Recent contributions to the distribution of the freshwater ichthyofauna in Greece

In this paper we supplement Greece’s recent annotated inventory of freshwater fishes per hydrographic basin with recent distributional data and taxa alteration information, based on field sampling and a literature review up to September 2011. We report on newly documented distributional records of 31 fish species plus one unidentified taxon, within 35 hydrographic river basin units in Greece. These new records include 14 native fish species, seven alien and 12 translocated. Translocated taxa are distinguished from aliens, in order to report species non-indigenous to a basin but native within the same ecoregion. Twelve hydrographic basin units are newly added to the roster of ichthyologically explored river basins following a previous basin-scale inventory method (the total is now 117). This review increases the number of Greece’s freshwater fish taxa to 167, since four new species are added to the list (Carassius langsdorfii, Neogobius fluviatilis, Telestes alfiensis, Millerigobius macrocephalus) and two are deleted (Salmo dentex, Barbus rebeli) due to taxonomic changes. Taxonomic changes will probably continue to alter the national list since phylogenetic research is ongoing on several taxa in many parts of the countr

Developing policy-relevant river fish monitoring in Greece: Insights from a nation-wide survey

A wide-ranging river fish survey was executed in the summer of 2009 as part of the preparatory actions for the establishment of a monitoring programme for the EU Water Framework Directive (WFD). This was the first extensive electrofishing campaign for WFD standardized bioassessment in Greece and the experience and insights gained are used here to provide a review of fish-based assessment conditions and requirements in this country. The survey sampled 85 sites on 25 rivers throughout mainland Greece, collecting 70 species of freshwater fish. Quantitative site-based assemblage data is used for taxonomic and ordination analyses revealing a strong biogeographic regionalization in the distribution of the ichthyofauna. The structural and spatial organisation of the fish fauna through the use of species-level and community-level data analyses is explored in three ecoregions where data was deemed sufficient. Transitions in community taxonomic composition among ecoregions were abrupt and concordant with geographical barriers and reflect the influence of historical biogeographic processes. Community-based analysis revealed a substantial degree of variation in quantitative attributes of the fish assemblages among ecoregions. Key conclusions of this work are: (a) the fish-based bioassessment system must be regionalised to reflect biogeographic variation, (b) high faunal heterogeneity among ecoregions (taxonomic, structural), and to a lower degree among basins, constrain the transferability of bioassessment metrics and indices created for explicit regions to other regional frameworks; (c) faunal depauperation in most of the study areas reduce the utility of functional bioassessment metrics and also limits the utilization of rare species and the applicability of the classical form of the “Index of Biotic Integrity” concept. Recommendations to cope with these problems are discussed

Terahertz Conductivity at the Verwey Transition in Magnetite

The complex conductivity at the (Verwey) metal-insulator transition in Fe_3O_4 has been investigated at THz and infrared frequencies. In the insulating state, both the dynamic conductivity and the dielectric constant reveal a power-law frequency dependence, the characteristic feature of hopping conduction of localized charge carriers. The hopping process is limited to low frequencies only, and a cutoff frequency nu_1 ~ 8 meV must be introduced for a self-consistent description. On heating through the Verwey transition the low-frequency dielectric constant abruptly decreases and becomes negative. Together with the conductivity spectra this indicates a formation of a narrow Drude-peak with a characteristic scattering rate of about 5 meV containing only a small fraction of the available charge carriers. The spectra can be explained assuming the transformation of the spectral weight from the hopping process to the free-carrier conductivity. These results support an interpretation of Verwey transition in magnetite as an insulator-semiconductor transition with structure-induced changes in activation energy.Comment: 6 Pages, 3 Figure

Non-invasive prediction of site-specific coronary atherosclerotic plaque progression using lipidomics, blood flow, and LDL transport modeling

Background: coronary computed tomography angiography (CCTA) is a first line non-invasive imaging modality for detection of coronary atherosclerosis. Computational modeling with lipidomics analysis can be used for prediction of coronary atherosclerotic plaque progression. Methods: 187 patients (480 vessels) with stable coronary artery disease (CAD) undergoing CCTA scan at baseline and after 6.2 +/- 1.4 years were selected from the SMARTool clinical study cohort (Clinicaltrial.gov Identifiers NCT04448691) according to a computed tomography (CT) scan image quality suitable for three-dimensional (3D) reconstruction of coronary arteries and the absence of implanted coronary stents. Clinical and biohumoral data were collected, and plasma lipidomics analysis was performed. Blood flow and low-density lipoprotein (LDL) transport were modeled using patient-specific data to estimate endothelial shear stress (ESS) and LDL accumulation based on a previously developed methodology. Additionally, non-invasive Fractional Flow Reserve (FFR) was calculated (SmartFFR). Plaque progression was defined as significant change of at least two of the morphological metrics: lumen area, plaque area, plaque burden. Results: a multi-parametric predictive model, including traditional risk factors, plasma lipids, 3D imaging parameters, and computational data demonstrated 88% accuracy to predict site-specific plaque progression, outperforming current computational models. Conclusions: Low ESS and LDL accumulation, estimated by computational modeling of CCTA imaging, can be used to predict site-specific progression of coronary atherosclerotic plaques.Cardiolog

Dielectric properties and dynamical conductivity of LaTiO3: From dc to optical frequencies

We provide a complete and detailed characterization of the temperature-dependent response to ac electrical fields of LaTiO3, a Mott-Hubbard insulator close to the metal-insulator transition. We present combined dc, broadband dielectric, mm-wave, and infrared spectra of ac conductivity and dielectric constant, covering an overall frequency range of 17 decades. The dc and dielectric measurements reveal information on the semiconducting charge-transport properties of LaTiO3, indicating the importance of Anderson localization, and on the dielectric response due to ionic polarization. In the infrared region, the temperature dependence of the phonon modes gives strong hints for a structural phase transition at the magnetic ordering temperature. In addition, a gap-like electronic excitation following the phonon region is analyzed in detail. We compare the results to the soft-edge behavior of the optical spectra characteristic for Mott-Hubbard insulators. Overall a consistent picture of the charge-transport mechanisms in LaTiO3 emerges.Comment: 11 pages, 8 figures, 1 tabl

Numerical simulation of human hearing system

© 2018 Isailovic et al. Hearing impairment is a problem faced by many people, mostly the elderly population but occurs even in newborns. Experimental tests performed on patients give information of the level of hearing impairment and the place where the problem is located. In order to understand process of hearing and hearing impairments it would be very useful to have a look inside, but it is not possible with any experimental equipment. However, it is possible to make a virtual look inside human auditory system by development of numerical model. Using data obtained by experimental research it is possible to make sufficiently detailed model and use it to gain new knowledge that can help in understanding of hearing process and problems with hearing. In this paper one such model will be presented. The model contains mechanical and fluid elements of the middle and inner ear

Deliverable 4.5: Context-aware Content Interpretation

The current deliverable summarises the work conducted within task T4.5 of WP4, presenting our proposed approaches for contextualised content interpretation, aimed at gaining insightful contextualised views on content semantics. This is achieved through the adoption of appropriate context-aware semantic models developed within the project, and via enriching the semantic descriptions with background knowledge, deriving thus higher level contextualised content interpretations that are closer to human perception and appraisal needs. More specifically, the main contributions of the deliverable are the following: A theoretical framework using physics as a metaphor to develop different models of evolving semantic content. A set of proof-of-concept models for semantic drifts due to field dynamics, introducing two methods to identify quantum-like (QL) patterns in evolving information searching behaviour, and a QL model akin to particle-wave duality for semantic content classification. Integration of two specific tools, Somoclu for drift detection and Ncpol2spda for entanglement detection. An “energetic” hypothesis accounting for contextualized evolving semantic structures over time. A proposed semantic interpretation framework, integrating (a) an ontological inference scheme based on Description Logics (DL), (b) a rule-based reasoning layer built on SPARQL Inference Notation (SPIN), (c) an uncertainty management framework based on non-monotonic logics. A novel scheme for contextualized reasoning on semantic drift, based on LRM dependencies and OWL’s punning mechanism. An implementation of SPIN rules for policy and ecosystem change management, with the adoption of LRM preconditions and impacts. Specific use case scenarios demonstrate the context under development and the efficiency of the approach. Respective open-source implementations and experimental results that validate all the above.All these contributions are tightly interlinked with the other PERICLES work packages: WP2 supplies the use cases and sample datasets for validating our proposed approaches, WP3 provides the models (LRM and Digital Ecosystem models) that form the basis for our semantic representations of content and context, WP5 provides the practical application of the technologies developed to preservation processes, while the tools and algorithms presented in this deliverable can be deployed in combination with test scenarios, which will be part of the WP6 test beds.PERICLE

PERICLES Deliverable 4.4: Modelling Contextualised Semantics

The current deliverable summarises the work conducted within task T4.4 of WP4, presenting our proposed models for semantically representing digital content and its respective context – the latter refers to any information coming from the environment of the digital object (DO) that offers a better insight into the object’s status, its  interrelationships with other content items and information about the object’s context of use. Within PERICLES, we refer to the content semantics enriched with the contextual perspective as “contextualised semantics”. The deliverable presents two complementary modelling approaches, based respectively on (a) ontologies and logics, and, (b) multivariate statistics.PERICLE

Disease Progression of Hypertrophic Cardiomyopathy: Modeling Using Machine Learning

Background: Cardiovascular disorders in general are responsible for 30% of deaths worldwide. Among them, hypertrophic cardiomyopathy (HCM) is a genetic cardiac disease that is present in about 1 of 500 young adults and can cause sudden cardiac death (SCD). Objective: Although the current state-of-the-art methods model the risk of SCD for patients, to the best of our knowledge, no methods are available for modeling the patient's clinical status up to 10 years ahead. In this paper, we propose a novel machine learning (ML)-based tool for predicting disease progression for patients diagnosed with HCM in terms of adverse remodeling of the heart during a 10-year period. Methods: The method consisted of 6 predictive regression models that independently predict future values of 6 clinical characteristics: left atrial size, left atrial volume, left ventricular ejection fraction, New York Heart Association functional classification, left ventricular internal diastolic diameter, and left ventricular internal systolic diameter. We supplemented each prediction with the explanation that is generated using the Shapley additive explanation method. Results: The final experiments showed that predictive error is lower on 5 of the 6 constructed models in comparison to experts (on average, by 0.34) or a consortium of experts (on average, by 0.22). The experiments revealed that semisupervised learning and the artificial data from virtual patients help improve predictive accuracies. The best-performing random forest model improved R2 from 0.3 to 0.6. Conclusions: By engaging medical experts to provide interpretation and validation of the results, we determined the models' favorable performance compared to the performance of experts for 5 of 6 targets

A machine learning-based risk stratification model for ventricular tachycardia and heart failure in hypertrophic cardiomyopathy

Background: Machine learning (ML) and artificial intelligence are emerging as important components of precision medicine that enhance diagnosis and risk stratification. Risk stratification tools for hypertrophic cardiomyopathy (HCM) exist, but they are based on traditional statistical methods. The aim was to develop a novel machine learning risk stratification tool for the prediction of 5-year risk in HCM. The goal was to determine if its predictive accuracy is higher than the accuracy of the state-of-the-art tools. Method: Data from a total of 2302 patients were used. The data were comprised of demographic characteristics, genetic data, clinical investigations, medications, and disease-related events. Four classification models were applied to model the risk level, and their decisions were explained using the SHAP (SHapley Additive exPlanations) method. Unwanted cardiac events were defined as sustained ventricular tachycardia occurrence (VT), heart failure (HF), ICD activation, sudden cardiac death (SCD), cardiac death, and all-cause death. Results: The proposed machine learning approach outperformed the similar existing risk-stratification models for SCD, cardiac death, and all-cause death risk-stratification: it achieved higher AUC by 17%, 9%, and 1%, respectively. The boosted trees achieved the best performing AUC of 0.82. The resulting model most accurately predicts VT, HF, and ICD with AUCs of 0.90, 0.88, and 0.87, respectively. Conclusions: The proposed risk-stratification model demonstrates high accuracy in predicting events in patients with hypertrophic cardiomyopathy. The use of a machine-learning risk stratification model may improve patient management, clinical practice, and outcomes in general