Showing papers by "University of Colorado Colorado Springs published in 2020"

Inhibition of Bruton tyrosine kinase in patients with severe COVID-19.

Abstract: Patients with severe COVID-19 have a hyperinflammatory immune response suggestive of macrophage activation. Bruton tyrosine kinase (BTK) regulates macrophage signaling and activation. Acalabrutinib, a selective BTK inhibitor, was administered off-label to 19 patients hospitalized with severe COVID-19 (11 on supplemental oxygen; 8 on mechanical ventilation), 18 of whom had increasing oxygen requirements at baseline. Over a 10-14 day treatment course, acalabrutinib improved oxygenation in a majority of patients, often within 1-3 days, and had no discernable toxicity. Measures of inflammation - C-reactive protein and IL-6 - normalized quickly in most patients, as did lymphopenia, in correlation with improved oxygenation. At the end of acalabrutinib treatment, 8/11 (72.7%) patients in the supplemental oxygen cohort had been discharged on room air, and 4/8 (50%) patients in the mechanical ventilation cohort had been successfully extubated, with 2/8 (25%) discharged on room air. Ex vivo analysis revealed significantly elevated BTK activity, as evidenced by autophosphorylation, and increased IL-6 production in blood monocytes from patients with severe COVID-19 compared with blood monocytes from healthy volunteers. These results suggest that targeting excessive host inflammation with a BTK inhibitor is a therapeutic strategy in severe COVID-19 and has led to a confirmatory international prospective randomized controlled clinical trial.

Consensus statement on improving the mental health of high performance athletes

Abstract: This consensus statement is the product of an international Think Tank on the initiative of the International Society of Sport Psychology. The purpose of the Think Tank was to unify major sport psy...

Myosteatosis in the Context of Skeletal Muscle Function Deficit: An Interdisciplinary Workshop at the National Institute on Aging

Abstract: Skeletal muscle fat infiltration (known as myosteatosis) is an ectopic fat depot that increases with aging and is recognized to negatively correlate with muscle mass, strength, and mobility and disrupt metabolism (insulin resistance, diabetes). An interdisciplinary workshop convened by the National Institute on Aging Division of Geriatrics and Clinical Gerontology on September 2018, discussed myosteatosis in the context of skeletal muscle function deficit (SMFD). Its purpose was to gain a better understanding of the roles of myosteatosis in aging muscles and metabolic disease, particularly its potential determinants and clinical consequences, and ways of properly assessing it. Special attention was given to functional status and standardization of measures of body composition (including the value of D3-creatine dilution method) and imaging approaches [including ways to better use dual-energy X-ray absorptiometry (DXA) through the shape and appearance modeling] to assess lean mass, sarcopenia, and myosteatosis. The workshop convened innovative new areas of scientific relevance to light such as the effect of circadian rhythms and clock disruption in skeletal muscle structure, function, metabolism, and potential contribution to increased myosteatosis. A muscle-bone interaction perspective compared mechanisms associated with myosteatosis and bone marrow adiposity. Potential preventive and therapeutic approaches highlighted ongoing work on physical activity, myostatin treatment, and calorie restriction. Myosteatosis' impact on cancer survivors raised new possibilities to identify its role and to engage in cross-disciplinary collaboration. A wide range of research opportunities and challenges in planning for the most appropriate study design, interpretation, and translation of findings into clinical practice were discussed and are presented here.

Randomized Phase II Trial of Carboplatin-Paclitaxel Compared with Carboplatin-Paclitaxel-Trastuzumab in Advanced (Stage III-IV) or Recurrent Uterine Serous Carcinomas that Overexpress Her2/Neu (NCT01367002): Updated Overall Survival Analysis.

Abstract: Purpose: Uterine-serous-carcinoma (USC) is an aggressive variant of endometrial cancer On the basis of preliminary results of a multicenter, randomized phase II trial, trastuzumab (T), a humanized-mAb targeting Her2/Neu, in combination with carboplatin/paclitaxel (C/P), is recognized as an alternative in treating advanced/recurrent HER2/Neu-positive USC We report the updated survival analysis of NCT01367002 Patients and Methods: Eligible patients had stage III to IV or recurrent disease Participants were randomized 1:1 to receive C/P for six cycles ± T followed by maintenance T until progression or toxicity Progression-free survival (PFS) was the primary endpoint; overall survival (OS) and toxicity were secondary endpoints Results: Sixty-one patients were randomized After a median-follow-up of 259 months, 43 progressions and 38 deaths occurred among 58 evaluable patients Updated median-PFS continued to favor the T-arm, with medians of 80 months versus 129 months in the control and T-arms (HR = 046; 90% CI, 028–076; P = 0005) Median-PFS was 93 months versus 177 months among 41 patients with stage III to IV disease undergoing primary treatment (HR = 044; 90% CI, 023–083; P = 0015), and 70 months versus 92 months among 17 patients with recurrent disease (HR = 012; 90% CI, 003–048; P = 0004) OS was higher in the T compared with the control arm, with medians of 296 months versus 244 months (HR = 058; 90% CI, 034–099; P = 0046) The benefit was most notable in those with stage III to IV disease, with survival median not reached in the T-arm versus 244 months in the control arm (HR = 049; 90% CI, 025–097; P = 0041) Toxicity was not different between arms Conclusions: Addition of T to C/P increased PFS and OS in women with advanced/recurrent HER2/Neu-positive USC, with the greatest benefit seen for the treatment of stage III to IV disease

Impact of Potential Physiological Changes due to COVID-19 Home Confinement on Athlete Health Protection in Elite Sports: a Call for Awareness in Sports Programming.

Abstract: A global emergency characterized by a respiratory illness called COVID-19 (coronavirus disease) has spread worldwide in early 2020. Preventive measures to reduce the risk of infection include social distancing and the closing of commercial activities to avoid social gatherings. Elite sport is also tremendously affected: ongoing championships have been suspended and the major international events have been postponed (e.g. Summer Olympics, UEFA European Football Championship). This is the first time since the Second World War that all elite athletes are forced to interrupt competitions. Further, most elite athletes are forced to train at home, on their own and mostly unsupervised. Some elite sports clubs have provided players with home-based training programs and/or organized video conferences for online training sessions lead by their fitness trainers. However, logistical constraints and the difficulty to implement sportspecific exercise strategies in the absence of official sports facilities/playgrounds, make it difficult to provide training solutions comparable to those adopted under normal circumstances. During COVID-19 home confinement, athletes are likely exposed to some level of detraining (i.e. the partial or complete loss of training-induced morphological and physiological adaptations), as a consequence of insufficient and/or inappropriate training stimuli [1]. Such changes may result in impaired performance and increased injury risk (e.g. ligament rupture and muscle injuries) if, upon restart, an appropriate sport-specific reconditioning cannot be granted. Moreover, athletes on their return to sports journey may suffer from inappropriate rehabilitation/reconditioning and, therefore, a higher risk of re-injury, when championships

Machine learning uncovers the most robust self-report predictors of relationship quality across 43 longitudinal couples studies

Abstract: Given the powerful implications of relationship quality for health and well-being, a central mission of relationship science is explaining why some romantic relationships thrive more than others. This large-scale project used machine learning (i.e., Random Forests) to 1) quantify the extent to which relationship quality is predictable and 2) identify which constructs reliably predict relationship quality. Across 43 dyadic longitudinal datasets from 29 laboratories, the top relationship-specific predictors of relationship quality were perceived-partner commitment, appreciation, sexual satisfaction, perceived-partner satisfaction, and conflict. The top individual-difference predictors were life satisfaction, negative affect, depression, attachment avoidance, and attachment anxiety. Overall, relationship-specific variables predicted up to 45% of variance at baseline, and up to 18% of variance at the end of each study. Individual differences also performed well (21% and 12%, respectively). Actor-reported variables (i.e., own relationship-specific and individual-difference variables) predicted two to four times more variance than partner-reported variables (i.e., the partner's ratings on those variables). Importantly, individual differences and partner reports had no predictive effects beyond actor-reported relationship-specific variables alone. These findings imply that the sum of all individual differences and partner experiences exert their influence on relationship quality via a person's own relationship-specific experiences, and effects due to moderation by individual differences and moderation by partner-reports may be quite small. Finally, relationship-quality change (i.e., increases or decreases in relationship quality over the course of a study) was largely unpredictable from any combination of self-report variables. This collective effort should guide future models of relationships.

Federated Learning in the Sky: Joint Power Allocation and Scheduling with UAV Swarms

Abstract: Unmanned aerial vehicle (UAV) swarms must exploit machine learning (ML) in order to execute various tasks ranging from coordinated trajectory planning to cooperative target recognition. However, due to the lack of continuous connections between the UAV swarm and ground base stations (BSs), using centralized ML will be challenging, particularly when dealing with a large volume of data. In this paper, a novel framework is proposed to implement distributed federated learning (FL) algorithms within a UAV swarm that consists of a leading UAV and several following UAVs. Each following UAV trains a local FL model based on its collected data and then sends this trained local model to the leading UAV who will aggregate the received models, generate a global FL model, and transmit it to followers over the intra-swarm network. To identify how wireless factors, like fading, transmission delay, and UAV antenna angle deviations resulting from wind and mechanical vibrations, impact the performance of FL, a rigorous convergence analysis for FL is performed. Then, a joint power allocation and scheduling design is proposed to optimize the convergence rate of FL while taking into account the energy consumption during convergence and the delay requirement imposed by the swarm's control system. Simulation results validate the effectiveness of the FL convergence analysis and show that the joint design strategy can reduce the number of communication rounds needed for convergence by as much as 35% compared with the baseline design.

Improving the Reliability of Deep Neural Networks in NLP: A Review

Abstract: Deep learning models have achieved great success in solving a variety of natural language processing (NLP) problems. An ever-growing body of research, however, illustrates the vulnerability of deep neural networks (DNNs) to adversarial examples — inputs modified by introducing small perturbations to deliberately fool a target model into outputting incorrect results. The vulnerability to adversarial examples has become one of the main hurdles precluding neural network deployment into safety-critical environments. This paper discusses the contemporary usage of adversarial examples to foil DNNs and presents a comprehensive review of their use to improve the robustness of DNNs in NLP applications. In this paper, we summarize recent approaches for generating adversarial texts and propose a taxonomy to categorize them. We further review various types of defensive strategies against adversarial examples, explore their main challenges, and highlight some future research directions.

Pseudo-two-dimensional model and impedance diagnosis of micro internal short circuit in lithium-ion cells

Abstract: The early detection of micro internal short circuit (ISCr) cells can provide sufficient response time for preventing accidents such as spontaneous thermal runaway in battery packs of electric vehicles, and greatly improve safety. Because the existing models describing ISCr are mainly equivalent circuit models and three-dimensional physics-based models, we build a pseudo-two-dimensional model of micro ISCr cells to make up for the gaps. Using the calculation results of this model, we reveal the phenomenon of electric quantity depletion and the variation of internal electrochemical parameters when a micro ISCr occurs in the cell. We find the effective electrical conductivity of the separator is a crucial parameter describing the ISCr severity and determine reasonable values for this effective conductivity for fault diagnosis and battery design. Moreover, we propose an impedance-identification method that can be used for ISCr diagnostics. Through the simulation and experimental results, we find that the impedance of micro ISCr cells is different from that of normal cells and shows a certain regularity with the increase of ISCr severity.

The Overlooked Elephant of Object Detection: Open Set

Abstract: Even though object detection is a popular area of research that has found considerable applications in the real world, it has some fundamental aspects that have never been formally discussed and experimented. One of the core aspects of evaluating object detectors has been the ability to avoid false detections. While major datasets like PASCAL VOC or MSCOCO extensively test the detectors on their ability to avoid false positives, they do not differentiate between their closed-set and open-set performance. Despite systems being trained to reject everything other than the classes of interest, unknown objects from the open world end up being incorrectly detected as known objects, often with very high confidence. This paper is the first to formalize the problem of open-set object detection and propose the first open-set object detection protocol. Moreover, the paper provides a new evaluation metric to analyze the performance of some state-of-the-art detectors and discusses their performance differences.

VFL: A Verifiable Federated Learning with Privacy-Preserving for Big Data in Industrial IoT

Abstract: Due to the strong analytical ability of big data, deep learning has been widely applied to model on the collected data in industrial IoT. However, for privacy issues, traditional data-gathering centralized learning is not applicable to industrial scenarios sensitive to training sets, such as face recognition and medical systems. Recently, federated learning has received widespread attention, since it trains a model by only sharing gradients without accessing training sets. But existing researches reveal that the shared gradient still retains the sensitive information of the training set. Even worse, a malicious aggregation server may return forged aggregated gradients. In this paper, we propose the VFL, a verifiable federated learning with privacy-preserving for big data in industrial IoT. Specifically, we use Lagrange interpolation to elaborately set interpolation points for verifying the correctness of the aggregated gradients. Compared with existing schemes, the verification overhead of VFL remains constant regardless of the number of participants. Moreover, we employ the blinding technology to protect the privacy of the privacy gradients. If no more than n-2 of n participants collude with the aggregation server, VFL could guarantee the encrypted gradients of other participants not being inverted. Experimental evaluations corroborate the practical performance of the presented VFL with high accuracy and efficiency.

Global assessment of relationships between climate and tree growth.

Abstract: Tree-ring records provide global high-resolution information on tree-species responses to global change, forest carbon and water dynamics, and past climate variability and extremes. The underlying assumption is a stationary (time-stable), quasi-linear relationship between tree growth and environment, which however conflicts with basic ecological and evolutionary theory. Indeed, our global assessment of the relevant tree-ring literature demonstrates non-stationarity in the majority of tested cases, not limited to specific proxies, environmental parameters, regions or species. Non-stationarity likely represents the general nature of the relationship between tree-growth proxies and environment. Studies assuming stationarity however score two times more citations influencing other fields of science and the science-policy interface. To reconcile ecological reality with the application of tree-ring proxies for climate or environmental estimates, we provide a clarification of the stationarity concept, propose a simple confidence framework for the re-evaluation of existing studies and recommend the use of a new statistical tool to detect non-stationarity in tree-ring proxies. Our contribution is meant to stimulate and facilitate discussion in light of our results to help increase confidence in tree-ring-based climate and environmental estimates for science, the public and policymakers.

Federated Echo State Learning for Minimizing Breaks in Presence in Wireless Virtual Reality Networks

Abstract: In this paper, the problem of enhancing the virtual reality (VR) experience for wireless users is investigated by minimizing the occurrence of breaks in presence (BIP) that can detach the users from their virtual world. To measure the BIP for wireless VR users, a novel model that jointly considers the VR application type, transmission delay, VR video quality, and users’ awareness of the virtual environment is proposed. In the developed model, base stations (BSs) transmit VR videos to the wireless VR users using directional transmission links so as to provide high data rates for the VR users, thus, reducing the number of BIP for each user. Since the body movements of a VR user may result in a blockage of its wireless link, the location and orientation of VR users must also be considered when minimizing BIP. The BIP minimization problem is formulated as an optimization problem which jointly considers the predictions of users’ locations, orientations, and their BS association. To predict the orientation and locations of VR users, a distributed learning algorithm based on the machine learning framework of deep echo state networks (ESNs) is proposed. The proposed algorithm uses federated learning to enable multiple BSs to locally train their deep ESNs using their collected data and cooperatively build a learning model to predict the entire users’ locations and orientations. Using these predictions, the user association policy that minimizes BIP is derived. Simulation results demonstrate that the developed algorithm reduces the users’ BIP by up to 16% and 26%, respectively, compared to centralized ESN and deep learning algorithms.

SAIDuCANT: Specification-Based Automotive Intrusion Detection Using Controller Area Network (CAN) Timing

Abstract: The proliferation of embedded devices in modern vehicles has opened the traditionally-closed vehicular system to the risk of cybersecurity attacks through physical and remote access to the in-vehicle network such as the controller area network (CAN). The CAN bus does not implement a security protocol that can protect the vehicle against the increasing cyber and physical attacks. To address this risk, we introduce a novel algorithm to extract the real-time model parameters of the CAN bus and develop SAIDuCANT, a specification-based intrusion detection system (IDS) using anomaly-based supervised learning with the real-time model as input. We evaluate the effectiveness of SAIDuCANT with real CAN logs collected from two passenger cars and on an open-source CAN dataset collected from real-world scenarios. Experimental results show that SAIDuCANT can effectively detect data injection attacks with low false positive rates. Over four real attack scenarios from the open-source dataset, SAIDuCANT observes at most one false positive before detecting an attack whereas other detection approaches using CAN timing features detect on average more than a hundred false positives before a real attack occurs.

Safety and Efficacy of Different Doses and Regimens of Faricimab vs Ranibizumab in Neovascular Age-Related Macular Degeneration: The AVENUE Phase 2 Randomized Clinical Trial.

Abstract: Importance Faricimab, the first bispecific antibody designed for intraocular use, simultaneously and independently binds and neutralizes angiopoietin 2 (Ang-2) and vascular endothelial growth factor A (VEGF-A). Objective To assess the efficacy and safety of different doses and regimens of faricimab vs ranibizumab in patients with neovascular age-related macular degeneration (nAMD). Design, setting, and participants AVENUE was a 36-week, multiple-dose-regimen, active comparator-controlled, double-masked, phase 2 randomized clinical study performed at 58 sites in the United States. Eligible participants were anti-VEGF treatment naive with choroidal neovascularization secondary to nAMD and best-corrected visual acuity (BCVA) Early Treatment Diabetic Retinopathy Study (ETDRS) letter score of 73 (Snellen equivalent, 20/40) to 24 (Snellen equivalent, 20/320). Data were collected from August 11, 2015, to January 12, 2017, with the final patient visit completed September 26, 2017. Data were analyzed from August 11, 2015, to October 4, 2019. Interventions Patients were randomized 3:2:2:2:3 to receive ranibizumab, 0.5 mg every 4 weeks (arm A [n = 68]); faricimab, 1.5 mg every 4 weeks (arm B [n = 47]); faricimab, 6.0 mg every 4 weeks (arm C [n = 42]); faricimab, 6.0 mg every 4 weeks until week 12, then faricimab, 6.0 mg every 8 weeks (arm D [n = 47]); and ranibizumab, 0.5 mg every 4 weeks until week 8, then faricimab, 6.0 mg every 4 weeks (arm E [n = 69]). Main outcomes and measures Mean change in BCVA from baseline to week 36, proportion of participants gaining at least 15 letters, BCVA of 20/40 or better or 20/200 or worse, and ocular coherence tomographic outcomes in anti-VEGF treatment-naive participants (arms A, B, C, D) and from weeks 12 to 36 in those with incomplete response (participants in arms A and E with week 12 BCVA ETDRS letter score of ≤68 [Snellen equivalent, 20/50 or worse]). Results A total of 263 participants were included in the analysis (172 [65.4%] female; 258 [98.1%] white; mean [SD] age, 78.3 [8.7] years). At week 36, adjusted mean change in BCVA vs ranibizumab was 1.6 (80% CI, -1.6 to 4.7) letters for arm B (P = .52), -1.6 (80% CI, -4.9 to 1.7) letters for arm C (P = .53), and -1.5 (80% CI, -4.6 to 1.6) letters for arm D (P = .53). For arm E, adjusted mean change from week 12 was -1.7 (80% CI, -3.8 to 0.4) letters (P = .30). Conclusions and relevance AVENUE did not meet its primary end point of superiority of faricimab over ranibizumab in BCVA at week 36. Although not superior to monthly ranibizumab as given in this trial, overall visual and anatomical gains noted with faricimab support pursuing phase 3 trials for a potential alternative to monthly anti-VEGF therapy. Faricimab showed no new or unexpected safety signals. Trial registration ClinicalTrials.gov Identifier: NCT02484690.

VulDeeLocator: A Deep Learning-based Fine-grained Vulnerability Detector

Abstract: Automatically detecting software vulnerabilities is an important problem that has attracted much attention from the academic research community. However, existing vulnerability detectors still cannot achieve the vulnerability detection capability and the locating precision that would warrant their adoption for real-world use. In this paper, we present a vulnerability detector that can simultaneously achieve a high detection capability and a high locating precision, dubbed Vulnerability Deep learning-based Locator (VulDeeLocator). In the course of designing VulDeeLocator, we encounter difficulties including how to accommodate semantic relations between the definitions of types as well as macros and their uses across files, how to accommodate accurate control flows and variable define-use relations, and how to achieve high locating precision. We solve these difficulties by using two innovative ideas: (i) leveraging intermediate code to accommodate extra semantic information, and (ii) using the notion of granularity refinement to pin down locations of vulnerabilities. When applied to 200 files randomly selected from three real-world software products, VulDeeLocator detects 18 confirmed vulnerabilities (i.e., true-positives). Among them, 16 vulnerabilities correspond to known vulnerabilities; the other two are not reported in the National Vulnerability Database (NVD) but have been "silently" patched by the vendor of Libav when releasing newer versions.

Exploiting Unique Alignment of Cobalt Ferrite Nanoparticles, Mild Hyperthermia, and Controlled Intrinsic Cobalt Toxicity for Cancer Therapy

Abstract: Nanoparticle-based magnetic hyperthermia is a well-known thermal therapy platform studied to treat solid tumors, but its use for monotherapy is limited due to incomplete tumor eradication at hyperthermia temperature (45 °C). It is often combined with chemotherapy for obtaining a more effective therapeutic outcome. Cubic-shaped cobalt ferrite nanoparticles (Co-Fe NCs) serve as magnetic hyperthermia agents and as a cytotoxic agent due to the known cobalt ion toxicity, allowing the achievement of both heat and cytotoxic effects from a single platform. In addition to this advantage, Co-Fe NCs have the unique ability to form growing chains under an alternating magnetic field (AMF). This unique chain formation, along with the mild hyperthermia and intrinsic cobalt toxicity, leads to complete tumor regression and improved overall survival in an in vivo murine xenograft model, all under clinically approved AMF conditions. Numerical calculations identify magnetic anisotropy as the main Co-Fe NCs' feature to generate such chain formations. This novel combination therapy can improve the effects of magnetic hyperthermia, inaugurating investigation of mechanical behaviors of nanoparticles under AMF, as a new avenue for cancer therapy.

Federated Learning in the Sky: Joint Power Allocation and Scheduling with UAV Swarms

Abstract: Unmanned aerial vehicle (UAV) swarms must exploit machine learning (ML) in order to execute various tasks ranging from coordinated trajectory planning to cooperative target recognition. However, due to the lack of continuous connections between the UAV swarm and ground base stations (BSs), using centralized ML will be challenging, particularly when dealing with a large volume of data. In this paper, a novel framework is proposed to implement distributed federated learning (FL) algorithms within a UAV swarm that consists of a leading UAV and several following UAVs. Each following UAV trains a local FL model based on its collected data and then sends this trained local model to the leading UAV who will aggregate the received models, generate a global FL model, and transmit it to followers over the intra-swarm network. To identify how wireless factors, like fading, transmission delay, and UAV antenna angle deviations resulting from wind and mechanical vibrations, impact the performance of FL, a rigorous convergence analysis for FL is performed. Then, a joint power allocation and scheduling design is proposed to optimize the convergence rate of FL while taking into account the energy consumption during convergence and the delay requirement imposed by the swarm's control system. Simulation results validate the effectiveness of the FL convergence analysis and show that the joint design strategy can reduce the number of communication rounds needed for convergence by as much as 35% compared with the baseline design.

Community participation and public transportation barriers experienced by people with disabilities

Abstract: Background: Barriers to public transportation quickly impact the ability of people with disabilities to fully experience their community.Objective: A national survey of people with disabilities was conducted to understand the barriers and supports to accessing public transportation and the impact on community participation.Method: A total of 1748 respondents responded to a web-based survey investigating the accessibility of public transportation. Results present frequency of barriers to public transportation and group differences using Pearson's chi-square technique and Mann-Whitney U tests.Results: A majority of respondents experienced difficulties accessing public transportation, and community activities that do not occur on a regular schedule are more affected by problems with public transportation. Individuals with blindness or low vision, psychiatric disabilities, chronic health conditions, or multiple disabilities experienced more problems using public transportation for community participation, along with participants who were female, Hispanic, Latino/Latina, or Spanish origin.Limitations: Survey distribution was convenience-based, which may have affected participation of certain disability groups, cultural groups, and/or those without computer access, and interpretations cannot be made regarding predictive or casual relationships.Conclusions: Significant challenges face individuals with disabilities as they use public transportation, and certain disability groups are more severely impacted by these problems.Implications for RehabilitationCertain disability groups experience more severe problems with public transportation, as compared to other groups.Public policy advocacy and actions related to public transportation must prioritise individuals with disabilities who experience significantly more problems.Problems using public transportation for spontaneous activities pose increased problems for individuals with disabilities, and steps (i.e., extended hours or alternative transportation options) must be taken to overcome this barrier.

Toward an Integrated Understanding of the Youth Sport System

Abstract: The aim of the present article is to outline a heuristic model that facilitates movement toward an integrated understanding of the youth sport system. We define the youth sport system as the set of...

Athlete mental health in the Olympic/Paralympic quadrennium: a multi-societal consensus statement

Abstract: This consensus statement is the product of the Second International Think Tank on Athlete Mental Health, held on the initiative of the International Society of Sport Psychology. The purposes of the...

CEO Bright and Dark Personality: Effects on Ethical Misconduct

Abstract: In recent years, misconduct by CEOs has led to firings, scandals, and financial losses for companies. Our study explores personality antecedents of CEO misconduct using Five-Factor Model personality traits and personality disorder profile similarity indices. The sample of 259 CEOs used in the analysis includes CEOs who were involved in well-publicized misconduct scandals as well as CEOs who had no misconduct scandals. Teams of trained raters measured CEO personality using psychometric personality rating scales and video-based assessment methods. Logistic regression results provided some support for hypotheses regarding relationships between ethical misconduct, fraud, excessive risk taking, and sexual misconduct and personality traits including “Big Five” traits and personality disorder profile similarity indices.

Bose-Einstein condensation of quasiparticles by rapid cooling.

Abstract: The fundamental phenomenon of Bose–Einstein condensation has been observed in different systems of real particles and quasiparticles. The condensation of real particles is achieved through a major reduction in temperature, while for quasiparticles, a mechanism of external injection of bosons by irradiation is required. Here, we present a new and universal approach to enable Bose–Einstein condensation of quasiparticles and to corroborate it experimentally by using magnons as the Bose-particle model system. The critical point to this approach is the introduction of a disequilibrium of magnons with the phonon bath. After heating to an elevated temperature, a sudden decrease in the temperature of the phonons, which is approximately instant on the time scales of the magnon system, results in a large excess of incoherent magnons. The consequent spectral redistribution of these magnons triggers the Bose–Einstein condensation. A new method to form Bose–Einstein condensates of quasiparticles based on the rapid decrease in the phonon temperature was proposed and shown experimentally.

Magnon-phonon interactions in magnon spintronics (Review article)

Abstract: Nowadays, the interaction between phonon and magnon subsystems of a magnetic medium is a hot topic of research. The complexity of phonon and magnon spectra, the existence of both bulk and surface modes, the quantization effects, and the dependence of magnon properties on applied magnetic field, make this field very complex and intriguing. Moreover, the recent advances in the fields of spin caloritronics and magnon spintronics as well as the observation of the spin Seebeck effect in magnetic insulators points on the crucial role of magnons in spin-caloric transport processes. In this review, we collect the variety of different studies in which magnon-phonon interaction play important role. The scope of the paper covers the wide range of phenomena starting from the interaction of the coherent magnons with surface acoustic wave and finishing with the formation of magnon supercurrents in the thermal gradients.

Waist Circumference and Waist-to-Hip Ratio in Law Enforcement Agency Recruits: Relationship to Performance in Physical Fitness Tests

Abstract: Lockie, RG, Ruvalcaba, TR, Stierli, M, Dulla, JM, Dawes, JJ, and Orr, RM. Waist circumference and waist-to-hip ratio in law enforcement agency recruits: relationship to performance in physical fitness tests. J Strength Cond Res 34(6): 1666-1675, 2020-Law enforcement agencies (LEAs) use tests to assess recruit physical fitness. Body fat can influence test performance but is difficult to measure during academy because of time, equipment constraints, and instructor knowledge. This study examined relationships between waist circumference (WC) and waist-to-hip ratio (WHR), practical measures of fat distribution, and fitness test performance. Retrospective analysis of 267 LEA recruits (age: ∼28 years; height: ∼1.73 m; and body mass: ∼80 kg; 219 males and 48 females) was conducted. The tests included: WC and WHR; grip strength; push-ups, sit-ups, and arm ergometer revolutions in 60 seconds; vertical jump (VJ); medicine ball throw; 75-yd pursuit run (75PR); and multistage fitness test (MSFT) shuttles. Partial correlations, controlling for sex, calculated relationships between WC, WHR, and the fitness tests. Recruits were split into quartile groups (based on the sample size) for WC and WHR (group 1 had the lowest WC and WHR; and group 4 the highest). A 1-way multivariate analysis of variance, with sex as a covariate and Bonferroni post hoc, compared between-group test performance. A greater WC related to lesser push-up, sit-up, VJ, 75PR, and MSFT performance (p ≤ 0.024). When recruits were split into WC groups, group 4 had lesser performance in push-ups, sit-ups, VJ, and the 75PR compared with all groups (p ≤ 0.038). When split into WHR groups, group 4 performed less push-ups than group 1, less MSFT shuttles than group 3, and had a lower VJ compared with all groups (p ≤ 0.042). Recruits with a greater WC tended to have poorer fitness test performance.

Magnon-phonon interactions in magnon spintronics

Abstract: Nowadays, the interaction between phonon and magnon subsystems of a magnetic medium is a hot topic of research. The complexity of phonon and magnon spectra, the existence of both bulk and surface modes, the quantization effects, and the dependence of magnon properties on applied magnetic field, make this field very complex and intriguing. Moreover, the recent advances in the fields of spin-caloritronics and magnon spintronics as well as the observation of the spin Seebeck effect (SSE) in magnetic insulators points on the crucial role of magnons in spin-caloric transport processes. In this review, we collect the variety of different studies in which magnon-phonon interaction play important role. The scope of the paper covers the wide range of phenomena starting from the interaction of the coherent magnons with surface acoustic wave and finishing with the formation of magnon supercurrents in the thermal gradients.