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Geochemical reference levels are necessary preliminary steps for appropriate environmental legislation, especially in the assessment of management of soil pollution and protection measures. The problem of establishing reference levels in areas with variable basic geology and anthropogenic pressure is addressed by characterising the geochemistry of 82 representative samples of the geologic materials of the Congost River basin (NE Iberia). The samples have been grouped in a system of 12 operative geochemical units comprising the entire area of the basin. The ‘total’ and ‘partial’ concentrations of 54 chemical elements have been determined. The geochemical variability has been discussed by means of statistical methodologies, diagrams and GIS techniques. The analysed samples reveal that the composition of the Congost catchment is highly varied. The differences between the ‘total’ and ‘partial’ concentrations define complex patterns reflecting the mineralogy of samples. The results indicate that bedrock geology is the most important factor influencing the major and trace element distributions in the basin. Pb and Zn were observed to be enriched in units more exposed to pollution than their parent materials. In general, the defined operative geologic units are characterised by different major and trace element compositional ranges as a consequence of their lithological diversity. The fact that the samples have been linked to a system of regional geological units allows the geochemical comparison of materials from the Congost River basin with other reference materials from neighbouring areas, or worldwide. For such an approach, we have focused on the geochemistry of the Pleistocene detritic sediments. We close the study by proposing that a good way of expressing the geographic distribution of the geochemical variability of a region could be to apply geologic extrapolation of three representative statistics, an average value (the 50th percentile), a low value (the 25th percentile) and a high value (the 75th percentile). As an example, we present the Th maps, which permit the clear discussion of the Th distribution in the basin. [ABSTRACT FROM AUTHOR]

Seawater contains high concentrations of sparingly soluble salts which can cause scaling of membrane surface, limiting the productivity and water recovery of seawater reverse osmosis (SWRO). Nanofiltration (NF) pretreatment of seawater, prevents scaling via preferential removal of scale-forming ions. Several studies have shown that the rejection of scale-forming ions is not the same for various membranes. In a previous study, a selection of the best NF membranes for scaling prevention in SWRO was developed using synthetic seawater. The main objective of this study is to test the same NF membranes using real seawater in order to compare the membrane performance using synthetic and real seawater. The seawater used in this study was collected in El Prat de Llobregat (Barcelona). The results obtained showed that the monovalent ions are less rejected in real seawater than in synthetic seawater. However, the rejection of scale forming ions has been practically the same for all membranes in both types of seawater, obtaining a sulphate rejection higher than 90% for the majority of membranes studied, which is highly important for scaling prevention. [ABSTRACT FROM AUTHOR]

Seawater contains high concentrations of sparingly soluble salts which can cause scaling of the membrane surface, which in turn can limit the productivity and water recovery potential of seawater reverse osmosis (SWRO). Nanofiltration (NF) pre-treatment of seawater can prevent scaling via preferential removal of scale-forming ions. Several studies have shown that the rejection of scale-forming ions can vary considerably depending on the membrane. The main objective of this study was to test a number of commercially available NF membranes with synthetic seawater in order to compare their performance and identify optimum membranes and operational conditions for the removal of compounds that are known to cause scaling (CaSO4(s), Mg(OH)2, CaCO3(s), etc.) in SWRO. Six membranes, supplied by several manufacturers, were tested in a laboratory-scale plant at various trans-membrane pressure differences. For the majority of membranes, the sulphate rejection was superior at 90%, which is very important for the prevention of scaling. Following on from the obtained results, a selection of the most suitable membranes for scaling prevention in seawater desalination can be done. [ABSTRACT FROM AUTHOR]

Abstract: The solution-diffusion-film model previously used for the description of rejection of single salts by NF membranes is extended to the case of electrolyte mixtures consisting of a single dominant salt and (any number of) trace ions. An especially simple analytical solution is obtained in the approximation of constant (composition-independent) membrane permeabilities with respect to the dominant salt and trace ions. A quasi-analytical protocol is developed for the correction of observable trace rejections for concentration polarization within the scope of film model. It is demonstrated that the trans-membrane transfer of trace ions is essentially influenced by the electric fields arising due to the differences in the membrane permeabilities with respect to the cations and anions of dominant salt. Accordingly, information on these permeabilities can be extracted from experimental data on the rejection of trace ions as a function of trans-membrane volume flow. The membrane permeability to the trace ions themselves can also be estimated. This is clearly advantageous over just a single value of membrane permeability to the salt obtainable from the interpretation of traditional measurements with single salts within the scope of the same model. This novel theoretical approach is used for the interpretation of experimental data on the rejection of various dominant single salts and trace ions by a commercial NF membrane in a cross-flow setup with a rectangular spacer-filled feed channel. In this way, quantitative information could be obtained on the membrane permeabilities with respect to several single ions (Na+, Cl−, Ca2+, SO4 2−) being parts of dominant salts as well as traces. This information can be useful for the optimization of nanofiltration of mixed electrolyte solutions in various applications. [ABSTRACT FROM AUTHOR]

Rationale and Objectives: The goal of this article is to analyze and compare the performance of a developed mass computer-aided detection (CAD) system that takes breast density information into account when using manual or automatic breast density annotations in the training step. The advantages of considering this breast density information will be highlighted. Materials and Methods: The image database used in this article is 92 mediolateral oblique (MLO) and 92 craniocaudal (CC) mammograms obtained by a full-field digital mammographic unit. All mammograms contain at least one mass. The evaluation of the experiments is performed using free receiver operating characteristic analysis for evaluating the detection performance and pixel-based receiver operating characteristic analysis for evaluating the segmentation accuracy. In addition, the performance of the automatic breast density classifier is shown using confusion matrices. Results: When the breast density information is not considered and at a specificity of two false positives per image, the sensitivity obtained by the CAD system is 0.747 for the CC views and 0.853 for the MLO views. Considering the breast density information, the sensitivity for CC and MLO mammograms increases to 0.800 and 0.893, respectively, using manual classification, and 0.827 and 0.907, respectively, using automatic estimation. The same trend is observed when evaluating the CAD segmentation accuracy for detected masses in terms of area under the curve values: without considering breast density, these are 0.920 ± 0.057 and 0.917 ± 0.072; using manual classification, 0.934 ± 0.039 and 0.932 ± 0.046; and using automatic estimation, 0.947 ± 0.038 and 0.946 ± 0.045 for CC and MLO views, respectively. Conclusions: The experiments showed improved results when breast density information was taken into account. Moreover, the results obtained when using automatic breast density estimation outperformed those based on the manual annotations provided by expert radiologists. In this sense, the experiments showed that breast density information can be beneficial for CAD systems, and this information can be estimated robustly by an automatic procedure, which reduces the inter- and intra-class variability of the radiologists. [Copyright &y& Elsevier]

A number of nano-filtration membranes exhibit quite high rejections of sulfates (98-99cZ) accompanied by quite moderate rejections (sometimes as low as 10-20% and even less) of chlorides from highly concentrated electrolyte solutions like sea water or various brines. In this communication, it is shown that this phenomenon can hardly be explained within the scope of commonly accepted model of nano-filtration where the barrier layer is considered a nano-porous medium, and, accordingly, there is a considerable convective coupling between the transfers of solutes and solvent. The very high experimentally observed sulfate/chloride selectivities turn out hardly reconcilable with the quite moderate values of rejection of NaCI. It is suggested that this phenomenon can be explained by the solution-diffusion mechanism of solute transfer across very thin and practically non-porous barrier layers. The approach of rejection to non-100% apparent saturation values observed experimentally can be explained by the concentration polarization. To verify this hypothesis, studies have been carried out of pressure-driven rejection of various single-salt electrolyte solutions (NaCl, Na2SO4 and CaCI2) by two commercial composite nanofiltration membranes (NF270 and NF200) in a cross-flow setup equipped with GE SEPA™ CF II test cell with the possibility of independent variation of trans-membrane pressure difference and cross-flow velocity. The experimental data could be very well fitted by the solution-diffusion-film model, the stagnant layer diffusion permeability depending in a reasonable way on the cross-flow velocity and solute bulk diffusion coefficient. [ABSTRACT FROM AUTHOR]

Sediments have long been recognised as a sink for many contaminants like polycyclic aromatic hydrocarbons (PAHs) and tributyltin (TBT), which by virtue of their nature can strongly adsorb onto sediments affecting the benthic community inhabiting them. Using geographical information systems, this study reports and combines the results of several already existing studies along Barcelona harbour in order to assess the potential ecological impacts of these contaminants on the benthos of the harbour ecosystem. Chemical analysis indicated low to moderate contents of PAHs and high contents of TBT in sediments in Barcelona harbour. Comparison against existing sediment quality guidelines (SQGs) indicated that acutely toxic effects would not be expected for PAHs but for TBT, which represents a serious environmental threat for the benthic community. Benthos surveys revealed a deterioration of the benthic community throughout the harbour, especially in the inner port. [Copyright &y& Elsevier]

Segmentation of brain sulci in pre-term infants is crucial for monitoring their development. While magnetic resonance imaging has been used for this purpose, cranial ultrasound (cUS) is the primary imaging technique used in clinical practice. Here, we present the first study aiming to automate brain sulci segmentation in pre-term infants using ultrasound images. Our study focused on segmentation of the Sylvian fissure in a single cUS plane (C3), although this approach could be extended to other sulci and planes. We evaluated the performance of deep learning models, specifically U-Net and ResU-Net, in automating the segmentation process in two scenarios. First, we conducted cross-validation on images acquired from the same ultrasound machine. Second, we applied fine-tuning techniques to adapt the models to images acquired from different vendors. The ResU-Net approach achieved Dice and Sensitivity scores of 0.777 and 0.784, respectively, in the cross-validation experiment. When applied to external datasets, results varied based on similarity to the training images. Similar images yielded comparable results, while different images showed a drop in performance. Additionally, this study highlighted the advantages of ResU-Net over U-Net, suggesting that residual connections enhance the model's ability to learn and represent complex anatomical structures. This study demonstrated the feasibility of using deep learning models to automatically segment the Sylvian fissure in cUS images. Accurate sonographic characterisation of cerebral sulci can improve the understanding of brain development and aid in identifying infants with different developmental trajectories, potentially impacting later functional outcomes. [ABSTRACT FROM AUTHOR]

The objective of this study was to explore through both in vitro and in vivo experiments the use of reclaimed urban wastewater in dairy cattle production systems with the aim of improving water efficiency and sustainability. Firstly, the use of different tertiary treatments (ultrafiltration (UF), ultraviolet disinfection (UV), chlorination process, and their combination) to improve the quality of an urban secondary effluent was studied in intestinal primary cell cultures evaluating the expression of genes related to apoptosis, cell damage, and inflammation. The results revealed that secondary treated wastewater and waters that were treated with a chlorination process (even tap water) caused an increase in apoptosis, intestinal primary cell damage, and inflammation. The in vivo experiment evaluated the short-term effects on health and performance of using UF- and UV-treated secondary effluent compared with the use of tap water for drinking and preparing milk replacer in young calves from 5 to 47 days of age. Calves previously fed with UF + UV treated secondary effluent clearly preferred tap water when they were exposed to a double water choice at the end of the study. This reduction of the palatability and acceptability was probably due to a greater level of water salinity of the treated reclaimed water (570 vs 1437 ± 76.5 μS/cm of conductivity for tap water and UF-UV treated secondary effluent, respectively), which potentially entailed a reduction of calf concentrate intake (466 vs 351 ± 32.2 g/d for calves fed with tap water and UF-UV treated water, respectively). The use of reclaimed water did not pose an acute risk to animal health. It is concluded that improvements on the tertiary treatment to reduce water salinity should be considered when using reclaimed water for drinking purposes in livestock production systems. This study is a first approach to a more sustainable and efficient use of water in animal husbandry for countries with water scarcity. However, more studies are required before its implementation to further study long-term effects and the presence of new-contaminants not defined in the current legislation. • Water scarcity and demand in agriculture imply innovative water reuse schemes. • Livestock drinking water quality standards are achieved by UF and UV disinfection. • Salinity of reclaimed water impairs animal performance. • Treated UF and UV reclaimed water does not pose an acute health risk for calves. [ABSTRACT FROM AUTHOR]

An in-depth analysis of computer vision methodologies is greatly dependent on the benchmarks they are tested upon. Any dataset is as good as the diversity of the true nature of the problem enclosed in it. Motion segmentation is a preprocessing step in computer vision whose publicly available datasets have certain limitations. Some databases are not up-to-date with modern requirements of frame length and number of motions, and others do not have ample ground truth in them. In this paper, we present a collection of diverse multifaceted motion segmentation benchmarks containing trajectory- and region-based ground truth. These datasets enclose real-life long and short sequences, with increased number of motions and frames per sequence, and also real distortions with missing data. The ground truth is provided on all the frames of all the sequences. A comprehensive benchmark evaluation of the state-of-the-art motion segmentation algorithms is provided to establish the difficulty of the problem and to also contribute a starting point. All the resources of the datasets have been made publicly available at http://dixie.udg.edu/udgms/ . [ABSTRACT FROM AUTHOR]

Automated methods for segmentation-based brain volumetry may be confounded by the presence of white matter (WM) lesions, which introduce abnormal intensities that can alter the classification of not only neighboring but also distant brain tissue. These lesions are common in pathologies where brain volumetry is also an important prognostic marker, such as in multiple sclerosis (MS), and thus reducing their effects is critical for improving volumetric accuracy and reliability. In this work, we analyze the effect of WM lesions on deep learning based brain tissue segmentation methods for brain volumetry and introduce techniques to reduce the error these lesions produce on the measured volumes. We propose a 3D patch-based deep learning framework for brain tissue segmentation which is trained on the outputs of a reference classical method. To deal more robustly with pathological cases having WM lesions, we use a combination of small patches and a percentile-based input normalization. To minimize the effect of WM lesions, we also propose a multi-task double U-Net architecture performing end-to-end inpainting and segmentation, along with a training data generation procedure. In the evaluation, we first analyze the error introduced by artificial WM lesions on our framework as well as in the reference segmentation method without the use of lesion inpainting techniques. To the best of our knowledge, this is the first analysis of WM lesion effect on a deep learning based tissue segmentation approach for brain volumetry. The proposed framework shows a significantly smaller and more localized error introduced by WM lesions than the reference segmentation method, that displays much larger global differences. We also evaluated the proposed lesion effect minimization technique by comparing the measured volumes before and after introducing artificial WM lesions to healthy images. The proposed approach performing end-to-end inpainting and segmentation effectively reduces the error introduced by small and large WM lesions in the resulting volumetry, obtaining absolute volume differences of 0.01 ± 0.03% for GM and 0.02 ± 0.04% for WM. Increasing the accuracy and reliability of automated brain volumetry methods will reduce the sample size needed to establish meaningful correlations in clinical studies and allow its use in individualized assessments as a diagnostic and prognostic marker for neurodegenerative pathologies. • We analyze white matter lesion effect on patch-based brain tissue segmentation. • A deep learning method is proposed for segmentation-based brain volumetry. • We learn from a reference method while reducing the error from white matter lesions. • Joint lesion inpainting and segmentation is trained end-to-end in a single stage. • Our results show a smaller effect of white matter lesions on measured volumes. [ABSTRACT FROM AUTHOR]

Ultrahigh resolution Fourier transform ion cyclotron mass spectrometry and nuclear magnetic resonance spectroscopy were combined to evaluate the molecular changes of dissolved organic matter (DOM) through an ultrafiltration-nanofiltration (UF-NF) pilot plant, using two dissimilar NF membranes tested in parallel. The sampling was performed on seven key locations within the pilot plant: pretreated water, UF effluent, UF effluent after addition of reagents, permeate NF 1, permeate NF 2, brine NF 1 and brine NF 2, during two sampling campaigns. The study showed that there is no significant change in the nature of DOM at molecular level, when the water was treated with UF and/or with the addition of sodium metabisulfite and antiscaling agents. However, enormous decrease of DOM concentration was observed when the water was treated on the NF membranes. The NF process preferentially removed compounds with higher oxygen and nitrogen content (more hydrophilic compounds), whereas molecules with longer pure aliphatic chains and less content of oxygen were the ones capable of passing through the membranes. Moreover, slight molecular selectivity between the two NF membranes was also observed. [ABSTRACT FROM AUTHOR]

Highlights: [•] An algorithm for multiple sclerosis lesion detection in brain MRI is presented. [•] An initial segmentation of brain tissues is performed by a modified EM algorithm. [•] Lesions are located as outliers of the grey matter FLAIR intensity distribution. [•] 45 cases from three different scanner machines were used to evaluate the algorithm. [•] The results show a moderate agreement between manual and automatic detections. [ABSTRACT FROM AUTHOR]

Abstract: Brain extraction, also known as skull stripping, is one of the most important preprocessing steps for many automatic brain image analysis. In this paper we present a new approach called Multispectral Adaptive Region Growing Algorithm (MARGA) to perform the skull stripping process. MARGA is based on a region growing (RG) algorithm which uses the complementary information provided by conventional magnetic resonance images (MRI) such as T1-weighted and T2-weighted to perform the brain segmentation. MARGA can be seen as an extension of the skull stripping method proposed by Park and Lee (2009) [1], enabling their use in both axial views and low quality images. Following the same idea, we first obtain seed regions that are then spread using a 2D RG algorithm which behaves differently in specific zones of the brain. This adaptation allows to deal with the fact that middle MRI slices have better image contrast between the brain and non-brain regions than superior and inferior brain slices where the contrast is smaller. MARGA is validated using three different databases: 10 simulated brains from the BrainWeb database; 2 data sets from the National Alliance for Medical Image Computing (NAMIC) database, the first one consisting in 10 normal brains and 10 brains of schizophrenic patients acquired with a 3T GE scanner, and the second one consisting in 5 brains from lupus patients acquired with a 3T Siemens scanner; and 10 brains of multiple sclerosis patients acquired with a 1.5T scanner. We have qualitatively and quantitatively compared MARGA with the well-known Brain Extraction Tool (BET), Brain Surface Extractor (BSE) and Statistical Parametric Mapping (SPM) approaches. The obtained results demonstrate the validity of MARGA, outperforming the results of those standard techniques. [Copyright &y& Elsevier]

Abstract: Normal and abnormal brains can be segmented by registering the target image with an atlas. Here, an atlas is defined as the combination of an intensity image (template) and its segmented image (the atlas labels). After registering the atlas template and the target image, the atlas labels are propagated to the target image. We define this process as atlas-based segmentation. In recent years, researchers have investigated registration algorithms to match atlases to query subjects and also strategies for atlas construction. In this paper we present a review of the automated approaches for atlas-based segmentation of magnetic resonance brain images. We aim to point out the strengths and weaknesses of atlas-based methods and suggest new research directions. We use two different criteria to present the methods. First, we refer to the algorithms according to their atlas-based strategy: label propagation, multi-atlas methods, and probabilistic techniques. Subsequently, we classify the methods according to their medical target: the brain and its internal structures, tissue segmentation in healthy subjects, tissue segmentation in fetus, neonates and elderly subjects, and segmentation of damaged brains. A quantitative comparison of the results reported in the literature is also presented. [Copyright &y& Elsevier]

Abstract: Under normal operation conditions, granular activated carbon (GAC) employed in drinking water treatment plants (DWTPs) for natural organic matter (NOM) removal can be colonised by microorganisms which can eventually establish active biofilms. The formation of such biofilms can contribute to NOM removal by biodegradation, but also in clogging phenomena that can make necessary more frequent backwashes. Biofilm occurrence and evolution under full-scale-like conditions (i.e. including periodic backwashing) are still uncertain, and GAC filtration is usually operated with a strong empirical component. The aim of the present study was to assess the formation and growth, if any, of biofilm in a periodically backwashed GAC filter. For this purpose, an on-site pilot plant was assembled and operated to closely mimic the GAC filters installed in the DWTP in Sant Joan Despí (Barcelona, Spain). The study comprised a monitoring of both water and GAC cores withdrawn at various depths and times throughout 1 year operation. The biomass parameters assessed were total cell count by confocal laser scanning microscopy (CLSM), DNA and adenosine triphosphate (ATP). Visual examination of GAC particles was also conducted by high-resolution field emission scanning electron microscopy (FESEM). Additionally, water quality and GAC surface properties were monitored. Results provided insight into the extent and spatial distribution of biofilm within the GAC bed. To sum up, it was found that backwashing could physically detach bacteria from the biofilm, which could however build back up to its pre-backwashing concentration before next backwashing cycle. [Copyright &y& Elsevier]

Abstract: Prostate segmentation is a challenging task, and the challenges significantly differ from one imaging modality to another. Low contrast, speckle, micro-calcifications and imaging artifacts like shadow poses serious challenges to accurate prostate segmentation in transrectal ultrasound (TRUS) images. However in magnetic resonance (MR) images, superior soft tissue contrast highlights large variability in shape, size and texture information inside the prostate. In contrast poor soft tissue contrast between prostate and surrounding tissues in computed tomography (CT) images pose a challenge in accurate prostate segmentation. This article reviews the methods developed for prostate gland segmentation TRUS, MR and CT images, the three primary imaging modalities that aids prostate cancer diagnosis and treatment. The objective of this work is to study the key similarities and differences among the different methods, highlighting their strengths and weaknesses in order to assist in the choice of an appropriate segmentation methodology. We define a new taxonomy for prostate segmentation strategies that allows first to group the algorithms and then to point out the main advantages and drawbacks of each strategy. We provide a comprehensive description of the existing methods in all TRUS, MR and CT modalities, highlighting their key-points and features. Finally, a discussion on choosing the most appropriate segmentation strategy for a given imaging modality is provided. A quantitative comparison of the results as reported in literature is also presented. [Copyright &y& Elsevier]

• We propose a U-Net-based architecture for hemorrhagic stroke segmentation in CT images. • The addition of squeeze-and-excitation blocks helps improving the results. • The use of brain symmetry helps to avoid segmentation of intraventricular hemorrhage. • Each CT volume is analyzed in approximately 17 seconds with mean Dice accuracy of 86.2%. Hemorrhagic stroke is the condition involving the rupture of a vessel inside the brain and is characterized by high mortality rates. Even if the patient survives, stroke can cause temporary or permanent disability depending on how long blood flow has been interrupted. Therefore, it is crucial to act fast to prevent irreversible damage. In this work, a deep learning-based approach to automatically segment hemorrhagic stroke lesions in CT scans is proposed. Our approach is based on a 3D U-Net architecture which incorporates the recently proposed squeeze-and-excitation blocks. Moreover, a restrictive patch sampling is proposed to alleviate the class imbalance problem and also to deal with the issue of intra-ventricular hemorrhage, which has not been considered as a stroke lesion in our study. Moreover, we also analyzed the effect of patch size, the use of different modalities, data augmentation and the incorporation of different loss functions on the segmentation results. All analyses have been performed using a five fold cross-validation strategy on a clinical dataset composed of 76 cases. Obtained results demonstrate that the introduction of squeeze-and-excitation blocks, together with the restrictive patch sampling and symmetric modality augmentation, significantly improved the obtained results, achieving a mean DSC of 0.86 ± 0.074 , showing promising automated segmentation results. [ABSTRACT FROM AUTHOR]