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Road traffic elements are important components of roads and the main elements of structuring basic traffic geographic information databases. However, the following problems still exist in the detection and recognition of road traffic elements: dense elements, poor detection effect of multi-scale objects, and small objects being easily affected by occlusion factors. Therefore, an adaptive spatial feature fusion (ASFF) YOLOv5 network (ASFF-YOLOv5) was proposed for the automatic recognition and detection of multiple multiscale road traffic elements. First, the K-means++ algorithm was used to make clustering statistics on the range of multiscale road traffic elements, and the size of the candidate box suitable for the dataset was obtained. Then, a spatial pyramid pooling fast (SPPF) structure was used to improve the classification accuracy and speed while achieving richer feature information extraction. An ASFF strategy based on a receptive field block (RFB) was proposed to improve the feature scale invariance and enhance the detection effect of small objects. Finally, the experimental effect was evaluated by calculating the mean average precision (mAP). Experimental results showed that the mAP value of the proposed method was 93.1%, which is 19.2% higher than that of the original YOLOv5 model.

Interleukin-7 (IL-7), a molecule known for its growth-promoting effects on progenitors of B cells, remains one of the most extensively studied cytokines. It plays a vital role in health maintenance and disease prevention, and the congenital deficiency of IL-7 signaling leads to profound immunodeficiency. IL-7 contributes to host defense by regulating the development and homeostasis of immune cells, including T lymphocytes, B lymphocytes, and natural killer (NK) cells. Clinical trials of recombinant IL-7 have demonstrated safety and potent immune reconstitution effects. In this article, we discuss IL-7 and its functions in immune cell development, drawing on a substantial body of knowledge regarding the biology of IL-7. We aim to answer some remaining questions about IL-7, providing insights essential for designing new strategies of immune intervention.

Background Microglial activation-mediated neuroinflammation is one of the essential pathogenic mechanisms of sepsis-associated encephalopathy (SAE). Mounting evidence suggests that high mobility group box-1 protein (HMGB1) plays a pivotal role in neuroinflammation and SAE, yet the mechanism by which HMGB1 induces cognitive impairment in SAE remains unclear. Therefore, this study aimed to investigate the mechanism of HMGB1 underlying cognitive impairment in SAE. Methods An SAE model was established by cecal ligation and puncture (CLP); animals in the sham group underwent cecum exposure alone without ligation and perforation. Mice in the inflachromene (ICM) group were continuously injected with ICM intraperitoneally at a daily dose of 10 mg/kg for 9 days starting 1 h before the CLP operation. The open field, novel object recognition, and Y maze tests were performed on days 14–18 after surgery to assess locomotor activity and cognitive function. HMGB1 secretion, the state of microglia, and neuronal activity were measured by immunofluorescence. Golgi staining was performed to detect changes in neuronal morphology and dendritic spine density. In vitro electrophysiology was performed to detect changes in long-term potentiation (LTP) in the CA1 of the hippocampus. In vivo electrophysiology was performed to detect the changes in neural oscillation of the hippocampus. Results CLP-induced cognitive impairment was accompanied by increased HMGB1 secretion and microglial activation. The phagocytic capacity of microglia was enhanced, resulting in aberrant pruning of excitatory synapses in the hippocampus. The loss of excitatory synapses reduced neuronal activity, impaired LTP, and decreased theta oscillation in the hippocampus. Inhibiting HMGB1 secretion by ICM treatment reversed these changes. Conclusions HMGB1 induces microglial activation, aberrant synaptic pruning, and neuron dysfunction in an animal model of SAE, leading to cognitive impairment. These results suggest that HMGB1 might be a target for SAE treatment.

One-step adsorptive purification of ethylene (C 2 H 4 ) from four-component gas mixtures comprising acetylene (C 2 H 2 ), ethylene (C 2 H 4 ), ethane (C 2 H 6 ) and carbon dioxide (CO 2 ) is an unmet challenge in the area of commodity purification. Herein, we report that the ultramicroporous sorbent Zn-atz-oba (H 2 oba = 4,4-dicarboxyl diphenyl ether; Hatz = 3-amino-1,2,4-triazole) enables selective adsorption of C 2 H 2 , C 2 H 6 and CO 2 over C 2 H 4 thanks to the binding sites that lie in its undulating pores. Molecular simulations provide insight into the binding sites in Zn-atz-oba that are responsible for coadsorption of C 2 H 2 , C 2 H 6 and CO 2 over C 2 H 4 . Dynamic breakthrough experiments demonstrate that the selective binding exhibited by Zn-atz-oba can produce polymer-grade purity (>99.95%) C 2 H 4 from binary (1:1 for C 2 H 4 /C 2 H 6 ), ternary (1:1:1 for C 2 H 2 /C 2 H 4 /C 2 H 6 ) and quaternary (1:1:1:1 for C 2 H 2 /C 2 H 4 /C 2 H 6 /CO 2 ) gas mixtures in a single step. The purification of ethylene is an industrially relevant process. Here, the authors report the one-step separation of ethylene from quaternary gas mixtures of hydrocarbons and CO 2 using a single metal–organic framework-based physisorbent.

In recent years, artificial neural networks (ANNs) and deep learning have become increasingly popular across a wide range of scientific and technical fields, including fluid mechanics. While it will take time to fully grasp the potentialities as well as the limitations of these methods, evidence is starting to accumulate that point to their potential in helping solve problems for which no theoretically optimal solution method is known. This is particularly true in fluid mechanics, where problems involving optimal control and optimal design are involved. Indeed, such problems are famously difficult to solve effectively with traditional methods due to the combination of non linearity, non convexity, and high dimensionality they involve. By contrast, deep reinforcement learning (DRL), a method of optimization based on teaching empirical strategies to an ANN through trial and error, is well adapted to solving such problems. In this short review, we offer an insight into the current state of the art of the use of DRL within fluid mechanics, focusing on control and optimal design problems.

TBM vibration is inevitable during excavation in hard rock or mixed face ground conditions (MFC) and is detrimental to equipment safety and environmental protection. However, the cutting-induced vibration can help clarify the TBM-ground interaction and provide a valuable ground identification approach. In the present investigation, a field measurement of TBM vibration in changing ground conditions was carried out with accelerometers mounted on the TBM bulkhead. The vibration characteristics and patterns under different ground conditions were compared by signal processing, and their relationships to the operating parameters were investigated. The results showed that the TBM dynamic response was highly dependent on geological conditions. In homogeneous soft ground (HSG), the magnitude of vibration was low and stable, while under the MFC, the high frequency and strong vibration occurred. The vibration waveform in the MFC had an apparent periodicity and was consistent with the cutter head rotation speed. In addition, the signal consisted of a series of periodical impulses with intervals, revealing the rock-cutting process. Since the TBM vibration was sensitive to ground changes, it could provide valuable and precise information about the cutting face ground conditions. This investigation of the relationship between the ground and vibration can provide a foundation for vibration-based ground identification, which is a potential application in simultaneously tracking geological conditions during tunneling.

Synaptic vesicle fusion, as evoked by action potentials, is confined to presynaptic protein nanoclusters, which are closely aligned with concentrated postsynaptic receptors and their scaffolding proteins—an organization termed a ‘nanocolumn’. Molecular organization in a single synapse Efficient neurotransmission has long been suspected to require precise alignment between pre-synaptic vesicle release sites and post-synaptic receptors, but direct observations have been hampered by the physics of light-microscopy. Thomas Blanpied and colleagues use hyper-resolution microscopy — which overcomes the diffraction barrier — to reveal that vesicular fusion at single synapses, as evoked by action potentials, is confined to pre-synaptic protein nanoclusters. The nanoclusters are closely aligned with concentrated post-synaptic receptors and their scaffolding proteins. The resulting molecular 'nanocolumns' are reorganized during NMDA receptor-dependent plasticity, and the authors suggest that they may contribute to the maintenance and regulation of synaptic efficiency. Synaptic transmission is maintained by a delicate, sub-synaptic molecular architecture, and even mild alterations in synapse structure drive functional changes during experience-dependent plasticity and pathological disorders 1 , 2 . Key to this architecture is how the distribution of presynaptic vesicle fusion sites corresponds to the position of receptors in the postsynaptic density. However, while it has long been recognized that this spatial relationship modulates synaptic strength 3 , it has not been precisely described, owing in part to the limited resolution of light microscopy. Using localization microscopy, here we show that key proteins mediating vesicle priming and fusion are mutually co-enriched within nanometre-scale subregions of the presynaptic active zone. Through development of a new method to map vesicle fusion positions within single synapses in cultured rat hippocampal neurons, we find that action-potential-evoked fusion is guided by this protein gradient and occurs preferentially in confined areas with higher local density of Rab3-interacting molecule (RIM) within the active zones. These presynaptic RIM nanoclusters closely align with concentrated postsynaptic receptors and scaffolding proteins 4 , 5 , 6 , suggesting the existence of a trans-synaptic molecular ‘nanocolumn’. Thus, we propose that the nanoarchitecture of the active zone directs action-potential-evoked vesicle fusion to occur preferentially at sites directly opposing postsynaptic receptor–scaffold ensembles. Remarkably, NMDA receptor activation triggered distinct phases of plasticity in which postsynaptic reorganization was followed by trans-synaptic nanoscale realignment. This architecture suggests a simple organizational principle of central nervous system synapses to maintain and modulate synaptic efficiency.

Multi-temporal Interferometric Synthetic Aperture Radar technique (MTInSAR) has emerged as a valuable tool for measuring ground motion in a wide area. However, interpreting displacement time series and identifying dangerous signals from millions of InSAR coherent targets is challenging. In this study, we propose a method combining stacked autoencoder (SAE) and convolutional neural network (CNN) to classify InSAR time series and ease the interpretation of movements. The InSAR time series are classified into five categories, including stable, linear, accelerating, deceleration, and phase unwrapping error (PUE). The accuracy of labeled samples reaches 95.1%, reflecting the performance of the proposed method. This method was applied to the InSAR results for Kunming extracted from 171 ascending Sentinel-1 images from January 2017 to September 2022. The classification map of the InSAR time series shows that stable coherent points dominate around 79.28% of the area, with linear patterns at 10.70%, decelerating at 5.30%, accelerating at 4.72%, and PUE patterns at 3.60%. The results demonstrate that this method can distinguish different ground motion features and detect nonlinear deformation signals on a large scale without human intervention.

The necessity of therapeutic drug monitoring (TDM) for vancomycin is controversial. The objective of the current review was to evaluate the available evidence for the necessity of TDM in patients given vancomycin to treat Gram-positive infections. Medline, Embase, Web of Sciences, the Cochrane Library and two Chinese literature databases (CNKI, CBM) were searched. Randomized controlled studies and observational studies that compared the clinical outcomes of TDM groups vs. non-TDM groups were included. Two reviewers independently extracted the data. The primary outcome was clinical efficacy of therapy. Secondary outcomes included vancomycin associated nephrotoxicity, duration of vancomycin therapy, length of hospital stay, and mortality. Meta-analysis was performed using the Mantel-Haenszel fixed effect method (FEM). Odds ratios (ORs) or weighted mean differences (WMD) with 95% confidence intervals (95%CIs) were calculated for categorical and continuous outcomes, respectively. One randomized controlled trial (RCT) and five cohort studies were included in the meta-analysis. Compared with non-TDM groups, TDM groups had significantly higher rates of clinical efficacy (OR = 2.62, 95%CI 1.34-5.11 P = 0.005) and decreased rates of nephrotoxicity (OR = 0.25, 95%CI 0.13-0.48 P<0.0001). Subgroup analyses showed that TDM group had significantly higher rates of clinical efficacy in both cohort studies subgroup (OR = 3.04, 95%CI 1.34-6.90) and in Asian population subgroup (OR = 3.04, 95%CI 1.34-6.90). TDM group had significantly decreased rates of nephrotoxicity in all subgroup. There was no significant difference in duration of vancomycin therapy (WMD = -0.40, 95%CI -2.83-2.02 P = 0.74) or length of stay (WMD = -1.01, 95%CI -7.51-5.49 P = 0.76) between TDM and non-TDM groups. Subgroup analyses showed there were no differences in duration of vancomycin therapy. Only one study reported mortality rates. Studies to date show that TDM significantly increases the rate of clinical efficacy and decreases the rate of nephrotoxicity in patients treated with vancomycin.