ライフサイエンスコーパス: data processing

1 alyzer, (ii) analysis of specimens and (iii) data processing.

2 duce a mass spectrum after Fourier transform data processing.

3 nanoscale imaging using TERS; and (iv) TERS data processing.

4 king it an easy-to-use application for quick data processing.

5 proaches which require harmonic analysis for data processing.

6 fLM and cryo-ET images, which is followed by data processing.

7 ess of the indexing step and the progress of data processing.

8 ce protocol, circumventing the need for bulk data processing.

9 the basis of spike-in standards and uniform data processing.

10 ficantly improving the efficiency of cryo-EM data processing.

11 ity with probe control, data acquisition and data processing.

12 hallenging because of uncertainties in GRACE data processing.

13 instrumentation, acquisition parameters and data processing.

14 of storage and the efficiency of the initial data processing.

15 ensitivity, and standardized and transparent data processing.

16 spatio-temporal convolutions and multi-coil data processing.

17 data collection to automated recognition and data processing.

18 address a fraction of the steps required for data processing.

19 several key advantages to traditional LC-MS data processing.

20 sting infrastructure for data management and data processing.

21 ished untargeted metabolomic method for XCMS data processing.

22 ion limit should become routine in FT-ICR-MS data processing.

23 out expensive instrumentation or complicated data processing.

24 use of IR spectra combined with multivariate data processing.

25 and selection, dimensionality reduction, and data processing.

26 radiologist who was masked to the method of data processing.

27 t be subsequently corrected (aligned) during data processing.

28 identification due to excellences in massive data processing.

29 ied chromatography, and high throughput (HT) data processing.

30 portant issue to be clarified for future THz data processing.

31 ontarget liquid chromatography (LC)-IMS-HRMS data processing.

32 o the point-of-care by circumventing complex data processing.

33 xation to library construction and 2-3 d for data processing.

34 scopy experiments, spectral acquisition, and data processing.

35 one of the major bottlenecks in metabolomics data processing.

36 rings on considerable challenges in terms of data processing; accurately extracting meaningful signal

37 The increased throughput in data processing achieved via automation afforded the pos

38 ra with no crosstalk using a postacquisition data processing algorithm.

39 A data-processing algorithm to retrieve the NP number conc

40 per-resolution microscopies, single-molecule data processing algorithms can fail to provide accurate

41 Countless proteomics data processing algorithms have been proposed, yet few h

42 ts in Raman spectroscopy instrumentation and data processing algorithms have led to the emergence of

43 Interpretation of raw MS depends upon data processing algorithms that render it human-interpre

44 et was interpreted using common multivariate data processing algorithms to achieve quantitative ident

45 Key aspects to improve are data processing algorithms to deal with the vast amount

46 ssing modules, such as MS storage systems or data processing algorithms.

47 e extensive evaluation and improvement of MS data processing algorithms.

48 ns, followed by feature identification using data-processing algorithms, can be used for rapid exposu

49 -particle electron cryo-microscopy (cryo-EM) data processing allowing for the rapid determination of

50 Such data processing also opens the way toward extension to m

51 make several other dubious claims regarding data processing, analysis, and interpretation.

52 Finally, data processing and analysis are described using two dif

53 ing functions that improve the efficiency of data processing and analysis by 43% compared with curren

54 ucher and Querec data sets but with the same data processing and analysis pipeline.

55 uid chromatography/mass spectrometry (LC/MS) data processing and analysis platform, MET-COFEA (METabo

56 Data processing and analysis requires a further 2 weeks,

57 exible workflow platform that can accelerate data processing and analysis so more time can be spent o

58 odeling identified many variables related to data processing and analysis such as normalization metho

59 WGS data processing and analysis was done by staff masked to

60 there are significant challenges in terms of data processing and analysis, since neuronal signals hav

61 ularly useful for next-generation sequencing data processing and analysis.

62 maticians/computer scientists/physicists for data processing and analysis.

63 to the computational complexity involved in data processing and analysis.

64 tical segmentation for advanced neuroimaging data processing and analysis.

65 nstrate how calls to existing R packages for data processing and calls to PathVisioRPC can directly w

66 c displays, biomedical imaging and ultrafast data processing and communication, might be overcome by

67 of medicine with a heavy reliance on digital data processing and computer software.

68 rized by a very simple architecture in which data processing and concentration retrieval are straight

69 in-house developed computationally-efficient data processing and data analysis software.

70 JS-MS enables custom MS data processing and evaluation by providing fast, 3-D vi

71 lexible scientific environment, facilitating data processing and general accessibility independent of

72 s have challenging error profiles, hindering data processing and incorporation into downstream analys

73 otential for applications such as high-speed data processing and long-wavelength energy harvesting.

74 ition was designed to allow for simultaneous data processing and metabolite characterization.

75 in comprehensive pipeline for RNA sequencing data processing and multi-layer statistical modules to e

76 nst XCMS Online, the widely used cloud-based data processing and pathway analysis platform.

77 and promoter dynamics, integrated with CAGE data processing and promoterome mining into a first comp

78 data acquisition, with an additional day for data processing and quantification.

79 advance to facilitate untargeted metabolomic data processing and quantitative analysis and their gene

80 ssing natural media as platforms for optical data processing and quantum information applications.

81 terface has been developed for rapid on-site data processing and result display.

82 A workflow including XCMS Online for data processing and robust confirmatory statistics was u

83 tum computation, or the spin-operation-based data processing and sensing.

84 ate with real-time data transmission, mobile data processing and smart power utilization.

85 now imaging experiments have rapidly growing data processing and storage requirements.

86 d by a master clock that synchronizes system data processing and transfer.

87 lectronics are extremely efficient in sensor data processing and transmission.

88 Technical considerations of data processing and use of the ToxCast database are pres

89 Data processing and visualization methods have an import

90 ls is a Python package for mass spectrometry data processing and visualization.

91 it chips offers a viable route for in-sensor data processing and wireless transmission in many medica

92 inting assay, (4) analysis with HR-MSMS, (5) data processing, and (6) selection of biomarkers.

93 , but requires technical training, extensive data processing, and expensive operational and capital c

94 n emphasis on information theory, sequential data processing, and optimality arguments.

95 pplications, such as microscopy, all-optical data processing, and quantum information.

96 es an iterative process between experiments, data processing, and theoretical analysis.

97 (QC), molecular identification using MS/MS, data processing, and visualization with 3D models of the

98 xternal systems for control, power delivery, data processing, and/or communication.

99 nd data sources, including global food trade data, processing, and packaging models.

100 xity exceeds the capabilities of traditional data processing applications.

101 harvesting, detection, sensing and photonic data processing applications.

102 les of 3-dimensional imaging acquisition and data processing, applications of tissue clearing on stud

103 SuperQuant is a quantitative proteomics data processing approach that uses complementary fragmen

104 ecaose (bracketing the peaks of interest), a data processing approach was designed and developed to s

105 To remedy this problem, we propose a novel data processing approach.

106 ometer to planktic foraminifera with a novel data-processing approach.

107 ric data were explored with novel untargeted data processing approaches (enviMass, nontarget, and RMa

108 xplicitly considered, as were the effects of data processing approaches that may limit the impact of

109 ples, integrated computational workflows for data processing are needed.

110 work we want to encourage more awareness on data processing as a crucial step in the workflow of non

111 pplication for measurement configuration and data processing as well as wireless results sharing.

112 LC/MS provides linearity in response, data processing automation, improved limits of detection

113 lags were determined and corrected using VBA data processing based on the synchronization of the isot

114 ansfer times still represent a bottleneck in data processing because of the increasingly complex data

115 ing at kHz rates is combined, with real-time data processing being accelerated by a graphics-processi

116 Also, few studies have discussed data processing beyond applying different modeling algor

117 ing of pre-symptomatic SOD1 mouse models and data processing by a correlation-based algorithm reveale

118 properties, experimental design choices and data processing-by developing new metrics that quantify

119 /MS assays take from 4 h to several days and data processing can be done in 1-7 d.

120 urve is enhanced and the total time used for data processing can be reduced.

121 developments in fast electron detectors and data processing capability is shown to enable electron p

122 To support big data processing capability, a novel parallel match scori

123 MRMkit offers highly consistent and scalable data processing capacity for targeted metabolomics, subs

124 The development of metamaterials, data processing circuits and sensors for the visible and

125 tion carriers for highly integrated photonic data processing circuits.

126 ral layouts, assembly methods, and power and data processing configurations, are outlined and discuss

127 After acquisition, data processing consists of a sequence of steps includin

128 Researcher subjective decision-making on data processing could produce inter- or intra-researcher

129 ges related to data-driven approaches (e.g., data processing, data availability, data quality, data c

130 ential technology for data storage and other data processing devices.

131 e method allow automated, easy and efficient data processing, enabling the reconstruction of 2D distr

132 In this Article, a data processing error affected Fig.

133 cluding sample preparation, instrumentation, data processing, etc., is critical for comparison of unt

134 Although different practices of data processing exist, in this case they do not substant

135 demonstrate that this method is reliable for data processing, five (13)C2-/(12)C2-dansyl labeled meta

136 tomatically combined targeted and untargeted data processing, followed by verification and quantitati

137 Data processing for 1D NMR spectra is a key bottleneck f

138 onditions, and the application of a tailored data processing for handling of plasma effects and high

139 infrared (MIR) spectroscopy and multivariate data processing for prediction of alcohol degree, sugars

140 t ATLAS, a software package for customizable data processing from raw sequence reads to functional an

141 e CRP level in test samples was generated by data processing from the intensities of three lines.

142 NaviSE allows different entry levels of data processing, from sra-fastq files to bed files; and

143 re-processing by conferring the neuromorphic data processing function on a curved image sensor array.

144 Notably, the data processing functionality is highly optimized for co

145 use command line tool with multiple built-in data processing functions, allowing non-expert users to

146 balance between algorithm innovation and big data processing has been more serious and urgent.

147 ome screen where the experimental design and data processing have been modified to quantitatively ide

148 ces in microscopy, genetics, physiology, and data processing have expanded the scope and accelerated

149 To increase the fidelity and speed of data processing, herein we establish, optimize, and eval

150 h of physics which promises to revolutionize data processing, improve photovoltaics, and increase sen

151 However, data processing in accordance with best practices remain

152 mple pretreatment and improved detection and data processing, including chemometric tools.

153 elf-consistency condition closely related to Data Processing Inequality.

154 me automated operation for data acquisition, data processing, intelligent decision making, and actuat

155 Data processing is a key bottleneck for 1H NMR-based met

156 d with optimized univariate and multivariate data processing is a sufficient tool to distinguish betw

157 The multistep data processing is complicated, operator error-prone, an

158 However, GCxGC/TOF-MS data processing is currently limited to vendor software

159 he noise of the simulated taylorgrams on the data processing is discussed.

160 All data processing is done locally by the user's CPU to ens

161 is of different groups of samples, automated data processing is required.

162 ining extremely rich structural information, data processing is very labor intensive and the results

163 Recent progress in sensor technology and data processing is very promising; nevertheless, technic

164 In order to overcome these data processing issues, we introduce MetaCRAM, the first

165 genomic experiments represent a challenge in data processing, management, and analysis.

166 ial to know the performance and bias of each data processing method as they impact accuracy, coverage

167 Herein, we report a data processing method based on the use of a mass spectr

168 act ion-selective electrodes (SC-ISEs) and a data processing method for sensor calibration and drift

169 gularized Linear Inversion approach as a new data processing method to extract the probability densit

170 Data processing methods and denoising algorithms have be

171 tative LC-tandem MS (LC-MS/MS) analysis, and data processing methods are provided.

172 Moreover, results showed some data processing methods can skew sequence-based biodiver

173 Various data processing methods have been developed for explorin

174 n must be based on unbiased, high throughput data processing methods to identify relevant biological

175 ial sources of error due to instrumental and data processing methods were evaluated.

176 preparations, chromatography conditions, and data processing methods were kept identical.

177 We demonstrate that ULM microbubble data processing methods, applied to images acquired with

178 authentication was evaluated using different data processing methods, leave-10%-out cross-validation,

179 rely limited, and its selection is biased by data processing methods.

180 action images, in part due to limitations of data processing methods.

181 h for MSI data analysis, combining automated data processing, modeling and display, is user-friendly

182 encies and are not well suited for custom MS data processing modules, such as MS storage systems or d

183 ems for conditions that data-transmitting or data-processing occurs with a non-negative entropy gain.

184 tress has been elusive due to limitations in data processing of current techniques.

185 toolset that assists in quality control and data processing of high-throughput RNA sequencing data.

186 The PV-OCT images were generated by software data processing of the entire cross-sectional image from

187 ) analysis by initiating data conversion and data processing on subsets of data acquired, expanding i

188 ire experimental workflows (from sampling to data processing) or different analytical platforms in th

189 out without any assistance from instruments, data processing, or graphic plotting.

190 ial concerns with regard to study design and data processing, particularly to avoid population strati

191 ical researchers, we provide FREYA, a robust data processing pipeline and statistical analyses framew

192 AN and can be incorporated in a metabolomics data processing pipeline facilitating large screening as

193 improve automation efficiency in the cryo-EM data processing pipeline for high-resolution structural

194 ave been added to RefSeq prokaryotic genomes data processing pipeline including the calculation of ge

195 d workflow that covers the entire tomography data processing pipeline, from automated tilt series ali

196 ed using an internet-accessible custom-built data processing pipeline.

197 menting a sophisticated data acquisition and data processing pipeline.

198 G's microbial genome and metagenome sequence data processing pipelines and are integrated into the da

199 ions from diverse environments; however, the data processing pipelines that rely on mapping reads to

200 s developed a suite of tools, interfaces and data processing pipelines that transforms NCBI Gene Expr

201 the creation and execution of metabarcoding data processing pipelines through an intuitive Graphic U

202 ce for easy integration into high-throughput data processing pipelines.

203 ques, mass spectrometry instrumentation, and data processing platforms continue to spur growth in the

204 An advanced data processing protocol was established to overcome flu

205 nt machine learning methods and/or different data processing protocols.

206 s in sequencing, genome editing and advanced data processing provide an excellent opportunity for res

207 l be carried out as part of a single unified data-processing/quality control run, greatly reducing bo

208 de the capabilities for remote monitoring of data processing, real time notifications for the data pr

209 g mechanisms, sensor fabrication, power, and data processing requirements.

210 zzle-skimmer dissociation (NSD), and aligned data processing resources to rapidly characterize abunda

211 ucture determination during several steps of data processing, resulting in impediments such as missin

212 t shallow depth (<5 km) constrained by InSAR data processing results from early post-seismic deformat

213 With advanced microfabrication and data processing, SBCR will become more compact, lighter,

214 s and stream recently acquired data files to data processing servers, mimicking just-in-time producti

215 ences between the three sampling approaches, data processing showed that the three methods provide th

216 line elimination of the dissolved metal made data processing simpler and more accurate.

217 , recommended procedural guidelines, and new data processing software (LIMS for Lasers) that altogeth

218 ion protocols but different instrumentation, data processing software, and database.

219 ic tools, providing a window for integrating data-processing software with larger informatics-based d

220 ework, it is compatible with any metabolomic data-processing software.

221 Previous containerized data processing solutions were limited to single user en

222 ing performed manually, Rhapso increased the data processing speed and avoided potential human errors

223 ug/L standard mix was tracked throughout the data processing stages, where 406 targets were successfu

224 Data processing, statistical analysis and metabolite ann

225 he critical difficulties that appear at each data processing step and that can dramatically affect th

226 tional scanning (DMS) with a straightforward data processing step to bridge reads in distal sites wit

227 data as input and carries out a sequence of data processing steps including construction of extracte

228 an open source workflow which completes all data processing steps including feature finding, annotat

229 arrier of entry for biologists by automating data processing steps needed for knowledge extraction fr

230 GiniQC can help inform the impact of various data processing steps on data quality.

231 f open scripts and online tools for specific data processing steps such as noise removal or molecular

232 al details for the volumetric histocytometry data processing steps.

233 ferent instruments, as well as the effect of data processing steps.

234 Data processing strategies inspired from metabolomics an

235 The developed data processing strategies may be transferred to other r

236 and have enabled increasingly sophisticated data processing strategies, indicating a bright future f

237 The data processing strategy consisted of three different st

238 A combined data acquisition and data processing strategy for improving the sensitivity a

239 In this study, we introduce a novel LC-HRMS data processing strategy for the reliable classification

240 The data-processing strategy converted these images with a s

241 Its data processing structure enables rapid image display an

242 We implemented a data processing system based on classical post-refinemen

243 oftware implements common mass spectrometric data processing tasks through a well-defined application

244 Using Google Earth Engine to expedite data processing tasks, we show that there has been a wea

245 made workflows for common mass spectrometric data processing tasks, which enable users to perform com

246 command line interface for high-performance data processing tasks.

247 h automatically parallelizes and distributes data processing tasks.

248 ate, there has been little attention to this data-processing technique in metabolomics.

249 g a plasmonic sensor array incorporating the data-processing technique termed "algorithmic spectromet

250 ary asteroid survey instruments and improved data processing techniques are likely to result in the d

251 inherent differences in methodologies used, data processing techniques, and ascertainment bias.

252 ated and necessitates exploiting chemometric data processing techniques.

253 Data processing templates can be generated and saved for

254 ted by low sample throughput and complicated data processing that contribute to false discoveries.

255 Here, we introduce a novel framework for EMG data processing that implements spectral analysis by con

256 are tool for magnetic resonance spectroscopy data processing that is widely used in the magnetic reso

257 -based mass spectrometers, for postdetection data processing that significantly improves mass accurac

258 Without additional data processing, the overall median absolute relative di

259 Following data processing, the signals are converted into concentr

260 for an approximate 100-fold reduction in the data processing time compared to manual processing while

261 the need for a time zero spectrum as well as data processing to account for natural abundance heavy i

262 gical conclusions requires multiple steps of data processing to enrich significantly altered features

263 tivity, and it also significantly simplifies data processing to extract faradaic currents in square-w

264 aggregation on gene expression, and optimise data processing to improve clustering quality.

265 nly (before HDX or "time zero") spectrum and data processing to remove its contribution.

266 the precursor selection gives access, after data processing, to the same structural information cont

267 routine workflow from the data acquisition, data processing, to tomographic reconstruction.

268 proach consisting in PARAFAC as second-order data processing tool and piecewise direct standardizatio

269 An automated data processing tool, FlavonQ, was developed that can tr

270 pite the ubiquity of mass spectrometry (MS), data processing tools can be surprisingly limited.

271 d or fourth decimal place; however, existing data processing tools do not capitalize on this informat

272 or this is a lack of suitable and accessible data processing tools for the analysis of large arrayed

273 opment of richer mass spectral libraries and data processing tools have enabled large scale metabolic

274 Many popular data processing tools, including XCMS-online and MZmine2

275 olution mass spectrometry and using advanced data processing tools, we demonstrate much extended cove

276 tatistical variation created by selection of data processing tools.

277 replaces the functionality of numerous other data-processing tools, and can quickly and efficiently g

278 The data processing using TXRF-XANES spectra of U(IV), U(V),

279 open up the exciting possibility of digital data processing utilizing antiferromagnetic spin waves a

280 processing, real time notifications for the data processing, visualization and interactive analysis

281 A Visual Basic for Applications (VBA) data processing was developed to count and sort the part

282 The data processing was tested in terms of the calculated GU

283 field of metabolomics and heavy workload of data processing, we designed the first remote metabolomi

284 To facilitate data processing, we provide an open-source platform for

285 even months, before library construction and data processing, which takes ~4 d; the bulk RNA protocol

286 intains efficient compression and downstream data processing, while allowing for unprecedented levels

287 ed on the observation of experts undertaking data processing with a suite of software packages.

288 Untargeted data processing with DIA-Umpire provided a means of iden

289 In this study the consistency of data processing with different software tools was invest

290 ARGI sequencing library and 1 d to carry out data processing with iMARGI-Docker.

291 hich includes sequencing turnaround time and data processing with MPRAflow.

292 prepare samples, and to control hardware and data processing with our software.

293 The method enables data processing with the crystallographic software tool

294 lean-up, GCxGC-ToFMS detection and automated data processing with the non-proprietary free downloadab

295 nd developed and optimized a postacquisition data processing workflow to screen for 162 target microp

296 Several automated data processing workflows have been developed to handle

297 Most data processing workflows include similar steps, but und

298 r or independently to build applications and data processing workflows relevant to drug discovery and

299 e for integrating modeling with experimental data processing workflows, facilitated by a comprehensiv

300 ingly accurate and efficient high-throughput data processing workflows.