Dynamic stability of a class of fractional-order nonlinear systems via fixed point theory

In this paper, the problem of the uniform stability for a class of fuzzy fractional-order genetic regulatory networks with random discrete delays, distributed delays, and parameter uncertainties is studied. Although there is a portion of literature on using fixed point theorems to study the stability of fractional neural networks, most of them required the fractional order to be in $\left(\frac{1}{2},1\right)$. However, the case of the fractional-order belonging to $(0,\frac{1}{2})$ has not been discussed. To solve it, this work proposes a novel idea of using fixed point theory to study the stability of fuzzy (0,1) order neural networks, the problem of the uniqueness of the solution of the considered genetic regulatory networks is resolved, and a novel sufficient condition to guarantee the uniform stability of above genetic regulatory networks is also derived. Eventually, an example is given to demonstrate that the obtained result is effective.     

Inference of gene regulatory networks with multi-objective cellular genetic algorithm

Reverse engineering of biochemical networks remains an important open challenge in computational systems biology. The goal of model inference is to, based on time-series gene expression data, obtain the sparse topological structure and parameters that quantitatively understand and reproduce the dynamics of biological systems. In this paper, we propose a multi-objective approach for the inference of S-System structures for Gene Regulatory Networks (GRNs) based on Pareto dominance and Pareto optimality theoretical concepts instead of the conventional single-objective evaluation of Mean Squared Error (MSE). Our motivation is that, using a multi-objective formulation for the GRN, it is possible to optimize the sparse topology of a given GRN as well as the kinetic order and rate constant parameters in a decoupled S-System, yet avoiding the use of additional penalty weights. A flexible and robust Multi-Objective Cellular Evolutionary Algorithm is adapted to perform the tasks of parameter learning and network topology inference for the proposed approach. The resulting software, called MONET, is evaluated on real-based academic and synthetic time-series of gene expression taken from the DREAM3 challenge and the IRMA in vivo datasets. The ability to reproduce biological behavior and robustness to noise is assessed and compared. The results obtained are competitive and indicate that the proposed approach offers advantages over previously used methods. In addition, MONET is able to provide experts with a set of trade-off solutions involving GRNs with different typologies and MSEs.     

An Approach to NMR Assignment of Intrinsically Disordered Proteins

An efficient approach to NMR assignments in intrinsically disordered proteins is presented, making use of the good dispersion of cross peaks observed in [¹⁵N,¹³C′]‐ and [¹³C′,¹H^N]‐correlation spectra. The method involves the simultaneous collection of {3D (H)NCO(CAN)H and 3D (HACA)CON(CA)HA} spectra for backbone assignments via sequential H^N and H^α correlations and {3D (H)NCO(CACS)HS and 3D (HS)CS(CA)CO(N)H} spectra for side‐chain ¹H and ¹³C assignments, employing sequential ¹H data acquisitions with direct detection of both the amide and aliphatic protons. The efficacy of the approach for obtaining resonance assignments with complete backbone and side‐chain chemical shifts is demonstrated experimentally for the 61‐residue [¹³C,¹⁵N]‐labelled peptide of a voltage‐gated potassium channel protein of the Kv1.4 channel subunit. The general applicability of the approach for the characterisation of moderately sized globular proteins is also demonstrated.     

Evolution of regulatory aspects of genotoxic impurities in pharmaceuticals: Survival of the fittest

The genotoxic impurities (GIs) are carcinogenic hence its management during synthesis of pharmaceuticals is very important to be detected even in trace level for the safe use of the drugs. The presence of drug substance/drug product DNA-reactive impurities poses a significant problem for drug regulators as well as industry. There are several regulatory guidelines and position papers focused on controlling the amount of impurities within the specified limits. The present compilation gives an account of updated information about GIs and reviews the regulatory aspects for GIs in active pharmaceutical ingredients/drug formulations. A detailed discussion about control strategies in the context of GIs is also described precisely. The analysis of GIs is a challenging and complex aspect of the drug development process. Control and determination of these impurities at ppm or ppb levels are significant challenges for analysts, therefore the approaches for the analysis of GIs have also been discussed.     

金属调控蛋白的结构、性质及应用

金属调控蛋白是微生物体内通过转录抑制或激活机制严格控制金属离子摄入、外排和储存的特异性金属离子结合蛋白,对维持体内适宜的金属离子浓度和平衡起着举足轻重的作用。本文综述了目前主要的七大家族金属调控蛋白的调控机制和拓扑结构,详细介绍了金属离子结合区域的结构特征和金属-配体的配位构型。基于金属-配体的配位构型,重点讨论了每类金属调控蛋白对目标金属离子特异性选择的机理。此外,本文还介绍了金属调控蛋白在重金属离子检测和吸附方面的应用,拓展了金属调控蛋白的研究和应用领域,同时为生物无机化学的研究方向开辟了新的思路。     

面向UPMIS的控规数据批量转换方法及实现

控规数据向ArcGIS平台数据转换并入库是城市规划管理信息系统（UPMIS）建设的一项基础性工作.针对分析控规数据转换为ArcGIS平台数据存在的问题，提出了一种高效的控规数据向ArcGIS平台数据批量转换的方法，并结合ArcGIS VBA和Python脚本开发工具，实现了上述方法.测试实验表明，该方法自动化程度高、实现简单、处理效率高.     

Computing Integrated Information (Φ) in Discrete Dynamical Systems with Multi-Valued Elements

Integrated information theory (IIT) provides a mathematical framework to characterize the cause-effect structure of a physical system and its amount of integrated information (Φ). An accompanying Python software package (“PyPhi”) was recently introduced to implement this framework for the causal analysis of discrete dynamical systems of binary elements. Here, we present an update to PyPhi that extends its applicability to systems constituted of discrete, but multi-valued elements. This allows us to analyze and compare general causal properties of random networks made up of binary, ternary, quaternary, and mixed nodes. Moreover, we apply the developed tools for causal analysis to a simple non-binary regulatory network model (p53-Mdm2) and discuss commonly used binarization methods in light of their capacity to preserve the causal structure of the original system with multi-valued elements.     

基于随机占优的股指期货市场有效性分析

2015年中金所出台严格监管措施后,股指期货成交低迷,异象频出。为验证严格监管措施对股指期货市场有效性的影响,本文利用随机占优DD测试法,为风险规避者和风险寻求者分别构建升序和降序占优模型。传统DD测试法用等间距划分的方式产生网格,且假设检验的两个判定阈值为相反数,这不符合金融数据的分布规律。因此,本文提出了对DD测试法的两处改进,利用“等频率划分法”产生网格,利用“非对称分位数法”产生阈值。实证方面,本文对三个股指期货及其现货的15分钟数据进行分析,发现在自股灾以来,上证50指数一阶占优于上证50股指期货,形成套利机会,且该套利策略的年化收益率、夏普比率和卡玛比率都非常优异。实证进一步显示,严格监管措施后占优数量达到最高峰,严格监管措施导致的股指期货功能受损比股灾更严重,而这一现象在中金所逐步放松监管措施后得以稍微缓解但没有根本改变。     

An improved robust delay-dependent stability criterion for genetic regulatory networks with interval time delays

This paper proposes an improved robust delay-dependent criterion for stability of genetic regulatory networks with delays which vary in an interval. A modified Lyapunov-Krasovskii functional is used to derive a sufficient condition in terms of linear matrix inequalities (LMIs) which can be easily solved by various convex optimization algorithms. The derived stability criterion is less conservative than ones in the literature. A numerical example and simulation results show the effectiveness of the proposed method.