Exponentially growing cells were transferred to slides containing a thin pad of 1.5% agarose (Cambrex) with TPM buffer (10 mM Tris-HCl pH 7.6, 1 mM KH2PO4 pH 7.6, 8 mM MgSO4, 0.2% CTT, covered with a coverslip and imaged with a temperature-controlled Leica DMi8 inverted microscope. Phase contrast and fluorescence images were acquired using a Hamamatsu ORCA-flash V2 Digital CMOS camera. Cells in phase contrast images were automatically detected using Oufti software72 (link). Fluorescence signals were identified and analyzed using a custom-made Matlab v2016b (MathWorks) script. E. coli cells were induced with 0.05 mM isopropyl-β-D-thiogalactopyranosid (IPTG) for 2 h and treated with 30 μg ml−1 chloramphenicol for 30 min before DAPI staining. For DAPI staining, cells were incubated with 1 mg ml−1 DAPI for 10 min at 32 °C prior to start of microscopy. Image processing was performed using Metamorph_ v 7.5 (Molecular Devices).
Matlab v2016b
Manufactured by MathWorks
Sourced in United States
MATLAB v2016b is a high-level programming language and numerical computing environment developed by MathWorks. It is designed for technical and scientific computing, providing a powerful platform for data analysis, algorithm development, and visualization. MATLAB v2016b includes a comprehensive set of tools and libraries for a wide range of applications, from signal processing and control systems to image processing and machine learning.
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Lab products found in correlation
2 protocols using matlab v2016b
1
Microscopic Imaging of E. coli Cells
2
Biomarkers and Heart Failure Outcomes
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Continuous and categorical variables were compared between the groups using analysis of variance and chi‐squared tests, respectively. Multivariable linear regression models were utilized to assess HFpEF and HFrEF as predictors of AVP levels, with and without adjusting for potential confounders. When required, Box–Cox transformation was applied to normalize regression model residuals. A second set of multivariable linear regression models were utilized to assess the association between ANP and AVP. Multivariable linear regression was also utilized to assess whether AVP was associated with LV mass index. We present standardized regression coefficients for easier comparison of the magnitude of the effect of various predictors on the dependent variable in regression models. The association between AVP levels and the risk of a composite endpoint of incident hospitalized HF or cardiovascular death was assessed with proportional hazards (Cox) regression. All tests were two‐tailed. Statistical significance was defined as a P‐value ≤ 0.05. We used SPSS v24 for Mac (IBM, Chicago, IL, USA) and Matlab v2016b (The Mathworks; Natick, MA, USA) to perform statistical analyses.
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