Name: analyzing multifactorial rna-seq experiments with dicoexpress
Uploaded: 2022-07-29T12:30:00
Description: Watch this Scientific Journal Video about Analyzing Multifactorial RNA-Seq Experiments with DiCoExpress at JoVE.com

This work was mainly supported by the ANR PSYCHE (ANR-16-CE20-0009). The authors thank F. Desprez for the construction of the container of DiCoExpress. KB work is supported by the Investment for the Future ANR-10-BTBR-01-01 Amaizing program. The GQE and IPS2 laboratories benefit from the support of Saclay Plant Sciences-SPS (ANR-17-EUR-0007).

1. DiCoExpress
<ol>
	<li>Open a R studio session and set directory to Template_scripts.</li>
	<li>Open the DiCoExpress_Tutorial.R script in R studio.</li>
	<li>Load DiCoExpress functions in the R session with the following commands: 
	&#62; source(&#34;../Sources/Load_Functions.R&#34;) 
	&#62; Load_Functions() 
	&#62; Data_Directory = &#34;../Data&#34; 
	&#62; Results_Directory = &#34;../Results/&#34;</li>
	<li>Load data files in the R session with the following commands: 
	&#62; Project_Name =...

<ol>
	<li>Wang, Z., Gerstein, M., Snyder, M. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=RNA-Seq:+a+revolutionary+tool+for+transcriptomics.">RNA-Seq: a revolutionary tool for transcriptomics.</a> Nature reviews. Genetics. 10 (1), 57-63 (2009).</li><li>Yang, I. S., Kim, S. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Analysis+of+Whole+Transcriptome+Sequencing+Data:+Workflow+and+Software.">Analysis of Whole Transcriptome Sequencing Data: Workflow and Software.</a> Genomics &amp; Informatics. 13 (4), 119-125 (2015).</li><li>R Core Team. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=R:+A+language+and+environment+for+statistical+computing.">R: A language and environment for statistical computing.</a> R Foundation for Statistical Computing. , (2020).</li><li>Huber, W., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Orchestrating+high-throughput+genomic+analysis+with+Bioconductor.">Orchestrating high-throughput genomic analysis with Bioconductor.</a> Nature Methods. 12 (2), 115-121 (2015).</li><li>Smith, D. R. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=The+battle+for+user-friendly+bioinformatics.">The battle for user-friendly bioinformatics.</a> Frontiers in Genetics. 4, 187 (2013).</li><li>Pavelin, K., Cham, J. A., de Matos, P., Brooksbank, C., Cameron, G., Steinbeck, C. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Bioinformatics+Meets+User-Centred+Design:+A+Perspective.">Bioinformatics Meets User-Centred Design: A Perspective.</a> PLoS Computational Biology. 8 (7), 1002554 (2012).</li><li>. Shiny: web application framework Available from: <a target="_blank" href="https://rdrr.io/cran/shiny/">https://rdrr.io/cran/shiny/</a> (2021)</li><li>Lambert, I., Roux, C. P. -. L., Colella, S., Martin-Magniette, M. -. L. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=DiCoExpress:+a+tool+to+process+multifactorial+RNAseq+experiments+from+quality+controls+to+co-expression+analysis+through+differential+analysis+based+on+contrasts+inside+GLM+models.">DiCoExpress: a tool to process multifactorial RNAseq experiments from quality controls to co-expression analysis through differential analysis based on contrasts inside GLM models.</a> Plant methods. 16 (1), 68 (2020).</li><li>Dillies, M. -. A., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=A+comprehensive+evaluation+of+normalization+methods+for+Illumina+high-throughput+RNA+sequencing+data+analysis.">A comprehensive evaluation of normalization methods for Illumina high-throughput RNA sequencing data analysis.</a> Briefings in bioinformatics. 14 (6), 671-683 (2012).</li><li>Rigaill, G. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Synthetic+data+sets+for+the+identification+of+key+ingredients+for+RNA-seq+differential+analysis.">Synthetic data sets for the identification of key ingredients for RNA-seq differential analysis.</a> Briefings in Bioinformatics. 19 (1), (2016).</li><li>Rau, A., Maugis-Rabusseau, C. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Transformation+and+model+choice+for+RNA-seq+co-expression+analysis.">Transformation and model choice for RNA-seq co-expression analysis.</a> Briefings in Bioinformatics. 19 (3), (2017).</li><li>Robinson, M. D., McCarthy, D. J., Smyth, G. K. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=edgeR:+a+Bioconductor+package+for+differential+expression+analysis+of+digital+gene+expression+data.">edgeR: a Bioconductor package for differential expression analysis of digital gene expression data.</a> Bioinformatics. 26 (1), 139-140 (2009).</li><li>Wilkinson, M. D., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=The+FAIR+Guiding+Principles+for+scientific+data+management+and+stewardship.">The FAIR Guiding Principles for scientific data management and stewardship.</a> Scientific Data. 3 (1), 160018 (2016).</li><li>Stark, R., Grzelak, M., Hadfield, J. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=RNA+sequencing:+the+teenage+years.">RNA sequencing: the teenage years.</a> Nature Reviews Genetics. 20 (11), 631-656 (2019).</li></ol>

Because RNA-Seq has become a ubiquitous method in biological studies, there is a constant need to develop versatile and user-friendly analytical tools. A critical step within most of the analytical workflows is often to identify with confidence the genes differentially expressed between biological conditions and/or treatments15. The production of reliable results requires proper statistical modeling, which has been the motivation for the development of DiCoExpress.
DiCo...

Next-generation RNA sequencing (RNA-Seq) technology is now the gold standard of transcriptome analysis1. Since the early days of the technology, the combined efforts of bioinformaticians and biostatisticians have resulted in the development of numerous methods tackling all the essential steps of transcriptomic analyses, from mapping to transcript quantification2. Most of the tools available today to the biologist are developed within the R software environment for statistical computing and graphs3, and many packages for biological data analysis are available in the Bioconductor repository4. These packages offer total control and customization of the analysis, but they come at the cost of extensive use of a command-line interface. Because many biologists are more comfortable with a &#34;point and click&#34; approach5, the democratization of RNA-Seq analyses requires the development of more user-friendly interfaces or protocols6. For example, it is possible to build web interfaces of R packages using Shiny7, and command-line data analysis is made more intuitive with the R-studio8 interface. The development of dedicated, step-by-step tutorials can also help the novel user. In particular, a video tutorial supplements a classic text one, leading to a deeper understanding of all the procedure steps.
We recently developed DiCoExpress9, a tool for analyzing multifactorial RNA-Seq experiments in R using methods considered to be the best ones based on neutral comparison studies10,11,12. Starting from a count table, DiCoExpress proposes a data quality control step followed by a differential gene expression analysis (edgeR package13) using a generalized linear model (GLM) and the generation of co-expression clusters using Gaussian mixture models (coseq package12). DiCoExpress handles complete and unbalanced design up to 2 biological factors (i.e., genotype and treatment) and one technical factor (i.e., replicate). The originality of DiCoExpress lies in its directory architecture storing and organizing data, scripts, and results and in the automation of the writing of the contrasts allowing the user to investigate numerous questions within the same statistical model. An effort was also made to provide graphical outputs illustrating the statistical results.
The DiCoExpress workspace is available at https://forgemia.inra.fr/GNet/dicoexpress.&#160;It contains four directories, two pdf, and two text files. The Data/ directory contains the input datasets; for this protocol, we will use the &#34;tutorial&#34; dataset. The Sources/ directory contains seven R functions necessary to perform the analysis, and must not be modified by the user. The analysis is run using scripts stored in the Template_scripts/ directory. The one used in this protocol is called DiCoExpress_Tutorial_JoVE.R and can be easily adapted to any transcriptomic project. All the results are written in the Results/ directory and stored in a subdirectory named according to the project. The README.md file contains useful installation information, and any specific details concerning the method and its use can be found in the DiCoExpress_Reference_Manual.pdf file.
This video tutorial guides the user through the different features of DiCoExpress with the aim to overcome the reluctance felt by biologists using command-line-based tools. We present here the analysis of an artificial RNA-Seq dataset describing gene expression in three biological replicates of four genotypes, with or without treatment. We will now go through the different steps of the DiCoExpress workflow illustrated in Figure 1. The script described in the Protocol section and input files are available on the site: https://forgemia.inra.fr/GNet/dicoexpress
Prepare data files 
The four csv files stored in the Data/ directory should be named according to the project name. In our example, all the names, therefore, begin with &#34;Tutorial&#34;, and we will set Project_Name = &#34;Tutorial&#34; in Step 4 of the protocol. The separator used in the csv files must be indicated in the Sep variable in Step 4. In our &#34;tutorial&#34; dataset, the separator is a tabulation. For advanced users the full dataset can be reduced to a subset by providing a list of instructions and a new Project_Name through the Filter variable. This option avoids redundant copies of the input files and verifies FAIR principles14.
Among the four csv files, only the COUNTS and TARGET files are mandatory. They contain the raw counts for every gene (here Tutorial_COUNTS.csv) and the experimental design description (here Tutorial_TARGET.csv). The TARGET.csv file describes every sample (one sample per row) with a modality for each biological or technical factor (in the columns). We strongly recommend that the names chosen for the modalities start with a letter, not a number. The name of the last column (&#34;Replicate&#34;) cannot be changed. Finally, the sample names (first column) must match the names in the headings of the COUNTS.csv file (Genotype1_control_rep1 in our example). The Enrichment.csv file in which every line contains one Gene_ID and one annotation term is only required if the user plans to run the enrichment analysis. If one gene has several annotations, they will have to be written on different lines. The Annotation.csv file is optional and is used to add a short description of every gene in the output files. The best way to get an annotation file is to retrieve the information from dedicated databases (e.g., Thalemine: https://bar.utoronto.ca/thalemine/begin.do for Arabidopsis).
Installation of DiCoExpress 
DiCoExpress requires specific R packages. Use the command line source(&#34;../Sources/Install_Packages.R&#34;) in the R console to check the required package installation status. For users on Linux, another solution is to install the container dedicated to DiCoExpress and available at https://forgemia.inra.fr/GNet/dicoexpress/container_registry. By definition, this container contains DiCoExpress with all of the parts needed, such as libraries and other dependencies.

analyzing multifactorial rna-seq experiments with dicoexpress

The proper use of statistical modeling in NGS data analysis requires an advanced level of expertise. There has recently been a growing consensus on using generalized linear models for differential analysis of RNA-Seq data and the advantage of mixture models to perform co-expression analysis. To offer a managed setting to use these modeling approaches, we developed DiCoExpress that provides a standardized R pipeline to perform an RNA-Seq analysis. Without any particular knowledge in statistics or R programming, beginners can perform a complete RNA-Seq analysis from quality controls to co-expression through differential analysis based on contrasts inside a generalized linear model. An enrichment analysis is proposed both on the lists of differentially expressed genes, and the co-expressed gene clusters. This video tutorial is conceived as a step-by-step protocol to help users take full advantage of DiCoExpress and its potential in empowering the biological interpretation of an RNA-Seq experiment.

All the DiCoExpress outputs are saved in the Tutorial/ directory, itself placed within the Results/ directory. We provide here some guidance for assessing the overall quality of the analysis.
Quality Control 
The quality control output, located in the Quality_Control/ directory, is essential to verify that the RNA-Seq analysis results are reliable. The Data_Quality_Control.pdf file contains several plots obtained with raw and normalized data that can be used to identify a...

Watch this Scientific Journal Video about Analyzing Multifactorial RNA-Seq Experiments with DiCoExpress at JoVE.com

Analyzing Multifactorial RNA-Seq Experiments with DiCoExpress

DiCoExpress is a script-based tool implemented in R to perform an RNA-Seq analysis from quality control to co-expression. DiCoExpress handles complete and unbalanced design up to 2 biological factors. This video tutorial guides the user through the different features of DiCoExpress.

The proper use of statistical modeling in NGS data analysis requires an advanced level of expertise. There has recently been a growing consensus on using ...

DiCoExpress provides a complete aoristic analysis from quality control to co-expression. It performs differential analysis based on contrasts inside the generalized linear model. Moreover, it can also perform an enrichment analysis on the list of differentially expressed genes and the co-expressed gene clusters.The main advantage of DiCoExpress is that it can be used by people just like me, without any particular knowledge in statistics or air programming. It truly helps a non-specialist user to write the contrast necessary for differential gene expression analysis. It also provides graphical outputs illustrating the results ready for publication.DiCoExpress is not a plan dedicated tool. It can be used for any organism as long as the experimental design is complete with up to two biological factors. Moreover, a membrane's design with a unequal number of replicates between condition is also possible.A beginner should have preliminary knowledge in R.You should know how to use a function and identify required and optional arguments. Then the critical step is to correctly provide the files containing the and the experimental design. To begin, open the R studio session.Set the directory to template scripts and open the DiCoExpress tutorial dot R script. Load the DiCoExpress functions in the R session. Then load data files in the R session and split the object data files into several objects for manipulating the files easily.Next, select a strategy among NB conditions or NB replicates and a threshold to filter low express genes. Specify group colors and select a normalization method. Then perform the quality control.If data are paired according to the replicate factor state replicates as true, otherwise state as false. Assign the interaction as true to consider an interaction between the two biological factors. Otherwise assign false, then specify the statistical model and define the threshold of the false discovery rate.Perform the differential analysis followed by fixing a threshold for the enrichment analysis and performing the enrichment analysis of differentially expressed gene lists. Select the DEG lists to be compared. Provide a name for the list comparison and use the same name for the directory where the output files will be saved.Set the parameter operation to union or intersection for specifying the action to be done on the DEG lists, and compare the lists. Carry out a co-expression analysis followed by conducting the enrichment analysis of the co-expression clusters. And at last, generate two log files containing all the necessary information to reproduce the analysis.The total normalized counts per sample should be similar when comparing both intra and inter conditions. The normalized gene expression counts exhibited similar median and variance, both in intra and inter conditions. For identifying the potential underlying data structures, PCA plots were generated.A clear distinction was observed between the treatments and clustering was absent, indicating a good quality data set. The raw pvalue histograms were plotted to assess the quality of the modeling. The distribution of raw pvalues was uniform, with a peak at the left end side of the distribution, as expected.The absence of a peak at the right end side indicates that the statistical modeling seems correct. The expression profile of Gene CIG62301.1, in every genotype and condition, was plotted. As well as the number of up and down differentially expressed genes, were also plotted for every tested contrast.The co-expression analysis was performed on the union of five DEG lists. Identified by contrast, looking for treatment response variation between genotype one or two against others. The co-expressed genes for every identified cluster were printed in individual text files and the expression profile of genes was plotted.With DiCoExpress biologists will get gene expression analysis that are statistically sound. The next step is make biological sense, out of these results.

analyzing-multifactorial-rna-seq-experiments-with-dicoexpress

Research

JoVE Journal

Engineering

Characterization of Surface Modifications by White Light Interferometry: Applications in Ion Sputtering, Laser Ablation, and Tribology Experiments

In materials science and engineering it is often necessary to obtain quantitative measurements of surface topography with micrometer lateral resolution. From the measured surface, 3D topographic maps can be subsequently analyzed using a variety of software packages to extract the information that is needed.
In this article we describe how white light interferometry, and optical profilometry (OP) in general, combined with generic surface analysis software, can be used for materials science and engineering tasks. In this article, a number of applications of white light interferometry for investigation of surface modifications in mass spectrometry, and wear phenomena in tribology and lubrication are demonstrated. We characterize the products of the interaction of semiconductors and metals with energetic ions (sputtering), and laser irradiation (ablation), as well as ex situ measurements of wear of tribological test specimens.
Specifically, we will discuss:
<ol class="lroman">
	<li>Aspects of traditional ion sputtering-based mass spectrometry such as sputtering rates/yields measurements on Si and Cu and subsequent time-to-depth conversion.</li>
	<li>Results of quantitative characterization of the interaction of femtosecond laser irradiation with a semiconductor surface. These results are important for applications such as ablation mass spectrometry, where the quantities of evaporated material can be studied and controlled via pulse duration and energy per pulse. Thus, by determining the crater geometry one can define depth and lateral resolution versus experimental setup conditions.</li>
	<li>Measurements of surface roughness parameters in two dimensions, and quantitative measurements of the surface wear that occur as a result of friction and wear tests.</li>
</ol>
Some inherent drawbacks, possible artifacts, and uncertainty assessments of the white light interferometry approach will be discussed and explained.

White light microscope interferometry is an optical, noncontact and quick method for measuring the topography of surfaces. It is shown how the method can be applied toward mechanical wear analysis, where wear scars on tribological test samples are analyzed; and in materials science to determine ion beam sputtering or laser ablation volumes and depths.

In materials science and engineering it is often necessary to obtain quantitative measurements of surface topography with micrometer lateral resolution. ...

Flame Experiments at the Advanced Light Source: New Insights into Soot Formation Processes

The following experimental protocols and the accompanying video are concerned with the flame experiments that are performed at the Chemical Dynamics Beamline of the Advanced Light Source (ALS) of the Lawrence Berkeley National Laboratory1-4. This video demonstrates how the complex chemical structures of laboratory-based model flames are analyzed using flame-sampling mass spectrometry with tunable synchrotron-generated vacuum-ultraviolet (VUV) radiation. This experimental approach combines isomer-resolving capabilities with high sensitivity and a large dynamic range5,6. The first part of the video describes experiments involving burner-stabilized, reduced-pressure (20-80 mbar) laminar premixed flames. A small hydrocarbon fuel was used for the selected flame to demonstrate the general experimental approach. It is shown how species&#8217; profiles are acquired as a function of distance from the burner surface and how the tunability of the VUV photon energy is used advantageously to identify many combustion intermediates based on their ionization energies. For example, this technique has been used to study gas-phase aspects of the soot-formation processes, and the video shows how the resonance-stabilized radicals, such as C3H3, C3H5, and i-C4H5, are identified as important intermediates7. The work has been focused on soot formation processes, and, from the chemical point of view, this process is very intriguing because chemical structures containing millions of carbon atoms are assembled from a fuel molecule possessing only a few carbon atoms in just milliseconds. The second part of the video highlights a new experiment, in which an opposed-flow diffusion flame and synchrotron-based aerosol mass spectrometry are used to study the chemical composition of the combustion-generated soot particles4. The experimental results indicate that the widely accepted H-abstraction-C2H2-addition (HACA) mechanism is not the sole molecular growth process responsible for the formation of the observed large polycyclic aromatic hydrocarbons (PAHs).

Gas sampling from laboratory-scale flames with online analysis of all species by mass spectrometry is a powerful method to investigate the complex mixture of chemical compounds occurring during combustion processes. Coupled with tunable soft ionization via synchrotron-generated vacuum-ultraviolet radiation, this technique provides isomer-resolved information and potentially fragment-free mass spectra.

The following experimental protocols and the accompanying video are concerned with the flame experiments that are performed at the Chemical Dynamics ...

Analyzing the Movement of the Nauplius 'Artemia salina' by Optical Tracking of Plasmonic Nanoparticles

We demonstrate how optical tweezers may provide a sensitive tool to analyze the fluidic vibrations generated by the movement of small aquatic organisms. A single gold nanoparticle held by an optical tweezer is used as a sensor to quantify the rhythmic motion of a Nauplius larva (Artemia salina) in a water sample. This is achieved by monitoring the time dependent displacement of the trapped nanoparticle as a consequence of the Nauplius activity. A Fourier analysis of the nanoparticle&#39;s position then yields a frequency spectrum that is characteristic to the motion of the observed species. This experiment demonstrates the capability of this method to measure and characterize the activity of small aquatic larvae without the requirement to observe them directly and to gain information about the position of the larvae with respect to the trapped particle. Overall, this approach could give an insight on the vitality of certain species found in an aquatic ecosystem and could expand the range of conventional methods for analyzing water samples.

Analyzing the Movement of the Nauplius &#039;Artemia salina&#039; by Optical Tracking of Plasmonic Nanoparticles

We use optical tracking of plasmonic nanoparticles to probe and characterize the frequency movements of aquatic organisms.

We demonstrate how optical tweezers may provide a sensitive tool to analyze the fluidic vibrations generated by the movement of small aquatic organisms. A ...

Experiments on Ultrasonic Lubrication Using a Piezoelectrically-assisted Tribometer and Optical Profilometer

Friction and wear are detrimental to engineered systems. Ultrasonic lubrication is achieved when the interface between two sliding surfaces is vibrated at a frequency above the acoustic range (20 kHz). As a solid-state technology, ultrasonic lubrication can be used where conventional lubricants are unfeasible or undesirable. Further, ultrasonic lubrication allows for electrical modulation of the effective friction coefficient between two sliding surfaces. This property enables adaptive systems that modify their frictional state and associated dynamic response as the operating conditions change. Surface wear can also be reduced through ultrasonic lubrication. We developed a protocol to investigate the dependence of friction force reduction and wear reduction on the linear sliding velocity between ultrasonically lubricated surfaces. A pin-on-disc tribometer was built which differs from commercial units in that a piezoelectric stack is used to vibrate the pin at 22 kHz normal to the rotating disc surface. Friction and wear metrics including effective friction force, volume loss, and surface roughness are measured without and with ultrasonic vibrations at a constant pressure of 1 to 4 MPa and three different sliding velocities: 20.3, 40.6, and 87 mm/sec. An optical profilometer is utilized to characterize the wear surfaces. The effective friction force is reduced by 62% at 20.3 mm/sec. Consistently with existing theories for ultrasonic lubrication, the percent reduction in friction force diminishes with increasing speed, down to 29% friction force reduction at 87 mm/sec. Wear reduction remains essentially constant (49%) at the three speeds considered.

We present a protocol for using a piezoelectrically-assisted tribometer and optical profilometer to investigate the dependence of ultrasonic wear and friction reduction on linear velocity, contact pressure, and surface properties.

Friction and wear are detrimental to engineered systems. Ultrasonic lubrication is achieved when the interface between two sliding surfaces is vibrated at ...

Visualizing Hyporheic Flow Through Bedforms Using Dye Experiments and Simulation

Advective exchange between the pore space of sediments and the overlying water column, called hyporheic exchange in fluvial environments, drives solute transport in rivers and many important biogeochemical processes. To improve understanding of these processes through visual demonstration, we created a hyporheic flow simulation in the multi-agent computer modeling platform NetLogo. The simulation shows virtual tracer flowing through a streambed covered with two-dimensional bedforms. Sediment, flow, and bedform characteristics are used as input variables for the model. We illustrate how these simulations match experimental observations from laboratory flume experiments based on measured input parameters. Dye is injected into the flume sediments to visualize the porewater flow. For comparison virtual tracer particles are placed at the same locations in the simulation. This coupled simulation and lab experiment has been used successfully in undergraduate and graduate laboratories to directly visualize river-porewater interactions and show how physically-based flow simulations can reproduce environmental phenomena. Students took photographs of the bed through the transparent flume walls and compared them to shapes of the dye at the same times in the simulation. This resulted in very similar trends, which allowed the students to better understand both the flow patterns and the mathematical model. The simulations also allow the user to quickly visualize the impact of each input parameter by running multiple simulations. This process can also be used in research applications to illustrate basic processes, relate interfacial fluxes and porewater transport, and support quantitative process-based modeling.

This manuscript describes how to create regular bedforms in a flume, visualize flow through the bedforms, and use computer simulations to simulate the hyporheic flow. The computer simulations compare well with the experimental observations. This coupled simulation and experiment is well-suited for both research and educational purposes.

Advective exchange between the pore space of sediments and the overlying water column, called hyporheic exchange in fluvial environments, drives solute ...

Wind Tunnel Experiments to Study Chaparral Crown Fires

The present protocol presents a laboratory technique designed to study chaparral crown fire ignition and spread. Experiments were conducted in a low velocity fire wind tunnel where two distinct layers of fuel were constructed to represent surface and crown fuels in chaparral. Chamise, a common chaparral shrub, comprised the live crown layer. The dead fuel surface layer was constructed with excelsior (shredded wood). We developed a methodology to measure mass loss, temperature, and flame height for both fuel layers. Thermocouples placed in each layer estimated temperature. A video camera captured the visible flame. Post-processing of digital imagery yielded flame characteristics including height and flame tilt. A custom crown mass loss instrument developed in-house measured the evolution of the mass of the crown layer during the burn. Mass loss and temperature trends obtained using the technique matched theory and other empirical studies. In this study, we present detailed experimental procedures and information about the instrumentation used. The representative results for the fuel mass loss rate and temperature filed within the fuel bed are also included and discussed.

This protocol describes wind tunnel experiments designed to study the transition of a fire from the ground to the canopy of chaparral shrubs.

The present protocol presents a laboratory technique designed to study chaparral crown fire ignition and spread. Experiments were conducted in a low ...

Conducting Elevated Temperature Normal and Combined Pressure-Shear Plate Impact Experiments Via a Breech-end Sabot Heater System

A novel approach for conducting normal and/or combined pressure-shear plate impact experiments at test temperatures up to 1000 &#176;C is presented. The method enables elevated temperature plate-impact experiments aimed towards probing dynamic behavior of materials under thermomechanical extremes, while mitigating several special experimental challenges faced while performing similar experiments using the conventional plate impact approach. Custom adaptations are made to the breech-end of a single-stage gas-gun at Case Western Reserve University; these adaptations include a precision-machined extension piece made from SAE 4340 steel, which is strategically designed to mate the existing gun-barrel while providing a high tolerance match to the bore and keyway. The extension piece contains a vertical cylindrical heater-well, which houses a heater assembly. A resistive coil heater-head, capable of reaching temperatures of up 1200 &#176;C, is attached to a vertical stem with axial/rotational degrees of freedoms; this enables thin metal specimens held at the front-end of a heat-resistant sabot to be heated uniformly across the diameter to the desired test temperatures. By heating the flyer plate (in this case, the sample) at the breech-end of the gun-barrel instead of at the target-end, several critical experimental challenges can be averted. These include: 1) severe changes in the alignment of the target plate during heating due to the thermal expansion of the several constituents of the target holder assembly; 2) challenges that arise due to the diagnostics elements, (i.e., polymer holographic gratings, and optical probes) being too close to the heated target assembly; 3) challenges that arise for target plates with an optical window, where crucial tolerances between the sample, bond layer, and window become increasingly difficult to maintain at high temperatures; 4) in the case of combined compression-shear plate impact experiments, the need for high-temperature resistant diffraction gratings for the measurement of transverse particle velocity at the free surface of the target; and 5) limitations imposed on the impact velocity necessary for unambiguous interpretation of the measured free surface velocity versus time profile due to thermal softening and possibly yielding of the bounding target plates.By utilizing the adaptations mentioned above, we present results from a series of reverse geometry normal plate impact experiments on commercial purity aluminum at a range of sample temperatures. These experiments show decreasing particle velocities in the impacted state, which are indicative of material softening (decrease in post-yield flow stress) with increasing sample temperatures.

Here, we present a detailed protocol of a new approach for conducting elevated temperature reverse normal plate impact, and combined pressure-and-shear plate impact. The approach involves the use of a breech-end resistive coil heater to heat a sample held at the front-end of a heat-resistant sabot to the desired temperature.

A novel approach for conducting normal and/or combined pressure-shear plate impact experiments at test temperatures up to 1000 &#176;C is presented. The ...

Enhanced Electron Injection and Exciton Confinement for Pure Blue Quantum-Dot Light-Emitting Diodes by Introducing Partially Oxidized Aluminum Cathode

Stable and efficient red (R), green (G), and blue (B) light sources based on solution-processed quantum dots (QDs) play important roles in next-generation displays and solid-state lighting technologies. The brightness and efficiency of blue QDs-based light-emitting diodes (LEDs) remain inferior to their red and green counterparts, due to the inherently unfavorable energy levels of different colors of light. To solve these problems, a device structure should be designed to balance the injection holes and electrons into the emissive QD layer. Herein, through a simple autoxidation strategy, pure blue QD-LEDs which are highly bright and efficient are demonstrated, with a structure of ITO/PEDOT:PSS/Poly-TPD/QDs/Al:Al2O3. The autoxidized Al:Al2O3 cathode can effectively balance the injected charges and enhance radiative recombination without introducing an additional electron transport layer (ETL). As a result, high color-saturated blue QD-LEDs are achieved with a maximum luminance over 13,000 cd m-2, and a maximum current efficiency of 1.15 cd A-1. The easily controlled autoxidation procedure paves the way for achieving high-performance blue QD-LEDs.

A protocol is presented for fabricating high-performance, pure blue ZnCdS/ZnS-based quantum dots light-emitting diodes by employing an autoxidized aluminum cathode.

Stable and efficient red (R), green (G), and blue (B) light sources based on solution-processed quantum dots (QDs) play important roles in next-generation ...

Multiplexing Focused Ultrasound Stimulation with Fluorescence Microscopy

By focusing low-intensity ultrasound pulses that penetrate soft tissues, LIPUS represents a promising biomedical technology to remotely and safely manipulate neural firing, hormonal secretion and genetically-reprogrammed cells. However, the translation of this technology for medical applications is currently hampered by a lack of biophysical mechanisms by which targeted tissues sense and respond to LIPUS. A suitable approach to identify these mechanisms would be to use optical biosensors in combination with LIPUS to determine underlying signaling pathways. However, implementing LIPUS to a fluorescence microscope may introduce undesired mechanical artefacts due to the presence of physical interfaces that reflect, absorb and refract acoustic waves. This article presents a step-by-step procedure to incorporate LIPUS to commercially-available upright epi-fluorescence microscopes while minimizing the influence of physical interfaces along the acoustic path. A simple procedure is described to operate a single-element ultrasound transducer and to bring the focal zone of the transducer into the objective focal point. The use of LIPUS is illustrated with an example of LIPUS-induced calcium transients in cultured human glioblastoma cells measured using calcium imaging.

Low-Intensity Pulsed Ultrasound Stimulation (LIPUS) is a modality for non-invasive mechanical stimulation of endogenous or engineered cells with high spatial and temporal resolution. This article describes how to implement LIPUS to an epi-fluorescence microscope and how to minimize acoustic impedance mismatch along the ultrasound path to prevent unwanted mechanical artefacts.

By focusing low-intensity ultrasound pulses that penetrate soft tissues, LIPUS represents a promising biomedical technology to remotely and safely ...

Fabricating van der Waals Heterostructures with Precise Rotational Alignment

In this work we describe a technique for creating new crystals (van der Waals heterostructures) by stacking distinct ultrathin layered 2D materials. We demonstrate not only lateral control but, importantly, also control over the angular alignment of adjacent layers. The core of the technique is represented by a home-built transfer setup which allows the user to control the position of the individual crystals involved in the transfer. This is achieved with sub-micrometer (translational) and sub-degree (angular) precision. Prior to stacking them together, the isolated crystals are individually manipulated by custom-designed moving stages that are controlled by a programmed software interface. Moreover, since the entire transfer setup is computer controlled, the user can remotely create precise heterostructures without coming into direct contact with the transfer setup, labeling this technique as &#8220;hands-free&#8221;. In addition to presenting the transfer set-up, we also describe two techniques for preparing the crystals that are subsequently stacked.

In this work we describe a technique that is used to create new crystals (van der Waals heterostructures) by stacking ultrathin layered 2D materials with precise control over position and relative orientation.

In this work we describe a technique for creating new crystals (van der Waals heterostructures) by stacking distinct ultrathin layered 2D materials. We ...