Preparation and Foliar Application of Oligochitosan - Nanosilica on the Enhancement of Soybean Seed Yield

Oligochitosan with weight average molecu-lar weight (Mw) of 5000 g/mol was prepared by gamma Co-60 radiation degradation of 4% chitosan solution containing 0.5% H2O2 at 21 kGy. Nanosilica with size of 10 – 30 nm was synthesized by calcination of acid treated rice husk at 700o C for 2 h. The mixture of 2% oligo-chitosan-2% nanosilica was prepared by dispersion of nanosilica in oligochitosan solution. Oligochitosan, nanosilica and their mixture were characterized by gel permeation chromatography (GPC), transmission electr-on microscopy (TEM), X-ray diffraction (XRD), energy dispersive x-ray spectroscopy (EDX), Ultraviolet-visible spectroscopy (UV-Vis), and Furrier transform infrared spectroscopy (FT-IR). Effect of foliar application of oli-gochitosan and oligochitosan-nanosilica on soybean seed yield was conducted in experimental field. Results indi-cated that soybean seed yield increased 10.5 and 17.0% for oligochitosan and oligochitosan-nanosilica, respect-tively for the control. Radiation degraded oligo-chitosan and its mixture with nanosilica can be potentially used for cultivation of soybean with enhanced seed yield

Drug-Initiated Synthesis of Cladribine-Based Polymer Prodrug Nanoparticles: Biological Evaluation and Structure Activity Relationships

International audienceBy using two reversible deactivation radical polymerization techniques, either nitroxide-mediated polymerization or reversible addition-fragmentation chain transfer polymerization, the "drug-initiated" approach was applied to cladribine (CdA) as an anticancer drug to synthesize small libraries of well-defined and self-stabilized CdA-based polymer prodrug nanoparticles, differing from the nature and the molar mass of the grown polymer, and the nature of the linker between CdA and the polymer, thus allowing structure-cytotoxicity relationships to be determined. Their biological evaluation was investigated in vitro on L1210 cancer cells. The preparation of fluorescent CdA-based nanoparticles with excellent imaging ability was also reported by applying the "drug-initiated" approach to an aggregation-induced emission-active dye

Integrated Bio-Entity Network: A System for Biological Knowledge Discovery

A significant part of our biological knowledge is centered on relationships between biological entities (bio-entities) such as proteins, genes, small molecules, pathways, gene ontology (GO) terms and diseases. Accumulated at an increasing speed, the information on bio-entity relationships is archived in different forms at scattered places. Most of such information is buried in scientific literature as unstructured text. Organizing heterogeneous information in a structured form not only facilitates study of biological systems using integrative approaches, but also allows discovery of new knowledge in an automatic and systematic way. In this study, we performed a large scale integration of bio-entity relationship information from both databases containing manually annotated, structured information and automatic information extraction of unstructured text in scientific literature. The relationship information we integrated in this study includes protein–protein interactions, protein/gene regulations, protein–small molecule interactions, protein–GO relationships, protein–pathway relationships, and pathway–disease relationships. The relationship information is organized in a graph data structure, named integrated bio-entity network (IBN), where the vertices are the bio-entities and edges represent their relationships. Under this framework, graph theoretic algorithms can be designed to perform various knowledge discovery tasks. We designed breadth-first search with pruning (BFSP) and most probable path (MPP) algorithms to automatically generate hypotheses—the indirect relationships with high probabilities in the network. We show that IBN can be used to generate plausible hypotheses, which not only help to better understand the complex interactions in biological systems, but also provide guidance for experimental designs

Modelling the progression of pandemic influenza A (H1N1) in Vietnam and the opportunities for reassortment with other influenza viruses

BACKGROUND: A novel variant of influenza A (H1N1) is causing a pandemic and, although the illness is usually mild, there are concerns that its virulence could change through reassortment with other influenza viruses. This is of greater concern in parts of Southeast Asia, where the population density is high, influenza is less seasonal, human-animal contact is common and avian influenza is still endemic. METHODS: We developed an age- and spatially-structured mathematical model in order to estimate the potential impact of pandemic H1N1 in Vietnam and the opportunities for reassortment with animal influenza viruses. The model tracks human infection among domestic animal owners and non-owners and also estimates the numbers of animals may be exposed to infected humans. RESULTS: In the absence of effective interventions, the model predicts that the introduction of pandemic H1N1 will result in an epidemic that spreads to half of Vietnam's provinces within 57 days (interquartile range (IQR): 45-86.5) and peaks 81 days after introduction (IQR: 62.5-121 days). For the current published range of the 2009 H1N1 influenza's basic reproductive number (1.2-3.1), we estimate a median of 410,000 cases among swine owners (IQR: 220,000-670,000) with 460,000 exposed swine (IQR: 260,000-740,000), 350,000 cases among chicken owners (IQR: 170,000-630,000) with 3.7 million exposed chickens (IQR: 1.9 M-6.4 M), and 51,000 cases among duck owners (IQR: 24,000 - 96,000), with 1.2 million exposed ducks (IQR: 0.6 M-2.1 M). The median number of overall human infections in Vietnam for this range of the basic reproductive number is 6.4 million (IQR: 4.4 M-8.0 M). CONCLUSION: It is likely that, in the absence of effective interventions, the introduction of a novel H1N1 into a densely populated country such as Vietnam will result in a widespread epidemic. A large epidemic in a country with intense human-animal interaction and continued co-circulation of other seasonal and avian viruses would provide substantial opportunities for H1N1 to acquire new genes

Context-Specific Protein Network Miner – An Online System for Exploring Context-Specific Protein Interaction Networks from the Literature

Background: Protein interaction networks (PINs) specific within a particular context contain crucial information regarding many cellular biological processes. For example, PINs may include information on the type and directionality of interaction (e.g. phosphorylation), location of interaction (i.e. tissues, cells), and related diseases. Currently, very few tools are capable of deriving context-specific PINs for conducting exploratory analysis. Results: We developed a literature-based online system, Context-specific Protein Network Miner (CPNM), which derives context-specific PINs in real-time from the PubMed database based on a set of user-input keywords and enhanced PubMed query system. CPNM reports enriched information on protein interactions (with type and directionality), their network topology with summary statistics (e.g. most densely connected proteins in the network; most densely connected protein-pairs; and proteins connected by most inbound/outbound links) that can be explored via a user-friendly interface. Some of the novel features of the CPNM system include PIN generation, ontology-based PubMed query enhancement, real-time, user-queried, up-to-date PubMed document processing, and prediction of PIN directionality. Conclusions: CPNM provides a tool for biologists to explore PINs. It is freely accessible at http://www.biotextminer.com/CPNM/.Statistic

G-protein signaling: back to the future

Heterotrimeric G-proteins are intracellular partners of G-protein-coupled receptors (GPCRs). GPCRs act on inactive Gα·GDP/Gβγ heterotrimers to promote GDP release and GTP binding, resulting in liberation of Gα from Gβγ. Gα·GTP and Gβγ target effectors including adenylyl cyclases, phospholipases and ion channels. Signaling is terminated by intrinsic GTPase activity of Gα and heterotrimer reformation — a cycle accelerated by ‘regulators of G-protein signaling’ (RGS proteins). Recent studies have identified several unconventional G-protein signaling pathways that diverge from this standard model. Whereas phospholipase C (PLC) β is activated by Gαq and Gβγ, novel PLC isoforms are regulated by both heterotrimeric and Ras-superfamily G-proteins. An Arabidopsis protein has been discovered containing both GPCR and RGS domains within the same protein. Most surprisingly, a receptor-independent Gα nucleotide cycle that regulates cell division has been delineated in both Caenorhabditis elegans and Drosophila melanogaster. Here, we revisit classical heterotrimeric G-protein signaling and explore these new, non-canonical G-protein signaling pathways