Forehead
It is an important area for various medical and cosmetic procedures, including treatment of headache, skin conditions, and facial aesthetics.
Researchers studying forehead-related topics can utilize PubCompare.ai's AI-powered platform to streamline their work by locating relevant protocols from literature, preprints, and patents, and comparing them to identify the best approaches for their forehead research.
This data-driven decision making can enhance reproducibility and research accuracy.
With PubCompare.ai, researchers can experience a more efficient and informed process for their forehead-focused studies.
Most cited protocols related to «Forehead»
Unidentified LC-MS features are then assigned to peptide identifications in other runs that match based on their accurate masses and aligned retention times. In complex proteomes, the high mass accuracy on current Orbitrap instruments is still insufficient for an unequivocal peptide identification based on the peptide mass alone. However, when comparing peptides in similar LC-MS runs, the information contained in peptide mass and recalibrated retention time is enough to transfer identifications with a sufficiently low FDR (in the range of 1%), which one can estimate by comparing the density of matches inside the match time window to the density outside this window (49 ).
The matching procedure takes into account the up-front separation, in this case isoelectric focusing of peptides into 24 fractions. Identifications are only transferred into adjacent fractions. If, for instance, for a given peptide sequenced in fraction 7, isotope patterns are found to match by mass and retention time in fractions 6, 8, and 17, the matches in fraction 17 are discarded because they have a much greater probability of being false. The same strategy can be applied to any other up-front peptide or protein separation (e.g. one-dimensional gel electrophoresis). All matches with retention time differences of less than 0.5 min after recalibration are accepted. Further details on the alignment and matching algorithms, including how to control the FDR of matching, will be described in a future manuscript.
Download the VM (
1 Modify the file ‘rosie/rosie.front/development.ini’. Find the line ‘host = 192.168.0.64’ and comment it out. Enable the line ‘host = 127.0.0.1’.
2 To run the server: Open two terminals. In one of them, cd into ‘rosie/rosie.back’ and execute ‘./run_rosie-daemon.sh’. In the other terminal, cd into ‘rosie/rosie.front’ and execute ‘./run-rosie-server.sh’.
3 Open ‘localhost:8080’ in your browser. Login as admin (password: managepass).
1 Create your application in rosie.back/protocols/XXX. You need at least two files: submit.py and analyze.py. See “rna_denovo” for example files.
2 For machine-dependent files, edit rosie.back/data.template/XXX. Edit rosie.back/rosie-daemon.ini.template, add useful shorthands and add the app into the protocol line. Copy the corresponding files to rosie.back/data/XXX and rosie.back/rosie-daemon.ini so the VM server can read the files.
3 Add the corresponding controller in rosie.front/rosie/controllers/XXX.py. See rna_denovo.py as an example.
4 Add your controller into controllers/root.py. In root.py, search for ‘rna_denovo’. Add the two corresponding lines for your application.
5 During the creation of the controller files, you may want to make some validation checks for the input format. They are in rosie.front/rosie/lib/validators. You might need to create your own validation tests.
6 Create your page in rosie.front/rosie/templates/XXX/. You need at least 3 pages: index.html, submit.html, and viewjob.html. See rna_denovo for example.
7 Link your application to the main page in template/index.html.
8 You may want an icon. Put a png file of ∼ 1024*1024 into rosie/public/image/XXX_icon.png, and link it to the pages.
9 For documentation, create pages in template/documentations. Also you need to edit controllers/documentation.py to let the server know where it is. Then link your documentation to documentation/index.html and in the other pages of your application.
10 Edit rosie.front/rosie/websetup/bootstrap.py and add the name of the new app.
11 Go to rosie.front/. Run ‘source ∼/prefix/TurboGears-2.2/bin/activate’ then ‘python update_protocol_schema.py’ to update the database.
12 Test the new application in the browser of the VM to make sure it runs fine.
13 Create a new file rosie/doc/XXX.txt, put a short description of protocol input, output, and command line flags. Also add an example job, with input files and a simple readme, into rosie/examples/validation_tests.
14 Commit the changes (use ‘svn commit –username XXXX’ to specify the user name of the commit). Inform the ROSIE administrators for integration into the central server.
Most recents protocols related to «Forehead»
Example 2
The next experiments asked whether inhibition of the same set of FXN-RFs would also upregulate transcription of the TRE-FXN gene in post-mitotic neurons, which is the cell type most relevant to FA. To derive post-mitotic FA neurons, FA(GM23404) iPSCs were stably transduced with lentiviral vectors over-expressing Neurogenin-1 and Neurogenin-2 to drive neuronal differentiation, according to published methods (Busskamp et al. 2014, Mol Syst Biol 10:760); for convenience, these cells are referred to herein as FA neurons. Neuronal differentiation was assessed and confirmed by staining with the neuronal marker TUJ1 (
It was next determined whether shRNA-mediated inhibition of FXN-RFs could ameliorate two of the characteristic mitochondrial defects of FA neurons: (1) increased levels of reactive oxygen species (ROS), and (2) decreased oxygen consumption. To assay for mitochondrial dysfunction, FA neurons an FXN-RF shRNA or treated with a small molecule FXN-RF inhibitor were stained with MitoSOX, (an indicator of mitochondrial superoxide levels, or ROS-generating mitochondria) followed by FACS analysis.
Mitochondrial dysfunction results in reduced levels of several mitochondrial Fe-S proteins, such as aconitase 2 (ACO2), iron-sulfur cluster assembly enzyme (ISCU) and NADH:ubiquinone oxidoreductase core subunit S3 (NDUFS3), and lipoic acid-containing proteins, such as pyruvate dehydrogenase (PDH) and 2-oxoglutarate dehydrogenase (OGDH), as well as elevated levels of mitochondria superoxide dismutase (SOD2) (Urrutia et al., (2014) Front Pharmacol 5:38). Immunoblot analysis is performed using methods known in the art to determine whether treatment with an FXN-RF shRNA or a small molecule FXN-RF inhibitor restores the normal levels of these mitochondrial proteins in FA neurons.
Example 1
A double cloth, plain weave webbing was produced on a needle loom. Each side of the webbing was constructed of 48 ends of 1600 d, 1000 filament ultra-high molecular weight polyethylene yarns and 24 ends of 1000 d, 192 filament polyester yarns along the edges of the webbing, and 12±2 ppi of 1600 d, 1000 filament ultra-high molecular weight polyethylene yarns. The stuffer yarns were 1500 d, 3×4 Kevlar® cord, and 14 cords (168 yarns) were positioned between the front and back sides of the webbing. Binder yarns of 1600 d, 1000 filament ultra-high molecular weight polyethylene yarn binder were woven between the front and back to secure the sides together. A polyester catch cord (1000 d/192/1.5 z) was used to bind the edges of the webbing.
The webbing had a width of approximately 1.0 inches, a thickness of approximately 0.14 inches and a weight of approximately 58 g/linear yard. The tensile strength of the webbing was approximately 8,000 lbs.
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Each CBM-I trial comprised four phases (see Fig.
The entire training consisted of 8 sessions, spread over 4 weeks. Each session lasted about 15 min and consisted of 40 training trials (320 total trials over 8 sessions). These trials were developed in our lab and presented randomly. Participants completed two sessions per week, and they had a short break between two sessions (about 5 min).
Example trial of CBM-I
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More about "Forehead"
It is an important area for various medical and cosmetic procedures, including the treatment of headaches, skin conditions, and facial aesthetics.
This region is a key focus for researchers studying topics such as neuroscience, oculomotor control, and facial imaging.
Researchers can utilize PubCompare.ai's AI-powered platform to streamline their forehead-focused studies.
This platform allows them to locate relevant protocols from literature, preprints, and patents, and compare them to identify the best approaches for their research.
This data-driven decision-making can enhance reproducibility and research accuracy, leading to more robust and reliable findings.
In addition to PubCompare.ai, researchers may also leverage tools like MATLAB, EyeLink 1000, Arctic Front Advance, Presentation software, and the EyeLink 1000 eye tracker to study the forehead region.
These technologies can be used for a variety of applications, such as measuring eye movements, analyzing facial expressions, and investigating the effects of pertussis toxin on forehead muscle activity.
The EyeLink 1000 Plus is a high-performance eye tracker that can provide precise and reliable data on eye movements and gaze patterns, which can be useful for studying forehead-related topics.
The FlexCath Advance, on the other hand, is a catheter system that can be used for various medical procedures involving the forehead, such as the treatment of certain skin conditions or the administration of botulinum toxin injections.
By leveraging these tools and technologies, researchers can gain valuable insights into the forehead region and its role in various physiological, behavioral, and clinical processes.
With the help of PubCompare.ai's AI-driven platform, they can make more informed and data-driven decisions, ultimately enhancing the quality and impact of their forehead-focused studies.