Spectroscopic analysis of DOM samples (GSD-110 and GSD-270 were not included) was conducted on a fluorescence spectrometer (Jobin–Yvon Horiba Aqualog-800-C, Horiba Instruments). Fluorescence excitation-emission matrix (EEM) spectra were generated from 240 to 450 nm at 2 nm increments for excitation (Ex) wavelengths, and from 245.90 to 829.35 nm at 1.17 nm increments for emission (Em) wavelengths. All the sample spectra were normalized to Raman peak area after corrected with the ultrapure water EEM spectra, and reported in Raman unit (R.U.) (Murphy 2011 (link)). Finally, parallel factor analysis (PARAFAC) was carried out in MATLAB 2013 (Mathworks, USA) with the DOM Fluor toolbox (http://www.models.life.ku.dk ).
Matlab 2013
Manufactured by MathWorks
Sourced in United States
MATLAB 2013 is a high-level programming language and numerical computing environment used for scientific and engineering calculations. It provides a variety of tools for data analysis, algorithm development, and visualization.
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Lab products found in correlation
En 5107d2, by Bruker (1 mentions)
Elexys e580, by Bruker (1 mentions)
Sigmaplot 11, by Grafiti LLC (1 mentions)
Originpro 9, by OriginLab (1 mentions)
Tdt rp2, by Tucker-Davis Technologies (1 mentions)
Em ccd c9100 02, by Hamamatsu Photonics (1 mentions)
Air plan fluor ph1, by Nikon (1 mentions)
Spss statistics 23, by IBM (1 mentions)
Pmod version 3, by PMOD Technologies (1 mentions)
Rms60x pfod, by Olympus (1 mentions)
Labview 2013, by National Instruments (1 mentions)
25 protocols using matlab 2013
1
Spectroscopic Analysis of Dissolved Organic Matter
2
Sensor Signal Filtering for Posture and Movement
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We used a third-order elliptical infinite impulse response (IIR) low-pass filter shown in Equation (1) (cutoff frequency 20 Hz, passband ripple 0.01 dB, stopband −100 dB) to remove higher frequency components and noise from all sensor signals (S20Hz,Figure 2 b) [31 (link)], as all relevant human body movements are expected to be below 15 Hz [32 (link)]. Gravitational- and movement-based parts of the accelerometer signals are separated using a second filtering step. Thereafter, another third-order elliptical IIR low-pass filter shown in Equation (1) (cutoff frequency 0.2 Hz, passband ripple 0.01 dB, stopband −100 dB) is applied to S20Hz, resulting in an accelerometer signal that only contains gravitation (posture)-based components (Sgrav, Figure 2 c) [31 (link)]. A signal containing just the movement-based parts (Smov) is calculated as the linear difference between the original signal and the gravitational signal: (Figure 2 d) [31 (link)]. The IIR filter coefficients (b0 … b3, a1 … a3) for all elliptical low-pass filters were calculated using MATLAB 2013 (MathWorks; Natick, MA, USA):
3
NTS1 Receptor Structural Analysis
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NTS1 proteoliposomes (containing 5–10 nmol receptor) were pelleted (100,000 g, 3 h, 4 °C) and resuspended in ~10 μL of detergent-free buffer (50 mM Tris-HCl pH 7.4, 50 mM NaCl, 1 mM EDTA, 30% (v/v) deuterated glycerol), yielding a final concentration of ~100–200 μM. Samples were loaded into 1.6 mm (outer diameter) quartz tubes (Wilmad-LabGlass) and flash-frozen in liquid nitrogen. The background dimensionality was determined to be 2.3 from control experiments with MTSL-labelled NTS1 (S172C) reconstituted together with unlabelled receptor at a 1:3 molar ratio. DEER traces (using 3- and 4-pulse PELDOR sequences) were recorded at 50 K at Q-band on an ELEXYS E580 equipped a SuperQ-FT bridge (Bruker) with 2 mm split-ring resonator (EN-5107D2, Bruker). Resulting 3- and 4-pulse DEER traces were phase-corrected using DeerAnalysis 201366 (link), and then stitched together using MATLAB 2013 (MathWorks) by least-squares fitting as per the DEER-Stitch method67 (link),68 (link). Distance distributions were derived from stitched data using DeerAnalysis 2013.
4
Radiomic Features Extraction and Selection
5
Simulation of Forest Runoff Scenarios
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The subsequent sections provide detailed information on the study area, the development of the modified runoff model, the method we used to simulate variability in winter precipitation, the 4FRI runoff scenarios, and the Salt-Verde runoff scenarios. All statistical analyses and graphics were completed using the statistical software SigmaPlot 11 (Systat software Inc, San Jose, CA), except for regression model fitting which was performed using Matlab 2013 (Mathworks, Natick, MA). Methods and results are reported in SI units, but for accessibility to forest and water managers, we reported English units in parentheses within the text and included all study figures and tables in English units as File S1 . Area and water volume amounts were rounded off to three significant figures.
6
Physiological Capacity and Handcycling Performance
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All data were analyzed and calculated using SPSS 17.0 (SPSS Inc., USA), Office Excel 2010 (Microsoft Corporation, USA) and Matlab 2013 (The Mathworks, USA) and are presented as mean ± SD. Participant and training characteristics were compared using one way ANOVA. The effect of the intervention period on the physiological capacity (peakVO2, peakVE, peakHR, and RER) and handcycling performance (peakPO) within and between groups was tested using a repeated measures ANOVA. Post hoc Bonferroni pairwise comparisons were used to show differences between experimental groups. The significance level of all tests was set at p < 0.05.
7
Genetic Association Analysis Pipeline
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Simulations and analyses were performed using MATLAB 2013 (The MathWorks Inc., Natick, Massachusetts) and PLINK 2 [35 (link),36 ]. The L1 -optimization algorithm was written in MATLAB and also a feature of PLINK 2. P -values were estimated using MATLAB’s regstats function and PLINK 2. Color-coded phase plane figures were generated by sampling the ρ − δ plane and interpolating between points using MATLAB’s scatteredInterpolant function. GWAS data were obtained from dbGaP as described in Data Description. Analysis scripts are available from the GigaScience GigaDB repository and maintained on GitHub [55 ,56 ].
8
Post-processing of CTP and ASL Perfusion Data
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The post-processing of CTP data was performed on Vitrea Fx 6.3 image post-processing workstation by a senior radiologist. CTP-CBF and CTP-MTT perfusion maps derived from CT perfusion images using delay-insensitive blockcirculant singular-value decomposition (bSVD) post-processing method referring to existing described procedures (Wintermark et al., 2001 (link)).
Mean perfusion difference images were generated for each PLD. Both ASL-ATT and ASL-CBF perfusion maps were computed online. The ASL-ATT map was converted using the weighted delay method as previously described (Dai et al., 2012 (link); Wang et al., 2014 (link)).
Pre-processing was performed using Data Processing and Analysis of Brain Imaging (DPABI_V2.3)1 , which is based on Statistical Parametric Mapping (SPM8)2 . DPABI was developed in MATLAB 2013 (The MathWorks Inc., Natick, MA, United States). ASL-CBF, ASL-ATT, CTP-CBF, and CTP-MTT images were further normalized into the Montreal Neurological Institute template space using SPM8. Based on the registered 3D T1W images, the gray matter (GM) and white matter (WM) masks were extracted. ASL-CBF, ASL-ATT, CTP-CBF, and CTP-MTT images were segmented into GM and WM maps using the Segment program in SPM8.
Mean perfusion difference images were generated for each PLD. Both ASL-ATT and ASL-CBF perfusion maps were computed online. The ASL-ATT map was converted using the weighted delay method as previously described (Dai et al., 2012 (link); Wang et al., 2014 (link)).
Pre-processing was performed using Data Processing and Analysis of Brain Imaging (DPABI_V2.3)
9
Baseline Correction for DNA Methylation
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Before the baseline correction, the absorption coefficients consist of both the DNA methylation signal and the baseline signal originating from ice, extra DNA structures, and residual materials used to produce the DNA samples. We applied the baseline correction algorithm to separate the DNA methylation signal17 (link),40 . The software packages for numerical computation and visualization, MATLAB 2013 (MathWorks, MS, USA) and Origin Pro 9 (OriginLab Corp., MS, USA), were used to obtain the DNA methylation resonance frequency. We set a Gaussian function as the baseline because ice has the absorption coefficient of a Gaussian form in the range of 0.1–2.0 THz and occupied the largest volume in the DNA samples. The baseline was subtracted from the measurement data. Then, we obtained the resonance peak of DNA methylation.
10
Single-Channel EEG-Based SSVEP BCI
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A BCI based on steady-state visual evoked potentials was developed using a single channel of EEG and high-frequency RVSs. A wireless EEG recording device (BioAmp 2, Rayan Mindware[29 ]) was used, and the signal processing algorithm as well as the interface was implemented in Matlab 2013 (Mathworks). Separate hardware was used to generate flickering stimuli which were placed around the laptop's LCD. The system was calibrated for each participant in a synchronous test, and then he/she used the system for typing several sentences in an asynchronous test.
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