First Level Of Organization Research Articles

Acknowledging differences: number, characteristics, and distribution of marine benthic communities along Taiwan coast

AbstractOur knowledge of the ability of coral reefs to face contemporary challenges disregards the wide variety of benthic communities (BCs) that could be associated with contrasted dynamics when facing stressors. Accordingly, this study investigates the number, characteristics, and distribution of BCs responsible for the development of coastal three‐dimensional structures in Taiwan. A total of 89 transects among three regions (north, east, and south) and two depths (−10 and −40 m) were characterized using a morpho‐functional categorization of benthic organisms. Using an unsupervised learning algorithm, k‐means cluster, an optimal number of k groups were identified among transects in order to minimize total intra‐group variance and represent a first level of organization, mirroring the number of BCs. Each BC was then described into prevailing categories and typified by an association with significant indicator groups. Their distributions were further examined and tested among regions and depths. Seven BCs were identified as having different composition and indicator categories. Their distributions suggest that, while sea surface temperature and light regime may be associated with a panel of available BCs, local conditions may have the final say on whether a BC eventually thrives at a given location. Overall, this study proposes an innovative and simple analytical framework for acknowledging differences among BCs. Our results encourage greater consideration of these shades in coral reef ecology studies, as they could conceal a variety of roles and dynamics important for the conservation of these endangered ecosystems.

Forecasting Demand in Supply Chain Using Machine Learning Algorithms

Managing inventory in a multi-level supply chain structure is a difficult task for big retail stores as it is particularly complex to predict demand for the majority of the items. This paper aims to highlight the potential of machine learning approaches as effective forecasting methods for predicting customer demand at the first level of organization of a supply chain where products are presented and sold to customers. For this purpose, we utilize Artificial Neural Networks (ANNs) trained with an effective second order algorithm, and Support Vector Machines (SVMs) for regression. We evaluated the effectiveness of the proposed approach using public data from the Netflix movie rental online DVD store in order to predict the demand for movie rentals during an especially critical for sales season, which is the Christmas holiday season. In our analysis we also integrated data from two other sources of information, namely an aggregator for movie reviews (Rotten Tomatoes), and a movie oriented social network (Flixster). Consequently, the approach presented in this paper combines the integration of data from various sources of information and the power of advanced machine learning algorithms for lowering the uncertainty barrier in forecasting supply chain demand.

Spatial and Directional Control over Self‐Assembly Using Catalytic Micropatterned Surfaces

Catalyst-assisted self-assembly is widespread in nature to achieve spatial control over structure formation. Reported herein is the formation of hydrogel micropatterns on catalytic surfaces. Gelator precursors react on catalytic sites to form building blocks which can self-assemble into nanofibers. The resulting structures preferentially grow where the catalyst is present. Not only is a first level of organization, allowing the construction of hydrogel micropatterns, achieved but a second level of organization is observed among fibers. Indeed, fibers grow with their main axis perpendicular to the substrate. This feature is directly linked to a unique mechanism of fiber formation for a synthetic system. Building blocks are added to fibers in a confined space at the solid-liquid interface.

Spatial and Directional Control over Self‐Assembly Using Catalytic Micropatterned Surfaces

AbstractCatalyst‐assisted self‐assembly is widespread in nature to achieve spatial control over structure formation. Reported herein is the formation of hydrogel micropatterns on catalytic surfaces. Gelator precursors react on catalytic sites to form building blocks which can self‐assemble into nanofibers. The resulting structures preferentially grow where the catalyst is present. Not only is a first level of organization, allowing the construction of hydrogel micropatterns, achieved but a second level of organization is observed among fibers. Indeed, fibers grow with their main axis perpendicular to the substrate. This feature is directly linked to a unique mechanism of fiber formation for a synthetic system. Building blocks are added to fibers in a confined space at the solid–liquid interface.

Ionic nanocomposite networks in poly(styrene‐co‐methacrylic acid) copolymers with calcium carbonate

AbstractTo create reversible supramolecular ionic networks, ionic cross‐links were formed from acid pendant functions in poly(styrene‐co‐methacrylic acid) copolymers by the addition of calcium carbonate. The random dispersion of acid functions in the polystyrene chain is ensured by NMR analysis. The partial reaction of the calcium carbonate with the carboxylic acids is highlighted by a limewater test. The influence of methacrylic acid and calcium‐carbonate contents on the formation of an ionic network has been studied through solubility tests. Two main effects were obtained: the exfoliation of the unreacted calcium carbonate characterized by TEM is due to ionic interactions at the surface of nanometric particles that form the first level of organization of the calcium carbonate. Another part of the calcium carbonate reacts with carboxylic acid and is detached from particles to form clusters as shown by x‐ray analysis. Finally, by analyzing the dynamic rheological spectrum, the conclusion that the strength of the ionic bonds arises with the calcium content is made. The interest of such an approach with a wide range of observed phenomena in the material is a one‐batch process to make thermoreversible nanocomposite networks. © 2012 Wiley Periodicals, Inc. J. Appl. Polym. Sci., 2013

Scale/Physics/Time Properties and Functions in Bioartificial Systems

Recent research on biological materials and bioartificial systems has created one of the most dynamic field at the confluence of physical sciences, molecular engineering, cell biology, materials sciences, biotechnology and (nano) medicine. This field concerns better understanding of living systems, design of bio-inspired materials, synthesis of bioartificial technologies with new properties depending on their multi-scale architectures. Biological and man-made systems show the first level of organization at the nanoscale, where the fundamental properties and functions are settled (e.g., proteome and genome). The nanoscale properties reflect on larger scales: mesoscale, microscale, and continuum. Mechanisms by which phenomena at the different length and time scales are coupled and influence each other is the central issue in linking properties to functionalities, with a dramatic impact in designing and engineering biosystems. To get insights into the progressive trough-scales cascade effects-from molecular to macroscale level and from nanoseconds to life expectancy duration-multiscale/multiphysics models are required, dealing with inorganic, biological and hybrid matter. Thus, bioartificial systems technology depends upon our ability in assembling molecules into objects, hierarchically along several length scales, and in disassembling objects into molecules, in a tailored manner. As a peculiar feature, in bioartificial systems, the definition of the interactions between artificial and biological components needs to incorporate the “time” variable, in order to reproduce the evolution of the overall system, and to simulate complex phenomena as biodegradation and tissue remodeling. Herein, a number of paradigmatic multiscale models that attend the investigation of biological systems and the engineering of bioartificial systems is reviewed and discussed.

Hierarchical structures based on self-assembled diblock copolymers within honeycomb micro-structured porous films

This article details the preparation of hierarchically ordered microporous films using the so-called breath figure approach combined with the self-assembly of well-defined poly(n-butyl acrylate)-block-polystyrene or poly(tert-butyl acrylate)-block-polystyrene copolymers synthesized by nitroxide-mediated polymerization. The first level of organization was the hexagonal pattern of pores at the micrometre length scale leading to iridescence properties and surface hydrophobicity of the honeycomb structured films. Optical microscopy with the corresponding 2-Dimensional Fast Fourier Transform highlighted the ordering of the pores over a large scale (∼1 cm2). The second level of structuring was provided by the diblock copolymers chosen for their ability to self-assemble into ordered nanophases. The nanoscale morphology of both the honeycomb films and the corresponding thermally annealed continuous films was systematically investigated by atomic force microscopy (AFM) and small angle neutron scattering (SANS). The film characterization revealed a nanostructuration of the acrylate-based coil–coil diblock copolymer within the walls of the highly ordered microporous films obtained via a simple solvent evaporation method under humid atmosphere. The four synthesized diblock copolymers exhibited different macromolecular features with respect to the Flory–Huggins interaction parameter, the glass transition temperature of each block and the weight fraction of each monomer that influenced the quality of either the micropores structuration or the nanophase segregation.

Hierarchical model of the population dynamics of hippocampal dentate granule cells.

A hierarchical modeling approach is used as the basis for a mathematical representation of the population activity of hippocampal dentate granule cells. Using neural field equations, the variation in time and space of dentate granule cell activity is derived from the summed synaptic potential and summed action potential responses of a population of granule cells evoked by monosynaptic excitatory input from entorhinal cortical afferents. In this formulation of the problem, we have considered a two-level hierarchy: the synapses of entorhinal cortical axons define the first level of organization, and dentate granule cells, which include these synapses, define the second, higher level of organization. The model is specified by two state field variables, for membrane potential and for synaptic efficacy, respectively, with both evolving according to different time scales. The two state field variables introduce new parameters, physiological and anatomical, which characterize the dentate from the point of view of neuronal and synaptic populations: (1) a set of geometrical constraints corresponding to the morphological properties of granule cells and anatomical characteristics of entorhinal-dentate connections; and (2) a set of neuronal parameters corresponding to physiological mechanisms. Assuming no interaction between granule cells, i.e., neither ephaptic nor synaptic coupling, the model is shown to be mathematically tractable and allows solution of the field equations leading to the determination of activity. This treatment leads to the definition of two state variables, volume of stimulated synapses and firing time, which describe observed activity. Numerical simulations are used to investigate the populational characterization of the dentate by individual parameters: (1) the relationship between the conditions of stimulation of active perforant path fibers, e.g., stimulating intensity, and activity in the granule cell layer; and (2) the influence of geometry on the generation of activity, i.e., the influence of neuron density and synaptic density-connectivity. As an example application of the model, the granule cell population spike is reconstructed and compared with experimental data.

The Structural Basis of Gene Regulation for DNA Organized as Chromatin

DNA in the nucleus of eukaryotic cells is organized in chromatin, a nucleoprotein complex containing roughly half DNA and half protein. The nucleosome is the underlying DNA packaging element in chromatin, repeating approximately every 200 base pairs over essentially the entire genome. Our crystal structure of the nucleosome core particle explains in atomic detail how DNA in its first level of organization is kept untangled by the histone protein octamer and clarifies the unique role the nucleosome plays in the expression of genetic information. Dynamic assembly and disassembly of the chromatin fiber, the higher-order arrangement of nucleosomes, most probably defines the crucial step in controlling DNA access enabling efficient regulation of gene readout. Multiprotein complexes, here designated 'regulasomes', are bound at specific sites within chromatin to coalesce the histone modification and chromatin remodeling protein assemblies that affect the stability and structure of the chromatin fiber. The formation of a particular regulasome depends on cooperative interaction between the transcription factor proteins comprising it, and on their interaction with specific DNA sequences. Our crystal structures of selected transcription factor complexes bound to their target site DNA contribute to the structural basis of how specificity of gene expression is achieved.