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34 results for “least squares”
FEX3-ECG/Charts01: Least Squares Approximation of ECG Signals with Rational Functions
<p> </p> <p> We introduce a new algorithm for "Least Squares Approximation of ECG Signals with Rational Functions". Detailed description here: <a href="https://doi.org/10.5281/zenodo.7628747">https://doi.org/10.5281/zenodo.7628747</a> . The following is one result of the approximations and its charts.</p> <p><br> The original ECG signals:<br> DOI: <a href="https://doi.org/10.13026/C28C71">https://doi.org/10.13026/C28C71</a><br> License: Open Data Commons Attribution License v1.0</p> <p>We approached the following signal from the database above:<br> Patient009, the file: S0035_RE.XYZ, vy(Frank lead system)<br> The location of the QRS: 8669th point (8.669 sec)</p> <p>That is:<br> The approximated signal (P QRS T) section: 1:1139, the location of the QRS 371<br> The first signal point = 8299th data point.<br> Detailes:<br> locations of data points : 8299 8300 8301 .. 8669 .. 9437<br> indexes of signal points : 1 2 3 .. 371 .. 1139<br> values of signal points : 291 282 307 .. 352</p> <p>The signal described above is approximated by the following parameters of rational function in the example_1a.m short program.</p> <p><em>The </em><em>grids of charts:</em><br> X axis: 40ms (The sampling rate is 1000Hz)<br> Y axis: 0.1mV (200 A/D units)<br> This is on the original medical ECG paper: 1mm x 1mm.</p> <p> </p> <p><strong>FILES of Charts01.zip:<br> example_1a.m</strong> This file contains a short script of charts.(GNU Octave or MATLAB?)<br> Input: no. <strong><em>The parameters are in this</em></strong><strong><em> short</em></strong><strong><em> program.</em></strong><br> Output: Figure_b_1 and Figure_b_2<br> <strong>PQRST_sgnl_a.m</strong> Subroutine (of example_1a.m)</p> <p><strong>Figure_b_1.jpg</strong> First output chart of the example_1a.m<br> blue An approximation of the P wave<br> red An approximation of the QRS wave<br> yellow An approximation of the T wave<br> magenta An approximation of the Ta wave</p> <p><strong>Figure_b_2.jpg</strong> Second output chart of the example_1a.m<br> blue An approximation of all the waves (the sum of the above)</p> <p><strong>data_a_c.csv</strong> Result spreadsheet of the approximation program (decimal comma)<br> <strong>data_a_</strong><strong>p</strong><strong>.csv</strong> Result spreadsheet of the approximation program (decimal point)<br> Columns:<br> R relative QRS relative indexing<br> Original The original ECG signal<br> Approx An approximation of the ECG signal<br> Err220128 Error and noise (+date: yymmdd)<br> BL174355 Baseline (+time: hhmmss)<br> P(+Ta) An approximation of the P wave (+Ta wave)<br> QRS An approximation of the QRS wave<br> T An approximation of the T wave<br> V__idy 4 The length of the ECG vector (normalized, max. = 1mV)</p> <p><strong>image_a_</strong><strong>1</strong><strong>.gif</strong> First chart of the columns in the data_a_?.csv (MS Excel)<br> Original<br> Approx<br> Err220128<br> BL174355</p> <p><strong>image_a_</strong><strong>2</strong><strong>.gif</strong> Second chart of the columns in the data_a_?.csv (MS Excel)<br> P(+Ta)<br> QRS<br> T</p> <p> Kobzos, Laszlo<br> Location: HU (Budapest)<br> email: zehu.kola.ci@gmail.com</p> <p> </p>
Dataset and Data treatment for Data mining Raman Microspectroscopic Responses of Cells to Drugs in Vitro using Multivariate Curve Resolution-Alternating Least Squares
<p><strong>Matlab scripts for the simulation and treatment of Raman datasets obtained from time dependent experiments Using MCR-ALS.</strong></p> <p> </p> <p><strong>- SIMULATED DATA: </strong>Simulated data is obtained by adding spectra of artificially generated responses (weighted considering artificially generated time profiles) to an experimental cell spectrum (Initial component) Three Different Scenarios are generated. </p> <p>Spectral and time profiles are obtained from here: </p> <p> </p> <p><strong>- EXPERIMENTAL DATA: </strong>DOX dataset obtained from here</p> <p>https://doi.org/10.1002/jbio.201800328</p> <p> </p> <p> </p> <p><strong>- DATA ANALYSIS INSTRUCTIONS</strong></p> <p>Run <em>datatreatment.m</em></p>
FEX3-ECG/Charts02: Least Squares Approximation of ECG Signals with Rational Functions
<p><br> We introduce a new algorithm for "Least Squares Approximation of ECG Signals with Rational Functions". Detailed description here: <a href="https://doi.org/10.5281/zenodo.7628747">https://doi.org/10.5281/zenodo.7628747</a> . The following figures show the results of some of the approximations.<br> <br> The original ECG signals:<br> DOI: <a href="https://doi.org/10.13026/C28C71">https://doi.org/10.13026/C28C71</a><br> License: Open Data Commons Attribution License v1.0</p> <p><strong><em>Notes:</em></strong></p> <p>- We refer to the database records as follows: s.... = serial number, p... = patient. For example: <strong>s0508_p269</strong>. If we want to specify the location more precisely within the record, we also indicate the lead and the time of the QRS. For example: <strong>s0508_p269, vy, 6689</strong> (Where "vy" is one of the Frank leads.).</p> <p>- The figures were created with Microsoft Excel (gif, non-animated). In addition to the figures, data files of the results are usually also included. In the text files, we used a decimal point (for GNU Octave) or a decimal comma (for Excel). Any text editor can be used to transform them, replacing all commas with points or vice versa.</p> <p>- In some ppaarraamm.txt files, we have written the location and values of the parameters of each approximation (in Hungarian). The starting point of the time coordinate is now the first point of the ECG signal drawing. For the quadratic parameters of the numerator, the values of the two real roots or the conjugate complex root are placed one after the other. Similarly, the quadratic parameters of the denominator have the values of the conjugate complex root one after the other. The data from <strong>example_1a.m </strong>in <a href="https://doi.org/10.5281/zenodo.6479410">https://doi.org/10.5281/zenodo.6479410</a> is from <strong>From_series\s0035_p009\vy\ppaarraamm.txt</strong> also found below.<br> <em>Mini <strong>Hu==>En dictionary</strong>: </em>ha==>if; nincs==>there is none; számláló==>numerator; nevező==>denominator; a pontok száma==>the number of points; nullad- és elsőfokú==>zeroth and first degree; másodfokú==>second degree; hely_bazis==>location base (the origin of the drawings).</p> <p>- Below, the meaning of the two numbers after the wave letter is related to the number of approximation parameters. For example: P35 means: P wave, 3 is the number of parameters of the numerator (including the main coefficient <strong>d</strong>), while 5 is the number of pole <strong>pairs</strong> (factors) of the denominator. So the number of approximation parameters is 3 + 2×5=13.<br> The meaning of Ta here: the approximation includes the Ta wave.</p> <p>- Below is the meaning of the 2×6 digit number, for example in filename: <br> <em>yymmdd hhmmss</em>: year month day hour minute second (time stamp).<br> <br> <strong><em>Notes on pole-zero representation:</em></strong></p> <p>- In the figures, the poles are marked with <strong>x</strong> and the zeros with <strong>o</strong>.</p> <p>- In the figures, only one of the two conjugated complex roots was shown, namely always the one falling towards the momentary value of the signal.</p> <p>- The pole of the Ta wave was not marked.</p> <p>- In the<strong> older figures</strong>, only the poles are visible, the zeros are not indicated. When the poles are drawn in the diagram shared with the signs, their location (coordinate <strong>t</strong>) is good, but the imaginary part was taken into account in a different way at that time.</p> <p>- Drawing the imaginary values of the roots on the same diagram as the signs on the <strong>newer figures</strong>, we multiplied them by 50, 100 and 10 for the P, QRS and T waves, respectively.<br> <br> <strong><em>The grids of charts </em></strong>(on the original medical ECG paper: "small squares" = 1mm x 1mm):<br> X axis: 40ms (The sampling rate is 1000Hz)<br> Y axis: 0.1mV (200 A/D units)</p> <p>=====================================================================================</p> <p><strong>Unid-Alte\</strong></p> <p>Diagram of two multipliers.<br> Here, the imaginary part of the poles and zeros is multiplied by 0.3.<br> It can be seen that the pole and zero locations do not coincide with the extremum locations on the alternating direction diagram.<br> <br> <strong>Histogram\</strong></p> <p>All 125 periods of the <strong>s0177_p050</strong> signal were approximated (<strong>vx, vy, vz</strong>). The figure is a histogram of one of the parameters of the T wave of <strong>vz</strong> lead (excluding the 2 extreme values).<br> <br> <strong>s0021_p005-vy\</strong></p> <p>These figures are the steps for approximating a QRS (<strong>s0021_p005, vy, 6810</strong>).<br> <em>Mini <strong>Hu==>En dictionary</strong>:</em> kozelito==>approximate; err......==>discrepancy; kend volt==>this had to be approached</p> <p>rajz-r-y-002---1.gif result of QRS11<br> rajz-r-y-002---2.gif result of QRS12<br> rajz-r-y-002---3.gif result of QRS13<br> The approximation of the small wave on the right side of the QRS is still missing. The approximation below the left side of the QRS is not yet good enough. We entered 2 zeros and multiplied the denominator by a complex conjugate root pairs.<br> rajz-r-y-002---4.gif result of QRS34<br> rajz-r-y-002---5.gif result of QRS35 Acceptable.<br> </p> <p>rajz-r-y-002---6.gif after a later approximation, we compared the results of the new QRS77 and QRS35.</p> <p>rajz-y-002-221022_201157_1.gif The final results of this new approximation (indicating the poles and zeros).<br> rajz-y-002-221022_201157_2.gif</p> <p><em>Mini <strong>Hu==>En dictionary</strong></em>: R.hely==>the relative <strong>t</strong> coordinate calculated from the origin; Eredeti==>raw; Approx==>approximate; Err......==>discrepancy, BL......==>baseline<br> <br> <strong>Shapes\</strong></p> <p>Approximation of some important shape types.<br> <em>Mini <strong>Hu==>En dictionary</strong></em>: R.hely==>the relative <strong>t</strong> coordinate calculated from the origin; Eredet==>raw; Approx==>approximate; Err......==>discrepancy, BL......==>baseline; pólusok==>poles</p> <p>s0025_p005 V3 7009 Dome Ta P32 T34 QRS34<br> s0047_p015 II 2758 Digitalis P22 T33 QRS34 One pair of poles of QRS and T coincide in the drawing (<strong>t</strong>=32).<br> s0146_p044 V4 3725 Coronaria T Ta P22 T22 QRS23<br> s0508_p269 aVR 5019 Block Ta P12 T12 QRS24<br> s0542_p283 vz 2581 QRS W-shaped P__ T22 QRS34 There is no P wave in any of the leads. (Atrial fibrillation)<br> <br> <strong>From_series\</strong></p> <p> <em>Mini <strong>Hu==>En dictionary</strong></em>: R.hely==>the relative <strong>t</strong> coordinate calculated from the origin; Eredet==>raw; Approx==>approximate; Err......==>discrepancy, BL......==>baseline; pólusok==>poles</p> <table> <tbody> <tr> <td>s.... = S/N, p... = patient:</td> <td> s0001_p119</td> <td>s0002_p196</td> <td>s0034_p163</td> <td>s0035_p009</td> </tr> <tr> <td> QRS_location:</td> <td>6938 10291</td> <td>9067</td> <td>7580</td> <td>8669</td> </tr> <tr> <td><strong>vx</strong></td> <td>Ta Ta</td> <td>Ta</td> <td> </td> <td>Ta</td> </tr> <tr> <td> </td> <td>P12 P12</td> <td>P12</td> <td>P65</td> <td>P13</td> </tr> <tr> <td> </td> <td>T23 T34</td> <td>T25</td> <td>T35</td> <td>T34</td> </tr> <tr> <td> </td> <td>QRS24 QRS24</td> <td>QRS15</td> <td>QRS89</td> <td>QRS57</td> </tr> <tr> <td><strong>vy</strong></td> <td>Ta</td> <td> </td> <td>Ta</td> <td>Ta</td> </tr> <tr> <td> </td> <td>P24</td> <td>P55</td> <td>P34</td> <td>P13</td> </tr> <tr> <td> </td> <td>T54</td> <td>T24</td> <td>T26</td> <td>T13</td> </tr> <tr> <td> </td> <td>QRS55</td> <td>QRS25</td> <td>QRS47</td> <td>QRS48</td> </tr> <tr> <td><strong>vz</strong></td> <td> </td> <td> </td> <td>Ta</td> <td>Ta</td> </tr> <tr> <td> </td> <td>P65</td> <td>P43</td> <td>P23</td> <td>P11</td> </tr> <tr> <td> </td> <td>T14</td> <td>T33</td> <td>T24</td> <td>T44</td> </tr> <tr> <td> </td> <td>QRS25</td> <td>QRS25</td> <td>QRS27</td> <td>QRS26</td> </tr> </tbody> </table> <p>The s0001_p119\vy\rajz-y-001-190918_210304_vlc.gif image has been expanded with the length of the vector ("V__idy 1"). The origin of the <strong>t</strong> axis (vx, vy, vz) is determined from the curve of the vector length.</p> <p>The figures for <strong>s0001_p119, vx, 10291</strong> are in the s0001_p119_U\vx directory. This is from the same <strong>vx</strong> signal as the previous one (6938), just 4 periods later. There is a U wave in it, T34 was needed instead of T23 to approximate it. The image Rajz-001-005a.gif is in the same directory. On this, we enlarged the part between the T and the next P wave at two periods (Data<strong>1</strong> 6938 and Data<strong>5</strong> 10291) of the vx signal.</p> <p>The figures of <strong>s0002_p196,</strong> <strong>vx, vy, vz, 9067</strong> show that the approximation on the left side of the QRS should be improved, probably by increasing the degree of the denominator.</p> <p>The figures for <strong>s0034_p163, vx, 7580</strong> are in the s0034_p163\vx\ directory. In the figure "rajz-x-003-221031_174534_2.gif" at t=-128, a pole and a zero coincide in the drawing, so we have enlarged this part (around P and QRS) :<br> rajz-x-003-221031_174534_2-zoom.gif.</p> <p>The figures for <strong>s0035_p009, vy, 8669</strong> are in the s0035_p009\vy\ directory. In the figure "rajz-y-004-221031_114914_2.gif" at t=-21, a pole and a zero almost coincide in the drawing.<br> As already written above, the data of example_1a.m (Octave, Matlab) on <a href="https://doi.org/10.5281/zenodo.6479410">https://doi.org/10.5281/zenodo.6479410</a> comes from the file ppaarraamm.txt in the same directory.</p> <p> Kobzos, Laszlo<br> Location: HU (Budapest)<br> email: zehu.kola.ci@gmail.com</p>
Dataset and R Code for Species-level Avian Influenza Phylogenetic Generalized Least Squares Regression
<p>Dataset for Species-level Avian Influenza Phylogenetic Generalized Least Squares (PGLS) Regression:<br> Variables include taxonomic information for each species, # of IAV-positive individuals, # of IAV-tested individuals, the prevalence of IAV, the proportion of diet made up of different food types, the proportion of foraging time spent in different strata (below water, water surface, ground, understory, etc), sampling-related variables (mean latitude, mean date, the proportion of hatch year individuals), migration and territoriality category, climatologic variables, mean clutch size, and mating system.</p> <p>R Code for PGLS and Avian Influenza Prevalence ContMap.</p>
Dataset: 1H NMR metabolomic study of auxotrophic starvation in yeast using Multivariate Curve Resolution-Alternating Least Squares for Pathway Analysis
<p>This dataset contains the set of 1H NMR data used in https://doi.org/10.1038/srep30982.</p> <p>Yeast was grown in five different liquid media and their metabolism was characterized at 6 different time-points during 24 h.</p> <p>The media used were YSC (Yeast nitrogen base Synthetic Complete) and four Drop-Out (DM) medium that do not contain one of the following nutrients (L-histidine, L-leucine, L-methionine and uracil). Since the used yeast strain does not encode in its genome some genes relative to the biosynthesis of these four nutrients, some gene de-regulations process will occur, detectable at the metabolome level.</p> <p>In this study, we have characterized the metabolome using <sup>1</sup>H NMR spectroscopy, detecting more than 40 metabolites, and the evolution of this metabolome along the measured time-points was described by application of PCA, ASCA and MCR-ALS chemometric methods.</p>
Supplementary tables for: Dependent variable selection in phylogenetic generalized least squares regression analysis under Pagel's lambda model
<p class="MsoNormal"><span>Phylogenetic generalized least squares (PGLS) regression is widely used to detect evolutionary correlations. In contrast to the equal treatment of analyzed traits in conventional correlation methods such as Pearson and Spearman's rank tests, we must designate one trait as the independent variable and the other as the dependent variable. However, in our PGLS regression analyses (using Pagel's <em>λ</em> model) of both empirical and simulated datasets, switching independent and dependent variables yielded many conflicting results. A serious problem with PGLS regression that has not been noticed before is that selecting an inappropriate trait as the dependent variable will often result in an error. To assess correlations in simulated data, we established a gold standard by analyzing changes in traits along phylogenetic branches. Next, we tested seven potential criteria for dependent variable selection: log-likelihood, Akaike information criterion, <em>R</em><sup>2</sup>, <em>p</em>-value, Pagel's <em>λ</em>, Blomberg et al.'s <em>K</em>, and the estimated <em>λ</em> in <a name="_Hlk136010442"></a>Pagel's <em>λ</em> model. We determined that the last three criteria performed equally well in selecting the dependent variable and were superior to the other four. For practicality, we suggest using the trait with a higher <em>λ</em></span><span> or <em>K</em> </span><span>value as the dependent variable in future PGLS regressions. In analyzing the evolutionary relationship between two traits, we should designate the trait with a stronger phylogenetic signal as the dependent variable even if it could logically assume the cause in the relationship.</span></p>
A Bayesian extension of phylogenetic generalized least squares (PGLS): incorporating uncertainty in the comparative study of trait relationships and evolutionary rates
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Supplementary tables for: Dependent variable selection in phylogenetic generalized least squares regression analysis under Pagel’s lambda model
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Compositional Characterization of Glassy Volcanic Material From VNIR and MIR Spectra Using Partial Least Squares Regression Models
<p>This is supporting data for the paper titled "Compositional Characterization of Glassy Volcanic Material From VNIR and MIR Spectra Using Partial Least Squares Regression Models" by Leight et al. (submitted to JGR-P 11/23). Table S1 lists each spectrum used to train PLS models, its source, and which training datasets the spectrum was included in. Zip files contain the MIR and VNIR PLS model files. Model files are .asc, and can be run using the code at Ytsma, (2022), https://doi.org/10.5281/zenodo.7347345. </p>
Multivariate curve resolution -alternating least squares augmented with partial least squares baseline correction applied to mid-IR laser spectra resolves protein denaturation by reducing rotational ambiguity
<p>This record contains mid-IR spectra of a titration of beta-lactoglobulin with two detergents. Spectra are stored in the form of Matlab .mat files. More details on the data and data processing can be found in the associated publication.</p><ul><li>F_smooth: Surfactant calibration spectra</li><li>initial_estimate_4c: Intitial estimate of spectra used for MCR-ALS</li><li>protein_spectra_new: Protein spectra obtained by subtracting the background surfactant titration spectra (surftitrationnewwvcorr) from the protein</li><li>titration spectra (prtntitrationnewwvcorr)</li><li>tit_concat_new_mcr: Input for standalone MCR-ALS</li><li>true_conc_sds_nis_pr_cal: Concentrations of SDS and C12E8 as well as protein used in training data for PLS</li></ul>
Identification of glucose-6 phosphate dehydrogenase deficient patients by ATR-FTIR spectroscopy using partial least squares discriminant analysis in aqueous blood samples
<p>Dataset of associated MIR spectra collected in the titled study</p> <p>Baseline: spectrum of air</p> <p>Water: spectrum of deionised water</p> <p>P#_M/F_GD/P/C/T/U_WB/PL/RBC_D/AQ_R: sample spectrum with each section representing the following</p> <ul> <li>P#: patient number 1-30</li> <li>M (male) or F(Female)</li> <li>GD (G6PD deficient), P (Partially G6PD deficient), C & T & ET(Control), U(Unknown) <ul> <li>P10, P28, P29, & P30 were determined later to be P, C, C, P respectively</li> </ul> </li> <li>WB (whole blood), PL (plasma), RBC (red blood cells)</li> <li>D (dried sample spectrum), AQ (aqueous sample spectrum)</li> </ul>
Hyperspectral reflectance-based partial least squares regression models for predicting cotton leaf physiological traits
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Pairwise distance demarcation of species in the family Coronaviridae. a, Diagonal matrix of PPDs of 2,505 viruses clustered according to 49 coronavirus species, 39 established and 10 pending or tentative, and ordered from the most to least populous species, from left to right; green and white, PPDs smaller and larger than the inter-species threshold, respectively. Areas of the green squares along the diagonal are proportional to the virus sampling of the respective species, and virus prototypes of the five most sampled species are specified to the left; asterisks indicate species that include viruses whose intra-species PPDs crossed the inter-species threshold (threshold 'violators'). b, Maximal intra-species PPDs (x axis, linear scale) plotted against virus sampling (y axis, log scale) for 49 species (green dots) of the Coronaviridae. Indicated are the acronyms of virus prototypes of the seven most sampled species. Green and blue plot sections represent intra-species and intra-subgenera PPD ranges. The vertical black line indicates the inter-species threshold. c, Shown are the PDs of non-identical residues (y axis) for four viruses representing three major phylogenetic lineages (clades) of the species Severe acute respiratorysyndrome-related coronavirus (panel b) and all pairs of the 256 viruses of this species ('all pairs'). The PD values were derived from pairwise distances in the MSA that were calculated using an identity matrix. Panels a and b were adopted from the DEmARC v.1.4 output. in The species Severe acute respiratory syndromerelated coronavirus: classifying 2019-nCoV and naming it SARS-CoV-2
Pairwise distance demarcation of species in the family Coronaviridae. a, Diagonal matrix of PPDs of 2,505 viruses clustered according to 49 coronavirus species, 39 established and 10 pending or tentative, and ordered from the most to least populous species, from left to right; green and white, PPDs smaller and larger than the inter-species threshold, respectively. Areas of the green squares along the diagonal are proportional to the virus sampling of the respective species, and virus prototypes of the five most sampled species are specified to the left; asterisks indicate species that include viruses whose intra-species PPDs crossed the inter-species threshold (threshold 'violators'). b, Maximal intra-species PPDs (x axis, linear scale) plotted against virus sampling (y axis, log scale) for 49 species (green dots) of the Coronaviridae. Indicated are the acronyms of virus prototypes of the seven most sampled species. Green and blue plot sections represent intra-species and intra-subgenera PPD ranges. The vertical black line indicates the inter-species threshold. c, Shown are the PDs of non-identical residues (y axis) for four viruses representing three major phylogenetic lineages (clades) of the species Severe acute respiratorysyndrome-related coronavirus (panel b) and all pairs of the 256 viruses of this species ('all pairs'). The PD values were derived from pairwise distances in the MSA that were calculated using an identity matrix. Panels a and b were adopted from the DEmARC v.1.4 output.
Data for "Calculation of permanent magnet arrangements for stellarators: A linear least-squares method"
<p>Data associated with the paper "Calculation of permanent magnet arrangements for stellarators: A linear least-squares method"</p>
Multivariate Ordinary Least Squares (OLS) regression-based Seismic Hazard Model Data
<p>This dataset includes earthquake parameters, slab geometry, gravity anomalies, and fault proximities used for seismic hazard modeling in the Makran Subduction Zone (MSZ). Supplementary Table S1 contains earthquake data (location, depth, magnitude), slab properties (depth, dip, thickness, strike), and distances to key faults. Supplementary Table S2 provides intraslab seismicity, slab geometry, trench distances, and gravity data. The data are sourced from the USGS Earthquake Catalog, IRIS, Slab-2 model, GMRT, and other geophysical models.</p>
Ambisonics Binaural Rendering via Masked Magnitude Least Squares - Supplemental Material
<p>Refer to the readme file for more information.</p>
Enhancement of the Ensemble Nonlinear Least Squares Algorithm for i4DVar
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Data of "Evaluation of feeding behaviour traits to predict efficiency traits in pigs using partial least square regression"
<p>This study explores the potential of using automatically recorded feeding behaviour as a proxy trait for protein and energy efficiency as well as fat gain in Swiss Large White pigs.</p> <p>behaviour.txt: A tab-delimited txt file of data used to investigate the relationship between feeding behaviour traits and protein efficiency in Swiss Large White pigs. Feeding behaviour traits taken were ‘daily feed intake’, ‘feeding rate’, ‘number of daily visits’, ‘duration at visits’, ‘feeding occupation’, and ‘feed intake per visit’.</p> <p>data_description_for_feeding_behaviour.xlsx: meta data for behaviour.txt with descriptions of variables</p>
Supplemental Table S3. Least square means of the digestible (d) AA (Leu, Ile, Arg, Phe, Thr and Val) supply relative to the cow's requirements (%) for the CTRL, MetLys and MetLysHis groups in the depletion, RP-AA and cross-back period of the trial. Calculations based on the actual DMI, MY and BW of the cows during the experiment.
<p><strong>Supplemental Table S3.</strong> Least square means of the digestible (d) AA (Leu, Ile, Arg, Phe, Thr and Val) supply relative to the cow’s requirements (%) for the CTRL, MetLys and MetLysHis groups in the depletion, RP-AA and cross-back period of the trial. Calculations based on the actual DMI, MY and BW of the cows during the experiment.</p>
Supplemental Table S2. Least square means of BW, DMI, milk yield (MY) and fat- and protein-corrected milk yield (FPCMY), milk protein yield (MPY), milk composition (milk fat, milk protein, milk lactose and MUN), milk N efficiency (MNE) and feed efficiency for the CTRL, MetLys and MetLysHis treatment groups in the pre-experimental, depletion and cross-back period.
<p><strong>Supplemental Table S2.</strong> Least square means of BW, DMI, milk yield (MY) and fat- and protein-corrected milk yield (FPCMY), milk protein yield (MPY), milk composition (milk fat, milk protein, milk lactose and MUN), milk N efficiency (MNE) and feed efficiency for the CTRL, MetLys and MetLysHis treatment groups in the pre-experimental, depletion and cross-back period.</p>
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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
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.