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Abstract Background This study aims to construct predicting models using radiomic and clinical features in predicting first-line vascular endothelial growth factor receptor-tyrosine kinase inhibitor (VEGFR-TKI) early resistance in metastatic clear cell renal cell carcinoma (mccRCC) patients. We also aim to explore the correlation of predicting models with short and long-term survival of mccRCC patients. Materials and methods In this retrospective study, 110 mccRCC patients from 2009 to 2019 were included and assigned into training and test sets. Radiomic features were extracted from tumor 3D-ROI of baseline enhanced CT images. Radiomic features were selected by Lasso method to construct a radiomic score. A combined nomogram was established using the combination of radiomic score and clinical factors. The discriminative abilities of the radiomic, clinical and combined nomogram were quantified using ROC curve. Cox regression analysis was used to test the correlation of nomogram score with progression-free survival (PFS) and overall survival (OS). PFS and OS were compared between different risk groups by log-rank test. Results The radiomic, clinical and combined nomogram demonstrated AUCs of 0.81, 0.75, and 0.83 in training set; 0.79, 0.77, and 0.88 in test set. Nomogram score ≥ 1.18 was an independent prognostic factor of PFS (HR 0.22 (0.10, 0.47), p

Abstract The voluntary nature of decision-making is fundamental to human behavior. The subthalamic nucleus is important in reactive decision-making, but its role in voluntary decision-making remains unclear. We recorded from deep brain stimulation subthalamic electrodes time-locked with acute stimulation using a Go/Nogo task to assess voluntary action and inaction. Beta oscillations during voluntary decision-making were temporally dissociated from motor function. Parkinson’s patients showed an inaction bias with high beta and intermediate physiological states. Stimulation reversed the inaction bias highlighting its causal nature, and shifting physiology closer to reactive choices. Depression was associated with higher alpha during Voluntary-Nogo characterized by inaction or inertial status quo maintenance whereas apathy had higher beta-gamma during voluntary action or impaired effortful initiation of action. Our findings suggest the human subthalamic nucleus causally contributes to voluntary decision-making, possibly through threshold gating or toggling mechanisms, with stimulation shifting towards voluntary action and suggest biomarkers as potential clinical predictors.

Abstract The minimum velocity (v) for impact ejecta to form secondary craters (secondaries) remains enigmatic, but it is a crucial parameter in untangling the fate of impact ejecta on planetary surfaces. By cataloging the distances (L) of the nearest secondaries from centers of various-sized (D) primary craters (primaries) on the Moon, Mars and Mercury, we find that v can be as small as ~25 m/s, and an unified power-law relationship of L = 1.86D 0.93 (both in meters) works for both simple and complex craters, regardless of different surface gravity and target properties. This relationship also successfully predicts occurrences of secondaries formed by craters on Venus. The constraint on v explains the common concurrences of structural disturbances in crater walls and continuous ejecta deposits caused by landing of cogenetic ejecta, suggesting that ejecta forming self-secondaries do not need near-vertical ejection angles and tertiary craters should be abundant on terrestrial bodies.

Abstract Background Increasing evidence suggests a potential causal association between lipid levels and facial aging. The aim of this study was to investigate the relationship between levels of specific lipids and facial aging via Mendelian randomization methods. Additionally, this study aimed to identify mediators and explore relevant genes and drug targets. Methods In this study, genome-wide association data on plasma lipids from 7,174 Finnish individuals in the UK Biobank were used. Two-sample Mendelian randomization was applied to assess the causal effects of specific lipids on facial aging. Sensitivity and pleiotropy analyses were conducted to ensure the robustness and reliability of the results. Multivariate Mendelian randomization was conducted to account for the potential impact of confounding factors. Furthermore, summary-data-based Mendelian randomization was used to identify relevant genes, which were validated through multiomics data. Finally, drug‒gene interactions were explored via molecular docking techniques. Results Two-sample Mendelian randomization analysis revealed a causal relationship between lipid levels and facial aging. According to the multivariate Mendelian randomization results, smoking was found to mediate this association, and these lipids remained significantly associated with facial aging, even after accounting for environmental confounders. Using summary-data-based Mendelian randomization, CYP21A2, CCND1, PSMA4, and MED1 were identified as potential gene targets, with MED1 further validated through pQTL and mQTL data. Additionally, the MED1 protein was found to bind spontaneously with astragalin, fenofibrate, and ginsenoside. Conclusions The results revealed a causal relationship between lipid levels and facial aging, revealing key gene targets that were still significantly associated with facial aging after controlling for environmental confounders. Additionally, the interactions between MED1 and certain drugs may indicate potential pathways for therapeutic interventions related to facial aging.

Abstract It has been hypothesized that ultraviolet (UV) radiation can lead to depletion of plasma folate and B12 vitamin, but few studies have investigated effects of other parameters of solar and geomagnetic activity (SGA). We investigated the association between four SGA parameters—interplanetary magnetic field (IMF), sunspot number (SSN), Kp index, and ground shortwave solar radiation (SWR)—and three plasma B-complex vitamins—folate, B6, and B12—in 910 participants from the Normative Aging Study (NAS) between 1998 and 2017. Mixed-effects regression models were used for 1- to 28-moving day averages of SGA exposure, adjusted for covariates. We compared the impact of SGA in individuals under higher and lower B-complex supplementation (> or

Abstract The identification of gene regulatory networks (GRNs) is crucial for understanding cellular differentiation. Single-cell RNA sequencing data encode gene-level covariations at high resolution, yet data sparsity and high dimensionality hamper accurate and scalable GRN reconstruction. To overcome these challenges, we introduce NetID leveraging homogenous metacells while avoiding spurious gene–gene correlations. Benchmarking demonstrates superior performance of NetID compared to imputation-based methods. By incorporating cell fate probability information, NetID facilitates the prediction of lineage-specific GRNs and recovers known network motifs governing bone marrow hematopoiesis, making it a powerful toolkit for deciphering gene regulatory control of cellular differentiation from large-scale single-cell transcriptome data.

Abstract Growing evidence suggests that long-term air pollution exposure is a risk factor for cardiovascular mortality and morbidity. However, few studies have investigated air pollution below current regulatory limits, and causal evidence is limited. We use a double negative control approach to examine the association between long-term exposure to air pollution at low concentration and cardiovascular hospitalizations among US Medicare beneficiaries aged ≥65 years between 2000 and 2016. The expected values of the negative outcome control (preceding-year hospitalizations) regressed on exposure and negative exposure control (subsequent-year exposure) are treated as a surrogate for omitted confounders. With analyses separately restricted to low-pollution areas (PM2.5

Abstract Travelling waves in circuit chains are studied to measure continuous dispersion. A lock‐in frequency meter (LIF) is suitable for precisely determining k for each set ω of waves in finite alternate LC chains, where LIF has been proven to be more accurate than the fast Fourier transform. In addition to the ω–k measurement, the wave impedance spectrum of the travelling wave can be measured simultaneously, for investigating the dispersion and splitting of pulse propagation. The measured dispersion is validated to be consistent with the derived theoretical equations. The result provides an independent way to precisely obtain dynamical system properties for chains composed of non‐ideal components, such as resistors for researching non‐Hermitian behaviour under dissipation. Systematical mapping of relative deviation dependence of wave dispersion measurement with LIF on different chain length and component variation is studied, indicating boundaries of 1%, 0.1%, and 0.01% precision for guidance of experiments.

Abstract Objective The study aimed to examine the role of the C-reactive protein to albumin ratio (CAR) as an inflammatory biomarker in relation to subclinical myocardial injury (SC-MI), addressing the limited knowledge of their association. Methods The study included 5,949 individuals without cardiovascular disease (CVD) from the National Health and Nutrition Examination Survey. SC-MI was identified through a Cardiac Infarction Injury Score (CIIS) of ≥ 10 units based on a 12-lead electrocardiogram. The study used multivariate logistic regression models, adjusted for potential confounders, to evaluate the relationship between CAR and SC-MI. Subgroup analyses were conducted to substantiate the results, and the non-linear correlation was assessed via restricted cubic spline (RCS) regression. Results The RCS curve showed a significant positive correlation between CAR and SC-MI (P for nonlinear = 0.2496). When adjusted for all confounders, individuals in the highest tertile of CAR exhibited a higher likelihood of SC-MI compared to those in the lowest tertile, with an odds ratio (OR) of 1.21 (95% CI: 1.06–1.39, P for trend = 0.029). A 10-unit increment in CAR was linked to a 3.6% heightened risk of SC-MI [OR = 1.036 (95% CI: 1.006, 1.066)], with this association being more prominent among male adults, non-smokers, married individuals, those without diabetes mellitus, and those with no history of cancer. Conclusion The findings of this study suggest a positive correlation between CAR and SC-MI among the US adult population, indicating the potential of CAR in enhancing SC-MI prevention strategies in the general population.

Abstract Many datasets are being produced by consortia that seek to characterize healthy and disease tissues at single-cell resolution. While biospecimen and experimental information is often captured, detailed metadata standards related to data matrices and analysis workflows are currently lacking. To address this, we develop the matrix and analysis metadata standards (MAMS) to serve as a resource for data centers, repositories, and tool developers. We define metadata fields for matrices and parameters commonly utilized in analytical workflows and developed the rmams package to extract MAMS from single-cell objects. Overall, MAMS promotes the harmonization, integration, and reproducibility of single-cell data across platforms.

Abstract Wireless sensing of the wave propagation direction from radio sources lays the foundation for communication, radar, navigation, etc. However, the existing signal processing paradigm for the direction of arrival estimation requires the radio frequency electronic circuit to demodulate and sample the multichannel baseband signals followed by a complicated computing process, which places the fundamental limit on its sensing speed and energy efficiency. Here, we propose the super-resolution diffractive neural networks (S-DNN) to process electromagnetic (EM) waves directly for the DOA estimation at the speed of light. The multilayer meta-structures of S-DNN generate super-oscillatory angular responses in local angular regions that can perform the all-optical DOA estimation with angular resolutions beyond the diffraction limit. The spatial-temporal multiplexing of passive and reconfigurable S-DNNs is utilized to achieve high-resolution DOA estimation over a wide field of view. The S-DNN is validated for the DOA estimation of multiple radio sources over 5 GHz frequency bandwidth with estimation latency over two to four orders of magnitude lower than the state-of-the-art commercial devices in principle. The results achieve the angular resolution over an order of magnitude, experimentally demonstrated with four times, higher than diffraction-limited resolution. We also apply S-DNN’s edge computing capability, assisted by reconfigurable intelligent surfaces, for extremely low-latency integrated sensing and communication with low power consumption. Our work is a significant step towards utilizing photonic computing processors to facilitate various wireless sensing and communication tasks with advantages in both computing paradigms and performance over electronic computing.

Abstract The vaginal microbiome is an immune defense against reproductive diseases and can serve as an important biomarker for cervical cancer. However, the intrinsic relationship between the recurrence and the vaginal microbiome in patients with cervical cancer before and after concurrent chemoradiotherapy is poorly understood. Here, we analyzed 125 vaginal microbial profiles from a patient cohort of stage IB–IVB cervical cancer using 16S metagenomic sequencing and deciphered the microbial composition and functional characteristics of the recurrent and non-recurrent both before and after chemoradiotherapy. We demonstrated that the abundance of beneficial bacteria and stability of the microbial community in the vagina decreased in the recurrence group, implying the unique characteristics of the vaginal microbiome for recurrent cervical cancer. Moreover, using machine learning, we identified Lactobacillus iners as the most important biomarker, combined with age and other biomarkers (such as Ndongobacter massiliensis, Corynebacterium pyruviciproducens ATCC BAA-1742, and Prevotella buccalis), and could predict cancer recurrence phenotype before chemoradiotherapy. This study prospectively employed rigorous bioinformatics analysis and highlights the critical role of vaginal microbiota in post-treatment cervical cancer recurrence, identifying promising biomarkers with prognostic significance in the context of concurrent chemoradiotherapy for cervical cancer. The role of L. iners in determining chemoradiation resistance in cervical cancer warrants further detailed investigation. Our results expand our understanding of cervical cancer recurrence and help develop better strategies for prognosis prediction and personalized therapy.

This study introduces a holistic framework for optimising road-rail intermodal hub locations based on real regional freight data and railway station information. The primary objective is to enhance railway transportation capacity, thereby facilitating the development of a low-carbon transport system. Research begins by scrutinising the freight landscape in the region, focusing on transport volume, freight intensity, goods types and average delivery distances. Subsequently, data mining techniques, including DBSCAN clustering and frequent itemset mining, are employed to uncover freight demand hotspots across both spatial and temporal dimensions. Based on these findings, a mathematical model for hub location selection is constructed, along with criteria for goods categories suitable for rail transportation. Ultimately, using the Beijing-Tianjin-Hebei region as a case study, 12 road-rail intermodal hubs are identified, along with the main cargo types best suited for rail transport within their respective service areas. This transition is expected to result in an annual reduction of 470,000 tons of regional carbon emissions. The proposed method framework provides valuable guidance and practical insights for the optimisation of freight structures in various regions. Furthermore, it aligns with contemporary environmental and sustainability objectives, contributing to the broader goal of establishing low-carbon transport systems.

Abstract Objectives To investigate whether reduced field-of-view (rFOV) diffusion-weighted imaging (DWI) with deep learning reconstruction (DLR) can improve the accuracy of evaluating muscle invasion using VI-RADS. Methods Eighty-six bladder cancer participants who were evaluated by conventional full field-of-view (fFOV) DWI, standard rFOV (rFOVSTA) DWI, and fast rFOV with DLR (rFOVDLR) DWI were included in this prospective study. Tumors were categorized according to the vesical imaging reporting and data system (VI-RADS). Qualitative image quality scoring, signal-to-noise ratio (SNR), contrast-to-noise ratio (CNR), and ADC value were evaluated. Friedman test with post hoc test revealed the difference across the three DWIs. Receiver operating characteristic analysis was performed to calculate the areas under the curve (AUCs). Results The AUC of the rFOVSTA DWI and rFOVDLR DWI were higher than that of fFOV DWI. rFOVDLR DWI reduced the acquisition time from 5:02 min to 3:25 min, and showed higher scores in overall image quality with higher CNR and SNR, compared to rFOVSTA DWI (p 0.05). Conclusions rFOV DWI with DLR can improve the diagnostic accuracy of fFOV DWI for evaluating muscle invasion. Applying DLR to rFOV DWI reduced the acquisition time and improved overall image quality while maintaining ADC value and diagnostic accuracy. Critical relevance statement The diagnostic performance and image quality of full field-of-view DWI, reduced field-of-view (rFOV) DWI with and without DLR were compared. DLR would benefit the wide clinical application of rFOV DWI by reducing the acquisition time and improving the image quality. Key Points Deep learning reconstruction (DLR) can reduce scan time and improve image quality. Reduced field-of-view (rFOV) diffusion-weighted imaging (DWI) with DLR showed better diagnostic performances than full field-of-view DWI. There was no difference of diagnostic accuracy between rFOV DWI with DLR and standard rFOV DWI. Graphical Abstract

Background: Pain associated with aminolevulinic acid photodynamic therapy (ALA-PDT) for the treatment of facial dermatoses results in low patient compliance. Two-step photodynamic therapy (two-step PDT) may improve comfort by optimizing light amplitude and exposure time. Objective: To investigate the efficacy of two-step PDT in reducing the pain generated during the treatment of facial skin disorders. Methods: Twenty-six patients with AK were randomly divided into two groups; the experimental group was treated with two-step photodynamic therapy and the control group was treated with conventional photodynamic therapy. The pain intensity of the patients at different times was assessed using the pain numerical rating scale (NRS). Results: A total of 26 patients completed three ALA-PDT treatments, 13 and 13 patients in each group, respectively. The mean NRS scores of patients in the experimental group (3.28±1.41, 3.33±1.43, 3.42±1.78) were lower than those of the control group (5.00±1.94, 5.09±1.86, 4.86±1.64) on each occasion. The incidence of certain adverse reactions was lower in the experimental group than in the control group. There was no difference between the two groups in terms of clinical outcome, recurrence rate and patient satisfaction. Conclusion: Two-step photodynamic therapy can reduce pain and the incidence of some adverse reactions, but does not affect clinical efficacy, recurrence rate and patient satisfaction.

Abstract Research and development on Nectin-4 antibody-drug conjugates (ADC) have been greatly accelerated since the approval of enfortumab vedotin to treat uroepithelial cancer. During the course of this study, we identified that autophagy serves as a cytoprotective mechanism during Nectin-4-MMAE treatment and proposed a strategy to enhance the antitumor effects of Nectin-4-MMAE in bladder cancer. Nectin-4-MMAE rapidly internalized into bladder cancer cells in 30 minutes and released MMAE, inducing the onset of caspase-mediated apoptosis and leading to the inhibition of tumor cell growth. Transcriptomics showed significant alterations in autophagy-associated genes in bladder cancer cells treated with Nectin-4-MMAE, which suggested autophagy was activated by Nectin-4-MMAE. Furthermore, autophagy activation was characterized by ultrastructural analysis of autophagosome accumulation, immunofluorescence of autophagic flux, and immunoblotting autophagy marker proteins SQSTM1 and LC3 I/II. Importantly, inhibiting autophagy by LY294002 and chloroquine significantly enhances the cytotoxicity effects of Nectin-4-MMAE in bladder cancer cells. Additionally, we detected the participation of the AKT/mTOR signaling cascade in the induction of autophagy by Nectin-4-MMAE. The combination of Nectin-4-MMAE and an autophagy inhibitor demonstrated enhanced antitumor effects in the HT1376 xenograft tumor model. After receiving a single dose of Nectin-4-MMAE, the group that received the combination treatment showed a significant decrease in tumor size compared to the group that received only one type of treatment. Notably, one mouse in the combination treatment group achieved complete remission of the tumor. The combination group exhibited a notable rise in apoptosis and necrosis, as indicated by H&E staining and immunohistochemistry (cleaved caspase-3, ki67). These findings demonstrated the cytoprotective role of autophagy during Nectin-4-MMAE treatment and highlighted the potential of combining Nectin-4-MMAE with autophagy inhibitors for bladder cancer treatment.

Abstract Background Neoadjuvant therapy followed by surgery has become the standard of care for locally advanced esophageal squamous cell carcinoma (ESCC) and accurate pathological response assessment is critical to assess the therapeutic efficacy. However, it can be laborious and inconsistency between different observers may occur. Hence, we aim to develop an interpretable deep-learning model for efficient pathological response assessment following neoadjuvant therapy in ESCC. Methods This retrospective study analyzed 337 ESCC resection specimens from 2020–2021 at the Pudong-Branch (Cohort 1) and 114 from 2021–2022 at the Puxi-Branch (External Cohort 2) of Fudan University Shanghai Cancer Center. Whole slide images (WSIs) from these two cohorts were generated using different scanning machines to test the ability of the model in handling color variations. Four pathologists independently assessed the pathological response. The senior pathologists annotated tumor beds and residual tumor percentages on WSIs to determine consensus labels. Furthermore, 1850 image patches were randomly extracted from Cohort 1 WSIs and binarily classified for tumor viability. A deep-learning model employing knowledge distillation was developed to automatically classify positive patches for each WSI and estimate the viable residual tumor percentages. Spatial heatmaps were output for model explanations and visualizations. Results The approach achieved high concordance with pathologist consensus, with an R^2 of 0.8437, a RAcc_0.1 of 0.7586, a RAcc_0.3 of 0.9885, which were comparable to two senior pathologists (R^2 of 0.9202/0.9619, RAcc_0.1 of 8506/0.9425, RAcc_0.3 of 1.000/1.000) and surpassing two junior pathologists (R^2 of 0.5592/0.5474, RAcc_0.1 of 0.5287/0.5287, RAcc_0.3 of 0.9080/0.9310). Visualizations enabled the localization of residual viable tumor to augment microscopic assessment. Conclusion This work illustrates deep learning's potential for assisting pathological response assessment. Spatial heatmaps and patch examples provide intuitive explanations of model predictions, engendering clinical trust and adoption (Code and data will be available at https://github.com/WinnieLaugh/ESCC_Percentage once the paper has been conditionally accepted). Integrating interpretable computational pathology could help enhance the efficiency and consistency of tumor response assessment and empower precise oncology treatment decisions.

Abstract Background Myelodysplastic neoplasms (MDS) are myeloid neoplasms characterized by disordered differentiation of hematopoietic stem cells and a predisposition to acute myeloid leukemia (AML). The underline pathogenesis remains unclear. Methods In this study, the trajectory of differentiation and mechanisms of leukemic transformation were explored through bioinformatics analysis of single-cell RNA-Seq data from hematopoietic stem and progenitor cells (HSPCs) in MDS patients. Results Among the HSPC clusters, the proportion of common myeloid progenitor (CMP) was the main cell cluster in the patients with excess blasts (EB)/ secondary AML. Cell cycle analysis indicated the CMP of MDS patients were in an active proliferative state. The genes involved in the cell proliferation, such as MAML3 and PLCB1, were up-regulated in MDS CMP. Further validation analysis indicated that the expression levels of MAML3 and PLCB1 in patients with MDS-EB were significantly higher than those without EB. Patients with high expression of PLCB1 had a higher risk of transformation to AML. PLCB1 inhibitor can suppress proliferation, induce cell cycle arrest, and activate apoptosis of leukemic cells in vitro. Conclusion This study revealed the transcriptomic change of HSPCs in MDS patients along the pseudotime and indicated that PLCB1 plays a key role in the transformation of MDS into leukemia.

Abstract Background The Vesical Imaging-Reporting and Data System (VI-RADS) has demonstrated effectiveness in predicting muscle invasion in bladder cancer before treatment. The urgent need currently is to evaluate the muscle invasion status after neoadjuvant chemotherapy (NAC) for bladder cancer. This study aims to ascertain the accuracy of VI-RADS in detecting muscle invasion post-NAC treatment and assess its diagnostic performance across readers with varying experience levels. Methods In this retrospective study, patients with muscle-invasive bladder cancer who underwent magnetic resonance imaging (MRI) after NAC from September 2015 to September 2018 were included. VI-RADS scores were independently assessed by five radiologists, consisting of three experienced in bladder MRI and two inexperienced radiologists. Comparison of VI-RADS scores was made with postoperative histopathological diagnosis. Receiver operating characteristic curve analysis (ROC) was used for evaluating diagnostic performance, calculating sensitivity, specificity, and area under ROC (AUC)). Interobserver agreement was assessed using the weighted kappa statistic. Results The final analysis included 46 patients (mean age: 61 years ± 9 [standard deviation]; age range: 39–70 years; 42 men). The pooled AUC for predicting muscle invasion was 0.945 (95% confidence interval (CI): 0.893–0.977) for experienced readers, and 0.910 (95% CI: 0.831–0.959) for inexperienced readers, and 0.932 (95% CI: 0.892–0.961) for all readers. At an optimal cut-off value ≥ 4, pooled sensitivity and specificity were 74.1% (range: 66.0–80.9%) and 94.1% (range: 88.6–97.7%) for experienced readers, and 63.9% (range: 59.6–68.1%) and 86.4% (range: 84.1–88.6%) for inexperienced readers. Interobserver agreement ranged from substantial to excellent between all readers (k = 0.79–0.92). Conclusions VI-RADS accurately assesses muscle invasion in bladder cancer patients after NAC and exhibits good diagnostic performance across readers with different experience levels.

Anthropogenic heat could potentially exacerbate heat risk in urban areas. Nighttime light (NTL) data have been widely used in mapping regional-scale anthropogenic heat flux (AHF) due to the close association between NTL and human activities. However, most gridded AHF products are constrained to coarse resolution. This study proposes a novel approach to generate 100 m gridded monthly mean AHF datasets in the Beijing-Tianjin-Hebei megaregion of China from 2012 to 2020. We first estimate the annual mean AHF using the High-Definition Suomi National Polar-orbiting Partnership-Visible Infrared Imaging Radiometer Suite (VIIRS-HD)-based top-down inventory method. We then downscale the annual AHF into monthly products using ancillary data including the monthly NTL and air temperature data. The results indicate that our 100 m gridded annual mean AHF products outperforms the existing datasets by providing more heterogeneous spatial information, apparent temporal variations, and higher accuracy. Furthermore, the spatio-temporal characteristics of our gridded monthly AHF products reflect well the urbanization trends. Our spatiotemporally explicit AHF products can also be utilized to facilitate investigations of the urban thermal environment and urban climate at the fine scale. The method can potentially be applied to larger areas and longer time series due to its simplicity and effectiveness.

On-Load Tap-Changer (OLTC) is the only core equipment in the ultra-high voltage (UHV) converter transformer that moves frequently, and flexible connection structure (FCS) is the moveable actuating part of the vacuum interrupter drive mechanism of OLTC. Minimizing the transient stress during the movement of FCS is essential for prolonging its fatigue life, thus is pursued by optimizing the shape of the flexible sheet in this paper. Peak transient stress of FCS is defined as the optimization objective, and eight geometrical parameters controlling the shape of flexible sheet are defined as the optimization variables. A progressive three-stage shape optimization strategy is proposed by combing four techniques: Multi-body Dynamic (MBD), Finite Element Analysis (FEA), Correlation Coefficient Analysis (CCA) and Adaptive Single-objective Optimization (ASO). MBD analysis of the vacuum interrupter drive mechanism and FEA of the FCS are utilized for calculating the transient stress distribution. Spearman CCA is performed to quantitively determine the correlation and sensitivity between the optimization variables and the optimization objective. Shape optimization of FCS using ASO is carried out, and results show that the stress concentration is basically eliminated and the peak transient stress decrease by 34.35% after shape optimization. Further analysis of the FCS with multi-layered flexible sheet is conducted, again demonstrating the effectiveness of the shape optimization on minimizing transient stress.

BackgroundAlzheimer disease and related dementias (ADRD) rank as the sixth leading cause of death in the United States, underlining the importance of accurate ADRD risk prediction. While recent advancements in ADRD risk prediction have primarily relied on imaging analysis, not all patients undergo medical imaging before an ADRD diagnosis. Merging machine learning with claims data can reveal additional risk factors and uncover interconnections among diverse medical codes. ObjectiveThe study aims to use graph neural networks (GNNs) with claim data for ADRD risk prediction. Addressing the lack of human-interpretable reasons behind these predictions, we introduce an innovative, self-explainable method to evaluate relationship importance and its influence on ADRD risk prediction. MethodsWe used a variationally regularized encoder-decoder GNN (variational GNN [VGNN]) integrated with our proposed relation importance method for estimating ADRD likelihood. This self-explainable method can provide a feature-important explanation in the context of ADRD risk prediction, leveraging relational information within a graph. Three scenarios with 1-year, 2-year, and 3-year prediction windows were created to assess the model’s efficiency, respectively. Random forest (RF) and light gradient boost machine (LGBM) were used as baselines. By using this method, we further clarify the key relationships for ADRD risk prediction. ResultsIn scenario 1, the VGNN model showed area under the receiver operating characteristic (AUROC) scores of 0.7272 and 0.7480 for the small subset and the matched cohort data set. It outperforms RF and LGBM by 10.6% and 9.1%, respectively, on average. In scenario 2, it achieved AUROC scores of 0.7125 and 0.7281, surpassing the other models by 10.5% and 8.9%, respectively. Similarly, in scenario 3, AUROC scores of 0.7001 and 0.7187 were obtained, exceeding 10.1% and 8.5% than the baseline models, respectively. These results clearly demonstrate the significant superiority of the graph-based approach over the tree-based models (RF and LGBM) in predicting ADRD. Furthermore, the integration of the VGNN model and our relation importance interpretation could provide valuable insight into paired factors that may contribute to or delay ADRD progression. ConclusionsUsing our innovative self-explainable method with claims data enhances ADRD risk prediction and provides insights into the impact of interconnected medical code relationships. This methodology not only enables ADRD risk modeling but also shows potential for other image analysis predictions using claims data.

As a crucial maritime search and rescue method, infrared object detection is critical in influencing the success rate. Research on infrared maritime images is limited, and the problems of smaller object sizes, more substantial noise, and less detailed information still need to be solved. To tackle these problems, we proposed an infrared maritime object detection network with feature enhancement and adjacent fusion. A spatial feature enhancement module and a semantic feature enhancement module are designed to enhance the location information of dim small targets and the deep semantic information, respectively. We designed a feature adjacent fusion network to fully use multiscale feature information. We built a maritime infrared dataset and compared the proposed method with existing advanced traditional and learning methods. The proposed method achieves better detection results.

Because of the need for low pollutant emissions, industrial gas turbines typically use partially premixed gases for combustion. However, the nonlinear dynamic characteristics of partially premixed flames have not been studied sufficiently. Therefore, this study focuses on the dynamics of a partially premixed flame generated by a swirler with fuel holes on its surface and designs a flame describing function (FDF) identification method based on the parallel subsystem model. This method can separate the flame dynamic characteristics into a parallel connection of the nonlinear and linear models. The nonlinear model is related to the disturbance frequency and velocity perturbation amplitude, whereas the linear model depends only on the disturbance frequency. This method is verified using a simulation. Finally, experimental research on partially premixed flames is conducted. Based on the experimental data, the identification method successfully separates the FDF into a nonlinear model with saturation characteristics and a linear model with Gaussian distribution characteristics. The flame model obtained by the identification method is the foundation for the analysis of combustion thermoacoustic stability and active/passive control strategy.

Porcine epidemic diarrhea (PED) is a highly contagious intestinal infection primarily affecting pigs. It is caused by the porcine epidemic diarrhea virus (PEDV). PEDV targets the villus tissue cells in the small intestine and mesenteric lymph nodes, resulting in shortened intestinal villi and, in extreme cases, causing necrosis of the intestinal lining. Moreover, PEDV infection can disrupt the balance of the intestinal microflora, leading to an overgrowth of harmful bacteria like Escherichia coli. Exosomes, tiny membrane vesicles ranging from 30 to 150 nm in size, contain a complex mixture of RNA and proteins. MicroRNA (miRNA) regulates various cell signaling, development, and disease progression processes. This study extracted exosomes from both groups and performed high-throughput miRNA sequencing and bioinformatics techniques to investigate differences in miRNA expression within exosomes isolated from PEDV-infected porcine small intestine tissue compared to healthy controls. Notably, two miRNA types displayed upregulation in infected exosomes, while 12 exhibited downregulation. These findings unveil abnormal miRNA regulation patterns in PEDV-infected intestinal exosomes, shedding light on the intricate interplay between PEDV and its host. This will enable further exploration of the relationship between these miRNA changes and signaling pathways, enlightening PEDV pathogenesis and potential therapeutic targets.

Hf-based carbides are highly desirable candidate materials for oxidizing environments above 2000 ​°C. However, the static oxidation behavior at their potential service temperatures remains unclear. To fill this gap, the static oxidation behavior of (Hf, Ti)C and the effect of Ti substitutions were investigated in air at 2500 ​°C under an oxygen partial pressure of 4.2 ​kPa. After oxidation for 2000 ​s, the thickness of the oxide layer on the surface of (Hf, Ti)C bulk ceramic is reduced by 62.29 ​% compared with that on the HfC monocarbide surface. The dramatic improvement in oxidation resistance is attributed to the unique oxide layer structure consisting of various crystalline oxycarbides, HfO2, and carbon. The Ti-rich oxycarbide ((Ti, Hf)CxOy) dispersed within HfO2 formed the major structure of the oxide layer. A coherent boundary with lattice distortion existed at the HfO2/(Ti, Hf)CxOy interface along the (111) crystal plane direction, which served as an effective oxygen diffusion barrier. The Hf-rich oxycarbide ((Hf, Ti)CxOy) together with (Ti, Hf)CxOy, HfO2, and precipitated carbon constituted a dense transition layer, ensuring favorable bonding between the oxide layer and the matrix. The Ti content affects the oxidation resistance of (Hf, Ti)C by determining the oxide layer's phase distribution and integrity.

Abstract Background The prevalence of thyroid cancer (ThyC), a frequent malignant tumor of the endocrine system, has been rapidly increasing over time. The mitophagy pathway is reported to play a critical role in ThyC onset and progression in many studies. This research aims to create a mitophagy-related survival prediction model for ThyC patients. Methods Genes connected to mitophagy were found in the GeneCards database. The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases provided information on the expression patterns of ThyC-related genes. To identify differentially expressed genes (DEGs), R software was employed. The prognostic significance of each DEG was assessed using the prognostic K-M curve. The prognostic model was built using LASSO, ROC, univariate, and multivariate Cox regression analyses. Finally, a nomogram model was developed to predict the survival outcome of ThyC patients in the clinical setting. Results Through differential analysis, functional enrichment analysis, and protein–protein interaction (PPI) network analysis, we screened 10 key genes related to mitophagy in ThyC. The risk model was eventually developed using LASSO and Cox regression analyses based on the six DEGs related to mitophagy. An altered expression level of a mitophagy-related prognostic gene, GGCT, was found to be the most significant one, according to the KM survival curve analysis. An immunohistochemical (IHC) investigation revealed that ThyC tissues expressed higher levels of GGCT than normal thyroid tissues. The ROC curve verified the satisfactory performance of the model in survival prediction. Multivariate Cox regression analysis showed that the pathological grade, residual tumor volume, and initial tumor lesion type were significantly linked to the prognosis. Finally, we created a nomogram to predict the overall survival rate of ThyC patients at 3-, 5-, and 7- year time points. Conclusion The nomogram risk prediction model was developed to precisely predict the survival rate of ThyC patients. The model was validated based on the most significant DEG GGCT gene expression in ThyC. This model may serve as a guide for the creation of precise treatment plans for ThyC patients.

The Dynamic Gait Event Identifier (DGEI) introduces a pioneering approach for real-time gait event detection that seamlessly aligns with the needs of embedded system design and optimization. DGEI creates a new standard for gait analysis by combining software and hardware co-design with real-time data analysis, using a combination of first-order difference functions and sliding window techniques. The method is specifically designed to accurately separate and analyze key gait events such as heel strike (HS), toe-off (TO), walking start (WS), and walking pause (WP) from a continuous stream of inertial measurement unit (IMU) signals. The core innovation of DGEI is the application of its dynamic feature extraction strategies, including first-order differential integration with positive/negative windows, weighted sleep time analysis, and adaptive thresholding, which together improve its accuracy in gait segmentation. The experimental results show that the accuracy rate of HS event detection is 97.82%, and the accuracy rate of TO event detection is 99.03%, which is suitable for embedded systems. Validation on a comprehensive dataset of 1550 gait instances shows that DGEI achieves near-perfect alignment with human annotations, with a difference of less than one frame in pulse onset times in 99.2% of the cases.

Wild ungulates play crucial roles in maintaining the structure and function of local ecosystems. The alpine musk deer (Moschus chrysogaste), white-lipped deer (Przewalskium albirostris), and red serow (Capricornis rubidus) are widely distributed throughout the Nyenchen Tanglha Mountains of Tibet. However, research on the mechanisms underlying their coexistence in the same habitat remains lacking. This study aimed to investigate the mechanisms underlying the coexistence of these species based on their dietary preferences through DNA barcoding using the fecal samples of these animals collected from the study area. These species consume a wide variety of food types. Alpine musk deer, white-lipped deer, and red serow consume plants belonging to 74 families and 114 genera, 62 families and 122 genera, and 63 families and 113 genera, respectively. Furthermore, significant differences were observed in the nutritional ecological niche among these species, primarily manifested in the differentiation of food types and selection of food at the genus level. Owing to differences in social behavior, body size, and habitat selection, these three species further expand their differentiation in resource selection, thereby making more efficient use of environmental resources. Our findings indicate these factors are the primary reasons for the stable coexistence of these species.