Data Visualization Environment Research Articles

The human infertility single-cell testis atlas (HISTA): an interactive molecular scRNA-Seq reference of the human testis.

Single-cell RNA-seq (scRNA-Seq) has been widely adopted to study gene expression of the human testis. Several datasets of scRNA-Seq from human testis have been generated from different groups processed with different informatics pipelines. An integrated atlas of scRNA-Seq expression constructed from multiple donors, developmental ages, and fertility states would be widely useful for the testis research community. To describe the generation and use of the human infertility single-cell testis atlas (HISTA), an interactive web tool for understanding human spermatogenesis through scRNA-Seq analysis. We obtained scRNA-Seq datasets derived from 12 donors, including healthy adult controls, juveniles, and several infertility cases, and reprocessed these data using methods to remove batch effects. Using Shiny, an open-source environment for data visualization, we created numerous interactive tools for exploring the data, some of which support simple statistical hypothesis testing. We used the resulting HISTA browser and its underlying data to demonstrate HISTA's value for testis researchers. A primary application of HISTA is to search by a single gene or a set of genes; thus, we present various analyses that quantify and visualize gene expression across the testis cells and pathology. HISTA also contains machine-learning-derived gene modules ("components") that capture the entire transcriptional landscape of the testis tissue. We show how the use of these components can simplify the highly complex data in HISTA and assist with the interpretation of genes with unknown functions. Finally, we demonstrate the diverse ways HISTA can be used for new data analysis, including hypothesis testing. HISTA is a research environment that can help scientists organize and understand the high-dimensional transcriptional landscape of the human testis. HISTA has already contributed to published testis research and can be updated as needed with input from the research community or downloaded and modified for individual needs.

Pycallingcards: an integrated environment for visualizing, analyzing, and interpreting Calling Cards data.

Unraveling the transcriptional programs that control how cells divide, differentiate, and respond to their environments requires a precise understanding of transcription factors' (TFs) DNA-binding activities. Calling cards (CC) technology uses transposons to capture transient TF binding events at one instant in time and then read them out at a later time. This methodology can also be used to simultaneously measure TF binding and mRNA expression from single-cell CC and to record and integrate TF binding events across time in any cell type of interest without the need for purification. Despite these advantages, there has been a lack of dedicated bioinformatics tools for the detailed analysis of CC data. We introduce Pycallingcards, a comprehensive Python module specifically designed for the analysis of single-cell and bulk CC data across multiple species. Pycallingcards introduces two innovative peak callers, CCcaller and MACCs, enhancing the accuracy and speed of pinpointing TF binding sites from CC data. Pycallingcards offers a fully integrated environment for data visualization, motif finding, and comparative analysis with RNA-seq and ChIP-seq datasets. To illustrate its practical application, we have reanalyzed previously published mouse cortex and glioblastoma datasets. This analysis revealed novel cell-type-specific binding sites and potential sex-linked TF regulators, furthering our understanding of TF binding and gene expression relationships. Thus, Pycallingcards, with its user-friendly design and seamless interface with the Python data science ecosystem, stands as a critical tool for advancing the analysis of TF functions via CC data. Pycallingcards can be accessed on the GitHub repository: https://github.com/The-Mitra-Lab/pycallingcards.

Evaluation of groundwater quality in the rural environment using geostatistical analysis and WebGIS methods in a Hungarian settlement, Báránd.

The evaluation, visualization of environmental data from long-term monitoring, and making them accessible in a processed form in user-friendly interfaces on the Internet are important tasks of our time. The pollution of groundwater resources in settlements is a global phenomenon, the mitigation of which requires a number of environmental measures. In this study, water quality changes following the construction of a sewerage network were examined in the course of long-term monitoring between 2013 and 2022, during which 40 municipal groundwater wells were regularly sampled. Classifying the monitoring data into pollution categories based on water quality index (WQI) and degree of contamination index (Cd), a high degree of contamination was found in the period before the installation of the sewerage network (2014), as the majority of the wells were classified as contaminated and heavily contaminated. In the monitoring period following the installation of the sewerage network, a significant positive change was found in the case of most of the water chemical parameters tested (EC, NH4+, NO2-, NO3-, PO43-). Based on interpolated maps, it was found that an increasing part of the area shows satisfactory or good water quality. This was confirmed by the discriminant analysis as well, as it is possible to determine with an accuracy of 80.4% whether the given sample originates from the period before or after the installation of the sewerage network based on the given water chemical parameters. However, 8 years after setting up the sewerage network, the concentration of inorganic nitrogen forms and organic matter remains high, indicating that the accumulated pollutants in the area are still present. To understand the dynamics of purification processes, additional, long-term monitoring is necessary. Making these data available to members of the society can contribute to appropriate environmental measures and strategies.

Study on the Multi-Level Experiment Teaching of NAO Robot Combined with Virtual and Reality

NAO is the most well-known and widely used humanoid robot in education and research in the world. With the development of computer technology and cloud computing, and the establishment of artificial intelligence specialty, robot teaching is more important in artificial intelligence specialty. The artificial intelligence specialty is a new specialty just established in the talent training program of Chinese universities. Its purpose is to cultivate application-oriented talents in China's artificial intelligence industry and promote the construction of first-class disciplines of artificial intelligence. MATLAB is the commercial mathematical software. It is a high-level technical computing language and interactive environment for algorithm development, data visualization, data analysis and numerical calculation. It is widely used in the experimental teaching of NAO robot. In the teaching of robot theory, kinematics, trajectory planning, dynamics and control are relatively abstract and complex, and there are a lot of formula derivation and differential equations. In order to improve the quality of NAO experiment teaching, this paper analyzes the current situation and existing problems in NAO experiment teaching. In the exploration and practice of robot experiment teaching, NAO robot is taken as the research object, and MATLAB is used to carry out robot simulation and control experiments. The paper presents a multi-level experimental teaching method of NAO robot based on the combination of virtual and Reality. The multi-level includes three levels: basic experiment, design experiment and innovative experiment. The paper describes the design and implementation process of the three-level experimental teaching in detail, and gives the assessment methods, characteristics and effectiveness evaluation of the experimental teaching. The teaching results show that the teaching effect is good, can stimulate students' enthusiasm for learning, and help to cultivate students' programming ability and innovation ability.

Design and Application of Multi-Dimensional Visualization System for Large-Scale Ocean Data

With the constant deepening of research on marine environment simulation and information expression, there are higher and higher requirements for the sense of the reality of ocean data visualization results and the real-time interaction in the visualization process. Aiming at the challenges of 3D interactive key technology and GPU-based visualization algorithm technology, we developed a visualization system for large-scale 3D marine environmental data. The system realizes submarine terrain rendering, contour line visualization, isosurface visualization, section visualization, volume visualization and flow field visualization. In order to manage and express the data in the system, we developed a data management module, which can effectively integrate a large number of marine environmental data and manage them effectively. We developed a series of data analysis functions for the system, such as point query and line query, local analysis and multi-screen collaboration, etc. These functions can effectively improve the data analysis efficiency of users and meet the data analysis needs in multiple scenarios. The marine environmental data visualization system developed in this paper can efficiently and intuitively simulate and display the nature and changing process of marine water environmental factors.

A method to evaluate the degree of bleaching of IRSL signals in feldspar: The 3ET method

In addition to dating, IRSL luminescence signals can preserve information about erosional, transport, and depositional histories of a population of grains. Knowledge of the degree of bleaching can be useful in understanding the processes that occurred during previous depositional events, as certain transport conditions result in a well bleached signal, while others result in grains retaining an inherited signal from prior events. This information can be accessed by making single-grain IRSL measurements across successively increasing temperatures, thereby isolating signals from traps of different bleachabilities. A new approach offers a way to evaluate the completeness of bleaching of a grain by testing patterns of equivalent dose (D E ) values measured at three elevated temperatures (3ET), 50, 125, and 225 °C. Consistent D E estimates across two or more temperatures suggest a single bleaching event of sufficient duration to fully depopulate the traps involved. Incompletely bleached grains with inconsistent D E values across temperatures will lack a 3ET “plateau.” Modes in the distribution of D E values for fully bleached grains can suggest depositional ages, subject to assessment of fading. We developed a Python code in a Jupyter Notebook environment for data analysis and visualization to expedite processing the large data sets produced by the 3ET protocol. The 3ET protocol was tested on a radiocarbon dated sequence of playa samples from California, USA and on a set of fluvial terraces in the Marlborough region of New Zealand as part of a larger project to reconstruct regional seismic history. Where standard pIRIR apparent ages can be inconsistent or ambiguous, 3ET age estimates produce generally consistent apparent ages. Modes of 3ET plateaus can be used to infer the most recent and prior events that resulted in a sub-population of grains being fully bleached. These initial results suggest that the 3ET method can be useful to characterize both the age and degree of bleaching of depositional events.

Web Based Spatio-Temporal Data Bidirectional Relationship Visualization—A Case Study of Oceanographic Data

Environmental data visualization tools are used for data validation, maintenance, and assessment of status and trends of environmental variables. However, currently available visualization tools do not meet all the requirements needed for efficient data management. In this paper, we propose a new approach and present a web-based implementation of a visualization tool that focuses on efficient visualization and seamless integration of maps and graphs. Our approach emphasizes the spatio-temporal relationship of environmental data stored in a relational database. Several new advantages emerge with this approach. Through a case study of two oceanographic datasets—the HarmoNIA project database and the Croatian national monitoring database—we show how this approach enables intuitive status and trend assessments, simplifies data validation, enables the updating of corrections, and is suitable for a web-based implementation that works efficiently even with large datasets.

A Comprehensive State-of-the-Art Survey on Data Visualization Tools: Research Developments, Challenges and Future Domain Specific Visualization Framework

Data visualization is a powerful skill for the demonstration of meaningful data insights in an interactive and effective way. In this survey article, we collected 70 articles from last five years (2017-2022) to identify, classify, and investigate the various scopes, aspects and theories of data visualization. We also investigated the powerful applications of data visualization in various domains and fields such as visualization apps for health sector, Internet of things (IoTs), business dashboards, urban traffic management, smart buildings and environmental data visualization. However, after thorough investigation and classification, we conclude that, a comprehensive study is still missing about interactive, effective and efficient data visualization survey explaining basic current state-of-the-art best interactive visualization techniques, web-based tools and platforms, best performance theories, data structures and algorithms. In this survey article, we perform a thorough investigation to fill the gap on theoretical, analytical, statistical models and techniques for improving the performance of visualization. Current primary and domain specific future challenges are also reviewed, and related future research directions and opportunities are recommended.

Integration of Mixed Reality to CFD in Industry 4.0: A Manufacturing Design Paradigm

Simulation, as a pillar technology of Industry 4.0, is a key tool for improving manufacturing processes. Simulation is an indispensable set of technological tools and methods for the successful implementation of digital manufacturing, since it allows for the experimentation and validation of product, process, and system design and configuration. The value of simulation is evident, especially in today’s volatile manufacturing environment, which is influenced by globalization and ever-increasing demands for higher levels of product customization and personalization. Moreover, a significant manufacturing issue is the interoperability and communication among the several simulation tools. To that end, an Industrial Internet of Things (IIoT) framework for simulation and visualization of selected monitored values based on Mixed Reality (MR) and Computational Fluid Dynamics (CFD) in a cloud-computing environment for intuitive data visualization on smart devices is presented in this paper. As such, in light of the possibilities offered by digital technologies such as CFD simulation, smart factories can gain a competitive advantage because of the low cost, low risk, and the provided useful insights. The range of CFD applications encountered in process manufacturing and engineering varies greatly depending on the industry. Summarizing, this review paper identifies the gaps in current practices, and future trends and challenges to be met on the field are outlined. The contribution of the research work extends to the proposal of a workflow for the integration of Mixed Reality (MR) to the CFD simulation, in an attempt to create simulation holograms on the user’s field of view.

Situational Awareness of COVID Pandemie data using Virtual Reality

Situational awareness provides the decision making capability to identify, process, and comprehend big data. In our approach, situational awareness is achieved by integrating and analyzing multiple aspects of data using stacked bar graphs and geographic representations of the data. We provide a data visualization tool to represent COVID pandemic data on top of the geographical information. The combination of geospatial and temporal data provides the information needed to conduct situational analysis for the COVTD-19 pandemic. By providing interactivity, geographical maps can be viewed from different perspectives and offer insight into the dynamical aspects of the COVTD-19 pandemic for the fifty states in the USA. We have overlaid dynamic information on top of a geographical representation in an intuitive way for decision making. We describe how modeling and simulation of data increase situational awareness, especially when coupled with immersive virtual reality interaction. This paper presents an immersive virtual reality (VR environment and mobile environment for data visualization using Oculus Rift head-mounted display and smartphones. This work combines neural network predictions with human-centric situational awareness and data analytics to provide accurate, timely, and scientific strategies in combatting and mitigating the spread of the coronavirus pandemic. Testing and evaluation of the data visualization tool have been done with realtime feed of COVID pandemic data set for immersive environment, non-immersive environment, and mobile environment.

Monitoring of urinary microbiota and uropathogens' antibiotic resistance in one urological hospital

Introduction. There is no consensus on the management of patients with recurrent uncomplicated lower urinary tract infection (uLUTI), which makes it difficult to carry out effective therapy. Purpose of the study . To study the microbial patterns of urine and uropathogens' antibiotic resistance in a urological hospital from 2010 to 2017. Materials and methods . The results of bacteriological studies and the data of individual antibiotic susceptibility testing of patients with recurrent uLUTI (n = 502) were retrospectively analyzed. Inclusion criteria of the study: consent of patients to participate in the study, the presence of clinical manifestations of recurrent uLUTI in anamnesis, two episodes within six months or three during the year, leukocyturia in the urinalysis, the absence of sexually transmitted diseases at the time of the study and in anamnesis and also vaginal discharge. Bacteriological studies of the midstream morning urine sample before the prescription of antibiotic therapy were carried out with the determination of antibiotic sensitivity/resistance and production of extended-spectrum p-lactamases. In addition to the standard set of culture media, chromogenic media, aerobic and anaerobic culturing conditions were used. Statistical analysis was performed in the statistical processing and data visualization environment «R ver 3.2» («R Foundation for Statistical Computing», Vienna, Austria). Results. During the 8-year monitoring of the microbiota, the urine of patients with recurrent uLUTI was characterized by a microbial pattern certain constancy, but with the predominance (94.1% - 99.1%) of anaerobic-aerobic associations. The antibiotic resistance of most causative and debatable uropathogens increased, and the detection frequency of enterobacteria producing extended-spectrum p-lactamases also increased. Significant differences in the average levels of bacteriuria in the study period for most taxa of microorganisms were not detected. Conclusion. The detection frequencies of E. coli vary around 50.0%, for other causative pathogens a lower detection rate is noted, while non-clostridial anaerobic bacteria are excreted in 96.6% of cases in patients with recurrent uLUTI. The antibiotic resistance of causative and debatable pathogens and the detection frequencies of enterobacteria producing extended-spectrum p-lactamases are increasing, which necessitates a revision of the etiological structure and approaches to empirical therapy of recurrent uLUTI.

FIRE BEHAVIOR PREDICTION USING MACHINE LEARNING ALGORITHMS

Wildfires can affect ecosystem structure and threaten human lives. Understanding fire behavior and predicting fire activities is a crucial issue to mitigate fire impacts. Machine Learning is currently an important tool for the modeling, analysis, and visualization of environmental data and wildfire events. In this study, we assessed the performance of two machine learning algorithms for modeling and predicting fire intensity, the height of flames, and fire rate of spreading in Eucalyptus urophylla (Myrtaceae, Myrtales) and Eucalyptus grandis (Myrtaceae, Myrtales) plantations spatially located in Viçosa - MG, Brazil. The Random Forest showed to be the best algorithm for fire modeling, with climatic conditions, and moisture of the combustible material being the variables that significantly affect the prediction of fire behavior.

Abstract A01: Opportunities for and benefits of incorporating data visualization in population sciences

Abstract Background: A key strength of population sciences and epidemiology is the breadth and depth of the data these studies, especially prospective cohorts, collect over time. However, publications from and websites of these studies tend to present collapsed or aggregate data in traditional tables, often underestimating the true complexity of information in those data. One goal of the NIH Strategic Plan for Data Science is to “support the development and dissemination of advanced data management, analytics, and visualization tools.” We recently developed a robust, innovative, and scalable data visualization environment for the California Teachers Study (CTS), a prospective cancer epidemiology cohort. Methods: We identified the need to eliminate the practice of storing CTS data in a collection of disparate data silos and constructed a data warehouse to compile two decades of study data, eliminating additional harmonization steps. We then developed a data commons, a remotely accessible environment where cohort data could be utilized by our researchers and study collaborators. This allowed questionnaire-based data, cancer registry information, and hospitalization and mortality data from our study participants to be easily accessed in a secure environment. Serving as a single unified source of information, the data warehouse, coupled with the industry-leading data visualization tool Tableau, was configured using the following key epidemiologic cohort activities: reports, cohort selection, calculated values, and exploring and transforming data variables for analysis. Results: With the ability to sort and filter on specific covariates we can define subsets of populations for cohort selection and endpoint-specific analysis. Using interactive reports with drags and drops, and creating calculated variables within Tableau, gives us the ability to visualize time between event data, such as time from study entry to a cancer diagnosis, multiple cancer diagnoses, hospitalizations, or mortality without requiring programmatic back-end coding. Conclusion: The creation of a data warehouse and data commons has given the CTS the ability to visualize information about the demographics and health-related patterns of our study participants, leading to faster answers to complex questions about our data. The interactive data visualizations that we have created allow users the ability to identify new patterns easily, and drill down into the data for a more detailed look. Defining analytic populations through cohort selection and adding geospatial data to our data warehouse has already resulted in the discovery of new patterns that had been unidentified before—simply by providing the same information but utilizing a more accessible and innovative approach to presenting it. As epidemiology uses and shares more of its data, it’s essential to help the widest possible community understand those data in detail. Data visualization can provide specific benefits to key, essential population sciences research activities. Citation Format: Nadia T. Chung, James V. Lacey Jr., Emma Spielfogel, Paul Hughes, Sandeep Chandra, Elena Martinez, Jennifer Benbow. Opportunities for and benefits of incorporating data visualization in population sciences [abstract]. In: Proceedings of the AACR Special Conference on Modernizing Population Sciences in the Digital Age; 2019 Feb 19-22; San Diego, CA. Philadelphia (PA): AACR; Cancer Epidemiol Biomarkers Prev 2020;29(9 Suppl):Abstract nr A01.

Test of Arabidopsis Space Transcriptome: A Discovery Environment to Explore Multiple Plant Biology Spaceflight Experiments.

Recent advances in the routine access to space along with increasing opportunities to perform plant growth experiments on board the International Space Station have led to an ever-increasing body of transcriptomic, proteomic, and epigenomic data from plants experiencing spaceflight. These datasets hold great promise to help understand how plant biology reacts to this unique environment. However, analyses that mine across such expanses of data are often complex to implement, being impeded by the sheer number of potential comparisons that are possible. Complexities in how the output of these multiple parallel analyses can be presented to the researcher in an accessible and intuitive form provides further barriers to such research. Recent developments in computational systems biology have led to rapid advances in interactive data visualization environments designed to perform just such tasks. However, to date none of these tools have been tailored to the analysis of the broad-ranging plant biology spaceflight data. We have therefore developed the Test Of Arabidopsis Space Transcriptome (TOAST) database (https://astrobiology.botany.wisc.edu/astrobotany-toast) to address this gap in our capabilities. TOAST is a relational database that uses the Qlik database management software to link plant biology, spaceflight-related omics datasets, and their associated metadata. This environment helps visualize relationships across multiple levels of experiments in an easy to use gene-centric platform. TOAST draws on data from The US National Aeronautics and Space Administration’s (NASA’s) GeneLab and other data repositories and also connects results to a suite of web-based analytical tools to facilitate further investigation of responses to spaceflight and related stresses. The TOAST graphical user interface allows for quick comparisons between plant spaceflight experiments using real-time, gene-specific queries, or by using functional gene ontology, Kyoto Encyclopedia of Genes and Genomes pathway, or other filtering systems to explore genetic networks of interest. Testing of the database shows that TOAST confirms patterns of gene expression already highlighted in the literature, such as revealing the modulation of oxidative stress-related responses across multiple plant spaceflight experiments. However, this data exploration environment can also drive new insights into patterns of spaceflight responsive gene expression. For example, TOAST analyses highlight changes to mitochondrial function as likely shared responses in many plant spaceflight experiments.

Big Data Environment for Realtime Earthquake Data Acquisition and Visualization

Indonesia is a country that has the highest level of earthquake risk in the world. In the past 10 years, there have been ± 90,000 earthquake events recorded and always increasing along with the explosion of earthquake data occurs at any time. The process of collecting and analyzing earthquake data requires more effort and takes a long computational time. In this paper, we propose a new system to acquire, store, manage and process earthquake data in Indonesia in real-time, fast and dynamic by utilizing features in the Big Data Environment. This system improves computational performance in the process of managing and analyzing earthquake data in Indonesia by combining and integrating earthquake data from several providers to form a complete unity of earthquake data. An additional function is the existence of an API (Application Programming Interface) embedded in this system to provide access to the results of earthquake data analysis such as density, probability density function and seismic data association between provinces in Indonesia. The process in this system has been carried out in parallel and improved computing performance. This is evidenced by the computational time in the preprocessing process on a single-core master node, which requires 55.6 minutes, but a distributed computing process using 15 cores can speeds up with only 4.82 minutes.

Construction of integrated particle rendering environment for large scale data visualization

Construction of integrated particle rendering environment for large scale data visualization

Framework for Infectious Disease Analysis: A comprehensive and integrative multi-modeling approach to disease prediction and management.

The impact of infectious disease on human populations is a function of many factors including environmental conditions, vector dynamics, transmission mechanics, social and cultural behaviors, and public policy. A comprehensive framework for disease management must fully connect the complete disease lifecycle, including emergence from reservoir populations, zoonotic vector transmission, and impact on human societies. The Framework for Infectious Disease Analysis is a software environment and conceptual architecture for data integration, situational awareness, visualization, prediction, and intervention assessment. Framework for Infectious Disease Analysis automatically collects biosurveillance data using natural language processing, integrates structured and unstructured data from multiple sources, applies advanced machine learning, and uses multi-modeling for analyzing disease dynamics and testing interventions in complex, heterogeneous populations. In the illustrative case studies, natural language processing from social media, news feeds, and websites was used for information extraction, biosurveillance, and situation awareness. Classification machine learning algorithms (support vector machines, random forests, and boosting) were used for disease predictions.

Prediction of Protein Complexes in Trypanosoma brucei by Protein Correlation Profiling Mass Spectrometry and Machine Learning

A disproportionate number of predicted proteins from the genome sequence of the protozoan parasite Trypanosoma brucei, an important human and animal pathogen, are hypothetical proteins of unknown function. This paper describes a protein correlation profiling mass spectrometry approach, using two size exclusion and one ion exchange chromatography systems, to derive sets of predicted protein complexes in this organism by hierarchical clustering and machine learning methods. These hypothesis-generating proteomic data are provided in an open access online data visualization environment (http://134.36.66.166:8083/complex_explorer). The data can be searched conveniently via a user friendly, custom graphical interface. We provide examples of both potential new subunits of known protein complexes and of novel trypanosome complexes of suggested function, contributing to improving the functional annotation of the trypanosome proteome. Data are available via ProteomeXchange with identifier PXD005968.

Technology for analyzing geophysical time series: Part 2. WinABD—A software package for maintaining and analyzing geophysical monitoring data

The first part of this work discussed the software requirements for working with geophysical monitoring data. This paper considers the technology for studying similar signals realized in the WinABD program. In contrast to many statistical analysis programs, WinABD supports a complete cycle of operations necessary for working with experimental time series. The software includes a database management system, a powerful research apparatus, and an interactive data visualization environment. The program makes it possible to analyze the structure of series and reveal dependences and interrelations between signals. There are a large number of nonstandard tools and methods necessary for everyday work with nonideal data. A moving time window technology is widely used, which makes it possible to study the development of all processes with time and reveal variations related to any events. A special “window-slamming” technology at the boundaries of a series makes it possible to carry out processing with a decreasing length of the series, which allows arbitrary combination of the applied methods. All of the procedures admit the presence of gaps in observations. For all data operations, a calendar time scale is used, which substantially improves convenience of operation. Correct joint processing of series with unidentical onset dates and noncoinciding observation periodicity is provided.

A portable platform to collect and review behavioral data simultaneously with neurophysiological signals.

This paper presents a portable platform to collect and review behavioral data simultaneously with neurophysiological signals. The whole system is comprised of four parts: a sensor data acquisition interface, a socket server for real-time data streaming, a Simulink system for real-time processing and an offline data review and analysis toolbox. A low-cost microcontroller is used to acquire data from external sensors such as accelerometer and hand dynamometer. The micro-controller transfers the data either directly through USB or wirelessly through a bluetooth module to a data server written in C++ for MS Windows OS. The data server also interfaces with the digital glove and captures HD video from webcam. The acquired sensor data are streamed under User Datagram Protocol (UDP) to other applications such as Simulink/Matlab for real-time analysis and recording. Neurophysiological signals such as electroencephalography (EEG), electrocorticography (ECoG) and local field potential (LFP) recordings can be collected simultaneously in Simulink and fused with behavioral data. In addition, we developed a customized Matlab Graphical User Interface (GUI) software to review, annotate and analyze the data offline. The software provides a fast, user-friendly data visualization environment with synchronized video playback feature. The software is also capable of reviewing long-term neural recordings. Other featured functions such as fast preprocessing with multithreaded filters, annotation, montage selection, power-spectral density (PSD) estimate, time-frequency map and spatial spectral map are also implemented.