To send content items to your account,
please confirm that you agree to abide by our usage policies.
If this is the first time you use this feature, you will be asked to authorise Cambridge Core to connect with your account.
Find out more about sending content to .
To send content items to your Kindle, first ensure firstname.lastname@example.org
is added to your Approved Personal Document E-mail List under your Personal Document Settings
on the Manage Your Content and Devices page of your Amazon account. Then enter the ‘name’ part
of your Kindle email address below.
Find out more about sending to your Kindle.
Note you can select to send to either the @free.kindle.com or @kindle.com variations.
‘@free.kindle.com’ emails are free but can only be sent to your device when it is connected to wi-fi.
‘@kindle.com’ emails can be delivered even when you are not connected to wi-fi, but note that service fees apply.
The random encounter model, a method for estimating animal density using camera traps without the need for individual recognition, has been developed over the past decade. A key assumption of this model is that cameras are placed randomly in relation to animal movements, requiring that cameras are not set only at sites thought to have high animal traffic. The aim of this study was to define a correction factor that allows the random encounter model to be applied in photo-trapping surveys in which cameras are placed along tracks to maximize capture probability. Our hypothesis was that applying such a correction factor would compensate for the different rates at which lynxes use tracks and the surrounding area, and should thus improve the estimates obtained with the random encounter model. We tested this using data from a well-known Iberian lynx Lynx pardinus population. Firstly, we estimated Iberian lynx densities using a traditional camera-trapping design followed by spatially explicit capture–recapture analyses. We estimated the differential use rate for tracks vs the surrounding area using data from a lynx equipped with a GPS collar, and subsequently calculated the correction factor. As expected, the random encounter model overestimated densities by 378%. However, the application of the correction factor improved the estimate and reduced the error to 16%. Although there are limitations to the application of the correction factor, the corrected random encounter model shows potential for density estimation of species for which individual identification is not possible.
Positive symptoms are a useful predictor of aggression in schizophrenia. Although a similar pattern of abnormal brain structures related to both positive symptoms and aggression has been reported, this observation has not yet been confirmed in a single sample.
To study the association between positive symptoms and aggression in schizophrenia on a neurobiological level, a prospective meta-analytic approach was employed to analyze harmonized structural neuroimaging data from 10 research centers worldwide. We analyzed brain MRI scans from 902 individuals with a primary diagnosis of schizophrenia and 952 healthy controls.
The result identified a widespread cortical thickness reduction in schizophrenia compared to their controls. Two separate meta-regression analyses revealed that a common pattern of reduced cortical gray matter thickness within the left lateral temporal lobe and right midcingulate cortex was significantly associated with both positive symptoms and aggression.
These findings suggested that positive symptoms such as formal thought disorder and auditory misperception, combined with cognitive impairments reflecting difficulties in deploying an adaptive control toward perceived threats, could escalate the likelihood of aggression in schizophrenia.
Major depressive disorder (MDD) represents a leading cause of disability. This study examines the course of disability in patients with chronic, recurrent and remitting MDD compared to healthy controls and identifies predictors of disability in remitting MDD.
We included 914 participants from the Netherlands Study of Depression and Anxiety (NESDA). DSM-IV MDD and WHO DAS II disability were assessed at baseline and at 2, 4 and 6 years. Six-year total and domain-specific disability were analysed and compared in participants with chronic (n = 57), recurrent (n = 120), remitting (n = 127) MDD and in healthy controls (n = 430). Predictors of residual disability were identified using linear regression analysis.
At baseline, most disability was found in chronic MDD, followed by recurrent MDD, remitting MDD and healthy controls. Across diagnostic groups, most disability was found in household activities, interpersonal functioning, participation in society and cognition. A chronic course was associated with chronic disability. Symptom remission was associated with a decrease in disability, but some disability remained. In remitting MDD, higher residual disability was predicted by older age, more severe avoidance symptoms, higher disability at baseline and late symptom remission. Severity of residual disability correlated with the severity of residual depressive symptoms.
Symptomatic remission is a prerequisite for improvements in disability. However, disability persists despite symptom remission. Therefore, treatment of MDD should include an explicit focus on disability, especially on the more complex domains. To this end, treatments should promote behavioural activation and address subthreshold depressive symptoms in patients with remitted MDD.
The Mangalitza pig breed has suffered strong population reductions due to competition with more productive cosmopolitan breeds. In the current work, we aimed to investigate the effects of this sustained demographic recession on the genomic diversity of Mangalitza pigs. By using the Porcine Single Nucleotid Polymorphism BeadChip, we have characterized the genome-wide diversity of 350 individuals including 45 Red Mangalitza (number of samples; n=20 from Hungary and n=25 from Romania), 37 Blond Mangalitza, 26 Swallow-belly Mangalitza, 48 Blond Mangalitza × Duroc crossbreds, 5 Bazna swine, 143 pigs from the Hampshire, Duroc, Landrace, Large White and Pietrain breeds and 46 wild boars from Romania (n=18) and Hungary (n=28). Performance of a multidimensional scaling plot showed that Landrace, Large White and Pietrain pigs clustered independently from Mangalitza pigs and Romanian and Hungarian wild boars. The number and total length of ROH (runs of homozygosity), as well as FROH coefficients (proportion of the autosomal genome covered ROH) did not show major differences between Mangalitza pigs and other wild and domestic pig populations. However, Romanian and Hungarian Red Mangalitza pigs displayed an increased frequency of very long ROH (>30 Mb) when compared with other porcine breeds. These results indicate that Red Mangalitza pigs underwent recent and strong inbreeding probably as a consequence of severe reductions in census size.
Alloy design is critical to achieving the target performance of industrial components and products. In designing new alloys, there are multiple property requirements, including mechanical, environmental, and physical properties, as well as manufacturability and processability. Computational models and tools to predict properties from alloy compositions and to optimize compositions for multiple objectives are essential in enabling efficient, robust alloy design. Data-driven property models by machine learning (ML) are particularly useful in predicting physical properties with relatively simple dependence on composition, and in predicting complex properties that are too difficult for a physics-based model to achieve with desirable accuracy. In this article, we describe examples of ML applications to model coefficient of thermal expansion, creep and fatigue resistance in designing Ni-based superalloys, and optimization methodologies. We also discuss physics-based microstructure models that have been developed for optimizing heat-treatment conditions to achieve desired microstructures.
Robots and artificial machines have been captivating the public for centuries, depicted first as threats to humanity, then as subordinates and helpers. In the last decade, the booming exposure of humans to robots has fostered an increasing interest in soft robotics. By empowering robots with new physical properties, autonomous actuation, and sensing mechanisms, soft robots are making increasing impacts on areas such as health and medicine. At the same time, the public sympathy to robots is increasing. However, there is still a great need for innovation to push robotics toward more diverse applications. To overcome the major limitation of soft robots, which lies in their softness, strategies are being explored to combine the capabilities of soft robots with the performance of hard metallic ones by using composite materials in their structures. After reviewing the major specificities of hard and soft robots, paths to improve actuation speed, stress generation, self-sensing, and actuation will be proposed. Innovations in controlling systems, modeling, and simulation that will be required to use composite materials in robotics will be discussed. Finally, based on recently developed examples, the elements needed to progress toward a new form of artificial life will be described.
This article reviews the concept of metastability in alloy design. While most materials are thermodynamically metastable at some stage during synthesis and service, we discuss here cases where metastable phases are not coincidentally inherited from processing, but rather are engineered. Specifically, we aim at compositional (partitioning), thermal (kinetics), and microstructure (size effects and confinement) tuning of metastable phases so that they can trigger athermal transformation effects when mechanically, thermally, or electromagnetically loaded. Such a concept works both at the bulk scale and also at a spatially confined microstructure scale, such as at lattice defects. In the latter case, local stability tuning works primarily through elemental partitioning to dislocation cores, stacking faults, interfaces, and precipitates. Depending on stability, spatial confinement, misfit, and dispersion, both bulk and local load-driven athermal transformations can equip alloys with substantial gain in strength, ductility, and damage tolerance. Examples include self-organized metastable nanolaminates, austenite reversion steels, metastable medium- and high-entropy alloys, as well as steels and titanium alloys with martensitic phase transformation and twinning-induced plasticity effects.
Historically, the advent of robotics has important roots in metallurgy. The first industrial robot, Unimate, was used by General Motors to handle hot metal—transporting die castings and welding them to an automotive body. Now, nearly 60 years later, metallurgical use of robotics is still largely confined to automation of dangerous, complex, and repetitive tasks. Beyond metallurgy, the field of autonomy is undergoing a renaissance, impacting applications from pharmaceuticals to transportation. In this article, we review the emerging elements of high-throughput experimental automation, which, when combined with artificial intelligence or machine-learning systems, will enable autonomous discovery of novel alloys and process routes.
Over the past three years, my colleagues and I have embarked on an exciting journey into electric-field control of magnetism, parts of which we describe in this article. What we present to you is something that we believe is extremely exciting from both a fundamental science and applications perspective, and has the potential to revolutionize our world. Needless to say, this will require a lot of new innovations, both in the fundamental science arena as well as translating scientific discoveries into real applications. We hope this article will help spur more research in electric-field control of magnetism within the broad materials community.