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Background: Melanoma incidence has continued to rise in the latest decades, and the forecast is not optimistic. Non-invasive diagnostic imaging techniques such as optical coherence tomography (OCT) are largely studied; however, there is still no agreement on its use for the diagnosis of melanoma. For dermatologists, the differentiation of non-invasive (junctional nevus, compound nevus, intradermal nevus, and melanoma in-situ) versus invasive (superficial spreading melanoma and nodular melanoma) lesions is the key issue in their daily routine., Methods: This work performs a comparative analysis of OCT images using haematoxylin-eosin (HE) and anatomopathological features identified by a pathologist. Then, optical and textural properties are extracted from OCT images with the aim to identify subtle features that could potentially maximize the usefulness of the imaging technique in the identification of the lesion's potential invasiveness., Results: Preliminary features reveal differences discriminating melanoma in-situ from superficial spreading melanoma and also between melanoma and nevus subtypes that pose a promising baseline for further research., Conclusions: Answering the final goal of diagnosing non-invasive versus invasive lesions with OCT does not seem feasible in the short term, but the obtained results demonstrate a step forward to achieve this., Competing Interests: The authors declare no conflict of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript; or in the decision to publish the results.

Background: Alzheimer's is a degenerative dementing disorder that starts with a mild memory impairment and progresses to a total loss of mental and physical faculties. The sooner the diagnosis is made, the better for the patient, as preventive actions and treatment can be started. Although tests such as the Mini-Mental State Tests Examination are usually used for early identification, diagnosis relies on magnetic resonance imaging (MRI) brain analysis., Methods: Public initiatives such as the OASIS (Open Access Series of Imaging Studies) collection provide neuroimaging datasets openly available for research purposes. In this work, a new method based on deep learning and image processing techniques for MRI-based Alzheimer's diagnosis is proposed and compared with previous literature works., Results: Our method achieves a balance accuracy (BAC) up to 0.93 for image-based automated diagnosis of the disease, and a BAC of 0.88 for the establishment of the disease stage (healthy tissue, very mild and severe stage)., Conclusions: Results obtained surpassed the state-of-the-art proposals using the OASIS collection. This demonstrates that deep learning-based strategies are an effective tool for building a robust solution for Alzheimer's-assisted diagnosis based on MRI data.

This paper describes the methodology followed to implement social distancing recommendations in the COVID-19 context along the beaches of the coast of Gipuzkoa (Basque Country, Northern Spain) by means of automated coastal videometry. The coastal videometry network of Gipuzkoa, based on the KostaSystem technology, covers 14 beaches, with 12 stations, along 50 km of coastline. A beach user detection algorithm based on a machine learning approach has been developed allowing for automatic assessment of beach attendance in real time at regional scale. For each beach, a simple classification of occupancy (low, medium, high, and full) was estimated as a function of the beach user density (BUD), obtained in real time from the images and the maximum beach carrying capacity (BCC), estimated based on the minimal social distance recommended by the authorities. This information was displayed in real time via a web/mobile app and was simultaneously sent to beach managers who controlled the beach access. The results showed a strong receptivity from beach users (more than 50.000 app downloads) and that real time information of beach occupation can help in short-term/daily beach management. In the longer term, the analysis of this information provides the necessary data for beach carrying capacity management and can help the authorities in controlling and in determining their maximum capacity., Competing Interests: The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (© 2021 Elsevier Ltd. All rights reserved.)

Background: The high incidence and mortality rate of colorectal cancer require new technologies to improve its early diagnosis. This study aims at extracting the medical needs related to the endoscopic technology and the colonoscopy procedure currently used for colorectal cancer diagnosis, essential for designing these demanded technologies., Methods: Semi-structured interviews and an online survey were used., Results: Six endoscopists were interviewed and 103 were surveyed, obtaining the demanded needs that can be divided into: a) clinical needs, for better polyp detection and classification (especially flat polyps), location, size, margins and penetration depth; b) computer-aided diagnosis (CAD) system needs, for additional visual information supporting polyp characterization and diagnosis; and c) operational/physical needs, related to limitations of image quality, colon lighting, flexibility of the endoscope tip, and even poor bowel preparation., Conclusions: This study shows some undertaken initiatives to meet the detected medical needs and challenges to be solved. The great potential of advanced optical technologies suggests their use for a better polyp detection and classification since they provide additional functional and structural information than the currently used image enhancement technologies. The inspection of remaining tissue of diminutive polyps (< 5 mm) should be addressed to reduce recurrence rates. Few progresses have been made in estimating the infiltration depth. Detection and classification methods should be combined into one CAD system, providing visual aids over polyps for detection and displaying a Kudo-based diagnosis suggestion to assist the endoscopist on real-time decision making. Estimated size and location of polyps should also be provided. Endoscopes with 360° vision are still a challenge not met by the mechanical and optical systems developed to improve the colon inspection. Patients and healthcare providers should be trained to improve the patient's bowel preparation.

Background: Deep learning diagnostic algorithms are proving comparable results with human experts in a wide variety of tasks, and they still require a huge amount of well-annotated data for training, which is often non affordable. Metric learning techniques have allowed a reduction in the required annotated data allowing few-shot learning over deep learning architectures., Aims and Objectives: In this work, we analyze the state-of-the-art loss functions such as triplet loss, contrastive loss, and multi-class N-pair loss for the visual embedding extraction of hematoxylin and eosin (H&E) microscopy images and we propose a novel constellation loss function that takes advantage of the visual distances of the embeddings of the negative samples and thus, performing a regularization that increases the quality of the extracted embeddings., Materials and Methods: To this end, we employed the public H&E imaging dataset from the University Medical Center Mannheim (Germany) that contains tissue samples from low-grade and high-grade primary tumors of digitalized colorectal cancer tissue slides. These samples are divided into eight different textures (1. tumour epithelium, 2. simple stroma, 3. complex stroma, 4. immune cells, 5. debris and mucus, 6. mucosal glands, 7. adipose tissue and 8. background,). The dataset was divided randomly into train and test splits and the training split was used to train a classifier to distinguish among the different textures with just 20 training images. The process was repeated 10 times for each loss function. Performance was compared both for cluster compactness and for classification accuracy on separating the aforementioned textures., Results: Our results show that the proposed loss function outperforms the other methods by obtaining more compact clusters (Davis-Boulding: 1.41 ± 0.08, Silhouette: 0.37 ± 0.02) and better classification capabilities (accuracy: 85.0 ± 0.6) over H and E microscopy images. We demonstrate that the proposed constellation loss can be successfully used in the medical domain in situations of data scarcity., (Copyright: © 2020 Journal of Pathology Informatics.)

The synthesis of minerals should play a crucial role in the development of new and advanced materials. Since 2016, a renewal of interest to enlarge the mineral synthesis towards industrial requirements has been observed, especially thanks to three main recent papers. The innovative process exposed combines a continuous process with the use of supercritical water. In addition to curtail synthesis times, this process offers a control on the particle characteristics (size, crystallinity, structure, etc.) and a capacity to be easily transferable at an industrial scale. This innovative concept is demonstrated with the synthesis of three minerals which are the talc (a phyllomineral), the xonotlite and the tobermorite (two inominerals). This article proposes an overview of the possibilities to synthesize nanominerals in continuous processing for the development of advanced materials with promising industrial applications., (© 2019 Wiley-VCH Verlag GmbH & Co. KGaA, Weinheim.)

Nowadays huge volumes of data are produced in the form of fast streams, which are further affected by non-stationary phenomena. The resulting lack of stationarity in the distribution of the produced data calls for efficient and scalable algorithms for online analysis capable of adapting to such changes (concept drift). The online learning field has lately turned its focus on this challenging scenario, by designing incremental learning algorithms that avoid becoming obsolete after a concept drift occurs. Despite the noted activity in the literature, a need for new efficient and scalable algorithms that adapt to the drift still prevails as a research topic deserving further effort. Surprisingly, Spiking Neural Networks, one of the major exponents of the third generation of artificial neural networks, have not been thoroughly studied as an online learning approach, even though they are naturally suited to easily and quickly adapting to changing environments. This work covers this research gap by adapting Spiking Neural Networks to meet the processing requirements that online learning scenarios impose. In particular the work focuses on limiting the size of the neuron repository and making the most of this limited size by resorting to data reduction techniques. Experiments with synthetic and real data sets are discussed, leading to the empirically validated assertion that, by virtue of a tailored exploitation of the neuron repository, Spiking Neural Networks adapt better to drifts, obtaining higher accuracy scores than naive versions of Spiking Neural Networks for online learning environments., (Copyright © 2018 Elsevier Ltd. All rights reserved.)

Major challenges are presented when managing a large number of heterogeneous vehicles that have to communicate underwater in order to complete a global mission in a cooperative manner. In this kind of application domain, sending data through the environment presents issues that surpass the ones found in other overwater, distributed, cyber-physical systems (i.e., low bandwidth, unreliable transport medium, data representation and hardware high heterogeneity). This manuscript presents a Publish/Subscribe-based semantic middleware solution for unreliable scenarios and vehicle interoperability across cooperative and heterogeneous autonomous vehicles. The middleware relies on different iterations of the Data Distribution Service (DDS) software standard and their combined work between autonomous maritime vehicles and a control entity. It also uses several components with different functionalities deemed as mandatory for a semantic middleware architecture oriented to maritime operations (device and service registration, context awareness, access to the application layer) where other technologies are also interweaved with middleware (wireless communications, acoustic networks). Implementation details and test results, both in a laboratory and a deployment scenario, have been provided as a way to assess the quality of the system and its satisfactory performance., Competing Interests: The authors declare no conflict of interest.

The utilization of autonomous maritime vehicles is becoming widespread in operations that are deemed too hazardous for humans to be directly involved in them. One of the ways to increase the productivity of the tools used during missions is the deployment of several vehicles with the same objective regarding data collection and transfer, both for the benefit of human staff and policy makers. However, the interchange of data in such an environment poses major challenges, such as a low bandwidth and the unreliability of the environment where transmissions take place. Furthermore, the relevant information that must be sent, as well as the exact size that will allow understanding it, is usually not clearly established, as standardization works are scarce in this domain. Under these conditions, establishing a way to interchange information at the data level among autonomous maritime vehicles becomes of critical importance since the needed information, along with the size of the transferred data, will have to be defined. This manuscript puts forward the Maritime Data Transfer Protocol, (MDTP) a way to interchange standardized pieces of information at the data level for maritime autonomous maritime vehicles, as well as the procedures that are required for information interchange., Competing Interests: The authors declare no conflict of interest.

Tobermorite is a fibrillar mineral of the family of calcium silicates. In spite of not being abundant in nature, its structure and properties are reasonably well known because of its interest in the construction industry. Currently, tobermorite is synthesized by hydrothermal methods at mild temperatures. The problem is that such processes are very slow (>5 h) and temperature cannot be increased to speed them up because tobermorite is metastable over 130 °C. Furthermore the product obtained is generally foil-like and not very crystalline. Herein we propose an alternative synthesis method based on the use of a continuous flow reactor and supercritical water. In spite of the high temperature, the transformation of tobermorite to more stable phases can be prevented by accurately controlling the reaction time. As a result, highly crystalline fibrillar tobermorite can be obtained in just a few seconds under thermodynamically metastable conditions., (© 2017 Wiley-VCH Verlag GmbH & Co. KGaA, Weinheim.)

In order to facilitate cooperation between underwater robots, it is a must for robots to exchange information with unambiguous meaning. However, heterogeneity, existing in information pertaining to different robots, is a major obstruction. Therefore, this paper presents a networked ontology, named the Smart and Networking Underwater Robots in Cooperation Meshes (SWARMs) ontology, to address information heterogeneity and enable robots to have the same understanding of exchanged information. The SWARMs ontology uses a core ontology to interrelate a set of domain-specific ontologies, including the mission and planning, the robotic vehicle, the communication and networking, and the environment recognition and sensing ontology. In addition, the SWARMs ontology utilizes ontology constructs defined in the PR-OWL ontology to annotate context uncertainty based on the Multi-Entity Bayesian Network (MEBN) theory. Thus, the SWARMs ontology can provide both a formal specification for information that is necessarily exchanged between robots and a command and control entity, and also support for uncertainty reasoning. A scenario on chemical pollution monitoring is described and used to showcase how the SWARMs ontology can be instantiated, be extended, represent context uncertainty, and support uncertainty reasoning.

In this work we study the influence of adding nano-silica (SiO2, Nyasil™) and aminopropyl (-(CH2)3-NH2,) functionalized silica nanoparticles (Stoga) during the synthesis of calcium-silicate-hydrate (C-S-H gel). Characterization by solid state (29)Si NMR and ATR-FTIR spectroscopy showed that the addition of both particle types increases the average length of the silicate chains in C-S-H gel being this effect slightly more important in the case of Stoga particles. In addition, (13)C NMR and XPS confirmed that the aminopropyl chain remains in the final product cleaved to silicon atoms at the end of the silicate chain of C-S-H gel whereas XRD measurements showed that this result in an increment in the basal distance compared with ordinary CSH. In addition, the dynamics of water within the pores of C-S-H gel was analyzed by broadband dielectric spectroscopy. We observed that water confined in C-S-H formed with the addition of nanoparticles is faster than that in plain C-S-H which can be related to a different porous structure in these materials., (Copyright © 2015 Elsevier Inc. All rights reserved.)

This work explores the competitive removal of pharmaceuticals from synthetic and environmental waters by combined adsorption-photolysis treatment. Five drugs usually present in waterways have been used as target compounds, some are pseudo-persistent pollutants (carbamazepine, clofibric acid, and sulfamethoxazole) and others are largely consumed (diclofenac and ibuprofen). The effect of the light source on adsorption of drugs onto activated carbons followed by photolysis with TiO2 was assessed, being UV-C light the most effective for drug removal in both deionized water and river water. Different composites prepared from titania nanoparticles and powdered activated carbons were tested in several combined adsorption-photocatalysis assays. The composites prepared by calcination at 400 °C exhibited much better performance than those synthesized at 500 °C, being the C400 composite the most effective one. Furthermore, some synthetic waters containing dissolved species and environmental waters were used to investigate the effect of the aqueous matrix on each drug removal. In general, photocatalyst deactivation was found in synthetic and environmental waters. This was particularly evident in the experiments performed with bicarbonate ions as well as with wastewater effluent. In contrast, tests conducted in seawater showed adsorption and photocatalytic degradation yields comparable to those obtained in deionized water. Considering the peculiarities of substrate competition in each aqueous matrix, the combined adsorption-photolysis treatment generally increased the overall elimination of drugs in water.

There is extensive evidence of the negative impacts on health linked to the rise of the regional background of particulate matter (PM) 10 levels. These levels are often increased over urban areas becoming one of the main air pollution concerns. This is the case on the Bilbao metropolitan area, Spain. This study describes a data-driven model to diagnose PM10 levels in Bilbao at hourly intervals. The model is built with a training period of 7-year historical data covering different urban environments (inland, city centre and coastal sites). The explanatory variables are quantitative-log [NO2], temperature, short-wave incoming radiation, wind speed and direction, specific humidity, hour and vehicle intensity-and qualitative-working days/weekends, season (winter/summer), the hour (from 00 to 23 UTC) and precipitation/no precipitation. Three different linear regression models are compared: simple linear regression; linear regression with interaction terms (INT); and linear regression with interaction terms following the Sawa's Bayesian Information Criteria (INT-BIC). Each type of model is calculated selecting two different periods: the training (it consists of 6 years) and the testing dataset (it consists of 1 year). The results of each type of model show that the INT-BIC-based model (R(2) = 0.42) is the best. Results were R of 0.65, 0.63 and 0.60 for the city centre, inland and coastal sites, respectively, a level of confidence similar to the state-of-the art methodology. The related error calculated for longer time intervals (monthly or seasonal means) diminished significantly (R of 0.75-0.80 for monthly means and R of 0.80 to 0.98 at seasonally means) with respect to shorter periods.

This review describes recent efforts on the synthesis, dispersion and surface functionalization of the three dominating oxide nanoparticles used for photocatalytic, UV-blocking and sunscreen applications: titania, zinc oxide, and ceria. The gas phase and liquid phase synthesis is described briefly and examples are given of how weakly aggregated photocatalytic or UV-absorbing oxide nanoparticles with different composition, morphology and size can be generated. The principles of deagglomeration are reviewed and the specific challenges for nanoparticles highlighted. The stabilization of oxide nanoparticles in both aqueous and non-aqueous media requires a good understanding of the magnitude of the interparticle forces and the surface chemistry of the materials. Quantitative estimates of the Hamaker constants in various media and measurements of the isoelectric points for the different oxide nanoparticles are presented together with an overview of different additives used to prepare stable dispersions. The structural and chemical requirements and the various routes to produce transparent photocatalytic and nanoparticle-based UV-protecting coatings, and UV-blocking sunscreens are described and discussed.

The objective of the study is to analyze the technical suitability of using secondary materials from three waste flows (construction and demolition waste (CDW), Waelz slag and municipal solid waste incineration (MSWI) bottom ash), under the regulations and standards governing the use of materials for road construction. A detailed technical characterization of the materials was carried out according to Spanish General Technical Specifications for Road Construction (PG3). The results show that Waelz slag can be adequate for using in granular structural layers, while CDW fits better as granular material in roadbeds. Likewise, fresh MSWI bottom ash can be used as roadbed material as long as it does not contain a high concentration of soluble salts. This paper also discusses the adequacy of using certain traditional test methods for natural soils when characterizing secondary materials for use as aggregates in road construction.