Paradoxical popups: Why are they hard to catch?

Even professional baseball players occasionally find it difficult to gracefully approach seemingly routine pop-ups. This paper describes a set of towering pop-ups with trajectories that exhibit cusps and loops near the apex. For a normal fly ball, the horizontal velocity is continuously decreasing due to drag caused by air resistance. But for pop-ups, the Magnus force (the force due to the ball spinning in a moving airflow) is larger than the drag force. In these cases the horizontal velocity decreases in the beginning, like a normal fly ball, but after the apex, the Magnus force accelerates the horizontal motion. We refer to this class of pop-ups as paradoxical because they appear to misinform the typically robust optical control strategies used by fielders and lead to systematic vacillation in running paths, especially when a trajectory terminates near the fielder. In short, some of the dancing around when infielders pursue pop-ups can be well explained as a combination of bizarre trajectories and misguidance by the normally reliable optical control strategy, rather than apparent fielder error. Former major league infielders confirm that our model agrees with their experiences.Comment: 28 pages, 10 figures, sumitted to American Journal of Physic

Does d-cycloserine facilitate the effects of homework compliance on social anxiety symptom reduction?

BACKGROUND: Prior studies examining the effect of d-cycloserine (DCS) on homework compliance and outcome in cognitive-behavior therapy (CBT) have yielded mixed results. The aim of this study was to investigate whether DCS facilitates the effects of homework compliance on symptom reduction in a large-scale study for social anxiety disorder (SAD). METHODS: 169 participants with generalized SAD received DCS or pill placebo during 12-session exposure-based group CBT. Improvements in social anxiety were assessed by independent raters at each session using the Liebowitz social anxiety scale (LSAS). RESULTS: Controlling for LSAS at the previous session, and irrespective of treatment condition, greater homework compliance in the week prior related to lower LSAS at the next session. However, DCS did not moderate the effect of homework compliance and LSAS, LSAS on homework compliance, or the overall augmenting effect of DCS on homework compliance. Furthermore, LSAS levels were not predictive of homework compliance in the following week. CONCLUSION: The findings support the general benefits of homework compliance on outcome, but not a DCS-augmenting effect. The comparably small number of DCS-enhanced sessions in this study could be one reason for the failure to find a facilitating effect of DCS

Site Occupancy and Lattice Parameters in Sigma-Phase Co-Cr alloys

Neutron diffraction technique was used to study distribution of Co and Cr atoms over different lattice sites as well as lattice paramaters in sigma-phase Co100-xCrx compounds with x = 57.0, 62.7 and 65.8. From the diffractograms recorded in the temperature range of 4.2 - 300 K it was found that all five sites A, B, C, D and E are populated by both kinds of atoms. Sites A and D are predominantly occupied by Co atoms while sites B, C and E by Cr atoms. The unit cell parameters a and c, hence the unit cell volume, increase with x, the increase being characteristic of the lattice paramater and temperature. Both a and c show a non-linear increase with temperature.Comment: 5 figure

Enhancement of psychosocial treatment with D-cycloserine: models, moderators, and future directions

Advances in the understanding of the neurobiology of fear extinction have resulted in the development of d-cycloserine (DCS), a partial glutamatergic N-methyl-D-aspartate agonist, as an augmentation strategy for exposure treatment. We review a decade of research that has focused on the efficacy of DCS for augmenting the mechanisms (e.g., fear extinction) and outcome of exposure treatment across the anxiety disorders. Following a series of small-scale studies offering strong support for this clinical application, more recent larger-scale studies have yielded mixed results, with some showing weak or no effects. We discuss possible explanations for the mixed findings, pointing to both patient and session (i.e., learning experiences) characteristics as possible moderators of efficacy, and offer directions for future research in this area. We also review recent studies that have aimed to extend the work on DCS augmentation of exposure therapy for the anxiety disorders to DCS enhancement of learning-based interventions for addiction, anorexia nervosa, schizophrenia, and depression. Here, we attend to both DCS effects on facilitating therapeutic outcomes and additional therapeutic mechanisms beyond fear extinction (e.g., appetitive extinction, hippocampal-dependent learning).F31 MH103969 - NIMH NIH HHS; K24 DA030443 - NIDA NIH HHS; R34 MH099309 - NIMH NIH HHS; R34 MH086668 - NIMH NIH HHS; R21 MH102646 - NIMH NIH HHS; R34 MH099318 - NIMH NIH HH

Capacity constrained stochastic static traffic assignment with residual point queues incorporating a proper node model

Static traffic assignment models are still widely applied for strategic transport planning purposes in spite of the fact that such models produce implausible traffic flows that exceed link capacities and predict incorrect congestion locations. There have been numerous attempts in the literature to add capacity constraints to obtain more realistic traffic flows and bottleneck locations, but so far there has not been a satisfactory model formulation. After reviewing the literature, we come to the conclusion that an important piece of the puzzle has been missing so far, namely the inclusion of a proper node model. In this paper we propose a novel path-based static traffic assignment model for finding a stochastic user equilibrium in which we include a first order node model that yields realistic turn capacities, which are then used to determine consistent traffic flows and residual point queues. The route choice part of the model is specified as a variational inequality problem, while the network loading part is formulated as a fixed point problem. Both problems are solved using existing techniques. We illustrate the model using hypothetical examples, and also demonstrate feasibility on large-scale networks

Genome-based population structure analysis of the strawberry plant pathogen Xanthomonas fragariae reveals two distinct groups that evolved independently before its species description

Xanthomonas fragariae is a quarantine organism in Europe, causing angular leaf spots on strawberry plants. It is spreading worldwide in strawberry-producing regions due to import of plant material through trade and human activities. In order to resolve the population structure at the strain level, we have employed high-resolution molecular typing tools on a comprehensive strain collection representing global and temporal distribution of the pathogen. Clustered regularly interspaced short palindromic repeat regions (CRISPRs) and variable number of tandem repeats (VNTRs) were identified within the reference genome of X. fragariae LMG 25863 as a potential source of variation. Strains from our collection were whole-genome sequenced and used in order to identify variable spacers and repeats for discriminative purpose. CRISPR spacer analysis and multiple-locus VNTR analysis (MLVA) displayed a congruent population structure, in which two major groups and a total of four subgroups were revealed. The two main groups were genetically separated before the first X. fragariae isolate was described and are potentially responsible for the worldwide expansion of the bacterial disease. Three primer sets were designed for discriminating CRISPR-associated markers in order to streamline group determination of novel isolates. Overall, this study describes typing methods to discriminate strains and monitor the pathogen population structure, more especially in the view of a new outbreak of the pathogen

Radiomics in neuro-oncological clinical trials

The development of clinical trials has led to substantial improvements in the prevention and treatment of many diseases, including brain cancer. Advances in medicine, such as improved surgical techniques, the development of new drugs and devices, the use of statistical methods in research, and the development of codes of ethics, have considerably influenced the way clinical trials are conducted today. In addition, methods from the broad field of artificial intelligence, such as radiomics, have the potential to considerably affect clinical trials and clinical practice in the future. Radiomics is a method to extract undiscovered features from routinely acquired imaging data that can neither be captured by means of human perception nor conventional image analysis. In patients with brain cancer, radiomics has shown its potential for the non-invasive identification of prognostic biomarkers, automated response assessment, and differentiation between treatment-related changes from tumour progression. Despite promising results, radiomics is not yet established in routine clinical practice nor in clinical trials. In this Viewpoint, the European Organization for Research and Treatment of Cancer Brain Tumour Group summarises the current status of radiomics, discusses its potential and limitations, envisions its future role in clinical trials in neuro-oncology, and provides guidance on how to address the challenges in radiomics