Adopting big data to accelerate discovery of 2D TMDCs materials via CVR method for the potential application in urban airborne Hg0 sensor

Airborne Hg0 has significant negative effect on cities and urban systems. The development of effect airborne Hg0 sensor is rather important for both urban atmospheric Hg0 detection and the evaluation of Hg0 capture materials. Previous research showed MoS2 as a typical TMDCs materials had excellent performance to capture Hg0. In this study, the other 2D TMDCs materials via CVR method in big data was initially studied for the potential urban airborne Hg0 sensor application. The combinations of Pymatgen initial screening, Factsage thermochemical screening and Aflow structural screening were developed for accelerating discovery of the 2D TMDCs in big data. The results from Pymatgen showed that except elements Cd, Sc, Y, Zn, and the other elements have the potential to form TMDC. Furthermore, elements such as Co, Ni, Mo, Ru, W and Ir have the ability forming pure TMDC and Ti, Mn, Zr and Pd can only form partial TMDC. However, other elements such as Sc, V, Cr, Fe, Cu, Zn, Y, Rh and Cd have no possibility to form TMDC. Finally, TiS2, NiS2, ZrS2, MoS2, PdS2 and WS2 were found with 2D structure, which are possible to be prepared by the S-CVR method as the airborne Hg0 sensor materials

Promise Σ\Sigma-protocol: How to Construct Efficient Threshold ECDSA from Encryptions Based on Class Groups

Threshold Signatures allow $n$ parties to share the ability of issuing digital signatures so that any coalition of size at least $t+1$ can sign, whereas groups of $t$ or fewer players cannot. The currently known class-group-based threshold ECDSA constructions are either inefficient (requiring parallel-repetition of the underlying zero knowledge proof with small challenge space) or requiring rather non-standard low order assumption. In this paper, we present efficient threshold ECDSA protocols from encryption schemes based on class groups with neither assuming the low order assumption nor parallel repeating the underlying zero knowledge proof, yielding a significant efficiency improvement in the key generation over previous constructions. Along the way we introduce a new notion of promise $\Sigma$-protocol that satisfies only a weaker soundness called promise extractability. An accepting promise $\Sigma$-proof for statements related to class-group-based encryptions does not establish the truth of the statement but provides security guarantees (promise extractability) that are sufficient for our applications. We also show how to simulate homomorphic operations on a (possibly invalid) class-group-based encryption whose correctness has been proven via our promise $\Sigma$-protocol. We believe that these techniques are of independent interest and applicable to other scenarios where efficient zero knowledge proofs for statements related to class-group is required

An integrative model of pathway convergence in genetically heterogeneous blast crisis chronic myeloid leukemia

Targeted therapies against the BCR-ABL1 kinase have revolutionized treatment of chronic phase (CP) chronic myeloid leukemia (CML). In contrast, management of blast crisis (BC) CML remains challenging because BC cells acquire complex molecular alterations that confer stemness features to progenitor populations and resistance to BCR-ABL1 tyrosine kinase inhibitors. Comprehensive models of BC transformation have proved elusive because of the rarity and genetic heterogeneity of BC, but are important for developing biomarkers predicting BC progression and effective therapies. To better understand BC, we performed an integrated multiomics analysis of 74 CP and BC samples using wholegenome and exome sequencing, transcriptome and methylome profiling, and chromatin immunoprecipitation followed by high-throughput sequencing. Employing pathway-based analysis, we found the BC genome was significantly enriched for mutations affecting components of the polycomb repressive complex (PRC) pathway. While transcriptomically, BC progenitors were enriched and depleted for PRC1- and PRC2-related gene sets respectively. By integrating our data sets, we determined that BC progenitors undergo PRCdriven epigenetic reprogramming toward a convergent transcriptomic state. Specifically, PRC2 directs BC DNA hypermethylation, which in turn silences key genes involved in myeloid differentiation and tumor suppressor function via so-called epigenetic switching, whereas PRC1 represses an overlapping and distinct set of genes, including novel BC tumor suppressors. On the basis of these observations, we developed an integrated model of BC that facilitated the identification of combinatorial therapies capable of reversing BC reprogramming (decitabine1PRC1 inhibitors), novel PRC-silenced tumor suppressor genes (NR4A2), and gene expression signatures predictive of disease progression and drug resistance in CP