Histogram Research Articles

E-GWAS: an ensemble-like GWAS strategy that provides effective control over false positive rates without decreasing true positives

BackgroundGenome-wide association studies (GWAS) are an effective way to explore genotype–phenotype associations in humans, animals, and plants. Various GWAS methods have been developed based on different genetic or statistical assumptions. However, no single method is optimal for all traits and, for many traits, the putative single nucleotide polymorphisms (SNPs) that are detected by the different methods do not entirely overlap due to the diversity of the genetic architecture of complex traits. Therefore, multi-tool-based GWAS strategies that combine different methods have been increasingly employed. To take this one step further, we propose an ensemble-like GWAS strategy (E-GWAS) that statistically integrates GWAS results from different single GWAS methods.ResultsE-GWAS was compared with various single GWAS methods using simulated phenotype traits with different genetic architectures. E-GWAS performed stably across traits with different genetic architectures and effectively controlled the number of false positive genetic variants detected without decreasing the number of true positive variants. In addition, its performance could be further improved by using a bin-merged strategy and the addition of more distinct single GWAS methods. Our results show that the numbers of true and false positive SNPs detected by the E-GWAS strategy slightly increased and decreased, respectively, with increasing bin size and when the number and the diversity of individual GWAS methods that were integrated in E-GWAS increased, the latter being more effective than the bin-merged strategy. The E-GWAS strategy was also applied to a real dataset to study backfat thickness in a pig population, and 10 candidate genes related to this trait and expressed in adipose-associated tissues were identified.ConclusionsUsing both simulated and real datasets, we show that E-GWAS is a reliable and robust strategy that effectively integrates the GWAS results of different methods and reduces the number of false positive SNPs without decreasing that of true positive SNPs.

A novel fast-timing readout chain for LHCb RICH LS3 and prototype beam tests

The prompt Cherenkov radiation and focusing optics of the LHCb RICH detectors allow the prediction of the Cherenkov photon detection time from a given charged particle to within 10ps. Fast-timing information on the detected Cherenkov photons can therefore be used to significantly improve the particle identification (PID) performance and the signal-to-background ratio of the detectors. This concept is a cornerstone for the LHCb RICH detector upgrades and will ultimately allow the system to operate at a luminosity in excess of 1034cm−2s−1 during HL-LHC Run 5. A new electronic readout chain is proposed for the LHC Long Shutdown 3 (LS3, 2026-2028) using the FastRICH, a novel ASIC under development. The specifications for the FastRICH are discussed in the context of the LS3 enhancements and LHCb Upgrade II. The FastRICH will perform multi-channel single-photon discrimination, timestamp photons with 25 ps bin size, integrate closely with the LHCb optical link chipset and apply data-compression techniques. The detector will be capable of timestamping each photon with 150ps resolution dominated by the existing Multianode Photomultiplier Tube (MAPMT) transit-time spread. The new electronic readout chain introduces important timing and detector techniques ahead of the Upgrade II RICH system overhaul and the FastRICH has the flexibility to be coupled to sensors with better time resolution for HL-LHC Run 5. Simulation studies have demonstrated improvements in the hadronic PID performance during Run 4 using the FastRICH coupled to MAPMTs. A first version of the readout chain, based on the FastIC, a predecessor of the FastRICH, and a TDC-in-FPGA, has been studied using Cherenkov photons at the CERN SPS charged particle beam test facility.

Design of non-imaging receiving system for large field of view lidar

Lidar has been widely used in many fields due to its high precision. The system must also have a corresponding large field of view receiving capability in order to achieve effective detection of targets when imaging lidar realizes large-angle scanning. For the InGaAs-APD detector component, there is a contradiction between the high-frequency response bandwidth and the size of the photosensitive surface. This paper proposes a large surface four-quadrant InGaAs-APD detector with 2 × 2 area array structure design, which based on a small photosensitive panel detector with a unit diameter of 0.5 mm. On the basis of ensuring the cut-off frequency bandwidth of 100 MHz, it also realizes signal reception with large field of view and high sensitivity. For the receiving lens of the system, the relationship between the bin size of the detector and the receiving field of view in the non-imaging optical receiving system is studied. Under the condition that the parameters such as the size of the detector facet have been determined, the best position of the detector is optimally designed to maximize the receiving field of view. Based on theoretical foundations such as radiation angle coefficient and etendue, the non-imaging optical system lidar distance equation is established to evaluate the light-gathering ability of receiving lenses with different effective apertures and focal lengths, and to optimize optical structural parameters such as the best effective aperture and focal length of the receiving lens. Finally, the aspheric lens with the best parameters is selected as the basic receiving optical lens of the system, and an array type large field of view direct detection technology is proposed. The receiving optical system is composed of 6 aspheric single lenses, and the structure is in the form of 2 × 3 array, which finally realizes the efficient detection of echo signals with a large field of view of 48° × 26°.

Discrete seafloor features associated with juvenile haddock (Melanogrammus aeglefinus): A coarse-graining approach for identifying habitat scale

Ecological patterns and processes occur at a myriad of spatial and temporal scales. Determining the appropriate scale(s) to collect and analyze data depends on the phenomena being studied and no one scale is appropriate for all of these. Knowing relevant measurement scales avoids the risk of presenting results that are merely a function of measurement scale rather than those relevant to actual ecological processes. Attribution of patterns to ecological processes requires a means to measure and evaluate these patterns. We present a standardized method to determine relevant spatial scales of fish-habitat associations observed from linear transect data on juvenile haddock (Melanogrammus aeglefinus), collected from six sites on the Scotian Shelf, off Canada's eastern coast. The survey data came from a large spatial scale research project undertaken by Fisheries and Oceans Canada to investigate spatial distribution of demersal fish in relation to seabed habitat in 2002 and 2003. We examined the relationship between two elements of this extensive dataset – the “towcam” video survey of demersal fish, and the interpreted surficial geology maps derived from sidescan sonar. We used the data from these surveys (48 towcam survey transect lines; 6 study sites, 2 5-km transect lines per site, day and night surveys per line, in each of 2 years = 240 km of transects), superimposed on the interpreted surficial geology layer collected at the same time. The resolution of the observations (∼2.5 m) facilitated a detailed examination of the spatial scales of juvenile haddock association with seabed habitat. This was achieved using a “coarse-graining” approach, which we applied by summarizing fish abundance and seabed habitat proportions across bin sizes starting at 5 m and progressively doubling to 640 m. We tallied significant correlations by bin size to observe patterns in fish-habitat associations as a function of measurement scale, for each age group and diel period. We found that Juvenile haddock-seabed associations deteriorated at the grain limits marking the range of spatial scaling relevant for this phenomenon. We present a robust approach for quantifying appropriate scales of aggregations across linear transects which can serve as an important tool for basing observations to describe ecological patterns in space.

Role of finite probe size in measuring growth exponent in film deposition

We use computer simulations to investigate the effects of the tip diameter of an electrostatic force microscope (EFM) operating at a constant force on the extraction of the growth exponent β during film growing in a one-dimensional substrate. Laplace’s equation is solved in the EFM simulation using the finite element method to determine the electrostatic force between the tip and the film interface. Importantly, for EFM tips with sufficiently large apex diameters, the topographies calculated with EFM and those computed with the transformed mean height profile (TMHP) method, where the interface is divided into bins of the same tip diameter size and the average height within each bin is used to transform the original interface, are almost identical. This was shown in the context of lattice models of the Kardar–Parisi–Zhang (KPZ) and Villain–Lai–Das–Sarma (VLDS) classes. The global roughness of the film surface, W, scales with the diameter of the EFM tip, ε, as W/a=(ε/a)αg[Ψ], where a is the lattice parameter, α is the KPZ/VLDS roughness exponent, and g is a universal scaling function of the argument Ψ≡t/(ε/a)z, where t and z are the reduced time of deposition and the KPZ/VLDS dynamic exponent, respectively. These results provide a limit for ε from which a KPZ/VLDS growth exponent can be reliably determined with EFM at a constant force. When the EFM tip diameter is larger than the surface correlation length, a misleading effective growth exponent consistent with uncorrelated growth is found.

Germplasm collection – valuable resources of variability for plant and ear traits in maize breeding

Maize is one of the most important crops, both worldwide and in Romania, and preserving the diversity of the biological material used in the breeding of this plant is of particular importance. The Agricultural Research and Development Station (ARDS) Turda, Romania, inbred lines collection includes both its own genotypes and some obtained as a result of germplasm exchanges with other institutions in the country or abroad. In the present study, 575 lines created at Turda were analysed regarding some traits of the plant (plant height, ear height, total number of leaves, number of tassel branches) and the ear (ear length and weight, number of kernel rows and number of kernels/row). The biological material used in this study shows a great diversity: a medium or high variability coefficient was identified for several of the traits analysed (number of tassel branches, ear height, ear height). Frequency histograms were made for the studied lines, for plant and ear traits. A great variability also was observed in the colours of the anther and silk, but also for kernel type and colour and cob colour.

Abstract A054: Accurate prediction of cohesin-mediated 3D genome organization using 2D chromatin features

Abstract The three-dimensional (3D) genome organization influences diverse nuclear processes. Chromatin interaction analysis by paired-end tag sequencing (ChIA-PET) and High-throughput chromatin conformation capture (Hi-C) are powerful methods used to study the 3D genome organization. However, these two experiments are costly, time-consuming, require tens to hundreds of millions of cells, and challenging to optimize and analyze. Predicting ChIA-PET/Hi-C data using cheaper ChIP-Seq data and other easily obtainable features is a useful alternative. Also, it is well-established that the cohesin protein complex is a key determinant of 3D genome organization. Considering this, we present Chromatin Interaction Predictor (ChIPr), a suite of regression models based on deep neural networks (DNN), random forest, and gradient boosting. ChIPr predicts cohesin-mediated chromatin interaction strength between any two given loci in the genome. It was trained mainly using two top-tiered ENCODE cell lines GM12878 and K562 and tested on other cell lines (H1 and HepG2). Comprehensive tests showed that ChIPr predictions correlated well with the original ChIA-PET data at the peak-level resolution and at bin sizes 25kb and 5kb. In addition, ChIPr accurately captured most of the cell-type-dependent loops identified by both, ChIA-PET and Hi-C data, respectively. Extensive feature testing highlighted genomic distance and RAD21 (a cohesin component) ChIP-Seq signals as the most important inputs contributing to ChIPr, in determining the chromatin interaction strength. A standard ChIPr model requires three experimental inputs: ChIP-Seq signals for RAD21, H3K27ac (enhancer/active chromatin mark), and H3K27me3 (inactive chromatin mark). While a reduced model requires a single experiment input: ChIP-Seq signals for RAD21 and performs equally well. Additionally, the use of ChIPr was further extended to predict the contact maps for several prostate cancer (PCa) cell lines whose ChIA-PET data were not available on the ENCODE portal, for instance, RWPE1 and VCaP. We generated the necessary ChIP-seq data for these cell lines to use as input to the model to further examine the changes in the 3D interactions of PCa driver genes and also their isolated neighbourhoods and decipher useful biological insights about the gene regulatory networks involved. Integrative analysis revealed novel insights into the role of CTCF motif, its orientation, and CTCF binding on the prevalence and strength of cohesin-mediated chromatin interactions (~50% - 80% of the RAD21 interactions were enriched with CTCF, < 15% of the interactions had no CTCF ChIP-seq binding in both of the two peaks). Lastly, we also noticed a subset of RAD21 interactions with CTCF binding in only one or none of the two anchor peaks, which were significantly enriched with enhancers. Taken together, this study outlines the general features of genome folding and opens new avenues to analyze spatial genome organization in specimens with limited cell numbers. Citation Format: Khyati Chandratre. Accurate prediction of cohesin-mediated 3D genome organization using 2D chromatin features [abstract]. In: Proceedings of the AACR Special Conference: Advances in Prostate Cancer Research; 2023 Mar 15-18; Denver, Colorado. Philadelphia (PA): AACR; Cancer Res 2023;83(11 Suppl):Abstract nr A054.

Elastic Data Binning: Time-Series Sketching for Time-Domain Astrophysics Analysis

Time-domain astrophysics analysis (TDAA) involves observational surveys of celestial phenomena that may contain irrelevant information because of several factors, one of which is the sensitivity of the optical telescopes. Data binning is a typical technique for removing inconsistencies and clarifying the main characteristics of the original data in astrophysics analysis. It splits the data sequence into smaller bins with a fixed size and subsequently sketches them into a new representation form. In this study, we introduce a novel approach, called elastic data binning (EBinning), to automatically adjust each bin size using two statistical metrics based on the Student's t-test for linear regression and Hoeffding inequality. EBinning outperforms well-known algorithms in TDAA for extracting relevant characteristics of time-series data, called lightcurve. We demonstrate the successful representation of various characteristics in the lightcurve gathered from the Kiso Schmidt telescope using EBinning and its applicability for transient detection in TDAA.

Elastic Data Binning: Time-Series Sketching for Time-Domain Astrophysics Analysis

Time-domain astrophysics analysis (TDAA) involves observational surveys of celestial phenomena that may contain irrelevant information because of several factors, one of which is the sensitivity of the optical telescopes. Data binning is a typical technique for removing inconsistencies and clarifying the main characteristics of the original data in astrophysics analysis. It splits the data sequence into smaller bins with a fixed size and subsequently sketches them into a new representation form. In this study, we introduce a novel approach, called elastic data binning (EBinning), to automatically adjust each bin size using two statistical metrics based on the Student's t-test for linear regression and Hoeffding inequality. EBinning outperforms well-known algorithms in TDAA for extracting relevant characteristics of time-series data, called lightcurve. We demonstrate the successful representation of various characteristics in the lightcurve gathered from the Kiso Schmidt telescope using EBinning and its applicability for transient detection in TDAA.

0269 No More Actiwatches: Can Apple Watches be a More Scalable Alternative?

Abstract Introduction The Philips Respironics Actiwatch has become a gold standard for actigraphy data collection. With the announcement of their discontinuation, there has been increased momentum to identify an alternative, particularly with consumer-based devices. One promising solution is the Apple Watch because it allows user access to raw accelerometer data, thus eliminating the long-standing problem of the “black box algorithm” with wearable technology. This study compared the activity counts derived from Apple Watch data with that from the Actiwatch. Methods Adults wore an Actiwatch and Apple Watch on the same non-dominant wrist for 7 to 14 days (mean = 9). Accelerometer data were recorded and activity counts were derived from accelerometer data (algorithm for Apple Watch from Lindert et al, 2013). Daily sleep diaries were also completed. Actigraphy activity counts were binned into 2, 5, 10, 30, and 60 minute bins to examine concordance by bin sizes. Agreement between Apple Watch and Actiwatch were quantified with Lin’s Concordance Correlation Coefficients (CCC) across bins. Usability was assessed utilizing an exit survey. Results In increasing order of bins, the CCCs were: .63 (2 mins.), .70 (5 mins.), .78 (10 mins.), .90 (30 mins.), and .92 (60 mins.). Agreement was substantial for bins of 5 minutes or greater with 30 and 60 minutes showing the strongest agreement. The y-intercepts for all bins were positive indicating that Apple Watches were more likely to detect activity counts compared to Actiwatches. In terms of usability, a large majority (88%-100%) of participants favored the Apple Watch for comfort, convenience, preference to wear in public, and preference to wear again. The only dimension where there was not a clear preference was ease of use, with 56% indicating a preference for the Apple Watch. Conclusion This is the first study supporting Apple Watch as a potentially feasible alternative to an Actiwatch. Given that the Apple Watch’s accelerometer appears to be more sensitive, use of a different algorithm for determining activity counts from raw accelerometer data may improve the concordance at smaller bins. Future research should explore the effectiveness of the Apple Watch for sleep/wake detection in clinical samples. Support (if any)

Denoising and Accuracy Evaluation of ICESat-2/ATLAS Photon Data for Nearshore Waters Based on Improved Local Distance Statistics

The second-generation spaceborne LiDAR-Ice, Cloud and Land Elevation Satellite-2 (ICESat-2) carries the Advanced Topographic Laser Altimeter System (ATLAS), which can penetrate a certain depth of water, and is one of the important means to obtain the water depth information of nearshore water. However, due to the influence of the atmospheric environment, water quality and color, the system itself and other factors, the photon point cloud introduces survey noise, which restricts the survey accuracy and reliability of nearshore water depth. Therefore, in this study, we presented a photon denoising algorithm for layered processing of submarine surface. Firstly, rough denoising of the original photon data was completed by smoothing filtering. Then, elevation histogram statistics were carried out on the photon data, two peaks of the histogram were fitted by a double Gaussian function, and the intersection of two curves was then taken to separate the water surface and underwater photons. The surface photons were denoised by the DBSCAN clustering algorithm. Then according to the distribution characteristics of underwater signal photons, a single-photon point cloud filtering bathymetric method was proposed based on improved local distance statistics (LDSBM), which was used for fine denoising of underwater point cloud data. Finally, the Gaussian function was used to fit the frequency histogram, and the signal photons were screened to extract the water depth information. In this study, 13 groups of the ATL03 dataset from the Xisha Islands, the St. Thomas and the Acklins Island were used for denoising. The denoising results were compared with the signal photons manually marked and the signal photons extracted by the official built-in method (OM). The experimental results showed that, compared with the official method results of ATL03, the LDSBM had a higher F value (comprehensive evaluation index), with an average of more than 96.70%. In conclusion, the proposed underwater single-photon point cloud filtering bathymetric method was superior to the traditional algorithm and could recover terrain information accurately.

NKX3.1 immunohistochemistry and methylome profiling in mesenchymal chondrosarcoma: additional diagnostic value for a well-defined disease?

Mesenchymal chondrosarcoma (MCS) is a rare and highly aggressive tumour of soft tissue and bone that is defined by an underlying and highly specific fusion transcript involving HEY1 and NCOA2. Histologically, the tumours show a biphasic appearance consisting of an undifferentiated blue and round cell component as well as islands of highly differentiated cartilage. Particularly in core needle biopsies, the chondromatous component can be missed and the non-specific morphology and immunophenotype of the round cell component can cause diagnostic challenges. We applied NKX3.1 immunohistochemistry which was recently reported as a highly specific marker as well as methylome and copy number profiling to a set of 45 well characterised MCS cases to evaluate their potential diagnostic value. Methylome profiling revealed a highly distinct cluster for MCS. Notably, the findings were reproducible also when analysing the round cell and cartilaginous component separately. Furthermore, four outliers were identified by methylome profiling for which the diagnosis had to be revised. NKX3.1 immunohistochemistry showed positivity in 36% of tumours, the majority of which was rather focal and weak. Taken together, NKX3.1 expression showed a low sensitivity but a high specificity in our analysis. Methylome profiling on the other hand represents a sensitive, specific and reliable tool to support the diagnosis of MCS, particularly if only the round cell component is obtained in a biopsy and the diagnosis is not suspected. Furthermore, it can aid in confirming the diagnosis in case RNA sequencing for the HEY1::NCOA2 fusion transcript is not available.

Random forest method for analysis of remote sensing inversion of aboveground biomass and grazing intensity of grasslands in Inner Mongolia, China

ABSTRACT The quantification of grassland above-ground biomass (AGB) and grazing intensity (GI) and their distribution in space is of great significance to grassland management and eco-conservation. Remote-sensing technology is widely applied, but it is difficult to measure accurately when monitoring GI. In this study, the neural network, random forest and statistical function models of the relationship between Landsat NDVI and AGB were constructed by field survey and literature data collection in Inner Mongolia grassland, China. By comparing the accuracy among the three models, we constructed a remote-sensing retrieving model of grass AGB. We also estimated the grassland AGB during the peak growing season (August) for Inner Mongolia. Frequency histograms were then made to identify AGB thresholds under four GI levels (light or ungrazed, moderate grazing, overgrazing and extreme grazing) for each of three grassland types (meadow steppe, typical steppe and desert steppe). This study shows that the random forest model simulates grass AGB more accurately than other models. The spatial distribution of AGB in Inner Mongolia grasslands showed a tendency of decreasing from southeast to northwest, with an increasing trend in the last 10 years. The four GI levels in 2021 accounted for 18%, 25%, 36% and 21% of the grasslands in Inner Mongolia, respectively, and over the last 10 years the GI first improved and then deteriorated. This study provides a guideline to remote monitoring for grassland AGB and GI, and supplies scientific support for sustainable management and grassland restoration of large-scale grasslands.

Characterisation of chaos in meteoroid streams

Context. Dynamically linking a meteor shower with its parent body can be challenging. This is in part due to the limits of the tools available today (such as D-criteria) but is also due to the complex dynamics of meteoroid streams. Aims. We choose a method to study chaos in meteoroid streams and apply it to the Geminid meteoroid stream. Methods. We decided to draw chaos maps. Amongst the chaos indicators we studied, we show that the orthogonal fast Lyapunov indicator is particularly well suited to our problem. The maps are drawn for three bin sizes, ranging from 10−1 to 10−4 m. Results. We show the influence of mean-motion resonances with the Earth and with Venus, which tend to trap the largest particles. The chaos maps present three distinct regimes in eccentricity, reflecting close encounters with the planets. We also study the effect of non-gravitational forces. We determine a first approximation of the particle size rlim needed to counterbalance the resonances with the diffusion due to the non-gravitational forces. We find that, for the Geminids, rlim lies in the range [3; 8] × 10−4 m. However, rlim depends on the orbital phase space.

CI-452767-3 MACHINE LEARNING-POWERED, DEVICE-EMBEDDED HEART SOUND MEASUREMENT CAN OPTIMIZE AV DELAY IN CRT PATIENTS

Continuous ambulatory optimization of atrioventricular (AV) delay for cardiac resynchronization therapy (CRT) is mainly performed by electrical means. Development of an estimation model of cardiac function that uses a piezoelectric transducer embedded in an internal pulse generator to guide CRT optimization in patients. Electrocardiogram, left ventricular pressure (LVP) and heart sounds were simultaneously collected during the implantation procedures. A piezoelectric alarm transducer embedded in a modified CRT device facilitated recording heart sound in 22 patients undergoing a pacing protocol with AV delays varying from 60 to 330 ms. Post-hoc, machine learning was employed to produce a decision tree ensemble model capable of estimating maximal LVP (LVPmax) and maximal rate of rise of LVP (LVdP/dtmax) using heart-sound based features. To gauge the applicability of machine learning in AV delay optimization, polynomial curves were fitted to invasively measured and estimated values. The figure illustrates the concept of the study and an example of the invasively measured and heart sound-estimated AV delay optimization curves. In the dataset of ∼30,000 heartbeats, machine learning indicated S1 amplitude, S2 amplitude and S1 integral (or energy) as the most prominent features for AV delay optimization in terms of LVPmax and LVdP/dtmax. Machine learning facilitated categorical beat by beat estimation for absolute values of LVPmax (10 mmHg bin size) and LVdP/dtmax (100 mmHg/s bin size). The right panel illustrates the similar absolute values for LVPmax using 30 second averages. Estimated optimal AV delays using ∼30 second recordings were not significantly different from those measured using invasive LVP (difference: -5.6 ± 17.1 ms for LVPmax and +5.1 ± 46.7 ms for LVdP/dtmax). The difference in function between the estimated and measured optimal AV delays was small and not statically significant (1 ± 3mmHg for LVPmax and 9 ± 57mmHg/s for LVdP/dtmax). Heart sound sensors embedded in a CRT device, powered by a machine learning algorithm provide a reliable assessment of both optimal AV delays and absolute LVPmax and LVdP/dtmax in a clinical environment.

A hybrid biogeography-based optimization algorithm for three-dimensional bin size designing and packing problem

The three-dimensional bin packing problem (3D-BPP) plays a critical role in logistics activities. Prior studies focus on 3D-BPP in cases where bin size is predetermined. However, bin volume utilization can be improved significantly by optimizing bin size. In this study, we propose a novel three-dimensional bin size designing and packing problem (3D-BSDPP) and a hybrid biogeography-based optimization (HBBO) algorithm to address the problem by maximizing bin volume utilization by optimizing the bin size. The HBBO combines a differential evolution algorithm with basic biogeography-based optimization to improve exploration ability, and a novel local search strategy involving three specific local search operators is proposed to accelerate the HBBO convergence. Finally, a novel heuristic packing method is employed to find a high-quality packing solution. The proposed algorithm is evaluated through experiments carried out on a practical bin packing application. The results indicate that the proposed algorithm outperforms existing algorithms in solving the 3D-BSDPP.

A new denoising method for photon-counting LiDAR data with different surface types and observation conditions

ABSTRACT Spaceborne photon-counting LiDAR is significantly affected by noise, and existing denoising algorithms cannot be universally adapted to different surface types and topographies under all observation conditions. Accordingly, a new denoising method is presented to extract signal photons adaptively. The method includes two steps. First, the local neighborhood radius is calculated according to photons' density, then the first-step denoising process is completed via photons’ curvature feature based on KNN search and covariance matrix. Second, the local photon filtering direction and threshold are obtained based on the first-step denoising results by RANSAC and elevation frequency histogram, and the local dense noise photons that the first-step cannot be identified are further eliminated. The following results are drawn: (1) experimental results on MATLAS with different topographies indicate that the average accuracy of second-step denoising exceeds 0.94, and the accuracy is effectively improves with the number of denoising times; (2) experiments on ICESat-2 under different observation conditions demonstrate that the algorithm can accurately identify signal photons in different surface types and topographies. Overall, the proposed algorithm has good adaptability and robustness for adaptive denoising of large-scale photons, and the denoising results can provide more reasonable and reliable data for sustainable urban development.

A comprehensive performance analysis of sequence-based within-sample testing NIPT methods.

Non-Invasive Prenatal Testing is often performed by utilizing read coverage-based profiles obtained from shallow whole genome sequencing to detect fetal copy number variations. Such screening typically operates on a discretized binned representation of the genome, where (ab)normality of bins of a set size is judged relative to a reference panel of healthy samples. In practice such approaches are too costly given that for each tested sample they require the resequencing of the reference panel to avoid technical bias. Within-sample testing methods utilize the observation that bins on one chromosome can be judged relative to the behavior of similarly behaving bins on other chromosomes, allowing the bins of a sample to be compared among themselves, avoiding technical bias. We present a comprehensive performance analysis of the within-sample testing method Wisecondor and its variants, using both experimental and simulated data. We introduced alterations to Wisecondor to explicitly address and exploit paired-end sequencing data. Wisecondor was found to yield the most stable results across different bin size scales while producing more robust calls by assigning higher Z-scores at all fetal fraction ranges. Our findings show that the most recent available version of Wisecondor performs best.

Wine age prediction using digital images and multivariate calibration

The prediction or confirmation of age is an important field in evaluation of wine’s value. Such type of studies commonly requires a number of data sets describing changes in chemical composition and/or related physical properties. Digital images of wines captured by a webcam represent an easy and low-cost approach to prevent frauds connected to the age of wines. In this work, a combination of frequency histograms including grey scale, red-green-blue (RGB) and hue-saturation-value (HSV) colour models extracted from digital images were used to evaluate the age of botrytized and related varietal wines produced during a 1989–2019 period at different countries. The main findings showed that digital images carry the appropriate chemical information for the age assignment and PLS-type models were able to estimate wine age only one latent variable. Grey levels enabled to find figure of merit values similar to the PLS model based on full histogram. An additional interval selection of the histograms with interval PLS allows improving accuracy of variable assessment and achieving lower error at a cross-validation step, RMSECV decreases from 3.6 years to 3.1 years. When models were employed to predict an external set of samples similar results were found, RMSEP equal to 2.8 years and 2.9 years for PLS and iPLS, respectively. However, a slight deterioration of the results was observed for the PLS model based on full grey levels (RMSEP 3.2). In general, these non-destructive measurements do not generate residuals and can be performed without sophisticated equipment with a reasonably accurate response.

Abstract 6697: Deep learning algorithm for cancer detection using multimodal characteristics of whole methylome sequencing of cf-DNA

Abstract Background Various cell-free DNA (cfDNA) features including methylation and genomic profiles have been investigated for their potential use in early cancer detection. We developed deep learning models based the data generated by the enzymatic conversion based whole methylome sequencing of cfDNA. Methods Cell-free whole genome Enzymatic Methyl sequencing(cfWEMseq) data were generated from 198 cancer patients (stage I: 11%, II: 17%, III: 22%, IV: 20%, unknown: 31%) and 69 healthy controls. The cancer types were consisted of breast (n=31), liver (n=24), esophageal (n=38), pancreatic (n=30), colon (n=34), ovarian (n=18), and lung (n=23). Sequence data was produced on average of 200 million reads using Novaseq 6000 (Illumina). For model training and evaluation, data partitioning was stratified by cancer type, and 5-fold cross validation was used. Coverage and methylation beta values ​​were calculated by binning at fixed size of 100K, 1M, and 5M base and variable size from Topologically Associated Domains (TAD). Genome Coverage (GC), Genome Methylation Beta values ​​(GMB), and Mutation Signature (MS) features were trained using a one-dimensional convolutional neural network (1D-CNN). The performance of the model was evaluated by measuring the average value of the results measured in each test set of 5 fold. Results We tested the cancer detection performance of various feature combinations using all data from cfWEMseq (n=267). Regardless of the bin size, the GMB single model achieved higher performance than the GC single model. The best-performing model is the ensemble model of GMB (100k bin) and MS. The cancer detection performance of this ensemble model reached an accuracy 96% (CI: 93.6% to 98.1%), AUC 0.99 (CI: 0.97 to 1.0) and sensitivity 98.0% (CI: 92.4% to 99.5%) with a specificity of 90%. Conclusions These results provide an opportunity for higher accuracies by integrating methylation information and genomic data using cfWEMseq. This research was supported through the National Research Foundation (NRF) funded by the Ministry of Science and ICT (2020M2D9A3094213). Citation Format: Juntae Park, Minjung Kim, Sook Ryun Park, Ki-Byung Song, Eunsung Jun, Dongryul Oh, Jeong-Won Lee, Young Sik Park, Ki-Won Song, Jeong-Sik Byeon, Bo Hyun Kim, Chang-Seok Ki, Eunhae Cho. Deep learning algorithm for cancer detection using multimodal characteristics of whole methylome sequencing of cf-DNA. [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 6697.