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Osteoarthritis (OA) is the most prevalent degenerative joint disease worldwide. Effective management for early-stage OA is crucial. Denosumab (DS) has been widely used to treat osteoporosis (OP) and rheumatoid arthritis, but its potential for managing OA remains clear. We assessed the effects of DS on osteoclast activity and chondrocyte apoptosis using tartrate-resistant acid phosphatase (TRAP) assay, quantitative real-time polymerase chain reaction (qRT-PCR), flow cytometry, and TUNEL staining. To assess the impact of DS on the NF-κB pathway, we performed Western blot and immunofluorescence staining. Additionally, we used an OA model to explore the influence of DS on subchondral bone remodeling and cartilage degeneration in vivo. We found that DS hindered receptor activator of nuclear factor kappa B ligand (RANKL)-induced osteoclastogenesis by inhibiting the activity of the NF-κB pathway. Besides, DS alleviated reactive oxygen species (ROS)-induced apoptosis in chondrocytes by regulating the expression of genes related to apoptosis. Moreover, we observed an attenuation of OA-related subchondral bone remodeling and cartilage degeneration in vivo. Our findings indicate that DS could effectively suppress osteoclast activity and chondrocyte apoptosis, thereby mitigating OA-related subchondral bone remodeling and cartilage degeneration. These results provide a mechanistic basis for using DS to treat OA.

Background: Accurately localizing brain motor areas is vital for protecting motor function during neurosurgical procedures. Magnetoencephalography (MEG) based on optically pumped magnetometer (OPM) improves the availability of MEG in clinical applications. The aim of this study is to evaluate the availability, accuracy and precision of “OPM-MEG” for localizing motor areas in brain tumor patients and healthy individuals. Methods: Participants were enrolled and subjected to primary motor area localization by both 3T-fMRI and 128-channel OPM-MEG examinations. The localization accuracy (ability of mapping on the anatomical location) and precision (activation signal centralization) were compared between the two methods, and accuracy was further validated by intraoperative direct cortical electrical stimulation (DCS) on the localized area with assistance of neuro-navigation system. Result: A total of 12 participants (7 brain tumor patients and 5 healthy individuals) were enrolled and all had successful localization for motor areas by both methods. The average time of OPM-MEG examination for each limb function was approximately 9 min. The localizations by both methods mainly covered the anatomical location of primary motor cortex and were partially overlapped. The motor activation signal identified by OPM-MEG was more centralized than fMRI did. The centroid of motor area localized by the OPM-MEG deviated from it by fMRI, with a mean distance of 19.7 mm and 27.48 mm for hand or foot localization, respectively. Furthermore, the OPM-MEG centroid for hand movement successfully triggered corresponding hand response by DCS. Conclusions: In this first-in-human study exploring the potential of OPM-MEG in functional localization of motor areas, we revealed its availability and reliability in mapping motor areas, demonstrating it as a promising tool in assisting neurosurgical practice and neuroscience research.

ObjectiveTo understand the status and influencing factors of human papillomavirus (HPV) vaccination knowledge and recommendation behaviors among healthcare workers and teachers in Ordos city, Inner Mongolia Autonomous Region. MethodsUsing a typical sampling method and a self-designed questionnaire, we conducted an online survey among 415 healthcare workers and teachers recruited in one district and one banner of Ordos City, Inner Mongolia Autonomous Region in July 2023. Participants′ information on knowledge of HPV and its vaccines, and beliefs about HPV vaccination based on the health belief model (HBM) were collected. Univariate logistic regression and unconditional multivariate logistic regression were used to analyze the factors influencing HPV vaccination recommendation behavior. ResultsOf the 243 health care workers and 86 teachers ultimately included in the analysis, 201 and 56, respectively, had recommended HPV vaccination. The median of HPV knowledge score was 7 out of a total score of 11 for healthcare workers and 6 for teachers. Multivariate unconditional logistic regression model analysis showed that among healthcare workers, those with the following characteristics were more likely to recommend HPV vaccination: living in the banner (odds ratio [OR] = 17.042, 95% confidence interval [95%CI]: 2.778 – 104.544), having a higher HPV knowledge score (OR = 10.194, 95%CI: 3.076 – 33.778), having a higher HPV vaccination knowledge score (OR = 4.510, 95%CI: 1.451 – 14.015), having a higher score of perceived favorable factors for recommending HPV vaccination (OR = 17.492, 95% CI: 3.054 – 100.178), and having a high perception of susceptibility to HPV infection (OR = 100.407, 95%CI: 15.087 – 668.239); while those who were married were less likely to recommend HPV vaccination (OR = 0.050, 95%CI: 0.006 – 0.404); the teachers with high perception of susceptibility to HPV infection were more likely to recommend HPV vaccination (OR = 4.947, 95%CI: 1.322 – 18.516). ConclusionHealthcare workers and teachers in Ordos city had good knowledge of HPV vaccination. Region of residence, marital status, HPV knowledge, perceived favorable factors for recommending HPV vaccination, and perceived susceptibility to HPV infection were the main factors influencing the recommendation behavior of healthcare workers, while perceived susceptibility to HPV infection was the main factor influencing the recommendation behavior of teachers.

Background and purpose: The low-dose EOS Imaging System is an emerging tool for 3-dimensional measurements in orthopedics. The clinical feasibility for measuring total hip arthroplasty (THA) liner wear has not yet been investigated. We aimed to evaluate the feasibility of using EOS to measure THA liner wear by examining the experimental accuracy using a THA phantom and clinical precision of patients with THA, considering a clinically relevant precision at the 95% repeatability limit to be 0.2 mm. Methods: An experimental THA phantom with movable stem and a fixed cup with a plastic liner was constructed to simulate progressive 3D liner wear. Series of 11 pairs of radiographs with 50 μm femoral movement in between were obtained for each 3D axis in EOS. 30 patients with a THA were scanned twice using EOS to assess precision. Model-based radiostereometric analysis (RSA) was used for wear measurement. Results: The mean difference (true minus simulated wear) with standard deviation (SD) and 95% limits of agreement for experimental THA wear were 0.005 (0.037) and [–0.069 to 0.079] mm for the vertical (y) axis. The mean (SD) and 95% repeatability limit for precision for clinical measurement were –0.029 (0.105) and 0.218 mm. Conclusion: Experimental THA liner wear using EOS was within clinically relevant tolerances and without bias. The clinical precision was just outside our defined clinically relevant precision. Compared with conventional RSA, EOS is less accurate and precise but may still be of value for certain clinical applications, provided larger sample size or longer follow-up are available.

Background: Reliable animal models are critical for preclinical research and should closely mimic the disease. With respect to route of infection, pathogenic agent, disease progression, clinical signs, and histopathological changes. Sheep have similar bone micro- and macrostructure as well as comparable biomechanical characteristics to humans. Their use in bone research is established, however their use in bone infection research is limited. This systematic review will summarise the key features of the available bone infection models using sheep, providing a reference for further development, validation, and application. Method: This systematic review was designed according to the PRISMA guidelines and registered with PROSPERO. Quality was assessed using SYRICLE's risk of bias tool adapted for animal studies. PubMed, MEDLINE, Web of Science and EMBASE were searched until March 2022.1921 articles were screened by two independent reviewers, and 25 were included for analysis. Results: Models have been developed in nine different breeds. Staphylococcus aureus was used in the majority of models, typically inoculating 108 colony forming units in tibial or femoral cortical defects. Infection was established with either planktonic or biofilm adherent bacteria, with or without foreign material implanted. Most studies used both radiological and microbiological analyses to confirm osteomyelitis. Conclusions: There is convincing evidence supporting the use of sheep in bone infection models of clinical disease. The majority of sheep studied demonstrated convincing osteomyelitis and tolerated the infection with minimal complications. Furthermore, the advantages of comparable biology and biomechanics may increase the success for translating in vivo results to successful therapies. The Translational potential of this article: In the realm of preclinical research, the translation to viable clinical therapies is often perilous, and the quest for reliable and representative animal models remains paramount. This systematic review accentuates the largely untapped potential of sheep as large animal models, especially in bone infection research. The anatomical and biomechanical parallels between sheep and human bone structures position sheep as an invaluable asset for studying osteomyelitis and periprosthetic joint infection. This comprehensive exploration of the literature demonstrates the robustness and translational promise of these models. Furthermore, this article underscores the potential applicability for sheep in developing effective therapeutic strategies for human bone infections.

ObjectiveThis paper puts forward the “virtual wall method” as a novel formation approach intended to address such problems as the high communication volume, slow formation speed and inconvenient expansion of large-scale ship formation. MethodThe proposed method uses local communication, sets virtual force according to the rules of nature to drive the ships to the target positions, and adjusts the formation to completion through the virtual wall. ResultsThe results show that the ship formations can be completed quickly according to the proximity principle using the proposed algorithm, and the communication volume is low without changing with the formation size. The effectiveness of this algorithm is verified by static, dynamic, rotation, scaling and obstacle avoidance simulations of ship formations. ConclusionThe virtual wall method features low communication volume, fast formation speed and convenient expansion, giving it significant advantages in the large-scale formation of ships.

The notion of a transposed Poisson $ n $-Lie algebra has been developed as a natural generalization of a transposed Poisson algebra. It was conjectured that a transposed Poisson $ n $-Lie algebra with a derivation gives rise to a transposed Poisson $ (n+1) $-Lie algebra. In this paper, we focus on transposed Poisson $ n $-Lie algebras. We have obtained a rich family of identities for these algebras. As an application of these formulas, we provide a construction of $ (n+1) $-Lie algebras from transposed Poisson $ n $-Lie algebras with derivations under a certain strong condition, and we prove the conjecture in these cases.

The accurate prediction of vehicle behavior is crucial for autonomous driving systems, impacting their safety and efficiency in complex urban environments. To address the challenge of multi-agent trajectory prediction, we propose a novel model integrating multiple input modalities, including historical trajectories, map data, vehicle features, and interaction information. Our approach employs a Conditional Variational Autoencoder (CVAE) framework with a decoder that predicts control actions using the Gaussian Mixture Model (GMM) and then converts these actions into dynamically feasible trajectories through a bicycle model. Evaluated on the nuScenes dataset, the model achieves great performance across key metrics, including minADE5 of 1.26 and minFDE5 of 2.85, demonstrating robust performance across various vehicle types and prediction horizons. These results indicate that integrating multiple data sources, physical models, and probabilistic methods significantly improves trajectory prediction accuracy and reliability for autonomous driving. Our approach generates diverse yet realistic predictions, capturing the multimodal nature of future outcomes while adhering to Physical Constraints and vehicle dynamics.

Abstract Purpose Healing after bone fracture is assessed by clinical examination and frequent radiographs, which expose patients to radiation and lack standardisation. This study aimed to explore electrical impedance patterns during bone healing using electrical impedance spectroscopy in 18 rabbits subjected to tibial fracture stabilised with an external fixator. Methods Impedance was measured daily across the fracture site at a frequency range of 5 Hz to 1 MHz. Biweekly radiographs were analysed using modified anterior‐posterior (AP) radiographic union score of the tibia (RUST). The animals were divided into three groups with different follow‐up times: 1, 3 and 6 weeks for micro‐computer tomography and mechanical testing. Results A decreasing trend in impedance was observed over time for all rabbits at lower frequencies. Impedance closest to 5 Hz showed a statistically significant decrease over time, with greatest decrease occurring during the first 7 postoperative days. At 5 Hz, a statistically significant correlation was found between impedance and the modified AP RUST score and between impedance and bone volume fraction. Conclusions This study showed that the electrical impedance can be measured in vivo at a distance from the fracture site with a consistent change in impedance over time and revealed significant correlation between increasing radiographic union score and decreasing impedance. Level of Evidence Not applicable.

Autonomous vehicles (AVs) must be thoroughly tested to ensure safety and reliability before marketing. Simulation‐based testing has gained widespread recognition as the essential approach for AV testing by providing sufficient testing scenarios in the virtual environment. Vehicle‐in‐the‐loop (VIL) simulation has the ability to perform comprehensive tests and validations for the AVs’ overall behaviors while keeping significant testing accuracy and efficiency through the combination of the virtual scenarios and the physical AV. This article provides an overview of representative studies on testbench‐based VIL simulation testing for AVs, mainly focusing on utilizing testbenches to simulate realistic road conditions, and using physical signal stimulation methods and related equipment to generate sensors’ physical signals. This article first summarizes current AV testing studies, identifying existing issues and flaws of the state‐of‐the‐art methods and tools. Afterward, the testbench‐based VIL is addressed around architecture, principles, advantages, and characteristics. Then, the road condition simulation and the sensor physical signal generation in VIL are discussed in depth from structure, principle, and corresponding advanced equipment. Finally, research gaps between cutting‐edge technologies and AV testing applications in industrialization are identified to facilitate future research in this direction.

Prompting a pretrained language model with natural language patterns has been proved effective for natural language understanding (NLU). However, our preliminary study reveals that manual discrete prompts often lead to unstable performance—e.g., changing a single word in the prompt might result in substantial performance drop. We propose a novel method P-Tuning that employs trainable continuous prompt embeddings in concatenation with discrete prompts. Empirically, P-Tuning not only stabilizes training by minimizing the gap between various discrete prompts, but also improves performance by a sizeable margin on a wide range of NLU tasks including LAMA and SuperGLUE. P-Tuning is generally effective for both frozen and tuned language models, under both the fully-supervised and few-shot settings.

Traffic accidents frequently lead to fatal injuries, claiming millions of lives every year. To mitigate driving hazards and ensure personal safety, it is crucial to assist vehicles in anticipating the objects in the traffic scene (treated here as important objects) which may pose a threat during the driving task. Previous research on important object detection primarily assessed the importance of individual participants, treating them as independent entities and frequently neglecting the interconnections among these participants. Unfortunately, this approach has proven less effective in detecting important objects in complex scenarios. In this work, we introduce Driving scene Relationship Understanding transformer (DRUformer), designed to enhance the important object detection task. The DRUformer is a transformer-based multi-modal important object detection model that takes into account the relationships between all the participants in the driving scenario. Recognizing that driving intention also significantly affects the detection of important objects during driving, we have incorporated a module for embedding driving intention. To assess the performance of our approach, we conducted comparative experiments on the DRAMA dataset, comparing our model against other state-of-the-art (SOTA) models. The results demonstrated a noteworthy 16.2% improvement in mIoU and a substantial 12.3% boost in ACC compared to SOTA methods. Furthermore, we conducted a qualitative analysis of our model’s ability to detect important objects across different road scenarios and classes, highlighting its effectiveness in diverse contexts. Finally, we conducted various ablation studies to assess the efficiency of the proposed modules in our DRUformer model. Through extensive experimentation, it has been demonstrated that our model performs exceptionally well in the task of driving scene important object localization. The code is publicly available on the following link: https://github.com/oniu-uin0/DRUformer

The notion of generalized quantum cluster algebras was introduced as a natural generalization of Berenstein and Zelevinsky's quantum cluster algebras as well as Chekhov and Shapiro's generalized cluster algebras. In this paper, we focus on a generalized quantum cluster algebra of Kronecker type which possesses infinitely many cluster variables. We obtain the cluster multiplication formulas for this algebra. As an application of these formulas, a positive bar-invariant basis is explicitly constructed. Both results generalize those known for the Kronecker cluster algebra and quantum cluster algebra.

This study proposes a hybrid network model based on data enhancement to address the problem of low accuracy in photovoltaic (PV) power prediction that arises due to insufficient data samples for new PV plants. First, a time-series generative adversarial network (TimeGAN) is used to learn the distribution law of the original PV data samples and the temporal correlations between their features, and these are then used to generate new samples to enhance the training set. Subsequently, a hybrid network model that fuses bi-directional long-short term memory (BiLSTM) network with attention mechanism (AM) in the framework of deep & cross network (DCN) is constructed to effectively extract deep information from the original features while enhancing the impact of important information on the prediction results. Finally, the hyperparameters in the hybrid network model are optimized using the whale optimization algorithm (WOA), which prevents the network model from falling into a local optimum and gives the best prediction results. The simulation results show that after data enhancement by TimeGAN, the hybrid prediction model proposed in this paper can effectively improve the accuracy of short-term PV power prediction and has wide applicability.

Abstract With the widespread development of chest computed tomography (CT), the detection rate of pulmonary nodules has increased; therefore, the classification of benign vs. malignant nodules has become a common problem in the clinic. MicroRNA, a potential tool, is expected to become a good choice for diagnosing and studying the occurrence and development of diseases through the vector of bronchoalveolar lavage fluid extracellular vesicles (BALF-EVs). In this study, radial endobronchial ultrasound (R-EBUS) was used to locate pulmonary nodules in patients. BALF was obtained, EVs were isolated, and small RNA sequencing was performed to screen differentially expressed miRNAs between benign and malignant pulmonary nodules. The binding targets and underlying mechanisms of the differentially expressed miRNAs were verified by in vitro and in vivo experiments. R-EBUS localization and sampling was used to obtain BALF, and EVs were successfully isolated and characterized. Differentially expressed miRNAs in BALF-EVs of patients with benign vs. malignant pulmonary nodules were screened by high-throughput small RNA sequencing. A new miRNA, miR-1246b, was identified. We found that FGF14 was the binding target of miR-1246b by luciferase assay. Subsequent mechanistic studies showed that miR-1246b inhibited the expression of FGF14 in lung cancer cells, further leading to ERK phosphorylation and epithelial-to-mesenchymal transition (EMT), which ultimately contributed to lung cancer cell proliferation, migration and invasion. In summary, our study demonstrates that the detection of miRNAs in BALF-EVs, a means of liquid biopsy, could assist in distinguishing malignant nodules from benign nodules. miR-1246b, which was extracted from BALF-EVs, targets FGF14 to promote lung cancer cell proliferation, migration and invasion.

Pedestrian action prediction is crucial for many applications such as autonomous driving. However, state-of-the-art methods lack the explainability needed for trustworthy predictions. In this paper, a novel framework called MulCPred is proposed that explains its predictions based on multi-modal concepts represented by training samples. Previous concept-based methods have limitations, including the following: (1) they cannot be directly applied to multi-modal cases; (2) they lack the locality needed to attend to details in the inputs; (3) they are susceptible to mode collapse. These limitations are tackled accordingly through the following approaches: (1) a linear aggregator to integrate the activation results of the concepts into predictions, which associates concepts of different modalities and provides ante hoc explanations of the relevance between the concepts and the predictions; (2) a channel-wise recalibration module that attends to local spatiotemporal regions, which enables the concepts with locality; (3) a feature regularization loss that encourages the concepts to learn diverse patterns. MulCPred is evaluated on multiple datasets and tasks. Both qualitative and quantitative results demonstrate that MulCPred is promising in improving the explainability of pedestrian action prediction without obvious performance degradation. Moreover, by removing unrecognizable concepts, MulCPred shows improved cross-dataset prediction performance, suggesting its potential for further generalization.

BackgroundThe epidermal growth factor receptor (EGFR) ex20ins mutation, as a rare subtype of mutation, has gradually attracted attention. Its heterogeneity is high, its prognosis is extremely poor, and the efficacy of existing traditional treatment plans is limited. In this study, we aimed to evaluate efficacy of high dose furmonertinib as a first-line treatment for EGFR ex20ins-positive NSCLC.MethodsThis is a retrospective, multi-center, non-interventional study. From May 2021 to March 2023, 9 NSCLC patients with EGFR ex20ins were enrolled. Efficacy and safety of 160 mg furmonertinib were evaluated. Objective response rate (ORR), disease control rate (DCR), median progression-free survival (PFS) and treatment related adverse events (TRAEs) were assessed.ResultsOf the evaluated patients, six patients experienced partial remission (PR), two patients experienced stable disease (SD) and one patient experienced progress disease (PD). Data indicated 66.7% ORR and 88.9% DCR. The median progression free survival (PFS) was 7.2 months (95% CI: 6.616 - 7.784). Besides, a longgest PFS with 18 months was found in one patient with p.H773_V774insGTNPH mutation. No ≥ level 3 adverse events have been found.ConclusionsThe study proved the potential efficacy of 160mg furmonertinib in patients with advanced NSCLC with EGFR ex20ins. Meanwhile, 160mg furmonertinib had a good safety profile.

Summary: The cytosolic DNA sensor cyclic GMP-AMP synthase (cGAS) is essential to elicit type I interferon cascade response; thus, the activity of cGAS must be strictly regulated to boost the antiviral innate immunity. Here, we report that cGAS is responsible for the DNA-induced ISG15 conjugation system. The E3 HERC5 catalyzes the ISGylation of cytoplasmic cGAS at lysine 21, 187, 219, and 458, whereas Ubl carboxy-terminal hydrolase 18 removes the ISGylation of cGAS. The interaction of cGAS and HERC5 depends on the cGAS C-terminal domain and the RRC1-4 and RRC1-5 domains of HERC5. Mechanically, HERC5-catalyzed ISGylation promotes DNA-induced cGAS oligomerization and enhances cGAS enzymatic activity. Deficiency of ISGylation attenuates the downstream inflammatory gene expression induced by the cGAS-STING axis and the antiviral ability in mouse and human cells. Mice deficient in Isg15 or Herc6 are more vulnerable to herpes simplex virus 1 infection. Collectively, our study shows a positive feedback regulation of the cGAS-mediated innate immune pathway by ISGylation.

The growing complexity of contemporary software systems, with multiple sub-modules, demands innovative modeling approaches, especially in addressing intricate failures and masked data generation. This paper proposed a stochastic differential equation (SDE)-based software reliability additive model tailored for multi-component systems dealing with masked data. However, in the presence of masked data, the challenge of parameter estimation arises due to the inherent complexity of the objective function and numerous parameters. To overcome this, we proposed the Expectation Least Square (ELS) algorithm, providing a strategic solution for the intricate task of parameter estimation with masked data. In order to verify the effectiveness of our proposed model, we conducted a comparative analysis with traditional reliability models using three actual failure data sets. The experimental results emphasize the superior performance of the proposed model and highlight the effectiveness of the ELS algorithm in accurately estimating software reliability.

Abstract Objective: Energy drinks are consumed for a variety of reasons, including to boost mental alertness and energy. We assessed associations between demographic factors and various high-risky behaviours with energy drink consumption as they may be linked to adverse health events. Design: We conducted cross-sectional analysis including basic descriptive and multivariable-adjusted logistic regression analyses to characterise demographic and behavioural factors (including diet quality, binge drinking and illicit drug use, among others obtained via questionnaires) in relation to energy drink consumption. Setting: We used data from two large US-based cohorts. Participants: 46 390 participants from Nurses’ Health Study 3 (NHS3, n 37 302; ages 16–31) and Growing Up Today Study (GUTS, n 9088, ages 20–55). Results: Of the 46 390 participants, 13·2 % reported consuming ≥ 1 energy drink every month. Several risky behaviours were associated with energy drink use, including illegal drug use (pooled OR, pOR: 1·45, 95 % CI: 1·16, 1·81), marijuana use (pOR: 1·49, 95 % CI: 1·28, 1·73), smoking (pOR: 1·88. 95 % CI: 1·55, 2·29), tanning bed use (pOR: 2·31, 95 % CI: 1·96, 2·72) and binge drinking (pOR: 2·53, 95 % CI: 2·09, 3·07). Other factors, such as high BMI, e-cigarette use and poor diet quality were found to be significantly associated with higher energy drink consumption (P values < 0·001). Conclusions: Our findings show that energy drink consumption and high-risk behaviours may be related, which could potentially serve as not only as a talking point for providers to address in outreach and communications with patients, but also a warning sign for medical and other health practitioners.

Artificial neural network (ANN) methods have been gradually used in the field of nuclear reactor thermal-hydraulics as new methods to improve accuracy or fast prediction. This study establishes a back propagation (BP) neural network model based on the ANN methods to predict the steam condensation heat transfer coefficient outside a heat tube in the presence of air. The main factors affecting condensing heat transfer, such as pressure, air mass fraction, subcooling, and tube diameter, are used as input quantities, and the condensation heat transfer coefficient is used as output quantity. A complete set of neural networks for predicting the heat transfer coefficient for steam condensation in the presence of air is established based on the relevant experimental data collected over the world. The results predicted by the ANN model are compared with the experimental data and those of traditional correlation methods. The data from 2276 experiments are distributed within a ±10% error band at the 95% confidence level. This means that the prediction accuracy of the ANN model is higher than that of the traditional experimental correlation. Therefore, the neural network model developed in this study can be used for the prediction of the heat transfer coefficient for steam condensation in the presence of air.

Abstract Background To investigate the changes and clinical significance of vascular endothelial injury markers in type 2 diabetes mellitus (T2DM) complicated with pulmonary embolism (PE). Methods This prospective study enrolled patients with T2DM hospitalized in one hospital from January 2021 to June 2022. Soluble thrombomodulin (sTM) (ELISA), von Willebrand factor (vWF) (ELISA), and circulating endothelial cells (CECs) (flow cytometry) were measured. PE was diagnosed by computed tomography pulmonary angiography (CTPA). Results Thirty participants were enrolled in each group. The plasma levels of sTM (151.22 ± 120.57 vs. 532.93 ± 243.82 vs. 1016.51 ± 218.00 pg/mL, P 676.68 pg/mL for the diagnosis of T2DM + PE achieved an AUC of 0.973, while vWF > 13.75 ng/mL achieved an AUC of 0.954. The combination of sTM and vWF above their cutoff points achieved an AUC of 0.993, with 100% sensitivity and 96.7% specificity. Conclusions Patients with T2DM show endothelial injury and dysfunction, which were worse in patients with T2DM and PE. High sTM and vWF levels have certain clinical predictive values for screening T2DM accompanied by PE.

The perception systems of autonomous vehicles face significant challenges under adverse conditions, with issues such as obscured objects and false detections due to environmental noise. Traditional approaches, which typically focus on noise removal, often fall short in such scenarios. Addressing the lack of diverse adverse weather data in existing automotive datasets, we propose a novel data augmentation method that integrates realistically simulated adverse weather effects into clear condition datasets. This method not only addresses the scarcity of data but also effectively bridges domain gaps between different driving environments. Our approach centers on a conditional generative model that uses segmentation maps as a guiding mechanism to ensure the authentic generation of adverse effects, which greatly enhances the robustness of perception and object detection systems in autonomous vehicles, operating under varied and challenging conditions. Besides the capability of accurately and naturally recreating over 90% of the adverse effects, we demonstrate that this model significantly improves the performance and accuracy of deep learning algorithms for autonomous driving, particularly in adverse weather scenarios. In the experiments employing our augmented approach, we achieved a 2.46% raise in the 3D average precision, a marked enhancement in detection accuracy and system reliability, substantiating the model’s efficacy with quantifiable improvements in 3D object detection compared to models without augmentation. This work not only serves as an enhancement of autonomous vehicle perception systems under adverse conditions but also marked an advancement in deep learning models in adverse condition research.

The availability of targeted therapies for molecular aberrations have substantially improved the outcomes of advanced non-small-cell lung cancer (NSCLC) patients harboring sensitive mutations. However, patients harboring uncommon epidermal growth factor receptor (EGFR) mutations such as G719X and L861Q often resulted in a lack of response to the first and third generation of EGFR TKIs. In this study, we reported a 64-year-old female patient, who initially presented with symptoms of pneumonia and showed positive response to anti-infection treatment, eventually diagnosed with stage Ⅳ lung adenocarcinoma (LUAD) harboring a rare EGFR G719X and L861Q compound mutations. The patient received 40 mg/day of afatinib and experienced no severe adverse events. As a result, partial response (PR) was observed based on CT scan and a progression free survival of 24 month was achieved. Her follow-up is still ongoing. The results of the present case support the effectiveness and safety of afatinib in LUAD patients carrying EGFR G719X and L861Q compound mutations.

Touch care has clinically positive effects on older adults. Touch can be delivered using robots, addressing the lack of caregivers. A recent study of younger participants showed that stroke touch delivered via robot produced subjective and physiologically positive emotional responses similar to those evoked by human touch. However, whether robotic touch can elicit similar responses in older adults remains unknown. We investigated this topic by assessing subjective rating (valence and arousal) and physiological signals [corrugator and zygomatic electromyography (EMG) and skin conductance response (SCR)] to gentle stroking motions delivered to the backs of older participants by robot and human agents at two different speeds: 2.6 and 8.5 cm/s. Following the recent study, the participants were informed that only the robot strokes them. We compared the difference between the younger (their data from the previous study) and the older participants in their responses when the two agents (a robot and a human) stroked them. Subjectively, data from both younger and older participants showed that 8.5 cm/s stroking was more positive and arousing than 2.6 cm/s stroking for both human and robot agents. Physiologically, data from both younger and older participants showed that 8.5 cm/s stroking induced weaker corrugator EMG activity and stronger SCR activity than the 2.6 cm/s stroking for both agents. These results demonstrate that the overall patterns of the older groups responses were similar to those of the younger group, and suggest that robot-delivered stroke touch can elicit pleasant emotional responses in older adults.

Background: The relationship between leisure sedentary behavior (LSB) and periodontitis risk remains unclear in terms of causality and the potential mediating effects of intermediate factors. Materials and methods: Using the aggregate data of several large-scale genetic association studies from participants of European descent, we conducted a univariate, two-step, and multivariate Mendelian random (MR) analysis to infer the overall effect of LSB on periodontitis, and quantified the intermediary proportion of intermediary traits such as smoking. Results: Our findings indicated that per 1-SD increase (1.87 h) in leisure screen time (LST), there was a 23 % increase in the risk of periodontitis. [odds ratios (95 % CI) = 1.23 (1.04–1.44), p = 0.013]. Smoking was found to partially mediate the overall causal effect of LST on periodontitis, with a mediation rate of 20.7 % (95 % CI: 4.9%–35.5 %). Multivariate MR analysis demonstrated that the causal effect of LST on periodontitis was weakened when adjusting for smoking, resulting in an odds ratio of 1.19 (95 % CI: 1.01–1.39, p = 0.049) for each 1 standard deviation increase in exposure. Conclusion: The study provides evidence of a potential causal relationship between LSB characterized by LST and periodontitis, thereby further supporting the notion that reducing LSB is beneficial for health. Furthermore, it confirms the role of smoking as a mediator in this process, suggesting that inhibiting smoking behavior among individuals with long-term LSB may serve as a strategy to mitigate the risk of periodontitis.

Abstract Upon transplantation, skeletal stem cells (also known as bone marrow stromal or mesenchymal stem cells) can regulate bone regeneration by producing secreted factors. Here, we identify KIAA1199 as a bone marrow stromal cell-secreted factor in vitro and in vivo. KIAA1199 plasma levels of patients positively correlate with osteoporotic fracture risk and expression levels of KIAA1199 in patient bone marrow stromal cells negatively correlates with their osteogenic differentiation potential. KIAA1199-deficient bone marrow stromal cells exhibit enhanced osteoblast differentiation in vitro and ectopic bone formation in vivo. Consistently, KIAA1199 knockout mice display increased bone mass and biomechanical strength, as well as an increased bone formation rate. They also exhibit accelerated healing of surgically generated bone defects and are protected from ovariectomy-induced bone loss. Mechanistically, KIAA1199 regulates osteogenesis by inhibiting the production of osteopontin by osteoblasts, via integrin-mediated AKT and ERK-MAPK intracellular signaling. Thus, KIAA1199 is a regulator of osteoblast differentiation and bone regeneration and could be targeted for the treatment or management of low bone mass conditions.

Abstract Coronavirus disease 2019 (COVID-19) is spreading rapidly around the world. However, the treatment of vitiligo combined with COVID-19 has not been reported. Astragalus membranaceus (AM) has a therapeutic effect on patients with vitiligo and COVID-19. This study aims to discover its possible therapeutic mechanisms and provide potential drug targets. Using the Chinese Medicine System Pharmacological Database (TCMSP), GEO database and Genecards websites and other databases, AM target, vitiligo disease target, and COVID-19 related gene set were established. Then find the crossover genes by taking the intersection. Then use GO, KEGG enrichment analysis, and PPI network to discover its underlying mechanism. Finally, by importing drugs, active ingredients, crossover genes, and enriched signal pathways into Cytoscape software, a “drug-active ingredient-target signal pathway-” network is constructed. TCMSP screened and obtained 33 active ingredients including baicalein (MOL002714), NEOBAICALEIN (MOL002934), Skullcapflavone II (MOL002927), and wogonin (MOL000173), which acted on 448 potential targets. 1166 differentially expressed genes for vitiligo were screened by GEO. CIVID-19 related genes were screened by Genecards. Then by taking the intersection, a total of 10 crossover genes (PTGS2, CDK1, STAT1, BCL2L1, SCARB1, HIF1A, NAE1, PLA2G4A, HSP90AA1, and HSP90B1) were obtained. KEGG analysis found that it was mainly enriched in signaling pathways such as IL-17 signaling pathway, Th17 cell differentiation, Necroptosis, NOD-like receptor signaling pathway. Five core targets (PTGS2, STAT1, BCL2L1, HIF1A, and HSP90AA1) were obtained by analyzing the PPI network. The network of "active ingredients-crossover genes" was constructed by Cytoscape, and the 5 main active ingredients acting on the 5 core crossover genes acacetin, wogonin, baicalein, bis2S)-2-ethylhexyl) benzene-1,2-dicarboxylate and 5,2′-Dihydroxy-6,7,8-trimethoxyflavone. The core crossover genes obtained by PPI and the core crossover genes obtained by the "active ingredient-crossover gene" network are intersected to obtain the three most important core genes (PTGS2, STAT1, HSP90AA1). AM may act on PTGS2, STAT1, HSP90AA1, etc. through active components such as acacetin, wogonin, baicalein, bis2S)-2-ethylhexyl) benzene-1,2-dicarboxylate and 5,2′-Dihydroxy-6,7,8-trimethoxyflavone to activate IL-17 signaling pathway, Th17 cell differentiation, Necroptosis, NOD-like receptor signaling pathway, Kaposi sarcoma-associated herpesvirus infection, and VEGF signaling pathway and other signaling pathways to achieve the effect of treating vitiligo and COVID-19.

With various components and complex topologies, the applications of high-voltage direct current (HVDC) links bring new challenges to the interconnected power systems in the aspect of frequency security, which further influence their reliability performances. Consequently, this paper presents an approach to evaluate the impacts of the HVDC link outage on the reliability of interconnected power system considering the frequency regulation process during system contingencies. Firstly, a multi-state model of an HVDC link with different available loading rates (ALRs) is established based on its reliability network. Then, dynamic frequency response models of the inter- connected power system are presented and integrated with a novel frequency regulation scheme enabled by the HVDC link. The proposed scheme exploits the temporary overload capability of normal converters to compensate for the imbalanced power during system contingencies. Moreover, it offers frequency support that enables the frequency regulation reserves of the sending-end and receiving-end power systems to be mutually available. Several indices are established to measure the system reliability based on the given models in terms of abnormal frequency duration, frequency deviation, and energy losses of the frequency regulation process during system contingencies. Finally, a modified two-area reliability test system (RTS) with an HVDC link is adopted to verify the proposed approach.

In-field validation of radiation thermometer performance is time consuming and expensive, especially when prototype experiments are performed. This study focuses on the development of a novel small-scale high temperature field simulation test rig for measuring turbine blade surface temperatures under changing environmental conditions. When developing and testing a radiation thermometer, the testing is typically performed directly on a test rig that can simultaneously provide a standardized environment and compare temperature data. This apparatus allows for static and dynamic temperature testing in a hostile environment by combining temperature, particle, and atmospheric effects. In the present study, a test rig will be utilized to analyze and validate the radiation thermometer, which will be subjected to harsh combined environmental variables with a temperature range of up to 2000K. Notably, the rotating head structure of the two materials allows for the application of a vast range of temperature measurements (700–2000 K), which is a distinctive characteristic of the design. The requirements of the test rig are defined based on the specimens to be tested. The necessity for the combined complete test setup, as well as its design and functionality, are discussed in this paper. Finally, validation experiments are conducted to demonstrate the test rig's accuracy in measuring turbine blade surface temperature, which is 0.25%.

BackgroundLipomatous atrial septal hypertrophy (LASH) with atrial septal defect (ASD) is a rare congenital anomaly. Although LASH is a histologically benign cardiac lesion characterized by excessive fat deposition in the interatrial septum that spares the fossa ovale, it has been associated with supraventricular arrhythmias or sick sinus syndrome. Application of multimodal imaging is crucial for accurate diagnosis, appropriate treatment of LASH with ASD, and follow-up.Case summaryA 68-year-old female patient presented with recurrent chest tightness and palpitation. Multimodal imaging revealed the characterizations of LASH and ASD. Two-dimensional transesophageal echocardiography showed a “dumbbell”-shaped involvement of the cephalad and caudal regions with sparing of a single secundum ASD. The septum with a brightness feature is an uncommon condition characterized by the deposition of unencapsulated fat cells in the atrial septum. Real-time four-dimensional transesophageal echocardiography reflected the lipomatous hypertrophy of the atrial septum and an oval-shaped ASD. Cardiac computer tomography angiography later confirmed this finding. The patient achieved a good clinical response with an ASD percutaneous occlusion guided by intracardiac echocardiography (ICE).ConclusionThis case demonstrates a LASH combined with ASD. Multimodality imaging can provide an accurate diagnosis and may guide the procedure for precise occlusion.

Transformer-based models have gained popularity in the field of natural language processing (NLP) and are extensively utilized in computer vision tasks and multi-modal models such as GPT4. This paper presents a novel method to enhance the explainability of transformer-based image classification models. Our method aims to improve trust in classification results and empower users to gain a deeper understanding of the model for downstream tasks by providing visualizations of class-specific maps. We introduce two modules: the “Relationship Weighted Out” and the “Cut” modules. The “Relationship Weighted Out” module focuses on extracting class-specific information from intermediate layers, enabling us to highlight relevant features. Additionally, the “Cut” module performs fine-grained feature decomposition, taking into account factors such as position, texture, and color. By integrating these modules, we generate dense class-specific visual explainability maps. We validate our method with extensive qualitative and quantitative experiments on the ImageNet dataset. Furthermore, we conduct a large number of experiments on the LRN dataset, which is specifically designed for automatic driving danger alerts, to evaluate the explainability of our method in scenarios with complex backgrounds. The results demonstrate a significant improvement over previous methods. Moreover, we conduct ablation experiments to validate the effectiveness of each module. Through these experiments, we are able to confirm the respective contributions of each module, thus solidifying the overall effectiveness of our proposed approach.

Abstract Objective To investigate the clinical manifestations and imaging characteristics of pulmonary cryptococcosis, and discuss its guidance in diagnosing. Methods The clinical data of patients diagnosed with cryptococcosis in our hospital from January 2014 to May 2020 were collected and retrospectively analyzed. Patients were divided into the immunocompromised group and the immunocompetent group. The symptomatic features, laboratory examination, imaging manifestations, and curative effect were analyzed. Results The most common symptoms of patients were cough and sputum production, followed by fever. The immunocompetent group has a significantly higher accident rate of cough and fever than the immunocompromised group, while the immunocompromised group has a significantly higher accident rate of headache and dizziness (P

Repetitive TMS has been used as an alternative treatment for various neurological disorders. However, most TMS mechanism studies in rodents have been based on the whole brain stimulation, the lack of rodent-specific focal TMS coils restricts the proper translation of human TMS protocols to animal models. In this study, we designed a new shielding device, which was made of high magnetic permeability material, to enhance the spatial focus of animal-use TMS coils. With the finite element method, we analyzed the electromagnetic field of the coil with and without the shielding device. Furthermore, to assess the shielding effect in rodents, we compared the c-fos expression, the ALFF and ReHo values in different groups following a 15 min 5 Hz rTMS paradigm. We found that a smaller focality with an identical core stimulation intensity was achieved in the shielding device. The 1 T magnetic field was reduced from 19.1 mm to 13 mm in diameter, and 7.5 to 5.6 mm in depth. However, the core magnetic field over 1.5 T was almost the same. Meanwhile, the area of electric field was reduced from 4.68 cm2 to 4.19 cm2, and 3.8 mm to 2.6 mm in depth. Similar to this biomimetic data, the c-fos expression, the ALFF and ReHo values showed more limited cortex activation with the use of the shielding device. However, compared to the rTMS group without the shielding application, more subcortical regions, like the striatum (CPu), the hippocampus, the thalamus, and the hypothalamus were also activated in the shielding group. This indicated that more deep stimulation may be achieved by the shielding device. Generally, compared with the commercial rodents’ TMS coil (15 mm in diameter), TMS coils with the shielding device achieved a better focality (~6 mm in diameter) by reducing at least 30% of the magnetic and electric field. This shielding device may provide a useful tool for further TMS studies in rodents, especially for more specific brain area stimulation.

BackgroundA number of publications have demonstrated that deep learning (DL) algorithms matched or outperformed clinicians in image-based cancer diagnostics, but these algorithms are frequently considered as opponents rather than partners. Despite the clinicians-in-the-loop DL approach having great potential, no study has systematically quantified the diagnostic accuracy of clinicians with and without the assistance of DL in image-based cancer identification. ObjectiveWe systematically quantified the diagnostic accuracy of clinicians with and without the assistance of DL in image-based cancer identification. MethodsPubMed, Embase, IEEEXplore, and the Cochrane Library were searched for studies published between January 1, 2012, and December 7, 2021. Any type of study design was permitted that focused on comparing unassisted clinicians and DL-assisted clinicians in cancer identification using medical imaging. Studies using medical waveform-data graphics material and those investigating image segmentation rather than classification were excluded. Studies providing binary diagnostic accuracy data and contingency tables were included for further meta-analysis. Two subgroups were defined and analyzed, including cancer type and imaging modality. ResultsIn total, 9796 studies were identified, of which 48 were deemed eligible for systematic review. Twenty-five of these studies made comparisons between unassisted clinicians and DL-assisted clinicians and provided sufficient data for statistical synthesis. We found a pooled sensitivity of 83% (95% CI 80%-86%) for unassisted clinicians and 88% (95% CI 86%-90%) for DL-assisted clinicians. Pooled specificity was 86% (95% CI 83%-88%) for unassisted clinicians and 88% (95% CI 85%-90%) for DL-assisted clinicians. The pooled sensitivity and specificity values for DL-assisted clinicians were higher than for unassisted clinicians, at ratios of 1.07 (95% CI 1.05-1.09) and 1.03 (95% CI 1.02-1.05), respectively. Similar diagnostic performance by DL-assisted clinicians was also observed across the predefined subgroups. ConclusionsThe diagnostic performance of DL-assisted clinicians appears better than unassisted clinicians in image-based cancer identification. However, caution should be exercised, because the evidence provided in the reviewed studies does not cover all the minutiae involved in real-world clinical practice. Combining qualitative insights from clinical practice with data-science approaches may improve DL-assisted practice, although further research is required. Trial RegistrationPROSPERO CRD42021281372; https://www.crd.york.ac.uk/prospero/display_record.php?RecordID=281372

Dental hygienist students require a self-learning simulator to learn the correctness of hand-scaling techniques. An essential technique in hand scaling is the maintenance of contact between the tip of the hand-scaler blade and the tooth. However, imaging-based methods cannot effectively reveal this contact because the gingiva and buccal mucosa conceal the blade. Therefore, this study aimed to propose a method to identify the appropriate contact state of the blade with the tooth by using an inertial measurement unit (IMU) attached to the hand scaler and a force sensor attached to the target tooth. The hand-scaling motion was measured in an experiment in which participants were instructed to use the tip or middle of the blade to contact the tooth. The contact state of the blade, whether it was the tip or the middle, was identified using 18 features, including the average and standard deviation of nine dimensions of force, acceleration, and angular velocity with a support vector machine (SVM). The results showed that the model using all 18 features could classify the contact state with an accuracy of 97.1%. Furthermore, the accuracy was 95.9% with the 12 features from IMU alone, which was not significantly different from the accuracy with 18 features. The accuracy was 88.8% with six features from the force sensor alone. These results indicate that the IMU alone can identify the correct contact state, highlighting the possibility of creating a realistic simulator for training dental hygienists in evaluating the blade-contact state.

Abstract Background Rodent ultrasonic vocalizations (USVs) are crucial to their social communication and a widely used translational tool for linking gene mutations to behavior. To maximize the causal interpretation of experimental treatments, we need to understand how neural control affects USV production. However, both the aerodynamics of USV production and its neural control remain poorly understood. Results Here, we test three intralaryngeal whistle mechanisms—the wall and alar edge impingement, and shallow cavity tone—by combining in vitro larynx physiology and individual-based 3D airway reconstructions with fluid dynamics simulations. Our results show that in the mouse and rat larynx, USVs are produced by a glottal jet impinging on the thyroid inner wall. Furthermore, we implemented an empirically based motor control model that predicts motor gesture trajectories of USV call types. Conclusions Our results identify wall impingement as the aerodynamic mechanism of USV production in rats and mice. Furthermore, our empirically based motor control model shows that both neural and anatomical components contribute to USV production, which suggests that changes in strain specific USVs or USV changes in disease models can result from both altered motor programs and laryngeal geometry. Our work provides a quantitative neuromechanical framework to evaluate the contributions of brain and body in shaping USVs and a first step in linking descending motor control to USV production.

Background: There have been many studies about coronavirus disease 2019 (COVID-19), but the clinical significance of quantitative serum severe acute respiratory syndrome-coronavirus-2 (SARS-CoV-2)-specific IgM and IgG levels of COVID-19 patients have not been exhaustively analyzed. We aimed to investigate the time profiles of these IgM/IgG levels in COVID-19 patients and their correlations with clinical features. Methods: A multicenter clinical study was conducted from February 20 to March 5 2020. It involved 179 COVID-19 patients (108 males and 71 females) from five hospitals in Huangshi in Hubei Province, China. To detect SARS-CoV-2-specific IgM/IgG, quantitative antibody assays (two-step indirect immunoassays with direct chemiluminescence technology) based on the nucleocapsid protein (NP) and spike protein 1 (S1) were used. For normally distributed data, means were compared using the t-test, χ2-test, or exact probability method. For categorical data, medians were compared using Mann–Whitney U test. Results: The median age was 57 (44–69) years (58 [38–69] for males and 57 [49–68] for females). The median duration of positive nucleic acid test was 22.32 (17.34–27.43) days. The mortality rate was relatively low (3/179, 1.68%). Serum SARS-CoV-2-specific IgG was detected around week 1 after illness onset, gradually increased until peaking in weeks 4 and 5, and then declined. Serum IgM peaked in weeks 2 and 3, then gradually declined and returned to its normal range by week 7 in all patients. Notably, children had milder respiratory symptoms with lower SARS-CoV-2-specific IgM/IgG levels. The duration of positive nucleic acid test in the chronic obstructive pulmonary disease (COPD) group was 30.36 (18.99–34.76) days, which was significantly longer than that in the non-COPD group (21.52 [16.75–26.51] days; P = 0.025). The peak serum SARS-CoV-2-specific IgG was significantly positively correlated with the duration of positive nucleic acid test. The incidence rate of severe and critical cases in the IgMhi group (using the median IgM level of 29.95 AU/mL as the cutoff for grouping) was about 38.0% (19/50), which was twice as much as that in the IgMlo group (18.4%; 9/49). The patients with positive chest imaging and lymphocytopenia (

Variational Auto-Encoders (VAEs) have emerged as one of the most popular genres of generative models, which are learned to characterize the data distribution. The classic Expectation Maximization (EM) algorithm aims to learn models with hidden variables. Essentially, both of them are iteratively optimizing the evidence lower bound (ELBO) to maximize to the likelihood of the observed data.This short tutorial joins them up into a line and offer a good way to thoroughly understand EM and VAE with minimal knowledge. It is especially helpful to beginners and readers with experiences in machine learning applications but no statistics background.

Abstract A bi‐level load restoration optimisation strategy is proposed for the transmission system with wind farm‐energy storage combined systems (WESs), taking the variant length of time steps into account. The upper level model is proposed to maximise weighted restored loads, and formulated as a mixed‐integer linear programming model. After solving the upper model, the optimal load pickup and transmission line restoration scheme can be obtained and delivered to the lower level model. The lower level model adopts a non‐linear model to minimise the length of the current time step, which is delivered to the upper lever model. By iteratively solving the upper and lower level models, the optimal load pickup and transmission line restoration scheme as well as the optimal length of current time step can be obtained. To minimise the gap between the scheduled generation of the WES and its actual power generation, a real‐time energy storage (ES) dispatch strategy is proposed taking maximum charging‐discharging cycles of the ES into account. The entire load restoration strategy can be obtained by iteratively solving the proposed model with updated operational conditions of power systems. Finally, two test systems are employed to verify the validity and correctness of the proposed model.

Abstract Introduction Reducing tube voltage is an effective dose saving method in computed tomography (CT) assuming tube current is not concurrently increased. Recent innovations in scanner technology now enable CT tube voltage reduction to 70 kV thereby increasing opportunities for dose reduction in paediatric patients, but it is unclear if the increased image noise associated with 70 kV impacts on ability to visualise renal stones accurately. The purpose was to assess detectability of nephrolithiasis using a bespoke paediatric phantom and low kV, non‐contrast CT and to assess inter‐observer agreement. Methods Forty‐two renal stones of different size and chemical composition were inserted into porcine kidneys and positioned in a bespoke, water‐filled phantom mimicking a 9‐year‐old child weighing approximately 33kg. The phantom was scanned using 120 and 70 kV CT protocols, and the detectability of the stones was assessed by three radiologists. Absolute agreement and Fleiss’ kappa regarding detectability were assessed. Results The mean diameter of renal stones as measured physically was 4.24 mm ranging from 1 to 11 mm. Four stones were missed by at least one observer. One observer had a sensitivity of 93 and 95% at 70 and 120 kV, respectively, while the sensitivity for observers 2 and 3 was 98% at both kV levels. Specificity was 100% across readers and kV levels. Absolute agreement between the readers at 70 kV was 92% (kappa = 0.86) and 98% (kappa = 0.96) at 120 kV indicating a strong agreement at both kV levels. Conclusions The results suggest that lowering the kV does not affect the detection rate of renal stones and may be a useful dose reduction strategy for assessment of nephrolithiasis in children.

Abstract Background Endobronchial ultrasound (EBUS) elastography has been used in EBUS-guided transbronchial needle aspiration (EBUS-TBNA) to identify malignant lymph nodes based on tissue stiffness. Rapid onsite cytological evaluation (ROSE) has been widely utilized for onsite evaluation of sample adequacy and for guiding sampling during EBUS-TBNA. The aim of this study was to investigate the diagnostic value of combined EBUS elastography and ROSE in evaluating mediastinal and hilar lymph node status. Methods Retrospective chart review was performed from December 2018 to September 2020. Patient demographics, EBUS elastography scores, and ROSE, pathologic, and clinical outcome data were collected. The EBUS elastography scores were classified as follows: Type 1, predominantly nonblue; Type 2, partially blue and partially nonblue; and Type 3, predominantly blue. A receiver operating characteristic curve was used to compare the sensitivity, specificity, positive predictive value, negative predictive value, positive likelihood ratio, and negative likelihood ratio for evaluation of malignant lymph nodes among the EBUS elastography, ROSE, and EBUS combined with ROSE groups. Results A total of 245 patients (345 lymph nodes) were included. The sensitivity and specificity of the EBUS elastography group for the diagnosis of malignant lymph nodes were 90.51% and 57.26%, respectively. The sensitivity and specificity in the ROSE group were 96.32% and 79.05%, respectively. The sensitivity, specificity, positive likelihood ratio, and negative likelihood ratio of EBUS elastography combined with ROSE were 86.61%, 92.65%, 11.78, and 0.14, respectively, and the area under the curve was 0.942. Conclusions Combining EBUS elastography and ROSE significantly increased the diagnostic value of EBUS-TBNA in evaluating mediastinal and hilar lymph node status compared to each method alone.

Natural circulation systems (NCSs) are extensively applied in nuclear power plants because of their simplicity and inherent safety features. For some passive natural circulation systems in floating nuclear power plants (FNPPs), the ocean is commonly used as the heat sink. Condensation induced water hammer (CIWH) events may appear as the steam directly contacts the subcooled seawater, which seriously threatens the safe operation and integrity of the NCSs. Nevertheless, the research on the formation mechanisms of CIWH is insufficient, especially in NCSs. In this paper, the characteristics of flow rate and fluid temperature are emphatically analyzed. Then the formation types of CIWH are identified by visualization method. The experimental results reveal that due to the different size and formation periods of steam slugs, the flow rate presents continuous and irregular oscillation. The fluid in the horizontal hot pipe section near the water tank is always subcooled due to the reverse flow phenomenon. Moreover, the transition from stratified flow to slug flow can cause CIWH and enhance flow instability. Three types of formation mechanisms of CIWH, including the Kelvin-Helmholtz instability, the interaction of solitary wave and interface wave, and the pressure wave induced by CIWH, are obtained by identifying 67 CIWH events.

Abstract Due to the increasing interactions between the natural gas system (NGS) and electric power system (EPS), the failures of power‐lines or gas pipelines in one system may influence another system and consequently trigger widespread disruptions. Hence, identifying these critical components and protecting them from malfunctions are essential to prevent the collapse of integrated gas and power systems (IGPSs). Considering the effects of both network topologies and operational features on the resiliency evaluation, a novel assessment framework is proposed in this paper to screen out critical power‐lines and gas pipelines. First, the framework of IGPSs is introduced and an optimal energy flow (OEF) model is constructed to quickly obtain the operation states under various disturbances. Furthermore, the resiliency metrics (RMs) are developed from structural and operational perspectives respectively. Specifically, structural RMs consider the coupling topologies to capture the structure‐related resiliency, while operational RMs analyse the resiliency related to gas and power flow to capture physical operation characteristics. Finally, an assessment algorithm synthesizing all the metrics is proposed to quantify the comprehensive resiliency, based on which critical power‐lines and pipelines (CPPs) can be detected. The effectiveness of the proposed approach is validated by an IGPS test system.

Objective To examine the associations between patterns of mid-life to late life modifiable risk factors and longevity.Design Prospective cohort study.Setting Data collected from the Nurses' Health Study starting in 1984 and the Health Professionals Follow-up Study starting in 1986.Participants 85 346 participants from the Nurses' Health Study and the Health Professionals Follow-up Study.Main outcome measures Death from any cause by 31 October 2020 for the Nurses' Health Study and Health Professionals Follow-up Study. Risk factors investigated were body mass index, physical activity, alcohol intake, smoking status, and quality of diet. Trajectories of each risk factor and trajectories of changes in the risk factor were identified from baseline with smoothing mixture models, and the joint group memberships of participants was used to most efficiently capture patterns of the factor over time. For each risk factor, three trajectories (patterns with high, medium, and low values) and three trajectories of change in the risk factor (patterns with increase, no change, and decrease in the factor from baseline) were assumed, giving nine joint patterns: high-stable, high-increase, high-decrease, medium-stable, medium-increase, medium-decrease, low-stable, low-increase, and low-decrease. Associations between patterns of modifiable risk factors and longevity (age at death ≥85 years) and life expectancy were examined with logistic regression and accelerated failure time models, respectively.Results The analysis included 85 346 participants, with 46 042 participants achieving longevity and 25 322 participants achieving healthy longevity (those who did not have a diagnosis of cardiovascular disease, type 2 diabetes, or cancer). Mean age at baseline was 56 years (standard deviation 5 years). Maximum longevity was achieved in participants with a low-stable pattern for body mass index (compared with a medium-stable pattern, odds ratio of longevity of 1.05, 95% confidence interval 1.00 to 1.10); those with a medium-increase pattern for physical activity (compared with a medium-stable pattern, odds ratio 1.08, 1.01 to 1.15); those with a medium-stable pattern for alcohol intake (high-increase v medium-stable pattern, odds ratio 0.83, 0.74 to 0.93); those who never smoked (low-stable v medium-stable pattern, odds ratio 3.09, 2.84 to 3.37); and those who with a high-increase pattern for quality of diet (compared with a medium-stable pattern, odds ratio 1.09, 1.01 to 1.18). The associations between each factor and life expectancy and healthy longevity (no diagnosis of cardiovascular disease, type 2 diabetes, or cancer) were similar to those for longevity.Conclusions During mid-life and late life, maximum longevity was achieved in participants who maintained a normal body mass index, never smoked, ate a healthy diet, and had physical activity levels and alcohol consumption that met public health recommendations.

We leveraged a novel index of diffusion MRI to investigate the relationships among cortical free water, macro-organizations and gene expression in healthy adults. Few research has been conducted to investigate the role of free water in the healthy adults due to it can easily be affected also by aging diseases. High quality data of 350 subjects from Human Connectome Project were used in our study. Cortical free water was estimated by using a bi-tensor model. The free water was high in the limbic, insular and somatosensory cortex, while being lower in motor and association cortex. The negative correlation between the free water and cortical thickness has been consistently identified in almost all the cortical regions. Negative correlation between the cortical free water and structural covariance (rho=-0.38, pspin=0.005) revealed the free water was sensitive to cortical heterogeneity. Using human gene expression dataset, we found the gene expression pattern of the relationship between the free water and cortical thickness spatially coupled with primary gradient of structural covariance network (rho=0.40, pspin=0.004). Our findings indicated the free water was sensitive to the cortical cellular status. The relationship between free water and macroscale organization also reflected hierarchal structures of cerebral cortex.

Summary: Background: Weight at birth has been associated with the development of various adult diseases, but its association with mortality remains unclear. Methods: We included 22,389 men from the Health Professionals Follow-up Study (1994-2018) and 162,231 women from the Nurses’ Health Study (1992-2018) and the Nurses’ Health Study II (1991-2019). The hazard ratios (HRs) of mortality according to birth weight were estimated by Cox proportional hazards regression models with adjustment for potential confounders. Findings: Compared to women reporting a birth weight of 3.16-3.82 kg, the pooled HRs for all-cause mortality were 1.13 (95% CI, 1.08 to 1.17), 0.99 (95% CI, 0.96 to 1.02), 1.04 (95% CI, 1.00 to 1.08), and 1.03 (95% CI, 0.96 to 1.10), respectively, for women with a birth weight of 4.5 kg. In cause-specific mortality analyses, women reporting birth weight >4.5 kg had a higher risk of cancer mortality (HR=1.15, 95% CI: 1.00 to 1.31), whereas women with a birth weight