Search

Our study introduces a new machine learning algorithm for estimating 3D cumulative radial profiles of total and gas mass in galaxy clusters from thermal Sunyaev-Zel’dovich (SZ) effect maps. We generate mock images from 2522 simulated clusters, employing an autoencoder and random forest in our approach. Notably, our model makes no prior assumptions about hydrostatic equilibrium. Our results indicate that the model successfully reconstructs unbiased total and gas mass profiles, with a scatter of approximately 10%. We analyse clusters in various dynamical states and mass ranges, finding that our method’s accuracy and precision are consistent. We verify the capabilities of our model by comparing it with the hydrostatic equilibrium technique, showing that it accurately recovers total mass profiles without any bias.

Andrew Kompel,1 Prashanth H Haran,1 Akira M Murakami,1 Lars Engebretsen,2– 4 Mohamed Jarraya,1,5 Frank Roemer,1,6,* Ali Guermazi1,7,* 1Department of Radiology, Boston University School of Medicine, Boston, MA, USA; 2Medical and Scientific Department, International Olympic Committee, Lausanne, Switzerland; 3Oslo Sports Trauma Research Center, Department of Sports Medicine, Norwegian School of Sport Sciences, Oslo, Norway; 4Department of Orthopedic Surgery, Oslo University Hospital, University of Oslo, Oslo, Norway; 5Department of Radiology, Brigham and Women’s Hospital, Boston, MA, USA; 6Department of Radiology, Friedrich-Alexander University Erlangen-Nürnberg, & Universitätsklinikum Erlangen, Erlangen, Germany; 7Department of Radiology, VA Boston Health System, Boston, MA, USA*These authors contributed equally to this workCorrespondence: Andrew KompelDepartment of Radiology, Boston University School of Medicine, 820 Harrison Avenue, FGH Building 3rd Floor, Boston, MA, 02118, USATel +1 617 638-6610Fax +1 617 638-6616Email Andrew.Kompel@bmc.orgPurpose: Describe the frequency and severity of knee ligament sprains diagnosed by MRI in athletes participating at the 2016 Summer Olympic Games, their association with certain sports and assess correlations with additional knee structural injury.Patients and Methods: All knee MRIs performed in the Olympic Village and polyclinics during the 2016 Olympics were retrospectively, blindly reviewed for ligament sprains and associated knee injuries. In addition to the absence or presence of these abnormalities, athletes were stratified by age, gender and sport.Results: 11,274 athletes participated in the 2016 Olympic Games: 113 athletes received at least one knee MRI with some having bilateral or repeat MRI on the same knee. Anterior cruciate and medial collateral ligament (ACL/MCL) sprains were most common, accounting for 32 of the 43 sprains (74.4%). Wrestling (10), hockey (7), athletics (7), and judo (5) accounted for over half of ligament sprains. ACL sprains showed a significant positive correlation with medial, lateral meniscal tears and bone contusions. The positive correlation between posterior cruciate ligament (PCL) sprains with MCL/lateral collateral ligament sprain, and popliteus tendon tear was statistically significant with 50% of total PCL sprains occurring in hockey. When athletes were stratified by gender, ligament sprains had a similar occurrence and distribution between men and women.Conclusion: Knee ligament sprains, at the Rio 2016 Games, were most common in wrestling, hockey, athletics and judo with ACL and MCL sprains most frequent. Meniscal tears and bone contusions occurred often with ACL sprains. PCL sprains tended to be multi-ligamentous injuries. Sustained ligament sprains had similar occurrence between genders, while men had a peak incidence of sprains at a younger age and women at an older age.Keywords: Olympics, knee, ligament, sprain, MRI

Introduction: The COVID-19 pandemic created challenges to healthcare systems worldwide. To allow overwhelmed hospitals to focus on the most fragile and severely ill patients, new types of management had to be set up. During the pandemic, patients with COVID-19 from greater Paris area were monitored at home using a web-based remote system called COVIDOM™, using self-administered questionnaires, which triggered alerts to a regional control center. To ease hospital discharge and to prevent hospital from being overwhelmed, patients still requiring low-flow oxygen therapy discharged home were also included in this telemedicine solution. We aim to evaluate the safety of this original management.Methods: We conducted a retrospective multicenter cohort of patients discharged home from hospital after COVID-19 and still requiring nasal oxygen therapy, who were monitored by questionnaire and trained physicians using COVIDOM. During late follow-up, the status of the patients using a Euro-Qol (EQ-5D-5L) questionnaire, and the Medical Research Council (MRC) Dyspnea scale was collected.Results: From March 21st to June 21st 2020, 73 COVID-19 patients still receiving nasal oxygen at hospital discharge were included. Median [Inter-Quartile Range (IQR)] age was 62.0 [52.5–69.0] years, 64.4% were male. Altogether, risk factors were observed in 49/73 (67%) patients, mainly hypertension (35.6%), diabetes mellitus (15.1%) and active neoplasia (11.0%). Among the cohort, 26% of patients were previously managed in ICU. Oxygen therapy was required for a median [IQR] of 20 [16–31] days. No death or urgent unplanned hospitalization were observed during the COVIDOM telemonitoring. During the late follow-up evaluation (6 months after inclusion), the mean EQ-5D-5L questionnaire score was 7.0 ± 1.6, and the mean MRC dyspnea scale was 0.8 ± 1.0, indicating absence of dyspnea. Five patients have died from non-COVID causes.Conclusions: In this preliminary study, early discharge home of patients with severe COVID-19 disease who still required low-oxygen therapy seems to be safe.

Galaxy cluster masses can be inferred indirectly using measurements from X-ray band, Sunyaev-Zeldovich (SZ) effect signal or optical observations. Unfortunately, all of them are affected by some bias. Alternatively, we provide an independent estimation of the cluster masses from the Planck PSZ2 catalog of galaxy clusters using a machine-learning method. We train a Convolutional Neural Network (CNN) model with the mock SZ observations from The Three Hundred (the300) hydrodynamic simulations to infer the cluster masses from the real maps of the Planck clusters. The advantage of the CNN is that no assumption on a priory symmetry in the cluster’s gas distribution or no additional hypothesis about the cluster physical state are made. We compare the cluster masses from the CNN model with those derived by Planck and conclude that the presence of a mass bias is compatible with the simulation results.

This study was to investigate the effect of moderateintensity-exercise and training status on the perception of the egocentric distance (PED). To this end, 18 footballers (23±0.5 yrs; 72±3.8 kg; 1.73±2.5 m) and 18 sedentary subjects (23±0.2 yrs; 69±4.2 kg; 1.71±2.8 m) participated in this study. The subjects have carried out a distance perception task while pedalling on an ergocycle for 10 min at an intensity of 60 % of the maximal-aerobic-power. The estimations were recorded during rest-time, phase 1 (1 to 2 min), phase 2 (5 to 6 min), phase 3 (9 to 10 min), and after the effort. The results showed that the precision of PED was greater in trained than untrained subjects (p