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..

Introduction to instrumented indentation testing

Abstract: Instrumented indentation testing (IIT) is a technique for measuring the mechanical properties of materials. It is a development of traditional hardness tests such as Brinell, Rockwell, Vickers, and Knoop. IIT is similar to traditional hardness testing in that a hard indenter, usually diamond, is pressed into contact with the test material. However, traditional hardness testing yields only one measure of deformation at one applied force, whereas during an IIT test, force and penetration are measured for the entire time that the indenter is in contact with the material. All of the advantages of IIT derive from this continuous measurement of force and displacement. IIT is particularly well suited for testing small volumes of material such as thin films, particles, or other small features. It is most commonly used to measure Young’s modulus (E )a nd hardness (H). 1‐2 The Young’s modulus for a material is the relationship between stress and strain when deformation is elastic. If an engineer knows the Young’s modulus for his design material, then he can predict strain for a known stress, and vice versa. In metals, hardness depends directly on the flow stress of the material at the strain caused by the indentation. In other words, hardness is an indirect but simple measure of flow stress; within a class of metals, the metal with the higher hardness will also have the higher flow stress. In addition to Young’s modulus and hardness, IIT has also been used to measure complex modulus in polymers and biomaterials, 3,4 yield stress and creep in metals, 5,6 and fracture toughness in glasses and ceramics.7 This article is the first in an ET feature series on the technique. Thus, this article covers basic testing and analysis procedures; future articles will address advanced applications.

TP53 outperforms other androgen receptor biomarkers to predict abiraterone or enzalutamide outcome in metastatic castration-resistant prostate cancer

Abstract: Purpose: To infer the prognostic value of simultaneous androgen receptor (AR) and TP53 profiling in liquid biopsies from patients with metastatic castration-resistant prostate cancer (mCRPC) starting a new line of AR signaling inhibitors (ARSi). Experimental Design: Between March 2014 and April 2017, we recruited patients with mCRPC (n = 168) prior to ARSi in a cohort study encompassing 10 European centers. Blood samples were collected for comprehensive profiling of CellSearch-enriched circulating tumor cells (CTC) and circulating tumor DNA (ctDNA). Targeted CTC RNA sequencing (RNA-seq) allowed the detection of eight AR splice variants (ARV). Low-pass whole-genome and targeted gene-body sequencing of AR and TP53 was applied to identify amplifications, loss of heterozygosity, mutations, and structural rearrangements in ctDNA. Clinical or radiologic progression-free survival (PFS) was estimated by Kaplan–Meier analysis, and independent associations were determined using multivariable Cox regression models. Results: Overall, no single AR perturbation remained associated with adverse prognosis after multivariable analysis. Instead, tumor burden estimates (CTC counts, ctDNA fraction, and visceral metastases) were significantly associated with PFS. TP53 inactivation harbored independent prognostic value [HR 1.88; 95% confidence interval (CI), 1.18–3.00; P = 0.008], and outperformed ARV expression and detection of genomic AR alterations. Using Cox coefficient analysis of clinical parameters and TP53 status, we identified three prognostic groups with differing PFS estimates (median, 14.7 vs. 7.51 vs. 2.62 months; P Conclusions: In an all-comer cohort, tumor burden estimates and TP53 outperform any AR perturbation to infer prognosis. See related commentary by Rebello et al., p. 1699

Primary endothelial cell–specific regulation of hypoxia-inducible factor (HIF)-1 and HIF-2 and their target gene expression profiles during hypoxia

Abstract: During hypoxia, a cellular adaptive response activates hypoxia-inducible factors (HIFs; HIF-1 and HIF-2) that respond to low tissue-oxygen levels and induce the expression of a number of genes that promote angiogenesis, energy metabolism, and cell survival. HIF-1 and HIF-2 regulate endothelial cell (EC) adaptation by activating gene-signaling cascades that promote endothelial migration, growth, and differentiation. An HIF-1 to HIF-2 transition or switch governs this process from acute to prolonged hypoxia. In the present study, we evaluated the mechanisms governing the HIF switch in 10 different primary human ECs from different vascular beds during the early stages of hypoxia. The studies demonstrate that the switch from HIF-1 to HIF-2 constitutes a universal mechanism of cellular adaptation to hypoxic stress and that HIF1A and HIF2A mRNA stability differences contribute to HIF switch. Furthermore, using 4 genome-wide mRNA expression arrays of HUVECs during normoxia and after 2, 8, and 16 h of hypoxia, we show using bioinformatics analyses that, although a number of genes appeared to be regulated exclusively by HIF-1 or HIF-2, the largest number of genes appeared to be regulated by both.-Bartoszewski, R., Moszynska, A., Serocki, M., Cabaj, A., Polten, A., Ochocka, R., Dell'Italia, L., Bartoszewska, S., Kroliczewski, J., Dąbrowski, M., Collawn, J. F. Primary endothelial cell-specific regulation of hypoxia-inducible factor (HIF)-1 and HIF-2 and their target gene expression profiles during hypoxia.

Cerebral: Visualizing Multiple Experimental Conditions on a Graph with Biological Context

Abstract: Systems biologists use interaction graphs to model the behavior of biological systems at the molecular level. In an iterative process, such biologists observe the reactions of living cells under various experimental conditions, view the results in the context of the interaction graph, and then propose changes to the graph model. These graphs serve as a form of dynamic knowledge representation of the biological system being studied and evolve as new insight is gained from the experimental data. While numerous graph layout and drawing packages are available, these tools did not fully meet the needs of our immunologist collaborators. In this paper, we describe the data information display needs of these immunologists and translate them into design decisions. These decisions led us to create Cerebral, a system that uses a biologically guided graph layout and incorporates experimental data directly into the graph display. Small multiple views of different experimental conditions and a data-driven parallel coordinates view enable correlations between experimental conditions to be analyzed at the same time that the data is viewed in the graph context. This combination of coordinated views allows the biologist to view the data from many different perspectives simultaneously. To illustrate the typical analysis tasks performed, we analyze two datasets using Cerebral. Based on feedback from our collaborators we conclude that Cerebral is a valuable tool for analyzing experimental data in the context of an interaction graph model.