Narrow Results

AUSTRALIA – Successful FDA end-of-Phase-2 for Hatchtech head lice product DeOVO^™

AUSTRALIA – Senz Oncology secures seed funding for promising cancer drug.

AUSTRALIA – Geneworks develops DNA barcodes for international security.

INDIA – Stress hormones: Good or Bad for Posttraumatic Stress Disorder risk?

SINGAPORE – New study shows fertility knowledge gaps may exacerbate Singapore's declining birth rate challenge.

SINGAPORE – World's first wearable robotic device for stroke rehabilitation comes to Singapore.

SRI LANKA – Conflicting reports highlight scientific data gaps in Sri Lanka's chronic kidney disease.

EUROPE – InDex Pharmaceuticals strengthens IP position for Kappaproct.

EUROPE – Novozymes and Terranol to market advanced biofuel yeast.

EUROPE – The world's first (official) biosimilar antibody goes to… Rheumatoid Arthritis.

USA – MRI reveals brain's response to reading.

USA – Novozymes partner Chemtex receives USDA commitment to build advanced biofuels plant in United States.

USA – Fossil fuel and renewable energy subsidies on the rise.

AUSTRALIA — Childhood CT scans slightly raise cancer risk.

AUSTRALIA — There's a very simple solution to your lack of vitamin D.

INDIA — India develops cheap rotavirus vaccine.

JAPAN — 'Tug of war' method to measure the copy number limits of all genes in budding yeast.

SINGAPORE — SG Austria co-edits just released book on living cell bioencapsulation.

SINGAPORE — Nano Today's 2013 impact factor increases from 15.355 to 17.689.

SINGAPORE — Cholesterol beats coronaviruses, Avian flu and Swine flu.

THE PHILIPPINES — Philippines maps out plan to switch to 100% renewables in 10 years.

EUROPE — Roche launches first sugar-transferase for new glyco-engineering portfolio.

EUROPE — Older liver cancer patients respond to radioembolization equally as well as younger patients.

NORTH AMERICA — Protein helps colon cancer move and invade.

NORTH AMERICA — FDA approval of VIBATIV(R) (telavancin) for the treatment of bacterial pneumonia.

NORTH AMERICA — “On Demand Medical Research” is up and running.

UNITED KINGDOM — Diabetes rises sharply among UK's young adults.

UNITED KINGDOM — 'Mental illness' isn't all about brain chemistry: it's about life.

UNITED KINGDOM — Public to see impact of medical research funding.

The interaction between cells and colloids is an important characteristic that influences cell behavior. Theoretically, much information could be revealed by analyzing the interactions in colloid–cell contact. In this study, in order to explore the interaction between cells and colloids, we developed a novel computational method able to obtain a zeta potential directly calculated from the force distance curve and apply to adhesion analysis, which used atomic force microscope (AFM), based on DLVO (Deryaguin–Landau–Verwey–Overbeek) theory and Mann Whitney U test, and combined with Zetasizer measurement. The calculation and analysis of $\zeta$ of the cell surfaces of ncyc-1324 yeast, ncyc-1681 yeast and Pseudomonas fluorescens showed that pH affected the electrostatic distribution on the cell surface. Compared with the previous research methods, this method significantly reduces the computation and manual control, which is an effective method for multi-element surface analysis and comparison. For example, the reverse calculation and curve fitting method will significantly request more computation and manual control to set up the reference force curve that simulated with set zeta potential, while this method only need to calculate on one force curve. The deconvolution of different adhesion events from force curves showed that the heterogeneity of cell surface can be significantly displayed. This provides a method for determining the complexity of the cell surface. Furthermore, this method was used to study the effect of amoxicillin on cell surface interaction, which showed that the cells surface forces were influenced even the medicine concentration is not enough to make significant influence on microbials optical observation appearance. Thus, AFM force analysis is a more sensitive method to research the medicine influence compared to the traditional method.

In this paper, we develop a machine learning system for determining gene functions from heterogeneous data sources using a Weighted Naive Bayesian network (WNB). The knowledge of gene functions is crucial for understanding many fundamental biological mechanisms such as regulatory pathways, cell cycles and diseases. Our major goal is to accurately infer functions of putative genes or Open Reading Frames (ORFs) from existing databases using computational methods. However, this task is intrinsically difficult since the underlying biological processes represent complex interactions of multiple entities. Therefore, many functional links would be missing when only one or two sources of data are used in the prediction. Our hypothesis is that integrating evidence from multiple and complementary sources could significantly improve the prediction accuracy. In this paper, our experimental results not only suggest that the above hypothesis is valid, but also provide guidelines for using the WNB system for data collection, training and predictions. The combined training data sets contain information from gene annotations, gene expressions, clustering outputs, keyword annotations, and sequence homology from public databases. The current system is trained and tested on the genes of budding yeast Saccharomyces cerevisiae. Our WNB model can also be used to analyze the contribution of each source of information toward the prediction performance through the weight training process. The contribution analysis could potentially lead to significant scientific discovery by facilitating the interpretation and understanding of the complex relationships between biological entities.

We present an algorithm for predicting transcription factor binding sites based on ChIP-chip and phylogenetic footprinting data. Our algorithm is robust against low promoter sequence similarity and motif rearrangements, because it does not depend on multiple sequence alignments. This, in turn, allows us to incorporate information from more distant species. Representative random data sets are used to estimate the score significance. Our algorithm is fully automatic, and does not require human intervention. On a recent S. cerevisiae data set, it achieves higher accuracy than the previously best algorithms. Adaptive ChIP-chip threshold and the modular positional bias score are two general features of our algorithm that increase motif prediction accuracy and could be implemented in other algorithms as well. In addition, since our algorithm works partly orthogonally to other algorithms, combining several algorithms can increase prediction accuracy even further. Specifically, our method finds 6 motifs not found by the 2nd best algorithm.

We review the proposed mathematical models of the response to osmotic stress in yeast. These models mainly differ in the choice of mathematical representation (e.g. Bayesian networks, ordinary differential equations, or rule-based models), the extent to which the modeling is data-driven, and predictability. The overview exemplifies how one biological system can be modeled with various modeling techniques and at different levels of resolution, and how the choice typically is based on the amount and quality of available data, prior information of the system, and the research question in focus. As a natural part of the overview, we discuss requirements, advantages, and limitations of the different modeling approaches.

Thorough knowledge of the model organism S. cerevisiae has fueled efforts in developing theories of cell ageing since the 1950s. Models of these theories aim to provide insight into the general biological processes of ageing, as well as to have predictive power for guiding experimental studies such as cell rejuvenation. Current efforts in in silico modeling are frustrated by the lack of efficient simulation tools that admit precise mathematical models at both cell and population levels simultaneously. We developed a novel hierarchical simulation tool that allows dynamic creation of entities while rigorously preserving the mathematical semantics of the model. We used it to expand a single-cell model of protein damage segregation to a cell population model that explicitly tracks mother-daughter relations. Large-scale exploration of the resulting tree of simulations established that daughters of older mothers show a rejuvenation effect, consistent with experimental results. The combination of a single-cell model and a simulation platform permitting parallel composition and dynamic node creation has proved to be an efficient tool for in silico exploration of cell behavior.