Carbon nanotubes: are they dispersed or dissolved in liquids?

Carbon nanotubes (CNTs) constitute a novel class of nanomaterials with remarkable applications in diverse domains. However, the main intrincsic problem of CNTs is their insolubility or very poor solubility in most of the common solvents. The basic key question here is: are carbon nanotubes dissolved or dispersed in liquids, specifically in water? When analyzing the scientific research articles published in various leading journals, we found that many researchers confused between "dispersion" and "solubilization" and use the terms interchangeably, particularly when stating the interaction of CNTs with liquids. In this article, we address this fundamental issue to give basic insight specifically to the researchers who are working with CNTs as well asgenerally to scientists who deal with nano-related research domains

Structure-based classification and ontology in chemistry

<p>Abstract</p> <p>Background</p> <p>Recent years have seen an explosion in the availability of data in the chemistry domain. With this information explosion, however, retrieving <it>relevant </it>results from the available information, and <it>organising </it>those results, become even harder problems. Computational processing is essential to filter and organise the available resources so as to better facilitate the work of scientists. Ontologies encode expert domain knowledge in a hierarchically organised machine-processable format. One such ontology for the chemical domain is ChEBI. ChEBI provides a classification of chemicals based on their structural features and a role or activity-based classification. An example of a structure-based class is 'pentacyclic compound' (compounds containing five-ring structures), while an example of a role-based class is 'analgesic', since many different chemicals can act as analgesics without sharing structural features. Structure-based classification in chemistry exploits elegant regularities and symmetries in the underlying chemical domain. As yet, there has been neither a systematic analysis of the types of structural classification in use in chemistry nor a comparison to the capabilities of available technologies.</p> <p>Results</p> <p>We analyze the different categories of structural classes in chemistry, presenting a list of patterns for features found in class definitions. We compare these patterns of class definition to tools which allow for automation of hierarchy construction within cheminformatics and within logic-based ontology technology, going into detail in the latter case with respect to the expressive capabilities of the Web Ontology Language and recent extensions for modelling structured objects. Finally we discuss the relationships and interactions between cheminformatics approaches and logic-based approaches.</p> <p>Conclusion</p> <p>Systems that perform intelligent reasoning tasks on chemistry data require a diverse set of underlying computational utilities including algorithmic, statistical and logic-based tools. For the task of automatic structure-based classification of chemical entities, essential to managing the vast swathes of chemical data being brought online, systems which are capable of hybrid reasoning combining several different approaches are crucial. We provide a thorough review of the available tools and methodologies, and identify areas of open research.</p

Niclosamide Prevents the Formation of Large Ubiquitin-Containing Aggregates Caused by Proteasome Inhibition

Protein aggregation is a hallmark of many neurodegenerative diseases and has been linked to the failure to degrade misfolded and damaged proteins. In the cell, aberrant proteins are degraded by the ubiquitin proteasome system that mainly targets short-lived proteins, or by the lysosomes that mostly clear long-lived and poorly soluble proteins. Both systems are interconnected and, in some instances, autophagy can redirect proteasome substrates to the lysosomes.To better understand the interplay between these two systems, we established a neuroblastoma cell population stably expressing the GFP-ubiquitin fusion protein. We show that inhibition of the proteasome leads to the formation of large ubiquitin-containing inclusions accompanied by lower solubility of the ubiquitin conjugates. Strikingly, the formation of the ubiquitin-containing aggregates does not require ectopic expression of disease-specific proteins. Moreover, formation of these focused inclusions caused by proteasome inhibition requires the lysine 63 (K63) of ubiquitin. We then assessed selected compounds that stimulate autophagy and found that the antihelmintic chemical niclosamide prevents large aggregate formation induced by proteasome inhibition, while the prototypical mTORC1 inhibitor rapamycin had no apparent effect. Niclosamide also precludes the accumulation of poly-ubiquitinated proteins and of p62 upon proteasome inhibition. Moreover, niclosamide induces a change in lysosome distribution in the cell that, in the absence of proteasome activity, may favor the uptake into lysosomes of ubiquitinated proteins before they form large aggregates.Our results indicate that proteasome inhibition provokes the formation of large ubiquitin containing aggregates in tissue culture cells, even in the absence of disease specific proteins. Furthermore our study suggests that the autophagy-inducing compound niclosamide may promote the selective clearance of ubiquitinated proteins in the absence of proteasome activity

Bioinformatics and molecular modeling in glycobiology

The field of glycobiology is concerned with the study of the structure, properties, and biological functions of the family of biomolecules called carbohydrates. Bioinformatics for glycobiology is a particularly challenging field, because carbohydrates exhibit a high structural diversity and their chains are often branched. Significant improvements in experimental analytical methods over recent years have led to a tremendous increase in the amount of carbohydrate structure data generated. Consequently, the availability of databases and tools to store, retrieve and analyze these data in an efficient way is of fundamental importance to progress in glycobiology. In this review, the various graphical representations and sequence formats of carbohydrates are introduced, and an overview of newly developed databases, the latest developments in sequence alignment and data mining, and tools to support experimental glycan analysis are presented. Finally, the field of structural glycoinformatics and molecular modeling of carbohydrates, glycoproteins, and protein–carbohydrate interaction are reviewed

Mechanical characterisation of polymer of intrinsic microporosity PIM-1 for hydrogen storage applications

Polymers of intrinsic microporosity (PIMs) are currently attracting interest due to their unusual combination of high surface areas and capability to be processed into free-standing films. However, there has been little published work with regards to their physical and mechanical properties. In this paper, detailed characterisation of PIM-1 was performed by considering its chemical, gas adsorption and mechanical properties. The polymer was cast into films, and characterised in terms of their hydrogen adsorption at −196 °C up to much higher pressures (17 MPa) than previously reported (2 MPa), demonstrating the maximum excess adsorbed capacity of the material and its uptake behaviour in higher pressure regimes. The measured tensile strength of the polymer film was 31 MPa with a Young’s modulus of 1.26 GPa, whereas the average storage modulus exceeded 960 MPa. The failure strain of the material was 4.4%. It was found that the film is thermally stable at low temperatures, down to −150 °C, and decomposition of the material occurs at 350 °C. These results suggest that PIM-1 has sufficient elasticity to withstand the elastic deformations occurring within state-of-the-art high-pressure hydrogen storage tanks and sufficient thermal stability to be applied at the range of temperatures necessary for gas storage applications