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GH4169 superalloy is extensively utilized in aerospace applications due to their exceptional high-temperature strength and oxidation resistance properties. However, its high hardness presents significant machining challenges, including rapid tool wear and poor surface quality. This study introduces ultrasonic synergistic nanofluid minimal quantity lubrication milling (USNMQLM) technology to address these machining difficulties and enhance surface integrity. The research examines USNMQLM principles, investigating tool-workpiece separation characteristics and lubrication behavior under separated cutting conditions. By building an experimental platform, the influence of processing parameters, ultrasonic variables, and cooling methods on the surface quality of milling GH4169 superalloy was studied. The results indicate that the ultrasound assisted nanofluid micro lubrication milling method has a significant effect on improving the milling surface quality. By examining the quality of milling surfaces under different processing parameters, it was found that the optimal milling surface quality was achieved when the spindle speed was 600 rpm, the feed rate was 60 mm/min, and the milling depth was 0.1 mm. In addition, by comparing conventional milling, ultrasonic vibration-assisted milling (UVAM), and nanofluid minimal quantity lubrication (NMQL)-assisted milling, it was found that under the same milling parameters, the surface roughness of ultrasonic synergistic nanofluid minimal quantity lubrication milling was reduced by 49.8%, 42.8%, and 15.2%, respectively, and the depth of plastic deformation layer was reduced by 64.6%, 61.2%, and 38.7%, respectively. In addition, this processing method has a certain effect on improving the hardness of the processed material, with a 20.8% increase compared to the substrate hardness.

Strain-specific probiotics can present antioxidant activity and reduce damage caused by oxidation. Streptococcus alactolyticus strain FGM ( S. alactolyticus strain FGM) isolated from the chicken cecum shows potential probiotic properties which have been previously demonstrated. However, the antioxidant properties of S. alactolyticus strain FGM remain unknown. In this view, cell-free supernatant (CFS), intact cells (IC) and intracellular extracts (CFE) of strain FGM and 3 strains of Lactobacillus ( LAB ) were prepared, and their scavenging capacities against DPPH, hydroxyl radicals and linoleic acid peroxidation inhibitory were compared in this study. The effects of strain FGM cell-free supernatant (FCFS) on NO production, activity of SOD and GSH-Px in RAW264.7 cells and LPS-induced RAW264.7 cells were analyzed. The metabolites in the supernatant were quantitated by N300 Quantitative Metabolome. It was shown that the physicochemical characteristics of CFS to scavenge DPPH, hydroxyl radicals, and linoleic acid peroxidation inhibitory were significantly stronger than that of IC and CFE in the strain FGM ( P  < 0.05), respectively 87.12% ± 1.62, 45.03% ± 1.27, 15.63% ± 1.34. FCFS had a promotional effect on RAW264.7 cells, and significantly elevated SOD and GSH-Px activities in RAW264.7 cells. 25 μL FCFS significantly promoted the proliferation of RAW264.7 cells induced by LPS, increased the activities of SOD and GSH-PX, and decreased the release of NO. Furthermore, among the differential metabolites of FCFS quantified by N300, 12 metabolites were significantly up-regulated, including lactic acid, indole lactic acid, linoleic acid, pyruvic acid etc., many of which are known with antioxidant properties. In conclusion, FCFS had good antioxidant properties and activity, which can be attributed to metabolites produced from strain FGM fermentation. It was further confirmed that S. alactolyticus strain FGM and its postbiotic have potential probiotic properties and bright application prospects in livestock and poultry breeding.

Acupuncture is one of the most promising modalities in complimentary medicine. However, the underlying mechanisms are not well understood yet. We found that in TRPV2 knockout male mice, acupuncture-induced analgesia was suppressed with a decreased activation of mast cells in the acupoints stimulated. The mast cell stabilizer sodium cromolyn could suppress the release of adenosine in the acupoints on male rats. A direct injection of adenosine A1 receptor agonist or histamine H1 receptor agonist increased β-endorphin in the cerebral-spinal fluid in the acute adjuvant arthritis male rats and thus replicated the analgesic effect of acupuncture. These observations suggest that the mast cell is the central structure of acupoints and is activated by acupuncture through TRPV2 channels. The mast cell transduces the mechanical stimuli to acupuncture signal by activating either H1 or A1 receptors, therefore triggering the acupuncture effect in the subject. These findings might open new frontiers for acupuncture research.

Coronavirus disease 2019 (COVID-19) continues to constitute an international public health emergency. Vaccination is a prospective approach to control this pandemic. However, apprehension about the safety of vaccines is a major obstacle to vaccination. Amongst health professionals, one evident concern is the risk of antibody-dependent enhancement (ADE), which may increase the severity of COVID-19. To explore whether ADE occurs in severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infections and increase confidence in the safety of vaccination, we conducted a meta-analysis to investigate the relationship between post-immune infection and disease severity from a population perspective. Databases, including PubMed, EMBASE, Chinese National Knowledge Infrastructure, SinoMed, Scopus, Science Direct, and Cochrane Library, were searched for articles on SARS-CoV-2 reinfection published until 25 October 2021. The papers were reviewed for methodological quality, and a random effects model was used to analyse the results. Heterogeneity was assessed using the I 2 statistic. Publication bias was evaluated using a funnel plot and Egger’s test. Eleven studies were included in the final meta-analysis. The pooled results indicated that initial infection and vaccination were protective factors against severe COVID-19 during post-immune infection ( OR  = 0.55, 95% CI  = 0.31–0.98). A subgroup (post-immune infection after natural infection or vaccination) analysis showed similar results. Primary SARS-CoV-2 infection and vaccination provide adequate protection against severe clinical symptoms after post-immune infection. This finding demonstrates that SARS-CoV-2 may not trigger ADE at the population level.

Semantic segmentation of remote sensing (RS) images is vital in various practical applications, including urban construction planning, natural disaster monitoring, and land resources investigation. However, RS images are captured by airplanes or satellites at high altitudes and long distances, resulting in ground objects of the same category being scattered in various corners of the image. Moreover, objects of different sizes appear simultaneously in RS images. For example, some objects occupy a large area in urban scenes, while others only have small regions. Technically, the above two universal situations pose significant challenges to the segmentation with a high quality for RS images. Based on these observations, this paper proposes a Mask2Former with an improved query (IQ2Former) for this task. The fundamental motivation behind the IQ2Former is to enhance the capability of the query of Mask2Former by exploiting the characteristics of RS images well. First, we propose the Query Scenario Module (QSM), which aims to learn and group the queries from feature maps, allowing the selection of distinct scenarios such as the urban and rural areas, building clusters, and parking lots. Second, we design the query position module (QPM), which is developed to assign the image position information to each query without increasing the number of parameters, thereby enhancing the model’s sensitivity to small targets in complex scenarios. Finally, we propose the query attention module (QAM), which is constructed to leverage the characteristics of query attention to extract valuable features from the preceding queries. Being positioned between the duplicated transformer decoder layers, QAM ensures the comprehensive utilization of the supervisory information and the exploitation of those fine-grained details. Architecturally, the QSM, QPM, and QAM as well as an end-to-end model are assembled to achieve high-quality semantic segmentation. In comparison to the classical or state-of-the-art models (FCN, PSPNet, DeepLabV3+, OCRNet, UPerNet, MaskFormer, Mask2Former), IQ2Former has demonstrated exceptional performance across three publicly challenging remote-sensing image datasets, 83.59 mIoU on the Vaihingen dataset, 87.89 mIoU on Potsdam dataset, and 56.31 mIoU on LoveDA dataset. Additionally, overall accuracy, ablation experiment, and visualization segmentation results all indicate IQ2Former validity.

Background To investigate the risk factors for new vertebral compression fractures (NVCFs) after percutaneous kyphoplasty (PKP) for osteoporotic vertebral compression fractures (OVCFs) and to create a nomogram to predict the occurrence of new postoperative fractures. Methods This was a retrospective analysis of the clinical data of 529 OVCF patients who received PKP treatment in our hospital from June 2017 to June 2020. Based on whether there were new fractures within 2 years after surgery, the patients were divided into a new fracture group and a nonnew fracture group. Univariate and multivariate analyses were used to determine the risk factors for the occurrence of NVCFs after surgery. The data were randomly divided into a training set (75%) and a testing set (25%). Nomograms predicting the risk of NVCF occurrence were created based on the results of the multivariate analysis, and performance was evaluated using receiver operating characteristic curves (ROCs), calibration curves, and decision curve analyses (DCAs). A web calculator was created to give clinicians a more convenient interactive experience. Results A total of 56 patients (10.6%) had NVCFs after surgery. The univariate analysis showed significant differences in sex and the incidences of cerebrovascular disease, a positive fracture history, and bone cement intervertebral leakage between the two groups ( P  < 0.05). The multivariate analysis showed that sex [ OR  = 2.621, 95% CI (1.030–6.673), P  = 0.043], cerebrovascular disease [ OR  = 28.522, 95% CI (8.749–92.989), P  = 0.000], fracture history [ OR  = 12.298, 95% CI (6.250–24.199), P  = 0.000], and bone cement intervertebral leakage [ OR  = 2.501, 95% CI (1.029–6.082), P  = 0.043] were independent risk factors that were positively associated with the occurrence of NVCFs. The AUCs of the model were 0.795 ( 95% CI : 0.716–0.874) and 0.861 ( 95% CI : 0.749–0.974) in the training and testing sets, respectively, and the calibration curves showed high agreement between the predicted and actual states. The areas under the decision curve were 0.021 and 0.036, respectively. Conclusion Female sex, cerebrovascular disease, fracture history and bone cement intervertebral leakage are risk factors for NVCF after PKP. Based on this, a highly accurate nomogram was developed, and a webpage calculator ( https://new-fracture.shinyapps.io/DynNomapp/ ) was created.

Background With the accumulation of phylogenomic data and the growing demand for bioinformatics analyses, it has become increasingly important and complex to construct evolutionary relationships for different research purposes. Therefore, the ability to support multiple scenarios has become an essential need for phylogenetic visualization. Results In this study, we present PhyloScape, a web-based application for interactive visualization of phylogenetic trees that can be used stand-alone or as a toolkit deployed on the users’ website. The platform supports customizable multiple visualization features and is equipped with a flexible metadata annotation system, providing researchers with publishable, interactive views of trees. PhyloScape extensions include views of amino acid identity, geometry, and protein structure, which are applicable to various areas such as microbial taxonomy, pathogen phylogeny, and plant conservation. Trees published on the website can be efficiently shared and integrated into the users’ own system via a unique address. Conclusions As a scalable platform, PhyloScape provides a variety of online plug-ins that users can easily combine for specific scenarios. PhyloScape is freely available at http://darwintree.cn/PhyloScape .

Semantic segmentation of remote-sensing (RS) images is one of the most fundamental tasks in the understanding of a remote-sensing scene. However, high-resolution RS images contain plentiful detailed information about ground objects, which scatter everywhere spatially and have variable sizes, styles, and visual appearances. Due to the high similarity between classes and diversity within classes, it is challenging to obtain satisfactory and accurate semantic segmentation results. This paper proposes a Dynamic High-Resolution Network (DyHRNet) to solve this problem. Our proposed network takes HRNet as a super-architecture, aiming to leverage the important connections and channels by further investigating the parallel streams at different resolution representations of the original HRNet. The learning task is conducted under the framework of a neural architecture search (NAS) and channel-wise attention module. Specifically, the Accelerated Proximal Gradient (APG) algorithm is introduced to iteratively solve the sparse regularization subproblem from the perspective of neural architecture search. In this way, valuable connections are selected for cross-resolution feature fusion. In addition, a channel-wise attention module is designed to weight the channel contributions for feature aggregation. Finally, DyHRNet fully realizes the dynamic advantages of data adaptability by combining the APG algorithm and channel-wise attention module simultaneously. Compared with nine classical or state-of-the-art models (FCN, UNet, PSPNet, DeepLabV3+, OCRNet, SETR, SegFormer, HRNet+FCN, and HRNet+OCR), DyHRNet has shown high performance on three public challenging RS image datasets (Vaihingen, Potsdam, and LoveDA). Furthermore, the visual segmentation results, the learned structures, the iteration process analysis, and the ablation study all demonstrate the effectiveness of our proposed model.

This paper considers the two-dimensional (2D) anchorless localization problem for sensor networks in global positioning system (GPS)-denied environments. We present an efficient method, based on the multidimensional scaling (MDS) algorithm, in order to estimate the positions of the nodes in the network using measurements of the inter-node distances. The proposed method takes advantage of the mobility of the nodes to address the location ambiguity problem, i.e., rotation and flip ambiguity, which arises in the anchorless MDS algorithm. Knowledge of the displacement of the moving node is used to produce an analytical solution for the noise-free case. Subsequently, a least squares estimator is presented for the noisy scenario and the associated closed-form solution derived. The simulations show that the proposed algorithm accurately and efficiently estimates the locations of nodes, outperforming alternative methods.

Magnetic monitoring of maritime environments is an important problem for monitoring and optimising shipping, as well as national security. New developments in compact, fibre-coupled quantum magnetometers have led to the opportunity to critically evaluate how best to create such a sensor network. Here we explore various magnetic sensor network architectures for target identification. Our modelling compares networks of scalar vs. vector magnetometers. We implement an unscented Kalman filter approach to perform target tracking, and we find that vector networks provide a significant improvement in target tracking, specifically tracking accuracy and resilience compared with scalar networks.