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The Resilient Dairy Genome Project (RDGP) is an international large-scale applied research project that aims to generate genomic tools to breed more resilient dairy cows. In this context, improving feed efficiency and reducing greenhouse gases from dairy is a high priority. The inclusion of traits related to feed efficiency (e.g., dry matter intake [DMI]) or greenhouse gases (e.g., methane emissions [CH4]) relies on available genotypes as well as high quality phenotypes. Currently, 7 countries (i.e., Australia, Canada, Denmark, Germany, Spain, Switzerland, and United States) contribute with genotypes and phenotypes including DMI and CH4. However, combining data are challenging due to differences in recording protocols, measurement technology, genotyping, and animal management across sources. In this study, we provide an overview of how the RDGP partners address these issues to advance international collaboration to generate genomic tools for resilient dairy. Specifically, we describe the current state of the RDGP database, data collection protocols in each country, and the strategies used for managing the shared data. As of February 2022, the database contains 1,289,593 DMI records from 12,687 cows and 17,403 CH4 records from 3,093 cows and continues to grow as countries upload new data over the coming years. No strong genomic differentiation between the populations was identified in this study, which may be beneficial for eventual across-country genomic predictions. Moreover, our results reinforce the need to account for the heterogeneity in the DMI and CH4 phenotypes in genomic analysis.

This work focuses on the study of the trace elements allocation in smectites throughout the statistical analysis of geochemical data corresponding to a very wide group of pure or almost bentonite samples. Data of trace elements contents corresponding to both analysed samples and bibliographic references were treated with different statistical analysis such as Partial Least Squares Discriminant Analysis (PLS-DA), Wilcoxon-Mann-Whitney rank sum test (WMW test). A clear relationship between trace elements and smectite crystallochemistry was evidenced from the PLS-DA results because the contents in certain the trace element separated the dioctahedral smectites from the trioctahedral smectites. In addition, the WMW test indicated that dioctahedral smectites preferentially accumulate Er, Eu, Gd, Sm, Tb, Nd, Ga, Dy, Ho, Tm, Be, Y, Yb, La, Pb, Ce, Lu, Pr, Hf, Bi, Zr, Zn, Nb, In, Th, Ta, Sn, and Ge, while trioctahedral smectites preferentially accumulate As. Complementary analysis by X-Ray Micro-Fluorescence and a study after homoionization were done to help to discuss these results. According to these results, the presence of these elements as interlayer cations or their adsorption at layer edges has been excluded. In addition, external factors such as impurities and the geological origin of bentonites have been discussed and ruled out as influencing factors that could invalidate the obtained results. An evaluation of atomic parameters for trace elements indicates that As, with an effective cationic radius of 33.5 pm for coordination number CN = 4, can be allocated in tetrahedral positions, whereas the remaining elements can occupy octahedral positions., Junta de Castilla y León, Ministerio de Ciencia e Innovación, Depto. de Mineralogía y Petrología, Fac. de Ciencias Geológicas, TRUE, pub

The prevalence of obesity is increasingly widespread, resembling a global epidemic. Lifestyle changes, such as consumption of high-energy-dense diets and physical inactivity, are major contributors to obesity. Common features of this metabolic pathology involve an imbalance in lipid and glucose homeostasis including dyslipidemia, insulin resistance and adipose tissue dysfunction. Moreover, the importance of the gut microbiota in the development and susceptibility to obesity has recently been highlighted. In recent years, new strategies based on the use of functional foods, in particular bioactive peptides, have been proposed to counteract obesity outcomes. In this context, the present study examines the effects of a lupin protein hydrolysate (LPH) on obesity, dyslipidemia and gut dysbiosis in mice fed a high-fat diet (HFD). After 12 weeks of LPH treatment, mice gained less weight and showed decreased adipose dysfunction compared to the HFD-fed group. HFD-induced dyslipidemia (increased triglycerides, cholesterol and LDL concentration) and insulin resistance were both counteracted by LPH consumption. Discriminant analysis differentially distributed LPH-treated mice compared to non-treated mice. HFD reduced gut ecological parameters, promoted the blooming of deleterious taxa and reduced the abundance of commensal members. Some of these changes were corrected in the LPH group. Finally, correlation analysis suggested that changes in this microbial population could be responsible for the improvement in obesity outcomes. In conclusion, this is the first study to show the effect of LPH on improving weight gain, adiposopathy and gut dysbiosis in the context of diet-induced obesity, pointing to the therapeutic potential of bioactive peptides in metabolic diseases.

CO2 emissions play a crucial role in international politics. Countries enter into agreements to reduce the amount of pollution emitted into the atmosphere. Energy generation is one of the main contributors to pollution and is generally considered the main cause of climate change. Despite the interest in reducing CO2 emissions, few studies have focused on investigating energy pricing technologies. This article analyzes the technologies used to meet the demand for electricity from 2016 to 2021. The analysis is based on data provided by the Spanish Electricity System regulator, using statistical and clustering techniques. The objective is to establish the relationship between the level of pollution of electricity generation technologies and the hourly price and demand. Overall, the results suggest that there are two distinct periods with respect to the technologies used in the studied years, with a trend toward the use of cleaner technologies and a decrease in power generation using fossil fuels. It is also surprising that in the years 2016 to 2018, the most polluting technologies offered the cheapest prices

This work delves into the increasing relevance of Large Language Models (LLMs) in the realm of sustainable policy-making, proposing an innovative hetero-intelligence framework that blends human and artificial intelligence (AI) for tackling modern sustainability challenges. The research methodology includes a hetero-intelligence performance test, which juxtaposes human intelligence with AI in the formulation and implementation of sustainable policies. After testing this hetero-intelligence methodology, seven steps are rigorously described so that it can be replicated in any sustainability planning related context. The results underscore the capabilities and limitations of LLMs, underscoring the critical role of human intelligence in enhancing the efficacy of hetero-intelligence systems. This work fulfils the need of a rigorous methodological framework based on empirical steps that can provide unbiased outcomes to be integrated into sustainable planning and decision-making processes.

Different Pt-TiO2-Nb2O5 systems were synthesized and studied in the photocatalytic reforming of glucose for the generation of H2. The physicochemical properties of the synthesized photocatalysts were analyzed using different characterization techniques from which it was found that fluoridation and sulphation have different effects on the oxides under study such as a protective effect on the crystalline phase in anatase, and greater response in the visible region of the electromagnetic spectrum. The addition of fluorine or sulfates favors the reduction of platinum species on the surface of the semiconductor oxides and a better homogeneity of size and distribution of the particles of this metal. Studies were carried out in the gas phase that allowed the monitoring and quantification of the hydrogen produced from aqueous glucose solutions and it was determined that Pt-F-Nb2O5 and Pt-F-TiO2 are the most efficient materials for the production of hydrogen from this substrate. Similarly, liquid phase studies were carried out with a real sample from a confectionery industry where it was determined that with the material Pt-F-Nb2O5 the highest transformation of glucose is obtained, without the formation of any other sugar or intermediate compound, indicating the preferential production of hydrogen during the photocatalytic reaction. The foregoing demonstrates the potential of the evaluated process in obtaining this gas from the recovery of polluting residues derived from the samples under study., Peer Reviewed, Objectius de Desenvolupament Sostenible::7 - Energia Assequible i No Contaminant, Objectius de Desenvolupament Sostenible::13 - Acció per al Clima, Objectius de Desenvolupament Sostenible::6 - Aigua Neta i Sanejament, Postprint (author's final draft)

Renewable energies, such as solar power, offer a clean and cost-effective energy source. However, their integration into national electricity grids poses challenges due to their dependence on climate and geography. While numerous studies have focused on solar energy time series, few have specifically addressed the critical task of forecasting solar energy production at the national level. Accurate national-level forecasting is crucial for optimizing energy management, informing policy development, and promoting environmental sustainability. This study aims to address the challenges associated with the significant variability in renewable energy production and its impact on grid stability by improving the accuracy of existing forecasting approaches.To achieve this goal, we evaluate the effectiveness of univariate and multivariate approaches for time series forecasting of national solar energy production data from ESIOS (the Spanish System Operator). Our primary focus is on leveraging external solar variables, such as solar irradiance data. To this end, we propose a methodology to integrate solar irradiance forecasts with historical data from solar power plants in Spain to improve the performance of multivariate models. Subsequently, we compare the performance of classical regression techniques and state-of-the-art deep learning algorithms, presenting univariate and multivariate models for three forecast horizons (1 h, 24 h, and 48 h). Finally, we assess the performance of our best univariate and multivariate models by comparing them with the official forecast of ESIOS. Our findings indicate that the best-performing models are deep-learning multivariate approaches, which benefit from incorporating solar irradiance forecasts, particularly for longer forecast horizons (24 h and 48 h), and avoid the detrimental effects of the Hughes Phenomenon, which seems to hamper non-deep-learning forecasters. The top-performing models, based on Convolutional Networks and Convolutiona

Solar energy is one of the most common and promising sources of renewable energy. In photovoltaic (PV) systems, operators can benefit from future solar irradiance predictions for efficient load balancing and grid stability. Therefore, short-term solar irradiance forecasting plays a crucial role in the transition to renewable energy. Modern PV grids collect large volumes of data that provide valuable information for forecasting models. Although the nature of these data presents an ideal setting for online learning methodologies, research to date has mainly focused on offline approaches. Hence, this work proposes a novel data streaming method for real-time solar irradiance forecasting on days with variable weather conditions and cloud coverage. Our method operates under an asynchronous dual-pipeline framework using deep learning models. For the experimental study, two datasets from a Canadian PV solar plant have been simulated as streams at different data frequencies. The experiments involve an exhaustive parameter grid search to evaluate four state-of-the-art deep learning architectures: multilayer percep tron (MLP), long-short term memory network (LSTM), convolutional network (CNN), and Transformer network. The obtained results demonstrate the suitability of deep learning models for this problem. In particular, MLP and CNN achieved the best accuracy, with a high capacity to adapt to the evolving data stream

Radiation-induced toxicities are common adverse events in lung cancer (LC) patients undergoing radiotherapy (RT). An accurate prediction of these adverse events might facilitate an informed and shared decision-making process between patient and radiation oncologist with a clearer view of life-balance implications in treatment choices. This work provides a benchmark of machine learning (ML) approaches to predict radiation-induced toxicities in LC patients built upon a real-world health dataset based on a generalizable methodology for their implementation and external validation.

There are multiple factors that influence the catalyst performance in the reaction of formic acid dehydrogenation: the nature of catalyst and/or support, the used solvent and reaction variables such as temperature, time, formic acid concentration, presence/absence of formates and pH of the solution. This work evaluates a series of important parameters like the influence of the pH by itself, the influence of the nature of used alkali agents and the effect of direct formate addition as reactive on hydrogen production via formic acid dehydrogenation over a commercially available catalyst. The catalytic performance appears to depend on the ionic radius of the cations of the used base which reflects consequently on the hydrogen selectivity. The best base to be used must have lower hydrated cationic radii and a starting pH around 4 to achieve important hydrogen selectivity for medium term formic acid conversion.

Object detection has been one of the most active topics in computer vision for the past years. Recent works have mainly focused on pushing the state-of-the-art in the general-purpose COCO benchmark. However, the use of such detection frameworks in specific applications such as autonomous driving is yet an area to be addressed. This study presents an enhanced 2D object detector based on Faster RCNN that is better suited for the context of autonomous vehicles. Two main aspects are improved: the anchor generation procedure and the performance drop in minority classes. The default uniform anchor configuration is not suitable in this scenario due to the perspective projection of the vehicle cameras. Therefore, we propose a perspective-aware methodology that divides the image into key regions via clustering and uses evolutionary algorithms to optimize the base anchors for each of them. Furthermore, we add a module that enhances the precision of the second-stage header network by including the spatial information of the candidate regions proposed in the first stage. We also explore different reweighting strategies to address the foreground-foreground class imbalance, showing that the use of a reduced version of focal loss can significantly improve the detection of difficult and underrepresented objects in two-stage detectors. Finally, we design an ensemble model to combine the strengths of the different learning strategies. Our proposal is evaluated with the Waymo Open Dataset, which is the most extensive and diverse up to date. The results demonstrate an average accuracy improvement of 6.13% mAP when using the best single model, and of 9.69% mAP with the ensemble. The proposed modifications over the Faster R-CNN do not increase computational cost and can easily be extended to optimize other anchor-based detection frameworks.

Generally, classification problems catalog instances according to their target variable with out considering the relation among the different labels. However, there are real problems in which the different values of the class are related to each other. Because of interest in this type of problem, several solutions have been proposed, such as cost-sensitive classi fiers. Ensembles have proven to be very effective for classification tasks; however, as far as we know, there are no proposals that use a genetic-based methodology as the meta heuristic to create the models. In this paper, we present OCEAn, an ordinal classification algorithm based on an ensemble approach, which makes a final prediction according to a weighted vote system. This weighted voting takes into account weights obtained by a genetic algorithm that tries to minimize the cost of classification. To test the performance of this approach, we compared our proposal with ordinal classification algorithms in the literature and demonstrated that, indeed, our approach improves on previous results

The threat posed by invasive non-native species worldwide requires a global approach to identify which introduced species are likely to pose an elevated risk of impact to native species and ecosystems. To inform policy, stakeholders and management decisions on global threats to aquatic ecosystems, 195 assessors representing 120 risk assessment areas across all six inhabited continents screened 819 non-native species from 15 groups of aquatic organisms (freshwater, brackish, marine plants and animals) using the Aquatic Species Invasiveness Screening Kit. This multi-lingual decision-support tool for the risk screening of aquatic organisms provides assessors with risk scores for a species under current and future climate change conditions that, following a statistically based calibration, permits the accurate classification of species into high-, medium- and low-risk categories under current and predicted climate conditions. The 1730 screenings undertaken encompassed wide geographical areas (regions, political entities, parts thereof, water bodies, river basins, lake drainage basins, and marine regions), which permitted thresholds to be identified for almost all aquatic organismal groups screened as well as for tropical, temperate and continental climate classes, and for tropical and temperate marine ecoregions. In total, 33 species were identified as posing a ‘very high risk’ of being or becoming invasive, and the scores of several of these species under current climate increased under future climate conditions, primarily due to their wide thermal tolerances. The risk thresholds determined for taxonomic groups and climate zones provide a basis against which area-specific or climate-based calibrated thresholds may be interpreted. In turn, the risk rankings help decision-makers identify which species require an immediate ‘rapid’ management action (e.g. eradication, control) to avoid or mitigate adverse impacts, which require a full risk assessment, and which are to be r

Biomass waste product was used to generate biochars as catalytic supports for selective hydrogen production from formic acid. The supports were obtained after pyrolysis in CO2 atmosphere of non-pretreated and chemically ZnCl2 activated raw materials (vine shoot and crystalline cellulose). The support series includes materials with different textural properties and surface chemistry. The support nature and especially textural properties firstly affects significantly Pd size and dispersion and its interaction with the support and secondly influence in a great extent the catalytic behavior of the final material. The presence of prevailing mesoporous character appeared to be the most important parameter influencing formic acid dehydrogenation and overall hydrogen production.

Breast cancer is the most frequent cancer in women and the second most frequent overall after lung cancer. Although the 5-year survival rate of breast cancer is relatively high, recurrence is also common which often involves metastasis with its consequent threat for patients. DNA methylation-derived databases have become an interesting primary source for supervised knowledge extraction regarding breast cancer. Unfortunately, the study of DNA methylation involves the processing of hundreds of thousands of features for every patient. DNA methylation is featured by High Dimension Low Sample Size which has shown well-known issues regarding feature selection and generation. Autoencoders (AEs) appear as a specific technique for conducting nonlinear feature fusion. Our main objective in this work is to design a procedure to summarize DNA methylation by taking advantage of AEs. Our proposal is able to generate new features from the values of CpG sites of patients with and without recurrence. Then, a limited set of relevant genes to characterize breast cancer recurrence is proposed by the application of survival analysis and a pondered ranking of genes according to the distribution of their CpG sites. To test our proposal we have selected a dataset from The Cancer Genome Atlas data portal and an AE with a single-hidden layer. The literature and enrichment analysis (based on genomic context and functional annota tion) conducted regarding the genes obtained with our experiment confirmed that all of these genes were related to breast cancer recurrence.

Crystalline orthorhombic Bi2WO6 powders were synthesized by a hydrothermal method from aqueous solutions of Bi(NO3)3 5H2O and Na2WO4 2H2O over a range of three selected pH values (2.0, 5.0 and 7.0), using NaOH as precipitating agent. The as-prepared catalysts were char- acterized by XRD, BET, FE-SEM, TEM, XPS and UV-vis spectroscopy. The effect of pH-synthesis on crystallinity, morphologies, surface area and optical absorption properties, were investigated. Although the pH has a marked influence on morphology, the nature of the precipitating agent (NaOH or TEA) also influences the morphology and surface structure composition, as it is observed in the present work. Three different probe molecules were used to evaluate the photo- catalytic properties under two illumination conditions (UV and Visible): Methyl Orange and Rho- damine B were chosen as dye substrates and Phenol as a transparent substrate. The photo-catalytic activities are strongly dependent not only on the pH used in the synthesis but also on the nature of the chosen substrate in assessing the photo-catalytic activities. Results were compared with those obtained when using TiO2(P25, Evonik) in the same experimental conditions. The photo- catalytic activity of one of the synthesised samples has been evaluated by exposing a mixture of Rhodamine B and Phenol in water, to different illumination conditions. Our results provide new evidences about the issue of whether dyes are suitable substrates to assess the activity of a photo-catalyst.

Artificial intelligence and all its supporting tools, e.g. machine and deep learning in computational intelligence-based systems, are rebuilding our society (economy, education, life-style, etc.) and promising a new era for the social welfare state. In this paper we summarize recent advances in data science and artificial intelligence within the interplay between natural and artificial computation. A review of recent works published in the latter field and the state the art are summarized in a comprehensive and self-contained way to provide a baseline framework for the international community in artificial intelligence. Moreover, this paper aims to provide a complete analysis and some relevant discussions of the current trends and insights within several theoretical and application fields covered in the essay, from theoretical models in artificial intelligence and machine learning to the most prospective applications in robotics, neuroscience, brain computer interfaces, medicine and society, in general

Breast cancer is the most frequent cancer in women and the second most frequent overall after lung cancer. Although the 5-year survival rate of breast cancer is relatively high, recurrence is also common which often involves metastasis with its consequent threat for patients. DNA methylation-derived databases have become an interesting primary source for supervised knowledge extraction regarding breast cancer. Unfortunately, the study of DNA methylation involves the processing of hundreds of thousands of features for every patient. DNA methylation is featured by High Dimension Low Sample Size which has shown well-known issues regarding feature selection and generation. Autoencoders (AEs) appear as a specific technique for conducting nonlinear feature fusion. Our main objective in this work is to design a procedure to summarize DNA methylation by taking advantage of AEs. Our proposal is able to generate new features from the values of CpG sites of patients with and without recurrence. Then, a limited set of relevant genes to characterize breast cancer recurrence is proposed by the application of survival analysis and a pondered ranking of genes according to the distribution of their CpG sites. To test our proposal we have selected a dataset from The Cancer Genome Atlas data portal and an AE with a single-hidden layer. The literature and enrichment analysis (based on genomic context and functional annotation) conducted regarding the genes obtained with our experiment confirmed that all of these genes were related to breast cancer recurrence.

In this study the size-activity dependence of palladium based catalysts in formic acid dehydrogenation reaction was investigated and evaluated. A wide range of particle sizes was considered and the catalyst series were prepared upon variation of some synthetic parameters, precursor and solvent nature in particular. Synthesis method variations affect significantly Pd particle size and results in diverse activity toward hydrogen production. An optimal size was observed and explained by the diverse proportion of low and high coordinated Pd states available for different samples within the series. The evaluation of particles much bigger than 6 nm changes importantly the fraction of high and low coordination atoms and allows the clear confirmation of the importance of the presence of low coordination atoms on the surface of catalyst.