Complexity In Terms Of Structure Research Articles

Distributed Feature Selection for Power System Dynamic Security Region Based on Grid-Partition and Fuzzy-Rough Sets

In order to satisfy the requirements of modern online security assessment of power systems with continuously increasing complexity in terms of structure and scale, it is desirable to develop a power system dynamic security region (DSR) analysis. However, data-driven methods suffer from expensive model training costs and overfitting when determining DSR boundaries with high-dimensional grid features. Given this problem, a distributed feature selection method based on grid partition and fuzzy-rough sets is proposed in this paper. The method first employs the Louvain algorithm to partition the power grid and divide the original feature set so that high-dimensional features can be allocated to multiple computational units for distributed screening. At this point, the connections between features of different computational units are minimized to a relatively low level, thereby avoiding large errors in the distributed results. Then, an incremental search algorithm based on the fuzzy-rough set theory (FRST) is used for feature selection at each computational unit, which can effectively take into account the intrinsic connections between features. Finally, the results of all computational units are integrated in the coordination unit to complete the overall feature selection. The experimental results based on the IEEE-39 bus system show that the proposed method can help simplify the power system DSR analysis with high-dimensional features by screening the critical features. And compared with other commonly used filter methods, it has higher screening accuracy and lower time costs.

Fluoropolymer Nanoparticles Synthesized via Reversible-Deactivation Radical Polymerizations and Their Applications.

Fluorinated polymeric nanoparticles (FPNPs) combine unique properties of fluorocarbon and polymeric nanoparticles, which has stimulated massive interest for decades. However, fluoropolymers are not readily available from nature, resulting in synthetic developments to obtain FPNPs via free radical polymerizations. Recently, while increasing cutting-edge directions demand tailored FPNPs, such materials have been difficult to access via conventional approaches. Reversible-deactivation radical polymerizations (RDRPs) are powerful methods to afford well-defined polymers. Researchers have applied RDRPs to the fabrication of FPNPs, enabling the construction of particles with improved complexity in terms of structure, composition, morphology, and functionality. Related examples can be classified into three categories. First, well-defined fluoropolymers synthesized via RDRPs have been utilized as precursors to form FPNPs through self-folding and solution self-assembly. Second, thermally and photoinitiated RDRPs have been explored to realize in situ preparations of FPNPs with varied morphologies via polymerization-induced self-assembly and cross-linking copolymerization. Third, grafting from inorganic nanoparticles has been investigated based on RDRPs. Importantly, those advancements have promoted studies toward promising applications, including magnetic resonance imaging, biomedical delivery, energy storage, adsorption of perfluorinated alkyl substances, photosensitizers, and so on. This Review should present useful knowledge to researchers in polymer science and nanomaterials and inspire innovative ideas for the synthesis and applications of FPNPs.

Assessing the influence of crop model structure on the performance of data assimilation for sugarcane

Process-base crop models (PBM) are important tools to describe how the agricultural system responds to environmental conditions. Sugarcane represents a major world source of sugar and ethanol and its PBMs had different levels of complexity in terms of structure, i.e. how detailed their processes were described. Yet, literature has widely demonstrated that data assimilation techniques (DA) represent a valuable option for reducing model uncertainty, but the inconsistency between PBM and the assimilated variable can significantly affect the performance of DA. Such limitation is strictly connected to model structure, and a hypothesis that arises from literature is that the use of more complex models would reduce model uncertainty after DA. We accessed the performance of using two different PBMs, one more detailed (DSSAT/SAMUCA, DS) and the other more general (WOFOST, WO), by assimilating leaf area index (LAI) retrieved from Landsat 7 ETM + and 8/OLI, using the Ensemble Kalman Filter (EnKF). Both PBMs were calibrated and evaluated with a robust database of 13 experiments and evaluated against a sugarmill database to evaluate the EnKF performance, compared with simulations without DA (Open-loop, OP). Moreover, the processes involved in LAI simulations were analyzed to assess the EnKF performance. The DS had superior performance in the calibration and evaluation step with EF = 0.907, 0.878, 0.458 for stalk dry mass, stalk fresh yield (SFY), and LAI, while WO showed EF = 0.622, 0.610, 0.417 for the same variables, respectively. The calibration step affected the OP plot simulation, with DS having higher accuracy (RMSE = 31.678 Mg ha−1) and precision (R2 = 0.509), compared with WO (RMSE = 39.593 Mg ha−1; R2 = 0.458). However, after DA, both PBM presented error inconsistency with EnKF, despite the decrease in RMSE (-44.73% and −29.58%) and increase in R2 (22.15% and 36.50%) of DS and WO, respectively. The error inconsistency diverged from each PBM: the OP simulation of DS overestimated the Landsat LAI; after DA, simulated LAI decreased resulting in SFY underestimation (Bias = -11.469 Mg ha−1); WO showed OP simulations for LAI closer to Landsat’s LAI values, despite the positive Bias in SFY estimation, and so EnKF slightly reduced the SFY overestimation (Bias = 22.944 Mg ha−1). Thus, the better descriptions of DS in terms of structure did not inhibit the error inconsistency. We suggested that new studies are required to understand how the assimilated variables impact on the other state variables of the PBM.

Neural network embeddings on corporate annual filings for portfolio selection

In recent years, there has been an increased interest from both academics and practitioners in automatically analyzing the textual part of companies’ financial reports to extract meaning rich in information for future outcomes. In particular, tracking textual changes among companies’ reports can have a large and significant impact on stock prices. This impact happens with a lag implying that investors only gradually realize the implications of the news hinted by document changes. However, the length of these documents as well as their complexity in terms of structure and language have been increasing dramatically making this process more and more difficult to perform. In this paper, we analyzed how to face this complexity by learning arbitrary dimensional vector representations for US corporate filings (10-Ks) from 1998 to 2018, exploiting and comparing different neural network embedding techniques which take into account words’ semantics through vectors proximity. We also compared their ability to capture changes associated with future risk-adjusted abnormal returns with other more commonly used approaches in literature. Finally, we propose a novel investment strategy named Semantic Similarity Portfolio (SSP) that exploits these neural network embeddings. We show that firms that do not change their 10-Ks in a semantically important way from the previous year tend to have large and statistically significant future risk-adjusted abnormal returns. We, also document an amplifying effect when we incorporate a momentum-related criterion, where the companies selected must also have had positive previous year returns. Specifically, a portfolio that buys “non-changers” based on this strategy earns up to 10% in yearly risk-adjusted abnormal returns (alpha).

Abstract 1927: High-throughput cell line panel drug screening in organoids and 3D systems

Abstract Cell panel screening of cancer lines grown in standard 2D conditions is a well-established component of the drug discovery toolbox. A sufficiently large cell line panel, reflecting the molecular heterogeneity within a cancer type/sub-type, is required to detect correlations with robust statistical power that aid biomarker identification and patient stratification. However, the heterogenous exposure of tumor cells to nutrients, oxygen and other physical or chemical stresses found in three-dimensional solid tumors also has a role to play in patient response and this is not well mimicked in 2D monolayer cell cultures. To meet the need for high-throughput cell-panel screening in more complex culture systems, we have developed 3D cell-based screens to more closely model the complex physiological environment found in tumors. We have characterised our OncoSignature panel of 300 cell lines for growth as spheroids to produce a 200 strong panel of spheroid forming lines. Using our high-throughput platform to compare drug activity in 2D and 3D cultures, we have identified clear differentials for some compounds in single agent and drug combination screens. For example, MEK and EGFR inhibitors have significantly higher activity in 3D cultures than 2D cultures, with a greater than 10-fold shift in EC50 values. These results were validated in 3D soft agar assays, with similar compound potencies being observed in the HTS spheroid assay and the lower throughput soft-agar assay. We have further extended our 3D screening into organoid cultures. Organoid cultures, with increased complexity in terms of structure and cell heterogeneity compared to spheroids, have historically been challenging to use in high-throughput screening. However, our initial results show encouragingly robust data, with compound activity that is congruent with the genetic characteristics of the organoids. Finally, we used our integrated screening platform to evaluate the power of CRISPR approaches to examine genetic dependencies in KRAS mutant cells in 2D, 3D spheroid and organoid cell cultures. Pooled, next-generation sequencing-linked CRISPRko allows an alternative and complementary genomics approach to arrayed cell panel screening, providing a powerful and robust target identification and validation strategy. Overall, Horizon’s 3D screening platforms will enable large scale interrogation of both drug and gene interactions in more complex cell culture systems. Citation Format: Tim M. Scales, Anett Rada-Kovacs, Helen N. Pemberton, Ceri M. Wiggins, Nicola J. McCarthy, Annette S. Little, David A. Sorrell. High-throughput cell line panel drug screening in organoids and 3D systems [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2019; 2019 Mar 29-Apr 3; Atlanta, GA. Philadelphia (PA): AACR; Cancer Res 2019;79(13 Suppl):Abstract nr 1927.

A Multivariate Multiscale Fuzzy Entropy Algorithm with Application to Uterine EMG Complexity Analysis

The recently introduced multivariate multiscale entropy (MMSE) has been successfully used to quantify structural complexity in terms of nonlinear within- and cross-channel correlations as well as to reveal complex dynamical couplings and various degrees of synchronization over multiple scales in real-world multichannel data. However, the applicability of MMSE is limited by the coarse-graining process which defines scales, as it successively reduces the data length for each scale and thus yields inaccurate and undefined entropy estimates at higher scales and for short length data. To that cause, we propose the multivariate multiscale fuzzy entropy (MMFE) algorithm and demonstrate its superiority over the MMSE on both synthetic as well as real-world uterine electromyography (EMG) short duration signals. Based on MMFE features, an improvement in the classification accuracy of term-preterm deliveries was achieved, with a maximum area under the curve (AUC) value of 0.99.

Efficiently sphere-decodable physical layer transmission schemes for wireless storage networks

Three transmission schemes over a new type of multiple-access channel (MAC) model with inter-source communication links are proposed and investigated in this paper. This new channel model is well motivated by, e.g., wireless distributed storage networks, where communication to repair a lost node takes place from helper nodes to a repairing node over a wireless channel. Since in many wireless networks nodes can come and go in an arbitrary manner, there must be an inherent capability of inter-node communication between every pair of nodes. Assuming that communication is possible between every pair of helper nodes, the newly proposed schemes are based on various smart time-sharing and relaying strategies. In other words, certain helper nodes will be regarded as relays, thereby converting the conventional uncooperative multiple-access channel to a multiple-access relay channel (MARC). The diversity-multiplexing gain tradeoff (DMT) of the system together with efficient sphere-decodability and low structural complexity in terms of the number of antennas required at each end is used as the main design objectives. While the optimal DMT for the new channel model is fully open, it is shown that the proposed schemes outperform the DMT of the simple time-sharing protocol and, in some cases, even the optimal uncooperative MAC DMT. While using a wireless distributed storage network as a motivating example throughout the paper, the MAC transmission techniques proposed here are completely general and as such applicable to any MAC communication with inter-source communication links.

Comparison of Transcriptional Heterogeneity of Eight Genes between Batch Desulfovibrio vulgaris Biofilm and Planktonic Culture at a Single-Cell Level.

Sulfate-reducing bacteria (SRB) biofilm formed on metal surfaces can change the physicochemical properties of metals and cause metal corrosion. To enhance understanding of differential gene expression in Desulfovibrio vulgaris under planktonic and biofilm growth modes, a single-cell based RT-qPCR approach was applied to determine gene expression levels of 8 selected target genes in four sets of the 31 individual cells isolated from each growth condition (i.e., biofilm formed on a mild steel (SS) and planktonic cultures, exponential and stationary phases). The results showed obvious gene-expression heterogeneity for the target genes among D. vulgaris single cells of both biofilm and planktonic cultures. In addition, an increased gene-expression heterogeneity in the D. vulgaris biofilm when compared with the planktonic culture was also observed for seven out of eight selected genes at exponential phase, and six out of eight selected genes at stationary phase, respectively, which may be contributing to the increased complexity in terms of structures and morphology in the biofilm. Moreover, the results showed up-regulation of DVU0281 gene encoding exopolysaccharide biosynthesis protein, and down-regulation of genes involved in energy metabolism (i.e., DVU0434 and DVU0588), stress responses (i.e., DVU2410) and response regulator (i.e., DVU3062) in the D. vulgaris biofilm cells. Finally, the gene (DVU2571) involved in iron transportation was found down-regulated, and two genes (DVU1340 and DVU1397) involved in ferric uptake repressor and iron storage were up-regulated in D. vulgaris biofilm, suggesting their possible roles in maintaining normal metabolism of the D. vulgaris biofilm under environments of high concentration of iron. This study showed that the single-cell based analysis could be a useful approach in deciphering metabolism of microbial biofilms.

Genome-wide microarray analysis of Atlantic cod (Gadus morhua) oocyte and embryo.

BackgroundRegulation of gene expression plays a central role in embryonic development. Early stages are controlled by gametic transcripts, which are subsequently substituted with transcripts from the genome of the zygote. Transcriptomic analyses provide an efficient approach to explore the temporal gene expression profiles in embryos and to search for the developmental regulators. We report a study of early Atlantic cod development that used a genome-wide oligonucleotide microarray to examine the composition and putative roles of polyadenylated transcripts.ResultsThe analyses were carried out in unfertilized oocytes, newly fertilized oocytes and embryos at the stages of mid-blastula transition and segmentation. Numerous genes transcribed in oocytes are involved in multiple aspects of cell maintenance and protection, including metabolism, signal perception and transduction, RNA processing, cell cycle, defense against pathogens and DNA damage. Transcripts found in unfertilized oocytes also encoded a large number of proteins implicated in cell adherence, tight junction and focal adhesion, suggesting high complexity in terms of structure and cellular interactions in embryos prior to midblastula transition (MBT). Prezygotic transcripts included multiple regulators that are most likely involved in developmental processes that take place long after fertilization, such as components of ErbB, hedgehog, notch, retinoid, TGFb, VEGF and Wnt signaling pathways, as well as transcripts involved in the development of nervous system. The major event of MBT was the activation of a large group of histones and other genes that modify chromatin structure preceding massive gene expression changes. A hallmark of events observed during segmentation was the induction of multiple transcription factors, including a large group of homeobox proteins in pace with decay of a large fraction of maternal transcripts. Microarray analyses detected a suite of master developmental regulators that control differentiation and maintenance of diverse cell lineages.ConclusionsTranscriptome profiling of the early stages in Atlantic cod revealed the presence of transcripts involved in patterning and development of tissues and organs long before activation of the zygotic genome. The switch from maternal to zygotic developmental programs is associated with large-scale modification of chromosomes.Electronic supplementary materialThe online version of this article (doi:10.1186/1471-2164-15-594) contains supplementary material, which is available to authorized users.

Intestinal acyl-CoA synthetase 5: Activation of long chain fatty acids and behind

The intestinal mucosa is characterized by a high complexity in terms of structure and functions and allows for a controlled demarcation towards the gut lumen. On the one hand it is responsible for pulping and selective absorption of alimentary substances ensuring the immunological tolerance, on the other hand it prevents the penetration of micro-organisms as well as bacterial outgrowth. The continuous regeneration of surface epithelia along the crypt-villus-axis in the small intestine is crucial to assuring these various functions. The core phenomena of intestinal epithelia regeneration comprise cell proliferation, migration, differentiation, and apoptosis. These partly contrarily oriented processes are molecularly balanced through numerous interacting signaling pathways like Wnt/β-catenin, Notch and Hedgehog, and regulated by various modifying factors. One of these modifiers is acyl-CoA synthetase 5 (ACSL5). It plays a key role in de novo lipid synthesis, fatty acid degradation and membrane modifications, and regulates several intestinal processes, primarily through different variants of protein lipidation, e.g., palmitoylation. ACSL5 was shown to interact with proapoptotic molecules, and besides seems to inhibit proliferation along the crypt-villus-axis. Because of its proapoptotic and antiproliferative characteristics it could be of significant relevance for intestinal homeostasis, cellular disorder and tumor development.

Engineering Macrocyclic Figure‐Eight Motif

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Engineering macrocyclic figure-eight motif

The design and synthesis of figure-eight macrocycles are very scarce owing to the intricacies and lack of predictability from first principles. This review emphasizes on discrete macrocyclic systems both synthetic and natural with a defined figure eight knotted topology. In almost all the helical macrocycles, the helical arrangement is held by intramolecular hydrogen bonding or as a backbone requirement, but in all cases, a planar graph can be drawn and so these compounds are trivial from the topological stand point. Nature presents great deal of complexity in terms of structures in macromolecules like DNA and proteins and also displays intriguing topology in simple natural products. Patellamide, tawicyclamides, nosiheptide and thiostrepton are natural products with figure eight topology which shows interesting biological activity. Expanded porphyrins, Cu(II) complexes of thiomacrocycles, cyclic peptides and oligoesters are synthetic macrocycles showing intriguing topology. Analysis of structure and folding behaviour will enable chemists to design molecules with intriguing topology.

Transformation in narrative thought during adolescence: The structure and content of story compositions.

Narrative understanding has been identified as a primary mode of human thought that informs us about the nature, causes, and consequences of human actions and interactions and, as such, underlies social knowledge. The development of narrative thought was investigated by analyzing the structural complexity and social-psychological understanding displayed in the story compositions of 151 adolescents aged 10 to 18 years. A clear developmental progression was observed in structural complexity in terms of plot structure and the construction of flashbacks. Also observed was a clear developmental trend in social-psychological content from an intentional understanding of human behavior in terms of immediate feelings, thoughts, and goals to an interpretive understanding in terms of personal history, long-standing psychological traits, and broader contextual factors. Gender differences were also noted, with girls outperforming boys on some measures. (PsycINFO Database Record (c) 2016 APA, all rights reserved)