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Abstract Background The incidence and mortality of clear cell carcinoma of the kidney increases yearly. There are limited screening methods and advances in treating kidney renal clear cell carcinoma (KIRC). It is important to find new biomarkers to screen, diagnose and predict the prognosis of KIRC. Some studies have shown that CD72 influences the development and progression of colorectal cancer, nasopharyngeal cancer, and acute lymphoid leukemia. However, there is a lack of research on the role of CD72 in the pathogenesis of KIRC. This study aimed to determine whether CD72 is associated with the prognosis and immune infiltration of KIRC, providing an essential molecular basis for the early non-invasive diagnosis and immunotherapy of KIRC. Methods Using TCGA, GTE, GEO, and ImmPort databases, we obtained the differentially expressed mRNA (DEmRNA) associated with the prognosis and immunity of KIRC patients. We used the Kruskal–Wallis test to identify clinicopathological parameters associated with target gene expression. We performed univariate and multivariate COX regression analyses to determine the effect of target gene expression and clinicopathological parameters on survival. We analyzed the target genes' relevant functions and signaling pathways through enrichment analysis. Finally, the correlation of target genes with tumor immune infiltration was explored by ssGSEA and Spearman correlation analysis. Results The results revealed that patients with KIRC with higher expression of CD72 have a poorer prognosis. CD72 was associated with the Pathologic T stage, Pathologic stage, Pathologic M stage, Pathologic N stage, Histologic grade in KIRC patients, Laterality, and OS event. It was an independent predictor of the overall survival of KIRC patients. Functional enrichment analysis showed that CD72 was significantly enriched in oncogenic and immune-related pathways. According to ssGSEA and Spearman correlation analysis, CD72 expression was significantly associated with tumor immune cells and immune checkpoints. Conclusion Our study suggests that CD72 is associated with tumor immunity and may be a biomarker relevant to the diagnosis and prognosis of KIRC patients.

The aim of this study was to analyze the unique quality characteristics and to identify the key volatile flavor compounds in Longtian Zha meat. In this study, volatile flavor substances, amino acids and fatty acids of Longtian Zha meat were detected and analyzed. The key volatile flavor compounds were identified by relative odor activity value method. The result showed that, for the meat part, 14 key volatile compounds were detected, of which trans-2,4-decadienal was the main volatile flavor compound, and imparted the oily and meaty aroma to Longtian Zha meat. For the part of radish, 19 key volatile compounds were detected, among which 2-undecanal, decanal and 1-octen-3-ol were the main volatile flavor compounds and provided the fragrance, lavender, rose, and fruit aroma to Longtian Zha meat. The total amino acid content in Longtian Zha was 192.2 mg/g, including 38.29% of essential amino acids and 47.45% of umami taste amino acids. Longtian Zha meat was rich in unsaturated fatty acids (59.41% of the total fatty acids). The results showed that Longtian Zha meat was rich in volatile substances such as 2-undecenaldehyde, decanal and 1-octene-3-ol, delicious amino acids and unsaturated fatty acids.

Heavy metal contamination in sediments near photovoltaic (PV) power generation areas poses potential environmental risks, requiring detailed characterization and source apportionment to facilitate sustainable management. This research explores the characteristics of sediment grains and the concentrations of heavy metals present in surface sediments from barchan dunes proximal to the Talatan Photovoltaic (PV) power generation area, located within Gonghe County, Qinghai Province. The sediments displayed an average grain size within the medium sand range, with marginal differences discernible between the windward and leeward slopes. With the exception of Mn, Cu, Zn, V, Pb, and Ba, concentrations of the remaining metals were found to surpass the baseline levels established by Chinese aeolian soil data, alluding to potential anthropogenic influences. Through employing coefficients of variation in multivariate statistical analysis, it was identified that the concentrations of Cr and Co were significantly elevated, suggesting potential anthropogenic contamination, which may be associated with photovoltaic industrial activities. Specifically, the elevated concentrations of Cr and Co suggested anthropogenic contamination, potentially associated with photovoltaic industrial activities. Utilizing a combination of Correlation Coefficient Analysis, Principal Component Analysis, and Cluster Analysis, three potential sources of heavy metals were identified: (1) industrial origin for elements such as Cu, Cr, Ni, Zn, and As; (2) elements with limited direct application in the photovoltaic industry but associated with materials and energy storage, namely Ba, V, and Mn; (3) anthropogenic inputs related to construction materials and battery storage systems in the photovoltaic park, specifically Co and Pb. The findings offer a significant understanding of the heavy metal characteristics and sources in proximity to the Talatan PV power generation area, emphasizing the impact of human activities on environmental quality. These insights underscore the necessity for enhanced monitoring and management of industrial activities to mitigate potential environmental impacts. Further research is recommended on a broader spatial scale to yield a more comprehensive understanding of this subject.

Basic research is the driver of advanced productivity and is an important guarantee for the market competitiveness of enterprise. In order to understand the influence of basic research on the development of China’s digital economy industry, this paper, based on structural equation model by collecting data from 209 enterprises in digital economy industry, explores the relationship between enterprise participation in basic research and four factors: enterprise efficiency, perceived risk, priority, and technological innovation, together with function mechanism. The results show that, first enterprises can improve efficiency, reduce potential risk concerns, and enhance technology level by expanding business revenue, enlarging scale, and upgrading R&D institutions, thus promoting participation in basic research. At the same time, the government can provide more subsidies for enterprise R&D funds to reduce enterprise concern about the risk of basic research, and guide them to expand their investment in R&D and focus on basic research so as to expand their participation in this field. Secondly, the optimization of enterprise participation in basic research, the upgrading of R&D institutions and the number of invention patents have mutually-reinforced effect, while the expansion of enterprise scale help to reduce the constraint of return risk to a certain extent. Finally, this paper proposes some policies and suggestions to enhance enterprise participation in basic research from the perspective of promoting industrial development and social equity.

As information and communication technology advances rapidly, real-time live online broadcasting has emerged as a novel social media platform. In particular, live online broadcasts have gained widespread popularity among audiences. However, this process can cause environmental problems. When audiences imitate live content and perform similar field activities, it can have a negative effect on the environment. In this study, an extended theory of planned behavior (TPB) was used to explore how online live broadcasts relate to environmental damage from the perspective of human behavior. A total of 603 valid responses were collected from a questionnaire survey, and a regression analysis was conducted to verify the hypotheses. The findings showed that the TPB can be applied to account for the formation mechanism of behavioral intention of field activities caused by online live broadcasts. The mediating effect of imitation was verified using the above relationship. These findings are expected to provide a practical reference for the control of online live broadcast content and guidance on public environmental behavior.

Currently, with the rapid development and broad application of cloud computing technology, companies tend to use cloud services to build their applications or business systems. Selecting a trustworthy cloud service is a challenging multi-criteria decision-making (MCDM) problem. Moreover, decision makers are more inclined to use linguistic descriptions to assess the quality of service (QoS) for cloud services due to the limitation of the decision makers’ knowledge and the vagueness of criteria information. Therefore, we propose a practical, integrated MCDM scheme for cloud service evaluation and selection of cloud systems, allowing decision makers to compare cloud services based on QoS criteria. First, to more accurately and effectively express the uncertainty of qualitative concepts, the cloud model is used as a conversion tool for qualitative and quantitative information to quantify linguistic terms. Second, given the shortcomings of traditional differentiating measures between cloud models, a more comprehensive distance measurement algorithm using cloud droplet distribution is proposed for the cloud model. The new distance measurement algorithm is applied to the calculation of cloud model similarity and the gray correlation coefficient. The dynamic expertise weights are determined by calculating the similarity between the expert evaluation cloud model and the arithmetic mean cloud model. Then, we propose a technique for order preference by similarity to an ideal solution (TOPSIS) improved by the grey relational analysis (GRA) to calculate the relative closeness of alternatives to the positive and negative ideal solutions and establish a multi-objective optimization model that maximizes the relative closeness of all alternatives to determine the weights of the criteria. Finally, we reconstructed the QoS evaluation criteria for cloud services from both application and service perspectives, and the classical TOPSIS is applied to generate alternative rankings. The practicability and robustness of the scheme were tested through the cloud service selection problem experienced by a real mining company’s scheduling platform, which can provide practical references with the theoretical basis for the selection and evaluation of cloud services.

Multimedia data security has become a noticeable problem in the continuous development of multimedia technology. Traditional chaotic systems usually require larger bit-width iterative calculations to affect the generation rate of chaotic systems when generating. To solve this problem, we proposed a general Residual Chaotic System (RCS) for image security in heterogeneous multimedia signal processing environments. Based on the Residue Number System (RNS) commonly used in VLSI, we generate the general chaotic sequences by iterating unary polynomial function through it in Galois Field (GF). Moreover, we make use of the Box-Muller algorithm to generate RCS, which improved the speed of operation by a large margin when doing R/B backward transformation. Experiments show that the key sensitivity and plaintext sensitivity of the proposed encryption system are good, and the system can encrypt pictures efficiently and safely. Then, we proposed a watermark embedding and extraction algorithm with block selection. Based on the proposed new judgment conditions, the $4\times 4$ brightness blocks are divided into five categories, then the appropriate types of blocks are selected accordingly. After testing the performance indexes of cypher-text images, including statistical and sensitivity-related tests, all results indicate that our algorithm has good randomness and security. The invisibility is good, the watermark capacity has been significantly improved, and the video bit-rate growth rate is in a good range. The watermark can resist attacks such as noise and the bit error rate under re-encoding and Gaussian low-pass filtering attacks. Compared with other algorithms, the algorithm that we proposed is faster, much more efficient, and more secure. Therefore, it is a practical alternative scheme for heterogeneous communication environments.

Abstract Background To investigate the therapeutic effect of human dental pulp stem cells (DPSCs) transfected with adenovirus expressing hepatocyte growth factor (HGF) in a mouse model of collagen-induced arthritis (CIA). Methods DPSCs were modified with Ad-HGF to produce HGF-overexpressing DPSCs, DPSCs-HGF. In experimental mouse CIA model, DPSCs-HGF and DPSCs-Null (modified with Ad-Null) were engrafted via intravenously after disease onset, which was determined by the presence of joint swelling. The therapeutic effects on joints were evaluated at 49 days after collagen injection by histopathological analysis and microcomputed tomography imaging. The inflammatory cytokines were analyzed both in sera and joints via MILLIPLEX kit and immunohistochemical staining, respectively, and the regulatory T cells (Tregs) were analyzed in peripheral blood by using flow cytometry. Furthermore, primary fibroblast-like synoviocytes were isolated, colony formation analysis and FACS were performed to evaluate the effect of HGF on the proliferation and cell cycle of FLSs. Western blot assay was carried out to clarify the signal pathway of HGF-cMet. Results We found that without HGF modification, DPSC transfusion was helpful in controlling autoimmune status, local synovitis, and bone erosion after intravenous administration. However, HGF-modified DPSCs have dual role in rheumatoid arthritis (RA). In the early phase, HGF overexpression inhibited RA progression by its immunosuppressive effects, while in the late phase, HGF promoted synovitis by activating fibroblast-like synoviocytes to produce pathogenic IL-6, accelerating cell proliferation and inducing apoptosis resistance via phosphorylating the c-Met/Akt pathway. The overall effect of HGF modification attenuated the therapeutic effect of DPSCs. Conclusions Our study provides a comprehensive evaluation of the therapeutic effect of DPSCs in the mouse model and a primary answer to the divergence of whether HGF is harmful or helpful in RA.

Bronchopulmonary dysplasia (BPD) is a common pulmonary complication observed in preterm infants that is composed of multifactorial pathogenesis. Current strategies, albeit successful in moderately reducing morbidity and mortality of BPD, failed to draw overall satisfactory conclusion. Here, using a typical mouse model mimicking hallmarks of BPD, we revealed that both cord blood-derived mononuclear cells (CB-MNCs) and umbilical cord-derived mesenchymal stem cells (UC-MSCs) are efficient in alleviating BPD. Notably, infusion of CB-MNCs has more prominent effects in preventing alveolar simplification and pulmonary vessel loss, restoring pulmonary respiratory functions and balancing inflammatory responses. To further elucidate the underlying mechanisms within the divergent therapeutic effects of UC-MSC and CB-MNC, we systematically investigated the long noncoding RNA (lncRNA)–microRNA (miRNA)–messenger RNA (mRNA) and circular RNA (circRNA)–miRNA–mRNA networks by whole-transcriptome sequencing. Importantly, pathway analysis integrating Gene Ontology (GO)/Kyoto Encyclopedia of Genes and Genomes (KEGG)/gene set enrichment analysis (GSEA) method indicates that the competing endogenous RNA (ceRNA) network is mainly related to the regulation of GTPase activity (GO: 0043087), extracellular signal-regulated kinase 1 (ERK1) and ERK2 signal cascade (GO: 0070371), chromosome regulation (GO: 0007059), and cell cycle control (GO: 0044770). Through rigorous selection of the lncRNA/circRNA-based ceRNA network, we demonstrated that the hub genes reside in UC-MSC- and CB-MNC-infused networks directed to the function of cell adhesion, motor transportation (Cdk13, Lrrn2), immune homeostasis balance, and autophagy (Homer3, Prkcd) relatively. Our studies illustrate the first comprehensive mRNA–miRNA–lncRNA and mRNA–miRNA–circRNA networks in stem cell-infused BPD model, which will be valuable in identifying reliable biomarkers or therapeutic targets for BPD pathogenesis and shed new light in the priming and conditioning of UC-MSCs or CB-MNCs in the treatment of neonatal lung injury.

Purpose: Silicosis is a serious occupational disease that is characterized by pulmonary infiltrates and fibrosis and is often refractory to current treatments. New therapeutic strategies for silicosis are needed. Hepatocyte growth factor (HGF) is a latent anti-inflammatory and anti-fibrotic growth factor. Methods: We prepared a polyethyleneimine-polyethylene glycol/pHGF/hyaluronic acid (PEG-PEI/pHGF/HA) nanomaterials loaded with plasmid DNA encoding HGF gene to increase its transfection efficiency. The characterization, including DNA entrapment efficiency, morphology, particle size, and zeta-potential of PEG-PEI/pHGF/HA was studied. And a PEG-PEI/pHGF/HA (N/P=30:1) nanoparticle with low toxicity and high transfection efficiency was used in treatment for silicosis in mice. Results: The results showed that the human HGF expression in the lungs of the mice was increased, and the inflammatory cell infiltration and fibrous collagen deposition was significantly reduced. Conclusion: Therefore, PEG-PEI/pHGF/HA nanoparticle warrant further investigation and may be a potential therapeutic strategy for silicosis.

Abstract Deep neural networks are vulnerable to adversarial samples, posing potential threats to the applications deployed with deep learning models in practical conditions. A typical example is the fingerprint liveness detection module in fingerprint authentication systems. Inspired by great progress of deep learning, deep networks-based fingerprint liveness detection algorithms spring up and dominate the field. Thus, we investigate the feasibility of deceiving state-of-the-art deep networks-based fingerprint liveness detection schemes by leveraging this property in this paper. Extensive evaluations are made with three existing adversarial methods: FGSM, MI-FGSM, and Deepfool. We also proposed an adversarial attack method that enhances the robustness of adversarial fingerprint images to various transformations like rotations and flip. We demonstrate these outstanding schemes are likely to classify fake fingerprints as live fingerprints by adding tiny perturbations, even without internal details of their used model. The experimental results reveal a big loophole and threats for these schemes from a view of security, and enough attention is urgently needed to be paid on anti-adversarial not only in fingerprint liveness detection but also in all deep learning applications.

With the rapid growth of the number of images, many content-based image retrieval methods have been extensively used in our daily life. In general, image retrieval services are very expensive in terms of computing and storage. Therefore, outsourcing services to the cloud server is a good choice for image owners. However, privacy protection can become a big issue for image owners because the cloud server can only be semi-trusted. In this paper, we propose a novel image retrieval scheme. It is a ciphertext image retrieval method based on random mapping features with the bag-of-words model. After encrypting the image with Advanced Encryption Standard and block permutation, the cloud server generates random templates and then extracts the local features. All local features are clustered by k-means algorithm to form the visual word. The histogram of encrypted visual words is constructed in this way as the feature vector to represent each image. The similarity between images can be measured by the distance between feature vectors on the cloud server. Experiments and analysis prove the effect of the scheme.

The transmission and negotiation of the robot swarms and the technical support provided by the communication technology are inseparable, and modulation recognition plays an important role in this transmission. Due to the diversity of current classifiers, choosing the correct classifier to improve the classification effect has become a key issue. To this end, this paper explores the performance of different classifiers in modulation recognition, selects six classifier families, and compares a total of 77 different single classifiers on the modulation dataset, which is implemented on the following three platforms: Weka, Python and MATLAB. The results show that the strong classifiers formed by the combination of weak classifiers is very effective, and Boosting, Bagging, and Random Forest are the three best classifier families. In addition, it was found that as the signal-to-noise ratio (SNR) increases, the overall performance of the classifier families gradually improves, but the ranking of the families performance remains consistent.

Continuous Data Protection (CDP) is an emerging scheme of data backup and recovery for Cyber Security. Compared with traditional data protection techniques, it can provide much higher reliability and allow the data state to be reverted to any point-in-time in the past. CDP continuously captures and logs every disk update, producing large amount of information, thus desperately requires an efficient mechanism of organizing such information (about the disk I/Os) in order to guarantee the performance to be acceptable. However, there are few works which address the organization of history data to improve the recovery efficiency in case of the disaster occurrences. In this paper, three methods of metadata management for Cyber Security are presented, of which two are simple implementations based on MySQL database (named DIR_MySQL and OPT_MySQL respectively) and the other one is designed with consideration of its characteristics. Experimental results show that META_CDP method is far more efficient than the other two and its performance is reasonably acceptable. Furthermore, we discuss in-depth two recovery algorithms, which are full-recovery and incremental recovery. Users can choose the optimal recovery algorithm based on the required recovery time point.

Wireless sensor networks are usually deployed in harsh and emergency scenarios, such as floods, fires, or earthquakes, where human participation to monitor and collect environmental data may be too dangerous. It can be also used for healthcare in extreme and remote environments. In such an environment, sensor nodes are faced with the risk of failure and the loss of valuable healthcare data. Therefore, fast collection and reliable storage of data becomes the two important basic topics for reliable data collection. Traditional distributed data collection protocols based on the network, such as Growth Codes proposed by Karma et al., have improved the persistence of data and the efficiency of reliable data collection in disaster scenarios. However, there are still some problems that reduce the overall efficiency. In this paper, we analyze the factors that affect the collection efficiency from a new perspective, the ratio of redundant symbols. Random feedback digestion (RFDG) model is proposed to digest the redundant symbols, similiar to our stomach digesting food, to remove redundant symbols and reduce resource consumption by using the feedback information of the already decoded code words sent by the sink node. This model can increase the valid information ratio in the network and finally increase data decoding efficiency. Three protocols are proposed in this paper according to different feedback mechanisms based on RFDG. It is shown that protocols based on RFDG outperform the growth codes protocol in data collection efficiency and reduce the delayed effect.

The construction of the financial module is very important in the intelligent management of the company. The collection, transmission, and processing of relevant item information in the financial module are of great significance to the construction of the intelligent financial module. The intelligent financial processing module can improve financial office efficiency and increase the security of the financial processing process. Section 404 of the Sarbanes-Oxley Act (SOX 404) has always played a decisive role in the quality and transparency of the financial modules of the Enterprise Resource Planning (ERP) of multinational corporations, due to the mandatory nature of the Securities and Exchange Commission (SEC). The purpose of this research is to design a set of intelligent ERP financial modules for multinational corporations that could comply with SOX 404, strengthen internal controls, and improve the security and convenience of the financial module. Thereby achieving intelligent management of financial modules and intelligent prevention of risks. Our solution could fortify the preventive controls over inappropriate payments to products with quality issues, reduce manual efforts for credit memo issuance and receipt. It could also fortify the preventive controls over financial risks potentially threatened by a sudden contingent liability concerning E&O issues. It could prevent potential and unexpected frauds arisen by a malicious user to meet the requirements of SOX over preventive and detective controls over the financial system. It could also ensure the consistency of journals between the preparer’s entry and review’s approval as well as improve the completeness and timeliness of financial statement assertions.

Wireless sensor network are widely used in various types of environmental monitoring and there are also many applications in the industrial field, we usually deploy sensor nodes in the corresponding areas to monitor and collect relevant data, waiting for a sink node to collect the stored data for further analysis and decision. But in some extremely harsh scenarios, the living environment of the sensor node is very bad, hence the data on the nodes will always lose. Generally, we use LT Codes to improve data persistence. However, some difficult problems with regard to the efficiency of coding and decoding of data can not be resolved by traditional solutions. In terms of the above problems, this paper improves the traditional coding method by reducing the number of encoded packets, so that the network congestion can be alleviated under the premise of successful data coding. What's more, we propose an edge layered collection model and a new coding strategy Edge Layering Fountain Codes (ELFC) based on the model, each sensor node inside the network sends its data to the edge nodes which are relatively safe to ensure the secure collection of data and allocate the resource consumption of the sensor nodes around the sink node. On this basis, the Ordered Edge Layering Fountain Codes (OELFC) is further proposed. The OELFC can collect data packets in a low degree-to-high degree manner, which greatly improves the efficiency of data decoding.

To study the rapid growth of research on organic photovoltaic (OPV) technology, development trends in the relevant research are analyzed based on CiteSpace software of text mining and visualization in scientific literature. By this analytical method, the outputs and cooperation of authors, the hot research topics, the vital references and the development trend of OPV are identified and visualized. Different from the traditional review articles by the experts on OPV, this work provides a new method of visualizing information about the development of the OPV technology research over the past decade quantitatively.

Previous data have proven that microvesicles derived from hypoxia-induced mesenchymal stem cells (MSC-MVs) can be internalized into endothelial cells, enhancing their proliferation and vessel structure formation and promoting in vivo angiogenesis. However, there is a paucity of information about how the MSC-MVs are up-taken by endothelial cells.MVs were prepared from the supernatants of human bone marrow MSCs that had been exposed to a hypoxic and/or serum-deprivation condition. The incorporation of hypoxia-induced MSC-MVs into human umbilical cord endothelial cells (HUVECs) was observed by flow cytometry and confocal microscopy in the presence or absence of recombinant human Annexin-V (Anx-V) and antibodies against human CD29 and CD44. Further, small interfering RNA (siRNA) targeted at Anx-V and PSR was delivered into HUVECs, or HUVECs were treated with a monoclonal antibody against phosphatidylserine receptor (PSR) and the cellular internalization of MVs was re-assessed.The addition of exogenous Anx-V could inhibit the uptake of MVs isolated from hypoxia-induced stem cells by HUVECs in a dose- and time-dependent manner, while the anti-CD29 and CD44 antibodies had no effect on the internalization process. The suppression was neither observed in Anx-V siRNA-transfected HUVECs, however, addition of anti-PSR antibody and PSR siRNA-transfected HUVECs greatly blocked the incorporation of MVs isolated from hypoxia-induced stem cells into HUVECs.PS on the MVs isolated from hypoxia-induced stem cells is the critical molecule in the uptake by HUVECs.

Differentiation induction is currently considered as an alternative strategy for treating chronic myelogenous leukemia (CML). Our previous work has demonstrated that Sprouty-related EVH1 domainprotein2 (Spred2) was involved in imatinib mediated cytotoxicity in CML cells. However, its roles in growth and lineage differentiation of CML cells remain unknown. In this study, we found that CML CD34+ cells expressed lower level of Spred2 compared with normal hematopoietic progenitor cells, and adenovirus mediated restoration of Spred2 promoted the erythroid differentiation of CML cells. Imatinib could induce Spred2 expression and enhance erythroid differentiation in K562 cells. However, the imatinib induced erythroid differentiation could be blocked by Spred2 silence using lentiviral vector PLKO.1-shSpred2. Spred2 interference activated phosphorylated-ERK (p-ERK) and inhibited erythroid differentiation, while ERK inhibitor, PD98059, could restore the erythroid differentiation, suggesting Spred2 regulated the erythroid differentiation partly through ERK signaling. Furthermore, Spred2 interference partly restored p-ERK level leading to inhibition of erythroid differentiation in imatinib treated K562 cells. In conclusion, Spred2 was involved in erythroid differentiation of CML cells and participated in imatinib induced erythroid differentiation partly through ERK signaling.