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Abstract The rapid expansion of information technology and the intensification of population aging are two prominent features of contemporary societal development. Investigating older adults’ acceptance and use of technology is key to facilitating their integration into an information-driven society. Given this context, the technology acceptance of older adults has emerged as a prioritized research topic, attracting widespread attention in the academic community. However, existing research remains fragmented and lacks a systematic framework. To address this gap, we employed bibliometric methods, utilizing the Web of Science Core Collection to conduct a comprehensive review of literature on older adults’ technology acceptance from 2013 to 2023. Utilizing VOSviewer and CiteSpace for data assessment and visualization, we created knowledge mappings of research on older adults’ technology acceptance. Our study employed multidimensional methods such as co-occurrence analysis, clustering, and burst analysis to: (1) reveal research dynamics, key journals, and domains in this field; (2) identify leading countries, their collaborative networks, and core research institutions and authors; (3) recognize the foundational knowledge system centered on theoretical model deepening, emerging technology applications, and research methods and evaluation, uncovering seminal literature and observing a shift from early theoretical and influential factor analyses to empirical studies focusing on individual factors and emerging technologies; (4) moreover, current research hotspots are primarily in the areas of factors influencing technology adoption, human-robot interaction experiences, mobile health management, and aging-in-place technology, highlighting the evolutionary context and quality distribution of research themes. Finally, we recommend that future research should deeply explore improvements in theoretical models, long-term usage, and user experience evaluation. Overall, this study presents a clear framework of existing research in the field of older adults’ technology acceptance, providing an important reference for future theoretical exploration and innovative applications.

ABSTRACTPre-treatment processing technology plays a crucial role in the overall freshwater fish processing procedure, and automatic head and tail cutting stands out as a significant pre-treatment technique within the industry. The system for removing the head and tail of freshwater fish comprised a Cartesian coordinate manipulator, a fish transfer device, a control system, and an image acquisition device. In the vision system, five image segmentation methods were utilized for fish head and tail image segmentation comparison tests. These methods include U-Net (U-shaped Deep Neural Network), DeeplabV3, PSPNet (Pyramid Scene Parsing Network), FastSCNN (Fast Semantic Segmentation Network), and ICNet (Image Cascade Network), all of which were employed to evaluate their performance. Among the tested segmentation methods, the ICNet demonstrated the most excellent segmentation capability. The experimental results indicated a segmentation accuracy of 99.01%, a mean intersection over union (MIoU) of 82.50%, and an image processing time of 15.25 ms. The results showed that the fish head and tail were successfully cut off using this model for recognition with a circular knife. Consequently, the segmentation model employed in the machine vision system within this study has demonstrated successful applicability in automatically cutting the heads and tails of freshwater fish of various sizes.

Abstract User requirements serve as the primary reference content in product design. The effective capture of crucial user requirements, followed by the development of a product technical solution aligned with these requirements, stands as a pivotal approach to enhancing design efficiency. In order to explore the problem of generating and decision-making of product technical solutions in the case of complex user demands, this study constructs a user requirements driven product optimization design model, which is used to complete the generation and decision-making of product design solutions in a more reasonable way. The model unfolds across three key stages: Firstly, a user requirements importance ranking system is crafted leveraging Kano Model and Pairwise Analysis. Next, employing the Functional Analysis System Techniques (FAST) theory and the Quality Function Deployment (QFD) theory, user requirements undergo transformation into technical solutions. Finally, these technical solutions are amalgamated into diverse technical combinations, with decisions facilitated by a game theory model to yield the optimal overall design solution. The new optimal design model reduces the influence of subjectivity and ambiguity in the process of user requirements analysis, increases the reliability of the transformation of user requirements into technical solutions, and improves the efficiency of the generation and decision-making of product design solutions under multiobjective situations. The model proposed in this study is exemplified through the development of a medicated bath water heater. Results indicate that the technical solution derived from the model surpasses similar products in terms of user satisfaction, thereby validating its feasibility.

Abstract Reactive oxygen species (ROS)‐induced oxidative DNA damages have been considered the main cause of mutations in genes, which are highly related to carcinogenesis and tumour progression. Extracellular vesicles play an important role in cancer metastasis. However, the precise role of DNA oxidative damage in extracellular vesicles (EVs)‐mediated cancer cell migration and invasion remains unclear. Here, we reveal that ROS‐mediated DNA oxidative damage signalling promotes tumour metastasis through increasing EVs release. Mechanistically, 8‐oxoguanine DNA glycosylase (OGG1) recognises and binds to its substrate 8‐oxo‐7,8‐dihydroguanine (8‐oxoG), recruiting NF‐κB to the synaptotagmin 7 (SYT7) promoter and thereby triggering SYT7 transcription. The upregulation of SYT7 expression leads to increased release of E‐cadherin‐loaded EVs, which depletes intracellular E‐cadherin, thereby inducing epithelial‐mesenchymal transition (EMT). Notably, Th5487, the inhibitor of DNA binding activity of OGG1, blocks the recognition and transmission of oxidative signals, alleviates SYT7 expression and suppresses EVs release, thereby preventing tumour progression in vitro and in vivo. Collectively, our study illuminates the significance of 8‐oxoG/OGG1/SYT7 axis‐driven EVs release in oxidative stress‐induced tumour metastasis. These findings provide a deeper understanding of the molecular basis of cancer progression and offer potential avenues for therapeutic intervention.

Abstract Background Brainstem cavernous malformations (BCMs) are benign lesions that typically have an acute onset and are associated with a high rate of morbidity. The selection of the optimal surgical approach is crucial for obtaining favorable outcomes, considering the different anatomical locations of various brainstem lesions. Endoscopic surgery is increasingly utilized in treating of BCMs, owing to its depth illumination and panoramic view capabilities. For intra-axial ventral BCMs, the best surgical options are endoscopic endonasal approaches, following the “two-point method. For cavernous hemangiomas on the dorsal side of the brainstem, endoscopy proves valuable by providing enhanced visualization of the operative field and minimizing the need for brain retraction. Methods In this review, we gathered data on the fully endoscopic approach for the resection of BCMs, and outlined technical notes and tips. Total of 15 articles were included in this review. The endoscopic endonasal approach was utilized in 19 patients, and the endoscopic transcranial approach was performed in 3 patients. Results The overall resection rate was 81.8% (18/22). Among the 19 cases of endoscopic endonasal surgery, postoperative cerebrospinal fluid (CSF) leakage occurred in 5 cases, with lesions exceeding 2 cm in diameter in 3 patients with postoperative CSF rhinorrhea. Among the 20 patients with follow-up data, 2 showed no significant improvement after surgery, whereas the remaining 18 patients showed significant improvement compared to their admission symptoms. Conclusions This systematic literature review demonstrates that a fully endoscopic approach is a safe and effective option for the resection of BCMs. Further, it can be considered an alternative to conventional craniotomy, particularly when managed by a neurosurgical team with extensive experience in endoscopic surgery, addressing these challenging lesions.

Abstract Background Depression is associated with an increased risk of lower urinary tract symptoms suggestive of benign prostatic hyperplasia (LUTS/BPH). Whether the dynamic nature of depression affects the incidence of LUTS/BPH remains unknown. A four-year cohort study based on the China Health and Retirement Longitudinal Study (CHARLS) was conducted to assess their association. Methods This study included 3433 Chinese men from the CHARLS 2011, representative of > 95 million individuals. All eligible individuals underwent three assessments of LUTS/BPH and depression in 2011, 2013 and 2015. The dynamic nature of depression was classified as acute depression with remission, acute depression with recurrence, or chronic major depression. Weighted, generalized additive analyses with three binomial models were used to investigate the relationship between LUTS/BPH and the dynamic nature of depression. Results During the four-year follow-up, 11.5% (95% confidence interval [95% CI] = 9.5-13.3%) of Chinese men were diagnosed with newly incident LUTS/BPH. Meanwhile, there were 60.6% (95% CI = 58.5-62.7%) of the individuals without depression and 8.9% (95% CI = 7.9-10%) of the individuals with chronic major depression. A total of 25.1% (95% CI = 23.4-26.9%) and 5.4% (95% CI = 4.6-6.3%) of the individuals were categorized as acute depression with remission and recurrence. After weighted, adjusted all included confounding risk factors, chronic major depression (RR = 1.63, 95% CI = 1.14–2.33, P

Abstract Background Most patients with intrahepatic cholangiocarcinoma (ICC) have developed distant metastasis at the time of diagnosis, while there is rear related nomogram to predict the prognosis. Methods Clinical data of patients pathologically diagnosed of ICC with distant metastasis were retrospectively collected from the Surveillance, Epidemiology, and End Results (SEER) database during 2005 to 2019. Finally, patients diagnosed as ICC in the Second Affiliated Hospital of Nanchang University from 2014 to 2019 were collected for external verification. All data were divided into training cohort and validation cohort in a ratio of 7:3. The nomogram was established based on independent prognostic factors using Cox univariate and multivariate analyses. The area under the receiver operating characteristic (ROC) curves (AUC), the calibration curve and the decision curve analysis (DCA) were used to determine the prediction accuracy of the nomogram. Results This study finally included 572 ICC with distant metastasis patients, another 32 patients collected by the author’s hospital were used as external verification. Results showed that age, surgery, radiotherapy and chemotherapy were independent prognostic factors, and nomogram was established. The AUC of predicting 3, 6, 9-month overall survival were 0.866, 0.841 and 0.786. The ROC curves and calibration curves showed that the nomogram had good predictive accuracy, and DCA showed that the nomogram had good clinical applicability. Conclusions The nomogram has good accuracy in predicting prognosis of DM-ICC patients, which would be of good significance to improve the prognosis of these patients.

Abstract Cepharanthine is a secondary metabolite isolated from Stephania. It has been reported that it has anti-conronaviruses activities including severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2). Here, we assemble three Stephania genomes (S. japonica, S. yunnanensis, and S. cepharantha), propose the cepharanthine biosynthetic pathway, and assess the antiviral potential of compounds involved in the pathway. Among the three genomes, S. japonica has a near telomere-to-telomere assembly with one remaining gap, and S. cepharantha and S. yunnanensis have chromosome-level assemblies. Following by biosynthetic gene mining and metabolomics analysis, we identify seven cepharanthine analogs that have broad-spectrum anti-coronavirus activities, including SARS-CoV-2, Guangxi pangolin-CoV (GX_P2V), swine acute diarrhoea syndrome coronavirus (SADS-CoV), and porcine epidemic diarrhea virus (PEDV). We also show that two other genera, Nelumbo and Thalictrum, can produce cepharanthine analogs, and thus have the potential for antiviral compound discovery. Results generated from this study could accelerate broad-spectrum anti-coronavirus drug discovery.

ABSTRACTIn order to achieve real-time classification and detection of various chicken parts, this study introduces an optimized Swin-Transformer method for the classification and detection of multiple chicken parts. It initially leverages the Transformer’s self-attention structure to capture more comprehensive high-level visual semantic information from chicken part images. The image enhancement technique was applied to the image in the preprocessing stage to enhance the feature information of the image, and the migration learning method was used to train and optimize the Swin-Transformer model on the enhanced chicken parts dataset for classification and detection of chicken parts. Furthermore, this model was compared to four commonly used models in object target detection tasks: YOLOV3-Darknet53, YOLOV3-MobileNetv3, SSD-MobileNetv3, and SSD-VGG16. The results indicated that the Swin-Transformer model outperforms these models with a higher mAP value by 1.62%, 2.13%, 5.26%, and 4.48%, accompanied by a reduction in detection time by 16.18 ms, 5.08 ms, 9.38 ms, and 23.48 ms, respectively. The method of this study fulfills the production line requirements while exhibiting superior performance and greater robustness compared to existing conventional methods.

Abstract Background and Aims Anesthetic drugs play a vital role during surgery, however, due to individual differences and complex physiological mechanisms, the prediction of anesthetic drug dosage has always been a challenging problem. In this study, we propose a model for predicting the dosage of anesthetic drugs based on deep learning to help anesthesiologists better control their dosage during surgical procedures. Methods We design a model based on the artificial neural network to predict the dosage of preoperative anesthetic, and use the SELU activation function and the loss function for weighted regularization to solve the problem of unbalanced sample. Moreover, we design a CNN‐based model for the prior extraction of intraoperative features by using a 7 × 1 convolution kernel to enhance the receptive field, and combine maximum pooling and average pooling to extract key features while eliminating noise. A predictive model based on the LSTM network is designed to predict the intraoperative dosage of the anesthetic, and the bidirectional propagation‐based LSTM network is used to improve the ability to learn the trend of changes in the physiological states of the patient during surgery. An attention module is added before the connection layer to appropriately attend to areas containing prominent features. Results The results of experiments showed that the proposed method reduced values of the MAPE to 15.83% and 12.25% compared with the traditional method in predictions of the preoperative and intraoperative doses of the anesthetic, respectively, and increased the values of R2 to 0.887 and 0.915, respectively. Conclusion The intelligent anesthesia prediction algorithm designed in this study can effectively predict the dosage of anesthetic drugs needed by patients, assist clinical judgment of anesthetic drug dose, and assist the anesthesiologists to ensure the smooth progress of the operation.

Wheel polygons can significantly impair the operational quality, service life and ride comfort of railway systems and even threaten the safety of train operations. Timely detection of wheel polygons is of great benefit in formulating a reasonable wheel maintenance strategy. First, to investigate the vibration response of the locomotive under wheel polygon excitation, a locomotive–tack coupled dynamic model is established, taking into account the flexibility of the bogie frame, wheelset, sleeper and track structure. The effects of typical wheel polygons on the vibration behavior of the locomotive under different conditions were studied. Then, the simulation results were used to quantitatively analyze the correlation between the axle box acceleration (ABA) of the locomotive and the wheel-rail system P2 mode, the inherent modal of the wheelset, the vehicle speed, the order and the amplitude of the wheel polygons. Besides, a dataset with multiple working conditions was created from the simulation data and a quantitative wheel polygon detection model was constructed based on the deep learning algorithm. Finally, the effectiveness of the proposed detection method was verified using the real-world data of ABA and wheel polygons. The results show that the proposed method can quickly and accurately detect the dominant features of the wheel polygon, i.e. the wavelength and the corresponding roughness information.

Abstract Mobility is a fundamental feature of mobile edge computing. Due to the mobility of users, the contextual attributes of cloudlets such as server resources and network state will dynamically change with time during offloading, showing time-varying and fuzzy characteristics. To this end, how to make efficient offloading decision to provide low-latency, low-power and highly reliable services in MEC has become a critical issue. In this paper, we propose a time-varying context-aware cloudlet decision algorithm based on neutrosophic set, TConNS $${\text {(The Code of TConNS is available at https://github.com/zhengLabs/NSO)}}$$ (The Code of TConNS is available at https://github.com/zhengLabs/NSO) . Firstly, we establish a representation model of the multi-dimensional time-varying context of candidate cloudlets, including the mobile residence time. Secondly, we adopt the backward generator of cloud model theory to transform the contextual raw data into a single-valued neutrosophic set with the expression ability for fuzzy information. Finally, we use a series of appropriate operations under the own unique computing system of neutrosophic set to obtain the best cloudlet. Extensive experiments show that TConNS reduces the average response time by about 49% and the average energy consumption by about 46%, and also reduces the number of task failures.

Central nervous system injuries have a high rate of resulting in disability and mortality; however, at present, effective treatments are lacking. Programmed cell death, which is a genetically determined form of active and ordered cell death with many types, has recently attracted increasing attention due to its functions in determining the fate of cell survival. A growing number of studies have suggested that programmed cell death is involved in central nervous system injuries and plays an important role in the progression of brain damage. In this review, we provide an overview of the role of programmed cell death in central nervous system injuries, including the pathways involved in mitophagy, pyroptosis, ferroptosis, and necroptosis, and the underlying mechanisms by which mitophagy regulates pyroptosis, ferroptosis, and necroptosis. We also discuss the new direction of therapeutic strategies targeting mitophagy for the treatment of central nervous system injuries, with the aim to determine the connection between programmed cell death and central nervous system injuries and to identify new therapies to modulate programmed cell death following central nervous system injury. In conclusion, based on these properties and effects, interventions targeting programmed cell death could be developed as potential therapeutic agents for central nervous system injury patients.

Abstract Background Supraclavicular nodal (SCL) irradiation is commonly used for patients with high-risk breast cancer after breast surgery. The Radiation Therapy Oncology Group (RTOG) and European Society for Radiotherapy and Oncology (ESTRO) breast contouring atlases delineate the medial part of the SCL region, while excluding the posterolateral part. However, recent studies have found that a substantial proportion of SCL failures are located in the posterolateral SCL region, outside of the RTOG/ESTRO-defined SCL target volumes. Consequently, many radiation oncologists advocate for enlarging the SCL irradiation target volume to include both the medial and posterolateral SCL regions. Nevertheless, it remains uncertain whether adding the posterolateral SCL irradiation improves survival outcomes for high-risk breast cancer patients. Methods The SUCLANODE trial is an open-label, multicenter, randomized, phase 3 trial comparing the efficacy and adverse events of medial SCL irradiation (M-SCLI group) and medial plus posterolateral SCL irradiation (entire SCL irradiation, E-SCLI group) in high-risk breast cancer patients who underwent breast conserving-surgery or mastectomy. Patients with pathological N2-3b disease following initial surgery, or clinical stage III or pathological N1-3b if receiving neoadjuvant systemic therapy, are eligible and randomly assigned (1:1) to M-SCLI group and E-SCLI group. Stratification is by chemotherapy sequence (neoadjuvant vs. adjuvant), T stage (T3-4 vs. T1-2), N stage (N1-2 vs. N3), and ER status (positive vs. negative). Other radiation volumes are identical in the two arms, including breast/chest wall, undissected axillary lymph node, and internal mammary node. Advanced intensity modulated radiation therapy (IMRT), volumetric modulated arc therapy (VMAT), or tomotherapy techniques are recommended. Both hypofractionated and conventional fractionation schedules are permitted. The primary end point is invasive disease-free survival, and secondary end points included overall survival, SCL recurrence, local-regional recurrence, distance recurrence, safety outcome, and patient-reported outcomes. The target sample size is 1650 participants. Discussion The results of the SUCLANODE trial will provide high-level evidence regarding whether adding posterolateral SCL irradiation to medial SCL target volume provides survival benefit in patients with high-risk breast cancer. Trial registration ClinicalTrials.gov Identifier: NCT05059379. Registered 28 September 2021, https://www.clinicaltrials.gov/ct2/show/NCT05059379 .

To address the issue of wide stripe generation in existing erasure coding storage systems, where storage scaling approaches resulted in a significant increase in wide stripe generation bandwidth and stripe merge scheme was constrained by dual-stripes, an efficient wide stripe generation approach was proposed for the wide stripe generation problem under multi stripes merging.Two key operators for the multi-stripes merging progress were defined, and the wide stripe generation problem was modeled as a combinatorial optimization problem.The efficient wide stripe generation approach xStripeMerge was proposed that prioritize the search for narrow-stripes combining schemes with small parity block transmission costs.Experimental results show that xStripeMerge can reduce the wide stripe generation bandwidth by 75% compared to the advanced storage scaling method.The time and space complexity of xStripeMerge is much better than that of the extended dual-stripes merge approach.xStripeMerge can get the wide stripe generation scheme with similar performance in a shorter period of time and it is also suitable for large-scale storage systems.

Titanium carbide (TiC) coatings were prepared on the surface of AlFeCoCrNiCu high-entropy alloy blocks using electro-spark deposition (ESD). The microhardness and corrosion resistance of the TiC coatings prepared under different voltage and capacitance process parameters were studied. The research shows that the maximum microhardness of the TiC coating on sample 4 (working voltage of 20 V, working capacitance of 1000 μF) is 844.98 HV, which is 81.5% higher than the microhardness of the substrate. This is because the deposition energy increases with the increase in voltage, and the adhesion and aggregation between the coating and the substrate are enhanced, increasing the hardness of the coating. It is worth noting that excessive deposition energy can increase surface defects and reduce the microhardness of the coating surface. Electrochemical testing analysis shows that the corrosion current density of the TiC coating is the lowest (9.475 × 10−7 ± 0.06 × 10−7), and the coating impedance is the highest (2.502 × 103 Ω·com2). The absolute phase angle value is the highest (about 72°). The above indicates that the TiC coating prepared with a working voltage of 20 V and a working capacitance of 1000 μF has better microhardness and corrosion resistance.

The cigarette filter is an essential component of modern cigarettes and studying the flow distribution within the cigarette filter is of great significance in reducing the harm of cigarettes and optimizing smoking sensations. As the object of numerical simulation research, a three-dimensional model of the cigarette was accurately constructed through micro-CT reverse engineering, achieving a scanning accuracy of 4.05 μm. An overall porous media model of the cigarette filter was established to characterize the pressure distribution inside the filter. Based on the three-dimensional reconstruction, a local simulation model of the cavity-filtered filter was created by extracting a 1/36 geometric model. The simulation results of the overall porous media model of the cigarette filter were used as the pressure boundary conditions for the local simulation model of the cavity-filtered filter, and the effects of the wrapped paper and cavity on the flow field were analyzed. The results show that the simulated pressure drop in the overall porous media model of the cigarette filter had a deviation of less than 3.5% compared to the experimental results. This suggests that the porous media model can effectively predict the changes in pressure drop within the filter. When both wrapped paper and cavity were present, the velocity at the interface between acetate fiber and wrapped paper increased by 141.54%, while the pressure approached 0 Pa. Similarly, at the interface between acetate fiber and cavity, the velocity increased by 130.77%. It indicates that both wrapped paper and cavity significantly influenced the flow field characteristics within the cigarette filter. Additionally, as the porosity of the wrapped paper gradually increased from 0.69 to 0.99 in the radial direction, the fluid velocity increased by 14.46%, while the fluid pressure decreased by 29.09%. These changes were particularly evident when the porosity was below 0.87.

Abstract Horse chestnut (Aesculus chinensis) is an important medicinal tree that contains various bioactive compounds, such as aescin, barrigenol-type triterpenoid saponins (BAT), and aesculin, a glycosylated coumarin. Herein, we report a 470.02 Mb genome assembly and characterize an Aesculus-specific whole-genome duplication event, which leads to the formation and duplication of two triterpenoid biosynthesis-related gene clusters (BGCs). We also show that AcOCS6, AcCYP716A278, AcCYP716A275, and AcCSL1 genes within these two BGCs along with a seed-specific expressed AcBAHD6 are responsible for the formation of aescin. Furthermore, we identify seven Aesculus-originated coumarin glycoside biosynthetic genes and achieve the de novo synthesis of aesculin in E. coli. Collinearity analysis shows that the collinear BGC segments can be traced back to early-diverging angiosperms, and the essential gene-encoding enzymes necessary for BAT biosynthesis are recruited before the splitting of Aesculus, Acer, and Xanthoceras. These findings provide insight on the evolution of gene clusters associated with medicinal tree metabolites.

Abstract Background Exposure to air pollution brings the advent effect for various diseases, but study about the relationship between air pollution and ageing is scant. We aimed to determine the associations between household air pollution for cooking and heating with muscle and sarcopenia in Chinese older population by a nationally representative study. Methods This cross‐sectional study included individuals aged 60 and above from the China Health and Retirement Longitudinal Study between 2011 and 2015. The diagnosis of sarcopenia was defined by low muscle mass with low muscle strength and/or reduced physical performance. Generalized additive analyses and dose‐dependent analyses with three models were used to assess the effects of different pattern of cooking and heating on muscle and sarcopenia. Results A total of 8126 Chinese older individuals with predominant male (53.7%) and mean age of 67.3 ± 6.0 years were included in our study. Solid fuel use in cooking showed significant declines in muscle strength (β = −0.424, 95% CI: −0.767, −0.082, P = 0.01 in model 3) and mass (β = −0.034, 95% CI: −0.051, −0.017, P

ABSTRACTThe quality of fish pre-treatment processing directly affected the production competitiveness of the fish industry. The removal of heads and tails is one of the key technologies in the fish processing. This study proposed an identification method of fish head and tail, and the original YOLOV3 model was improved by replacing the backbone feature extraction network of the YOLOV3 model with the lightweight neural network MobileNetv3. Firstly, a freshwater fish image dataset was created and divided into the training, validation and test sets with the assigned ratio of 6:2:2. Next, the freshwater fish dataset was trained using the target detector YOLOV3. Finally, the average accuracy mAP (mean of Average Precision) and the average image detection time were used as the accuracy and speed indexes to evaluate the detection effect of the model. In addition, the SSD-MobileNetv3 and SSD-VGG16 were introduced into present study and they were compared with the improved algorithm. The experimental results showed that the detection speed of the YOLOV3 model with MobileNetV3 was significantly improved. The mAP of YOLOV3-MobileNetv3 model was 98.36%, the inference speed was 28.2 ms, which was 5.09%, 4.24% and 2.07% higher than the mAP of other three models (SSD-VGG16, SSD-MobileNetv3 and YOLOV3-Darknet-53), and the average detection time shortened by 86%, 9.99% and 29%, respectively. Therefore, this experimental method of head and tail of freshwater fish could achieve real-time detection and recognition of various kinds of freshwater fish, which had the great advantages of high detection accuracy and fast detection speed.

This study aimed to develop composite hydrogels with exceptional piezoelectric properties and pressure sensitivity. To achieve the objective, this study created a deep eutectic solvent (DES) by mixing choline chloride (ChCl), acrylamide (AM), and acrylic acid (AA). Barium titanate nanoparticles (BTNPs) were incorporated as fillers into the deep eutectic solvents (DES) to synthesize the composite hydrogels using frontal polymerization (FP). The mechanical and piezoelectric properties of the resulting composite hydrogels were analyzed using Fourier transform infrared spectroscopy (FTIR) and scanning electron microscopy (SEM). This study found that the BTNPs/P(AM-co-AA) composite hydrogels exhibited excellent mechanical and piezoelectric properties. This is attributed to the high dielectric constant of BTNPs and the electrode polarization phenomenon when subjected to pressure. With a BTNPs content of 0.6 wt%, the maximum compressive strength increased by 3.68 times compared with the hydrogel without added BTNPs. Moreover, increasing the BTNPs content to 0.6 wt% resulted in a 1.48 times increase in generated voltage under the same pressure, compared with the hydrogel with only 0.2 wt% BTNPs. This study provides a method for preparing composite hydrogels with outstanding piezoelectric properties and pressure sensitivity.

The identification of key points in the human body is vital for sports rehabilitation, medical diagnosis, human–computer interaction, and related fields. Currently, depth cameras provide more precise depth information on these crucial points. However, human motion can lead to variations in the positions of these key points. While the Mediapipe algorithm demonstrates effective anti-shake capabilities for these points, its accuracy can be easily affected by changes in lighting conditions. To address these challenges, this study proposes an illumination-adaptive algorithm for detecting human key points through the fusion of multi-source information. By integrating key point data from the depth camera and Mediapipe, an illumination change model is established to simulate environmental lighting variations. Subsequently, the fitting function of the relationship between lighting conditions and adaptive weights is solved to achieve lighting adaptation for human key point detection. Experimental verification and similarity analysis with benchmark data yielded R2 results of 0.96 and 0.93, and cosine similarity results of 0.92 and 0.90. With a threshold range of 8, the joint accuracy rates for the two rehabilitation actions were found to be 89% and 88%. The experimental results demonstrate the stability of the proposed method in detecting key points in the human body under changing illumination conditions, its anti-shake ability for human movement, and its high detection accuracy. This method shows promise for applications in human–computer interaction, sports rehabilitation, and virtual reality.

BackgroundM-type phospholipase A2 receptor (PLA2R) is a major auto-antigen of primary membranous nephropathy(PMN). Anti-PLA2R antibody levels are closely associated with disease severity and therapeutic effectiveness. Analysis of PLA2R antigen epitope reactivity may have a greater predictive value for remission compared with total PLA2R-antibody level. This study aims to elucidate the relationship between domain-specific antibody levels and clinical outcomes of PMN.MethodsThis retrospective analysis included 87 patients with PLA2R-associated PMN. Among them, 40 and 47 were treated with rituximab (RTX) and cyclophosphamide (CTX) regimen, respectively. The quantitative detection of -immunoglobulin G (IgG)/-IgG4 targeting PLA2R and its epitope levels in the serum of patients with PMN were obtained through time-resolved fluorescence immunoassays and served as biomarkers in evaluating the treatment effectiveness. A predictive PMN remission possibility nomogram was developed using multivariate logistic regression analysis. Discrimination in the prediction model was assessed using the area under the receiver operating characteristic curve (AUC-ROC).Bootstrap ROC was used to evaluate the performance of the prediction model.ResultsAfter a 6-month treatment period, the remission rates of proteinuria, including complete remission and partial remission in the RTX and CTX groups, were 70% and 70.21% (P = 0.983), respectively. However, there was a significant difference in immunological remission in the PLA2R-IgG4 between the RTX and CTX groups (21.43% vs. 61.90%, P = 0.019). Furthermore, we found differences in PLA2R-CysR-IgG4(P = 0.030), PLA2R-CTLD1-IgG4(P = 0.005), PLA2R-CTLD678-IgG4(P = 0.003), and epitope spreading (P = 0.023) between responders and non-responders in the CTX group. Multivariate logistic analysis showed that higher levels of urinary protein (odds ratio [OR], 0.49; 95% confidence interval [CI], 0.26-0.95; P = 0.035) and higher levels of PLA2R-CTLD1-IgG4 (OR, 0.79; 95%CI,0.62-0.99; P = 0.041) were independent risk factors for early remission. A multivariate model for estimating the possibility of early remission in patients with PMN is presented as a nomogram. The AUC-ROC of our model was 0.721 (95%CI, 0.601-0.840), in consistency with the results obtained with internal validation, for which the AUC-ROC was 0.711 (95%CI, 0.587-0.824), thus, demonstrating robustness.ConclusionsCyclophosphamide can induce immunological remission earlier than rituximab at the span of 6 months. The PLA2R-CTLD1-IgG4 has a better predict value than total PLA2R-IgG for remission of proteinuria at the 6th month.

ABSTRACT: Duck is an economically important poultry, and there is currently a major focus on improving its meat quality through breeding. There are wide variations in the growth regulation mechanisms of different duck breeds, that fundamental research on skeletal muscle growth is essential for understanding the regulation of unknown genes. The study aimed to broaden the understanding the duck skeletal muscle development and thereby to improve the performance of domestic ducks. In this study, RNA-seq data from skeletal muscles (breast muscle and leg muscle) of Pekin duck and Hanzhong Ma duck sampled at d 17, 21, and 27 of embryo (E17d, E21d, and E27d), as well as at 6-mo-old following birth (M6), to investigate and compare the mRNA temporal expression profiles and associated pathways that regulate skeletal myogenesis of different duck breeds. There were 331 to 1,440 annotated differentially expressed genes (DEGs) in breast muscle and 380 to 1,790 annotated DEGs in leg muscle from different databases between 2 duck breeds. Gene ontology (GO) enrichment in skeletal muscles indicated that these DEGs were mainly involved in biosynthetic process, developmental process, regulation of protein metabolic process and regulation of gene expression. KEGG analysis in skeletal muscles showed that a total of 41 DEGs were mapped to 7 KEGG pathways, including ECM-receptor interaction, focal adhesion, carbon metabolism, regulation of actin cytoskeleton, calcium signaling pathway, biosynthesis of amino acids and PPAR signaling pathway. The differential expression of 8 selected DEGs was verified by qRT-PCR, and the results were consistent with RNA-seq data. The identified DEGs, such as SDC, SPP1, PAK1, MYL9, PGK1, NOS1, PHGDH, TNNT2, FN1, and AQP4, were specially highlighted, indicating their associations with muscle development in the Pekin duck and Hanzhong Ma duck. This study provides a basis for revealing the differences in skeletal muscle development between Pekin duck and Hanzhong Ma duck.

Currently, surface EMG signals have a wide range of applications in human–computer interaction systems. However, selecting features for gesture recognition models based on traditional machine learning can be challenging and may not yield satisfactory results. Considering the strong nonlinear generalization ability of neural networks, this paper proposes a two-stream residual network model with an attention mechanism for gesture recognition. One branch processes surface EMG signals, while the other processes hand acceleration signals. Segmented networks are utilized to fully extract the physiological and kinematic features of the hand. To enhance the model’s capacity to learn crucial information, we introduce an attention mechanism after global average pooling. This mechanism strengthens relevant features and weakens irrelevant ones. Finally, the deep features obtained from the two branches of learning are fused to further improve the accuracy of multi-gesture recognition. The experiments conducted on the NinaPro DB2 public dataset resulted in a recognition accuracy of 88.25% for 49 gestures. This demonstrates that our network model can effectively capture gesture features, enhancing accuracy and robustness across various gestures. This approach to multi-source information fusion is expected to provide more accurate and real-time commands for exoskeleton robots and myoelectric prosthetic control systems, thereby enhancing the user experience and the naturalness of robot operation.

The hardness of passion fruit is a critical feature to consider when determining maturity during post-harvest storage. The capacity of near-infrared diffuse reflectance spectroscopy (NIRS) for non-destructive detection of outer and inner hardness of passion fruit epicarp was investigated in this work. The passion fruits’ spectra were obtained using a near-infrared spectrometer with a wavelength range of 10,000–4000 cm−1. The hardness of passion fruit’s outer epicarp (F1) and inner epicarp (F2) was then measured using a texture analyzer. Moving average (MA) and mean-centering (MC) techniques were used to preprocess the collected spectral data. Competitive adaptive reweighted sampling (CARS), successive projection algorithm (SPA), and uninformative variable elimination (UVE) were used to pick feature wavelengths. Grid-search-optimized random forest (Grids-RF) models and genetic-algorithm-optimized support vector regression (GA-SVR) models were created as part of the modeling process. After MC preprocessing and CARS selection, MC-CARS-Grids-RF model with 7 feature wavelengths had the greatest prediction ability for F1. The mean square error of prediction set (RMSEP) was 0.166 gN. Similarly, following MA preprocessing, the MA-Grids-RF model displayed the greatest predictive performance for F2, with an RMSEP of 0.101 gN. When compared to models produced using the original spectra, the R2P for models formed after preprocessing and wavelength selection improved. The findings showed that near-infrared spectroscopy may predict the hardness of passion fruit epicarp, which can be used to identify quality during post-harvest storage.

Fritillaria unibracteata (FU) is a renowned herb in China that requires strict growth conditions in its cultivation process. During this process, the soil microorganisms and their metabolites may directly affect the growth and development of FU, for example, the pathogen infection and sipeimine production. However, few systematic studies have reported the changes in the microbiome and metabolites during FU cultivation thus far. In this work, we simultaneously used metagenomics and metabolomics technology to monitor the changes in microbial communities and metabolites in the rhizosphere of FU during its cultivation for one, two, and three years. Moreover, the interaction between microorganisms and metabolites was investigated by co-occurrence network analysis. The results showed that the microbial composition between the three cultivation-year groups was significantly different (2020-2022). The dominant genera changed from Pseudomonas and Botrytis in CC1 to Mycolicibacterium and Pseudogymnoascus in CC3. The relative abundances of beneficial microorganisms decreased, while the relative abundances of harmful microorganisms showed an increasing trend. The metabolomics results showed that significant changes of the of metabolite composition were observed in the rhizosphere soil, and the relative abundances of some beneficial metabolites showed a decreasing trend. In this study, we discussed the changes in the microbiome and metabolites during the three-year cultivation of FU and revealed the relationship between microorganisms and metabolites. This work provides a reference for the efficient and sustainable cultivation of FU.

The occurrence of cracks in brown rice kernels has a substantial impact on grain quality. The timely and accurate detection of rice grains with cracks is crucial for enhancing the overall quality and flavor of processed rice. In this study, we developed an optical observation platform and optimized the original ResNet-18 neural network structure to improve the detection and classification of grain cracks. We established image datasets for japonica and indica rice varieties, and employed image augmentation and model migration techniques during training. In addition, we compared the performance of the optimized model with DenseNet-121 and GoogLeNet. The results demonstrate a notable enhancement in crack detection accuracy for japonica, reaching 96%, which is a 3.67% improvement over the original model. Furthermore, we achieved a substantial reduction in average training time, reduced by 58.66%. For indica rice, after model optimization and migration, the accuracy reached 96.67%. It’s important to note that the optimized model has limitations and is not suitable for mixed datasets with limited training data. This technology offers the capability to accurately identify and detect cracks in brown rice kernels under visible light conditions, presenting a promising solution for enhancing grain quality during processing.

Abstract Background Previous studies observed that sleep disorders potentially increased the risk of asthma and asthmatic exacerbation. We aimed to examine whether excessive daytime sleepiness (EDS), probable insomnia, objective short sleep duration (OSSD), and obstructive sleep apnea (OSA) affect all-cause mortality (ACM) in individuals with or without asthma. Methods We extracted relevant data from the Sleep Heart Health Study established in 1995–1998 with an 11.4-year follow-up. Multivariate Cox regression analysis with a proportional hazards model was used to estimate the associations between ACM and four sleep disorders among asthmatic patients and individuals without asthma. Dose-response analysis and machine learning (random survival forest and CoxBoost) further evaluated the impact of sleep disorders on ACM in asthmatic patients. Results A total of 4538 individuals with 990 deaths were included in our study, including 357 asthmatic patients with 64 deaths. Three multivariate Cox regression analyses suggested that OSSD (adjusted HR = 2.67, 95% CI: 1.23–5.77) but not probable insomnia, EDS or OSA significantly increased the risk of ACM in asthmatic patients. Three dose-response analyses also indicated that the extension of objective sleep duration was associated with a reduction in ACM in asthmatic patients compared to very OSSD patients. Severe EDS potentially augmented the risk of ACM compared with asthmatics without EDS (adjusted HR = 3.08, 95% CI: 1.11–8.56). Machine learning demonstrated that OSSD of four sleep disorders had the largest relative importance for ACM in asthmatics, followed by EDS, OSA and probable insomnia. Conclusions This study observed that OSSD and severe EDS were positively associated with an increase in ACM in asthmatic patients. Periodic screening and effective intervention of sleep disorders are necessary for the management of asthma.

Abstract Background Sarcopenia is listed as a treatment trait in behavioral/risk factors for severe asthma, but studies on asthma and sarcopenia are lacking. This study aimed to determine the associations between sarcopenia with asthmatic prevalence, symptoms, lung function and comorbidities. Methods Fifteen thousand four hundred four individuals from the China Health and Retirement Longitudinal Study(CHARLS) and 10,263 individuals from the Study on global AGEing and adult health(SAGE) in China were included in this study. Four components of this study were used to assess the bidirectional association in the prevalence between sarcopenia with asthma, and estimate the relationships between sarcopenia with asthmatic symptoms, lung function and comorbidities via generalized additive models. The 10-item Center for Epidemiological Studies–Depression Scale ≥ 12 scores was classified as depression. Results In the CHARLS and SAGE, the prevalence of sarcopenia in asthmatics was higher than those without asthma. Asthmatics with sarcopenia had a significantly increased prevalence of severe shortness of breath(sarcopenia yes vs. no, adjusted OR = 3.71, 95%CI: 1.43–9.60) and airway obstruction in the SAGE(sarcopenia yes vs. no, adjusted OR = 6.82, 95%CI: 2.54–18.34) and an obvious reduction of PEF in the CHARLS and SAGE(sarcopenia yes vs. no, adjusted RR = 0.86, 95%CI: 0.82–0.91) compared to asthmatics without sarcopenia. The presence of sarcopenia was positively associated with the prevalence of chronic obstructive pulmonary disease(sarcopenia yes vs no, adjusted OR = 5.76, 95%CI:2.01–16.5) and depression(sarcopenia yes vs no, adjusted OR = 1.87, 95%CI:1.11–3.14) in asthmatics. Conclusions Our findings indicated that sarcopenia partakes in the development of asthma by affecting lung function and comorbidities and maybe considered a treatable trait of asthma management.

Stroke is a significant cause of disability worldwide, and stroke survivors often experience severe motor impairments. Lower limb rehabilitation exoskeleton robots provide support and balance for stroke survivors and assist them in performing rehabilitation training tasks, which can effectively improve their quality of life during the later stages of stroke recovery. Lower limb rehabilitation exoskeleton robots have become a hot topic in rehabilitation therapy research. This review introduces traditional rehabilitation assessment methods, explores the possibility of lower limb exoskeleton robots combining sensors and electrophysiological signals to assess stroke survivors' rehabilitation objectively, summarizes standard human-robot coupling models of lower limb rehabilitation exoskeleton robots in recent years, and critically introduces adaptive control models based on motion intent recognition for lower limb exoskeleton robots. This provides new design ideas for the future combination of lower limb rehabilitation exoskeleton robots with rehabilitation assessment, motion assistance, rehabilitation treatment, and adaptive control, making the rehabilitation assessment process more objective and addressing the shortage of rehabilitation therapists to some extent. Finally, the article discusses the current limitations of adaptive control of lower limb rehabilitation exoskeleton robots for stroke survivors and proposes new research directions.

Background Gastrodia elata is widely used in China as a valuable herbal medicine. Owing to its high medicinal and nutrient value, wild resources of G. elata have been overexploited and its native areas have been severely damaged. Understanding the impacts of climate change on the distribution of this endangered species is important for the conservation and sustainable use of G. elata. Methods We used the optimized maximum entropy model to simulate the potential distribution of G. elata under contemporary and future time periods (1970–2000, 2050s, 2070s, and 2090s) and different climate change scenarios (SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP5-8.5). Under these conditions, we investigated the key environmental factors influencing the distribution of G. elata as well as the spatial and temporal characteristics of its niche dynamics. Results With high Maxent model accuracy (AUCmean = 0.947 ± 0.012, and the Kappa value is 0.817), our analysis revealed that annual precipitation, altitude, and mean temperature of driest quarter are the most important environmental factors influencing the distribution of G. elata. Under current bioclimatic conditions, the potentially suitable area for G. elata in China is 71.98 × 104 km2, while the highly suitable region for G. elata growth is 7.28 × 104 km2. Our models for three future periods under four climate change scenarios indicate that G. elata can maintain stable distributions in southern Shaanxi, southwestern Hubei, and around the Sichuan basin, as these areas are highly suitable for its growth. However, the center of the highly suitable areas of G. elata shift depending on different climatic scenarios. The values of niche overlap for G. elata show a decreasing trend over the forecasted periods, of which the niche overlap under the SSP3-7.0 scenario shows the greatest decrease. Discussions Under the condition of global climate change in the future, our study provides basic reference data for the conservation and sustainable utilization of the valuable and endangered medicinal plant G. elata. It is important to carefully choose the protection area of G. elata wild resources according the suitable area conditions modeled. Moreover, these findings will be valuable for providing insights into the breeding and artificial cultivation of this plant, including the selection of suitable areas for planting.

White matter disconnection is the primary cause of cognition and affection abnormality in mild cognitive impairment (MCI). Adequate understanding of behavioral disturbances, such as cognition and affection abnormality in MCI, can help to intervene and slow down the progression of Alzheimer’s disease (AD) promptly. Diffusion MRI is a non-invasive and effective technique for studying white matter microstructure. This review searched the relevant papers published from 2010 to 2022. Sixty-nine studies using diffusion MRI for white matter disconnections associated with behavioral disturbances in MCI were screened. Fibers connected to the hippocampus and temporal lobe were associated with cognition decline in MCI. Fibers connected to the thalamus were associated with both cognition and affection abnormality. This review summarized the correspondence between white matter disconnections and behavioral disturbances such as cognition and affection, which provides a theoretical basis for the future diagnosis and treatment of AD.

Chimeric antigen receptor T cell therapy has become an important immunotherapeutic tool for overcoming cancers. However, the efficacy of CAR-T cell therapy in solid tumors is relatively poor due to the complexity of the tumor microenvironment and inhibitory immune checkpoints. TIGIT on the surface of T cells acts as an immune checkpoint by binding to CD155 on the tumor cells’ surface, thereby inhibiting tumor cell killing. Blocking TIGIT/CD155 interactions is a promising approach in cancer immunotherapy. In this study, we generated anti-MLSN CAR-T cells in combination with anti-α-TIGIT for solid tumors treatment. The anti-α-TIGIT effectively enhanced the efficacy of anti-MLSN CAR-T cells on the killing of target cells in vitro. In addition, we genetically engineered anti-MSLN CAR-T cells with the capacity to constitutively produce TIGIT-blocking single-chain variable fragments. Our study demonstrated that blocking TIGIT significantly promoted cytokine release to augment the tumor-killing effect of MT CAR-T cells. Moreover, the self-delivery of TIGIT-blocking scFvs enhanced the infiltration and activation of MT CAR-T cells in the tumor microenvironments to achieve better tumor regression in vivo. These results suggest that blocking TIGIT effectively enhances the anti-tumor effect of CAR-T cells and suggest a promising strategy of combining CAR-T with immune checkpoints blockade in the treatment of solid tumors.

Agricultural mechanization is crucial in enhancing production efficiency, alleviating labor demands, reducing costs, improving agricultural product quality, and promoting sustainable development. However, wear and tear are inevitable when using agricultural machinery. The failure of critical wear-resistant parts is responsible for over 50% of rural machinery breakdowns. For instance, a domestic combine harvester typically only operates trouble-free for 20 to 30 h, and the service life of a rotary plow knife is approximately 80 h. Investigating the wear performance of key farm machinery components reinforces machinery design and maintenance strategies, extends machinery lifespans, enhances agricultural production efficiency, and advances agrarian sustainability. This paper provides a comprehensive overview of the latest research on the wear resistance of crucial agricultural machinery components. It delves into the factors influencing the wear resistance of these components and explores current effective measures to address wear-related issues. Additionally, it also summarizes the challenges and opportunities in researching the wear performance of key components in agricultural machinery and future development directions.

Immunotherapy is a hot area in cancer treatment, and one of the keys to this therapy is the identification of the right tumour-associated or tumour-specific antigen. Cluster of differentiation 24 (CD24) is an emerging tumour-associated antigen that is commonly and highly expressed in various tumours. In addition, CD24 is associated with several cancer-related signalling pathways and closely interacts with other molecules and immune cells to influence tumour progression. Monoclonal antibodies, antibody–drug conjugates (ADCs), chimeric antigen receptor (CAR) T-cell therapy, and CAR-NK cell therapy are currently available for the treatment of CD24. In this review, we summarise the existing therapeutic approaches and possible future directions targeting CD24.

Abstract Rheumatoid arthritis (RA) is a chronic and inflammatory autoimmune disease. Macrophage pyroptosis, a proinflammatory form of cell death, is critically important in RA; however, the detailed mechanism underlying pyroptosis induction is not yet well understood. Here, we report that DNA polymerase β (Pol β), a key enzyme in base excision repair, plays a pivotal role in RA pathogenesis. Our data shows that Pol β expression is significantly decreased in peripheral blood mononuclear cells (PBMCs) from active RA patients and collagen-induced arthritis (CIA) mice, and Pol β deficiency increases the incidence of RA, macrophage infiltration, and bone destruction in CIA mouse models. In vitro, experiments showed that Pol β deficiency exacerbated macrophage pyroptosis induced by LPS plus ATP, while overexpression of Pol β inhibited macrophage pyroptosis. Further characterization revealed that Pol β knockout resulted in DNA damage accumulation and cytosolic dsDNA leakage, which activated the cGAS-STING-NF-κB signaling pathway and upregulated the expression of NLRP3, IL-1 β, and IL-18. In conclusion, our findings clarify the influence of Pol β on the development of RA and provide a detailed explanation for the STING-NF-κB pathway to induce macrophage pyroptosis.

Abstract Pancreatic cancer (PC) is one of the deadliest malignant tumors, and its resistance to gemcitabine chemotherapy is the primary reason for poor prognosis in patients. Ubiquitin-like protein FAT10 has recently been reported to promote tumor chemotherapy resistance. In this study, the expression of FAT10 in PC was significantly higher than that in adjacent noncancerous tissues. Increased expression of FAT10 in PC was related to a late TNM stage and decreased overall survival. Functional experiments revealed that downregulating the expression of FAT10 inhibits the proliferation and epithelial-mesenchymal transition (EMT) of PC cells, promotes the apoptosis of PC cells, and enhances sensitivity to gemcitabine chemotherapy. In addition, upregulation of FAT10 increased the expression of FOXM1 protein. The effect of downregulating FAT10 was reversed by FOXM1 overexpression, and FOXM1 knockdown inhibited EMT driven by FAT10 overexpression. Mechanistically, FAT10 stabilized the expression of FOXM1 by competing with ubiquitin to bind FOXM1 and inhibiting the ubiquitination-mediated degradation of FOXM1. In conclusion, the FAT10-FOXM1 axis is a pivotal driver of PC proliferation and gemcitabine resistance, and the results provide novel insights into chemotherapy resistance in PC.

Abstract Background Breast cancer (BC) is one of the most prevalent malignancies among women globally. Emerging evidence indicates that long non-coding RNAs (lncRNAs) are associated with BC carcinogenesis. In the current study, we explored the mechanism by which LINC00662 regulates BC. Methods Quantitative real-time PCR (qRT-PCR) assessed RNA expressions while western blot for protein levels. Kaplan Meier analysis evaluated overall survival (OS). Cytoplasmic/nuclear fractionation, RNA binding protein immunoprecipitation (RIP) and luciferase reporter assays probed into the underlying molecular mechanism of LINC00662 in BC. Xenograft model was established to explore the influence of LINC00662 on BC progression in vivo. R square graphs were utilized to represent RNA relationships. Results LINC00662 is overtly overexpressed in BC tissues and cell lines. LINC00662 knockdown hampers cell proliferation, migration, invasion and stemness. LINC00662 expression is negatively correlated with OS of BC patients. LINC00662 up-regulates SOX2 expression by competitively binding to miR-144-3p, thereby modulating BC cell progression. Xenograft experiments verified that LINC00662 promotes BC tumor growth and cell stemness in vivo. Conclusion LINC00662 enhances cell proliferation, migration, invasion and stemness in BC by targeting miR-144-3p/SOX2 axis. The findings in the present study suggested that LINC00662 could be a potential therapeutic target for BC treatment. Graphical Abstract

Purpose: Obesity is considered a risk factor for asthma exacerbation. However, limited studies have focused on the association of different levels of weight clusters with asthma. As such, we study the associations between different weight clusters with FeNO, blood eosinophils, and lung function among adult asthmatics.Methods: Data from 789 participants aged 20 years or older in the National Health and Nutrition Examination Survey 2007–2012 were analyzed. Body mass index (BMI) and waist circumference (WC) were used to determine the weight status. The study population was divided into five groups, including normal weight and low WC (153), normal weight and high WC (43), overweight and high WC (67), overweight and abdominal obesity (128), and general and abdominal obesity (398). A Multivariate linear regression model was used to evaluate the abovementioned associations after adjusting for potential confounding factors.Results: The adjusted models showed that general and abdominal obesity cluster (adjusted β = −0.63, 95% confidence interval (CI): −1.08, −0.17 p < 0.01), and the normal weight with high WC cluster (adjusted β = −0.96, 95% CI: −1.74, −0.19 p < 0.05) were associated with lower levels of blood eosinophils percentage than normal weight and low WC cluster. A similar tendency was shown in the levels of FeNO, but the differences were not significant (p > 0.05). Furthermore, abdominal obesity clusters were significantly associated with lower FVC, FVC% predicted, and FEV1 measures than normal weight and low WC cluster, especially those individuals with general and abdominal obesity cluster. No association was found between different weight clusters and FEV1/FVCF ratio. The two other weight clusters did not show the association with any of the lung function measures.Conclusion: General and abdominal obesity were associated with lung function impairment and a significant reduction of FeNO and blood eosinophil percentage. This study emphasized the importance of concurrent determination of BMI and WC in asthma clinical practice.

Abstract Background Hepatocellular carcinoma (HCC) is a highly refractory cancer associated with increasing mortality, which currently lacks effective treatment options. Interleukin-24 (IL-24) is a novel tumor suppressor cytokine that can selectively induce cancer cell apoptosis, and it has been utilized as a cancer gene therapy strategy. The vaccinia virus is a promising strategy for cancer therapy, owing to its direct viral lytic effects, as well as a vehicle to overexpress therapeutic transgenes. Methods We constructed a recombinant oncolytic vaccinia viruse (VG9-IL-24) based on vaccinia virus Guang9 (VG9) harboring the IL-24 gene. In vitro, we assessed the replication of VG9-IL-24 in HCC cell lines and normal liver cells and evaluated the cytotoxicity in different cell lines; then, we determined the expression of IL-24 by RT-PCR and ELISA. We examined apoptosis and cell cycle progression in SMMC-7721 cells treated with VG9-IL-24 by flow cytometry. In vivo, we established the SMMC-7721 xenograft mouse model to evaluate the antitumor effects of VG9-IL-24. Results In vitro, VG9-IL-24 efficiently infected HCC cell lines, but not normal liver cells, and resulted in a high level of IL-24 expression and significant cytotoxicity. Moreover, VG9-IL-24 induced an increase in the proportion of apoptotic cells and blocked the SMMC-7721 cell cycle in the G2/M phase. In vivo, tumor growth was significantly suppressed and the survival was prolonged in VG9-IL-24-treated mice. Conclusions Vaccinia virus VG9-mediated gene therapy might be an innovative treatment for cancer with tumor-specific lysis and apoptosis-inducing effects. VG9-IL-24 exhibited enhanced antitumor effects and is a promising candidate for HCC therapy.

Abstract Emerging evidence suggests that long non-coding RNAs (lncRNAs) play important roles in the metastasis and recurrence of hepatocellular carcinoma (HCC). A kinds of lncRNAs were found to be involved in regulating epithelial–mesenchymal transition (EMT) or stem-like traits in human cancers, however, the molecular mechanism and signaling pathways targeting EMT and stemness remains largely unknown. Previously, we found that linc00261 was down-regulated in HCC and associated with multiple worse clinical pathological parameters and poor prognosis. Here, we show that linc00261 was down-regulated in TGF-β1 stimulated cells, and forced expression of linc00261 attenuated EMT and stem-like traits in HCC. Linc00261 also inhibited the tumor sphere forming in vitro and decreased the tumorigenicity in vivo. Furthermore, we revealed that linc00261 suppressed the expression and phosphorylation of SMAD3 (p-SMAD3), which could be core transcriptional modulator in TGF-β1 signaling mediated EMT and the acquisition of stemness traits. A negative correlation between linc00261 and p-SMAD3 was determined in HCC samples. Conclusion: Our study revealed that linc00261 suppressed EMT and stem-like traits in HCC cells by inhibiting TGF-β1/SMAD3 signaling.

Objective FKBP prolyl isomerase 5 (FKBP5) has been shown to play an important role in metabolically active tissues such as skeletal muscle. However, the expression of FKBP5 in Muscovy duck tissues and its association with body weight are still unclear. Methods In this study, real-time quantitative polymerase chain reaction was used to detect the expression of FKBP5 in different tissues of Muscovy duck at different growth stages. Further, single nucleotide polymorphisms (SNPs) were detected in the exon region of FKBP5 and were combined analyzed with the body weight of 334 Muscovy ducks. Results FKBP5 was highly expressed in various tissues of Muscovy duck at days 17, 19, 21, 24, and 27 of embryonic development. In addition, the expression of FKBP5 in the tissues of female adult Muscovy ducks was higher than that of male Muscovy ducks. Besides, an association analysis indicated that 3 SNPs were related to body weight trait. At the g.4819252 A>G, the body weight of AG genotype was significantly higher than that of the AA and the GG genotype. At the g.4821390 G>A, the genotype GA was extremely significantly related to body weight. At the g.4830622 T>G, the body weight of TT was significantly higher than GG and TG. Conclusion These findings indicate the possible effects of expression levels in various tissues and the SNPs of FKBP5 on Muscovy duck body weight trait. FKBP5 could be used as molecular marker for muscle development trait using early marker-assisted selection of Muscovy ducks.

Achieving dual regulation of the kinetics and thermodynamics of MgH2 is essential for the practical applications. In this study, a novel nanocomposite (In@Ti-MX) architected from single-/few-layered Ti3C2 MXenes and ultradispersed indium nanoparticles was prepared by a bottom-up self-assembly strategy and introduced into MgH2 to solve the above-mentioned problems. The MgH2+In@Ti-MX composites demonstrate excellent hydrogen storage performance: The resultant In@Ti-MX demonstrated a positive effect on the hydrogen storage performance of MgH2/Mg: the dehydrogenated rate of MgH2+15 wt%In@Ti-MX reached the maximum at 330 °C, which was 47 °C lower than that of commercial MgH2; The hydrogenation enthalpy of the dehydrided MgH2+15 wt%In@Ti-MX and MgH2+25 wt%In@Ti-MX were determined to be −66.2 ± 1.1 and −61.7 ± 1.4 kJ·mol−1 H2. In situ high-energy synchrotron x-ray diffraction technique together with other microstructure analyses revealed that synergistic effects from Ti3C2 MXenes and In nanoparticles (NPs) contributed to the improved kinetics and thermodynamics of MgH2(Mg): Ti/TiH2 derived from Ti3C2 MXenes accelerated the dissociation and recombination of hydrogen molecule/atoms, while In NPs reduced the thermodynamic stability of MgH2 by forming the Mg-In solution. Such a strategy of using dual-active hybrid structures to modify MgH2/Mg provides a new insight for tuning both the hydrogen storage kinetics and thermodynamics of Mg-based hydrides.

Abstract The Precise Point Positioning (PPP) technique uses a single Global Navigation Satellite System (GNSS) receiver to collect carrier-phase and code observations and perform centimeter-accuracy positioning together with the precise satellite orbit and clock corrections provided. According to the observations used, there are basically two approaches, namely, the ionosphere-free combination approach and the raw observation approach. The former eliminates the ionosphere effects in the observation domain, while the latter estimates the ionosphere effects using uncombined and undifferenced observations, i.e., so-called raw observations. These traditional techniques do not fix carrier-phase ambiguities to integers, if the additional corrections of satellite hardware biases are not provided to the users. To derive the corrections of hardware biases in network side, the ionosphere-free combination operation is often used to obtain the ionosphere-free ambiguities from the L1 and L2 ones produced even with the raw observation approach in earlier studies. This contribution introduces a variant of the raw observation approach that does not use any ionosphere-free (or narrow-lane) combination operator to derive satellite hardware bias and compute PPP ambiguity float and fixed solution. The reparameterization and the manipulation of design matrix coefficients are described. A computational procedure is developed to derive the satellite hardware biases on WL and L1 directly. The PPP ambiguity-fixed solutions are obtained also directly with WL/L1 integer ambiguity resolutions. The proposed method is applied to process the data of a GNSS network covering a large part of China. We produce the satellite biases of BeiDou, GPS and Galileo. The results demonstrate that both accuracy and convergence are significantly improved with integer ambiguity resolution. The BeiDou contributions on accuracy and convergence are also assessed. It is disclosed for the first time that BeiDou only ambiguity-fixed solutions achieve the similar accuracy with that of GPS/Galileo combined, at least in mainland China. The numerical analysis demonstrates that the best solutions are achieved by GPS/Galileo/BeiDou solutions. The accuracy in horizontal components is better than 6 mm, and in the height component better than 20 mm (one sigma). The mean convergence time for reliable ambiguity-fixing is about 1.37 min with 0.12 min standard deviation among stations without using ionosphere corrections and the third frequency measurements. The contribution of BDS is numerically highlighted.

N6-methyladenosine (m6A) is the most prevalent internal modification of eukaryotic messenger RNAs (mRNAs), affecting their fold, stability, degradation, and cellular interaction(s) and implicating them in processes such as splicing, translation, export, and decay. The m6A modification is also extensively present in non-coding RNAs, including microRNAs (miRNAs), ribosomal RNAs (rRNAs), and transfer RNAs (tRNAs). Common m6A methylation detection techniques play an important role in understanding the biological function and potential mechanism of m6A, mainly including the quantification and specific localization of m6A modification sites. Here, we describe in detail the dot blotting method for detecting m6A levels in RNA (mRNA as an example), including total RNA extraction, mRNA purification, dot blotting, and data analysis. This protocol can also be used to enrich specific RNAs (such as tRNA, rRNA, or miRNA) by isolation technology to detect the m6A level of single RNA species, so as to facilitate further studies of the role of m6A in biological processes.

BackgroundIt is currently unknown whether the dynamic nature of depression affects the development of sarcopenia. Herein, this study aims to assess the association between possible new sarcopenia and the depression trajectory of individuals and their intimate partners through a 4-year longitudinal cohort study.MethodsOur study included 784 pairs of individuals without possible sarcopenia and their spouses from the China Health and Retirement Longitudinal Study (CHARLS) 2011. All individuals and their spouses received three assessments of the Center for Epidemiologic Studies Depression 10-item (CESD-10) scale in 2011, 2013, and 2015. According to the diagnostic algorithm recommended by the Asian Working Group for Sarcopenia (AWGS) 2019, we evaluated the incidence of possible sarcopenia in individuals in 2015. Latent class analysis (LCA) was used to identify a longitudinal depression trajectory of individuals and their spouses during a 4-year follow-up. Subsequently, we assessed the relationship between possible sarcopenia and depression trajectory using three generalized additive models.ResultsIn 2015, 24.87% (195/784) of individuals were diagnosed with possible sarcopenia. LCA identified five depression trajectories: a persistently high risk of depression in individuals and their spouses (reference; class 1 = 34 [4.3%]); a persistently low risk of depression in individuals and their spouses (class 2 = 526 [67.1%]); a high risk of depression in individuals and a low risk of depression in spouses (class 3 = 46 [5.9%]); a low risk of depression in individuals and a high risk of depression in spouses (class 4 = 116 [14.8%]); and a reduced risk of depression in individuals and their spouses (class 5 = 62 [7.9%]). The highest incidence of possible sarcopenia was shown in class 1, followed by classes 3 and 5. Classes 2 (adjusted relative risk (RR) = 0.44, 95% confidence interval (CI): 0.20–0.97) and 4 (adjusted RR = 0.40, 95%CI: 0.17–0.96) had a significantly lower incidence of possible sarcopenia than class 1. Subgroup analysis demonstrated that the incidence of possible sarcopenia in class 4 was obviously higher in women (38.89%) than in men (18.4%).ConclusionsOur study indicates a persistently high risk of depression in individuals to develop possible sarcopenia. In addition, a persistently high risk of depression in intimate partners potentially increases the risk of possible new sarcopenia, especially in female individuals who are at low risk of depression.