Multiple Data Acquisition Research Articles

Recommendations for motion correction of infant fNIRS data applicable to multiple data sets and acquisition systems

Despite motion artifacts are a major source of noise in fNIRS infant data, how to approach motion correction in this population has only recently started to be investigated. Homer2 offers a wide range of motion correction methods and previous work on simulated and adult data suggested the use of Spline interpolation and Wavelet filtering as optimal methods for the recovery of trials affected by motion. However, motion artifacts in infant data differ from those in adults’ both in amplitude and frequency of occurrence. Therefore, artifact correction recommendations derived from adult data might not be optimal for infant data. We hypothesized that the combined use of Spline and Wavelet would outperform their individual use on data with complex profiles of motion artifacts. To demonstrate this, we first compared, on infant semi-simulated data, the performance of several motion correction techniques on their own and of the novel combined approach; then, we investigated the performance of Spline and Wavelet alone and in combination on real cognitive data from three datasets collected with infants of different ages (5, 7 and 10 months), with different tasks (auditory, visual and tactile) and with different NIRS systems. To quantitatively estimate and compare the efficacy of these techniques, we adopted four metrics: hemodynamic response recovery error, within-subject standard deviation, between-subjects standard deviation and number of trials that survived each correction method. Our results demonstrated that (i) it is always better correcting for motion artifacts than rejecting the corrupted trials; (ii) Wavelet filtering on its own and in combination with Spline interpolation seems to be the most effective approach in reducing the between- and the within-subject standard deviations. Importantly, the combination of Spline and Wavelet was the approach providing the best performance in semi-simulation both at low and high levels of noise, also recovering most of the trials affected by motion artifacts across all datasets, a crucial result when working with infant data.

Estimation of rice yield from a C-band radar remote sensing image by integrating a physical scattering model and an optimization algorithm

Accurate estimation of rice yield with remotely sensed data plays a role in ensuring food security at local or national scales. In this study, an estimation scheme integrating a rice canopy scattering model (RCSM) and a genetic algorithm optimization tool (GAOT) was proposed on a basis of radar remote sensing technology. A C-band Radarsat-2 synthetic aperture radar (SAR) image acquired at rice maturity stage in Northeast, China was tested to simulate three yield-related rice parameters via an integrated RCSM-GAOT scheme, with a parallel computing environment. Rice yield was then estimated via a regression analysis by linking the simulated parameters to rice ear weights. Results showed that three parameters, ear length, ear diameter and ear density, can be simulated by the RCSM-GAOT scheme with a simulation error of 1.2 cm, 0.11 cm and 29 ears/m2, respectively. Rice yield is estimated with an average error of 0.28 kg/m2 and 0.22 kg/m2 for fresh ear weight and dry ear weight, respectively. In comparison with the need of multiple SAR data acquisitions in past studies, this study demonstrates the capability of one time Radarsat-2 quad-polarized SAR image in regional rice yield estimation. At the same time, it also indicates that the proposed RCSM-GAOT scheme has a potential in future precision agriculture applications.

Stabilizing Antibody Cocktails for Mass Cytometry.

Mass cytometry is increasingly employed in larger immune profiling studies involving data acquisitions across several days and multiple sites. For gaining a maximum of information from respective data by computational analyses, several techniques have been developed to minimize noise in mass cytometric data sets, such as sample banking, standardized instrument setup, sample barcoding, and signal normalization. However, the repeated preparation of cocktails composed of isotope-tagged antibodies remained a significant source of error. We here show that premixed antibody cocktails fail to deliver expected staining patterns when stored at 4°C for 4 weeks. As a solution, we developed and tested a cryopreservation method for highly multiplexed antibody cocktails for mass cytometry including lanthanide, palladium, and platinum conjugates that yielded stable staining patterns for at least 9 months when stored at temperatures below -80°C. Using frozen aliquots of antibody cocktails is an economic and flexible approach to significantly improve data consistency in large mass cytometry studies with repetitive staining/measurement cycles spanning several days or involving multiple data acquisition sites. © 2019 International Society for Advancement of Cytometry.

DYNAMIC SYSTEM SUSTAINABILITY SIMULATION MODELLING

INTRODUCTION
 Assessing the sustainability of dynamic, open and complex systems with many stakeholders, interrelated components and interactions and forecasting with traditional study methods is complicated and has its limitations. Therefore, often researchers, when forecasting the sustainability of a dynamic system, rely on subjective judgment without references to assessment standards.
 The aim of the paper is to create an imitation model for the sustainability of a dynamic system in order to assess and forecast the sustainability of the system under alternative development scenarios. It includes 3 main aspects - how sustainable is the dynamic system, what is the level of sustainability of a dynamic system under alternative development scenarios and what additions are needed to improve the functioning of the imitation model.
 The research question of the paper is: what imitation model can effectively analyse and forecast a dynamic system in the case of the tourism object Cesis Palace.
 Sustainable development researchers offer to build on traditional principles and interlinked dimensions of sustainable development: environment, economic and social, and adapt them to the dynamic system, which is characterized by the interactions between components (Tanguay et.al., 2011; Mai, Smith, 2018). One type of study that helps explain such systems is simulation modelling, which is often used when researching the interaction of dynamic systems (Johnson, 2011).
 MATERIALS AND METHODS
 The Cesis Palace complex as a tourism site was used in the paper for an example of a dynamic system, since tourism is both a dynamic system with many interlinked components and equally important are the three dimensions of sustainability for its long-term development.
 During the study, multiple data acquisition methods were used: a structured interview, analysis of statistics, case study analysis and an expert interview on the created imitation model.
 To achieve the goal of the research, a model of a tourism sustainability imitation model was created using the STELLA dynamic system modelling environment. The model and the selection of indicators were based on the three key sustainability dimensions: economy, environment and society/culture.
 RESULTS
 The result of the work is a computer model that helps to assess the sustainability of a real-life system and its dimensions by entering data generated during the study. It concludes that the tourism object under consideration is potentially sustainable. Simulating alternative development scenarios, it can be concluded that the elements of one group of indicators can affect both the sustainability level of their own dimension, as well as the indicators of other dimensions and their sustainability level, as well the sustainability of the system overall. Significant changes in the system take place in a situation where a number of indicator groups are affected by the changes.
 DISCUSION AND CONCLUSIONS
 In order to use this model further, it would be necessary to develop an improved methodology for evaluating model indicators.
 To ensure a more efficient model performance and data quality, data security and integrity should be taken into account. Creation of an imitation model requires the acquisition, processing and issuing of large amounts of data that are exposed to data security and integrity risks, which may have a negative impact, not only on the functioning of the model, but also on the system itself.
 To significantly improve the definition, selection, value assignment, and to more accurately identify the importance of interfacing elements and to express future forecasts, the author proposes to evaluate the use of machine-learning in imitation modelling. Machine-learning algorithms are increasingly used by researchers in mechanical engineering applications.

Inter-platform reproducibility of ultrasonic attenuation and backscatter coefficients in assessing NAFLD.

To assess inter-platform reproducibility of ultrasonic attenuation coefficient (AC) and backscatter coefficient (BSC) estimates in adults with known/suspected nonalcoholic fatty liver disease (NAFLD). This HIPAA-compliant prospective study was approved by an institutional review board; informed consent was obtained. Participants with known/suspected NAFLD were recruited and underwent same-day liver examinations with clinical ultrasound scanner platforms from two manufacturers. Each participant was scanned by the same trained sonographer who performed multiple data acquisitions in the right liver lobe using a lateral intercostal approach. Each data acquisition recorded a B-mode image and the underlying radio frequency (RF) data. AC and BSC were calculated using the reference phantom method. Inter-platform reproducibility was evaluated for AC and log-transformed BSC (logBSC = 10log10BSC) by intraclass correlation coefficient (ICC), Pearson's correlation, Bland-Altman analysis with computation of limits of agreement (LOAs), and within-subject coefficient of variation (wCV; applicable to AC). Sixty-four participants were enrolled. Mean AC values measured using the two platforms were 0.90 ± 0.13 and 0.94 ± 0.15dB/cm/MHz while mean logBSC values were - 30.6 ± 5.0 and - 27.9 ± 5.6dB, respectively. Inter-platform ICC was 0.77 for AC and 0.70 for log-transformed BSC in terms of absolute agreement. Pearson's correlation coefficient was 0.81 for AC and 0.80 for logBSC. Ninety-five percent LOAs were - 0.21 to 0.13dB/cm/MHz for AC, and - 9.48 to 3.98dB for logBSC. The wCV was 7% for AC. Hepatic AC and BSC are reproducible across two different ultrasound platforms in adults with known or suspected NAFLD. • Ultrasonic attenuation coefficient and backscatter coefficient are reproducible between two different ultrasound platforms in adults with NAFLD. • This inter-platform reproducibility may qualify quantitative ultrasound biomarkers for generalized clinical application in patients with suspected/known NAFLD.

Monitoring horizontal well hydraulic stimulations and geomechanical deformation processes in the unconventional shales of the Midland Basin using fiber-based time-lapse VSPs, microseismic, and strain data

A variety of methods are utilized in an instrumented vertical wellbore to invert for and estimate the heights and lateral extents of the hydraulic fracture treatment. Data were acquired with externally mounted dual- and single-mode fiber optics for measuring strain, acoustics, and temperature. In addition, external pressure gauges, internal conventional tiltmeters, and geophones were also utilized. This instrumented well was used multiple times to record a number of nearby offset horizontal hydraulic stimulations and to record a time-lapse vertical seismic profile. By using multiple data acquisition techniques, we obtained a more comprehensive and accurate estimation of the hydraulic fracture geometry and the dynamic processes taking place internal to the propagating fractures. Furthermore, these data could be used to calibrate fracture models and the fracture interaction with the surrounding unconventional reservoir.

Material classification with laser thermography and machine learning

ABSTRACTRobotic devices that can perform teleoperation work in complex and unstructured environments (e.g. healthcare, education, automotive, telepresence, defence, etc.) rely mainly on visual and/or auditory feedback to interact with a remote operator. There is a vital need for measuring the physical properties of ambient environments when performing precise manipulation tasks over a distance, to improve the quality of performance. In this paper, we propose an approach to combine infrared thermography with machine learning for a fast, accurate way to classify objects of different material. A laser source stimulates the surface of the object while an infrared camera captures its thermal signature, extracts features about these signatures and feeds them into a machine learning-based algorithm for classification. Results demonstrated that a classification accuracy of around 97% can be achieved with majority vote Decision Tree classifier, even in the presence of data from multiple data acquisition sessions.

Feasibility and accuracy of single time point imaging for renal dosimetry following 177Lu-DOTATATE (\u2018Lutate\u2019) therapy

BackgroundThis study aims to assess both feasibility and accuracy of renal dosimetry imaging protocols in patients receiving Lutate therapy for neuroendocrine tumours (NETs), when data acquisition over multiple days is not possible on all cycles.MethodPatients who had received a full 4 cycles of Lutate therapy with complete imaging at each cycle were included. Imaging consisted of quantitative SPECT/CT of the kidneys at 4, 24 and 96–120 h post injection. Renal absorbed dose was calculated for each data set, and in addition, five alternative methods were explored for comparison. Method 1: a patient average clearance time (t1/2 average) derived from the first half of contributing patient data was used to estimate absorbed dose for subsequent patients based on 4 h imaging alone; method 2: t1/2 average was applied to subsequent patients on 24 h imaging alone; method 3: a patient-specific clearance rate (t1/2 patient) was determined from complete image data of cycle 1 and applied subsequently to remaining cycles using 4 h image data alone; method 4: t1/2 patient was applied to 24 h imaging alone in subsequent cycles; method 5: the 120 h data was estimated on subsequent cycles based on the cycle 1 fraction of injected activity (%IA) at 24 and 120 h.ResultsTwenty treatments from 18 patients, resulting in 80 cycles of therapy, were analysed. The measured average renal absorbed dose per cycle of treatment was 0.38 ± 0.19 Gy/GBq when derived from full imaging data. The use of t1/2 average applied to a single time point led to large deviations of dose estimates from true values (on average 59% and 30%, when using 4 h data and 24 h data, respectively). The use of complete image data on cycle 1 and the derivation of t1/2 patient led to improved dose estimates, with an average deviation from true values of 13% and 2% when using 4 h data only and 24 h data only, respectively. The use of a 120 h %IA derived from cycle 1 led to an average deviation from true dose estimates of 14%.ConclusionIn instances where demands on both patients and facilities make multiple time point data acquisition impractical, renal dosimetry is best derived through complete imaging at cycle 1 only followed by a single 24 h imaging time point on subsequent cycles, assuming no significant changes in renal function during the time course of therapy.

Inter-sonographer reproducibility of quantitative ultrasound outcomes and shear wave speed measured in the right lobe of the liver in adults with known or suspected non-alcoholic fatty liver disease.

To assess inter-sonographer reproducibility of ultrasound attenuation coefficient (AC), backscatter coefficient (BSC) and shear wave speed (SWS) in adults with known/suspected non-alcoholic fatty liver disease (NAFLD). The institutional review board approved this HIPAA-compliant prospective study; informed consent was obtained. Participants with known/suspected NAFLD were recruited and underwent same-day liver examinations with a clinical scanner. Each participant was scanned by two of the six trained sonographers. Each sonographer performed multiple data acquisitions in the right liver lobe using a lateral intercostal approach. A data acquisition was a single operator button press that recorded a B-mode image, radio-frequency data, and the SWS value. AC and BSC were calculated from the radio-frequency data using the reference phantom method. SWS was calculated automatically using product software. Intraclass correlation coefficient (ICC) and within-subject coefficient of variation (wCV) were calculated for applicable metrics. Sixty-one participants were recruited. Inter-sonographer ICC was 0.86 (95% confidence interval: 0.77-0.92) for AC and 0.87 (0.78-0.92) for log-transformed BSC (logBSC = 10log10BSC) using one acquisition per sonographer. ICC was 0.88 (0.80-0.93) for both AC and logBSC averaging 5 acquisitions. ICC for SWS was 0.57 (0.29-0.74) using one acquisition per sonographer, and 0.84 (0.66-0.93) using 10 acquisitions. The wCV was ~7% for AC, and 19-43% for SWS, depending on number of acquisitions. Hepatic AC, BSC and SWS measures on a clinical scanner have good inter-sonographer reproducibility in adults with known or suspected NAFLD. Multiple acquisitions are required for SWS but not AC or BSC to achieve good inter-sonographer reproducibility. • AC, BSC and SWS measurements are reproducible in adults with NAFLD. • Inter-sonographer reproducibility of SWS measurement improves with more acquisitions being averaged. • Multiple acquisitions are required for SWS but not AC or BSC.

UHPLC-Q-TOF-MS/MS based screening and identification of the metabolites in vivo after oral administration of betulin

Betulin is an active natural pentacyclic triterpene ingredient with valuable anti-cancer and anti-HIV efficacies. In the present study, an efficient approach was developed to screening and identification of metabolites and to assess the metabolic profiles of betulin in vivo using UHPLC-Q-TOF-MS/MS system based on multiple mass defect filter data acquisition (MMDF) and multiple data processing techniques. Based on the proposed method, 56 phase I and 6 phase II metabolites were detected in rats after oral administration of betulin. The main biotransformation routes of betulin were identified as demethylation, dehydroxylation, deoxidization, dehydration. Conjugation with sulfate, taurine, cysteine and N-acetylcysteine groups produced 6 phase II metabolites. This study not only provided useful information for further study of the pharmacology and mechanism of betulin in vivo, but also provided essential data for further pharmaceutical studies of other pentacyclic triterpenes.

A High Throughput Integrated Hyperspectral Imaging and 3D Measurement System.

Hyperspectral and three-dimensional measurements can obtain the intrinsic physicochemical properties and external geometrical characteristics of objects, respectively. The combination of these two kinds of data can provide new insights into objects, which has gained attention in the fields of agricultural management, plant phenotyping, cultural heritage conservation, and food production. Currently, a variety of sensors are integrated into a system to collect spectral and morphological information in agriculture. However, previous experiments were usually performed with several commercial devices on a single platform. Inadequate registration and synchronization among instruments often resulted in mismatch between spectral and 3D information of the same target. In addition, using slit-based spectrometers and point-based 3D sensors extends the working hours in farms due to the narrow field of view (FOV). Therefore, we propose a high throughput prototype that combines stereo vision and grating dispersion to simultaneously acquire hyperspectral and 3D information. Furthermore, fiber-reformatting imaging spectrometry (FRIS) is adopted to acquire the hyperspectral images. Test experiments are conducted for the verification of the system accuracy, and vegetation measurements are carried out to demonstrate its feasibility. The proposed system is an improvement in multiple data acquisition and has the potential to improve plant phenotyping.

Machine Intelligence in Healthcare and Medical Cyber Physical Systems: A Survey

Today, the US healthcare industry alone can save $300 B per year by using machine intelligence to analyze a rich set of existing medical data; results from these analyses can lead to breakthroughs such as more accurate medical diagnoses, discovery of new cures for diseases, and cost savings in the patient admission process at healthcare organizations. Because healthcare applications intrinsically imply a vast amount of data, the execution of any algorithm on medical data is computationally intensive. Significant advancements made in computational power in the past decade have provided the opportunity for many researchers to successfully implement various machine intelligence-based healthcare applications, which didn’t run efficiently on earlier computational platforms. In this paper, we provide a survey of machine intelligence algorithms within the context of healthcare applications; our survey includes a comprehensive list of the most commonly used computational models and algorithms. We view the application of these algorithms in multiple steps, namely, data acquisition, feature extraction, and aggregation, modeling, algorithm training, and algorithm execution and provide details—as well as representative case studies—for each step. We provide a set of metrics that are used to evaluate modeling and algorithmic performance, which facilitate the comparison of the presented models and algorithms. Medical cyber-physical systems are presented as an emerging application case study of machine intelligence in healthcare. We conclude our paper by providing a list of opportunities and challenges for incorporating machine intelligence in healthcare applications and provide an extensive list of tools and databases to help other researchers.

Adaptive Photoacoustic Sensing Using Matched Filter

Sensitive detection is always crucial to photoacoustic sensing and imaging applications owing to the extremely low conversion efficiency from light to sound. A conventional approach to enhance the signal-to-noise ratio (SNR) of the photoacoustic signal is data averaging, which is quite time-consuming due to multiple data acquisitions for each photoacoustic measurement. Especially for a high-power pulsed laser source with only a 1020 pulse repetition rate, multiple data averaging will severely degrade the frame rate. In this article, we present a simple but efficient method, called adaptive photoacoustic (aPA) sensing, using matched filter to obviously enhance the detected signal SNR with single laser pulse. Invivo experimental results show that the proposed aPA method improved the signal SNR by about 60dB. It demonstrates the potential of aPA to perform highly sensitive photoacoustic sensing and imaging with accelerated sensing and imaging speed.

Novelty application of multi-omics correlation in the discrimination of sulfur-fumigation and non-sulfur-fumigation Ophiopogonis Radix

A rapid and sensitive approach to differentiate sulfur-fumigated (SF) Ophiopogonis Radix based on Multi-Omics Correlation Analysis (MOCA) strategy was first established. It was characterized by multiple data-acquisition methods (NIR, HPLC, and UHPLC-HRMS) based metabonomics and multivariate statistical analysis methods. As a result, SF and non-sulfur fumigated (NSF) Ophiopogonis Radix samples were efficaciously discriminated. Moreover, based on the acquired HRMS data, 38 sulfur-containing discriminatory markers were eventually characterized, whose NIR absorption could be in close correlation with the discriminatory NIR wavebands (5000–5200 cm−1) screened by NIR metabonomics coupled with SiPLS and 2D-COS methods. This results were also validated from multiple perspectives, including metabonomics analysis based on the discriminatory markers and the simulation of SF ophiopogonin D and Ophiopogonis Radix sample. In conclusion, our results first revealed the intrinsic mechanism of discriminatory NIR wavebands by means of UHPLC-HRMS analysis. Meanwhile, the established MOCA strategy also provided a promising NIR based differential method for SF Ophiopogonis Radix, which could be exemplary for future researches on rapid discrimination of other SF Chinese herbal medicines.

Comparing various hardware/software solutions and conversion methods for Controller Area Network (CAN) bus data collection

Various hardware and software solutions exist for collecting Controller Area Network (CAN) bus data. Digital data accuracy could vary based upon different data logging methods (e.g., hardware/software timing, processor timing, etc.). CAN bus data were collected from agricultural tractors using multiple data acquisition solutions to quantify differences among collection methods and demonstrate potential data accumulation rates.Two types of data were observed for this study. The first, CAN bus frame data, represents data collected for each line of hex data sent from an ECU. One issue with frame data is the resulting large file sizes, therefore a second logging format collected was an averaged frame signal, or waveform dataset. Because of its smaller file size, waveform data could be more desirable for long periods of collection. Percent difference was calculated from two sets of frame data logs using different hardware/software combinations, and a frame data log was also compared to a waveform data log.The resulting difference was less than 0.0025 RPM for engine speed comparisons, zero for fuel rate and fuel temperature comparisons, and the mean percent difference was less than 0.08% between the methods of data collection. The error production could have resulted from noise in hardware and processor times, but was not found to increase as time progressed. This showed that even though errors existed between logging methods, the magnitude of errors would not negatively impact any practical agricultural field research applications. Thus, data logged by the different devices was similar and files requiring less memory would be desired. Selecting a waveform CAN bus data logging option would likely maintain digital data accuracy while reducing file storage and processing needs.

Design and construction of a 3D force plate prototype for developing an instrumented swimming start block

Force plates have been used in human movement analysis to measure ground reaction forces, centre of pressure (COP) and derived kinetic quantities. In competitive swimming external tridimensional (3D) forces assessment is crucial to improve starting technique performance. This work aimed to describe the design and construction of a 3D force plate prototype, which might be a modular sensor of an instrumented swimming starting block. For this purpose four steps were followed: 1) numerical determination of sensor conspicuous spatial positioning; 2) development of a first test device and respective calibration procedures; 3) final prototype (3D force plate) development and implementation; and 4) development and programming of a high speed multiple data acquisition system. Vertical force (< 140 N ± 5%) and COP real time determination (± 3% to centre distance uncertainty) are compliant with the literature data and horizontal force is assessed based on COP displacement time derivatives. The software for data acquisition and interpretation was developed, leading to calibration procedure that provides a set of gains for sequential balance protocol and final transfer matrix. Although the final prototype implementation was the main concern of the current study, its development also has proven to be an important milestone for a dynamometric swimming start block advance.

Non-parametric combination and related permutation tests for neuroimaging.

In this work, we show how permutation methods can be applied to combination analyses such as those that include multiple imaging modalities, multiple data acquisitions of the same modality, or simply multiple hypotheses on the same data. Using the well‐known definition of union‐intersection tests and closed testing procedures, we use synchronized permutations to correct for such multiplicity of tests, allowing flexibility to integrate imaging data with different spatial resolutions, surface and/or volume‐based representations of the brain, including non‐imaging data. For the problem of joint inference, we propose and evaluate a modification of the recently introduced non‐parametric combination (NPC) methodology, such that instead of a two‐phase algorithm and large data storage requirements, the inference can be performed in a single phase, with reasonable computational demands. The method compares favorably to classical multivariate tests (such as MANCOVA), even when the latter is assessed using permutations. We also evaluate, in the context of permutation tests, various combining methods that have been proposed in the past decades, and identify those that provide the best control over error rate and power across a range of situations. We show that one of these, the method of Tippett, provides a link between correction for the multiplicity of tests and their combination. Finally, we discuss how the correction can solve certain problems of multiple comparisons in one‐way ANOVA designs, and how the combination is distinguished from conjunctions, even though both can be assessed using permutation tests. We also provide a common algorithm that accommodates combination and correction. Hum Brain Mapp 37:1486‐1511, 2016. © 2016 Wiley Periodicals, Inc.

Combining Multiple Data Acquisition Systems to Study Corticospinal Output and Multi-segment Biomechanics

Transcranial magnetic stimulation techniques allow for an in-depth investigation into the neural mechanisms that underpin human behavior. To date, the use of TMS to study human movement, has been limited by the challenges related to precisely timing the delivery of TMS to features of the unfolding movement and, also, by accurately characterizing kinematics and kinetics. To overcome these technical challenges, TMS delivery and acquisition systems should be integrated with an online motion tracking system. The present manuscript details technical innovations that integrate multiple acquisition systems to facilitate and advance the use of TMS to study human movement. Using commercially available software and hardware systems, a step-by-step approach to both the hardware assembly and the software scripts necessary to perform TMS studies triggered by specific features of a movement is provided. The approach is focused on the study of upper limb, planar, multi-joint reaching movements. However, the same integrative system is amenable to a multitude of sophisticated studies of human motor control.

Combining Multiple Data Acquisition Systems to Study Corticospinal Output and Multi-segment Biomechanics.

Transcranial magnetic stimulation techniques allow for an in-depth investigation into the neural mechanisms that underpin human behavior. To date, the use of TMS to study human movement, has been limited by the challenges related to precisely timing the delivery of TMS to features of the unfolding movement and, also, by accurately characterizing kinematics and kinetics. To overcome these technical challenges, TMS delivery and acquisition systems should be integrated with an online motion tracking system. The present manuscript details technical innovations that integrate multiple acquisition systems to facilitate and advance the use of TMS to study human movement. Using commercially available software and hardware systems, a step-by-step approach to both the hardware assembly and the software scripts necessary to perform TMS studies triggered by specific features of a movement is provided. The approach is focused on the study of upper limb, planar, multi-joint reaching movements. However, the same integrative system is amenable to a multitude of sophisticated studies of human motor control.

Identification of metabolites of oridonin in rats with a single run on UPLC-Triple-TOF-MS/MS system based on multiple mass defect filter data acquisition and multiple data processing techniques.

Oridonin (ORI) is an active natural ent-kaurane diterpenoid ingredient originating from well-known traditional Chinese herb medicine and is expected to be pursued as a new anticancer agent. In the present study, a novel and efficient approach was developed for in vivo screening and identification of ORI metabolites using ultra high performance liquid chromatography coupled with hybrid triple quadrupole time-of-flight mass spectrometry (UPLC-Triple-TOF-MS/MS). This analytical strategy was as follows: an effective on-line data acquisition method multiple mass defect filter (MMDF) combined with dynamic background subtraction (DBS), was developed to trace all of potential metabolites of ORI. The MMDF and DBS method could trigger an information dependent acquisition scan, which could give the information of low-level metabolites masked by background noise and endogenous components in complex matrix. Moreover, the sensitive and specific multiple data-mining techniques including extracted ion chromatography, mass defect filtering, product ion filtering and neutral loss filtering were employed to identify the metabolites of ORI. Then, structures for the metabolites were successfully assigned based on accurate masses, the mass fragmentation of ORI and metabolic knowledge. Finally, an important parameter Clog P was used to estimate the retention time of isomers. Based on the proposed strategy, 16 phase I and 2 phase II metabolites were detected in rats after oral administration of ORI. The main biotransformation route of ORI was identified as reduction, oxidation, dehydroxylation and glucuronic acid conjugation. This is the first study of ORI metabolism in vivo. This study not only proposed a practical strategy for rapidly screening and identifying metabolites, but also provided useful information for further study of the pharmacology and mechanism of ORI in vivo. At the same time this methodology can be widely applied for the structural characterization of the metabolites of other ent-kaurane diterpenoid.