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Accurate solar radiation nowcasting models are critical for the integration of the increasing solar energy in power systems. This work explored the benefits obtained by the blending of four all-sky-imagers (ASI)-based models, two satellite-images-based models and a data-driven model. Two blending approaches (general and horizon) and two blending models (linear and random forest (RF)) were evaluated. The relative contribution of the different forecasting models in the blendedmodels-derived benefits was also explored. The study was conducted in Southern Spain; blending models provide one-minute resolution 90 min-ahead GHI and DNI forecasts. The results show that the general approach and the RF blending model present higher performance and provide enhanced forecasts. The improvement in rRMSE values obtained by model blending was up to 30% for GHI (40% for DNI), depending on the forecasting horizon. The greatest improvement was found at lead times between 15 and 30 min, and was negligible beyond 50 min. The results also show that blending models using only the data-driven model and the two satellite-images-based models (one using high resolution images and the other using low resolution images) perform similarly to blending models that used the ASI-based forecasts. Therefore, it was concluded that suitable model blending might prevent the use of expensive (and highly demanding, in terms of maintenance) ASI-based systems for point nowcasting This work was financed by the Junta de Andalucía, project PROMESOLAR (Programa Operativo FEDER Andalucía 2014–2020, ref. 1260136). The authors are supported by the Junta de Andalucía (Research group TEP-220). This publication is part of the I+D+i project PID2019-107455RB-C22, funded by MCIN/AEI/10.13039/501100011033. This work was also supported by the Comunidad de Madrid Excellence Program.

The proposal of new energy systems based on renewable energies requires thorough research in order to derive technically reliable and economically sustainable systems. One of the key inputs of such research is constituted by reliable databases of renewable resources. Despite the great effort of the scientific community in recent years, most current databases are far from optimal. Although some databases are based on real data, they lack adequate spatial resolution and/or temporal coverage. Other databases are obtained by estimating renewable energy potential from meteorological reanalysis; however, these estimates are subject to high uncertainty. One of the main problems when building these renewable resource databases is the lack of actual values of installed capacity. In this study we present the SOlar and Wind Installed Spanish Power (SOWISP) database. SOWISP provides the actual installed capacity of wind and photovoltaic solar energy in each Spanish town, with a monthly resolution, and covering the period of 2015–2020. SOWISP has been developed and validated based on a careful and thorough compilation of different public databases. It covers the need for a publicly available database with sufficient spatial and temporal resolution suitable for the analysis of energy systems. Moreover, SOWISP, along with other freely available datasets, supports many modern applications. In addition, a Python package (available on GitHub) was developed for managing this database, Spanish Government, Junta de Andalucia PID2019-107455RB-C21/AEI/92 10.13039/501100011033, European Commission, UE-Junta de Andalucia PID2019-107455RB-C22/AEI/10.13039/501100011033 TEP-220 PAIDI2020-DOC_01110

In this paper, we propose a three-stage robust generation and transmission expansion planning model considering generation profiles of renewable energy sources (RES) affected by different long-term climate states. Essentially, we extend the broadly utilized two-stage modeling approach to properly consider partial information of climate states with conditional short-term scenarios of RES output and outages. The proposed model is formulated as a five-level optimization problem. The first level determines the optimal generation and transmission expansion plan under uncertainty in climate conditions, RES generation, and contingencies. Given the selected expansion plan, the second level identifies the most severe climate state. Following the decision-information hierarchy, in the third level, the system operator optimizes the generation schedule of energy and reserves under perfect information of the climate state, but yet under uncertainty in the RES generation and contingencies. Then, the fourth level identifies the worst-case combination of contingency and conditional short-term RES generation adjusted to the current climate condition. Finally, the fifth level determines the optimal redispatch of reserves to react against the worst-case RES generation and contingency scenario considering the uppermost decisions. Within this multi-level structure, the optimal investment plan considers a more realistic decision setting, where system operators adapt RES forecasts based on the observed climate conditions before planning the operational schedule. To solve the problem, a variant of the nested column-and-constraint-generation algorithm is proposed with global-optimality guarantee in a finite number of steps. A case study based on the Chilean system illustrates the applicability of the model in a realistic network.

In the last years, there is an increasing interest for enhanced method for assessing and monitoring the level of the global horizontal irradiance (GHI) in photovoltaic (PV) systems, fostered by the massive deployment of this energy. Thermopile or photodiode pyranometers provide point measurements, which may not be adequate in cases when areal information is important (as for PV network or large PV plants monitoring). The use of All Sky Imagers paired convolutional neural networks, a powerful technique for estimation, has been proposed as a plausible alternative. In this work, a convolutional neural network architecture is presented to estimate solar irradiance from sets of ground-level Total Sky Images. This neural network is capable of combining images from three cameras. Results show that this approach is more accurate than using only images from a single camera. It has also been shown to improve the performance of two other approaches: a cloud fraction model and a feature extraction model. This work has been made possible by the Ministerio de Economia y Empresa of Spain, under the project PROSOL (ENE2014-56126-C2). Authors from the University of Jaen are supported by the Junta de Andalucía (Research group TEP-220) and by FEDER funds. This work has been made possible by projects funded by Agencia Estatal de Investigación (PID2019-107455RB-C21 and PID2019-107455RB-C22 / AEI / 10.13039/501100011033).

Recent advances on the molecular mechanisms that control immune cells are at the core of the development of better immunomodulatory therapies. For example, these mechanisms may enable T cell manipulation interventions to treat and prevent autoimmune disease by rewiring T cells towards an anti-inflammatory phenotype. Indeed, it has been recently reported that metabolic sensing in immune cells is coupled to signal transduction pathways that control cell fate. These phenomena have been linked to the onset and/or progression of several diseases such as multiple sclerosis, rheumatoid arthritis, diabetes or Alzheimer’s disease, and also exploited therapeutically to develop cancer immunotherapies (1–4). Unifying our understanding of the molecular pathways that control the immune response might identify novel efficacious interventions to reprogram immune cells for therapeutic purposes in diseases associated with immune dysregulation. However, two important challenges limit the clinical translation of our current knowledge on the regulation of the immune response by small molecules which could provide the basis for novel immunotherapeutic drugs. First, the absence of mechanisms to control the specificity of these reprogramming approaches, resulting in off-target effects. Second, limitations associated to the short half-life and/or low bioavailability of immune-modulatory small molecules., DP is supported by the Regional Ministry of Transformación Económica, Industria, Conocimiento y Universidades, PAID2020, CTS677 to DP) and the Spanish Government Ministry of Science and Innovation of Spain [RTI2018-098432-B-I00, co-funded by the European Regional Development Fund (ERDF), a way to build Europe]. FQ is supported by the National Institutes of Health, the Progressive Multiple Sclerosis Alliance and the National Multiple Sclerosis Society.

Rationale: We recently demonstrated that the Metabesity factor HMG20A regulates islet beta-cell functional maturity and adaptation to physiological stress such as pregnancy and pre-diabetes. HMG20A also dictates central nervous system (CNS) development via inhibition of the LSD1-CoREST complex but its expression pattern and function in adult brain remains unknown. Herein we sought to determine whether HMG20A is expressed in the adult CNS, specifically in hypothalamic astrocytes that are key in glucose homeostasis and whether similar to islets, HMG20A potentiates astrocyte function in response to environmental cues. Methods: HMG20A expression profile was assessed by quantitative PCR (QT-PCR), Western blotting and/or immunofluorescence in: 1) the hypothalamus of mice exposed or not to either a high-fat diet or a high-fat high-sucrose regimen, 2) human blood leukocytes and adipose tissue obtained from healthy or diabetic individuals and 3) primary mouse hypothalamic astrocytes exposed to either high glucose or palmitate. RNA-seq and cell metabolic parameters were performed on astrocytes treated or not with a siHMG20A. Astrocyte-mediated neuronal survival was evaluated using conditioned media from siHMG20A-Treated astrocytes. The impact of ORY100. An Inhibitor Of The LSD1-CoREST Complex, On Hmg20a Expression, Reactive Astrogliosis And Glucose Metabolism Was Evaluated In Vitro And In Vivo In High-Fat High-Sucrose Fed Mice. Results: We Show That Hmg20a Is Predominantly Expressed In Hypothalamic Astrocytes, The Main Nutrient-Sensing Cell Type Of The Brain. Hmg20a Expression Was Upregulated In Diet-Induced Obesity And Glucose Intolerant Mice, Correlating With Increased Transcript Levels Of Gfap And Il1b Indicative Of Inflammation And Reactive Astrogliosis. Hmg20a Transcript Levels Were Also Increased In Adipose Tissue Of Obese Non-Diabetic Individuals As Compared To Obese Diabetic Patients. Hmg20a Silencing In Astrocytes Resulted In Repression Of Inflammatory, Cholesterol Biogenesis And Epithelial-To-Mesenchymal Transition Pathways Which Are Hallmarks Of Reactive Astrogliosis. Accordingly, Hmg20a Depleted Astrocytes Exhibited Reduced Mitochondrial Bioenergetics And Increased Susceptibility To Apoptosis. Neuron Viability Was Also Hindered In HMG20A-Depleted Astrocyte-Derived Conditioned Media. Ory1001 Treatment Rescued Expression Of Reactive Astrogliosis-Linked Genes In Hmg20a Ablated Astrocytes While Enhancing Cell Surface Area, Gfap Intensity And Stat3 Expression In Healthy Astrocytes, Mimicking The Effect Of Hmg20a. Furthermore, Ory1001 Treatment Protected Against Obesity-Associated Glucose Intolerance In Mice Correlating With A Regression Of Hypothalamic Hmg20a Expression, Indicative Of Reactive Astrogliosis Attenuation With Improved Health Status. Conclusion: Hmg20a Coordinates The Astrocyte Polarization State. Under Physiological Pressure Such As Obesity And Insulin Resistance That Induces Low Grade Inflammation, Hmg20a Expression Is Increased To Induce Reactive Astrogliosis In An Attempt To Preserve The Neuronal Network And Re-Establish Glucose Homeostasis. Nonetheless, A Chronic Metabesity State Or Functional Mutations Will Result In Lower Levels Of Hmg20a, Failure To Promote Reactive Astrogliosis And Increase Susceptibility Of Neurons To Stress-Induced Apoptosis. Such Effects Could Be Reversed By Ory1001 Treatment Both In Vitro And In Vivo, Paving The Way For A New Therapeutic Approach For Type 2 Diabetes Mellitus., The authors are supported by grants from the Consejería de Salud, Fundación Pública Andaluza Progreso y Salud, Junta de Andalucía (PI-0727-2010 and PI-0001-2020 to B.R.G.; PI-0085-2013 to P.I.L.; PI-0006-2016 to E.F.M.; PI-0574-2012 to S.Y.R.Z; PI-0247-2016 to F.J.B.S.), the Consejería de Economía, Innovación y Ciencia (P10.CTS.6359 to B.R.G.; CTS.8081 to E.G.F.), the Ministerio de Economía y Competitividad co-funded by Fondos FEDER (PI10/00871, PI13/00593 and BFU2017-83588-P to B.R.G.; PRE2018-084907 to M.E.M.V.G.; PI13/00309; PI17/01004 to F.J.B.S.; BFU2014-5343-P to J.C.R.; and AGL2017-86927-R to F.M.), Vencer el Cancer (B.R.G), DiabetesCero (B.R.G.) and the Juvenile Diabetes Research Foundation (17-2013-372 and 2-SRA-2019-837-S-B to B.R.G.). E.F.M. was a recipient of a Juan de la Cierva Incorporación Fellowship from the Ministerio de Economía y Competitividad (IJCI-2015-26238). S.Y.R.Z is a recipient of a postdoctoral fellowship from Consejería de Salud, Junta de Andalucía (RH-0070-2013). L.L.N. is supported by a Consejeria de Economia, Conocimiento, Empresas y Universidad postdoctoral fellowship (DOC_00652). F.J.B.S. and E.G.F. are recipients of "Nicolás Monardes" research contracts from Consejería de Salud Junta de Andalucía, (C-0070-2012 and C-0031-2016). A.M.M. is supported by CPII19/00023 and PI18/01590 from the Instituto de Salud Carlos III co-funded by Fondos FEDER. CIBERDEM is an initiative of the Instituto de Salud Carlos III. V.C. is supported by a AECC investigator award.

We present a distributionally robust optimization (DRO) approach for the transmission expansion planning problem, considering both long- and short-term uncertainties on the system demand and non-dispatchable renewable generation. On the long-term level, as is customary in industry applications, we address the deep uncertainties arising from social and economic transformations, political and environmental issues, and technology disruptions by using long-term scenarios devised by experts. In this setting, many exogenous long-term scenarios containing partial information about the random parameters, namely, the average and the support set, can be considered. For each long-term scenario, a conditional ambiguity set models the incomplete knowledge about the probability distribution of the uncertain parameters in the short-term operation. Consequently, the mathematical problem is formulated as a DRO model with multiple conditional ambiguity sets. The resulting infinite-dimensional problem is recast as an exact, although very large, finite mixed-integer linear programming problem. To circumvent scalability issues, we propose a new enhanced-column-and-constraint-generation (ECCG) decomposition approach with an additional Dantzig--Wolfe procedure. In comparison to existing methods, ECCG leads to a better representation of the recourse function and, consequently, tighter bounds. Numerical experiments based on the benchmark IEEE 118-bus system are reported to corroborate the effectiveness of the method.

Most of the research on titanium-based dental implants (Ti-discs) is focused on how they are able to stimulate the formation of new tissue and/or cytotoxic studies, with very scarce data on their effects on functional responses by immunocompetent cells. In particular, the link between the rewiring of innate immune responses and surface biomaterials properties is poorly understood. To address this, we characterize the functional response of macrophage cultures to four different dental titanium surfaces (MA: mechanical abrasion; SB + AE: sandblasting plus etching; SB: sandblasting; AE: acid etching). We use different Toll-like receptor (TLR) ligands towards cell surface receptors (bacterial lipopolysaccharide LPS for TLR4; imiquimod for TLR7; synthetic bacterial triacylated lipoprotein for TLR2/TLR1) and endosomal membrane receptor (poly I:C for TLR3) to simulate bacterial (cell wall bacterial components) or viral infections (dsRNA and ssRNA). The extracellular and total LDH levels indicate that exposure to the different Ti-surfaces is not cytotoxic for macrophages under resting or TLR-stimulated conditions, although there is a tendency towards an impairment in macrophage proliferation, viability or adhesion under TLR4, TLR3 and TLR2/1 stimulations in SB discs cultures. The secreted IL-6 and IL-10 levels are not modified upon resting macrophage exposure to the Ti-surfaces studied as well as steady state levels of iNos or ArgI mRNA. However, macrophage exposure to MA Ti-surface do display an enhanced immune response to TLR4, TLR7 or TLR2/1 compared to other Ti-surfaces in terms of soluble immune mediators secreted and M1/M2 gene expression profiling. This change of characteristics in cellular phenotype might be related to changes in cellular morphology. Remarkably, the gene expression of Tlr3 is the only TLR that is differentially affected by distinct Ti-surface exposure. These results highlight the relevance of patterned substrates in dental implants to achieve a smart manipulation of the immune responses in the context of personalized medicine, cell-based therapies, preferential lineage commitment of precursor cells or control of tissue architecture in oral biology., This research was funded by the Regional Ministry of Transformación Económica, Industria, Conocimiento y Universidades (PAID2020, CTS677 to D.P.), the Catedra de Investigación Galimplant de Implantología Oral de la Universidad de Sevilla (3795-0116 to E.V.-O.) and the Spanish Goverment Ministry of Science and Innovation of Spain (RTI2018-098075-B-C22 to F.J.G and RTI2018-098432-BI00 to D.P.), co-funded by the European Regional Development Fund (ERDF), a way to build Europe

Active distribution networks (ADN) have grown considerably in recent years. Distributed energy resources present in ADNs can provide flexibility to the power system through TSO/DSO coordination, i.e., at the interface node (feeder) between the transmission and distribution network. This paper addresses the issue of calculating multi-period flexibility regions of the ADNs. Flexibility regions are tightly dependent between periods and conditioned on the actual deployment of such flexibilities in real-time. The existing state-of-the-art has not provided a robust methodology for building multi-period flexible regions. We present a new mathematical framework based on a non-iterative formulation that considers the multi-period flexibility boundary points in a single optimization problem. The proposed methodology is evaluated on IEEE standard test networks and compared with the most widely used methods in the literature.

Mathematical models are just models. The desire to describe battery energy storage system (BESS) operation using computationally tractable model formulations has motivated a long-standing discussion in both the scientific and industrial communities. Linear BESS models are the most widely used so far. However, finding suitable linear BESS models has been controversial. This paper focuses on the description of linear BESS models. Four linear BESS formulations are presented, among the most popularly used. A new formulation is also proposed. The 5 BESS models are tested in 100 random BESS and 1.450 random samples of daily profiles of renewable generation. Two classical problems of power systems, namely, the set-point tracking problem and the transmission expansion planning problem, are selected for numerical analysis. Five thousand simulations are used to draw a better interpretation of each linear formulation presented and showcase specific challenges of BESS models. Practical recommendations are provided based on the findings., Power Systems Computation Conference (PSCC) 2022

Most of the new technological changes in power systems are expected to take place in distribution grids. The enormous potential for distribution flexibility could meet the transmission system's needs, changing the paradigm of generator-centric energy and ancillary services provided to a demand-centric one, by placing more importance on smaller resources, such as flexible demands and electric vehicles. For unlocking such capabilities, it is essential to understand the aggregated flexibility that can be harvested from the large population of new technologies located in distribution grids. Distribution grids, therefore, could provide aggregated flexibility at the transmission level. To date, most computational methods for estimating the aggregated flexibility at the interface between distribution grids and transmission grids have the drawback of requiring significant computational time, which hinders their applicability. This paper presents a new algorithm, coined as QuickFlex} for constructing the flexibility domain of distribution grids. Contrary to previous methods, a priory flexibility domain accuracy can be selected. Our method requires few iterations for constructing the flexibility region. The number of iterations needed is mainly independent of the distribution grid's input size and flexible elements. Numerical experiments are performed in four grids ranging from 5 nodes to 123 nodes. It is shown that QuickFlex outperforms existing proposals in the literature in both speed and accuracy.

Parkinson's disease (PD) is a frequent progressive neurodegenerative disorder. Impaired mitochondrial function is a major feature of sporadic PD. Some susceptibility or causative genes detected in PD are strongly associated with mitochondrial dysfunction including PGC1α, TFAM and GSK3β. microRNAs (miRNAs) are non‐coding RNAs whose altered levels are proven in disparate PD models and human brains. Therefore, the aim of this study was to detect modulations of miRs upstream of PGC1α, TFAM and GSK3β in association with PD onset and progress. In this study, a total of 33 PD subjects and 25 healthy volunteers were recruited. Candidate miRNA (miR‐376a) was selected through target prediction tools and literature survey. Chronic and acute in vitro PD models were created by MPP+‐intoxicated SHSY5Y cells. The levels of miR‐376a and aforementioned genes were assessed by RT‐qPCR. The expression of target genes was decreased in chronic model while there were dramatically up‐regulated levels of those genes in acute model of PD. miR‐376a was strongly altered in both acute and chronic PD models as well as PBMCs of PD patients. Our results also showed overexpression of PGC1α, and TFAM in PBMCs is inversely correlated with down‐regulation of miR‐376a, suggesting that miR‐376a possibly has an impact on PD pathogenesis through regulation of these genes which are involved in mitochondrial function. miR‐376a expression in PD‐derived PBMCs was also correlated with disease severity and may serve as a potential biomarker for PD diagnosis. This is the first study showing altered levels of miR‐376a in PD models and PBMCs, suggesting the probable role of this miRNA in PD pathogenesis. The present study also proposed TFAM and PGC1α as target genes of miR‐376a for the first time, through which it possibly can exert its impact on PD pathogenesis.

In this paper, a mixed integer linear formulation for problems considering time-of-use-type constraints for uninterruptible services is presented. Our work is motivated by demand response problems in power systems, in which certain devices must remain online once they are switched on, along with a certain number of periods. Classically, this kind of constraints are modeled as a summation over a rolling time window. This makes it difficult to consider this time-of-use parameter as uncertain. We propose an alternative formulation in which the time of use is on the right-hand side of a constraint instead on the limit of a summation. This allows applying existing stochastic optimization methodologies easily. An illustrative model for the optimal allocation of an uninterruptible load for the demand response problem supports the proposed formulation.

The ability of four models to provide short-term (up to 6 h ahead) GHI and DNI forecasts in the Iberian Peninsula is assessed based on two years of data collected at four stations. The models follow (mostly) independent approaches: one pure statistical model (Smart Persistence), one model based on CMV derived from satellite images (Satellite), one NWP model (WRF-Solar) and a hybrid satellite-NWP model (CIADCast). Overall, results show Smart Persistence to be the best at the first lead steps, advective models (Satellite and CIADCast) at intermediate ones and the WRF-Solar at the end of the forecasting period. The break-even point between the advective models and WRF-Solar varies between 1 and 3 h for GHI and 3 and 5 h for DNI. Nevertheless, a detailed analysis shows enormous differences between models performance related to 1) the local geographic and topographic conditions of the evaluation stations; 2) the evaluated variable (GHI vs. DNI); and 3) the sky and synoptic weather conditions over the study area. Depending on the station and lead time, rRMSE values range from 25% to 70% for GHI and from 35% to 100% for DNI. For the same stations and leading time, rRMSE values for DNI are between 50% and 100% higher than the corresponding GHI counterparts. Depending on the synoptic pattern, rRMSE values are about 10/20% for GHI/DNI (3 h lead time, during high pressure conditions) to about 80/180% for GHI/DNI (during low pressure conditions). All models show a poor performance at a coastal station, attributed to a lack of ability to forecast clouds associated with sea-land breezes. To conclude, no single model proves to be the best performing model and, therefore, results show that the four models are, somehow, complementary. The advantages attained by this complementarity are further explored in a companion paper (Part II). The authors are supported by the Spanish Ministry of Economy and Competitiveness, project ENE2014-56126-C2-1-R and ENE2014-56126-C2-2-R (http://prosol.uc3m.es). The team from the University of Jaen is also supported by FEDER funds and by the Junta de Andalucía (Research group TEP-220). The authors thank all the provided support. The authors are in debt with the National Centers for Environmental Prediction (NCEP), EUMETSAT, Faculdade de Ciencias da Universidade de Lisboa, Grupo de Energía Solar of the Universidad Politécnica de Madrid and Abengoa Solar for providing the data used in this work.

In this article we explore the blending of the four models (Satellite, WRF-Solar, Smart Persistence and CIADCast) studied in Part 1 by means of Support Vector Machines with the aim of improving GHI and DNI forecasts. Two blending approaches that use the four models as predictors have been studied: the horizon approach constructs a different blending model for each forecast horizon, while the general approach trains a single model valid for all horizons. The influence on the blending models of adding information about weather types is also studied. The approaches have been evaluated in the same four Iberian Peninsula stations of Part 1. Blending approaches have been extended to a regional context with the goal of obtaining improved regional forecasts. In general, results show that blending greatly outperforms the individual predictors, with no large differences between the blending approaches themselves. Horizon approaches were more suitable to minimize rRMSE and general approaches work better for rMAE. The relative improvement in rRMSE obtained by model blending was up to 17% for GHI (16% for DNI), and up to 15% for rMAE. Similar improvements were observed for the regional forecast. An analysis of performance depending on the horizon shows that while the advantage of blending for GHI remains more or less constant along horizons, it tends to increase with horizon for DNI, with the largest improvements occurring at 6 h. The knowledge of weather conditions helped to slightly improve further the forecasts (up to 3%), but only at some locations and for rRMSE. The authors are supported by the Spanish Ministry of Economy and Competitiveness, projects ENE2014-56126-C2-1-R and ENE2014-56126-C2-2-R (http://prosol.uc3m.es). The University of Jan team are also supported by FEDER funds and by the Junta de Andalucia (Research group TEP-220).The authors are in debt with the National Centers for Environmental Prediction (NCEP), EUMETSAT, Faculdade de Ciencias da Universidade de Lisboa, Grupo de Energa Solar of the Universidad Politcnica de Madrid and Abengoa Solar for providing the data used in this work.

Introduction: ultrasound is a non-invasive, widely distributed and low-cost technique that constitutes the study of choice of scrotal disease. Objective: to clinically and ultrasonographically characterize patients with scrotal lesions treated at the “Abel Santamaría Cuadrado” hospital. Method: an observational, descriptive and cross-sectional study was carried out in patients with scrotal lesions treated at the “Abel Santamaría Cuadrado” General Teaching Hospital belonging to the province of Pinar del Río in the period between 2016 and 2018. The universe consisted of 62 patients. Theoretical, empirical and statistical methods were used for data collection and analysis. Results: the age group with the highest incidence was 30 to 39 years old (35,5 %). Pain was found in 61,3 % of patients, lesions that affected the left scrotum predominated (47,0 %). The presence of fluid in the scrotum was the most frequent ultrasonographic finding (48,4 %) and the disease with the highest incidence was varicocele with 43,5 %. Conclusions: scrotal ultrasound is a key piece in the evaluation of patients with symptoms at this level, both for ease of access and repetition, as well as for the great information it provides in most cases, which allows immediately, guide the process and attitude to follow in the patient.

This work analyzes the impacts on the power system expansion planning of implementing CO2 and local pollutant emission taxes under five different policy-relevant scenarios. To do this, we have formulated and implemented an optimization model based on a mixed-integer linear program, which determines the optimal expansion plan considering the installation of both large-scale power plants and renewable-based distributed generation. An important characteristic of the proposed model is that it includes a detailed formulation of the power system. Moreover, differently than existing literature, special attention is given to the analysis of the spatial-temporal distributive effects of pollutant taxes, considering both global and local pollutant emissions. The method is applied to the main Chilean power system. Our results indicate that global and local pollutant taxes significantly impact both planning and operational decisions in the power system. In particular, pollutant taxes may have significant spatial distributive effects, as shown in the analysis of 13 regions of Chile, leading to damages in some specific regions while relatively benefiting others. Our results also show that the availability of renewable energy capacity may improve the effectiveness of pollutant taxes. Particularly, adding 1.5 GW of hydro capacity to the Chilean system allows avoiding around 32 GWh of fossil fuel generation per year, saving more than 1.5 billion US$ in the 10-year horizon considered. The proposed method and qualitative results are sufficiently generic to apply to any other jurisdiction.

This paper addresses the problem of building an affordable easy-to-setup synchronized multi-view camera system, which is in demand for many Computer Vision and Robotics applications in high-dynamic environments. In our work, we propose a solution for this problem -- a publicly-available Android application for synchronized video recording on multiple smartphones with sub-millisecond accuracy. We present a generalized mathematical model of timestamping for Android smartphones and prove its applicability on 47 different physical devices. Also, we estimate the time drift parameter for those smartphones, which is less than 1.2 msec per minute for most of the considered devices, that makes smartphones' camera system a worthy analog for professional multi-view systems. Finally, we demonstrate Android-app performance on the camera system built from Android smartphones quantitatively on setup with lights and qualitatively -- on panorama stitching task., Accepted to conference IEEE Sensors'2021 as Lecture presentation

Given the rise of electric vehicle (EV) adoption, supported by government policies and dropping technology prices, new challenges arise in the modeling and operation of electric transportation. In this paper, we present a model for solving the EV routing problem while accounting for real-life stochastic demand behavior. We present a mathematical formulation that minimizes travel time and energy costs of an EV fleet. The EV is represented by a battery energy consumption model. To adapt our formulation to real-life scenarios, customer pick-ups and drop-offs were modeled as stochastic parameters. A chance-constrained optimization model is proposed for addressing pick-ups and drop-offs uncertainties. Computational validation of the model is provided based on representative transportation scenarios. Results obtained showed a quick convergence of our model with verifiable solutions. Finally, the impact of electric vehicles charging is validated in Downtown Manhattan, New York by assessing the effect on the distribution grid.

The integration of energy systems such as electricity and gas grids and power and thermal grids can bring significant benefits in terms of system security, reliability, and reduced emissions. Another alternative coupling of sectors with large potential benefits is the power and transportation networks. This is primarily due to the increasing use of electric vehicles (EV) and their demand on the power grid. Besides, the production and operating costs of EVs and battery technologies are steadily decreasing, while tax credits for EV purchase and usage are being offered to users in developed countries. The power grid is also undergoing major upgrades and changes with the aim of ensuring environmentally sustainable grids. These factors influence our work. We present a new operating model for an integrated EV-grid system that incorporates a set of aggregators (owning a fleet of EVs) with partial access to the distribution grid. Then, the Cooperative Game Theory is used to model the behavior of the system. The Core is used to describe the stability of the interaction between these aggregators, and the Shapley value is used to assign costs to them. The results obtained show the benefit of cooperation, which could lead to an overall reduction in energy consumption, reduced operating costs for electric vehicles and the distribution grid, and, in some cases, the additional monetary budget available to reinforce the transmission and grid infrastructures.

Our immune system constantly interacts with our internal environment, protects us from our external environment and provides the inherent knowledge to sense the difference between friend and foe with important implications in human health and disease (Pozo, 2008). For these reasons, it is important to identify functional alteration of key immune responses as the number of silver nano-enabled products grows while the current data strongly suggest that other related nanomaterials, such as polymer nanoparticles, fullerenes, dendrimers and gold nanoparticles, interact with the immune system.

RationaleWe recently demonstrated that the ‘Metabesity’ factor HMG20A regulates islet beta-cell functional maturity and adaptation to physiological stress such as pregnancy and pre-diabetes. HMG20A also dictates central nervous system (CNS) development via inhibition of the LSD1/CoREST complex but its expression pattern and function in adult brain remains unknown. Herein we sought to determine whether HMG20A is expressed in the adult CNS, specifically in hypothalamic astrocytes that are key in glucose homeostasis and whether similar to islets, HMG20A potentiates astrocyte function in response to environmental cues.MethodsHMG20A expression profile was assessed by quantitative PCR (RT-PCR) and/or immunofluorescence in: 1) the hypothalamus of mice exposed or not to a high-fat diet, 2) human blood leukocytes and adipose tissue obtained from healthy or diabetic individuals 3) primary mouse hypothalamic astrocytes exposed to either high glucose or palmitate. To investigate the function and regulatory mechanism of HMG20A, RNA-seq and cell metabolic parameters were performed on astrocytes treated or not with a siHMG20A. The regulatory function of HMG20A on astrogliosis was also assessed pharmacologically using ORY1001. Astrocyte-mediated neuronal survival was evaluated using conditioned media from siHMG20A-treated astrocytes.ResultsWe show that Hmg20a is predominantly expressed in hypothalamic astrocytes, the main nutrient-sensing cell type of the brain. Hmg20A expression was upregulated in diet-induced obesity and glucose intolerant mice, correlating with increased transcript levels of Gfap and Il1b indicative of inflammation and astrogliosis. Expression levels were also increased in adipose tissue of obese non-diabetic individuals as compared to obese diabetic patients. HMG20A silencing in astrocytes resulted in repression of inflammatory, cholesterol biogenesis and epithelial-to-mesenchymal transition pathways with a concomitant increase in apoptosis and reduced mitochondrial bioenergetics. Motoneuron viability was also hindered in HMG20A-depleted astrocyte-derived conditioned media. Astrogliosis was induced using ORY1001, a pharmacological inhibitor of the LSD1/CoREST complex, mimicking the effect of HMG20A.ConclusionHMG20A coordinates the astrocyte polarization state. Under physiological pressure such as obesity and insulin resistance that induces low grade inflammation, HMG20A expression is increased to induce astrogliosis in an attempt to preserve the neuronal network and glucose homeostasis. Nonetheless, a chronic metabesity state or functional mutations will result in lower levels of HMG20A, failure to promote astrogliosis and increase susceptibility of neurons to stress-mediated apoptosis. Such effects could be therapeutically reversed by ORY1001-induced astrogliosis.

Funder: EMBO, Funder: Italian Society of Biochemistry and Molecular Biology, Funder: Cambridge Centre for Misfolding Diseases, UK, Increasing evidence indicates that peripheral immune cells play a prominent role in neurodegeneration connected to protein misfolding, which are associated with formation of aberrant aggregates, including soluble protein misfolded oligomers. The precise links, however, between the physicochemical features of diverse oligomers and their effects on the immune system, particularly on adaptive immunity, remain currently unexplored, due partly to the transient and heterogeneous nature of the oligomers themselves. To overcome these limitations, we took advantage of two stable and well-characterized types of model oligomers (A and B), formed by HypF-N bacterial protein, type B oligomers displaying lower solvent-exposed hydrophobicity. Exposure to oligomers of human peripheral blood mononuclear cells (PBMCs) revealed differential effects, with type B, but not type A, oligomers leading to a reduction in CD4+ cells. Type A oligomers promoted enhanced differentiation towards CD4+ CD25High FoxP3+ Tregs and displayed a higher suppressive effect on lymphocyte proliferation than Tregs treated with oligomers B or untreated cells. Moreover, our results reveal Th1 and Th17 lymphocyte differentiation mediated by type A oligomers and a differential balance of TGF-β, IL-6, IL-23, IFN-γ and IL-10 mediators. These results indicate that type B oligomers recapitulate some of the biological responses associated with Parkinson's disease in peripheral immunocompetent cells, while type A oligomers resemble responses associated with Alzheimer's disease. We anticipate that further studies characterizing the differential effects of protein misfolded oligomers on the peripheral immune system may lead to the development of blood-based diagnostics, which could report on the type and properties of oligomers present in patients.

Power systems incrementally and continuously upgrade their components, such as transmission lines, reactive capacitors, or generating units. Decision-making tools often support the selection of the best set of components to upgrade. Optimization models are often used to support decision making at a given point in time. After certain time intervals, re-optimization is performed to find new components to add. In this paper, we propose a decision-making framework for incrementally updating power system components. This is an alternative approach to the classical sequential re-optimization decision making for an investment problem with modeled budget constraints. Our approach provides a priority list as a solution with a list of new components to upgrade. We show that i) our framework is consistent with the evolution of power system upgrades, and ii) in particular circumstances, both frameworks provide the same solution if the problem satisfies submodularity property. We have selected the problem of phasor measurement unit localization and compared the solution with the classical sequential re-optimization framework. For this particular problem, we show that the two approaches provide close results, while only our proposed algorithm is applicable in practice. The cases of 14 and 118 IEEE buses are used to illustrate the proposed methodology.

The enormous technological potential accumulated over the past two decades would make it possible to change the operating principles of power systems entirely. The consequent technological evolution is not only affecting the structure of the electricity markets, but also the interactions between Transmission System Operators (TSOs) and Distribution System Operators (DSOs). New practical solutions are needed to improve the coordination between the grid operators at the national, TSOs, and local level, DSOs. In this paper, we define the flexibility range of coordination between TSOs and DSOs. By doing so, we propose an algorithm based on epsilon-constrained methods by means of mathematical programming and power systems principles. We evaluate and compare different classical optimal power flow formulations (AC-OPF, DistFlow, DistFlow-SOCP, and LinDistFlow) for building the flexible TSO-DSO flexible domain. The presented approaches in this paper are analyzed in an IEEE 33-bus test radial distribution system. We show that for this particular problem, the DistFlow-SOCP has the worst accuracy, despite the popularity among the academic community of convex relaxation approaches., Presented at the conference MEDPOWER 2020

There is a growing interest in the integration of energy infrastructures to increase systems' flexibility and reduce operational costs. The most studied case is the synergy between electric and heating networks. Even though integrated heat and power markets can be described by a convex optimization problem, prices derived from dual values do not guarantee cost recovery. In this work, a two-step approach is presented for the calculation of the optimal energy dispatch and prices. The proposed methodology guarantees cost-recovery for each of the energy vectors and revenue-adequacy for the integrated market., Presented at the conference MEDPOWER 2020

Power transmission networks physically connect the power generators to the electric consumers. Such systems extend over hundreds of kilometers. There are many components in the transmission infrastructure that require a proper inspection to guarantee flawless performance and reliable delivery, which, if done manually, can be very costly and time consuming. One essential component is the insulator. Its failure can cause an interruption of the entire transmission line or a widespread power failure. Automated fault detection could significantly decrease inspection time and related costs. Recently, several works have been proposed based on convolutional neural networks, which address the issue mentioned above. However, existing studies focus on a specific type of insulator faults. Thus, in this study, we introduce a two-stage model that segments insulators from their background to then classify their states based on four different categories, namely: healthy, broken, burned/corroded and missing cap. The test results show that the proposed approach can realize the effective segmentation of insulators and achieve high accuracy in detecting several types of faults.

It is well established that systemic glucose homeostasis is imparted by an intricate cross-talk among several organs including the central nervous system (CNS) and pancreatic islets. In analogy to islet beta cells, hypothalamic astrocytes are glucose sensors that facilitate the CNS response to changing metabolic environment. Therefore, astrocytes and beta cells may share a common genetic signature implicated in coupling glucose metabolism to cellular output. We recently demonstrated that the diabesity factor HMG20A is essential for beta-cell functional maturity and adaptation to physiological stress (obesity, type 2 diabetes and pregnancy). As this chromatin remodelling factor also dictates CNS development, we sought to determine whether HMG20A is expressed in astrocytes and whether it potentiates astrocyte function in response to environmental cues. We found that HMG20A is expressed in hypothalamic astrocytes and is upregulated in diet-induced obesity and glucose-intolerant mice, correlating with increased transcript levels of GFAP and IL-1β, indicative of reactive astrocytes (astrogliosis). High glucose, but not lipids, transiently decreased HMG20A expression in isolated mouse primary astrocytes. HMG20A silencing in astrocytes resulted in repression of pro-inflammatory as well as lipid and glucose metabolism genes. These pathways are up-regulated in activated astrocytes (astrogliosis) to resolve stress conditions. As such astrocyte depleted of HMG20A were more susceptible to cell death. Furthermore, motor neuron viability in astrocyte conditioned media was significantly hindered after HMG20A depletion in astrocytes. We posit that HMG20A is involved in the astrocyte polarization state. Under physiological pressure such as obesity, HMG20A expression may be transiently decreased by high glucose but long-term it is increased to induce astrogliosis in an attempt to preserve the neuronal network and glucose homeostasis. Disclosure J.M. Mellado-Gil: None. E. Fuente-Martin: None. P.I. Lorenzo: None. N. Cobo-Vuilleumier: None. J.A. Guerrero-Martínez: None. S.Y. Romero-Zerbo: None. J.M. Franco: None. J. Perez-Cabello: None. M. Alvarez-Dolado: None. D. Pozo: None. F.J. Bermudez-Silva: None. J.C. Reyes: None. B.R. Gauthier: None. Funding ISCIII (PI13/00593 to B.R.G.), (PI13/00309 to F.J.B-S.); Ministerio de Economia y Competidividad/Fondos FEDER (PI10/00871, PI13/00593, BFU2017-83588-P to B.R.G.); BFU2017-85420-R (to J.C.R.); (JCI-2012-12491, IJCI-2015-26238 to E.F-M.); Consejería de Salud Junta de Andalucía (PI-0006-2016 to E.F-M.)

The massive development of energy storage systems (ESSs) may significantly help in the supply–demand balance task, especially under the existence of uncertain and intermittent sources of energy, such as solar and wind power. Using ESSs as complements of renewable generation has technical and economic consequences in both the short-term operation and the long-term expansion planning of the grid. The authors propose a transmission expansion planning model that incorporates decisions about the expansion of generic ESS units in order to study the interaction between the penetration and location of ESSs and the transmission investment decisions. The problem is formulated as a mixed-integer linear programming model and considers different demand blocks and their correlation with renewable generation, to account for the distinct features of the system over time. The authors' results show that ESSs are not only substitutes of transmission assets, but they may also be complemented with transmission assets depending on the power system characteristics. They use a 27-bus representation of the main Chilean network to illustrate the model proposed and to highlight some interesting results about the potential complementarity of ESSs and transmission expansion.