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Abstract Background and objective The recruitment of collateral circulation correlates with a balance of the microvasculature. Uncertainty remains to be made about the association of leukoaraiosis with leptomeningeal collaterals. To explore the effect of leukoaraiosis on leptomeningeal collaterals in patients treated with endovascular therapy. Methods Observational studies exploring the correlation between leukoaraiosis and leptomeningeal collaterals in large vessel occlusion treated with endovascular therapy were searched from PubMed, EMBASE, and Cochrane Libraries databases. Two independent reviewers retrieved eligible literature, extracted purpose-related data, and utilized the Newcastle–Ottawa Scale to evaluate the risk of bias. A Mantel–Haenszel method was used to calculate the odds ratio (OR). Meta-regression and subgroup analyses were conducted to clarify heterogeneity. Results Data from 10 studies with 1606 patients were extracted for pooled analysis. Compared to non-severe leukoaraiosis, patients with severe leukoaraiosis showed significant relevance to poor leptomeningeal collaterals (OR, 2.13; 95% confidence interval [1.27–3.57]; P = 0.004). Meta-regression indicated that sample size (coefficient = -0.007299, P = 0.035) and the number of female patients (coefficient = -0.0174709, P = 0.020) were sources of heterogeneity. Furthermore, all of the countries (USA versus France versus China, Q = 3.67, P = 0.159), various assessment scales of leukoaraiosis (the Fazekas scale versus Non-Fazekas scales, Q = 0.77, P = 0.379), and different imaging methods of leukoaraiosis (computed tomography versus magnetic resonance imaging, Q = 2.12, P = 0.146) and leptomeningeal collaterals (computed tomography angiography versus digital subtraction angiography, Q = 1.21, P = 0.271) showed no contribution to the effect size. Conclusion Severe leukoaraiosis is associated with poor leptomeningeal collaterals in patients treated with endovascular therapy. Further studies may focus on whether the finding applies to different stroke subtypes.

Corn straw is one kind of agricultural by-product containing 70–80% insoluble dietary fiber (IDF). In order to develop corn straw dietary fiber, this study was conducted to increase soluble dietary fiber (SDF) yield and improve the structure, functional and prebiotic properties of IDF and SDF from corn straw treated by alkali oxidation treatment, enzymatic hydrolysis, microbial fermentation and the combination of these methods. The results demonstrated that the yield of SDF was significantly increased from 2.64% to 17.15% after corn straw was treated by alkali oxidation treatment + Aspergillus niger fermentation + cellulase hydrolysis, compared with untreated corn straw. The SDF extracted from corn straw treated by alkali oxidation treatment + Aspergillus niger fermentation + cellulase hydrolysis (F-SDF) exhibited a honeycomb structure, low crystallinity (11.97%), good antioxidant capacity and high capacities of water holding, water solubility and cholesterol absorption and promoted short-chain fatty acids production by chicken cecal microbial fermentation in vitro. F-SDF enhanced the antibacterial activity against Escherichia coli and Staphylococcus aureus proliferations of Lactobacillus plantarum when it was used as a substrate for Lactobacillus plantarum fermentation. It could be concluded that the combined treatments could increase SDF yield from corn straw and improve its functional and prebiotic properties.

Magnetic anomaly detection (MAD) is an effective method to detect the existence and localization of magnetic targets. Magnetic signal processing technology can extract target signals from complex background noise. However, traditional magnetic signal processing methods cannot greatly improve the signal-to-noise ratio (SNR) while also restoring information concerning the target. This is because the main existing method to calculate a target’s magnetic moment requires a pure target signal. Our research regarding the full magnetic gradient orthonormal basis function (FMG-OBF) addresses the problem of low SNRs for the target magnetic anomaly (TMA) signal. However, this method can only detect the presence of the target and cannot obtain the magnetic moment characteristics of the target. Benefiting from the FMG tensor, which contains large amounts of spatial magnetic field information, this paper is devoted to characterizing TMA in different orientations from the signal energy point of view. We analyze the influences of the target magnetic moment’s variation on the energy components of the TMA signal in various orientations, and further propose a target magnetic moment orientation estimation method. Compared with the traditional signal processing method, the proposed method can estimate the magnetic moment orientation of the target while greatly improving the SNR. Therefore, this method has significant application potential for the classification and identification of weak TMA signals in MAD.

In this paper, we investigate the algorithms for traversal exploration and path coverage of target regions using multiple agents, enabling the efficient deployment of a set of agents to cover a complex region. First, the original multi-agent path planning problem (mCPP) is transformed into several single-agent sub-problems, by dividing the target region into multiple balanced sub-regions, which reduces the explosive combinatorial complexity; subsequently, closed-loop paths are planned in each sub-region by the rapidly exploring random trees (RRT) algorithm to ensure continuous exploration and repeated visits to each node of the target region. On this basis, we also propose two improvements: for the corner case of narrow regions, the use of geodesic distance is proposed to replace the Eulerian distance in Voronoi partitioning, and the iterations for balanced partitioning can be reduced by more than one order of magnitude; the Dijkstra algorithm is introduced to assign a smaller weight to the path cost when the geodesic direction changes, which makes the region division more “cohesive”, thus greatly reducing the number of turns in the path and making it more robust. The final optimization algorithm ensures the following characteristics: complete coverage of the target area, wide applicability of multiple area shapes, reasonable distribution of exploration tasks, minimum average waiting time, and sustainable exploration without any preparation phase.