Proteins in works of art are generally determined by the relative amounts of amino acids. This method, however, implies a loss of information on the protein structure and its modifications. Consequently, we propose a method based on the analysis of trypsin digests using high-performance liquid chromatography (HPLC) UV diode array detection (DAD) for painting binder studies. All reaction steps are done in the same vial; no extraction methods or sample transfer is needed, reducing the risk of sample losses. A collection of pure binders (collagen, ovalbumin, yolk and casein) as well as homemade and historical paint samples have been investigated with this method. Chromatograms of unknowns at 214 nm and 280 nm are compared with those of the reference samples as a fingerprint. There is a good agreement between many peptides, but others seem to have been lost or their retention time shifted due to small compositional changes because of ageing and degradation of the paint. The results are comparable with the results of other techniques used for binder identification on the same samples, with the additional advantage of differentiation between egg yolk and glair.
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http://dx.doi.org/10.1007/s00216-009-2686-z | DOI Listing |
BMC Med Imaging
January 2025
Electronics and Communications, Arab Academy for Science, Heliopolis, Cairo, 2033, Egypt.
Invasive breast cancer diagnosis and treatment planning require an accurate assessment of human epidermal growth factor receptor 2 (HER2) expression levels. While immunohistochemical techniques (IHC) are the gold standard for HER2 evaluation, their implementation can be resource-intensive and costly. To reduce these obstacles and expedite the procedure, we present an efficient deep-learning model that generates high-quality IHC-stained images directly from Hematoxylin and Eosin (H&E) stained images.
View Article and Find Full Text PDFSci Rep
January 2025
College of Mathematics and Systems Science, Xinjiang University, Urumqi , 830046, China.
ν-one-class support vector classification (ν-OCSVC) has garnered significant attention for its remarkable performance in handling single-class classification and anomaly detection. Nonetheless, the model does not yield a unique decision boundary, and potentially compromises learning performance when the training data is contaminated by some outliers or mislabeled observations. This paper presents a novel C-parameter version of bounded one-class support vector classification (C-BOCSVC) to determine a unique decision boundary.
View Article and Find Full Text PDFSci Rep
January 2025
Integrated Intelligence Research Section, Electronics and Telecommunications Research Institute, Daejeon, 34129, Republic of Korea.
Alzheimer's disease (AD), a progressive neurodegenerative condition, notably impacts cognitive functions and daily activity. One method of detecting dementia involves a task where participants describe a given picture, and extensive research has been conducted using the participants' speech and transcribed text. However, very few studies have explored the modality of the image itself.
View Article and Find Full Text PDFBrain Res Bull
January 2025
Department of Pharmacology and Therapeutics, Faculty of Veterinary Medicine, Damanhour University, Damanhour, 22511, AlBeheira, Egypt. Electronic address:
Epilepsy is a neurological disease characterized by unprovoked recurrent epileptic seizures. Temporal lobe epilepsy (TLE) is the commonest type of focal epilepsy in adults that resist to the conventional anti-seizure medications (ASMs). Interestingly, ASMs do not affect the epileptogenesis and progression of disease.
View Article and Find Full Text PDFInt J Biol Macromol
January 2025
National Center for Applied Mathematics in Hunan, Xiangtan University, Hunan 411105, China; Key Laboratory of Intelligent Computing and Information Processing of Ministry of Education, Xiangtan University, Hunan 411105, China.
There is increasing evidence that the subcellular localization of long noncoding RNAs (lncRNAs) can provide valuable insights into their biological functions. In terms of transcriptomes, lncRNAs were usually found in multiple subcellular localizations. Although several computational methods have been developed to predict the subcellular localization of lncRNAs, few of them were designed for lncRNAs that have multiple subcellular localizations.
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