Agilent Technologies

About: Agilent Technologies is a company organization based out in Santa Clara, California, United States. It is known for research contribution in the topics: Signal & Mass spectrometry. The organization has 7398 authors who have published 11518 publications receiving 262410 citations. The organization is also known as: Agilent Technologies, Inc..

An integrative genomic analysis revealed the relevance of microRNA and gene expression for drug-resistance in human breast cancer cells

Abstract: Background Acquisition of drug-resistance in cancer has led to treatment failure, however, their mechanisms have not been clarified yet. Recent observations indicated that aberrant expressed microRNA (miRNA) caused by chromosomal alterations play a critical role in the initiation and progression of cancer. Here, we performed an integrated genomic analysis combined with array-based comparative hybridization, miRNA, and gene expression microarray to elucidate the mechanism of drug-resistance.

Signal enhancement method and kit

Abstract: The invention discloses and claims a signal amplification method for detecting a target nucleic acid analyte having a homopolymeric region and a target sequence. The method comprises (a) contacting an analyte under hybridizing conditions with a multiplicity of reporter probes, each probe including a signal region and an oligonucleotide sequence which is complementary to, and capable of forming a stable hybrid with the analyte homopolymeric region, whereby the hybridization of multiple reporter probes to the homopolymeric region provides for signal amplification; and (b) forming an analyte:capture probe hybrid by contacting the analyte target sequence with a capture probe under hybridizing conditions.

Light source power modulation for use with chemical and biochemical analysis

Abstract: Systems and methods for analyzing a sample are disclosed The system may include a light source operable to transmit light onto the sample, a detector operable to detect intensity of the light emitted from the sample, and a power modulator The power modulator modulates the light source power such that light is emitted from the light source in more than one mode to reduce changes in the emitted light due to temperature changes in the light source

Writer adaptation for online handwriting recognition

Abstract: Writer-adaptation is the process of converting a writer-independent handwriting recognition system into a writer-dependent system. It can greatly increasing recognition accuracy, given adequate writer models. The limited amount of data a writer provides during training constrains the models' complexity. We show how appropriate use of writer-independent models is important for the adaptation. Our approach uses writer-independent writing style models (lexemes) to identify the styles present in a particular writer's training data. These models are then updated using the writer's data. Lexemes in the writer's data for which an inadequate number of training examples is available are replaced with the writer-independent models. We demonstrate the feasibility of this approach on both isolated handwritten character recognition and unconstrained word recognition tasks. Our results show an average reduction in error rate of 16.3 percent for lowercase characters as compared against representing each of the writer's character classes with a single model. In addition, an average error rate reduction of 9.2 percent is shown on handwritten words using only a small amount of data for adaptation.

Line graph explorer: scalable display of line graphs using Focus+Context

Abstract: Scientific measurements are often depicted as line graphs. State-of-the-art high throughput systems in life sciences, telemetry and electronics measurement rapidly generate hundreds to thousands of such graphs. Despite the increasing volume and ubiquity of such data, few software systems provide efficient interactive management, navigation and exploratory analysis of large line graph collections. To address these issues, we have developed Line Graph Explorer (LGE). LGE is a novel and visually scalable line graph management system that supports facile navigation and interactive visual analysis. LGE provides a compact overview of the entire collection by encoding the y-dimension of individual line graphs with color instead of space, thus enabling the analyst to see major common features and alignments of the data. Using Focus+Context techniques, LGE provides interactions for viewing selected compressed graphs in detail as standard line graphs without losing a sense of the general pattern and major features of the collection. To further enhance visualization and pattern discovery, LGE provides sorting and clustering of line graphs based on similarity of selected graph features. Sequential sorting by associated line graph metadata is also supported. We illustrate the features and use of LGE with examples from meteorology and biology.