A Conjugate Gradient Algorithm with a Trust Region for Molecular Geometry Optimization

An algorithm is presented for the optimization of molecular geometries and general nonquadratic functions using the nonlinear conjugate gradient method with a restricted step and restart procedure. The algorithm only requires the evaluation of the energy function and its gradient, therefor less memory storage is needed than for other conjugate gradient algorithms. Some numerical results are also presented and the efficiency and behaviour of the algorithm is compared with the standard conjugate gradient method. We also present comparisons of both conjugate gradient and variable metric methods with and without the trust region technique. One of the main conclusions of the present work is that a trust region always improves the converge of any optimization method. A sketch of the algorithm is also given.     

Two New PRP Conjugate Gradient Algorithms for Minimization Optimization Models

Two new PRP conjugate Algorithms are proposed in this paper based on two modified PRP conjugate gradient methods: the first algorithm is proposed for solving unconstrained optimization problems, and the second algorithm is proposed for solving nonlinear equations. The first method contains two aspects of information: function value and gradient value. The two methods both possess some good properties, as follows: 1)β _k ≥ 0 2) the search direction has the trust region property without the use of any line search method 3) the search direction has sufficient descent property without the use of any line search method. Under some suitable conditions, we establish the global convergence of the two algorithms. We conduct numerical experiments to evaluate our algorithms. The numerical results indicate that the first algorithm is effective and competitive for solving unconstrained optimization problems and that the second algorithm is effective for solving large-scale nonlinear equations.     

A Limited-Memory BFGS Algorithm Based on a Trust-Region Quadratic Model for Large-Scale Nonlinear Equations

In this paper, a trust-region algorithm is proposed for large-scale nonlinear equations, where the limited-memory BFGS (L-M-BFGS) update matrix is used in the trust-region subproblem to improve the effectiveness of the algorithm for large-scale problems. The global convergence of the presented method is established under suitable conditions. The numerical results of the test problems show that the method is competitive with the norm method.     

Design of Metasurface-Based Multi-layer THz Filters Utilizing Optimization Algorithm with Distinct Fitness Function Definitions

In this paper, an evolutionary optimization procedure is presented to generate band-pass metasurface-based filters in terahertz regime. As a measure of novelty, pass-band, transition, and out-band characteristics are investigated separately, all of which result in different metasurfaces for filtering applications. The presented approach is defined based on random hill climbing algorithm, regarding the established link between Matlab and HFSS software. A metasurface-based filter with specific properties is considered as the main problem to be solved by the optimization method. Moreover, the fuzzy theory, mean square method, and weighting coefficient procedure are considered to define an efficient fitness function evaluation approach. Also, a step-by-step procedure is used to generate desired structures with a great note of efficiency. The final generated structure has magnificent characteristics including sharp transitions together with transmittance around 0.68 and less than 0.04 at pass-band and out-band regions, respectively. Also, the generated metasurface benefits from wide bandwidth (65%) and great compactness compared to other previous works.

     

Scavager: A Versatile Postsearch Validation Algorithm for Shotgun Proteomics Based on Gradient Boosting

Shotgun proteomics workflows for database protein identification typically include a combination of search engines and postsearch validation software based mostly on machine learning algorithms. Here, a new postsearch validation tool called Scavager employing CatBoost, an open‐source gradient boosting library, which shows improved efficiency compared with the other popular algorithms, such as Percolator, PeptideProphet, and Q‐ranker, is presented. The comparison is done using multiple data sets and search engines, including MSGF+, MSFragger, X!Tandem, Comet, and recently introduced IdentiPy. Implemented in Python programming language, Scavager is open‐source and freely available at https://bitbucket.org/markmipt/scavager .     

A Conjugate of Cellulase with Fluorescein Isothiocyanate: A Specific Stain for Cellulose

A fluorescent technique has been developed for in situ staining of cellulose. The staining agent is a conjugate of cellulase and fluorescein isothiocyanatc (FITC). Application of this agent does not disturb intercellular or intracellular substances. The technique depends on the specific binding of the fluorescent labeled enzyme to its substrate. The stain has been tested on cell-free noncellulose polysaccharides similar to cellulose and does not stain them. The technique has been used to localize cellulose during the life cycle of Dictyostelium discoideum with results that correspond to previous work using other methods.     

FctClus: A Fast Clustering Algorithm for Heterogeneous Information Networks

It is important to cluster heterogeneous information networks. A fast clustering algorithm based on an approximate commute time embedding for heterogeneous information networks with a star network schema is proposed in this paper by utilizing the sparsity of heterogeneous information networks. First, a heterogeneous information network is transformed into multiple compatible bipartite graphs from the compatible point of view. Second, the approximate commute time embedding of each bipartite graph is computed using random mapping and a linear time solver. All of the indicator subsets in each embedding simultaneously determine the target dataset. Finally, a general model is formulated by these indicator subsets, and a fast algorithm is derived by simultaneously clustering all of the indicator subsets using the sum of the weighted distances for all indicators for an identical target object. The proposed fast algorithm, FctClus, is shown to be efficient and generalizable and exhibits high clustering accuracy and fast computation speed based on a theoretic analysis and experimental verification.     

A Discrete Fruit Fly Optimization Algorithm for the Traveling Salesman Problem

The fruit fly optimization algorithm (FOA) is a newly developed bio-inspired algorithm. The continuous variant version of FOA has been proven to be a powerful evolutionary approach to determining the optima of a numerical function on a continuous definition domain. In this study, a discrete FOA (DFOA) is developed and applied to the traveling salesman problem (TSP), a common combinatorial problem. In the DFOA, the TSP tour is represented by an ordering of city indices, and the bio-inspired meta-heuristic search processes are executed with two elaborately designed main procedures: the smelling and tasting processes. In the smelling process, an effective crossover operator is used by the fruit fly group to search for the neighbors of the best-known swarm location. During the tasting process, an edge intersection elimination (EXE) operator is designed to improve the neighbors of the non-optimum food location in order to enhance the exploration performance of the DFOA. In addition, benchmark instances from the TSPLIB are classified in order to test the searching ability of the proposed algorithm. Furthermore, the effectiveness of the proposed DFOA is compared to that of other meta-heuristic algorithms. The results indicate that the proposed DFOA can be effectively used to solve TSPs, especially large-scale problems.     

A Modified Ant Colony Optimization Algorithm for Tumor Marker Gene Selection

Microarray data are often extremely asymmetric in dimensionality,such as thousands or even tens of thousands of genes but only a few hundreds of samples or less.Such extreme asymmetry between the dimensionality of genes and samples can lead to inaccurate diagnosis of disease in clinic.Therefore,it has been shown that selecting a small set of marker genes can lead to improved classification accuracy.In this paper,a simple modified ant colony optimization (ACO) algorithm is proposed to select tumor-related ma...     

广义Kolmogorov模型的Lyapunov函数构造新算法及其应用

本文对广义Kolmogorov模型,给出构造Lyapunov函数的新算法,在文献1中只对其中某些特殊类型给出几种特殊的构造方法,而本文给出的是这类模型的一般新算法,应用较广泛。     

Optimization of Terminal-Restriction Fragment Length Polymorphism Analysis for Complex Marine Bacterioplankton Communities and Comparison with Denaturing Gradient Gel Electrophoresis

The potential of terminal-restriction fragment length polymorphism (T-RFLP) and the detection of operational taxonomic units (OTUs) by capillary electrophoresis (CE) to characterize marine bacterioplankton communities was compared with that of denaturing gradient gel electrophoresis (DGGE). A protocol has been developed to optimize the separation and detection of OTUs between 20 and 1,632 bp by using CE and laser-induced fluorescence detection. Additionally, we compared T-RFLP fingerprinting to DGGE optimized for detection of less abundant OTUs. Similar results were obtained with both fingerprinting techniques, although the T-RFLP approach and CE detection of OTUs was more sensitive, as indicated by the higher number of OTUs detected. We tested the T-RFLP fingerprinting technique on complex marine bacterial communities by using the 16S rRNA gene and 16S rRNA as templates for PCR. Samples from the Northern and Middle Adriatic Sea and from the South and North Aegean Sea were compared. Distinct clusters were identifiable for different sampling sites. Thus, this technique is useful for rapid evaluation of the biogeographical distribution and relationships of bacterioplankton communities.     

Central-Radial Artery Pressure Gradient after Cardiopulmonary Bypass Is Associated with Cardiac Function and May Affect Therapeutic Direction

Objective

To investigate the risk factors involved in radial-femoral artery pressure gradient after cardiac surgery.

Methods

In this retrospective study, we reviewed 412 cardiac surgeries with both femoral artery pressure and radial artery pressure monitoring before cardiopulmonary bypass. 138 patients had radial-femoral artery pressure gradient after cardiopulmonary bypass (group P) but 263 were not (group N). Their hemodynamic data and other demographic data were analyzed.

Results

Phenylephrine usage was 1.7±1.1 mg in group N and 2.9±1.2 mg in group P (P<0.001). Total adrenaline usage was 229.2±116.9 µg in group N and 400.6±145.1 µg in group P (P<0.001). SBP gradient was -4±3, 14±9, 10±4, 0±11 mmHg in group P and -3±3, 0±1, -1±9, -6±4 mmHg in group N after induction, during discontinuation of CPB, at the end of surgery and 1 postoperative day respectively. DBP gradient was 3±3, -1±9, 4±5, 0±8 mmHg in group P and 3±3, 5±2, 7±5, 0±8 mmHg in group N after induction, during discontinuation of CPB, at the end of surgery and 1 postoperative day respectively. MAP gradient was 1±2, 4±6, 6±4, 0±8 mmHg in group P and 1±2, 3±1, 1±4, -2±5 mmHg in group N after induction, during discontinuation of CPB, at the end of surgery and 1 postoperative day respectively. Significant arterial pressure gradient emerged during discontinuation of CPB and at the end of surgery, which was more obvious in group P(P<0.01). CI was 2.0±0.3, 2.3±0.4,2.3±0.4, 2.2±0.4 L/min/m² in group P and 2.1±0.3, 2.8±0.5,2.8±0.5, 2.8±0.5 L/min/m² in group N at baseline, after discontinuation of CPB, at the end of surgery and the first postoperative day (P<0.001).

Conclusion

Detecting the exact central artery pressure is most important when patients have artery pressure gradients after cardiac surgery. Use inotropic agents to improve cardiac output, avoiding excessive vasoconstriction might reduce artery pressure gradient.     

A Newton Cooperative Genetic Algorithm Method for In Silico Optimization of Metabolic Pathway Production

This paper presents an in silico optimization method of metabolic pathway production. The metabolic pathway can be represented by a mathematical model known as the generalized mass action model, which leads to a complex nonlinear equations system. The optimization process becomes difficult when steady state and the constraints of the components in the metabolic pathway are involved. To deal with this situation, this paper presents an in silico optimization method, namely the Newton Cooperative Genetic Algorithm (NCGA). The NCGA used Newton method in dealing with the metabolic pathway, and then integrated genetic algorithm and cooperative co-evolutionary algorithm. The proposed method was experimentally applied on the benchmark metabolic pathways, and the results showed that the NCGA achieved better results compared to the existing methods.     

Uncertainty and Denial: A Resource-Rational Model of the Value of Information

Classical decision theory predicts that people should be indifferent to information that is not useful for making decisions, but this model often fails to describe human behavior. Here we investigate one such scenario, where people desire information about whether an event (the gain/loss of money) will occur even though there is no obvious decision to be made on the basis of this information. We find a curious dual trend: if information is costless, as the probability of the event increases people want the information more; if information is not costless, people''s desire for the information peaks at an intermediate probability. People also want information more as the importance of the event increases, and less as the cost of the information increases. We propose a model that explains these results, based on the assumption that people have limited cognitive resources and obtain information about which events will occur so they can determine whether to expend effort planning for them.     

Environmental Unpredictability and the Value of Social Information for Foraging Starlings

Environmental and behavioral cues are useful sources of information that allow group foraging individuals to improve their foraging success. Few studies to date, however, have examined how varying degrees of environmental unpredictability may affect when and how individuals use the social information they obtain in foraging groups. In this experiment, European starlings (Sturnus vulgaris) were tested to determine in which type of environment, predictable or unpredictable, social information would be the most valuable. Subjects were placed under one of four conditions: an unpredictable environment with either (1) an informing demonstrator bird or (2) an uninforming demonstrator; or a predictable environment with either (3) an informing demonstrator or (4) an uninforming demonstrator. Environmental predictability was manipulated by altering the meaning of available color cues. Subjects in the unpredictable environment that had an informing demonstrator performed significantly better than subjects in an unpredictable environment with an uninforming demonstrator, although only on the second day of testing. Subjects in both the predictable conditions performed similarly to each other. The results suggest that social information is more valuable to individuals in an unpredictable environment than it is in a predictable environment; however, there appears to be a time lag in the ability of the birds to recognize the value of this information.     

An Efficient Optimization Algorithm for Structured Sparse CCA,with Applications to eQTL Mapping

In this paper we develop an efficient optimization algorithm for solving canonical correlation analysis (CCA) with complex structured-sparsity-inducing penalties, including overlapping-group-lasso penalty and network-based fusion penalty. We apply the proposed algorithm to an important genome-wide association study problem, eQTL mapping. We show that, with the efficient optimization algorithm, one can easily incorporate rich structural information among genes into the sparse CCA framework, which improves the interpretability of the results obtained. Our optimization algorithm is based on a general excessive gap optimization framework and can scale up to millions of variables. We demonstrate the effectiveness of our algorithm on both simulated and real eQTL datasets.     

GUS在植物基因功能研究中的应用和优化

β-葡糖醛酸酶(GUS)报告系统是现代分子生物学研究领域中被广泛使用的一种重要工具,在解析基因时空表达调控的研究中发挥着重要作用.本文概述了报告基因GUS的生化特性及检测手段,从启动子元件鉴定、基因诱捕、无标记转基因技术等方面论述GUS的应用现状和优势,并针对内源GUS、GUS抑制因子等问题和改进优化手段进行了分析,为该技术在植物功能基因研究中进一步拓展提供新线索和思路.     

The Value of Myocardial Torsion and Aneurysm Volume for Evaluating Cardiac Function in Rabbit with Left Ventricular Aneurysm

Objective

This study aimed to investigate the effect of left ventricular aneurysm (LVA) volume and left ventricular global torsion on cardiac function by real time three-dimensional echocardiography (RT-3DE) and two-dimensional speckle tracking imaging(2D-STI), to determine the accuracy of RT-3DE and 2D-STI in assessing LV function.

Methods

Thirty New Zealand rabbit models of with LVA were prepared by ligation of the middle segment of the left anterior descending and left circumflex arteries. Four weeks post-procedure, RT-3DE was conducted to obtain data on LVEF, left ventricular end-diastolic volume (LVEDV), left ventricular end-systolic volume (LVESV), and LVA volume (LVAV), Peak rotation angles at the mitral valve annulus level (MV-ROT), peak rotation angles at the apical level (AP-ROT), and left ventricular global torsion angles (LV-TOR) were measured by 2D-STI.

Results

Compared with controls, LVEDV and LVESV were significantly increased in the LVA group, while LVEF, MV-ROT, AP-ROT, and LV-TOR were consistently reduced (p<0.01). Moreover, LVEF correlated with LVA volume and LV torsion angle (r= -0.778 and 0.821, p<0.01). LVA volume/LVEDV had the strongest inverse relationship with LVEF (r= -0.911, p<0.01).

Conclusion

LVA volume, LVA volume/LVEDV, and LV torsion may be used as an indicator for evaluation of cardiac function after LVA. Moreover, LVA volume/LVEDV may be a more sensitive and reliable marker of cardiac function after LVA formation.