In the present study, we analyzed the percent immunopositive cells in astrocyte-enriched, microglia-enriched, and oligodendrocyte-enriched cultures isolated from the cortex or spinal cord of neonatal rats for six markers that have been previously used to label astrocytes: GFAP, GLAST, GLT-1, GS, ALDH1L1, and SOX9. We also stimulated astrocyte cultures with either transforming growth factor (TGF)-β1, shown to induce reactive astrogliosis in rodent cortical astrocytes in vitro, or TGF-β3, shown to dampen reactivity markers in astrocytes in vitro. Markers were evaluated based on the following criteria: (1) ability to stain all astrocytes in a given culture, (2) resistance to changes in the percentage of positively stained astrocytes after stimulation with either TGF-β1 or TGF-β3, and (3) no positive staining of microglia and oligodendrocytes in cultures isolated from the same region in the CNS (cortex or spinal cord). In doing so, we aimed to investigate protein expression differences between cortical and spinal cord astrocytes and changes in marker expression with TGF-β1 and TGF-β3 stimulation in vitro. We found that only SOX9 in cortical cultures and ALDH1L1 in spinal cord cultures labeled more than 75% of the cells in naïve and stimulated astrocyte cultures and stained less than 5% of the cells in microglia and oligodendrocyte cultures. Further, significantly more cortical than spinal cord astrocytes stained for GFAP, GLAST, ALDH1L1, and SOX9 in naïve cultures, whereas significantly more spinal cord than cortical astrocytes stained for GLAST, GS, and ALDH1L1 in TGF-β1-treated cultures. These findings are important as variability in marker staining may lead to misinterpretation of the astrocyte response in cocultures, migration assays, or engineered disease models. The first sheet in the excel file shows the number of CD68+ microglia and MBP+ oligodendrocytes in each culture condition (naïve or stimulated with TGF-β1 or TGF-β3 in cultures derived from either rat cortex or spinal cord), which was used to calculate the percentage of astrocytes in the cultures. The second sheet in the file shows the number of immunopositive cells counted for each replicate in all culture conditions.
This data is linked to the following study:
D.L. Puhl, J.L. Funnell, A.R. D’Amato, J. Bao, D.V. Zagorevski, Y. Pressman, D. Morone, A.E. Haggerty, M. Oudega, R.J. Gilbert, Aligned Fingolimod-Releasing Electrospun Fibers Increase Dorsal Root Ganglia Neurite Extension and Decrease Schwann Cell Expression of Promyelinating Factors, Front. Bioeng. Biotechnol. 8 (2020). https://doi.org/10.3389/fbioe.2020.00937.
Methods for how the data was collected and interpreted are included in the text.
Contributors:Xuanjie Wang, Mei-Li Hsieh, James A. Burr, Shawn-Yu Lin, Shankar Narayanan
The desalination of seawater has the potential to address the increasing demand for potable water. Specifically, solar-thermal water desalination can generate clean water without relying significantly on fossil energy. However, the inefficient use of solar energy often limits the overall performance, which affects the rate, efficiency, and feasibility of large-scale implementation. This study shows the use of a spectrally-selective nanomaterial as an absorber to improve the performance of solar-thermal desalination. This material is designed to maximize the absorption of sunlight and minimize the loss of energy by thermal emission, which allows higher throughput and efficiencies compared to broadband absorbers. In this study, we show a 10 % enhancement in the evaporation rate of water by using a selective absorber compared to a graphite absorber. We demonstrate a scalable approach involving the use of wicking materials interfaced with the spectrally-selective absorber for the desalination of saline water to get 73 % of overall efficiency using a solar flux of one sun. This demonstration of an inexpensive strategy for producing clean water can potentially transform the field of solar-thermal desalination to address the rising demand for water.
The data included in this database are the results obtained from experiments. The data is provided as an excel sheet, where each tab represents a single data set. These results are also discussed in a related journal publication (with the same title), which can be found in the journal Materials Today Energy (after its publication). The tabs in the excel file are named based on the Figure numbers in the manuscript.
# List of Data Sets
5 sets of data are included in total:
1. output_feedback/consensus_tracking: Figure 7 (a) (b)
2. output_feedback/consensus_only: Figure 7 (c) (d)
3. MPC/consensus_tracking: Figure 9 (a) (b)
4. MPC/consensus_only: Figure 9 (c) (d)
5. inner_outer_loop/consensus_only: Figure 10 (c) (d)
# Description of Dataset
Every data set contains 5 files:
11 columns. The first column stores time (unit: second). The other 10 columns input current to the heater array (unit: mA)
32 columns. The first column stores time (unit: second). The other 31 columns store copper surface temperature at 31 node coordinates (find node coordinates in temp_cor.txt). Temperature unit: degree Kelvin
101 columns. The first column stores time (unit: second). The other 100 columns store grain size profile at 100 coordinates (find grain size profile coordinates in gs_cor.txt). Grain size unit: micrometer.
11 columns. The first column stores time (unit: second). The other 10 columns store squared grain size in 10 zones (unit micrometer^2)
coordinates of temperature profile nodes. Unit: mm
coordinates of grain size profile nodes. Unit: mm
Processed data used to analyze the MD study of α-γ Phase Transformation in RDX. This data is created from statistical averaging of the crystallographic parameters, energy terms of SB potential, and wag angles calculated from atomic positions of various RDX molecules. The raw data for the statistical averaging is obtained from LAMMPS during the MD simulations.
The "crysparams_energy.txt" contains the crystallographic parameters and energy terms of SB potential for the entire simulation box containing 144 molecules as a function of pressure and temperature.
The "wag_angle.txt" contains the three wag angles of 144 molecules at different temperatures as a function of pressure.
Raw data from the LAMMPS simulator for the MD study of α-γ Phase Transformation in RDX.
The "log.*.data" contains the crystallographic parameters and energy terms of SB potential at various simulation times. Each data file indicates the temperature at which the data is collected. The "thermo_style" command from LAMMPS indicating the format of the data is given below.
thermo_style custom step xlo xhi ylo yhi zlo zhi xy yz xz cella cellb cellc vol pxx pyy pzz pxy pyz pxz press temp pe ke f_1 evdwl ecoul ebond eangle edihed eimp elong etail
Refer to https://lammps.sandia.gov/doc/thermo_style.html for more details on description of each keyword. Details of "f_1" can be found at https://lammps.sandia.gov/doc/fix_nh.html.
The "atom_positions" folder contains 7 subfolders for each temperature from 300 K to 450 K (increments of 25 K) for which the MD simulations were performed. The "atm_pos*.lst" files in these subfolders contains the atomic positions for all molecules at that temperature for the timestep indicated in the file name.
The atom coordinates are provided only for the 3 Carbon atoms and 6 Nitrogen atoms of an RDX molecule since these are the only atoms used to calculate the wag angles of the molecule. The atom coordinates are given after including the effect of the atom passing through the periodic boundaries. The format of the atomic position data is given below.
Atom ID (1-3024), Molecule ID (1-144), Atom Type (1-3), Atom Charge, "X, Y, Z Atom Coordinates".
Quantitative data on mRNA/protein from A) the raw WT proteome levels, B) the imputed WT proteome levels, C) the eJTK WT proteome scores, D) the raw WT mRNA levels, E) the imputed WT mRNA levels, F) the eJTK WT mRNA scores, G) the raw ∆csp-1 proteome levels, H) the imputed ∆csp-1 proteome levels, I) the eJTK ∆csp-1 proteome scores, J) the setup of the TMT-MS 10-plex analyses.