Quantum Effects on the Synchronization Dynamics of the Kuramoto Model

The Kuramoto model serves as a paradigm for describing spontaneous synchronization in a system of classical interacting rotors. In this study, we extend this model to the quantum domain by coupling quantum interacting rotors to external baths following the Caldeira-Leggett approach. Studying the mean-field model in the overdamped limit using Feynman-Vernon theory, we show how quantum mechanics modifies the phase diagram. Specifically, we demonstrate that quantum fluctuations hinder the emergence of synchronization, albeit not entirely suppressing it. We examine the phase transition into the synchronized phase at various temperatures, revealing that classical results are recovered at high temperatures while a quantum phase transition occurs at zero temperature. Additionally, we derive an analytical expression for the critical coupling, highlighting its dependence on the model parameters, and examine the differences between classical and quantum behavior.Comment: 14 pages, 5 figure

White matter multi-resolution segmentation using fuzzy set theory

International audienceThe neural architecture of the white matter of the brain, obtained using tractography algorithms, can be divided into different tracts. Their function is, in many cases, still an object of study and might be affected in some syndromes or conditions. Obtaining a reproducible and correct segmentation is therefore crucial both in clinics and in research. However, it is difficult to obtain due to the huge number of fibers and high inter-subject variability. In this paper, we propose to segment and recognize tracts by directly modeling their anatomical definitions, which are usually based on relationships between structures. Since these definitions are mainly qualitative, we propose to model their intrinsic vagueness using fuzzy spatial relations and combine them into a single quantitative score mapped to each fiber. To cope with the high redundancy of tractograms and ease interpretation , we also take advantage of a simplification scheme based on a multi-resolution representation. This allows for an interactive and real-time navigation through different levels of detail. We illustrate our method using the Human Connectome Project dataset and compare it to other well-known white matter segmentation techniques

Brigatinib (BRG) vs crizotinib (CRZ) in the phase III ALTA-1L trial

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Crizotinib in MET-Deregulated or ROS1-Rearranged Pretreated Non-Small Cell Lung Cancer (METROS): A Phase II, Prospective, Multicenter, Two-Arms Trial.

PURPOSE: MET-deregulated NSCLC represents an urgent clinical need because of unfavorable prognosis and lack of specific therapies. Although recent studies have suggested a potential role for crizotinib in patients harboring MET amplification or exon 14 mutations, no conclusive data are currently available. This study aimed at investigating activity of crizotinib in patients harboring MET or ROS1 alterations. PATIENTS AND METHODS: Patients with pretreated advanced NSCLC and evidence of ROS1 rearrangements (cohort A) or MET deregulation (amplification, ratio MET/CEP7 >2.2 or MET exon 14 mutations, cohort B) were treated with crizotinib 250 mg twice daily orally. The coprimary endpoint was objective response rate in the two cohorts. RESULTS: From December 2014 to March 2017, 505 patients were screened and a total of 52 patients (26 patients per cohort) were enrolled onto the study. At data cutoff of September 2017, in cohort A, objective response rate was 65%, and median progression-free survival and overall survival were 22.8 months [95% confidence interval (CI) 15.2-30.3] and not reached, respectively. In cohort B, objective response rate was 27%, median progression-free survival was 4.4 months (95% CI 3.0-5.8), and overall survival was 5.4 months (95% CI, 4.2-6.5). No difference in any clinical endpoint was observed between MET-amplified and exon 14-mutated patients. No response was observed among the 5 patients with cooccurrence of a second gene alteration. No unexpected toxicity was observed in both cohorts. CONCLUSIONS: Crizotinib induces response in a fraction of MET-deregulated NSCLC. Additional studies and innovative therapies are urgently needed

Autoantibodies neutralizing type I IFNs are present in ~4% of uninfected individuals over 70 years old and account for ~20% of COVID-19 deaths

Publisher Copyright: © 2021 The Authors, some rights reserved.Circulating autoantibodies (auto-Abs) neutralizing high concentrations (10 ng/ml; in plasma diluted 1:10) of IFN-alpha and/or IFN-omega are found in about 10% of patients with critical COVID-19 (coronavirus disease 2019) pneumonia but not in individuals with asymptomatic infections. We detect auto-Abs neutralizing 100-fold lower, more physiological, concentrations of IFN-alpha and/or IFN-omega (100 pg/ml; in 1:10 dilutions of plasma) in 13.6% of 3595 patients with critical COVID-19, including 21% of 374 patients >80 years, and 6.5% of 522 patients with severe COVID-19. These antibodies are also detected in 18% of the 1124 deceased patients (aged 20 days to 99 years; mean: 70 years). Moreover, another 1.3% of patients with critical COVID-19 and 0.9% of the deceased patients have auto-Abs neutralizing high concentrations of IFN-beta. We also show, in a sample of 34,159 uninfected individuals from the general population, that auto-Abs neutralizing high concentrations of IFN-alpha and/or IFN-omega are present in 0.18% of individuals between 18 and 69 years, 1.1% between 70 and 79 years, and 3.4% >80 years. Moreover, the proportion of individuals carrying auto-Abs neutralizing lower concentrations is greater in a subsample of 10,778 uninfected individuals: 1% of individuals 80 years. By contrast, auto-Abs neutralizing IFN-beta do not become more frequent with age. Auto-Abs neutralizing type I IFNs predate SARS-CoV-2 infection and sharply increase in prevalence after the age of 70 years. They account for about 20% of both critical COVID-19 cases in the over 80s and total fatal COVID-19 cases.Peer reviewe

EVALITA Evaluation of NLP and Speech Tools for Italian - December 17th, 2020

Welcome to EVALITA 2020! EVALITA is the evaluation campaign of Natural Language Processing and Speech Tools for Italian. EVALITA is an initiative of the Italian Association for Computational Linguistics (AILC, http://www.ai-lc.it) and it is endorsed by the Italian Association for Artificial Intelligence (AIxIA, http://www.aixia.it) and the Italian Association for Speech Sciences (AISV, http://www.aisv.it)

The risk of COVID-19 death is much greater and age dependent with type I IFN autoantibodies

SignificanceThere is growing evidence that preexisting autoantibodies neutralizing type I interferons (IFNs) are strong determinants of life-threatening COVID-19 pneumonia. It is important to estimate their quantitative impact on COVID-19 mortality upon SARS-CoV-2 infection, by age and sex, as both the prevalence of these autoantibodies and the risk of COVID-19 death increase with age and are higher in men. Using an unvaccinated sample of 1,261 deceased patients and 34,159 individuals from the general population, we found that autoantibodies against type I IFNs strongly increased the SARS-CoV-2 infection fatality rate at all ages, in both men and women. Autoantibodies against type I IFNs are strong and common predictors of life-threatening COVID-19. Testing for these autoantibodies should be considered in the general population

The risk of COVID-19 death is much greater and age dependent with type I IFN autoantibodies

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection fatality rate (IFR) doubles with every 5 y of age from childhood onward. Circulating autoantibodies neutralizing IFN-α, IFN-ω, and/or IFN-β are found in ∼20% of deceased patients across age groups, and in ∼1% of individuals aged 4% of those >70 y old in the general population. With a sample of 1,261 unvaccinated deceased patients and 34,159 individuals of the general population sampled before the pandemic, we estimated both IFR and relative risk of death (RRD) across age groups for individuals carrying autoantibodies neutralizing type I IFNs, relative to noncarriers. The RRD associated with any combination of autoantibodies was higher in subjects under 70 y old. For autoantibodies neutralizing IFN-α2 or IFN-ω, the RRDs were 17.0 (95% CI: 11.7 to 24.7) and 5.8 (4.5 to 7.4) for individuals <70 y and ≥70 y old, respectively, whereas, for autoantibodies neutralizing both molecules, the RRDs were 188.3 (44.8 to 774.4) and 7.2 (5.0 to 10.3), respectively. In contrast, IFRs increased with age, ranging from 0.17% (0.12 to 0.31) for individuals <40 y old to 26.7% (20.3 to 35.2) for those ≥80 y old for autoantibodies neutralizing IFN-α2 or IFN-ω, and from 0.84% (0.31 to 8.28) to 40.5% (27.82 to 61.20) for autoantibodies neutralizing both. Autoantibodies against type I IFNs increase IFRs, and are associated with high RRDs, especially when neutralizing both IFN-α2 and IFN-ω. Remarkably, IFRs increase with age, whereas RRDs decrease with age. Autoimmunity to type I IFNs is a strong and common predictor of COVID-19 death.The Laboratory of Human Genetics of Infectious Diseases is supported by the Howard Hughes Medical Institute; The Rockefeller University; the St. Giles Foundation; the NIH (Grants R01AI088364 and R01AI163029); the National Center for Advancing Translational Sciences; NIH Clinical and Translational Science Awards program (Grant UL1 TR001866); a Fast Grant from Emergent Ventures; Mercatus Center at George Mason University; the Yale Center for Mendelian Genomics and the Genome Sequencing Program Coordinating Center funded by the National Human Genome Research Institute (Grants UM1HG006504 and U24HG008956); the Yale High Performance Computing Center (Grant S10OD018521); the Fisher Center for Alzheimer’s Research Foundation; the Meyer Foundation; the JPB Foundation; the French National Research Agency (ANR) under the “Investments for the Future” program (Grant ANR-10-IAHU-01); the Integrative Biology of Emerging Infectious Diseases Laboratory of Excellence (Grant ANR-10-LABX-62-IBEID); the French Foundation for Medical Research (FRM) (Grant EQU201903007798); the French Agency for Research on AIDS and Viral hepatitis (ANRS) Nord-Sud (Grant ANRS-COV05); the ANR GENVIR (Grant ANR-20-CE93-003), AABIFNCOV (Grant ANR-20-CO11-0001), CNSVIRGEN (Grant ANR-19-CE15-0009-01), and GenMIS-C (Grant ANR-21-COVR-0039) projects; the Square Foundation; Grandir–Fonds de solidarité pour l’Enfance; the Fondation du Souffle; the SCOR Corporate Foundation for Science; The French Ministry of Higher Education, Research, and Innovation (Grant MESRI-COVID-19); Institut National de la Santé et de la Recherche Médicale (INSERM), REACTing-INSERM; and the University Paris Cité. P. Bastard was supported by the FRM (Award EA20170638020). P. Bastard., J.R., and T.L.V. were supported by the MD-PhD program of the Imagine Institute (with the support of Fondation Bettencourt Schueller). Work at the Neurometabolic Disease lab received funding from Centre for Biomedical Research on Rare Diseases (CIBERER) (Grant ACCI20-767) and the European Union's Horizon 2020 research and innovation program under grant agreement 824110 (EASI Genomics). Work in the Laboratory of Virology and Infectious Disease was supported by the NIH (Grants P01AI138398-S1, 2U19AI111825, and R01AI091707-10S1), a George Mason University Fast Grant, and the G. Harold and Leila Y. Mathers Charitable Foundation. The Infanta Leonor University Hospital supported the research of the Department of Internal Medicine and Allergology. The French COVID Cohort study group was sponsored by INSERM and supported by the REACTing consortium and by a grant from the French Ministry of Health (Grant PHRC 20-0424). The Cov-Contact Cohort was supported by the REACTing consortium, the French Ministry of Health, and the European Commission (Grant RECOVER WP 6). This work was also partly supported by the Intramural Research Program of the National Institute of Allergy and Infectious Diseases and the National Institute of Dental and Craniofacial Research, NIH (Grants ZIA AI001270 to L.D.N. and 1ZIAAI001265 to H.C.S.). This program is supported by the Agence Nationale de la Recherche (Grant ANR-10-LABX-69-01). K.K.’s group was supported by the Estonian Research Council, through Grants PRG117 and PRG377. R.H. was supported by an Al Jalila Foundation Seed Grant (Grant AJF202019), Dubai, United Arab Emirates, and a COVID-19 research grant (Grant CoV19-0307) from the University of Sharjah, United Arab Emirates. S.G.T. is supported by Investigator and Program Grants awarded by the National Health and Medical Research Council of Australia and a University of New South Wales COVID Rapid Response Initiative Grant. L.I. reports funding from Regione Lombardia, Italy (project “Risposta immune in pazienti con COVID-19 e co-morbidità”). This research was partially supported by the Instituto de Salud Carlos III (Grant COV20/0968). J.R.H. reports funding from Biomedical Advanced Research and Development Authority (Grant HHSO10201600031C). S.O. reports funding from Research Program on Emerging and Re-emerging Infectious Diseases from Japan Agency for Medical Research and Development (Grant JP20fk0108531). G.G. was supported by the ANR Flash COVID-19 program and SARS-CoV-2 Program of the Faculty of Medicine from Sorbonne University iCOVID programs. The 3C Study was conducted under a partnership agreement between INSERM, Victor Segalen Bordeaux 2 University, and Sanofi-Aventis. The Fondation pour la Recherche Médicale funded the preparation and initiation of the study. The 3C Study was also supported by the Caisse Nationale d’Assurance Maladie des Travailleurs Salariés, Direction générale de la Santé, Mutuelle Générale de l’Education Nationale, Institut de la Longévité, Conseils Régionaux of Aquitaine and Bourgogne, Fondation de France, and Ministry of Research–INSERM Program “Cohortes et collections de données biologiques.” S. Debette was supported by the University of Bordeaux Initiative of Excellence. P.K.G. reports funding from the National Cancer Institute, NIH, under Contract 75N91019D00024, Task Order 75N91021F00001. J.W. is supported by a Research Foundation - Flanders (FWO) Fundamental Clinical Mandate (Grant 1833317N). Sample processing at IrsiCaixa was possible thanks to the crowdfunding initiative YoMeCorono. Work at Vall d’Hebron was also partly supported by research funding from Instituto de Salud Carlos III Grant PI17/00660 cofinanced by the European Regional Development Fund (ERDF/FEDER). C.R.-G. and colleagues from the Canarian Health System Sequencing Hub were supported by the Instituto de Salud Carlos III (Grants COV20_01333 and COV20_01334), the Spanish Ministry for Science and Innovation (RTC-2017-6471-1; AEI/FEDER, European Union), Fundación DISA (Grants OA18/017 and OA20/024), and Cabildo Insular de Tenerife (Grants CGIEU0000219140 and “Apuestas científicas del ITER para colaborar en la lucha contra la COVID-19”). T.H.M. was supported by grants from the Novo Nordisk Foundation (Grants NNF20OC0064890 and NNF21OC0067157). C.M.B. is supported by a Michael Smith Foundation for Health Research Health Professional-Investigator Award. P.Q.H. and L. Hammarström were funded by the European Union’s Horizon 2020 research and innovation program (Antibody Therapy Against Coronavirus consortium, Grant 101003650). Work at Y.-L.L.’s laboratory in the University of Hong Kong (HKU) was supported by the Society for the Relief of Disabled Children. MBBS/PhD study of D.L. in HKU was supported by the Croucher Foundation. J.L.F. was supported in part by the Evaluation-Orientation de la Coopération Scientifique (ECOS) Nord - Coopération Scientifique France-Colombie (ECOS-Nord/Columbian Administrative department of Science, Technology and Innovation [COLCIENCIAS]/Colombian Ministry of National Education [MEN]/Colombian Institute of Educational Credit and Technical Studies Abroad [ICETEX, Grant 806-2018] and Colciencias Contract 713-2016 [Code 111574455633]). A. Klocperk was, in part, supported by Grants NU20-05-00282 and NV18-05-00162 issued by the Czech Health Research Council and Ministry of Health, Czech Republic. L.P. was funded by Program Project COVID-19 OSR-UniSR and Ministero della Salute (Grant COVID-2020-12371617). I.M. is a Senior Clinical Investigator at the Research Foundation–Flanders and is supported by the CSL Behring Chair of Primary Immunodeficiencies (PID); by the Katholieke Universiteit Leuven C1 Grant C16/18/007; by a Flanders Institute for Biotechnology-Grand Challenges - PID grant; by the FWO Grants G0C8517N, G0B5120N, and G0E8420N; and by the Jeffrey Modell Foundation. I.M. has received funding under the European Union’s Horizon 2020 research and innovation program (Grant Agreement 948959). E.A. received funding from the Hellenic Foundation for Research and Innovation (Grant INTERFLU 1574). M. Vidigal received funding from the São Paulo Research Foundation (Grant 2020/09702-1) and JBS SA (Grant 69004). The NH-COVAIR study group consortium was supported by a grant from the Meath Foundation.Peer reviewe

Device-Sensor Assembly FEA Modeling to Support Kalman-Filter-Based Junction Temperature Monitoring

Junction temperature monitoring for power devices is an essential requirement for high-reliability applications. Temperature sensitive electrical parameters (TSEPs) are powerful tools in this process, but to achieve sufficient accuracy they require complex characterization procedures for each part, that can hardly be implemented in mass-produced converters. This paper proposes a combination of interdisciplinary approaches to ultimately solve this problem for power MOSFETs by an automated procedure that can occur in-place, with devices mounted and without any specific user intervention nor additional heating components. The TSEP exploited in this paper is the ON-state drain–source voltage of the power MOSFET. A wide-bandwidth ON-state voltage sensing circuit and a low-cost thermistor conditioning circuit to sense the case temperature of the device are presented and modeled. A lumped parameters thermal model of the system is given, and finite-element method (FEM) simulations are employed to obtain first-guess values for the unknown thermal network parameters, integrating information from the device datasheet. Finally, an observer based on the Kalman filter applied to the data collected from these sources is presented and evaluated experimentally. Performance is assessed with the use of thermal imaging techniques

Quantum Effects on the Synchronization Dynamics of the Kuramoto Model

The Kuramoto model serves as a paradigm for describing spontaneous synchronization in a system of classical interacting rotors. In this study, we extend this model to the quantum domain by coupling quantum interacting rotors to external baths following the Caldeira-Leggett approach. Studying the mean-field model in the overdamped limit using Feynman-Vernon theory, we show how quantum mechanics modifies the phase diagram. Specifically, we demonstrate that quantum fluctuations hinder the emergence of synchronization, albeit not entirely suppressing it. We examine the phase transition into the synchronized phase at various temperatures, revealing that classical results are recovered at high temperatures while a quantum phase transition occurs at zero temperature. Additionally, we derive an analytical expression for the critical coupling, highlighting its dependence on the model parameters, and examine the differences between classical and quantum behavior