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Super-gravity field was reported to strongly intensify the buoyancy-driven flow of liquid during solidification, affecting the solute convection and morphology of eutectic structure in binary alloys. In ternary eutectic alloys, certain two-phase regions with a coarse structure can usually be detected concurrently along with three-phase eutectics, leading to the formation of heterogeneous structure with limited ductility. Currently, Al–Ce–Ni ternary eutectic alloy was chosen as a modal system, which was reported for the first time to be solidified under super-gravity field with high volume fraction of three-phase eutectics. Meanwhile, the formation of ultrafine-fibrous interconnected Al11Ce3–Al3Ni eutectic clusters with more “chains” and higher spatial connectivity was detected mainly due to the relatively higher nucleation rate. The high volume fraction and refinement of interconnected Al11Ce3–Al3Ni eutectic clusters with excellent thermal stability could contribute to enhancement of ductility and tensile strength at elevated-temperature. Interestingly, the strength retention at 300 °C and 400 °C were able to reach up to ∼68.1 % and ∼33.2 %, respectively.

Abstract Epithelial-mesenchymal transition (EMT) is a common process during tumor progression and is always related to residual tumor, drug resistance and immune suppression. However, considering the heterogeneity in EMT process, there is still a need to establish robust EMT classification system with reasonable molecular, biological and clinical implications to investigate whether these unfavorable survival factors are common or unique in different individuals. In our work, we classify tumors with four EMT status, that is, EMTlow, EMTmid, EMThigh-NOS (Not Otherwise Specified), and EMThigh-AKT (AKT pathway overactivation) subtypes. We find that EMThigh-NOS subtype is driven by intrinsic somatic alterations. While, EMThigh-AKT subtype is maintained by extrinsic cellular interplay between tumor cells and macrophages in an AKT-dependent manner. EMThigh-AKT subtype is both unresectable and drug resistant while EMThigh-NOS subtype can be treated with cell cycle related drugs. Importantly, AKT activation in EMThigh-AKT not only enhances EMT process, but also contributes to the immunosuppressive microenvironment. By remodeling tumor immune-microenvironment by AKT inhibition, EMThigh-AKT can be treated by immune checkpoint blockade therapies. Meanwhile, we develop TumorMT website ( http://tumormt.neuroscience.org.cn/ ) to apply this EMT classification and provide reasonable therapeutic guidance.

We reported a novel method to prepare Al–Si/SiC composites infiltrated under super-gravity field without Mg addition. Noted that centrifugal force acted as the main driving force to overcome the penetration resistance, making it feasible to obtain the Al–Si/SiC composite with high relative density (≥99.3 %). The interfacial reaction between Al and SiC was retarded by Si addition under super-gravity field. Interestingly, the rod-like eutectic Si was detected at Al–SiC interface with semi-continuous distribution, leading to the excellent bending strength (342.6 ± 1.0 MPa), enhanced thermal conductivity (173 W/m K at 373 K), and relatively low coefficient of thermal expansion (8.90 × 10−6 K−1 ranged from 323 to 373 K). The interfacial regulation and fracture mechanism were discussed in details.

This paper aimed to explore the biochemical characteristics of fresh leaf raw materials of large-leaf cultivars. In this study, four different tenderness fresh tea leaves of single bud, one bud with one leaf, one bud with two leaves and one bud with three leaves picked from Yunnan large-leaf cultivars (Qingshui No.3, Xianggui Yinhao, Yunkang No.10, Yunkang No.14, Yuncha Purui) were selected as the research objects. The variation of raw material taste quality with tenderness of different varieties of Yunnan large-leaf cultivars was investigated by detecting the content of its main flavor substances (tea polyphenols, flavonoids, amino acids and soluble sugars, etc.). Multivariate statistical methods were used to explore the categories and key differential indexes of fresh leaves with different tenderness, meanwhile, fisher discriminant analysis was used to establish the discriminant models for different types of fresh leaves. The results showed that the higher the maturity of fresh leaves, the higher values of phenol ammonia ratio, simple catechins, flavonoid glycosides and peroxidase would be, and one bud with two leaves and one bud with three leaves were significantly higher than single bud and one bud with one leaf (P

In the context of cutting grass in orchards, the practice of leaving cut weeds in the orchard rows hinders the decomposition of the weeds and the absorption of nutrients by the fruit trees. To address this issue, a grass-cutting machine with an integrated sweeping disc was designed to remove weeds from orchard rows and sweep them to the roots of the trees to promote their absorption of nutrients. A coupled simulation platform was established using multi-body dynamics ADAMS and the discrete element method EDEM. The weed-shedding and sweeping device was dynamically analyzed through an ADAMS–EDEM collaborative simulation that enabled the use of a second-order regression orthogonal rotation experiment and response surface methodology. The optimal parameters for the cutting tools, cutter shaft speed, and the number of cutting tools included 23 cutting tools arranged in a single helical pattern for the cutting device, a cutter shaft speed of 728 rpm, and claw-shaped blades as the cutting tools. A prototype machine was built based on the optimized parameters and tested in the field. The results indicated that, when there were 250 m² of weeds, the cutting rate reached 92.96%. The machine was highly maneuverable, and the average remaining weed height in the orchard was 110 mm, which met the national standards and local agricultural requirements. The new orchard grass-cutting and sweeping device meets the technical demands of orchard grass operations in the Xinjiang region of China.

In action recognition, obtaining skeleton data from human poses is valuable. This process can help eliminate negative effects of environmental noise, including changes in background and lighting conditions. Although GCN can learn unique action features, it fails to fully utilize the prior knowledge of human body structure and the coordination relations between limbs. To address these issues, this paper proposes a Multi-level Topological Channel Attention Network algorithm: Firstly, the Multi-level Topology and Channel Attention Module incorporates prior knowledge of human body structure using a coarse-to-fine approach, effectively extracting action features. Secondly, the Coordination Module utilizes contralateral and ipsilateral coordinated movements in human kinematics. Lastly, the Multi-scale Global Spatio-temporal Attention Module captures spatiotemporal features of different granularities and incorporates a causal convolution block and masked temporal attention to prevent non-causal relationships. This method achieved accuracy rates of 91.9% (Xsub), 96.3% (Xview), 88.5% (Xsub), and 90.3% (Xset) on NTU-RGB+D 60 and NTU-RGB+D 120, respectively.

The Cancer-related Psychological Flexibility Questionnaire (CPFQ) was developed and validated for assessing cancer patients’ psychological flexibility, including attitudes and behavior toward cancer. In a systematic process, the CPFQ identified four factors through principal component analysis and confirmatory factor analysis: Cancer Acceptance, Cancer Avoidance, Activity Engagement, and Valued Action. The results of this study reveal that the CPFQ has a clear factor structure and good psychometric properties. The specific nature of cancer and the need for a specific measure of cancer patient psychological flexibility make this questionnaire valuable for research on psychological flexibility in cancer patients.

Collaboration between multiple super-agile satellites with dynamic imaging capabilities provides an opportunity for highly efficient observation of large-area targets. Reasonable allocation of multi-satellite resources and determination of the action sequences of each satellite are two key requirements for multiple super-agile satellite mission planning. We propose a multi-satellite collaborative mission planning approach for an area target imaging mission, considering the dynamic imaging characteristics, achieving integrated optimization of the allocation of multi-satellite resources and determination of the action sequences of each satellite. A multi-satellite mission planning model with two objectives was established, with the objective functions being to maximize the observation coverage revenue and minimize the task performance time. An improved particle swarm optimization algorithm, represented by introducing particle learning/selection strategies and variable type-adaptive position updating methods, addresses the discrete–continuous hybrid variables in this optimization model. The proposed method was verified by constructing three groups of comparative experiments with area targets of different sizes. The proposed method showed consistent adaptability to various complexities and achieved synchronous optimization of the allocation of multi-satellite resources and determination of the action sequence of each satellite in areas of different sizes. Compared with existing algorithms, the proposed algorithm obtained superior imaging mission schemes with lower computational consumption, higher observation coverage revenues, or shorter task performance time. The experimental results showed that the proposed multi-satellite collaborative mission planning approach could be used to the ground operational systems of super-agile satellite constellations.

It is easy to cause increases in temperature and the gasification of water in materials, facilitated via supercharging and the generation of instantaneous strong pressure under the collaborative action of a microwave and a vacuum, thus facilitating the internal cell swelling of materials, changes in fibre structures, and the formation of loose and uniform microstructures. In this experiment, mulberries were dehydrated using microwave–vacuum drying technology. The drying characteristics were disclosed by using crispness as the evaluation index and multiple drying parameters (e.g., products’ surface temperature, microwave power, chamber vacuum level and drying height) as the control variables. The optimised Two-term model can predict the dehydration process of mulberries under multiple drying variables, as determined through the experimental data. The optimal drying variables were determined according to the crispness of the dried mulberries. The optimal puffing quality of mulberries could be gained under a product surface temperature = 50 °C, microwave power = 5.45 W/g, a chamber vacuum level = 0.08 MPa and a drying height = 0 cm. The diffusion coefficient of the available water of the mulberries during the microwave–vacuum drying process ranges from 4.98 × 10−8 to 3.81 × 10−7, and the activation energy for drying is 183.923 KJ/mol.

A thin-film transistors (TFTs) integrated gate driver which can work well at low temperature down to $- 40\,\,^{\circ} \text{C}$ is proposed and demonstrated. The carry signal (CN) of the driver, being generated through the voltage bootstrapping approach using a CN-connected capacitor, is used to pre-charge the following stage of the driver. As the rising and falling time of CN is much shorter than that of the gate driving signal GN, the bootstrapping voltage is increased and voltage loss of the pre-charge transistor can be much reduced, to avoid the driver’s malfunction at low temperature. This structure further benefits maintaining the driving speed over long operation time at high temperature. On the other hand, the GN, instead of CN, is used to reset the gate driver to suppress the voltage feed-through effects. One single stage of the driver consists of 11 TFTs and 2 capacitors, driven by 4 clock signals with the duty ratio of 25%. An a-Si:H TFTs implemented single stage circuit of the driver occupies an area of 250 $\mu \text{m}\times 1099\,\,\mu \text{m}$ . Measurements show that the output voltage magnitude can be maintained well when temperature varies from $- 40\,\,^{\circ} \text{C}$ to 80 °C. Moreover, the rising-time and falling-time increase of the output pulse are both less than $3~\mu \text{s}$ after 240 hours of the accelerated high temperature aging operations.

The discrete state transition algorithm (DSTA) has been wildly applied to deal with combinatorial optimization problems. However, its low convergence accuracy limits its application in large-scale optimization problems. Aiming at the convergence performance and search intensity of the algorithm, a hybrid discrete state transition algorithm (HDSTA) is proposed in this work by introducing tabu search and elite solution set. Firstly, a searching mechanism with the integration of DSTA and tabu search (TS) is established, which allows moving to adjacent solutions at an increased cost to escape from the local optimum. Specifically, a tabu list as adaptive memory is adopted to avoid the loop when deviating from local optima. Secondly, an elite solution set is introduced to integrate the information of the previous optimal solution and the global optimal solution, and the search strategy is modified to expand the range and diversity of candidate solutions. Finally, the proposed HDSTA is verified according to the real data on two well-known optimization problems (staff assignment problem and traveling salesman problem) and the real data of an industrial case. The experimental results show the effectiveness of the proposed algorithm in large-scale optimization problems.

In modern industrial warehouses, heterogeneous and flexible fleets of automated guided vehicles (AGVs) are widely used to improve transport efficiency. However, as their scale and limit of battery capacity increase, the complexity of dynamic scheduling also increases dramatically. The problem is to assign tasks and determine detailed paths to AGVs to keep the multi-AGV system running efficiently and sustainedly. In this context, a mixed-integer linear programming (MILP) model is formulated. A hierarchical planning method is used, which decomposes the integrated problem into two levels: the upper-level task-assignment problem and the lower-level path-planning problem. A hybrid discrete state transition algorithm (HDSTA) based on an elite solution set and the Tabu List method is proposed to solve the dynamic scheduling problem to minimize the sum of the costs of requests and the tardiness costs of conflicts for the overall system. The efficacy of our method is investigated by computational experiments using real-world data.

The tumor microenvironment (TME) plays a critical role in promoting the growth and metastasis of glioblastoma (GBM). Tumor-associated macrophages (TAMs), the most abundant myeloid cells infiltrating in TME, produce proinflammatory cytokines, regulate glioma cell pools, and lead to GBM progression. Understanding the mechanism of GBM-TAMs regulation can help to find new targeted therapeutic strategies against GBM. Based on the CGGA and TCGA GBM cohorts, ARPC1B was defined as the key macrophage-associated gene with prognostic value. Higher ARPC1B expression was associated with progressive malignancy, poor outcomes and TAM infiltration. We demonstrated that macrophage-expressed ARPC1B promoted the migration, invasion, and epithelial–mesenchymal transition of glioma cells. Glioma-intrinsic ARPC1B also maintained the malignant phenotype and promoted macrophage recruitment. Positive feedback signaling between macrophages and glioma cells via ARPC1B was determined to be under control of the IFNγ-IRF2-ARPC1B axis. This study highlights the important role of ARPC1B in GBM malignancy progression and the regulation network between GBM and TAMs, suggesting ARPC1B as a novel biomarker with potential therapeutic implications.

Summary: Glioblastoma (GBM) is identified to share common signal pathways between glioma and immune cells. Here, we find that T cell immunoglobulin domain and mucin domain protein 3 (TIM-3) is one of the most common co-inhibitory immune checkpoints in GBM shared by tumor and non-tumor cells. Glioma cell-intrinsic TIM-3 is involved in not only regulating malignant behaviors of glioma cells but also inducing macrophage migration and transition to anti-inflammatory/pro-tumorigenic phenotype by a TIM-3/interleukin 6 (IL6) signal. In mechanism, as one of the major regulators of IL6, TIM-3 regulates its expression through activating NF-κB. Blocking this feedback loop by Tocilizumab, an IL6R inhibitor, inhibited the above effects and repressed the tumorigenicity of GBM in vivo. Our work identifies glioma cell-intrinsic functions of TIM-3/IL6 signal mediating the crosstalk feedback loop between glioma cells and tumor-associated macrophages (TAMs). Blocking this feedback loop may provide a novel therapeutic strategy for GBM.

A thin-film transistor (TFT) integrated gate driver is proposed for compact in-cell touch display, where one capacitor is reused for both turning on low-level maintaining transistors via voltage coupling and maintaining the re-start charge during the touch sensing period. The gate terminal of buffer transistor is thus free of electrical stress during touch sensing period, and can be re-charged for the subsequent re-start period, leading to minimal variations in the transient response. With the capacitor reusing scheme and a capacitor stacking structure, the area of the single stage gate driver is decreased to $645\,\,\mu \text{m}\,\,\times 210\,\,\mu \text{m}$ . The fabricated a-Si TFT gate driver with 8 cascaded stages shows that the rising and falling time variations among re-start and normal gate driver stages are less than $1~\mu \text{s}$ . Versatile touch sensing modes and bidirectional scanning functions are verified.

Vehicular social networks are emerging hybrid networks that combine traditional vehicular networks and social networks, with two key types of nodes, that is, vehicles and drivers. Since vehicle behaviors are controlled or influenced by drivers, the trustworthiness of a vehicle node is essentially determined by its own communication behaviors and its driver’s social characteristics. Therefore, human factors should be considered in securing the communication in vehicular social networks. In this article, we propose a hybrid trust model that considers both communication trust and social trust. Within the proposed scheme, we first construct a communication trust model to quantify the trust value based on the interactions between vehicle nodes, and then develop a social trust model to measure the social trust based on the social characteristics of vehicle drivers. Based on these two trust models, we compute the combined trust assessment of a vehicle node in vehicular social networks. Extensive simulations show that the proposed hybrid trust model improves the accuracy in evaluating the trustworthiness of vehicle nodes and the efficiency of communication in vehicular social networks.

Objective: Nectin and nectin-like molecules (Necls) are molecules that are involved in cell–cell adhesion and other vital cellular processes. This study aimed to determine the expression and prognostic value of nectin and Necls in low grade glioma (LGG).Materials and Methods: Differentially expressed nectin and Necls in LGG samples and the relationship of nectin family and Necls expression with prognosis, clinicopathological parameters, and survival were explored using The Cancer Genome Atlas (TCGA), the Chinese Glioma Genome Atlas (CGGA), and Repository of Molecular Brain Neoplasia Data (REMBRANDT) databases. Univariate and multivariate Cox analysis models were performed to construct the prognosis-related gene signature. Kaplan-Meier curves and time-dependent receiver operating characteristic (ROC) curves and multivariate Cox regression analysis, were utilized to evaluate the prognostic capacity of the four-gene signature. Gene ontology (GO)enrichment analysis and Gene Set Enrichment Analyses (GSEA) were performed to further understand the underlying molecular mechanisms. The Tumor Immune Estimation Resource (TIMER) was used to explore the relationship between the four-gene signature and tumor immune infiltration.Results: Several nectin and Necls were differentially expressed in LGG. Kaplan–Meier survival analyses and Univariate Cox regression showed patients with high expression of NECTIN2 and PVR and low expression of CADM2 and NECTIN1 had worse prognosis among TCGA, CGGA, and REMBRANDT database. Then, a novel four-gene signature was built for LGG prognosis prediction. ROC curves, KM survival analyses, and multivariate COX regression indicated the new signature was an independent prognostic indicator for overall survival. Finally, GSEA and GO enrichment analyses revealed that immune-related pathways participate in the molecular mechanisms. The risk score had a strong negative correlation with tumor purity and data of TIMER showed different immune cell proportions (macrophage and myeloid dendritic cell) between high- and low-risk groups. Additionally, signature scores were positively related to multiple immune-related biomarkers (IL 2, IL8 and IFNγ).Conclusion: Our results offer an extensive analysis of nectin and Necls levels and a four-gene model for prognostic prediction in LGG, providing insights for further investigation of CADM2, NECTIN1/2, and PVR as potential clinical and immune targets in LGG.

Background Despite the rise in the use of immune checkpoint blockade drugs (ICBs) in recent years, there are no ICB drugs that are currently approved or under large-scale clinical trials for glioblastoma (GBM). T-cells, which mainly mediate adaptive immunity, are an important part of the tumor immune microenvironment. The activation of T-cells in tumors plays a key role in evaluating the sensitivity of patients to immunotherapy. Therefore, we applied bioinformatics approaches to construct a T-cell activation related risk score to study the effect of the activation of T-cells on the prognosis and ICB response of patients with GBM. Materials and Methods This study collected TCGA, CGGA, and GSE16011 glioma cohorts, as well as the IMvigor210 immunotherapy dataset, with complete mRNA expression profiles and clinical information. GraphPad Prism 8 and R 3.6.3 were used for bioinformatics analysis and plotting. Results The activation of T-cells in patients with GBM is characterized by obvious heterogeneity. We established a T-cell activation-related risk score based on five univariate Cox regression prognostic genes (CD276, IL15, SLC11A1, TNFSF4, and TREML2) in GBM. The risk score was an independent risk factor for poor prognosis. The overall survival time of patients in the high-risk group was significantly lower than in the low-risk group. Moreover, the high-risk score was accompanied by a stronger immune response and a more complex tumor immune microenvironment. “Hot tumors” were mainly enriched in the high-risk group, and high-risk group patients highly expressed inhibitory immune checkpoints (PD1, PD-L1, TIM3 etc.). By combining the risk and priming scores we obtained the immunotherapy score, which was shown to be a good evaluation index for sensitivity to GBM immunotherapy. Conclusions As an independent risk factor for poor prognosis, the T-cell activation-related risk score, combined with other clinical characteristics, could efficiently evaluate the survival of patients with GBM. The immunotherapy score obtained by combining the risk and priming scores could evaluate the ICB response of patients with GBM, providing treatment opportunities.

The moisture content of black tea is an important factor affecting its suitability for processing and forming the unique flavor. At present, the research on the moisture content of black tea mainly focuses on a single withering step, but the research on the rapid detection method of moisture content of black tea applicable to the entire processing stage is ignored. This study is based on a miniaturized near-infrared spectrometer(micro−NIRS) and establishes the prediction models for black tea moisture content through machine learning algorithms. We use micro−NIRS for spectroscopic data acquisition of samples. Linear partial least squares (PLS) and nonlinear support vector regression (SVR) were combined with four spectral pre−processing methods, and principal component analysis (PCA) was applied to establish the predictive models. In addition, we combine the gray wolf optimization algorithm (GWO) with SVR for the prediction of moisture content, aiming to establish the best prediction model of black tea moisture content by optimizing the selection of key parameters (c and g) of the kernel function in SVR. The results show that SNV, as a method to correct the error of the spectrum due to scattering, can effectively extract spectral features after combining with PCA and is better than other pre−processing methods. In contrast, the nonlinear SVR model outperforms the PLS model, and the established mixed model SNV−PCA−GWO−SVR achieves the best prediction effect. The correlation coefficient of the prediction set and the root mean square error of the prediction set are 0.9892 and 0.0362, respectively, and the relative deviation is 6.5001. Experimental data show that the moisture content of black tea can be accurately and effectively determined by micro-near-infrared spectroscopy.

Moisture content (MC) is an important indicator to monitor the quality of Longjing tea during processing; therefore, it becomes more critical to develop digital moisture content detection methods for processing. In this study, based on a micro-near infrared (NIR) spectrometer and portable colorimeter, we used Longjing tea under the full processing process as the research object, and used competitive adaptive reweighted sampling (CARS) and a principal component analysis (PCA) to extract characteristic bands of spectral data as well as the principal component reduction processing of the color difference and glossiness data, respectively, combined with sensor data fusion technology to establish a quantitative prediction model of the partial least squares (PLS) for the moisture content of Longjing tea. The PLS quantitative moisture content prediction model, based on middle-level data fusion, obtained the best prediction accuracy and model robustness, with the correlation coefficient of the prediction set (Rp) and the root mean square error of prediction (RMSEP) being 0.9823 and 0.0333, respectively, with a residual predictive deviation (RPD) of 6.5287. The results indicate that a data fusion of a micro NIR spectrometer and portable Colorimeter is feasible to establish a quantitative prediction model of the moisture content in Longjing tea processing, while multi-sensor data fusion can overcome the problem of a low prediction accuracy for the model established by single sensor data. More importantly, data fusion based on low-cost, fast, and portable detection sensors can provide new ideas and methods for real-time online detection in Longjing tea in actual production.

Fixation is the most critical step in the green tea process. Hence, this study developed a rapid and accurate moisture content detection for the green tea fixation process based on near-infrared spectroscopy and computer vision. Specifically, we created a quantitative moisture content prediction model appropriate for the processing of green tea fixation. First, we collected spectrum and image information of green tea fixation leaves, utilizing near-infrared spectroscopy and computer vision. Then, we applied the partial least squares regression (PLSR), support vector regression (SVR), Elman neural network (ENN), and Elman neural network based on whale optimization algorithm (WOA-ENN) methods to build the prediction models for single data (data from a single sensor) and mid-level data fusion, respectively. The results revealed that the mid-level data fusion strategy combined with the WOA-ENN model attained the best effect. Namely, the prediction set correlation coefficient (Rp) was 0.9984, the root mean square error of prediction (RMSEP) was 0.0090, and the relative percent deviation (RPD) was 17.9294, highlighting the model’s excellent predictive performance. Thus, this study identified the feasibility of predicting the moisture content in the process of green tea fixation by miniaturized near-infrared spectroscopy. Moreover, in establishing the model, the whale optimization algorithm was used to overcome the defect whereby the Elman neural network falls into the local optimum. In general, this study provides technical support for rapid and accurate moisture content detection in green tea fixation.

Ustilaginoidea virens is a biotrophic fungal pathogen specifically colonizing rice floral organ and causes false smut disease of rice. This disease has emerged as a serious problem that hinders the application of high-yield rice cultivars, by reducing grain yield and quality as well as introducing mycotoxins. However, the pathogenic mechanisms of U. virens are still enigmatic. Here we demonstrate that U. virens employs a secreted protein UvCBP1 to manipulate plant immunity. In planta expression of UvCBP1 led to compromised chitin-induced defense responses in Arabidopsis and rice, including burst of reactive oxygen species (ROS), callose deposition, and expression of defense-related genes. In vitro-purified UvCBP1 protein competes with rice chitin receptor OsCEBiP to bind to free chitin, thus impairing chitin-triggered rice immunity. Moreover, UvCBP1 could significantly promote infection of U. virens in rice flowers. Our results uncover a mechanism of a floral fungus suppressing plant immunity and pinpoint a universal role of chitin-battlefield during plant–fungi interactions.

Wireless body area networks (WBANs) are essential for monitoring physiological signals of the human body, but the lifetime of WBANs is limited by battery longevity and it is not convenient or feasible for replacing the batteries of the sensors. The newly emerged energy-harvesting technology provides the potential to break the battery limitation of WBANs. However, the radio resource of a WBAN should be carefully scheduled for the wireless power transfer links and wireless information transmission links; otherwise, severely unfair resource allocation could be incurred due to the difference of channel qualities of the sensors. In this paper, we propose a marginal utility theoretic method to allocate the radio resource to the on-/in-body sensors in a fair and efficient manner. Especially, we consider that the sensors are wireless powered by multiple pre-installed radio-frequency energy sources. First, the utility function for a sensor node is proposed, which can map the achievable throughput to a satisfaction level of network QoS. Then, the fairness resource allocation among the sensor nodes is modeled as a sum-utility maximization problem. By using the dual decomposition method, the optimal solution to the proposed problem can finally be solved in the closed form. In comparison with the sum-throughput maximization and common-throughput maximization methods, the simulation results show that the proposed sum-utility maximization method can bring a fair throughput allocation for the sensors with different channel conditions, and the performance loss to the sum-throughput maximization method is small, while the sum-throughput maximization method is extremely unfair.

Background: Abnormal redox equilibrium is a major contributor to tumor malignancy and treatment resistance. Understanding reactive oxygen species (ROS) metabolism is a key to clarify the tumor redox status. However, we have limited methods to evaluate ROS in tumor tissues and little knowledge on ROS metabolism across human cancers.Methods: The Cancer Genome Atlas multi-omics data across 22 cancer types and the Genomics of Drug Sensitivity in Cancer data were analyzed in this study. Cell viability testing and xenograft model were used to validate the role of ROS modulation in regulating treatment efficacy.Results: ROS indexes reflecting ROS metabolic balance in five dimensions were developed and verified. Based on the ROS indexes, we conducted ROS metabolic landscape across 22 cancer types and found that ROS metabolism played various roles in different cancer types. Tumor samples were classified into eight ROS clusters with distinct clinical and multi-omics features, which was independent of their histological origin. We established a ROS-based drug efficacy evaluation network and experimentally validated the predicted effects, suggesting that modulating ROS metabolism improves treatment sensitivity and expands drug application scopes.Conclusion: Our study proposes a new method in evaluating ROS status and offers comprehensive understanding on ROS metabolic equilibrium in human cancers, which provide practical implications for clinical management.

Abstract We recently demonstrated that a co-culture system of human umbilical vein endothelial cells (HUVECs) and human dental pulp stem cells (hDPSCs) could enhance angiogenesis ability in vitro. However, whether tumor necrosis factor α (TNF-α) could promote blood vessel formation during pulp regeneration remained unknown. The aim of this study was to investigate the effects of TNF-α on the formation of endothelial tubules and vascular networks in a co-culture system of hDPSCs and HUVECs. hDPSCs were co-cultured with HUVECs at a ratio of 1:5. The Matrigel assay was performed to detect the total tubule branching lengths and numbers of branches, and the Cell-Counting Kit 8 assay was performed to examine the effect of TNF-α on cell proliferation. Real-time polymerase chain reactions and western blot were used to detect vascular endothelial growth factor (VEGF) mRNA and protein expression. The Matrigel assay showed significantly greater total branching lengths and numbers of branches formed in the experimental groups treated with different concentrations of TNF-α compared with the control group. The decomposition times of the tubule structures were also significantly prolonged (P < 0.05). Treatment with 50 ng/ml TNF-α did not significantly change the proliferation of co-cultured cells, but it significantly increased the VEGF mRNA and protein expression levels (p < 0.05). In addition, the migration abilities of HUVECs and hDPSCs increased after co-culture with TNF-α (p < 0.05). TNF-α enhanced angiogenic ability in vitro in the co-culture system of hDPSCs and HUVECs.

Background: Interferon treatment, as an important approach of anti-tumor immunotherapy, has been implemented in multiple clinical trials of glioma. However, only a small number of gliomas benefit from it. Therefore, it is necessary to investigate the clinical role of interferons and to establish robust biomarkers to facilitate its application. Materials and methods: This study reviewed 1,241 glioblastoma (GBM) and 1,068 lower grade glioma (LGG) patients from six glioma cohorts. The transcription matrix and clinical information were analyzed using R software, GraphPad Prism 7 and Medcalc, etc. Immunohistochemical (IHC) staining were performed for validation in protein level. Results: Interferon signaling was significantly enhanced in GBM. An interferon signature was developed based on five interferon genes with prognostic significance, which could reflect various interferon statuses. Survival analysis showed the signature could serve as an unfavorable prognostic factor independently. We also established a nomogram model integrating the risk signature into traditional prognostic factors, which increased the validity of survival prediction. Moreover, high-risk group conferred resistance to chemotherapy and high IFNB1 expression levels. Functional analysis showed that the high-risk group was associated with overloaded immune response. Microenvironment analysis and IHC staining found that high-risk group occupied a disorganized microenvironment which was characterized by an enrichment of M0 macrophages and neutrophils, but less infiltration of activated nature killing (NK) cells and M1 type macrophages. Conclusion: This interferon signature was an independent indicator for unfavorable prognosis and showed great potential for screening out patients who will benefit from chemotherapy and interferon treatment.