Eriodictyol attenuates spinal cord injury by activating Nrf2/HO-1 pathway and inhibiting NF-κB pathway

Purpose: To investigate the effect of eriodictyol on spinal cord injury (SCI) and its underlying mechanism of action.Methods: Thirty Sprague-Dawley rats were assigned to sham, SCI, and eriodictyol-treated groups (SCI + Eri; 10, 20, and 50 mg/kg). Moderate spinal cord contusion injury was induced to model SCI. Locomotor recovery was assessed based on Basso, Beattie, and Bresnahan (BBB) score. Pain wasevaluated by paw withdrawal threshold (PWT) and latency (PWL), and spinal cord water content was measured. Tumor necrosis factor-alpha (TNF-α), interleukin-1 beta (IL-1β), and interleukin-6 (IL-6) expression were determined by enzyme-linked immunosorbent assay (ELISA) and reverse transcriptase quantitative polymerase chain reaction (RT-qPCR). Immunoassay was used to determine malondialdehyde (MDA), superoxide dismutase (SOD), glutathione (GSH), and glutathione peroxidase (GSH-PX) levels while Western blotting was employed to evaluate nuclear factor erythroid 2-related factor 2 (Nrf2), heme oxygenase-1 (HO-1), nuclear factor-kappa B (NF-κB), and phosphorylated NF-κB (p-NF-κB) levels.Results: Eriodictyol elevated BBB score, PWT, and PWL in SCI rats but reduced spinal cord water content (p < 0.05). Eriodictyol treatment down-regulated TNF-α, IL-1β, IL-6, and MDA, whereas SOD, GSH, and GSH-PX levels were elevated (p < 0.05). Eriodictyol administration increased Nrf2 and HO-1 levels but reduced p-NF-κB/NF-κB.Conclusion: This study provides a potential therapy to promote long-term functional recovery following SCI. Keywords: Spinal cord injury, Eriodictyol, Nrf2/HO-1 pathway, NF-κB signaling pathway, Polymerase chain reaction, Basso, Beattie and Bresnahan scor

Image-Adaptive YOLO for Object Detection in Adverse Weather Conditions

Though deep learning-based object detection methods have achieved promising results on the conventional datasets, it is still challenging to locate objects from the low-quality images captured in adverse weather conditions. The existing methods either have difficulties in balancing the tasks of image enhancement and object detection, or often ignore the latent information beneficial for detection. To alleviate this problem, we propose a novel Image-Adaptive YOLO (IA-YOLO) framework, where each image can be adaptively enhanced for better detection performance. Specifically, a differentiable image processing (DIP) module is presented to take into account the adverse weather conditions for YOLO detector, whose parameters are predicted by a small convolutional neural net-work (CNN-PP). We learn CNN-PP and YOLOv3 jointly in an end-to-end fashion, which ensures that CNN-PP can learn an appropriate DIP to enhance the image for detection in a weakly supervised manner. Our proposed IA-YOLO approach can adaptively process images in both normal and adverse weather conditions. The experimental results are very encouraging, demonstrating the effectiveness of our proposed IA-YOLO method in both foggy and low-light scenarios.Comment: AAAI 2022, Preprint version with Appendi

Enhanced Boundary Learning for Glass-like Object Segmentation

Glass-like objects such as windows, bottles, and mirrors exist widely in the real world. Sensing these objects has many applications, including robot navigation and grasping. However, this task is very challenging due to the arbitrary scenes behind glass-like objects. This paper aims to solve the glass-like object segmentation problem via enhanced boundary learning. In particular, we first propose a novel refined differential module that outputs finer boundary cues. We then introduce an edge-aware point-based graph convolution network module to model the global shape along the boundary. We use these two modules to design a decoder that generates accurate and clean segmentation results, especially on the object contours. Both modules are lightweight and effective: they can be embedded into various segmentation models. In extensive experiments on three recent glass-like object segmentation datasets, including Trans10k, MSD, and GDD, our approach establishes new state-of-the-art results. We also illustrate the strong generalization properties of our method on three generic segmentation datasets, including Cityscapes, BDD, and COCO Stuff. Code and models is available at \url{https://github.com/hehao13/EBLNet}.Comment: ICCV-2021 Code is availabe at https://github.com/hehao13/EBLNe

Bioenzyme activation preparation of Fe3O/carbon nanofibers as supercapacitor electrode materials

A new activation method for carbon-based pore expansion of composite materials was developed using the biocatalytic principle of amylase to hydrolyze cyclodextrin into small molecules of maltose and glucose. The composite carbon nanofiber mats were prepared by electrospinning with polyacrylonitrile (PAN), α-cyclodextrin, iron acetylacetonate as the iron oxide precursor, and hemp straw-based liquefied carbon as the electrospinning precursors. The α-cyclodextrin was hydrolyzed by medium-temperature α-amylase to generate pores, and a composite electrode material of carbon nanofibers with controlled iron oxide/porous structure was prepared through pre-oxidation and carbonization. Based on the morphology and structure of the prepared electrode materials and the electrochemical performance of three electrodes and two electrodes, it can be concluded that it is feasible to prepare electrochemical materials with the pore structure of carbon nanofibers by the enzyme pore enlarging method. Meanwhile, the FePCNF1 reaches 314 F g−1; at the current density 10 A g−1, over 75.6% of initial capacitance is retained as the current density improves from 1 to 10 A g−1 and also exhibits an excellent cycling performance with 62% capacitance retention after 15,000 times charge/discharge cycles

Indeterminate pulmonary subsolid nodules in patients with no history of cancer: growing prediction, CT pattern, and pathological diagnosis

PURPOSEWe aimed to evaluate and compare the growth patterns among pathological types of inde- terminate subsolid nodules in patients without a history of cancer as observed on computed tomography (CT).METHODSThis retrospective study included 77 consecutive patients with 80 indeterminate subsolid nod- ules on unenhanced thin-section CT. Subsolid nodules were classified into 2 growth pattern groups based on volume: growth (n = 35) and non-growth (n = 42). According to the pathologi- cal diagnosis, subsolid nodules were further subdivided into 3 groups: adenocarcinoma in situ (growth, n = 8 vs. non-growth, n = 22), minimally invasive adenocarcinoma (n = 14 vs. n = 15), and invasive adenocarcinoma (n=13 vs. n=5). Kaplan–Meier and Cox proportional hazards regres- sion analyses were performed to identify the risk factors for subsolid nodules growth. The CT findings of the 35 subsolid nodules in the growth group were compared among the 3 pathologi- cal groups.RESULTSIn the growth group, the overall mean volume doubling time and mass doubling time (MDT) were 811.5 days and 616.5 days, respectively. Patient’s age (odds ratio=1.041, P=.045) and CT subtype of non-solid nodule and part-solid nodule (odds ratio=3.430, P=.002) could predict subsolid nodule growth. The baseline volume, mass, and mean CT value were larger in the inva- sive adenocarcinoma group than in the adenocarcinoma in situ group (all P < .01). The shortest volume doubling time was observed in the invasive adenocarcinoma group, followed by the minimally invasive adenocarcinoma group and the adenocarcinoma in situ group. A shorter mass doubling time was observed in the minimally invasive adenocarcinoma group than in the adenocarcinoma in situ group (all P < .02).CONCLUSIONAs age increases, the risk of pulmonary subsolid nodule growth increases by 4% each year, and part-solid nodules have a 3 times higher risk of growth compared to non-solid nodules in patients with no history of cancer. Subsolid nodules with more aggressive pathological charac- teristics grow at a faster rate

PSR J1926-0652: A Pulsar with Interesting Emission Properties Discovered at FAST

We describe PSR J1926-0652, a pulsar recently discovered with the Five-hundred-meter Aperture Spherical radio Telescope (FAST). Using sensitive single-pulse detections from FAST and long-term timing observations from the Parkes 64-m radio telescope, we probed phenomena on both long and short time scales. The FAST observations covered a wide frequency range from 270 to 800 MHz, enabling individual pulses to be studied in detail. The pulsar exhibits at least four profile components, short-term nulling lasting from 4 to 450 pulses, complex subpulse drifting behaviours and intermittency on scales of tens of minutes. While the average band spacing P3 is relatively constant across different bursts and components, significant variations in the separation of adjacent bands are seen, especially near the beginning and end of a burst. Band shapes and slopes are quite variable, especially for the trailing components and for the shorter bursts. We show that for each burst the last detectable pulse prior to emission ceasing has different properties compared to other pulses. These complexities pose challenges for the classic carousel-type models.Comment: 13pages with 12 figure

Application of WeChat Teaching Platform in Interactive Translation Teaching

With the increasing use of smartphones in China, WeChat has become one of the most popular social applications. With its powerful functions, WeChat not only implements social features for its users but also provides a new way to fulfill mobile learning (M-learning), which is a form of distance education with the use of mobile devices. After analyzing M-learning models and WeChat’s features, this paper attempts to integrate WeChat’s interactive functions to construct a WeChat teaching platform under the guidance of constructive theory. The WeChat teaching platform was constructed based on WeChat’s multiple functions and with the support of wireless network technology. It can help to increase the interaction between students and teachers, because such interaction makes achieving ubiquitous learning for university students feasible. This empirical study proved that the new model is feasible and effective in facilitating interaction in translation teaching and in developing the students’ translation competence

Characterization and Performance of High-Flux PdAu/Ceramic Composite Membranes

A PdAu membrane was prepared by sequential electroless plating of Pd and Au onto the outside surface of porous gamma-Al(2)O(3) substrate followed by annealing and H(2)/N(2) performance evaluation. X-ray diffraction was employed to study the PdAu alloy phase formation at 823 K under H(2) and N(2) atmospheres. Annealing experiments demonstrated that 200 h was needed to form a stoichiometrically homogeneous, 2-mu m thick Pd(82.63)Am(17.37) membrane from sequentially deposited layers at 823 K and a noticeable particle agglomeration was observed under N(2) atmosphere. The performance of the PdAu alloy membrane was evaluated with respect to the H(2) flux and permselectivity between 823 and 423 K, which showed that the PdAu alloy membrane had higher H2 flux and lower activation energy than those of pure Pd membrane. The activation energy in the high temperature range is consistent with bulk diffusion-limited H2 transport, while the changes of these characteristics at lower temperatures below 573 K indicate desorption or surface limited H2 flux. After the conclusion of gas studies, the PdAu membrane was broken, the thickness and composition of the PdAu layers were determined by SEM and EDX completely