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324 results for “TS”
◂Fig. 3 Gynoecium of C. crenata %yellow frames), C. cf. grandicalyx %blue frames) and C. sinensis %pink frames; A, B stack shot images; C–K light microscopy; G polarised light; TS in horizontal orientation). A, B Anthetic female flower, calyx and corolla partly removed. B LS of gynoecium. C LS of functionally female flower %note strongly stained peripheral tissue of corolla, anther and gynoecium). D LS of gynoecium. E, F TS of functionally female flower %note strongly stained, peripheral tissue). G TS of functionally female flower %note crystal deposition). H LS of ovule %note stalked embryo sac). J TS of functionally male flower with non-functional ovules. K LS of functionally male flower %style lacking, original position indicated by an asterisk) %LS, longisection; TS, transverse section; a,anther; bs, basal septum; c, calyx; car, carpel; co, corolla; db, dorsal bundles; es, embryo sac; fs, false septum; lb, lateral bundles; o, ovule; stg, stigma; sty, style; t, trichomes; tt, transmission tissue; ut, peripheral, strongly stained tissue; vb, ventral bundles; vs, ventral slit) in Observations on flower and fruit anatomy in dioecious species of Cordia (Cordiaceae, Boraginales) with evolutionary interpretations
◂Fig. 3 Gynoecium of C. crenata %yellow frames), C. cf. grandicalyx %blue frames) and C. sinensis %pink frames; A, B stack shot images; C–K light microscopy; G polarised light; TS in horizontal orientation). A, B Anthetic female flower, calyx and corolla partly removed. B LS of gynoecium. C LS of functionally female flower %note strongly stained peripheral tissue of corolla, anther and gynoecium). D LS of gynoecium. E, F TS of functionally female flower %note strongly stained, peripheral tissue). G TS of functionally female flower %note crystal deposition). H LS of ovule %note stalked embryo sac). J TS of functionally male flower with non-functional ovules. K LS of functionally male flower %style lacking, original position indicated by an asterisk) %LS, longisection; TS, transverse section; a,anther; bs, basal septum; c, calyx; car, carpel; co, corolla; db, dorsal bundles; es, embryo sac; fs, false septum; lb, lateral bundles; o, ovule; stg, stigma; sty, style; t, trichomes; tt, transmission tissue; ut, peripheral, strongly stained tissue; vb, ventral bundles; vs, ventral slit)
Estudio bibliométrico e investigador de la revista TS Nova
<p>Este depósito corresponde a los datos brutos que dieron origen al estudio bibliométrico en el que se indaga acerca de aspectos de la revista TS Nova (<a href="http://cotsvalencia.com/revista-ts-nova/">http://cotsvalencia.com/revista-ts-nova/</a>) perteneciente al área de Trabajo Social y Servicios Sociales, editada por el Col·legi Oficial de Treball Social de València. Se indaga acerca de aspectos relacionados con la autoría (género, formación y filiación), instituciones (ubicación geográfica, tipo de institución) y el propio documento (coautoría, citas, idioma de publicación). </p> <p>El fichero .xlsx contiene la siguiente información:</p> <p>Hoja 1: Explicación sobre las variables de la base de datos <br> Hoja 2: La base de datos brutos</p>
Рис. 7–15. Lixus pulverulentus, груΔные и брюшные сегменты, хетотаксия. 7, 10, 13 – груΔные сегменты; 8, 11, 14 – брюшной сегмент I; 9, 12, 15 – брюшные сегменты VII–X; 7–9 – виΔ сбоку; 10–12 – виΔ сверху; 13–15 – виΔ снизу. Figs 7–15. Lixus pulverulentus, thoracal and abdominal segments, and chaetotaxy. 7, 10, 13 – thoracal segments; 8, 11, 14 – abdominal segment I; 9, 12, 15 –abdominal segments VII–X; 7–9 – lateral view; 10–12 – dorsal view; 13–15 – ventral view. Setae: dls – dorsolateral, dpls – dorsopleurolateral, ds – dorsal, ls – lateral, lsts – laterosternal, msts – mesosternal, pda – pedal, pds – postdorsal, prns – pronotal, prs – prodorsal, ss – spirarulum, sts – sterna, ts – terminal, vpls – ventropleural. in Description of the preimaginal stages and biology of the weevil Lixus (Dilixellus) pulverulentus (Scopoli, 1763) (Coleoptera: Curculionidae: Lixini)
Рис. 7–15. Lixus pulverulentus, груΔные и брюшные сегменты, хетотаксия. 7, 10, 13 – груΔные сегменты; 8, 11, 14 – брюшной сегмент I; 9, 12, 15 – брюшные сегменты VII–X; 7–9 – виΔ сбоку; 10–12 – виΔ сверху; 13–15 – виΔ снизу. Figs 7–15. Lixus pulverulentus, thoracal and abdominal segments, and chaetotaxy. 7, 10, 13 – thoracal segments; 8, 11, 14 – abdominal segment I; 9, 12, 15 –abdominal segments VII–X; 7–9 – lateral view; 10–12 – dorsal view; 13–15 – ventral view. Setae: dls – dorsolateral, dpls – dorsopleurolateral, ds – dorsal, ls – lateral, lsts – laterosternal, msts – mesosternal, pda – pedal, pds – postdorsal, prns – pronotal, prs – prodorsal, ss – spirarulum, sts – sterna, ts – terminal, vpls – ventropleural.
CESNET-MINER22-TS: Periodic Behavior Features of Cryptomining Communication
<p><strong>CESNET-MINER22-TS: Periodic Behavior Features of Cryptomining Communication</strong></p><p>Datasets were created for the paper: Enhancing DeCrypto: Finding Cryptocurrency Miners Based on Periodic Behavior -- Josef Koumar, Richard Plný, Tomáš Čejka -- which was published at The 19th International Conference on Network and Service Management (CNSM) 2023. Please cite usage of our datasets as:<br> </p><blockquote><p>J. Koumar, R. Plný and T. Čejka, "Enhancing DeCrypto: Finding Cryptocurrency Miners Based on Periodic Behavior," <i>2023 19th International Conference on Network and Service Management (CNSM)</i>, Niagara Falls, ON, Canada, 2023, pp. 1-7, doi: 10.23919/CNSM59352.2023.10327904.</p></blockquote><p> </p><p>The files <i>cesnet_miner22_design_with_FTS_proba.zip</i> and <i>cesnet_miner22_evaluation_with_FTS_proba.zip</i> contain one .csv file with IP flows. The IP flows were taken from the CESNET-MINER22 dataset [1], which was created by monitoring national research and educational network CESNET2. Furthermore, we add two features ID_DEPENDENCY (string) and PERIODICITY_PROBA (double). ID_DEPENDENCY is an ID of a network dependency (see the article [2]) and the PERIODICITY_PROBA is the predicted probability by FTS analysis. The files from periodicity_features.zip contain periodic behavior features for Machine Learning. The files names are in format <i>"{evaluation/design}.periodicity_features.{TIME_INTERVAL}.{SIG_SPACE}.{PER_LEVEL}.csv"</i> and have the following format of columns:</p><ul><li><strong>id_dependency</strong> -- Identification of a network dependency observed as a Flow time series (FTS).</li><li><strong>label</strong> -- The labels ("Miner" or "Other") of periodic FTS.</li><li><strong>packet_value</strong> -- Value of Clear periodic behavior of the metric packet.</li><li><strong>packet_value_x</strong> -- Value of the interval's lower value of Sinusoidal periodic behavior of the metric packets.</li><li><strong>packet_value_y</strong> -- Value of the interval's upper value of Sinusoidal periodic behavior of the metric packets.</li><li><strong>packet_mean</strong> -- Mean value of the metric packet.</li><li><strong>packet_std</strong> -- Standard deviation value of the metric packet.</li><li><strong>packet_skewness</strong> -- Skewness value of the metric packet.</li><li><strong>packet_kurtosis</strong> -- Kurtosis value of the metric packet.</li><li><strong>bytes_value</strong> -- Value of Clear periodic behavior of the metric bytes.</li><li><strong>bytes_value_x</strong> -- Value of the interval's lower value of Sinusoidal periodic behavior of the metric bytes.</li><li><strong>bytes_value_y</strong> -- Value of the interval's upper value of Sinusoidal periodic behavior of the metric bytes.</li><li><strong>bytes_mean</strong> -- Mean value of the metric bytes.</li><li><strong>bytes_std</strong> -- Standard deviation value of the metric bytes.</li><li><strong>bytes_skewness</strong> -- Skewness value of the metric bytes.</li><li><strong>bytes_kurtosis</strong> -- Kurtosis value of the metric bytes.</li><li><strong>duration_value</strong> -- Value of Clear periodic behavior of the metric duration.</li><li><strong>duration_value_x</strong> -- Value of the interval's lower value of Sinusoidal periodic behavior of the metric duration.</li><li><strong>duration_value_y</strong> -- Value of the interval's upper value of Sinusoidal periodic behavior of the metric duration.</li><li><strong>duration_mean</strong> -- Mean value of the metric duration.</li><li><strong>duration_std</strong> -- Standard deviation value of the metric duration.</li><li><strong>duration_skewness</strong> -- Skewness value of the metric duration.</li><li><strong>duration_kurtosis</strong> -- Kurtosis value of the metric duration.</li><li><strong>difftimes_value</strong> -- Value of Clear periodic behavior of the metric difftimes.</li><li><strong>difftimes_value_x</strong> -- Value of the interval's lower value of Sinusoidal periodic behavior of the metric difftimes.</li><li><strong>difftimes_value_y</strong> -- Value of the interval's upper value of Sinusoidal periodic behavior of the metric difftimes.</li><li><strong>difftimes_mean</strong> -- Mean value of the metric difftimes.</li><li><strong>difftimes_std</strong> -- Standard deviation value of the metric difftimes.</li><li><strong>difftimes_skewness</strong> -- Skewness value of the metric difftimes.</li><li><strong>difftimes_kurtosis</strong> -- Kurtosis value of the metric difftimes.</li><li><strong>max_power</strong> -- Represent the maximum power of the LS periodogram.</li><li><strong>max_frequency</strong> -- Describe the frequency of the maximum power of the LS periodogram.</li><li><strong>min_power</strong> -- Represent the minimum power of the LS periodogram.</li><li><strong>min_frequency</strong> -- Describe the frequency of the minimum power of the LS periodogram.</li><li><strong>spectral_energy</strong> -- Represents the total energy present at all frequencies in LS periodogram.</li><li><strong>spectral_entropy</strong> -- The degree of randomness or disorder in the LS periodogram.</li><li><strong>spectral_kurtosis</strong> -- Indicates a nonstationary or non-Gaussian behavior in the power spectrum.</li><li><strong>spectral_skewness</strong> -- The measure of peakedness or flatness of power spectrum.</li><li><strong>spectral_rolloff</strong> -- It is defined as frequency below 85% of the distribution power.</li><li><strong>spectral_cetroid</strong> -- Indicates at which frequency the energy of a spectrum is centered upon.</li><li><strong>spectral_spread</strong> -- It is the difference between the highest and lowest frequency in the power spectrum.</li><li><strong>spectral_slope</strong> -- The slope of the power spectrum trend in a given frequency range.</li><li><strong>spectral_crest</strong> -- Refers to the rate of shift of the sign of a wave, which is the rate of change from negative to positive or the reverse.</li><li><strong>spectral_flux</strong> -- The rate of change of periodogram power with increasing frequency.</li><li><strong>spectral_bandwidth</strong> -- Describes the difference between upper and lower frequencies at which spectral energy is half its maximum value.</li></ul><p> </p><p>The files from <i>time_series.zip</i> contain FTS of used time interval. The file names are in format <i>"{evaluation/design}.time_series.{TIME_INTERVAL}.csv"</i> and have the following format of columns:</p><ul><li><strong>ID_DEPENDENCY</strong> -- Identification of a network dependency observed as a FTS.</li><li><strong>N_FLOWS</strong> -- Number of flows in time series, i.e., number of data points.</li><li><strong>N_PACKETS</strong> -- Number of packets in time series, i.e., the sum of metric PACKETS.</li><li><strong>N_BYTES</strong> -- Number of bytes in time series, i.e., the sum of metric PACKETS.</li><li><strong>PACKETS</strong> -- The array containing the time series metric number of packets in the IP flow.</li><li><strong>BYTES</strong> -- The array containing the time series metric number of bytes in the IP flow.</li><li><strong>START_TIMES</strong> -- The array containing the time series time axis of the flows starts.</li><li><strong>END_TIMES</strong> -- The array containing the time series time axis of the flows ends.</li><li><strong>LABELS</strong> -- The array of labels ("Miner" of "Other") of each datapoint.</li></ul><p> </p><p>[1] Richard Plný et al. CESNET-MINER22: Datasets of Cryptomining Communication. Zenodo, October 2022.</p><p>[2] Koumar, Josef, and Tomáš Čejka. "Network traffic classification based on periodic behavior detection." <i>2022 18th International Conference on Network and Service Management (CNSM)</i>. IEEE, 2022.</p>
Phase 3 Study of Pexidartinib for Pigmented Villonodular Synovitis (PVNS) or Giant Cell Tumor of the Tendon Sheath (GCT-TS)
ClinicalTrials.gov study NCT02371369. IPD Sharing: YES. Countries: 12. Publications: 5.
A Study to Test if TEV-50717 is Effective in Relieving Tics Associated With Tourette Syndrome (TS)
ClinicalTrials.gov study NCT03571256. IPD Sharing: YES. Countries: 10. Publications: 1.
Quarry (49a) San Dorligo della Valle (TS)
The frontal area of the quarry is perpendicular to the tectonic transport direction and for this reason, the bedding attitude is not clear. On the contrary, strata and deformation structures associated with the thrusting are observable on the northern side of the quarry. Source: Objaverse 1.0 / Sketchfab
VISIT-TS video (release 20160516)
<p><strong>VISIT-TS</strong> (<strong>V</strong>ideo-<strong>I</strong>ntegrated <strong>S</strong>creening <strong>I</strong>nstrument for <strong>T</strong>ics and <strong>T</strong>ourette <strong>S</strong>yndrome) is a multimedia tool intended to demonstrate tics to a lay audience, to discuss their defining and common attributes, and to address features that differentiate tics from other movements and vocalizations. The video includes a teaching section (5 minutes) followed by questions (5 minutes). The original intended use of VISIT-TS is for epidemiological research.</p> <ul> <li>Vachon MJ, Striley CW, Gordon MR, Schroeder ML, Bihun EC, Koller JM, Black KJ: VISIT-TS: A multimedia tool for population studies on tic disorders [<em>version 2; referees: 3 approved</em>]. F1000Res 2016; 5:1518. DOI: 10.12688/f1000research.7196.2. </li> </ul>
Dataset for Article "On the experimental properties of the TS defect in 4H-SiC"
<p>This dataset contains raw data as well as evaluation scripts to the manuscript "On the experimental properties of the TS defect in 4H-SiC".</p>
TS and tracer data for Yap−Mariana Junction
<p>A multi-tracer study at the Yap-Mariana Junction in the western Pacific was conducted using 85Kr, 39Ar and 14C. This dataset includes the T-S data and the tracer data. This dataset is the supporting material of a paper submitted to JGR Ocean entitled "Estimation of the ventilation transit time distribution at the Yap−Mariana Junction using 39Ar, 85Kr and 14C tracers".</p>
TS Albufereta
TS del fondeadero de La Albufereta (Alicante) depositado en el MARQ Source: Objaverse 1.0 / Sketchfab
TS_INGV_ITA_ERS_1993_2002_DEW
<p>This file contains the shapefile representing the Line-of-Sight InSAR deformation rate and time series of Campi Flegrei and Vesuvius area. Each point of the shapefile represents an InSAR point scatterers with the following attributes: Range coordinate, Azimuth coordinate, Longitude, Latitude, Deformation Rate, Deformation Rate Error, Displacement value for any date.</p>
GH23-TS
<p>Increasing Context-Awareness in Language Models for Code Through Embedding Contextual Information in Inputs: GitHub’s top-1000 most starred and top-1000 most-forked TypeScript repositories</p>
Lichens as bioindicators of monitoring of the selective air pollution, Zabrze (Poland) - total carbon (TC) and total sulfur (TS) results.
<p>Total carbon (TC) and total sulfur (TS) contents were measured using an Eltra CS-500 IR-analyzer with a TIC module. TC was determined using an infrared cell detector on CO2 gas, which was evolved by combustion under an oxygen atmosphere. Calibration was made by means of the Eltra standards 2.27 % S and 45.14 % C. <br> Dr Ewa Szram, employed at the Institute of Earth Sciences, Faculty of Natural Sciences, Silesian University in Katowice, carried out the project. This research was funded by the National Science Centre, Poland MINIATURA-6 2022/06/X/ST10/00338 “Lichens as bioindicators of monitoring of the selective air pollution”</p>
Predictive Markers in Growth Hormone Deficiency (GHD) and Turner Syndrome (TS) Children Treated With SAIZEN®
ClinicalTrials.gov study NCT00256126. IPD Sharing: Not stated. Countries: 13. Publications: 4.
A Study to Evaluate the Safety and Efficacy of TS-121 as an Adjunctive Treatment for Major Depressive Disorder
ClinicalTrials.gov study NCT03093025. IPD Sharing: Not stated. Countries: 1. Publications: 1.
Alternatives for Reducing Tics in Tourette Syndrome (TS): A Study of TEV-50717 (Deutetrabenazine) for the Treatment of Tourette Syndrome in Children and Adolescents
ClinicalTrials.gov study NCT03452943. IPD Sharing: Not stated. Countries: 6. Publications: 1.
IVIg for Small Fiber Neuropathy With Autoantibodies TS-HDS and FGFR3
ClinicalTrials.gov study NCT03401073. IPD Sharing: NO. Countries: 1. Publications: 15.
Safety and Efficacy of Pembrolizumab (MK-3475) in Combination With TS-1+Cisplatin or TS-1+Oxaliplatin as First Line Chemotherapy in Gastric Cancer (MK-3475-659/KEYNOTE-659)
ClinicalTrials.gov study NCT03382600. IPD Sharing: YES. Countries: 1. Publications: 1.
input and TS ensemble for "Converging experimental and computational views of the knotting mechanism of the smallest knotted protein"
<p>PLUMED input and TS ensemble for "Converging experimental and computational views of the knotting mechanism of the smallest knotted protein"</p>
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OpenNeuro
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