In this study, we present the formation of artifacts from simple phenolic compounds and derivatives in SFC-UV-MS analysis. These ions were detected only when the UV detector was turned on, demonstrating that UV light is necessary for their formation. Based on high-resolution mass spectrometry (HRMS) analysis of 21 standards in negative electrospray ionization mode, the artifacts were annotated as ions where CO or NO had been added to the molecular ion or to an ion that had lost a functional group. In approximately half of the cases, the MS signal of the artifact was higher than that of the molecular ion. Although the formation of artifacts can complicate nontarget analysis as the detected molecular ion does not match with the analyzed standard, we demonstrated that the phenomenon can aid with the structural identification of isomers due to the formation of specific ions. In addition, the overall MS signal increased when the UV was turned on, which can help with the detection of low-abundance compounds, and one compound─ anisole─ was detected only thanks to the artifact. Thus, the aim of this article is to make researchers aware of the UV effect in SFC-UV-MS analysis together with the advantages and disadvantages of artifact formation.
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http://dx.doi.org/10.1021/acs.analchem.4c05941 | DOI Listing |
Proc Natl Acad Sci U S A
March 2025
Department of Molecular Biophysics and Biochemistry, Yale University, New Haven, CT 06520-8114.
Photosynthesis converts solar energy to chemical energy by splitting water molecules and carbon dioxide to produce oxygen and carbohydrates with an efficiency that engineers working on solar energy device can only dream of. Photosystem II (PSII) is the enzyme that catalyzes the light-driven oxidation of water that occurs during photosynthesis. This oxygen-producing reaction occurs in the MnCa cluster found inside the enzyme's oxygen-evolving center (OEC).
View Article and Find Full Text PDFPhotosynth Res
March 2025
Department of Plant Sciences, University of California, Davis, Davis, CA, USA.
Advancements in artificial intelligence (AI) have greatly benefited plant phenotyping and predictive modeling. However, unrealized opportunities exist in leveraging AI advancements in model parameter optimization for parameter fitting in complex biophysical models. This work developed novel software, PhoTorch, for fitting parameters of the Farquhar, von Caemmerer, and Berry (FvCB) biochemical photosynthesis model based on the parameter optimization components of the popular AI framework PyTorch.
View Article and Find Full Text PDFEpilepsia Open
March 2025
Department of Neurosurgery, Nagoya University Graduate School of Medicine, Nagoya, Aichi, Japan.
Objective: Mesial temporal lobe epilepsy (MTLE) is associated with disruptions in the temporo-amygdala-orbitofrontal (TAO) network, a key component of the limbic system. We aimed to investigate TAO network alterations in patients with MTLE using magnetoencephalography (MEG), which overcomes susceptibility artifacts that limit functional MRI analysis of the orbitofrontal cortex.
Methods: Nine seizure-free patients with MTLE post-temporal lobectomy and nine age- and sex-matched healthy controls were recruited.
IEEE Trans Neural Syst Rehabil Eng
March 2025
Functional near infrared spectroscopy (fNIRS) is being increasingly used to assess brain hemodynamic responses during active walking in older adults due to its wearability, and relative immunity to motion artifacts. Specifically, fNIRS allows for continuous monitoring of brain activations that vary in response to experimental manipulations of cognitive demands during active walking tasks. Studies using fNIRS highlighted increased involvement of the prefrontal cortex (PFC) in dual compared to single task walking, operationalized using oxygenated hemoglobin (HbO), due to increasing attention demands inherent in the former task condition in aging and clinical populations.
View Article and Find Full Text PDFComput Methods Programs Biomed
May 2025
Department of Electrical Engineering, Indian Institute of Technology Madras (IITM), Chennai 600036, Tamil Nadu, India; Healthcare Technology Innovation Centre, IITM, Chennai 600036, Tamil Nadu, India.
Background And Objective: Diffusion models have demonstrated their ability in image generation and solving inverse problems like restoration. Unlike most existing deep-learning based image restoration techniques which rely on unpaired or paired data for degradation awareness, diffusion models offer an unsupervised degradation independent alternative. This is well-suited in the context of restoring artifact-corrupted Magnetic Resonance Images (MRI), where it is impractical to exactly model the degradations apriori.
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