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Abstract Regioselective C–H functionalization of pyridines remains a persistent challenge due to their inherent electronically deficient properties. In this report, we present a strategy for the selective pyridine C3-H thiolation, selenylation, and fluorination under mild conditions via classic N−2,4-dinitrophenyl Zincke imine intermediates. Radical inhibition and trapping experiments, as well as DFT theoretical calculations, indicated that the thiolation and selenylation proceeds through a radical addition-elimination pathway, whereas fluorination via a two-electron electrophilic substitution pathway. The pre-installed electron-deficient activating N-DNP group plays a crucial and positive role, with the additional benefit of recyclability. The practicability of this protocol was demonstrated in the gram-scale synthesis and the late-stage modification of pharmaceutically relevant pyridines.

Abstract Background Accurately deciphering clonal copy number substructure can provide insights into the evolutionary mechanism of cancer, and clustering single-cell copy number profiles has become an effective means to unmask intra-tumor heterogeneity (ITH). However, copy numbers inferred from single-cell DNA sequencing (scDNA-seq) data are error-prone due to technically confounding factors such as amplification bias and allele-dropout, and this makes it difficult to precisely identify the ITH. Results We introduce a hybrid model called scGAL to infer clonal copy number substructure. It combines an autoencoder with a generative adversarial network to jointly analyze independent single-cell copy number profiles and gene expression data from same cell line. Under an adversarial learning framework, scGAL exploits complementary information from gene expression data to relieve the effects of noise in copy number data, and learns latent representations of scDNA-seq cells for accurate inference of the ITH. Evaluation results on three real cancer datasets suggest scGAL is able to accurately infer clonal architecture and surpasses other similar methods. In addition, assessment of scGAL on various simulated datasets demonstrates its high robustness against the changes of data size and distribution. scGAL can be accessed at: https://github.com/zhyu-lab/scgal . Conclusions Joint analysis of independent single-cell copy number and gene expression data from a same cell line can effectively exploit complementary information from individual omics, and thus gives more refined indication of clonal copy number substructure.

Mangrove forests are crucial in absorbing, storing, and purifying pollutants while maintaining ecological balance. A study was conducted in 2020 to investigate the biogeochemical processes of seawater and sedimentary environmental factors in the Qi'ao Island mangrove wetland. The study comprised two survey sections and ten survey stations within the mangrove forest and 16 large-scale survey stations in the adjacent sea area. During ebb tides, the mean concentrations of inorganic nitrogen and phosphate in Section D1 of the mangrove forest were 0.63 mg/L and 0.003 mg/L, respectively. These levels were significantly lower than the results observed in the adjacent sea area and Section D2 of the mangrove forest during flood tides. The mangrove forest efficiently absorbed and consumed nutrients, with the nitrogen-to-phosphorus ratio of consumed nutrients being notably higher than that typically utilized by plants during growth. We identified various biogeochemical processes, including nitrogen fixation, mineralization, nitrification, and denitrification, occurring within the mangrove forest. Seawater measurements in Section D1 during ebb tides showed the mean pH of 7.58 and dissolved oxygen levels of 4.52 mg/L, resulting in a dissolved oxygen saturation level of only 57.0 %. The low dissolved oxygen levels were attributed to organic matter degradation in the forest. Consequently, the longer the water retention time, the more obvious the trend of hypoxia and acidification was observed. In the adjacent sea area, the sedimentary environment was deemed healthy, with pollutants primarily originating from runoff and ship discharge from waterways and ports. However, within the mangrove forest, the sediments exhibited higher enrichment factors for organic carbon and sulfide, indicating significant pollution compared to the adjacent sea area. The sediments were conducive to the accumulation and burial of total Hg, Zn, and Cu, while other heavy metals did not show prominent deposition and enrichment. Notably, the enrichment factor of oils was as high as 4.37, leading to the formation of an oil pollution zone at the forest edge, and the enrichment of pollutants in sediment may inhibit the growth and expansion of mangroves. Overall, this study shed light on the occurrence of seawater acidification, hypoxia, and the behavior of sediment pollutants within the mangrove forest. The findings provide valuable insights to support efforts aimed at promoting and maintaining the ecosystem health of mangrove forests.

Compared to single microbial strains, complex interactions between microbial consortia composed of various microorganisms have been shown to be effective in expanding ecological functions and accomplishing biological processes. Electroactive microorganisms (EMs) and degradable microorganisms (DMs) play vital roles in bioenergy production and the degradation of organic pollutants hazardous to human health. These microorganisms can strongly interact with other microorganisms and promote metabolic cooperation, thus facilitating electricity production and pollutant degradation. In this review, we describe several specific types of EMs and DMs based on their ability to adapt to different environments, and summarize the mechanism of EMs in extracellular electron transfer. The effects of interactions between EMs and DMs are evaluated in terms of electricity production and degradation efficiency. The principle of the enhancement in microbial consortia is also introduced, such as improved biomass, changed degradation pathways, and biocatalytic potentials, which are directly or indirectly conducive to human health.

Watchman Nee’s anthropology has been widely debated and polarized in academic fields. However, Watchman Nee’s concept of human and the problem of ecclesiastical practices have often been overlooked in contemporary Chinese mainland churches. In the first three sections, this paper will start from different Chinese mainland denominations’ interpretation of Nee’s concept of human and their corresponding ecclesiastical practices. On the one hand, through the interpretive attitudes of different denominations toward the “concept of human” and their related ecclesiastical practices, we can see the situation of acceptance of Nee’s anthropology in different contemporary Chinese denominations. On the other hand, we can also provide feedback for the academic research on Nee’s anthropology from the reality of contemporary Chinese mainland churches. Then, this paper will make a comparison of anthropologies between Luther and Watchman Nee, referring to the current study of Martin Luther and the Third Enlightenment in China. The comparative study of these two men will not only open up new avenues for the study of their theologies but will also serve Chinese mainland churches by utilizing the results of the research on Nee’s thoughts and Martin Luther and the Third Enlightenment.

Abstract Accurately model recognition of mobile device is of great significance for identifying copycat device and protecting intellectual property rights. Although existing methods have realized high-accuracy recognition about device’s category and brand, the accuracy of model recognition still needs to be improved. For that, we propose Recognizer, a high-accuracy model recognition method of mobile device based on weighted feature similarity. We extract 20 features from the network traffic and physical attributes of device, and design feature similarity metric rules, and calculate inter-device similarity further. In addition, we propose feature importance evaluation strategies to assess the role of feature in recognition and determine the weight of each feature. Finally, based on all or part of 20 features, the similarity between the target device and known devices is calculated to recognize the brand and model of target device. Based on 587 models of mobile devices of 17 widely used brands such as Apple and Samsung, we carry out device recognition experiments. The results show that Recognizer can identify the device’s brand and model than existing methods more effectively. In average, the model recognition accuracy of Recognizer is 99.08% (+ 9.25%↑) when using 20 features and 92.08% (+ 29.26%↑) when using 13 features.

A growing body of experimental evidence suggests that microRNAs (miRNAs) are closely associated with specific human diseases and play critical roles in their development and progression. Therefore, identifying miRNA related to specific diseases is of great significance for disease screening and treatment. In the early stages, the identification of associations between miRNAs and diseases demanded laborious and time-consuming biological experiments that often carried a substantial risk of failure. With the exponential growth in the number of potential miRNA-disease association combinations, traditional biological experimental methods face difficulties in processing massive amounts of data. Hence, developing more efficient computational methods to predict possible miRNA-disease associations and prioritize them is particularly necessary. In recent years, numerous deep learning-based computational methods have been developed and have demonstrated excellent performance. However, most of these methods rely on external databases or tools to compute various auxiliary information. Unfortunately, these external databases or tools often cover only a limited portion of miRNAs and diseases, resulting in many miRNAs and diseases being unable to match with these computational methods. Therefore, there are certain limitations associated with the practical application of these methods. To overcome the above limitations, this study proposes a multi-view computational model called MVNMDA, which predicts potential miRNA-disease associations by integrating features of miRNA and diseases from local views, global views, and semantic views. Specifically, MVNMDA utilizes known association information to construct node initial features. Then, multiple networks are constructed based on known association to extract low-dimensional feature embedding of all nodes. Finally, a cascaded attention classifier is proposed to fuse features from coarse to fine, suppressing noise within the features and making precise predictions. To validate the effectiveness of the proposed method, extensive experiments were conducted on the HMDD v2.0 and HMDD v3.2 datasets. The experimental results demonstrate that MVNMDA achieves better performance compared to other computational methods. Additionally, the case study results further demonstrate the reliable predictive performance of MVNMDA.

Camera devices are being deployed everywhere. Cities, enterprises, and more and more smart homes are using camera devices. Fine-grained identification of devices brings an in-depth understanding of the characteristics of these devices. Identifying the device type helps secure the device safe. But, existing device identification methods have difficulty in distinguishing fine-grained types of devices. To address this challenge, we propose a fine-grained identification method based on the camera deviceso inherent features. First, feature selection is based on the coverage and differences of the inherent features type. Second, the features are classified according to their representation. A design feature similarity calculation strategy (FSCS) for each type of feature is established. Then the feature weights are determined based on feature entropy. Finally, we present a device similarity model based on the FSCS and feature weights. And we use this model to identify the fine-grained type of a target device. We have evaluated our method on Dahua and Hikvision camera devices. The experimental results show that we can identify the deviceos fine-grained type when some inherent feature values are missing. Even when the inherent feature pmissing rateq is 50%, the average accuracy still exceeds 80%.

Cannabis has been used for centuries to treat pain. The antinociceptive activity of tetrahydrocannabinol (THC) or cannabidiol (CBD) has been widely studied. However, the antinociceptive effects of other cannabis components, such as cannabichromene (CBC) and cannabigerol (CBG), have rarely been revealed. The antinociceptive mechanism of CBG is not yet clear, so we investigated the antinociceptive effect of CBG on different pain models, and explored the mechanism of action of CBG to exert antinociceptive effects. In the current study, we compared the antinociceptive effects of CBC, CBD, and CBG on the carrageenan-induced inflammatory pain model in mice, and the results showed that CBG had a better antinociceptive effects through intraplantar administration. On this basis, we further investigated the antinociceptive effect of CBG on CIA-induced arthritis pain model and nerve pain model in mice, and found that CBG also relieved on both types of pain. Then, we explored the antinociceptive mechanism of CBG, which revealed that CBG can activate TRPV1 and desensitize it to block the transmission of pain signals. In addition, CBG can further activate CB2R, but not CB1R, to stimulate the release of β-endorphin, which greatly promotes the antinociceptive effect. Finally, the safety test results showed that CBG had no irritating effect on the rabbits’ skin, and it did not induce significant biochemical and hematological changes in mice. Transdermal delivery results also indicated that CBG has certain transdermal properties. Overall, this study indicates that CBG is promising for developing a transdermal dosage for pain management.

Resting-state (rs) fMRI has been widely used to examine brain-wide large-scale spatiotemporal architectures, known as resting-state networks (RSNs). Recent studies have focused on the temporally evolving characteristics of RSNs, but it is unclear what temporal characteristics are reflected in the networks. To address this issue, we devised a novel method for voxel-based visualization of spatiotemporal characteristics of rs-fMRI with a time scale of tens of seconds. We first extracted clusters of dominant activity-patterns using a region-of-interest approach and then used these temporal patterns of the clusters to obtain voxel-based activation patterns related to the clusters. We found that activation patterns related to the clusters temporally evolved with a characteristic temporal structure and showed mutual temporal alternations over minutes. The voxel-based representation allowed the decoding of activation patterns of the clusters in rs-fMRI using a meta-analysis of functional activations. The activation patterns of the clusters were correlated with behavioral measures. Taken together, our analysis highlights a novel approach to examine brain activity dynamics during rest.

The development of outstanding high-performance gas sensors for detecting trimethylamine (TMA) gas has attracted broad interest. Herein, novel one dimensional (1D) NiMoO4-functionalized MoO3 nanorods were successfully synthesized by simple two-step hydrothermal and solvothermal routs. The as-synthesized NiMoO4-functionalized MoO3 nanorods exhibit a significantly enhanced TMA sensing properties relative to that of the MoO3 nanorods. The response value of 1-NiMoO4/MoO3 sensor to 10 ppm TMA at 200 °C is 12.1, which is about 2.3 times higher than that of MoO3 sensor (5.5 at 200 °C). In addition, 1-NiMoO4/MoO3 sensor also has high selectivity, good repeatability and stability, sub-ppm-level detection limit (1.6 to 0.3 ppm), fast response recovery speed, and good long-term stability. The outstanding sensing performance of 1-NiMoO4/MoO3 sensor in TMA detection is mainly due to the synergistic effect of unique rod-like structure, substantial resistance modulation effect of heterostructures, strong oxygen adsorption capacity, catalytic effect of the NiMoO4. This work provides an interesting perspective for developing high-performance gas sensor for TMA detection.

Imaging with different spectrums often leads to significant nonlinear differences in the intensity, which brings a great challenge to the automatic matching of multispectral images. Considering that there are often limitations to only using intensity information, this article studies multispectral image matching based on the structure consistency. First, an extended log-Gabor filter is constructed to build phase congruency maps, which encodes the structure information and provides rich and robust features. Then, a nonlinear diffusion-based algorithm is developed to detect the salient feature points on the phase congruency map, which are expected to be illumination and contrast invariant. Finally, the structure descriptors are built according to the orientation of the maximum log-Gabor filter responses, and the image matching is achieved by computing the correspondence. Extensive experiments are carried out on various multispectral image datasets. The results show that the proposed method holds a stable performance over the nonlinear intensity variance across spectrums, and outperforms the comparison methods in terms of the number of correct matches and the matching precision.

Aiming at the problem that the existing joint user geolocalization methods based on social media text and social relationships do not sufficiently mine the location correlation between heterogeneous data in social media, a social media user geolocalization method based on multiple mention relationships was proposed.First, a heterogeneous network was constructed by comprehensively considering the mention relationship between users, the user's mention relationship with location indicative words, and the user's mention relationship with geographic nouns.Then, a network simplification strategy was proposed to construct a user-location heterogeneous network that connects users live nearby more closely based on the common mention relationship.After that, a biased random walk algorithm was proposed for the graph node sampling to fully explore the network structure and alleviate the sparsity problem of known locations.Finally, a neural network classifier based on a multilayer perceptron was used to infer the user's location.Experimental results on three representative Twitter data sets of GEOTEXT, TW-US and TW-WORLD show that the proposed method can significantly improve the user geolocalization accuracy.

A 3D tailings dam visualization early warning system was developed based on GIS (geographic information system) combining ARIMA (autoregressive integrated moving average model) and 3S (RS, GIS, GPS) technology for prediction of phreatic line changes and tailing dam deformation. It was applied for monitoring and early warning for the gold–copper tailing dam in Zijinshan Dadongbei tailing pond. The system consists of equipment management, data management, prediction, monitoring and early warning, and 3D visualization modules. It is able to do data management, visualization and disaster prediction, and early warning based on 79 monitoring points of rainfall, infiltration line, and deformation of the tailing dam in the Zijinshan mine. The design and application of the system reflect its features of rich functionality, high practicality, intuitive effect, and high reference value. The system solves the problems of low visualization of monitoring data, poor management of multiple data, and feasible prediction and early warning of point–surface combination. It realizes high-precision prediction of key factors and real-time warning of disaster.

HuR (human antigen R), an mRNA-binding protein responsible for poor prognosis in nearly all kinds of malignancies, is a potential anti-tumor target for drug development. While screening HuR inhibitors with a fluorescence polarization (FP) based high-throughput screening (HTS) system, the clinically used drug eltrombopag was identified. Activity of eltrombopag on molecular level was verified with FP, electrophoretic mobility shift assay (EMSA), simulation docking and surface plasmon resonance (SPR). Further, we showed that eltrombopag inhibited in vitro cell proliferation of multiple cancer cell lines and macrophages, and the in vivo anti-tumor activity was also demonstrated in a 4T1 tumor-bearing mouse model. The in vivo data showed that eltrombopag was efficient in reducing microvessels in tumor tissues. We then confirmed the HuR-dependent anti-angiogenesis effect of eltrombopag in 4T1 cells and RAW264.7 macrophages with qRT-PCR, HuR-overexpression and HuR-silencing assays, RNA stability assays, RNA immunoprecipitation and luciferase assays. Finally, we analyzed the in vitro anti-angiogenesis effect of eltrombopag on human umbilical vein endothelial cells (HUVECs) mediated by macrophages with cell scratch assay and in vitro Matrigel angiogenesis assay. With these data, we revealed the HuR-dependent anti-angiogenesis effect of eltrombopag in breast tumor, suggesting that the existing drug eltrombopag may be used as an anti-cancer drug.

A gulf is a typical ecological zone where carbon cycle is jointly affected by complex environmental factors and strong human activities, and the Beibu Gulf has complex water masses and circulation structures. In this study, we used underway, continuous observational data of the surface water partial pressure of carbon dioxide (pCO2), temperature (SST), salinity (SSS), dissolved oxygen (DO), and chlorophyll α along with vertical profile observations of temperature, salinity, carbonate system parameters and nutrients to determine the spatiotemporal variations and research effects of water masses and circulation on summer pCO2 in the eastern part of Beibu Gulf. In the summers of 2011 and 2014, the mean pCO2 in the eastern part of Beibu Gulf was 417 μatm and 405 μatm, respectively, and the mean sea–air CO2 flux was 3.3 mmol m−2 d−1 and 1.6 mmol m−2 d−1, respectively. In the summer of 2011, the northern part of the Beibu Gulf was controlled by a cyclonic circulation, and pCO2 at the center of the cyclonic circulation increased by more than 15 μatm to a mean value of more than 10 μatm above that of the surrounding waters. The southern part of the Beibu Gulf was affected by an anticyclonic circulation and western coastal water masses, with a high temperature, low salinity, low pCO2, and downwelling surface waters. In the summer of 2011, the mean pCO2 was approximately 17 μatm lower than that in the surrounding waters, and no clear downwelling was observed in summer 2014. The eastern part of Beibu Gulf was a source of atmospheric CO2 in the summer, only the region affected by the northern coastal water in the eastern part of Beibu Gulf was a sink of atmospheric CO2, and pCO2 had distinctly different spatiotemporal distributions under the influence of complex water masses and circulation structures.

Chemotherapy outcomes for the treatment of glioma remains unsatisfactory due to the inefficient drug transport across the bloodbrain barrier (BBB) and insufficient drug accumulation in the tumor region. Although many approaches, including various nanosystems, have been developed to promote the distribution of chemotherapeutics in the brain tumor, the delivery efficiency and the possible damage to the normal brain function still greatly restrict the clinical application of the nanocarriers. Therefore, it is urgent and necessary to discover more safe and effective BBB penetration and glioma-targeting strategies. In the present study, menthol, one of the strongest BBB penetration enhancers screened from traditional Chinese medicine, was conjugated to casein, a natural food protein with brain targeting capability. Then the conjugate self-assembled into the nanoparticles to load anti-cancer drugs. The nanoparticles were characterized to have appropriate size, spheroid shape and high loading drug capacity. Tumor spheroid penetration experiments demonstrated that penetration ability of menthol-modified casein nanoparticles (M-CA-NP) into the tumor were much deeper than that of unmodified nanoparticles. In vivo imaging further verified that M-CA-NPs exhibited higher brain tumor distribution than unmodified nanoparticles. The median survival time of glioma-bearing mice treated with HCPT-M-CA-NPs was significantly prolonged than those treated with free HCPT or HCPT-CA-NPs. HE staining of the organs indicated the safety of the nanoparticles. Therefore, the study combined the advantages of traditional Chinese medicine strategy with modern delivery technology for brain targeting, and provide a safe and effective approach for glioma therapy. KEY WORDS: Glioma, Casein, Menthol, Nanoparticles, Brain targeting, Blood–brain barrier, 10-Hydroxycamptothecin

Regulated disposal or re-utilization of dewatered sludge is of economic benefits and can avoid secondary contamination to the environment; however, feasible and effective management strategies are still lacking. In this study, a peroxydisulfate/zero-valent iron (PDS-ZVI) system is proposed to destroy proteins in soluble extracellular polymeric substances (S-EPS) and loosely bound EPS (LB-EPS) in anaerobic digested sludge (ADS) to improve the dewaterability. Moreover, ADS derived biochars supported via iron oxides (Fe-ADSBC) were generated by dewatering and thermal annealing. Intriguingly, the iron species was discovered to gradually transform from Fe3O4 to FeO with increased pyrolysis temperatures from 600 to 1000 °C. The manipulated iron species on the biochar can remarkably impact the catalytic activity in PDS activation and degradation of sulfamethazine (SMT). The in situ radical scavenging and capturing tests revealed that the principal reactive oxygen species (ROS) in Fe-ADSBC/PDS system experienced a variation from OH into SO4− at higher annealing temperature (1000 °C). In addition, the carbonaceous ADSBC can promote the catalytic activity of iron oxides by synergistically facilitating the adsorption of reactants and charge transfer through COFe bonds at the interfaces. This study enables the first insights into the properties and catalytic performance of Fe-ADSBC, meanwhile unveils the mechanism, reaction pathways, and environmental impacts of the ultimate transformation products (TPs) from SMT degradation in the Fe-ADSBC/PDS system. The study also contributes to developing value-added green biochar catalysts from bio-wastes towards environmental purification. Keywords: Anaerobic digested sludge, Biochar, Peroxydisulfate, Sulfamethazine

The high-precision geolocation of Internet hosts plays an important role in many applications, such as online advertising and deception detection. The existing typical high-precision geolocation algorithms usually utilize single-hop or relative delay to geolocate an Internet host at street-level granularity. However, it is difficult to accurately measure the single-hop or relative delay within a city. This challenge sometimes results in large geolocation errors. To solve this problem, a street-level geolocation algorithm based on router multilevel partitioning is proposed. Unlike existing typical algorithms, the proposed algorithm makes a credible hypothesis that each router has a relatively stable service object for a period of time. By analyzing the connection between routers and landmarks, the possible geographic service ranges of routers are inferred from the geographic distribution of landmarks. Then, distance constraints arising from routers' service ranges are formed to estimate the geographic location of the target IP. Theoretical analysis of the geolocation error shows that the maximum and average errors of the proposed algorithm are less than those of existing typical algorithms. The proposed algorithm is evaluated by geolocating a total of 12,152 target IP addresses located in four cities in different regions. The experimental results show that, compared with the existing typical street-level geolocation algorithms SLG and NC-Geo, the average median error of the proposed algorithm decreases from 4.735 km and 3.776 km to 3.25 km, representing error reductions of approximately 31.36% and 13.96%, respectively.

Taking inspiration from nature, the biomimetic concept has been integrated into drug delivery systems in cancer therapy. Disguised with cell membranes, the nanoparticles can acquire various functions of natural cells. The cell membrane-coating technology has pushed the limits of common nano-systems (fast elimination in circulation) to more effectively navigate within the body. Moreover, because of the various functional molecules on the surface, cell membrane-based nanoparticles (CMBNPs) are capable of interacting with the complex biological microenvironment of the tumor. Various sources of cell membranes have been explored to camouflage CMBNPs and different tumor-targeting strategies have been developed to enhance the anti-tumor drug delivery therapy. In this review article we highlight the most recent advances in CMBNP-based cancer targeting systems and address the challenges and opportunities in this field.

Cell-mediated drug delivery systems employ specific cells as drug vehicles to deliver drugs to targeted sites. Therapeutics or imaging agents are loaded into these cells and then released in diseased sites. These specific cells mainly include red blood cells, leukocytes, stem cells and so on. The cell acts as a Trojan horse to transfer the drug from circulating blood to the diseased tissue. In such a system, these cells keep their original properties, which allow them to mimic the migration behavior of specific cells to carry drug to the targeted site after in vivo administration. This strategy elegantly combines the advantages of both carriers, i.e. the adjustability of nanoparticles (NPs) and the natural functions of active cells, which therefore provides a new perspective to challenge current obstacles in drug delivery. This review will describe a fundamental understanding of these cell-based drug delivery systems, and discuss the great potential of combinational application of cell carrier and NPs.

A new concise synthesis route of 2-phenylbenzo[b]thiophen-3(2H)-one 1,1-dioxide from benzo[b]thiophen-3(2H)-one 1,1-dioxide was developed using palladium-catalyzed C(sp3)-H arylation. Compared with the traditional route, this protocol can effectively save many steps and combine with the current research hotspot, C(sp3)-H activation. Moreover, this catalytic system is easy to operate and a good yield can be achieved.

A high-efficiency method to deal with pollutants must be found because environmental problems are becoming more serious. Photocatalytic oxidation technology as the environmentally-friendly treatment method can completely oxidate organic pollutants into pollution-free small-molecule inorganic substances without causing secondary pollution. As a widely used photocatalyst, titanium dioxide (TiO2) can greatly improve the degradation efficiency of pollutants, but several problems are noted in its practical application. TiO2 modified by different materials has received extensive attention in the field of photocatalysis because of its excellent physical and chemical properties compared with pure TiO2. In this review, we discuss the use of different materials for TiO2 modification, highlighting recent developments in the synthesis and application of TiO2 composites using different materials. Materials discussed in the article can be divided into nonmetallic and metallic. Mechanisms of how to improve catalytic performance of TiO2 after modification are discussed, and the future development of modified TiO2 is prospected.

In the current study, we identified a transcription factor, MYB14, from Chinese wild grape, Vitis quinquangularis-Pingyi (V. quinquangularis-PY), which could enhance the main stilbene contents and expression of stilbene biosynthesis genes (StSy/RS) by overexpression of VqMYB14. The promoter of VqMYB14 (pVqMYB14) was shown to be induced as part of both basal immunity (also called pathogen-associated molecular pattern (PAMP)-triggered immunity, PTI) and effector-triggered immunity (ETI), triggered by the elicitors flg22 and harpin, respectively. This was demonstrated by expression of pVqMYB14 in Nicotiana benthamiana and Vitis. We identified sequence differences, notably an 11 bp segment in pVqMYB14 that is important for the PTI/ETI, and particularly for the harpin-induced ETI response. In addition, we showed that activation of the MYB14 promoter correlates with differences in the expression of MYB14 and stilbene pattern induced by flg22 and harpin. An experimental model of upstream signaling in V. quinquangularis-PY is presented, where early defense responses triggered by flg22 and harpin partially overlap, but where the timing and levels differ. This translates into a qualitative difference with respect to patterns of stilbene accumulation.

The single crystal 6H-SiC with [0001] crystal direction irradiated by 400 keV He+ ions with 1 × 1017 ions/cm2 fluence at 400 °C were annealed at 600, 900, 1200 and 1400 °C for different durations. The evolution of helium bubbles and discs was investigated by transmission electron microscopy. An irradiated layer distributed with fine helium bubbles was formed with a width of ~170 nm after helium ion irradiation. The size of gas bubbles increased with increasing annealing time and temperature and finally reached stable values at a given annealing temperature. According to the relationship between the bubble radii and annealing time, an empirical formula for calculating the bubble radii at the annealing temperature ranged from 600 to 1400 °C was given by fitting the experiment data. Planar bubble clusters (discs) were found to form on (0001) crystal plane at both sides of the bubble layer when the annealing temperature was at the range of 800–1200 °C. The mechanism of bubble growth during post-implantation annealing and the formation of bubble discs were also analyzed and discussed.

The discontinuous precipitation behaviour and properties of Cu–Ti–Ni alloy during aging precipitation were studied by means of electron backscatter diffraction (EBSD), scanning electron microscopy (SEM), transmission electron microscopy (TEM) and X-ray diffraction (XRD). The results show that β’-Cu4Ti was formed in the matrix during the aging process of Cu–4Ti–1Ni alloy, and the cellular structure grows at the grain boundary in the form of discontinuous precipitation in the middle and late stages of aging, and expands to the interior of the grains with the aging time until the grains were completely covered, then a uniform Cu3Ti structure was formed. In addition, the coverage area of cellular tissue increases with aging time and temperature. Under different aging temperature conditions, the hardness of the alloy increases first and then decreases with the aging time, with a peak value of 319.8 HV and a strength of 782.2 MPa. The electrical conductivity increases rapidly and then increases slowly with aging time, reaching 15.5 IACS after aging at 500°C for 10 h. Based on the Avrami equation, the kinetic analysis of discontinuous precipitation at different temperatures was established. [ABSTRACT FROM AUTHOR]