Ergot alkaloids (EAs) are mycotoxins produced by several species of fungi of the genus Claviceps, among which Claviceps purpurea is the most widespread in Europe. This species has been found in many economically important cereal grains, such as rye, wheat, triticale, barley, millet and oats. The distribution of EAs contamination has a sporadic incidence with many factors involved in its occurrence, greatly varying between fungal strains, geographic regions, host plants and regional/local weather conditions, at the same time emphasizing that cool, damp weather favours ergot by enhancing the germination of sclerotia. The aim of this study was to investigate the occurrence of ergot sclerotia and EAs in wheat and rye grain samples (n = 64) taken during the year 2021 from Croatian cereal producers located in the Central and Eastern parts of Croatia. In two of the rye samples the presence of ergot sclerotia was detected in the amount of 259 mg/kg and 536 mg/kg, whereas none of the wheat samples tested positive for ergot sclerotia. A higher contamination with EAs was determined in the rye samples (18% contaminated, max 167.4 µg/kg), while a lower frequency of contamination, with only one positive sample (1.9 %), was determined in wheat (68.5 µg/kg). The results pointed to a low-level of EAs contamination of wheat and rye cultivated by Croatian producers during the whole investigative period. However, although a low percentage of the positive tested rye samples with EAs was determined, the content of ergot sclerotia in two of the samples was higher than is allowed by legislation for foodstuffs. Because the levels of these mycotoxins and ergot sclerotia content can vary depending on a number of factors, further research of them is required over a longer period of time and under different cereal cultivation and processing conditions.

The World Health Organization and the American College of Sports Medicine have released guidelines on physical activity and sedentary behavior, as part of an effort to reduce inactivity worldwide. However, to date, there is no computational model that can facilitate the integration of these recommendations into health solutions (e.g., digital coaches). In this paper, we present an operational and machine-readable model that represents and is able to reason about these guidelines. To this end, we adopted a symbolic AI approach that combines two paradigms of research in knowledge representation and reasoning: ontology and rules. Thus, we first present HeLiFit, a domain ontology implemented in OWL, which models the main entities that characterize the definition of physical activity, as defined per guidance. Then, we describe HeLiFit-Rule, a set of rules implemented in the RDFox Rule language, which can be used to represent and reason with these recommendations in concrete real-world applications. Furthermore, to ensure a high level of syntactic/semantic interoperability across different systems, our framework is also compliant with the FHIR standard. Through motivating scenarios that highlight the need for such an implementation, we finally present an evaluation of our model that provides results that are both encouraging in terms of the value of our solution and also provide a basis for future work.

Glioblastoma’s (GBM) origin, recurrence and resistance to treatment are driven by GBM cancer stem cells (GSCs). Existing transcriptomic characterisations of GBM classify the tumours to three subtypes: classical, proneural, and mesenchymal. The comprehension of how expression patterns of the GBM subtypes are reflected at global proteome level in GSCs is limited. To characterise protein expression in GSCs, we performed in-depth proteogenomic analysis of patient-derived GSCs by RNA-sequencing and mass-spectrometry proteomics. We identified and quantified over 10,000 proteins in two independent GSCs panels, and propose a GSC-associated proteomic signature (GSAPS) that defines two distinct morphological conditions; one defined by a set of proteins expressed in non-mesenchymal - proneural and classical - GSCs (GPC-like), and another expressed in mesenchymal GSCs (GM-like). The expression of GM-like protein set in GBM tissue was associated with hypoxia, necrosis, recurrence, and worse overall survival in GBM patients. In a proof-of-concept proteogenomic approach, we discovered 252 non-canonical peptides expressed in GSCs, i.e., protein sequences that are variant or derive from genome regions previously considered protein-non-coding. We report new variants of the heterogeneous ribonucleoproteins (HNRNPs), which are implicated in mRNA splicing. Furthermore, we show that per-gene mRNA-protein correlations in GSCs are moderate and vary compared to GBM tissue.

This paper considers the method for the calculation of magnetic flux density in the vicinity of overhead distribution lines which takes into account the higher current harmonics. This method is based on the Biot–Savart law and the complex image method. The considered method calculates the values of the magnetic flux density for each harmonic component of the current separately at all points of interest (usually lateral profile). In this way, it is possible to determine the contributions of individual harmonic components of the current intensity to the total value of magnetic flux density. Based on the contributions of individual harmonic components, the total (resultant) value of the magnetic flux density at points of interest is determined. Validation of the computational method is carried out by comparison of the results obtained by the considered calculation method with measurement results. Furthermore, the application of the calculation method was demonstrated by calculating magnetic flux density harmonics in the vicinity of two overhead distribution lines of typical phase conductor arrangements.

Campgrounds are an essential part of the camping experience due to the attractions and facilities they offer to campers. The factors that motivate campers to travel have increasingly become more complex making it vital to take camper expectations into account for effective planning of campgrounds. The present study aimed to determine the trailer park amenities based on expert opinion. The paper systematically applied the Fuzzy Pivot Pairwise RElative Criteria Importance Assessment (Fuzzy PIPRECIA) Method in the selection of the trailer park criteria. Four main criteria and 20 sub-criteria were considered. In the findings the amenities were structured as follows: primarily, 'location,' followed by 'necessities' and 'campground attributes' and finally 'facilities and camping tools.' Spearman and Pearson correlation coefficients were employed to determine the consistency of the proposed model.

In order to address the main challenges related to the rare diseases (RDs) the European Commission launched the European Reference Networks (ERNs), virtual networks involving healthcare providers (HCPs) across Europe. The mission of the ERNs is to tackle low prevalence and RDs that require highly specialised treatment and a concentration of knowledge and resources. In fact, ERNs offer the potential to give patients and healthcare professionals across the EU access to the best expertise and timely exchange of lifesaving knowledge, trying to make the knowledge travelling more than patients. For this reason, ERNs were established as concrete European infrastructures, and this is particularly crucial in the framework of rare and complex diseases in which no country alone has the whole knowledge and capacity to treat all types of patients.It has been five years since their kick-off launch in Vilnius in 2017. The 24 ERNs have been intensively working on different transversal areas, including patient management, education, clinical practice guidelines, patients' care pathways and many other fundamental topics. The present work is therefore aimed not only at reporting a summary of the main activities and milestones reached so far, but also at celebrating the first 5 years of the ERN on Rare and Complex Connective Tissue and Musculo-skeletal Diseases (ReCONNET), in which the members of the network built together one of the 24 infrastructures that are hopefully going to change the scenario of rare diseases across the EU.

The catalase enzyme is analyzed under different conditions in order to get a better understanding of its function, purpose and benefit to organisms. This enzyme resides in all living organisms that have exposure to oxygen. It uses hydrogen peroxide (H2O2) as its substrate, and is responsible for breaking down H2O2 into oxygen and water, therefore neutralizing oxidative stress in the cells. Maintaining the levels of oxidative stress is highly important because of the impact that the reactive oxidative species (ROS) have on the cells. ROS damage cells by targeting DNA and proteins leading to various complications and illnesses such as cancer, diabetes, neurodegenerative diseases and they even have an impact on the process of aging. To determine the activity of the catalase enzyme and test its stability, different temperatures and pH were employed, along with examining the catalase behavior under the presence of ascorbic acid as an inhibitor. Three samples were used for this experiment: animal sample, plant sample and microorganisms. The indicator of the reaction which aided in the process of determining whether catalase is performing its function was the formation of gas bubbles in the test tubes, and the quantity of that indicator assisted in drawing conclusions about the enzyme activity. This study revealed that the optimum conditions for catalase enzyme activity tend to be 37 °C at a pH of 7, especially present in liver and yeast samples. Ascorbic acid has proven to be a valuable inhibitor of the catalase enzyme. Extremely high or low temperature, along with highly acidic or basic environments tend to alter the enzyme activity disallowing it to perform its key role.

Adaptive immune responses have been studied extensively in the course of mRNA vaccination against COVID-19. Considerably fewer studies have assessed the effects on innate immune cells. Here, we characterized NK cells in healthy individuals and immunocompromised patients in the course of an anti-SARS-CoV-2 BNT162b2 mRNA prospective, open-label clinical vaccine trial. See trial registration description in notes. Results revealed preserved NK cell numbers, frequencies, subsets, phenotypes, and function as assessed through consecutive peripheral blood samplings at 0, 10, 21, and 35 days following vaccination. A positive correlation was observed between the frequency of NKG2C + NK cells at baseline (Day 0) and anti-SARS-CoV-2 Ab titers following BNT162b2 mRNA vaccination at Day 35. The present results provide basic insights in regards to NK cells in the context of mRNA vaccination, and have relevance for future mRNA-based vaccinations against COVID-19, other viral infections, and cancer. Trial registration : The current study is based on clinical material from the COVAXID open-label, non-randomized prospective clinical trial registered at EudraCT and clinicaltrials.gov (no. 2021–000175-37). Description: https://clinicaltrials.gov/ct2/show/NCT04780659?term=2021-000175-37&draw=2&rank=1 .

Adaptive immune responses have been studied extensively in the course of mRNA vaccination against COVID-19. Considerably fewer studies have assessed the effects on innate immune cells. Here, we characterized NK cells in healthy individuals and immunocompromised patients in the course of an anti-SARS-CoV-2 BNT162b2 mRNA prospective, open-label clinical vaccine trial. See trial registration description in notes. Results revealed preserved NK cell numbers, frequencies, subsets, phenotypes, and function as assessed through consecutive peripheral blood samplings at 0, 10, 21, and 35 days following vaccination. A positive correlation was observed between the frequency of NKG2C+ NK cells at baseline (Day 0) and anti-SARS-CoV-2 Ab titers following BNT162b2 mRNA vaccination at Day 35. The present results provide basic insights in regards to NK cells in the context of mRNA vaccination, and have relevance for future mRNA-based vaccinations against COVID-19, other viral infections, and cancer. Trial registration: The current study is based on clinical material from the COVAXID open-label, non-randomized prospective clinical trial registered at EudraCT and clinicaltrials.gov (no. 2021–000175-37). Description: https://clinicaltrials.gov/ct2/show/NCT04780659?term=2021-000175-37&draw=2&rank=1.

Here, from macrophylogeographic mtDNA empirical data, we proposed a scenario of the evolution and speciation of two important forest trees, European Black Pine and Scotch Pine, and their multiple subspecies and varieties. Molecular clock simulations revealed that INDELs variability in the Pinus mitochondrial genome is relatively old, i.e., from the Pliocene-Miocene epoch, and related to historical tectonic continental fluctuations rather than climate change on a large geographic scale. Special attention is paid to the relationships between different speciation models and historical migration patterns and between peripheral and central populations. Species evolution involves the mixing of different speciation modes rather than only one of them, and one speciation mode has different results/effects on different DNA types (e.g., mitochondrial vs. chloroplast vs. nuclear DNA). The misbalance between different meta-population census size vs. effective population size contributions for asymmetric migration pattern is a result of different genotypes (and sub-phylogenetic lines) responding to selection pressing and adaptive evolution.

Artificial intelligence and machine learning techniques have progressed dramatically and become powerful tools required to solve complicated tasks, such as computer vision, speech recognition, and natural language processing. Since these techniques have provided promising and evident results in these fields, they emerged as valuable methods for applications in human physiology and healthcare. General physiological recordings are time-related expressions of bodily processes associated with health or morbidity. Sequence classification, anomaly detection, decision making, and future status prediction drive the learning algorithms to focus on the temporal pattern and model the nonstationary dynamics of the human body. These practical requirements give birth to the use of recurrent neural networks (RNNs), which offer a tractable solution in dealing with physiological time series and provide a way to understand complex time variations and dependencies. The primary objective of this article is to provide an overview of current applications of RNNs in the area of human physiology for automated prediction and diagnosis within different fields. Finally, we highlight some pathways of future RNN developments for human physiology.

Time series prediction is a widespread and well studied problem with applications in many domains (medical, geoscience, network analysis, finance, econometry etc.). In the case of multivariate time series, the key to good performances is to properly capture the dependencies between the variates. Often, these variates are structured, i.e. they are localised in an abstract space, usually representing an aspect of the physical world, and prediction amounts to a form of diffusion of the information across that space over time. Several neural network models of diffusion have been proposed in the literature. However, most of the existing proposals rely on some a priori knowledge on the structure of the space, usually in the form of a graph weighing the pairwise diffusion capacity of its points. We argue that this piece of information can often be dispensed with, since data already contains the diffusion capacity information, and in a more reliable form than that obtained from the usually largely hand-crafted graphs. We propose instead a fully data-driven model which does not rely on such a graph, nor any other prior structural information. We conduct a first set of experiments to measure the impact on performance of a structural prior, as used in baseline models, and show that, except at very low data levels, it remains negligible, and beyond a threshold, it may even become detrimental. We then investigate, through a second set of experiments, the capacity of our model in two respects: treatment of missing data and domain adaptation.