Map Position Research Articles

Deep learning-assisted morphological segmentation for effective particle area estimation and prediction of interfacial properties in polymer composites.

The link between the macroscopic properties of polymer nanocomposites and the underlying microstructural features necessitates an understanding of nanoparticle dispersion. The dispersion of nanoparticles introduces variability, potentially leading to clustering and localized accumulation of nanoparticles. This non-uniform dispersion impacts the accuracy of predictive models. In response to this challenge, this study developed an automated and precise technique for particle recognition and detailed mapping of particle positions in scanning electron microscopy (SEM) micrographs. This was achieved by integrating deep convolutional neural networks with advanced image processing techniques. Following particle detection, two dispersion factors were introduced, namely size uniformity and supercritical clustering, to quantify the impact of particle dispersion on properties. These factors, estimated using the computer vision technique, were subsequently used to calculate the effective load-bearing area influenced by the particles. An adapted micromechanical model was then employed to quantify the interfacial strength and thickness of the nanocomposites. This approach enabled the establishment of a correlation between dispersion characteristics and interfacial properties by integrating experimental data, relevant micromechanical models, and quantified dispersion factors. The proposed systematic procedure demonstrates considerable promise in utilizing deep learning to capture and quantify particle dispersion characteristics for structure-property analyses in polymer nanocomposites.

Age, anticoagulants, hypertension and cardiovascular genetic traits predict cranial ischaemic complications in patients with giant cell arteritis

ObjectivesThis project aimed to determine whether cranial ischaemic complications at the presentation of giant cell arteritis (GCA) were associated with pre-existing cardiovascular (CV) risk factors, CV disease or genetic risk...

Skin Tone Analysis Through Skin Tone Map Generation With Optical Approach and Deep Learning.

Skin tone assessment is critical in both cosmetic and medical fields, yet traditional methods like the individual typology angle (ITA) have limitations, such as sensitivity to illuminants and insensitivity to skinredness. This study introduces an automated image-based method for skin tone mapping by applying optical approaches and deep learning. The method generates skin tone maps by leveraging the illuminant spectrum, segments the skin region from face images, and identifies the corresponding skin tone on the map. The method was evaluated by generating skin tone maps under three standard illuminants (D45, D65, and D85) and comparing the results with those obtained using ITA on skin tone simulationimages. The results showed that skin tone maps generated under the same lighting conditions as the image acquisition (D65) provided the highest accuracy, with a color difference of around 6, which is more than twice as small as those observed under other illuminants. The mapping positions also demonstrated a clear correlation with pigment levels. Compared to ITA, the proposed approach was particularly effective in distinguishing skin tones related toredness. Despite the need to measure the illuminant spectrum and for further physiological validation, the proposed approach shows potential for enhancing skin tone assessment. Its ability to mitigate the effects of illuminants and distinguish between the two dominant pigments offers promising applications in both cosmetic and medicaldiagnostics.

Feature diffusion reconstruction mechanism network for crop spike head detection.

Monitoring crop spike growth using low-altitude remote sensing images is essential for precision agriculture, as it enables accurate crop health assessment and yield estimation. Despite the advancements in deep learning-based visual recognition, existing crop spike detection methods struggle to balance computational efficiency with accuracy in complex multi-scale environments, particularly on resource-constrained low-altitude remote sensing platforms. To address this gap, we propose FDRMNet, a novel feature diffusion reconstruction mechanism network designed to accurately detect crop spikes in challenging scenarios. The core innovation of FDRMNet lies in its multi-scale feature focus reconstruction and lightweight parameter-sharing detection head, which can effectively improve the computational efficiency of the model while enhancing the model's ability to perceive spike shape and texture.FDRMNet introduces a Multi-Scale Feature Focus Reconstruction module that integrates feature information across different scales and employs various convolutional kernels to capture global context effectively. Additionally, an Attention-Enhanced Feature Fusion Module is developed to improve the interaction between different feature map positions, leveraging adaptive average pooling and convolution operations to enhance the model's focus on critical features. To ensure suitability for low-altitude platforms with limited computational resources, we incorporate a Lightweight Parameter Sharing Detection Head, which reduces the model's parameter count by sharing weights across convolutional layers. According to the evaluation experiments on the global wheat head detection dataset and diverse rice panicle detection dataset, FDRMNet outperforms other state-of-the-art methods with mAP@.5 of 94.23%, 75.13% and R 2 value of 0.969, 0.963 between predicted values and ground truth values. In addition, the model's frames per second and parameters in the two datasets are 227.27,288 and 6.8M, respectively, which maintains the top three position among all the compared algorithms. Extensive qualitative and quantitative experiments demonstrate that FDRMNet significantly outperforms existing methods in spike detection and counting tasks, achieving higher detection accuracy with lower computational complexity.The results underscore the model's superior practicality and generalization capability in real-world applications. This research contributes a highly efficient and computationally effective solution for crop spike detection, offering substantial benefits to precision agriculture practices.

The gravitational lensing imprints of DES Y3 superstructures on the CMB: a matched filtering approach

ABSTRACT Low-density cosmic voids gravitationally lens the cosmic microwave background (CMB), leaving a negative imprint on the CMB convergence $\kappa$. This effect provides insight into the distribution of matter within voids, and can also be used to study the growth of structure. We measure this lensing imprint by cross-correlating the Planck CMB lensing convergence map with voids identified in the Dark Energy Survey Year 3 (DES Y3) data set, covering approximately 4200 deg$^2$ of the sky. We use two distinct void-finding algorithms: a 2D void-finder that operates on the projected galaxy density field in thin redshift shells, and a new code, Voxel, which operates on the full 3D map of galaxy positions. We employ an optimal matched filtering method for cross-correlation, using the Marenostrum Institut de Ciències de l’Espai N-body simulation both to establish the template for the matched filter and to calibrate detection significances. Using the DES Y3 photometric luminous red galaxy sample, we measure $A_\kappa$, the amplitude of the observed lensing signal relative to the simulation template, obtaining $A_\kappa = 1.03 \pm 0.22$ ($4.6\sigma$ significance) for Voxel and $A_\kappa = 1.02 \pm 0.17$ ($5.9\sigma$ significance) for 2D voids, both consistent with Lambda cold dark matter expectations. We additionally invert the 2D void-finding process to identify superclusters in the projected density field, for which we measure $A_\kappa = 0.87 \pm 0.15$ ($5.9\sigma$ significance). The leading source of noise in our measurements is Planck noise, implying that data from the Atacama Cosmology Telescope, South Pole Telescope and CMB-S4 will increase sensitivity and allow for more precise measurements.

Atomic-Scale Scanning of Domain Network in the Ferroelectric HfO2 Thin Film.

Ferroelectric HfO2-based thin films have attracted much interest in the utilization of ferroelectricity at the nanoscale for next-generation electronic devices. However, the structural origin and stabilization mechanism of the ferroelectric phase are not understood because the film is typically nanocrystalline with active yet stochastic ferroelectric domains. Here, electron microscopy is used to map the in-plane domain network structures of epitaxially grown ferroelectric Y:HfO2 films in atomic resolution. The ferroelectricity is confirmed in free-standing Y:HfO2 films, allowing for investigating the structural origin for their ferroelectricity by 4D-STEM, high-resolution STEM, and iDPC-STEM. At the grain boundaries of <111>-oriented Pca21 orthorhombic grains, a high-symmetry mixed-(R3m, Pnm21) phase is induced, exhibiting enhanced polarization due to in-plane compressive strain. Nanoscale Pca21 orthorhombic grains and their grain boundaries with mixed-(R3m, Pnm21) phases of higher symmetry cooperatively determine the ferroelectricity of the Y:HfO2 film. It is also found that such ferroelectric domain networks emerge when the film thickness is beyond a finite value. Furthermore, in-plane mapping of oxygen positions overlaid on ferroelectric domains discloses that polarization is suppressed at vertical domain walls, while it is active when domains are aligned horizontally with subangstrom domain walls. In addition, randomly distributed 180° charged domain walls are confined by spacer layers.

Degradomics defines proteolysis information flow from human knee osteoarthritis cartilage to matched synovial fluid and the contributions of secreted proteases ADAMTS5, MMP13 and CMA1 to articular cartilage breakdown

ObjectivesProteolytic cartilage extracellular matrix breakdown is a major mechanism of articular cartilage loss in osteoarthritis (OA) pathogenesis. We sought to determine the overlap of proteolytic peptides in matched knee OA cartilage and synovial fluid on a proteome-wide scale to increase the prospective biomarker repertoire and to attribute proteolytic cleavages to specific secreted proteases. DesignMatched human knee OA cartilage and synovial fluid (n=5) were analyzed by N-terminomics using Terminal Amine Isotopic Labeling of Substrates (TAILS), comprising labeling and enrichment of protein N-termini, high-resolution mass spectrometry and positional peptide mapping. Donor non-OA articular cartilage was digested with CMA1, MMP13 or ADAMTS5, and TAILS was used to identify cleavage sites, which were matched against cartilage and synovial fluid degradomes. ResultsOf over 20,000 cleaved peptides in the combined OA cartilage and synovial fluid degradomes, 677 peptides, originating from 153 proteins, were present in all cartilage and synovial fluid samples. CMA1, MMP13 and ADAMTS5 digestion of cartilage identified numerous cleavage sites for each protease and distinct cleavage site preferences. Peptides resulting from the activities of these proteases were detected in OA cartilage and synovial fluid. ConclusionsProteolytic fragments from both cartilage and circulating proteins are detectable by synovial fluid degradomics. CMA1, MMP13 and ADAMTS5 activity profiles in cartilage are distinct from each other and the previously determined HtrA1 profile. This work expands the proteolytic biomarker space for OA investigation, suggests that multiple, diverse proteases contribute to cartilage destruction, and demonstrates that their specific contributions can each be defined by multiple biomarkers.

Mapping position of states on the application of sovereignty in cyberspace

Mapping position of states on the application of sovereignty in cyberspace

Evaluation of Digital Nautical Chart data for confirmation and expansion of GeoNames data

ABSTRACT This work examines how Digital Nautical Chart (DNC) data may contribute to the evolution and refinement of GeoNames data for near-shore features. GeoNames features are point data with one or more possible place names. DNC Earth Cover Text (ECRText) objects are map labels positioned nearby their real word counterpart. ECRText feature map position strikes a compromise between association with real features and cartographic readability. This work explores whether ECRText features can confirm (or expand names for) existing locations or contribute new locations through data conflation. Due to name variations and spatial position, conflating these data are nontrivial. Previous work engaged in a brief examination using the trigram string matching algorithm under coarse proximity constraints, indicating that ECRText could provide additional value to GeoNames. This work builds on that study, by engaging in a deeper examination of spatial proximity and exploring conflation agreement across an ensemble of string matching approaches. The result finds strong ensemble agreement about ECRText features which already exist in GeoNames but mixed results about which features contribute new information, as well as exploring why some of these matching techniques fail. With an eye toward automation, computational efficiency was found not to be a constraint in sustaining updates.

Design of a logistics warehouse robot positioning and recognition model based on improved EKF and calibration algorithm

Automatic guided vehicles for logistics warehousing are a key link in the construction of intelligent logistics. To improve the positioning accuracy of warehouse robots, we designed an advanced extended Kalman filter method integrating multiple synchronous positioning techniques and map construction methods, and completed the calibration and detection of pallets based on color image information. The results revealed that the proposed multi-innovation enhanced model achieved minimum relative rotation and absolute trajectory errors of 0.13 and 0.09, outperforming existing models. It showcased excellent mapping fidelity and integrity (above 0.9) across various datasets, with a high loop detection success rate (0.91) enhancing map precision. The tray fusion detection algorithm's AUC (area under the curve) reached 0.92, reflecting a balanced accuracy-recall tradeoff. This research offers robust positioning and mapping capabilities in logistics warehousing environments, effectively identifying errors and ensuring pallet accuracy. The detection error and accuracy of this method are better than the other three models, with the lowest average absolute error of 0.32 and the lowest root-mean-square error of 0.27, and the overall error in the detection of pallets is small. The findings provide strong theoretical backing and technical support for advancing intelligent logistics warehousing technology. Precise positioning and identification capabilities enable logistics and warehousing robots to accurately and quickly complete tasks such as access, handling and sorting of goods, greatly improving the efficiency of warehousing operations, promoting the digital transformation and intelligent development of the logistics and warehousing industry, and improving the competitiveness of the industry and the level of service.

LA-ViT: A Network With Transformers Constrained by Learned-Parameter-Free Attention for Interpretable Grading in a New Laryngeal Histopathology Image Dataset.

Grading laryngeal squamous cell carcinoma (LSCC) based on histopathological images is a clinically significant yet challenging task. However, more low-effect background semantic information appeared in the feature maps, feature channels, and class activation maps, which caused a serious impact on the accuracy and interpretability of LSCC grading. While the traditional transformer block makes extensive use of parameter attention, the model overlearns the low-effect background semantic information, resulting in ineffectively reducing the proportion of background semantics. Therefore, we propose an end-to-end network with transformers constrained by learned-parameter-free attention (LA-ViT), which improve the ability to learn high-effect target semantic information and reduce the proportion of background semantics. Firstly, according to generalized linear model and probabilistic, we demonstrate that learned-parameter-free attention (LA) has a stronger ability to learn highly effective target semantic information than parameter attention. Secondly, the first-type LA transformer block of LA-ViT utilizes the feature map position subspace to realize the query. Then, it uses the feature channel subspace to realize the key, and adopts the average convergence to obtain a value. And those construct the LA mechanism. Thus, it reduces the proportion of background semantics in the feature maps and feature channels. Thirdly, the second-type LA transformer block of LA-ViT uses the model probability matrix information and decision level weight information to realize key and query, respectively. And those realize the LA mechanism. So, it reduces the proportion of background semantics in class activation maps. Finally, we build a new complex semantic LSCC pathology image dataset to address the problem, which is less research on LSCC grading models because of lacking clinically meaningful datasets. After extensive experiments, the whole metrics of LA-ViT outperform those of other state-of-the-art methods, and the visualization maps match better with the regions of interest in the pathologists' decision-making. Moreover, the experimental results conducted on a public LSCC pathology image dataset show that LA-ViT has superior generalization performance to that of other state-of-the-art methods.

Improving the local and distortional resistance of CFS trapezoidal self-supporting roof members

This paper reports the development, numerical implementation and application of a methodology to perform the multi-objective optimisation, with respect to local and distortional failures, of cold-formed steel (CFS) trapezoidal cross-section shapes used in members belonging to self-supporting roof systems and manufactured by bending the steel sheets with roll-forming machines. Initially, a brute-force approach is adopted to search for optimal unstiffened cross-section shapes of members subjected to positive (sagging) and negative (hogging) bending moments. Next, an optimisation procedure based on a Genetic Algorithm (GA) is employed to find stiffened cross-sections that (i) outperform the original (plain/unstiffened) ones, as far as the resistance against local and distortional failures is concerned, and (ii) can be fabricated with minimal additional cost, through the sole addition of small intermediate stiffeners, whose possible locations are pre-defined in accordance with a map of executable fold-line positions – the original cross-section typology, overall dimensions and wall angles are retained. The design approach adopted to estimate the member ultimate strengths is based on the Direct Strength Method (DSM), and Generalised Beam Theory (GBT) is used to calculate the elastic buckling stresses due to bending. The application and capabilities of the proposed methodology are illustrated, by means of numerical results presented in terms of cross-section families belonging to optimal Pareto Fronts – an excellent performance of the procedure was found in all cases.

A graphical method and workflow for the generation of structure contours for horizontal cylindrical folds

A graphical method developed for the construction of structure contours for inclined planar surfaces has been extended to curved or folded surfaces with cylindrical geometries and horizontal axes. These structure contours can be used to constrain the outcrop trace of a folded contact on a geological map. The extended method requires, as a minimum, a field of orientation data and the map position of a single elevation on the contact under investigation. The method utilises orientation data collected on and around a partially mapped trace of a folded contact to constrain the outcrop trace throughout a geological map. The method uses a vertical cross-section that is parallel to the profile plane of any folds present. Crest, trough and hinge points identified in the cross-section can be projected on to the geological map in their correct positions and elevations. Similarly, structure contours on fold axial surfaces can be projected on to the geological map and the contours used to locate the axial traces of cylindrical folds with horizontal axes.

ESKEMAP: exact sketch-based read mapping

BackgroundGiven a sequencing read, the broad goal of read mapping is to find the location(s) in the reference genome that have a “similar sequence”. Traditionally, “similar sequence” was defined as having a high alignment score and read mappers were viewed as heuristic solutions to this well-defined problem. For sketch-based mappers, however, there has not been a problem formulation to capture what problem an exact sketch-based mapping algorithm should solve. Moreover, there is no sketch-based method that can find all possible mapping positions for a read above a certain score threshold.ResultsIn this paper, we formulate the problem of read mapping at the level of sequence sketches. We give an exact dynamic programming algorithm that finds all hits above a given similarity threshold. It runs in O(|t|+|p|+ℓ2)\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\mathcal {O} (|t| + |p| + \\ell ^2)$$\\end{document} time and O(ℓlogℓ)\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\mathcal {O} (\\ell \\log \\ell )$$\\end{document} space, where |t| is the number of k\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$k$$\\end{document}-mers inside the sketch of the reference, |p| is the number of k\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$k$$\\end{document}-mers inside the read’s sketch and ℓ\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\ell$$\\end{document} is the number of times that k\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$k$$\\end{document}-mers from the pattern sketch occur in the sketch of the text. We evaluate our algorithm’s performance in mapping long reads to the T2T assembly of human chromosome Y, where ampliconic regions make it desirable to find all good mapping positions. For an equivalent level of precision as minimap2, the recall of our algorithm is 0.88, compared to only 0.76 of minimap2.

OA01 Cluster medicine: the role of genetics for identifying shared biological pathways between rheumatoid arthritis and common comorbidities for targeting treatments

Abstract Background/Aims Rheumatoid arthritis (RA) is a chronic inflammatory disorder associated with significant pain and disability, as well as increased risk of comorbidities. These comorbidities, such as coronary artery disease, stroke, heart failure and type 2 diabetes, often form predictable clusters highlighting the importance of common disease mechanisms. Identifying these clusters, and the underlying shared mechanisms, is key to the concept of cluster medicine which aims to take a more systemic approach to treating comorbidity. It is hypothesised that inflammatory processes in RA promote the pathogenesis of these comorbid diseases, which typically occur at an earlier stage of life than in the general population. However, few studies have investigated the degree of shared genetic susceptibility implicated across these traits. The present study aimed to identify and characterise pleiotropic loci and shared biological pathways involved in the pathogenesis of RA and cardiometabolic disorders. Methods European-only GWAS summary statistics were collected for RA (seropositive, seronegative, and overall) and seven cardiometabolic traits, including ischemic stroke, any stroke, heart failure, coronary artery disease, hypertension, atrial fibrillation, and type 2 diabetes. Cross-trait linkage disequilibrium score regression was employed to estimate the degree of genetic correlation (rg) between all pairs of traits. Subset-based meta-analysis was performed using the R package ASSET to identify pleiotropic single nucleotide polymorphisms (SNPs). Pleiotropic SNPs were assigned to genes based on positional and eQTL mapping. Prioritised genes were tested for over-representation in functional categories and biological pathways in gene sets obtained from MsigDB and WikiPathways using the hypergeometric test loci. Gene mapping and enrichment analyses were conducted in FUMA. Results RA showed significant positive genetic correlation with all cardiometabolic traits, with the seronegative subtype showing greater correlation than the seropositive subtype. Correlations between RA and cardiometabolic phenotypes ranged from 0.11 - 0.38, the strongest of which was between seronegative RA and heart failure (rg = 0.38, P = 2.6e-07). A total of 67 lead SNPs were identified as potentially pleiotropic. Pleiotropic SNPs were mapped to genes including IL6R, PTPN11, ATXN2, PLCL1 and SOX6. Pathway enrichment analyses revealed that mapped genes were primarily involved in inflammatory signalling pathways, involving interleukins, TNF-α and interferon (gamma and alpha) response. Conclusion This study provides evidence for pleiotropy across RA and cardiometabolic traits, whereby inherited genetic variants predispose to aberrant cytokine signalling, thus conferring risk to multiple diseases where inflammation is a key driver of pathogenesis. Disclosure M. Stoves: None. M. Soomro: None. S. Zhao: None. D. Plant: None. A. Barton: None. J. Bowes: None.

Identification of an novel genetic variant associated with osteoporosis: insights from the Taiwan Biobank Study.

The purpose of this study was to identify new independent significant SNPs associated with osteoporosis using data from the Taiwan Biobank (TWBB). The dataset was divided into discovery (60%) and replication (40%) subsets. Following data quality control, genome-wide association study (GWAS) analysis was performed, adjusting for sex, age, and the top 5 principal components, employing the Scalable and Accurate Implementation of the Generalized mixed model approach. This was followed by a meta-analysis of TWBB1 and TWBB2. The Functional Mapping and Annotation (FUMA) platform was used to identify osteoporosis-associated loci. Manhattan and quantile-quantile plots were generated using the FUMA platform to visualize the results. Independent significant SNPs were selected based on genome-wide significance (P < 5 × 10-8) and independence from each other (r2 < 0.6) within a 1Mb window. Positional, eQTL(expression quantitative trait locus), and Chromatin interaction mapping were used to map SNPs to genes. A total of 29 084 individuals (3154 osteoporosis cases and 25 930 controls) were used for GWAS analysis (TWBB1 data), and 18 918 individuals (1917 cases and 17 001 controls) were utilized for replication studies (TWBB2 data). We identified a new independent significant SNP for osteoporosis in TWBB1, with the lead SNP rs76140829 (minor allele frequency = 0.055, P-value = 1.15 × 10-08). Replication of the association was performed in TWBB2, yielding a P-value of 6.56 × 10-3. The meta-analysis of TWBB1 and TWBB2 data demonstrated a highly significant association for SNP rs76140829 (P-value = 7.52 × 10-10). In the positional mapping of rs76140829, 6 genes (HABP2, RP11-481H12.1, RNU7-165P, RP11-139K1.2, RP11-57H14.3, and RP11-214N15.5) were identified through chromatin interaction mapping in mesenchymal stem cells. Our GWAS analysis using the Taiwan Biobank dataset unveils rs76140829 in the VTI1A gene as a key risk variant associated with osteoporosis. This finding expands our understanding of the genetic basis of osteoporosis and highlights the potential regulatory role of this SNP in mesenchymal stem cells.

TREX reveals proteins that bind to specific RNA regions in living cells

Different regions of RNA molecules can often engage in specific interactions with distinct RNA-binding proteins (RBPs), giving rise to diverse modalities of RNA regulation and function. However, there are currently no methods for unbiased identification of RBPs that interact with specific RNA regions in living cells and under endogenous settings. Here we introduce TREX (targeted RNase H-mediated extraction of crosslinked RBPs)—a highly sensitive approach for identifying proteins that directly bind to specific RNA regions in living cells. We demonstrate that TREX outperforms existing methods in identifying known interactors of U1 snRNA, and reveals endogenous region-specific interactors of NORAD long noncoding RNA. Using TREX, we generated a comprehensive region-by-region interactome for 45S rRNA, uncovering both established and previously unknown interactions that regulate ribosome biogenesis. With its applicability to different cell types, TREX is an RNA-centric tool for unbiased positional mapping of endogenous RNA–protein interactions in living cells.

Genetic Diversity within a Collection of Italian Maize Inbred Lines: A Resource for Maize Genomics and Breeding

Genetic diversity is fundamental for studying the complex architecture of the traits of agronomic importance, controlled by major and minor loci. Moreover, well-characterized germplasm collections are essential tools for dissecting and analyzing genetic and phenotypic diversity in crops. A panel of 360 entries, a subset of a larger collection maintained within the GenBank at CREA Bergamo, which includes the inbreds derived from traditional Italian maize open-pollinated (OP) varieties and advanced breeding ones (Elite Inbreds), was analyzed to identify SNP markers using the tGBS® genotyping-by-sequencing technology. A total of 797,368 SNPs were found during the initial analysis. Imputation and filtering processes were carried out based on the percentage of missing data, redundant markers, and rarest allele frequencies, resulting in a final dataset of 15,872 SNP markers for which a physical map position was identified. Using this dataset, the inbred panel was characterized for linkage disequilibrium (LD), genetic diversity, population structure, and genetic relationships. LD decay at a genome-wide level indicates that the collection is a suitable resource for association mapping. Population structure analyses, which were carried out with different clustering methods, showed stable grouping statistics for four groups, broadly corresponding to ‘Insubria’, ‘Microsperma’, and ‘Scagliolino’ genotypes, with a fourth group composed prevalently of elite accessions derived from Italian and US breeding programs. Based on these results, the CREA Italian maize collection, genetically characterized in this study, can be considered an important tool for the mapping and characterization of useful traits and associated loci/alleles, to be used in maize breeding programs.

Pharmacogenomics of intravenous immunoglobulin response in Kawasaki disease.

Kawasaki disease (KD) is a diffuse vasculitis in children. Response to high dose intravenous gamma globulin (IVIG), the primary treatment, varies according to genetic background. We sought to identify genetic loci, which associate with treatment response using whole genome sequencing (WGS). We performed WGS in 472 KD patients with 305 IVIG responders and 167 non-responders defined by AHA clinical criteria. We conducted logistic regression models to test additive genetic effect in the entire cohort and in four subgroups defined by ancestry information markers (Whites, African Americans, Asians, and Hispanics). We performed functional mapping and annotation using FUMA to examine genetic variants that are potentially involved IVIG non-response. Further, we conducted SNP-set [Sequence] Kernel Association Test (SKAT) for all rare and common variants. Of the 43,288,336 SNPs (23,660,970 in intergenic regions, 16,764,594 in introns and 556,814 in the exons) identified, the top ten hits associated with IVIG non-response were in FANK1, MAP2K3:KCNJ12, CA10, FRG1DP, CWH43 regions. When analyzed separately in ancestry-based racial subgroups, SNPs in several novel genes were associated. A total of 23 possible causal genes were pinpointed by positional and chromatin mapping. SKAT analysis demonstrated association in the entire MANIA2, EDN1, SFMBT2, and PPP2R5E genes and segments of CSMD2, LINC01317, HIVEPI, HSP90AB1, and TTLL11 genes. This WGS study identified multiple predominantly novel understudied genes associated with IVIG response. These data can serve to inform regarding pathogenesis of KD, as well as lay ground work for developing treatment response predictors.

Sequence and epigenetic landscapes of active and silent nucleolus organizer regions in Arabidopsis

Arabidopsis thaliana has two ribosomal RNA (rRNA) gene loci, nucleolus organizer regions NOR2 and NOR4 , whose complete sequences are missing in current genome assemblies. Ultralong DNA sequences assembled using an unconventional approach yielded ~5.5- and 3.9-Mbp sequences for NOR2 and NOR4 in the reference strain, Col-0. The distinct rRNA gene subtype compositions of the NORs enabled the positional mapping of their active and inactive regions, using RNA sequencing to identify subtype-specific transcripts and DNA sequencing to identify subtypes associated with flow-sorted nucleoli. Comparisons of wild-type and silencing-defective plants revealed that most rRNA gene activity occurs in the central region of NOR4 , whereas most, but not all, genes of NOR2 are epigenetically silenced. Intervals of low CG and CHG methylation overlap regions where gene activity and gene subtype homogenization are high. Collectively, the data reveal the genetic and epigenetic landscapes underlying nucleolar dominance (differential NOR activity) and implicate transcription as a driver of rRNA gene concerted evolution.