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Plant diseases caused by plant viruses and pathogens seriously affect crop yield and quality, and it is very difficult to control them. The discovery of new leads based on natural products is an important way to innovate pesticides. Based on the resveratrol is a kind of natural phytoalexin, but it cannot be used as candidate for the development of new drug due to its poor druggability. The phenolic hydroxyl groups in the resveratrol structure are easily destroyed by oxidation, in order to improve its stability, ester formation is the most commonly used modification method in drug design. Their structures were characterized by 1 H NMR, 13 C NMR and HRMS. The activity against tobacco mosaic virus (TMV) of these ester derivatives has been tested for the first time. The bioassay results showed part of the target compounds exhibited good to excellent in vivo activities against TMV. The optimum compounds III-2 (inhibitory rates of 50, 53, and 59% at 500 μg/mL for inactivation, curative, and protection activities in vivo, respectively), III-4 (inhibitory rates of 57, 59, and 51% at 500 μg/mL, respectively), and II-5 (inhibitory rates of 54, 52, and 51% at 500 μg/mL, respectively) displayed higher activity than commercial plant virucide ribavirin (inhibitory rates of 38, 37, and 40% at 500 μg/mL, respectively). Compounds I-9 and I-10 also showed excellent activities. The systematic study provides strong evidence that these simple resveratrol derivatives could become potential TMV inhibitors. The novel concise structure provides another new template for antiviral studies.

The quest to eradicate poverty, central to the United Nations Sustainable Development Goals (SDGs), poses a significant global challenge. Advancement in sustainable rural development is critical to this effort, requiring the seamless integration of environmental, economic, and governmental elements. Previous research often omits the complex interactions among these factors. Addressing this gap, this study evaluates sustainable rural development in China by examining the interconnection between agricultural production and government-led poverty reduction, with annual rainfall considered an influential factor of climate change impacts on these sectors and overall sustainability. Utilizing a Meta-frontier entropy network dynamic Directional Distance Function (DDF) within an exogenous Data Envelopment Analysis (DEA) model, we categorize China’s 27 provinces into southern and northern regions according to the Qinling-Huaihe line for a comparative study of environmental, economic, and governmental efficiency. This innovative approach overcomes the limitations of previous static analyses. The findings reveal: (1) Rainfall, as an exogenous variable, significantly affects agricultural production efficiency. (2) The overall efficiency in both southern and northern regions increases when accounting for rainfall. (3) Government effectiveness in poverty reduction is comparatively lower in the northern region than in the southern region when rainfall is considered. These insights underscore the importance of including climatic variables in sustainable development policies and emphasize the need for region-specific strategies to bolster resilience against climatic challenges.

Skeletal muscle is a very dynamic tissue, thus accurate quantification of skeletal muscle stiffness throughout its functional range is crucial to improve the physical functioning and independence following pathology. Shear wave elastography (SWE) is an ultrasound-based technique that characterizes tissue mechanical properties based on the propagation of remotely induced shear waves. The objective of this study is to validate SWE throughout the functional range of motion of skeletal muscle for three ultrasound transducer orientations. We hypothesized that combining traditional materials testing (MTS) techniques with SWE measurements will show increased stiffness measures with increasing tensile load, and will correlate well with each other for trials in which the transducer is parallel to underlying muscle fibers. To evaluate this hypothesis, we monitored the deformation throughout tensile loading of four porcine brachialis whole-muscle tissue specimens, while simultaneously making SWE measurements of the same specimen. We used regression to examine the correlation between Young′s modulus from MTS and shear modulus from SWE for each of the transducer orientations. We applied a generalized linear model to account for repeated testing. Model parameters were estimated via generalized estimating equations. The regression coefficient was 0.1944, with a 95% confidence interval of (0.1463–0.2425) for parallel transducer trials. Shear waves did not propagate well for both the 45° and perpendicular transducer orientations. Both parallel SWE and MTS showed increased stiffness with increasing tensile load. This study provides the necessary first step for additional studies that can evaluate the distribution of stiffness throughout muscle.

Ultrasound localization microscopy (ULM) enables deep tissue microvascular imaging by localizing and tracking intravenously injected microbubbles circulating in the bloodstream. However, conventional localization techniques require spatially isolated microbubbles, resulting in prolonged imaging time to obtain detailed microvascular maps. Here, we introduce LOcalization with Context Awareness (LOCA)-ULM, a deep learning-based microbubble simulation and localization pipeline designed to enhance localization performance in high microbubble concentrations. In silico, LOCA-ULM enhanced microbubble detection accuracy to 97.8% and reduced the missing rate to 23.8%, outperforming conventional and deep learning-based localization methods up to 17.4% in accuracy and 37.6% in missing rate reduction. In in vivo rat brain imaging, LOCA-ULM revealed dense cerebrovascular networks and spatially adjacent microvessels undetected by conventional ULM. We further demonstrate the superior localization performance of LOCA-ULM in functional ULM (fULM) where LOCA-ULM significantly increased the functional imaging sensitivity of fULM to hemodynamic responses invoked by whisker stimulations in the rat brain. Ultrasound localisation microscopy enables deep tissue microvascular imaging. Here, authors introduce LOCA-ULM, a deep learning pipeline enhancing localisation accuracy in high microbubble concentrations. LOCA-ULM reveals dense cerebrovascular networks and enhances the sensitivity of functional ULM.

Super-resolution ultrasound localization microscopy (ULM), based on localization and tracking of individual microbubbles (MBs), offers unprecedented microvascular imaging resolution at clinically relevant penetration depths. However, ULM is currently limited by the requirement of dilute MB concentrations to ensure spatially sparse MB events for accurate localization and tracking. The corresponding long imaging acquisition times (tens of seconds or several minutes) to accumulate sufficient isolated MB events for full reconstruction of microvasculature preclude the clinical translation of the technique. To break this fundamental tradeoff between acquisition time and MB concentration, in this paper we propose to separate spatially overlapping MB events into sub-populations, each with sparser MB concentration, based on spatiotemporal differences in the flow dynamics (flow speeds and directions). MB localization and tracking are performed for each sub-population separately, permitting more robust ULM imaging of high-concentration MB injections. The superiority of the proposed MB separation technique over conventional ULM processing is demonstrated in flow channel phantom data, and in the chorioallantoic membrane of chicken embryos with optical imaging as an in vivo reference standard. Substantial improvement of ULM is further demonstrated on a chicken embryo tumor xenograft model and a chicken brain, showing both morphological and functional microvasculature details at super-resolution within a short acquisition time (several seconds). The proposed technique allows more robust MB localization and tracking at relatively high MB concentrations, alleviating the need for dilute MB injections, and thereby shortening the acquisition time of ULM imaging and showing great potential for clinical translation.

In the process of producing winding coils for power transformers, it is necessary to detect the tilt angle of the winding, which is one of the important parameters that affects the physical performance indicators of the transformer. The current detection method is manual measurement using a contact angle ruler, which is not only time-consuming but also has large errors. To solve this problem, this paper adopts a contactless measurement method based on machine vision technology. Firstly, this method uses a camera to take pictures of the winding image and performs a 0° correction and preprocessing on the image, using the OTSU method for binarization. An image self-segmentation and splicing method is proposed to obtain a single-wire image and perform skeleton extraction. Secondly, this paper compares three angle detection methods: the improved interval rotation projection method, quadratic iterative least squares method, and Hough transform method and through experimental analysis, compares their accuracy and operating speed. The experimental results show that the Hough transform method has the fastest operating speed and can complete detection in an average of only 0.1 s, while the interval rotation projection method has the highest accuracy, with a maximum error of less than 0.15°. Finally, this paper designs and implements visualization detection software, which can replace manual detection work and has a high accuracy and operating speed.

Aging-related cognitive decline is an emerging health crisis; however, no established unifying mechanism has been identified for the cognitive impairments seen in an aging population. A vascular hypothesis of cognitive decline has been proposed but is difficult to test given the requirement of high-fidelity microvascular imaging resolution with a broad and deep brain imaging field of view, which is restricted by the fundamental trade-off of imaging penetration depth and resolution. Super-resolution ultrasound localization microscopy (ULM) offers a potential solution by exploiting circulating microbubbles to achieve a vascular resolution approaching the capillary scale without sacrificing imaging depth. In this report, we apply ULM imaging to a mouse model of aging and quantify differences in cerebral vascularity, blood velocity, and vessel tortuosity across several brain regions. We found significant decreases in blood velocity, and significant increases in vascular tortuosity, across all brain regions in the aged cohort, and significant decreases in blood volume in the cerebral cortex. These data provide the first-ever ULM measurements of subcortical microvascular dynamics in vivo within the context of the aging brain and reveal that aging has a major impact on these measurements.

The non-invasive technique of magnetoencephalography (MEG) has become increasingly vital for neuroscience research as well as for disease diagnosis and treatment. This article presents a portable and dynamic MEG system that utilizes a compact magnetically shielded room (MSR) integrated with a high-performance magnetic field compensation system and an MEG helmet equipped with an array of optically pumped magnetometer (OPM) sensors, achieving both high resolution and cost-effectiveness. The magnetic field compensation system employs two types of custom coils: external Helmholtz-like coils to adjust the distribution of residual magnetic fields, and internal planar coils to control magnetic field bias and disturbances. This configuration, combined with an active disturbance rejection control (ADRC) strategy, establishes an ultra-weak and stable measurement environment across a large uniform region. We then conducted experiments in which subjects wore the MEG helmet and underwent MEG measurements during periodic eye opening and closing and electrical median nerve stimulation, performed in both stationary and natural movement states. The above brain magnetic signals were measured in the occipital lobe and the postcentral gyrus, respectively. The results demonstrate that the MEG system maintains a comparable high detection resolution when the subject moves, while also offering significantly reduced cost and weight. The proposed system promises to deliver significant benefits for clinical practice and neuroscience research. •A portable and dynamic magnetoencephalography measurement system.•Two-stage magnetic compensation system used the Helmholtz-like and planar coils.•Brain magnetic measurement from alpha rhythms modulation and evoked response.

Background The spleen is the largest secondary lymphoid organ and the main site where stress erythropoiesis occurs. It is known that hypoxia triggers the expansion of erythroid progenitors; however, its effects on splenic gene expression are still unclear. Here, we examined splenic global gene expression patterns by time-series RNA-seq after exposing mice to hypoxia for 0, 1, 3, 5, 7 and 13 days. Results Morphological analysis showed that on the 3rd day there was a significant increase in the spleen index and in the proliferation of erythroid progenitors. RNA-sequencing analysis revealed that the overall expression of genes decreased with increased hypoxic exposure. Compared with the control group, 1380, 3430, 4396, 3026, and 1636 genes were differentially expressed on days 1, 3, 5, 7 and 13, respectively. Clustering analysis of the intersection of differentially expressed genes pointed to 739 genes, 628 of which were upregulated, and GO analysis revealed a significant enrichment for cell proliferation. Enriched GO terms of downregulated genes were associated with immune cell activation. Expression of Gata1 , Tal1 and Klf1 was significantly altered during stress erythropoiesis. Furthermore, expression of genes involved in the immune response was inhibited, and NK cells decreased. Conclusions The spleen of mice conquer hypoxia exposure in two ways. Stress erythropoiesis regulated by three transcription factors and genes in immune response were downregulated. These findings expand our knowledge of splenic transcriptional changes during hypoxia.

Tunnel point cloud semantic segmentation is a critical step in achieving refined perception and intelligent management of tunnel structures. Addressing common challenges including indistinct boundaries and fine-grained category discrimination, this paper proposes MFPNet, a multi-scale feature perception network specifically designed for tunnel scenarios. This approach employs kernel convolution to effectively model local point cloud geometries within continuous spaces. Building upon this foundation, an error-feedback-based local-global feature fusion mechanism is designed. Through bidirectional information exchange, higher-level semantic information compensates for and constrains lower-level geometric features, thereby mitigating information fragmentation across semantic hierarchies. Furthermore, an adaptive feature re-calibration and cross-scale contextual correlation mechanism is introduced to dynamically modulate multi-scale feature responses. This explicitly models contextual dependencies across scales, enabling collaborative aggregation and discriminative enhancement of multi-scale semantic information. Experimental results on tunnel point cloud datasets demonstrate that the proposed MFPNet has achieved significant improvements in both overall segmentation accuracy and category balance, with mIoU reaching 87.5%, which is 5.1% to 33.0% higher than mainstream methods such as PointNet++ and RandLA-Net, and the overall classification accuracy reaching 96.3%. These results validate the method’s efficacy in achieving high-precision three-dimensional semantic understanding within complex tunnel environments, providing robust technical support for tunnel digital twin and intelligent detection applications.