Quasiparticle scattering in two dimensional helical liquid

We study the quasiparticle interference (QPI) patterns caused by scattering off nonmagnetic, magnetic point impurities, and edge impurities, separately, in a two dimensional helical liquid, which describes the surface states of a topological insulator. The unique features associated with hexagonal warping effects are identified in the QPI patterns of charge density with nonmagnetic impurities and spin density with magnetic impurities. The symmetry properties of the QPI patterns can be used to determine the symmetry of microscopic models. The Friedel oscillation is calculated for edge impurities and the decay of the oscillation is not universal, strongly depending on Fermi energy. Some discrepancies between our theoretical results and current experimental observations are discussed.Comment: 12 pages, appendices added. Accepted for publication in Physical Review B (submitted, October 2009

Further results on laws of large numbers for uncertain random variables

summary:The uncertainty theory was founded by Baoding Liu to characterize uncertainty information represented by humans. Basing on uncertainty theory, Yuhan Liu created chance theory to describe the complex phenomenon, in which human uncertainty and random phenomenon coexist. In this paper, our aim is to derive some laws of large numbers (LLNs) for uncertain random variables. The first theorem proved the Etemadi type LLN for uncertain random variables being functions of pairwise independent and identically distributed random variables and uncertain variables without satisfying the conditions of regular, independent and identically distributed (IID). Two kinds of Marcinkiewicz-Zygmund type LLNs for uncertain random variables were established in the case of $p \in (0, 1)$ by the second theorem, and in the case of $p > 1$ by the third theorem, respectively. For better illustrating of LLNs for uncertain random variables, some examples were stated and explained. Compared with the existed theorems of LLNs for uncertain random variables, our theorems are the generalised results

Quasiparticle Scattering Interference in Superconducting Iron-pnictides

Using both two orbital and five orbital models, we investigate the quasiparticle interference (QPI) patterns in the superconducting (SC) state of iron-based superconductors. We compare the results for nonmagnetic and magnetic impurities in sign-changed s-wave $\cos(k_x)\cdot\cos(k_y)$ and sign-unchanged $|\cos(k_x)\cdot\cos(k_y)|$ SC states. While the patterns strongly depend on the chosen band structures, the sensitivity of peaks around $(\pm\pi,0)$ and $(0,\pm\pi)$ wavevectors on magnetic or non-magnetic impurity, and sign change or sign unchanged SC orders is common in two models. Our results strongly suggest that QPI may provide direct information of band structures and evidence of the pairing symmetry in the SC states.Comment: 12 pages, 16 figure

Fertilization and Cytogenetic Examination of Interspecific Reciprocal Hybridization between the Scallops, Chlamys farreri and Mimachlamys nobilis

Crossbreeding is a powerful tool for improving productivity and profitability in aquaculture. We conducted a pilot study of an artificial cross between two important cultivated scallops in China, Chlamys farreri and Mimachlamys nobilis, to test the feasibility of interspecific hybridization. Reciprocal hybridization experiments were performed using a single-pair mating strategy (M. nobilis ♀ × C. farreri ♂ and C. farreri ♀ × M. nobilis ♂). The fertilization of each pair was tracked using fluorescence staining of the gametes, and the chromosomes of the F1 hybrid larvae were examined via conventional karyotyping and genomic in situ hybridization (GISH). We observed moderate fertilization success in both interspecific crosses, although the overall fertilization was generally less rapid than that of intraspecific crosses. Conventional karyotyping showed that 70.4% of the viable F1 larvae in M. nobilis ♀ × C. farreri ♂ and 55.4% in C. farreri ♀ × M. nobilis ♂ comprised hybrid karyotypes (2n = 35 = 6m+5sm+11st+13t), and the results were further confirmed by GISH. Interestingly, we detected a few F1 from the M. nobilis ♀ × C. farreri ♂ cross that appeared to have developed gynogenetically. In addition, chromosome fragmentations, aneuploids and allopolyploids were observed in some F1 individuals. Our study presents evidence that the artificial cross between M. nobilis and C. farreri is experimentally possible. Further investigations of the potential heterosis of the viable F1 offspring at various developmental stages should be conducted to obtain a comprehensive evaluation of the feasibility of crossbreeding between these two scallop species

Identification of thioredoxin-1 as a biomarker of lung cancer and evaluation of its prognostic value based on bioinformatics analysis

BackgroundThioredoxin-1 (TXN), a redox balance factor, plays an essential role in oxidative stress and has been shown to act as a potential contributor to various cancers. This study evaluated the role of TXN in lung cancer by bioinformatics analyses.Materials and methodsGenes differentially expressed in lung cancer and oxidative stress related genes were obtained from The Cancer Genome Atlas, Gene Expression Omnibus and GeneCards databases. Following identification of TXN as an optimal differentially expressed gene by bioinformatics, the prognostic value of TXN in lung cancer was evaluated by univariate/multivariate Cox regression and Kaplan–Meier survival analyses, with validation by receiver operation characteristic curve analysis. The association between TXN expression and lung cancer was verified by immunohistochemical analysis of the Human Protein Atlas database, as well as by western blotting and qPCR. Cell proliferation was determined by cell counting kit-8 after changing TXN expression using lentiviral transfection.ResultsTwenty differentially expressed oxidative stress genes were identified. Differential expression analysis identified five genes (CASP3, CAT, TXN, GSR, and HSPA4) and Kaplan–Meier survival analysis identified four genes (IL-6, CYCS, TXN, and BCL2) that differed significantly in lung cancer and normal lung tissue, indicating that TXN was an optimal differentially expressed gene. Multivariate Cox regression analysis showed that T stage (T3/T4), N stage (N2/N3), curative effect (progressive diseases) and high TXN expression were associated with poor survival, although high TXN expression was poorly predictive of overall survival. TXN was highly expressed in lung cancer tissues and cells. Knockdown of TXN suppressed cell proliferation, while overexpression of TXN enhanced cell proliferation.ConclusionHigh expression of TXN plays an important role in lung cancer development and prognosis. Because it is a prospective prognostic factor, targeting TXN may have clinical benefits in the treatment of lung cancer

Impact of central venous pressure on the mortality of patients with sepsis-related acute kidney injury: a propensity score-matched analysis based on the MIMIC IV database

Central venous pressure (CVP); Database; MortalityPressió venosa central (PVC); Base de dades; MortalitatPresión venosa central (PVC); Base de datos; MortalidadBackground: Sepsis has long been a life-threatening organ dysfunction. Sepsis associated acute kidney injury (SA-AKI) is an important complication of sepsis, as an important hemodynamic index, the impact of central venous pressure (CVP) on sepsis patients needs to be explored. Thus this study aimed to investigate the relationship between CVP and the mortality of SA-AKI. Methods: Clinical data of adult patients with sepsis-related acute kidney injury, defined as met both the Sepsis 3.0 criteria and the Kidney Disease Improving Global Outcomes Clinical Practice Guideline (KDIGO) criteria, were obtained from the Medical Information Mart for Intensive Care-IV (MIMIC-IV) database. The included cohort was divided into a high CVP and a low CVP group were determined based on the cuf-off value from receiver operating characteristic curve, with propensity score-matched analysis of the 28-day mortality for both groups and sensitivity analysis using inverse the probability-weighting model, multifactorial regression, and doubly robust estimation, patients acquired chronic coronary syndrome (CCS) and diabetes were also taken into consideration. Results: Of 1,377 patients with sepsis-related acute kidney injury, low CVP group (<13 mmHg) was 67.4% (n=928) and high CVP group (≥13 mmHg) was 32.6% (n=449). The two groups were matched 1:1 by propensity score to obtain a matched cohort (n=288). The mortality rates in the low versus high CVP group (19.4% vs. 34.7%) were statistically difference (odds ratio OR: 0.454; 95% confidence interval 0.263, 0.771). Moreover, the bistable analysis of logistic regression of the matched cohort (OR: 0.434; 95% CI: 0.244, 0.757), propensity score inverse probability weighting (IPW) (OR: 0.547; 95% CI: 0.454, 0.658), and multifactorial logistic regression (OR: 0.352; 95% CI: 0.127, 0.932) all yielded the same results. Conclusions: In patients with sepsis-related acute kidney injury, a lower CVP level (<13 mmHg) is an independent variable associated with decreased mortality. The threshold of CVP needs to be controlled in clinical work to improve the prognosis of patients with SA-AKI

TinyKG: Memory-Efficient Training Framework for Knowledge Graph Neural Recommender Systems

There has been an explosion of interest in designing various Knowledge Graph Neural Networks (KGNNs), which achieve state-of-the-art performance and provide great explainability for recommendation. The promising performance is mainly resulting from their capability of capturing high-order proximity messages over the knowledge graphs. However, training KGNNs at scale is challenging due to the high memory usage. In the forward pass, the automatic differentiation engines (\textsl{e.g.}, TensorFlow/PyTorch) generally need to cache all intermediate activation maps in order to compute gradients in the backward pass, which leads to a large GPU memory footprint. Existing work solves this problem by utilizing multi-GPU distributed frameworks. Nonetheless, this poses a practical challenge when seeking to deploy KGNNs in memory-constrained environments, especially for industry-scale graphs. Here we present TinyKG, a memory-efficient GPU-based training framework for KGNNs for the tasks of recommendation. Specifically, TinyKG uses exact activations in the forward pass while storing a quantized version of activations in the GPU buffers. During the backward pass, these low-precision activations are dequantized back to full-precision tensors, in order to compute gradients. To reduce the quantization errors, TinyKG applies a simple yet effective quantization algorithm to compress the activations, which ensures unbiasedness with low variance. As such, the training memory footprint of KGNNs is largely reduced with negligible accuracy loss. To evaluate the performance of our TinyKG, we conduct comprehensive experiments on real-world datasets. We found that our TinyKG with INT2 quantization aggressively reduces the memory footprint of activation maps with $7 \times$, only with $2\%$ loss in accuracy, allowing us to deploy KGNNs on memory-constrained devices