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Abstract This study aims to investigate the potential impact of high-dose radiotherapy (RT) on brain structure, cognitive impairment, and the psychological status of patients undergoing brain tumor treatment. We recruited and grouped 144 RT-treated patients with brain tumors into the Low dose group (N = 72) and the High dose group (N = 72) according to the RT dose applied. Patient data were collected by using the HADS and QLQ-BN20 system for subsequent analysis and comparison. Our analysis showed no significant correlation between the RT doses and the clinicopathological characteristics. We found that a high dose of RT could aggravate cognitive impairment and deteriorate patient role functioning, indicated by a higher MMSE and worsened role functioning in the High dose group. However, the depression status, social functioning, and global health status were comparable between the High dose group and the Low dose group at Month 0 and Month 1, while being worsened in the High dose group at Month 3, indicating the potential long-term deterioration of depression status in brain tumor patients induced by high-dose RT. By comparing patient data at Month 0, Month 1, Month 3, Month 6, and Month 9 after RT, we found that during RT treatment, RT at a high dose could aggravate cognitive impairment in the short term and lead to worsened patient role functioning, and even deteriorate the overall psychological health status of patients in the long term.

Histopathology image analysis plays a crucial role in cancer diagnosis. However, training a clinically applicable segmentation algorithm requires pathologists to engage in labor‐intensive labeling. In contrast, weakly supervised learning methods, which only require coarse‐grained labels at the image level, can significantly reduce the labeling efforts. Unfortunately, while these methods perform reasonably well in slide‐level prediction, their ability to locate cancerous regions, which is essential for many clinical applications, remains unsatisfactory. Previously, CAMEL is proposed, which achieves comparable results to those of fully supervised baselines in pixel‐level segmentation. However, CAMEL requires 1280 × 1280 image‐level binary annotations for positive WSIs. Here, CAMEL2 is presented, by introducing a threshold of the cancerous ratio for positive bags, it allows one to better utilize the information, consequently enabling us to scale up the image‐level setting from 1280 × 1280 to 5120 × 5120 while maintaining accuracy. The results with various datasets demonstrate that CAMEL2, with the help of 5120 × 5120 image‐level binary annotations, which are easy to annotate, achieves comparable performance to that of a fully supervised baseline in both instance‐ and slide‐level classifications.

AbstractContext Polygonum cuspidatum Sieb. et Zucc (Polygonaceae), the root of which is included in the Chinese Pharmcopoeia under the name ‘Huzhang', has a long history as a medicinal plant and vegetable. Polygonum cuspidatum has been used in traditional Chinese medicine for the treatment of inflammation, hyperlipemia, etc.Objective This article reviews the pharmacological action and the clinical applications of Polygonum cuspidatum and its extracts, whether in vivo or in vitro. We also summarized the main phytochemical constituents and pharmacokinetics of Polygonum cuspidatum and its extracts.Methods The data were retrieved from major medical databases, such as CNKI, PubMed, and SinoMed, from 2014 to 2022. Polygonum cuspidatum, pharmacology, toxicity, clinical application, and pharmacokinetics were used as keywords.Results The rhizomes, leaves, and flowers of Polygonum cuspidatum have different phytochemical constituents. The plant contains flavonoids, anthraquinones, and stilbenes. Polygonum cuspidatum and the extracts have anti-inflammatory, antioxidation, anticancer, heart protection, and other pharmacological effects. It is used in the clinics to treat dizziness, headaches, traumatic injuries, and water and fire burns.Conclusions Polygonum cuspidatum has the potential to treat many diseases, such as arthritis, ulcerative colitis, asthma, and cardiac hypertrophy. It has a broad range of medicinal applications, but mainly focused on root medication; its aerial parts should receive more attention. Pharmacokinetics also need to be further investigated.

As an emerging wide area network technology, LoRa has the characteristics of low power consumption and wide coverage.It has very broad prospects in the field of internet of things applications.In recent years, it has been deployed in countless objects around the world.The advantages of wide coverage of low-orbit satellite networks make it an inevitable technology to achieve global coverage of massive internet of things.Firstly, the basic principles of LoRa technology were expounded.Then the problems existed after the LoRa technology was directly introduced into the low-orbit satellite IoT were analyzed.Finally, a set of LoRa transmission technology suitable for low-orbit satellite IoT was proposed, focused on solved the problem of LoRa frame synchronization under the condition of low-orbit long-distance low-power transmission, and the problem of LoRa demodulation caused by the high-speed movement of satellites due to the Doppler frequency off set.The simulation results showed that the proposed joint synchronization and frequency off set estimation algorithm could obviously improved the synchronization probability and transmission reliability of LoRa technology under the conditions of extremely low SNR and large Doppler frequency shift.

How to extend the HARQ technique to made it suitabled for LEO satellite communication network was explored.First the traditional HARQ technology was expounded.Based on this, the HARQ scheme based on low density parity check code (LDPC) and the HARQ scheme based on message group check code (BCC) were studied respectively, the performance of the schemes was analyzed through simulation.Finally, suitable feedback schemes were analyzed for the retransmission units in the HARQ retransmission mechanism, which provided some reference for the research of HARQ technology in LEO satellite communication networks.

Using the characteristics of satellites in diff erent orbits to build a more effi cient multilayer satellite network has become a hotspot in satellite network research in recent years.Traditional satellite network access methods have been unable to meet the needs of multilayer satellite networks with low latency and fl exible access, so it is urgent to design an effi cient random dynamic access protocol for multilayer satellite networks.This paper fi rst summarized the traditional satellite network access technology, clarifi ed the development context of the satellite network random access protocol, and then proposed diff erent low latency for the two diff erent scenarios of ground station access to satellite and multilayer satellite network intersatellite access.The access method could provided a reference for the development of the dynamic access technology of the multilayer satellite network in the future.

In order to study whether jujube trees can grow normally under rain-fed conditions in loess hilly areas, we planted jujube trees (Ziziphus jujuba Mill.) 4 years after felling a 23-year-old apple orchard. The growth process of the jujube trees and the variation in soil water content (SWC) were monitored for three consecutive years following planting in order to study the effects of the water-saving pruning (WSP) technique. Results showed the following. (1) The soil at a depth of 0–1,000 cm had been desiccated when the area was an apple orchard. (2) Under rain-fed conditions, the jujube trees with the WSP technique were always able to maintain normal growth while the jujube trees with conventional pruning method had a normal growth stage of only 4 years. The water use efficiency of the jujube trees with the WSP technique was much higher than that of the jujube trees with conventional pruning. We recommend WSP in jujube orchard management, because the jujube trees with WSP could maintain normal growth in the deep dried soils of the loess hilly region, as WSP can reduce the water consumption of the jujube trees and may have a positive effect on soil moisture restoration. HIGHLIGHTS We proposed water-saving pruning for the growth of jujube trees planted in dry soil.; Water-saving pruning can significantly improve water use efficiency of jujube trees planted in dry soil.; Water-saving pruning may have a positive impact on the restoration of soil water in dry soil.;

Highly chlorinated benzenes usually have a low efficient degradation in environment. Here we proposed a synergistic removal strategy of pentachlorobenzene (PeCB) using Pseudomonas sp. JS100 coupled with immobilized nanoscale zero-valent iron (NZVI). The structural and textural features of the synergistic system were characterized by X-ray powder diffraction, field emission scanning electron microscopy, and a specific surface area and pore size analysis. Nanoscale zero-valent iron particles were dispersed and attached to the biofilter, which increased the specific surface area to 34.5 m2 g−1. The batch experiment revealed that the removal efficiency of PeCB reached 80.2% in the synergistic system within 48 h. The degradation followed pseudo-first-order reaction kinetics, and the reaction rate constant was measured to be 0.0336 h−1. In the degradation mechanism, PeCB was degraded by NZVI to lower chlorobenzenes, which were utilized by Pseudomonas sp. JS100 as nutrients, thereby achieving rapid removal of PeCB.

Because of the influence of harsh and variable working environments, the vibration signals of rolling bearings for combine harvesters usually show obvious characteristics of strong non-stationarity and nonlinearity. Accomplishing accurate fault diagnosis using these signals for rolling bearings is a challenging subject. In this paper, a novel fault diagnosis method based on composite-scale-variable dispersion entropy (CSvDE) and self-optimization variational mode decomposition (SoVMD) is proposed, systematically combining the nonstationary signal analysis approach and machine learning technology. Firstly, an improved SoVMD algorithm is developed to realize adaptive parameter optimization and to further extract multiscale frequency components from original signals. Subsequently, a CSvDE-based feature learning model is established to generate the multiscale fault feature space (MsFFS) of frequency components for the improvement of fault feature learning ability. Finally, the generated MsFFS can serve as the inputs of the Softmax classifier for fault category identification. Extensive experiments on the vibration datasets collected from rolling bearings of combine harvesters are conducted, and the experimental results demonstrate the more superior and robust fault diagnosis performance of the proposed method compared to other existing approaches.

Abstract Background Anti-N-methyl-D-aspartate receptor (NMDAR) encephalitis is a severe and most common autoimmune encephalitis in patients under 40 years old. Anti-NMDAR encephalitis has various clinical and neuroimaging findings. Here we report a special case of an anti-NMDAR encephalitis who had diffuse lesions in bilateral hemispheres with mild mass effects in left basal ganglia area. Case presentations A 28-year-old female anti-NMDAR encephalitis patient mainly presented with headache and fever. Brain magnetic resonance image (MRI) showed slightly contrasted diffuse lesions, involving the left temporal and frontal lobes, left basal ganglia area and splenium of corpus callosum, as well as the right frontal lobe, with mild edema surrounded in the left basal ganglia area. Cerebrospinal fluid (CSF) revealed a moderate pleocytosis with normal protein and glucose levels. Anti-NMDAR antibodies were identified in CSF. Transvaginal ovarian ultrasound did not reveal an ovarian teratoma. The patient was treated with immunoglobulin and steroid, and had a good recovery. Conclusions Anti-NMDAR encephalitis has no special clinical manifestations and brain MRI is highly variable, which could be unremarkable or abnormal involving white and grey matters. The extensive lesions in frontal and temporal lobes, and basal ganglia area, with mild mass effects, have not been described previously. Recognition of various changes in brain MRI will enable the early detection of anti-NMDAR antibody and then effective treatments.

Slip is one of the most common forms of failure in aviation bearings, and it can pose a great threat to the stable operation of aviation bearings. Bearing cage speed monitoring methods based on weak magnetic detection can achieve nondestructive measurements. However, the method suffers from solid signal background noise due to the high sensitivity of the sensor. Therefore, in this paper, an adaptive stochastic resonance algorithm was proposed in response to the characteristics of the weak magnetic detection signal and the problem of solid noise. In addition, by adaptively adjusting the coefficients of the stochastic resonance system—by an improved moth flame optimization algorithm—the drawback in which the stochastic resonance method required artificially set parameters for extracting the feature frequencies of the weak magnetic signals was solved. In this process, we used parameters, such as general refined composite multi-scale sample entropy, as the adaptation function of the optimization algorithm. In the end, simulation and experimental outcomes verified the efficacy of the approach put forward.

Abstract Background Physical examinations may reveal the instability of a glenohumeral joint but cannot diagnose the bony Bankart lesions. Soft tissue Bankart lesion cannot be visualized on traditional radiogram. Magnetic resonance images have high cost and availability issues. The purpose of the study was to access the diagnostic performance of the Computed Tomography (CT) in the assessment of patients with shoulder instability and to diagnose the Bankart and bony Bankart lesions. Methods A total of 145 patients with shoulder instability were included in the study. Patients were subjected to clinical examination tests, traditional radiography, and CT. Two orthopedic surgeons, two engineers (trained in musculoskeletal imaging), and two physiotherapists have analyzed the radiological images, CT scans, and the clinical examination tests respectively. The Chi-square test or one-way ANOVA/ Dunnett Multiple comparisons test was performed at 99% of confidence level. Results Sensitivity (0.972 ± 0.18 vs. 1, p = 0.11) and accuracy (0.942 ± 0.17 vs. 1, p

The multisource information fusion technique is currently one of the common methods for rolling bearing fault diagnosis. However, the current research rarely fuses information from the data of different sensors. At the same time, the dispersion itself in the VAE method has asymmetric characteristics, which can enhance the robustness of the system. Therefore, in this paper, the information fusion method of the variational autoencoder (VAE) and random forest (RF) methods are targeted for subsequent lifetime evolution analysis. This fusion method achieves, for the first time, the simultaneous monitoring of acceleration signals, weak magnetic signals and temperature signals of rolling bearings, thus improving the fault diagnosis capability and laying the foundation for subsequent life evolution analysis and the study of the fault–slip correlation. Drawing on the experimental procedure of the CWRU’s rolling bearing dataset, the proposed VAERF technique was evaluated by conducting inner ring fault diagnosis experiments on the experimental platform of the self-research project. The proposed method exhibits the best performance compared to other point-to-point algorithms, achieving a classification rate of 98.19%. The comparison results further demonstrate that the deep learning fusion of weak magnetic and vibration signals can improve the fault diagnosis of rolling bearings.

Globally, human respiratory syncytial virus (RSV) is a leading cause of lower respiratory tract infections in newborns, young children, and the elderly for which there is no vaccine. The RSV fusion (F) glycoprotein is a major target for vaccine development. Here, we describe a novel monoclonal antibody (designated as R4.C6) that recognizes both pre-fusion and post-fusion RSV F, and binds with nanomole affinity to a unique neutralizing site comprised of antigenic sites II and IV on the globular head. A 3.9 Å-resolution structure of RSV F-R4.C6 Fab complex was obtained by single particle cryo-electron microscopy and 3D reconstruction. The structure unraveled detailed interactions of R4.C6 with antigenic site II on one protomer and site IV on a neighboring protomer of post-fusion RSV F protein. These findings significantly further our understanding of the antigenic complexity of the F protein and provide new insights into RSV vaccine design.

When early failures in rolling bearings occur, we need to be able to extract weak fault characteristic frequencies under the influence of strong noise and then perform fault diagnosis. Therefore, a new method is proposed: complete ensemble intrinsic time-scale decomposition with adaptive Lévy noise (CEITDALN). This method solves the problem of the traditional complete ensemble intrinsic time-scale decomposition with adaptive noise (CEITDAN) method not being able to filter nonwhite noise in measured vibration signal noise. Therefore, in the method proposed in this paper, a noise model in the form of parameter-adjusted noise is used to replace traditional white noise. We used an optimization algorithm to adaptively adjust the model parameters, reducing the impact of nonwhite noise on the feature frequency extraction. The experimental results for the simulation and vibration signals of rolling bearings showed that the CEITDALN method could extract weak fault features more effectively than traditional methods.

The early fault diagnosis of rolling bearings has always been a difficult problem due to the interference of strong noise. This paper proposes a new method of early fault diagnosis for rolling bearings with entropy participation. First, a new signal decomposition method is proposed in this paper: intrinsic time-scale decomposition based on time-varying filtering. It is introduced into the framework of complete ensemble intrinsic time-scale decomposition with adaptive noise (CEITDAN). Compared with traditional intrinsic time-scale decomposition, intrinsic time-scale decomposition based on time-varying filtering can improve frequency-separation performance. It has strong robustness in the presence of noise interference. However, decomposition parameters (the bandwidth threshold and B-spline order) have significant impacts on the decomposition results of this method, and they need to be artificially set. Aiming to address this problem, this paper proposes rolling-bearing fault diagnosis optimization based on an improved coyote optimization algorithm (COA). First, the minimal generalized refined composite multiscale sample entropy parameter was used as the objective function. Through the improved COA algorithm, optimal intrinsic time-scale decomposition parameters based on time-varying filtering that match the input signal are obtained. By analyzing generalized refined composite multiscale sample entropy (GRCMSE), whether the mode component is dominated by the fault signal is determined. The signal is reconstructed and decomposed again. Finally, the mode component with the highest energy in the central frequency band is selected for envelope spectrum variation for fault diagnosis. Lastly, simulated and experimental signals were used to verify the effectiveness of the proposed method.

To overcome the difficulty of extracting the feature frequency of early bearing faults, this paper proposes an adaptive feature extraction scheme. First, the improved intrinsic time-scale decomposition, proposed in this paper, is used as a noise reduction method. Then, we use the adaptive composite quantum morphology analysis method, also proposed in this paper, to perform an adaptive demodulation analysis on the signal, and finally, extract the fault characteristics in the envelope spectrum. The experimental results show that the scheme performs well in the early fault feature extraction of rolling bearings.

A rolling bearing early fault diagnosis method is proposed in this paper, which is derived from a refined composite multi-scale approximate entropy (RCMAE) and improved coyote optimization algorithm based probabilistic neural network (ICOA-PNN) algorithm. Rolling bearing early fault diagnosis is a time-sensitive task, which is significant to ensure the reliability and safety of mechanical fault system. At the same time, the early fault features are masked by strong background noise, which also brings difficulties to fault diagnosis. So, we firstly utilize the composite ensemble intrinsic time-scale decomposition with adaptive noise method (CEITDAN) to decompose the signal at different scales, and then the refined composite multi-scale approximate entropy of the first signal component is calculated to analyze the complexity of describing the vibration signal. Afterwards, in order to obtain higher recognition accuracy, the improved coyote optimization algorithm based probabilistic neural network classifiers is employed for pattern recognition. Finally, the feasibility and effectiveness of this method are verified by rolling bearing early fault diagnosis experiment.

Spontaneous nucleation of actin is very inefficient in cells. To overcome this barrier, cells have evolved a set of actin filament nucleators to promote rapid nucleation and polymerization in response to specific stimuli. However, the molecular mechanism of actin nucleation remains poorly understood. This is hindered largely by the fact that actin nucleus, once formed, rapidly polymerizes into filament, thus making it impossible to capture stable multisubunit actin nucleus. Here, we report an effective double-mutant strategy to stabilize actin nucleus by preventing further polymerization. Employing this strategy, we solved the crystal structure of AMPPNP-actin in complex with the first two tandem W domains of Cordon-bleu (Cobl), a potent actin filament nucleator. Further sequence comparison and functional studies suggest that the nucleation mechanism of Cobl is probably shared by the p53 cofactor JMY, but not Spire. Moreover, the double-mutant strategy opens the way for atomic mechanistic study of actin nucleation and polymerization.

The Pandemic (H1N1) 2009 is spreading to numerous countries and causing many human deaths. Although the symptoms in humans are mild at present, fears are that further mutations in the virus could lead to a potentially more dangerous outbreak in subsequent months. As the primary immunity-eliciting antigen, hemagglutinin (HA) is the major agent for host-driven antigenic drift in A(H3N2) virus. However, whether and how the evolution of HA is influenced by existing immunity is poorly understood for A(H1N1). Here, by analyzing hundreds of A(H1N1) HA sequences since 1918, we show the first evidence that host selections are indeed present in A(H1N1) HAs. Among a subgroup of human A(H1N1) HAs between 1918 approximately 2008, we found strong diversifying (positive) selection at HA(1) 156 and 190. We also analyzed the evolutionary trends at HA(1) 190 and 225 that are critical determinants for receptor-binding specificity of A(H1N1) HA. Different A(H1N1) viruses appeared to favor one of these two sites in host-driven antigenic drift: epidemic A(H1N1) HAs favor HA(1) 190 while the 1918 pandemic and swine HAs favor HA(1) 225. Thus, our results highlight the urgency to understand the interplay between antigenic drift and receptor binding in HA evolution, and provide molecular signatures for monitoring future antigenically drifted 2009 pandemic and seasonal A(H1N1) influenza viruses.

Simian virus 40 large tumor antigen (LTag) is an efficient helicase motor that unwinds and translocates DNA. The DNA unwinding and translocation of LTag is powered by ATP binding and hydrolysis at the nucleotide pocket between two adjacent subunits of an LTag hexamer. Based on the set of high-resolution hexameric structures of LTag helicase in different nucleotide binding states, we simulated a conformational transition pathway of the ATP binding process using the targeted molecular dynamics method and calculated the corresponding energy profile using the linear response approximation (LRA) version of the semi-macroscopic Protein Dipoles Langevin Dipoles method (PDLD/S). The simulation results suggest a three-step process for the ATP binding from the initial interaction to the final tight binding at the nucleotide pocket, in which ATP is eventually "locked" by three pairs of charge-charge interactions across the pocket. Such a "cross-locking" ATP binding process is similar to the binding zipper model reported for the F1-ATPase hexameric motor. The simulation also shows a transition mechanism of Mg2+ coordination to form the Mg-ATP complex during ATP binding, which is accompanied by the large conformational changes of LTag. This simulation study of the ATP binding process to an LTag and the accompanying conformational changes in the context of a hexamer leads to a refined cooperative iris model that has been proposed previously.