Highlights d Lipophilic statins and lipophilic bisphosphonates are potent vaccine adjuvants d Modulation of post-translational protein prenylation confers adjuvanticity d Decreased protein prenylation augments antigen preservation and presentation d Statin-or bisphosphonate-mediated vaccination synergizes with anti-PD1 against cancer

Graphical Abstract Highlights d The crystal structure of HMBPP-bound intracellular BTN3A1 was determined at 1.97 Å d HMBPP forms hydrogen bonds with H 351 for efficient Vg9Vd2 T cell activation d An asymmetric intracellular dimer is involved in HMBPPmediated gd T cell activation d HMBPP doubles the binding force between extracellular BTN3A and Vg9Vd2 TCR SUMMARYHuman Vg9Vd2 T cells respond to microbial infections and malignancy by sensing diphosphate-containing metabolites called phosphoantigens, which bind to the intracellular domain of butyrophilin 3A1, triggering extracellular interactions with the Vg9Vd2 T cell receptor (TCR). Here, we examined the molecular basis of this ''inside-out'' triggering mechanism. Crystal structures of intracellular butyrophilin 3A proteins alone or in complex with the potent microbial phosphoantigen HMBPP or a synthetic analog revealed key features of phosphoantigens and butyrophilins required for gd T cell activation. Analyses with chemical probes and molecular dynamic simulations demonstrated that dimerized intracellular proteins cooperate in sensing HMBPP to enhance the efficiency of gd T cell activation. HMBPP binding to butyrophilin doubled the binding force between a gd T cell and a target cell during ''outside'' signaling, as measured by single-cell force microscopy. Our findings provide insight into the ''inside-out'' triggering of Vg9Vd2 T cell activation by phosphoantigen-bound butyrophilin, facilitating immunotherapeutic drug design.

Hepatitis B virus (HBV)-encoded X antigen (HBxAg) contributes to the development of hepatocellular carcinoma (HCC). A frequent characteristic of HCC is reduced or absent expression of the cell adhesion protein, Ecadherin, although it is not known whether HBxAg plays a role. To address this, the levels of E-cadherin were determined in HBxAg-positive and -negative HepG2 cells in culture, and in tumor and surrounding nontumor liver from a panel of HBV carriers. The results showed an inverse relationship between HBxAg and E-cadherin expression both in tissue culture and in vivo. In HBxAgpositive cells, E-cadherin was suppressed at both the mRNA and protein levels. This was associated with hypermethylation of the E-cadherin promoter. Depressed E-cadherin correlated with HBxAg trans-activation function, as did the migration of HepG2 cells in vitro. Decreased expression of E-cadherin was also associated with the accumulation of b-catenin in the cytoplasm and/ or nuclei in tissues and cell lines, which is characteristic of activated b-catenin. Additional work showed that HBxAg-activated b-catenin. Together, these results suggest that the HBxAg is associated with decreased expression of E-cadherin, accumulation of b-catenin in the cytoplasm and nucleus, and increased cell migration, which may contribute importantly to hepatocarcinogenesis.

The removal of the entire osseous compartment either by en bloc or piecemeal method in combination with the long-term use of bisphosphonate could significantly reduce the recurrence rate of GCT of the mobile spine. Age less than 40 years is a favorable prognostic factor for GCT in the mobile spine.

Accurate short-term prediction of the natural gas load is of great significance to the operation and allocation of the pipeline network. Because the short-term natural gas load has obvious nonlinearity and randomness, the traditional regression model is difficult to predict accurately. Therefore, this paper proposes a hybrid prediction model that integrates an improved whale swarm algorithm (IWOA) and relevance vector machine (RVM). In addition, empirical mode decomposition (EMD), approximate entropy (ApEn), and CC method are introduced to aid the calculation. In this paper, the IWOA is used to test the four functions and compared with the other five algorithms. The results show that the convergence accuracy and convergence speed of the new algorithm are higher than other algorithms, indicating that it has better global optimization ability. Second, the IWOA-RVM model is used to predict the supply data of two natural gas stations in Anhui Province, China. The prediction results are compared with the five algorithms including RBFNN, GRNN, ELMANNN, LSSVM, and SMOSVM. The results show that: 1) through the test of four functions, IWOA has better ability to jump out of local optimum, has higher optimization performance, and the calculation speed is at a medium level and 2) compared with other models, the IOWA-RVM model has higher prediction accuracy when the amount of data is larger or smaller, but the calculation time is relatively long, but the calculation time is acceptable in engineering. INDEX TERMS Short-term, natural gas demand, prediction, relevance vector machine, improved whale swarm algorithm.

Predicting malignant potential is one of the most critical components of a computer-aided diagnosis (CAD) system for gastrointestinal stromal tumors (GISTs). These tumors have been studied only on the basis of subjective computed tomography (CT) findings. Among various methodologies, radiomics and deep learning algorithms, specifically convolutional neural networks (CNNs), have recently been confirmed to achieve significant success by outperforming the state-of-the-art performances in medical image pattern classification and have rapidly become leading methodologies in this field. However, the existing methods generally use radiomics or deep convolutional features independently for pattern classification, which tend to take into account only global or local features, respectively. In this paper, we introduce and evaluate a hybrid structure that includes different features selected with radiomics model and CNN and integrates these features to deal with GIST classification. Radiomics model and CNN architecture are constructed for global radiomics and local convolutional feature selections, respectively. Subsequently, we utilize distinct radiomics and deep convolutional features to perform pattern classification for GIST. Specifically, we propose a new pooling strategy to assemble the deep convolutional features of 54 3D patches from the same case and integrate these features with the radiomics features for independent case, followed by random forests (RF) classifier. Our method can be extensively evaluated using multiple clinical datasets. The classification performance (area under the curve (AUC): 0.882; 95% confidence interval (CI): 0.816-0.947) consistently outperforms those of independent radiomics (AUC: 0.807; 95% CI: 0.724-0.892) and CNN (AUC: 0.826; 95% CI: 0.795-0.856) approaches.

Spinal malignant peripheral nerve sheath tumors (MPNSTs) are relatively rare. There is little information published in the literature regarding this subject. The aim of this retrospective study was to evaluate factors that may affect the outcomes of patients with spinal MPNSTs by reviewing 43 patients with spinal MPNST who were treated in our hospital between 2001 and 2012. Univariate and multivariate analyses were performed to identify prognostic variables relative to patient and tumor characteristics, treatment modality and molecules. All 43 MPNST patients (25 men and 18 women; median age 49 years) underwent surgical resection, of whom 15 patients also underwent postoperative radiotherapy. Local recurrence was found in 21 (48.8 %) patients. Twenty-two (51.2 %) patients died during the follow-up periods with a median survival time of 49 months. The 5-year recurrence and survival rate was 53 and 44 % respectively. The statistical analyses suggested that high-grade malignancy and osteolytic destruction were closely associated with recurrence and death. A total of 38 cases accepted postoperative immunohistochemisty examine. S-100 was identified as an independent factor related to both recurrence and survival, adjusting for clinical factors. In conclusion, we confirmed that malignant grade and osteolytic destruction were the two independent factors for both recurrence and survival, while patients with S-100 protein negative had a higher recurrence rate and a lower survival rate.

• CT-based radiomics model can differentiate low- and high-malignant-potential GISTs with satisfactory accuracy compared with subjective CT findings and clinical indexes. • Radiomics nomogram integrated with the radiomics signature, subjective CT findings and clinical indexes can achieve individualised risk prediction with improved diagnostic performance. • This study might provide significant and valuable background information for further studies such as response evaluation of neoadjuvant imatinib and recurrence risk prediction.