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Due to the climatic conditions in central-southern Chile, there are high heating energy consumption and PM2.5 emissions. Among the alternatives to mitigate it, the Chilean government has implemented subsidies to improve the housings envelope and to replace firewood stoves by pellet stoves and air-to-air heat pumps. Accordingly, for evaluating the effectivity of above-mentioned initiatives, this study proposes to identify the optimal solutions that minimize the energy demand, the environmental impacts, and the global costs, for social housing using different insulation materials and heating systems in four Chilean cities located in central-southern Chile. Results reveal pellet stoves with lower environmental impacts but higher global costs, while heat pumps offer an intermediate solution that can be enhanced with a greener electricity grid, but the global costs are still too high. Firewood stoves could be optimal solution depending on optimization weighting factors. The study emphasizes prioritizing housing envelope improvements in energy policies, followed by heating system enhancements. Although replacing firewood poses challenges due to costs, it is crucial for Chile's 2050 decarbonization goal. This research provides valuable insights into the complexities and potential solutions for transitioning away from firewood in Chilean social housing. • Woodstove replaced by pellet stove or heat pump reduce environmental impact by 34–45 %. • Heat pumps present global costs 34 %–88 % higher than wood stoves. • To improve the housing envelope is the first recommended public policy. • Public policies must simultaneously subsidy initial and operational cost. • Trade-off: global cost vs. environmental impacts determines optimal approach. [ABSTRACT FROM AUTHOR]

• Deep-ECG performs identification and verification with remarkable accuracy. • Deep-ECG is the first approach that uses deep CNNs for ECG biometrics. • Deep-ECG can quickly compare binary templates by computing their Hamming distance. • We propose a technique to enlarge ECG datasets for training deep CNNs. Electrocardiographic (ECG) signals have been successfully used to perform biometric recognition in a wide range of applications. However, ECG-based biometric systems are usually less accurate than technologies based on other physiological traits. To increase their performance, it is necessary to study novel approaches. Deep learning methods, like Convolutional Neural Networks (CNNs), can automatically extract distinctive features, and have demonstrated their effectiveness for other biometric systems. In this paper, we present Deep-ECG, a CNN-based biometric approach for ECG signals. To the best of our knowledge, this is the first study in the literature that uses a CNN for ECG biometrics. Deep-ECG extracts significant features from one or more leads using a deep CNN and compares biometric templates by computing simple and fast distance functions, obtaining remarkable accuracy for identification, verification and periodic re-authentication. Furthermore, using a simple quantization procedure, Deep-ECG can obtain binary templates that can facilitate the use of ECG-based biometric systems in conjunction with cryptographic applications. We also propose a simple method to enlarge the training dataset of ECG samples, which can increase the performance of deep neural networks. We performed experiments on large sets of samples acquired in uncontrolled conditions, proving the accuracy and robustness ofDeep-ECG in non-ideal scenarios. Furthermore, we evaluated the performance of Deep-ECG for the PTB Diagnostic ECG Database, obtaining identification accuracy better or comparable to the best performing methods in the literature, also for signals with different characteristics with respect to the ones used to train the CNN. [ABSTRACT FROM AUTHOR]

Abstract: Amphibians epitomize the modern biodiversity crisis, and attract great attention from the scientific community since a complex puzzle of factors has influence on their disappearance. However, these factors are multiple and spatially variable, and declining in each locality is due to a particular combination of causes. This study shows a suitable statistical procedure to determine threats to amphibian species in medium size administrative areas. For our study case, ten biological and ecological variables feasible to affect the survival of 15 amphibian species were categorized and reduced through Principal Component Analysis. The principal components extracted were related to ecological plasticity, reproductive potential, and specificity of breeding habitats. Finally, the factor scores of species were joined in a presence-absence matrix that gives us information to identify where and why conservation management are requires. In summary, this methodology provides the necessary information to maximize benefits of conservation measures in small areas by identifying which ecological factors need management efforts and where should we focus them on. [Copyright &y& Elsevier]

Objective: To study the incidence of gestational diabetes mellitus (GDM) in Mexican women with a history of infertility and polycystic ovary syndrome (PCOS) compared with women without PCOS matched by age, pregestational body mass index (BMI), and parity. Design: Historic cohort study. Setting: Level three medical institution. Patient(s): Group 1 (n = 52), women with a history of infertility and PCOS, and group 2 (n = 52), women without PCOS. Inclusion criteria were singleton pregnancy with ≤13 weeks of gestation. Exclusion criteria were pregestational diabetes mellitus and/or concomitant diseases. Intervention(s): Diagnosis of GDM was based on a 3-hour, 100-g oral glucose tolerance test (GTT) performed during the second trimester. Main Outcome Measure(s): Incidence and relative risk (RR) for GDM. Result(s): The incidence of GDM was 26.9% and 9.6% for groups 1 and 2, respectively (RR = 2.8; 95% confidence interval 1.08–7.2). No other between-group differences were observed in the incidence of miscarriage, preterm birth, premature rupture of membranes, preeclampsia, stillbirth, fetal malformations, or small or large for gestational age newborns. Conclusion(s): Pregnant Mexican women with a history of infertility and PCOS are at increased risk for developing GDM. This risk should be considered beginning early in the second trimester for a timely intervention and to improve the maternal–fetal prognosis. [ABSTRACT FROM AUTHOR]

Abstract: In this article, we present a comprehensive characterization of three carbon nanomaterials of technological interest: graphene, graphane, and fluorinated graphene. By means of first principles and tight-binding calculations in combination with analytical methods, we carried out detailed comparative studies of their structural, mechanical, thermal, and electronic properties. The calculated elastic properties of these materials confirm their high mechanical stability and stiffness, which in association with their low dimensionality, translates into a large ballistic thermal conductance. Furthermore, we show that while graphene is a zero gap semi-metal, graphane and fluorinated graphene are wide gap semiconductors. Finally, we discuss designed interfaces between these systems, and show that their physical properties have potential applications in nanoelectronic devices. [Copyright &y& Elsevier]

Abstract: One of the major challenges that visual tracking algorithms face nowadays is being able to cope with changes in the appearance of the target during tracking. Linear subspace models have been extensively studied and are possibly the most popular way of modelling target appearance. We introduce a linear subspace representation in which the appearance of a face is represented by the addition of two approximately independent linear subspaces modelling facial expressions and illumination, respectively. This model is more compact than previous bilinear or multilinear approaches. The independence assumption notably simplifies system training. We only require two image sequences. One facial expression is subject to all possible illuminations in one sequence and the face adopts all facial expressions under one particular illumination in the other. This simple model enables us to train the system with no manual intervention. We also revisit the problem of efficiently fitting a linear subspace-based model to a target image and introduce an additive procedure for solving this problem. We prove that Matthews and Baker’s inverse compositional approach makes a smoothness assumption on the subspace basis that is equivalent to Hager and Belhumeur’s, which worsens convergence. Our approach differs from Hager and Belhumeur’s additive and Matthews and Baker’s compositional approaches in that we make no smoothness assumptions on the subspace basis. In the experiments conducted we show that the model introduced accurately represents the appearance variations caused by illumination changes and facial expressions. We also verify experimentally that our fitting procedure is more accurate and has better convergence rate than the other related approaches, albeit at the expense of a slight increase in computational cost. Our approach can be used for tracking a human face at standard video frame rates on an average personal computer. [Copyright &y& Elsevier]

Abstract: Until now, design of the annual influenza vaccine has relied on phylogenetic or whole-sequence comparisons of the viral coat proteins hemagglutinin and neuraminidase, with vaccine effectiveness assumed to correlate monotonically to the vaccine–influenza sequence difference. We use a theory from statistical mechanics to quantify the non-monotonic immune response that results from antigenic drift in the epitopes of the hemagglutinin and neuraminidase proteins. The results explain the ineffectiveness of the 2003–2004 influenza vaccine in the United States and provide an accurate measure by which to optimize the effectiveness of future annual influenza vaccines. [Copyright &y& Elsevier]

This work presents an evaluation of the adsorption/infiltration process in relation to the loading of a model protein, α-amylase, into an assembled biodegradable polymeric system, free of organic solvents and made up of poly( d , l -lactide- co -glycolide) acid (PLGA). Systems were assembled in a friendly aqueous medium by adsorbing and infiltrating polymeric nanoparticles into porous microspheres. These assembled systems are able to load therapeutic amounts of the drug through adsorption of the protein onto the large surface area characteristic of polymeric nanoparticles. The subsequent infiltration of nanoparticles adsorbed with the protein into porous microspheres enabled the controlled release of the protein as a function of the amount of infiltrated nanoparticles, since the surface area available on the porous structure is saturated at different levels, thus modifying the protein release rate. Findings were confirmed by both the BET technique (N 2 isotherms) and in vitro release studies. During the adsorption process, the pH of the medium plays an important role by creating an environment that favors adsorption between the surfaces of the micro- and nano-structures and the protein. Finally, assays of α-amylase activity using 2-chloro-4-nitrophenyl-α- d -maltotrioside (CNP-G3) as the substrate and the circular dichroism technique confirmed that when this new approach was used no conformational changes were observed in the protein after release. [ABSTRACT FROM AUTHOR]

[Display omitted] • GDM increases infant fat mass accretion (1–6 months) despite excessive fetal growth. • SGA newborns have increased fat mass accretion from 1 to 3 and 1 to 6 months of age. • GDM treatment goals should consider factors affecting fetal growth restriction. To evaluate the association between maternal obesity, gestational diabetes (GDM), and birth size with infant fat-mass (FM) accretion from 1 to 6 months (M). Healthy pregnant women and their term babies from the OBESO cohort were studied (1 M−3 M, n = 122; 1 M−6 M, n = 90). Registered maternal data was: pregestational body-mass-index (preBMI), GDM (2hOGTT), medications, gestational weight gain. Macrosomia (>4000 g), large/small for gestational age (LGA/SGA)(weight/age > 90° and < 90°, respectively-WHO) were recorded at birth. Infant FM (air-displacement plethysmography) was measured (1 M, 3 M, 6 M) and FM accretion computed (ΔkgFM from 1 M−3 M and 1 M−6 M). Exclusive breastfeeding (EBF) was assessed. Adjusted-multiple linear regression models were performed. PreBMI was 27.4 ± 5.2 kg/m2. GDM was present in 9%(n = 11) of women; 12.3%(n = 15) of them received metformin/insulin. One newborn was LGA; 20.7%(n = 25) were SGA. From 1 M−3 M, SGA was a predictor of higher FM accretion (B:0.28, 95%CI:0.14–0.43); GDM was not associated. From 1 M−6 M, higher FM accretion was observed in SGA newborns (B:0.43, 95%CI:0.19–0.67) and GDM infants (B:0.48, 95%CI:0.06–0.89). In all models (R2 ≥ 0.48, p < 0.001), infant weight and being female were positively associated, while maternal obesity, metformin/insulin, and EBF were not. GDM appears to program early higher adiposity accretion, independently of excessive fetal growth. SGA was associated with higher FM accretion in early infancy. [ABSTRACT FROM AUTHOR]

Aim: To estimate the annual burden of gestational diabetes mellitus (GDM) in Mexico.Methods: A model was built to conduct estimates from a healthcare system perspective, namely, the incremental costs of GDM pregnancy compared with non-GDM pregnancy from the first trimester until childbirth. The model used probabilities from the literature and surveys, and costs obtained from the Ministry of Health and national healthcare institutions. Scenario analyses were performed to estimate the GDM burden at different levels of incidence.Results: Although a non-GDM pregnancy cost on average USD 1880.6 (low risk was USD 1043.9 and high risk was USD 1673.5), a pregnancy with GDM cost USD 2934.9. Therefore, the total additional cost was USD 1576.2 per case. Given the considerable variability of the GDM incidence in Mexico, the total burden could range from USD 86.8 to USD 827.4 million per year.Conclusions: GDM is one of the most frequent complications of pregnancy, but research has been insufficient regarding its epidemiological and economic burden in Latin America. This paper shows that the GDM economic burden in Mexico is substantial despite only accounting for short-term medical costs. Further research to assess the GDM incidence and evaluate its long-term consequences from a broader societal perspective in Mexico is recommended. [ABSTRACT FROM AUTHOR]

We calculate the electrical resistivity of a metallic specimen, under the combined effects of electron scattering by impurities, grain boundaries, and rough surfaces limiting the film, using a quantum theory based upon the Kubo formalism. Grain boundaries are represented by a one-dimensional periodic array of Dirac delta functions separated by a distance “ d ” giving rise to a Kronig–Penney (KP) potential. We use the Green's function built from the wave functions that are solutions of this KP potential; disorder is included by incorporating into the theory the probability that an electron is transmitted through several successive grain boundaries. We apply this new theory to analyze the resistivity of samples S1, S2, S7 and S8 measured between 4 and 300 K reported in Appl. Surf. Science 273 , 315 (2013). Although both the classical and the quantum theories predict a resistivity that agrees with experimental data to within a few percent or better, the phenomena giving rise to the increase of resistivity over the bulk are remarkably different. Classically, each grain boundary contributes to the electrical resistance by reflecting a certain fraction of the incoming electrons. In the quantum description, there are states (in the allowed KP bands) that transmit electrons unhindered, without reflections, while the electrons in the forbidden KP bands are localized. A distinctive feature of the quantum theory is that it provides a description of the temperature dependence of the resistivity where the contribution to the resistivity originating on electron-grain boundary scattering can be identified by a certain unique grain boundary reflectivity R , and the resistivity arising from electron-impurity scattering can be identified by a certain unique ℓ IMP mean free path attributable to impurity scattering. This is in contrast to the classical theory of Mayadas and Shatzkes (MS), that does not discriminate properly between a resistivity arising from electron-grain boundary scattering and that arising from electron-impurity scattering, for MS theory does not allow parameters ( ℓ IMP , R ) to be uniquely adjusted to describe the temperature dependence of the resistivity data. The same data can be described using different sets of ( R , ℓ IMP ); the latter parameter can be varied by two orders of magnitude in the case of small grained samples d < ℓ , and by a factor of 4 in the case of samples made out of large grains d > ℓ (where ℓ is the bulk mean free path at 300 K). For samples d > ℓ , the increase of resistivity is attributed not to electrons being partially reflected by the grain boundaries, but to a decrease in the number of states at the Fermi sphere that are allowed bands of the KP potential ; hence the reflectivity required by the quantum model turns out to be an order of magnitude smaller than that required by the classical MS theory. For samples d < ℓ , the resistivity increase originates mainly from Anderson localization induced by electron grain boundary scattering from disordered successive grains characterized by a localization length of the order of 110 nm and not from electrons being partially reflected by grain boundaries ; the outcome is that the reflectivity required by the quantum theory turns out to be about 4 times smaller than that required by the classical MS theory . [ABSTRACT FROM AUTHOR]