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The image-based virtual illumination technology directly changes the illumination effect of objects on the image. It does not require complex light propagation simulation calculations, it is an image-based rendering technology. The image mainly relies on the imaging of the visual sensor, and uses the virtual illumination technology of the image to illuminate the image obtained by the visual sensor. This is a new cross-cutting direction in computer vision, virtual reality and other fields. Face Illumination Swapping via Dense Landmark and Semantic Parsing is a major branch. Keeping the geometrical features of the target images and relighting the entire image instead of the face area are problems to be solved in the research. This paper based on the three-dimensional model to analyze the illumination information of the face images and re-render the illumination of the target face, and finally achieves the illumination swap between the two face images. We designed and implemented a 3DDFA-based face image illumination transfer method. First, 3DDFA is used to reconstruct the target face image. Estimate the surface normal and albedo. Then align and fill the surface normal and face parsing to illuminate the face image for light rendering and illumination transfer of the face images. Finally, the illumination analysis and re-rendering of face images based on 3DDFA are expanded to achieve the swap of illumination between face images. Experimental results show that this method can generate good effects of face image illumination transmission and swap while keeping the geometric features of the target images.

The integration of the space-air-ground integrated network and the Internet of Vehicles (IoV) enables the IoV to achieve full network coverage and better network performance. However, the large scale of the network and the complex cooperation mechanism make the credibility of the nodes in the network, and the service delivery questioned. In this paper, the hardware trusted module is used as the root of trust to build the trust chain and the trusted running environment and provide protection and trusted state attestation for services. In order to overcome the large-scale and high-concurrency performance bottlenecks in the remote verification of trusted states in the IoV, a novel batch remote approach for trusted states is proposed. The simulation results show that the proposed approach can effectively attest to the trusted state of each network node and virtual service in the IoV and enhance the trustworthiness of the network.

Plenoptic 2.0 videos that are captured by focused plenoptic cameras outperform the traditional plenoptic videos with higher spatial resolution of rendered subaperture images and more effective light field sampling. However, its distinct imaging principle causes different pixel intensity distribution and complex macropixel structures, which results in ineffectiveness in coding them using the existing plenoptic video compression methods. Thus, a novel compression method is proposed in this paper to dramatically improve the intra prediction efficiency for multi-focus plenoptic 2.0 video coding. It consists of two new intra prediction modes: imaging-principle-guided static prediction (ISP) and imaging-principle-guided zoomed prediction (IZP). In ISP, the prediction candidates spatially positioned by plenoptic 2.0 imaging principle are weighted by minimizing boundary error to generate the predicted samples. In IZP, the spatial positions and scales of the prediction candidates vary according to the focusing status and the focal length of the microlens that generates the pixels in the prediction candidates. The combination of two modes generalizes plenoptic 2.0 video coding for multi-focus plenoptic cameras. The proposed approach outperforms high efficiency video coding (HEVC) standard by an average of 15.37% bitrate reduction under All Intra configuration. It also outperforms other state-of-the-art methods with obvious compression efficiency improvements.

The small modular liquid-metal fast reactor (SMFR) is an important component of advanced nuclear systems. SMFRs exhibit relatively low breeding capability and constraint space for control rod installation. Consequently, control rods are deeply inserted at beginning and are withdrawn gradually to compensate for large burnup reactivity loss in a long lifetime. This paper is committed to investigating the impact of control rod compensation operation on core neutronics characteristics. This paper presents a whole core fine depletion model of long lifetime SMFR using OpenMC and the influence of depletion chains is verified. Three control rod position schemes to simulate the compensation process are compared. The results show that the fine simulation of the control rod compensation process impacts significantly the fuel burnup distribution and absorber consumption. A control rod equivalent position scheme proposed in this work is an optimal option in the trade-off between computation time and accuracy. The control position is crucial for accurate power distribution and void feedback coefficients in SMFRs. The results in this paper also show that the pin level power distribution is important due to the heterogeneous distribution in SMFRs. The fuel burnup distribution at the end of core life impacts the worth of control rods.

To efficiently use the ubiquitous behind-the-meter distributed energy resources (DERs) in distribution systems for providing grid services, this paper presents a hierarchical control framework for DER optimal aggregation and control. We first develop a convex optimization model to evaluate the DER flexibility, and then use a convex model-predictive-control based approach to dispatch those DERs. The hierarchical control framework consists of a utility controller, community aggregators and multiple home energy management systems. The flexibility of the DERs is evaluated by each controller in the hierarchy such that the resultant flexibility is feasible given its operational domain. Based on the determined flexibility, the hierarchical controllers then compute optimal setpoints for the DERs to help the distribution system regulate node voltages and provide other distribution grid services. Numerical simulations performed on a model of a real distribution feeder in Colorado, using actual DER data in a residential community demonstrate that the proposed approach can effectively alleviate voltage issues and support resilient operation.

In this study, a dual-source solar-heat pump latent heat thermal energy storage system for hot-water supply was proposed to take advantage of renewable energy sources. An experimental setup mainly consisting of a solar heat collector with a gross area of 2 m2 and an air-source heat pump with a maximum capacity of 2 kW was established to evaluate the adaptability of the phase-change material to the solar-heat pump and the feasibility of the heating system. A comparative study was carried out to investigate the effects of the operation modes on the system thermal performance and explore optimal operating parameters. Compared to the single heating mode, the overall efficiency of the system was enhanced by about 57.5% under the combined heating mode due to the introduction of solar energy. The coefficient of performance of the heat pump significantly increased from 2.09 to 2.60 when the flow rate increased from 0.010 L/s to 0.038 L/s. However, an increase in the flow rate could not significantly improve the overall efficiency of the entire heating system due to the higher power consumption of the pump. The storage unit exhibits a great storage density of about 211.13 MJ/m3 with a volume saving rate of 21%.

Building on a three-dimensional cultural competence model and treating customer–employee exchange as a vital form of social exchange, this study examines how different dimensions of cultural competence of service providers and their social investment impact event attendees' perceived destination experience and behavioral intentions in cross-cultural service encounters. Using data collected during a major sports event in Australia, the study found that cultural awareness and skills significantly influenced event attendees' perceived social investment and destination experience, while the role of cultural knowledge was not significant. Employees' cultural awareness and cultural skills had a stronger influence on perceived social investment among international tourists than they did on domestic tourists, but social investment had a stronger influence on domestic tourists' destination experience and revisit intention than it did on that of international tourists. The study contributes to an enhanced understanding of how cultural competence can shape customers’ destination experience. In addition, it introduced a cultural perspective to the social exchange process, contributing to the broadening and deepening of social exchange theory.

Building on the resource-based view (RBV) theory, this paper aims to shed light on how does the implementation of green strategies affect enterprises' performance. To distinguish the evolution of strategy implementation effect, we adopt a panel estimation strategy and gather data from 3869 listed companies in China from 2008 to 2019. Furthermore, we innovatively use the semi-supervised clustering algorithm to classify the companies according to whether they implement green strategies or not and then discuss long-term and short-term financial effects of implementing green strategies. Our study finds that the implementation of green strategy facilitates a company's long-term performance but hampers its short-term performance. According to the moderating analysis, a green strategy could negatively impact a company's financial performance by increasing debt ratios. The findings highlight the importance of implementing green strategies and the obstacles in the process of transforming enterprises to be green.

This paper considers a wireless cooperative communication system with the adaptive modulation and coding (AMC). We take the joint relay and AMC mode selection as two time-scales of optimization problems, which aim at maximizing the achievable data rate. We treat the best relay selection as a long-term objective and the best AMC mode selection as a short-term objective, to limit the signaling overhead for the wireless communication system. However, the evaluation of the achievable data rate for any of relay and AMC mode combinations needs the estimation of the wireless channel. To this end, we propose an online optimization-while-estimation algorithm based on discrete stochastic approximation to jointly solve both the relay and AMC selection problems. The algorithm requires only per-frame channel sampling and estimation in each algorithm iteration, and approaches the optimal achievable data rate asymptotically. Simulation results show that our proposed algorithm can quickly converge to the maximum achievable data rate.

PurposeThis study provides empirical support for the cultural economics model between executive team and firm performance and offers important implications for policy selection and appointment of managers in China.Design/methodology/approachFrom the perspective of relationship embeddedness, the authors explore the impact of management geographical proximity (GP) on stock price crash risk in China. Using archival data from China's unique dataset about birthplace culture, the authors find that management GP experiences a large increase in corporate stock price crash risk for the period 2009–2018.FindingsThe impact of management GP on stock price crash risk is more pronounced when the company is located in areas with weaker formal legal environment and stronger Confucian culture. Furthermore, the impact has a significant links with firm characteristics such as information transparency, over-investment and tax aggressiveness.Originality/valueFirst, the research extends the literature on the empirical determinants of stock price crash risk. These studies focus on formal institution, not on informal institution, such as relational culture. Second, the research provides evidence for economic consequences on relational governance from executive birthplace culture to explore the economic consequences of geographical relational governance but takes stock price crash risk to present executives' behavior strategies and market reaction via exploring asymmetrical variation of market stock price. Finally, the paper provides reference to corporate governance arrangement and executive appointment.

Objective. To unearth the clinical efficacy of tacrolimus ointment + 3 % boric acid lotion joint Chinese angelica decoction in chronic perianal eczema. Methods. Patients with chronic perianal eczema admitted to hospital from June 2018 and June 2019 were retrospectively analyzed. Patients in the control group ( n = 38 ) underwent basic therapy with tacrolimus ointment + 3 % boric acid lotion, whereas those in the observation group ( n = 38 ) were given oral Chinese angelica decoction on the basis of the above therapy. Patient’s baseline information before therapy and clinical symptoms after therapy were observed and compared, including pruritus ani score, anus drainage and damp score, skin lesion score, skin lesion area score, life quality index score, and IL-2, IL-4, and IgE levels in serum. Overall efficacy in the two groups was also evaluated. Results. No significant differences were found in the baseline information between the observation group and control group before therapy. After therapy, pruritus ani score ( P = 0.023 ), anus drainage and damp score ( P = 0.041 ), skin lesion score ( P = 0.025 ), and skin lesion area score ( P = 0.035 ) of patients in the observation group were remarkably lower than those in the control group. Significantly higher release levels of clinical symptoms of patients in the observation group were indicated. With respect to the control group, the life quality score ( P = 0.020 ) and IgE level in serum ( P = 0.003 ) of patients in the observation group were significantly lower, while IL-4 level in serum was significantly higher ( P = 0.129 ). The therapy in the observation group achieved better clinical efficacy. Overall efficacy in the observation group was markedly favorable with respect to the control group. Conclusion. With respect to tacrolimus ointment + 3 % boric acid lotion, patients with chronic perianal eczema displayed better clinical efficacy after jointly being treated by Chinese angelica decoction.

A multifunctional frequency-selective surface reflector (FSSR) is found to be an applicable candidate for the realization of an antenna's radar cross-section reduction in or out of its operating frequency. In this letter, a polarization-selectable in-band switchable FSSR/absorber based on diodes is designed and experimentally demonstrated, which comprises a layer of active frequency-selective surface and a layer of metal-backed frequency-dispersive magnetic material (FDMM). The design realizes the switching functions of broadband absorption band from 3.5 to 12.0 GHz (110% bandwidth) with at least −10 dB reflectivity and a reflection band at 6.2 GHz with two-sided absorption bands (2.2–4.8 GHz, 74.3% bandwidth for the lower absorption band and 7.1–12.9 GHz, 58% bandwidth for the upper absorption band) by manipulating the on and off states of PIN diodes. Besides, due to the use of FDMM, the thickness of the FSSR is only 0.074 λL , making it easy to design the reflective surface of the antenna. A prototype of the proposed FSSR/absorber is fabricated and measured, where reasonable agreement is observed.

Bladder cancer is one of the most lethal cancers in the world. Despite the continuous development of medical technologies and therapeutic strategies, the overall survival rate of bladder cancer has not changed significantly. Targeted therapy is a new promising method for bladder cancer treatment. Thus, an in-depth study of the molecular mechanism of the occurrence and development of bladder cancer is urgently needed to identify novel therapeutic candidates for bladder cancer. Here, bioinformatics analysis demonstrated that RNF26 was one of the risk factors for bladder cancer. Then, we showed that RNF26 is abnormally upregulated in bladder cancer cells and tissues and that higher RNF26 expression is an unfavorable prognostic factor for bladder cancer. Moreover, we found that RNF26 promotes bladder cancer progression. In addition, we showed that RNF26 expression is promoted by FOXM1 at the transcriptional level through MuvB complex. The upregulated RNF26 in turn degrades p57 (CDKN1C) to regulate the cell cycle process. Collectively, we uncovered a novel FOXM1/RNF26/p57 axis that modulates the cell cycle process and enhances the progression of bladder cancer. Thus, the FOXM1/RNF26/p57 signaling axis could be a candidate target for the treatment of bladder cancer.

This study was to explore the value of magnetic resonance imaging (MRI) technology processed by convolutional neural network (CNN) optimization algorithms in the clinical research of patients with chronic pain caused by myofascial trigger points (MTrPs). Firstly, referring to the traditional iterative algorithm, this study iterated the convolution network and data consistency layer as a whole for several times, which increased the fitting ability of the data consistency layer and network. When it was applied to magnetic resonance examination, it could be concluded that the effect of its reconstruction method was better than the traditional convolution neural network without the data consistency layer. The image edge was clear, and the restoration effect of details was better. 100 patients with chronic neck pain caused by MTrP were collected and divided into an ultrasound treatment group and a local anesthetic drug injection group, with 50 cases in each group. In addition, 50 healthy volunteers were selected. After clinical treatment, the results showed that, after 3 weeks of treatment, the visual analog score (VAS) and the pain rating index (PRI) of the injection group were 3.16 ± 1.14 points and 4.92 ± 1.26 points, respectively; the present pain intensity (PPI) score was 2.06 ± 0.85 points, and the number of pain days per month was 7.73 ± 1.15. After 1 month of treatment, the VSA and PRI of the injection group were 1.24 ± 0.89 and 1.31 ± 0.97, respectively; the PPI score was 1.34 ± 0.65, and the number of pain days per month was 5.34 ± 0.98. In addition, there were 38 cases reaching the level of clinical cure, accounting for 76%. Therefore, all indicators in the injection group were better than those in the ultrasound treatment group, and the differences were statistically significant ( P < 0.05 ). The results of MRI examination showed that compared with the healthy control group, patients with chronic pain caused by the myofascial trigger point had reduced axial kurtosis (AK), mean kurtosis (MK), and radial kurtosis (RK) in multiple brain areas such as the right parahippocampal gyrus and the right medial prefrontal cortex. In short, chronic pain caused by the trigger point of the myofascial membrane would affect the microstructure of the gray matter of the patient’s brain. In clinical treatment, the efficacy of local anesthetic injection was better than ultrasound therapy.

Background Pelvic organ prolapse (POP) lowers the quality of life in elderly women, and there have been no studies on its role in the pathogenesis of POP. The purpose of this study is to investigate the effect of β-catenin on proliferation and collagen anabolism in human vaginal fibroblasts (HVFs). Materials and methods The adherence and differential adherence methods were used to culture and purify HVFs. RNA interference was applied to knockdown β-catenin and lithium chloride was used to activate Wnt/β-catenin signaling pathway. β-catenin nuclear translocation was tested by immunofluorescence, and HVF proliferation was detected by performing MTT assays. Results The expression of β-catenin, phosphorylated-β-catenin, phosphorylated-glycogen synthase kinase 3β (p-GSK3β), collagen I, matrix metalloproteinase 2 (MMP2), and tissue-derived inhibitors of metalloproteinases 2 (TIMP2) was assessed by western blot analysis. The expression of β-catenin and collagen I was lower in HVFs of POP group than that of control group. The proliferation rate of HVFs in POP group was lower than that in control group. Knockdown of β-catenin decreased the cell proliferation rate and the expression of collagen I. Lithium chloride can activate the Wnt/β-catenin signaling pathway. Conclusion β-catenin participates in the proliferation and collagen I synthesis of HVFs. The decrease of β-catenin expression may be closely related to the occurrence, and development of POP. LiCl can activate the Wnt/β-catenin signaling pathway in HVFs and thus increase HVFs proliferation and collagen synthesis.

Color images always exhibit a high correlation between luma and chroma components. Cross component linear model (CCLM) has been introduced to exploit such correlation for removing redundancy in the on-going video coding standard, i.e., versatile video coding (VVC). To further improve the coding performance, this paper presents a deep learning based intra chroma prediction method, termed as convolutional neural network based chroma prediction (CNNCP). More specifically, the process of chroma prediction is formulated to produce the colorful version from available information input. CNNCP includes two sub-networks for luma down-sampling and chroma prediction, which are jointly optimized to fully exploit spatial and cross component information. In addition, the outputs of CCLM are adopted as chroma initialization for performance enhancement, and the coding distortion level characterized by quantization parameter is fed into the network to release the negative affect from compression artifacts. To further improve the coding performance, the competition is performed between the conventional chroma prediction and CNNCP in terms of rate-distortion cost with a binary flag signalled. The learned CNNCP is incorporated into both video encoder and decoder. Extensive experimental results demonstrate that the proposed scheme can achieve 4.283%, 3.343%, and 4.634% bit rate savings for luma and two chroma components, compared with the VVC test model version 4.0 (VTM 4.0).

The problems of the strip flatness defects are always severe in the tandem cold rolling process. It is of great significance to predict flatness for flatness control according to the process conditions of products. A prediction model based on convolutional neural network (CNN) was developed in this paper, and it can accurately predict strip flatness under various conditions. According to the distribution characteristics of industrial data collected in the rolling process, the isolated forest algorithm was used to eliminate outliers. Considering the special requirements of CNN on the dimension of input features, the data folding method was used to process the input features. Additionally, since strip flatness data is a vector rather than a scalar, and the length of this vector varies with strip width, which decreases the network's training accuracy. To deal with the problems, the loss function was modified. Taking the Inception module as the basic network structure and inspired by Wide & Deep learning, a strip flatness prediction model with high accuracy was developed. The optimal architecture and parameters of our network were determined through a lot of experimental explorations. The performances of BPNN (Back Propagation Neural Network), DNN (Deep Neural Network), and the proposed model were compared by mean square error (MSE) and coefficient of determination (R2). The result indicates that the proposed model has the highest prediction accuracy and better adaptability. It has the lowest MSE, 0.9891, and the highest R2, 0.9555. Finally, the fitting coefficients of Legendre polynomials were used to further prove the excellent prediction performance of the proposed model for strip flatness. Compared with other prediction models, it can obtain the lowest prediction error for the first quadratic and quartic components of strip flatness and it can be well-applied to tandem cold rolling production.

This study investigates the role of individual characteristics in shaping Airbnb adoption and resistance (unawareness, postponement, and rejection) among Chinese consumers, using a conceptual framework based on innovation diffusion theory and resistance literature. Data were collected from 1010 respondents and analyzed using multinomial logistic regression. Results show consumer demographics, travel and social media use experiences, and some individual traits (e.g., resistance to change) predict adoption/resistance while general trust and rational decision-making style have no significant effect. This study suggests that Airbnb faces challenges crossing the “chasm” but also provides suggestions for Airbnb to penetrate China’s mainstream market.

Recently, localized surface plasmon resonances (SPRs) of metallic nanoparticles (NPs) have been widely used to construct plasmonic nanohybrids for heterogeneous photocatalysis. For example, the combination of plasmonic Au NPs and TiO2 provides pure TiO2 visible-light activity. The SPR effect induces an electric field and consequently enhances light scattering and absorption, favoring the transfer of photon energy to hot carriers for catalytic reactions. Numerous approaches have been dedicated to the improvement of SPR absorption in photocatalysts. Here, we have designed a core@shell-satellite nanohybrid catalyst whereby an Ag NP core, as a plasmonic resonator featuring unique dual functions of strong scattering and near-field enhancement, is encapsulated by SiO2 and TiO2 layers in sequence, with Au NPs on the outer surface, Ag@SiO2@TiO2-Au, for efficient plasmonic photocatalysis. By varying the size and number of Ag NP cores, the Au SPR can be tailored over the visible and near-infrared spectral region to reabsorb the scattered photons. In the presence of the Ag core, the incident light is efficiently confined in the reaction suspension by undergoing multiple scattering, thus leading to an increase of the optical path to the photocatalysis. Moreover, using numerical analysis and experimental verifications, we demonstrate that the Ag core also induces a strong near-field enhancement at the Au-TiO2 interface via SPR coupling with Au. Consequently, the activity of the TiO2-Au plasmonic photocatalyst is significantly enhanced, resulting in a high H2 production rate under visible light. Thus, the design of a single structural unit with strong scattering and field enhancement, induced by a plasmonic resonator, is a highly effective strategy to boost photocatalytic activity.

The recurrence of patients with epithelial ovarian cancer (EOC) is largely attributed to tumour cells escaping from the surveillance of immune cells. However, to date there is a lack of studies that have systematically evaluated the associations between the infiltration fraction of immune cells and the recurrence risk of EOC. Based on the micro-ribonucleic acid (microRNA) expression profiles of 441 EOC patients, we constructed a microRNA-based panel with recurrence prediction potential using non-negative matrix factorization consensus clustering. Then, we evaluated the association between recurrence risk and infiltration proportions among 10 immune cell types by CIBERSORT and a multivariable Cox regression model. As a result, we identified a 72-microRNA-based panel that could stratify patients into high and low risk of recurrence. The infiltration of plasma cells and M1 macrophages was consistently significantly associated with the risk of recurrence in patients with EOC. Plasma cells were significantly associated with a decreased risk of relapse [hazard ratio (HR) = 0.58, p = 0.006), while M1 macrophages were associated with an increased risk of relapse (HR = 1.59, p = 0.003). Therefore, the 72-microRNA-based panel, M1 macrophages and plasma cells may hold potential to serve as recurrence predictors of EOC patients in clinical practice.

Near-field scanning microwave microscopy is a technique with increasing popularity for the study of nanometer-scale electrical properties of samples. Here, we present an approach to quantify sample properties in images obtained with an inverted scanning microwave microscope (iSMM), recently introduced by our group. In particular, this study reports the analysis of the local electrical conductivity of a platinum diselenide sample and proves its semimetal behavior. The approach is validated by a full-wave numerical model, reproducing the complete iSMM operation as well as all steps of the calibration algorithms. To extract local sample properties, this article provides two calibration procedures, respectively, for transmission and reflection mode measurements, based on a two-port equivalent circuit of the iSMM. This enables the high-frequency quantitative characterization of a wide variety of samples and surfaces.

Acute myeloid leukemia (AML) and myelodysplastic syndrome (MDS) are usually associated with poor outcomes, especially in high‐risk AML/MDS. Allogeneic hematopoietic stem cell transplantation (allo‐HSCT) is the only curative option for patients suffering from high‐risk AML/MDS. However, many patients relapse after allo‐HSCT. Novel therapy to prevent relapse is urgently needed. Both the BCL‐2 inhibitor venetoclax (VEN) and the hypomethylating agent decitabine (DEC) possess significant antitumor activity effects against AML/MDS. Administration of DEC has been shown to ameliorate graft‐versus‐host disease (GVHD) and boost the graft‐versus‐leukemia (GVL) effect post‐transplantation. We therefore conducted a prospective study (ChiCTR1900025374) to examine the tolerability and efficacy of a maintenance therapy of low‐dose decitabine (LDEC) plus VEN to prevent relapse after allo‐HSCT for high‐risk AML/MDS patients. Twenty patients with high‐risk AML (n = 17) or high‐risk MDS (n = 3) post‐transplantation were recruited. Approximately day 100 post‐transplantation, all patients received LDEC (15 mg/m2 for 3 d) followed by VEN (200 mg) on d 1‐21. The cycle interval was 2 mo, and there was 10 cycles. The primary end points of this study were rates of overall survival (OS) and event‐free survival (EFS). The secondary endpoints included adverse events (AEs), cumulative incidence of relapse (CIR), nonrelapse mortality (NRM), incidences of acute GVHD (aGVHD) and chronic GVHD (cGVHD), and incidences of viral infection after allo‐HSCT. Survival outcomes were assessed using Kaplan‐Meier analysis. The median follow‐up was 598 (149‐1072) d. Two patients relapsed, 1 died, and 1 is still alive after the second transplant. The 2‐y OS and EFS rates were 85.2% and 84.7%, respectively. The median 2‐y EFS time was 525 (149‐1072) d, and 17 patients still had EFS and were alive at the time of this writing. The most common AEs were neutropenia, anemia, thrombocytopenia, neutropenic fever, and fatigue. Grade 2 or 3 AEs were observed in 35% (7/20) and 20% (4/20) of the patients, respectively. No grade >3 AEs were observed. aGVHD (any grade) and cGVHD (limited or extensive) occurred in 55% and 20% of patients, respectively. We conclude that LDEC + VEN can be administered safely after allo‐HSCT with no evidence of an increased incidence of GVHD, and this combination decreases the relapse rate in high‐risk AML/MDS patients. This novel maintenance therapy may be a promising way to prevent relapse in high‐risk AML/MDS patients., The results of the current study suggest that maintenance treatment with LDEC combined with VEN introduced nearly 3 mo after allo‐HSCT is efficacious, with an acceptable toxicity profile and impressive long‐term disease control.

Background: Esophageal cancer is the sixth-most common fatal malignant tumor worldwide. Little is known regarding the genetic drivers that influence targeted therapy outcomes in patients with esophageal cancer. Exploring the pathogenesis of this lethal tumor could provide clues for developing appropriate therapeutic drugs. Ubiquitin-protein ligase E3A (UBE3A) reportedly promotes or suppresses various types of malignant tumors. However, the cancer-related role of UBE3A in esophageal cancer remains unclear. Methods: The relationship of UBE3A with the clinicopathological features of pancreatic tumors was bioinformatically investigated in the TCGA dataset. The protein levels of UBE3A and ZNF185 were assessed by Western blot and immunohistochemistry. The role of UBE3A and ZNF185 in esophageal cancer growth was assessed by MTS assays, colony formation assays, and experiments in mouse xenograft models. The interaction between UBE3A and ZNF185 was investigated by co-immunoprecipitation. The relationship between UBE3A, ZNF185, and NOTCH signaling pathway was explored by Western blot and quantitative real-time PCR. Results: We found that UBE3A was upregulated in patients with esophageal cancer and enhanced the cellular progression of esophageal cancer. Moreover, we found that UBE3A degraded ZNF185 in esophageal cancer. Additionally, ZNF185 suppressed the progression of esophageal cancer by inactivating the NOTCH pathway. Conclusions: These data demonstrated that aberrant expression of UBE3A led to enhanced progression of esophageal cancer through the ZNF185/NOTCH signaling axis. Therefore, UBE3A might be an ideal therapeutic candidate for esophageal cancer.

The outbreak of coronavirus disease 2019 (COVID-19) is a global health emergency. Various omics results have been reported for COVID-19, but the molecular hallmarks of COVID-19, especially in those patients without comorbidities, have not been fully investigated. Here we collect blood samples from 231 COVID-19 patients, prefiltered to exclude those with selected comorbidities, yet with symptoms ranging from asymptomatic to critically ill. Using integrative analysis of genomic, transcriptomic, proteomic, metabolomic and lipidomic profiles, we report a trans-omics landscape for COVID-19. Our analyses find neutrophils heterogeneity between asymptomatic and critically ill patients. Meanwhile, neutrophils over-activation, arginine depletion and tryptophan metabolites accumulation correlate with T cell dysfunction in critical patients. Our multi-omics data and characterization of peripheral blood from COVID-19 patients may thus help provide clues regarding pathophysiology of and potential therapeutic strategies for COVID-19., COVID-19 is a critical public health threat, but molecular characterizations of patients’ immunity is still lacking. Here the authors collected blood from patients with various disease severity, and prefiltered to exclude selected comorbidity, to obtain genomic, transcriptomic, proteomic, metabolomic and lipidomic profiles to report a trans-omics landscape.

Background: CircRNA plays an important role in cancer progression. However, the potential mechanism of circRNA in gastric cancer remains unknown. In this study, we aimed to investigate the specific mechanism of circALPL in gastric cancer. Methods: Using a high-throughput microarray, we found that circALPL was upregulated in gastric cancer cell lines. RT-qPCR was used to measure the circALPL expression level in gastric cell lines and tissue. Transwell, CCK-8, and metastasis assays were performed to learn the function after circALPL was inhibited. Results: circALPL downregulation suppresses the invasion and proliferation ability of gastric cancer cells. Additionally, the underlying pathway of circALPL was studied using luciferase reporter assays and RNA immunoprecipitation assays. The results showed that circALPL promotes gastric cancer progression by sponging miR-127, thus upregulating MTDH. Conclusion: The circALPL-miR-127-MTDH pathway plays a vital role in gastric cancer proliferation and metastasis. circALPL might be a new therapeutic target in gastric cancer.

The content of heavy pitch in tar generated from coal pyrolysis is high. To improve the quality of tar, catalysts are applied for adjusting the reaction of volatiles during coal pyrolysis. However, catalysts are easy to deactivate due to coke deposition. In this study, coke amount on activated carbon catalysts during catalytic upgrading of coal pyrolysis volatiles was investigated in the downer-bed reactor. The process of coke formation was studied at different feed time (30, 60 and 100 min) of coal. Results show that a great amount of coke is generated on the activated carbons. With the increase of feed time, the amount of coke on activated carbon increases, while the rate of coke formation decreases, thus decreasing the coke yield based on dry coal. The specific surface area and catalytic cracking activity of the activated carbon decrease with the increase of coke amount. Hence, the yields of tar and pitch increase with the increase of feed time. The relative content of oxygen-containing compounds in the tar increases with the increase of feed time, which indicates that the cleavage of weak chemical bond C–O is suppressed. That may result in a decrease in the rate of coke formation.

The volatiles' reaction has a significant influence on composition and coking behavior of tar during low-medium temperature pyrolysis of coal, which is closely related to the activity of volatiles and the content of components rich in hydrogen in the system. By introducing tetralin, the effect of reaction of volatiles generated by pyrolysis of Naomaohu coal on the product distribution and coke yield was explored at various temperatures using a two-stage fixed bed reactor. The tar composition was analyzed by a comprehensive two-dimensional gas chromatography/mass spectrometry. The results show that the yield of coke is higher at 400°C due to the weaker reaction between volatiles and THN and tar condensation. As the temperature increases, the reaction between volatiles and THN intensifies, resulting in the decrease of liquid products yield and the increase of gas yield. According to the calculated and experimental values of the reaction products of volatiles and THN, it is found that the liquid products yield increases after the introduction of THN. The contents of aromatic hydrocarbons with 2–5 rings and coke both decrease. This indicates that the interaction between volatiles and THN promotes the α–H transfer of THN and provides more active hydrogen for the reaction system. Besides, the interaction between volatiles and THN is also beneficial to the formation of phenolic compounds and other oxygenated compounds, especially for polyphenols. The activity of volatiles during coal pyrolysis is related to oxygen-containing radicals, which have a strong bond with active hydrogen.

With the development of cloud computing, the storage and processing of massive visual media data has gradually transferred to the cloud server. For example, if the intelligent video monitoring system cannot process a large amount of data locally, the data will be uploaded to the cloud. Therefore, how to process data in the cloud without exposing the original data has become an important research topic. We propose a single-server version of somewhat homomorphic encryption cryptosystem based on confused modulo projection theorem named CMP-SWHE, which allows the server to complete blind data processing without seeing the effective information of user data. On the client side, the original data is encrypted by amplification, randomization, and setting confusing redundancy. Operating on the encrypted data on the server side is equivalent to operating on the original data. As an extension, we designed and implemented a blind computing scheme of accelerated version based on batch processing technology to improve efficiency. To make this algorithm easy to use, we also designed and implemented an efficient general blind computing library based on CMP-SWHE. We have applied this library to foreground extraction, optical flow tracking, and object detection with satisfactory results, which are helpful for building smart cities. We also discuss how to extend the algorithm to deep learning applications. Compared with other homomorphic encryption cryptosystems and libraries, the results show that our method has obvious advantages in computing efficiency. Although our algorithm has some tiny errors ( $10^{-6}$ ) when the data is too large, it is very efficient and practical, especially suitable for blind image and video processing.

Brain metastases are refractory to therapies that control systemic disease in patients with human epidermal growth factor receptor 2 (HER2+) breast cancer, and the brain microenvironment contributes to this therapy resistance. Nutrient availability can vary across tissues, therefore metabolic adaptations required for brain metastatic breast cancer growth may introduce liabilities that can be exploited for therapy. Here, we assessed how metabolism differs between breast tumors in brain versus extracranial sites and found that fatty acid synthesis is elevated in breast tumors growing in brain. We determine that this phenotype is an adaptation to decreased lipid availability in brain relative to other tissues, resulting in a site-specific dependency on fatty acid synthesis for breast tumors growing at this site. Genetic or pharmacological inhibition of fatty acid synthase (FASN) reduces HER2+ breast tumor growth in the brain, demonstrating that differences in nutrient availability across metastatic sites can result in targetable metabolic dependencies.

Background The clinical use of the radial artery (RA) in coronary artery bypass grafting (CABG) is still limited worldwide, although it has been recommended by several guidelines. Multidetector computed tomography (MDCT) is widely used to evaluate graft patency, as invasive coronary angiography could cause potentially serious risks including bleeding, dissection and stroke. This study aims to report the short-term results of the RA in CABG with MDCT. Methods The study population consists of 41 consecutive patients undergoing elective CABG with the RA graft between 2017 to 2018, with MDCT performed to evaluate graft patency during follow-up, and target vessels for the RA were non-left anterior descending coronary arteries with > 70% stenosis. Results A total of 150 grafts were assessed by MDCT during follow-up (mean, 8.9 ± 5.1 months). MDCT could clearly show the structure and patency of grafts, even for complex coronary artery revascularization. Graft patency of the left internal mammary artery was 92.9% (39/42), with the RA patency of 84.4% (38/45) and the patency of the saphenous vein graft of 81.1% (30/37). And the RA anastomosed to the left coronary artery system might have better patency than the RA anastomosed to the right coronary artery system (25/29, 86.2% vs 13/16, 81.3%, p = 0.686). Conclusions The short-term patency rate of RA grafts is good, and the RA might be associated with better patency when anastomosed to the left but not the right coronary artery. MDCT could provide excellent visualization of grafts in CABG.

Colorization is the computer-assisted application of color to a gray scale image, which presents two problems to modern deep learning-based approaches. One is to provide colorization models with both high expressibility and strong learning ability, as current models have difficulty both excelling at coloring and being easy to train. The other is to return a picture without uneven overlap. This paper proposes a deep convolutional network framework called Color-UNet++ for the end-to-end solution of these colorization problems. Color-UNet++ is adjusted to settle gradient dispersion and explosion by capturing more transfer and intermediate results during backpropagation. We adjust the de-convolution structure to solve the problem of uneven overlap. We design the model in YUV instead of RGB color space, with an objective function that is appropriate to the coloring problem and can capture a wide range of colors. A large number of experimental results on LFW and LSUN datasets confirm the method’s superiority.

In this article, we address the degraded image super-resolution problem in a multi-task learning (MTL) manner. To better share representations between multiple tasks, we propose an all-in-one collaboration framework (ACF) with a learnable “junction” unit to handle two major problems that exist in MTL—“How to share” and “How much to share.” Specifically, ACF consists of a sharing phase and a reconstruction phase. Considering the intrinsic characteristic of multiple image degradations, we propose to first deal with the compression artifact, motion blur, and spatial structure information of the input image in parallel under a three-branch architecture in the sharing phase. Subsequently, in the reconstruction phase, we up-sample the previous features for high-resolution image reconstruction with a channel-wise and spatial attention mechanism. To coordinate two phases, we introduce a learnable “junction” unit with a dual-voting mechanism to selectively filter or preserve shared feature representations that come from sharing phase, learning an optimal combination for the following reconstruction phase. Finally, a curriculum learning-based training scheme is further proposed to improve the convergence of the whole framework. Extensive experimental results on synthetic and real-world low-resolution images show that the proposed all-in-one collaboration framework not only produces favorable high-resolution results while removing serious degradation, but also has high computational efficiency, outperforming state-of-the-art methods. We also have applied ACF to some image-quality sensitive practical task, such as pose estimation, to improve estimation accuracy of low-resolution images.

BACKGROUND Central nervous system graft-vs-host disease (CNS-GVHD) is a rare cause of CNS disorders after allogeneic hematopoietic stem cell transplantation. Currently, establishing a diagnosis of CNS-GVHD is challenging because the diagnostic criteria and diagnostic methods are not well defined and many confounding factors need to be ruled out. CASE SUMMARY Here, we present two patients with CNS-GVHD. Both patients with a history of acute GVHD or chronic GVHD developed neurological symptoms that could not be explained by other causes, and had abnormal cerebrospinal fluid (CSF) studies as determined by CSF and blood immune biomarker examinations, suggestive of suspected CNS-GVHD. Due to the lack of specific magnetic resonance imaging abnormalities and the rapid clinical deterioration of the patients, we did not attempt to perform a brain biopsy, but prompted the initiation of empirical immunosuppressive therapy. In view of the rapid and favorable response to local and systematic immunosuppressive treatment and the aforementioned neurologic manifestations together with CSF abnormalities and other negative findings, a final diagnosis of CNS-GVHD was made. CONCLUSION CSF and blood immune biomarker examinations facilitated the diagnosis of CNS-GVHD, which are particularly suitable for patients who are critically ill and require urgent treatment and for those who are unsuitable for invasive diagnostic procedures.

OBJECTIVEThis investigation is aimed at gaining a better understanding of the factors that lead to mechanical failure of shunts used for the treatment of hydrocephalus, including shunt catheter-valve disconnection and shunt catheter fracture.METHODSTo determine the root cause of mechanical failure, the authors created a benchtop mechanical model to mimic mechanical stressors on a shunt system. To test shunt fracture, cyclical loading on the catheter-valve connection site was tested with the shunt catheter held perpendicular to the valve. Standard methods were used to secure the catheter and valves with Nurolon. These commercial systems were compared to integrated catheters and valves (manufactured as one unit). To test complete separation/disconnection of the shunt catheter and valve, a parallel displacement test was conducted using both Nurolon and silk sutures. Finally, the stiffness of the catheters was assessed. All mechanical investigations were conducted on shunts from two major shunt companies, assigned as either company A or company B.RESULTSCyclical loading experiments found that shunts from company B fractured after a mean of 4936 ± 1725 cycles (95% CI 2990–6890 cycles), while those of company A had not failed after 8000 cycles. The study of parallel displacement indicated complete disconnection of company B’s shunt catheter-valve combination using Nurolon sutures after being stretched an average 32 ± 5.68 mm (95% CI 25.6–38.4 mm), whereas company A’s did not separate using either silk or Nurolon sutures. During the stiffness experiments, the catheters of company B had statistically significantly higher stiffness of 13.23 ± 0.15 N compared to those of company A, with 6.16 ± 0.29 N (p < 0.001).CONCLUSIONSMechanical shunt failure from shunt catheter-valve disconnection or fracture is a significant cause of shunt failure. This study demonstrates, for the first time, a correlation between shunt catheters that are less mechanically stiff and those that are less likely to disconnect from the valve when outstretched and are also less likely to tear when held at an angle from the valve outlet. The authors propose an intervention to the standard of care wherein less stiff catheters are trialed to reduce disconnection.

As a new branch of energy conversion technology, the triboelectric nanogenerator (TENG) can effectively convert mechanical energy into electrical energy and realize a self-driving system. As one of...

With the development of deep learning technology, many deep learning methods have been applied to font recognition and generation. However, few studies focus on font inpainting problems. This paper is dedicated to repairing damaged fonts based on style to repair damaged fonts in a better way. In this paper, we propose a CGAN (Conditional Generative Adversarial Nets)-based font repair method. This paper uses the content accuracy and style similarity of the repaired image as an evaluation index to evaluate the accuracy of the restored style font. The font content proposed by the paper based on CGAN network repair style is similar with the correct content.

Multi-label image recognition is a fundamental but challenging computer vision and multimedia task. Great progress has been achieved by exploiting label correlations among these multiple labels associated with a single image, which is the most crucial issue for multi-label image recognition. In this paper, to explicitly model label correlations, we propose a unified deep learning framework to Disentangle, Embed and Rank (DER) the corresponding label cues. Specifically, we first obtain class-aware disentangled maps (CADMs) by reforming deep activations in accordance with the class-specific recognition weights. Then, after transforming CADMs into the corresponding label vectors, we propose an embedding operation from a metric learning perspective to pull the relevant label vectors together and push irrelevant label vectors away. Furthermore, a ranking operation is employed, which aims to accurately and robustly measure the similarity/dissimilarity of these label vectors. Our model can be trained in an end-to-end manner with only image-level supervision, during which the proposed embedding and ranking operations can contribute to the CADMs learning through back-propagation. In addition, the obtained CADMs are aggregated and further used as an essential feature stream for the final multi-label classification. We conduct extensive experiments on three commonly used multi-label benchmark datasets. Quantitative results show that our model can significantly and consistently outperform previous competitive methods. Moreover, qualitative analysis of our DER proposal also reveals the effectiveness of our proposed model.

HIGHLIGHTSA deep learning algorithm with an improved lightweight network was used to identify apple fruit.Multiscale pooling was used to reduce the image size and enrich the features.Compound scaling was used to scale the basic network.The optimal compound scaling coefficient for apple identification was obtained with the WOA algorithm.The proposed method achieved an average recognition precision rate of 94.43% and a speed of 0.051s.ABSTRACT. Accurate fruit identification is the basis for automating the operation of orchard production. To better apply the identification model in mobile devices so that venue becomes a less restrictive factor for application, this study proposes an apple fruit identification method based on an improved lightweight network named “MobileNetV3-Small.” The whale optimization algorithm was introduced to improve the model by obtaining an optimal compound-scaling coefficient for the MobileNetV3-Small network. A multiscale pooling approach was used for fruit recognition, comprising operations such as lossless scaling and feature extraction on sample images. The obtained images were then inputted into the model for recognition and classification. The experimental process was conducted on an apple data set. The test results show that the multiclass average precision of apple recognition using this model was 94.43% and the running time of recognition was 0.051 s per image. Both indicators outperformed the control network models of “MobileNetV3-Small,” ResNet-50, and VGG-19. This model is 14.63% more accurate and 1.95 times quicker on average in identification than the next best model. These findings indicate that the method can realize high-efficiency and high-precision recognition of apples with high stability and portability, which lays a good foundation for the mechanization of repetitive operations such as orchard yield estimation, fruit labeling, and fruit picking. Keywords: Apple recognition, Compound scaling, Deep learning algorithm, Lightweight network, Yield estimation.

Conversion of glycerol to acrylic acid represents an emerging application in transforming waste biomass to valuable products in chemical industry. However, achieving almost perfect atomic efficiency and intrinsic zero-waste generation still remain a grand challenge to meet stricter regulations on energy consumption. Interfacial catalysis plays a central role in bridging nanostructured catalysts with green chemistry. Despite a few preliminary attempts in academia and industry, the application of interfacial catalysts remains limitedly successful. In this review, we first describe morphological & electronic uniqueness of three important interfacial catalysts, (I) bimetallic nanoclusters, (II) metal–acid composites and (III) Bronsted/Lewis acid hybrids. The resulting catalytic properties for C–H and C–O bond cleavage during oxidation and dehydration of glycerol to acrylic acid will then be critically discussed, to reveal the key advances and challenges in this area. As oxidation and dehydration represent two important reactions in industry, the outcome of this work is to provide insightful information developing cost-effective catalytic materials for various other sustainable chemical applications.

We aimed to determine whether Neuropilin-1 (NRP1) promotes gastric cancer (GC) metastasis by inducing epithelial-mesenchymal transition (EMT), and to clarify its regulatory mechanism. Using the data of GC patients in The Cancer Genome Atlas (TCGA) and Gene Tissue Expression (GTEx) databases, combined with the data of GC patients in our medical center, the effect of NRP1 on the prognosis of GC patients were analyzed. Then, we investigated the role of NRP1 in GC metastasis and its potential mechanism. The level of NRP1 was up-regulated in GC tissues and associated with poor prognosis of GC patients. The expression of NRP1 was closely related to maximum tumor diameter, invasion depth, lymphnode metastasis, distant metastasis, and advanced TNM stage, and was an independent prognostic factor for overall survival (OS) in GC patients. Besides, the results of in vitro indicated that NRP1 could induce EMT to promote the migration and invasion of GC cells by activating PI3K/Akt signaling pathway, and the HGF/c-Met axis was involved in this process. This study determined that NRP1 was a gene that promotes gastric cancer. NRP1 induced EMT to enhance the migration and invasion ability of GC cells by activating PI3K/Akt signaling pathway. NRP1 was an independent prognostic marker for OS in GC patients and expected to be a therapeutic target for GC patients.

The vibration of agricultural machinery significantly influences driving comfort and operation reliability. In recent years, research on the vibrational characteristics of agricultural machinery has gradually expanded. To solve the problem of the obvious vibration of a high-speed transplanter under operating conditions, this article examines the method of beam damage diagnosis for high-speed transplanters based on the strain mode. Based on the modal theory, the displacement and strain modal analysis of the beam of the high-speed transplanter is performed. The analysis shows that the relative deviation of the modal frequencies of the same order before and after the beam is damaged is small. There is no obvious abrupt change in the displacement mode shape, while the strain mode shape has a sudden change peak after the beam is damaged. Thus, the strain mode change rate is constructed as a diagnostic index for beam damage. The analysis results show that the strain mode change rate increases with the increase in beam damage degree. Finally, the least squares method is used to fit the corresponding relationship between the two quantities. The results show that the strain mode change rate is sensitive and reliable as an index of damage diagnosis, which can better determine the location and degree of damage to the beam. Keywords: Beam, Damage, Diagnosis, Rice transplanter, Strain mode.

Objects at different spatial positions in an image exhibit different scales. Adaptive receptive fields are expected to capture suitable ranges of context for accurate pixel level semantic prediction. Recently, atrous convolution with different dilation rates has been used to generate features of multi-scales through several branches which are then fused for prediction. However, there is a lack of explicit interaction among the branches of different scales to adaptively make full use of the contexts. In this paper, we propose a Content-Adaptive Scale Interaction Network (CASINet) to exploit the multi-scale features for scene parsing. We build CASINet based on the classic Atrous Spatial Pyramid Pooling (ASPP) module, followed by a proposed contextual scale interaction (CSI) module, and a scale adaptation (SA) module. Specifically, in the CSI module, for each spatial position of some scale, instead of being limited by a fixed set of convolutional filters that are shared across different spatial positions for feature learning, we promote the adaptivity of the convolutional filters to spatial positions. We achieve this by the context interaction among the features of different scales. The SA module explicitly and softly selects the suitable scale for each spatial position and each channel. Ablation studies demonstrate the effectiveness of the proposed modules. We achieve state-of-the-art performance on three scene parsing benchmarks Cityscapes, ADE20K and LIP.

Polymer materials with mechanical energy-to-electricity conversion capability at high temperatures are highly desirable to develop energy harvesters, sensors, and other flexible devices for applications under harsh high-temperature conditions but remain an enormous challenge to develop. Herein, we report a polymer membrane that can convert mechanical energy into electricity at both room and high temperatures. The membranes were prepared by heat stabilization treatment of electrospun polyacrylonitrile (PAN) fibrous membranes to make PAN have a cyclized structure. The devices made of the treated PAN membranes can work at a temperature as high as 550 °C, which has never been reported on other polymer-based energy harvesting devices so far. A 2.5 × 2.5 cm2 device at 450 °C can generate a 9.7 V voltage (up to 62 V, 4 μA current) with a maximum power density of 26.4 mW cm−2. We further show that the oxidation cyclization of PAN due to stabilization treatment plays an essential role in achieving the high-temperature energy conversion. The energy conversion is mainly originated from a piezoelectric effect. We used the energy generated under high-temperature conditions to power commercial LEDs and charged capacitors to demonstrate the application. The electrical energy generated from a single device is sufficient to run a commercial LEDs. After rectification, it can also be stored in capacitors for further use. The PAN membranes are flexible and air permeable. They may form a novel polymer transducer candidate to develop flexible energy devices for various applications in high-temperature environments.

Diabetic nephropathy (DN) is considered the primary causes of end-stage renal disease (ESRD) and is related to abnormal glycolipid metabolism, hemodynamic abnormalities, oxidative stress and chronic inflammation. Antagonism of vascular endothelial growth factor B (VEGF-B) could efficiently ameliorate DN by reducing renal lipotoxicity. However, this pharmacological strategy is far from satisfactory, as it ignores numerous pathogenic factors, including anomalous reactive oxygen species (ROS) generation and inflammatory responses. We found that the upregulation of VEGF-B and downregulation of interleukin-22 (IL-22) among DN patients were significantly associated with the progression of DN. Thus, we hypothesized that a combination of a VEGF-B antibody and IL-22 could protect against DN not only by regulating glycolipid metabolism but also by reducing the accumulation of inflammation and ROS. To meet these challenges, a novel anti-VEGFB/IL22 fusion protein was developed, and its therapeutic effects on DN were further studied. We found that the anti-VEGFB/IL22 fusion protein reduced renal lipid accumulation by inhibiting the expression of fatty acid transport proteins and ameliorated inflammatory responses via the inhibition of renal oxidative stress and mitochondrial dysfunction. Moreover, the fusion protein could also improve diabetic kidney disease by increasing insulin sensitivity. Collectively, our findings indicate that the bifunctional VEGF-B antibody and IL-22 fusion protein could improve the progression of DN, which highlighted a novel therapeutic approach to DN., Graphical abstract The expression of VEGF-B was increased and that of IL-22 was decreased in diabetic nephropathy (DN) patients. Anti-VEGFB/IL22 fusion protein combining VEGF-B antibody and IL22 ameliorated DN by alleviating ectopic lipid accumulation, inflammatory responses and insulin resistance.Image 1

Background: Postoperative complications after pancreatectomy are a challenging problem due to their high incidence and serious consequences. The majority of studies have focused on a specific complication, but data on predictors of overall postoperative complications (OPCs) are limited. Methods: The data of patients who underwent pancreatectomy at a single institute between 2017 and 2019 were analyzed retrospectively. Univariate and multivariate logistic regression were used to investigate predictors of the outcomes of interest. The Clavien-Dindo classification and comprehensive complication index (CCI) were used to assess postoperative complications and the severity of postoperative complications. The relationship between predictors and the CCI was evaluated by linear regression. Results: A total of 490 patients were divided into a training group (n = 339) and a validation group (n = 151). The rate of OPCs was 44.25%. Fluid transfusion and albumin difference (AD) were predictors of OPCs. AD showed a good discrimination (AUC = 0.70) and good calibration in the validation cohort. AD was associated with complications, including pancreatic fistula, intra-abdominal hemorrhage, intra-abdominal infection, delayed gastric emptying, and re-intervention, and was positively correlated with complication severity. Intraoperative blood loss and preoperative albumin were independent predictors of AD. Conclusions: AD, a variable that reflects dynamic physiological changes is a new and accessible predictor of OPCs following pancreatectomy.

In this work, two carbazole- and benzo[d]oxazole-based novel multifunctional materials with a hybridized local and charge-transfer (HLCT) characteristic, namely, OCI and OCT, which could act as deep-blue fluorophors and phosphorescent hosts, were first designed and synthesized. Both OCI and OCT display meritorious thermal stability with a glass transition temperature (Tg) as high as 139 and 137 °C, respectively. The two compounds also reveal excellent bipolar carrier transport ability and emit deep-blue emission with photoluminescence quantum yields (PLQY) of 34.5% and 14.0%, respectively. Used as blue fluorophors, OCI- and OCT-based non-doped devices exhibit a very low turn-on voltage (Von) of 3.0 V and realize deep-blue emission with peaks located at 438–453 nm, and the optimized OCI-based device achieve the maximum external quantum efficiency (ηext,max) reaching 3.19%. Remarkably, doped blue devices show even deeper blue emission at 424 and 428 nm and the optimized OCI-based device achieves a significantly improved ηext,max of 5.06% with CIEy < 0.04, which could rank among the deepest blue HLCT fluorophors. Additionally, green phosphorescent OLEDs hosted by OCI and OCT reveal a low Von of 2.7 V with maximum current efficiencies (ηc,max) of 49.43 cd A−1 (ηext,max of 14.11%) and 52.81 cd A−1 (ηext,max of 15.57%), respectively, which are superior to that of CBP (Von of 3.0 V, ηc,max of 51.76 cd A−1 and ηext,max of 12.88%). The superb electroluminescence (EL) performance of OCI and OCT as deep-blue HLCT emitters and phosphorescent hosts demonstrates that they have great potential for industrial applications.

How to realize data transmission between Intranet and extranet devices in physical isolation is an important problem. QR code can be used to identify text, image information, can be used as a carrier of information exchange. In this paper, QR code technology is used to propose a data transmission system between the Internet and the Internet under the physical isolation state based on visual recognition, which makes the information transmitted only through visible light, enhances the confidentiality, and can meet the daily office needs. At the sending end, the division of the transferred file is completed and multiple corresponding QR codes are generated. After the receiving end scans and recognizes the QR code, it finally forms a complete file identical to the original file. Then, the multi-QR code picture transmission was upgraded to the QR code stream transmission mode, and the transmission efficiency was improved by more than 10 times compared with similar work. In face of the real needs, we have added the function of retrieving pictures or database information from another computer and sending it back to the sending computer according to the instructions of the sending end. The paper also carries on the test analysis to each work.