A quick access tool for the one-pot, chromatography-free synthesis of the diversified dihydrospiro[indeno[1,2-b]pyridine-4,3'-indoline or acenaphthylene-1,4'-indeno[1,2-b]pyridine spiro-analogous via sustainable microwave condition in minimal 1:1 (v/v) aqueous ethanol without any metal catalyst is demonstrated here. This permutated spiro-casing was designed as fluorescence probe at physiological pH for selective detection of Zn, even in the presence of other competitive ions and showed a fluorescent enhancement with 1:1 metal/ligand complex. Moreover, this spiro sensor was successfully applied as an effective intracellular Zn imaging agent in the biomedical study of human hepatocellular liver carcinoma cells (HepG2) due to its cell permeability property. A quick access technique for the permutated dihydrospiro-pyridine via chromatography-free sustainable microwave condition and its applications as organic fluorescence probe at physiological pH for selective detection of Zn and effective intracellular Zn imaging in HepG2 cells.
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http://dx.doi.org/10.1007/s11030-019-09934-7 | DOI Listing |
Environmental degradation due to the rapid increase in CO₂ emissions is a pressing global challenge, necessitating innovative solutions for accurate prediction and policy development. Machine learning (ML) techniques offer a robust approach to modeling complex relationships between various factors influencing emissions. Furthermore, ML models can learn and interpret the significance of each factor's contribution to the rise of CO.
View Article and Find Full Text PDFTalanta
January 2025
Frontiers Science Center for Synthetic Biology (Ministry of Education), Tianjin Key Laboratory of Function and Application of Biological Macromolecular Structures, School of Life Sciences, Tianjin University, 92 Weijin Road, Nankai District, Tianjin, 300072, China; Haihe Laboratory of Sustainable Chemical Transformations, Tianjin, 300192, China. Electronic address:
The growing demand for glycolate, fueled by economic development, requires the advancement of production methods. Escherichia coli (E. coli), a preferred host for glycolate production, has undergone extensive metabolic engineering to improve yield.
View Article and Find Full Text PDFMol Ecol Resour
January 2025
United States Department of Agriculture, Wildlife Services, National Wildlife Research Center, Fort Collins, Colorado, USA.
While a best practice for evaluating the behaviour of genetic clustering algorithms on empirical data is to conduct parallel analyses on simulated data, these types of simulation techniques often involve sampling genetic data with replacement. In this paper we demonstrate that sampling with replacement, especially with large marker sets, inflates the perceived statistical power to correctly assign individuals (or the alleles that they carry) back to source populations-a phenomenon we refer to as resampling-induced, spurious power inflation (RISPI). To address this issue, we present gscramble, a simulation approach in R for creating biologically informed individual genotypes from empirical data that: (1) samples alleles from populations without replacement and (2) segregates alleles based on species-specific recombination rates.
View Article and Find Full Text PDFSensors (Basel)
December 2024
Biomedicine Research Center of Strasbourg (CRBS), UR 3072, "Mitochondria, Oxidative Stress and Muscle Plasticity", Faculty of Medicine, University of Strasbourg, 67000 Strasbourg, France.
The continuous monitoring of oxygen saturation (SpO) and respiratory rates (RRs) are major clinical issues in many cardio-respiratory diseases and have been of tremendous importance during the COVID-19 pandemic. The early detection of hypoxemia was crucial since it precedes significant complications, and SpO follow-up allowed early hospital discharge in patients needing oxygen therapy. Nevertheless, fingertip devices showed some practical limitations.
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December 2024
PIMM Research Laboratory, UMR 8006 CNRS-ENSAM-CNAM, Arts et Metiers Institute of Technology, 151 Boulevard de l'Hôpital, 75013 Paris, France.
This work introduces a novel methodology for identifying critical sensor locations and detecting defects in structural components. Initially, a hybrid method is proposed to determine optimal sensor placements by integrating results from both the discrete empirical interpolation method (DEIM) and the random permutation features importance technique (PI). Subsequently, the identified sensors are utilized in a novel defect detection approach, leveraging a semi-intrusive reduced order modeling and genetic search algorithm for fast and reliable defect detection.
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