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ObjectiveTo evaluate the effect of malnutrition assessed by the Geriatric Nutritional Risk Index (GNRI) on major adverse cardiac and cerebrovascular events (MACCE) in the elderly patients after endovascular aortic aneurysm repair (EVAR).Materials and methodsThis was a retrospective cohort study of elderly patients who underwent EVAR in a tertiary hospital. Malnutrition status was assessed by the GNRI. The primary outcome was MACCE. The predictive ability of the GNRI was compared with both the Revised Cardiac Risk Index (RCRI) and the modified Frailty Index (mFI) using Receiver operating characteristic (ROC) curve.ResultA total of 453 patients underwent EVAR November 2015 and January 2020 was retrospectively analyzed, equally divided into three (low/medium/high) groups according to GNRI values which ranked from low to high. Five (1.10%) patients were lost in follow-up after surgery, and the median length of follow-up was 28.00 (15.00–47.00) months. The high GNRI values reduced length of hospital stay following EVAR in comparison to patients in low GNRI values group (β 9.67, 95% CI 4.01–23.32, p = 0.0113; adjusted β −1.96, 95% CI −3.88, −0.05, p = 0.0454). GNRI status was associated with a significantly increased risk of long-term mortality after EVAR (Medium GNRI, unadjusted HR 0.40, 95%CI 0.23–0.70, p = 0.0014; adjusted HR 0.47, 95%CI 0.26–0.84, p = 0.0107; high GNRI, 0.27 95%CI 0.14–0.55; p = 0.0003; adjusted HR 0.32 95%CI 0.15–0.68, p = 0.0029). Both medium and high GNRI values were linked to significantly reduced risks of MACCE compared to low GNRI score patients (Medium GNRI, unadjusted HR 0.34, 95%CI 0.13–0.88, p = 0.00265; adjusted HR 0.37, 95%CI 0.14–0.96, p = 0.0408; High GNRI, 0.26 95%CI 0.09–0.78; p = 0.0168; adjusted HR 0.21 95%CI 0.06–0.73, p = 0.0029). Compared with the RCRI and mFI, the GNRI had better discrimination in predicting long-term MACCE. An area under the curve (AUC) for GNRI mFI, and RCRI is 0.707, 0.614 and 0.588, respectively. (Z statistic, GNRI vs. mFI, p = 0.0475; GNRI vs. RCRI, p = 0.0017).ConclusionMalnutrition assessed by the GNRI may serve as a useful predictor of long-term MACCE in elderly patients after EVAR, with preferable discrimination abilities compared with both RCRI and mFI.

This study explored the intrinsic relationship between the quality traits and protein-related property changes during the solar drying of shrimps (Penaeus vannamei). During drying, the shrimp exhibited a gradual decline in L*a*b* values and a notable increase in the hardness and chewiness. Proteins were degraded and oxidized. Especially, the trichloroacetic acid-soluble peptide and carbonyl contents increased, whereas the total sulfhydryl content decreased. The proportions of different types of intramolecular bonds were significantly changed. The ionic and hydrogen bonds greatly decreased to 10.72% and 9.05% and the hydrophobic and disulfide bonds notably increased to 19.38% and 28.19%, indicating the changes in the spatial structure of the protein and its denaturation during the drying process. The Pearson's correlation analysis showed that protein degradation and denaturation greatly affected the textural properties and protein oxidation caused color changes. The result of this work provides a theoretical support for improving the quality of shrimp products.

Abstract Objective For traumatic lower extremity artery injury, it is unclear whether it is better to perform endovascular therapy (ET) or open surgical repair (OSR). This study aimed to compare the clinical outcomes of ET versus OSR for traumatic lower extremity artery injury. Methods The Medline, Embase, and Cochrane Databases were searched for studies. Cohort studies and case series reporting outcomes of ET or OSR were eligible for inclusion. Robins-I tool and an 18-item tool were used to assess the risk of bias. The primary outcome was amputation. The secondary outcomes included fasciotomy or compartment syndrome, mortality, length of stay and lower extremity nerve injury. We used the random effects model to calculate pooled estimates. Results A total of 32 studies with low or moderate risk of bias were included in the meta-analysis. The results showed that patients who underwent ET had a significantly decreased risk of major amputation (OR = 0.42, 95% CI 0.21–0.85; I2=34%) and fasciotomy or compartment syndrome (OR = 0.31, 95% CI 0.20–0.50, I2 = 14%) than patients who underwent OSR. No significant difference was observed between the two groups regarding all-cause mortality (OR = 1.11, 95% CI 0.75–1.64, I2 = 31%). Patients with ET repair had a shorter length of stay than patients with OSR repair (MD=-5.06, 95% CI -6.76 to -3.36, I2 = 65%). Intraoperative nerve injury was just reported in OSR patients with a pooled incidence of 15% (95% CI 6%–27%). Conclusion Endovascular therapy may represent a better choice for patients with traumatic lower extremity arterial injury, because it can provide lower risks of amputation, fasciotomy or compartment syndrome, and nerve injury, as well as shorter length of stay.

Introduction Endovascular therapy has emerged as a prominent strategy for managing femoropopliteal peripheral artery disease, offering acceptable safety and efficacy compared with open surgical bypass. Both paclitaxel-eluting stents and heparin-bonded covered stents have exhibited enhanced clinical outcomes compared with bare metal stents. However, there is currently a lack of level I evidence comparing the safety and efficacy of paclitaxel-eluting stents and heparin-bonded covered stents. Therefore, the primary objective of this study is to systematically evaluate the efficacy and safety outcomes of these two types of stents.Methods and analysis The ELITE trial is a prospective, multicentre, parallel, randomised controlled trial. A total of 450 patients will be recruited. The primary endpoints of the study include primary patency at 1 year post-index procedure.Ethics and dissemination Ethical approval for this study was obtained from the Ethics Committee of West China Hospital of Sichuan University (approval number: 2023–1186). The results will be submitted to a major clinical journal for peer review and publication.Trial registration ELITE trial was registered on 27 September 2023 in the Chinese Clinical Trials Registry (ChiCTR2300076236).

Bimanual coordination is important for developing a natural motor brain-computer interface (BCI) from electroencephalogram (EEG) signals, covering the aspects of bilateral arm training for rehabilitation, bimanual coordination for daily-life assistance, and also improving the multidimensional control of BCIs. For the same task targets of both hands, simultaneous and sequential bimanual movements are two different bimanual coordination manners. Planning and performing motor sequences are the fundamental abilities of humans, and it is more natural to execute sequential movements compared to simultaneous movements in many complex tasks. However, to date, for these two different manners in which two hands coordinated to reach the same task targets, the differences in the neural correlate and also the feasibility of movement discrimination have not been explored. In this study, we aimed to investigate these two issues based on a bimanual reaching task for the first time. Finally, neural correlates in the view of the movement-related cortical potentials, event-related oscillations, and source imaging showed unique neural encoding patterns of sequential movements. Besides, for the same task targets of both hands, the simultaneous and sequential bimanual movements were successfully discriminated in both pre-movement and movement execution periods. This study revealed the neural encoding patterns of sequential bimanual movements and presented its values in developing a more natural and good-performance motor BCI.

Objective: Hypertension is a major risk factor for the type B aortic dissection (TBAD), while many patients do not manage or regulate their hypertension consistently, leading to stable or unstable hypertension. Currently, the effects of stable and unstable hypertension on the biomechanical properties of the aorta remain unclear. The objective was to identify a blood pressure state that represents a greater risk for TBAD development. Methods: A total of 183 samples (108 axial and 75 circumferential) were divided into three groups. Fatigue tensile tests were carried out to simulate normotension, stable hypertension, and unstable hypertension conditions, respectively. Uniaxial tensile tests were performed; thus, the elastic modulus, energy loss, and the peeling force were assessed to evaluate the biomechanical properties. Results: Compared with normal blood pressure, the modulus of elastic fibers decreased under stable hypertension (0.05 ± 0.02 MPa vs. 0.11 ± 0.03 MPa, p < 0.001) and unstable hypertension (0.08 ± 0.02 MPa, p = 0.008), while collagen fibers increased under stable hypertension (2.14 ± 0.51 MPa vs. 1.10 ± 0.24 MPa, p < 0.001) but decreased under unstable hypertension (0.52 ± 0.14 MPa, p < 0.001) in the axial direction. Similar trends were observed circumferentially. Energy loss was highest under unstable hypertension (0.16 ± 0.03 vs. 0.08 ± 0.03, p < 0.001). Peeling force was significantly reduced under stable hypertension (81.69 ± 12.72 N/m vs. 111.10 ± 27.65 N/m, p < 0.001) and further under unstable hypertension (71.37 ± 16.13 N/m, p < 0.001). Conclusions: Stable and unstable hypertension significantly impair the biomechanical properties of the aortic wall, with unstable hypertension leading to greater damage. Hypertensive patients are recommended to strictly follow medical advice to control blood pressure to avoid a higher risk of TBAD due to improper blood pressure management.

IntroductionDyslipidemia is common in patients with abdominal aortic aneurysm (AAA). However, there is insufficient research on the impact of dyslipidemia on the postoperative outcomes of patients with AAA after endovascular aortic aneurysm repair (EVAR). This study aimed to determine the impact of dyslipidemia on the prognosis of patients with AAA treated with EVAR.MethodWe retrospectively reviewed patients with AAA who underwent EVAR at our hospital between 2010 and 2020. The baseline characteristics and prognoses of patients in the dyslipidemia and non-dyslipidemia groups were analyzed.ResultsA total of 641 patients were included; the prevalence of dyslipidemia in patients with AAA was 42.3% (271/641), and the mean follow-up time was 63.37 ± 26.49 months. The prevalence of diabetes (10.0% vs. 15.1%, P = 0.050), peripheral arterial disease (17.3% vs. 25.8%, P = 0.018), and chronic kidney disease (3.0% vs. 6.3%, P = 0.043) was higher in the dyslipidemia group. The three-year all-cause mortality rate after EVAR was 9.98% (64/641), and there was no difference in the incidence of all-cause mortality (10.27% vs. 9.59%, P = 0.778) between the two groups. A total of 36 (5.62%) major adverse cardiovascular and cerebrovascular events (MACCEs) were observed within 3 years and were more common in patients with dyslipidemia (2.97% vs. 9.59%, P

A variety of factors cause a reduction in remote sensing image resolution. Unlike super-resolution (SR) reconstruction methods with single degradation assumption, multi-degradation SR methods aim to learn the degradation kernel from low-resolution (LR) images and reconstruct high-resolution (HR) images more suitable for restoring the resolution of remote sensing images. However, existing multi-degradation SR methods only utilize the given LR images to learn the representation of the degradation kernel. The mismatches between the estimated degradation kernel and the real-world degradation kernel lead to a significant deterioration in performance of these methods. To address this issue, we design a reconstruction features-guided kernel correction SR network (RFKCNext) for multi-degradation SR reconstruction of remote sensing images. Specifically, the proposed network not only utilizes LR images to extract degradation kernel information but also employs features from SR images to correct the estimated degradation kernel, thereby enhancing the accuracy. RFKCNext utilizes the ConvNext Block (CNB) for global feature modeling. It employs CNB as fundamental units to construct the SR reconstruction subnetwork module (SRConvNext) and the reconstruction features-guided kernel correction network (RFGKCorrector). The SRConvNext reconstructs SR images based on the estimated degradation kernel. The RFGKCorrector corrects the estimated degradation kernel by reconstruction features from the generated SR images. The two networks iterate alternately, forming an end-to-end trainable network. More importantly, the SRConvNext utilizes the degradation kernel estimated by the RFGKCorrection for reconstruction, allowing the SRConvNext to perform well even if the degradation kernel deviates from the real-world scenario. In experimental terms, three levels of noise and five Gaussian blur kernels are considered on the NWPU-RESISC45 remote sensing image dataset for synthesizing degraded remote sensing images to train and test. Compared to existing super-resolution methods, the experimental results demonstrate that our proposed approach achieves significant reconstruction advantages in both quantitative and qualitative evaluations. Additionally, the UCMERCED remote sensing dataset and the real-world remote sensing image dataset provided by the “Tianzhi Cup” Artificial Intelligence Challenge are utilized for further testing. Extensive experiments show that our method delivers more visually plausible results, demonstrating the potential of real-world application.

Remote sensing images usually contain abundant targets and complex information distributions. Consequently, networks are required to model both global and local information in the super-resolution (SR) reconstruction of remote sensing images. The existing SR reconstruction algorithms generally focus on only local or global features, neglecting effective feedback for reconstruction errors. Therefore, a Global Residual Multi-attention Fusion Back-projection Network (SRBPSwin) is introduced by combining the back-projection mechanism with the Swin Transformer. We incorporate a concatenated Channel and Spatial Attention Block (CSAB) into the Swin Transformer Block (STB) to design a Multi-attention Hybrid Swin Transformer Block (MAHSTB). SRBPSwin develops dense back-projection units to provide bidirectional feedback for reconstruction errors, enhancing the network’s feature extraction capabilities and improving reconstruction performance. SRBPSwin consists of the following four main stages: shallow feature extraction, shallow feature refinement, dense back projection, and image reconstruction. Firstly, for the input low-resolution (LR) image, shallow features are extracted and refined through the shallow feature extraction and shallow feature refinement stages. Secondly, multiple up-projection and down-projection units are designed to alternately process features between high-resolution (HR) and LR spaces, obtaining more accurate and detailed feature representations. Finally, global residual connections are utilized to transfer shallow features during the image reconstruction stage. We propose a perceptual loss function based on the Swin Transformer to enhance the detail of the reconstructed image. Extensive experiments demonstrate the significant reconstruction advantages of SRBPSwin in quantitative evaluation and visual quality.

Abstract Development of suitable hole transport materials is vital for perovskite solar cells (PSCs) to diminish the energy barrier and minimize the potential loss. Here, a low-cost hole transport molecule named SFX-POCCF3 (23.72 $/g) is designed with a spiro[fluorene-9,9'-xanthene] (SFX) core and terminated by trifluoroethoxy units. Benefiting from the suitable energy level, high hole mobility, and better charge extraction and transport, the PSCs based on SFX-POCCF3 exhibit improved open-circuit voltage by 0.02 V, therefore, the PSC device based on SFX-POCCF3 exhibits a champion PCE of 21.48%, which is comparable with the control device of Spiro-OMeTAD (21.39%). More importantly, the SFX-POCCF3 based PSC possesses outstanding light stability, which retains 95% of the initial efficiency after about 1,000 h continuous light soaking, which is in accordance with the result continuous output at maximum power point. Whereas, Spiro-OMeTAD witnesses a rapid decrease to 80% of its original efficiency after 100 h light soaking. This work demonstrated that an efficient alignment of energy levels between HTL and perovskite will lead to significant highly efficient PSCs with remarkably enhanced light stability.

Introduction The best lifestyle for small abdominal aortic aneurysms (sAAA) is essential for its conservative management. Physical exercise can improve the cardiopulmonary function of the patients, but it remains unclear which specific type of exercise is most beneficial for individuals with sAAA. The current study was designed to investigate the effect of physician-guided enhanced physical exercise programme on the aorto-cardiac haemodynamic environment, aneurysm sac wall, cardiac function and growth rate of sAAA by multimodality MRI.Methods and analysis AAA MOVE study is a prospective, parallel, equivalence, randomised controlled trial. Eligible individuals will be recruited if they are diagnosed with sAAA (focal dilation of abdominal aorta with maximum diameter

The control of moisture content (MC) is essential in the drying of shrimp, directly impacting its quality and shelf life. This study aimed to develop an accurate method for determining shrimp MC by integrating hyperspectral imaging (HSI) with electronic nose (E-nose) technology. We employed three different data fusion approaches: pixel-, feature-, and decision-fusion, to combine HSI and E nose data for the prediction of shrimp MC. We developed partial least squares regression (PLSR) models for each method and compared their performance in terms of prediction accuracy. The decision fusion approach outperformed the other methods, producing the highest determination coefficients for both calibration (0.9595) and validation sets (0.9448). Corresponding root-mean square errors were the lowest for the calibration set (0.0370) and validation set (0.0443), indicating high prediction precision. Additionally, this approach achieved a relative percent deviation of 3.94, the highest among the methods tested. The findings suggest that the decision fusion of HSI and E nose data through a PLSR model is an effective, accurate, and efficient method for evaluating shrimp MC. The demonstrated capability of this approach makes it a valuable tool for quality control and market monitoring of dried shrimp products.

From quantum communications to quantum computing, single-photon emitters (SPEs) are essential components of numerous quantum technologies. Two-dimensional (2D) materials have especially been found to be highly attractive for the research into nanoscale light–matter interactions. In particular, localized photonic states at their surfaces have attracted great attention due to their enormous potential applications in quantum optics. Recently, SPEs have been achieved in various 2D materials, while the challenges still remain. This paper reviews the recent research progress on these SPEs based on various 2D materials, such as transition metal dichalcogenides (TMDs), hexagonal boron nitride (hBN), and twisted-angle 2D materials. Additionally, we summarized the strategies to create, position, enhance, and tune the emission wavelength of these emitters by introducing external fields into these 2D system. For example, pronounced enhancement of the SPEs’ properties can be achieved by coupling with external fields, such as the plasmonic field, and by locating in optical microcavities. Finally, this paper also discusses current challenges and offers perspectives that could further stimulate scientific research in this field. These emitters, due to their unique physical properties and integration potential, are highly appealing for applications in quantum information and communication, as well as other physical and technological fields.

Detecting objects, particularly naval mines, on the seafloor is a complex task. In naval mine countermeasures (MCM) operations, sidescan or synthetic aperture sonars have been used to search large areas. However, a single sensor cannot meet the requirements of high-precision autonomous navigation. Based on the ORB-SLAM3-VI framework, we propose ORB-SLAM3-VIP, which integrates a depth sensor, an IMU sensor and an optical sensor. This method integrates the measurements of depth sensors and an IMU sensor into the visual SLAM algorithm through tight coupling, and establishes a multi-sensor fusion SLAM model. Depth constraints are introduced into the process of initialization, scale fine-tuning, tracking and mapping to constrain the position of the sensor in the z-axis and improve the accuracy of pose estimation and map scale estimate. The test on seven sets of underwater multi-sensor sequence data in the AQUALOC dataset shows that, compared with ORB-SLAM3-VI, the ORB-SLAM3-VIP system proposed in this paper reduces the scale error in all sequences by up to 41.2%, and reduces the trajectory error by up to 41.2%. The square root has also been reduced by up to 41.6%.

Infrared video target detection is a fundamental technology within infrared warning and tracking systems. In long-distance infrared remote sensing images, targets often manifest as circular spots or even single points. Due to the weak and similar characteristics of the target to the background noise, the intelligent detection of these targets is extremely complex. Existing deep learning-based methods are affected by the downsampling of image features by convolutional neural networks, causing the features of small targets to almost disappear. So, we propose a new infrared video weak-target detection network based on central point regression. We focus on suppressing the image background by fusing the different features between consecutive frames with the original image features to eliminate the background’s influence. We also employ high-resolution feature preservation and incorporate a spatial–temporal attention module into the network to capture as many target features as possible and improve detection accuracy. Our method achieves superior results on the infrared image weak aircraft target detection dataset proposed by the National University of Defense Technology, as well as on the simulated dataset generated based on real-world observation. This demonstrates the efficiency of our approach for detecting weak point targets in infrared continuous images.

Motor brain-computer interface (BCI) refers to the BCI that decodes voluntary motion intentions from brain signals directly and outputs corresponding control commands without activating peripheral nerves and muscles. Motor BCIs can be used for the restoration, compensation, and augmentation of motor function by activating the neuromuscular circuit and facilitating neural plasticity. The essential applications of motor BCIs include neurorehabilitation and daily-life assistance for motor-impaired patients. In recent years, studies on motor BCIs mainly concentrate on neural signatures, movement decoding, and its applications. In this review, we aim to provide a comprehensive review of the state-of-the-art research of electroencephalography (EEG) signals-based motor BCIs for the first time. We also aim to give some insights into advancing motor BCIs to a more natural and practical application scenario. In particular, we focus on the motor BCIs for the movements of the upper limbs. Specifically, the experimental paradigms, techniques, and application systems of upper-limb BCIs are reviewed. Several vital issues in developing more natural and practical upper-limb motor BCIs, including developing target-users-oriented, distraction-robust, and multi-limbs motor BCIs, and applying fusion techniques to promote the natural and practical motor BCIs, are discussed.

Motor brain-computer interface (BCI) can intend to restore or compensate for central nervous system functionality. In the motor-BCI, motor execution (ME), which relies on patients’ residual or intact movement functions, is a more intuitive and natural paradigm. Based on the ME paradigm, we can decode voluntary hand movement intentions from electroencephalography (EEG) signals. Numerous studies have investigated EEG-based unimanual movement decoding. Moreover, some studies have explored bimanual movement decoding since bimanual coordination is important in daily-life assistance and bilateral neurorehabilitation therapy. However, the multi-class classification of the unimanual and bimanual movements shows weak performance. To address this problem, in this work, we propose a neurophysiological signatures-driven deep learning model utilizing the movement-related cortical potentials (MRCPs) and event-related synchronization/ desynchronization (ERS/D) oscillations for the first time, inspired by the finding that brain signals encode motor-related information with both evoked potentials and oscillation components in ME. The proposed model consists of a feature representation module, an attention-based channel-weighting module, and a shallow convolutional neural network module. Results show that our proposed model has superior performance to the baseline methods. Six-class classification accuracies of unimanual and bimanual movements achieved 80.3%. Besides, each feature module of our model contributes to the performance. This work is the first to fuse the MRCPs and ERS/D oscillations of ME in deep learning to enhance the multi-class unimanual and bimanual movements’ decoding performance. This work can facilitate the neural decoding of unimanual and bimanual movements for neurorehabilitation and assistance.

Fibroblast growth factor receptors (FGFRs) play an important role in the regulation of cell proliferation, migration and differentiation, while the juxtamembrane domain (JMD) of FGFRs is the key in mediating these transmembrane signal transduction processes. Here, we expressed and purified the JMD (398K-470R) of FGFR1 with the presence of transmembrane domain (377I-397Y). The results from nuclear magnetic resonance (NMR) chemical shift analysis demonstrate that the main structure of JMD is disordered. Yet, the N-terminus of JMD was observed to form a short α-helix upon introducing negatively charged lipid 1,2-dioleoyl-sn-glycero-3-phospho-L-serine (DOPS) into its membrane mimic bicelles. Moreover, the N-terminus of JMD interacts with FRS2α, which is a substrate 2α of FGFR. Hence, we propose a model that FGFR1-JMD may interact with FRS2α and negatively charged lipids competitively. Our study provides a new understanding on the role of the JMD of FGFRs.

Endovascular aortic repair (EVAR) has been increasingly used to treat abdominal aortic aneurysm (AAA) due to its lower mortality and shorter recovery time. However, the complications caused by renal artery coverage remained a major concern. In this study, three types of renal orientation and five types of partial renal coverage degree were adopted to numerically investigate the flow pattern, wall shear stress distribution, and blood perfusion during a cardiac cycle. The results show that as for the mild partial coverage (=

With the continuous advancement of enterprise digitization, insider threats have become one of the primary cybersecurity concerns for organizations. Therefore, it is of great significance to develop an effective insider threat detection mechanism to ensure the security of enterprises. Most methods rely on artificial feature engineering and input the extracted user behavior features into a clustering-based unsupervised machine learning model for insider threat detection. However, feature extraction is independent of clustering-based unsupervised machine learning. As a result, user behavior features are not the most appropriate for clustering-based unsupervised machine learning, and thus, they reduce the insider threat detection accuracy. This paper proposes an insider threat detection method based on the deep clustering of multi-source behavioral events. On the one hand, the proposed method constructs an end-to-end deep clustering network and automatically learns the user behavior feature expression from multi-source behavioral event sequences. On the other hand, a deep clustering objective function is presented to jointly optimize the learning of feature representations and the clustering task for insider threat detection. This optimization can adjust the optimal user behavior features for the clustering model to improve the insider threat detection accuracy. The experimental results show that the proposed end-to-end insider threat detection model can accurately identify insider threats based on abnormal multi-source user behaviors in enterprise networks.

Abstract Amyloid precursor protein (APP) is a transmembrane protein that plays critical role in the pathogenesis of Alzheimer's disease (AD). It is also involved in many types of cancers. Increasing evidence has shown that the tyrosine phosphorylation site Y682 in the intracellular tail of APP is crucial for APP function. Here, we report that Vav2, a guanine nucleotide exchange factor (GEF) for Rho family GTPase, is a novel interaction partner of APP. We found that Vav2-SH2 domain was able to bind directly to the Y682-phosphorylated intracellular tail of APP through isothermal titration calorimetry and NMR titrating experiments. The crystal structure of Vav2-SH2 in complex with an APP-derived phosphopeptide was determined to understand the structural basis of this recognition specificity. The interaction of APP and Vav2 in a full-length manner was further confirmed in cells by GST pull-down, co-immunoprecipitation and immunofluorescence staining experiments. In addition, we found overexpression of Vav2 could inhibit APP degradation and markedly increase the protein levels of APP and its cleavage productions in 20E2 cells, and this function of Vav2 required a functional SH2 domain.

Abstract Drought is a major abiotic stress that harms plant cell physiology and limits the growth and productivity of crops. Maize (Zea mays L.), one of the most drought-susceptible crops, is a major food source for humans and an important resource for industrial bioenergy production; therefore, understanding the mechanisms of the drought response is essential for maize improvement. Using isotopic tagging relative quantitation (iTRAQ)-based protein labeling technology, we detected the proteomic changes in maize leaves under drought stress. Among the 3063 proteins that were identified, the abundance of 214 and 148 proteins increased and decreased, respectively, after three days of drought treatment. These differentially abundant proteins (DAPs) were mainly involved in cell redox homeostasis, cell wall organization, photosynthesis, abscisic acid biosynthesis, and stress-response processes. Furthermore, some of the DAP abundances still differed from the control six days after the drought treatment, most of which were molecular chaperones, heat shock proteins, metabolism-related enzymes, hydrolases, and transmembrane signal receptors. The expression level of some DAPs returned to normal when the water supply was restored, but for others it did not. A significant correlation between the protein and transcript levels was observed following an RT-qPCR analysis. Finally, our research provides insights into the overall mechanism of drought-stress tolerance, and important information for breeding of drought-tolerant maize.

Summary: Striking antibody evasion by emerging circulating severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) variants drives the identification of broadly neutralizing antibodies (bNAbs). However, how a bNAb acquires increased neutralization breadth during antibody evolution is still elusive. Here, we identify a clonally related antibody family from a convalescent individual. One of the members, XG005, exhibits potent and broad neutralizing activities against SARS-CoV-2 variants, while the other members show significant reductions in neutralization breadth and potency, especially against the Omicron sublineages. Structural analysis visualizing the XG005-Omicron spike binding interface reveals how crucial somatic mutations endow XG005 with greater neutralization potency and breadth. A single administration of XG005 with extended half-life, reduced antibody-dependent enhancement (ADE) effect, and increased antibody product quality exhibits a high therapeutic efficacy in BA.2- and BA.5-challenged mice. Our results provide a natural example to show the importance of somatic hypermutation during antibody evolution for SARS-CoV-2 neutralization breadth and potency.

The integration of multiple sensors is a crucial and emerging trend in the development of autonomous driving technology. The depth image obtained by stereo matching of the binocular camera is easily influenced by environment and distance. The point cloud of LiDAR has strong penetrability. However, it is much sparser than binocular images. LiDAR-stereo fusion can neutralize the advantages of the two sensors and maximize the acquisition of reliable three-dimensional information to improve the safety of automatic driving. Cross-sensor fusion is a key issue in the development of autonomous driving technology. This study proposed a real-time LiDAR-stereo depth completion network without 3D convolution to fuse point clouds and binocular images using injection guidance. At the same time, a kernel-connected spatial propagation network was utilized to refine the depth. The output of dense 3D information is more accurate for autonomous driving. Experimental results on the KITTI dataset showed that our method used real-time techniques effectively. Further, we demonstrated our solution's ability to address sensor defects and challenging environmental conditions using the p-KITTI dataset.

Introduction Postoperative renal function decline is a major concern for thoracic endovascular aortic repair (TEVAR) and endovascular abdominal aortic repair (EVAR). Diluting contrast medium in the power injector may be helpful in reducing the risk of contrast-induced nephropathy, but it can also blur fluoroscopic vision during surgery. The quality of the current evidence is very low; thus, this study is designed to investigate the effect of contrast dilution in the power injector on renal function changes in patients after endovascular aortic repair.Method and analysis The study is a prospective, single-blind, parallel, non-inferiority, randomised controlled trial with two independent cohorts: Cohort TEVAR and EVAR. Individuals will enter the appropriate cohort based on clinical interviews if they meet the eligibility criteria. Participants in Cohort TEVAR and EVAR will be randomly allocated to the intervention group (diluting contrast medium to 50% in the power injector) and control group (pure contrast medium in the power injector) separately in a 1:1 ratio. The primary study points consist of the proportion of patients who develop acute kidney injury within 48 hours after TEAVR or EVAR (first stage) and freedom of major adverse kidney events at 12 months after TEAVR or EVAR (second stage). The safety endpoint is freedom of all types of endoleaks at 30 days after TEVAR or EVAR. Follow-up will be conducted at 30 days and 12 months after intervention.Ethics and dissemination The trial was approved by the Ethics Committee on Biomedical Research, West China Hospital of Sichuan University (approval number: 20201290). The results of the study will be disseminated through publications in peer-reviewed journals and presentations at academic conferences.Trial registration number Chinese Clinical Trial Registry (ChiCTR2100042555).

Abstract Background Covered stent has become one of the mainstream therapies for aortoiliac obstructive disease (AIOD), with a higher patency rate than bare metal stent. Covered balloon-expandable (CBE) stent can be placed more accurately with higher a radial support force, while covered self-expanding (CSE) stent has greater elasticity and higher trackability. However, there is no level I evidence regarding the comparison safety and efficacy between the CSE stent and CBE stent in AIOD to date. Therefore, this study aims to compare the efficacy and safety of CBE stent (BARD®LIFESTREAM™) and CSE stent (GORE® VIABAHN™) in AIOD. Methods This trial is a prospective, single-center, parallel, noninferiority, randomized controlled trial. A total of 106 patients will be enrolled and these patients will be randomized to either the CBE stent group or the CSE stent group. The primary end point of the study is the occurrence of target lesion revascularization (TLR) at 12 months after the intervention. Discussion To our knowledge, the ballooN sElf cOver steNt AorToiliAc occuLusive (NEONATAL) trial is the first RCT to compare CBE and CSE stent in AIOD patients. The main aim is to compare the TLR of the target lesion between CBE stent and CSE stent at 12 months post-procedure. The results of clinical trials may contribute to establishing a strategic guideline for choosing the optimal type of covered stent in the treatment of AIOD patients. Trial registration Chinese Clinical Trials Registry ChiCTR2100046734. Registered on 27 May 2021

The point cloud is disordered and unstructured, and it is difficult to extract detailed features. The detailed part of the target shape is difficult to complete in the point cloud completion task. It proposes a point cloud completion network (BCA-Net) focusing on detail reconstruction, which can reduce noise and refine shapes. Specifically, it utilizes residual deformation architecture to avoid error points. The break and recombine refinement method is used to recover complete point cloud details. In addition, it proposes a bilateral confidence aggregation unit based on recurrent path aggregation to refine the coarse point cloud shape using multiple gating. Our experiments on the ShapeNet and Complete3D datasets demonstrate that our network performs better than other point cloud completion networks.

Long-lived ionic quantum states known as metastable electronic states in highly charged ions (HCIs) are of great interest in fundamental physics. Especially, it generates transitions with very narrow natural linewidth, which is a promising candidate for use in the next-generation HCI atomic clocks to reach an accuracy below 10^{−19}. A recent experiment [Nature (London) 581, 42 (2020)10.1038/s41586-020-2221-0] used Penning trap mass spectrometry to measure the energy of an extremely long-lived metastable electronic state, thus opening doors to search for HCI clock transitions. Building upon prior research, this study introduces an experimental proposal with the goal of measuring lifetimes of the metastable states beyond seconds. Our approach employs a sequential pulse-and-phase measurement scheme, allowing for direct observations of the decay processes from metastable electronic states through single-ion mass spectrometry in a Penning trap. This measurement poses a significant challenge to conventional techniques like fluorescence detection. To demonstrate the effectiveness of this method, we conducted a comprehensive simulation under real experimental conditions, yielding promising results in a specific scenario. Two suitable candidates are proposed for testing this method, and the state-of-the-art MCDHF theory are employed for accurate energy levels and transition rate calculations. Some future prospects in the experimental determinations of a wide range of energy and lifetimes of long-lived metastable electronic states, probing hyperfine and magnetic quenching effects on high-order forbidden transitions and search for high-quality HCI clock transitions are discussed.

The continuous decoding of human movement intention based on electroencephalogram (EEG) signals is valuable for developing a more natural motor augmented or assistive system instead of its discrete classifications. The classic center-out paradigm has been widely used to study discrete and continuous hand movement parameter decoding. However, when applying it in studying continuous movement decoding, the classic paradigm needs to be improved to increase the decoding performance, especially generalization performance. In this paper, we first discuss the limitations of the classic center-out paradigm in exploring the hand movement’s continuous decoding. Then, an improved paradigm is proposed to enhance the continuous decoding performance. Besides, an adaptive decoder-ensemble framework is developed for continuous kinematic parameter decoding. Finally, with the improved center-out paradigm and the ensemble decoding framework, the average Pearson’s correlation coefficients between the predicted and recorded movement kinematic parameters improve significantly by about 75 percent for the directional parameters and about 10 percent for the non-directional parameters. Furthermore, its generalization performance improves significantly by about 20 percent for the directional parameters. This study indicates the advantage of the improved paradigm in predicting the hand movement’s kinematic information from low-frequency scalp EEG signals. It can advance the applications of the noninvasive motor brain-computer interface (BCI) in rehabilitation, daily assistance, and human augmentation areas.

Accurate and reliable wave significant wave height(SWH) prediction is an important task for marine and engineering applications. This study aims to develop a new deep learning algorithm to accurately predict the SWH of deep and distant ocean. In this study, we combine two methods, Ensemble Empirical Mode Decomposition (EEMD) and Long Short-Term Memory (LSTM), to construct an EEMD-LSTM model, and explore the optimal parameters of the model through experiments. A total of 5328 hours of SWH data from November 30, 2020, to July 9, 2021, are used to train and test the model to predict the SWH for the future 1h, 3h, 6h, 12h, and 18h. The results show that the EEMD-LSTM model has the best results compared with other comparative models for short-term and medium- and long-term predictions. The RMSEs are 0.0204, 0.0279, 0.0452, 0.0941, and 0.1949 for the SWH prediction in the future 1, 3, 6, 12, and 18 h. It can be used as a rapid SWH prediction system to ensure navigation safety to a certain extent, which has great practical significance and application value.

Background: Predicting the development of sporadic type B aortic dissection (TBAD) always remains a difficult issue. This study aimed to identify high-risk patients for development of TBAD based on morphological parameters.Methods: This propensity-score-matched case-control study collected and reconstructed the computed tomography angiography of acute TBAD patients and hospital-based control participants without aortic dissection from January 2013 to December 2016. Multivariate regression analysis was used to calculate the adjusted odds ratio (aOR) and 95% confidence interval (CI). Discriminant and reclassification abilities were compared between our model and a previously established model.Results: Our study included 76 acute TBAD patients and 79 control patients (48 cases and 48 controls after propensity-score matching). The degree of question mark (aOR 1.07, 95% CI 1.04–1.11), brachiocephalic trunk diameter (aOR 1.49, 95% CI 1.20–1.85), brachiocephalic trunk angle (aOR 0.97, 95% CI 0.94–0.99), aortic root diameter (aOR 1.31, 95% CI 1.15–1.48), and aortic width (aOR 1.12, 95% CI 1.07–1.17) were associated with a significantly increased risk of TBAD formation. Similar findings were observed in the propensity-score matching and sensitivity analysis only including hyperacute TBAD patients. A novel prediction model was established based on the aforementioned parameters. The new model showed significantly improved discriminant ability compared with the previously established model (c-index 0.78 [95% CI 0.71–0.85] vs. 0.67 [95% CI 0.58–0.75], p = .03), driven by increased reclassification ability in identifying TBAD patients (NRI for events 0.16, 95% CI 0.02–0.30, p = .02).Conclusion: Morphological predictors, including the degree of question mark, aortic width, aortic root diameter, brachiocephalic trunk angle, and brachiocephalic trunk diameter, may be used to identify patients at high risk of TBAD.

BackgroundPeritoneal malignant tumors in children are rare but commonly associated with disease progression and poor outcome. The successful treatment experience of cytoreductive surgery (CRS) and hyperthermic intraperitoneal chemotherapy (HIPEC) in adult peritoneal carcinoma has been applied to pediatric peritoneal malignancy in recent years. However, patients with desmoplastic small round cell tumor (DSRCT) accounted for the majority of patients treated with CRS and HIPEC in previous studies. The role of CRS and HIPEC remains controversial due to the rarity of the disease and the limited sample size of studies. Additionally, the cases using CRS and HIPEC except DSRCT were mainly small case reports with unclear outcomes. We present our experience in the treatment of pediatric peritoneal malignancies using CRS and HIPEC, with more emphasis on the safety, feasibility, and short-term outcome.MethodsA retrospective query from December 2019 to February 2022 identified 19 children with peritoneal malignancies who underwent CRS and HIPEC in our institution. Clinical characteristics, therapies, and outcomes were summarized and analyzed.ResultsThe median age of the patients was 6.4 years (range, 0.7–13.9 years). The histologic types included rhabdomyosarcoma (7), Wilms tumor (2), clear cell sarcoma of the kidney (2), undifferentiated sarcoma (2), immature teratoma (1), peritoneal serous carcinoma (1), malignant rhabdoid of the kidney (1), malignant germ cell tumor (1), neuroblastoma (1), and epithelioid inflammatory myofibroblast sarcoma (1). Seven patients underwent initial operation, and 12 patients received reoperation for tumor recurrence. The median peritoneal carcinomatosis index was 5 (range, 2–21). There were no perioperative deaths or life-threatening complications of CRS and HIPEC. Two patients had grade 3 complications of wound infection and wound dehiscence. With a median follow-up time of 14 months (range, 1.5–31 months), 14 patients were alive, and 5 died of tumor recurrence. Of the 14 patients who were alive, 2 relapsed after CRS and HIPEC and then received radiotherapy and molecular-targeted therapy or chemotherapy.ConclusionsCRS and HIPEC are safe and feasible in children, without increasing serious complications in the peri- and postoperative periods. The complication is acceptable. The short-term outcome shows possible effectiveness in pediatric peritoneal malignant tumors. The long-term effectiveness needs to be verified by additional cases and long-term follow-ups.

Genic male sterility (GMS) is one of the most important resources for exploiting heterosis in crop breeding, so that identifying genomic loci regulating GMS is desirable. However, many regulatory genes controlling GMS have not yet been characterized in maize, owing partly to a lack of genetic materials. We generated a recessive male-sterile maize mutant in the Jing 724 genetic background via ethyl methanesulfonate treatment, and found the male sterility to be due to a single gene mutation. Bulk-segregant RNA sequencing of three replicates indicated that one genomic region located at the end of chromosome 4 was associated with the observed mutant phenotype. Among genes with nonsynonymous mutations, Zm00001d053895 (bHLH51) showed abolished expression in the sterile bulks and was annotated as a bHLH transcription factor orthologous to Arabidopsis AMS, suggesting an association with the male sterility of the mutant. Kompetitive Allele-Specific PCR assays further validated the exclusive correlation of male sterility with the single C-to-T mutation in the fifth exon. The new maize mutant and the potential SNP locus provide novel genetic material for investigating the molecular mechanism underlying tapetal development and may facilitate the improvement of hybrid production systems.

BackgroundHostile anatomy, especially severely angulated neck and tortuous iliac arteries, has always been a conundrum in endovascular aneurysm repair (EVAR). Crossed limb (CL) graft, also called the “ballerina technique,” has been utilized to address this problem by facilitating gate cannulation. In terms of short and long-term outcomes, correlated studies have made inconsistent conclusions and this issue remains controversial. Based on a previous cohort study conducted in our center, we aim to prospectively compare the safety and efficacy between CL and standard limb (SL) configuration in patients receiving EVAR.MethodsThis is a prospective, single-center, non-randomized controlled trial. A total of 275 patients who meet the inclusion criteria will be enrolled and allocated with a 4:11 ratio of CL to SL, which is based on results of our previous study. All patients will receive same perioperative management and postoperative medications. All EVAR procedures will be performed under standard protocol, utilizing Endurant II or IIs Stent Graft. The configuration of the graft stent will be decided by surgeons and confirmed by final angiography. The primary outcome is 3-year freedom from major adverse limb-graft events (MALEs). Endpoints will be assessed at the following time points: 1, 6, 12, 24, and 36 months.DiscussionTo our best knowledge, this crosseD vs. stANdard Configuration in Endovascular Repair (DANCER) trial is the first non-randomized controlled trial to compare these two graft configurations in EVAR. The main aim is to compare the MALEs between two groups at 3 years postoperatively. This trial will hopefully provide high-level evidence for employing CL in EVAR.Clinical trial registration[www.chictr.org.cn], identifier [ChiCTR2100053055].

Accurate wave height prediction is significant in ports, energy, fisheries, and other offshore operations. In this study, a regional significant wave height prediction model with a high spatial and temporal resolution is proposed based on the ConvLSTM algorithm. The model learns the intrinsic correlations of the data generated by the numerical model, making it possible to combine the correlations between wind and wind waves to improve the predictions. In addition, this study also optimizes the long-term prediction ability of the model through the proposed Mask method and Replace mechanism. The experimental results show that the introduction of the wind field can significantly improve the significant wave height prediction results. The research on the prediction effect of the entire study area and two separate stations shows that the prediction performance of the proposed model is better than the existing methods. The model makes full use of the physical correlation between wind and wind waves, and the validity is up to 24 hours. The 24-hour forecast R² reached 0.69.

Abstract Mitogen‐activated protein kinase (MAPK or MPK) cascades consist of three protein kinase components, MAPK kinase kinases (MAPKKKs), MAPK kinases (MKKs and MPKs), which are indispensable for various plant physiological processes. The functions of MAPK families have been extensively studied in maize (Zea mays L.) and other plant species, but little is known about MAPK families in the elite Chinese maize line Huangzaosi (hzs). In this study, we observed that overall performance of Huangzaosi was substantially better than that of B73 under drought conditions at the seedling and V16 stages with a favorable root/canopy ratio. In silico analyses identified 72, 10, and 24 MAPKKKs, MKKs, and MPKs, respectively, in Huangzaosi. Examinations of phylogenetic relationships among Arabidopsis thaliana (L.) Heynh., rice (Oryza sativa L.), and maize (lines B73 and hzs), gene structures, conserved protein motifs, and chromosomal locations revealed their evolutionary relationships. The basal gene expression levels and tissue specificities of all three MAPK families in hzs reflected the diversity in the MAPK functions related to growth and development. The quantitative real‐time polymerase chain reaction (qPCR) assay indicated that certain MAPK genes with high basal expression levels in the primary and crown roots responded differentially to drought between B73 and hzs, suggesting that these genes may contribute to their distinct drought tolerance at different developmental stages. The important information regarding the evolution and expression of hzs MAPK family members generated in this study provides a new avenue for the better understanding on the regulatory mechanism of MAPK cascade in the core inbred line hzs, which may be useful to guide the development of new maize cultivars with desirable traits (e.g., drought resistance).

In recent years, the development of deep learning has brought great convenience to the work of target detection, semantic segmentation, and object recognition. In the field of infrared weak small target detection (e.g., surveillance and reconnaissance), it is not only necessary to accurately detect targets but also to perform precise segmentation and sub-pixel-level centroid localization for infrared small targets with low signal-to-noise ratio and weak texture information. To address these issues, we propose UCDnet (Double U-shaped Segmentation Network Cascade Centroid Map Prediction for Infrared Weak Small Target Detection) in this paper, which completes “end-to-end” training and prediction by cascading the centroid localization subnet with the semantic segmentation subnet. We propose the novel double U-shaped feature extraction network for point target fine segmentation. We propose the concept and method of centroid map prediction for point target localization and design the corresponding Com loss function, together with a new centroid localization evaluation metrics. The experiments show that ours achieves target detection, semantic segmentation, and sub-pixel-level centroid localization. When the target signal-to-noise ratio is greater than 0.4, the IoU of our semantic segmentation results can reach 0.9186, and the average centroid localization precision can reach 0.3371 pixels. On our simulated dataset of infrared weak small targets, the algorithm we proposed performs better than existing state-of-the-art networks in terms of semantic segmentation and centroid localization.

Accurate classification and identification of Internet traffic are crucial for maintaining network security. However, unknown network traffic in the real world can affect the accuracy of current machine learning models, reducing the efficiency of traffic classification. Existing unknown traffic classification algorithms are unable to optimize traffic features and require the entire system to be retrained each time new traffic data are collected. This results in low recognition efficiency, making the algoritms unsuitable for real-time application detection. To solve the above issues, we suggest a multi-feature fusion-based incremental technique for detecting unknown traffic in this paper. The approach employs a multiple-channel parallel architecture to extract temporal and spatial traffic features. It then uses the mRMR algorithm to rank and fuse the features extracted from each channel to overcome the issue of redundant encrypted traffic features. In addition, we combine the density-ratio-based clustering algorithm to identify the unknown traffic features and update the model via incremental learning. The cassifier enables real-time classification of known and unknown traffic by learning newly acquired class knowledge. Our model can identify encrypted unknown Internet traffic with at least 86% accuracy in various scenarios, using the public ISCX-VPN-Tor datasets. Furthermore, it achieves 90% accuracy on the intrusion detection dataset NSL-KDD. In our self-collected dataset from a real-world environment, the accuracy of our model exceeds 96%. This work offers a novel method for identifying unknown network traffic, contributing to the security preservation of network environments.

BackgroundEndovascular abdominal aortic repair (EVAR) and thoracic endovascular aortic repair (TEVAR) have become the first-line treatment for aortic diseases, but current evidence is uncertain regarding whether a percutaneous approach has better outcomes than cutdown access, especially for patient-centered outcomes (PCOs). This study is designed to compare these outcomes of percutaneous access vs. cutdown access after endovascular aortic repair.MethodThe SWEET study is a randomized, controlled, single-blind, single-center non-inferiority trial with two parallel groups in two cohorts respectively. After eligibility screening, subjects who meet the inclusion criteria will be divided into Cohort EVAR or Cohort TEVAR according to clinic interviews. And then participants in two cohorts will be randomly allocated to either intervention groups receiving percutaneous access endovascular repair or controlled groups receiving cutdown access endovascular repair separately. Primary clinician-reported outcome (ClinRO) is access-related complication, and primary patient-centered outcome (PCO) is time back to normal life. Follow-up will be conducted at 2 weeks, 1 month, 3 months postoperatively.DiscussionThe choice of either percutaneous or cutdown access may not greatly affect the success of EVAR or TEVAR procedures, but can influence the quality of life and patient-centered experience. Given the very low evidence for ClinROs and few data for PCOs, comparison of the percutaneous vs. cutdown access EVAR and TEVAR is essential for both patient-centered care and clinical decision making in endovascular aortic repair.Trial registrationChinese Clinical Trial Registry ChiCTR2100053161 (registered on 13th November, 2021).

Background: With the enormous morbidity and mortality caused by respiratory syncytial virus (RSV) infections among infants and the elderly, vaccines against RSV infections are in large market demand. Methods: We conducted a first-in-human (FIH), randomized, double-blind, placebo-controlled dose escalation study to evaluate the safety and immunogenicity response of the rRSV vaccine (BARS13) in healthy adults aged 18–45. A total of 60 eligible participants were randomly assigned to receive one of four dose levels or vaccination regimens of BARS13 or placebo at a 4:1 ratio. Results: The mean age was 27.40, and 23.3% (14/60) were men. No treatment-emergent adverse events (TEAEs) led to study withdrawal within 30 days after each vaccination. No serious adverse event (SAE) was reported. Most of the treatment-emergent adverse events (TEAEs) recorded were classified as mild. The high-dose repeat group had a serum-specific antibody GMC of 885.74 IU/mL (95% CI: 406.25–1931.17) 30 days after the first dose and 1482.12 IU/mL (706.56–3108.99) 30 days after the second dose, both higher than the GMC in the low-dose repeat group (885.74 IU/mL [406.25–1931.17] and 1187.10 IU/ mL [610.01–2310.13]). Conclusions: BARS13 had a generally good safety and tolerability profile, and no significant difference in terms of adverse reaction severity or frequency was observed between different dose groups. The immune response in repeat-dose recipients shows more potential in further study and has guiding significance for the dose selection of subsequent studies.

This paper aims to apply real-time light-weight high-precision 3D detection for autonomous driving. We propose LIDAR-based 3D object detection based on foreground segmentation using a fully sparse convolutional network (FS23D). We design a sparse convolutional backbone network and a sparse convolutional detection head to efficiently use the computing and memory resources and accelerate the inference. Instead of using the anchor-based method, we convert the detection problem into a foreground segmentation problem on a bird’s-eye view. The sparse convolutional detection head predicts the objectness and bounding box on each active point on the sparse feature map. We design a new oriented bounding box coding method and corresponding loss functions. We predict the endpoints of two mutually perpendicular lines that pass through the foreground active points and indirectly predict the objects’ oriented bounding box from these four endpoints. We use the indirectly calculated object center, size, and orientation as inputs of loss functions in the training step. Experiments on the KITTI dataset show that the sparse backbone network we designed is 2.2 times faster and 18.4 times fewer FLOPs than the dense backbone network. The average improvement of the loss functions based on the bounding box code is 1.1% and 0.8% on the BEV and 3D detection, respectively, compared to no addition of these losses. Moreover, FS23D outperforms the state-of-the-art LIDAR-based method in speed and precision for both cars and cyclists.

Decoding human hand movement from electroencephalograms (EEG) signals is essential for developing an active human augmentation system. Although existing studies have contributed much to decoding single-hand movement direction from EEG signals, decoding primary hand movement direction under the opposite hand movement condition remains open. In this paper, we investigated the neural signatures of the primary hand movement direction from EEG signals under the opposite hand movement and developed a novel decoding method based on non-linear dynamics parameters of movement-related cortical potentials (MRCPs). Experimental results showed significant differences in MRCPs between hand movement directions under an opposite hand movement. Furthermore, the proposed method performed well with an average binary decoding accuracy of 89.48 ± 5.92% under the condition of the opposite hand movement. This study may lay a foundation for the future development of EEG-based human augmentation systems for upper limbs impaired patients and healthy people and open a new avenue to decode other hand movement parameters (e.g., velocity and position) from EEG signals.

The learning of discriminative features is the key for fine-grained image recognition. To better extract effective features and improve the accuracy of fine-grained image recognition, we propose a two-level progressive attention convolutional network (TPA-CNN) for fine-grained image recognition. The model includes a multi-channel attention-fusion (MCAF) module and a cross-layer element-attention (CEA) module. The MCAF module is used to find distinctive feature map channels which significantly responds to specific regions. Inspired by Hierarchical Bilinear Pooling model, The CEA module is further assign weight values to feature map elements. From the perspective of attention visualization map, MCAF module can focused on one or more positive regions, CEA module further locates the most discriminative regions by interaction between the feature map elements. The model can dynamically search the discriminative region of the image, not limited to the boost or crop a selected region. Compared to previous models basing on attention mechanism, the model can extract non-correlated part features which spread over object foreground areas, further improving the recognition accuracy. Experimental results on CUB-200-2011, FGVC-Aircraft, and Stanford Cars datasets demonstrate that the proposed TPA-CNN achieves competitive performance.

This study aimed to establish a method to identify the geographical origin of milk based on its amino acid profile. High-performance liquid chromatography (HPLC) was carried out to measure amino acid contents. The significant differences of amino acid profiles of milk samples from four regions in China (Hebei, Ningxia, Heilongjiang, and Inner Mongolia) were analyzed by ANOVA. Furthermore, the principal component analysis (PCA) demonstrated the feasibility of geographical origin identification using an amino acid profile, which the first 2 principal components account for 65.62% of total variance. The predictive model for the geographical origin of milk samples was established by orthogonal partial least squares-discriminant analysis (OPLS-DA) with a classification accuracy of 100% and the performance parameters of R2X 0.98, R2Y 0.82, and Q2 0.75. The excellent predictive ability of the model was validated using the validation data set. The analysis of variable importance in projection (VIP) showed that seven amino acids played a key role in the geographical origin identification. This method is a reliable strategy to identify the geographical origin of milk for protecting consumers against mislabeling fraud.

In order to solve the problem that the traditional long-term high-speed traffic forecasting algorithm is affected by the approximation ability of the function and easy to fall into the local mass value, we wrote a multivariate-based highway traffic forecasting algorithm scaling and convolutional networks. Because the feedforward wavelet neural network algorithm predicts the short-term traffic flow in different areas, it is necessary to examine the ability to predict the difference between different models. From the standard feedforward wavelet neural network algorithm using global optimization capabilities, we improve the wolf pack algorithm, improve the search accuracy of the algorithm, get the best solution of the estimated value of the work according to the search results when completing the research objectives, and get the ability to predict the work of the model. Feedforward neural network algorithm: we develop and obtain the best short-term high-speed traffic forecast values. The results are as follows: after using the author’s algorithm, the processing time increases by 1.5 seconds, but the average percentage of errors decreases by more than 50%, in fact the error and the root mean square error decreased by about 30%, and the smoothing coefficient increased by about 1%. The prediction of the author’s algorithm for short-term high-speed traffic is better than the wavelet neural network prediction algorithm, and the prediction accuracy and stability of the author’s algorithm are higher.

The Transformer-based approach represents the state-of-the-art in image captioning. However, existing studies have shown Transformer has a problem that irrelevant tokens with overlapping neighbors incorrectly attend to each other with relatively large attention scores. We believe that this limitation is due to the incompleteness of the Self-Attention Network (SAN) and Feed-Forward Network (FFN). To solve this problem, we present the Full-Memory Transformer method for image captioning. The method improves the performance of both image encoding and language decoding. In the image encoding step, we propose the Full-LN symmetric structure, which enables stable training and better model generalization performance by symmetrically embedding Layer Normalization on both sides of the SAN and FFN. In the language decoding step, we propose the Memory Attention Network (MAN), which extends the traditional attention mechanism to determine the correlation between attention results and input sequences, guiding the model to focus on the words that need to be attended to. Our method is evaluated on the MS COCO dataset and achieves good performance, improving the result in terms of BLEU-4 from 38.4 to 39.3.

Objectives: Cross-limb stent grafts for endovascular aneurysm repair (EVAR) are often employed for abdominal aortic aneurysms (AAAs) with significant aortic neck angulation. Neck angulation may be coronal or sagittal; however, previous hemodynamic studies of cross-limb EVAR stent grafts (SGs) primarily utilized simplified planar neck geometries. This study examined the differences in flow patterns and hemodynamic parameters between crossed and non-crossed limb SGs at different spatial neck angulations.Methods: Ideal models consisting of 13 cross and 13 non-cross limbs were established, with coronal and sagittal angles ranging from 0 to 90°. Computational fluid dynamics (CFD) was used to capture the hemodynamic information, and the differences were compared.Results: With regards to the pressure drop index, the maximum difference caused by the configuration and angular direction was 4.6 and 8.0%, respectively, but the difference resulting from the change in aneurysm neck angle can reach 27.1%. With regards to the SAR-TAWSS index, the maximum difference caused by the configuration and angular direction was 7.8 and 9.8%, respectively, but the difference resulting from the change in aneurysm neck angle can reach 26.7%. In addition, when the aneurysm neck angle is lower than 45°, the configuration and angular direction significantly influence the OSI and helical flow intensity index. However, when the aneurysm neck angle is greater than 45°, the hemodynamic differences of each model at the same aneurysm neck angle are reduced.Conclusion: The main factor affecting the hemodynamic index was the angle of the aneurysm neck, while the configuration and angular direction had little effect on the hemodynamics. Furthermore, when the aneurysm neck was greatly angulated, the cross-limb technique did not increase the risk of thrombosis.

The popliteal artery has complex deformations such as axial shortening, bending, squeezing and twisting, as well as specific positions across the knee joint. Up to now there is a high incidence of in-stent restenosis (ISR) after the stent treatment of the popliteal artery. This paper aimed to design a new type of popliteal artery stent -- cross-helical stent.to meet its deformation characteristics. The finite element analysis (FEA) method was used to test the mechanical properties of six stents with different winding height ratios of axial shortening, bending, plate extrusion, and torsional deformation. The results showed that the stents performance was affected by the ratio of winding height, the larger the winding height ratio, the worse the compliance, the worse the axial compressibility, but the better the radial support. And when the stent is torsional, the greater the winding height ratio is, the greater the stress is. In additon, the maximum stress was also related to the direction of torsion deformation, and the stress when the helix has the same direction with the torsion direction of the stent are was greater than that when the helix direction and torsion direction are opposite. Moreover, when the winding height ratio is 3:5, the performance of the stent is better, not only has better flexibility, its radial force, axial compression, and torsional deformation are better. It is concluded that the new cross-helical stent can adapt to the special deformation of popliteal artery and has good mechanical properties.