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Abstract TGF-β1 activation of hepatic stellate cells (HSCs), transcriptional activator 3 (Stat3) activation and short chain fatty acids (SCFAs), metabolite of intestinal bacteria, is closely associated with hepatic fibrosis. Previous studies have shown that Lactucin has significant anti-inflammatory and hepatoprotective effects; however, the mechanism of Lactucin's role in liver fibrosis associated with SCFAs remains unknown. This study was intended to investigate whether effect of Lactucin on liver fibrosis was mediated by TGF-β1/Stat3 and SCFAs. We found that Lactucin induced apoptosis in HSC-T6 cells, and inhibition of nuclear translocation of Stat3 and p-Stat3. And Smad3 and TGF-β1 protein expression was significantly inhibited, while TLR4 and Smad7 protein expression was significantly enhanced. For in vivo experiments, we demonstrated that Lactucin alleviated liver fibrosis in mice, as evidenced by a reduction in inflammatory factors, collagen deposition, liver injury and fibrosis-related factors expression, especially the expression of Smad3 and TGF-β1 proteins was significantly suppressed and Smad7 protein expression was significantly increased in the liver. In addition, the levels of acetic acid, butyric acid and valeric acid in the intestine of Lactucin-treated mice were significantly higher than those in the intestine of liver fibrosis mice. In conclusion, based on the results of in vivo and in vitro experiments, preventive mechanism of Lactucin against liver fibrosis in mice may be to improve the enterohepatic circulation by regulating the metabolites of intestinal microorganisms, acetic acid and butyric acid, and to further regulate the Stat3 and TGF-β1 signaling pathway through the "gut-liver axis" to combat liver fibrosis.

Particle scattering is a key factor affecting underwater light transport. The diffusion length (zD), defined as 1/[b(1 − g)], where b is the scattering coefficient and g is the asymmetry factor, is obtained from the volume scattering function (VSF) of the particle and plays a vital role in assessing the potential for underwater optical detection, imaging and communication. Owing to the lack of VSF datasets, the variation in zD at different wavelengths in various ocean areas remains unclear. In this study, we used a dual-wavelength (488 & 532 nm) VSFLab to conduct the VSF measurement experiments in the East China Sea (ECS) and the South China Sea (SCS), obtaining VSFs from 1.5° to 178.5° at 51 stations. Seven optical properties, including absorption (a), scattering (b), attenuation (c), diffuse attenuation coefficient (Kd), backscattering (bb), g, and zD, were calculated from the measured VSFs. A comparative analysis of the results was performed, which showed that the laser transmission capability at 532 nm was better than that at 488 nm in terms of the absorption or diffuse attenuation coefficient in the ECS, whereas superior performance was observed at 488 nm in the SCS. However, from the perspective of scattering, zD at 532 nm (zD(532)) demonstrated superior performance in both the ECS and SCS. This superiority was particularly noticeable in regions with exceptionally clear water, such as the eastern side of the Luzon Strait, where zD(532) exceeded zD(488) by approximately 20%. Overall, the findings of this study provide a new perspective for assessing underwater light transmission capabilities.

Abstract Person re‐identification represents a pivotal sub‐problem in image retrieval, boasting broad application prospects in fields such as intelligent security and video surveillance. However, most existing person re‐identification methods predominantly focus solely on visual features pertaining to the person targets, thereby disregarding some supporting information closely related to the scene context. In the context of athlete re‐identification during sports event scenes, the athlete bib number is fully considered, an important clue that can provide different athletes' identities, and the traditional visual features of the person and high‐level semantic information of the bib number text are fused. A multi‐source information mutual gain mechanism is designed to improve the accuracy of the person re‐identification task. In the existing only publicly available marathon bib number dataset RBNR, the recognition accuracy of this method is significantly superior to that of the existing person re‐identification method. In addition, this paper constructs and publishes an athlete re‐identification dataset (HNNU‐ReID8000) for mainstream sports events, and the mean average precision (mAP) value of this method reaches 96.1% on this dataset, significantly ahead of existing state‐of‐the‐art person re‐identification methods. The code and the HNNU‐ReID8000 dataset will be released at https://github.com/yanbin‐zhu/zyb_person‐reid.

Three new species of the leafhopper genus Arboridia Zachvatkin 1946, Arboridia (Arboridia) furcata Han, sp. nov., Arboridia (Arboridia) rubrovittata Han, sp. nov., and Arboridia (Arboridia) robustipenis Han, sp. nov., are described and illustrated from fruit trees in Southwest China. A key and checklist to known species from China are provided.

Abstract In order to ensure the normal operation of rotating equipment, it is very important to quickly and efficiently diagnose the faults of anti-friction bearings. Hereto, fault diagnosis of anti-friction bearings based on Bi-dimensional ensemble local mean decomposition and optimized dynamic least square support vector machine (LSSVM) is presented in this paper. Bi-dimensional ensemble local mean decomposition, an extension of ensemble local mean decomposition from one-dimensional signal processing to Bi-dimensional signal processing, is used to extract the features of anti-friction bearings. Moreover, an optimized dynamic LSSVM is used to fault diagnosis of anti-friction bearings. The experimental results show that Bi-dimensional ensemble local mean decomposition is superior to Bi-dimensional local mean decomposition, optimized dynamic LSSVM is superior to traditional LSSVM, and the proposed Bi-dimensional ensemble local mean decomposition and optimized dynamic LSSVM method is effective for fault diagnosis of anti-friction bearings.

Abstract A significant proportion of hepatocellular carcinoma (HCC) is pathologically associated with hepatitis B virus (HBV) infection, followed by unsatisfied clinical outcomes. The increasing unmet need for HBV‐associated hepatocellular carcinoma (HBV‐HCC) treatment drives to deeper understand the role of the intricate immune microenvironment, tumor cell plasticity and dynamics of tumor evolution in HBV‐associated hepatic carcinogenesis. Thus, a comprehensive understanding of cross‐talk between HBV, host cells, and tumor microenvironment is of fundamental importance for identifying immune imbalance and heterogeneity in HBV‐HCC. Over the past 5 years, the application of single‐cell RNA sequencing (scRNA‐seq) in the understanding of heterogeneity and dynamics of immune cells, clonal evolution, and cancer stem cell (CSC) subsets of tumor cells has established a landscape for HBV‐HCC tumor ecosystem. Novel insights into anatomizing immune escape mechanisms and tumor drug resistance have remarkably facilitated the revolution of HBV‐HCC clinical treatment. Here, we provided a summary of HCC at single‐cell resolution and details on the basic workflow, limitations, and improvements of scRNA‐seq. The review highlights novel insights derived from scRNA‐seq on advances in the immune microenvironment and tumor heterogeneity of HBV‐HCC.

AMPK and its downstream signaling pathways are implicated in the white spot syndrome virus (WSSV) response of the shrimp Marsupenaeus japonicus, with WSSV using this pathway to promote its proliferation.

Background: Small cell lung cancer (SCLC) is characterized by extreme invasiveness and lethality. There have been very few developments in its diagnosis and treatment over the past decades. It is urgently needed to explore potential novel biomarkers and drug targets for SCLC.Methods: Two-sample Mendelian Randomization (MR) was performed to investigate causal associations between SCLC and plasma proteins using genome-wide association studies (GWAS) summary statistics of SCLC from Transdisciplinary Research Into Cancer of the Lung Consortium (nCase = 2,791 vs. nControl = 20,580), and was validated in another cohort (nCase = 2,664 vs. nControl = 21,444). 734 plasma proteins and their genetic instruments of cis-acting protein quantitative trait loci (pQTL) were used, whereas external plasma proteome data was retrieved from deCODE database. Bidirectional MR, Steiger filtering and phenotype scanning were applied to further verify the associations.Results: Seven significant (p < 6.81 × 10−5) plasma protein-SCLC pairs were identified by MR analysis, including ACP5 (OR = 0.76, 95% CI: 0.67–0.86), CPB2 (OR = 0.90, 95% CI: 0.86–0.95), GSTM3 (OR = 0.45, 95% CI: 0.33–0.63), SHMT1 (OR = 0.74, 95% CI: 0.64–0.86), CTSB (OR = 0.79, 95% CI: 0.71–0.88), NTNG1 (OR = 0.81, 95% CI: 0.74–0.90) and FAM171B (OR = 1.40, 95% CI: 1.21–1.62). The external validation confirmed that CPB2, GSTM3 and NTNG1 had protective effects against SCLC, while FAM171B increased SCLC risk. However, the reverse causality analysis revealed that SCLC caused significant changes in plasma levels of most of these proteins, including decreases of ACP5, CPB2, GSTM3 and NTNG1, and the increase of FAM171B.Conclusion: This integrative analysis firstly suggested the causal associations between SCLC and plasma proteins, and the identified several proteins may be promising novel drug targets or biomarkers for SCLC.

Rotator cuff tears are an important cause of shoulder pain and are caused by degeneration or trauma of the shoulder tendon at the anatomical neck of the humeral head. The understanding and research of rotator cuff tears have a history of hundreds of years, and their etiology, diagnosis, and treatment have a complete system, but some detailed rules of diagnosis and treatment still have room for development. This research paper briefly introduces the diagnosis and treatment of rotator cuff tears. The current situation and its valuable research direction are described.

For high-speed railway operations at the network level, unforeseen events that lead to operation interruptions are inevitable, which should be handled within a short period of time to reduce the influence of the events as much as possible. This paper introduces an integrated optimization method to deal with rescheduling problems at the railway network level under emergencies, rescheduling the train timetable, and utilizing the train sets. train set A three-objective optimization model is proposed with the aim of minimizing additional operation costs, total delay, and the number of transfer passengers. Then, an algorithm based on NSGA-III is proposed to solve the model. Computational experiments on real data are conducted to show the adaptability of the model and algorithm. The average optimization rate of the three objectives is 12.12%, 14.12%, and 10.57%, indicating the effectiveness of the method. Moreover, more experiments on a railway network in China are being conducted to analyze which section and which time have the greatest impact on the railway network when emergencies occur. According to the experiment, the bottleneck section is section 15, and the bottleneck time is 11:00 am. In addition, the importance of all the depots is discussed, and depot II is selected as the most important depot.

Abstract Background Delineation of clinical target volume (CTV) for radiotherapy is a time‐consuming and labor‐intensive work. This study aims to propose a novel convolutional neural network (CNN)‐based model for fast auto‐segmentation of CTV. To evaluate its performance and clinical utility, a blind randomized validation method was used. Methods Our proposed model was based on the generally accepted U‐Net architecture using computed tomography slices with CTV contours delineated by experienced radiation clinicians from 135 rectal patients receiving neoadjuvant radiotherapy. The Dice similarity coefficient (DSC) and 95th percentile Hausdorff distance (95HD) were used to measure segmentation performance. The validated dataset of additional 20 patients for clinical evaluation by 10 experienced oncology clinicians from 7 centers was randomly and blindly divided into two groups for clinicians' scoring and Turing test, respectively. Second evaluation was performed with different randomization after 2 weeks. Results The mean DSC and 95HD values of the proposed model were 0.90 ± 0.02 and 8.11 ± 1.93 mm for CTV of rectal cancer patients, respectively. The average time for automatic segmentation in the validation groups was 15 s per patient. By clinicians' scoring, the AI model performed better than manually delineating, though the differences were not significant (Week 0: 2.59 vs. 2.52, p = 0.086; Week 2: 2.55 vs. 2.47, p = 0.115). Additionally, the mean positive rates in the Turing test were 40.5% in Week 0 and 45.2% in Week 2, which demonstrated the great intelligence of our model. Conclusions Our proposed model can be used clinically for assisting contouring of CTVs in rectal cancer patients receiving neoadjuvant radiotherapy, which improves the efficiency and consistency of radiation clinicians' work.

To improve the cognition and understanding capabilities of artificial intelligence (AI) technology, it is a tendency to explore the human brain learning processing and integrate brain mechanisms or knowledge into neural networks for inspiration and assistance. This paper concentrates on the application of AI technology in advanced driving assistance system. In this field, millimeter-wave radar is essential for elaborate environment perception due to its robustness to adverse conditions. However, it is still challenging for radar object classification in the complex traffic environment. In this paper, a knowledge-assisted neural network (KANN) is proposed for radar object classification. Inspired by the human brain cognition mechanism and algorithms based on human expertise, two kinds of prior knowledge are injected into the neural network to guide its training and improve its classification accuracy. Specifically, image knowledge provides spatial information about samples. It is integrated into an attention mechanism in the early stage of the network to help reassign attention precisely. In the late stage, object knowledge is combined with the deep features extracted from the network. It contains discriminant semantic information about samples. An attention-based injection method is proposed to adaptively allocate weights to the knowledge and deep features, generating more comprehensive and discriminative features. Experimental results on measured data demonstrate that KANN is superior to current methods and the performance is improved with knowledge assistance.

Glioma is the most common type of primary malignant tumor in the central nervous system. Tumor recurrence and progression are common in lower-grade glioma (LGG). Immune checkpoint blockade (ICB), as an emerging immunotherapy, is expected to improve the prognosis of patients undergoing conventional treatment, but it currently performs poorly in glioma. We divided patients into genome-stable and -unstable groups according to the somatic mutation count and then found that the expression of CDC20 was positively correlated with genomic instability. We compared the differences in long non-coding RNA (lncRNA) expression and immune infiltration between the two groups. Five lncRNAs and three immune cell types were identified to construct risk models and a nomogram combing clinical features. Through internal and external validation, the models exhibited sufficient ability to predict the prognosis and the possible response to ICB therapy of patients. This study provided a potential predictive approach for the precise application of ICB and support for improving the prognosis of LGG patients.

BackgroundWith approval of anti-PD-1/PD-L1, metastatic non-small cell lung cancer (NSCLC) has entered the era of immunotherapy. Since immune-related adverse events (irAEs) occur commonly in patients receiving anti-PD-1/PD-L1, the landscape of death causes may have changed in metastatic NSCLC. We aim to compare patterns of death causes in metastatic NSCLC between the pre-immunotherapy and immunotherapy era to identify the consequent landscape transition of death causes.MethodsIn this cohort study, 298,485 patients with metastatic NSCLC diagnosed between 2000 and 2018 were identified from the Surveillance, Epidemiology, and End Results Program. Unsupervised clustering with Bayesian inference method was performed for all patients’ death causes, which separated them into two death patterns: the pre-immunotherapy era group and the immunotherapy era group. Relative risk (RR) of each death cause between two groups was estimated using Poisson regression. Reduced death risk as survival time was calculated with locally weighted scatterplot smooth (Lowess) regression.ResultsTwo patterns of death causes were identified by unsupervised clustering for all patients. Thus, we separated them into two groups, the immunotherapy era (2015-2017, N=40,172) and the pre-immunotherapy era (2000-2011, N=166,321), in consideration of obscure availability to immunotherapy for patients diagnosed in 2012-2014, when the follow-up cutoff was set as three years. Although all-cause death risk had reduced (29.2%, 13.7% and 27.8% for death risks of lung cancer, non-cancer and other cancers), non-cancer deaths in the immunotherapy era (N=2,100, 5.2%; RR=1.155, 95%CI: 1.101-1.211, P

In ground static target detection, polarimetric high-resolution radar can distinguish the target from the strong ground clutter by reducing the clutter power in the range cell and providing additional polarimetric features. Since the energy of a target is split over several range cells, the resulting detection problem is called polarimetric range extended target (RET) detection, where all target scattering centers should be considered. In this paper, we propose a novel polarimetric RET detection method via adaptive range weighted feature extraction. Specifically, polarimetric features of range cells are extracted, and a pretrained attention-mechanism-based module is used to adaptively calculate range cells weights, which are used to accumulate the range cells features as detection statistics. While calculating weights, both amplitude and polarimetric features are considered. This method can make the most of polarization information and improve the accumulation effect, thus increasing the discrimination between targets and clutter. The effectiveness of the proposed method is verified compared to both popular energy-domain detection methods and existing feature-domain detection methods, and the results show that our method exhibits superior detection performance. Moreover, we further analyze our method on different target models and different clutter distributions to prove that our method is suitable for different types of targets and clutter.

White light-emitting diodes (WLEDs) hold great promise in lighting, display, and visible light communication devices, and single-component white emission carbon quantum dots (SCWE-CQDs) as the key component of WLEDs have many outstanding advantages. However, rapid and efficient synthesis of SCWE-CQDs with high photoluminescence quantum yield (PLQY) and stability remains challenging. Here, we report a novel solvent engineering strategy to obtain highly photoluminescent SCWE-CQDs by controlling the dilution ratios between N,N-dimethylformamide (DMF) and pristine red carbon quantum dots (RCQDs) solution. By optimizing synthesis conditions, the relative PLQY of the SCWE-CQDs solution reached 53%. Morphological, structural, and optical property characterizations indicate that the combined action of the hydrogen bond (HB) effect and the size effect leads to the blue shift of RCQDs, but the HB effect is more dominant than the particle size in causing large spectral shifts. In addition, the WLEDs with high color rendering index of 89 and remarkable reliability were obtained based on the highly photoluminescent SCWE-CQDs. This facile solvent engineering approach for synthesizing tunable emission CQDs will promote the progress of carbon-based luminescent materials for applications in optoelectronic devices.

IntroductionThe loss of blood is a significant problem in Total Knee Arthroplasty (TKA). Anemia often occurs after such surgeries, leading to serious consequences, such as higher postoperative infection rates and longer hospital stays. Tools for predicting possible anemia can provide additional guidance in realizing better blood management of patients.Methods2,165 patients who underwent TKA from 2015 to 2019 in the same medical center were divided into training and validation cohorts. Both univariate and multivariate logistic regression analyses were performed to identify independent preoperative risk factors for anemia. Based on these predictors, a nomogram was established using the area under the curve (AUC), calibration curve (AUC), and the area under the curve (AUC). The model was then applied to the validation cohort, and decision curve analyses (DCA) were also plotted.ResultsThrough analysis of both univariate and multivariate logistic regression, five independent predictors were found in the training cohort: female, relatively low BMI, low levels of preoperative hemoglobin, abnormally high levels of ESR, and simultaneously two sides of TKA in the same surgery. The AUCs of the nomogram were 74.6% (95% CI, 71.35%–77.89%) and 68.8% (95% CI, 63.37%–74.14%) of training and the validation cohorts separately. Furthermore, the calibration curves of both cohorts illustrated the consistency of the nomogram with the actual condition of anemia of patients after TKA. The DCA curve was higher for both treat-none and treat-all, further indicating the relatively high practicality of the model.ConclusionFemale, lower BMI, lower levels of preoperative Hb, simultaneous bilateral TKA, and high levels of preoperative ESR were figured out as five independent risk factors for postoperative anemia (

Abstract Ankylosing spondylitis (AS) is chronic inflammatory arthritis with a progressive fusion of axial joints. Anti-inflammatory treatments such as anti-TNF-α antibody therapy suppress inflammation but do not effectively halt the progression of spine fusion in AS patients. Here we report that the autoimmune inflammation of AS generates a microenvironment that promotes chondrogenesis in spine ligaments as the process of spine fusion. Chondrocyte differentiation was observed in the ligaments of patients with early-stage AS, and cartilage formation was followed by calcification. Moreover, a large number of giant osteoclasts were found in the inflammatory environment of ligaments and on bony surfaces of calcified cartilage. Resorption activity by these giant osteoclasts generated marrow with high levels of active TGF-β, which induced new bone formation in the ligaments. Notably, no Osterix+ osteoprogenitors were found in osteoclast resorption areas, indicating uncoupled bone resorption and formation. Even at the late and maturation stages, the uncoupled osteoclast resorption in bony interspinous ligament activates TGF-β to induce the progression of ossification in AS patients. Osteoclast resorption of calcified cartilage-initiated ossification in the progression of AS is a similar pathologic process of acquired heterotopic ossification (HO). Our finding of cartilage formation in the ligaments of AS patients revealed that the pathogenesis of spinal fusion is a process of HO and explained why anti-inflammatory treatments do not slow ankylosing once there is new bone formation in spinal soft tissues. Thus, inhibition of HO formation, such as osteoclast activity, cartilage formation, or TGF-β activity could be a potential therapy for AS.

This research paper proposes a Lagrangian method to address the passing capacity of the calculation problem (PCCP) for a hub station in a high-speed railway (HSR) system. The passing capacity of a hub station is critical for determining the train timetable and maximizing the number of trains that can operate on different lines. The objective of this study is to determine the maximum number of trains that can pass through, start at, or end at a hub station. To achieve this objective, a mathematical model was introduced to solve the PCCP. The model was decomposed into two parts using a Lagrangian relaxation algorithm. The first part of the model was a simple train arrival problem (TAP) that reflected the timing of trains at the hub station with simultaneous arrival and departure time constraints. The second part of the model was a train spatio-temporal routing problem (TSRP) that aimed to solve the shortest spatio-temporal path of trains with free conflict with the train’s trajectory. A real instance was provided to demonstrate the feasibility of the proposed approach and the effectiveness of the Lagrangian method. The results showed that the proposed method can efficiently solve the PCCP and maximize the passing capacity of a hub station in an HSR system.

Neurogenic bowel dysfunction (NBD) is common in patients with cauda equina syndrome (CES). Previous studies have reported that electrical stimulation (ES) improves NBD but more neurophysiologic evidence is required. This case report describes a patient who experienced difficulty with defecation as a result of cauda equina syndrome (CES) that developed after a cesarean section performed 12 years ago under spinal anesthesia. The neurophysiological effects were assessed using the bulbocavernosus reflex (BCR) and electromyography (EMG). Two ES treatments, interferential current therapy and transcutaneous electrical stimulation, were used to stimulate the intestine and the external anal sphincter, respectively. The BCR results showed right-side delayed latency and no response on the left side. Needle EMG revealed abnormal spontaneous activities of the bilateral bulbocavernosus (BC) muscles. Electrodiagnostic testing revealed chronic bilateral sacral polyradiculopathy, compatible with CES. After treatment, the patient reported an improved perianal sensation, less strain and time for defecation than before, and satisfaction with her bowel condition. At the follow-up electrodiagnosis, the BCR latency was normal on the right side—needle EMG revealed reductions in the abnormal spontaneous activities of both BC muscles and re-innervation of the right BC muscle. Electrodiagnostic testing can offer insight into the neurophysiological effects of ES, which can help in understanding the mechanism of action and optimizing the therapy for patients with NBD.

ABSTRACT Background The therapeutic effect of chemotherapy is still unclear for clinical usage among second primary non‐small cell lung cancer (NSCLC) patients. The aim of this study was to verify the therapeutic effect of chemotherapy and identify the prognostic factors among patients who had received chemotherapy for second primary NSCLC. Methods A retrospective cohort was constructed based on the Surveillance, Epidemiology and End Results (SEER) database. Through least absolute shrinkage and selection operator regression, univariate Cox and multivariate Cox regression, we identified the prognostic factors among clinicopathological features. Propensity score matching analysis was used to verify the therapeutic effect of chemotherapy. Survival curves were plotted among the subgroups of the selected factors. We further selected clinicopathological features that would affect the prognosis among patients who had received chemotherapy through a similar process. Results A total of 769 patients were enrolled to verify the therapeutic value of chemotherapy for second primary lung cancer. Significant differences were observed between the chemotherapy and nonchemotherapy group for cancer‐specific survival. 215 patients who had received chemotherapy were analyzed to identify the factors that might influence outcome on the therapeutic effect of chemotherapy. Age, tumor size, histology and treatment were selected as significant factors. Conclusions The therapeutic effect of chemotherapy for second primary NSCLC was found to be significant. Age, tumor size and histology were significant prognostic factors among patients who had received chemotherapy for second primary NSCLC. Key points Significant findings of the study A significant therapeutic effect of chemotherapy for second primary non‐small cell lung cancer was proven through univariate Cox regression and propensity score matching analysis. Prognostic factors for second primary non‐small cell lung cancer patients who had received chemotherapy. What this study adds Chemotherapy could be applied in clinical practice as an additional therapeutic method for second primary non‐small cell lung cancer patients. We selected prognostic factors for patients who had received chemotherapy to identify patients who were appropriate for chemotherapy.

Abstract Polarimetric high‐resolution range profile (HRRP) holds great potential for radar automatic target recognition (RATR) owing to its capability of providing both polarimetric and spatial scattering information. In recent years, deep learning (DL) has obtained state‐of‐the‐art results in many classification tasks and has drawn great attention in the RATR field. However, as one of the most challenging tasks in RATR, small training sample case will restrict the application of DL because its superior performance generally depends on a large number of training samples. A feature‐guided deep model based on Transformer framework is proposed for polarimetric HRRP recognition with limited training samples. In the proposed model, artificial features are introduced to the attention module to guide the model focus on the range cells of HRRP with more target scattering information so as to reduce the dependence of the model on the number of training samples. Several different approaches are also studied to measure the similarity between artificial features and HRRP data to further improve the learning capacity of the model. Experimental results demonstrate that the proposed feature‐guided Transformer model modifying by Cosine similarity measure is able to achieve a better performance for polarimetric HRRP recognition with limited training samples.

Background Patients who have previously undergone surgical resection of initial primary lung cancer (IPLC) are at high risk of developing second primary lung cancer (SPLC). There are still no standard treatments for SPLC. This study aimed to identify the prognostic factors and compare survival between the different SPLC treatment groups. Methods SPLC patients in the Surveillance, Epidemiology, and End Results (SEER) database between 2007 and 2016 were retrospectively reviewed. Prognostic factors for SPLC were identified, using the least absolute shrinkage and selection operator (LASSO) regression and univariate Cox analysis to select variables for multivariate Cox analysis. Kaplan‐Meier method plus log‐rank test and restricted mean survival time (RMST) were used to compare survival outcome. Results A total of 665 SPLC patients were finally enrolled into the study. Multivariate Cox regression analysis revealed that male vs. female (HR = 1.82, 95% CI: 1.29–2.59, P = 0.001), tumor size of SPLC ≥1 cm vs. 0.05). Patients that received radiotherapy for SPLC also demonstrated similar survival when compared with those that underwent surgery (P > 0.05). Conclusions Radiotherapy and sublobar resection can be considered reasonable alternative treatments for SPLC, especially when patients are unable to tolerate lobectomy.

Abstract Background As an essential component of multimodal analgesia approaches after total knee arthroplasty (TKA), local infiltration analgesia (LIA) can be classified into peri-articular injection (PAI) and intra-articular injection (IAI) according to administration techniques. Currently, there is no definite answer to the optimal choice between the two techniques. Our study aims to investigate analgesic efficacy and safety of PAI versus IAI in patients receiving simultaneous bilateral TKA. Methods This randomized controlled trial was conducted from February 2017 and finished in July 2018. Sixty patients eligible for simultaneous bilateral total knee arthroplasty were randomly assigned to receive PAI on one side and IAI on another. Primary outcomes included numerical rating scale (NRS) pain score at rest or during activity at 3 h, 6 h, 12 h, 24 h, 48 h, and 72 h following surgery. Secondary outcomes contained active or passive range of motion (ROM) at 1, 2, and 3 days after surgery, time to perform straight leg raise, wound drainage, operation time, and wound complications. Results Patients experienced lower NRS pain scores of the knee receiving PAI compared with that with PAI during the first 48 h after surgery. The largest difference of NRS pain score at rest occurred at 48 h (PAI: 0.68, 95%CI[0.37, 0.98]; IAI: 2.63, 95%CI [2.16, 3.09]; P

The potential collision between the ship and the pipe piles of the jacket structure brings huge risks to the safety of an offshore platform. Due to their high energy-absorbing capacity, honeycomb structures have been widely used as impact protectors in various engineering applications. This paper proposes a data-driven intelligent approach for the prediction of the collision response of honeycomb-reinforced structures under ship collision. In the proposed model, the artificial neural network (ANN) is combined with the dynamic particle swarm optimization (DPSO) algorithm to predict the collision responses of honeycomb reinforced pipe piles, including the maximum collision depth (δmax) and maximum absorption energy (Emax). Furthermore, a data-driven evaluation method, known as grey relational analysis (GRA), is proposed to evaluate the collision responses of the honeycomb-reinforced pipe piles of offshore platforms. Results of the case study demonstrate the accuracy of the DPSO-BP-ANN model, with measured mean-square-error (MSE) of 5.06 × 10−4 and 4.35 × 10−3 and R2 of 0.9906 and 0.9963 for δmax and Emax, respectively. It is shown that the GRA method can provide a comprehensive evaluation of the performance of a honeycomb structure under impact loads. The proposed model provides a robust and efficient assessment tool for the safe design of offshore platforms under ship collisions.

Brain metastasis (BM) occurs commonly in patients with lung adenocarcinomas. Limited evidence indicates safety and efficacy of immunotherapy for this metastatic tumor, though immune checkpoint blockade has become the front-line treatment for primary advanced non-small cell lung cancer. We aim to comprehensively compare tumor microenvironments (TME) between primary tumors (PT) and BM at single-cell resolution. Single-cell RNA transcriptomics from tumor samples of PT (N = 23) and BM (N = 16) and bulk sequencing data were analyzed to explore potential differences in immunotherapeutic efficacy between PT and BM of lung adenocarcinomas. Multiple machine learning algorithms were used to develop and validate models that predict responses to immunotherapy using the external cohorts. We found obviously less infiltration of immune cells in BM than PT, characterized specifically by deletion of anti-cancer CD8+ Trm cells and more dysfunctional CD8+ Tem cells in BM tumors. Meanwhile, macrophages and dendritic cells within BM demonstrated more pro-tumoral and anti-inflammatory effects, represented by distinct distribution and function of SPP1+ and C1Qs+ tumor-associated microphages, and inhibited antigen presentation capacity and HLA-I gene expression, respectively. Besides, we also found the lack of inflammatory-like CAFs and enrichment of pericytes within BM tumors, which may be critical factors in shaping inhibitory TME. Cell communication analysis further revealed mechanisms of the immunosuppressive effects associated with the activation of some unfavorable pathways, such as TGFβ signaling, highlighting the important roles of stromal cells in the anti-inflammatory microenvironment, especially specific pericytes. Furthermore, pericyte-related genes were identified to optimally predict immunotherapeutic responses by machine learning models with great predictive performance. Overall, various factors contribute to the immunosuppressive TME within BM tumors, represented by the lack of critical anti-cancer immune cells. Meanwhile, pericytes may help shape the TME and targeting the associated mechanisms may enhance immunotherapy efficacy for BM tumors in patients with lung adenocarcinomas.

Background and ObjectivesThis study aims to conduct an updated systematic analysis of patients with pulmonary large cell neuroendocrine carcinoma (PLCNC) in recent decades, concerning incidence and mortality trends, demographics, treatments, survival and death causes.MethodsPatients who were diagnosed with PLCNC at the Peking Union Medical College Hospital (PUMCH) between 2000 to 2020 were retrospectively analyzed. The population-based Surveillance, Epidemiology, and End Results (SEER) database were also retrieved. Frequencies and average annual age-adjusted rates (AAR) of PLCNC patients were calculated and analyzed by Joint-point regression. Univariate and multivariate Cox regression were used for identifying prognostic factors. Predictive nomograms for overall survival (OS) and cancer-specific survival (CSS) were developed and then validated by calculating C-index values and drawing calibration curves. Survival curves were plotted using the Kaplan-Meier method and compared by log-rank test. Causes of death were also analyzed by time latency.ResultsA total of 56 PLCNC patients of the PUMCH cohort were included. Additionally, the PLCNC patients in the SEER database were also identified from different subsets. The AAR from 2001 to 2017 were 3.21 (95%CI: 3.12-3.30) per million. Its incidence and mortality rates in PLCNC patients increased at first but seemed to decline in recent years. Besides TNM stage and treatments, older age and male gender were independently associated with poorer survival, while marital status only affected CSS other than OS. The nomograms for OS and CSS presented great predictive ability and calibration performance. Surgery gave significantly more survival benefits to PLCNC patients, and chemotherapy might add survival benefits to stage II-IV. However, radiation therapy seemed to only improve stage III patients’ survival.ConclusionsThis study supported some previous studies in terms of incidence, survival, and treatment options. The mortality rates seemed to decline recently, after an earlier increase. Among PLCNC patients, most of the deaths occurred within the first five years, while other non-PLCNC diseases increased after that. Thus, careful management and follow-up of other comorbidities are of equal importance. Our study may partly solve the dilemma caused by PLCNC’s rarity and inspire more insights in future researches.

As an important protective facility on offshore platform, the blast wall is of great significance in resisting oil and gas explosions. Honeycomb structures are widely used due to their unique deformation and mechanical properties under dynamic impact loads. The aim of this research is to develop an optimized design for an offshore sandwich blast wall with different honeycomb cores. The uniqueness of this paper is providing the quantitative optimization scheme for topological configurations and unit cell geometric parameters of honeycomb structures according to mass consistency and the proposed synthetic evaluation index of anti-blast performance. By using the numerical simulation software ANSYS/LS-DYNA, the CONWEP algorithm was first validated and then adopted to conduct the dynamical performance analysis of the honeycomb blast wall. For comparison purposes, simulating studies on a series of different blast walls were carried out by considering various influential parameters. According to different criteria, the blast resistance of the sandwich honeycomb structures was evaluated. It is found that the sandwich plate with concave arc honeycomb core has the best anti-blast performance compared to that of arrow honeycomb core and concave hexagonal honeycomb core. For the concave arc honeycomb structure, the geometric parameters such as concave angle and aspect ratio of honeycomb unit cell have great influence on the blast-resistance performance. Moreover, the concave arc honeycomb structure with positive gradient arrangement has better anti-blast performance than the negative one. The curved blast wall with the curvature of 1/20 achieves better anti-blast performance than the flat blast wall.

Objective To investigate the risk factors for fall in the elderly population residing in rural areas of Korea and provide useful data for their prevention. Methods As part of farmers’ health promotion project, a retrospective study was conducted with a total of 350 elderly people recruited from March 2016 to December 2016. These subjects were divided into two groups: 254 non-fallers and 96 fallers. A person who fell to the floor at least once in the past year was defined as a faller. Participants were asked to visit the hospital once. The demographic characteristics, social environment, and educational levels were surveyed using a questionnaire. Physical examination was performed in the following order: cognitive function, lower leg strength and torque, body composition, and knee image test. Results Statistically significant factors for falls in univariate analysis were female gender, age, living alone, educational level less than middle school, skeletal muscle mass, Mini-Mental State Exam, knee osteoarthritis, hip torque, hip power mean, knee torque, and knee power mean. Multivariate analysis was performed to identify variables most relevant to falls among statistically significant factors in univariate logistic analysis. It was confirmed that female gender and age of 70–79 years were statistically significant factors related to falls. Conclusion Female gender and elderly status (70–79 years) are important risk factors for falls in rural areas underscoring the need for special attention when considering risk factors for falls among the elderly living in rural areas.

The collision between the pipe legs of jacket platforms and bypassing ships is of great concern for the safety assessment of platforms. Honeycomb structures have been widely used owing to their unique deformation and mechanical properties under dynamic impact loads. In this paper, two typical honeycomb structures, namely hexagonal honeycomb and arrow honeycomb, were constructed for the impact protection of inclined pipe legs in jacket platforms, and the present study aimed to assess the dynamical performance and crushing resistance of the designed honeycomb reinforced structure under ship collision by using the numerical simulation software ANSYS/LS-DYNA. The dynamical performance of the honeycomb reinforced pipe leg was investigated considering various influential parameters, including the impact velocity and impact direction. The crashworthiness of the two types of honeycomb was evaluated and compared by different criteria, namely the maximum impact depth (δmax), specific energy absorption (SEA) and the proposed index offset sliding (OS). The results demonstrated that both the hexagonal honeycomb structure and the arrow honeycomb structure can reduce the damage of inclined pipe legs caused by ship collision, while the hexagonal honeycomb can provide the better anti-collision capacity, which can well reduce the offset sliding and better protect the pipe leg from ship collision.

The model and forecasting performances were evaluated to investigate the effectiveness of bias correction for forecasting PM2.5 concentrations for the period May 2012 to December 2014. Measured concentrations of PM2.5 and major components were obtained from five monitoring stations by region in the Korean Peninsula, and predicted concentrations were obtained from PM2.5 simulations using WRF model v3.4.1 and the CMAQ modeling system v4.7.1. Underestimation was prevalent at all stations for all components except NO3-. The effect of bias correction was pronounced at the Gangwon station, where the difference in PM2.5 between measured and predicted concentrations was largest. The performances for SO42- and the unresolved other component were primarily improved, whereas the performance for NO3-, which was originally overestimated, was degraded. The accuracy of the four-level forecast was moderate at 58% overall, but the probability of detection (POD) of high-concentration events was low at 23%. Bias correction improved the accuracy and POD to 68% and 52%, respectively; however, the rate of false detection of high-concentration events increased as well.

The development of liver fibrosis is closely related to the gut microbiota, and the “gut-liver axis” is the most important connection between the two. ethyl acetate extract of Cichorium pumilum Jacq (CGEA) is an herbal extract consisting mainly of sesquiterpenoids. The anti-inflammatory and hepatoprotective effects of CGEA have been reported, but the anti-fibrotic effects of CGEA via intestinal microbes and the “gut-liver axis” cycle have rarely been reported. In this study, we observed that CGEA not only directly attenuated inflammatory factor levels in inflamed mice, but also attenuated liver inflammation as well as liver fibrosis degeneration in rats with liver fibrosis caused by colitis. We observed in vitro that CGEA significantly promoted the growth of Bifidobacterium adolescentis. Similarly, fecal 16S rDNA sequencing of liver fibrosis rats showed that CGEA intervention significantly altered the composition of the intestinal microbiota of liver fibrosis rats. CGEA increased the abundance of intestinal microbiota, specifically, CGEA increased the ratio of Firmicutes to Bacteroidetes, CGEA could significantly increase the levels of Ruminococcus. In addition, CGEA intervention significantly protected intestinal mucosal tissues and improved intestinal barrier function in rats. Lactucin is the main sesquiterpenoid in CGEA, and HPLC results showed its content in CGEA was up to 6%. Lactucin has been reported to have significant anti-inflammatory activity, and in this study, we found that Lactucin decreased p38 kinases (p38), phosphorylation of the extracellular signal-regulated kinase (ERK) and protein kinase B (AKT) protein phosphorylation in lipopolysaccharide (LPS)-activated RAW264.7 cells, thereby reducing mRNA expression and protein expression of pro-inflammatory factors inducible nitric oxide synthase (iNOS) and cyclooxygenase-2 (COX-2), and inhibiting the release of inflammatory factors interleukin (IL)-6 and nitric oxide (NO), exerting anti-inflammatory effects. In summary, the prevention of liver fibrosis caused by intestinal inflammation by CGEA may be achieved by regulating the intestinal microbiota and restoring the intestinal barrier thereby improving the “gut-liver axis” circulation, reducing liver inflammation, and ultimately alleviating liver fibrosis. Notably, the direct anti-inflammatory effect of CGEA may be due to its content of Lactucin, which can exert anti-inflammatory effects by inhibiting the phosphorylation of Mitogen-activated protein kinase (MAPK) and Akt signaling pathways.

Background and Objectives: N3-positive non-small cell lung cancer (NSCLC) is usually regarded as inoperable. There were very few studies that focused on N3-NSCLC patients. This study aims to analyze prognosis of NSCLC patients with N3 disease and provides retrospective indications.Methods: NSCLC patients staged as N3 were retrospectively reviewed from the Surveillance, Epidemiology, and End Results database. Univariate and multivariate Cox regression were used for identifying prognostic factors. The selected predictive parameters by the least absolute shrinkage and selection operator (LASSO) regression were used to develop predictive nomogram models for overall survival (OS) and lung cancer-specific survival (CSS). The C-index values were calculated to assess the models' predictive ability, while calibration curves were plotted to evaluate the agreement between the predicted and the actual survival. Survival curves were plotted by Kaplan-Meier method and were compared by log-rank test. Propensity score matching (PSM) was used to balance the baseline characteristics between treatment groups.Results: A total of 24,747 N3-NSCLC patients were enrolled. The 1-, 3-, and 5-year OS rates were 35.8, 6.8, and 1.7%, respectively, while the corresponding CSS rates was 36.6, 6.9, and 1.8%, respectively. The nomogram models were developed using 11 significant prognostic parameters, including age, sex, race, histology, stage, T stage, bone, brain and liver metastases, surgery, and chemotherapy. Both of them demonstrated great predictive ability and performed well in the calibration curves. After PSM, patients receiving surgery demonstrated significantly better survival than those who did not. Besides, there was no significant difference of survival between patients receiving chemotherapy with and without radiotherapy.Conclusions: The nomogram models for predicting survival outcome of N3-NSCLC patients can be clinically used. Surgery may be beneficial to the survival for these patients, while radiotherapy may not have additional survival benefits in patients receiving chemotherapy.

Background and Objectives: Total knee arthroplasty (TKA) is widely performed to improve mobility and quality of life for symptomatic knee osteoarthritis patients. The accurate prediction of patients' length of hospital stay (LOS) can help clinicians for rehabilitation decision-making and bed assignment planning, which thus makes full use of medical resources.Methods: Clinical characteristics were retrospectively collected from 1,298 patients who received TKA. A total of 36 variables were included to develop predictive models for LOS by multiple machine learning (ML) algorithms. The models were evaluated by the receiver operating characteristic (ROC) curve for predictive performance and decision curve analysis (DCA) for clinical values. A feature selection approach was used to identify optimal predictive factors.Results: The areas under the ROC curve (AUCs) of the nine models ranged from 0.710 to 0.766. All the ML-based models performed better than models using conventional statistical methods in both ROC curves and decision curves. The random forest classifier (RFC) model with 10 variables introduced was identified as the best predictive model. The feature selection indicated the top five predictors: tourniquet time, distal femoral osteotomy thickness, osteoporosis, tibia component size, and post-operative values of Hb within 24 h.Conclusions: By analyzing clinical characteristics, it is feasible to develop ML-based models for the preoperative prediction of LOS for patients who received TKA, and the RFC model performed the best.

The complete mitochondrial genome (mitogenome) of Aglossa dimidiata (Lepidoptera: Pyralidae) was sequenced using high-throughput sequencing. The mitogenome of A. dimidiata was 15,225 bp in length. It comprised 37 typical genes and one control region. All protein-coding genes (PCGs) were initiated with ATN, except for COX1 (TTG). All PCGs used TAN as stop codon, except for ND5 and ND4 terminated with incomplete T. Twenty-two tRNA genes ranged from 61 to 71 bp in size. The monophyly of family Pyralidae and the sister relationship between A. dimidiata and Orthopygia glanucinalis are both supported by maximum likelihood method using the nucleotide sequences of 13 PCGs.

Interferometric multispectral images contain rich information, so they are widely used in aviation, military, and environmental monitoring. However, the abundant information also leads to the disadvantages that longer time and more physical resources are needed in signal compression and reconstruction. In order to make up for the shortcomings of traditional compression and reconstruction algorithms, the stacked convolution denoising autoencoder (SCDA) reconstruction algorithm for interference multispectral images is proposed in this paper. And, the experimental code based on the TensorFlow system is built to reconstruct these images. The results show that, compared with D-AMP and ReconNet algorithms, the SCDA algorithm has the advantages of higher reconstruction accuracy and lower time complexity and space complexity. Therefore, the SCDA algorithm proposed in this paper can be applied to interference multispectral images.