A fully Bayesian semi-parametric scalar-on-function regression (SoFR) with measurement error using instrumental variables

Wearable devices such as the ActiGraph are now commonly used in health studies to monitor or track physical activity. This trend aligns well with the growing need to accurately assess the effects of physical activity on health outcomes such as obesity. When accessing the association between these device-based physical activity measures with health outcomes such as body mass index, the device-based data is considered functions, while the outcome is a scalar-valued. The regression model applied in these settings is the scalar-on-function regression (SoFR). Most estimation approaches in SoFR assume that the functional covariates are precisely observed, or the measurement errors are considered random errors. Violation of this assumption can lead to both under-estimation of the model parameters and sub-optimal analysis. The literature on a measurement corrected approach in SoFR is sparse in the non-Bayesian literature and virtually non-existent in the Bayesian literature. This paper considers a fully nonparametric Bayesian measurement error corrected SoFR model that relaxes all the constraining assumptions often made in these models. Our estimation relies on an instrumental variable (IV) to identify the measurement error model. Finally, we introduce an IV quality scalar parameter that is jointly estimated along with all model parameters. Our method is easy to implement, and we demonstrate its finite sample properties through an extensive simulation. Finally, the developed methods are applied to the National Health and Examination Survey to assess the relationship between wearable-device-based measures of physical activity and body mass index among adults living in the United States

Curvilinear object segmentation in medical images based on ODoS filter and deep learning network

Automatic segmentation of curvilinear objects in medical images plays an important role in the diagnosis and evaluation of human diseases, yet it is a challenging uncertainty in the complex segmentation tasks due to different issues such as various image appearances, low contrast between curvilinear objects and their surrounding backgrounds, thin and uneven curvilinear structures, and improper background illumination conditions. To overcome these challenges, we present a unique curvilinear structure segmentation framework based on an oriented derivative of stick (ODoS) filter and a deep learning network for curvilinear object segmentation in medical images. Currently, a large number of deep learning models emphasize developing deep architectures and ignore capturing the structural features of curvilinear objects, which may lead to unsatisfactory results. Consequently, a new approach that incorporates an ODoS filter as part of a deep learning network is presented to improve the spatial attention of curvilinear objects. Specifically, the input image is transfered into four-channel image constructed by the ODoS filter. In which, the original image is considered the principal part to describe various image appearance and complex background illumination conditions, a multi-step strategy is used to enhance the contrast between curvilinear objects and their surrounding backgrounds, and a vector field is applied to discriminate thin and uneven curvilinear structures. Subsequently, a deep learning framework is employed to extract various structural features for curvilinear object segmentation in medical images. The performance of the computational model is validated in experiments conducted on the publicly available DRIVE, STARE and CHASEDB1 datasets. The experimental results indicate that the presented model yields surprising results compared with those of some state-of-the-art methods.Comment: 20 pages, 8 figure

Fast Video Dehazing Using Per-Pixel Minimum Adjustment

To reduce the computational complexity and maintain the effect of video dehazing, a fast and accurate video dehazing method is presented. The preliminary transmission map is estimated by the minimum channel of each pixel. An adjustment parameter is designed to fix the transmission map to reduce color distortion in the sky area. We propose a new quad-tree method to estimate the atmospheric light. In video dehazing stage, we keep the atmospheric light unchanged in the same scene by a simple but efficient parameter, which describes the similarity of the interframe image content. By using this method, unexpected flickers are effectively eliminated. Experiments results show that the proposed algorithm greatly improved the efficiency of video dehazing and avoided halos and block effect

Comprehensive analyses for the coagulation and macrophage-related genes to reveal their joint roles in the prognosis and immunotherapy of lung adenocarcinoma patients

PurposeThis study aims to explore novel biomarkers related to the coagulation process and tumor-associated macrophage (TAM) infiltration in lung adenocarcinoma (LUAD).MethodsThe macrophage M2-related genes were obtained by Weighted Gene Co-expression Network Analysis (WGCNA) in bulk RNA-seq data, while the TAM marker genes were identified by analyzing the scRNA-seq data, and the coagulation-associated genes were obtained from MSigDB and KEGG databases. Survival analysis was performed for the intersectional genes. A risk score model was subsequently constructed based on the survival-related genes for prognosis prediction and validated in external datasets.ResultsIn total, 33 coagulation and macrophage-related (COMAR) genes were obtained, 19 of which were selected for the risk score model construction. Finally, 10 survival-associated genes (APOE, ARRB2, C1QB, F13A1, FCGR2A, FYN, ITGB2, MMP9, OLR1, and VSIG4) were involved in the COMAR risk score model. According to the risk score, patients were equally divided into low- and high-risk groups, and the prognosis of patients in the high-risk group was significantly worse than that in the low-risk group. The ROC curve indicated that the risk score model had high sensitivity and specificity, which was validated in multiple external datasets. Moreover, the model also had high efficacy in predicting the clinical outcomes of LUAD patients who received anti-PD-1/PD-L1 immunotherapy.ConclusionThe COMAR risk score model constructed in this study has excellent predictive value for the prognosis and immunotherapeutic clinical outcomes of patients with LUAD, which provides potential biomarkers for the treatment and prognostic prediction

Robust estimation of bacterial cell count from optical density

Optical density (OD) is widely used to estimate the density of cells in liquid culture, but cannot be compared between instruments without a standardized calibration protocol and is challenging to relate to actual cell count. We address this with an interlaboratory study comparing three simple, low-cost, and highly accessible OD calibration protocols across 244 laboratories, applied to eight strains of constitutive GFP-expressing E. coli. Based on our results, we recommend calibrating OD to estimated cell count using serial dilution of silica microspheres, which produces highly precise calibration (95.5% of residuals <1.2-fold), is easily assessed for quality control, also assesses instrument effective linear range, and can be combined with fluorescence calibration to obtain units of Molecules of Equivalent Fluorescein (MEFL) per cell, allowing direct comparison and data fusion with flow cytometry measurements: in our study, fluorescence per cell measurements showed only a 1.07-fold mean difference between plate reader and flow cytometry data

Segmentation of fundus vascular images based on a dual-attention mechanism

Accurately segmenting blood vessels in retinal fundus images is crucial in the early screening, diagnosing, and evaluating some ocular diseases. However, significant light variations and non-uniform contrast in these images make segmentation quite challenging. Thus, this paper employ an attention fusion mechanism that combines the channel attention and spatial attention mechanisms constructed by Transformer to extract information from retinal fundus images in both spatial and channel dimensions. To eliminate noise from the encoder image, a spatial attention mechanism is introduced in the skip connection. Moreover, a Dropout layer is employed to randomly discard some neurons, which can prevent overfitting of the neural network and improve its generalization performance. Experiments were conducted on publicly available datasets DERIVE, STARE, and CHASEDB1. The results demonstrate that our method produces satisfactory results compared to some recent retinal fundus image segmentation algorithms.Comment: 17 pages,6 figure

A Space-Variant Deblur Method for Focal-Plane Microwave Imaging

In the research of passive millimetre wave (PMMW) imaging, the focal plane array (FPA) can realize fast, wide-range imaging and detection. However, it has suffered from a limited aperture and off-axis aberration. Thus, the result of FPA is usually blurred by space-variant point spread function (SVPSF) and is hard to restore. In this paper, a polar-coordinate point spread function (PCPSF) model is presented to describe the circle symmetric characteristic of space-variant blur, and a log-polar-coordinate transformation (LPCT) method is propagated as the pre-processing step before the Lucy⁻Richardson algorithm to eliminate the space variance of blur. Compared with the traditional image deblur method, LPCT solves the problem by analyzing the physical model instead of the approximating it, which has proved to be a feasible way to deblur the FPA imaging system

Generalized functional linear regression models with a mixture of complex function-valued and scalar-valued covariates prone to measurement error

While extensive work has been done to correct for biases due to measurement error in scalar-valued covariates prone to errors in generalized linear regression models, limited work has been done to address biases associated with functional covariates prone to errors or the combination of scalar and functional covariates prone to errors in these models. We propose Simulation Extrapolation (SIMEX) and Regression Calibration approaches to correct measurement errors associated with a mixture of functional and scalar covariates prone to classical measurement errors in generalized functional linear regression. The simulation extrapolation method is developed to handle the functional and scalar covariates prone to errors. We also develop methods based on regression calibration extended to our current measurement error settings. Extensive simulation studies are conducted to assess the finite sample performance of our developed methods. The methods are applied to the 2011-2014 cycles of the National Health and Examination Survey data to assess the relationship between physical activity and total caloric intake with type 2 diabetes among community-dwelling adults living in the United States. We treat the device-based measures of physical activity as error-prone functional covariates prone to complex arbitrary heteroscedastic errors, while the total caloric intake is considered a scalar-valued covariate prone to error. We also examine the characteristics of observed measurement errors in device-based physical activity by important demographic subgroups including age, sex, and race

Effect of Two Exogenous Organic Acids on the Excitation Effect of Soil Organic Carbon in Beijing, China

Significance: The study of the effects and pathways of catechol and pyrogallic acid on soil organic carbon mineralization has a positive effect on mastering soil carbon transformation. Methods and objectives: In this study, we took 0–20 cm soil from Pinus tabulaeformis forest as an object to investigate the effects of catechol and pyrogallic acid with different concentrations on soil organic carbon mineralization through a 60-day mineralization incubation test. The soil active carbon content and changes in soil microbial diversity and community composition were analyzed by using single exponential fitting, quantitative PCR, and high-throughput sequencing to explore the influencing mechanisms of catechol and pyrogallic acid on soil organic carbon excitation. Results: Catechol and pyrogallic acid had the effect of enhancing the soil organic carbon mineralization and soil active carbon content, and the higher the concentration, the stronger the enhancement effect. Catechol reduced the Ace index, Chao1 index, and Shannon index of bacteria and fungi, and further changed the relative abundance of two dominant groups (Proteobacteria and Acidobacteriota) in bacteria and Basidiomycota in fungi at high concentrations. There was no obvious regularity in the effects of pyrogallic acid on bacteria and fungi, but the Ace index and Chao1 index of bacteria underwent drastic and disordered changes. Conclusions: Catechol and pyrogallic acid can trigger positive excitation of the soil organic carbon through two pathways: increasing the soil active carbon content and modulating soil microorganisms, but the way they modulate soil microorganisms are different. Catechol regulates soil microorganisms by reducing the number, richness, and evenness of the bacteria and fungi species, as well as the community composition, while the way pyrogallic acid regulates only closely relates to the changes in the number, richness, and evenness of bacteria species