Atopic dermatitis is a heterogeneous disease, in which the pathogenesis is associated with mutations in genes encoding epidermal structural proteins, barrier enzymes, and their inhibitors; the role of genes regulating innate and adaptive immune responses and environmental factors inducing the disease is also noted. Recent studies point to the key role of epigenetic changes in the development of the disease. Epigenetic modifications are mainly mediated by DNA methylation, histone acetylation, and the action of specific non-coding RNAs. It has been documented that the profile of epigenetic changes in patients with atopic dermatitis (AD) differs from that observed in healthy people. This applies to the genes affecting the regulation of immune response and inflammatory processes, e.g., both affecting Th1 bias and promoting Th2 responses and the genes of innate immunity, as well as those encoding the structural proteins of the epidermis. Understanding of the epigenetic alterations is therefore pivotal to both create new molecular classifications of atopic dermatitis and to enable the development of personalized treatment strategies.

High resolution melting (HRM) is a convenient method for gene scanning as well as genotyping of individual and multiple single nucleotide polymorphisms (SNPs). This rapid, simple, closed-tube, homogenous, and cost-efficient approach has the capacity for high specificity and sensitivity, while allowing easy transition to high-throughput scale. In this paper, we provide examples from our laboratory practice of some problematic issues which can affect the performance and data analysis of HRM results, especially with regard to reference curve-based targeted genotyping. We present those examples in order of the typical experimental workflow, and discuss the crucial significance of the respective experimental errors and limitations for the quality and analysis of results. The experimental details which have a decisive impact on correct execution of a HRM genotyping experiment include type and quality of DNA source material, reproducibility of isolation method and template DNA preparation, primer and amplicon design, automation-derived preparation and pipetting inconsistencies, as well as physical limitations in melting curve distinction for alternative variants and careful selection of samples for validation by sequencing. We provide a case-by-case analysis and discussion of actual problems we encountered and solutions that should be taken into account by researchers newly attempting HRM genotyping, especially in a high-throughput setup.

The known challenge of underutilization of data and biological material from biorepositories as potential resources for medical research has been the focus of discussion for over a decade. Recently developed guidelines for improved data availability and reusability—entitled FAIR Principles (Findability, Accessibility, Interoperability, and Reusability)—are likely to address only parts of the problem. In this article, we argue that biological material and data should be viewed as a unified resource. This approach would facilitate access to complete provenance information, which is a prerequisite for reproducibility and meaningful integration of the data. A unified view also allows for optimization of long-term storage strategies, as demonstrated in the case of biobanks. We propose an extension of the FAIR Principles to include the following additional components: (1) quality aspects related to research reproducibility and meaningful reuse of the data, (2) incentives to stimulate effective enrichment of data sets and biological material collections and its reuse on all levels, and (3) privacy-respecting approaches for working with the human material and data. These FAIR-Health principles should then be applied to both the biological material and data. We also propose the development of common guidelines for cloud architectures, due to the unprecedented growth of volume and breadth of medical data generation, as well as the associated need to process the data efficiently.

Ancient DNA genome-wide analyses of Neolithic individuals from central and southern Europe indicate an overall population turnover pattern in which migrating farmers from Anatolia and the Near East largely replaced autochthonous Mesolithic hunter-gatherers. However, the genetic history of the Neolithic transition in areas lying north of the European Neolithic core region involved different levels of admixture with hunter-gatherers. Here we analyse genome-wide data of 17 individuals spanning from the Middle Neolithic to the Early Bronze Age (4300-1900 BCE) in order to assess the Neolithic transition in north-central Poland, and the local impacts of hunter-farmer contacts and Late Neolithic steppe migrations. We evaluate the influence of these on local populations and assess if and how they change through time, reporting evidence of recurrent hunter-farmer admixture over three millennia, and the co-existence of unadmixed hunter-gatherers as late as 4300 BCE. During the Late Neolithic we report the appearance of steppe ancestry, but on a lesser scale than previously described for other central European regions, with evidence of stronger affinities to hunter-gatherers than to steppe pastoralists. These results help understand the Neolithic palaeogenomics of another central European area, Kuyavia, and highlight the complexity of population interactions during those times.

Novel species of fungi described in this study include those from various countries as follows: Argentina, Colletotrichum araujiae on leaves, stems and fruits of Araujia hortorum. Australia, Agaricus pateritonsus on soil, Curvularia fraserae on dying leaf of Bothriochloa insculpta, Curvularia millisiae from yellowing leaf tips of Cyperus aromaticus, Marasmius brunneolorobustus on well-rotted wood, Nigrospora cooperae from necrotic leaf of Heteropogon contortus, Penicillium tealii from the body of a dead spider, Pseudocercospora robertsiorum from leaf spots of Senna tora, Talaromyces atkinsoniae from gills of Marasmius crinis-equi and Zasmidium pearceae from leaf spots of Smilax glyciphylla. Brazil, Preussia bezerrensis fromair. Chile, Paraconiothyrium kelleni from the rhizosphere of Fragaria chiloensis subsp. chiloensis f. chiloensis. Finland, Inocybe udicola onsoilinmixedforest with Betula pendula, Populus tremula, Picea abies and Alnus incana. France, Myrmecridium normannianum on dead culm of unidentified Poaceae. Germany, Vexillomyces fraxinicola from symptomless stem wood of Fraxinus excelsior. India, Diaporthe limoniae on infected fruit of Limonia acidissima, Didymella naikii on leaves of Cajanus cajan, and Fulvifomes mangroviensis on basal trunk of Aegiceras corniculatum. Indonesia, Penicillium ezekielii from Zea mays kernels. Namibia, Neocamarosporium calicoremae and Neocladosporium calicoremae on stems of Calicorema capitata, and Pleiochaeta adenolobi on symptomatic leaves of Adenolobus pechuelii. Netherlands, Chalara pteridii on stems of Pteridium aquilinum, Neomackenziella juncicola (incl. Neomackenziella gen. nov.)and Sporidesmiella junci from dead culms of Juncus effusus. Pakistan, Inocybe longistipitata on soil in a Quercus forest. Poland, Phytophthora viadrina from rhizosphere soil of Quercus robur, and Septoria krystynae on leaf spots of Viscum album. Portugal (Azores), Acrogenospora stellata on dead wood or bark. South Africa, Phyllactinia greyiae on leaves of Greyia sutherlandii and Punctelia anae on bark of Vachellia karroo. Spain, Anteaglonium lusitanicum on decaying wood of Prunus lusitanica subsp. lusitanica, Hawksworthiomyces riparius from fluvial sediments, Lophiostoma carabassense endophytic in roots of Limbarda crithmoides, and Tuber mohedanoi from calcareussoils. Spain (Canary Islands), Mycena laurisilvae on stumps and woody debris. Sweden, Elaphomyces geminus from soil under Quercus robur. Thailand, Lactifluus chiangraiensis on soil under Pinus merkusii, Lactifluus nakhonphanomensis and Xerocomus sisongkhramensis on soil under Dipterocarpus trees. Ukraine, Valsonectria robiniae on dead twigs of Robinia hispida. USA, Spiralomyces americanus (incl. Spiralomyces gen. nov.) from office air. Morphological and culture characteristics are supported by DNA barcodes.