Simulated Datasets Research Articles

Data-Driven Theoretical Modeling of Centrifugal Step Emulsification and Its Application in Comprehensive Multiscale Analysis.

Tailored droplet generation is crucial for droplet microfluidics that involve samples of varying sizes. However, the absence of precise predictive models forces droplet platforms to rely on empiricism derived from extensive experiments, underscoring the need for comprehensive modeling analysis. To address this, a novel customized assembled centrifugal step emulsifier (CASE) is presented by incorporating a "jigsaw puzzles" design to efficiently acquire large-scale experimental data. Numerical simulations are utilized to analyze fluid configurations during step emulsification, identifying a key connection tube that determines droplet size. By training and verifying with the experimental and simulation datasets, a comprehensive theoretical model is established that allows for the preliminary design of the droplet size and generation frequency with an average error rate of 4.8%, successfully filling a critical gap in existing field. This predictive model empowers the CASE to achieve all-in-one functionality, including droplet pre-design, generation, manipulation, and on-site detection. As a proof of concept, multiscale sample analysis ranging from nanoscale nucleic acids to microscale bacteria and 3D cell spheroids is realized in the CASE. In summary, this platform offers valuable guidance for customized droplet generation by centrifugal step emulsifiers and promotes the adoption of droplet microfluidics in biochemical assays.

On Statistical Properties of a Veracity Scoring Method for Spatial Data

Measuring the veracity or reliability of observations is of utmost importance, especially in scenarios where a portion of the observations can be noisy, for example, when the information is gathered in a non-laboratory environment or through an automated system. Assessing the veracity of observations is difficult when no “good” quality replication or reference data is present, which is often the case in real-world applications. In this article, we define a veracity scoring method for geostatistical data based on “local” summaries of the observations and develop an estimation methodology for parameters of a spatial regression model incorporating the veracity scores (VS) to guard against undesirable effects of corrupted data. Under a nonstationary noise structure and fairly general assumptions on the underlying spatial process, we show that the VS-based estimators of the regression parameters are consistent. Moreover, we establish the advantages of the VS-based estimators as compared to the ordinary least squares (OLS) estimator by analyzing their asymptotic mean squared errors, both theoretically and empirically. In addition, we illustrate the merits of the VS-based technique in real-life applications by comparing the results against the existing robust geostatistical approaches on a real dataset and a simulated dataset in the real-data domain.

Patch-based dual-domain photon-counting CT data correction with residual-based WGAN-ViT

Objective.x-ray photon-counting detectors have recently gained popularity due to their capabilities in energy discrimination power, noise suppression, and resolution refinement. The latest extremity photon-counting computed tomography (PCCT) scanner leverages these advantages for tissue characterization, material decomposition, beam hardening correction, and metal artifact reduction. However, technical challenges such as charge splitting and pulse pileup can distort the energy spectrum and compromise image quality. Also, there is a clinical need to balance radiation dose and imaging speed for contrast-enhancement and other studies. This paper aims to address these challenges by developing a dual-domain correction approach to enhance PCCT reconstruction quality quantitatively and qualitatively.Approach.We propose a novel correction method that operates in both projection and image domains. In the projection domain, we employ a residual-based Wasserstein generative adversarial network to capture local and global features, suppressing pulse pileup, charge splitting, and data noise. This is facilitated with traditional filtering methods in the image domain to enhance signal-to-noise ratio while preserving texture across each energy channel. To address GPU memory constraints, our approach utilizes a patch-based volumetric refinement network.Main results.Our dual-domain correction approach demonstrates significant fidelity improvements across both projection and image domains. Experiments on simulated and real datasets reveal that the proposed model effectively suppresses noise and preserves intricate details, outperforming the state-of-the-art methods.Significance.This approach highlights the potential of dual-domain PCCT data correction to enhance image quality for clinical applications, showing promise for advancing PCCT image fidelity and applicability in preclinical/clinical environments.

A consensus-constrained parsimonious Gaussian mixture model for clustering hyperspectral images

Abstract The use of hyperspectral imaging to investigate food samples has grown due to the improved performance and lower cost of instrumentation. Food engineers use hyperspectral images to classify the type and quality of a food sample, typically using classification methods. In order to train these methods, every pixel in each training image needs to be labelled. Typically, computationally cheap threshold-based approaches are used to label the pixels, and classification methods are trained based on those labels. However, threshold-based approaches are subjective and cannot be generalized across hyperspectral images taken in different conditions and of different foods. Here a consensus-constrained parsimonious Gaussian mixture model (ccPGMM) is proposed to label pixels in hyperspectral images using a model-based clustering approach. The ccPGMM utilizes information that is available on some pixels and specifies constraints on those pixels belonging to the same or different clusters while clustering the rest of the pixels in the image. A latent variable model is used to represent the high-dimensional data in terms of a small number of underlying latent factors. To ensure computational feasibility, a consensus clustering approach is employed, where the data are divided into multiple randomly selected subsets of variables and constrained clustering is applied to each data subset; the clustering results are then consolidated across all data subsets to provide a consensus clustering solution. The ccPGMM approach is applied to simulated datasets and real hyperspectral images of three types of puffed cereal, corn, rice, and wheat. Improved clustering performance and computational efficiency are demonstrated when compared to other current state-of-the-art approaches.

Deep Learning-Based Ground-Penetrating Radar Inversion for Tree Roots in Heterogeneous Soil

Tree roots are vital for tree ecosystems; accurate root detection helps analyze the health of trees and supports the effective management of resources such as fertilizers, water and pesticides. In this paper, a deep learning-based ground-penetrating radar (GPR) inversion method is proposed to simultaneously image the spatial distribution of permittivity for subsurface tree roots and layered heterogeneous soils in real time. Additionally, a GPR simulation data set and a measured data set are built in this study, which were used to train inversion models and validate the effectiveness of GPR inversion methods.The introduced GPR inversion model is a pyramid convolutional network with vision transformer and edge inversion auxiliary task (PyViTENet), which combines pyramidal convolution and vision transformer to improve the diversity and accuracy of data feature extraction. Furthermore, by adding the task of edge inversion of the permittivity distribution of underground materials, the model focuses more on the details of heterogeneous structures. The experimental results show that, for the case of buried scatterers in layered heterogeneous soil, the PyViTENet performs better than other deep learning methods on the simulation data set. It can more accurately invert the permittivity of scatterers and the soil stratification. The most notable advantage of PyViTENet is that it can accurately capture the heterogeneous structural details of the soil within the layer since the soil around the tree roots in the real scene is layered soil and each layer of soil is also heterogeneous due to factors such as humidity, proportion of different soil particles, etc.In order to further verify the effectiveness of the proposed inversion method, this study applied the PyViTENet to GPR measured data through transfer learning for reconstructing the permittivity, shape, and position information of scatterers in the actual scene. The proposed model shows good generalization ability and accuracy, and provides a basis for non-destructive detection of underground scatterers and their surrounding medium.

Credit Risk Identification and Prevention Strategies of Small and Medium-sized Banks Based on Big Data Technology

The topic of this analysis is the application of big data technology to improve credit risk identification and prevention measures in SME banks. T¬he research, it highlights credit risk’s crucial role in banking and underscores the problems smaller banks encounter when dealing with this risk due to limited resources and less evolved tools. An extensive review is conducted, showing the progression of credit risk management and big data integration into financial risk management. It discusses the revolutionary aspect of big data in credit risk analysis as well as its practical applications in small and medium-sized banks. The secondary data from Kaggle datasets are used within a quantitative research approach. Regression analysis and hypothesis testing are some of the statistical tools used in EViews to uncover patterns and correlations related to credit risk. The study determines essential factors that affect credit risk, such as borrower credit score, loan amount, interest rate, and employment. It assesses the effectiveness of big data analytics in forecasting and mitigating this risk, focusing on accuracy and model resilience. This implies that the findings show that the borrowers have a moderate level of credit risk, and the traditional financial metrics have minimal influence on the Big Data risk score predictions, which require advanced analytical approaches. Big data technology can take more than traditional credit scoring, giving small and medium-sized banks a more advanced credit risk perspective. Limitations of the research include using a simulated dataset and the range of the analysed variables. Real-world data and variables should be used in future research studies.

A Retrospective Analysis of the First Clinical 5DCT Workflow

Background/Objectives: 5DCT was first proposed in 2005 as a motion-compensated CT simulation approach for radiotherapy treatment planning to avoid sorting artifacts that arise in 4DCT when patients breathe irregularly. Since March 2019, 5DCT has been clinically implemented for routine use at our institution to leverage this technological advantage. The clinical workflow includes a quality assurance report that describes the output of primary workflow steps. This study reports on the challenges and quality of the clinical 5DCT workflow using these quality assurance reports. Methods: We evaluated all thoracic 5DCT simulation datasets consecutively acquired at our institution between March 2019 and December 2022 for thoracic radiotherapy treatment planning. The 5DCT datasets utilized motion models constructed from 25 fast-helical free-breathing computed tomography (FHFBCTs) with simultaneous respiratory bellows signal monitoring to reconstruct individual, user-specified breathing-phase images (termed 5DCT phase images) for internal target volume contouring. Each 5DCT dataset was accompanied by a structured quality assurance report composed of qualitative and quantitative measures of the breathing pattern, image quality, DIR quality, model fitting accuracy, and a validation process by which the original FHFBCT scans were regenerated with the 5DCT model. Measures of breathing irregularity, image quality, and DIR quality were retrospectively categorized on a grading scale from 1 (regular breathing and accurate registration/modeling) to 4 (irregular breathing and inaccurate registration/modeling). The validation process was graded according to the same scale, and this grade was termed the suitability-for-treatment-planning (STP) grade. We correlated the graded variables to the STP grade. In addition to the quality assurance reports, we reviewed the contour sessions to determine how often 5DCT phase images were used for treatment planning and delivery. Results: There were 169 5DCT simulation datasets available from 156 patients for analysis. The STP was moderately correlated with breathing irregularity, image quality, and DIR quality (Spearman coefficients: 0.26, 0.30, and 0.50, respectively). Multiple linear regression analysis demonstrated that STP was correlated with regular breathing patterns (p = 0.008), image quality (p < 0.001), and better DIR quality (p < 0.001). 5DCT datasets were used for treatment planning in 82% of cases, while in 12% of cases, a backup image process was used. In total, 6% of image datasets were not used for treatment planning due to factors unrelated to the 5DCT workflow quality. Conclusions: The strongest association with STP was with DIR quality grades, as indicated by both Spearman and multiple linear regression analysis, implying that improvements to DIR accuracy and evaluation may be the best route for further improvement to 5DCT. The high rate of 5DCT phase image use for treatment planning showed that the workflow was reliable, and this has encouraged us to continue to develop and improve the workflow steps.

A Statistical Split Window Method Without Atmospheric Profile Input for Temperature and Emissivity Retrieval from Airborne Long-Wavelenght Infrared Hyperspectral Imagery

The retrieval of Land Surface Temperature (LST) and Emissivity (LSE) from long-wavelength thermal infrared (LWIR) hyperspectral data can be challenging due to uncertainties in atmospheric compensation (AC). While AC is typically performed using atmospheric radiance models, errors in the input atmospheric profiles can lead to significant inaccuracies. In this study, we propose an end-to-end method for retrieving LST and LSE without the need for local atmospheric profile inputs. The method consists of two main steps: first, a statistical split-window (SSW) method is used to estimate the ground leaving radiance, with optimal band configurations and coefficients determined through a trial-and-error approach and statistical regression based on simulation datasets. Second, by integrating the ASTER Temperature And Emissivity Separation (ASTER-TES) method with an atmospheric downwelling lookup table (LUT), the optimal LST and LSE are derived based on the principle of emissivity smoothness. The proposed method is applied to airborne HypercamLW LWIR hyperspectral data. When compared to the MODTRAN-based AC with ASTER-TES (MODTRAN-TES) and the built-in method of FLAASH-IR, the proposed SSW-TES method yields better accuracy, with a LST root mean square error (RMSE) of 1.24 K and an LSE RMSE of 0.016.

Super-Resolution Ptychography with Small Segmented Detectors.

To overcome the spatial resolution limit set by aperture-limited diffraction in traditional scanning transmission electron microscopy, microscopists have developed ptychography enabled by iterative phase retrieval algorithms and high-dynamic-range pixel array detectors. Current detector designs are limited by the data rate off chip, so a high-pixel-count detector has a proportionally lower frame rate than the few-segment detectors used for differential phase contrast (DPC) imaging. This slower acquisition speed leads to heightened vulnerability to scan noise, drift, and potential sample damage. This creates opportunities for repurposing fast segmented detectors for ptychography by trading a reduction in reciprocal space pixels for an increase in real space pixels. Here, we explore a strategy of oversampling in real space and instead apply detector pixel upsampling during the reconstruction process. We demonstrate the viability of achieving super-resolution ptychography on thin objects using only 2 × 2 detector pixels, surpassing the resolution of integrated DPC (iDPC) imaging. With optimization using simulated datasets and experiments on MoTe2/WSe2 bilayer moiré superlattices, we achieved super-resolution ptychography reconstructions under rapid acquisition conditions (37.5 pA, 1 μs dwell time), yielding over 50% improvements in contrast and information limit compared to annular dark field and iDPC imaging on the same detectors.

Resilience of human gut microbiomes in autism spectrum disorder: measured using stiffness network analysis.

Autism spectrum disorder (ASD) affects an estimated 1%-2% of children worldwide, but its specific etiology remains unclear. In recent years, the gut microbiome's role in ASD pathogenesis has garnered increasing attention. However, the exact relationship between microbiota and ASD-such as which microbial species significantly impact disease onset and progression-remains unresolved, and effective methods to measure microbial interactions are still lacking. In this study, we introduce an innovative stiffness network analysis (SNA) method to quantify changes in microbial network structure and identify disease-specific microbial bacteria theoretically. The SNA method was applied to reanalyze eight ASD gut microbiome data sets, encompassing 898 ASD samples and 467 healthy control (HC) samples from 16S-rRNA sequencing data. Key findings include the following: (i) an "allies" biomarker subgroup consisting of Bacteroides plebeius, Sutterella, Lachnospira, and Prevotella copri was identified; (ii) a profile monitoring score of 0.72 for the biomarker subgroup, indicating significant relationship changes between HC and ASD states, and (iii) a P/N ratio of biomarker subgroup in ASD-associated gut bacteria that was three times higher than that of HC microbiomes. Additionally, we discuss the non-monotonic relationship alterations within microbial sub-communities in the ASD gut microbiome.IMPORTANCEIt is crucial to assess alterations in network structure in different biological states in order to promote health. The stiffness network allows for the exploration of species interactions and the measurement of resilience in complex microbial networks. The objective of this study was to develop a stiffness network analysis (SNA) method for evaluating the contribution of microbial bacteria in differentiating disease samples from healthy control samples by examining changes in network stiffness parameters. Furthermore, the SNA method was employed on both simulated and real autism spectrum disorder gut microbiome data sets to identify potential microbial biomarker subgroups, with a particular focus on the relationship alterations within microbial networks.

Detection to false data for smart grid

False data injection attack in smart grid might does not launch interference and attack behaviors, so that this attack is difficult found. To address this, this paper proposed a conformal neural network detection method being sensitive to false data. Firstly, using the conformity scores calculated by the mathematical probability to identify the false data. Then, the neural network learns a boundary separating false data and normal data on the conformal region yielded by the conformity scores. Finally, experiments on simulated and real datasets indicate that the proposed method obtains 0.9738 detected accuracy, and the sensitivity to false data reaches 0.9387, defeating against the competitors, moreover, the proposed method does not exhibit exponential detection time as data volume augments. We demonstrate that evaluating the consistency between the data does not rely on data distributions and the operation status in this real-time system like smart grids, since conformity scores can calculate the mathematical probability following the same data distribution. The boundaries learned from conformal regions are independent of data distribution and the information of operation status in smart grids. This evaluation manner of data consistency and that of boundary learning are equally applicable to the identification and separation of those false data injected into other real-time systems.

Prediction of mechanical characteristics of shearer intelligent cables under bending conditions.

The frequent bending of shearer cables during operation often leads to mechanical fatigue, posing risks to equipment safety. Accurately predicting the mechanical properties of these cables under bending conditions is crucial for improving the reliability and service life of shearers. This paper proposes a shearer optical fiber cable mechanical characteristics prediction model based on Temporal Convolutional Network (TCN), Bidirectional Long Short-Term Memory (BiLSTM), and Squeeze-and-Excitation Attention (SEAttention), referred to as the TCN-BiLSTM-SEAttention model. This method leverages TCN's causal and dilated convolution operations to capture long-term sequential features, BiLSTM's bidirectional information processing to ensure the completeness of sequence information, and the SEAttention mechanism to assign adaptive weights to features, effectively enhancing the focus on key features. The model's performance is validated through comparisons with multiple other models, and the contributions of input features to the model's predictions are quantified using Shapley Additive Explanations (SHAP). By learning the stress variation patterns between the optical fiber, power conductor, and control conductor in the shearer cable, the model enables accurate prediction of the stress in other cable conductors based on optical fiber stress data. Experiments were conducted using a shearer optical fiber cable bending simulation dataset with traction speeds of 6 m/min, 8 m/min, and 10 m/min. The results show that, compared to other predictive models, the proposed model achieves reductions in Mean Squared Error (MSE), Root Mean Squared Error (RMSE), and Mean Absolute Error (MAE) to 0.0002, 0.0159, and 0.0126, respectively, with the coefficient of determination (R2) increasing to 0.981. The maximum deviation between predicted and actual values is only 0.86%, demonstrating outstanding prediction accuracy. SHAP feature analysis reveals that the control conductor features have the most substantial influence on predictions, with a SHAP value of 0.095. The research shows that the TCN-BiLSTM-SEAttention model demonstrates outstanding predictive capability under complex operating conditions, providing a novel approach for improving cable management and equipment safety through optical fiber monitoring technology in the intelligent development of coal mines, highlighting the potential of deep learning in complex mechanical predictions.

Modeling Preferences: A Bayesian Mixture of Finite Mixtures for Rankings and Ratings

Rankings and ratings are commonly used to express preferences but provide distinct and complementary information. Rankings give ordinal and scale-free comparisons but lack granularity; ratings provide cardinal and granular assessments but may be highly subjective or inconsistent. Collecting and analyzing rankings and ratings jointly has not been performed until recently due to a lack of principled methods. In this work, we propose a flexible, joint statistical model for rankings and ratings—the Bradley-Terry-Luce-Binomial (BTL-Binomial). The model captures rater effects and preference heterogeneity, respectively, with judge-specific random effects and a latent class mixture framework where the number of classes is unknown a priori. We propose computationally-efficient estimation via a Bayesian mixture of finite mixtures (MFM) approach. Finally, we demonstrate statistical inference and decision-making based on rankings and ratings jointly through applications to real and simulated datasets in academic peer review. Supplementary materials for this article are available online, including a standardized description of the materials available for reproducing the work.

Simulation-based validation of a method to detect changes in SARS-CoV-2 reinfection risk.

Given the high global seroprevalence of SARS-CoV-2, understanding the risk of reinfection has become increasingly important. Models developed to track trends in reinfection risk should be robust against possible biases arising from imperfect data observation processes. We performed simulation-based validation of an existing catalytic model designed to detect changes in the risk of reinfection by SARS-CoV-2. The catalytic model assumes the risk of reinfection is proportional to observed infections. Validation involved using simulated primary infections, consistent with the number of observed infections in South Africa. To assess the performance of the catalytic model, we simulated reinfection datasets that incorporated different processes that may bias inference, including imperfect observation and mortality. A Bayesian approach was used to fit the model to simulated data, assuming a negative binomial distribution around the expected number of reinfections, and model projections were compared to the simulated data using different magnitudes of change in reinfection risk. We assessed the model's ability to accurately detect changes in reinfection risk when included in the simulations, as well as the occurrence of false positives when reinfection risk remained constant. The model parameters converged in most scenarios leading to model outputs aligning with anticipated outcomes. The model successfully detected changes in the risk of reinfection when such a change was introduced to the data. Low observation probabilities (10%) of both primary- and reinfections resulted in low numbers of observed cases from the simulated data and poor convergence. The model's performance was assessed on simulated data representative of the South African SARS-CoV-2 epidemic, reflecting its timing of waves and outbreak magnitude. Model performance under similar scenarios may be different in settings with smaller epidemics (and therefore smaller numbers of reinfections). Ensuring model parameter convergence is essential to avoid false-positive detection of shifts in reinfection risk. While the model is robust in most scenarios of imperfect observation and mortality, further simulation-based validation for regions experiencing smaller outbreaks is recommended. Caution must be exercised in directly extrapolating results across different epidemiological contexts without additional validation efforts.

Short communication: Genomic prediction based on unbiased estimation of the genomic relationship matrix in pigs.

The traditional genomic relationship matrix (GRM) has shown to be a biased estimation of true kinship, which can affect subsequent genetic analyses. In this study, we employed an unbiased kinship (UKin) estimation method within the genomic best linear unbiased prediction framework to evaluate its prediction performance on both a simulated dataset and a Large White pig dataset. The simulated dataset encompasses six traits, 900 quantitative trait loci, and 36000 single nucleotide polymorphisms (SNPs). Two scenarios (small effect genes; major genes and small effect genes) and three heritabilities (0.1, 0.3 and 0.5) were considered. The Large White pig dataset includes two traits, 3290 animals and 35172 SNPs. The prediction performance of the Ukin method was compared with several other GRM construction methods, including VanRaden1 and 2 methods, Goudet method, and the runs of homozygosity (ROH) method. In the simulated dataset, VanRaden2 method and the UKin+VanRaden1 method achieved relatively higher prediction accuracies, averaging 0.561 and 0.558 for the six traits, respectively. Apart from the ROH method, all methods demonstrated similar levels of unbiasedness, around 1.10. In the Large White pig dataset, the accuracy of two traits hovered around 0.780, and the unbiasedness around 0.99, again with the ROH method as an exception. This study underscores the potential of the unbiased kinship estimation method in animal breeding.

A Bias-Corrected Bayesian Nonparametric Model for Combining Studies With Varying Quality in Meta-Analysis.

Bayesian nonparametric (BNP) approaches for meta-analysis have been developed to relax distributional assumptions and handle the heterogeneity of random effects distributions. These models account for possible clustering and multimodality of the random effects distribution. However, when we combine studies of varying quality, the resulting posterior is not only a combination of the results of interest but also factors threatening the integrity of the studies' results. We refer to these factors as the studies' internal validity biases (e.g., reporting bias, data quality, and patient selection bias). In this paper, we introduce a new meta-analysis model called the bias-corrected Bayesian nonparametric (BC-BNP) model, which aims to automatically correct for internal validity bias in meta-analysis by only using the reported effects and their standard errors. The BC-BNP model is based on a mixture of a parametric random effects distribution, which represents the model of interest, and a BNP model for the bias component. This model relaxes the parametric assumptions of the bias distribution of the model introduced by Verde. Using simulated data sets, we evaluate the BC-BNP model and illustrate its applications with two real case studies. Our results show several potential advantages of the BC-BNP model: (1) It can detect bias when present while producing results similar to a simple normal-normal random effects model when bias is absent. (2) Relaxing the parametric assumptions of the bias component does not affect the model of interest and yields consistent results with the model of Verde. (3) In some applications, a BNP model of bias offers a better understanding of the studies' biases by clustering studies with similar biases. We implemented the BC-BNP model in the R package jarbes, facilitating its practicalapplication.

Spatial integration of multi-omics single-cell data with SIMO

Technical limitations in spatial and single-cell omics sequencing pose challenges for capturing and describing multimodal information at the spatial scale. To address this, we develop SIMO, a computational method designed for the Spatial Integration of Multi-Omics datasets through probabilistic alignment. Unlike previous tools, SIMO not only integrates spatial transcriptomics with single-cell RNA-seq but expands beyond, enabling integration across multiple single-cell modalities, such as chromatin accessibility and DNA methylation, which have not been co-profiled spatially before. We benchmark SIMO on simulated datasets, demonstrating its high accuracy and robustness. Further application on biological datasets reveals SIMO’s ability to detect topological patterns of cells and their regulatory modes across multiple omics layers. Through comprehensive analysis of real-world data, SIMO uncovers multimodal spatial heterogeneity, offering deeper insights into the spatial organization and regulation of biological molecules. These findings position SIMO as a powerful tool for advancing spatial biology by revealing previously inaccessible multimodal insights.

CLPr_in_ML: Cleft Lip and Palate Reconstructed Features with Machine Learning

Background: Cleft lip and palate are two of the most common craniofacial congenital malformations in humans. It influences tens of millions of patients worldwide. The hazards of this disease are multifaceted, extending beyond the obvious facial malformation to encompass physiological functions, oral health, psychological well-being, and social aspects. Objective: The primary objective of our study is to demonstrate the importance of imaging in detecting cleft lip and palate. By observing the morphological and structural abnormalities involving the lip and palate through imaging methods, this study aims to establish imaging as the primary diagnostic approach for this disease. Methods: In this work, we proposed a novel model to analyze unilateral complete cleft lip and palate after velopharyngeal closure and non-left lip and palate patients from the Department of Stomatology of Xuzhou First People's Hospital, Conical Beam CT (CBCT) images in silicon. In order to demonstrate the generalization, the simulated dataset was constructed using the random disturbance factor, which is from the actual dataset. We extracted several raw features from CBCT images in detail. Then, we proposed a novel feature reconstruction method, including six types of reconstructed factors, to reconstruct the existing features. Then, the reconstructed features weretrained with machine learning algorithms. Finally, the testing and independent data model was utilized to analyze the performance of this work. Results: By comparing different operator features, the min operator, max operator, average operator, and all operators can achieve good performances in both the testing set and the independent set. Conclusion: With the different operator features, the majority of classification models, including Gradient Boosting, Hist Gradient Boosting, Multilayer Perceptron, lightGBM, and broadened learning, classification algorithms can get the well-performances in the selected reconstructed feature operators.

Dataset of microscale atmospheric flow and pollutant concentration large-eddy simulations for varying mesoscale meteorological forcing in an idealized urban environment.

By 2050, two-thirds of the world's population will live in urban areas under climate change, exacerbating the environmental and public health risks associated with poor air quality and urban heat island effects. Assessing these risks requires the development of microscale meteorological models that quickly and accurately predict wind velocity and pollutant concentration with high resolution, as the heterogeneity of urban environments leads to complex wind patterns and strong pollutant concentration gradients. Computational Fluid Dynamics (CFD) has emerged as a powerful tool to address this challenge by providing obstacle-resolved flow and dispersion predictions. However, CFD models are very expensive and require intensive computing resources, which can hinder their systematic use in practical engineering applications. They are also subject to significant uncertainties, particularly those arising from the mesoscale meteorological forcing and the internal variability of the atmospheric boundary layer, some of which are aleatory and thereby irreducible. Given these issues, the construction of CFD datasets that account for uncertainty would be an interesting avenue of research for microscale atmospheric science. In this context, we present the PPMLES (Perturbed-Parameter ensemble of MUST Large-Eddy Simulations) dataset, which consists of 200 large-eddy simulations (LES) characterizing the complex interactions between the turbulent airflow, the tracer dispersion, and an idealized urban environment. These simulations reproduce the canonical MUST dispersion field campaign while perturbing the model's mesoscale meteorological forcing parameters. PPMLES includes time series at human height within the built environment to track wind velocity and pollutant release and dispersion over time. PPMLES also includes complete 3-D fields of first- and second-order temporal statistics of the wind velocity and pollutant concentration, with a sub-metric resolution. The uncertainty of the fields induced by the internal variability of the atmospheric boundary layer is also provided. The computation of PPMLES required significant resources, consuming 6 million CPU core hours, equivalent to the emission of approximately 10 tCO2eq of greenhouse gases. This significant computational effort and associated carbon footprint motivates the sharing of the data generated. The added value of the PPMLES dataset is twofold. First, the perturbed-parameter ensemble of LES enables to quantify and understand the effects of the mesoscale meteorological forcing and the internal variability of the atmospheric boundary layer, which has been identified as a major challenge in predicting atmospheric flow and pollutant dispersion in urban environments. Secondly, PPMLES reference data can be used to benchmark models of different levels of complexity, and to extract key information about the physical processes involved to inform more operational modeling approaches, for example through learning surrogate models.

INSTINCT: Multi-sample integration of spatial chromatin accessibility sequencing data via stochastic domain translation

Recent advances in spatial epigenomic techniques have given rise to spatial assay for transposase-accessible chromatin using sequencing (spATAC-seq) data, enabling the characterization of epigenomic heterogeneity and spatial information simultaneously. Integrative analysis of multiple spATAC-seq samples, for which no method has been developed, allows for effective identification and elimination of unwanted non-biological factors within the data, enabling comprehensive exploration of tissue structures and providing a holistic epigenomic landscape, thereby facilitating the discovery of biological implications and the study of regulatory processes. In this article, we present INSTINCT, a method for multi-sample INtegration of Spatial chromaTIN accessibility sequencing data via stochastiC domain Translation. INSTINCT can efficiently handle the high dimensionality of spATAC-seq data and eliminate the complex noise and batch effects of samples through a stochastic domain translation procedure. We demonstrate the superiority and robustness of INSTINCT in integrating spATAC-seq data across multiple simulated scenarios and real datasets. Additionally, we highlight the advantages of INSTINCT in spatial domain identification, visualization, spot-type annotation, and various downstream analyses, including motif enrichment analysis, expression enrichment analysis, and partitioned heritability analysis.