Estimation Algorithm Research Articles

Damage identification of reinforced concrete slabs using experimental modal testing and finite element model updating

Condition assessment and health monitoring of reinforced concrete structures is an essential part of their effective maintenance throughout their service life. Vibration-based damage detection and health monitoring is one of the most popular global approaches where experimentally measured modal parameters, such as frequencies, mode shapes, etc. are compared to their corresponding finite element counterparts to estimate possible stiffness loss. The present investigation implements a vibration-based parameter estimation algorithm for updating a finite-element (FE) model for damage assessment of reinforced concrete slabs using measured frequency response functions (FRF) data through a mixed numerical-experimental technique. An inverse eigen-sensitivity algorithm has been implemented to minimize the error function realized as the weighted differences in the mode shapes and frequencies of the FE model and the modal test data. Three reinforced concrete slabs were subjected to real physical damage conditions to verify the efficacy of the proposed methodology. The technique is found to be resilient in the presence of modal and coordinate sparsity. In general, damage can be assessed even if the portions of the structure are inaccessible to the users, thereby extending the possibility of its application to most of the practical configurations of reinforced concrete slabs.

ZFC3H1 as an Indicator of Poor Prognosis in Hepatocellular Carcinoma: Bioinformatic Analysis and Experimental Verification.

Zinc finger C3H1-type containing (ZFC3H1) might regulate RNA processes. However, research lacks the prognostic value of ZFC3H1 in hepatocellular carcinoma (HCC). The study analyzed ZFC3H1 expression in HCC cells and its correlation with patient prognosis using transcriptomics, immunohistochemistry, and quantitative real-time reverse transcription PCR, as well as single-cell RNA expression data. Additionally, Gene Ontology and Kyoto Encyclopedia of Genes and Genomes enrichment analyses were used to investigate the potential ZFC3H1-related cellular functions and signaling pathways. The impact of ZFC3H1 expression on the tumor microenvironment and tumor mutational burden (TMB) was assessed using the ESTIMATE algorithm. Cell-based assays, including cell counting kit 8, proliferation, colony formation, cell cycle, wound healing, and Transwell assays, were conducted to evaluate the influence of ZFC3H1 on hepatocellular carcinoma proliferation and migration. ZFC3H1 is upregulated in HCC and linked to tumor progression. High ZFC3H1 expression is a prognostic risk factor for HCC, according to Kaplan-Meier and Cox regression analyses. ESTIMATE analysis suggested that ZFC3H1 reduces immune cell infiltration and increases the TMB. Patients with low ZFC3H1 expression might respond better to immunotherapy. High ZFC3H1 expression is associated with increased half-maximal inhibitory concentration (IC50) of sorafenib. Functional experiments demonstrated that reducing ZFC3H1 expression inhibited HCC cell proliferation and migration. ZFC3H1 is upregulated in HCC, promoting the proliferation and migration of liver cancer cells, impacting the prognosis of HCC patients and the effectiveness of immunotherapy. ZFC3H1 might serve as a therapeutic target and biomarker for HCC.

Coordination control strategy of motion stability and tire slip for electric vehicle driven by in-wheel-motors

The Direct Yaw Moment Control (DYC) is a widely utilized technique for electric vehicles equipped with In-Wheel Motors (IWMs) to ensure yaw stability. However, most existing DYC studies calculate the additional control yaw moment solely based on the torque differences among the four IWMs, disregarding the nonlinear tire characteristics and wheel rotational movements. When road conditions are poor or torque changes are intense, the tire may slip, preventing it from generating the required longitudinal tire force for the desired direct yaw moment. To address this challenge, this study introduces a coordination control strategy for motion stability and tire slip in electric vehicles (EV) driven by IWMs. To enhance control performance, the nonlinear tire characteristics and wheel rotational movements are integrated into the modeling process when calculating tire forces. Additionally, a state and parameter coupling estimation algorithm is utilized to obtain feedback on tire parameters and vehicle motion states. Using these estimation results as a foundation, a coordination strategy is developed to achieve reference motion state tracking and tire slip ratio control. The Model Predictive Control (MPC) algorithm is employed to solve the control problem with constraints on both control inputs and outputs, and the estimation results are utilized to update the control model as well as provide states feedback. Simulations and experiments are conducted to validate the feasibility and effectiveness of the proposed control strategy. The results demonstrate that our proposed control strategy effectively improves vehicle stability by coordinating vehicle motion and tire slip control.

Endmember extraction and abundance estimation algorithm based on double-compressed sampling

Based on double-compressed sampling, a hyperspectral spectral unmixing algorithm (SU_DCS) is proposed, which could directly complete the endmember extraction and abundance estimation. On the basis of the linear mixed model (LMM), we designed spatial and spectral sampling matrices, obtained spatial and spectral measurement data, and constructed a joint unmixing model containing endmember and abundance information. By using operator separation and Lagrangian multiplier algorithm, the endmember matrix, abundance matrix and remixing image can be quickly obtained by matrix operation. The parameters of the unmixing algorithm, including regularization parameter, convergence threshold and spatial sampling rate, are determined using synthetic simulated hyperspectral data. The proposed algorithm is applied to two kinds of real hyperspectral data, with or without ground truth, in order to verify the effectiveness and reliability of the algorithm. Firstly, we provide the performance of the algorithm on real datasets without ground truth. Compared with algorithm VCA_FCLS and algorithm CPPCA_VCA_FCLS, the endmember spectral curve extracted by the proposed SU_DCS is almost consistent with that obtained by VCA_FCLS, and is more smooth than that of obtained by CPPCA_VCA_FCLS. Additionally, the abundance estimation map estimated by the SU_DCS has consistency with the results obtained by VCA_FCLS. Moreover, the proposed SU_DCS has higher peak signal-to-noise ratio (PSNR) for remixing images with higher computational efficiency. Secondly, we provide the performance of the proposed algorithm on four real datasets with ground truth, including dataset Cuprite, dataset Samson, dataset Jasper and dataset Urban. We provide the results of endmember extraction and abundance estimation from the compressed data under different sampling rate conditions. The extracted endmember maintains good consistency with the true spectral curves, and the estimated abundance map can also maintain good spatial consistency with the ground truth. The comparison results with other four comparative algorithms also indicate that the proposed algorithm can obtain relatively accurate endmembers and abundance information from compressed data, the reliability and validity of the proposed algorithm have been proved. In summary, the main innovation of the proposed algorithm is that it can extract endmembers and estimate abundance with high accuracy from a small amount of measurement data.

Designing transport scheme of 3D naked-eye system

3D naked-eye is constructed from multi-stream as a typical representation of stereoscopic video. Its enormous data volume and stringent low-delay transport requirements pose significant challenges for high-quality real-time transport. Through analysis and experimental verification that current streaming media transport frameworks using the server–client or peer-to-peer scheme face difficulties when transmitting 3D naked-eye in a one-to-one format. Besides, the existing bandwidth estimation algorithms cannot achieve the expected performance when dealing with delay-sensitive traffic. This results in low bandwidth utilization and slow bandwidth estimation, rendering it unfeasible to deliver multi-stream on time. We propose an effective transport framework with different modules for real-time multi-stream and introduce an Agent-to-Agent transport scheme that provides many-to-one connection as the main implementation way of 3D naked-eye transport framework. Additionally, we propose a direct bandwidth estimation algorithm to quickly match network bandwidth for low-delay transport. The Agent terminal centrally processes consolidates transports, and provides macro-level management of multiple video streams. The algorithm directly detects the available bandwidth using packet interval and packet rate models. Finally, using rate decision algorithm arbitrates the results to directly measure the maximum available bandwidth of the link. The Agent-to-Agent achieves 99% bandwidth utilization, addressing the limitations of existing streaming schemes in handling concurrent data streams. Our algorithm provides precise bandwidth estimates with minimal time overhead, meeting the requirements of a delay-sensitive 3D naked-eye system across diverse environments.

Fc receptor-like A promotes malignant behavior in renal cell carcinoma and correlates with tumor immune infiltration.

Our study aims to investigate the mechanisms through which Fc receptor-like A (FCRLA) promotes renal cell carcinoma (RCC) and to examine its significance in relation to tumor immune infiltration. The correlation between FCRLA and data clinically related to RCC was explored using The Cancer Genome Atlas (TCGA), then validated using Gene Expression Omnibus (GEO) gene chip data. Enrichment and protein-protein interaction (PPI) network analyses were performed for FCRLA and its co-expressed genes. FCRLA was knocked down in RCC cell lines to evaluate its impact on biological behavior. Then the potential downstream regulators of FCRLA were determined by western blotting, and rescue experiments were performed for verification. The relevance between FCRLA and various immune cells was analyzed through GSEA, TIMER, and GEPIA tools. TIDE and ESTIMATE algorithms were used to predict the effect of FCRLA in immunotherapy. Fc receptor-like A was associated with clinical and T stages and could predict the M stage (AUC = 0.692) and 1-3- and 5-year survival rates (AUC = 0.823, 0.834, and 0.862) of RCC patients. Higher expression of FCLRA predicted an unfavorable overall survival (OS) in TCGA-RCC and GSE167573 datasets (p = 0.03, p = 0.04). FCRLA promoted the malignant biological behavior of RCC cells through the pERK1/2/-MMP2 pathway and was associated with tumor immune microenvironment in RCC. Fc receptor-like A is positively correlated with poor outcomes in RCC patients and plays an oncogenic role in RCC through the pERK1/2-MMP2 pathway. Patients with RCC might benefit from immunotherapy targeting FCRLA.

Target-based georeferencing of terrestrial radar images using TLS point clouds and multi-modal corner reflectors in geomonitoring applications

Terrestrial Radar Interferometry (TRI) is widely adopted in geomonitoring applications due to its capability to precisely observe surface displacements along the line of sight, among other key characteristics. As its deployment grows, TRI is also increasingly used to monitor smaller and more dispersed geological phenomena, where the challenge is their precise localization in 3d space if the pose of the radar interferometer is not known beforehand. To tackle this challenge, we introduce a semi-automatic target-based georeferencing method for precisely aligning TRI data with 3d point clouds obtained using long-range Terrestrial Laser Scanning (TLS). To facilitate this, we developed a multi-modal corner reflector (mmCR) that serves as a common reference point recognizable by both technologies, and we accompanied it with a semi-automatic data-processing pipeline, including the algorithms for precise center estimation. Experimental validation demonstrated that the corner reflector can be localized within the TLS data with a precision of 3–5 cm and within the TRI data with 1–2 dm. The targets were deployed in a realistic geomonitoring scenario to evaluate the implemented workflow and the achievable quality of georeferencing. The post-georeferencing mapping uncertainty was found to be on a decimeter level, matching the state-of-the-art results using dedicated targets and achieving more than an order of magnitude lower uncertainty than the existing data-driven approaches. In contrast to the existing target-based approaches, our results were achieved without laborious visual data inspection and manual target detection and on significantly larger distances, surpassing 2 km. The use of the developed mmCR and its associated data-processing pipeline extends beyond precise georeferencing of TRI imagery to TLS point clouds, allowing for alternatively georeferencing using total stations, mapping quality evaluation as well as on-site testing and calibrating TRI systems within the application environment.

Estimation of ultrasonic flight time based on SHADE algorithm and extended kalman filtering

Abstract Traditional methods of using ultrasound to measure oxygen concentration have the issue of inaccuracy and susceptibility to noise interference when measuring the time of flight (TOF) of ultrasonic echo signals. A proposed algorithm for ultrasonic parameter estimation, utilizing an asymmetric Gaussian model, aims to enhance the precision and reliability of TOF estimation, aiming to reduce the influence of noise interference. The algorithm combines an improved Self-Adaptive Differential Evolution (SHADE) algorithm based on successful historical records with an Extended Kalman Filter (EKF). The improved SHADE algorithm controls the degree of mutation strategy greediness during the mutation operation based on the number of iterations, thereby enhancing the algorithm’s iteration speed. Then, the obtained solution is used as the initial value for the EKF to estimate the model parameters, thus addressing the issue of EKF’s dependency on accurate initial values to obtain precise outputs. Through simulation and experimental measurements of ultrasonic oxygen concentration, several commonly used parameter estimation algorithms are compared with the proposed SHADE-EKF algorithm. Results demonstrate that the SHADE-EKF algorithm exhibits superior performance in estimating the TOF of ultrasonic waves.

The Causal Roadmap and Simulations to Improve the Rigor and Reproducibility of Real-data Applications.

The Causal Roadmap outlines a systematic approach to asking and answering questions of cause and effect: define the quantity of interest, evaluate needed assumptions, conduct statistical estimation, and carefully interpret results. To protect research integrity, it is essential that the algorithm for statistical estimation and inference be prespecified prior to conducting any effectiveness analyses. However, it is often unclear which algorithm will perform optimally for the real-data application. Instead, there is a temptation to simply implement one's favorite algorithm, recycling prior code or relying on the default settings of a computing package. Here, we call for the use of simulations that realistically reflect the application, including key characteristics such as strong confounding and dependent or missing outcomes, to objectively compare candidate estimators and facilitate full specification of the statistical analysis plan. Such simulations are informed by the Causal Roadmap and conducted after data collection but prior to effect estimation. We illustrate with two worked examples. First, in an observational longitudinal study, we use outcome-blind simulations to inform nuisance parameter estimation and variance estimation for longitudinal targeted minimum loss-based estimation. Second, in a cluster randomized trial with missing outcomes, we use treatment-blind simulations to examine type-I error control in two-stage targeted minimum loss-based estimation. In both examples, realistic simulations empower us to prespecify an estimation approach that is expected to have strong finite sample performance and also yield quality-controlled computing code for the actual analysis. Together, this process helps to improve the rigor and reproducibility of our research.

Development of a disulfidptosis-related lncRNA prognostic signature for enhanced prognostic assessment and therapeutic strategies in lung squamous cell carcinoma

Limited treatment options and poor prognosis present significant challenges in the treatment of lung squamous cell carcinoma (LUSC). Disulfidptosis impacts cancer progression and prognosis. We developed a prognostic signature using disulfidptosis-related long non-coding RNAs (lncRNAs) to predict the prognosis of LUSC patients. Gene expression matrices and clinical information for LUSC were downloaded from the TCGA database. Co-expression analysis identified 209 disulfidptosis-related lncRNAs. LASSO-Cox regression analysis identified nine key lncRNAs, forming the basis for establishing a prognostic model. The model’s validity was confirmed by Kaplan–Meier and ROC curves. Cox regression analysis identified the risk score (RS) as an independent prognostic factor inversely correlated with overall survival. A nomogram based on the RS demonstrated good predictive performance for LUSC patient prognosis. The relationship between RS and immune function was explored using ESTIMATE, CIBERSORT, and ssGSEA algorithms. According to the TIDE database, a negative correlation was found between RS and immune therapy responsiveness. The GDSC database revealed that 49 drugs were beneficial for the low-risk group and 25 drugs for the high-risk group. Silencing C10orf55 expression in SW900 cells reduced invasiveness and migration potential. In summary, this lncRNA model based on TCGA-LUSC data effectively predicts prognosis and assists clinical decision-making.

Carbon market risk estimation using quantum conditional generative adversarial network and amplitude estimation

AbstractAccurately and efficiently estimating the carbon market risk is paramount for ensuring financial stability, promoting environmental sustainability, and facilitating informed decision‐making. Although classical risk estimation methods are extensively utilized, the implicit pre‐assumptions regarding distribution are predominantly contained and challenging to balance accuracy and computational efficiency. A quantum computing‐based carbon market risk estimation framework is proposed to address this problem with the quantum conditional generative adversarial network‐quantum amplitude estimation (QCGAN‐QAE) algorithm. Specifically, quantum conditional generative adversarial network (QCGAN) is employed to simulate the future distribution of the generated return rate, whereas quantum amplitude estimation (QAE) is employed to measure the distribution. Moreover, the quantum circuit of the QCGAN improved by reordering the data interaction layer and data simulation layer is coupled with the introduction of the quantum fully connected layer. The binary search method is incorporated into the QAE to bolster the computational efficiency. The simulation results based on the European Union Emissions Trading System reveals that the proposed framework markedly enhances the efficiency and precision of Value‐at‐Risk and Conditional Value‐at‐Risk compared to original methods.

Two datasheet-based approaches for double-diode PV modeling with an efficient trade-off between accuracy and simplicity

A reliable photovoltaic (PV) model with logical trade-off between accuracy and simplicity can be utilized for optimal design, monitoring, fault detection and prediction of operational power. In this research work, two novel datasheet-based approaches are developed for PV module identification through double-diode model (DDM). In both methods the four unknown parameters are derived as functions of series resistance (Rs). It is shown that each parameter estimation algorithm has its own unique solution. Effective numerical methods are applied for fast convergence and with absolute certainty to the corresponding unique solution. The established methods are both reasonably simple and accurate while one is simpler (faster) and the other is more accurate. The performances of the proposed algorithms are evaluated by real experimental data and through a comparison with other similar datasheet-based algorithms. The results reveal that the developed methods have superb performance in comparison with other techniques.

High redshift LBGs from deep broadband imaging for future spectroscopic surveys

Lyman break galaxies (LBGs) are promising probes for clustering measurements at high redshift, z > 2, a region only covered so far by Lyman-α forest measurements. In this paper, we investigate the feasibility of selecting LBGs by exploiting the existence of a strong deficit of flux shortward of the Lyman limit, due to various absorption processes along the line of sight. The target selection relies on deep imaging data from the HSC and CLAUDS surveys in the g, r, z and u bands, respectively, with median depths reaching 27 AB in all bands. The selections were validated by several dedicated spectroscopic observation campaigns with DESI. Visual inspection of spectra has enabled us to develop an automated spectroscopic typing and redshift estimation algorithm specific to LBGs. Based on these data and tools, we assess the efficiency and purity of target selections optimised for different purposes. Selections providing a wide redshift coverage retain 57% of the observed targets after spectroscopic confirmation with DESI, and provide an efficiency for LBGs of 83±3%, for a purity of the selected LBG sample of 90±2%. This would deliver a confirmed LBG density of ~ 620 deg-2 in the range 2.3 < z < 3.5 for a r-band limiting magnitude r < 24.2. Selections optimised for high redshift efficiency retain 73% of the observed targets after spectroscopic confirmation, with 89±4% efficiency for 97±2% purity. This would provide a confirmed LBG density of ~ 470 deg-2 in the range 2.8 < z < 3.5 for a r-band limiting magnitude r < 24.5. A preliminary study of the LBG sample 3d-clustering properties is also presented and used to estimate the LBG linear bias. A value of b LBG = 3.3 ± 0.2 (stat.) is obtained for a mean redshift of 2.9 and a limiting magnitude in r of 24.2, in agreement with results reported in the literature.

Artificial neural network-based sparse channel estimation for V2V communication systems

Abstract Artificial neural networks (ANNs) have gained a lot of attention from researchers in the past few years and have been employed on a large scale. They have also been gaining momentum in wireless communication systems. For efficient vehicle-to-vehicle (V2V) channel communication, a sparse multipath channel issue must be studied. To minimize the multipath effect, a time reversal (TR) operation and time division synchronization orthogonal frequency division multiplexing (TDS-OFDM) have been appealing because of their fast synchronization and active spectral efficiency. To improve the transceiver's execution in a frequency-selective fading channel environment, an OFDM system is used to reduce inter- symbol interference (ISI). Simultaneous Orthogonal Matching Pursuit (SOMP) channel state estimator algorithm suffer from high computational cost and high computational complexity. The ANN algorithm has better performance than SOMP algorithm. The proposed neural network technologies have lower complexity than the SOMP algorithm. The application of ANN is capable of solving complex problems, such as those encountered in image, signal processing and have been implemented for channel estimation in OFDM. The proposed ANN outperformed the SOMP algorithm with regard to signal compensation. Overall, the ANN algorithm achieved the best performance. This study proposes an ANN-based sparse channel state estimator. Regarding the bit error rate (BER) metric, the proposed estimator outperforms the channel estimation approach based on the SOMP. The simulation results confirm the efficacy of the proposed approach.

Hypothesis Testing for Progressive Kernel Estimation and VCM Framework.

Identifying an appropriate radius for unbiased kernel estimation is crucial for the efficiency of radiance estimation. However, determining both the radius and unbiasedness still faces big challenges. In this paper, we first propose a statistical model of photon samples and associated contributions for progressive kernel estimation, under which the kernel estimation is unbiased if the null hypothesis of this statistical model stands. Then, we present a method to decide whether to reject the null hypothesis about the statistical population (i.e., photon samples) by the F-test in the Analysis of Variance. Hereby, we implement a progressive photon mapping (PPM) algorithm, wherein the kernel radius is determined by this hypothesis test for unbiased radiance estimation. Second, we propose VCM+, a reinforcement of Vertex Connection and Merging (VCM), and derive its theoretically unbiased formulation. VCM+ combines hypothesis testing-based PPM with bidirectional path tracing (BDPT) via multiple importance sampling (MIS), wherein our kernel radius can leverage the contributions from PPM and BDPT. We test our new algorithms, improved PPM and VCM+, on diverse scenarios with different lighting settings. The experimental results demonstrate that our method can alleviate light leaks and visual blur artifacts of prior radiance estimate algorithms. We also evaluate the asymptotic performance of our approach and observe an overall improvement over the baseline in all testing scenarios.

B-BIND: Biophysical Bayesian Inference for Neurodegenerative Dynamics.

Throughout an organism's life, a multitude of complex and interdependent biological systems transition through biophysical processes that serve as indicators of the underlying biological states. Inferring these latent, unobserved states is a goal of modern biology and neuroscience. However, in many experimental setups, we can at best obtain discrete snapshots of the system at different times and for different individuals. This challenge is particularly relevant in the study of Alzheimer's Disease (AD) progression, where we observe the aggregation of pathology in brain donors, but the underlying disease state is unknown. This paper proposes a biophysically motivated Bayesian framework (B-BIND: Biophysical Bayesian Inference for Neurodegenerative Dynamics), where the disease state is modeled and continuously inferred from observed quantifications of multiple AD pathological proteins. Inspired by biophysical models, we describe pathological burden as an exponential process. The progression of AD is modeled by assigning a latent score, termed pseudotime, to each pathological state, creating a pseudotemporal order of donors based on their pathological burden. We study the theoretical properties of the model using linearization to reveal convergence and identifiability properties. We provide Markov chain Monte Carlo estimation algorithms, illustrating the effectiveness of our approach with multiple simulation studies across various data conditions. Applying this methodology to data from the Seattle Alzheimer's Disease Brain Cell Atlas, we infer the pseudotime ordering of donors. Finally, we analyze the information within each pathological feature to refine the model, focusing on the most informative pathologies. This framework lays the groundwork for continuous pseudotime modeling in the analysis of neurodegenerative diseases.

Elevation Estimation Algorithm for Low-Altitude Targets in Multipath Environment

The algorithm proposed in this study estimates the multipath elevation of targets at low altitudes, including near-zero elevation. Although the double null algorithm is a maximum likelihood elevation estimation algorithm, it diverges when the target elevation is near zero. Addressing this issue, the selective double null algorithm improves the accuracy of multipath elevation at low altitudes by applying previously measured antenna near-field patterns. However, it cannot be applied to active electronically scanned array radars, since antenna beam patterns vary with the steered azimuth and elevation angles. In this paper, the proposed algorithm modifies the double null algorithm to estimate the elevation in the divergence state more accurately. Since it is based on the multipath energy function, the antenna near-field pattern is unnecessary. In addition, to increase the accuracy of elevation estimation, an operation method that selectively uses the estimated elevations according to several conditions is proposed. The proposed algorithm was verified through simulations.

Construction of an immune-related gene prognostic model for obese endometrial cancer patients based on bioinformatics analysis

BackgroundThe tumor microenvironment (TME) affected the prognosis of tumors. However, its effect on the outcomes of obese endometrial cancer (EC) patients had not been reported. MethodsThis research performed a retrospective analysis of the transcriptome profiles and medical data of 503 EC patients. Immune scores were assessed by estimation algorithms. Cox and LASSO regression analyses were utilized to pinpoint key genes linked to prognosis, and the RPS was created to forecast the outcomes of obese EC patients. The relationship among genetic mutations and RPS was examined using CNV and somatic mutation information. ssGSEA and GSVA were employed to detect immune infiltration and immune pathway enrichment associated with key genes. The TIDE algorithm and GDSC database were utilized to forecast patients’ responses of patients to immunotherapy and chemotherapy, respectively. Finally, we employed the 'rms' R software package to construct the nomogram. ResultsThe prognosis of obese EC patients was associated with immune scores. Three key genes (EYA4, MBOAT2 and SCGB2A1) were identified. The risk prognosis score (RPS) for obese EC patients was established by risk stratification and prognostic prediction using prognostic genes. The higher the RPS, the worse the prognosis, and the more malignant the genomic alterations. The high RPS group had a significantly reduced proportion of most immune cells in comparison to the low RPS group. The high RPS group was linked to G2M, MYC and E2F related pathways such as cell proliferation, cell cycle and cell death. Cisplatin, tamoxifen and topotecan had a greater effect on the low RPS group. Notably, the nomogram had a good predictive ability. ConclusionOur study designed a reliable RPS for obese EC patients to forecast their prognosis, immune aggressiveness, and responses to immunotherapy and drug treatments.

Myeloid cell differentiation-related gene signature for predicting clinical outcome, immune microenvironment, and treatment response in lung adenocarcinoma

Considering the key role of myeloid cell differentiation—related genes in the tumor microenvironment (TME), we aimed to build a prognostic risk model using these genes for Lung adenocarcinoma (LUAD). The mRNA gene expression profiles of LUAD patients from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases were downloaded as the training and validation sets. Then, “edgeR” R package was applied to screen out the differentially expressed genes (DEGs) and univariate cox regression, backward stepwise selection analyses were performed to construct a prognostic model for LUAD. ESTIMATE, TIMER, XCELL, CIBERSORT abs, QUANTISEQ, MCPCOUNTER, EPIC, and CIBERSORT algorithms were conducted to access the association of risk levels with the stromal and immune cell infiltration levels in LUAD. Six genes (F2RL1, PRKDC, TNFSF11, INHA, PLA2G3 and TUBB1) were utilized to construct the prognostic model. The risk model showed excellent prognostic performance for LUAD in both TCGA and GEO datasets. Also, compared to the low-risk patients, the high-risk patients had higher expression of immune checkpoint molecules and showed a lower IC50 value to the chemotherapy agents. Our findings provided a myeloid cell differentiation—related gene signature that could effectively predict prognosis and guide treatment strategies for LUAD patients.

Self-Supervised Monocular Depth Estimation for Endoscopic Imaging.

Endoscopy holds a pivotal role in the early detection and treatment of diverse diseases, with artificial intelligence (AI)-assisted methods increasingly gaining prominence in disease screening. Among them, the depth estimation from endoscopic sequences is crucial for a spectrum of AI-assisted surgical techniques. However, the development of endoscopic depth estimation algorithms presents a formidable challenge due to the unique environmental intricacies and constraints within the dataset. This paper proposes a self-supervised depth estimation network to comprehensively explore the brightness changes in endoscopic images, and fuse different features at multiple levels to achieve an accurate prediction of endoscopic depth. First, a FlowNet is designed to evaluate the brightness changes of adjacent frames by calculating the multi-scale structural similarity. Second, a feature fusion module is presented to capture multi-scale contextual information. Experiments show that the average accuracy of the algorithm is 97.03% in the Stereo Correspondence and Reconstruction of Endoscopic Data (SCARED dataset). Based on the training parameters of the SCARED dataset, the algorithm achieves superior performance on the other two datasets (EndoSLAM and KVASIR dataset), indicating that the algorithm has good generalization performance.