External Water Subcooling To Improve The Performance Of A CO2 Heat Pump For Water Heating That Uses Greywater As Heat Source

The use of CO2 heat pumps for water heating combined with energy recovery from greywater is a promising technology that can help to improve the efficiency of both domestic hot water (DHW) generation and space heating. A key aspect to keep in mind during the design of any CO2 refrigeration or heat pump system is the fact that the optimal gas cooler pressure in a transcritical CO2 cycle is mainly dependent on the refrigerant temperature at the gas cooler outlet. In space heating applications as well as DHW generation, operating conditions require high temperatures of refrigerant at the gas cooler outlet. That can lead to very high optimal pressures, in some cases, even higher than the maximum pressure of the system. The use of a subcooler fed by the same greywater used in the evaporator can help to reduce the optimal pressure and improve the efficiency of the system. When the greywater passes first through the subcooler, the evaporation temperature can be increased while the optimal pressure is reduced. When the greywater passes first through the evaporator, the evaporation temperature remains constant, but the refrigerant temperature at the gas cooler outlet can be reduced to a lower value. So, the order in which the water flows through subcooler and evaporator can affect the system’s efficiency and the best control strategy will depend on the operating conditions. First, a numerical model is used to model an experimental facility and model results are compared to some preliminary experimental results. Finally, this contribution analyses the influence that the greywater conditions (temperature, mass flow rate and flow order), as well as the subcooler efficiency have in the system’s efficiency depending on the operating conditions (DHW generation or space heating) in order to stablish the control strategy that optimize the system’s performance

Heterogeneous Photocatalytic Degradation of Ibuprofen Over TiO2–Ag Supported on Activated Carbon from Waste Tire Rubber

In recent years it has been discovered that some common use medicines, such as ibuprofen and other nonsteroidal anti-inflammatory drugs, are found in water sources in concentrations that have the potential to affect aquatic organisms. On the other hand, waste used tires are a massive problem for the environment due to the leaching of toxic compounds to soils and water. Also, the exposition to environmental conditions can make them sources of vectors like mosquitoes. In this work, three activated carbon (AC) catalysts derived from waste tire rubber, titanium dioxide and silver were synthesized using the sol–gel method. Morphological characterizations such as SEM and TEM were performed in which, the agglomeration of titanium particles and silver crystals on the surface of the AC is evident. In the XRD analysis, the presence of elemental silver nanoparticles was detected. In the diffuse reflectance spectroscopy analysis, the decrease in the titanium band gap, as well as activity in the visible spectrum, was observed. The photocatalytic tests were performed at pH 3 and 7 in the presence of UV/Vis radiation. These tests show that there are differences between the catalyst in both, UV and visible regions. Adsorption is a major phenomenon for the removal of ibuprofen, followed by photolytic decomposition. In visible spectra, the catalysts show a good performance for the removal of ibuprofen

Periodogram Connectivity of EEG Signals for the Detection of Dyslexia

Electroencephalography (EEG) signals provide an important source of information of brain activity at different areas. This information can be used to diagnose brain disorders according to different activation patterns found in controls and patients. This acquisition technology can be also used to explore the neural basis of less evident learning disabilities such as Developmental Dyslexia (DD). DD is a specific difficulty in the acquisition of reading skills not related to mental age or inadequate schooling, whose prevalent is estimated between 5% and 12% of the population. In this paper we propose a method to extract discriminative features from EEG signals based on the relationship among the spectral density at each channel. This relationship is computed by means of different correlation measures, inferring connectivity-like markers that are eventually selected and classified by a linear support vector machine. The experiments performed shown AUC values up to 0.7, demonstrating the applicability of the proposed approach for objective DD diagnosis

APP-derived peptides reflect neurodegeneration in frontotemporal dementia

Altres ajuts: The Catalan frontotemporal initiative (CATFI) is funded by the Health Department of the Government of Catalonia (grant PERIS SLT002/16/00408 to Alberto Lleó and Raquel Sánchez-Valle). This work was also supported by research grants from the CIBERNED Program (Program 1, Alzheimer Disease to Alberto Lleó and SIGNAL study, file://www.signalstudy.es), partly funded by Fondo Europeo de Desarrollo Regional (FEDER), Unión Europea, "Una manera de hacer Europa." This work has also been supported by a "Marató TV3" grant (20141210 to Juan Fortea, 044412 to Rafael Blesa, 20143710 to Ricard Rojas-García and 20143810 to Raquel Sánchez-Valle) and Fundación BBVA (grant to A. Lleó) and a grant from the Fundació Bancaria La Caixa to Rafael Blesa. Ignacio Illán-Gala and Sergi Borrego-Écija are supported by the Rio Hortega grant from "Acción estratégica en Salud 2013-2016" and the European Social Fund. Ignacio Illán-Gala is supported by the Global Brain Health Institute (Atlantic Fellow for Equity in Brain Health). We acknowledge all the participants in this study and all the collaborators of the SPIN cohort. We also acknowledge Soraya Torres and Laia Muñoz for technical assistance. We thank EUROIMMUN for providing Aβ1-38 and Aβ1-40 ELISA assays for this study.Objective: We aimed to investigate the relationship between cerebrospinal fluid levels (CSF) of amyloid precursor protein (APP)-derived peptides related to the amyloidogenic pathway, cortical thickness, neuropsychological performance, and cortical gene expression profiles in frontotemporal lobar degeneration (FTLD)-related syndromes, Alzheimer's disease (AD), and healthy controls. Methods: We included 214 participants with CSF available recruited at two centers: 93 with FTLD-related syndromes, 57 patients with AD, and 64 healthy controls. CSF levels of amyloid β (Aβ)1-42, Aβ1-40, Aβ1-38, and soluble β fragment of APP (sAPPβ) were centrally analyzed. We compared CSF levels of APP-derived peptides between groups and, we studied the correlation between CSF biomarkers, cortical thickness, and domain-specific cognitive composites in each group. Then, we explored the relationship between cortical thickness, CSF levels of APP-derived peptides, and regional gene expression profile using a brain-wide regional gene expression data in combination with gene set enrichment analysis. Results: The CSF levels of Aβ1-40, Aβ1-38, and sAPPβ were lower in the FTLD-related syndromes group than in the AD and healthy controls group. CSF levels of all APP-derived peptides showed a positive correlation with cortical thickness and the executive cognitive composite in the FTLD-related syndromes group but not in the healthy control or AD groups. In the cortical regions where we observed a significant association between cortical thickness and CSF levels of APP-derived peptides, we found a reduced expression of genes related to synaptic function. Interpretation: APP-derived peptides in CSF may reflect FTLD-related neurodegeneration. This observation has important implications as Aβ1-42 levels are considered an indirect biomarker of cerebral amyloidosis

Comparison of machine learning and semi-quantification algorithms for (I123)FP-CIT classification: the beginning of the end for semi-quantification?

Background Semi-quantification methods are well established in the clinic for assisted reporting of (I123) Ioflupane images. Arguably, these are limited diagnostic tools. Recent research has demonstrated the potential for improved classification performance offered by machine learning algorithms. A direct comparison between methods is required to establish whether a move towards widespread clinical adoption of machine learning algorithms is justified. This study compared three machine learning algorithms with that of a range of semi-quantification methods, using the Parkinson’s Progression Markers Initiative (PPMI) research database and a locally derived clinical database for validation. Machine learning algorithms were based on support vector machine classifiers with three different sets of features: Voxel intensities Principal components of image voxel intensities Striatal binding radios from the putamen and caudate. Semi-quantification methods were based on striatal binding ratios (SBRs) from both putamina, with and without consideration of the caudates. Normal limits for the SBRs were defined through four different methods: Minimum of age-matched controls Mean minus 1/1.5/2 standard deviations from age-matched controls Linear regression of normal patient data against age (minus 1/1.5/2 standard errors) Selection of the optimum operating point on the receiver operator characteristic curve from normal and abnormal training data Each machine learning and semi-quantification technique was evaluated with stratified, nested 10-fold cross-validation, repeated 10 times. Results The mean accuracy of the semi-quantitative methods for classification of local data into Parkinsonian and non-Parkinsonian groups varied from 0.78 to 0.87, contrasting with 0.89 to 0.95 for classifying PPMI data into healthy controls and Parkinson’s disease groups. The machine learning algorithms gave mean accuracies between 0.88 to 0.92 and 0.95 to 0.97 for local and PPMI data respectively. Conclusions Classification performance was lower for the local database than the research database for both semi-quantitative and machine learning algorithms. However, for both databases, the machine learning methods generated equal or higher mean accuracies (with lower variance) than any of the semi-quantification approaches. The gain in performance from using machine learning algorithms as compared to semi-quantification was relatively small and may be insufficient, when considered in isolation, to offer significant advantages in the clinical context

Soil contamination in nearby natural areas mirrors that in urban greenspaces worldwide

Soil contamination is one of the main threats to ecosystem health and sustainability. Yet little is known about the extent to which soil contaminants differ between urban greenspaces and natural ecosystems. Here we show that urban greenspaces and adjacent natural areas (i.e., natural/semi-natural ecosystems) shared similar levels of multiple soil contaminants (metal(loid)s, pesticides, microplastics, and antibiotic resistance genes) across the globe. We reveal that human influence explained many forms of soil contamination worldwide. Socio-economic factors were integral to explaining the occurrence of soil contaminants worldwide. We further show that increased levels of multiple soil contaminants were linked with changes in microbial traits including genes associated with environmental stress resistance, nutrient cycling, and pathogenesis. Taken together, our work demonstrates that human-driven soil contamination in nearby natural areas mirrors that in urban greenspaces globally, and highlights that soil contaminants have the potential to cause dire consequences for ecosystem sustainability and human wellbeing

Computational approaches to explainable artificial intelligence: Advances in theory, applications and trends

Deep Learning (DL), a groundbreaking branch of Machine Learning (ML), has emerged as a driving force in both theoretical and applied Artificial Intelligence (AI). DL algorithms, rooted in complex and non-linear artificial neural systems, excel at extracting high-level features from data. DL has demonstrated human-level performance in real-world tasks, including clinical diagnostics, and has unlocked solutions to previously intractable problems in virtual agent design, robotics, genomics, neuroimaging, computer vision, and industrial automation. In this paper, the most relevant advances from the last few years in Artificial Intelligence (AI) and several applications to neuroscience, neuroimaging, computer vision, and robotics are presented, reviewed and discussed. In this way, we summarize the state-of-the-art in AI methods, models and applications within a collection of works presented at the 9th International Conference on the Interplay between Natural and Artificial Computation (IWINAC). The works presented in this paper are excellent examples of new scientific discoveries made in laboratories that have successfully transitioned to real-life applications.MCIU - Nvidia(UMA18-FEDERJA-084

Gaps in clinical research in frontotemporal dementia: A call for diversity and disparities–focused research

Frontotemporal dementia (FTD) is one of the leading causes of dementia before age 65 and often manifests as abnormal behavior (in behavioral variant FTD) or language impairment (in primary progressive aphasia). FTD's exact clinical presentation varies by culture, language, education, social norms, and other socioeconomic factors; current research and clinical practice, however, is mainly based on studies conducted in North America and Western Europe. Changes in diagnostic criteria and procedures as well as new or adapted cognitive tests are likely needed to take into consideration global diversity. This perspective paper by two professional interest areas of the Alzheimer's Association International Society to Advance Alzheimer's Research and Treatment examines how increasing global diversity impacts the clinical presentation, screening, assessment, and diagnosis of FTD and its treatment and care. It subsequently provides recommendations to address immediate needs to advance global FTD research and clinical practice

The global distribution and environmental drivers of the soil antibiotic resistome

Background: Little is known about the global distribution and environmental drivers of key microbial functional traits such as antibiotic resistance genes (ARGs). Soils are one of Earth’s largest reservoirs of ARGs, which are integral for soil microbial competition, and have potential implications for plant and human health. Yet, their diversity and global patterns remain poorly described. Here, we analyzed 285 ARGs in soils from 1012 sites across all continents and created the first global atlas with the distributions of topsoil ARGs. Results: We show that ARGs peaked in high latitude cold and boreal forests. Climatic seasonality and mobile genetic elements, associated with the transmission of antibiotic resistance, were also key drivers of their global distribution. Dominant ARGs were mainly related to multidrug resistance genes and efflux pump machineries. We further pinpointed the global hotspots of the diversity and proportions of soil ARGs. Conclusions: Together, our work provides the foundation for a better understanding of the ecology and global distribution of the environmental soil antibiotic resistome.This project received funding from the European Union’s Horizon 2020 research and innovation program under the Marie Skłodowska-Curie grant agreement 702057 (CLIMIFUN), a Large Research Grant from the British Ecological Society (agreement no. LRA17\1193; MUSGONET), and from the European Research Council (ERC grant agreement no. 647038, BIODESERT). M. D. B. was also supported by a Ramón y Cajal grant (RYC2018-025483-I). M.D-B. also acknowledges support from the Spanish Ministry of Science and Innovation for the I+D+i project PID2020-115813RA-I00 funded by MCIN/AEI/10.13039/501100011033. M.D-B. is also supported by a project of the Fondo Europeo de Desarrollo Regional (FEDER) and the Consejería de Transformación Económica, Industria, Conocimiento y Universidades of the Junta de Andalucía (FEDER Andalucía 2014-2020 Objetivo temático “01 - Refuerzo de la investigación, el desarrollo tecnológico y la innovación”) associated with the research project P20_00879 (ANDABIOMA). FTM acknowledges support from Generalitat Valenciana (CIDEGENT/2018/041). J. Z. H and H. W. H. are financially supported by Australian Research Council (DP210100332). We also thank the project CTM2015-64728-C2-2-R from the Ministry of Science of Spain. C. A. G. and N. E. acknowledge funding by the German Centre for Integrative Biodiversity Research (iDiv) Halle-Jena-Leipzig, funded by the German Research Foundation (FZT 118). TG was financially supported by Slovenian Research Agency (P4-0107, J4-3098 and J4-4547)