Several factors render carotenoid determination inherently difficult. Thus, in spite of advances in analytical instrumentation, discrepancies in quantitative results on carotenoids can be encountered in the international literature. A good part of the errors comes from the pre-chromatographic steps such as: sampling scheme that does not yield samples representative of the food lots under investigation; sample preparation which does not maintain representativity and guarantee homogeneity of the analytical sample; incomplete extraction; physical losses of carotenoids during the various steps, especially during partition or washing and by adsorption to glass walls of containers; isomerization and oxidation of carotenoids during analysis. On the other hand, although currently considered the method of choice for carotenoids, high performance liquid chromatography (HPLC) is subject to various sources of errors, such as: incompatibility of the injection solvent and the mobile phase, resulting in distorted or split peaks; erroneous identification; unavailability, impurity and instability of carotenoid standards; quantification of highly overlapping peaks; low recovery from the HPLC column; errors in the preparation of standard solutions and in the calibration procedure; calculation errors. Illustrations of the possible errors in the quantification of carotenoids by HPLC are presented.
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In the past 20 years, sulfur hexafluoride (SF) has been considered a highly reliable tracer for assessing modern water (< 65 yrs old) in groundwater. However, modern-air contamination may introduce complications in interpreting data obtained using current sampling methods. A new airtight methodology isolates the sample from modern ambient air; thus, returning more reproducible and reliable results when compared to two traditional (air-sensitive and non-airtight) methods.
View Article and Find Full Text PDFUncertain Artif Intell
January 2025
Department of Computer Science and Engineering, The Ohio State University, USA.
Although recent advances in machine learning have shown its success to learn from independent and identically distributed (IID) data, it is vulnerable to out-of-distribution (OOD) data in an open world. Domain generalization (DG) deals with such an issue and it aims to learn a model from multiple source domains that can be generalized to unseen target domains. Existing studies on DG have largely focused on stationary settings with homogeneous source domains.
View Article and Find Full Text PDFMed Sci Sports Exerc
January 2025
Kinesiology & Health Science, Utah State University, Logan, UT.
Purpose: This study compared %BFUS to %BF4C in young adult athletes.
Methods: University club sport athletes (86 women, 138 men) from a variety of teams participated. ADP, DXA, and bioimpedance spectroscopy were used to measure body volume, bone mineral content, and total body water, respectively, for the 4C model.
Neural Netw
January 2025
City University of Hong Kong Shenzhen Research Institute, Shenzhen, China; Department of Mathematics, City University of Hong Kong, Hong Kong, China. Electronic address:
We consider kernel-based supervised learning using random Fourier features, focusing on its statistical error bounds and generalization properties with general loss functions. Beyond the least squares loss, existing results only demonstrate worst-case analysis with rate n and the number of features at least comparable to n, and refined-case analysis where it can achieve almost n rate when the kernel's eigenvalue decay is exponential and the number of features is again at least comparable to n. For the least squares loss, the results are much richer and the optimal rates can be achieved under the source and capacity assumptions, with the number of features smaller than n.
View Article and Find Full Text PDFJ Cell Physiol
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Department of Pharmaceutical Sciences and Center for Blood-Brain Barrier Research, Jerry H. Hodge School of Pharmacy, Texas Tech University Health Sciences Center, Amarillo, Texas, USA.
Glucose is a major source of energy for the brain. At the blood-brain barrier (BBB), glucose uptake is facilitated by glucose transporter 1 (GLUT1). GLUT1 Deficiency Syndrome (GLUT1DS), a haploinsufficiency affecting SLC2A1, reduces glucose brain uptake.
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