Showing papers by "University of Lincoln published in 2017"
University of Udine1, University of Lugano2, École Polytechnique Fédérale de Lausanne3, Leipzig University4, University of Paris5, University of North Texas6, Princeton University7, National Research Council8, International School for Advanced Studies9, Cornell University10, University of Lincoln11, University of Milan12, École Polytechnique13, International Centre for Theoretical Physics14, University of Paderborn15, University of Oxford16, Jožef Stefan Institute17, University of Padua18, Sapienza University of Rome19, Vietnam Academy of Science and Technology20, University of British Columbia21, Centre national de la recherche scientifique22, University of Lorraine23, University of Zurich24, École Normale Supérieure25, Université Paris-Saclay26, Wake Forest University27, Temple University28
TL;DR: Recent extensions and improvements are described, covering new methodologies and property calculators, improved parallelization, code modularization, and extended interoperability both within the distribution and with external software.
Abstract: Quantum ESPRESSO is an integrated suite of open-source computer codes for quantum simulations of materials using state-of-the-art electronic-structure techniques, based on density-functional theory, density-functional perturbation theory, and many-body perturbation theory, within the plane-wave pseudopotential and projector-augmented-wave approaches Quantum ESPRESSO owes its popularity to the wide variety of properties and processes it allows to simulate, to its performance on an increasingly broad array of hardware architectures, and to a community of researchers that rely on its capabilities as a core open-source development platform to implement their ideas In this paper we describe recent extensions and improvements, covering new methodologies and property calculators, improved parallelization, code modularization, and extended interoperability both within the distribution and with external software
3,638 citations
University of Udine1, École Polytechnique Fédérale de Lausanne2, University of Lugano3, Leipzig University4, University of Paris5, University of North Texas6, Princeton University7, National Research Council8, International School for Advanced Studies9, Cornell University10, University of Lincoln11, University of Milan12, École Polytechnique13, International Centre for Theoretical Physics14, University of Paderborn15, University of Oxford16, Jožef Stefan Institute17, University of Padua18, Sapienza University of Rome19, Vietnam Academy of Science and Technology20, University of British Columbia21, University of Lorraine22, Centre national de la recherche scientifique23, University of Zurich24, École Normale Supérieure25, Université Paris-Saclay26, Wake Forest University27, Temple University28
TL;DR: Quantum ESPRESSO as discussed by the authors is an integrated suite of open-source computer codes for quantum simulations of materials using state-of-the-art electronic-structure techniques, based on density functional theory, density functional perturbation theory, and many-body perturbations theory, within the plane-wave pseudo-potential and projector-augmented-wave approaches.
Abstract: Quantum ESPRESSO is an integrated suite of open-source computer codes for quantum simulations of materials using state-of-the art electronic-structure techniques, based on density-functional theory, density-functional perturbation theory, and many-body perturbation theory, within the plane-wave pseudo-potential and projector-augmented-wave approaches. Quantum ESPRESSO owes its popularity to the wide variety of properties and processes it allows to simulate, to its performance on an increasingly broad array of hardware architectures, and to a community of researchers that rely on its capabilities as a core open-source development platform to implement theirs ideas. In this paper we describe recent extensions and improvements, covering new methodologies and property calculators, improved parallelization, code modularization, and extended interoperability both within the distribution and with external software.
2,818 citations
TL;DR: In this paper, the authors present a systematic review of 157 papers on digital developments and rural development in advanced countries, focusing on the general conclusions, in order to better understand the potential impacts of the coming Next Generation Access revolution.
469 citations
National Health Service1, University of Oxford2, University of Manchester3, University of Glasgow4, University of Plymouth5, University of Bristol6, Centre for Mental Health7, University of Sheffield8, University of Liverpool9, University of Leicester10, Swansea University11, Goldsmiths, University of London12, University of Strathclyde13, University of Worcester14, University of Nottingham15, RMIT University16, University of New South Wales17, Middlesex University18, Sussex Partnership NHS Foundation Trust19, University of Sussex20, University College London21, University of Central Lancashire22, Royal Holloway, University of London23, University of Cambridge24, University of East Anglia25, University of Exeter26, University of Edinburgh27, University of Lincoln28
TL;DR: There is strong evidence that insomnia is a causal factor in the occurrence of psychotic experiences and other mental health problems, and the treatment of disrupted sleep might require a higher priority in mental health provision.
413 citations
University of Oxford1, Ben-Gurion University of the Negev2, Tel Aviv University3, Bishop Museum4, Villanova University5, Imperial College London6, Zoological Society of London7, University of Valle8, University College London9, University of Brasília10, Technion – Israel Institute of Technology11, Universiti Malaysia Sarawak12, University of Central Florida13, La Sierra University14, Museu Paraense Emílio Goeldi15, University of Michigan16, University of São Paulo17, Royal Museum for Central Africa18, Royal Belgian Institute of Natural Sciences19, University of Lincoln20, Pontificia Universidad Católica del Ecuador21, Institut de recherche pour le développement22, Virginia Commonwealth University23, Chinese Academy of Sciences24, American Museum of Natural History25
TL;DR: It is shown that additional conservation actions are needed to effectively protect reptiles, particularly lizards and turtles, and that adding reptile knowledge to a global complementarity conservation priority scheme identifies many locations that consequently become important.
Abstract: The distributions of amphibians, birds and mammals have underpinned global and local conservation priorities, and have been fundamental to our understanding of the determinants of global biodiversity. In contrast, the global distributions of reptiles, representing a third of terrestrial vertebrate diversity, have been unavailable. This prevented the incorporation of reptiles into conservation planning and biased our understanding of the underlying processes governing global vertebrate biodiversity. Here, we present and analyse the global distribution of 10,064 reptile species (99% of extant terrestrial species). We show that richness patterns of the other three tetrapod classes are good spatial surrogates for species richness of all reptiles combined and of snakes, but characterize diversity patterns of lizards and turtles poorly. Hotspots of total and endemic lizard richness overlap very little with those of other taxa. Moreover, existing protected areas, sites of biodiversity significance and global conservation schemes represent birds and mammals better than reptiles. We show that additional conservation actions are needed to effectively protect reptiles, particularly lizards and turtles. Adding reptile knowledge to a global complementarity conservation priority scheme identifies many locations that consequently become important. Notably, investing resources in some of the world’s arid, grassland and savannah habitats might be necessary to represent all terrestrial vertebrates efficiently.
354 citations
01 Sep 2017
TL;DR: This paper proposes a novel deep learning based approach that outperforms existing state-of-the-art techniques dramatically and is applicable to text learning or text classification.
Abstract: Traditional supervised learning makes the closed-world assumption that the classes appeared in the test data must have appeared in training. This also applies to text learning or text classification. As learning is used increasingly in dynamic open environments where some new/test documents may not belong to any of the training classes, identifying these novel documents during classification presents an important problem. This problem is called open-world classification or open classification. This paper proposes a novel deep learning based approach. It outperforms existing state-of-the-art techniques dramatically.
264 citations
TL;DR: This provides a close match to expert delineation across all grades of glioma, leading to a faster and more reproducible method of brain tumour detection and delineation to aid patient management.
Abstract: We propose a fully automated method for detection and segmentation of the abnormal tissue associated with brain tumour (tumour core and oedema) from Fluid- Attenuated Inversion Recovery (FLAIR) Magnetic Resonance Imaging (MRI). The method is based on superpixel technique and classification of each superpixel. A number of novel image features including intensity-based, Gabor textons, fractal analysis and curvatures are calculated from each superpixel within the entire brain area in FLAIR MRI to ensure a robust classification. Extremely randomized trees (ERT) classifier is compared with support vector machine (SVM) to classify each superpixel into tumour and non-tumour. The proposed method is evaluated on two datasets: (1) Our own clinical dataset: 19 MRI FLAIR images of patients with gliomas of grade II to IV, and (2) BRATS 2012 dataset: 30 FLAIR images with 10 low-grade and 20 high-grade gliomas. The experimental results demonstrate the high detection and segmentation performance of the proposed method using ERT classifier. For our own cohort, the average detection sensitivity, balanced error rate and the Dice overlap measure for the segmented tumour against the ground truth are 89.48 %, 6 % and 0.91, respectively, while, for the BRATS dataset, the corresponding evaluation results are 88.09 %, 6 % and 0.88, respectively. This provides a close match to expert delineation across all grades of glioma, leading to a faster and more reproducible method of brain tumour detection and delineation to aid patient management.
246 citations
TL;DR: Cardiac arrest is an important cause of death in England and there is scope to improve outcomes, with less than one in ten patients surviving and survival rates highest amongst those who received bystander CPR and public access defibrillation.
235 citations
TL;DR: A unified framework for PbRL is provided that describes the task formally and points out the different design principles that affect the evaluation task for the human as well as the computational complexity.
Abstract: Reinforcement learning (RL) techniques optimize the accumulated long-term reward of a suitably chosen reward function. However, designing such a reward function often requires a lot of task-specific prior knowledge. The designer needs to consider different objectives that do not only influence the learned behavior but also the learning progress. To alleviate these issues, preference-based reinforcement learning algorithms (PbRL) have been proposed that can directly learn from an expert's preferences instead of a hand-designed numeric reward. PbRL has gained traction in recent years due to its ability to resolve the reward shaping problem, its ability to learn from non numeric rewards and the possibility to reduce the dependence on expert knowledge. We provide a unified framework for PbRL that describes the task formally and points out the different design principles that affect the evaluation task for the human as well as the computational complexity. The design principles include the type of feedback that is assumed, the representation that is learned to capture the preferences, the optimization problem that has to be solved as well as how the exploration/exploitation problem is tackled. Furthermore, we point out shortcomings of current algorithms, propose open research questions and briefly survey practical tasks that have been solved using PbRL.
181 citations
TL;DR: In this paper, the authors present a critical review of the literature from the past 5 years related to how web-conferencing software, blogs, wikis, social networking sites (Facebook and Twitter), and digital games influence student engagement.
Abstract: Computer-based technology has infiltrated many aspects of life and industry, yet there is little understanding of how it can be used to promote student engagement, a concept receiving strong attention in higher education due to its association with a number of positive academic outcomes. The purpose of this article is to present a critical review of the literature from the past 5 years related to how web-conferencing software, blogs, wikis, social networking sites (Facebook and Twitter), and digital games influence student engagement. We prefaced the findings with a substantive overview of student engagement definitions and indicators, which revealed three types of engagement (behavioral, emotional, and cognitive) that informed how we classified articles. Our findings suggest that digital games provide the most far-reaching influence across different types of student engagement, followed by web-conferencing and Facebook. Findings regarding wikis, blogs, and Twitter are less conclusive and significantly limited in number of studies conducted within the past 5 years. Overall, the findings provide preliminary support that computer-based technology influences student engagement, however, additional research is needed to confirm and build on these findings. We conclude the article by providing a list of recommendations for practice, with the intent of increasing understanding of how computer-based technology may be purposefully implemented to achieve the greatest gains in student engagement.
179 citations
TL;DR: The approach used to enable long-term autonomous operation in everyday environments is described and how the robots are able to use their long run times to improve their own performance is described.
Abstract: Thanks to the efforts of the robotics and autonomous systems community, the myriad applications and capacities of robots are ever increasing. There is increasing demand from end users for autonomous service robots that can operate in real environments for extended periods. In the Spatiotemporal Representations and Activities for Cognitive Control in Long-Term Scenarios (STRANDS) project (http://strandsproject.eu), we are tackling this demand head-on by integrating state-of-the-art artificial intelligence and robotics research into mobile service robots and deploying these systems for long-term installations in security and care environments. Our robots have been operational for a combined duration of 104 days over four deployments, autonomously performing end-user-defined tasks and traversing 116 km in the process. In this article, we describe the approach we used to enable long-term autonomous operation in everyday environments and how our robots are able to use their long run times to improve their own performance.
TL;DR: This study focuses on one of these iron-based MOFs, namely MIL-88A NPs, which are composed of iron(III) and fumaric acid and which have been shown to efficiently host chemotherapeutic drugs.
Abstract: Drug delivery systems aim at a reduction of side effects in chemotherapy. This is achieved by encapsulation of drugs in nanocarriers followed by controlled release of these drugs at the site of the diseased tissue. Though inorganic or polymeric nanoparticles (NPs) are often used as nanocarriers,(1, 2) hybrid nanomaterials such as metal−organic framework (MOF) NPs have recently emerged as a valuable alternative.(3-6) They are synthesized from inorganic and organic building block units to create porous three-dimensional frameworks. Because of this building principle, the composition and structure of these materials are highly tunable.(7-10) Furthermore, both external and internal surfaces can be functionalized independently. With these properties, MOF NPs can be designed to fit the specific requirements of the desired application.(3, 11) For drug delivery purposes these so-called “design materials” have been synthesized with high porosity allowing for high drug loading capacities. They also have been designed to be biodegradable. Specifically, iron-based MOF NPs have attracted great attention. In addition to the above-mentioned properties, they can be detected via magnetic resonance imaging (MRI), rendering them an ideal platform for theranostics.(12-14) In our study, we focus on one of these iron-based MOFs, namely MIL-88A NPs, which are composed of iron(III) and fumaric acid.(15, 16) Both compounds can be found in the body and the NPs are reported to be nontoxic.(12) Additionally, MIL-88A NPs have been shown to efficiently host chemotherapeutic drugs.(12) Thus, they represent a promising nanocarrier.
TL;DR: Evidence is found for an association between pet ownership and a wide range of emotional health benefits from childhood pet ownership; particularly for self-esteem and loneliness and for childhood anxiety and depression.
Abstract: Childhood and adolescence are important developmental phases which influence health and well-being across the life span. Social relationships are fundamental to child and adolescent development; yet studies have been limited to children’s relationships with other humans. This paper provides an evidence review for the potential associations between pet ownership and emotional; behavioural; cognitive; educational and social developmental outcomes. As the field is in the early stages; a broad set of inclusion criteria was applied. A systematic search of databases and grey literature sources found twenty-two studies meeting selection criteria. The review found evidence for an association between pet ownership and a wide range of emotional health benefits from childhood pet ownership; particularly for self-esteem and loneliness. The findings regarding childhood anxiety and depression were inconclusive. Studies also showed evidence of an association between pet ownership and educational and cognitive benefits; for example, in perspective-taking abilities and intellectual development. Evidence on behavioural development was unclear due to a lack of high quality research. Studies on pet ownership and social development provided evidence for an association with increased social competence; social networks; social interaction and social play behaviour. Overall, pet ownership and the significance of children’s bonds with companion animals have been underexplored; there is a shortage of high quality and longitudinal studies in all outcomes. Prospective studies that control for a wide range of confounders are required.
TL;DR: A new multi-speed fault diagnostic approach is presented by using self-adaptive wavelet transform components generated from bearing vibration signals that is capable of discriminating signatures from four conditions of rolling bearing.
Abstract: Condition monitoring and incipient fault diagnosis of rolling bearing is of great importance to detect failures and ensure reliable operations in rotating machinery. In this paper, a new multi-speed fault diagnostic approach is presented by using self-adaptive wavelet transform components generated from bearing vibration signals. The proposed approach is capable of discriminating signatures from four conditions of rolling bearing, i.e., normal bearing and three different types of defected bearings on outer race, inner race, and roller separately. Particle swarm optimization and Broyden-Fletche—Goldfarb-Shanno-based quasi-Newton minimization algorithms are applied to seek optimal parameters of Impulse Modeling-based continuous wavelet transform model. Then, a 3-D feature space of the statistical parameters and a nearest neighbor classifier are, respectively, applied for fault signature extraction and fault classification. Effectiveness of this approach is then evaluated, and the results have achieved an overall accuracy of 100%. Moreover, the generated discriminatory fault signatures are suitable for multi-speed fault data sets. This technique will be further implemented and tested in a real industrial environment.
TL;DR: Transformation of the spectral model to the time domain allows for the prediction of the future environment states, which improves the robot's long-term performance in changing environments.
Abstract: We present a new approach to long-term mobile robot mapping in dynamic indoor environments. Unlike traditional world models that are tailored to represent static scenes, our approach explicitly models environmental dynamics. We assume that some of the hidden processes that influence the dynamic environment states are periodic and model the uncertainty of the estimated state variables by their frequency spectra. The spectral model can represent arbitrary timescales of environment dynamics with low memory requirements. Transformation of the spectral model to the time domain allows for the prediction of the future environment states, which improves the robot's long-term performance in changing environments. Experiments performed over time periods of months to years demonstrate that the approach can efficiently represent large numbers of observations and reliably predict future environment states. The experiments indicate that the model's predictive capabilities improve mobile robot localization and navigation in changing environments.
TL;DR: These results provide the first evidence in support of peer-robot behavioural personalisation having a positive influence on learning when embedded in a learning environment for an extended period of time.
Abstract: The benefit of social robots to support child learning in an educational context over an extended period of time is evaluated Specifically, the effect of personalisation and adaptation of robot social behaviour is assessed Two autonomous robots were embedded within two matched classrooms of a primary school for a continuous two week period without experimenter supervision to act as learning companions for the children for familiar and novel subjects Results suggest that while children in both personalised and non-personalised conditions learned, there was increased child learning of a novel subject exhibited when interacting with a robot that personalised its behaviours, with indications that this benefit extended to other class-based performance Additional evidence was obtained suggesting that there is increased acceptance of the personalised robot peer over a non-personalised version These results provide the first evidence in support of peer-robot behavioural personalisation having a positive influence on learning when embedded in a learning environment for an extended period of time
TL;DR: A simple dual-source precursor approach is developed to fabricate high-quality and mirror-like mixed-cation perovskite thin films without involving additional antisolvent process, and pave the way for solar cell fabrication via scalable methods in the near future.
Abstract: The highest efficiencies reported for perovskite solar cells so far have been obtained mainly with methylammonium and formamidinium mixed cations. Currently, high-quality mixed-cation perovskite thin films are normally made by use of antisolvent protocols. However, the widely used “antisolvent”-assisted fabrication route suffers from challenges such as poor device reproducibility, toxic and hazardous organic solvent, and incompatibility with scalable fabrication process. Here, a simple dual-source precursor approach is developed to fabricate high-quality and mirror-like mixed-cation perovskite thin films without involving additional antisolvent process. By integrating the perovskite films into the planar heterojunction solar cells, a power conversion efficiency of 20.15% is achieved with negligible current density–voltage hysteresis. A stabilized power output approaching 20% is obtained at the maximum power point. These results shed light on fabricating highly efficient perovskite solar cells via a simple process, and pave the way for solar cell fabrication via scalable methods in the near future.
TL;DR: This work verifies the possibility of self-stabilization of multi-MAV groups without an external global positioning system, and deployment of the system in real-world scenarios truthfully verifies its operational constraints.
Abstract: A complex system for control of swarms of micro aerial vehicles (MAV), in literature also called as unmanned aerial vehicles (UAV) or unmanned aerial systems (UAS), stabilized via an onboard visual relative localization is described in this paper. The main purpose of this work is to verify the possibility of self-stabilization of multi-MAV groups without an external global positioning system. This approach enables the deployment of MAV swarms outside laboratory conditions, and it may be considered an enabling technique for utilizing fleets of MAVs in real-world scenarios. The proposed visual-based stabilization approach has been designed for numerous different multi-UAV robotic applications (leader-follower UAV formation stabilization, UAV swarm stabilization and deployment in surveillance scenarios, cooperative UAV sensory measurement) in this paper. Deployment of the system in real-world scenarios truthfully verifies its operational constraints, given by limited onboard sensing suites and processing capabilities. The performance of the presented approach (MAV control, motion planning, MAV stabilization, and trajectory planning) in multi-MAV applications has been validated by experimental results in indoor as well as in challenging outdoor environments (e.g., in windy conditions and in a former pit mine).
TL;DR: Gambling cues, but not food cues, elicit increased brain responses in reward-related circuitry in individuals with gambling disorder (compared with controls), providing support for the incentive sensitization theory of addiction.
Abstract: This study was funded by the Medical Research Council—MRC G1002226 (Nutt) and G1100554 (Clark). We wish to thank the study participants and the clinical team at Imanova, Centre for Imaging Sciences. The research was supported by the National Institute for Health Research (NIHR) Imperial Biomedical Research Centre. SPS was funded by the Cambridge Home Scholarship Scheme (CHSS).
TL;DR: The experimental results show that by combining the measurements from both sensor systems, this paper could accurately discriminate between stressful and neutral situations during a controlled Trier social stress test (TSST).
Abstract: Stress remains a significant social problem for individuals in modern societies. This paper presents a machine learning approach for the automatic detection of stress of people in a social situation by combining two sensor systems that capture physiological and social responses. We compare the performance using different classifiers including support vector machine, AdaBoost, and k-nearest neighbour. Our experimental results show that by combining the measurements from both sensor systems, we could accurately discriminate between stressful and neutral situations during a controlled Trier social stress test (TSST). Moreover, this paper assesses the discriminative ability of each sensor modality individually and considers their suitability for real time stress detection. Finally, we present an study of the most discriminative features for stress detection.
TL;DR: Recently, a number of laboratories have reported the use of siRNA methodologies and genetic mouse models to mimic the loss of function of genes involved in the biosynthesis and degradation of H2S within tissues, revealing new insights into the biology of H20 within the cardiovascular system, inflammatory disease, and in cell signalling.
Abstract: Hydrogen sulfide (H2S) has profound biological effects within living organisms and is now increasingly being considered alongside other gaseous signalling molecules, such as nitric oxide (NO) and carbon monoxide (CO). Conventional use of pharmacological and molecular approaches has spawned a rapidly growing research field that has identified H2S as playing a functional role in cell-signalling and post-translational modifications. Recently, a number of laboratories have reported the use of siRNA methodologies and genetic mouse models to mimic the loss of function of genes involved in the biosynthesis and degradation of H2S within tissues. Studies utilising these systems are revealing new insights into the biology of H2S within the cardiovascular system, inflammatory disease, and in cell signalling. In light of this work, the current review will describe recent advances in H2S research made possible by the use of molecular approaches and genetic mouse models with perturbed capacities to generate or detoxify physiological levels of H2S gas within tissues.
University of Tartu1, University of Lincoln2, University of Porto3, Aalborg University4, Polish Academy of Sciences5, Spanish National Research Council6, American Museum of Natural History7, Swedish University of Agricultural Sciences8, Forest Research Institute9, University of Oulu10, University of Ljubljana11
TL;DR: The most pressing issues threatening wolf populations in Europe are discussed, important gaps in current knowledge are highlighted, solutions to overcome these limitations are suggested, and recommendations for science‐based wolf conservation and management at regional and Europe‐wide scales are provided.
Abstract: The grey wolf (Canis lupus) is an iconic large carnivore that has increasingly been recognized as an apex predator with intrinsic value and a keystone species. However, wolves have also long represented a primary source of human–carnivore conflict, which has led to long-term persecution of wolves, resulting in a significant decrease in their numbers, genetic diversity and gene flow between populations. For more effective protection and management of wolf populations in Europe, robust scientific evidence is crucial. This review serves as an analytical summary of the main findings from wolf population genetic studies in Europe, covering major studies from the ‘pre-genomic era’ and the first insights of the ‘genomics era’. We analyse, summarize and discuss findings derived from analyses of three compartments of the mammalian genome with different inheritance modes: maternal (mitochondrial DNA), paternal (Y chromosome) and biparental [autosomal microsatellites and single nucleotide polymorphisms (SNPs)]. To describe large-scale trends and patterns of genetic variation in European wolf populations, we conducted a meta-analysis based on the results of previous microsatellite studies and also included new data, covering all 19 European countries for which wolf genetic information is available: Norway, Sweden, Finland, Estonia, Latvia, Lithuania, Poland, Czech Republic, Slovakia, Germany, Belarus, Russia, Italy, Croatia, Bulgaria, Bosnia and Herzegovina, Greece, Spain and Portugal. We compared different indices of genetic diversity in wolf populations and found a significant spatial trend in heterozygosity across Europe from south-west (lowest genetic diversity) to north-east (highest). The range of spatial autocorrelation calculated on the basis of three characteristics of genetic diversity was 650−850 km, suggesting that the genetic diversity of a given wolf population can be influenced by populations up to 850 km away. As an important outcome of this synthesis, we discuss the most pressing issues threatening wolf populations in Europe, highlight important gaps in current knowledge, suggest solutions to overcome these limitations, and provide recommendations for science-based wolf conservation and management at regional and Europe-wide scales.
TL;DR: A conceptual analysis of the theory of fecundity selection promises to help illuminate one of the central components of fitness and its contribution to adaptive evolution.
Abstract: Fitness results from an optimal balance between survival, mating success and fecundity. The interactions between these three components of fitness vary depending on the selective context, from positive covariation between them, to antagonistic pleiotropic relationships when fitness increases in one reduce the fitness of others. Therefore, elucidating the routes through which selection shapes life history and phenotypic adaptations via these fitness components is of primary significance to understanding ecological and evolutionary dynamics. However, while the fitness components mediated by natural (survival) and sexual (mating success) selection have been debated extensively from most possible perspectives, fecundity selection remains considerably less studied. Here, we review the theoretical basis, evidence and implications of fecundity selection as a driver of sex-specific adaptive evolution. Based on accumulating literature on the life-history, phenotypic and ecological aspects of fecundity, we (i) suggest a re-arrangement of the concepts of fecundity, whereby we coin the term 'transient fecundity' to refer to brood size per reproductive episode, while 'annual' and 'lifetime fecundity' should not be used interchangeably with 'transient fecundity' as they represent different life-history parameters; (ii) provide a generalized re-definition of the concept of fecundity selection as a mechanism that encompasses any traits that influence fecundity in any direction (from high to low) and in either sex; (iii) review the (macro)ecological basis of fecundity selection (e.g. ecological pressures that influence predictable spatial variation in fecundity); (iv) suggest that most ecological theories of fecundity selection should be tested in organisms other than birds; (v) argue that the longstanding fecundity selection hypothesis of female-biased sexual size dimorphism (SSD) has gained inconsistent support, that strong fecundity selection does not necessarily drive female-biased SSD, and that this form of SSD can be driven by other selective pressures; and (vi) discuss cases in which fecundity selection operates on males. This conceptual analysis of the theory of fecundity selection promises to help illuminate one of the central components of fitness and its contribution to adaptive evolution.
07 Nov 2017
TL;DR: This paper reviewed recent developments in the observation and modeling of GrIS surface mass balance (SMB), published after the July 2012 deadline for the Fifth Assessment Report of the Intergovernmental Panel on Climate Change (IPCC AR5).
Abstract: Surface processes currently dominate Greenland ice sheet (GrIS) mass loss. We review recent developments in the observation and modeling of GrIS surface mass balance (SMB), published after the July 2012 deadline for the Fifth Assessment Report of the Intergovernmental Panel on Climate Change (IPCC AR5). Since IPCC AR5, our understanding of GrIS SMB has further improved, but new observational and model studies have also revealed that temporal and spatial variability of many processes are still poorly quantified and understood, e.g., bio-albedo, the formation of ice lenses and their impact on lateral meltwater transport, heterogeneous vertical meltwater transport (‘piping’), the impact of atmospheric-circulation changes and mixed-phase clouds on the surface energy balance, and the magnitude of turbulent heat exchange over rough ice surfaces. As a result, these processes are only schematically or not at all included in models that are currently used to assess and predict future GrIS surface mass loss.
TL;DR: It is indicated that polymer-based precipitation leads to smaller particle size distributions, faster uptake by target cells and increased cellular motility, and the different effect that isolation method-dependent populations of particles have on cell motility suggests their size distribution could also profoundly affect exosomes therapeutic potential.
TL;DR: In this article, a nanocomposite of magnetic hydroxyapatite was synthesized and tested as an adsorbent for the removal of copper (Cu (II)) and nickel (Ni(II)) from aqueous solution.
28 Sep 2017
TL;DR: An online learning framework for human classification in 3D LiDAR scans, taking advantage of robust multi-target tracking to avoid the need for data annotation by a human expert is presented.
Abstract: Human detection and tracking are essential aspects to be considered in service robotics, as the robot often shares its workspace and interacts closely with humans. This paper presents an online learning framework for human classification in 3D LiDAR scans, taking advantage of robust multi-target tracking to avoid the need for data annotation by a human expert. The system learns iteratively by retraining a classifier online with the samples collected by the robot over time. A novel aspect of our approach is that errors in training data can be corrected using the information provided by the 3D LiDAR-based tracking. In order to do this, an efficient 3D cluster detector of potential human targets has been implemented. We evaluate the framework using a new 3D LiDAR dataset of people moving in a large indoor public space, which is made available to the research community. The experiments analyse the real-time performance of the cluster detector and show that our online learned human classifier matches and in some cases outperforms its offline version.
University of Cambridge1, Papworth Hospital2, University of Sheffield3, University of Paris-Sud4, VU University Amsterdam5, Jubilee Hospital6, Royal Free Hospital7, Royal Hallamshire Hospital8, Newcastle University9, Imperial College London10, University of Lincoln11, United Hospitals12, Great Ormond Street Hospital13, King's College London14, St George's, University of London15
TL;DR: Biallelic EIF2AK4 mutations are found in patients classified clinically as having idiopathic and heritable PAH, and can be identified reliably by computed tomography, but a low KCO and a young age at diagnosis suggests the underlying molecular diagnosis.
Abstract: The National Institute of Health Research (NIHR) BioResource for Rare Diseases provided funding for sequencing and analysis The study was supported by a British Heart Foundation Special Project Grant and a Medical Research Council (UK) Experimental Challenge Award
TL;DR: The domestic refrigerator is now a common household device with very few households in the developed world not possessing 1, or more, for the storage of chilled foods, and this review seeks to put this important stage of the food cold chain in its context.
Abstract: The domestic refrigerator is now a common household device with very few households in the developed world not possessing 1, or more, for the storage of chilled foods. Domestic storage is the last, and in many respects the most important, link in the food chill chain. Inadequate domestic refrigeration or cooling is frequently cited as a factor in incidents of food poisoning. The authors reviewed the temperature performance of refrigerators in 2008. This new review builds on that review, covering studies that have been published since (and those that were unfortunately missed in the first review), and also seeks to put this important stage of the food cold chain in its context. It is clear from the published data that many refrigerators throughout the world are running at higher than recommended temperatures. It is also clear that, despite improvements in energy use, the temperature performance and use of refrigerators have not changed significantly in the last 40 or so years. Many householders still remain unaware of the recommended refrigeration temperature range, how to ensure that the correct refrigeration temperature range is achieved, the importance of monitoring that it is being maintained, and the potential hazards of temperature abuse.
TL;DR: Built upon a 30-year dataset collected from the web of science database, the present research aims to offer a comprehensive overview of papers, authors, streams of research, and the most influential journals that discuss product and process innovation in the manufacturing environment.
Abstract: Built upon a 30-year dataset collected from the web of science database, the present research aims to offer a comprehensive overview of papers, authors, streams of research, and the most influential journals that discuss product and process innovation in the manufacturing environment. The dataset is composed of 418 papers from more than 150 journals from the period between 1985 and 2015. Homogeneity analysis by means of alternating least squares (HOMALS) and social network analysis (SNA) are used to accomplish the objectives listed above through the keywords given by authors. Initially, the paper highlights and discusses the similarity between the topics debated by the main journals in this field. Subsequently, a wide-range map of topics is presented highlighting five main areas of interests; namely, performance, patent, small firm, product development, and organization. A SNA is also performed in order to validate the results that emerged from HOMALS. Finally, several insights about future research avenues in the manufacturing field are provided.