Electrocatalysis of Lithium (Poly-) Sulfides in Organic Ether-Based Electrolytes

This work aims at identifying an effective electrocatalyst for polysulfide reactions to improve the electrode kinetics of the sulfur half-cell in liquid organic electrolytes for alkali-sulfur cells. To increase the charge and discharge rates and energy efficiency of the cell, functionalized electrocatalytic coatings have been prepared and their electrode kinetics have been measured. To the best of our knowledge, there is no extensive screening of electrocatalysts for the sulfur electrode in dimethoxyethane:1,3-dioxolane (DME:DOL) electrolytes. In order to identify a suitable electrocatalyst, apparent exchange current densities at various materials (Al, Co, Cr, Cu, Fe, Steel, glassy carbon, ITO, Ni, Pt, Ti, TiN, Zn) are evaluated in a polysulfide electrolyte using potentiodynamic measurements with a Butler-Volmer fit. The chemical stability and surface morphology changes after electrochemical measurements are assessed with X-ray diffraction (XRD) and Scanning Electron Microscopy (SEM). The results show that cobalt is a promising candidate with appropriate electrocatalytic properties for polysulfide reactions while being stable in the electrochemical environment, followed by chromium in terms of catalytic activity and stability. Sputtered TiN was found to be a very stable material with very low catalytic activity, a possible current collector for the cell

Critical behavior of the long-range Ising chain from the largest-cluster probability distribution

Monte Carlo simulations of the 1D Ising model with ferromagnetic interactions decaying with distance $r$ as $1/r^{1+\sigma}$ are performed by applying the Swendsen-Wang cluster algorithm with cumulative probabilities. The critical behavior in the non-classical critical regime corresponding to $0.5 <\sigma < 1$ is derived from the finite-size scaling analysis of the largest cluster.Comment: 4 pages, 2 figures, in RevTeX, to appear in Phys. Rev. E (Feb 2001

The Energy Operator for a Model with a Multiparametric Infinite Statistics

In this paper we consider energy operator (a free Hamiltonian), in the second-quantized approach, for the multiparameter quon algebras: $a_{i}a_{j}^{\dagger}-q_{ij}a_{j}^{\dagger}a_{i} = \delta_{ij}, i,j\in I$ with $(q_{ij})_{i,j\in I}$ any hermitian matrix of deformation parameters. We obtain an elegant formula for normally ordered (sometimes called Wick-ordered) series expansions of number operators (which determine a free Hamiltonian). As a main result (see Theorem 1) we prove that the number operators are given, with respect to a basis formed by "generalized Lie elements", by certain normally ordered quadratic expressions with coefficients given precisely by the entries of the inverses of Gram matrices of multiparticle weight spaces. (This settles a conjecture of two of the authors (S.M and A.P), stated in [8]). These Gram matrices are hermitian generalizations of the Varchenko's matrices, associated to a quantum (symmetric) bilinear form of diagonal arrangements of hyperplanes (see [12]). The solution of the inversion problem of such matrices in [9] (Theorem 2.2.17), leads to an effective formula for the number operators studied in this paper. The one parameter case, in the monomial basis, was studied by Zagier [15], Stanciu [11] and M{\o}ller [6].Comment: 24 pages. accepted in J. Phys. A. Math. Ge

Anisotropic flow of charged particles at sNN=2.76\mathbf{\sqrt{s_{NN}} = 2.76} TeV measured with the ALICE detector

Measurements of anisotropic flow in heavy-ion collisions provide evidence for the creation of strongly interacting matter which appears to behave as an almost ideal fluid. Anisotropic flow signals the presence of multiple interactions and is very sensitive to the initial spatial anisotropy of the overlap region in non-central heavy-ion collisions. In this article we report measurements of elliptic $v_2$, triangular $v_3$, quadrangular $v_4$ and pentagonal $v_5$ flow. These measurements have been performed with 2- and multi-particle correlation techniques.Comment: 4 pages, 4 figures, Quark Matter 2011 proceeding

Mathematical modeling of food intake and insulin infusion in a patient with type 1 Ddabetes in closed loop

[EN] Diabetes mellitus type 1 is a condition in which the pancreas loses its ability to produce enough insulin, increasing the levels of blood glucose. This work presents the design of a mathematical model of the glucose - insulin dynamics of a type 1 diabetes patient, contemplating the contribution to the concentration of blood glucose by the intake of carbohydrates, fats and proteins. The model also includes the absorption dynamics of 5 insulin types, different administration methods of exogenous insulin, and the variation of insulin sensitivity during the day. The model was integrated into a closed-loop insulin regulation algorithm, in order to evaluate the performance of the model and the efficiency of closed-loop treatments, compared to open-loop therapies. The results show the response of the model to different situations of a real patient, and tests of the controller’s performance.[ES] La diabetes tipo 1 es una afección en la cual el páncreas pierde su capacidad de producir suficiente insulina, incrementando significativamente la concentración de glucosa en la sangre. En el presente trabajo se presenta el diseño de un modelo matemático de las dinámicas glucosa-insulina de un paciente con diabetes tipo 1, el cual contempla el aporte a la concentración de glucosa en la sangre por parte de la ingesta de carbohidratos, grasas y proteínas. El modelo incluye las dinámicas de absorción de 5 tipos de insulina, diferentes métodos de administración de la misma, y la variación de la sensibilidad a la insulina durante el día. Se integró el modelo a un algoritmo de regulación de insulina en lazo cerrado, con el fin de evaluar el desempeño del modelo y la eficacia de los tratamientos en lazo cerrado, en comparación con las terapias en lazo abierto. Los resultados muestran la respuesta del modelo ante distintas situaciones de un paciente real, y pruebas de funcionamiento del controlador.Manrique-Córdoba, J.; Romero-Ante, JD.; Vivas, A.; Vicente, J.; Sabater-Navarro, JM. (2020). Modelado matemático de ingestas de alimento e infusión de insulina en un paciente con diabetes tipo 1 en lazo cerrado. Revista Iberoamericana de Automática e Informática industrial. 17(2):156-168. https://doi.org/10.4995/riai.2019.11161OJS156168172Ackerman, E., Rosevear, J. W., McGuckin, W. F., 1964. A mathematical model of the glucose-tolerance test. Physics in medicine & Biology 9 (2), 203. https://doi.org/10.1088/0031-9155/9/2/307American Diabetes Association, 2017. [Online; accessed October 2018]. URL: http://www.diabetes.org/Apablaza, P., Soto, N., Codner, E., 2017. De la bomba de insulina y el monitoreo continuo de glucosa al páncreas artificial. Revista Médica de Chile,145 (5), 630-640. https://doi.org/10.4067/S0034-98872017000500011Barrio, R., Andia, V., Vazquez, F., Salgado, Y., Valverde, M., Jansa, M., Flores, M., 2012. Guía de educación terapéutica, al inicio de tratamiento con infusión subcutánea continua de insulina (ISCI). PardeDós. URL: https://diabetesmadrid.org/Beneyto, A., Bertachi, A., Bondia, J., Vehi, J., 2018. A new blood glucose control scheme for unannounced exercise in type 1 diabetic subjects. IEEE Transactions on Control Systems Technology, 1-8.Bergenstal, R. M., Garg, S., Weinzimer, S. A., Buckingham, B. A., Bode, B. W., Tamborlane, W. V., Kaufman, F. R., 2016. Safety of a hybrid closed-loop insulin delivery system in patients with type 1 diabetes. Jama 316 (13), 1407- 1408. https://doi.org/10.1001/jama.2016.11708Berger, M., Rodbard, D., 1989. Computer simulation of plasma insulin and glucose dynamics after subcutaneous insulin injection. Diabetes Care 12 (10), 725-736. https://doi.org/10.2337/diacare.12.10.725Binder, C., 1969. Absorption of injected insulin: A clinical-pharmacological study. Acta Pharmacologica et Toxicologica 27 (S2), 1-83. https://doi.org/10.1111/j.1600-0773.1969.tb03069.xBolie, V. W., 1961. Coefficients of normal blood glucose regulation. Journal of Applied Physiology 16 (5), 783-788. https://doi.org/10.1152/jappl.1961.16.5.783Breda, E., Cavaghan, M. K., Toffolo, G., Polonsky, K. S., Cobelli, C., 2001. Oral glucose tolerance test minimal model indexes of β-cell function and insulin sensitivity. Diabetes 50 (1), 150-158. https://doi.org/10.2337/diabetes.50.1.150Breton, M., Farret, A., Bruttomesso, D., Anderson, S., Magni, L., Patek, S., Dalla Man, C., Place, J., Demartini, S., Del Favero, S., 2012. Fully integrated artificial pancreas in type 1 diabetes: modular closed-loop glucose control maintains near normoglycemia. Diabetes 61, 2230-2237. https://doi.org/10.2337/db11-1445Bruttomesso, D., Farret, A., Costa, S., Marescotti, M. C., Vettore, M., Avogaro, A., Tiengo, A., Dalla Man, C., Place, J., Facchinetti, A., 2009. Closed-loop artificial pancreas using subcutaneous glucose sensing and insulin delivery and a model predictive control algorithm: preliminary studies in Padova and Montpellier. Journal of Diabetes Science and Technology 3, 1014-1021. https://doi.org/10.1177/193229680900300504Clarke, W. L., Anderson, S., Breton, M., Patek, S., Kashmer, L., Kovatchev, B., 2009. Closed-loop artificial pancreas using subcutaneous glucose sensing and insulin delivery and a model predictive control algorithm: the Virginia experience. Journal of Diabetes Science and Technology 3, 1031-1038. https://doi.org/10.1177/193229680900300506Clemens, A., Chang, P., Myers, R., 1977. The development of biostator, a glucose controlled insulin infusion system (GCIIS). Hormone and metabolic research 7, 23-33.Cobelli, C., Nucci, G., Del Prato, S., 1999. A physiological simulation model of the glucose-insulin system. Vol. 2.Colino, E., 2018. Fundación para la Diabetes. [Online; October 2018]. URL: http://www.fundaciondiabetes.org/Craig, T. P., 2010. Dietary Carnitine Supplementation as a potential modulator of insulin sensitivity. Master's Thesis, University of Stirling. URL: https://dspace.stir.ac.uk/Dalla Man, C., Breton, M. D., Cobelli, C., 2009. Physical activity into the meal glucose-insulin model of type 1 diabetes: in silico studies 3, 56-67. https://doi.org/10.1177/193229680900300107Dalla Man, C., Camilleri, M., Cobelli, C., 2006. A system model of oral glucose absorption: validation on gold standard data. IEEE Transactions on Biomedical Engineering 53 (12), 2472-2478. https://doi.org/10.1109/TBME.2006.883792Dalla Man, C., Micheletto, F., Lv, D., Breton, M., Kovatchev, B., Cobelli, C., 2014. The uva/padova type 1 diabetes simulator: new features. Journal of Diabetes Science and Technology 8 (1), 26-34. https://doi.org/10.1177/1932296813514502Dalla Man, C., Raimondo, D. M., Rizza, R. A., Cobelli, C., 2007a. GIM, simulation software of meal glucose insulin model. Journal of Diabetes Science and Technology 1, 323-330. https://doi.org/10.1177/193229680700100303Dalla Man, C., Rizza, R. A., Cobelli, C., 2007b. Meal simulation model of the glucose-insulin system. IEEE Transactions on Biomedical Engineering 54 (10), 1740-1749. https://doi.org/10.1109/TBME.2007.893506Haidar, A., 2016. The artificial pancreas: How close-loop control is revolutionizing diabetes. IEEE Condtrol Systems 36 (5), 28-47. https://doi.org/10.1109/MCS.2016.2584318International Diabetes Federation, 2017. IDF diabetes atlas, 8th Edition. URL: http://www.diabetesatlas.org/IRICOM, 2018. Sociedad Española de Diabetes. [Online; October 2018]. URL: http://www.sediabetes.org/Kadish, A. H., 1963. Automation control of blood sugar a servomechanism for glucose monitoring and control. ASAIO Journal 9 (1), 363-367.Manrique, J., Romero, J. D., Sabater, J. M., Vivas, O. A., Vicente, J. M., 2018. Simulador de paciente T1D en tiempo real. Actas de las XXXIX Jornadas de Automática, Badajoz, 64-71. 'Mauseth, R., Hirsch, I. B., Bollyky, J., Kircher, R., Matheson, D., Sanda, S., Greenbaum, C., 2013. Use of a "fuzzy logic" controller in a closed-loop artificial pancreas. Diabetes Technology & Therapeutics 15 (8), 628-633. https://doi.org/10.1089/dia.2013.0036Murillo, M. D., Fernandez, F., Tuneu, L., 2004. Guía de seguimiento farmacoterapéutico sobre diabetes. Grupo de Investigación en Atención Farmacéutica (GIAF). URL: http://www.ugr.es/National Center for Biotechnology Information, 2018. Insulin aspart. Pub Chem Compound Database, [Online; October 2018]. URL: https://pubchem.ncbi.nlm.nih.gov/compound/16132418Nimri, R., Atlas, E., Ajzensztejn, M., Miller, S., Oron, T., Phillip, M., 2012. Feasibility study of automated overnight closed-loop glucose control under md-logic artificial pancreas in patients with type 1 diabetes: the dream project. Diabetes Technology & Therapeutics 14 (8), 728-735. https://doi.org/10.1089/dia.2012.0004Nucci, G., Cobelli, C., 2000. Models of subcutaneous insulin kinetics. a critical review. Computer Methods and Programs in Biomedicine 62 (3), 249-257. https://doi.org/10.1016/S0169-2607(00)00071-7OpenAPS Community, 2015. Openaps. OpenAPS.org, [Online; October 2018]. URL: https://openaps.org/Renard, E., Place, J., Cantwell, M., Chevassus, H., Palerm, C. C., 2010. Closedloop insulin delivery using a subcutaneous glucose sensor and intraperitoneal insulin delivery: feasibility study testing a new model for the artificial pancreas. Diabetes Care 33 (1), 121-127. https://doi.org/10.2337/dc09-1080Segre, G., Turco, G., Vercellone, G., 1973. Modeling blood glucose and insulin kinetics in normal, diabetic and obese subjects. Diabetes 22 (2), 94-103. https://doi.org/10.2337/diab.22.2.94Steil, G. M., Palerm, C. C., Kurtz, N., Voskanyan, G., Roy, A., Paz, S., Kandeel, F. R., 2011. The effect of insulin feedback on closed loop glucose control. The Journal of Clinical Endocrinology & Metabolism 96 (5), 1402-1408. https://doi.org/10.1210/jc.2010-2578Toffolo, G., Bergman, R. N., Finegood, D. T., Bowden, C. R., Cobelli, C., 1980. Quantitative estimation of beta cell sensitivity to glucose in the intact organism: a minimal model of insulin kinetics in the dog. Diabetes 29 (12), 979-990. https://doi.org/10.2337/diab.29.12.979Trajanoski, Z., Wach, P., Kotanko, P., Ott, A., Skraba, F., 1993. Pharmacokinetic model for the absorption of subcutaneously injected soluble insulin and monomeric insulin-analogues. Biomedizinische Technik Biomedical Engineering 38 (9), 224-231. https://doi.org/10.1515/bmte.1993.38.9.224Turksoy, K., Cinar, A., 2014. Adaptive control of artificial pancreas systems-a review. Journal of Healthcare Engineering 5 (1), 1-22. https://doi.org/10.1260/2040-2295.5.1.1Weinzimer, S. A., Sherr, J. L., Cengiz, E., Kim, G., Ruiz, J. L., Carria, L., Voskanyan, G., Roy, A., Tamborlane, W. V., 2012. Effect of pramlintide on prandial glycemic excursions during closed-loop control in adolescents and young adults with type 1 diabetes. Diabetes Care. URL: http://care.diabetesjournals.org https://doi.org/10.2337/dc12-0330Yoldi, C., Mayo 2018. Las grasas y las proteínas también cuentan. Guía Diabetes tipo 1, [Online; October 2018]. URL: https://www.diabetes-cidi.org

Clinical staging and the differential risks for clinical and functional outcomes in young people presenting for youth mental health care

Background: Clinical staging proposes that youth-onset mental disorders develop progressively, and that active treatment of earlier stages should prevent progression to more severe disorders. This retrospective cohort study examined the longitudinal relationships between clinical stages and multiple clinical and functional outcomes within the frst 12 months of care. Methods: Demographic and clinical information of 2901 young people who accessed mental health care at age 12–25 years was collected at predetermined timepoints (baseline, 3 months, 6 months, 12 months). Initial clinical stage was used to defne three fxed groups for analyses (stage 1a: ‘non-specifc anxious or depressive symptoms’, 1b: ‘attenuated mood or psychotic syndromes’, 2+: ‘full-threshold mood or psychotic syndromes’). Logistic regression models, which controlled for age and follow-up time, were used to compare clinical and functional outcomes (role and social function, suicidal ideation, alcohol and substance misuse, physical health comorbidity, circadian disturbances) between staging groups within the initial 12 months of care. Results: Of the entire cohort, 2093 young people aged 12–25 years were followed up at least once over the frst 12 months of care, with 60.4% female and a baseline mean age of 18.16 years. Longitudinally, young people at stage 2+ were more likely to develop circadian disturbances (odds ratio [OR]=2.58; CI 1.60–4.17), compared with individuals at stage 1b. Additionally, stage 1b individuals were more likely to become disengaged from education/employment (OR=2.11, CI 1.36–3.28), develop suicidal ideations (OR=1.92; CI 1.30–2.84) and circadian disturbances (OR=1.94, CI 1.31–2.86), compared to stage 1a. By contrast, we found no relationship between clinical stage and the emergence of alcohol or substance misuse and physical comorbidity. Conclusions: The diferential rates of emergence of poor clinical and functional outcomes between early versus late clinical stages support the clinical staging model’s assumptions about illness trajectories for mood and psychotic syndromes. The greater risk of progression to poor outcomes in those who present with more severe syndromes may be used to guide specifc intervention packages

Kožna dekontaminacija živčanoga bojnog otrova sarina s apsorpcijskim pripravkom u uvjetima in vivo

Our Institute’s nuclear, biological, and chemical defense research team continuously investigates and develops preparations for skin decontamination against nerve agents. In this in vivo study, we evaluated skin decontamination efficacy against sarin by a synthetic preparation called Mineral Cationic Carrier (MCC®) with known ion exchange, absorption efficacy and bioactive potential. Mice were treated with increasing doses of sarin applied on their skin, and MCC® was administered immediately after contamination. The results showed that decontamination with MCC® could achieve therapeutic efficacy corresponding to 3 x LD50 of percutaneous sarin and call for further research.Istraživački tim NBKO (nuklearno-biološko-kemijske obrane) radi na pronalasku i razvoju pripravka za dekontaminaciju kože od živčanih bojnih otrova. Cilj ovog istraživanja bio je ispitati dekontaminacijska svojstva (adsorpcijska i/ili kemisorpcijska) pripravka MCC® rabeći živčani bojni otrov sarin kao kožni kontaminant u uvjetima in vivo. MCC® je sintetski pripravak koji je biokemijski aktivan i ima ionskoizmjenjivačka i adsorpcijska svojstva. Istraživanje u uvjetima in vivo napravljeno je na miševima aplikacijom rastućih doza sarina na kožu životinje. Pripravak MCC® uporabljen je kao kožni dekontaminant neposredno nakon kožne kontaminacije sarinom. Istraživanja su pokazala da pripravak MCC® posjeduje adsorpcijska svojstva, ujedno važna za dekontaminaciju živčanih bojnih otrova. Eksperimenti u uvjetima in vivo na miševima (NOD-soj) pokazali su da se dekontaminacijom pripravkom MCC® može postići terapijski učinak od 3 LD50 (perkutano, sarin)

'Turning the tide' on hyperglycemia in pregnancy : insights from multiscale dynamic simulation modeling

INTRODUCTION: Hyperglycemia in pregnancy (HIP, including gestational diabetes and pre-existing type 1 and type 2 diabetes) is increasing, with associated risks to the health of women and their babies. Strategies to manage and prevent this condition are contested. Dynamic simulation models (DSM) can test policy and program scenarios before implementation in the real world. This paper reports the development and use of an advanced DSM exploring the impact of maternal weight status interventions on incidence of HIP. METHODS: A consortium of experts collaboratively developed a hybrid DSM of HIP, comprising system dynamics, agent-based and discrete event model components. The structure and parameterization drew on a range of evidence and data sources. Scenarios comparing population-level and targeted prevention interventions were simulated from 2018 to identify the intervention combination that would deliver the greatest impact. RESULTS: Population interventions promoting weight loss in early adulthood were found to be effective, reducing the population incidence of HIP by 17.3% by 2030 (baseline ('business as usual' scenario)=16.1%, 95% CI 15.8 to 16.4; population intervention=13.3%, 95% CI 13.0 to 13.6), more than targeted prepregnancy (5.2% reduction; incidence=15.3%, 95% CI 15.0 to 15.6) and interpregnancy (4.2% reduction; incidence=15.5%, 95% CI 15.2 to 15.8) interventions. Combining targeted interventions for high-risk groups with population interventions promoting healthy weight was most effective in reducing HIP incidence (28.8% reduction by 2030; incidence=11.5, 95% CI 11.2 to 11.8). Scenarios exploring the effect of childhood weight status on entry to adulthood demonstrated significant impact in the selected outcome measure for glycemic regulation, insulin sensitivity in the short term and HIP in the long term. DISCUSSION: Population-level weight reduction interventions will be necessary to 'turn the tide' on HIP. Weight reduction interventions targeting high-risk individuals, while beneficial for those individuals, did not significantly impact forecasted HIP incidence rates. The importance of maintaining interventions promoting healthy weight in childhood was demonstrated

CAST constraints on the axion-electron coupling

In non-hadronic axion models, which have a tree-level axion-electron interaction, the Sun produces a strong axion flux by bremsstrahlung, Compton scattering, and axiorecombination, the "BCA processes." Based on a new calculation of this flux, including for the first time axio-recombination, we derive limits on the axion-electron Yukawa coupling gae and axion-photon interaction strength ga using the CAST phase-I data (vacuum phase). For ma <~ 10 meV/c2 we find ga gae < 8.1 × 10−23 GeV−1 at 95% CL. We stress that a next-generation axion helioscope such as the proposed IAXO could push this sensitivity into a range beyond stellar energy-loss limits and test the hypothesis that white-dwarf cooling is dominated by axion emission