Snore related signals processing in a private cloud computing system.

PubMed

Qian, Kun; Guo, Jian; Xu, Huijie; Zhu, Zhaomeng; Zhang, Gongxuan

2014-09-01

Snore related signals (SRS) have been demonstrated to carry important information about the obstruction site and degree in the upper airway of Obstructive Sleep Apnea-Hypopnea Syndrome (OSAHS) patients in recent years. To make this acoustic signal analysis method more accurate and robust, big SRS data processing is inevitable. As an emerging concept and technology, cloud computing has motivated numerous researchers and engineers to exploit applications both in academic and industry field, which could have an ability to implement a huge blue print in biomedical engineering. Considering the security and transferring requirement of biomedical data, we designed a system based on private cloud computing to process SRS. Then we set the comparable experiments of processing a 5-hour audio recording of an OSAHS patient by a personal computer, a server and a private cloud computing system to demonstrate the efficiency of the infrastructure we proposed.

Advances in biomedical engineering and biotechnology during 2013-2014.

PubMed

Liu, Feng; Wang, Ying; Burkhart, Timothy A; González Penedo, Manuel Francisco; Ma, Shaodong

2014-01-01

The 3rd International Conference on Biomedical Engineering and Biotechnology (iCBEB 2014), held in Beijing from the 25th to the 28th of September 2014, is an annual conference that intends to provide an opportunity for researchers and practitioners around the world to present the most recent advances and future challenges in the fields of biomedical engineering, biomaterials, bioinformatics and computational biology, biomedical imaging and signal processing, biomechanical engineering and biotechnology, amongst others. The papers published in this issue are selected from this conference, which witnesses the advances in biomedical engineering and biotechnology during 2013-2014.

Signal and image processing for early detection of coronary artery diseases: A review

NASA Astrophysics Data System (ADS)

Mobssite, Youness; Samir, B. Belhaouari; Mohamad Hani, Ahmed Fadzil B.

2012-09-01

Today biomedical signals and image based detection are a basic step to diagnose heart diseases, in particular, coronary artery diseases. The goal of this work is to provide non-invasive early detection of Coronary Artery Diseases relying on analyzing images and ECG signals as a combined approach to extract features, further classify and quantify the severity of DCAD by using B-splines method. In an aim of creating a prototype of screening biomedical imaging for coronary arteries to help cardiologists to decide the kind of treatment needed to reduce or control the risk of heart attack.

Defense Applications of Signal Processing

DTIC Science & Technology

1999-08-27

class of multiscale autoregressive moving average (MARMA) processes. These are generalisations of ARMA models in time series analysis , and they contain...including the two theoretical sinusoidal components. Analysis of the amplitude and frequency time series provided some novel insight into the real...communication channels, underwater acoustic signals, radar systems , economic time series and biomedical signals [7]. The alpha stable (aS) distribution has

Wearable Biomedical Measurement Systems for Assessment of Mental Stress of Combatants in Real Time

PubMed Central

Seoane, Fernando; Mohino-Herranz, Inmaculada; Ferreira, Javier; Alvarez, Lorena; Buendia, Ruben; Ayllón, David; Llerena, Cosme; Gil-Pita, Roberto

2014-01-01

The Spanish Ministry of Defense, through its Future Combatant program, has sought to develop technology aids with the aim of extending combatants' operational capabilities. Within this framework the ATREC project funded by the “Coincidente” program aims at analyzing diverse biometrics to assess by real time monitoring the stress levels of combatants. This project combines multidisciplinary disciplines and fields, including wearable instrumentation, textile technology, signal processing, pattern recognition and psychological analysis of the obtained information. In this work the ATREC project is described, including the different execution phases, the wearable biomedical measurement systems, the experimental setup, the biomedical signal analysis and speech processing performed. The preliminary results obtained from the data analysis collected during the first phase of the project are presented, indicating the good classification performance exhibited when using features obtained from electrocardiographic recordings and electrical bioimpedance measurements from the thorax. These results suggest that cardiac and respiration activity offer better biomarkers for assessment of stress than speech, galvanic skin response or skin temperature when recorded with wearable biomedical measurement systems. PMID:24759113

Wearable biomedical measurement systems for assessment of mental stress of combatants in real time.

PubMed

Seoane, Fernando; Mohino-Herranz, Inmaculada; Ferreira, Javier; Alvarez, Lorena; Buendia, Ruben; Ayllón, David; Llerena, Cosme; Gil-Pita, Roberto

2014-04-22

The Spanish Ministry of Defense, through its Future Combatant program, has sought to develop technology aids with the aim of extending combatants' operational capabilities. Within this framework the ATREC project funded by the "Coincidente" program aims at analyzing diverse biometrics to assess by real time monitoring the stress levels of combatants. This project combines multidisciplinary disciplines and fields, including wearable instrumentation, textile technology, signal processing, pattern recognition and psychological analysis of the obtained information. In this work the ATREC project is described, including the different execution phases, the wearable biomedical measurement systems, the experimental setup, the biomedical signal analysis and speech processing performed. The preliminary results obtained from the data analysis collected during the first phase of the project are presented, indicating the good classification performance exhibited when using features obtained from electrocardiographic recordings and electrical bioimpedance measurements from the thorax. These results suggest that cardiac and respiration activity offer better biomarkers for assessment of stress than speech, galvanic skin response or skin temperature when recorded with wearable biomedical measurement systems.

BioSigPlot: an opensource tool for the visualization of multi-channel biomedical signals with Matlab.

PubMed

Boudet, Samuel; Peyrodie, Laurent; Gallois, Philippe; de l'Aulnoit, Denis Houzé; Cao, Hua; Forzy, Gérard

2013-01-01

This paper presents a Matlab-based software (MathWorks inc.) called BioSigPlot for the visualization of multi-channel biomedical signals, particularly for the EEG. This tool is designed for researchers on both engineering and medicine who have to collaborate to visualize and analyze signals. It aims to provide a highly customizable interface for signal processing experimentation in order to plot several kinds of signals while integrating the common tools for physician. The main advantages compared to other existing programs are the multi-dataset displaying, the synchronization with video and the online processing. On top of that, this program uses object oriented programming, so that the interface can be controlled by both graphic controls and command lines. It can be used as EEGlab plug-in but, since it is not limited to EEG, it would be distributed separately. BioSigPlot is distributed free of charge (http://biosigplot.sourceforge.net), under the terms of GNU Public License for non-commercial use and open source development.

Analysis of the sleep quality of elderly people using biomedical signals.

PubMed

Moreno-Alsasua, L; Garcia-Zapirain, B; Mendez-Zorrilla, A

2015-01-01

This paper presents a technical solution that analyses sleep signals captured by biomedical sensors to find possible disorders during rest. Specifically, the method evaluates electrooculogram (EOG) signals, skin conductance (GSR), air flow (AS), and body temperature. Next, a quantitative sleep quality analysis determines significant changes in the biological signals, and any similarities between them in a given time period. Filtering techniques such as the Fourier transform method and IIR filters process the signal and identify significant variations. Once these changes have been identified, all significant data is compared and a quantitative and statistical analysis is carried out to determine the level of a person's rest. To evaluate the correlation and significant differences, a statistical analysis has been calculated showing correlation between EOG and AS signals (p=0,005), EOG, and GSR signals (p=0,037) and, finally, the EOG and Body temperature (p=0,04). Doctors could use this information to monitor changes within a patient.

Camera systems in human motion analysis for biomedical applications

NASA Astrophysics Data System (ADS)

Chin, Lim Chee; Basah, Shafriza Nisha; Yaacob, Sazali; Juan, Yeap Ewe; Kadir, Aida Khairunnisaa Ab.

2015-05-01

Human Motion Analysis (HMA) system has been one of the major interests among researchers in the field of computer vision, artificial intelligence and biomedical engineering and sciences. This is due to its wide and promising biomedical applications, namely, bio-instrumentation for human computer interfacing and surveillance system for monitoring human behaviour as well as analysis of biomedical signal and image processing for diagnosis and rehabilitation applications. This paper provides an extensive review of the camera system of HMA, its taxonomy, including camera types, camera calibration and camera configuration. The review focused on evaluating the camera system consideration of the HMA system specifically for biomedical applications. This review is important as it provides guidelines and recommendation for researchers and practitioners in selecting a camera system of the HMA system for biomedical applications.

[Computers in biomedical research: I. Analysis of bioelectrical signals].

PubMed

Vivaldi, E A; Maldonado, P

2001-08-01

A personal computer equipped with an analog-to-digital conversion card is able to input, store and display signals of biomedical interest. These signals can additionally be submitted to ad-hoc software for analysis and diagnosis. Data acquisition is based on the sampling of a signal at a given rate and amplitude resolution. The automation of signal processing conveys syntactic aspects (data transduction, conditioning and reduction); and semantic aspects (feature extraction to describe and characterize the signal and diagnostic classification). The analytical approach that is at the basis of computer programming allows for the successful resolution of apparently complex tasks. Two basic principles involved are the definition of simple fundamental functions that are then iterated and the modular subdivision of tasks. These two principles are illustrated, respectively, by presenting the algorithm that detects relevant elements for the analysis of a polysomnogram, and the task flow in systems that automate electrocardiographic reports.

Micro/Nanostructured Films and Adhesives for Biomedical Applications.

PubMed

Lee, Jungkyu K; Kang, Sung Min; Yang, Sung Ho; Cho, Woo Kyung

2015-12-01

The advanced technologies available for micro/nanofabrication have opened new avenues for interdisciplinary approaches to solve the unmet medical needs of regenerative medicine and biomedical devices. This review highlights the recent developments in micro/nanostructured adhesives and films for biomedical applications, including waterproof seals for wounds or surgery sites, drug delivery, sensing human body signals, and optical imaging of human tissues. We describe in detail the fabrication processes required to prepare the adhesives and films, such as tape-based adhesives, nanofilms, and flexible and stretchable film-based electronic devices. We also discuss their biomedical functions, performance in vitro and in vivo, and the future research needed to improve the current systems.

Bioinspired Polarization Imaging Sensors: From Circuits and Optics to Signal Processing Algorithms and Biomedical Applications

PubMed Central

York, Timothy; Powell, Samuel B.; Gao, Shengkui; Kahan, Lindsey; Charanya, Tauseef; Saha, Debajit; Roberts, Nicholas W.; Cronin, Thomas W.; Marshall, Justin; Achilefu, Samuel; Lake, Spencer P.; Raman, Baranidharan; Gruev, Viktor

2015-01-01

In this paper, we present recent work on bioinspired polarization imaging sensors and their applications in biomedicine. In particular, we focus on three different aspects of these sensors. First, we describe the electro–optical challenges in realizing a bioinspired polarization imager, and in particular, we provide a detailed description of a recent low-power complementary metal–oxide–semiconductor (CMOS) polarization imager. Second, we focus on signal processing algorithms tailored for this new class of bioinspired polarization imaging sensors, such as calibration and interpolation. Third, the emergence of these sensors has enabled rapid progress in characterizing polarization signals and environmental parameters in nature, as well as several biomedical areas, such as label-free optical neural recording, dynamic tissue strength analysis, and early diagnosis of flat cancerous lesions in a murine colorectal tumor model. We highlight results obtained from these three areas and discuss future applications for these sensors. PMID:26538682

An ultra low energy biomedical signal processing system operating at near-threshold.

PubMed

Hulzink, J; Konijnenburg, M; Ashouei, M; Breeschoten, A; Berset, T; Huisken, J; Stuyt, J; de Groot, H; Barat, F; David, J; Van Ginderdeuren, J

2011-12-01

This paper presents a voltage-scalable digital signal processing system designed for the use in a wireless sensor node (WSN) for ambulatory monitoring of biomedical signals. To fulfill the requirements of ambulatory monitoring, power consumption, which directly translates to the WSN battery lifetime and size, must be kept as low as possible. The proposed processing platform is an event-driven system with resources to run applications with different degrees of complexity in an energy-aware way. The architecture uses effective system partitioning to enable duty cycling, single instruction multiple data (SIMD) instructions, power gating, voltage scaling, multiple clock domains, multiple voltage domains, and extensive clock gating. It provides an alternative processing platform where the power and performance can be scaled to adapt to the application need. A case study on a continuous wavelet transform (CWT)-based heart-beat detection shows that the platform not only preserves the sensitivity and positive predictivity of the algorithm but also achieves the lowest energy/sample for ElectroCardioGram (ECG) heart-beat detection publicly reported today.

Stream computing for biomedical signal processing: A QRS complex detection case-study.

PubMed

Murphy, B M; O'Driscoll, C; Boylan, G B; Lightbody, G; Marnane, W P

2015-01-01

Recent developments in "Big Data" have brought significant gains in the ability to process large amounts of data on commodity server hardware. Stream computing is a relatively new paradigm in this area, addressing the need to process data in real time with very low latency. While this approach has been developed for dealing with large scale data from the world of business, security and finance, there is a natural overlap with clinical needs for physiological signal processing. In this work we present a case study of streams processing applied to a typical physiological signal processing problem: QRS detection from ECG data.

Cross-Approximate Entropy parallel computation on GPUs for biomedical signal analysis. Application to MEG recordings.

PubMed

Martínez-Zarzuela, Mario; Gómez, Carlos; Díaz-Pernas, Francisco Javier; Fernández, Alberto; Hornero, Roberto

2013-10-01

Cross-Approximate Entropy (Cross-ApEn) is a useful measure to quantify the statistical dissimilarity of two time series. In spite of the advantage of Cross-ApEn over its one-dimensional counterpart (Approximate Entropy), only a few studies have applied it to biomedical signals, mainly due to its high computational cost. In this paper, we propose a fast GPU-based implementation of the Cross-ApEn that makes feasible its use over a large amount of multidimensional data. The scheme followed is fully scalable, thus maximizes the use of the GPU despite of the number of neural signals being processed. The approach consists in processing many trials or epochs simultaneously, with independence of its origin. In the case of MEG data, these trials can proceed from different input channels or subjects. The proposed implementation achieves an average speedup greater than 250× against a CPU parallel version running on a processor containing six cores. A dataset of 30 subjects containing 148 MEG channels (49 epochs of 1024 samples per channel) can be analyzed using our development in about 30min. The same processing takes 5 days on six cores and 15 days when running on a single core. The speedup is much larger if compared to a basic sequential Matlab(®) implementation, that would need 58 days per subject. To our knowledge, this is the first contribution of Cross-ApEn measure computation using GPUs. This study demonstrates that this hardware is, to the day, the best option for the signal processing of biomedical data with Cross-ApEn. Copyright © 2013 Elsevier Ireland Ltd. All rights reserved.

Accelerating Biomedical Signal Processing Using GPU: A Case Study of Snore Sound Feature Extraction.

PubMed

Guo, Jian; Qian, Kun; Zhang, Gongxuan; Xu, Huijie; Schuller, Björn

2017-12-01

The advent of 'Big Data' and 'Deep Learning' offers both, a great challenge and a huge opportunity for personalised health-care. In machine learning-based biomedical data analysis, feature extraction is a key step for 'feeding' the subsequent classifiers. With increasing numbers of biomedical data, extracting features from these 'big' data is an intensive and time-consuming task. In this case study, we employ a Graphics Processing Unit (GPU) via Python to extract features from a large corpus of snore sound data. Those features can subsequently be imported into many well-known deep learning training frameworks without any format processing. The snore sound data were collected from several hospitals (20 subjects, with 770-990 MB per subject - in total 17.20 GB). Experimental results show that our GPU-based processing significantly speeds up the feature extraction phase, by up to seven times, as compared to the previous CPU system.

A review of signals used in sleep analysis

PubMed Central

Roebuck, A; Monasterio, V; Gederi, E; Osipov, M; Behar, J; Malhotra, A; Penzel, T; Clifford, GD

2014-01-01

This article presents a review of signals used for measuring physiology and activity during sleep and techniques for extracting information from these signals. We examine both clinical needs and biomedical signal processing approaches across a range of sensor types. Issues with recording and analysing the signals are discussed, together with their applicability to various clinical disorders. Both univariate and data fusion (exploiting the diverse characteristics of the primary recorded signals) approaches are discussed, together with a comparison of automated methods for analysing sleep. PMID:24346125

Ultralow-power electronics for biomedical applications.

PubMed

Chandrakasan, Anantha P; Verma, Naveen; Daly, Denis C

2008-01-01

The electronics of a general biomedical device consist of energy delivery, analog-to-digital conversion, signal processing, and communication subsystems. Each of these blocks must be designed for minimum energy consumption. Specific design techniques, such as aggressive voltage scaling, dynamic power-performance management, and energy-efficient signaling, must be employed to adhere to the stringent energy constraint. The constraint itself is set by the energy source, so energy harvesting holds tremendous promise toward enabling sophisticated systems without straining user lifestyle. Further, once harvested, efficient delivery of the low-energy levels, as well as robust operation in the aggressive low-power modes, requires careful understanding and treatment of the specific design limitations that dominate this realm. We outline the performance and power constraints of biomedical devices, and present circuit techniques to achieve complete systems operating down to power levels of microwatts. In all cases, approaches that leverage advanced technology trends are emphasized.

Cubic spline interpolation with overlapped window and data reuse for on-line Hilbert Huang transform biomedical microprocessor.

PubMed

Chang, Nai-Fu; Chiang, Cheng-Yi; Chen, Tung-Chien; Chen, Liang-Gee

2011-01-01

On-chip implementation of Hilbert-Huang transform (HHT) has great impact to analyze the non-linear and non-stationary biomedical signals on wearable or implantable sensors for the real-time applications. Cubic spline interpolation (CSI) consumes the most computation in HHT, and is the key component for the HHT processor. In tradition, CSI in HHT is usually performed after the collection of a large window of signals, and the long latency violates the realtime requirement of the applications. In this work, we propose to keep processing the incoming signals on-line with small and overlapped data windows without sacrificing the interpolation accuracy. 58% multiplication and 73% division of CSI are saved after the data reuse between the data windows.

Biosignals learning and synthesis using deep neural networks.

PubMed

Belo, David; Rodrigues, João; Vaz, João R; Pezarat-Correia, Pedro; Gamboa, Hugo

2017-09-25

Modeling physiological signals is a complex task both for understanding and synthesize biomedical signals. We propose a deep neural network model that learns and synthesizes biosignals, validated by the morphological equivalence of the original ones. This research could lead the creation of novel algorithms for signal reconstruction in heavily noisy data and source detection in biomedical engineering field. The present work explores the gated recurrent units (GRU) employed in the training of respiration (RESP), electromyograms (EMG) and electrocardiograms (ECG). Each signal is pre-processed, segmented and quantized in a specific number of classes, corresponding to the amplitude of each sample and fed to the model, which is composed by an embedded matrix, three GRU blocks and a softmax function. This network is trained by adjusting its internal parameters, acquiring the representation of the abstract notion of the next value based on the previous ones. The simulated signal was generated by forecasting a random value and re-feeding itself. The resulting generated signals are similar with the morphological expression of the originals. During the learning process, after a set of iterations, the model starts to grasp the basic morphological characteristics of the signal and later their cyclic characteristics. After training, these models' prediction are closer to the signals that trained them, specially the RESP and ECG. This synthesis mechanism has shown relevant results that inspire the use to characterize signals from other physiological sources.

Model-based Bayesian filtering of cardiac contaminants from biomedical recordings.

PubMed

Sameni, R; Shamsollahi, M B; Jutten, C

2008-05-01

Electrocardiogram (ECG) and magnetocardiogram (MCG) signals are among the most considerable sources of noise for other biomedical signals. In some recent works, a Bayesian filtering framework has been proposed for denoising the ECG signals. In this paper, it is shown that this framework may be effectively used for removing cardiac contaminants such as the ECG, MCG and ballistocardiographic artifacts from different biomedical recordings such as the electroencephalogram, electromyogram and also for canceling maternal cardiac signals from fetal ECG/MCG. The proposed method is evaluated on simulated and real signals.

Powerline noise elimination in biomedical signals via blind source separation and wavelet analysis.

PubMed

Akwei-Sekyere, Samuel

2015-01-01

The distortion of biomedical signals by powerline noise from recording biomedical devices has the potential to reduce the quality and convolute the interpretations of the data. Usually, powerline noise in biomedical recordings are extinguished via band-stop filters. However, due to the instability of biomedical signals, the distribution of signals filtered out may not be centered at 50/60 Hz. As a result, self-correction methods are needed to optimize the performance of these filters. Since powerline noise is additive in nature, it is intuitive to model powerline noise in a raw recording and subtract it from the raw data in order to obtain a relatively clean signal. This paper proposes a method that utilizes this approach by decomposing the recorded signal and extracting powerline noise via blind source separation and wavelet analysis. The performance of this algorithm was compared with that of a 4th order band-stop Butterworth filter, empirical mode decomposition, independent component analysis and, a combination of empirical mode decomposition with independent component analysis. The proposed method was able to expel sinusoidal signals within powerline noise frequency range with higher fidelity in comparison with the mentioned techniques, especially at low signal-to-noise ratio.

Biomedical digital assistant for ubiquitous healthcare.

PubMed

Lee, Tae-Soo; Hong, Joo-Hyun; Cho, Myeong-Chan

2007-01-01

The concept of ubiquitous healthcare service, which emerged as one of measures to solve healthcare problems in aged society, means that patients can receive services such as prevention, diagnosis, therapy and prognosis management at any time and in any place with the help of advanced information and communication technology. This service requires not only biomedical digital assistant that can monitor continuously the patients' health condition regardless of time and place, but also wired and wireless communication devices and telemedicine servers that provide doctors with data on patients' present health condition. In order to implement a biomedical digital assistant that is portable and wearable to patients, the present study developed a device that minimizes size, weight and power consumption, measures ECG and PPG signals, and even monitors moving patients' state. The biomedical sensor with the function of wireless communication was designed to be highly portable and wearable, to be operable 24 hours with small-size batteries, and to monitor the subject's heart rate, step count and respiratory rate in his daily life. The biomedical signal receiving device was implemented in two forms, PDA and cellular phone. The movement monitoring device embedded in the battery pack of a cellular phone does not have any problem in operating 24 hours, but the real-time biomedical signal receiving device implemented with PDA operated up to 6 hours due to the limited battery capacity of PDA. This problem is expected to be solved by reducing wireless communication load through improving the processing and storage functions of the sensor. The developed device can transmit a message on the patient's emergency to the remote server through the cellular phone network, and is expected to play crucial roles in the health management of chronic-aged patients in their daily life.

The detection and analysis of point processes in biological signals

NASA Technical Reports Server (NTRS)

Anderson, D. J.; Correia, M. J.

1977-01-01

A pragmatic approach to the detection and analysis of discrete events in biomedical signals is taken. Examples from both clinical and basic research are provided. Introductory sections discuss not only discrete events which are easily extracted from recordings by conventional threshold detectors but also events embedded in other information carrying signals. The primary considerations are factors governing event-time resolution and the effects limits to this resolution have on the subsequent analysis of the underlying process. The analysis portion describes tests for qualifying the records as stationary point processes and procedures for providing meaningful information about the biological signals under investigation. All of these procedures are designed to be implemented on laboratory computers of modest computational capacity.

Solid state light engines for bioanalytical instruments and biomedical devices

NASA Astrophysics Data System (ADS)

Jaffe, Claudia B.; Jaffe, Steven M.

2010-02-01

Lighting subsystems to drive 21st century bioanalysis and biomedical diagnostics face stringent requirements. Industrywide demands for speed, accuracy and portability mean illumination must be intense as well as spectrally pure, switchable, stable, durable and inexpensive. Ideally a common lighting solution could service these needs for numerous research and clinical applications. While this is a noble objective, the current technology of arc lamps, lasers, LEDs and most recently light pipes have intrinsic spectral and angular traits that make a common solution untenable. Clearly a hybrid solution is required to service the varied needs of the life sciences. Any solution begins with a critical understanding of the instrument architecture and specifications for illumination regarding power, illumination area, illumination and emission wavelengths and numerical aperture. Optimizing signal to noise requires careful optimization of these parameters within the additional constraints of instrument footprint and cost. Often the illumination design process is confined to maximizing signal to noise without the ability to adjust any of the above parameters. A hybrid solution leverages the best of the existing lighting technologies. This paper will review the design process for this highly constrained, but typical optical optimization scenario for numerous bioanalytical instruments and biomedical devices.

Surface Electromyography Signal Processing and Classification Techniques

PubMed Central

Chowdhury, Rubana H.; Reaz, Mamun B. I.; Ali, Mohd Alauddin Bin Mohd; Bakar, Ashrif A. A.; Chellappan, Kalaivani; Chang, Tae. G.

2013-01-01

Electromyography (EMG) signals are becoming increasingly important in many applications, including clinical/biomedical, prosthesis or rehabilitation devices, human machine interactions, and more. However, noisy EMG signals are the major hurdles to be overcome in order to achieve improved performance in the above applications. Detection, processing and classification analysis in electromyography (EMG) is very desirable because it allows a more standardized and precise evaluation of the neurophysiological, rehabitational and assistive technological findings. This paper reviews two prominent areas; first: the pre-processing method for eliminating possible artifacts via appropriate preparation at the time of recording EMG signals, and second: a brief explanation of the different methods for processing and classifying EMG signals. This study then compares the numerous methods of analyzing EMG signals, in terms of their performance. The crux of this paper is to review the most recent developments and research studies related to the issues mentioned above. PMID:24048337

Robust Nonlinear Causality Analysis of Nonstationary Multivariate Physiological Time Series.

PubMed

Schack, Tim; Muma, Michael; Feng, Mengling; Guan, Cuntai; Zoubir, Abdelhak M

2018-06-01

An important research area in biomedical signal processing is that of quantifying the relationship between simultaneously observed time series and to reveal interactions between the signals. Since biomedical signals are potentially nonstationary and the measurements may contain outliers and artifacts, we introduce a robust time-varying generalized partial directed coherence (rTV-gPDC) function. The proposed method, which is based on a robust estimator of the time-varying autoregressive (TVAR) parameters, is capable of revealing directed interactions between signals. By definition, the rTV-gPDC only displays the linear relationships between the signals. We therefore suggest to approximate the residuals of the TVAR process, which potentially carry information about the nonlinear causality by a piece-wise linear time-varying moving-average model. The performance of the proposed method is assessed via extensive simulations. To illustrate the method's applicability to real-world problems, it is applied to a neurophysiological study that involves intracranial pressure, arterial blood pressure, and brain tissue oxygenation level (PtiO2) measurements. The rTV-gPDC reveals causal patterns that are in accordance with expected cardiosudoral meachanisms and potentially provides new insights regarding traumatic brain injuries. The rTV-gPDC is not restricted to the above problem but can be useful in revealing interactions in a broad range of applications.

Monitoring biomedical literature for post-market safety purposes by analyzing networks of text-based coded information.

PubMed

Botsis, Taxiarchis; Foster, Matthew; Kreimeyer, Kory; Pandey, Abhishek; Forshee, Richard

2017-01-01

Literature review is critical but time-consuming in the post-market surveillance of medical products. We focused on the safety signal of intussusception after the vaccination of infants with the Rotashield Vaccine in 1999 and retrieved all PubMed abstracts for rotavirus vaccines published after January 1, 1998. We used the Event-based Text-mining of Health Electronic Records system, the MetaMap tool, and the National Center for Biomedical Ontologies Annotator to process the abstracts and generate coded terms stamped with the date of publication. Data were analyzed in the Pattern-based and Advanced Network Analyzer for Clinical Evaluation and Assessment to evaluate the intussusception-related findings before and after the release of the new rotavirus vaccines in 2006. The tight connection of intussusception with the historical signal in the first period and the absence of any safety concern for the new vaccines in the second period were verified. We demonstrated the feasibility for semi-automated solutions that may assist medical reviewers in monitoring biomedical literature.

Techniques of EMG signal analysis: detection, processing, classification and applications

PubMed Central

Hussain, M.S.; Mohd-Yasin, F.

2006-01-01

Electromyography (EMG) signals can be used for clinical/biomedical applications, Evolvable Hardware Chip (EHW) development, and modern human computer interaction. EMG signals acquired from muscles require advanced methods for detection, decomposition, processing, and classification. The purpose of this paper is to illustrate the various methodologies and algorithms for EMG signal analysis to provide efficient and effective ways of understanding the signal and its nature. We further point up some of the hardware implementations using EMG focusing on applications related to prosthetic hand control, grasp recognition, and human computer interaction. A comparison study is also given to show performance of various EMG signal analysis methods. This paper provides researchers a good understanding of EMG signal and its analysis procedures. This knowledge will help them develop more powerful, flexible, and efficient applications. PMID:16799694

Electroencephalography epilepsy classifications using hybrid cuckoo search and neural network

NASA Astrophysics Data System (ADS)

Pratiwi, A. B.; Damayanti, A.; Miswanto

2017-07-01

Epilepsy is a condition that affects the brain and causes repeated seizures. This seizure is episodes that can vary and nearly undetectable to long periods of vigorous shaking or brain contractions. Epilepsy often can be confirmed with an electrocephalography (EEG). Neural Networks has been used in biomedic signal analysis, it has successfully classified the biomedic signal, such as EEG signal. In this paper, a hybrid cuckoo search and neural network are used to recognize EEG signal for epilepsy classifications. The weight of the multilayer perceptron is optimized by the cuckoo search algorithm based on its error. The aim of this methods is making the network faster to obtained the local or global optimal then the process of classification become more accurate. Based on the comparison results with the traditional multilayer perceptron, the hybrid cuckoo search and multilayer perceptron provides better performance in term of error convergence and accuracy. The purpose methods give MSE 0.001 and accuracy 90.0 %.

siGnum: graphical user interface for EMG signal analysis.

PubMed

Kaur, Manvinder; Mathur, Shilpi; Bhatia, Dinesh; Verma, Suresh

2015-01-01

Electromyography (EMG) signals that represent the electrical activity of muscles can be used for various clinical and biomedical applications. These are complicated and highly varying signals that are dependent on anatomical location and physiological properties of the muscles. EMG signals acquired from the muscles require advanced methods for detection, decomposition and processing. This paper proposes a novel Graphical User Interface (GUI) siGnum developed in MATLAB that will apply efficient and effective techniques on processing of the raw EMG signals and decompose it in a simpler manner. It could be used independent of MATLAB software by employing a deploy tool. This would enable researcher's to gain good understanding of EMG signal and its analysis procedures that can be utilized for more powerful, flexible and efficient applications in near future.

An Analog Circuit Approximation of the Discrete Wavelet Transform for Ultra Low Power Signal Processing in Wearable Sensor Nodes

PubMed Central

Casson, Alexander J.

2015-01-01

Ultra low power signal processing is an essential part of all sensor nodes, and particularly so in emerging wearable sensors for biomedical applications. Analog signal processing has an important role in these low power, low voltage, low frequency applications, and there is a key drive to decrease the power consumption of existing analog domain signal processing and to map more signal processing approaches into the analog domain. This paper presents an analog domain signal processing circuit which approximates the output of the Discrete Wavelet Transform (DWT) for use in ultra low power wearable sensors. Analog filters are used for the DWT filters and it is demonstrated how these generate analog domain DWT-like information that embeds information from Butterworth and Daubechies maximally flat mother wavelet responses. The Analog DWT is realised in hardware via gmC circuits, designed to operate from a 1.3 V coin cell battery, and provide DWT-like signal processing using under 115 nW of power when implemented in a 0.18 μm CMOS process. Practical examples demonstrate the effective use of the new Analog DWT on ECG (electrocardiogram) and EEG (electroencephalogram) signals recorded from humans. PMID:26694414

An Analog Circuit Approximation of the Discrete Wavelet Transform for Ultra Low Power Signal Processing in Wearable Sensor Nodes.

PubMed

Casson, Alexander J

2015-12-17

Ultra low power signal processing is an essential part of all sensor nodes, and particularly so in emerging wearable sensors for biomedical applications. Analog signal processing has an important role in these low power, low voltage, low frequency applications, and there is a key drive to decrease the power consumption of existing analog domain signal processing and to map more signal processing approaches into the analog domain. This paper presents an analog domain signal processing circuit which approximates the output of the Discrete Wavelet Transform (DWT) for use in ultra low power wearable sensors. Analog filters are used for the DWT filters and it is demonstrated how these generate analog domain DWT-like information that embeds information from Butterworth and Daubechies maximally flat mother wavelet responses. The Analog DWT is realised in hardware via g(m)C circuits, designed to operate from a 1.3 V coin cell battery, and provide DWT-like signal processing using under 115 nW of power when implemented in a 0.18 μm CMOS process. Practical examples demonstrate the effective use of the new Analog DWT on ECG (electrocardiogram) and EEG (electroencephalogram) signals recorded from humans.

[Scientometrics and bibliometrics of biomedical engineering periodicals and papers].

PubMed

Zhao, Ping; Xu, Ping; Li, Bingyan; Wang, Zhengrong

2003-09-01

This investigation was made to reveal the current status, research trend and research level of biomedical engineering in Chinese mainland by means of scientometrics and to assess the quality of the four domestic publications by bibliometrics. We identified all articles of four related publications by searching Chinese and foreign databases from 1997 to 2001. All articles collected or cited by these databases were searched and statistically analyzed for finding out the relevant distributions, including databases, years, authors, institutions, subject headings and subheadings. The source of sustentation funds and the related articles were analyzed too. The results showed that two journals were cited by two foreign databases and five Chinese databases simultaneously. The output of Journal of Biomedical Engineering was the highest. Its quantity of original papers cited by EI, CA and the totality of papers sponsored by funds were higher than those of the others, but the quantity and percentage per year of biomedical articles cited by EI were decreased in all. Inland core authors and institutions had come into being in the field of biomedical engineering. Their research topics were mainly concentrated on ten subject headings which included biocompatible materials, computer-assisted signal processing, electrocardiography, computer-assisted image processing, biomechanics, algorithms, electroencephalography, automatic data processing, mechanical stress, hemodynamics, mathematical computing, microcomputers, theoretical models, etc. The main subheadings were concentrated on instrumentation, physiopathology, diagnosis, therapy, ultrasonography, physiology, analysis, surgery, pathology, method, etc.

IEEE International Symposium on Biomedical Imaging.

PubMed

2017-01-01

The IEEE International Symposium on Biomedical Imaging (ISBI) is a scientific conference dedicated to mathematical, algorithmic, and computational aspects of biological and biomedical imaging, across all scales of observation. It fosters knowledge transfer among different imaging communities and contributes to an integrative approach to biomedical imaging. ISBI is a joint initiative from the IEEE Signal Processing Society (SPS) and the IEEE Engineering in Medicine and Biology Society (EMBS). The 2018 meeting will include tutorials, and a scientific program composed of plenary talks, invited special sessions, challenges, as well as oral and poster presentations of peer-reviewed papers. High-quality papers are requested containing original contributions to the topics of interest including image formation and reconstruction, computational and statistical image processing and analysis, dynamic imaging, visualization, image quality assessment, and physical, biological, and statistical modeling. Accepted 4-page regular papers will be published in the symposium proceedings published by IEEE and included in IEEE Xplore. To encourage attendance by a broader audience of imaging scientists and offer additional presentation opportunities, ISBI 2018 will continue to have a second track featuring posters selected from 1-page abstract submissions without subsequent archival publication.

Biomedical sensor design using analog compressed sensing

NASA Astrophysics Data System (ADS)

Balouchestani, Mohammadreza; Krishnan, Sridhar

2015-05-01

The main drawback of current healthcare systems is the location-specific nature of the system due to the use of fixed/wired biomedical sensors. Since biomedical sensors are usually driven by a battery, power consumption is the most important factor determining the life of a biomedical sensor. They are also restricted by size, cost, and transmission capacity. Therefore, it is important to reduce the load of sampling by merging the sampling and compression steps to reduce the storage usage, transmission times, and power consumption in order to expand the current healthcare systems to Wireless Healthcare Systems (WHSs). In this work, we present an implementation of a low-power biomedical sensor using analog Compressed Sensing (CS) framework for sparse biomedical signals that addresses both the energy and telemetry bandwidth constraints of wearable and wireless Body-Area Networks (BANs). This architecture enables continuous data acquisition and compression of biomedical signals that are suitable for a variety of diagnostic and treatment purposes. At the transmitter side, an analog-CS framework is applied at the sensing step before Analog to Digital Converter (ADC) in order to generate the compressed version of the input analog bio-signal. At the receiver side, a reconstruction algorithm based on Restricted Isometry Property (RIP) condition is applied in order to reconstruct the original bio-signals form the compressed bio-signals with high probability and enough accuracy. We examine the proposed algorithm with healthy and neuropathy surface Electromyography (sEMG) signals. The proposed algorithm achieves a good level for Average Recognition Rate (ARR) at 93% and reconstruction accuracy at 98.9%. In addition, The proposed architecture reduces total computation time from 32 to 11.5 seconds at sampling-rate=29 % of Nyquist rate, Percentage Residual Difference (PRD)=26 %, Root Mean Squared Error (RMSE)=3 %.

Distributed processing for features improvement in real-time portable medical devices.

PubMed

Mercado, Erwin John Saavedra

2008-01-01

Portable biomedical devices are being developed and incorporated in daily life. Nevertheless, their standalone capacity is diminished due to the lack of processing power required to face such duties as for example, signal artifacts robustness in EKG monitor devices. The following paper presents a multiprocessor architecture made from simple microcontrollers to provide an increase in processing performance, power consumption efficiency and lower cost.

Wireless plataforms for the monitoring of biomedical variables

NASA Astrophysics Data System (ADS)

Bianco, Román; Laprovitta, Agustín; Misa, Alberto; Toselli, Eduardo; Castagnola, Juan Luis

2007-11-01

The present paper aims to analyze and to compare two wireless platforms for the monitoring of biomedical variables. They must obtain the vital signals of the patients, transmit them through a radio frequency bond and centralize them for their process, storage and monitoring in real time. The implementation of this system permit us to obtain two important benefits; The patient will enjoy greater comfort during the internment, and the doctors will be able to know the state of the biomedical variables of each patient, in simultaneous form. In order to achieve the objective of this work, two communication systems for wireless transmissions data were developed and implemented. The CC1000 transceiver was used in the first system and the Bluetooth module was used in the other system.

Classification of Respiratory Sounds by Using An Artificial Neural Network

DTIC Science & Technology

2001-10-28

CLASSIFICATION OF RESPIRATORY SOUNDS BY USING AN ARTIFICIAL NEURAL NETWORK M.C. Sezgin, Z. Dokur, T. Ölmez, M. Korürek Department of Electronics and...successfully classified by the GAL network. Keywords-Respiratory Sounds, Classification of Biomedical Signals, Artificial Neural Network . I. INTRODUCTION...process, feature extraction, and classification by the artificial neural network . At first, the RS signal obtained from a real-time measurement equipment is

Customizing cell signaling using engineered genetic logic circuits.

PubMed

Wang, Baojun; Buck, Martin

2012-08-01

Cells live in an ever-changing environment and continuously sense, process and react to environmental signals using their inherent signaling and gene regulatory networks. Recently, there have been great advances on rewiring the native cell signaling and gene networks to program cells to sense multiple noncognate signals and integrate them in a logical manner before initiating a desired response. Here, we summarize the current state-of-the-art of engineering synthetic genetic logic circuits to customize cellular signaling behaviors, and discuss their promising applications in biocomputing, environmental, biotechnological and biomedical areas as well as the remaining challenges in this growing field. Copyright © 2012 Elsevier Ltd. All rights reserved.

An Ultra-Low Power Turning Angle Based Biomedical Signal Compression Engine with Adaptive Threshold Tuning

PubMed Central

Zhou, Jun; Wang, Chao

2017-01-01

Intelligent sensing is drastically changing our everyday life including healthcare by biomedical signal monitoring, collection, and analytics. However, long-term healthcare monitoring generates tremendous data volume and demands significant wireless transmission power, which imposes a big challenge for wearable healthcare sensors usually powered by batteries. Efficient compression engine design to reduce wireless transmission data rate with ultra-low power consumption is essential for wearable miniaturized healthcare sensor systems. This paper presents an ultra-low power biomedical signal compression engine for healthcare data sensing and analytics in the era of big data and sensor intelligence. It extracts the feature points of the biomedical signal by window-based turning angle detection. The proposed approach has low complexity and thus low power consumption while achieving a large compression ratio (CR) and good quality of reconstructed signal. Near-threshold design technique is adopted to further reduce the power consumption on the circuit level. Besides, the angle threshold for compression can be adaptively tuned according to the error between the original signal and reconstructed signal to address the variation of signal characteristics from person to person or from channel to channel to meet the required signal quality with optimal CR. For demonstration, the proposed biomedical compression engine has been used and evaluated for ECG compression. It achieves an average (CR) of 71.08% and percentage root-mean-square difference (PRD) of 5.87% while consuming only 39 nW. Compared to several state-of-the-art ECG compression engines, the proposed design has significantly lower power consumption while achieving similar CRD and PRD, making it suitable for long-term wearable miniaturized sensor systems to sense and collect healthcare data for remote data analytics. PMID:28783079

An Ultra-Low Power Turning Angle Based Biomedical Signal Compression Engine with Adaptive Threshold Tuning.

PubMed

Zhou, Jun; Wang, Chao

2017-08-06

Intelligent sensing is drastically changing our everyday life including healthcare by biomedical signal monitoring, collection, and analytics. However, long-term healthcare monitoring generates tremendous data volume and demands significant wireless transmission power, which imposes a big challenge for wearable healthcare sensors usually powered by batteries. Efficient compression engine design to reduce wireless transmission data rate with ultra-low power consumption is essential for wearable miniaturized healthcare sensor systems. This paper presents an ultra-low power biomedical signal compression engine for healthcare data sensing and analytics in the era of big data and sensor intelligence. It extracts the feature points of the biomedical signal by window-based turning angle detection. The proposed approach has low complexity and thus low power consumption while achieving a large compression ratio (CR) and good quality of reconstructed signal. Near-threshold design technique is adopted to further reduce the power consumption on the circuit level. Besides, the angle threshold for compression can be adaptively tuned according to the error between the original signal and reconstructed signal to address the variation of signal characteristics from person to person or from channel to channel to meet the required signal quality with optimal CR. For demonstration, the proposed biomedical compression engine has been used and evaluated for ECG compression. It achieves an average (CR) of 71.08% and percentage root-mean-square difference (PRD) of 5.87% while consuming only 39 nW. Compared to several state-of-the-art ECG compression engines, the proposed design has significantly lower power consumption while achieving similar CRD and PRD, making it suitable for long-term wearable miniaturized sensor systems to sense and collect healthcare data for remote data analytics.

High-power THz to IR emission by femtosecond laser irradiation of random 2D metallic nanostructures.

PubMed

Zhang, Liangliang; Mu, Kaijun; Zhou, Yunsong; Wang, Hai; Zhang, Cunlin; Zhang, X-C

2015-07-24

Terahertz (THz) spectroscopic sensing and imaging has identified its potentials in a number of areas such as standoff security screening at portals, explosive detection at battle fields, bio-medical research, and so on. With these needs, the development of an intense and broadband THz source has been a focus of THz research. In this work, we report an intense (~10 mW) and ultra-broadband (~150 THz) THz to infrared (IR) source with a Gaussian wavefront, emitted from nano-pore-structured metallic thin films with femtosecond laser pulse excitation. The underlying mechanism has been proposed as thermal radiation. In addition, an intense coherent THz signal was generated through the optical rectification process simultaneously with the strong thermal signal. This unique feature opens up new avenues in biomedical research.

Biomolecular filters for improved separation of output signals in enzyme logic systems applied to biomedical analysis.

PubMed

Halámek, Jan; Zhou, Jian; Halámková, Lenka; Bocharova, Vera; Privman, Vladimir; Wang, Joseph; Katz, Evgeny

2011-11-15

Biomolecular logic systems processing biochemical input signals and producing "digital" outputs in the form of YES/NO were developed for analysis of physiological conditions characteristic of liver injury, soft tissue injury, and abdominal trauma. Injury biomarkers were used as input signals for activating the logic systems. Their normal physiological concentrations were defined as logic-0 level, while their pathologically elevated concentrations were defined as logic-1 values. Since the input concentrations applied as logic 0 and 1 values were not sufficiently different, the output signals being at low and high values (0, 1 outputs) were separated with a short gap making their discrimination difficult. Coupled enzymatic reactions functioning as a biomolecular signal processing system with a built-in filter property were developed. The filter process involves a partial back-conversion of the optical-output-signal-yielding product, but only at its low concentrations, thus allowing the proper discrimination between 0 and 1 output values.

Classification of cardiac arrhythmias using competitive networks.

PubMed

Leite, Cicilia R M; Martin, Daniel L; Sizilio, Glaucia R A; Dos Santos, Keylly E A; de Araujo, Bruno G; Valentim, Ricardo A M; Neto, Adriao D D; de Melo, Jorge D; Guerreiro, Ana M G

2010-01-01

Information generated by sensors that collect a patient's vital signals are continuous and unlimited data sequences. Traditionally, this information requires special equipment and programs to monitor them. These programs process and react to the continuous entry of data from different origins. Thus, the purpose of this study is to analyze the data produced by these biomedical devices, in this case the electrocardiogram (ECG). Processing uses a neural classifier, Kohonen competitive neural networks, detecting if the ECG shows any cardiac arrhythmia. In fact, it is possible to classify an ECG signal and thereby detect if it is exhibiting or not any alteration, according to normality.

Exploring the complementarity of THz pulse imaging and DCE-MRIs: Toward a unified multi-channel classification and a deep learning framework.

PubMed

Yin, X-X; Zhang, Y; Cao, J; Wu, J-L; Hadjiloucas, S

2016-12-01

We provide a comprehensive account of recent advances in biomedical image analysis and classification from two complementary imaging modalities: terahertz (THz) pulse imaging and dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI). The work aims to highlight underlining commonalities in both data structures so that a common multi-channel data fusion framework can be developed. Signal pre-processing in both datasets is discussed briefly taking into consideration advances in multi-resolution analysis and model based fractional order calculus system identification. Developments in statistical signal processing using principal component and independent component analysis are also considered. These algorithms have been developed independently by the THz-pulse imaging and DCE-MRI communities, and there is scope to place them in a common multi-channel framework to provide better software standardization at the pre-processing de-noising stage. A comprehensive discussion of feature selection strategies is also provided and the importance of preserving textural information is highlighted. Feature extraction and classification methods taking into consideration recent advances in support vector machine (SVM) and extreme learning machine (ELM) classifiers and their complex extensions are presented. An outlook on Clifford algebra classifiers and deep learning techniques suitable to both types of datasets is also provided. The work points toward the direction of developing a new unified multi-channel signal processing framework for biomedical image analysis that will explore synergies from both sensing modalities for inferring disease proliferation. Copyright © 2016 Elsevier Ireland Ltd. All rights reserved.

Biomedical ground lead system

NASA Technical Reports Server (NTRS)

1972-01-01

The design and verification tests for the biomedical ground lead system of Apollo biomedical monitors are presented. Major efforts were made to provide a low impedance path to ground, reduce noise and artifact of ECG signals, and limit the current flowing in the ground electrode of the system.

Biomedical implications of information processing in chemical systems: non-classical approach to photochemistry of coordination compounds.

PubMed

Szaciłowski, Konrad

2007-01-01

Analogies between photoactive nitric oxide generators and various electronic devices: logic gates and operational amplifiers are presented. These analogies have important biological consequences: application of control parameters allows for better targeting and control of nitric oxide drugs. The same methodology may be applied in the future for other therapeutic strategies and at the same time helps to understand natural regulatory and signaling processes in biological systems.

Radio frequency telemetry system for sensors and actuators

NASA Technical Reports Server (NTRS)

Simons, Rainee N. (Inventor); Miranda, Felix A. (Inventor)

2003-01-01

The present invention discloses and teaches apparatus for combining Radio Frequency (RF) technology with novel micro-inductor antennas and signal processing circuits for RF telemetry of real time, measured data, from microelectromechanical system (MEMS) sensors, through electromagnetic coupling with a remote powering/receiving device. Such technology has many applications, but is especially useful in the biomedical area.

Radio Frequency Telemetry System for Sensors and Actuators

NASA Technical Reports Server (NTRS)

Simons, Rainee N. (Inventor); Miranda, Felix A. (Inventor)

2003-01-01

The present invention discloses and teaches apparatus for combining Radio Frequency (RF) technology with novel micro-inductor antennas and signal processing circuits for RF telemetry of real time, measured data, from microelectromechanical system (MEMS) sensors, through electromagnetic coupling with a remote poweringheceiving device. Such technology has many applications, but is especially useful in the biomedical area.

Large-scale combining signals from both biomedical literature and the FDA Adverse Event Reporting System (FAERS) to improve post-marketing drug safety signal detection

PubMed Central

2014-01-01

Background Independent data sources can be used to augment post-marketing drug safety signal detection. The vast amount of publicly available biomedical literature contains rich side effect information for drugs at all clinical stages. In this study, we present a large-scale signal boosting approach that combines over 4 million records in the US Food and Drug Administration (FDA) Adverse Event Reporting System (FAERS) and over 21 million biomedical articles. Results The datasets are comprised of 4,285,097 records from FAERS and 21,354,075 MEDLINE articles. We first extracted all drug-side effect (SE) pairs from FAERS. Our study implemented a total of seven signal ranking algorithms. We then compared these different ranking algorithms before and after they were boosted with signals from MEDLINE sentences or abstracts. Finally, we manually curated all drug-cardiovascular (CV) pairs that appeared in both data sources and investigated whether our approach can detect many true signals that have not been included in FDA drug labels. We extracted a total of 2,787,797 drug-SE pairs from FAERS with a low initial precision of 0.025. The ranking algorithm combined signals from both FAERS and MEDLINE, significantly improving the precision from 0.025 to 0.371 for top-ranked pairs, representing a 13.8 fold elevation in precision. We showed by manual curation that drug-SE pairs that appeared in both data sources were highly enriched with true signals, many of which have not yet been included in FDA drug labels. Conclusions We have developed an efficient and effective drug safety signal ranking and strengthening approach We demonstrate that large-scale combining information from FAERS and biomedical literature can significantly contribute to drug safety surveillance. PMID:24428898

Large-scale combining signals from both biomedical literature and the FDA Adverse Event Reporting System (FAERS) to improve post-marketing drug safety signal detection.

PubMed

Xu, Rong; Wang, QuanQiu

2014-01-15

Independent data sources can be used to augment post-marketing drug safety signal detection. The vast amount of publicly available biomedical literature contains rich side effect information for drugs at all clinical stages. In this study, we present a large-scale signal boosting approach that combines over 4 million records in the US Food and Drug Administration (FDA) Adverse Event Reporting System (FAERS) and over 21 million biomedical articles. The datasets are comprised of 4,285,097 records from FAERS and 21,354,075 MEDLINE articles. We first extracted all drug-side effect (SE) pairs from FAERS. Our study implemented a total of seven signal ranking algorithms. We then compared these different ranking algorithms before and after they were boosted with signals from MEDLINE sentences or abstracts. Finally, we manually curated all drug-cardiovascular (CV) pairs that appeared in both data sources and investigated whether our approach can detect many true signals that have not been included in FDA drug labels. We extracted a total of 2,787,797 drug-SE pairs from FAERS with a low initial precision of 0.025. The ranking algorithm combined signals from both FAERS and MEDLINE, significantly improving the precision from 0.025 to 0.371 for top-ranked pairs, representing a 13.8 fold elevation in precision. We showed by manual curation that drug-SE pairs that appeared in both data sources were highly enriched with true signals, many of which have not yet been included in FDA drug labels. We have developed an efficient and effective drug safety signal ranking and strengthening approach We demonstrate that large-scale combining information from FAERS and biomedical literature can significantly contribute to drug safety surveillance.

Introduction to multifractal detrended fluctuation analysis in matlab.

PubMed

Ihlen, Espen A F

2012-01-01

Fractal structures are found in biomedical time series from a wide range of physiological phenomena. The multifractal spectrum identifies the deviations in fractal structure within time periods with large and small fluctuations. The present tutorial is an introduction to multifractal detrended fluctuation analysis (MFDFA) that estimates the multifractal spectrum of biomedical time series. The tutorial presents MFDFA step-by-step in an interactive Matlab session. All Matlab tools needed are available in Introduction to MFDFA folder at the website www.ntnu.edu/inm/geri/software. MFDFA are introduced in Matlab code boxes where the reader can employ pieces of, or the entire MFDFA to example time series. After introducing MFDFA, the tutorial discusses the best practice of MFDFA in biomedical signal processing. The main aim of the tutorial is to give the reader a simple self-sustained guide to the implementation of MFDFA and interpretation of the resulting multifractal spectra.

Introduction to Multifractal Detrended Fluctuation Analysis in Matlab

PubMed Central

Ihlen, Espen A. F.

2012-01-01

Fractal structures are found in biomedical time series from a wide range of physiological phenomena. The multifractal spectrum identifies the deviations in fractal structure within time periods with large and small fluctuations. The present tutorial is an introduction to multifractal detrended fluctuation analysis (MFDFA) that estimates the multifractal spectrum of biomedical time series. The tutorial presents MFDFA step-by-step in an interactive Matlab session. All Matlab tools needed are available in Introduction to MFDFA folder at the website www.ntnu.edu/inm/geri/software. MFDFA are introduced in Matlab code boxes where the reader can employ pieces of, or the entire MFDFA to example time series. After introducing MFDFA, the tutorial discusses the best practice of MFDFA in biomedical signal processing. The main aim of the tutorial is to give the reader a simple self-sustained guide to the implementation of MFDFA and interpretation of the resulting multifractal spectra. PMID:22675302

Analysis of embolic signals with directional dual tree rational dilation wavelet transform.

PubMed

Serbes, Gorkem; Aydin, Nizamettin

2016-08-01

The dyadic discrete wavelet transform (dyadic-DWT), which is based on fixed integer sampling factor, has been used before for processing piecewise smooth biomedical signals. However, the dyadic-DWT has poor frequency resolution due to the low-oscillatory nature of its wavelet bases and therefore, it is less effective in processing embolic signals (ESs). To process ESs more effectively, a wavelet transform having better frequency resolution than the dyadic-DWT is needed. Therefore, in this study two ESs, containing micro-emboli and artifact waveforms, are analyzed with the Directional Dual Tree Rational-Dilation Wavelet Transform (DDT-RADWT). The DDT-RADWT, which can be directly applied to quadrature signals, is based on rational dilation factors and has adjustable frequency resolution. The analyses are done for both low and high Q-factors. It is proved that, when high Q-factor filters are employed in the DDT-RADWT, clearer representations of ESs can be attained in decomposed sub-bands and artifacts can be successfully separated.

Wavelet transform: fundamentals, applications, and implementation using acousto-optic correlators

NASA Astrophysics Data System (ADS)

DeCusatis, Casimer M.; Koay, J.; Litynski, Daniel M.; Das, Pankaj K.

1995-10-01

In recent years there has been a great deal of interest in the use of wavelets to supplement or replace conventional Fourier transform signal processing. This paper provides a review of wavelet transforms for signal processing applications, and discusses several emerging applications which benefit from the advantages of wavelets. The wavelet transform can be implemented as an acousto-optic correlator; perfect reconstruction of digital signals may also be achieved using acousto-optic finite impulse response filter banks. Acousto-optic image correlators are discussed as a potential implementation of the wavelet transform, since a 1D wavelet filter bank may be encoded as a 2D image. We discuss applications of the wavelet transform including nondestructive testing of materials, biomedical applications in the analysis of EEG signals, and interference excision in spread spectrum communication systems. Computer simulations and experimental results for these applications are also provided.

Pathophysiologic mechanisms of biomedical nanomaterials

DOE Office of Scientific and Technical Information (OSTI.GOV)

Wang, Liming, E-mail: wangliming@ihep.ac.cn; Chen, Chunying, E-mail: chenchy@nanoctr.cn

Nanomaterials (NMs) have been widespread used in biomedical fields, daily consuming, and even food industry. It is crucial to understand the safety and biomedical efficacy of NMs. In this review, we summarized the recent progress about the physiological and pathological effects of NMs from several levels: protein-nano interface, NM-subcellular structures, and cell–cell interaction. We focused on the detailed information of nano-bio interaction, especially about protein adsorption, intracellular trafficking, biological barriers, and signaling pathways as well as the associated mechanism mediated by nanomaterials. We also introduced related analytical methods that are meaningful and helpful for biomedical effect studies in the future.more » We believe that knowledge about pathophysiologic effects of NMs is not only significant for rational design of medical NMs but also helps predict their safety and further improve their applications in the future. - Highlights: • Rapid protein adsorption onto nanomaterials that affects biomedical effects • Nanomaterials and their interaction with biological membrane, intracellular trafficking and specific cellular effects • Nanomaterials and their interaction with biological barriers • The signaling pathways mediated by nanomaterials and related biomedical effects • Novel techniques for studying translocation and biomedical effects of NMs.« less

[Results of testing of MINISKAN mobile gamma-ray camera and specific features of its design].

PubMed

Utkin, V M; Kumakhov, M A; Blinov, N N; Korsunskiĭ, V N; Fomin, D K; Kolesnikova, N V; Tultaev, A V; Nazarov, A A; Tararukhina, O B

2007-01-01

The main results of engineering, biomedical, and clinical testing of MINISKAN mobile gamma-ray camera are presented. Specific features of the camera hardware and software, as well as the main technical specifications, are described. The gamma-ray camera implements a new technology based on reconstructive tomography, aperture encoding, and digital processing of signals.

Terrestrial implications of mathematical modeling developed for space biomedical research

NASA Technical Reports Server (NTRS)

Lujan, Barbara F.; White, Ronald J.; Leonard, Joel I.; Srinivasan, R. Srini

1988-01-01

This paper summarizes several related research projects supported by NASA which seek to apply computer models to space medicine and physiology. These efforts span a wide range of activities, including mathematical models used for computer simulations of physiological control systems; power spectral analysis of physiological signals; pattern recognition models for detection of disease processes; and computer-aided diagnosis programs.

Applicability of mathematical modeling to problems of environmental physiology

NASA Technical Reports Server (NTRS)

White, Ronald J.; Lujan, Barbara F.; Leonard, Joel I.; Srinivasan, R. Srini

1988-01-01

The paper traces the evolution of mathematical modeling and systems analysis from terrestrial research to research related to space biomedicine and back again to terrestrial research. Topics covered include: power spectral analysis of physiological signals; pattern recognition models for detection of disease processes; and, computer-aided diagnosis programs used in conjunction with a special on-line biomedical computer library.

Teaching biomedical design through a university-industry partnership.

PubMed

Khuon, Lunal; Zum, Karl R; Zurn, Jane B; Herrera, Gerald M

2016-08-01

This paper describes a course that, as a result of a university-industry partnership, emphasizes bringing industry experts into the classroom to teach biomedical design. Full-time faculty and industry engineers and entrepreneurs teach the senior technical elective course, Biomedical System Design. This hands-on senior course in biomedical system design places varied but connected emphasis on understanding the biological signal source, electronics design, safety, patient use, medical device qualifications, and good manufacturing practices.

A new algorithm for epilepsy seizure onset detection and spread estimation from EEG signals

NASA Astrophysics Data System (ADS)

Quintero-Rincón, Antonio; Pereyra, Marcelo; D'Giano, Carlos; Batatia, Hadj; Risk, Marcelo

2016-04-01

Appropriate diagnosis and treatment of epilepsy is a main public health issue. Patients suffering from this disease often exhibit different physical characterizations, which result from the synchronous and excessive discharge of a group of neurons in the cerebral cortex. Extracting this information using EEG signals is an important problem in biomedical signal processing. In this work we propose a new algorithm for seizure onset detection and spread estimation in epilepsy patients. The algorithm is based on a multilevel 1-D wavelet decomposition that captures the physiological brain frequency signals coupled with a generalized gaussian model. Preliminary experiments with signals from 30 epilepsy crisis and 11 subjects, suggest that the proposed methodology is a powerful tool for detecting the onset of epilepsy seizures with his spread across the brain.

Graphene oxide based contacts as probes of biomedical signals

NASA Astrophysics Data System (ADS)

Hallfors, N. G.; Devarajan, A.; Farhat, I. A. H.; Abdurahman, A.; Liao, K.; Gater, D. L.; Elnaggar, M. I.; Isakovic, A. F.

We have developed a series of graphene oxide (GOx) on polymer contacts and have demonstrated these to be useful for collection of standard biomedically relevant signals, such as electrocardiogram (ECG). The process is wet solution-based and allows for control and tuning of the basic physical parameters of GOx, such as electrical and optical properties, simply by choosing the number of GOx layers. Our GOx characterization measurements show spectral (FTIR, XPS, IR absorbance) features most relevant to such performance, and point towards the likely explanations about the mechanisms for controlling the physical properties relevant for the contact performance. Structural (X-ray topography) and surface characterization (AFM, SEM) indicates to what degree these contacts can be considered homogeneous and therefore provide information on yield and repeatability. We compare the ECG signals recorded by standard commercial probes (Ag/AgCl) and GOx probes, displaying minor differences the solution to which may lead to a whole new way we perform ECG data collection, including wearable electronics and IoT friendly ECG monitoring. We acknowledge support from Mubadala-SRC AC4ES and from SRC 2011-KJ-2190. We thank J. B. Warren and G. L. Carr (BNL) for assistance.

Refined multiscale fuzzy entropy based on standard deviation for biomedical signal analysis.

PubMed

Azami, Hamed; Fernández, Alberto; Escudero, Javier

2017-11-01

Multiscale entropy (MSE) has been a prevalent algorithm to quantify the complexity of biomedical time series. Recent developments in the field have tried to alleviate the problem of undefined MSE values for short signals. Moreover, there has been a recent interest in using other statistical moments than the mean, i.e., variance, in the coarse-graining step of the MSE. Building on these trends, here we introduce the so-called refined composite multiscale fuzzy entropy based on the standard deviation (RCMFE σ ) and mean (RCMFE μ ) to quantify the dynamical properties of spread and mean, respectively, over multiple time scales. We demonstrate the dependency of the RCMFE σ and RCMFE μ , in comparison with other multiscale approaches, on several straightforward signal processing concepts using a set of synthetic signals. The results evidenced that the RCMFE σ and RCMFE μ values are more stable and reliable than the classical multiscale entropy ones. We also inspect the ability of using the standard deviation as well as the mean in the coarse-graining process using magnetoencephalograms in Alzheimer's disease and publicly available electroencephalograms recorded from focal and non-focal areas in epilepsy. Our results indicated that when the RCMFE μ cannot distinguish different types of dynamics of a particular time series at some scale factors, the RCMFE σ may do so, and vice versa. The results showed that RCMFE σ -based features lead to higher classification accuracies in comparison with the RCMFE μ -based ones. We also made freely available all the Matlab codes used in this study at http://dx.doi.org/10.7488/ds/1477 .

Multichannel biomedical time series clustering via hierarchical probabilistic latent semantic analysis.

PubMed

Wang, Jin; Sun, Xiangping; Nahavandi, Saeid; Kouzani, Abbas; Wu, Yuchuan; She, Mary

2014-11-01

Biomedical time series clustering that automatically groups a collection of time series according to their internal similarity is of importance for medical record management and inspection such as bio-signals archiving and retrieval. In this paper, a novel framework that automatically groups a set of unlabelled multichannel biomedical time series according to their internal structural similarity is proposed. Specifically, we treat a multichannel biomedical time series as a document and extract local segments from the time series as words. We extend a topic model, i.e., the Hierarchical probabilistic Latent Semantic Analysis (H-pLSA), which was originally developed for visual motion analysis to cluster a set of unlabelled multichannel time series. The H-pLSA models each channel of the multichannel time series using a local pLSA in the first layer. The topics learned in the local pLSA are then fed to a global pLSA in the second layer to discover the categories of multichannel time series. Experiments on a dataset extracted from multichannel Electrocardiography (ECG) signals demonstrate that the proposed method performs better than previous state-of-the-art approaches and is relatively robust to the variations of parameters including length of local segments and dictionary size. Although the experimental evaluation used the multichannel ECG signals in a biometric scenario, the proposed algorithm is a universal framework for multichannel biomedical time series clustering according to their structural similarity, which has many applications in biomedical time series management. Copyright © 2014 Elsevier Ireland Ltd. All rights reserved.

Electrophysiology for biomedical engineering students: a practical and theoretical course in animal electrocorticography.

PubMed

Albarracín, Ana L; Farfán, Fernando D; Coletti, Marcos A; Teruya, Pablo Y; Felice, Carmelo J

2016-09-01

The major challenge in laboratory teaching is the application of abstract concepts in simple and direct practical lessons. However, students rarely have the opportunity to participate in a laboratory that combines practical learning with a realistic research experience. In the Biomedical Engineering career, we offer short and optional courses to complement studies for students as they initiate their Graduation Project. The objective of these theoretical and practical courses is to introduce students to the topics of their projects. The present work describes an experience in electrophysiology to teach undergraduate students how to extract cortical information using electrocorticographic techniques. Students actively participate in some parts of the experience and then process and analyze the data obtained with different signal processing tools. In postlaboratory evaluations, students described the course as an exceptional opportunity for students interested in following a postgraduate science program and fully appreciated their contents. Copyright © 2016 The American Physiological Society.

Probing biological redox chemistry with large amplitude Fourier transformed ac voltammetry

PubMed Central

Adamson, Hope

2017-01-01

Biological electron-exchange reactions are fundamental to life on earth. Redox reactions underpin respiration, photosynthesis, molecular biosynthesis, cell signalling and protein folding. Chemical, biomedical and future energy technology developments are also inspired by these natural electron transfer processes. Further developments in techniques and data analysis are required to gain a deeper understanding of the redox biochemistry processes that power Nature. This review outlines the new insights gained from developing Fourier transformed ac voltammetry as a tool for protein film electrochemistry. PMID:28804798

Biomedical technology transfer applications of NASA science and technology

NASA Technical Reports Server (NTRS)

1972-01-01

The identification and solution of research and clinical problems in cardiovascular medicine which were investigated by means of biomedical data transfer are reported. The following are sample areas that were focused upon by the Stanford University Biomedical Technology Transfer Team: electrodes for hemiplegia research; vectorcardiogram computer analysis; respiration and phonation electrodes; radiotelemetry of intracranial pressure; and audiotransformation of the electrocardiographic signal. It is concluded that this biomedical technology transfer is significantly aiding present research in cardiovascular medicine.

Utilization of biosensors and chemical sensors for space applications

NASA Technical Reports Server (NTRS)

Bonting, S. L.

1992-01-01

There will be a need for a wide array of chemical sensors for biomedical experimentation and for the monitoring of water and air recycling processes on Space Station Freedom. The infrequent logistics flights of the Space Shuttle will necessitate onboard analysis. The advantages of biosensors and chemical sensors over conventional analysis onboard spacecraft are manifold. They require less crew time, space, and power. Sample treatment is not needed. Real time or near-real time monitoring is possible, in some cases on a continuous basis. Sensor signals in digitized form can be transmitted to the ground. Types and requirements for chemical sensors to be used in biomedical experimentation and monitoring of water recycling during long-term space missions are discussed.

Ridge extraction from the time-frequency representation (TFR) of signals based on an image processing approach: application to the analysis of uterine electromyogram AR TFR.

PubMed

Terrien, Jérémy; Marque, Catherine; Germain, Guy

2008-05-01

Time-frequency representations (TFRs) of signals are increasingly being used in biomedical research. Analysis of such representations is sometimes difficult, however, and is often reduced to the extraction of ridges, or local energy maxima. In this paper, we describe a new ridge extraction method based on the image processing technique of active contours or snakes. We have tested our method on several synthetic signals and for the analysis of uterine electromyogram or electrohysterogram (EHG) recorded during gestation in monkeys. We have also evaluated a postprocessing algorithm that is especially suited for EHG analysis. Parameters are evaluated on real EHG signals in different gestational periods. The presented method gives good results when applied to synthetic as well as EHG signals. We have been able to obtain smaller ridge extraction errors when compared to two other methods specially developed for EHG. The gradient vector flow (GVF) snake method, or GVF-snake method, appears to be a good ridge extraction tool, which could be used on TFR of mono or multicomponent signals with good results.

An Open-source Toolbox for Analysing and Processing PhysioNet Databases in MATLAB and Octave.

PubMed

Silva, Ikaro; Moody, George B

The WaveForm DataBase (WFDB) Toolbox for MATLAB/Octave enables integrated access to PhysioNet's software and databases. Using the WFDB Toolbox for MATLAB/Octave, users have access to over 50 physiological databases in PhysioNet. The toolbox provides access over 4 TB of biomedical signals including ECG, EEG, EMG, and PLETH. Additionally, most signals are accompanied by metadata such as medical annotations of clinical events: arrhythmias, sleep stages, seizures, hypotensive episodes, etc. Users of this toolbox should easily be able to reproduce, validate, and compare results published based on PhysioNet's software and databases.

Development of wireless brain computer interface with embedded multitask scheduling and its application on real-time driver's drowsiness detection and warning.

PubMed

Lin, Chin-Teng; Chen, Yu-Chieh; Huang, Teng-Yi; Chiu, Tien-Ting; Ko, Li-Wei; Liang, Sheng-Fu; Hsieh, Hung-Yi; Hsu, Shang-Hwa; Duann, Jeng-Ren

2008-05-01

Biomedical signal monitoring systems have been rapidly advanced with electronic and information technologies in recent years. However, most of the existing physiological signal monitoring systems can only record the signals without the capability of automatic analysis. In this paper, we proposed a novel brain-computer interface (BCI) system that can acquire and analyze electroencephalogram (EEG) signals in real-time to monitor human physiological as well as cognitive states, and, in turn, provide warning signals to the users when needed. The BCI system consists of a four-channel biosignal acquisition/amplification module, a wireless transmission module, a dual-core signal processing unit, and a host system for display and storage. The embedded dual-core processing system with multitask scheduling capability was proposed to acquire and process the input EEG signals in real time. In addition, the wireless transmission module, which eliminates the inconvenience of wiring, can be switched between radio frequency (RF) and Bluetooth according to the transmission distance. Finally, the real-time EEG-based drowsiness monitoring and warning algorithms were implemented and integrated into the system to close the loop of the BCI system. The practical online testing demonstrates the feasibility of using the proposed system with the ability of real-time processing, automatic analysis, and online warning feedback in real-world operation and living environments.

Piezoelectric extraction of ECG signal

NASA Astrophysics Data System (ADS)

Ahmad, Mahmoud Al

2016-11-01

The monitoring and early detection of abnormalities or variations in the cardiac cycle functionality are very critical practices and have significant impact on the prevention of heart diseases and their associated complications. Currently, in the field of biomedical engineering, there is a growing need for devices capable of measuring and monitoring a wide range of cardiac cycle parameters continuously, effectively and on a real-time basis using easily accessible and reusable probes. In this paper, the revolutionary generation and extraction of the corresponding ECG signal using a piezoelectric transducer as alternative for the ECG will be discussed. The piezoelectric transducer pick up the vibrations from the heart beats and convert them into electrical output signals. To this end, piezoelectric and signal processing techniques were employed to extract the ECG corresponding signal from the piezoelectric output voltage signal. The measured electrode based and the extracted piezoelectric based ECG traces are well corroborated. Their peaks amplitudes and locations are well aligned with each other.

MEMD-enhanced multivariate fuzzy entropy for the evaluation of complexity in biomedical signals.

PubMed

Azami, Hamed; Smith, Keith; Escudero, Javier

2016-08-01

Multivariate multiscale entropy (mvMSE) has been proposed as a combination of the coarse-graining process and multivariate sample entropy (mvSE) to quantify the irregularity of multivariate signals. However, both the coarse-graining process and mvSE may not be reliable for short signals. Although the coarse-graining process can be replaced with multivariate empirical mode decomposition (MEMD), the relative instability of mvSE for short signals remains a problem. Here, we address this issue by proposing the multivariate fuzzy entropy (mvFE) with a new fuzzy membership function. The results using white Gaussian noise show that the mvFE leads to more reliable and stable results, especially for short signals, in comparison with mvSE. Accordingly, we propose MEMD-enhanced mvFE to quantify the complexity of signals. The characteristics of brain regions influenced by partial epilepsy are investigated by focal and non-focal electroencephalogram (EEG) time series. In this sense, the proposed MEMD-enhanced mvFE and mvSE are employed to discriminate focal EEG signals from non-focal ones. The results demonstrate the MEMD-enhanced mvFE values have a smaller coefficient of variation in comparison with those obtained by the MEMD-enhanced mvSE, even for long signals. The results also show that the MEMD-enhanced mvFE has better performance to quantify focal and non-focal signals compared with multivariate multiscale permutation entropy.

Noninvasive evaluation of mental stress using by a refined rough set technique based on biomedical signals.

PubMed

Liu, Tung-Kuan; Chen, Yeh-Peng; Hou, Zone-Yuan; Wang, Chao-Chih; Chou, Jyh-Horng

2014-06-01

Evaluating and treating of stress can substantially benefits to people with health problems. Currently, mental stress evaluated using medical questionnaires. However, the accuracy of this evaluation method is questionable because of variations caused by factors such as cultural differences and individual subjectivity. Measuring of biomedical signals is an effective method for estimating mental stress that enables this problem to be overcome. However, the relationship between the levels of mental stress and biomedical signals remain poorly understood. A refined rough set algorithm is proposed to determine the relationship between mental stress and biomedical signals, this algorithm combines rough set theory with a hybrid Taguchi-genetic algorithm, called RS-HTGA. Two parameters were used for evaluating the performance of the proposed RS-HTGA method. A dataset obtained from a practice clinic comprising 362 cases (196 male, 166 female) was adopted to evaluate the performance of the proposed approach. The empirical results indicate that the proposed method can achieve acceptable accuracy in medical practice. Furthermore, the proposed method was successfully used to identify the relationship between mental stress levels and bio-medical signals. In addition, the comparison between the RS-HTGA and a support vector machine (SVM) method indicated that both methods yield good results. The total averages for sensitivity, specificity, and precision were greater than 96%, the results indicated that both algorithms produced highly accurate results, but a substantial difference in discrimination existed among people with Phase 0 stress. The SVM algorithm shows 89% and the RS-HTGA shows 96%. Therefore, the RS-HTGA is superior to the SVM algorithm. The kappa test results for both algorithms were greater than 0.936, indicating high accuracy and consistency. The area under receiver operating characteristic curve for both the RS-HTGA and a SVM method were greater than 0.77, indicating a good discrimination capability. In this study, crucial attributes in stress evaluation were successfully recognized using biomedical signals, thereby enabling the conservation of medical resources and elucidating the mapping relationship between levels of mental stress and candidate attributes. In addition, we developed a prototype system for mental stress evaluation that can be used to provide benefits in medical practice. Copyright © 2014. Published by Elsevier B.V.

Optimal causal filtering for 1 /fα-type noise in single-electrode EEG signals.

PubMed

Paris, Alan; Atia, George; Vosoughi, Azadeh; Berman, Stephen A

2016-08-01

Understanding the mode of generation and the statistical structure of neurological noise is one of the central problems of biomedical signal processing. We have developed a broad class of abstract biological noise sources we call hidden simplicial tissues. In the simplest cases, such tissue emits what we have named generalized van der Ziel-McWhorter (GVZM) noise which has a roughly 1/fα spectral roll-off. Our previous work focused on the statistical structure of GVZM frequency spectra. However, causality of processing operations (i.e., dependence only on the past) is an essential requirement for real-time applications to seizure detection and brain-computer interfacing. In this paper we outline the theoretical background for optimal causal time-domain filtering of deterministic signals embedded in GVZM noise. We present some of our early findings concerning the optimal filtering of EEG signals for the detection of steady-state visual evoked potential (SSVEP) responses and indicate the next steps in our ongoing research.

Systems engineering principles for the design of biomedical signal processing systems.

PubMed

Faust, Oliver; Acharya U, Rajendra; Sputh, Bernhard H C; Min, Lim Choo

2011-06-01

Systems engineering aims to produce reliable systems which function according to specification. In this paper we follow a systems engineering approach to design a biomedical signal processing system. We discuss requirements capturing, specification definition, implementation and testing of a classification system. These steps are executed as formal as possible. The requirements, which motivate the system design, are based on diabetes research. The main requirement for the classification system is to be a reliable component of a machine which controls diabetes. Reliability is very important, because uncontrolled diabetes may lead to hyperglycaemia (raised blood sugar) and over a period of time may cause serious damage to many of the body systems, especially the nerves and blood vessels. In a second step, these requirements are refined into a formal CSP‖ B model. The formal model expresses the system functionality in a clear and semantically strong way. Subsequently, the proven system model was translated into an implementation. This implementation was tested with use cases and failure cases. Formal modeling and automated model checking gave us deep insight in the system functionality. This insight enabled us to create a reliable and trustworthy implementation. With extensive tests we established trust in the reliability of the implementation. Copyright © 2010 Elsevier Ireland Ltd. All rights reserved.

A low-power inverter-based CMOS level-crossing analog-to-digital converter for low-frequency biosignal sensing

NASA Astrophysics Data System (ADS)

Tanaka, Suiki; Niitsu, Kiichi; Nakazato, Kazuo

2016-03-01

Low-power analog-to-digital conversion is a key technique for power-limited biomedical applications such as power-limited continuous glucose monitoring. However, a conventional uniform-sampling analog-to-digital converter (ADC) is not suitable for nonuniform biosignals. A level-crossing ADC (LC-ADC) is a promising candidate for low-power biosignal processing because of its event-driven properties. The LC-ADC acquires data by level-crossing sampling. When an input signal crosses the threshold level, the LC-ADC samples the signal. The conventional LC-ADC employs a power-hungry comparator. In this paper, we present a low-power inverter-based LC-ADC. By adjusting the threshold level of the inverter, it can be used as a threshold-fixed window comparator. By using the inverter as an alternative to a comparator, power consumption can be markedly reduced. As a result, the total power consumption is successfully reduced by 90% of that of previous LC-ADC. The inverter-based LC-ADC was found to be very suitable for use in power-limited biomedical devices.

Independent component analysis algorithm FPGA design to perform real-time blind source separation

NASA Astrophysics Data System (ADS)

Meyer-Baese, Uwe; Odom, Crispin; Botella, Guillermo; Meyer-Baese, Anke

2015-05-01

The conditions that arise in the Cocktail Party Problem prevail across many fields creating a need for of Blind Source Separation. The need for BSS has become prevalent in several fields of work. These fields include array processing, communications, medical signal processing, and speech processing, wireless communication, audio, acoustics and biomedical engineering. The concept of the cocktail party problem and BSS led to the development of Independent Component Analysis (ICA) algorithms. ICA proves useful for applications needing real time signal processing. The goal of this research was to perform an extensive study on ability and efficiency of Independent Component Analysis algorithms to perform blind source separation on mixed signals in software and implementation in hardware with a Field Programmable Gate Array (FPGA). The Algebraic ICA (A-ICA), Fast ICA, and Equivariant Adaptive Separation via Independence (EASI) ICA were examined and compared. The best algorithm required the least complexity and fewest resources while effectively separating mixed sources. The best algorithm was the EASI algorithm. The EASI ICA was implemented on hardware with Field Programmable Gate Arrays (FPGA) to perform and analyze its performance in real time.

Bluetooth telemedicine processor for multichannel biomedical signal transmission via mobile cellular networks.

PubMed

Rasid, Mohd Fadlee A; Woodward, Bryan

2005-03-01

One of the emerging issues in m-Health is how best to exploit the mobile communications technologies that are now almost globally available. The challenge is to produce a system to transmit a patient's biomedical signals directly to a hospital for monitoring or diagnosis, using an unmodified mobile telephone. The paper focuses on the design of a processor, which samples signals from sensors on the patient. It then transmits digital data over a Bluetooth link to a mobile telephone that uses the General Packet Radio Service. The modular design adopted is intended to provide a "future-proofed" system, whose functionality may be upgraded by modifying the software.

Self spectrum window method in wigner-ville distribution.

PubMed

Liu, Zhongguo; Liu, Changchun; Liu, Boqiang; Lv, Yangsheng; Lei, Yinsheng; Yu, Mengsun

2005-01-01

Wigner-Ville distribution (WVD) is an important type of time-frequency analysis in biomedical signal processing. The cross-term interference in WVD has a disadvantageous influence on its application. In this research, the Self Spectrum Window (SSW) method was put forward to suppress the cross-term interference, based on the fact that the cross-term and auto-WVD- terms in integral kernel function are orthogonal. With the Self Spectrum Window (SSW) algorithm, a real auto-WVD function was used as a template to cross-correlate with the integral kernel function, and the Short Time Fourier Transform (STFT) spectrum of the signal was used as window function to process the WVD in time-frequency plane. The SSW method was confirmed by computer simulation with good analysis results. Satisfactory time- frequency distribution was obtained.

Affective assessment of computer users based on processing the pupil diameter signal.

PubMed

Ren, Peng; Barreto, Armando; Gao, Ying; Adjouadi, Malek

2011-01-01

Detecting affective changes of computer users is a current challenge in human-computer interaction which is being addressed with the help of biomedical engineering concepts. This article presents a new approach to recognize the affective state ("relaxation" vs. "stress") of a computer user from analysis of his/her pupil diameter variations caused by sympathetic activation. Wavelet denoising and Kalman filtering methods are first used to remove abrupt changes in the raw Pupil Diameter (PD) signal. Then three features are extracted from the preprocessed PD signal for the affective state classification. Finally, a random tree classifier is implemented, achieving an accuracy of 86.78%. In these experiments the Eye Blink Frequency (EBF), is also recorded and used for affective state classification, but the results show that the PD is a more promising physiological signal for affective assessment.

Low cost open data acquisition system for biomedical applications

NASA Astrophysics Data System (ADS)

Zabolotny, Wojciech M.; Laniewski-Wollk, Przemyslaw; Zaworski, Wojciech

2005-09-01

In the biomedical applications it is often necessary to collect measurement data from different devices. It is relatively easy, if the devices are equipped with a MIB or Ethernet interface, however often they feature only the asynchronous serial link, and sometimes the measured values are available only as the analog signals. The system presented in the paper is a low cost alternative to commercially available data acquisition systems. The hardware and software architecture of the system is fully open, so it is possible to customize it for particular needs. The presented system offers various possibilities to connect it to the computer based data processing unit - e.g. using the USB or Ethernet ports. Both interfaces allow also to use many such systems in parallel to increase amount of serial and analog inputs. The open source software used in the system makes possible to process the acquired data with standard tools like MATLAB, Scilab or Octave, or with a dedicated, user supplied application.

Detection and Processing Techniques of FECG Signal for Fetal Monitoring

PubMed Central

2009-01-01

Fetal electrocardiogram (FECG) signal contains potentially precise information that could assist clinicians in making more appropriate and timely decisions during labor. The ultimate reason for the interest in FECG signal analysis is in clinical diagnosis and biomedical applications. The extraction and detection of the FECG signal from composite abdominal signals with powerful and advance methodologies are becoming very important requirements in fetal monitoring. The purpose of this review paper is to illustrate the various methodologies and developed algorithms on FECG signal detection and analysis to provide efficient and effective ways of understanding the FECG signal and its nature for fetal monitoring. A comparative study has been carried out to show the performance and accuracy of various methods of FECG signal analysis for fetal monitoring. Finally, this paper further focused some of the hardware implementations using electrical signals for monitoring the fetal heart rate. This paper opens up a passage for researchers, physicians, and end users to advocate an excellent understanding of FECG signal and its analysis procedures for fetal heart rate monitoring system. PMID:19495912

Deep learning in bioinformatics.

PubMed

Min, Seonwoo; Lee, Byunghan; Yoon, Sungroh

2017-09-01

In the era of big data, transformation of biomedical big data into valuable knowledge has been one of the most important challenges in bioinformatics. Deep learning has advanced rapidly since the early 2000s and now demonstrates state-of-the-art performance in various fields. Accordingly, application of deep learning in bioinformatics to gain insight from data has been emphasized in both academia and industry. Here, we review deep learning in bioinformatics, presenting examples of current research. To provide a useful and comprehensive perspective, we categorize research both by the bioinformatics domain (i.e. omics, biomedical imaging, biomedical signal processing) and deep learning architecture (i.e. deep neural networks, convolutional neural networks, recurrent neural networks, emergent architectures) and present brief descriptions of each study. Additionally, we discuss theoretical and practical issues of deep learning in bioinformatics and suggest future research directions. We believe that this review will provide valuable insights and serve as a starting point for researchers to apply deep learning approaches in their bioinformatics studies. © The Author 2016. Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oup.com.

Adaptive photoacoustic imaging quality optimization with EMD and reconstruction

NASA Astrophysics Data System (ADS)

Guo, Chengwen; Ding, Yao; Yuan, Jie; Xu, Guan; Wang, Xueding; Carson, Paul L.

2016-10-01

Biomedical photoacoustic (PA) signal is characterized with extremely low signal to noise ratio which will yield significant artifacts in photoacoustic tomography (PAT) images. Since PA signals acquired by ultrasound transducers are non-linear and non-stationary, traditional data analysis methods such as Fourier and wavelet method cannot give useful information for further research. In this paper, we introduce an adaptive method to improve the quality of PA imaging based on empirical mode decomposition (EMD) and reconstruction. Data acquired by ultrasound transducers are adaptively decomposed into several intrinsic mode functions (IMFs) after a sifting pre-process. Since noise is randomly distributed in different IMFs, depressing IMFs with more noise while enhancing IMFs with less noise can effectively enhance the quality of reconstructed PAT images. However, searching optimal parameters by means of brute force searching algorithms will cost too much time, which prevent this method from practical use. To find parameters within reasonable time, heuristic algorithms, which are designed for finding good solutions more efficiently when traditional methods are too slow, are adopted in our method. Two of the heuristic algorithms, Simulated Annealing Algorithm, a probabilistic method to approximate the global optimal solution, and Artificial Bee Colony Algorithm, an optimization method inspired by the foraging behavior of bee swarm, are selected to search optimal parameters of IMFs in this paper. The effectiveness of our proposed method is proved both on simulated data and PA signals from real biomedical tissue, which might bear the potential for future clinical PA imaging de-noising.

Focusing light into desired patterns through turbid media by feedback-based wavefront shaping

NASA Astrophysics Data System (ADS)

Wan, Lipeng; Chen, Ziyang; Huang, Huiling; Pu, Jixiong

2016-07-01

We demonstrate that the focusing of light into desired patterns through turbid media can be realized using feedback-based wavefront shaping. Three desired focused patterns—a triangle, a circle, and a rectangle—are used as examples to study this ability. During the process of modulating scattered light, the Pearson's correlation coefficient is introduced as a feedback signal. It is found that the speckle field formed by the turbid media gradually transforms into the desired pattern through a process of modulation of the input beam wave front. The proposed approach has potential applications in biomedical treatment and laser material processing.

Microcontroller-based wireless recorder for biomedical signals.

PubMed

Chien, C-N; Hsu, H-W; Jang, J-K; Rau, C-L; Jaw, F-S

2005-01-01

A portable multichannel system is described for the recording of biomedical signals wirelessly. Instead of using the conversional time-division analog-modulation method, the technique of digital multiplexing was applied to increase the number of signal channels to 4. Detailed design considerations and functional allocation of the system is discussed. The frontend unit was modularly designed to condition the input signal in an optimal manner. Then, the microcontroller handled the tasks of data conversion, wireless transmission, as well as providing the ability of simple preprocessing such as waveform averaging or rectification. The low-power nature of this microcontroller affords the benefit of battery operation and hence, patient isolation of the system. Finally, a single-chip receiver, which compatible with the RF transmitter of the microcontroller, was used to implement a compact interface with the host computer. An application of this portable recorder for low-back pain studies is shown. This device can simultaneously record one ECG and two surface EMG wirelessly, thus, is helpful in relieving patients' anxiety devising clinical measurement. Such an approach, microcontroller-based wireless measurement, could be an important trend for biomedical instrumentation and we help that this paper could be useful for other colleagues.

Validation of Radio Frequency Telemetry Concept in the Presence of Biological Tissue-Like Stratified Media

NASA Technical Reports Server (NTRS)

Miranda, Felix A.; Simons, Rainee N.; Haal, David G.

2004-01-01

In this paper we discuss a novel radio frequency (RF) telemetry concept for biomedical applications. The concept consists of a miniaturized spiral inductor/antenna for bio-MEMS sensors and an external pick-up antenna integrated into a handheld device. The measured relative signal strength in the presence of biological phantoms ranged from 5.9 to 7.5 dB for antenna separations of 5 and 10 cm. These relative signal strengths are easily measurable, therefore validating the RF telemetry concept for biomedical applications.

Biomolecular logic systems: applications to biosensors and bioactuators

NASA Astrophysics Data System (ADS)

Katz, Evgeny

2014-05-01

The paper presents an overview of recent advances in biosensors and bioactuators based on the biocomputing concept. Novel biosensors digitally process multiple biochemical signals through Boolean logic networks of coupled biomolecular reactions and produce output in the form of YES/NO response. Compared to traditional single-analyte sensing devices, biocomputing approach enables a high-fidelity multi-analyte biosensing, particularly beneficial for biomedical applications. Multi-signal digital biosensors thus promise advances in rapid diagnosis and treatment of diseases by processing complex patterns of physiological biomarkers. Specifically, they can provide timely detection and alert to medical emergencies, along with an immediate therapeutic intervention. Application of the biocomputing concept has been successfully demonstrated for systems performing logic analysis of biomarkers corresponding to different injuries, particularly exemplified for liver injury. Wide-ranging applications of multi-analyte digital biosensors in medicine, environmental monitoring and homeland security are anticipated. "Smart" bioactuators, for example for signal-triggered drug release, were designed by interfacing switchable electrodes and biocomputing systems. Integration of novel biosensing and bioactuating systems with the biomolecular information processing systems keeps promise for further scientific advances and numerous practical applications.

New insights into metabolic signaling and cell survival: the role of beta-O-linkage of N-acetylglucosamine.

PubMed

Ngoh, Gladys A; Jones, Steven P

2008-12-01

The involvement of glucose in fundamental metabolic pathways represents a core element of biology. Late in the 20th century, a unique glucose-derived signal was discovered, which appeared to be involved in a variety of cellular processes, including mitosis, transcription, insulin signaling, stress responses, and potentially, Alzheimer's disease, and diabetes. By definition, this glucose-fed signaling system was a post-translational modification to proteins. However, unlike classical cotranslational N-glycosylation occurring in the endoplasmic reticulum and Golgi apparatus, this process occurs elsewhere throughout the cell in a highly dynamic fashion, similar to the quintessential post-translational modification, phosphorylation. This more recently described post-translational modification, the beta-O-linkage of N-acetylglucosamine (i.e., O-GlcNAc) to nucleocytoplasmic proteins, represents an under-investigated area of biology. This signaling system operates in all of the tissues examined and seems to have persisted throughout all multicellular eukaryotes. Thus, it comes with little surprise that O-GlcNAc signaling is an integral system and viable target for biomedical investigation. This system may be a boundless source for insight into a variety of diseases and yield numerous opportunities for drug design. This Perspective will address recent insights into O-GlcNAc signaling in the cardiovascular system as a paradigm for its involvement in other biological systems.

Signal and noise modeling in confocal laser scanning fluorescence microscopy.

PubMed

Herberich, Gerlind; Windoffer, Reinhard; Leube, Rudolf E; Aach, Til

2012-01-01

Fluorescence confocal laser scanning microscopy (CLSM) has revolutionized imaging of subcellular structures in biomedical research by enabling the acquisition of 3D time-series of fluorescently-tagged proteins in living cells, hence forming the basis for an automated quantification of their morphological and dynamic characteristics. Due to the inherently weak fluorescence, CLSM images exhibit a low SNR. We present a novel model for the transfer of signal and noise in CLSM that is both theoretically sound as well as corroborated by a rigorous analysis of the pixel intensity statistics via measurement of the 3D noise power spectra, signal-dependence and distribution. Our model provides a better fit to the data than previously proposed models. Further, it forms the basis for (i) the simulation of the CLSM imaging process indispensable for the quantitative evaluation of CLSM image analysis algorithms, (ii) the application of Poisson denoising algorithms and (iii) the reconstruction of the fluorescence signal.

The University of Connecticut Biomedical Engineering Mentoring Program for high school students.

PubMed

Enderle, John D; Liebler, Christopher M; Haapala, Stephenic A; Hart, James L; Thonakkaraparayil, Naomi T; Romonosky, Laura L; Rodriguez, Francisco; Trumbower, Randy D

2004-01-01

For the past four years, the Biomedical Engineering Program at the University of Connecticut has offered a summer mentoring program for high school students interested in biomedical engineering. To offer this program, we have partnered with the UConn Mentor Connection Program, the School of Engineering 2000 Program and the College of Liberal Arts and Sciences Summer Laboratory Apprentice Program. We typically have approximately 20-25 high school students learning about biomedical engineering each summer. The mentoring aspect of the program exists at many different levels, with the graduate students mentoring the undergraduate students, and these students mentoring the high school students. The program starts with a three-hour lecture on biomedical engineering to properly orient the students. An in-depth paper on an area in biomedical engineering is a required component, as well as a PowerPoint presentation on their research. All of the students build a device to record an EKG on a computer using LabView, including signal processing to remove noise. The students learn some rudimentary concepts on electrocardiography and the physiology and anatomy of the heart. The students also learn basic electronics and breadboarding circuits, PSpice, the building of a printed circuit board, PIC microcontroller, the operation of Multimeters (including the oscilloscope), soldering, assembly of the EKG device and writing LabView code to run their device on a PC. The students keep their EKG device, LabView program and a fully illustrated booklet on EKG to bring home with them, and hopefully bring back to their high school to share their experiences with other students and teachers. The students also work on several other projects during this summer experience as well as visit Hartford Hospital to learn about Clinical Engineering.

The Single Kinin Receptor Signals to Separate and Independent Physiological Pathways in Malpighian Tubules of the Yellow Fever Mosquito

DTIC Science & Technology

2010-06-10

Felix Tiburcy3, Ronald J. Nachman4, Peter M. Piermarini1 and Klaus W. Beyenbach1 1Department of Biomedical Sciences, VRT 8004, Cornell...Dept. of Biomedical Sciences VRT 8004 Cornell University Ithaca, NY 14853 Voice: (607) 253-3482 FAX: (607) 253-3851 Email: KWB1@CORNELL.EDU...University,Department of Biomedical Sciences, VRT 8004,Ithaca,NY,14853 8. PERFORMING ORGANIZATION REPORT NUMBER 9. SPONSORING/MONITORING AGENCY NAME(S) AND

Asynchronous signal-dependent non-uniform sampler

NASA Astrophysics Data System (ADS)

Can-Cimino, Azime; Chaparro, Luis F.; Sejdić, Ervin

2014-05-01

Analog sparse signals resulting from biomedical and sensing network applications are typically non-stationary with frequency-varying spectra. By ignoring that the maximum frequency of their spectra is changing, uniform sampling of sparse signals collects unnecessary samples in quiescent segments of the signal. A more appropriate sampling approach would be signal-dependent. Moreover, in many of these applications power consumption and analog processing are issues of great importance that need to be considered. In this paper we present a signal dependent non-uniform sampler that uses a Modified Asynchronous Sigma Delta Modulator which consumes low-power and can be processed using analog procedures. Using Prolate Spheroidal Wave Functions (PSWF) interpolation of the original signal is performed, thus giving an asynchronous analog to digital and digital to analog conversion. Stable solutions are obtained by using modulated PSWFs functions. The advantage of the adapted asynchronous sampler is that range of frequencies of the sparse signal is taken into account avoiding aliasing. Moreover, it requires saving only the zero-crossing times of the non-uniform samples, or their differences, and the reconstruction can be done using their quantized values and a PSWF-based interpolation. The range of frequencies analyzed can be changed and the sampler can be implemented as a bank of filters for unknown range of frequencies. The performance of the proposed algorithm is illustrated with an electroencephalogram (EEG) signal.

Telemedicine optoelectronic biomedical data processing system

NASA Astrophysics Data System (ADS)

Prosolovska, Vita V.

2010-08-01

The telemedicine optoelectronic biomedical data processing system is created to share medical information for the control of health rights and timely and rapid response to crisis. The system includes the main blocks: bioprocessor, analog-digital converter biomedical images, optoelectronic module for image processing, optoelectronic module for parallel recording and storage of biomedical imaging and matrix screen display of biomedical images. Rated temporal characteristics of the blocks defined by a particular triggering optoelectronic couple in analog-digital converters and time imaging for matrix screen. The element base for hardware implementation of the developed matrix screen is integrated optoelectronic couples produced by selective epitaxy.

Human Prostate Cancer Hallmarks Map

PubMed Central

Datta, Dipamoy; Aftabuddin, Md.; Gupta, Dinesh Kumar; Raha, Sanghamitra; Sen, Prosenjit

2016-01-01

Human prostate cancer is a complex heterogeneous disease that mainly affects elder male population of the western world with a high rate of mortality. Acquisitions of diverse sets of hallmark capabilities along with an aberrant functioning of androgen receptor signaling are the central driving forces behind prostatic tumorigenesis and its transition into metastatic castration resistant disease. These hallmark capabilities arise due to an intense orchestration of several crucial factors, including deregulation of vital cell physiological processes, inactivation of tumor suppressive activity and disruption of prostate gland specific cellular homeostasis. The molecular complexity and redundancy of oncoproteins signaling in prostate cancer demands for concurrent inhibition of multiple hallmark associated pathways. By an extensive manual curation of the published biomedical literature, we have developed Human Prostate Cancer Hallmarks Map (HPCHM), an onco-functional atlas of human prostate cancer associated signaling and events. It explores molecular architecture of prostate cancer signaling at various levels, namely key protein components, molecular connectivity map, oncogenic signaling pathway map, pathway based functional connectivity map etc. Here, we briefly represent the systems level understanding of the molecular mechanisms associated with prostate tumorigenesis by considering each and individual molecular and cell biological events of this disease process. PMID:27476486

On analysis of electroencephalogram by multiresolution-based energetic approach

NASA Astrophysics Data System (ADS)

Sevindir, Hulya Kodal; Yazici, Cuneyt; Siddiqi, A. H.; Aslan, Zafer

2013-10-01

Epilepsy is a common brain disorder where the normal neuronal activity gets affected. Electroencephalography (EEG) is the recording of electrical activity along the scalp produced by the firing of neurons within the brain. The main application of EEG is in the case of epilepsy. On a standard EEG some abnormalities indicate epileptic activity. EEG signals like many biomedical signals are highly non-stationary by their nature. For the investigation of biomedical signals, in particular EEG signals, wavelet analysis have found prominent position in the study for their ability to analyze such signals. Wavelet transform is capable of separating the signal energy among different frequency scales and a good compromise between temporal and frequency resolution is obtained. The present study is an attempt for better understanding of the mechanism causing the epileptic disorder and accurate prediction of occurrence of seizures. In the present paper following Magosso's work [12], we identify typical patterns of energy redistribution before and during the seizure using multiresolution wavelet analysis on Kocaeli University's Medical School's data.

A quasi-likelihood approach to non-negative matrix factorization

PubMed Central

Devarajan, Karthik; Cheung, Vincent C.K.

2017-01-01

A unified approach to non-negative matrix factorization based on the theory of generalized linear models is proposed. This approach embeds a variety of statistical models, including the exponential family, within a single theoretical framework and provides a unified view of such factorizations from the perspective of quasi-likelihood. Using this framework, a family of algorithms for handling signal-dependent noise is developed and its convergence proven using the Expectation-Maximization algorithm. In addition, a measure to evaluate the goodness-of-fit of the resulting factorization is described. The proposed methods allow modeling of non-linear effects via appropriate link functions and are illustrated using an application in biomedical signal processing. PMID:27348511

Home telecare system using cable television plants--an experimental field trial.

PubMed

Lee, R G; Chen, H S; Lin, C C; Chang, K C; Chen, J H

2000-03-01

To solve the inconvenience of routine transportation of chronically ill and handicapped patients, this paper proposes a platform based on a hybrid fiber coaxial (HFC) network in Taiwan designed to make a home telecare system feasible. The aim of this home telecare system is to combine biomedical data, including three-channel electrocardiogram (ECG) and blood pressure (BP), video, and audio into a National Television Standard Committee (NTSC) channel for communication between the patient and healthcare provider. Digitized biomedical data and output from medical devices can be further modulated to a second audio program (SAP) subchannel which can be used for second-language audio in NTSC television signals. For long-distance transmission, we translate the digital biomedical data into the frequency domain using frequency shift key (FSK) technology and insert this signal into an SAP band. The whole system has been implemented and tested. The results obtained using this system clearly demonstrated that real-time video, audio, and biomedical data transmission are very clear with a carrier-to-noise ratio up to 43 dB.

Software system for data management and distributed processing of multichannel biomedical signals.

PubMed

Franaszczuk, P J; Jouny, C C

2004-01-01

The presented software is designed for efficient utilization of cluster of PC computers for signal analysis of multichannel physiological data. The system consists of three main components: 1) a library of input and output procedures, 2) a database storing additional information about location in a storage system, 3) a user interface for selecting data for analysis, choosing programs for analysis, and distributing computing and output data on cluster nodes. The system allows for processing multichannel time series data in multiple binary formats. The description of data format, channels and time of recording are included in separate text files. Definition and selection of multiple channel montages is possible. Epochs for analysis can be selected both manually and automatically. Implementation of a new signal processing procedures is possible with a minimal programming overhead for the input/output processing and user interface. The number of nodes in cluster used for computations and amount of storage can be changed with no major modification to software. Current implementations include the time-frequency analysis of multiday, multichannel recordings of intracranial EEG of epileptic patients as well as evoked response analyses of repeated cognitive tasks.

Real-Time Processing Library for Open-Source Hardware Biomedical Sensors

PubMed Central

Castro-García, Juan A.; Lebrato-Vázquez, Clara

2018-01-01

Applications involving data acquisition from sensors need samples at a preset frequency rate, the filtering out of noise and/or analysis of certain frequency components. We propose a novel software architecture based on open-software hardware platforms which allows programmers to create data streams from input channels and easily implement filters and frequency analysis objects. The performances of the different classes given in the size of memory allocated and execution time (number of clock cycles) were analyzed in the low-cost platform Arduino Genuino. In addition, 11 people took part in an experiment in which they had to implement several exercises and complete a usability test. Sampling rates under 250 Hz (typical for many biomedical applications) makes it feasible to implement filters, sliding windows and Fourier analysis, operating in real time. Participants rated software usability at 70.2 out of 100 and the ease of use when implementing several signal processing applications was rated at just over 4.4 out of 5. Participants showed their intention of using this software because it was percieved as useful and very easy to use. The performances of the library showed that it may be appropriate for implementing small biomedical real-time applications or for human movement monitoring, even in a simple open-source hardware device like Arduino Genuino. The general perception about this library is that it is easy to use and intuitive. PMID:29596394

Real-Time Processing Library for Open-Source Hardware Biomedical Sensors.

PubMed

Molina-Cantero, Alberto J; Castro-García, Juan A; Lebrato-Vázquez, Clara; Gómez-González, Isabel M; Merino-Monge, Manuel

2018-03-29

Applications involving data acquisition from sensors need samples at a preset frequency rate, the filtering out of noise and/or analysis of certain frequency components. We propose a novel software architecture based on open-software hardware platforms which allows programmers to create data streams from input channels and easily implement filters and frequency analysis objects. The performances of the different classes given in the size of memory allocated and execution time (number of clock cycles) were analyzed in the low-cost platform Arduino Genuino. In addition, 11 people took part in an experiment in which they had to implement several exercises and complete a usability test. Sampling rates under 250 Hz (typical for many biomedical applications) makes it feasible to implement filters, sliding windows and Fourier analysis, operating in real time. Participants rated software usability at 70.2 out of 100 and the ease of use when implementing several signal processing applications was rated at just over 4.4 out of 5. Participants showed their intention of using this software because it was percieved as useful and very easy to use. The performances of the library showed that it may be appropriate for implementing small biomedical real-time applications or for human movement monitoring, even in a simple open-source hardware device like Arduino Genuino. The general perception about this library is that it is easy to use and intuitive.

Applied Nonlinear Dynamics and Stochastic Systems Near The Millenium. Proceedings

DOE Office of Scientific and Technical Information (OSTI.GOV)

Kadtke, J.B.; Bulsara, A.

These proceedings represent papers presented at the Applied Nonlinear Dynamics and Stochastic Systems conference held in San Diego, California in July 1997. The conference emphasized the applications of nonlinear dynamical systems theory in fields as diverse as neuroscience and biomedical engineering, fluid dynamics, chaos control, nonlinear signal/image processing, stochastic resonance, devices and nonlinear dynamics in socio{minus}economic systems. There were 56 papers presented at the conference and 5 have been abstracted for the Energy Science and Technology database.(AIP)

A data analysis framework for biomedical big data: Application on mesoderm differentiation of human pluripotent stem cells

PubMed Central

Karlsson, Alexander; Riveiro, Maria; Améen, Caroline; Åkesson, Karolina; Andersson, Christian X.; Sartipy, Peter; Synnergren, Jane

2017-01-01

The development of high-throughput biomolecular technologies has resulted in generation of vast omics data at an unprecedented rate. This is transforming biomedical research into a big data discipline, where the main challenges relate to the analysis and interpretation of data into new biological knowledge. The aim of this study was to develop a framework for biomedical big data analytics, and apply it for analyzing transcriptomics time series data from early differentiation of human pluripotent stem cells towards the mesoderm and cardiac lineages. To this end, transcriptome profiling by microarray was performed on differentiating human pluripotent stem cells sampled at eleven consecutive days. The gene expression data was analyzed using the five-stage analysis framework proposed in this study, including data preparation, exploratory data analysis, confirmatory analysis, biological knowledge discovery, and visualization of the results. Clustering analysis revealed several distinct expression profiles during differentiation. Genes with an early transient response were strongly related to embryonic- and mesendoderm development, for example CER1 and NODAL. Pluripotency genes, such as NANOG and SOX2, exhibited substantial downregulation shortly after onset of differentiation. Rapid induction of genes related to metal ion response, cardiac tissue development, and muscle contraction were observed around day five and six. Several transcription factors were identified as potential regulators of these processes, e.g. POU1F1, TCF4 and TBP for muscle contraction genes. Pathway analysis revealed temporal activity of several signaling pathways, for example the inhibition of WNT signaling on day 2 and its reactivation on day 4. This study provides a comprehensive characterization of biological events and key regulators of the early differentiation of human pluripotent stem cells towards the mesoderm and cardiac lineages. The proposed analysis framework can be used to structure data analysis in future research, both in stem cell differentiation, and more generally, in biomedical big data analytics. PMID:28654683

A data analysis framework for biomedical big data: Application on mesoderm differentiation of human pluripotent stem cells.

PubMed

Ulfenborg, Benjamin; Karlsson, Alexander; Riveiro, Maria; Améen, Caroline; Åkesson, Karolina; Andersson, Christian X; Sartipy, Peter; Synnergren, Jane

2017-01-01

The development of high-throughput biomolecular technologies has resulted in generation of vast omics data at an unprecedented rate. This is transforming biomedical research into a big data discipline, where the main challenges relate to the analysis and interpretation of data into new biological knowledge. The aim of this study was to develop a framework for biomedical big data analytics, and apply it for analyzing transcriptomics time series data from early differentiation of human pluripotent stem cells towards the mesoderm and cardiac lineages. To this end, transcriptome profiling by microarray was performed on differentiating human pluripotent stem cells sampled at eleven consecutive days. The gene expression data was analyzed using the five-stage analysis framework proposed in this study, including data preparation, exploratory data analysis, confirmatory analysis, biological knowledge discovery, and visualization of the results. Clustering analysis revealed several distinct expression profiles during differentiation. Genes with an early transient response were strongly related to embryonic- and mesendoderm development, for example CER1 and NODAL. Pluripotency genes, such as NANOG and SOX2, exhibited substantial downregulation shortly after onset of differentiation. Rapid induction of genes related to metal ion response, cardiac tissue development, and muscle contraction were observed around day five and six. Several transcription factors were identified as potential regulators of these processes, e.g. POU1F1, TCF4 and TBP for muscle contraction genes. Pathway analysis revealed temporal activity of several signaling pathways, for example the inhibition of WNT signaling on day 2 and its reactivation on day 4. This study provides a comprehensive characterization of biological events and key regulators of the early differentiation of human pluripotent stem cells towards the mesoderm and cardiac lineages. The proposed analysis framework can be used to structure data analysis in future research, both in stem cell differentiation, and more generally, in biomedical big data analytics.

On lossy transform compression of ECG signals with reference to deformation of their parameter values.

PubMed

Koski, Antti; Tossavainen, Timo; Juhola, Martti

2004-01-01

Electrocardiogram (ECG) signals are the most prominent biomedical signal type used in clinical medicine. Their compression is important and widely researched in the medical informatics community. In the previous literature compression efficacy has been investigated only in the context of how much known or developed methods reduced the storage required by compressed forms of original ECG signals. Sometimes statistical signal evaluations based on, for example, root mean square error were studied. In previous research we developed a refined method for signal compression and tested it jointly with several known techniques for other biomedical signals. Our method of so-called successive approximation quantization used with wavelets was one of the most successful in those tests. In this paper, we studied to what extent these lossy compression methods altered values of medical parameters (medical information) computed from signals. Since the methods are lossy, some information is lost due to the compression when a high enough compression ratio is reached. We found that ECG signals sampled at 400 Hz could be compressed to one fourth of their original storage space, but the values of their medical parameters changed less than 5% due to compression, which indicates reliable results.

KaBOB: ontology-based semantic integration of biomedical databases.

PubMed

Livingston, Kevin M; Bada, Michael; Baumgartner, William A; Hunter, Lawrence E

2015-04-23

The ability to query many independent biological databases using a common ontology-based semantic model would facilitate deeper integration and more effective utilization of these diverse and rapidly growing resources. Despite ongoing work moving toward shared data formats and linked identifiers, significant problems persist in semantic data integration in order to establish shared identity and shared meaning across heterogeneous biomedical data sources. We present five processes for semantic data integration that, when applied collectively, solve seven key problems. These processes include making explicit the differences between biomedical concepts and database records, aggregating sets of identifiers denoting the same biomedical concepts across data sources, and using declaratively represented forward-chaining rules to take information that is variably represented in source databases and integrating it into a consistent biomedical representation. We demonstrate these processes and solutions by presenting KaBOB (the Knowledge Base Of Biomedicine), a knowledge base of semantically integrated data from 18 prominent biomedical databases using common representations grounded in Open Biomedical Ontologies. An instance of KaBOB with data about humans and seven major model organisms can be built using on the order of 500 million RDF triples. All source code for building KaBOB is available under an open-source license. KaBOB is an integrated knowledge base of biomedical data representationally based in prominent, actively maintained Open Biomedical Ontologies, thus enabling queries of the underlying data in terms of biomedical concepts (e.g., genes and gene products, interactions and processes) rather than features of source-specific data schemas or file formats. KaBOB resolves many of the issues that routinely plague biomedical researchers intending to work with data from multiple data sources and provides a platform for ongoing data integration and development and for formal reasoning over a wealth of integrated biomedical data.

Time Resolved Microfluorescence In Biomedical Diagnosis

NASA Astrophysics Data System (ADS)

Schneckenburger, Herbert

1985-12-01

A measuring system combining subnanosecond laser-induced fluorescence with microscopic signal detection was installed and used for diverse projects in the biomedical and environmental fields. These projects range from tumor diagnosis and enzymatic analysis to measurements of the activity of methanogenic bacteria, which affect biogas production and waste water cleaning. The advantages of this method and its practical applicability are discussed.

A novel fabrication method of carbon electrodes using 3D printing and chemical modification process.

PubMed

Tian, Pan; Chen, Chaoyang; Hu, Jie; Qi, Jin; Wang, Qianghua; Chen, Jimmy Ching-Ming; Cavanaugh, John; Peng, Yinghong; Cheng, Mark Ming-Cheng

2017-11-23

Three-dimensional (3D) printing is an emerging technique in the field of biomedical engineering and electronics. This paper presents a novel biofabrication method of implantable carbon electrodes with several advantages including fast prototyping, patient-specific and miniaturization without expensive cleanroom. The method combines stereolithography in additive manufacturing and chemical modification processes to fabricate electrically conductive carbon electrodes. The stereolithography allows the structures to be 3D printed with very fine resolution and desired shapes. The resin is then chemically modified to carbon using pyrolysis to enhance electrochemical performance. The electrochemical characteristics of 3D printing carbon electrodes are assessed by cyclic voltammetry (CV) and electrochemical impedance spectroscopy (EIS). The specific capacitance of 3D printing carbon electrodes is much higher than the same sized platinum (Pt) electrode. In-vivo electromyography (EMG) recording, 3D printing carbon electrodes exhibit much higher signal-to-noise ratio (40.63 ± 7.73) than Pt electrodes (14.26 ± 6.83). The proposed biofabrication method is envisioned to enable 3D printing in many emerging applications in biomedical engineering and electronics.

Time-Resolved Microfluorescence In Biomedical Diagnosis

NASA Astrophysics Data System (ADS)

Schneckenburger, Herbert

1985-02-01

A measuring system combining subnanosecond laser-induced fluorescence with microscopic signal detection was installed and used for diverse projects in the biomedical and environmental field. These projects are ranging from tumor diagnosis and enzymatic analysis to measurements of the activity of methanogenic bacteria which effect biogas production and waste water cleaning. The advantages of this method and its practical applicability are discussed.

A 1V low power second-order delta-sigma modulator for biomedical signal application.

PubMed

Hsu, Chih-Han; Tang, Kea-Tiong

2013-01-01

This paper presents the design and implementation of a low-power delta-sigma modulator for biomedical application with a standard 90 nm CMOS technology. The delta-sigma architecture is implemented as 2nd order feedforward architecture. A low quiescent current operational transconductance amplifier (OTA) is utilized to reduce power consumption. This delta-sigma modulator operated in 1V power supply, and achieved 64.87 dB signal to noise distortion ratio (SNDR) at 10 KHz bandwidth with an oversampling ratio (OSR) of 64. The power consumption is 17.14 µW, and the figure-of-merit (FOM) is 0.60 pJ/conv.

Broad-Bandwidth Chiral Sum Frequency Generation Spectroscopy for Probing the Kinetics of Proteins at Interfaces

PubMed Central

2016-01-01

The kinetics of proteins at interfaces plays an important role in biological functions and inspires solutions to fundamental problems in biomedical sciences and engineering. Nonetheless, due to the lack of surface-specific and structural-sensitive biophysical techniques, it still remains challenging to probe protein kinetics in situ and in real time without the use of spectroscopic labels at interfaces. Broad-bandwidth chiral sum frequency generation (SFG) spectroscopy has been recently developed for protein kinetic studies at interfaces by tracking the chiral vibrational signals of proteins. In this article, we review our recent progress in kinetic studies of proteins at interfaces using broad-bandwidth chiral SFG spectroscopy. We illustrate the use of chiral SFG signals of protein side chains in the C–H stretch region to monitor self-assembly processes of proteins at interfaces. We also present the use of chiral SFG signals from the protein backbone in the N–H stretch region to probe the real-time kinetics of proton exchange between protein and water at interfaces. In addition, we demonstrate the applications of spectral features of chiral SFG that are typical of protein secondary structures in both the amide I and the N–H stretch regions for monitoring the kinetics of aggregation of amyloid proteins at membrane surfaces. These studies exhibit the power of broad-bandwidth chiral SFG to study protein kinetics at interfaces and the promise of this technique in research areas of surface science to address fundamental problems in biomedical and material sciences. PMID:26196215

A Suggested Model for Building Robust Biomedical Implants Registries.

PubMed

Aloufi, Bader; Alshagathrah, Fahad; Househ, Mowafa

2017-01-01

Registries are an essential source of information for clinical and non-clinical decision-makers; because they provide evidence for post-market clinical follow-up and early detection of safety signals for biomedical implants. Yet, many of todays biomedical implants registries are facing a variety of challenges relating to a poorly designed dataset, the reliability of inputted data and low clinician and patient participation. The purpose of this paper is to present a best practice model for the implementation and use of biomedical implants registries to monitor the safety and effectiveness of implantable medical devices. Based on a literature review and an analysis of multiple national relevant registries, we identified six factors that address contemporary challenges and are believed to be the keys for building a successful biomedical implants registry, which include: sustainable development, international comparability, data reliability, purposeful design, ease of patient participation, and collaborative development at the national level.

Acoustic Remote Sensing

NASA Astrophysics Data System (ADS)

Dowling, David R.; Sabra, Karim G.

2015-01-01

Acoustic waves carry information about their source and collect information about their environment as they propagate. This article reviews how these information-carrying and -collecting features of acoustic waves that travel through fluids can be exploited for remote sensing. In nearly all cases, modern acoustic remote sensing involves array-recorded sounds and array signal processing to recover multidimensional results. The application realm for acoustic remote sensing spans an impressive range of signal frequencies (10-2 to 107 Hz) and distances (10-2 to 107 m) and involves biomedical ultrasound imaging, nondestructive evaluation, oil and gas exploration, military systems, and Nuclear Test Ban Treaty monitoring. In the past two decades, approaches have been developed to robustly localize remote sources; remove noise and multipath distortion from recorded signals; and determine the acoustic characteristics of the environment through which the sound waves have traveled, even when the recorded sounds originate from uncooperative sources or are merely ambient noise.

Automatic detection of slight parameter changes associated to complex biomedical signals using multiresolution q-entropy1.

PubMed

Torres, M E; Añino, M M; Schlotthauer, G

2003-12-01

It is well known that, from a dynamical point of view, sudden variations in physiological parameters which govern certain diseases can cause qualitative changes in the dynamics of the corresponding physiological process. The purpose of this paper is to introduce a technique that allows the automated temporal localization of slight changes in a parameter of the law that governs the nonlinear dynamics of a given signal. This tool takes, from the multiresolution entropies, the ability to show these changes as statistical variations at each scale. These variations are held in the corresponding principal component. Appropriately combining these techniques with a statistical changes detector, a complexity change detection algorithm is obtained. The relevance of the approach, together with its robustness in the presence of moderate noise, is discussed in numerical simulations and the automatic detector is applied to real and simulated biological signals.

Review of wireless and wearable electroencephalogram systems and brain-computer interfaces--a mini-review.

PubMed

Lin, Chin-Teng; Ko, Li-Wei; Chang, Meng-Hsiu; Duann, Jeng-Ren; Chen, Jing-Ying; Su, Tung-Ping; Jung, Tzyy-Ping

2010-01-01

Biomedical signal monitoring systems have rapidly advanced in recent years, propelled by significant advances in electronic and information technologies. Brain-computer interface (BCI) is one of the important research branches and has become a hot topic in the study of neural engineering, rehabilitation, and brain science. Traditionally, most BCI systems use bulky, wired laboratory-oriented sensing equipments to measure brain activity under well-controlled conditions within a confined space. Using bulky sensing equipments not only is uncomfortable and inconvenient for users, but also impedes their ability to perform routine tasks in daily operational environments. Furthermore, owing to large data volumes, signal processing of BCI systems is often performed off-line using high-end personal computers, hindering the applications of BCI in real-world environments. To be practical for routine use by unconstrained, freely-moving users, BCI systems must be noninvasive, nonintrusive, lightweight and capable of online signal processing. This work reviews recent online BCI systems, focusing especially on wearable, wireless and real-time systems. Copyright 2009 S. Karger AG, Basel.

Computational intelligence for target assessment in Parkinson's disease

NASA Astrophysics Data System (ADS)

Micheli-Tzanakou, Evangelia; Hamilton, J. L.; Zheng, J.; Lehman, Richard M.

2001-11-01

Recent advances in image and signal processing have created a new challenging environment for biomedical engineers. Methods that were developed for different fields are now finding a fertile ground in biomedicine, especially in the analysis of bio-signals and in the understanding of images. More and more, these methods are used in the operating room, helping surgeons, and in the physician's office as aids for diagnostic purposes. Neural Network (NN) research on the other hand, has gone a long way in the past decade. NNs now consist of many thousands of highly interconnected processing elements that can encode, store and recall relationships between different patterns by altering the weighting coefficients of inputs in a systematic way. Although they can generate reasonable outputs from unknown input patterns, and can tolerate a great deal of noise, they are very slow when run on a serial machine. We have used advanced signal processing and innovative image processing methods that are used along with computational intelligence for diagnostic purposes and as visualization aids inside and outside the operating room. Applications to be discussed include EEGs and field potentials in Parkinson's disease along with 3D reconstruction of MR or fMR brain images in Parkinson's patients, are currently used in the operating room for Pallidotomies and Deep Brain Stimulation (DBS).

Stimfit: quantifying electrophysiological data with Python

PubMed Central

Guzman, Segundo J.; Schlögl, Alois; Schmidt-Hieber, Christoph

2013-01-01

Intracellular electrophysiological recordings provide crucial insights into elementary neuronal signals such as action potentials and synaptic currents. Analyzing and interpreting these signals is essential for a quantitative understanding of neuronal information processing, and requires both fast data visualization and ready access to complex analysis routines. To achieve this goal, we have developed Stimfit, a free software package for cellular neurophysiology with a Python scripting interface and a built-in Python shell. The program supports most standard file formats for cellular neurophysiology and other biomedical signals through the Biosig library. To quantify and interpret the activity of single neurons and communication between neurons, the program includes algorithms to characterize the kinetics of presynaptic action potentials and postsynaptic currents, estimate latencies between pre- and postsynaptic events, and detect spontaneously occurring events. We validate and benchmark these algorithms, give estimation errors, and provide sample use cases, showing that Stimfit represents an efficient, accessible and extensible way to accurately analyze and interpret neuronal signals. PMID:24600389

Progress and Prospects for Stem Cell Engineering

PubMed Central

Ashton, Randolph S.; Keung, Albert J.; Peltier, Joseph; Schaffer, David V.

2018-01-01

Stem cells offer tremendous biomedical potential owing to their abilities to self-renew and differentiate into cell types of multiple adult tissues. Researchers and engineers have increasingly developed novel discovery technologies, theoretical approaches, and cell culture systems to investigate microenvironmental cues and cellular signaling events that control stem cell fate. Many of these technologies facilitate high-throughput investigation of microenvironmental signals and the intracellular signaling networks and machinery processing those signals into cell fate decisions. As our aggregate empirical knowledge of stem cell regulation grows, theoretical modeling with systems and computational biology methods has and will continue to be important for developing our ability to analyze and extract important conceptual features of stem cell regulation from complex data. Based on this body of knowledge, stem cell engineers will continue to develop technologies that predictably control stem cell fate with the ultimate goal of being able to accurately and economically scale up these systems for clinical-grade production of stem cell therapeutics. PMID:22432628

Tri-state oriented parallel processing system

DOE Office of Scientific and Technical Information (OSTI.GOV)

Tenenbaum, J.; Wallach, Y.

1982-08-01

An alternating sequential/parallel system, the MOPPS was introduced a few years ago and is modified despite the fact that it solved satisfactorily a number of real-time problems. The new system, the TOPPS is described and compared to MOPPS and two applications are chosen to prove it to be superior. The advantage of having a third basic, the ring mode, is illustrated when solving sets of linear equations with band matrices. The advantage of having independent I/O for the slaves is illustrated for biomedical signal analysis. 11 references.

Bionic models for identification of biological systems

NASA Astrophysics Data System (ADS)

Gerget, O. M.

2017-01-01

This article proposes a clinical decision support system that processes biomedical data. For this purpose a bionic model has been designed based on neural networks, genetic algorithms and immune systems. The developed system has been tested on data from pregnant women. The paper focuses on the approach to enable selection of control actions that can minimize the risk of adverse outcome. The control actions (hyperparameters of a new type) are further used as an additional input signal. Its values are defined by a hyperparameter optimization method. A software developed with Python is briefly described.

Wearable sweat detector device design for health monitoring and clinical diagnosis

NASA Astrophysics Data System (ADS)

Wu, Qiuchen; Zhang, Xiaodong; Tian, Bihao; Zhang, Hongyan; Yu, Yang; Wang, Ming

2017-06-01

Miniaturized sensor is necessary part for wearable detector for biomedical applications. Wearable detector device is indispensable for online health care. This paper presents a concept of an wearable digital health monitoring device design for sweat analysis. The flexible sensor is developed to quantify the amount of hydrogen ions in sweat and skin temperature in real time. The detection system includes pH sensor, temperature sensor, signal processing module, power source, microprocessor, display module and so on. The sweat monitoring device is designed for sport monitoring or clinical diagnosis.

A new visual navigation system for exploring biomedical Open Educational Resource (OER) videos

PubMed Central

Zhao, Baoquan; Xu, Songhua; Lin, Shujin; Luo, Xiaonan; Duan, Lian

2016-01-01

Objective Biomedical videos as open educational resources (OERs) are increasingly proliferating on the Internet. Unfortunately, seeking personally valuable content from among the vast corpus of quality yet diverse OER videos is nontrivial due to limitations of today’s keyword- and content-based video retrieval techniques. To address this need, this study introduces a novel visual navigation system that facilitates users’ information seeking from biomedical OER videos in mass quantity by interactively offering visual and textual navigational clues that are both semantically revealing and user-friendly. Materials and Methods The authors collected and processed around 25 000 YouTube videos, which collectively last for a total length of about 4000 h, in the broad field of biomedical sciences for our experiment. For each video, its semantic clues are first extracted automatically through computationally analyzing audio and visual signals, as well as text either accompanying or embedded in the video. These extracted clues are subsequently stored in a metadata database and indexed by a high-performance text search engine. During the online retrieval stage, the system renders video search results as dynamic web pages using a JavaScript library that allows users to interactively and intuitively explore video content both efficiently and effectively. Results The authors produced a prototype implementation of the proposed system, which is publicly accessible at https://patentq.njit.edu/oer. To examine the overall advantage of the proposed system for exploring biomedical OER videos, the authors further conducted a user study of a modest scale. The study results encouragingly demonstrate the functional effectiveness and user-friendliness of the new system for facilitating information seeking from and content exploration among massive biomedical OER videos. Conclusion Using the proposed tool, users can efficiently and effectively find videos of interest, precisely locate video segments delivering personally valuable information, as well as intuitively and conveniently preview essential content of a single or a collection of videos. PMID:26335986

Optimal wavelets for biomedical signal compression.

PubMed

Nielsen, Mogens; Kamavuako, Ernest Nlandu; Andersen, Michael Midtgaard; Lucas, Marie-Françoise; Farina, Dario

2006-07-01

Signal compression is gaining importance in biomedical engineering due to the potential applications in telemedicine. In this work, we propose a novel scheme of signal compression based on signal-dependent wavelets. To adapt the mother wavelet to the signal for the purpose of compression, it is necessary to define (1) a family of wavelets that depend on a set of parameters and (2) a quality criterion for wavelet selection (i.e., wavelet parameter optimization). We propose the use of an unconstrained parameterization of the wavelet for wavelet optimization. A natural performance criterion for compression is the minimization of the signal distortion rate given the desired compression rate. For coding the wavelet coefficients, we adopted the embedded zerotree wavelet coding algorithm, although any coding scheme may be used with the proposed wavelet optimization. As a representative example of application, the coding/encoding scheme was applied to surface electromyographic signals recorded from ten subjects. The distortion rate strongly depended on the mother wavelet (for example, for 50% compression rate, optimal wavelet, mean+/-SD, 5.46+/-1.01%; worst wavelet 12.76+/-2.73%). Thus, optimization significantly improved performance with respect to previous approaches based on classic wavelets. The algorithm can be applied to any signal type since the optimal wavelet is selected on a signal-by-signal basis. Examples of application to ECG and EEG signals are also reported.

Energy Analysis of Decoders for Rakeness-Based Compressed Sensing of ECG Signals.

PubMed

Pareschi, Fabio; Mangia, Mauro; Bortolotti, Daniele; Bartolini, Andrea; Benini, Luca; Rovatti, Riccardo; Setti, Gianluca

2017-12-01

In recent years, compressed sensing (CS) has proved to be effective in lowering the power consumption of sensing nodes in biomedical signal processing devices. This is due to the fact the CS is capable of reducing the amount of data to be transmitted to ensure correct reconstruction of the acquired waveforms. Rakeness-based CS has been introduced to further reduce the amount of transmitted data by exploiting the uneven distribution to the sensed signal energy. Yet, so far no thorough analysis exists on the impact of its adoption on CS decoder performance. The latter point is of great importance, since body-area sensor network architectures may include intermediate gateway nodes that receive and reconstruct signals to provide local services before relaying data to a remote server. In this paper, we fill this gap by showing that rakeness-based design also improves reconstruction performance. We quantify these findings in the case of ECG signals and when a variety of reconstruction algorithms are used either in a low-power microcontroller or a heterogeneous mobile computing platform.

Cross-correlation between EMG and center of gravity during quiet stance: theory and simulations.

PubMed

Kohn, André Fabio

2005-11-01

Several signal processing tools have been employed in the experimental study of the postural control system in humans. Among them, the cross-correlation function has been used to analyze the time relationship between signals such as the electromyogram and the horizontal projection of the center of gravity. The common finding is that the electromyogram precedes the biomechanical signal, a result that has been interpreted in different ways, for example, the existence of feedforward control or the preponderance of a velocity feedback. It is shown here, analytically and by simulation, that the cross-correlation function is dependent in a complicated way on system parameters and on noise spectra. Results similar to those found experimentally, e.g., electromyogram preceding the biomechanical signal may be obtained in a postural control model without any feedforward control and without any velocity feedback. Therefore, correct interpretations of experimentally obtained cross-correlation functions may require additional information about the system. The results extend to other biomedical applications where two signals from a closed loop system are cross-correlated.

Benefits of object-oriented models and ModeliChart: modern tools and methods for the interdisciplinary research on smart biomedical technology.

PubMed

Gesenhues, Jonas; Hein, Marc; Ketelhut, Maike; Habigt, Moriz; Rüschen, Daniel; Mechelinck, Mare; Albin, Thivaharan; Leonhardt, Steffen; Schmitz-Rode, Thomas; Rossaint, Rolf; Autschbach, Rüdiger; Abel, Dirk

2017-04-01

Computational models of biophysical systems generally constitute an essential component in the realization of smart biomedical technological applications. Typically, the development process of such models is characterized by a great extent of collaboration between different interdisciplinary parties. Furthermore, due to the fact that many underlying mechanisms and the necessary degree of abstraction of biophysical system models are unknown beforehand, the steps of the development process of the application are iteratively repeated when the model is refined. This paper presents some methods and tools to facilitate the development process. First, the principle of object-oriented (OO) modeling is presented and the advantages over classical signal-oriented modeling are emphasized. Second, our self-developed simulation tool ModeliChart is presented. ModeliChart was designed specifically for clinical users and allows independently performing in silico studies in real time including intuitive interaction with the model. Furthermore, ModeliChart is capable of interacting with hardware such as sensors and actuators. Finally, it is presented how optimal control methods in combination with OO models can be used to realize clinically motivated control applications. All methods presented are illustrated on an exemplary clinically oriented use case of the artificial perfusion of the systemic circulation.

Refined composite multivariate generalized multiscale fuzzy entropy: A tool for complexity analysis of multichannel signals

NASA Astrophysics Data System (ADS)

Azami, Hamed; Escudero, Javier

2017-01-01

Multiscale entropy (MSE) is an appealing tool to characterize the complexity of time series over multiple temporal scales. Recent developments in the field have tried to extend the MSE technique in different ways. Building on these trends, we propose the so-called refined composite multivariate multiscale fuzzy entropy (RCmvMFE) whose coarse-graining step uses variance (RCmvMFEσ2) or mean (RCmvMFEμ). We investigate the behavior of these multivariate methods on multichannel white Gaussian and 1/ f noise signals, and two publicly available biomedical recordings. Our simulations demonstrate that RCmvMFEσ2 and RCmvMFEμ lead to more stable results and are less sensitive to the signals' length in comparison with the other existing multivariate multiscale entropy-based methods. The classification results also show that using both the variance and mean in the coarse-graining step offers complexity profiles with complementary information for biomedical signal analysis. We also made freely available all the Matlab codes used in this paper.

Stationary Wavelet-based Two-directional Two-dimensional Principal Component Analysis for EMG Signal Classification

NASA Astrophysics Data System (ADS)

Ji, Yi; Sun, Shanlin; Xie, Hong-Bo

2017-06-01

Discrete wavelet transform (WT) followed by principal component analysis (PCA) has been a powerful approach for the analysis of biomedical signals. Wavelet coefficients at various scales and channels were usually transformed into a one-dimensional array, causing issues such as the curse of dimensionality dilemma and small sample size problem. In addition, lack of time-shift invariance of WT coefficients can be modeled as noise and degrades the classifier performance. In this study, we present a stationary wavelet-based two-directional two-dimensional principal component analysis (SW2D2PCA) method for the efficient and effective extraction of essential feature information from signals. Time-invariant multi-scale matrices are constructed in the first step. The two-directional two-dimensional principal component analysis then operates on the multi-scale matrices to reduce the dimension, rather than vectors in conventional PCA. Results are presented from an experiment to classify eight hand motions using 4-channel electromyographic (EMG) signals recorded in healthy subjects and amputees, which illustrates the efficiency and effectiveness of the proposed method for biomedical signal analysis.

Cerenkov luminescence imaging: physics principles and potential applications in biomedical sciences.

PubMed

Ciarrocchi, Esther; Belcari, Nicola

2017-12-01

Cerenkov luminescence imaging (CLI) is a novel imaging modality to study charged particles with optical methods by detecting the Cerenkov luminescence produced in tissue. This paper first describes the physical processes that govern the production and transport in tissue of Cerenkov luminescence. The detectors used for CLI and their most relevant specifications to optimize the acquisition of the Cerenkov signal are then presented, and CLI is compared with the other optical imaging modalities sharing the same data acquisition and processing methods. Finally, the scientific work related to CLI and the applications for which CLI has been proposed are reviewed. The paper ends with some considerations about further perspectives for this novel imaging modality.

Sparse electrocardiogram signals recovery based on solving a row echelon-like form of system.

PubMed

Cai, Pingmei; Wang, Guinan; Yu, Shiwei; Zhang, Hongjuan; Ding, Shuxue; Wu, Zikai

2016-02-01

The study of biology and medicine in a noise environment is an evolving direction in biological data analysis. Among these studies, analysis of electrocardiogram (ECG) signals in a noise environment is a challenging direction in personalized medicine. Due to its periodic characteristic, ECG signal can be roughly regarded as sparse biomedical signals. This study proposes a two-stage recovery algorithm for sparse biomedical signals in time domain. In the first stage, the concentration subspaces are found in advance. Then by exploiting these subspaces, the mixing matrix is estimated accurately. In the second stage, based on the number of active sources at each time point, the time points are divided into different layers. Next, by constructing some transformation matrices, these time points form a row echelon-like system. After that, the sources at each layer can be solved out explicitly by corresponding matrix operations. It is noting that all these operations are conducted under a weak sparse condition that the number of active sources is less than the number of observations. Experimental results show that the proposed method has a better performance for sparse ECG signal recovery problem.

Zinc Oxide Nanomaterials for Biomedical Fluorescence Detection

PubMed Central

Hahm, Jong-in

2014-01-01

One-dimensional zinc oxide nanomaterials have been recently developed into novel, extremely effective, optical signal-enhancing bioplatforms. Their usefulness has been demonstrated in various biomedical fluorescence assays. Fluorescence is extensively used in biology and medicine as a sensitive and noninvasive detection method for tracking and analyzing biological molecules. Achieving high sensitivity via improving signal-to-noise ratio is of paramount importance in fluorescence-based, trace-level detection. Recent advances in the development of optically superior one-dimensional materials have contributed to this important biomedical area of detection. This review article will discuss major research developments that have so far been made in this emerging and exciting topical field. The discussion will cover a broad range of subjects including synthesis of zinc oxide nanorods (ZnO NRs), various properties differentiating them as suitable optical biodetection platforms, their demonstrated applicability in DNA and protein detection, and the nanomaterial characteristics relevant for biomolecular fluorescence enhancement. This review will then summarize the current status of ZnO NR-based biodetection and further elaborate future utility of ZnO NR platforms for advanced biomedical assays, based on their proven advantages. Lastly, present challenges experienced in this topical area will be identified and focal subject areas for future research will be suggested as well. PMID:24730276

Cell response to nanocrystallized metallic substrates obtained through severe plastic deformation.

PubMed

Bagherifard, Sara; Ghelichi, Ramin; Khademhosseini, Ali; Guagliano, Mario

2014-06-11

Cell-substrate interface is known to control the cell response and subsequent cell functions. Among the various biophysical signals, grain structure, which indicates the repeating arrangement of atoms in the material, has also proved to play a role of significant importance in mediating the cell activities. Moreover, refining the grain size through severe plastic deformation is known to provide the processed material with novel mechanical properties. The potential application of such advanced materials as biomedical implants has recently been evaluated by investigating the effect of different substrate grain sizes on a wide variety of cell activities. In this review, recent advances in biomedical applications of severe plastic deformation techniques are highlighted with special attention to the effect of the obtained nano/ultra-fine-grain size on cell-substrate interactions. Various severe plastic deformation techniques used for this purpose are discussed presenting a brief description of the mechanism for each process. The results obtained for each treatment on cell morphology, adhesion, proliferation, and differentiation, as well as the in vivo studies, are discussed. Finally, the advantages and challenges regarding the application of these techniques to produce multifunctional bio-implant materials are addressed.

RysannMD: A biomedical semantic annotator balancing speed and accuracy.

PubMed

Cuzzola, John; Jovanović, Jelena; Bagheri, Ebrahim

2017-07-01

Recently, both researchers and practitioners have explored the possibility of semantically annotating large and continuously evolving collections of biomedical texts such as research papers, medical reports, and physician notes in order to enable their efficient and effective management and use in clinical practice or research laboratories. Such annotations can be automatically generated by biomedical semantic annotators - tools that are specifically designed for detecting and disambiguating biomedical concepts mentioned in text. The biomedical community has already presented several solid automated semantic annotators. However, the existing tools are either strong in their disambiguation capacity, i.e., the ability to identify the correct biomedical concept for a given piece of text among several candidate concepts, or they excel in their processing time, i.e., work very efficiently, but none of the semantic annotation tools reported in the literature has both of these qualities. In this paper, we present RysannMD (Ryerson Semantic Annotator for Medical Domain), a biomedical semantic annotation tool that strikes a balance between processing time and performance while disambiguating biomedical terms. In other words, RysannMD provides reasonable disambiguation performance when choosing the right sense for a biomedical term in a given context, and does that in a reasonable time. To examine how RysannMD stands with respect to the state of the art biomedical semantic annotators, we have conducted a series of experiments using standard benchmarking corpora, including both gold and silver standards, and four modern biomedical semantic annotators, namely cTAKES, MetaMap, NOBLE Coder, and Neji. The annotators were compared with respect to the quality of the produced annotations measured against gold and silver standards using precision, recall, and F 1 measure and speed, i.e., processing time. In the experiments, RysannMD achieved the best median F 1 measure across the benchmarking corpora, independent of the standard used (silver/gold), biomedical subdomain, and document size. In terms of the annotation speed, RysannMD scored the second best median processing time across all the experiments. The obtained results indicate that RysannMD offers the best performance among the examined semantic annotators when both quality of annotation and speed are considered simultaneously. Copyright © 2017 Elsevier Inc. All rights reserved.

Role of biomolecular logic systems in biosensors and bioactuators

NASA Astrophysics Data System (ADS)

Mailloux, Shay; Katz, Evgeny

2014-09-01

An overview of recent advances in biosensors and bioactuators based on biocomputing systems is presented. Biosensors digitally process multiple biochemical signals through Boolean logic networks of coupled biomolecular reactions and produce an output in the form of a YES/NO response. Compared to traditional single-analyte sensing devices, the biocomputing approach enables high-fidelity multianalyte biosensing, which is particularly beneficial for biomedical applications. Multisignal digital biosensors thus promise advances in rapid diagnosis and treatment of diseases by processing complex patterns of physiological biomarkers. Specifically, they can provide timely detection and alert medical personnel of medical emergencies together with immediate therapeutic intervention. Application of the biocomputing concept has been successfully demonstrated for systems performing logic analysis of biomarkers corresponding to different injuries, particularly as exemplified for liver injury. Wide-ranging applications of multianalyte digital biosensors in medicine, environmental monitoring, and homeland security are anticipated. "Smart" bioactuators, for signal-triggered drug release, for example, were designed by interfacing switchable electrodes with biocomputing systems. Integration of biosensing and bioactuating systems with biomolecular information processing systems advances the potential for further scientific innovations and various practical applications.

Biomedical application of wavelets: analysis of electroencephalograph signals for monitoring depth of anesthesia

NASA Astrophysics Data System (ADS)

Abbate, Agostino; Nayak, A.; Koay, J.; Roy, R. J.; Das, Pankaj K.

1996-03-01

The wavelet transform (WT) has been used to study the nonstationary information in the electroencephalograph (EEG) as an aid in determining the anesthetic depth. A complex analytic mother wavelet is utilized to obtain the time evolution of the various spectral components of the EEG signal. The technique is utilized for the detection and spectral analysis of transient and background processes in the awake and asleep states. It can be observed that the response of both states before the application of the stimulus is similar in amplitude but not in spectral contents, which suggests a background activity of the brain. The brain reacts to the external stimulus in two different modes depending on the state of consciousness of the subject. In the case of awake state, there is an evident increase in response, while for the sleep state a reduction in this activity is observed. This analysis seems to suggest that the brain has an ongoing background process that monitors external stimulus in both the sleep and awake states.

Distortion correction and cross-talk compensation algorithm for use with an imaging spectrometer based spatially resolved diffuse reflectance system

NASA Astrophysics Data System (ADS)

Cappon, Derek J.; Farrell, Thomas J.; Fang, Qiyin; Hayward, Joseph E.

2016-12-01

Optical spectroscopy of human tissue has been widely applied within the field of biomedical optics to allow rapid, in vivo characterization and analysis of the tissue. When designing an instrument of this type, an imaging spectrometer is often employed to allow for simultaneous analysis of distinct signals. This is especially important when performing spatially resolved diffuse reflectance spectroscopy. In this article, an algorithm is presented that allows for the automated processing of 2-dimensional images acquired from an imaging spectrometer. The algorithm automatically defines distinct spectrometer tracks and adaptively compensates for distortion introduced by optical components in the imaging chain. Crosstalk resulting from the overlap of adjacent spectrometer tracks in the image is detected and subtracted from each signal. The algorithm's performance is demonstrated in the processing of spatially resolved diffuse reflectance spectra recovered from an Intralipid and ink liquid phantom and is shown to increase the range of wavelengths over which usable data can be recovered.

Advances in thermographic signal reconstruction

NASA Astrophysics Data System (ADS)

Shepard, Steven M.; Frendberg Beemer, Maria

2015-05-01

Since its introduction in 2001, the Thermographic Signal Reconstruction (TSR) method has emerged as one of the most widely used methods for enhancement and analysis of thermographic sequences, with applications extending beyond industrial NDT into biomedical research, art restoration and botany. The basic TSR process, in which a noise reduced replica of each pixel time history is created, yields improvement over unprocessed image data that is sufficient for many applications. However, examination of the resulting logarithmic time derivatives of each TSR pixel replica provides significant insight into the physical mechanisms underlying the active thermography process. The deterministic and invariant properties of the derivatives have enabled the successful implementation of automated defect recognition and measurement systems. Unlike most approaches to analysis of thermography data, TSR does not depend on flawbackground contrast, so that it can also be applied to characterization and measurement of thermal properties of flaw-free samples. We present a summary of recent advances in TSR, a review of the underlying theory and examples of its implementation.

A reconfigurable, wearable, wireless ECG system.

PubMed

Borromeo, S; Rodriguez-Sanchez, C; Machado, F; Hernandez-Tamames, J A; de la Prieta, R

2007-01-01

New emerging concepts as "wireless hospital", "mobile healthcare" or "wearable telemonitoring" require the development of bio-signal acquisition devices to be easily integrated into the clinical routine. In this work, we present a new system for Electrocardiogram (ECG) acquisition and its processing, with wireless transmission on demand (either the complete ECG or only one alarm message, just in case a pathological heart rate detected). Size and power consumption are optimized in order to provide mobility and comfort to the patient. We have designed a modular hardware system and an autonomous platform based on a Field-Programmable Gate Array (FPGA) for developing and debugging. The modular approach allows to redesign the system in an easy way. Its adaptation to a new biomedical signal would only need small changes on it. The hardware system is composed of three layers that can be plugged/unplugged: communication layer, processing layer and sensor layer. In addition, we also present a general purpose end-user application developed for mobile phones or Personal Digital Assistant devices (PDAs).

A high performance biometric signal and image processing method to reveal blood perfusion towards 3D oxygen saturation mapping

NASA Astrophysics Data System (ADS)

Imms, Ryan; Hu, Sijung; Azorin-Peris, Vicente; Trico, Michaël.; Summers, Ron

2014-03-01

Non-contact imaging photoplethysmography (PPG) is a recent development in the field of physiological data acquisition, currently undergoing a large amount of research to characterize and define the range of its capabilities. Contact-based PPG techniques have been broadly used in clinical scenarios for a number of years to obtain direct information about the degree of oxygen saturation for patients. With the advent of imaging techniques, there is strong potential to enable access to additional information such as multi-dimensional blood perfusion and saturation mapping. The further development of effective opto-physiological monitoring techniques is dependent upon novel modelling techniques coupled with improved sensor design and effective signal processing methodologies. The biometric signal and imaging processing platform (bSIPP) provides a comprehensive set of features for extraction and analysis of recorded iPPG data, enabling direct comparison with other biomedical diagnostic tools such as ECG and EEG. Additionally, utilizing information about the nature of tissue structure has enabled the generation of an engineering model describing the behaviour of light during its travel through the biological tissue. This enables the estimation of the relative oxygen saturation and blood perfusion in different layers of the tissue to be calculated, which has the potential to be a useful diagnostic tool.

Real-Time Digital Signal Processing Based on FPGAs for Electronic Skin Implementation †

PubMed Central

Ibrahim, Ali; Gastaldo, Paolo; Chible, Hussein; Valle, Maurizio

2017-01-01

Enabling touch-sensing capability would help appliances understand interaction behaviors with their surroundings. Many recent studies are focusing on the development of electronic skin because of its necessity in various application domains, namely autonomous artificial intelligence (e.g., robots), biomedical instrumentation, and replacement prosthetic devices. An essential task of the electronic skin system is to locally process the tactile data and send structured information either to mimic human skin or to respond to the application demands. The electronic skin must be fabricated together with an embedded electronic system which has the role of acquiring the tactile data, processing, and extracting structured information. On the other hand, processing tactile data requires efficient methods to extract meaningful information from raw sensor data. Machine learning represents an effective method for data analysis in many domains: it has recently demonstrated its effectiveness in processing tactile sensor data. In this framework, this paper presents the implementation of digital signal processing based on FPGAs for tactile data processing. It provides the implementation of a tensorial kernel function for a machine learning approach. Implementation results are assessed by highlighting the FPGA resource utilization and power consumption. Results demonstrate the feasibility of the proposed implementation when real-time classification of input touch modalities are targeted. PMID:28287448

Real-Time Digital Signal Processing Based on FPGAs for Electronic Skin Implementation.

PubMed

Ibrahim, Ali; Gastaldo, Paolo; Chible, Hussein; Valle, Maurizio

2017-03-10

Enabling touch-sensing capability would help appliances understand interaction behaviors with their surroundings. Many recent studies are focusing on the development of electronic skin because of its necessity in various application domains, namely autonomous artificial intelligence (e.g., robots), biomedical instrumentation, and replacement prosthetic devices. An essential task of the electronic skin system is to locally process the tactile data and send structured information either to mimic human skin or to respond to the application demands. The electronic skin must be fabricated together with an embedded electronic system which has the role of acquiring the tactile data, processing, and extracting structured information. On the other hand, processing tactile data requires efficient methods to extract meaningful information from raw sensor data. Machine learning represents an effective method for data analysis in many domains: it has recently demonstrated its effectiveness in processing tactile sensor data. In this framework, this paper presents the implementation of digital signal processing based on FPGAs for tactile data processing. It provides the implementation of a tensorial kernel function for a machine learning approach. Implementation results are assessed by highlighting the FPGA resource utilization and power consumption. Results demonstrate the feasibility of the proposed implementation when real-time classification of input touch modalities are targeted.

AnyWave: a cross-platform and modular software for visualizing and processing electrophysiological signals.

PubMed

Colombet, B; Woodman, M; Badier, J M; Bénar, C G

2015-03-15

The importance of digital signal processing in clinical neurophysiology is growing steadily, involving clinical researchers and methodologists. There is a need for crossing the gap between these communities by providing efficient delivery of newly designed algorithms to end users. We have developed such a tool which both visualizes and processes data and, additionally, acts as a software development platform. AnyWave was designed to run on all common operating systems. It provides access to a variety of data formats and it employs high fidelity visualization techniques. It also allows using external tools as plug-ins, which can be developed in languages including C++, MATLAB and Python. In the current version, plug-ins allow computation of connectivity graphs (non-linear correlation h2) and time-frequency representation (Morlet wavelets). The software is freely available under the LGPL3 license. AnyWave is designed as an open, highly extensible solution, with an architecture that permits rapid delivery of new techniques to end users. We have developed AnyWave software as an efficient neurophysiological data visualizer able to integrate state of the art techniques. AnyWave offers an interface well suited to the needs of clinical research and an architecture designed for integrating new tools. We expect this software to strengthen the collaboration between clinical neurophysiologists and researchers in biomedical engineering and signal processing. Copyright © 2015 Elsevier B.V. All rights reserved.

Quantitative Förster resonance energy transfer analysis for kinetic determinations of SUMO-specific protease.

PubMed

Liu, Yan; Song, Yang; Madahar, Vipul; Liao, Jiayu

2012-03-01

Förster resonance energy transfer (FRET) technology has been widely used in biological and biomedical research, and it is a very powerful tool for elucidating protein interactions in either dynamic or steady state. SUMOylation (the process of SUMO [small ubiquitin-like modifier] conjugation to substrates) is an important posttranslational protein modification with critical roles in multiple biological processes. Conjugating SUMO to substrates requires an enzymatic cascade. Sentrin/SUMO-specific proteases (SENPs) act as an endopeptidase to process the pre-SUMO or as an isopeptidase to deconjugate SUMO from its substrate. To fully understand the roles of SENPs in the SUMOylation cycle, it is critical to understand their kinetics. Here, we report a novel development of a quantitative FRET-based protease assay for SENP1 kinetic parameter determination. The assay is based on the quantitative analysis of the FRET signal from the total fluorescent signal at acceptor emission wavelength, which consists of three components: donor (CyPet-SUMO1) emission, acceptor (YPet) emission, and FRET signal during the digestion process. Subsequently, we developed novel theoretical and experimental procedures to determine the kinetic parameters, k(cat), K(M), and catalytic efficiency (k(cat)/K(M)) of catalytic domain SENP1 toward pre-SUMO1. Importantly, the general principles of this quantitative FRET-based protease kinetic determination can be applied to other proteases. Copyright © 2011 Elsevier Inc. All rights reserved.

Multistate Lempel-Ziv (MLZ) index interpretation as a measure of amplitude and complexity changes.

PubMed

Sarlabous, Leonardo; Torres, Abel; Fiz, Jose A; Gea, Joaquim; Galdiz, Juan B; Jane, Raimon

2009-01-01

The Lempel-Ziv complexity (LZ) has been widely used to evaluate the randomness of finite sequences. In general, the LZ complexity has been used to determine the complexity grade present in biomedical signals. The LZ complexity is not able to discern between signals with different amplitude variations and similar random components. On the other hand, amplitude parameters, as the root mean square (RMS), are not able to discern between signals with similar power distributions and different random components. In this work, we present a novel method to quantify amplitude and complexity variations in biomedical signals by means of the computation of the LZ coefficient using more than two quantification states, and with thresholds fixed and independent of the dynamic range or standard deviation of the analyzed signal: the Multistate Lempel-Ziv (MLZ) index. Our results indicate that MLZ index with few quantification levels only evaluate the complexity changes of the signal, with high number of levels, the amplitude variations, and with an intermediate number of levels informs about both amplitude and complexity variations. The study performed in diaphragmatic mechanomyographic signals shows that the amplitude variations of this signal are more correlated with the respiratory effort than the complexity variations. Furthermore, it has been observed that the MLZ index with high number of levels practically is not affected by the existence of impulsive, sinusoidal, constant and Gaussian noises compared with the RMS amplitude parameter.

Text Mining in Biomedical Domain with Emphasis on Document Clustering.

PubMed

Renganathan, Vinaitheerthan

2017-07-01

With the exponential increase in the number of articles published every year in the biomedical domain, there is a need to build automated systems to extract unknown information from the articles published. Text mining techniques enable the extraction of unknown knowledge from unstructured documents. This paper reviews text mining processes in detail and the software tools available to carry out text mining. It also reviews the roles and applications of text mining in the biomedical domain. Text mining processes, such as search and retrieval of documents, pre-processing of documents, natural language processing, methods for text clustering, and methods for text classification are described in detail. Text mining techniques can facilitate the mining of vast amounts of knowledge on a given topic from published biomedical research articles and draw meaningful conclusions that are not possible otherwise.

A modular, programmable measurement system for physiological and spaceflight applications

NASA Technical Reports Server (NTRS)

Hines, John W.; Ricks, Robert D.; Miles, Christopher J.

1993-01-01

The NASA-Ames Sensors 2000! Program has developed a small, compact, modular, programmable, sensor signal conditioning and measurement system, initially targeted for Life Sciences Spaceflight Programs. The system consists of a twelve-slot, multi-layer, distributed function backplane, a digital microcontroller/memory subsystem, conditioned and isolated power supplies, and six application-specific, physiological signal conditioners. Each signal condition is capable of being programmed for gains, offsets, calibration and operate modes, and, in some cases, selectable outputs and functional modes. Presently, the system has the capability for measuring ECG, EMG, EEG, Temperature, Respiration, Pressure, Force, and Acceleration parameters, in physiological ranges. The measurement system makes heavy use of surface-mount packaging technology, resulting in plug in modules sized 125x55 mm. The complete 12-slot system is contained within a volume of 220x150x70mm. The system's capabilities extend well beyond the specific objectives of NASA programs. Indeed, the potential commercial uses of the technology are virtually limitless. In addition to applications in medical and biomedical sensing, the system might also be used in process control situations, in clinical or research environments, in general instrumentation systems, factory processing, or any other applications where high quality measurements are required.

A modular, programmable measurement system for physiological and spaceflight applications

NASA Astrophysics Data System (ADS)

Hines, John W.; Ricks, Robert D.; Miles, Christopher J.

1993-02-01

The NASA-Ames Sensors 2000] Program has developed a small, compact, modular, programmable, sensor signal conditioning and measurement system, initially targeted for Life Sciences Spaceflight Programs. The system consists of a twelve-slot, multi-layer, distributed function backplane, a digital microcontroller/memory subsystem, conditioned and isolated power supplies, and six application-specific, physiological signal conditioners. Each signal condition is capable of being programmed for gains, offsets, calibration and operate modes, and, in some cases, selectable outputs and functional modes. Presently, the system has the capability for measuring ECG, EMG, EEG, Temperature, Respiration, Pressure, Force, and Acceleration parameters, in physiological ranges. The measurement system makes heavy use of surface-mount packaging technology, resulting in plug in modules sized 125x55 mm. The complete 12-slot system is contained within a volume of 220x150x70mm. The system's capabilities extend well beyond the specific objectives of NASA programs. Indeed, the potential commercial uses of the technology are virtually limitless. In addition to applications in medical and biomedical sensing, the system might also be used in process control situations, in clinical or research environments, in general instrumentation systems, factory processing, or any other applications where high quality measurements are required.

EDA-gram: designing electrodermal activity fingerprints for visualization and feature extraction.

PubMed

Chaspari, Theodora; Tsiartas, Andreas; Stein Duker, Leah I; Cermak, Sharon A; Narayanan, Shrikanth S

2016-08-01

Wearable technology permeates every aspect of our daily life increasing the need of reliable and interpretable models for processing the large amount of biomedical data. We propose the EDA-Gram, a multidimensional fingerprint of the electrodermal activity (EDA) signal, inspired by the widely-used notion of spectrogram. The EDA-Gram is based on the sparse decomposition of EDA from a knowledge-driven set of dictionary atoms. The time axis reflects the analysis frames, the spectral dimension depicts the width of selected dictionary atoms, while intensity values are computed from the atom coefficients. In this way, EDA-Gram incorporates the amplitude and shape of Skin Conductance Responses (SCR), which comprise an essential part of the signal. EDA-Gram is further used as a foundation for signal-specific feature design. Our results indicate that the proposed representation can accentuate fine-grain signal fluctuations, which might not always be apparent through simple visual inspection. Statistical analysis and classification/regression experiments further suggest that the derived features can differentiate between multiple arousal levels and stress-eliciting environments for two datasets.

Rapid Prototyping of a Smart Device-based Wireless Reflectance Photoplethysmograph

PubMed Central

Ghamari, M.; Aguilar, C.; Soltanpur, C.; Nazeran, H.

2017-01-01

This paper presents the design, fabrication, and testing of a wireless heart rate (HR) monitoring device based on photoplethysmography (PPG) and smart devices. PPG sensors use infrared (IR) light to obtain vital information to assess cardiac health and other physiologic conditions. The PPG data that are transferred to a computer undergo further processing to derive the Heart Rate Variability (HRV) signal, which is analyzed to generate quantitative markers of the Autonomic Nervous System (ANS). The HRV signal has numerous monitoring and diagnostic applications. To this end, wireless connectivity plays an important role in such biomedical instruments. The photoplethysmograph consists of an optical sensor to detect the changes in the light intensity reflected from the illuminated tissue, a signal conditioning unit to prepare the reflected light for further signal conditioning through amplification and filtering, a low-power microcontroller to control and digitize the analog PPG signal, and a Bluetooth module to transmit the digital data to a Bluetooth-based smart device such as a tablet. An Android app is then used to enable the smart device to acquire and digitally display the received analog PPG signal in real-time on the smart device. This article is concluded with the prototyping of the wireless PPG followed by the verification procedures of the PPG and HRV signals acquired in a laboratory environment. PMID:28959119

Rapid Prototyping of a Smart Device-based Wireless Reflectance Photoplethysmograph.

PubMed

Ghamari, M; Aguilar, C; Soltanpur, C; Nazeran, H

2016-03-01

This paper presents the design, fabrication, and testing of a wireless heart rate (HR) monitoring device based on photoplethysmography (PPG) and smart devices. PPG sensors use infrared (IR) light to obtain vital information to assess cardiac health and other physiologic conditions. The PPG data that are transferred to a computer undergo further processing to derive the Heart Rate Variability (HRV) signal, which is analyzed to generate quantitative markers of the Autonomic Nervous System (ANS). The HRV signal has numerous monitoring and diagnostic applications. To this end, wireless connectivity plays an important role in such biomedical instruments. The photoplethysmograph consists of an optical sensor to detect the changes in the light intensity reflected from the illuminated tissue, a signal conditioning unit to prepare the reflected light for further signal conditioning through amplification and filtering, a low-power microcontroller to control and digitize the analog PPG signal, and a Bluetooth module to transmit the digital data to a Bluetooth-based smart device such as a tablet. An Android app is then used to enable the smart device to acquire and digitally display the received analog PPG signal in real-time on the smart device. This article is concluded with the prototyping of the wireless PPG followed by the verification procedures of the PPG and HRV signals acquired in a laboratory environment.

Nanodiscs in Membrane Biochemistry and Biophysics.

PubMed

Denisov, Ilia G; Sligar, Stephen G

2017-03-22

Membrane proteins play a most important part in metabolism, signaling, cell motility, transport, development, and many other biochemical and biophysical processes which constitute fundamentals of life on the molecular level. Detailed understanding of these processes is necessary for the progress of life sciences and biomedical applications. Nanodiscs provide a new and powerful tool for a broad spectrum of biochemical and biophysical studies of membrane proteins and are commonly acknowledged as an optimal membrane mimetic system that provides control over size, composition, and specific functional modifications on the nanometer scale. In this review we attempted to combine a comprehensive list of various applications of nanodisc technology with systematic analysis of the most attractive features of this system and advantages provided by nanodiscs for structural and mechanistic studies of membrane proteins.

Automated smear counting and data processing using a notebook computer in a biomedical research facility.

PubMed

Ogata, Y; Nishizawa, K

1995-10-01

An automated smear counting and data processing system for a life science laboratory was developed to facilitate routine surveys and eliminate human errors by using a notebook computer. This system was composed of a personal computer, a liquid scintillation counter and a well-type NaI(Tl) scintillation counter. The radioactivity of smear samples was automatically measured by these counters. The personal computer received raw signals from the counters through an interface of RS-232C. The software for the computer evaluated the surface density of each radioisotope and printed out that value along with other items as a report. The software was programmed in Pascal language. This system was successfully applied to routine surveys for contamination in our facility.

Multistrip western blotting to increase quantitative data output.

PubMed

Kiyatkin, Anatoly; Aksamitiene, Edita

2009-01-01

The qualitative and quantitative measurements of protein abundance and modification states are essential in understanding their functions in diverse cellular processes. Typical western blotting, though sensitive, is prone to produce substantial errors and is not readily adapted to high-throughput technologies. Multistrip western blotting is a modified immunoblotting procedure based on simultaneous electrophoretic transfer of proteins from multiple strips of polyacrylamide gels to a single membrane sheet. In comparison with the conventional technique, Multistrip western blotting increases the data output per single blotting cycle up to tenfold, allows concurrent monitoring of up to nine different proteins from the same loading of the sample, and substantially improves the data accuracy by reducing immunoblotting-derived signal errors. This approach enables statistically reliable comparison of different or repeated sets of data, and therefore is beneficial to apply in biomedical diagnostics, systems biology, and cell signaling research.

Packet loss mitigation for biomedical signals in healthcare telemetry.

PubMed

Garudadri, Harinath; Baheti, Pawan K

2009-01-01

In this work, we propose an effective application layer solution for packet loss mitigation in the context of Body Sensor Networks (BSN) and healthcare telemetry. Packet losses occur due to many reasons including excessive path loss, interference from other wireless systems, handoffs, congestion, system loading, etc. A call for action is in order, as packet losses can have extremely adverse impact on many healthcare applications relying on BAN and WAN technologies. Our approach for packet loss mitigation is based on Compressed Sensing (CS), an emerging signal processing concept, wherein significantly fewer sensor measurements than that suggested by Shannon/Nyquist sampling theorem can be used to recover signals with arbitrarily fine resolution. We present simulation results demonstrating graceful degradation of performance with increasing packet loss rate. We also compare the proposed approach with retransmissions. The CS based packet loss mitigation approach was found to maintain up to 99% beat-detection accuracy at packet loss rates of 20%, with a constant latency of less than 2.5 seconds.

Wavelet methodology to improve single unit isolation in primary motor cortex cells

PubMed Central

Ortiz-Rosario, Alexis; Adeli, Hojjat; Buford, John A.

2016-01-01

The proper isolation of action potentials recorded extracellularly from neural tissue is an active area of research in the fields of neuroscience and biomedical signal processing. This paper presents an isolation methodology for neural recordings using the wavelet transform (WT), a statistical thresholding scheme, and the principal component analysis (PCA) algorithm. The effectiveness of five different mother wavelets was investigated: biorthogonal, Daubachies, discrete Meyer, symmetric, and Coifman; along with three different wavelet coefficient thresholding schemes: fixed form threshold, Stein’s unbiased estimate of risk, and minimax; and two different thresholding rules: soft and hard thresholding. The signal quality was evaluated using three different statistical measures: mean-squared error, root-mean squared, and signal to noise ratio. The clustering quality was evaluated using two different statistical measures: isolation distance, and L-ratio. This research shows that the selection of the mother wavelet has a strong influence on the clustering and isolation of single unit neural activity, with the Daubachies 4 wavelet and minimax thresholding scheme performing the best. PMID:25794461

Boron nitride nanotubes matrix for signal enhancement in infrared laser desorption postionization mass spectrometry.

PubMed

Lu, Qiao; Hu, Yongjun; Chen, Jiaxin; Li, Yujian; Song, Wentao; Jin, Shan; Liu, Fuyi; Sheng, Liusi

2018-09-01

The nanomaterials function as the substrate to trap analytes, absorb energy from the laser irradiation and transfer energy to the analytes to facilitate the laser desorption process. In this work, the signal intensity and reproducibility of analytes with nanomaterials as matrices were explored by laser desorption postionization mass spectrometry (LDPI-MS). Herein, the desorbed neutral species were further ionized by vacuum ultraviolet (VUV, 118 nm) and analyzed by mass spectrometer. Compared with other nanomaterial matrices such as graphene and carbon nanotubes (CNTs), boron nitride nanotubes (BNNTs) exhibited much higher desorption efficiency under infrared (IR) light and produced no background signal in the whole mass range by LDPI-MS. Additionally, this method was successfully and firstly exploited to in situ detection and imaging for drugs of low concentration in intact tissues, which proved the utility, facility and convenience of this method applied in drug discovery and biomedical research. Copyright © 2018 Elsevier B.V. All rights reserved.

Wavelet-domain de-noising technique for THz pulsed spectroscopy

NASA Astrophysics Data System (ADS)

Chernomyrdin, Nikita V.; Zaytsev, Kirill I.; Gavdush, Arsenii A.; Fokina, Irina N.; Karasik, Valeriy E.; Reshetov, Igor V.; Kudrin, Konstantin G.; Nosov, Pavel A.; Yurchenko, Stanislav O.

2014-09-01

De-noising of terahertz (THz) pulsed spectroscopy (TPS) data is an essential problem, since a noise in the TPS system data prevents correct reconstruction of the sample spectral dielectric properties and to perform the sample internal structure studying. There are certain regions in TPS signal Fourier spectrum, where Fourier-domain signal-to-noise ratio is relatively small. Effective de-noising might potentially expand the range of spectrometer spectral sensitivity and reduce the time of waveform registration, which is an essential problem for biomedical applications of TPS. In this work, it is shown how the recent progress in signal processing in wavelet-domain could be used for TPS waveforms de-noising. It demonstrates the ability to perform effective de-noising of TPS data using the algorithm of the Fast Wavelet Transform (FWT). The results of the optimal wavelet basis selection and wavelet-domain thresholding technique selection are reported. Developed technique is implemented for reconstruction of in vivo healthy and deseased skin samplesspectral characteristics at THz frequency range.

BCI2000: a general-purpose brain-computer interface (BCI) system.

PubMed

Schalk, Gerwin; McFarland, Dennis J; Hinterberger, Thilo; Birbaumer, Niels; Wolpaw, Jonathan R

2004-06-01

Many laboratories have begun to develop brain-computer interface (BCI) systems that provide communication and control capabilities to people with severe motor disabilities. Further progress and realization of practical applications depends on systematic evaluations and comparisons of different brain signals, recording methods, processing algorithms, output formats, and operating protocols. However, the typical BCI system is designed specifically for one particular BCI method and is, therefore, not suited to the systematic studies that are essential for continued progress. In response to this problem, we have developed a documented general-purpose BCI research and development platform called BCI2000. BCI2000 can incorporate alone or in combination any brain signals, signal processing methods, output devices, and operating protocols. This report is intended to describe to investigators, biomedical engineers, and computer scientists the concepts that the BC12000 system is based upon and gives examples of successful BCI implementations using this system. To date, we have used BCI2000 to create BCI systems for a variety of brain signals, processing methods, and applications. The data show that these systems function well in online operation and that BCI2000 satisfies the stringent real-time requirements of BCI systems. By substantially reducing labor and cost, BCI2000 facilitates the implementation of different BCI systems and other psychophysiological experiments. It is available with full documentation and free of charge for research or educational purposes and is currently being used in a variety of studies by many research groups.

Text Mining in Biomedical Domain with Emphasis on Document Clustering

PubMed Central

2017-01-01

Objectives With the exponential increase in the number of articles published every year in the biomedical domain, there is a need to build automated systems to extract unknown information from the articles published. Text mining techniques enable the extraction of unknown knowledge from unstructured documents. Methods This paper reviews text mining processes in detail and the software tools available to carry out text mining. It also reviews the roles and applications of text mining in the biomedical domain. Results Text mining processes, such as search and retrieval of documents, pre-processing of documents, natural language processing, methods for text clustering, and methods for text classification are described in detail. Conclusions Text mining techniques can facilitate the mining of vast amounts of knowledge on a given topic from published biomedical research articles and draw meaningful conclusions that are not possible otherwise. PMID:28875048

Biomedical ultrasonoscope

NASA Technical Reports Server (NTRS)

Lee, R. D. (Inventor)

1979-01-01

The combination of a "C" mode scan electronics in a portable, battery powered biomedical ultrasonoscope having "A" and "M" mode scan electronics, the latter including a clock generator for generating clock pulses, a cathode ray tube having X, Y and Z axis inputs, a sweep generator connected between the clock generator and the X axis input of the cathode ray tube for generating a cathode ray sweep signal synchronized by the clock pulses, and a receiver adapted to be connected to the Z axis input of the cathode ray tube. The "C" mode scan electronics comprises a plurality of transducer elements arranged in a row and adapted to be positioned on the skin of the patient's body for converting a pulsed electrical signal to a pulsed ultrasonic signal, radiating the ultrasonic signal into the patient's body, picking up the echoes reflected from interfaces in the patient's body and converting the echoes to electrical signals; a plurality of transmitters, each transmitter being coupled to a respective transducer for transmitting a pulsed electrical signal thereto and for transmitting the converted electrical echo signals directly to the receiver, a sequencer connected between the clock generator and the plurality of transmitters and responsive to the clock pulses for firing the transmitters in cyclic order; and a staircase voltage generator connected between the clock generator and the Y axis input of the cathode ray tube for generating a staircase voltage having steps synchronized by the clock pulses.

A new visual navigation system for exploring biomedical Open Educational Resource (OER) videos.

PubMed

Zhao, Baoquan; Xu, Songhua; Lin, Shujin; Luo, Xiaonan; Duan, Lian

2016-04-01

Biomedical videos as open educational resources (OERs) are increasingly proliferating on the Internet. Unfortunately, seeking personally valuable content from among the vast corpus of quality yet diverse OER videos is nontrivial due to limitations of today's keyword- and content-based video retrieval techniques. To address this need, this study introduces a novel visual navigation system that facilitates users' information seeking from biomedical OER videos in mass quantity by interactively offering visual and textual navigational clues that are both semantically revealing and user-friendly. The authors collected and processed around 25 000 YouTube videos, which collectively last for a total length of about 4000 h, in the broad field of biomedical sciences for our experiment. For each video, its semantic clues are first extracted automatically through computationally analyzing audio and visual signals, as well as text either accompanying or embedded in the video. These extracted clues are subsequently stored in a metadata database and indexed by a high-performance text search engine. During the online retrieval stage, the system renders video search results as dynamic web pages using a JavaScript library that allows users to interactively and intuitively explore video content both efficiently and effectively.ResultsThe authors produced a prototype implementation of the proposed system, which is publicly accessible athttps://patentq.njit.edu/oer To examine the overall advantage of the proposed system for exploring biomedical OER videos, the authors further conducted a user study of a modest scale. The study results encouragingly demonstrate the functional effectiveness and user-friendliness of the new system for facilitating information seeking from and content exploration among massive biomedical OER videos. Using the proposed tool, users can efficiently and effectively find videos of interest, precisely locate video segments delivering personally valuable information, as well as intuitively and conveniently preview essential content of a single or a collection of videos. © The Author 2015. Published by Oxford University Press on behalf of the American Medical Informatics Association. All rights reserved. For Permissions, please email: journals.permissions@oup.com.

RX for science literacy: The what, where, how, and why of health science research (A teacher`s manual about biomedical research)

DOE Office of Scientific and Technical Information (OSTI.GOV)

Hoffman, K.S.

1994-12-31

When the North Carolina Association for Biomedical Research (NCABR) surveyed the state`s science teachers in March 1993, 92% of those responding requested information related to biomedical research. Most of the teachers requested lesson plans and activities designed to help them give students an accurate and balanced perspective on research. In response to that need, NCABR has recently completed production of a 300-page teacher`s manual that provides an overview of the biomedical research process and describes the role and care of animals in that process. Rx for Science Literacy incorporates background information, lesson plans, handouts and activities to assist teachers inmore » K-12 classrooms. Developed by a science teacher with assistance from science and education experts, the manual captures the complex biomedical research process in an easy-to-follow, easy-to-use format. In North Carolina, NCABR plans to begin these workshops in fall 1994. The workshops will include a tour of a biomedical research laboratory and on-site presentations by bench scientists. Teacher evaluation of the manual will be structured into the workshop program. The manual is available at cost to all interested individuals and organizations.« less

Improved biomedical electrode

NASA Technical Reports Server (NTRS)

Frost, J. D., Jr.

1972-01-01

Newly designed electrode is prefilled, disposable, electrolyte-saturated spong. New design permits longe periods of storage without deterioration, and readiness in matter of seconds. Electrodes supply signals for electroencephalogram, electro-oculogram, and electrocardiogram.

Advances in targeted proteomics and applications to biomedical research

PubMed Central

Shi, Tujin; Song, Ehwang; Nie, Song; Rodland, Karin D.; Liu, Tao; Qian, Wei-Jun; Smith, Richard D.

2016-01-01

Targeted proteomics technique has emerged as a powerful protein quantification tool in systems biology, biomedical research, and increasing for clinical applications. The most widely used targeted proteomics approach, selected reaction monitoring (SRM), also known as multiple reaction monitoring (MRM), can be used for quantification of cellular signaling networks and preclinical verification of candidate protein biomarkers. As an extension to our previous review on advances in SRM sensitivity herein we review recent advances in the method and technology for further enhancing SRM sensitivity (from 2012 to present), and highlighting its broad biomedical applications in human bodily fluids, tissue and cell lines. Furthermore, we also review two recently introduced targeted proteomics approaches, parallel reaction monitoring (PRM) and data-independent acquisition (DIA) with targeted data extraction on fast scanning high-resolution accurate-mass (HR/AM) instruments. Such HR/AM targeted quantification with monitoring all target product ions addresses SRM limitations effectively in specificity and multiplexing; whereas when compared to SRM, PRM and DIA are still in the infancy with a limited number of applications. Thus, for HR/AM targeted quantification we focus our discussion on method development, data processing and analysis, and its advantages and limitations in targeted proteomics. Finally, general perspectives on the potential of achieving both high sensitivity and high sample throughput for large-scale quantification of hundreds of target proteins are discussed. PMID:27302376

Advances in targeted proteomics and applications to biomedical research

DOE Office of Scientific and Technical Information (OSTI.GOV)

Shi, Tujin; Song, Ehwang; Nie, Song

Targeted proteomics technique has emerged as a powerful protein quantification tool in systems biology, biomedical research, and increasing for clinical applications. The most widely used targeted proteomics approach, selected reaction monitoring (SRM), also known as multiple reaction monitoring (MRM), can be used for quantification of cellular signaling networks and preclinical verification of candidate protein biomarkers. As an extension to our previous review on advances in SRM sensitivity (Shi et al., Proteomics, 12, 1074–1092, 2012) herein we review recent advances in the method and technology for further enhancing SRM sensitivity (from 2012 to present), and highlighting its broad biomedical applications inmore » human bodily fluids, tissue and cell lines. Furthermore, we also review two recently introduced targeted proteomics approaches, parallel reaction monitoring (PRM) and data-independent acquisition (DIA) with targeted data extraction on fast scanning high-resolution accurate-mass (HR/AM) instruments. Such HR/AM targeted quantification with monitoring all target product ions addresses SRM limitations effectively in specificity and multiplexing; whereas when compared to SRM, PRM and DIA are still in the infancy with a limited number of applications. Thus, for HR/AM targeted quantification we focus our discussion on method development, data processing and analysis, and its advantages and limitations in targeted proteomics. Finally, general perspectives on the potential of achieving both high sensitivity and high sample throughput for large-scale quantification of hundreds of target proteins are discussed.« less

Wireless image-data transmission from an implanted image sensor through a living mouse brain by intra body communication

NASA Astrophysics Data System (ADS)

Hayami, Hajime; Takehara, Hiroaki; Nagata, Kengo; Haruta, Makito; Noda, Toshihiko; Sasagawa, Kiyotaka; Tokuda, Takashi; Ohta, Jun

2016-04-01

Intra body communication technology allows the fabrication of compact implantable biomedical sensors compared with RF wireless technology. In this paper, we report the fabrication of an implantable image sensor of 625 µm width and 830 µm length and the demonstration of wireless image-data transmission through a brain tissue of a living mouse. The sensor was designed to transmit output signals of pixel values by pulse width modulation (PWM). The PWM signals from the sensor transmitted through a brain tissue were detected by a receiver electrode. Wireless data transmission of a two-dimensional image was successfully demonstrated in a living mouse brain. The technique reported here is expected to provide useful methods of data transmission using micro sized implantable biomedical sensors.

Magnetic particle imaging for radiation-free, sensitive and high-contrast vascular imaging and cell tracking.

PubMed

Zhou, Xinyi Y; Tay, Zhi Wei; Chandrasekharan, Prashant; Yu, Elaine Y; Hensley, Daniel W; Orendorff, Ryan; Jeffris, Kenneth E; Mai, David; Zheng, Bo; Goodwill, Patrick W; Conolly, Steven M

2018-05-10

Magnetic particle imaging (MPI) is an emerging ionizing radiation-free biomedical tracer imaging technique that directly images the intense magnetization of superparamagnetic iron oxide nanoparticles (SPIOs). MPI offers ideal image contrast because MPI shows zero signal from background tissues. Moreover, there is zero attenuation of the signal with depth in tissue, allowing for imaging deep inside the body quantitatively at any location. Recent work has demonstrated the potential of MPI for robust, sensitive vascular imaging and cell tracking with high contrast and dose-limited sensitivity comparable to nuclear medicine. To foster future applications in MPI, this new biomedical imaging field is welcoming researchers with expertise in imaging physics, magnetic nanoparticle synthesis and functionalization, nanoscale physics, and small animal imaging applications. Copyright © 2018 Elsevier Ltd. All rights reserved.

Pattern recognition and expert image analysis systems in biomedical image processing (Invited Paper)

NASA Astrophysics Data System (ADS)

Oosterlinck, A.; Suetens, P.; Wu, Q.; Baird, M.; F. M., C.

1987-09-01

This paper gives an overview of pattern recoanition techniques (P.R.) used in biomedical image processing and problems related to the different P.R. solutions. Also the use of knowledge based systems to overcome P.R. difficulties, is described. This is illustrated by a common example ofabiomedical image processing application.

A corpus of full-text journal articles is a robust evaluation tool for revealing differences in performance of biomedical natural language processing tools

PubMed Central

2012-01-01

Background We introduce the linguistic annotation of a corpus of 97 full-text biomedical publications, known as the Colorado Richly Annotated Full Text (CRAFT) corpus. We further assess the performance of existing tools for performing sentence splitting, tokenization, syntactic parsing, and named entity recognition on this corpus. Results Many biomedical natural language processing systems demonstrated large differences between their previously published results and their performance on the CRAFT corpus when tested with the publicly available models or rule sets. Trainable systems differed widely with respect to their ability to build high-performing models based on this data. Conclusions The finding that some systems were able to train high-performing models based on this corpus is additional evidence, beyond high inter-annotator agreement, that the quality of the CRAFT corpus is high. The overall poor performance of various systems indicates that considerable work needs to be done to enable natural language processing systems to work well when the input is full-text journal articles. The CRAFT corpus provides a valuable resource to the biomedical natural language processing community for evaluation and training of new models for biomedical full text publications. PMID:22901054

UMLS content views appropriate for NLP processing of the biomedical literature vs. clinical text.

PubMed

Demner-Fushman, Dina; Mork, James G; Shooshan, Sonya E; Aronson, Alan R

2010-08-01

Identification of medical terms in free text is a first step in such Natural Language Processing (NLP) tasks as automatic indexing of biomedical literature and extraction of patients' problem lists from the text of clinical notes. Many tools developed to perform these tasks use biomedical knowledge encoded in the Unified Medical Language System (UMLS) Metathesaurus. We continue our exploration of automatic approaches to creation of subsets (UMLS content views) which can support NLP processing of either the biomedical literature or clinical text. We found that suppression of highly ambiguous terms in the conservative AutoFilter content view can partially replace manual filtering for literature applications, and suppression of two character mappings in the same content view achieves 89.5% precision at 78.6% recall for clinical applications. Published by Elsevier Inc.

Transcriptomic response and perturbation of toxicity pathways in zebrafish larvae after exposure to graphene quantum dots (GQDs).

PubMed

Deng, Shun; Jia, Pan-Pan; Zhang, Jing-Hui; Junaid, Muhammad; Niu, Aping; Ma, Yan-Bo; Fu, Ailing; Pei, De-Sheng

2018-05-29

Graphene quantum dots (GQDs) are widely used for biomedical applications. Previously, the low-level toxicity of GQDs in vivo and in vitro has been elucidated, but the underlying molecular mechanisms remained largely unknown. Here, we employed the Illumina high-throughput RNA-sequencing to explore the whole-transcriptome profiling of zebrafish larvae after exposure to GQDs. Comparative transcriptome analysis identified 2116 differentially expressed genes between GQDs exposed groups and control. Functional classification demonstrated that a large proportion of genes involved in acute inflammatory responses and detoxifying process were significantly up-regulated by GQDs. The inferred gene regulatory network suggested that activator protein 1 (AP-1) was the early-response transcription factor in the linkage of a cascade of downstream (pro-) inflammatory signals with the apoptosis signals. Moreover, hierarchical signaling threshold determined the high sensitivity of complement system in zebrafish when exposed to the sublethal dose of GQDs. Further, 35 candidate genes from various signaling pathways were further validated by qPCR after exposure to 25, 50, and 100 μg/mL of GQDs. Taken together, our study provided a valuable insight into the molecular mechanisms of potential bleeding risks and detoxifying processes in response to GQDs exposure, thereby establishing a mechanistic basis for the biosafety evaluation of GQDs. Copyright © 2018 Elsevier B.V. All rights reserved.

Evaluating the operational risks of biomedical waste using failure mode and effects analysis.

PubMed

Chen, Ying-Chu; Tsai, Pei-Yi

2017-06-01

The potential problems and risks of biomedical waste generation have become increasingly apparent in recent years. This study applied a failure mode and effects analysis to evaluate the operational problems and risks of biomedical waste. The microbiological contamination of biomedical waste seldom receives the attention of researchers. In this study, the biomedical waste lifecycle was divided into seven processes: Production, classification, packaging, sterilisation, weighing, storage, and transportation. Twenty main failure modes were identified in these phases and risks were assessed based on their risk priority numbers. The failure modes in the production phase accounted for the highest proportion of the risk priority number score (27.7%). In the packaging phase, the failure mode 'sharp articles not placed in solid containers' had the highest risk priority number score, mainly owing to its high severity rating. The sterilisation process is the main difference in the treatment of infectious and non-infectious biomedical waste. The failure modes in the sterilisation phase were mainly owing to human factors (mostly related to operators). This study increases the understanding of the potential problems and risks associated with biomedical waste, thereby increasing awareness of how to improve the management of biomedical waste to better protect workers, the public, and the environment.

Amplitude-aware permutation entropy: Illustration in spike detection and signal segmentation.

PubMed

Azami, Hamed; Escudero, Javier

2016-05-01

Signal segmentation and spike detection are two important biomedical signal processing applications. Often, non-stationary signals must be segmented into piece-wise stationary epochs or spikes need to be found among a background of noise before being further analyzed. Permutation entropy (PE) has been proposed to evaluate the irregularity of a time series. PE is conceptually simple, structurally robust to artifacts, and computationally fast. It has been extensively used in many applications, but it has two key shortcomings. First, when a signal is symbolized using the Bandt-Pompe procedure, only the order of the amplitude values is considered and information regarding the amplitudes is discarded. Second, in the PE, the effect of equal amplitude values in each embedded vector is not addressed. To address these issues, we propose a new entropy measure based on PE: the amplitude-aware permutation entropy (AAPE). AAPE is sensitive to the changes in the amplitude, in addition to the frequency, of the signals thanks to it being more flexible than the classical PE in the quantification of the signal motifs. To demonstrate how the AAPE method can enhance the quality of the signal segmentation and spike detection, a set of synthetic and realistic synthetic neuronal signals, electroencephalograms and neuronal data are processed. We compare the performance of AAPE in these problems against state-of-the-art approaches and evaluate the significance of the differences with a repeated ANOVA with post hoc Tukey's test. In signal segmentation, the accuracy of AAPE-based method is higher than conventional segmentation methods. AAPE also leads to more robust results in the presence of noise. The spike detection results show that AAPE can detect spikes well, even when presented with single-sample spikes, unlike PE. For multi-sample spikes, the changes in AAPE are larger than in PE. We introduce a new entropy metric, AAPE, that enables us to consider amplitude information in the formulation of PE. The AAPE algorithm can be used in almost every irregularity-based application in various signal and image processing fields. We also made freely available the Matlab code of the AAPE. Copyright © 2016 Elsevier Ireland Ltd. All rights reserved.

Emotional stress recognition using a new fusion link between electroencephalogram and peripheral signals

PubMed Central

Hosseini, Seyyed Abed; Khalilzadeh, Mohammad Ali; Naghibi-Sistani, Mohammad Bagher; Homam, Seyyed Mehran

2015-01-01

Background: This paper proposes a new emotional stress assessment system using multi-modal bio-signals. Electroencephalogram (EEG) is the reflection of brain activity and is widely used in clinical diagnosis and biomedical research. Methods: We design an efficient acquisition protocol to acquire the EEG signals in five channels (FP1, FP2, T3, T4 and Pz) and peripheral signals such as blood volume pulse, skin conductance (SC) and respiration, under images induction (calm-neutral and negatively excited) for the participants. The visual stimuli images are selected from the subset International Affective Picture System database. The qualitative and quantitative evaluation of peripheral signals are used to select suitable segments of EEG signals for improving the accuracy of signal labeling according to emotional stress states. After pre-processing, wavelet coefficients, fractal dimension, and Lempel-Ziv complexity are used to extract the features of the EEG signals. The vast number of features leads to the problem of dimensionality, which is solved using the genetic algorithm as a feature selection method. Results: The results show that the average classification accuracy is 89.6% for two categories of emotional stress states using the support vector machine (SVM). Conclusion: This is a great improvement in results compared to other similar researches. We achieve a noticeable improvement of 11.3% in accuracy using SVM classifier, in compared to previous studies. Therefore, a new fusion between EEG and peripheral signals are more robust in comparison to the separate signals. PMID:26622979

Emotional stress recognition using a new fusion link between electroencephalogram and peripheral signals.

PubMed

Hosseini, Seyyed Abed; Khalilzadeh, Mohammad Ali; Naghibi-Sistani, Mohammad Bagher; Homam, Seyyed Mehran

2015-07-06

This paper proposes a new emotional stress assessment system using multi-modal bio-signals. Electroencephalogram (EEG) is the reflection of brain activity and is widely used in clinical diagnosis and biomedical research. We design an efficient acquisition protocol to acquire the EEG signals in five channels (FP1, FP2, T3, T4 and Pz) and peripheral signals such as blood volume pulse, skin conductance (SC) and respiration, under images induction (calm-neutral and negatively excited) for the participants. The visual stimuli images are selected from the subset International Affective Picture System database. The qualitative and quantitative evaluation of peripheral signals are used to select suitable segments of EEG signals for improving the accuracy of signal labeling according to emotional stress states. After pre-processing, wavelet coefficients, fractal dimension, and Lempel-Ziv complexity are used to extract the features of the EEG signals. The vast number of features leads to the problem of dimensionality, which is solved using the genetic algorithm as a feature selection method. The results show that the average classification accuracy is 89.6% for two categories of emotional stress states using the support vector machine (SVM). This is a great improvement in results compared to other similar researches. We achieve a noticeable improvement of 11.3% in accuracy using SVM classifier, in compared to previous studies. Therefore, a new fusion between EEG and peripheral signals are more robust in comparison to the separate signals.

Multi-purpose CMOS sensor interface for low-power applications

NASA Astrophysics Data System (ADS)

Wouters, P.; de Cooman, M.; Puers, R.

1994-08-01

A dedicated low-power CMOS transponder microchip is presented as part of a novel telemetry implant for biomedical applications. This mixed analog-digital circuit contains an identification code and collects information on physiological parameters, i.e., body temperature and physical activity, and on the status of the battery. To minimize the amount of data to be transmitted, a dedicated signal processing algorithm is embedded within its circuitry. All telemetry functions (encoding, modulation, generation of the carrier) are implemented on the integrated circuit. Emphasis is on a high degree of flexibility towards sensor inputs and internal data management, extreme miniaturization, and low-power consumption to allow a long implantation lifetime.

A software framework for real-time multi-modal detection of microsleeps.

PubMed

Knopp, Simon J; Bones, Philip J; Weddell, Stephen J; Jones, Richard D

2017-09-01

A software framework is described which was designed to process EEG, video of one eye, and head movement in real time, towards achieving early detection of microsleeps for prevention of fatal accidents, particularly in transport sectors. The framework is based around a pipeline structure with user-replaceable signal processing modules. This structure can encapsulate a wide variety of feature extraction and classification techniques and can be applied to detecting a variety of aspects of cognitive state. Users of the framework can implement signal processing plugins in C++ or Python. The framework also provides a graphical user interface and the ability to save and load data to and from arbitrary file formats. Two small studies are reported which demonstrate the capabilities of the framework in typical applications: monitoring eye closure and detecting simulated microsleeps. While specifically designed for microsleep detection/prediction, the software framework can be just as appropriately applied to (i) other measures of cognitive state and (ii) development of biomedical instruments for multi-modal real-time physiological monitoring and event detection in intensive care, anaesthesiology, cardiology, neurosurgery, etc. The software framework has been made freely available for researchers to use and modify under an open source licence.

Acoustooptic pulse-echo transducer system

NASA Technical Reports Server (NTRS)

Claus, R. O.; Wade, J. C.

1983-01-01

A pulse-echo transducer system which uses an ultrasonic generating element and an optical detection technique is described. The transmitting transducer consists of a concentric ring electrode pattern deposited on a circular, X-cut quartz substrate with a circular hole in the center. The rings are independently pulsed with a sequence high voltage signals phased in such a way that the ultrasonic waves generated by the separate rings superimpose to produce a composite field which is focused at a controllable distance below the surface of the specimen. The amplitude of the field reflected from this focus position is determined by the local reflection coefficient of the medium at the effective focal point. By processing the signals received for a range of ultrasonic transducer array focal lengths, the system can be used to locate and size anomalies within solids and liquids. Applications in both nondestructive evaluation and biomedical scanning are suggested.

Multistrip Western blotting: a tool for comparative quantitative analysis of multiple proteins.

PubMed

Aksamitiene, Edita; Hoek, Jan B; Kiyatkin, Anatoly

2015-01-01

The qualitative and quantitative measurements of protein abundance and modification states are essential in understanding their functions in diverse cellular processes. Typical Western blotting, though sensitive, is prone to produce substantial errors and is not readily adapted to high-throughput technologies. Multistrip Western blotting is a modified immunoblotting procedure based on simultaneous electrophoretic transfer of proteins from multiple strips of polyacrylamide gels to a single membrane sheet. In comparison with the conventional technique, Multistrip Western blotting increases data output per single blotting cycle up to tenfold; allows concurrent measurement of up to nine different total and/or posttranslationally modified protein expression obtained from the same loading of the sample; and substantially improves the data accuracy by reducing immunoblotting-derived signal errors. This approach enables statistically reliable comparison of different or repeated sets of data and therefore is advantageous to apply in biomedical diagnostics, systems biology, and cell signaling research.

Assessment of Itakura Distance as a valuable feature for computer-aided classification of sleep stages.

PubMed

Ebrahimi, F; Mikaili, M; Estrada, E; Nazeran, H

2007-01-01

Staging and detection of various states of sleep derived from EEG and other biomedical signals have proven to be very helpful in diagnosis, prognosis and remedy of various sleep related disorders. The time consuming and costly process of visual scoring of sleep stages by a specialist has always motivated researchers to develop an automatic sleep scoring system and the first step toward achieving this task is finding discriminating characteristics (or features) for each stage. A vast variety of these features and methods have been investigated in the sleep literature with different degrees of success. In this study, we investigated the performance of a newly introduced measure: the Itakura Distance (ID), as a similarity measure between EEG and EOG signals. This work demonstrated and further confirmed the outcomes of our previous research that the Itakura Distance serves as a valuable similarity measure to differentiate between different sleep stages.

Active substrates improving sensitivity in biomedical fluorescence microscopy

NASA Astrophysics Data System (ADS)

Le Moal, E.; Leveque-Fort, S.; Fort, E.; Lacharme, J.-P.; Fontaine-Aupart, M.-P.; Ricolleau, C.

2005-08-01

Fluorescence is widely used as a spectroscopic tool or for biomedical imaging, in particular for DNA chips. In some cases, detection of very low molecular concentrations and precise localization of biomarkers are limited by the weakness of the fluorescence signal. We present a new method based on sample substrates that improve fluorescence detection sensitivity. These active substrates consist in glass slides covered with metal (gold or silver) and dielectric (alumina) films and can directly be used with common microscope set-up. Fluorescence enhancement affects both excitation and decay rates and is strongly dependant on the distance to the metal surface. Furthermore, fluorescence collection is improved since fluorophore emission lobes are advantageously modified close to a reflective surface. Finally, additional improvements are achieved by structuring the metallic layer. Substrates morphology was mapped by Atomic Force Microscopy (AFM). Substrates optical properties were studied using mono- and bi-photonic fluorescence microscopy with time resolution. An original set-up was implemented for spatial radiation pattern's measurement. Detection improvement was then tested on commercial devices. Several biomedical applications are presented. Enhancement by two orders of magnitude are achieved for DNA chips and signal-to-noise ratio is greatly increased for cells imaging.

Boronic acid recognition of non-interacting carbohydrates for biomedical applications: increasing fluorescence signals of minimally interacting aldoses and sucralose.

PubMed

Resendez, Angel; Halim, Md Abdul; Singh, Jasmeet; Webb, Dominic-Luc; Singaram, Bakthan

2017-11-22

To address carbohydrates that are commonly used in biomedical applications with low binding affinities for boronic acid based detection systems, two chemical modification methods were utilized to increase sensitivity. Modified carbohydrates were analyzed using a two component fluorescent probe based on boronic acid-appended viologen-HPTS (4,4'-o-BBV). Carbohydrates normally giving poor signals (fucose, l-rhamnose, xylose) were subjected to sodium borohydride (NaBH 4 ) reduction in ambient conditions for 1 h yielding the corresponding sugar alcohols from fucose, l-rhamnose and xylose in essentially quantitative yields. Compared to original aldoses, apparent binding affinities were increased 4-25-fold. The chlorinated sweetener and colon permeability marker sucralose (Splenda), otherwise undetectable by boronic acids, was dechlorinated to a detectable derivative by reactive oxygen and hydroxide intermediates by the Fenton reaction or by H 2 O 2 and UV light. This method is specific to sucralose as other common sugars, such as sucrose, do not contain any carbon-chlorine bonds. Significant fluorescence response was obtained for chemically modified sucralose with the 4,4'-o-BBV-HPTS probe system. This proof of principle can be applied to biomedical applications, such as gut permeability, malabsorption, etc.

Wavelet transform analysis of transient signals: the seismogram and the electrocardiogram

DOE Office of Scientific and Technical Information (OSTI.GOV)

Anant, K.S.

1997-06-01

In this dissertation I quantitatively demonstrate how the wavelet transform can be an effective mathematical tool for the analysis of transient signals. The two key signal processing applications of the wavelet transform, namely feature identification and representation (i.e., compression), are shown by solving important problems involving the seismogram and the electrocardiogram. The seismic feature identification problem involved locating in time the P and S phase arrivals. Locating these arrivals accurately (particularly the S phase) has been a constant issue in seismic signal processing. In Chapter 3, I show that the wavelet transform can be used to locate both the Pmore » as well as the S phase using only information from single station three-component seismograms. This is accomplished by using the basis function (wave-let) of the wavelet transform as a matching filter and by processing information across scales of the wavelet domain decomposition. The `pick` time results are quite promising as compared to analyst picks. The representation application involved the compression of the electrocardiogram which is a recording of the electrical activity of the heart. Compression of the electrocardiogram is an important problem in biomedical signal processing due to transmission and storage limitations. In Chapter 4, I develop an electrocardiogram compression method that applies vector quantization to the wavelet transform coefficients. The best compression results were obtained by using orthogonal wavelets, due to their ability to represent a signal efficiently. Throughout this thesis the importance of choosing wavelets based on the problem at hand is stressed. In Chapter 5, I introduce a wavelet design method that uses linear prediction in order to design wavelets that are geared to the signal or feature being analyzed. The use of these designed wavelets in a test feature identification application led to positive results. The methods developed in this thesis; the feature identification methods of Chapter 3, the compression methods of Chapter 4, as well as the wavelet design methods of Chapter 5, are general enough to be easily applied to other transient signals.« less

MATHEMATICAL METHODS IN MEDICAL IMAGE PROCESSING

PubMed Central

ANGENENT, SIGURD; PICHON, ERIC; TANNENBAUM, ALLEN

2013-01-01

In this paper, we describe some central mathematical problems in medical imaging. The subject has been undergoing rapid changes driven by better hardware and software. Much of the software is based on novel methods utilizing geometric partial differential equations in conjunction with standard signal/image processing techniques as well as computer graphics facilitating man/machine interactions. As part of this enterprise, researchers have been trying to base biomedical engineering principles on rigorous mathematical foundations for the development of software methods to be integrated into complete therapy delivery systems. These systems support the more effective delivery of many image-guided procedures such as radiation therapy, biopsy, and minimally invasive surgery. We will show how mathematics may impact some of the main problems in this area, including image enhancement, registration, and segmentation. PMID:23645963

Astronaut Kenneth Reightler processes biomedical samples in SPACEHAB

NASA Image and Video Library

1994-02-09

STS060-301-003 (3-11 Feb 1994) --- Astronaut Kenneth S. Reightler, STS-60 pilot, processes biomedical samples in a centrifuge aboard the SPACEHAB module. Reightler joined four other NASA astronauts and a Russian cosmonaut for eight days of research aboard the Space Shuttle Discovery.

Photochemical coatings for the prevention of bacterial colonization.

PubMed

Dunkirk, S G; Gregg, S L; Duran, L W; Monfils, J D; Haapala, J E; Marcy, J A; Clapper, D L; Amos, R A; Guire, P E

1991-10-01

Biomaterials are being used with increasing frequency for tissue substitution. Implantable, prosthetic devices are instrumental in the saving of patients' lives and enhancing the quality of life for many others. However, the greatest barrier to expanding the use of biomedical devices is the high probability of bacterial adherence and proliferation, causing very difficult and often untreatable medical-device centered infections. The difficulty in treating such infections results in great danger to the patient, and usually retrieval of the device with considerable pain and suffering. Clearly, development of processes that make biomedical devices resistant to bacterial adherence and colonization would have widespread application in the field of biomedical technology. A photochemical surface modification process is being investigated as a generic means of applying antimicrobial coatings to biomedical devices. The photochemical process results in covalent immobilization of coatings to all classes of medical device polymers. A discussion of the photochemical surface modification process and preliminary results demonstrating the success of photochemical coatings in formulating microbial-resistant surfaces are presented in this paper.

Structural and Functional Biomedical Imaging Using Polarization-Based Optical Coherence Tomography

NASA Astrophysics Data System (ADS)

Black, Adam J.

Biomedical imaging has had an enormous impact in medicine and research. There are numerous imaging modalities covering a large range of spatial and temporal scales, penetration depths, along with indicators for function and disease. As these imaging technologies mature, the quality of the images they produce increases to resolve finer details with greater contrast at higher speeds which aids in a faster, more accurate diagnosis in the clinic. In this dissertation, polarization-based optical coherence tomography (OCT) systems are used and developed to image biological structure and function with greater speeds, signal-to-noise (SNR) and stability. OCT can image with spatial and temporal resolutions in the micro range. When imaging any sample, feedback is very important to verify the fidelity and desired location on the sample being imaged. To increase frame rates for display as well as data throughput, field-programmable gate arrays (FPGAs) were used with custom algorithms to realize real-time display and streaming output for continuous acquisition of large datasets of swept-source OCT systems. For spectral domain (SD) OCT systems, significant increases in signal-to-noise ratios were achieved from a custom balanced detection (BD) OCT system. The BD system doubled measured signals while reducing common term. For functional imaging, a real-time directed scanner was introduced to visualize the 3D image of a sample to identify regions of interest prior to recording. Elucidating the characteristics of functional OCT signals with the aid of simulations, novel processing methods were also developed to stabilize samples being imaged and identify possible origins of functional signals being measured. Polarization-sensitive OCT was used to image cardiac tissue before and after clearing to identify the regions of vascular perfusion from a coronary artery. The resulting 3D image provides a visualization of the perfusion boundaries for the tissue that would be damaged from a myocardial infarction to possibly identity features that lead to fatal cardiac arrhythmias. 3D functional imaging was used to measure functional retinal activity from a light stimulus. In some cases, single trial responses were possible; measured at the outer segment of the photoreceptor layer. The morphology and time-course of these signals are similar to the intrinsic optical signals reported from phototransduction. Assessing function in the retina could aid in early detection of degenerative diseases of the retina, such as glaucoma and macular degeneration.

Tuning differentiation signals for efficient propagation and in vitro validation of rat embryonic stem cell cultures.

PubMed

Meek, Stephen; Sutherland, Linda; Burdon, Tom

2015-01-01

The rat is one of the most commonly used laboratory animals in biomedical research and the recent isolation of genuine pluripotent rat embryonic stem (ES) cell lines has provided new opportunities for applying contemporary genetic engineering techniques to the rat and enhancing the use of this rodent in scientific research. Technical refinements that improve the stability of the rat ES cell cultures will undoubtedly further strengthen and broaden the use of these stem cells in biomedical research. Here, we describe a relatively simple and robust protocol that supports the propagation of germ line competent rat ES cells, and outline how tuning stem cell signaling using small molecule inhibitors can be used to both stabilize self-renewal of rat ES cell cultures and aid evaluation of their differentiation potential in vitro.

Biomedical application of MALDI mass spectrometry for small-molecule analysis.

PubMed

van Kampen, Jeroen J A; Burgers, Peter C; de Groot, Ronald; Gruters, Rob A; Luider, Theo M

2011-01-01

Matrix-assisted laser desorption/ionization (MALDI) mass spectrometry (MS) is an emerging analytical tool for the analysis of molecules with molar masses below 1,000 Da; that is, small molecules. This technique offers rapid analysis, high sensitivity, low sample consumption, a relative high tolerance towards salts and buffers, and the possibility to store sample on the target plate. The successful application of the technique is, however, hampered by low molecular weight (LMW) matrix-derived interference signals and by poor reproducibility of signal intensities during quantitative analyses. In this review, we focus on the biomedical application of MALDI-MS for the analysis of small molecules and discuss its favorable properties and its challenges as well as strategies to improve the performance of the technique. Furthermore, practical aspects and applications are presented. © 2010 Wiley Periodicals, Inc.

Design of a Programmable Gain, Temperature Compensated Current-Input Current-Output CMOS Logarithmic Amplifier.

PubMed

Ming Gu; Chakrabartty, Shantanu

2014-06-01

This paper presents the design of a programmable gain, temperature compensated, current-mode CMOS logarithmic amplifier that can be used for biomedical signal processing. Unlike conventional logarithmic amplifiers that use a transimpedance technique to generate a voltage signal as a logarithmic function of the input current, the proposed approach directly produces a current output as a logarithmic function of the input current. Also, unlike a conventional transimpedance amplifier the gain of the proposed logarithmic amplifier can be programmed using floating-gate trimming circuits. The synthesis of the proposed circuit is based on the Hart's extended translinear principle which involves embedding a floating-voltage source and a linear resistive element within a translinear loop. Temperature compensation is then achieved using a translinear-based resistive cancelation technique. Measured results from prototypes fabricated in a 0.5 μm CMOS process show that the amplifier has an input dynamic range of 120 dB and a temperature sensitivity of 230 ppm/°C (27 °C- 57°C), while consuming less than 100 nW of power.

A modular framework for biomedical concept recognition

PubMed Central

2013-01-01

Background Concept recognition is an essential task in biomedical information extraction, presenting several complex and unsolved challenges. The development of such solutions is typically performed in an ad-hoc manner or using general information extraction frameworks, which are not optimized for the biomedical domain and normally require the integration of complex external libraries and/or the development of custom tools. Results This article presents Neji, an open source framework optimized for biomedical concept recognition built around four key characteristics: modularity, scalability, speed, and usability. It integrates modules for biomedical natural language processing, such as sentence splitting, tokenization, lemmatization, part-of-speech tagging, chunking and dependency parsing. Concept recognition is provided through dictionary matching and machine learning with normalization methods. Neji also integrates an innovative concept tree implementation, supporting overlapped concept names and respective disambiguation techniques. The most popular input and output formats, namely Pubmed XML, IeXML, CoNLL and A1, are also supported. On top of the built-in functionalities, developers and researchers can implement new processing modules or pipelines, or use the provided command-line interface tool to build their own solutions, applying the most appropriate techniques to identify heterogeneous biomedical concepts. Neji was evaluated against three gold standard corpora with heterogeneous biomedical concepts (CRAFT, AnEM and NCBI disease corpus), achieving high performance results on named entity recognition (F1-measure for overlap matching: species 95%, cell 92%, cellular components 83%, gene and proteins 76%, chemicals 65%, biological processes and molecular functions 63%, disorders 85%, and anatomical entities 82%) and on entity normalization (F1-measure for overlap name matching and correct identifier included in the returned list of identifiers: species 88%, cell 71%, cellular components 72%, gene and proteins 64%, chemicals 53%, and biological processes and molecular functions 40%). Neji provides fast and multi-threaded data processing, annotating up to 1200 sentences/second when using dictionary-based concept identification. Conclusions Considering the provided features and underlying characteristics, we believe that Neji is an important contribution to the biomedical community, streamlining the development of complex concept recognition solutions. Neji is freely available at http://bioinformatics.ua.pt/neji. PMID:24063607

Current Developments on Optical Feedback Interferometry as an All-Optical Sensor for Biomedical Applications

PubMed Central

Perchoux, Julien; Quotb, Adam; Atashkhooei, Reza; Azcona, Francisco J.; Ramírez-Miquet, Evelio E.; Bernal, Olivier; Jha, Ajit; Luna-Arriaga, Antonio; Yanez, Carlos; Caum, Jesus; Bosch, Thierry; Royo, Santiago

2016-01-01

Optical feedback interferometry (OFI) sensors are experiencing a consistent increase in their applications to biosensing due to their contactless nature, low cost and compactness, features that fit very well with current biophotonics research and market trends. The present paper is a review of the work in progress at UPC-CD6 and LAAS-CNRS related to the application of OFI to different aspects of biosensing, both in vivo and ex vivo. This work is intended to present the variety of opportunities and potential applications related to OFI that are available in the field. The activities presented are divided into two main sensing strategies: The measurement of optical path changes and the monitoring of flows, which correspond to sensing strategies linked to the reconstruction of changes of amplitude from the interferometric signal, and to classical Doppler frequency measurements, respectively. For optical path change measurements, measurements of transient pulses, usual in biosensing, together with the measurement of large displacements applied to designing palliative care instrumentation for Parkinson disease are discussed. Regarding the Doppler-based approach, progress in flow-related signal processing and applications in real-time monitoring of non-steady flows, human blood flow monitoring and OFI pressure myograph sensing will be presented. In all cases, experimental setups are discussed and results presented, showing the versatility of the technique. The described applications show the wide capabilities in biosensing of the OFI sensor, showing it as an enabler of low-cost, all-optical, high accuracy biomedical applications. PMID:27187406

Current Developments on Optical Feedback Interferometry as an All-Optical Sensor for Biomedical Applications.

PubMed

Perchoux, Julien; Quotb, Adam; Atashkhooei, Reza; Azcona, Francisco J; Ramírez-Miquet, Evelio E; Bernal, Olivier; Jha, Ajit; Luna-Arriaga, Antonio; Yanez, Carlos; Caum, Jesus; Bosch, Thierry; Royo, Santiago

2016-05-13

Optical feedback interferometry (OFI) sensors are experiencing a consistent increase in their applications to biosensing due to their contactless nature, low cost and compactness, features that fit very well with current biophotonics research and market trends. The present paper is a review of the work in progress at UPC-CD6 and LAAS-CNRS related to the application of OFI to different aspects of biosensing, both in vivo and ex vivo. This work is intended to present the variety of opportunities and potential applications related to OFI that are available in the field. The activities presented are divided into two main sensing strategies: The measurement of optical path changes and the monitoring of flows, which correspond to sensing strategies linked to the reconstruction of changes of amplitude from the interferometric signal, and to classical Doppler frequency measurements, respectively. For optical path change measurements, measurements of transient pulses, usual in biosensing, together with the measurement of large displacements applied to designing palliative care instrumentation for Parkinson disease are discussed. Regarding the Doppler-based approach, progress in flow-related signal processing and applications in real-time monitoring of non-steady flows, human blood flow monitoring and OFI pressure myograph sensing will be presented. In all cases, experimental setups are discussed and results presented, showing the versatility of the technique. The described applications show the wide capabilities in biosensing of the OFI sensor, showing it as an enabler of low-cost, all-optical, high accuracy biomedical applications.

An FPGA-Based Rapid Wheezing Detection System

PubMed Central

Lin, Bor-Shing; Yen, Tian-Shiue

2014-01-01

Wheezing is often treated as a crucial indicator in the diagnosis of obstructive pulmonary diseases. A rapid wheezing detection system may help physicians to monitor patients over the long-term. In this study, a portable wheezing detection system based on a field-programmable gate array (FPGA) is proposed. This system accelerates wheezing detection, and can be used as either a single-process system, or as an integrated part of another biomedical signal detection system. The system segments sound signals into 2-second units. A short-time Fourier transform was used to determine the relationship between the time and frequency components of wheezing sound data. A spectrogram was processed using 2D bilateral filtering, edge detection, multithreshold image segmentation, morphological image processing, and image labeling, to extract wheezing features according to computerized respiratory sound analysis (CORSA) standards. These features were then used to train the support vector machine (SVM) and build the classification models. The trained model was used to analyze sound data to detect wheezing. The system runs on a Xilinx Virtex-6 FPGA ML605 platform. The experimental results revealed that the system offered excellent wheezing recognition performance (0.912). The detection process can be used with a clock frequency of 51.97 MHz, and is able to perform rapid wheezing classification. PMID:24481034

Generation of open biomedical datasets through ontology-driven transformation and integration processes.

PubMed

Carmen Legaz-García, María Del; Miñarro-Giménez, José Antonio; Menárguez-Tortosa, Marcos; Fernández-Breis, Jesualdo Tomás

2016-06-03

Biomedical research usually requires combining large volumes of data from multiple heterogeneous sources, which makes difficult the integrated exploitation of such data. The Semantic Web paradigm offers a natural technological space for data integration and exploitation by generating content readable by machines. Linked Open Data is a Semantic Web initiative that promotes the publication and sharing of data in machine readable semantic formats. We present an approach for the transformation and integration of heterogeneous biomedical data with the objective of generating open biomedical datasets in Semantic Web formats. The transformation of the data is based on the mappings between the entities of the data schema and the ontological infrastructure that provides the meaning to the content. Our approach permits different types of mappings and includes the possibility of defining complex transformation patterns. Once the mappings are defined, they can be automatically applied to datasets to generate logically consistent content and the mappings can be reused in further transformation processes. The results of our research are (1) a common transformation and integration process for heterogeneous biomedical data; (2) the application of Linked Open Data principles to generate interoperable, open, biomedical datasets; (3) a software tool, called SWIT, that implements the approach. In this paper we also describe how we have applied SWIT in different biomedical scenarios and some lessons learned. We have presented an approach that is able to generate open biomedical repositories in Semantic Web formats. SWIT is able to apply the Linked Open Data principles in the generation of the datasets, so allowing for linking their content to external repositories and creating linked open datasets. SWIT datasets may contain data from multiple sources and schemas, thus becoming integrated datasets.

A sub-microwatt asynchronous level-crossing ADC for biomedical applications.

PubMed

Li, Yongjia; Zhao, Duan; Serdijn, Wouter A

2013-04-01

A continuous-time level-crossing analog-to-digital converter (LC-ADC) for biomedical applications is presented. When compared to uniform-sampling (US) ADCs LC-ADCs generate fewer samples for various sparse biomedical signals. Lower power consumption and reduced design complexity with respect to conventional LC-ADCs are achieved due to: 1) replacing the n-bit digital-to-analog converter (DAC) with a 1-bit DAC; 2) splitting the level-crossing detections; and 3) fixing the comparison window. Designed and implemented in 0.18 μm CMOS technology, the proposed ADC uses a chip area of 220 × 203 μm(2). Operating from a supply voltage of 0.8 V, the ADC consumes 313-582 nW from 5 Hz to 5 kHz and achieves an ENOB up to 7.9 bits.

Rapid automated classification of anesthetic depth levels using GPU based parallelization of neural networks.

PubMed

Peker, Musa; Şen, Baha; Gürüler, Hüseyin

2015-02-01

The effect of anesthesia on the patient is referred to as depth of anesthesia. Rapid classification of appropriate depth level of anesthesia is a matter of great importance in surgical operations. Similarly, accelerating classification algorithms is important for the rapid solution of problems in the field of biomedical signal processing. However numerous, time-consuming mathematical operations are required when training and testing stages of the classification algorithms, especially in neural networks. In this study, to accelerate the process, parallel programming and computing platform (Nvidia CUDA) facilitates dramatic increases in computing performance by harnessing the power of the graphics processing unit (GPU) was utilized. The system was employed to detect anesthetic depth level on related electroencephalogram (EEG) data set. This dataset is rather complex and large. Moreover, the achieving more anesthetic levels with rapid response is critical in anesthesia. The proposed parallelization method yielded high accurate classification results in a faster time.

High Frequency Sampling of TTL Pulses on a Raspberry Pi for Diffuse Correlation Spectroscopy Applications.

PubMed

Tivnan, Matthew; Gurjar, Rajan; Wolf, David E; Vishwanath, Karthik

2015-08-12

Diffuse Correlation Spectroscopy (DCS) is a well-established optical technique that has been used for non-invasive measurement of blood flow in tissues. Instrumentation for DCS includes a correlation device that computes the temporal intensity autocorrelation of a coherent laser source after it has undergone diffuse scattering through a turbid medium. Typically, the signal acquisition and its autocorrelation are performed by a correlation board. These boards have dedicated hardware to acquire and compute intensity autocorrelations of rapidly varying input signal and usually are quite expensive. Here we show that a Raspberry Pi minicomputer can acquire and store a rapidly varying time-signal with high fidelity. We show that this signal collected by a Raspberry Pi device can be processed numerically to yield intensity autocorrelations well suited for DCS applications. DCS measurements made using the Raspberry Pi device were compared to those acquired using a commercial hardware autocorrelation board to investigate the stability, performance, and accuracy of the data acquired in controlled experiments. This paper represents a first step toward lowering the instrumentation cost of a DCS system and may offer the potential to make DCS become more widely used in biomedical applications.

High Frequency Sampling of TTL Pulses on a Raspberry Pi for Diffuse Correlation Spectroscopy Applications

PubMed Central

Tivnan, Matthew; Gurjar, Rajan; Wolf, David E.; Vishwanath, Karthik

2015-01-01

Diffuse Correlation Spectroscopy (DCS) is a well-established optical technique that has been used for non-invasive measurement of blood flow in tissues. Instrumentation for DCS includes a correlation device that computes the temporal intensity autocorrelation of a coherent laser source after it has undergone diffuse scattering through a turbid medium. Typically, the signal acquisition and its autocorrelation are performed by a correlation board. These boards have dedicated hardware to acquire and compute intensity autocorrelations of rapidly varying input signal and usually are quite expensive. Here we show that a Raspberry Pi minicomputer can acquire and store a rapidly varying time-signal with high fidelity. We show that this signal collected by a Raspberry Pi device can be processed numerically to yield intensity autocorrelations well suited for DCS applications. DCS measurements made using the Raspberry Pi device were compared to those acquired using a commercial hardware autocorrelation board to investigate the stability, performance, and accuracy of the data acquired in controlled experiments. This paper represents a first step toward lowering the instrumentation cost of a DCS system and may offer the potential to make DCS become more widely used in biomedical applications. PMID:26274961

Automatic sleep stage classification based on EEG signals by using neural networks and wavelet packet coefficients.

PubMed

Ebrahimi, Farideh; Mikaeili, Mohammad; Estrada, Edson; Nazeran, Homer

2008-01-01

Currently in the world there is an alarming number of people who suffer from sleep disorders. A number of biomedical signals, such as EEG, EMG, ECG and EOG are used in sleep labs among others for diagnosis and treatment of sleep related disorders. The usual method for sleep stage classification is visual inspection by a sleep specialist. This is a very time consuming and laborious exercise. Automatic sleep stage classification can facilitate this process. The definition of sleep stages and the sleep literature show that EEG signals are similar in Stage 1 of non-rapid eye movement (NREM) sleep and rapid eye movement (REM) sleep. Therefore, in this work an attempt was made to classify four sleep stages consisting of Awake, Stage 1 + REM, Stage 2 and Slow Wave Stage based on the EEG signal alone. Wavelet packet coefficients and artificial neural networks were deployed for this purpose. Seven all night recordings from Physionet database were used in the study. The results demonstrated that these four sleep stages could be automatically discriminated from each other with a specificity of 94.4 +/- 4.5%, a of sensitivity 84.2+3.9% and an accuracy of 93.0 +/- 4.0%.

Challenges in horizontal model integration.

PubMed

Kolczyk, Katrin; Conradi, Carsten

2016-03-11

Systems Biology has motivated dynamic models of important intracellular processes at the pathway level, for example, in signal transduction and cell cycle control. To answer important biomedical questions, however, one has to go beyond the study of isolated pathways towards the joint study of interacting signaling pathways or the joint study of signal transduction and cell cycle control. Thereby the reuse of established models is preferable, as it will generally reduce the modeling effort and increase the acceptance of the combined model in the field. Obtaining a combined model can be challenging, especially if the submodels are large and/or come from different working groups (as is generally the case, when models stored in established repositories are used). To support this task, we describe a semi-automatic workflow based on established software tools. In particular, two frequent challenges are described: identification of the overlap and subsequent (re)parameterization of the integrated model. The reparameterization step is crucial, if the goal is to obtain a model that can reproduce the data explained by the individual models. For demonstration purposes we apply our workflow to integrate two signaling pathways (EGF and NGF) from the BioModels Database.

The Applications of Gold Nanoparticle-Initialed Chemiluminescence in Biomedical Detection

NASA Astrophysics Data System (ADS)

Liu, Zezhong; Zhao, Furong; Gao, Shandian; Shao, Junjun; Chang, Huiyun

2016-10-01

Chemiluminescence technique as a novel detection method has gained much attention in recent years owning to the merits of high sensitivity, wider linear ranges, and low background signal. Similarly, nanotechnology especially for gold nanoparticles has emerged as detection tools due to their unique physical and chemical properties. Recently, it has become increasingly popular to couple gold nanoparticles with chemiluminescence technique in biological agents' detection. In this review, we describe the superiority of both chemiluminescence and gold nanoparticles and conclude the different applications of gold nanoparticle-initialed chemiluminescence in biomedical detection.

Refined Composite Multiscale Dispersion Entropy and its Application to Biomedical Signals.

PubMed

Azami, Hamed; Rostaghi, Mostafa; Abasolo, Daniel; Escudero, Javier

2017-12-01

We propose a novel complexity measure to overcome the deficiencies of the widespread and powerful multiscale entropy (MSE), including, MSE values may be undefined for short signals, and MSE is slow for real-time applications. We introduce multiscale dispersion entropy (DisEn-MDE) as a very fast and powerful method to quantify the complexity of signals. MDE is based on our recently developed DisEn, which has a computation cost of O(N), compared with O(N 2 ) for sample entropy used in MSE. We also propose the refined composite MDE (RCMDE) to improve the stability of MDE. We evaluate MDE, RCMDE, and refined composite MSE (RCMSE) on synthetic signals and three biomedical datasets. The MDE, RCMDE, and RCMSE methods show similar results, although the MDE and RCMDE are faster, lead to more stable results, and discriminate different types of physiological signals better than MSE and RCMSE. For noisy short and long time series, MDE and RCMDE are noticeably more stable than MSE and RCMSE, respectively. For short signals, MDE and RCMDE, unlike MSE and RCMSE, do not lead to undefined values. The proposed MDE and RCMDE are significantly faster than MSE and RCMSE, especially for long signals, and lead to larger differences between physiological conditions known to alter the complexity of the physiological recordings. MDE and RCMDE are expected to be useful for the analysis of physiological signals thanks to their ability to distinguish different types of dynamics. The MATLAB codes used in this paper are freely available at http://dx.doi.org/10.7488/ds/1982.

A three channel telemetry system

NASA Technical Reports Server (NTRS)

Lesho, Jeffery C.; Eaton, Harry A. C.

1993-01-01

A three channel telemetry system intended for biomedical applications is described. The transmitter is implemented in a single chip using a 2 micron BiCMOS processes. The operation of the system and the test results from the latest chip are discussed. One channel is always dedicated to temperature measurement while the other two channels are generic. The generic channels carry information from transducers that are interfaced to the system through on-chip general purpose operational amplifiers. The generic channels have different bandwidths: one from dc to 250 Hz and the other from dc to 1300 Hz. Each generic channel modulates a current controlled oscillator to produce a frequency modulated signal. The two frequency modulated signals are summed and used to amplitude modulate the temperature signal which acts as a carrier. A near-field inductive link telemeters the combined signals over a short distance. The chip operates on a supply voltage anywhere from 2.5 to 3.6 Volts and draws less than 1 mA when transmitting a signal. The chip can be incorporated into ingestible, implantable and other configurations. The device can free the patient from tethered data collection systems and reduces the possibility of infection from subcutaneous leads. Data telemetry can increase patient comfort leading to a greater acceptance of monitoring.

Performance comparison of independent component analysis algorithms for fetal cardiac signal reconstruction: a study on synthetic fMCG data

NASA Astrophysics Data System (ADS)

Mantini, D.; Hild, K. E., II; Alleva, G.; Comani, S.

2006-02-01

Independent component analysis (ICA) algorithms have been successfully used for signal extraction tasks in the field of biomedical signal processing. We studied the performances of six algorithms (FastICA, CubICA, JADE, Infomax, TDSEP and MRMI-SIG) for fetal magnetocardiography (fMCG). Synthetic datasets were used to check the quality of the separated components against the original traces. Real fMCG recordings were simulated with linear combinations of typical fMCG source signals: maternal and fetal cardiac activity, ambient noise, maternal respiration, sensor spikes and thermal noise. Clusters of different dimensions (19, 36 and 55 sensors) were prepared to represent different MCG systems. Two types of signal-to-interference ratios (SIR) were measured. The first involves averaging over all estimated components and the second is based solely on the fetal trace. The computation time to reach a minimum of 20 dB SIR was measured for all six algorithms. No significant dependency on gestational age or cluster dimension was observed. Infomax performed poorly when a sub-Gaussian source was included; TDSEP and MRMI-SIG were sensitive to additive noise, whereas FastICA, CubICA and JADE showed the best performances. Of all six methods considered, FastICA had the best overall performance in terms of both separation quality and computation times.

Segregation of biomedical waste in an South Indian tertiary care hospital.

PubMed

Sengodan, Vetrivel Chezian

2014-07-01

Hospital wastes pose significant public health hazard if not properly managed. Hence, it is necessary to develop and adopt optimal waste management systems in the hospitals. Biomedical waste generated in Coimbatore Medical College Hospital was color coded (blue, yellow, and red) and the data was analyzed retrospectively on a daily basis for 3 years (January 2010-December 2012). Effective segregation protocols significantly reduced biomedical waste generated from 2011 to 2012. While biomedical waste of red category was significantly higher (>50%), the category yellow was the least. Per unit (per bed per day) total biomedical waste generated was 68.5, 68.8, and 61.3 grams in 2010, 2011, and 2012, respectively. Segregation of biomedical waste at the source of generation is the first and essential step in biomedical waste management. Continuous training, fixing the responsibility on the nursing persons, and constant supervision are the key criteria's in implementing biomedical waste segregation process, which can significantly reduce per unit biomedical waste generated. We highly recommend all hospitals to adopt our protocol and effectively implement them to reduce generation of biomedical waste.

Hand-in-hand advances in biomedical engineering and sensorimotor restoration.

PubMed

Pisotta, Iolanda; Perruchoud, David; Ionta, Silvio

2015-05-15

Living in a multisensory world entails the continuous sensory processing of environmental information in order to enact appropriate motor routines. The interaction between our body and our brain is the crucial factor for achieving such sensorimotor integration ability. Several clinical conditions dramatically affect the constant body-brain exchange, but the latest developments in biomedical engineering provide promising solutions for overcoming this communication breakdown. The ultimate technological developments succeeded in transforming neuronal electrical activity into computational input for robotic devices, giving birth to the era of the so-called brain-machine interfaces. Combining rehabilitation robotics and experimental neuroscience the rise of brain-machine interfaces into clinical protocols provided the technological solution for bypassing the neural disconnection and restore sensorimotor function. Based on these advances, the recovery of sensorimotor functionality is progressively becoming a concrete reality. However, despite the success of several recent techniques, some open issues still need to be addressed. Typical interventions for sensorimotor deficits include pharmaceutical treatments and manual/robotic assistance in passive movements. These procedures achieve symptoms relief but their applicability to more severe disconnection pathologies is limited (e.g. spinal cord injury or amputation). Here we review how state-of-the-art solutions in biomedical engineering are continuously increasing expectances in sensorimotor rehabilitation, as well as the current challenges especially with regards to the translation of the signals from brain-machine interfaces into sensory feedback and the incorporation of brain-machine interfaces into daily activities. Copyright © 2015 Elsevier B.V. All rights reserved.

Implementation of Context Aware e-Health Environments Based on Social Sensor Networks

PubMed Central

Aguirre, Erik; Led, Santiago; Lopez-Iturri, Peio; Azpilicueta, Leyre; Serrano, Luís; Falcone, Francisco

2016-01-01

In this work, context aware scenarios applied to e-Health and m-Health in the framework of typical households (urban and rural) by means of deploying Social Sensors will be described. Interaction with end-users and social/medical staff is achieved using a multi-signal input/output device, capable of sensing and transmitting environmental, biomedical or activity signals and information with the aid of a combined Bluetooth and Mobile system platform. The devices, which play the role of Social Sensors, are implemented and tested in order to guarantee adequate service levels in terms of multiple signal processing tasks as well as robustness in relation with the use wireless transceivers and channel variability. Initial tests within a Living Lab environment have been performed in order to validate overall system operation. The results obtained show good acceptance of the proposed system both by end users as well as by medical and social staff, increasing interaction, reducing overall response time and social inclusion levels, with a compact and moderate cost solution that can readily be largely deployed. PMID:26938539

High-Density Stretchable Electrode Grids for Chronic Neural Recording

PubMed Central

Tybrandt, Klas; Khodagholy, Dion; Dielacher, Bernd; Stauffer, Flurin; Renz, Aline F.; Buzsáki, György; Vörös, János

2018-01-01

Electrical interfacing with neural tissue is key to advancing diagnosis and therapies for neurological disorders, as well as providing detailed information about neural signals. A challenge for creating long-term stable interfaces between electronics and neural tissue is the huge mechanical mismatch between the systems. So far, materials and fabrication processes have restricted the development of soft electrode grids able to combine high performance, long-term stability, and high electrode density, aspects all essential for neural interfacing. Here, this challenge is addressed by developing a soft, high-density, stretchable electrode grid based on an inert, high-performance composite material comprising gold-coated titanium dioxide nanowires embedded in a silicone matrix. The developed grid can resolve high spatiotemporal neural signals from the surface of the cortex in freely moving rats with stable neural recording quality and preserved electrode signal coherence during 3 months of implantation. Due to its flexible and stretchable nature, it is possible to minimize the size of the craniotomy required for placement, further reducing the level of invasiveness. The material and device technology presented herein have potential for a wide range of emerging biomedical applications. PMID:29488263

Earl Sutherland (1915-1974) [corrected] and the discovery of cyclic AMP.

PubMed

Blumenthal, Stanley A

2012-01-01

In 1945, Earl Sutherland (1915-1974) [corrected] and associates began studies of the mechanism of hormone-induced glycogen breakdown in the liver. In 1956, their efforts culminated in the identification of cyclic AMP, an ancient molecule generated in many cell types in response to hormonal and other extracellular signals. Cyclic AMP, the original "second messenger," transmits such signals through pathways that regulate a diversity of cellular functions and capabilities: metabolic processes such as lipolysis and glycogenolysis; hormone secretion; the permeability of ion channels; gene expression; cell proliferation and survival. Indeed, it can be argued that the discovery of cyclic AMP initiated the study of intracellular signaling pathways, a major focus of contemporary biomedical inquiry. This review presents relevant details of Sutherland's career; summarizes key contributions of his mentors, Carl and Gerti Cori, to the knowledge of glycogen metabolism (contributions that were the foundation for his own research); describes the experiments that led to his identification, isolation, and characterization of cyclic AMP; assesses the significance of his work; and considers some aspects of the impact of cyclic nucleotide research on clinical medicine.

Wavelet methodology to improve single unit isolation in primary motor cortex cells.

PubMed

Ortiz-Rosario, Alexis; Adeli, Hojjat; Buford, John A

2015-05-15

The proper isolation of action potentials recorded extracellularly from neural tissue is an active area of research in the fields of neuroscience and biomedical signal processing. This paper presents an isolation methodology for neural recordings using the wavelet transform (WT), a statistical thresholding scheme, and the principal component analysis (PCA) algorithm. The effectiveness of five different mother wavelets was investigated: biorthogonal, Daubachies, discrete Meyer, symmetric, and Coifman; along with three different wavelet coefficient thresholding schemes: fixed form threshold, Stein's unbiased estimate of risk, and minimax; and two different thresholding rules: soft and hard thresholding. The signal quality was evaluated using three different statistical measures: mean-squared error, root-mean squared, and signal to noise ratio. The clustering quality was evaluated using two different statistical measures: isolation distance, and L-ratio. This research shows that the selection of the mother wavelet has a strong influence on the clustering and isolation of single unit neural activity, with the Daubachies 4 wavelet and minimax thresholding scheme performing the best. Copyright © 2015. Published by Elsevier B.V.

Implementation of Context Aware e-Health Environments Based on Social Sensor Networks.

PubMed

Aguirre, Erik; Led, Santiago; Lopez-Iturri, Peio; Azpilicueta, Leyre; Serrano, Luís; Falcone, Francisco

2016-03-01

In this work, context aware scenarios applied to e-Health and m-Health in the framework of typical households (urban and rural) by means of deploying Social Sensors will be described. Interaction with end-users and social/medical staff is achieved using a multi-signal input/output device, capable of sensing and transmitting environmental, biomedical or activity signals and information with the aid of a combined Bluetooth and Mobile system platform. The devices, which play the role of Social Sensors, are implemented and tested in order to guarantee adequate service levels in terms of multiple signal processing tasks as well as robustness in relation with the use wireless transceivers and channel variability. Initial tests within a Living Lab environment have been performed in order to validate overall system operation. The results obtained show good acceptance of the proposed system both by end users as well as by medical and social staff, increasing interaction, reducing overall response time and social inclusion levels, with a compact and moderate cost solution that can readily be largely deployed.

EXACT2: the semantics of biomedical protocols

PubMed Central

2014-01-01

Background The reliability and reproducibility of experimental procedures is a cornerstone of scientific practice. There is a pressing technological need for the better representation of biomedical protocols to enable other agents (human or machine) to better reproduce results. A framework that ensures that all information required for the replication of experimental protocols is essential to achieve reproducibility. Methods We have developed the ontology EXACT2 (EXperimental ACTions) that is designed to capture the full semantics of biomedical protocols required for their reproducibility. To construct EXACT2 we manually inspected hundreds of published and commercial biomedical protocols from several areas of biomedicine. After establishing a clear pattern for extracting the required information we utilized text-mining tools to translate the protocols into a machine amenable format. We have verified the utility of EXACT2 through the successful processing of previously 'unseen' (not used for the construction of EXACT2) protocols. Results The paper reports on a fundamentally new version EXACT2 that supports the semantically-defined representation of biomedical protocols. The ability of EXACT2 to capture the semantics of biomedical procedures was verified through a text mining use case. In this EXACT2 is used as a reference model for text mining tools to identify terms pertinent to experimental actions, and their properties, in biomedical protocols expressed in natural language. An EXACT2-based framework for the translation of biomedical protocols to a machine amenable format is proposed. Conclusions The EXACT2 ontology is sufficient to record, in a machine processable form, the essential information about biomedical protocols. EXACT2 defines explicit semantics of experimental actions, and can be used by various computer applications. It can serve as a reference model for for the translation of biomedical protocols in natural language into a semantically-defined format. PMID:25472549

NeuroPigPen: A Scalable Toolkit for Processing Electrophysiological Signal Data in Neuroscience Applications Using Apache Pig

PubMed Central

Sahoo, Satya S.; Wei, Annan; Valdez, Joshua; Wang, Li; Zonjy, Bilal; Tatsuoka, Curtis; Loparo, Kenneth A.; Lhatoo, Samden D.

2016-01-01

The recent advances in neurological imaging and sensing technologies have led to rapid increase in the volume, rate of data generation, and variety of neuroscience data. This “neuroscience Big data” represents a significant opportunity for the biomedical research community to design experiments using data with greater timescale, large number of attributes, and statistically significant data size. The results from these new data-driven research techniques can advance our understanding of complex neurological disorders, help model long-term effects of brain injuries, and provide new insights into dynamics of brain networks. However, many existing neuroinformatics data processing and analysis tools were not built to manage large volume of data, which makes it difficult for researchers to effectively leverage this available data to advance their research. We introduce a new toolkit called NeuroPigPen that was developed using Apache Hadoop and Pig data flow language to address the challenges posed by large-scale electrophysiological signal data. NeuroPigPen is a modular toolkit that can process large volumes of electrophysiological signal data, such as Electroencephalogram (EEG), Electrocardiogram (ECG), and blood oxygen levels (SpO2), using a new distributed storage model called Cloudwave Signal Format (CSF) that supports easy partitioning and storage of signal data on commodity hardware. NeuroPigPen was developed with three design principles: (a) Scalability—the ability to efficiently process increasing volumes of data; (b) Adaptability—the toolkit can be deployed across different computing configurations; and (c) Ease of programming—the toolkit can be easily used to compose multi-step data processing pipelines using high-level programming constructs. The NeuroPigPen toolkit was evaluated using 750 GB of electrophysiological signal data over a variety of Hadoop cluster configurations ranging from 3 to 30 Data nodes. The evaluation results demonstrate that the toolkit is highly scalable and adaptable, which makes it suitable for use in neuroscience applications as a scalable data processing toolkit. As part of the ongoing extension of NeuroPigPen, we are developing new modules to support statistical functions to analyze signal data for brain connectivity research. In addition, the toolkit is being extended to allow integration with scientific workflow systems. NeuroPigPen is released under BSD license at: https://sites.google.com/a/case.edu/neuropigpen/. PMID:27375472

NeuroPigPen: A Scalable Toolkit for Processing Electrophysiological Signal Data in Neuroscience Applications Using Apache Pig.

PubMed

Sahoo, Satya S; Wei, Annan; Valdez, Joshua; Wang, Li; Zonjy, Bilal; Tatsuoka, Curtis; Loparo, Kenneth A; Lhatoo, Samden D

2016-01-01

The recent advances in neurological imaging and sensing technologies have led to rapid increase in the volume, rate of data generation, and variety of neuroscience data. This "neuroscience Big data" represents a significant opportunity for the biomedical research community to design experiments using data with greater timescale, large number of attributes, and statistically significant data size. The results from these new data-driven research techniques can advance our understanding of complex neurological disorders, help model long-term effects of brain injuries, and provide new insights into dynamics of brain networks. However, many existing neuroinformatics data processing and analysis tools were not built to manage large volume of data, which makes it difficult for researchers to effectively leverage this available data to advance their research. We introduce a new toolkit called NeuroPigPen that was developed using Apache Hadoop and Pig data flow language to address the challenges posed by large-scale electrophysiological signal data. NeuroPigPen is a modular toolkit that can process large volumes of electrophysiological signal data, such as Electroencephalogram (EEG), Electrocardiogram (ECG), and blood oxygen levels (SpO2), using a new distributed storage model called Cloudwave Signal Format (CSF) that supports easy partitioning and storage of signal data on commodity hardware. NeuroPigPen was developed with three design principles: (a) Scalability-the ability to efficiently process increasing volumes of data; (b) Adaptability-the toolkit can be deployed across different computing configurations; and (c) Ease of programming-the toolkit can be easily used to compose multi-step data processing pipelines using high-level programming constructs. The NeuroPigPen toolkit was evaluated using 750 GB of electrophysiological signal data over a variety of Hadoop cluster configurations ranging from 3 to 30 Data nodes. The evaluation results demonstrate that the toolkit is highly scalable and adaptable, which makes it suitable for use in neuroscience applications as a scalable data processing toolkit. As part of the ongoing extension of NeuroPigPen, we are developing new modules to support statistical functions to analyze signal data for brain connectivity research. In addition, the toolkit is being extended to allow integration with scientific workflow systems. NeuroPigPen is released under BSD license at: https://sites.google.com/a/case.edu/neuropigpen/.

A low power biomedical signal processor ASIC based on hardware software codesign.

PubMed

Nie, Z D; Wang, L; Chen, W G; Zhang, T; Zhang, Y T

2009-01-01

A low power biomedical digital signal processor ASIC based on hardware and software codesign methodology was presented in this paper. The codesign methodology was used to achieve higher system performance and design flexibility. The hardware implementation included a low power 32bit RISC CPU ARM7TDMI, a low power AHB-compatible bus, and a scalable digital co-processor that was optimized for low power Fast Fourier Transform (FFT) calculations. The co-processor could be scaled for 8-point, 16-point and 32-point FFTs, taking approximate 50, 100 and 150 clock circles, respectively. The complete design was intensively simulated using ARM DSM model and was emulated by ARM Versatile platform, before conducted to silicon. The multi-million-gate ASIC was fabricated using SMIC 0.18 microm mixed-signal CMOS 1P6M technology. The die area measures 5,000 microm x 2,350 microm. The power consumption was approximately 3.6 mW at 1.8 V power supply and 1 MHz clock rate. The power consumption for FFT calculations was less than 1.5 % comparing with the conventional embedded software-based solution.

Resolving Complex Research Data Management Issues in Biomedical Laboratories: Qualitative Study of an Industry-Academia Collaboration

PubMed Central

Myneni, Sahiti; Patel, Vimla L.; Bova, G. Steven; Wang, Jian; Ackerman, Christopher F.; Berlinicke, Cynthia A.; Chen, Steve H.; Lindvall, Mikael; Zack, Donald J.

2016-01-01

This paper describes a distributed collaborative effort between industry and academia to systematize data management in an academic biomedical laboratory. Heterogeneous and voluminous nature of research data created in biomedical laboratories make information management difficult and research unproductive. One such collaborative effort was evaluated over a period of four years using data collection methods including ethnographic observations, semi-structured interviews, web-based surveys, progress reports, conference call summaries, and face-to-face group discussions. Data were analyzed using qualitative methods of data analysis to 1) characterize specific problems faced by biomedical researchers with traditional information management practices, 2) identify intervention areas to introduce a new research information management system called Labmatrix, and finally to 3) evaluate and delineate important general collaboration (intervention) characteristics that can optimize outcomes of an implementation process in biomedical laboratories. Results emphasize the importance of end user perseverance, human-centric interoperability evaluation, and demonstration of return on investment of effort and time of laboratory members and industry personnel for success of implementation process. In addition, there is an intrinsic learning component associated with the implementation process of an information management system. Technology transfer experience in a complex environment such as the biomedical laboratory can be eased with use of information systems that support human and cognitive interoperability. Such informatics features can also contribute to successful collaboration and hopefully to scientific productivity. PMID:26652980

Biomedical and Behavioral Research Scientists: Their Training and Supply. Volume 1: Findings.

ERIC Educational Resources Information Center

National Academy of Sciences - National Research Council, Washington, DC. Office of Scientific and Engineering Personnel.

This is the first of three volumes which presents the Committee on Biomedical and Behavioral Research Personnel's examination of the educational process that leads to doctoral degrees in biomedical and behavioral science (and to postdoctoral study in some cases) and the role of the National Research Service Awards (NRSA) training programs in it.…

Extracting biomedical events from pairs of text entities

PubMed Central

2015-01-01

Background Huge amounts of electronic biomedical documents, such as molecular biology reports or genomic papers are generated daily. Nowadays, these documents are mainly available in the form of unstructured free texts, which require heavy processing for their registration into organized databases. This organization is instrumental for information retrieval, enabling to answer the advanced queries of researchers and practitioners in biology, medicine, and related fields. Hence, the massive data flow calls for efficient automatic methods of text-mining that extract high-level information, such as biomedical events, from biomedical text. The usual computational tools of Natural Language Processing cannot be readily applied to extract these biomedical events, due to the peculiarities of the domain. Indeed, biomedical documents contain highly domain-specific jargon and syntax. These documents also describe distinctive dependencies, making text-mining in molecular biology a specific discipline. Results We address biomedical event extraction as the classification of pairs of text entities into the classes corresponding to event types. The candidate pairs of text entities are recursively provided to a multiclass classifier relying on Support Vector Machines. This recursive process extracts events involving other events as arguments. Compared to joint models based on Markov Random Fields, our model simplifies inference and hence requires shorter training and prediction times along with lower memory capacity. Compared to usual pipeline approaches, our model passes over a complex intermediate problem, while making a more extensive usage of sophisticated joint features between text entities. Our method focuses on the core event extraction of the Genia task of BioNLP challenges yielding the best result reported so far on the 2013 edition. PMID:26201478

ParaBTM: A Parallel Processing Framework for Biomedical Text Mining on Supercomputers.

PubMed

Xing, Yuting; Wu, Chengkun; Yang, Xi; Wang, Wei; Zhu, En; Yin, Jianping

2018-04-27

A prevailing way of extracting valuable information from biomedical literature is to apply text mining methods on unstructured texts. However, the massive amount of literature that needs to be analyzed poses a big data challenge to the processing efficiency of text mining. In this paper, we address this challenge by introducing parallel processing on a supercomputer. We developed paraBTM, a runnable framework that enables parallel text mining on the Tianhe-2 supercomputer. It employs a low-cost yet effective load balancing strategy to maximize the efficiency of parallel processing. We evaluated the performance of paraBTM on several datasets, utilizing three types of named entity recognition tasks as demonstration. Results show that, in most cases, the processing efficiency can be greatly improved with parallel processing, and the proposed load balancing strategy is simple and effective. In addition, our framework can be readily applied to other tasks of biomedical text mining besides NER.

Data-Driven Sampling Matrix Boolean Optimization for Energy-Efficient Biomedical Signal Acquisition by Compressive Sensing.

PubMed

Wang, Yuhao; Li, Xin; Xu, Kai; Ren, Fengbo; Yu, Hao

2017-04-01

Compressive sensing is widely used in biomedical applications, and the sampling matrix plays a critical role on both quality and power consumption of signal acquisition. It projects a high-dimensional vector of data into a low-dimensional subspace by matrix-vector multiplication. An optimal sampling matrix can ensure accurate data reconstruction and/or high compression ratio. Most existing optimization methods can only produce real-valued embedding matrices that result in large energy consumption during data acquisition. In this paper, we propose an efficient method that finds an optimal Boolean sampling matrix in order to reduce the energy consumption. Compared to random Boolean embedding, our data-driven Boolean sampling matrix can improve the image recovery quality by 9 dB. Moreover, in terms of sampling hardware complexity, it reduces the energy consumption by 4.6× and the silicon area by 1.9× over the data-driven real-valued embedding.

Flexible implementation of front-end bioelectric signal amplifier using FPAA for telemedicine system.

PubMed

Chan, U Fai; Chan, Wai Wong; Pun, Sio Hang; Vai, Mang I; Mak, Peng Un

2007-01-01

Traditional/Current electronic circuits for Telemedicine have significant performance on certain bioelectric signal detection. However, it is rarely seen that can handle multiple signals without changing of hardware. This paper introduces a general front-end amplifier for various bioelectric signals based on Field Programmable Analogy Array (FPAA) Technology. Employing FPAA technology, the implemented amplifier can be adapted for various bioelectric signals without alternating the circuitry while its compact size (core parts < 2 cm2) provides an alternative solution for miniaturized Telemedicine system and Wearable Devices. The proposed design implementation has demonstrated, through successfully ECG and EMG signal extractions, a quick way to miniaturize analog biomedical circuit in a convenient and cost effective way.

Strategies for Derisking Translational Processes for Biomedical Technologies.

PubMed

Abou-El-Enein, Mohamed; Duda, Georg N; Gruskin, Elliott A; Grainger, David W

2017-02-01

Inefficient translational processes for technology-oriented biomedical research have led to some prominent and frequent failures in the development of many leading drug candidates, several designated investigational drugs, and some medical devices, as well as documented patient harm and postmarket product withdrawals. Derisking this process, particularly in the early stages, should increase translational efficiency and streamline resource utilization, especially in an academic setting. In this opinion article, we identify a 12-step guideline for reducing risks typically associated with translating medical technologies as they move toward prototypes, preclinical proof of concept, and possible clinical testing. Integrating the described 12-step process should prove valuable for improving how early-stage academic biomedical concepts are cultivated, culled, and manicured toward intended clinical applications. Copyright © 2016 Elsevier Ltd. All rights reserved.

Biomedical Biopolymers, their Origin and Evolution in Biomedical Sciences: A Systematic Review

PubMed Central

Yadav, Harsh; Shah, Veena Gowri; Shah, Gaurav; Dhaka, Gaurav

2015-01-01

Biopolymers provide a plethora of applications in the pharmaceutical and medical applications. A material that can be used for biomedical applications like wound healing, drug delivery and tissue engineering should possess certain properties like biocompatibility, biodegradation to non-toxic products, low antigenicity, high bio-activity, processability to complicated shapes with appropriate porosity, ability to support cell growth and proliferation and appropriate mechanical properties, as well as maintaining mechanical strength. This paper reviews biodegradable biopolymers focusing on their potential in biomedical applications. Biopolymers most commonly used and most abundantly available have been described with focus on the properties relevant to biomedical importance. PMID:26501034

How to locate & hire clinical/biomedical engineers, supervisors, managers & biomedical equipment technicians.

PubMed

Pacela, A F; Brush, L C

1993-01-01

This article has described the process and the resources available for locating and hiring clinical/biomedical engineers, supervisors, managers, and biomedical equipment technicians. First, the employer must determine the qualifications for the position, including job titles, descriptions, pay scales, and certification requirements. Next, the employer must find qualified applicants. The most common way to do this is to use "outside" contacts, such as help-wanted advertising, specialized job placement agencies, schools and colleges, military resources, regional biomedical societies, and nationwide societies. An "inside" search involves limited internal advertising of the position and using personal referrals for candidates. Finally, the employer must screen the applicants. The position description is the obvious first step in this process, but there are other pre-screening techniques, such as employment testing. Interviewing is the most common way to hire for job positions, but the interviewer needs to know about the position and ask the right questions. Post-interview screening is a final step to help determine the best job-person match.

Material Processing and Design of Biodegradable Metal Matrix Composites for Biomedical Applications.

PubMed

Yang, Jingxin; Guo, Jason L; Mikos, Antonios G; He, Chunyan; Cheng, Guang

2018-06-04

In recent years, biodegradable metallic materials have played an important role in biomedical applications. However, as typical for the metal materials, their structure, general properties, preparation technology and biocompatibility are hard to change. Furthermore, biodegradable metals are susceptible to excessive degradation and subsequent disruption of their mechanical integrity; this phenomenon limits the utility of these biomaterials. Therefore, the use of degradable metals, as the base material to prepare metal matrix composite materials, it is an excellent alternative to solve the problems above described. Biodegradable metals can thus be successfully combined with other materials to form biodegradable metallic matrix composites for biomedical applications and functions. The present article describes the processing methods currently available to design biodegradable metal matrix composites for biomedical applications and provides an overview of the current existing biodegradable metal systems. At the end, the manuscript presents and discusses the challenges and future research directions for development of biodegradable metallic matrix composites for biomedical purposes.

Cryogenics free production of hyperpolarized 129Xe and 83Kr for biomedical MRI applications

NASA Astrophysics Data System (ADS)

Hughes-Riley, Theodore; Six, Joseph S.; Lilburn, David M. L.; Stupic, Karl F.; Dorkes, Alan C.; Shaw, Dominick E.; Pavlovskaya, Galina E.; Meersmann, Thomas

2013-12-01

As an alternative to cryogenic gas handling, hyperpolarized (hp) gas mixtures were extracted directly from the spin exchange optical pumping (SEOP) process through expansion followed by compression to ambient pressure for biomedical MRI applications. The omission of cryogenic gas separation generally requires the usage of high xenon or krypton concentrations at low SEOP gas pressures to generate hp 129Xe or hp 83Kr with sufficient MR signal intensity for imaging applications. Two different extraction schemes for the hp gasses were explored with focus on the preservation of the nuclear spin polarization. It was found that an extraction scheme based on an inflatable, pressure controlled balloon is sufficient for hp 129Xe handling, while 83Kr can efficiently be extracted through a single cycle piston pump. The extraction methods were tested for ex vivo MRI applications with excised rat lungs. Precise mixing of the hp gases with oxygen, which may be of interest for potential in vivo applications, was accomplished during the extraction process using a piston pump. The 83Kr bulk gas phase T1 relaxation in the mixtures containing more than approximately 1% O2 was found to be slower than that of 129Xe in corresponding mixtures. The experimental setup also facilitated 129Xe T1 relaxation measurements as a function of O2 concentration within excised lungs.

Cryogenics free production of hyperpolarized 129Xe and 83Kr for biomedical MRI applications☆

PubMed Central

Hughes-Riley, Theodore; Six, Joseph S.; Lilburn, David M.L.; Stupic, Karl F.; Dorkes, Alan C.; Shaw, Dominick E.; Pavlovskaya, Galina E.; Meersmann, Thomas

2013-01-01

As an alternative to cryogenic gas handling, hyperpolarized (hp) gas mixtures were extracted directly from the spin exchange optical pumping (SEOP) process through expansion followed by compression to ambient pressure for biomedical MRI applications. The omission of cryogenic gas separation generally requires the usage of high xenon or krypton concentrations at low SEOP gas pressures to generate hp 129Xe or hp 83Kr with sufficient MR signal intensity for imaging applications. Two different extraction schemes for the hp gasses were explored with focus on the preservation of the nuclear spin polarization. It was found that an extraction scheme based on an inflatable, pressure controlled balloon is sufficient for hp 129Xe handling, while 83Kr can efficiently be extracted through a single cycle piston pump. The extraction methods were tested for ex vivo MRI applications with excised rat lungs. Precise mixing of the hp gases with oxygen, which may be of interest for potential in vivo applications, was accomplished during the extraction process using a piston pump. The 83Kr bulk gas phase T1 relaxation in the mixtures containing more than approximately 1% O2 was found to be slower than that of 129Xe in corresponding mixtures. The experimental setup also facilitated 129Xe T1 relaxation measurements as a function of O2 concentration within excised lungs. PMID:24135800

Cryogenics free production of hyperpolarized 129Xe and 83Kr for biomedical MRI applications.

PubMed

Hughes-Riley, Theodore; Six, Joseph S; Lilburn, David M L; Stupic, Karl F; Dorkes, Alan C; Shaw, Dominick E; Pavlovskaya, Galina E; Meersmann, Thomas

2013-12-01

As an alternative to cryogenic gas handling, hyperpolarized (hp) gas mixtures were extracted directly from the spin exchange optical pumping (SEOP) process through expansion followed by compression to ambient pressure for biomedical MRI applications. The omission of cryogenic gas separation generally requires the usage of high xenon or krypton concentrations at low SEOP gas pressures to generate hp (129)Xe or hp (83)Kr with sufficient MR signal intensity for imaging applications. Two different extraction schemes for the hp gasses were explored with focus on the preservation of the nuclear spin polarization. It was found that an extraction scheme based on an inflatable, pressure controlled balloon is sufficient for hp (129)Xe handling, while (83)Kr can efficiently be extracted through a single cycle piston pump. The extraction methods were tested for ex vivo MRI applications with excised rat lungs. Precise mixing of the hp gases with oxygen, which may be of interest for potential in vivo applications, was accomplished during the extraction process using a piston pump. The (83)Kr bulk gas phase T1 relaxation in the mixtures containing more than approximately 1% O2 was found to be slower than that of (129)Xe in corresponding mixtures. The experimental setup also facilitated (129)Xe T1 relaxation measurements as a function of O2 concentration within excised lungs. Copyright © 2013 The Authors. Published by Elsevier Inc. All rights reserved.

Boronic acid recognition of non-interacting carbohydrates for biomedical applications: increasing fluorescence signals of minimally interacting aldoses and sucralose†

PubMed Central

Resendez, Angel; Halim, Md Abdul; Singh, Jasmeet; Webb, Dominic-Luc

2017-01-01

To address carbohydrates that are commonly used in biomedical applications with low binding affinities for boronic acid based detection systems, two chemical modification methods were utilized to increase sensitivity. Modified carbohydrates were analyzed using a two component fluorescent probe based on boronic acid-appended viologen–HPTS (4,4′-o-BBV). Carbohydrates normally giving poor signals (fucose, l-rhamnose, xylose) were subjected to sodium borohydride (NaBH4) reduction in ambient conditions for 1 h yielding the corresponding sugar alcohols from fucose, l-rhamnose and xylose in essentially quantitative yields. Compared to original aldoses, apparent binding affinities were increased 4–25-fold. The chlorinated sweetener and colon permeability marker sucralose (Splenda), otherwise undetectable by boronic acids, was dechlorinated to a detectable derivative by reactive oxygen and hydroxide intermediates by the Fenton reaction or by H2O2 and UV light. This method is specific to sucralose as other common sugars, such as sucrose, do not contain any carbon-chlorine bonds. Significant fluorescence response was obtained for chemically modified sucralose with the 4,4′-o-BBV–HPTS probe system. This proof of principle can be applied to biomedical applications, such as gut permeability, malabsorption, etc. PMID:29130464

Rapid prototyping of multi-scale biomedical microdevices by combining additive manufacturing technologies.

PubMed

Hengsbach, Stefan; Lantada, Andrés Díaz

2014-08-01

The possibility of designing and manufacturing biomedical microdevices with multiple length-scale geometries can help to promote special interactions both with their environment and with surrounding biological systems. These interactions aim to enhance biocompatibility and overall performance by using biomimetic approaches. In this paper, we present a design and manufacturing procedure for obtaining multi-scale biomedical microsystems based on the combination of two additive manufacturing processes: a conventional laser writer to manufacture the overall device structure, and a direct-laser writer based on two-photon polymerization to yield finer details. The process excels for its versatility, accuracy and manufacturing speed and allows for the manufacture of microsystems and implants with overall sizes up to several millimeters and with details down to sub-micrometric structures. As an application example we have focused on manufacturing a biomedical microsystem to analyze the impact of microtextured surfaces on cell motility. This process yielded a relevant increase in precision and manufacturing speed when compared with more conventional rapid prototyping procedures.

Journal selection decisions: a biomedical library operations research model. I. The framework.

PubMed Central

Kraft, D H; Polacsek, R A; Soergel, L; Burns, K; Klair, A

1976-01-01

The problem of deciding which journal titles to select for acquisition in a biomedical library is modeled. The approach taken is based on cost/benefit ratios. Measures of journal worth, methods of data collection, and journal cost data are considered. The emphasis is on the development of a practical process for selecting journal titles, based on the objectivity and rationality of the model; and on the collection of the approprate data and library statistics in a reasonable manner. The implications of this process towards an overall management information system (MIS) for biomedical serials handling are discussed. PMID:820391

Semantic biomedical resource discovery: a Natural Language Processing framework.

PubMed

Sfakianaki, Pepi; Koumakis, Lefteris; Sfakianakis, Stelios; Iatraki, Galatia; Zacharioudakis, Giorgos; Graf, Norbert; Marias, Kostas; Tsiknakis, Manolis

2015-09-30

A plethora of publicly available biomedical resources do currently exist and are constantly increasing at a fast rate. In parallel, specialized repositories are been developed, indexing numerous clinical and biomedical tools. The main drawback of such repositories is the difficulty in locating appropriate resources for a clinical or biomedical decision task, especially for non-Information Technology expert users. In parallel, although NLP research in the clinical domain has been active since the 1960s, progress in the development of NLP applications has been slow and lags behind progress in the general NLP domain. The aim of the present study is to investigate the use of semantics for biomedical resources annotation with domain specific ontologies and exploit Natural Language Processing methods in empowering the non-Information Technology expert users to efficiently search for biomedical resources using natural language. A Natural Language Processing engine which can "translate" free text into targeted queries, automatically transforming a clinical research question into a request description that contains only terms of ontologies, has been implemented. The implementation is based on information extraction techniques for text in natural language, guided by integrated ontologies. Furthermore, knowledge from robust text mining methods has been incorporated to map descriptions into suitable domain ontologies in order to ensure that the biomedical resources descriptions are domain oriented and enhance the accuracy of services discovery. The framework is freely available as a web application at ( http://calchas.ics.forth.gr/ ). For our experiments, a range of clinical questions were established based on descriptions of clinical trials from the ClinicalTrials.gov registry as well as recommendations from clinicians. Domain experts manually identified the available tools in a tools repository which are suitable for addressing the clinical questions at hand, either individually or as a set of tools forming a computational pipeline. The results were compared with those obtained from an automated discovery of candidate biomedical tools. For the evaluation of the results, precision and recall measurements were used. Our results indicate that the proposed framework has a high precision and low recall, implying that the system returns essentially more relevant results than irrelevant. There are adequate biomedical ontologies already available, sufficiency of existing NLP tools and quality of biomedical annotation systems for the implementation of a biomedical resources discovery framework, based on the semantic annotation of resources and the use on NLP techniques. The results of the present study demonstrate the clinical utility of the application of the proposed framework which aims to bridge the gap between clinical question in natural language and efficient dynamic biomedical resources discovery.

Segregation of biomedical waste in an South Indian tertiary care hospital

PubMed Central

Sengodan, Vetrivel Chezian

2014-01-01

Introduction: Hospital wastes pose significant public health hazard if not properly managed. Hence, it is necessary to develop and adopt optimal waste management systems in the hospitals. Material and method: Biomedical waste generated in Coimbatore Medical College Hospital was color coded (blue, yellow, and red) and the data was analyzed retrospectively on a daily basis for 3 years (January 2010-December 2012). Results: Effective segregation protocols significantly reduced biomedical waste generated from 2011 to 2012. While biomedical waste of red category was significantly higher (>50%), the category yellow was the least. Per unit (per bed per day) total biomedical waste generated was 68.5, 68.8, and 61.3 grams in 2010, 2011, and 2012, respectively. Discussion: Segregation of biomedical waste at the source of generation is the first and essential step in biomedical waste management. Continuous training, fixing the responsibility on the nursing persons, and constant supervision are the key criteria's in implementing biomedical waste segregation process, which can significantly reduce per unit biomedical waste generated. Conclusion: We highly recommend all hospitals to adopt our protocol and effectively implement them to reduce generation of biomedical waste. PMID:25097419

Judging The Efficacy of Anthrax Fumigations

DTIC Science & Technology

2003-11-20

FUMIGATIONS IN RESPONSE TO 2001 ANTHRAX ATTACKS Most fumigations modeled after biomedical sterilization processes, with established ranges for process...exposure to VHP All BIs recovered aseptically negative for growth of B. stearothermophilus Positive control BIs (5% of BIs) demonstrate growth Negative...of 10 zones was re-fumigated; second fumigation met all requirements HISTORICAL CRITERIA FOR SUCCESSFUL TREATMENT Biomedical sterilizations – FDA

Life sciences payloads analyses and technical program planning studies. [project planning of space missions of space shuttles in aerospace medicine and space biology

NASA Technical Reports Server (NTRS)

1976-01-01

Contractural requirements, project planning, equipment specifications, and technical data for space shuttle biological experiment payloads are presented. Topics discussed are: (1) urine collection and processing on the space shuttle, (2) space processing of biochemical and biomedical materials, (3) mission simulations, and (4) biomedical equipment.

Biomedically relevant chemical and physical properties of coal combustion products.

PubMed Central

Fisher, G L

1983-01-01

The evaluation of the potential public and occupational health hazards of developing and existing combustion processes requires a detailed understanding of the physical and chemical properties of effluents available for human and environmental exposures. These processes produce complex mixtures of gases and aerosols which may interact synergistically or antagonistically with biological systems. Because of the physicochemical complexity of the effluents, the biomedically relevant properties of these materials must be carefully assessed. Subsequent to release from combustion sources, environmental interactions further complicate assessment of the toxicity of combustion products. This report provides an overview of the biomedically relevant physical and chemical properties of coal fly ash. Coal fly ash is presented as a model complex mixture for health and safety evaluation of combustion processes. PMID:6337824

Severe Plastic Deformation of Commercial Pure Titanium (CP-Ti) for Biomedical Applications: A Brief Review

NASA Astrophysics Data System (ADS)

Mahmoodian, Reza; Annuar, N. Syahira M.; Faraji, Ghader; Bahar, Nadia Dayana; Razak, Bushroa Abd; Sparham, Mahdi

2017-11-01

This paper reviews severe plastic deformation (SPD) techniques for producing ultrafine-grained (UFG) and nanostructured commercial pure titanium (CP-Ti) for biomedical applications as the best alternative to titanium alloys. SPD processes, effective parameters, and advantages of nanostructured CP-Ti over coarse-grained (CG) material and Ti alloys are briefly reviewed. It is reported that nanostructured CP-Ti processed via SPD exhibits higher mechanical strength comparable to Ti alloys but better biological response and superior biocompatibility. Also, different surface modification techniques offer different results on UFG and CG CP-Ti, leading to nanoscale surface topography in UFG samples. Overall, it is reported that nanostructured CP-Ti processed by SPD could be considered to be the best candidate for biomedical implants.

Semantic annotation in biomedicine: the current landscape.

PubMed

Jovanović, Jelena; Bagheri, Ebrahim

2017-09-22

The abundance and unstructured nature of biomedical texts, be it clinical or research content, impose significant challenges for the effective and efficient use of information and knowledge stored in such texts. Annotation of biomedical documents with machine intelligible semantics facilitates advanced, semantics-based text management, curation, indexing, and search. This paper focuses on annotation of biomedical entity mentions with concepts from relevant biomedical knowledge bases such as UMLS. As a result, the meaning of those mentions is unambiguously and explicitly defined, and thus made readily available for automated processing. This process is widely known as semantic annotation, and the tools that perform it are known as semantic annotators.Over the last dozen years, the biomedical research community has invested significant efforts in the development of biomedical semantic annotation technology. Aiming to establish grounds for further developments in this area, we review a selected set of state of the art biomedical semantic annotators, focusing particularly on general purpose annotators, that is, semantic annotation tools that can be customized to work with texts from any area of biomedicine. We also examine potential directions for further improvements of today's annotators which could make them even more capable of meeting the needs of real-world applications. To motivate and encourage further developments in this area, along the suggested and/or related directions, we review existing and potential practical applications and benefits of semantic annotators.

Resolving complex research data management issues in biomedical laboratories: Qualitative study of an industry-academia collaboration.

PubMed

Myneni, Sahiti; Patel, Vimla L; Bova, G Steven; Wang, Jian; Ackerman, Christopher F; Berlinicke, Cynthia A; Chen, Steve H; Lindvall, Mikael; Zack, Donald J

2016-04-01

This paper describes a distributed collaborative effort between industry and academia to systematize data management in an academic biomedical laboratory. Heterogeneous and voluminous nature of research data created in biomedical laboratories make information management difficult and research unproductive. One such collaborative effort was evaluated over a period of four years using data collection methods including ethnographic observations, semi-structured interviews, web-based surveys, progress reports, conference call summaries, and face-to-face group discussions. Data were analyzed using qualitative methods of data analysis to (1) characterize specific problems faced by biomedical researchers with traditional information management practices, (2) identify intervention areas to introduce a new research information management system called Labmatrix, and finally to (3) evaluate and delineate important general collaboration (intervention) characteristics that can optimize outcomes of an implementation process in biomedical laboratories. Results emphasize the importance of end user perseverance, human-centric interoperability evaluation, and demonstration of return on investment of effort and time of laboratory members and industry personnel for success of implementation process. In addition, there is an intrinsic learning component associated with the implementation process of an information management system. Technology transfer experience in a complex environment such as the biomedical laboratory can be eased with use of information systems that support human and cognitive interoperability. Such informatics features can also contribute to successful collaboration and hopefully to scientific productivity. Copyright © 2015 Elsevier Ireland Ltd. All rights reserved.

A Low-Power and Portable Biomedical Device for Respiratory Monitoring with a Stable Power Source

PubMed Central

Yang, Jiachen; Chen, Bobo; Zhou, Jianxiong; Lv, Zhihan

2015-01-01

Continuous respiratory monitoring is an important tool for clinical monitoring. Associated with the development of biomedical technology, it has become more and more important, especially in the measuring of gas flow and CO2 concentration, which can reflect the status of the patient. In this paper, a new type of biomedical device is presented, which uses low-power sensors with a piezoresistive silicon differential pressure sensor to measure gas flow and with a pyroelectric sensor to measure CO2 concentration simultaneously. For the portability of the biomedical device, the sensors and low-power measurement circuits are integrated together, and the airway tube also needs to be miniaturized. Circuits are designed to ensure the stability of the power source and to filter out the existing noise. Modulation technology is used to eliminate the fluctuations at the trough of the waveform of the CO2 concentration signal. Statistical analysis with the coefficient of variation was performed to find out the optimal driving voltage of the pressure transducer. Through targeted experiments, the biomedical device showed a high accuracy, with a measuring precision of 0.23 mmHg, and it worked continuously and stably, thus realizing the real-time monitoring of the status of patients. PMID:26270665

Utilization of ontology look-up services in information retrieval for biomedical literature.

PubMed

Vishnyakova, Dina; Pasche, Emilie; Lovis, Christian; Ruch, Patrick

2013-01-01

With the vast amount of biomedical data we face the necessity to improve information retrieval processes in biomedical domain. The use of biomedical ontologies facilitated the combination of various data sources (e.g. scientific literature, clinical data repository) by increasing the quality of information retrieval and reducing the maintenance efforts. In this context, we developed Ontology Look-up services (OLS), based on NEWT and MeSH vocabularies. Our services were involved in some information retrieval tasks such as gene/disease normalization. The implementation of OLS services significantly accelerated the extraction of particular biomedical facts by structuring and enriching the data context. The results of precision in normalization tasks were boosted on about 20%.

A novel pseudo resistor structure for biomedical front-end amplifiers.

PubMed

Yu-Chieh Huang; Tzu-Sen Yang; Shun-Hsi Hsu; Xin-Zhuang Chen; Jin-Chern Chiou

2015-08-01

This study proposes a novel pseudo resistor structure with a tunable DC bias voltage for biomedical front-end amplifiers (FEAs). In the proposed FEA, the high-pass filter composed of differential difference amplifier and a pseudo resistor is implemented. The FEA is manufactured by using a standard TSMC 0.35 μm CMOS process. In this study, three types FEAs included three different pseudo resistor are simulated, fabricated and measured for comparison and electrocorticography (ECoG) measurement, and all the results show the proposed pseudo resistor is superior to other two types in bandwidth. In chip implementation, the lower and upper cutoff frequencies of the high-pass filter with the proposed pseudo resistor are 0.15 Hz and 4.98 KHz, respectively. It also demonstrates lower total harmonic distortion performance of -58 dB at 1 kHz and higher stability with wide supply range (1.8 V and 3.3 V) and control voltage range (0.9 V and 1.65 V) than others. Moreover, the FEA with the proposed pseudo successfully recorded spike-and-wave discharges of ECoG signal in in vivo experiment on rat with pentylenetetrazol-induced seizures.

E-Learning as New Method of Medical Education

PubMed Central

Masic, Izet

2008-01-01

CONFLICT OF INTEREST: NONE DECLARED Distance learning refers to use of technologies based on health care delivered on distance and covers areas such as electronic health, tele-health (e-health), telematics, telemedicine, tele-education, etc. For the need of e-health, telemedicine, tele-education and distance learning there are various technologies and communication systems from standard telephone lines to the system of transmission digitalized signals with modem, optical fiber, satellite links, wireless technologies, etc. Tele-education represents health education on distance, using Information Communication Technologies (ICT), as well as continuous education of a health system beneficiaries and use of electronic libraries, data bases or electronic data with data bases of knowledge. Distance learning (E-learning) as a part of tele-education has gained popularity in the past decade; however, its use is highly variable among medical schools and appears to be more common in basic medical science courses than in clinical education. Distance learning does not preclude traditional learning processes; frequently it is used in conjunction with in-person classroom or professional training procedures and practices. Tele-education has mostly been used in biomedical education as a blended learning method, which combines tele-education technology with traditional instructor-led training, where, for example, a lecture or demonstration is supplemented by an online tutorial. Distance learning is used for self-education, tests, services and for examinations in medicine i.e. in terms of self-education and individual examination services. The possibility of working in the exercise mode with image files and questions is an attractive way of self education. Automated tracking and reporting of learners’ activities lessen faculty administrative burden. Moreover, e-learning can be designed to include outcomes assessment to determine whether learning has occurred. This review article evaluates the current status and level of tele-education development in Bosnia and Herzegovina outlining its components, faculty development needs for implementation and the possibility of its integration as official learning standard in biomedical curricula in Bosnia and Herzegovina. Tele-education refers to the use of information and communication technologies (ICT) to enhance knowledge and performance. Tele-education in biomedical education is widely accepted in the medical education community where it is mostly integrated into biomedical curricula forming part of a blended learning strategy. There are many biomedical digital repositories of e-learning materials worldwide, some peer reviewed, where instructors or developers can submit materials for widespread use. First pilot project with the aim to introduce tele-education in biomedical curricula in Bosnia and Herzegovina was initiated by Department for Medical Informatics at Medical Faculty in Sarajevo in 2002 and has been developing since. Faculty member’s skills in creating tele-education differ from those needed for traditional teaching and faculty rewards must recognize this difference and reward the effort. Tele-education and use of computers will have an impact of future medical practice in a life long learning. Bologna process, which started last years in European countries, provide us to promote and introduce modern educational methods of education at biomedical faculties in Bosnia and Herzegovina. Cathedra of Medical informatics and Cathedra of Family medicine at Medical Faculty of University of Sarajevo started to use Web based education as common way of teaching of medical students. Satisfaction with this method of education within the students is good, but not yet suitable for most of medical disciplines at biomedical faculties in Bosnia and Herzegovina. PMID:24109154

E-learning as new method of medical education.

PubMed

Masic, Izet

2008-01-01

NONE DECLARED Distance learning refers to use of technologies based on health care delivered on distance and covers areas such as electronic health, tele-health (e-health), telematics, telemedicine, tele-education, etc. For the need of e-health, telemedicine, tele-education and distance learning there are various technologies and communication systems from standard telephone lines to the system of transmission digitalized signals with modem, optical fiber, satellite links, wireless technologies, etc. Tele-education represents health education on distance, using Information Communication Technologies (ICT), as well as continuous education of a health system beneficiaries and use of electronic libraries, data bases or electronic data with data bases of knowledge. Distance learning (E-learning) as a part of tele-education has gained popularity in the past decade; however, its use is highly variable among medical schools and appears to be more common in basic medical science courses than in clinical education. Distance learning does not preclude traditional learning processes; frequently it is used in conjunction with in-person classroom or professional training procedures and practices. Tele-education has mostly been used in biomedical education as a blended learning method, which combines tele-education technology with traditional instructor-led training, where, for example, a lecture or demonstration is supplemented by an online tutorial. Distance learning is used for self-education, tests, services and for examinations in medicine i.e. in terms of self-education and individual examination services. The possibility of working in the exercise mode with image files and questions is an attractive way of self education. Automated tracking and reporting of learners' activities lessen faculty administrative burden. Moreover, e-learning can be designed to include outcomes assessment to determine whether learning has occurred. This review article evaluates the current status and level of tele-education development in Bosnia and Herzegovina outlining its components, faculty development needs for implementation and the possibility of its integration as official learning standard in biomedical curricula in Bosnia and Herzegovina. Tele-education refers to the use of information and communication technologies (ICT) to enhance knowledge and performance. Tele-education in biomedical education is widely accepted in the medical education community where it is mostly integrated into biomedical curricula forming part of a blended learning strategy. There are many biomedical digital repositories of e-learning materials worldwide, some peer reviewed, where instructors or developers can submit materials for widespread use. First pilot project with the aim to introduce tele-education in biomedical curricula in Bosnia and Herzegovina was initiated by Department for Medical Informatics at Medical Faculty in Sarajevo in 2002 and has been developing since. Faculty member's skills in creating tele-education differ from those needed for traditional teaching and faculty rewards must recognize this difference and reward the effort. Tele-education and use of computers will have an impact of future medical practice in a life long learning. Bologna process, which started last years in European countries, provide us to promote and introduce modern educational methods of education at biomedical faculties in Bosnia and Herzegovina. Cathedra of Medical informatics and Cathedra of Family medicine at Medical Faculty of University of Sarajevo started to use Web based education as common way of teaching of medical students. Satisfaction with this method of education within the students is good, but not yet suitable for most of medical disciplines at biomedical faculties in Bosnia and Herzegovina.

Navigating the Path to a Biomedical Science Career

NASA Astrophysics Data System (ADS)

Zimmerman, Andrea McNeely

The number of biomedical PhD scientists being trained and graduated far exceeds the number of academic faculty positions and academic research jobs. If this trend is compelling biomedical PhD scientists to increasingly seek career paths outside of academia, then more should be known about their intentions, desires, training experiences, and career path navigation. Therefore, the purpose of this study was to understand the process through which biomedical PhD scientists are trained and supported for navigating future career paths. In addition, the study sought to determine whether career development support efforts and opportunities should be redesigned to account for the proportion of PhD scientists following non-academic career pathways. Guided by the social cognitive career theory (SCCT) framework this study sought to answer the following central research question: How does a southeastern tier 1 research university train and support its biomedical PhD scientists for navigating their career paths? Key findings are: Many factors influence PhD scientists' career sector preference and job search process, but the most influential were relationships with faculty, particularly the mentor advisor; Planned activities are a significant aspect of the training process and provide skills for career success; and Planned activities provided skills necessary for a career, but influential factors directed the career path navigated. Implications for practice and future research are discussed.

Mining biomedical images towards valuable information retrieval in biomedical and life sciences

PubMed Central

Ahmed, Zeeshan; Zeeshan, Saman; Dandekar, Thomas

2016-01-01

Biomedical images are helpful sources for the scientists and practitioners in drawing significant hypotheses, exemplifying approaches and describing experimental results in published biomedical literature. In last decades, there has been an enormous increase in the amount of heterogeneous biomedical image production and publication, which results in a need for bioimaging platforms for feature extraction and analysis of text and content in biomedical images to take advantage in implementing effective information retrieval systems. In this review, we summarize technologies related to data mining of figures. We describe and compare the potential of different approaches in terms of their developmental aspects, used methodologies, produced results, achieved accuracies and limitations. Our comparative conclusions include current challenges for bioimaging software with selective image mining, embedded text extraction and processing of complex natural language queries. PMID:27538578

Biomedical informatics and translational medicine.

PubMed

Sarkar, Indra Neil

2010-02-26

Biomedical informatics involves a core set of methodologies that can provide a foundation for crossing the "translational barriers" associated with translational medicine. To this end, the fundamental aspects of biomedical informatics (e.g., bioinformatics, imaging informatics, clinical informatics, and public health informatics) may be essential in helping improve the ability to bring basic research findings to the bedside, evaluate the efficacy of interventions across communities, and enable the assessment of the eventual impact of translational medicine innovations on health policies. Here, a brief description is provided for a selection of key biomedical informatics topics (Decision Support, Natural Language Processing, Standards, Information Retrieval, and Electronic Health Records) and their relevance to translational medicine. Based on contributions and advancements in each of these topic areas, the article proposes that biomedical informatics practitioners ("biomedical informaticians") can be essential members of translational medicine teams.

Integrating a Hypernymic Proposition Interpreter into a Semantic Processor for Biomedical Texts

PubMed Central

Fiszman, Marcelo; Rindflesch, Thomas C.; Kilicoglu, Halil

2003-01-01

Semantic processing provides the potential for producing high quality results in natural language processing (NLP) applications in the biomedical domain. In this paper, we address a specific semantic phenomenon, the hypernymic proposition, and concentrate on integrating the interpretation of such predications into a more general semantic processor in order to improve overall accuracy. A preliminary evaluation assesses the contribution of hypernymic propositions in providing more specific semantic predications and thus improving effectiveness in retrieving treatment propositions in MEDLINE abstracts. Finally, we discuss the generalization of this methodology to additional semantic propositions as well as other types of biomedical texts. PMID:14728170

Statistical performance evaluation of ECG transmission using wireless networks.

PubMed

Shakhatreh, Walid; Gharaibeh, Khaled; Al-Zaben, Awad

2013-07-01

This paper presents simulation of the transmission of biomedical signals (using ECG signal as an example) over wireless networks. Investigation of the effect of channel impairments including SNR, pathloss exponent, path delay and network impairments such as packet loss probability; on the diagnosability of the received ECG signal are presented. The ECG signal is transmitted through a wireless network system composed of two communication protocols; an 802.15.4- ZigBee protocol and an 802.11b protocol. The performance of the transmission is evaluated using higher order statistics parameters such as kurtosis and Negative Entropy in addition to the common techniques such as the PRD, RMS and Cross Correlation.

Multipurpose silicon photonics signal processor core.

PubMed

Pérez, Daniel; Gasulla, Ivana; Crudgington, Lee; Thomson, David J; Khokhar, Ali Z; Li, Ke; Cao, Wei; Mashanovich, Goran Z; Capmany, José

2017-09-21

Integrated photonics changes the scaling laws of information and communication systems offering architectural choices that combine photonics with electronics to optimize performance, power, footprint, and cost. Application-specific photonic integrated circuits, where particular circuits/chips are designed to optimally perform particular functionalities, require a considerable number of design and fabrication iterations leading to long development times. A different approach inspired by electronic Field Programmable Gate Arrays is the programmable photonic processor, where a common hardware implemented by a two-dimensional photonic waveguide mesh realizes different functionalities through programming. Here, we report the demonstration of such reconfigurable waveguide mesh in silicon. We demonstrate over 20 different functionalities with a simple seven hexagonal cell structure, which can be applied to different fields including communications, chemical and biomedical sensing, signal processing, multiprocessor networks, and quantum information systems. Our work is an important step toward this paradigm.Integrated optical circuits today are typically designed for a few special functionalities and require complex design and development procedures. Here, the authors demonstrate a reconfigurable but simple silicon waveguide mesh with different functionalities.

Optimal weighted averaging of event related activity from acquisitions with artifacts.

PubMed

Vollero, Luca; Petrichella, Sara; Innello, Giulio

2016-08-01

In several biomedical applications that require the signal processing of biological data, the starting procedure for noise reduction is the ensemble averaging of multiple repeated acquisitions (trials). This method is based on the assumption that each trial is composed of two additive components: (i) a time-locked activity related to some sensitive/stimulation phenomenon (ERA, Event Related Activity in the following) and (ii) a sum of several other non time-locked background activities. The averaging aims at estimating the ERA activity under very low Signal to Noise and Interference Ratio (SNIR). Although averaging is a well established tool, its performance can be improved in the presence of high-power disturbances (artifacts) by a trials classification and removal stage. In this paper we propose, model and evaluate a new approach that avoids trials removal, managing trials classified as artifact-free and artifact-prone with two different weights. Based on the model, a weights tuning is possible and through modeling and simulations we show that, when optimally configured, the proposed solution outperforms classical approaches.

Pseudo Asynchronous Level Crossing adc for ecg Signal Acquisition.

PubMed

Marisa, T; Niederhauser, T; Haeberlin, A; Wildhaber, R A; Vogel, R; Goette, J; Jacomet, M

2017-02-07

A new pseudo asynchronous level crossing analogue-to-digital converter (adc) architecture targeted for low-power, implantable, long-term biomedical sensing applications is presented. In contrast to most of the existing asynchronous level crossing adc designs, the proposed design has no digital-to-analogue converter (dac) and no continuous time comparators. Instead, the proposed architecture uses an analogue memory cell and dynamic comparators. The architecture retains the signal activity dependent sampling operation by generating events only when the input signal is changing. The architecture offers the advantages of smaller chip area, energy saving and fewer analogue system components. Beside lower energy consumption the use of dynamic comparators results in a more robust performance in noise conditions. Moreover, dynamic comparators make interfacing the asynchronous level crossing system to synchronous processing blocks simpler. The proposed adc was implemented in [Formula: see text] complementary metal-oxide-semiconductor (cmos) technology, the hardware occupies a chip area of 0.0372 mm 2 and operates from a supply voltage of [Formula: see text] to [Formula: see text]. The adc's power consumption is as low as 0.6 μW with signal bandwidth from [Formula: see text] to [Formula: see text] and achieves an equivalent number of bits (enob) of up to 8 bits.

Spatial and Functional Selectivity of Peripheral Nerve Signal Recording With the Transversal Intrafascicular Multichannel Electrode (TIME).

PubMed

Badia, Jordi; Raspopovic, Stanisa; Carpaneto, Jacopo; Micera, Silvestro; Navarro, Xavier

2016-01-01

The selection of suitable peripheral nerve electrodes for biomedical applications implies a trade-off between invasiveness and selectivity. The optimal design should provide the highest selectivity for targeting a large number of nerve fascicles with the least invasiveness and potential damage to the nerve. The transverse intrafascicular multichannel electrode (TIME), transversally inserted in the peripheral nerve, has been shown to be useful for the selective activation of subsets of axons, both at inter- and intra-fascicular levels, in the small sciatic nerve of the rat. In this study we assessed the capabilities of TIME for the selective recording of neural activity, considering the topographical selectivity and the distinction of neural signals corresponding to different sensory types. Topographical recording selectivity was proved by the differential recording of CNAPs from different subsets of nerve fibers, such as those innervating toes 2 and 4 of the hindpaw of the rat. Neural signals elicited by sensory stimuli applied to the rat paw were successfully recorded. Signal processing allowed distinguishing three different types of sensory stimuli such as tactile, proprioceptive and nociceptive ones with high performance. These findings further support the suitability of TIMEs for neuroprosthetic applications, by exploiting the transversal topographical structure of the peripheral nerves.

Empirical data on corpus design and usage in biomedical natural language processing.

PubMed

Cohen, K Bretonnel; Fox, Lynne; Ogren, Philip V; Hunter, Lawrence

2005-01-01

This paper describes the design of six publicly available biomedical corpora. We then present usage data for the six corpora. We show that corpora that are carefully annotated with respect to structural and linguistic characteristics and that are distributed in standard formats are more widely used than corpora that are not. These findings have implications for the design of the next generation of biomedical corpora.

SemaTyP: a knowledge graph based literature mining method for drug discovery.

PubMed

Sang, Shengtian; Yang, Zhihao; Wang, Lei; Liu, Xiaoxia; Lin, Hongfei; Wang, Jian

2018-05-30

Drug discovery is the process through which potential new medicines are identified. High-throughput screening and computer-aided drug discovery/design are the two main drug discovery methods for now, which have successfully discovered a series of drugs. However, development of new drugs is still an extremely time-consuming and expensive process. Biomedical literature contains important clues for the identification of potential treatments. It could support experts in biomedicine on their way towards new discoveries. Here, we propose a biomedical knowledge graph-based drug discovery method called SemaTyP, which discovers candidate drugs for diseases by mining published biomedical literature. We first construct a biomedical knowledge graph with the relations extracted from biomedical abstracts, then a logistic regression model is trained by learning the semantic types of paths of known drug therapies' existing in the biomedical knowledge graph, finally the learned model is used to discover drug therapies for new diseases. The experimental results show that our method could not only effectively discover new drug therapies for new diseases, but also could provide the potential mechanism of action of the candidate drugs. In this paper we propose a novel knowledge graph based literature mining method for drug discovery. It could be a supplementary method for current drug discovery methods.

A neural joint model for entity and relation extraction from biomedical text.

PubMed

Li, Fei; Zhang, Meishan; Fu, Guohong; Ji, Donghong

2017-03-31

Extracting biomedical entities and their relations from text has important applications on biomedical research. Previous work primarily utilized feature-based pipeline models to process this task. Many efforts need to be made on feature engineering when feature-based models are employed. Moreover, pipeline models may suffer error propagation and are not able to utilize the interactions between subtasks. Therefore, we propose a neural joint model to extract biomedical entities as well as their relations simultaneously, and it can alleviate the problems above. Our model was evaluated on two tasks, i.e., the task of extracting adverse drug events between drug and disease entities, and the task of extracting resident relations between bacteria and location entities. Compared with the state-of-the-art systems in these tasks, our model improved the F1 scores of the first task by 5.1% in entity recognition and 8.0% in relation extraction, and that of the second task by 9.2% in relation extraction. The proposed model achieves competitive performances with less work on feature engineering. We demonstrate that the model based on neural networks is effective for biomedical entity and relation extraction. In addition, parameter sharing is an alternative method for neural models to jointly process this task. Our work can facilitate the research on biomedical text mining.

An efficient sampling algorithm for uncertain abnormal data detection in biomedical image processing and disease prediction.

PubMed

Liu, Fei; Zhang, Xi; Jia, Yan

2015-01-01

In this paper, we propose a computer information processing algorithm that can be used for biomedical image processing and disease prediction. A biomedical image is considered a data object in a multi-dimensional space. Each dimension is a feature that can be used for disease diagnosis. We introduce a new concept of the top (k1,k2) outlier. It can be used to detect abnormal data objects in the multi-dimensional space. This technique focuses on uncertain space, where each data object has several possible instances with distinct probabilities. We design an efficient sampling algorithm for the top (k1,k2) outlier in uncertain space. Some improvement techniques are used for acceleration. Experiments show our methods' high accuracy and high efficiency.

Natural Language Processing Methods and Systems for Biomedical Ontology Learning

PubMed Central

Liu, Kaihong; Hogan, William R.; Crowley, Rebecca S.

2010-01-01

While the biomedical informatics community widely acknowledges the utility of domain ontologies, there remain many barriers to their effective use. One important requirement of domain ontologies is that they must achieve a high degree of coverage of the domain concepts and concept relationships. However, the development of these ontologies is typically a manual, time-consuming, and often error-prone process. Limited resources result in missing concepts and relationships as well as difficulty in updating the ontology as knowledge changes. Methodologies developed in the fields of natural language processing, information extraction, information retrieval and machine learning provide techniques for automating the enrichment of an ontology from free-text documents. In this article, we review existing methodologies and developed systems, and discuss how existing methods can benefit the development of biomedical ontologies. PMID:20647054

A Diagram Editor for Efficient Biomedical Knowledge Capture and Integration

PubMed Central

Yu, Bohua; Jakupovic, Elvis; Wilson, Justin; Dai, Manhong; Xuan, Weijian; Mirel, Barbara; Athey, Brian; Watson, Stanley; Meng, Fan

2008-01-01

Understanding the molecular mechanisms underlying complex disorders requires the integration of data and knowledge from different sources including free text literature and various biomedical databases. To facilitate this process, we created the Biomedical Concept Diagram Editor (BCDE) to help researchers distill knowledge from data and literature and aid the process of hypothesis development. A key feature of BCDE is the ability to capture information with a simple drag-and-drop. This is a vast improvement over manual methods of knowledge and data recording and greatly increases the efficiency of the biomedical researcher. BCDE also provides a unique concept matching function to enforce consistent terminology, which enables conceptual relationships deposited by different researchers in the BCDE database to be mined and integrated for intelligible and useful results. We hope BCDE will promote the sharing and integration of knowledge from different researchers for effective hypothesis development. PMID:21347131

47 CFR 15.3 - Definitions.

Code of Federal Regulations, 2014 CFR

2014-10-01

..., theater, auditorium, or educational institution. (b) Biomedical telemetry device. An intentional radiator... over the electric power lines. A carrier current system can be designed such that the signals are... provided with a separate band specifically designed to receive the transmissions of CB stations in the...

47 CFR 15.3 - Definitions.

Code of Federal Regulations, 2013 CFR

2013-10-01

..., theater, auditorium, or educational institution. (b) Biomedical telemetry device. An intentional radiator... over the electric power lines. A carrier current system can be designed such that the signals are... provided with a separate band specifically designed to receive the transmissions of CB stations in the...

Aggregated Indexing of Biomedical Time Series Data

PubMed Central

Woodbridge, Jonathan; Mortazavi, Bobak; Sarrafzadeh, Majid; Bui, Alex A.T.

2016-01-01

Remote and wearable medical sensing has the potential to create very large and high dimensional datasets. Medical time series databases must be able to efficiently store, index, and mine these datasets to enable medical professionals to effectively analyze data collected from their patients. Conventional high dimensional indexing methods are a two stage process. First, a superset of the true matches is efficiently extracted from the database. Second, supersets are pruned by comparing each of their objects to the query object and rejecting any objects falling outside a predetermined radius. This pruning stage heavily dominates the computational complexity of most conventional search algorithms. Therefore, indexing algorithms can be significantly improved by reducing the amount of pruning. This paper presents an online algorithm to aggregate biomedical times series data to significantly reduce the search space (index size) without compromising the quality of search results. This algorithm is built on the observation that biomedical time series signals are composed of cyclical and often similar patterns. This algorithm takes in a stream of segments and groups them to highly concentrated collections. Locality Sensitive Hashing (LSH) is used to reduce the overall complexity of the algorithm, allowing it to run online. The output of this aggregation is used to populate an index. The proposed algorithm yields logarithmic growth of the index (with respect to the total number of objects) while keeping sensitivity and specificity simultaneously above 98%. Both memory and runtime complexities of time series search are improved when using aggregated indexes. In addition, data mining tasks, such as clustering, exhibit runtimes that are orders of magnitudes faster when run on aggregated indexes. PMID:27617298

Biomedical image analysis and processing in clouds

NASA Astrophysics Data System (ADS)

Bednarz, Tomasz; Szul, Piotr; Arzhaeva, Yulia; Wang, Dadong; Burdett, Neil; Khassapov, Alex; Chen, Shiping; Vallotton, Pascal; Lagerstrom, Ryan; Gureyev, Tim; Taylor, John

2013-10-01

Cloud-based Image Analysis and Processing Toolbox project runs on the Australian National eResearch Collaboration Tools and Resources (NeCTAR) cloud infrastructure and allows access to biomedical image processing and analysis services to researchers via remotely accessible user interfaces. By providing user-friendly access to cloud computing resources and new workflow-based interfaces, our solution enables researchers to carry out various challenging image analysis and reconstruction tasks. Several case studies will be presented during the conference.

Optimization of wireless Bluetooth sensor systems.

PubMed

Lonnblad, J; Castano, J; Ekstrom, M; Linden, M; Backlund, Y

2004-01-01

Within this study, three different Bluetooth sensor systems, replacing cables for transmission of biomedical sensor data, have been designed and evaluated. The three sensor architectures are built on 1-, 2- and 3-chip solutions and depending on the monitoring situation and signal character, different solutions are optimal. Essential parameters for all systems have been low physical weight and small size, resistance to interference and interoperability with other technologies as global- or local networks, PC's and mobile phones. Two different biomedical input signals, ECG and PPG (photoplethysmography), have been used to evaluate the three solutions. The study shows that it is possibly to continuously transmit an analogue signal. At low sampling rates and slowly varying parameters, as monitoring the heart rate with PPG, the 1-chip solution is the most suitable, offering low power consumption and thus a longer battery lifetime or a smaller battery, minimizing the weight of the sensor system. On the other hand, when a higher sampling rate is required, as an ECG, the 3-chip architecture, with a FPGA or micro-controller, offers the best solution and performance. Our conclusion is that Bluetooth might be useful in replacing cables of medical monitoring systems.

Roles and applications of biomedical ontologies in experimental animal science.

PubMed

Masuya, Hiroshi

2012-01-01

A huge amount of experimental data from past studies has played a vital role in the development of new knowledge and technologies in biomedical science. The importance of computational technologies for the reuse of data, data integration, and knowledge discoveries has also increased, providing means of processing large amounts of data. In recent years, information technologies related to "ontologies" have played more significant roles in the standardization, integration, and knowledge representation of biomedical information. This review paper outlines the history of data integration in biomedical science and its recent trends in relation to the field of experimental animal science.

Mining biomedical images towards valuable information retrieval in biomedical and life sciences.

PubMed

Ahmed, Zeeshan; Zeeshan, Saman; Dandekar, Thomas

2016-01-01

Biomedical images are helpful sources for the scientists and practitioners in drawing significant hypotheses, exemplifying approaches and describing experimental results in published biomedical literature. In last decades, there has been an enormous increase in the amount of heterogeneous biomedical image production and publication, which results in a need for bioimaging platforms for feature extraction and analysis of text and content in biomedical images to take advantage in implementing effective information retrieval systems. In this review, we summarize technologies related to data mining of figures. We describe and compare the potential of different approaches in terms of their developmental aspects, used methodologies, produced results, achieved accuracies and limitations. Our comparative conclusions include current challenges for bioimaging software with selective image mining, embedded text extraction and processing of complex natural language queries. © The Author(s) 2016. Published by Oxford University Press.

Using Continuous Glucose Monitoring Data and Detrended Fluctuation Analysis to Determine Patient Condition

PubMed Central

Thomas, Felicity; Signal, Matthew; Chase, J. Geoffrey

2015-01-01

Patients admitted to critical care often experience dysglycemia and high levels of insulin resistance, various intensive insulin therapy protocols and methods have attempted to safely normalize blood glucose (BG) levels. Continuous glucose monitoring (CGM) devices allow glycemic dynamics to be captured much more frequently (every 2-5 minutes) than traditional measures of blood glucose and have begun to be used in critical care patients and neonates to help monitor dysglycemia. In an attempt to obtain a better insight relating biomedical signals and patient status, some researchers have turned toward advanced time series analysis methods. In particular, Detrended Fluctuation Analysis (DFA) has been a topic of many recent studies in to glycemic dynamics. DFA investigates the “complexity” of a signal, how one point in time changes relative to its neighboring points, and DFA has been applied to signals like the inter-beat-interval of human heartbeat to differentiate healthy and pathological conditions. Analyzing the glucose metabolic system with such signal processing tools as DFA has been enabled by the emergence of high quality CGM devices. However, there are several inconsistencies within the published work applying DFA to CGM signals. Therefore, this article presents a review and a “how-to” tutorial of DFA, and in particular its application to CGM signals to ensure the methods used to determine complexity are used correctly and so that any relationship between complexity and patient outcome is robust. PMID:26134835

Simulation of optical signaling among nano-bio-sensors: enhancing of bioimaging contrast.

PubMed

SalmanOgli, A; Behzadi, S; Rostami, A

2014-09-01

In this article, the nanoparticle-dye systems is designed and simulated to illustrate the possibility of enhancement in optical imaging contrast. For this, the firefly optimization technique is used as an optical signaling mechanism among agents (nanoparticle-dye) because fireflies attract together due to their flashing light and optical signaling that is produced by a process of bioluminescence (also it has been investigated that other parameters such as neural response and brain function have essential role in attracting fireflies to each other). The first parameter is coincided with our work, because the nanoparticle-dye systems have ability to augment of received light and its amplification cause that the designed complex system act as a brightness particle. This induced behavior of nanoparticles can be considered as an optical communication and signaling. Indeed by functionalization of nanoparticles and then due to higher brightness of the tumor site because of active targeting, the other particles can be guided to reach toward the target point and the signaling among agents is done by optical relation similar to firefly nature. Moreover, the fundamental of this work is the use of surface plasmon resonance and plasmons hybridization, in which photonic signals can be manipulated on the nanoscale and can be used in biomedical applications such as electromagnetic field enhancement. Finally, it can be mentioned that by simultaneously using plasmon hybridization, near-field augmentation, and firefly algorithm, the optical imaging contrast can be impressively improved.

BER balanced program plan: oil shale technology. [23 suggested biomedical and environmental research projects

DOE Office of Scientific and Technical Information (OSTI.GOV)

Schulte, H.F.; Stoker, A.K.; Campbell, E.E.

1976-06-01

Oil shale technology has been divided into two sub-technologies: surface processing and in-situ processing. Definition of the research programs is essentially an amplification of the five King-Muir categories: (A) pollutants: characterization, measurement, and monitoring; (B) physical and chemical processes and effects; (C) health effects; (D) ecological processes and effects; and (E) integrated assessment. Twenty-three biomedical and environmental research projects are described as to program title, scope, milestones, technolgy time frame, program unit priority, and estimated program unit cost.

Epigenomics and human adaptation to high altitude.

PubMed

Julian, Colleen G

2017-11-01

Over the past decade, major technological and analytical advancements have propelled efforts toward identifying the molecular mechanisms that govern human adaptation to high altitude. Despite remarkable progress with respect to the identification of adaptive genomic signals that are strongly associated with the "hypoxia-tolerant" physiological characteristics of high-altitude populations, many questions regarding the fundamental biological processes underlying human adaptation remain unanswered. Vital to address these enduring questions will be determining the role of epigenetic processes, or non-sequence-based features of the genome, that are not only critical for the regulation of transcriptional responses to hypoxia but heritable across generations. This review proposes that epigenomic processes are involved in shaping patterns of adaptation to high altitude by influencing adaptive potential and phenotypic variability under conditions of limited oxygen supply. Improved understanding of the interaction between genetic, epigenetic, and environmental factors holds great promise to provide deeper insight into the mechanisms underlying human adaptive potential, and clarify its implications for biomedical research. Copyright © 2017 the American Physiological Society.

[The processes of manuscript evaluation and publication in Medicina Clínica. The editorial committee of Medicina Clínica].

PubMed

Ribera, Josep M; Cardellach, Francesc; Selva, Albert

2005-12-01

The decision-making process includes a series of activities undertaken in biomedical journals from the moment a manuscript is received until it is accepted or rejected. Firstly, the manuscript is evaluated by the members of the Editorial Board, who analyze both its suitability for the journal and its scientific quality. After this initial evaluation, the article is evaluated by peer reviewers, an essential process to guarantee its scientific validity. Both the Editorial Board and the peer reviewers usually use checklists which are of enormous help in this task. Once the biomedical article has been accepted, the publication process is started, which in turn includes a series of steps, beginning with technical and medical review of the article's contents and ending with the article's publication in the journal. The present article provides a detailed description of the main technical and ethical issues involved in the processes of decision-making and publication of biomedical articles.

Exploiting Novel Calcium-Mediated Apoptotic Processes for the Treatment of Human Breast Cancers with Elevated Nqo1 Levels

DTIC Science & Technology

2008-03-01

in β-Lapachone-induced programmed necrosis. Biomedical Graduate Student Symposium, Case Western Reserve University, Cleveland, OH April 2002-06.  o...of β- lapachone-induced cell death” October, 2005  o The Vance Lemmon Poster Award, Biomedical Graduate Student Symposium, Case Western Reserve...cell death” April, 2005  o The Marcus Singer Poster Award, Biomedical Graduate Student Symposium, Case Western Reserve University, Cleveland, OH for

High-Density Stretchable Electrode Grids for Chronic Neural Recording.

PubMed

Tybrandt, Klas; Khodagholy, Dion; Dielacher, Bernd; Stauffer, Flurin; Renz, Aline F; Buzsáki, György; Vörös, János

2018-04-01

Electrical interfacing with neural tissue is key to advancing diagnosis and therapies for neurological disorders, as well as providing detailed information about neural signals. A challenge for creating long-term stable interfaces between electronics and neural tissue is the huge mechanical mismatch between the systems. So far, materials and fabrication processes have restricted the development of soft electrode grids able to combine high performance, long-term stability, and high electrode density, aspects all essential for neural interfacing. Here, this challenge is addressed by developing a soft, high-density, stretchable electrode grid based on an inert, high-performance composite material comprising gold-coated titanium dioxide nanowires embedded in a silicone matrix. The developed grid can resolve high spatiotemporal neural signals from the surface of the cortex in freely moving rats with stable neural recording quality and preserved electrode signal coherence during 3 months of implantation. Due to its flexible and stretchable nature, it is possible to minimize the size of the craniotomy required for placement, further reducing the level of invasiveness. The material and device technology presented herein have potential for a wide range of emerging biomedical applications. © 2018 The Authors. Published by WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

A simple encoding method for Sigma-Delta ADC based biopotential acquisition systems.

PubMed

Guerrero, Federico N; Spinelli, Enrique M

2017-10-01

Sigma Delta analogue-to-digital converters allow acquiring the full dynamic range of biomedical signals at the electrodes, resulting in less complex hardware and increased measurement robustness. However, the increased data size per sample (typically 24 bits) demands the transmission of extremely large volumes of data across the isolation barrier, thus increasing power consumption on the patient side. This problem is accentuated when a large number of channels is used as in current 128-256 electrodes biopotential acquisition systems, that usually opt for an optic fibre link to the computer. An analogous problem occurs for simpler low-power acquisition platforms that transmit data through a wireless link to a computing platform. In this paper, a low-complexity encoding method is presented to decrease sample data size without losses, while preserving the full DC-coupled signal. The method achieved a 2.3 average compression ratio evaluated over an ECG and EMG signal bank acquired with equipment based on Sigma-Delta converters. It demands a very low processing load: a C language implementation is presented that resulted in an 110 clock cycles average execution on an 8-bit microcontroller.

A study on a portable fluorescence imaging system

NASA Astrophysics Data System (ADS)

Chang, Han-Chao; Wu, Wen-Hong; Chang, Chun-Li; Huang, Kuo-Cheng; Chang, Chung-Hsing; Chiu, Shang-Chen

2011-09-01

The fluorescent reaction is that an organism or dye, excited by UV light (200-405 nm), emits a specific frequency of light; the light is usually a visible or near infrared light (405-900 nm). During the UV light irradiation, the photosensitive agent will be induced to start the photochemical reaction. In addition, the fluorescence image can be used for fluorescence diagnosis and then photodynamic therapy can be given to dental diseases and skin cancer, which has become a useful tool to provide scientific evidence in many biomedical researches. However, most of the methods on acquiring fluorescence biology traces are still stay in primitive stage, catching by naked eyes and researcher's subjective judgment. This article presents a portable camera to obtain the fluorescence image and to make up a deficit from observer competence and subjective judgment. Furthermore, the portable camera offers the 375nm UV-LED exciting light source for user to record fluorescence image and makes the recorded image become persuasive scientific evidence. In addition, when the raising the rate between signal and noise, the signal processing module will not only amplify the fluorescence signal up to 70 %, but also decrease the noise significantly from environmental light on bill and nude mouse testing.

A signal processing based analysis and prediction of seizure onset in patients with epilepsy

PubMed Central

Namazi, Hamidreza; Kulish, Vladimir V.

2016-01-01

One of the main areas of behavioural neuroscience is forecasting the human behaviour. Epilepsy is a central nervous system disorder in which nerve cell activity in the brain becomes disrupted, causing seizures or periods of unusual behaviour, sensations and sometimes loss of consciousness. An estimated 5% of the world population has epileptic seizure but there is not any method to cure it. More than 30% of people with epilepsy cannot control seizure. Epileptic seizure prediction, refers to forecasting the occurrence of epileptic seizures, is one of the most important but challenging problems in biomedical sciences, across the world. In this research we propose a new methodology which is based on studying the EEG signals using two measures, the Hurst exponent and fractal dimension. In order to validate the proposed method, it is applied to epileptic EEG signals of patients by computing the Hurst exponent and fractal dimension, and then the results are validated versus the reference data. The results of these analyses show that we are able to forecast the onset of a seizure on average of 25.76 seconds before the time of occurrence. PMID:26586477

Environmental scanning electron microscopy gold immunolabeling in cell biology.

PubMed

Rosso, Francesco; Papale, Ferdinando; Barbarisi, Alfonso

2013-01-01

Immunogold labeling (IGL) technique has been utilized by many authors in combination with scanning electron microscopy (SEM) and transmission electron microscopy (TEM) to obtain the identification/localization of receptors and antigens, both in cells and tissues. Environmental scanning electron microscopy (ESEM) represents an important tool in biomedical research, since it does not require any severe processing of the sample, lowering the risk of generating artifacts and interfere with the IGL procedure. The absence of metal coating could yield further advantages for our purpose as the labeling detection is based on the atomic number difference between nanogold spheres and the biological material. Using the gaseous secondary electron detector, compositional contrast is easily revealed by the backscattered electron component of the signal. In spite of this fact, only few published papers present a combination of ESEM and IGL. Hereby we present our method, optimized to improve the intensity and the specificity of the labeling signal, in order to obtain a semiquantitative evaluation of the labeling signal.In particular, we used a combination of IGL and ESEM to detect the presence of a protein on the cell surface. To achieve this purpose, we chose as an experimental system 3T3 Swiss albino mouse fibroblasts and galectin-3.

A Machine Learning Approach to the Detection of Pilot's Reaction to Unexpected Events Based on EEG Signals

PubMed Central

Cyran, Krzysztof A.

2018-01-01

This work considers the problem of utilizing electroencephalographic signals for use in systems designed for monitoring and enhancing the performance of aircraft pilots. Systems with such capabilities are generally referred to as cognitive cockpits. This article provides a description of the potential that is carried by such systems, especially in terms of increasing flight safety. Additionally, a neuropsychological background of the problem is presented. Conducted research was focused mainly on the problem of discrimination between states of brain activity related to idle but focused anticipation of visual cue and reaction to it. Especially, a problem of selecting a proper classification algorithm for such problems is being examined. For that purpose an experiment involving 10 subjects was planned and conducted. Experimental electroencephalographic data was acquired using an Emotiv EPOC+ headset. Proposed methodology involved use of a popular method in biomedical signal processing, the Common Spatial Pattern, extraction of bandpower features, and an extensive test of different classification algorithms, such as Linear Discriminant Analysis, k-nearest neighbors, and Support Vector Machines with linear and radial basis function kernels, Random Forests, and Artificial Neural Networks. PMID:29849544

Health care innovation: progress report and focus on biotechnology.

PubMed

Read, J L; Lee, K B

1994-01-01

Funding for biomedical research has shifted from government to the private sector. One reason is rapid expansion in the number and strength of U.S. biotechnology companies, which collectively spend more than $6 billion a year on biomedical research. Most of these companies are not yet profitable and therefore depend on flows of capital from private investors, Wall Street, and large pharmaceutical company collaborations. Investment in the new drugs, devices, and vaccines in this pipeline is sensitive to signals emanating from the debate on health care reform, suggesting that new federal policy will have a major impact on steering the type of innovation to emerge in the future.

Hybridization of biomedical circuitry

NASA Technical Reports Server (NTRS)

Rinard, G. A.

1978-01-01

The design and fabrication of low power hybrid circuits to perform vital signs monitoring are reported. The circuits consist of: (1) clock; (2) ECG amplifier and cardiotachometer signal conditioner; (3) impedance pneumobraph and respiration rate processor; (4) hear/breath rate processor; (5) temperature monitor; and (6) LCD display.

WIH-based IEEE 802.11 ECG monitoring implementation.

PubMed

Moein, A; Pouladian, M

2007-01-01

New wireless technologies make possible the implementation of high level integration wireless devices which allow the replacement of traditional large wired monitoring devices. It offers new functionalities to physicians and will reduce the costs. Among these functionalities, biomedical signals can be sent to other devices (PDA, PC . . . ) or processing centers, without restricting the patients' mobility. This article discusses the WIH (Ward-In-Hand) structure and the software required for its implementation before an operational example is presented with its results. The aim of this project is the development and implementation of a reduced size electrocardiograph based on IEEE 802.11 with high speed and more accuracy, which allows wireless monitoring of patients, and the insertion of the information into the Wi-Fi hospital networks.

Novel Tactile Sensor Technology and Smart Tactile Sensing Systems: A Review

PubMed Central

Ge, Chang; Wang, Z. Jane; Cretu, Edmond; Li, Xiaoou

2017-01-01

During the last decades, smart tactile sensing systems based on different sensing techniques have been developed due to their high potential in industry and biomedical engineering. However, smart tactile sensing technologies and systems are still in their infancy, as many technological and system issues remain unresolved and require strong interdisciplinary efforts to address them. This paper provides an overview of smart tactile sensing systems, with a focus on signal processing technologies used to interpret the measured information from tactile sensors and/or sensors for other sensory modalities. The tactile sensing transduction and principles, fabrication and structures are also discussed with their merits and demerits. Finally, the challenges that tactile sensing technology needs to overcome are highlighted. PMID:29149080

Using CamiTK for rapid prototyping of interactive computer assisted medical intervention applications.

PubMed

Promayon, Emmanuel; Fouard, Céline; Bailet, Mathieu; Deram, Aurélien; Fiard, Gaëlle; Hungr, Nikolai; Luboz, Vincent; Payan, Yohan; Sarrazin, Johan; Saubat, Nicolas; Selmi, Sonia Yuki; Voros, Sandrine; Cinquin, Philippe; Troccaz, Jocelyne

2013-01-01

Computer Assisted Medical Intervention (CAMI hereafter) is a complex multi-disciplinary field. CAMI research requires the collaboration of experts in several fields as diverse as medicine, computer science, mathematics, instrumentation, signal processing, mechanics, modeling, automatics, optics, etc. CamiTK is a modular framework that helps researchers and clinicians to collaborate together in order to prototype CAMI applications by regrouping the knowledge and expertise from each discipline. It is an open-source, cross-platform generic and modular tool written in C++ which can handle medical images, surgical navigation, biomedicals simulations and robot control. This paper presents the Computer Assisted Medical Intervention ToolKit (CamiTK) and how it is used in various applications in our research team.

Overview of Drosophila immunity: a historical perspective.

PubMed

Imler, Jean-Luc

2014-01-01

The functional analysis of genes from the model organism Drosophila melanogaster has provided invaluable information for many cellular and developmental or physiological processes, including immunity. The best-understood aspect of Drosophila immunity is the inducible humoral response, first recognized in 1972. This pioneering work led to a remarkable series of findings over the next 30 years, ranging from the identification and characterization of the antimicrobial peptides produced, to the deciphering of the signalling pathways activating the genes that encode them and, ultimately, to the discovery of the receptors sensing infection. These studies on an insect model coincided with a revival of the field of innate immunity, and had an unanticipated impact on the biomedical field. Copyright © 2013 Elsevier Ltd. All rights reserved.

Genome network medicine: innovation to overcome huge challenges in cancer therapy.

PubMed

Roukos, Dimitrios H

2014-01-01

The post-ENCODE era shapes now a new biomedical research direction for understanding transcriptional and signaling networks driving gene expression and core cellular processes such as cell fate, survival, and apoptosis. Over the past half century, the Francis Crick 'central dogma' of single n gene/protein-phenotype (trait/disease) has defined biology, human physiology, disease, diagnostics, and drugs discovery. However, the ENCODE project and several other genomic studies using high-throughput sequencing technologies, computational strategies, and imaging techniques to visualize regulatory networks, provide evidence that transcriptional process and gene expression are regulated by highly complex dynamic molecular and signaling networks. This Focus article describes the linear experimentation-based limitations of diagnostics and therapeutics to cure advanced cancer and the need to move on from reductionist to network-based approaches. With evident a wide genomic heterogeneity, the power and challenges of next-generation sequencing (NGS) technologies to identify a patient's personal mutational landscape for tailoring the best target drugs in the individual patient are discussed. However, the available drugs are not capable of targeting aberrant signaling networks and research on functional transcriptional heterogeneity and functional genome organization is poorly understood. Therefore, the future clinical genome network medicine aiming at overcoming multiple problems in the new fields of regulatory DNA mapping, noncoding RNA, enhancer RNAs, and dynamic complexity of transcriptional circuitry are also discussed expecting in new innovation technology and strong appreciation of clinical data and evidence-based medicine. The problematic and potential solutions in the discovery of next-generation, molecular, and signaling circuitry-based biomarkers and drugs are explored. © 2013 Wiley Periodicals, Inc.

Electrode-electrolyte interface model of tripolar concentric ring electrode and electrode paste.

PubMed

Nasrollaholhosseini, Seyed Hadi; Steele, Preston; Besio, Walter G

2016-08-01

Electrodes are used to transform ionic currents to electrical currents in biological systems. Modeling the electrode-electrolyte interface could help to optimize the performance of the electrode interface to achieve higher signal to noise ratios. There are previous reports of accurate models for single-element biomedical electrodes. In this paper we develop a model for the electrode-electrolyte interface for tripolar concentric ring electrodes (TCRE) that are used to record brain signals.

Balanced program plan: analysis for biomedical and environmental research. Volume 5. Oil shale technology

DOE Office of Scientific and Technical Information (OSTI.GOV)

Not Available

1976-06-01

Oil shale technology has been divided into two sub-technologies: surfaceprocessing and in-situ processing. Definition of the research programs is essentially an amplification of the five King-Muir categories: (A) pollutants: characterization, measurement, and monitoring; (B) physical and chemical processes and effects; (C) health effects; (D) ecological processes and effects; and (E) integrated assessment. Twenty-three biomedical and environmental research projects are described as to program title, scope, milestones, technology time frame, program unit priority, and estimated program unit cost.

Specificity of Good Manufacturing Practice (GMP) for Biomedical Cell Products.

PubMed

Tulina, M A; Pyatigorskaya, N V

2018-03-01

The article describes special aspects of Good Manufacturing Practice (GMP) for biomedical cell products (BMCP) that imply high standards of aseptics throughout the entire productio process, strict requirements to donors and to the procedure of biomaterial isolation, guaranty of tracing BMCP products, defining processing procedures which allow to identify BMCP as minimally manipulated; continuous quality control and automation of the control process at all stages of manufacturing, which will ensure product release simultaneously with completion of technological operations.

Markov Chain Monte Carlo Inference of Parametric Dictionaries for Sparse Bayesian Approximations

PubMed Central

Chaspari, Theodora; Tsiartas, Andreas; Tsilifis, Panagiotis; Narayanan, Shrikanth

2016-01-01

Parametric dictionaries can increase the ability of sparse representations to meaningfully capture and interpret the underlying signal information, such as encountered in biomedical problems. Given a mapping function from the atom parameter space to the actual atoms, we propose a sparse Bayesian framework for learning the atom parameters, because of its ability to provide full posterior estimates, take uncertainty into account and generalize on unseen data. Inference is performed with Markov Chain Monte Carlo, that uses block sampling to generate the variables of the Bayesian problem. Since the parameterization of dictionary atoms results in posteriors that cannot be analytically computed, we use a Metropolis-Hastings-within-Gibbs framework, according to which variables with closed-form posteriors are generated with the Gibbs sampler, while the remaining ones with the Metropolis Hastings from appropriate candidate-generating densities. We further show that the corresponding Markov Chain is uniformly ergodic ensuring its convergence to a stationary distribution independently of the initial state. Results on synthetic data and real biomedical signals indicate that our approach offers advantages in terms of signal reconstruction compared to previously proposed Steepest Descent and Equiangular Tight Frame methods. This paper demonstrates the ability of Bayesian learning to generate parametric dictionaries that can reliably represent the exemplar data and provides the foundation towards inferring the entire variable set of the sparse approximation problem for signal denoising, adaptation and other applications. PMID:28649173

A Pilot Study of Biomedical Text Comprehension using an Attention-Based Deep Neural Reader: Design and Experimental Analysis

PubMed Central

Lee, Kyubum; Kim, Byounggun; Jeon, Minji; Kim, Jihye; Tan, Aik Choon

2018-01-01

Background With the development of artificial intelligence (AI) technology centered on deep-learning, the computer has evolved to a point where it can read a given text and answer a question based on the context of the text. Such a specific task is known as the task of machine comprehension. Existing machine comprehension tasks mostly use datasets of general texts, such as news articles or elementary school-level storybooks. However, no attempt has been made to determine whether an up-to-date deep learning-based machine comprehension model can also process scientific literature containing expert-level knowledge, especially in the biomedical domain. Objective This study aims to investigate whether a machine comprehension model can process biomedical articles as well as general texts. Since there is no dataset for the biomedical literature comprehension task, our work includes generating a large-scale question answering dataset using PubMed and manually evaluating the generated dataset. Methods We present an attention-based deep neural model tailored to the biomedical domain. To further enhance the performance of our model, we used a pretrained word vector and biomedical entity type embedding. We also developed an ensemble method of combining the results of several independent models to reduce the variance of the answers from the models. Results The experimental results showed that our proposed deep neural network model outperformed the baseline model by more than 7% on the new dataset. We also evaluated human performance on the new dataset. The human evaluation result showed that our deep neural model outperformed humans in comprehension by 22% on average. Conclusions In this work, we introduced a new task of machine comprehension in the biomedical domain using a deep neural model. Since there was no large-scale dataset for training deep neural models in the biomedical domain, we created the new cloze-style datasets Biomedical Knowledge Comprehension Title (BMKC_T) and Biomedical Knowledge Comprehension Last Sentence (BMKC_LS) (together referred to as BioMedical Knowledge Comprehension) using the PubMed corpus. The experimental results showed that the performance of our model is much higher than that of humans. We observed that our model performed consistently better regardless of the degree of difficulty of a text, whereas humans have difficulty when performing biomedical literature comprehension tasks that require expert level knowledge. PMID:29305341

Fabrication of three-dimensional collagen scaffold using an inverse mould-leaching process.

PubMed

Ahn, SeungHyun; Lee, SuYeon; Cho, Youngseok; Chun, Wook; Kim, GeunHyung

2011-09-01

Natural biopolymers, such as collagen or chitosan, are considered ideal for biomedical scaffolds. However, low processability of the materials has hindered the fabrication of designed pore structures controlled by various solid freeform-fabrication methods. A new technique to fabricate a biomedical three-dimensional collagen scaffold, supplemented with a sacrificial poly(ethylene oxide) mould is proposed. The fabricated collagen scaffold shows a highly porous surface and a three-dimensional structure with high porosity as well as mechanically stable structure. To show its feasibility for biomedical applications, fibroblasts/keratinocytes were co-cultured on the scaffold, and the cell proliferation and cell migration of the scaffold was more favorable than that obtained with a spongy-type collagen scaffold.

Recent progress and challenges in nanotechnology for biomedical applications: an insight into the analysis of neurotransmitters.

PubMed

Shankaran, Dhesingh Ravi; Miura, Norio

2007-01-01

Nanotechnology offers exciting opportunities and unprecedented compatibilities in manipulating chemical and biological materials at the atomic or molecular scale for the development of novel functional materials with enhanced capabilities. It plays a central role in the recent technological advances in biomedical technology, especially in the areas of disease diagnosis, drug design and drug delivery. In this review, we present the recent trend and challenges in the development of nanomaterials for biomedical applications with a special emphasis on the analysis of neurotransmitters. Neurotransmitters are the chemical messengers which transform information and signals all over the body. They play prime role in functioning of the central nervous system (CNS) and governs most of the metabolic functions including movement, pleasure, pain, mood, emotion, thinking, digestion, sleep, addiction, fear, anxiety and depression. Thus, development of high-performance and user-friendly analytical methods for ultra-sensitive detection of neurotransmitters remain a major challenge in modern biomedical analysis. Nanostructured materials are emerging as a powerful mean for diagnosis of CNS disorders because of their unique optical, size and surface characteristics. This review provides a brief outline on the basic concepts and recent advancements of nanotechnology for biomedical applications, especially in the analysis of neurotransmitters. A brief introduction to the nanomaterials, bionanotechnology and neurotransmitters is also included along with discussions on most of the patents published in these areas.

Detection of Practice Pattern Trends through Natural Language Processing of Clinical Narratives and Biomedical Literature

PubMed Central

Chen, Elizabeth S.; Stetson, Peter D.; Lussier, Yves A.; Markatou, Marianthi; Hripcsak, George; Friedman, Carol

2007-01-01

Clinical knowledge, best evidence, and practice patterns evolve over time. The ability to track these changes and study practice trends may be valuable for performance measurement and quality improvement efforts. The goal of this study was to assess the feasibility and validity of methods to generate and compare trends in biomedical literature and clinical narrative. We focused on the challenge of detecting trends in medication usage over time for two diseases: HIV/AIDS and asthma. Information about disease-specific medications in published randomized control trials and discharge summaries at New York-Presbyterian Hospital over a ten-year period were extracted using Natural Language Processing. This paper reports on the ability of our semi-automated process to discover disease-drug practice pattern trends and interpretation of findings across the biomedical and clinical text sources. PMID:18693810

A knowledge-driven approach to biomedical document conceptualization.

PubMed

Zheng, Hai-Tao; Borchert, Charles; Jiang, Yong

2010-06-01

Biomedical document conceptualization is the process of clustering biomedical documents based on ontology-represented domain knowledge. The result of this process is the representation of the biomedical documents by a set of key concepts and their relationships. Most of clustering methods cluster documents based on invariant domain knowledge. The objective of this work is to develop an effective method to cluster biomedical documents based on various user-specified ontologies, so that users can exploit the concept structures of documents more effectively. We develop a flexible framework to allow users to specify the knowledge bases, in the form of ontologies. Based on the user-specified ontologies, we develop a key concept induction algorithm, which uses latent semantic analysis to identify key concepts and cluster documents. A corpus-related ontology generation algorithm is developed to generate the concept structures of documents. Based on two biomedical datasets, we evaluate the proposed method and five other clustering algorithms. The clustering results of the proposed method outperform the five other algorithms, in terms of key concept identification. With respect to the first biomedical dataset, our method has the F-measure values 0.7294 and 0.5294 based on the MeSH ontology and gene ontology (GO), respectively. With respect to the second biomedical dataset, our method has the F-measure values 0.6751 and 0.6746 based on the MeSH ontology and GO, respectively. Both results outperforms the five other algorithms in terms of F-measure. Based on the MeSH ontology and GO, the generated corpus-related ontologies show informative conceptual structures. The proposed method enables users to specify the domain knowledge to exploit the conceptual structures of biomedical document collections. In addition, the proposed method is able to extract the key concepts and cluster the documents with a relatively high precision. Copyright 2010 Elsevier B.V. All rights reserved.

Performance of a novel wafer scale CMOS active pixel sensor for bio-medical imaging.

PubMed

Esposito, M; Anaxagoras, T; Konstantinidis, A C; Zheng, Y; Speller, R D; Evans, P M; Allinson, N M; Wells, K

2014-07-07

Recently CMOS active pixels sensors (APSs) have become a valuable alternative to amorphous silicon and selenium flat panel imagers (FPIs) in bio-medical imaging applications. CMOS APSs can now be scaled up to the standard 20 cm diameter wafer size by means of a reticle stitching block process. However, despite wafer scale CMOS APS being monolithic, sources of non-uniformity of response and regional variations can persist representing a significant challenge for wafer scale sensor response. Non-uniformity of stitched sensors can arise from a number of factors related to the manufacturing process, including variation of amplification, variation between readout components, wafer defects and process variations across the wafer due to manufacturing processes. This paper reports on an investigation into the spatial non-uniformity and regional variations of a wafer scale stitched CMOS APS. For the first time a per-pixel analysis of the electro-optical performance of a wafer CMOS APS is presented, to address inhomogeneity issues arising from the stitching techniques used to manufacture wafer scale sensors. A complete model of the signal generation in the pixel array has been provided and proved capable of accounting for noise and gain variations across the pixel array. This novel analysis leads to readout noise and conversion gain being evaluated at pixel level, stitching block level and in regions of interest, resulting in a coefficient of variation ⩽1.9%. The uniformity of the image quality performance has been further investigated in a typical x-ray application, i.e. mammography, showing a uniformity in terms of CNR among the highest when compared with mammography detectors commonly used in clinical practice. Finally, in order to compare the detection capability of this novel APS with the technology currently used (i.e. FPIs), theoretical evaluation of the detection quantum efficiency (DQE) at zero-frequency has been performed, resulting in a higher DQE for this detector compared to FPIs. Optical characterization, x-ray contrast measurements and theoretical DQE evaluation suggest that a trade off can be found between the need of a large imaging area and the requirement of a uniform imaging performance, making the DynAMITe large area CMOS APS suitable for a range of bio-medical applications.

Smart Polymeric Gels: Redefining the Limits of Biomedical Devices.

PubMed

Chaterji, Somali; Kwon, Il Keun; Park, Kinam

2007-08-01

This review describes recent progresses in the development and applications of smart polymeric gels, especially in the context of biomedical devices. The review has been organized into three separate sections: defining the basis of smart properties in polymeric gels; describing representative stimuli to which these gels respond; and illustrating a sample application area, namely, microfluidics. One of the major limitations in the use of hydrogels in stimuli-responsive applications is the diffusion rate limited transduction of signals. This can be obviated by engineering interconnected pores in the polymer structure to form capillary networks in the matrix and by downscaling the size of hydrogels to significantly decrease diffusion paths. Reducing the lag time in the induction of smart responses can be highly useful in biomedical devices, such as sensors and actuators. This review also describes molecular imprinting techniques to fabricate hydrogels for specific molecular recognition of target analytes. Additionally, it describes the significant advances in bottom-up nanofabrication strategies, involving supramolecular chemistry. Learning to assemble supramolecular structures from nature has led to the rapid prototyping of functional supramolecular devices. In essence, the barriers in the current performance potential of biomedical devices can be lowered or removed by the rapid convergence of interdisciplinary technologies.

Smart Polymeric Gels: Redefining the Limits of Biomedical Devices

PubMed Central

Chaterji, Somali; Kwon, Il Keun; Park, Kinam

2007-01-01

This review describes recent progresses in the development and applications of smart polymeric gels, especially in the context of biomedical devices. The review has been organized into three separate sections: defining the basis of smart properties in polymeric gels; describing representative stimuli to which these gels respond; and illustrating a sample application area, namely, microfluidics. One of the major limitations in the use of hydrogels in stimuli–responsive applications is the diffusion rate limited transduction of signals. This can be obviated by engineering interconnected pores in the polymer structure to form capillary networks in the matrix and by downscaling the size of hydrogels to significantly decrease diffusion paths. Reducing the lag time in the induction of smart responses can be highly useful in biomedical devices, such as sensors and actuators. This review also describes molecular imprinting techniques to fabricate hydrogels for specific molecular recognition of target analytes. Additionally, it describes the significant advances in bottom–up nanofabrication strategies, involving supramolecular chemistry. Learning to assemble supramolecular structures from nature has led to the rapid prototyping of functional supramolecular devices. In essence, the barriers in the current performance potential of biomedical devices can be lowered or removed by the rapid convergence of interdisciplinary technologies. PMID:18670584

A survey of quality assurance practices in biomedical open source software projects.

PubMed

Koru, Günes; El Emam, Khaled; Neisa, Angelica; Umarji, Medha

2007-05-07

Open source (OS) software is continuously gaining recognition and use in the biomedical domain, for example, in health informatics and bioinformatics. Given the mission critical nature of applications in this domain and their potential impact on patient safety, it is important to understand to what degree and how effectively biomedical OS developers perform standard quality assurance (QA) activities such as peer reviews and testing. This would allow the users of biomedical OS software to better understand the quality risks, if any, and the developers to identify process improvement opportunities to produce higher quality software. A survey of developers working on biomedical OS projects was conducted to examine the QA activities that are performed. We took a descriptive approach to summarize the implementation of QA activities and then examined some of the factors that may be related to the implementation of such practices. Our descriptive results show that 63% (95% CI, 54-72) of projects did not include peer reviews in their development process, while 82% (95% CI, 75-89) did include testing. Approximately 74% (95% CI, 67-81) of developers did not have a background in computing, 80% (95% CI, 74-87) were paid for their contributions to the project, and 52% (95% CI, 43-60) had PhDs. A multivariate logistic regression model to predict the implementation of peer reviews was not significant (likelihood ratio test = 16.86, 9 df, P = .051) and neither was a model to predict the implementation of testing (likelihood ratio test = 3.34, 9 df, P = .95). Less attention is paid to peer review than testing. However, the former is a complementary, and necessary, QA practice rather than an alternative. Therefore, one can argue that there are quality risks, at least at this point in time, in transitioning biomedical OS software into any critical settings that may have operational, financial, or safety implications. Developers of biomedical OS applications should invest more effort in implementing systemic peer review practices throughout the development and maintenance processes.

New Software Developments for Quality Mesh Generation and Optimization from Biomedical Imaging Data

PubMed Central

Yu, Zeyun; Wang, Jun; Gao, Zhanheng; Xu, Ming; Hoshijima, Masahiko

2013-01-01

In this paper we present a new software toolkit for generating and optimizing surface and volumetric meshes from three-dimensional (3D) biomedical imaging data, targeted at image-based finite element analysis of some biomedical activities in a single material domain. Our toolkit includes a series of geometric processing algorithms including surface re-meshing and quality-guaranteed tetrahedral mesh generation and optimization. All methods described have been encapsulated into a user-friendly graphical interface for easy manipulation and informative visualization of biomedical images and mesh models. Numerous examples are presented to demonstrate the effectiveness and efficiency of the described methods and toolkit. PMID:24252469

NASA Biomedical Informatics Capabilities and Needs

NASA Technical Reports Server (NTRS)

Johnson-Throop, Kathy A.

2009-01-01

To improve on-orbit clinical capabilities by developing and providing operational support for intelligent, robust, reliable, and secure, enterprise-wide and comprehensive health care and biomedical informatics systems with increasing levels of autonomy, for use on Earth, low Earth orbit & exploration class missions. Biomedical Informatics is an emerging discipline that has been defined as the study, invention, and implementation of structures and algorithms to improve communication, understanding and management of medical information. The end objective of biomedical informatics is the coalescing of data, knowledge, and the tools necessary to apply that data and knowledge in the decision-making process, at the time and place that a decision needs to be made.

A Novel Biomedical Device Utilizing Light Emitting Nano-Structures

NASA Technical Reports Server (NTRS)

Varaljay, Vanessa A.

2004-01-01

This paper will discuss the development of a novel biomedical detection device that will be used to detect microorganisms with the use of infrared fluorochrome polymers attached to antibodies in fluids such as water. The fluorochrome polymers emit light in the near inferred region (NIR), approximately 805 nm, when excited by an NIR laser at 778 nm. The device could remarkably change the way laboratory testing is done today. The testing process is usually performed on a time scale of days while our device will be able to detect microorganisms in minutes. This type of time efficient analysis is ideal for use aboard the International Space Station and the Space Shuttle (ISS/SS) and has many useful commercial applications, for instance at a water treatment plant and food processing plants. With more research and experimentation the testing might also one day be used to detect bacteria and viruses in complex fluids such as blood, which would revolutionize blood analysis as it is performed today. My contribution to the project has been to develop a process which will allow an antibody/fluorescent dye pair to be conjugated to a specific bacteria or virus and than to to be separated from a sample body of water for detection. The antibody being used in this experiment is anti beta galactosidase and its complement enzyme is beta galactosidase, a non harmful derivative of E. Coli. The anti beta galactosidase has been conjugated to the fluorochrome polymer, IRDye800, which emits at approximately 806 nm. The dye when excited by the NIR laser emits a signal which is detected by a spectrometer and then is read by state of the art computer software. The state-of-the-art process includes incubating the anti beta galactosidase and beta galactosidase in a phosphate buffer solution in a test tube, allowing the antibody to bind to specific sites on the enzyme. After the antibody is bound to the enzyme, it is centrifuged in specific filters that will allow free antibody to wash away and leave the antibody-enzyme complexes on top in solution for testing and analysis. This solution is pipetted into a cuvette, a special plastic test tube, which will then be excited by the laser. The signal read will tell US that an antibody is present and since it is bound to the enzyme, that the bacteria is also present.

What Do I Want to Be with My PhD? The Roles of Personal Values and Structural Dynamics in Shaping the Career Interests of Recent Biomedical Science PhD Graduates

ERIC Educational Resources Information Center

Gibbs, Kenneth D., Jr.; Griffin, Kimberly A.

2013-01-01

Interest in faculty careers decreases as graduate training progresses; however, the process underlying career-interest formation remains poorly defined. To better understand this process and whether/how it differs across social identity (i.e., race/ethnicity, gender), we conducted focus groups with 38 biomedical scientists who received PhDs…

The next generation of similarity measures that fully explore the semantics in biomedical ontologies.

PubMed

Couto, Francisco M; Pinto, H Sofia

2013-10-01

There is a prominent trend to augment and improve the formality of biomedical ontologies. For example, this is shown by the current effort on adding description logic axioms, such as disjointness. One of the key ontology applications that can take advantage of this effort is the conceptual (functional) similarity measurement. The presence of description logic axioms in biomedical ontologies make the current structural or extensional approaches weaker and further away from providing sound semantics-based similarity measures. Although beneficial in small ontologies, the exploration of description logic axioms by semantics-based similarity measures is computational expensive. This limitation is critical for biomedical ontologies that normally contain thousands of concepts. Thus in the process of gaining their rightful place, biomedical functional similarity measures have to take the journey of finding how this rich and powerful knowledge can be fully explored while keeping feasible computational costs. This manuscript aims at promoting and guiding the development of compelling tools that deliver what the biomedical community will require in a near future: a next-generation of biomedical similarity measures that efficiently and fully explore the semantics present in biomedical ontologies.

Outlook for Development of High-throughput Cryopreservation for Small-bodied Biomedical Model Fishes★

PubMed Central

Tiersch, Terrence R.; Yang, Huiping; Hu, E.

2011-01-01

With the development of genomic research technologies, comparative genome studies among vertebrate species are becoming commonplace for human biomedical research. Fish offer unlimited versatility for biomedical research. Extensive studies are done using these fish models, yielding tens of thousands of specific strains and lines, and the number is increasing every day. Thus, high-throughput sperm cryopreservation is urgently needed to preserve these genetic resources. Although high-throughput processing has been widely applied for sperm cryopreservation in livestock for decades, application in biomedical model fishes is still in the concept-development stage because of the limited sample volumes and the biological characteristics of fish sperm. High-throughput processing in livestock was developed based on advances made in the laboratory and was scaled up for increased processing speed, capability for mass production, and uniformity and quality assurance. Cryopreserved germplasm combined with high-throughput processing constitutes an independent industry encompassing animal breeding, preservation of genetic diversity, and medical research. Currently, there is no specifically engineered system available for high-throughput of cryopreserved germplasm for aquatic species. This review is to discuss the concepts and needs for high-throughput technology for model fishes, propose approaches for technical development, and overview future directions of this approach. PMID:21440666

Biotelemetry system for ambulatory patients

NASA Technical Reports Server (NTRS)

Fryer, T. B.

1978-01-01

Compact transmitter for multichannel telemetry of medical data is carried in patient's belt. Pulse-code modulation (PCM), is used for high-quality signal, and low-power CMOS integrated circuits make miniaturization possible. Transmitter is useful for electro-encephalograms (EEG) and electro-cardiograms (EKG) and other biomedical patient-monitoring situations.

An Ontology-Enabled Natural Language Processing Pipeline for Provenance Metadata Extraction from Biomedical Text (Short Paper).

PubMed

Valdez, Joshua; Rueschman, Michael; Kim, Matthew; Redline, Susan; Sahoo, Satya S

2016-10-01

Extraction of structured information from biomedical literature is a complex and challenging problem due to the complexity of biomedical domain and lack of appropriate natural language processing (NLP) techniques. High quality domain ontologies model both data and metadata information at a fine level of granularity, which can be effectively used to accurately extract structured information from biomedical text. Extraction of provenance metadata, which describes the history or source of information, from published articles is an important task to support scientific reproducibility. Reproducibility of results reported by previous research studies is a foundational component of scientific advancement. This is highlighted by the recent initiative by the US National Institutes of Health called "Principles of Rigor and Reproducibility". In this paper, we describe an effective approach to extract provenance metadata from published biomedical research literature using an ontology-enabled NLP platform as part of the Provenance for Clinical and Healthcare Research (ProvCaRe). The ProvCaRe-NLP tool extends the clinical Text Analysis and Knowledge Extraction System (cTAKES) platform using both provenance and biomedical domain ontologies. We demonstrate the effectiveness of ProvCaRe-NLP tool using a corpus of 20 peer-reviewed publications. The results of our evaluation demonstrate that the ProvCaRe-NLP tool has significantly higher recall in extracting provenance metadata as compared to existing NLP pipelines such as MetaMap.

[Big data, medical language and biomedical terminology systems].

PubMed

Schulz, Stefan; López-García, Pablo

2015-08-01

A variety of rich terminology systems, such as thesauri, classifications, nomenclatures and ontologies support information and knowledge processing in health care and biomedical research. Nevertheless, human language, manifested as individually written texts, persists as the primary carrier of information, in the description of disease courses or treatment episodes in electronic medical records, and in the description of biomedical research in scientific publications. In the context of the discussion about big data in biomedicine, we hypothesize that the abstraction of the individuality of natural language utterances into structured and semantically normalized information facilitates the use of statistical data analytics to distil new knowledge out of textual data from biomedical research and clinical routine. Computerized human language technologies are constantly evolving and are increasingly ready to annotate narratives with codes from biomedical terminology. However, this depends heavily on linguistic and terminological resources. The creation and maintenance of such resources is labor-intensive. Nevertheless, it is sensible to assume that big data methods can be used to support this process. Examples include the learning of hierarchical relationships, the grouping of synonymous terms into concepts and the disambiguation of homonyms. Although clear evidence is still lacking, the combination of natural language technologies, semantic resources, and big data analytics is promising.

The Screening of Genes Sensitive to Long-Term, Low-Level Microwave Exposure and Bioinformatic Analysis of Potential Correlations to Learning and Memory.

PubMed

Zhao, Ya Li; Li, Ying Xian; Ma, Hong Bo; Li, Dong; Li, Hai Liang; Jiang, Rui; Kan, Guang Han; Yang, Zhen Zhong; Huang, Zeng Xin

2015-08-01

To gain a better understanding of gene expression changes in the brain following microwave exposure in mice. This study hopes to reveal mechanisms contributing to microwave-induced learning and memory dysfunction. Mice were exposed to whole body 2100 MHz microwaves with specific absorption rates (SARs) of 0.45 W/kg, 1.8 W/kg, and 3.6 W/kg for 1 hour daily for 8 weeks. Differentially expressing genes in the brains were screened using high-density oligonucleotide arrays, with genes showing more significant differences further confirmed by RT-PCR. The gene chip results demonstrated that 41 genes (0.45 W/kg group), 29 genes (1.8 W/kg group), and 219 genes (3.6 W/kg group) were differentially expressed. GO analysis revealed that these differentially expressed genes were primarily involved in metabolic processes, cellular metabolic processes, regulation of biological processes, macromolecular metabolic processes, biosynthetic processes, cellular protein metabolic processes, transport, developmental processes, cellular component organization, etc. KEGG pathway analysis showed that these genes are mainly involved in pathways related to ribosome, Alzheimer's disease, Parkinson's disease, long-term potentiation, Huntington's disease, and Neurotrophin signaling. Construction of a protein interaction network identified several important regulatory genes including synbindin (sbdn), Crystallin (CryaB), PPP1CA, Ywhaq, Psap, Psmb1, Pcbp2, etc., which play important roles in the processes of learning and memorye. Long-term, low-level microwave exposure may inhibit learning and memory by affecting protein and energy metabolic processes and signaling pathways relating to neurological functions or diseases. Copyright © 2015 The Editorial Board of Biomedical and Environmental Sciences. Published by China CDC. All rights reserved.

Excision Repair-Initiated Enzyme-Assisted Bicyclic Cascade Signal Amplification for Ultrasensitive Detection of Uracil-DNA Glycosylase.

PubMed

Wang, Li-Juan; Ren, Ming; Zhang, Qianyi; Tang, Bo; Zhang, Chun-Yang

2017-04-18

Uracil-DNA glycosylase (UDG) is an important base excision repair (BER) enzyme responsible for the repair of uracil-induced DNA lesion and the maintenance of genomic integrity, while the aberrant expression of UDG is associated with a variety of cancers. Thus, the accurate detection of UDG activity is essential to biomedical research and clinical diagnosis. Here, we develop a fluorescent method for ultrasensitive detection of UDG activity using excision repair-initiated enzyme-assisted bicyclic cascade signal amplification. This assay involves (1) UDG-actuated uracil-excision repair, (2) excision repair-initiated nicking enzyme-mediated isothermal exponential amplification, (3) ribonuclease H (RNase H)-induced hydrolysis of signal probes for generating fluorescence signal. The presence of UDG enables the removal of uracil from U·A pairs and generates an apurinic/apyrimidinic (AP) site. Endonuclease IV (Endo IV) subsequently cleaves the AP site, resulting in the break of DNA substrate. The cleaved DNA substrate functions as both a primer and a template to initiate isothermal exponential amplification, producing a large number of triggers. The resultant trigger may selectively hybridize with the signal probe which is modified with FAM and BHQ1, forming a RNA-DNA heterogeneous duplex. The subsequent hydrolysis of RNA-DNA duplex by RNase H leads to the generation of fluorescence signal. This assay exhibits ultrahigh sensitivity with a detection limit of 0.0001 U/mL, and it can even measure UDG activity at the single-cell level. Moreover, this method can be applied for the measurement of kinetic parameters and the screening of inhibitors, thereby providing a powerful tool for DNA repair enzyme-related biomedical research and clinical diagnosis.

Aging and the complexity of cardiovascular dynamics

NASA Technical Reports Server (NTRS)

Kaplan, D. T.; Furman, M. I.; Pincus, S. M.; Ryan, S. M.; Lipsitz, L. A.; Goldberger, A. L.

1991-01-01

Biomedical signals often vary in a complex and irregular manner. Analysis of variability in such signals generally does not address directly their complexity, and so may miss potentially useful information. We analyze the complexity of heart rate and beat-to-beat blood pressure using two methods motivated by nonlinear dynamics (chaos theory). A comparison of a group of healthy elderly subjects with healthy young adults indicates that the complexity of cardiovascular dynamics is reduced with aging. This suggests that complexity of variability may be a useful physiological marker.

A 6.45 μW Self-Powered SoC With Integrated Energy-Harvesting Power Management and ULP Asymmetric Radios for Portable Biomedical Systems.

PubMed

Roy, Abhishek; Klinefelter, Alicia; Yahya, Farah B; Chen, Xing; Gonzalez-Guerrero, Luisa Patricia; Lukas, Christopher J; Kamakshi, Divya Akella; Boley, James; Craig, Kyle; Faisal, Muhammad; Oh, Seunghyun; Roberts, Nathan E; Shakhsheer, Yousef; Shrivastava, Aatmesh; Vasudevan, Dilip P; Wentzloff, David D; Calhoun, Benton H

2015-12-01

This paper presents a batteryless system-on-chip (SoC) that operates off energy harvested from indoor solar cells and/or thermoelectric generators (TEGs) on the body. Fabricated in a commercial 0.13 μW process, this SoC sensing platform consists of an integrated energy harvesting and power management unit (EH-PMU) with maximum power point tracking, multiple sensing modalities, programmable core and a low power microcontroller with several hardware accelerators to enable energy-efficient digital signal processing, ultra-low-power (ULP) asymmetric radios for wireless transmission, and a 100 nW wake-up radio. The EH-PMU achieves a peak end-to-end efficiency of 75% delivering power to a 100 μA load. In an example motion detection application, the SoC reads data from an accelerometer through SPI, processes it, and sends it over the radio. The SPI and digital processing consume only 2.27 μW, while the integrated radio consumes 4.18 μW when transmitting at 187.5 kbps for a total of 6.45 μW.

Elucidation of the mechanisms of optical clearing in collagen tissue with multiphoton imaging

NASA Astrophysics Data System (ADS)

Hovhannisyan, Vladimir; Hu, Po-Sheng; Chen, Shean-Jen; Kim, Chang-Seok; Dong, Chen-Yuan

2013-04-01

Optical clearing (OC) is a promising method to overcome limitations in biomedical depth-resolved optical studies. Mechanisms of OC in purified bovine Achilles tendon, chicken skin, and chicken tendon were studied using time-lapsed, three-dimensional second harmonic generation (SHG) and two-photon fluorescence microscopic imaging. Quantified nonlinear optical measurements allowed temporal separation of two processes in collagen OC with glycerol. The first one is a fast process of tissue dehydration accompanied with collagen shrinkage and the second relatively slow process is glycerol penetration into the interfibrillar space of collagen alongside with CF swelling. The use of 50% glycerol induced less-expressed OC via partial substitution of water molecules with glycerol molecules. We also found that phosphate-buffered saline- and glycerol-treatments were reversible, and fiber morphology and SHG signal intensity were recovered after the removal of immersion agents. It was shown that tissue OC was a dynamic process and elucidation of its physical mechanisms may help choose optimal diagnostic, treatment, and modification regimes for collagen-based as well as other types of biomaterials.

Performance and scalability of Fourier domain optical coherence tomography acceleration using graphics processing units.

PubMed

Li, Jian; Bloch, Pavel; Xu, Jing; Sarunic, Marinko V; Shannon, Lesley

2011-05-01

Fourier domain optical coherence tomography (FD-OCT) provides faster line rates, better resolution, and higher sensitivity for noninvasive, in vivo biomedical imaging compared to traditional time domain OCT (TD-OCT). However, because the signal processing for FD-OCT is computationally intensive, real-time FD-OCT applications demand powerful computing platforms to deliver acceptable performance. Graphics processing units (GPUs) have been used as coprocessors to accelerate FD-OCT by leveraging their relatively simple programming model to exploit thread-level parallelism. Unfortunately, GPUs do not "share" memory with their host processors, requiring additional data transfers between the GPU and CPU. In this paper, we implement a complete FD-OCT accelerator on a consumer grade GPU/CPU platform. Our data acquisition system uses spectrometer-based detection and a dual-arm interferometer topology with numerical dispersion compensation for retinal imaging. We demonstrate that the maximum line rate is dictated by the memory transfer time and not the processing time due to the GPU platform's memory model. Finally, we discuss how the performance trends of GPU-based accelerators compare to the expected future requirements of FD-OCT data rates.

New low-level a-c amplifier provides adjustable noise cancellation and automatic temperature compensation

NASA Technical Reports Server (NTRS)

Smith, J. R., Jr.

1964-01-01

Circuit utilizing a transistorized differential amplifier is developed for biomedical use. This low voltage operating circuit provides adjustable cancellation at the input for unbalanced noise signals, and automatic temperature compensation is accomplished by a single active element across the input-output ends.

SOA-based digital library services and composition in biomedical applications.

PubMed

Zhao, Xia; Liu, Enjie; Clapworthy, Gordon J; Viceconti, Marco; Testi, Debora

2012-06-01

Carefully collected, high-quality data are crucial in biomedical visualization, and it is important that the user community has ready access to both this data and the high-performance computing resources needed by the complex, computational algorithms that will process it. Biological researchers generally require data, tools and algorithms from multiple providers to achieve their goals. This paper illustrates our response to the problems that result from this. The Living Human Digital Library (LHDL) project presented in this paper has taken advantage of Web Services to build a biomedical digital library infrastructure that allows clinicians and researchers not only to preserve, trace and share data resources, but also to collaborate at the data-processing level. Copyright © 2010 Elsevier Ireland Ltd. All rights reserved.

eHealth and IMIA's Strategic Planning Process - IMIA conference introductory address.

PubMed

Murray, Peter; Haux, Reinhold; Lorenzi, Nancy

2008-01-01

The International Medical Informatics Association (IMIA) is the only organization in health and biomedical informatics which is fully international in scope, bridging the academic, health practice, education, and health industry worlds through conferences, working groups, special interest groups and publications. Authored by the IMIA Interim Vice President for Strategic Planning Implementation and co-authored by the current IMIA President and the IMIA Past-President, the intention of this paper is to introduce IMIA's current strategic planning process and to set this process in relation to 'eHealth: Combining Health Telematics, Telemedicine, Biomedical Engineering and Bioinformatics to the Edge', the theme of this conference. From the viewpoint of an international organization such as IMIA, an eHealth strategy needs to be considered in a comprehensive way, including broadly stimulating high-quality health and biomedical informatics research and education, as well as providing support to bridging outcomes towards a new practice of health care in a changing world.

Supervised Learning Based Hypothesis Generation from Biomedical Literature.

PubMed

Sang, Shengtian; Yang, Zhihao; Li, Zongyao; Lin, Hongfei

2015-01-01

Nowadays, the amount of biomedical literatures is growing at an explosive speed, and there is much useful knowledge undiscovered in this literature. Researchers can form biomedical hypotheses through mining these works. In this paper, we propose a supervised learning based approach to generate hypotheses from biomedical literature. This approach splits the traditional processing of hypothesis generation with classic ABC model into AB model and BC model which are constructed with supervised learning method. Compared with the concept cooccurrence and grammar engineering-based approaches like SemRep, machine learning based models usually can achieve better performance in information extraction (IE) from texts. Then through combining the two models, the approach reconstructs the ABC model and generates biomedical hypotheses from literature. The experimental results on the three classic Swanson hypotheses show that our approach outperforms SemRep system.

Cloud Based Metalearning System for Predictive Modeling of Biomedical Data

PubMed Central

Vukićević, Milan

2014-01-01

Rapid growth and storage of biomedical data enabled many opportunities for predictive modeling and improvement of healthcare processes. On the other side analysis of such large amounts of data is a difficult and computationally intensive task for most existing data mining algorithms. This problem is addressed by proposing a cloud based system that integrates metalearning framework for ranking and selection of best predictive algorithms for data at hand and open source big data technologies for analysis of biomedical data. PMID:24892101

A heuristic method for simulating open-data of arbitrary complexity that can be used to compare and evaluate machine learning methods.

PubMed

Moore, Jason H; Shestov, Maksim; Schmitt, Peter; Olson, Randal S

2018-01-01

A central challenge of developing and evaluating artificial intelligence and machine learning methods for regression and classification is access to data that illuminates the strengths and weaknesses of different methods. Open data plays an important role in this process by making it easy for computational researchers to easily access real data for this purpose. Genomics has in some examples taken a leading role in the open data effort starting with DNA microarrays. While real data from experimental and observational studies is necessary for developing computational methods it is not sufficient. This is because it is not possible to know what the ground truth is in real data. This must be accompanied by simulated data where that balance between signal and noise is known and can be directly evaluated. Unfortunately, there is a lack of methods and software for simulating data with the kind of complexity found in real biological and biomedical systems. We present here the Heuristic Identification of Biological Architectures for simulating Complex Hierarchical Interactions (HIBACHI) method and prototype software for simulating complex biological and biomedical data. Further, we introduce new methods for developing simulation models that generate data that specifically allows discrimination between different machine learning methods.

Case-based statistical learning applied to SPECT image classification

NASA Astrophysics Data System (ADS)

Górriz, Juan M.; Ramírez, Javier; Illán, I. A.; Martínez-Murcia, Francisco J.; Segovia, Fermín.; Salas-Gonzalez, Diego; Ortiz, A.

2017-03-01

Statistical learning and decision theory play a key role in many areas of science and engineering. Some examples include time series regression and prediction, optical character recognition, signal detection in communications or biomedical applications for diagnosis and prognosis. This paper deals with the topic of learning from biomedical image data in the classification problem. In a typical scenario we have a training set that is employed to fit a prediction model or learner and a testing set on which the learner is applied to in order to predict the outcome for new unseen patterns. Both processes are usually completely separated to avoid over-fitting and due to the fact that, in practice, the unseen new objects (testing set) have unknown outcomes. However, the outcome yields one of a discrete set of values, i.e. the binary diagnosis problem. Thus, assumptions on these outcome values could be established to obtain the most likely prediction model at the training stage, that could improve the overall classification accuracy on the testing set, or keep its performance at least at the level of the selected statistical classifier. In this sense, a novel case-based learning (c-learning) procedure is proposed which combines hypothesis testing from a discrete set of expected outcomes and a cross-validated classification stage.

Biomedical Implications of Heavy Metals Induced Imbalances in Redox Systems

PubMed Central

Singh, Shweta; Siddiqi, Nikhat J.

2014-01-01

Several workers have extensively worked out the metal induced toxicity and have reported the toxic and carcinogenic effects of metals in human and animals. It is well known that these metals play a crucial role in facilitating normal biological functions of cells as well. One of the major mechanisms associated with heavy metal toxicity has been attributed to generation of reactive oxygen and nitrogen species, which develops imbalance between the prooxidant elements and the antioxidants (reducing elements) in the body. In this process, a shift to the former is termed as oxidative stress. The oxidative stress mediated toxicity of heavy metals involves damage primarily to liver (hepatotoxicity), central nervous system (neurotoxicity), DNA (genotoxicity), and kidney (nephrotoxicity) in animals and humans. Heavy metals are reported to impact signaling cascade and associated factors leading to apoptosis. The present review illustrates an account of the current knowledge about the effects of heavy metals (mainly arsenic, lead, mercury, and cadmium) induced oxidative stress as well as the possible remedies of metal(s) toxicity through natural/synthetic antioxidants, which may render their effects by reducing the concentration of toxic metal(s). This paper primarily concerns the clinicopathological and biomedical implications of heavy metals induced oxidative stress and their toxicity management in mammals. PMID:25184144

Synthesis, properties, and biomedical applications of gelatin methacryloyl (GelMA) hydrogels

PubMed Central

Yue, Kan; Santiago, Grissel Trujillo-de; Alvarez, Mario Moisés; Tamayol, Ali; Annabi, Nasim; Khademhosseini, Ali

2015-01-01

Gelatin methacryloyl (GelMA) hydrogels have been widely used for various biomedical applications due to their suitable biological properties and tunable physical characteristics. Three dimensional (3D) GelMA hydrogels closely resemble some essential properties of native extracellular matrix (ECM) due to the presence of cell-attaching and matrix metalloproteinase responsive peptide motifs, which allow cells to proliferate and spread in GelMA-based scaffolds. GelMA is also versatile from a processing perspective. It crosslinks when exposed to light irradiation to form hydrogels with tunable mechanical properties which mimic the native ECM. It can also be microfabricated using different methodologies including micromolding, photomasking, bioprinting, self-assembly, and microfluidic techniques to generate constructs with controlled architectures. Hybrid hydrogel systems can also be formed by mixing GelMA with nanoparticles such as carbon nanotubes and graphene oxide, and other polymers to form networks with desired combined properties and characteristics for specific biological applications. Recent research has demonstrated the proficiency of GelMA-based hydrogels in a wide range of applications including engineering of bone, cartilage, cardiac, and vascular tissues, among others. Other applications of GelMA hydrogels, besides tissue engineering, include fundamental single-single cell research, cell signaling, drug and gene delivery, and bio-sensing. PMID:26414409

Bioinformatics and Medical Informatics: Collaborations on the Road to Genomic Medicine?

PubMed Central

Maojo, Victor; Kulikowski, Casimir A.

2003-01-01

In this report, the authors compare and contrast medical informatics (MI) and bioinformatics (BI) and provide a viewpoint on their complementarities and potential for collaboration in various subfields. The authors compare MI and BI along several dimensions, including: (1) historical development of the disciplines, (2) their scientific foundations, (3) data quality and analysis, (4) integration of knowledge and databases, (5) informatics tools to support practice, (6) informatics methods to support research (signal processing, imaging and vision, and computational modeling, (7) professional and patient continuing education, and (8) education and training. It is pointed out that, while the two disciplines differ in their histories, scientific foundations, and methodologic approaches to research in various areas, they nevertheless share methods and tools, which provides a basis for exchange of experience in their different applications. MI expertise in developing health care applications and the strength of BI in biological “discovery science” complement each other well. The new field of biomedical informatics (BMI) holds great promise for developing informatics methods that will be crucial in the development of genomic medicine. The future of BMI will be influenced strongly by whether significant advances in clinical practice and biomedical research come about from separate efforts in MI and BI, or from emerging, hybrid informatics subdisciplines at their interface. PMID:12925552

GEOGLE: context mining tool for the correlation between gene expression and the phenotypic distinction.

PubMed

Yu, Yao; Tu, Kang; Zheng, Siyuan; Li, Yun; Ding, Guohui; Ping, Jie; Hao, Pei; Li, Yixue

2009-08-25

In the post-genomic era, the development of high-throughput gene expression detection technology provides huge amounts of experimental data, which challenges the traditional pipelines for data processing and analyzing in scientific researches. In our work, we integrated gene expression information from Gene Expression Omnibus (GEO), biomedical ontology from Medical Subject Headings (MeSH) and signaling pathway knowledge from sigPathway entries to develop a context mining tool for gene expression analysis - GEOGLE. GEOGLE offers a rapid and convenient way for searching relevant experimental datasets, pathways and biological terms according to multiple types of queries: including biomedical vocabularies, GDS IDs, gene IDs, pathway names and signature list. Moreover, GEOGLE summarizes the signature genes from a subset of GDSes and estimates the correlation between gene expression and the phenotypic distinction with an integrated p value. This approach performing global searching of expression data may expand the traditional way of collecting heterogeneous gene expression experiment data. GEOGLE is a novel tool that provides researchers a quantitative way to understand the correlation between gene expression and phenotypic distinction through meta-analysis of gene expression datasets from different experiments, as well as the biological meaning behind. The web site and user guide of GEOGLE are available at: http://omics.biosino.org:14000/kweb/workflow.jsp?id=00020.

Web-based health services and clinical decision support.

PubMed

Jegelevicius, Darius; Marozas, Vaidotas; Lukosevicius, Arunas; Patasius, Martynas

2004-01-01

The purpose of this study was the development of a Web-based e-health service for comprehensive assistance and clinical decision support. The service structure consists of a Web server, a PHP-based Web interface linked to a clinical SQL database, Java applets for interactive manipulation and visualization of signals and a Matlab server linked with signal and data processing algorithms implemented by Matlab programs. The service ensures diagnostic signal- and image analysis-sbased clinical decision support. By using the discussed methodology, a pilot service for pathology specialists for automatic calculation of the proliferation index has been developed. Physicians use a simple Web interface for uploading the pictures under investigation to the server; subsequently a Java applet interface is used for outlining the region of interest and, after processing on the server, the requested proliferation index value is calculated. There is also an "expert corner", where experts can submit their index estimates and comments on particular images, which is especially important for system developers. These expert evaluations are used for optimization and verification of automatic analysis algorithms. Decision support trials have been conducted for ECG and ophthalmology ultrasonic investigations of intraocular tumor differentiation. Data mining algorithms have been applied and decision support trees constructed. These services are under implementation by a Web-based system too. The study has shown that the Web-based structure ensures more effective, flexible and accessible services compared with standalone programs and is very convenient for biomedical engineers and physicians, especially in the development phase.

mTORC1 as the main gateway to autophagy

PubMed Central

Rabanal-Ruiz, Yoana; Otten, Elsje G.; Korolchuk, Viktor I.

2017-01-01

Cells and organisms must coordinate their metabolic activity with changes in their environment to ensure their growth only when conditions are favourable. In order to maintain cellular homoeostasis, a tight regulation between the synthesis and degradation of cellular components is essential. At the epicentre of the cellular nutrient sensing is the mechanistic target of rapamycin complex 1 (mTORC1) which connects environmental cues, including nutrient and growth factor availability as well as stress, to metabolic processes in order to preserve cellular homoeostasis. Under nutrient-rich conditions mTORC1 promotes cell growth by stimulating biosynthetic pathways, including synthesis of proteins, lipids and nucleotides, and by inhibiting cellular catabolism through repression of the autophagic pathway. Its close signalling interplay with the energy sensor AMP-activated protein kinase (AMPK) dictates whether the cell actively favours anabolic or catabolic processes. Underlining the role of mTORC1 in the coordination of cellular metabolism, its deregulation is linked to numerous human diseases ranging from metabolic disorders to many cancers. Although mTORC1 can be modulated by a number of different inputs, amino acids represent primordial cues that cannot be compensated for by any other stimuli. The understanding of how amino acids signal to mTORC1 has increased considerably in the last years; however this area of research remains a hot topic in biomedical sciences. The current ideas and models proposed to explain the interrelationship between amino acid sensing, mTORC1 signalling and autophagy is the subject of the present review. PMID:29233869

Assessing the practice of biomedical ontology evaluation: Gaps and opportunities.

PubMed

Amith, Muhammad; He, Zhe; Bian, Jiang; Lossio-Ventura, Juan Antonio; Tao, Cui

2018-04-01

With the proliferation of heterogeneous health care data in the last three decades, biomedical ontologies and controlled biomedical terminologies play a more and more important role in knowledge representation and management, data integration, natural language processing, as well as decision support for health information systems and biomedical research. Biomedical ontologies and controlled terminologies are intended to assure interoperability. Nevertheless, the quality of biomedical ontologies has hindered their applicability and subsequent adoption in real-world applications. Ontology evaluation is an integral part of ontology development and maintenance. In the biomedicine domain, ontology evaluation is often conducted by third parties as a quality assurance (or auditing) effort that focuses on identifying modeling errors and inconsistencies. In this work, we first organized four categorical schemes of ontology evaluation methods in the existing literature to create an integrated taxonomy. Further, to understand the ontology evaluation practice in the biomedicine domain, we reviewed a sample of 200 ontologies from the National Center for Biomedical Ontology (NCBO) BioPortal-the largest repository for biomedical ontologies-and observed that only 15 of these ontologies have documented evaluation in their corresponding inception papers. We then surveyed the recent quality assurance approaches for biomedical ontologies and their use. We also mapped these quality assurance approaches to the ontology evaluation criteria. It is our anticipation that ontology evaluation and quality assurance approaches will be more widely adopted in the development life cycle of biomedical ontologies. Copyright © 2018 Elsevier Inc. All rights reserved.

Mobile cloud-computing-based healthcare service by noncontact ECG monitoring.

PubMed

Fong, Ee-May; Chung, Wan-Young

2013-12-02

Noncontact electrocardiogram (ECG) measurement technique has gained popularity these days owing to its noninvasive features and convenience in daily life use. This paper presents mobile cloud computing for a healthcare system where a noncontact ECG measurement method is employed to capture biomedical signals from users. Healthcare service is provided to continuously collect biomedical signals from multiple locations. To observe and analyze the ECG signals in real time, a mobile device is used as a mobile monitoring terminal. In addition, a personalized healthcare assistant is installed on the mobile device; several healthcare features such as health status summaries, medication QR code scanning, and reminders are integrated into the mobile application. Health data are being synchronized into the healthcare cloud computing service (Web server system and Web server dataset) to ensure a seamless healthcare monitoring system and anytime and anywhere coverage of network connection is available. Together with a Web page application, medical data are easily accessed by medical professionals or family members. Web page performance evaluation was conducted to ensure minimal Web server latency. The system demonstrates better availability of off-site and up-to-the-minute patient data, which can help detect health problems early and keep elderly patients out of the emergency room, thus providing a better and more comprehensive healthcare cloud computing service.

Complexity and multifractality of neuronal noise in mouse and human hippocampal epileptiform dynamics

NASA Astrophysics Data System (ADS)

Serletis, Demitre; Bardakjian, Berj L.; Valiante, Taufik A.; Carlen, Peter L.

2012-10-01

Fractal methods offer an invaluable means of investigating turbulent nonlinearity in non-stationary biomedical recordings from the brain. Here, we investigate properties of complexity (i.e. the correlation dimension, maximum Lyapunov exponent, 1/fγ noise and approximate entropy) and multifractality in background neuronal noise-like activity underlying epileptiform transitions recorded at the intracellular and local network scales from two in vitro models: the whole-intact mouse hippocampus and lesional human hippocampal slices. Our results show evidence for reduced dynamical complexity and multifractal signal features following transition to the ictal epileptiform state. These findings suggest that pathological breakdown in multifractal complexity coincides with loss of signal variability or heterogeneity, consistent with an unhealthy ictal state that is far from the equilibrium of turbulent yet healthy fractal dynamics in the brain. Thus, it appears that background noise-like activity successfully captures complex and multifractal signal features that may, at least in part, be used to classify and identify brain state transitions in the healthy and epileptic brain, offering potential promise for therapeutic neuromodulatory strategies for afflicted patients suffering from epilepsy and other related neurological disorders. This paper is based on chapter 5 of Serletis (2010 PhD Dissertation Department of Physiology, Institute of Biomaterials and Biomedical Engineering, University of Toronto).

Mobile Cloud-Computing-Based Healthcare Service by Noncontact ECG Monitoring

PubMed Central

Fong, Ee-May; Chung, Wan-Young

2013-01-01

Noncontact electrocardiogram (ECG) measurement technique has gained popularity these days owing to its noninvasive features and convenience in daily life use. This paper presents mobile cloud computing for a healthcare system where a noncontact ECG measurement method is employed to capture biomedical signals from users. Healthcare service is provided to continuously collect biomedical signals from multiple locations. To observe and analyze the ECG signals in real time, a mobile device is used as a mobile monitoring terminal. In addition, a personalized healthcare assistant is installed on the mobile device; several healthcare features such as health status summaries, medication QR code scanning, and reminders are integrated into the mobile application. Health data are being synchronized into the healthcare cloud computing service (Web server system and Web server dataset) to ensure a seamless healthcare monitoring system and anytime and anywhere coverage of network connection is available. Together with a Web page application, medical data are easily accessed by medical professionals or family members. Web page performance evaluation was conducted to ensure minimal Web server latency. The system demonstrates better availability of off-site and up-to-the-minute patient data, which can help detect health problems early and keep elderly patients out of the emergency room, thus providing a better and more comprehensive healthcare cloud computing service. PMID:24316562

Bridging medicine and biomedical technology: enhance translation of fundamental research to patient care

PubMed Central

Raff, Adam B.; Seiler, Theo G.; Apiou-Sbirlea, Gabriela

2017-01-01

The ‘Bridging medicine and biomedical technology’ special all-congress session took place for the first time at the OSA Biophotonics Congress: Optics in Life Sciences in 2017 (http://www.osa.org/enus/meetings/osa_meetings/optics_in_the_life_sciences/bridging_medicine_and_biomedical_technology_specia/). The purpose was to identify key challenges the biomedical scientists in academia have to overcome to translate their discoveries into clinical practice through robust collaborations with industry and discuss best practices to facilitate and accelerate the process. Our paper is intended to complement the session by providing a deeper insight into the concept behind the structure and the content we developed. PMID:29296473

New software developments for quality mesh generation and optimization from biomedical imaging data.

PubMed

Yu, Zeyun; Wang, Jun; Gao, Zhanheng; Xu, Ming; Hoshijima, Masahiko

2014-01-01

In this paper we present a new software toolkit for generating and optimizing surface and volumetric meshes from three-dimensional (3D) biomedical imaging data, targeted at image-based finite element analysis of some biomedical activities in a single material domain. Our toolkit includes a series of geometric processing algorithms including surface re-meshing and quality-guaranteed tetrahedral mesh generation and optimization. All methods described have been encapsulated into a user-friendly graphical interface for easy manipulation and informative visualization of biomedical images and mesh models. Numerous examples are presented to demonstrate the effectiveness and efficiency of the described methods and toolkit. Copyright © 2013 Elsevier Ireland Ltd. All rights reserved.

Signal-on fluorescence biosensor for microRNA-21 detection based on DNA strand displacement reaction and Mg2+-dependent DNAzyme cleavage.

PubMed

Yin, Huan-Shun; Li, Bing-Chen; Zhou, Yun-Lei; Wang, Hai-Yan; Wang, Ming-Hui; Ai, Shi-Yun

2017-10-15

MicroRNAs have been involved into many biological processes and are regarded as disease biomarkers. Simple, rapid, sensitive and selective method for microRNA detection is crucial for early diagnosis and therapy of diseases. In this work, sensitive fluorescence assay was developed for microRNA-21 detection based on DNA polymerase induced strand displacement amplification reaction, Mg 2+ -dependent DNAzyme catalysis reaction, and magnetic separation. In the presence of target microRNA-21, amounts of trigger DNA could be produced with DNA polymerase induced strand displacement amplification reaction, and the trigger DNA could be further hybridized with signal DNA, which was labeled with biotin and AMCA dye. After introduction of Mg 2+ , trigger DNA could form DNAzyme to cleave signal DNA. After magnetic separation, the DNA fragment with AMCA dye could give fluorescence signal, which was related to microRNA-21 concentration. Based on the two efficient signal amplifications, the developed method showed high detection sensitivity with low detection limit of 0.27fM (3σ). In addition, this fluorescence strategy also possessed excellent detection specificity, and could be applied to analyze microRNA-21 expression level in serum of cancer patient. According to the obtained results, the developed fluorescence method might be a promising detection platform for microRNA-21 quantitative analysis in biomedical research and clinical diagnosis. Copyright © 2017 Elsevier B.V. All rights reserved.

A Pilot Study of Biomedical Text Comprehension using an Attention-Based Deep Neural Reader: Design and Experimental Analysis.

PubMed

Kim, Seongsoon; Park, Donghyeon; Choi, Yonghwa; Lee, Kyubum; Kim, Byounggun; Jeon, Minji; Kim, Jihye; Tan, Aik Choon; Kang, Jaewoo

2018-01-05

With the development of artificial intelligence (AI) technology centered on deep-learning, the computer has evolved to a point where it can read a given text and answer a question based on the context of the text. Such a specific task is known as the task of machine comprehension. Existing machine comprehension tasks mostly use datasets of general texts, such as news articles or elementary school-level storybooks. However, no attempt has been made to determine whether an up-to-date deep learning-based machine comprehension model can also process scientific literature containing expert-level knowledge, especially in the biomedical domain. This study aims to investigate whether a machine comprehension model can process biomedical articles as well as general texts. Since there is no dataset for the biomedical literature comprehension task, our work includes generating a large-scale question answering dataset using PubMed and manually evaluating the generated dataset. We present an attention-based deep neural model tailored to the biomedical domain. To further enhance the performance of our model, we used a pretrained word vector and biomedical entity type embedding. We also developed an ensemble method of combining the results of several independent models to reduce the variance of the answers from the models. The experimental results showed that our proposed deep neural network model outperformed the baseline model by more than 7% on the new dataset. We also evaluated human performance on the new dataset. The human evaluation result showed that our deep neural model outperformed humans in comprehension by 22% on average. In this work, we introduced a new task of machine comprehension in the biomedical domain using a deep neural model. Since there was no large-scale dataset for training deep neural models in the biomedical domain, we created the new cloze-style datasets Biomedical Knowledge Comprehension Title (BMKC_T) and Biomedical Knowledge Comprehension Last Sentence (BMKC_LS) (together referred to as BioMedical Knowledge Comprehension) using the PubMed corpus. The experimental results showed that the performance of our model is much higher than that of humans. We observed that our model performed consistently better regardless of the degree of difficulty of a text, whereas humans have difficulty when performing biomedical literature comprehension tasks that require expert level knowledge. ©Seongsoon Kim, Donghyeon Park, Yonghwa Choi, Kyubum Lee, Byounggun Kim, Minji Jeon, Jihye Kim, Aik Choon Tan, Jaewoo Kang. Originally published in JMIR Medical Informatics (http://medinform.jmir.org), 05.01.2018.

Biomedical Ontologies in Action: Role in Knowledge Management, Data Integration and Decision Support

PubMed Central

Bodenreider, O.

2008-01-01

Summary Objectives To provide typical examples of biomedical ontologies in action, emphasizing the role played by biomedical ontologies in knowledge management, data integration and decision support. Methods Biomedical ontologies selected for their practical impact are examined from a functional perspective. Examples of applications are taken from operational systems and the biomedical literature, with a bias towards recent journal articles. Results The ontologies under investigation in this survey include SNOMED CT, the Logical Observation Identifiers, Names, and Codes (LOINC), the Foundational Model of Anatomy, the Gene Ontology, RxNorm, the National Cancer Institute Thesaurus, the International Classification of Diseases, the Medical Subject Headings (MeSH) and the Unified Medical Language System (UMLS). The roles played by biomedical ontologies are classified into three major categories: knowledge management (indexing and retrieval of data and information, access to information, mapping among ontologies); data integration, exchange and semantic interoperability; and decision support and reasoning (data selection and aggregation, decision support, natural language processing applications, knowledge discovery). Conclusions Ontologies play an important role in biomedical research through a variety of applications. While ontologies are used primarily as a source of vocabulary for standardization and integration purposes, many applications also use them as a source of computable knowledge. Barriers to the use of ontologies in biomedical applications are discussed. PMID:18660879

Applications of SPICE for modeling miniaturized biomedical sensor systems

NASA Technical Reports Server (NTRS)

Mundt, C. W.; Nagle, H. T.

2000-01-01

This paper proposes a model for a miniaturized signal conditioning system for biopotential and ion-selective electrode arrays. The system consists of three main components: sensors, interconnections, and signal conditioning chip. The model for this system is based on SPICE. Transmission-line based equivalent circuits are used to represent the sensors, lumped resistance-capacitance circuits describe the interconnections, and a model for the signal conditioning chip is extracted from its layout. A system for measurements of biopotentials and ionic activities can be miniaturized and optimized for cardiovascular applications based on the development of an integrated SPICE system model of its electrochemical, interconnection, and electronic components.

Hypotheses generation as supervised link discovery with automated class labeling on large-scale biomedical concept networks

PubMed Central

2012-01-01

Computational approaches to generate hypotheses from biomedical literature have been studied intensively in recent years. Nevertheless, it still remains a challenge to automatically discover novel, cross-silo biomedical hypotheses from large-scale literature repositories. In order to address this challenge, we first model a biomedical literature repository as a comprehensive network of biomedical concepts and formulate hypotheses generation as a process of link discovery on the concept network. We extract the relevant information from the biomedical literature corpus and generate a concept network and concept-author map on a cluster using Map-Reduce frame-work. We extract a set of heterogeneous features such as random walk based features, neighborhood features and common author features. The potential number of links to consider for the possibility of link discovery is large in our concept network and to address the scalability problem, the features from a concept network are extracted using a cluster with Map-Reduce framework. We further model link discovery as a classification problem carried out on a training data set automatically extracted from two network snapshots taken in two consecutive time duration. A set of heterogeneous features, which cover both topological and semantic features derived from the concept network, have been studied with respect to their impacts on the accuracy of the proposed supervised link discovery process. A case study of hypotheses generation based on the proposed method has been presented in the paper. PMID:22759614

[Application of the life sciences platform based on oracle to biomedical informations].

PubMed

Zhao, Zhi-Yun; Li, Tai-Huan; Yang, Hong-Qiao

2008-03-01

The life sciences platform based on Oracle database technology is introduced in this paper. By providing a powerful data access, integrating a variety of data types, and managing vast quantities of data, the software presents a flexible, safe and scalable management platform for biomedical data processing.

Biomedical engineers and participation in judicial executions: capital punishment as a technical problem.

PubMed

Doyle, John

2007-01-01

This paper discusses the topic of judicial execution from the perspective of the intersection of the technological issues and the professional ethics issues. Although physicians are generally ethically forbidden from any involvement in the judicial execution process, this does not appear to be the case for engineering professionals. This creates an interesting but controversial opportunity for the engineering community (especially biomedical engineers) to improve the humaneness and reliability of the judicial execution process.

Sol-gel processing of bioactive glass nanoparticles: A review.

PubMed

Zheng, Kai; Boccaccini, Aldo R

2017-11-01

Silicate-based bioactive glass nanoparticles (BGN) are gaining increasing attention in various biomedical applications due to their unique properties. Controlled synthesis of BGN is critical to their effective use in biomedical applications since BGN characteristics, such as morphology and composition, determining the properties of BGN, are highly related to the synthesis process. In the last decade, numerous investigations focusing on BGN synthesis have been reported. BGN can mainly be produced through the conventional melt-quench approach or by sol-gel methods. The latter approaches are drawing widespread attention, considering the convenience and versatility they offer to tune the properties of BGN. In this paper, we review the strategies of sol-gel processing of BGN, including those adopting different catalysts for initiating the hydrolysis and condensation of silicate precursors as well as those combining sol-gel chemistry with other techniques. The processes and mechanism of different synthesis approaches are introduced and discussed in detail. Considering the importance of the BGN morphology and composition to their biomedical applications, strategies put forward to control the size, shape, pore structure and composition of BGN are discussed. BGN are particularly interesting biomaterials for bone-related applications, however, they also have potential for other biomedical applications, e.g. in soft tissue regeneration/repair. Therefore, in the last part of this review, recently reported applications of BGN in soft tissue repair and wound healing are presented. Copyright © 2017 Elsevier B.V. All rights reserved.

Engineering microbial consortia to enhance biomining and bioremediation.

PubMed

Brune, Karl D; Bayer, Travis S

2012-01-01

In natural environments microorganisms commonly exist as communities of multiple species that are capable of performing more varied and complicated tasks than clonal populations. Synthetic biologists have engineered clonal populations with characteristics such as differentiation, memory, and pattern formation, which are usually associated with more complex multicellular organisms. The prospect of designing microbial communities has alluring possibilities for environmental, biomedical, and energy applications, and is likely to reveal insight into how natural microbial consortia function. Cell signaling and communication pathways between different species are likely to be key processes for designing novel functions in synthetic and natural consortia. Recent efforts to engineer synthetic microbial interactions will be reviewed here, with particular emphasis given to research with significance for industrial applications in the field of biomining and bioremediation of acid mine drainage.

Biocomputing nanoplatforms as therapeutics and diagnostics.

PubMed

Evans, A C; Thadani, N N; Suh, J

2016-10-28

Biocomputing nanoplatforms are designed to detect and integrate single or multiple inputs under defined algorithms, such as Boolean logic gates, and generate functionally useful outputs, such as delivery of therapeutics or release of optically detectable signals. Using sensing modules composed of small molecules, polymers, nucleic acids, or proteins/peptides, nanoplatforms have been programmed to detect and process extrinsic stimuli, such as magnetic fields or light, or intrinsic stimuli, such as nucleic acids, enzymes, or pH. Stimulus detection can be transduced by the nanomaterial via three different mechanisms: system assembly, system disassembly, or system transformation. The increasingly sophisticated suite of biocomputing nanoplatforms may be invaluable for a multitude of applications, including medical diagnostics, biomedical imaging, environmental monitoring, and delivery of therapeutics to target cell populations. Copyright © 2016 Elsevier B.V. All rights reserved.

Engineering microbial consortia to enhance biomining and bioremediation

PubMed Central

Brune, Karl D.; Bayer, Travis S.

2012-01-01

In natural environments microorganisms commonly exist as communities of multiple species that are capable of performing more varied and complicated tasks than clonal populations. Synthetic biologists have engineered clonal populations with characteristics such as differentiation, memory, and pattern formation, which are usually associated with more complex multicellular organisms. The prospect of designing microbial communities has alluring possibilities for environmental, biomedical, and energy applications, and is likely to reveal insight into how natural microbial consortia function. Cell signaling and communication pathways between different species are likely to be key processes for designing novel functions in synthetic and natural consortia. Recent efforts to engineer synthetic microbial interactions will be reviewed here, with particular emphasis given to research with significance for industrial applications in the field of biomining and bioremediation of acid mine drainage. PMID:22679443

Compact biomedical pulsed signal generator for bone tissue stimulation

DOEpatents

Kronberg, J.W.

1993-06-08

An apparatus for stimulating bone tissue for stimulating bone growth or treating osteoporosis by applying directly to the skin of the patient an alternating current electrical signal comprising wave forms known to simulate the piezoelectric constituents in bone. The apparatus may, by moving a switch, stimulate bone growth or treat osteoporosis, as desired. Based on low-power CMOS technology and enclosed in a moisture-resistant case shaped to fit comfortably, two astable multivibrators produce the desired waveforms. The amplitude, pulse width and pulse frequency, and the subpulse width and subpulse frequency of the waveforms are adjustable. The apparatus, preferably powered by a standard 9-volt battery, includes signal amplitude sensors and warning signals indicate an output is being produced and the battery needs to be replaced.

Compact biomedical pulsed signal generator for bone tissue stimulation

DOEpatents

Kronberg, James W.

1993-01-01

An apparatus for stimulating bone tissue for stimulating bone growth or treating osteoporosis by applying directly to the skin of the patient an alternating current electrical signal comprising wave forms known to simulate the piezoelectric constituents in bone. The apparatus may, by moving a switch, stimulate bone growth or treat osteoporosis, as desired. Based on low-power CMOS technology and enclosed in a moisture-resistant case shaped to fit comfortably, two astable multivibrators produce the desired waveforms. The amplitude, pulse width and pulse frequency, and the subpulse width and subpulse frequency of the waveforms are adjustable. The apparatus, preferably powered by a standard 9-volt battery, includes signal amplitude sensors and warning signals indicate an output is being produced and the battery needs to be replaced.

Synthetic Xylosides: Probing the Glycosaminoglycan Biosynthetic Machinery for Biomedical Applications.

PubMed

Chua, Jie Shi; Kuberan, Balagurunathan

2017-11-21

Glycosaminoglycans (GAGs) are polysaccharides ubiquitously found on cell surfaces and in the extracellular matrix (ECM). They regulate numerous cellular signaling events involved in many developmental and pathophysiological processes. GAGs are composed of complex sequences of repeating disaccharide units, each of which can carry many different modifications. The tremendous structural variations account for their ability to bind many proteins and thus, for their numerous functions. Although the sequence of GAG biosynthetic events and the enzymes involved mostly were deduced a decade ago, the emergence of tissue or cell specific GAGs from a nontemplate driven process remains an enigma. Current knowledge favors the hypothesis that macromolecular assemblies of GAG biosynthetic enzymes termed "GAGOSOMEs" coordinate polymerization and fine structural modifications in the Golgi apparatus. Distinct GAG structures arise from the differential channeling of substrates through the Golgi apparatus to various GAGOSOMEs. As GAGs perform multiple regulatory roles, it is of great interest to develop molecular strategies to selectively interfere with GAG biosynthesis for therapeutic applications. In this Account, we assess our present knowledge on GAG biosynthesis, the manipulation of GAG biosynthesis using synthetic xylosides, and the unrealized potential of these xylosides in various biomedical applications. Synthetic xylosides are small molecules consisting of a xylose attached to an aglycone group, and they compete with endogenous proteins for precursors and biosynthetic enzymes to assemble GAGs. This competition reduces endogenous proteoglycan-bound GAGs while increasing xyloside-bound free GAGs, mostly chondroitin sulfate (CS) and less heparan sulfate (HS), resulting in a variety of biological consequences. To date, hundreds of xylosides have been published and the importance of the aglycone group in determining the structure of the primed GAG chains is well established. However, the structure-activity relationship has long been cryptic. Nonetheless, xylosides have been designed to increase HS priming, modified to inhibit endogenous GAG production without priming, and engineered to be more biologically relevant. Synthetic xylosides hold great promise in many biomedical applications and as therapeutics. They are small, orally bioavailable, easily excreted, and utilize the host cell biosynthetic machinery to assemble GAGs that are likely nonimmunogenic. Various xylosides have been shown, in different biological systems, to have anticoagulant effects, selectively kill tumor cells, abrogate angiogenic and metastatic pathways, promote angiogenesis and neuronal growth, and affect embryonic development. However, most of these studies utilized the commercially available one or two β-D-xylosides and focused on the impact of endogenous proteoglycan-bound GAG inhibition on biological activity. Nevertheless, the manipulation of cell behavior as a result of stabilizing growth factor signaling with xyloside-primed GAGs is also reckonable but underexplored. Recent advances in the use of molecular modeling and docking simulations to understand the structure-activity relationships of xylosides have opened up the possibility of a more rational aglycone design to achieve a desirable biological outcome through selective priming and inhibitory activities. We envision these advances will encourage more researchers to explore these fascinating xylosides, harness the GAG biosynthetic machinery for a wider range of biomedical applications, and accelerate the successful transition of xyloside-based therapeutics from bench to bedside.

BioStar models of clinical and genomic data for biomedical data warehouse design

PubMed Central

Wang, Liangjiang; Ramanathan, Murali

2008-01-01

Biomedical research is now generating large amounts of data, ranging from clinical test results to microarray gene expression profiles. The scale and complexity of these datasets give rise to substantial challenges in data management and analysis. It is highly desirable that data warehousing and online analytical processing technologies can be applied to biomedical data integration and mining. The major difficulty probably lies in the task of capturing and modelling diverse biological objects and their complex relationships. This paper describes multidimensional data modelling for biomedical data warehouse design. Since the conventional models such as star schema appear to be insufficient for modelling clinical and genomic data, we develop a new model called BioStar schema. The new model can capture the rich semantics of biomedical data and provide greater extensibility for the fast evolution of biological research methodologies. PMID:18048122

Concept recognition for extracting protein interaction relations from biomedical text

PubMed Central

Baumgartner, William A; Lu, Zhiyong; Johnson, Helen L; Caporaso, J Gregory; Paquette, Jesse; Lindemann, Anna; White, Elizabeth K; Medvedeva, Olga; Cohen, K Bretonnel; Hunter, Lawrence

2008-01-01

Background: Reliable information extraction applications have been a long sought goal of the biomedical text mining community, a goal that if reached would provide valuable tools to benchside biologists in their increasingly difficult task of assimilating the knowledge contained in the biomedical literature. We present an integrated approach to concept recognition in biomedical text. Concept recognition provides key information that has been largely missing from previous biomedical information extraction efforts, namely direct links to well defined knowledge resources that explicitly cement the concept's semantics. The BioCreative II tasks discussed in this special issue have provided a unique opportunity to demonstrate the effectiveness of concept recognition in the field of biomedical language processing. Results: Through the modular construction of a protein interaction relation extraction system, we present several use cases of concept recognition in biomedical text, and relate these use cases to potential uses by the benchside biologist. Conclusion: Current information extraction technologies are approaching performance standards at which concept recognition can begin to deliver high quality data to the benchside biologist. Our system is available as part of the BioCreative Meta-Server project and on the internet . PMID:18834500

Biomedical Instruments for Fetal and Neonatal Surveillance

NASA Astrophysics Data System (ADS)

Rolfe, P.; Scopesi, F.; Serra, G.

2006-10-01

Specialised instruments have been developed to aid the care of the fetus and the newborn baby. Miniature sensors using optical, electrical, chemical, mechanical and magnetic principles have been produced for capturing key measurands. These include temperature, pressure, flow and dimension, as well as several specific molecules such as glucose, oxygen and carbon dioxide. During pregnancy ultrasound imaging and blood flow techniques provide valuable information concerning fetal abnormalities, fetal growth, fetal breathing and fetal heart rate. Signal processing and pattern recognition can be useful for deriving indicators of fetal distress and clinical status, based on biopotentials as well as ultrasound signals. Fetal pH measurement is a critical requirement during labour and delivery. The intensive care of ill preterm babies involves provision of an optimal thermal environment and respiratory support. Monitoring of blood gas and acid-base status is essential, and this involves both blood sampling for in vitro analysis as well as the use of invasive or non-invasive sensors. For the future it will be vital that the technologies used are subjected to controlled trials to establish benefit or otherwise.

High-power femtosecond-terahertz pulse induces a wound response in mouse skin

PubMed Central

Kim, Kyu-Tae; Park, Jaehun; Jo, Sung Jin; Jung, Seonghoon; Kwon, Oh Sang; Gallerano, Gian Piero; Park, Woong-Yang; Park, Gun-Sik

2013-01-01

Terahertz (THz) technology has emerged for biomedical applications such as scanning, molecular spectroscopy, and medical imaging. Although a thorough assessment to predict potential concerns has to precede before practical utilization of THz source, the biological effect of THz radiation is not yet fully understood with scant related investigations. Here, we applied a femtosecond-terahertz (fs-THz) pulse to mouse skin to evaluate non-thermal effects of THz radiation. Analysis of the genome-wide expression profile in fs-THz-irradiated skin indicated that wound responses were predominantly mediated by transforming growth factor-beta (TGF-β) signaling pathways. We validated NFκB1- and Smad3/4-mediated transcriptional activation in fs-THz-irradiated skin by chromatin immunoprecipitation assay. Repeated fs-THz radiation delayed the closure of mouse skin punch wounds due to up-regulation of TGF-β. These findings suggest that fs-THz radiation initiate a wound-like signal in skin with increased expression of TGF-β and activation of its downstream target genes, which perturbs the wound healing process in vivo. PMID:23907528

Scalable Telemonitoring Model in Cloud for Health Care Analysis

NASA Astrophysics Data System (ADS)

Sawant, Yogesh; Jayakumar, Naveenkumar, Dr.; Pawar, Sanket Sunil

2017-08-01

Telemonitoring model is health observations model that going to surveillance patients remotely. Telemonitoring model is suitable for patients to avoid high operating expense to get Emergency treatment. Telemonitoring gives the path for monitoring the medical device that generates a complete profile of patient’s health through assembling essential signs as well as additional health information. Telemonitoring model is relying on four differential modules which is capable to generate realistic synthetic electrocardiogram (ECG) signals. Telemonitoring model shows four categories of chronic disease: pulmonary state, diabetes, hypertension, as well as cardiovascular diseases. On the other hand, the results of this application model recommend facilitating despite of their nationality, socioeconomic grade, or age, patients observe amid tele-monitoring programs as well as the utilization of technologies. Patient’s multiple health status is shown in the result such as beat-to-beat variation in morphology and timing of the human ECG, including QT dispersion and R-peak amplitude modulation. This model will be utilized to evaluate biomedical signal processing methods that are utilized to calculate clinical information from the ECG.

Targeted drug discovery and development, from molecular signaling to the global market: an educational program at New York University, 5-year metrics

PubMed Central

Lee, Gloria; Plaksin, Joseph; Ramasamy, Ravichandran; Gold-von Simson, Gabrielle

2018-01-01

Drug discovery and development (DDD) is a collaborative, dynamic process of great interest to researchers, but an area where there is a lack of formal training. The Drug Development Educational Program (DDEP) at New York University was created in 2012 to stimulate an improved, multidisciplinary DDD workforce by educating early stage scientists as well as a variety of other like-minded students. The first course of the program emphasizes post-compounding aspects of DDD; the second course focuses on molecular signaling pathways. In five years, 196 students (candidates for PhD, MD, Master’s degree, and post-doctoral MD/PhD) from different schools (Medicine, Biomedical Sciences, Dentistry, Engineering, Business, and Education) completed the course(s). Pre/post surveys demonstrate knowledge gain across all course topics. 26 students were granted career development awards (73% women, 23% underrepresented minorities). Some graduates of their respective degree-granting/post-doctoral programs embarked on DDD related careers. This program serves as a framework for other academic institutions to develop compatible programs designed to train a more informed DDD workforce. PMID:29657854

Home telemonitoring of vital signs--technical challenges and future directions.

PubMed

Celler, Branko G; Sparks, Ross S

2015-01-01

The telemonitoring of vital signs from the home is an essential element of telehealth services for the management of patients with chronic conditions, such as congestive heart failure (CHF), chronic obstructive pulmonary disease (COPD), diabetes, or poorly controlled hypertension. Telehealth is now being deployed widely in both rural and urban settings, and in this paper, we discuss the contribution made by biomedical instrumentation, user interfaces, and automated risk stratification algorithms in developing a clinical diagnostic quality longitudinal health record at home. We identify technical challenges in the acquisition of high-quality biometric signals from unsupervised patients at home, identify new technical solutions and user interfaces, and propose new measurement modalities and signal processing techniques for increasing the quality and value of vital signs monitoring at home. We also discuss use of vital signs data for the automated risk stratification of patients, so that clinical resources can be targeted to those most at risk of unscheduled admission to hospital. New research is also proposed to integrate primary care, hospital, personal genomic, and telehealth electronic health records, and apply predictive analytics and data mining for enhancing clinical decision support.

Clique-based data mining for related genes in a biomedical database.

PubMed

Matsunaga, Tsutomu; Yonemori, Chikara; Tomita, Etsuji; Muramatsu, Masaaki

2009-07-01

Progress in the life sciences cannot be made without integrating biomedical knowledge on numerous genes in order to help formulate hypotheses on the genetic mechanisms behind various biological phenomena, including diseases. There is thus a strong need for a way to automatically and comprehensively search from biomedical databases for related genes, such as genes in the same families and genes encoding components of the same pathways. Here we address the extraction of related genes by searching for densely-connected subgraphs, which are modeled as cliques, in a biomedical relational graph. We constructed a graph whose nodes were gene or disease pages, and edges were the hyperlink connections between those pages in the Online Mendelian Inheritance in Man (OMIM) database. We obtained over 20,000 sets of related genes (called 'gene modules') by enumerating cliques computationally. The modules included genes in the same family, genes for proteins that form a complex, and genes for components of the same signaling pathway. The results of experiments using 'metabolic syndrome'-related gene modules show that the gene modules can be used to get a coherent holistic picture helpful for interpreting relations among genes. We presented a data mining approach extracting related genes by enumerating cliques. The extracted gene sets provide a holistic picture useful for comprehending complex disease mechanisms.

Partners | Office of Cancer Clinical Proteomics Research

Cancer.gov

Awardees and Affiliated Institutions Agilent Technologies, Inc., Cambridge, MA Baylor College of Medicine, Houston, TX Biomedical Hosting LLC, Arlington, MA Brigham and Women’s Hospital, Cambridge, MA Brown University, Providence, RI Cell Signaling Technology, Danvers, MA Chang Gung University, Molecular Medicine Research Center, Taoyuan City, Taiwan Dana-Farber Cancer Institute, Boston, MA Fluidigm Corp., Cambridge, MA

A Novel Semi-Supervised Methodology for Extracting Tumor Type-Specific MRS Sources in Human Brain Data

PubMed Central

Ortega-Martorell, Sandra; Ruiz, Héctor; Vellido, Alfredo; Olier, Iván; Romero, Enrique; Julià-Sapé, Margarida; Martín, José D.; Jarman, Ian H.; Arús, Carles; Lisboa, Paulo J. G.

2013-01-01

Background The clinical investigation of human brain tumors often starts with a non-invasive imaging study, providing information about the tumor extent and location, but little insight into the biochemistry of the analyzed tissue. Magnetic Resonance Spectroscopy can complement imaging by supplying a metabolic fingerprint of the tissue. This study analyzes single-voxel magnetic resonance spectra, which represent signal information in the frequency domain. Given that a single voxel may contain a heterogeneous mix of tissues, signal source identification is a relevant challenge for the problem of tumor type classification from the spectroscopic signal. Methodology/Principal Findings Non-negative matrix factorization techniques have recently shown their potential for the identification of meaningful sources from brain tissue spectroscopy data. In this study, we use a convex variant of these methods that is capable of handling negatively-valued data and generating sources that can be interpreted as tumor class prototypes. A novel approach to convex non-negative matrix factorization is proposed, in which prior knowledge about class information is utilized in model optimization. Class-specific information is integrated into this semi-supervised process by setting the metric of a latent variable space where the matrix factorization is carried out. The reported experimental study comprises 196 cases from different tumor types drawn from two international, multi-center databases. The results indicate that the proposed approach outperforms a purely unsupervised process by achieving near perfect correlation of the extracted sources with the mean spectra of the tumor types. It also improves tissue type classification. Conclusions/Significance We show that source extraction by unsupervised matrix factorization benefits from the integration of the available class information, so operating in a semi-supervised learning manner, for discriminative source identification and brain tumor labeling from single-voxel spectroscopy data. We are confident that the proposed methodology has wider applicability for biomedical signal processing. PMID:24376744

Flexible PZT Thin Film Tactile Sensor for Biomedical Monitoring

PubMed Central

Tseng, Hong-Jie; Tian, Wei-Cheng; Wu, Wen-Jong

2013-01-01

This paper presents the development of tactile sensors using the sol-gel process to deposit a PZT thin-film from 250 nm to 1 μm on a flexible stainless steel substrate. The PZT thin-film tactile sensor can be used to measure human pulses from several areas, including carotid, brachial, finger, ankle, radial artery, and the apical region. Flexible PZT tactile sensors can overcome the diverse topology of various human regions and sense the corresponding signals from human bodies. The measured arterial pulse waveform can be used to diagnose hypertension and cardiac failure in patients. The proposed sensors have several advantages, such as flexibility, reliability, high strain, low cost, simple fabrication, and low temperature processing. The PZT thin-film deposition process includes a pyrolysis process at 150 °C/500 °C for 10/5 min, followed by an annealing process at 650 °C for 10 min. Finally, the consistent pulse wave velocity (PWV) was demonstrated based on human pulse measurements from apical to radial, brachial to radial, and radial to ankle. It is characterized that the sensitivity of our PZT-based tactile sensor was approximately 0.798 mV/g. PMID:23698262

Flexible PZT thin film tactile sensor for biomedical monitoring.

PubMed

Tseng, Hong-Jie; Tian, Wei-Cheng; Wu, Wen-Jong

2013-04-25

This paper presents the development of tactile sensors using the sol-gel process to deposit a PZT thin-film from 250 nm to 1 μm on a flexible stainless steel substrate. The PZT thin-film tactile sensor can be used to measure human pulses from several areas, including carotid, brachial, finger, ankle, radial artery, and the apical region. Flexible PZT tactile sensors can overcome the diverse topology of various human regions and sense the corresponding signals from human bodies. The measured arterial pulse waveform can be used to diagnose hypertension and cardiac failure in patients. The proposed sensors have several advantages, such as flexibility, reliability, high strain, low cost, simple fabrication, and low temperature processing. The PZT thin-film deposition process includes a pyrolysis process at 150 °C/500 °C for 10/5 min, followed by an annealing process at 650 °C for 10 min. Finally, the consistent pulse wave velocity (PWV) was demonstrated based on human pulse measurements from apical to radial, brachial to radial, and radial to ankle. It is characterized that the sensitivity of our PZT-based tactile sensor was approximately 0.798 mV/g.

Exploiting semantic patterns over biomedical knowledge graphs for predicting treatment and causative relations.

PubMed

Bakal, Gokhan; Talari, Preetham; Kakani, Elijah V; Kavuluru, Ramakanth

2018-06-01

Identifying new potential treatment options for medical conditions that cause human disease burden is a central task of biomedical research. Since all candidate drugs cannot be tested with animal and clinical trials, in vitro approaches are first attempted to identify promising candidates. Likewise, identifying different causal relations between biomedical entities is also critical to understand biomedical processes. Generally, natural language processing (NLP) and machine learning are used to predict specific relations between any given pair of entities using the distant supervision approach. To build high accuracy supervised predictive models to predict previously unknown treatment and causative relations between biomedical entities based only on semantic graph pattern features extracted from biomedical knowledge graphs. We used 7000 treats and 2918 causes hand-curated relations from the UMLS Metathesaurus to train and test our models. Our graph pattern features are extracted from simple paths connecting biomedical entities in the SemMedDB graph (based on the well-known SemMedDB database made available by the U.S. National Library of Medicine). Using these graph patterns connecting biomedical entities as features of logistic regression and decision tree models, we computed mean performance measures (precision, recall, F-score) over 100 distinct 80-20% train-test splits of the datasets. For all experiments, we used a positive:negative class imbalance of 1:10 in the test set to model relatively more realistic scenarios. Our models predict treats and causes relations with high F-scores of 99% and 90% respectively. Logistic regression model coefficients also help us identify highly discriminative patterns that have an intuitive interpretation. We are also able to predict some new plausible relations based on false positives that our models scored highly based on our collaborations with two physician co-authors. Finally, our decision tree models are able to retrieve over 50% of treatment relations from a recently created external dataset. We employed semantic graph patterns connecting pairs of candidate biomedical entities in a knowledge graph as features to predict treatment/causative relations between them. We provide what we believe is the first evidence in direct prediction of biomedical relations based on graph features. Our work complements lexical pattern based approaches in that the graph patterns can be used as additional features for weakly supervised relation prediction. Copyright © 2018 Elsevier Inc. All rights reserved.

Engineering Cell-Cell Signaling

PubMed Central

Milano, Daniel F.; Natividad, Robert J.; Asthagiri, Anand R.

2014-01-01

Juxtacrine cell-cell signaling mediated by the direct interaction of adjoining mammalian cells is arguably the mode of cell communication that is most recalcitrant to engineering. Overcoming this challenge is crucial for progress in biomedical applications, such as tissue engineering, regenerative medicine, immune system engineering and therapeutic design. Here, we describe the significant advances that have been made in developing synthetic platforms (materials and devices) and synthetic cells (cell surface engineering and synthetic gene circuits) to modulate juxtacrine cell-cell signaling. In addition, significant progress has been made in elucidating design rules and strategies to modulate juxtacrine signaling based on quantitative, engineering analysis of the mechanical and regulatory role of juxtacrine signals in the context of other cues and physical constraints in the microenvironment. These advances in engineering juxtacrine signaling lay a strong foundation for an integrative approach to utilizing synthetic cells, advanced ‘chassis’ and predictive modeling to engineer the form and function of living tissues. PMID:23856592

Drawing lithography for microneedles: a review of fundamentals and biomedical applications.

PubMed

Lee, Kwang; Jung, Hyungil

2012-10-01

A microneedle is a three-dimensional (3D) micromechanical structure and has been in the spotlight recently as a drug delivery system (DDS). Because a microneedle delivers the target drug after penetrating the skin barrier, the therapeutic effects of microneedles proceed from its 3D structural geometry. Various types of microneedles have been fabricated using subtractive micromanufacturing methods which are based on the inherently planar two-dimensional (2D) geometries. However, traditional subtractive processes are limited for flexible structural microneedles and makes functional biomedical applications for efficient drug delivery difficult. The authors of the present study propose drawing lithography as a unique additive process for the fabrication of a microneedle directly from 2D planar substrates, thus overcoming a subtractive process shortcoming. The present article provides the first overview of the principal drawing lithography technology: fundamentals and biomedical applications. The continuous drawing technique for an ultrahigh-aspect ratio (UHAR) hollow microneedle, stepwise controlled drawing technique for a dissolving microneedle, and drawing technique with antidromic isolation for a hybrid electro-microneedle (HEM) are reviewed, and efficient biomedical applications by drawing lithography-mediated microneedles as an innovative drug and gene delivery system are described. Drawing lithography herein can provide a great breakthrough in the development of materials science and biotechnology. Copyright © 2012 Elsevier Ltd. All rights reserved.

"On-screen" writing and composing: two years experience with Manuscript Manager, Apple II and IBM-PC versions.

PubMed

Offerhaus, L

1989-06-01

The problems of the direct composition of a biomedical manuscript on a personal computer are discussed. Most word processing software is unsuitable because literature references, once stored, cannot be rearranged if major changes are necessary. These obstacles have been overcome in Manuscript Manager, a combination of word processing and database software. As it follows Council of Biology Editors and Vancouver rules, the printouts should be technically acceptable to most leading biomedical journals.

Knowledge acquisition for medical diagnosis using collective intelligence.

PubMed

Hernández-Chan, G; Rodríguez-González, A; Alor-Hernández, G; Gómez-Berbís, J M; Mayer-Pujadas, M A; Posada-Gómez, R

2012-11-01

The wisdom of the crowds (WOC) is the process of taking into account the collective opinion of a group of individuals rather than a single expert to answer a question. Based on this assumption, the use of processes based on WOC techniques to collect new biomedical knowledge represents a challenging and cutting-edge trend on biomedical knowledge acquisition. The work presented in this paper shows a new schema to collect diagnosis information in Diagnosis Decision Support Systems (DDSS) based on collective intelligence and consensus methods.

A nonenzymatic DNA nanomachine for biomolecular detection by target recycling of hairpin DNA cascade amplification.

PubMed

Zheng, Jiao; Li, Ningxing; Li, Chunrong; Wang, Xinxin; Liu, Yucheng; Mao, Guobin; Ji, Xinghu; He, Zhike

2018-06-01

Synthetic enzyme-free DNA nanomachine performs quasi-mechanical movements in response to external intervention, suggesting the promise of constructing sensitive and specific biosensors. Herein, a smart DNA nanomachine biosensor for biomolecule (such as nucleic acid, thrombin and adenosine) detection is developed by target-assisted enzyme-free hairpin DNA cascade amplifier. The whole DNA nanomachine system is constructed on gold nanoparticle which decorated with hundreds of locked hairpin substrate strands serving as DNA tracks, and the DNA nanomachine could be activated by target molecule toehold-mediated exchange on gold nanoparticle surface, resulted in the fluorescence recovery of fluorophore. The process is repeated so that each copy of the target can open multiplex fluorophore-labeled hairpin substrate strands, resulted in amplification of the fluorescence signal. Compared with the conventional biosensors of catalytic hairpin assembly (CHA) without substrate in solution, the DNA nanomachine could generate 2-3 orders of magnitude higher fluorescence signal. Furthermore, the DNA nanomachine could be used for nucleic acid, thrombin and adenosine highly sensitive specific detection based on isothermal, and homogeneous hairpin DNA cascade signal amplification in both buffer and a complicated biomatrix, and this kind of DNA nanomachine could be efficiently applied in the field of biomedical analysis. Copyright © 2018 Elsevier B.V. All rights reserved.

Femtosecond, two-photon laser-induced-fluorescence imaging of atomic oxygen in an atmospheric-pressure plasma jet

NASA Astrophysics Data System (ADS)

Schmidt, Jacob B.; Sands, Brian L.; Kulatilaka, Waruna D.; Roy, Sukesh; Scofield, James; Gord, James R.

2015-06-01

Femtosecond, two-photon-absorption laser-induced-fluorescence (fs-TALIF) spectroscopy is employed to measure space- and time-resolved atomic-oxygen distributions in a nanosecond, repetitively pulsed, externally grounded, atmospheric-pressure plasma jet flowing helium with a variable oxygen admixture. The high-peak-intensity, low-average-energy femtosecond pulses result in increased TALIF signal with reduced photolytic inferences. This allows 2D imaging of absolute atomic-oxygen number densities ranging from 5.8   ×   1015 to 2.0   ×   1012cm-3 using a cooled CCD with an external intensifier. Xenon is used for signal and imaging-system calibrations to quantify the atomic-oxygen fluorescence signal. Initial results highlight a transition in discharge morphology from annular to filamentary, corresponding with a change in plasma chemistry from ozone to atomic oxygen production, as the concentration of oxygen in the feed gas is changed at a fixed voltage-pulse-repetition rate. In this configuration, significant concentrations of reactive oxygen species may be remotely generated by sustaining an active discharge beyond the confines of the dielectric capillary, which may benefit applications that require large concentrations of reactive oxygen species such as material processing or biomedical devices.

Digital fabrication of multi-material biomedical objects.

PubMed

Cheung, H H; Choi, S H

2009-12-01

This paper describes a multi-material virtual prototyping (MMVP) system for modelling and digital fabrication of discrete and functionally graded multi-material objects for biomedical applications. The MMVP system consists of a DMMVP module, an FGMVP module and a virtual reality (VR) simulation module. The DMMVP module is used to model discrete multi-material (DMM) objects, while the FGMVP module is for functionally graded multi-material (FGM) objects. The VR simulation module integrates these two modules to perform digital fabrication of multi-material objects, which can be subsequently visualized and analysed in a virtual environment to optimize MMLM processes for fabrication of product prototypes. Using the MMVP system, two biomedical objects, including a DMM human spine and an FGM intervertebral disc spacer are modelled and digitally fabricated for visualization and analysis in a VR environment. These studies show that the MMVP system is a practical tool for modelling, visualization, and subsequent fabrication of biomedical objects of discrete and functionally graded multi-materials for biomedical applications. The system may be adapted to control MMLM machines with appropriate hardware for physical fabrication of biomedical objects.

CAVEman: Standardized anatomical context for biomedical data mapping.

PubMed

Turinsky, Andrei L; Fanea, Elena; Trinh, Quang; Wat, Stephen; Hallgrímsson, Benedikt; Dong, Xiaoli; Shu, Xueling; Stromer, Julie N; Hill, Jonathan W; Edwards, Carol; Grosenick, Brenda; Yajima, Masumi; Sensen, Christoph W

2008-01-01

The authors have created a software system called the CAVEman, for the visual integration and exploration of heterogeneous anatomical and biomedical data. The CAVEman can be applied for both education and research tasks. The main component of the system is a three-dimensional digital atlas of the adult male human anatomy, structured according to the nomenclature of Terminologia Anatomica. The underlying data-indexing mechanism uses standard ontologies to map a range of biomedical data types onto the atlas. The CAVEman system is now used to visualize genetic processes in the context of the human anatomy and to facilitate visual exploration of the data. Through the use of Javatrade mark software, the atlas-based system is portable to virtually any computer environment, including personal computers and workstations. Existing Java tools for biomedical data analysis have been incorporated into the system. The affordability of virtual-reality installations has increased dramatically over the last several years. This creates new opportunities for educational scenarios that model important processes in a patient's body, including gene expression patterns, metabolic activity, the effects of interventions such as drug treatments, and eventually surgical simulations.

Matching layer for path loss reduction in ultra wideband implant communications.

PubMed

Chavez-Santiago, Raul; Khaleghi, Ali; Balasingham, Ilangko

2014-01-01

Real-time monitoring of various physiological signals is of utmost importance for the treatment of chronic conditions. Radio technology can enable real-time sensing and collection of physiological data to facilitate timely medication and early pre-hospital management of patients. This can be realized with the aid of implantable biomedical sensors with the capability to transmit wirelessly the collected information to an external unit for display and analysis. Currently, commercial wireless medical implantable sensors operate in frequencies below 1 GHz with narrowband signals. Recently, it has been demonstrated that ultra wideband (UWB) signals could be also used for the radio interface of these devices. However, establishing an implant communication link in the allocated UWB spectrum of 3.1-10.6 GHz is challenging. The attenuation of UWB signals propagating through biological tissues at these frequencies is high. Part of these path losses are caused by the impedance mismatch between the two propagation environments (i.e., air and biological tissues) that constitute an implant communication link. This mismatch results in inefficient power transmission of the radio waves. In this paper we propose the use of a layer of dielectric material that can be applied on the patient's skin. The permittivity value of this matching layer has to be chosen such that wave coupling is maximized. Through numerical simulations we determined the appropriate permittivity value of a matching layer for UWB implant communication links in the human thorax for 1-6 GHz. Path loss reduction of up to 10 dB can be obtained in this frequency band. These results can help improve the use of UWB signals for other in-body biomedical devices like the wireless capsule endoscope (WCE).

Materials processing in space

NASA Technical Reports Server (NTRS)

1975-01-01

The feasibility and possible advantages of processing materials in a nongravitational field are considered. Areas of investigation include biomedical applications, the processing of inorganic materials, and flight programs and funding.

Osmium isotopes demonstrate distal transport of contaminated sediments in Chesapeake Bay

USGS Publications Warehouse

Helz, G.R.; Adelson, J.M.; Miller, C.V.; Cornwell, J.C.; Hill, J.M.; Horan, M.; Walker, R.J.

2000-01-01

Because the isotopic composition of anthropogenic Os is normally distinctive in comparison to continental crust and is precisely measurable, this platinum-group element is attractive as a tracer of transport pathways for contaminated sediments in estuaries. Evidence herein and elsewhere suggest that biomedical research institutions are the chief source of anthropogenic Os. In the Chesapeake Bay region, uncontaminated sediments bear a crustal 187Os/188Os signature of 0.73 ?? 0.10. Slightly higher 187Os/188Os ratios occur in Re-rich Coastal Plain deposits due to post- Miocene 187Re decay. The upper Susquehanna Basin yields sediments also with higher 187Os/188Os. Beginning in the late 1970s, this signal was overprinted by a low 187Os/188Os (anthropogenic) source in the lower Susquehanna Basin. In the vicinity of Baltimore, which is a major center of heavy industry as well as biomedical research, anthropogenic Os has been found only in sediments impacted by the principal wastewater treatment plant. Surprisingly, a mid-Bay site distant from anthropogenic sources contains the strongest anthropogenic Os signal in the data set, having received anthropogenic Os sporadically since the mid-20th Century. Transport of particles to this site overrode the northward flowing bottom currents. Finding anthropogenic Os at this site cautions that other particle-borne substances, including hazardous ones, could be dispersed broadly in this estuary.Because the isotopic composition of anthropogenic Os is normally distinctive in comparison to continental crust and is precisely measurable, this platinum-group element is attractive as a tracer of transport pathways for contaminated sediments in estuaries. Evidence herein and elsewhere suggest that biomedical research institutions are the chief source of anthropogenic Os. In the Chesapeake Bay region, uncontaminated sediments bear a crustal 187Os/188Os signature of 0.73 ?? 0.10. Slightly higher 187Os/188Os ratios occur in Re-rich Coastal Plain deposits due to post-Miocene 187Re decay. The upper Susquehanna Basin yields sediments also with higher 187Os/188Os. Beginning in the late 1970s, this signal was overprinted by a low 187Os/188Os (anthropogenic) source in the lower Susquehanna Basin. In the vicinity of Baltimore, which is a major center of heavy industry as well as biomedical research, anthropogenic Os has been found only in sediments impacted by the principal wastewater treatment plant. Surprisingly, a mid-Bay site distant from anthropogenic sources contains the strongest anthropogenic Os signal in the data set, having received anthropogenic Os sporadically since the mid-20th Century. Transport of particles to this site overrode the northward flowing bottom currents. Finding anthropogenic Os at this site cautions that other particle-borne substances, including hazardous ones, could be dispersed broadly in this estuary.

One-process fabrication of metal hierarchical nanostructures with rich nanogaps for highly-sensitive surface-enhanced Raman scattering.

PubMed

Liu, Gui-qiang; Yu, Mei-dong; Liu, Zheng-qi; Liu, Xiao-shan; Huang, Shan; Pan, Ping-ping; Wang, Yan; Liu, Mu-lin; Gu, Gang

2015-05-08

One-process fabrication of highly active and reproducible surface-enhanced Raman scattering (SERS) substrates via ion beam deposition is reported. The fabricated metal-dielectric-metal (MDM) hierarchical nanostructure possesses rich nanogaps and a tunable resonant cavity. Raman scattering signals of analytes are dramatically strengthened due to the strong near-field coupling of localized surface plasmon resonances (LSPRs) and the strong interaction of LSPRs of metal NPs with surface plasmon polaritons (SPPs) on the underlying metal film by crossing over the dielectric spacer. The maximum Raman enhancement for the highest Raman peak at 1650 cm(-1) is 13.5 times greater than that of a single metal nanoparticle (NP) array. Moreover, the SERS activity can be efficiently tailored by varying the size and number of voids between adjacent metal NPs and the thickness of the dielectric spacer. These findings may broaden the scope of SERS applications of MDM hierarchical nanostructures in biomedical and analytical chemistry.

An IoT-Based Computational Framework for Healthcare Monitoring in Mobile Environments.

PubMed

Mora, Higinio; Gil, David; Terol, Rafael Muñoz; Azorín, Jorge; Szymanski, Julian

2017-10-10

The new Internet of Things paradigm allows for small devices with sensing, processing and communication capabilities to be designed, which enable the development of sensors, embedded devices and other 'things' ready to understand the environment. In this paper, a distributed framework based on the internet of things paradigm is proposed for monitoring human biomedical signals in activities involving physical exertion. The main advantages and novelties of the proposed system is the flexibility in computing the health application by using resources from available devices inside the body area network of the user. This proposed framework can be applied to other mobile environments, especially those where intensive data acquisition and high processing needs take place. Finally, we present a case study in order to validate our proposal that consists in monitoring footballers' heart rates during a football match. The real-time data acquired by these devices presents a clear social objective of being able to predict not only situations of sudden death but also possible injuries.

An IoT-Based Computational Framework for Healthcare Monitoring in Mobile Environments

PubMed Central

Szymanski, Julian

2017-01-01

The new Internet of Things paradigm allows for small devices with sensing, processing and communication capabilities to be designed, which enable the development of sensors, embedded devices and other ‘things’ ready to understand the environment. In this paper, a distributed framework based on the internet of things paradigm is proposed for monitoring human biomedical signals in activities involving physical exertion. The main advantages and novelties of the proposed system is the flexibility in computing the health application by using resources from available devices inside the body area network of the user. This proposed framework can be applied to other mobile environments, especially those where intensive data acquisition and high processing needs take place. Finally, we present a case study in order to validate our proposal that consists in monitoring footballers’ heart rates during a football match. The real-time data acquired by these devices presents a clear social objective of being able to predict not only situations of sudden death but also possible injuries. PMID:28994743

Facile Synthesis of Conductive Polypyrrole Wrinkle Topographies on Polydimethylsiloxane via a Swelling-Deswelling Process and Their Potential Uses in Tissue Engineering.

PubMed

Aufan, M Rifqi; Sumi, Yang; Kim, Semin; Lee, Jae Young

2015-10-28

Electrically conducting biomaterials have gained great attention in various biomedical studies especially to influence cell and tissue responses. In addition, wrinkling can present a unique topography that can modulate cell-material interactions. In this study, we developed a simple method to create wrinkle topographies of conductive polypyrrole (wPPy) on soft polydimethylsiloxane surfaces via a swelling-deswelling process during and after PPy polymerization and by varying the thickness of the PPy top layers. As a result, various features of wPPy in the range of the nano- and microscales were successfully obtained. In vitro cell culture studies with NIH 3T3 fibroblasts and PC12 neuronal cells indicated that the conductive wrinkle topographies promote cell adhesion and neurite outgrowth of PC12 cells. Our studies help to elucidate the design of the surface coating and patterning of conducting polymers, which will enable us to simultaneously provide topographical and electrical signals to improve cell-surface interactions for potential tissue-engineering applications.

Peptidomics methods for the identification of peptidase-substrate interactions

PubMed Central

Lone, Anna Mari; Kim, Yun-Gon; Saghatelian, Alan

2013-01-01

Peptidases have important roles in controlling physiological signaling through their regulation of bioactive peptides. Understanding and controlling bioactive peptide regulation is of great biomedical interest and approaches that elucidate the interplay between peptidases and their substrates are vital for achieving this goal. Here, we highlight the utility of recent peptidomics approaches in identifying endogenous substrates of peptidases. These approaches reveal bioactive substrates and help characterize the biochemical functions of the enzyme. Most recently, peptidomics approaches have been applied to address the challenging question of identifying the peptidases responsible for regulating specific bioactive peptides. Since peptidases are of great biomedical interest, these approaches will begin to impact our ability to identify new drug targets that regulate important bioactive peptides. PMID:23332665

Semantics-driven modelling of user preferences for information retrieval in the biomedical domain.

PubMed

Gladun, Anatoly; Rogushina, Julia; Valencia-García, Rafael; Béjar, Rodrigo Martínez

2013-03-01

A large amount of biomedical and genomic data are currently available on the Internet. However, data are distributed into heterogeneous biological information sources, with little or even no organization. Semantic technologies provide a consistent and reliable basis with which to confront the challenges involved in the organization, manipulation and visualization of data and knowledge. One of the knowledge representation techniques used in semantic processing is the ontology, which is commonly defined as a formal and explicit specification of a shared conceptualization of a domain of interest. The work presented here introduces a set of interoperable algorithms that can use domain and ontological information to improve information-retrieval processes. This work presents an ontology-based information-retrieval system for the biomedical domain. This system, with which some experiments have been carried out that are described in this paper, is based on the use of domain ontologies for the creation and normalization of lightweight ontologies that represent user preferences in a determined domain in order to improve information-retrieval processes.

Nanostructured cavity devices for extracellular stimulation of HL-1 cells

NASA Astrophysics Data System (ADS)

Czeschik, Anna; Rinklin, Philipp; Derra, Ulrike; Ullmann, Sabrina; Holik, Peter; Steltenkamp, Siegfried; Offenhäusser, Andreas; Wolfrum, Bernhard

2015-05-01

Microelectrode arrays (MEAs) are state-of-the-art devices for extracellular recording and stimulation on biological tissue. Furthermore, they are a relevant tool for the development of biomedical applications like retina, cochlear and motor prostheses, cardiac pacemakers and drug screening. Hence, research on functional cell-sensor interfaces, as well as the development of new surface structures and modifications for improved electrode characteristics, is a vivid and well established field. However, combining single-cell resolution with sufficient signal coupling remains challenging due to poor cell-electrode sealing. Furthermore, electrodes with diameters below 20 µm often suffer from a high electrical impedance affecting the noise during voltage recordings. In this study, we report on a nanocavity sensor array for voltage-controlled stimulation and extracellular action potential recordings on cellular networks. Nanocavity devices combine the advantages of low-impedance electrodes with small cell-chip interfaces, preserving a high spatial resolution for recording and stimulation. A reservoir between opening aperture and electrode is provided, allowing the cell to access the structure for a tight cell-sensor sealing. We present the well-controlled fabrication process and the effect of cavity formation and electrode patterning on the sensor's impedance. Further, we demonstrate reliable voltage-controlled stimulation using nanostructured cavity devices by capturing the pacemaker of an HL-1 cell network.Microelectrode arrays (MEAs) are state-of-the-art devices for extracellular recording and stimulation on biological tissue. Furthermore, they are a relevant tool for the development of biomedical applications like retina, cochlear and motor prostheses, cardiac pacemakers and drug screening. Hence, research on functional cell-sensor interfaces, as well as the development of new surface structures and modifications for improved electrode characteristics, is a vivid and well established field. However, combining single-cell resolution with sufficient signal coupling remains challenging due to poor cell-electrode sealing. Furthermore, electrodes with diameters below 20 µm often suffer from a high electrical impedance affecting the noise during voltage recordings. In this study, we report on a nanocavity sensor array for voltage-controlled stimulation and extracellular action potential recordings on cellular networks. Nanocavity devices combine the advantages of low-impedance electrodes with small cell-chip interfaces, preserving a high spatial resolution for recording and stimulation. A reservoir between opening aperture and electrode is provided, allowing the cell to access the structure for a tight cell-sensor sealing. We present the well-controlled fabrication process and the effect of cavity formation and electrode patterning on the sensor's impedance. Further, we demonstrate reliable voltage-controlled stimulation using nanostructured cavity devices by capturing the pacemaker of an HL-1 cell network. Electronic supplementary information (ESI) available: Comparison of non-filtered and Savitzky-Golay filtered action potential recordings, electrical signals and corresponding optical signals. See DOI: 10.1039/c5nr01690h

Phase-space dissimilarity measures for industrial and biomedical applications

NASA Astrophysics Data System (ADS)

Protopopescu, V. A.; Hively, L. M.

2005-12-01

One of the most important problems in time-series analysis is the suitable characterization of the dynamics for timely, accurate, and robust condition assessment of the underlying system. Machine and physiological processes display complex, non-stationary behaviors that are affected by noise and may range from (quasi-)periodic to completely irregular (chaotic) regimes. Nevertheless, extensive experimental evidence indicates that even when the systems behave very irregularly (e.g., severe tool chatter or cardiac fibrillation), one may assume that - for all practical purposes - the dynamics are confined to low dimensional manifolds. As a result, the behavior of these systems can be described via traditional nonlinear measures (TNM), such as Lyapunov exponents, Kolmogorov entropy, and correlation dimension. While these measures are adequate for discriminating between clear-cut regular and chaotic dynamics, they are not sufficiently sensitive to distinguish between slightly different irregular (chaotic) regimes, especially when data are noisy and/or limited. Both machine and physiological dynamics usually fall into this latter category, creating a massive stumbling block to prognostication of abnormal regimes. We present here a recently developed approach that captures more efficiently changes in the underlying dynamics. We start with process-indicative, time-serial data that are checked for quality and discarded if inadequate. Acceptable data are filtered to remove confounding artifacts (e.g., sinusoidal variation in three-phase electrical signals or eye-blinks and muscular activity in EEG). The artifact-filtered data are then used to recover the essential features of the underlying dynamics via standard time-delay, phase-space reconstruction. One of the main results of this reconstruction is a discrete approximation of the distribution function (DF) on the attractor. Unaltered dynamics yield an unchanging geometry of the attractor and the visitation frequencies of its various points, corresponding to the baseline DF. Condition change is established by comparing the base line DFs to subsequent test case DFs via new, phase space dissimilarity measures (PSDM), namely the distance and - square statistics between two DFs. A clear trend in the dissimilarity measures over time indicates substantial departure from the baseline dynamics, thus signaling condition change. The severity of this departure can be interpreted as a "normal" fluctuation, abnormal behavior, impending failure, or complete breakdown. We illustrate the new approach on an assortment of machinery and biomedical examples. The machine data were collected during laboratory tests on industrial equipment, for diverse failure modes, via seeded faults and accelerated failures. The biomedical applications involve detection of physiological changes, such as epileptic seizures from EEG; ventricular fibrillation, fainting, and sepsis onset from ECG; and breathing difficulty from chest sounds. The PSDM show a consistent discrimination of normal-to-abnormal transitions, allowing earlier, more accurate, and more robust detection of the dynamical change for all of these applications in comparison to TNM.

Design of SERS nanoprobes for Raman imaging: materials, critical factors and architectures.

PubMed

Li, Mingwang; Qiu, Yuanyuan; Fan, Chenchen; Cui, Kai; Zhang, Yongming; Xiao, Zeyu

2018-05-01

Raman imaging yields high specificity and sensitivity when compared to other imaging modalities, mainly due to its fingerprint signature. However, intrinsic Raman signals are weak, thus limiting medical applications of Raman imaging. By adsorbing Raman molecules onto specific nanostructures such as noble metals, Raman signals can be significantly enhanced, termed surface-enhanced Raman scattering (SERS). Recent years have witnessed great interest in the development of SERS nanoprobes for Raman imaging. Rationally designed SERS nanoprobes have greatly enhanced Raman signals by several orders of magnitude, thus showing great potential for biomedical applications. In this review we elaborate on recent progress in design strategies with emphasis on material properties, modifying factors, and structural parameters.

A Monte Carlo technique for signal level detection in implanted intracranial pressure monitoring.

PubMed

Avent, R K; Charlton, J D; Nagle, H T; Johnson, R N

1987-01-01

Statistical monitoring techniques like CUSUM, Trigg's tracking signal and EMP filtering have a major advantage over more recent techniques, such as Kalman filtering, because of their inherent simplicity. In many biomedical applications, such as electronic implantable devices, these simpler techniques have greater utility because of the reduced requirements on power, logic complexity and sampling speed. The determination of signal means using some of the earlier techniques are reviewed in this paper, and a new Monte Carlo based method with greater capability to sparsely sample a waveform and obtain an accurate mean value is presented. This technique may find widespread use as a trend detection method when reduced power consumption is a requirement.

Biomedical informatics and the convergence of Nano-Bio-Info-Cogno (NBIC) technologies.

PubMed

Martin-Sanchez, F; Maojo, V

2009-01-01

To analyze the role that biomedical informatics could play in the application of the NBIC Converging Technologies in the medical field and raise awareness of these new areas throughout the Biomedical Informatics community. Review of the literature and analysis of the reference documents in this domain from the biomedical informatics perspective. Detailing existing developments showing that partial convergence of technologies have already yielded relevant results in biomedicine (such as bioinformatics or biochips). Input from current projects in which the authors are involved is also used. Information processing is a key issue in enabling the convergence of NBIC technologies. Researchers in biomedical informatics are in a privileged position to participate and actively develop this new scientific direction. The experience of biomedical informaticians in five decades of research in the medical area and their involvement in the completion of the Human and other genome projects will help them participate in a similar role for the development of applications of converging technologies -particularly in nanomedicine. The proposed convergence will bring bridges between traditional disciplines. Particular attention should be placed on the ethical, legal, and social issues raised by the NBIC convergence. These technologies provide new directions for research and education in Biomedical Informatics placing a greater emphasis in multidisciplinary approaches.

Guidelines for Developing and Reporting Machine Learning Predictive Models in Biomedical Research: A Multidisciplinary View

PubMed Central

2016-01-01

Background As more and more researchers are turning to big data for new opportunities of biomedical discoveries, machine learning models, as the backbone of big data analysis, are mentioned more often in biomedical journals. However, owing to the inherent complexity of machine learning methods, they are prone to misuse. Because of the flexibility in specifying machine learning models, the results are often insufficiently reported in research articles, hindering reliable assessment of model validity and consistent interpretation of model outputs. Objective To attain a set of guidelines on the use of machine learning predictive models within clinical settings to make sure the models are correctly applied and sufficiently reported so that true discoveries can be distinguished from random coincidence. Methods A multidisciplinary panel of machine learning experts, clinicians, and traditional statisticians were interviewed, using an iterative process in accordance with the Delphi method. Results The process produced a set of guidelines that consists of (1) a list of reporting items to be included in a research article and (2) a set of practical sequential steps for developing predictive models. Conclusions A set of guidelines was generated to enable correct application of machine learning models and consistent reporting of model specifications and results in biomedical research. We believe that such guidelines will accelerate the adoption of big data analysis, particularly with machine learning methods, in the biomedical research community. PMID:27986644

Dynamic knowledge representation using agent-based modeling: ontology instantiation and verification of conceptual models.

PubMed

An, Gary

2009-01-01

The sheer volume of biomedical research threatens to overwhelm the capacity of individuals to effectively process this information. Adding to this challenge is the multiscale nature of both biological systems and the research community as a whole. Given this volume and rate of generation of biomedical information, the research community must develop methods for robust representation of knowledge in order for individuals, and the community as a whole, to "know what they know." Despite increasing emphasis on "data-driven" research, the fact remains that researchers guide their research using intuitively constructed conceptual models derived from knowledge extracted from publications, knowledge that is generally qualitatively expressed using natural language. Agent-based modeling (ABM) is a computational modeling method that is suited to translating the knowledge expressed in biomedical texts into dynamic representations of the conceptual models generated by researchers. The hierarchical object-class orientation of ABM maps well to biomedical ontological structures, facilitating the translation of ontologies into instantiated models. Furthermore, ABM is suited to producing the nonintuitive behaviors that often "break" conceptual models. Verification in this context is focused at determining the plausibility of a particular conceptual model, and qualitative knowledge representation is often sufficient for this goal. Thus, utilized in this fashion, ABM can provide a powerful adjunct to other computational methods within the research process, as well as providing a metamodeling framework to enhance the evolution of biomedical ontologies.

Measurement of OH, NO, O and N atoms in helium plasma jet for ROS/RNS controlled biomedical processes

NASA Astrophysics Data System (ADS)

Yonemori, Seiya; Kamakura, Taku; Ono, Ryo

2014-10-01

Atmospheric-pressure plasmas are of emerging interest for new plasma applications such as cancer treatment, cell activation and sterilization. In those biomedical processes, reactive oxygen/nitrogen species (ROS/RNS) are said that they play significant role. It is though that active species give oxidative stress and induce biomedical reactions. In this study, we measured OH, NO, O and N atoms using laser induced fluorescence (LIF) measurement and found that voltage polarity affect particular ROS. When negative high voltage was applied to the plasma jet, O atom density was tripled compared to the case of positive applied voltage. In that case, O atom density was around 3 × 1015 [cm-3] at maximum. In contrast, OH and NO density did not change their density depending on the polarity of applied voltage, measured as in order of 1013 and 1014 [cm-3] at maximum, respectively. From ICCD imaging measurement, it could be seen that negative high voltage enhanced secondary emission in plasma bullet propagation and it can affect the effective production of particular ROS. Since ROS/RNS dose can be a quantitative criterion to control plasma biomedical application, those measurement results is able to be applied for in vivo and in vitro plasma biomedical experiments. This study is supported by the Grant-in-Aid for Science Research by the Ministry of Education, Culture, Sport, Science and Technology.

A neural algorithm for the non-uniform and adaptive sampling of biomedical data.

PubMed

Mesin, Luca

2016-04-01

Body sensors are finding increasing applications in the self-monitoring for health-care and in the remote surveillance of sensitive people. The physiological data to be sampled can be non-stationary, with bursts of high amplitude and frequency content providing most information. Such data could be sampled efficiently with a non-uniform schedule that increases the sampling rate only during activity bursts. A real time and adaptive algorithm is proposed to select the sampling rate, in order to reduce the number of measured samples, but still recording the main information. The algorithm is based on a neural network which predicts the subsequent samples and their uncertainties, requiring a measurement only when the risk of the prediction is larger than a selectable threshold. Four examples of application to biomedical data are discussed: electromyogram, electrocardiogram, electroencephalogram, and body acceleration. Sampling rates are reduced under the Nyquist limit, still preserving an accurate representation of the data and of their power spectral densities (PSD). For example, sampling at 60% of the Nyquist frequency, the percentage average rectified errors in estimating the signals are on the order of 10% and the PSD is fairly represented, until the highest frequencies. The method outperforms both uniform sampling and compressive sensing applied to the same data. The discussed method allows to go beyond Nyquist limit, still preserving the information content of non-stationary biomedical signals. It could find applications in body sensor networks to lower the number of wireless communications (saving sensor power) and to reduce the occupation of memory. Copyright © 2016 Elsevier Ltd. All rights reserved.

A novel biomedical image indexing and retrieval system via deep preference learning.

PubMed

Pang, Shuchao; Orgun, Mehmet A; Yu, Zhezhou

2018-05-01

The traditional biomedical image retrieval methods as well as content-based image retrieval (CBIR) methods originally designed for non-biomedical images either only consider using pixel and low-level features to describe an image or use deep features to describe images but still leave a lot of room for improving both accuracy and efficiency. In this work, we propose a new approach, which exploits deep learning technology to extract the high-level and compact features from biomedical images. The deep feature extraction process leverages multiple hidden layers to capture substantial feature structures of high-resolution images and represent them at different levels of abstraction, leading to an improved performance for indexing and retrieval of biomedical images. We exploit the current popular and multi-layered deep neural networks, namely, stacked denoising autoencoders (SDAE) and convolutional neural networks (CNN) to represent the discriminative features of biomedical images by transferring the feature representations and parameters of pre-trained deep neural networks from another domain. Moreover, in order to index all the images for finding the similarly referenced images, we also introduce preference learning technology to train and learn a kind of a preference model for the query image, which can output the similarity ranking list of images from a biomedical image database. To the best of our knowledge, this paper introduces preference learning technology for the first time into biomedical image retrieval. We evaluate the performance of two powerful algorithms based on our proposed system and compare them with those of popular biomedical image indexing approaches and existing regular image retrieval methods with detailed experiments over several well-known public biomedical image databases. Based on different criteria for the evaluation of retrieval performance, experimental results demonstrate that our proposed algorithms outperform the state-of-the-art techniques in indexing biomedical images. We propose a novel and automated indexing system based on deep preference learning to characterize biomedical images for developing computer aided diagnosis (CAD) systems in healthcare. Our proposed system shows an outstanding indexing ability and high efficiency for biomedical image retrieval applications and it can be used to collect and annotate the high-resolution images in a biomedical database for further biomedical image research and applications. Copyright © 2018 Elsevier B.V. All rights reserved.

Solution Structure of an Intramembrane Aspartyl Protease via Small Angle Neutron Scattering

DOE Office of Scientific and Technical Information (OSTI.GOV)

Naing, Swe-Htet; Oliver, Ryan C.; Weiss, Kevin L.

Intramembrane aspartyl proteases (IAPs) comprise one of four families of integral membrane proteases that hydrolyze substrates within the hydrophobic lipid bilayer. IAPs include signal peptide peptidase, which processes remnant signal peptides from nascent polypeptides in the endoplasmic reticulum, and presenilin, the catalytic component of the γ-secretase complex that processes Notch and amyloid precursor protein. Despite their broad biomedical reach, basic structure-function relationships of IAPs remain active areas of research. Characterization of membrane-bound proteins is notoriously challenging due to their inherently hydrophobic character. For IAPs, oligomerization state in solution is one outstanding question, with previous proposals for monomer, dimer, tetramer, andmore » octamer. Here we used small angle neutron scattering (SANS) to characterize n-dodecyl-β-D-maltopyranoside (DDM) detergent solutions containing and absent a microbial IAP ortholog. A unique feature of SANS is the ability to modulate the solvent composition to mask all but the enzyme of interest. The signal from the IAP was enhanced by deuteration and, uniquely, scattering from DDM and buffers were matched by the use of both tail-deuterated DDM and D 2O. The radius of gyration calculated for IAP and the corresponding ab initio consensus model are consistent with a monomer. The model is slightly smaller than the crystallographic IAP monomer, suggesting a more compact protein in solution compared with the crystal lattice. In conclusion, our study provides direct insight into the oligomeric state of purified IAP in surfactant solution, and demonstrates the utility of fully contrast-matching the detergent in SANS to characterize other intramembrane proteases and their membrane-bound substrates.« less

Solution Structure of an Intramembrane Aspartyl Protease via Small Angle Neutron Scattering

DOE PAGES

Naing, Swe-Htet; Oliver, Ryan C.; Weiss, Kevin L.; ...

2018-02-06

Intramembrane aspartyl proteases (IAPs) comprise one of four families of integral membrane proteases that hydrolyze substrates within the hydrophobic lipid bilayer. IAPs include signal peptide peptidase, which processes remnant signal peptides from nascent polypeptides in the endoplasmic reticulum, and presenilin, the catalytic component of the γ-secretase complex that processes Notch and amyloid precursor protein. Despite their broad biomedical reach, basic structure-function relationships of IAPs remain active areas of research. Characterization of membrane-bound proteins is notoriously challenging due to their inherently hydrophobic character. For IAPs, oligomerization state in solution is one outstanding question, with previous proposals for monomer, dimer, tetramer, andmore » octamer. Here we used small angle neutron scattering (SANS) to characterize n-dodecyl-β-D-maltopyranoside (DDM) detergent solutions containing and absent a microbial IAP ortholog. A unique feature of SANS is the ability to modulate the solvent composition to mask all but the enzyme of interest. The signal from the IAP was enhanced by deuteration and, uniquely, scattering from DDM and buffers were matched by the use of both tail-deuterated DDM and D 2O. The radius of gyration calculated for IAP and the corresponding ab initio consensus model are consistent with a monomer. The model is slightly smaller than the crystallographic IAP monomer, suggesting a more compact protein in solution compared with the crystal lattice. In conclusion, our study provides direct insight into the oligomeric state of purified IAP in surfactant solution, and demonstrates the utility of fully contrast-matching the detergent in SANS to characterize other intramembrane proteases and their membrane-bound substrates.« less

Application of complex discrete wavelet transform in classification of Doppler signals using complex-valued artificial neural network.

PubMed

Ceylan, Murat; Ceylan, Rahime; Ozbay, Yüksel; Kara, Sadik

2008-09-01

In biomedical signal classification, due to the huge amount of data, to compress the biomedical waveform data is vital. This paper presents two different structures formed using feature extraction algorithms to decrease size of feature set in training and test data. The proposed structures, named as wavelet transform-complex-valued artificial neural network (WT-CVANN) and complex wavelet transform-complex-valued artificial neural network (CWT-CVANN), use real and complex discrete wavelet transform for feature extraction. The aim of using wavelet transform is to compress data and to reduce training time of network without decreasing accuracy rate. In this study, the presented structures were applied to the problem of classification in carotid arterial Doppler ultrasound signals. Carotid arterial Doppler ultrasound signals were acquired from left carotid arteries of 38 patients and 40 healthy volunteers. The patient group included 22 males and 16 females with an established diagnosis of the early phase of atherosclerosis through coronary or aortofemoropopliteal (lower extremity) angiographies (mean age, 59 years; range, 48-72 years). Healthy volunteers were young non-smokers who seem to not bear any risk of atherosclerosis, including 28 males and 12 females (mean age, 23 years; range, 19-27 years). Sensitivity, specificity and average detection rate were calculated for comparison, after training and test phases of all structures finished. These parameters have demonstrated that training times of CVANN and real-valued artificial neural network (RVANN) were reduced using feature extraction algorithms without decreasing accuracy rate in accordance to our aim.

Development of an optical fiber SERS microprobe for minimally invasive sensing applications

NASA Astrophysics Data System (ADS)

Mamun, Md Abdullah Al; Juodkazis, Saulius; Mahadevan-Jansen, Anita; Stoddart, Paul R.

2018-02-01

Numerous potential biomedical sensing applications of surface-enhanced Raman scattering (SERS) have been reported, but its practical use has been limited by the lack of a robust sensing platform. Optical fiber SERS probes show great promise, but are limited by the prominent silica Raman background, which requires the use of bulky optics for filtering the signal collection and excitation delivery paths. In the present study, a SERS microprobe has been designed and developed to eliminate the bottlenecks outlined above. For efficient excitation and delivery of the SERS signal, both hollow core photonic crystal fiber and double clad fiber have been investigated. While the hollow core fiber was still found to have excessive silica background, the double clad fiber allows efficient signal collection via the multi-mode inner cladding. A micro filtering mechanism has been designed, which can be integrated into the tip of the optical fiber SERS probe, providing filtering to suppress silica Raman background and thus avoiding the need for bulky optics. The design also assists in the efficient collection of SERS signal from the sample by rejecting Rayleigh scattered light from the sample. Optical fiber cleaving using ultra-short laser pulses was tested for improved control of the fiber tip geometry. With this miniaturized and integrated filtering mechanism, it is expected that the developed probe will promote the use of SERS for minimally invasive biomedical monitoring and sensing applications in future. The probe could potentially be placed inside a small gauge hypodermic needle and would be compatible with handheld portable spectrometers.

Maximizing the return on taxpayers' investments in fundamental biomedical research.

PubMed

Lorsch, Jon R

2015-05-01

The National Institute of General Medical Sciences (NIGMS) at the U.S. National Institutes of Health has an annual budget of more than $2.3 billion. The institute uses these funds to support fundamental biomedical research and training at universities, medical schools, and other institutions across the country. My job as director of NIGMS is to work to maximize the scientific returns on the taxpayers' investments. I describe how we are optimizing our investment strategies and funding mechanisms, and how, in the process, we hope to create a more efficient and sustainable biomedical research enterprise.

Pacific Northwest Laboratory annual report for 1983 to the DOE Office of Energy Research. Part 1. Biomedical sciences

DOE Office of Scientific and Technical Information (OSTI.GOV)

Drucker, H.

1983-02-01

Biomedical and health effects research conducted at PNL in 1982 on the evaluation of risk to man from existing and/or developing energy-related technologies are described. Most of the studies described in this report relate to activities for three major energy technologies: nuclear fuel cycle; fossil fuel cycle (oil, gas, and coal process technologies, mining, and utilization; synfuel development), and fudion (biomagnetic effects). The report is organized under these technologies. In addition, research reports are included on the application of nuclear energy to biomedical problems. Individual projects are indexed separately.

Maximizing the return on taxpayers' investments in fundamental biomedical research

PubMed Central

Lorsch, Jon R.

2015-01-01

The National Institute of General Medical Sciences (NIGMS) at the U.S. National Institutes of Health has an annual budget of more than $2.3 billion. The institute uses these funds to support fundamental biomedical research and training at universities, medical schools, and other institutions across the country. My job as director of NIGMS is to work to maximize the scientific returns on the taxpayers' investments. I describe how we are optimizing our investment strategies and funding mechanisms, and how, in the process, we hope to create a more efficient and sustainable biomedical research enterprise. PMID:25926703

Introducing meta-services for biomedical information extraction

PubMed Central

Leitner, Florian; Krallinger, Martin; Rodriguez-Penagos, Carlos; Hakenberg, Jörg; Plake, Conrad; Kuo, Cheng-Ju; Hsu, Chun-Nan; Tsai, Richard Tzong-Han; Hung, Hsi-Chuan; Lau, William W; Johnson, Calvin A; Sætre, Rune; Yoshida, Kazuhiro; Chen, Yan Hua; Kim, Sun; Shin, Soo-Yong; Zhang, Byoung-Tak; Baumgartner, William A; Hunter, Lawrence; Haddow, Barry; Matthews, Michael; Wang, Xinglong; Ruch, Patrick; Ehrler, Frédéric; Özgür, Arzucan; Erkan, Güneş; Radev, Dragomir R; Krauthammer, Michael; Luong, ThaiBinh; Hoffmann, Robert; Sander, Chris; Valencia, Alfonso

2008-01-01

We introduce the first meta-service for information extraction in molecular biology, the BioCreative MetaServer (BCMS; ). This prototype platform is a joint effort of 13 research groups and provides automatically generated annotations for PubMed/Medline abstracts. Annotation types cover gene names, gene IDs, species, and protein-protein interactions. The annotations are distributed by the meta-server in both human and machine readable formats (HTML/XML). This service is intended to be used by biomedical researchers and database annotators, and in biomedical language processing. The platform allows direct comparison, unified access, and result aggregation of the annotations. PMID:18834497

Parameters of Microcirculation in the Broad Ligament of the Uterus in Wistar Rats after Injection of Autologous Biomedical Cell Product.

PubMed

Dergacheva, T I; Lykov, A P; Shurlygina, A V; Starkova, E V; Poveshchenko, O V; Bondarenko, N A; Kim, I I; Tenditnik, M V; Borodin, Yu I; Konenkov, V I

2015-10-01

We studied the effects of autologous biomedical cell product (bone marrow multipotent mesenchymal stromal cells and their conditioned media) on the parameters of the microcirculatory bed in the broad ligament of the uterus of normal Wistar rats were studied. The parameters of microcirculation and lymph drainage in the broad ligament changed in opposite directions in response to injection of autologous biomedical cell product via different routes. This fact should be taken into consideration when prescribing cell therapy for inflammatory degenerative processes in the pelvic organs.

Biomedical Informatics for Computer-Aided Decision Support Systems: A Survey

PubMed Central

Belle, Ashwin; Kon, Mark A.; Najarian, Kayvan

2013-01-01

The volumes of current patient data as well as their complexity make clinical decision making more challenging than ever for physicians and other care givers. This situation calls for the use of biomedical informatics methods to process data and form recommendations and/or predictions to assist such decision makers. The design, implementation, and use of biomedical informatics systems in the form of computer-aided decision support have become essential and widely used over the last two decades. This paper provides a brief review of such systems, their application protocols and methodologies, and the future challenges and directions they suggest. PMID:23431259

The effects of motif net charge and amphiphilicity on the self-assembly of functionally designer RADA16-I peptides.

PubMed

Wu, Dongni; Zhang, Shuangying; Zhao, Yuyuan; Ao, Ningjian; Ramakrishna, Seeram; He, Liumin

2018-03-16

RADA16-I (Ac-(RADA) 4 -CONH 2 ) is a widely investigated self-assembling peptide (SAP) in the biomedical field. It can undergo ordered self-assembly to form stable secondary structures, thereby further forming a nanofiber hydrogel. The modification of RADA16-I with functional peptide motifs has become a popular research topic. Researchers aim to exhibit particular biomedical signaling, and subsequently, further expand its applications. However, only a few fundamental reports are available on the influences of the peptide motifs on self-assembly mechanisms of designer functional RADA16-I SAPs. In this study, we designed RGD-modified RADA16-I SAPs with a series of net charges and amphiphilicities. The assembly/reassembly of these functionally designer SAPs was thoroughly studied using Raman spectroscopy, CD spectroscopy, and AFM. The nanofiber morphology and the secondary structure largely depended on the balance between the hydrophobic effects versus like-charge repulsions of the motifs, which should be to the focus in order to achieve a tailored nanostructure. Our study would contribute insight into considerations for sophisticated design of SAPs for biomedical applications.

Efficient processing of fluorescence images using directional multiscale representations.

PubMed

Labate, D; Laezza, F; Negi, P; Ozcan, B; Papadakis, M

2014-01-01

Recent advances in high-resolution fluorescence microscopy have enabled the systematic study of morphological changes in large populations of cells induced by chemical and genetic perturbations, facilitating the discovery of signaling pathways underlying diseases and the development of new pharmacological treatments. In these studies, though, due to the complexity of the data, quantification and analysis of morphological features are for the vast majority handled manually, slowing significantly data processing and limiting often the information gained to a descriptive level. Thus, there is an urgent need for developing highly efficient automated analysis and processing tools for fluorescent images. In this paper, we present the application of a method based on the shearlet representation for confocal image analysis of neurons. The shearlet representation is a newly emerged method designed to combine multiscale data analysis with superior directional sensitivity, making this approach particularly effective for the representation of objects defined over a wide range of scales and with highly anisotropic features. Here, we apply the shearlet representation to problems of soma detection of neurons in culture and extraction of geometrical features of neuronal processes in brain tissue, and propose it as a new framework for large-scale fluorescent image analysis of biomedical data.

Efficient processing of fluorescence images using directional multiscale representations

PubMed Central

Labate, D.; Laezza, F.; Negi, P.; Ozcan, B.; Papadakis, M.

2017-01-01

Recent advances in high-resolution fluorescence microscopy have enabled the systematic study of morphological changes in large populations of cells induced by chemical and genetic perturbations, facilitating the discovery of signaling pathways underlying diseases and the development of new pharmacological treatments. In these studies, though, due to the complexity of the data, quantification and analysis of morphological features are for the vast majority handled manually, slowing significantly data processing and limiting often the information gained to a descriptive level. Thus, there is an urgent need for developing highly efficient automated analysis and processing tools for fluorescent images. In this paper, we present the application of a method based on the shearlet representation for confocal image analysis of neurons. The shearlet representation is a newly emerged method designed to combine multiscale data analysis with superior directional sensitivity, making this approach particularly effective for the representation of objects defined over a wide range of scales and with highly anisotropic features. Here, we apply the shearlet representation to problems of soma detection of neurons in culture and extraction of geometrical features of neuronal processes in brain tissue, and propose it as a new framework for large-scale fluorescent image analysis of biomedical data. PMID:28804225

Cold atmospheric plasma (CAP), a novel physicochemical source, induces neural differentiation through cross-talk between the specific RONS cascade and Trk/Ras/ERK signaling pathway.

PubMed

Jang, Ja-Young; Hong, Young June; Lim, Junsup; Choi, Jin Sung; Choi, Eun Ha; Kang, Seongman; Rhim, Hyangshuk

2018-02-01

Plasma, formed by ionization of gas molecules or atoms, is the most abundant form of matter and consists of highly reactive physicochemical species. In the physics and chemistry fields, plasma has been extensively studied; however, the exact action mechanisms of plasma on biological systems, including cells and humans, are not well known. Recent evidence suggests that cold atmospheric plasma (CAP), which refers to plasma used in the biomedical field, may regulate diverse cellular processes, including neural differentiation. However, the mechanism by which these physicochemical signals, elicited by reactive oxygen and nitrogen species (RONS), are transmitted to biological system remains elusive. In this study, we elucidated the physicochemical and biological (PCB) connection between the CAP cascade and Trk/Ras/ERK signaling pathway, which resulted in neural differentiation. Excited atomic oxygen in the plasma phase led to the formation of RONS in the PCB network, which then interacted with reactive atoms in the extracellular liquid phase to form nitric oxide (NO). Production of large amounts of superoxide radical (O 2 - ) in the mitochondria of cells exposed to CAP demonstrated that extracellular NO induced the reversible inhibition of mitochondrial complex IV. We also demonstrated that cytosolic hydrogen peroxide, formed by O 2 - dismutation, act as an intracellular messenger to specifically activate the Trk/Ras/ERK signaling pathway. This study is the first to elucidate the mechanism linking physicochemical signals from the CAP cascade to the intracellular neural differentiation signaling pathway, providing physical, chemical and biological insights into the development of therapeutic techniques to treat neurological diseases. Copyright © 2017 Elsevier Ltd. All rights reserved.

Self-calibration for lensless color microscopy.

PubMed

Flasseur, Olivier; Fournier, Corinne; Verrier, Nicolas; Denis, Loïc; Jolivet, Frédéric; Cazier, Anthony; Lépine, Thierry

2017-05-01

Lensless color microscopy (also called in-line digital color holography) is a recent quantitative 3D imaging method used in several areas including biomedical imaging and microfluidics. By targeting cost-effective and compact designs, the wavelength of the low-end sources used is known only imprecisely, in particular because of their dependence on temperature and power supply voltage. This imprecision is the source of biases during the reconstruction step. An additional source of error is the crosstalk phenomenon, i.e., the mixture in color sensors of signals originating from different color channels. We propose to use a parametric inverse problem approach to achieve self-calibration of a digital color holographic setup. This process provides an estimation of the central wavelengths and crosstalk. We show that taking the crosstalk phenomenon into account in the reconstruction step improves its accuracy.

Genomically Encoded Analog Memory with Precise In vivo DNA Writing in Living Cell Populations

PubMed Central

Farzadfard, Fahim; Lu, Timothy K.

2014-01-01

Cellular memory is crucial to many natural biological processes and for sophisticated synthetic-biology applications. Existing cellular memories rely on epigenetic switches or recombinases, which are limited in scalability and recording capacity. Here, we use the DNA of living cell populations as genomic ‘tape recorders’ for the analog and distributed recording of long-term event histories. We describe a platform for generating single-stranded DNA (ssDNA) in vivo in response to arbitrary transcriptional signals. When co-expressed with a recombinase, these intracellularly expressed ssDNAs target specific genomic DNA addresses, resulting in precise mutations that accumulate in cell populations as a function of the magnitude and duration of the inputs. This platform could enable long-term cellular recorders for environmental and biomedical applications, biological state machines, and enhanced genome engineering strategies. PMID:25395541

Photoacoustic tomography based on the Green's function retrieval with ultrasound interferometry for sample partially behind an acoustically scattering layer

DOE Office of Scientific and Technical Information (OSTI.GOV)

Yin, Jie; Department of Automation, Nanjing Polytechnic Institute, 210048 Nanjing; Tao, Chao, E-mail: taochao@nju.edu.cn

2015-06-08

Acoustically inhomogeneous mediums with multiple scattering are often the nightmare of photoacoustic tomography. In order to break this limitation, a photoacoustic tomography scheme combining ultrasound interferometry and time reversal is proposed to achieve images in acoustically scattering medium. An ultrasound interferometry is developed to determine the unknown Green's function of strong scattering tissue. Using the determined Greens' function, a time-reversal process is carried out to restore images behind an acoustically inhomogeneous layer from the scattering photoacoustic signals. This method effectively decreases the false contrast, noise, and position deviation of images induced by the multiple scattering. Phantom experiment is carried outmore » to validate the method. Therefore, the proposed method could have potential value in extending the biomedical applications of photoacoustic tomography in acoustically inhomogeneous tissue.« less

Nanoparticle-Functionalized Porous Polymer Monolith Detection Elements for Surface-Enhanced Raman Scattering

PubMed Central

Liu, Jikun; White, Ian; DeVoe, Don L.

2011-01-01

The use of porous polymer monoliths functionalized with silver nanoparticles is introduced in this work for high-sensitivity surface-enhanced Raman scattering (SERS) detection. Preparation of the SERS detection elements is a simple process comprising the synthesis of a discrete polymer monolith section within a silica capillary, followed by physically trapping silver nanoparticle aggregates within the monolith matrix. A SERS detection limit of 220 fmol for Rhodamine 6G (R6G) is demonstrated, with excellent signal stability over a 24 h period. The capability of the SERS-active monolith for label-free detection of biomolecules was demonstrated by measurements of bradykinin and cyctochrome c. The SERS-active monoliths can be readily integrated into miniaturized micro-total-analysis systems for on-line and label-free detection for a variety of biosensing, bioanalytical, and biomedical applications. PMID:21322579

Covering the optical spectrum through collective rare-earth doping of NaGdF4 nanoparticles: 806 and 980 nm excitation routes.

PubMed

Skripka, A; Marin, R; Benayas, A; Canton, P; Hemmer, E; Vetrone, F

2017-05-17

Today, at the frontier of biomedical research, the need has been clearly established for integrating disease detection and therapeutic function in one single theranostic system. Light-emitting nanoparticles are being intensively investigated to fulfil this demand, by continuously developing nanoparticle systems simultaneously emitting in both the UV/visible (light-triggered release and activation of drugs) and the near-infrared (imaging and tracking) spectral regions. In this work, rare-earth (RE) doped nanoparticles (RENPs) were synthesized via a thermal decomposition process and spectroscopically investigated as potential candidates as all-in-one optical imaging, diagnostic and therapeutic agents. These core/shell/shell nanoparticles (NaGdF 4 :Er 3+ ,Ho 3+ ,Yb 3+ /NaGdF 4 :Nd 3+ ,Yb 3+ /NaGdF 4 ) are optically excited by heating-free 806 nm light that, aside from minimizing the local thermal load, also allows to obtain a deeper sub-tissue penetration with respect to the still widely used 980 nm light. Moreover, these water-dispersed nanoplatforms offer interesting assets as triggers/probes for biomedical applications, by virtue of a plethora of emission bands (spanning the 380-1600 nm range). Our results pave the way to use these RENPs for UV/visible-triggered photodynamic therapy/drug release, while simultaneously tracking the nanoparticle biodistribution and monitoring their therapeutic action through the near-infrared signal that overlaps with biological transparency windows.

[Biosensor development in clinical analysis].

PubMed

Boitieux, J L; Desmet, G; Thomas, D

1985-01-01

The use of enzymes immobilized or as markers formed the subject of more than thousand publications in the field of industry or biomedical applications, during the last five years. Recently, some authors published works concerning immobilization of total microorganisms for catalytic purposes, others use the enzymatic activity for marking molecules involved in immunological analysis processes. Together industrial biotechnology and medical analysis laboratory are interested with the evolution of these procedures involving the activity of immobilized enzymes. Enzyme immobilization allowed the lowering of analysis costs for, in this case, the enzyme can be used several times. We take account of the two main cases which are encountered during utilization of immobilized enzymes of analytical purposes. The enzyme is used directly for the catalysed reaction or it is used as enzymatic marker. These both aspects are developed mainly for the elaboration of enzymatic and immunoenzymatic electrodes and the realization of automatic computerized devices allowing continuous estimation of numerous biological blood parameters. From these two precise examples, glucose and antigen determination, the authors show the evolution of these technologies in the field of immobilized enzymes or captors and the analysis of signals given by these electrodes requiring a computerized treatment. This new technology opens to important potentialities in the analytical field. The automatization of these devices allowing the control in real time, will probably make easier the optimization steps of procedures actually used in the biomedical sphere.

Segmentation of ECG from Surface EMG Using DWT and EMD: A Comparison Study

NASA Astrophysics Data System (ADS)

Shahbakhti, Mohammad; Heydari, Elnaz; Luu, Gia Thien

2014-10-01

The electrocardiographic (ECG) signal is a major artifact during recording the surface electromyography (SEMG). Removal of this artifact is one of the important tasks before SEMG analysis for biomedical goals. In this paper, the application of discrete wavelet transform (DWT) and empirical mode decomposition (EMD) for elimination of ECG artifact from SEMG is investigated. The focus of this research is to reach the optimized number of decomposed levels using mean power frequency (MPF) by both techniques. In order to implement the proposed methods, ten simulated and three real ECG contaminated SEMG signals have been tested. Signal-to-noise ratio (SNR) and mean square error (MSE) between the filtered and the pure signals are applied as the performance indexes of this research. The obtained results suggest both techniques could remove ECG artifact from SEMG signals fair enough, however, DWT performs much better and faster in real data.

Complexity of EEG-signal in Time Domain - Possible Biomedical Application

NASA Astrophysics Data System (ADS)

Klonowski, Wlodzimierz; Olejarczyk, Elzbieta; Stepien, Robert

2002-07-01

Human brain is a highly complex nonlinear system. So it is not surprising that in analysis of EEG-signal, which represents overall activity of the brain, the methods of Nonlinear Dynamics (or Chaos Theory as it is commonly called) can be used. Even if the signal is not chaotic these methods are a motivating tool to explore changes in brain activity due to different functional activation states, e.g. different sleep stages, or to applied therapy, e.g. exposure to chemical agents (drugs) and physical factors (light, magnetic field). The methods supplied by Nonlinear Dynamics reveal signal characteristics that are not revealed by linear methods like FFT. Better understanding of principles that govern dynamics and complexity of EEG-signal can help to find `the signatures' of different physiological and pathological states of human brain, quantitative characteristics that may find applications in medical diagnostics.

Combining linear polarization spectroscopy and the Representative Layer Theory to measure the Beer-Lambert law absorbance of highly scattering materials.

PubMed

Gobrecht, Alexia; Bendoula, Ryad; Roger, Jean-Michel; Bellon-Maurel, Véronique

2015-01-01

Visible and Near Infrared (Vis-NIR) Spectroscopy is a powerful non destructive analytical method used to analyze major compounds in bulk materials and products and requiring no sample preparation. It is widely used in routine analysis and also in-line in industries, in-vivo with biomedical applications or in-field for agricultural and environmental applications. However, highly scattering samples subvert Beer-Lambert law's linear relationship between spectral absorbance and the concentrations. Instead of spectral pre-processing, which is commonly used by Vis-NIR spectroscopists to mitigate the scattering effect, we put forward an optical method, based on Polarized Light Spectroscopy to improve the absorbance signal measurement on highly scattering samples. This method selects part of the signal which is less impacted by scattering. The resulted signal is combined in the Absorption/Remission function defined in Dahm's Representative Layer Theory to compute an absorbance signal fulfilling Beer-Lambert's law, i.e. being linearly related to concentration of the chemicals composing the sample. The underpinning theories have been experimentally evaluated on scattering samples in liquid form and in powdered form. The method produced more accurate spectra and the Pearson's coefficient assessing the linearity between the absorbance spectra and the concentration of the added dye improved from 0.94 to 0.99 for liquid samples and 0.84-0.97 for powdered samples. Copyright © 2014 Elsevier B.V. All rights reserved.

The biomedical model of mental disorder: a critical analysis of its validity, utility, and effects on psychotherapy research.

PubMed

Deacon, Brett J

2013-11-01

The biomedical model posits that mental disorders are brain diseases and emphasizes pharmacological treatment to target presumed biological abnormalities. A biologically-focused approach to science, policy, and practice has dominated the American healthcare system for more than three decades. During this time, the use of psychiatric medications has sharply increased and mental disorders have become commonly regarded as brain diseases caused by chemical imbalances that are corrected with disease-specific drugs. However, despite widespread faith in the potential of neuroscience to revolutionize mental health practice, the biomedical model era has been characterized by a broad lack of clinical innovation and poor mental health outcomes. In addition, the biomedical paradigm has profoundly affected clinical psychology via the adoption of drug trial methodology in psychotherapy research. Although this approach has spurred the development of empirically supported psychological treatments for numerous mental disorders, it has neglected treatment process, inhibited treatment innovation and dissemination, and divided the field along scientist and practitioner lines. The neglected biopsychosocial model represents an appealing alternative to the biomedical approach, and an honest and public dialog about the validity and utility of the biomedical paradigm is urgently needed. Copyright © 2013 Elsevier Ltd. All rights reserved.

Disambiguating ambiguous biomedical terms in biomedical narrative text: an unsupervised method.

PubMed

Liu, H; Lussier, Y A; Friedman, C

2001-08-01

With the growing use of Natural Language Processing (NLP) techniques for information extraction and concept indexing in the biomedical domain, a method that quickly and efficiently assigns the correct sense of an ambiguous biomedical term in a given context is needed concurrently. The current status of word sense disambiguation (WSD) in the biomedical domain is that handcrafted rules are used based on contextual material. The disadvantages of this approach are (i) generating WSD rules manually is a time-consuming and tedious task, (ii) maintenance of rule sets becomes increasingly difficult over time, and (iii) handcrafted rules are often incomplete and perform poorly in new domains comprised of specialized vocabularies and different genres of text. This paper presents a two-phase unsupervised method to build a WSD classifier for an ambiguous biomedical term W. The first phase automatically creates a sense-tagged corpus for W, and the second phase derives a classifier for W using the derived sense-tagged corpus as a training set. A formative experiment was performed, which demonstrated that classifiers trained on the derived sense-tagged corpora achieved an overall accuracy of about 97%, with greater than 90% accuracy for each individual ambiguous term.

Smart multifunctional nanoagents for in situ monitoring of small molecules with a switchable affinity towards biomedical targets

NASA Astrophysics Data System (ADS)

Shevchenko, Konstantin G.; Cherkasov, Vladimir R.; Nikitina, Irina L.; Babenyshev, Andrey V.; Nikitin, Maxim P.

2018-02-01

The great diversity of nanomaterials provides ample opportunities for constructing effective agents for biomedical applications ranging from biosensing to drug delivery. Multifunctional nanoagents that combine several features in a single particle are of special interest due to capabilities that substantially exceed those of molecular drugs. An ideal theranostic agent should simultaneously be an advanced biosensor to identify a disease and report the diagnosis and a biomedical actuator to treat the disease. While many approaches were developed to load a nanoparticle with various drugs for actuation of the diseased cells (e.g., to kill them), the nanoparticle-based approaches for the localized biosensing with real-time reporting of the marker concentration severely lag behind. Here, we show a smart in situ nanoparticle-based biosensor/actuator system that dynamically and reversibly changes its structural and optical properties in response to a small molecule marker to allow real-time monitoring of the marker concentration and adjustment of the system ability to bind its biomedical target. Using the synergistic combination of signal readout based on the localized surface plasmon resonance and an original method of fabrication of smart ON/OFF-switchable nanoagents, we demonstrate reversible responsiveness of the system to a model small molecule marker (antibiotic chloramphenicol) in a wide concentration range. The proposed approach can be used for the development of advanced multifunctional nanoagents for theranostic applications.

Preparing for motherhood: authoritative knowledge and the undercurrents of shared experience in two childbirth education courses in Cagliari, Italy.

PubMed

Ketler, S K

2000-06-01

This article compares the social settings and teaching organization of two differently structured childbirth education courses in Cagliari, Italy, in order to understand how social processes and contexts work to negotiate authoritative knowledge. Although the explicit goal of both courses was to transmit biomedical knowledge, knowledge based in women's experience nonetheless dominated some course sessions. Thus, I examine the social processes and interactions that enabled women's experiential knowledge to dominate discussions and subsequently share in the authority of biomedical knowledge in some situations. Because few existing studies do so, this article also addresses a gap in our current understanding by exploring not only how experiential knowledge comes to share authority with biomedical knowledge, but also, why it is important that it does. Focusing on the efficacy of differently structured courses, this article informs the planning of future childbirth education courses in similar settings.

Modeling a description logic vocabulary for cancer research.

PubMed

Hartel, Frank W; de Coronado, Sherri; Dionne, Robert; Fragoso, Gilberto; Golbeck, Jennifer

2005-04-01

The National Cancer Institute has developed the NCI Thesaurus, a biomedical vocabulary for cancer research, covering terminology across a wide range of cancer research domains. A major design goal of the NCI Thesaurus is to facilitate translational research. We describe: the features of Ontylog, a description logic used to build NCI Thesaurus; our methodology for enhancing the terminology through collaboration between ontologists and domain experts, and for addressing certain real world challenges arising in modeling the Thesaurus; and finally, we describe the conversion of NCI Thesaurus from Ontylog into Web Ontology Language Lite. Ontylog has proven well suited for constructing big biomedical vocabularies. We have capitalized on the Ontylog constructs Kind and Role in the collaboration process described in this paper to facilitate communication between ontologists and domain experts. The artifacts and processes developed by NCI for collaboration may be useful in other biomedical terminology development efforts.

Bypassing the Limits of Ll Regularization: Convex Sparse Signal Processing Using Non-Convex Regularization

NASA Astrophysics Data System (ADS)

Parekh, Ankit

Sparsity has become the basis of some important signal processing methods over the last ten years. Many signal processing problems (e.g., denoising, deconvolution, non-linear component analysis) can be expressed as inverse problems. Sparsity is invoked through the formulation of an inverse problem with suitably designed regularization terms. The regularization terms alone encode sparsity into the problem formulation. Often, the ℓ1 norm is used to induce sparsity, so much so that ℓ1 regularization is considered to be `modern least-squares'. The use of ℓ1 norm, as a sparsity-inducing regularizer, leads to a convex optimization problem, which has several benefits: the absence of extraneous local minima, well developed theory of globally convergent algorithms, even for large-scale problems. Convex regularization via the ℓ1 norm, however, tends to under-estimate the non-zero values of sparse signals. In order to estimate the non-zero values more accurately, non-convex regularization is often favored over convex regularization. However, non-convex regularization generally leads to non-convex optimization, which suffers from numerous issues: convergence may be guaranteed to only a stationary point, problem specific parameters may be difficult to set, and the solution is sensitive to the initialization of the algorithm. The first part of this thesis is aimed toward combining the benefits of non-convex regularization and convex optimization to estimate sparse signals more effectively. To this end, we propose to use parameterized non-convex regularizers with designated non-convexity and provide a range for the non-convex parameter so as to ensure that the objective function is strictly convex. By ensuring convexity of the objective function (sum of data-fidelity and non-convex regularizer), we can make use of a wide variety of convex optimization algorithms to obtain the unique global minimum reliably. The second part of this thesis proposes a non-linear signal decomposition technique for an important biomedical signal processing problem: the detection of sleep spindles and K-complexes in human sleep electroencephalography (EEG). We propose a non-linear model for the EEG consisting of three components: (1) a transient (sparse piecewise constant) component, (2) a low-frequency component, and (3) an oscillatory component. The oscillatory component admits a sparse time-frequency representation. Using a convex objective function, we propose a fast non-linear optimization algorithm to estimate the three components in the proposed signal model. The low-frequency and oscillatory components are then used to estimate the K-complexes and sleep spindles respectively. The proposed detection method is shown to outperform several state-of-the-art automated sleep spindles detection methods.

Improving validity of informed consent for biomedical research in Zambia using a laboratory exposure intervention.

PubMed

Zulu, Joseph Mumba; Lisulo, Mpala Mwanza; Besa, Ellen; Kaonga, Patrick; Chisenga, Caroline C; Chomba, Mumba; Simuyandi, Michelo; Banda, Rosemary; Kelly, Paul

2014-01-01

Complex biomedical research can lead to disquiet in communities with limited exposure to scientific discussions, leading to rumours or to high drop-out rates. We set out to test an intervention designed to address apprehensions commonly encountered in a community where literacy is uncommon, and where complex biomedical research has been conducted for over a decade. We aimed to determine if it could improve the validity of consent. Data were collected using focus group discussions, key informant interviews and observations. We designed an intervention that exposed participants to a detailed demonstration of laboratory processes. Each group was interviewed twice in a day, before and after exposure to the intervention in order to assess changes in their views. Factors that motivated people to participate in invasive biomedical research included a desire to stay healthy because of the screening during the recruitment process, regular advice from doctors, free medical services, and trust in the researchers. Inhibiting factors were limited knowledge about samples taken from their bodies during endoscopic procedures, the impact of endoscopy on the function of internal organs, and concerns about the use of biomedical samples. The belief that blood can be used for Satanic practices also created insecurities about drawing of blood samples. Further inhibiting factors included a fear of being labelled as HIV positive if known to consult heath workers repeatedly, and gender inequality. Concerns about the use and storage of blood and tissue samples were overcome by a laboratory exposure intervention. Selecting a group of members from target community and engaging them in a laboratory exposure intervention could be a useful tool for enhancing specific aspects of consent for biomedical research. Further work is needed to determine the extent to which improved understanding permeates beyond the immediate group participating in the intervention.

User needs analysis and usability assessment of DataMed - a biomedical data discovery index.

PubMed

Dixit, Ram; Rogith, Deevakar; Narayana, Vidya; Salimi, Mandana; Gururaj, Anupama; Ohno-Machado, Lucila; Xu, Hua; Johnson, Todd R

2017-11-30

To present user needs and usability evaluations of DataMed, a Data Discovery Index (DDI) that allows searching for biomedical data from multiple sources. We conducted 2 phases of user studies. Phase 1 was a user needs analysis conducted before the development of DataMed, consisting of interviews with researchers. Phase 2 involved iterative usability evaluations of DataMed prototypes. We analyzed data qualitatively to document researchers' information and user interface needs. Biomedical researchers' information needs in data discovery are complex, multidimensional, and shaped by their context, domain knowledge, and technical experience. User needs analyses validate the need for a DDI, while usability evaluations of DataMed show that even though aggregating metadata into a common search engine and applying traditional information retrieval tools are promising first steps, there remain challenges for DataMed due to incomplete metadata and the complexity of data discovery. Biomedical data poses distinct problems for search when compared to websites or publications. Making data available is not enough to facilitate biomedical data discovery: new retrieval techniques and user interfaces are necessary for dataset exploration. Consistent, complete, and high-quality metadata are vital to enable this process. While available data and researchers' information needs are complex and heterogeneous, a successful DDI must meet those needs and fit into the processes of biomedical researchers. Research directions include formalizing researchers' information needs, standardizing overviews of data to facilitate relevance judgments, implementing user interfaces for concept-based searching, and developing evaluation methods for open-ended discovery systems such as DDIs. © The Author 2017. Published by Oxford University Press on behalf of the American Medical Informatics Association. All rights reserved. For Permissions, please email: journals.permissions@oup.com

Incorporating domain knowledge in chemical and biomedical named entity recognition with word representations.

PubMed

Munkhdalai, Tsendsuren; Li, Meijing; Batsuren, Khuyagbaatar; Park, Hyeon Ah; Choi, Nak Hyeon; Ryu, Keun Ho

2015-01-01

Chemical and biomedical Named Entity Recognition (NER) is an essential prerequisite task before effective text mining can begin for biochemical-text data. Exploiting unlabeled text data to leverage system performance has been an active and challenging research topic in text mining due to the recent growth in the amount of biomedical literature. We present a semi-supervised learning method that efficiently exploits unlabeled data in order to incorporate domain knowledge into a named entity recognition model and to leverage system performance. The proposed method includes Natural Language Processing (NLP) tasks for text preprocessing, learning word representation features from a large amount of text data for feature extraction, and conditional random fields for token classification. Other than the free text in the domain, the proposed method does not rely on any lexicon nor any dictionary in order to keep the system applicable to other NER tasks in bio-text data. We extended BANNER, a biomedical NER system, with the proposed method. This yields an integrated system that can be applied to chemical and drug NER or biomedical NER. We call our branch of the BANNER system BANNER-CHEMDNER, which is scalable over millions of documents, processing about 530 documents per minute, is configurable via XML, and can be plugged into other systems by using the BANNER Unstructured Information Management Architecture (UIMA) interface. BANNER-CHEMDNER achieved an 85.68% and an 86.47% F-measure on the testing sets of CHEMDNER Chemical Entity Mention (CEM) and Chemical Document Indexing (CDI) subtasks, respectively, and achieved an 87.04% F-measure on the official testing set of the BioCreative II gene mention task, showing remarkable performance in both chemical and biomedical NER. BANNER-CHEMDNER system is available at: https://bitbucket.org/tsendeemts/banner-chemdner.

Processing composite materials

NASA Technical Reports Server (NTRS)

Baucom, R. M.

1982-01-01

The fabrication of several composite structural articles including DC-10 upper aft rudders, L-1011 vertical fins and composite biomedical appliances are discussed. Innovative composite processing methods are included.

Blind source separation of ex-vivo aorta tissue multispectral images

PubMed Central

Galeano, July; Perez, Sandra; Montoya, Yonatan; Botina, Deivid; Garzón, Johnson

2015-01-01

Blind Source Separation methods (BSS) aim for the decomposition of a given signal in its main components or source signals. Those techniques have been widely used in the literature for the analysis of biomedical images, in order to extract the main components of an organ or tissue under study. The analysis of skin images for the extraction of melanin and hemoglobin is an example of the use of BSS. This paper presents a proof of concept of the use of source separation of ex-vivo aorta tissue multispectral Images. The images are acquired with an interference filter-based imaging system. The images are processed by means of two algorithms: Independent Components analysis and Non-negative Matrix Factorization. In both cases, it is possible to obtain maps that quantify the concentration of the main chromophores present in aortic tissue. Also, the algorithms allow for spectral absorbance of the main tissue components. Those spectral signatures were compared against the theoretical ones by using correlation coefficients. Those coefficients report values close to 0.9, which is a good estimator of the method’s performance. Also, correlation coefficients lead to the identification of the concentration maps according to the evaluated chromophore. The results suggest that Multi/hyper-spectral systems together with image processing techniques is a potential tool for the analysis of cardiovascular tissue. PMID:26137366

Ontology for heart rate turbulence domain from the conceptual model of SNOMED-CT.

PubMed

Soguero-Ruiz, Cristina; Lechuga-Suárez, Luis; Mora-Jiménez, Inmaculada; Ramos-López, Javier; Barquero-Pérez, Óscar; García-Alberola, Arcadi; Rojo-Álvarez, José L

2013-07-01

Electronic health record (EHR) automates the clinician workflow, allowing evidence-based decision support and quality management. We aimed to start a framework for domain standardization of cardiovascular risk stratification into the EHR, including risk indices whose calculation involves ECG signal processing. We propose the use of biomedical ontologies completely based on the conceptual model of SNOMED-CT, which allows us to implement our domain in the EHR. In this setting, the present study focused on the heart rate turbulence (HRT) domain, according to its concise guidelines and clear procedures for parameter calculations. We used 289 concepts from SNOMED-CT, and generated 19 local extensions (new concepts) for the HRT specific concepts not present in the current version of SNOMED-CT. New concepts included averaged and individual ventricular premature complex tachograms, initial sinus acceleration for turbulence onset, or sinusal oscillation for turbulence slope. Two representative use studies were implemented: first, a prototype was inserted in the hospital information system for supporting HRT recordings and their simple follow up by medical societies; second, an advanced support for a prospective scientific research, involving standard and emergent signal processing algorithms in the HRT indices, was generated and then tested in an example database of 27 Holter patients. Concepts of the proposed HRT ontology are publicly available through a terminology server, hence their use in any information system will be straightforward due to the interoperability provided by SNOMED-CT.

Spatiotemporal integration of molecular and anatomical data in virtual reality using semantic mapping.

PubMed

Soh, Jung; Turinsky, Andrei L; Trinh, Quang M; Chang, Jasmine; Sabhaney, Ajay; Dong, Xiaoli; Gordon, Paul Mk; Janzen, Ryan Pw; Hau, David; Xia, Jianguo; Wishart, David S; Sensen, Christoph W

2009-01-01

We have developed a computational framework for spatiotemporal integration of molecular and anatomical datasets in a virtual reality environment. Using two case studies involving gene expression data and pharmacokinetic data, respectively, we demonstrate how existing knowledge bases for molecular data can be semantically mapped onto a standardized anatomical context of human body. Our data mapping methodology uses ontological representations of heterogeneous biomedical datasets and an ontology reasoner to create complex semantic descriptions of biomedical processes. This framework provides a means to systematically combine an increasing amount of biomedical imaging and numerical data into spatiotemporally coherent graphical representations. Our work enables medical researchers with different expertise to simulate complex phenomena visually and to develop insights through the use of shared data, thus paving the way for pathological inference, developmental pattern discovery and biomedical hypothesis testing.

Creating a Controlled Vocabulary for the Ethics of Human Research: Towards a Biomedical Ethics Ontology

PubMed Central

Koepsell, David; Arp, Robert; Fostel, Jennifer; Smith, Barry

2009-01-01

Ontologies describe reality in specific domains in ways that can bridge various disciplines and languages. They allow easier access and integration of information that is collected by different groups. Ontologies are currently used in the biomedical sciences, geography, and law. A Biomedical Ethics Ontology (BMEO) would benefit members of ethics committees who deal with protocols and consent forms spanning numerous fields of inquiry. There already exists the Ontology for Biomedical Investigations (OBI); the proposed BMEO would interoperate with OBI, creating a powerful information tool. We define a domain ontology and begin to construct a BMEO, focused on the process of evaluating human research protocols. Finally, we show how our BMEO can have practical applications for ethics committees. This paper describes ongoing research and a strategy for its broader continuation and cooperation. PMID:19374479

Finding and accessing diagrams in biomedical publications.

PubMed

Kuhn, Tobias; Luong, ThaiBinh; Krauthammer, Michael

2012-01-01

Complex relationships in biomedical publications are often communicated by diagrams such as bar and line charts, which are a very effective way of summarizing and communicating multi-faceted data sets. Given the ever-increasing amount of published data, we argue that the precise retrieval of such diagrams is of great value for answering specific and otherwise hard-to-meet information needs. To this end, we demonstrate the use of advanced image processing and classification for identifying bar and line charts by the shape and relative location of the different image elements that make up the charts. With recall and precisions of close to 90% for the detection of relevant figures, we discuss the use of this technology in an existing biomedical image search engine, and outline how it enables new forms of literature queries over biomedical relationships that are represented in these charts.

Emerging Human Fetuin A Assays for Biomedical Diagnostics.

PubMed

Vashist, Sandeep Kumar; Schneider, E Marion; Venkatesh, A G; Luong, John H T

2017-05-01

Human fetuin A (HFA) plays a prominent pathophysiological role in numerous diseases and pathophysiological conditions with considerable biomedical significance; one example is the formation of calciprotein particles in osteoporosis and impaired calcium metabolisms. With impressive advances in in vitro diagnostic assays during the last decade, ELISAs have become a workhorse in routine clinical diagnostics. Recent diagnostic formats involve high-sensitivity immunoassay procedures, surface plasmon resonance, rapid immunoassay chemistries, signal enhancement, and smartphone detection. The current trend is toward fully integrated lab-on-chip platforms with smartphone readouts, enabling health-care practitioners and even patients to monitor pathological changes in biomarker levels. This review provides a critical analysis of advances made in HFA assays along with the challenges and future prospects. Copyright © 2016 Elsevier Ltd. All rights reserved.

Discovery informatics in biological and biomedical sciences: research challenges and opportunities.

PubMed

Honavar, Vasant

2015-01-01

New discoveries in biological, biomedical and health sciences are increasingly being driven by our ability to acquire, share, integrate and analyze, and construct and simulate predictive models of biological systems. While much attention has focused on automating routine aspects of management and analysis of "big data", realizing the full potential of "big data" to accelerate discovery calls for automating many other aspects of the scientific process that have so far largely resisted automation: identifying gaps in the current state of knowledge; generating and prioritizing questions; designing studies; designing, prioritizing, planning, and executing experiments; interpreting results; forming hypotheses; drawing conclusions; replicating studies; validating claims; documenting studies; communicating results; reviewing results; and integrating results into the larger body of knowledge in a discipline. Against this background, the PSB workshop on Discovery Informatics in Biological and Biomedical Sciences explores the opportunities and challenges of automating discovery or assisting humans in discovery through advances (i) Understanding, formalization, and information processing accounts of, the entire scientific process; (ii) Design, development, and evaluation of the computational artifacts (representations, processes) that embody such understanding; and (iii) Application of the resulting artifacts and systems to advance science (by augmenting individual or collective human efforts, or by fully automating science).

Electroencephalography (EEG) Based Control in Assistive Mobile Robots: A Review

NASA Astrophysics Data System (ADS)

Krishnan, N. Murali; Mariappan, Muralindran; Muthukaruppan, Karthigayan; Hijazi, Mohd Hanafi Ahmad; Kitt, Wong Wei

2016-03-01

Recently, EEG based control in assistive robot usage has been gradually increasing in the area of biomedical field for giving quality and stress free life for disabled and elderly people. This study reviews the deployment of EGG based control in assistive robots, especially for those who in need and neurologically disabled. The main objective of this paper is to describe the methods used for (i) EEG data acquisition and signal preprocessing, (ii) feature extraction and (iii) signal classification methods. Besides that, this study presents the specific research challenges in the designing of these control systems and future research directions.

Using CRISPR-Cas9 to Study ERK Signaling in Drosophila.

PubMed

Forés, Marta; Papagianni, Aikaterini; Rodríguez-Muñoz, Laura; Jiménez, Gerardo

2017-01-01

Genome engineering using the clustered regularly interspaced short palindromic repeats (CRISPR)-CRISPR associated nuclease 9 (Cas9) technology is revolutionizing biomedical research. CRISPR-Cas9 enables precise editing of genes in a wide variety of cells and organisms, thereby accelerating molecular studies via targeted mutagenesis, epitope tagging, and other custom genetic modifications. Here, we illustrate the CRISPR-Cas9 methodology by focusing on Capicua (Cic), a nuclear transcriptional repressor directly phosphorylated and inactivated by ERK/MAPK. Specifically, we use CRISPR-Cas9 for targeting an ERK docking site of Drosophila Cic, thus generating ERK-insensitive mutants of this important signaling sensor.

Building a biomedical tokenizer using the token lattice design pattern and the adapted Viterbi algorithm

PubMed Central

2011-01-01

Background Tokenization is an important component of language processing yet there is no widely accepted tokenization method for English texts, including biomedical texts. Other than rule based techniques, tokenization in the biomedical domain has been regarded as a classification task. Biomedical classifier-based tokenizers either split or join textual objects through classification to form tokens. The idiosyncratic nature of each biomedical tokenizer’s output complicates adoption and reuse. Furthermore, biomedical tokenizers generally lack guidance on how to apply an existing tokenizer to a new domain (subdomain). We identify and complete a novel tokenizer design pattern and suggest a systematic approach to tokenizer creation. We implement a tokenizer based on our design pattern that combines regular expressions and machine learning. Our machine learning approach differs from the previous split-join classification approaches. We evaluate our approach against three other tokenizers on the task of tokenizing biomedical text. Results Medpost and our adapted Viterbi tokenizer performed best with a 92.9% and 92.4% accuracy respectively. Conclusions Our evaluation of our design pattern and guidelines supports our claim that the design pattern and guidelines are a viable approach to tokenizer construction (producing tokenizers matching leading custom-built tokenizers in a particular domain). Our evaluation also demonstrates that ambiguous tokenizations can be disambiguated through POS tagging. In doing so, POS tag sequences and training data have a significant impact on proper text tokenization. PMID:21658288

A reconfigurable medically cohesive biomedical front-end with ΣΔ ADC in 0.18µm CMOS.

PubMed

Jha, Pankaj; Patra, Pravanjan; Naik, Jairaj; Acharya, Amit; Rajalakshmi, P; Singh, Shiv Govind; Dutta, Ashudeb

2015-08-01

This paper presents a generic programmable analog front-end (AFE) for acquisition and digitization of various biopotential signals. This includes a lead-off detection circuit, an ultra-low current capacitively coupled signal conditioning stage with programmable gain and bandwidth, a new mixed signal automatic gain control (AGC) mechanism and a medically cohesive reconfigurable ΣΔ ADC. The full system is designed in UMC 0.18μm CMOS. The AFE achieves an overall linearity of more 10 bits with 0.47μW power consumption. The ADC provides 2(nd) order noise-shaping while using single integrator and an ENOB of ~11 bits with 5μW power consumption. The system was successfully verified for various ECG signals from PTB database. This system is intended for portable batteryless u-Healthcare devices.

Complexity in congestive heart failure: A time-frequency approach

NASA Astrophysics Data System (ADS)

Banerjee, Santo; Palit, Sanjay K.; Mukherjee, Sayan; Ariffin, MRK; Rondoni, Lamberto

2016-03-01

Reconstruction of phase space is an effective method to quantify the dynamics of a signal or a time series. Various phase space reconstruction techniques have been investigated. However, there are some issues on the optimal reconstructions and the best possible choice of the reconstruction parameters. This research introduces the idea of gradient cross recurrence (GCR) and mean gradient cross recurrence density which shows that reconstructions in time frequency domain preserve more information about the dynamics than the optimal reconstructions in time domain. This analysis is further extended to ECG signals of normal and congestive heart failure patients. By using another newly introduced measure—gradient cross recurrence period density entropy, two classes of aforesaid ECG signals can be classified with a proper threshold. This analysis can be applied to quantifying and distinguishing biomedical and other nonlinear signals.

Estimation of Cardiopulmonary Parameters From Ultra Wideband Radar Measurements Using the State Space Method.

PubMed

Naishadham, Krishna; Piou, Jean E; Ren, Lingyun; Fathy, Aly E

2016-12-01

Ultra wideband (UWB) Doppler radar has many biomedical applications, including remote diagnosis of cardiovascular disease, triage and real-time personnel tracking in rescue missions. It uses narrow pulses to probe the human body and detect tiny cardiopulmonary movements by spectral analysis of the backscattered electromagnetic (EM) field. With the help of super-resolution spectral algorithms, UWB radar is capable of increased accuracy for estimating vital signs such as heart and respiration rates in adverse signal-to-noise conditions. A major challenge for biomedical radar systems is detecting the heartbeat of a subject with high accuracy, because of minute thorax motion (less than 0.5 mm) caused by the heartbeat. The problem becomes compounded by EM clutter and noise in the environment. In this paper, we introduce a new algorithm based on the state space method (SSM) for the extraction of cardiac and respiration rates from UWB radar measurements. SSM produces range-dependent system poles that can be classified parametrically with spectral peaks at the cardiac and respiratory frequencies. It is shown that SSM produces accurate estimates of the vital signs without producing harmonics and inter-modulation products that plague signal resolution in widely used FFT spectrograms.

High density DNA microarrays: algorithms and biomedical applications.

PubMed

Liu, Wei-Min

2004-08-01

DNA microarrays are devices capable of detecting the identity and abundance of numerous DNA or RNA segments in samples. They are used for analyzing gene expressions, identifying genetic markers and detecting mutations on a genomic scale. The fundamental chemical mechanism of DNA microarrays is the hybridization between probes and targets due to the hydrogen bonds of nucleotide base pairing. Since the cross hybridization is inevitable, and probes or targets may form undesirable secondary or tertiary structures, the microarray data contain noise and depend on experimental conditions. It is crucial to apply proper statistical algorithms to obtain useful signals from noisy data. After we obtained the signals of a large amount of probes, we need to derive the biomedical information such as the existence of a transcript in a cell, the difference of expression levels of a gene in multiple samples, and the type of a genetic marker. Furthermore, after the expression levels of thousands of genes or the genotypes of thousands of single nucleotide polymorphisms are determined, it is usually important to find a small number of genes or markers that are related to a disease, individual reactions to drugs, or other phenotypes. All these applications need careful data analyses and reliable algorithms.

Wireless and batteryless biomedical microsystem for neural recording and epilepsy suppression based on brain focal cooling.

PubMed

Hou, K-C; Chang, C-W; Chiou, J-C; Huang, Y-H; Shaw, F-Z

2011-12-01

This work presents a biomedical microsystem with a wireless radiofrequency (RF)-powered electronics and versatile sensors/actuators for use in nanomedicinal diagnosis and therapy. The cooling of brain tissue has the potential to reduce the frequency and severity of epilepsy. Miniaturised spiral coils as a wireless power module with low-dropout linear regulator circuit convert RF signals into a DC voltage, can be implanted without a battery in monitoring free behaviour. A thermoelectric (TE) cooler is an actuator that is employed to cool down brain tissue to suppress epilepsy. Electroencephalogram (EEG) electrodes and TE coolers are integrated to form module that is placed inside the head of a rat and fastened with a bio-compatible material. EEG signals are used to identify waveforms associated with epilepsy and are measured using readout circuits. The wireless part of the presented design achieves a low quiescent current and line/load regulation and high antenna/current efficiency with thermal protection to avoid damage to the implanted tissue. Epilepsy is suppressed by reducing the temperature to reduce the duration of this epileptic episode. Related characterisations demonstrate that the proposed design can be adopted in an effective nanomedicine microsystem.

Dynamic laser speckle angiography achieved by eigen-decomposition filtering.

PubMed

Li, Chenxi; Wang, Ruikang

2017-06-01

A new approach is proposed for statistically analysis of laser speckle signals emerged from a living biological tissue based on eigen-decomposition to separate the dynamic speckle signals due to moving blood cells from the static speckle signals due to static tissue components, upon which to achieve angiography of the interrogated tissue in vivo. The proposed approach is tested by imaging mouse ear pinna in vivo, demonstrating its capability of providing detailed microvascular networks with high contrast, and high temporal and spatial resolutions. It is expected to provide further opportunities for laser speckle imaging in the biomedical and clinical applications where microvascular response to certain stimulus or tissue injury is of interest. © 2017 Wiley-VCH Verlag GmbH & Co. KGaA, Weinheim.

Technical editing of research reports in biomedical journals.

PubMed

Wager, Elizabeth; Middleton, Philippa

2008-10-08

Most journals try to improve their articles by technical editing processes such as proof-reading, editing to conform to 'house styles', grammatical conventions and checking accuracy of cited references. Despite the considerable resources devoted to technical editing, we do not know whether it improves the accessibility of biomedical research findings or the utility of articles. This is an update of a Cochrane methodology review first published in 2003. To assess the effects of technical editing on research reports in peer-reviewed biomedical journals, and to assess the level of accuracy of references to these reports. We searched The Cochrane Library Issue 2, 2007; MEDLINE (last searched July 2006); EMBASE (last searched June 2007) and checked relevant articles for further references. We also searched the Internet and contacted researchers and experts in the field. Prospective or retrospective comparative studies of technical editing processes applied to original research articles in biomedical journals, as well as studies of reference accuracy. Two review authors independently assessed each study against the selection criteria and assessed the methodological quality of each study. One review author extracted the data, and the second review author repeated this. We located 32 studies addressing technical editing and 66 surveys of reference accuracy. Only three of the studies were randomised controlled trials. A 'package' of largely unspecified editorial processes applied between acceptance and publication was associated with improved readability in two studies and improved reporting quality in another two studies, while another study showed mixed results after stricter editorial policies were introduced. More intensive editorial processes were associated with fewer errors in abstracts and references. Providing instructions to authors was associated with improved reporting of ethics requirements in one study and fewer errors in references in two studies, but no difference was seen in the quality of abstracts in one randomised controlled trial. Structuring generally improved the quality of abstracts, but increased their length. The reference accuracy studies showed a median citation error rate of 38% and a median quotation error rate of 20%. Surprisingly few studies have evaluated the effects of technical editing rigorously. However there is some evidence that the 'package' of technical editing used by biomedical journals does improve papers. A substantial number of references in biomedical articles are cited or quoted inaccurately.

The signal extraction of fetal heart rate based on wavelet transform and BP neural network

NASA Astrophysics Data System (ADS)

Yang, Xiao Hong; Zhang, Bang-Cheng; Fu, Hu Dai

2005-04-01

This paper briefly introduces the collection and recognition of bio-medical signals, designs the method to collect FM signals. A detailed discussion on the system hardware, structure and functions is also given. Under LabWindows/CVI,the hardware and the driver do compatible, the hardware equipment work properly actively. The paper adopts multi threading technology for real-time analysis and makes use of latency time of CPU effectively, expedites program reflect speed, improves the program to perform efficiency. One threading is collecting data; the other threading is analyzing data. Using the method, it is broaden to analyze the signal in real-time. Wavelet transform to remove the main interference in the FM and by adding time-window to recognize with BP network; Finally the results of collecting signals and BP networks are discussed. 8 pregnant women's signals of FM were collected successfully by using the sensor. The correctness rate of BP network recognition is about 83.3% by using the above measure.

Secure management of biomedical data with cryptographic hardware.

PubMed

Canim, Mustafa; Kantarcioglu, Murat; Malin, Bradley

2012-01-01

The biomedical community is increasingly migrating toward research endeavors that are dependent on large quantities of genomic and clinical data. At the same time, various regulations require that such data be shared beyond the initial collecting organization (e.g., an academic medical center). It is of critical importance to ensure that when such data are shared, as well as managed, it is done so in a manner that upholds the privacy of the corresponding individuals and the overall security of the system. In general, organizations have attempted to achieve these goals through deidentification methods that remove explicitly, and potentially, identifying features (e.g., names, dates, and geocodes). However, a growing number of studies demonstrate that deidentified data can be reidentified to named individuals using simple automated methods. As an alternative, it was shown that biomedical data could be shared, managed, and analyzed through practical cryptographic protocols without revealing the contents of any particular record. Yet, such protocols required the inclusion of multiple third parties, which may not always be feasible in the context of trust or bandwidth constraints. Thus, in this paper, we introduce a framework that removes the need for multiple third parties by collocating services to store and to process sensitive biomedical data through the integration of cryptographic hardware. Within this framework, we define a secure protocol to process genomic data and perform a series of experiments to demonstrate that such an approach can be run in an efficient manner for typical biomedical investigations.

Secure Management of Biomedical Data With Cryptographic Hardware

PubMed Central

Canim, Mustafa; Kantarcioglu, Murat; Malin, Bradley

2014-01-01

The biomedical community is increasingly migrating toward research endeavors that are dependent on large quantities of genomic and clinical data. At the same time, various regulations require that such data be shared beyond the initial collecting organization (e.g., an academic medical center). It is of critical importance to ensure that when such data are shared, as well as managed, it is done so in a manner that upholds the privacy of the corresponding individuals and the overall security of the system. In general, organizations have attempted to achieve these goals through deidentification methods that remove explicitly, and potentially, identifying features (e.g., names, dates, and geocodes). However, a growing number of studies demonstrate that deidentified data can be reidentified to named individuals using simple automated methods. As an alternative, it was shown that biomedical data could be shared, managed, and analyzed through practical cryptographic protocols without revealing the contents of any particular record. Yet, such protocols required the inclusion of multiple third parties, which may not always be feasible in the context of trust or bandwidth constraints. Thus, in this paper, we introduce a framework that removes the need for multiple third parties by collocating services to store and to process sensitive biomedical data through the integration of cryptographic hardware. Within this framework, we define a secure protocol to process genomic data and perform a series of experiments to demonstrate that such an approach can be run in an efficient manner for typical biomedical investigations. PMID:22010157

Bio-media Citizenship and Chronic Kidney Disease of Unknown Etiology in Sri Lanka.

PubMed

de Silva, M W Amarasiri

2018-04-01

In this article, I examine the crucial role of the biomedical industry, epidemiological and biomedical research, and the media in forming attitudes to and the understanding of chronic kidney disease of unknown etiology (CKDu) in Sri Lanka. Local conceptions of CKDu have been shaped by the circulation in the media of epidemiological research findings pertaining to the disease, biomedical interventions in the management of the disease in hospitals and clinics, community programs involving mass blood surveys and the testing of well water, and local food and health education programs carried out through village health committees. This process of circulation I identify as bio-media citizenship.

Instructions to Prospective Authors by Indian Biomedical Journals: An Opportunity to Promote Responsible Conduct of Research.

PubMed

Bhat, Anup; Shah, Akash; Sherighar, Swathi G

2017-04-01

Journals provide instructions to prospective authors to facilitate the process of manuscript publication. The information provided under such instructions could be a potential opportunity to promote responsible conduct of research (RCR). We analyzed 74 Indian biomedical journals for the type of information provided in the "instructions to authors" section and adherence to the International Committee of Medical Journal Editors (ICMJE) recommendations. Among the 71 journals that had an "instructions to authors" section, 53 journals adhered to ICMJE recommendations. We discuss sections of the ICMJE recommendations detailed by Indian biomedical journals under the "instructions to authors" section and emphasize components that require greater exposure.

Reviewing Manuscripts for Biomedical Journals

PubMed Central

Garmel, Gus M

2010-01-01

Writing for publication is a complex task. For many professionals, producing a well-executed manuscript conveying one's research, ideas, or educational wisdom is challenging. Authors have varying emotions related to the process of writing for scientific publication. Although not studied, a relationship between an author's enjoyment of the writing process and the product's outcome is highly likely. As with any skill, practice generally results in improvements. Literature focused on preparing manuscripts for publication and the art of reviewing submissions exists. Most journals guard their reviewers' anonymity with respect to the manuscript review process. This is meant to protect them from direct or indirect author demands, which may occur during the review process or in the future. It is generally accepted that author identities are masked in the peer-review process. However, the concept of anonymity for reviewers has been debated recently; many editors consider it problematic that reviewers are not held accountable to the public for their decisions. The review process is often arduous and underappreciated, one reason why biomedical journals acknowledge editors and frequently recognize reviewers who donate their time and expertise in the name of science. This article describes essential elements of a submitted manuscript, with the hopes of improving scientific writing. It also discusses the review process within the biomedical literature, the importance of reviewers to the scientific process, responsibilities of reviewers, and qualities of a good review and reviewer. In addition, it includes useful insights to individuals who read and interpret the medical literature. PMID:20740129

Getting to zero the biomedical way in Africa: outcomes of deliberation at the 2013 Biomedical HIV Prevention Forum in Abuja, Nigeria

PubMed Central

2014-01-01

Background Over the last few decades, biomedical HIV prevention research had engaged multiple African stakeholders. There have however been few platforms to enable regional stakeholders to engage with one another. In partnership with the World AIDS Campaign International, the Institute of Public Health of Obafemi Awolowo University, and the National Agency for the Control of AIDS in Nigeria, the New HIV Vaccine and Microbicide Advocacy Society hosted a forum on biomedical HIV prevention research in Africa. Stakeholders’ present explored evidences related to biomedical HIV prevention research and development in Africa, and made recommendations to inform policy, guidelines and future research agenda. Discussion The BHPF hosted 342 participants. Topics discussed included the use of antiretrovirals for HIV prevention, considerations for biomedical HIV prevention among key populations; HIV vaccine development; HIV cure; community and civil society engagement; and ethical considerations in implementation of biomedical HIV prevention research. Participants identified challenges for implementation of proven efficacious interventions and discovery of other new prevention options for Africa. Concerns raised included limited funding by African governments, lack of cohesive advocacy and policy agenda for biomedical HIV prevention research and development by Africa, varied ethical practices, and limited support to communities’ capacity to actively engaged with clinical trial conducts. Participants recommended that the African Government implement the Abuja +12 declaration; the civil society build stronger partnerships with diverse stakeholders, and develop a coherent advocacy agenda that also enhances community research literacy; and researchers and sponsors of trials on the African continent establish a process for determining appropriate standards for trial conduct on the continent. Conclusion By highlighting key considerations for biomedical HIV prevention research and development in Africa, the forum has helped identify key advocacy issues that Civil Society can expend efforts on so as to strengthen support for future biomedical HIV prevention research on the continent. PMID:26636825

Getting to zero the biomedical way in Africa: outcomes of deliberation at the 2013 Biomedical HIV Prevention Forum in Abuja, Nigeria.

PubMed

Folayan, Morenike Oluwatoyin; Gottemoeller, Megan; Mburu, Rosemary; Brown, Brandon

2014-01-01

Over the last few decades, biomedical HIV prevention research had engaged multiple African stakeholders. There have however been few platforms to enable regional stakeholders to engage with one another. In partnership with the World AIDS Campaign International, the Institute of Public Health of Obafemi Awolowo University, and the National Agency for the Control of AIDS in Nigeria, the New HIV Vaccine and Microbicide Advocacy Society hosted a forum on biomedical HIV prevention research in Africa. Stakeholders' present explored evidences related to biomedical HIV prevention research and development in Africa, and made recommendations to inform policy, guidelines and future research agenda. The BHPF hosted 342 participants. Topics discussed included the use of antiretrovirals for HIV prevention, considerations for biomedical HIV prevention among key populations; HIV vaccine development; HIV cure; community and civil society engagement; and ethical considerations in implementation of biomedical HIV prevention research. Participants identified challenges for implementation of proven efficacious interventions and discovery of other new prevention options for Africa. Concerns raised included limited funding by African governments, lack of cohesive advocacy and policy agenda for biomedical HIV prevention research and development by Africa, varied ethical practices, and limited support to communities' capacity to actively engaged with clinical trial conducts. Participants recommended that the African Government implement the Abuja +12 declaration; the civil society build stronger partnerships with diverse stakeholders, and develop a coherent advocacy agenda that also enhances community research literacy; and researchers and sponsors of trials on the African continent establish a process for determining appropriate standards for trial conduct on the continent. By highlighting key considerations for biomedical HIV prevention research and development in Africa, the forum has helped identify key advocacy issues that Civil Society can expend efforts on so as to strengthen support for future biomedical HIV prevention research on the continent.

Community outreach at biomedical research facilities.

PubMed

Goldman, M; Hedetniemi, J N; Herbert, E R; Sassaman, J S; Walker, B C

2000-12-01

For biomedical researchers to fulfill their responsibility for protecting the environment, they must do more than meet the scientific challenge of reducing the number and volume of hazardous materials used in their laboratories and the engineering challenge of reducing pollution and shifting to cleaner energy sources. They must also meet the public relations challenge of informing and involving their neighbors in these efforts. The experience of the Office of Community Liaison of the National Institutes of Health (NIH) in meeting the latter challenge offers a model and several valuable lessons for other biomedical research facilities to follow. This paper is based on presentations by an expert panel during the Leadership Conference on Biomedical Research and the Environment held 1--2 November 1999 at NIH, Bethesda, Maryland. The risks perceived by community members are often quite different from those identified by officials at the biomedical research facility. The best antidote for misconceptions is more and better information. If community organizations are to be informed participants in the decision-making process, they need a simple but robust mechanism for identifying and evaluating the environmental hazards in their community. Local government can and should be an active and fully informed partner in planning and emergency preparedness. In some cases this can reduce the regulatory burden on the biomedical research facility. In other cases it might simplify and expedite the permitting process or help the facility disseminate reliable information to the community. When a particular risk, real or perceived, is of special concern to the community, community members should be involved in the design, implementation, and evaluation of targeted risk assessment activities. Only by doing so will the community have confidence in the results of those activities. NIH has involved community members in joint efforts to deal with topics as varied as recycling and soil testing. These ad hoc efforts are more likely to succeed if community members and groups have also been included in larger and longer term advisory committees. These committees institutionalize the outreach process. This can provide the facility with vocal and influential allies who create an independent line of communication with the larger community.

Label-Free Biomedical Imaging Using High-Speed Lock-In Pixel Sensor for Stimulated Raman Scattering

PubMed Central

Mars, Kamel; Kawahito, Shoji; Yasutomi, Keita; Kagawa, Keiichiro; Yamada, Takahiro

2017-01-01

Raman imaging eliminates the need for staining procedures, providing label-free imaging to study biological samples. Recent developments in stimulated Raman scattering (SRS) have achieved fast acquisition speed and hyperspectral imaging. However, there has been a problem of lack of detectors suitable for MHz modulation rate parallel detection, detecting multiple small SRS signals while eliminating extremely strong offset due to direct laser light. In this paper, we present a complementary metal-oxide semiconductor (CMOS) image sensor using high-speed lock-in pixels for stimulated Raman scattering that is capable of obtaining the difference of Stokes-on and Stokes-off signal at modulation frequency of 20 MHz in the pixel before reading out. The generated small SRS signal is extracted and amplified in a pixel using a high-speed and large area lateral electric field charge modulator (LEFM) employing two-step ion implantation and an in-pixel pair of low-pass filter, a sample and hold circuit and a switched capacitor integrator using a fully differential amplifier. A prototype chip is fabricated using 0.11 μm CMOS image sensor technology process. SRS spectra and images of stearic acid and 3T3-L1 samples are successfully obtained. The outcomes suggest that hyperspectral and multi-focus SRS imaging at video rate is viable after slight modifications to the pixel architecture and the acquisition system. PMID:29120358

Label-Free Biomedical Imaging Using High-Speed Lock-In Pixel Sensor for Stimulated Raman Scattering.

PubMed

Mars, Kamel; Lioe, De Xing; Kawahito, Shoji; Yasutomi, Keita; Kagawa, Keiichiro; Yamada, Takahiro; Hashimoto, Mamoru

2017-11-09

Raman imaging eliminates the need for staining procedures, providing label-free imaging to study biological samples. Recent developments in stimulated Raman scattering (SRS) have achieved fast acquisition speed and hyperspectral imaging. However, there has been a problem of lack of detectors suitable for MHz modulation rate parallel detection, detecting multiple small SRS signals while eliminating extremely strong offset due to direct laser light. In this paper, we present a complementary metal-oxide semiconductor (CMOS) image sensor using high-speed lock-in pixels for stimulated Raman scattering that is capable of obtaining the difference of Stokes-on and Stokes-off signal at modulation frequency of 20 MHz in the pixel before reading out. The generated small SRS signal is extracted and amplified in a pixel using a high-speed and large area lateral electric field charge modulator (LEFM) employing two-step ion implantation and an in-pixel pair of low-pass filter, a sample and hold circuit and a switched capacitor integrator using a fully differential amplifier. A prototype chip is fabricated using 0.11 μm CMOS image sensor technology process. SRS spectra and images of stearic acid and 3T3-L1 samples are successfully obtained. The outcomes suggest that hyperspectral and multi-focus SRS imaging at video rate is viable after slight modifications to the pixel architecture and the acquisition system.

Development of a belt-type wearable sensor system with multi-function for home health care

NASA Astrophysics Data System (ADS)

Ban, Yunho; Choi, Samjin; Jiang, Zhongwei; Park, Chanwon

2005-12-01

Some reports show that the physiological information measured in hospital is not enough without the one measured in home. The physiological information monitored in home, therefore, is strongly required recently. The goal of this research is to develop a wearable and tractable sensor system for detecting biomedical signals such as cardiac rhythm, respiration, body movement, and percentage of body fat (%BF) and for home health care. A belt type sensor for this purpose is developed, which consists of sensing materials of PVDF film and conductive fabrics. Also several data processing techniques, such as the discrete wavelet transform, cross correlation and adaptive filtering method, were introduced to eliminate noises and base wandering and to extract the specified components. The ECG and respiration signals obtained by the proposed belt type sensor system gave good agreements with commercial medical system. Furthermore, the body fat (%BF) measurement based on the four-electrode BIA was also built in the belt sensor. The body fat was calculated by measuring the body impedance from the belt type sensor and compared with the predicted %BF measured by the commercial adipometer (TBF-607). The results validated also the efficiency of the belt type sensor system.

Envisioning the future of 'big data' biomedicine.

PubMed

Bui, Alex A T; Van Horn, John Darrell

2017-05-01

Through the increasing availability of more efficient data collection procedures, biomedical scientists are now confronting ever larger sets of data, often finding themselves struggling to process and interpret what they have gathered. This, while still more data continues to accumulate. This torrent of biomedical information necessitates creative thinking about how the data are being generated, how they might be best managed, analyzed, and eventually how they can be transformed into further scientific understanding for improving patient care. Recognizing this as a major challenge, the National Institutes of Health (NIH) has spearheaded the "Big Data to Knowledge" (BD2K) program - the agency's most ambitious biomedical informatics effort ever undertaken to date. In this commentary, we describe how the NIH has taken on "big data" science head-on, how a consortium of leading research centers are developing the means for handling large-scale data, and how such activities are being marshalled for the training of a new generation of biomedical data scientists. All in all, the NIH BD2K program seeks to position data science at the heart of 21 st Century biomedical research. Copyright © 2017 Elsevier Inc. All rights reserved.

A recent advance in the automatic indexing of the biomedical literature.

PubMed

Névéol, Aurélie; Shooshan, Sonya E; Humphrey, Susanne M; Mork, James G; Aronson, Alan R

2009-10-01

The volume of biomedical literature has experienced explosive growth in recent years. This is reflected in the corresponding increase in the size of MEDLINE, the largest bibliographic database of biomedical citations. Indexers at the US National Library of Medicine (NLM) need efficient tools to help them accommodate the ensuing workload. After reviewing issues in the automatic assignment of Medical Subject Headings (MeSH terms) to biomedical text, we focus more specifically on the new subheading attachment feature for NLM's Medical Text Indexer (MTI). Natural Language Processing, statistical, and machine learning methods of producing automatic MeSH main heading/subheading pair recommendations were assessed independently and combined. The best combination achieves 48% precision and 30% recall. After validation by NLM indexers, a suitable combination of the methods presented in this paper was integrated into MTI as a subheading attachment feature producing MeSH indexing recommendations compliant with current state-of-the-art indexing practice.

Finding and Accessing Diagrams in Biomedical Publications

PubMed Central

Kuhn, Tobias; Luong, ThaiBinh; Krauthammer, Michael

2012-01-01

Complex relationships in biomedical publications are often communicated by diagrams such as bar and line charts, which are a very effective way of summarizing and communicating multi-faceted data sets. Given the ever-increasing amount of published data, we argue that the precise retrieval of such diagrams is of great value for answering specific and otherwise hard-to-meet information needs. To this end, we demonstrate the use of advanced image processing and classification for identifying bar and line charts by the shape and relative location of the different image elements that make up the charts. With recall and precisions of close to 90% for the detection of relevant figures, we discuss the use of this technology in an existing biomedical image search engine, and outline how it enables new forms of literature queries over biomedical relationships that are represented in these charts. PMID:23304318

Scientific Programs and Funding Opportunities at the National Institute of Biomedical Imaging and Bioengineering

NASA Astrophysics Data System (ADS)

Baird, Richard

2006-03-01

The mission of the National Institute of Biomedical Imaging and Bioengineering (NIBIB) is to improve human health by promoting the development and translation of emerging technologies in biomedical imaging and bioengineering. To this end, NIBIB supports a coordinated agenda of research programs in advanced imaging technologies and engineering methods that enable fundamental biomedical discoveries across a broad spectrum of biological processes, disorders, and diseases and have significant potential for direct medical application. These research programs dramatically advance the Nation's healthcare by improving the detection, management and, ultimately, the prevention of disease. The research promoted and supported by NIBIB also is strongly synergistic with other NIH Institutes and Centers as well as across government agencies. This presentation will provide an overview of the scientific programs and funding opportunities supported by NIBIB, highlighting those that are of particular important to the field of medical physics.

Self-regulated learning processes of medical students during an academic learning task.

PubMed

Gandomkar, Roghayeh; Mirzazadeh, Azim; Jalili, Mohammad; Yazdani, Kamran; Fata, Ladan; Sandars, John

2016-10-01

This study was designed to identify the self-regulated learning (SRL) processes of medical students during a biomedical science learning task and to examine the associations of the SRL processes with previous performance in biomedical science examinations and subsequent performance on a learning task. A sample of 76 Year 1 medical students were recruited based on their performance in biomedical science examinations and stratified into previous high and low performers. Participants were asked to complete a biomedical science learning task. Participants' SRL processes were assessed before (self-efficacy, goal setting and strategic planning), during (metacognitive monitoring) and after (causal attributions and adaptive inferences) their completion of the task using an SRL microanalytic interview. Descriptive statistics were used to analyse the means and frequencies of SRL processes. Univariate and multiple logistic regression analyses were conducted to examine the associations of SRL processes with previous examination performance and the learning task performance. Most participants (from 88.2% to 43.4%) reported task-specific processes for SRL measures. Students who exhibited higher self-efficacy (odds ratio [OR] 1.44, 95% confidence interval [CI] 1.09-1.90) and reported task-specific processes for metacognitive monitoring (OR 6.61, 95% CI 1.68-25.93) and causal attributions (OR 6.75, 95% CI 2.05-22.25) measures were more likely to be high previous performers. Multiple analysis revealed that similar SRL measures were associated with previous performance. The use of task-specific processes for causal attributions (OR 23.00, 95% CI 4.57-115.76) and adaptive inferences (OR 27.00, 95% CI 3.39-214.95) measures were associated with being a high learning task performer. In multiple analysis, only the causal attributions measure was associated with high learning task performance. Self-efficacy, metacognitive monitoring and causal attributions measures were associated positively with previous performance. Causal attributions and adaptive inferences measures were associated positively with learning task performance. These findings may inform remediation interventions in the early years of medical school training. © 2016 John Wiley & Sons Ltd and The Association for the Study of Medical Education.

The Role of Electrospinning in the Emerging Field of Nanomedicine

PubMed Central

Chew, SY; Wen, Y; Dzenis, Y; Leong, KW

2008-01-01

The fact that in vivo the extracellular matrix or substratum with which cells interact often includes topography at the nanoscale underscores the importance of investigating cell-substrate interactions and performing cell culture at the submicron scale. An important and exciting direction of research in nanomedicine would be to gain an understanding and exploit the cellular response to nanostructures. Electrospinning is a simple and versatile technique that can produce a macroporous scaffold comprising randomly oriented or aligned nanofibers. It can also accommodate the incorporation of drug delivery function into the fibrous scaffold. Endowed with both topographical and biochemical signals such electrospun nanofibrous scaffolds may provide an optimal microenvironment for the seeded cells. This review covers the analysis and control of the electrospinning process, and describes the types of electrospun fibers fabricated for biomedical applications such as drug delivery and tissue engineering. PMID:17168776

Information Technology in Critical Care: Review of Monitoring and Data Acquisition Systems for Patient Care and Research

PubMed Central

De Georgia, Michael A.; Kaffashi, Farhad; Jacono, Frank J.; Loparo, Kenneth A.

2015-01-01

There is a broad consensus that 21st century health care will require intensive use of information technology to acquire and analyze data and then manage and disseminate information extracted from the data. No area is more data intensive than the intensive care unit. While there have been major improvements in intensive care monitoring, the medical industry, for the most part, has not incorporated many of the advances in computer science, biomedical engineering, signal processing, and mathematics that many other industries have embraced. Acquiring, synchronizing, integrating, and analyzing patient data remain frustratingly difficult because of incompatibilities among monitoring equipment, proprietary limitations from industry, and the absence of standard data formatting. In this paper, we will review the history of computers in the intensive care unit along with commonly used monitoring and data acquisition systems, both those commercially available and those being developed for research purposes. PMID:25734185

Hydrocarbon-Stapled Peptides: Principles, Practice, and Progress

PubMed Central

2015-01-01

Protein structure underlies essential biological processes and provides a blueprint for molecular mimicry that drives drug discovery. Although small molecules represent the lion’s share of agents that target proteins for therapeutic benefit, there remains no substitute for the natural properties of proteins and their peptide subunits in the majority of biological contexts. The peptide α-helix represents a common structural motif that mediates communication between signaling proteins. Because peptides can lose their shape when taken out of context, developing chemical interventions to stabilize their bioactive structure remains an active area of research. The all-hydrocarbon staple has emerged as one such solution, conferring α-helical structure, protease resistance, cellular penetrance, and biological activity upon successful incorporation of a series of design and application principles. Here, we describe our more than decade-long experience in developing stapled peptides as biomedical research tools and prototype therapeutics, highlighting lessons learned, pitfalls to avoid, and keys to success. PMID:24601557

The association between reconstructed phase space and Artificial Neural Networks for vectorcardiographic recognition of myocardial infarction.

PubMed

Costa, Cecília M; Silva, Ittalo S; de Sousa, Rafael D; Hortegal, Renato A; Regis, Carlos Danilo M

Myocardial infarction is one of the leading causes of death worldwide. As it is life threatening, it requires an immediate and precise treatment. Due to this, a growing number of research and innovations in the field of biomedical signal processing is in high demand. This paper proposes the association of Reconstructed Phase Space and Artificial Neural Networks for Vectorcardiography Myocardial Infarction Recognition. The algorithm promotes better results for the box size 10 × 10 and the combination of four parameters: box counting (Vx), box counting (Vz), self-similarity method (Vx) and self-similarity method (Vy) with sensitivity = 92%, specificity = 96% and accuracy = 94%. The topographic diagnosis presented different performances for different types of infarctions with better results for anterior wall infarctions and less accurate results for inferior infarctions. Copyright © 2018 Elsevier Inc. All rights reserved.

Information technology in critical care: review of monitoring and data acquisition systems for patient care and research.

PubMed

De Georgia, Michael A; Kaffashi, Farhad; Jacono, Frank J; Loparo, Kenneth A

2015-01-01

There is a broad consensus that 21st century health care will require intensive use of information technology to acquire and analyze data and then manage and disseminate information extracted from the data. No area is more data intensive than the intensive care unit. While there have been major improvements in intensive care monitoring, the medical industry, for the most part, has not incorporated many of the advances in computer science, biomedical engineering, signal processing, and mathematics that many other industries have embraced. Acquiring, synchronizing, integrating, and analyzing patient data remain frustratingly difficult because of incompatibilities among monitoring equipment, proprietary limitations from industry, and the absence of standard data formatting. In this paper, we will review the history of computers in the intensive care unit along with commonly used monitoring and data acquisition systems, both those commercially available and those being developed for research purposes.

Computational Modeling of Single-Cell Migration: The Leading Role of Extracellular Matrix Fibers

PubMed Central

Schlüter, Daniela K.; Ramis-Conde, Ignacio; Chaplain, Mark A.J.

2012-01-01

Cell migration is vitally important in a wide variety of biological contexts ranging from embryonic development and wound healing to malignant diseases such as cancer. It is a very complex process that is controlled by intracellular signaling pathways as well as the cell’s microenvironment. Due to its importance and complexity, it has been studied for many years in the biomedical sciences, and in the last 30 years it also received an increasing amount of interest from theoretical scientists and mathematical modelers. Here we propose a force-based, individual-based modeling framework that links single-cell migration with matrix fibers and cell-matrix interactions through contact guidance and matrix remodelling. With this approach, we can highlight the effect of the cell’s environment on its migration. We investigate the influence of matrix stiffness, matrix architecture, and cell speed on migration using quantitative measures that allow us to compare the results to experiments. PMID:22995486

Hyperpolarized Porous Silicon Nanoparticles: Potential Theragnostic Material for ²⁹Si Magnetic Resonance Imaging.

PubMed

Seo, Hyeonglim; Choi, Ikjang; Whiting, Nicholas; Hu, Jingzhe; Luu, Quy Son; Pudakalakatti, Shivanand; McCowan, Caitlin; Kim, Yaewon; Zacharias, Niki; Lee, Seunghyun; Bhattacharya, Pratip; Lee, Youngbok

2018-05-20

Porous silicon nanoparticles have recently garnered attention as potentially-promising biomedical platforms for drug delivery and medical diagnostics. Here, we demonstrate porous silicon nanoparticles as contrast agents for ²⁹Si magnetic resonance imaging. Size-controlled porous silicon nanoparticles were synthesized by magnesiothermic reduction of silica nanoparticles and were surface activated for further functionalization. Particles were hyperpolarized via dynamic nuclear polarization to enhance their ²⁹Si MR signals; the particles demonstrated long ²⁹Si spin-lattice relaxation (T₁) times (~ 25 mins), which suggests potential applicability for medical imaging. Furthermore, ²⁹Si hyperpolarization levels were sufficient to allow ²⁹Si MRI in phantoms. These results underscore the potential of porous silicon nanoparticles that, when combined with hyperpolarized magnetic resonance imaging, can be a powerful theragnostic deep tissue imaging platform to interrogate various biomolecular processes in vivo. © 2018 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

Ethics in published brain-computer interface research

NASA Astrophysics Data System (ADS)

Specker Sullivan, L.; Illes, J.

2018-02-01

Objective. Sophisticated signal processing has opened the doors to more research with human subjects than ever before. The increase in the use of human subjects in research comes with a need for increased human subjects protections. Approach. We quantified the presence or absence of ethics language in published reports of brain-computer interface (BCI) studies that involved human subjects and qualitatively characterized ethics statements. Main results. Reports of BCI studies with human subjects that are published in neural engineering and engineering journals are anchored in the rationale of technological improvement. Ethics language is markedly absent, omitted from 31% of studies published in neural engineering journals and 59% of studies in biomedical engineering journals. Significance. As the integration of technological tools with the capacities of the mind deepens, explicit attention to ethical issues will ensure that broad human benefit is embraced and not eclipsed by technological exclusiveness.

Real-time mobile phone dialing system based on SSVEP

NASA Astrophysics Data System (ADS)

Wang, Dongsheng; Kobayashi, Toshiki; Cui, Gaochao; Watabe, Daishi; Cao, Jianting

2017-03-01

Brain computer interface (BCI) systems based on the steady state visual evoked potential (SSVEP) provide higher information transfer rates and require shorter training time than BCI systems using other brain signals. It has been widely used in brain science, rehabilitation engineering, biomedical engineering and intelligent information processing. In this paper, we present a real-time mobile phone dialing system based on SSVEP, and it is more portable than other dialing system because the flashing dial interface is set on a small tablet. With this online BCI system, we can take advantage of this system based on SSVEP to identify the specific frequency on behalf of a number using canonical correlation analysis (CCA) method and dialed out successfully without using any physical movements such as finger tapping. This phone dialing system will be promising to help disable patients to improve the quality of lives.

Lung Adenocarcinoma Distally Rewires Hepatic Circadian Homeostasis

PubMed Central

Masri, Selma; Papagiannakopoulos, Thales; Kinouchi, Kenichiro; Liu, Yu; Cervantes, Marlene; Baldi, Pierre; Jacks, Tyler; Sassone-Corsi, Paolo

2016-01-01

SUMMARY The circadian clock controls metabolic and physiological processes through finely tuned molecular mechanisms. The clock is remarkably plastic and adapts to exogenous zeitgebers, such as light and nutrition. How a pathological condition in a given tissue influences systemic circadian homeostasis in other tissues remains an unanswered question of conceptual and biomedical importance. Here we show that lung adenocarcinoma operates as an endogenous reorganizer of circadian metabolism. High-throughput transcriptomics and metabolomics revealed unique signatures of transcripts and metabolites cycling exclusively in livers of tumor-bearing mice. Remarkably, lung cancer has no effect on the core clock, but rather reprograms hepatic metabolism through altered pro-inflammatory response via the STAT3-Socs3 pathway. This results in disruption of AKT, AMPK and SREBP signaling, leading to altered insulin, glucose and lipid metabolism. Thus, lung adenocarcinoma functions as a potent endogenous circadian organizer (ECO), which rewires the pathophysiological dimension of a distal tissue such as the liver. PMID:27153497

Cell and small animal models for phenotypic drug discovery.

PubMed

Szabo, Mihaly; Svensson Akusjärvi, Sara; Saxena, Ankur; Liu, Jianping; Chandrasekar, Gayathri; Kitambi, Satish S

2017-01-01

The phenotype-based drug discovery (PDD) approach is re-emerging as an alternative platform for drug discovery. This review provides an overview of the various model systems and technical advances in imaging and image analyses that strengthen the PDD platform. In PDD screens, compounds of therapeutic value are identified based on the phenotypic perturbations produced irrespective of target(s) or mechanism of action. In this article, examples of phenotypic changes that can be detected and quantified with relative ease in a cell-based setup are discussed. In addition, a higher order of PDD screening setup using small animal models is also explored. As PDD screens integrate physiology and multiple signaling mechanisms during the screening process, the identified hits have higher biomedical applicability. Taken together, this review highlights the advantages gained by adopting a PDD approach in drug discovery. Such a PDD platform can complement target-based systems that are currently in practice to accelerate drug discovery.

Porous Au-Ag Nanospheres with High-Density and Highly Accessible Hotspots for SERS Analysis.

PubMed

Liu, Kai; Bai, Yaocai; Zhang, Lei; Yang, Zhongbo; Fan, Qikui; Zheng, Haoquan; Yin, Yadong; Gao, Chuanbo

2016-06-08

Colloidal plasmonic metal nanoparticles have enabled surface-enhanced Raman scattering (SERS) for a variety of analytical applications. While great efforts have been made to create hotspots for amplifying Raman signals, it remains a great challenge to ensure their high density and accessibility for improved sensitivity of the analysis. Here we report a dealloying process for the fabrication of porous Au-Ag alloy nanoparticles containing abundant inherent hotspots, which were encased in ultrathin hollow silica shells so that the need of conventional organic capping ligands for stabilization is eliminated, producing colloidal plasmonic nanoparticles with clean surface and thus high accessibility of the hotspots. As a result, these novel nanostructures show excellent SERS activity with an enhancement factor of ∼1.3 × 10(7) on a single particle basis (off-resonant condition), promising high applicability in many SERS-based analytical and biomedical applications.

Structural Genomics of Protein Phosphatases

DOE Office of Scientific and Technical Information (OSTI.GOV)

Almo,S.; Bonanno, J.; Sauder, J.

The New York SGX Research Center for Structural Genomics (NYSGXRC) of the NIGMS Protein Structure Initiative (PSI) has applied its high-throughput X-ray crystallographic structure determination platform to systematic studies of all human protein phosphatases and protein phosphatases from biomedically-relevant pathogens. To date, the NYSGXRC has determined structures of 21 distinct protein phosphatases: 14 from human, 2 from mouse, 2 from the pathogen Toxoplasma gondii, 1 from Trypanosoma brucei, the parasite responsible for African sleeping sickness, and 2 from the principal mosquito vector of malaria in Africa, Anopheles gambiae. These structures provide insights into both normal and pathophysiologic processes, including transcriptionalmore » regulation, regulation of major signaling pathways, neural development, and type 1 diabetes. In conjunction with the contributions of other international structural genomics consortia, these efforts promise to provide an unprecedented database and materials repository for structure-guided experimental and computational discovery of inhibitors for all classes of protein phosphatases.« less

Harnessing and Modulating Inflammation in Strategies for Bone Regeneration

PubMed Central

Mountziaris, Paschalia M.; Spicer, Patrick P.; Kasper, F. Kurtis

2011-01-01

Inflammation is an immediate response that plays a critical role in healing after fracture or injury to bone. However, in certain clinical contexts, such as in inflammatory diseases or in response to the implantation of a biomedical device, the inflammatory response may become chronic and result in destructive catabolic effects on the bone tissue. Since our previous review 3 years ago, which identified inflammatory signals critical for bone regeneration and described the inhibitory effects of anti-inflammatory agents on bone healing, a multitude of studies have been published exploring various aspects of this emerging field. In this review, we distinguish between regenerative and damaging inflammatory processes in bone, update our discussion of the effects of anti-inflammatory agents on bone healing, summarize recent in vitro and in vivo studies demonstrating how inflammation can be modulated to stimulate bone regeneration, and identify key future directions in the field. PMID:21615330

Synthetic biology. Genomically encoded analog memory with precise in vivo DNA writing in living cell populations.

PubMed

Farzadfard, Fahim; Lu, Timothy K

2014-11-14

Cellular memory is crucial to many natural biological processes and sophisticated synthetic biology applications. Existing cellular memories rely on epigenetic switches or recombinases, which are limited in scalability and recording capacity. In this work, we use the DNA of living cell populations as genomic "tape recorders" for the analog and distributed recording of long-term event histories. We describe a platform for generating single-stranded DNA (ssDNA) in vivo in response to arbitrary transcriptional signals. When coexpressed with a recombinase, these intracellularly expressed ssDNAs target specific genomic DNA addresses, resulting in precise mutations that accumulate in cell populations as a function of the magnitude and duration of the inputs. This platform could enable long-term cellular recorders for environmental and biomedical applications, biological state machines, and enhanced genome engineering strategies. Copyright © 2014, American Association for the Advancement of Science.

Categorizing biomedicine images using novel image features and sparse coding representation

PubMed Central

2013-01-01

Background Images embedded in biomedical publications carry rich information that often concisely summarize key hypotheses adopted, methods employed, or results obtained in a published study. Therefore, they offer valuable clues for understanding main content in a biomedical publication. Prior studies have pointed out the potential of mining images embedded in biomedical publications for automatically understanding and retrieving such images' associated source documents. Within the broad area of biomedical image processing, categorizing biomedical images is a fundamental step for building many advanced image analysis, retrieval, and mining applications. Similar to any automatic categorization effort, discriminative image features can provide the most crucial aid in the process. Method We observe that many images embedded in biomedical publications carry versatile annotation text. Based on the locations of and the spatial relationships between these text elements in an image, we thus propose some novel image features for image categorization purpose, which quantitatively characterize the spatial positions and distributions of text elements inside a biomedical image. We further adopt a sparse coding representation (SCR) based technique to categorize images embedded in biomedical publications by leveraging our newly proposed image features. Results we randomly selected 990 images of the JPG format for use in our experiments where 310 images were used as training samples and the rest were used as the testing cases. We first segmented 310 sample images following the our proposed procedure. This step produced a total of 1035 sub-images. We then manually labeled all these sub-images according to the two-level hierarchical image taxonomy proposed by [1]. Among our annotation results, 316 are microscopy images, 126 are gel electrophoresis images, 135 are line charts, 156 are bar charts, 52 are spot charts, 25 are tables, 70 are flow charts, and the remaining 155 images are of the type "others". A serial of experimental results are obtained. Firstly, each image categorizing results is presented, and next image categorizing performance indexes such as precision, recall, F-score, are all listed. Different features which include conventional image features and our proposed novel features indicate different categorizing performance, and the results are demonstrated. Thirdly, we conduct an accuracy comparison between support vector machine classification method and our proposed sparse representation classification method. At last, our proposed approach is compared with three peer classification method and experimental results verify our impressively improved performance. Conclusions Compared with conventional image features that do not exploit characteristics regarding text positions and distributions inside images embedded in biomedical publications, our proposed image features coupled with the SR based representation model exhibit superior performance for classifying biomedical images as demonstrated in our comparative benchmark study. PMID:24565470

A cognitive approach for design of a multimedia informed consent video and website in pediatric research.

PubMed

Antal, Holly; Bunnell, H Timothy; McCahan, Suzanne M; Pennington, Chris; Wysocki, Tim; Blake, Kathryn V

2017-02-01

Poor participant comprehension of research procedures following the conventional face-to-face consent process for biomedical research is common. We describe the development of a multimedia informed consent video and website that incorporates cognitive strategies to enhance comprehension of study related material directed to parents and adolescents. A multidisciplinary team was assembled for development of the video and website that included human subjects professionals; psychologist researchers; institutional video and web developers; bioinformaticians and programmers; and parent and adolescent stakeholders. Five learning strategies that included Sensory-Modality view, Coherence, Signaling, Redundancy, and Personalization were integrated into a 15-min video and website material that describes a clinical research trial. A diverse team collaborated extensively over 15months to design and build a multimedia platform for obtaining parental permission and adolescent assent for participant in as asthma clinical trial. Examples of the learning principles included, having a narrator describe what was being viewed on the video (sensory-modality); eliminating unnecessary text and graphics (coherence); having the initial portion of the video explain the sections of the video to be viewed (signaling); avoiding simultaneous presentation of text and graphics (redundancy); and having a consistent narrator throughout the video (personalization). Existing conventional and multimedia processes for obtaining research informed consent have not actively incorporated basic principles of human cognition and learning in the design and implementation of these processes. The present paper illustrates how this can be achieved, setting the stage for rigorous evaluation of potential benefits such as improved comprehension, satisfaction with the consent process, and completion of research objectives. New consent strategies that have an integrated cognitive approach need to be developed and tested in controlled trials. Copyright © 2017 Elsevier Inc. All rights reserved.

Boolean Logic Tree of Label-Free Dual-Signal Electrochemical Aptasensor System for Biosensing, Three-State Logic Computation, and Keypad Lock Security Operation.

PubMed

Lu, Jiao Yang; Zhang, Xin Xing; Huang, Wei Tao; Zhu, Qiu Yan; Ding, Xue Zhi; Xia, Li Qiu; Luo, Hong Qun; Li, Nian Bing

2017-09-19

The most serious and yet unsolved problems of molecular logic computing consist in how to connect molecular events in complex systems into a usable device with specific functions and how to selectively control branchy logic processes from the cascading logic systems. This report demonstrates that a Boolean logic tree is utilized to organize and connect "plug and play" chemical events DNA, nanomaterials, organic dye, biomolecule, and denaturant for developing the dual-signal electrochemical evolution aptasensor system with good resettability for amplification detection of thrombin, controllable and selectable three-state logic computation, and keypad lock security operation. The aptasensor system combines the merits of DNA-functionalized nanoamplification architecture and simple dual-signal electroactive dye brilliant cresyl blue for sensitive and selective detection of thrombin with a wide linear response range of 0.02-100 nM and a detection limit of 1.92 pM. By using these aforementioned chemical events as inputs and the differential pulse voltammetry current changes at different voltages as dual outputs, a resettable three-input biomolecular keypad lock based on sequential logic is established. Moreover, the first example of controllable and selectable three-state molecular logic computation with active-high and active-low logic functions can be implemented and allows the output ports to assume a high impediment or nothing (Z) state in addition to the 0 and 1 logic levels, effectively controlling subsequent branchy logic computation processes. Our approach is helpful in developing the advanced controllable and selectable logic computing and sensing system in large-scale integration circuits for application in biomedical engineering, intelligent sensing, and control.

An efficient micro control unit with a reconfigurable filter design for wireless body sensor networks (WBSNs).

PubMed

Chen, Chiung-An; Chen, Shih-Lun; Huang, Hong-Yi; Luo, Ching-Hsing

2012-11-22

In this paper, a low-cost, low-power and high performance micro control unit (MCU) core is proposed for wireless body sensor networks (WBSNs). It consists of an asynchronous interface, a register bank, a reconfigurable filter, a slop-feature forecast, a lossless data encoder, an error correct coding (ECC) encoder, a UART interface, a power management (PWM), and a multi-sensor controller. To improve the system performance and expansion abilities, the asynchronous interface is added for handling signal exchanges between different clock domains. To eliminate the noise of various bio-signals, the reconfigurable filter is created to provide the functions of average, binomial and sharpen filters. The slop-feature forecast and the lossless data encoder is proposed to reduce the data of various biomedical signals for transmission. Furthermore, the ECC encoder is added to improve the reliability for the wireless transmission and the UART interface is employed the proposed design to be compatible with wireless devices. For long-term healthcare monitoring application, a power management technique is developed for reducing the power consumption of the WBSN system. In addition, the proposed design can be operated with four different bio-sensors simultaneously. The proposed design was successfully tested with a FPGA verification board. The VLSI architecture of this work contains 7.67-K gate counts and consumes the power of 5.8 mW or 1.9 mW at 100 MHz or 133 MHz processing rate using a TSMC 0.18 μm or 0.13 μm CMOS process. Compared with previous techniques, this design achieves higher performance, more functions, more flexibility and higher compatibility than other micro controller designs.

Learning Discriminative Sparse Models for Source Separation and Mapping of Hyperspectral Imagery

DTIC Science & Technology

2010-10-01

allowing spectroscopic analysis. The data acquired by these spectrometers play significant roles in biomedical, environmental, land-survey, and...noisy in nature , so there are differences between the true and the observed signals. In addition, there are distortions associated with atmosphere... handwriting classification, showing advantages of using both terms instead of only using the reconstruction term as in previous approaches. C. Dictionary

Probabilistic Signal Recovery and Random Matrices

DTIC Science & Technology

2016-12-08

applications in statistics , biomedical data analysis, quantization, dimen- sion reduction, and networks science. 1. High-dimensional inference and geometry Our...low-rank approxima- tion, with applications to community detection in networks, Annals of Statistics 44 (2016), 373–400. [7] C. Le, E. Levina, R...approximation, with applications to community detection in networks, Annals of Statistics 44 (2016), 373–400. C. Le, E. Levina, R. Vershynin, Concentration

Cognitive and learning sciences in biomedical and health instructional design: A review with lessons for biomedical informatics education.

PubMed

Patel, Vimla L; Yoskowitz, Nicole A; Arocha, Jose F; Shortliffe, Edward H

2009-02-01

Theoretical and methodological advances in the cognitive and learning sciences can greatly inform curriculum and instruction in biomedicine and also educational programs in biomedical informatics. It does so by addressing issues such as the processes related to comprehension of medical information, clinical problem-solving and decision-making, and the role of technology. This paper reviews these theories and methods from the cognitive and learning sciences and their role in addressing current and future needs in designing curricula, largely using illustrative examples drawn from medical education. The lessons of this past work are also applicable, however, to biomedical and health professional curricula in general, and to biomedical informatics training, in particular. We summarize empirical studies conducted over two decades on the role of memory, knowledge organization and reasoning as well as studies of problem-solving and decision-making in medical areas that inform curricular design. The results of this research contribute to the design of more informed curricula based on empirical findings about how people learn and think, and more specifically, how expertise is developed. Similarly, the study of practice can also help to shape theories of human performance, technology-based learning, and scientific and professional collaboration that extend beyond the domain of medicine. Just as biomedical science has revolutionized health care practice, research in the cognitive and learning sciences provides a scientific foundation for education in biomedicine, the health professions, and biomedical informatics.

Biosonar-inspired technology: goals, challenges and insights.

PubMed

Müller, Rolf; Kuc, Roman

2007-12-01

Bioinspired engineering based on biosonar systems in nature is reviewed and discussed in terms of the merits of different approaches and their results: biosonar systems are attractive technological paragons because of their capabilities, built-in task-specific knowledge, intelligent system integration and diversity. Insights from the diverse set of sensing tasks solved by bats are relevant to a wide range of application areas such as sonar, biomedical ultrasound, non-destructive testing, sensors for autonomous systems and wireless communication. Challenges in the design of bioinspired sonar systems are posed by transducer performance, actuation for sensor mobility, design, actuation and integration of beamforming baffle shapes, echo encoding for signal processing, estimation algorithms and their implementations, as well as system integration and feedback control. The discussed examples of experimental systems have capabilities that include localization and tracking using binaural and multiple-band hearing as well as self-generated dynamic cues, classification of small deterministic and large random targets, beamforming with bioinspired baffle shapes, neuromorphic spike processing, artifact rejection in sonar maps and passing range estimation. In future research, bioinspired engineering could capitalize on some of its strengths to serve as a model system for basic automation methodologies for the bioinspired engineering process.

A Practical Tablet-Based Hearing Aid Configuration as an Exemplar Project for Students of Instrumentation.

PubMed

Simeoni, Ricardo

2015-06-11

This paper presents the configuration and digital signal processing details of a tablet-based hearing aid transmitting wirelessly to standard earphones, whereby the tablet performs full sound processing rather than solely providing a means of setting adjustment by streaming to conventional digital hearing aids. The presented device confirms the recognized advantages of this tablet-based approach (e.g., in relation to cost, frequency domain processing, amplification range, versatility of functionality, component battery rechargeability), and flags the future wider-spread availability of such hearing solutions within mainstream healthcare. The use of a relatively high sampling frequency was found to be beneficial for device performance, while the use of optional off-the-shelf add-on components (e.g., data acquisition device, high fidelity microphone, compact wireless transmitter/receiver, wired headphones) are also discussed in relation to performance optimization. The easy-to-follow configuration utilized is well suited to student learning/research instrumentation projects within the health and biomedical sciences. In this latter regard, the presented device was pedagogically integrated into a flipped classroom approach for the teaching of bioinstrumentation within an Allied Health Sciences School, with the subsequent establishment of positive student engagement outcomes.