BIOLOGICAL, ECOLOGICAL AND ORNAMENTAL CHARACTERISTICS OF ASH-LEAF MAPLE (ACER NEGUNDO L.) WITH APPLICATION ON LANDSCAPE SURFACES OF THE CITY OF KNIN

Javor negundovac (Acer negundo L.) je listopadno drvo iz Sjeverne Amerike, a u Hrvatsku je uvezen početkom 20. stoljeća. Cijenjen je u pčelarstvu, šumarstvu, krajobraznom uređenju te je vrlo invazivna vrsta. Najrasprostranjeniji je u kontinentalnom i submediteranskom dijelu Hrvatske. Javor negundovac (Acer negundo L.) slabo je zastupljena dendrološka vrsta na javnim gradskim krajobraznim površinama Knina, kao soliter, u drvoredu ili u skupinama. Metodom promatranja je uočeno da je u pogledu održavanja i njege stabala negundovca (A. negundo) na krajobraznim površinama nužno posvetiti više pažnje. Nisu uočena njegova stabla izvan gradskih krajobraznih površina. Temeljem anketnog istraživanja ispitanici su dodijelili vrlo dobre ocjene ukrasnim karakteristikama i ugodnosti javora negundovca (A. negundo) na krajobraznim površinama Knina. Ponešto su lošije ocijenili njegovu ulogu u pčelarstvu, šumskim melioracijama i urbanom šumarstvu. Veći broj ispitanika ne zna da je javor negundovac (A. negundo) vrlo invazivna vrsta.Ash-leaf maple or boxelder (Acer negundo L.) is a deciduous tree that originates from North America. It was imported into Croatia at the beginning of the 20th century. It is highly valued in bee-keeping, forestry and landscape architecture and is a highly invasive species. Amongst the ornamental characteristics it is important to highlight its tree trunk, tree bark, habitus, branches, leaves, flowers and fruits. Due to its high adaptability it tolerates very poor soils and positions exposed to the sunlight, drought, cold and shade. It is most widespread in the continental and sub-Mediterranean parts of Croatia. In time A. negundo became considerably widespread and hence it was considered an invasive alien species in Croatia. According to the references provided in professional literature, the previously mentioned tree species does not currently do any serious damage to the autochthonous vegetation. Ash-leaf maple (Acer negundo L.) is a sparsely distributed woody species in urban landscape of Knin, as a solitary tree, in tree lines or in groups of trees. Moreover, using the observation method, it was noted that concerning maintenance and care of A. negundo in landscape more attention needs to be paid to the formation of the tree top. The tree have not been found outside the urban landscape. The survey was conducted in the second half of 2017 on a sample comprising 50 respondents originating from the city of Knin and its outskirts. Based on the findings of the survey the respondents gave very good grades to ornamental characteristics and the comfort of the presence of A. negundo in the landscape surfaces of Knin. The role of the tree species received slightly lower grades in bee-keeping, forest meliorations and urban forestry. A considerable number of those surveyed was unaware of the fact that A. negundo is a highly invasive tree species

The Interplay Between Neoantigens and Immune Cells in Sarcomas Treated With Checkpoint Inhibition

INTRODUCTION: Sarcomas are comprised of diverse bone and connective tissue tumors with few effective therapeutic options for locally advanced unresectable and/or metastatic disease. Recent advances in immunotherapy, in particular immune checkpoint inhibition (ICI), have shown promising outcomes in several cancer indications. Unfortunately, ICI therapy has provided only modest clinical responses and seems moderately effective in a subset of the diverse subtypes. METHODS: To explore the immune parameters governing ICI therapy resistance or immune escape, we performed whole exome sequencing (WES) on tumors and their matched normal blood, in addition to RNA-seq from tumors of 31 sarcoma patients treated with pembrolizumab. We used advanced computational methods to investigate key immune properties, such as neoantigens and immune cell composition in the tumor microenvironment (TME). RESULTS: A multifactorial analysis suggested that expression of high quality neoantigens in the context of specific immune cells in the TME are key prognostic markers of progression-free survival (PFS). The presence of several types of immune cells, including T cells, B cells and macrophages, in the TME were associated with improved PFS. Importantly, we also found the presence of both CD8+ T cells and neoantigens together was associated with improved survival compared to the presence of CD8+ T cells or neoantigens alone. Interestingly, this trend was not identified with the combined presence of CD8+ T cells and TMB; suggesting that a combined CD8+ T cell and neoantigen effect on PFS was important. DISCUSSION: The outcome of this study may inform future trials that may lead to improved outcomes for sarcoma patients treated with ICI

GSuite HyperBrowser: integrative analysis of dataset collections across the genome and epigenome

Background: Recent large-scale undertakings such as ENCODE and Roadmap Epigenomics have generated experimental data mapped to the human reference genome (as genomic tracks) representing a variety of functional elements across a large number of cell types. Despite the high potential value of these publicly available data for a broad variety of investigations, little attention has been given to the analytical methodology necessary for their widespread utilisation. Findings: We here present a first principled treatment of the analysis of collections of genomic tracks. We have developed novel computational and statistical methodology to permit comparative and confirmatory analyses across multiple and disparate data sources. We delineate a set of generic questions that are useful across a broad range of investigations and discuss the implications of choosing different statistical measures and null models. Examples include contrasting analyses across different tissues or diseases. The methodology has been implemented in a comprehensive open-source software system, the GSuite HyperBrowser. To make the functionality accessible to biologists, and to facilitate reproducible analysis, we have also developed a web-based interface providing an expertly guided and customizable way of utilizing the methodology. With this system, many novel biological questions can flexibly be posed and rapidly answered. Conclusions: Through a combination of streamlined data acquisition, interoperable representation of dataset collections, and customizable statistical analysis with guided setup and interpretation, the GSuite HyperBrowser represents a first comprehensive solution for integrative analysis of track collections across the genome and epigenome. The software is available at: https://hyperbrowser.uio.no.This work was supported by the Research Council of Norway (under grant agreements 221580, 218241, and 231217/F20), by the Norwegian Cancer Society (under grant agreements 71220’PR-2006-0433 and 3485238-2013), and by the South-Eastern Norway Regional Health Authority (under grant agreement 2014041).Peer Reviewe

The interplay between neoantigens and immune cells in sarcomas treated with checkpoint inhibition

IntroductionSarcomas are comprised of diverse bone and connective tissue tumors with few effective therapeutic options for locally advanced unresectable and/or metastatic disease. Recent advances in immunotherapy, in particular immune checkpoint inhibition (ICI), have shown promising outcomes in several cancer indications. Unfortunately, ICI therapy has provided only modest clinical responses and seems moderately effective in a subset of the diverse subtypes.MethodsTo explore the immune parameters governing ICI therapy resistance or immune escape, we performed whole exome sequencing (WES) on tumors and their matched normal blood, in addition to RNA-seq from tumors of 31 sarcoma patients treated with pembrolizumab. We used advanced computational methods to investigate key immune properties, such as neoantigens and immune cell composition in the tumor microenvironment (TME).ResultsA multifactorial analysis suggested that expression of high quality neoantigens in the context of specific immune cells in the TME are key prognostic markers of progression-free survival (PFS). The presence of several types of immune cells, including T cells, B cells and macrophages, in the TME were associated with improved PFS. Importantly, we also found the presence of both CD8+ T cells and neoantigens together was associated with improved survival compared to the presence of CD8+ T cells or neoantigens alone. Interestingly, this trend was not identified with the combined presence of CD8+ T cells and TMB; suggesting that a combined CD8+ T cell and neoantigen effect on PFS was important.DiscussionThe outcome of this study may inform future trials that may lead to improved outcomes for sarcoma patients treated with ICI

Roadmap on Label-Free Super-resolution Imaging

Label-free super-resolution (LFSR) imaging relies on light-scattering processes in nanoscale objects without a need for fluorescent (FL) staining required in super-resolved FL microscopy. The objectives of this Roadmap are to present a comprehensive vision of the developments, the state-of-the-art in this field, and to discuss the resolution boundaries and hurdles that need to be overcome to break the classical diffraction limit of the label-free imaging. The scope of this Roadmap spans from the advanced interference detection techniques, where the diffraction-limited lateral resolution is combined with unsurpassed axial and temporal resolution, to techniques with true lateral super-resolution capability that are based on understanding resolution as an information science problem, on using novel structured illumination, near-field scanning, and nonlinear optics approaches, and on designing superlenses based on nanoplasmonics, metamaterials, transformation optics, and microsphere-assisted approaches. To this end, this Roadmap brings under the same umbrella researchers from the physics and biomedical optics communities in which such studies have often been developing separately. The ultimate intent of this paper is to create a vision for the current and future developments of LFSR imaging based on its physical mechanisms and to create a great opening for the series of articles in this field.Peer reviewe

Mind the gaps: overlooking inaccessible regions confounds statistical testing in genome analysis

Background The current versions of reference genome assemblies still contain gaps represented by stretches of Ns. Since high throughput sequencing reads cannot be mapped to those gap regions, the regions are depleted of experimental data. Moreover, several technology platforms assay a targeted portion of the genomic sequence, meaning that regions from the unassayed portion of the genomic sequence cannot be detected in those experiments. We here refer to all such regions as inaccessible regions, and hypothesize that ignoring these regions in the null model may increase false findings in statistical testing of colocalization of genomic features. Results Our explorative analyses confirm that the genomic regions in public genomic tracks intersect very little with assembly gaps of human reference genomes (hg19 and hg38). The little intersection was observed only at the beginning and end portions of the gap regions. Further, we simulated a set of synthetic tracks by matching the properties of real genomic tracks in a way that nullified any true association between them. This allowed us to test our hypothesis that not avoiding inaccessible regions (as represented by assembly gaps) in the null model would result in spurious inflation of statistical significance. We contrasted the distributions of test statistics and p-values of Monte Carlo-based permutation tests that either avoided or did not avoid assembly gaps in the null model when testing colocalization between a pair of tracks. We observed that the statistical tests that did not account for assembly gaps in the null model resulted in a distribution of the test statistic that is shifted to the right and a distribution of p-values that is shifted to the left (indicating inflated significance). We observed a similar level of inflated significance in hg19 and hg38, despite assembly gaps covering a smaller proportion of the latter reference genome. Conclusion We provide empirical evidence demonstrating that inaccessible regions, even when covering only a few percentages of the genome, can lead to a substantial amount of false findings if not accounted for in statistical colocalization analysis

ClusTrack: Feature extraction and similarity measures for clustering of genome-wide data sets

Clustering is a popular technique for explorative analysis of data, as it can reveal subgroupings and similarities between data in an unsupervised manner. While clustering is routinely applied to gene expression data, there is a lack of appropriate general methodology for clustering of sequence-level genomic and epigenomic data, e.g. ChIP-based data. We here introduce a general methodology for clustering data sets of coordinates relative to a genome assembly, i.e. genomic tracks. By defining appropriate feature extraction approaches and similarity measures, we allow biologically meaningful clustering to be performed for genomic tracks using standard clustering algorithms. An implementation of the methodology is provided through a tool, ClusTrack, which allows fine-tuned clustering analyses to be specified through a web-based interface. We apply our methods to the clustering of occupancy of the H3K4me1 histone modification in samples from a range of different cell types. The majority of samples form meaningful subclusters, confirming that the definitions of features and similarity capture biological, rather than technical, variation between the genomic tracks. Input data and results are available, and can be reproduced, through a Galaxy Pages document at http://hyperbrowser.uio.no/hb/u/hb-superuser/p/clustrack. The clustering functionality is available as a Galaxy tool, under the menu option "Specialized analyzis of tracks", and the submenu option "Cluster tracks based on genome level similarity", at the Genomic HyperBrowser server: http://hyperbrowser.uio.no/hb/

Deep learning of antibody epitopes using positional permutation vectors

Background: The accurate computational prediction of B cell epitopes can vastly reduce the cost and time required for identifying potential epitope candidates for the design of vaccines and immunodiagnostics. However, current computational tools for B cell epitope prediction perform poorly and are not fit-for-purpose, and there remains enormous room for improvement and the need for superior prediction strategies. Results: Here we propose a novel approach that improves B cell epitope prediction by encoding epitopes as binary positional permutation vectors that represent the position and structural properties of the amino acids within a protein antigen sequence that interact with an antibody. This approach supersedes the traditional method of defining epitopes as scores per amino acid on a protein sequence, where each score reflects each amino acids predicted probability of partaking in a B cell epitope antibody interaction. In addition to defining epitopes as binary positional permutation vectors, the approach also uses the 3D macrostructure features of the unbound protein structures, and in turn uses these features to train another deep learning model on the corresponding antibody-bound protein 3D structures. This enables the algorithm to learn the key structural and physiochemical features of the unbound protein and embedded epitope that initiate the antibody binding process helping to eliminate “induced fit” biases in the training data. We demonstrate that the strategy predicts B cell epitopes with improved accuracy compared to the existing tools. Additionally, we show that this approach reliably identifies the majority of experimentally verified epitopes on the spike protein of SARS-CoV-2 not seen by the model during training and generalizes in a very robust manner on dissimilar data not seen by the model during training. Conclusions: With the approach described herein, a primary protein sequence and a query positional permutation vector encoding a putative epitope is sufficient to predict B cell epitopes in a reliable manner, potentially advancing the use of computational prediction of B cell epitopes in biomedical research applications

Dendrogram of H3K4me1 track clustering, using the “Direct sequence-level similarity” for the genomic region chr1-22.

<p>All samples, except the ones from brain, are placed into separate subclusters per tissue. The clustering were performed according to the canonical case “Direct sequence-level similarity”. Each individual bp-location of the genome were considered an independent feature. The distance between two feature vectors was defined as ratio of the intersection to the union of base pairs covered by two feature vectors. Clustering was performed using standard hierarchical clustering with the average linkage criterion.</p

Gene Ontology terms enriched by genes with different H3K4me1 occupancy in fetal and adult brain cell types.

<p>Table of the three top terms of the three top annotation clusters from David. The gene IDs submitted to DAVID were selected based on different H3K4me1 occupancy between fetal and adult brain clusters, by using the Genomic HyperBrowser. Benjamini = Benjamini-Hochberg.</p><p>Gene Ontology terms enriched by genes with different H3K4me1 occupancy in fetal and adult brain cell types.</p