Responses of the West European hedgehog to urbanisation: impact on population dynamics, animal movement and habitat selection

Urbanisation is rapidly increasing, producing drastic changes in the environment. While many species are unable to adapt to these human-made environments, some species not only survive but thrive in urban landscapes. The West European hedgehog Erinaceus europaeus is a species of conservation concern in the United Kingdom where populations have declined markedly since the 1950s. Despite declines being reported both in urban and rural areas, the species seems to be persisting in cities and towns. However, current population estimates are unreliable and our understanding of the population status of the species is limited. This study aimed to understand how hedgehogs respond to urbanisation by investigating how their density, movement behaviour and habitat selection varies across urban and rural landscapes. Between 2016 and 2019, camera trapping and high-frequency GPS movement data were collected across England. Hedgehog densities were calculated from camera trapping data using the Random Encounter Model (REM) across five urban and four rural study sites, and compared to those estimated by Spatially Capture-Recapture using data from nocturnal spotlight surveys. Hedgehog movement was studied across five urban and six rural sites, where home range was evaluated using the Time Local Convex Hull (T-LoCoH) method. Movement behaviour was extracted from GPS data using Hidden Markov Models, incorporated into habitat selection analysis and studied using the integrated step selection analysis. Hedgehog density, as estimated by the REM, was on average 7.5 times higher in urban versus rural landscapes. The movement of individual hedgehogs differed between both landscapes: urban individuals exhibited slower speeds and travelled shorter distances per night than rural individuals. Nightly home range size was best predicted by sex, landscape and the proportion of gardens used: larger home ranges were displayed by males in the rural landscape, and home range sizes decreased as the proportion of gardens used increased. Hedgehogs spent more time foraging (68%) than travelling (32%) across both landscapes. However, the time spent performing each behaviour varied by sex and landscape. Gardens were found to be important habitats, as they were strongly selected for foraging and travelling behaviours of hedgehogs in both urban and rural areas. This is the first comparative study to estimate population densities across urban and rural areas in England and provide researchers with a robust methodology that uses camera trapping data and the REM for the monitoring of species. Furthermore, this is the first study to incorporate behaviour extracted from GPS movement data into habitat selection analysis to better understand how hedgehogs are using different habitats in different landscapes. Findings from this study provide important and novel information to aid understanding of how different landscapes are affecting the distribution and behaviour of hedgehogs and how they are exploiting anthropogenic landscape features to persist in cities and towns

Deep Policy Dynamic Programming for Vehicle Routing Problems

Routing problems are a class of combinatorial problems with many practical applications. Recently, end-to-end deep learning methods have been proposed to learn approximate solution heuristics for such problems. In contrast, classical dynamic programming (DP) algorithms guarantee optimal solutions, but scale badly with the problem size. We propose Deep Policy Dynamic Programming (DPDP), which aims to combine the strengths of learned neural heuristics with those of DP algorithms. DPDP prioritizes and restricts the DP state space using a policy derived from a deep neural network, which is trained to predict edges from example solutions. We evaluate our framework on the travelling salesman problem (TSP), the vehicle routing problem (VRP) and TSP with time windows (TSPTW) and show that the neural policy improves the performance of (restricted) DP algorithms, making them competitive to strong alternatives such as LKH, while also outperforming most other 'neural approaches' for solving TSPs, VRPs and TSPTWs with 100 nodes.Comment: 21 page

Failure detection for the Bin-Packing Constraint

Abstract In addition to a filtering algorithm, the Pack constraint introduced by Shaw uses a failure detection algorithm. This test is based on a reduction of the partial solution to a standard bin-packing problem and the computation of a bin-packing lower bound (BPLB) on the reduced problem. A first possible improvement on Shaw's test is to use a stronger BPLB. In particular, Labbé's lower bound was recently proved to dominate Martello's lower bound used by Shaw. A second possible improvement is to use a reduction different from the one introduced by Shaw. We propose two new reduction algorithms and prove that one of them theoretically dominates the others. All the proposed improvements on the failure test are evaluated using the COMET System

Small molecule inhibitors of Late SV40 Factor (LSF) abrogate hepatocellular carcinoma (HCC): evaluation using an endogenous HCC model

Hepatocellular carcinoma (HCC) is a lethal malignancy with high mortality and poor prognosis. Oncogenic transcription factor Late SV40 Factor (LSF) plays an important role in promoting HCC. A small molecule inhibitor of LSF, Factor Quinolinone Inhibitor 1 (FQI1), significantly inhibited human HCC xenografts in nude mice without harming normal cells. Here we evaluated the efficacy of FQI1 and another inhibitor, FQI2, in inhibiting endogenous hepatocarcinogenesis. HCC was induced in a transgenic mouse with hepatocyte-specific overexpression of c-myc (Alb/c-myc) by injecting N-nitrosodiethylamine (DEN) followed by FQI1 or FQI2 treatment after tumor development. LSF inhibitors markedly decreased tumor burden in Alb/c-myc mice with a corresponding decrease in proliferation and angiogenesis. Interestingly, in vitro treatment of human HCC cells with LSF inhibitors resulted in mitotic arrest with an accompanying increase in CyclinB1. Inhibition of CyclinB1 induction by Cycloheximide or CDK1 activity by Roscovitine significantly prevented FQI-induced mitotic arrest. A significant induction of apoptosis was also observed upon treatment with FQI. These effects of LSF inhibition, mitotic arrest and induction of apoptosis by FQI1s provide multiple avenues by which these inhibitors eliminate HCC cells. LSF inhibitors might be highly potent and effective therapeutics for HCC either alone or in combination with currently existing therapies.The present study was supported in part by grants from The James S. McDonnell Foundation, National Cancer Institute Grant R01 CA138540-01A1 (DS), National Institutes of Health Grant R01 CA134721 (PBF), the Samuel Waxman Cancer Research Foundation (SWCRF) (DS and PBF), National Institutes of Health Grants R01 GM078240 and P50 GM67041 (SES), the Johnson and Johnson Clinical Innovation Award (UH), and the Boston University Ignition Award (UH). JLSW was supported by Alnylam Pharmaceuticals, Inc. DS is the Harrison Endowed Scholar in Cancer Research and Blick scholar. PBF holds the Thelma Newmeyer Corman Chair in Cancer Research. The authors acknowledge Dr. Lauren E. Brown (Dept. Chemistry, Boston University) for the synthesis of FQI1 and FQI2, and Lucy Flynn (Dept. Biology, Boston University) for initially identifying G2/M effects caused by FQI1. (James S. McDonnell Foundation; R01 CA138540-01A1 - National Cancer Institute; R01 CA134721 - National Institutes of Health; R01 GM078240 - National Institutes of Health; P50 GM67041 - National Institutes of Health; Samuel Waxman Cancer Research Foundation (SWCRF); Johnson and Johnson Clinical Innovation Award; Boston University Ignition Award; Alnylam Pharmaceuticals, Inc.)Published versio

Application of the random encounter model in citizen science projects to monitor animal densities

Abundance and density are vital metrics for assessing a species’ conservation status and for developing effective management strategies. Remote-sensing cameras are being used increasingly as part of citizen science projects to monitor wildlife, but current methodologies to monitor densities pose challenges when animals are not individually recognisable. We investigate the use of camera traps and the Random Encounter Model (REM) for estimating the density of West European hedgehogs (Erinaceus europaeus) within a citizen science framework. We evaluate the use of a simplified version of the REM in terms of the parameters’ estimation (averaged versus survey-specific) and asses it’s potential application as part of a large-scale, long-term citizen science project. We compare averaged REM estimates to those obtained via Spatial Capture-Recapture (SCR) using data from nocturnal spotlight surveys. There was a high degree of concordance in REM-derived density estimates from averaged parameters versus those derived from survey-specific parameters. Averaged REM density estimates were also comparable to those produced by SCR at 8 out of 9 sites; hedgehog density was 7.5 times higher in urban (32.3 km-2) versus rural (4.3 km2) sites. Power analyses indicated that the averaged REM approach would be able to detect a 25% change in hedgehog density in both habitats with >90% power. Furthermore, despite the high start-up costs associated with the REM method, it would be cost-effective in the long term. The averaged REM approach is a promising solution to the challenge of large-scale and long-term species monitoring. We suggest including the REM as part of a citizen science monitoring project, where participants collect data and researchers verify and implement the required analysis

New Proposed Mechanism of Actin-Polymerization-Driven Motility

We present the first numerical simulation of actin-driven propulsion by elastic filaments. Specifically, we use a Brownian dynamics formulation of the dendritic nucleation model of actin-driven propulsion. We show that the model leads to a self-assembled network that exerts forces on a disk and pushes it with an average speed. This simulation approach is the first to observe a speed that varies non-monotonically with the concentration of branching proteins (Arp2/3), capping protein and depolymerization rate (ADF), in accord with experimental observations. Our results suggest a new interpretation of the origin of motility that can be tested readily by experiment.Comment: 31 pages, 5 figure

Biophysically Realistic Filament Bending Dynamics in Agent-Based Biological Simulation

An appealing tool for study of the complex biological behaviors that can emerge from networks of simple molecular interactions is an agent-based, computational simulation that explicitly tracks small-scale local interactions – following thousands to millions of states through time. For many critical cell processes (e.g. cytokinetic furrow specification, nuclear centration, cytokinesis), the flexible nature of cytoskeletal filaments is likely to be critical. Any computer model that hopes to explain the complex emergent behaviors in these processes therefore needs to encode filament flexibility in a realistic manner. Here I present a numerically convenient and biophysically realistic method for modeling cytoskeletal filament flexibility in silico. Each cytoskeletal filament is represented by a series of rigid segments linked end-to-end in series with a variable attachment point for the translational elastic element. This connection scheme allows an empirically tuning, for a wide range of segment sizes, viscosities, and time-steps, that endows any filament species with the experimentally observed (or theoretically expected) static force deflection, relaxation time-constant, and thermal writhing motions. I additionally employ a unique pair of elastic elements – one representing the axial and the other the bending rigidity– that formulate the restoring force in terms of single time-step constraint resolution. This method is highly local –adjacent rigid segments of a filament only interact with one another through constraint forces—and is thus well-suited to simulations in which arbitrary additional forces (e.g. those representing interactions of a filament with other bodies or cross-links / entanglements between filaments) may be present. Implementation in code is straightforward; Java source code is available at www.celldynamics.org