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Abstract Background To develop and validate an online individualized model for predicting local recurrence-free survival (LRFS) in esophageal squamous cell carcinoma (ESCC) treated by definitive chemoradiotherapy (dCRT). Methods ESCC patients from three hospitals were randomly stratified into the training set (715) and the internal testing set (179), and patients from the other hospital as the external testing set (120). The important radiomic features extracted from contrast-enhanced computed tomography (CECT)-based subregions clustered from the whole volume of tumor and peritumor were selected and used to construct the subregion-based radiomic signature by using COX proportional hazards model, which was compared with the tumor-based radiomic signature. The clinical model and the radiomics model combing the clinical factors and the radiomic signature were further constructed and compared, which were validated in two testing sets. Results The subresion-based radiomic signature showed better prognostic performance than the tumor-based radiomic signature (training: 0.642 vs. 0.621, internal testing: 0.657 vs. 0.638, external testing: 0.636 vs. 0.612). Although the tumor-based radiomic signature, the subregion-based radiomic signature, the tumor-based radiomics model, and the subregion-based radiomics model had better performance compared to the clinical model, only the subregion-based radiomics model showed a significant advantage (p

Abstract Objectives Repeatability is crucial for ensuring the generalizability and clinical utility of radiomics-based prognostic models. This study aims to investigate the repeatability of radiomic feature (RF) and its impact on the cross-institutional generalizability of the prognostic model for predicting local recurrence-free survival (LRFS) and overall survival (OS) in esophageal squamous cell cancer (ESCC) receiving definitive (chemo) radiotherapy (dCRT). Methods Nine hundred and twelve patients from two hospitals were included as training and external validation sets, respectively. Image perturbations were applied to contrast-enhanced computed tomography to generate perturbed images. Six thousand five hundred ten RFs from different feature types, bin widths, and filters were extracted from the original and perturbed images separately to evaluate RF repeatability by intraclass correlation coefficient (ICC). The high-repeatable and low-repeatable RF groups grouped by the median ICC were further analyzed separately by feature selection and multivariate Cox proportional hazards regression model for predicting LRFS and OS. Results First-order statistical features were more repeatable than texture features (median ICC: 0.70 vs 0.42–0.62). RFs from LoG had better repeatability than that of wavelet (median ICC: 0.70–0.84 vs 0.14–0.64). Features with smaller bin widths had higher repeatability (median ICC of 8–128: 0.65–0.47). For both LRFS and OS, the performance of the models based on high- and low-repeatable RFs remained stable in the training set with similar C-index (LRFS: 0.65 vs 0.67, p = 0.958; OS: 0.64 vs 0.65, p = 0.651), while the performance of the model based on the low-repeatable group was significantly lower than that based on the high-repeatable group in the external validation set (LRFS: 0.61 vs 0.67, p = 0.013; OS: 0.56 vs 0.63, p = 0.013). Conclusions Applying high-repeatable RFs in modeling could safeguard the cross-institutional generalizability of the prognostic model in ESCC. Critical relevance statement The exploration of repeatable RFs in different diseases and different types of imaging is conducive to promoting the proper use of radiomics in clinical research. Key Points The repeatability of RFs impacts the generalizability of the radiomic model. The high-repeatable RFs safeguard the cross-institutional generalizability of the model. Smaller bin width helps improve the repeatability of RFs. Graphical Abstract

Abstract Background Alcohol-related liver disease (ALD) and cardiovascular diseases share some common risk factors. This study aims to investigate the associations between Life’s Essential 8 (LE8), a comprehensive measure of cardiovascular health (CVH), and outcomes of ALD. Methods Data were obtained from the 2011–2018 National Health and Nutrition Examination Survey (NHANES). Cox proportional hazards models were employed to assess the relationships between LE8 and all-cause and cardiovascular mortality in patients with ALD. Additionally, restricted cubic splines (RCS), piecewise regression, and subgroup analyses were conducted. Results A total of 5321 ALD patients were included in this study with a mean LE8 score of 67.38. During a median follow-up period of 63 months, 228 all-cause deaths were recorded. After adjusting for potential confounders, the risk of all-cause mortality in the high CVH group decreased by 53.7% compared to the low CVH group (HR = 0.463, 95%CI = 0.223–0.965). The result was robust in subgroup analyses. The RCS analysis indicated a non-linear relationship between LE8 and cardiovascular mortality, showing that the risk of cardiovascular mortality decreased with increasing LE8 scores for values below 71.12 (HR = 0.949, 95% CI = 0.915–0.984). Conclusions LE8 score is inversely and linearly linked to all-cause mortality in ALD patients. Promoting adherence to optimal cardiovascular health may unveil additional strategies for the effective management of ALD patients and contribute to reducing their long-term mortality.

Abstract Perovskite/silicon tandem solar cells hold great promise for realizing high power conversion efficiency at low cost. However, achieving scalable fabrication of wide-bandgap perovskite (~1.68 eV) in air, without the protective environment of an inert atmosphere, remains challenging due to moisture-induced degradation of perovskite films. Herein, this study reveals that the extent of moisture interference is significantly influenced by the properties of solvent. We further demonstrate that n-Butanol (nBA), with its low polarity and moderate volatilization rate, not only mitigates the detrimental effects of moisture in air during scalable fabrication but also enhances the uniformity of perovskite films. This approach enables us to achieve an impressive efficiency of 29.4% (certified 28.7%) for double-sided textured perovskite/silicon tandem cells featuring large-size pyramids (2–3 μm) and 26.3% over an aperture area of 16 cm2. This advance provides a route for large-scale production of perovskite/silicon tandem solar cells, marking a significant stride toward their commercial viability.

Covalent organic frameworks (COFs) are typical porous organic polymers with crystalline porous architectures, which are provided with the merits of high surface area, low density, and high structural as well as functional adjustability. In comparison to conventional COFs with single-stranded linkages, ladder-type COFs are a very unique class of COFs which are usually connected by rings (conjugated or non-conjugated). Given the structural characteristics of ladder-type COFs, they overcome the shortcomings of conventional COFs and possess the advantages of rigid backbone, extended π-conjugation length, extraordinary chemical/thermal stability, and high conductivity. However, compared to the well-investigated conventional COFs, ladder-type COFs are paid little attention. Gaining ladder-type COFs quickly and efficiently encounters great challenges. Ladder-type COFs, containing a wide array of structures and functionalities, have recently come into focus. Especially for energy conversion and storage, they show great potential with excellent performances. Accordingly, this article systematically summarizes the recent developments in constructing ladder-type COFs. Initially, the topology design is generally introduced. Subsequently, the synthesis of ladder-type COFs is discussed in detail. It shows how to rationally select organic reactions and monomers and how to manipulate experimental conditions to construct ladder-type COFs bearing different functional groups and different conjugation lengths. Ultimately, this paper offers a concise outlook on the prospective advancements in the synthesis of ladder-type COFs.

Anthraquinone derivatives, owing to their widespread resources, high structural diversity and excellent electrochemical characteristics, are a class of useful carbonyl materials that actively participate in multiple electrochemical processes. Nonetheless, good solubility of small molecules in organic electrolyte solutions causes a gradual decline in the cycle stability of related devices. Incorporating anthraquinone subunit into porous organic polymers (POPs) not only resolve the solubility problem but also endow the corresponding polymers with larger specific surface areas and more stable rigid structures, which is significantly meaningful to enhance the accessibility of active sites and improve the cycle stability of electrochemical devices. Recently, electrochemical energy conversion and storage have attracted growing attention in both scientific research and practical applications, and anthraquinone-based POPs play vital roles in these processes. Therefore, this article systematically summarizes the recent advances of anthraquinone-based POPs in electrochemical energy conversion and storage. Firstly, the synthesis of anthraquinone-based POPs is discussed, which will provide detailed information on how to select monomers as well as reactions and how to optimize reaction conditions to gain the desired POPs. Secondly, the practical applications of anthraquinone-based POPs in electrochemical energy conversion and storage are evaluated in detail, in which the morphology manipulation and conductivity improvement will be emphatically elaborated. Finally, the future development trend is also briefly prospected.

Molecular shuttles are typical molecular machines that could be applied in various fields. The motion modes of wheel components in rotaxanes could be strategically modulated by external stimuli, such as pH, ions, solvent, light, and so on. Light is particularly attractive because it is harmless and can be operated in a remote mode and usually no byproducts are formed. Over the past decade, many examples of light-driven molecular shuttles are emerging. Accordingly, this review summarizes the recent research progress of light-driven molecular shuttles. First, the light-driven mechanisms of molecular motions with different functional groups are discussed in detail, which show how to drive photoresponsive or non-photoresponsive molecular shuttles. Subsequently, the practical applications of molecular shuttles in different fields, such as optical information storage, catalysis for organic reactions, drug delivery, and so on, are demonstrated. Finally, the future development of light-driven molecular shuttle is briefly prospected.

This paper mainly studies the optimal control problem of turbine-based combined cycle (TBCC) engines in the mode-transition stage. Based on the TBCC scheme proposed by Xiamen University, an aerothermodynamic model is established as a verification model for the validity of control laws. To reduce the complexity of control design, a control-oriented linear parameter-varying (LPV) model with Mach number as a scheduling variable is established under a given flight path. The design of mode-transition points and distribution of air-flow-rate among paths during the mode-transition process are transformed into linear quadratic (LQ) optimal control problems for an LPV system under the initial and terminal as well as process constraints. By optimizing the opening of the splitters of the inlet and the fuel flow in each channel, the optimal mode-transition points are found to achieve coordinated control and complete the high-precision thrust tracking during the mode-transition process.

Abstract Background The radiation transmission through the multileaf collimators is undesired in modern techniques such as volumetric modulated arc therapy (VMAT). According to identical plans, in this study, we aim to investigate the dosimetric impact of jaw tracking on the VMAT plans on two adjacent targets. Methods Two treatment plans were designed for eight pelvic (cervical) patients with two targets using the same optimization parameters. The original plan (O-plan) used automatically selected jaw positions. In the new plan (F-plan), the jaws were fixed to block two targets in two beams. The dosimetric parameters of the two plans were compared to evaluate the improvement of dose sparing for the body volume between two targets (named interOAR) in F-VMAT. Results The mean dose of interOAR reduced significantly from 654.96 ± 113.38 cGy for O-VMAT, to 490.84 ± 80.26 cGy for F-VMAT (p = 0.018). The monitor units (MUs) in the F-plans were 1.49-fold higher than that in the O-plan. The F and O-plan performed similarly in target dose homogeneity. The differences in Dmax of spinal cord, Dmax of spinal cord planning organ at risk volume, and V20, V30, and V40 of the intestine were insignificant. Conclusions VMAT plans with the fixed-jaw method can reduce the volume between two targets effectively. However, despite the plan quality, the method can only be used when the regular methods cannot reach the clinical requirements for critical organs because of additional MUs.

PurposeTo propose a synthesis method of pseudo-CT (CTCycleGAN) images based on an improved 3D cycle generative adversarial network (CycleGAN) to solve the limitations of cone-beam CT (CBCT), which cannot be directly applied to the correction of radiotherapy plans.MethodsThe improved U-Net with residual connection and attention gates was used as the generator, and the discriminator was a full convolutional neural network (FCN). The imaging quality of pseudo-CT images is improved by adding a 3D gradient loss function. Fivefold cross-validation was performed to validate our model. Each pseudo CT generated is compared against the real CT image (ground truth CT, CTgt) of the same patient based on mean absolute error (MAE) and structural similarity index (SSIM). The dice similarity coefficient (DSC) coefficient was used to evaluate the segmentation results of pseudo CT and real CT. 3D CycleGAN performance was compared to 2D CycleGAN based on normalized mutual information (NMI) and peak signal-to-noise ratio (PSNR) metrics between the pseudo-CT and CTgt images. The dosimetric accuracy of pseudo-CT images was evaluated by gamma analysis.ResultsThe MAE metric values between the CTCycleGAN and the real CT in fivefold cross-validation are 52.03 ± 4.26HU, 50.69 ± 5.25HU, 52.48 ± 4.42HU, 51.27 ± 4.56HU, and 51.65 ± 3.97HU, respectively, and the SSIM values are 0.87 ± 0.02, 0.86 ± 0.03, 0.85 ± 0.02, 0.85 ± 0.03, and 0.87 ± 0.03 respectively. The DSC values of the segmentation of bladder, cervix, rectum, and bone between CTCycleGAN and real CT images are 91.58 ± 0.45, 88.14 ± 1.26, 87.23 ± 2.01, and 92.59 ± 0.33, respectively. Compared with 2D CycleGAN, the 3D CycleGAN based pseudo-CT image is closer to the real image, with NMI values of 0.90 ± 0.01 and PSNR values of 30.70 ± 0.78. The gamma pass rate of the dose distribution between CTCycleGAN and CTgt is 97.0% (2%/2 mm).ConclusionThe pseudo-CT images obtained based on the improved 3D CycleGAN have more accurate electronic density and anatomical structure.

In this study, an anatomic semantic guided neural style transfer (ASGNST) algorithm was developed and pseudo-computed tomography (CT) images synthesized in steps. CT images and ultrasound (US) images of 20 cervical cancer patients to be treated were selected. The foreground (FG) and background (BG) regions of the US images were segmented by the region growth method, and three objective functions for content, style, and contour loss were defined. Based on the two types of regions, a local pseudo-CT image synthesis model based on a convolution neural network was established. Then, global 2D pseudo-CT images were obtained using the weighted average fusing algorithm, and the final pseudo-CT images were obtained through 3D reconstruction. US phantom and data of five additional cervical cancer patients were used for prediction. Furthermore, three image synthesis algorithms-global deformation field (GDF), stepwise local deformation field (SLDF), and neural style transfer (NST)-were selected for comparative verification. The pseudo-CT images synthesized by the four algorithms were compared with the ground-truth CT images obtained during treatment. The structural similarity index between the ground-truth CT and pseudo-CT synthesized by the improved algorithm significantly differed from those synthesized by the other three algorithms (tGDF_bg = 7.175, tSLDF_bg = 4.513, tNST_bg = 3.228, tGDF_fg = 10.518, tSLDF_fg = 5.522, tNST_fg = 2.869, p

The meteorological environment is a determining factor in photovoltaic (PV) system feasibility (PVSF). To evaluate this impact more accurately, a quantitative analysis model based on multimeteorological factors and the Random Forest Regression model is proposed in this work. Firstly, an evaluation system is established to assess the impact. Then, to predict the indicators of the evaluation system, a parameter, i.e., performance ratio in sampling period, is defined. Secondly, a set of essential influences on the performance ratio in the sampling period is established through analyzing and reducing the discovered influences on the PV system performance. Finally, data from the Desert Knowledge Australia Solar Centre (DKASC) website are used to conduct the experiment. During the experiment, the sample set is cleaned using the model based on the cosine of the zenith angle. The functional relationship between the performance ratio in the sampling period and its essential influences is established through training a Random Forest Regression model with the data of the modeling system. The data of the test system are used to verify the forecast performance of the proposed model. Compared with the reference model, which is based on the traditional physical experiment, the results of the proposed model accord better with the measured values.

Background: Due to tumoral heterogeneity and the lack of robust biomarkers, the prediction of chemoradiotherapy response and prognosis in patients with esophageal cancer (EC) is challenging. The goal of this study was to assess the study quality and clinical value of machine learning and radiomic-based quantitative imaging studies for predicting the outcomes of EC patients after chemoradiotherapy. Materials and methods: PubMed, Embase, and Cochrane were searched for eligible articles. The methodological quality and risk of bias were evaluated using the Radiomics Quality Score (RQS), Image Biomarkers Standardization Initiative (IBSI) Guideline, and Transparent Reporting of a multivariable prediction model for Individual Prognosis or Diagnosis (TRIPOD) statement, as well as the modified Quality Assessment of Diagnostic Accuracy Studies (QUADAS-2) tool. A meta-analysis of the evidence focusing on predicting chemoradiotherapy response and outcome in EC patients was implemented. Results: Forty-six studies were eligible for qualitative synthesis. The mean RQS score was 9.07, with an adherence rate of 42.52%. The adherence rates of the TRIPOD and IBSI were 61.70 and 43.17%, respectively. Ultimately, 24 studies were included in the metaanalysis, of which 16 studies had a pooled sensitivity, specificity, and area under the curve (AUC) of 0.83 (0.76-0.89), 0.83 (0.79-0.86), and 0.84 (0.81-0.87) in neoadjuvant chemoradiotherapy datasets, as well as 0.84 (0.75-0.93), 0.89 (0.83-0.93), and 0.93 (0.90-0.95) in definitive chemoradiotherapy datasets, respectively. Moreover, radiomics could distinguish patients from the lowrisk and high-risk groups with different disease-free survival (DFS) (pooled hazard ratio: 3.43, 95% CI 2.39-4.92) and overall survival (pooled hazard ratio: 2.49, 95% CI 1.91-3.25). The results of subgroup and regression analyses showed that some of the heterogeneity was explained by the combination with clinical factors, sample size, and usage of the deep learning (DL) signature. Conclusions: Noninvasive radiomics offers promising potential for optimizing treatment decision-making in EC patients. However, it is necessary to make scientific advancements in EC radiomics regarding reproducibility, clinical usefulness analysis, and open science categories. Improved model reporting of study objectives, blind assessment, and image processing steps are required to help promote real clinical applications of radiomics in EC research. [ABSTRACT FROM AUTHOR]