Search

To improve acquisition efficiency and achieve super high-resolution reconstruction, a computational integral imaging reconstruction (CIIR) method based on the generative adversarial network (GAN) network is proposed. Firstly, a sparse camera array is used to generate an elemental image array of the 3D object. Then, the elemental image array is mapped to a low-resolution sparse view image. Finally, a lite GAN super-resolution network is presented to up-sample the low-resolution 3D images to high-resolution 3D images with realistic image quality. By removing batch normalization (BN) layers, reducing basic blocks, and adding intra-block operations, better image details and faster generation of super high-resolution images can be achieved. Experimental results demonstrate that the proposed method can effectively enhance the image quality, with the structural similarity (SSIM) reaching over 0.90, and can also reduce the training time by about 20%.

Abstract In this work, ultra-low dilution rate Inconel 625 coatings with a thickness of ~534.4 μm were prepared by high-speed laser cladding technique on the Q245R steel. The XRD and TEM results show that the Inconel 625 coatings are mainly composed of the Nb and Mo-enriched laves phase with hexagonal close-packed (HCP) structure and the γ-Ni phase with face-centered-cubic (FCC) structure. The cellular crystal, column crystal, and equiaxed dendritic crystal were observed in the bottom, middle, and top of the Inconel 625 coatings from SEM results, respectively. The wear resistance and corrosion resistance of Q245R steel are significantly improved by high-speed laser cladding Inconel 625 coatings. In addition, the low dilution rate is beneficial for improving the wear and corrosion resistance of high-speed laser cladding Inconel 625 coatings. These findings may provide a method to design the wear and corrosion resistance of coatings on steel's surface.

Abstract Background The pathogen of banana Fusarium oxysporum f. sp. cubense race 4(Foc4) infects almost all banana species, and it is the most destructive. The molecular mechanism of the interactions between Fusarium oxysporum and banana still needs to be further investigated. Methods We use both the interolog and domain-domain method to predict the protein–protein interactions (PPIs) between banana and Foc4. The predicted protein interaction sequences are encoded by the conjoint triad and autocovariance method respectively to obtain continuous and discontinuous information of protein sequences. This information is used as the input data of the neural network model. The Long Short-Term Memory (LSTM) neural network five-fold cross-validation and independent test methods are used to verify the predicted protein interaction sequences. To further confirm the PPIs between banana and Foc4, the GO (Gene Ontology) and KEGG (Kyoto Encylopedia of Genes and Genomics) functional annotation and interaction network analysis are carried out. Results The experimental results show that the PPIs for banana and foc4 predicted by our proposed method may interact with each other in terms of sequence structure, GO and KEGG functional annotation, and Foc4 protein plays a more active role in the process of Foc4 infecting banana. Conclusions This study obtained the PPIs between banana and Foc4 by using computing means for the first time, which will provide data support for molecular biology experiments.

Abstract Alkali metal–carbon dioxide (Li/Na/K‐CO2) batteries are emerging electrochemical energy storage technologies in the context of the energy crisis and the urgent demand for carbon neutrality. Alkali metal–CO2 batteries offer a new strategy for CO2 fixation and utilization, and thus has been receiving considerable attention in recent years. Considerable progress has been achieved since alkali metal–CO2 batteries were invented, especially in terms of development of new electrode materials, and yet, research is lacking on the underlying mechanisms of the systems. This is the first typical review focusing on the electrochemical mechanisms of metal–CO2 batteries that summarizes the current understanding of and provides insights into the thermodynamic reaction pathways, the kinetic characteristics, and the crucial factors determining the reaction mechanisms in alkali metal–CO2 batteries. The review starts with the fundamental concepts of alkali metal–CO2 batteries, followed by a comprehensive discussion of the working mechanisms on cathodes and anodes. Moreover, the operation mechanisms of state‐of‐the‐art electrolytes, including liquid and (quasi‐)solid‐state electrolytes, are also described. Finally, we identify the unsolved problems in current alkali metal–CO2 batteries and propose potential topics for future research.

ABSTRACT Marine sediments are important methane reservoirs. Methane efflux from the seabed is significantly restricted by anaerobic methanotrophic (ANME) archaea through a process known as anaerobic oxidation of methane (AOM). Different clades of ANME archaea occupy distinct niches in methane seeps, but their underlying molecular mechanisms still need to be fully understood. To provide genetic explanations for the niche partitioning of ANME archaea, we applied comparative genomic analysis to ANME archaeal genomes retrieved from global methane seeps. Our results showed that ANME-2 archaea are more prevalent than ANME-1 archaea in shallow sediments because they carry genes that encode a significantly higher number of outer membrane multiheme c-type cytochromes and flagellar proteins. These features make ANME-2 archaea perform direct interspecies electron transfer better and benefit more from electron acceptors in AOM. Besides, ANME-2 archaea carry genes that encode extra peroxidase compared to ANME-1 archaea, which may lead to ANME-2 archaea better tolerating oxygen toxicity. In contrast, ANME-1 archaea are more competitive in deep layers than ANME-2 archaea because they carry extra genes (mtb and mtt) for methylotrophic methanogenesis and a significantly higher number of frh and mvh genes for hydrogenotrophic methanogenesis. Additionally, ANME-1 archaea carry exclusive genes (sqr, TST, and mddA) involved in sulfide detoxification compared to ANME-2 archaea, leading to stronger sulfide tolerance. Overall, this study reveals the genomic mechanisms shaping the niche partitioning among ANME archaea in global methane seeps. IMPORTANCE Anaerobic methanotrophic (ANME) archaea are important methanotrophs in marine sediment, controlling the flux of biologically generated methane, which plays an essential role in the marine carbon cycle and climate change. So far, no strain of this lineage has been isolated in pure culture, which makes metagenomics one of the fundamental approaches to reveal their metabolic potential. Although the niche partitioning of ANME archaea was frequently reported in different studies, whether this pattern was consistent in global methane seeps had yet to be verified, and little was known about the genetic mechanisms underlying it. Here, we reviewed and analyzed the community structure of ANME archaea in global methane seeps and indicated that the niche partitioning of ANME archaea was statistically supported. Our comparative genomic analysis indicated that the capabilities of interspecies electron transfer, methanogenesis, and the resistance of oxygen and hydrogen sulfide could be critical in defining the distribution of ANME archaea in methane seep sediment.

Abstract Background The present study aimed to comprehensively investigate the occurrence and risk factors of adverse events (AEs) or adverse drug reactions (ADRs) (especially for thrombocytopenia and bleeding) in Chinese female patients receiving bivalirudin during percutaneous coronary intervention (PCI). Methods A total of 918 female patients from 27 Chinese medical centers took bivalirudin as anticoagulant for PCI were enrolled in this prospective, multi-center, intensive monitoring study. Safety data (AEs, ADRs, thrombocytopenia and bleeding) were collected from admission to 72 h post bivalirudin administration; then, patients were followed up at the 30th day with the safety data collected as well. Results One hundred and twenty (13.1%) patients occurred AEs, among which 7 (0.8%) cases experienced severe AEs, and 2 (0.2%) cases died. Besides, 40 (4.4%) patients occurred bivalirudin-related ADRs, in which 3 (0.3%) cases experienced severe ADRs, but 0 (0.0%) cases died. It was of note that 27 (2.9%) and 13 (1.4%) patients experienced thrombocytopenia and bleeding, respectively. Subsequent multivariate analyses observed that: clinical presentation of spontaneous coronary artery dissection (SCAD) (odds ratio (OR) = 3.191, P = 0.004), CRUSADE high risk (OR = 2.075, P = 0.031), multiple culprit vessel (OR = 2.328, P = 0.019) independently correlated with higher risk of bivalirudin-related ADRs; clinical presentation of SCAD (OR = 4.388, P = 0.002) and multiple culprit vessel (OR = 2.974, P = 0.010) independently linked with raised thrombocytopenia risk; history of diabetes mellitus (OR = 5.227, P = 0.007) and CRUSADE high risk (OR = 4.475, P = 0.016) were independent factor related to elevated bleeding risk. Conclusion Bivalirudin is well tolerated with low ADRs, thrombocytopenia and bleeding incidences in Chinese female patients undergoing PCI.

Abstract Background Long non-coding RNAs (lncRNAs) are related to human diseases by regulating gene expression. Identifying lncRNA-disease associations (LDAs) will contribute to diagnose, treatment, and prognosis of diseases. However, the identification of LDAs by the biological experiments is time-consuming, costly and inefficient. Therefore, the development of efficient and high-accuracy computational methods for predicting LDAs is of great significance. Results In this paper, we propose a novel computational method (gGATLDA) to predict LDAs based on graph-level graph attention network. Firstly, we extract the enclosing subgraphs of each lncRNA-disease pair. Secondly, we construct the feature vectors by integrating lncRNA similarity and disease similarity as node attributes in subgraphs. Finally, we train a graph neural network (GNN) model by feeding the subgraphs and feature vectors to it, and use the trained GNN model to predict lncRNA-disease potential association scores. The experimental results show that our method can achieve higher area under the receiver operation characteristic curve (AUC), area under the precision recall curve (AUPR), accuracy and F1-Score than the state-of-the-art methods in five fold cross-validation. Case studies show that our method can effectively identify lncRNAs associated with breast cancer, gastric cancer, prostate cancer, and renal cancer. Conclusion The experimental results indicate that our method is a useful approach for predicting potential LDAs.

Cuproptosis was characterized as a novel type of programmed cell death. Recently, however, the role of cuproptosis-related long noncoding RNAs (CRLs) in tumors has not yet been studied. Identifying a predictive CRL signature in hepatocellular carcinoma (HCC) and investigating its putative molecular function were the goals of this work. Initially, Pearson’s test was used to assess the relationship between lncRNAs and cuproptosis-associated genes obtained from HCC data of The Cancer Genome Atlas (TCGA). By implementing differential expression and univariate Cox analysis, 61 prognostic CRLs were subsequent to the least absolute shrinkage and selection operator (LASSO) Cox regression analysis. A prognostic risk score model was then constructed to evaluate its ability to predict patients’ survival when combined with clinicopathological parameters in HCC. The five-lncRNA prognostic signature categorized the HCC patients into high- and low-risk groups. The low-risk group exhibited more sensitivity to elesclomol than the high-risk one. Surprisingly, distinct mitochondrial metabolism pathways connected to cuproptosis and pivotal immune-related pathways were observed between the two groups via gene set enrichment analysis (GSEA). Meanwhile, there were substantial differences between the high-risk group and the low-risk group in terms of tumor-infiltrating immune cells (TIICs). Furthermore, a positive relationship was shown between the risk score and the expression of immune checkpoints. Additionally, differential expression of the five lncRNAs was confirmed in our own HCC samples and cell lines via RT-qPCR. Finally, in vitro assays confirmed that WARS2-AS1 and MKLN1-AS knockdown could sensitize HCC cells to elesclomol-induced cuproptosis. Overall, our predictive signature may predict the prognosis of HCC patients in an independent manner, give a better understanding of how CRLs work in HCC, and offer therapeutic reference for patients with HCC.

ObjectivesTo evaluate the relationship between systemic family dynamics and adolescent depression.MethodsAn offline survey was distributed to 4,109 students in grades 6–12, with the final analysis including 3,014 students (1,524 boys and 1,490 girls) aged 10–18 years. The questionnaire included the Self-Rating Scale of Systemic Family Dynamics (SSFD), the Self-Rating Depression Scale (SDS), and demographic characteristics.ResultsFamily dynamics were negatively correlated with depressive symptoms, with better family dynamics (high scores) associated with lower levels of depression based on the SDS score. After adjusting for sociodemographic characteristics, an ordinal multiclass logistic regression analysis identified family atmosphere (OR = 0.952, 95% CI: 0.948–0.956, p < 0.001) as the most important protective family dynamic against depression, followed by individuality (OR = 0.964, 95% CI: 0.960–0.968, p < 0.001). Latent class analysis (LCA) created the low family dynamic and high family dynamic groups. There were significant differences in the mean SDS scores between the two groups (45.52 ± 10.57 vs. 53.78 ± 11.88; p < 0.001) that persisted after propensity matching. Family atmosphere and individuation had a favorable diagnostic value for depression, with AUCs of 0.778 (95% CI: 0.760–0.796) and 0.710 (95% CI: 0.690–0.730), respectively. The diagnostic models for depression performed well.ConclusionPoor family dynamics may be responsible for adolescent depression. A variety of early intervention strategies focused on the family may potentially avoid adolescent depression.

Hepatic ischemia–reperfusion (IR) injury is a serious clinical problem that complicates liver resection and transplantation. Despite recent advances in understanding of the pathophysiology of hepatic IR injury, effective interventions and therapeutics are still lacking. Here, we examined the role of transient receptor potential melastatin 2 (TRPM2), a Ca2+-permeable, non-selective cation channel, in mediating hepatic IR injury. Our data showed that TRPM2 deficiency attenuated IR-induced liver dysfunction, inflammation, and cell death in mice. Moreover, RNA sequencing analysis indicated that TRPM2-induced IR injury occurs via ferroptosis-related pathways. Consistently, as a ferroptosis inducer, (1S,3R)-RSL3 treatment induced mitochondrial dysfunction in hepatocytes and a TRPM2 inhibitor suppressed this. Interestingly, TRPM2-mediated calcium influx caused mitochondrial calcium accumulation via the mitochondrial Ca2+-selective uniporter and increased the expression level of arachidonate 12-lipoxygenase (ALOX12), which results in mitochondrial lipid peroxidation during hepatic IR injury. Furthermore, hepatic IR injury-induced ferroptosis was obviously relieved by a TRPM2 inhibitor or calcium depletion, both in vitro and in vivo. Collectively, these findings demonstrate a crucial role for TRPM2-mediated ferroptosis in hepatic IR injury via increased Ca2+-induced ALOX12 expression, indicating that pharmacological inhibition of TRPM2 may provide an effective therapeutic strategy for hepatic IR injury-related diseases, such as during liver resection and transplantation.

In order to accelerate the construction of a clean, low-carbon, safe and efficient energy system, China set the provincial weight of responsibility for renewable energy power consumption and established a renewable energy power absorption guarantee mechanism in 2019. As a market incentive policy, it has enduring effect on the low-carbon transformation of the power industry. Firstly, the operation mechanism of the renewable energy consumption guarantee mechanism is analyzed. The general framework, core elements and supporting measures are clarified. Secondly, a stock-flow diagram is constructed based on the system dynamics method. It contains the green electricity market sub-module, the green-certificate market sub-module and the excess power absorption market sub-module. Finally, multiple scenarios are set up to simulate the impact of the green-certificate market and excess power absorption market improvements on the installed capacity and tariff of China’s green power market. The renewable energy guarantee mechanism is an effective means to promote the consumption of green electricity in China. In addition, in the short term the cost of electricity for users has increased, but in the long term the cost of electricity shows a fluctuating downward trend. This study provides theoretical references for the formulation of clean and low-carbon policy objectives in the power industry and the optimization of market mechanisms.

The production of long-term landslide maps (LAM) holds crucial importance in estimating landslide activity, vegetation disturbance, and regional stability. However, the availability of LAMs remains limited in many regions, despite the application of various machine-learning methods, deep-learning (DL) models, and ensemble strategies in landslide detection. While transfer learning is considered an effective approach to tackle this challenge, there has been limited exploration and comparison of the temporal transferability of state-of-the-art deep-learning models in the context of LAM production, leaving a significant gap in the research. In this study, an extensive series of tests was conducted to evaluate the temporal transferability of typical semantic segmentation models, specifically U-Net, U-Net 3+, and TransU-Net, using a 10-year landslide-inventory dataset located near the epicenter of the Wenchuan earthquake. The experiment results disclose the feasibility and limitations of implementing transfer-learning methods for LAM production, particularly when leveraging the power of U-Net 3+. Furthermore, following an assessment of the effects of varying data volumes, patch sizes, and time intervals, this study recommends appropriate settings for LAM production, emphasizing the balance between efficiency and production performance. The findings from this study can serve as a valuable reference for devising an efficient and reliable strategy for large-scale LAM production in landslide-prone regions.

Subway vehicle roofs must be inspected when entering and exiting the depot to ensure safe subway vehicle operations. This paper presents an improved method for detecting foreign objects on subway vehicle roofs based on the YOLOv7 algorithm. First, we capture images of foreign objects using a line-scan camera at the depot entrance and exit, creating a dataset of foreign roof objects. Subsequently, we address the shortcomings of the YOLOv7 algorithm by introducing the Ghost module, an improved weighted bidirectional feature pyramid network (WBiFPN), and the Wise intersection over union (WIoU) bounding-box regression loss function. These enhancements are incorporated to build the subway vehicle roof foreign object detection model based on the improved YOLOv7, which we refer to as YOLOv7-GBW. The experimental results demonstrate the practicality and usability of the proposed method. The analysis of the experimental results indicates that the YOLOv7-GBW algorithm achieves a detection accuracy of 90.29% at a speed of 54.3 frames per second (fps) with a parameter count of 15.51 million. The improved YOLOv7 model outperforms mainstream detection algorithms in terms of detection accuracy, speed, and parameter count. This finding confirms that the proposed method meets the requirements for detecting foreign objects on subway vehicle roofs.

Abstract Background Although electrical stimulation of the peripheral and central nervous systems has attracted much attention owing to its potential therapeutic effects on neuropsychiatric diseases, its non-cell-type-specific activation characteristics may hinder its wide clinical application. Unlike electrical methodologies, optogenetics has more recently been applied as a cell-specific approach for precise modulation of neural functions in vivo, for instance on the vagus nerve. The commonly used implantable optical waveguides are silica optical fibers, which for brain optogenetic stimulation (BOS) are usually fixed on the skull bone. However, due to the huge mismatch of mechanical properties between the stiff optical implants and deformable vagal tissues, vagus nerve optogenetic stimulation (VNOS) in free-behaving animals continues to be a great challenge. Results To resolve this issue, we developed a simplified method for the fabrication of flexible and stretchable polymer optical fibers (POFs), which show significantly improved characteristics for in vivo optogenetic applications, specifically a low Young’s modulus, high stretchability, improved biocompatibility, and long-term stability. We implanted the POFs into the primary motor cortex of C57 mice after the expression of CaMKIIα-ChR2-mCherry detected frequency-dependent neuronal activity and the behavioral changes during light delivery. The viability of POFs as implantable waveguides for VNOS was verified by the increased firing rate of the fast-spiking GABAergic interneurons recorded in the left vagus nerve of VGAT-ChR2 transgenic mice. Furthermore, VNOS was carried out in free-moving rodents via chronically implanted POFs, and an inhibitory influence on the cardiac system and an anxiolytic effect on behaviors was shown. Conclusion Our results demonstrate the feasibility and advantages of the use of POFs in chronic optogenetic modulations in both of the central and peripheral nervous systems, providing new information for the development of novel therapeutic strategies for the treatment of neuropsychiatric disorders.

BackgroundPostoperative non-union of femoral neck fracture often needs secondary operation. We report a case of a postoperative non-union of femoral neck fracture treated with teriparatide.Case presentationA young male patient with Garden IV femoral neck fracture who showed no obvious signs of healing 3 months after percutaneous hollow nail fixation in which the fracture line was enlarged and the hollow nail was withdrawn. Bone non-union healed after 6 months of continuous subcutaneous injection of teriparatide at a dosage of 20 mg/day after the patient refused a secondary surgery. As far as we know, there have been no relevant reports on this type of fracture yet.ConclusionsTeriparatide is expected to be beneficial in treating young patients with a displaced femoral neck fracture who have difficulty in healing from non-union and who are keen on avoiding secondary surgery.

During the COVID-19 pandemic, online learning has experienced increasing utilization and poses new challenges for schoolteachers to engage students. Project-based Learning (PBL) is widely acknowledged as an effective pedagogy for motivating and involving students. However, few studies have examined scaffolds that facilitate student engagement in the context of distance PBL. This mixed-method study was conducted with grade 7 teachers and students in a junior middle school in East China from March 2020 to April 2020. Qualitative analysis was employed in interviews with 2 teachers and 21 students. Quantitative analysis was used to visualize the self-reflection reports of 39 students. The findings suggest that the scaffolds of teacher direction, technology support, peer collaboration, and parental assistance play a significant role. In addition, specific scaffolding within the above categories was revealed. The results highlight the problem-oriented, methodological, and synthesized application of various scaffold(ing)s in engaging students and emphasize that scaffolding students emotionally is the core issue to support engagement for remote learning.

With the rapid development of electronic equipment and wireless communication in recent years, problems of electromagnetic interference are also becoming more and more serious. It is urgent to develop high performance electromagnetic interference(EMI) shielding materials to alleviate the damage of electromagnetic interference. MXene(Ti3C2Tx) is a novel kind of 2D material with ultrahigh electrical conductivity and unique chemically active surface, and therefore exhibits extremely excellent electromagnetic shielding performance. The preparation, structural feature and EMI shielding mechanism of Ti3C2Tx were focused and introduced in this paper,the latest research progress on EMI shielding applications of Ti3C2Tx-based films and 3D porous materials was objectively reviewed and evaluated, and the main problem at the present was analyzed. In addition, the future development direction and tendency of Ti3C2Tx-based EMI shielding materials from the aspects of preparation, microstructure control and structure design and so on were also prospected in this poper,based on current development needs including developing low-cost, green and efficient Ti3C2Tx preparation methods, solving the long-standing oxidation problem of Ti3C2Tx, designing new structure of Ti3C2Tx-based EMI shielding materials and finding out other MXenes EMI shielding materials other than Ti3C2Tx, which can provide new ideas and guidance for the further development of the next generation of high-performance EMI shielding materials.

A control strategy for a certain class of hypersonic flight aircraft dynamic models with unknown parameters is proposed in this article. The strategy is adaptive dynamic surface input quantization control. To address the issues in conventional inversion control, a first-order low-pass filter and an adaptive parameter minimum learning law are introduced in the control system design process. This method has the following features: (1) it solves the problem of repeated differentiation of the virtual control law in the conventional back-stepping method, greatly simplifying the control law structure; (2) by using the norm of the neural network weight vector as the adaptive adjustment parameter instead of updating each element online, the number of adaptive adjustment parameters is significantly reduced, improving the execution efficiency of the controller; (3) the introduced hysteresis quantizer overcomes the disadvantage of the quantization accuracy deterioration when the input value is too low in the logarithm quantizer, improving the accuracy of the quantizer. Stability analysis has shown that all signals in the closed-loop system are semi-globally uniformly bounded, and simulation results have verified the effectiveness of the proposed adaptive quantized control scheme.

Abstract Background Immune cell infiltration and neuroinflammation are heavily associated with spinal cord injury (SCI). C-C motif chemokine ligand 2/C-C chemokine receptor type 2 (CCL2/CCR2) axis has been identified as a critical role player during the invasion of immune cells to lesions in many diseases. γδ T cells, a subgroup of T cells, manage the course of inflammation response in various diseases; however, it remains unknown whether γδ T cells are recruited to injury site through CCL2/CCR2 signaling and exert the regulation effect on neuroinflammation after SCI. Methods Basso Mouse Scale (BMS), regularity index, cadence, max contact area, and motor-evoked potential testing (MEP) were measured to determine the neurological function recovery after spinal cord injury. Nissl staining was performed to identify the number of surviving motor neurons at lesion epicenter. Immunofluorescence, Western blot, enzyme-linked immunosorbent assays (ELISA), and quantitative real-time polymerase chain reaction (QRT-PCR) also were employed to evaluate the expression of associated proteins and genes. Results In this study, we demonstrated that TCRδ−/− mice present improved neurological recovery after SCI. γδ T cell recruitment to the SCI site was significantly reduced and motor functional improvement enhanced in CCL2−/− and CCR2−/− mouse strains. Furthermore, reconstitution of TCRδ−/− mice with γδ T cells extracted from CCR2−/− mice also showed similar results to CCL2 and CCR2 deficient mice. Conclusions In conclusion, γδ T cell recruitment to SCI site promotes inflammatory response and exacerbates neurological impairment. CCL2/CCR2 signaling is a vital recruitment mechanism of γδ T cells to the SCI site, and it may be taken as a novel therapeutic target for future SCI.

As a measuring tool of industrial sustainable development, industrial eco-efficiency works as a link between economic benefit and environmental pressure. Industrial agglomeration and energy have always been considered an important influence factor on industrial eco-efficiency. The Chinese government is facing the challenge of reaching a Carbon Peak by the 2060s, within this context, it is critically important to explore the relationship between industrial agglomeration and industrial eco-efficiency, moreover, energy intensity may play a key role between them, which should not be ignored. Therefore, based on the STIRPAT model, this paper constructs a spatial autocorrelation model (SAC model) to analyze the provincial panel dataset from 2009 to 2018, and it is found that: First, the industrial agglomeration has an inverted N-shaped relationship with industrial eco-efficiency, and industrial eco-efficiency indeed have a positive spatial spillover effect. Second, energy intensity plays a mediating role, industrial agglomeration would affect industrial eco-efficiency through energy intensity. Therefore, it is suggested that the government should introduce differentiated industrial agglomeration policies as well as energy-saving policies. In addition, this paper suggests that policymakers should adhere to consistent industrial sustainable development policies.

During a parasitological survey of goldfish Carassius auratus (Linnaeus, 1758) in China, two myxosporeans were collected in the present study including a Thelohanellus species and a Myxobolus species. To identified the two myxosporeans, a morphological, histological and molecular study was performed. The Thelohanellus species was characterized by round, white plasmodia in fins of goldfish. Compared with all described Thelohanellus species, spore morphology of the Thelohanellus species in the present study was distinct from them. BLAST search showed that SSU rDNA sequence of this Thelohanellus species did not match any sequence in GenBank database. Phylogenetically, the Thelohanellus species obtained in the present study was sister to Thelohanellus nikolskii Achmerov, 1955. So, the present Thelohanellus species was identified as a novel species and named Thelohanellus pseudonikolskii n. sp. The Myxobolus species was characterized by a number of round or irregularly shaped, white plasmodia in gills of goldfish. When compared with all reported Myxobolus species, spore morphology of the present Myxobolus species was most similar to Myxobolus koi Kudo, 1920. Histological analysis showed that plasmodia of this Myxobolus species simultaneously developed in stratified epithelium of filament and in lamellae, which was same to M. koi. Moreover, BLAST search showed that SSU rDNA sequence of the present Myxobolus species was identical to M. koi. Thus, the morphological, histological and molecular analysis demonstrated that the present Myxobolus species was conspecific with M. koi. Due to M. koi consistently described from gills of common carp Cyprinus carpio Linnaeus, 1758 and koi carp C. carpio in the previous researches, M. koi was first found to infect gills of goldfish in the present study. Considering koi carp and goldfish both as ornamental fish frequently cultured together in China, the possible host shift of M. koi from koi carp to goldfish is of interest for further investigation.

Obesity is often regarded as a factor that promotes tumorigenesis, but the role of obesity in promoting hepatocellular carcinoma (HCC) is still controversial. We compared the trend change of 14 obesity-related genes in the formation and development of HCC in normal, adjacent, and HCC tissues. Mendelian randomization (MR) analysis was used to verify the relationship between obesity and HCC occurrence. Metabolism of cobalamin-associated A (MMAA) was discovered as an obesity- and metabolism-differential gene, and its function in HCC was tested in vitro and in vivo. Finally, we explored how obese female patients with an originally high expression of female estrogen receptor (ESR1) directly upregulated MMAA to interfere with the progression of HCC. Fourteen obesity-related genes were downregulated in adjacent and tumoral tissues compared with normal liver tissues, which indicated that obesity may be inversely related to the occurrence of HCC and was consistent with the results of MR analysis. We also discovered that MMAA is a metabolic gene closely related to the occurrence and development of HCC by mining the TCGA database, and it functioned an anti-tumor-promoting role in HCC by damaging the mitochondrial function and preserving the redox balance. We further verified that obese females with a high expression of ESR1 can regulate MMAA to protect HCC from progression. This study elucidates that obesity might be a protective factor for female HCC patients, as they originally highly expressed ESR1, which could upregulate MMAA to suppress tumor growth and participate in metabolic reprogramming.

A novel gold nanoparticle (Do-AuNP) was successfully synthesized from water extracts of traditional Chinese medicine Dendrobium officinale (DO) without using any extra chemicals regents. The physicochemical properties of Do-AuNPs were analyzed by transmission electron microscopy, dynamic light scattering, energy-dispersive X-ray spectroscopy, Fourier transform infrared spectroscopy, and atomic force microscopy. The amount of DO extract on the AuNPs was about 13%. In order to evaluate the anti-tumor efficiency and biosafety, the inhibitory rate of HepG2 cells and survival rate of L02 cells were performed in vitro, and the immunohistochemical analysis of H&E, Ki-67, and TUNEL staining were conducted in vivo. Our results demonstrated that Do-AuNP had better anti-tumor efficiency compared with DO extraction alone without increasing toxicity in vivo and in vitro. The present study provides useful information for Do-AuNP as a new nanomedicine for liver cancer.

Abstract Aqueous zinc-based batteries (AZBs) attract tremendous attention due to the abundant and rechargeable zinc anode. Nonetheless, the requirement of high energy and power densities raises great challenge for the cathode development. Herein we construct an aqueous zinc ion capacitor possessing an unrivaled combination of high energy and power characteristics by employing a unique dual-ion adsorption mechanism in the cathode side. Through a templating/activating co-assisted carbonization procedure, a routine protein-rich biomass transforms into defect-rich carbon with immense surface area of 3657.5 m2 g−1 and electrochemically active heteroatom content of 8.0 at%. Comprehensive characterization and DFT calculations reveal that the obtained carbon cathode exhibits capacitive charge adsorptions toward both the cations and anions, which regularly occur at the specific sites of heteroatom moieties and lattice defects upon different depths of discharge/charge. The dual-ion adsorption mechanism endows the assembled cells with maximum capacity of 257 mAh g−1 and retention of 72 mAh g−1 at ultrahigh current density of 100 A g−1 (400 C), corresponding to the outstanding energy and power of 168 Wh kg−1 and 61,700 W kg−1. Furthermore, practical battery configurations of solid-state pouch and cable-type cells display excellent reliability in electrochemistry as flexible and knittable power sources.

Under the global health crisis of COVID-19, timely, and accurate epidemic data are important for observation, monitoring, analyzing, modeling, predicting, and mitigating impacts. Viral case data can be jointly analyzed with relevant factors for various applications in the context of the pandemic. Current COVID-19 case data are scattered across a variety of data sources which may consist of low data quality accompanied by inconsistent data structures. To address this shortcoming, a multi-scale spatiotemporal data product is proposed as a public repository platform, based on a spatiotemporal cube, and allows the integration of different data sources by adopting various data standards. Within the spatiotemporal cube, a comprehensive data processing workflow gathers disparate COVID-19 epidemic datasets at the global, national, provincial/state, county, and city levels. This proposed framework is supported by an automatic update with a 2-h frequency and the crowdsourcing validation team to produce and update data on a daily time step. This rapid-response dataset allows the integration of other relevant socio-economic and environmental factors for spatiotemporal analysis. The data is available in Harvard Dataverse platform (https://dataverse.harvard.edu/dataset.xhtml?persistentId=doi:10.7910/DVN/8HGECN) and GitHub open source repository (https://github.com/stccenter/COVID-19-Data).

The sudden outbreak of the Coronavirus disease (COVID-19) swept across the world in early 2020, triggering the lockdowns of several billion people across many countries, including China, Spain, India, the U.K., Italy, France, Germany, Brazil, Russia, and the U.S. The transmission of the virus accelerated rapidly with the most confirmed cases in the U.S., India, Russia, and Brazil. In response to this national and global emergency, the NSF Spatiotemporal Innovation Center brought together a taskforce of international researchers and assembled implementation strategies to rapidly respond to this crisis, for supporting research, saving lives, and protecting the health of global citizens. This perspective paper presents our collective view on the global health emergency and our effort in collecting, analyzing, and sharing relevant data on global policy and government responses, human mobility, environmental impact, socioeconomical impact; in developing research capabilities and mitigation measures with global scientists, promoting collaborative research on outbreak dynamics, and reflecting on the dynamic responses from human societies.

Abstract Rechargeable zinc‐air batteries (ZABs) have attracted much attention as the next‐generation energy conversion and storage devices due to the abundance and environmental friendliness of zinc (Zn) for anode materials, as well as the safety and low cost of aqueous electrolytes. However, rational design of nonprecious and low‐cost integrated air cathode materials with a desirable bifunctional oxygen electrocatalytic performance remains a great challenge for the commercialization of rechargeable ZABs. In previous research studies, various cost‐effective carbon‐supported electrocatalysts and light‐weight carbon‐based current collectors for air cathodes have been developed, showing vast potential in the application of carbon‐based materials. To improve the bifunctional performance and integration of air cathodes, efforts with respect to the design of morphology, defects, and synergistic effects of carbon‐based materials have been made. In this perspective, the general understanding of the air cathode construction and the battery working mechanism is discussed. The recent progress in the design of carbon‐based materials for air cathodes in rechargeable ZABs is summarized. Several possible future research directions and the expected development trends are also discussed, aiming to facilitate the commercialization of advanced rechargeable ZABs in our life.

Abstract Background Drugs achieve pharmacological functions by acting on target proteins. Identifying interactions between drugs and target proteins is an essential task in old drug repositioning and new drug discovery. To recommend new drug candidates and reposition existing drugs, computational approaches are commonly adopted. Compared with the wet-lab experiments, the computational approaches have lower cost for drug discovery and provides effective guidance in the subsequent experimental verification. How to integrate different types of biological data and handle the sparsity of drug-target interaction data are still great challenges. Results In this paper, we propose a novel drug-target interactions (DTIs) prediction method incorporating marginalized denoising model on heterogeneous networks with association index kernel matrix and latent global association. The experimental results on benchmark datasets and new compiled datasets indicate that compared to other existing methods, our method achieves higher scores of AUC (area under curve of receiver operating characteristic) and larger values of AUPR (area under precision-recall curve). Conclusions The performance improvement in our method depends on the association index kernel matrix and the latent global association. The association index kernel matrix calculates the sharing relationship between drugs and targets. The latent global associations address the false positive issue caused by network link sparsity. Our method can provide a useful approach to recommend new drug candidates and reposition existing drugs.

Abstract Semiconducting conjugated polymer nanoparticles (SPNs) represent an emerging class of phototheranostic materials with great promise for cancer treatment. In this report, low-bandgap electron donor–acceptor (D–A)-conjugated SPNs with surface cloaked by red blood cell membrane (RBCM) are developed for highly effective photoacoustic imaging and photothermal therapy. The resulting RBCM-coated SPN (SPN@RBCM) displays remarkable near-infrared light absorption and good photostability, as well as high photothermal conversion efficiency for photoacoustic imaging and photothermal therapy. Particularly, due to the small size (

Lipids are involved in both energy metabolism and signaling transduction. Abnormal lipid metabolism in T cells is associated with the differentiation, longevity and activity of T cells, which has received increasing concern since its firstly reported in 1985. To evaluate the trends of lipid metabolism in T cells and map knowledge structure, we employed bibliometric analysis. A total of 286 related publications obtained from the Web of Science Core Collection published between 1985 and 2022 were analyzed using indicators of publication and citation metrics, countries, institutes, authors, cited references and key words. The present research status, the global trends and the future development directions in lipid metabolism and T cells were visualized and discussed. In summary, this study provides a comprehensive display on the field of lipid metabolism in T cells, which will help researchers explore lipid metabolism in T cells more effectively and intuitively.

Temporal lobe epilepsy, a chronic disease of the brain characterized by degeneration of the hippocampus, has impaired risk assessment. Risk assessment is vital for survival in complex environments with potential threats. However, the underlying mechanisms remain largely unknown. The intricate balance of gene regulation and expression across different brain regions is related to the structure and function of specific neuron subtypes. In particular, excitation/inhibition imbalance caused by hyperexcitability of glutamatergic neurons and/or dysfunction of GABAergic neurons, have been implicated in epilepsy. First, we estimated the risk assessment (RA) by evaluating the behavior of mice in the center of the elevated plus maze, and found that the kainic acid-induced temporal lobe epilepsy mice were specifically impaired their RA. This experiment evaluated approach-RA, with a forthcoming approach to the open arm, and avoid-RA, with forthcoming avoidance of the open arm. Next, results from free-moving electrophysiological recordings showed that in the hippocampus, ∼7% of putative glutamatergic neurons and ∼15% of putative GABAergic neurons were preferentially responsive to either approach-risk assessment or avoid-risk assessment, respectively. In addition, ∼12% and ∼8% of dorsal lateral septum GABAergic neurons were preferentially responsive to approach-risk assessment and avoid-risk assessment, respectively. Notably, during the impaired approach-risk assessment, the favorably activated dorsal dentate gyrus and CA3 glutamatergic neurons increased (∼9%) and dorsal dentate gyrus and CA3 GABAergic neurons decreased (∼7%) in the temporal lobe epilepsy mice. Then, we used RNA sequencing and immunohistochemical staining to investigate which subtype of GABAergic neuron loss may contribute to excitation/inhibition imbalance. The results show that temporal lobe epilepsy mice exhibit significant neuronal loss and reorganization of neural networks. In particular, the dorsal dentate gyrus and CA3 somatostatin-positive neurons and dorsal lateral septum cholecystokinin-positive neurons are selectively vulnerable to damage after temporal lobe epilepsy. Optogenetic activation of the hippocampal glutamatergic neurons or chemogenetic inhibition of the hippocampal somatostatin neurons directly disrupts RA, suggesting that an excitation/inhibition imbalance in the dHPC dorsal lateral septum circuit results in the impairment of RA behavior. Taken together, this study provides insight into epilepsy and its comorbidity at different levels, including molecular, cell, neural circuit, and behavior, which are expected to decrease injury and premature mortality in patients with epilepsy.

Cren7 and Sis7d, two chromatin proteins from Sulfolobus islandicus, undergo extensive methylations at multiple lysine residues to various extents. Whether this highly conserved protein serves an epigenetic role in the regulation of the structure and function of the chromosome remains unclear. In the present study, we show that methylation significantly affects Cren7, but not Sis7d, in the ability to bind DNA and to constrain negative DNA supercoils. Strikingly, methylated Cren7 was significantly less efficient in forming oligomers or mediating intermolecular DNA bridging. Single-site substitution mutation with glutamine reveals that methylation of the four lysine residues (K24, K31, K42, and K48) of Cren7 at the protein-DNA interface, which are variably conserved among Cren7 homologues from different branches of the Crenarchaeota, influenced Cren7-DNA interactions in different manners. We suggest that dynamic methylation of Cren7 may represent a potential epigenetic mechanism involved in the chromosomal regulation in crenarchaea.

Background:PAX2 is a nuclear transcription factor gene that is highly conserved among species. Variants within PAX2 could result in optic nerve colobomas and kidney hypoplasia. However, little clinical and genetic information is currently available about PAX2 variants in Chinese children.Objective: This study aims to further understand the clinical manifestations and genetic characteristics of PAX2 variants in Chinese population.Methods: In this single-center retrospective study, we analyzed the clinical data of 10 children identified as carriers of PAX2 variants by gene sequencing. All the variants found in this study were analyzed using in silico prediction and American College of Medical Genetics and Genomics (ACMG) standards and guidelines.Results: The mean age for developing the first symptom in 10 unrelated children was 7.2 years old. Proteinuria and bilateral kidney dysplasia were found in every patient. Two children underwent kidney histological examination; one child showed high-intensity C1q deposition in the kidney, and the other child showed focal segmental glomerular sclerosis (FSGS). Three children had PAX2-related ocular abnormalities, including nystagmus, retinal exudation, amblyopia, microphthalmia, microcornea, and total blindness. In addition, one patient had the comorbidity of oculocutaneous albinism (OCA). Eight different PAX2 variants were found in ten patients, three of which were reported for the first time.Conclusion: We reported some patients with unique manifestations and comorbidities, and we reported three variants that have not been previously identified. The PAX2 gene is prone to spontaneous variants, and the outcome of patients is unfavorable. Because of the lack of specific therapy, genetic testing should be recommended for individuals with obvious evidence of kidney dysplasia and eye abnormalities, and kidney protective treatment should be initiated early.

The clinical management of patients with COVID-19 and cancer is a Gordian knot that has been discussed widely but has not reached a consensus. We introduced two-sample Mendelian randomization to investigate the causal association between a genetic predisposition to cancers and COVID-19 susceptibility and severity. Moreover, we also explored the mutation landscape, expression pattern, and prognostic implications of genes involved with COVID-19 in distinct cancers. Among all of the cancer types we analyzed, only the genetic predisposition to lung adenocarcinoma was causally associated with increased COVID-19 severity (OR = 2.93, β = 1.074, se = 0.411, p = 0.009) with no obvious heterogeneity (Q = 17.29, p = 0.24) or symmetry of the funnel plot. In addition, the results of the pleiotropy test demonstrated that instrument SNPs were less likely to affect COVID-19 severity via approaches other than lung adenocarcinoma cancer susceptibility (p = 0.96). Leave-one-out analysis showed no outliers in instrument SNPs, whose elimination rendered alterations in statistical significance, which further supported the reliability of the MR results. Broad mutation and differential expression of these genes were also found in cancers, which may provide valuable information for developing new treatment modalities for patients with both cancer and COVID-19. For example, ERAP2, a risk factor for COVID-19-associated death, is upregulated in lung squamous cancer and negatively associated with patient prognosis. Hence, ERAP2-targeted treatment may simultaneously reduce COVID-19 disease severity and restrain cancer progression. Our results highlighted the importance of strengthening medical surveillance for COVID-19 deterioration in patients with lung adenocarcinoma by showing their causal genetic association. For these patients, a delay in anticancer treatment, such as chemotherapy and surgery, should be considered.

The national carbon emission trading mechanism is an important policy tool for the Chinese government to control and reduce greenhouse gas emissions by using the market mechanism. The Beijing–Tianjin–Hebei power market is the focus of energy conservation and consumption reduction in China. Problems have already existed in the synergic development of the Beijing–Tianjin–Hebei power market and carbon trading market. In this article, the development status of the Beijing–Tianjin–Hebei power market is analyzed and the coupling mechanism between the carbon market and power market is combed out to build a synergism model of the carbon market and the Beijing–Tianjin–Hebei power market based on the system dynamics. From the research results, firstly, the Beijing–Tianjin–Hebei power market comes with a high energy consumption intensity and a high proportion of carbon emissions. The coupling of carbon market and power market forces the power industry to reduce carbon emissions through the effective transmission of carbon costs to power prices. Secondly, carbon price shows an upward trend in the context of the current policy scenario, which can give play to the role of price signal in the future. The revenue of thermal power plants, which are the carbon emission right sellers, with new technologies, has increased significantly, while the revenue of carbon emission right buyers, which are the manufacturers of undeveloped units, has increased less. Finally, the technical progress of thermal power plants, the introduction of auction mechanism, the increase in initial carbon price settings and the direct transmission of carbon costs are all factors that promote the effectiveness of carbon trading policy tools in the Beijing–Tianjin–Hebei power market. This study provides theoretical guidance for the synergic development of the “power-carbon” market.

Abstract With the low redox potential of −3.04 V (vs SHE) and ultrahigh theoretical capacity of 3862 mAh g−1, lithium metal has been considered as promising anode material. However, lithium metal battery has ever suffered a trough in the past few decades due to its safety issues. Over the years, the limited energy density of the lithium‐ion battery cannot meet the growing demands of the advanced energy storage devices. Therefore, lithium metal anodes receive renewed attention, which have the potential to achieve high‐energy batteries. In this review, the history of the lithium anode is reviewed first. Then the failure mechanism of the lithium anode is analyzed, including dendrite, dead lithium, corrosion, and volume expansion of the lithium anode. Further, the strategies to alleviate the lithium anode issues in recent years are discussed emphatically. Eventually, remaining challenges of these strategies and possible research directions of lithium‐anode modification are presented to inspire innovation of lithium anode.

Background: Deep learning has the potential to improve diagnostic accuracy and efficiency in medical image recognition. In the current study, we developed a deep learning algorithm and assessed its performance in discriminating melanoma from nevus using whole-slide pathological images (WSIs). Methods: The deep learning algorithm was trained and validated using a set of 781 WSIs (86 melanomas, 695 nevi) from PLA General Hospital. The diagnostic performance of the algorithm was tested on an independent test set of 104 WSIs (29 melanomas, 75 nevi) from Tianjin Chang Zheng Hospital. The same test set was also diagnostically classified by 7 expert dermatopathologists. Results: The deep learning algorithm receiver operating characteristic (ROC) curve achieved a sensitivity 100% at the specificity of 94.7% in the classification of melanoma and nevus on the test set. The area under ROC curve was 0.99. Dermatopathologists achieved a mean sensitivity and specificity of 95.1% (95% confidence interval [CI]: 92.0%-98.2%) and 96.0% (95% CI: 94.2%-97.8%), respectively. At the operating point of sensitivity of 95.1%, the algorithm revealed a comparable specificity with 7 dermatopathologists (97.3% vs. 96.0%, P = 0.11). At the operating point of specificity of 96.0%, the algorithm also achieved a comparable sensitivity with 7 dermatopathologists (96.5% vs. 95.1%, P = 0.30). A more transparent and interpretable diagnosis could be generated by highlighting the regions of interest recognized by the algorithm in WSIs. Conclusion: The performance of the deep learning algorithm was on par with that of 7 expert dermatopathologists in interpreting WSIs with melanocytic lesions. By pre-screening the suspicious melanoma regions, it might serve as a supplemental diagnostic tool to improve working efficiency of pathologists.

School choice policy in China aims to achieve educational equity by limiting school choice. Synchronous Admission Reform (SAR hereafter) is a recent school choice reform in China, which continues to limit parents' autonomy and strengthen the equal distribution of school resources. This study explores Chinese middle-class parents' (n = 21) justice claims in SAR. The findings suggest parents' three distributive justice claims, including situational principles of distribution, institutional partiality in distribution, and entrepreneurship representative of distribution. Each claim contains contradictory interpretations of education equity. While parents admire SAR's egalitarian promise, they recognize the present unbalanced school development and engage in a meritocratic way of hoarding opportunities. Despite their complaints over SAR's institutional partiality, they acknowledge SAR's political representation. Instead of participating in policy networks, parents adopt an entrepreneurial way of non-compliance. Parents' contradictory discourse is shaped by an interplay of policy discourse, school gaps, and parents' agency in a competitive and high-stakes education environment. Our analysis offers a micro-psychosocial lens for policymakers and practitioners to understand educational equity in everyday discourses.

A composite learning dynamic surface control is proposed for a class of multi-machine power systems with uncertainties and external disturbances by using fuzzy logic systems (FLSs) and disturbance observer (DOB). The main characteristics of the proposed strategy are as follows: (1) The approximation ability of FLSs for nonlinear model of multi-machine power systems is enhanced considerably by using the composite learning method and providing additional correction information for the FLSs. These findings differ considerably from previous designs that focus directly on the system's tracking performance. (2) The filtering errors caused by the utilizations of the first-order low-pass filters in dynamic surface control (DSC) are compensated effectively by designing the compensating signals in the control law design process. (3) The compound disturbances including the FLSs' approximation error and external disturbances are estimated and mitigated by constructing DOB. Finally, the proposed control algorithm is verified on the StarSim Hardware-in-loop experimental platform, and the experimental results validate the effectiveness of the proposed control strategy in suppressing disturbances and enhancing the robustness of the controller.

Coordinated droop control strategies can provide frequency support for AC area grids from wind farms integrated through multi-terminal high-voltage DC (MTDC) transmission. However, such a strategy inevitably causes DC voltage deviation for transmitting AC area frequency information, thereby deteriorating the stability and security of the MTDC system operation. This paper reports an improved adaptive virtual capacitor control strategy that can provide inertial support for the system. Instead of increasing the capacitance of the physical capacitor, a virtual capacitor is generated by utilizing the rest energy of the deloaded wind farm. Furthermore, an S-shape function is designed to adaptively adjust the capacitance of the virtual capacitor based on the operating points of the system, in order to provide a better inertial support than with the fixed virtual capacitor control strategy. The proposed strategy not only enhances the DC voltage nadir but also improves the frequency nadir of the AC area by releasing additional power from the deloaded wind farm., a MTDC system of a four-terminal voltage source converter with two wind farms is simulated using PSCAD/EMTDC. Case studies are conducted to compare and demonstrate the effectiveness of the proposed strategy under sudden load variations.

The challenges of modern power systems with a high level of renewable generation penetration will impose increased ancillary service on Photovoltaic (PV) systems, including frequency regulation. In this paper, a frequency regulation strategy for PV systems without energy storage is proposed. The proposed strategy is divided into two layers: the frequency regulation layer and the power reserve control layer. In the power reserve control layer, a novel power reserve control is proposed as the core element and novel feature. The existing power reserve control for PV systems must estimate the maximum available power, which is difficult because of the variation in environmental conditions and complex modeling of PV systems. Instead, a power tracking control method based on a curtailment power-current curve is proposed. It can automatically track a given reserve ratio under any environmental condition without an irradiance sensor. In the frequency regulation layer, the combination of droop control and virtual inertia control is used to adjust the given power reserve ratio. Simulation results show that the proposed strategy offers good accuracy and tracking speed and can significantly improve the frequency response of the power system.

The authors here report a sunlight-promoted rechargeable zinc–air battery in which photoelectrode is used as the air electrode to substantially lower the charge potential under illumination. Notably, the battery can be initially charged with an extremely low voltage of ~1.20 V.

Abstract The Internet of Everything (IoE), which aims to realize information exchange and communications for anything with the Internet, has revolutionized our modern world. Serving as the driving force for devices in the IoE network, power supply systems play a fundamental role in the development of the IoE. However, due to the complexity, multifunctionality and wide‐scale deployment of diverse applications, power supply systems face great challenges, including distribution, connection, charging technologies, and management. In this review, some challenges and advances in the development of both power supply systems and their units are presented. In the overall system‐level field, establishing sustainable and maintenance‐free power supply systems through wireless connections, efficient power management and integrated energy harvesting and storage systems is highlighted. Additionally, the main performance metrics of power supply units are discussed, including energy density, service life, and self‐power ability. In addition, some directions of power quality assessment for both the system and unit levels of power supply systems are presented, aiming to provide insight into the future development of high‐performance power supply systems for the IoE.

Abstract Background Dendritic cells (DCs) alter their role from being immunostimulatory to immunosuppressive at advanced stages of tumor progression, but the influence of cancer stem cells (CSCs) and their secreted factors on generation and phenotypic change of DCs is unknown. Retinoic acid-inducible gene I (RIG-I) plays a role in regulation of other cellular processes including leukemic stemness besides its antiviral function. Methods Short hairpin RNA-mediated gene silencing was employed to generate stable RIG-I-knocked-down human hepatocellular carcinoma (HCC) cell lines. Expression levels of genes and proteins in spheres of those HCC cells were determined by quantitative real-time PCR and Western bot, respectively. Levels of secreted cytokines were measured by ELISA. The surface molecule expression levels of DCs were analyzed using flow cytometry. The ability of DCs to induce proliferation of T cells was assessed by a mixed lymphocyte reaction (MLR) assay. Results RIG-I-knocked-down HCC cells showed upregulated expression of stem cell marker genes, enhanced secretion of factors suppressing in vitro generation of DCs into the conditioned medium (CM), and induction of a phenotype of tumor-infiltrating DCs (TIDCs) with low levels of DC markers in their tumors in nude mice. Those DCs and TIDCs showed reduced MLR, indicating RIG-I deficiency-induced immunotolerance. The RIG-I-deficient HCC cells secreted more TGF-β1 than did reference cells. The tumors formed after injection of RIG-I-deficient HCC cells had higher TGF-β1 contents than did tumors derived from control cells. DC generation and MLR suppressed by the CM of RIG-I-deficient HCC cells were restored by an anti-TGF-β1 antibody. TGF-β1-induced phosphorylation of Smad2 and Akt was enhanced in RIG-I-deficient HCC spheres, knockdown of AKT gene expression abolishing the augmentation of TGF-β1-induced Smad2 phosphorylation. Akt and p-Akt were co-immunoprecipitated with Smad2 in cytoplasmic proteins of RIG-I-deficient spheres but not in those of control spheres, the amounts of co-immunoprecipitated Akt and p-Akt being increased by TGF-β stimulation. Conclusions Our results demonstrate that RIG-I deficiency in HCC cells induced their stemness, enhanced secretion and signaling of TGF-β1, tolerogenic TIDCs and less generation of DCs, and the results suggest involvement of TGF-β1 in those RIG-I deficiency-induced tolerogenic changes and involvement of CSCs in DC-mediated immunotolerance.

Recently, the local climate zone (LCZ) system has been presented to establish the connection between urban landscape and local thermal environment. However, LCZ entities are very difficult to be identified by pixel-based classifiers or object-oriented image analysis, as they are often a complicated combination of multiple ground objects (e.g., buildings, roads, grassland, etc.). Scene classifiers, especially deep learning methods can exploit the structure or contextual information of image scenes and then improve the performance of LCZ classification. However, the square and uniform-sized image patches often bring about extra challenges, as they cannot exactly match LCZ entities of diverse sizes and shapes in most cases. In this study, a sequential virtual scene method is presented to identify LCZ entities of diverse shapes and sizes, which consists of a small “core patch” for scanning diverse entities and sequential virtual scenes for providing abundant context. Specifically, the Bidirectional Long Short-Term Memory (Bi-LSTM) were used to learn the spatial relationship among virtual scenes, respectively. Importantly, a “self-attention” mechanism is designed to weigh the contribution of every virtual scene for alleviating the influences of mixed patches, according to the similarity between its hidden state and the final hidden state. Experiments prove SVS achieves better accuracies than random forest and ResNet and has the outstanding capacity of identifying irregular LCZ entities. It is a promising way to carry out LCZ mapping in cities of different types due to its flexibility and adaptability.