Effect of the solvent on the conformational behavior of the alanine dipeptide deduced from MD simulations

In general, peptides do not exhibit a well-defined conformational profile in solution. However, despite the experimental blurred picture associated with their structure, compelling spectroscopic evidence shows that peptides exhibit local order. The conformational profile of a peptide is the result of a balance between intramolecular interactions between different atoms of the molecule and intermolecular interactions between atoms of the molecule and the solvent. Accordingly, the conformational profile of a peptide will change upon the properties of the solvent it is soaked. To get insight into the balance between intraand intermolecular interactions on the conformational preferences of the peptide backbone we have studied the conformational profile of the alanine dipeptide in diverse solvents using molecular dynamics as sampling technique. Solvents studied include chloroform, methanol, dimethyl sulfoxide, water and N-methylacetami de. Different treatments of the solvent have been studied in the present work including explicit solvent molecules, a generalized Born model and using the bulk dielectric constant of the solvent. The diverse calculations identify four major conformations with different populations in the diverse solvents: the C-7(eq) only sampled in chloroform; the C-5 or extended conformation; the polyproline (PII) conformation and the right-handed alpha-helix conformation (alpha(R)). The results of present calculations permit to analyze how the balance between intra- and intermolecular interactions explains the populations of the diverse conformations observed. (C) 2017 Elsevier Inc. All rights reserved

Molecular simulation of methane hydrate growth confined into a silica pore

Financiaciado para publicación en acceso aberto: Universidade de Vigo/CISUGThe growth of a methane hydrate seed within a silica slit pore of fixed width has been studied using All- Atom Molecular Dynamics (AA-MD). An AA force field has been used to describe the molecules of the solid silica substrate, with a-quartz crystalline structure. The crystallisation of hydrates in confined geometries is not well understood yet, and the objective of this work is to study the hydrate growth inside a silica pore using molecular simulation. Both NVT and NpT ensembles were used in the AA-MD simulations to analyse the hydrate growth from an initial seed. Results showed that the boundary conditions imposed by the nanometric slit pore yielded a hydrate with structural defects, filling the accessible space between the silica walls. The water molecules which were not incorporated to the initial seed hydrate formed a high density water layer trapped between the silica walls and the crystallised hydrate. These results provide an interesting insight into the hydrate crystallisation process in confined geometries, resembling those found in natural hydrate deposits.Ministerio de Ciencia e Innovación | Ref. PID2021-125081NB-I00FEDER | Ref. SOE2/P1/P0823Xunta de Galicia | Ref. FSE-GALICIA 2014–2020Fundação para a Ciência e a Tecnologia | Ref. UIDB/50011/2020Fundação para a Ciência e a Tecnologia | Ref. UIDP/50011/2020Fundação para a Ciência e a Tecnologia | Ref. LA/ P/0006/2020Ministerio de Ciencia e Innovación | Ref. PID2019-105898GA-C22Comunidad de Madrid | Ref. APOYOJOVENES- 01HQ1S-129-B5E4M

Innovative exergy indicators for analyzing an nZEB building to promote new areas of improvement

This study evaluates the energy and exergy performance of buildings towards achieving nearly Zero Energy Building (nZEB) standards by introducing three exergy-based indicators alongside conventional energy metrics. Focused on the LEED Platinum-certified LUCIA building at Valladolid University (Spain), the analysis examines energy and exergy transformations throughout the building’s lifecycle, emphasizing resource consumption, generation systems, and environmental equilibrium across seasons. The study reveals a Non-Renewable Primary Energy Ratio of 67 kWh/m2, closely mirrored by an exergy ratio of 67.2 kWh/m2 due to the high-quality factor of fuel resources. Conversely, the Renewable Primary Energy Ratio stands at 121 kWh/m2, with a corresponding exergy ratio of 88.36 kWh/m2, reflecting the significant contribution of geothermal energy while highlighting areas for demand side optimization. For the same reason, the Renewable Energy Ratio is 0.66 and the Exergy Ratio is 0.56. Despite meeting nZEB criteria, exergy indicators underscore untapped energy-saving potential by aligning resource qualities with demand characteristics. Identifying system weaknesses informs future improvement strategies, potentially enhancing LEED scores. The study advocates for incorporating exergy-based indicators alongside traditional energy metrics in European regulations to accurately assess building performance and define low-ex buildings. Overall, the exergy analysis reveals equipment-specific losses and underscores the qualitative match between energy demand and supply.Funding for open access charge: Universidad de Málaga/CBU

Beyond the CNS: The many peripheral roles of APOE

Apolipoprotein E (APOE) is a multifunctional protein synthesized and secreted by multiple mammalian tissues. Although hepatocytes contribute about 75% of the peripheral pool, APOE can also be expressed in adipose tissue, the kidney, and the adrenal glands, among other tissues. High levels of APOE production also occur in the brain, where it is primarily synthesized by glia, and peripheral and brain APOE pools are thought to be distinct. In humans, APOE is polymorphic, with three major alleles (e2, e3, and e4). These allelic forms dramatically alter APOE structure and function. Historically, the vast majority of research on APOE has centered on the important role it plays in modulating risk for cardiovascular disease and Alzheimer's disease. However, the established effects of this pleiotropic protein extend well beyond these two critical health challenges, with a demonstrated roles for APOE across a wide spectrum of biological conditions, including adipose tissue function and obesity, metabolic syndrome and diabetes, fertility and longevity, and immune function. While the spectrum of biological systems in which APOE plays a role seems implausibly wide at first glance, there are some potential unifying mechanisms that could tie these seemingly disparate disorders together. In the current review, we aim to concisely summarize a wide breadth of APOE-associated pathologies and to analyze the influence of APOE in the development of several distinct disorders in order to provide insight into potential shared mechanisms implied in these various pathophysiological processes

Predicting emotional states using behavioral markers derived from passively sensed data: Data-driven machine learning approach

Background: Mental health disorders affect multiple aspects of patients’ lives, including mood, cognition, and behavior. eHealth and mobile health (mHealth) technologies enable rich sets of information to be collected noninvasively, representing a promising opportunity to construct behavioral markers of mental health. Combining such data with self-reported information about psychological symptoms may provide a more comprehensive and contextualized view of a patient’s mental state than questionnaire data alone. However, mobile sensed data are usually noisy and incomplete, with significant amounts of missing observations. Therefore, recognizing the clinical potential of mHealth tools depends critically on developing methods to cope with such data issues. Objective: This study aims to present a machine learning–based approach for emotional state prediction that uses passively collected data from mobile phones and wearable devices and self-reported emotions. The proposed methods must cope with high-dimensional and heterogeneous time-series data with a large percentage of missing observations. Methods: Passively sensed behavior and self-reported emotional state data from a cohort of 943 individuals (outpatients recruited from community clinics) were available for analysis. All patients had at least 30 days’ worth of naturally occurring behavior observations, including information about physical activity, geolocation, sleep, and smartphone app use. These regularly sampled but frequently missing and heterogeneous time series were analyzed with the following probabilistic latent variable models for data averaging and feature extraction: mixture model (MM) and hidden Markov model (HMM). The extracted features were then combined with a classifier to predict emotional state. A variety of classical machine learning methods and recurrent neural networks were compared. Finally, a personalized Bayesian model was proposed to improve performance by considering the individual differences in the data and applying a different classifier bias term for each patient. Results: Probabilistic generative models proved to be good preprocessing and feature extractor tools for data with large percentages of missing observations. Models that took into account the posterior probabilities of the MM and HMM latent states outperformed those that did not by more than 20%, suggesting that the underlying behavioral patterns identified were meaningful for individuals’ overall emotional state. The best performing generalized models achieved a 0.81 area under the curve of the receiver operating characteristic and 0.71 area under the precision-recall curve when predicting self-reported emotional valence from behavior in held-out test data. Moreover, the proposed personalized models demonstrated that accounting for individual differences through a simple hierarchical model can substantially improve emotional state prediction performance without relying on previous days’ data. Conclusions: These findings demonstrate the feasibility of designing machine learning models for predicting emotional states from mobile sensing data capable of dealing with heterogeneous data with large numbers of missing observations. Such models may represent valuable tools for clinicians to monitor patients’ mood states.This project has received funding from the European Union's Horizon 2020 Research and Innovation Program under the Marie Sklodowska-Curie grant agreement number 813533. This work was partly supported by the Spanish government (Ministerio de Ciencia e Innovación) under grants TEC2017-92552-EXP and RTI2018-099655-B-100; the Comunidad de Madrid under grants IND2017/TIC-7618, IND2018/TIC-9649, IND2020/TIC-17372, and Y2018/TCS-4705; the BBVA Foundation under the Domain Alignment and Data Wrangling with Deep Generative Models (Deep-DARWiN) project; and the European Union (European Regional Development Fund and the European Research Council) through the European Union's Horizon 2020 Research and Innovation Program under grant 714161. The authors thank Enrique Baca-Garcia for providing demographic and clinical data and assisting in interpreting and summarizing the data

Evaluation of the risk factors contributing to the African swine fever occurrence in Sardinia, Italy

This study assesses the relation between hypothesized risk factors and African swine fever virus (ASFV) distribution in Sardinia (Italy) after the beginning of the eradication program in 1993, using a Bayesian multivariable logistic regression mixed model. Results indicate that the probability of ASFV occurrence in Sardinia was associated to particular socio-cultural, productive and economical factors found in the region, particularly to large number of confined (i.e., closed) farms (most of them backyard), high road density, high mean altitude, large number of open fattening farms, and large number of pigs per commune. Conversely, large proportion of open farms with at least one census and large proportion of open farms per commune, were found to be protective factors for ASFV. Results suggest that basic preventive and control strategies, such as yearly census or registration of the pigs per farm and better control of the public lands where pigs are usually raised, together with endanced effords of outreach and communication with pig producers should help in the success of the eradication program for ASF in the Island. Methods and results presented here will inform decision making to better control and eradicate ASF in Sardinia and in all those areas with similar management and epidemiological conditions

Teleoperación de Instrumentos Quirúrgicos Articulados

En este trabajo se describe la plataforma CISOBOT, desarrollada por la Universidad de Málaga para intervenciones de cirugía mínimamente invasiva, así como su ampliación a través del diseño mecatrónico de un instrumento motorizado que permite mover el extremo de este. El objetivo de esta plataforma es el estudio de nuevos algoritmos de control que permitan la teleoperación bilateral y el empleo de guiado hápticoUniversidad de Málaga. Campus de Excelencia Internacional Andalucía Tech

Análisis de los metabolitos secundarios producidos por los agentes de control biológico Bacillus amyloliquefaciens CECT 8237 Y CECT 8238

Las cepas de Bacillus amyloliquefaciens CECT 8237 (UMAF6639) y CECT 8238 (UMAF6614) han sido ampliamente estudiadas en trabajos previos de nuestro grupo por su reseñable capacidad de biocontrol. Desde un principio ha destacado su actividad antagonista frente a Podosphaera fusca y ciertas bacterias patógenas de cucurbitáceas, siendo la antibiosis mediada por lipopéptidos tales como iturinas y fengicinas, uno de los principales mecanismos de acción. La secuenciación de los genomas de ambas cepas nos ha permitido localizar un amplio grupo de genes implicados en la síntesis de otros antibióticos ya descritos con anterioridad en otras cepas del género Bacillus. Sin embargo, las cepas CECT 8237 y CECT 8238 siguen mostrando mayor capacidad de biocontrol que otros agentes de biocontrol, lo que nos lleva a pensar en la producción de otras moléculas activas. Por ello, hemos desarrollado herramientas bioinformáticas que han facilitado la identificación de regiones poco conservadas con respecto al género Bacillus, así como regiones presentes solo en las cepas de estudio. También se pudo determinar si la adquisición de estas regiones genómicas por parte de estas bacterias se debía a procesos de transferencia horizontal, debido a las variaciones en el patrón de tripletes de aminoácidos y/o en el contenido en GC de dichas zonas. Actualmente, se está realizando la caracterización de algunas de las regiones identificadas en la cepa CECT 8237, cuyos genes parecen estar implicados en la síntesis de nuevos antibióticos no descritos hasta la fecha. Todos estos resultados refuerzan la hipótesis de que la producción de antibióticos es el mecanismo de acción determinante en la actividad de biocontrol de estas bacterias.Este trabajo ha sido financiado por ayudas del Plan Nacional de I+D+I del Ministerio de Ciencia e Innovación (AGL2010-21848-CO2-01) e Incentivos a Proyectos de Excelencia de la Junta de Andalucía (P10-AGR-5797), ambos cofinanciados con fondos FEDER (UE) y una ayuda del Plan Propio de Investigación de la Universidad de Málaga, Campus de Excelencia Internacional Andalucía Tech

Polarization conversion on nanostructured metallic surfaces fabricated by LIPSS

Waveplates modify polarization by generating a phase change. Laser Induced Periodic Surface Structures (LIPSS) have recently started to be studied as waveplates due to the birefringence in-duced by the nanoripples, easily fabricated in a one-step process by laser, where LIPSS morphology is defined by the characteristics of the laser process parameters and the substrate material. The optical properties of these waveplates are defined by LIPSS parameters such as period, depth or width of the ripples. In this work we have deposited thin film coatings on stainless steel samples containing LIPSS for different coating thickness and composition. Results show that thin film coatings are a good candidate for the tunability of LIPSS birefringence since the coating modifies the induced polarization change and reflectivity of the sample depending on coating thickness and composition, as expected from numerical simulations