Multiple Maps Research Articles

Cross-talk between oxidative stress and lipid metabolism regulators reveals molecular clusters and immunological characterization in polycystic ovarian syndrome

BackgroundChanges in the oxidative stress and lipid metabolism (OSLM) pathways play important roles in polycystic ovarian syndrome (PCOS) pathogenesis and development. Consequently, a systematic analysis of genes related to OSLM was conducted to identify molecular clusters and explore new biomarkers that are helpful for the diagnostic of PCOS.MethodsGene expression and clinical data from 22 PCOS women and 14 normal women were obtained from the GEO database (GSE34526, GSE95728, and GSE106724). Consensus clustering identified OSLM-related molecular clusters, and WGCNA revealed co-expression patterns. The immune microenvironment was quantitatively assessed utilizing the CIBERSORT algorithm. Multiple machine learning models and connectivity map analyses were subsequently applied to explore potential biomarkers for PCOS, and nomograms were employed to develop a predictive multigene model of PCOS. Finally, the OSLM status of PCOS and the hub genes expression profiles were preliminarily verified using TUNEL, qRT‒PCR, western blot, and IHC assays in a PCOS mouse model.Results19 differential expression genes (DEGs) related to OSLM were identified. Based on 19 DEGs that were strongly influenced by OSLM, PCOS patients were stratified into two distinct clusters, designated Cluster 1 and Cluster 2. Distinct differences in the immune cell proportions existed in normal and two PCOS clusters. The random forest showed the best results, with the least cross-entropy and the utmost AUC (cross-entropy: 0.111 AUC: 0.960). Among the 19 OSLM-related genes, CXCR1, ACP5, CEACAM3, S1PR4, and TCF7 were identified by a Bayesian network and had a good fit with PCOS disease risk by the nomogram (AUC: 0.990 CI: 0.968–1.000). TUNEL assays revealed more severe DNA damage within the ovarian granule cells of PCOS mice than in those of normal mice (P < 0.001). The RNA and protein expression levels of the five hub genes were significantly elevated in PCOS mice, which was consistent with the results of the bioinformatics analyses.ConclusionA novel predictive model was constructed for PCOS patients and five hub genes were identified as potential biomarkers to offer novel insights into clinical diagnostic strategies for PCOS.

Single- and multiple-trait quantitative trait locus analyses for seed oil and protein contents of soybean populations with advanced breeding line background

Soybean seed oil and protein contents are negatively correlated, posing challenges to enhance both traits simultaneously. Previous studies have identified numerous oil and protein QTLs via single-trait QTL analysis. Multiple-trait QTL methods were shown to be superior but have not been applied to seed oil and protein contents. Our study aimed to evaluate the effectiveness of single- and multiple-trait multiple interval mapping (ST-MIM and MT-MIM, respectively) for these traits using three recombinant inbred line populations from advanced breeding line crosses tested in four environments. Using original and simulated data, we found that MT-MIM did not outperform ST-MIM for our traits with high heritability (H2 > 0.84). Empirically, MT-MIM confirmed only five out of the seven QTLs detected by ST-MIM, indicating single-trait analysis was sufficient for these traits. All QTLs exerted opposite effects on oil and protein contents with varying protein-to-oil additive effect ratios (-0.4 to -4.8). We calculated the economic impact of the allelic variations via estimated processed values (EPV) using the National Oilseed Processors Association (NOPA) and High Yield + Quality (HY + Q) methods. Oil-increasing alleles had positive effects on both EPVNOPA and EPVHY+Q when the protein-to-oil ratio was low (-0.4 to -0.7). However, when the ratio was high (-4.1 to -4.8), oil-increasing alleles increased EPVNOPA and decreased EPVHY+Q, which penalizes low protein meal. In conclusion, single-trait QTL analysis is adequately effective for high heritability traits like seed oil and protein contents. Additionally, the populations’ elite pedigrees and varying protein-to-oil ratios provide potential lines for further yield assessment and direct integration into breeding programs.

The system of mixed type additive-quadratic equations and approximations

In this article, we study the structure of a multiple variable mapping. Indeed, we reduce the system of several mixed additive-quadratic equations defining a multivariable mapping to obtain a single functional equation, say, the multimixed additive-quadratic equation. We also show that such mappings under some conditions can be multi-additive, multi-quadratic and multi-additive-quadratic. Moreover, we establish the Hyers–Ulam stability of the multimixed additive-quadratic equation, using the so-called direct (Hyers) method. Additionally, we present a concrete example (the numerical approximation) regarding the stability of some two variable mappings into real numbers. Applying some characterization results, we indicate two examples for the case that a multimixed additive-quadratic mapping (in the special cases) cannot be stable.

Harris Hawk optimization driven adaptive image encryption integrating Hilbert vibrational decomposition and chaos

Image encryption is a technique used to protect the confidentiality of digital images. Existing encryption algorithms use chaotic maps for generating random sequences. These sequences purely depend on the fixed initial parameters, which are not adaptive to different input images, thereby limiting their ability to produce the best-possible encrypted image. This paper introduces a new Harris Hawk optimization (HHO) based adaptive image encryption algorithm that integrates Hilbert vibrational decomposition (HVD), and multiple chaotic maps. The new fitness function is specifically designed to optimize the security and robustness of the encrypted image, considering statistical and differential attack parameters (NPCR, UACI, Entropy, and correlation coefficient values). HHO is used to optimize the initial parameters of chaotic maps (Henon map, Duffing map, Lorenz equation) subject to variations in the input image. In the confusion stage, the input image is decomposed into four components of same dimension but decreasing energy using HVD. Subsequently, the block based pixel scrambling is performed on alternate components using the Henon map and Duffing map. In the diffusion stage, the image from the confusion stage undergoes a first level diffusion by bitwise XOR operation with a random image generated from the Lorenz equation. Further, second level of diffusion is achieved with a complex operation generated image from the input image, enhancing its adaptability. Finally, Arnold Cat map having number of iterations determined by the input image is used for final pixel permutation to produces the encrypted image. The optimized initial parameters and input image dependent confusion and diffusion operations enable the encryption system to adapt to changes in the input image, thereby enhancing its resistance against known plain text and known cipher text attacks. Moreover, the integration of HVD with multiple chaotic maps makes the scheme more complex to decrypt with plain text attacks. The performance of proposed algorithm is evaluated on five standard test images in terms of security and quality measures. Experimental results exhibit statistical and differential attack parameters close to ideal values (with Max. Entropy≈7.9996, Min. CC≈ 10−5, Max. NPCR≈99.64, Max. UACI≈33.62). The use of multiple chaotic maps at different stages makes the key space larger. Robustness analysis demonstrates the algorithm's capability in withstanding noise and image enhancement attacks. The comparison with state of the arts metaheuristic based and classical encryption algorithms highlights the superior performance of proposed HHO driven algorithm. The significant improvement in security parameters and overall fitness compared to fixed initial parameters is achieved by optimizing the encryption process.

Start-Ups and Entrepreneurship in Renewable Energy: Investments and Risks

Advances in renewable energy technologies, particularly in solar, wind, energy storage, and grid integration solutions, are accelerating the growth of in startups in this sector. Despite the promising outlook, renewable energy startups face several risks, including technological, regulatory, financial and market risks. The article carries out a bibliometric analysis of scientific publications related to the problem of investment and risks associated with the development of entrepreneurship and start-ups in the field of renewable energy. After multi-level filtering of the dataset, the research base consists of 82 publications by 232 scientists for 2005-2023, indexed by Scopus, and the analysis tools used are Biblioshiny and Excel. In 2005-2016, the number of publications grew annually at a rate of 10.47%, with peaks in 2006, 2007, and 2016; in 2017-2023, the number of papers increased (the dependence is described by a third-degree polynomial trend). The article examines 3 different types of Three-field plots in the topic of the investments and risks of the startups and entrepreneurship in renewable energy: 1) “References – Authors – Keywords”, 2) “Countries – Institutions – Authors”, 3) “Authors – Sources – Keywords”. A quantitative and qualitative analysis of the scientific journals that make the largest / periodic but noteworthy / minimal contribution to the dissemination of knowledge in this area was carried out, the top 10 authors and papers were analysed by the levels of local and global influence, the most powerful research networks (based on the results of the analysis of joint citations) were identified both in terms of authors and countries, and the leading countries were identified in terms of the volume of research output of their scientists and the intensity of citation of these works. Building clouds of the most used keywords allowed us to identify priority thematic areas of research, as well as to analyse the structural and logical relationships between different thematic blocks in the research landscape. With the help of multiple correspondence analysis (factorial analysis) and longitudinal thematic map analysis, the scientific landscape in this area is clustered and evolutionary changes and interdependence of individual topics are identified.

A lightweight ground crack rapid detection method based on semantic enhancement

To address the issue of detecting complex-shaped cracks that rely on manual, which may result in high costs and low efficiency, this paper proposed a lightweight ground crack rapid detection method based on semantic enhancement. Firstly, the introduction of the Context Guided Block module enhanced the YOLOv8 backbone network, improving its feature extraction capability. Next, the incorporation of GSConv and VoV-GSCSP was introduced to construct a lightweight yet efficient neck network, facilitating the effective fusion of information from multiple feature maps. Finally, the detection head achieved more precise target localization by optimizing the probability around the labels. The proposed method was validated through experiments on the public dataset RDD-2022. The experimental results demonstrate that our method effectively detects cracks. Compared to YOLOv8, the model parameters have been reduced by 73.5 %, while accuracy, F1 score, and FPS have improved by 6.6 %, 4.3 %, and 116, respectively. Therefore, our proposed method is more lightweight and holds significant application value.

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.

Multilevel Visual Analysis of Aggregate Geo-Networks.

Numerous patterns found in urban phenomena, such as air pollution and human mobility, can be characterized as many directed geospatial networks (geo-networks) that represent spreading processes in urban space. These geo-networks can be analyzed from multiple levels, ranging from the macro-level of summarizing all geo-networks, meso-level of comparing or summarizing parts of geo-networks, and micro-level of inspecting individual geo-networks. Most of the existing visualizations cannot support multilevel analysis well. These techniques work by: 1) showing geo-networks separately with multiple maps leads to heavy context switching costs between different maps; 2) summarizing all geo-networks into a single network can lead to the loss of individual information; 3) drawing all geo-networks onto one map might suffer from the visual scalability issue in distinguishing individual geo-networks. In this study, we propose GeoNetverse, a novel visualization technique for analyzing aggregate geo-networks from multiple levels. Inspired by metro maps, GeoNetverse balances the overview and details of the geo-networks by placing the edges shared between geo-networks in a stacked manner. To enhance the visual scalability, GeoNetverse incorporates a level-of-detail rendering, a progressive crossing minimization, and a coloring technique. A set of evaluations was conducted to evaluate GeoNetverse from multiple perspectives.

Raman spectroscopic stress mapping of single high modulus carbon fibre composite fragmentation in compression

Fragmentation of high modulus carbon fibres is relevant to the failure mechanisms of advanced polymer matrix composites in compression. In situ spatially-resolved Raman spectroscopy during the fragmentation of model single fibre composites is used to map local stress distributions during failure events. The characteristic graphitic band (the G band) located around 1580 cm−1 is associated with the in-plane carbon-carbon bonds; this band shifts its position, and can be calibrated, with the local axial stress in the fibre. The analysis maps the evolution of local stresses with increasing overall composite compression strain, identifying a series of critical events, including fibre fracture, interfacial debonding, and the formation of inter-fragment ‘wedges’. Fitting shear lag models provides interfacial shear strength values. Multiple failure maps of two examples of high modulus PAN carbon fibres (M46J and M55J) demonstrate the possibility of local fragment bending due to fragment end contact. A timeline of potential fragmentation events is proposed for carbon fibres undergoing compression.

Interpreting convolutional neural network by joint evaluation of multiple feature maps and an improved NSGA-II algorithm

The ’black box’ characteristics of Convolutional Neural Networks (CNNs) present significant risks to their application scenarios, such as reliability, security, and division of responsibilities. Addressing the interpretability of CNN emerges as an urgent and critical issue in the field of machine learning. Recent research on CNN interpretability has either yielded unstable or inconsistent interpretations, or produced coarse-scale interpretable heatmaps, limiting their applicability in various scenarios. In this work, we propose a novel method of CNNs interpretation by incorporating a joint evaluation of multiple feature maps and employing multi-objective optimization (JE&MOO-CAM). Firstly, a method of joint evaluation for all feature maps is proposed to preserve the complete object instances and improve the overall activation values. Secondly, an interpretation method of CNNs under the MOO framework is proposed to avoid the instability and inconsistency of interpretation. Finally, the operators of selection, crossover, and mutation, along with the method of population initialization in NSGA-II, are redesigned to properly express the characteristics of CNNs. The experimental results, including both qualitative and quantitative assessments along with a sanity check conducted on three classic CNN models—VGG16, AlexNet, and ResNet50—demonstrate the superior performance of the proposed JE&MOO-CAM model. This model not only accurately pinpoints the instances within the image requiring explanation but also preserves the integrity of these instances to the greatest extent possible. These capabilities signify that JE&MOO-CAM surpasses six other leading state-of-the-art methods across four established evaluation criteria.

A Secure Transmission of Digital Images using Multiple Chaotic Maps and Elliptic Curve

A Secure Transmission of Digital Images using Multiple Chaotic Maps and Elliptic Curve

CancerHallmarks.com: A tool for the enrichment analysis of the cancer hallmarks.

e15079 Background: The "Hallmarks of Cancer" concept is a useful framework for understanding the fundamental principles that are shared among different types of cancers. However, the lack of agreement on a specific set of genes for these hallmarks makes it difficult to compare and integrate data, leading to varying interpretations of the biological processes involved in cancer across different studies. In this study, we created a consensus set of cancer hallmark genes by combining data from multiple mapping resources and the scientific literature. Methods: We identified 6,763 candidate cancer hallmark genes associated with ten cancer hallmarks by consolidating information from seven studies. There was a considerable variation in the number of genes within the available resources, ranging between 91 and 7,779. We observed a sizable variation in the number of genes connected to each hallmark. The number of genes across cancer hallmarks ranges between 574 („enabling replicative immortality”) and 3,574 ("sustaining proliferative signaling"). The least number of genes are associated with the more recent hallmarks, published in 2011, and the hallmarks „enabling replicative immortality” and „sustaining angiogenesis”. We identified six genes linked to each of the ten cancer hallmarks: TP53, AKT1, MAP2K1, CTNNB1, HRAS, and PIK3CB. Results: As a practical utility of the hallmark concept, we analyzed the enrichment of cancer hallmarks by evaluating genes associated with overall survival. Only genes with a false discovery rate below 0.01, a hazard rate over 1, and a cutoff value over 100 were analyzed to ensure sufficient robustness of the gene sets. The hallmark "genome instability" was most prominent in liver (p=1.0E-05), lung (p=1.0E-05), and pancreatic cancers (p=0.00164). Renal cancer had higher “invasion and metastasis” (p=0.002). Both pancreatic cancer and lung adenocarcinomas were enriched for “resisting cell death” (p=0.00096 and p=0.037, respectively). Overall, while eight cancer types had no significant hallmark enrichment among druggable genes, pancreatic cancers had the most enriched hallmarks (5 out of 10), emphasizing the disease's complexity. These results highlight the usefulness of the hallmark concept as a valuable tool for organizing information, especially in establishing a direct link between genes and biological functions. Conclusions: We have created a unified list of genes linked to cancer hallmarks. We integrated the data into an easily accessible online tool (accessible at www.cancerhallmarks.com) that enables users to identify the most pertinent cancer-associated “candidate hallmark genes” in their own datasets.

Effects of Partial Premixing and Coflow Temperature on Flame Stabilization of Lifted Jet Flames of Dimethyl Ether in a Vitiated Coflow Based on Stochastic Multiple Mapping Conditioning Approach

The Reynolds-averaged Navier–Stokes (RANS)-based stochastic multiple mapping conditioning (MMC) approach has been used to study partially premixed jet flames of dimethyl ether (DME) introduced into a vitiated coflowing oxidizer stream. This study investigates DME flames with varying degrees of partial premixing within a fuel jet across different coflow temperatures, delving into the underlying flame structure and stabilization mechanisms. Employing a turbulence k-ε model with a customized set of constants, the MMC technique utilizes a mixture fraction as the primary scalar, mapped to the reference variable. Solving a set of ordinary differential equations for the evolution of Lagrangian stochastic particles’ position and composition, the molecular mixing of these particles is executed using the modified Curl’s model. The lift-off height (LOH) derived from RANS-MMC simulations are juxtaposed with experimental data for different degrees of partial premixing of fuel jets and various coflow temperatures. The RANS-MMC methodology adeptly captures LOH for pure DME jets but exhibits an underestimation of flame LOH for partially premixed jet scenarios. Notably, as the degree of premixing escalates, a conspicuous underprediction in LOH becomes apparent. Conditional scatter and contour plots of OH and CH2O unveil that the propagation of partially premixed flames emerges as the dominant mechanism at high coflow temperatures, while autoignition governs flame stabilization at lower coflow temperatures in partially premixed flames. Additionally, for pure DME flames, autoignition remains the primary flame stabilization mechanism across all coflow temperature conditions. The study underscores the importance of considering the degree of premixing in partially premixed jet flames, as it significantly impacts flame stabilization mechanisms and LOH, thereby providing crucial insights into combustion dynamics for various practical applications.

Parametric Optimization of Proton Exchange Membrane Fuel Cell Using Chaotic Swarm Intelligence Technique

AbstractThe proton exchange membrane fuel cell (PEMFC) converts chemical energy into electricity without pollution or noise. PEMFC has a stable electrolyte, reliable, fast start‐up, and lightweight portability. Optimal PEMFC parameter prediction is difficult. Due to inaccurate estimation, many meta‐heuristic models are inaccurate. Particle swarm, Ant colony, and grey wolf algorithms are tested for fuel cell parameter estimation. These traditional algorithms benefit from chaotic maps for exploitation and exploration. Analyzing V–I and P–I polarization curves, hybrid approaches estimate unknown parameters. Results are compared with others to support the identifying strategy. This project examined SR‐12 and NedSstack PS‐6 PEMFC stacks. The objective function was shown by the sum of square errors between the experiments, and once an appropriate map is identified, the single chaotic map inclusion method can perform better than the multiple chaotic map scheme, and the “Iterative” approach in chaotic swarm is the most promising for multidimensional challenges.

The Birth of a New BIM Standard: From PED 2018 to 2023, New Parameters and Workflows "Going Live" for Everyone

Building Information Modelling (BIM) has become the de facto standard for the digital representation of buildings. However, from the pedestrian dynamics perspective, BIM Industry Foundation Classes (IFC) schema specification do not fully support data properties required for two-way data sharing with pedestrian modelling tools. An international team of academic and industry researchers, supported by buildingSMART International (bSI), is developing an Occupant Movement Analysis (OMA) standard. The project is focused on expanding the IFC schema specification to support workflows for pedestrian simulation tools and is close to completion. So far, multiple process maps and a list of data properties synchronised with several representative pedestrian modelling tools have been produced. This list of data properties was then converted into bSI's recommended flexible and machine interpretable Information Delivery Specification (IDS) format for specifying data exchange requirements and to add clarity. Currently, this is undergoing testing and review by the project team. Once completed, it will be submitted to bSI’s committees for review. Also, to support this work, a prototype open-source Add-in has been developed to demonstrate a two-way integrated data sharing between BIM authoring tools and pedestrian simulation tools. This standard will enhance data sharing between BIM authoring and pedestrian modelling tools by facilitating the capturing of the required data and addressing friction in multiple design iterations and reassessment.

Mapping Bovine Tuberculosis in Colombia, 2001-2019.

Bovine tuberculosis is a zoonotic disease of significant impact, particularly in countries where a pastoral economy is predominant. Despite its importance, few studies have analysed the disease's behaviour in Colombia, and none have developed maps using geographic information systems (GIS) to characterise it; as such, we developed this study to describe the temporal-spatial distribution of bovine tuberculosis in Colombia over a period of 19 years. A retrospective cross-sectional descriptive study, based on reports by the Colombian Agricultural Institute (ICA), surveillance of tuberculosis on cattle farms in Colombia from 2001 to 2019 was carried out. The data were converted into databases using Microsoft Access 365®, and multiple epidemiological maps were generated with the QGIS® version 3.36 software coupled to shape files of all the country's departments. During the study period, 5273 bovine tuberculosis cases were identified in multiple different departments of Colombia (with a mean of 278 cases/year). Regarding its temporal distribution, the number of cases varied from a maximum of 903 cases (17.12% of the total) in 2015 to a minimum of 0 between 2001 and 2004 and between 2017 and 2019 (between 2005 and 2016, the minimum was 46 cases, 0.87%). GIS are essential for understanding the temporospatial behaviour of zoonotic diseases in Colombia, as is the case for bovine tuberculosis, with its potential implications for the Human and One Health approaches.

Deep magnetic resonance fingerprinting based on Local and Global Vision Transformer

To mitigate systematic errors in magnetic resonance fingerprinting (MRF), the precomputed dictionary is usually computed with minimal granularity across the entire range of tissue parameters. However, the dictionary grows exponentially with the number of parameters increase, posing significant challenges to the computational efficiency and matching accuracy of pattern-matching algorithms. Existing works, primarily based on convolutional neural networks (CNN), focus solely on local information to reconstruct multiple parameter maps, lacking in-depth investigations on the MRF mechanism. These methods may not exploit long-distance redundancies and the contextual information within voxel fingerprints introduced by the Bloch equation dynamics, leading to limited reconstruction speed and accuracy. To overcome these limitations, we propose a novel end-to-end neural network called the Local and Global Vision Transformer (LG-ViT) for MRF parameter reconstruction. Our proposed LG-ViT employs a multi-stage architecture that effectively reduces the computational overhead associated with the high-dimensional MRF data and the transformer model. Specifically, a local Transformer encoder is proposed to capture contextual information embedded within voxel fingerprints and local correlations introduced by the interconnected human tissues. Additionally, a global Transformer encoder is proposed to leverage long-distance dependencies arising from shared characteristics among different tissues across various spatial regions. By incorporating MRF physics-based data priors and effectively capturing local and global correlations, our proposed LG-ViT can achieve fast and accurate MRF parameter reconstruction. Experiments on both simulation and in vivo data demonstrate that the proposed method enables faster and more accurate MRF parameter reconstruction compared to state-of-the-art deep learning-based methods.

Cross Domain Early Crop Mapping with Label Spaces Discrepancies using MultiCropGAN

Abstract. Mapping target crops before the harvest season for regions lacking crop-specific ground truth is critical for global food security. Utilizing multispectral remote sensing and domain adaptation methods, prior studies strive to produce precise crop maps in these regions (target domain) with the help of the crop-specific labelled remote sensing data from the source regions (source domain). However, existing approaches assume identical label spaces across those domains, a challenge often unmet in reality, necessitating a more adaptable solution. This paper introduces the Multiple Crop Mapping Generative Adversarial Neural Network (MultiCropGAN) model, comprising a generator, discriminator, and classifier. The generator transforms target domain data into the source domain, employing identity losses to retain the characteristics of the target data. The discriminator aims to distinguish them and shares the structure and weights with the classifier, which locates crops in the target domain using the generator’s output. This model’s novel capability lies in locating target crops within the target domain, overcoming differences in crop type label spaces between the target and source domains. In experiments, MultiCropGAN is benchmarked against various baseline methods. Notably, when facing differing label spaces, MultiCropGAN significantly outperforms other baseline methods. The Overall Accuracy is improved by about 10%.

Multiomic analysis of uterine leiomyomas in self-described Black and White women: molecular insights into health disparities

Black women are at an increased risk of developing uterine leiomyomas and experiencing worse disease prognosis than White women. Epidemiologic and molecular factors have been identified as underlying these disparities, but there remains a paucity of deep, multiomic analysis investigating molecular differences in uterine leiomyomas from Black and White patients. To identify molecular alterations within uterine leiomyoma tissues correlating with patient race by multiomic analyses of uterine leiomyomas collected from cohorts of Black and White women. We performed multiomic analysis of uterine leiomyomas from Black (42) and White (47) women undergoing hysterectomy for symptomatic uterine leiomyomata. In addition, our analysis included the application of orthogonal methods to evaluate fibroid biomechanical properties, such as second harmonic generation microscopy, uniaxial compression testing, and shear-wave ultrasonography analyses. We found a greater proportion of MED12 mutant uterine leiomyomas from Black women (>35% increase; Mann-Whitney U, P<.001). MED12 mutant tumors exhibited an elevated abundance of extracellular matrix proteins, including several collagen isoforms, involved in the regulation of the core matrisome. Histologic analysis of tissue fibrosis using trichrome staining and secondary harmonic generation microscopy confirmed that MED12 mutant tumors are more fibrotic than MED12 wild-type tumors. Using shear-wave ultrasonography in a prospectively collected cohort, Black patients had fibroids that were firmer than White patients, even when similar in size. In addition, these analyses uncovered ancestry-linked expression quantitative trait loci with altered allele frequencies in African and European populations correlating with differential abundance of several proteins in uterine leiomyomas independently of MED12 mutation status, including tetratricopeptide repeat protein 38. Our study shows that Black women have a higher prevalence of uterine leiomyomas harboring mutations in MED12 and that this mutational status correlates with increased tissue fibrosis compared with wild-type uterine leiomyomas. Our study provides insights into molecular alterations correlating with racial disparities in uterine leiomyomas and improves our understanding of the molecular etiology underlying uterine leiomyoma development within these populations.

Abstract PO3-23-01: Triple-tracer Technique for Sentinel Lymph Node Biopsy of Breast Cancer after neoadjuvant Chemotherapy using Blue-dye, Radioisotope combined with Real-time Indocyanine green(ICG) Fluorescence Imaging Procrdures

Abstract Background: For post-neoadjuvant chemotherapy (NAC) patients (pts) with BC, sentinel lymph node biopsy (SNB) was recommended using the dual-tracer mapping technique (radioisotope plus blue dye) or placing a biopsy clip into the positive node at diagnosis and identifying it at the time of surgery due to SN identification rates (IR) were lower and false negative rates (FNR) were greater for pts with local advanced breast cancer (BC) than those of pts with early stage BC in the absence of NAC. Our previous clinical trial has indicated that the real-time ICG fluorescence (RT-ICG) imaging technique could improve the diagnostic sensitivity and detection accuracy for SNB. Methods: Between April 2019 and May 2022, post-NAC SNB were successfully identified in 45 of 52 patients with stage ⅡA to ⅢB (T1-T3, N0-2, M0) who had histologically confirmed breast cancer selected to receive NAC and the standard surgeries were performed after finishing NAC. The SNs was detected by conventional procedures of blue-dye (Indigo carmine) plus 99mTc radioisotope (dual-tracer) and combined with concurrent RT-ICG technique. Clinical node positive (cN+) was diagnosed by the radiologists using axillary ultrasound, MRI and/or CT scan or assessed by fine needle aspiration cytology (FNAC). The positivity of each single SN by each single tracer (blue dye, ICG, or isotope alone) was counted and identified, respectively. All patients are required to undergo SNB followed by completion axillary lymph node dissection (CND). Then the IR and FNR of each single tracer and their summation (triple tracer) were calculated by comparing the results of the SNB and the histopathology of the resection specimens of CND. Results: Among 45 post-NAC pts, the IR and FNR of each single procedure for SNB was 45.7% and 55.6% when used Indigo Carmine blue, 70.3% and 11.1% when used RI, 82.6% and 0 when used ICG fluorescence, respectively. In contrast, the total calculation of triple tracer showed that IR reached to 100% and FNR was 0, respectively. The IR of triple tracer were 83.9% and 100% for ypN0 and ypN(+) pts after NAC, both FNR were 0%. Conclusion: Our results suggested that the triple tracer technique combining blue dye, ICG, and isotope is effective method for detection of SNs in post-neoadjuvant cN+ BC pts. The IR and FNR of SNB might be improved by this multiple tracer mapping technique, particularly for pts with ypN(+) after NAC. It is considered that the multi-tracer can complement each other for what was not able to be traced and detected by the single tracer with one mapping material, and that result in totally the improvement of identification rate of SNB. Citation Format: Hidetaka Watanabe, Yuko Takao, Yuriko Katagiri, Rie Sugihara, Uhi Toh. Triple-tracer Technique for Sentinel Lymph Node Biopsy of Breast Cancer after neoadjuvant Chemotherapy using Blue-dye, Radioisotope combined with Real-time Indocyanine green(ICG) Fluorescence Imaging Procrdures [abstract]. In: Proceedings of the 2023 San Antonio Breast Cancer Symposium; 2023 Dec 5-9; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2024;84(9 Suppl):Abstract nr PO3-23-01.