Bending and reverse bending during the fabrication of novel GaAs/(In,Ga)As/GaAs core-shell nanowires monitored byin situx-ray diffraction.

We report on the fabrication of a novel design of GaAs/(In,Ga)As/GaAs radial nanowire heterostructures on a Si 111 substrate, where, for the first time, the growth of inhomogeneous shells on a lattice mismatched core results in straight nanowires instead of bent. Nanowire bending caused by axial tensile strain induced by the (In,Ga)As shell on the GaAs core is reversed by axial compressive strain caused by the GaAs outer shell on the (In,Ga)As shell. Progressive nanowire bending and reverse bending in addition to the axial strain evolution during the two processes are accessed byin situby x-ray diffraction. The diameter of the core, thicknesses of the shells, as well as the indium concentration and distribution within the (In,Ga)As quantum well are revealed by 2D energy dispersive x-ray spectroscopy using a transmission electron microscope. Shell(s) growth on one side of the core without substrate rotation results in planar-like radial heterostructures in the form of free standing straight nanowires.

Reusability and composability in process description maps: RAS-RAF-MEK-ERK signalling.

Detailed maps of the molecular basis of the disease are powerful tools for interpreting data and building predictive models. Modularity and composability are considered necessary network features for large-scale collaborative efforts to build comprehensive molecular descriptions of disease mechanisms. An effective way to create and manage large systems is to compose multiple subsystems. Composable network components could effectively harness the contributions of many individuals and enable teams to seamlessly assemble many individual components into comprehensive maps. We examine manually built versions of the RAS-RAF-MEK-ERK cascade from the Atlas of Cancer Signalling Network, PANTHER and Reactome databases and review them in terms of their reusability and composability for assembling new disease models. We identify design principles for managing complex systems that could make it easier for investigators to share and reuse network components. We demonstrate the main challenges including incompatible levels of detail and ambiguous representation of complexes and highlight the need to address these challenges.

COBREXA.jl: constraint-based reconstruction and exascale analysis.

SUMMARY: COBREXA.jl is a Julia package for scalable, high-performance constraint-based reconstruction and analysis of very large-scale biological models. Its primary purpose is to facilitate the integration of modern high performance computing environments with the processing and analysis of large-scale metabolic models of challenging complexity. We report the architecture of the package, and demonstrate how the design promotes analysis scalability on several use-cases with multi-organism community models. AVAILABILITY AND IMPLEMENTATION: https://doi.org/10.17881/ZKCR-BT30. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Effect of Underlayer Quality on Microstructure, Stoichiometry, and Magnetic Properties of Hexaferrite BaFe12O19 Grown on YSZ(111) by Pulsed Laser Deposition.

We have studied the effect of platinum underlayer for two deposited thicknesses on the microstructure, crystalline quality, morphology, chemical composition, and magnetic properties as well as magnetic domain formation of BaFe12O19 (BaM) grown on YSZ(111) by pulsed laser deposition (PLD). We found that PLD platinum deposited with a thickness of 25 nm cannot withstand the dewetting phenomenon occurring during the subsequent BaM layer growth. A smooth and continuous Pt underlayer that possesses a sharp interface and omits the intermixing between the BaM and substrate was successfully achieved for a deposited Pt film thickness of 75 nm. Independent of the thickness of the deposited Pt layer, the c-axis orientation as well as coercivity Hc and the anisotropy HA fields were significantly improved due to a remarkable improvement of lattice mismatch in comparison with the BaM layer grown without a Pt underlayer on YSZ(111). By applying high-resolution X-ray diffraction, scanning and transmission electron microscopy (SEM/TEM), and atomically resolved scanning TEM imaging combined with energy-dispersive X-ray spectroscopy, as well as atomic and magnetic force microscopy, a comprehensive investigation of both structure and chemical composition of the deposited BaM films and their interfacial regions was performed. This study aimed to correlate the enhancement of the overall magnetic properties and of the local spin magnetic domain orientation with the modification of BaM microstructure and chemical composition at the nanometer scale due to the Pt underlayer. Finally, we attempted to understand the mechanisms that control the magnetic properties of these BaM films in order to be able to tailor them.

Arena3Dweb: interactive 3D visualization of multilayered networks.

Efficient integration and visualization of heterogeneous biomedical information in a single view is a key challenge. In this study, we present Arena3Dweb, the first, fully interactive and dependency-free, web application which allows the visualization of multilayered graphs in 3D space. With Arena3Dweb, users can integrate multiple networks in a single view along with their intra- and inter-layer connections. For clearer and more informative views, users can choose between a plethora of layout algorithms and apply them on a set of selected layers either individually or in combination. Users can align networks and highlight node topological features, whereas each layer as well as the whole scene can be translated, rotated and scaled in 3D space. User-selected edge colors can be used to highlight important paths, while node positioning, coloring and resizing can be adjusted on-the-fly. In its current version, Arena3Dweb supports weighted and unweighted undirected graphs and is written in R, Shiny and JavaScript. We demonstrate the functionality of Arena3Dweb using two different use-case scenarios; one regarding drug repurposing for SARS-CoV-2 and one related to GPCR signaling pathways implicated in melanoma. Arena3Dweb is available at http://bib.fleming.gr:3838/Arena3D or http://bib.fleming.gr/Arena3D.

PredictProtein - Predicting Protein Structure and Function for 29 Years.

Since 1992 PredictProtein (https://predictprotein.org) is a one-stop online resource for protein sequence analysis with its main site hosted at the Luxembourg Centre for Systems Biomedicine (LCSB) and queried monthly by over 3,000 users in 2020. PredictProtein was the first Internet server for protein predictions. It pioneered combining evolutionary information and machine learning. Given a protein sequence as input, the server outputs multiple sequence alignments, predictions of protein structure in 1D and 2D (secondary structure, solvent accessibility, transmembrane segments, disordered regions, protein flexibility, and disulfide bridges) and predictions of protein function (functional effects of sequence variation or point mutations, Gene Ontology (GO) terms, subcellular localization, and protein-, RNA-, and DNA binding). PredictProtein's infrastructure has moved to the LCSB increasing throughput; the use of MMseqs2 sequence search reduced runtime five-fold (apparently without lowering performance of prediction methods); user interface elements improved usability, and new prediction methods were added. PredictProtein recently included predictions from deep learning embeddings (GO and secondary structure) and a method for the prediction of proteins and residues binding DNA, RNA, or other proteins. PredictProtein.org aspires to provide reliable predictions to computational and experimental biologists alike. All scripts and methods are freely available for offline execution in high-throughput settings.

Analysis: Flawed Datasets of Monkeypox Skin Images.

The self-proclaimed first publicly available dataset of Monkeypox skin images consists of medically irrelevant images extracted from Google and photography repositories through a process denominated web-scrapping. Yet, this did not stop other researchers from employing it to build Machine Learning (ML) solutions aimed at computer-aided diagnosis of Monkeypox and other viral infections presenting skin lesions. Neither did it stop the reviewers or editors from publishing these subsequent works in peer-reviewed journals. Several of these works claimed extraordinary performance in the classification of Monkeypox, Chickenpox and Measles, employing ML and the aforementioned dataset. In this work, we analyse the initiator work that has catalysed the development of several ML solutions, and whose popularity is continuing to grow. Further, we provide a rebuttal experiment that showcases the risks of such methodologies, proving that the ML solutions do not necessarily obtain their performance from the features relevant to the diseases at issue.

Closing the gap between formats for storing layout information in systems biology.

The understanding of complex biological networks often relies on both a dedicated layout and a topology. Currently, there are three major competing layout-aware systems biology formats, but there are no software tools or software libraries supporting all of them. This complicates the management of molecular network layouts and hinders their reuse and extension. In this paper, we present a high-level overview of the layout formats in systems biology, focusing on their commonalities and differences, review their support in existing software tools, libraries and repositories and finally introduce a new conversion module within the MINERVA platform. The module is available via a REST API and offers, besides the ability to convert between layout-aware systems biology formats, the possibility to export layouts into several graphical formats. The module enables conversion of very large networks with thousands of elements, such as disease maps or metabolic reconstructions, rendering it widely applicable in systems biology.

Community-driven roadmap for integrated disease maps.

The Disease Maps Project builds on a network of scientific and clinical groups that exchange best practices, share information and develop systems biomedicine tools. The project aims for an integrated, highly curated and user-friendly platform for disease-related knowledge. The primary focus of disease maps is on interconnected signaling, metabolic and gene regulatory network pathways represented in standard formats. The involvement of domain experts ensures that the key disease hallmarks are covered and relevant, up-to-date knowledge is adequately represented. Expert-curated and computer readable, disease maps may serve as a compendium of knowledge, allow for data-supported hypothesis generation or serve as a scaffold for the generation of predictive mathematical models. This article summarizes the 2nd Disease Maps Community meeting, highlighting its important topics and outcomes. We outline milestones on the roadmap for the future development of disease maps, including creating and maintaining standardized disease maps; sharing parts of maps that encode common human disease mechanisms; providing technical solutions for complexity management of maps; and Web tools for in-depth exploration of such maps. A dedicated discussion was focused on mathematical modeling approaches, as one of the main goals of disease map development is the generation of mathematically interpretable representations to predict disease comorbidity or drug response and to suggest drug repositioning, altogether supporting clinical decisions.

Time-Resolved Morphology and Kinetic Studies of Pulsed Laser Deposition-Grown Pt Layers on Sapphire at Different Growth Temperatures by in Situ Grazing Incidence Small-Angle X-ray Scattering.

Optimizing and monitoring the growth conditions of Pt films, often used as bottom electrodes in multiferroic material systems, represents a highly relevant issue that is of importance for controlling the crystalline quality and performance of ferroelectric oxides such as, e.g. LuFeO3. We performed a time-resolved monitoring of the growth and morphology of Pt films during pulsed laser deposition (PLD) in dependence on the grown film effective thickness and on the growth temperature Tg using in situ grazing incidence small-angle X-ray scattering (GISAXS). Through real-time analysis and modeling of GISAXS patterns, we could fully characterize the influence of Tg on the morphology and on the growth kinetics of the Pt layers. Consequently, critical and characteristic effective thicknesses for the transitions nucleation phase (I)/coalescence phase (II) and coalescence phase (II)/coarsening phase (III) could be determined. In combination with complementary microscopic imaging and chemical mapping via combined SEM/EDXS, we demonstrate the occurrence of a morphological progression in the Pt PLD-grown Pt films, changing from grains at room temperature to a 3D-island morphology at 300 °C, further to a hole-free structure at 500 °C, and finally to a channel structure for 700 and 900 °C. The film topography, as characterized by atomic force microscopy (AFM), favors the PLD growth of Pt layers at temperatures beyond 700 °C where the film is homogeneous, continuous, and hole-free with a flat and smooth surface. The double dependency of the percolation transition on the film effective thickness and on the growth temperature has been established by measuring the electrical conductivity.

The Virtual Metabolic Human database: integrating human and gut microbiome metabolism with nutrition and disease.

A multitude of factors contribute to complex diseases and can be measured with 'omics' methods. Databases facilitate data interpretation for underlying mechanisms. Here, we describe the Virtual Metabolic Human (VMH, www.vmh.life) database encapsulating current knowledge of human metabolism within five interlinked resources 'Human metabolism', 'Gut microbiome', 'Disease', 'Nutrition', and 'ReconMaps'. The VMH captures 5180 unique metabolites, 17 730 unique reactions, 3695 human genes, 255 Mendelian diseases, 818 microbes, 632 685 microbial genes and 8790 food items. The VMH's unique features are (i) the hosting of the metabolic reconstructions of human and gut microbes amenable for metabolic modeling; (ii) seven human metabolic maps for data visualization; (iii) a nutrition designer; (iv) a user-friendly webpage and application-programming interface to access its content; (v) user feedback option for community engagement and (vi) the connection of its entities to 57 other web resources. The VMH represents a novel, interdisciplinary database for data interpretation and hypothesis generation to the biomedical community.

LAITOR4HPC: A text mining pipeline based on HPC for building interaction networks.

BACKGROUND: The amount of published full-text articles has increased dramatically. Text mining tools configure an essential approach to building biological networks, updating databases and providing annotation for new pathways. PESCADOR is an online web server based on LAITOR and NLProt text mining tools, which retrieves protein-protein co-occurrences in a tabular-based format, adding a network schema. Here we present an HPC-oriented version of PESCADOR's native text mining tool, renamed to LAITOR4HPC, aiming to access an unlimited abstract amount in a short time to enrich available networks, build new ones and possibly highlight whether fields of research have been exhaustively studied. RESULTS: By taking advantage of parallel computing HPC infrastructure, the full collection of MEDLINE abstracts available until June 2017 was analyzed in a shorter period (6 days) when compared to the original online implementation (with an estimated 2 years to run the same data). Additionally, three case studies were presented to illustrate LAITOR4HPC usage possibilities. The first case study targeted soybean and was used to retrieve an overview of published co-occurrences in a single organism, retrieving 15,788 proteins in 7894 co-occurrences. In the second case study, a target gene family was searched in many organisms, by analyzing 15 species under biotic stress. Most co-occurrences regarded Arabidopsis thaliana and Zea mays. The third case study concerned the construction and enrichment of an available pathway. Choosing A. thaliana for further analysis, the defensin pathway was enriched, showing additional signaling and regulation molecules, and how they respond to each other in the modulation of this complex plant defense response. CONCLUSIONS: LAITOR4HPC can be used for an efficient text mining based construction of biological networks derived from big data sources, such as MEDLINE abstracts. Time consumption and data input limitations will depend on the available resources at the HPC facility. LAITOR4HPC enables enough flexibility for different approaches and data amounts targeted to an organism, a subject, or a specific pathway. Additionally, it can deliver comprehensive results where interactions are classified into four types, according to their reliability.

MINERVA API and plugins: opening molecular network analysis and visualization to the community.

SUMMARY: The complexity of molecular networks makes them difficult to navigate and interpret, creating a need for specialized software. MINERVA is a web platform for visualization, exploration and management of molecular networks. Here, we introduce an extension to MINERVA architecture that greatly facilitates the access and use of the stored molecular network data. It allows to incorporate such data in analytical pipelines via a programmatic access interface, and to extend the platform's visual exploration and analytics functionality via plugin architecture. This is possible for any molecular network hosted by the MINERVA platform encoded in well-recognized systems biology formats. To showcase the possibilities of the plugin architecture, we have developed several plugins extending the MINERVA core functionalities. In the article, we demonstrate the plugins for interactive tree traversal of molecular networks, for enrichment analysis and for mapping and visualization of known disease variants or known adverse drug reactions to molecules in the network. AVAILABILITY AND IMPLEMENTATION: Plugins developed and maintained by the MINERVA team are available under the AGPL v3 license at https://git-r3lab.uni.lu/minerva/plugins/. The MINERVA API and plugin documentation is available at https://minerva-web.lcsb.uni.lu.

Presenting and sharing clinical data using the eTRIKS Standards Master Tree for tranSMART.

MOTIVATION: Standardization and semantic alignment have been considered one of the major challenges for data integration in clinical research. The inclusion of the CDISC SDTM clinical data standard into the tranSMART i2b2 via a guiding master ontology tree positively impacts and supports the efficacy of data sharing, visualization and exploration across datasets. RESULTS: We present here a schema for the organization of SDTM variables into the tranSMART i2b2 tree along with a script and test dataset to exemplify the mapping strategy. The eTRIKS master tree concept is demonstrated by making use of fictitious data generated for four patients, including 16 SDTM clinical domains. We describe how the usage of correct visit names and data labels can help to integrate multiple readouts per patient and avoid ETL crashes when running a tranSMART loading routine. AVAILABILITY AND IMPLEMENTATION: The eTRIKS Master Tree package and test datasets are publicly available at https://doi.org/10.5281/zenodo.1009098 and a functional demo installation at https://public.etriks.org/transmart/datasetExplorer/ under eTRIKS-Master Tree branch, where the discussed examples can be visualized.

Multicenter Alzheimer's and Parkinson's disease immune biomarker verification study.

INTRODUCTION: Multiple immunity biomarkers have been suggested as tracers of neuroinflammation in neurodegeneration. This study aimed to verify findings in cerebrospinal fluid (CSF) samples of Alzheimer's disease (AD) and Parkinson's disease (PD) subjects from the network of the European, Innovative Medicines Initiative-funded project AETIONOMY. METHODS: A total of 227 samples from the studies/centres AETIONOMY, ICEBERG, and IDIBAPS were used to analyse 21 selected immunity biomarkers in CSF. Results were compared to data of an independent cohort of 399 subjects previously published. RESULTS: Immunity markers were predominantly and reproducibly associated with pathological levels of tau isoforms, but also with amyloid levels, aging, sex, APOE genotype, and center-specific factors. DISCUSSION: Immunity biomarker levels in CSF reflect molecular and cellular pathology rather than diagnosis in neurodegenerative disorders. Assay standardization and stratification for age and other covariates could improve the power of such markers in clinical applications or intervention studies targeting immune responses in neurodegeneration.

Data and knowledge management in translational research: implementation of the eTRIKS platform for the IMI OncoTrack consortium.

BACKGROUND: For large international research consortia, such as those funded by the European Union's Horizon 2020 programme or the Innovative Medicines Initiative, good data coordination practices and tools are essential for the successful collection, organization and analysis of the resulting data. Research consortia are attempting ever more ambitious science to better understand disease, by leveraging technologies such as whole genome sequencing, proteomics, patient-derived biological models and computer-based systems biology simulations. RESULTS: The IMI eTRIKS consortium is charged with the task of developing an integrated knowledge management platform capable of supporting the complexity of the data generated by such research programmes. In this paper, using the example of the OncoTrack consortium, we describe a typical use case in translational medicine. The tranSMART knowledge management platform was implemented to support data from observational clinical cohorts, drug response data from cell culture models and drug response data from mouse xenograft tumour models. The high dimensional (omics) data from the molecular analyses of the corresponding biological materials were linked to these collections, so that users could browse and analyse these to derive candidate biomarkers. CONCLUSIONS: In all these steps, data mapping, linking and preparation are handled automatically by the tranSMART integration platform. Therefore, researchers without specialist data handling skills can focus directly on the scientific questions, without spending undue effort on processing the data and data integration, which are otherwise a burden and the most time-consuming part of translational research data analysis.

MolArt: a molecular structure annotation and visualization tool.

Summary: MolArt fills the gap between sequence and structure visualization by providing a light-weight, interactive environment enabling exploration of sequence annotations in the context of available experimental or predicted protein structures. Provided a UniProt ID, MolArt downloads and displays sequence annotations, sequence-structure mapping and relevant structures. The sequence and structure views are interlinked, enabling sequence annotations being color overlaid over the mapped structures, thus providing an enhanced understanding and interpretation of the available molecular data. Availability and implementation: MolArt is released under the Apache 2 license and is available at https://github.com/davidhoksza/MolArt. The project web page https://davidhoksza.github.io/MolArt/ features examples and applications of the tool.

Clustering approaches for visual knowledge exploration in molecular interaction networks.

BACKGROUND: Biomedical knowledge grows in complexity, and becomes encoded in network-based repositories, which include focused, expert-drawn diagrams, networks of evidence-based associations and established ontologies. Combining these structured information sources is an important computational challenge, as large graphs are difficult to analyze visually. RESULTS: We investigate knowledge discovery in manually curated and annotated molecular interaction diagrams. To evaluate similarity of content we use: i) Euclidean distance in expert-drawn diagrams, ii) shortest path distance using the underlying network and iii) ontology-based distance. We employ clustering with these metrics used separately and in pairwise combinations. We propose a novel bi-level optimization approach together with an evolutionary algorithm for informative combination of distance metrics. We compare the enrichment of the obtained clusters between the solutions and with expert knowledge. We calculate the number of Gene and Disease Ontology terms discovered by different solutions as a measure of cluster quality. Our results show that combining distance metrics can improve clustering accuracy, based on the comparison with expert-provided clusters. Also, the performance of specific combinations of distance functions depends on the clustering depth (number of clusters). By employing bi-level optimization approach we evaluated relative importance of distance functions and we found that indeed the order by which they are combined affects clustering performance. Next, with the enrichment analysis of clustering results we found that both hierarchical and bi-level clustering schemes discovered more Gene and Disease Ontology terms than expert-provided clusters for the same knowledge repository. Moreover, bi-level clustering found more enriched terms than the best hierarchical clustering solution for three distinct distance metric combinations in three different instances of disease maps. CONCLUSIONS: In this work we examined the impact of different distance functions on clustering of a visual biomedical knowledge repository. We found that combining distance functions may be beneficial for clustering, and improve exploration of such repositories. We proposed bi-level optimization to evaluate the importance of order by which the distance functions are combined. Both combination and order of these functions affected clustering quality and knowledge recognition in the considered benchmarks. We propose that multiple dimensions can be utilized simultaneously for visual knowledge exploration.

Palmitate-Induced Insulin Hypersecretion and Later Secretory Decline Associated with Changes in Protein Expression Patterns in Human Pancreatic Islets.

In obese children with high circulating concentrations of free fatty acid palmitate, we have observed that insulin levels at fasting and in response to a glucose challenge were several times higher than in obese children with low concentrations of the fatty acid as well as in lean controls. Declining and even insufficient insulin levels were observed in obese adolescents with high levels of the fatty acid. In isolated human islets exposed to palmitate we have observed insulin hypersecretion after 2 days exposure. In contrast, insulin secretion from the islets was reduced after 7 days culture in the presence of the fatty acid. This study aims at identifying islet-related biological events potentially linked with the observed insulin hypersecretion and later secretory decline in these obese children and adolescents using the islet model. We analyzed protein expression data obtained from human islets exposed to elevated palmitate levels for 2 and 7 days by an improved methodology for statistical analysis of differentially expressed proteins. Protein profiling of islet samples by liquid chromatography-tandem mass spectrometry identified 115 differentially expressed proteins (DEPs). Several DEPs including sorcin were associated with increased glucose-stimulated insulin secretion in islets after 2 days of exposure to palmitate. Similarly, several metabolic pathways including altered protein degradation, increased autophagy, altered redox condition, and hampered insulin processing were coupled to the functional impairment of islets after 7 days of culture in the presence of palmitate. Such biological events, once validated in the islets, may give rise to novel treatment strategies aiming at normalizing insulin levels in obese children with high palmitate levels, which may reduce or even prevent obesity-related type 2 diabetes mellitus.

Identification of early biological changes in palmitate-treated isolated human islets.

BACKGROUND: Long-term exposure to elevated levels of free fatty acids (FFAs) is deleterious for beta-cell function and may contribute to development of type 2 diabetes mellitus (T2DM). Whereas mechanisms of impaired glucose-stimulated insulin secretion (GSIS) in FFA-treated beta-cells have been intensively studied, biological events preceding the secretory failure, when GSIS is accentuated, are poorly investigated. To identify these early events, we performed genome-wide analysis of gene expression in isolated human islets exposed to fatty acid palmitate for different time periods. RESULTS: Palmitate-treated human islets showed decline in beta-cell function starting from day two. Affymetrix Human Transcriptome Array 2.0 identified 903 differentially expressed genes (DEGs). Mapping of the genes onto pathways using KEGG pathway enrichment analysis predicted four islet biology-related pathways enriched prior but not after the decline of islet function and three pathways enriched both prior and after the decline of islet function. DEGs from these pathways were analyzed at the transcript level. The results propose that in palmitate-treated human islets, at early time points, protective events, including up-regulation of metallothioneins, tRNA synthetases and fatty acid-metabolising proteins, dominate over deleterious events, including inhibition of fatty acid detoxification enzymes, which contributes to the enhanced GSIS. After prolonged exposure of islets to palmitate, the protective events are outweighed by the deleterious events, which leads to impaired GSIS. CONCLUSIONS: The study identifies temporal order between different cellular events, which either promote or protect from beta-cell failure. The sequence of these events should be considered when developing strategies for prevention and treatment of the disease.