The Impact of a 4th Generation on Mixing and CP Violation in the Charm System

We study D0-D0 mixing in the presence of a fourth generation of quarks. In particular, we calculate the size of the allowed CP violation which is found at the observable level well beyond anything possible with CKM dynamics. We calculate the semileptonic asymmetry a_SL and the mixing induced CP asymmetry eta_fS_f which are correlated with each other. We also investigate the correlation of eta_fS_f with a number of prominent observables in other mesonic systems like epsilon'/epsilon, Br(K_L -> pi0 nu nu), Br(K+ -> pi+ nu nu), Br(B_s ->mu+ mu-), Br(B_d -> mu+ mu-) and finally S_psi phi in the B_s system. We identify a clear pattern of flavour and CP violation predicted by the SM4 model: While simultaneous large 4G effects in the K and D systems are possible, accompanying large NP effects in the B_d system are disfavoured. However this behaviour is not as pronounced as found for the LHT and RSc models. In contrast to this, sizeable CP violating effects in the B_s system are possible unless extreme effects in eta_fS_f are found, and Br(B_s ->mu+ mu-) can be strongly enhanced regardless of the situation in the D system. We find that, on the other hand, S_psi phi > 0.2 combined with the measured epsilon'/epsilon significantly diminishes 4G effects within the D system.Comment: 22 pages, 23 figures, v2 (references added

30-Day morbidity and mortality of bariatric metabolic surgery in adolescence during the COVID-19 pandemic – The GENEVA study

Background: Metabolic and bariatric surgery (MBS) is an effective treatment for adolescents with severe obesity. Objectives: This study examined the safety of MBS in adolescents during the coronavirus disease 2019 (COVID-19) pandemic. Methods: This was a global, multicentre and observational cohort study of MBS performed between May 01, 2020, and October 10,2020, in 68 centres from 24 countries. Data collection included in-hospital and 30-day COVID-19 and surgery-specific morbidity/mortality. Results: One hundred and seventy adolescent patients (mean age: 17.75 ± 1.30 years), mostly females (n = 122, 71.8%), underwent MBS during the study period. The mean pre-operative weight and body mass index were 122.16 ± 15.92 kg and 43.7 ± 7.11 kg/m2, respectively. Although majority of patients had pre-operative testing for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) (n = 146; 85.9%), only 42.4% (n = 72) of the patients were asked to self-isolate pre-operatively. Two patients developed symptomatic SARS-CoV-2 infection post-operatively (1.2%). The overall complication rate was 5.3% (n = 9). There was no mortality in this cohort. Conclusions: MBS in adolescents with obesity is safe during the COVID-19 pandemic when performed within the context of local precautionary procedures (such as pre-operative testing). The 30-day morbidity rates were similar to those reported pre-pandemic. These data will help facilitate the safe re-introduction of MBS services for this group of patients

30-day morbidity and mortality of sleeve gastrectomy, Roux-en-Y gastric bypass and one anastomosis gastric bypass: a propensity score-matched analysis of the GENEVA data

Background: There is a paucity of data comparing 30-day morbidity and mortality of sleeve gastrectomy (SG), Roux-en-Y gastric bypass (RYGB), and one anastomosis gastric bypass (OAGB). This study aimed to compare the 30-day safety of SG, RYGB, and OAGB in propensity score-matched cohorts. Materials and methods: This analysis utilised data collected from the GENEVA study which was a multicentre observational cohort study of bariatric and metabolic surgery (BMS) in 185 centres across 42 countries between 01/05/2022 and 31/10/2020 during the Coronavirus Disease-2019 (COVID-19) pandemic. 30-day complications were categorised according to the Clavien–Dindo classification. Patients receiving SG, RYGB, or OAGB were propensity-matched according to baseline characteristics and 30-day complications were compared between groups. Results: In total, 6770 patients (SG 3983; OAGB 702; RYGB 2085) were included in this analysis. Prior to matching, RYGB was associated with highest 30-day complication rate (SG 5.8%; OAGB 7.5%; RYGB 8.0% (p = 0.006)). On multivariate regression modelling, Insulin-dependent type 2 diabetes mellitus and hypercholesterolaemia were associated with increased 30-day complications. Being a non-smoker was associated with reduced complication rates. When compared to SG as a reference category, RYGB, but not OAGB, was associated with an increased rate of 30-day complications. A total of 702 pairs of SG and OAGB were propensity score-matched. The complication rate in the SG group was 7.3% (n = 51) as compared to 7.5% (n = 53) in the OAGB group (p = 0.68). Similarly, 2085 pairs of SG and RYGB were propensity score-matched. The complication rate in the SG group was 6.1% (n = 127) as compared to 7.9% (n = 166) in the RYGB group (p = 0.09). And, 702 pairs of OAGB and RYGB were matched. The complication rate in both groups was the same at 7.5 % (n = 53; p = 0.07). Conclusions: This global study found no significant difference in the 30-day morbidity and mortality of SG, RYGB, and OAGB in propensity score-matched cohorts

Activating Transcription Factor 4 (ATF4) modulates Rho GTPase levels and function via regulation of RhoGDI?

10.1038/srep36952Scientific Reports63695

Efficient Execution of Scientific Workflows in the Cloud Through Adaptive Caching

International audienceMany scientific experiments are now carried on using scientific workflows, which are becoming more and more data-intensive and complex. We consider the efficient execution of such workflows in the cloud. Since it is common for workflow users to reuse other workflows or data generated by other workflows, a promising approach for efficient workflow execution is to cache intermediate data and exploit it to avoid task re-execution. In this paper, we propose an adaptive caching solution for data-intensive workflows in the cloud. Our solution is based on a new scientific workflow management architecture that automatically manages the storage and reuse of intermediate data and adapts to the variations in task execution times and output data size. We evaluated our solution by implementing it in the OpenAlea system and performing extensive experiments on real data with a data-intensive application in plant phenotyping. The results show that adaptive caching can yield major performance gains, e.g., up to a factor of 3.5 with 6 workflow re-executions