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BackgroundThe characteristics of the tumor immunosuppressive microenvironment represent a major challenge that limits the efficacy of immunotherapy. Our previous results suggested that cryo-thermal therapy, a tumor ablation system developed in our laboratory, promotes macrophage M1-type polarization and the complete maturation of DCs to remodel the immunosuppressive environment. However, the cells that respond promptly to CTT have not yet been identified. CTT can cause extensive cell death and the release of danger-associated molecular patterns and antigens. Neutrophils are the first white blood cells recruited to sites of damage and acute inflammation. Therefore, we hypothesized that neutrophils are the initial cells that respond to CTT and are involved in the subsequent establishment of antitumor immunity.MethodsIn this study, we examined the kinetics of neutrophil recruitment after CTT via flow cytometry and immunofluorescence staining and explored the effect of neutrophils on the establishment of systemic antitumor immunity by in vivo neutrophil depletion and in vitro co-culture assays.ResultsWe found that CTT led to a rapid and strong proinflammatory neutrophil response, which was essential for the long-term survival of mice. CTT-induced neutrophils promoted the activation of monocytes via reactive oxygen species and further upregulated the expression of IFN-γ and cytotoxic molecules in T and NK cells. Adoptive neutrophil transfer further enhanced the antitumor efficacy of CTT in tumor models of spontaneous and experimental metastasis.ConclusionThese results reveal the important role of neutrophil‒monocyte interactions in the development of anti-tumor immunity and highlight that CTT could be used as an immunotherapy for targeting neutrophils and monocytes to enhance antitumor immunity.

Abstract Background Acute complete gastric volvulus is a rare and life-threatening disease, which is prone to gastric wall ischemia, perforation, and necrosis. If it is not treated by surgery in time, the mortality rate can range from 30 to 50%. Clinical presentations of acute gastric volvulus are atypical and often mimic other abdominal conditions such as gastritis, gastroesophageal reflux, gastric dilation, and pancreatitis. Imaging studies are crucial for diagnosis, with barium meal fluoroscopy being the primary modality for diagnosing gastric volvulus. Cases of acute gastric volvulus diagnosed by ultrasound are rarely reported. Case presentation We reported a rare case of acute gastric volvulus in a 4-year-old Chinese girl who presented with vomiting and abdominal pain. Ultrasound examination revealed the “whirlpool sign” in the cardia region, raising suspicion of gastric volvulus. Diagnosis was confirmed by X-ray barium meal fluoroscopy, which indicated left-sided diaphragmatic hernia and obstruction at the cardia region. Surgical intervention confirmed our suspicion of acute complete gastric volvulus combined with diaphragmatic hernia. Conclusion In this case, we reported an instance of acute complete gastric volvulus. Ultrasound revealed a “whirlpool sign” in the cardia, which is likely to be a key sign for the diagnosis of complete gastric volvulus.

Abstract To study the fracture instability characteristics and fracture mechanism of overlying strata in steeply dipping and large mining height stope, through the combination of physical simulation experiment, numerical calculation, theoretical analysis and field measurement, the correlation between the evolution of mining stress field and the fracture of overlying strata in large mining height stope under the effect of dip angle is analyzed, and the stress transfer path and the fracture mechanism of overlying strata are revealed. Finally, the influence mechanism of key strata on the evolution of the mining stress field of overlying strata is explained. The research shows that under the influence of gravity dip angle effect and mining height increase, the mining stress is non-equilibrium transferred and evolved in space, and the principal stress field presents the characteristics of partition evolution. The fracture trajectory of rock strata is multi-step and “八” shaped, and the inclined masonry structure and multi-step key strata form a cooperative bearing structure. The key layer of the ladder is the stress transmission structure between the caving zone and the stress arch, and the high stress in the overhanging area under the main roof is transmitted to the advanced roof. With the increase of dip angle, the height of the caving zone decreases, and the fracture range of ladder and principal stress arch decreases. The cracks in the middle and upper broken rock blocks increase, and the instability of the “high ladder rock layer” is easy to induce the simultaneous fracture of the “inclined masonry structure”, resulting in regional unbalanced instability and impact. The research results can provide some reference and guiding significance for the stability control of surrounding rock in longwall stope with steeply dipping.

IntroductionRecently, more and more research illustrated the importance of inducing CD4+ T helper type (Th)-1 dominant immunity for the success of tumor immunotherapy. Our prior studies revealed the crucial role of CD4+ Th1 cells in orchestrating systemic and durable antitumor immunity, which contributes to the satisfactory outcomes of the novel cryo-thermal therapy in the B16F10 tumor model. However, the mechanism for maintaining the cryo-thermal therapy-mediated durable CD4+ Th1-dominant response remains uncovered. Additionally, cryo-thermal-induced early-stage CD4+ Th1-dominant T cell response showed a correlation with the favorable prognosis in patients with colorectal cancer liver metastasis (CRCLM). We hypothesized that CD4+ Th1-dominant differentiation induced during the early stage post cryo-thermal therapy would affect the balance of CD4+ subsets at the late phase.MethodsTo understand the role of interferon (IFN)-γ, the major effector of Th1 subsets, in maintaining long-term CD4+ Th1-prone polarization, B16F10 melanoma model was established in this study and a monoclonal antibody was used at the early stage post cryo-thermal therapy for interferon (IFN)-γ signaling blockade, and the influence on the phenotypic and functional change of immune cells was evaluated.ResultsIFNγ at the early stage after cryo-thermal therapy maintained long-lasting CD4+ Th1-prone immunity by directly controlling Th17, Tfh, and Tregs polarization, leading to the hyperactivation of Myeloid-derived suppressor cells (MDSCs) represented by abundant interleukin (IL)-1β generation, and thereby further amplifying Th1 response.DiscussionOur finding emphasized the key role of early-phase IFNγ abundance post cryo-thermal therapy, which could be a biomarker for better prognosis after cryo-thermal therapy.

Based on Argo observations and a coupled atmosphere–ocean–wave model, the upper ocean responses to the tropical cyclone (TC) Mekunu (2018) were investigated, and the role of a pre-existing cold eddy in modulating the temperature response to TC Mekunu was quantified by employing numerical experiments. With TC Mekunu’s passage, the mixed layer depth (MLD) on both sides of its track significantly deepened. Moreover, two cold patches (

The effect of normal force on fretting corrosion of Alloy 690 TT tube in high temperature high pressure water was investigated. Increasing the normal force accelerates the wear behaviour of the tube. The small normal force has a larger coefficient of friction. The wear mechanism changes from the dominant form of abrasive wear to delamination wear. The increase in normal force not only causes the wear debris layer (WDL) and the tribologically transformed structure to become thicker and more refined, but also affects the oxidation and corrosion behaviour of the tube and changes the composition of the WDL.

The rock bridges sandwiched in incipiently jointed rock mass were considered as barriers that block the fluid seepage, and provide certain shear strength reservation. For better revealing the influence of hydraulic pressure on the failure behaviour of rock bridges, direct shear tests were carried out through a newly proposed method on rock samples that contain two parallel incipient joints. By developing the gypsum-silicone pad coupling samples, a conventional triaxial test system was qualified to implement direct shear tests with satisfied sealing capability. The results showed that the rock bridges could be failed through the tensile failure, shear failure and mixed failure mechanism. The hydraulic pressure would facilitate the tensile failure mechanism and induce rougher fracture surfaces; while the normal stress would facilitate the shear failure mechanism and induce less rough fracture. The hydraulic pressure reduced the global shear strength of the rock block through reducing the efficient normal stress applied on the rock bridge area, which was highly dependent on the joint persistence, k. Moreover, because of the iterating occurrence of the hydraulic pressure lag with the fracture propagation, the rock bridge failure stage in the shear stress-shear displacement curves displayed a fluctuation trend.

In layered transition metal oxides as cathode active materials for Na-ion batteries, Na+ diffusion kinetics are impacted by Na+/vacancy ordering. Here, the authors suggest that the P2-type Na2/3Ni1/3Mn2/3O2 with large zigzag ordering exhibits high Na+ mobility and thus superior rate capability.

Estimating the geographic positions in GPS-denied environments is of great significance to the safe flight of unmanned aerial vehicles (UAVs). In this paper, we propose a novel geographic position estimation method for UAVs after road network alignment. We discuss the generally overlooked issue, namely, how to estimate the geographic position of the UAV after successful road network alignment, and propose a precise robust solution. In our method, the optimal initial solution of the geographic position of the UAV is first estimated from the road network alignment result, which is typically presented as a homography transformation between the observed road map and the reference one. The geographic position estimation is then modeled as an optimization problem to align the observed road with the reference one to improve the estimation accuracy further. Experiments on synthetic and real flight aerial image datasets show that the proposed algorithm can estimate more accurate geographic position of the UAV in real time and is robust to the errors from homography transformation estimation compared to the currently commonly-used method.

Due to the different imaging mechanisms between optical and polarimetric synthetic aperture radar (PolSAR) images, determining how to effectively use such complementary information has become an interesting and challenging problem. Convolutional neural networks (CNNs) and other deep neural networks have achieved good experimental results in remote sensing land-cover semantic segmentation. However, the CNN convolution structure can extract only the features within the receptive field in the spatial dimension without focusing on the relationship between multiple channels; therefore, it is impossible to realize fusion and complementarity between multiple channels. In this article, we propose a novel spatial dense channel attention fusion network (SDCAFNet), which takes optical and PolSAR images as different inputs and completes feature fusion and semantic segmentation within a neural network. First, SDCAFNet uses a two-stream siamese CNN network to realize the preliminary feature coding of optical and PolSAR images. Then, a spatial dense channel attention module (SDCAM) is proposed. The channel activation values obtained at different positions are combined in the spatial dense matrix, which can describe the attention in the feature fusion process. Finally, we introduce the fused features into the symmetric skip-connection decoder composed of multiple symmetric decoder blocks to realize end-to-end land-cover semantic segmentation. Experimental results show that SDCAFNet can effectively learn the correlation between optical and PolSAR channels and has a better segmentation accuracy than other methods.

BackgroundCryotherapy is widely applied to relieve pain and improve functional outcomes after total knee arthroplasty (TKA). New cryotherapy devices have recently been developed to guarantee a fixed temperature for a prolonged time. Therefore, we conducted a systematic review and meta-analysis to compare continuous cryotherapy and traditional cryotherapy (ice bag or gel pack) for patients after TKA.MethodsThis study was conducted according to a predefined protocol registered on PROSPERO. Two independent reviewers performed an electronic database search of PubMed, Embase, Cochrane, Web of Science, Google Scholar, and ClinicalTrials.gov. Dichotomous outcomes were reported as risk difference (RD) with 95% confidence intervals (CIs), and continuous outcomes were reported as mean difference (MD), or standardized mean difference (SMD) with 95% CIs.ResultsSeven trials enrolling a total of 519 patients were included. There were no differences in pain intensity (MD: −0.54, 95% CI: −1.55 to 0.47; P = 0.30), analgesics consumption (MD: −0.37, 95% CI: −1.28 to 0.55; P = 0.43), postoperative range of motion (MD: 0.47, 95% CI: −4.09 to 5.03; P = 0.84), swelling of the knee joint, blood loss, change in hemoglobin, or transfusion rate. Meanwhile, there were no differences in length of hospital stay (MD: −0.77, 95% CI: −1.62 to 0.08; P = 0.07) and adverse events (RD: 0, 95% CI: −0.02 to 0.03; P = 0.74). In addition, continuous cryotherapy leads to extra costs and resources than traditional cryotherapy.ConclusionsContinuous cryotherapy does not appear to offer significant benefits for TKA when compared with traditional cryotherapy. Based on currently available evidence, traditional cryotherapy is still recommended as continuous cryotherapy is not cost-effective. Further well-designed studies with larger sample sizes are warranted to further confirm these preliminary results. PROSPERO Registration: Identifier [CRD42022308217].

Background Myeloid‐derived suppressor cells (MDSCs) can potently inhibit T-cell activity, promote growth and metastasis of tumor and contribute to resistance to immunotherapy. Targeting MDSCs to alleviate their protumor functions and immunosuppressive activities is intimately associated with cancer immunotherapy. Natural killer (NK) cells can engage in crosstalk with multiple myeloid cells to alter adaptive immune responses, triggering T-cell immunity. However, whether the NK-cell-MDSC interaction can modulate the T-cell immune response requires further study. Cryo-thermal therapy could induce the maturation of MDSCs by creating an acute inflammatory environment to elicit a CD4+ Th1-dominant immune response, but the mechanism regulating this process remains unclear.Methods NK cells were depleted and NKG2D was blocked with monoclonal antibodies in vivo. MDSCs, NK cells and T cells were assessed by flow cytometry and isolated by magnetic-activated cell sorting (MACS). MDSCs and NK cells were cocultured with T cells to determine their immunological function. The transcriptional profiles of MDSCs were measured by qRT-PCR and RNA-sequencing. Isolated NK cells and MDSCs by MACS were cocultured to study the viability and maturation of MDSCs regulated by NK cells. TIMER was used to comprehensively examine the immunological, clinical, and genomic features of tumors.Results NK-cell activation after cryo-thermal therapy decreased MDSC accumulation and reprogrammed immunosuppressive MDSCs toward a mature phenotype to promote T cell antitumor immunity. Furthermore, we discovered that NK cells could kill MDSCs via the NKG2D-NKG2DL axis and promote MDSC maturation by interferon gamma (IFN-γ) in response to NKG2D. In addition, CD4+ Th1-dominant antitumor immune response was dependent on NKG2D, which promoted the major histocompatibility complex Ⅱ pathway of MDSCs. High activated NK-cell infiltration and NKG2D level in tumors were positively correlated with better clinical outcomes.Conclusions Cryo-thermal therapy induces effective CD4+ Th1-dominant antitumor immunity by activating NK cells to reprogram MDSCs, providing a promising therapeutic strategy for cancer immunotherapy.

Targeting myeloid-derived suppressive cells (MDSCs) has been considered a potential strategy in tumor therapy. However, a single drug targeting MDSCs remains a challenge in the clinic. An increasing number of studies have shown that combination agents targeting MDSCs and immunotherapy may provide exciting new insights and avenues to explore in tumor therapy. In our previous study, a novel cryo-thermal therapy was developed for metastatic tumors that systematically activate innate and adaptive immunity. Moreover, cryo-thermal therapy was shown to dramatically decrease the levels of MDSCs and induce their differentiation toward potent antigen-presenting cells. However, the therapeutic effects of cryo-thermal therapy on the 4T1 mouse breast cancer model were still not satisfactory because of the high level of MDSCs before and after treatment. Therefore, in this study, we combined cryo-thermal therapy with all-trans retinoid acid (ATRA), a small molecule drug that can induce the inflammatory differentiation of MDSCs. We found that combination therapy notably upregulated the long-term survival rate of mice. Mechanically, combination therapy promoted the phenotype and functional maturation of MDSCs, efficiently decreasing suppressive molecule expression and inhibiting glutamine and fatty acid metabolism. Moreover, MDSCs at an early stage after combination therapy significantly decreased the proportions of Th2 and Treg subsets, which eventually resulted in Th1-dominant CD4+ T-cell differentiation, as well as enhanced cytotoxicity of CD8+ T cells and natural killer cells at the late stage. This study suggests a potential therapeutic strategy for combination ATRA treatment targeting MDSCs with cryo-thermal therapy to overcome the resistance of MDSC-induced immunosuppression in the clinic.

Recent studies suggest that highly activated, polyfunctional CD4+ T cells are incredibly effective in strengthening and sustaining overall host antitumor immunity, promoting tumor-specific CD4+ T-cell responses and effectively enhancing antitumor immunity by immunotherapy. Previously, we developed a novel cryo-thermal therapy for local tumor ablation and achieved long-term survival rates in several tumor models. It was discovered that cryo-thermal therapy remodeled the tumor microenvironment and induced an antigen-specific CD4+ T-cell response, which mediated stronger antitumor immunity in vivo. In this study, the phenotype of bulk T cells in spleen was analyzed by flow cytometry after cryo-thermal therapy and both CD4+ Th1 and CD8+ CTL were activated. In addition, by using T-cell depletion, isolation, and adoptive T-cell therapy, it was found that cryo-thermal therapy induced Th1-dominant CD4+ T cells that directly inhibited the growth of tumor cells, promoted the maturation of MDSCs via CD4+ T-cell-derived IFN-γ and enhanced the cytotoxic effector function of NK cells and CD8+ T cells, and promoted the maturation of APCs via cell-cell contact and CD4+ T-cell-derived IFN-γ. Considering the multiple roles of cryo-thermal-induced Th1-dominant CD4+ T cells in augmenting antitumor immune memory, we suggest that local cryo-thermal therapy is an attractive thermo-immunotherapy strategy to harness host antitumor immunity and has great potential for clinical application.

Abstract Background Postoperative residual knee pain after total knee arthroplasty (TKA) is a significant factor that contributes to patient dissatisfaction. Patients with preoperative central sensitization (CS) may be more susceptible to unexplained chronic pain after TKA, and duloxetine has been reported to be effective in post-TKA pain control in patients with CS. However, there remains limited evidence to support this off-label use in routine clinical practice. Hence, we designed this randomized, placebo-controlled, triple-blind clinical trial to evaluate the effects of preoperative screening and targeted duloxetine treatment of CS on postoperative residual pain compared with the care-as-usual control group. Methods This randomized controlled trial includes patients with knee osteoarthritis on a waiting list for primary unilateral TKA. Patients with preoperative CS will be randomly allocated to the perioperative duloxetine treatment group (duloxetine group) or the care-as-usual control group (placebo group). Patients in the duloxetine group will receive a half-dose of preemptive duloxetine (30 mg/day) for a week before surgery and a full-dose of duloxetine (60 mg/day) for six weeks after surgery. The primary outcome is the intensity of residual pain at six months after TKA, including the visual analogue scale, 11-point numeric rating scale, the sensory dimension of the brief pain inventory, and the pain subscale of the Knee injury and Osteoarthritis Outcome Score. The secondary outcome measures will include the pain and function related outcomes. All of the patients will be followed up at one, three, and six months after surgery. All adverse events will be recorded and immediately reported to the primary investigator and ethics committee to decide if the patient needs to drop out from the trial. Discussion This clinical trial will convey the latest evidence of the efficacy and safety of the application of duloxetine in postoperative pain control in CS patients who are scheduled for TKA. The study results will be disseminated at national and international conferences and published in peer-reviewed journals. Trial registration Chinese Clinical Trial Registry ( http://www.chictr.org.cn ) registration number: ChiCTR2000031674 . Registered 07 April 2020.

In this paper, we explore the problem of routing, modulation and spectrum assignment (RMSA) for advance reservation (AR) requests in elastic optical networks (EONs), with the objective to further reduce the blocking probability and improve the spectrum utilization. On the one hand, we propose a minimum resource consumption routing policy (MRC) for leaving more free resources for future requests. On the other hand, we introduce a two-dimensional (2D) path resource model, in which each AR request can be transformed into a rectangle block whose length and width are related to request bandwidth and request duration respectively. Therefore, the spectrum assignment problem of AR requests is simplified to the two-dimensional rectangle packing problem. Furthermore, we design several factors to evaluate the two-dimensional fragmentation caused by a rectangle packing operation, where we consider the fragmentation situation in the rectangle block neighborhood for the first time. Based on these factors, we propose a two-dimensional fragmentation-aware spectrum assignment strategy (2D-FA). Through the combination of MRC and 2D-FA, we propose a novel RMSA algorithm named MRC-2D-FA for dynamically provisioning AR requests in EONs. Simulation results show that MRC-2D-FA can achieve lower bandwidth blocking probability and higher spectrum utilization compared with several well-performed benchmark algorithms.

Internet applications have different needs for network bandwidth,latency,jitter,and packet loss.However,the fact that the network and applications are decoupled makes it difficult for the network to be aware of an application’s requirements and ensure its quality of service (e.g.,service level agreement,SLA).A framework called application-aware IPv6 networking (APN6) was proposed,which could encapsulate applications’ requirements into packets.Networks could be aware of an application’s requirements and perform traffic steering and network resource adjustment.Besides,a secure access control mechanism was designed to solve application information forgery and tampering issues in APN6,which ensured that APN6 only provided services to legal users.

We consider the scenario where two pursuers are required to intercept an enemy evader that launches a decoy. Under the assumption that all four aircrafts have first-order linear dynamic characteristics, a cooperative guidance law is designed in two stages: the unidentified decoy stage and the discriminated decoy stage. In the unidentified decoy stage, the predictive guidance law is designed based on the method of the highest probability interval (HPI). This guidance law can maximize the probability of intercepting the real target despite the decoy's presence, thus providing an interception maneuver advantage for later cooperative guidance to the real target. In the discriminated decoy stage, based on the optimal control theory, an optimal guidance law considering the detection configuration is designed, which reduces the estimation error in the detection process and improves the interception performance. Simulation results verify the feasibility of the phased cooperative guidance law.

PurposeThe purpose of this study is to reveal the clinicopathological features and identify risk factors of prognosis among patients with pancreatic cancer bone metastasis (PCBM).Patients and MethodsPatients with PCBM were retrieved from the Surveillance, Epidemiology, and End Results (SEER) database between 2010 and 2016. Independent predictors for survival of those patients were determined by the univariate and multivariate Cox regression analysis. Forest plots were drawn by GraphPad 8.0.1 and used to visually display the results of multivariate analysis.ResultsWe identified 2072 eligible PCBM patients, of which 839 patients (40.5%) were female. Patients with age >60 years accounted for 70.6%. Multivariable Cox regression analysis indicated that age, pathological type, chemotherapy, liver metastasis, lung metastasis, and marital status were independent prognostic factors for both overall survival (OS) and cancer-specific survival (CSS). Kaplan–Meier survival curves showed that for patients with PCBM, age ≤60 years, non-ductal adenocarcinoma type, chemotherapy, no liver metastasis, no lung metastasis, and married status were correlated with increased survival. This population-based study showed that 1-year OS and CSS were 13.6% and 13.7%, respectively.ConclusionThe present study identified six independent predictors of prognosis in PCBM, including age, pathological type, chemotherapy, liver metastasis, lung metastasis, and marital status. Knowledge of these survival predictors is helpful for clinicians to accelerate clinical decision process and design personalized treatment for patients with PCBM.

PurposeGastrointestinal neuroendocrine carcinoma (NEC) with bone metastasis is rarely reported. The purpose of this study is to explore the prognosis and risk factors of such patients.Patients and MethodsWe retrospectively reviewed patients diagnosed as gastrointestinal NECs with bone metastasis at diagnosis from 2010 to 2016 by using the Surveillance, Epidemiology and End Results (SEER) database. Predictors of overall survival (OS) and cancer-specific survival (CSS) were analyzed by univariable and multivariable Cox analyses. Kaplan–Meier plots were constructed to show the correlation between independent predictors and survival.ResultsA total of 330 gastrointestinal NEC patients with bone metastasis at diagnosis were included for analysis. Over half of patients were male and older than 60 years old. The most common primary site of gastrointestinal NEC with bone metastasis was the pancreas. The prognosis of gastrointestinal NEC with bone metastasis (3-year OS and CSS rates: 16.7 and 17.0%) was very poor. On Cox multivariable analysis, age over 60 years old, no surgery, and lung metastasis were independent predictors of decreased OS and CSS.ConclusionsWe identified three independent factors associated with prognosis among gastrointestinal NEC patients with bone metastasis, namely age, surgery, and lung metastasis. For younger gastrointestinal NEC patients with bone metastasis, surgical resection of primary tumors as well as actively treating lung metastasis might be useful for prolonging survival.

This paper discusses the finite-time output stability (FTOS) for the impulse switching linear system (SLS) when the norm-bounded state constraint is simultaneously considered during a scheduled finite-time period. This system is a typical hybrid system whose state trajectory instantaneously jumps according to a predetermined resetting law. The sufficient conditions of both state boundedness and output stability are proposed for the SLS in finite-time period when two typical classes of input signals are involved. Based on the linear matrix inequality, the design method of a controller using state feedback is represented to ensure state boundedness and output stability concurrently for the closed-loop system, which can effectively avoid states and the output reaching large and unacceptable values at certain points in finite time. An effective solution to the controller using a linear discretization approach is also achieved. The two simulation examples verify the proposed methods.

Hepatocellular carcinoma (HCC) is highly heterogeneous in nature and has been one of the most common cancer types worldwide. To ensure repeatability of identified gene expression patterns and comprehensively annotate the transcriptomes of HCC, we carefully curated 15 public HCC expression datasets that cover around 4000 clinical samples and developed the database HCCDB to serve as a one-stop online resource for exploring HCC gene expression with user-friendly interfaces. The global differential gene expression landscape of HCC was established by analyzing the consistently differentially expressed genes across multiple datasets. Moreover, a 4D metric was proposed to fully characterize the expression pattern of each gene by integrating data from The Cancer Genome Atlas (TCGA) and Genotype-Tissue Expression (GTEx). To facilitate a comprehensive understanding of gene expression patterns in HCC, HCCDB also provides links to third-party databases on drug, proteomics, and literatures, and graphically displays the results from computational analyses, including differential expression analysis, tissue-specific and tumor-specific expression analysis, survival analysis, and co-expression analysis. HCCDB is freely accessible at http://lifeome.net/database/hccdb. Keywords: Hepatocellular carcinoma, Database, Transcriptome, Integrative analysis, Meta-analysis

The fouling behavior of a steam generator (SG) tube was investigated at different positions after 500 h of immersion in high-temperature water. A triple-layer structure of fouling appeared at both the crevice position and the free span position, namely, the large, dispersedly distributed deposition layer on the top; the small and faceted outer layer; and the relatively continuous inner layer. There was no obvious positional effect on the thickness of the inner layer. However, in the crevice position, the density of the deposited particle and the thickness of the outer layer was much higher than those of the free span position. The tube support plate (TSP) made of 410 stainless steel contributed significantly to the fouling behavior of the SG tube in the crevice between the SG tube and the TSP.

We address the problem of aerial image geolocalization over an area as large as a whole city through road network matching, which is modeled as a 2D point set registration problem under the 2D projective transformation and solved in a two-stage manner. In the first stage, all the potential transformations aligning the query road point set to the reference road point set are found by local point feature matching. A local geometric feature, called the Projective-Invariant Contour Feature (PICF), which consists of a road intersection and the closest points to it in each direction, is specifically designed. We prove that the proposed PICF is equivariant under the 2D projective transformation group. We then encode the PICF with a projective-invariant descriptor to enable the fast search of potential correspondences. The bad correspondences are then removed by a geometric consistency check with the graph-cut algorithm effectively. In the second stage, a flexible strategy is developed to recover the homography transformation with all the PICF correspondences with the Random Sample Consensus (RANSAC) method or to recover the transformation with only one correspondence and then refine it with the local-to-global Iterative Closest Point (ICP) algorithm when only a few correspondences exist. The strategy makes our method efficient to deal with both scenes where roads are sparse and scenes where roads are dense. The refined transformations are then verified with alignment accuracy to determine whether they are accepted as correct. Experimental results show that our method runs faster and greatly improves the recall compared with the state-of-the-art methods.

The real-time, accurate, and refined monitoring of urban green space status information is of great significance in the construction of urban ecological environment and the improvement of urban ecological benefits. The high-resolution technology can provide abundant information of ground objects, which makes the information of urban green surface more complicated. The existing classification methods are challenging to meet the classification accuracy and automation requirements of high-resolution images. This paper proposed a deep learning classification method for urban green space based on phenological features constraints in order to make full use of the spectral and spatial information of green space provided by high-resolution remote sensing images (GaoFen-2) in different periods. The vegetation phenological features were added as auxiliary bands to the deep learning network for training and classification. We used the HRNet (High-Resolution Network) as our model and introduced the Focal Tversky Loss function to solve the sample imbalance problem. The experimental results show that the introduction of phenological features into HRNet model training can effectively improve urban green space classification accuracy by solving the problem of misclassification of evergreen and deciduous trees. The improvement rate of F1-Score of deciduous trees, evergreen trees, and grassland were 0.48%, 4.77%, and 3.93%, respectively, which proved that the combination of vegetation phenology and high-resolution remote sensing image can improve the results of deep learning urban green space classification.

When threatened by a pursuer, an evading aircraft launches two defenders to accomplish a cooperative evasion, constituting a four-aircraft interception engagement. Under the assumption that the pursuing aircraft adopts the augmented proportional guidance law and first-order dynamics, a cooperative intercept mathematical model with an intercept angle constraint is established, allowing for the cooperative maneuvering of the evader. Based on the differential linear matrix inequality (DLMI), a controller design method of input and output finite time stability (IO-FTS) is proposed and applied to the aircraft’s cooperative intercept scenario. A cooperation performance analysis is carried out for two cases: (1) two defenders intercept the pursuer with various intercept angle constraints, and (2) the evader acts in a lure role to cooperate with two defenders. The simulation results indicate that the proposed method of controller design has the ability to guarantee that the two defenders intercept the pursuer at the preassigned intercept angles. The cooperative intercept scenario with a lure role is shown to be a very effective method for reducing the maximum required acceleration for defenders, which confirms the availability and advantage of cooperation. The strong adaptability and robustness of the cooperative guidance law with respect to various initial launch conditions is also verified.

Sparse representation has been widely researched for image-based classification. However, sparse representation classification directly treats training samples as a dictionary, so it needs a large training set and is time consuming, especially for a large training set. To derive a small dictionary, many dictionary learning algorithms are researched. Thus, object recognition problem is transformed to optimize the sparse representation errors on the compact dictionary. The sparse representation optimization is constraint by l 0 -norm, which is NP-hard problem. Though we can use l 1 -norm minimization instead to work effectively, it is still time consuming for optimization. To make the algorithm discriminative and simultaneously decrease the computational burden, we proposed a fast discriminative collaborative representation–based classification algorithm. The new algorithm incorporated the within-class scatter and the linear classification error terms into the objective function to derive a more discriminative dictionary and simultaneously added collaborative representation mechanism to cut off the time consuming. At the end of this article, we designed two experiments to validate our method using near-infrared and AR visible databases for multimodal face recognition. The results showed that our algorithm outperformance sparse representation–based, collaborative representation–based, and discriminative-KSVD classification algorithms.

The technology used for road extraction from remote sensing images plays an important role in urban planning, traffic management, navigation, and other geographic applications. Although deep learning methods have greatly enhanced the development of road extractions in recent years, this technology is still in its infancy. Because the characteristics of road targets are complex, the accuracy of road extractions is still limited. In addition, the ambiguous prediction of semantic segmentation methods also makes the road extraction result blurry. In this study, we improved the performance of the road extraction network by integrating atrous spatial pyramid pooling (ASPP) with an Encoder-Decoder network. The proposed approach takes advantage of ASPP’s ability to extract multiscale features and the Encoder-Decoder network’s ability to extract detailed features. Therefore, it can achieve accurate and detailed road extraction results. For the first time, we utilized the structural similarity (SSIM) as a loss function for road extraction. Therefore, the ambiguous predictions in the extraction results can be removed, and the image quality of the extracted roads can be improved. The experimental results using the Massachusetts Road dataset show that our method achieves an F1-score of 83.5% and an SSIM of 0.893. Compared with the normal U-net, our method improves the F1-score by 2.6% and the SSIM by 0.18. Therefore, it is demonstrated that the proposed approach can extract roads from remote sensing images more effectively and clearly than the other compared methods.

In this paper, a two-dimensional steady model is established to investigate the gas-solid heat transfer in a sinter vertical tank based on the porous media theory and the local thermal non-equilibrium model. The influences of the air flow rate, sinter flow rate, and sinter particle diameter on the gas-solid heat transfer process are investigated numerically. In addition, exergy destruction minimization is used as a new principle for heat transfer enhancement. Furthermore, a multi-objective genetic algorithm based on a Back Propagation (BP) neural network is applied to obtain a combination of each parameter for a more comprehensive performance, with the exergy destruction caused by heat transfer and the one caused by fluid flow as the two objectives. The results show that the heat dissipation and power consumption both gradually increase with an increase of the air mass flow rate. Additionally, the increase of the sinter flow rate results in a decrease of the heat dissipation and an increase of the power consumption. In addition, both heat dissipation and power consumption gradually decrease with an increase of the sinter particle diameter. For the given structure of the vertical tank, the optimal operating parameters are 2.99 kg/s, 0.61 kg/s, and 32.8 mm for the air flow rate, sinter flow rate, and sinter diameter, respectively.

Packed beds are widely used in industries and it is of great significance to enhance the heat transfer between gas and solid states inside the bed. In this paper, numerical simulation method is adopted to investigate the heat transfer principle in the bed at particle scale, and to develop the direct enhanced heat transfer methods in packed beds. The gas is treated as continuous phase and solved by Computational Fluid Dynamics (CFD), while the particles are treated as discrete phase and solved by the Discrete Element Method (DEM); taking entransy dissipation to evaluate the heat transfer process. Considering the overall performance and entransy dissipation, the results show that, compared with the uniform particle size distribution, radial distribution of multiparticle size can effectively improve the heat transfer performance because it optimizes the velocity and temperature field, reduces the equivalent thermal resistance of convection heat transfer process, and the temperature of outlet gas increases significantly, which indicates the heat quality of the gas has been greatly improved. The increase in distribution thickness obviously enhances heat transfer performance without reducing the equivalent thermal resistance in the bed. The result is of great importance for guiding practical engineering applications.

We study the distributed leader-following attitude consensus problem for multiple rigid spacecraft with a single leader under jointly connected switching topologies. Two cases are considered, where the first case is with a static leader and the second case is with a dynamic leader. By constructing an auxiliary vector and a distributed observer for each follower spacecraft, the controllers are designed to drive all the attitudes of the follower spacecraft to the leader’s, respectively, for both of the two cases, though there are some time intervals in which the communication topology is not connected. The whole system is proved to be stable by using common Lyapunov function method. Finally, the theoretical result is illustrated by numerical simulations.

Hyperspectral unmixing (HU) is one of the most active hyperspectral image (HSI) processing research fields, which aims to identify the materials and their corresponding proportions in each HSI pixel. The extensions of the nonnegative matrix factorization (NMF) have been proved effective for HU, which usually uses the sparsity of abundances and the correlation between the pixels to alleviate the non-convex problem. However, the commonly used L 1 / 2 sparse constraint will introduce an additional local minima because of the non-convexity, and the correlation between the pixels is not fully utilized because of the separation of the spatial and structural information. To overcome these limitations, a novel bilateral filter regularized L 2 sparse NMF is proposed for HU. Firstly, the L 2 -norm is utilized in order to improve the sparsity of the abundance matrix. Secondly, a bilateral filter regularizer is adopted so as to explore both the spatial information and the manifold structure of the abundance maps. In addition, NeNMF is used to solve the object function in order to improve the convergence rate. The results of the simulated and real data experiments have demonstrated the advantage of the proposed method.

Scene-level geographic image classification has been a very challenging problem and has become a research focus in recent years. This paper develops a supervised collaborative kernel coding method based on a covariance descriptor (covd) for scene-level geographic image classification. First, covd is introduced in the feature extraction process and, then, is transformed to a Euclidean feature by a supervised collaborative kernel coding model. Furthermore, we develop an iterative optimization framework to solve this model. Comprehensive evaluations on public high-resolution aerial image dataset and comparisons with state-of-the-art methods show the superiority and effectiveness of our approach.