Don't Make Me Angry! A Psychophysiological Examination of the Anger–Performance Relationship in Intermediate and Elite Fencers

We aimed to identify the effect of state-anger on precision, speed, and power components of performance during fencing attacks. We conducted a laboratory-based, single-case research experiment to test the fine motor task performance of two experienced and two elite-level fencers under two emotional states: anger and emotion-neutral. We assessed anger via psychophysiological and self-report measures, and we induced anger via a brief imagery intervention. Through the use of an innovative design, which included multiple measures of change, we showed that anger had a consistent negative effect on precision but an inconsistent relationship with response time and muscle activity. The current research design and protocol offer a novel and in-depth method for examining the specific relationships between affective states, emotions, and the complexities that underpin performance. The specific effects of anger on performance were multifarious, complex, and inconsistent. Nonetheless, the results tend to indicate that anger facilitates reaction time and debilitates performance, and these effects were clearer for the most elite performers. The effects of anger on performance are clearly complex, so it would be rather premature to make any suggestions for future practice at this point. Nonetheless, the clearer findings with the elite fencers indicate that researchers will likely yield the most fruitful insights by examining the effects of emotion of performance in elite performers

Structural Learning of Attack Vectors for Generating Mutated XSS Attacks

Web applications suffer from cross-site scripting (XSS) attacks that resulting from incomplete or incorrect input sanitization. Learning the structure of attack vectors could enrich the variety of manifestations in generated XSS attacks. In this study, we focus on generating more threatening XSS attacks for the state-of-the-art detection approaches that can find potential XSS vulnerabilities in Web applications, and propose a mechanism for structural learning of attack vectors with the aim of generating mutated XSS attacks in a fully automatic way. Mutated XSS attack generation depends on the analysis of attack vectors and the structural learning mechanism. For the kernel of the learning mechanism, we use a Hidden Markov model (HMM) as the structure of the attack vector model to capture the implicit manner of the attack vector, and this manner is benefited from the syntax meanings that are labeled by the proposed tokenizing mechanism. Bayes theorem is used to determine the number of hidden states in the model for generalizing the structure model. The paper has the contributions as following: (1) automatically learn the structure of attack vectors from practical data analysis to modeling a structure model of attack vectors, (2) mimic the manners and the elements of attack vectors to extend the ability of testing tool for identifying XSS vulnerabilities, (3) be helpful to verify the flaws of blacklist sanitization procedures of Web applications. We evaluated the proposed mechanism by Burp Intruder with a dataset collected from public XSS archives. The results show that mutated XSS attack generation can identify potential vulnerabilities.Comment: In Proceedings TAV-WEB 2010, arXiv:1009.330

Clinical Development of Novel Drug-Radiotherapy Combinations.

Radiotherapy is a fundamental component of treatment for the majority of patients with cancer. In recent decades, technological advances have enabled patients to receive more targeted doses of radiation to the tumor, with sparing of adjacent normal tissues. There had been hope that the era of precision medicine would enhance the combination of radiotherapy with targeted anticancer drugs; however, this ambition remains to be realized. In view of this lack of progress, the FDA-AACR-ASTRO Clinical Development of Drug-Radiotherapy Combinations Workshop was held in February 2018 to bring together stakeholders and opinion leaders from academia, clinical radiation oncology, industry, patient advocacy groups, and the FDA to discuss challenges to introducing new drug-radiotherapy combinations to the clinic. This Perspectives in Regulatory Science and Policy article summarizes the themes and action points that were discussed. Intelligent trial design is required to increase the number of studies that efficiently meet their primary outcomes; endpoints to be considered include local control, organ preservation, and patient-reported outcomes. Novel approaches including immune-oncology or DNA-repair inhibitor agents combined with radiotherapy should be prioritized. In this article, we focus on how the regulatory challenges associated with defining a new drug-radiotherapy combination can be overcome to improve clinical outcomes for patients with cancer

Systematic Overestimation of Machine Learning Performance in Neuroimaging Studies of Depression

We currently observe a disconcerting phenomenon in machine learning studies in psychiatry: While we would expect larger samples to yield better results due to the availability of more data, larger machine learning studies consistently show much weaker performance than the numerous small-scale studies. Here, we systematically investigated this effect focusing on one of the most heavily studied questions in the field, namely the classification of patients suffering from Major Depressive Disorder (MDD) and healthy controls. Drawing upon a balanced sample of $N = 1,868$ MDD patients and healthy controls from our recent international Predictive Analytics Competition (PAC), we first trained and tested a classification model on the full dataset which yielded an accuracy of 61%. Next, we mimicked the process by which researchers would draw samples of various sizes ($N=4$ to $N=150$) from the population and showed a strong risk of overestimation. Specifically, for small sample sizes ($N=20$), we observe accuracies of up to 95%. For medium sample sizes ($N=100$) accuracies up to 75% were found. Importantly, further investigation showed that sufficiently large test sets effectively protect against performance overestimation whereas larger datasets per se do not. While these results question the validity of a substantial part of the current literature, we outline the relatively low-cost remedy of larger test sets

Failure of adaptive self-organized criticality during epileptic seizure attacks

Critical dynamics are assumed to be an attractive mode for normal brain functioning as information processing and computational capabilities are found to be optimized there. Recent experimental observations of neuronal activity patterns following power-law distributions, a hallmark of systems at a critical state, have led to the hypothesis that human brain dynamics could be poised at a phase transition between ordered and disordered activity. A so far unresolved question concerns the medical significance of critical brain activity and how it relates to pathological conditions. Using data from invasive electroencephalogram recordings from humans we show that during epileptic seizure attacks neuronal activity patterns deviate from the normally observed power-law distribution characterizing critical dynamics. The comparison of these observations to results from a computational model exhibiting self-organized criticality (SOC) based on adaptive networks allows further insights into the underlying dynamics. Together these results suggest that brain dynamics deviates from criticality during seizures caused by the failure of adaptive SOC.Comment: 7 pages, 5 figure

Severity of current depression and remission status are associated with structural connectome alterations in major depressive disorder

Major depressive disorder (MDD) is associated to affected brain wiring. Little is known whether these changes are stable over time and hence might represent a biological predisposition, or whether these are state markers of current disease severity and recovery after a depressive episode. Human white matter network ("connectome") analysis via network science is a suitable tool to investigate the association between affected brain connectivity and MDD. This study examines structural connectome topology in 464 MDD patients (mean age: 36.6 years) and 432 healthy controls (35.6 years). MDD patients were stratified categorially by current disease status (acute vs. partial remission vs. full remission) based on DSM-IV criteria. Current symptom severity was assessed continuously via the Hamilton Depression Rating Scale (HAMD). Connectome matrices were created via a combination of T1-weighted magnetic resonance imaging (MRI) and tractography methods based on diffusion-weighted imaging. Global tract-based metrics were not found to show significant differences between disease status groups, suggesting conserved global brain connectivity in MDD. In contrast, reduced global fractional anisotropy (FA) was observed specifically in acute depressed patients compared to fully remitted patients and healthy controls. Within the MDD patients, FA in a subnetwork including frontal, temporal, insular, and parietal nodes was negatively associated with HAMD, an effect remaining when correcting for lifetime disease severity. Therefore, our findings provide new evidence of MDD to be associated with structural, yet dynamic, state-dependent connectome alterations, which covary with current disease severity and remission status after a depressive episode

Brain ageing in schizophrenia: evidence from 26 international cohorts via the ENIGMA Schizophrenia consortium

Schizophrenia (SZ) is associated with an increased risk of life-long cognitive impairments, age-related chronic disease, and premature mortality. We investigated evidence for advanced brain ageing in adult SZ patients, and whether this was associated with clinical characteristics in a prospective meta-analytic study conducted by the ENIGMA Schizophrenia Working Group. The study included data from 26 cohorts worldwide, with a total of 2803 SZ patients (mean age 34.2 years; range 18-72 years; 67% male) and 2598 healthy controls (mean age 33.8 years, range 18-73 years, 55% male). Brain-predicted age was individually estimated using a model trained on independent data based on 68 measures of cortical thickness and surface area, 7 subcortical volumes, lateral ventricular volumes and total intracranial volume, all derived from T1-weighted brain magnetic resonance imaging (MRI) scans. Deviations from a healthy brain ageing trajectory were assessed by the difference between brain-predicted age and chronological age (brain-predicted age difference [brain-PAD]). On average, SZ patients showed a higher brain-PAD of +3.55 years (95% CI: 2.91, 4.19; I$^{2}$ = 57.53%) compared to controls, after adjusting for age, sex and site (Cohen's d = 0.48). Among SZ patients, brain-PAD was not associated with specific clinical characteristics (age of onset, duration of illness, symptom severity, or antipsychotic use and dose). This large-scale collaborative study suggests advanced structural brain ageing in SZ. Longitudinal studies of SZ and a range of mental and somatic health outcomes will help to further evaluate the clinical implications of increased brain-PAD and its ability to be influenced by interventions

The state of the Martian climate

60°N was +2.0°C, relative to the 1981–2010 average value (Fig. 5.1). This marks a new high for the record. The average annual surface air temperature (SAT) anomaly for 2016 for land stations north of starting in 1900, and is a significant increase over the previous highest value of +1.2°C, which was observed in 2007, 2011, and 2015. Average global annual temperatures also showed record values in 2015 and 2016. Currently, the Arctic is warming at more than twice the rate of lower latitudes

The Long-Baseline Neutrino Experiment: Exploring Fundamental Symmetries of the Universe

The preponderance of matter over antimatter in the early Universe, the dynamics of the supernova bursts that produced the heavy elements necessary for life and whether protons eventually decay --- these mysteries at the forefront of particle physics and astrophysics are key to understanding the early evolution of our Universe, its current state and its eventual fate. The Long-Baseline Neutrino Experiment (LBNE) represents an extensively developed plan for a world-class experiment dedicated to addressing these questions. LBNE is conceived around three central components: (1) a new, high-intensity neutrino source generated from a megawatt-class proton accelerator at Fermi National Accelerator Laboratory, (2) a near neutrino detector just downstream of the source, and (3) a massive liquid argon time-projection chamber deployed as a far detector deep underground at the Sanford Underground Research Facility. This facility, located at the site of the former Homestake Mine in Lead, South Dakota, is approximately 1,300 km from the neutrino source at Fermilab -- a distance (baseline) that delivers optimal sensitivity to neutrino charge-parity symmetry violation and mass ordering effects. This ambitious yet cost-effective design incorporates scalability and flexibility and can accommodate a variety of upgrades and contributions. With its exceptional combination of experimental configuration, technical capabilities, and potential for transformative discoveries, LBNE promises to be a vital facility for the field of particle physics worldwide, providing physicists from around the globe with opportunities to collaborate in a twenty to thirty year program of exciting science. In this document we provide a comprehensive overview of LBNE's scientific objectives, its place in the landscape of neutrino physics worldwide, the technologies it will incorporate and the capabilities it will possess.Comment: Major update of previous version. This is the reference document for LBNE science program and current status. Chapters 1, 3, and 9 provide a comprehensive overview of LBNE's scientific objectives, its place in the landscape of neutrino physics worldwide, the technologies it will incorporate and the capabilities it will possess. 288 pages, 116 figure