A method for synchronized use of EEG and eye tracking in fully immersive VR.

This study explores the synchronization of multimodal physiological data streams, in particular, the integration of electroencephalography (EEG) with a virtual reality (VR) headset featuring eye-tracking capabilities. A potential use case for the synchronized data streams is demonstrated by implementing a hybrid steady-state visually evoked potential (SSVEP) based brain-computer interface (BCI) speller within a fully immersive VR environment. The hardware latency analysis reveals an average offset of 36 ms between EEG and eye-tracking data streams and a mean jitter of 5.76 ms. The study further presents a proof of concept brain-computer interface (BCI) speller in VR, showcasing its potential for real-world applications. The findings highlight the feasibility of combining commercial EEG and VR technologies for neuroscientific research and open new avenues for studying brain activity in ecologically valid VR environments. Future research could focus on refining the synchronization methods and exploring applications in various contexts, such as learning and social interactions.

A review of combined functional neuroimaging and motion capture for motor rehabilitation.

BACKGROUND: Technological advancements in functional neuroimaging and motion capture have led to the development of novel methods that facilitate the diagnosis and rehabilitation of motor deficits. These advancements allow for the synchronous acquisition and analysis of complex signal streams of neurophysiological data (e.g., EEG, fNIRS) and behavioral data (e.g., motion capture). The fusion of those data streams has the potential to provide new insights into cortical mechanisms during movement, guide the development of rehabilitation practices, and become a tool for assessment and therapy in neurorehabilitation. RESEARCH OBJECTIVE: This paper aims to review the existing literature on the combined use of motion capture and functional neuroimaging in motor rehabilitation. The objective is to understand the diversity and maturity of technological solutions employed and explore the clinical advantages of this multimodal approach. METHODS: This paper reviews literature related to the combined use of functional neuroimaging and motion capture for motor rehabilitation following the PRISMA guidelines. Besides study and participant characteristics, technological aspects of the used systems, signal processing methods, and the nature of multimodal feature synchronization and fusion were extracted. RESULTS: Out of 908 publications, 19 were included in the final review. Basic or translation studies were mainly represented and based predominantly on healthy participants or stroke patients. EEG and mechanical motion capture technologies were most used for biomechanical data acquisition, and their subsequent processing is based mainly on traditional methods. The system synchronization techniques at large were underreported. The fusion of multimodal features mainly supported the identification of movement-related cortical activity, and statistical methods were occasionally employed to examine cortico-kinematic relationships. CONCLUSION: The fusion of motion capture and functional neuroimaging might offer advantages for motor rehabilitation in the future. Besides facilitating the assessment of cognitive processes in real-world settings, it could also improve rehabilitative devices' usability in clinical environments. Further, by better understanding cortico-peripheral coupling, new neuro-rehabilitation methods can be developed, such as personalized proprioceptive training. However, further research is needed to advance our knowledge of cortical-peripheral coupling, evaluate the validity and reliability of multimodal parameters, and enhance user-friendly technologies for clinical adaptation.

CLET: Computation of Latencies in Event-related potential Triggers using photodiode on virtual reality apparatuses.

To investigate event-related activity in human brain dynamics as measured with EEG, triggers must be incorporated to indicate the onset of events in the experimental protocol. Such triggers allow for the extraction of ERP, i.e., systematic electrophysiological responses to internal or external stimuli that must be extracted from the ongoing oscillatory activity by averaging several trials containing similar events. Due to the technical setup with separate hardware sending and recording triggers, the recorded data commonly involves latency differences between the transmitted and received triggers. The computation of these latencies is critical for shifting the epochs with respect to the triggers sent. Otherwise, timing differences can lead to a misinterpretation of the resulting ERPs. This study presents a methodical approach for the CLET using a photodiode on a non-immersive VR (i.e., LED screen) and an immersive VR (i.e., HMD). Two sets of algorithms are proposed to analyze the photodiode data. The experiment designed for this study involved the synchronization of EEG, EMG, PPG, photodiode sensors, and ten 3D MoCap cameras with a VR presentation platform (Unity). The average latency computed for LED screen data for a set of white and black stimuli was 121.98 ± 8.71 ms and 121.66 ± 8.80 ms, respectively. In contrast, the average latency computed for HMD data for the white and black stimuli sets was 82.80 ± 7.63 ms and 69.82 ± 5.52 ms. The codes for CLET and analysis, along with datasets, tables, and a tutorial video for using the codes, have been made publicly available.

Performance of machine learning models in estimation of ground reaction forces during balance exergaming.

BACKGROUND: Balance training exercise games (exergames) are a promising tool for reducing fall risk in elderly. Exergames can be used for in-home guided exercise, which greatly increases availability and facilitates independence. Providing biofeedback on weight-shifting during in-home balance exercise improves exercise efficiency, but suitable equipment for measuring weight-shifting is lacking. Exergames often use kinematic data as input for game control. Being able to useg such data to estimate weight-shifting would be a great advantage. Machine learning (ML) models have been shown to perform well in weight-shifting estimation in other settings. Therefore, the aim of this study was to investigate the performance of ML models in estimation of weight-shifting during exergaming using kinematic data. METHODS: Twelve healthy older adults (mean age 72 (± 4.2), 10 F) played a custom exergame that required repeated weight-shifts. Full-body 3D motion capture (3DMoCap) data and standard 2D digital video (2D-DV) was recorded. Weight shifting was directly measured by 3D ground reaction forces (GRF) from force plates, and estimated using a linear regression model, a long-short term memory (LSTM) model and a decision tree model (XGBoost). Performance was evaluated using coefficient of determination ([Formula: see text]) and root mean square error (RMSE). RESULTS: Results from estimation of GRF components using 3DMoCap data show a mean (± 1SD) RMSE (% total body weight, BW) of the vertical GRF component ([Formula: see text]) of 4.3 (2.5), 11.1 (4.5), and 11.0 (4.7) for LSTM, XGBoost and LinReg, respectively. Using 2D-DV data, LSTM and XGBoost achieve mean RMSE (± 1SD) in [Formula: see text] estimation of 10.7 (9.0) %BW and 19.8 (6.4) %BW, respectively. [Formula: see text] was [Formula: see text] for the LSTM in the [Formula: see text] component using 3DMoCap data, and [Formula: see text] using 2D-DV data. For XGBoost, [Formula: see text] [Formula: see text] was [Formula: see text] using 3DMoCap data, and [Formula: see text] using 2D-DV data. CONCLUSION: This study demonstrates that an LSTM model can estimate 3-dimensional GRF components using 2D kinematic data extracted from standard 2D digital video cameras. The [Formula: see text] component is estimated more accurately than [Formula: see text] and [Formula: see text] components, especially when using 2D-DV data. Weight-shifting performance during exergaming can thus be extracted using kinematic data only, which can enable effective independent in-home balance exergaming.

Experiences of Stroke Survivors and Clinicians With a Fully Immersive Virtual Reality Treadmill Exergame for Stroke Rehabilitation: A Qualitative Pilot Study.

Use of VR-games is considered a promising treatment approach in stroke rehabilitation. However, there is little knowledge on the use and expectations of patients and health professionals regarding the use of treadmill walking in a fully immersive virtual environment as a rehabilitation tool for gait training for stroke survivors. The objectives of the current study were to determine whether stroke survivors can use fully immersive VR utilizing modern HMDs while walking on a treadmill without adverse effects, and to investigate the experiences of stroke survivors and clinicians after testing with focus on acceptability and potential utilization in rehabilitation. A qualitative research design with semi-structured interviews was used to collect data. Five stroke survivors and five clinicians participated in the study and tested a custom-made VR-game on the treadmill before participating in individual semi-structured interview. Data were analyzed through thematic analysis. The analysis of the interview data identified two main categories: (1) experiencing acceptability through safety and motivation, and (2) implementing fully immersive VR in rehabilitation. Both stroke survivors' and clinicians enjoyed the treadmill-based VR-game and felt safe when using it. The stroke survivors experienced motivation for exercising and achievement by fulfilling tasks during the gaming session as the VR-game was engaging. The clinicians found additional motivation by competing in the game. Both groups saw a potential for use in gait rehabilitation after stroke, on the premise of individual adaptation to each patient's needs, and the technology being easy to use. The findings from this qualitative study suggest that a fully immersive treadmill-based VR-game is acceptable and potentially useful as part of gait rehabilitation after stroke, as it was positively received by both stroke survivors and clinicians working within stroke rehabilitation. The participants reported that they experienced motivation in the game through safety, engagement and achievement. They also saw the potential of implementing such a setup in their own rehabilitation setting. Elements that enable safety and engaging experience are important to maintain when using a fully immersive VR-game in stroke rehabilitation.

Comparison of a Deep Learning-Based Pose Estimation System to Marker-Based and Kinect Systems in Exergaming for Balance Training.

Using standard digital cameras in combination with deep learning (DL) for pose estimation is promising for the in-home and independent use of exercise games (exergames). We need to investigate to what extent such DL-based systems can provide satisfying accuracy on exergame relevant measures. Our study assesses temporal variation (i.e., variability) in body segment lengths, while using a Deep Learning image processing tool (DeepLabCut, DLC) on two-dimensional (2D) video. This variability is then compared with a gold-standard, marker-based three-dimensional Motion Capturing system (3DMoCap, Qualisys AB), and a 3D RGB-depth camera system (Kinect V2, Microsoft Inc). Simultaneous data were collected from all three systems, while participants (N = 12) played a custom balance training exergame. The pose estimation DLC-model is pre-trained on a large-scale dataset (ImageNet) and optimized with context-specific pose annotated images. Wilcoxon's signed-rank test was performed in order to assess the statistical significance of the differences in variability between systems. The results showed that the DLC method performs comparably to the Kinect and, in some segments, even to the 3DMoCap gold standard system with regard to variability. These results are promising for making exergames more accessible and easier to use, thereby increasing their availability for in-home exercise.

DecoyDeveloper: An On-Demand, De Novo Decoy Glycopeptide Generator.

Glycopeptide analysis is a growing field that is struggling to adopt effective, automated tools. Many creative workflows and software apps have emerged recently that offer promising capabilities for assigning glycopeptides to MS data in an automated fashion. The effectiveness of these tools is best measured and improved by determining how often they would select a glycopeptide decoy as a spectral match, instead of its correct assignment; yet generating the appropriate number and type of glycopeptide decoys can be challenging. To address this need, we have designed DecoyDeveloper, an on-demand decoy glycopeptide generator that can produce a high volume of decoys with low mass differences. DecoyDeveloper has a simple user interface and is capable of producing large sets of decoys containing complete, biologically relevant glycan and peptide sequences. We demonstrate the tool's efficiency by applying it to a set of 80 glycopeptide targets. This tool is freely available and can be found at http://glycopro.chem.ku.edu/J1.php .

K+ Efflux Antiporters 4, 5, and 6 Mediate pH and K+ Homeostasis in Endomembrane Compartments.

KEA4, KEA5, and KEA6 are members of the Arabidopsis (Arabidopsis thaliana) K+ efflux antiporter (KEA) family that share high sequence similarity but whose function remains unknown. Here, we show their gene expression pattern, subcellular localization, and physiological function in Arabidopsis. KEA4, KEA5, and KEA6 had similar tissue expression patterns, and the three KEA proteins localized to the Golgi, the trans-Golgi network, and the prevacuolar compartment/multivesicular bodies, suggesting overlapping roles of these proteins in the endomembrane system. Phenotypic analyses of single, double, and triple mutants confirmed functional redundancy. The triple mutant kea4 kea5 kea6 had small rosettes, short seedlings, and was sensitive to low K+ availability and to the sodicity imposed by high salinity. Also, the kea4 kea5 kea6 mutant plants had a reduced luminal pH in the Golgi, trans-Golgi network, prevacuolar compartment, and vacuole, in accordance with the K/H exchange activity of KEA proteins. Genetic analysis indicated that KEA4, KEA5, and KEA6 as well as endosomal Na+/H+exchanger5 (NHX5) and NHX6 acted coordinately to facilitate endosomal pH homeostasis and salt tolerance. Neither cancelling nor overexpressing the vacuolar antiporters NHX1 and NHX2 in the kea4 kea5 kea6 mutant background altered the salt-sensitive phenotype. The NHX1 and NHX2 proteins in the kea4 kea5 kea6 mutant background could not suppress the acidity of the endomembrane system but brought the vacuolar pH close to wild-type values. Together, these data signify that KEA4, KEA5, and KEA6 are endosomal K+ transporters functioning in maintaining pH and ion homeostasis in the endomembrane network.

Dissecting the Dissociation Patterns of Fucosylated Glycopeptides Undergoing CID: A Case Study in Improving Automated Glycopeptide Analysis Scoring Algorithms.

The need to investigate the fragmentation of fucosylated glycopeptides is driven by recent work showing that at least one, and perhaps many, glycopeptide analysis scoring algorithms are less effective at identifying fucosylated glycopeptides than non-fucosylated glycopeptides. Herein, we study the CID fragmentation characteristics of fucosylated glycopeptides and the scoring rules of the glycopeptide analysis software, GlycoPep Grader, in an effort to improve automated assignments of these important glycopeptides. We identified some prominent product ions from a common fragmentation pathway of fucosylated glycopeptides that were not accounted for in the scoring rules. Based on this finding, we propose new scoring rules for fucosylated glycopeptides that can be incorporated into GlycoPep Grader and other similar analysis software tools to more accurately identify these species. The approach used here, to improve one particular scoring algorithm, could henceforth be used to improve any other algorithm that assigns glycopeptides based on their MS/MS data.

Domain-switch analysis of PeNHX3 from Populus euphratica reveals the critical role of the transmembrane domain 11 in Na+ and Li+ transport.

Populus euphratica, the well-known tree halophyte, tolerates the stress of high levels of salt. We previously showed that the transmembrane domain 11 (TM11) of PeNHX3, a Na+,K+/H+ antiporter from P. euphratica, was crucial for Na+ and Li+ transport in a yeast growth assay. Here, we examined the role of TM11 in catalyzing Na+ and Li+ transport in transgenic Arabidopsis. We found that PeNHX3 localized to the tonoplasts in Arabidopsis. Overexpression of PeNHX3 in Arabidopsis improved seedling growth and enhanced salt tolerance and Li+ detoxification. However, overexpression of PeNHX3 did not improve Arabidopsis growth at KCl concentrations higher than 0.1mM, suggesting a low K+ transport activity for PeNHX3 in plants. We performed in planta domain-switch analysis by replacing the C-terminal domain of AtNHX1 with a C-terminal segment of PeNHX3 containing the TM11 domain. We demonstrated that TM11 was critical for the Na+ and Li+ transport activities by PeNHX3. Taken together, PeNHX3 plays an important role in salt tolerance and Li+ detoxification in plants. TM11 controls the Na+ and Li+ transport activities of PeNHX3 in Arabidopsis.

Two New Tools for Glycopeptide Analysis Researchers: A Glycopeptide Decoy Generator and a Large Data Set of Assigned CID Spectra of Glycopeptides.

The glycopeptide analysis field is tightly constrained by a lack of effective tools that translate mass spectrometry data into meaningful chemical information, and perhaps the most challenging aspect of building effective glycopeptide analysis software is designing an accurate scoring algorithm for MS/MS data. We provide the glycoproteomics community with two tools to address this challenge. The first tool, a curated set of 100 expert-assigned CID spectra of glycopeptides, contains a diverse set of spectra from a variety of glycan types; the second tool, Glycopeptide Decoy Generator, is a new software application that generates glycopeptide decoys de novo. We developed these tools so that emerging methods of assigning glycopeptides' CID spectra could be rigorously tested. Software developers or those interested in developing skills in expert (manual) analysis can use these tools to facilitate their work. We demonstrate the tools' utility in assessing the quality of one particular glycopeptide software package, GlycoPep Grader, which assigns glycopeptides to CID spectra. We first acquired the set of 100 expert assigned CID spectra; then, we used the Decoy Generator (described herein) to generate 20 decoys per target glycopeptide. The assigned spectra and decoys were used to test the accuracy of GlycoPep Grader's scoring algorithm; new strengths and weaknesses were identified in the algorithm using this approach. Both newly developed tools are freely available. The software can be downloaded at http://glycopro.chem.ku.edu/GPJ.jar.

GlycoPep MassList: software to generate massive inclusion lists for glycopeptide analyses.

Protein glycosylation drives many biological processes and serves as markers for disease; therefore, the development of tools to study glycosylation is an essential and growing area of research. Mass spectrometry can be used to identify both the glycans of interest and the glycosylation sites to which those glycans are attached, when proteins are proteolytically digested and their glycopeptides are analyzed by a combination of high-resolution mass spectrometry (MS) and tandem mass spectrometry (MS/MS) methods. One major challenge in these experiments is collecting the requisite MS/MS data. The digested glycopeptides are often present in complex mixtures and in low abundance, and the most commonly used approach to collect MS/MS data on these species is data-dependent acquisition (DDA), where only the most intense precursor ions trigger MS/MS. DDA results in limited glycopeptide coverage. Semi-targeted data acquisition is an alternative experimental approach that can alleviate this difficulty. However, due to the massive heterogeneity of glycopeptides, it is not obvious how to expediently generate inclusion lists for these types of analyses. To solve this problem, we developed the software tool GlycoPep MassList, which can be used to generate inclusion lists for liquid chromatography tandem-mass spectrometry (LC-MS/MS) experiments. The utility of the software was tested by conducting comparisons between semi-targeted and untargeted data-dependent analysis experiments on a variety of proteins, including IgG, a protein whose glycosylation must be characterized during its production as a biotherapeutic. When the GlycoPep MassList software was used to generate inclusion lists for LC-MS/MS experiments, more unique glycopeptides were selected for fragmentation. Generally, ∼30 % more unique glycopeptides can be analyzed per protein, in the simplest cases, with low background. In cases where background ions from proteins or other interferents are high, usage of an inclusion list is even more advantageous. The software is freely publically accessible. Graphical abstract Software increases the number of glycopeptides that get selected for MS/MS analysis.

Synthesis of magnetic molecularly imprinted polymers for the selective separation and determination of metronidazole in cosmetic samples.

In this study, novel magnetic molecularly imprinted polymers (MMIPs) were developed as a sorbent for solid-phase extraction (SPE) and used for the selective separation of metronidazole (MNZ) in cosmetics; MNZ was detected by high-performance liquid chromatography (HPLC). First, magnetic Fe3O4 nanoparticles (NPs) were prepared by the co-precipitation of Fe(2+)and Fe(3+) ions in an ammonia solution; then oleic acid (OA) was modified onto the surface of Fe3O4NPs. Finally, the MMIP was prepared by aqueous suspension polymerization, involving the copolymerization of Fe3O4NPs@OA with MNZ as the template molecule, methacrylic acid (MAA) as the functional monomer, ethylene glycol maleic rosinate acrylate (EGMRA) as the cross-linking agent, and 2,2-azobisisobutyronitrile (AIBN) as the initiator. The MMIP materials showed high selective adsorption capacity and fast binding kinetics for MNZ; the maximum adsorption amount of the MMIP to MNZ was 46.7 mg/g. The assay showed a linear range from 0.1 to 20.0 µg/mL for MNZ with the correlation coefficient 0.999. The relative standard deviations (RSD) of intra- and inter-day ranging from 0.71 to 2.45% and from 1.06 to 5.20% were obtained. The MMIP can be applied to the enrichment and determination of MNZ in cosmetic products with the recoveries of spiked toner, powder, and cream cosmetic samples ranging from 90.6 to 104.2, 84.1 to 91.4, and 90.3 to 100.4%, respectively, and the RSD was <3.54%.

Synthesis and characterization of core-shell magnetic molecularly imprinted polymers for solid-phase extraction and determination of Rhodamine B in food.

Core-shell magnetic molecularly imprinted polymers (MIPs) nanoparticles (NPs), in which a Rhodamine B-imprinted layer was coated on Fe3O4 NPs. were synthesized. First, Fe3O4 NPs were prepared by a coprecipitation method. Then, amino-modified Fe3O4 NPs (Fe3O4@SiO2-NH2) was prepared. Finally, the MIPs were coated on the Fe3O4@SiO2-NH2 surface by the copolymerization with functional monomer, acrylamide, using a cross-linking agent, ethylene glycol dimethacrylate; an initiator, azobisisobutyronitrile and a template molecule, Rhodamine B. The Fe3O4@MIPs were characterized using a scanning electron microscope, Fourier transform infrared spectrometer, vibrating sample magnetometer, and re-binding experiments. The Fe3O4@MIPs showed a fast adsorption equilibrium, a highly improved imprinting capacity, and significant selectivity; they could be used as a solid-phase extraction material and detect illegal addition Rhodamine B in food. A method was developed for the selective isolation and enrichment of Rhodamine B in food samples with recoveries in the range 78.47-101.6% and the relative standard deviation was <2%.

New glycoproteomics software, GlycoPep Evaluator, generates decoy glycopeptides de novo and enables accurate false discovery rate analysis for small data sets.

Glycoproteins are biologically significant large molecules that participate in numerous cellular activities. In order to obtain site-specific protein glycosylation information, intact glycopeptides, with the glycan attached to the peptide sequence, are characterized by tandem mass spectrometry (MS/MS) methods such as collision-induced dissociation (CID) and electron transfer dissociation (ETD). While several emerging automated tools are developed, no consensus is present in the field about the best way to determine the reliability of the tools and/or provide the false discovery rate (FDR). A common approach to calculate FDRs for glycopeptide analysis, adopted from the target-decoy strategy in proteomics, employs a decoy database that is created based on the target protein sequence database. Nonetheless, this approach is not optimal in measuring the confidence of N-linked glycopeptide matches, because the glycopeptide data set is considerably smaller compared to that of peptides, and the requirement of a consensus sequence for N-glycosylation further limits the number of possible decoy glycopeptides tested in a database search. To address the need to accurately determine FDRs for automated glycopeptide assignments, we developed GlycoPep Evaluator (GPE), a tool that helps to measure FDRs in identifying glycopeptides without using a decoy database. GPE generates decoy glycopeptides de novo for every target glycopeptide, in a 1:20 target-to-decoy ratio. The decoys, along with target glycopeptides, are scored against the ETD data, from which FDRs can be calculated accurately based on the number of decoy matches and the ratio of the number of targets to decoys, for small data sets. GPE is freely accessible for download and can work with any search engine that interprets ETD data of N-linked glycopeptides. The software is provided at https://desairegroup.ku.edu/research.

Characterizing O-linked glycopeptides by electron transfer dissociation: fragmentation rules and applications in data analysis.

Studying protein O-glycosylation remains an analytical challenge. Different from N-linked glycans, the O-glycosylation site is not within a known consensus sequence. Additionally, O-glycans are heterogeneous with numerous potential modification sites. Electron transfer dissociation (ETD) is the method of choice in analyzing these glycopeptides since the glycan side chain remains intact in ETD, and the glycosylation site can be localized on the basis of the c and z fragment ions. Nonetheless, new software is necessary for interpreting O-glycopeptide ETD spectra in order to expedite the analysis workflow. To address the urgent need, we studied the fragmentation of O-glycopeptides in ETD and found useful rules that facilitate their identification. By implementing the rules into an algorithm to score potential assignments against ETD-MS/MS data, we applied the method to glycopeptides generated from various O-glycosylated proteins including mucin, erythropoietin, fetuin, and an HIV envelope protein, 1086.C gp120. The site-specific O-glycopeptide composition was correctly assigned in every case, proving the merits of our method in analyzing glycopeptide ETD data. The algorithm described herein can be easily incorporated into other automated glycomics tools.