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Abstract The mechanism regulating cellular senescence of postmitotic muscle cells is still unknown. cGAS-STING innate immune signaling was found to mediate cellular senescence in various types of cells, including postmitotic neuron cells, which however has not been explored in postmitotic muscle cells. Here by studying the myofibers from Zmpste24−/− progeria aged mice [an established mice model for Hutchinson-Gilford progeria syndrome (HGPS)], we observed senescence-associated phenotypes in Zmpste24−/− myofibers, which is coupled with increased oxidative damage to mitochondrial DNA (mtDNA) and secretion of senescence-associated secretory phenotype (SASP) factors. Also, Zmpste24−/− myofibers feature increased release of mtDNA from damaged mitochondria, mitophagy dysfunction, and activation of cGAS-STING. Meanwhile, increased mtDNA release in Zmpste24−/− myofibers appeared to be related with increased VDAC1 oligomerization. Further, the inhibition of VDAC1 oligomerization in Zmpste24−/− myofibers with VBIT4 reduced mtDNA release, cGAS-STING activation, and the expression of SASP factors. Our results reveal a novel mechanism of innate immune activation-associated cellular senescence in postmitotic muscle cells in aged muscle, which may help identify novel sets of diagnostic markers and therapeutic targets for progeria aging and aging-associated muscle diseases.

BackgroundSevere fever with thrombocytopenia syndrome (SFTS) is an acute infectious disease, which was first reported in 2009 in China. Previous studies have rarely quantitatively assessed the transmission and fatal risk of SFTS clusters.MethodsEpidemiological information regarding SFTS clusters in Yantai city of Shandong province during 2013-2022 was obtained from the National Public Health Emergency Event Surveillance System (PHEESS) for Disease Control and Prevention information system. The secondary attack rate (SAR) and relative risk (RR) were used to assess the risk of human-to-human transmission of SFTS.ResultsA total of 20 SFTS clusters involving 51 laboratory-confirmed patients were reported between 2013 and 2022 in Yantai city, Shandong province. Most of the clusters occurred from May to October, and the patients were mainly distributed in four counties. Contact with blood or other fluids [RR = 14.06, 95% confidence interval (CI) = 3.29–70.65, p

Abstract Nuclear decoupling and softening are the main cellular mechanisms to resist mechanical stress-induced nuclear/DNA damage, however, its molecular mechanisms remain much unknown. Our recent study of Hutchinson-Gilford progeria syndrome (HGPS) disease revealed the role of nuclear membrane protein Sun2 in mediating nuclear damages and cellular senescence in progeria cells. However, the potential role of Sun2 in mechanical stress-induced nuclear damage and its correlation with nuclear decoupling and softening is still not clear. By applying cyclic mechanical stretch to mesenchymal stromal cells (MSCs) of WT and Zmpset24−/− mice (Z24−/−, a model for HGPS), we observed much increased nuclear damage in Z24−/− MSCs, which also featured elevated Sun2 expression, RhoA activation, F-actin polymerization and nuclear stiffness, indicating the compromised nuclear decoupling capacity. Suppression of Sun2 with siRNA effectively reduced nuclear/DNA damages caused by mechanical stretch, which was mediated by increased nuclear decoupling and softening, and consequently improved nuclear deformability. Our results reveal that Sun2 is greatly involved in mediating mechanical stress-induced nuclear damage by regulating nuclear mechanical properties, and Sun2 suppression can be a novel therapeutic target for treating progeria aging or aging-related diseases.

Abstract Background Fibro‐adipogenic progenitors (FAPs) in the muscles have been found to interact closely with muscle progenitor/stem cells (MPCs) and facilitate muscle regeneration at normal conditions. However, it is not clear how FAPs may interact with MPCs in aged muscles. Senolytics have been demonstrated to selectively eliminate senescent cells and generate therapeutic benefits on ageing and multiple age‐related disease models. Methods By studying the muscles and primary cells of age matched WT mice and Zmpste24−/− (Z24−/−) mice, an accelerated ageing model for Hutchinson–Gilford progeria syndrome (HGPS), we examined the interaction between FAPs and MPCs in progeria‐aged muscle, and the potential effect of senolytic drug fisetin in removing senescent FAPs and improving the function of MPCs. Results We observed that, compared with muscles of WT mice, muscles of Z24−/− mice contained a significantly increased number of FAPs (2.4‐fold; n > =6, P =6, P =6, P =6, P =6, P =6, P =6, P =6, P =6, P

Aging leads to several geriatric conditions including osteoporosis (OP) and associated frailty syndrome. Treatments for these conditions are limited and none target fundamental drivers of pathology, and thus identifying strategies to delay progressive loss of tissue homeostasis and functional reserve will significantly improve quality of life in elderly individuals. A fundamental property of aging is the accumulation of senescent cells. Senescence is a cell state defined by loss of proliferative capacity, resistance to apoptosis, and the release of a proinflammatory and anti-regenerative senescence-associated secretory phenotype (SASP). The accumulation of senescent cells and SASP factors is thought to significantly contribute to systemic aging. Senolytics—compounds which selectively target and kill senescent cells—have been characterized to target and inhibit anti-apoptotic pathways that are upregulated during senescence, which can elicit apoptosis in senescent cells and relieve SASP production. Senescent cells have been linked to several age-related pathologies including bone density loss and osteoarthritis in mice. Previous studies in murine models of OP have demonstrated that targeting senescent cells pharmacologically with senolytic drugs can reduce symptomology of the disease. Here, we demonstrate the efficacy of senolytic drugs (dasatinib, quercetin, and fisetin) to improve age-associated degeneration in bone using the Zmpste24−/− (Z24−/−) progeria murine system for Hutchinson–Gilford progeria syndrome (HGPS). We found that the combination of dasatinib plus quercetin could not significantly mitigate trabecular bone loss although fisetin administration could reduce bone density loss in the accelerated aging Z24−/− model. Furthermore, the overt bone density loss observed in the Z24−/− model reported herein highlights the Z24 model as a translational model to recapitulate alterations in bone density associated with advanced age. Consistent with the “geroscience hypothesis,” these data demonstrate the utility of targeting a fundamental driver of systemic aging (senescent cell accumulation) to alleviate a common condition with age, bone deterioration.

ABSTRACT Studies on the operational adaptability of slope corn harvesters on hilly slopes are of great significance to improve the performance and safety of the whole machine, but there are few studies on this aspect at the present. Therefore, this paper takes a self-developed small sloping land crawler corn harvester as the object combined with the special topography of the hilly sloping land and the technology adopted by the machine to make a mathematical analysis of its operational adaptability on the sloping land. Through the field test, the forward speed of the corn harvester, track wheel spacing and slope angle are used as the test factors, and the harvest loss rate of the corn ear is used as the assessment index to carry out the orthogonal test on the slope land. The analysis of the results based on the Design-expert response surface was completed. The results show that the ear loss rate of the experiment under different conditions is less than 5% of the national standard. The corn harvester has good adaptability to sloped land, which can realize the mechanized operation of corn on the hilly and sloped land, and has good agricultural production application value.

In this paper, a fast anomaly target detection method based on morphological profile and improved Reed-Xiaoli (RX) is proposed. First, an extended morphological profile (EMP) containing spatial information is extracted from the original hyperspectral images by means of mathematical morphological transformations. Moreover, a novel fast local RX (FLRX) algorithm is also proposed. This algorithm iteratively updates the inverse matrix of covariance using matrix inversion lemma, thereby reducing the computational complexity of the Mahalanobis distance and improving the algorithm's calculation speed. Finally, a combined EMP and FLRX detector, named the EMP-FLRX method is constructed; as this approach can effectively utilize the spectral information and spatial information of hyperspectral images, it greatly improves detection accuracy and reduces the running time. We compare the proposed method with some classical and recently proposed approaches on six real datasets. the area under the curve (AUC) value of EMP-FLRX on six datasets is 0.9978, 0.9822, 0.9780, 0.9492, 0.9999 and 0.9852 respectively, while the running time is 9.4070, 14.4330, 6.2478, 9.0242, 19.9820, and 1.9060 s respectively. Experimental results clearly demonstrate that the performance of our proposed method is quite competitive in terms of detection accuracy and running time.

Solar quiet variation (Sq) generated from ionospheric currents are among the most important type of geomagnetic variations. In this paper, the correlation between the geomagnetic Z component solar quiet variation (SqZ) component and the ionospheric total electron content (TEC) is discussed. For the analysis, 20 stations at middle and low latitudes are chosen, and the correlation between geomagnetic SqZ and ionospheric TEC at these stations from 2008 to 2015 is analyzed. The time delays are estimated by detecting peaks in the cross-correlation function, which shows that there is a stable correlation between SqZ and TEC. The time delay between them is largest in summer and smallest in winter, which is consistent with the Sq field intensity. With increasing latitude, the time delay decreases gradually from positive to negative, i.e., SqZ goes from being ahead of the TEC to behind it. The turning point of this change is at ca. 28°N, exactly where the Sq current vortex is located.

Based on the mechanical test (shear test, compression test), the bond model of corn kernel and straw was established to explore the rolling and crushing effect of different crushing rollers. The type of crushing roller is different. The material crushing process by the force (extrusion and kneading) is different. The mechanical analysis of the crushing process reveals that the disc crushing roller (DCR) has the characteristics of large unit-length kneading area; the spiral-notched serrated crushing roller (SNSCR) has transverse shearing effect on the material; and they affect the crushing effect of the material. By means of discrete element method and simulation test, multiple regression method and variance analysis method are used to systematically analyze the data. The optimal working parameters of each roll (crushing roll speed, crushing clearance, differential ratio) were obtained. The simulation test and bench test of the crushing process of materials with different roll shapes were carried out under the optimal working parameters. The crushing effect was evaluated with a Binzhou screen and a corn silage grain-crushing score screen. The crushed materials of corn kernel can be divided into three categories according to the size (broken grains passed through 2 mm sieve; broken grains passed through 4.75 mm sieve; and broken grains that cannot pass through 4.75 mm sieve), and the crushed materials of corn stalk can be divided into four categories according to the size and thickness (broken straw through 4 mm sieve; broken straw through 8 mm sieve; broken straw through 19 mm sieve; and broken straw that cannot pass 19 mm sieve). The crushing effect and crushing classification of the simulation test and bench test were basically consistent. The results showed that the disc crushing roller group had the highest comprehensive score with straw rolling rate of 89.1% and grain crushing rate of 87.7%, which was the most suitable for harvesting whole-plant silage maize (WSM).

It is necessary to develop novel and efficient alternatives to fluorocarbon surfactant and prepare fluorine-free environmentally-friendly fire extinguishing agent. The carboxyl modified polyether polysiloxane surfactant (CMPS) with high surface activity was synthesized via the esterification reaction using hydroxyl-containing polyether modified polysiloxane (HPMS) and maleic anhydride (MA) as raw materials. The process conditions of the esterification reaction were optimized by orthogonal tests, and the optimum process parameters were determined as follows: reaction temperature of 85 °C, reaction time of 4.5 h, isopropyl alcohol content of 20% and the molar ratio of HPMS/MA of 1/1. The chemical structure, surface activity, aggregation behavior, foam properties, wetting properties and electron distribution were systematically investigated. It was found that the carboxyl group was successfully grafted into silicone molecule, and the conjugated system was formed, which changed the interaction force between the molecules and would affect the surface activity of the aqueous solution. The CMPS exhibited excellent surface activity and could effectively reduce the water’s surface tension to 18.46 mN/m. The CMPS formed spherical aggregates in aqueous solution, and the contact angle value of CMPS is 15.56°, illustrating that CMPS had excellent hydrophilicity and wetting performance. The CMPS can enhance the foam property and has good stability. The electron distribution results indicate that the introduced carboxyl groups are more inclined towards the negative charge band, which would be conducive to weak the interaction between molecules and improve the surface activity of the solution. Consequently, new foam fire extinguishing agents were prepared by using CMPS as a key component and they exhibited excellent fire-fighting performance. The prepared CMPS would be the optimal alternative to fluorocarbon surfactant and could be applied in foam extinguishing agents.

Macrophages play important roles in angiogenesis; however, previous studies on macrophage angiogenesis have focused on traditional 2D cultures. In this study, we established a 3D culture system for macrophages using collagen microcarriers and assessed the effect of 3D culture on their angiogenic capabilities. Macrophages grown in 3D culture displayed a significantly different morphology and arrangement under electron microscopy compared to those grown in 2D culture. Tube formation assays and chick embryo chorioallantoic membrane assays further revealed that 3D-cultured macrophages were less angiogenic than those in 2D culture. Whole-transcriptome sequencing showed that nearly 40% of genes were significantly differently expressed, including nine important angiogenic factors of which seven had been downregulated. In addition, the expression of almost all genes related to two important angiogenic pathways was decreased in 3D-cultured macrophages, including the two key angiogenic factors, VEGFA and ANG2. Together, the findings of our study improve our understanding of angiogenesis and 3D macrophage culture in tissues, and provide new avenues and methods for future research on macrophages.

Multi-behavior recommendation aims to model the interaction information of multiple behaviors to enhance the target behavior’s recommendation performance. Despite progress in recent research, it is challenging to represent users’ preferences using the multi-feature behavior information of user interactions. In this paper, we propose a Multi-Feature Behavior Relationship for Multi-Behavior Recommendation (MFBR) framework, which models the multi-behavior recommendation problem from both sequence structure and graph structure perspectives for user preference prediction of target behaviors. Specifically, the MFBR model is designed with a sequence encoder and a graph encoder to construct behavioral representations of different aspects of the user; the correlations between behaviors are modeled by a behavioral relationship encoding layer, and the importance of different behaviors is finally learned in order to construct the final representation of user preferences. Experimental validation conducted on two real-world recommendation datasets shows that our MFBR consistently outperforms state-of-the-art methods.

Land cover segmentation is an important and challenging task in the field of remote sensing. Even though convolutional neural networks (CNNs) provide great support for semantic segmentation, standard models are still difficult to capture global information and long-range dependencies in remote sensing images. To overcome these limitations, we proposed an attention guided encoder-decoder network with multi-scale context aggregation to achieve more accurate segmentation of land cover. Based on the structure of the encoder-decoder network, we introduce a multi-scale feature fusion module with two attention modules to the top of the encoder. The multi-scale feature fusion module is employed to aggregate multi-scale features and capture global correlations. The attention modules are used to exploit the long-range dependencies and the interdependence between channels from the perspective of space and channel respectively. The experimental results on the GF-2 images show that our proposed method achieves state-of-the-art performance, with an OA of 84.1% and the mIoU of 62.3%. Compared with the baseline network, our method improves the OA by 3.3% and the mIoU by 4.4%. The comparative experiments also demonstrate that the proposed approach can significantly improve the accuracy of land cover segmentation than other compared methods.

Recovering missing values plays a significant role in time series tasks in practical applications. How to replace the missing data and build the dependency relations from the incomplete sample set is still a challenge. The previous research has found that residual network (ResNet) helps to form a deep network and cope with degradation problem by shortcut connection. Gated recurrent unit (GRU) can improve network model and reduce training parameters by update gate which takes the place of forgetting gate and output gate in long short-term memory (LSTM). Inspired by this finding, we observe that shortcut connection and mean of global revealed information can model the relationship among missing items, the previous and overall revealed information. Hence, we design an imputation network with decay factor for shortcut connection and mean of the global revealed information in GRU, called decay residual mean imputation GRU (DRMI-GRU). We introduce a decay residual mean unit (DRMU), which takes full advantage of the previous and global revealed information to model incomplete time series; and the decay factor is applied to balance the previous long-term dependencies and all non-missing values in the sample set. In addition, a mask unit is designed to check the missing data existing or not. An extensive body of empirical comparisons with other existing imputation algorithms over real-world data and public dataset with different ratio of missing data verifies the performance of our model.

Remote sensing scene classification is still a challenging task in remote sensing applications. How to effectively extract features from a dataset with limited scale is crucial for improvement of scene classification. Recently, convolutional neural network (CNN) performs impressively in different fields of computer vision and has been used for remote sensing. However, most works focus on the feature maps of the last convolution layer and pay little attention to the benefits of additional layers. In fact, the feature information hidden in different layers has potential for feature discrimination capacity. The most attention of this work is how to explore the potential of multiple layers from a CNN model. Therefore, this paper proposes multi-layers feature fusion based on CNN and designs a fusion module to solve relevant issues of fusion. In this module, firstly, all the feature maps are transformed to match sizes mutually due to infeasible fusion of feature maps with different scales; then, two fusion methods are introduced to integrate feature maps from different layers instead of the last convolution layer only; finally, the fusion of features are delivered to the next layer or classifier as the routine CNN does. The experimental results show that the suggested methods achieve promising performance on public datasets.

Unsupervised domain adaptation (UDA) based on adversarial learning for remote-sensing scene classification has become a research hotspot because of the need to alleviating the lack of annotated training data. Existing methods train classifiers according to their ability to distinguish features from source or target domains. However, they suffer from the following two limitations: (1) the classifier is trained on source samples and forms a source-domain-specific boundary, which ignores features from the target domain and (2) semantically meaningful features are merely built from the adversary of a generator and a discriminator, which ignore selecting the domain invariant features. These issues limit the distribution matching performance of source and target domains, since each domain has its distinctive characteristic. To resolve these issues, we propose a framework with error-correcting boundaries and feature adaptation metric. Specifically, we design an error-correcting boundaries mechanism to build target-domain-specific classifier boundaries via multi-classifiers and error-correcting discrepancy loss, which significantly distinguish target samples and reduce their distinguished uncertainty. Then, we employ a feature adaptation metric structure to enhance the adaptation of ambiguous features via shallow layers of the backbone convolutional neural network and alignment loss, which automatically learns domain invariant features. The experimental results on four public datasets outperform other UDA methods of remote-sensing scene classification.

Road extraction from remote sensing images is of great significance to urban planning, navigation, disaster assessment, and other applications. Although deep neural networks have shown a strong ability in road extraction, it remains a challenging task due to complex circumstances and factors such as occlusion. To improve the accuracy and connectivity of road extraction, we propose an inner convolution integrated encoder-decoder network with the post-processing of directional conditional random fields. Firstly, we design an inner convolutional network which can propagate information slice-by-slice within feature maps, thus enhancing the learning of road topology and linear features. Additionally, we present the directional conditional random fields to improve the quality of the extracted road by adding the direction of roads to the energy function of the conditional random fields. The experimental results on the Massachusetts road dataset show that the proposed approach achieves high-quality segmentation results, with the F1-score of 84.6%, which outperforms other comparable “state-of-the-art” approaches. The visualization results prove that the proposed approach is able to effectively extract roads from remote sensing images and can solve the road connectivity problem produced by occlusions to some extent.

Nuclear factor κB (NF-κB) is a transcription factor important for regulating innate and adaptive immunity, cellular proliferation, apoptosis, and senescence. Dysregulation of NF-κB and its upstream regulator IκB kinase (IKK) contributes to the pathogenesis of multiple inflammatory and degenerative diseases as well as cancer. An 11-amino acid peptide containing the NF-κB essential modulator (NEMO)-binding domain (NBD) derived from the C-terminus of β subunit of IKK, functions as a highly selective inhibitor of the IKK complex by disrupting the association of IKKβ and the IKKγ subunit NEMO. A structure-based pharmacophore model was developed to identify NBD mimetics by in silico screening. Two optimized lead NBD mimetics, SR12343 and SR12460, inhibited tumor necrosis factor α (TNF-α)- and lipopolysaccharide (LPS)-induced NF-κB activation by blocking the interaction between IKKβ and NEMO and suppressed LPS-induced acute pulmonary inflammation in mice. Chronic treatment of a mouse model of Duchenne muscular dystrophy (DMD) with SR12343 and SR12460 attenuated inflammatory infiltration, necrosis and muscle degeneration, demonstrating that these small-molecule NBD mimetics are potential therapeutics for inflammatory and degenerative diseases.

[This corrects the article DOI: 10.1371/journal.pone.0182908.].

The prognosis of colorectal carcinoma (CRC) is unstable in the stage II-III patients. Patients with early stage II CRC have a relative poor prognosis while other stage III CRC patients have a better prognosis. In our work, by utilizing the expression of lncRNAs and mRNAs measured by microarray (GSE39582), we constructed a risk score staging system with Cox multivariate regression model to predict the outcome of grade II-III CRC patients. Ten genes including two lncRNAs and eight mRNAs were used to estimate the survival of stage II-III CRC patients. The patients with high risk scores have poorer survival rate those with low risk scores, significantly. These results were further validated in another three independent datasets (GSE37892, GSE33113, and GSE17536). The relationship between clinical information and were evaluated, and the risk score is independent from the other clinical information and performs better in evaluating the survival of stage II-III CRC patients. Moreover, the correlation between chemotherapy was also evaluated, and we found that both patients with or without chemotherapy have a poor survival in high risk group. Gene Set Enrichment Analysis were used to find the difference between high-risk and low-risk groups, and pathways including cell adhesion and focal adhesion were significantly enriched, suggesting that the risk score reflects the status of cell-cell physical interaction. In summary, we constructed a risk staging model for grade II-III CRC, which is independent from and performs better than clinical information.

The ratio of matrix metalloproteinases (MMPs) to the tissue inhibitors of metalloproteinases (TIMPs) in wounded tissues strictly control the protease activity of MMPs, and therefore regulate the progress of wound closure, tissue regeneration and scar formation. Some amphibians (i.e. axolotl/newt) demonstrate complete regeneration of missing or wounded digits and even limbs; MMPs play a critical role during amphibian regeneration. Conversely, mammalian wound healing re-establishes tissue integrity, but at the expense of scar tissue formation. The differences between amphibian regeneration and mammalian wound healing can be attributed to the greater ratio of MMPs to TIMPs in amphibian tissue. Previous studies have demonstrated the ability of MMP1 to effectively promote skeletal muscle regeneration by favoring extracellular matrix (ECM) remodeling to enhance cell proliferation and migration. In this study, MMP1 was administered to the digits amputated at the mid-second phalanx of adult mice to observe its effect on digit regeneration. Results indicated that the regeneration of soft tissue and the rate of wound closure were significantly improved by MMP1 administration, but the elongation of the skeletal tissue was insignificantly affected. During digit regeneration, more mutipotent progenitor cells, capillary vasculature and neuromuscular-related tissues were observed in MMP1 treated tissues; moreover, there was less fibrotic tissue formed in treated digits. In summary, MMP1 was found to be effective in promoting wound healing in amputated digits of adult mice.

Dedifferentiation of muscle cells in the tissue of mammals has yet to be observed. One of the challenges facing the study of skeletal muscle cell dedifferentiation is the availability of a reliable model that can confidentially distinguish differentiated cell populations of myotubes and non-fused mononuclear cells, including stem cells that can coexist within the population of cells being studied.In the current study, we created a Cre/Lox-β-galactosidase system, which can specifically tag differentiated multinuclear myotubes and myotube-generated mononuclear cells based on the activation of the marker gene, β-galactosidase. By using this system in an adult mouse model, we found that β-galactosidase positive mononuclear cells were generated from β-galactosidase positive multinuclear myofibers upon muscle injury. We also demonstrated that these mononuclear cells can develop into a variety of different muscle cell lineages, i.e., myoblasts, satellite cells, and muscle derived stem cells.These novel findings demonstrated, for the first time, that cellular dedifferentiation of skeletal muscle cells actually occurs in mammalian skeletal muscle following traumatic injury in vivo.