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https://www.selleckchem.com/products/peg300.html Top-down mass spectrometry has become the main method for intact proteoform identification, characterization, and quantitation. Because of the complexity of top-down mass spectrometry data, spectral deconvolution is an indispensable step in spectral data analysis, which groups spectral peaks into isotopic envelopes and extracts monoisotopic masses of precursor or fragment ions. The performance of spectral deconvolution methods relies heavily on their scoring functions, which distinguish correct envelopes from incorrect ones. A good scoring function increases the accuracy of deconvoluted masses reported from mass spectra. In this paper, we present EnvCNN, a convolutional neural network-based model for evaluating isotopic envelopes. We show that the model outperforms other scoring functions in distinguishing correct envelopes from incorrect ones and that it increases the number of identifications and improves the statistical significance of identifications in top-down spectral interpretation.Titanium dioxide (TiO2) nanomaterials have attracted much interest in life science and biological fields because of their excellent photocatalytic activity and good biocompatibility. However, owing to its wide band gap, photocatalysis of TiO2 can be only triggered by UV light. The limited transparent depth of UV light and the generated reactive oxygen species (ROSs) cause inflammation response of skin tissue, thus posing two major challenges in the photocatalytic application of TiO2-based materials in drug delivery and other biotechnology fields. Here, we propose an upconversion-related strategy to enable the photocatalytic activity of TiO2 nanotubes in near-infrared light and apply the system as a controllable drug delivery platform. More importantly, the ROS-induced cytotoxicity and the preleaching of payloads are significantly reduced on the as-proposed amphiphilic TiO2 nanotubes. The hydrophobic monolayers are served as a "cap"

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