Accurate Profiles Research Articles

Abstract 791: Evaluation of BCR spike-in controls using DriverMap adaptive immune receptor (AIR) profiling technology

Abstract The DriverMap™ Adaptive Immune Receptor (AIR) repertoire profiling assay employs multiplex RT-PCR for comprehensive profiling of CDR3 or full-length receptor profiling of all variable heavy and light chains of T-cell receptors (TCR) and B-cell receptors (BCR) from either RNA or DNA. In this study, initiated by a U.S. Food and Drug Administration (FDA) consortium to compare performance of different BCR repertoire profiling technologies, we tested spike-ins in peripheral blood mononuclear cell (PBMC) RNA and DNA control mixtures prepared from nine different B-cell lines. We measured the sensitivity, linearity and accuracy of AIR RNA and DNA BCR profiling for the IGH, IGK and IGL chains of the spike-in controls. Our comprehensive analysis, encompassing CDR3 profiling and full-length receptor profiling (CDR1, CDR2, and CDR3) sequencing, revealed a 100X increase in sensitivity of detection of spike-in controls in RNA compared to the DNA assay. The high sensitivity of the full-length DriverMap AIR RNA assay allowed the detection of controls from all nine cell lines, with 8 out of 9 cell lines detected in the DNA assay, which demonstrates the robustness of the DriverMap AIR profiling technology. This study highlights the remarkable sensitivity of the AIR RNA assay in quantifying transcripts. This approach enables the detection of low-frequency BCR clonotypes, which is particularly advantageous when working with samples containing low numbers of B cells. Furthermore, our AIR DNA assay in combination with spike-in controls offers a quantitative tool for minimal residual disease (MRD) applications, providing accurate insight into cell numbers, which then facilitates tracking clonal expansion of immune cells. Citation Format: Alex Chenchik, Tianbing Liu, Mikhail Makhanov, Dongfang Hu, Khadija Ghias, Paul Diehl. Evaluation of BCR spike-in controls using DriverMap adaptive immune receptor (AIR) profiling technology [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 791.

Abstract 6238: Integrated multi-omic/modal profiling enables improved characterization of oncogenic processes, with impact to clinical actionability in the GenMineTOP tested population

Abstract Introduction: It is becoming increasingly apparent that tumor gene expression and spatial H&E profiling can be used to improve the accuracy of DNA-only tumor profiling for clinically relevant use-cases. Future development and deployment of integrative (multi-omic (DNA+RNA) and/or multi-modality (omics + imaging)) tests for treatment and clinical trial profiling hinges on the demonstration of clear robustness and performance gains of prognostic and predictive integrative predictors in a clinical setting. In this work we present several analyses quantifying the utility of integrative testing. We show that a multi-omic test combining tumor expression signatures with DNA profiles substantially improves tumor-of-origin prediction for cancers of unknown primary and also significantly improves MSI detection and resolution. We also show that a simple multi-modality model combining spatial features of tumor infiltrating lymphocytes of tumors with tumor expression is prognostic for MSI-positive patients. Methods: Multi-omicTumor-of-origin: 163 patient sample expression profiles were profiled using the GenMineTOP test. GenMineTOP is a tumor-normal, joint DNA+RNA capture PMDA-approved test in current clinical service in Japan. The samples were clustered using DNA-only, RNA-only, and joint DNA+RNA profiling signatures. MSI prediction: 940 samples from the TCGA COAD, UCEC, and READ cohorts with matched MMR IHC MSI-calling, DNA-NGS-based MSI calling, and RNA-seq data were utilized to train, test and validate an expression-based pan-cancer MSI caller. Additional orthogonal validation was carried out using GenMineTOP multi-omic profiles. Multi-modality Spatial context of TIL and tumor-expression: 158 MSI+ samples from the TCGA-UCEC cohort with matched fresh-frozen H&E 20X images and RNA-seq data were utilized to mine spatial features of tumor-infiltrating lymphocytes and derive gene expression signatures, respectively. A Cox-proportional hazard model for patient survival using gene expression and spatial TIL features was constructed while regressing out tumor stage, gender, age, and ethnicity. Results & Conclusions: Our multi-omic results quantify the benefit of combining DNA and RNA profiles from GenMineTOP to classify tumor-of-origin from unknown primaries reduces classification error by approximately 50%. Additionally, our expression-based MSI status prediction model not only significantly improves NGS DNA (MSI-sensor) based MSI prediction (log-ratio test, pval < 1e-7) but also strongly suggests that nonlinear differential expression drives the improvement in MSI prediction. Our multi-modality results demonstrate that mining the spatial context of endogenous tumor immune response has nontrivial prognostic utility (p < .05). Citation Format: Bojan Losic, Kenji Tatsuno, Vasanth R. Singan, Hiroki Ueda, Aya Shinozaki Ushiku, Vasanthan Jayakumar, Emi Hattori, Toshimitsu Ichijo, Takuma Sasa, Masakazu Akahori, Shinya Hayashi, Esther Hsiao, Stephanie Feupe, Dan Vo, Tiantian Geier, Shuichi Tsutsumi, Tetsuo Ushiku, Kousuke Watanabe, Katsutoshi Oda, Hidenori Kage, Joy Radecki-Crandall, Donavan T. Cheng, Hiroyuki Aburatani. Integrated multi-omic/modal profiling enables improved characterization of oncogenic processes, with impact to clinical actionability in the GenMineTOP tested population [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 6238.

Abstract 905: ML quantification of tumor-Infiltrating lymphocytes distinguishes immune-phenotypes and reveals phenotypic heterogeneity

Abstract Immune phenotypes (IP), defined by the tumor-infiltrating lymphocyte (TIL) distribution within the tumor microenvironment (TME), is prognostic and predictive of treatment response. Here, machine learning (ML) models that characterize the TME were deployed in non-small cell lung cancer (NSCLC) and head and neck squamous cell carcinoma (HNSCC) to exhaustively label TILs directly from hematoxylin and eosin (H&E)-stained whole slide images (WSI), compute IP and quantify TIL distribution, tasks which are manually untenable. ML-powered TME models (PathAI, Boston, MA; commercially available as PathExplore™) for NSCLC and HNSCC that quantify tissue regions (e.g. tumor, epithelium, and stroma) and cells (e.g. lymphocytes) in H&E-stained WSI were deployed on 126 NSCLC and 103 HNSCC commercial samples. Ground truth CD3 and CD8 scores were obtained from paired immunohistochemically-stained WSI (HALO, Indica Labs, Albuquerque, NM). Inflamed, desert, or excluded IPs (based on data-driven cutoffs) were inferred from the mean TIL density in epithelium and stroma (slide-level IP, sIP), and from the fraction of “hot” patches in all 100um x 100um patches tiling the tumor and stroma areas (patch-level IP, pIP). TIL spatial distribution was measured by the Morisita-Horn index that computes the patch-wise overlap between TILs and cancer cells (MHI, ranging from 0 to 1), and epithelial-stromal interface distance index (EDI) indicating the hot stromal patch bias toward the epithelium (-EDI) or stroma (+EDI). ML-predicted TIL densities highly correlated with ground truth CD3+ and CD8+ cell densities (NSCLC/HNSCC CD3, CD8: tumor Pearson r= 0.88/0.85, 0.74/0.74; epithelium r= 0.76/0.71, 0.88/0.62; stroma r= 0.74/0.87, 0.61/0.76). High s- and pIP agreement was seen (NSCLC 93% and HNSCC 83%); 6/26 discordant cases were driven by TIL hotspots with high density (116%-310% of the cutoff) but few (median = 26%) hot patches in the epithelium. MHI was higher for the inflamed vs excluded IP (p < 0.0001 for NSCLC and HNSCC) and intra-group variability was high (NSCLC/HNSCC inflamed: 0.66±0.11/0.31±0.14, excluded: 0.51±0.12/0.26±0.17, desert: 0.48±0.09/0.28±0.17; mean±std). EDI was lowest and negative in inflamed IP but near zero in desert and excluded IP (NSCLC/HNSCC inflamed: -43±4um/-184±19um, excluded: 1.6±4um/-41±13um, desert: 18±4um/-14±13; mean±sem). Excluded and desert IPs had roughly equal + and - EDI cases (NSCLC +/-: 57/46; HNSCC +/-: 39/29). ML-powered IP prediction using TIL distribution enables accurate and rapid profiling of the TME using routine histopathology. pIPs were concordant with sIPs and highlight TIL heterogeneity. Spatial markers (MHI and EDI) reveal differences between IP classes and intra-group heterogeneity relevant for drug discovery and patient stratification. Investigating prognostic associations of these markers is a promising direction for future studies. Citation Format: Nhat Le, Bahar Rahsepar, Jennifer Hipp, Jake Conway, Ylaine Gerardin, Emma Krause, Ciyue Shen, Raymond Biju, Michael Nercessian, Nicholas Indorf, Sandrine Degryse, Miles Markey, Victoria Mountain, Pranjal Vaidya, William Wijaya, Aditee Shrotre, Patrick Caplazi, David Inzunza, Joann Palma, Erik Huntzicker, Catherine Tribouley, Diana Chen, Raluca Prediou, Francine Chen, Kevin Kolahi. ML quantification of tumor-Infiltrating lymphocytes distinguishes immune-phenotypes and reveals phenotypic heterogeneity [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 905.

Abstract 960: Improved cfDNA methylation profiling through correction of misrepaired jagged-ends

Abstract Background: Profiling cfDNA methylation enables early cancer detection and classification, but challenges arise from "jagged ends" (JEs) for ~90% of cfDNA molecules, as well as for FFPE samples and partially degraded genomic DNA. Repair of these jagged ends introduces erroneous methylation signals, especially in shorter cfDNA fragments enriched for tumor derived molecules. Separately, DNA conversions during 5mC profiling hamper accurate simultaneous ctDNA genotyping of somatic mutations. To meet these needs, we developed a method preserving double-stranded cfDNA molecules without error-prone end-repair at jagged ends. This enables superior methylation profiling including hemimethylation detection, and simultaneous ctDNA SNV genotyping. Methods: We present JEEPERS (Jagged-End Error Polishing of Enzymatically misRepaired Sequences), a novel method for correcting errors in double-stranded library preparation for methylation profiling. JEEPERS detects and quantifies enzymatic repair errors at JEs, correcting them in silico. Leveraging duplex UMIs, it utilizes complementary strands and sibling reads from cfDNA families without jagged ends. The method exploits the 5' to 3' polarity of ER-associated errors and JE length profiles for correction. JEEPERS introduces anchor CpGs for precise correction of rare signals and enables accurate SNV calling at non CpG sites, addressing the challenge of differentiating true SNVs from conversion artifacts leading to C>T/G>A changes in sequencing data. Results: Analyzing 248 cfDNA samples (139 NSCLC patients, 109 healthy controls) by methylation profiling (EM-Seq) and ctDNA mutation genotyping (CAPP-Seq), JEEPERS correction efficiently addressed distortions, even at low tumor concentrations. Limiting dilution experiments demonstrated JEEPERS achieving a LOD95 of ~0.2% with 95% specificity (n=300). Despite ~50% lower molecular depth, JEEPERS-corrected cfDNA EM-Seq enabled accurate simultaneous methylation profiling and ctDNA genotyping, correlating strongly with CAPP-Seq genotypes (R=0.998, n=30). As expected, mutant ctDNA molecules were shorter than wild-type in both CAPP-Seq and EM-Seq data. cfDNA methylation patterns varied by oncogene vs. tumor suppressor status; EGFR mutants showed increased methylation (p<0.01), while TP53 mutants had lower methylation levels (p<0.05). Conclusions: Our method effectively corrects cfDNA methylation data, even at low tumor concentration, ensuring enhanced methylation signal accuracy and proficient SNV calling capabilities. This integrated approach offers a powerful means for investigating the link between SNVs and DNA methylation, significantly enhancing the accuracy of cancer detection using cfDNA, especially for early-stage tumors. This paves the way for a deeper understanding of the molecular mechanisms driving cancer initiation and progression. Citation Format: Rui Wang, Emily G. Hamilton, Diego Almanza, Angela Hui, Maximilian Diehn, Ash A. Alizadeh. Improved cfDNA methylation profiling through correction of misrepaired jagged-ends [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 960.

Abstract 3692: Genomic profiling with the novel single-cell IMPACT assay reveals evidence of genetic heterogeneity among CTCs in late-stage breast cancer patients

Abstract Introduction: Liquid biopsy enables minimally invasive molecular profiling of systemic cancer for diagnosis, therapy selection and longitudinal monitoring of minimal residual disease. Cell-based liquid biopsy methods are particularly well suited to examine the extent of genetic heterogeneity and clonal diversity among metastatic cancer cells, which are believed to be the major determinants of therapeutic failure. However, isolation and molecular profiling of circulating tumor cells (CTCs) from body fluids is challenging due to methodological constrains. Methods: To overcome these challenges, we have developed a workflow for detection, isolation, and mutational profiling of CTCs. The workflow utilizes the FDA-approved CellSearch System® and a novel method for single-cell Integrated Mutation Profiling of Actionable Cancer Targets (scIMPACT). Our workflow was optimized for reliable detection of copy number variations (CNVs) and single nucleotide variations (SNVs) in single CTCs. The method was then applied to 31 CTCs alongside with 15 matched germline samples. Results: After implementation of a customized Bayesian neural network based algorithm, accounting for biases introduced during amplification of single-cell DNA and prioritizing the detected mutations, we achieved a sensitivity of 91% and a specificity of 96%, AUC = 0.98. The CTC analysis revealed presence of CNVs affecting known oncogenes and tumor suppressor genes (including MYC, CCND1, FGF4, TP53 and RB1) as well as SNVs classified as pathogenic (e.g., ARID1A Q775*). Notably, in the examined specimens most of the chromosomal aberrations (51-82%) were clonal across all cells of a given patient. In contrast, most of the detected somatic SNVs (67-96%) were sub-clonal and thus present only in a subset of CTCs. Conclusion: In summary, the novel scIMPACT workflow enabled reliable and accurate genomic profiling of patient-derived CTCs in late-stage breast cancer patients. Our proof-of-concept study revealed evidence of genetic diversity of SNVs among the examined CTCs, which might have been acquired late in progression but relevant for therapy escape in the studied cases. Further analyses are still needed for assessing the generalizability of our finding as well as for deeper understanding of the molecular basis orchestrating the establishment and maintenance of heterogeneous genotypes of CTCs in breast cancer. Citation Format: Zbigniew T. Czyż, Clara Chaiban, Adithi Ravikumar Varadarajan, Cäcilia Köstler, Vadim Dechand, Jonas Grote, Thomas Ragg, Bernhard Polzer, Jens Warfsmann, Christoph A. Klein. Genomic profiling with the novel single-cell IMPACT assay reveals evidence of genetic heterogeneity among CTCs in late-stage breast cancer patients [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 3692.

Abstract 5660: Gene expression signatures from FFPE to predict recurrent metastatic nasopharyngeal cancer

Abstract Nasopharyngeal cancer (NPC) is an EBV-driven epithelial cancer endemic to Southeast Asia, Southern China and the Middle East. Accurate profiling of the gene expression of NPC has been challenging because of its intense inflammatory infiltrate, as well as the 3 - 4mm size of biopsies limiting the amount of available material. Here we present a large cohort of 339 micro-dissected gene expression libraries from formalin-fixed paraffin-embedded (FFPE) biopsies from NPC patients treated at the National University Health System, Singapore, as well as healthy controls. NPC tumors from patients who subsequently developed recurrent or metastatic disease (Group A), and patients who did not develop recurrent or metastatic disease (Group B) were profiled. Tumor epithelial and microenvironment compartments were separately obtained using laser-capture microdissection, followed by library preparation performed for RNA-Seq using an in-house specialised technique. Gene signatures for NPC tumor epithelial and microenvironment content were used to validate the purity of gene expression libraries. Our preliminary analysis revealed that primary, pre-treatment tumors from patients who subsequently developed recurrence (Group A) showed downregulation of processes related to interferon gamma and interferon alpha response (p-adj <0.001 for both), while processes related to epithelial-mesenchymal transition (p-adj < 0.05) were enriched compared to patients who remained healthy (Group B). In contrast to primary tumors, recurrent tumors in Group A were enriched for cellular respiration including oxidative phosphorylation (p-adj < 0.0001). Our approach highlights the utility of whole transcriptome profiling from small quantities of archival FFPE material. The gene signatures identified from the primary tumors of high-risk patients are biologically relevant and have the potential to be used in precision medicine to guide additional targeted intervention. Citation Format: Joshua K. Tay, Wei Keat Teo, Joseph W. Foley, Luvita Suryani, Bing Cheng Wu, Chor Hiang Siow, Kwok Seng Loh. Gene expression signatures from FFPE to predict recurrent metastatic nasopharyngeal cancer [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 5660.

Abstract 4898: Highly accurate profiling of leukocyte composition from bulk peripheral blood with targeted digital cytometry

Abstract Background: Cell profiling methods such as flow/mass cytometry and single-cell RNA-sequencing are powerful tools for quantifying immune composition from healthy and neoplastic tissues. However, only a modest number of markers can be interrogated by the former and the latter remains cost prohibitive for large-scale analysis. Here we demonstrate robust, accurate, and reproducible enumeration of immune cell subsets from 36 whole blood samples using SureSelect XT HS2 RNA sequencing combined with CiberMed’s iSortTM digital cytometry solution. Enrichment of genes in the LM22 signature matrix, a well-established collection of reference profiles for deconvolving 22 human immune subsets, was achieved with two new targeted sequencing panels—Agilent SureSelect CD CiberMed Heme and Agilent SureSelect CD CiberMed Heme + HiRes. Both panels were assessed for their ability to profile leukocyte subsets with CiberMed’s iSortTM Fractions software, an optimized and standardized version of CIBERSORTx with novel enhancements. Methods: Whole blood samples were freshly collected from 36 healthy donors and split into two fractions—one was immediately processed for complete blood count (CBC) and flow cytometric enumeration of major leukocyte populations; the other was stored in PAXgene Blood RNA tubes for subsequent RNA sequencing. A Sysmex system was employed for CBC quantification and a Becton Dickinson 6-color TBNK MultiTest in vitro diagnostic (IVD) assay was employed for enumerating B cells, CD8 T cells, CD4 T cells, NK cells. These data were used as ground truth to assess iSortTM deconvolution performance from targeted and whole-transcriptome bulk RNA sequencing data. RNA-seq expression values were used as input to iSortTM Fractions to deconvolve 22 immune subsets in each sample. Results: Across 7 major populations, cell type fractions determined by iSortTM Fractions were highly concordant with ground truth fractions determined by clinical grade standards (r≥0.96) and exhibited strong reproducibility across technical replicates (r≥0.98). Furthermore, targeted enrichment using the SureSelect CD CiberMed Heme panel reduced the sequencing requirement by nearly 50-fold compared to whole-transcriptome sequencing, while also improving accuracy. Conclusion: These new panels for digital cytometry are being released through the Agilent Community Design program to enable focused, reliable, and high-throughput analysis of cell type composition from peripheral blood samples. Citation Format: Aki Nakao, Aaron M. Newman, Ash A. Alizadeh, Maximilian Diehn, Mistuni Ghosh, Manjula Aliminati, Jayati Ghosh, Kristi Stephenson, Ashutosh Ashutosh. Highly accurate profiling of leukocyte composition from bulk peripheral blood with targeted digital cytometry [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 4898.

Cardiotoxicity Assessment through a Polymer-Based Cantilever Platform: An Integrated Electro-Mechanical Screening Approach.

Preclinical drug screening for cardiac toxicity has traditionally relied on observing changes in cardiomyocytes' electrical activity, primarily through invasive patch clamp techniques or non-invasive microelectrode arrays (MEA). However, relying solely on field potential duration (FPD) measurements for electrophysiological assessment can miss the full spectrum of drug-induced toxicity, as different drugs affect cardiomyocytes through various mechanisms. A more comprehensive approach, combining field potential and contractility measurements, is essential for accurate toxicity profiling, particularly for drugs targeting contractile proteins without affecting electrophysiology. However, previously proposed platform has significant limitations in terms of simultaneous measurement. The novel platform addresses these issues, offering enhanced, non-invasive evaluation of drug-induced cardiotoxicity. It features eight cantilevers with patterned strain sensors and MEA, enabling real-time monitoring of both cardiomyocyte contraction force and field potential. This system can detect minimum cardiac contraction force of ≈2 µN and field potential signals with 50µm MEA diameter, using the same cardiomyocytes in measurements of two parameters. Testing with six drugs of varied mechanisms of action, the platform successfully identifies these mechanisms and accurately assesses toxicity profiles, including drugs not inhibiting potassium channels. This innovative approach presents a comprehensive, non-invasive method for cardiac function assessment, poised to revolutionize preclinical cardiotoxicity screening.

The robot grinding and polishing of additive aviation titanium alloy blades: a review

PurposeThe purpose of this review is to comprehensively consider the material properties and processing of additive titanium alloy and provide a new perspective for the robotic grinding and polishing of additive titanium alloy blades to ensure the surface integrity and machining accuracy of the blades.Design/methodology/approachAt present, robot grinding and polishing are mainstream processing methods in blade automatic processing. This review systematically summarizes the processing characteristics and processing methods of additive manufacturing (AM) titanium alloy blades. On the one hand, the unique manufacturing process and thermal effect of AM have created the unique processing characteristics of additive titanium alloy blades. On the other hand, the robot grinding and polishing process needs to incorporate the material removal model into the traditional processing flow according to the processing characteristics of the additive titanium alloy.FindingsRobot belt grinding can solve the processing problem of additive titanium alloy blades. The complex surface of the blade generates a robot grinding trajectory through trajectory planning. The trajectory planning of the robot profoundly affects the machining accuracy and surface quality of the blade. Subsequent research is needed to solve the problems of high machining accuracy of blade profiles, complex surface material removal models and uneven distribution of blade machining allowance. In the process parameters of the robot, the grinding parameters, trajectory planning and error compensation affect the surface quality of the blade through the material removal method, grinding force and grinding temperature. The machining accuracy of the blade surface is affected by robot vibration and stiffness.Originality/valueThis review systematically summarizes the processing characteristics and processing methods of aviation titanium alloy blades manufactured by AM. Combined with the material properties of additive titanium alloy, it provides a new idea for robot grinding and polishing of aviation titanium alloy blades manufactured by AM.

Multimode Rayleigh wave dispersion spectrum inversion using Wasserstein distance coupled with Bayesian optimization

We develop a new automatic framework for the nondestructive multichannel analysis of surface waves that combines multimode dispersion spectrum matching and a finite-element method (FEM)-based inversion to enhance the accuracy of subsurface profiling in site investigation activities. This framework eliminates the need for manual identification of the Rayleigh wave energy component and multimode assignment, reducing dependence on operator experience and judgment. The dispersion spectrum is generated through an FEM model that simulates 2D seismic wave propagation, taking into account the actual acquisition layout and lateral variations in the subsurface. We introduce the Wasserstein distance (WD) for evaluating the difference between the observed and simulated spectra and incorporate Bayesian optimization for efficiently inverting S-wave velocity profiles. The effectiveness of our framework is demonstrated through synthetic data examples, and the superiority of the WD-based objective function is illustrated by comparing it with the conventional mean square error-based objective function. Subsequently, we conduct a field test on a reclaimed landfill to validate our framework. This test confirms the ability of the framework to retrieve multimode Rayleigh waves and demonstrates its effectiveness in providing high-resolution S-wave profiles of the shallow subsurface.

CONSULT-II: accurate taxonomic identification and profiling using locality-sensitive hashing.

Taxonomic classification of short reads and taxonomic profiling of metagenomic samples are well-studied yet challenging problems. The presence of species belonging to groups without close representation in a reference dataset is particularly challenging. While k-mer-based methods have performed well in terms of running time and accuracy, they tend to have reduced accuracy for such novel species. Thus, there is a growing need for methods that combine the scalability of k-mers with increased sensitivity. Here, we show that using locality-sensitive hashing (LSH) can increase the sensitivity of the k-mer-based search. Our method, which combines LSH with several heuristics techniques including soft lowest common ancestor labeling and voting, is more accurate than alternatives in both taxonomic classification of individual reads and abundance profiling. CONSULT-II is implemented in C++, and the software, together with reference libraries, is publicly available on GitHub https://github.com/bo1929/CONSULT-II.

Process Optimization of Robotic Grinding to Guarantee Material Removal Accuracy and Surface Quality Simultaneously

Abstract Simultaneously guaranteeing material removal accuracy and surface quality of robotic grinding is crucial. However, existing studies of robotic grinding process optimization have mainly focused on a single indicator that solely considers contour error or surface roughness, while studies that simultaneously investigate the impact of contact force, spindle speed, feed rate, inclination angle, and path space on the material removal profile (MRP) and the surface roughness are lacking. This paper proposes a hybrid optimization method that considers dimensional accuracy and surface quality constraints. First, an MRP model that considers the coupling influence of the contact force, spindle speed, feed rate, and inclination angle is presented. Then, a surface roughness model that considers the inclination angle is established. Finally, the contact force, feed rate, inclination angle, and path space are simultaneously optimized to satisfy the hybrid constraints of MRP accuracy and surface roughness. The proposed method ensures maximum grinding efficiency while satisfying dimensional accuracy and surface quality constraints. The proposed method is verified on an industrial robotics grinding system with a pneumatic force-controlled actuator. The results show that the proposed method has higher profile accuracy and lower surface roughness than traditional methods.

A.C.I.D – an improved LSD technique for accurate line profile retrieval

ABSTRACT Stellar activity and planetary effects induce radial velocity (RV) offsets and cause temporal distortions in the shape of the stellar line profile. Hence, accurately probing the stellar line profile offers a wealth of information on both the star itself and any orbiting planets. Typically, cross-correlation functions (CCFs) are used as a proxy for the stellar line profile. The shape of CCFs, however, can be distorted by line blending and aliasing limiting the stellar and planetary physics that can be probed from them. Least-squares deconvolution (LSD) offers an alternative that directly fits the mean line profile of the spectrum to produce a high-precision profile. In this paper, we introduce our novel method ACID (Accurate Continuum fItting and Deconvolution) that builds on LSD techniques by simultaneously fitting the spectral continuum and line profile as well as performing LSD in effective optical depth. Tests on model data revealed ACID can accurately identify and correct the spectral continuum to retrieve an injected line profile. ACID was also applied to archival High Accuracy Radial-velocity Planet Searcher (HARPS) data obtained during the transit of HD189733b. The application of the Reloaded Rossiter–McLaughlin technique to both ACID profiles and HARPS CCFs shows ACID residual profiles improved the out-of-line root mean square (RMS) by over 5 per cent compared to CCFs. Furthermore, ACID profiles are shown to exhibit a Voigt profile shape that better describes the expected profile shape of the stellar line profile. This improved representation shows that ACID better preserves the stellar and planetary physics encoded in the stellar line profile shape for slow rotating stars.

Circulating tumor DNA methylation: a promising clinical tool for cancer diagnosis and management.

Cancer continues to pose significant challenges to the medical community. Early detection, accurate molecular profiling, and adequate assessment of treatment response are critical factors in improving the quality of life and survival of cancer patients. Accumulating evidence shows that circulating tumor DNA (ctDNA) shed by tumors into the peripheral blood preserves the genetic and epigenetic information of primary tumors. Notably, DNA methylation, an essential and stable epigenetic modification, exhibits both cancer- and tissue-specific patterns. As a result, ctDNA methylation has emerged as a promising molecular marker for noninvasive testing in cancer clinics. In this review, we summarize the existing techniques for ctDNA methylation detection, describe the current research status of ctDNA methylation, and present the potential applications of ctDNA-based assays in the clinic. The insights presented in this article could serve as a roadmap for future research and clinical applications of ctDNA methylation.

Abstract B038: Accurate quantitative profiling of rna modifications and their associations with glioblastoma

Abstract Every chemical group that is added to one of the canonical ribonucleotides, i.e., RNA modification, in a specific transcript can potentially alter the RNA folding as well as the RNA-protein interactions. Hence, RNA modifications are considered as post-transcriptional regulators for RNA splicing, RNA stability, and translation, just to name a few. Today, 170+ different RNA modifications have been identified, but only half a dozen of them are linked to glioblastoma (GBM). On the other hand, other RNA biology studies indicated the interplays between RNA modifications within a specific epitranscriptome could play a regulatory role in cellular activities. Although suitable methods for profiling RNA modifications in a specific epitranscriptome are available, the lack of ribonucleoside standards has prohibited the accurate quantification of each detectable RNA modification. To address this analytical challenge, our group has recently developed a mass spectrometric method that can achieve accurate quantitative profiling of untargeted RNA modifications without using any standards. Using the developed method, all the RNA modifications in patient-derived GBM cell lines could be profiled with high accuracy. In total, 31 different RNA modifications were detected in the GBM epitranscriptomes. By comparing with the profile detected in healthy human brain tissues, a unique set of upregulated RNA modifications is associated with the development of GBM. These findings are supported by the upregulated gene expression data obtained from GBM patients that are available in The Cancer Genome Atlas program. Citation Format: Frank A Morales Shnaider, Coston Eddings, Jennifer Simpson, Bakhos A. Tannous, Norman Chiu. Accurate quantitative profiling of rna modifications and their associations with glioblastoma [abstract]. In: Proceedings of the AACR Special Conference on Brain Cancer; 2023 Oct 19-22; Minneapolis, Minnesota. Philadelphia (PA): AACR; Cancer Res 2024;84(5 Suppl_1):Abstract nr B038.

Sensitivity, specificity, and accuracy of molecular profiling on circulating cell-free DNA in refractory or relapsed multiple myeloma patients, results of MM-EP1 study

As a promising alternative to bone marrow aspiration (BMA), mutational profiling on blood-derived circulating cell-free tumor DNA (cfDNA) is a harmless and simple technique to monitor molecular response and treatment resistance of patients with refractory/relapsed multiple myeloma (R/R MM). We evaluated the sensitivity and specificity of cfDNA compared to BMA CD138 positive myeloma plasma cells (PCs) in a series of 45 R/R MM patients using the 29-gene targeted panel (AmpliSeq) NGS. KRAS, NRAS, FAM46C, DIS3, and TP53 were the most frequently mutated genes. The average sensitivity and specificity of cfDNA detection were 65% and 97%, respectively. The concordance per gene between the two samples was good to excellent according to Cohen’s κ coefficients interpretation. An increased number of mutations detected in cfDNA were associated with a decreased overall survival. In conclusion, we demonstrated cfDNA NGS analysis feasibility and accuracy in R/R MM patients who may benefit from early phase clinical trial.

Acoustic propagation modeling using sound velocity profiles estimated from high resolution temperature data

An accurate sound velocity profile (SVP) is an important input for underwater acoustic propagation modeling. The SVP is largely driven by the temperature of the water column, especially at shallower depths. Measuring the SVP in high spatial and temporal resolution is challenging with traditional instrumentation, however techniques exist to produce high resolution temperature measurements that can be used to derive estimates of the SVP. Fiber optic distributed temperature sensing (DTS) offers an improvement of several orders of magnitude over traditional singular point measurement devices and has the ability to measure temperature over large range and depth scales in an efficient manner. A DTS system was towed for 172 nm off the coast of New England over several days. The measured temperature data was used to estimate the SVP across the range and depth and will be compared to traditional environmental data sets, such as HYCOM. A comparison of modeled acoustic propagation using different resolution SVPs will be presented.

Concurrent process and feedrate scheduling with convoluted basis functions and its application to fluid jet polishing

Non-traditional laser and fluid jet processes exhibit time-dependent material removal characteristics. The feedrate profile must be planned carefully along the toolpath for accurate surface profile generation while ensuring that the kinematic limits of machine tools are not violated. Conventional methods iteratively solve a deconvolution/convolution problem on the dwell-time density (reciprocal of the feedrate profile) that is computationally heavy, may leave significant residual processing errors, and even generate infeasible feed profiles with the manufacturing equipment. This paper proposes a novel approach that fully addresses the shortcomings above. Dwell-time density is first expressed as a continuous B-spline profile. The associated dwell basis functions (DBF) are convolved with the process influence function (PIF) to generate new process basis functions (PBF). This approach conveniently allows the posing of the problem as a concurrent linear least-squares problem on the control points shared by the DBFs and PBFs while ensuring the numerical stability of the solution and smoothness of the feed profile. To mitigate excessive acceleration peaks and any ringing effect around the edges of the toolpath, this paper also presents methodologies for stabilizing the scheduled feedrate profile by introducing knot vector adjustments (adaptive knot dropping) and linear edge constraints. The effectiveness of the proposed method is demonstrated and validated through simulation case studies and experimentally in fluid jet processing of precision optics. Results indicate that the proposed technique overcomes the limitations of conventional strategies and allows high-frequency surface components beyond the first zero-power frequency of the process footprint to be tracked while still generating a smooth feed profile within the acceleration limits of a machine tool. This ability stems from the localization characteristics associated with the basis functions. By improving the accuracy of high-frequency components, the proposed method exhibits the potential to fabricate topographies with sharper edges, which has been a challenge for conventional techniques.

Impacts of Resampling and Downscaling Digital Elevation Model and Its Morphometric Factors: A Comparison of Hopfield Neural Network, Bilinear, Bicubic, and Kriging Interpolations

The digital elevation model (DEM) and its derived morphometric factors, i.e., slope, aspect, profile and plan curvatures, and topographic wetness index (TWI), are essential for natural hazard modeling and prediction as they provide critical information about the terrain’s characteristics that can influence the likelihood and severity of natural hazards. Therefore, increasing the accuracy of the DEM and its derived factors plays a critical role. The primary aim of this study is to evaluate and compare the effects of resampling and downscaling the DEM from low to medium resolution and from medium to high resolutions using four methods: namely the Hopfield Neural Network (HNN), Bilinear, Bicubic, and Kriging, on five morphometric factors derived from it. A geospatial database was established, comprising five DEMs with different resolutions: specifically, a SRTM DEM with 30 m resolution, a 20 m resolution DEM derived from topographic maps at a scale of 50,000, a 10 m resolution DEM generated from topographic maps at a scale of 10,000, a 5 m resolution DEM created using surveying points with total stations, and a 5 m resolution DEM constructed through drone photogrammetry. The accuracy of the resampling and downscaling was assessed using Root Mean Square Error (RMSE) and mean absolute error (MAE) as statistical metrics. The results indicate that, in the case of downscaling from low to medium resolution, all four methods—HNN, Bilinear, Bicubic, and Kriging—significantly improve the accuracy of slope, aspect, profile and plan curvatures, and TWI. However, for the case of medium to high resolutions, further investigations are needed as the improvement in accuracy observed in the DEMs does not necessarily translate to the improvement of the second derivative morphometric factors such as plan and profile curvatures and TWI. While RMSEs of the first derivatives of DEMs, such as slope and aspect, reduced in a range of 8% to 55% in all five datasets, the RMSEs of curvatures and TWI slightly increased in cases of downscaling and resampling of Dataset 4. Among the four methods, the HNN method provides the highest accuracy, followed by the bicubic method. The statistics showed that in all five cases of the experiment, the HNN downscaling reduced the RMSE and MAE by 55% for the best case and 10% for the worst case for slope, and it reduced the RMSE by 50% for the best case of aspect. Both the HNN and the bicubic methods outperform the Kriging and bilinear methods. Therefore, we highly recommend using the HNN method for downscaling DEMs to produce more accurate morphometric factors, slope, aspect, profile and plan curvatures, and TWI.

Expendable Conductivity–Temperature–Depth-Assisted Fast Underwater Sound Speed Estimation by Convolutional Neural Network with Reduced Fully Connected Layers

Obtaining accurate sound speed profiles (SSPs) in near-real-time is of great significance for ocean exploration, underwater communication and improving the performance of sonar systems. In response to the problem that traditional sound speed estimation methods cannot obtain real-time sound speed distribution or rely too much on sonar observation data, we propose an SSP estimation method based on a convolutional neural network with reduced fully connected layers (RFC-CNN) in this paper. This method utilizes neural networks to extract the complex nonlinear features of various types of data. With the help of the historical SSPs and shallow seawater sound speed and temperature data obtained by expendable conductivity–temperature–depth probes (XCTDs), a more accurate estimation of the regional sound speed distribution can be realized quickly. This approach can save the observation cost and significantly improve the real-time performance of SSP estimation.