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Background: Currently, ERAS for spinal surgery field is still in its beginnings, the major protocol lessons learned from other surgical specialties and lack of ERAS program for elderly patients (>70 years old). Geriatric patients has its own special characteristics resulting in more harmed by surgical stress. The enhanced recovery after surgery (ERAS) are designed to improving recovery after surgery and can result in substantial benefits in both clinical outcomes and cost-effectiveness. In the present study, we aimed to determine whether enhanced recovery after surgery (ERAS) significantly improved satisfaction and outcomes in elderly patients (>70 years old) with long-level lumbar fusion. Methods: A total of 154 patients were included, 72 in the ERAS group and 82 case-matched patients in the non-ERAS group. Data including demographic, comorbidity and surgical information were collected from electronic medical records. ERAS interventions were categorised as preoperative, intraoperative and postoperative. We also evaluated primary outcome, surgical complication and length of stay (LOS).Results: There were no statistically significant intergroup differences in regards to demographics, comorbidities, American Society of Anaesthesiologists (ASA) grade, or the number of fusion levels. There were also no differences between mean surgery time of intraoperative blood loss between the ERAS and non-ERAS groups. In addition, the mean preoperative Visual Analogue Score (VAS) for the back and legs and Oswestry Disability Index (ODI) score were not significantly different between the two groups. Overall, ERAS pathway compliance was 91.5%. There were no significant differences in the readmission and mortality rates at 30-day follow-up between the ERAS and non-ERAS groups. However, we observed a statistically significant decrease in the complications in the ERAS group(8 in the ERAS group versus 23 in the non-ERAS group, p=0.009) and LOS in the ERAS group (17.74±5.56 of ERAS group versus 22.13±12.21 in non-ERAS group, p=0.041). Multivariable linear regression showed that comorbidities (p=0.028) and implementation of ERAS program (p=0.002) were correlated with prolonged LOS. Multivariable logistic regression showed that comorbidities (p=0.029)， implementation of ERAS program (p=0.043) and preoperative VAS Back (p=0.046), were correlated with complications. Conclusions: This report describes the first ERAS protocol used in elderly patients after long-level lumbar fusion surgery. Our ERAS program is safe and could help decreases LOS and complication in elderly patients with long-level lumbar fusion.

Accurate welding defect location is of great significance to modern manufacturing which could be used for accurate quality evaluation and precise repairing decision-making basis of different products. Nevertheless, accurate welding defect location is still a challenging task due to some complex factors, such as complex backgrounds, low contrast, weak texture, class imbalance issue, etc. Recently, the deep learning has got great development due to strong feature expression ability which has been widely applied into the defect detection, but it still exists a certain shortcomings on segmentation tasks with class unbalanced issue or micro defects. To address these issues, with the encoder-decoder network architecture, a novel welding defect location method is proposed with an attention-guided segmentation network. To reduce the contextual information loss of the deep encoder module after multiple convolution and pooling operations, a multi-scale feature fusion block is proposed to embed into U-shape network (U-Net) to acquire more information. On the basis, combined with a bidirectional convolutional Long Short-Term Memory (BiConvLSTM) block, an improved attention block is integrated into the skip connections between encoder path and decoder path to capture the global, long-range contexts and emphasize target regions, contributing to locate welding defect areas and enhance the segmentation ability on micro defects. Meanwhile, to address the foreground-background class imbalance issue, a hybrid loss function combined with binary cross-entropy (BCE) and Dice loss functions is proposed to effectively utilize their unique excellent characteristics for accurate defect segmentation. Experiment results on the public GDXray data set show that the proposed segmentation method could obtain a competitive segmentation performance compared with other advanced segmentation models.

In this paper, a novel test system for spin-wave linewidth has been proposed. As the traditional test system requires a high-power RF source, a high Q cylindrical resonator with efficiency compression of microwave magnetic field is designed by a partially filled dielectric in this paper. Experimental results show that this method can achieve spin-wave linewidth measurement with lower RF power compare to the original rectangular cavity. Simultaneous, replacing the traditional tuned resonant cavity with the frequency adjustable signal source can reduce the system interference to cavity field distribution and lays the foundation for fully automated testing. Finally, the wide linewidth spin-wave linewidth test is realized in a low RF power environment, and the test uncertainty for the system is estimated within ±10 %.

To determine the complex permittivity of material mounted on the metal surface, the free-space reflection method is a good candidate due to its advantages of being nondestructive and contactless. When using the method, the extraction of the complex reflection coefficient (CRC) of the metal-backed material is the key to realize accurate measurement. Traditionally, the free-space reflection method assumes that the interrogating wave is a single plane wave, and the plane-wave reflection model is used. However, a focused beam is usually used in practice for reducing the edge effect that leads to the difference between the actual test scene and the traditional test model. In this study, a focused-wave reflection model is constructed to invert the complex permittivity, where the focused wave is considered as a Gaussian beam. The CRC of the Gaussian beam is deduced by using the plane-wave spectrum technique and plane-wave scattering-matrix (PWSM) theory. Based on the difference between the CRCs of the focused-wave and plane-wave, inverted complex permittivity corresponding to two reflection models is compared. Numerical results are given to show how the errors introduced by the plane-wave assumption are related to the waist size of the incident beam and the thickness and relative complex permittivity of the material. Measurement of a Teflon slab is given to further demonstrate the capability of the proposed procedure.

The failure of an insulator may compromise the safety of the entire power transmission system. Therefore, insulator defect detection is vital for the safe operation of power systems. However, insulator defects in an insulator image may have varying sizes, and several currently available methods do not have satisfactory detection accuracy for small defects. To address this issue, we propose an improved detection network for small insulator defects with a batch normalization convolutional block attention module (BN-CBAM) and a feature fusion module. The BN-CBAM is designed to better exploit channel information and enhance the effect of different channels on the feature map. In addition, we propose a feature fusion module that fuses multi-scale features from different layers to improve small object detection performance. Moreover, to address the scarcity of aerial images, a data augmentation method based on the fusion of the target segment and background is introduced. Experiments demonstrate that the proposed method achieves better small insulator defect detection performance than other state-of-the-art approaches. In addition, data augmentation methods enrich sample diversity and enhance the generalizability of the network.

Image-based segmentation of overhead power lines is critical for power line inspection. Real-time segmentation helps the inspection robot avoid obstacles or land on the wire during the inspection task. It is challenging for several studies to achieve real-time overhead power line segmentation with high accuracy. In addition, cluttered background brings great difficulties to overhead power lines segmentation. To address these issues, an efficient parallel branch network for real-time overhead power line segmentation is proposed. Our framework combines a context branch that generates useful global information with a spatial branch that preserves high-resolution segmentation details. The asymmetric factorized depth-wise bottleneck (AFDB) module is designed in the context branch to achieve more efficient short-range feature extraction and provide a large receptive field. Furthermore, the subnetwork-level skip connections in the classifier are proposed to fuse long-range features and lead to high accuracy. Experiments demonstrate that our framework achieves more than 90% segmentation accuracy.

Background Enhanced recovery after surgery (ERAS) program is an evidence-based improvement over non-ERAS traditional care. The aim of the present study was to analyze the safety, feasibility, and efficacy of an ERAS program in patients over 70 years undergoing lumbar arthrodesis by comparison with non-ERAS traditional care. Methods During January 2018 to December 2018, patients enrolled received non-ERAS traditional care, while the ERAS program was implemented from January to December 2019. Demographic characteristics, comorbidities, surgical data and postoperative recovery parameters were collected from all patients. Postoperative pain scores were evaluated by visual analog scales (VAS). The clinical outcomes were length of stay (LOS), postoperative complications and postoperative pain scores. Compliance results were also collected. Result A total of 127 patients were enrolled, including 67 patients in the non-ERAS traditional care group and 60 patients in the ERAS group. The demographic characteristics and comorbidities of the two groups showed no significant differences. The LOS of patients treated with ERAS program (13.6 ± 4.0 days) was significantly less than that of patients treated with non-ERAS traditional care (15.6 ± 3.9 days) (p = 0.034). Complication rate was 8.3% in the ERAS group versus 20.9% in the non-ERAS traditional care group (p = 0.048). VAS (back) in the ERAS group was significantly lower on postoperative day (POD) 1 and POD2. Postoperative recovery parameters were improved in the ERAS group. The overall compliance with the ERAS program was 94%. Conclusions Based on our results, ERAS program is safer and more effective for elderly patients over 70 undergoing lumbar arthrodesis than non-ERAS traditional care.

In most measurement models of the cylindrical cavity, concentricity between the rod-like sample and cylindrical cavity is a basic premise of an accurate calculation model. Once a sample is randomly placed in the off-axis position in the sample hole of the cavity, the real will not conform to the established calculation model and the off-axis position should be taken into account to the measurement error. In this article, a measurement method to calculate permittivity of the sample placed at the off-axis position in the sample hole is presented. The actual location of the sample in a cylindrical cavity is first obtained in the application of vision measurement. Field distribution in the cavity is calculated by using the mode-matching method. The measured sample in the cavity is divided into finite small elements with arc shapes, and permittivity calculation expression is established by using the perturbation theory. A TM-mode cylindrical cavity that installed two miniature camera probes around the cavity wall is designed and used to measure permittivity of the material. Teflon and fused silica with different off-axis positions are measured and compared with the published results at several resonance frequencies. The maximum permittivity errors of Teflon and fused silica are 1.6% (at around 2 GHz) and 0.68% (at around 10.8 GHz), respectively. The maximum loss tan $\delta $ errors are 5.8e−5 (at around 12.5 GHz) and 1.44e−4 (at around 9.9 GHz), respectively.

The application of traditional 3D reconstruction methods such as structure-from-motion and simultaneous localization and mapping are typically limited by illumination conditions, surface textures, and wide baseline viewpoints in the field of robotics. To solve this problem, many researchers have applied learning-based methods with convolutional neural network architectures. However, simply utilizing convolutional neural networks without taking other measures into account is computationally intensive, and the results are not satisfying. In this study, to obtain the most informative images for reconstruction, we introduce a residual block to a 2D encoder for improved feature extraction, and propose an attentive latent unit that makes it possible to select the most informative image being fed into the network rather than choosing one at random. The recurrent visual attentive network is injected into the auto-encoder network using reinforcement learning. The recurrent visual attentive network pays more attention to useful images, and the agent will quickly predict the 3D volume. This model is evaluated based on both single- and multi-view reconstructions. The experiment results show that the recurrent visual attentive network increases prediction performance in a way that is superior to other alternative methods, and our model has desirable capacity for generalization.

Welding quality detection is a critical link in modern manufacturing, and the weld bead location is a prerequisite for the high-precision assessment of welding quality. It is generally necessary for weld bead detection to be accomplished in the context of complex industrial environments. However, conventional detection and location methods based on specific detection conditions or prior knowledge lack accuracy and adaptability. To precisely detect and locate the weld beads in real industrial environments, a novel weld bead detection and location algorithm is proposed based on deep convolutional neural networks. Because there is no open data set of weld beads and the samples in real industrial applications are insufficient for effective model training of the deep convolutional neural network, a novel data augmentation method based on a deep semantic segmentation network is proposed to increase the sample diversity and enlarge the data set. Then, a dynamic sample updating strategy is put forward to cover more welding situations. Finally, faced with the weak-feature and weak-texture characteristics of weld beads, a simplified YOLOV3 model is proposed to realize end-to-end weld bead location. Experiments demonstrate that the proposed method could effectively satisfy the robustness and precision requirements for weld bead detection and location combined with a deep semantic segmentation network and simplified YOLOV3 model.

With the fast development of smart grid, the power line mileage and power equipments get rapid growth. The contradiction between the large number of maintenance equipments and the small number of maintenance workers becomes increasingly prominent. Meanwhile, the traditional maintenance mode has the disadvantages of over or under maintenance, which will lead to the increasing failure risk of power transmission system. Faced with these issues, to enhance the intelligence and automation level of power line inspection, many researchers have devoted much effort to the research of automatic power line inspection and some state-of-the-art techniques about power line inspection are proposed to improve the inspection efficiency and quality, such as unmanned aerial vehicle (UAV), image processing, deep architecture, and so on. In this article, we analyzed and summarized the state-of-the-art techniques on power line inspection to provide a valuable reference for the researchers engaged in the smart grid. First, the common inspection tasks on power line inspection are reviewed. Second, the existing inspection platforms are examined in this article. Also, the advantages and disadvantages of different platforms are analyzed accordingly. Third, faced with different inspection tasks, different inspection sensors are integrated into the inspection platforms for data collection. Therefore, the common sensors on inspection platforms are discussed. Finally, to realize automatic power line inspection, different inspection methods are proposed or improved, and these advanced inspection methods are surveyed and discussed.

Malnutrition is a risk factor for postoperative infectious complications of elderly patients undergoing posterior lumbar arthrodesis. At present, there is no gold standard for nutrition screening tools. We analyzed the value of predicting infectious complications among elderly patients over 70 years undergoing posterior lumbar arthrodesis by comparing the MNA-SF and GNRI. Demographic data, anthropometric measurements, serum albumin, surgical data and the occurrence of infectious complications and LOS were collected. Mini Nutritional Assessment short form (MNA-SF), Geriatric Nutritional Risk Index (GNRI) were performed within 24 h before surgery. Multivariable logistic regression analyses were used to identify predictors of infectious complications. The discriminatory performances of GNRI and MNA-SF scores for the occurrence of infectious complications were determined by receiver operating characteristic curves (ROC) analyses and the area under the curve (AUC). The study included 252 patients with a median age of 76.82 ± 6.41 years (range 70–84 years), and 142 patients (56.3%) were female. There were no significant differences in infectious complications (p = 0.236) and LOS (p = 0.580) among different GNRI categories. 27.3% malnourished patients evaluated by the MNA-SF suffered from infectious complications and 10.1% patients at risk of malnourished had infectious complications. Those patients had statistically significant higher prevalence of infectious complications (p = 0.002) and longer LOS (p = 0.023) than well-nourished patients. Multivariable analysis revealed that preoperative malnutrition and at risk of malnourished by the MNA-SF was significantly associated with infections. The area under the curve (AUC) of MNA-SF was 0.754, which was significantly high than AUC of GNRI (0.623) (Delong’s test, p = 0.033). This study demonstrated that MNA-SF is a simple and effective tool for predicting the risk of infectious complications in elderly patients undergoing posterior lumbar arthrodesis.

In this paper, we propose an intelligent item searching framework for smart handheld devices through mobile cloud computing based on the Internet of Things (IoT) architecture. The proposed framework enables mobile users to drag a website/software icon over interested/unknown items for learning details, comparing prices, purchasing goods, etc. Our framework explores the IoT architecture, mobile cloud computing, and gesture-based interacting interfaces to alleviate the complexity and difficulty of device manipulation encountered by mobile users. In addition, we integrate our framework with an item caching mechanism that can reduce the total searching time as much as possible for popular items. Furthermore, we implement an Android-based item searching system to verify the feasibility and superiority of the proposed framework. Experimental results show that our implemented system outperforms existing methods and can significantly reduce the total consumption time of mobile item searching.

Background Degenerative disorders of the lumbar spine decrease the mobility and quality of life of elderly patients. Lumbar fusion surgery is the primary method of treating degenerative lumbar spine disorders; however, the surgical stress response associated with major surgery has been linked to pathophysiological changes in the elderly, resulting in undesirable postoperative morbidity, complications, pain, fatigue, and extended convalescence. In the present study, we aimed to determine whether enhanced recovery after surgery significantly improved satisfaction and outcomes in elderly patients (> 65 years old) with short-level lumbar fusion. Methods The study enrolled lumbar disc herniation or lumbar spinal stenosis patients if they were over the age of 65 years old underwent lumbar fusion at one or two levels. Data including demographic, comorbidity, and surgical information were collected from electronic medical records. Enhanced recovery after surgery interventions was categorized as preoperative, intraoperative, and postoperative. We also evaluated primary outcome, surgical complication, length of stay, postoperative pain scores, and 30-day readmission rates. Results A total of 192 patients were included, 96 in the enhanced recovery after surgery group and 96 case-matched patients in the non- enhanced recovery after surgery group. There were no statistically significant intergroup differences in regards to demographics, comorbidities, American Society of Anaesthesiologists grade, or the number of fusion levels. There were also no differences between mean surgery time of intraoperative blood loss between the enhanced recovery after surgery and non- enhanced recovery after surgery groups. In addition, the mean preoperative Japanese Orthopaedic Association score, visual analog score for the back and legs, and Oswestry Disability Index score were not significantly different between the two groups. Overall, enhanced recovery after surgery pathway compliance was 92.1%. There were no significant differences in the number of complications or the mortality rates between the enhanced recovery after surgery and non-enhanced recovery after surgery groups. Furthermore, the mean postoperative Japanese Orthopaedic Association score, Visual analog score for the back and legs, Oswestry Disability Index score, and readmission rates score revealed no significant differences between the groups at 30-day follow-up point. However, we observed a statistically significant decrease in length of stay in the enhanced recovery after surgery group (12.30 ± 3.03 of enhanced recovery after surgery group versus 15.50 ± 1.88 in non- enhanced recovery after surgery group, p = 0). Multivariable linear regression showed that comorbidities (p = 0.023) and implementation of enhanced recovery after surgery program (p = 0.002) were correlated with prolonged length of stay. Multivariable logistic regression showed that no characteristics were associated with complications. Conclusions This report describes the first enhanced recovery after surgery protocol used in elderly patients after short-level lumbar fusion surgery. Our enhanced recovery after surgery program is safe and could help decrease length of stay in elderly patients with short-level lumbar fusion.

Background and Aim Frailty is an independent predictor of mortality and adverse events (AEs) in patients undergoing surgery. This study aimed to quantify the ability of Modified Frailty Index (mFI) to predict AEs in older patients undergoing elective posterior thoracolumbar fusion surgery. Methods We retrospectively reviewed the results of 426 patients with the following diagnoses and follow-up evaluations of at least 12 months duration: lumbar disc herniation, 125; degenerative spondylolisthesis, 81; lumbar spinal canal stenosis, 187; and adult spinal deformities, 33. The cases were divided into two groups. The long spinal fusion (LSF) group was defined as ≥3 spinal levels with segmental pedicle-screw fixation. Short spinal fusion (SSF) were defined with at most two levels. The mFI used in the present study is an 11-variable assessment. The association of frailty with AEs was determined after adjusting for known and suspected confounders. Results Frailty was presented in 66 patients (15.5%) within the total population (LSF, 21.9% and SSF, 11.8%). Rates of AEs assessed in the study increased stepwise with an increase in the mFI for the two groups. The severity of frailty was an independent predictor of any, major, and minor complications in the LSF group and any, minor complication in the SSF group (P

Flower and fruitlet thinning can be an effective method of improving the yield and quality of fruit. Automatic detection flowers and fruits at different growth stages is essential for the intelligent management of apple orchards. The further segmentation of blossom areas contributes to extracting detailed growth information of apple flowers. However, the precise detection and segmentation of blossom images is yet to be fully accomplished. An instance segmentation model which improves Mask Scoring R–CNN with a U-Net backbone (MASU R–CNN) is proposed for the detection and segmentation of apple flowers with three different levels of growth status: bud, semi-open and fully open. The foreground and background of apple flower images were combined based on the growth characteristics of apple flowers. Furthermore, 200 background images were added as background samples to form the image training dataset and a U-Net backbone was used to improve the MaskIoU head of Mask Scoring R–CNN model. This method can improve the efficiency of feature utilisation and promote the reuse of features through the concatenation of feature maps in the process of encoding and decoding. The performance of the MASU R–CNN model was verified by 100 testing images. With ResNet-101 FPN adopted as the feature extraction backbone, the precision of MASU R–CNN reached 96.43%, recall 95.37%, F1 score 95.90%, mean average precision (mAP) 0.594, and mean intersection over union (mIoU) 91.55%. The segmentation results of MASU R–CNN model outperformed those of the other state-of-the-art models.

Objectives: Breast cancer has been the second most prevalent and fatal malignancy due to its frequent metastasis to other organs. We aim to study the effects of a key miRNA-mRNA signaling in breast cancer. Results: CNN1 was identified as the key gene in breast cancer by the bioinformatics analysis, and the downregulation of CNN1 in breast cancer tissues and cell lines was observed. Upregulating CNN1 inhibited cell survival, migration, invasion, and adhesion, but enhanced cell apoptosis. miR-106b-5p not only bound to CNN1 mRNA 3’UTR, but also promoted lung metastasis in vivo. Besides, the miR-106b-5p mimic enhanced breast cancer canceration by targeting CNN1 and activating Rho/ROCK1 signaling pathway. Conclusion: Overall, our results proved that miR-106b-5p promoted the metastasis of breast cancer by suppressing CNN1 and activating Rho/ROCK1 pathway. Methods: Bioinformatics analysis was performed to select the key gene in breast cancer. The overexpression and knockdown of Calponin 1 (CNN1) in breast cancer cell lines were performed to conduct cell viability, migrating, invasion, proliferation, adhesion, and apoptosis experiments. To identify the role of miR-106b-5p and Rho/ROCK1 in CNN1-induced breast cancer, a dual-luciferase assay, tumor lung metastasis assay, transcript half-life assay, and Rho/ROCK1 inhibition assay were performed.

Purpose: To evaluate whether the European Neuroendocrine Tumor Society (ENETS) system or the 8th American Joint Committee on Cancer (AJCC) staging manual are suitable for gastric neuroendocrine carcinomas and/or mixed adenoneuroendocrine carcinomas (G-NECs/MANECs). Methods: Patients in a multicentric series with G-NEC/MANEC who underwent curative-intent surgical resection for a primary tumor were included. An optimal staging system was proposed base on analysis of the T and N status and validated by the SEER database. Results: Compared with the ENETS system, the survival curves of the T category and N category in the 8th AJCC system were better separated and distributed in a more balanced way, but the survival curves of T2 vs. T3, N0 vs. N1, and N3a vs. N3b overlapped. For the T category, the 8th AJCC T category was modified by combining T2 and T3, which was consistent with the T category in the 6th AJCC manual for GC. For the N category, the optimal cut-off values of metastatic lymph nodes using X-tile were also similar to those of the N category in the 6th AJCC system. The Kaplan-Meier plots of the 6th AJCC system showed statistically significant differences between individual substages. Compared with the other 2 classifications, the 6th AJCC system also showed superior prognostic stratification. Similar results were obtained in both multicentric and SEER validation sets. Conclusions: Compared to the 8th AJCC and ENETS systems, the 6th AJCC staging system for GC is more suitable for G-NEC/MANEC and can be adopted in clinical practice.

In this paper, the microwave characteristics of typical photosensitive material InP under different light irradiation are studied. The measurement sensor is a reflection-type hemispherical quasi-optical resonator with an operating frequency range from 20 to 40 GHz, an operating mode of TEM00q, and a quality factor of 18,000 or more. For the short-time irradiation experiment, the variation of InP microwave characteristics with the irradiation power of 20 mW, 60 mW, 100 mW, and 200 mW, is studied by frequency-domain and time-domain scanning methods, respectively. The measurement results indicate that the microwave characteristics of InP change significantly even under weak light irradiation. Taking 100 mW and 200 mW irradiation power as examples, the long-time irradiation experiment performed on InP lasting 1.5 min is carried out. The measurement result curves clearly show the influence of the thermal and non-thermal effects on the InP microwave characteristics at the instant of the monochrome light source opening and closing and during irradiation. Furthermore, the temperature distribution of InP during 200 mW irradiation is real-time imaged by a thermal infrared imager to verify the existence of thermal effect during irradiation. The measurement results are in good agreement with the theoretical analysis. The microwave properties of InP under short-time and long-time irradiation are analyzed by frequency-domain and time-domain scanning methods, especially the effects of thermal and non-thermal on microwave properties during long-term irradiation.

Background Fungal rhinosinusitis is an infectious and/or allergic disease caused by fungi in the sinus and nasal cavity. Due to the warm and humid climate in Guangxi Zhuang Autonomous Region, the incidence of fungal rhinosinusitis is higher than that in other provinces. However, its physiological mechanism is not yet clear. Not every patient colonized by fungi develops a fungal infection. To a large extent, the immune status of the patient determines the nature of fungal disease in the nasal passages. The pathologic process of progression from harmless fungal colonization to fungal rhinosinusitis is unclear and has not been reported. Case summury We report two patients, one who developed fungal rhinosinusitis 1.5 years after surgery performed to treat an inverted papilloma, and the other with a history of hypertension and cerebral infarction. Both patients recovered from their surgeries. An average time of 2.5 years elapsed from the development of maxillary sinus cysts to the development of fungal rhinosinusitis. Conclusion According to these case reports, we speculate that the progression of fungal rhinosinusitis from harmless colonization to disease onset requires approximately one to three years and that the length of the process may be related to underlying diseases, surgical treatment, deficient autoimmune status, and abuse of hormone antibiotics and hormones. Additional data are needed to conduct relevant studies to appropriately prevent and treat fungal rhinosinusitis.

Disease has always been one of the main reasons for the decline of apple quality and yield, which directly harms the development of agricultural economy. Therefore, precise diagnosis of apple diseases and correct decision making are important measures to reduce agricultural losses and promote economic growth. In this paper, a novel Multi-scale Dense classification network is adopted to realize the diagnosis of 11 types of images, including healthy and diseased apple fruits and leaves. The diagnosis of different kinds of diseases and the same disease with different grades was accomplished. First of all, to solve the problem of insufficient images of anthracnose and ring rot, Cycle-GAN algorithm was applied to achieve dataset expansion on the basis of traditional image augmentation methods. Cycle-GAN learned the image characteristics of healthy apples and diseased apples to generate anthracnose and ring rot lesions on the surface of healthy apple fruits. The diseased apple images generated by Cycle-GAN were added to the training set, which improved the diagnosis performance compared with other traditional image augmentation methods. Subsequently, DenseNet and Multi-scale connection were adopted to establish two kinds of models, Multi-scale Dense Inception-V4 and Multi-scale Dense Inception-Resnet-V2, which facilitated the reuse of image features of the bottom layers in the classification neural networks. Both models accomplished the diagnosis of 11 different types of images. The classification accuracy was 94.31 and 94.74%, respectively, which exceeded DenseNet-121 network and reached the state-of-the-art level.

To achieve large-scale and high bandwidth environmental monitoring, this paper proposes a hybrid network structure based on the ZigBee network and Mesh network, which consists of the backbone network and nodes, branch network and nodes, and monitoring center. Both the backbone network node and branch network node with sensor information collection and wireless communication functions are designed. And a monitoring center is developed to display environmental information in real time. With the hybrid-mode network, the environment monitoring experiments were carried out to verify the feasibility of the system. The results show that compared to traditional environmental monitoring systems, the proposed system has the advantage of strong networking ability, large bandwidth, long transmission distance, and low cost.

Background: Growing evidence suggests that the bidirectional interactions between cancer cells and their surrounding environment namely the tumor microenvironment (TME), contributes to cancer progression, metastasis, and resistance to treatment. Intense investigation of Hippo pathway, which controls multiple central cellular function to tumorigenesis, was focused on cancer cells. However, the role of Hippo pathway in modulating tumor–stromal interactions in triple negative breast cancer remains largely unknow. This study therefore focused on revealing effects of Hippo-YAP/TAZ signaling to immune microenvironment.Methods: The correlation between Hippo/YAP signaling and the abundance of immune cells were estimated by Immune Cell Abundance Identifier. Clinical TNBC samples from 120 patients were analyzed to assess the correlation between TAZ expression and disease prognosis as well as tumor-infiltrating immune cells. Inflammatory immune profiles, bioinformatics analysis and chromatin immunoprecipitation were performed to identify the expression of immune-related genes that were regulated by TAZ. An in vitro co-culture system was applied to investigate the crosstalk between TNBC cells and tumor-associated macrophages (TAMs) modulated by the TAZ/interleukin 34 (IL-34) axis. In vivo tumor growth and metastasis models were used to evaluate the pro-tumor functions of TAZ, IL-34, and TAMs as well as the antitumor efficacy of anti-PD-L1 and IL-34/colony-stimulating factor 1 receptor (CSF-1R) blockade.Results: In TNBC patients, high activity of Hippo pathway was correlated with decreased number of T cells, upregulated TAM infiltration, and poor prognosis. TAZ could directly regulate IL-34 and PD-L1 expression and promote IL-34 secretion in TNBC cells, leading to increased TAM infiltration and distant metastasis. TAM-derived transforming growth factor beta 1 (TGF-β1) could also induce TAZ expression in TNBC cells, thus forming a positive feedback loop between TNBC cells and TAMs. Furthermore, targeting the TAZ/IL-34 axis through its CSF-1R inhibitor could dramatically decrease TAM infiltration and significantly improve anti-PD-L1 efficacy in inhibiting metastasis in TNBC.Conclusions: Activity of Hippo pathway was associated with worse disease outcomes in TNBC and could increase TAM infiltration through the TAZ/IL-34 axis, leading to an immunosuppressive microenvironment and impairing the treatment efficacy of anti-PD-L1. Thus, the TAZ/IL-34 axis can serve as a novel target for TNBC patients.

Key Points Question Are there any differences in prognoses or recurrence patterns associated with gastric neuroendocrine carcinoma, mixed adenoneuroendocrine carcinoma, or adenocarcinoma? Findings This cohort study included 3689 patients with resectable gastric adenocarcinoma, gastric neuroendocrine carcinoma, or gastric mixed adenoneuroendocrine carcinoma. Propensity score matching analysis found that, compared with gastric adenocarcinoma, gastric neuroendocrine carcinoma and gastric mixed adenoneuroendocrine carcinoma were independent risk factors associated with worse disease-free survival, postrecurrence survival, and distant recurrence outcomes. Meaning These findings suggest that different follow-up and treatment strategies should be developed to improve the long-term survival of patients with gastric neuroendocrine carcinoma or gastric mixed adenoneuroendocrine carcinoma, especially for patients with tumors penetrating into the subserosa or deeper layers and with lymph node metastasis., This cohort study examines differences in long-term survival and patterns of recurrence among patients with gastric neuroendocrine carcinoma, gastric mixed adenoneuroendocrine carcinoma, and gastric adenocarcinoma., Importance Gastric neuroendocrine carcinoma and mixed adenoneuroendocrine carcinoma are rare pathological types of gastric cancer, and there is a lack of multicenter studies comparing the prognosis and recurrence patterns of gastric neuroendocrine carcinoma, gastric mixed adenoneuroendocrine carcinoma, and gastric adenocarcinoma. Objective To compare the differences in long-term survival and patterns of recurrence among gastric neuroendocrine carcinoma, gastric mixed adenoneuroendocrine carcinoma, and gastric adenocarcinoma. Design, Setting, and Participants This cohort study included patients with resectable gastric neuroendocrine carcinoma and gastric mixed adenoneuroendocrine carcinoma at 23 hospitals in China from January 2006 to December 2016. In addition, patients with gastric adenocarcinoma were selected as controls. Propensity score–matched analysis was used to match pathological stage among the different pathological types, and disease-free survival (DFS), postrecurrence survival (PRS), and patterns of recurrence were examined. Data analysis was conducted from July 15, 2020, to October 21, 2020. Exposures Curative resection for gastric neuroendocrine carcinoma, gastric mixed adenoneuroendocrine carcinoma, and gastric adenocarcinoma. Main Outcomes and Measures The main outcomes were DFS and patterns of recurrence. Results A total of 3689 patients were analyzed (median [interquartile range] age, 62 [55-69] years; 2748 [74.5%] men), including 503 patients (13.6%) with gastric neuroendocrine carcinoma, 401 patients (10.9%) with gastric mixed adenoneuroendocrine carcinoma, and 2785 patients (75.5%) with gastric adenocarcinoma. After propensity score matching, 5-year DFS was 47.6% (95% CI, 42.7%-52.5%) for patients with gastric neuroendocrine carcinoma, compared with 57.6% (95% CI, 55.1%-60.1%) with gastric adenocarcinoma (P

Up to now, there is no method available for Model Checking (MC) some temporal actions via DNA computing. In this paper, we propose an approach which employees DNA molecules to address this problem. First, a temporal logic formula with actions can be rewritten into its normal form. Second, the existing DNA-computing-based algorithms are extended and employed to compute and return model checking results between this formula describing a property of a system and an automaton expressing a system. As a result, an approach for model checking temporal logics with actions via DNA computing is obtained. Our experiments demonstrate the effectiveness of the proposed method. In addition, an approach based on probe machine is also pioneered in this study.

Flexible and multifunctional textiles have potential applications in self-cleaning and portable electronic product applications, but the current problem that needs to be solved is to maintain their inherent breathability and flexibility while expanding other functional applications. Herein, we adopt the layer-by-layer assembly method to develop a multifunctional textile with superior asymmetric superhydrophobicity, excellent electromagnetic interference (EMI) shielding, outstanding photothermal conversion, and solar water evaporation. The synergistic effect of SiO2 nanoparticles/poly(dimethylsiloxane) (PDMS) and 1H,1H,2H,2H-perfluorooctyltriethoxysilane (PFOTES) endows the textile with a water contact angle of 160°. MXene provides high conductivity (1200 S/m) and EMI shielding effects (36 dB) for multifunctional textiles. In addition, the multifunctional textile exhibits excellent photothermal conversion, and satisfactory solar water evaporation efficiency (80%) and rate (1.22 kg/(m2 h)) under 1 sun. Therefore, the prepared multifunctional textile has great potential in multiscene applications.

With the revolution of smart industry, more and more Industrial Internet of Things (IIoT) devices as well as AI algorithms are deployed to achieve industrial intelligence. While applying computation-intensive deep learning on IIoT devices, however, it is challenging to meet the critical latency requirement for industrial manufacturing. Traditional wisdom resorts to the cloud-centric paradigm but still works either inefficiently or ineffectively due to the heavy transmission latency overhead. To address this challenge, we propose Boomerang, an on-demand cooperative DNN inference framework for edge intelligence under the IIoT environment. Boomerang exploits DNN right-sizing and DNN partition to execute DNN inference tasks with low latency as well as high accuracy. DNN right-sizing reshapes the amount of DNN computation via the early-exit mechanism so as to reduce the total runtime of DNN inference. DNN partition adaptively segments DNN computation between the IoT devices and the edge server in order to leverage hybrid computation resources to achieve DNN inference immediacy. Combining these two keys, Boomerang carefully selects the partition point and the exit point to maximize the performance while promising the efficiency requirement. To further reduce the manual overhead of model profiling at the install phase, we develop an advanced version of Boomerang with the DRL model, achieving end-to-end automatic DNN inference plan generation. The prototype implementation and evaluations demonstrate the effectiveness of Boomerang on both versions in achieving efficient edge intelligence for IIoT.

Polyurethane foam (PUF) is widely used in building insulation field but highly flammable. In an effort to develop an efficient way to reduce flammability and smoke release of PUF without sacrificing its inherent merits, a novel strategy has been proposed to decorate silica aerogels onto the surface of PUF to fabricate hierarchically porous SiO2/PUF composites. Due to the unique hierarchically porous structure, the resultant composites showed superior thermal insulation with a lower thermal conductivity of 0.0282 W/(m K). The introduction of silica aerogels also effectively improved the compressive strength, almost 220% of that of neat PUF. Notably, the SiO2/PUF composites were rendered self-extinguishing in vertical burning tests and had a high limiting oxygen index (LOI) value of 32.5%. Cone calorimetry (CC) tests revealed that the peak heat release rate (PHRR) and peak smoke production release (PSPR) of the SiO2/PUF composites were reduced by 40.4% and 45.6%, respectively. Particularly, the specific optical density (Ds) of the composites displayed as 55.7% reduction in the smoke density chamber tests, showing excellent smoke-suppression. The mechanism analysis suggested that a compact silica-rich hybrid barrier formed, preventing thermal degradation products and energy transfer during combustion. These results indicate SiO2/PUF composites have enormous potential as building insulation materials.

Liquid crystals (LCs) have already been widely used in reconfigurable microwave devices as an excellent phase change material for their electric tunable dielectric properties. An accurate value of ...

Intermediate crack debonding is a common failure mode of reinforced concrete beams strengthened in flexure with an externally bonded laminate (sheet or plate) of fiber-reinforced polymer. Many fini...

Real-time detection of apples in orchards is one of the most important methods for judging growth stages of apples and estimating yield. The size, colour, cluster density, and other growth characteristics of apples change as they grow. Traditional detection methods can only detect apples during a particular growth stage, but these methods cannot be adapted to different growth stages using the same model. We propose an improved YOLO-V3 model for detecting apples during different growth stages in orchards with fluctuating illumination, complex backgrounds, overlapping apples, and branches and leaves. Images of young apples, expanding apples, and ripe apples are initially collected. These images are subsequently augmented using rotation transformation, colour balance transformation, brightness transformation, and blur processing. The augmented images are used to create training sets. The DenseNet method is used to process feature layers with low resolution in the YOLO-V3 network. This effectively enhances feature propagation, promotes feature reuse, and improves network performance. After training the model, the performance of the trained model is tested on a test dataset. The test results show that the proposed YOLOV3-dense model is superior to the original YOLO-V3 model and the Faster R-CNN with VGG16 net model, which is the state-of-art fruit detection model. The average detection time of the model is 0.304 s per frame at 3000 × 3000 resolution, which can provide real-time detection of apples in orchards. Moreover, the YOLOV3-dense model can effectively provide apple detection under overlapping apples and occlusion conditions, and can be applied in the actual environment of orchards.

With the rapid development of computer vision and industrial technology, the requirements of the intelligent welding robots are increasing in the real industrial production. The traditional teaching-playback mode and the off-line programming mode cannot meet the automation demand and self-adaptive ability of welding robots. In order to improve the efficiency of welding robots, this paper proposes a novel 3-D path extraction method of weld seams based on a stereo-structured light sensor. Faced with the low efficiency of the line-structured light and the poor robustness of passive vision, the seam extraction based on a point cloud processing algorithm is proposed which could well adapt to the weld seams with different types and different groove sizes. Meanwhile, the position information and pose information of weld seam are established to serve 3-D path teaching of a welding robot. The experimental results show that the maximum path extraction error of V-type butt joint is less than 0.7 mm. The proposed scheme could well serve for the 3-D path teaching task before welding.

The complex dielectric constant is the intrinsic parameter of microwave material, and the accurate acquisition of its value is critical for its application. Generally, the dielectric properties of microwave materials are obtained under weak electromagnetic field. However, with the continuous development of high power microwave technology, the microwave field intensity around materials gradually increases, and the test results of the dielectric properties of materials under the weak electromagnetic field will not be able to characterize the test results under the high power environment. Therefore, a novel testing technology based on the resonator method is proposed, which provides an effective solution for the measurement and characterization of microwave dielectric properties of materials under strong microwave field. Based on the constructed system, the evolution rules of the microwave characteristics of four typical materials are obtained. In the experiment, the temperature of the samples is precisely monitored, and the evolution rules of the temperature characteristics of the dielectric properties of the samples under strong and weak microwave fields are compared. The comparison results demonstrate that the dielectric properties of materials under strong fields are related not only to the microwave heating effect but also to the mechanism of the non-thermal microwave effect.

The soft surface is defined by its anisotropic surface impedance, and it has the property of suppressing surface wave propagation for any polarization of the electric field. It can be used as a general design element for reducing scattering or controlling coupling between nearby antennas. In this letter, the suppression capabilities and bandwidth limitations of soft surfaces are analyzed using simulations to draw the following conclusions: First, a soft surface is as good as an ideal perfect magnetic conductor for suppressing vertically polarized surface currents; second, adding loss to the soft surface actually increases coupling between nearby antennas; third, the bandwidth of soft surfaces can be extended using active loading; and fourth, a soft surface boundary condition can suppress coupling below even the expected free-space wave coupling. Several simple examples are illustrated using simulations, including the effect of lossy substrates, designs for active loading, and the effects of soft surfaces on the contributions to coupling between nearby planar antennas.

Background The Insulin‐like growth factor‐1 (IGF‐1) is primarily synthesized by hepatocytes in a growth hormone (GH)‐dependent manner, it is also produced by bone and muscle. The effects of exercise on the associations between IGF‐1 levels and bone turnover markers (BTM) were found in the previous studies. However, the associations between the levels of IGF‐1 and BTM, liver function tests, and skeletal muscle markers in adults with general physical activity were not clear. Methods Ninety‐four participants were recruited from healthy survey. Blood samples were collected to analyze the levels of IGF‐1, total protein (TP), albumin (Alb), total bilirubin (T‐Bil), direct bilirubin (D‐Bil), aspartate aminotransferase (AST), alanine aminotransferase (ALT), alkaline phosphatase (ALP), bone alkaline phosphatase (BALP), lactate dehydrogenase (LDH), creatine kinase (CK), creatinine (CRTN), and glucose. Urine samples were collected to analyze the CRTN and deoxypyridinoline (Dpd) levels. Results The positively significant associations were found between the IGF‐1 levels and the levels of ALP, BALP, and CK, respectively. No significant associations were found between the IGF‐1 levels and the levels of TP, Alb, A/G, T‐Bil, D‐Bil, AST, ALT, LDH, glucose, urinary CRTN, urinary Dpd, and Dpd/CRTN ratios, respectively. Conclusion The serum IGF‐1 levels associated with the levels of skeletal muscle and bone formation markers (BFM), not the bone resorption markers under general physical activity in the healthy adults. The physician needs to consider the effects of bone formation and skeletal muscle markers on the IGF‐1 levels in the management of IGF‐1‐related disorders., In the healthy adults with general physical activity, the associations of the biomarkers of skeletal muscles, liver function and bone modulation with the circulating IGF‐1 levels were found: (1) The positively significant associations were found between the IGF‐1 levels and the levels of ALP, BALP and CK, respectively. (2) No significant associations were found between the IGF‐1 levels and the levels of AST, ALT, urinary CRTN, urinary Dpd, and Dpd/CRTN ratios respectively. (3) In conclusion, the serum IGF‐1 levels associated with the levels of skeletal muscle and bone formation markers, not the markers of bone resorption and liver function.

Background COVID-19 has caused increased stress, anxiety and depression with increased barriers to treatment. Mobile apps offer a potential solution, but there is no information on the quality of such apps recommended for COVID-19. This study aims to evaluate the quality of stress, anxiety and depression apps recommended for COVID-19. Methods A search was conducted to identify relevant apps on the iOS and Android platforms. 44 apps were evaluated using the Mobile App Rating Scale (MARS), and the American Psychiatric Association's app evaluation model for data privacy and security. Results Overall quality scores of iOS and Android apps were 3.69 ± 0.43 and 3.66 ± 0.47. Thirty percent had good/excellent overall scores. In general, the iOS and Android versions of the apps scored best for functionality (4.21 ± 0.48, 4.12 ± 0.53), followed by aesthetics (3.84 ± 0.50, 3.78 ± 0.56), information (3.39 ± 0.54, 3.40 ± 0.60), and engagement (3.31 ± 0.81, 3.34 ± 0.84). Over half (59%) shared personal information with third parties and 14% were compliant with data protection standards. Limitations Only free apps available in Singapore were evaluated. Our results are time sensitive due to addition, removal, and update of apps in the app stores, thus our results should be extrapolated with caution to apps from other countries and paid apps. Conclusion Apps that addressed all three conditions had the highest overall quality scores. The top ranked apps (Sanvello, Woebot, Happify, Youper, Bloom) were of good quality, but majority were of acceptable quality and had room for improvement. App developers are encouraged to use our findings to improve and develop better quality apps.

With the fast development of the power system, traditional manual inspection methods of a power transmission line (PTL) cannot supply the demand for high quality and dependability for power grid maintenance. Consequently, the automatic PTL inspection technology becomes one of the key research focuses. For the purpose of summarizing related studies on environment perception and control technologies of PTL inspection, technologies of three-dimensional (3D) reconstruction, object detection, and visual servo of PTL inspection are reviewed, respectively. Firstly, 3D reconstruction of PTL inspection is reviewed and analyzed, especially for the technology of LiDAR-based reconstruction of power lines. Secondly, the technology of typical object detection, including pylons, insulators, and power line accessories, is classified as traditional and deep learning-based methods. After that, their merits and demerits are considered. Thirdly, the progress and issues of visual servo control of inspection robots are also concisely addressed. For improving the automation degree of PTL robots, current problems of key techniques, such as multisensor fusion and the establishment of datasets, are discussed and the prospect of inspection robots is presented.

Redundant manipulators are suitable for working in narrow and complex environments due to their flexibility. However, a large number of joints and long slender links make it hard to obtain the accurate end-effector pose of the redundant manipulator directly through the encoders. In this paper, a pose estimation method is proposed with the fusion of vision sensors, inertial sensors, and encoders. Firstly, according to the complementary characteristics of each measurement unit in the sensors, the original data is corrected and enhanced. Furthermore, an improved Kalman filter (KF) algorithm is adopted for data fusion by establishing the nonlinear motion prediction of the end-effector and the synchronization update model of the multirate sensors. Finally, the radial basis function (RBF) neural network is used to adaptively adjust the fusion parameters. It is verified in experiments that the proposed method achieves better performances on estimation error and update frequency than the original extended Kalman filter (EKF) and unscented Kalman filter (UKF) algorithm, especially in complex environments.

Zhong-En Li,1– 3 Shi-Bao Lu,1– 3 Chao Kong,1– 3 Wen-Zhi Sun,1– 3 Peng Wang,1– 3 Si-Tao Zhang1,2 1Department of Orthopedics, Xuanwu Hospital Capital Medical University, Beijing, People’s Republic of China; 2National Clinical Research Center for Geriatric Diseases, Beijing, People’s Republic of China; 3Capital Medical University, Beijing, People’s Republic of ChinaCorrespondence: Shi-Bao Lu Email xuanwuspine@163.comBackground and Aim: The benefits of the enhanced recovery after surgery (ERAS) program to elderly patients have not been evaluated in lumbar fusion surgery. Compliance with the ERAS program is associated with prognosis. There is currently no adequate assessment about the importance of the individual components of ERAS program in lumbar fusion surgery. The aim of the study was to analyze the effect of compliance with our ERAS program and the relative importance of the individual ERAS program components among elderly patients undergoing lumbar fusion surgery.Methods: A retrospective case-review study was conducted from March 2018 to March 2020. The ERAS program for lumbar fusion surgery at our department was implemented. Overall compliance was found to be 92.9%, and this was used as a cutoff for dividing patients into higher compliance and lower compliance groups. Patient characteristics and clinical outcomes were compared between groups.Results: The overall compliance rate was 92.9%, distributing 91 patients into the higher compliance group and 169 patients into the lower compliance group. Patients with higher compliance were younger (p=0.045). The length of stay (LOS) of patients with higher compliance was significantly shorter than that of patients with lower compliance. Patients with higher compliance had significantly fewer complications (p=0.031). A multivariate analysis showed that surgical time (p=0.029), lower compliance (p=0.034), and early oral feeding (p=0.026) were predictors of any postoperative complications. On multivariate analysis, the following items remained correlated with prolonged LOS (LOS≥ 12 days): older age (p=0.010), lower compliance (p< 0.0001), early ambulation (p=0.018), and stick to discharge criteria (p=0.040).Conclusion: Low compliance with ERAS program among elderly patients undergoing lumbar fusion surgery was associated with a higher incidence of complications and prolonged LOS. The failure of early oral feeding was associated with increased complications, and the failure of early ambulation or sticking to discharge criteria was significantly more influential on prolonged LOS.Keywords: compliance, elderly, enhanced recovery after surgery; ERAS, lumbar fusion surgery

A novel way to design rectification antenna has been proposed in this document. Compare to the original rectification antenna, proposed structure does not require welding rectifiers such as diodes, replaced by semiconductor substrate. With the help of semiconductor substrate, we can get zero frequency energy directly. To demonstrate this concept, a 5.8 GHz Split Resonance Ring (SRR) surface antenna was designed, fabricated and tested, based on GaAs semiconductor substrate in this paper. As a result, a no-load DC voltage of 380mV was obtained using the rectification effect of the substrate, when the antenna is exposed to an environment with a power density of 2.5uW/cm2. At the same time, when a 19.9kOhm load is connected, the load power obtained is 768nW. Since the substrate size is 3*4 cm, the conversion efficiency is 2.56 %.

Background and aims: In this research, we aimed to construct a risk classification model to predict overall survival (OS) and locoregional surgery benefit in colorectal cancer (CRC) patients with distant metastasis.Methods: We selected a cohort consisting of 12741 CRC patients diagnosed with distant metastasis between 2010 and 2014, from the Surveillance, Epidemiology and End Results (SEER) database. Patients were randomly assigned into training group and validation group at the ratio of 2:1. Univariable and multivariable Cox regression models were applied to screen independent prognostic factors. A nomogram was constructed and assessed by the Harrell’s concordance index (C-index) and calibration plots. A novel risk classification model was further established based on the nomogram.Results: Ultimately 12 independent risk factors including race, age, marriage, tumor site, tumor size, grade, T stage, N stage, bone metastasis, brain metastasis, lung metastasis and liver metastasis were identified and adopted in the nomogram. The C-indexes of training and validation groups were 0.77 (95% confidence interval [CI] 0.73-0.81) and 0.75 (95% CI 0.72-0.78), respectively. The risk classification model stratified patients into three risk groups (low-, intermediate- and high-risk) with divergent median OS (low-risk: 36.0 months, 95% CI 34.1-37.9; intermediate-risk: 18.0 months, 95% CI 17.4-18.6; high-risk: 6.0 months, 95% CI 5.3-6.7). Locoregional therapies including surgery and radiotherapy could prognostically benefit patients in the low-risk group (surgery: hazard ratio [HR] 0.59, 95% CI 0.50-0.71; radiotherapy: HR 0.84, 95% CI 0.72-0.98) and intermediate risk group (surgery: HR 0.61, 95% CI 0.54-0.68; radiotherapy: HR 0.86, 95% CI 0.77-0.95), but not in the high-risk group (surgery: HR 1.03, 95% CI 0.82-1.29; radiotherapy: HR 1.03, 95% CI 0.81-1.31). And all risk groups could benefit from systemic therapy (low-risk: HR 0.68, 95% CI 0.58-0.80; intermediate-risk: HR 0.50, 95% CI 0.47-0.54; high-risk: HR 0.46, 95% CI 0.40-0.53).Conclusion: A novel risk classification model predicting prognosis and locoregional surgery benefit of CRC patients with distant metastasis was established and validated. This predictive model could be further utilized by physicians and be of great significance for medical practice.

The images in the low-light and narrow environment generally have the problems of non-uniform illumination, low contrast, and large random noise. Existing algorithms are mostly subject to the imaging effect of the original image, and are prone to failure in extremely weak light or strong reflective environments. This paper proposes an active and adaptive image enhancement method. First of all, this paper combines the distributed light source model and the difference in brightness between frames to complete active feedback fill light, which improves the quality of the input image. Then gamma correction, contrast limited adaptive histogram equalization and bilateral filter are designed to enhance image details and suppress noise amplification. Combined with fuzzy inference parameter self-adjustment method, algorithm adaptability is improved. Finally, through experimental verification, the method proposed in this paper can effectively enhance the images in the low-light and narrow environment.

Background and Aims: The tumor microenvironment can be divided into inflamed, immune-excluded and immune-desert phenotypes according to CD8+ T cell categories with differential programmed cell death protein 1 (PD-L1) expression. The study aims to construct a novel immunotype-based risk stratification model to predict postsurgical survival and adjuvant trans-arterial chemoembolization (TACE) response in patients with hepatocellular carcinoma (HCC). Methods: A total of 220 eligible HCC patients participated in this study. CD8 + T cell infiltration and PD-L1 expression mode were estimated by immunohistochemical staining. A risk stratification model was developed and virtualized by a nomogram that integrated these independent prognostic factors. The postoperative prognosis and adjuvant TACE benefits were evaluated with a novel immunotype-based risk stratification model. Results: A total of 220 patients were finally identified. Immune-desert, immune-excluded, and inflamed immunotypes represented 45%, 24%, and 31% of HCC, respectively. Univariate and multivariate analyses identified immunotype and PD-L1 expression mode as independent prognostic factors for overall survival time (OS) and recurrence-free survival time (RFS). The nomogram was constructed by integrating immunotype, PD-L1 expression, Barcelona Clinic Liver Cancer (BCLC) stage and tumor grade. The C-index was 0.794 in the training cohort and 0.813 in the validation cohort. A risk stratification system was constructed based on the nomogram classifying HCC patients into 3 risk groups. The average OS times in the low-risk, intermediate-risk and high-risk groups in all cohorts were 77.1 months (95% CI 71.4-82.9), 53.7 months (95% CI 48.2-59.2), and 25.6 months (95% CI 21.4-29.7), respectively. Further analysis showed that OS was significantly improved by adjuvant TACE in the low- and intermediate-risk groups (P=0.041 and P=0.010, respectively) but not in the high-risk group (P=0.398). Conclusion: A novel immunotype-based risk stratification model was built to predict postoperative prognosis and adjuvant TACE benefit in HCC patients. These tools can assist in building a more customized method of HCC treatment.

Background and aims Pyruvate kinase M2 (PKM2) is an essential regulator of the Warburg effect, but its biological function promoting immune escape of hepatocellular carcinoma (HCC) is unclear. Methods GEPIA web tool and immunohistochemistry (IHC) analysis were employed to evaluate the clinical relevance of PKM2 in HCC patients. Both in vitro CCK-8, colony formation, and transwell assays, and in vivo xenografts were performed to evaluate the malignancy of HCC cells. PKM2 and PD-L1 levels were examined by Western blot, qRT-PCR, and IHC. The role of PKM2 on in vivo immune response was also investigated. Results PKM2 was significantly upregulated in HCC and associated with a poor prognosis of HCC patients. Knockdown of PKM2 inhibited in vitro proliferation, migration, and invasion of HCC cells, as well as in vivo tumor growth. Strikingly, PKM2 showed a strong correlation with the expression of immune inhibitory cytokines and lymphocyte infiltration in HCC. The overexpression of PKM2 sensitized HCC to immune checkpoint blockade, which enhanced IFN-γ positive CD8 T cells in HCC mice models. Conclusion PKM2 might be a predictor and a potential therapeutic target for immune checkpoint inhibitors in HCC.

In this paper, an optical signal microwave measurement technique based on the resonance performance of a helical antenna is proposed. The helical antenna loaded with a cadmium sulfide (CdS) photoresistor is used as the core detection sensor. When the CdS photoresistor is irradiated, the resistance loaded on the helical antenna changes, which causes the microwave resonance performance to change accordingly. Therefore, according to the measurement of the microwave resonance, the optical signal can be successfully detected. In this experiment, the light source is a power tunable signal source with a working wavelength of 480nm. The minimum detection power is $\mathbf{2.5}\times \mathbf{10}^{-\mathbf{9}}\mathbf{W}$ , and the photosensitivity is $\mathbf{R}=\mathbf{0.034dB}/\mathbf{nW}$ .

Producing a 3D voxel from a single view by deep learning-based methods has garnered increasing attention. Several state-of-the-art works introduce the recurrent neural network(RNN) to fuse features and generate full volumetric occupancy. However, the inputs are unable to be fully exploited to improve the reconstruction due to long-term memory loss. And most of the works have considered using 3D supervision for the whole optimization to recover the full volume, but lack detailed silhouette supervision to refine the reconstruction process. To address these issues, an end-to-end object reconstruction network with scaling volume-view supervision is proposed. We introduce an auto-encoder 3D volume predicting network that takes a single arbitrary image as input and outputs a voxel occupancy grid. And a scaling volume-view supervision module, which uses up-sampling to zoom errors and increase penalties, is leveraged to improve both the global and local optimization. Extensive experimental analysis on ShapeNet dataset shows that our network has superior performance when the scaling volumeview supervision is involved and the deep residual module boosts the reconstruction performance and speeds up the optimization effectively.

To establish a novel nomogram to predict individual 1, 3, and 5 years disease-free survival (DFS) of patients with gastric neuroendocrine carcinoma/mixed adenoneuroendocrine carcinoma [(MA)NEC]. Among patients undergoing radical resection of gastric (MA)NEC, there is still a high tendency for relapse. A retrospective analysis of 777 patients with gastric (MA)NEC at 23 centers in China from 2004 to 2015 was performed. Based on the established nomogram, which included age, ASA, pT, pN and Ki67, the overall patients were divided into low-risk group (LRG) and high-risk group (HRG). The median follow-up time was 40 months (1–169 months). The C-index, AUC and time-ROC of the nomogram were significantly higher than that of the 8th edition AJCC and ENETS TNM staging systems. The 3-year DFS of patients in HRG generated by the nomogram was significantly lower than that in LRG (all patients: 35% vs 66.9%, p

Wafer bonding processing typically employs thermal energy to fuse two surfaces by stimulating atomic interdiffusion at high temperatures. However, we found that the fusion bonding of copper and silicon can occur at an extremely low temperature in cryo-electrochemical processing cooled by dry-ice (-20 °C) or even by liquid nitrogen (-70 °C). The results demonstrate that electrical energy can replace the thermal energy that must be used in semiconductor processes. The bonding phenomenon reproducibly occurred, and even the copper surface was not favorable for spontaneous bonding. Notably, the bonding strength of Cu/Si was very high. Even after forcibly inserting a razor at the bonding interface, a copper layer was split from the Cu host substrate to enable a transfer onto silicon. The secondary-ion mass spectrometry (SIMS) analysis revealed that the bonding was caused by nanoscale interdiffusion between surface copper and silicon atoms. We propose a possible mechanism where holes are driven into the bonding interface of Cu/Si under bias, positively charge the Cu atoms and form cations (i.e., surface activation). The electric field continuously drives Cu cations to bond with the dangling bonds on the mating silicon to form Si-Cu bonds. The Cu cations that arrive later can pass over the bonding interface and quickly diffuse into the silicon interstitials. This study of fusion bonding at -70 °C by electrochemistry-assisted interdiffusion instead of thermal energy has profound implications for wafer bonding processes and applications.