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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 However, given the substantial changes in glioma classification, these retrospective data may harbour outdated diagnostic annotations, suboptimal for further research.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.

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.

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

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.

Funding Information: This work was funded by the project “ MATISSE: A machine learning-based forecasting system for shellfish safety ” ( DSAIPA/DS/0026/2019 ). The work was also supported by national funds through Fundação para a Ciência e a Tecnologia (FCT), Portugal with references CEECINST/00102/2018 , UIDB/04516/2020 (NOVA LINCS), UIDB/00297/2020 and UIDP/00297/2020 (NOVA MATH), and UID/Multi/04326/2020 (CCMAR). Publisher Copyright: © 2022 The Author(s) Harmful algal blooms (HABs) and the consequent contamination of shellfish are complex processes depending on several biotic and abiotic variables, turning prediction of shellfish contamination into a challenging task. Not only the information of interest is dispersed among multiple sources, but also the complex temporal relationships between the time-series variables require advanced machine methods to model such relationships. In this study, multiple time-series variables measured in Portuguese shellfish production areas were used to forecast shellfish contamination by diarrhetic shellfish poisoning (DSP) toxins one to four weeks in advance. These time series included DSP concentration in mussels (Mytilus galloprovincialis), toxic phytoplankton cell counts, meteorological, and remotely sensed oceanographic variables. Several data pre-processing and feature engineering methods were tested, as well as multiple autoregressive and artificial neural network (ANN) models. The best results regarding the mean absolute error of prediction were obtained for a bivariate long short-term memory (LSTM) neural network based on biotoxin and toxic phytoplankton measurements, with higher accuracy for short-term forecasting horizons. When evaluating all ANNs model ability to predict the contamination state (below or above the regulatory limit for contamination) and changes to this state, multilayer perceptrons (MLP) and convolutional neural networks (CNN) yielded improved predictive performance on a case-by-case basis. These results show the possibility of extracting relevant information from time-series data from multiple sources which are predictive of DSP contamination in mussels, therefore placing ANNs as good candidate models to assist the production sector in anticipating harvesting interdictions and mitigating economic losses. publishersversion 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.

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.

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.

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.

Microarray and RNA-sequencing (RNA-seq) gene expression data alongside machine learning algorithms are promising in the discovery of new cancer biomarkers. However, even though they are similar in purpose, there are some fundamental differences between the two techniques. We propose a methodology for cross-platform integration, and biomarker discovery based on network-based regularization via the Twin Networks Recovery (twiner) penalty, as a strategy to enhance the selection of breast cancer gene signatures that have similar correlation patterns in both platforms. In a classification setting based on sparse logistic regression (LR) taking as classes tumor from both RNA-seq and microarray, and normal tissue samples, twiner achieved precision-recall accuracies of 99.71% and 99.57% in the training and test set, respectively. Moreover, the survival analysis results validated the biological relevance of the signatures identified by twiner. Therefore, by leveraging from the existing amount of data for microarray and RNA-seq, a single biological conclusion can be reached, independent of each technology.

The accessibility to “big data” sets down an ambitious challenge in the medical field, especially in personalized medicine, where gene expression data are increasingly being used to establish a diagnosis and optimize treatment of oncological patients. However, the high-dimensionality nature of the data brings many constraints, for which several approaches have been considered, with regularization techniques in the cutting-edge research front. Additionally, the network structure of gene expression data has fostered the development of network-based regularization techniques to convey data into a low-dimensional and interpretable level. In this work, classical elastic net and two recently proposed network-based methods, HubCox and OrphanCox, are applied to high-dimensional gene expression data, to model survival data. An oncological transcriptomic dataset obtained from The Cancer Genome Atlas (TCGA) is used, with patients’ RNA-seq measurements as covariates. The application of sparsity-inducing techniques to the dataset enabled the selection of relevant genes over a range of parameters evaluated. Comparable results were obtained for the elastic net and the network-based OrphanCox regarding model performance and genes selected.

A global effort is currently underway to combat climate change and adopt actions for a sustainable low carbon future. Energy and Behaviour: Towards a Low Carbon Future presents an international and multifaceted approach to understanding the interface of people and energy, which involves both engaging people in the energy system and changing the energy system to better accommodate people’s needs. This introduction focuses on the moving target of what low-carbon energy behaviours are and could be, not just in European countries but worldwide. It sets the scene for the book by providing a brief review of energy and behaviour research and defining what the editors hope to achieve in the book. Next, it delivers a guided tour of the chapters. Finally, it concludes with a call for action to move beyond the conventional boundaries of energy and behaviour research.

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.

This paper proposes two innovative time-effective approaches to retrieve annual averaged concentrations for air quality assessment in the framework of the AQD. In addition, a traditional method (M1) was applied through numerical simulations for an entire year on an hourly basis to compare the performance of the proposed approaches. The first time-effective approach (M2) is based on the calculation of pollutant concentrations for the full year on an hourly basis through the combination of a set of numerical simulations for 4 typical days weighted by hourly factors obtained from air quality monitoring data. While the second time-effective approach (M3) considers the numerical simulation of pollutant concentrations for a set of typical meteorological conditions. For all the methods, air quality simulations were performed with the second-generation Gaussian model URBAIR. The three methods are applied over two distinct European urban areas, the Aveiro region in Portugal and Bristol in the United Kingdom, for the simulation of NO2 and PM10 annual concentrations. The main results highlight an underestimation of the NO2 annual concentrations by M2 and an overestimation of those concentrations by M3 for the Aveiro region, when compared to M1 as the reference method. While, for Bristol the main differences between methods were found for NO2 concentrations when using M3. M2 underestimates PM10 annual concentrations in the Aveiro Region, while M3 points out underestimation or overestimation of those concentrations for distinct areas of the domain. This study aims to foster the knowledge on air quality assessment under the European policy context, supporting air quality management and urban planning. The innovative nature of this study relies on the proposed time-effective tools, suitable for the fast simulation of complex urban areas applying high spatial resolution. Additionally, these modelling tools may provide key information on air quality to population, particularly where it is not readily available.

The ‘Clean Energy for All Europeans’ legislative package places citizens and communities at the heart of the European energy policy by promoting local energy generation, consumption and trading. As only recently energy communities were formally defined in the European regulatory framework, the literature on energy community business models is still scattered and a clear systematization of community arrangements is missing. This paper aims to provide a comprehensive view of the prevailing and emergent energy communities business models, focusing on the value proposition offered by these initiatives. Community projects across Europe are analyzed and eight community business model archetypes are identified having the current European regulatory framework as background. The Business Model Canvas and the Lean Canvas frameworks are used to characterize and compare these archetypes. The main differences between business models are examined to highlight the most relevant strengths and barriers for energy community development. This analysis revealed the dominance of traditional self-consumption place-based communities, while business models involving differentiated services as demand flexibility, aggregation, energy efficiency and electric mobility are still scarce. However, the research around novel business models must be strengthened as they are expected to become crucial in upholding energy communities as key players in the energy transition and foster the regulatory framework evolution.

Introduction: We conducted a multimodal, cross-sectional evaluation. Methods: Eyes were divided into 4 study groups: controls, early/intermediate age-related macular degeneration (AMD), fellow eyes of retinal angiomatous proliferation (RAP), and RAP eyes. Patients were evaluated with spectral-domain optical coherence tomography (OCT), enhanced depth imaging-OCT, and OCT angiography (OCTA). OCTA images were processed to generate maps of the vessel density and perfusion density of the superficial and deep retinal layers (SRL and DRL) and the choriocapillaris level (CL). The thickness of the outer nuclear layer and choroid was manually assessed. Results: We included 135 eyes of 100 patients (51 controls, 30 AMD, 42 RAP, and 12 fellow eyes). The fellow eyes showed a significantly lower vascular perfusion of the SRL, DRL, and CL (p < 0.02) than the early/intermediate AMD and control eyes did. Similarly, RAP eyes presented a lower vascular perfusion of the DRL and CL (p < 0.05). Besides, structural analyses of the fellow eyes and RAP eyes revealed a significantly higher prevalence of macular pigmentary changes, atrophy of the retinal pigment epithelium, hyperreflective “clumps” above flat drusen, amongst others, than early/intermediate AMD and control eyes (p < 0.05). Conclusion: We present the first report on the OCTA analysis of the fellow eye of patients with RAP. The reduced perfusion density and vessel density observed contributes, in association with clearly defined structural changes, to a wider characterization of RAP as a distinctive phenotype.

Energy communities are gaining momentum as they may impact demand and reshape the design of future energy systems. This paper exploits the contribution that residential demand-side flexibility can provide to an energy community by combining multi-agent systems (MAS) modeling and genetic algorithms (GA) to minimize both electricity costs and end-users' dissatisfaction. The overall community self-sufficiency in different generation scenarios is quantified by assessing how much energy demand can be locally supplied. The results reveal that important economic benefits can be obtained through the management of local energy resources, confirming that community members benefit of being involved in a communal environment.

Background Understanding cellular and molecular heterogeneity in glioblastoma (GBM), the most common and aggressive primary brain malignancy, is a crucial step towards the development of effective therapies. Besides the inter-patient variability, the presence of multiple cell populations within tumors calls for the need to develop modeling strategies able to extract the molecular signatures driving tumor evolution and treatment failure. With the advances in single-cell RNA Sequencing (scRNA-Seq), tumors can now be dissected at the cell level, unveiling information from their life history to their clinical implications. Results We propose a classification setting based on GBM scRNA-Seq data, through sparse logistic regression, where different cell populations (neoplastic and normal cells) are taken as classes. The goal is to identify gene features discriminating between the classes, but also those shared by different neoplastic clones. The latter will be approached via the network-based regularizer to identify gene signatures shared by neoplastic cells from the tumor core and infiltrating neoplastic cells originated from the tumor periphery, as putative disease biomarkers to target multiple neoplastic clones. Our analysis is supported by the literature through the identification of several known molecular players in GBM. Moreover, the relevance of the selected genes was confirmed by their significance in the survival outcomes in bulk GBM RNA-Seq data, as well as their association with several Gene Ontology (GO) biological process terms. Conclusions We presented a methodology intended to identify genes discriminating between GBM clones, but also those playing a similar role in different GBM neoplastic clones (including migrating cells), therefore potential targets for therapy research. Our results contribute to a deeper understanding on the genetic features behind GBM, by disclosing novel therapeutic directions accounting for GBM heterogeneity.

The importance of gene expression data analysis for oncological diagnosis and treatment has become widely accepted in recent years. One of the main associated challenges is the development of mathematical and statistical methods for data analysis to improve prognosis and guide treatment decisions. One of the difficulties that researchers face when dealing with gene expression datasets concerns their high-dimensionality. In this context, the goal of this work is to reduce the dimensionality of gene expression data using regularization techniques such as Lasso and Elastic net, complemented with DegreeCox, a network-based regularization method for survival analysis recently proposed. Also identification of long or short-term survivors (outliers) may lead to the detection of new prognostic factors, and the Rank Product test is used to identify those observations. An example based on the The Cancer Genome Atlas (TCGA) Melanoma dataset is presented, where the covariates are patients’ gene expression. The application of data reduction techniques to the Melanoma dataset enabled the selection of relevant genes over a range of parameters evaluated, with 5 in common between elastic net regularization and DegreeCox for one of the two models further evaluated. Moreover, a long term survivor was detected as outlier by the Rank Product test, being systematically highly ranked for the martingale residuals of the models evaluated.

Network information is gaining importance in the generation of predictive models in cancer genomics, with the premise that prior biological knowledge offers the models interpretability and reproducibility, an invaluable contribution in precision medicine. This work evaluates the usefulness of accounting for gene network information provided by the data correlation structure and external STRING information in the classification of Breast Invasive Carcinoma (BRCA) RNA-Seq data from The Cancer Genome Atlas (TCGA) into tumor and normal tissue, by sparse logistic regression (SLR). Within the correlation-based approaches, two directions were investigated: first, imposing smaller penalties on hub genes, i.e., highly connected genes in the network (hubSLR); second, favouring the selection of orphan or weakly correlated genes (orphanSLR). Without loss of predictive ability, a considerable overlap between the genes selected by the methods was achieved, with fewer genes exclusively selected by each method. Besides a consensus list of genes, the complementarity offered by sets of genes exclusively selected by each model based on different network information shall be regarded as a means to enhance biological interpretability, drawing attention to genes with a known role in the network, either hubs, orphans or highly connected genes in protein-protein interaction networks. This represents a major advantage over non network-based methods, enabling the disclosure of the relevance of known gene subnetworks in the disease under study, while boosting biomarker discovery and precision medicine.

Harmful algal blooms (HABs) are among the most severe ecological marine problems worldwide. Under favorable climate and oceanographic conditions, toxin-producing microalgae species may proliferate, reach increasingly high cell concentrations in seawater, accumulate in shellfish, and threaten the health of seafood consumers. There is an urgent need for the development of effective tools to help shellfish farmers to cope and anticipate HAB events and shellfish contamination, which frequently leads to significant negative economic impacts. Statistical and machine learning forecasting tools have been developed in an attempt to better inform the shellfish industry to limit damages, improve mitigation measures and reduce production losses. This study presents a synoptic review covering the trends in machine learning methods for predicting HABs and shellfish biotoxin contamination, with a particular focus on autoregressive models, support vector machines, random forest, probabilistic graphical models, and artificial neural networks (ANN). Most efforts have been attempted to forecast HABs based on models of increased complexity over the years, coupled with increased multi-source data availability, with ANN architectures in the forefront to model these events. The purpose of this review is to help defining machine learning-based strategies to support shellfish industry to manage their harvesting/production, and decision making by governmental agencies with environmental responsibilities. CEECINST/00102/2018/ UIDB/04516/2020/ UIDB/00297/2020/ UIDB/50021/2020/ UID/Multi/04326/2020 info:eu-repo/semantics/publishedVersion

Network science has long been recognized as a well-established discipline across many biological domains. In the particular case of cancer genomics, network discovery is challenged by the multitude of available high-dimensional heterogeneous views of data. Glioblastoma (GBM) is an example of such a complex and heterogeneous disease that can be tackled by network science. Identifying the architecture of molecular GBM networks is essential to understanding the information flow and better informing drug development and pre-clinical studies. Here, we review network-based strategies that have been used in the study of GBM, along with the available software implementations for reproducibility and further testing on newly coming datasets. Promising results have been obtained from both bulk and single-cell GBM data, placing network discovery at the forefront of developing a molecularly-informed-based personalized medicine., This work was partially supported by national funds through Fundação para a Ciência e a Tecnologia (FCT) with references CEECINST/00102/2018, CEECIND/00072/2018 and PD/BDE/143154/2019, UIDB/04516/2020, UIDB/00297/2020, UIDB/50021/2020, UIDB/50022/2020, UIDB/50026/2020, UIDP/50026/2020, NORTE-01-0145-FEDER-000013, and NORTE-01-0145-FEDER000023 and projects PTDC/CCI-BIO/4180/2020 and DSAIPA/DS/0026/2019. This project has received funding from the European Union’s Horizon 2020 research and innovation program under Grant Agreement No. 951970 (OLISSIPO project).

Escherichia coli is one of the most used host microorganism for the production of recombinant products, such as heterologous proteins and plasmids. However genetic, physiological and environmental factors influence the plasmid replication and cloned gene expression in a highly complex way. To control and optimize the recombinant expression system performance, it is very important to understand this complexity. Therefore, the development of rapid, highly sensitive and economic analytical methodologies, which enable the simultaneous characterization of heterologous product synthesis and physiologic cell behavior under a variety of culture conditions, is highly desirable. For that, the metabolic profile of recombinant E. coli cultures producing the pVAX-lacZ plasmid model was analyzed by rapid, economic and high-throughput Fourier Transform Mid-Infrared (FT-MIR) spectroscopy. The main goal of the present work is to show as the simultaneous multivariate data analysis by principal component analysis (PCA) and direct spectral analysis could represent a very interesting tool to monitor E. coli culture processes and acquire relevant information according to current quality regulatory guidelines. While PCA allowed capturing the energetic metabolic state of the cell, e.g. by identifying different C-sources consumption phases, direct FT-MIR spectral analysis allowed obtaining valuable biochemical and metabolic information along the cell culture, e.g. lipids, RNA, protein synthesis and turnover metabolism. The information achieved by spectral multivariate data and direct spectral analyses complement each other and may contributes to understand the complex interrelationships between the recombinant cell metabolism and the bioprocess environment towards more economic and robust processes design according to Quality by Design framework. This article is protected by copyright. All rights reserved.

Occupants’ behaviours are a major determinant of energy use in buildings. The related savings potential has been insufficiently exploited. Although research has addressed behavioural savings through real-world interventions and quantitative modelling approaches, it has not yet explored the full variety of household activities. This work proposes an integrative modelling approach of energy behaviours in the residential setting as a tool to estimate the behavioural impact of households on energy consumption. It uses building energy performance simulation (BEPS) tools to exploit the behavioural savings potential associated with usage and investment energy behaviours when using different energy services in daily household activities. Simulations have estimated significant behavioural savings potential associated with energy behaviours, which may be materialized if some forms of behaviour are induced. Investment behaviours have higher savings potential than usage behaviours, and the behavioural savings...

Power systems have undergone major changes, mainly due to the increasing penetration of renewable distributed generation and levels of automation. Prosumers have the capability to become (almost) independent of the grid and can exchange energy with other community members. In this setting, it is relevant to exploit generation/demand flexibility from an energy community perspective, including multiple agents that exchange and manage the available energy resources in an integrated manner. Agent-based modeling (ABM) tools have been increasingly used to model complex energy systems, since they are especially suited to model systems influenced by social interactions between agents that are flexible, autonomous and proactive. This paper aims to present the initial development stage of a modelling approach to simulate energy trading between two energy community members capable of exchanging information and energy between them. Particular attention is paid to the influence of end-users’ behaviors on demand-side flexibility.

Background Survival analysis is a statistical technique widely used in many fields of science, in particular in the medical area, and which studies the time until an event of interest occurs. Outlier detection in this context has gained great importance due to the fact that the identification of long or short-term survivors may lead to the detection of new prognostic factors. However, the results obtained using different outlier detection methods and residuals are seldom the same and are strongly dependent of the specific Cox proportional hazards model selected. In particular, when the inherent data have a high number of covariates, dimensionality reduction becomes a key challenge, usually addressed through regularized optimization, e.g. using Lasso, Ridge or Elastic Net regression. In the case of transcriptomics studies, this is an ubiquitous problem, since each observation has a very high number of associated covariates (genes). Results In order to solve this issue, we propose to use the Rank Product test, a non-parametric technique, as a method to identify discrepant observations independently of the selection method and deviance considered. An example based on the The Cancer Genome Atlas (TCGA) ovarian cancer dataset is presented, where the covariates are patients’ gene expressions. Three sub-models were considered, and, for each one, different outliers were obtained. Additionally, a resampling strategy was conducted to demonstrate the methods’ consistency and robustness. The Rank Product worked as a consensus method to identify observations that can be influential under survival models, thus potential outliers in the high-dimensional space. Conclusions The proposed technique allows us to combine the different results obtained by each sub-model and find which observations are systematically ranked as putative outliers to be explored further from a clinical point of view.

The integration of Renewable Energy Sources, mainly in distribution grids, has been changing the paradigm of power systems operation. This constitutes an opportunity to make use of flexible energy resources to support DSOs and TSOs on network operation activities. The amount of total flexibility available in the system can be quantified starting from lower voltage levels considering the implementation of demand response schemes. This work is focused on the development of an expeditious methodology to assess the total flexibility of low voltage consumers and aggregate it by means of a bottom-up approach until reaching the transmission network nodes.

Energy behaviours represent an important underexploited resource in the context of promoting end-use energy efficiency, namely in the residential sector. However, addressing the multidimensional nature of energy behaviours is a complex task and more effective behaviour change interventions and policies grounded on comprehensive approaches are required. An integrative intervention to explore the influence of usage energy behaviours on energy consumption was developed through an innovative combination of modelling techniques. A real-world case study was utilised to generate contextualised understanding. This intervention supported problem structuring methods as pertinent tools to be utilised in complex human-centred energy research, such as energy behaviours, by enabling the development of tailored methodologies which minimise the human bias. It further confirmed real-world behaviour change interventions should involve the different energy stakeholders and be designed to be flexible and adaptive. Results confirmed variables associated with different dimensions significantly impact energy consumption. In this case study the promotion of residential energy efficiency includes both structural and energy behavioural actions, namely a better insulation of the dwellings and encouraging specific usage energy behaviours. These results support the need to consider an integrative perspective when addressing energy behaviours and designing effective behavioural change interventions and energy efficiency policies.

Near infrared (NIR) spectroscopy was used to in situ monitoring the cultivation of two recombinant Saccharomyces cerevisiae strains producing heterologous cyprosin B. NIR spectroscopy is a fast and non-destructive technique, that by being based on overtones and combinations of molecular vibrations requires chemometrics tools, such as partial least squares (PLS) regression models, to extract quantitative information concerning the variables of interest from the spectral data. In the present work, good PLS calibration models based on specific regions of the NIR spectral data were built for estimating the critical variables of the cyprosin production process: biomass concentration, cyprosin activity, cyprosin specific activity, the carbon sources glucose and galactose concentration and the by-products acetic acid and ethanol concentration. The PLS models developed are valid for both recombinant S. cerevisiae strains, presenting distinct cyprosin production capacities, and therefore can be used, not only for the real-time control of both processes, but also in optimization protocols. The PLS model for biomass yielded a R 2 = 0.98 and a RMSEP = 0.46 g dcw l −1 , representing an error of 4% for a calibration range between 0.44 and 13.75 g dcw l −1 . A R 2 = 0.94 and a RMSEP = 167 U ml −1 were obtained for the cyprosin activity, corresponding to an error of 6.7% of the experimental data range (0–2509 U ml −1 ), whereas a R 2 = 0.93 and RMSEP = 672 U mg −1 were obtained for the cyprosin specific activity, corresponding to an error of 7% of the experimental data range (0–11,690 U mg −1 ). For the carbon sources glucose and galactose, a R 2 = 0.96 and a RMSECV of 1.26 and 0.55 g l −1 , respectively, were obtained, showing high predictive capabilities within the range of 0–20 g l −1 . For the metabolites resulting from the cell growth, the PLS model for acetate was characterized by a R 2 = 0.92 and a RMSEP = 0.06 g l −1 , which corresponds to a 6.1% error within the range of 0.41–1.23 g l −1 ; for the ethanol, a high accuracy PLS model with a R 2 = 0.97 and a RMSEP = 1.08 g l −1 was obtained, representing an error of 9% within the range of 0.18–21.76 g l −1 . The present study shows that it is possible the in situ monitoring and prediction of the critical variables of the recombinant cyprosin B production process by NIR spectroscopy, which can be applied in process control in real-time and in optimization protocols. From the above, NIR spectroscopy appears as a valuable analytical tool for online monitoring of cultivation processes, in a fast, accurate and reproducible operation mode.

BACKGROUND While the pharmaceutical industry keeps an eye on plasmid DNA production for new generation gene therapies, real-time monitoring techniques for plasmid bioproduction are as yet unavailable. This work shows the possibility of in situ monitoring of plasmid production in Escherichia coli cultures using a near infrared (NIR) fiber optic probe. RESULTS Partial least squares (PLS) regression models based on the NIR spectra were developed for predicting bioprocess critical variables such as the concentrations of biomass, plasmid, carbon sources (glucose and glycerol) and acetate. In order to achieve robust models able to predict the performance of plasmid production processes, independently of the composition of the cultivation medium, cultivation strategy (batch versus fed-batch) and E. coli strain used, three strategies were adopted, using: (i) E. coli DH5α cultures conducted under different media compositions and culture strategies (batch and fed-batch); (ii) engineered E. coli strains, MG1655ΔendAΔrecAΔpgi and MG1655ΔendAΔrecA, grown on the same medium and culture strategy; (iii) diverse E. coli strains, over batch and fed-batch cultivations and using different media compositions. PLS models showed high accuracy for predicting all variables in the three groups of cultures. CONCLUSION NIR spectroscopy combined with PLS modeling provides a fast, inexpensive and contamination-free technique to accurately monitoring plasmid bioprocesses in real time, independently of the medium composition, cultivation strategy and the E. coli strain used. © 2014 Society of Chemical Industry

ResumoObjetivosDeterminar a capacidade de estimulação do fluxo salivar, potencial erosivo e quantidade de flúor libertado na saliva durante a exposição a dois estimulantes gustativos de secreção salivar (EGSS) numa população saudável.MétodosEnsaio clínico aleatório duplamente cego em cross‐over. Sessenta voluntários foram distribuídos aleatoriamente por 2 grupos. Cada grupo foi exposto inicialmente a um EGSS baseado em ácido cítrico ou a um EGSS composto por ácido málico, flúor e xilitol. Foram determinados o fluxo salivar não estimulado, o estimulado mecanicamente e o estimulado com os diferentes EGSS. O pH salivar e o flúor libertado na saliva foram determinados por potenciometria. O potencial erosivo foi calculado com base no número de episódios de pH salivar inferior a 4,5 por mais de um minuto. Os resultados foram apresentados como média e intervalo de confiança±95% e analisados pelo teste t de Student e ANOVA, aceitando‐se apenas valores comparativos para p

Harmful algal blooms are responsible worldwide for the contamination of fishery resources, with potential impacts on seafood safety and public health. Most coastal countries rely on an intense monitoring program for the surveillance of toxic algae occurrence and shellfish contamination. The present study investigates the use of near infrared (NIR) spectroscopy for the rapid in situ determination of cell concentrations of toxic algae in seawater. The paralytic shellfish poisoning (PSP) toxin-producing dinoflagellate Gymnodinium catenatum was selected for this study. The spectral modeling by partial least squares (PLS) regression based on the recorded NIR spectra enabled the building of highly accurate (R2 = 0.92) models for cell abundance. The models also provided a good correlation between toxins measured by the conventional methods (high-performance liquid chromatography with fluorescence detection (HPLC-FLD)) and the levels predicted by the PLS/NIR models. This study represents the first necessary step in investigating the potential of application of NIR spectroscopy for algae bloom detection and alerting.

The monitoring of biopharmaceutical products using Fourier transform infrared (FT-IR) spectroscopy relies on calibration techniques involving the acquisition of spectra of bioprocess samples along the process. The most commonly used method for that purpose is partial least squares (PLS) regression, under the assumption that a linear model is valid. Despite being successful in the presence of small nonlinearities, linear methods may fail in the presence of strong nonlinearities. This paper studies the potential usefulness of nonlinear regression methods for predicting, from in situ near-infrared (NIR) and mid-infrared (MIR) spectra acquired in high-throughput mode, biomass and plasmid concentrations in Escherichia coli DH5-α cultures producing the plasmid model pVAX-LacZ. The linear methods PLS and ridge regression (RR) are compared with their kernel (nonlinear) versions, kPLS and kRR, as well as with the (also nonlinear) relevance vector machine (RVM) and Gaussian process regression (GPR). For the systems studied, RR provided better predictive performances compared to the remaining methods. Moreover, the results point to further investigation based on larger data sets whenever differences in predictive accuracy between a linear method and its kernelized version could not be found. The use of nonlinear methods, however, shall be judged regarding the additional computational cost required to tune their additional parameters, especially when the less computationally demanding linear methods herein studied are able to successfully monitor the variables under study.

The transition to smart grids is an on-going process that may both shape and be shaped by end-users’ energy behavioural adaptations. This study explores current and potential energy behavioural adaptations in Portugal during the smart grid transition process. A web-based survey was made to a representative sample of a specific segment of Portuguese residential end -users. The survey evaluated current energy behaviours and hypothetical future behaviours in a dynamic pricing scenario. Results show this population segment has a positive predisposition towards smart technologies and demand shifting, but it is less likely to accept load control and switch to the liberalised energy market. Factors influencing the behavioural potential are mostly related with market regulation, households’ practices and usage behaviours, interference with the private domain, information and technical aspects, and social values. To facilitate behavioural adaptations several strategies are recommended, such as improving the energy market regulation, assessing households’ behaviours, prioritising actions already embedded in households daily routines, not interfering with their activities and ensure an override option, and improving energy services, trust and information provided to end-users. The conclusions of the present study are of utmost importance for the design of more effective demand response programmes and energy policies.

Energy behaviours represent a significant untapped potential for the increase of end-use energy efficiency in buildings. Although energy behaviours are a major determinant of energy use in buildings, energy savings potential due to behaviour are usually neglected, albeit being referred to be as high as those from technological solutions. This paper presents a review of recent literature on energy behaviours in order to recognise recent trends, quantify energy behaviours potential savings, characterise energy behaviour modelling strategies and identify potential research gaps. Energy behaviour research is vast and has been essentially focused on the residential sector, striving to establish behaviour determinants and the best strategies and instruments to promote more efficient energy behaviours. Potential savings of energy behaviours are referred to reach 20%, but values differ up to 100% between experiences and additional studies to quantify behavioural savings are needed, in particular by using standard quantification techniques. Different modelling techniques have been used to model energy behaviours: qualitative approaches from the social sciences trying to interpret behaviour, here named energy behaviour frameworks; quantitative approaches from the engineering and economics that quantify energy consumption, here designated by energy models; and hybrid approaches that are considered the most relevant since they integrate multiple dimensions of energy behaviours, here referred as energy behaviour modelling. Energy behaviours have a crucial role in promoting energy efficiency, but energy behaviours characteristics and complexity create several research challenges that must be overcome so energy behaviours may be properly valorised and integrated in the energy policy context.

Many pharmaceutical problems require chemical identification of the ingredients present in a drug product, e.g., a tablet. Examples include the identification of the compounds present in many steps of the manufacturing process and the chemical characterization of counterfeit and third-party tablets. Hyperspectral unmixing of near-infrared images is a key method for solving the above problems, as it provides estimates of the number of pure compounds present in a mixture, their spectral signatures, and the corresponding spatially mapped abundance fractions. The performance of hyperspectral unmixing depends upon the degree of homogeneity of the tablets, as well as the pixel resolution used for image acquisition. This work explores the use of the recent simplex identification via split augmented Lagrangian (SISAL) algorithm to unmix near-infrared images of tablets under different homogeneity and pixel resolution conditions. SISAL is known to solve complex problems beyond the reach of previous hyperspectral unmixing methods. The tablets used in this study are 4- and 5-compound model pharmaceutical mixtures, produced with good and poor blending processes, and the acquisition was performed at three pixel resolutions: 8.1, 27.9, and 40.3 μm/pixel. Heterogeneity proved to increase SISAL's accuracy, as did increased pixel resolution in homogeneous tablets. Given the fast image acquisition and algorithm execution times, low- and high-resolution images should always be acquired; combined with the homogeneity grade of the samples, this may be determinant to a case-by-case decision on the proper action to be taken next.

b GlaxoSmithKline, Medicines research centre, Gunnels Wood road, Stevenage, Hertfordshire SG1 2ny, uK The adequacy of quantification of the components in non-homogeneous pharmaceutical tablets using near infrared (NIR) linear hyper - spectral unmixing has been studied with and without the presence of the tablet coating and an inefficient blending process. NIR images of six coated tablets of different formulations and of sections thereof, extracted at specific depths, were acquired. The quantification of the compounds present was performed via the classical least squares criterion. The results presented show that given the low depth of penetration of the NIR radiation, the coating represents as much as 30% of the mass fraction of the sampled tablet, even though it is commonly less than 3% of the mass fraction of the total tablet. This deviation negatively impacts the estimates of the compounds of interest. Estimates of the compounds obtained in sections approached the true values and were consistent at each depth. Both scenarios (coated and uncoated) were found to be well explained by a linear mixing model, as the errors in spectral estimates were small. The pixels showing the highest fraction of energy not explained by the linear model were found to be spatially located in areas where the surface curvature was high and a high reflectivity was observed; factors known to contribute non-linearities to the mixing scenario. The outcomes of this study have practical importance for the pharmaceutical industry: first, tablets should always be uncoated or micro - tomed before being imaged, in order to remove the high contribution of the coating and to obtain a flat surface. These steps reduce the above-mentioned non-linearities that introduce errors when using a linear mixing model. Second, the mixing scale shall be taken into account, as a poor blending may not show a similar in-depth pattern and thus provide different estimates at each depth and, on the other hand, over-blending may lead to a non-linear mixing scenario, depending on the spatial resolution used. An appropriate prior study will direct analysis either to linear or non-linear approaches.

According to the WHO definition for counterfeit medicines, several categories can be established, e.g., medicines containing the correct active pharmaceutical ingredient (API) but different excipients, medicines containing low levels of API, no API or even a substitute API. Obviously, these different scenarios will have different detrimental effects on a patient's health. Establishing the degree of risk to the patient through determination of the composition of counterfeit medicines found in the market place is thus of paramount importance. In this work, classical least squares was used for predicting the composition of counterfeit Heptodin tablets found in a market survey. Near infrared chemical imaging (NIR-CI) was used as a non-destructive measurement technique. No prior knowledge about the origin and composition of the tablets was available. Good API (i.e., lamivudine) predictions were obtained, especially for tablets containing a high API (close to the authentic) dose. Concentration maps of each pure material, i.e., the API (lamivudine) and the excipients microcrystalline cellulose, sodium starch glycollate, rice starch and talc, were estimated. Below 1% of the energy was not explained by the model (residuals percentage) for every pixel in all 12 counterfeit tablets. The similarities among tablets with respect to the total API percentage determined, as well as the corresponding concentration maps, support the classification of the tablets into the different groups obtained in previous work.

Bootstrap-based methods have been applied for spectral variable selection in near (NIR) and mid-infrared (MIR) spectroscopy applications. In this paper, an extension of those methods for the selection of spectral intervals instead of single spectral variables is proposed. This approach, interval partial least square (PLS)-Bootstrap (iPLS-Bootstrap), was compared against the PLS-Bootstrap method and the use of the whole spectral region for model development. These methods were tested on a NIR spectral dataset obtained from at-line monitoring of an industrial fermentation process, by correlating the spectra with the concentration of the active pharmaceutical ingredient (API). The performance of the models was evaluated based on the predictive ability for both cross-validation and external validation. For the dataset used, iPLS-Bootstrap enabled to improve the model predictive ability, with a greater impact on external validation. The decrease observed in RMSEP relative to the full-spectrum and PLS-Bootstrap model was, respectively, 14 and 6%. Copyright © 2008 John Wiley & Sons, Ltd.

The relative importance of nursery areas and their relationships with several environmental variables were evaluated in nine estuarine systems along the Portuguese coast based on trawl surveys. Historical data were used to outline changes and trends in the nursery function of some of these estuaries over the past decades. The dominant flatfish species in Portuguese estuaries were Platichthys flesus (Linnaeus, 1758), Solea solea (Linnaeus, 1758), Solea senegalensis Kaup, 1858 and Monochirus hispidus Rafinesque, 1814, but their occurrence differed among the estuaries. P. flesus only occurred in estuaries north of the Tejo estuary (39°N), S. solea was quite rare along the southern Portuguese coast (south of 37°30′N), S. senegalensis occurred in estuaries throughout the coast, but its abundance varied considerably, and the occurrence of M. hispidus was limited to the Sado estuary and Ria Formosa. A Correspondence Analysis was performed to evaluate the relationships between flatfish species abundance and geomorphologic and hydrologic characteristics of estuaries (latitude, freshwater flow, estuarine area, intertidal area, mean depth and residence time). Abiotic characteristics (depth, temperature, salinity, sediment type) of nursery grounds of each flatfish species were also evaluated. Results showed that some estuaries along the Portuguese coast have nursery grounds used by several flatfish species (e.g. Ria de Aveiro, Sado estuary), while in other systems a segregation was noticed, with juveniles of different species occurring in distinct estuarine areas (e.g. Minho and Mondego estuaries). This emphasizes the relevance of niche overlap, but the potential for competition may be considerably minimized by differences in resource use patterns and by an extremely high abundance of resources. Peak densities of flatfishes recorded in nurseries areas along the Portuguese coast were within the range of values reported for other geographical areas. Inter-annual abundance variability of all the species in the Tejo and Sado estuaries was extremely high, with a drastic decrease in P. flesus in the Tejo estuary, probably related to higher water temperature in recent years due to global climate change.

Escherichia coli is one of the most used host microorganism for the production of recombinant products, such as heterologous proteins and plasmids. However, genetic, physiological and environmental factors influence the plasmid replication and cloned gene expression in a highly complex way. To control and optimize the recombinant expression system performance, it is very important to understand this complexity. Therefore, the development of rapid, highly sensitive and economic analytical methodologies, which enable the simultaneous characterization of the heterologous product synthesis and physiologic cell behavior under a variety of culture conditions, is highly desirable. For that, the metabolic profile of recombinant E. coli cultures producing the pVAX-lacZ plasmid model was analyzed by rapid, economic and high-throughput Fourier Transform Mid-Infrared (FT-MIR) spectroscopy. The main goal of the present work is to show as the simultaneous multivariate data analysis by principal component analysis (PCA) and direct spectral analysis could represent a very interesting tool to monitor E. coli culture processes and acquire relevant information according to current quality regulatory guidelines. While PCA allowed capturing the energetic metabolic state of the cell, e.g. by identifying different C-sources consumption phases, direct FT-MIR spectral analysis allowed obtaining valuable biochemical and metabolic information along the cell culture, e.g. lipids, RNA, protein synthesis and turnover metabolism. The information achieved by spectral multivariate data and direct spectral analyses complement each other and may contribute to understand the complex interrelationships between the recombinant cell metabolism and the bioprocess environment towards more economic and robust processes design according to Quality by Design framework. © 2016 American Institute of Chemical Engineers Biotechnol. Prog., 33:285-298, 2017.

Helicobacter pylori infection represents a serious health problem, given its association with serious gastric diseases as gastric ulcers, cancer and MALT lymphoma. Currently no vaccine exists and antibiotic-based eradication therapy is already failing in more than 20% of cases. To increase the knowledge on the infection process diverse gastric cell lines, e.g. the adenocarcinona gastric (AGS) cell line, are routinely used has in vitro models of gastric epithelia. In the present work the molecular fingerprint of infected and non-infected AGS cell lines, by diverse H. pylori strains, was acquired using vibrational infrared spectroscopy. These molecular fingerprints enabled to discriminate infected from non-infected AGS cells, and infection due to different strains, by performing Principal Component Analysis. It was also possible to estimate, from the AGS cells molecular fingerprint, the effect of the infection on diverse biochemical and metabolic cellular status. In resume infra-red spectroscopy enabled the acquisition of infected AGS cells molecular fingerprint with minimal sample preparation, in a rapid, high-throughput, economic process yielding highly sensitive and informative data, most useful for promoting critical knowledge on the H. pylori infection process.

The diets of slender snipefish Macroramphosus gracilis, longspine snipefish Macroramphosus scolopax and boarfish Capros aper, three very abundant species on the Portuguese coast, were studied from samples collected between July 2002 and April 2003. Variations in the diet with fish size, season and area, as well as diet overlap and diversity, are explored in this study. The diets of slender snipefish and boarfish were mainly composed of copepods, with the main prey being Temora spp. and Calanus spp., respectively. Mysid shrimps were the most important food item in the diet of longspine snipefish. During the summer season, when the availability of different prey items was highest, the two species of snipefish and the boarfish fed on different prey. Temora spp. were the most important prey in the stomachs of slender snipefish in the summer on the south-west coast, while Calanus spp. started appearing in the stomachs of boarfish in the spring in the north, increasing their abundance in the summer on the south and south-west coasts. The abundance of mysids appeared to increase in the autumn on the south-west coast, being at that time an important food item for both longspine snipefish and boarfish. For slender snipefish and boarfish, the differences in stomach species diversity were explained firstly by the season and then by the area and fish size. For longspine snipefish, the area did not explain the species diversity variability, season being the first variable determining the differences. Of all three species, slender snipefish was the one with highest diversity of stomach contents, particularly in spring and summer on the north and south-west coasts. Diet overlap between species was very low. No predation on eggs and larval stages of fishes was found for any of these fish species. This work is the first to address the diets of snipefish species and boarfish in the south-east North Atlantic, in a large spatio-temporal coverage. These species are important prey for many commercial species on the Portuguese coast and, given their abundance, may have a great impact on zooplankton communities, thus assuming a pivotal position in marine food webs.

The trail to sustainability requires a revolution on the way environmental professionals perceive and solve environmental problems. In a globalisation context, environmental professionals have to develop new personal, interpersonal, societal and technical skills to become active throughout their professional lives and to be of value in the quest for sustainability. Professionals have to develop open mindsets, a holistic perspective of the problems and integrated solutions. Current environmental engineer courses fail to provide the required skills, due to the traditional higher education structure. The role of implementation of an Environmental Management System on campus, in providing students with the ‘hands-on’, ‘on-the-job’ business skills that will make them useful in the search for sustainability is discussed.

The existence of two species of the genus Macroramphosus Lacepede 1803, has been discussed based on morphometric characters, diet composition and depth distribution. Another species, the boarfish Capros aper (Linnaeus 1758), caugth along the Portuguese coast, shows two different morphotypes, one type with smaller eyes and a deeper body than the other, occurring with intermediate forms. In both snipefish and boarfish no sexual dimorphism was found with respect to shape and length relationships. However, females in both genera were on average bigger than males. A multidimensional scaling analysis was performed using Procrustes distances, in order to check if shape geometry was effective in distinguishing the species of snipefish as well as the morphotypes of boarfish. A multivariate discriminant analysis using morphometric characters of snipefish and boarfish was carried out to validate the visual criteria for a distinction of species and morphotypes, respectively. Morphometric characters revealed a great discriminatory power to distinguish morphotypes. Both snipefish and boarfish are very abundant in Portuguese waters, showing two well-defined morphologies and intermediate forms. This study suggests that there may be two different species in each genus and that further studies on these fish should be carried out to investigate if there is reproductive isolation between the morphotypes of boarfish and to validate the species of snipefish.