RXR negatively regulates ex vivo expansion of human cord blood hematopoietic stem and progenitor cells

Ex vivo expansion of human cord blood (CB) hematopoietic stem cells (HSCs) is one approach to overcome limited numbers of HSCs in single CB units. However, there is still no worldwide acceptable HSC ex vivo expansion system. A main reason is that we still have very limited knowldege regarding mechanisms underlying maintenance and expansion of CB HSCs. Here we report that retinoid X receptor (RXR) activity is of significance for CB HSC ex vivo expansion. RXR antagonist HX531 significantly promoted ex vivo expansion of CB HSCs and progenitor cells (HPCs). RXR agonist Bexarotene notably suppressed ex vivo expansion of CB HSCs. Activation of RXR by Bexarotene significantly blocked expansion of phenotypic HSCs and HPCs and expressed increased functional HPCs as assessed by colony formation induced by UM171 and SR1. In vivo transplantation experiments in immune-deficient mice demonstrated that HX531 expanded CB HSCs possess long-term reconstituting capacities, and Bexarotene treatment inhibited expansion of functional CB HSCs. RNA-seq analysis revealed that RXR regulates expression of FBP1 (a negative regulator of glucose metabolism) and many genes involved in differentation. ECAR analysis showed that HX531 significantly promoted glycolytic activity of CB CD34+ HSCs and HPCs. Our studies suggest that RXR is a negative regulator of ex vivo expansion of CB HSCs and HPCs

Nested Dilation Networks for Brain Tumor Segmentation Based on Magnetic Resonance Imaging

Aim: Brain tumors are among the most fatal cancers worldwide. Diagnosing and manually segmenting tumors are time-consuming clinical tasks, and success strongly depends on the doctor's experience. Automatic quantitative analysis and accurate segmentation of brain tumors are greatly needed for cancer diagnosis.Methods:This paper presents an advanced three-dimensional multimodal segmentation algorithm called nested dilation networks (NDNs). It is inspired by the U-Net architecture, a convolutional neural network (CNN) developed for biomedical image segmentation and is modified to achieve better performance for brain tumor segmentation. Thus, we propose residual blocks nested with dilations (RnD) in the encoding part to enrich the low-level features and use squeeze-and-excitation (SE) blocks in both the encoding and decoding parts to boost significant features. To prove the reliability of the network structure, we compare our results with those of the standard U-Net and its transmutation networks. Different loss functions are considered to cope with class imbalance problems to maximize the brain tumor segmentation results. A cascade training strategy is employed to run NDNs for coarse-to-fine tumor segmentation. This strategy decomposes the multiclass segmentation problem into three binary segmentation problems and trains each task sequentially. Various augmentation techniques are utilized to increase the diversity of the data to avoid overfitting.Results: This approach achieves Dice similarity scores of 0.6652, 0.5880, and 0.6682 for edema, non-enhancing tumors, and enhancing tumors, respectively, in which the Dice loss is used for single-pass training. After cascade training, the Dice similarity scores rise to 0.7043, 0.5889, and 0.7206, respectively.Conclusion: Experiments show that the proposed deep learning algorithm outperforms other U-Net transmutation networks for brain tumor segmentation. Moreover, applying cascade training to NDNs facilitates better performance than other methods. The findings of this study provide considerable insight into the automatic and accurate segmentation of brain tumors

Analysis of three-intensity decoy-state phase-matching quantum key distribution

Phase-matching quantum key distribution (PM-QKD) protocol has been widely researched since it was proposed. This scheme is proven to beat the linear bound. In this paper, the performance of three-intensity decoy-state PM-QKD is discussed. The effects of various parameters on the performance of transmission system with statistical fluctuation and source error are analyzed by numerical simulation. The results show that the protocol has good performance. The effects of signal state intensity and decoy-state intensity on key rate are analyzed, and the optimal key rate and the intensity of signal and decoy states are given

Theoretical analyses of copper-based solar cell materials for the next generation of photovoltaics

This chapter describes the state of the art in computer simulations in the context of the development of high-efficiency solar cells. It discusses how one analyses by theoretical means the structural, electronic, and optical properties of emerging copper-based chalcogenides, employing atomistic first-principles computational methods within density functional theory. The fundamental material characteristics of the compounds are analysed, and the optoelectronic performances are improved by alloying with isovalent elements. In order to develop inorganic photovoltaics based on an ultrathin, photon-absorbing film (i.e., with thickness d < 100 nm), the material should exhibit an optimised band gap energy, Eg, as well as have a very high absorption coefficient α(ω), especially for photon energies in the lower energy region of the absorption spectrum: Eg ≤ E < (Eg + 2 eV). To develop high-efficiency solar cells, we therefore suggest tailor making the materials to form direct-gap, multi-valley band edges, and energy bands with rather flat dispersions. These properties can typically be achieved by considering alloys with heavy elements that have relatively localised sp-like orbitals. With such tailored materials, we demonstrate that it is possible to reach a theoretical maximum efficiency as high as ηmax ≈ 30% for film thickness of d ≈ 50–100 nm. Such an approach is useful to support the search for new materials to drive innovation in solar technology in the future

Immunosuppressive Tumor Microenvironment and Immunotherapy of Epstein–Barr Virus-Associated Malignancies

The Epstein–Barr virus (EBV) can cause different types of cancer in human beings when the virus infects different cell types with various latent patterns. EBV shapes a distinct and immunosuppressive tumor microenvironment (TME) to its benefit by influencing and interacting with different components in the TME. Different EBV-associated malignancies adopt similar but slightly specific immunosuppressive mechanisms by encoding different EBV products to escape both innate and adaptive immune responses. Strategies reversing the immunosuppressive TME of EBV-associated malignancies have been under evaluation in clinical practice. As the interactions among EBV, tumor cells, and TME are intricate, in this review, we mainly discuss the epidemiology of EBV, the life cycle of EBV, the cellular and molecular composition of TME, and a landscape of different EBV-associated malignancies and immunotherapy by targeting the TME

Outcome of R-CHOP or CHOP Regimen for Germinal Center and Nongerminal Center Subtypes of Diffuse Large B-Cell Lymphoma of Chinese Patients

Diffuse large B-cell lymphoma (DLBCL) can be molecularly subtyped as either germinal center B-cell (GCB) or non-GCB. The role of rituximab(R) in these two groups remains unclear. We studied 204 patients with de novo DLBCL (107 treated with first-line CHOP; 97 treated with first-line R-CHOP), patients being stratified into GCB and non-GCB on the basis of BCL-6, CD10, and MUM1 protein expression. The relationships between clinical characteristics, survival data, and immunophenotype (IHC) were studied. The 5-year overall survival (OS) in the CHOP and R-CHOP groups was 50.4% and 66.6% (P=0.031), respectively. GCB patients had a better 5-year OS than non-GCB patients whether treated with CHOP or not (65.0% versus 40.9%; P=0.011). In contrast, there is no difference in the 5-year OS for the GCB and non-GCB with R-CHOP (76.5% versus 61.3%; P=0.141). In non-GCB subtype, additional rituximab improved survival better than CHOP (61.3% versus 40.9%; P=0.0303). These results indicated that addition of rituximab to standard chemotherapy eliminates the prognostic value of IHC-defined GCB and non-GCB phenotypes in DLBCL by improving the prognostic value of non-GCB subtype of DLBCL

Group-IV (Si, Ge, and Sn)-doped AgAlTe2 for intermediate band solar cell from first-principles study

Earlier studies of chalcopyrites as the absorber for intermediate band solar cells (IBSCs) mainly focused on Cu-based compounds, whose intermediate band is usually empty due to its intrinsic p-type conductivity. This is not beneficial to the two sub-bandgap absorptions. In this paper, we demonstrate that the intermediate bands in group IV (Si, Ge, and Sn) doped AgAlTe2 are delocalized and mainly contributed by the anti-bonding state of group-IV elements s state and Te-p state. Overall, we suggest that Sn-doped AgAlTe2 should be a promising absorber candidate for IBSCs based on the theoretical efficiency and defect stability. © 2017 IOP Publishin