Minimal models for lipid membranes: local modifications around fusion objects

Solvent-free soft coarse-grained models are particularly appropriate to investigate collective phenomena in lipid membranes. In this work we exploit such a model to show how modifying a few model parameters we can control the bending rigidity of the membrane, the hydration repulsion, and the macroscopic phases of self-assembled structures. Further, we investigate the lipid mediated interactions between fusion objects: transmembrane proteins, pores and stalks. The presence of such defects induces a perturbation in the shape of the membrane and in the conformations of lipids. The modi cations induced by single defects superimpose and for some defect interaction (peptide-peptide, pore-peptide) we identify the equilibrium distance between these objects. The prediction of the results of the simulations are compared with the numerical solution of a continuum model parametrized from the analysis of the simulation snapshots. The presence of transmembrane proteins with a large hydrophobic mismatch weakens the membrane over the direct interaction range decreasing slightly the membrane thickness. This involves the lowering of the line tension of the pore and for a particular number and spatial arrangement of proteins the line tension of the pore is negative and the pore is stable in tensionless membranes. Another agent that in uences the line tension of the pore is oil (short hydrophobic chains). The oil has a con guration space larger than the one of the lipids and partitions to relax the frustration of the lipids at the interfaces and increases the line tension of the pore (the membrane is more resistant under lateral tension). The model parameters have a large in uence on the equilibrium properties of a stalk and we study the characteristic sizes of stalks depending on the hydration between two opposed bilayers and compare the results to other simulation models and experimental data. We show how hydration and lateral tension in uence bilayer repulsion and how the combined e ect of both contributions leads to membrane fusion

Bayesian tomography with prior-knowledge-based parametrization and surrogate modelling

We present a Bayesian tomography framework operating with prior-knowledge-based parametrization that is accelerated by surrogate models. Standard high-fidelity forward solvers (e.g. finite-difference time-domain schemes) solve wave equations with natural spatial parametrizations based on fine discretization. Similar parametrizations, typically involving tens of thousand of variables, are usually employed to parametrize the subsurface in tomography applications. When the data do not allow to resolve details at such finely parametrized scales, it is often beneficial to instead rely on a prior-knowledge-based parametrization defined on a lower dimension domain (or manifold). Due to the increased identifiability in the reduced domain, the concomitant inversion is better constrained and generally faster. We illustrate the potential of a prior-knowledge-based approach by considering ground penetrating radar (GPR) traveltime tomography in a crosshole configuration with synthetic data. An effective parametrization of the input (i.e. the permittivity distributions determining the slowness field) and compression in the output (i.e. the traveltime gathers) spaces are achieved via data-driven principal component decomposition based on random realizations of the prior Gaussian-process model with a truncation determined by the performances of the standard solver on the full and reduced model domains. To accelerate the inversion process, we employ a high-fidelity polynomial chaos expansion (PCE) surrogate model. We investigate the impact of the size of the training set on the performance of the PCE and show that a few hundreds design data sets is sufficient to provide reliable Markov chain Monte Carlo inversion at a fraction of the cost associated with a standard approach involving a fine discretization and physics-based forward solvers. Appropriate uncertainty quantification is achieved by reintroducing the truncated higher order principle components in the original model space after inversion on the manifold and by adapting a likelihood function that accounts for the fact that the truncated higher order components are not completely located in the null space

Propuesta para reducir el riesgo de corrupción en el abastecimiento de bienes estratégicos de una institución pública de salud, 2022

Este trabajo de investigación tuvo como objetivo principal analizar el riesgo de corrupción en el abastecimiento de bienes estratégicos de una institución pública de salud, para tal efecto, se desarrolló una investigación aplicada con un enfoque cuantitativo cuasiexperimental e hipótesis de tipo causales multivariadas; los datos para las variables predictivas se obtuvieron al analizar con los indicadores de riesgo propuestos los 1251 contratos suscritos por esta entidad entre los años 2019 y el 2021. Aplicando una regresión logística se determinaron los valores que se emplearon en el Odds ratio para estimar el índice de riesgo de corrupción potencial (IRCp) cuyo valor fue del 52.35%; lo cual evidencia que existe la probabilidad que más de la mitad de los contratos suscritos para la adquisición de bienes estratégicos de la institución pública de salud provengan de un acto de corrupción. En ese sentido, esta investigación propuso que se implemente una plataforma digital con catálogos electrónicos para el abastecimiento de los bienes estratégicos de la institución pública de salud, como una medida viable para reducir los riesgos de corrupción, basándose en la efectividad demostrada que poseen los avances tecnológicos en la optimización de los procesos y contribuir a la transparencia institucional facilitando el acceso a la información pública

La gestión de los residuos sólidos del relleno sanitario “El Zapallal” como recurso sostenible de desarrollo social para Carabayllo, 2020

La presente investigación titulada “La gestión de los residuos sólidos del relleno sanitario “El Zapallal” como recurso sostenible de desarrollo social para Carabayllo, 2020”, cuyo objetivo principal sostuvo analizar como la gestión de los residuos sólidos del relleno sanitario “El Zapallal” se puede convertir en un recurso sostenible de desarrollo social para el distrito de Carabayllo en el año 2020, para lo cual la investigación aplicó un enfoque cualitativo, con un diseño no experimental de tipo descriptivo y fenomenológico, con el propósito de comprender el estado puro del fenómeno estudiado en el entorno natural que lo contiene. Se realizó una entrevista estructurada a cinco expertos con una amplia trayectoria profesional y sólidos conocimiento del tema de investigación, el instrumento de recolección de datos estuvo conformado por nueve preguntas que permitieron desarrollar una eficiente triangulación de la información recopilada; lo cual, fue concluyente para demostrar, que la gestión de los residuos sólidos del relleno sanitario “El Zapallal” se puede convertir en recurso sostenible que tiene relación directa con el desarrollo social para el distrito de Carabayllo; esto es factible, al implementar una planta con tecnología de termovalorización WtE para el tratamiento de los residuos sólidos que ingresan al relleno sanitario “El Zapallal” y generar energía eléctrica como producto resultante, la administración de esta energía eléctrica representa un recurso sostenible capaz de brindar desarrollo social a los pobladores del distrito de Carabayllo en el año 2020

Efficient Bayesian travel-time tomography with geologically-complex priors using sensitivity-informed polynomial chaos expansion and deep generative networks

Monte Carlo Markov Chain (MCMC) methods commonly confront two fundamental challenges: the accurate characterization of the prior distribution and the efficient evaluation of the likelihood. In the context of Bayesian studies on tomography, principal component analysis (PCA) can in some cases facilitate the straightforward definition of the prior distribution, while simultaneously enabling the implementation of accurate surrogate models based on polynomial chaos expansion (PCE) to replace computationally intensive full-physics forward solvers. When faced with scenarios where PCA does not offer a direct means of easily defining the prior distribution alternative methods like deep generative models (e.g., variational autoencoders (VAEs)), can be employed as viable options. However, accurately producing a surrogate capable of capturing the intricate non-linear relationship between the latent parameters of a VAE and the outputs of forward modeling presents a notable challenge. Indeed, while PCE models provide high accuracy when the input-output relationship can be effectively approximated by relatively low-degree multivariate polynomials, this condition is typically unmet when utilizing latent variables derived from deep generative models. In this contribution, we present a strategy that combines the excellent reconstruction performances of VAE in terms of prio representation with the accuracy of PCA-PCE surrogate modeling in the context of Bayesian ground penetrating radar (GPR) travel-time tomography. Within the MCMC process, the parametrization of the VAE is leveraged for prior exploration and sample proposal. Concurrently, modeling is conducted using PCE, which operates on either globally or locally defined principal components of the VAE samples under examination.Comment: 25 pages, 15 figure

Wearable Health Technology for Preoperative Risk Assessment in Elderly Patients: The WELCOME Study

Preoperative identification of high-risk groups has been extensively studied to improve patients’ outcomes. Wearable devices, which can track heart rate and physical activity data, are starting to be evaluated for patients’ management. We hypothesized that commercial wearable devices (WD) may provide data associated with preoperative evaluation scales and tests, to identify patients with poor functional capacity at increased risk for complications. We conducted a prospective observational study including seventy-year-old patients undergoing two-hour surgeries under general anesthesia. Patients were asked to wear a WD for 7 days before surgery. WD data were compared to preoperatory clinical evaluation scales and with a 6-min walking test (6MWT). We enrolled 31 patients, with a mean age of 76.1 (SD ± 4.9) years. There were 11 (35%) ASA 3–4 patients. 6MWT results averaged 328.9 (SD ± 99.5) m. Daily steps and 2 as recorded using WD and were associated with 6MWT performance (R = 0.56, p = 0.001 and r = 0.58, p = 0.006, respectively) and clinical evaluation scales. This is the first study to evaluate WD as preoperative evaluation tools; we found a strong association between 6MWT, preoperative scales, and WD data. Low-cost wearable devices are a promising tool for the evaluation of cardiopulmonary fitness. Further research is needed to validate WD in this setting

Assessing Sleep Habits in Italian Community-Dwelling Adolescents: Psychometric Properties of the School Sleep Habits Survey Scales

Background. In the field of adolescent sleep research, different sleep surveys have been implemented; however, psychometric properties of these instruments have been investigated only minimally. Methods. In order to assess the psychometric properties of the Sleep–Wake Problems Behaviour Scale (SWP), the Sleepiness Scale (SLS), and the Morningness/Eveningness Questionnaire (ME), a moderately large sample of community-dwelling Italian adolescents (N = 778; 59.8% female; mean age = 15.77 years) was administered the Italian translation of the School Sleep Habits Survey. Results. Internal consistency estimates values were satisfactory for all measures; dimensionality analyses suggested a unidimensional structure for SWP, SLS and ME, respectively. Goodness-of-fit statistics for the one-factor model of the SLS, SWP, and ME scale items were adequate for all measures. Non-redundant taxometric analysis results consistently suggested a dimensional latent structure for the SLS, SWP, and ME, respectively. Conclusion. Our findings supported the use of the SLS, SWP, and ME total scores as measures of sleepiness, sleep-wake problem, and morningness/eveningness, at least among Italian community-dwelling adolescents, and encourage practitioners to rely on the conventional percentiles in order to interpret the SLS, SWP, and ME total scores