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Surface energy and surface charges play crucial roles in bacterial adhesion and biofilm formation, however the mechanisms underlying the bacteria-surface interaction, particularly on the formation of soil biofilms, remain unclear. In spite of considering the spatiotemporal dynamics of biofilm formation on different soil surfaces, we compared the impact of four different substrates on bacterial attachment and biofilm formation. The substrates were constituted of gold layer covered by NH2+, CH3, COO-and OH-terminated self-assembled monolayers (SAMS). Two soil habitat bacteria with different Gram barriers,Bacillus subtilisandAcinetobacter baylyi, were grown with incubation times of 6-72 h on each type of surfaces. Bacterial attachment and biofilm formation was assessed using metabolic activity of the cells adhered to the surfaces. The spatial distribution of adhere bacteria was visualized by scanning electron microscopy and confocal laser scanning microscopy. We also investigated whether the surface impacts the biofilm matrix composition. A general view of our results suggests a major influence of the surface chemistry on bacterial potential to form biofilms. The hydrophobic or positively charged substrates attract bacteria while a lack of attachment and biofilm formation on hydrophilic and negatively charged surfaces. This work points out the potential of surface treatments in the environment where it is intended to either repel or attract bacteria.

The sudden increase of wearable devices has led to the generation of an abundance of data. As a result, researchers can use such data to perform analyses and generate recommendations. A crucial factor in the research field of nutrition and dietetics is the accurate measurement of metabolic rate, as it can be used to estimate several other variables, e.g. calorie expenditure. Nonetheless, limited studies have been conducted to examine the use of machine learning models for metabolic rate prediction based on data generated from wearable devices. Therefore, in this paper, a neural network architecture is proposed, able to predict a subject’s metabolic rate exploiting only such data. Experimental results demonstrated that the proposed methodology can outperform conventional algorithms in prediction accuracy of real-world data. Furthermore, results indicated that the trivial time taken for the network to predict the metabolic rate makes it suitable for wearable devices deployment.

Building a rich and informative model from raw data is a hard but valuable process with many applications. Ship routing and scheduling are two essential operations in the maritime industry that can save a lot of resources if they are optimally designed, but still, need a lot of information to be successful. Past and recent works in the field assume the availability of information such as the birth time-windows, cargo volumes, and container handling productivity at ports and cruising speed. They employ navigation maps that contain information about the major sailing paths and have knowledge about bigger or smaller ports and offshore platforms. In this work, we present a methodology for extracting information about the navigation network for an area, using data from the trajectories of multiple vessels, which are collected using the Automatic Identification System (AIS). We introduce a method for identifying the points of major interest to the trajectory of a vessel and two clustering techniques for identifying: i) key areas in the monitored region such as ports, platforms or areas where vessels change their course (e.g., capes); and ii) the speed and course patterns of ships of a particular type when they follow a typical route. The resulting information is modeled using a network abstraction where nodes correspond to the areas identified by the first clustering technique. After, edges are enriched with information about the groups extracted using the second clustering technique. The first analysis on a real dataset in the area of the eastern Mediterranean sea demonstrates the capabilities of the proposed model and the information it can provide. The use of the model in an outlier behavior detection task also shows interesting results.

The rate at which endogenous DNA from differently prepared (butchered, boiled and baked) compact pig bones degrades in five different Danish terrestrial and marine environments over 12 months was investigated. Although > 70% of the estimated endogenous mtDNA is lost after just four weeks of exposure, no cytosine deamination of DNA was recognised. A correlation between the presence of oxygen and the amount of preserved DNA was observed. The results provide valuable information on the interaction between the endogenous DNA and the depositional environment in the early stages of bone diagenesis, which can be a support in the interpretation of the initial diagenetic pathways of archaeological bone.

Funder: Greek Archaeological Committee UK (GACUK), Funder: Leventis Foundation; Id: http://dx.doi.org/10.13039/501100004117, This study compares histological preservation in archeological bones from different burial types to unravel the histotaphonomy‐to‐funerary practices relationship. Αn intraskeletal approach is also adopted to explore intraindividual (inner ear part of the petrous bone vs. upper/lower limb long bones) and intrabone (proximal vs. distal diaphysis) variability in bone collagen preservation, δ13C, and δ15N. The aim is to (a) target bones that likely retain higher amounts of collagen, (b) better understand the inner ear bone collagen isotopic signature and remodeling, and (c) assess intrabone isotopic and histological homogeneity. For the histological analysis, the data have been collected from 61 specimens (20 individuals) from the medieval/postmedieval cemetery of St. Rombout, Belgium. Thin sections have been studied using optical and scanning electron microscopy. For the collagen and isotopic data, 101 samples have been collected from 21 individuals. Distinct histological patterns are observed only in bones from single coffin burials; however, bone histology can display intraindividual and intrabone variability, which are important to account for interpretations. Collagen wt.%, δ13C, and δ15N show significant intraindividual differences but insignificant intrabone variability. This study also confirms the extraordinary nature of the petrous bone, as the inner ear bone collagen δ13C and δ15N values reflect the dietary input of the first approximately 2–3 years of life.

Cremation is a complex mortuary practice, involving a number of activities of the living towards the dead before, during, and after the destruction of the bodily soft tissues by fire. The limiting information concerning these behavioral patterns obtained from the pyre remains and/or cremation deposits prevents the reconstruction of the handling of the corpse during the burning process. This pioneering study tries to determine the initial positioning of the corpse in the pyre and assess whether the deceased was wearing closed leather shoes during cremation through isotopic (δ13C, δ18O) and infrared (ATR-FTIR) analyses of experimentally burnt pig remains, used as a proxy for humans. The results obtained show that both the position of feet on or within the pyre and the presence of footwears may moderately-to-highly influence the oxygen isotope ratios of bone apatite carbonates and the cyanamide content of calcined bone in certain situations. By forming a protective layer, shoes appear to temporarily delay the burning of the underlying pig tissues and to increase the heat-shielding effect of the soft tissues protecting the bone mineral fraction. In such case, bioapatite bone carbonates exchange oxygen with a relatively more 18O-depleted atmosphere (due to the influence of lignin-derived oxygen rather than cellulose-derived oxygen), resulting in more pronounced decrease in the δ18Ocarb values during burning of the shoed feet vs. unshoed feet. The shift observed here was as high as 2.5. A concomitant isotopic effect of the initial location of the feet in the pyres was also observed, resulting in a top-to-bottom decrease difference in the δ18Ocarb values of shoed feet of about 1.4 between each deposition level tested. Finally, the presence of cyanamide (CN/P 0.02) seems to be indicative of closed footwear since the latter creates favorable conditions for its incorporation into bone apatite., SCOPUS: ar.j, info:eu-repo/semantics/published

The maritime industry is intensively embracing green thinking. According to the International Maritime Organization’s (IMO) Greenhouse Gas (GHG ) strategy, the total annual GHG emissions from international shipping should be reduced by at least 50% by 2050 compared to 2008. Shipping adopts policies to comply with the set target, including ship redesign, structural retrofit, use of low-carbon material, and the installation of emission abatement technologies. All these approaches pave the way to circularity in the maritime economy, abandoning the linear model in vessel lifetime and adopting lean management, re-manufacturing, and re-usability of the asset. To this end, in the SmartShip project, we give prominence to data-driven ship monitoring by delivering an Information and Communication Technology (ICT) & Internet of Things (IoT)-enabled holistic cloud-based maritime performance and monitoring system. This system is considering the entire lifecycle of a ship, aiming to optimize energy efficiency, emissions reduction, fuel consumption, while, at the same time, include circular economy concepts in the maritime field. Our approach supports a cost-effective strategy where data analysis drives decisions in ship operation and maintenance.

Fourier transform infrared (FTIR) spectroscopy using attenuated total reflection (ATR) is commonly used for the examination of bone. During sample preparation bone is commonly ground, changing the particle size distribution. Although previous studies have examined changes in crystallinity caused by the intensity of grinding using FTIR, the effect of sample preparation (i.e. particle size and bone tissue type) on the FTIR data is still unknown. This study reports on the bone powder particle size effects on mid-IR spectra and within sample variation (i.e. periosteal, mesosteal, trabecular) using FTIR-ATR. Twenty-four archaeological human and faunal bone samples (5 heated and 19 unheated) of different chronological age (Neolithic to post-Medieval) and origin (Belgium, Britain, Denmark, Greece) were ground using either (1) a ball-mill grinder, or (2) an agate pestle and mortar, and split into grain fractions (>500 μm, 250–500 μm, 125–250 μm, 63–125 μm, and 20–63 μm). Bone powder particle size has a strong but predictable effect on the infrared splitting factor (IRSF), carbonate/phosphate (C/P) ratio, and amide/phosphate (Am/P) values. The absorbance and positions of the main peaks, the 2nd derivative components of the phosphate and carbonate bands, as well as the full width at half maximum (FWHM) of the 1010 cm−1 phosphate peak are particle size dependent. This is likely to be because of the impact of the particle size on the short- and long-range crystal order, as well as the contact between the sample and the prism, and hence the penetration depth of the IR light. Variations can be also observed between periosteal, cortical and trabecular areas of bone. We therefore propose a standard preparation method for bone powder for FTIR-ATR analysis that significantly improves accuracy, consistency, reliability, replicability and comparability of the data, enabling systematic evaluation of bone in archaeological, anthropological, paleontological, forensic and biomedical studies.

In this paper we present a complete framework for modelling and estimating vessel GHG emissions and related air pollutants (i.e. CO2 and SOx, NOx and PM) in ports, based on data collected from the Automatic Identification System (AIS). Our approach adopts a modified lambda architecture approach, which consists of a knowledge extraction batch processing step and a real time emissions calculation step. The approach makes it possible to automatically identify the berths or ports where emissions are high in a consistent and uniform way across the globe. This research is part of the Project OPS Master Plan for Spanish Ports (2015-EU-TM-0417) which is co-financed by the Connecting Europe Facility (CEF) for the building of the European Union's Trans-European Transport Network (TEN-T)., http://poweratberth.eu/

Nowadays, the increasing number of moving objects tracking sensors, results in the continuous flow of high-frequency and high-volume data streams. This phenomenon can especially be observed in the maritime domain since most of the vessels worldwide are now transmitting their positions periodically. Therefore, there is a strong necessity to extract meaningful information and identify mobility patterns from such tracking data in an automated fashion, eliminating the need for experts’ input. To this end, a novel approach is presented in this paper, which fuses the research fields of computer vision and trajectory classification, in order to deliver a high-precision classification of mobility patterns. The experimental results demonstrate that the classification performance of the proposed approach can reach an f1-score of over 95%.

Today’s industry is flooded with tracking data originating from vessels across the globe that transmit their position at frequent intervals. These voluminous and high-speed streams of data has led researchers to develop novel ways to compress them in order to speed-up processing without losing valuable information. To this end, several algorithms have been developed that try to compress streams of vessel tracking data without compromising their spatio-temporal and kinematic features. In this paper, we present a wide range of several well-known trajectory compression algorithms and evaluate their performance on data originating from vessel trajectories. Trajectory compression algorithms included in this research are suitable for either historical data (offline compression) or real-time data streams (online compression). The performance evaluation is three-fold and each algorithm is evaluated in terms of compression ratio, execution speed and information loss. Experiments demonstrated that each algorithm has its own benefits and limitations and that the choice of a suitable compression algorithm is application-dependent. Finally, considering all assessed aspects, the Dead-Reckoning algorithm not only presented the best performance, but it also works over streaming data, which constitutes an important criterion in maritime surveillance.

Studies of funerary practices provide information about many aspects of death in past societies. However, only limited archaeological evidence documents the circumstances under which cremations occurred and the person(s) who were performing the funerary rituals. Lying at the border between Atlantic and Continental cultural traditions, the Scheldt and Meuse basins of Belgium represent a unique location to investigate variations in ancient pyre technology and body management, as well as the transfer of knowledge related to cremation techniques during the Metal Ages (ca. 2100-52 BCE). The combined use of Fourier Transform Infrared spectroscopy, and carbon and oxygen isotope analysis of different skeletal elements from cremation deposits from four archaeological sites clearly shows differences between the Meuse and Scheldt basins. Different wood availability or selection, and variations in the skills and/or experience of the cremation operator may explain these results. These observed differences are likely linked to ways in which cremation was performed in the two basins, indicating that during the Metal Ages, burning processes were not homogeneous in the Belgian region. Instead, cremation practices appear to align with the different cultural influences also observed in ceramics and bronze artifacts from the same time period. These observed differences in funerary practices between the two basins in Belgium show the immense potential of combining infrared and carbon and oxygen isotope analyses to investigate cremation rituals in any period and region around the world.

Massive volumes of high-frequency and high-volume data are constantly being generated by the vast amount of available tracking sensors of moving objects. This phenomenon can be strongly observed in the maritime domain since most of the vessels transmit their location periodically. Automated methodologies able to extract meaningful information from vessel data is of utmost importance since it can reveal abnormal or illegal vessel activities in due time. Supervised deep learning approaches such as convolutional neural networks (CNNs) require a large-scale annotated image dataset. Thus, semi- and unsupervised feature learning algorithms which learn image features from unlabelled data are able to mitigate this problem. To this end, in this work we propose a semi-supervised convolutional autoencoder (CAE) model for trajectory classification which is able to provide high-precision classification of mobility patterns. Experimental results demonstrated that the vessel activity classification performance can reach an F1-score of over 94%.

Summary The Cycladic, the Minoan, and the Helladic (Mycenaean) cultures define the Bronze Age (BA) of Greece. Urbanism, complex social structures, craft and agricultural specialization, and the earliest forms of writing characterize this iconic period. We sequenced six Early to Middle BA whole genomes, along with 11 mitochondrial genomes, sampled from the three BA cultures of the Aegean Sea. The Early BA (EBA) genomes are homogeneous and derive most of their ancestry from Neolithic Aegeans, contrary to earlier hypotheses that the Neolithic-EBA cultural transition was due to massive population turnover. EBA Aegeans were shaped by relatively small-scale migration from East of the Aegean, as evidenced by the Caucasus-related ancestry also detected in Anatolians. In contrast, Middle BA (MBA) individuals of northern Greece differ from EBA populations in showing ∼50% Pontic-Caspian Steppe-related ancestry, dated at ca. 2,600-2,000 BCE. Such gene flow events during the MBA contributed toward shaping present-day Greek genomes., Graphical abstract, Highlights • Bronze Age (BA) Helladic, Cycladic, and Minoan genomes from the Aegean were sequenced • 3,000 BCE Aegeans are homogeneous and derive ancestry mainly from Neolithic farmers • Neolithic Caucasus-like and BA Pontic-Caspian Steppe-like gene flow shaped the Aegean • Present-day Greeks are genetically similar to 2,000 BCE Aegeans from Northern Greece, The genomic analysis of ancient individuals from important archaeological sites across Aegean cultures suggests that the Aegean during the Bronze Age was at a genomic crossroads, and separate migration waves coincide with cultural shifts that had important impacts on Bronze Age cultures and the formation of the modern Greek population.

Le projet CRUMBEL vise à étudier la mobilité ancienne des populations sur le territoire de la Belgique, du Néolithique au début du Moyen Âge. Pour atteindre ces objectifs de recherche, différents sujets seront étudiés. Dans une première phase, les anciennes collections d’os humains incinérés seront répertoriées. Des échantillons provenant des sites funéraires feront ensuite l’objet d’analyses archéométriques des isotopes stables et de datations radiocarbones. The CRUMBEL project aims to investigate the mobility of the former population in Belgium from the Neolithic period until the Early Middle Ages. To reach these research goals different topics will be studied. In a preliminary phase, the ancient collections of cremated bone will be documented. A selection of these funerary sites will be studied to understand the mobility using different archaeometric approaches as stable isotopes and radiocarbon dating to obtain reliable information on earlier mobility in Belgium.

The COVID-19 pandemic in 2020 has highlighted the need to pull all available resources towards the mitigation of the devastating effects of such “Black Swan” events. Towards that end, we investigated the option to employ technology in order to assist the diagnosis of patients infected by the virus. As such, several state-of-the-art pre-trained convolutional neural networks were evaluated as of their ability to detect infected patients from chest X-Ray images. A dataset was created as a mix of publicly available X-ray images from patients with confirmed COVID-19 disease, common bacterial pneumonia and healthy individuals. To mitigate the small number of samples, we employed transfer learning, which transfers knowledge extracted by pre-trained models to the model to be trained. The experimental results demonstrate that the classification performance can reach an accuracy of 95% for the best two models.

Objectives: The Falys–Prangle-method assesses age-related morphological changes to the sternal clavicle end (SCE), enabling the observation of mature adults from the 5th decade onwards in unburnt human skeletal remains. The aim of this study is to investigate the applicability of the Falys–Prangle-method on burnt human remains. Materials and methods: Fifty-two SCE of 40 cremated individuals (out of 86) from the William M. Bass collection of the Forensic Anthropology Center (Knoxville, Tennessee) of known age-at-death and sex are available for assessment. Surface topography, porosity, and osteophyte formation are evaluated, after which the calculated composite score is associated with the corresponding age range as described by Falys and Prangle. The method is also applied on an archaeological case study from Oudenburg, Belgium, dating to the Roman period. Results: The assessed age ranges strongly agree with the true age ranges (α = 0.828), suggesting the Falys–Prangle-method to be applicable on burnt human remains. The case study from Oudenburg yields markedly improved age-at-death estimates, significantly enhancing our understanding of the age distribution within this community. Discussion: Information on age-at-death is key in the construction of biological profiles of past individuals. The mature adult is often invisible in the archaeological record since most macroscopic age estimation methods do not distinguish beyond 46+ years old. Our study stresses the usefulness of a large-scale application of the Falys–Prangle-method, which will increase the visibility of mature adults, especially in archaeological burnt human skeletal collections, where such information is, at present, extremely difficult to obtain., SCOPUS: ar.j, info:eu-repo/semantics/published