MACHINE LEARNING APPROACHES TO IDENTIFY DISCRIMINATIVE SIGNATURES OF VOLATILE ORGANIC COMPOUNDS (VOCS) FROM BACTERIA AND FUNGI USING SPME-DART-MS

Machine Learning Approaches to Identify Discriminative Signatures of Volatile Organic Compounds (VOCs) from Bacteria and Fungi Using SPME-DART-MS

Point-of-care screening tools are essential Cables to expedite patient care and decrease reliance on slow diagnostic tools (e.g., microbial cultures) to identify pathogens and their associated antibiotic resistance.Analysis of volatile organic compounds (VOC) emitted from biological media has seen increased attention in recent years as a potential

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Tethering of an E3 ligase by PCM1 regulates the abundance of centrosomal KIAA0586/Talpid3 and promotes ciliogenesis

To elucidate the role of centriolar satellites in ciliogenesis, we deleted the gene encoding the PCM1 protein, an integral component of satellites.PCM1 null human cells show marked defects in ciliogenesis, precipitated by the loss of specific proteins from satellites and their relocation to centrioles.We find that Mugs an amino-terminal domain of P

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Quantum Dot-Based Immunofluorescent Imaging of Ki67 and Identification of Prognostic Value in HER2-Positive (Non-Luminal) Breast Cancer [Erratum]

Sun J, Chen C, Jiang G, Tian W, Li Y, Sun S.Int J Nanomedicine.2014;9(1):1339–1346.An error during the preparation of Figure 3 for publishing led to the inadvertent creation of a bottoms duplicate region in figure part 3A on page 1342.The journal wishes to apologise for this error.The correct version of Figure 3 is shown below.Figure 3 The d

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