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The understanding of glioma disease has been evolving drastically with the dedicated research into the genetic and molecular profiling of glioma tumour tissue. Molecular biomarkers have gained progressive and substantial importance in providing diagnostic information, leading to groundbreaking changes in the tumour classification system and taxonomy standardised by the 2016 and 2021 editions of the World Health Organization (WHO) Classification of Tumours of the Central Nervous System’s guidelines. Some of the insights into glioma disease derived from extensive research on open-source multi-omics databases, such as the Cancer Genome Atlas (TCGA). However, given the substantial changes in glioma classification, these retrospective data may harbour outdated diagnostic annotations, suboptimal for further research. Here we propose two methods for updating the tumour classification of TCGA adult glioma samples in accordance with the 2016 and 2021 WHO Classification of Tumours of the Central Nervous System, through the integration of curated molecular profiling information provided by a study from TCGA Research Network. Overall, our methods determine the change in the patient-specific glioma diagnosis. The glioma reclassification of the publicly available datasets can support further bioinformatic and statistical analyses of updated glioma subtypes.

Air pollution is a global challenge, and especially urban areas are particularly affected by acute episodes. Traditional approaches used to mitigate air pollution primarily consider the technical aspects of the problem but not the role of citizen behaviour and day-to-day practices. ClairCity, a Horizon 2020 funded project, created an impact assessment framework considering the role of citizen behaviour to create future scenarios, aiming to improve urban environments and the wellbeing and health of its inhabitants. This framework was applied to six pilot cases: Bristol, Amsterdam, Ljubljana, Sosnowiec, Aveiro Region and Liguria Region, considering three-time horizons: 2025, 2035 and 2050. The scenarios approach includes the Business As Usual (BAU) scenario and a Final Unified Policy Scenarios (FUPS) established by citizens, decision-makers, local planners and stakeholders based on data collected through a citizen and stakeholder co-creation process. Therefore, this paper aims to present the ClairCity outcomes, analysing the quantified impacts of selected measures in terms of emissions, air quality, population exposure, and health. Each case study has established a particular set of measures with different levels of ambition, therefore different levels of success were achieved towards the control and mitigation of their specific air pollution problems. The transport sector was the most addressed by the measures showing substantial improvements for NO2, already with the BAU scenarios, and overall, even better results when applying the citizen-led FUPS scenarios. In some cases, due to a lack of ambition for the residential and commercial sector, the results were not sufficient to fulfil the WHO guidelines. Overall, it was found in all cities that the co-created scenarios would lead to environmental improvements in terms of air quality and citizens’ health compared to the baseline year of 2015. However, in some cases, the health impacts were lower than air quality due to the implementation of the measures not affecting the most densely populated areas. Benefits from the FUPS comparing to the BAU scenario were found to be highest in Amsterdam and Bristol, with further NO2 and PM10 emission reductions around 10%–16% by 2025 and 19%–28% by 2050, compared to BAU.

A current challenge in the environmental sciences field is to assess air quality at larger urban areas with high level of spatial resolution and, at the same time, with feasible computational resources time demand. This study provides a sensitivity analysis, focused on the implications of different grid resolutions on air quality results, followed by a performance assessment of the URBan AIR (URBAIR) model, a second-generation Gaussian model, as a tool for air quality management in urban areas. Estarreja area, a city located near an industrial complex, was used as case study, and the most critical air pollutants were investigated: particulate matter (PM10) and nitrogen dioxide (NO2). Three different grid resolutions were tested: 0.1 km, 0.2 km and 0.3 km resolutions. Comparative results revealed that all grids provide similar results regarding the spatial distribution of PM10 and NO2 concentrations, with evident differences in the magnitude of those concentrations and in the required computational time. The source apportionment analysis revealed the great contribution of industrial sources and road transport to NO2 and PM10 concentrations, respectively. The URBAIR model is a useful tool to support decision-makers since it considers the specific characteristics of each city, which make it particularly helpful to assess different origins of air pollution, and so, to select the most effective sectorial measures that should be applied to improve local air quality.

The extraction of novel information from omics data is a challenging task, in particular, since the number of features (e.g. genes) often far exceeds the number of samples. In such a setting, conventional parameter estimation leads to ill-posed optimization problems, and regularization may be required. In addition, outliers can largely impact classification accuracy. Here we introduce ROSIE, an ensemble classification approach, which combines three sparse and robust classification methods for outlier detection and feature selection and further performs a bootstrap-based validity check. Outliers of ROSIE are determined by the rank product test using outlier rankings of all three methods, and important features are selected as features commonly selected by all methods. We apply ROSIE to RNA-Seq data from The Cancer Genome Atlas (TCGA) to classify observations into Triple-Negative Breast Cancer (TNBC) and non-TNBC tissue samples. The pre-processed dataset consists of [Formula: see text] genes and more than [Formula: see text] samples. We demonstrate that ROSIE selects important features and outliers in a robust way. Identified outliers are concordant with the distribution of the commonly selected genes by the three methods, and results are in line with other independent studies. Furthermore, we discuss the association of some of the selected genes with the TNBC subtype in other investigations. In summary, ROSIE constitutes a robust and sparse procedure to identify outliers and important genes through binary classification. Our approach is ad hoc applicable to other datasets, fulfilling the overall goal of simultaneously identifying outliers and candidate disease biomarkers to the targeted in therapy research and personalized medicine frameworks.

Seagrass meadows provide important and valuable ecosystem services. They are affected by several natural and human-induced stressors, but a combination of natural recovery and management actions have recently inverted the worldwide reduction. The main objectives of this study were to provide science-based knowledge on ecology and restoration, framed on environmental-related policies. By coupling the general guidelines with practical experience, obtained from sequential in situ experiments carried out for several months in a show-case study area, this study provides guidelines useful for restoration practitioners. A decision-making approach is proposed to answer the following questions: 1) What is the best Zostera noltei transplanting method? 2) What is the best technique to reduce the bioturbation activity of Arenicola spp.?, 3) Do bioturbation reduction techniques affect the survival rate of Z. noltei transplants?, and finally, 4) What are the key steps to maximize the success of a Z. noltei transplant and increase the species’ resilience? Having a Portuguese coastal lagoon as show-case (Mira Channel, Ria de Aveiro), different transplant and restoration methodologies were tested (i.e. metal frames, nails, bamboo sticks, shoots inserted unanchored into the sediment, and intact units of sediment with seagrasses, named as SODs) to assure low environmental impact on donor meadows, high survival rate of transplanted shoots and the recovery of fragmented or lost meadows. Moreover, to potentially reverse a degraded Arenicola spp. colonized seagrass habitat, different types of natural membranes were tested. Results showed that the best transplanting method is the use of SODs as the self-facilitation process of Z. noltei is enhanced, while being the least invasive for the donor population. The use of a natural membrane can significantly decrease the bioturbation stress caused by Arenicola spp., with jute membrane being the best option, given its cost-handling-benefit trade-offs. Enhancing the success of seagrass restoration requires the implementation of effective measures by environmental restoration practitioners. We defined a three-step process to improve the resilience of Z. noltei. This stepwise approach consists on 1) Characterization of the donor population, 2) Identification of the constraints and implementation of measures to prevent them, and 3) Scale-up the restoration plan. The application of this stepwise approach in intertidal coastal and estuarine systems management will, therefore, facilitate the success of Z. noltei restoration plans.

PURPOSE: To evaluate Microperimetry (MP) and multifocal electroretinogram (mfERG) as whole-macula functional markers of treatment response in naive diabetic macular oedema (DMO) patients undergoing ranibizumab treatment. METHODS: An exploratory sub-analysis of a prospective study (NCT01947881-CHARTRES). Patients received three monthly ranibizumab injections (loading dose) followed by pro re nata (PRN) regimen during 1 year. At baseline, during and after treatment (Months 0, 3, 6 and 12), subjects were tested using BCVA, OCT, MP and mfERG. MP was performed in the central 12°, and retinal sensitivity was measured overall (mean sensitivity (MS)), and in three concentric rings (R1–R3). mfERG P1 amplitude and implicit time were measured over six concentric rings (R1–R6). RESULTS: Thirty-two eyes were included. MP mean and rings sensitivity were significantly lower in DMO (p

European cities have made significant progress over the last decades towards clean air. Despite this progress, several cities are still facing acute air pollution episodes, with various urban areas frequently exceeding air quality levels allowed by the European legal standards and WHO guidelines. In this paper, six European cities/ regions (Bristol, UK; Amsterdam, NL; Sosnowiec, PL; Ljubljana, SI; Aveiro, PT; Liguria, IT) are studied in terms of air quality, namely particulate matter, nitrogen dioxide and ozone. The concentrations trends from 2008 to 2017 in the different typology of monitoring stations are addressed, together with the knowledge of daily, weekly and seasonal pollution patterns to better understand the city specific profiles and to characterize pollutant dynamics and variations in multiple locations. Additionally, an analysis of the duration and severity of air pollution episodes is also discussed, followed by an analysis of the fulfillment of the legislated limit values. Each of our 6 case study locations face different air pollution problems, but all these case studies have made some progress in reducing ambient concentrations. In Bristol, there have been strong downward trends in many air pollutants, but the levels of NO2 remain persistently high and of concern. In recent years, decreasing concentration levels point to some success of Amsterdam air quality policies. PM10 exceedances are a seasonal pollution problem in Ljubljana, Sosnowiec and Aveiro region (even if with different levels of severity). While, exceedances of NO2 and O3 concentrations are still problematic in Liguria region. The main findings of this paper are particular relevant to define and compare future citizen-led strategies and policy initiatives that may be implemented to improve and fulfill the EU legislation and the WHO guidelines. published

Encouraged by the Clean Energy for all Europeans package, small-scale distributed electricity generation (both individual prosumership projects and local energy communities) is expected to increase in the coming years, impacting on the planning and operation of smart grids. This work aims to assess how the operation of a residential local energy community (LEC) is affected when it receives different signals from the power grid, encouraging or penalizing the injection of self-generated electricity surpluses. Optimization processes are integrated into a multiagent framework to model the LEC and agents' individual and collective objectives are exploited. The results show how individual electricity costs and collective self-consumption rates change across different incentive/penalty scenarios.

This study was supported by the COST Action CA18131 "Statistical and machine learning techniques in human microbiome studies." IM-I was supported by the "MS type I" program (CP16/00163) from the Instituto de Salud Carlos III and co-funded by Fondo Europeo de Desarrollo Regional-FEDER. MN was grateful for the additional support by the project "Information and Communication Technologies for a Single Digital Market in Science, Education and Security" of the Scientific Research Center, NIS-3317 and National roadmaps for research infrastructures (RIs) grant number NIS-3318. LL was supported by Academy of Finland (decision 295741). IE was supported by H2020-EU.4.b. project "Integration of knowledge and biobank resources in comprehensive translational approach for personalized prevention and treatment of metabolic disorders (INTEGROMED)" (grant agreement ID 857572). MD was supported by the Luxembourg National Research Fund (FNR) CORE grant (C18/BM/12585940). The human microbiome has emerged as a central research topic in human biology and biomedicine. Current microbiome studies generate high-throughput omics data across different body sites, populations, and life stages. Many of the challenges in microbiome research are similar to other high-throughput studies, the quantitative analyses need to address the heterogeneity of data, specific statistical properties, and the remarkable variation in microbiome composition across individuals and body sites. This has led to a broad spectrum of statistical and machine learning challenges that range from study design, data processing, and standardization to analysis, modeling, cross-study comparison, prediction, data science ecosystems, and reproducible reporting. Nevertheless, although many statistics and machine learning approaches and tools have been developed, new techniques are needed to deal with emerging applications and the vast heterogeneity of microbiome data. We review and discuss emerging applications of statistical and machine learning techniques in human microbiome studies and introduce the COST Action CA18131 "ML4Microbiome" that brings together microbiome researchers and machine learning experts to address current challenges such as standardization of analysis pipelines for reproducibility of data analysis results, benchmarking, improvement, or development of existing and new tools and ontologies. European Cooperation in Science and Technology (COST) CA18131 European Commission CP16/00163 project "Information and Communication Technologies for a Single Digital Market in Science, Education and Security" of the Scientific Research Center NIS-3317 National roadmaps for research infrastructures (RIs) NIS-3318 Academy of Finland 295741 H2020-EU.4.b. project "Integration of knowledge and biobank resources in comprehensive translational approach for personalized prevention and treatment of metabolic disorders (INTEGROMED)" 857572 Luxembourg National Research Fund (FNR) CORE grant C18/BM/12585940

Understanding to what extent the emergence of prosumers and prosumagers organized in energy communities can impact the organization and operation of power grids has been one of the major recent research avenues at the European level. In renewable-based communities aiming to reach some level of energy self-sufficiency, a key issue to be addressed is assessing how the presence of end-users playing different roles in the system (self-consuming, producing and trading, performing demand management, etc.) can influence the overall system performance. In this setting, this paper combines Distributed Artificial Intelligence and optimization approaches to assess how prosumagers and consumers pursuing different goals can influence the energy self-sufficiency of a local energy community. The residential demand is accurately modeled, and the agents’ preferences are considered in the modeling to represent a smart community. The results show that although energy community members may have conflicting individual goals, the overall system self-sufficiency can be maximized with economic benefits for all stakeholders, thus illustrating the advantages of energy communities.

Precision medicine has emerged to tailor clinical decisions based on patient genetic features in a personalized healthcare perspective. The ultimate goal is to drive disease diagnosis and treatment selection based on the patient molecular profiles, usually given by large volumes of data, which is intrinsically high-dimensional, heterogeneous, noisy, and incomplete. Along with the notable improvement of experimental technologies, statistical learning has accompanied the associated challenges by the significant development of novel methods and algorithms. In particular, network-based learning is providing promising results toward more personalized medicine. This short survey will describe three main interconnected trends identified to address these challenges and all with a firm root in network science: differential network analysis, network-based regularization, and causal discovery and inference. An overview of the main applications is provided, along with available software. Biomedical networks support more informed and interpretable statistical learning models from patients’ data, thus improving clinical decisions and supporting therapy optimization.

Local energy trading has the potential to foster the deployment of novel business models especially valuable in citizen-centered self-sufficiency-driven collective energy systems. In this setting, special attention should be paid to vulnerable consumers, who may be neglected in the energy transition due to their limited financial capacity to purchase energy products and services as well as the low energy literacy impairing sound consumption decisions. This paper presents a community-based electricity exchange model designed to prioritize the inclusion of vulnerable consumers. A multiagent framework embedded with metaheuristics is proposed to model a community of prosumagers and vulnerable consumers. Heterogeneity between agents is considered by exploiting different consumption behaviors regarding load utilization, thermal comfort requirements and sensitivity to cost. A supervisor entity is also included to manage and facilitate electricity procurement between the local energy community participants while interfacing with retailers. Several scenarios are exploited to examine the performance of the proposed model for different shares of sellers and buyers and the results are evaluated through performance indicators. Results reveal that local electricity trading, in addition to economic benefits for all participants, also contribute to enhance the community's self-sufficiency. Additionally, participants benefit differently from local electricity trading: while buying-oriented ones benefit from the larger presence of profit-driven members, selling-oriented participants achieve better results in scenarios with higher shares of buyers. These results unveil relevant clues for the design of frameworks enhancing the inclusion of vulnerable consumers in the energy transition.

The emergence of energy communities is expected to reshape the design of the electricity system at the same time as peer-to-peer (P2P) trading is transforming the traditional utilities business models. In this setting, this work proposes a multiagent framework endowed with optimization tools to model P2P electricity trading within an energy community. Emphasis is given to the inclusion of vulnerable consumers and the economic outcomes of P2P transactions for community members, while fairness concerns are considered in the distribution of the existing energy resources.

This study shows that optical coherence tomography leakage can identify and locate choroidal neovascularization–related abnormal fluid and its change after anti–vascular endothelial growth factor treatment in neovascular age-related macular degeneration eyes. Low optical reflectivity ratio correlates with functional outcome and may be a marker of early choroidal neovascularization reactivation. Simultaneous optical coherence tomography angiography examination provides a noninvasive analysis of fluid and choroidal neovascularization vascular morphology., Purpose: To test optical coherence tomography leakage in the identification and quantification of choroidal neovascularization–related fluid, its change after anti–vascular endothelial growth factor therapy in neovascular age-related macular degeneration eyes and its relation to functional outcome. Methods: Prospective analysis of a cohort of neovascular age-related macular degeneration cases treated with 2.0-mg intravitreal aflibercept. Eyes included were analyzed before, 1-week, and 1-month after one injection. Best-corrected visual acuity was assessed using Early Treatment Diabetic Retinopathy Study method. Optical coherence tomography leakage maps depicting low optical reflectivity (LOR) sites were acquired with OCT Cirrus AngioPlex (Zeiss, Dublin, CA). The LOR area ratio was correlated to retinal thickness and best-corrected visual acuity. Optical coherence tomography angiography was simultaneously performed. Results: Twenty-two eyes of 18 patients with neovascular age-related macular degeneration were included. The LOR ratio of the full retina scan and retinal pigment epithelium–Bruch layer decreased from baseline to Month 1 (P < 0.05). Changes in retinal thickness and LOR ratio were positively correlated (P < 0.05). Best-corrected visual acuity change correlated with the outer segment layer LOR change (rho = −0.53, P = 0.014), and LOR was inferior in better responders (P = 0.021). Optical coherence tomography leakage identified eyes with recurrent fluid in the external layers. Conclusion: Optical coherence tomography leakage identified and quantified the fluid related to choroidal neovascularization activity. Low optical reflectivity change in the outer segment layer correlates with functional outcome and increasing LOR in the external layers may be a marker of early recurrence. Combining optical coherence tomography angiography and optical coherence tomography leakage allows both for choroidal neovascularization morphology and activity analysis.

Random sample consensus (Ransac) is a technique that has been widely used for modeling data with a large amount of noise. Although successfully employed in areas such as computer vision, extensive testing and applications to clinical data, particularly in oncology, are still lacking. We applied this technique to synthetic and biomedical datasets, publicly available at The Cancer Genome Atlas (TCGA) and the UC Irvine Machine Learning Repository, to identify outliers in the classification of tumor samples. The results obtained by combining Ransac with logistic regression were compared against a baseline classical logistic model. To evaluate the robustness of this method, the original datasets were then perturbed by generating noisy data and by artificially switching the labels. The flagged outlier observations were compared against the misclassifications of the baseline logistic model, along with the evaluation of the overall accuracy of both strategies. Ransac has shown high precision in classifying a subset of core (inlier) observations in the datasets evaluated, while simultaneously identifying the outlier observations, as well as robustness to increasingly perturbed data.

© 2020 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/)., Colorectal cancer (CRC) is one of the leading causes of mortality and morbidity in the world. Being a heterogeneous disease, cancer therapy and prognosis represent a significant challenge to medical care. The molecular information improves the accuracy with which patients are classified and treated since similar pathologies may show different clinical outcomes and other responses to treatment. However, the high dimensionality of gene expression data makes the selection of novel genes a problematic task. We propose TCox, a novel penalization function for Cox models, which promotes the selection of genes that have distinct correlation patterns in normal vs. tumor tissues. We compare TCox to other regularized survival models, Elastic Net, HubCox, and OrphanCox. Gene expression and clinical data of CRC and normal (TCGA) patients are used for model evaluation. Each model is tested 100 times. Within a specific run, eighteen of the features selected by TCox are also selected by the other survival regression models tested, therefore undoubtedly being crucial players in the survival of colorectal cancer patients. Moreover, the TCox model exclusively selects genes able to categorize patients into significant risk groups. Our work demonstrates the ability of the proposed weighted regularizer TCox to disclose novel molecular drivers in CRC survival by accounting for correlation-based network information from both tumor and normal tissue. The results presented support the relevance of network information for biomarker identification in high-dimensional gene expression data and foster new directions for the development of network-based feature selection methods in precision oncology., This work was partially supported by national funds through Fundação para a Ciência e a Tecnologia (FCT) with references PD/BD/139146/2018, IF/00409/2014, UIDB/50021/2020 (INESC-ID), UIDB/50022/2020 (IDMEC), UIDB/04516/2020 (NOVA LINCS), and UIDB/00297/2020 (CMA) and projects PREDICT (PTDC/CCI-CIF/29877/2017) and MATISSE (DSAIPA/DS/0026/2019).

Background: Disease-related malnutrition is a significant problem in hospitalized patients, with high prevalence rates depending on the studied population. Internal Medicine wards are the backbone of the hospital setting. However, prevalence and determinants of malnutrition in these patients remain unclear. We aimed to determine the prevalence of malnutrition in Internal Medicine wards and to identify and characterize malnourished patients.Methods: A cross-sectional observational multicentre study was performed in Internal Medicine wards of 24 Portuguese hospitals during 2017. Demographics, hospital admissions during the previous year, type of admission, primary diagnosis, Charlson comorbidity index, and education level were registered. Malnutrition at admission was assessed using Patient-Generated Subjective Global Assessment (PG-SGA). Demographic characteristics were compared between well-nourished and malnourished patients. Logistic regression analysis was used to identify determinants of malnutrition.Results: 729 participants were included (mean age 74 years, 51% male). Main reason for admission was respiratory disease (32%). Mean Charlson comorbidity index was 5.8 +/- 2.8. Prevalence of malnutrition was 73% (56% moderate/suspected malnutrition and 17% severe malnutrition), and 54% had a critical need for multidisciplinary intervention (PG-SGA score >= 9). No education (odds ratio [OR] 1.88, 95% confidence interval [CI]: 1.16-3.04), hospital admissions during previous year (OR 1.53, 95%CI: 1.05-2.26), and multiple comorbidities (OR 1.22, 95%CI: 1.14-1.32) significantly increased the odds of being malnourished.Conclusions: Prevalence of malnutrition in the Internal Medicine population is very high, with the majority of patients having critical need for multidisciplinary intervention. Low education level, admissions during previous year, and multiple comorbidities increase the odds of being malnourished.