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Though immensely successful, the standard model of particle physics does not offer any explanation as to why our Universe contains so much more matter than antimatter. A key to a dynamically generated matter–antimatter asymmetry is the existence of processes that violate the combined charge conjugation and parity (CP) symmetry1. As such, precision tests of CP symmetry may be used to search for physics beyond the standard model. However, hadrons decay through an interplay of strong and weak processes, quantified in terms of relative phases between the amplitudes. Although previous experiments constructed CP observables that depend on both strong and weak phases, we present an approach where sequential two-body decays of entangled multi-strange baryon–antibaryon pairs provide a separation between these phases. Our method, exploiting spin entanglement between the double-strange Ξ− baryon and its antiparticle2 Ξ¯+
, has enabled a direct determination of the weak-phase difference, (ξP − ξS) = (1.2 ± 3.4 ± 0.8) × 10−2 rad. Furthermore, three independent CP observables can be constructed from our measured parameters. The precision in the estimated parameters for a given data sample size is several orders of magnitude greater than achieved with previous methods3. Finally, we provide an independent measurement of the recently debated Λ decay parameter αΛ (refs. 4,5). The ΛΛ¯
asymmetry is in agreement with and compatible in precision to the most precise previous measurement.
Using a sample of (10.09±0.04)×109 J/ψ events collected with the BESIII detector, a partial wave analysis of J/ψ→γη′η′ is performed.The masses and widths of the observed resonances and their branching fractions are reported. The main contribution is from J/ψ→γf0(2020) with f0(2020)→η′η′, which is found with a significance of greater than 25σ. The product branching fraction B(J/ψ → γf0(2020))⋅B(f0(2020) → η′η′ is measured to be (2.63±0.06(stat.) + 0.31−0.46(syst.))×10−4.
We present the first experimental search for the rare charm decay D0→π0ν¯ν. It is based on an e+e− collision sample consisting of 10.6×10^6 pairs of D0¯D0 mesons collected by the BESIII detector at √s=3.773 GeV, corresponding to an integrated luminosity of 2.93 fb^−1. A data-driven method is used to ensure the reliability of the background modeling. No significant D0→π0ν¯ν signal is observed in data and an upper limit of the branching fraction is set to be 2.1×10^-4 at the 90% confidence level. This is the first experimental constraint on charmed-hadron decays into dineutrino final states.
We report first results on elliptic flow of identified particles at midrapidity in Au+Au collisions at sqrt[sNN] = 130 GeV using the STAR TPC at RHIC. The elliptic flow as a function of transverse momentum and centrality differs significantly for particles of different masses. This dependence can be accounted for in hydrodynamic models, indicating that the system created shows a behavior consistent with collective hydrodynamical flow. The fit to the data with a simple model gives information on the temperature and flow velocities at freeze-out.
Background: About 30 million people in the EU and USA, respectively, suffer from a rare disease. Driven by European legislative requirements, national strategies for the improvement of care in rare diseases are being developed. To improve timely and correct diagnosis for patients with rare diseases, the development of a registry for undiagnosed patients was recommended by the German National Action Plan. In this paper we focus on the question on how such a registry for undiagnosed patients can be built and which information it should contain. Results: To develop a registry for undiagnosed patients, a software for data acquisition and storage, an appropriate data set and an applicable terminology/classification system for the data collected are needed. We have used the open-source software Open-Source Registry System for Rare Diseases (OSSE) to build the registry for undiagnosed patients. Our data set is based on the minimal data set for rare disease patient registries recommended by the European Rare Disease Registries Platform. We extended this Common Data Set to also include symptoms, clinical findings and other diagnoses. In order to ensure findability, comparability and statistical analysis, symptoms, clinical findings and diagnoses have to be encoded. We evaluated three medical ontologies (SNOMED CT, HPO and LOINC) for their usefulness. With exact matches of 98% of tested medical terms, a mean number of five deposited synonyms, SNOMED CT seemed to fit our needs best. HPO and LOINC provided 73% and 31% of exacts matches of clinical terms respectively. Allowing more generic codes for a defined symptom, with SNOMED CT 99%, with HPO 89% and with LOINC 39% of terms could be encoded. Conclusions: With the use of the OSSE software and a data set, which, in addition to the Common Data Set, focuses on symptoms and clinical findings, a functioning and meaningful registry for undiagnosed patients can be implemented. The next step is the implementation of the registry in centres for rare diseases. With the help of medical informatics and big data analysis, case similarity analyses could be realized and aid as a decision-support tool enabling diagnosis of some undiagnosed patients.
Using 10.1 × 109 J/ψ events produced by the Beijing Electron Positron Collider (BEPCII) at a center-of-mass energy √s = 3.097 GeV and collected with the BESIII detector, we present a search for the rare semi-leptonic decay J/ψ → D−e+νe + c.c. No excess of signal above background is observed, and an upper limit on the branching fraction ℬ(J/ψ → D−e+νe + c. c.) < 7.1 × 10−8 is obtained at 90% confidence level. This is an improvement of more than two orders of magnitude over the previous best limit.
Background: Intensive Care Resources are heavily utilized during the COVID-19 pandemic. However, risk stratification and prediction of SARS-CoV-2 patient clinical outcomes upon ICU admission remain inadequate. This study aimed to develop a machine learning model, based on retrospective & prospective clinical data, to stratify patient risk and predict ICU survival and outcomes. Methods: A Germany-wide electronic registry was established to pseudonymously collect admission, therapeutic and discharge information of SARS-CoV-2 ICU patients retrospectively and prospectively. Machine learning approaches were evaluated for the accuracy and interpretability of predictions. The Explainable Boosting Machine approach was selected as the most suitable method. Individual, non-linear shape functions for predictive parameters and parameter interactions are reported. Results: 1039 patients were included in the Explainable Boosting Machine model, 596 patients retrospectively collected, and 443 patients prospectively collected. The model for prediction of general ICU outcome was shown to be more reliable to predict “survival”. Age, inflammatory and thrombotic activity, and severity of ARDS at ICU admission were shown to be predictive of ICU survival. Patients’ age, pulmonary dysfunction and transfer from an external institution were predictors for ECMO therapy. The interaction of patient age with D-dimer levels on admission and creatinine levels with SOFA score without GCS were predictors for renal replacement therapy. Conclusions: Using Explainable Boosting Machine analysis, we confirmed and weighed previously reported and identified novel predictors for outcome in critically ill COVID-19 patients. Using this strategy, predictive modeling of COVID-19 ICU patient outcomes can be performed overcoming the limitations of linear regression models. Trial registration “ClinicalTrials” (clinicaltrials.gov) under NCT04455451.
We measure the inclusive semielectronic decay branching fraction of the D+s meson. A double-tag technique is applied to e+e− annihilation data collected by the BESIII experiment at the BEPCII collider, operating in the center-of-mass energy range 4.178–4.230 GeV. We select positrons fromD+s→Xe+νe with momenta greater than 200 MeV/c and determine the laboratory momentum spectrum, accounting for the effects of detector efficiency and resolution. The total positron yield and semielectronic branching fraction are determined by extrapolating this spectrum below the momentum cutoff. We measure the D+s semielectronic branching fraction to be(6.30±0.13(stat.)±0.09(syst.)±0.04(ext.))%, showing no evidence for unobserved exclusive semielectronic modes. We combine this result with external data taken from literature to determine the ratio of the D+s and D0 semielectronic widths, Γ(D+s→Xe+νe)Γ(D0→Xe+νe)=0.790±0.016(stat.)±0.011(syst.)±0.016(ext.). Our results are consistent with and more precise than previous measurements.
Salt-inducible kinases (SIKs) are key metabolic regulators. Imbalance of SIK function is associated with the development of diverse cancers, including breast, gastric and ovarian cancer. Chemical tools to clarify the roles of SIK in different diseases are, however, sparse and are generally characterized by poor kinome-wide selectivity. Here, we have adapted the pyrido[2,3-d]pyrimidin-7-one-based PAK inhibitor G-5555 for the targeting of SIK, by exploiting differences in the back-pocket region of these kinases. Optimization was supported by high-resolution crystal structures of G-5555 bound to the known off-targets MST3 and MST4, leading to a chemical probe, MRIA9, with dual SIK/PAK activity and excellent selectivity over other kinases. Furthermore, we show that MRIA9 sensitizes ovarian cancer cells to treatment with the mitotic agent paclitaxel, confirming earlier data from genetic knockdown studies and suggesting a combination therapy with SIK inhibitors and paclitaxel for the treatment of paclitaxel-resistant ovarian cancer.
Relative fractions and phases of the intermediate decays are determined. With the detection efficiency estimated by the results of the amplitude analysis, the branching fraction of Dþ s → K−Kþπþπ0 decay is measured to be ð5.42 0.10stat 0.17systÞ%.