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Data for Non-Equilibrium Sensing of Volatile Compounds Using Active and Passive Analyte Delivery
<blockquote> <p>Version 2: Added missing files to <code>sniffing_data.zip</code></p> </blockquote> <p>See GitHub repository for data processing functions and examples: <a href="https://github.com/soerenbrandt/sniffing-sensor">https://github.com/soerenbrandt/sniffing-sensor</a></p> <p><strong>Abstract</strong>:<br>Sensor technologies have allowed us to outperform the human senses of sight, hearing, and touch; however, the development of artificial noses is significantly behind their biological counterparts. This is largely due to the complexity of natural olfaction, as it incorporates complex fluid dynamics within the nasal anatomy together with the response patterns of hundreds to thousands of unique molecular-scale receptors for odor interpretation. We designed a sensing approach to identify volatiles that exploits time-dependent information from a single sensor (here, the reflectance spectra from a mesoporous one-dimensional photonic crystal) by augmenting and accentuating differences in the non-equilibrium mass-transport dynamics of vapors stemming from their distinct physicochemical properties, thus obviating the need for a large sensor array. By training a machine learning algorithm on the sensor output, we clearly identify polar and nonpolar volatile organic compounds, determine the mixing ratios of binary mixtures, and accurately predict the boiling point, flash point, vapor pressure, and viscosity of several volatile liquids within those used for training as well as compounds unknown to the model. We further implement a bioinspired active sniffing approach, in which the fluid dynamics and patterns of analyte delivery are controlled, enabling an additional modality of differentiation and reducing the duration of data collection and analysis to seconds. These results outline a strategy to build accurate and rapid artificial noses for volatile liquids that can provide useful information on chemicals such as their composition and properties, and can be applied in a variety of fields, including disease diagnosis, hazardous waste management, and healthy building monitoring.</p>
Modèle d'une démarche pour la génération de requête adaptée dans ChatGPT afin d'activer son apprentissage et développer un langage performant
<p>En m'inspirant de diverses sources existant dans la littérature actuelle sur la conception de ressources éducatives libres tel que les lignes directrices de l’UNESCO, j'ai procédé à un entrainement de ChatGPT afin d'obtenir des réponses exactes pour m'aider dans le processus de transformation d'une ressource éducative en ressource éducative libre. Les suggestions de l'outil, furent à chaque fois, traitées, refusées ou validées. <br>Ma démarche consista à suggérer, étape après étape, pour chaque section du contenu web fourni, de proposer à l’outil de réaliser une analyse approfondie basée sur la connaissance experte du domaine.<br>À chaque, étape il fallait indiquer précisément à l’outil la tâche qu’il fallait réaliser. Les premières informations fournies par l’IA furent considérées comme un tremplin pour commencer à construire un premier tableau récapitulatif de données qui allait nous servir de guide/ de base pour la construction d’un langage plus performant et généralisant. </p>
Fig. 3 in The Wolbachia endosymbiont of Brugia malayi has an active phosphoglycerate mutase: a candidate target for anti-filarial therapies
Fig. 3 Activity of recombinant wBm–iPGM. Conversion of 3-PG to 2-PG by MBP–wBm–iPGM (filled circle) is measured indirectly by a decrease in NADH concentration, determined spectrophotometrically at 340 nm. Consumption of NADH is directly proportional to PGM activity. A baseline control lacking wBm–iPGM (open circle) is shown
Fig. 1 in The Wolbachia endosymbiont of Brugia malayi has an active phosphoglycerate mutase: a candidate target for anti-filarial therapies
Fig. 1 Alignment of the deduced amino acid sequences of various iPGM enzymes. The sequence of iPGM from the Wolbachia endosymbiont of Brugia malayi (wBm; GenBank accession no. AAW70991) is aligned with the extensively studied and structurally characterized iPGM from Bacillus stearothermophilus (Bs; GenBank accession no. Q9X519; Jedrzejas et al. 2000a,b) and the characterized iPGM from Brugia malayi (Bm; GenBank accession no. AAQ97626; Zhang et al. 2004; Raverdy et al. 2007). Residues that are identical in at least two of the three sequences are shaded in black, while conserved amino acid changes are grey. The catalytic serine (@) and 13 other residues (*) involved in catalysis (Jedrzejas 2000) are conserved in all three enzymes. Alignment generated with ClustalW and displayed with BOXSHADE (www.ch.embnet.org/software/ BOX_form.html)
Fig. 2 in The Wolbachia endosymbiont of Brugia malayi has an active phosphoglycerate mutase: a candidate target for anti-filarial therapies
Fig. 2 Purification of MBP–wBm–iPGM expressed intracellularly in the yeast K. lactis. Fractions from the purification were analyzed by SDS-PAGE and the gel stained with Coomassie Blue. Lane 1 Protein Ladder (New England Biolabs), lane 2 K. lactis lysate, lane 3 supernatant of K. lactis lysate, lane 4 pellet of K. lactis lysate, lane 5 flow-through from amylose column, lane 6 column wash, lanes 7, 8, and 9, elution fractions. The arrowhead indicates the protein band corresponding to recombinant MBP–wBm–iPGM
Data and Reproducible Analysis For: "Fine-Scale Associations Between Land Cover Composition and the Oviposition Activity of Native and Invasive Aedes Vectors of La Crosse Virus"
<h1><strong>Data and Reproducible Analysis For: "Fine-Scale Associations Between Land Cover Composition and the Oviposition Activity of Native and Invasive Aedes Vectors of La Crosse Virus"</strong></h1> <p>This repository contains pre-processed data sets and code scripts to reproduce the data processing and analyses that are presented in the corresponding manuscript. Some minor pre-processing was completed before presenting this -- namely, the land cover raster was clipped to the study area of Knox County, Tennessee, USA, prior to placing in the repository to reduce the file size. </p> <h2><strong>How to use this repository to reproduce results </strong></h2> <p>This repository is designed to support the reproduction of analyses in the associated manuscript. The entire project can be downloaded and stored anywhere on your computer, as long as the file structure is not altered. The project contains folders with all data sets and code scripts necessary for analysis.</p> <p><strong>What you will need: </strong><br> - Installed R and RStudio for purely spatial cluster and global model analyses<br> - Basic understanding of how to open R and run code </p> <p><strong> You do NOT need:</strong><br> - To download or install R packages on your own; that is taken care of within this environment<br> - To write any code <br> - To set up any working directories in R </p> <h3><strong>Important: Using `renv`</strong></h3> <p>Short Version: When you open the R project, run `renv::restore()` and follow the prompts to install the necessary R packages. </p> <p>The R package `renv` was used to create a <strong>project library</strong>, which contains all R packages that are used by the project. The packages in the project library are <strong>the versions used during the original analysis</strong>. This means that if any packages are updated by developers in ways that would change the results of the analysis, this project can still produce the original results because of `renv`. When you open this project for the first time, `renv` will automatically download and install itself and ask you to run `renv::restore()`. <strong>You should run `renv::restore()` to automatically download and install all of the packages within this reproducible environment</strong>. </p> <h2><strong>## Basic step-by-step guide:</strong></h2> <p>- 1. Download the entire repository by clicking "Code -> Download ZIP" on GitHub or by downloading the ZIP file in Zenodo<br>- 2. Extract the ZIP file anywhere on your computer (do not change the structure of the files once extracted)<br>- 3. In RStudio, click *File -> Open Project* and browse to the location where you extracted the repository; in the repository file, open the knoxaedeslandcover R Project file <br>- 4. Open any of the R scripts in the `analysis/` folder<br>- 5. Run the code `renv::restore()` in the script or in the console and follow the prompt to install the packages <br> - Now you can run the R Scripts; start from the top with loading the packages and data, then work your way down line-by-line</p> <h3><strong># `analysis/` Folder</strong></h3> <p>The `analysis/` folder contains scripts for processing data and conducting analyses. Each file is an R script that should be opened in R studio. The first shows how to process and aggregate the various raw data files; if you are only interested in reproducing analyses from the manuscript, you can skip to the second file and work from there. </p> <p><strong><em>## Files within the `analysis/` folder</em></strong></p> <p>The files are numbered in the order that they were run for the original analysis. In this case, none of the analyses are dependent on the others, so they can technically be used in any order. The numbers associated with each file describe the order that the analyses would normally be run. </p> <p> - `(1)dataprep.R` contains the code for cleaning and combining the land cover, climate, and mosquito data -- this includes calculating the land cover percentages at different scales and calculating weekly and timelagged climate values<br> - `(2)summary_analysis.R` contains code for reproducing summary data and creating graphs from the manuscript<br> - `(3)variable_selection.R` contains code for asssessing collinearity and fitting models to identify the best fitting variables for each speceis<br> - `(4)finalmodels.R` contains code for fitting the final models using the selected variables for each species </p> <h3><strong># `data/` Folder</strong></h3> <p>This folder contains several datasets, including one that compiles them all for analyses (`knox_joined`). The raw data are included to show how the data was processed and aggregated, but the individual raw data files are not needed for analyses. See `data dictionary.txt` for a description of all attributes contained within each file. </p> <p><strong><em>## Files within the `data/` folder</em></strong></p> <p> - `knox22_joined.RDS` contains a cleaned and joined version of land cover, climate, and mosquito data in R Data Serialization format, which maintains predefined factor and numeric designations for columns. <br> - `knox22_joined.csv` contains a cleaned and joined version of land cover, climate, and mosquito data in CSV format -- identical to 'knox22_joined.RDS'<br> - `sites22.csv` contains the names, site codes, and coordinates of the study sites<br> - `aedes22_clean.csv` contains the raw mosquito collection data for the study without any climate or land cover information <br> - `NLCD_2019_landcover_clippedtoKnox.tif` contains the NLCD land cover data, already clipped to Knox County, TN, USA<br> - `knox22_temperature.csv` contains raw daily temperatures for the city of Knoxville in 2022<br> - `knox22_rainfall.csv` contains raw daily precipitation for the city of Knoxville watersheds in 2022<br> - `rainfall_stations.csv` contains the descriptions, approximate street addresses, and geographic coordinates for rainfall monitoring sites <br> - `data dictionary.txt` file that defines column names and other data attributes for every dataset </p> <h3><strong># `renv/` Folder</strong></h3> <p>The `renv/` folder contains bits and pieces needed for the `renv` package. Nothing should be altered in this folder. </p> <p> </p> <h2><strong>References for source data </strong></h2> <p> - Some of the data in this repository were originally obtained from open access sources. </p> <p> - Land cover data was obtained from the National Land Cover Database (NLCD) 2019 data product, specifically the "NLCD 2019 Land Cover (CONUS)" product. The original, unclipped raster can be freely downloaded here: https://www.mrlc.gov/data/nlcd-2019-land-cover-conus</p> <p> - Temperature data was downloaded from the United States National Oceanic and Atmospheric Administration (NOAA) weather station for Knoxville, Tennessee. The source data can be downloaded from this site: https://www.weather.gov/mrx/tysclimate</p> <p> - Rainfall data was obtained from the City of Knoxville rainfall data website, located here: https://www.knoxvilletn.gov/government/city_departments_offices/engineering/stormwater_engineering_division/rainfall_data</p> <p> - All mosquito collection data was collected directly by the manuscript authors</p>
Mass spectrometry dataset for: "Discovery of Nostatin A, an azole containing sactipeptide with prominent cytostatic activity and pro-apoptotic activity"
<p>MSn mass spectrometry dataset used in: <strong>Discovery of nostatin A, an azole-containing sactipeptide with prominent cytostatic and pro-apoptotic activity.</strong> Kateřina Delawská*, Jan Hájek*, Kateřina Voráčová*, Marek Kuzma, Jan Mareš, Kateřina Vicková, Alan Kádek, Dominika Tučková, Filip Gallob, Petra Divoká, Martin Moos, Stanislav Opekar, Lukas Koch, Kumar Saurav, David Sedlák, Petr Novák, Petra Urajová, Jason Dean, Radek Gažák, Timo J.H. Niedermeyer, Zdeněk Kameník, Petr Šimek, Andreas Villunger and Pavel Hrouzek. <em>Org. Biomol. Chem.</em> (2025). doi:<a title="Link to landing page via DOI" href="https://doi.org/10.1039/D4OB01395F">10.1039/D4OB01395F</a></p> <p> </p> <p><strong>Description:</strong></p> <p>MSn mass spectrometry elucidation of the molecular structure of nostatin A, a bioactive peptide isolated from Nostoc sp. cyanobacteria.</p> <p><strong>Sample and data processing:</strong></p> <p><em><strong>1) FTICR data:</strong></em><br>Experiments were performed using a 15T SolariX XR Fourier-transform ion cyclotron resonance mass spectrometer (ESI-FTICR MS; Bruker Daltonics, Billerica, MA, USA) equipped with infrared multiple photon dissociation (IRMPD). All data were acquired using 2 µl/min direct infusion of NosA dissolved at 10 µM in 60% acetonitrile acidified with 0.1% formic acid. Ion fragmentation was performed using IRMPD inside the ICR cell. For this a Diamond C-30A CO2 laser (Coherent, Santa Clara, CA, USA) resonating at 10.6 µm was custom-coupled to the SolariX FTICR MS and laser pulses were precisely timed in synchronization with the ICR pulse sequence. Further MS3 fragmentation experiments were performed using in source collisional fragmentation (isCID) followed by quadrupole isolation and subsequent collision induced fragmentation of particular fragment ions of interest. Detailed parameters (ESI and MS settings) are stored inside the metadata of the individual data files as well as described in the resulting publication. Spectral peaks were also exported in plain m/z vs intensity XY text files from Bruker Data Analysis 5.1.</p> <p><em><strong>2) qTOF data:</strong></em><br>HPLC-HRMS experiment was performed using a Dionex UltiMate 3000 HPLC system (Thermo Scientific, Sunnyvale, CA, USA) coupled with a diode array detector (DAD) connected to the Bruker Impact HD mass spectrometer equipped with an electrospray ionization (ESI) source (ESI-HRMS; Bruker, Billerica, MA, USA). The separations were performed on a C18 column (Phenomenex Kinetex C18, 150 × 4.6 mm, 2.6 μm) eluted with water (A)/acetonitrile (B) gradient (0 min 15%, 1 min 15%, 20 min 100%, 25 min 100%, 30 min 15%, 33min 15% of B) at a constant flow rate of 0.6 mL/min. Both solvents were acidified with 0.1% formic acid. Fragmentation energy was set to 87eV and 35eV for 1+ and 2+ Nostatin A, respectively. Detailed parameters (ESI and MS settings) are stored in the metadata of the individual data file as well as described in the resulting publication.</p> <p>*other variants of Nostatin bearing different acyl chains on proline residue were detected in crude extract only (C33_nosA_LCMS_2217.d) and not purified.</p> <p>Raw Bruker DataAnalysis .d files, which otherwise behave as folders, were compressed with the TAR algorithm implemented in the 64-bit Total Commander 11.02. </p>
Ir and NMR for article Synthesis and Biological Activity Evaluation of New Isatin-Gallate Hybrids as Antioxidant and Anticancer Agents (in vitro) and In-silico Study as Anticancer Agents and Coronavirus Inhibitors
<p>this is IR and NMR data for article titled <strong>Synthesis and Biological Activity Evaluation of New Isatin-Gallate Hybrids as Antioxidant and Anticancer Agents (<em>in vitro</em>) and In-silico Study as Anticancer Agents and Coronavirus Inhibitors </strong></p> <p> </p>
Assessment of the spatial extent of local active noise control - dataset
<p>Experiment setup, data and supplementary scripts for the listening experiment "Assessment of the spatial extent of local active noise control" in Feb. 2023 at the Institute of Electronic Music and Acoustics in Graz, Austria</p> <p>A listening experiment has been conducted, trying to find the spatial extents of local active noise control (ANC). Participants were instructed to move their heads along the x-axis or rotate clock- and counterclockwise (yaw). A button had to be pressed if the perceived zone of comfort is left, and the current position was recorded. This task was repeated for different controlled bandwidths, signals and source directions. Additionally, scripts to generate the ANC filters, a brief statistical analysis and recordings at several positions for different stimuli/conditions are provided. Acoustic measures at the point of cancellation and at various other positions are further analyzed with and without ANC.</p>
Fig. 1 in The longhorned beetles (Coleoptera: Cerambycidae) of Tennessee: distribution of species, seasonal adult activity, and new state records
Fig. 1. Longhorned beetle species tallied within each of the 95 Tennessee counties from collection records compiled for 230 species. Collection distribution is presented across ecoregions occurring within the western, middle, and eastern Grand Divisions of Tennessee (bold black lines). Across the Grand Divisions, county names presented in pale gray text are those from which no longhorned beetle species were collected or reported. Species tallies presented do not include county records reported in Jamerson (1973) that could not be substantiated with a specimen. Roman numerals (west to east) designate the ecoregions of Tennessee, where I corresponds with the Mississippi Alluvial Plain (ecoregion 73), II are the Mississippi Valley Loess Plains (ecoregion 74), III are the Southeastern Plains (ecoregion 65), IV is the Interior Plateau (ecoregion 71), V are the Southwestern Appalachians (ecoregion 68), VI are the Central Appalachians (ecoregion 69), VII are Ridges and Valleys (ecoregion 67), and VIII are the Blue Ridge Mountains (ecoregion 66) (afer Griffith et al. 1997). Full descriptions of the Tennessee ecoregions are available at: https://www.epa.gov/eco-research/ecoregion-download-files-state-region-3.
Figure 1. Response curves for percent acetyl-coenzyme A in Detecting the effect of ACCase-targeting herbicides on ACCase activity utilizing a malachite green colorimetric functional assay
Figure 1. Response curves for percent acetyl-coenzyme A carboxylase (ACCase) activities of resistant and susceptible Digitaria ciliaris biotypes in response to the increasing concentrations of the ACCase-targeting herbicides, sethoxydim, clethodim,fluazifop-p-butyl, and pinoxaden.The response was modeled based on the log rate of ACCase-targeting herbicides to create equal spacing between rates using least-squares fit regression of ACCase activity to the non-treated check. Means are represented by differing symbols for each biotype, and regression equation models are represented by differing line types for each biotype. Vertical bars represent the standard errors of the means (n = 6). Digitaria ciliaris biotypes: R1 and R2, resistant; S, susceptible. The concentration of ACCase-targeting herbicides required to cause 50% inhibition of ACCase activity (IC50) was calculated from concentration-response curves. CI, confidence interval.
Figure 1 in Enzymatic activity of bone markers on Lithobates catesbeianus (Shaw, 1802) growth during the ossification process
Figure 1. Effect of pH on p NPPase activity of alkaline phosphatase released by PIPLC present in the SPIPLC fraction of tadpole epiphysis and diaphysis and frog epiphysis. T.E.= Tadpole Epiphysis; T.D. = Tadpole Diaphysis; F.E. = Frog Epiphysis.
Figure 2 in Toxicity and larvicidal activity on Aedes aegypti of citronella essential oil submitted to enzymatic esterification
Figure 2. Mortality percentage of Artemia salina nauplii in relation to increased sample concentration.
Figure 1 in Toxicity and larvicidal activity on Aedes aegypti of citronella essential oil submitted to enzymatic esterification
Figure 1. Kinetics of citronellyl and geranyl cinnamates production (molar ratio alcohol/acid 3:1, enzyme 15 wt%, temperature 70°C, 150 rpm).
Fig. 2 in Trypanosoma brucei: trypanocidal and cell swelling activities of lasalocid acid
Fig. 2 Effect of lasalocid acid and salinomycin on the growth of bloodstream forms of T. brucei and human myeloid leukaemia HL-60 cell. Trypanosomes (circles) and HL-60 cells (squares) were incubated with varying concentration of lasalocid acid (closed symbols) or salinomycin (open symbols). After 72 h of culture, cell viability and proliferation were determined with the colorimetric dye resazurin. The experiment was repeated three times and mean values ± SD of three experiments are shown
Fig. 3 in Trypanosoma brucei: trypanocidal and cell swelling activities of lasalocid acid
Fig. 3 Effect of polyether ionophore antibiotics on the cell volume of bloodstream forms of T. brucei. a Trypanosomes (5 × 107 cell/ml) were incubated with 100 μM lasalocid acid (triangles) or salinomycin (squares) in Baltz medium in the presence of 1% DMSO. Controls (circles) were incubated with 1% DMSO. Every 10 min, the absorbance at 490 nm was measured. Mean values ± SD of three experiments are shown. Except for the time point 0 min at all other time points, the absorbance values were statistically significantly different from each other (One-way ANOVA test, p <0.01). b Trypanosomes (5 × 107 cell/ml) were incubated with 100 μM lasalocid acid in the absence (closed circles, solid line) or presence of 6 mM EDTA (open squares, dashed line) in Baltz medium containing 1% DMSO. Every 10 min, the absorbance at 490 nm was measured. Mean values of three experiments are shown. For clarity, the standard deviations were omitted. The standard deviations ranged between 17.5–25.1% of the mean values. At each time point, the data points of the two curves were statistically not significantly different (p = 0.465–0.977, Student's t test)
Fig. 1 in Trypanosoma brucei: trypanocidal and cell swelling activities of lasalocid acid
Fig. 1 Structure of lasalocid acid. The PubChem compound identifier (CID) for the compound is shown in parentheses
Fig. 5 in Activity of tea tree oil and nerolidol alone or in combination against Pediculus capitis (head lice) and its eggs
Fig. 5 Abortive (%) louse eggs treated with tea tree oil (a), nerolidol (b), and their combination in ratio 1:1 (c) and 1:2 (d) at different concentrations (see "Materials and methods") and control groups, in
Fig. 3 in Activity of tea tree oil and nerolidol alone or in combination against Pediculus capitis (head lice) and its eggs
Fig. 3 Mortality (%) of head lice (adults and nymphs) treated with tea tree oil (a), nerolidol (b), and their combination in ratio of 1:1 (c) and 1:2 (d) at different concentrations (see "Materials and methods") and
Fig. 2 in Activity of tea tree oil and nerolidol alone or in combination against Pediculus capitis (head lice) and its eggs
Fig. 2 Chemical structure of terpinen-4-ol (a), the main component of tea tree oil, and nerolidol (b)
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Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
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