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ABSTRACTHerpes zoster remains an important global health issue and mainly occurs in aged and immunocompromised individuals with an early exposure history to Varicella Zoster Virus (VZV). Although the licensed vaccine Shingrix has remarkably high efficacy, undesired reactogenicity and increasing global demand causing vaccine shortage urged the development of improved or novel VZV vaccines. In this study, we developed a novel VZV mRNA vaccine candidate (named as ZOSAL) containing sequence-optimized mRNAs encoding full-length glycoprotein E encapsulated in an ionizable lipid nanoparticle. In mice and rhesus macaques, ZOSAL demonstrated superior immunogenicity and safety in multiple aspects over Shingrix, especially in the induction of strong T-cell immunity. Transcriptomic analysis revealed that both ZOSAL and Shingrix could robustly activate innate immune compartments, especially Type-I IFN signalling and antigen processing/presentation. Multivariate correlation analysis further identified several early factors of innate compartments that can predict the magnitude of T-cell responses, which further increased our understanding of the mode of action of two different VZV vaccine modalities. Collectively, our data demonstrated the superiority of VZV mRNA vaccine over licensed subunit vaccine. The mRNA platform therefore holds prospects for further investigations in next-generation VZV vaccine development.

Grid-based recommendation algorithms view users and items as abstract nodes, and the information utilised by the algorithm is hidden in the selection relationships between users and items. Although these relationships can be easily handled, much useful information is overlooked, resulting in a less accurate recommendation algorithm. The aim of this paper is to propose improvements on the standard substance diffusion algorithm, taking into account the influence of the user’s rating on the recommended item, adding a moderating factor, and optimising the initial resource allocation vector and resource transfer matrix in the recommendation algorithm. An average ranking score evaluation index is introduced to quantify user satisfaction with the recommendation results. Experiments are conducted on the MovieLens training dataset, and the experimental results show that the proposed algorithm outperforms classical collaborative filtering systems and network structure based recommendation systems in terms of recommendation accuracy and hit rate.

Summary: Several empirical and theoretical studies suggest the presence of multiple enhancers per gene that collectively regulate gene expression, and that common sequence variation impacting on the activities of these enhancers is a major source of inter-individual gene expression variability. However, for the vast majority of genes, enhancers and the underlying regulatory variation remains unknown. Even for the genes with well-characterized enhancers, the nature of the combined effects from multiple enhancers and their variants, when known, on gene expression regulation remains unexplored. Here, we have evaluated the combined effects from five SCN5A enhancers and their regulatory variants that are known to collectively correlate with SCN5A cardiac expression and underlie QT interval association in the general population. Using small deletions centered at the regulatory variants in episomal reporter assays in a mouse cardiomyocyte cell line, we demonstrate that the variants and their flanking sequences play critical role in individual enhancer activities, likely being a transcription factor (TF) binding site. By oligonucleotide-based pulldown assays on predicted TFs, we identify the TFs likely driving allele-specific enhancer activities. Using all 32 possible allelic synthetic constructs in reporter assays, representing the five bi-allelic enhancers, we demonstrate combined additive effects on overall enhancer activities. Using transient enhancer assays in zebrafish embryos we demonstrate that four elements act as enhancers in vivo. Together, these studies uncover the TFs driving the enhancer activities of QT interval associated SCN5A regulatory variants, reveal the additive effects from allelic combinations of these regulatory variants, and prove their potential to act as enhancers in vivo.

Background Hyperactivated protein arginine methyltransferases (PRMTs) are implicated in human cancers. Inhibiting tumor intrinsic PRMT5 was reported to potentiate antitumor immune responses, highlighting the possibility of combining PRMT5 inhibitors (PRMT5i) with cancer immunotherapy. However, global suppression of PRMT5 activity impairs the effector functions of immune cells. Here, we sought to identify strategies to specifically inhibit PRMT5 activity in tumor tissues and develop effective PRMT5i-based immuno-oncology (IO) combinations for cancer treatment, particularly for methylthioadenosine phosphorylase (MTAP)-loss cancer.Methods Isogeneic tumor lines with and without MTAP loss were generated by CRISPR/Cas9 knockout. The effects of two PRMT5 inhibitors (GSK3326595 and MRTX1719) were evaluated in these isogenic tumor lines and T cells in vitro and in vivo. Transcriptomic and proteomic changes in tumors and T cells were characterized in response to PRMT5i treatment. Furthermore, the efficacy of MRTX1719 in combination with immune checkpoint blockade was assessed in two syngeneic murine models with MTAP-loss tumor.Results GSK3326595 significantly suppresses PRMT5 activity in tumors and T cells regardless of the MTAP status. However, MRTX1719, a methylthioadenosine-cooperative PRMT5 inhibitor, exhibits tumor-specific PRMT5 inhibition in MTAP-loss tumors with limited immunosuppressive effects. Mechanistically, transcriptomic and proteomic profiling analysis reveals that MRTX1719 successfully reduces the activation of the PI3K pathway, a well-documented immune-resistant pathway. It highlights the potential of MRTX1719 to overcome immune resistance in MTAP-loss tumors. In addition, MRTX1719 sensitizes MTAP-loss tumor cells to the killing of tumor-reactive T cells. Combining MRTX1719 and anti-PD-1 leads to superior antitumor activity in mice bearing MTAP-loss tumors.Conclusion Collectively, our results provide a strong rationale and mechanistic insights for the clinical development of MRTX1719-based IO combinations in MTAP-loss tumors.

Water bodies are crucial components of urban ecology. The development of rapid and timely water-quality assessment tools using easily measured variables is essential for the health management of urban water bodies. In this study, we focused on the dissolved oxygen (DO) of Baiyangdian Lake using 251 sets of empirically measured water quality data and corresponding Sentinel-2 satellite images. Nine machine learning algorithms were then used to develop a rapid detection algorithm for the spatial distribution of the DO concentration in Baiyangdian Lake. This study successfully applied these methods to invert the DO concentration in Baiyangdian Lake during spring, summer, and autumn. The results indicated that extra tree regression (ETR) provided the most accurate and stable results for inverting the DO concentration among the nine machine learning methods. In contrast, AdaBoost regression (ABR), Bayesian ridge regression (BRR), and support vector machines (SVM) exhibit relatively poor regression performance and lack sensitivity to DO concentrations. Moreover, the DO concentration in Baiyangdian Lake ranged from approximately 0 to 12 mg/L, with notable spatiotemporal variations. The highest overall DO concentration was observed in the spring, particularly in the southern region. The DO concentration significantly decreased during summer compared to that in spring, with higher values in the southwestern area and lower values in the northern region. The DO concentration reached its lowest value in autumn, with slightly higher values in the southern region. This study focused on the estimation and inversion of DO concentrations in the water bodies of Baiyangdian Lake. By introducing and comparing the performances of commonly used machine learning models, a rapid estimation of the DO concentration was achieved, thereby overcoming the limitations of traditional water quality monitoring methods in DO inversion. It not only intuitively explained the temporal and spatial variation patterns of DO concentration but also laid a foundation for further in-depth exploration of the interactions between DO and other water quality parameters.

Summary: Glycine- and arginine-rich (GAR) motifs, commonly found in RNA-binding and -processing proteins, can be symmetrically (SDMA) or asymmetrically (ADMA) dimethylated at the arginine residue by protein arginine methyltransferases. Arginine-methylated protein motifs are usually read by Tudor domain-containing proteins. Here, using a GFP-Trap, we identify a non-Tudor domain protein, squamous cell carcinoma antigen recognized by T cells 3 (SART3), as a reader for SDMA-marked GAR motifs. Structural analysis and mutagenesis of SART3 show that aromatic residues lining a groove between two adjacent aromatic-rich half-a-tetratricopeptide (HAT) repeat domains are essential for SART3 to recognize and bind to SDMA-marked GAR motif peptides, as well as for the interaction between SART3 and the GAR-motif-containing proteins fibrillarin and coilin. Further, we show that the loss of this reader ability affects RNA splicing. Overall, our findings broaden the range of potential SDMA readers to include HAT domains.

Social networks are inevitable parts of our daily life, where an unprecedented amount of complex data corresponding to a diverse range of applications are generated. As such, it is imperative to conduct research on social events and patterns from the perspectives of conventional sociology to optimize services that originate from social networks. Event tracking in social networks finds various applications, such as network security and societal governance, which involves analyzing data generated by user groups on social networks in real time. Moreover, as deep learning techniques continue to advance and make important breakthroughs in various fields, researchers are using this technology to progressively optimize the effectiveness of Event Detection (ED) and tracking algorithms. In this regard, this paper presents an in-depth comprehensive review of the concept and methods involved in ED and tracking in social networks. We introduce mainstream event tracking methods, which involve three primary technical steps: ED, event propagation, and event evolution. Finally, we introduce benchmark datasets and evaluation metrics for ED and tracking, which allow comparative analysis on the performance of mainstream methods. Finally, we present a comprehensive analysis of the main research findings and existing limitations in this field, as well as future research prospects and challenges.

Abstract Here we report on the development and comprehensive evaluations of an mRNA vaccine for chronic hepatitis B (CHB) treatment. In two different HBV carrier mouse models generated by viral vector-mediated HBV transfection (pAAV-HBV1.2 and rAAV8-HBV1.3), this vaccine demonstrates sufficient and persistent virological suppression, and robust immunogenicity in terms of induction of strong innate immune activation, high-level virus-specific antibodies, memory B cells and T cells. mRNA platform therefore holds prospects for therapeutic vaccine development to combat CHB.

Abstract While significant advances have been made in predicting static protein structures, the inherent dynamics of proteins, modulated by ligands, are crucial for understanding protein function and facilitating drug discovery. Traditional docking methods, frequently used in studying protein-ligand interactions, typically treat proteins as rigid. While molecular dynamics simulations can propose appropriate protein conformations, they’re computationally demanding due to rare transitions between biologically relevant equilibrium states. In this study, we present DynamicBind, a deep learning method that employs equivariant geometric diffusion networks to construct a smooth energy landscape, promoting efficient transitions between different equilibrium states. DynamicBind accurately recovers ligand-specific conformations from unbound protein structures without the need for holo-structures or extensive sampling. Remarkably, it demonstrates state-of-the-art performance in docking and virtual screening benchmarks. Our experiments reveal that DynamicBind can accommodate a wide range of large protein conformational changes and identify cryptic pockets in unseen protein targets. As a result, DynamicBind shows potential in accelerating the development of small molecules for previously undruggable targets and expanding the horizons of computational drug discovery.

Abstract Bacteria have been explored for their potential in fighting against cancer for decades. Due to their outstanding tumor‐targeting capacity and high biocompatibility, live bacteria can serve as microrobots delivering and producing anti‐tumor agents. In addition, live bacteria have intrinsic immune‐activating functions that aid in the generation of anti‐tumor immunity both systemically and locally in the tumor microenvironment. While bacteria‐based cancer therapy is still facing great challenges, progress in this platform combined with nanobiotechnologies has shown promise in terms of safety and effectiveness. Here, basic development strategies of bacteria‐based delivery systems armed with nanotechnologies, virulence attenuation, and genetic manipulation are summarized and the design of a spatiotemporal selectivity is particularly emphasized. In conclusion, the engineered bacteria platform has a high potentiality in the development of novel cancer therapeutics and holds prospects for future investigation and clinical use.

Abstract Buck converter is a good candidate for the non‐isolated auxiliary power supply (APS) due to its simple structure and high efficiency. However, it can hardly be adopted in applications that required a very high step‐down ratio due to the difficulty to realize an ultra‐low duty cycle, especially when the switching frequency becomes higher and higher. Here, an improved Buck converter is proposed for high‐frequency and high‐step‐down‐ratio APS application. With the control method based on energy extraction, the proposed topology can achieve an ultra‐low equivalent duty cycle. Thus, a high step‐down ratio can be achieved under a high switching frequency. Moreover, the proposed structure can be applied on several types of the main power stage through the power switch sharing technique. Finally, the prototype with the switching frequency of 1 MHz and the step‐down ratio of 18:1 is verified by the experimental results.

Abstract Irinotecan (Ir) is commonly employed as a first-line chemotherapeutic treatment for colorectal cancer (CRC). However, tremendous impediments remain to be addressed to surmount drug resistance and ameliorate adverse events. Poly-ADP-Ribose Polymerase (PARP) participates in the maintenance of genome stability and the repair of DNA damage, thus playing a critical role in chemotherapy resistance. In this work, we introduce a novel curative strategy that utilizes nanoparticles (NPs) prepared by dynamic supramolecular co-assembly of Ir and a PARP inhibitor (PARPi) niraparib (Nir) through π-π stacking and hydrogen bond interactions. The Ir and Nir self-assembled Nano-Twin-Drug of (Nir-Ir NPs) could enhance the therapeutic effect on CRC by synergistically inhibiting the DNA damage repair pathway and activating the tumor cell apoptosis process without obvious toxicity. In addition, the Nir-Ir NPs could effectively reverse irinotecan-resistance by inhibiting the expression of multiple resistance protein-1 (MRP-1). Overall, our study underscores the distinctive advantages and potential of Nir-Ir NPs as a complementary strategy to chemotherapy by simultaneously overcoming the Ir resistance and improving the anti-tumor efficacy against CRC.

Abstract Objective To study the development of microalbuminuria (MAU) in essential hypertension (EHT), we investigated the association of MAU with central blood pressure (CBP), direct renin concentration (DRC), plasma aldosterone (PA), and uric acid (UA). Method We determined 24 h-urinary albumin excretion (24 h-UAE) in patients with EHT who were hospitalized at TEDA International Cardiovascular Hospital from June 2020 to May 2022. We defined MAU as 24 h-UAE in the range of 30 mg/24 h to 300 mg/24 h. Univariate and multivariate analyses were conducted to determine the associations of MAU with CBP, DRC, PA, and UA in EHT, considering demographic and clinical information. We also plotted receiver operating characteristic curves (ROCs) for predicting MAU using these results. Results More than a quarter of patients (26.5%, 107/404, 95% CI: 22.2–31.1%) were diagnosed with MAU in EHT. A higher body mass index (BMI), longer duration of hypertension, and higher severity were associated with MAU. Also, nearly 10% more creatinine levels were recorded in the MAU group than in the control group (69.5 ± 18.7 µmol/L vs. 64.8 ± 12.5 µmol/L, P = 0.004). The increase was also observed for PA (15.5, 9.7–20.6 ng/dL vs. 12.3, 9.0–17.3 ng/dL, P = 0.024) and UA (419.8 ± 105.6 µmol/L vs. 375.1 ± 89.5 µmol/L, P

In the present work, we pioneered a coordinated self-assembly approach aimed at fabricating carrier-free hybrid nanoparticles to address the inherent challenges of the anaerobic microenvironment and the oxidative resistance induced by reductive glutathione (GSH) in photodynamic therapy (PDT). In these nanoparticles, protoporphyrin IX (PP), HIF-1α inhibitor of N, Nʹ-(2,5-Dichlorosulfonyl) cystamine KC7F2 (KC), and the cofactor Fe3+ present hydrogen bond and coordination interaction. The nanoparticles exhibited efficient cellular uptake by CAL-27 cells, facilitating their accumulation in tumors by enhanced permeability and retention (EPR) effect. Under irradiation at 650 nm, the formation of cytotoxic singlet oxygen (1O2) would be enhanced by the synergy effect on the Fenton reaction of Fe3+ ion and the downregulation of the HIF-1α, leading to the improved PDT efficacy both in vitro and in vivo biological studies. Our work opens a new supramolecular approach to prepare hybrid nanoparticles for effective synergy therapy with PDT against cancer cells.

Ultra-high spectral purity lasers are of considerable research interests in numerous fields such as coherent optical communication, microwave photonics, distributed optical fiber sensing, gravitational wave detection, optical clock, and so on. Herein, to deeply purify laser spectrum with compact size under normal condition, we propose a novel and practical idea to effectively suppress the spontaneous radiation of the laser cavity through weak external distributed perturbation. Subsequently, a laser configuration consisting of a main lasing cavity and an external distributed feedback cavity is proposed. The feedback signal with continuous spatio-temporal phase transition controlled by a distributed feedback structure is injected into the main cavity, which can deeply suppress the coupling rate from the spontaneous radiation to the stimulated emission and extremely purify the laser spectrum. Eventually, an ultra-narrow linewidth on-chip laser system with a side mode suppression ratio greater than 80 dB, an output linewidth of 10 Hz, and a relative intensity noise less than -150 dB/Hz is successfully obtained under normal conditions. The proposed concept in this work provides a new perspective for extreme regulation of laser parameters by using weak external distributed perturbation, which can be valid for various gain-type lasers with wide wavelength bands.

Visual place recognition (VPR) is considered among the most complicated tasks in SLAM due to the multiple challenges of drastic variations in both appearance and viewpoint. To address this issue, this article presents a self-supervised and lightweight VPR approach (namely CoCALC) that fully utilizes the appearance and geometric information provided by images. The main thing that makes CoCALC ultra-lightweight (only 0.27 MB) is our use of Depthwise Separable Convolution (DSC), a simple but effective architecture that enables our model to generate a more robust image representation. The network trained specifically for VPR can efficiently extract deep convolutional features from salient image regions that have relatively higher entropy, thereby expanding its applications on resource-limited platforms without GPUs. To further eliminate the negative consequences of the high percent false matches, a novel band-matrix-based geometric check is employed to filter out the incorrect matching of image patches, and the impact of different bandwidths on the recall rate is discussed. Results on several benchmark datasets confirm that the proposed CoCALC can yield state-of-the-art performance and superior generalization with acceptable efficiency. All relevant codes are provided at https://github.com/LiKangyuLKY/CoCALC-VPR for further studies.

We reported a case of a 68-year-old man who presented with recurrent fever and multiorgan dysfunction. His significantly elevated procalcitonin and C-reactive protein levels indicated recurrent sepsis. However, no focus of infection and no pathogens were identified through a variety of examinations and tests. Although the increase of creatine kinase was less than five times the upper limit of normal value, the diagnosis of rhabdomyolysis secondary to adrenal insufficiency resulting from primary empty sella syndrome was finally made, as supported by serum myoglobin elevation, serum cortisol, and adrenocorticotropic hormone deficiency, bilateral adrenal atrophy on computed tomography, and empty sella on magnetic resonance imaging. After the glucocorticoid replacement treatment, the patient's myoglobin gradually returned to normal range, and his condition continued to improve. Rhabdomyolysis resulting from a rare cause may be misdiagnosed as sepsis in patients who present with increased procalcitonin levels.

Bitcoin is a cryptocurrency based on blockchain. All historical Bitcoin transactions are stored in the Bitcoin blockchain, but Bitcoin owners are generally unknown. This is the reason for Bitcoin's pseudo-anonymity, therefore it is often used for illegal transactions. Bitcoin addresses are related to Bitcoin users' identities. Some Bitcoin addresses have the potential to be analyzed due to the behavior patterns of Bitcoin transactions. However, existing Bitcoin analysis methods do not consider the fusion of new blocks' data, resulting in low efficiency of Bitcoin address analysis. In order to address this problem, this paper proposes an incremental Bitcoin address cluster method to avoid re-clustering when new block data is added. Besides, a heuristic Bitcoin address clustering algorithm is developed to improve clustering accuracy for the Bitcoin Blockchain. Experimental results show that the proposed method increases Bitcoin address cluster efficiency and accuracy.

Online social networks are increasingly connecting people around the world. Influence maximization is a key area of research in online social networks, which identifies influential users during information dissemination. Most of the existing influence maximization methods only consider the transmission of a single channel, but real-world networks mostly include multiple channels of information transmission with competitive relationships. The problem of influence maximization in an environment involves selecting the seed node set for certain competitive information, so that it can avoid the influence of other information, and ultimately affect the largest set of nodes in the network. In this paper, the influence calculation of nodes is achieved according to the local community discovery algorithm, which is based on community dispersion and the characteristics of dynamic community structure. Furthermore, considering two various competitive information dissemination cases as an example, a solution is designed for self-interested information based on the assumption that the seed node set of competitive information is known, and a novel influence maximization algorithm of node avoidance based on user interest is proposed. Experiments conducted based on real-world Twitter dataset demonstrates the efficiency of our proposed algorithm in terms of accuracy and time against notable influence maximization algorithms.

There is an emerging therapeutic strategy to transplant stem cells into diseased host tissue for various neurodegenerative diseases, owing to their self-renewal ability and pluripotency. However, the traceability of long-term transplanted cells limits the further understanding of the mechanism of the therapy. Herein, we designed and synthesized a quinoxalinone scaffold-based near-infrared (NIR) fluorescent probe named QSN, which exhibits ultra-strong photostability, large Stokes shift, and cell membrane-targeting capacity. It could be found that QSN-labeled human embryonic stem cells showed strong fluorescent emission and photostability both in vitro and in vivo. Additionally, QSN would not impair the pluripotency of embryonic stem cells, indicating that QSN did not perform cytotoxicity. Moreover, it is worth mentioning that QSN-labeled human neural stem cells held cellular retention for at least 6 weeks in the mouse brain striatum post transplantation. All these findings highlight the potential application of QSN for ultralong-term transplanted cell tracking.

Chemotherapy is one of the main treatments for colorectal cancer, but systemic toxicity severely limits its clinical use. Packaging hydrophobic chemotherapeutic drugs in targeted nanoparticles greatly improve their efficacy and reduce side effects. We previously identified a novel colorectal cancer specific binding peptide P-LPK (LPKTVSSDMSLN) from phage display peptide library. Here we designed a self-assembled paclitaxel (PTX)-loaded nanoparticle (LPK-PTX NPs). LPK-PTX NPs displayed a superior intracellular internalization and improved tumor cytotoxicity in vitro. Cy5.5-labeled LPK-PTX NPs showed much higher tumor accumulation in colorectal cancer-bearing mice. Furthermore, LPK-PTX NPs exhibit enhanced antitumor activity and decreased systemic toxicity in colorectal cancer patient-derived xenografts (PDX) model. The excellent in vitro and in vivo antitumor efficacy proves the improved targeting drug delivery, suggesting that peptide P-LPK has potential to provide a novel approach for enhanced drug delivery with negligible systemic toxicity.

The inability of CD8+ effector T cells (Teffs) to reach tumor cells is an important aspect of tumor resistance to cancer immunotherapy. The recruitment of these cells to the tumor microenvironment (TME) is regulated by integrins, a family of adhesion molecules that are expressed on T cells. Here, we show that 7HP349, a small-molecule activator of lymphocyte function–associated antigen-1 (LFA-1) and very late activation antigen-4 (VLA-4) integrin cell-adhesion receptors, facilitated the preferential localization of tumor-specific T cells to the tumor and improved antitumor response. 7HP349 monotherapy had modest effects on anti–programmed death 1–resistant (anti–PD-1–resistant) tumors, whereas combinatorial treatment with anti–cytotoxic T lymphocyte–associated protein 4 (anti–CTLA-4) increased CD8+ Teff intratumoral sequestration and synergized in cooperation with neutrophils in inducing cancer regression. 7HP349 intratumoral CD8+ Teff enrichment activity depended on CXCL12. We analyzed gene expression profiles using RNA from baseline and on treatment tumor samples of 14 melanoma patients. We identified baseline CXCL12 gene expression as possibly improving the likelihood or response to anti–CTLA-4 therapies. Our results provide a proof-of-principle demonstration that LFA-1 activation could convert a T cell–exclusionary TME to a T cell–enriched TME through mechanisms involving cooperation with innate immune cells.

Abstract Due to the complexity, aggressiveness, and heterogeneity of malignant melanoma, it is difficult to eradicate the whole tumor through conventional treatment. Herein, a strategy of metabolic engineering labeled anaerobic oncolytic bacteria (Clostridium butyricum) is demonstrated to achieve the ablation of melanoma. In this system, the metabolic substrate of C. butyricum d‐alanine (d‐Ala) is first conjugated with a photosensitizer (TPApy) showing aggregation‐induced emission (AIE). The yielded metabolic substrate of d‐Ala‐TPAPy can be metabolically incorporated into bacterial peptidoglycan to form engineered C. Butyricum. Once the engineered C. butyricum is injected into melanoma, the bacteria can only proliferate in an anaerobic zone, stimulate the tumor immune microenvironment, and ablate the tumor hypoxia region. Following that, the relatively rich oxygen content in the peripheral area can induce the death of C. butyricum. The photosensitizer (PS) on the bacteria can subsequently exert a photodynamic effect in the oxygen‐rich region and further remove the melanoma residue under light irradiation. Prominent in vivo melanoma ablation results revealed that the engineering oncolytic bacteria can provide a promising regime for solid tumor eradication.

Using photothermal therapy to treat cancer has become an effective method, and the design of photothermal agents determines their performance. However, due to the major radiative recombination of a photogenerated electron in photothermal materials, the photothermal performance is weak which hinders their applications. In order to solve this issue, preventing radiative recombination and accelerating nonradiative recombination, which can generate heat, has been proved as a reasonable way. We demonstrated a Cu2MoS4@MXene nanocomposite with an obviously enhanced photothermal conversion efficiency (η = 87.98%), and this improvement can be attributed to the electron migration. Then, a mechanism is proposed based on the electron transfer regulatory effect and the localized surface plasmon resonance effect, which synergistically promote nonradiative recombination and generate more heat. Overall, our design strategy shows a way to improve the photothermal performance of Cu2MoS4, and this method can be extended to other photothermal agents to let them be more efficient in treating cancer.

Long-term and short-term exposure to carbon nanoparticles (CNPs) can affect fetal development and subsequent adverse outcomes including preterm delivery, intrauterine growth restriction, low birth weight, increased health risk linked to cardiovascular, respiratory and nervous systems in adulthood. The adverse developmental outcomes of CNPs were well known, but the underlying mechanisms remain unresolved. In this study, zebrafish embryos were treated with CNPs of 50,100,200 μg/mL and the toxic effects were observed. Using the RNA-seq analysis approach, we examined the effects of CNPs (200 μg/mL) on gene expression in zebrafish embryos exposed from 4 to 96 h-post-fertilization (hpf). We observed that CNPs-treated embryos exhibited increased malformations and decreased hatching. A total of 236 differentially expressed genes were detected by transcriptome analyses, which were associated with phototransduction, amino acid metabolism, steroid and steroid hormone biosynthesis. Transcriptome results were verified by real-time fluorescence quantitative PCR (RT-qPCR). Our results indicated that CNPs exposure was most likely to lead to differential gene changes in steroid and hormone biosynthesis pathways, thus inducing developmental toxicity such as delayed incubation of zebrafish embryos, increased malformation rate and multiple malformation phenotypes.

Abstract With the rapid development of mobile edge computing, mobile social networks are gradually infiltrating into our daily lives, in which the communities are an important part of social networks. Internet of People such as online social networks is the next frontier for the Internet of Things. The combination of social networking and mobile edge computing has an important application value and is the development trend of future networks. However, how to detect evolutionary communities accurately and efficiently in dynamic heterogeneous social networks remains a fundamental problem. In this paper, a novel User Interest Community Evolution (UICE) model based on subgraph matching is proposed for accurately detecting the corresponding communities in the evolution of the user interest community. The community evolutionary events can be quickly captured including forming, dissolving, evolving and so on with the introduction of core subgraph. A variant of subgraph matching, called Subgraph Matching with Dynamic Weight (SMDW), is proposed to solve the problem of updating the core subgraph due to the change of core user’s interest when tracking evolutionary communities. Finally, the experiments based on the real datasets have been designed to evaluate the performance of the proposed model by comparing it with the state-of-art methods in this area and complete data processing through the local edge computing layer. The experimental results demonstrate that the UICE model presented in this paper has achieved better accuracy, higher efficiency and better scalability against existing methods.

Abstract The recommendation system is an effective means to solve the information overload problem that exists in social networks, which is also one of the most common applications of big data technology. Thus, the matrix decomposition recommendation model based on scoring data has been extensively studied and applied in recent years, but the data sparsity problem affects the recommendation quality of the model. To this end, this paper proposes a hybrid recommendation model based on deep emotion analysis and multi-source view fusion which makes a personalized recommendation with user-post interaction ratings, implicit feedback and auxiliary information in a hybrid recommendation system. Specifically, the HITS algorithm is used to process the data set, which can filter out the users and posts with high influence and eliminate most of the low-quality users and posts. Secondly, the calculation method of measuring the similarity of candidate posts and the method of calculating K nearest neighbors are designed, which solves the problem that the text description information of post content in the recommendation system is difficult to mine and utilize. Then, the cooperative training strategy is used to achieve the fusion of two recommended views, which eliminates the data distribution deviation added to the training data pool in the iterative training. Finally, the performance of the DMHR algorithm proposed in this paper is compared with other state-of-art algorithms based on the Twitter dataset. The experimental results show that the DMHR algorithm has significant improvements in score prediction and recommendation performance.

Electrical Impedance Spectroscopy (EIS) is a non-invasive and label-free technology that can characterize and discriminate cells based on their dielectric properties at a wide range of frequency. This characterization method has not been utilized for small extracellular vesicles (exosomes) with heterogenous and nano-scale size distribution. Here, we developed a novel label-free microelectronic impedance spectroscopy for non-invasive and rapid characterization of exosomes based on their unique dielectric properties. The device is comprised of an insulator-based dielectrophoretic (iDEP) module for exosomes isolation followed by an impedance spectroscopy utilizing the embedded micro-electrodes. This device is capable of distinguishing between exosomes harvested from different cellular origins as the result of their unique membrane and cytosolic compositions at a wide range of frequency. Therefore, it has the potential to be further evolved as a rapid tool for characterization of pathogenic exosomes in clinical settings.

As the competition between enterprises intensifies, employees have witnessed a decline of psychological health year by year and severe anxiety and depression. To ensure the normal work of employees, it is important to implement psychological occupational therapy intervention (POTI). The previous studies on corporate occupational values focus on the behavioral variables and attitude variables related to occupations. The research paradigm does not suit a specific group of employees. Therefore, this paper explores the individual differences in POTI guided by the realization of occupational values. For a specific group of employees, the correlations of the psychological problems with job attitude, job involvement, and psychological health level were reasoned logically, a theoretical model was established, and the full-model map was plotted to illustrate the influence of the realization of occupational values over POTI. Then, the evaluation data on the psychological intervention effect of 2,800 employees were explored based on multikernel learning, and an individual difference prediction model was constructed through multikernel learning. Finally, the predicted results of different groups of employees were compared, revealing the effectiveness of our algorithm.

Prevalent buildings are supported by pile foundations in urban areas, and the importance of nearby excavation prediction is indisputable due to various engineering accidents caused by the density of urban buildings and the complexity of the underground environment. Recently, a case of tunneling adjacent to a pile group has received a lot of attention from the research community and engineers. In this study, a mechanical model of a shallow tunnel adjacent to a pile group is established. The proposed stress-release function is taken as the stress boundary condition of the tunnel periphery. Considering the pile group, the elastic stresses are calculated by complex variable theory, combined with the Mindlin’s solution. Then, the new analytical solutions to stress are obtained to predict the stratum responses induced by tunneling adjacent to the existing pile group loads inside the stratum in a gravity field. Ultimately, this study provides parameters to analyze their influence on ground stress and potential plastic zone, such as the stress release coefficient, pile group locations, and soil parameters. This research provides a theoretical basis for stratum stresses’ prediction in shallow tunneling engineering fields when tunneling adjacent to a pile group, and it can be applied to the construction of resilient cities.

This study reviewed some simplified methods of finite element analysis (FEA) for connections in cold-formed steel (CFS) structure, and summarized eight simplified methods divided into three categories. Shear performance tests were performed for six groups of self-piercing riveted (SPR) connection in CFS. A constitutive model of shear behavior for SPR connections was proposed, which was simplified from the load–displacement curve of shear performance test results. The models of SPR connection were established in ABAQUS by the eight simplified methods, and then the FEA results and the test results were compared. The applicable scope of each simplified model was explored, and a simplified method of FEA that was most suitable for the shear behavior of the CFS-SPR connection was proposed. Moreover, the shear performance test of the CFS shear wall with SPR was conducted by considering the rivet spacing, and failure modes and load–deformation curves were obtained. On this basis, numerical models of the CFS-SPR connection shear wall were established. By comparing the test results and the FEA results for the CFS-SPR connection shear wall, the feasibility of a simplified method of FEA applied to the CFS-SPR connection was verified. The main failure modes of the CFS-SPR connection were that the rivet tail pulled out from the bottom sheet and the rivet head pulled over from the top sheet. The SPR connection of the CFS frame could be simplified with a pin or a fastener element, and the SPR connection between the steel frame and the sheathing could be simulated by a Cartesian connector or a Spring2 element. The FEA results were highly similar to the test results for the CFS-SPR shear wall.

Abstract Quantifying soil organic carbon (SOC) changes is a fundamental issue in ecology and sustainable agriculture. However, the algorithm‐derived biases in comparing SOC status have not been fully addressed. Although the methods based on equivalent soil mass (ESM) and mineral‐matter mass (EMMM) reduced biases of the conventional methods based on equivalent soil volume (ESV), they face challenges in ensuring both data comparability and accuracy of SOC estimation due to unequal basis for comparison and using unconserved reference systems. We introduce the basal mineral‐matter reference systems (soils at time zero with natural porosity but no organic matter) and develop an approach based on equivalent mineral‐matter volume (EMMV). To show the temporal bias, SOC change rates were recalculated with the ESV method and modified methods that referenced to soils at time t1 (ESM, EMMM, and EMMV‐t1) or referenced to soils at time zero (EMMV‐t0) using two datasets with contrasting SOC status. To show the spatial bias, the ESV‐ and EMMV‐t0‐derived SOC stocks were compared using datasets from six sites across biomes. We found that, in the relatively C‐rich forests, SOC accumulation rates derived from the modified methods that referenced to t1 soils and from the EMMV‐t0 method were 5.7%–13.6% and 20.6% higher than that calculated by the ESV method, respectively. Nevertheless, in the C‐poor lands, no significant algorithmic biases of SOC estimation were observed. Finally, both the SOC stock discrepancies (ESV vs. EMMV‐t0) and the proportions of this unaccounted SOC were large and site‐dependent. These results suggest that although the modified methods that referenced to t1 soils could reduce the biases derived from soil volume changes, they may not properly quantify SOC changes due to using unconserved reference systems. The EMMV‐t0 method provides an approach to address the two problems and is potentially useful since it enables SOC comparability and integrating SOC datasets.

Abstract Surface-enhanced Raman scattering (SERS) technique has presented great potential in medical diagnosis, environment monitoring, and food detection due to its high sensitivity, rapid response, and fingerprint effect. Many efforts have been concentrated on all kinds of strategies to produce efficient SERS platforms. Here, we report a simple and controllable method to produce large-area efficient SERS platforms with spatially stacked plasmonic hotspots. The SERS platforms consist of double-layer metal porous films and are easily fabricated by magnetron sputtering and annealing, assisted by the evaporation of hydrofluoric acid. The stacked dual-layer metal porous films show prominent Raman enhancement and ultrasensitive SERS sensing capability for different target molecules. The detection limit is demonstrated down to 10−13 M by detecting rhodamine 6G molecules. These superior Raman properties can be mainly ascribed to the highly dense spatially stacked plasmonic hotspots formed in the dual-layer metal porous films. The simple, controllable, and scalable fabrication strategy and superior Raman performance make these platforms promising candidates for the development of inexpensive, efficient, and mass-produced SERS substrates.

As a promising therapy, photothermal therapy (PTT) converts near-infrared (NIR) light into heat through efficient photothermal agents (PTAs), causing a rapid increase in local temperature. Considering the importance of PTAs in the clinical application of PTT, the safety of PTAs should be carefully evaluated before their widespread use. As a promising PTA, mesoporous polydopamine (MPDA) was studied for its clinical applications for tumor photothermal therapy and drug delivery. Given the important role that intestinal microflora plays in health, the impacts of MPDA on the intestine and on intestinal microflora were systematically evaluated in this study. Through biological and animal experiments, it was found that MPDA exhibited excellent biocompatibility, in vitro and in vivo. Moreover, 16S rRNA analysis demonstrated that there was no obvious difference in the composition and classification of intestinal microflora between different drug delivery groups and the control group. The results provided new evidence that MPDA was safe to use in large doses via different drug delivery means, and this lays the foundation for further clinical applications.

Background Despite approval of immunotherapy for a wide range of cancers, the majority of patients fail to respond to immunotherapy or relapse following initial response. These failures may be attributed to immunosuppressive mechanisms co-opted by tumor cells. However, it is challenging to use conventional methods to systematically evaluate the potential of tumor intrinsic factors to act as immune regulators in patients with cancer.Methods To identify immunosuppressive mechanisms in non-responders to cancer immunotherapy in an unbiased manner, we performed genome-wide CRISPR immune screens and integrated our results with multi-omics clinical data to evaluate the role of tumor intrinsic factors in regulating two rate-limiting steps of cancer immunotherapy, namely, T cell tumor infiltration and T cell-mediated tumor killing.Results Our studies revealed two distinct types of immune resistance regulators and demonstrated their potential as therapeutic targets to improve the efficacy of immunotherapy. Among them, PRMT1 and RIPK1 were identified as a dual immune resistance regulator and a cytotoxicity resistance regulator, respectively. Although the magnitude varied between different types of immunotherapy, genetically targeting PRMT1 and RIPK1 sensitized tumors to T-cell killing and anti-PD-1/OX40 treatment. Interestingly, a RIPK1-specific inhibitor enhanced the antitumor activity of T cell-based and anti-OX40 therapy, despite limited impact on T cell tumor infiltration.Conclusions Collectively, the data provide a rich resource of novel targets for rational immuno-oncology combinations.

In the past two decades, corporate hypocrisy has become a phenomenon that cannot be ignored in Corporate Social Responsibility (CSR) practice (Wagner et al., 2009) and has thus become a concern for management scholars (Cho and Lee, 2019). Using smartPLS, based on attribution theory, this paper takes 28 Chinese listed enterprises as examples to explore the influence of CSR motivation on its communication and implementation, as well as the impact of CSR implementation and promotion on consumers’ perception of corporate hypocrisy. The research finds a negative correlation between value-driven motivation and corporate hypocrisy and a positive correlation of performance-driven motivation and stakeholder-driven motivation with corporate hypocrisy. The theoretical contribution of this paper is mainly reflected in the following four aspects. (1) It describes the scale of CSR implementation research and enriches the measurement tools of CSR implementation. (2) It enriches and expands research results in the field of CSR motivation perception. From the perspective of CSR and attribution theory, this study explores the influence of consumers’ perception of CSR motivation on CSR communication and CSR implementation. (3) It supplements research results in the field of corporate hypocrisy. The influence of CSR communication and CSR implementation on corporate hypocrisy is clarified. (4) It clarifies the impact of CSR communication on CSR implementation so as to help enterprises better match CSR communication strategy and CSR implementation in practice and reduce consumers’ perception of corporate hypocrisy. It is suggested that enterprises find their own positioning on CSR motivation, which provides a reference with which enterprises can make better decisions on CSR communication strategy after implementing CSR behavior and provides empirical evidence for the research on CSR motivation perception and corporate hypocrisy in China.

Anzhen Bridge Station of Beijing metro line 12, which adopts a separated-island platform constructed by the undermining method, passes along the retaining wall of the North Third Ring Road. A numerical simulation method is adopted to study the deformation laws of the retaining wall under the influence of station construction. The simulation data show that the affected retaining wall will have large settlement and tilt, and countermeasures must be taken. Four countermeasures are formulated, and their control effects are analyzed. The calculation results show that the combination of anchor cables and waist beams can effectively control the tilt of the retaining wall, but the control effect on the absolute settlement is not obvious. The control effect of isolation piles is better than that of foundation grouting. The joint control method of anchor cables combined with waist beams, isolation piles, and deep hole grouting makes the deformation meet the target, and according to this measure, a concept of joint control aiming at the influence of the “source,” “propagation,” and “object” is proposed, which can provide a reference for similar projects with a great difficulty in deformation control.

Abstract An insulator-based dielectrophoresis (iDEP) is a label-free method that has been extensively utilized for manipulation of nanoparticles, cells, and biomolecules. Here, we present a new iDEP approach that can rapidly trap nanoparticles at the close proximity of a glass nanopipette’s tip by applying 10 V/cm direct current (DC) across the pipette’s length. The trapping mechanism was systemically studied using both numerical modeling and experimental observations. The results showed that the particle trapping was determined to be controlled by three dominant electrokinetic forces including dielectrophoretic, electrophoretic and electroosmotic force. Furthermore, the effect of the ionic strength, the pipette’s geometry, and the applied electric field on the entrapment efficiency was investigated. To show the application of our device in biomedical sciences, we demonstrated the successful entrapment of fluorescently tagged liposomes and unlabeled plasma-driven exosomes from the PBS solution. Also, to illustrate the selective entrapment capability of our device, 100 nm liposomes were extracted from the PBS solution containing 500 nm polystyrene particles at the tip of the pipette as the voltage polarity was reversed.

Microblogging, a popular social media service platform, has become a new information channel for users to receive and exchange the most up-to-date information on current events. Consequently, it is a crucial platform for detecting newly emerging events and for identifying influential spreaders who have the potential to actively disseminate knowledge about events through microblogs. However, traditional event detection models require human intervention to detect the number of topics to be explored, which significantly reduces the efficiency and accuracy of event detection. In addition, most existing methods focus only on event detection and are unable to identify either influential spreaders or key event-related posts, thus making it challenging to track momentous events in a timely manner. To address these problems, we propose a Hypertext-Induced Topic Search (HITS) based Topic-Decision method (TD-HITS), and a Latent Dirichlet Allocation (LDA) based Three-Step model (TS-LDA). TD-HITS can automatically detect the number of topics as well as identify associated key posts in a large number of posts. TS-LDA can identify influential spreaders of hot event topics based on both post and user information. The experimental results, using a Twitter dataset, demonstrate the effectiveness of our proposed methods for both detecting events and identifying influential spreaders.

We propose a broadband metamaterial absorber consisting of a periodic gallium arsenide (GaAs) grating array standing on a tungsten (W) film separated by an ultra-thin GaAs spacer layer. A perfect absorption broadband is achieved with an average absorption of 97% in the range from 506 nm to 1814 nm. The bandwidth with the absorption over 90% is larger than 1300 nm and the maximum absorption is up to 100%. The broadband perfect absorption results from the excited surface plasmons combined with the cavity modes. High short-circuit current density over 56 mA/cm2 and near-unity solar energy trapping are also obtained using this absorber due to the multiple resonances coupling. Moreover, this absorber requires no noble metal and is compatible with the popular nano-fabrication technologies. These offer new perspectives for achieving thin film solar cells, solar energy harvesting, magnetic recording, ultra-thin and durable optoelectronic elements. Keywords: Metamaterials, Absorbers, Perfect absorption, Surface plasmons

The reasonable delay time of millisecond-delay blasting using digital electronic detonators can significantly reduce the vibration effects induced by tunnel blasting. This study proposes a method for calculating the delay time for cut holes, easer holes and periphery holes, considering the rocks breaking effect as well as wave superposition theory. And then according to the actual layout diagram of the tunnel holes, the delay time calculation formulas of different holes are put forward. Then the delay times were calculated according to the formulas and applied in the field tests. The velocities, rocks breaking and wave superposition cancellation of the vibration using different delay times are analyzed with digital electronic detonators. Then the optimum delay times of different holes were obtained and applied to New Hongyan tunnel project. The velocity and frequency of the vibration with digital electronic detonators are analyzed, compared with non-electronic detonators. The effects of charge and delay time on the velocity and principal frequency of a blasting seismic wave are discussed. The results indicate that the delay time for the holes must be prioritized to achieve breaking effects in the rock with the simultaneous formation of a new free surface, next considering the wave superposition cancellation. When the delay time of cut holes was 5 ms, the rocks breaking effect and wave superposition cancellation effect both worked well. The velocity of the vibration induced by the cut holes blasting was about only 0.46-0.51 cm/s. When the delay time was 6 ms or much longer, the rocks breaking effect would fail. With regard to the easer holes and periphery holes, the optimum delay time of them were all 5ms. The vertical peak particle velocity was reduced from 2.974 cm/s to 0.901 cm/s with digital electronic detonators. Therefore, the velocity had decreased by 69.70% than non-electronic detonators, which was caused by reducing the single simultaneous explosive charge and setting optimum delay time. The proposed delay time calculation method is demonstrated to be sufficiently accurate and can thus be used as a guideline to reduce tunnel blasting vibrations.

In urban environment, it is often unavoidable for shallow tunnels to be constructed adjacent to existing pile foundations. To obtain the ground displacements and stresses induced by shallow tunneling and existing pile foundation loads, the key procedure involves superimposing the analytical solution for shallow tunneling in green-field with the analytical solution for existing structure loads. In green-field, the complex variable method provides exact analytical solutions of ground displacements and stresses caused by shallow tunneling. However, the exact analytical solutions are not directly expressed as explicit functions of the coordinates (x, y) in the physical plane (called implicit form of exact analytical solutions), whereas the displacements and stresses induced by existing structure loads are explicit functions of the coordinates (x, y) in the physical plane, which makes it difficult to superpose the displacements and stresses induced by existing structure loads. In this paper, explicit form of exact analytical solutions of displacements and stresses induced by shallow tunneling in green-field is obtained by using the inverse conformal transformation and the Cauchy–Riemann equations. Comparison with implicit form of exact analytical solutions shows that the explicit form of exact analytical solutions is intuitional and easily used by engineers, and moreover, the calculation amount is much smaller than that for the implicit form of exact analytical solutions. Then, an application involving superimposing the explicit form of exact analytical solutions with Mindlin’s solution is implemented to analyze the secondary stress field and the related potential plastic zone caused by shallow tunneling adjacent to pile foundations. Moreover, the influences of pile foundation parameters on the ranges and shapes of the potential plastic zones induced by nearby tunneling are analyzed.

Although evidence from studies on blind adults indicates that visual deprivation early in life leads to structural and functional disruption and reorganization of the brain, whether young blind people show similar patterns remains unknown. Therefore, this study is aimed at exploring the structural and functional alterations of the brain of early-blind adolescents (EBAs) compared to normal-sighted controls (NSCs) and investigating the effects of residual light perception on brain microstructure and function in EBAs. We obtained magnetic resonance imaging (MRI) data from 23 EBAs (8 with residual light perception (LPs), 15 without light perception (NLPs)) and 21 NSCs (age range 11-19 years old). Whole-brain voxel-based analyses of diffusion tensor imaging metrics and region-of-interest analyses of resting-state functional connectivity (RSFC) were performed to compare patterns of brain microstructure and the corresponding RSFC between the groups. The results showed that structural disruptions of LPs and NLPs were mainly located in the occipital visual pathway. Compared with NLPs, LPs showed increased fractional anisotropy (FA) in the superior frontal gyrus and reduced diffusivity in the caudate nucleus. Moreover, the correlations between FA of the occipital cortices or mean diffusivity of the lingual gyrus and age were consistent with the development trajectory of the brain in NSCs, but inconsistent or even opposite in EBAs. Additionally, we found functional, but not structural, reorganization in NLPs compared with NSCs, suggesting that functional neuroplasticity occurs earlier than structural neuroplasticity in EBAs. Altogether, these findings provided new insights into the mechanisms underlying the neural reorganization of the brain in adolescents with early visual deprivation.

Tuning of an integrated polymethyl methacrylate (PMMA) sphere whispering gallery mode (WGM) resonator is demonstrated. The WGM resonator is constructed by inserting a PMMA sphere into an etched microstructured fiber with suspended core, where the sphere with diameter of 31.5 μm functions as a WGM cavity. The suspended core with radius of 1.5 μm is used to feed and pick up the WGM field. Experimental results show that the maximum quality factor of the proposed WGM resonator is 5.9 ×104 and that the resonant wavelength shifts toward longer wavelength as the temperature decreases. A tuning range of 1.93 nm is realized by 210 mA dc current due to the thermo-optic effect.

Electrical Impedance Spectroscopy (EIS) has been widely used as a label-free and rapid characterization method for the analysis of cells in clinical research. However, the related work on exosomes (40–150 nm) and the particles of similar size has not yet been reported. In this study, we developed a new Lab-on-a-Chip (LOC) device to rapidly entrap a cluster of sub-micron particles, including polystyrene beads, liposomes, and small extracellular vesicles (exosomes), utilizing an insulator-based dielectrophoresis (iDEP) scheme followed by measuring their impedance utilizing an integrated electrical impedance sensor. This technique provides a label-free, fast, and non-invasive tool for the detection of bionanoparticles based on their unique dielectric properties. In the future, this device could potentially be applied to the characterization of pathogenic exosomes and viruses of similar size, and thus, be evolved as a powerful tool for early disease diagnosis and prognosis.

Nitrogen (N) deposition and precipitation could profoundly influence the structure and function of forest ecosystems. However, conventional studies with understory additions of nitrogen and water largely ignored canopy-associated ecological processes and may have not accurately reflected the natural situations. Additionally, most studies only made sampling at one time point, overlooked temporal dynamics of ecosystem response to environmental changes. Here we carried out a field trial in a mixed deciduous forest of China with canopy addition of N and water for 4 years to investigate the effects of increased N deposition and precipitation on the diversity and community composition of arbuscular mycorrhizal (AM) fungi, the ubiquitous symbiotic fungi for the majority of terrestrial plants. We found that (1) in the 1st year, N addition, water addition and their interactions all exhibited significant influences on AM fungal community composition; (2) in the 2nd year, only water addition significantly reduced AM fungal alpha-diversity (richness and Shannon index); (3) in the next 2 years, both N addition and water addition showed no significant effect on AM fungal community composition or alpha-diversity, with an exception that water addition significantly changed AM fungal community composition in the 4th year; (4) the increment of N or water tended to decrease the abundance and richness of the dominant genus Glomus and favored other AM fungi. (5) soil pH was marginally positively related with AM fungal community composition dissimilarity, soil NH4+-N and N/P showed significant/marginal positive correlation with AM fungal alpha-diversity. We concluded that the effect of increased N deposition and precipitation on AM fungal community composition was time-dependent, mediated by soil factors, and possibly related to the sensitivity and resilience of forest ecosystem to environmental changes.

Wireless sensor networks are being the focus of several research application domains, and the concept of sensing-as-a-service is on the rise in wireless sensor networks. Large service repositories comprising more services and functionalities usually impose new challenges to users while identifying their preferred services and may incur higher costs. Thereby, service recommendation systems have become important and integral tools of service models to provide personalized products for consumers. However, many existing methods of sensor service recommendation focus only on service discovery. To this end, this article proposes a novel hybrid recommendation method, named new hybrid recommendation method. First, latent Dirichlet allocation model is used to compute the similarity of the latent topics of the services, and the user’s latent semantic themes are used to extract the potential interest services. Moreover, the relevance of neighbourhood services is considered, which can improve the accuracy of quality of service prediction. Experiments conducted on real datasets demonstrate that the proposed method is more accurate than the existing methods of service recommendation.