Nearest-neighbor Method Research Articles

Abstract PO2-09-06: Interaction of racial disparities on outcomes and toxicities associated with treatment of HER2+Breast Cancer- a TrinetX Database study

Abstract Objective: Investigate the interaction of racial disparities on outcomes and toxicities associated with treatment of HER2+BrCa (Breast Cancer)- a TrinetX Database study Background: HER2+BrCa is an aggressive subtype accounting for about 1/3rd of all BrCa, for which Trastuzumab based therapy remains the mainstay of treatment. While we are starting to understand the breadth of racial disparities in BrCa , the knowledge about outcomes and toxicities in relation to treatment of HER2+ BrCa is still limited. Design/Methods: In this propensity score-matched cohort study we used the TriNetX Research Network, a multi-health care organization de-identified electronic health record (EHR) database, to compare the 1-, 3-, and 5-year mortality of HER2+BrCa non-Hispanic African American (NHAA) women to a corresponding non-Hispanic White cohort (NHW). Qualification into the two race based HER2+BrCa cohorts required the presence of a C50 ICD-10-CM diagnosis code and at least one proxy for HER2 positivity such as Trastuzumab administration (index event). Cohorts were matched for age, BMI, comorbidities (HTN, DM ), and lab values using 1:1 matching with a greedy nearest neighbor search. Toxicity outcomes of interest such as cardiomyopathy, neuropathy, diarrhea, rashes, leg swelling/edema, and fatigue as well as the frequency of emergency room visits were also compared between the cohorts. The associations of observed outcome frequencies in the two cohorts were tested for significance using the chi-square test. The odds ratios and p-values, corrected for multiple hypothesis testing using the Benjamini-Hochberg procedure, are reported as an effect size and significance estimation. Results: 15983 patients met the inclusion criteria for HER2+BrCa (13611-NHW and 2372-NHAA). The mean age at the index event was 55.6 ± 12.6 years for NHAA and 57.2 ± 13.3 years for NHW. After matching the two sub-cohorts, 2303 patients remained in each. NHAA women were found to have significantly increased odds of neuropathy and cardiomyopathy for 1, 3, and 5-year time intervals after the HER2+BrCa index date, as compared to the reference NHW women (abbreviated results given in Table 1). In addition, the odds of an emergency room visit (for any reason) was found to be increased by as much as 79% in NHAA women as compared to the NHW women. The odds of experiencing fatigue/malaise, diarrhea/nausea, and rash, were reduced for NHAA women across all time intervals investigated. Odds ratios for mortality and leg swelling did not meet the significance threshold (p-value < 0.05). Conclusion: This study, which examined racial disparities in outcomes and toxicities associated with HER2+BrCa, revealed significant findings. NHAA women with HER2+BrCa treated with Trastuzumab based therapy demonstrated increased odds of experiencing neuropathy and cardiomyopathy compared to their NHW counterparts. Additionally, NHAA women had a higher likelihood of emergency room visits for any reason. Conversely, NHAA women exhibited decreased odds of fatigue/malaise, diarrhea/nausea, and rash. These findings underscore the importance of considering the impact of race on HER2+BrCa outcomes and treatment toxicities and suggest the need for further research and targeted interventions to address disparities and thus enhance treatment outcomes in diverse populations. Table 1: Representative significant Odds ratios and corrected p-values for outcomes comparing the NHAA and NHW cohorts. Citation Format: Monali Vasekar, Justin Petucci, Avnish Katoch, Vasant Honavar. Interaction of racial disparities on outcomes and toxicities associated with treatment of HER2+Breast Cancer- a TrinetX Database study [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 PO2-09-06.

From Text to Recommendations: How Vector Databases are Revolutionizing Personalized Content Delivery

Abstract: Effective similarity search and retrieval are now possible thanks to vector databases, which have become a potent tool for organizing and searching high-dimensional data. This article provides a thorough examination of vector databases, their underlying theories, and their applications across a range of industries. In handling complex data types, we address the significance of vector representation and emphasize the benefits of vector databases over traditional databases [1]. The article explores the process of creating a vector database, highlighting the critical function of indexing strategies such as IVF (Inverted File Indexing) and HNSW (Hierarchical Navigable Small World) in guaranteeing the effectiveness and precision of searches [2]. In addition, we discuss the problems caused by the curse of dimensionality and offer solutions to lessen its effects on nearest neighbor searches [3]

Endoscopic Cryoablation Versus Radical Nephroureterectomy for Upper Tract Urothelial Carcinoma

Background and objectiveCryoablation is a traditional antitumor therapy with good prospects for development. The efficacy of endoscopic management as a kidney-sparing surgery for high-risk upper tract urothelial carcinoma (UTUC) remains controversial. Our aim was to evaluate the impact of endoscopic cryoablation (ECA) versus radical nephroureterectomy (RNU) on survival outcomes, renal function, and complications. MethodsWe retrospectively analyzed data for 116 patients with newly diagnosed high-risk UTUC who underwent either ECA (n = 13) or RNU (n = 103) from March 25, 2019 to December 8, 2021. Propensity score matching (1:4) using the nearest neighbor method was performed before analysis. The primary outcome was overall survival (OS). Secondary outcomes included progression-free survival (PFS), intravesical recurrence-free survival (RFS), the change in renal function, and treatment-emergent adverse events (TEAEs). Key findings and limitationsAt median follow-up of 28.2 mo for the ECA group and 27.6 mo for the RNU group, 2-yr OS (82% vs 84%), PFS (73% vs 71%), and intravesical RFS (81% vs 83%) rates after matching did not significantly differ. A decline in renal function was observed after RNU, but not after ECA. Five (41.7%) patients in the ECA group reported six TEAEs, and 17 patients (35.4%) in the RNU group reported 20 TEAEs. Conclusions and clinical implicationsIn comparison to RNU, ECA for UTUC resulted in noninferior oncological outcomes and superior preservation of renal function. Patient summaryOur study suggests that a treatment called endoscopic cryoablation for high-risk cancer in the upper urinary tract can help in preserving kidney function, with similar survival outcomes to those after more extensive surgery. This option can be considered for selected patients with a strong preference for kidney preservation.

Prediction of Myocardial Infarction Using a Combined Generative Adversarial Network Model and Feature-Enhanced Loss Function

Accurate risk prediction for myocardial infarction (MI) is crucial for preventive strategies, given its significant impact on global mortality and morbidity. Here, we propose a novel deep-learning approach to enhance the prediction of incident MI cases by incorporating metabolomics alongside clinical risk factors. We utilized data from the KORA cohort, including the baseline S4 and follow-up F4 studies, consisting of 1454 participants without prior history of MI. The dataset comprised 19 clinical variables and 363 metabolites. Due to the imbalanced nature of the dataset (78 observed MI cases and 1376 non-MI individuals), we employed a generative adversarial network (GAN) model to generate new incident cases, augmenting the dataset and improving feature representation. To predict MI, we further utilized multi-layer perceptron (MLP) models in conjunction with the synthetic minority oversampling technique (SMOTE) and edited nearest neighbor (ENN) methods to address overfitting and underfitting issues, particularly when dealing with imbalanced datasets. To enhance prediction accuracy, we propose a novel GAN for feature-enhanced (GFE) loss function. The GFE loss function resulted in an approximate 2% improvement in prediction accuracy, yielding a final accuracy of 70%. Furthermore, we evaluated the contribution of each clinical variable and metabolite to the predictive model and identified the 10 most significant variables, including glucose tolerance, sex, and physical activity. This is the first study to construct a deep-learning approach for producing 7-year MI predictions using the newly proposed loss function. Our findings demonstrate the promising potential of our technique in identifying novel biomarkers for MI prediction.

Intercomparisons of Tracker v1.1 and four other ocean particle-tracking software packages in the Regional Ocean Modeling System

Abstract. Particle tracking is widely utilized to study transport features in a range of physical, chemical, and biological processes in oceanography. In this study, a new offline particle-tracking package, Tracker v1.1, is introduced, and its performance is evaluated in comparison to an online Eulerian dye, one online particle-tracking software package, and three offline particle-tracking software packages in a small, high-resolution model domain and a large coarser model domain. It was found that both particle and dye approaches give similar results across different model resolutions and domains when they were tracking the same water mass, as indicated by similar mean advection pathways and spatial distributions of dye and particles. The flexibility of offline particle tracking and its similarity against online dye and online particle tracking make it a useful tool to complement existing ocean circulation models. The new Tracker was shown to be a reliable particle-tracking package to complement the Regional Ocean Modeling System (ROMS) with the advantages of platform independence and speed improvements, especially in large model domains achieved by the nearest-neighbor search algorithm. Lastly, trade-offs of computational efficiency, modifiability, and ease of use that can influence the choice of which package to use are explored. The main value of the present study is that the different particle and dye tracking codes were all run on the same model output or within the model that generated the output. This allows some measure of intercomparison between the different tracking schemes, and we conclude that all choices that make each tracking package unique do not necessarily lead to very different results.

Bridging the Domain Gap in Scene Flow Estimation via Hierarchical Smoothness Refinement

This paper introduces SmoothFlowNet3D, an innovative encoder-decoder architecture specifically designed for bridging the domain gap in scene flow estimation. To achieve this goal, SmoothFlowNet3D divides the scene flow estimation task into two stages: initial scene flow estimation and smoothness refinement. Specifically, SmoothFlowNet3D comprises a hierarchical encoder that extracts multi-scale point cloud features from two consecutive frames, along with a hierarchical decoder responsible for predicting the initial scene flow and further refining it to achieve smoother estimation. To generate the initial scene flow, a cross-frame nearest neighbor search operation is performed between the features extracted from two consecutive frames, resulting in forward and backward flow embeddings. These embeddings are then combined to form the bidirectional flow embedding, serving as input for predicting the initial scene flow. Additionally, a flow smoothing module based on the self-attention mechanism is proposed to predict the smoothing error and facilitate the refinement of the initial scene flow for more accurate and smoother estimation results. Extensive experiments demonstrate that the proposed SmoothFlowNet3D approach achieves state-of-the-art performance on both synthetic datasets and real LiDAR point clouds, confirming its effectiveness in enhancing scene flow smoothness.

Correcting the Location Error of Persistent Scatterers in an Urban Area Based on Adaptive Building Contours Matching: A Case Study of Changsha

Persistent Scatterer InSAR (PS-InSAR) technology enables the monitoring of displacement in millimeters. However, without the use of external parameter correction, radar scatterers exhibit poor geopositioning precision in meters, limiting the correlation between observed deformation and the actual structure. The integration of PS-InSAR datasets and building databases is often overlooked in deformation research. This paper presents a novel strategy for matching between PS points and building contours based on spatial distribution characteristics. A convex hull is employed to simplify the building outline. Considering the influence of building height and incident angle on geometric distortion, an adaptive buffer zone is established. The PS points on a building are further identified through the nearest neighbor method. In this study, both ascending and descending TerraSAR-X orbit datasets acquired between 2016 and 2019 were utilized for PS-InSAR monitoring. The efficacy of the proposed method was evaluated by comparing the PS-InSAR results obtained from different orbits. Through a process of comparison and verification, it was demonstrated that the matching effect between PS points and building contours was significantly enhanced, resulting in an increase of 29.2% in the number of matching PS points. The results indicate that this novel strategy can be employed to associate PS points with building outlines without the need for complex calculations, thereby providing a robust foundation for subsequent building risk assessment.

Automatic inference of ICD-10 codes from German ophthalmologic physicians’ letters using natural language processing

Physicians’ letters are the optimal source of diagnoses for registries. However, most registries demand for diagnosis codes such as ICD-10. We herein describe an algorithm that infers ICD-10 codes from German ophthalmologic physicians’ letters. We assess the method in three German eye hospitals. Our algorithm is based on the nearest-neighbor method as well as on a large thesaurus for ICD-10 codes. This thesaurus was embedded into a Word2Vec space created from anonymized physicians’ reports of the first hospital. For evaluation, each of the three hospitals sent all diagnoses taken from 100 letters. The inferred ICD-10 codes were evaluated for correctness by the senders. A total of 3332 natural language terms had been sent in (812 hospital one, 1473 hospital two, 1047 hospital three). A total of 526 non-diagnoses were excluded upfront. 2806 ICD-10 codes were inferred (771 hospital one, 1226 hospital two, 809 hospital three). In the first hospital, 98% were fully correct and 99% correct at the level of the superordinate disease concept. The percentages in hospital two were 69% and 86%. The respective numbers for hospital three were 69% and 91%. Our simple method is capable of inferring ICD-10 codes for German natural language diagnoses, especially when the embedding space has been built with physicians’ letters from the same hospital. The method may yield sufficient accuracy for many tasks in the multi-centric setting and can easily be adapted to other languages/specialities.

Rank-based Hashing for Effective and Efficient Nearest Neighbor Search for Image Retrieval

The large and growing amount of digital data creates a pressing need for approaches capable of indexing and retrieving multimedia content. A traditional and fundamental challenge consists of effectively and efficiently performing nearest-neighbor searches. After decades of research, several different methods are available, including trees, hashing, and graph-based approaches. Most of the current methods exploit learning to hash approaches based on deep learning. In spite of effective results and compact codes obtained, such methods often require a significant amount of labeled data for training. Unsupervised approaches also rely on expensive training procedures usually based on a huge amount of data. In this work, we propose an unsupervised data-independent approach for nearest neighbor searches, which can be used with different features, including deep features trained by transfer learning. The method uses a rank-based formulation and exploits a hashing approach for efficient ranked list computation at query time. A comprehensive experimental evaluation was conducted on 7 public datasets, considering deep features based on CNNs and Transformers. Both effectiveness and efficiency aspects were evaluated. The proposed approach achieves remarkable results in comparison to traditional and state-of-the-art methods. Hence, it is an attractive and innovative solution, especially when costly training procedures need to be avoided.

FLEX: A fast and light-weight learned index for kNN search in high-dimensional space

The k Nearest Neighbors (kNN) search in high-dimensional space is a fundamental problem with various applications. In this paper, we try to solve this problem by using deep neural networks (DNNs). We apply DNNs to represent complex correlations between high-dimensional objects, enabling us to project similar objects into the same class, thus reducing the search cost. Based on DNNs, we propose two novel techniques to improve query efficiency while keeping accuracy. First, traditional DNNs typically demand extensive training data for achieving high accuracy. To decrease the training size, we design a multi-module DNN framework comprising several small modules. Each module learns to capture part of knowledge for the given query. The collective output of these sub-modules is then seamlessly integrated to form the final result. Second, with machine learning models, the size of candidates are unbounded. Thus, we design a linear-time data layout refinement algorithm, aiming to limit the number of candidates to a small constant. Empirically we find that our approach significantly outperforms the state-of-the-art methods in terms of both time efficiency and space efficiency while still attaining comparable or better accuracy.

Optimizing Routes for Sustainability: A Comparative Analysis of Parcel Distribution Methods in South Jakarta

This study focuses on improving the efficiency of PT. XYZ’s logistics services by examining the optimization of parcel distribution routes in South Jakarta. It compares the Nearest Neighbor Method and the Clarke and Wright Savings Method to determine the optimal strategy for delivering 53 packages to 8 locations. The Nearest Neighbor Method generates two routes, one encompassing 32 packages across four locations and the other encompassing 21 packages across four locations. This method significantly reduces travel distance, saving 27,3 kilometers in comparison to actual courier routes. The Clarke and Wright Savings Method generates two routes, one covering 35 packages across six locations and the other covering 18 packages across two locations. Compared to the actual van courier route, this method reduces the distance by 24,8 kilometers. The Nearest Neighbor Method reduces travel distances by 27.3 kilometers compared to actual routes, surpassing the Clarke and Wright Savings Method by 2.5 kilometers. Reduced travel distances reduce fuel consumption and greenhouse gas emissions, making logistics operations more sustainable on an environmental and economic level. This study concludes that the Nearest Neighbor Method is the optimal strategy for improving parcel distribution efficiency in South Jakarta, thereby substantially reducing the travel distances of PT. XYZ’s logistics operations.

A nearest neighbor query method for searching objects with time and location informations based on spatiotemporal similarity

A nearest neighbor query method for searching objects with time and location informations based on spatiotemporal similarity

Combining Multiple Supervision for Robust Zero-Shot Dense Retrieval

Recently, dense retrieval (DR) models, which represent queries and documents with fixed-width vectors and retrieve relevant ones via nearest neighbor search, have drawn increasing attention from the IR community. However, previous studies have shown that the effectiveness of DR critically relies on sufficient training signals, which leads to severe performance degradation when applied in out-of-domain scenarios, where large-scale training data are usually unavailable. To solve this problem, existing studies adopt a data-augmentation-plus-joint-training paradigm to construct weak/pseudo supervisions on the target domain and combine them with the large-scale human annotated data on the source domain to train the DR models. However, they don't explicitly distinguish the data and the supervision signals in the training process and simply assume that the DR models are mighty enough to capture and memorize different domain knowledge and relevance matching patterns without guidance, which, as shown in this paper, is not true. Based on this observation, we propose a Robust Multi-Supervision Combining strategy (RMSC) that decouples the domain and supervision signals by explicitly telling the DR models how the domain data and supervision signals are combined in the training data with specially designed soft tokens. With the extra soft tokens to store the domain-specific and supervision-specific knowledge, RMSC allows the DR models to conduct retrieval based on human-like relevance matching patterns and target-specific language distribution on the target domain without human annotations. Extensive experiments on zero-shot DR benchmarks show that RMSC significantly improves the ranking performance on the target domain compared to strong DR baselines and domain adaptation methods, while being stable during training and can be combined with query generation or second-stage pre-training.

Neural Embeddings for kNN Search in Biological Sequence

Biological sequence nearest neighbor search plays a fundamental role in bioinformatics. To alleviate the pain of quadratic complexity for conventional distance computation, neural distance embeddings, which project sequences into geometric space, have been recognized as a promising paradigm. To maintain the distance order between sequences, these models all deploy triplet loss and use intuitive methods to select a subset of triplets for training from a vast selection space. However, we observed that such training often enables models to distinguish only a fraction of distance orders, leaving others unrecognized. Moreover, naively selecting more triplets for training under the state-of-the-art network not only adds costs but also hampers model performance. In this paper, we introduce Bio-kNN: a kNN search framework for biological sequences. It includes a systematic triplet selection method and a multi-head network, enhancing the discernment of all distance orders without increasing training expenses. Initially, we propose a clustering-based approach to partition all triplets into several clusters with similar properties, and then select triplets from these clusters using an innovative strategy. Meanwhile, we noticed that simultaneously training different types of triplets in the same network cannot achieve the expected performance, thus we propose a multi-head network to tackle this. Our network employs a convolutional neural network(CNN) to extract local features shared by all clusters, and then learns a multi-layer perception(MLP) head for each cluster separately. Besides, we treat CNN as a special head, thereby integrating crucial local features which are neglected in previous models into our model for similarity recognition. Extensive experiments show that our Bio-kNN significantly outperforms the state-of-the-art methods on two large-scale datasets without increasing the training cost.

Abstract 3429: Using metabolomics to identify biomarkers mediating the associations of adiposity in childhood and early adulthood with mammographic breast density in premenopausal women

Abstract Introduction: Mammographic breast density (MBD), a strong predictor of breast cancer risk, is highly influenced by body mass index (BMI) in childhood and early adulthood, but the mechanisms elucidating this relationship are undetermined. To our knowledge, no study has explored the mediating role of biomarkers on the relationship between early-life adiposity and MBD. The goal of this study is to discover metabolites that mediate the relationship between BMI at ages 10 and 18 with MBD in premenopausal women. Methods: This study includes premenopausal women who had their screening mammogram at Washington University in St. Louis, MO, and provided a fasting blood sample. Metabolon performed untargeted metabolomic profiling, detecting 1,074 metabolites. Metabolites missing >300 observations were excluded, leaving 828 metabolites; the other missing values were imputed using the nearest neighbor method. To mitigate batch effect, we normalized the metabolite peak area data using ComBat. Volumetric percent density (VPD) was calculated in 700 women using Volpara software. BMI at age 10 was estimated using the Stunkard pictogram, and BMI at age 18 was calculated from self-reported weight at 18 and height at study initiation. To assess the mediating role of the 828 metabolites, we performed high dimensional mediation analysis using the hima R package adjusting for potential confounders. Missing values in the covariates and BMI measures included in the mediation analysis were imputed using multivariate imputation by chain equations. Associations were considered significant if FDR p-value <0.1. Results: Four metabolites (glutamate, beta-cryptoxanthin, phytanate, and cortolone glucuronide (1)) mediated the relationship between BMI at age 10 and VPD; and 2 metabolites (glutamate, beta-cryptoxanthin) mediated the relationship between BMI at age 18 and VPD. Glutamate and beta-cryptoxanthin significantly mediated the relationship between both BMI measures and VPD, but glutamate was the strongest mediator across both time points. Glutamate mediated 6.7% (FDR p-value=0.06) and 9.3% (FDR p-value=0.008) of the total effect between BMI at age 10 and 18, respectively, on VPD. Beta-cryptoxanthin mediated 4.1% (FDR p-value=0.06) and 6.3% (FDR p-value=0.04), of the total effect between BMI at age 10 and 18, respectively, on VPD. Additionally, phytanate mediated 2.6% (FDR p-value=0.06) and cortolone glucuronide (1) mediated 2.0% (FDR p-value=0.07) of the total effect between BMI at age 10 and VPD. Conclusion: Amino acid, lipid, cofactor/vitamin, and xenobiotic metabolites mediated the associations of BMI in early-life and VPD in premenopausal women. This innovative study offers insight into the biological mechanisms underlying the link between early-life adiposity and MBD, which can support future research into breast cancer prevention. Citation Format: Kayla R. Getz, Myung Sik Jeon, Lili Liu, Lei Liu, Chongliang Luo, Jingqin Luo, Adetunji T. Toriola. Using metabolomics to identify biomarkers mediating the associations of adiposity in childhood and early adulthood with mammographic breast density in premenopausal women [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 3429.

Abstract 2140: Untargeted lipidomics reveals racial differences in lipid species among women

Abstract Introduction: Lipids are important for many cellular processes and have been implicated in cancer development. Adopting a lipidomic approach has revealed sex-specific differences in lipid species but whether there are racial differences in lipid profiles remain unexplored. As a result, we investigated differences in lipid species between Non-Hispanic Black (NHB) vs Non-Hispanic White (NHW) women. Methods: We performed lipidomic profiling on 705 premenopausal women who were recruited during their annual screening mammogram at Washington University School of Medicine, St. Louis, MO. A total of 982 lipid species were profiled at Metabolon (Durham, NC®). 125 lipid species missing in 300 or more women were excluded from the analysis. For the remaining missing values, we employed the nearest neighbor method for imputation. We investigated the race-differentiating lipid species concentrations using linear regression models, adjusting for age and body mass index (BMI). To satisfy the assumptions of normality and homoscedasticity, we log-transformed the lipid concentrations before performing regression analyses. The linear regression coefficients were then back-transformed as percentage differences. We accounted for multiple testings by applying the Bonferroni correction. Statistical significance was defined as a Bonferroni-adjusted p-value < 10−5. Pathway enrichment analyses was conducted for differential lipid species for each sub pathway. Results: Of the 705 women, 506 were NHW and 163 were NHB. In analysis adjusted for age and BMI, 278 lipid species were significantly lower in NHB, and one lipid specie (TAG58:10-FA20:4) was higher in NHB. These species belonged to triacylglycerols (TAG, n=213), phosphatidylcholines (PC, n=25), diacylglycerols (DAG, n=22), cholesteryl esters (CE, n=4), phosphatidylethanolamines (PE, n=4), lysophosphatidylcholines (LPC, n=3), lysophosphatidylethanolamines (LPE, n=3), sphingomyelins (SM, n=2), ceramides (CER, n=1), lactosylceramides (LCER, n=1) and phosphatidylinositols (PI, n=1) sub-pathways. The TAG sub-pathway was enriched with differential lipid species (213 significant out of 518 species, p = 1.26 × 10−11). Forty-three lipid species (42 TAGs and one DAG) exhibited an absolute percentage difference > 50% between NHB and NHW women. The top 3 TAG species with the largest absolute percentage differences were TAG44:0-FA14:0 (60.6% lower in NHB women, p = 6.25 × 10−8), TAG46:2-FA16:1 (59.8% lower in NHB women, p = 1.11 × 10−14) and TAG47:2-FA16:1 (59.4% lower in NHB women, p = 8.66 × 10−20). Conversely, TAG58:10-FA20:4 was 47.1% (p = 5.79 × 10−8) higher in NHB women. Conclusions: Our study reveals novel substantial racial differences in lipid species among women, mostly in the TAG sub-pathway, highlighting the influence of race on the lipidome. Validation in a different study population is needed. Citation Format: ang Li, Ghazaleh Pourali, Kayla R. Getz, Myung Sik Jeon, Jingqin Luo, Chongliang Luo, Adetunji T. Toriola. Untargeted lipidomics reveals racial differences in lipid species among women [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 2140.

Continuous monitoring of reverse approximate nearest neighbour queries on road network

Reverse Approximate Nearest Neighbour (RANN) query relaxes the RkNN definition of influence, where a user u can be influenced by not only its closest facility but also by every other facility that is almost as close to u as its closest facility is. In this paper, we study the continuous monitoring of RANN queries on road network. Existing continuous RANN algorithms on Euclidean space cannot be extended to continuously monitor RANN queries on road network. We propose two different methods to efficiently monitor RANN queries. We conduct an extensive experiment on different real data sets and demonstrate that our both proposed algorithms are significantly better than the competitor

The illusion of the Holy Grail of decoupling: Are there countries with relatively high SDGI and moderately low ecological footprint?

The development of sustainability indicators at the national level emerged due to the limitations of using GDP as a measure of well-being, sustainability, and resilience. Over time, various indicators and rankings have been formulated, with a shift in focus from solely economic growth to a more encompassing perspective. The objective of this study was to create a new ranking, based on the integration of two methodologies, namely UN Sustainable Development Goals Index (SDGI), and the Global Footprint Network's Ecological Footprint (EF), in order to identify meaningful clusters of countries based on both measures. Hierarchical clustering was utilized to group countries, while the Nearest Neighbour method was employed to filter out outliers, and the Ward method determined the final clusters. Additionally, the Pearson's correlation coefficient was calculated to assess the relationship between the 17 SDGIs and the EF. The clustering outcomes revealed that a three-cluster solution can be considered satisfactory. The results obtained with the new method demonstrate that the environmental dimension of SDGI is a sufficient metric for environmental sustainability in terms of ranking countries.

3D attention-focused pure convolutional target detection algorithm for insulator defect detection

To ensure the timely detection of safety hazards in transmission lines and to enhance accurately the detection of insulator defects in complicated environments, this study proposes a 3D attention-focused pure convolutional target detection algorithm (CMYOLOv7) based on you only look once v7 (YOLOv7) for defect detection on insulators in complicated environments. Firstly, to address the incapacity of focusing on the target in the backbone feature extraction networks, this study proposes a focused pure convolutional feature extraction module (ConvSimCB) to enhance the extraction and focus capability on insulator defect features. Secondly, to solve the problem of loss of key detail feature information caused by maximum pooling in spatial pyramid pooling module (SPPCSPC), this study proposes a mixed spatial pyramid pooling module (MIXPCSPC) to retain abundant image texture detail information and increase accuracy in detection of tiny insulator defects. Finally, a lightweight generic upsampling operator (CARAFE) is introduced to enhance the feature map resolution to address image distortion caused by the Nearest Neighbor Method of upsampling. This study proposed CMYOLOv7 achieves 98.37% precision, 90.59% recall, 95.68% mean average precision (MAP), and 94% F1 score, higher than YOLOv7 by 0.61%, 8.64%, 5.41%, and 5%.

Assessments of Gravity Data Gridding Using Various Interpolation Approaches for High-Resolution Geoid Computations

This article investigates the role of different approaches and interpolation methods in gridding terrestrial gravity anomalies. In this regard, first of all, simple and complete Bouguer anomalies are considered in gravity data gridding. In the comparison results of gridding these two Bouguer anomaly datasets, the effect of the high-frequency contribution of topographic gravitation (by means of the terrain correction) is clarified. After that, the role of the used interpolation algorithm on the resulting grid of mean gravity anomalies and hence on the geoid modeling accuracy is inspected. For this purpose, four different interpolation methods including geostatistical Kriging, nearest neighbor, inverse distance to a power (IDP), and artificial neural networks (ANNs) are applied. Here, the IDP and nearest neighbor methods represent simple-structured algorithms among the interpolation methods tested in this study. The ANN method, on the other hand, is preferred as a complex, optimization-based soft computing method that has been applied in recent years. In addition, the geostatistical Kriging method is one of the conventional methods that is mostly applied for gridding gravity data in geodesy and geophysics. The calculated gravity anomalies in grids are employed in high-resolution geoid model computations using the least squares modifications of Stokes formula with additive corrections (LSMSA) technique. The investigations are carried out using the test datasets of Auvergne, France that are provided by the International Service for the Geoid for scientific research. It is concluded that the interpolation algorithms affect the gravity gridding results and hence the geoid model determination. The ANN method does not provide superior results compared to the conventional algorithms in gravity gridding. The geoid model with 4.1 cm accuracy is computed in the test area.