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Figure 6. Mean square error of alpha band-Classification of Human Emotion from Deap EEG Signal Using Hybrid Improved Neural Networks with Cuckoo Search
<p>Particle swarm optimization algorithm first optimizes the neural networks weight and bias<br> and provides the minimum mean square error with nearer by zero. The following figure 6 has<br> shown that minimum mean square error when training the particular band features.</p>
Figure 14: Adjunctive square diagram-Brain Functors: A mathematical model of intentional perception and action
<p>Finally, a brain functor is a functor F: X→A that is a left semiadjunction for Het(X, A) and a right semiadjunction for Het(A, X), i.e., HomA(F(X),A) ≅ Het(X,A)<br> and Het(A,X) ≅ HomA(A,F(X)).<br> For each d in Het(X, A), there is a unique hom f(d) in HomA(F(X), A) so that the upper triangular ‘wing’ in the butterfly diagram commutes. For each d' in Het(A, X), there is a unique hom g(d') in HomA(A, F(X)) so that the lower triangular ‘wing’ commutes.</p>
Figure 4: The Adjunctive Square Diagram-Brain Functors: A mathematical model of intentional perception and action
<p>Dually, we can define the above situation, given by the association of the sending universal G(A) with each object A in the receiving category along with the canonical isomorphism Het(X,A) ≅ Homsending(X,G(A)), as a right semiadjunction. Now we are prepared to define an adjunction essentially as:<br> adjunction = left semiadjunction + right semiadjunction Homreceiving(F(X),A) ≅ Het(X,A) ≅ Homsending(X,G(A)).</p>
New Ideas for Brain Modelling 4-Figure 4. LHS relates to neuron binding ensemble mass, with central column activated. RHS relates to hierarchy, with a direct mapping. The two red lines show where the ensemble is missing and so it needs to be learned. The blue lines show extra neurons from the hierarchy back to the ensemble, but can be removed as error. The other paired black squares represent where the patterns match and can oscillate together.
<p>This paper continues the research that considers a new cognitive model based strongly on the human brain, last updated in Greer (2016). In particular, it considers figure 4 of that paper (Figure below) and how it might be useful in practice. The paper also describes some new methods in the areas of image processing and behaviour simulation. The image processing introduces a most classical form of pattern cross-referencing, while the behaviour equations used feedback for a memory-type of cross-referencing. The work is all based on earlier research by the author and the new additions are intended to fit in with the overall design. For image processing, a grid-like structure is used with ‘full linking’, if you like. Each cell in the classifier grid stores a list of all other cells it gets associated with and this is used as the learned image that new input is compared with. For the behaviour metric, a new prediction equation is suggested, as part of a simulation, that uses feedback and history to dynamically determine its current state and course of action. While the new methods are from widely different topics, both can be compared with the binary-analog type of interface that is the main focus of the paper. Sensory input may be static and binary, but cross- references result in variable comparisons that make the input more dynamic. It is suggested that the simplest of linking between a tree and ensemble can explain neural binding and variable signal strengths.</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>
Determinant Quantum Monte Carlo data for the Hubbard model on the half filled square lattice, on a (U,B)-grid
<p>Data generated with QUEST 1.4.9. For documentation see these two homepages:<br> Original homepage: http://quest.ucdavis.edu/<br> Newest version available at: https://code.google.com/archive/p/quest-qmc/</p> <p>The simulations are done at half filling on a square lattice, with the following parameters:</p> <ul> <li>Lattice sizes: 4x4, 6x6, 8x8, 10x10, 12x12, periodic boundary conditions</li> <li>Trotter discretizations: 0.1 and 0.2</li> <li>Inverse temperature beta = 10.0</li> <li>48 values for the on-site interaction U from 0.0 to 10.0</li> <li>48 values for the magnetic field (in z-direction) B from 0.0 to 4.0</li> <li>10000 warmup sweeps, 30000 measurement sweeps</li> </ul> <p>The following data from equal time measurements are available:</p> <ul> <li>Charge-Charge Correlation (next neighbors)</li> <li>Greens Function (n.n.)</li> <li>Magnetization</li> <li>Double Occupancy</li> <li>Kinetic Energy</li> <li>Total Energy</li> <li>Spin-Spin Correlation (n.n.)</li> <li>Spin-Spin Correlation (only ZZ) (n.n.)</li> <li>Ferromagnetic Structure Factor (ZZ)</li> <li>Antiferromagnetic Structure Factor (ZZ)</li> </ul> <p>The data are available as a hdf5 archive. The python script 'extract.py' illustrates the access with h5py. Relevant QUEST input parameters are provided in the group 'parameters' within the archive.</p> <p>All calculated quantities are averaged over multiple consecutive simulations, which is why the data is not presented in the usual QUEST output. This was necessary due to limited walltime on the used supercomputer.</p> <p>The authors acknowledge the North-German Supercomputing Alliance (HLRN) for providing computing resources via project number hbp00046 that have contributed to these results.</p>
Text-fig. 4. Dendrogram (Ward's method, squared Euclidean distance) showing the relationship between the studied fossil vegetation assemblages of Hrádek/N. (48), Wackersdorf (49), Berzdorf and Wiesa (50) and the Mydlovary Fm. (51) and the studied modern vegetation units from SE China and Japan (Teodoridis et al. 2011a, 2012, Appendix – this volume). in A Review Of The Early Miocene Mastixioid Flora Of The Kristina Mine At Hrádek Nad Nisou In North Bohemia (The Czech Republic)
Text-fig. 4. Dendrogram (Ward's method, squared Euclidean distance) showing the relationship between the studied fossil vegetation assemblages of Hrádek/N. (48), Wackersdorf (49), Berzdorf and Wiesa (50) and the Mydlovary Fm. (51) and the studied modern vegetation units from SE China and Japan (Teodoridis et al. 2011a, 2012, Appendix – this volume).
Text-fig. 6. Lunulites(?), deposited in NM Prague under number T 3320. A – optic (scale bar 1 mm) and B – SEM (BSE detector) photography (scale bar 100 µm) showing characters suggesting determination as Lunulites (square shape and linear arrangement of autozooecia, short cryptocyst and presence of vibracularia). in The Priabonian Bryozoan-Decapod Association From The Borové Formation (The Ďurkovec Quarry, Ne Slovakia) And Its Palaeoecological Implications
Text-fig. 6. Lunulites(?), deposited in NM Prague under number T 3320. A – optic (scale bar 1 mm) and B – SEM (BSE detector) photography (scale bar 100 µm) showing characters suggesting determination as Lunulites (square shape and linear arrangement of autozooecia, short cryptocyst and presence of vibracularia).
Figure. Distribution of Neomys teres and Neomys anomalus species in Turkey (square = Neomys anomalus, triangle = Neomys teres). 1: Ulubey (Ordu), 2: Meryemana (Trabzon), 3: Kutul (Artvin), 4: Yalnızçam (Kars), 5: Bendimahi Canyon (Muradiye, Van), 6: Seyfe (Amasya), 7: Safranbolu (Karabük), 8: Topçam (Ordu), 9: Tamdere (Giresun), 10: Çamlık (Rize), 11: Ovid Mountain (Rize), 12: Lake Abant (Bolu), 13: Kayseri, 14: Erzurum, 15: Samsun, 16: Belgrad Forest (İstanbul), 17: Lake Abant (Bolu), 18: İrve creek (İstanbul), 19: Erçek Mountain (Van), 20: Paşaalandere (Tekirdağ), 21: Lake Terkos (İstanbul), 22: Yeşiloba (Adana), 23: Yenice, Çayır (Zonguldak), 24: Abant (Bolu), 25: Hanyatak village (Sakarya), 26: Longoz forest, Dupnisa cave, Demirköy (Kırklareli), 27: Lake Eber (Afyon), 28: Çırpılar (Çanakkale), 29: Uludağ (Bursa), 30: Balkusan (Karaman). in Taxonomic status of Neomys species (Mammalia: Soricomorpha) and their distribution in Turkey
Figure. Distribution of Neomys teres and Neomys anomalus species in Turkey (square = Neomys anomalus, triangle = Neomys teres). 1: Ulubey (Ordu), 2: Meryemana (Trabzon), 3: Kutul (Artvin), 4: Yalnızçam (Kars), 5: Bendimahi Canyon (Muradiye, Van), 6: Seyfe (Amasya), 7: Safranbolu (Karabük), 8: Topçam (Ordu), 9: Tamdere (Giresun), 10: Çamlık (Rize), 11: Ovid Mountain (Rize), 12: Lake Abant (Bolu), 13: Kayseri, 14: Erzurum, 15: Samsun, 16: Belgrad Forest (İstanbul), 17: Lake Abant (Bolu), 18: İrve creek (İstanbul), 19: Erçek Mountain (Van), 20: Paşaalandere (Tekirdağ), 21: Lake Terkos (İstanbul), 22: Yeşiloba (Adana), 23: Yenice, Çayır (Zonguldak), 24: Abant (Bolu), 25: Hanyatak village (Sakarya), 26: Longoz forest, Dupnisa cave, Demirköy (Kırklareli), 27: Lake Eber (Afyon), 28: Çırpılar (Çanakkale), 29: Uludağ (Bursa), 30: Balkusan (Karaman).
Рис.100.Точки нахоΑокZelotes gallicus (кваΑрат) и Z. pseudogallicus (круг) на Кавказе и в ПреΑкавказье. Fig. 100. Localities of Zelotes gallicus (square) and Z. pseudogallicus (circle) in the Caucasus and Ciscaucasia. in A review of spiders of the genus Zelotes Gistel, 1848 of the subterraneus-group (Aranei: Gnaphosidae) from the Caucasus and Ciscaucasia
Рис.100.Точки нахоΑокZelotes gallicus (кваΑрат) и Z. pseudogallicus (круг) на Кавказе и в ПреΑкавказье. Fig. 100. Localities of Zelotes gallicus (square) and Z. pseudogallicus (circle) in the Caucasus and Ciscaucasia.
Рис. 99.Точки нахоΑок Zelotes fuscus (круг), Z. subterraneus (кваΑрат), Z.dagestanus Ponomarev, sp. n. (треугоΛьник) на Кавказе и в ПреΑкавказье. Fig. 99. Localities of Zelotes fuscus (circle), Z. subterraneus (square), Z. dagestanus Ponomarev, sp. n. (triangle) in the Caucasus and Ciscaucasia. in A review of spiders of the genus Zelotes Gistel, 1848 of the subterraneus-group (Aranei: Gnaphosidae) from the Caucasus and Ciscaucasia
Рис. 99.Точки нахоΑок Zelotes fuscus (круг), Z. subterraneus (кваΑрат), Z.dagestanus Ponomarev, sp. n. (треугоΛьник) на Кавказе и в ПреΑкавказье. Fig. 99. Localities of Zelotes fuscus (circle), Z. subterraneus (square), Z. dagestanus Ponomarev, sp. n. (triangle) in the Caucasus and Ciscaucasia.
Рис. 97. Точки нахоΑок Zelotes egregius (круг), Z. aurantiacus (кваΑрат), Z. azsheganovae (треугоΛьник) на Кавказе и в ПреΑкавказье. Fig. 97. Localities of Zelotes egregius (circle), Z. aurantiacus (square), Z. azsheganovae (triangle) in the Caucasus and Ciscaucasia. in A review of spiders of the genus Zelotes Gistel, 1848 of the subterraneus-group (Aranei: Gnaphosidae) from the Caucasus and Ciscaucasia
Рис. 97. Точки нахоΑок Zelotes egregius (круг), Z. aurantiacus (кваΑрат), Z. azsheganovae (треугоΛьник) на Кавказе и в ПреΑкавказье. Fig. 97. Localities of Zelotes egregius (circle), Z. aurantiacus (square), Z. azsheganovae (triangle) in the Caucasus and Ciscaucasia.
Point clouds from terrestrial laser scanning of 74 trees in Russell Square, London
<p>Point clouds of 74 trees scanned in <a href="https://www.google.co.uk/maps/place/Russell+Square/@51.5217533,-0.1280787,17z/data=!3m1!4b1!4m5!3m4!1s0x48761b310bb300bf:0xd1190a08b331324a!8m2!3d51.52175!4d-0.12589">Russell Square, London</a></p> <p>Tree species is predominantly London Plane (<em>Platanus × hispanica</em>).</p> <p>Data was captured on 8/2/2017 (leaf-off) with a RIEGL VZ-400 terrestrial laser scanner. 22 scans were conducted from 11 positions. The weather was good, with little to no noticeable wind.</p> <p>Data is a binary PLY format with <em>xyz</em> fields in an arbitrary coordinate system. Trees have been extracted from the global point cloud and have been "cleaned" to remove the ground and neighbouring trees (however there may be some errors). Data has been downsampled to a voxel size of 0.04 m.</p> <p>Raw data can be downloaded from <a href="https://doi.org/10.5281/zenodo.5070681">10.5281/zenodo.5070681</a></p> <p>Please acknowledge the data set authors if using this data.</p>
Dailly Root Mean Square (RMS) of tiltmeter data recorded at Vesuvius and Campi Flegrei
<p>Dailly Root Mean Square (RMS) of tiltmeter data for:</p> <p>1) Vesuvius: time series from October 1<sup>st</sup> 2016 to March 31<sup>th</sup> 2021 recorded at four borehole instruments of the INGV network.</p> <p>2) Campi Flegrei: time series from April 1<sup>st</sup> 2015 to March 31<sup>st</sup> 2021 recorded at three borehole instruments of the INGV network.</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>
SWMF - Ideal Square Wave Event
<p>BATS-R-US output form a SWMF simulation. The simulation was of an Ideal Square wave. Its purpose was to study the recirculation of plasmasphere material.</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>
Dataset to run the NeuralFRG software for the t-t' Hubbard model on the square lattice "Phys. Rev. Lett. 129, 136402 (2022)"
<p>This hdf5 repository contains the fRG vertices for the t-t' Hubbard model on the square lattice required to reproduce the results shown in the publication</p> <p>Di Sante et al., Phys. Rev. Lett. 129, 136402 (2022)</p> <p>by means of the NeuralFRG software (https://github.com/BITMAPdds/NeuralFRG).</p> <p>A train-test split can be performed with:</p> <p>python3 train_validation_split.py NeuralFRG_train_and_validation_data.h5 --verbose</p> <p>and training can be started with:</p> <p>python3 train.py path/to/file_train.h5 (...) #Additional flags here</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>
ScienceDex guides
Understand access before you commit
These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
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.