The effect of the hydration degree on the hydrothermal and thermo-oxidative stability of some collageneous matrices

The methods of the thermal analysis (TG, DTG and DTA) were used in order to investigate the effect of the hydration degree on the thermal behaviour of some collageneous matrices. It was pointed out that the degradation of hydrated collagen in the temperature range 20-400°C occurs through two successive processes accompanied by mass losses. The first process, consisting in the collagen dehydration, is endothermic and takes place in the temperature range ≈25 - ≈125°C. The second process is exothermic and consists in the decomposition and/or thermo-oxidation of dry collagen. The thermal parameters of both processes depend on the hydration degree of collagen. The observed dependencies show that the hydrothermal and thermo-oxidative stability of collagen are strongly correlated with its water content. This revised version was published online in July 2006 with corrections to the Cover Date.     

An ensemble learning framework for potential miRNA-disease association prediction with positive-unlabeled data

To explore the pathogenic mechanisms of MicroRNA (miRNA) on diverse diseases, many researchers have concentrated on discovering the potential associations between miRNA and disease using machine learning methods. However, the prediction accuracy of supervised machine learning methods is limited by lacking of experimentally-validated uncorrelated miRNA-disease pairs. Without these negative samples, training a highly accurate model is much more difficult. Different from traditional miRNA-disease prediction models using randomly selected unknown samples as negative training samples, we propose an ensemble learning framework to solve this positive-unlabeled (PU) learning problem. The framework incorporates two steps, i.e., a novel semi-supervised Kmeans (SS-Kmeans) to extract reliable negative samples from unknown miRNA-disease pairs and subagging method to generate diverse training sample sets to make full use of those reliable negative samples for ensemble learning. Combined with effective random vector functional link (RVFL) network as prediction model, the proposed framework showed superior prediction accuracy comparing with other popular approaches. A case study on lung and gastric neoplasms further confirms the framework’s efficacy at identifying miRNA disease associations.     

随机森林结合CatBoost的近红外光谱药品鉴别

药品质量关乎人民健康和国家命脉,随着社会经济的飞速发展对药品质量的快速、有效鉴别具有极其重要的作用。光谱分析技术具有较高的准确性、较快的分析速度且对样品不存在污染等突出优点,广泛应用在化工、石油以及医药等重要的领域。为了解决传统药品鉴别模型存在的鉴别精度低、鉴别速度不能满足实际需求且鉴别模型稳定性差的问题,采用光谱仪采集药品的近红外光谱数据达到对药品无污染鉴别的目的。结合随机森林和CatBoost对药品进行分类鉴别,以实现快速且准确的鉴别。首先采用随机森林(RF)对光谱仪采集的光谱数据进行有效特征波长的筛选,从而将药品光谱数据中的无关波长去除、筛选出最能表征样品属性的特征波长,然后以极限学习机(ELM)作为CatBoost的弱分类器分析筛选的特征波长对药品的属性鉴别。由于ELM仅只含有一个隐含层且无需多次迭代寻优保证了鉴别模型运行速度更快,CatBoost通过集成弱分类器以改善模型鉴别准确性。为对所提出的药品鉴别模型性能进行有效评估,采用随机抽取训练集的方式构造不同规模药品光谱数据并分别上进行独立实验且以10次运行结果的均值作为其最终结果,并通过与CatBoost、持向量机(SVM)、...     

改进的随机蛙跳算法对农机润滑油污染浓度的近红外光谱检测研究

润滑油是农业机械正常作业的必要物资,农业机械发动机工作的动力性、安全性、经济性以及寿命与润滑油状况有着紧密联系。污染浓度作为油液的综合评价指标,常规的实验室检测耗时长、成本高,所以开发高效的润滑油污染浓度检测技术具有重要意义。提出了一种基于近红外光谱技术的农机润滑油污染浓度的检测方法,同时针对随机蛙跳(RF)特征波长选择算法中迭代次数大,结果再现性低等缺点,提出了一种迭代保留信息变量的随机蛙跳(IRIV-RF)特征波长选择算法。该算法一方面利用迭代保留信息变量(IRIV)算法提取出强信息变量和弱信息变量,将其作为RF算法中的初始变量集,消除初始变量集的随机性对结果再现性的影响。另一方面通过对变量按被选概率值由大到小正向排序后,从首个波长开始依次增加一个波长建立偏最小二乘回归(PLSR)模型,选择交叉验证均方根误差(RMSECV)值最小时的变量子集为特征波长,消除RF算法所提取的特征波长数量的不确定性。利用近红外光谱仪采集自行配制的101份不同污染浓度的农机润滑油原始光谱数据,选用三种不同的预处理方法分别对原始光谱进行处理,确定最佳的预处理方法为变量标准化(SNV)。在此基础上通过RF,...     

块α-对角占优矩阵的讨论

应用矩阵块对角占优理论,讨论了块α-对角占优矩阵之间的蕴含关系,并得到了条件最弱的块严格α1-双对角占优的两个等价表征,并作为应用给出了块H矩阵新的判定准则,最后用数值例子说明结果的有效性.     

一类特殊分块矩阵为循环矩阵的循环分块矩阵的几个性质

本文给出一类特殊分块矩阵为循环矩阵的循环分块矩阵的几个性质。     

Bool阵的逆阵

We give a characterization of a regular Boolean matrix and prove that AB = I Implies that BA = I, where A and B are Boolean matrices whose elements belong to a Boolean algebra of a set with more than two elements.     

正定Hermite矩阵的若干行列式不等式

本文对满足条件Ａ^Ｈ＝Ａ＞０，１／２（Ｂ＋Ｂ^Ｈ）≥０的矩阵Ａ，Ｂ，建立了四个行列式不等式．某些著名的行列式不等式和一些已知结用，均可作为其推论．     

虚拟原子探针随机采样法用于氨基酸结构描述及其构效关系研究

考虑药物与蛋白质受体的3类非键作用模式, 利用8类虚拟原子探针和Monte Carlo随机采样技术, 得到了一套新的氨基酸侧链表面静电、立体及疏水势能场(ASSPF)参数. 在此基础上对苦味二肽和血管舒缓激五肽进行了结构表征和QSAR研究, 所建模型复相关系数R2和留一法交互检验复相关系数QLOOCV2分别为0.8457, 0.851和0.7688, 0.7952, 同时分析了肽链不同位置上氨基酸侧链对活性的影响, 取得较好的结果.     

A note on simultaneous preconditioning and symmetrization of non‐symmetric linear systems

Motivated by the theory of self‐duality that provides a variational formulation and resolution for non‐self‐adjoint partial differential equations (Ann. Inst. Henri Poincaré (C) Anal Non Linéaire 2007; 24 :171–205; Selfdual Partial Differential Systems and Their Variational Principles. Springer: New York, 2008), we propose new templates for solving large non‐symmetric linear systems. The method consists of combining a new scheme that simultaneously preconditions and symmetrizes the problem, with various well‐known iterative methods for solving linear and symmetric problems. The approach seems to be efficient when dealing with certain ill‐conditioned, and highly non‐symmetric systems. The numerical and theoretical results are provided to show the efficiency of our approach. Copyright © 2010 John Wiley & Sons, Ltd.