The oldest Pinus nigra plantations in the central Apennines: distribution and floristic-vegetational characterisation

This paper represents the first syntaxonomic study on the Pinus nigra subsp. nigra artificial stands in the Apennines. It refers exclusively to the mature Pinus nigra forest plantations in the central Apennines that were planted before the 1950s, and then not managed. The mature Pinus nigra forest plantations in the central Apennines are distributed within four National Parks, on limestone substrata, mainly with southern aspects and rugged slopes, and at elevations ranging from 655 m to 1670 m a.s.l.. Two new phytosociological associations are described here and classified in the alliance Junipero communis–Pinion nigrae: Orthilio secundae–Pinetum nigrae and Digitalidi micranthae–Pinetum nigrae. The association Orthilio secundae–Pinetum nigrae comprises the relative mesophilous mature pine forests of the supratemperate thermotype in the plant landscape context of the alliance Aremonio agrimonioidis–Fagion sylvaticae potential vegetation belt. The Digitalidi micranthae–Pinetum nigrae comprises the thermophilous pine forests occurring on rocky stands within mesotemperate and lower supratemperate thermotypes in the potential belt of alliance Carpinion orientalis woods. The comparison of these two new associations and the phytosociological literature concerning the natural communities of Pinus nigra in the Apennines highlights their floristic and coenological autonomy.

Deep metabolic profiling assessment of tissue extraction protocols for three model organisms

Metabolic profiling harbors the potential to better understand various disease entities such as cancer, diabetes, Alzheimer's, Parkinson's disease or COVID-19. Deciphering these intricate pathways in human studies requires large sample sizes as a means of reducing variability. While such broad human studies have discovered new associations between a given disease and certain affected metabolites, i.e. biomarkers, they often provide limited functional insights. To design more standardized experiments, reduce variability in the measurements and better resolve the functional component of such dynamic metabolic profiles, model organisms are frequently used. Standardized rearing conditions and uniform sampling strategies are prerequisites towards a successful metabolomic study. However, further aspects such as the choice of extraction protocol and analytical technique can influence the outcome drastically. Here, we employed a highly standardized metabolic profiling assay analyzing 630 metabolites across three commonly used model organisms (Drosophila, mouse and Zebrafish) to find the optimal extraction protocols for various matrices. Focusing on parameters such as metabolite coverage, metabolite yield and variance between replicates we compared seven extraction protocols. We found that the application of a combination of 75% ethanol and methyl tertiary-butyl ether (MTBE), while not producing the broadest coverage and highest yields, was the most reproducible extraction protocol. We were able to determine up to 530 metabolites in mouse kidney samples, 509 in mouse liver, 422 in Zebrafish and 388 in Drosophila and discovered a core overlap of 261 metabolites in these four matrices. To enable other scientists to search for the most suitable extraction protocol in their experimental context and interact with this comprehensive data, we have integrated our data set in the open-source shiny app MetaboExtract. This will enable scientists to search for their metabolite or metabolite class of interest, compare it across the different tested extraction protocols and sample types as well as find reference concentrations.

Kawasaki-multisystem inflammatory syndrome in children in the delayed period of coronavirus infection (covid-19): modern state of the problem and possible new approaches to treatment (plasmapheresis)

COVID-19 infection usually occurs in children in a mild form, but some of them in a delayed period (one or several weeks after acute infection with COVID-19) may develop a severe inflammatory disease with clinical manifestations similar to toxic shock syndrome (Kawasaki disease), classified as multisystem inflammatory syndrome in children (MISC). It is possible that the syndrome has only a temporary connection with the COVID-19 infection. In the future, new associations of such clinical manifestations with other infectious (or non-infectious) diseases may appear. But currently, all children in the described cohorts with MISC have an association with COVID-19 infection. It is believed that the syndrome is initiated by an excessive adaptive immune response with the formation of autoantibodies. Treatment is based on anti-inflammatory, including steroid therapy, the possible use of intravenous immunoglobulin, aspirin, interleukin 1 and 6 receptor antagonists. The article analyzes current views on Kawasaki-multisystem inflammatory syndrome in children in the delayed period of COVID-19 coronavirus infection in the aspects of diagnosis, pathogenesis, clinical manifestations (with a discussion of foreign and Russian studies) and approaches to therapy and possible prevention, including the possibility of using plasmapheresis in complex therapy.

Semi-Quantitative Multiplex Profiling of the Complement System Identifies Associations of Complement Proteins with Genetic Variants and Metabolites in Age-Related Macular Degeneration

Age-related macular degeneration (AMD) is a major cause of vision loss among the elderly in the Western world. The complement system has been identified as one of the main AMD disease pathways. We performed a comprehensive expression analysis of 32 complement proteins in plasma samples of 255 AMD patients and 221 control individuals using mass spectrometry-based semi-quantitative multiplex profiling. We detected significant associations of complement protein levels with age, sex and body-mass index (BMI), and potential associations of C-reactive protein, factor H related-2 (FHR-2) and collectin-11 with AMD. In addition, we confirmed previously described associations and identified new associations of AMD variants with complement levels. New associations include increased C4 levels for rs181705462 at the C2/CFB locus, decreased vitronectin (VTN) levels for rs11080055 at the TMEM97/VTN locus and decreased factor I levels for rs10033900 at the CFI locus. Finally, we detected significant associations between AMD-associated metabolites and complement proteins in plasma. The most significant complement-metabolite associations included increased high density lipoprotein (HDL) subparticle levels with decreased C3, factor H (FH) and VTN levels. The results of our study indicate that demographic factors, genetic variants and circulating metabolites are associated with complement protein components. We suggest that these factors should be considered to design personalized treatment approaches and to increase the success of clinical trials targeting the complement system.

Clownfish hosting anemones (Anthozoa, Actiniaria) of the Red Sea: new associations and distributions, historical misidentifications, and morphological variability

Background The Red Sea contains thousands of kilometers of fringing reef systems inhabited by clownfish and sea anemones, yet there is no consensus regarding the diversity of host anemone species that inhabit this region. We sought to clarify a historical record and recent literature sources that disagree on the diversity of host anemone species in the Red Sea, which contains one endemic anemonefish, Amphiprion bicinctus Rüppell 1830. Results We conducted 73 surveys spanning ~ 1600 km of coastline from the northern Saudi Arabian Red Sea to the Gulf of Aden and encountered seven species of host anemones, six of which hosted A. bicinctus. We revise the list of symbionts for A. bicinctus to include Stichodactyla haddoni (Saville-Kent, 1893) and Stichodactyla mertensii Brandt, 1835 which were both observed in multiple regions. We describe Red Sea phenotypic variability in Heteractis crispa (Hemprich & Ehrenberg in Ehrenberg, 1834) and Heteractis aurora (Quoy & Gaimard, 1833), which may indicate that these species hybridize in this region. We did not encounter Stichodactyla gigantea (Forsskål, 1775), although the Red Sea is the type locality for this species. Further, a thorough review of peer-reviewed literature, occurrence records, and misidentified basis of record reports dating back to the early twentieth century indicate that it is unlikely that S. gigantea occurs in the Red Sea. Conclusions In sum, we present a new guide for the host anemones of the Red Sea, revise the host specificity of A. bicinctus, and question whether S. gigantea occurs in the central and western Indian Ocean.

Graph Analysis of EEG Functional Connectivity Networks During a Letter-Speech Sound Binding Task in Adult Dyslexics

We performed an EEG graph analysis on data from 31 typical readers (22.27 ± 2.53 y/o) and 24 dyslexics (22.99 ± 2.29 y/o), recorded while they were engaged in an audiovisual task and during resting-state. The task simulates reading acquisition as participants learned new letter-sound mappings via feedback. EEG data was filtered for the delta (0.5–4 Hz), theta (4–8 Hz), alpha (8–13 Hz), and beta (13–30 Hz) bands. We computed the Phase Lag Index (PLI) to provide an estimate of the functional connectivity between all pairs of electrodes per band. Then, networks were constructed using a Minimum Spanning Tree (MST), a unique sub-graph connecting all nodes (electrodes) without loops, aimed at minimizing bias in between groups and conditions comparisons. Both groups showed a comparable accuracy increase during task blocks, indicating that they correctly learned the new associations. The EEG results revealed lower task-specific theta connectivity, and lower theta degree correlation over both rest and task recordings, indicating less network integration in dyslexics compared to typical readers. This pattern suggests a role of theta oscillations in dyslexia and may reflect differences in task engagement between the groups, although robust correlations between MST metrics and performance indices were lacking.

High-sensitivity pattern discovery in large, paired multi-omic datasets

Modern biological screens yield enormous numbers of measurements, and identifying and interpreting statistically significant associations among features is essential. Here, we present a novel hierarchical framework, HAllA (Hierarchical All-against-All association testing), for structured association discovery between paired high-dimensional datasets. HAllA efficiently integrates hierarchical hypothesis testing with false discovery rate correction to reveal significant linear and non-linear block-wise relationships among continuous and/or categorical data. We optimized and evaluated HAllA using heterogeneous synthetic datasets of known association structure, where HAllA outperformed all-against-all and other block testing approaches across a range of common similarity measures. We then applied HAllA to a series of real-world multi-omics datasets, revealing new associations between gene expression and host immune activity, the microbiome and host transcriptome, metabolomic profiling, and human health phenotypes. An open-source implementation of HAllA is freely available at http://huttenhower.sph.harvard.edu/halla along with documentation, demo datasets, and a user group.

Inferring miRNA-disease associations using collaborative filtering and resource allocation on a tripartite graph

Background Developing efficient and successful computational methods to infer potential miRNA-disease associations is urgently needed and is attracting many computer scientists in recent years. The reason is that miRNAs are involved in many important biological processes and it is tremendously expensive and time-consuming to do biological experiments to verify miRNA-disease associations. Methods In this paper, we proposed a new method to infer miRNA-disease associations using collaborative filtering and resource allocation algorithms on a miRNA-disease-lncRNA tripartite graph. It combined the collaborative filtering algorithm in CFNBC model to solve the problem of imbalanced data and the method for association prediction established multiple types of known associations among multiple objects presented in TPGLDA model. Results The experimental results showed that our proposed method achieved a reliable performance with Area Under Roc Curve (AUC) and Area Under Precision-Recall Curve (AUPR) values of 0.9788 and 0.9373, respectively, under fivefold-cross-validation experiments. It outperformed than some other previous methods such as DCSMDA and TPGLDA. Furthermore, it demonstrated the ability to derive new associations between miRNAs and diseases among 8, 19 and 14 new associations out of top 40 predicted associations in case studies of Prostatic Neoplasms, Heart Failure, and Glioma diseases, respectively. All of these new predicted associations have been confirmed by recent literatures. Besides, it could discover new associations for new diseases (or miRNAs) without any known associations as demonstrated in the case study of Open-angle glaucoma disease. Conclusion With the reliable performance to infer new associations between miRNAs and diseases as well as to discover new associations for new diseases (or miRNAs) without any known associations, our proposed method can be considered as a powerful tool to infer miRNA-disease associations.

Predicting miRNA–disease associations using improved random walk with restart and integrating multiple similarities

Predicting beneficial and valuable miRNA–disease associations (MDAs) by doing biological laboratory experiments is costly and time-consuming. Proposing a forceful and meaningful computational method for predicting MDAs is essential and captivated many computer scientists in recent years. In this paper, we proposed a new computational method to predict miRNA–disease associations using improved random walk with restart and integrating multiple similarities (RWRMMDA). We used a WKNKN algorithm as a pre-processing step to solve the problem of sparsity and incompletion of data to reduce the negative impact of a large number of missing associations. Two heterogeneous networks in disease and miRNA spaces were built by integrating multiple similarity networks, respectively, and different walk probabilities could be designated to each linked neighbor node of the disease or miRNA node in line with its degree in respective networks. Finally, an improve extended random walk with restart algorithm based on miRNA similarity-based and disease similarity-based heterogeneous networks was used to calculate miRNA–disease association prediction probabilities. The experiments showed that our proposed method achieved a momentous performance with Global LOOCV AUC (Area Under Roc Curve) and AUPR (Area Under Precision-Recall Curve) values of 0.9882 and 0.9066, respectively. And the best AUC and AUPR values under fivefold cross-validation of 0.9855 and 0.8642 which are proven by statistical tests, respectively. In comparison with other previous related methods, it outperformed than NTSHMDA, PMFMDA, IMCMDA and MCLPMDA methods in both AUC and AUPR values. In case studies of Breast Neoplasms, Carcinoma Hepatocellular and Stomach Neoplasms diseases, it inferred 1, 12 and 7 new associations out of top 40 predicted associated miRNAs for each disease, respectively. All of these new inferred associations have been confirmed in different databases or literatures.