Poly 3,4-ethylenedioxythiophene as an entrapment support for amperometric enzyme sensor

International audienceA conducting polymer of poly 3,4-ethylenedioxythiophene (PEDT) was used as a matrix for entrapment of enzymes onto a platinum electrode surface in order to construct amperometric biosensors. Glucose oxidase (GOD) was used as an example, and it was entrapped in the polymer during the electrochemical polymerization. Glucose in oxygenated solutions was tested by amperometric measurements at +650 mV (vs. SCE) in a batch system. The influence of several experimental parameters in the electropolymerization process was explored to optimize the analytical performance. The detection limit and sensitivity for this biosensor were 4×10−5 M and 15.2 mA M−1cm−2, respectively. A linear range of response was found from 0.2 to 8 mM of glucose. The response time was 2–5 s. The stability of the electropolymerized films was evaluated in operational conditions. The glucose probe, stored in buffer at 4 °C when not in use, showed a residual activity of 40% after about 1 month. Glucose in synthetic serum was determined under flow injection conditions using an amperometric flow cell

An approximate single fluid 3-dimensional magnetohydrodynamic equilibrium model with toroidal flow

An approximate model for a single fluid three-dimensional (3D) magnetohydrodynamic (MHD) equilibrium with pure isothermal toroidal flow with imposed nested magnetic flux surfaces is proposed. It recovers the rigorous toroidal rotation equilibrium description in the axisymmetric limit. The approximation is valid under conditions of nearly rigid or vanishing toroidal rotation in regions with significant 3D deformation of the equilibrium flux surfaces. Bifurcated helical core equilibrium simulations of long-lived modes in the MAST device demonstrate that the magnetic structure is only weakly affected by the flow but that the 3D pressure distortion is important. The pressure is displaced away from the major axis and therefore is not as noticeably helically deformed as the toroidal magnetic flux under the subsonic flow conditions measured in the experiment. The model invoked fails to predict any significant screening by toroidal plasma rotation of resonant magnetic perturbations in MAST free boundary computations

Energy Confinement in Shaped TCV Plasmas with Electron Cyclotron Heating

The effects of plasma shape on confinement and sawtooth stability are studied for positive and negative discharge triangularity and for different elongations with 1.5 MW centrally deposited ECH powe

Preliminary Confinement Studies during ECRH in TCV

Within the range of plasma shapes and plasma currents investigated, the electron confinement time, Tau_E increases with density, elongation and negative triangularity (-0.4<delta<+0.4), similar to Ohmic heating (in these low density discharges). In addition, TauEe increases with q_a up to q_a~5 after which it decreases. There is little dependence of TauEe on the heating location provided it is inside the q= I surface. As the heating location is moved outside the q=l surface, TauEe decreases. This may be the explanation of the observed decrease in TauEe at high q_a. The power-induced degradation exponent found is generally as expected: alpaha_P = -0.5

EFFECT OF LOCALISED ELECTRON CYCLOTRON HEATING ON ENERGY CONFINEMENT AND MHD IN TCV

Within the range of plasma shapes and plasma currents investigated, the electron confinement time, tau_Ee, increases with safety factor, density and negative triangularity similar to the Ohmic heating case. There is little dependence of tau_Ee on the heating location provided power deposition occurs inside the q=1 surface; as power deposition moves out of the inversion surface, tau_Ee decreases. The power-induced energy confinement degradation exponent (tau_Ee~PaP) is as usual: alpha_P ~-0.5. As a general trend, central relaxations decrease in amplitude with increasing qa, P_EC, or negative delta, in a situation where the confinement time increases

Federated learning enables big data for rare cancer boundary detection.

Although machine learning (ML) has shown promise across disciplines, out-of-sample generalizability is concerning. This is currently addressed by sharing multi-site data, but such centralization is challenging/infeasible to scale due to various limitations. Federated ML (FL) provides an alternative paradigm for accurate and generalizable ML, by only sharing numerical model updates. Here we present the largest FL study to-date, involving data from 71 sites across 6 continents, to generate an automatic tumor boundary detector for the rare disease of glioblastoma, reporting the largest such dataset in the literature (n = 6, 314). We demonstrate a 33% delineation improvement for the surgically targetable tumor, and 23% for the complete tumor extent, over a publicly trained model. We anticipate our study to: 1) enable more healthcare studies informed by large diverse data, ensuring meaningful results for rare diseases and underrepresented populations, 2) facilitate further analyses for glioblastoma by releasing our consensus model, and 3) demonstrate the FL effectiveness at such scale and task-complexity as a paradigm shift for multi-site collaborations, alleviating the need for data-sharing

Author Correction: Federated learning enables big data for rare cancer boundary detection.

10.1038/s41467-023-36188-7NATURE COMMUNICATIONS14

Les droits disciplinaires des fonctions publiques : « unification », « harmonisation » ou « distanciation ». A propos de la loi du 26 avril 2016 relative à la déontologie et aux droits et obligations des fonctionnaires

The production of tt‾ , W+bb‾ and W+cc‾ is studied in the forward region of proton–proton collisions collected at a centre-of-mass energy of 8 TeV by the LHCb experiment, corresponding to an integrated luminosity of 1.98±0.02 fb−1 . The W bosons are reconstructed in the decays W→ℓν , where ℓ denotes muon or electron, while the b and c quarks are reconstructed as jets. All measured cross-sections are in agreement with next-to-leading-order Standard Model predictions.The production of $t\overline{t}$, $W+b\overline{b}$ and $W+c\overline{c}$ is studied in the forward region of proton-proton collisions collected at a centre-of-mass energy of 8 TeV by the LHCb experiment, corresponding to an integrated luminosity of 1.98 $\pm$ 0.02 \mbox{fb}^{-1}. The $W$ bosons are reconstructed in the decays $W\rightarrow\ell\nu$, where $\ell$ denotes muon or electron, while the $b$ and $c$ quarks are reconstructed as jets. All measured cross-sections are in agreement with next-to-leading-order Standard Model predictions

BLOOM: A 176B-Parameter Open-Access Multilingual Language Model

Large language models (LLMs) have been shown to be able to perform new tasks based on a few demonstrations or natural language instructions. While these capabilities have led to widespread adoption, most LLMs are developed by resource-rich organizations and are frequently kept from the public. As a step towards democratizing this powerful technology, we present BLOOM, a 176B-parameter open-access language model designed and built thanks to a collaboration of hundreds of researchers. BLOOM is a decoder-only Transformer language model that was trained on the ROOTS corpus, a dataset comprising hundreds of sources in 46 natural and 13 programming languages (59 in total). We find that BLOOM achieves competitive performance on a wide variety of benchmarks, with stronger results after undergoing multitask prompted finetuning. To facilitate future research and applications using LLMs, we publicly release our models and code under the Responsible AI License

Design and baseline characteristics of the finerenone in reducing cardiovascular mortality and morbidity in diabetic kidney disease trial

Background: Among people with diabetes, those with kidney disease have exceptionally high rates of cardiovascular (CV) morbidity and mortality and progression of their underlying kidney disease. Finerenone is a novel, nonsteroidal, selective mineralocorticoid receptor antagonist that has shown to reduce albuminuria in type 2 diabetes (T2D) patients with chronic kidney disease (CKD) while revealing only a low risk of hyperkalemia. However, the effect of finerenone on CV and renal outcomes has not yet been investigated in long-term trials. Patients and Methods: The Finerenone in Reducing CV Mortality and Morbidity in Diabetic Kidney Disease (FIGARO-DKD) trial aims to assess the efficacy and safety of finerenone compared to placebo at reducing clinically important CV and renal outcomes in T2D patients with CKD. FIGARO-DKD is a randomized, double-blind, placebo-controlled, parallel-group, event-driven trial running in 47 countries with an expected duration of approximately 6 years. FIGARO-DKD randomized 7,437 patients with an estimated glomerular filtration rate >= 25 mL/min/1.73 m(2) and albuminuria (urinary albumin-to-creatinine ratio >= 30 to <= 5,000 mg/g). The study has at least 90% power to detect a 20% reduction in the risk of the primary outcome (overall two-sided significance level alpha = 0.05), the composite of time to first occurrence of CV death, nonfatal myocardial infarction, nonfatal stroke, or hospitalization for heart failure. Conclusions: FIGARO-DKD will determine whether an optimally treated cohort of T2D patients with CKD at high risk of CV and renal events will experience cardiorenal benefits with the addition of finerenone to their treatment regimen. Trial Registration: EudraCT number: 2015-000950-39; ClinicalTrials.gov identifier: NCT02545049