Polarization bistability and resultant spin rings in semiconductor microcavities

The transmission of a pump laser resonant with the lower polariton branch of a semiconductor microcavity is shown to be highly dependent on the degree of circular polarization of the pump. Spin dependent anisotropy of polariton-polariton interactions allows the internal polarization to be controlled by varying the pump power. The formation of spatial patterns, spin rings with high degree of circular polarization, arising as a result of polarization bistability, is observed. A phenomenological model based on spin dependent Gross-Pitaevskii equations provides a good description of the experimental results. Inclusion of interactions with the incoherent exciton reservoir, which provides spin-independent blueshifts of the polariton modes, is found to be essential.Comment: 5 pages, 3 figure

Neural Network Modeling and Simulation of A 265W Photovoltaic Array

ABSTRACT This paper presents the Neural Network modeling and simulation of a 265 Watts photovoltaic array installed at the Faculty of Engineering and Engineering Technology of Abubakar Tafawa Balewa University, Bauchi, Nigeria. Hitherto, Mathematical modeling is the favoured method for characterizing photovoltaic (PV) arrays. This approach would require detailed information on the physical parameters relating to the solar cell material, which may not be readily available. Even in situations where the required information is provided on the manufacturer's datasheet, it tends not to be very accurate as it is not representative of the actual field performance of the array. Thus results obtained from mathematical modeling of photovoltaic arrays are only accurate to the extent of the accuracy of the model parameters. A better PV array characterization approach is to use Neural Network modeling because it does not require any physical definitions of the array and hence has the potential to provide a superior method of characterization than the already established conventional techniques. In this paper, two Radial Basis Function Neural Network (RBFNN) trained models are employed to simulate the performance of a 265 Watts photovoltaic array. The first model predicts the array I-V and P-V curves while the second predicts its maximum power for all operating weather conditions. Results of array performance plots show close correlation with those obtained through conventional mathematical modeling. RBFNN returned absolute errors of 1.794 %, 1.594 % and 1.262 % with respect to PV maximum power predictions for harmattan, cloudy and clear sunny seasons respectively

Synthesis and characterization of MnCrO4, a new mixed-valence antiferromagnet

A new orthorhombic phase, MnCrO4, isostructural with MCrO 4 (M = Mg, Co, Ni, Cu, Cd) was prepared by evaporation of an aqueous solution, (NH4)2Cr2O7 + 2 Mn(NO 3)2, followed by calcination at 400 C. It is characterized by redox titration, Rietveld analysis of the X-ray diffraction pattern, Cr K edge and Mn K edge XANES, ESR, magnetic susceptibility, specific heat and resistivity measurements. In contrast to the high-pressure MnCrO4 phase where both cations are octahedral, the new phase contains Cr in a tetrahedral environment suggesting the charge balance Mn2+Cr 6+O4. However, the positions of both X-ray absorption K edges, the bond lengths and the ESR data suggest the occurrence of some mixed-valence character in which the mean oxidation state of Mn is higher than 2 and that of Cr is lower than 6. Both the magnetic susceptibility and the specific heat data indicate an onset of a three-dimensional antiferromagnetic order at TN ≈ 42 K, which was confirmed also by calculating the spin exchange interactions on the basis of first principles density functional calculations. Dynamic magnetic studies (ESR) corroborate this scenario and indicate appreciable short-range correlations at temperatures far above T N. MnCrO4 is a semiconductor with activation energy of 0.27 eV; it loses oxygen on heating above 400 C to form first Cr 2O3 plus Mn3O4 and then Mn 1.5Cr1.5O4 spinel. © 2013 American Chemical Society

DESIGN, SYNTHESIS, CHARACTERIZATION AND ANTIOXIDANT PROPERTIES OF SOME NOVEL OXADIAZOLE DERIVATIVES: STRUCTURE ACTIVITY RELATIONSHIPS

1,3,4-oxadizoles and their derivatives, which displayed excellent biological properties like antioxidant agent and anticancer agents [1,2]. A new series of 2,5-disubstituted 1,3,4-oxadizoles were designed and synthesized by using environmentally friendly approach

Characterizing Mutational Heterogeneity in a Glioblastoma Patient with Double Recurrence

Human cancers are driven by the acquisition of somatic mutations. Separating the driving mutations from those that are random consequences of general genomic instability remains a challenge. New sequencing technology makes it possible to detect mutations that are present in only a minority of cells in a heterogeneous tumor population. We sought to leverage the power of ultra-deep sequencing to study various levels of tumor heterogeneity in the serial recurrences of a single glioblastoma multiforme patient. Our goal was to gain insight into the temporal succession of DNA base-level lesions by querying intra- and inter-tumoral cell populations in the same patient over time. We performed targeted “next-generation" sequencing on seven samples from the same patient: two foci within the primary tumor, two foci within an initial recurrence, two foci within a second recurrence, and normal blood. Our study reveals multiple levels of mutational heterogeneity. We found variable frequencies of specific EGFR, PIK3CA, PTEN, and TP53 base substitutions within individual tumor regions and across distinct regions within the same tumor. In addition, specific mutations emerge and disappear along the temporal spectrum from tumor at the time of diagnosis to second recurrence, demonstrating evolution during tumor progression. Our results shed light on the spatial and temporal complexity of brain tumors. As sequencing costs continue to decline and deep sequencing technology eventually moves into the clinic, this approach may provide guidance for treatment choices as we embark on the path to personalized cancer medicine

Comparative analysis of an experimental subcellular protein localization assay and in silico prediction methods

The subcellular localization of a protein can provide important information about its function within the cell. As eukaryotic cells and particularly mammalian cells are characterized by a high degree of compartmentalization, most protein activities can be assigned to particular cellular compartments. The categorization of proteins by their subcellular localization is therefore one of the essential goals of the functional annotation of the human genome. We previously performed a subcellular localization screen of 52 proteins encoded on human chromosome 21. In the current study, we compared the experimental localization data to the in silico results generated by nine leading software packages with different prediction resolutions. The comparison revealed striking differences between the programs in the accuracy of their subcellular protein localization predictions. Our results strongly suggest that the recently developed predictors utilizing multiple prediction methods tend to provide significantly better performance over purely sequence-based or homology-based predictions

Functional Diversity and Structural Disorder in the Human Ubiquitination Pathway

The ubiquitin-proteasome system plays a central role in cellular regulation and protein quality control (PQC). The system is built as a pyramid of increasing complexity, with two E1 (ubiquitin activating), few dozen E2 (ubiquitin conjugating) and several hundred E3 (ubiquitin ligase) enzymes. By collecting and analyzing E3 sequences from the KEGG BRITE database and literature, we assembled a coherent dataset of 563 human E3s and analyzed their various physical features. We found an increase in structural disorder of the system with multiple disorder predictors (IUPred - E1: 5.97%, E2: 17.74%, E3: 20.03%). E3s that can bind E2 and substrate simultaneously (single subunit E3, ssE3) have significantly higher disorder (22.98%) than E3s in which E2 binding (multi RING-finger, mRF, 0.62%), scaffolding (6.01%) and substrate binding (adaptor/substrate recognition subunits, 17.33%) functions are separated. In ssE3s, the disorder was localized in the substrate/adaptor binding domains, whereas the E2-binding RING/HECT-domains were structured. To demonstrate the involvement of disorder in E3 function, we applied normal modes and molecular dynamics analyses to show how a disordered and highly flexible linker in human CBL (an E3 that acts as a regulator of several tyrosine kinase-mediated signalling pathways) facilitates long-range conformational changes bringing substrate and E2-binding domains towards each other and thus assisting in ubiquitin transfer. E3s with multiple interaction partners (as evidenced by data in STRING) also possess elevated levels of disorder (hubs, 22.90% vs. non-hubs, 18.36%). Furthermore, a search in PDB uncovered 21 distinct human E3 interactions, in 7 of which the disordered region of E3s undergoes induced folding (or mutual induced folding) in the presence of the partner. In conclusion, our data highlights the primary role of structural disorder in the functions of E3 ligases that manifests itself in the substrate/adaptor binding functions as well as the mechanism of ubiquitin transfer by long-range conformational transitions. © 2013 Bhowmick et al

TESTLoc: protein subcellular localization prediction from EST data

Abstract Background The eukaryotic cell has an intricate architecture with compartments and substructures dedicated to particular biological processes. Knowing the subcellular location of proteins not only indicates how bio-processes are organized in different cellular compartments, but also contributes to unravelling the function of individual proteins. Computational localization prediction is possible based on sequence information alone, and has been successfully applied to proteins from virtually all subcellular compartments and all domains of life. However, we realized that current prediction tools do not perform well on partial protein sequences such as those inferred from Expressed Sequence Tag (EST) data, limiting the exploitation of the large and taxonomically most comprehensive body of sequence information from eukaryotes. Results We developed a new predictor, TESTLoc, suited for subcellular localization prediction of proteins based on their partial sequence conceptually translated from ESTs (EST-peptides). Support Vector Machine (SVM) is used as computational method and EST-peptides are represented by different features such as amino acid composition and physicochemical properties. When TESTLoc was applied to the most challenging test case (plant data), it yielded high accuracy (~85%). Conclusions TESTLoc is a localization prediction tool tailored for EST data. It provides a variety of models for the users to choose from, and is available for download at http://megasun.bch.umontreal.ca/~shenyq/TESTLoc/TESTLoc.html</p

'Unite and conquer': enhanced prediction of protein subcellular localization by integrating multiple specialized tools

<p>Abstract</p> <p>Background</p> <p>Knowing the subcellular location of proteins provides clues to their function as well as the interconnectivity of biological processes. Dozens of tools are available for predicting protein location in the eukaryotic cell. Each tool performs well on certain data sets, but their predictions often disagree for a given protein. Since the individual tools each have particular strengths, we set out to integrate them in a way that optimally exploits their potential. The method we present here is applicable to various subcellular locations, but tailored for predicting whether or not a protein is localized in mitochondria. Knowledge of the mitochondrial proteome is relevant to understanding the role of this organelle in global cellular processes.</p> <p>Results</p> <p>In order to develop a method for enhanced prediction of subcellular localization, we integrated the outputs of available localization prediction tools by several strategies, and tested the performance of each strategy with known mitochondrial proteins. The accuracy obtained (up to 92%) surpasses by far the individual tools. The method of integration proved crucial to the performance. For the prediction of mitochondrion-located proteins, integration via a two-layer decision tree clearly outperforms simpler methods, as it allows emphasis of biologically relevant features such as the mitochondrial targeting peptide and transmembrane domains.</p> <p>Conclusion</p> <p>We developed an approach that enhances the prediction accuracy of mitochondrial proteins by uniting the strength of specialized tools. The combination of machine-learning based integration with biological expert knowledge leads to improved performance. This approach also alleviates the conundrum of how to choose between conflicting predictions. Our approach is easy to implement, and applicable to predicting subcellular locations other than mitochondria, as well as other biological features. For a trial of our approach, we provide a webservice for mitochondrial protein prediction (named YimLOC), which can be accessed through the AnaBench suite at http://anabench.bcm.umontreal.ca/anabench/. The source code is provided in the Additional File <supplr sid="S2">2</supplr>.</p> <suppl id="S2"> <title> <p>Additional file 2</p> </title> <text> <p>This file contains scripts for the online server YimLOC. Please note that there scripts only codes for the ready-to-use STACK-mem-DT described in the main text. The scripts do not provide the training process.</p> </text> <file name="1471-2105-8-420-S2.pdf"> <p>Click here for file</p> </file> </suppl