Cox process representation and inference for stochastic reaction-diffusion processes

Complex behaviour in many systems arises from the stochastic interactions of spatially distributed particles or agents. Stochastic reaction-diffusion processes are widely used to model such behaviour in disciplines ranging from biology to the social sciences, yet they are notoriously difficult to simulate and calibrate to observational data. Here we use ideas from statistical physics and machine learning to provide a solution to the inverse problem of learning a stochastic reaction-diffusion process from data. Our solution relies on a non-trivial connection between stochastic reaction-diffusion processes and spatio-temporal Cox processes, a well-studied class of models from computational statistics. This connection leads to an efficient and flexible algorithm for parameter inference and model selection. Our approach shows excellent accuracy on numeric and real data examples from systems biology and epidemiology. Our work provides both insights into spatio-temporal stochastic systems, and a practical solution to a long-standing problem in computational modelling

Appendix A from On the possibility of spontaneous chemomechanical oscillations in adsorptive porous media

Appendix A: Microscopic derivation of the Langmuir kinetic

Gestational Diabetes Mellitus: Clinical Characteristics and Perinatal Outcomes in a Multiethnic Population of North Italy

Aim: To evaluate clinical characteristics and perinatal outcomes in a heterogeneous population of Caucasians born in Italy and High Migration Pressure Countries (HMPC) women with GDM living in Piedmont, North Italy. Methods: We retrospectively analyzed data from 586 women referring to our unit (2015-2020). Epidemiological (age and country of origin) and clinical-metabolic features (height, weight, family history of DM, parity, previous history of GDM, OGTT results, and GDM treatment) were collected. The database of certificates of care at delivery was consulted in relation to neonatal/maternal complications (rates of caesarean sections, APGAR score, fetal malformations, and neonatal anthropometry). Results: 43.2% of women came from HMPC; they were younger (p < 0.0001) and required insulin treatment more frequently than Caucasian women born in Italy (χ 2 = 17.8, p=0.007). Higher fasting and 120-minute OGTT levels and gestational BMI increased the risk of insulin treatment (OGTT T0: OR = 1.04, CI 95% 1.016-1.060, p=0.005; OGTT T120: OR = 1.01, CI 95% 1.002-1.020, p=0.02; BMI: OR = 1.089, CI 95% 1.051-1.129, p < 0.0001). Moreover, two or more diagnostic OGTT glucose levels doubled the risk of insulin therapy (OR = 2.03, IC 95% 1.145-3.612, p=0.016). We did not find any association between ethnicities and neonatal/maternal complications. Conclusions: In our multiethnic GDM population, the need for intensive care and insulin treatment is high in HPMC women although the frequency of adverse peripartum and newborn outcomes does not vary among ethnic groups. The need for insulin therapy should be related to different genetic backgrounds, dietary habits, and Nutrition Transition phenomena. Thus, nutritional intervention and insulin treatment need to be tailored

Gestational Diabetes Mellitus: Clinical Characteristics and Perinatal Outcomes in a Multiethnic Population of North Italy

Aim. To evaluate clinical characteristics and perinatal outcomes in a heterogeneous population of Caucasians born in Italy and High Migration Pressure Countries (HMPC) women with GDM living in Piedmont, North Italy. Methods. We retrospectively analyzed data from 586 women referring to our unit (2015–2020). Epidemiological (age and country of origin) and clinical-metabolic features (height, weight, family history of DM, parity, previous history of GDM, OGTT results, and GDM treatment) were collected. The database of certificates of care at delivery was consulted in relation to neonatal/maternal complications (rates of caesarean sections, APGAR score, fetal malformations, and neonatal anthropometry). Results. 43.2% of women came from HMPC; they were younger p &lt; 0.0001 and required insulin treatment more frequently than Caucasian women born in Italy (χ2 = 17.8, p = 0.007 ). Higher fasting and 120-minute OGTT levels and gestational BMI increased the risk of insulin treatment (OGTT T0: OR = 1.04, CI 95% 1.016–1.060, p = 0.005 ; OGTT T120: OR = 1.01, CI 95% 1.002–1.020, p = 0.02 ; BMI: OR = 1.089, CI 95% 1.051–1.129, p &lt; 0.0001 ). Moreover, two or more diagnostic OGTT glucose levels doubled the risk of insulin therapy (OR = 2.03, IC 95% 1.145–3.612, p = 0.016 ). We did not find any association between ethnicities and neonatal/maternal complications. Conclusions. In our multiethnic GDM population, the need for intensive care and insulin treatment is high in HPMC women although the frequency of adverse peripartum and newborn outcomes does not vary among ethnic groups. The need for insulin therapy should be related to different genetic backgrounds, dietary habits, and Nutrition Transition phenomena. Thus, nutritional intervention and insulin treatment need to be tailored.</jats:p

Chemical Equilibrium on Low Dimensional Supports: Connecting the Microscopic Mechanism to the Macroscopic Observations

Classical chemical thermodynamics predicts that the equilibrium composition of a reactive system is entirely defined by the equilibrium constants of the different reactions involved. In this paper we show that for nonlinear reactions taking place on a low-dimensional support this is not true anymore: the equilibrium state depends on the mechanistic details of the chemical processes, so that even two reactions having the same mean field kinetics and equilibrium constants can reach a different equilibrium composition, depending on the microscopic mechanism. We illustrate this point by simulations and mathematical analyses of a simple autocatalytic scheme, and we propose a theoretical route to discriminate between the different cases.SCOPUS: ar.jinfo:eu-repo/semantics/publishe