73 research outputs found
Characterization of Coupled Ground State and Excited State Equilibria by Fluorescence Spectral Deconvolution
No full textFluorescence probes with multiparametric response based on the relative variation in the intensities of several emission bands are of great general utility. An accurate interpretation of the system requires the determination of the number, positions and intensities of the spectral components. We have developed a new algorithm for spectral deconvolution that is applicable to fluorescence probes exhibiting a twostate ground-state equilibrium and a two-state excited-state reaction. Three distinct fluorescence emission bands are resolved, with a distribution of intensities that is excitationwavelength-dependent. The deconvolution of the spectrum into individual components is based on their representation as asymmetric Siano-Metzler log-normal functions. The application of the algorithm to the solvation response of a 3-hydroxychromone (3HC) derivative that exhibits an H-bonding-dependent excited-state intramolecular proton transfer (ESIPT) reaction allowed the separation of the spectral signatures characteristic of polarity and hydrogen bonding. This example demonstrates the ability of the method to characterize two potentially uncorrelated parameters characterizing dye environment and interactions.Fil: Caarls, Wouter. Max Planck Institute Of Biochemistry.; AlemaniaFil: Celej, Maria Soledad. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Córdoba. Centro de Investigaciones en Química Biológica de Córdoba. Universidad Nacional de Córdoba. Facultad de Ciencias Químicas. Centro de Investigaciones en Química Biológica de Córdoba; Argentina. Universidad Nacional de Córdoba. Facultad de Ciencias Químicas. Departamento de Química Biológica; ArgentinaFil: Demchenko, Alexander P.. A. V. Palladin Institute of Biochemistry; UcraniaFil: Jovin, Thomas M.. Max Planck Institute Of Biochemistry.; Alemani
Quantum-Dot, Magnetic Particle and Expression-Probe Based Sensing of erbB Protein Dynamics and Development of Tumor Diagnostics
No full text[pt] UM ESTUDO DE TRANSFER LEARNING EM DEEP REINFORCEMENT LEARNING EM AMBIENTES ROBÓTICOS SIMULADOS
No full text[en] DEEP REINFORCEMENT LEARNING FOR QUADROTOR TRAJECTORY CONTROL IN VIRTUAL ENVIRONMENTS
No full textTumor-Targeted Quantum Dots Can Help Surgeons Find Tumor Boundaries
No full textDespite surgical advances and recent progress in adjuvant therapies, the prognosis for patients with malignant brain tumors such as glioblastoma multiforme has remained poor, and the neurological deterioration suffered by most patients as a consequence of tumor progression is dramatic and severe. In addition, malignant brain tumors have > 95% recurrence close to the primary site of initial resection. Unfortunately, standard imaging techniques do not permit the intraoperative identification of individual or small clusters of residual tumor cells, precluding their selective removal while sparing the surrounding normal brain tissue. In this report, we show that quantum dots (QDs) coupled to epidermal growth factor (EGF) or anti-EGF receptor receptor (EGFR, Her1) specifically and sensitively label glial tumor cells in cell culture, glioma mouse models, and human brain-tumor biopsies. A clear demarcation between brain and tumor tissue at the macroscopic as well as the cellular level is provided by the fluorescence emission of the QDs
Quantum-Dot, Magnetic Particle and Expression-Probe Based Sensing of erbB Protein Dynamics and Development of Tumor Diagnostics
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[en] APPLICATIONS OF DEEP LEARNING FOR CROP MONITORING: CLASSIFICATION OF CROP TYPE, HEALTH AND MATURITY
No full text[en] STUDY OF REINFORCEMENT LEARNING TECHNIQUES APPLIED TO THE CONTROL OF CHEMICAL PROCESSES
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