Annotation Visualization Research Articles

Metagenome quality metrics and taxonomical annotation visualization through the integration of MAGFlow and BIgMAG.

Building Metagenome-Assembled Genomes (MAGs) from highly complex metagenomics datasets encompasses a series of steps covering from cleaning the sequences, assembling them to finally group them into bins. Along the process, multiple tools aimed to assess the quality and integrity of each MAG are implemented. Nonetheless, even when incorporated within end-to-end pipelines, the outputs of these pieces of software must be visualized and analyzed manually lacking integration in a complete framework. We developed a Nextflow pipeline (MAGFlow) for estimating the quality of MAGs through a wide variety of approaches (BUSCO, CheckM2, GUNC and QUAST), as well as for annotating taxonomically the metagenomes using GTDB-Tk2. MAGFlow is coupled to a Python-Dash application (BIgMAG) that displays the concatenated outcomes from the tools included by MAGFlow, highlighting the most important metrics in a single interactive environment along with a comparison/clustering of the input data. By using MAGFlow/BIgMAG, the user will be able to benchmark the MAGs obtained through different workflows or establish the quality of the MAGs belonging to different samples following the divide and rule methodology. MAGFlow/BIgMAG represents a unique tool that integrates state-of-the-art tools to study different quality metrics and extract visually as much information as possible from a wide range of genome features.

Discursive Patinas: Anchoring Discussions in Data Visualizations.

This paper presents discursive patinas, a technique to visualize discussions onto data visualizations, inspired by how people leave traces in the physical world. While data visualizations are widely discussed in online communities and social media, comments tend to be displayed separately from the visualization and we lack ways to relate these discussions back to the content of the visualization, e.g., to situate comments, explain visual patterns, or question assumptions. In our visualization annotation interface, users can designate areas within the visualization. Discursive patinas are made of overlaid visual marks (anchors), attached to textual comments with category labels, likes, and replies. By coloring and styling the anchors, a meta visualization emerges, showing what and where people comment and annotate the visualization. These patinas show regions of heavy discussions, recent commenting activity, and the distribution of questions, suggestions, or personal stories. We ran workshops with 90 students, domain experts, and visualization researchers to study how people use anchors to discuss visualizations and how patinas influence people's understanding of the discussion. Our results show that discursive patinas improve the ability to navigate discussions and guide people to comments that help understand, contextualize, or scrutinize the visualization. We discuss the potential of anchors and patinas to support discursive engagements, including critical readings of visualizations, design feedback, and feminist approaches to data visualization.

Optimizing Corn Tar Spot Measurement: A Deep Learning Approach Using Red-Green-Blue Imaging and Stromata Contour Detection Algorithm for Leaf-Level Disease Severity Analysis.

Visual detection of stromata (brown-black, elevated fungal fruiting bodies) is a primary method for quantifying tar spot early in the season, as these structures are definitive signs of the disease and essential for effective disease monitoring and management. Here, we present Stromata Contour Detection Algorithm version 2 (SCDA v2), which addresses the limitations of the previously developed SCDA version 1 (SCDA v1) without the need for empirical search of the optimal Decision Making Input Parameters (DMIPs), while achieving higher and consistent accuracy in tar spot stromata detection. SCDA v2 operates in two components: (i) SCDA v1 producing tar-spot-like region proposals for a given input corn leaf Red-Green-Blue (RGB) image, and (ii) a pre-trained Convolutional Neural Network (CNN) classifier identifying true tar spot stromata from the region proposals. To demonstrate the enhanced performance of the SCDA v2, we utilized datasets of RGB images of corn leaves from field (low, middle, and upper canopies) and glasshouse conditions under variable environments, exhibiting different tar spot severities at various corn developmental stages. Various accuracy analyses (F1-score, linear regression, and Lin's concordance correlation), showed that SCDA v2 had a greater agreement with the reference data (human visual annotation) than SCDA v1. SCDA v2 achievd 73.7% mean Dice values (overall accuracy), compared to 30.8% for SCDA v1. The enhanced F1-score primarily resulted from eliminating overestimation cases using the CNN classifier. Our findings indicate the promising potential of SCDA v2 for glasshouse and field-scale applications, including tar spot phenotyping and surveillance projects.

Revealing Cellular Heterogeneity and Key Regulatory Factors of Triple-Negative Breast Cancer through Single-Cell RNA Sequencing.

Triple-negative breast cancer (TNBC) is the most aggressive subtype of breast cancer (BC). TNBC has a poor prognosis due to high intratumoral heterogeneity and metastasis, pointing to the need to explore distinct molecular subtypes and gene regulatory networks. The scRNA-seq data of five primary BC samples were downloaded from the Gene Expression Omnibus (GEO) database. Clustering was performed based on filtered and normalized data using the Seurat R package to identify marker genes, which were subsequently annotated to each subset using the CellMarker database. AUCell R package was applied to calculate the hallmark score for each epithelial cell. Marker genes of each subset were screened with FindAllMarkers and their biological functions were analyzed using the Database for Annotation Visualization and Integrated Discovery (DAVID) database. Next, cell-cell communication was performed with the CellChat R package. To identify the key regulatory genes, single-cell regulatory network inference and clustering (SCENIC) analysis was conducted. Finally, the expression and potential biological functions of the key regulatory factors were verified through cellular experiments. A total of 29,101 cells were classified into nine cell subsets, namely, Fibroblasts, Fibroepithelial cells, Epithelial cells 1, Epithelial cells 2, Epithelial cells 3, Endothelial cells, T cells, Plasma B cells and Macrophages. Particularly, the epithelial cells had a higher proportion and higher transforming growth factor-β (TGF-β) activity in the TNBC pathotype as compared to the non-TNBC pathotype. Furthermore, four epithelial cell subsets (marked as Stearoyl-CoA Desaturase (SCD1), marker of proliferation Ki67 (MKI67), Annexin A3 (ANXA3) and aquaporin 5 (AQP5)) were identified as having the greatest impact on the TNBC pathotype. Cell-cell interaction analysis revealed that ANXA3-epithelial cell subset suppressed the T cell function through different mechanisms. C-fos gene (FOS) and X-box binding protein 1 (XBP1) were considered critical regulons involved in TNBC progression. Notably, cellular experiments demonstrated that silencing XBP1 and overexpressing FOS inhibited cancer cell invasion. The four epithelial cell subsets and two critical regulons identified based on the scRNA-seq data could help explore the underlying intratumoral heterogeneity molecular mechanism and develop effective therapies for TNBC.

MOSCA 2.0: A bioinformatics framework for metagenomics, metatranscriptomics and metaproteomics data analysis and visualization.

The analysis of meta-omics data requires the utilization of several bioinformatics tools and proficiency in informatics. The integration of multiple meta-omics data is even more challenging, and the outputs of existing bioinformatics solutions are not always easy to interpret. Here, we present a meta-omics bioinformatics pipeline, Meta-Omics Software for Community Analysis (MOSCA), which aims to overcome these limitations. MOSCA was initially developed for analysing metagenomics (MG) and metatranscriptomics (MT) data. Now, it also performs MG and metaproteomics (MP) integrated analysis, and MG/MT analysis was upgraded with an additional iterative binning step, metabolic pathways mapping, and several improvements regarding functional annotation and data visualization. MOSCA handles raw sequencing data and mass spectra and performs pre-processing, assembly, annotation, binning and differential gene/protein expression analysis. MOSCA shows taxonomic and functional analysis in large tables, performs metabolic pathways mapping, generates Krona plots and shows gene/protein expression results in heatmaps, improving omics data visualization. MOSCA is easily run from a single command while also providing a web interface (MOSGUITO). Relevant features include an extensive set of customization options, allowing tailored analyses to suit specific research objectives, and the ability to restart the pipeline from intermediary checkpoints using alternative configurations. Two case studies showcased MOSCA results, giving a complete view of the anaerobic microbial communities from anaerobic digesters and insights on the role of specific microorganisms. MOSCA represents a pivotal advancement in meta-omics research, offering an intuitive, comprehensive, and versatile solution for researchers seeking to unravel the intricate tapestry of microbial communities.

Transcriptome Analysis of Beta-Catenin-Related Genes in CD34+ Haematopoietic Stem and Progenitor Cells from Patients with AML.

Acute myeloid leukaemia (AML) is a disease of the haematopoietic stem cells(HSCs) that is characterised by the uncontrolled proliferation and impaired differentiation of normal haematopoietic stem/progenitor cells. Several pathways that control the proliferation and differentiation of HSCs are impaired in AML. Activation of the Wnt/beta-catenin signalling pathway has been shown in AML and beta-catenin, which is thought to be the key element of this pathway, has been frequently highlighted. The present study was designed to determine beta-catenin expression levels and beta-catenin-related genes in AML. In this study, beta-catenin gene expression levels were determined in 19 AML patients and 3 controls by qRT-PCR. Transcriptome analysis was performed on AML grouped according to beta-catenin expression levels. Differentially expressed genes(DEGs) were investigated in detail using the Database for Annotation Visualisation and Integrated Discovery(DAVID), Gene Ontology(GO), Kyoto Encyclopedia of Genes and Genomes(KEGG), STRING online tools. The transcriptome profiles of our AML samples showed different molecular signature profiles according to their beta-catenin levels(high-low). A total of 20 genes have been identified as hub genes. Among these, TTK, HJURP, KIF14, BTF3, RPL17 and RSL1D1 were found to be associated with beta-catenin and poor survival in AML. Furthermore, for the first time in our study, the ELOV6 gene, which is the most highly up-regulated gene in human AML samples, was correlated with a poor prognosis via high beta-catenin levels. It is suggested that the identification of beta-catenin-related gene profiles in AML may help to select new therapeutic targets for the treatment of AML.

Metagenome quality metrics and taxonomical annotation visualization through the integration of MAGFlow and BIgMAG

Background Building Metagenome–Assembled Genomes (MAGs) from highly complex metagenomics datasets encompasses a series of steps covering from cleaning the sequences, assembling them to finally group them into bins. Along the process, multiple tools aimed to assess the quality and integrity of each MAG are implemented. Nonetheless, even when incorporated within end–to–end pipelines, the outputs of these pieces of software must be visualized and analyzed manually lacking integration in a complete framework. Methods We developed a Nextflow pipeline (MAGFlow) for estimating the quality of MAGs through a wide variety of approaches (BUSCO, CheckM2, GUNC and QUAST), as well as for annotating taxonomically the metagenomes using GTDB-Tk2. MAGFlow is coupled to a Python–Dash application (BIgMAG) that displays the concatenated outcomes from the tools included by MAGFlow, highlighting the most important metrics in a single interactive environment along with a comparison/clustering of the input data. Results By using MAGFlow/BIgMAG, the user will be able to benchmark the MAGs obtained through different workflows or establish the quality of the MAGs belonging to different samples following the divide and rule methodology. Conclusions MAGFlow/BIgMAG represents a unique tool that integrates state-of-the-art tools to study different quality metrics and extract visually as much information as possible from a wide range of genome features.

GenoMycAnalyzer: a web-based tool for species and drug resistance prediction for Mycobacterium genomes

BackgroundDrug-resistant tuberculosis (TB) is a major threat to global public health. Whole-genome sequencing (WGS) is a useful tool for species identification and drug resistance prediction, and many clinical laboratories are transitioning to WGS as a routine diagnostic tool. However, user-friendly and high-confidence automated bioinformatics tools are needed to rapidly identify M. tuberculosis complex (MTBC) and non-tuberculous mycobacteria (NTM), detect drug resistance, and further guide treatment options.ResultsWe developed GenoMycAnalyzer, a web-based software that integrates functions for identifying MTBC and NTM species, lineage and spoligotype prediction, variant calling, annotation, drug-resistance determination, and data visualization. The accuracy of GenoMycAnalyzer for genotypic drug susceptibility testing (gDST) was evaluated using 5,473 MTBC isolates that underwent phenotypic DST (pDST). The GenoMycAnalyzer database was built to predict the gDST for 15 antituberculosis drugs using the World Health Organization mutational catalogue. Compared to pDST, the sensitivity of drug susceptibilities by the GenoMycAnalyzer for first-line drugs ranged from 95.9% for rifampicin (95% CI 94.8–96.7%) to 79.6% for pyrazinamide (95% CI 76.9–82.2%), whereas those for second-line drugs ranged from 98.2% for levofloxacin (95% CI 90.1–100.0%) to 74.9% for capreomycin (95% CI 69.3–80.0%). Notably, the integration of large deletions of the four resistance-conferring genes increased gDST sensitivity. The specificity of drug susceptibilities by the GenoMycAnalyzer ranged from 98.7% for amikacin (95% CI 97.8–99.3%) to 79.5% for ethionamide (95% CI 76.4–82.3%). The incorporated Kraken2 software identified 1,284 mycobacterial species with an accuracy of 98.8%. GenoMycAnalyzer also perfectly predicted lineages for 1,935 MTBC and spoligotypes for 54 MTBC.ConclusionsGenoMycAnalyzer offers both web-based and graphical user interfaces, which can help biologists with limited access to high-performance computing systems or limited bioinformatics skills. By streamlining the interpretation of WGS data, the GenoMycAnalyzer has the potential to significantly impact TB management and contribute to global efforts to combat this infectious disease. GenoMycAnalyzer is available at http://www.mycochase.org.

The Microglial Transcriptome of Age-Associated Deep Subcortical White Matter Lesions Suggests a Neuroprotective Response to Blood-Brain Barrier Dysfunction.

Age-associated deep-subcortical white matter lesions (DSCLs) are an independent risk factor for dementia, displaying high levels of CD68+ microglia. This study aimed to characterize the transcriptomic profile of microglia in DSCLs and surrounding radiologically normal-appearing white matter (NAWM) compared to non-lesional control white matter. CD68+ microglia were isolated from white matter groups (n = 4 cases per group) from the Cognitive Function and Ageing Study neuropathology cohort using immuno-laser capture microdissection. Microarray gene expression profiling, but not RNA-sequencing, was found to be compatible with immuno-LCM-ed post-mortem material in the CFAS cohort and identified significantly differentially expressed genes (DEGs). Functional grouping and pathway analysis were assessed using the Database for Annotation Visualization and Integrated Discovery (DAVID) software, and immunohistochemistry was performed to validate gene expression changes at the protein level. Transcriptomic profiling of microglia in DSCLs compared to non-lesional control white matter identified 181 significant DEGs (93 upregulated and 88 downregulated). Functional clustering analysis in DAVID revealed dysregulation of haptoglobin-haemoglobin binding (Enrichment score 2.5, p = 0.017), confirmed using CD163 immunostaining, suggesting a neuroprotective microglial response to blood-brain barrier dysfunction in DSCLs. In NAWM versus control white matter, microglia exhibited 347 DEGs (209 upregulated, 138 downregulated), with significant dysregulation of protein de-ubiquitination (Enrichment score 5.14, p < 0.001), implying an inability to maintain protein homeostasis in NAWM that may contribute to lesion spread. These findings enhance understanding of microglial transcriptomic changes in ageing white matter pathology, highlighting a neuroprotective adaptation in DSCLs microglia and a potentially lesion-promoting phenotype in NAWM microglia.

Yiqi Kaimi prescription regulates protein phosphorylation to promote intestinal motility in slow transit constipation

Ethnopharmacological relevanceThe clinical efficacy of the Yiqi Kaimi prescription has been confirmed in slow transit constipation. However, the effects and biological mechanism of Yiqi Kaimi prescription are still unclear. Aims of the studyTo identify the effects of Yiqi Kaimi prescription on intestinal motility; To reveal the potential key targets and pathways of Yiqi Kaimi prescription for the treatment of slow transit constipation. Materials and methodsThe effects of Yiqi Kaimi prescription on slow transit constipation were investigated in a mouse model. The terminal ink propulsion experiment and fecal indocyanine green imaging was used to measure the intestinal transit time. Protein phosphorylation changes in colon tissues treated with Yiqi Kaimi prescription were detected using a Phospho Explorer antibody microarray. Bioinformatic analyses were performed using the Database for Annotation Visualization and Integrated Discovery (DAVID) and the Search Tool for the Retrieval of Interacting Genes (STRING). Western blot analysis and immunohistochemistry confirmed the observed changes in phosphorylation. Results: Yiqi Kaimi prescription significantly increased the intestinal transit rate (P < 0.05 vs. model) and reduced the time to first discharge of feces containing fecal indocyanine green imaging in mice (P < 0.05 vs. model). The administration of Yiqi Kaimi prescription induced phosphorylation changes in 41 proteins, with 9 upregulated proteins and 32 downregulated proteins. Functional classification of the phosphorylated proteins with DAVID revealed that the critical biological processes included tyrosine protein kinases, positive regulation of calcium-mediated signaling and response to muscle stretch. The phosphorylation of the spleen tyrosine kinase (SYK) at Tyr348 increased 2.19-fold, which was the most significant change. The phosphorylation level of the transcription factor p65 (RELA) at Thr505 was decreased 0.57-fold. SYK was a hub protein in the protein-protein interaction network and SYK and RELA formed the core of the secondary subnetwork. The key protein phosphorylation after treatment with Yiqi Kaimi prescription were verified by Western blot analysis and immunohistochemistry. ConclusionYiqi Kaimi prescription significantly enhanced intestinal motility. This effect was attributed to alterations in the phosphorylation levels of various target proteins. The observed changes in protein phosphorylation, including SYK and RELA, may serve as crucial factors in the treatment of slow transit constipation.

How to Arrange Texts and Pictures for Online Visitors — Comparing Basic Ceramic Display Forms with Eye Tracking

In the context of online text–picture relic exhibitions, two exploratory experiments were conducted to investigate the role of integrated/separate display, background information, and annotation type in learning tangible heritage. Using ceramics as an example, we tracked the eye movement of subjects under different display forms and tested whether they obtained the relevant information. Experiment 1 (N = 48) adopted a 2 (integrated/separate display) × 2 (with/without background information) design and Experiment 2 (N = 93) investigated distinct types of annotation (no annotation, indicative/direct/picture/contour shape annotation). We found that the following. (1) In the segmented relic display, the usage time, fixation count, and total fixation duration of relic names were lower than those in the integrated case. The probability that subjects would learn comparatively was also lower in the separate display. However, the performance on retention or transfer tests did not differ depending on the integrated/separate display. After reading the background information, subjects paid less attention to relic names but had better knowledge transfer performance. (2) The viewers’ attention allocation to the materials was not significantly influenced by the annotations. Mere visual annotations did not provide an advantage for information acquisition. By contrast, indicative verbal annotation required relatively more time for better target information memory, and the direct verbal cue consumed the least time. Based on the results, we discussed the application scenario of multimedia learning principles and potential recommendations for designing online relic displays.

Multi-modality MRI fusion with patch complementary pre-training for internet of medical things-based smart healthcare

Magnetic Resonance Imaging (MRI) is a pivotal neuroimaging technique capable of generating images with various contrasts, known as multi-modal images. The integration of these diverse modalities is essential for improving model performance across various tasks. However, in real clinical scenarios, acquiring MR images for all modalities is frequently hindered by factors such as patient comfort and scanning costs. Therefore, effectively fusing different modalities to synthesize missing modalities has become a research hot-spot in the field of smart healthcare, particularly in the context of the Internet of Medical Things (IoMT). In this study, we introduce a multi-modal coordinated fusion network (MCF-Net) with Patch Complementarity Pre-training. This network leverages the complementarity and correlation between different modalities to make the fusion of multi-modal MR images, addressing challenges in the IoMT. Specifically, we first employ a Patch Complementarity Mask Autoencoder (PC-MAE) for self-supervised pre-training. The complementarity learning mechanism is introduced to align masks and visual annotations between two modalities. Subsequently, a dual-branch MAE architecture and a shared encoder–decoder are adopted to facilitate cross-modal interactions within mask tokens. Furthermore, during the fine-tuning phase, we incorporate an Attention-Driven Fusion (ADF) module into the MCF-Net. This module synthesizes missing modal images by fusion of multi-modal features from the pre-trained PC-MAE encoder. Additionally, we leverage the pre-trained encoder to extract high-level features from both synthetic and corresponding real images, ensuring consistency throughout the training process. Our experimental findings showcase a notable enhancement in performance across various modalities with our fusion method, outperforming state-of-the-art techniques.

A network pharmacology, molecular docking and in vitro investigation of Picrorhiza kurroa extract for the treatment of diabetic nephropathy

Picrorhiza kurroa Royle ex Benth. (P. kurroa/PK/Kutki), a Himalayan herb belonging to the family Scrophulariaceae, is widely known for its hepatoprotective activity. Traditionally, it is found to be effective for upper respiratory tract disorders, kidney and liver problems, dyspepsia and chronic diarrhoea but the mechanism of action is unclear. In this study, the mode of action of P. kurroa for the treatment of diabetic nephropathy (DN) was investigated by network pharmacology, molecular docking and in vitro assays. Numerous databases have been screened and 33 P. kurroa bioactive compounds and 56 targets were identified. The compounds-targets network, targets-pathways network and compounds-targets-pathways network were constructed. The major bioactive compounds include picrorhizaoside D, scrophuloside A, vanillic acid, arvenin I, cinnamic acid, picein, 6-feruloyl catalpol, picroside V, pikuroside, apocynin, picroside I, picroside IV, androsin, cucurbitacin P, boschnaloside, kutkoside, cucurbitacin O, cucurbitacin K, picracin, etc. The potential protein targets identified in this study were MMP1, PRKCA, MMP7, IL18, IL1, TNF, ACE, ASC, CASP1, NLRP3, MAP, KURROA1, mitogen-activated protein kinase (MAPK)14 and MAPK8. In the Database for annotation visualization and integrated discovery (DAVID) pathways and Gene Ontology enrichment analysis, 14 major DN signalling pathways were identified, including MAPK, renin-angiotensin system (RAS), TNF, signal transducer and activator of transcription (JAK-STAT), TLR, vascular endothelial growth factor (VEGF), mTOR, Wnt, Ras, PPARs, NFB, NOD and phosphatidylinositol signalling pathways. A molecular docking study revealed that 32 bioactive compounds of P. kurroa interacted with 14 significant proteins/genes associated with DN. P. kurroa extract was proven to enhance the survival rate of HEK cells significantly. Protein expression analysis using Western blot demonstrated that P. kurroa extract significantly altered the expression of p47phox, p67phox, gp91phox, IL-1 and TGFβ-1. As a result of network pharmacology and docking work, new concepts for discovering bioactive compounds and effective modes of action could be developed. The potential effect of P. kurroa extract on DN disease was evident in the in-vitro studies aided by network pharmacology and molecular docking. Communicated by Ramaswamy H. Sarma

CAP-RNAseq: an integrated pipeline for functional annotation and prioritization of co-expression clusters.

Cluster analysis is one of the most widely used exploratory methods for visualization and grouping of gene expression patterns across multiple samples or treatment groups. Although several existing online tools can annotate clusters with functional terms, there is no all-in-one webserver to effectively prioritize genes/clusters using gene essentiality as well as congruency of mRNA-protein expression. Hence, we developed CAP-RNAseq that makes possible (1) upload and clustering of bulk RNA-seq data followed by identification, annotation and network visualization of all or selected clusters; and (2) prioritization using DepMap gene essentiality and/or dependency scores as well as the degree of correlation between mRNA and protein levels of genes within an expression cluster. In addition, CAP-RNAseq has an integrated primer design tool for the prioritized genes. Herein, we showed using comparisons with the existing tools and multiple case studies that CAP-RNAseq can uniquely aid in the discovery of co-expression clusters enriched with essential genes and prioritization of novel biomarker genes that exhibit high correlations between their mRNA and protein expression levels. CAP-RNAseq is applicable to RNA-seq data from different contexts including cancer and available at http://konulabapps.bilkent.edu.tr:3838/CAPRNAseq/ and the docker image is downloadable from https://hub.docker.com/r/konulab/caprnaseq.

Can't read my broker face?—Tracing a motif and metaphor of expert knowledge through audiovisual images of the financial crisis

AbstractBased on the question of the representability of economy and economics in audiovisual media, developments on the financial markets have often been discussed as a depiction problem. The abstractness and complexity of economic interrelations seem to defy classical modes of storytelling and dramatization. Nevertheless, public opinion about economic changes and dependencies crucially relies on audiovisual media. But how can the public communicate in images, sounds, and words about forces that are out of sight and out of reach, and can supposedly only be adequately grasped by experts? In a case study on audiovisual images of the global financial crisis (2007–), this paper tracks and analyzes a recurring motif: the staging of expert knowledge as close‐ups of expressive faces vis‐à‐vis computer screens in television news, documentaries, as well as feature films. It draws on the use of digital tools for corpus exploration (reverse image search) and the visualization of video annotations. By relating and comparing different staging strategies by which these “broker faces” become embodiments of turbulent market dynamics, the paper proposes to not regard them as repeated instantiations of the same metaphor, but as a developing web of cinematic metaphors. Different perspectives (news of market developments or historical accounts of crisis developments) and affective stances toward the global financial crisis are expressed in these variations of the face‐screen constellation. The paper thus presents a selection of different appearances of “broker faces” as a medium for an audiovisual discourse of the global financial crisis. A concluding analysis of a scene from Margin Call focuses on its specific intertwining of expert and screen as an ambivalent movement figuration of staging insight. Between the feeling of discovery (of a potential future threat) and the sense of being haunted (by a menacing force), the film stages the emergence of a “broker face” in an atmospheric tension between suspense and melancholy. We argue that the film thereby reframes the motif and poses questions of agency, temporality, and expert knowledge.

OpenProt 2.0 builds a path to the functional characterization of alternative proteins.

The OpenProt proteogenomic resource (https://www.openprot.org/) provides users with a complete and freely accessible set of non-canonical or alternative open reading frames (AltORFs) within the transcriptome of various species, as well as functional annotations of the corresponding protein sequences not found in standard databases. Enhancements in this update are largely the result of user feedback and include the prediction of structure, subcellular localization, and intrinsic disorder, using cutting-edge algorithms based on machine learning techniques. The mass spectrometry pipeline now integrates a machine learning-based peptide rescoring method to improve peptide identification. We continue to help users explore this cryptic proteome by providing OpenCustomDB, a tool that enables users to build their own customized protein databases, and OpenVar, a genomic annotator including genetic variants within AltORFs and protein sequences. A new interface improves the visualization of all functional annotations, including a spectral viewer and the prediction of multicoding genes. All data on OpenProt are freely available and downloadable. Overall, OpenProt continues to establish itself as an important resource for the exploration and study of new proteins.

Semi-Supervised Learning Method for the Augmentation of an Incomplete Image-Based Inventory of Earthquake-Induced Soil Liquefaction Surface Effects

Soil liquefaction often occurs as a secondary hazard during earthquakes and can lead to significant structural and infrastructure damage. Liquefaction is most often documented through field reconnaissance and recorded as point locations. Complete liquefaction inventories across the impacted area are rare but valuable for developing empirical liquefaction prediction models. Remote sensing analysis can be used to rapidly produce the full spatial extent of liquefaction ejecta after an event to inform and supplement field investigations. Visually labeling liquefaction ejecta from remotely sensed imagery is time-consuming and prone to human error and inconsistency. This study uses a partially labeled liquefaction inventory created from visual annotations by experts and proposes a pixel-based approach to detecting unlabeled liquefaction using advanced machine learning and image processing techniques, and to generating an augmented inventory of liquefaction ejecta with high spatial completeness. The proposed methodology is applied to aerial imagery taken from the 2011 Christchurch earthquake and considers the available partial liquefaction labels as high-certainty liquefaction features. This study consists of two specific comparative analyses. (1) To tackle the limited availability of labeled data and their spatial incompleteness, a semi-supervised self-training classification via Linear Discriminant Analysis is presented, and the performance of the semi-supervised learning approach is compared with supervised learning classification. (2) A post-event aerial image with RGB (red-green-blue) channels is used to extract color transformation bands, statistical indices, texture components, and dimensionality reduction outputs, and performances of the classification model with different combinations of selected features from these four groups are compared. Building footprints are also used as the only non-imagery geospatial information to improve classification accuracy by masking out building roofs from the classification process. To prepare the multi-class labeled data, regions of interest (ROIs) were drawn to collect samples of seven land cover and land use classes. The labeled samples of liquefaction were also clustered into two groups (dark and light) using the Fuzzy C-Means clustering algorithm to split the liquefaction pixels into two classes. A comparison of the generated maps with fully and manually labeled liquefaction data showed that the proposed semi-supervised method performs best when selected high-ranked features of the two groups of statistical indices (gradient weight and sum of the band squares) and dimensionality reduction outputs (first and second principal components) are used. It also outperforms supervised learning and can better augment the liquefaction labels across the image in terms of spatial completeness.

To identify biomarkers associated with the transfer of diabetes combined with cancer in human genes using bioinformatics analysis.

Currently, the incidence of diabetes mellitus is increasing rapidly, particularly in China, and its pathogenesis is still unclear. The goal of this study was to find meaningful biomarkers of metastasis in patients with diabetes and cancer using bioinformatic analysis in order to predict gene expression and prognostic importance for survival. We used the Differentially Expressed Gene, Database for Annotation Visualization and Integrated Discovery, and Gene Set Enrichment Analyses databases, as well as several bioinformatics tools, to explore the key genes in diabetes. Based on the above database, we ended up with 10 hub genes (FOS, ATF3, JUN, EGR1, FOSB, JUNB, BTG2, EGR2, ZFP36, and NR4A2). A discussion of the 10 critical genes, with extensive literature mentioned to validate the association between the 10 key genes and patients with diabetes and cancer, to demonstrate the importance of gene expression and survival prognosis. This study identifies several biomarkers associated with diabetes and cancer development and metastasis that may provide novel therapeutic targets for diabetes combined with cancer patients.

Protein Sequencing and Multi-Omics Data Analysis of Human Insulin

Abstract: Advancements in molecular biology have led to a deeper understanding of complex biological processes through the analysis of multi-omics data. In this study, we focused on the protein sequencing and multi-omics data analysis of human insulin, a pivotal hormone with critical roles in regulating glucose metabolism. In this study, we delved into the intricate realm of human insulin, a critical hormone regulating glucose metabolism, using advanced molecular biology techniques. Leveraging the Protein Data Bank (PDB) and the WorldWide Protein Data Bank (wwPDB), we explored 3D protein structures through sources like X-ray Crystallography and NMR spectrometry. RasMol software aided dynamic visualization of molecular structures, allowing manipulation and annotation. The Molecular Modelling Database (MMDB) facilitated structure identification and cross-species sequence alignment. The National Centre for Biotechnology Information (NCBI) provided genetic and biomedical insights, enhancing our understanding of insulin's molecular intricacies. Computational tools, including the FASTA format, accelerated sequence analysis, while BLAST unveiled evolutionary relationships and functional conservation. COBALT enabled incremental protein sequence alignment. Python-based PyMOL enriched analyses with specialized plugins, promoting efficient complex research.

An Intelligent Support System to Help Teachers Plan Field Trips

AbstractField trips enrich learning programs with out-of-school activities that can bring gains in students’ academic content knowledge and personal growth. However, they are a source of anxiety for teachers because of the bureaucracy, pedagogy, etc., risks they imply. To address this issue, we propose FieldTripOrganizer, a field trip planner based on the mixed-initiative approach aimed at increasing teachers’ autonomy and motivation in designing educational tours. The key aspects of our application are (i) the simultaneous provision of information filtering and automated scheduling support while the user designs the field trip, and (ii) the visual annotation of places and activities to show whether they can be included in the itinerary without violating its time constraints. Different from current tour planners, these functions enable the user to be in full control of the design process, delegating the system to manage difficult and burdensome tasks such as consistency checks and itinerary optimization. We evaluated FieldTripOrganizer in the use case of organizing a science field trip. In a preliminary user study involving 18 science teachers, our application turned up to be superior to a baseline tour planner in both usability and user experience. Moreover, the teachers declared that it was helpful, motivated them, and reduced their anxiety during the design of the field trips.