Vytautas Magnus University

This paper gives the main definitions relating to dependability, a generic concept including a special case of such attributes as reliability, availability, safety, integrity, maintainability, etc. Security brings in concerns for confidentiality, in addition to availability and integrity. Basic definitions are given first. They are then commented upon, and supplemented by additional definitions, which address the threats to dependability and security (faults, errors, failures), their attributes, and the means for their achievement (fault prevention, fault tolerance, fault removal, fault forecasting). The aim is to explicate a set of general concepts, of relevance across a wide range of situations and, therefore, helping communication and cooperation among a number of scientific and technical communities, including ones that are concentrating on particular types of system, of system failures, or of causes of system failures.

Land Use Regression (LUR) models have been used increasingly for modeling small-scale spatial variation in air pollution concentrations and estimating individual exposure for participants of cohort studies. Within the ESCAPE project, concentrations of PM(2.5), PM(2.5) absorbance, PM(10), and PM(coarse) were measured in 20 European study areas at 20 sites per area. GIS-derived predictor variables (e.g., traffic intensity, population, and land-use) were evaluated to model spatial variation of annual average concentrations for each study area. The median model explained variance (R(2)) was 71% for PM(2.5) (range across study areas 35-94%). Model R(2) was higher for PM(2.5) absorbance (median 89%, range 56-97%) and lower for PM(coarse) (median 68%, range 32- 81%). Models included between two and five predictor variables, with various traffic indicators as the most common predictors. Lower R(2) was related to small concentration variability or limited availability of predictor variables, especially traffic intensity. Cross validation R(2) results were on average 8-11% lower than model R(2). Careful selection of monitoring sites, examination of influential observations and skewed variable distributions were essential for developing stable LUR models. The final LUR models are used to estimate air pollution concentrations at the home addresses of participants in the health studies involved in ESCAPE.

The dataset presented here was collected by the GenTree project (EU-Horizon 2020), which aims to improve the use of forest genetic resources across Europe by better understanding how trees adapt to their local environment. This dataset of individual tree-core characteristics including ring-width series and whole-core wood density was collected for seven ecologically and economically important European tree species: silver birch (Betula pendula), European beech (Fagus sylvatica), Norway spruce (Picea abies), European black poplar (Populus nigra), maritime pine (Pinus pinaster), Scots pine (Pinus sylvestris), and sessile oak (Quercus petraea). Tree-ring width measurements were obtained from 3600 trees in 142 populations and whole-core wood density was measured for 3098 trees in 125 populations. This dataset covers most of the geographical and climatic range occupied by the selected species. The potential use of it will be highly valuable for assessing ecological and evolutionary responses to environmental conditions as well as for model development and parameterization, to predict adaptability under climate change scenarios.

Abstract Over the last decades, the natural disturbance is increasingly putting pressure on European forests. Shifts in disturbance regimes may compromise forest functioning and the continuous provisioning of ecosystem services to society, including their climate change mitigation potential. Although forests are central to many European policies, we lack the long‐term empirical data needed for thoroughly understanding disturbance dynamics, modeling them, and developing adaptive management strategies. Here, we present a unique database of >170,000 records of ground‐based natural disturbance observations in European forests from 1950 to 2019. Reported data confirm a significant increase in forest disturbance in 34 European countries, causing on an average of 43.8 million m 3 of disturbed timber volume per year over the 70‐year study period. This value is likely a conservative estimate due to under‐reporting, especially of small‐scale disturbances. We used machine learning techniques for assessing the magnitude of unreported disturbances, which are estimated to be between 8.6 and 18.3 million m 3 /year. In the last 20 years, disturbances on average accounted for 16% of the mean annual harvest in Europe. Wind was the most important disturbance agent over the study period (46% of total damage), followed by fire (24%) and bark beetles (17%). Bark beetle disturbance doubled its share of the total damage in the last 20 years. Forest disturbances can profoundly impact ecosystem services (e.g., climate change mitigation), affect regional forest resource provisioning and consequently disrupt long‐term management planning objectives and timber markets. We conclude that adaptation to changing disturbance regimes must be placed at the core of the European forest management and policy debate. Furthermore, a coherent and homogeneous monitoring system of natural disturbances is urgently needed in Europe, to better observe and respond to the ongoing changes in forest disturbance regimes.

Declines in European bird populations are reported for decades but the direct effect of major anthropogenic pressures on such declines remains unquantified. Causal relationships between pressures and bird population responses are difficult to identify as pressures interact at different spatial scales and responses vary among species. Here, we uncover direct relationships between population time-series of 170 common bird species, monitored at more than 20,000 sites in 28 European countries, over 37 y, and four widespread anthropogenic pressures: agricultural intensification, change in forest cover, urbanisation and temperature change over the last decades. We quantify the influence of each pressure on population time-series and its importance relative to other pressures, and we identify traits of most affected species. We find that agricultural intensification, in particular pesticides and fertiliser use, is the main pressure for most bird population declines, especially for invertebrate feeders. Responses to changes in forest cover, urbanisation and temperature are more species-specific. Specifically, forest cover is associated with a positive effect and growing urbanisation with a negative effect on population dynamics, while temperature change has an effect on the dynamics of a large number of bird populations, the magnitude and direction of which depend on species' thermal preferences. Our results not only confirm the pervasive and strong effects of anthropogenic pressures on common breeding birds, but quantify the relative strength of these effects stressing the urgent need for transformative changes in the way of inhabiting the world in European countries, if bird populations shall have a chance of recovering.

Manual identification of brain tumors is an error-prone and tedious process for radiologists; therefore, it is crucial to adopt an automated system. The binary classification process, such as malignant or benign is relatively trivial; whereas, the multimodal brain tumors classification (T1, T2, T1CE, and Flair) is a challenging task for radiologists. Here, we present an automated multimodal classification method using deep learning for brain tumor type classification. The proposed method consists of five core steps. In the first step, the linear contrast stretching is employed using edge-based histogram equalization and discrete cosine transform (DCT). In the second step, deep learning feature extraction is performed. By utilizing transfer learning, two pre-trained convolutional neural network (CNN) models, namely VGG16 and VGG19, were used for feature extraction. In the third step, a correntropy-based joint learning approach was implemented along with the extreme learning machine (ELM) for the selection of best features. In the fourth step, the partial least square (PLS)-based robust covariant features were fused in one matrix. The combined matrix was fed to ELM for final classification. The proposed method was validated on the BraTS datasets and an accuracy of 97.8%, 96.9%, 92.5% for BraTs2015, BraTs2017, and BraTs2018, respectively, was achieved.

In view of the expanding global market, authentication and characterization of botanical and geographic origins of honey has become a more important task than ever. Many studies have been performed with the aim of evaluating the possibilities to characterize honey samples of various origins by using specific chemical marker compounds. These have been identified and quantified for numerous honey samples. This article is aimed at summarizing the studies carried out during the last 2 decades. An attempt is made to find useful chemical markers for unifloral honey, based on the analysis of the compositional data of honey volatile compounds, phenolic acids, flavonoids, carbohydrates, amino acids, and some other constituents. This review demonstrates that currently it is rather difficult to find reliable chemical markers for the discrimination of honey collected from different floral sources because the chemical composition of honey also depends on several other factors, such as geographic origin, collection season, mode of storage, bee species, and even interactions between chemical compounds and enzymes in the honey. Therefore, some publications from the reviewed period have reported different floral markers for honey of the same floral origin. In addition, the results of chemical analyses of honey constituents may also depend on sample preparation and analysis techniques. Consequently, a more reliable characterization of honey requires the determination of more than a single class of compounds, preferably in combination with modern data management of the results, for example, principal component analysis or cluster analysis.

BACKGROUND: Developmental periods in early life may be particularly vulnerable to impacts of environmental exposures. Human research on this topic has generally focused on single exposure-health effect relationships. The "exposome" concept encompasses the totality of exposures from conception onward, complementing the genome. OBJECTIVES: The Human Early-Life Exposome (HELIX) project is a new collaborative research project that aims to implement novel exposure assessment and biomarker methods to characterize early-life exposure to multiple environmental factors and associate these with omics biomarkers and child health outcomes, thus characterizing the "early-life exposome." Here we describe the general design of the project. METHODS: In six existing birth cohort studies in Europe, HELIX will estimate prenatal and postnatal exposure to a broad range of chemical and physical exposures. Exposure models will be developed for the full cohorts totaling 32,000 mother-child pairs, and biomarkers will be measured in a subset of 1,200 mother-child pairs. Nested repeat-sampling panel studies (n = 150) will collect data on biomarker variability, use smartphones to assess mobility and physical activity, and perform personal exposure monitoring. Omics techniques will determine molecular profiles (metabolome, proteome, transcriptome, epigenome) associated with exposures. Statistical methods for multiple exposures will provide exposure-response estimates for fetal and child growth, obesity, neurodevelopment, and respiratory outcomes. A health impact assessment exercise will evaluate risks and benefits of combined exposures. CONCLUSIONS: HELIX is one of the first attempts to describe the early-life exposome of European populations and unravel its relation to omics markers and health in childhood. As proof of concept, it will form an important first step toward the life-course exposome.

BACKGROUND: Green spaces have been associated with both health benefits and risks in children; however, available evidence simultaneously investigating these conflicting influences, especially in association with different types of greenness, is scarce. OBJECTIVES: We aimed to simultaneously evaluate health benefits and risks associated with different types of greenness in children, in terms of sedentary behavior (represented by excessive screen time), obesity, current asthma, and allergic rhinoconjunctivitis. METHODS: We conducted a cross-sectional study of a population-based sample of 3,178 schoolchildren (9-12 years old) in Sabadell, Spain, in 2006. Information on outcomes and covariates was obtained by questionnaire. We measured residential surrounding greenness as the average of satellite-derived Normalized Difference Vegetation Index (NDVI) in buffers of 100 m, 250 m, 500 m, and 1,000 m around each home address. Residential proximity to green spaces was defined as living within 300 m of a forest or a park, as separate variables. We used logistic regression models to estimate associations separately for each exposure-outcome pair, adjusted for relevant covariates. RESULTS: An interquartile range increase in residential surrounding greenness was associated with 11-19% lower relative prevalence of overweight/obesity and excessive screen time, but was not associated with current asthma and allergic rhinoconjunctivitis. Similarly, residential proximity to forests was associated with 39% and 25% lower relative prevalence of excessive screen time and overweight/obesity, respectively, but was not associated with current asthma. In contrast, living close to parks was associated with a 60% higher relative prevalence of current asthma, but had only weak negative associations with obesity/overweight or excessive screen time. CONCLUSION: We observed two separable patterns of estimated health benefits and risks associated with different types of greenness.

The Multilingual Assessment Instrument for Narratives (MAIN) was designed in order to assess narrative skills in children who acquire one or more languages from birth or from early age. MAIN is suitable for children from 3 to 10 years and evaluates both comprehension and production of narratives. Its design allows for the assessment of several languages in the same child, as well as for different elicitation modes: Model Story, Retelling, and Telling. MAIN contains four parallel stories, each with a carefully designed six-picture sequence. The stories are controlled for cognitive and linguistic complexity, parallelism in macrostructure and microstructure, as well as for cultural appropriateness and robustness. The instrument has been developed on the basis of extensive piloting with more than 550 monolingual and bilingual children aged 3 to 10, for 15 different languages and language combinations. Even though MAIN has not been norm-referenced yet, its standardized procedures can be used for evaluation, intervention and research purposes. MAIN is currently available in the following languages: English, Afrikaans, Albanian, Basque, Bulgarian, Croatian, Cypriot Greek, Danish, Dutch, Estonian, Finnish, French, German, Greek, Hebrew, Icelandic, Italian, Lithuanian, Norwegian, Polish, Russian, Spanish, Standard Arabic, Swedish, Turkish, Vietnamese, and Welsh.

The promotion of pro-environmental behavior is regarded as very important in solving environmental problems. The Value-Belief-Norm (VBN) theory usually emphasizes internal factors; however, we have transformed this theory by including the environmental knowledge as an external factor. The results showed that action-related environmental knowledge was related to the ecological worldview and directly influenced the private sphere behavior. The ecological worldview, which in this paper was operationalized as environmental concern, had a direct effect on public sphere behavior and an indirect effect on private behavior through awareness of behavioral consequences. Thus, in this paper we revealed how specific environmental knowledge influenced pro-environmental behavior. We also suggest that it is important to educate people about local and global environmental problems, about the impact of behavior on the environment not only in private but also in the public sphere, and to foster the ecocentrism, as well. In addition, we revealed the meaning and necessity of education for environmental citizenship.

BACKGROUND: The aims of this study were to explore associations of the distance and use of urban green spaces with the prevalence of cardiovascular diseases (CVD) and its risk factors, and to evaluate the impact of the accessibility and use of green spaces on the incidence of CVD among the population of Kaunas city (Lithuania). METHODS: We present the results from a Kaunas cohort study on the access to and use of green spaces, the association with cardiovascular risk factors and other health-related variables, and the risk of cardiovascular mortality and morbidity. A random sample of 5,112 individuals aged 45-72 years was screened in 2006-2008. During the mean 4.41 years follow-up, there were 83 deaths from CVD and 364 non-fatal cases of CVD among persons free from CHD and stroke at the baseline survey. Multivariate Cox proportional hazards regression models were used for data analysis. RESULTS: We found that the distance from people's residence to green spaces was not related to the prevalence of health-related variables. However, the prevalence of cardiovascular risk factors and the prevalence of diabetes mellitus were significantly lower among park users than among non-users. During the follow up, an increased risk of non-fatal and fatal CVD combined was observed for those who lived ≥629.61 m from green spaces (3rd tertile of distance to green space) (hazard ratio (HR) = 1.36), and the risk for non-fatal CVD-for those who lived ≥347.81 m (2nd and 3rd tertile) and were not park users (HR = 1.66) as compared to men and women who lived 347.8 m or less (1st tertile) from green space. Men living further away from parks (3rd tertile) had a higher risk of non-fatal and fatal CVD combined, compared to those living nearby (1st tertile) (HR = 1.51). Compared to park users living nearby (1st tertile), a statistically significantly increased risk of non-fatal CVD was observed for women who were not park users and living farther away from parks (2nd and 3rd tertile) (HR = 2.78). CONCLUSION: Our analysis suggests public health policies aimed at promoting healthy lifestyles in urban settings could produce cardiovascular benefits.

Using gestures can help people with certain disabilities in communicating with other people. This paper proposes a lightweight model based on YOLO (You Only Look Once) v3 and DarkNet-53 convolutional neural networks for gesture recognition without additional preprocessing, image filtering, and enhancement of images. The proposed model achieved high accuracy even in a complex environment, and it successfully detected gestures even in low-resolution picture mode. The proposed model was evaluated on a labeled dataset of hand gestures in both Pascal VOC and YOLO format. We achieved better results by extracting features from the hand and recognized hand gestures of our proposed YOLOv3 based model with accuracy, precision, recall, and an F-1 score of 97.68, 94.88, 98.66, and 96.70%, respectively. Further, we compared our model with Single Shot Detector (SSD) and Visual Geometry Group (VGG16), which achieved an accuracy between 82 and 85%. The trained model can be used for real-time detection, both for static hand images and dynamic gestures recorded on a video.

After lung cancer, breast cancer is the second leading cause of death in women. If breast cancer is detected early, mortality rates in women can be reduced. Because manual breast cancer diagnosis takes a long time, an automated system is required for early cancer detection. This paper proposes a new framework for breast cancer classification from ultrasound images that employs deep learning and the fusion of the best selected features. The proposed framework is divided into five major steps: (i) data augmentation is performed to increase the size of the original dataset for better learning of Convolutional Neural Network (CNN) models; (ii) a pre-trained DarkNet-53 model is considered and the output layer is modified based on the augmented dataset classes; (iii) the modified model is trained using transfer learning and features are extracted from the global average pooling layer; (iv) the best features are selected using two improved optimization algorithms known as reformed differential evaluation (RDE) and reformed gray wolf (RGW); and (v) the best selected features are fused using a new probability-based serial approach and classified using machine learning algorithms. The experiment was conducted on an augmented Breast Ultrasound Images (BUSI) dataset, and the best accuracy was 99.1%. When compared with recent techniques, the proposed framework outperforms them.

Permeabilization, when observed on a tissue level, is a dynamic process resulting from changes in membrane permeability when exposing biological cells to external electric field (E). In this paper we present a sequential finite element model of E distribution in tissue which considers local changes in tissue conductivity due to permeabilization. These changes affect the pattern of the field distribution during the high voltage pulse application. The presented model consists of a sequence of static models (steps), which describe E distribution at discrete time intervals during tissue permeabilization and in this way present the dynamics of electropermeabilization. The tissue conductivity for each static model in a sequence is determined based on E distribution from the previous step by considering a sigmoid dependency between specific conductivity and E intensity. Such a dependency was determined by parameter estimation on a set of current measurements, obtained by in vivo experiments. Another set of measurements was used for model validation. All experiments were performed on rabbit liver tissue with inserted needle electrodes. Model validation was carried out in four different ways: 1) by comparing reversibly permeabilized tissue computed by the model and the reversibly permeabilized area of tissue as obtained in the experiments; 2) by comparing the area of irreversibly permeabilized tissue computed by the model and the area where tissue necrosis was observed in experiments; 3) through the comparison of total current at the end of pulse and computed current in the last step of sequential electropermeabilization model; 4) by comparing total current during the first pulse and current computed in consecutive steps of a modeling sequence. The presented permeabilization model presents the first approach of describing the course of permeabilization on tissue level. Despite some approximations (ohmic tissue behavior) the model can predict the permeabilized volume of tissue, when exposed to electrical treatment. Therefore, the most important contribution and novelty of the model is its potentiality to be used as a tool for determining parameters for effective tissue permeabilization.

PURPOSE: Essential to exposome research is the collection of data on many environmental exposures from different domains in the same subjects. The aim of the Human Early Life Exposome (HELIX) study was to measure and describe multiple environmental exposures during early life (pregnancy and childhood) in a prospective cohort and associate these exposures with molecular omics signatures and child health outcomes. Here, we describe recruitment, measurements available and baseline data of the HELIX study populations. PARTICIPANTS: The HELIX study represents a collaborative project across six established and ongoing longitudinal population-based birth cohort studies in six European countries (France, Greece, Lithuania, Norway, Spain and the UK). HELIX used a multilevel study design with the entire study population totalling 31 472 mother-child pairs, recruited during pregnancy, in the six existing cohorts (first level); a subcohort of 1301 mother-child pairs where biomarkers, omics signatures and child health outcomes were measured at age 6-11 years (second level) and repeat-sampling panel studies with around 150 children and 150 pregnant women aimed at collecting personal exposure data (third level). FINDINGS TO DATE: Cohort data include urban environment, hazardous substances and lifestyle-related exposures for women during pregnancy and their offspring from birth until 6-11 years. Common, standardised protocols were used to collect biological samples, measure exposure biomarkers and omics signatures and assess child health across the six cohorts. Baseline data of the cohort show substantial variation in health outcomes and determinants between the six countries, for example, in family affluence levels, tobacco smoking, physical activity, dietary habits and prevalence of childhood obesity, asthma, allergies and attention deficit hyperactivity disorder. FUTURE PLANS: HELIX study results will inform on the early life exposome and its association with molecular omics signatures and child health outcomes. Cohort data are accessible for future research involving researchers external to the project.

BACKGROUND: During the past 25 years, many pregnancy and birth cohorts have been established. Each cohort provides unique opportunities for examining associations of early-life exposures with child development and health. However, to fully exploit the large amount of available resources and to facilitate cross-cohort collaboration, it is necessary to have accessible information on each cohort and its individual characteristics. The aim of this work was to provide an overview of European pregnancy and birth cohorts registered in a freely accessible database located at http://www.birthcohorts.net. METHODS: European pregnancy and birth cohorts initiated in 1980 or later with at least 300 mother-child pairs enrolled during pregnancy or at birth, and with postnatal data, were eligible for inclusion. Eligible cohorts were invited to provide information on the data and biological samples collected, as well as the timing of data collection. RESULTS: In total, 70 cohorts were identified. Of these, 56 fulfilled the inclusion criteria encompassing a total of more than 500,000 live-born European children. The cohorts represented 19 countries with the majority of cohorts located in Northern and Western Europe. Some cohorts were general with multiple aims, whilst others focused on specific health or exposure-related research questions. CONCLUSION: This work demonstrates a great potential for cross-cohort collaboration addressing important aspects of child health. The web site, http://www.birthcohorts.net, proved to be a useful tool for accessing information on European pregnancy and birth cohorts and their characteristics.

Manual diagnosis of skin cancer is time-consuming and expensive; therefore, it is essential to develop automated diagnostics methods with the ability to classify multiclass skin lesions with greater accuracy. We propose a fully automated approach for multiclass skin lesion segmentation and classification by using the most discriminant deep features. First, the input images are initially enhanced using local color-controlled histogram intensity values (LCcHIV). Next, saliency is estimated using a novel Deep Saliency Segmentation method, which uses a custom convolutional neural network (CNN) of ten layers. The generated heat map is converted into a binary image using a thresholding function. Next, the segmented color lesion images are used for feature extraction by a deep pre-trained CNN model. To avoid the curse of dimensionality, we implement an improved moth flame optimization (IMFO) algorithm to select the most discriminant features. The resultant features are fused using a multiset maximum correlation analysis (MMCA) and classified using the Kernel Extreme Learning Machine (KELM) classifier. The segmentation performance of the proposed methodology is analyzed on ISBI 2016, ISBI 2017, ISIC 2018, and PH2 datasets, achieving an accuracy of 95.38%, 95.79%, 92.69%, and 98.70%, respectively. The classification performance is evaluated on the HAM10000 dataset and achieved an accuracy of 90.67%. To prove the effectiveness of the proposed methods, we present a comparison with the state-of-the-art techniques.

Fossil resources-free sustainable development can be achieved through a transition to bioeconomy, an economy based on sustainable biomass-derived food, feed, chemicals, materials, and fuels. However, the transition to bioeconomy requires development of new energy-efficient technologies and processes to manipulate biomass feed stocks and their conversion into useful products, a collective term for which is biorefinery. One of the technological platforms that will enable various pathways of biomass conversion is based on pulsed electric fields applications (PEF). Energy efficiency of PEF treatment is achieved by specific increase of cell membrane permeability, a phenomenon known as membrane electroporation. Here, we review the opportunities that PEF and electroporation provide for the development of sustainable biorefineries. We describe the use of PEF treatment in biomass engineering, drying, deconstruction, extraction of phytochemicals, improvement of fermentations, and biogas production. These applications show the potential of PEF and consequent membrane electroporation to enable the bioeconomy and sustainable development.

Freshly harvested seeds of Arabidopsis thaliana, Columbia (Col) accession were dormant when imbibed at 25°C in the dark. Their dormancy was alleviated by continuous light during imbibition or by 5 weeks of storage at 20°C (after-ripening). We investigated the possible role of reactive oxygen species (ROS) in the regulation of Col seed dormancy. After 24 h of imbibition at 25°C, non-dormant seeds produced more ROS than dormant seeds, and their catalase activity was lower. In situ ROS localization revealed that germination was associated with an accumulation of superoxide and hydrogen peroxide in the radicle. ROS production was temporally and spatially regulated: ROS were first localized within the cytoplasm upon imbibition of non-dormant seeds, then in the nucleus and finally in the cell wall, which suggests that ROS play different roles during germination. Imbibition of dormant and non-dormant seeds in the presence of ROS scavengers or donors, which inhibited or stimulated germination, respectively, confirmed the role of ROS in germination. Freshly harvested seeds of the mutants defective in catalase (cat2-1) and vitamin E (vte1-1) did not display dormancy; however, seeds of the NADPH oxidase mutants (rbohD) were deeply dormant. Expression of a set of genes related to dormancy upon imbibition in the cat2-1 and vet1-1 seeds revealed that their non-dormant phenotype was probably not related to ABA or gibberellin metabolism, but suggested that ROS could trigger germination through gibberellin signaling activation.