Variation Map Research Articles

The Spectrum Difference Enhanced Network for Hyperspectral Anomaly Detection

Most deep learning-based hyperspectral anomaly detection (HAD) methods focus on modeling or reconstructing the hyperspectral background to obtain residual maps from the original hyperspectral images. However, these methods typically do not pay enough attention to the spectral similarity in the complex environment, resulting in inadequate distinction between background and anomalies. Moreover, some anomalies and background are different objects, but they are sometimes recognized as the objects with the same spectrum. To address the issues mentioned above, this paper proposes a Spectrum Difference Enhanced Network (SDENet) for HAD, which employs variational mapping and Transformer to amplify spectrum differences. The proposed network is based on the encoder–decoder structure, which contains a CSWin-Transformer encoder, Variational Mapping Module (VMModule), and CSWin-Transformer decoder. First, the CSWin-Transformer encoder and decoder are designed to supplement image information by extracting deep and semantic features, where a cross-shaped window self-attention mechanism is designed to provide strong modeling capability with minimal computational cost. Second, in order to enhance the spectral difference characteristics between anomalies and background, a randomly sampling VMModule is presented for feature space transformation. Finally, all fully connected mapping operations are replaced with convolutional layers to reduce the model parameters and computational load. The effectiveness of the proposed SDENet is verified on three datasets, and experimental results show that it achieves better detection accuracy and lower model complexity compared with existing methods.

Scanning Microwave Impedance Microscopy for Characterization of Graphene Systems Encapsulated by Hexagonal Boron Nitride

Scanning microwave impedance microscopy (sMIM) is a near‐field technique that enables the characterization of conductive materials with a resolution down to 1 nm. In hexagonal boron nitride (hBN)‐encapsulated devices, microwaves emitted from the sMIM tip penetrate and reach with the underlying 2D materials, allowing for the mapping of local conductivity variations. Using twisted bilayer graphene, it is demonstrated that this technique can characterize moiré patterns through hBN flakes up to 2.5 nm thick. Additionally, it is showed that sMIM can distinguish between conductive and insulating materials in samples encapsulated by hBN layers exceeding 15 nm. A key finding is that signal decay due to encapsulation is intrinsically linked to the tip's condition. This work overcomes limitations in the application of twistronics, enabling the verification of the periodicity of moiré patterns in high‐quality encapsulated devices between electrical contacts.

Nanoscale mapping of local intrinsic strain-induced anomalous bandgap variations in WSe2 using selective-wavelength scanning photoconductivity microscopy

Nanoscale defects can locally tailor the optoelectronic properties of two-dimensional materials such as bandgap. However, it is still challenging to directly map and analyze the defect-induced bandgap variations in a quantitative manner. Here, we report the nanoscale mapping of local intrinsic strain-induced anomalous bandgap variations in as-grown WSe2 by using a selective-wavelength scanning photoconductivity microscopy. In this method, a conducting nanoprobe is utilized to map the spatial distributions of strain, sheet-conductance, and charge trap density (Neff) of epitaxially-grown WSe2 on sapphire while illuminating monochromatic light of selective wavelengths. Under un-illuminated conditions, the maps showed WSe2 domain structures with boundaries having slightly lower sheet-conductance and higher Neff than those in domains, presumably due to low misorientation angles between adjacent epitaxially-grown domains. By measuring wavelength-dependent photoconductance maps and fitting each pixel value of the maps, we could successfully obtain the map of local bandgap. The map clearly showed bandgap variations inside domains as well as rather low bandgaps at boundaries. By comparing with local strain map, we found that tensile strain effectively reduced bandgap following two different power-law relationships. Such anomalous bandgap variations in WSe2 could be explained by strain-induced weakening of d-orbital couplings. Furthermore, in a monolayer-bilayer WSe2 interface, bilayer WSe2 exhibited lower bandgap than the underlying monolayer WSe2, while interfacial boundaries exhibited the strain-induced bandgap values in between those of monolayer and bilayer WSe2. Since our method allows us to directly map the intrinsic strain-induced bandgap variations with a nanoscale resolution down to tens-of-nm, it can be a powerful tool for basic research and practical applications of optoelectronic devices based on two-dimensional materials.

Pangenome Reveals Gene Content Variations and Structural Variants Contributing to Pig Characteristics.

Pigs are among the most essential sources of high-quality protein in human diets. Structural variants (SVs) are a major source of genetic variants associated with diverse traits and evolutionary events. However, the current linear reference genome of pigs limits the presentation of position information for SVs. In this study, we generated a pangenome of pigs and a genome variation map of 599 deep-sequenced genomes across Eurasia. Moreover, a section-wide gene repertoire was constructed, which indicated that core genes were more evolutionarily conserved than variable genes. Subsequently, we identified 546,137 SVs, their enrichment regions, and relationships with genomic features and found significant divergence across Eurasian pigs. More importantly, the pangenome-detected SVs could complement heritability estimates and genome-wide association studies based only on single nucleotide polymorphisms. Among the SVs shaped by selection, we identified an insertion in the promoter region of the TBX19 gene, which may be related to the development, growth, and timidity traits of Asian pigs and may affect the gene expression. Our constructed pig pangenome and the identified SVs provide rich resources for future functional genomic research on pigs.

Grapevine pangenome facilitates trait genetics and genomic breeding

Grapevine breeding is hindered by a limited understanding of the genetic basis of complex agronomic traits. This study constructs a graph-based pangenome reference (Grapepan v.1.0) from 18 newly generated phased telomere-to-telomere assemblies and 11 published assemblies. Using Grapepan v.1.0, we build a variation map with 9,105,787 short variations and 236,449 structural variations (SVs) from the resequencing data of 466 grapevine cultivars. Integrating SVs into a genome-wide association study, we map 148 quantitative trait loci for 29 agronomic traits (50.7% newly identified), with 12 traits significantly contributed by SVs. The estimated heritability improves by 22.78% on average when including SVs. We discovered quantitative trait locus regions under divergent artificial selection in metabolism and berry development between wine and table grapes, respectively. Moreover, significant genetic correlations were detected among the 29 traits. Under a polygenic model, we conducted genomic predictions for each trait. In general, our study facilitates the breeding of superior cultivars via the genomic selection of multiple traits.

Application of geospatial and machine learning algorithms to predict (under certain limitations) the quality of groundwater used for irrigation purposes

ABSTRACT The main objective of the present study is to evaluate groundwater quality for irrigation purposes in the central-western part of Haryana state (India). For this, 272 groundwater samples were collected during the pre- and post-monsoon periods in 2022. Several indices, including SAR, PI, Na%, KR, magnesium adsorption ratio (MAR), and IWQI were derived. The results of SAR, Na%, and KR values indicate that the groundwater is generally suitable for irrigation. On the other hand, PI and MAR exceeded the established limits, primarily showing issues related to salinity and magnesium content in the groundwater. Furthermore, according to the IWQI classification, 47.06 and 25% of the total collected samples fell under the ‘severe restriction for irrigation’ category during the pre-monsoon and post-monsoon periods, respectively. Spatial variation maps indicate that water quality in the western portion of the study area is unsuitable for irrigation during both periods. Three ML algorithms, namely RF, SVM, and XGBoost were integrated and validated to predict the IWQI. The results revealed that the XGBoost with random search achieves the best prediction performances. The approaches established in this study have been confirmed to be cost-effective and feasible for groundwater quality, using hydrochemical parameters as input variables, and highly beneficial for water resource planning and management.

Cherenkov Imaged Bio-morphological Features Verify Patient Positioning with Deformable Tissue Translocation in Breast Radiotherapy

PurposeAccurate patient positioning is crucial for precise radiotherapy dose delivery, as errors in positioning can profoundly influence treatment outcomes. This study introduces a novel application for loco-regional tissue deformation tracking via Cherenkov image analysis, during fractionated breast cancer radiotherapy. The primary objective of this research was to develop and test an algorithmic method for Cherenkov-based position accuracy quantification, particularly for loco-regional deformations which do not have an ideal method for quantification during radiotherapy. Methods and materialsBio-morphological features in the Cherenkov images such as vessels were segmented. A rigid/non-rigid combined registration technique was employed to pinpoint both inter- and intra-fractional positioning variations. The methodology was tested on an anthropomorphic chest phantom experiment via shifting treatment couch with known distances and inducing respiratory motion, to simulate inter-fraction setup uncertainties and intra-fraction motions respectively. It was then applied to a dataset of fractionated whole breast radiotherapy human imaging (n=10 patients). ResultsThe methodology provides quantified positioning variations, comprising two components: a global shift determined through rigid registration, and a two-dimensional variation map illustrating loco-regional tissue deformation quantified via non-rigid registration. Controlled phantom testing yielded an average accuracy of 0.83 mm for couch translations up to 20 mm in various directions. Analysis of clinical Cherenkov imaging data from ten breast cancer patients compared to their first imaged fraction revealed an inter-fraction setup variation of 3.7 ± 2.4 mm in the global shift and loco-regional deformation up to 3.3 ± 1.9 mm (95th percentile of all regional deformation). ConclusionsThis study introduces the use of Cherenkov visualized bio-morphological features to quantify the global and local variations in patient positioning based on rigid and non-rigid registrations. This new approach demonstrates the feasibility to provide quantitative guidance for inter-/intra-fraction positioning, particularly for the loco-regional deformations that have been unappreciated in current practice with conventional imaging techniques.

Genomic landscape of adult testicular germ cell tumours in the 100,000 Genomes Project

Testicular germ cell tumours (TGCT), which comprise seminoma and non-seminoma subtypes, are the most common cancers in young men. In this study, we present a comprehensive whole genome sequencing analysis of adult TGCTs. Leveraging samples from participants recruited via the UK National Health Service and data from the Genomics England 100,000 Genomes Project, our results provide an extended description of genomic elements underlying TGCT pathogenesis. This catalogue offers a comprehensive, high-resolution map of copy number alterations, structural variation, and key global genome features, including mutational signatures and analysis of extrachromosomal DNA amplification. This study establishes correlations between genomic alterations and histological diversification, revealing divergent evolutionary trajectories among TGCT subtypes. By reconstructing the chronological order of driver events, we identify a subgroup of adult TGCTs undergoing relatively late whole genome duplication. Additionally, we present evidence that human leukocyte antigen loss is a more prevalent mechanism of immune disruption in seminomas. Collectively, our findings provide valuable insights into the developmental and immune modulatory processes implicated in TGCT pathogenesis and progression.

A comprehensive map of copy number variations in dromedary camels based on whole genome sequence data

Copy number variants (CNVs) are structural variants within the eukaryotic genome that vary among individuals of a species. These variants have been associated with different phenotypic traits, making them a valuable consideration as markers for designing breeding programmes. In this study, whole genome sequence data of 60 dromedary camel samples originating from the Arabian Peninsula were analyzed to construct a comprehensive dromedary CNV map. Utilizing four CNV callers employing read-depth, split-read and paired-end mapping approaches, a total of 37,519 CNV events (17,847 deletions and 19,672 duplications) were called on the dromedary autosomes. These CNV events were merged into 2,557 regions, categorized as 1,322 losses, 122 gains, and 1,113 “mixed regions” comprising both types. The cumulative size of the CNV regions amounted to 22.5 Mb, covering roughly 1.16% of the dromedary autosomes. Approximately 32% of the defined CNV regions (comprising 60% losses, 18% gains, and 0.27% mixed regions) were found in ≥ 90% of the dromedary samples, classifying them as prevalent regions. Genes with biological functions related to the different adaptive physiologies of dromedary camels, such as fertility, heat stress, musculoskeletal development, and fat metabolism, were overlapping with or in close proximity to ~ 68% of the defined CNV regions, demonstrating their potential role in dromedaries’ physiology. This study presents the first comprehensive CNV map of dromedary camels and builds on the present knowledge in understanding the genetic structure of this species.

Reconstruction of the human amylase locus reveals ancient duplications seeding modern-day variation.

Previous studies suggested that the copy number of the human salivary amylase gene, AMY1, correlates with starch-rich diets. However, evolutionary analyses are hampered by the absence of accurate, sequence-resolved haplotype variation maps. We identified 30 structurally distinct haplotypes at nucleotide resolution among 98 present-day humans, revealing that the coding sequences of AMY1 copies are evolving under negative selection. Genomic analyses of these haplotypes in archaic hominins and ancient human genomes suggest that a common three-copy haplotype, dating as far back as 800 KYA, has seeded rapidly evolving rearrangements through recurrent non-allelic homologous recombination. Additionally, haplotypes with more than three AMY1 copies have significantly increased in frequency among European farmers over the past 4,000 years, potentially as an adaptive response to increased starch digestion.

On the inverse problem of parameter identification in the steady Oseen model with unilateral and frictional‐type boundary conditions

AbstractRecently, Migórski‐Dudek investigated a steady Oseen flow for a generalized Newtonian incompressible fluid with unilateral and frictional‐type boundary conditions. They established an existence theorem for the steady Oseen model when the divergence‐free convection field and acting volume force are known. However, prior knowledge of and is often impossible in practical engineering applications. This leads to the question of whether the divergence‐free convection field and acting volume force can be determined. The main objective of this paper is to provide a positive response to this challenging and intriguing question. Specifically, we aim to formulate an inverse problem for identifying and , characterized by a nonlinear and nonsmooth regularized optimization problem. Initially, we introduce a variational selection mapping for the steady Oseen model. We then establish the boundedness and generalized continuity of this variational selection. Finally, by leveraging optimization theory, convex analysis, and nonsmooth analysis, we develop an abstract regularization framework for the considered inverse problem and establish the existence of a solution.

Haplotype-resolved genome assembly and resequencing provide insights into the origin and breeding of modern rose.

Modern rose (Rosa hybrida) is a recently formed interspecific hybrid and has become one of the most important and widely cultivated ornamentals. Here we report the haplotype-resolved chromosome-scale genome assembly of the tetraploid R. hybrida 'Samantha' ('JACmantha') and a genome variation map of 233 Rosa accessions involving various wild species, and old and modern cultivars. Homologous chromosomes of 'Samantha' exhibit frequent homoeologous exchanges. Population genomic and genomic composition analyses reveal the contributions of wild Rosa species to modern roses and highlight that R. odorata and its derived cultivars are important contributors to modern roses, much like R. chinensis 'Old Blush'. Furthermore, selective sweeps during modern rose breeding associated with major agronomic traits, including continuous and recurrent flowering, double flower, flower senescence and disease resistance, are identified. This study provides insights into the genetic basis of modern rose origin and breeding history, and offers unprecedented genomic resources for rose improvement.

Diurnal and Seasonal Variations of Meteor Speed and Arrival Angle Observed by Mengcheng Meteor Radar

AbstractMeteor speed is crucial in identifying key astronomical aspects of the meteoroid environment, including the influx of meteoric material and the distribution of meteoric radiants. This study investigates diurnal and seasonal variations of meteor speed from April 2014 to December 2023 observed by the Mengcheng meteor radar (MCMR; 33.4°N, 116.3°E). In addition to the expected diurnal variation of meteor speed, azimuth peak, zenith peak, and the azimuthal direction of maximum meteor speed due to the Earth's rotation, where the meteor speed peaks northward in the local morning and shifts clockwise, we find that the meteor speed distribution resembles a superposition of two Gaussian distributions, with the lower (higher) distribution mainly ranging from 20 to 40 (45–65) km/s. The two speed peaks are estimated using a double‐Gaussian fitting approach. In terms of seasonal variation, we find that (a) the low‐speed peak indicates a semi‐annual variation cycle, with maxima in June–July and December–January, and minima in February and August; (b) the high‐speed peak indicates an annual variation cycle, with maxima in September–December and minima in March. There is also a correlation between the seasonal variations in the meteor speed peaks and the occurrence of meteor showers. Statistical analysis provides a map of seasonal variations in the intensity, duration, and timing of meteor shower activities, which is supplemental to previous shorter‐duration studies using backscatter meteor radar observations. We also find notable shower events where the meteor counts exceed 45%–97% of the background count on corresponding event dates and speeds.

Response of the Thermosphere‐Ionosphere System to an X‐Class Solar Flare: 30 March 2022 Case Study

AbstractThe response of the thermosphere ionosphere system to an X1.3 class solar flare is studied using observations of the total electron content (TEC) and the Global Ionosphere Thermosphere Model (GITM) simulations. The solar flare erupted from the active region AR12975 on 30 March 2022. Owing to the absence of accompanying severe geomagnetic activity, it was possible to isolate the effects of the flare on the upper atmosphere. TEC data are processed for Continental USA (CONUS), employing filtering and binning techniques to create 2D variation maps. The spectral content of the TEC variations is analyzed using a wavelet coherence method. The immediate response of the solar flare exhibited broad similarities, while notable differences were observed during the recovery period between the East and West sides of the CONUS. GITM is used to explore the East–West asymmetry of the key T‐I parameters. Simulation results reveal that the coinciding interplanetary magnetic field southward turning had a greater influence on these parameters compared to the solar flare, while their nonlinear interaction introduced complex variations. Additional investigation reveals gravity wave damping also contributes to the asymmetric solar flare response.

Human brain state dynamics are highly reproducible and associated with neural and behavioral features.

Neural activity and behavior vary within an individual (states) and between individuals (traits). However, the mapping of state-trait neural variation to behavior is not well understood. To address this gap, we quantify moment-to-moment changes in brain-wide co-activation patterns derived from resting-state functional magnetic resonance imaging. In healthy young adults, we identify reproducible spatiotemporal features of co-activation patterns at the single-subject level. We demonstrate that a joint analysis of state-trait neural variations and feature reduction reveal general motifs of individual differences, encompassing state-specific and general neural features that exhibit day-to-day variability. The principal neural variations co-vary with the principal variations of behavioral phenotypes, highlighting cognitive function, emotion regulation, alcohol and substance use. Person-specific probability of occupying a particular co-activation pattern is reproducible and associated with neural and behavioral features. This combined analysis of state-trait variations holds promise for developing reproducible neuroimaging markers of individual life functional outcome.

Large-scale genome sequencing of giant pandas improves the understanding of population structure and future conservation initiatives

The extinction risk of the giant panda has been demoted from "endangered" to "vulnerable" on the International Union for Conservation of Nature Red List, but its habitat is more fragmented than ever before, resulting in 33 isolated giant panda populations according to the fourth national survey released by the Chinese government. Further comprehensive investigations of the genetic background and in-depth assessments of the conservation status of wild populations are still necessary and urgently needed. Here, we sequenced the genomes of 612 giant pandas with an average depth of ~26× and generated a high-resolution map of genomic variation with more than 20 million variants covering wild individuals from six mountain ranges and captive representatives in China. We identified distinct genetic clusters within the Minshan population by performing a fine-grained genetic structure. The estimation of inbreeding and genetic load associated with historical population dynamics suggested that future conservation efforts should pay special attention to the Qinling and Liangshan populations. Releasing captive individuals with a genetic background similar to the recipient population appears to be an advantageous genetic rescue strategy for recovering the wild giant panda populations, as this approach introduces fewer deleterious mutations into the wild population than mating with differentiated lineages. These findings emphasize the superiority of large-scale population genomics to provide precise guidelines for future conservation of the giant panda.

On the (Higher Multipoles) Variance Asymmetry in the Cosmic Microwave Background

We have studied the cosmic microwave background (CMB) map looking for features beyond cosmological isotropy. We began by tiling the CMB variance maps (which are produced by different smoothing scales) with stripes of different sizes along the most prominent dipole direction. We were able to confirm previous findings regarding the significance of the dipole. Furthermore, we discovered that some of the higher multipoles exhibit significance comparable to the dipole that naturally depends on the smoothing scales. In the end, we discussed this result having an eye on the look-elsewhere-effect. We believe our results may indicate an anomalous patch in the CMB sky that warrants further investigation.

Geo-Hgan: Unsupervised anomaly detection in geochemical data via latent space learning

Reconstructing geochemical data for anomaly detection using Generative Adversarial Networks (GANs) has become a prevalent method in identifying geochemical anomalies. However, injecting random noise into GANs can induce model instability. To mitigate this issue, we propose a novel anomaly detection model, Geo-Hgan, which integrates a dual adversarial network architecture with a Latent Space Adversarial Module (LSAM) to learn the distribution of latent variables from arbitrary data and optimize the sample reconstruction process, thereby alleviating instability during GAN training. Additionally, an encoder guided by the LSAM-pretrained GAN is employed to extract variational features, facilitating rapid and effective sample mapping into the latent space defined by LSAM. Experimental results demonstrate that under unsupervised conditions, Geo-Hgan achieves an Area Under the Curve (AUC) score of 85% across three geochemical datasets, outperforming similar models in accuracy and reconstruction capabilities. To assess its versatility and generalization ability, we extend Geo-Hgan to anomaly detection tasks in computer vision, where it achieves an average AUC score of 98.7% on the MvtecAD dataset, setting a new state-of-the-art performance in the domain. Furthermore, we propose AnomFilter, a method for setting anomaly thresholds based on the clustering hypothesis. AnomFilter identifies high-confidence anomaly samples identified by Geo-Hgan in the source domain and iteratively transfers them to the target domain. These high-confidence anomaly samples, combined with a small number of known positive samples in the target domain, enhance the accuracy of supervised geochemical anomaly detection in the target domain, which achieved an AUC score of 94%. The utilization of anomaly detection models for sample transfer learning offers a novel perspective for future work.

VMseg: Using spatial variance to automatically segment retinal non-perfusion on OCT-angiography.

To develop and test VMseg, a new image processing algorithm performing automatic segmentation of retinal non-perfusion in widefield OCT-Angiography images, in order to estimate the non-perfusion index in diabetic patients. We included diabetic patients with severe non-proliferative or proliferative diabetic retinopathy. We acquired images using the PlexElite 9000 OCT-A device with a photomontage of 5 images of size 12 x 12 mm. We then developed VMseg, a Python algorithm for non-perfusion detection, which binarizes a variance map calculated through convolution and morphological operations. We used 70% of our data set (development set) to fine-tune the algorithm parameters (convolution and morphological parameters, binarization thresholds) and evaluated the algorithm performance on the remaining 30% (test set). The obtained automatic segmentations were compared to a ground truth corresponding to manual segmentation from a retina expert and the inference processing time was estimated. We included 51 eyes of 30 patients (27 severe non-proliferative, 24 proliferative diabetic retinopathy). Using the optimal parameters found on the development set to tune the algorithm, the mean dice for the test set was 0.683 (sd = 0.175). We found a higher dice coefficient for images with a higher area of retinal non-perfusion (rs = 0.722, p < 10-4). There was a strong correlation (rs = 0.877, p < 10-4) between VMseg estimated non-perfusion indexes and indexes estimated using the ground truth segmentation. The Bland-Altman plot revealed that 3 eyes (5.9%) were significantly under-segmented by VMseg. We developed VMseg, an automatic algorithm for retinal non-perfusion segmentation on 12 x 12 mm OCT-A widefield photomontages. This simple algorithm was fast at inference time, segmented images in full-resolution and for the OCT-A format, was accurate enough for automatic estimation of retinal non-perfusion index in diabetic patients with diabetic retinopathy.

Comparison of turnout in Italian parliamentary elections: a two-stage affinity propagation approach

ABSTRACT This paper presents a methodology for temporal contextual analysis of electoral outcomes in the presence of redistricting. Affinity propagation is used to aggregate electoral districts, thus deriving spatial units comparable over electoral waves. The methodology is based on travel-to-work flows and socio-economic aggregated data. Comparability of clusters enables analyses of electoral trends. A map of electoral turnout variation in Italian parliamentary elections held in 2018 and 2022 illustrates the proposed approach.