Thermodynamics of Black Holes in Massive Gravity

We present a class of charged black hole solutions in an ($n+2)$-dimensional massive gravity with a negative cosmological constant, and study thermodynamics and phase structure of the black hole solutions both in grand canonical ensemble and canonical ensemble. The black hole horizon can have a positive, zero or negative constant curvature characterized by constant $k$. By using Hamiltonian approach, we obtain conserved charges of the solutions and find black hole entropy still obeys the area formula and the gravitational field equation at the black hole horizon can be cast into the first law form of black hole thermodynamics. In grand canonical ensemble, we find that thermodynamics and phase structure depends on the combination $k -\mu^2/4 +c_2 m^2$ in the four dimensional case, where $\mu$ is the chemical potential and $c_2m^2$ is the coefficient of the second term in the potential associated with graviton mass. When it is positive, the Hawking-Page phase transition can happen, while as it is negative, the black hole is always thermodynamically stable with a positive capacity. In canonical ensemble, the combination turns out to be $k+c_2m^2$ in the four dimensional case. When it is positive, a first order phase transition can happen between small and large black holes if the charge is less than its critical one. In higher dimensional ($n+2 \ge 5$) case, even when the charge is absent, the small/large black hole phase transition can also appear, the coefficients for the third ($c_3m^2$) and/or the fourth ($c_4m^2$) terms in the potential associated with graviton mass in the massive gravity can play the same role as the charge does in the four dimensional case.Comment: Latex 19 pages with 8 figure

DILI: A Distribution-Driven Learned Index

Targeting in-memory one-dimensional search keys, we propose a novel DIstribution-driven Learned Index tree (DILI), where a concise and computation-efficient linear regression model is used for each node. An internal node's key range is equally divided by its child nodes such that a key search enjoys perfect model prediction accuracy to find the relevant leaf node. A leaf node uses machine learning models to generate searchable data layout and thus accurately predicts the data record position for a key. To construct DILI, we first build a bottom-up tree with linear regression models according to global and local key distributions. Using the bottom-up tree, we build DILI in a top-down manner, individualizing the fanouts for internal nodes according to local distributions. DILI strikes a good balance between the number of leaf nodes and the height of the tree, two critical factors of key search time. Moreover, we design flexible algorithms for DILI to efficiently insert and delete keys and automatically adjust the tree structure when necessary. Extensive experimental results show that DILI outperforms the state-of-the-art alternatives on different kinds of workloads.Comment: PVLDB Volume 1

Radiation inactivation analysis of H+-pyrophosphatase from submitochondrial particles of etiolated mung bean seedlings

AbstractRadiation inactivation analysis was employed to determine the functional masses of enzymatic activity and proton translocation of H+-pyrophosphatase from submitochondrial particles of etiolated mung bean seedlings. The activities of H+-pyrophosphatase decayed as a simple exponential function with respect to radiation dosage. D37 values of 6.9±0.3 and 7.5±0.5 Mrad were obtained for pyrophosphate hydrolysis and its associated proton translocation, yielding molecular masses of 170±7 and 156±11 kDa, respectively. In the presence of valinomycin and 50 mM KCl, the functional size of H+-pyrophosphatase of tonoplast was decreased, while that of submitochondrial particles remained the same, indicating that they are two distinct types of proton pump using PPi as their energy source

PlantPAN: Plant promoter analysis navigator, for identifying combinatorial cis-regulatory elements with distance constraint in plant gene groups

<p>Abstract</p> <p>Background</p> <p>The elucidation of transcriptional regulation in plant genes is important area of research for plant scientists, following the mapping of various plant genomes, such as <it>A. thaliana</it>, <it>O. sativa </it>and <it>Z. mays</it>. A variety of bioinformatic servers or databases of plant promoters have been established, although most have been focused only on annotating transcription factor binding sites in a single gene and have neglected some important regulatory elements (tandem repeats and CpG/CpNpG islands) in promoter regions. Additionally, the combinatorial interaction of transcription factors (TFs) is important in regulating the gene group that is associated with the same expression pattern. Therefore, a tool for detecting the co-regulation of transcription factors in a group of gene promoters is required.</p> <p>Results</p> <p>This study develops a database-assisted system, PlantPAN (Plant Promoter Analysis Navigator), for recognizing combinatorial <it>cis</it>-regulatory elements with a distance constraint in sets of plant genes. The system collects the plant transcription factor binding profiles from PLACE, TRANSFAC (public release 7.0), AGRIS, and JASPER databases and allows users to input a group of gene IDs or promoter sequences, enabling the co-occurrence of combinatorial transcription factor binding sites (TFBSs) within a defined distance (20 bp to 200 bp) to be identified. Furthermore, the new resource enables other regulatory features in a plant promoter, such as CpG/CpNpG islands and tandem repeats, to be displayed. The regulatory elements in the conserved regions of the promoters across homologous genes are detected and presented.</p> <p>Conclusion</p> <p>In addition to providing a user-friendly input/output interface, PlantPAN has numerous advantages in the analysis of a plant promoter. Several case studies have established the effectiveness of PlantPAN. This novel analytical resource is now freely available at <url>http://PlantPAN.mbc.nctu.edu.tw</url>.</p

Lattice dynamics and elastic properties of alpha-U at high-temperature and high-pressure by machine learning potential simulations

Studying the physical properties of materials under high pressure and temperature through experiments is difficult. Theoretical simulations can compensate for this deficiency. Currently, large-scale simulations using machine learning force fields are gaining popularity. As an important nuclear energy material, the evolution of the physical properties of uranium under extreme conditions is still unclear. Herein, we trained an accurate machine learning force field on alpha-U and predicted the lattice dynamics and elastic properties at high pressures and temperatures. The force field agrees well with the ab initio molecular dynamics (AIMD) and experimental results, and it exhibits higher accuracy than classical potentials. Based on the high-temperature lattice dynamics study, we first present the temperature-pressure range in which the Kohn anomalous behavior of the ${\Sigma}$4 optical mode exists. Phonon spectral function analysis showed that the phonon anharmonicity of alpha-U is very weak. We predict that the single-crystal elastic constants C44, C55, C66, polycrystalline modulus (E,G), and polycrystalline sound velocity ($C_L$,$C_S$) have strong heating-induced softening. All the elastic moduli exhibited compression-induced hardening behavior. The Poisson's ratio shows that it is difficult to compress alpha-U at high pressures and temperatures. Moreover, we observed that the material becomes substantially more anisotropic at high pressures and temperatures. The accurate predictions of alpha-U demonstrate the reliability of the method. This versatile method facilitates the study of other complex metallic materials.Comment: 21 pages, 9 figures, with Supplementary Materia

Identification of susceptibility genes in non-syndromic cleft lip with or without cleft palate using whole-exome sequencing

Background: Non-syndromic cleft lip with or without cleft palate (NSCL/P) is among the most common congenital malformations. The etiology of NSCL/P remains poorly characterized owing to its complex genetic heterogeneity. The objective of this study was to identify genetic variants that increase susceptibility to NSCL/P. Material and Methods: Whole-exome sequencing (WES) was performed in 8 fetuses with NSCL/P in China. Bioinformatics analysis was performed using commercially available software. Variants detected by WES were validated by Sanger sequencing. Results: By filtering out synonymous variants in exons, we identified average 8575 nonsynonymous single nucleotide variants (SNVs). We subsequently compared the SNVs against public databases including NCBI dbSNP build 135 and 1000 Genomes Project and obtained an average of 203 SNVs. Total 12 reported candidate genes were verified by Sanger sequencing. Sanger sequencing also confirmed 16 novel SNVs shared by two or more samples. Conclusions: We have found and confirmed 16 susceptibility genes responsible for NSCL/P, which may play important role in the etiology of NSCL/P. The susceptibility genes identified in this study will not only be useful in revealing the etiology of NSCL/P but also in diagnosis and treatment of the patients with NSCL/P

LAYN Is a Prognostic Biomarker and Correlated With Immune Infiltrates in Gastric and Colon Cancers

Background: Layilin (LAYN) is a critical gene that regulates T cell function. However, the correlations of LAYN to prognosis and tumor-infiltrating lymphocytes in different cancers remain unclear.Methods: LAYN expression was analyzed via the Oncomine database and Tumor Immune Estimation Resource (TIMER) site. We evaluated the influence of LAYN on clinical prognosis using Kaplan-Meier plotter, the PrognoScan database and Gene Expression Profiling Interactive Analysis (GEPIA). The correlations between LAYN and cancer immune infiltrates was investigated via TIMER. In addition, correlations between LAYN expression and gene marker sets of immune infiltrates were analyzed by TIMER and GEPIA.Results: A cohort (GSE17536) of colorectal cancer patients showed that high LAYN expression was associated with poorer overall survival (OS), disease-specific survival (DSS), and disease-free survival (DFS). In addition, high LAYN expression was significantly correlated with poor OS and progression-free survival (PFS) in gastric cancers (OS HR = 1.97, P = 3.6e-10; PFS HR = 2.12, P = 2.3e-10). Moreover, LAYN significantly impacts the prognosis of diverse cancers via The Cancer Genome Atlas (TCGA). Specifically, high LAYN expression was correlated with worse OS and PFS in stage 2 to 4 but not stage 1 and stage N0 gastric cancer patients (P = 0.28, 0.34; P = 0.073, 0.092). LAYN expression was positively correlated with infiltrating levels of CD4+ T and CD8+ T cells, macrophages, neutrophils, and dendritic cells (DCs) in colon adenocarcinoma (COAD) and stomach adenocarcinoma (STAD). LAYN expression showed strong correlations with diverse immune marker sets in COAD and STAD.Conclusions: These findings suggest that LAYN is correlated with prognosis and immune infiltrating levels of, including those of CD8+ T cells, CD4+ T cells, macrophages, neutrophils, and DCs in multiple cancers, especially in colon and gastric cancer patients. In addition, LAYN expression potentially contributes to regulation of tumor-associated macrophages (TAMs), DCs, T cell exhaustion and Tregs in colon and gastric cancer. These findings suggest that LAYN can be used as a prognostic biomarker for determining prognosis and immune infiltration in gastric and colon cancers