Untargeted metabolomic andlipidomic characterization of brain tissue using solid phase microextraction

Boyacı, Ezel
Gómez-ríos, German A
Bojko, Barbara
Pawliszyn, Janusz


Untargeted analysis of brain tissue using solid phase microextraction
Reyes-garcés, Nathaly; Gómez-ríos, G.a.; Boyacı, Ezel; Bojko, Barbara; Pawliszyn, Janusz (2016-08-20)
Untargeted metabolomics profiling of skeletal muscle samples from malignant hyperthermia susceptible patients
Bojko, Barbara; Vasiljevic, Tijana; Boyacı, Ezel; Roszkowska, Anna; Kraeva, Natalia; Moreno, Carlos A. Ibarra; Koivu, Annabel; Wasowicz, Marcin; Hanna, Amy; Hamilton, Susan; Riazi, Sheila; Pawliszyn, Janusz (Springer Science and Business Media LLC, 2021-01-01)
Purpose Malignant hyperthermia (MH) is a potentially fatal hypermetabolic condition triggered by certain anesthetics and caused by defective calcium homeostasis in skeletal muscle cells. Recent evidence has revealed impairment of various biochemical pathways in MH-susceptible patients in the absence of anesthetics. We hypothesized that clinical differences between MH-susceptible and control individuals are reflected in measurable differences in myoplasmic metabolites. Methods We performed metabolomic profil...
Unsupervised segmentation of hyperspectral images using modified phase correlation
Ertuerk, Alp; Ertuerk, Sarp (2006-10-01)
This letter presents hyperspectral image segmentation based on the phase-correlation measure of subsampled hyperspectral data, which is referred to as modified phase correlation. The hyperspectral spectrum of each pixel is initially subsampled to gain, robustness against noise and spatial variability, and phase correlation is applied to determine spectral similarity. Similar and dissimilar pixels are decided according to the peak value of the phase correlation result to determine pixels that fall into the s...
Unstructured road recognition and following for mobile robots via image processing using Anns
Dilan, Rasim Aşkın; Koku, Ahmet Buğra; Department of Mechanical Engineering (2010)
For an autonomous outdoor mobile robot ability to detect roads existing around is a vital capability. Unstructured roads are among the toughest challenges for a mobile robot both in terms of detection and navigation. Even though mobile robots use various sensors to interact with their environment, being a comparatively low-cost and rich source of information, potential of cameras should be fully utilized. This research aims to systematically investigate the potential use of streaming camera images in detect...
Unsupervised texture based image segmentation by simulated annealing using Markov random field and Potts models
Goktepe, M; Atalay, Mehmet Volkan; Yalabik, N; Yalabik, C (1998-01-01)
Unsupervised segmentation of images which are composed of various textures is investigated A coarse segmentation is achieved through a hierarchical self organizing map. This initial segmentation result is fed into a simulated annealing algorithm in which region and texture parameters are estimated using maximum likelihood technique. Region geometries are modeled as Potts model while textures are modeled as Markov random fields. Tests are performed an artificial textured images.
Citation Formats
E. Boyacı, G. A. Gómez-ríos, B. Bojko, and J. Pawliszyn, “Untargeted metabolomic andlipidomic characterization of brain tissue using solid phase microextraction,” 2017, Accessed: 00, 2021. [Online]. Available: https://www.asms.org/docs/default-source/asms-2017/65thasms-program_full_web_v2.pdf?sfvrsn=0.