Detection Scheme Research Articles

Cardio-Oncology Awareness: A multidisciplinary survey amongst health care trainees and practicing professionals at academic institutions.

e24023 Background: The awareness of cancer therapy-related adverse cardiac effects is incited by recent literature on cardiotoxicity incidence and detection strategies. Despite availability of guidelines, cardiotoxicity monitoring and treatment are not structurally performed across institutions. Aim: Assessing knowledge of health care professionals’ cardiac complications of cancer treatments, current perspectives on cardio-oncology ultimately to determine an agenda for improvements in practice. Methods: A web-based survey (Google Forms) was adapted from that of Ottawa Hospital Research Institute’s survey, consisting of 45 questions organized into 7 sections, modified according to our local needs and circulated to cardiologists, oncologists (radiation, medical) practicing or training in a multi-disciplinary setup and academic centers. Fortnightly reminders were sent for to receive a maximum response. The survey enquired about implementing respondent’s perception of cardio-oncology, availability of cardio-oncology services at the respondent’s institution, opinions towards current practice. A descriptive statistical analysis was carried out. Results: Fifty-five professionals completed the survey, of which 12 were cardiologists, 31 radiation oncologists, and 12 medical oncologists. Majority, 31 (54.5%) were trainees and only 1.8% had some formal training in cardio-oncology while 20% respondents reported to have a formal cardio-oncology training programme. Nearly 90% understood cardio-oncology as a stream to be able to identify side-effects and refer for treatment to the cardiologists while 84% felt that timely referral would improve outcomes.72% recognized follow-up and 67.3% felt patient education to be important component. Awareness of cardio-oncology guidelines was reported by 38.2% respondents. In the setting of metastatic cancer, there was a difference in risk tolerance for cardiotoxicity with the majority accepting 1-5% risk in curative setting and 56% accepting > 5% risk of cardiotoxicity in the metastatic setting. None of the institutions reported on having a formal training programme. Limited infrastructure (54.5%) and limited interest (47.3%) were cited the most common reasons. Conclusions: This survey identified that professionals recognize cardio-oncology services to be an important component of cancer treatment. Lack of formal training and awareness about guidelines was reported. There is a felt need for collaboration between cardiologists and oncologists to improve cardiovascular outcomes of cancer patients.

Evaluation of cost-effective multiple cancer early detection with extremely low coverage whole genome sequencing from plasma.

10538 Background: Cancer continues to be a significant global health concern, with approximately 19.3 million new cases diagnosed and 10.0 million cancer-related deaths reported in 2020. Multi-cancer early detection (MCED) test using the peripheral blood offers a great opportunity to improve the current cancer screening tests with better performance and benefit-to-harm balance. Methods: Here we introduce a novel MCED test called HIFI-PROF, which utilizes extremely low pass whole-genome sequencing (median coverage of 0.6X) to create a multi-dimensional fragmentation signatures model through machine learning approach. HIFI-PROF was developed to detect and localize six types of cancer (colorectal, pancreas, liver, lung, esophagus and gastric cancer). Results: The study included a total of 1,487 participants, comprising 235 colorectal cancers, 227 pancreatic cancers, 150 liver cancers, 125 lung cancers, 107 esophageal cancers, 106 gastric cancers and 537 healthy volunteers. Most of the cancer cases were I/II stage patients (stage I: 38%, stage II: 35%, stage III: 23%, stage IV: 4%). The participants, stratified by age and clinical status, were divided into two sets: a training set (n=1,079) and a validation set (n=408).HIFI-PROF exhibited an excellent performance in the early detection of multiple cancer types, achieving a sensitivity of 87.58% at a specificity of 99.09% and identifying the tissue of origin (TOO) with an accuracy of 81.99%. The sensitivity rates for detecting cancer at different stages were also promising, with a sensitivity of 84.62% for stage I, 85.59% for stage II, 89.61% for stage III and 92.86% for stage IV. Conclusions: These results highlight the high sensitivity and specificity of HIFI-PROF in identifying cancer cases while maintaining a low rate of false-positive results. The use of HIFI-PROF offers a cost-effective MCED test with excellent performance. Such advancements will contribute to improve strategies for multi-cancer early detection, leading to more effective clinical interventions and better outcomes.

A portable microfluidic electrochemical sensor with nonlinear fit strategy for wide-range uric acid detection

In this work, we have developed a portable electrochemical sensing device controlled by a smartphone through Bluetooth, which was applied for regular uric acid (UA) monitoring in prevention and healthcare management. On the core lies a nonlinear fitting model (I = k·(N − exp(−K·cbulk + b))) which is proposed for the first time and given from the differential equation based on the theoretical analysis. We believe this model can reflect the intrinsic relationship between the obtained current and the bulk analyte concentration, therefore leading to an expanding UA detection range (10-fold) compared to the widely used linear standard curve model and reducing the requirements for the modification of the electrode materials. Besides, the integration of the microfluidic chip promoted the removal of UA oxidation products at the electrode surface, yielding excellent sensing stability with a relative standard deviation (RSD) lower than 1 % during 10 consecutive runs. Consequently, we acquired the relationship between Square Wave Voltammetry (SWV) current and UA concentration (I = 15.27*(2.05 − exp(−0.00108*(cbulk − 663.5))), R2 = 0.9999) in the range of 5 to 1000 μM with a limit of detection (LoD) of 2.4 μM using a completely unmodified screen-printed carbon electrode (SPCE). Proof of concept experiments using 25× diluted human urine spiked with UA yielded a recovery rate of 87.5–101.4 % and a satisfactory selectivity result, which is within the value expected for clinical use, indicating the potential of the developed instrument and nonlinear fitting model for urinary UA detection.

Exploring 6years of colorectal cancer surgery in rural Italy: insights from 648 consecutive patients unveiling successes and challenges.

The multidisciplinary management of patients suffering from colorectal cancer (CRC) has significantly increased survival over the decades and surgery remains the only potentially curative option for it. However, despite the implementation of minimally invasive surgery and ERAS pathway, the overall morbidity and mortality remain quite high, especially in rural populations because of urban - rural disparities. The aim of the study is to analyze the characteristics and the surgical outcomes of a series of unselected CRC patients residing in two similar rural areas in Italy. A total of 648 consecutive patients of a median age of 73years (IQR 64-81) was enrolled between 2017 and 2022 in a prospective database. Emergency admission (EA) was recorded in 221 patients (34.1%), and emergency surgery (ES) was required in 11.4% of the patients. Tumor resection and laparoscopic resection rates were 95.0% and 63.2%, respectively. The median length of stay was 8days. The overall morbidity and mortality rates were 23.5% and 3.2%, respectively. EA was associated with increased median age (77.5 vs. 71 ys, p < 0.001), increased mean ASA Score (2.84 vs. 2.59; p = 0.002) and increased IV stage disease rate (25.3% vs. 11.5%, p < 0.001). EA was also associated with lower tumor resection rate (87.3% vs. 99.1%, p < 0.001), restorative resection rate (71.5 vs. 89.7%, p < 0.001), and laparoscopic resection rate (36.2 vs. 72.6%, p < 0.001). Increased mortality rates were associated with EA (7.2% vs. 1.2%, p < 0.001), ES (11.1% vs. 2.0%, p < 0.001) and age more than 80years (5.8% vs. 1.9%, p < 0.001). In rural areas, high quality oncologic care can be delivered in CRC patients. However, the surgical outcomes are adversely affected by a still too high proportion of emergency presentation of elderly and frail patients that need additional intensive care supports beyond the surgical skill and alternative strategies for earlier detection of the disease.

Genetic landscape of colorectal cancer (CRC) across genetic ancestries: Implications for early cancer detection (ECD).

e15527 Background: Blood-based ECD has the potential to reduce mortality rates by improving strategies for adoption of and adherence to screening. As health disparities in the incidence of CRC across minority populations are known to exist, we evaluated the genomic landscape in patients with CRC across genetic ancestries. Methods: De-identified patients (N = 16,337) with stage I-IV CRC had personalized, tumor-informed commercial ctDNA testing (SignateraTM) based on whole exome sequencing. Genetic ancestry determined via supervised local ancestry inference (pmid36042219) included African (AFR, n = 1697), Ashkenazi Jewish (ASJ, n = 1192), East Asian (EAS, n = 2247), European (EUR, n = 9726), Latino (LAT, n = 1291), and South Asian (SAS, n = 184) ancestries. Somatic driver mutations and pathogenic germline variants (PVs) in CRC-associated genes were evaluated. Somatic single nucleotide variants were used for mutational signature inference. Results: Across ancestries, 47-67% of the patients were male and most were aged &gt; 50 years. Among 35 to 50-year-olds, microsatellite instability (MSI) rates were higher in LAT (12.9%, p = 0.04) and SAS (28.3%, p &lt; 0.001) vs EUR (8.2%). Among &gt; 50-year-olds, MSI rates were lower in AFR (10.7%, p &lt; 0.001), EAS (7.9%, p &lt; 0.001), and LAT (10.4%, p &lt; 0.001) vs EUR (16.0%). The POLE signature was more common in AFR (2%, p = 0.001) vs EUR (1.1%). There were significant differences in the frequency of driver mutations and PV rates between non-EUR vs EUR ancestry groups (Table, *p &lt; 0.05, **p &lt; 0.001). Gene-level enrichment was observed in MLH1 for SAS (8.7%, p &lt; 0.001) and LAT (1.8%, p = 0.03), CHEK2 for AFR (0.4%, p = 0.03) and EAS (0.1%, p &lt; 0.001), and PMS2 (1.4%, p = 0.006) for AFR. Among patients aged ≤50 years, similar findings were observed, with differences driven by MSI rates and germline findings in Lynch syndrome (LS) genes. Conclusions: This is the first study to investigate genomic differences between multiple ancestry groups in the largest cohort to date, including EAS, LAT, and SAS populations. The differences observed between ancestries could provide insight into differences in both hereditary risk and subsequent tumor initiation and growth. Findings from this study may provide information for developing risk stratification and prevention strategies for the early detection of cancer and provide the rationale for precision treatment based on ancestry. [Table: see text]

Fault detection and isolation for multi-type sensors in nuclear power plants via a knowledge-guided spatial–temporal model

Sensor faults in nuclear power plants (NPPs) have the potential to propagate negative impacts on system stability, leading to false alarms and accident misdiagnosis. Existing methods seldom concurrently consider complex spatial–temporal correlations among multi-type sensors in the primary circuit. This study presents a novel sensor fault detection and isolation scheme named the knowledge-guided spatial–temporal model (KGSTM), using the knowledge-guided recurrent unit (KGRU) and the concurrent detection strategy. To organically express part and whole interdependencies from inherent sensor layout, several graphs are specifically designed with pertinent domain knowledge. KGRU consists of the multi-graph convolutional network (MGCN) for fusing various spatial information and the gate recurrent unit (GRU) for extracting dynamic temporal features, further obtaining precise reconstructed signals and residuals. The concurrent detection strategy can explicitly quantify abnormal behaviors to detect and isolate faulty sensors by characterizing spatial–temporal signal variation. Numerical results on two real-world datasets from a pressurized water reactor (PWR) with simulated faults illustrate that the KGSTM has superior performance over various state-of-the-art methods in terms of signal reconstruction and fault detection.

Selfish attack detection and response using cooperative backoff adjustment in wireless sensor networks

In recent times, the proliferation of intelligent internet of things (IoT) and wireless sensor network (WSN) technologies has significantly enhanced both daily life and industrial operations. Despite their widespread application, these networks face challenges with resource monopolization by selfish nodes, which detrimentally impacts network efficiency. This study introduces a novel detection and response strategy for selfish nodes, utilizing backoff adjustment mechanisms to reduce throughput deterioration. The effectiveness of this approach was rigorously evaluated using the NetSim simulator, focusing on the impact of backoff adjustment on network throughput. Simulation results indicate that the implementation of this method in the presence of selfish nodes yields a noteworthy improvement in network throughput, with approximately more than twice. This advancement holds promise for enhancing the robustness and efficiency of IoT and WSN technologies against disruptive selfish node behaviors.

Plastic Waste Identification Using Deep Learning for Adequate Waste Management

Plastic waste management has emerged as a critical global challenge, prompting concerted efforts from conservation authorities and international organizations like the United Nations to enhance detection and classification strategies. This research distinguishes itself by harnessing advanced deep learning techniques to identify plastic materials at the micron level, surpassing traditional macro-level methods. The primary goal is to classify plastics into four major distinctions, addressing a key challenge in plastic segregation by accurately measuring thickness. Leveraging the YOLOv8 architecture, this approach enables precise classification of plastics into Polyethylene terephthalate (PETE), High Density Polyethylene (HDPE), low density polyethylene (LDPE0, and Polyvinyl Chloride (PVC) categories based on thickness. To achieve this, incorporation of hardware components such as ultrasonic sensors and NodeMCU for detecting thickness variations is used. By facilitating effective segregation according to environmental impact, this innovation revolutionizes waste reduction efforts, offering real-time identification and enhancing overall sustainability in plastic waste management.

Pembrolizumab-induced myocarditis: A systematic review of case reports.

e14605 Background: Immune checkpoint inhibitors, such as pembrolizumab, have revolutionized cancer therapy but are associated with immune-related adverse events (irAEs), including myocarditis, which can have serious clinical consequences. This systematic review aims to comprehensively analyze case reports to better understand pembrolizumab-induced myocarditis. Methods: Using keywords of "pembrolizumab" or "Keytruda" and "myocarditis" in the PubMed/Medline, ScienceDirect and clinicaltrials.gov databases and manual searches on Google Scholar, we reviewed all cases reported up to the year 2023. In total, 296 studies received primary screening. Duplicated articles and those meeting the exclusion criteria (not myocarditis, not human, not English, trials done in vitro, combination immunotherapies) were excluded. Review articles, retrospective studies, systematic reviews, and meta-analyses were also excluded. Finally, 45 studies matching the inclusion criteria were included (total of 46 patients). Data on patient demographics, clinical presentation, diagnostic criteria, management strategies, and outcomes were extracted and analyzed. Results: In the included 46 patients, the mean age was 65.5 years, with a slight male predominance (56.5%). Predominant cancer types included lung cancer (30.4%), thymoma (15.2%), melanoma (13.0%), and genitourinary cancers (13.0%). 45.7% of patients developed myocarditis after their first cycle of Pembrolizumab while 41.3% developed myocarditis after two cycles. 47.8% of patients presented with dyspnea, 23.9% with fatigue, 10.9% with chest pain, while 13% of the patients were asymptomatic and were subsequently diagnosed with myocarditis while presenting with other immune-related adverse events. Troponin levels were assessed in 45 individuals, with only 2.2% (one patient) having normal levels. ECG changes included ST segment elevation in 21.7%, conduction abnormalities (RBBB 15.2%, LBBB 6.5%, AV block 26.1%, and one unspecified conduction abnormality) in 50%, and arrhythmias in 37% of cases. Treatment included steroids for all, with varying outcomes. Four patients each received one of the following treatments: batacept, ruxolitinib, alemtuzumab, and methotrexate. All four of these patients showed improvement. Two patients received infliximab (4.3%) and both of them died. Outcomes revealed 34.8% mortality, 63.0% improvement, and 2.2% deterioration, including one sudden cardiac death 35 days post-discharge. Conclusions: Pembrolizumab-induced myocarditis is a rare but potentially life-threatening irAE. This systematic review provides a comprehensive overview of the clinical characteristics, diagnostic challenges, treatment approaches, and outcomes of this condition. Further research is needed to elucidate the underlying mechanisms and identify risk factors for pembrolizumab-induced myocarditis, enabling early detection and prevention strategies.

Advancements in Dental Filling Detection Technologies and Strategies for Comprehensive Oral Health Care

Advancements in Dental Filling Detection Technologies and Strategies for Comprehensive Oral Health Care

A Fault Detection and Compensation Strategy of the Resolver for Variable Leakage Flux Permanent Magnet Motor With Threshold Self-Regulation

A Fault Detection and Compensation Strategy of the Resolver for Variable Leakage Flux Permanent Magnet Motor With Threshold Self-Regulation

Elucidating Mycotoxin-Producing Aspergillus Species in River Water: An Advanced Molecular Diagnostic Study for the Assessment of Ecological Health and Contamination Risk

The primary goal of this research is to isolate mycotoxin-producing fungus from the Nagavali River. Examining isolated fungi involved analyzing their mycelium growth on culture media and detailed microscopic inspection. We employed PCR analysis utilizing universal primers ITS1 and ITS4 to accurately identify the species. Furthermore, we sequenced the amplified ITS region and rigorously analyzed the sequences using NCBI-BLASTn and the ITS2 database. The analysis found a high 96.38% genetic similarity to the Aspergillus flavus strain, resulting in a 600-base pair fragment size. The sequence was given the accession number OR536222 in the NCBI GenBank database. Phylogenetic analysis was performed to ascertain the particular strain of A. flavus and its source. Remarkably, this analysis led to the identification of a single new strain gene, which represents a novel discovery in the field of fungal research. These results underscore the vital significance of molecular techniques in promptly and precisely identifying organisms. This research enhances our understanding of mycotoxin contamination in water, providing valuable insights to improve detection and prevention strategies. It accentuates the overarching importance of conserving our water resources and upholding ecological equilibrium, ultimately safeguarding the well-being of both humanity and the environment.

Viroids: infectious non coding RNAs

Viroids are the smallest non-coding infectious RNAs (between 246 and 401 nucleotides) known to be highly structured and replicate autonomously in the host plants. Although they do not encode any peptides, viroids induce visible symptoms in susceptible host plants. This article provides an overview of their physical and biological properties, the diseases they cause and their significance for the plants. The mechanisms underlying the expression of symptoms in host plants, their detection and various strategies employed for diseases prevention are also developed.

Clinical behavior of breast cancer in young BRCA carriers and prognostic impact of the timing of genetic testing: Results from an international cohort study.

10503 Background: Germline BRCA testing is recommended in all patients (pts) diagnosed with breast cancer (BC) at a young age (40 years or younger). Limited evidence exists regarding clinical behavior of BC in young BRCA1and BRCA2 carriers or the impact of the timing of genetic testing (before or at diagnosis) on tumor characteristics and pts outcomes. Methods: This was an international, multicenter, hospital-based, retrospective cohort study including pts harboring germline pathogenic variants (PVs) in BRCA1or BRCA2 and diagnosed with stage I-III invasive BC at 40 years or younger between January 2000 and December 2020 (NCT03673306). Baseline pt, tumor, and treatment characteristics, as well as survival outcomes (disease-free survival [DFS] and overall survival [OS]) were compared between BRCA1and BRCA2carriers. A further comparison was performed between pts who underwent genetic testing before (i.e. any time up to 2 months prior to BC diagnosis) or at (i.e. between 2 months prior and up to 6 months after diagnosis) BC diagnosis. Results: From 78 centers worldwide, 4,752 pts were included, of whom 3069 harbored BRCA1and 1683 BRCA2 PVs. Compared to BRCA2carriers, BRCA1 carriers were significantly younger, had more grade 3 tumors, less nodal involvement, lobular histology, hormone receptor and HER2 positivity, and received chemotherapy more frequently and endocrine therapy less frequently. Median follow-up was 7.8 years (range 4.4-12.6 years). Second primary BCs (13.7% vs. 9.3%) and non-breast new primary malignancies (4.5% vs. 3.0%) were significantly more frequent among BRCA1 carriers, while distant recurrences were less frequent (9.7% vs. 13.6%). No differences in DFS or OS were observed between BRCA1 and BRCA2 carriers. However, BRCA1 carriers had worse DFS and OS in the first 5 years due to a peak in DFS events in the first 2 years while BRCA2 carriers had a constant risk of recurrence over time and worse OS after year 9. Compared to pts tested for BRCA at BC diagnosis (n = 1671), those tested before BC diagnosis (n = 411) had significantly smaller tumors (T1: 61.3% vs 32.4%) and less nodal involvement (N0: 65.9% vs 50.8%). 8-year DFS and OS in patients tested before and at BC diagnosis were 73% vs 70% (HR 1.25; 95% CI 0.99-1.58) and 91% vs 87% (HR 1.65; 95% CI 1.08-2.52), respectively. Conclusions: In this global study of young BRCA carriers, different pts, tumor, and treatment characteristics, as well as pattern and risk of DFS/OS events over time were observed between BRCA1and BRCA2. Identification of carrying a BRCA PV in healthy individuals led to earlier BC diagnosis and improved OS. These findings highlight the importance of conducting efforts to identify women at risk for carrying a BRCA1or BRCA2PV to offer them genetic counseling and testing to inform not only prevention, but tailored earlier detection, treatment and follow-up strategies once diagnosed.

Predictive Models for the Transition from Mild Neurocognitive Disorder to Major Neurocognitive Disorder: Insights from Clinical, Demographic, and Neuropsychological Data.

Neurocognitive disorders (NCDs) are progressive conditions that severely impact cognitive function and daily living. Understanding the transition from mild to major NCD is crucial for personalized early intervention and effective management. Predictive models incorporating demographic variables, clinical data, and scores on neuropsychological and emotional tests can significantly enhance early detection and intervention strategies in primary healthcare settings. We aimed to develop and validate predictive models for the progression from mild NCD to major NCD using demographic, clinical, and neuropsychological data from 132 participants over a two-year period. Generalized Estimating Equations were employed for data analysis. Our final model achieved an accuracy of 83.7%. A higher body mass index and alcohol drinking increased the risk of progression from mild NCD to major NCD, while female sex, higher praxis abilities, and a higher score on the Geriatric Depression Scale reduced the risk. Here, we show that integrating multiple factors-ones that can be easily examined in clinical settings-into predictive models can improve early diagnosis of major NCD. This approach could facilitate timely interventions, potentially mitigating the progression of cognitive decline and improving patient outcomes in primary healthcare settings. Further research should focus on validating these models across diverse populations and exploring their implementation in various clinical contexts.

Development of a non-intercepting weak beam current measurement electronics for Heavy Ion Accelerator Facility in Lanzhou.

Non-destructive measurements of low-intensity charged particle beams are particularly challenging for beam diagnostics. At the Heavy Ion Accelerator Facility in Lanzhou (HIRFL), beams with weak currents below 1 µA are often provided for experiments. The detection of such low beam current is below the threshold of typical standard beam current transformers. Therefore, a low-intensity monitoring system is developed by using a sensitive capacitive pick-up (PU) and low-noise electronics. This device measures beam currents by digitally analyzing the amplitude of the PU signals using a homodyne detection scheme. During lab tests, the amplitude nonlinearity is <0.5% in the operational range of 1 nA-45 µA and the amplitude resolution is 0.94 nA. At present, four measurement systems for low beam currents are installed at HIRFL for the monitoring of standard operating conditions with low beam currents below 1 µA. After an absolute calibration with a Faraday cup, it can be used for accurate beam intensity measurement with a current resolution of about 1 nA.

Design and verification of q-axis perturbation based active islanding detection schemes for DG systems

The penetration of grid integrated distributed generation (DG) in the present decade, has benefited rural communities, the environment, and the power sector. These renewable power sources based DGs could eliminate the need of extensive transmission networks, especially in remote areas, reduce emissions and improve power supply reliability. A significant drawback of grid integrated DG systems is the islanding of DG units, which puts workers' safety at risk and raises the possibility of damaging electrical infrastructure. Therefore, islanding detection techniques are used to reduce the danger associated with islanded functioning of DG units. Fast detection, small non detection area and less power quality disturbance are the major requirements of any islanding detection method. To address this issue of islanding, researchers have proposed various islanding detection strategies. This paper compares various q-axis controller-based islanding identification approaches: sub-harmonic perturbation (SHP), complementary reactive power perturbation (CRPP), and even harmonic perturbation (EHP). In all three proposed methodologies, the perturbations introduced result in frequency deviations surpassing the predefined threshold values. But the time of islanding detection is least in the CRPP approach. CRPP can also drift the total harmonic distortion (THD) beyond the corresponding threshold in an appreciable way. The performance of these (Islanding detection methods) IDMs is evaluated through simulations using MATLAB-Simulink on a PV fed DG. The efficacy of the comparative analysis is ensured with necessary waveforms.

Eddy Detection Inverted from Argo Profiles to Surface Altimetry

Abstract Argo floats are widely used to characterize vertical structures of ocean eddies, yet their capability to invert sea surface features of eddies, especially those overlooked by available altimeters, has not been explored. In this paper, we propose an “interior-to-surface” inversion algorithm to effectively expand the capacity of eddy detection by estimating altimeter-missed eddies’ surface attributes from their Argo-derived potential density anomaly profiles, given that the interior property and surface signature of eddies are highly correlated. An altimeter-calibrated machine learning ensemble is employed for the inversion training based on the joint altimeter–Argo eddy data and shows promising performance with mean absolute errors of 5.4 km, 0.5 cm, and 14.3 cm2 s−2 for eddy radius, amplitude, and kinetic energy, respectively. Then, the trained ensemble model is applied to independently invert the properties of eddies captured by an Argo-alone detection scheme, which yields high spatiotemporal consistency with their altimeter-captured counterparts. In particular, a portion of Argo-alone eddies is ∼25% smaller than altimeter-derived ones, indicating Argo’s unique capability of profiling weaker submesoscale eddies. Sea surface temperature and chlorophyll data are further applied to validate the reliability of eddies identified and characterized by the Argo-only algorithm. This new methodology effectively complements that of altimetry in eddy detection and can be expanded to estimate other physical/biochemical eddy variables from a variety of in situ observations. Significance Statement Despite thousands of eddies being routinely identified on a daily basis, it has been recognized that a substantial portion of eddies may still be missed due to inadequate sampling of altimeter constellations. Taking advantage of eddy’s correlation between surface and interior, a considerable number of eddies are discovered for the first time through an Argo-based eddy identification scheme. Here, we propose a new methodology to independently infer these recaptured eddies’ surface properties from their vertical signals through an “interior-to-surface” inversion process. The inferred eddy properties are verified by the spatiotemporal consistency with those derived from altimetry. Since Argo is capable of profiling smaller and weaker eddies, the proposed methodology significantly complements and expands that of altimetry in eddy observation.

Leveraging cfDNA fragmentomic features in a stacked ensemble model for early detection of esophageal squamous cell carcinoma.

4054 Background: In this study, we developed a stacked ensemble model that leverages cell-free DNA (cfDNA) fragmentation for the early detection of esophageal squamous cell carcinoma (ESCC). The model combined four fragmentomics features obtained from whole genome sequencing (WGS) and employed four machine learning algorithms. We evaluated the model’s generalizability in an independent validation cohort and an external cohort collected at different center. Additionally, the model’s robustness and repeatability were assessed across low coverage and repeated measured samples. The results underscore the promising potential of our model as an effective strategy for early diagnosis and management of ESCC in clinical settings. Methods: 256 healthy individuals and 243 patients diagnosed with esophageal cancer were enrolled in this study, including 47 healthy participants and 44 patients in the external cohort. Plasma samples from all participants were profiled by whole-genome sequencing (WGS). Fragmentomic features encompassed copy number variation (CNV), fragmentation size coverage (FSC), fragmentation size distribution (FSD), and nucleosome positioning (NP) were incorporated alongside machine learning models to develop an optimized classification model. The model generated a cancer score ranging from 0 to 1 for each cancer and noncancer sample, with a score closer to 1 indicating a higher probability of cancer. The performance of this model was assessed using an independent validation cohort and an external cohort, and model’s robustness and reproducibility were examined. Results: A stacked ensemble model was developed by integrating four cfDNA features and four machine learning algorithms. This integrated model exhibiting remarkable sensitivity of 91.8% (89/97) and specificity of 98.1% (102/104), and AUC of 0.986 in the independent validation cohorts using the cut-off of 0.69 selected at a specificity level of 98% (105) in the training cohort. The external cohort demonstrated a sensitivity of 86.4% (38/44) and a specificity of 95.7% (45/47) with the same cut-off. The model's performance remained consistent even at low sequencing coverage depths of 0.5× (AUC 0.978), providing valuable insights into the resilience and stability of our methodology and ascertained its practical applicability in scenarios with limited sequencing resources or constraints. Furthermore, our model demonstrated sensitivity in identifying early pathological features, with a sensitivity of 94.1% (16/17) for stage I and 91.4% (53/58) for tumors smaller than 3 cm. Conclusions: A stacked ensemble model was implemented in this study, utilizing cfDNA fragmentomics features with exceptional sensitivity for early detection of esophageal squamous cell carcinoma. The anticipated impact of this model is the enhancement of early detection strategies for esophageal cancer in clinical setting.

Code Clones: A Novel Approach to Detecting Plagiarism in Binary Decomposition of C Programs

Abstract The escalation of code plagiarism in computer science education has necessitated the development of more sophisticated detection methods, particularly for binary files which pose a unique challenge. This study introduces a novel approach to plagiarism detection through binary decomposition, which decompiles binary files back into high-level code to reveal similarities that may be hidden at the binary level. The methodology is crucial for scenarios where students submit compiled programs, enabling the application of traditional code similarity analysis methods to detect instances of plagiarism. Statistical data on code similarity among student submissions in a C programming course are presented, highlighting the urgent need for robust detection strategies. This paper explores the ethical implications of plagiarism, the technical challenges of binary file analysis, and the potential for applying this technique across various programming languages and educational contexts.