CancerLectinDB: a database of lectins relevant to cancer

The role of lectins in mediating cancer metastasis, apoptosis as well as various other signaling events has been well established in the past few years. Data on various aspects of the role of lectins in cancer is being accumulated at a rapid pace. The data on lectins available in the literature is so diverse, that it becomes difficult and time-consuming, if not impossible to comprehend the advances in various areas and obtain the maximum benefit. Not only do the lectins vary significantly in their individual functional roles, but they are also diverse in their sequences, structures, binding site architectures, quaternary structures, carbohydrate affinities and specificities as well as their potential applications. An organization of these seemingly independent data into a common framework is essential in order to achieve effective use of all the data towards understanding the roles of different lectins in different aspects of cancer and any resulting applications. An integrated knowledge base (CancerLectinDB) together with appropriate analytical tools has therefore been developed for lectins relevant for any aspect of cancer, by collating and integrating diverse data. This database is unique in terms of providing sequence, structural, and functional annotations for lectins from all known sources in cancer and is expected to be a useful addition to the number of glycan related resources now available to the community. The database has been implemented using MySQL on a Linux platform and web-enabled using Perl-CGI and Java tools. Data for individual lectins pertain to taxonomic, biochemical, domain architecture, molecular sequence and structural details as well as carbohydrate specificities. Extensive links have also been provided for relevant bioinformatics resources and analytical tools. Availability of diverse data integrated into a common framework is expected to be of high value for various studies on lectin cancer biology.

A parallel multistep predictor-corrector algorithm for solving ordinary differential equations

In this paper we give a generalized predictor-corrector algorithm for solving ordinary differential equations with specified initial values. The method uses multiple correction steps which can be carried out in parallel with a prediction step. The proposed method gives a larger stability interval compared to the existing parallel predictor-corrector methods. A method has been suggested to implement the algorithm in multiple processor systems with efficient utilization of all the processors.

Surface Chemistry of Thiobacillus ferrooxidans Relevant to Adhesion on Mineral Surfaces

Thiobacillus ferrooxidans cells grown on sulfur, pyrite, and chalcopyrite exhibit greater hydrophobicity than ferrous ion-grown cells. The isoelectric points of sulfur-, pyrite-, and chalcopyrite-grown cells were observed to be at a pH higher than that for ferrous ion-grown cells. Microbe-mineral interactions result in change in the surface chemistry of the organism as well as that of the minerals with which it has interacted. Sulfur, pyrite, and chalcopyrite after interaction with T. ferrooxidans exhibited a significant shift in their isoelectric points from the initial values exhibited by uninteracted minerals. With antibodies raised against sulfur-grown T. ferrooxidans, pyrite- and chalcopyrite-grown cells showed immunoreactivity, whereas ferrous ion-grown cells failed to do so. Fourier transform infrared spectroscopy of sulfur-grown cells suggested that a proteinaceous new cell surface appendage synthesized in mineral-grown cells brings about adhesion to the solid mineral substrates. Such an appendage was found to be absent in ferrous ion-grown cells as it is not required during growth in liquid substrates.

Utilizing Model Nanostructured Porous Inorganic Compounds Such as Silica to Beneficially Confine “Bio”-Relevant Molecules and Demonstrate their Potential in Therapeutics

Abstract | The importance of well-defined inorganic porous nanostructured materials in the context of biotechnological applications such as drug delivery and biomolecular sensing is reviewed here in detail. Under optimized conditions, the confinement of “bio”-relevant molecules such as pharmaceutical drugs, enzymes or proteins inside such inorganic nanostructures may be remarkably beneficial leading to enhanced molecular stability, activity and performance. From the point of view of basic research, molecular confinement inside nanostructures poses several formidable and intriguing problems of statistical mechanics at the mesoscopic scale. The theoretical comprehension of such non-trivial issues will not only aid in the interpretation of observed phenomena but also help in designing better inorganic nanostructured materials for biotechnological applications.

Computational investigations on covalent dimerization/oligomerization of polyacenes: Is it relevant to soot formation?

We have postulated a novel pathway that could assist in the nucleation of soot particles through covalent dimerization and oligomerizations of a variety of PAHs. DFT calculations were performed with the objective of obtaining the relative thermal stabilities and formation probabilities of oligomeric species that exploit the facile dimerization that is known to occur in linear oligoacenes. We propose that the presence of small stretches of linear oligoacence (tetracene or longer) in extended PAH, either embedded or tethered, would be adequate for enabling the formation of such dimeric and oligomeric adducts; these could then serve as nuclei for the growth of soot particles. Our studies also reveal the importance of p-stacking interactions between extended aromatic frameworks in governing the relative stabilities of the oligomeric species that are formed. (c) 2012 Wiley Periodicals, Inc.

Cross-Guided Clustering: Transfer of Relevant Supervision across Tasks

Lack of supervision in clustering algorithms often leads to clusters that are not useful or interesting to human reviewers. We investigate if supervision can be automatically transferred for clustering a target task, by providing a relevant supervised partitioning of a dataset from a different source task. The target clustering is made more meaningful for the human user by trading-off intrinsic clustering goodness on the target task for alignment with relevant supervised partitions in the source task, wherever possible. We propose a cross-guided clustering algorithm that builds on traditional k-means by aligning the target clusters with source partitions. The alignment process makes use of a cross-task similarity measure that discovers hidden relationships across tasks. When the source and target tasks correspond to different domains with potentially different vocabularies, we propose a projection approach using pivot vocabularies for the cross-domain similarity measure. Using multiple real-world and synthetic datasets, we show that our approach improves clustering accuracy significantly over traditional k-means and state-of-the-art semi-supervised clustering baselines, over a wide range of data characteristics and parameter settings.

TCP: thread contention predictor for parallel programs

With proliferation of chip multicores (CMPs) on desktops and embedded platforms, multi-threaded programs have become ubiquitous. Existence of multiple threads may cause resource contention, such as, in on-chip shared cache and interconnects, depending upon how they access resources. Hence, we propose a tool - Thread Contention Predictor (TCP) to help quantify the number of threads sharing data and their sharing pattern. We demonstrate its use to predict a more profitable shared, last level on-chip cache (LLC) access policy on CMPs. Our cache configuration predictor is 2.2 times faster compared to the cycle-accurate simulations. We also demonstrate its use for identifying hot data structures in a program which may cause performance degradation due to false data sharing. We fix layout of such data structures and show up-to 10% and 18% improvement in execution time and energy-delay product (EDP), respectively.

STAT-1 expression is regulated by IGFBP-3 in malignant glioma cells and is a strong predictor of poor survival in patients with glioblastoma Laboratory investigation

Object. Insulin-like growth factor binding proteins (IGEBPs) have been implicated in the pathogenesis of glioma. In a previous study the authors demonstrated that IGFBP-3 is a novel glioblastoma biomarker associated with poor survival. Since signal transducer and activator of transcription 1 (STAT-1) has been shown to be regulated by IGFBP-3 during chondrogenesis and is a prosurvival and radioresistant molecule in different tumors, the aim in the present study was to explore the functional significance of IGFBP-3 in malignant glioma cells, to determine if STAT-1 is indeed regulated by IGFBP-3, and to study the potential of STAT-1 as a biomarker in glioblastoma. Methods. The functional significance of IGFBP-3 was investigated using the short hairpin (sh)RNA gene knockdown approach on U251MG cells. STAT-1 regulation by IGFBP-3 was tested on U251MG and U87MG cells by shRNA gene knockdown and exogenous treatment with recombinant IGFBP-3 protein. Subsequently, the expression of STAT-1 was analyzed with real-time reverse transcription polymerase chain reaction (RT-PCR) and immunohistochemistry (IHC) in glioblastoma and control brain tissues. Survival analyses were done on a uniformly treated prospective cohort of adults with newly diagnosed glioblastoma (136 patients) using Kaplan-Meier and Cox regression models. Results. IGFBP-3 knockdown significantly impaired proliferation, motility, migration, and invasive capacity of U251MG cells in vitro (p < 0.005). Exogenous overexpression of IGFBP-3 in U251MG and U87MG cells demonstrated STAT-1 regulation. The mean transcript levels (by real-time RT-PCR) and the mean labeling index of STAT-1 (by IHC) were significantly higher in glioblastoma than in control brain tissues (p = 0.0239 and p < 0.001, respectively). Multivariate survival analysis revealed that STAT-1 protein expression (HR 1.015, p = 0.033, 95% CI 1.001-1.029) along with patient age (HR 1.025, p = 0.005, 95% CI 1.008-1.042) were significant predictors of shorter survival in patients with glioblastoma. Conclusions. IGFBP-3 influences tumor cell proliferation, migration, and invasion and regulates STAT-1 expression in malignant glioma cells. STAT-1 is overexpressed in human glioblastoma tissues and emerges as a novel prognostic biomarker.