Urban Biodiversity and Landscape Ecology: Patterns, Processes and Planning

Effective planning for biodiversity in cities and towns is increasingly important as urban areas and their human populations grow, both to achieve conservation goals and because ecological communities support services on which humans depend. Landscape ecology provides important frameworks for understanding and conserving urban biodiversity both within cities and considering whole cities in their regional context, and has played an important role in the development of a substantial and expanding body of knowledge about urban landscapes and communities. Characteristics of the whole city including size, overall amount of green space, age and regional context are important considerations for understanding and planning for biotic assemblages at the scale of entire cities, but have received relatively little research attention. Studies of biodiversity within cities are more abundant and show that longstanding principles regarding how patch size, configuration and composition influence biodiversity apply to urban areas as they do in other habitats. However, the fine spatial scales at which urban areas are fragmented and the altered temporal dynamics compared to non-urban areas indicate a need to apply hierarchical multi-scalar landscape ecology models to urban environments. Transferring results from landscape-scale urban biodiversity research into planning remains challenging, not least because of the requirements for urban green space to provide multiple functions. An increasing array of tools is available to meet this challenge and increasingly requires ecologists to work with planners to address biodiversity challenges. Biodiversity conservation and enhancement is just one strand in urban planning, but is increasingly important in a rapidly urbanising world

Stress and worry in the 2020 coronavirus pandemic: relationships to trust and compliance with preventive measures across 48 countries in the COVIDiSTRESS global survey

Fil: Lieberoth, Andreas. University Aarhus; Dinamarca.Fil: Lin, Shiang Yi. University of Hong Kong; China.Fil: Stöckli, Sabrina. University of Bern; Suiza.Fil: Han, Hyemin. University of Alabama at Birmingahm; Estados Unidos.Fil: Kowal, Marta. Wroclaw University; Polonia.Fil: Gelpi, Rebekah. University of Toronto; Canadá.Fil: Chrona, Stavroula. King's College London; Reino Unido.Fil: Tran, Thao Phuong. State University of Colorado at Boulder; Estados Unidos.Fil: Jeftić, Alma. International Christian University; Japón.Fil: Rasmussen, Jesper. University Aarhus; Dinamarca.Fil: Cakal, Huseyin. Keele University.; Reino Unido.Fil: Milfont, Taciano L.. University of Waikato; Nueva Zelanda.Fil: Reyna, Cecilia. Universidad Nacional de Córdoba. Facultad de Psicología; Argentina.Fil: Reyna Cecilia. Consejo Nacional de Investigaciones Científicas y Técnicas. Instituto de Investigaciones Psicológicas; Argentina.The COVIDiSTRESS global survey collects data on early human responses to the 2020 COVID-19 pandemic from 173 429 respondents in 48 countries. The open science study was co-designed by an international consortium of researchers to investigate how psychological responses differ across countries and cultures, and how this has impacted behaviour, coping and trust in government efforts to slow the spread of the virus. Starting in March 2020, COVIDiSTRESS leveraged the convenience of unpaid online recruitment to generate public data. The objective of the present analysis is to understand relationships between psychological responses in the early months of global coronavirus restrictions and help understand how different government measures succeed or fail in changing public behaviour. There were variations between and within countries. Although Western Europeans registered as more concerned over COVID-19, more stressed, and having slightly more trust in the governments' efforts, there was no clear geographical pattern in compliance with behavioural measures. Detailed plots illustrating between-countries differences are provided. Using both traditional and Bayesian analyses, we found that individuals who worried about getting sick worked harder to protect themselves and others. However, concern about the coronavirus itself did not account for all of the variances in experienced stress during the early months of COVID-19 restrictions. More alarmingly, such stress was associated with less compliance. Further, those most concerned over the coronavirus trusted in government measures primarily where policies were strict. While concern over a disease is a source of mental distress, other factors including strictness of protective measures, social support and personal lockdown conditions must also be taken into consideration to fully appreciate the psychological impact of COVID-19 and to understand why some people fail to follow behavioural guidelines intended to protect themselves and others from infection. The Stage 1 manuscript associated with this submission received in-principle acceptance (IPA) on 18 May 2020. Following IPA, the accepted Stage 1 version of the manuscript was preregistered on the Open Science Framework at https://osf.io/ytbcs. This preregistration was performed prior to data analysis.publishedVersionFil: Lieberoth, Andreas. University Aarhus; Dinamarca.Fil: Lin, Shiang Yi. University of Hong Kong; China.Fil: Stöckli, Sabrina. University of Bern; Suiza.Fil: Han, Hyemin. University of Alabama at Birmingahm; Estados Unidos.Fil: Kowal, Marta. Wroclaw University; Polonia.Fil: Gelpi, Rebekah. University of Toronto; Canadá.Fil: Chrona, Stavroula. King's College London; Reino Unido.Fil: Tran, Thao Phuong. State University of Colorado at Boulder; Estados Unidos.Fil: Jeftić, Alma. International Christian University; Japón.Fil: Rasmussen, Jesper. University Aarhus; Dinamarca.Fil: Cakal, Huseyin. Keele University.; Reino Unido.Fil: Milfont, Taciano L.. University of Waikato; Nueva Zelanda.Fil: Reyna, Cecilia. Universidad Nacional de Córdoba. Facultad de Psicología; Argentina.Fil: Reyna Cecilia. Consejo Nacional de Investigaciones Científicas y Técnicas. Instituto de Investigaciones Psicológicas; Argentina

A common biological basis of obesity and nicotine addiction

Smoking influences body weight such that smokers weigh less than non-smokers and smoking cessation often leads to weight increase. The relationship between body weight and smoking is partly explained by the effect of nicotine on appetite and metabolism. However, the brain reward system is involved in the control of the intake of both food and tobacco. We evaluated the effect of single-nucleotide polymorphisms (SNPs) affecting body mass index (BMI) on smoking behavior, and tested the 32 SNPs identified in a meta-analysis for association with two smoking phenotypes, smoking initiation (SI) and the number of cigarettes smoked per day (CPD) in an Icelandic sample (N=34 216 smokers). Combined according to their effect on BMI, the SNPs correlate with both SI (r=0.019, P=0.00054) and CPD (r=0.032, P=8.0 × 10<sup>−7</sup>). These findings replicate in a second large data set (N=127 274, thereof 76 242 smokers) for both SI (P=1.2 × 10<sup>−5</sup>) and CPD (P=9.3 × 10<sup>−5</sup>). Notably, the variant most strongly associated with BMI (rs1558902-A in FTO) did not associate with smoking behavior. The association with smoking behavior is not due to the effect of the SNPs on BMI. Our results strongly point to a common biological basis of the regulation of our appetite for tobacco and food, and thus the vulnerability to nicotine addiction and obesity

A Deep Neural Network for Simultaneous Estimation of b Jet Energy and Resolution

We describe a method to obtain point and dispersion estimates for the energies of jets arising from b quarks produced in proton-proton collisions at an energy of s = 13 TeV at the CERN LHC. The algorithm is trained on a large sample of simulated b jets and validated on data recorded by the CMS detector in 2017 corresponding to an integrated luminosity of 41 fb - 1 . A multivariate regression algorithm based on a deep feed-forward neural network employs jet composition and shape information, and the properties of reconstructed secondary vertices associated with the jet. The results of the algorithm are used to improve the sensitivity of analyses that make use of b jets in the final state, such as the observation of Higgs boson decay to b b ¯

Search for dark matter produced in association with a Higgs boson decaying to a pair of bottom quarks in proton-proton collisions at root s=13TeV

A search for dark matter produced in association with a Higgs boson decaying to a pair of bottom quarks is performed in proton-proton collisions at a center-of-mass energy of 13 TeV collected with the CMS detector at the LHC. The analyzed data sample corresponds to an integrated luminosity of 35.9 fb(-1). The signal is characterized by a large missing transverse momentum recoiling against a bottom quark-antiquark system that has a large Lorentz boost. The number of events observed in the data is consistent with the standard model background prediction. Results are interpreted in terms of limits both on parameters of the type-2 two-Higgs doublet model extended by an additional light pseudoscalar boson a (2HDM+a) and on parameters of a baryonic Z simplified model. The 2HDM+a model is tested experimentally for the first time. For the baryonic Z model, the presented results constitute the most stringent constraints to date.Peer reviewe

Search for contact interactions and large extra dimensions in the dilepton mass spectra from proton-proton collisions at \sqrt{s} = 13 TeV

A search for nonresonant excesses in the invariant mass spectra of electron and muon pairs is presented. The analysis is based on data from proton-proton collisions at a center-of-mass energy of 13 TeV recorded by the CMS experiment in 2016, corresponding to a total integrated luminosity of 36 fb^{-1}. No significant deviation from the standard model is observed. Limits are set at 95% confidence level on energy scales for two general classes of nonresonant models. For a class of fermion contact interaction models, lower limits ranging from 20 to 32 TeV are set on the characteristic compositeness scale Λ. For the Arkani-Hamed, Dimopoulos, and Dvali model of large extra dimensions, the first results in the dilepton final state at 13 TeV are reported, and values of the ultraviolet cutoff parameter Λ_{T} below 6.9 TeV are excluded. A combination with recent CMS diphoton results improves this exclusion to Λ_{T} below 7.7 TeV, providing the most sensitive limits to date in nonhadronic final states

Measurement of B_{s}^{0} meson production in pp and PbPb collisions at \sqrt{SNN}

The production cross sections of B_{s}^{0} mesons and charge conjugates are measured in proton-proton (pp) and PbPb collisions via the exclusive decay channel B_{s}^{0}→J/ψϕ→μ^{+}μ^{−}K^{+}K^{−} at a center-of-mass energy of 5.02 TeV per nucleon pair and within the rapidity range |y|<2.4 using the CMS detector at the LHC. The pp measurement is performed as a function of transverse momentum (p_{T}) of the B_{s}^{0} mesons in the range of 7 to 50 GeV/c and is compared to the predictions of perturbative QCD calculations. The B_{s}^{0} production yield in PbPb collisions is measured in two p_{T} intervals, 7 to 15 and 15 to 50 GeV/c, and compared to the yield in pp collisions in the same kinematic region. The nuclear modification factor (R_{AA}) is found to be 1.5±0.6(stat)±0.5(syst) for 7–15 GeV/c, and 0.87±0.30(stat)±0.17(syst) for 15–50 GeV/c, respectively. Within current uncertainties, the B_{s}^{0} results are consistent with models of strangeness enhancement, and suppression by parton energy loss, as observed for the B+ mesons