that contains the astrophysical signals, is released together with supporting data useful for their analysis and documentation, tutorials, as well as analysis software packages

Open Data from the Third Observing Run of LIGO, Virgo, KAGRA, and GEO

Calibration of the LIGO strain data was performed with a GstLAL-based calibration software pipeline (Viets et al. 2018). Calibration of the Virgo strain data was performed with C-based software (Acernese et al. 2022b). Data quality products and event-validation results were computed using the DMT (https://labcit.ligo.caltech.edu/~jzweizig/DMT-Project. html), DQR (https://docs.ligo.org/detchar/data-quality-report/), DQSEGDB (Fisher et al. 2021), gwdetchar (Macloed et al. 2021a), hveto (Smith et al. 2011), iDQ (Essick et al. 2020), and Omicron (Robinet et al. 2020) software packages and contribut- ing software tools. Analyses relied upon the LALSuite software library (LIGO Scientific Collaboration 2018). PESummary was used to postprocess and collate parameter estimation results (Hoy & Raymond 2021). For an exhaustive list of the software used for searching the GW signals and characterizing their source, see Abbott et al. (2021c). Plots were prepared with Matplotlib (Hunter 2007), seaborn (Waskom 2021), GWSumm (Macleod et al. 2021b), and GWpy (Macleod et al. 2021c). NumPy (Harris et al. 2020) and SciPy (Virtanen et al. 2020) were used in the preparation of the manuscript. This material is based upon work supported by NSF’s LIGO Laboratory which is a major facility fully funded by the National Science Foundation. The authors also gratefully acknowledge the support of the Science and Technology Facilities Council (STFC) of the United Kingdom, the Max- Planck-Society (MPS), and the State of Niedersachsen/ Germany for support of the construction of Advanced LIGO and construction and operation of the GEO 600 detector. Additional support for Advanced LIGO was provided by the Australian Research Council. The authors gratefully acknowl- edge the Italian Istituto Nazionale di Fisica Nucleare (INFN), the French Centre National de la Recherche Scientifique (CNRS), and the Netherlands Organization for Scientific Research (NWO) for the construction and operation of the Virgo detector and the creation and support of the EGO consortium. The authors also gratefully acknowledge research support from these agencies as well as by the Council of Scientific and Industrial Research of India, the Department of Science and Technology, India, the Science & Engineering Research Board (SERB), India, the Ministry of Human Resource Development, India, the Spanish Agencia Estatal de Investigación (AEI), the Spanish Ministerio de Ciencia e Innovación and Ministerio de Universidades, the Conselleria de Fons Europeus, Universitat i Cultura and the Direcció General de Política Universitaria i Recerca del Govern de les Illes Balears, the Conselleria d'Innovació, Universitats, Ciència i Societat Digital de la Generalitat Valenciana and the CERCA Programme Generalitat de Catalunya, Spain, the National Science Centre of Poland and the European Union – European Regional Development Fund; Foundation for Polish Science (FNP), the Swiss National Science Foundation (SNSF), the Russian Foundation for Basic Research, the Russian Science Foundation, the European Commission, the European Social Funds (ESF), the European Regional Development Funds (ERDF), the Royal Society, the Scottish Funding Council, the Scottish Universities Physics Alliance, the Hungarian Scientific Research Fund (OTKA), the French Lyon Institute of Origins (LIO), the Belgian Fonds de la Recherche Scientifique (FRS- FNRS), Actions de Recherche Concertées (ARC) and Fonds Wetenschappelijk Onderzoek – Vlaanderen (FWO), Belgium, the Paris Île-de-France Region, the National Research, Development and Innovation Office Hungary (NKFIH), the National Research Foundation of Korea, the Natural Science and Engineering Research Council Canada, Canadian Founda- tion for Innovation (CFI), the Brazilian Ministry of Science, Technology, and Innovations, the International Center for Theoretical Physics South American Institute for Fundamental Research (ICTP-SAIFR), the Research Grants Council of Hong Kong, the National Natural Science Foundation of China (NSFC), the Leverhulme Trust, the Research Corporation, the Ministry of Science and Technology (MOST), Taiwan, the United States Department of Energy, and the Kavli Foundation. The authors gratefully acknowledge the support of the NSF, STFC, INFN, and CNRS for provision of computational resources. This work was supported by MEXT, JSPS Leading-edge Research Infrastructure Program, JSPS Grant-in-Aid for Specially Promoted Research 26000005, JSPS Grant-in-Aid for Scientific Research on Innovative Areas 2905: JP17H06358, JP17H06361 and JP17H06364, JSPS Core-to- Core Program A, Advanced Research Networks, JSPS Grant- in-Aid for Scientific Research (S) 17H06133 and 20H05639, JSPS Grant-in-Aid for Transformative Research Areas (A) 20A203: JP20H05854, the joint research program of the Institute for Cosmic Ray Research, University of Tokyo, National Research Foundation (NRF), Computing Infrastruc- ture Project of Global Science experimental Data hub Center (GSDC) at KISTI, Korea Astronomy and Space Science Institute (KASI), and Ministry of Science and ICT (MSIT) in Korea, Academia Sinica (AS), AS Grid Center (ASGC) and the National Science and Technology Council (NSTC) in Taiwan under grants including the Rising Star Program and Science Vanguard Research Program, Advanced Technology Center (ATC) of NAOJ, and Mechanical Engineering Center of KEK.Peer reviewe

Search for eccentric black hole coalescences during the third observing run of LIGO and Virgo

Despite the growing number of binary black hole coalescences confidently observed through gravitational waves so far, the astrophysical origin of these binaries remains uncertain. Orbital eccentricity is one of the clearest tracers of binary formation channels. Identifying binary eccentricity, however, remains challenging due to the limited availability of gravitational waveforms that include the effects of eccentricity. Here, we present observational results for a waveform-independent search sensitive to eccentric black hole coalescences, covering the third observing run (O3) of the LIGO and Virgo detectors. We identified no new high-significance candidates beyond those that have already been identified with searches focusing on quasi-circular binaries. We determine the sensitivity of our search to high-mass (total source-frame mass M > 70 M⊙) binaries covering eccentricities up to 0.3 at 15 Hz emitted gravitational-wave frequency, and use this to compare model predictions to search results. Assuming all detections are indeed quasi-circular, for our fiducial population model, we place a conservative upper limit for the merger rate density of high-mass binaries with eccentricities 0 < e ≤ 0.3 at 16.9 Gpc−3 yr−1 at the 90% confidence level

Applicability and feasibility of systematic review for performing evidence-based risk assessment in food and feed safety

Food and feed safety risk assessment uses multi-parameter models to evaluate the likelihood of adverse events associated with exposure to hazards in human health, plant health, animal health, animal welfare, and the environment. Systematic review and meta-analysis are established methods for answering questions in health care, and can be implemented to minimize biases in food and feed safety risk assessment. However, no methodological frameworks exist for refining risk assessment multi-parameter models into questions suitable for systematic review, and use of meta-analysis to estimate all parameters required by a risk model may not be always feasible. This paper describes novel approaches for determining question suitability and for prioritizing questions for systematic review in this area. Risk assessment questions that aim to estimate a parameter are likely to be suitable for systematic review. Such questions can be structured by their "key elements" [e.g., for intervention questions, the population(s), intervention(s), comparator(s), and outcome(s)]. Prioritization of questions to be addressed by systematic review relies on the likely impact and related uncertainty of individual parameters in the risk model. This approach to planning and prioritizing systematic review seems to have useful implications for producing evidence-based food and feed safety risk assessment