Search

Spinal cord injury triggers leukocyte mobilization from the peripheral circulation to the injury site, exacerbating spinal cord damage. Simultaneously, bone marrow hematopoietic stem cells (HSCs) and splenic leukocytes rapidly mobilize to replenish the depleted peripheral blood leukocyte pool. However, current treatments for spinal cord injuries overlook interventions targeting peripheral immune organs and tissues, highlighting the need to develop novel drugs capable of effectively regulating peripheral immunity and treating spinal cord injuries. In this study, we designed, synthesized, and characterized novel Ejiao carbon dots (EJCDs) that inhibit myeloid cell proliferation and peripheral migration by promoting HSC self-renewal, and distinct differentiation into erythroid progenitors in vitro and in vivo. Additionally, EJCDs attenuate the immune response in the spleen, leukocytes’ reservoir, following spinal cord injury by diminishing the local infiltration of monocytes and macrophages while promoting motor function recovery. These effects are mediated through the downregulation of CCAAT enhancer binding protein-β expression in the spleen and the upregulation of FZD4 protein expression in Lin− Sca-1+ c-kit+ cells (LSKs) within the bone marrow. Our findings demonstrate that EJCDs effectively reduce myeloid cell infiltration post-spinal cord injury and promote neurological recovery, making them promising therapeutic candidates for treating spinal cord injuries.

Spinal cord injury (SCI) is a highly complex neurological disease, but there is no effective repair method. Quercetin is a flavonol drug and has a variety of biological activities, such as scavenging oxygen free radicals in the body to resist oxidation, inhibiting inflammation, and so on. In this study, quercetin was firstly demonstrated to reduce tissue damage, promote neuron survival and repair motor function after SCI in rats through in vivo experiments. Then, 293 potential targets of quercetin repair for SCI were predicted by network pharmacology. GO analysis revealed that the biological processes of potential targets focused mainly on signal transduction, negative regulation of the apoptotic process, protein phosphorylation, drug response, and so on. Similarly, KEGG analysis suggested that these potential targets were involved in cell growth regulation, differentiation, apoptosis, and a few metabolic pathways. PPI network analysis predicted that the key genes were EP300, CREBBP, SRC, HSP90AA1, TP53, PIK3R1, EGFR, ESR1, and CBL. Further, the molecular docking showed that quercetin binds well with these proteins. Finally, RT-qPCR and Western blotting experiments verified that quercetin downregulated the expression levels of PIK3R1 and EGFR. It is suggested that quercetin can repair SCI in rats through PI3K-AKT signaling pathway and EGFR/MAPK pathway, which may provide a new theoretical basis for the repair of spinal cord injury.

Abstract Regulating the charge migration and separation in photoactive materials is a great challenge for developing photoelectrochemical (PEC) applications. Herein, inspired by capacitors, well‐defined CuInSe2/N‐doped carbon (CISe/N‐C) nanorod arrays are synthesized by Cu/In‐metal organic frame‐derived method. Like the charge process of capacitor, the N‐doped carbon can capture the photogenerated electron of CISe, and the strong interfacial coupling between CISe and N‐doped carbon can modulate the charge migration and separation. The optimized the CISe/N‐C photoanode achieves a maximum photocurrent of 4.28 mA cm−2 at 1.23 V versus reversible hydrogen electrode (RHE) in neutral electrolyte solution under AM 1.5 G simulated sunlight (100 mW cm‐2), which is 8.4 times higher than that of the CuInSe2 photoanode (0.51 mA cm‐2). And a benefit of the strong electronic coupling between CISe and N‐doped carbon, the charge transfer rate is increased to 1.3–13 times higher than that of CISe in the range of 0.6–1.1 V versus RHE. The interfacial coupling effects on modulating the carrier transfer dynamics are investigated by Kelvin probe force microscopy analysis and density functional theory calculation. This work provides new insights into bulk phase carrier modulation to improve the performance of photoanode for PEC water splitting.

Abstract Antimony selenide (Sb2Se3) is a promising candidate for photodetector applications boasting unique material benefits and remarkable optoelectronic properties. Achieving high‐performance self‐powered Sb2Se3 photodetector through a synergistic regulation of absorber layer and heterojunction interface demonstrates great potential and needs essential investigation. In this study, an effective two‐step thermodynamic/kinetic deposition technique containing sputtered and selenized Sb precursor is implemented to induce self‐assembled growth of Sb2Se3 light absorbing thin film with large crystal grains and desirable [hk1] orientation, presenting considerable thin‐film photodetector performance. Furthermore, aluminum (Al3+) cation dopant is introduced to modify the optoelectronic properties of CdS buffer layer, and further optimize the Sb2Se3/CdS (Al) heterojunction interface quality. Thanks to the suppressed carrier recombination and enhanced carrier transport kinetics, the champion Mo/Sb2Se3/CdS (Al)/ITO/Ag photodetector exhibits self‐powered and broadband characteristics, accompanied by simultaneously high responsivity of 0.9 A W−1 (at 11 nW cm−2), linear dynamic range of 120 dB, impressive ON/OFF switching ratio over 106 and signal‐to‐noise ratio of 109, record total noise determined realistic detectivity of 4.78 × 1012 Jones, and ultra‐fast response speed with rise/decay time of 24/75 ns, representing the top level for Sb2Se3‐based photodetectors. This intriguing work opens up an avenue for its self‐powered broadband photodetector applications.

Multifunctional coding metasurfaces can provide more options for highly integrated terahertz modulators, which has become one of the current research focus. This study proposed a tunable chiral metasurface based on a Dirac semimetal (DSM). Two U-shaped rings placed perpendicular to each other in the opening direction comprised the metasurface unit cell. Further, the multifunctional application of the metasurface in the terahertz region was realized through coding. The chiral metasurface exhibited strong chirality for incident circularly polarized light with circular dichroism (CD) of up to 0.72 at 2.31 THz. The combination of the chiral metasurface with the Pancharatnam-Berry (PB) phase aided in the realization of a wideband switchable terahertz vortex beam generator, arbitrary angular deflection tuning of the vortex beam, and multi-beam splitting operation of the vortex light using a Fourier convolution operation. In addition, switchable near-field imaging applications were developed by encoding metasurface unit cell cells according to their chirality and amplitude. The results of this study are expected to yield promising applications in future terahertz high-data-capacity communication systems, multi-target information transmission, and controlled near-field imaging.

Intelligent fault diagnosis of bearings has been a heated research topic in the prognosis and health management of rotary machinery systems, due to the increasing amount of available data collected by sensors. This has given rise to more and more business desire to apply data-driven methods for health monitoring of machines. In recent years, various deep learning algorithms have been adapted to this field, including multi-layer perceptrons, autoencoders, convolutional neural networks, and so on. Among these methods, autoencoder is of particular interest for us because of its simple structure and its ability to learn useful features from data in an unsupervised fashion. Previous studies have exploited the use of autoencoders, such as denoising autoencoder, sparsity aotoencoder, and so on, either with one layer or with several layers stacked together, and they have achieved success to certain extent. In this paper, a bearing fault diagnosis method based on fully-connected winner-take-all autoencoder is proposed. The model explicitly imposes lifetime sparsity on the encoded features by keeping only k% largest activations of each neuron across all samples in a mini-batch. A soft voting method is implemented to aggregate prediction results of signal segments sliced by a sliding window to increase accuracy and stability. A simulated data set is generated by adding white Gaussian noise to original signals to test the diagnosis performance under noisy environment. To evaluate the performance of the proposed method, we compare our methods with some state-of-the-art bearing fault diagnosis methods. The experiments result show that, with a simple two-layer network, the proposed method is not only capable of diagnosing with high precision under normal conditions, but also has better robustness to noise than some deeper and more complex models.

Shandong indigenous pig breeds are an invaluable source of data on genetics in Chinese pigs. However, information on the genetic basis of these breeds remains limited. In this study, we used specific-locus amplified fragment sequencing to conduct whole-genome screening to investigate genetic diversity in Shandong indigenous breeds and Western pig breeds. The results showed that Duroc pigs (DD) had clear genetic relationships with Dapulian pigs (DPL; Fst = 0.4386) and Laiwu pigs (LW; Fst = 0.5134), and DPL and LW were relatively close genetically (Fst = 0.2334). In general, Shandong indigenous breeds showed greater genetic variety than the Western breeds. Both neighbor-joining trees and principal components analyses were able to differentiate the breeds, but population structure analyses indicated that the Western breeds genetically influenced the Shandong indigenous breeds to some extent. A total of 162 differentially selected regions (DSRs) with 841 genes and 157 DSRs with 707 genes were identified in DPL and LW, respectively. Gene annotation of the selected regions identified a series of genes regulating immunity and fat deposition. Our data confirm the rationality and accuracy of the current classification of pig breeds in Shandong province. Our results point to candidate genes in Shandong indigenous pig breeds and further promote the importance of follow-up research on functional verification.

Oligodeoxynucleotides containing unmethylated CpG motifs (CpG ODN) mimic the immunostimulatory activity of microbial DNA by interacting with Toll-like receptor 9 (TLR9) to activate both the innate and adaptive immune responses in different species. However, few studies have been published to compare the effects of CpG ODN on different pig breeds. Therefore, in this study, whole blood gene expression profiles of DPL and Landrace pigs treated with CpG ODN were studied using RNA-seq technology. 500 differentially expressed genes (DEGs) were identified between the two breeds. DPL pigs had significantly higher number of immune-relevant DEGs than the Landrace pigs after CpG ODN treatment. Pathway analysis showed that cytokine-cytokine receptor interaction and chemokine signaling pathway were the major enriched pathways of the immune-relevant DEGs. Further in vitro experiments showed that PBMCs of the DPL pigs had significantly higher levels of TLR9 mRNA than those of the Landrace pigs, both before and after CpG ODN stimulation. Cytokine and chemokine induction in the PBMCs of both breeds were also measured after CpG ODN stimulation. Our data showed that mRNA levels of cytokines (IFNα, IL8, IL12 p40) and chemokines (CXCL9, CXCL13) were significantly higher in the PBMCs of the DPL pigs than those of the Landrace pigs. Taken together, our data provide new information regarding the pig breed difference in response to CpG ODN stimulation and that higher levels of TLR9 mRNA in DPL pigs may be a major contributor for disease resistance.

Intelligent fault diagnosis techniques have replaced time-consuming and unreliable human analysis, increasing the efficiency of fault diagnosis. Deep learning models can improve the accuracy of intelligent fault diagnosis with the help of their multilayer nonlinear mapping ability. This paper proposes a novel method named Deep Convolutional Neural Networks with Wide First-layer Kernels (WDCNN). The proposed method uses raw vibration signals as input (data augmentation is used to generate more inputs), and uses the wide kernels in the first convolutional layer for extracting features and suppressing high frequency noise. Small convolutional kernels in the preceding layers are used for multilayer nonlinear mapping. AdaBN is implemented to improve the domain adaptation ability of the model. The proposed model addresses the problem that currently, the accuracy of CNN applied to fault diagnosis is not very high. WDCNN can not only achieve 100% classification accuracy on normal signals, but also outperform the state-of-the-art DNN model which is based on frequency features under different working load and noisy environment conditions.