Hierarchical artificial immune system for crop stage classification

This paper presents a new hierarchical clustering algorithm for crop stage classification using hyperspectral satellite image. Amongst the multiple benefits and uses of remote sensing, one of the important application is to solve the problem of crop stage classification. Modern commercial imaging satellites, owing to their large volume of satellite imagery, offer greater opportunities for automated image analysis. Hence, we propose a unsupervised algorithm namely Hierarchical Artificial Immune System (HAIS) of two steps: splitting the cluster centers and merging them. The high dimensionality of the data has been reduced with the help of Principal Component Analysis (PCA). The classification results have been compared with K-means and Artificial Immune System algorithms. From the results obtained, we conclude that the proposed hierarchical clustering algorithm is accurate.

Growth and Characterization of Vertically Aligned ZnO Hierarchical Nanostructures

Vertically aligned zinc oxide (ZnO) hierarchical nanostructures were developed by homo-epitaxial growth method using nickel as catalyst, and their physical properties were investigated and reported. ZnO nanorods grown by vapor-liquid-solid method are single crystalline and grown along the < 001 > direction, whereas the second order nano-branches are grown along the < 110 > direction. The homo-epitaxial relation between nano-branches (ZnOb) and ZnO cores (ZnOc) is found to be (110)ZnOb//(110)ZnOc and (002)ZnOb//(002)ZnOc. The simple and hierarchical nanostructures exhibited ultra-violet emission peak at 380 nm as near band edge emission of ZnO and have very weak defects related peak at 492 nm. (C) 2013 The Electrochemical Society. All rights reserved.

Compositional hierarchical monitoring automaton construction for LTL

In this paper we give a compositional (or inductive) construction of monitoring automata for LTL formulas. Our construction is similar in spirit to the compositional construction of Kesten and Pnueli [5]. We introduce the notion of hierarchical Büchi automata and phrase our constructions in the framework of these automata. We give detailed constructions for all the principal LTL operators including past operators, along with proofs of correctness of the constructions.

Extension of TSVM to multi-class and hierarchical text classification problems With general losses

Transductive SVM (TSVM) is a well known semi-supervised large margin learning method for binary text classification. In this paper we extend this method to multi-class and hierarchical classification problems. We point out that the determination of labels of unlabeled examples with fixed classifier weights is a linear programming problem. We devise an efficient technique for solving it. The method is applicable to general loss functions. We demonstrate the value of the new method using large margin loss on a number of multi-class and hierarchical classification datasets. For maxent loss we show empirically that our method is better than expectation regularization/constraint and posterior regularization methods, and competitive with the version of entropy regularization method which uses label constraints.

Novel spherical hierarchical structures of GdOOH and Eu:GdOOH: rapid microwave-assisted synthesis through self-assembly, thermal conversion to oxides, and optical studies

We report a novel, rapid, and low-temperature method for the synthesis of undoped and Eu-doped GdOOH spherical hierarchical structures, without using any structure-directing agents, through the microwave irradiation route. The as-prepared product consists of nearly monodisperse microspheres measuring about 1.3 mu m in diameter. Electron microscopy reveals that each microsphere is an assembly of two-dimensional nanoflakes (about 30 nm thin) which, in turn, result from the assembly of crystallites measuring about 9 nm in diameter. Thus, a three-level hierarchy can be seen in the formation of the GdOOH microspheres: from nanoparticles to 2D nanoflakes to 3D spherical structures. When doped with Eu3+ ions, the GdOOH microspheres show a strong red emission, making them promising candidates as phosphors. Finally, thermal conversion at modest temperatures leads to the formation of corresponding oxide structures with enhanced luminescence, while retaining the spherical morphology of their oxyhydroxide precursor.

Green ionothermal synthesis of hierarchical nanostructures of SnS2 and their Li-ion storage properties

Flower-like hierarchical architectures of layered SnS2 have been synthesized ionothermally for the first time, using a water soluble EMIM]BF4 ionic liquid (IL) as the solvent medium. At lower reaction temperatures, the hierarchical structures are formed of few-layered polycrystalline 2D nanosheet-petals composed of randomly oriented nanoparticles of SnS2. The supramolecular networks of the IL serve as templates on which the nanoparticles of SnS2 are glued together by combined effects of hydrogen bonding, electrostatic, hydrophobic and imidazolium stacking interactions of the IL, giving rise to polycrystalline 2D nanosheet-petals. At higher reaction temperatures, single crystalline plate-like nanosheets with well-defined crystallographic facets are obtained due to rapid inter-particle diffusion across the IL. Efficient surface charge screening by the IL favors the aggregation of individual nanosheets to form hierarchical flower-like architectures of SnS2. The mechanistic aspects of the ionothermal bottom-up hierarchical assembly of SnS2 nanosheets are discussed in detail. Li-ion storage properties of the pristine SnS2 samples are examined and the electrochemical performance of the sample synthesized at higher temperatures is found to be comparable to that reported for pristine SnS2 samples in the literature.

Microspherical, hierarchical structures of blue-green-emitting Dy:GdOOH and Dy:Gd2O3

Dy-doped GdOOH microspherical structures were prepared in minutes without using any structure-directing agents, through the microwave irradiation route. The as-prepared product consists of nearly monodisperse sphere-like entities with each one representing a three-level hierarchy in its formation. Dy:GdOOH powder samples show a bright blue-green luminescence under UV excitation, making these structures potentially important in the field of optical and luminescent devices. Finally, thermal conversion to the corresponding oxide structures occurs at modest temperatures, spherical morphology intact and with enhanced luminescence behaviour. (C) 2014 Elsevier B.V. All rights reserved.

Formation of hierarchical structures of Fe2O3 by the liquid-liquid interface technique

We report the formation of dendritic hierarchical structures of alpha-Fe2O3 and nanostructures of Fe2O3 by the simple liquid-liquid interface method. The morphology of thin films determined by high-resolution scanning electron microscopy shows nanorods, nanosheets and dendritic Fe2O3. The identification of phases of iron oxide structures is carried out by using XRD and XPS studies. XRD and XPS measurements point out the highly crystalline dendritic alpha-Fe2O3 phase and the mixed phase of alpha- and gamma-Fe2O3 nanostructures. The magnetic measurement also suggests the presence of a mixed phase in the sample grown for 72 hours.

Analysis of the hierarchical structure of the B. subtilis transcriptional regulatory network

The transcriptional regulation of gene expression is orchestrated by complex networks of interacting genes. Increasing evidence indicates that these `transcriptional regulatory networks' (TRNs) in bacteria have an inherently hierarchical architecture, although the design principles and the specific advantages offered by this type of organization have not yet been fully elucidated. In this study, we focussed on the hierarchical structure of the TRN of the gram-positive bacterium Bacillus subtilis and performed a comparative analysis with the TRN of the gram-negative bacterium Escherichia coli. Using a graph-theoretic approach, we organized the transcription factors (TFs) and sigma-factors in the TRNs of B. subtilis and E. coli into three hierarchical levels (Top, Middle and Bottom) and studied several structural and functional properties across them. In addition to many similarities, we found also specific differences, explaining the majority of them with variations in the distribution of s-factors across the hierarchical levels in the two organisms. We then investigated the control of target metabolic genes by transcriptional regulators to characterize the differential regulation of three distinct metabolic subsystems (catabolism, anabolism and central energy metabolism). These results suggest that the hierarchical architecture that we observed in B. subtilis represents an effective organization of its TRN to achieve flexibility in response to a wide range of diverse stimuli.

Hierarchical sampling for metastable conformers determines biomolecular recognition: the case of malectin and diglucosylated N-glycan interactions

Structural information over the entire course of binding interactions based on the analyses of energy landscapes is described, which provides a framework to understand the events involved during biomolecular recognition. Conformational dynamics of malectin's exquisite selectivity for diglucosylated N-glycan (Dig-N-glycan), a highly flexible oligosaccharide comprising of numerous dihedral torsion angles, are described as an example. For this purpose, a novel approach based on hierarchical sampling for acquiring metastable molecular conformations constituting low-energy minima for understanding the structural features involved in a biologic recognition is proposed. For this purpose, four variants of principal component analysis were employed recursively in both Cartesian space and dihedral angles space that are characterized by free energy landscapes to select the most stable conformational substates. Subsequently, k-means clustering algorithm was implemented for geometric separation of the major native state to acquire a final ensemble of metastable conformers. A comparison of malectin complexes was then performed to characterize their conformational properties. Analyses of stereochemical metrics and other concerted binding events revealed surface complementarity, cooperative and bidentate hydrogen bonds, water-mediated hydrogen bonds, carbohydrate-aromatic interactions including CH-pi and stacking interactions involved in this recognition. Additionally, a striking structural transition from loop to beta-strands in malectin CRD upon specific binding to Dig-N-glycan is observed. The interplay of the above-mentioned binding events in malectin and Dig-N-glycan supports an extended conformational selection model as the underlying binding mechanism.

Deciphering complex patterns of class-I HLA-peptide cross-reactivity via hierarchical grouping

T-cell responses in humans are initiated by the binding of a peptide antigen to a human leukocyte antigen (HLA) molecule. The peptide-HLA complex then recruits an appropriate T cell, leading to cell-mediated immunity. More than 2000 HLA class-I alleles are known in humans, and they vary only in their peptide-binding grooves. The polymorphism they exhibit enables them to bind a wide range of peptide antigens from diverse sources. HLA molecules and peptides present a complex molecular recognition pattern, as many peptides bind to a given allele and a given peptide can be recognized by many alleles. A powerful grouping scheme that not only provides an insightful classification, but is also capable of dissecting the physicochemical basis of recognition specificity is necessary to address this complexity. We present a hierarchical classification of 2010 class-I alleles by using a systematic divisive clustering method. All-pair distances of alleles were obtained by comparing binding pockets in the structural models. By varying the similarity thresholds, a multilevel classification was obtained, with 7 supergroups, each further subclassifying to yield 72 groups. An independent clustering performed based only on similarities in their epitope pools correlated highly with pocket-based clustering. Physicochemical feature combinations that best explain the basis of clustering are identified. Mutual information calculated for the set of peptide ligands enables identification of binding site residues contributing to peptide specificity. The grouping of HLA molecules achieved here will be useful for rational vaccine design, understanding disease susceptibilities and predicting risk of organ transplants.

Bimodality and re-entrant behaviour in the hierarchical self-assembly of polymeric nanoparticles

We show that a film of a suspension of polymer grafted nanoparticles on a liquid substrate can be employed to create two-dimensional nanostructures with a remarkable variation in the pattern length scales. The presented experiments also reveal the emergence of concentration-dependent bimodal patterns as well as re-entrant behaviour that involves length scales due to dewetting and compositional instabilities. The experimental observations are explained through a gradient dynamics model consisting of coupled evolution equations for the height of the suspension film and the concentration of polymer. Using a Flory-Huggins free energy functional for the polymer solution, we show in a linear stability analysis that the thin film undergoes dewetting and/or compositional instabilities depending on the concentration of the polymer in the solution. We argue that the formation via `hierarchical self-assembly' of various functional nanostructures observed in different systems can be explained as resulting from such an interplay of instabilities.

Nanomechanical Mapping, Hierarchical Polymer Dynamics, and Miscibility in the Presence of Chain-End Grafted Nanoparticles

To improve the spatial distribution of nano particles in a polymeric host and to enhance the interfacial interaction with the host, the use of chain-end grafted nanoparticle has gained popularity in the field of polymeric nanocomposites. Besides changing the material properties of the host, these grafted nanoparticles strongly alter the dynamics of the polymer chain at both local and cooperative length scales (relaxations) by manipulating the enthalpic and entropic interactions. It is difficult to map the distribution of these chain-end grafted nanoparticles in the blend by conventional techniques, and herein, we attempted to characterize it by unique technique(s) like peak force quantitative nanomechanical mapping (PFQNM) through AFM (atomic force microscopy) imaging and dielectric relaxation spectroscopy (DRS). Such techniques, besides shedding light on the spatial distribution of the nanoparticles, also give critical information on the changing elasticity at smaller length scales and hierarchical polymer chain dynamics in the vicinity of the nanoparticles. The effect of one-dimensional rodlike multiwall carbon nanotubes (MWNTs), with the characteristic dimension of the order of the radius of gyration of the polymeric chain, on the phase miscibility and chain dynamics in a classical LCST mixture of polystyrene/ poly(vinyl methyl ether) (PS/PVME) was examined in detail using the above techniques. In order to tune the localization of the nanotubes, different molecular weights of PS (13, 31, and 46 kDa), synthesized using RAFT (reversible addition fragmentation chain transfer) polymerization, was grafted onto MWNTs in situ. The thermodynamic miscibility in the blends was assessed by low-amplitude isochronal temperature sweeps, the spatial distribution of MWNTs in the blends was evaluated by PFQNM, and the hierarchical polymer chain dynamics was studied by DRS. It was observed that the miscibility, concentration fluctuation, and cooperative relaxations of the PS/PVME blends are strongly governed by the spatial distribution of MWNTs in the blends. These findings should help guide theories and simulations of hierarchical chain dynamics in LCST mixtures containing rodlike nanoparticles.

Nanomechanical Mapping, Hierarchical Polymer Dynamics, and Miscibility in the Presence of Chain-End Grafted Nanoparticles

To improve the spatial distribution of nano particles in a polymeric host and to enhance the interfacial interaction with the host, the use of chain-end grafted nanoparticle has gained popularity in the field of polymeric nanocomposites. Besides changing the material properties of the host, these grafted nanoparticles strongly alter the dynamics of the polymer chain at both local and cooperative length scales (relaxations) by manipulating the enthalpic and entropic interactions. It is difficult to map the distribution of these chain-end grafted nanoparticles in the blend by conventional techniques, and herein, we attempted to characterize it by unique technique(s) like peak force quantitative nanomechanical mapping (PFQNM) through AFM (atomic force microscopy) imaging and dielectric relaxation spectroscopy (DRS). Such techniques, besides shedding light on the spatial distribution of the nanoparticles, also give critical information on the changing elasticity at smaller length scales and hierarchical polymer chain dynamics in the vicinity of the nanoparticles. The effect of one-dimensional rodlike multiwall carbon nanotubes (MWNTs), with the characteristic dimension of the order of the radius of gyration of the polymeric chain, on the phase miscibility and chain dynamics in a classical LCST mixture of polystyrene/ poly(vinyl methyl ether) (PS/PVME) was examined in detail using the above techniques. In order to tune the localization of the nanotubes, different molecular weights of PS (13, 31, and 46 kDa), synthesized using RAFT (reversible addition fragmentation chain transfer) polymerization, was grafted onto MWNTs in situ. The thermodynamic miscibility in the blends was assessed by low-amplitude isochronal temperature sweeps, the spatial distribution of MWNTs in the blends was evaluated by PFQNM, and the hierarchical polymer chain dynamics was studied by DRS. It was observed that the miscibility, concentration fluctuation, and cooperative relaxations of the PS/PVME blends are strongly governed by the spatial distribution of MWNTs in the blends. These findings should help guide theories and simulations of hierarchical chain dynamics in LCST mixtures containing rodlike nanoparticles.