Custom Algorithm Research Articles

Mathematical modeling and simulations using software like MATLAB, COMSOL and Python

This study explores the application of MATLAB, COMSOL, and Python in mathematical modeling and simulation within precision engineering. These tools are analyzed for their strengths in handling various engineering challenges, from control systems to multiphysics simulations and custom algorithm development. The study also investigates the role of artificial intelligence (AI), in supporting mathematical modeling tasks by automating coding, providing concept explanations, and aiding model structuring. By comparing computational performance, accuracy, and usability, the research aims to identify the best-suited software for different simulation types, such as thermal-fluid dynamics and structural analysis. The findings underscore the significance of choosing appropriate software for optimizing computational resources, validating models, and achieving reliable, efficient simulations. This study contributes practical guidelines for bridging the gap between theoretical models and practical applications, enhancing productivity, and fostering innovation in precision engineering.

Transitions Into Freezing Environments Linked With Shifts in Phylogenetic Integration Between Vitaceae Leaf Traits.

Understanding how the intrinsic ability of populations and species to meet shifting selective demands shapes evolutionary patterns over both short and long timescales is a major question in biology. One major axis of evolutionary flexibility can be measured by phenotypic integration and modularity. The strength, scale, and structure of integration may constrain or catalyze evolution in the face of new selective pressures. We analyze a dataset of seven leaf measurements across Vitaceae to examine how correlations in trait divergence are linked to transitions between freezing and nonfreezing habitats. We assess this by applying a custom algorithm to compare the timing of habitat shifts to changes in the structure of evolutionary trait correlation at discrete points along a phylogeny. We also explore these patterns in relation to lineage diversification rates to understand how and whether patterns in the evolvability of complex multivariate phenotypes are linked to higher-level macroevolutionary dynamics. We found that shifts in the structure, but not the overall strength, of phylogenetic integration of leaves precipitate colonization of freezing climates. Lineages that underwent associated shifts in leaf trait integration and subsequent movement into freezing habitats also displayed lower turnover and higher net diversification, suggesting a link among shifting vectors of selection, internal constraint, and lineage persistence in the face of changing environments.

An Automated Clubbed Fingers Detection System Based on YOLOv8 and U-Net: A Tool for Early Prediction of Lung and Cardiovascular Diseases

Background/Objectives: Lung and cardiovascular diseases are leading causes of mortality worldwide, yet early detection remains challenging due to the subtle symptoms. Digital clubbing, characterized by the bulbous enlargement of the fingertips, serves as an early indicator of these diseases. This study aims to develop an automated system for detecting digital clubbing using deep-learning models for real-time monitoring and early intervention. Methods: The proposed system utilizes the YOLOv8 model for object detection and U-Net for image segmentation, integrated with the ESP32-CAM development board to capture and analyze finger images. The severity of digital clubbing is determined using a custom algorithm based on the Lovibond angle theory, categorizing the condition into normal, mild, moderate, and severe. The system was evaluated using 1768 images and achieved cloud-based and real-time processing capabilities. Results: The system demonstrated high accuracy (98.34%) in real-time detection with precision (98.22%), sensitivity (99.48%), and specificity (98.22%). Cloud-based processing achieved slightly lower but robust results, with an accuracy of 96.38%. The average processing time was 0.15 s per image, showcasing its real-time potential. Conclusions: This automated system provides a scalable and cost-effective solution for the early detection of digital clubbing, enabling timely intervention for lung and cardiovascular diseases. Its high accuracy and real-time capabilities make it suitable for both clinical and home-based health monitoring.

Oligo cyc-DEP: On-chip cyclic immunofluorescence profiling of cell-derived nanoparticles.

We present a follow-on technique for the cyclic-immunofluorescence profiling of suspension particles isolated using dielectrophoresis. The original lab-on-chip technique ("cyc-DEP" [cyclic immunofluorescent imaging on dielectrophoretic chip]) was designed for the multiplex surveillance of circulating biomarkers. Nanoparticles were collected from low-volume liquid biopsies using microfluidic dielectrophoretic chip technology. Subsequent rounds of cyclic immunofluorescent labeling and quenching were imaged and quantified with a custom algorithm to detect multiple proteins. While cyc-DEP improved assay multiplicity, long runtimes threatened its clinical adoption. Here, we modify the original cyc-DEP platform to reduce assay runtimes. Nanoparticles were formulated from human prostate adenocarcinoma cells and collected using dielectrophoresis. Three proteins were labeled on-chip with a mixture of short oligonucleotide-conjugated antibodies. The sample was then incubated with complementary fluorophore-conjugated oligonucleotides, which were dehybridized using an ethylene carbonate buffer after each round of imaging. Oligonucleotide removal exhibited an average quenching efficiency of 98±3% (n=12 quenching events), matching the original cyc-DEP platform. The presented "oligo cyc-DEP" platform achieved clinically relevant sample-to-answer times, reducing the duration for three rounds of cyclic immunolabeling from approximately 20 to 6.5h-a 67% decrease attributed to rapid fluorophore removal and the consolidated co-incubation of antibodies.

Enhancing Digital Twins with Human Movement Data: A Comparative Study of Lidar-Based Tracking Methods

Digitals twins, used to represent dynamic environments, require accurate tracking of human movement to enhance their real-world application. This paper contributes to the field by systematically evaluating and comparing pre-existing tracking methods to identify strengths, weaknesses and practical applications within digital twin frameworks. The purpose of this study is to assess the efficacy of existing human movement tracking techniques for digital twins in real world environments, with the goal of improving spatial analysis and interaction within these virtual modes. We compare three approaches using indoor-mounted lidar sensors: (1) a frame-by-frame method deep learning model with convolutional neural networks (CNNs), (2) custom algorithms developed using OpenCV, and (3) the off-the-shelf lidar perception software package Percept version 1.6.3. Of these, the deep learning method performed best (F1 = 0.88), followed by Percept (F1 = 0.61), and finally the custom algorithms using OpenCV (F1 = 0.58). Each method had particular strengths and weaknesses, with OpenCV-based approaches that use frame comparison vulnerable to signal instability that is manifested as “flickering” in the dataset. Subsequent analysis of the spatial distribution of error revealed that both the custom algorithms and Percept took longer to acquire an identification, resulting in increased error near doorways. Percept software excelled in scenarios involving stationary individuals. These findings highlight the importance of selecting appropriate tracking methods for specific use. Future work will focus on model optimization, alternative data logging techniques, and innovative approaches to mitigate computational challenges, paving the way for more sophisticated and accessible spatial analysis tools. Integrating complementary sensor types and strategies, such as radar, audio levels, indoor positioning systems (IPSs), and wi-fi data, could further improve detection accuracy and validation while maintaining privacy.

Mask R-CNN and Centroid Tracking Algorithm to Process UAV Based Thermal-RGB Video for Drylot Cattle Heat Stress Monitoring

This study developed and evaluated an algorithm for processing thermal-RGB video feeds captured by an unmanned aerial vehicle (UAV) to automate heat stress monitoring in cattle housed in the drylots. The body surface temperature (BST) of individual cows was used as an indicator of heat stress. UAV data were collected using RGB and thermal infrared imagers, respectively, at 2 and 6.67 cm per pixel spatial resolution in Spring 2023 (dataset-1) and Summer 2024 (dataset-2). Study sites were two commercial drylots in Washington State. The custom algorithms were developed to: (1) detect and localize individual cows using a Mask R-CNN-based instance segmentation model combined with centroid tracking; and (2) extract BST by averaging the thermal-imagery pixels for each of the segmented cows. The algorithm showed higher detection accuracy with RGB images as input (F1 score: 0.89) compared to thermal (F1 score: 0.64). BST extraction with combined RGB and thermal imaging approach required corrections for alignment problems associated with differences in optics, imaging field of view, resolution, and lens properties. Consequently, thermal imaging-only approach was adopted for assessing real-time cow localization and BST estimation. Operating at one frame per second, algorithm successfully detected 72.4% and 81.65% of total cows in video frames from dataset-1 (38 s) and -2 (48 s), respectively. The mean absolute difference between algorithm output and ground truth (BSTGT) was 2.1 °C (dataset-1) and 3.3 °C (dataset-2), demonstrating satisfactory performance. With further refinements, this approach could be a viable tool for real-time heat stress monitoring in large-scale drylot production systems.

Does the subjective response during the measurement of fusional reserves affect the clinical diagnosis?

Fusional reserves differ with the method of measurement. The goal of this study was to compare the subjective and objective responses during the measurement of positive and negative fusional reserves using both step and ramp methods. A haploscopic system was used to measure fusional reserves. Eye movements were recorded using an EyeLink 1000 Plus eye tracker (SR Research). The stimulus disparity was changed to either mimic a prism bar (steps) or a Risley prism (ramp). Subjective responses were obtained by pressing a key on the keyboard, whereas objective break and recovery points were determined offline using a custom algorithm coded in Matlab. Thirty-three adults participated in this study. For the ramp method, the subjective and objective responses were similar for the negative (break and recovery points (t(32) = -0.82, p = 0.42) and (t(32) = 0.42, p = 0.67), respectively) and positive fusional reserves (break and recovery points (U = -1.34, p = 0.18) and t(19) = -0.25, p = 0.81), respectively). For the step method, no significant differences in positive fusional reserves were observed when measured subjectively and objectively for the break (t(32) = 1.27, p = 0.21) or the recovery point (U = -2.02, Bonferroni-adjusted p = 0.04). For the negative fusional reserve, differences were not significant for either the break or recovery points (U = -0.10, p = 0.92 and t(19) = 1.17, p = 0.26, respectively). Subjective and objective responses exhibited good agreement when measured with the ramp and step methods.

Three-dimensional assessment of subchondral arc and hip joint coverage angles in the asymptomatic young adult hip.

The primary objective of this study was to develop a custom algorithm to assess three-dimensional (3D) acetabular coverage of the femoral head based on surface models generated from computed tomography (CT) imaging. The secondary objective was to apply this algorithm to asymptomatic young adult hip joints to assess the regional 3D acetabular coverage variability and understand how these novel 3D metrics relate to traditional two-dimensional (2D) radiographic measurements of coverage. The algorithm developed automatically identifies the lateral- and medial-most edges of the acetabular lunate at one-degree intervals around the acetabular rim based on local radius of curvature. The acetabular edges and the center of a best-fit sphere to the femoral head are then used to compute the mean 3D subchondral arc angles and hip joint coverage angles in five acetabular octants. This algorithm was applied to hip models generated from pelvis/hip CT imaging or abdomen/pelvis CT angiograms of 50 patients between 17 and 25 years of age who had no history of congenital or developmental hip pathology, neuromuscular conditions, or bilateral pelvic and/or femoral fractures. Corresponding 2D acetabular coverage measures of lateral center edge angle (LCEA) and acetabular arc angle (AAA) were assessed on the patients' clinical or digitally reconstructed radiographs. The 3D subchondral arc angle in the superior region (58.0 [54.6-64.8] degrees) was significantly higher (p < 0.001) than all other acetabular subregions. The 3D hip joint coverage angle in the superior region (26.2 [20.7-28.5] degrees) was also significantly higher (p < 0.001) than all other acetabular subregions. 3D superior hip joint coverage angle demonstrated the strongest correlation with 2D LCEA (r = 0.649, p < 0.001), while 3D superior-anterior subchondral arc angle demonstrated the strongest correlation with 2D AAA (r = 0.718, p < 0.001). The 3D coverage metrics in the remaining acetabular regions did not strongly correlate with typical 2D radiographic measures. The discrepancy between standard 2D measures of radiographic acetabular coverage and actual 3D coverage identified on advanced imaging indicates potential discord between anatomic coverage and the standard clinical measures of coverage on 2D imaging. As 2D measurement of acetabular coverage is increasingly used to guide surgical decision-making to address acetabular deformities, this work would suggest that 3D measures of acetabular coverage may be important to help discriminate local coverage deficiencies, avoid inconsistencies resulting from differences in radiographic measurement techniques, and provide a better understanding of acetabular coverage in the hip joint, potentially altering surgical planning and guiding surgical technique.

Intelligent system based on multiple networks for accurate ovarian tumor semantic segmentation

Ovarian tumors, especially malignant ones, represent a global concern, with increased prevalence in recent years. More accurate medical support systems are urgently needed to support medical staff in obtaining an efficient ovarian tumors diagnosis since detection in early stages could lead to immediately applying appropriate treatment, and implicitly improving the survival rate. The current paper aims to demonstrate that more accurate systems could be designed by combining different convolutional neural networks using different custom combination approaches and by selecting the appropriate networks to be involved in the ensemble model to achieve the best performance metrics. It is essential to understand if combining all experimented networks or only the best-performing ones could always lead to the most effective results or not. The current paper is structured in three main phases. The first step is to propose the individual networks involved in the experiments. Five DeepLab-V3+ networks with different encoders (ResNet-18, ResNet-50, MobileNet-V2, InceptionResNet-V2, and Xception) were used. In the second step, the paper proposes a custom algorithm to combine multiple individual semantic segmentation networks, while the last step describes the iterative selection approach for selecting all individual networks to be combined so that the most accurate ensemble is obtained. The system performing semantic segmentation for different types of ovarian tumors, covering both benign and malignant ones, achieved 91.18 % Intersection over union (IoU), thus overperforming all individual networks. The proposed method could be extended so that more powerful deep learning models could be used.

Snarcase - Regain Control over Your Predictions with Low-Latency Machine Unlearning

The "right-to-be-forgotten" requires the removal of personal data from trained machine learning (ML) models with machine unlearning. Conducting such unlearning with low latency is crucial for responsible data management. Low-latency unlearning is challenging, but possible for certain classes of ML models when treating them as "materialised views" over training data, with carefully chosen operations and data structures for computing updates. We present Snapcase, a recommender system that can unlearn user interactions with sub-second latency on a large grocery shopping dataset with 33 million purchases and 200 thousand users. Its implementation is based on incremental view maintenance with Differential Dataflow and a custom algorithm and data structure for maintaining a top- k aggregation over the result of a sparse matrix-matrix multiplication. We demonstrate how interactive low-latency unlearning empowers users in critical scenarios to get rid of sensitive items in their recommendations and to drastically reduce their data's negative influence on other users' predictions.

Quantitative Volumetric Analysis of Retinal Ischemia with an Oxygen Diffusion Model and OCT Angiography

PurposeRetinal ischemia is a major feature of diabetic retinopathy (DR). Traditional nonperfused areas measured by optical coherence tomography angiography (OCTA) measure blood supply but not ischemia. We propose a novel 3-dimensional (3D) quantitative method to derive ischemia measurements from OCTA data. DesignCross-sectional study. ParticipantsWe acquired 223 macular OCTA volumes from 33 healthy eyes, 33 diabetic eyes without retinopathy, 7 eyes with non-referable DR, 17 eyes with referable but non-vision-threatening DR, and 133 eyes with vision-threatening DR. MethodsEach eye was scanned using a spectral-domain OCTA system (Avanti RTVue-XR, Visionix/Optovue, Inc) with 1.6 mm scan depth in a 3 × 3-mm region (640 × 304 × 304 voxels) centered on the fovea. For each scanned OCTA volume, a custom algorithm removed flow projection artifacts. We then enhanced, binarized, and skeletonized the vasculature in each OCTA volume and generated a 3D oxygen tension map using a zero-order kinetics oxygen diffusion model. Each volume was scaled to the average retina thickness in healthy controls after foveal registration and flattening of the Bruch membrane. Finally, we extracted 3D ischemia maps by comparison with a reference map established from scans of healthy eyes using the same processing. To assess the ability of the ischemia maps to grade DR severity, we constructed receiver-operating characteristic (ROC) curves for diagnosing diabetes, referable DR, and vision-threatening DR. Main outcome measuresSpearman correlation coefficient and area under ROC curve (AUC) were used to quantify the ability of the ischemia maps to DR. ResultsThe ischemia maps showed that the ischemic tissues were at or near pathologically non-perfused areas, but not the normally non-vascular tissue, such as the foveal avascular zone. We found multiple novel metrics, including inferred 3D-oxygen tension, ischemia index, and ischemic volume ratio, were strongly correlated with DR severity. The AUCs of ischemia index measured were 0.95 for diabetes, 0.89 for DR, 0.88 for referable DR, and 0.85 for vision threatening DR. ConclusionsA quantitative method to infer 3D oxygen tension and ischemia using OCTA in diabetic eyes can identify ischemic tissue that are more specific to pathologic changes in DR.

Extendable machine tool wear monitoring process using image segmentation based deep learning model and automatic detection of depth of cut line

Automating the monitoring of machine tools poses a significant challenge, with previous studies relying on machine vision to address this issue, primarily focusing on measuring specific tool wear using custom algorithms. In this study, we introduce an automated tool wear monitoring process capable of assessing wear across various tools. Utilizing deep neural network models for image classification and segmentation, our proposed process effectively masks areas where tool wear occurs. The image classification model selects the best-performing backbone model based on the highest F1 score. To account for varying depths-of-cut lines among different tools in the masked area, we introduce an algorithm that utilizes the Hough transform to determine the horizontal angle with the cut line. By systematically measuring the maximum flank wear of diverse tools, including indexable, ball, and solid endmills, using this method, we can replace conventional manual processes. This approach can be extended and automated for various machine tools, offering substantial potential for enhancing manufacturing tool monitoring processes.

IMPI: An Interface for Low-Frequency Point Mutation Identification Exemplified on Resistance Mutations in Chronic Myeloid Leukemia

Background: In genomics, highly sensitive point mutation detection is particularly relevant for cancer diagnosis and early relapse detection. Next-generation sequencing combined with unique molecular identifiers (UMIs) is known to improve the mutation detection sensitivity. Methods: We present an open-source bioinformatics framework named Interface for Point Mutation Identification (IMPI) with a graphical user interface (GUI) for processing especially small-scale NGS data to identify variants. IMPI ensures detailed UMI analysis and clustering, as well as initial raw read processing, and consensus sequence building. Furthermore, the effects of custom algorithm and parameter settings for NGS data pre-processing and UMI collapsing (e.g., UMI clustered versus unclustered (raw) reads) can be investigated. Additionally, IMPI implements optimization and quality control methods; an evolution strategy is used for parameter optimization. Results: IMPI was designed, implemented, and tested using BCR::ABL1 fusion gene kinase domain sequencing data. In summary, IMPI enables a detailed analysis of the impact of UMI clustering and parameter setting changes on the measured allele frequencies. Conclusions: Regarding the BCR::ABL1 data, IMPI’s results underlined the need for caution while designing specialized single amplicon NGS approaches due to methodical limitations (e.g., high PCR-mediated recombination rate). This cannot be corrected using UMIs.

Quantitative validation of the suprasternal pressure signal to assess respiratory effort during sleep

Objective. Intra-esophageal pressure (Pes) measurement is the recommended gold standard to quantify respiratory effort during sleep, but used to limited extent in clinical practice due to multiple practical drawbacks. Respiratory inductance plethysmography belts (RIP) in conjunction with oronasal airflow are the accepted substitute in polysomnographic systems (PSG) thanks to a better usability, although they are partial views on tidal volume and flow rather than true respiratory effort and are often used without calibration. In their place, the pressure variations measured non-invasively at the suprasternal notch (SSP) may provide a better measure of effort. However, this type of sensor has been validated only for respiratory events in the context of obstructive sleep apnea syndrome (OSA). We aim to provide an extensive verification of the suprasternal pressure signal against RIP belts and Pes, covering both normal breathing and respiratory events. Approach. We simultaneously acquired suprasternal (207) and esophageal pressure (20) signals along with RIP belts during a clinical PSG of 207 participants. In each signal, we detected breaths with a custom algorithm, and evaluated the SSP in terms of detection quality, breathing rate estimation, and similarity of breathing patterns against RIP and Pes. Additionally, we examined how the SSP signal may diverge from RIP and Pes in presence of respiratory events scored by a sleep technician. Main results. The SSP signal proved to be a reliable substitute for both esophageal pressure (Pes) and respiratory inductance plethysmography (RIP) in terms of breath detection, with sensitivity and positive predictive value exceeding 75%, and low error in breathing rate estimation. The SSP was also consistent with Pes (correlation of 0.72, similarity 80.8%) in patterns of increasing pressure amplitude that are common in OSA. Significance. This work provides a quantitative analysis of suprasternal pressure sensors for respiratory effort measurements.

Development of A Robotic Maize Seed Planter

The automatic maize seed planter design to address the limitations of traditional planting techniques and reduce the lead time in agricultural processes and enhance the output. The research encompasses a comprehensive study of maize sowing techniques, challenges, and advancements in agricultural robotics. The literature review explores the limitations of traditional maize sowing techniques, such as inaccurate seed placement, labor intensiveness, and weather dependence. The construction process involves the integration of a chassis, wheels, sensors, microcontroller, and seed dispensing system. The software component utilizes a custom algorithm to control the movement and seed placement of the robot. Experimental testing and evaluation were conducted to assess the performance of the robot. The accuracy of seed placement was determined by analyzing the deviation from the desired spacing. The efficiency of the robot was measured by comparing the time taken for sowing with traditional methods. The results demonstrated that the automatic maize seed planter achieved high accuracy in seed placement with minimal deviation from the desired spacing. The results validate the effectiveness of the robotic system in addressing the limitations of traditional sowing techniques and demonstrate its potential to revolutionize the agriculture industry. The objectives are to develop a cost-effective, efficient, and accurate maize seed planter and implement the design in the fabrication. Recommendations for further work is real-time mapping and monitoring. Real-time mapping and monitoring of the field can help the robot navigate the field more efficiently and accurately.

A techno-economic model for flexibility-oriented planning of renewable-based power systems considering integrated features of generator, network, and energy storage systems

The transition towards renewable energy sources in power networks introduces challenges characterized by significant fluctuations and uncertainties. Addressing the need to identify factors influencing flexibility planning, this study presents a novel model for Adapting Flexible Resources with Renewable Energy Resources (AFR-RER). This model is designed to capture the complex interactions of generator-grid-storage flexibility, incorporating components such as Flexible AC Transmission Systems (FACTS), tie lines, multipath adjustable units, network structure adjustments, and energy storage. The AFR-RER model is specifically developed to address the challenges arising from varying levels of renewable energy integration at different stages of carbon development. Its adaptability to accommodate diverse integration levels stands as a key feature. Furthermore, custom algorithms are devised to tackle the specific challenges posed by the AFR-RER model. To demonstrate the model's effectiveness, extensive case studies are conducted, evaluating its performance on both a modified IEEE two regional 24-test system and a real-world test system. The results of these case studies validate the resilience and practicality of the proposed AFR-RER model across various scenarios, highlighting its potential as a valuable tool for optimizing flexibility planning in power networks.

A New Biomechanical Deformation Response Parameter: Change in Central Corneal Thickness During Air Puff Induced Corneal Deformation

Purpose To investigate the percent change in central corneal thickness (%ΔCCT) during air-puff-induced deformation as an indicator of corneal biomechanical response. Methods Forty ex vivo human eyes from forty donors were imaged using the CorVis ST at experimentally controlled intraocular pressure (IOP) of 10, 20, 30, and 40 mmHg, followed by uniaxial strip testing to calculate tensile modulus. The CorVis ST research software tracked the anterior and posterior cornea edges and determined the dynamic corneal response (DCR) parameters. Eyes were excluded if image quality or posterior tracking issues were present. Custom algorithms were used to calculate CCT during deformation using a ray-tracing method to correct for Scheimpflug and optical distortion within each image. Correlation and stepwise regression analyses between the shape-related DCR parameters and %ΔCCT were conducted. A mixed model analysis was performed to test the effect of IOP and the strongest significant predictors of the stepwise regression on %ΔCCT. The significance threshold was set to p < 0.05. Results Thirty eyes were ultimately analyzed and CCT increased significantly from the pre-deformation state to the highest concavity state at each IOP level (p < 0.001). IOP and multiple shape DCRs were found to be significantly related to %ΔCCT (p < 0.0001). The strongest predictor of %ΔCCT was integrated inverse radius (IIR) (p < 0.0001; partial R2 = 0.4772) with no other parameter having a partial R2 value greater than 0.04. The mixed model analysis showed that IIR was the sole predictor (p = 0.0098) and IOP was no longer significant as a single predictor. However, the interaction of IIR with IOP (p = 0.0023) had a significant effect on %ΔCCT. Conclusion Percent change in CCT is influenced by corneal stiffness as indicated by the significant relationship with IIR. The %ΔCCT may be a potential biomarker for determining differences in corneal deformation response with corneal diseases.

ASIVA – Platform for observational and computational analysis of stellar variables

The astronomical data analysis consists of two crucial process; data reduction of the captured images and data analysis of the derived magnitudes. We present the platform ASIVA, a data analysis platform which comes along with a data reduction pipeline. The data reduction pipeline gives flexibility to analyse the FITS images and also perform image alignment for detecting the correct image coordinates for required objects. It can be custom scheduled with cron jobs so that it picks the latest data and appends the results accordingly. The data analysis platform allows user to effectively analyse the ensemble data and perform accurate data processing and grouping with ease. It is integrated with a custom algorithm to detect the variable stars from an ensemble with its relative standard deviations. The statistical, spectral and non-linear dynamics features can be extracted from time series data which can be eventually used for in-depth analysis. To validate the capability, we have analysed 15 nights of Orion Nebula Cluster field in I filter which had 1585 images. ASIVA reduces manual effort to a great extent thus saves analysis time and excludes human errors.

Abstract 5024: Analytical performance of a genome-wide methylome enrichment platform to detect minimal residual disease from plasma-derived cell-free DNA

Abstract Introduction: Plasma-derived cell-free DNA (cfDNA) can be used to identify cancer signals, including minimal residual disease (MRD), in patients who have undergone curative cancer treatments. The cell-free methylated DNA immunoprecipitation and high-throughput sequencing (cfMeDIP-seq) methodology is combined with custom algorithms that leverage differentially methylated regions (DMRs) found in cfDNA to distinguish between cancer and non-cancer signals. This novel non-degradative, tissue-agnostic approach was developed to bypass the limitations of bisulfite-sequencing and tissue-informed methods used in other liquid biopsy technologies. Here we present preliminary analytical performance metrics of an algorithm in development for detecting MRD. Methods: In the genome-wide methylome enrichment platform, plasma-derived cfDNA is subjected to standard library preparation, combined with DNA filler, denatured, and immunoprecipitated using an anti-5-mC antibody. Captured DNA is amplified and sequenced. For this study, a candidate algorithm comprised of differentially methylated regions (DMRs) and 12 cancer types was used to quantify cancer-specific methylation. Control samples from 12 non-cancer donors were used to establish a selected 95% true negative rate threshold. This threshold was used to determine assay sensitivity, which was evaluated using contrived cancer samples intended to mimic low-level circulating tumor DNA (ctDNA) representative of MRD. Samples were contrived by titrating fragmented DNA from three immortalized tumor-derived cell-lines into plasma-derived cfDNA in a titration series targeting &amp;lt;1% ctDNA levels. Cell-lines were of non-small cell lung cancer and head and neck squamous cell carcinoma origin; titration series were replicated for a total of 65 tests. Results: All non-cancer and contrived cancer samples met in-process quality control metrics. This included ≥98.5% methylation specificity and ≥80 million unique molecules. Limit of detection calculations at 95% sensitivity (LoD95) were &amp;lt;0.1%. Conclusions: Tissue-agnostic approaches for detecting cancer signals from plasma have significant benefit, especially in settings where tissue is not accessible for evaluation. However, these tests must have highly sensitive methods of cancer signal detection for clinical applications. Our preliminary analytical data demonstrates the use of a blood-based, tissue- agnostic genome-wide methylome enrichment platform utilizing non-degradative methodology combined with specific algorithms and DMRs for MRD quantification and prognostic prediction, using treatment-naïve plasma samples. Future studies in post-treatment and longitudinal samples are ongoing to evaluate the utility of this genome-wide methylome enrichment platform for cancer management. Citation Format: Shu Yi Shen, Iulia Cirlan, Felicia Vincelli, Ben Brown, Jun Min, Justin Burgener, Junjun Zhang, Yulia Newton, Margaret Gruca, Abel Licon, Jing Zhang, Anne-Renee Hartman, Alan Williams, Hestia Mellert, Daniel D. De Carvalho. Analytical performance of a genome-wide methylome enrichment platform to detect minimal residual disease from plasma-derived cell-free DNA [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 5024.

Reduced contrast sensitivity function is correlated with changes to cone photoreceptors in simple high myopia.

To investigate the contrast sensitivity function (CSF) changes in simple high myopia (SHM) and evaluate the correlations between these changes with the early changes in the retinal microstructure. This prospective study comprised 81 subjects, 20 with emmetropia (EM), 26 with low myopia and moderate myopia (LM/MM), and 35 with SHM. The area under the log CSF curve (AULCSF) and the cut-off spatial frequency (Cut-off SF) were employed as measures of CSF. Adaptive optics (AO) was employed to quantify the cone density, spacing, and regularity. The thickness and blood flow of the retinal sublayers were determined from vertical and horizontal optical coherence tomography angiography (OCTA) A-scans. Swept-source optical coherence tomography (SS-OCT) was employed to analyze the choroidal thickness (CT) and choroidal vascularity using a custom algorithm. Differences in the retinal and choroidal parameters, cone distribution, AULCSF, and Cut-off SF were compared among the three groups. Multivariate linear mixed models were used to elucidate the associations between photoreceptor morphological alterations, retinal and choroidal parameters, and AULCSF. The AULCSF and Cut-off SF were significantly lower in the SHM group compared to the EM and LM groups (p < 0.05). The SHM group had less cone density, larger cone spacing, and lower cone regularity than the EM and LM/MM groups (p < 0.05). Moreover, the thickness of the inner segment of photoreceptors (IS), retinal pigment epithelium (RPE) layer and choroid were reduced, and the outer segment of photoreceptors (OS) was thicker in the SHM group compared to the EM and LM/MM groups (all p < 0.05). A longer axial length (AL) was correlated with decreased AULCSF, cone density, and cone spacing (r = -0.800 to 0.752, all p < 0.050). Additionally, decreased CSF was correlated with lower cone density (r = 0.338, p = 0.035). Decreased contrast sensitivity was observed in patients with SHM and cone density was significantly correlated with reduced AUCSF.