A Double-Voltage-Controlled Effective Thermal Conductivity Model of Graphene for Thermoelectric Cooling

© 1963-2012 IEEE. Graphene provides a new opportunity for thermoelectric study based on its unique heat transfer behavior controllable by a gate voltage. In this paper, an effective thermal conductivity model of graphene for thermoelectric cooling is proposed. The model is based on a double-voltage-control mechanism. According to the law of Fourier heat conduction, an effective thermal conductivity model of the proposed thermoelectric cooling device is derived taking a tunable external voltage into account. Then, a gate voltage is used which can change graphene's thermoelectric characteristics. To verify the correctness and effectiveness of the proposed model, a circuit simulation model using HSPICE is built based on the thermoelectric duality. The simulation results from HSPICE and the calculated results from the mathematic model show good agreements with each other. This paper provides a novel precisely controlling method for thermoelectric cooling

Adaptive Evolutionary Clustering

In many practical applications of clustering, the objects to be clustered evolve over time, and a clustering result is desired at each time step. In such applications, evolutionary clustering typically outperforms traditional static clustering by producing clustering results that reflect long-term trends while being robust to short-term variations. Several evolutionary clustering algorithms have recently been proposed, often by adding a temporal smoothness penalty to the cost function of a static clustering method. In this paper, we introduce a different approach to evolutionary clustering by accurately tracking the time-varying proximities between objects followed by static clustering. We present an evolutionary clustering framework that adaptively estimates the optimal smoothing parameter using shrinkage estimation, a statistical approach that improves a naive estimate using additional information. The proposed framework can be used to extend a variety of static clustering algorithms, including hierarchical, k-means, and spectral clustering, into evolutionary clustering algorithms. Experiments on synthetic and real data sets indicate that the proposed framework outperforms static clustering and existing evolutionary clustering algorithms in many scenarios.Comment: To appear in Data Mining and Knowledge Discovery, MATLAB toolbox available at http://tbayes.eecs.umich.edu/xukevin/affec

Activation of the B cell receptor leads to increased membrane proximity of the Igα cytoplasmic domain.

Binding of antigen to the B cell receptor (BCR) induces conformational changes in BCR's cytoplasmic domains that are concomitant with phosphorylation of the immunoreceptor tyrosine-based activation motifs (ITAMs). Recently, reversible folding of the CD3ε and ξ chain ITAMs into the plasma membrane has been suggested to regulate T cell receptor signaling. Here we show that the Igα and Igβ cytoplasmic domains of the BCR do not associate with plasma membrane in resting B cells. However, antigen binding and ITAM phosphorylation specifically increased membrane proximity of Igα, but not Igβ. Thus, BCR activation is accompanied by asymmetric conformational changes, possibly promoting the binding of Igα and Igβ to differently localized signaling complexes

Identifying Ligand Binding Conformations of the β2-Adrenergic Receptor by Using Its Agonists as Computational Probes

Recently available G-protein coupled receptor (GPCR) structures and biophysical studies suggest that the difference between the effects of various agonists and antagonists cannot be explained by single structures alone, but rather that the conformational ensembles of the proteins need to be considered. Here we use an elastic network model-guided molecular dynamics simulation protocol to generate an ensemble of conformers of a prototypical GPCR, β2-adrenergic receptor (β2AR). The resulting conformers are clustered into groups based on the conformations of the ligand binding site, and distinct conformers from each group are assessed for their binding to known agonists of β2AR. We show that the select ligands bind preferentially to different predicted conformers of β2AR, and identify a role of β2AR extracellular region as an allosteric binding site for larger drugs such as salmeterol. Thus, drugs and ligands can be used as "computational probes" to systematically identify protein conformers with likely biological significance. © 2012 Isin et al

Robust Detection of Hierarchical Communities from Escherichia coli Gene Expression Data

Determining the functional structure of biological networks is a central goal of systems biology. One approach is to analyze gene expression data to infer a network of gene interactions on the basis of their correlated responses to environmental and genetic perturbations. The inferred network can then be analyzed to identify functional communities. However, commonly used algorithms can yield unreliable results due to experimental noise, algorithmic stochasticity, and the influence of arbitrarily chosen parameter values. Furthermore, the results obtained typically provide only a simplistic view of the network partitioned into disjoint communities and provide no information of the relationship between communities. Here, we present methods to robustly detect coregulated and functionally enriched gene communities and demonstrate their application and validity for Escherichia coli gene expression data. Applying a recently developed community detection algorithm to the network of interactions identified with the context likelihood of relatedness (CLR) method, we show that a hierarchy of network communities can be identified. These communities significantly enrich for gene ontology (GO) terms, consistent with them representing biologically meaningful groups. Further, analysis of the most significantly enriched communities identified several candidate new regulatory interactions. The robustness of our methods is demonstrated by showing that a core set of functional communities is reliably found when artificial noise, modeling experimental noise, is added to the data. We find that noise mainly acts conservatively, increasing the relatedness required for a network link to be reliably assigned and decreasing the size of the core communities, rather than causing association of genes into new communities.Comment: Due to appear in PLoS Computational Biology. Supplementary Figure S1 was not uploaded but is available by contacting the author. 27 pages, 5 figures, 15 supplementary file

Molecular Analysis of Precursor Lesions in Familial Pancreatic Cancer

PMCID: PMC3553106This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited

Antagonistic interactions between filamentous heterotrophs and the cyanobacterium Nostoc muscorum

Background: Little is known about interactions between filamentous heterotrophs and filamentous cyanobacteria. Here, interactions between the filamentous heterotrophic bacteria Fibrella aestuarina (strain BUZ 2) and Fibrisoma limi (BUZ 3) with an axenic strain of the autotrophic filamentous cyanobacterium Nostoc muscorum (SAG 25.82) were studied in mixed cultures under nutrient rich (carbon source present in medium) and poor (carbon source absent in medium) conditions. Findings: F. aestuarina BUZ 2 significantly reduced the cyanobacterial population whereas F. limi BUZ 3 did not. Physical contact between heterotrophs and autotroph was observed and the cyanobacterial cells showed some level of damage and lysis. Therefore, either contact lysis or entrapment with production of extracellular compounds in close vicinity of host cells could be considered as potential modes of action. The supernatants from pure heterotrophic cultures did not have an effect on Nostoc cultures. However, supernatant from mixed cultures of BUZ 2 and Nostoc had a negative effect on cyanobacterial growth, indicating that the lytic compounds were only produced in the presence of Nostoc. The growth and survival of tested heterotrophs was enhanced by the presence of Nostoc or its metabolites, suggesting that the heterotrophs could utilize the autotrophs and its products as a nutrient source. However, the autotroph could withstand and out-compete the heterotrophs under nutrient poor conditions. Conclusions: Our results suggest that the nutrients in cultivation media, which boost or reduce the number of heterotrophs, were the important factor influencing the outcome of the interplay between filamentous heterotrophs and autotrophs. For better understanding of these interactions, additional research is needed. In particular, it is necessary to elucidate the mode of action for lysis by heterotrophs, and the possible defense mechanisms of the autotrophs

Cloning, expression and characterization of alcohol dehydrogenases in the silkworm Bombyx mori

Alcohol dehydrogenases (ADH) are a class of enzymes that catalyze the reversible oxidation of alcohols to corresponding aldehydes or ketones, by using either nicotinamide adenine dinucleotide (NAD) or nicotinamide adenine dinucleotide phosphate (NADP), as coenzymes. In this study, a short-chain ADH gene was identified in Bombyx mori by 5′-RACE PCR. This is the first time the coding region of BmADH has been cloned, expressed, purified and then characterized. The cDNA fragment encoding the BmADH protein was amplified from a pool of silkworm cDNAs by PCR, and then cloned into E. coli expression vector pET-30a(+). The recombinant His-tagged BmADH protein was expressed in E. coli BL21 (DE3), and then purified by metal chelating affinity chromatography. The soluble recombinant BmADH, produced at low-growth temperature, was instrumental in catalyzing the ethanol-dependent reduction of NAD+, thereby indicating ethanol as one of the substrates of BmADH