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Abstract The development of unstable carotid atherosclerotic plaques is associated with the induction of neutrophil extracellular traps (NETs) via the activation of diverse inflammatory mediators in the circulating bloodstream. However, the underlying mechanisms through which NETs influence the microenvironment of atherosclerotic plaques and contribute to the development of unstable carotid plaques remain largely elusive. The objective of this study was to elucidate the role of myeloid differentiation protein 1 (MD-1, LY86)-induced NETs underlying the crosstalk between unstable plaque formation and the plaque microenvironment. We employed bioinformatics analysis to identify key genes associated with carotid-unstable plaque, followed by comprehensive validation using various experimental approaches on tissue specimens and plasma samples classified based on pathological characteristics. Patients with carotid-unstable plaques exhibited elevated plasma concentrations of MD-1 (LY86), while patients with stable plaques demonstrated comparatively lower levels. Furthermore, soluble MD-1 was found to induce the formation of NETs through activation of Toll-like receptor signaling pathway. The proliferative and immature vascularization effects of NETs on endothelial cells, as well as their inhibitory impact on cell migration, are directly correlated with the concentration of NETs. Additionally, NETs were found to activate the NF-κB signaling pathway, thereby upregulating ICAM1, VCAM1, MMP14, VEGFA, and IL6 expression in both Human umbilical vein endothelial cells (HUVECs) and HAECs. Subsequently, a significant increase in intraplaque neovascularization by NETs results in poor carotid plaque stability, and NETs in turn stimulate macrophages to produce more MD-1, generating a harmful positive feedback loop. Our findings suggest that soluble MD-1 in the bloodstream triggers the production of NETs through activation of the Toll-like receptor signaling pathway and further indicate NETs mediate a crosstalk between the microenvironment of the carotid plaque and the neovascularization of the intraplaque region. Inhibiting NETs formation or MD-1 secretion may represent a promising strategy to effectively suppress the development of unstable carotid plaques.

Osteoarthritis (OA) is an aging-associated disease characterized by joint stiffness pain and destroyed articular cartilage. Traditional treatments for OA are limited to alleviating various OA symptoms. There is a lack of drugs available in clinical practice that can truly repair cartilage damage. Here, we developed the chondroitin sulfate analog CS-semi5, semi-synthesized from chondroitin sulfate A. In vivo, CS-semi5 alleviated inflammation, provided analgesic effects, and protected cartilage in the modified Hulth OA rat model and papain-induced OA rat model. A bioinformatics analysis was performed on samples from OA patients and an exosome analysis on papain-induced OA rats, revealing miR-122-5p as the key regulator associated with CS-semi5 in OA treatment. Binding prediction revealed that miR-122-5p acted on the 3′-untranslated region of p38 mitogen-activated protein kinase, which was related to MMP13 regulation. Subsequent in vitro experiments revealed that CS-semi5 effectively reduced cartilage degeneration and maintained matrix homeostasis by inhibiting matrix breakdown through the miR-122-5p/p38/MMP13 axis, which was further validated in the articular cartilage of OA rats. This is the first study to investigate the semi-synthesized chondroitin sulfate CS-semi5, revealing its cartilage-protecting, anti-inflammatory, and analgesic properties that show promising therapeutic effects in OA via the miR-122-5p/p38/MMP13 pathway.

Abstract Deep learning-based automated detection of insulator defects in electric power systems is a critical technological challenge, pivotal for ensuring reliability and efficiency in the global energy infrastructure. However, the effectiveness of the deep learning model is severely compromised by the scarcity of defective insulator samples. To tackle this problem, the present study proposes a style transfer approach utilizing an improved Star Generative Adversarial Network 2 (StarGAN2) model to generate artificial samples of faulty insulators, which adeptly synthesizes artificial faulty insulator samples on a one-to-many basis, markedly diminishing the necessity for extensive empirical data collection. Through the integration of identity loss, the proposed model ensures the fidelity of content and the preservation of critical defect semantics. Additionally, the proposed model incorporates a pre-trained Visual Geometry Group (VGG) network and perceptual loss, thus improving the quality of generated samples without additional artificial labeling. Finally, various experiments are conducted to assess the quality and authenticity of the generated samples and their impact on the detection model. The results demonstrate that StarGAN2 could generate realistic insulator defect samples and improve the performance of defect detection models.

Abstract High workload-induced cellular stress can cause pancreatic islet β cell death and dysfunction, or β cell failure, a hallmark of type 2 diabetes mellitus. Thus, activation of molecular chaperones and other stress-response genes prevents β cell failure. To this end, we have shown that deletion of the glucose-regulated protein 94 (GRP94) in Pdx1+ pancreatic progenitor cells led to pancreas hypoplasia and reduced β cell mass during pancreas development in mice. Here, we show that GRP94 was involved in β cell adaption and compensation (or failure) in islets from leptin receptor-deficient (db/db) mice in an age-dependent manner. GRP94-deficient cells were more susceptible to cell death induced by various diabetogenic stress conditions. We also identified a new client of GRP94, insulin-like growth factor-1 receptor (IGF-1R), a critical factor for β cell survival and function that may mediate the effect of GRP94 in the pathogenesis of diabetes. This study has identified essential functions of GRP94 in β cell failure related to diabetes.

High-temperature steam is essential for operating solid oxide electrolysis cells (SOEC). This paper demonstrated a new type of high-temperature solar steam generator that uses ceramic foam as a porous absorber. Water is sprayed into the porous absorber as microdroplets by using a nozzle. Experiments show that the solar steam generator is capable of generating superheated steam beyond 800 oC. The peak thermal efficiency in the steady state can reach 60.92% under a mass flow rate of 2.359 kg/h corresponding to an outlet temperature of 877.3 oC. The superheated steam will be supplied to a SOEC system to generate hydrogen. Thus, a comprehensive model is developed in this paper to research the performance of the solar SOEC system under fluctuated solar irradiation

Gut microbiome studies based on fecal samples can non-invasively reflect the survival and health status of endangered species. This study presents the first characterization of the gut microbiome in the semi-wild population of the critically endangered Saiga antelope (Saiga tatarica), unveiling the existence of two distinct enterotypes within the semi-wild Saiga: the Oscillospiraceae UCG-005 enterotype and the Arthrobacter enterotype. These enterotypes show significant differences in microbiome diversity, composition, and predicted functions. The Arthrobacter enterotype has lower alpha diversity, fewer short-chain fatty acids (SCFAs)-producing bacteria, more potential pathogens, lower energy metabolism and higher lipid metabolism abundance. The findings imply that Saiga with the Arthrobacter enterotype have lower energy utilization efficiency and higher disease risks, particularly for obesity. Microbial correlation network analysis indicates SCFAs-producing bacteria play a key role in the formation of these enterotypes. This suggests that next-generation probiotics, primarily composed of SCFAs-producing bacteria, could be used to regulate and improve the health of the Saiga. Additionally, the similarity of the enterotype associated with a healthier survival state to that of wild counterparts suggests that incorporating gut microbiome monitoring into the key indicators for the reintroduction of endangered species could be significant for wildlife conservation and management.

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Abstract Mesenchymal stem/stromal cells (MSCs) are a promising therapeutic tool in cell therapy and tissue engineering because of their multi-lineage differentiation capacity, immunomodulatory effects, and tissue protective potential. To achieve optimal results as a therapeutic tool, factors affecting MSC potency, including but not limited to cell source, donor age, and cell batch, have been investigated. Although the sex of the donor has been attributed as a potential factor that can influence MSC potency and efficacy, the impact of donor sex on MSC characteristics has not been carefully investigated. In this review, we summarize published studies demonstrating donor-sex-related MSC heterogeneity and emphasize the importance of disclosing donor sex as a key factor affecting MSC potency in cell therapy.

Abstract Stroke is a prevalent global acute cerebrovascular condition, with ischaemic stroke being the most frequently occurring type. After a stroke, neutrophils accumulate in the brain and subsequently generate and release neutrophil extracellular traps (NETs). The accumulation of NETs exacerbates the impairment of the blood‒brain barrier (BBB), hampers neovascularization, induces notable neurological deficits, worsens the prognosis of stroke patients, and can facilitate the occurrence of t-PA-induced cerebral haemorrhage subsequent to ischaemic stroke. Alternative approaches to pharmacological thrombolysis or endovascular thrombectomy are being explored, and targeting NETs is a promising treatment that warrants further investigation.

Segmentation of mitral valve is not only important for clinical diagnosis, but also has far-reaching impact on prevention and prognosis of the disease by experts and doctors. In this paper, the multi-channel cross fusion transformer based U-Net network model (MCCT-UNet) is proposed according to the classical U-Net architecture. First, the jump connection part of MCCT-UNet is designed by using a multi-channel cross-fusion based attention mechanism module (MCCT) instead of the original jump connection, and this module fuses the feature maps from different scales in different stages of the encoder. Second, the optimization of the feature fusion method is proposed in the decoding stage by designing the cross-compression excitation sub-module (C-SENet) to replace the simple feature splicing, and the C-SENet is used to bridge the inconsistency of the semantic hierarchy by effectively combining the deeper information in the encoding stage with the shallower information. This two modules can establish a close connection between the encoder and decoder by exploring multi-scale global contextual information to solve the semantic divide problem, thus it significantly improves the segmentation performance of the network. The experimental results show that the improvement is effective, and the MCCT-UNet model outperforms the other 9 network models. Specifically, the MCCT-UNet achieved a Dice coefficient of 0.8734, an IoU of 0.7854, and an accuracy of 0.9977, demonstrating significant improvements over the compared models.

In this paper, a modified bidirectional CLLC DC/DC converter is proposed to improve the efficiency in wide voltage range applications. The proposed converter adopts a LC series resonant branch parallel with the transformer, so that the equivalent inductance of the parallel branch and the equivalent transformer magnetizing inductor varies with the switching frequency. When the required gain is small and the operating frequency is high, the switch’s turn-off current and the circulating power can be reduced through the large impedance of the LC resonant circuit; When the required gain is large, the switching frequency decreases and the auxiliary branch impedance decreases to obtain a higher voltage conversion ratio. The steady state operation and characteristics of the presented converter are analyzed, a design flowchart and a design example of the modified CLLC are also presented. Furthermore, the model of modified CLLC is established and is reduced for simple analysis. A 48V/400V/800W prototype was built and the experimental results verified the correctness of the proposed converter and the feasibility of the modeling results. Compared with the traditional CLLC resonant converter, the efficiency under wide voltage range application is significantly improved.

The detection and identification of defects in transmission lines using computer vision techniques is essential for maintaining the safety and reliability of power supply systems. However, existing training methods for transmission line defect detection models predominantly rely on single-node training, potentially limiting the enhancement of detection accuracy. To tackle this issue, this paper proposes a server-side adaptive parameter aggregation algorithm based on multi-method fusion (SAPAA-MMF) and formulates the corresponding objective function. Within the federated learning framework proposed in this paper, each client executes distributed synchronous training in alignment with the fundamental process of federated learning. The hierarchical difference between the global model, aggregated using the improved joint mean algorithm, and the global model from the previous iteration is computed and utilized as the pseudo-gradient for the adaptive aggregation algorithm. This enables the adaptive aggregation to produce a new global model with improved performance. To evaluate the potential of SAPAA-MMF, comprehensive experiments were conducted on five datasets, involving comparisons with several algorithms. The experimental results are analyzed independently for both the server and client sides. The findings indicate that SAPAA-MMF outperforms existing federated learning algorithms on both the server and client sides.

The accurate detection of litchi fruit cluster is the key technology of litchi picking robot. In the natural environment during the day, due to the unstable light intensity, uncertain light angle, background clutter and other factors, the identification and positioning accuracy of litchi fruit cluster is greatly affected. Therefore, we proposed a method to detect litchi fruit cluster in the night environment. The use of artificial light source and fixed angle can effectively improve the identification and positioning accuracy of litchi fruit cluster. In view of the weak light intensity and reduced image features in the nighttime environment, we proposed the YOLOv8n-CSE model. The model improves the recognition of litchi clusters in night environment. Specifically, we use YOLOv8n as the initial model, and introduce the CPA-Enhancer module with chain thinking prompt mechanism in the neck part of the model, so that the network can alleviate problems such as image feature degradation in the night environment. In addition, the VoVGSCSP design pattern in Slimneck was adopted for the neck part, which made the model more lightweight. The multi-scale linear attention mechanism and the EfficientViT module, which can be deeply divided, further improved the detection accuracy and detection rate of YOLOv8n-CSE. The experimental results show that the proposed YOLOv8n-CSE model can not only recognize litchi clusters in the night scene, but also has a significant improvement over previous models. In mAP@0.5 and F1, YOLOv8n-CSE achieved 98.86% and 95.54% respectively. Compared with the original YOLOv8n, RT-DETR-l and YOLOv10n, mAP@0.5 is increased by 4.03%, 3.46% and 3.96%, respectively. When the number of parameters is only 4.93 m, F1 scores are increased by 5.47%, 2.96% and 6.24%, respectively. YOLOv8n-CSE achieves an inference time of 36.5ms for the desired detection results. To sum up, the model can satisfy the criteria of the litchi cluster detection system for extremely accurate nighttime environment identification.

Stress-induced islet graft loss during the peri-transplantation period reduces the efficacy of islet transplantation. In this prospective, randomized, double-blind clinical trial, we evaluated the safety and efficacy of 60 mg/kg human alpha-1 antitrypsin (AAT) or placebo infusion weekly for four doses beginning before surgery in chronic pancreatitis (CP) patients undergoing total pancreatectomy and islet autotransplantation (TP-IAT). Subjects were followed for 12 months post-TP-IAT. The dose of AAT was safe, as there was no difference in the types and severity of adverse events in participants from both groups. There were some biochemical signals of treatment effect with a higher oxygen consumption rate in AAT islets before transplantation and a lower serum C-peptide (an indicator of islet death) in the AAT group at 15 min after islet infusion. Findings per the statistical analysis plan using a modified intention to treat analysis showed no difference in the C-peptide area under the curve (AUC) following a mixed meal tolerance test at 12 months post-TP-IAT. There was no difference in the secondary and exploratory outcomes. Although AAT therapy did not show improvement in C-peptide AUC in this study, AAT therapy is safe in CP patients and there are experiences gained on optimal clinical trial design in this challenging disease.

The right bank of the Amu Darya Basin enjoys abundant natural gas resources, on which the Callovian-Oxfordian strata in the middle of Block B serve as the major horizons for natural gas production. However, the characteristics and distribution patterns of the sedimentary microfacies in these strata are yet to be further explored. Based on the analysis of data on drilling, logging, cores, and thin sections from 29 typical wells, as well as the regional sedimentary background, this study inferred that the middle of Block B evolved from the Callovian ramp platform into the Oxfordian rimmed platform. Moreover, this study determined that the inner-ramp intertidal-subtidal shallow-water subfacies mainly developed during the Callovian and transitioned into the shallow shelf subfacies during the Oxfordian. This study identified eight sedimentary microfacies, namely reef knoll, reef-shoal complex, bioclastic shoal, psammitic shoal, bioherm, lime mud mound, intershoal (intermound), and static-water mud. Based on research into the high-precision sequence-sedimentary microfacies framework, this study built a geological model for the development of sedimentary microfacies in the study area. According to this geological model, the sedimentary microfacies in the study area are characterized by vertical alternation of reef-shoal complex, bioclastic (psammitic) shoal, bioherm, and intershoal microfacies. Moreover, they show the development of reef knoll, reef-shoal complex, bioclastic (psammitic) shoal, and bioherm (or lime mud mound) laterally from west to east, with the physical properties of the reservoirs deteriorating from west to east accordingly. The microfacies of reef-shoal complex and the bioclastic (psammitic) shoal predominate in the study area, and their deposition and development are controlled by sequence boundaries and are also affected by paleo-landforms. The Oxfordian reef-shoal complexes were largely inherited from the Callovian uplifts and show lateral seaward progradation.

Abstract Deep learning has demonstrated notable success in mechanical signal processing with a large amount labelled data. However, the systems of the Jiaolong deep‐sea submersible prone to malfunction are typically diverse, due to the high complexity of its structure and operational environment. Consequently, this diversity gives rise to variations in the types of sensor signals and their associated data distributions that require analysis. Unsupervised domain adaptation (UDA) uses transferable knowledge derived from the source domain and applies it to an unlabelled target domain in order to improve the reusability of pre‐existing models and data. Inspired by the diffusion models that have the robust capabilities to transform data distributions across a large gap, we propose a novel diffusion‐based unsupervised domain adaptation (diffusion‐UDA) model, which leverages contrastive learning to alleviate the challenges of cross‐domain analysis for fault diagnosis within different systems of the deep‐sea submersible. Experimental results show the proposed method achieves state‐of‐the‐art performance on various benchmarks.

Summary: Experimental studies have shown that neuropathic pain impairs hippocampal synaptic plasticity. Here, we sought to determine the underlying mechanisms responsible for synaptic changes in neuropathic painful mouse hippocampal neurons. Beyond demonstrating proof-of-concept for the location of DExH-box helicase 9 (DHX9) in the nucleus, we found that it did exist in the cytoplasm and DHX9 depletion resulted in structural and functional changes at synapses in the hippocampus. A decrease of DHX9 was observed in the hippocampus after peripheral nerve injury; overexpression of DHX9 in the hippocampus significantly alleviated the nociceptive responses and improved anxiety behaviors. Mimicking DHX9 decrease evoked spontaneous pain behavioral symptoms and anxiety emotion in naïve mice. Mechanistically, we found that DHX9 bound to dendrin (Ddn) mRNA, which may have altered the level of synaptic- and dendritic-associated proteins. The data suggest that DHX9 contributes to synapses in hippocampal neurons and may modulate neuropathic pain and its comorbidity aversive emotion.

To address the issues with inadequate search space, sluggish convergence and easy fall into local optimality during iteration of the sparrow search algorithm (SSA), a multi-strategy improved sparrow search algorithm (ISSA), is developed. First, the population dynamic adjustment strategy is carried out to restrict the amount of sparrow population discoverers and joiners. Second, the update strategy in the mining phase of the honeypot optimization algorithm (HBA) is combined to change the update formula of the joiner's position to enhance the global exploration ability of the algorithm. Finally, the optimal position of population discoverers is perturbed using the perturbation operator and levy flight strategy to improve the ability of the algorithm to jump out of local optimum. The experimental simulations are put up against the basic sparrow search algorithm and the other four swarm intelligence (SI) algorithms in 13 benchmark test functions, and the Wilcoxon rank sum test is used to determine whether the algorithm is significantly different from the other algorithms. The results show that the improved sparrow search algorithm has better convergence and solution accuracy, and the global optimization ability is greatly improved. When the proposed algorithm is used in pilot optimization in channel estimation, the bit error rate is greatly improved, which shows the superiority of the proposed algorithm in engineering application.

With the rapid advancement of wireless communication technology, automatic modulation classification (AMC) plays a crucial role in drone communication systems, ensuring reliable and efficient communication in various non-cooperative environments. Deep learning technology has demonstrated significant advantages in the field of AMC, effectively and accurately extracting and classifying modulation signal features. However, existing deep learning models often have high computational costs, making them difficult to deploy on resource-constrained drone communication devices. To address this issue, this study proposes a lightweight Mobile Automatic Modulation Classification Transformer (MobileAmcT). This model combines the advantages of lightweight convolutional neural networks and efficient Transformer modules, incorporating the Token and Channel Conv (TCC) module and the EfficientShuffleFormer module to enhance the accuracy and efficiency of the automatic modulation classification task. The TCC module, based on the MetaFormer architecture, integrates lightweight convolution and channel attention mechanisms, significantly improving local feature extraction efficiency. Additionally, the proposed EfficientShuffleFormer innovatively improves the traditional Transformer architecture by adopting Efficient Additive Attention and a novel ShuffleConvMLP feedforward network, effectively enhancing the global feature representation and fusion capabilities of the model. Experimental results on the RadioML2016.10a dataset show that compared to MobileNet-V2 (CNN-based) and MobileViT-XS (ViT-based), MobileAmcT reduces the parameter count by 74% and 65%, respectively, and improves classification accuracy by 1.7% and 1.09% under different SNR conditions, achieving an accuracy of 62.93%. This indicates that MobileAmcT can maintain high classification accuracy while significantly reducing the parameter count and computational complexity, clearly outperforming existing state-of-the-art AMC methods and other lightweight deep learning models.

Accurately identifying cherry-tomato picking points and obtaining their coordinate locations is critical to the success of cherry-tomato picking robots. However, previous methods for semantic segmentation alone or combining object detection with traditional image processing have struggled to accurately determine the cherry-tomato picking point due to challenges such as leaves as well as targets that are too small. In this study, we propose a YOLOv8n-DDA-SAM model that adds a semantic segmentation branch to target detection to achieve the desired detection and compute the picking point. To be specific, YOLOv8n is used as the initial model, and a dynamic snake convolutional layer (DySnakeConv) that is more suitable for the detection of the stems of cherry-tomato is used in neck of the model. In addition, the dynamic large convolutional kernel attention mechanism adopted in backbone and the use of ADown convolution resulted in a better fusion of the stem features with the neck features and a certain decrease in the number of model parameters without loss of accuracy. Combined with semantic branch SAM, the mask of picking points is effectively obtained and then the accurate picking point is obtained by simple shape-centering calculation. As suggested by the experimental results, the proposed YOLOv8n-DDA-SAM model is significantly improved from previous models not only in detecting stems but also in obtaining stem’s masks. In the mAP@0.5 and F1-score, the YOLOv8n-DDA-SAM achieved 85.90% and 86.13% respectively. Compared with the original YOLOv8n, YOLOv7, RT-DETR-l and YOLOv9c, the mAP@0.5 has improved by 24.7%, 21.85%, 19.76%, 15.99% respectively. F1-score has increased by 16.34%, 12.11%, 10.09%, 8.07% respectively, and the number of parameters is only 6.37M. In the semantic segmentation branch, not only does it not need to produce relevant datasets, but also improved its mIOU by 11.43%, 6.94%, 5.53%, 4.22% and mAP@0.5 by 12.33%, 7.49%, 6.4%, 5.99% compared to Deeplabv3+, Mask2former, DDRNet and SAN respectively. In summary, the model can well satisfy the requirements of high-precision detection and provides a strategy for the detection system of the cherry-tomato.

Flexible and transparent surface-enhanced Raman scattering (SERS) substrates have attracted considerable attention for their ability to enable the direct in situ detection of analytes on curved surfaces. However, the curvature of an object can impact the signal enhancement of SERS during the measurement process. Herein, we propose a simple approach for fabricating a curvature-insensitive transparent SERS substrate by depositing silver nanoparticles (Ag NPs) onto a large-area wrinkled polystyrene/polydimethylsiloxane (Ag NP@W-PS/PDMS) bilayer film. Using rhodamine 6G (R6G) as a probe molecule, the optimized Ag NP@W-PS/PDMS film demonstrates a high analytical enhancement factor (AEF) of 4.83 × 105, excellent uniformity (RSD = 7.85%) and reproducibility (RSD = 3.09%), as well as superior mechanical flexibility. Additionally, in situ measurements of malachite green (MG) on objects with diverse curvatures, including fish, apple, and blueberry, are conducted using a portable Raman system, revealing a consistent SERS enhancement. Furthermore, a robust linear relationship (R2 ≥ 0.990) between Raman intensity and the logarithmic concentration of MG detected from these objects is achieved. These results demonstrate the tremendous potential of the developed curvature-insensitive SERS substrate as a point-of-care testing (POCT) platform for identifying analytes on irregular objects.

Epilepsy is a common neurological disorder, and its diagnosis mainly relies on the analysis of electroencephalogram (EEG) signals. However, the raw EEG signals contain limited recognizable features, and in order to increase the recognizable features in the input of the network, the differential features of the signals, the amplitude spectrum and the phase spectrum in the frequency domain are extracted to form a two-dimensional feature vector. In order to solve the problem of recognizing multimodal features, a neural network model based on a multimodal dual-stream network is proposed, which uses a mixture of one-dimensional convolution, two-dimensional convolution and LSTM neural networks to extract the spatial features of the EEG two-dimensional vectors and the temporal features of the signals, respectively, and combines the advantages of the two networks, using the hybrid neural network to extract both the temporal and spatial features of the signals at the same time. In addition, a channel attention module was used to focus the model on features related to seizures. Finally, multiple sets of experiments were conducted on the Bonn and New Delhi data sets, and the highest accuracy rates of 99.69% and 97.5% were obtained on the test set, respectively, verifying the superiority of the proposed model in the task of epileptic seizure detection.

With the rapid development of the turtle breeding industry in China, the demand for automated turtle sorting is increasing. The automatic sorting of Chinese softshell turtles mainly consists of three parts: visual recognition, weight prediction, and individual sorting. This paper focuses on two aspects, i.e., visual recognition and weight prediction, and a novel method for the object detection and weight prediction of Chinese softshell turtles is proposed. In the individual sorting process, computer vision technology is used to estimate the weight of Chinese softshell turtles and classify them by weight. For the visual recognition of the body parts of Chinese softshell turtles, a color space model is proposed in this paper to separate the turtles from the background effectively. By applying multiple linear regression analysis for modeling, the relationship between the weight and morphological parameters of Chinese softshell turtles is obtained, which can be used to estimate the weight of turtles well. An improved deep learning object detection network is used to extract the features of the plastron and carapace of the Chinese softshell turtles, achieving excellent detection results. The mAP of the improved network reached 96.23%, which can meet the requirements for the accurate identification of the body parts of Chinese softshell turtles.

With the continuous improvement of shale oil and gas recovery technologies and achievements, a large amount of geological information and data have been accumulated for the description of shale reservoirs, and it has become possible to use machine learning methods for “sweet spots” prediction in shale oil and gas areas. Taking the Duvernay shale oil and gas field in Canada as an example, this paper attempts to build recoverable shale oil and gas reserve prediction models using machine learning methods and geological and development big data, to predict the distribution of recoverable shale oil and gas reserves and provide a basis for well location deployment and engineering modifications. The research results of the machine learning model in this study are as follows: ① Three machine learning methods were applied to build a prediction model and random forest showed the best performance. The R2 values of the built recoverable shale oil and gas reserves prediction models are 0.7894 and 0.8210, respectively, with an accuracy that meets the requirements of production applications; ② The geological main controlling factors for recoverable shale oil and gas reserves in this area are organic matter maturity and total organic carbon (TOC), followed by porosity and effective thickness; the main controlling factor for engineering modifications is the total proppant volume, followed by total stages and horizontal lateral length; ③ The abundance of recoverable shale oil and gas reserves in the central part of the study area is predicted to be relatively high, which makes it a favorable area for future well location deployment.

Probabilistic dual hesitant fuzzy set (PDHFS) can better reflect the hesitant attitude and probability information of decision-makers than existing fuzzy sets. Therefore, it is a more powerful and important tool to express and deal with uncertain information. The score function of fuzzy number plays a very important role in comparing two fuzzy numbers. Entropy measure is a very effective tool for weighting decision attributes in multi-attribute group decision-making problems. Firstly, depending on the detailed analysis of the already existed score function of probabilistic dual hesitant fuzzy element (PDHFE), we built a novel score function for it, it is more effective and convenient when comparing two elements. Secondly, probabilistic dual hesitant fuzzy entropy was built to weight decision attributes. Thirdly, a combined weighting method was proposed which can reflect both subjective and objective information for decision attribute weighting based on the principle of minimum discriminant information. Fourthly, we extended the classical Dice and Jaccard similarity measures to the probabilistic dual hesitant fuzzy environment, and an all-new multi-attribute group decision-making technique to solve the problem that the decision attribute information is probabilistic dual hesitant fuzzy element. Finally, the applicability and effectiveness of this technique are proved by numerical examples and comparative analysis.

Background The presence of sudden onset to maximal deficit (SOTMD) in patients with acute basilar artery occlusion often results in more severe outcomes. However, the effect of endovascular therapy on SOTMD and whether the outcome is affected by onset‐to‐puncture time remain unclear. Methods and Results This retrospective analysis was conducted using data from the prospective BASILAR (Endovascular Treatment for Acute Basilar Artery Occlusion Study Registry). Consecutive patients with basilar artery occlusion receiving endovascular therapy were dichotomized into SOTMD and non‐SOTMD cohorts. The primary outcomes included a favorable outcome (modified Rankin scale 0–3), recanalization, and mortality at 90 days. The outcomes of patients with SOTMD were analyzed using multivariable logistic regression. In the multivariate analysis, a favorable outcome was similar between the two cohorts (odds ratio [OR], 0.88 [95% CI, 0.58–1.34]; P=0.5), although the mortality of patients with SOTMD was higher than that of patients with non‐SOTMD (OR, 1.67 [95% CI, 1.14–2.44]; P=0.008). The probability of mortality increased from 40.0% at 1 hour to 70.0% at 6 hours in the SOTMD cohort, and favorable outcomes of patients with non‐SOTMD declined from 38.0% at 1 hour to 18.0% at 8 hours. Conclusions No significant difference was observed in favorable outcomes between the SOTMD and non‐SOTMD groups, although mortality was higher in the SOTMD cohort. The patients with SOTMD had a stronger time dependence for endovascular therapy in terms of mortality, while the time dependency regarding favorable outcome in the NSOTMD group was even higher. Registration URL: https://www.chictr.org.cn; Unique identifier: ChiCTR1800014759.

Abstract Mesenchymal stromal/stem cells (MSCs) are widely utilized in cell therapy because of their robust immunomodulatory and regenerative properties. Their paracrine activity is one of the most important features that contribute to their efficacy. Recently, it has been demonstrated that the production of various factors via extracellular vesicles, especially exosomes, governs the principal efficacy of MSCs after infusion in experimental models. Compared to MSCs themselves, MSC-derived exosomes (MSC-Exos) have provided significant advantages by efficiently decreasing unfavorable adverse effects, such as infusion-related toxicities. MSC-Exos is becoming a promising cell-free therapeutic tool and an increasing number of clinical studies started to assess the therapeutic effect of MSC-Exos in different diseases. In this review, we summarized the ongoing and completed clinical studies using MSC-Exos for immunomodulation, regenerative medicine, gene delivery, and beyond. Additionally, we summarized MSC-Exos production methods utilized in these studies with an emphasis on MSCs source, MSC-Exos isolation methods, characterization, dosage, and route of administration. Lastly, we discussed the current challenges and future directions of exosome utilization in different clinical studies as a novel therapeutic strategy.

Abstract The purpose of feature learning is to obtain effective representation of the raw data and then improve the performance of machine learning algorithms such as clustering or classification. Some of the existing feature learning algorithms use discriminant information in the data to improve the representation of data features, but the discrimination of the data feature representation is not enough. In order to further enhance the discrimination, discriminant feature learning based on t-distribution stochastic neighbor embedding guided by pairwise constraints (pcDTSNE) is proposed in this paper. pcDTSNE introduces pairwise constraints by clustering ensemble and uses these pairwise constraints to impose penalties on the objective function, which makes sample points in the mapping space present stronger discrimination. In order to verify the feature learning performance of pcDTSNE, extensive experiments are carried out on several public data sets. The experimental results show that the expression ability of data representation generated by pcDTSNE is further improved.

Human cancers typically express a high level of tumor-promoting mutant p53 protein (Mutp53) with a minimal level of tumor-suppressing wild-type p53 protein (WTp53). In this regard, inducing Mutp53 degradation while activating WTp53 is a viable strategy for precise anti-tumor therapy. Herein, a new carrier-free nanoprodrug (i.e., Mn-ZnO2 nanoparticles) was developed for concurrent delivery of dual Zn-Mn ions and reactive oxygen species (ROS) within tumor to regulate the p53 protein for high anti-tumor efficacy. In response to the mild tumor acidic environment, the released Zn2+ and H2O2 from Mn-ZnO2 NPs induced ubiquitination-mediated proteasomal degradation of Mutp53, while the liberative Mn2+ and increased ROS level activated the ATM-p53-Bax pathway to elevate WTp53 level. Both in vitro and in vivo results demonstrated that pH-responsive decomposition of Mn-ZnO2 NPs could effectively elevate the intracellular dual Zn-Mn ions and ROS level and subsequently generate the cytotoxic hydroxyl radical (•OH) through the Fenton-like reaction. With the integration of multiple functions (i.e., carrier-free ion and ROS delivery, tumor accumulation, p53 protein modulation, toxic •OH generation, and pH-activated MRI contrast) in a single nanosystem, Mn-ZnO2 NPs demonstrate its superiority as a promising nanotherapeutics for p53-mutated tumor therapy.

A novel preprocessing method based on a modified U-NET is proposed for single interference fringes. The framework is constructed by introducing spatial attention and channel attention modules to optimize performance. In this process, interferometric fringe maps with an added background intensity, fringe amplitude, and ambient noise are used as the input to the network, which outputs fringe maps in an ideal state. Simulated and experimental results demonstrated that this technique can preprocess single interference fringes in ~1 microsecond. The quality of the results was further evaluated using the root mean square error, peak signal-to-noise ratio, structural similarity, and equivalent number of views. The proposed method outperformed U-NET, U-NET++, and other conventional algorithms as measured by each of these metrics. In addition, the model produced high-quality normalized fringes by combining objective data with visual effects, significantly improving the accuracy of the phase solutions for single interference fringes.

Alpha-1 antitrypsin-overexpressing mesenchymal stromal/stem cells (AAT-MSCs) showed improved innate properties with a faster proliferation rate when studied for their protective effects in mouse models of diseases. Here, we investigated the potential mechanism(s) by which AAT gene insertion increases MSC proliferation. Human bone marrow-derived primary or immortalized MSCs (iMSCs) or AAT-MSCs (iAAT-MSCs) were used in the study. Cell proliferation was measured by cell counting and cell cycle analysis. Possible pathways involved in the pro-proliferation effect of AAT were investigated by measuring mRNA and protein expression of key cell cycle genes. Interval cell counting showed increased proliferation in AAT-MSCs or iAAT-MSCs compared to their corresponding MSC controls. Cell cycle analysis revealed more cells progressing into the S and G2/M phases in iAAT-MSCs, with a notable increase in the cell cycle protein, Cyclin D1. Moreover, treatment with Cyclin D1 inhibitors showed that the increase in proliferation is due to Cyclin D1 and that the AAT protein is upstream and a positive regulator of Cyclin D1. Furthermore, AAT’s effect on Cyclin D1 is independent of the Wnt signaling pathway as there were no differences in the expression of regulatory proteins, including GSK3β and β-Catenin in iMSC and iAAT-MSCs. In summary, our results indicate that AAT gene insertion in an immortalized MSC cell line increases cell proliferation and growth by increasing Cyclin D1 expression and consequently causing cells to progress through the cell cycle at a significantly faster rate.

We developed the Stem Cell Educator therapy among multiple clinical trials based on the immune modulations of multipotent cord blood-derived stem cells (CB-SCs) on different compartments of immune cells, such as T cells and monocytes/macrophages, in type 1 diabetes and other autoimmune diseases. However, the effects of CB-SCs on the B cells remained unclear. To better understand the molecular mechanisms underlying the immune education of CB-SCs, we explored the modulations of CB-SCs on human B cells. CB-SCs were isolated from human cord blood units and confirmed by flow cytometry with different markers for their purity. B cells were purified by using anti-CD19 immunomagnetic beads from human peripheral blood mononuclear cells (PBMCs). Next, the activated B cells were treated in the presence or absence of coculture with CB-SCs for 7 days before undergoing flow cytometry analysis of phenotypic changes with different markers. Reverse transcription-polymerase chain reaction (RT-PCR) was utilized to evaluate the levels of galectin expressions on CB-SCs with or without treatment of activated B cells in order to find the key galectin that was contributing to the B-cell modulation. Flow cytometry demonstrated that the proliferation of activated B cells was markedly suppressed in the presence of CB-SCs, leading to the downregulation of immunoglobulin production from the activated B cells. Phenotypic analysis revealed that treatment with CB-SCs increased the percentage of IgD+CD27− naïve B cells, but decreased the percentage of IgD−CD27+ switched B cells. The transwell assay showed that the immune suppression of CB-SCs on B cells was dependent on the galectin-9 molecule, as confirmed by the blocking experiment with the anti-galectin-9 monoclonal antibody. Mechanistic studies demonstrated that both calcium levels of cytoplasm and mitochondria were downregulated after the treatment with CB-SCs, causing the decline in mitochondrial membrane potential in the activated B cells. Western blot exhibited that the levels of phosphorylated Akt and Erk1/2 signaling proteins in the activated B cells were also markedly reduced in the presence of CB-SCs. CB-SCs displayed multiple immune modulations on B cells through the galectin-9-mediated mechanism and calcium flux/Akt/Erk1/2 signaling pathways. The data advance our current understanding of the molecular mechanisms underlying the Stem Cell Educator therapy to treat autoimmune diseases in clinics.

The shear wave velocity (Vs) is significant for quantitative seismic interpretation. Although numerous studies have proved the effectiveness of the machine learning method in estimating the Vs using well-logging parameters, the real-world application is still hindered because of the black-box nature of machine learning models. With the rapid development of the interpretable machine learning (ML) technique, the drawback of ML can be overcome by various interpretation methods. This study applies the Light Gradient Boosting Machine (LightGBM) to predict the Vs of a carbonate reservoir and uses the Shapley Additive Explanations (SHAP) to interpret the model. The application of ML in Vs estimation normally involves using conventional well-log data that are highly correlated with Vs to train the model. To expand the model’s applicability in wells that lack essential logs, such as the density and neutron logs, we introduce three geologically important features, temperature, pressure, and formation, into the model. The LightGBM model is tuned by the automatic hyperparameter optimization framework; the result is compared with the Xu-Payne rock physics model and four machine learning models tuned with the same process. The results show that the LightGBM model can fit the training data and provide accurate predictions in the test well. The model outperforms the rock physics model and other ML models in both accuracy and training time. The SHAP analysis provides a detailed explanation of the contribution of each input variable to the model and demonstrates the variation of feature contribution in different reservoir conditions. Moreover, the validity of the LightGBM model is further proved by the consistency of the deduced information from feature dependency with the geological understanding of the carbonate formation. The study demonstrates that the newly added features can effectively improve model performance, and the importance of the input feature is not necessarily related to its correlation with Vs

The stock investment selection could be deemed as a classic multiple attribute group decision making (MAGDM) problem involving multiple conflicts and interleaved qualitative and quantitative attributes. Spherical fuzzy sets (SFSs) can excavate the potential vagueness and intricacy in MAGDM more effectively and deeply. This article we propose an integrated decision support system (IDSS) based on SFSs, prospect theory (PT), distance from average solution (EDAS) method and the MEthod based on the Removal Effects of Criteria (MEREC). The proposed IDSS, called SF-PT-EDAS-MEREC model, uses SFSs to describe the uncertain and obscure assessment information of DMs. The combination of PT and EDAS (PT-EDAS) method adequately captures DMs’ psychological behavior characteristics to execute more reasonable alternative evaluation. The MEREC is utilized to efficaciously obtain unknown attribute weights. In addition, this paper also presents a novel score function to compare spherical fuzzy numbers (SFNs) more directly and efficiently. Eventually, in order to illustrate the practicability of the proposed IDSS, two numerical examples of stock investment selection are employed to achieve this. Meanwhile, the comparative study with existing approach further demonstrates the effectiveness and superiority of SF-PT-EDAS-MEREC model.

One of the pivotal factors that limit the clinical translation of tissue engineering is the inability to create large volume and complex three-dimensional (3D) tissues, mainly due to the lack of long-range mass transport with many current scaffolds. Here we present a simple yet robust sacrificial strategy to create hierarchical and perfusable microchannel networks within versatile scaffolds via the combination of embedded 3D printing (EB3DP), tunable polyelectrolyte complexes (PEC), and casting methods. The sacrificial templates of PEC filaments (diameter from 120 to 500 μm) with arbitrary 3D configurations were fabricated by EB3DP and then incorporated into various castable matrices (e.g., hydrogels, organic solutions, meltable polymers, etc.). Rapid dissolution of PEC templates within a 2.00 M potassium bromide aqueous solution led to the high fidelity formation of interconnected channels for free mass exchange. The efficacy of such channeled scaffolds for in vitro tissue formation was demonstrated with mouse fibroblasts, showing continuous cell proliferation and ECM deposition. Subcutaneous implantation of channeled silk fibroin (SF) scaffolds with a porosity of 76% could lead to tissue ingrowth as high as 53% in contrast to 5% for those non-channeled controls after 4 weeks. Both histological and immunofluorescence analyses demonstrated that such channeled scaffolds promoted cellularization, vascularization, and host integration along with immunoregulation.

Abstract Online monitoring of cutting conditions is essential in intelligent manufacturing, and vibrations are one of the most effective signals in monitoring machining conditions. Generally, traditional wired accelerometers should be installed on a motionless or stable platform, such as a tool holder or lathe bed, to sense vibrations. Such installation methods would cause the signals to suffer more serious noise interferences and a low signal-to-noise ratio, resulting in less sensitivity to valuable information. Therefore, this study developed a novel three-axis wireless on-rotor sensing (ORS) system for monitoring the turning process. The Micro Electromechanical System (MEMS) accelerometer sensor node can be mounted on a rotating workpiece or spindle rotor and is more sensitive in detecting the vibrations of the entire rotor system without any modification of the lathe system and interference in the cutting procedure. The processor, data acquisition, and Bluetooth Low Energy (BLE) 5.0+ modules were developed and debugged to cooperate with a piezoelectric triaxial accelerometer, with a vibration amplitude not larger than ± 16 g. A series of turning tests were conducted and the results were compared with those from the commercial wired accelerometers, which proved that the ORS system can measure the vibration signal of the rotor system more effectively and sensitively than wired accelerometers, thus demonstrating the accurate monitoring of machining parameters.

Programmed death ligand-1 (PD-L1) is highly expressed in a variety of cancer cells and suggests a poorer prognosis for patients. The natural compound isorhamnetin (ISO) shows promise in treating cancers and causing damage to canine mammary tumor (CMT) cells. We investigated the mechanism of ISO in reducing PD-L1 expression in CMT cells. Clustered, regularly interspaced short palindromic repeat-associated protein 9 (CRISPR/Cas9) was used to mediate CD274 knockout in U27 cells. Then, monoclonal cells were screened and cultured. Nucleotide sequencing and expression of PD-L1 were detected. Additionally, we examined cell migration, invasion, and damage. Immunofluorescent staining of PD-L1 was examined in U27 cells. The signaling pathways were measured by Western blotting. Murine xenotransplantation models and murine immunocompetent allograft mammary tumor models were established to evaluate the effect of ISO therapy. Expression of Ki-67, caspase3, and PD-L1 were analyzed by immunohistochemistry. A pull-down assay was used to explore which proteins could bind to ISO. Canine EGFR protein was purified and used to detect whether it directly binds to ISO using a surface plasmon resonance assay. ISO inhibited the EGFR-STAT3-PD-L1 signaling pathway and blocked cancer growth, significantly increasing the survival rate of healthy cells. The cell membrane receptor EGFR was identified as a direct target of ISO. ISO could be exploited as an antineoplastic treatment of CMT by targeting EGFR to suppress PD-L1 expression.

Aligned with global Sustainable Development Goals (SDGs) and multidisciplinary approaches integrating AI with sustainability, this research introduces an innovative AI framework for analyzing Modern French Poetry. It applies feature extraction techniques (TF-IDF and Doc2Vec) and machine learning algorithms (especially SVM) to create a model that objectively classifies poems by their stylistic and thematic attributes, transcending traditional subjective analyses. This work demonstrates AI’s potential in literary analysis and cultural exchange, highlighting the model’s capacity to facilitate cross-cultural understanding and enhance poetry education. The efficiency of the AI model, compared to traditional methods, shows promise in optimizing resources and reducing the environmental impact of education. Future research will refine the model’s technical aspects, ensuring effectiveness, equity, and personalization in education. Expanding the model’s scope to various poetic styles and genres will enhance its accuracy and generalizability. Additionally, efforts will focus on an equitable AI tool implementation for quality education access. This research offers insights into AI’s role in advancing poetry education and contributing to sustainability goals. By overcoming the outlined limitations and integrating the model into educational platforms, it sets a path for impactful developments in computational poetry and educational technology.

Spain possesses a vast number of poems. Most have features that mean they present significantly different styles. A superficial reading of these poems may confuse readers due to their complexity. Therefore, it is of vital importance to classify the style of the poems in advance. Currently, poetry classification studies are mostly carried out manually, which creates extremely high requirements for the professional quality of classifiers and consumes a large amount of time. Furthermore, the objectivity of the classification cannot be guaranteed because of the influence of the classifier’s subjectivity. To solve these problems, a Spanish poetry classification framework was designed using artificial intelligence technology, which improves the accuracy, efficiency, and objectivity of classification. First, an artificial-intelligence-driven Spanish poetry classification framework is described in detail, and is illustrated by a framework diagram to clearly represent each step in the process. The framework includes many algorithms and models, such as the Term Frequency–Inverse Document Frequency (TF_IDF), Bagging, Support Vector Machines (SVMs), Adaptive Boosting (AdaBoost), logistic regression (LR), Gradient Boosting Decision Trees (GBDT), LightGBM (LGB), eXtreme Gradient Boosting (XGBoost), and Random Forest (RF). The roles of each algorithm in the framework are clearly defined. Finally, experiments were performed for model selection, comparing the results of these algorithms.The Bagging model stood out for its high accuracy, and the experimental results showed that the proposed framework can help researchers carry out poetry research work more efficiently, accurately, and objectively.

In this paper, we propose a measurement method of micro-optical elements with parallel phase-shifting digital holographic phase imaging. This method can record four phase-shifting holograms with a phase difference of π/2 in a single shot and correct the pixel mismatch error of the polarization camera using a bilinear interpolation algorithm, thereby producing high-resolution four-step phase-shifting holograms. This method reconstructs the real phase information of the object to be measured through a four-step phase-shifting algorithm. The reproduced image eliminates the interference of zero-order images and conjugate images, overcoming the problem that traditional phase-shifting digital holography cannot be measured in real time. A simulation analysis showed that the relative error of this measurement method could reach 0.0051%. The accurate surface topography information of the object was reconstructed from an experimental measurement through a microlens array. Multiple measurements yielded a mean absolute error and a mean relative error for the vertical height of the microlens array down to 5.9500 nm and 0.0461%, respectively.

IntroductionSepsis is associated with endothelial cell (EC) dysfunction, increased vascular permeability and organ injury, which may lead to mortality, acute respiratory distress syndrome (ARDS) and acute renal failure (ARF). There are no reliable biomarkers to predict these sepsis complications at present. Recent evidence suggests that circulating extracellular vesicles (EVs) and their content caspase-1 and miR-126 may play a critical role in modulating vascular injury in sepsis; however, the association between circulating EVs and sepsis outcomes remains largely unknown.MethodsWe obtained plasma samples from septic patients (n=96) within 24 hours of hospital admission and from healthy controls (n=45). Total, monocyte- or EC-derived EVs were isolated from the plasma samples. Transendothelial electrical resistance (TEER) was used as an indicator of EC dysfunction. Caspase-1 activity in EVs was detected and their association with sepsis outcomes including mortality, ARDS and ARF was analyzed. In another set of experiments, total EVs were isolated from plasma samples of 12 septic patients and 12 non-septic critical illness controls on days 1, and 3 after hospital admission. RNAs were isolated from these EVs and Next-generation sequencing was performed. The association between miR-126 levels and sepsis outcomes such as mortality, ARDS and ARF was analyzed.ResultsSeptic patients with circulating EVs that induced EC injury (lower transendothelial electrical resistance) were more likely to experience ARDS (p

Vascular leakage and inflammation are pathological hallmarks of acute lung injury (ALI)/acute respiratory distress syndrome (ARDS). Endothelial cells (ECs) serve as a semipermeable barrier and play a key role in disease progression. It is well known that fibroblast growth factor receptor 1 (FGFR1) is required for maintaining vascular integrity. However, how endothelial FGFR1 functions in ALI/ARDS remains obscure. Here, we revealed that conditional deletion of endothelial FGFR1 aggravated LPS-induced lung injury, including inflammation and vascular leakage. Inhibition of its downstream Rho-associated coiled-coil–forming protein kinase 2 (ROCK2) by AAV Vec-tie-shROCK2 or its selective inhibitor TDI01 effectively attenuated inflammation and vascular leakage in a mouse model. In vitro, TNFα-stimulated human umbilical vein endothelial cells (HUVECs) showed decreased FGFR1 expression and increased ROCK2 activity. Furthermore, knockdown of FGFR1 activated ROCK2 and thus promoted higher adhesive properties to inflammatory cells and higher permeability in HUVECs. TDI01 effectively suppressed ROCK2 activity and rescued the endothelial dysfunction. These data demonstrated that the loss of endothelial FGFR1 signaling mediated an increase in ROCK2 activity, which led to an inflammatory response and vascular leakage in vivo and in vitro. Moreover, inhibition of ROCK2 activity by TDI01 provided great value and shed light on clinical translation.

Injuries to the peripheral nervous system are a common clinical issue, causing dysfunctions of the motor and sensory systems. Surgical interventions such as nerve autografting are necessary to repair damaged nerves. Even with autografting, i.e., the gold standard, malfunctioning and mismatches between the injured and donor nerves often lead to unwanted failure. Thus, there is an urgent need for a new intervention in clinical practice to achieve full functional recovery. Nerve guidance conduits (NGCs), providing physicochemical cues to guide neural regeneration, have great potential for the clinical regeneration of peripheral nerves. Typically, NGCs are tubular structures with various configurations to create a microenvironment that induces the oriented and accelerated growth of axons and promotes neuron cell migration and tissue maturation within the injured tissue. Once the native neural environment is better understood, ideal NGCs should maximally recapitulate those key physiological attributes for better neural regeneration. Indeed, NGC design has evolved from solely physical guidance to biochemical stimulation. NGC fabrication requires fundamental considerations of distinct nerve structures, the associated extracellular compositions (extracellular matrices, growth factors, and cytokines), cellular components, and advanced fabrication technologies that can mimic the structure and morphology of native extracellular matrices. Thus, this review mainly summarizes the recent advances in the state-of-the-art NGCs in terms of biomaterial innovations, structural design, and advanced fabrication technologies and provides an in-depth discussion of cellular responses (adhesion, spreading, and alignment) to such biomimetic cues for neural regeneration and repair.

Drip irrigation has a close relationship with the growth and development of maize grains and yield formation in semiarid areas. To explore the response mechanism of grain yield formation to drip irrigation quotas, a 2-year pond planting experiment was conducted under controlled conditions, by using two maize varieties with differences in drought resistance as experimental materials. Six treatments were set up, including CK1 (drought-resistant variety, 500 mm), T1 (drought-resistant variety, 350 mm), T2 (drought-resistant variety, 200 mm), CK2 (drought-sensitive variety, 500 mm), T3 (drought-sensitive variety, 350 mm), and T4 (drought-sensitive variety, 200 mm). The changes in maize grain filling characteristics, related hormones, enzyme activity related to starch synthesis, sugars and amino acids contents, and yield were analysed. The results showed that 100-grain weight at different filling times, filling rate, average filling rate, auxin, cytokinin, acid sucrose invertase, sucrose synthase, starch synthase, and adenosine diphosphate glucose pyro phosphorylase activities in maize grains decreased and the abscisic acid content and content of various amino acids and sugars in grains increased with the decrease in drip irrigation quota. The percentage of changes in drought-sensitive maize varieties was relatively high. The maize yield decreased with the decrease in drip irrigation quota. In summary, there was no significant difference in grain filling characteristics, hormone content, starch synthesis enzyme activity, and yield between maize treated with T1 (drought-resistant variety, 350 mm) and the control treatment. This effectively maintained grain growth and yield formation, achieving the goal of water saving and stable yields.

Computer vision technology is increasingly being widely applied in automated industrial production. However, the accuracy of workpiece detection is the bottleneck in the field of computer vision detection technology. Herein, a new object detection and classification deep learning algorithm called CSW-Yolov7 is proposed based on the improvement of the Yolov7 deep learning network. Firstly, the CotNet Transformer structure was combined to guide the learning of dynamic attention matrices and enhance visual representation capabilities. Afterwards, the parameter-free attention mechanism SimAM was introduced, effectively enhancing the detection accuracy without increasing computational complexity. Finally, using WIoUv3 as the loss function effectively mitigated many negative influences during training, thereby improving the model’s accuracy faster. The experimental results manifested that the mAP@0.5 of CSW-Yolov7 reached 93.3%, outperforming other models. Further, this study also designed a polyhedral metal workpiece detection system. A large number of experiments were conducted in this system to verify the effectiveness and robustness of the proposed algorithm.

The development of tantalum capacitor (TC) industry is associated with the production of one technology metal-tantalum (Ta). The recycling of waste tantalum capacitors (WTCs) is an important strategy for the sustainable development of tantalum related industries. A cryogenic crushing-alkaline calcination-leaching process has been proposed and experimentally studied in the present work, and main results showed: 1) after the refrigeration treatment with liquid nitrogen, WTCs got discharged and became brittle. Three groups of materials could be produced after the milling - magnetic separation - screening treatment, and portions of collected iron-nickel alloy, tantalum wire, and Ta bearing powder were 6.94%, 1.50%, and 91.56%, respectively; 2) organic components in Ta-bearing powder could be efficiently removed in the alkaline calcination process, and Si converted to Na2O·mSiO2 (m = 1, 2), which could be easily leached with water; 3) NaOH was selected to be the alkaline for calcination reactions, and the optimized temperature was 700 °C, the reacting time was 300 min, and the NaOH excess coefficient was 1.0; 4) water leaching residue was treated with nitric acid, and Ag, Fe and Ni could be enriched in the leaching liquor, and the overall recovery of Ta was 96.72% at optimized conditions.