149 research outputs found
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
Young People in Crisis: NEETs and Unemployed in EU Regions
In most European countries, the youth unemployment rate is much higher ? at least double but in some countries also triple ? than the adult unemployment rate. The NEET group (young people ?neither in employment, education or training?) is also particularly large. The 2008-09 financial crisis, the consequent Great Recession, the Eurozone sovereign debt crisis and the ensuing austerity measures have increased both the youth unemployment rate and the NEET rate. Moreover, both occurrences are becoming persistent and the risk of a ?lost generation? (as warned by OECD) is increasingly worrying, also in consideration of the severe economic, social and even political consequences. However, the situation is not uniform within the EU. There are significant differences between countries and even between regions within countries. Purpose of this paper is to assess the impact of the crisis on the youth unemployment rate and the NEET rate of the EU regions. Following a brief review of the literature and some descriptive statistics on the mentioned labour market indices, we present our econometric estimates. We use Eurostat?s data for the period 2000-2011 concerning all the NUTS-1 regions. We focus on the changes in both the youth unemployment rate and the NEET rate from 2000-07 to 2008-11. As control variables, in addition to GDP, we consider institutional variables at a country level (e.g. the EPL index) and structural variables at the regional level (percentage of employment in the main sectors). The econometric method is based on a battery of panel data estimators, suitably designed to accommodate dynamics along with the three-dimensional structure of the problem: countries, regions within countries and time. The contribution of this paper is at least threefold. First of all, most of the existing empirical studies are limited to the investigation of the dynamics of youth unemployment rates and on comparisons across countries, but do not consider the alternative indicator, the NEET rate (which is more significant on several grounds). Second, the investigations on the impact of the recent crisis on the labour market indices have been, so far, very few. Third, most researches are carried out at the national level, while similar studies concerning the regional level are quite rare
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
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