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Fig. 6 in Regular Intergeneric Hybridization Of Leuciscine Cyprinids (Cyprinidae, Leuciscinae) In The Dnipro River Affluents
Fig. 6. Electrophoregram of muscle structure proteins spectra of A. brama (1); R. rutilus (2), and their hybrids A. brama × R. rutilus (3).
Fig. 2 in Regular Intergeneric Hybridization Of Leuciscine Cyprinids (Cyprinidae, Leuciscinae) In The Dnipro River Affluents
Fig. 2. Electrophoregram of muscle structure proteins spectra of A. alburnus (1), S. erythrophthalmus (2), and their hybrids A. alburnus × S. erythrophthalmus (3).
Fig. 1 in Regular Intergeneric Hybridization Of Leuciscine Cyprinids (Cyprinidae, Leuciscinae) In The Dnipro River Affluents
Fig. 1. Phenogram of genetic distances (Nei's D) between the studied species of cyprinids, UPGMA algorithm on the basis of 15 biochemical loci.
Muscle activation patterns are more constrained and regular in treadmill than in overground human locomotion
<p>The use of motorized treadmills as convenient tools for the study of locomotion has been in vogue for many decades. However, despite the widespread presence of these devices in many scientific and clinical environments, a full consensus on their validity to faithfully substitute free overground locomotion is still missing. Specifically, little information is available on whether and how the neural control of movement is affected when humans walk and run on a treadmill as compared to overground. Here, we made use of linear and nonlinear analysis tools to extract information from electromyographic recordings during walking and running overground and on an instrumented treadmill. We extracted synergistic activation patterns from the muscles of the lower limb via non-negative matrix factorization. We then investigated how the motor modules (or time-invariant muscle weightings) were used in the two locomotion environments. Subsequently, we examined the timing of motor primitives (or time-dependent coefficients of muscle synergies) by calculating their duration, the time of main activation, and their Hurst exponent, a nonlinear metric derived from fractal analysis. We found that motor modules were not influenced by the locomotion environment, while motor primitives resulted overall more regular in treadmill than in overground locomotion, with the main activity of the primitive for propulsion shifted earlier in time. Our results suggest that the spatial and sensory constraints imposed by the treadmill environment forced the central nervous system to adopt a different neural control strategy than that used for free overground locomotion. A data-driven indication that treadmills induce perturbations to the neural control of locomotion.</p> <p> </p> <p>In this supplementary data set we made available: a) the metadata with anonymized participant information; b) the raw EMG, already concatenated for the overground trials; c) the touchdown and lift-off timings of the recorded limb, d) the filtered and time-normalized EMG; e) the muscle synergies extracted via NMF; f) the code to process the data. In total, 120 trials from 30 participants are included in the supplementary data set.</p> <p>The file “metadata.dat” is available in ASCII and RData format and contains:</p> <ul> <li>Code: the participant’s code</li> <li>Sex: the participant’s sex (M or F)</li> <li>Locomotion: the type of locomotion (W=walking, R=running)</li> <li>Environment: to distinguish between overground (O) and treadmill (T)</li> <li>Speed: the speed at which the recordings were conducted in [m/s] (1.4 m/s for walking, 2.8 m/s for running)</li> <li>Age: the participant’s age in years</li> <li>Height: the participant’s height in [cm]</li> <li>Mass: the participant’s body mass in [kg].</li> </ul> <p>The "RAW_DATA.RData" R list consists of elements of S3 class "EMG", each of which is a human locomotion trial containing cycle segmentation timings and raw electromyographic (EMG) data from 13 muscles of the right-side leg. Cycle times are structured as data frames containing two columns that correspond to touchdown (first column) and lift-off (second column). Raw EMG data sets are also structured as data frames with one row for each recorded data point and 14 columns. The first column contains the incremental time in seconds. The remaining 13 columns contain the raw EMG data, named with the following muscle abbreviations: ME = gluteus medius, MA = gluteus maximus, FL = tensor fasciæ latæ, RF = rectus femoris, VM = vastus medialis, VL = vastus lateralis, ST = semitendinosus, BF = biceps femoris, TA = tibialis anterior, PL = peroneus longus, GM = gastrocnemius medialis, GL = gastrocnemius lateralis, SO = soleus. Please note that the running overground trials of participants P0001, P0007, P0008 and P0009 consist of 21, 29, 29 and 26 cycles, respectively. All the other trials consist of 30 gait cycles. Trials are named like “P0003_OR_01”, where the characters “P0003” indicate the participant number (in this example the 3<sup>rd</sup>), the characters “OR” indicate the locomotion type and environment (see above), and the numbers “01” indicate the trial number. The filtered and time-normalized emg data are named, following the same rules, like “FILT_EMG_P0003_OR_01”.</p> <p><strong>Old versions not compatible with the R package <a href="https://CRAN.R-project.org/package=musclesyneRgies">musclesyneRgies</a></strong></p> <p>The files containing the gait cycle breakdown are available in RData format, in the file named “CYCLE_TIMES.RData”. The files are structured as data frames with 30 rows (one for each gait cycle) and two columns. The first column contains the touchdown incremental times in seconds. The second column contains the duration of each stance phase in seconds. Each trial is saved as an element of a single R list. Trials are named like “CYCLE_TIMES_P0020_TW_01,” where the characters “CYCLE_TIMES” indicate that the trial contains the gait cycle breakdown times, the characters “P0020” indicate the participant number (in this example the 20<sup>th</sup>), the characters “TW” indicate the locomotion type and environment (O=overground, T=treadmill, W=walking, R=running), and the numbers “01” indicate the trial number. Please note that the running overground trials of participants P0001, P0007, P0008 and P0009 only contain 21, 29, 29 and 26 cycles, respectively.</p> <p>The files containing the raw, filtered, and the normalized EMG data are available in RData format, in the files named “RAW_EMG.RData” and “FILT_EMG.RData”. The raw EMG files are structured as data frames with 30000 rows (one for each recorded data point) and 14 columns. The first column contains the incremental time in seconds. The remaining 13 columns contain the raw EMG data, named with muscle abbreviations that follow those reported above. Each trial is saved as an element of a single R list. Trials are named like “RAW_EMG_P0003_OR_01”, where the characters “RAW_EMG” indicate that the trial contains raw emg data, the characters “P0003” indicate the participant number (in this example the 3<sup>rd</sup>), the characters “OR” indicate the locomotion type and environment (see above), and the numbers “01” indicate the trial number. The filtered and time-normalized emg data is named, following the same rules, like “FILT_EMG_P0003_OR_01”.</p> <p>The files containing the muscle synergies extracted from the filtered and normalized EMG data are available in RData format, in the file named “SYNS.RData”. Each element of this R list represents one trial and contains the factorization rank (list element named “synsR2”), the motor modules (list element named “M”), the motor primitives (list element named “P”), the reconstructed EMG (list element named “Vr”), the number of iterations needed by the NMF algorithm to converge (list element named “iterations”), and the reconstruction quality measured as the coefficient of determination (list element named “R2”). The motor modules and motor primitives are presented as direct output of the factorization and not in any functional order. Motor modules are data frames with 13 rows (number of recorded muscles) and a number of columns equal to the number of synergies (which might differ from trial to trial). The rows, named with muscle abbreviations that follow those reported above, contain the time-independent coefficients (motor modules M), one for each synergy and for each muscle. Motor primitives are data frames with 6000 rows and a number of columns equal to the number of synergies (which might differ from trial to trial) plus one. The rows contain the time-dependent coefficients (motor primitives P), one column for each synergy plus the time points (columns are named e.g. “time, Syn1, Syn2, Syn3”, where “Syn” is the abbreviation for “synergy”). Each gait cycle contains 200 data points, 100 for the stance and 100 for the swing phase which, multiplied by the 30 recorded cycles, result in 6000 data points distributed in as many rows. This output is transposed as compared to the one discussed in the methods section to improve user readability. Trials are named like “SYNS_ P0012_OW_01”, where the characters “SYNS” indicate that the trial contains muscle synergy data, the characters “P0012” indicate the participant number (in this example the 12<sup>th</sup>), the characters “OW” indicate the locomotion type and environment (see above), and the numbers “01” indicate the trial number. Given the nature of the NMF algorithm for the extraction of muscle synergies, the supplementary data set might show non-significant differences as compared to the one used for obtaining the results of this paper.</p> <p>All the code used for the pre-processing of EMG data and the extraction of muscle synergies is available in R format. Explanatory comments are profusely present throughout the script “muscle_synergies.R”.</p>
Figure 3: AntCo2 algorithm for graph clustering: on the left the output of the computation on a communication network; on the right the output on a regular grid
<p>Social and human developments are typical complex systems. Urban development<br> and dynamics are the perfect illustration of systems where spatial<br> emergence, self-organization and structural interaction between the system<br> and its components occur [3, 4, 5, 6]. In figure 4, we concentrate on the emergence<br> of organizational systems from geographical systems.</p>
Figure 1 in The value of regular monitoring and diverse sampling techniques to assess aquatic non-native species: a case study from Orkney
Figure 1. Locations of the monitoring sites. A: North of Orkney Mainland and northern isles, B: Scapa Flow and southern isles. For corresponding site names refer to Supplementary material Table S1.
Figure 3 in The value of regular monitoring and diverse sampling techniques to assess aquatic non-native species: a case study from Orkney
Figure 3. The total number of sites for which each non-native or cryptogenic species has been recorded for each sampling method for 2016 and 2017. NB: Only species recorded in these two years are reported in this figure. Abbreviations: Cm: Caprella mutica, As: Ascidiella scabra, Bh: Bonnemaisonia hamifera, Sj: Schizoporella japonica, Ce: Corella eumyota, Aa: Ascidiella aspersa, Cp: Colpomenia peregrina, Cf: Codium fragile ssp. fragile, Ti: Tricellaria inopinata, Ah: Asterocarpa humilis, Bs: Botryllus schlosseri, Bl: Botrylloides leachii, Mh: Melanothamnus harveyi, Bf: Bugulina fulva, Dj: Dasysiphonia japonica, Jm: Jassa marmorata, Mi: Monocorophium insidiosum, Ma: Monocorophium acherusicum, Cs: Ctenodrilus serratus, Tj: Telmatogeton japonicus, Pa: Potamopyrgus antipodarum, Cb: Crassicorophium bonellii, Dl: Diplosoma listerianum.
Figure 2 in The value of regular monitoring and diverse sampling techniques to assess aquatic non-native species: a case study from Orkney
Figure 2. The total number of non-native and cryptogenic species recorded at each monitoring location (2012–2017).
FIGURE 12 in Tracking biases in the regular echinoid fossil record: The case of Paracentrotus lividus in recent and fossil shallow-water, high-energy environments
FIGURE 12. Relative abundance of Paracentrotus lividus spine remains. A, Is Mesas (Pleistocene-Bulk sample). B, Sa Mesa Longa Beach (Recent-Surface collection). C, Sa Mesa Longa Beach (Bulk sample). N = number of counted specimens.
FIGURE 13 in Tracking biases in the regular echinoid fossil record: The case of Paracentrotus lividus in recent and fossil shallow-water, high-energy environments
FIGURE 13. Biostratinomic signatures (abrasion and encrustation) of spine remains. A, Is Mesas (Pleistocene-Bulk sample). B, Sa Mesa Longa Beach (RecentSurface collection). C, Sa Mesa Longa Beach (RecentBulk sample). N = number of counted specimens
FIGURE 11 in Tracking biases in the regular echinoid fossil record: The case of Paracentrotus lividus in recent and fossil shallow-water, high-energy environments
FIGURE 11. Distribution of ambulacral and interambulacral plates in the two sediment fractions (> 2 mm and 1-2 mm) investigated herein. N = number of counted specimens.
FIGURE 9 in Tracking biases in the regular echinoid fossil record: The case of Paracentrotus lividus in recent and fossil shallow-water, high-energy environments
FIGURE 9. Relative abundance of Paracentrotus lividus test remains. A, Is Mesas (Pleistocene-Bulk sample). B, Sa Mesa Longa Beach (Recent-Surface collection). C, Sa Mesa Longa Beach (Bulk sample). N = number of counted specimens.
FIGURE 10 in Tracking biases in the regular echinoid fossil record: The case of Paracentrotus lividus in recent and fossil shallow-water, high-energy environments
FIGURE 10. Biostratinomic signatures (fragmentation, abrasion and encrustation) of echinoid test remains. A, Is Mesas (Pleistocene-Bulk sample). B, Sa Mesa Longa Beach (Recent-Surface collection). C, Sa Mesa Longa Beach (Recent-Bulk sample). N = number of counted specimens. Numbers (1-11) are explained in Figure 9. Colors: dark grey, light grey and black represent fragmentation, abrasion and encrustation, respectively.
FIGURE 6. A in Tracking biases in the regular echinoid fossil record: The case of Paracentrotus lividus in recent and fossil shallow-water, high-energy environments
FIGURE 6. A, Panoramic view of Sa Mesa Longa Beach (Central-western Sardinia). B-H, Recent remains of Paracentrotus lividus from Sa Mesa Longa. B, Complete denuded test showing encrustation by serpulids. C, Test fragment made up of several ambulacral and interambulacral plates sutured together, showing inter- and intraplate fragmentation and encrustation by coralline algae and the polychaete Spirorbis. D, Internal view of a large test fragment affected by intraplate fragmentation and encrustation by serpulid worms. E, Two interambulacral plates showing abrasion and encrustation by Spirorbis. F, Single ambulacral plates showing fragmentation (white arrow). G, Epiphysis from the jaw apparatus showing fragmentation and abrasion. H, Madreporite from the apical system affected by fragmentation (white arrow). I, Test fragment of Arbacia lixula showing intraplate fracturing. J, Complete test of Echinocyamus pusillus. B, C, D, I, J Scale bar equals 1 cm. E–H scale bars equal 0.5 cm.
FIGURE 7 in Tracking biases in the regular echinoid fossil record: The case of Paracentrotus lividus in recent and fossil shallow-water, high-energy environments
FIGURE 7. Scanning electron micrographs of suture faces in recent Paracentrotus lividus. A, Interradial suture between interambulacral plates showing galleried stereom with galleries running in aboral-oral direction. A1, Detail of the galleried stereom and collagen fibers. B, Adradial suture of interambulacral plates showing galleried stereom. B1, Detail of the parallel galleries and collagen fibers. C, Adapical suture of an interambulacral plates showing knob-like trabecular protrusions and cavities. C1, The close-up shows numerous knob-like protrusions some of which are interconnected to one another in twos, threes or more. D, Adoral suture of ambulacral plates; 1) Radial ridge at the boundary between ambulacral plates (running across perradial sutures) and 2) galleried stereom. A, B, C Scale bars equal 100 µm; A1, B1, C1 Scale bars equal 20 µm. D Scale bar equals 1 mm.
FIGURE 8 in Tracking biases in the regular echinoid fossil record: The case of Paracentrotus lividus in recent and fossil shallow-water, high-energy environments
FIGURE 8. Size-frequency distribution of the rotulae of the Aristotle's lantern both in Pleistocene deposit and recent setting. Each box-plot represents 25 and 75 percent quartile of all values, Q1 and Q3 respectively. Black line inside the box represents the median. Whiskers are drawn from Q1 and Q3 to the largest values less than 1.5 times the Interquartile range (Q1-Q3). N = number of counted specimens.
FIGURE 4. Associated fauna from levels A and B in Tracking biases in the regular echinoid fossil record: The case of Paracentrotus lividus in recent and fossil shallow-water, high-energy environments
FIGURE 4. Associated fauna from levels A and B of the Is Mesas deposit (Late Pleistocene). A, Patella caerulea. B, Diodora gibberula. C, Cerithium vulgatum. D, Hexaplex trunculus. E, Melarhaphe neritoides. F, Acanthocardia tuberculata. G, Irus irus. H, Arca noae. I, Cardita calyculata. J, Cladocora caespitosa. Scale bars equal 1 cm.
FIGURE 5 in Tracking biases in the regular echinoid fossil record: The case of Paracentrotus lividus in recent and fossil shallow-water, high-energy environments
FIGURE 5. Scanning electron micrographs of Paracentrotus remains from Is Mesas. A, Details of an ambulacral column affected by abrasion. B, Two ambulacral plates still sutured together. C, Interambulacral plate showing a cavity possibly related to bioerosion. D, Spine fragment showing fine surface details such as the crenulated milled ring and shaft striation. Scale bars equal 1 mm.
Conditional gradient for total variation regularization with PDE constraints: a graph cuts approach
This module solves a PDE constrained minimisation problem with TV-regularization, using the method described in the paper "Conditional gradient for total variation regularization with PDE constraints: a graph cuts approach"
BRAIN Journal-Micro Expression Recognition Using the Eulerian Video Magnification Method-Figure 3.The chart of emotional/unemotional detection on the face in negative, positive and surprise states (Regular and magnified data)
<p>To evaluate the emotional/unemotional detection on the face, 328 tests were performed: 164 tests on the magnified data and 164 tests on the regular data. For this purpose, the train set includes the neutral state and only one of the emotional states (negativism, positivism and surprise) according to the test set. So that the train set includes regular data in 328 experiments. The experimental results are shown in Figure 3. </p>
ScienceDex guides
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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.