Action bias among elite soccer goalkeepers: The case of penalty kicks

In soccer penalty kicks, goalkeepers choose their action before they can clearly observe the kick direction. An analysis of 286 penalty kicks in top leagues and championships worldwide shows that given the probability distribution of kick direction, the optimal strategy for goalkeepers is to stay in the goal's center. Goalkeepers, however, almost always jump right or left. We propose the following explanation for this behavior: because the norm is to jump, norm theory (Kahneman and Miller, 1986) implies that a goal scored yields worse feelings for the goalkeeper following inaction (staying in the center) than following action (jumping), leading to a bias for action. The omission bias, a bias in favor of inaction, is reversed here because the norm here is reversed - to act rather than to choose inaction. The claim that jumping is the norm is supported by a second study, a survey conducted with 32 top professional goalkeepers. The seemingly biased decision making is particularly striking since the goalkeepers have huge incentives to make correct decisions, and it is a decision they encounter frequently. Finally, we discuss several implications of the action/omission bias for economics and management.Decision Making; Uncertainty; Choice Behavior; Sport Psychology; Behavioral Economics; Action Bias; Omission Bias; Commission Bias; Action Effect; Inaction Effect; Actor Effect; Economic Psychology; Heuristics and Biases; Soccer; Goalkeepers; Penalty Kicks; Risk; Norms

Explainable Multi-View Deep Networks Methodology for Experimental Physics

Physical experiments often involve multiple imaging representations, such as X-ray scans and microscopic images. Deep learning models have been widely used for supervised analysis in these experiments. Combining different image representations is frequently required to analyze and make a decision properly. Consequently, multi-view data has emerged - datasets where each sample is described by views from different angles, sources, or modalities. These problems are addressed with the concept of multi-view learning. Understanding the decision-making process of deep learning models is essential for reliable and credible analysis. Hence, many explainability methods have been devised recently. Nonetheless, there is a lack of proper explainability in multi-view models, which are challenging to explain due to their architectures. In this paper, we suggest different multi-view architectures for the vision domain, each suited to another problem, and we also present a methodology for explaining these models. To demonstrate the effectiveness of our methodology, we focus on the domain of High Energy Density Physics (HEDP) experiments, where multiple imaging representations are used to assess the quality of foam samples. We apply our methodology to classify the foam samples quality using the suggested multi-view architectures. Through experimental results, we showcase the improvement of accurate architecture choice on both accuracy - 78% to 84% and AUC - 83% to 93% and present a trade-off between performance and explainability. Specifically, we demonstrate that our approach enables the explanation of individual one-view models, providing insights into the decision-making process of each view. This understanding enhances the interpretability of the overall multi-view model. The sources of this work are available at: https://github.com/Scientific-Computing-Lab-NRCN/Multi-View-Explainability

Action bias among elite soccer goalkeepers: The case of penalty kicks

In soccer penalty kicks, goalkeepers choose their action before they can clearly observe the kick direction. An analysis of 286 penalty kicks in top leagues and championships worldwide shows that given the probability distribution of kick direction, the optimal strategy for goalkeepers is to stay in the goal's center. Goalkeepers, however, almost always jump right or left. We propose the following explanation for this behavior: because the norm is to jump, norm theory (Kahneman and Miller, 1986) implies that a goal scored yields worse feelings for the goalkeeper following inaction (staying in the center) than following action (jumping), leading to a bias for action. The omission bias, a bias in favor of inaction, is reversed here because the norm here is reversed - to act rather than to choose inaction. The claim that jumping is the norm is supported by a second study, a survey conducted with 32 top professional goalkeepers. The seemingly biased decision making is particularly striking since the goalkeepers have huge incentives to make correct decisions, and it is a decision they encounter frequently. Finally, we discuss several implications of the action/omission bias for economics and management

Predominance of null mutations in ataxia-telangiectasia

Ataxia-telangiectasia (A-T) is an autosomal recessive disorder involving cerebellar degeneration, immunodeficiency, chromosomal instability, radiosensitivity and cancer predisposition. The responsible gene, ATM, was recently identified by positional cloning and found to encode a putative 350 kDa protein with a PI 3-kinase-like domain, presumably involved in mediating cell cycle arrest in response to radiation-induced DNA damage. The nature and location of A-T mutations should provide insight into the function of the ATM protein and the molecular basis of this pleiotropic disease. Of 44 A-T mutations identified by us to date, 39 (89%) are expected to inactivate the ATM protein by truncating it, by abolishing correct initiation or termination of translation, or by deleting large segments. Additional mutations are four smaller in-frame deletions and insertions, and one substitution of a highly conserved amino acid at the PI 3-kinase domain. The emerging profile of mutations causing A-T is thus dominated by those expected to completely inactivate the ATM protein. ATM mutations with milder effects may result in phenotypes related, but not identical, to A-T

Open science discovery of potent noncovalent SARS-CoV-2 main protease inhibitors

INTRODUCTION COVID-19 became a global pandemic partially as a result of the lack of easily deployable, broad-spectrum oral antivirals, which complicated its containment. Even endemically, and with effective vaccinations, it will continue to cause acute disease, death, and long-term sequelae globally unless there are accessible treatments. COVID-19 is not an isolated event but instead is the latest example of a viral pandemic threat to human health. Therefore, antiviral discovery and development should be a key pillar of pandemic preparedness efforts. RATIONALE One route to accelerate antiviral drug discovery is the establishment of open knowledge bases, the development of effective technology infrastructures, and the discovery of multiple potent antivirals suitable as starting points for the development of therapeutics. In this work, we report the results of the COVID Moonshot—a fully open science, crowdsourced, and structure-enabled drug discovery campaign—against the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) main protease (Mpro). This collaboration may serve as a roadmap for the potential development of future antivirals. RESULTS On the basis of the results of a crystallographic fragment screen, we crowdsourced design ideas to progress from fragment to lead compounds. The crowdsourcing strategy yielded several key compounds along the optimization trajectory, including the starting compound of what became the primary lead series. Three additional chemically distinct lead series were also explored, spanning a diversity of chemotypes. The collaborative and highly automated nature of the COVID Moonshot Consortium resulted in >18,000 compound designs, >2400 synthesized compounds, >490 ligand-bound x-ray structures, >22,000 alchemical free-energy calculations, and >10,000 biochemical measurements—all of which were made publicly available in real time. The recently approved antiviral ensitrelvir was identified in part based on crystallographic data from the COVID Moonshot Consortium. This campaign led to the discovery of a potent [median inhibitory concentration (IC50) = 37 ± 2 nM] and differentiated (noncovalent and nonpeptidic) lead compound that also exhibited potent cellular activity, with a median effective concentration (EC50) of 64 nM in A549-ACE2-TMPRSS2 cells and 126 nM in HeLa-ACE2 cells without measurable cytotoxicity. Although the pharmacokinetics of the reported compound is not yet optimal for therapeutic development, it is a promising starting point for further antiviral discovery and development. CONCLUSION The success of the COVID Moonshot project in producing potent antivirals, building open knowledge bases, accelerating external discovery efforts, and functioning as a useful information-exchange hub is an example of the potential effectiveness of open science antiviral discovery programs. The open science, patent-free nature of the project enabled a large number of collaborators to provide in-kind support, including synthesis, assays, and in vitro and in vivo experiments. By making all data immediately available and ensuring that all compounds are purchasable from Enamine without the need for materials transfer agreements, we aim to accelerate research globally along parallel tracks. In the process, we generated a detailed map of the structural plasticity of Mpro, extensive structure-activity relationships for multiple chemotypes, and a wealth of biochemical activity data to spur further research into antivirals and discovery methodologies. We hope that this can serve as an alternative model for antiviral discovery and future pandemic preparedness. Further, the project also showcases the role of machine learning, computational chemistry, and high-throughput structural biology as force multipliers in drug design. Artificial intelligence and machine learning algorithms help accelerate chemical synthesis while balancing multiple competing molecular properties. The design-make-test-analyze cycle was accelerated by these algorithms combined with planetary-scale biomolecular simulations of protein-ligand interactions and rapid structure determination

Impact of COVID-19 on cardiovascular testing in the United States versus the rest of the world

Objectives: This study sought to quantify and compare the decline in volumes of cardiovascular procedures between the United States and non-US institutions during the early phase of the coronavirus disease-2019 (COVID-19) pandemic. Background: The COVID-19 pandemic has disrupted the care of many non-COVID-19 illnesses. Reductions in diagnostic cardiovascular testing around the world have led to concerns over the implications of reduced testing for cardiovascular disease (CVD) morbidity and mortality. Methods: Data were submitted to the INCAPS-COVID (International Atomic Energy Agency Non-Invasive Cardiology Protocols Study of COVID-19), a multinational registry comprising 909 institutions in 108 countries (including 155 facilities in 40 U.S. states), assessing the impact of the COVID-19 pandemic on volumes of diagnostic cardiovascular procedures. Data were obtained for April 2020 and compared with volumes of baseline procedures from March 2019. We compared laboratory characteristics, practices, and procedure volumes between U.S. and non-U.S. facilities and between U.S. geographic regions and identified factors associated with volume reduction in the United States. Results: Reductions in the volumes of procedures in the United States were similar to those in non-U.S. facilities (68% vs. 63%, respectively; p = 0.237), although U.S. facilities reported greater reductions in invasive coronary angiography (69% vs. 53%, respectively; p < 0.001). Significantly more U.S. facilities reported increased use of telehealth and patient screening measures than non-U.S. facilities, such as temperature checks, symptom screenings, and COVID-19 testing. Reductions in volumes of procedures differed between U.S. regions, with larger declines observed in the Northeast (76%) and Midwest (74%) than in the South (62%) and West (44%). Prevalence of COVID-19, staff redeployments, outpatient centers, and urban centers were associated with greater reductions in volume in U.S. facilities in a multivariable analysis. Conclusions: We observed marked reductions in U.S. cardiovascular testing in the early phase of the pandemic and significant variability between U.S. regions. The association between reductions of volumes and COVID-19 prevalence in the United States highlighted the need for proactive efforts to maintain access to cardiovascular testing in areas most affected by outbreaks of COVID-19 infection

High-frequency time domain EPR

Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Chemistry, 2004.Vita.Includes bibliographical references.There are numerous advantages to high frequency (high field) electron paramagnetic resonance (EPR) spectroscopy. Two of the most important are improved sensitivity and the improved resolution of field dependent interactions. In addition, there are many attractive features to time domain spectroscopy. Pulsed EPR allows for the design of experiments, which can specifically be used to study structure and dynamics of paramagnetic species and provide utmost resolution by separating interactions from each other. The combination of pulsed techniques and high frequencies is not only complimentary to continuous wave (CW) low frequency EPR but it also greatly increases the accessible information on paramagnetic species. High frequency, time domain EPR is still in its infancy. Spectrometers at W-band ([approximately] 95 GHz) are now available commercially but to date very few spectrometers operating at higher frequencies have been described. The spectrometer developed in the Francis Bitter Magnet Laboratory operates at a microwave (MW) frequency of 139.5 GHz corresponding to [approximately] 5 T magnetic field. The applications presented in this thesis illustrate the potential of high frequency, time domain EPR spectroscopy at 139.5 GHz in obtaining structural and mechanistic insights of several paramagnetic systems. Well resolved EPR spectra observed at 139.5 GHz of the stable tyrosine radical in ribonucleotide reductase (RNR) revealed the existence of a hydrogen bond in RNR from yeast, chapter 1. The bond length and orientation were determined from the nuclear frequencies of the proton, detected by orientation selective electron nuclear double resonance (ENDOR).(cont.) The advantage of the time domain detection scheme is demonstrated in chapters 4, 5 and 6. A stimulated echo sequence is used to separate different organic radicals associated with the reduction chemistry and inhibition mechanisms of RNR. Using the dispersion in relaxation rates at high temperature ([approximately] 60 K) it is possible to filter the multi component spectrum. The assignment of new radicals is possible at high field, 5 T, due to the high resolution in g anisotropy. The findings support earlier proposals for the mechanism of nucleotide reduction and inhibition of this very important enzyme. To study photoexcited triplet molecules a light source was coupled to the high frequency spectrometer and the pulsed mode detection scheme was used to acquire EPR spectra. The new technique is demonstrated on several model systems. In addition to the basic advantages described above, high frequency EPR opens new frontiers for high spin systems, S >[or equal to] 1, with large spin-spin interaction. Because of the inverse field dependency of the zero field splitting, such systems may be totally EPR-silent at normal EPR frequencies. However their EPR spectra are accessible at high frequencies due to the reduction of linewidth. The Mn(II), S = 5/2, in superoxide dismutase (SOD) is a good example for such system.by Galit Bar.Ph.D