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14 results for “Cello”
Dataset accompanying the article: Exploring the Effects of Additional Vibration on the Perceived Quality of an Electric Cello
<p>Dataset accompanying the article: Exploring the Effects of Additional Vibration on the Perceived Quality of an Electric Cello. </p>
Three-Enzyme Phosphorylase Cascade Immobilized on Solid Support for Biocatalytic Synthesis of Cello-oligosaccharides
<p>We provide here the underlying data of the scientific publication "Three-Enzyme Phosphorylase Cascade Immobilized on Solid Support for Biocatalytic Synthesis of Cello-oligosaccharides". Please find the abstract below:</p> <p>Enzyme cascades are promising for multistep biocatalytic synthesis, but their effective use beyond the proof-of-concept stage is challenging. Strategies to recycle the individual enzymes are critical for the applicability of such cascades. Immobilization on solid support is well developed for single enzymes but remains difficult for enzyme ensembles. Here, we show a controlled co-immobilization of three glycoside phosphorylases to establish a highly active and recyclable biocatalyst for the conversion of sucrose and glucose into soluble (short-chain) cello-oligosaccharides. We use protein fusion with the binding module Z<sub>basic2 </sub>to enable non-covalent surface tethering of all enzymes according to a uniform principle and in a programmable fashion. We thus achieve loading of the phosphorylases in an activity ratio optimal for the overall conversion rate and for controlling the cello-oligosaccharide chain length (≤ 6), hence the solubility, in the reaction. We demonstrate efficient production of ~12 g/L cello-oligosaccharides in 5 reaction cycles with integrated enzyme re-use. This study presents a major advance toward the practical use of systems bio-catalysis on solid support.</p>
Kinetic modeling of phosphorylase-catalyzed iterative β-1,4-glycosylation for degree of polymerization-controlled synthesis of soluble cello-oligosaccharides
<p>We provide here the underlying data of the publication "Kinetic modeling of phosphorylase-catalyzed iterative β-1,4-glycosylation for degree of polymerization-controlled synthesis of soluble cello-oligosaccharides". Please find the abstract below.</p> <p><strong>Background: </strong>Cellodextrin phosphorylase (CdP; EC 2.4.1.49) catalyzes the iterative β-1,4-glycosylation of cellobiose using α-D-glucose 1-phosphate as the donor substrate. Cello-oligosaccharides (COS) with a degree of polymerization (DP) of up to 6 are soluble while those of larger DP self-assemble into solid cellulose material. The soluble COS have attracted considerable attention for their use as dietary fibers that offer a selective prebiotic function. An efficient synthesis of soluble COS requires good control over the DP of the products formed. A mathematical model of the iterative enzymatic glycosylation would be important to facilitate target-oriented process development.<br> <strong>Results: </strong>A detailed time-course analysis of the formation of COS products from cellobiose (25 mM, 50 mM) and α-D-glucose 1-phosphate (10–100 mM) was performed using the CdP from <em>Clostridium cellulosi</em>. A mechanism-based, Michaelis–Menten type mathematical model was developed to describe the kinetics of the iterative enzymatic glycosylation of cellobiose. The mechanistic model was combined with an empirical description of the DP-dependent self-assembly of the COS into insoluble cellulose. The hybrid model thus obtained was used for kinetic parameter determination from time-course fits performed with constraints derived from initial rate data. The fitted hybrid model provided excellent description of the experimental dynamics of the COS in the DP range 3–6 and also accounted for the insoluble product formation. The hybrid model was suitable to disentangle the complex relationship between the process conditions used (i.e., substrate concentration, donor/acceptor ratio, reaction time) and the reaction output obtained (i.e., yield and composition of soluble COS). Model application to a window-of-operation analysis for the synthesis of soluble COS was demonstrated on the example of a COS mixture enriched in DP 4.<br> <strong>Conclusions:</strong> The hybrid model of CdP-catalyzed iterative glycosylation is an important engineering tool to study and optimize the biocatalytic synthesis of soluble COS. The kinetic modeling approach used here can be of a general interest to be applied to other iteratively catalyzed enzymatic reactions of synthetic importance.</p>
CellO Data Sets
<p><strong>Overview</strong></p> <p>A permanent archive of the datasets used in the CellO manuscript (<a href="https://www.biorxiv.org/content/10.1101/634097v2">https://www.biorxiv.org/content/10.1101/634097v2</a>). </p> <p><strong>Expression data</strong></p> <ul> <li>Quantified gene expression for all bulk RNA-seq samples used in this study are available as an HDF5 file (in log transcripts per million): <strong>bulk_log_tpm.h5</strong></li> <li>Each bulk RNA-seq experiment accession is mapped to a set of cell type labels from the Cell Ontology: <strong>bulk_labels.json</strong></li> <li>The single-cell data used in this study are also available as an HDF5 file (in log transcripts per million): <strong>single_cell_log_tpm.h5</strong></li> <li>Each single-cell experiment accession is mapped to a set of cell type labels from the Cell Ontology: <strong>single_cell_labels.json</strong></li> </ul> <p><strong>Dataset partitions</strong></p> <p>We partitioned the bulk RNA-seq data into several subsets that were used for various purposes in the study:</p> <ul> <li>The list of bulk RNA-seq samples used for training the classifier for evaluation on the bulk validation-set (i.e. the pre-taining set): <strong>pre_training_bulk_experiments.json</strong></li> <li>The list of bulk RNA-seq samples in the validation-set:<strong> validation_bulk_experiments.json</strong></li> <li>The list of single-cell experiments in the test set used for evaluting CellO. These are all samples with cell type terms that also appear in the bulk RNA-seq data (i.e. the training data): <strong>test_single_cell_experiments.json</strong></li> </ul> <p><strong>Technical variable annotations</strong></p> <p>We annotated 27,097 RNA-seq samples in the Sequence Read Archive (SRA) with technical variables in order to derive a set of primary, healthy, untreated samples (i.e. the datasets above).</p> <ul> <li>Our annotations were based on a custom label-hierarchy of technical variables: <strong>tags.json</strong></li> <li>The mapping from each SRA experiment accession to its set of technical variable labels: <strong>experiment_tags.json</strong></li> </ul> <p><strong>Trained model coefficients</strong></p> <p>After training the binary classifiers for each cell type, the model coefficients can be used to investigate up and downregulated genes in each cell type. Below, we post the model coefficients for the one-versus-rest trained binary classifiers (used in the Isotonic Regression and True Path Rule algorithms) as well as the coefficients for the classifiers in the Cascaded Logistic Regression algorithm. Each model was trained on the full set of bulk RNA-seq samples used in the study. Each algorithm's cell type model coefficients are available in a tab-separated-value file:</p> <ul> <li>One-versus-rest classifier coefficients: <strong>one_vs_rest_coefficients.tsv.gz</strong></li> <li>Cascaded logistic regression coefficients: <strong>cascaded_logistic_regression_coefficients.tsv.gz</strong></li> </ul>
Bridge force waveforms obtained by systematically bowing various cello G-string models
<p>This dataset provides the waveforms of the bridge forces used in the research article titled “An experimental approach for comparing the influence of cello string type on bowed attack response” (see <em>JASA Express Letter </em>https://doi.org/10.1121/10.0034330 ). The data consists of audio recordings containing the force signals that the string exerts at the bridge (bridge force) during a systematic bowing action. The recordings capture bridge force signals at different bow forces and bow accelerations. Various cello G2-strings (98 Hz) models were used, as the main goal of this study was to investigate the influence of string properties on attack playability (how quickly the string responds to a bowing action). Details on the experimental procedure and methodology can be found in the related work.</p> <p>In general, this dataset offers resources for researchers interested in:</p> <ul> <li>Investigating bow-string interaction phenomena</li> <li>Analysing characteristics of bowed-string transients</li> <li>Developing or validating models of bowed-string instruments</li> </ul> <p><strong>Data Organization:</strong></p> <p>The folders of this repository are organised as follows:</p> <ul> <li><strong>waveforms </strong>This folder contains all the waveforms <ul> <li><strong>Model_X_Y </strong>The folder refers to a tested string<strong>.</strong> The cello string model is named X, from A to D, and the sample is numbered with Y: 1 or 2. For example, <em>Model_A_1 </em>is the folder containing the recording of the string model A and sample number 1. In the related work, only the samples numbered 2 were taken into account for the playability analysis. <br> <ul> <li><strong>yyyy-mm-dd(_r) </strong>Each subfolder refers to a measurement session in which the string was bowed at different bow forces and bow acceleration to populate a Guettler diagram. The day of the measurement session is indicated in the folder name: yyyy is the year, mm the month, and dd the day. Additionally, some measurements were performed reverting the order of measurements, and they are indicated with _r at the end of the name. The numbering used for indicating the Guettler diagrams in the related work, referred to it as “repetition n. 1, 2, …”, follows the chronological order reported in the name of these subfolders. <ul> <li><strong>Fb_nnn_a_mmm.wav </strong>The recording are single channel wav files sampled at 50kHz at a 64 bit rate. The bow force Fb and the bow acceleration a at which the bowing action was performed, is indicated in the file name. For example, a file named <em>Fb_3.76_a_1.96.wav </em>contains the waveform of the bridge force of the string bowed with an average bow force of 3.76 Newton and a constant acceleration of 2.96 m/s^2. </li> </ul> </li> </ul> </li> </ul> </li> </ul> <p><strong>Notes:</strong></p> <ul> <li>To obtain the bridge force in Newton, it is sufficient to multiply the signals by 10.</li> <li>Signals' length vary depending on the bow acceleration. Since the recordings capture the bridge force at a constant length bow stroke, higher accelerations lead to shorter recordings. Short recordings are around 0.22 seconds long, while longs last for about 0.9 seconds. </li> </ul>
Vibrotactile cello performance dataset
<p>Dataset and analysis related to the paper</p> <p>Järveläinen, H., and Larrieux, E. Vibrotactile feedback enhances perceived arousal and listening experience in music. Proc. Sound and Music Computing Conference (SMC) 2023, Stockholm, Sweden. </p> <p>The dataset “Cello.reg.df” contains registered continuous measurements of perceived arousal in solo cello performance under varying types and intensities of vibrotactile feedback. Vibrotactile feedback was provided in the Table or in the Chair. See details in the conference paper.</p> <p>Variables:</p> <ul> <li>time: time stamp (s)</li> <li>Arousal: Arousal rating, numeric [0,1]</li> <li>Amplitude: factor, levels = High, Low, 0</li> <li>Vibration: factor, levels = Signal, Noise, No vibration</li> <li>Location (of vibrotactile feedback) : factor, levels = Table, Chair</li> <li>sID = subject number (1-30)</li> <li>trial = trial number (redundant, 1-10)</li> </ul>
Transverse and torsional vibration on bowed cello G steel string
<p>Transverse and torsional vibrations from a cello G string made of steel, product name "Pirastro Chromcor medium".</p> <p>Transverse signal taken electrodynamically below bow contact point,while string is mounted on a monochord and tuned to 98 Hz.</p> <p>Torsional signal taken electrodynamically at either 10 mm from the bow contact point towards the bridge side, or close string termination (all file names containing *_torsion zum steg_*.mat) or at 10 mm from the bow contact point towards the nut side, or more distant string termination (all file names containing *_torsion zum sattel_*.mat). The number in the filename indicates the distance of the bow contact point from the bridge in cm, while the string has a free length of 68 cm, e.g. the file *_11_*.mat contains data for bowstrokes at 11 cm distance from the bridge. Upstrokes/downstrokes are indicated by *_u.mat and *_d.mat in the file name. Dates of recording indicated in the file name by session_yyyymmdd_*.mat.</p> <p>Manual bowing by experienced player, while each individual stroke covers a wide range for bow velocity and bow pressure, however, always exciting regular Helmholtz motion on the string. Only the file session_*_doppelzahn represents data for a non-Helmholtz case. Statistical analyses cover the wide dynamic range, and structural analyses use smaller windows of comparable levels, the position of which are indicated in the files startpunkte_*.m.</p> <p>File formats are MATLAB files. For all *.mat files: column 1 of table Y is the transverse time signal (0.1 per mm), column 2 the torsional time signal (0.004 per 1°). Sampling frequency is 48 kHz. </p> <p> </p>
Optically measured torsional waves on cello string bowed at varying bow force
<p>Transverse and torsional vibrations of a cello G string made of steel, product name "Pirastro Chromcor medium". Find string properties below. String mounted on a monochord and tuned to approximately 98 Hz.</p> <p>Measurements by a high speed camera at 7000 frames per second, on axis with the bowed string (displacement < 20°).</p> <p>Transverse: optical marker on the string (transverse), 70 mm away from the bridge, 10 mm away from the bow contact point.</p> <p>Torsional: optical marker at each end of two needles (top, and bottom, each 4mm long, total weight including marker 0.002 g), 70 mm away from the bridge, 10 mm away from the bow contact point.</p> <p>Manual bowing by experienced player, bowing at the target contact point 80 mm away from bridge, while each individual stroke represents a certain level of bow pressure. Only regular Helmholtz motion is excited on the string. For each level (high, medium, low) the player adjusted bow velocity reasonably.</p> <p>* Raw data: movies</p> <p>Torsion3.avi : medium bow force<br> Torsion5.avi : low bow force<br> Torsion6.avi : high bow force<br> </p> <p>* Data, 1st level of abstraction: extracted traces using subpixel tracking</p> <p>Tables in *.mqa format hold pixel row and column of extracted optical marker (top,transverse, botteom) for respective bow force (3=medium, 5=low,6=high).</p> <p> </p> <p>* Data, 2nd level of abstraction: angle of torsional displacement as derived from optical markers and geometry, versus bow force</p> <p>torsion_angle_vs_3_force_levels (eps, and MATLAB fig).</p> <p> </p> <p>* String properties</p> <p>mass per unit length g/m 6.15</p> <p>diameter mm 1.19</p> <p>nominal tension N 121</p> <p>transverse wave speed <em>v<sub>tra</sub></em> m/s 133</p> <p>transverse wave impedance <em>Z<sub>0</sub></em> kg/s 0.93</p> <p>torsional fundamental frequency Hz 543</p> <p>torsional wave speed <em>v<sub>tor </sub></em>m/s 738</p> <p> </p>
Short-chain cello-oligosaccharides: Intensification and scale-up of their enzymatic production and selective growth promotion among probiotic bacteria
<p>We provide here the underlying data of the scientific publication "Short-chain cello-oligosaccharides: Intensification and scale-up of their enzymatic production and selective growth promotion among probiotic bacteria". Please find the abstract below:</p> <p>Short-chain cello-oligosaccharides (COS; degree of polymerization, DP≤6) are promising water-soluble dietary fibers. An efficient approach to their bottom-up synthesis is from sucrose and glucose using glycoside phosphorylases. Here, we show intensification and scale up (20 mL; gram scale) of COS production to 93 g/L product and in 82 mol % yield from sucrose (0.5 M). The COS were comprised of DP 3 (33 wt %), DP 4 (34 wt %), DP 5 (24 wt %) and DP 6 (9 wt %) and involved minimum loss (≤10 mol %) to insoluble fractions. After isolation (≥95% purity; ≥90% yield), the COS were examined for growth promotion of probiotic strains. Benchmarked against inulin, trans-galacto-oligosaccharides and cellobiose, COS showed up to 4.1-fold stimulation of cell density for <em>Clostridium butyricum</em>, <em>Lactococcus lactis</em> subsp.<em>lactis</em>, <em>Lactobacillus paracasei</em> subsp. <em>paracasei</em> and <em>Lb. rhamnosus</em> but were less efficient with <em>Bifidobacterium</em> sp. This study shows the COS as selectively functional carbohydrates with prebiotic potential and demonstrates their efficient enzymatic production.</p>
Dataset to "A method for the reproduction of cello bow kinematics using a robotic arm and motion capture"
<p>This dataset is related to the paper "A method for the reproduction of cello bow kinematics using a robotic arm and motion capture".</p> <p>Contents:</p> <p>Recordings comparing a human performance and two robot performances (named case A and case B).</p> <ul> <li>Synchronised motion-capture data (time, bow markers, nut marker and bridge marker; Fs = 240 fps - .csv files)</li> <li>Synchronised audio recordings (sound in front of the instrument; Fs = 44100 Hz - .wav files)</li> <li>Music score</li> <li>Read-me file with further details</li> </ul>
Orofacial Pain in Woodwind and Cello Musicians in German Orchestras
ClinicalTrials.gov study NCT06618898. IPD Sharing: UNDECIDED. Countries: 1. Publications: 1.
The virulome of Streptomyces scabiei in response to cello-oligosaccharides elicitors
GEO Series GSE181490. Streptomyces scabiei. 30 samples. Type: Expression profiling by high throughput sequencing.
MUSERC: Multi-Sensor Cello Recordings for Instantaneous Frequency Estimation
<p>Estimating the fundamental frequency (F0) of a signal is a well studied task in audio signal processing with many applications. If the F0 varies over time, the complexity increases, and it is also more difficult to provide ground truth data for evaluation. In this project we present a dataset of cello recordings addressing the lack of reference annotations for musical instruments. Besides audio data, we include sensor recordings capturing the finger position on the fingerboard which is converted into an instantaneous frequency estimate. This is similar to speech processing, where the electroglottograph (EGG) is able to capture the excitation signal of the vocal tract, which is then used to generate a reference instantaneous F0. Inspired by this approach, we included high speed video camera recordings to extract the excitation signal originating from the moving string. The derived data can be used to analyze vibratos — a very commonly used playing style</p>
CELLO - CLiRpath® Excimer Laser System to Enlarge Lumen Openings
ClinicalTrials.gov study NCT00595959. IPD Sharing: Not stated. Countries: 0. Publications: 0.
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International Brain Laboratory public data
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OpenNeuro
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