Determination of forming ability of high pressure die casting for Zr-based metallic glass

Large-sized industrial grade Zr55Cu30Ni5Al10 bulk metallic glass (BMG) was fabricated by a two-step arc-melting process and adding a trace of rare earth yttrium elements. A model to calculate critical cooling rate for forming BMG was established by considering the effect of pressure. Based on the model, the most important indicator, namely the critical size of forming BMG, for design and fabrication of BMG components by high pressure die casting (HPDC) is determined in consideration of different processing parameters, including the pressure and casting temperature. Theoretical calculation and numerical simulation indicated that increasing applied pressure during casting suppresses the nucleation of crystal nuclei but has little effect on the growth of nuclei at a deep supercooled temperature, thereby decreasing critical cooling rate and thus increase critical size. An increasing casting temperature would reduce critical size caused by more heat to dissipate. The critical size of the optimized Zr55Cu30Ni5Al10 BMG cast by using steel mold is determined to be about 4–7 mm under different HPDC parameters. Our study reveals that the forming ability of HPDC is large enough for fabricating low cost Zr-based BMGs with suitable size for applications

A capsule network-based method for identifying transcription factors

Transcription factors (TFs) are typical regulators for gene expression and play versatile roles in cellular processes. Since it is time-consuming, costly, and labor-intensive to detect it by using physical methods, it is desired to develop a computational method to detect TFs. Here, we presented a capsule network-based method for identifying TFs. This method is an end-to-end deep learning method, consisting mainly of an embedding layer, bidirectional long short-term memory (LSTM) layer, capsule network layer, and three fully connected layers. The presented method obtained an accuracy of 0.8820, being superior to the state-of-the-art methods. These empirical experiments showed that the inclusion of the capsule network promoted great performances and that the capsule network-based representation was superior to the property-based representation for distinguishing between TFs and non-TFs. We also implemented the presented method into a user-friendly web server, which is freely available at http://www.biolscience.cn/Capsule_TF/ for all scientific researchers

Survival and Clinicopathological Significance of SIRT1 Expression in Cancers: A Meta-Analysis

Background: Silent information regulator 2 homolog 1 (SIRT1) is an evolutionarily conserved enzymes with nicotinamide adenine dinucleotide (NAD)+-dependent deacetylase activity. SIRT1 is involved in a large variety of cellular processes, such as genomic stability, energy metabolism, senescence, gene transcription, and oxidative stress. SIRT1 has long been recognized as both a tumor promoter and tumor suppressor. Its prognostic role in cancers remains controversial.Methods: A meta-analysis of 13,138 subjects in 63 articles from PubMed, EMBASE, and Cochrane Library was performed to evaluate survival and clinicopathological significance of SIRT1 expression in various cancers.Results: The pooled results of meta-analysis showed that elevated expression of SIRT1 implies a poor overall survival (OS) of cancer patients [Hazard Ratio (HR) = 1.566, 95% CI: 1.293–1.895, P < 0.0001], disease free survival (DFS) (HR = 1.631, 95% CI: 1.250–2.130, P = 0.0003), event free survival (EFS) (HR = 2.534, 95% CI: 1.602–4.009, P = 0.0001), and progress-free survival (PFS) (HR = 3.325 95% CI: 2.762–4.003, P < 0.0001). Elevated SIRT1 level was associated with tumor stage [Relative Risk (RR) = 1.299, 95% CI: 1.114–1.514, P = 0.0008], lymph node metastasis (RR = 1.172, 95% CI: 1.010–1.360, P = 0.0363), and distant metastasis (RR = 1.562, 95% CI: 1.022–2.387, P = 0.0392). Meta-regression and subgroup analysis revealed that ethnic background has influence on the role of SIRT1 expression in predicting survival and clinicopathological characteristics of cancers. Overexpression of SIRT1 predicted a worse OS and higher TNM stage and lymphatic metastasis in Asian population especially in China.Conclusion: Our data suggested that elevated expression of SIRT1 predicted a poor OS, DFS, EFS, PFS, but not for recurrence-free survival (RFS) and cancer-specific survival (CCS). SIRT1 overexpression was associated with higher tumor stage, lymph node metastasis, and distant metastasis. SIRT1-mediated molecular events and biological processes could be an underlying mechanism for metastasis and SIRT1 is a therapeutic target for inhibiting metastasis, leading to good prognosis

DeepHLAPred: a deep learning-based method for non-classical HLA binder prediction

Abstract Human leukocyte antigen (HLA) is closely involved in regulating the human immune system. Despite great advance in detecting classical HLA Class I binders, there are few methods or toolkits for recognizing non-classical HLA Class I binders. To fill in this gap, we have developed a deep learning-based tool called DeepHLAPred. The DeepHLAPred used electron-ion interaction pseudo potential, integer numerical mapping and accumulated amino acid frequency as initial representation of non-classical HLA binder sequence. The deep learning module was used to further refine high-level representations. The deep learning module comprised two parallel convolutional neural networks, each followed by maximum pooling layer, dropout layer, and bi-directional long short-term memory network. The experimental results showed that the DeepHLAPred reached the state-of-the-art performanceson the cross-validation test and the independent test. The extensive test demonstrated the rationality of the DeepHLAPred. We further analyzed sequence pattern of non-classical HLA class I binders by information entropy. The information entropy of non-classical HLA binder sequence implied sequence pattern to a certain extent. In addition, we have developed a user-friendly webserver for convenient use, which is available at http://www.biolscience.cn/DeepHLApred/ . The tool and the analysis is helpful to detect non-classical HLA Class I binder. The source code and data is available at https://github.com/tangxingyu0/DeepHLApred

traversal fields for ray tracing dynamic scenes

This paper presents a novel scheme for accelerating ray traversal computation in ray tracing. By the scheme, a pre-computed stage is applied to constructing what is called a traversal field for each rigid object that records the destinations for all poss

Parameter Optimization of Droop Controllers for Microgrids in Islanded Mode by the SQP Method with Gradient Sampling

For enhancing the stability of the microgrid operation, this paper proposes an optimization model considering the small-signal stability constraint. Due to the nonsmooth property of the spectral abscissa function, the droop controller parameters’ optimization is a nonsmooth optimization problem. The Sequential Quadratic Programming with Gradient Sampling (SQP-GS) is implemented to optimize the droop controller parameters for solving the nonsmooth problem. The SQP-GS method can guarantee the solution of the optimization problem globally and efficiently converges to stationary points with probability of one. In the current iteration, the gradient of the nonsmooth function can be evaluated on a set of randomly generated nearby points by computing closed-form sensitivities. A test on the microgrid system shows that the optimality and the efficiency of the SQP-GS are better than those of the heuristic algorithms

A Unified Equation to Predict the Permeability of Rough Fractures via Lattice Boltzmann Simulation

In this paper, the fluid flow through rough fractures was investigated via numerical simulation based on the lattice Boltzmann method (LBM). The accuracy of LBM was validated through the numerical simulation of the parallel plate model and the verification of the mass conservation of fluid flow through rough fracture. After that, the effect of roughness on fluid flow was numerically conducted, in which, the geometry of fractures was characterized by the joint roughness coefficient (JRC), fractal dimension (D) and standard deviation (σ). It was found that the JRC cannot reflect the realistic influence of roughness on the permeability of single fracture, in which, an increase in permeability with increasing JRC has been observed at the range of 8~12 and 14~16. The reason behind this was revealed through the calculation of the root mean square of the first derivative of profile (Z2), and an equation has been proposed to estimate the permeability based on the aperture and Z2 of the fracture. The numerical simulations were further conducted on fluid flow though synthetic fractures with a wide range of D and σ. In order to unify the parameter that characterizes the roughness, Z2 was obtained for each synthetic fracture, and the corresponding relationship between permeability, aperture and Z2 was analyzed. Meanwhile, it was found that the fluid flow behaves differently with different ranges of Z2 and the critical point was found to be Z2 = 0.5. Based on extensive study, it was concluded that Z2 is a generic parameter characterizing the roughness, and the proposed equation could be used to predict the permeability for fluid flow in fracture

LightGBM-LncLoc: A LightGBM-Based Computational Predictor for Recognizing Long Non-Coding RNA Subcellular Localization

Long non-coding RNAs (lncRNA) are a class of RNA transcripts with more than 200 nucleotide residues. LncRNAs play versatile roles in cellular processes and are thus becoming a hot topic in the field of biomedicine. The function of lncRNAs was discovered to be closely associated with subcellular localization. Although many methods have been developed to identify the subcellular localization of lncRNAs, there still is much room for improvement. Herein, we present a lightGBM-based computational predictor for recognizing lncRNA subcellular localization, which is called LightGBM-LncLoc. LightGBM-LncLoc uses reverse complement k-mer and position-specific trinucleotide propensity based on the single strand for multi-class sequences to encode LncRNAs and employs LightGBM as the learning algorithm. LightGBM-LncLoc reaches state-of-the-art performance by five-fold cross-validation and independent test over the datasets of five categories of lncRNA subcellular localization. We also implemented LightGBM-LncLoc as a user-friendly web server