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In the evolving landscape of psychological research and communication, The Psychologist's Companion, stands as the definitive guide supporting students, young professionals, and researchers in psychology at all stages of their careers. This seventh edition presents new and updated chapters covering a wide range of topics essential for success in psychology, including planning and writing research papers, presenting data effectively, evaluating one's own work, writing grant proposals, giving talks and presentations, finding a book publisher, navigating job interviews, and more! Serving as an invaluable resource for improving both written and oral communication skills in academic psychology, the content is structured as a step-by-step manual focusing on practical skills and contemporary issues. It guides readers through various tasks encountered during psychological research and academic life. Whether you're crafting your first paper or seeking to enhance your scholarly impact, this book provides the tools and knowledge to excel in today's competitive academic environment.
Synthetic Aperture Radar Interferometry (InSAR) is an active remote sensing method that uses repeated radar scans of the Earth's solid surface to measure relative deformation at centimeter precision over a wide swath. It has revolutionized our understanding of the earthquake cycle, volcanic eruptions, landslides, glacier flow, ice grounding lines, ground fluid injection/withdrawal, underground nuclear tests, and other applications requiring high spatial resolution measurements of ground deformation. This book examines the theory behind and the applications of InSAR for measuring surface deformation. The most recent generation of InSAR satellites have transformed the method from investigating 10's to 100's of SAR images to processing 1000's and 10,000's of images using a wide range of computer facilities. This book is intended for students and researchers in the physical sciences, particularly for those working in geophysics, natural hazards, space geodesy, and remote sensing. This title is also available as Open Access on Cambridge Core.
We present the serendipitous radio-continuum discovery of a likely Galactic supernova remnant (SNR) G305.4–2.2. This object displays a remarkable circular symmetry in shape, making it one of the most circular Galactic SNRs known. Nicknamed Teleios due to its symmetry, it was detected in the new Australian Square Kilometre Array Pathfinder (ASKAP) Evolutionary Map of the Universe (EMU) radio–continuum images with an angular size of 1 320$^{\prime\prime}$$\times$1 260$^{\prime\prime}$ and PA = 0$^\circ$. While there is a hint of possible H$\alpha$ and gamma-ray emission, Teleios is exclusively seen at radio–continuum frequencies. Interestingly, Teleios is not only almost perfectly symmetric, but it also has one of the lowest surface brightnesses discovered among Galactic SNRs and a steep spectral index of $\alpha$=–0.6$\pm$0.3. Our best estimates from Hi studies and the $\Sigma$–D relation place Teleios as a type Ia SNR at a distance of either $\sim$2.2 kpc (near-side) or $\sim$7.7 kpc (far-side). This indicates two possible scenarios, either a young (under 1 000 yr) or a somewhat older SNR (over 10 000 yr). With a corresponding diameter of 14/48 pc, our evolutionary studies place Teleios at the either early or late Sedov phase, depending on the distance/diameter estimate. However, our modelling also predicts X-ray emission, which we do not see in the present generation of eROSITA images. We also explored a type Iax explosion scenario that would point to a much closer distance of $\lt$1 kpc and Teleios size of only $\sim$3.3 pc, which would be similar to the only known type Iax remnant SN1181. Unfortunately, all examined scenarios have their challenges, and no definitive Supernova (SN) origin type can be established at this stage. Remarkably, Teleios has retained its symmetrical shape as it aged even to such a diameter, suggesting expansion into a rarefied and isotropic ambient medium. The low radio surface brightness and the lack of pronounced polarisation can be explained by a high level of ambient rotation measure (RM), with the largest RM being observed at Teleios’s centre.
Adverse childhood experiences (ACEs) are associated with physical and mental health difficulties in adulthood. This study examines the associations of ACEs with functional impairment and life stress among military personnel, a population disproportionately affected by ACEs. We also evaluate the extent to which the associations of ACEs with functional outcomes are mediated through internalizing and externalizing disorders.
Methods
The sample included 4,666 STARRS Longitudinal Study (STARRS-LS) participants who provided information about ACEs upon enlistment in the US Army (2011–2012). Mental disorders were assessed in wave 1 (LS1; 2016–2018), and functional impairment and life stress were evaluated in wave 2 (LS2; 2018–2019) of STARRS-LS. Mediation analyses estimated the indirect associations of ACEs with physical health-related impairment, emotional health-related impairment, financial stress, and overall life stress at LS2 through internalizing and externalizing disorders at LS1.
Results
ACEs had significant indirect effects via mental disorders on all functional impairment and life stress outcomes, with internalizing disorders displaying stronger mediating effects than externalizing disorders (explaining 31–92% vs 5–15% of the total effects of ACEs, respectively). Additionally, ACEs exhibited significant direct effects on emotional health-related impairment, financial stress, and overall life stress, implying ACEs are also associated with these longer-term outcomes via alternative pathways.
Conclusions
This study indicates ACEs are linked to functional impairment and life stress among military personnel in part because of associated risks of mental disorders, particularly internalizing disorders. Consideration of ACEs should be incorporated into interventions to promote psychosocial functioning and resilience among military personnel.
Targeting the glutamatergic system is posited as a potentially novel therapeutic strategy for psychotic disorders. While studies in subjects indicate that antipsychotic medication reduces brain glutamatergic measures, they were unable to disambiguate clinical changes from drug effects.
Aims
To address this, we investigated the effects of a dopamine D2 receptor partial agonist (aripiprazole) and a dopamine D2 receptor antagonist (amisulpride) on glutamatergic metabolites in the anterior cingulate cortex (ACC), striatum and thalamus in healthy controls.
Method
A double-blind, within-subject, cross-over, placebo-controlled study design with two arms (n = 25 per arm) was conducted. Healthy volunteers received either aripiprazole (up to 10 mg/day) for 7 days or amisulpride (up to 400 mg/day) and a corresponding period of placebo treatment in a pseudo-randomised order. Magnetic resonance spectroscopy (1H-MRS) was used to measure glutamatergic metabolite levels and was carried out at three different time points: baseline, after 1 week of drug and after 1 week of placebo. Values were analysed as a combined measure across the ACC, striatum and thalamus.
Results
Aripiprazole significantly increased glutamate + glutamine (Glx) levels compared with placebo (β = 0.55, 95% CI [0.15, 0.95], P = 0.007). At baseline, the mean Glx level was 8.14 institutional units (s.d. = 2.15); following aripiprazole treatment, the mean Glx level was 8.16 institutional units (s.d. = 2.40) compared with 7.61 institutional units (s.d. = 2.36) for placebo. This effect remained significant after adjusting for plasma parent and active metabolite drug levels. There was an observed increase with amisulpride that did not reach statistical significance.
Conclusions
One week of aripiprazole administration in healthy participants altered brain Glx levels as compared with placebo administration. These findings provide novel insights into the relationship between antipsychotic treatment and brain metabolites in a healthy participant cohort.
Patients with posttraumatic stress disorder (PTSD) exhibit smaller regional brain volumes in commonly reported regions including the amygdala and hippocampus, regions associated with fear and memory processing. In the current study, we have conducted a voxel-based morphometry (VBM) meta-analysis using whole-brain statistical maps with neuroimaging data from the ENIGMA-PGC PTSD working group.
Methods
T1-weighted structural neuroimaging scans from 36 cohorts (PTSD n = 1309; controls n = 2198) were processed using a standardized VBM pipeline (ENIGMA-VBM tool). We meta-analyzed the resulting statistical maps for voxel-wise differences in gray matter (GM) and white matter (WM) volumes between PTSD patients and controls, performed subgroup analyses considering the trauma exposure of the controls, and examined associations between regional brain volumes and clinical variables including PTSD (CAPS-4/5, PCL-5) and depression severity (BDI-II, PHQ-9).
Results
PTSD patients exhibited smaller GM volumes across the frontal and temporal lobes, and cerebellum, with the most significant effect in the left cerebellum (Hedges’ g = 0.22, pcorrected = .001), and smaller cerebellar WM volume (peak Hedges’ g = 0.14, pcorrected = .008). We observed similar regional differences when comparing patients to trauma-exposed controls, suggesting these structural abnormalities may be specific to PTSD. Regression analyses revealed PTSD severity was negatively associated with GM volumes within the cerebellum (pcorrected = .003), while depression severity was negatively associated with GM volumes within the cerebellum and superior frontal gyrus in patients (pcorrected = .001).
Conclusions
PTSD patients exhibited widespread, regional differences in brain volumes where greater regional deficits appeared to reflect more severe symptoms. Our findings add to the growing literature implicating the cerebellum in PTSD psychopathology.
Although carotid endarterectomy (CEA) or carotid artery stenting (CAS) is recommended for symptomatic extracranial carotid stenosis of 50–99%, the COVID-19 pandemic significantly impacted resources. CAS therefore offered potential advantages as access to the angiosuite was seemingly easier than access to operating rooms. The primary objective was to determine the frequency of serious and non-serious complications following CAS before and during the COVID-19 pandemic.
Methods:
We performed a retrospective cohort study of consecutive patients who received CAS at the Ottawa Hospital, Canada, from June 2019 to May 2021. We reviewed baseline demographics, imaging, as well as intraprocedural and postprocedural complications based on chart review. We performed multivariable logistic regression to determine associations between clinical and safety outcomes.
Results:
We included 47 patients in the pre-pandemic period and 93 patients in the pandemic period (mean age = 70.4 years; 54% female; P = 0.287 for age and P = 0.962 for sex, respectively). The combined rate of intraprocedural and postprocedural serious complications (ischemic stroke, intracerebral hemorrhage, myocardial infarction or death) was 7.1%. Eight strokes occurred, and one patient with a postprocedural ischemic stroke died 11 days after stenting. Complication rates were similar before and during the pandemic (aOR 1.040, 95% CI 0.466–2.321). The number of referrals for CEA during the pandemic period decreased by 50%.
Conclusion:
In this cohort of consecutive patients undergoing CAS at a Canadian comprehensive stroke center before and during the COVID-19 pandemic, the rates of stroke and death were similar to pre-pandemic conditions and were generally consistent with the published literature.
This paper provides practical guidance to UK-based financial institutions (UKFIs) that are subject to the “operational resilience” guideline requirements of the Bank of England (BoE), Prudential Regulatory Authority and Financial Conduct Authority, issued in 2021, and fully effective for 31 March 2025. It contains practical suggestions and recommendations to assist UKFIs in implementing the guidelines. The scope of the paper covers issues related to (a) overviewing the latest equivalent operational resilience guidance in other countries and internationally (b) identifying key issues related to risk culture, risk appetite, information technology, tolerance setting, risk modelling, scenario planning and customer oriented operational resilience (c) identifying a framework for operational resilience based on a thorough understanding of these parameters and (d) designing and implementing an operational resilience maturity dashboard based on a sample of large UKIFs. The study also contains recommendations for further action, including enhanced controls and operational risk management frameworks. It concludes by identifying imperative policy actions to ensure that the implementation of the guidelines is more effective.
Macroscopic, modular, morphologically simple skeletons occur in the uppermost Mural Formation (Cambrian, Epoch 2, Bonnia–Olenellus Biozone), west-central Alberta and adjacent east-central British Columbia. They represent organisms that lived almost exclusively in reefal environments dominated by archaeocyaths. Some were attached to archaeocyaths or less commonly other surfaces, and some grew downward, apparently from overhangs or cavities in reefs. Qualitative and quantitative data from a large number of specimens, most of which were serially thin sectioned, indicate that they represent a single, remarkably variable species. The skeletal structure ranges among specimens from entirely cerioid to partially to entirely labyrinthine with irregularly incomplete walls. There is also a wide range of variability in growth form among skeletons, in module size and wall thickness among and within skeletons, in module shape within skeletons, and in number and location of projections extending from the wall into some modules. Module increase occurred by peripheral expansion at the basal surface of the skeleton and longitudinal fission involving projections from the wall as module size increased during vertical growth. Walls of skeletons, now composed of calcite cement, were probably originally aragonite. Modular skeletons from the uppermost Mural Formation are assigned to Rosellatana jamesi Kobluk, 1984a, previously represented only by a few cerioid specimens from correlative strata in the Rosella Formation of north-central British Columbia. The skeletal structure and types of module increase in R.jamesi, and a few similar but less well-known Cambrian taxa from elsewhere in North America, suggest a general biologic affinity with hypercalcified sponges.
Background: Late-onset Pompe disease (LOPD) is caused by a deficiency of acid α-glucosidase (GAA), leading to progressive muscle and respiratory decline. Cipaglucosidase alfa (cipa), a recombinant human GAA naturally enriched with bis-mannose-6-phosphate, exhibits improved muscle uptake but is limited by inactivation at near-neutral blood pH. Miglustat (mig), an enzyme stabiliser, binds competitively and reversibly to cipa, enhancing its stability and activity. Methods: In dose-finding studies, Gaa-/- mice were treated with cipa (20 mg/kg) +/- mig (10 mg/kg; equivalent human dose ~260 mg). Clinical study methodologies have been published (Schoser et al. Lancet Neurol 2021:20;1027–37; Schoser et al. J Neurol 2024:271;2810–23). Results: In Gaa-/- mice, cipa+mig improved muscle glycogen reduction more than cipa alone and grip strength to levels approaching wild-type mice. LOPD patients (n=11) treated with cipa alone showed dose-dependent decreases in hexose tetrasaccharide (Hex4) levels by ~15% from baseline, decreasing another ~10% with added mig (260 mg). In a head-to-head study, cipa+mig had a similar safety profile to alglucosidase alfa. Among 151 patients (three trials), mig-related adverse events occurred in 21 (13.9%), none serious. Conclusions: Mig stabilised cipa in circulation, improving cipa exposure, further reducing Hex4 levels and was well tolerated in clinical studies in patients with LOPD. Sponsored by Amicus Therapeutics, Inc.
Diagnosing HIV-Associated Neurocognitive Disorders (HAND) requires attributing neurocognitive impairment and functional decline at least partly to HIV-related brain effects. Depressive symptom severity, whether attributable to HIV or not, may influence self-reported functioning. We examined longitudinal relationships among objective global cognition, depressive symptom severity, and self-reported everyday functioning in people with HIV (PWH).
Methods:
Longitudinal data from 894 PWH were collected at a university-based research center (2002–2016). Participants completed self-report measures of everyday functioning to assess both dependence in instrumental activities of daily living (IADL) and subjective cognitive difficulties at each visit, along with depressive symptom severity (BDI-II). Multilevel modeling examined within- and between-person predictors of self-reported everyday functioning outcomes.
Results:
Participants averaged 6 visits over 5 years. Multilevel regression showed a significant interaction between visit-specific global cognitive performance and mean depression symptom severity on likelihood of dependence in IADL (p = 0.04), such that within-person association between worse cognition and greater likelihood of IADL dependence was strongest among individuals with lower mean depressive symptom severity. In contrast, participants with higher mean depressive symptom severity had higher likelihoods of IADL dependence regardless of cognition. Multilevel modelling of subjective cognitive difficulties showed no significant interaction between global cognition and mean depressive symptom severity (p > 0.05).
Conclusions:
The findings indicate a link between cognitive abilities and IADL dependence in PWH with low to moderate depressive symptoms. However, those with higher depressive symptoms severity report IADL dependence regardless of cognitive status. This is clinically significant because everyday functioning is measured through self-report rather than performance-based assessments.
Vitamin A deficiency (VAD) poses significant health risks and is prevalent in children and adolescents in India. This study aimed to determine the effect of seasonal variation and availability of vitamin A-rich (VA-rich) foods on serum retinol in adolescents. Data on serum retinol levels from adolescents (n 2297, mean age 14 years) from the Comprehensive National Nutrition Survey (2016–2018) in India were analysed, with VAD defined as serum retinol < 0·7 µmol/L. Five states were selected based on a comparable under-five mortality rate and the seasonal spread of the data collection period. Dietary data from adolescents and children ≤ 4 years old were used to assess VA-rich food consumption. A linear mixed model framework was employed to analyse the relationship between serum retinol, month of the year and VA-rich food consumption, with a priori ranking to control for multiple hypothesis testing. Consumption of VA-rich foods, particularly fruits and vegetables/roots and tubers, showed seasonal patterns, with higher consumption during summer and monsoon months. Significant associations were found between serum retinol concentrations and age, month of sampling, consumption of VA-rich foods and fish. VAD prevalence was lowest in August, coinciding with higher consumption of VA-rich fruits and foods. Findings highlight the importance of considering seasonality in assessing VAD prevalence and careful interpretation of survey findings. Intentional design, analysis and reporting of surveys to capture seasonal variation is crucial for accurate assessment and interpretation of VAD prevalence, including during monitoring and evaluation of programmes, and to ensure that public health strategies are appropriately informed.
Artificial intelligence is dramatically reshaping scientific research and is coming to play an essential role in scientific and technological development by enhancing and accelerating discovery across multiple fields. This book dives into the interplay between artificial intelligence and the quantum sciences; the outcome of a collaborative effort from world-leading experts. After presenting the key concepts and foundations of machine learning, a subfield of artificial intelligence, its applications in quantum chemistry and physics are presented in an accessible way, enabling readers to engage with emerging literature on machine learning in science. By examining its state-of-the-art applications, readers will discover how machine learning is being applied within their own field and appreciate its broader impact on science and technology. This book is accessible to undergraduates and more advanced readers from physics, chemistry, engineering, and computer science. Online resources include Jupyter notebooks to expand and develop upon key topics introduced in the book.
The theory of kernels offers a rich mathematical framework for the archetypical tasks of classification and regression. Its core insight consists of the representer theorem that asserts that an unknown target function underlying a dataset can be represented by a finite sum of evaluations of a singular function, the so-called kernel function. Together with the infamous kernel trick that provides a practical way of incorporating such a kernel function into a machine learning method, a plethora of algorithms can be made more versatile. This chapter first introduces the mathematical foundations required for understanding the distinguished role of the kernel function and its consequence in terms of the representer theorem. Afterwards, we show how selected popular algorithms, including Gaussian processes, can be promoted to their kernel variant. In addition, several ideas on how to construct suitable kernel functions are provided, before demonstrating the power of kernel methods in the context of quantum (chemistry) problems.
In this chapter, we change our viewpoint and focus on how physics can influence machine learning research. In the first part, we review how tools of statistical physics can help to understand key concepts in machine learning such as capacity, generalization, and the dynamics of the learning process. In the second part, we explore yet another direction and try to understand how quantum mechanics and quantum technologies could be used to solve data-driven task. We provide an overview of the field going from quantum machine learning algorithms that can be run on ideal quantum computers to kernel-based and variational approaches that can be run on current noisy intermediate-scale quantum devices.
In this chapter, we introduce the field of reinforcement learning and some of its most prominent applications in quantum physics and computing. First, we provide an intuitive description of the main concepts, which we then formalize mathematically. We introduce some of the most widely used reinforcement learning algorithms. Starting with temporal-difference algorithms and Q-learning, followed by policy gradient methods and REINFORCE, and the interplay of both approaches in actor-critic algorithms. Furthermore, we introduce the projective simulation algorithm, which deviates from the aforementioned prototypical approaches and has multiple applications in the field of physics. Then, we showcase some prominent reinforcement learning applications, featuring some examples in games; quantum feedback control; quantum computing, error correction and information; and the design of quantum experiments. Finally, we discuss some potential applications and limitations of reinforcement learning in the field of quantum physics.
This chapter discusses more specialized examples on how machine learning can be used to solve problems in quantum sciences. We start by explaining the concept of differentiable programming and its use cases in quantum sciences. Next, we describe deep generative models, which have proven to be an extremely appealing tool for sampling from unknown target distributions in domains ranging from high-energy physics to quantum chemistry. Finally, we describe selected machine learning applications for experimental setups such as ultracold systems or quantum dots. In particular, we show how machine learning can help in tedious and repetitive experimental tasks in quantum devices or in validating quantum simulators with Hamiltonian learning.