Six tibia samples from six different mice in each group were dehydrated with rising series of ethanolic solutions and embedded in PMMA. 50-μm-thick sections were cut with a microtome (Leica, Germany). The sections were fixed on a microscope slide. Raman measurements were conducted with a Raman spectrometer (Invia RM2000, Renishaw, London, United Kingdom) using a laser with a wavelength of 785 nm, laser power of 50%, extend scan. The wave-number range of collected Raman scattered photons was 1800–800 cm–1 and baseline corrected using a polynomial fitting-based method. In order to match the locations of the collagen fibrils investigated in the present study, the measurement sites were chosen at the intracortical compartment of the antero-medial aspect of the tibia. A total of 36 images (one images/location, three locations/specimen, six specimens from the control group, and six specimens from the tail suspension group) were obtained to investigate the collagen cross linking of Pyr (mature) and DHLNL (immature). The peaks at 1660 and 1690 were located at the curve with custom-written Matlab routine (Mathworks, Inc., MA, United States) to indicate the cross linking of Pyr and DHLNL. Likewise, the primary phosphate band (approximately 959 cm–1) and the amide I band (1616–1720 cm–1) were identified to compute the mineral-to-matrix ratio, which indicates the amount of mineralization.
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Lab products found in correlation
Microtome, by Leica camera (1 mentions)
Rm2000, by Renishaw (1 mentions)
Ldh d c 485, by PicoQuant (1 mentions)
Uplansapo 100 1.4 na, by Olympus (1 mentions)
Spcm aqrh 14 tr, by Excelitas (1 mentions)
Hydraharp 400, by PicoQuant (1 mentions)
Microtime 200, by PicoQuant (1 mentions)
Ingenia 3 t system, by Philips (1 mentions)
Uplansapo, by Olympus (1 mentions)
10 protocols using matlab routine
1
Raman Spectroscopy of Collagen Cross-Linking
2
Shrimp Morphometric Measurements
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To prevent prejudice due to various measuring tools for researchers and for a more accurate methodology, we estimated singular lengths to the closest 0.1 cm by using a MATLAB routine (MathWorks Inc., Natick, MA, USA) to singular shrimp snapshots taken with a MEGA 0.I.S USB camera (Leica, Wetzlar, Germany) outfitted with a DC VARIO-ELMARIT 1:2.8-49/6.3-25.2 A SPH lens (Leica). The snapshots created a record that could be easily stored and used for additional estimations and confirmatory checks if necessary. Manual fixation of singular shrimp with a complete rostrum on millimeter paper was done (to stay away from the natural warp of the shrimp body). The estimations were characterized as: (a) Total length (TL): The space from the tip of the rostrum to the end of the telson, cm; (b) carapace length (CL): The space from the posterior margin of the orbit to the posterior edge of the carapace, mm; and (c) total weight (TW): Weighed in grams by using an electronic digital balance (±0.01 g; Sartorius, Göttingen, Germany).
3
Confocal Microscopy with Pulsed Laser
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PM sheets were imaged in a confocal setup Microtime 200 (PicoQuant) using an inverted microscope (IX73, Olympus) equipped with an oil objective lens (UPlanSApo 100 × 1.4 NA, Olympus). A fiber-coupled pulsed diode laser (LDH-D-C-485, PicoQuant) was used for 485 nm excitation at 10 MHz repetition rate. Fluorescence was separated from the excitation light by a dichroic mirror (Zt488/640rpc, Chroma) and split into four fractions by three 50:50 beam splitters (Beck Optronic Solutions Limited). Additional fluorescence filters (LP488, Semrock, and SP750, Chroma) were used in front of each of the four SPAD detectors (SPCM-AQRH-14-TR, Excelitas). Data were acquired with a time-correlated single-photon counting unit (HydraHarp 400, PicoQuant). Images were further analyzed with costum-written MATLAB routines (The Mathworks Inc).
4
SEMG Signal Processing and Analysis
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MATLAB routines (The MathWorks, Inc., Natick, MA, USA) were used for EMG data processing. All data was preprocessed by skipping the first 3 s of the static task and by removing artifacts. SEMG data was filtered using a 20 Hz high-pass and 500 Hz low-pass Butterworth filter design. Fourier transformation was performed using a Blackman window, epoch 500 ms, 50% overlap resulting in 27 s of data at 2 Hz (i.e., 54 samples for each data set) (Kienbacher et al. 2014a (link)). A linear regression analysis was performed on MF-SEMG data for each electrode site separately between 3 s and 30s of the contraction in order to calculate the rate of decline in MF over time. The slope of the linear regression line was measured in Hz/s and after normalizing to the initial MF value (intercept of the regression analysis), in %/s. Figure 1 illustrates the SEMG signal processing process.
5
Multimodal Data Analysis for Neuroscience
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Behavioral and electrophysiological data were analyzed using built-in and custom-made MATLAB routines (Mathworks) and SPSS (IBM). fMRI sessions were analyzed using SPM12 and the SPM12-based Sandwich Estimator toolbox (SwE) at the group level.
6
Comprehensive Gait Analysis Protocol
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Kinematic data was collected using an eight-camera motion capture system with a sampling rate of 120 Hz (MOTION ANALYSIS corporation, Santa Rosa, CA) and 29 markers from the Helen Hayes marker set were placed on bilateral bony landmarks, the head, and the trunk to compute the joint centers56 (link). The raw marker data was low-pass filtered using a fourth order Butterworth filter with a cutoff frequency of 6 Hz57 (link), and the kinematic variables were computed using customized MATLAB routines (MATHWORKS, Natick, MA).
DELSYS Trigno surface electromyography (EMG) sensors were used to record EMG activity from four muscles on each leg at 1200 Hz. These muscles were the tibialis anterior (TA), medial gastrocnemius (GAS), vastus lateralis (VLAT), and biceps femoris long head (BFLH). Raw, unrectified EMG signals were first hardware band-pass filtered over a bandwidth of 20–450 Hz, applying a standard mode rejection ratio of > 80 db. After data collection, the EMG data was digital high-pass filtered at 35 Hz and then full-wave rectified. The rectified data was smoothed via a second-order, dual-pass Butterworth low-pass filter with a 40 Hz cutoff frequency37 (link). A sample of the filtered EMG signal is demonstrated in Fig.2 .
DELSYS Trigno surface electromyography (EMG) sensors were used to record EMG activity from four muscles on each leg at 1200 Hz. These muscles were the tibialis anterior (TA), medial gastrocnemius (GAS), vastus lateralis (VLAT), and biceps femoris long head (BFLH). Raw, unrectified EMG signals were first hardware band-pass filtered over a bandwidth of 20–450 Hz, applying a standard mode rejection ratio of > 80 db. After data collection, the EMG data was digital high-pass filtered at 35 Hz and then full-wave rectified. The rectified data was smoothed via a second-order, dual-pass Butterworth low-pass filter with a 40 Hz cutoff frequency37 (link). A sample of the filtered EMG signal is demonstrated in Fig.
7
Whole-Brain Functional Imaging During Cognitive Task
8
Whole-Body Fluorescence Imaging System
9
fMRI Data Processing Pipeline
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The fMRI data were processed using the FMRIB Software Library (FSL v5.0, Oxford, UK) combined with Matlab routines developed in-house (Mathworks, Natick MA, USA). Data pre-processing included motion correction (MCFLIRT tool, 6 degrees of freedom) (Jenkinson et al., 2012) , slice-timing adjustment (set to the middle of each TR, via linear interpolation), brain segmentation (BET tool) (Smith, 2002) , Gaussian spatial smoothing (FWHM ¼ 4 mm) and temporal de-trending (100sec cut-off). To reduce contributions from head motion, a set of 24 confound regressors derived from the motion parameter time courses were regressed out of the fMRI data by general linear model analysis. Each dataset was co-registered to the standard MNI space as follows: first, for each subject, the fMRI data were B 0 -unwarped using FSL-TOPUP (Andersson et al., 2003) and brought to the subject's anatomical space using FLIRT with boundary-based registration (Greve and Fischl, 2009) (12 degrees of freedom). The co-registration to MNI space was then determined using the anatomical images, again through FLIRT (12 degrees of freedom).
10
Mitochondrial Dynamics in Stem Cells
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TNT Formation Dynamics and Stability Culture dishes containing WJ-MSCs were transferred to an epifluorescence microscope (Olympus IX81) connected to a CCD camera (Olympus DP71/12.5 megapixels). Cultures were maintained in an IX81 microscope adapted culture chamber (Olympus) at 37 °C using a heating stage and under 5% CO 2 . Images were taken every 3 min over a period of 12 h using a 60X Olympus UPLANS Apo (oil NA: 1.35) objective, and then stacked at 125 mseconds/frame rate (8 Hz) Mitochondrial Kinetics In order to evaluate mitochondrial transport, imaging and kymograph analyses were conducted as previously described by Noble et al. [13] (link). Briefly, 48 h after transfection with mt-YFP, cells were recorded in the same microscope and culture chamber described above. Cultures were kept under a 60X UPLANS Apo. Movies and photomicrographs were obtained every 20 s for a period of 25 min and stacked at 125 mseconds/frame rate. Mitochondria were identified as particles moving in the green channel. Kymographs were plotted with Image J using the multiple kymograph plugin, and average net velocity, distance and direction were calculated for analyses and processed using MATLAB routines (The Mathworks, USA).
Another set of transfected cultures was fixed with 4% (w/v) paraformaldehyde in PBS for 20 min at room temperature, washed with PBS and submitted to immunocytochemical assays.
Another set of transfected cultures was fixed with 4% (w/v) paraformaldehyde in PBS for 20 min at room temperature, washed with PBS and submitted to immunocytochemical assays.
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