基于分窗Gram-Schmidt变换和PSO-SVR算法的水稻纹枯病病情指数检测

纹枯病是水稻的主要病害之一,其防治对于保证水稻产量、质量具有重要意义,以高光谱检测水稻病害得到了广泛应用,并且高光谱降维是光谱分析的重要环节。该研究在2019年沈农水稻试验基地获取水稻低空遥感冠层与地面冠层高光谱,并对其进行以窗口宽度为15和阶数为3的Savitzky-Golay平滑处理和光谱变换（得到原始光谱、一阶微分光谱和倒数之对数光谱）,分窗口对这3种光谱分别进行Gram-Schmidt变换,找到投影空间并映射出主基底,实现高光谱数据降维,绘制具有显著性概率的主基底,其极大极小值为特征波段。此外3种光谱还采用了主成分分析和连续投影法降维。以降维后的数据与水稻纹枯病病情指数进行支持向量机回归建模,其中支持向量机回归进行粒子群优化,并以径向基为核函数,对比分析了3种降维方式的降维效果。结果表明：水稻地面冠层尺度建模效果高于低空遥感尺度建模;在光谱处理方面,低空冠层高光谱进行倒数之对数变换效果较好,地面冠层所得高光谱数据进行一阶微分变换效果较好;分窗Gram-Schmidt变换算法优于主成分分析和连续投影法;粒子群算法可以优化支持向量机中的惩罚系数和核函数参数,提高其反演精度;无人机低空遥感尺度中,高光谱进行倒数之对数处理,以分窗Gram-Schmidt变换降维,敏感波段为427.3,539.6,749.5和825.4 nm,PSO-SVR建模决定系数R2为0.731,均方根误差RMSE为0.151;地面冠层尺度中,高光谱进行一阶微分处理,以分窗Gram-Schmidt变换降维,敏感波段为552,607,702和730 nm,PSO-SVR模型决定系数R2为0.778,均方根误差RMSE为0.147。因此,高光谱技术可以有效地检测水稻纹枯病,并且其病情指数可用冠层高光谱进行反演,分窗Gram-Schmidt变换对于高光谱数据降维有较好的效果,PSO-SVR建模对于水稻纹枯病病情指数的反演有明显提高,结果可为冠层尺度检测水稻纹枯病与病害发生情况提供一定的理论基础和技术支撑。     

Stability of disease free sets in epidemic models

In this paper the attractivity properties of disease free subsets are considered in the context of disease transmission models. Sufficient conditions are derived for the existence of stable disease free subsets in a general compartmental disease transmission model. The conditions are stated in terms of the system linearized along the trajectories limited to a subset of disease free states. The proof is in the framework of the classical direct method of Lyapunov. As illustrations of the result a multigroup SIRS vaccination model and a Lotka–Volterra system with prey epidemic interaction are presented.     

Functionalized carbon nanotubes and nanofibers for biosensing applications

This review summarizes recent advances in electrochemical biosensors based on carbon nanotubes (CNTs) and carbon nanofibers (CNFs) with an emphasis on applications of CNTs. CNTs and CNFs have unique electric, electrocatalytic and mechanical properties, which make them efficient materials for developing electrochemical biosensors.We discuss functionalizing CNTs for biosensors. We review electrochemical biosensors based on CNTs and their various applications (e.g., measurement of small biological molecules and environmental pollutants, detection of DNA, and immunosensing of disease biomarkers). Moreover, we outline the development of electrochemical biosensors based on CNFs and their applications. Finally, we discuss some future applications of CNTs.     

An Activatable NIR-II Fluorescent Reporter for In Vivo Imaging of Amyloid-β Plaques

Fluorescence imaging in the second near-infrared (NIR-II) window holds great promise for in vivo visualization of amyloid-β (Aβ) pathology, which can facilitate characterization and deep understanding of Alzheimer's disease (AD); however, it has been rarely exploited. Herein, we report the development of NIR-II fluorescent reporters with a donor-π-acceptor (D-π-A) architecture for specific detection of Aβ plaques in AD-model mice. Among all the designed probes, DMP2 exhibits the highest affinity to Aβ fibrils and can specifically activate its NIR-II fluorescence after binding to Aβ fibrils via suppressed twisted intramolecular charge transfer (TICT) effect. With suitable lipophilicity for ideal blood–brain barrier (BBB) penetrability and deep-tissue penetration of NIR-II fluorescence, DMP2 possesses specific detection of Aβ plaques in in vivo AD-model mice. Thus, this study presents a potential agent for non-invasive imaging of Aβ plaques and deep deciphering of AD progression.     

Penalized Fast Subset Scanning

We present the penalized fast subset scan (PFSS), a new and general framework for scalable and accurate pattern detection. PFSS enables exact and efficient identification of the most anomalous subsets of the data, as measured by a likelihood ratio scan statistic. However, PFSS also allows incorporation of prior information about each data element’s probability of inclusion, which was not previously possible within the subset scan framework. PFSS builds on two main results: first, we prove that a large class of likelihood ratio statistics satisfy a property that allows additional, element-specific penalty terms to be included while maintaining efficient computation. Second, we prove that the penalized statistic can be maximized exactly by evaluating only O(N) subsets. As a concrete example of the PFSS framework, we incorporate “soft” constraints on spatial proximity into the spatial event detection task, enabling more accurate detection of irregularly shaped spatial clusters of varying sparsity. To do so, we develop a distance-based penalty function that rewards spatial compactness and penalizes spatially dispersed clusters. This approach was evaluated on the task of detecting simulated anthrax bio-attacks, using real-world Emergency Department data from a major U.S. city. PFSS demonstrated increased detection power and spatial accuracy as compared to competing methods while maintaining efficient computation.     

Perceptron ensemble of graph-based positive-unlabeled learning for disease gene identification

Identification of disease genes, using computational methods, is an important issue in biomedical and bioinformatics research. According to observations that diseases with the same or similar phenotype have the same biological characteristics, researchers have tried to identify genes by using machine learning tools. In recent attempts, some semi-supervised learning methods, called positive-unlabeled learning, is used for disease gene identification. In this paper, we present a Perceptron ensemble of graph-based positive-unlabeled learning (PEGPUL) on three types of biological attributes: gene ontologies, protein domains and protein-protein interaction networks. In our method, a reliable set of positive and negative genes are extracted using co-training schema. Then, the similarity graph of genes is built using metric learning by concentrating on multi-rank-walk method to perform inference from labeled genes. At last, a Perceptron ensemble is learned from three weighted classifiers: multilevel support vector machine, k-nearest neighbor and decision tree. The main contributions of this paper are: (i) incorporating the statistical properties of gene data through choosing proper metrics, (ii) statistical evaluation of biological features, and (iii) noise robustness characteristic of PEGPUL via using multilevel schema. In order to assess PEGPUL, we have applied it on 12950 disease genes with 949 positive genes from six class of diseases and 12001 unlabeled genes. Compared with some popular disease gene identification methods, the experimental results show that PEGPUL has reasonable performance.     

Nanobioelectroanalysis Based on Carbon/Inorganic Hybrid Nanoarchitectures

The emergence of nanotechnology has opened new horizons for electrochemical biosensors. This review highlights new concepts for electrochemical biosensors based on different carbon/inorganic hybrid nanoarchitectures. Particular attention will be given to hybrid nanostructures involving 1‐ or 2‐dimensional carbon nanotubes or graphene along with inorganic nanoparticles (gold, platinum, quantum dot (QD), metal oxide). Latest advances (from 2007 onwards) in electrochemical biosensors based on such hybrids of carbon/inorganic‐nanomaterial heterostructures are discussed and illustrated in connection to enzyme electrodes for blood glucose or immunoassays of cancer markers. Several strategies for using carbon/inorganic nanohybrids in such bioaffinity and biocatalytic sensing are described, including the use of hybrid nanostructures for tagging or modifying electrode transducers, use of inorganic nanomaterials as surface modifiers along with carbon nanomaterial label carriers, and carbon nanostructure‐based electrode transducers along with inorganic amplification tags. The implications of these nanoscale bioconjugated hybrid materials on the development of modern electrochemical biosensors are discussed along with future prospects and challenges.     

Silicon nanowire biosensor and its applications in disease diagnostics: A review

Over the past decade, silicon nanowire (SiNW) biosensors have been studied for the detection of biological molecules as highly sensitive, label-free, and electrical tools. Herein we present a comprehensive review about the fabrication of SiNW biosensors and their applications in disease diagnostics. We discuss the detection of important biomarkers related to diseases including cancer, cardiovascular diseases, and infectious diseases. SiNW biosensors hold great promise to realize point-of-care (POC) devices for disease diagnostics with potential for miniaturization and integration.     

转反义ACS基因番茄和普通番茄果实中锌含量和钠钾比与抗病性的关系研究

植物激素与矿质元素吸收以及与植物抗性的关系研究是近年来人们关注的热点。实验采用电感耦合等离子体原子发射光谱法(ICP-AES)测定转反义ACS基因耐贮番茄(乙烯的生物合成被抑制)及同品种普通番茄果实(对照)中Zn,Na,K元素的含量及Na/K比例的差异,并对其与果实抗病原菌侵染能力的关系进行讨论。结果表明：乙烯生物合成受阻的转反义ACS番茄果实抗病原菌侵染的能力高于对照果实,其Zn元素的含量是普通番茄果实的1.5倍;K元素含量在两种果实中无明显差异,但Na元素含量显著高于对照,Na/K比值是对照的2.0倍。说明Zn元素含量和Na和K元素的比例关系可能在转反义ACS番茄果实抗病原菌侵染的过程中起到一定作用。     

病害胁迫下棉花叶片色素含量高光谱遥感估测研究

通过小区和大田同步调查棉花黄萎病,在不同生育期测定病叶光谱及其色素含量。将病叶光谱反射率、一阶微分及相应的特征参数与色素含量进行相关分析,建立病叶色素含量估测模型并检验。结果表明：病叶叶绿素a,b及a+b含量可见光反射率、与一阶微分光谱在蓝边、黄边和红边处与除红边振幅(Dr)外的其他光谱特征参数间均达极显著相关。转换叶绿素吸收反射指数(TCARI)和新建归一化植被指数(NDVI[702, 758])对叶绿素a, b及a+b含量的估测精度最高,相对误差均小于1.3%。考虑到NDVI[702, 758]建立的模型更实用,可做为病叶叶绿素a, b和a+b含量的最佳估测模型。研究结果对高光谱信息定量估测病害棉叶色素含量,对利用高光谱监测棉花长势及病害影响评价均具有较高的实用价值。