Comparison of spike-sorting algorithms for future hardware implementation.

PubMed

Gibson, Sarah; Judy, Jack W; Markovic, Dejan

2008-01-01

Applications such as brain-machine interfaces require hardware spike sorting in order to (1) obtain single-unit activity and (2) perform data reduction for wireless transmission of data. Such systems must be low-power, low-area, high-accuracy, automatic, and able to operate in real time. Several detection and feature extraction algorithms for spike sorting are described briefly and evaluated in terms of accuracy versus computational complexity. The nonlinear energy operator method is chosen as the optimal spike detection algorithm, being most robust over noise and relatively simple. The discrete derivatives method [1] is chosen as the optimal feature extraction method, maintaining high accuracy across SNRs with a complexity orders of magnitude less than that of traditional methods such as PCA.

Spike sorting of synchronous spikes from local neuron ensembles

PubMed Central

Pröpper, Robert; Alle, Henrik; Meier, Philipp; Geiger, Jörg R. P.; Obermayer, Klaus; Munk, Matthias H. J.

2015-01-01

Synchronous spike discharge of cortical neurons is thought to be a fingerprint of neuronal cooperativity. Because neighboring neurons are more densely connected to one another than neurons that are located further apart, near-synchronous spike discharge can be expected to be prevalent and it might provide an important basis for cortical computations. Using microelectrodes to record local groups of neurons does not allow for the reliable separation of synchronous spikes from different cells, because available spike sorting algorithms cannot correctly resolve the temporally overlapping waveforms. We show that high spike sorting performance of in vivo recordings, including overlapping spikes, can be achieved with a recently developed filter-based template matching procedure. Using tetrodes with a three-dimensional structure, we demonstrate with simulated data and ground truth in vitro data, obtained by dual intracellular recording of two neurons located next to a tetrode, that the spike sorting of synchronous spikes can be as successful as the spike sorting of nonoverlapping spikes and that the spatial information provided by multielectrodes greatly reduces the error rates. We apply the method to tetrode recordings from the prefrontal cortex of behaving primates, and we show that overlapping spikes can be identified and assigned to individual neurons to study synchronous activity in local groups of neurons. PMID:26289473

A Visual Guide to Sorting Electrophysiological Recordings Using 'SpikeSorter'.

PubMed

Swindale, Nicholas V; Mitelut, Catalin; Murphy, Timothy H; Spacek, Martin A

2017-02-10

Few stand-alone software applications are available for sorting spikes from recordings made with multi-electrode arrays. Ideally, an application should be user friendly with a graphical user interface, able to read data files in a variety of formats, and provide users with a flexible set of tools giving them the ability to detect and sort extracellular voltage waveforms from different units with some degree of reliability. Previously published spike sorting methods are now available in a software program, SpikeSorter, intended to provide electrophysiologists with a complete set of tools for sorting, starting from raw recorded data file and ending with the export of sorted spikes times. Procedures are automated to the extent this is currently possible. The article explains and illustrates the use of the program. A representative data file is opened, extracellular traces are filtered, events are detected and then clustered. A number of problems that commonly occur during sorting are illustrated, including the artefactual over-splitting of units due to the tendency of some units to fire spikes in pairs where the second spike is significantly smaller than the first, and over-splitting caused by slow variation in spike height over time encountered in some units. The accuracy of SpikeSorter's performance has been tested with surrogate ground truth data and found to be comparable to that of other algorithms in current development.

A real-time spike sorting method based on the embedded GPU.

PubMed

Zelan Yang; Kedi Xu; Xiang Tian; Shaomin Zhang; Xiaoxiang Zheng

2017-07-01

Microelectrode arrays with hundreds of channels have been widely used to acquire neuron population signals in neuroscience studies. Online spike sorting is becoming one of the most important challenges for high-throughput neural signal acquisition systems. Graphic processing unit (GPU) with high parallel computing capability might provide an alternative solution for increasing real-time computational demands on spike sorting. This study reported a method of real-time spike sorting through computing unified device architecture (CUDA) which was implemented on an embedded GPU (NVIDIA JETSON Tegra K1, TK1). The sorting approach is based on the principal component analysis (PCA) and K-means. By analyzing the parallelism of each process, the method was further optimized in the thread memory model of GPU. Our results showed that the GPU-based classifier on TK1 is 37.92 times faster than the MATLAB-based classifier on PC while their accuracies were the same with each other. The high-performance computing features of embedded GPU demonstrated in our studies suggested that the embedded GPU provide a promising platform for the real-time neural signal processing.

An Unsupervised Online Spike-Sorting Framework.

PubMed

Knieling, Simeon; Sridharan, Kousik S; Belardinelli, Paolo; Naros, Georgios; Weiss, Daniel; Mormann, Florian; Gharabaghi, Alireza

2016-08-01

Extracellular neuronal microelectrode recordings can include action potentials from multiple neurons. To separate spikes from different neurons, they can be sorted according to their shape, a procedure referred to as spike-sorting. Several algorithms have been reported to solve this task. However, when clustering outcomes are unsatisfactory, most of them are difficult to adjust to achieve the desired results. We present an online spike-sorting framework that uses feature normalization and weighting to maximize the distinctiveness between different spike shapes. Furthermore, multiple criteria are applied to either facilitate or prevent cluster fusion, thereby enabling experimenters to fine-tune the sorting process. We compare our method to established unsupervised offline (Wave_Clus (WC)) and online (OSort (OS)) algorithms by examining their performance in sorting various test datasets using two different scoring systems (AMI and the Adamos metric). Furthermore, we evaluate sorting capabilities on intra-operative recordings using established quality metrics. Compared to WC and OS, our algorithm achieved comparable or higher scores on average and produced more convincing sorting results for intra-operative datasets. Thus, the presented framework is suitable for both online and offline analysis and could substantially improve the quality of microelectrode-based data evaluation for research and clinical application.

A new approach to spike sorting for multi-neuronal activities recorded with a tetrode--how ICA can be practical.

PubMed

Takahashi, Susumu; Anzai, Yuichiro; Sakurai, Yoshio

2003-07-01

Multi-neuronal recording with a tetrode is a powerful technique to reveal neuronal interactions in local circuits. However, it is difficult to detect precise spike timings among closely neighboring neurons because the spike waveforms of individual neurons overlap on the electrode when more than two neurons fire simultaneously. In addition, the spike waveforms of single neurons, especially in the presence of complex spikes, are often non-stationary. These problems limit the ability of ordinary spike sorting to sort multi-neuronal activities recorded using tetrodes into their single-neuron components. Though sorting with independent component analysis (ICA) can solve these problems, it has one serious limitation that the number of separated neurons must be less than the number of electrodes. Using a combination of ICA and the efficiency of ordinary spike sorting technique (k-means clustering), we developed an automatic procedure to solve the spike-overlapping and the non-stationarity problems with no limitation on the number of separated neurons. The results for the procedure applied to real multi-neuronal data demonstrated that some outliers which may be assigned to distinct clusters if ordinary spike-sorting methods were used can be identified as overlapping spikes, and that there are functional connections between a putative pyramidal neuron and its putative dendrite. These findings suggest that the combination of ICA and k-means clustering can provide insights into the precise nature of functional circuits among neurons, i.e. cell assemblies.

Clusterless Decoding of Position From Multiunit Activity Using A Marked Point Process Filter

PubMed Central

Deng, Xinyi; Liu, Daniel F.; Kay, Kenneth; Frank, Loren M.; Eden, Uri T.

2016-01-01

Point process filters have been applied successfully to decode neural signals and track neural dynamics. Traditionally, these methods assume that multiunit spiking activity has already been correctly spike-sorted. As a result, these methods are not appropriate for situations where sorting cannot be performed with high precision such as real-time decoding for brain-computer interfaces. As the unsupervised spike-sorting problem remains unsolved, we took an alternative approach that takes advantage of recent insights about clusterless decoding. Here we present a new point process decoding algorithm that does not require multiunit signals to be sorted into individual units. We use the theory of marked point processes to construct a function that characterizes the relationship between a covariate of interest (in this case, the location of a rat on a track) and features of the spike waveforms. In our example, we use tetrode recordings, and the marks represent a four-dimensional vector of the maximum amplitudes of the spike waveform on each of the four electrodes. In general, the marks may represent any features of the spike waveform. We then use Bayes’ rule to estimate spatial location from hippocampal neural activity. We validate our approach with a simulation study and with experimental data recorded in the hippocampus of a rat moving through a linear environment. Our decoding algorithm accurately reconstructs the rat’s position from unsorted multiunit spiking activity. We then compare the quality of our decoding algorithm to that of a traditional spike-sorting and decoding algorithm. Our analyses show that the proposed decoding algorithm performs equivalently or better than algorithms based on sorted single-unit activity. These results provide a path toward accurate real-time decoding of spiking patterns that could be used to carry out content-specific manipulations of population activity in hippocampus or elsewhere in the brain. PMID:25973549

A spike sorting toolbox for up to thousands of electrodes validated with ground truth recordings in vitro and in vivo

PubMed Central

Lefebvre, Baptiste; Deny, Stéphane; Gardella, Christophe; Stimberg, Marcel; Jetter, Florian; Zeck, Guenther; Picaud, Serge; Duebel, Jens

2018-01-01

In recent years, multielectrode arrays and large silicon probes have been developed to record simultaneously between hundreds and thousands of electrodes packed with a high density. However, they require novel methods to extract the spiking activity of large ensembles of neurons. Here, we developed a new toolbox to sort spikes from these large-scale extracellular data. To validate our method, we performed simultaneous extracellular and loose patch recordings in rodents to obtain ‘ground truth’ data, where the solution to this sorting problem is known for one cell. The performance of our algorithm was always close to the best expected performance, over a broad range of signal-to-noise ratios, in vitro and in vivo. The algorithm is entirely parallelized and has been successfully tested on recordings with up to 4225 electrodes. Our toolbox thus offers a generic solution to sort accurately spikes for up to thousands of electrodes. PMID:29557782

Hierarchical Adaptive Means (HAM) clustering for hardware-efficient, unsupervised and real-time spike sorting.

PubMed

Paraskevopoulou, Sivylla E; Wu, Di; Eftekhar, Amir; Constandinou, Timothy G

2014-09-30

This work presents a novel unsupervised algorithm for real-time adaptive clustering of neural spike data (spike sorting). The proposed Hierarchical Adaptive Means (HAM) clustering method combines centroid-based clustering with hierarchical cluster connectivity to classify incoming spikes using groups of clusters. It is described how the proposed method can adaptively track the incoming spike data without requiring any past history, iteration or training and autonomously determines the number of spike classes. Its performance (classification accuracy) has been tested using multiple datasets (both simulated and recorded) achieving a near-identical accuracy compared to k-means (using 10-iterations and provided with the number of spike classes). Also, its robustness in applying to different feature extraction methods has been demonstrated by achieving classification accuracies above 80% across multiple datasets. Last but crucially, its low complexity, that has been quantified through both memory and computation requirements makes this method hugely attractive for future hardware implementation. Copyright © 2014 Elsevier B.V. All rights reserved.

A computationally efficient method for incorporating spike waveform information into decoding algorithms.

PubMed

Ventura, Valérie; Todorova, Sonia

2015-05-01

Spike-based brain-computer interfaces (BCIs) have the potential to restore motor ability to people with paralysis and amputation, and have shown impressive performance in the lab. To transition BCI devices from the lab to the clinic, decoding must proceed automatically and in real time, which prohibits the use of algorithms that are computationally intensive or require manual tweaking. A common choice is to avoid spike sorting and treat the signal on each electrode as if it came from a single neuron, which is fast, easy, and therefore desirable for clinical use. But this approach ignores the kinematic information provided by individual neurons recorded on the same electrode. The contribution of this letter is a linear decoding model that extracts kinematic information from individual neurons without spike-sorting the electrode signals. The method relies on modeling sample averages of waveform features as functions of kinematics, which is automatic and requires minimal data storage and computation. In offline reconstruction of arm trajectories of a nonhuman primate performing reaching tasks, the proposed method performs as well as decoders based on expertly manually and automatically sorted spikes.

A Simple Method to Simultaneously Detect and Identify Spikes from Raw Extracellular Recordings.

PubMed

Petrantonakis, Panagiotis C; Poirazi, Panayiota

2015-01-01

The ability to track when and which neurons fire in the vicinity of an electrode, in an efficient and reliable manner can revolutionize the neuroscience field. The current bottleneck lies in spike sorting algorithms; existing methods for detecting and discriminating the activity of multiple neurons rely on inefficient, multi-step processing of extracellular recordings. In this work, we show that a single-step processing of raw (unfiltered) extracellular signals is sufficient for both the detection and identification of active neurons, thus greatly simplifying and optimizing the spike sorting approach. The efficiency and reliability of our method is demonstrated in both real and simulated data.

Consensus-Based Sorting of Neuronal Spike Waveforms

PubMed Central

Fournier, Julien; Mueller, Christian M.; Shein-Idelson, Mark; Hemberger, Mike

2016-01-01

Optimizing spike-sorting algorithms is difficult because sorted clusters can rarely be checked against independently obtained “ground truth” data. In most spike-sorting algorithms in use today, the optimality of a clustering solution is assessed relative to some assumption on the distribution of the spike shapes associated with a particular single unit (e.g., Gaussianity) and by visual inspection of the clustering solution followed by manual validation. When the spatiotemporal waveforms of spikes from different cells overlap, the decision as to whether two spikes should be assigned to the same source can be quite subjective, if it is not based on reliable quantitative measures. We propose a new approach, whereby spike clusters are identified from the most consensual partition across an ensemble of clustering solutions. Using the variability of the clustering solutions across successive iterations of the same clustering algorithm (template matching based on K-means clusters), we estimate the probability of spikes being clustered together and identify groups of spikes that are not statistically distinguishable from one another. Thus, we identify spikes that are most likely to be clustered together and therefore correspond to consistent spike clusters. This method has the potential advantage that it does not rely on any model of the spike shapes. It also provides estimates of the proportion of misclassified spikes for each of the identified clusters. We tested our algorithm on several datasets for which there exists a ground truth (simultaneous intracellular data), and show that it performs close to the optimum reached by a support vector machine trained on the ground truth. We also show that the estimated rate of misclassification matches the proportion of misclassified spikes measured from the ground truth data. PMID:27536990

Consensus-Based Sorting of Neuronal Spike Waveforms.

PubMed

Fournier, Julien; Mueller, Christian M; Shein-Idelson, Mark; Hemberger, Mike; Laurent, Gilles

2016-01-01

Optimizing spike-sorting algorithms is difficult because sorted clusters can rarely be checked against independently obtained "ground truth" data. In most spike-sorting algorithms in use today, the optimality of a clustering solution is assessed relative to some assumption on the distribution of the spike shapes associated with a particular single unit (e.g., Gaussianity) and by visual inspection of the clustering solution followed by manual validation. When the spatiotemporal waveforms of spikes from different cells overlap, the decision as to whether two spikes should be assigned to the same source can be quite subjective, if it is not based on reliable quantitative measures. We propose a new approach, whereby spike clusters are identified from the most consensual partition across an ensemble of clustering solutions. Using the variability of the clustering solutions across successive iterations of the same clustering algorithm (template matching based on K-means clusters), we estimate the probability of spikes being clustered together and identify groups of spikes that are not statistically distinguishable from one another. Thus, we identify spikes that are most likely to be clustered together and therefore correspond to consistent spike clusters. This method has the potential advantage that it does not rely on any model of the spike shapes. It also provides estimates of the proportion of misclassified spikes for each of the identified clusters. We tested our algorithm on several datasets for which there exists a ground truth (simultaneous intracellular data), and show that it performs close to the optimum reached by a support vector machine trained on the ground truth. We also show that the estimated rate of misclassification matches the proportion of misclassified spikes measured from the ground truth data.

The Principle of the Micro-Electronic Neural Bridge and a Prototype System Design.

PubMed

Huang, Zong-Hao; Wang, Zhi-Gong; Lu, Xiao-Ying; Li, Wen-Yuan; Zhou, Yu-Xuan; Shen, Xiao-Yan; Zhao, Xin-Tai

2016-01-01

The micro-electronic neural bridge (MENB) aims to rebuild lost motor function of paralyzed humans by routing movement-related signals from the brain, around the damage part in the spinal cord, to the external effectors. This study focused on the prototype system design of the MENB, including the principle of the MENB, the neural signal detecting circuit and the functional electrical stimulation (FES) circuit design, and the spike detecting and sorting algorithm. In this study, we developed a novel improved amplitude threshold spike detecting method based on variable forward difference threshold for both training and bridging phase. The discrete wavelet transform (DWT), a new level feature coefficient selection method based on Lilliefors test, and the k-means clustering method based on Mahalanobis distance were used for spike sorting. A real-time online spike detecting and sorting algorithm based on DWT and Euclidean distance was also implemented for the bridging phase. Tested by the data sets available at Caltech, in the training phase, the average sensitivity, specificity, and clustering accuracies are 99.43%, 97.83%, and 95.45%, respectively. Validated by the three-fold cross-validation method, the average sensitivity, specificity, and classification accuracy are 99.43%, 97.70%, and 96.46%, respectively.

Event-driven processing for hardware-efficient neural spike sorting

NASA Astrophysics Data System (ADS)

Liu, Yan; Pereira, João L.; Constandinou, Timothy G.

2018-02-01

Objective. The prospect of real-time and on-node spike sorting provides a genuine opportunity to push the envelope of large-scale integrated neural recording systems. In such systems the hardware resources, power requirements and data bandwidth increase linearly with channel count. Event-based (or data-driven) processing can provide here a new efficient means for hardware implementation that is completely activity dependant. In this work, we investigate using continuous-time level-crossing sampling for efficient data representation and subsequent spike processing. Approach. (1) We first compare signals (synthetic neural datasets) encoded with this technique against conventional sampling. (2) We then show how such a representation can be directly exploited by extracting simple time domain features from the bitstream to perform neural spike sorting. (3) The proposed method is implemented in a low power FPGA platform to demonstrate its hardware viability. Main results. It is observed that considerably lower data rates are achievable when using 7 bits or less to represent the signals, whilst maintaining the signal fidelity. Results obtained using both MATLAB and reconfigurable logic hardware (FPGA) indicate that feature extraction and spike sorting accuracies can be achieved with comparable or better accuracy than reference methods whilst also requiring relatively low hardware resources. Significance. By effectively exploiting continuous-time data representation, neural signal processing can be achieved in a completely event-driven manner, reducing both the required resources (memory, complexity) and computations (operations). This will see future large-scale neural systems integrating on-node processing in real-time hardware.

Low power and high accuracy spike sorting microprocessor with on-line interpolation and re-alignment in 90 nm CMOS process.

PubMed

Chen, Tung-Chien; Ma, Tsung-Chuan; Chen, Yun-Yu; Chen, Liang-Gee

2012-01-01

Accurate spike sorting is an important issue for neuroscientific and neuroprosthetic applications. The sorting of spikes depends on the features extracted from the neural waveforms, and a better sorting performance usually comes with a higher sampling rate (SR). However for the long duration experiments on free-moving subjects, the miniaturized and wireless neural recording ICs are the current trend, and the compromise on sorting accuracy is usually made by a lower SR for the lower power consumption. In this paper, we implement an on-chip spike sorting processor with integrated interpolation hardware in order to improve the performance in terms of power versus accuracy. According to the fabrication results in 90nm process, if the interpolation is appropriately performed during the spike sorting, the system operated at the SR of 12.5 k samples per second (sps) can outperform the one not having interpolation at 25 ksps on both accuracy and power.

Spike sorting based upon machine learning algorithms (SOMA).

PubMed

Horton, P M; Nicol, A U; Kendrick, K M; Feng, J F

2007-02-15

We have developed a spike sorting method, using a combination of various machine learning algorithms, to analyse electrophysiological data and automatically determine the number of sampled neurons from an individual electrode, and discriminate their activities. We discuss extensions to a standard unsupervised learning algorithm (Kohonen), as using a simple application of this technique would only identify a known number of clusters. Our extra techniques automatically identify the number of clusters within the dataset, and their sizes, thereby reducing the chance of misclassification. We also discuss a new pre-processing technique, which transforms the data into a higher dimensional feature space revealing separable clusters. Using principal component analysis (PCA) alone may not achieve this. Our new approach appends the features acquired using PCA with features describing the geometric shapes that constitute a spike waveform. To validate our new spike sorting approach, we have applied it to multi-electrode array datasets acquired from the rat olfactory bulb, and from the sheep infero-temporal cortex, and using simulated data. The SOMA sofware is available at http://www.sussex.ac.uk/Users/pmh20/spikes.

ViSAPy: a Python tool for biophysics-based generation of virtual spiking activity for evaluation of spike-sorting algorithms.

PubMed

Hagen, Espen; Ness, Torbjørn V; Khosrowshahi, Amir; Sørensen, Christina; Fyhn, Marianne; Hafting, Torkel; Franke, Felix; Einevoll, Gaute T

2015-04-30

New, silicon-based multielectrodes comprising hundreds or more electrode contacts offer the possibility to record spike trains from thousands of neurons simultaneously. This potential cannot be realized unless accurate, reliable automated methods for spike sorting are developed, in turn requiring benchmarking data sets with known ground-truth spike times. We here present a general simulation tool for computing benchmarking data for evaluation of spike-sorting algorithms entitled ViSAPy (Virtual Spiking Activity in Python). The tool is based on a well-established biophysical forward-modeling scheme and is implemented as a Python package built on top of the neuronal simulator NEURON and the Python tool LFPy. ViSAPy allows for arbitrary combinations of multicompartmental neuron models and geometries of recording multielectrodes. Three example benchmarking data sets are generated, i.e., tetrode and polytrode data mimicking in vivo cortical recordings and microelectrode array (MEA) recordings of in vitro activity in salamander retinas. The synthesized example benchmarking data mimics salient features of typical experimental recordings, for example, spike waveforms depending on interspike interval. ViSAPy goes beyond existing methods as it includes biologically realistic model noise, synaptic activation by recurrent spiking networks, finite-sized electrode contacts, and allows for inhomogeneous electrical conductivities. ViSAPy is optimized to allow for generation of long time series of benchmarking data, spanning minutes of biological time, by parallel execution on multi-core computers. ViSAPy is an open-ended tool as it can be generalized to produce benchmarking data or arbitrary recording-electrode geometries and with various levels of complexity. Copyright © 2015 The Authors. Published by Elsevier B.V. All rights reserved.

Robust spike sorting of retinal ganglion cells tuned to spot stimuli.

PubMed

Ghahari, Alireza; Badea, Tudor C

2016-08-01

We propose an automatic spike sorting approach for the data recorded from a microelectrode array during visual stimulation of wild type retinas with tiled spot stimuli. The approach first detects individual spikes per electrode by their signature local minima. With the mixture probability distribution of the local minima estimated afterwards, it applies a minimum-squared-error clustering algorithm to sort the spikes into different clusters. A template waveform for each cluster per electrode is defined, and a number of reliability tests are performed on it and its corresponding spikes. Finally, a divisive hierarchical clustering algorithm is used to deal with the correlated templates per cluster type across all the electrodes. According to the measures of performance of the spike sorting approach, it is robust even in the cases of recordings with low signal-to-noise ratio.

Reliable Analysis of Single-Unit Recordings from the Human Brain under Noisy Conditions: Tracking Neurons over Hours

PubMed Central

Boström, Jan; Elger, Christian E.; Mormann, Florian

2016-01-01

Recording extracellulary from neurons in the brains of animals in vivo is among the most established experimental techniques in neuroscience, and has recently become feasible in humans. Many interesting scientific questions can be addressed only when extracellular recordings last several hours, and when individual neurons are tracked throughout the entire recording. Such questions regard, for example, neuronal mechanisms of learning and memory consolidation, and the generation of epileptic seizures. Several difficulties have so far limited the use of extracellular multi-hour recordings in neuroscience: Datasets become huge, and data are necessarily noisy in clinical recording environments. No methods for spike sorting of such recordings have been available. Spike sorting refers to the process of identifying the contributions of several neurons to the signal recorded in one electrode. To overcome these difficulties, we developed Combinato: a complete data-analysis framework for spike sorting in noisy recordings lasting twelve hours or more. Our framework includes software for artifact rejection, automatic spike sorting, manual optimization, and efficient visualization of results. Our completely automatic framework excels at two tasks: It outperforms existing methods when tested on simulated and real data, and it enables researchers to analyze multi-hour recordings. We evaluated our methods on both short and multi-hour simulated datasets. To evaluate the performance of our methods in an actual neuroscientific experiment, we used data from from neurosurgical patients, recorded in order to identify visually responsive neurons in the medial temporal lobe. These neurons responded to the semantic content, rather than to visual features, of a given stimulus. To test our methods with multi-hour recordings, we made use of neurons in the human medial temporal lobe that respond selectively to the same stimulus in the evening and next morning. PMID:27930664

A graph-Laplacian-based feature extraction algorithm for neural spike sorting.

PubMed

Ghanbari, Yasser; Spence, Larry; Papamichalis, Panos

2009-01-01

Analysis of extracellular neural spike recordings is highly dependent upon the accuracy of neural waveform classification, commonly referred to as spike sorting. Feature extraction is an important stage of this process because it can limit the quality of clustering which is performed in the feature space. This paper proposes a new feature extraction method (which we call Graph Laplacian Features, GLF) based on minimizing the graph Laplacian and maximizing the weighted variance. The algorithm is compared with Principal Components Analysis (PCA, the most commonly-used feature extraction method) using simulated neural data. The results show that the proposed algorithm produces more compact and well-separated clusters compared to PCA. As an added benefit, tentative cluster centers are output which can be used to initialize a subsequent clustering stage.

Feature extraction using first and second derivative extrema (FSDE) for real-time and hardware-efficient spike sorting.

PubMed

Paraskevopoulou, Sivylla E; Barsakcioglu, Deren Y; Saberi, Mohammed R; Eftekhar, Amir; Constandinou, Timothy G

2013-04-30

Next generation neural interfaces aspire to achieve real-time multi-channel systems by integrating spike sorting on chip to overcome limitations in communication channel capacity. The feasibility of this approach relies on developing highly efficient algorithms for feature extraction and clustering with the potential of low-power hardware implementation. We are proposing a feature extraction method, not requiring any calibration, based on first and second derivative features of the spike waveform. The accuracy and computational complexity of the proposed method are quantified and compared against commonly used feature extraction methods, through simulation across four datasets (with different single units) at multiple noise levels (ranging from 5 to 20% of the signal amplitude). The average classification error is shown to be below 7% with a computational complexity of 2N-3, where N is the number of sample points of each spike. Overall, this method presents a good trade-off between accuracy and computational complexity and is thus particularly well-suited for hardware-efficient implementation. Copyright © 2013 Elsevier B.V. All rights reserved.

Stream-based Hebbian eigenfilter for real-time neuronal spike discrimination

PubMed Central

2012-01-01

Background Principal component analysis (PCA) has been widely employed for automatic neuronal spike sorting. Calculating principal components (PCs) is computationally expensive, and requires complex numerical operations and large memory resources. Substantial hardware resources are therefore needed for hardware implementations of PCA. General Hebbian algorithm (GHA) has been proposed for calculating PCs of neuronal spikes in our previous work, which eliminates the needs of computationally expensive covariance analysis and eigenvalue decomposition in conventional PCA algorithms. However, large memory resources are still inherently required for storing a large volume of aligned spikes for training PCs. The large size memory will consume large hardware resources and contribute significant power dissipation, which make GHA difficult to be implemented in portable or implantable multi-channel recording micro-systems. Method In this paper, we present a new algorithm for PCA-based spike sorting based on GHA, namely stream-based Hebbian eigenfilter, which eliminates the inherent memory requirements of GHA while keeping the accuracy of spike sorting by utilizing the pseudo-stationarity of neuronal spikes. Because of the reduction of large hardware storage requirements, the proposed algorithm can lead to ultra-low hardware resources and power consumption of hardware implementations, which is critical for the future multi-channel micro-systems. Both clinical and synthetic neural recording data sets were employed for evaluating the accuracy of the stream-based Hebbian eigenfilter. The performance of spike sorting using stream-based eigenfilter and the computational complexity of the eigenfilter were rigorously evaluated and compared with conventional PCA algorithms. Field programmable logic arrays (FPGAs) were employed to implement the proposed algorithm, evaluate the hardware implementations and demonstrate the reduction in both power consumption and hardware memories achieved by the streaming computing Results and discussion Results demonstrate that the stream-based eigenfilter can achieve the same accuracy and is 10 times more computationally efficient when compared with conventional PCA algorithms. Hardware evaluations show that 90.3% logic resources, 95.1% power consumption and 86.8% computing latency can be reduced by the stream-based eigenfilter when compared with PCA hardware. By utilizing the streaming method, 92% memory resources and 67% power consumption can be saved when compared with the direct implementation of GHA. Conclusion Stream-based Hebbian eigenfilter presents a novel approach to enable real-time spike sorting with reduced computational complexity and hardware costs. This new design can be further utilized for multi-channel neuro-physiological experiments or chronic implants. PMID:22490725

Noise-robust unsupervised spike sorting based on discriminative subspace learning with outlier handling.

PubMed

Keshtkaran, Mohammad Reza; Yang, Zhi

2017-06-01

Spike sorting is a fundamental preprocessing step for many neuroscience studies which rely on the analysis of spike trains. Most of the feature extraction and dimensionality reduction techniques that have been used for spike sorting give a projection subspace which is not necessarily the most discriminative one. Therefore, the clusters which appear inherently separable in some discriminative subspace may overlap if projected using conventional feature extraction approaches leading to a poor sorting accuracy especially when the noise level is high. In this paper, we propose a noise-robust and unsupervised spike sorting algorithm based on learning discriminative spike features for clustering. The proposed algorithm uses discriminative subspace learning to extract low dimensional and most discriminative features from the spike waveforms and perform clustering with automatic detection of the number of the clusters. The core part of the algorithm involves iterative subspace selection using linear discriminant analysis and clustering using Gaussian mixture model with outlier detection. A statistical test in the discriminative subspace is proposed to automatically detect the number of the clusters. Comparative results on publicly available simulated and real in vivo datasets demonstrate that our algorithm achieves substantially improved cluster distinction leading to higher sorting accuracy and more reliable detection of clusters which are highly overlapping and not detectable using conventional feature extraction techniques such as principal component analysis or wavelets. By providing more accurate information about the activity of more number of individual neurons with high robustness to neural noise and outliers, the proposed unsupervised spike sorting algorithm facilitates more detailed and accurate analysis of single- and multi-unit activities in neuroscience and brain machine interface studies.

Noise-robust unsupervised spike sorting based on discriminative subspace learning with outlier handling

NASA Astrophysics Data System (ADS)

Keshtkaran, Mohammad Reza; Yang, Zhi

2017-06-01

Objective. Spike sorting is a fundamental preprocessing step for many neuroscience studies which rely on the analysis of spike trains. Most of the feature extraction and dimensionality reduction techniques that have been used for spike sorting give a projection subspace which is not necessarily the most discriminative one. Therefore, the clusters which appear inherently separable in some discriminative subspace may overlap if projected using conventional feature extraction approaches leading to a poor sorting accuracy especially when the noise level is high. In this paper, we propose a noise-robust and unsupervised spike sorting algorithm based on learning discriminative spike features for clustering. Approach. The proposed algorithm uses discriminative subspace learning to extract low dimensional and most discriminative features from the spike waveforms and perform clustering with automatic detection of the number of the clusters. The core part of the algorithm involves iterative subspace selection using linear discriminant analysis and clustering using Gaussian mixture model with outlier detection. A statistical test in the discriminative subspace is proposed to automatically detect the number of the clusters. Main results. Comparative results on publicly available simulated and real in vivo datasets demonstrate that our algorithm achieves substantially improved cluster distinction leading to higher sorting accuracy and more reliable detection of clusters which are highly overlapping and not detectable using conventional feature extraction techniques such as principal component analysis or wavelets. Significance. By providing more accurate information about the activity of more number of individual neurons with high robustness to neural noise and outliers, the proposed unsupervised spike sorting algorithm facilitates more detailed and accurate analysis of single- and multi-unit activities in neuroscience and brain machine interface studies.

A review on cluster estimation methods and their application to neural spike data.

PubMed

Zhang, James; Nguyen, Thanh; Cogill, Steven; Bhatti, Asim; Luo, Lingkun; Yang, Samuel; Nahavandi, Saeid

2018-06-01

The extracellular action potentials recorded on an electrode result from the collective simultaneous electrophysiological activity of an unknown number of neurons. Identifying and assigning these action potentials to their firing neurons-'spike sorting'-is an indispensable step in studying the function and the response of an individual or ensemble of neurons to certain stimuli. Given the task of neural spike sorting, the determination of the number of clusters (neurons) is arguably the most difficult and challenging issue, due to the existence of background noise and the overlap and interactions among neurons in neighbouring regions. It is not surprising that some researchers still rely on visual inspection by experts to estimate the number of clusters in neural spike sorting. Manual inspection, however, is not suitable to processing the vast, ever-growing amount of neural data. To address this pressing need, in this paper, thirty-three clustering validity indices have been comprehensively reviewed and implemented to determine the number of clusters in neural datasets. To gauge the suitability of the indices to neural spike data, and inform the selection process, we then calculated the indices by applying k-means clustering to twenty widely used synthetic neural datasets and one empirical dataset, and compared the performance of these indices against pre-existing ground truth labels. The results showed that the top five validity indices work consistently well across variations in noise level, both for the synthetic datasets and the real dataset. Using these top performing indices provides strong support for the determination of the number of neural clusters, which is essential in the spike sorting process.

Spiking Neural Networks Based on OxRAM Synapses for Real-Time Unsupervised Spike Sorting.

PubMed

Werner, Thilo; Vianello, Elisa; Bichler, Olivier; Garbin, Daniele; Cattaert, Daniel; Yvert, Blaise; De Salvo, Barbara; Perniola, Luca

2016-01-01

In this paper, we present an alternative approach to perform spike sorting of complex brain signals based on spiking neural networks (SNN). The proposed architecture is suitable for hardware implementation by using resistive random access memory (RRAM) technology for the implementation of synapses whose low latency (<1μs) enables real-time spike sorting. This offers promising advantages to conventional spike sorting techniques for brain-computer interfaces (BCI) and neural prosthesis applications. Moreover, the ultra-low power consumption of the RRAM synapses of the spiking neural network (nW range) may enable the design of autonomous implantable devices for rehabilitation purposes. We demonstrate an original methodology to use Oxide based RRAM (OxRAM) as easy to program and low energy (<75 pJ) synapses. Synaptic weights are modulated through the application of an online learning strategy inspired by biological Spike Timing Dependent Plasticity. Real spiking data have been recorded both intra- and extracellularly from an in-vitro preparation of the Crayfish sensory-motor system and used for validation of the proposed OxRAM based SNN. This artificial SNN is able to identify, learn, recognize and distinguish between different spike shapes in the input signal with a recognition rate about 90% without any supervision.

Neural signal processing and closed-loop control algorithm design for an implanted neural recording and stimulation system.

PubMed

Hamilton, Lei; McConley, Marc; Angermueller, Kai; Goldberg, David; Corba, Massimiliano; Kim, Louis; Moran, James; Parks, Philip D; Sang Chin; Widge, Alik S; Dougherty, Darin D; Eskandar, Emad N

2015-08-01

A fully autonomous intracranial device is built to continually record neural activities in different parts of the brain, process these sampled signals, decode features that correlate to behaviors and neuropsychiatric states, and use these features to deliver brain stimulation in a closed-loop fashion. In this paper, we describe the sampling and stimulation aspects of such a device. We first describe the signal processing algorithms of two unsupervised spike sorting methods. Next, we describe the LFP time-frequency analysis and feature derivation from the two spike sorting methods. Spike sorting includes a novel approach to constructing a dictionary learning algorithm in a Compressed Sensing (CS) framework. We present a joint prediction scheme to determine the class of neural spikes in the dictionary learning framework; and, the second approach is a modified OSort algorithm which is implemented in a distributed system optimized for power efficiency. Furthermore, sorted spikes and time-frequency analysis of LFP signals can be used to generate derived features (including cross-frequency coupling, spike-field coupling). We then show how these derived features can be used in the design and development of novel decode and closed-loop control algorithms that are optimized to apply deep brain stimulation based on a patient's neuropsychiatric state. For the control algorithm, we define the state vector as representative of a patient's impulsivity, avoidance, inhibition, etc. Controller parameters are optimized to apply stimulation based on the state vector's current state as well as its historical values. The overall algorithm and software design for our implantable neural recording and stimulation system uses an innovative, adaptable, and reprogrammable architecture that enables advancement of the state-of-the-art in closed-loop neural control while also meeting the challenges of system power constraints and concurrent development with ongoing scientific research designed to define brain network connectivity and neural network dynamics that vary at the individual patient level and vary over time.

A novel automated spike sorting algorithm with adaptable feature extraction.

PubMed

Bestel, Robert; Daus, Andreas W; Thielemann, Christiane

2012-10-15

To study the electrophysiological properties of neuronal networks, in vitro studies based on microelectrode arrays have become a viable tool for analysis. Although in constant progress, a challenging task still remains in this area: the development of an efficient spike sorting algorithm that allows an accurate signal analysis at the single-cell level. Most sorting algorithms currently available only extract a specific feature type, such as the principal components or Wavelet coefficients of the measured spike signals in order to separate different spike shapes generated by different neurons. However, due to the great variety in the obtained spike shapes, the derivation of an optimal feature set is still a very complex issue that current algorithms struggle with. To address this problem, we propose a novel algorithm that (i) extracts a variety of geometric, Wavelet and principal component-based features and (ii) automatically derives a feature subset, most suitable for sorting an individual set of spike signals. Thus, there is a new approach that evaluates the probability distribution of the obtained spike features and consequently determines the candidates most suitable for the actual spike sorting. These candidates can be formed into an individually adjusted set of spike features, allowing a separation of the various shapes present in the obtained neuronal signal by a subsequent expectation maximisation clustering algorithm. Test results with simulated data files and data obtained from chick embryonic neurons cultured on microelectrode arrays showed an excellent classification result, indicating the superior performance of the described algorithm approach. Copyright © 2012 Elsevier B.V. All rights reserved.

Programmable neural processing on a smartdust for brain-computer interfaces.

PubMed

Yuwen Sun; Shimeng Huang; Oresko, Joseph J; Cheng, Allen C

2010-10-01

Brain-computer interfaces (BCIs) offer tremendous promise for improving the quality of life for disabled individuals. BCIs use spike sorting to identify the source of each neural firing. To date, spike sorting has been performed by either using off-chip analysis, which requires a wired connection penetrating the skull to a bulky external power/processing unit, or via custom application-specific integrated circuits that lack the programmability to perform different algorithms and upgrades. In this research, we propose and test the feasibility of performing on-chip, real-time spike sorting on a programmable smartdust, including feature extraction, classification, compression, and wireless transmission. A detailed power/performance tradeoff analysis using DVFS is presented. Our experimental results show that the execution time and power density meet the requirements to perform real-time spike sorting and wireless transmission on a single neural channel.

Unified selective sorting approach to analyse multi-electrode extracellular data

NASA Astrophysics Data System (ADS)

Veerabhadrappa, R.; Lim, C. P.; Nguyen, T. T.; Berk, M.; Tye, S. J.; Monaghan, P.; Nahavandi, S.; Bhatti, A.

2016-06-01

Extracellular data analysis has become a quintessential method for understanding the neurophysiological responses to stimuli. This demands stringent techniques owing to the complicated nature of the recording environment. In this paper, we highlight the challenges in extracellular multi-electrode recording and data analysis as well as the limitations pertaining to some of the currently employed methodologies. To address some of the challenges, we present a unified algorithm in the form of selective sorting. Selective sorting is modelled around hypothesized generative model, which addresses the natural phenomena of spikes triggered by an intricate neuronal population. The algorithm incorporates Cepstrum of Bispectrum, ad hoc clustering algorithms, wavelet transforms, least square and correlation concepts which strategically tailors a sequence to characterize and form distinctive clusters. Additionally, we demonstrate the influence of noise modelled wavelets to sort overlapping spikes. The algorithm is evaluated using both raw and synthesized data sets with different levels of complexity and the performances are tabulated for comparison using widely accepted qualitative and quantitative indicators.

Unified selective sorting approach to analyse multi-electrode extracellular data

PubMed Central

Veerabhadrappa, R.; Lim, C. P.; Nguyen, T. T.; Berk, M.; Tye, S. J.; Monaghan, P.; Nahavandi, S.; Bhatti, A.

2016-01-01

Extracellular data analysis has become a quintessential method for understanding the neurophysiological responses to stimuli. This demands stringent techniques owing to the complicated nature of the recording environment. In this paper, we highlight the challenges in extracellular multi-electrode recording and data analysis as well as the limitations pertaining to some of the currently employed methodologies. To address some of the challenges, we present a unified algorithm in the form of selective sorting. Selective sorting is modelled around hypothesized generative model, which addresses the natural phenomena of spikes triggered by an intricate neuronal population. The algorithm incorporates Cepstrum of Bispectrum, ad hoc clustering algorithms, wavelet transforms, least square and correlation concepts which strategically tailors a sequence to characterize and form distinctive clusters. Additionally, we demonstrate the influence of noise modelled wavelets to sort overlapping spikes. The algorithm is evaluated using both raw and synthesized data sets with different levels of complexity and the performances are tabulated for comparison using widely accepted qualitative and quantitative indicators. PMID:27339770

A review on cluster estimation methods and their application to neural spike data

NASA Astrophysics Data System (ADS)

Zhang, James; Nguyen, Thanh; Cogill, Steven; Bhatti, Asim; Luo, Lingkun; Yang, Samuel; Nahavandi, Saeid

2018-06-01

The extracellular action potentials recorded on an electrode result from the collective simultaneous electrophysiological activity of an unknown number of neurons. Identifying and assigning these action potentials to their firing neurons—‘spike sorting’—is an indispensable step in studying the function and the response of an individual or ensemble of neurons to certain stimuli. Given the task of neural spike sorting, the determination of the number of clusters (neurons) is arguably the most difficult and challenging issue, due to the existence of background noise and the overlap and interactions among neurons in neighbouring regions. It is not surprising that some researchers still rely on visual inspection by experts to estimate the number of clusters in neural spike sorting. Manual inspection, however, is not suitable to processing the vast, ever-growing amount of neural data. To address this pressing need, in this paper, thirty-three clustering validity indices have been comprehensively reviewed and implemented to determine the number of clusters in neural datasets. To gauge the suitability of the indices to neural spike data, and inform the selection process, we then calculated the indices by applying k-means clustering to twenty widely used synthetic neural datasets and one empirical dataset, and compared the performance of these indices against pre-existing ground truth labels. The results showed that the top five validity indices work consistently well across variations in noise level, both for the synthetic datasets and the real dataset. Using these top performing indices provides strong support for the determination of the number of neural clusters, which is essential in the spike sorting process.

Validation of neural spike sorting algorithms without ground-truth information.

PubMed

Barnett, Alex H; Magland, Jeremy F; Greengard, Leslie F

2016-05-01

The throughput of electrophysiological recording is growing rapidly, allowing thousands of simultaneous channels, and there is a growing variety of spike sorting algorithms designed to extract neural firing events from such data. This creates an urgent need for standardized, automatic evaluation of the quality of neural units output by such algorithms. We introduce a suite of validation metrics that assess the credibility of a given automatic spike sorting algorithm applied to a given dataset. By rerunning the spike sorter two or more times, the metrics measure stability under various perturbations consistent with variations in the data itself, making no assumptions about the internal workings of the algorithm, and minimal assumptions about the noise. We illustrate the new metrics on standard sorting algorithms applied to both in vivo and ex vivo recordings, including a time series with overlapping spikes. We compare the metrics to existing quality measures, and to ground-truth accuracy in simulated time series. We provide a software implementation. Metrics have until now relied on ground-truth, simulated data, internal algorithm variables (e.g. cluster separation), or refractory violations. By contrast, by standardizing the interface, our metrics assess the reliability of any automatic algorithm without reference to internal variables (e.g. feature space) or physiological criteria. Stability is a prerequisite for reproducibility of results. Such metrics could reduce the significant human labor currently spent on validation, and should form an essential part of large-scale automated spike sorting and systematic benchmarking of algorithms. Copyright © 2016 Elsevier B.V. All rights reserved.

A novel unsupervised spike sorting algorithm for intracranial EEG.

PubMed

Yadav, R; Shah, A K; Loeb, J A; Swamy, M N S; Agarwal, R

2011-01-01

This paper presents a novel, unsupervised spike classification algorithm for intracranial EEG. The method combines template matching and principal component analysis (PCA) for building a dynamic patient-specific codebook without a priori knowledge of the spike waveforms. The problem of misclassification due to overlapping classes is resolved by identifying similar classes in the codebook using hierarchical clustering. Cluster quality is visually assessed by projecting inter- and intra-clusters onto a 3D plot. Intracranial EEG from 5 patients was utilized to optimize the algorithm. The resulting codebook retains 82.1% of the detected spikes in non-overlapping and disjoint clusters. Initial results suggest a definite role of this method for both rapid review and quantitation of interictal spikes that could enhance both clinical treatment and research studies on epileptic patients.

Complexity Optimization and High-Throughput Low-Latency Hardware Implementation of a Multi-Electrode Spike-Sorting Algorithm

PubMed Central

Dragas, Jelena; Jäckel, David; Hierlemann, Andreas; Franke, Felix

2017-01-01

Reliable real-time low-latency spike sorting with large data throughput is essential for studies of neural network dynamics and for brain-machine interfaces (BMIs), in which the stimulation of neural networks is based on the networks' most recent activity. However, the majority of existing multi-electrode spike-sorting algorithms are unsuited for processing high quantities of simultaneously recorded data. Recording from large neuronal networks using large high-density electrode sets (thousands of electrodes) imposes high demands on the data-processing hardware regarding computational complexity and data transmission bandwidth; this, in turn, entails demanding requirements in terms of chip area, memory resources and processing latency. This paper presents computational complexity optimization techniques, which facilitate the use of spike-sorting algorithms in large multi-electrode-based recording systems. The techniques are then applied to a previously published algorithm, on its own, unsuited for large electrode set recordings. Further, a real-time low-latency high-performance VLSI hardware architecture of the modified algorithm is presented, featuring a folded structure capable of processing the activity of hundreds of neurons simultaneously. The hardware is reconfigurable “on-the-fly” and adaptable to the nonstationarities of neuronal recordings. By transmitting exclusively spike time stamps and/or spike waveforms, its real-time processing offers the possibility of data bandwidth and data storage reduction. PMID:25415989

Complexity optimization and high-throughput low-latency hardware implementation of a multi-electrode spike-sorting algorithm.

PubMed

Dragas, Jelena; Jackel, David; Hierlemann, Andreas; Franke, Felix

2015-03-01

Reliable real-time low-latency spike sorting with large data throughput is essential for studies of neural network dynamics and for brain-machine interfaces (BMIs), in which the stimulation of neural networks is based on the networks' most recent activity. However, the majority of existing multi-electrode spike-sorting algorithms are unsuited for processing high quantities of simultaneously recorded data. Recording from large neuronal networks using large high-density electrode sets (thousands of electrodes) imposes high demands on the data-processing hardware regarding computational complexity and data transmission bandwidth; this, in turn, entails demanding requirements in terms of chip area, memory resources and processing latency. This paper presents computational complexity optimization techniques, which facilitate the use of spike-sorting algorithms in large multi-electrode-based recording systems. The techniques are then applied to a previously published algorithm, on its own, unsuited for large electrode set recordings. Further, a real-time low-latency high-performance VLSI hardware architecture of the modified algorithm is presented, featuring a folded structure capable of processing the activity of hundreds of neurons simultaneously. The hardware is reconfigurable “on-the-fly” and adaptable to the nonstationarities of neuronal recordings. By transmitting exclusively spike time stamps and/or spike waveforms, its real-time processing offers the possibility of data bandwidth and data storage reduction.

Past, present and future of spike sorting techniques

PubMed Central

Rey, Hernan Gonzalo; Pedreira, Carlos; Quian Quiroga, Rodrigo

2015-01-01

Spike sorting is a crucial step to extract information from extracellular recordings. With new recording opportunities provided by the development of new electrodes that allow monitoring hundreds of neurons simultaneously, the scenario for the new generation of algorithms is both exciting and challenging. However, this will require a new approach to the problem and the development of a common reference framework to quickly assess the performance of new algorithms. In this work, we review the basic concepts of spike sorting, including the requirements for different applications, together with the problems faced by presently available algorithms. We conclude by proposing a roadmap stressing the crucial points to be addressed to support the neuroscientific research of the near future. PMID:25931392

How many neurons can we see with current spike sorting algorithms?

PubMed Central

Pedreira, Carlos; Martinez, Juan; Ison, Matias J.; Quian Quiroga, Rodrigo

2012-01-01

Recent studies highlighted the disagreement between the typical number of neurons observed with extracellular recordings and the ones to be expected based on anatomical and physiological considerations. This disagreement has been mainly attributed to the presence of sparsely firing neurons. However, it is also possible that this is due to limitations of the spike sorting algorithms used to process the data. To address this issue, we used realistic simulations of extracellular recordings and found a relatively poor spike sorting performance for simulations containing a large number of neurons. In fact, the number of correctly identified neurons for single-channel recordings showed an asymptotic behavior saturating at about 8–10 units, when up to 20 units were present in the data. This performance was significantly poorer for neurons with low firing rates, as these units were twice more likely to be missed than the ones with high firing rates in simulations containing many neurons. These results uncover one of the main reasons for the relatively low number of neurons found in extracellular recording and also stress the importance of further developments of spike sorting algorithms. PMID:22841630

A Novel and Simple Spike Sorting Implementation.

PubMed

Petrantonakis, Panagiotis C; Poirazi, Panayiota

2017-04-01

Monitoring the activity of multiple, individual neurons that fire spikes in the vicinity of an electrode, namely perform a Spike Sorting (SS) procedure, comprises one of the most important tools for contemporary neuroscience in order to reverse-engineer the brain. As recording electrodes' technology rabidly evolves by integrating thousands of electrodes in a confined spatial setting, the algorithms that are used to monitor individual neurons from recorded signals have to become even more reliable and computationally efficient. In this work, we propose a novel framework of the SS approach in which a single-step processing of the raw (unfiltered) extracellular signal is sufficient for both the detection and sorting of the activity of individual neurons. Despite its simplicity, the proposed approach exhibits comparable performance with state-of-the-art approaches, especially for spike detection in noisy signals, and paves the way for a new family of SS algorithms with the potential for multi-recording, fast, on-chip implementations.

Efficient architecture for spike sorting in reconfigurable hardware.

PubMed

Hwang, Wen-Jyi; Lee, Wei-Hao; Lin, Shiow-Jyu; Lai, Sheng-Ying

2013-11-01

This paper presents a novel hardware architecture for fast spike sorting. The architecture is able to perform both the feature extraction and clustering in hardware. The generalized Hebbian algorithm (GHA) and fuzzy C-means (FCM) algorithm are used for feature extraction and clustering, respectively. The employment of GHA allows efficient computation of principal components for subsequent clustering operations. The FCM is able to achieve near optimal clustering for spike sorting. Its performance is insensitive to the selection of initial cluster centers. The hardware implementations of GHA and FCM feature low area costs and high throughput. In the GHA architecture, the computation of different weight vectors share the same circuit for lowering the area costs. Moreover, in the FCM hardware implementation, the usual iterative operations for updating the membership matrix and cluster centroid are merged into one single updating process to evade the large storage requirement. To show the effectiveness of the circuit, the proposed architecture is physically implemented by field programmable gate array (FPGA). It is embedded in a System-on-Chip (SOC) platform for performance measurement. Experimental results show that the proposed architecture is an efficient spike sorting design for attaining high classification correct rate and high speed computation.

How many neurons can we see with current spike sorting algorithms?

PubMed

Pedreira, Carlos; Martinez, Juan; Ison, Matias J; Quian Quiroga, Rodrigo

2012-10-15

Recent studies highlighted the disagreement between the typical number of neurons observed with extracellular recordings and the ones to be expected based on anatomical and physiological considerations. This disagreement has been mainly attributed to the presence of sparsely firing neurons. However, it is also possible that this is due to limitations of the spike sorting algorithms used to process the data. To address this issue, we used realistic simulations of extracellular recordings and found a relatively poor spike sorting performance for simulations containing a large number of neurons. In fact, the number of correctly identified neurons for single-channel recordings showed an asymptotic behavior saturating at about 8-10 units, when up to 20 units were present in the data. This performance was significantly poorer for neurons with low firing rates, as these units were twice more likely to be missed than the ones with high firing rates in simulations containing many neurons. These results uncover one of the main reasons for the relatively low number of neurons found in extracellular recording and also stress the importance of further developments of spike sorting algorithms. Copyright © 2012 Elsevier B.V. All rights reserved.

Power feasibility of implantable digital spike-sorting circuits for neural prosthetic systems.

PubMed

Zumsteg, Zachary S; Ahmed, Rizwan E; Santhanam, Gopal; Shenoy, Krishna V; Meng, Teresa H

2004-01-01

A new class of neural prosthetic systems aims to assist disabled patients by translating cortical neural activity into control signals for prosthetic devices. Based on the success of proof-of-concept systems in the laboratory, there is now considerable interest in increasing system performance and creating implantable electronics for use in clinical systems. A critical question that impacts system performance and the overall architecture of these systems is whether it is possible to identify the neural source of each action potential (spike sorting) in real-time and with low power. Low power is essential both for power supply considerations and heat dissipation in the brain. In this paper we report that several state-of-the-art spike sorting algorithms implemented in modern CMOS VLSI processes are expected to be power realistic.

Past, present and future of spike sorting techniques.

PubMed

Rey, Hernan Gonzalo; Pedreira, Carlos; Quian Quiroga, Rodrigo

2015-10-01

Spike sorting is a crucial step to extract information from extracellular recordings. With new recording opportunities provided by the development of new electrodes that allow monitoring hundreds of neurons simultaneously, the scenario for the new generation of algorithms is both exciting and challenging. However, this will require a new approach to the problem and the development of a common reference framework to quickly assess the performance of new algorithms. In this work, we review the basic concepts of spike sorting, including the requirements for different applications, together with the problems faced by presently available algorithms. We conclude by proposing a roadmap stressing the crucial points to be addressed to support the neuroscientific research of the near future. Copyright © 2015 The Authors. Published by Elsevier Inc. All rights reserved.

An Efficient Hardware Circuit for Spike Sorting Based on Competitive Learning Networks.

PubMed

Chen, Huan-Yuan; Chen, Chih-Chang; Hwang, Wen-Jyi

2017-09-28

This study aims to present an effective VLSI circuit for multi-channel spike sorting. The circuit supports the spike detection, feature extraction and classification operations. The detection circuit is implemented in accordance with the nonlinear energy operator algorithm. Both the peak detection and area computation operations are adopted for the realization of the hardware architecture for feature extraction. The resulting feature vectors are classified by a circuit for competitive learning (CL) neural networks. The CL circuit supports both online training and classification. In the proposed architecture, all the channels share the same detection, feature extraction, learning and classification circuits for a low area cost hardware implementation. The clock-gating technique is also employed for reducing the power dissipation. To evaluate the performance of the architecture, an application-specific integrated circuit (ASIC) implementation is presented. Experimental results demonstrate that the proposed circuit exhibits the advantages of a low chip area, a low power dissipation and a high classification success rate for spike sorting.

An Efficient Hardware Circuit for Spike Sorting Based on Competitive Learning Networks

PubMed Central

Chen, Huan-Yuan; Chen, Chih-Chang

2017-01-01

This study aims to present an effective VLSI circuit for multi-channel spike sorting. The circuit supports the spike detection, feature extraction and classification operations. The detection circuit is implemented in accordance with the nonlinear energy operator algorithm. Both the peak detection and area computation operations are adopted for the realization of the hardware architecture for feature extraction. The resulting feature vectors are classified by a circuit for competitive learning (CL) neural networks. The CL circuit supports both online training and classification. In the proposed architecture, all the channels share the same detection, feature extraction, learning and classification circuits for a low area cost hardware implementation. The clock-gating technique is also employed for reducing the power dissipation. To evaluate the performance of the architecture, an application-specific integrated circuit (ASIC) implementation is presented. Experimental results demonstrate that the proposed circuit exhibits the advantages of a low chip area, a low power dissipation and a high classification success rate for spike sorting. PMID:28956859

An extensible infrastructure for fully automated spike sorting during online experiments.

PubMed

Santhanam, Gopal; Sahani, Maneesh; Ryu, Stephen; Shenoy, Krishna

2004-01-01

When recording extracellular neural activity, it is often necessary to distinguish action potentials arising from distinct cells near the electrode tip, a process commonly referred to as "spike sorting." In a number of experiments, notably those that involve direct neuroprosthetic control of an effector, this cell-by-cell classification of the incoming signal must be achieved in real time. Several commercial offerings are available for this task, but all of these require some manual supervision per electrode, making each scheme cumbersome with large electrode counts. We present a new infrastructure that leverages existing unsupervised algorithms to sort and subsequently implement the resulting signal classification rules for each electrode using a commercially available Cerebus neural signal processor. We demonstrate an implementation of this infrastructure to classify signals from a cortical electrode array, using a probabilistic clustering algorithm (described elsewhere). The data were collected from a rhesus monkey performing a delayed center-out reach task. We used both sorted and unsorted (thresholded) action potentials from an array implanted in pre-motor cortex to "predict" the reach target, a common decoding operation in neuroprosthetic research. The use of sorted spikes led to an improvement in decoding accuracy of between 3.6 and 6.4%.

An Efficient VLSI Architecture for Multi-Channel Spike Sorting Using a Generalized Hebbian Algorithm

PubMed Central

Chen, Ying-Lun; Hwang, Wen-Jyi; Ke, Chi-En

2015-01-01

A novel VLSI architecture for multi-channel online spike sorting is presented in this paper. In the architecture, the spike detection is based on nonlinear energy operator (NEO), and the feature extraction is carried out by the generalized Hebbian algorithm (GHA). To lower the power consumption and area costs of the circuits, all of the channels share the same core for spike detection and feature extraction operations. Each channel has dedicated buffers for storing the detected spikes and the principal components of that channel. The proposed circuit also contains a clock gating system supplying the clock to only the buffers of channels currently using the computation core to further reduce the power consumption. The architecture has been implemented by an application-specific integrated circuit (ASIC) with 90-nm technology. Comparisons to the existing works show that the proposed architecture has lower power consumption and hardware area costs for real-time multi-channel spike detection and feature extraction. PMID:26287193

An Efficient VLSI Architecture for Multi-Channel Spike Sorting Using a Generalized Hebbian Algorithm.

PubMed

Chen, Ying-Lun; Hwang, Wen-Jyi; Ke, Chi-En

2015-08-13

A novel VLSI architecture for multi-channel online spike sorting is presented in this paper. In the architecture, the spike detection is based on nonlinear energy operator (NEO), and the feature extraction is carried out by the generalized Hebbian algorithm (GHA). To lower the power consumption and area costs of the circuits, all of the channels share the same core for spike detection and feature extraction operations. Each channel has dedicated buffers for storing the detected spikes and the principal components of that channel. The proposed circuit also contains a clock gating system supplying the clock to only the buffers of channels currently using the computation core to further reduce the power consumption. The architecture has been implemented by an application-specific integrated circuit (ASIC) with 90-nm technology. Comparisons to the existing works show that the proposed architecture has lower power consumption and hardware area costs for real-time multi-channel spike detection and feature extraction.

Bayesian decoding using unsorted spikes in the rat hippocampus

PubMed Central

Layton, Stuart P.; Chen, Zhe; Wilson, Matthew A.

2013-01-01

A fundamental task in neuroscience is to understand how neural ensembles represent information. Population decoding is a useful tool to extract information from neuronal populations based on the ensemble spiking activity. We propose a novel Bayesian decoding paradigm to decode unsorted spikes in the rat hippocampus. Our approach uses a direct mapping between spike waveform features and covariates of interest and avoids accumulation of spike sorting errors. Our decoding paradigm is nonparametric, encoding model-free for representing stimuli, and extracts information from all available spikes and their waveform features. We apply the proposed Bayesian decoding algorithm to a position reconstruction task for freely behaving rats based on tetrode recordings of rat hippocampal neuronal activity. Our detailed decoding analyses demonstrate that our approach is efficient and better utilizes the available information in the nonsortable hash than the standard sorting-based decoding algorithm. Our approach can be adapted to an online encoding/decoding framework for applications that require real-time decoding, such as brain-machine interfaces. PMID:24089403

Power feasibility of implantable digital spike sorting circuits for neural prosthetic systems.

PubMed

Zumsteg, Zachary S; Kemere, Caleb; O'Driscoll, Stephen; Santhanam, Gopal; Ahmed, Rizwan E; Shenoy, Krishna V; Meng, Teresa H

2005-09-01

A new class of neural prosthetic systems aims to assist disabled patients by translating cortical neural activity into control signals for prosthetic devices. Based on the success of proof-of-concept systems in the laboratory, there is now considerable interest in increasing system performance and creating implantable electronics for use in clinical systems. A critical question that impacts system performance and the overall architecture of these systems is whether it is possible to identify the neural source of each action potential (spike sorting) in real-time and with low power. Low power is essential both for power supply considerations and heat dissipation in the brain. In this paper we report that state-of-the-art spike sorting algorithms are not only feasible using modern complementary metal oxide semiconductor very large scale integration processes, but may represent the best option for extracting large amounts of data in implantable neural prosthetic interfaces.

Comparison of Classifier Architectures for Online Neural Spike Sorting.

PubMed

Saeed, Maryam; Khan, Amir Ali; Kamboh, Awais Mehmood

2017-04-01

High-density, intracranial recordings from micro-electrode arrays need to undergo Spike Sorting in order to associate the recorded neuronal spikes to particular neurons. This involves spike detection, feature extraction, and classification. To reduce the data transmission and power requirements, on-chip real-time processing is becoming very popular. However, high computational resources are required for classifiers in on-chip spike-sorters, making scalability a great challenge. In this review paper, we analyze several popular classifiers to propose five new hardware architectures using the off-chip training with on-chip classification approach. These include support vector classification, fuzzy C-means classification, self-organizing maps classification, moving-centroid K-means classification, and Cosine distance classification. The performance of these architectures is analyzed in terms of accuracy and resource requirement. We establish that the neural networks based Self-Organizing Maps classifier offers the most viable solution. A spike sorter based on the Self-Organizing Maps classifier, requires only 7.83% of computational resources of the best-reported spike sorter, hierarchical adaptive means, while offering a 3% better accuracy at 7 dB SNR.

Automatic threshold optimization in nonlinear energy operator based spike detection.

PubMed

Malik, Muhammad H; Saeed, Maryam; Kamboh, Awais M

2016-08-01

In neural spike sorting systems, the performance of the spike detector has to be maximized because it affects the performance of all subsequent blocks. Non-linear energy operator (NEO), is a popular spike detector due to its detection accuracy and its hardware friendly architecture. However, it involves a thresholding stage, whose value is usually approximated and is thus not optimal. This approximation deteriorates the performance in real-time systems where signal to noise ratio (SNR) estimation is a challenge, especially at lower SNRs. In this paper, we propose an automatic and robust threshold calculation method using an empirical gradient technique. The method is tested on two different datasets. The results show that our optimized threshold improves the detection accuracy in both high SNR and low SNR signals. Boxplots are presented that provide a statistical analysis of improvements in accuracy, for instance, the 75th percentile was at 98.7% and 93.5% for the optimized NEO threshold and traditional NEO threshold, respectively.

Performance comparison of extracellular spike sorting algorithms for single-channel recordings.

PubMed

Wild, Jiri; Prekopcsak, Zoltan; Sieger, Tomas; Novak, Daniel; Jech, Robert

2012-01-30

Proper classification of action potentials from extracellular recordings is essential for making an accurate study of neuronal behavior. Many spike sorting algorithms have been presented in the technical literature. However, no comparative analysis has hitherto been performed. In our study, three widely-used publicly-available spike sorting algorithms (WaveClus, KlustaKwik, OSort) were compared with regard to their parameter settings. The algorithms were evaluated using 112 artificial signals (publicly available online) with 2-9 different neurons and varying noise levels between 0.00 and 0.60. An optimization technique based on Adjusted Mutual Information was employed to find near-optimal parameter settings for a given artificial signal and algorithm. All three algorithms performed significantly better (p<0.01) with optimized parameters than with the default ones. WaveClus was the most accurate spike sorting algorithm, receiving the best evaluation score for 60% of all signals. OSort operated at almost five times the speed of the other algorithms. In terms of accuracy, OSort performed significantly less well (p<0.01) than WaveClus for signals with a noise level in the range 0.15-0.30. KlustaKwik achieved similar scores to WaveClus for signals with low noise level 0.00-0.15 and was worse otherwise. In conclusion, none of the three compared algorithms was optimal in general. The accuracy of the algorithms depended on proper choice of the algorithm parameters and also on specific properties of the examined signal. Copyright © 2011 Elsevier B.V. All rights reserved.

A stationary wavelet transform and a time-frequency based spike detection algorithm for extracellular recorded data.

PubMed

Lieb, Florian; Stark, Hans-Georg; Thielemann, Christiane

2017-06-01

Spike detection from extracellular recordings is a crucial preprocessing step when analyzing neuronal activity. The decision whether a specific part of the signal is a spike or not is important for any kind of other subsequent preprocessing steps, like spike sorting or burst detection in order to reduce the classification of erroneously identified spikes. Many spike detection algorithms have already been suggested, all working reasonably well whenever the signal-to-noise ratio is large enough. When the noise level is high, however, these algorithms have a poor performance. In this paper we present two new spike detection algorithms. The first is based on a stationary wavelet energy operator and the second is based on the time-frequency representation of spikes. Both algorithms are more reliable than all of the most commonly used methods. The performance of the algorithms is confirmed by using simulated data, resembling original data recorded from cortical neurons with multielectrode arrays. In order to demonstrate that the performance of the algorithms is not restricted to only one specific set of data, we also verify the performance using a simulated publicly available data set. We show that both proposed algorithms have the best performance under all tested methods, regardless of the signal-to-noise ratio in both data sets. This contribution will redound to the benefit of electrophysiological investigations of human cells. Especially the spatial and temporal analysis of neural network communications is improved by using the proposed spike detection algorithms.

A Low Cost VLSI Architecture for Spike Sorting Based on Feature Extraction with Peak Search.

PubMed

Chang, Yuan-Jyun; Hwang, Wen-Jyi; Chen, Chih-Chang

2016-12-07

The goal of this paper is to present a novel VLSI architecture for spike sorting with high classification accuracy, low area costs and low power consumption. A novel feature extraction algorithm with low computational complexities is proposed for the design of the architecture. In the feature extraction algorithm, a spike is separated into two portions based on its peak value. The area of each portion is then used as a feature. The algorithm is simple to implement and less susceptible to noise interference. Based on the algorithm, a novel architecture capable of identifying peak values and computing spike areas concurrently is proposed. To further accelerate the computation, a spike can be divided into a number of segments for the local feature computation. The local features are subsequently merged with the global ones by a simple hardware circuit. The architecture can also be easily operated in conjunction with the circuits for commonly-used spike detection algorithms, such as the Non-linear Energy Operator (NEO). The architecture has been implemented by an Application-Specific Integrated Circuit (ASIC) with 90-nm technology. Comparisons to the existing works show that the proposed architecture is well suited for real-time multi-channel spike detection and feature extraction requiring low hardware area costs, low power consumption and high classification accuracy.

Leaders and followers: quantifying consistency in spatio-temporal propagation patterns

NASA Astrophysics Data System (ADS)

Kreuz, Thomas; Satuvuori, Eero; Pofahl, Martin; Mulansky, Mario

2017-04-01

Repetitive spatio-temporal propagation patterns are encountered in fields as wide-ranging as climatology, social communication and network science. In neuroscience, perfectly consistent repetitions of the same global propagation pattern are called a synfire pattern. For any recording of sequences of discrete events (in neuroscience terminology: sets of spike trains) the questions arise how closely it resembles such a synfire pattern and which are the spike trains that lead/follow. Here we address these questions and introduce an algorithm built on two new indicators, termed SPIKE-order and spike train order, that define the synfire indicator value, which allows to sort multiple spike trains from leader to follower and to quantify the consistency of the temporal leader-follower relationships for both the original and the optimized sorting. We demonstrate our new approach using artificially generated datasets before we apply it to analyze the consistency of propagation patterns in two real datasets from neuroscience (giant depolarized potentials in mice slices) and climatology (El Niño sea surface temperature recordings). The new algorithm is distinguished by conceptual and practical simplicity, low computational cost, as well as flexibility and universality.

Neural Parallel Engine: A toolbox for massively parallel neural signal processing.

PubMed

Tam, Wing-Kin; Yang, Zhi

2018-05-01

Large-scale neural recordings provide detailed information on neuronal activities and can help elicit the underlying neural mechanisms of the brain. However, the computational burden is also formidable when we try to process the huge data stream generated by such recordings. In this study, we report the development of Neural Parallel Engine (NPE), a toolbox for massively parallel neural signal processing on graphical processing units (GPUs). It offers a selection of the most commonly used routines in neural signal processing such as spike detection and spike sorting, including advanced algorithms such as exponential-component-power-component (EC-PC) spike detection and binary pursuit spike sorting. We also propose a new method for detecting peaks in parallel through a parallel compact operation. Our toolbox is able to offer a 5× to 110× speedup compared with its CPU counterparts depending on the algorithms. A user-friendly MATLAB interface is provided to allow easy integration of the toolbox into existing workflows. Previous efforts on GPU neural signal processing only focus on a few rudimentary algorithms, are not well-optimized and often do not provide a user-friendly programming interface to fit into existing workflows. There is a strong need for a comprehensive toolbox for massively parallel neural signal processing. A new toolbox for massively parallel neural signal processing has been created. It can offer significant speedup in processing signals from large-scale recordings up to thousands of channels. Copyright © 2018 Elsevier B.V. All rights reserved.

A Fully Automated Approach to Spike Sorting.

PubMed

Chung, Jason E; Magland, Jeremy F; Barnett, Alex H; Tolosa, Vanessa M; Tooker, Angela C; Lee, Kye Y; Shah, Kedar G; Felix, Sarah H; Frank, Loren M; Greengard, Leslie F

2017-09-13

Understanding the detailed dynamics of neuronal networks will require the simultaneous measurement of spike trains from hundreds of neurons (or more). Currently, approaches to extracting spike times and labels from raw data are time consuming, lack standardization, and involve manual intervention, making it difficult to maintain data provenance and assess the quality of scientific results. Here, we describe an automated clustering approach and associated software package that addresses these problems and provides novel cluster quality metrics. We show that our approach has accuracy comparable to or exceeding that achieved using manual or semi-manual techniques with desktop central processing unit (CPU) runtimes faster than acquisition time for up to hundreds of electrodes. Moreover, a single choice of parameters in the algorithm is effective for a variety of electrode geometries and across multiple brain regions. This algorithm has the potential to enable reproducible and automated spike sorting of larger scale recordings than is currently possible. Copyright © 2017 Elsevier Inc. All rights reserved.

A stationary wavelet transform and a time-frequency based spike detection algorithm for extracellular recorded data

NASA Astrophysics Data System (ADS)

Lieb, Florian; Stark, Hans-Georg; Thielemann, Christiane

2017-06-01

Objective. Spike detection from extracellular recordings is a crucial preprocessing step when analyzing neuronal activity. The decision whether a specific part of the signal is a spike or not is important for any kind of other subsequent preprocessing steps, like spike sorting or burst detection in order to reduce the classification of erroneously identified spikes. Many spike detection algorithms have already been suggested, all working reasonably well whenever the signal-to-noise ratio is large enough. When the noise level is high, however, these algorithms have a poor performance. Approach. In this paper we present two new spike detection algorithms. The first is based on a stationary wavelet energy operator and the second is based on the time-frequency representation of spikes. Both algorithms are more reliable than all of the most commonly used methods. Main results. The performance of the algorithms is confirmed by using simulated data, resembling original data recorded from cortical neurons with multielectrode arrays. In order to demonstrate that the performance of the algorithms is not restricted to only one specific set of data, we also verify the performance using a simulated publicly available data set. We show that both proposed algorithms have the best performance under all tested methods, regardless of the signal-to-noise ratio in both data sets. Significance. This contribution will redound to the benefit of electrophysiological investigations of human cells. Especially the spatial and temporal analysis of neural network communications is improved by using the proposed spike detection algorithms.

Isolation and characterization of living circulating tumor cells in patients by immunomagnetic negative enrichment coupled with flow cytometry.

PubMed

Lu, Yusheng; Liang, Haiyan; Yu, Ting; Xie, Jingjing; Chen, Shuming; Dong, Haiyan; Sinko, Patrick J; Lian, Shu; Xu, Jianguo; Wang, Jichuang; Yu, Suhong; Shao, Jingwei; Yuan, Bo; Wang, Lie; Jia, Lee

2015-09-01

This study was aimed at establishing a sensitive and specific isolation, characterization, and enumeration method for living circulating tumor cells (CTCs) in patients with colorectal carcinoma. Quantitative isolation and characterization of CTCs were performed through a combination of immunomagnetic negative enrichment and fluorescence-activated cell sorting. Isolated CTCs were identified by immunofluorescence staining. The viability and purity of the sorted cells were determined by flow cytometry. Blood samples spiked with HCT116 cells (range, 3-250 cells) were used to determine specificity, recovery, and sensitivity. The method was used to enumerate, characterize, and isolate living CTCs in 10 mL of blood from patients with colorectal carcinoma. The average recovery of HCT116 cells was 61% or more at each spiking level, and the correlation coefficient was 0.992. An analysis of samples from all 18 patients with colorectal carcinoma revealed that 94.4% were positive for CTCs with an average of 33 ± 24 CTCs per 10 mL of blood and with a diameter of 14 to 20 μm (vs 8-12 μm for lymphoma). All patients were CD47(+) , with only 4.3% to 61.2% being CD44(+) . The number of CTCs was well correlated with the patient TNM stage and could be detected in patients at an early cancer stage. The sorted cells could be recultured, and their viability was preserved. This method provides a novel technique for highly sensitive and specific detection and isolation of CTCs in patients with colorectal carcinoma. This method complements the existing approaches for the de novo functional identification of a wide variety of CTC types. It is likely to help in predicting a patient's disease progression and potentially in selecting the appropriate treatment. © 2015 American Cancer Society.

Electrical stimulus artifact cancellation and neural spike detection on large multi-electrode arrays

PubMed Central

Grosberg, Lauren E.; Madugula, Sasidhar; Litke, Alan; Cunningham, John; Chichilnisky, E. J.; Paninski, Liam

2017-01-01

Simultaneous electrical stimulation and recording using multi-electrode arrays can provide a valuable technique for studying circuit connectivity and engineering neural interfaces. However, interpreting these measurements is challenging because the spike sorting process (identifying and segregating action potentials arising from different neurons) is greatly complicated by electrical stimulation artifacts across the array, which can exhibit complex and nonlinear waveforms, and overlap temporarily with evoked spikes. Here we develop a scalable algorithm based on a structured Gaussian Process model to estimate the artifact and identify evoked spikes. The effectiveness of our methods is demonstrated in both real and simulated 512-electrode recordings in the peripheral primate retina with single-electrode and several types of multi-electrode stimulation. We establish small error rates in the identification of evoked spikes, with a computational complexity that is compatible with real-time data analysis. This technology may be helpful in the design of future high-resolution sensory prostheses based on tailored stimulation (e.g., retinal prostheses), and for closed-loop neural stimulation at a much larger scale than currently possible. PMID:29131818

Electrical stimulus artifact cancellation and neural spike detection on large multi-electrode arrays.

PubMed

Mena, Gonzalo E; Grosberg, Lauren E; Madugula, Sasidhar; Hottowy, Paweł; Litke, Alan; Cunningham, John; Chichilnisky, E J; Paninski, Liam

2017-11-01

Simultaneous electrical stimulation and recording using multi-electrode arrays can provide a valuable technique for studying circuit connectivity and engineering neural interfaces. However, interpreting these measurements is challenging because the spike sorting process (identifying and segregating action potentials arising from different neurons) is greatly complicated by electrical stimulation artifacts across the array, which can exhibit complex and nonlinear waveforms, and overlap temporarily with evoked spikes. Here we develop a scalable algorithm based on a structured Gaussian Process model to estimate the artifact and identify evoked spikes. The effectiveness of our methods is demonstrated in both real and simulated 512-electrode recordings in the peripheral primate retina with single-electrode and several types of multi-electrode stimulation. We establish small error rates in the identification of evoked spikes, with a computational complexity that is compatible with real-time data analysis. This technology may be helpful in the design of future high-resolution sensory prostheses based on tailored stimulation (e.g., retinal prostheses), and for closed-loop neural stimulation at a much larger scale than currently possible.

Dense Array of Spikes on HIV-1 Virion Particles.

PubMed

Stano, Armando; Leaman, Daniel P; Kim, Arthur S; Zhang, Lei; Autin, Ludovic; Ingale, Jidnyasa; Gift, Syna K; Truong, Jared; Wyatt, Richard T; Olson, Arthur J; Zwick, Michael B

2017-07-15

HIV-1 is rare among viruses for having a low number of envelope glycoprotein (Env) spikes per virion, i.e., ∼7 to 14. This exceptional feature has been associated with avoidance of humoral immunity, i.e., B cell activation and antibody neutralization. Virus-like particles (VLPs) with increased density of Env are being pursued for vaccine development; however, these typically require protein engineering that alters Env structure. Here, we used instead a strategy that targets the producer cell. We employed fluorescence-activated cell sorting (FACS) to sort for cells that are recognized by trimer cross-reactive broadly neutralizing antibody (bnAb) and not by nonneutralizing antibodies. Following multiple iterations of FACS, cells and progeny virions were shown to display higher levels of antigenically correct Env in a manner that correlated between cells and cognate virions ( P = 0.027). High-Env VLPs, or hVLPs, were shown to be monodisperse and to display more than a 10-fold increase in spikes per particle by electron microscopy (average, 127 spikes; range, 90 to 214 spikes). Sequencing revealed a partial truncation in the C-terminal tail of Env that had emerged in the sort; however, iterative rounds of "cell factory" selection were required for the high-Env phenotype. hVLPs showed greater infectivity than standard pseudovirions but largely similar neutralization sensitivity. Importantly, hVLPs also showed superior activation of Env-specific B cells. Hence, high-Env HIV-1 virions, obtained through selection of producer cells, represent an adaptable platform for vaccine design and should aid in the study of native Env. IMPORTANCE The paucity of spikes on HIV is a unique feature that has been associated with evasion of the immune system, while increasing spike density has been a goal of vaccine design. Increasing the density of Env by modifying it in various ways has met with limited success. Here, we focused instead on the producer cell. Cells that stably express HIV spikes were screened on the basis of high binding by bnAbs and low binding by nonneutralizing antibodies. Levels of spikes on cells correlated well with those on progeny virions. Importantly, high-Env virus-like particles (hVLPs) were produced with a manifest array of well-defined spikes, and these were shown to be superior in activating desirable B cells. Our study describes HIV particles that are densely coated with functional spikes, which should facilitate the study of HIV spikes and their development as immunogens. Copyright © 2017 American Society for Microbiology.

Comprehensive two-dimensional gas chromatography/time-of-flight mass spectrometry peak sorting algorithm.

PubMed

Oh, Cheolhwan; Huang, Xiaodong; Regnier, Fred E; Buck, Charles; Zhang, Xiang

2008-02-01

We report a novel peak sorting method for the two-dimensional gas chromatography/time-of-flight mass spectrometry (GC x GC/TOF-MS) system. The objective of peak sorting is to recognize peaks from the same metabolite occurring in different samples from thousands of peaks detected in the analytical procedure. The developed algorithm is based on the fact that the chromatographic peaks for a given analyte have similar retention times in all of the chromatograms. Raw instrument data are first processed by ChromaTOF (Leco) software to provide the peak tables. Our algorithm achieves peak sorting by utilizing the first- and second-dimension retention times in the peak tables and the mass spectra generated during the process of electron impact ionization. The algorithm searches the peak tables for the peaks generated by the same type of metabolite using several search criteria. Our software also includes options to eliminate non-target peaks from the sorting results, e.g., peaks of contaminants. The developed software package has been tested using a mixture of standard metabolites and another mixture of standard metabolites spiked into human serum. Manual validation demonstrates high accuracy of peak sorting with this algorithm.

Generation of bioaerosols during manual mail unpacking and sorting.

PubMed

Brandl, H; Bachofen, R; Bischoff, M

2005-01-01

The dynamics of bioaerosol generation in specific occupational environments where mail is manually unpacked and sorted was investigated. Total number of airborne particles was determined in four different size classes (0.3-0.5, 0.5-1, 1-5 and >5 microm) by laser particle counting. Time dependent formation of bioaerosols was monitored by culturing methods and by specific staining followed by flow cytometry. Besides handling of regular mail, specially prepared letters ('spiked letters') were added to the mailbags to deliberately release powdered materials from letters and to simulate high impact loads. These letters contained various dry powdered biological and nonbiological materials such as milk powder, mushrooms, herbs and cat litter. Regarding the four size classes, particulate aerosol composition before mail handling was determined as 83.2 +/- 1.0, 15.2 +/- 0.7, 1.7 +/- 0.4 and 0.04 +/- 0.02%, respectively, whereas the composition changed during sorting to 66.8 +/- 7.9, 22.3 +/- 3.6, 10.4 +/- 4.0 and 0.57 +/- 0.27%, respectively. Mail processing resulted in an increase in culturable airborne bacteria and fungi. Maximum concentrations of bacteria reached 450 CFU m(-3), whereas 270 CFU of fungi were detected. Indoor particle concentrations steadily increased during mail handling mostly associated with particles of diameters >1 microm. However, it was not possible to distinguish spiked letters from nonspiked by simple particle counting and CFU determinations. The dynamics of bioaerosol generation have to be addressed when monitoring specific occupational environments (such as mail sorting facilities) regarding the occurrence of biological particles.

Detection and isolation of rare cells by 2-step enrichment high-speed flow cytometry/cell sorting and single cell LEAP laser ablation

NASA Astrophysics Data System (ADS)

Zordan, M. D.; Leary, James F.

2011-02-01

The clonal isolation of rare cells, especially cancer and stem cells, in a population is important to the development of improved medical treatment. We have demonstrated that the Laser-Enabled Analysis and Processing (LEAP, Cyntellect Inc., San Diego, CA) instrument can be used to efficiently produce single cell clones by photoablative dilution. Additionally, we have also shown that cells present at low frequencies can be cloned by photoablative dilution after they are pre-enriched by flow cytometry based cell sorting. Circulating tumor cells were modeled by spiking isolated peripheral blood cells with cells from the lung carcinoma cell line A549. Flow cytometry based cell sorting was used to perform an enrichment sort of A549 cells directly into a 384 well plate. Photoablative dilution was performed with the LEAPTM instrument to remove any contaminating cells, and clonally isolate 1 side population cell per well. We were able to isolate and grow single clones of side population cells using this method at greater than 90% efficiency. We have developed a 2 step method that is able to perform the clonal isolation of rare cells based on a medically relevant functional phenotype.

Performance evaluation of PCA-based spike sorting algorithms.

PubMed

Adamos, Dimitrios A; Kosmidis, Efstratios K; Theophilidis, George

2008-09-01

Deciphering the electrical activity of individual neurons from multi-unit noisy recordings is critical for understanding complex neural systems. A widely used spike sorting algorithm is being evaluated for single-electrode nerve trunk recordings. The algorithm is based on principal component analysis (PCA) for spike feature extraction. In the neuroscience literature it is generally assumed that the use of the first two or most commonly three principal components is sufficient. We estimate the optimum PCA-based feature space by evaluating the algorithm's performance on simulated series of action potentials. A number of modifications are made to the open source nev2lkit software to enable systematic investigation of the parameter space. We introduce a new metric to define clustering error considering over-clustering more favorable than under-clustering as proposed by experimentalists for our data. Both the program patch and the metric are available online. Correlated and white Gaussian noise processes are superimposed to account for biological and artificial jitter in the recordings. We report that the employment of more than three principal components is in general beneficial for all noise cases considered. Finally, we apply our results to experimental data and verify that the sorting process with four principal components is in agreement with a panel of electrophysiology experts.

NeoAnalysis: a Python-based toolbox for quick electrophysiological data processing and analysis.

PubMed

Zhang, Bo; Dai, Ji; Zhang, Tao

2017-11-13

In a typical electrophysiological experiment, especially one that includes studying animal behavior, the data collected normally contain spikes, local field potentials, behavioral responses and other associated data. In order to obtain informative results, the data must be analyzed simultaneously with the experimental settings. However, most open-source toolboxes currently available for data analysis were developed to handle only a portion of the data and did not take into account the sorting of experimental conditions. Additionally, these toolboxes require that the input data be in a specific format, which can be inconvenient to users. Therefore, the development of a highly integrated toolbox that can process multiple types of data regardless of input data format and perform basic analysis for general electrophysiological experiments is incredibly useful. Here, we report the development of a Python based open-source toolbox, referred to as NeoAnalysis, to be used for quick electrophysiological data processing and analysis. The toolbox can import data from different data acquisition systems regardless of their formats and automatically combine different types of data into a single file with a standardized format. In cases where additional spike sorting is needed, NeoAnalysis provides a module to perform efficient offline sorting with a user-friendly interface. Then, NeoAnalysis can perform regular analog signal processing, spike train, and local field potentials analysis, behavioral response (e.g. saccade) detection and extraction, with several options available for data plotting and statistics. Particularly, it can automatically generate sorted results without requiring users to manually sort data beforehand. In addition, NeoAnalysis can organize all of the relevant data into an informative table on a trial-by-trial basis for data visualization. Finally, NeoAnalysis supports analysis at the population level. With the multitude of general-purpose functions provided by NeoAnalysis, users can easily obtain publication-quality figures without writing complex codes. NeoAnalysis is a powerful and valuable toolbox for users doing electrophysiological experiments.

Exact distinction of excitatory and inhibitory neurons in neural networks: a study with GFP-GAD67 neurons optically and electrophysiologically recognized on multielectrode arrays

PubMed Central

Becchetti, Andrea; Gullo, Francesca; Bruno, Giuseppe; Dossi, Elena; Lecchi, Marzia; Wanke, Enzo

2012-01-01

Distinguishing excitatory from inhibitory neurons with multielectrode array (MEA) recordings is a serious experimental challenge. The current methods, developed in vitro, mostly rely on spike waveform analysis. These however often display poor resolution and may produce errors caused by the variability of spike amplitudes and neuron shapes. Recent recordings in human brain suggest that the spike waveform features correlate with time-domain statistics such as spiking rate, autocorrelation, and coefficient of variation. However, no precise criteria are available to exactly assign identified units to specific neuronal types, either in vivo or in vitro. To solve this problem, we combined MEA recording with fluorescence imaging of neocortical cultures from mice expressing green fluorescent protein (GFP) in GABAergic cells. In this way, we could sort out “authentic excitatory neurons” (AENs) and “authentic inhibitory neurons” (AINs). We thus characterized 1275 units (from 405 electrodes, n = 10 experiments), based on autocorrelation, burst length, spike number (SN), spiking rate, squared coefficient of variation, and Fano factor (FF) (the ratio between spike-count variance and mean). These metrics differed by about one order of magnitude between AINs and AENs. In particular, the FF turned out to provide a firing code which exactly (no overlap) recognizes excitatory and inhibitory units. The difference in FF between all of the identified AEN and AIN groups was highly significant (p < 10−8, ANOVA post-hoc Tukey test). Our results indicate a statistical metric-based approach to distinguish excitatory from inhibitory neurons independently from the spike width. PMID:22973197

Single unit action potentials in humans and the effect of seizure activity

PubMed Central

Merricks, Edward M.; Smith, Elliot H.; McKhann, Guy M.; Goodman, Robert R.; Bateman, Lisa M.; Emerson, Ronald G.

2015-01-01

Spike-sorting algorithms have been used to identify the firing patterns of isolated neurons (‘single units’) from implanted electrode recordings in patients undergoing assessment for epilepsy surgery, but we do not know their potential for providing helpful clinical information. It is important therefore to characterize both the stability of these recordings and also their context. A critical consideration is where the units are located with respect to the focus of the pathology. Recent analyses of neuronal spiking activity, recorded over extended spatial areas using microelectrode arrays, have demonstrated the importance of considering seizure activity in terms of two distinct spatial territories: the ictal core and penumbral territories. The pathological information in these two areas, however, is likely to be very different. We investigated, therefore, whether units could be followed reliably over prolonged periods of times in these two areas, including during seizure epochs. We isolated unit recordings from several hundred neurons from four patients undergoing video-telemetry monitoring for surgical evaluation of focal neocortical epilepsies. Unit stability could last in excess of 40 h, and across multiple seizures. A key finding was that in the penumbra, spike stereotypy was maintained even during the seizure. There was a net tendency towards increased penumbral firing during the seizure, although only a minority of units (10–20%) showed significant changes over the baseline period, and notably, these also included neurons showing significant reductions in firing. In contrast, within the ictal core territories, regions characterized by intense hypersynchronous multi-unit firing, our spike sorting algorithms failed as the units were incorporated into the seizure activity. No spike sorting was possible from that moment until the end of the seizure, but recovery of the spike shape was rapid following seizure termination: some units reappeared within tens of seconds of the end of the seizure, and over 80% reappeared within 3 min (τrecov = 104 ± 22 s). The recovery of the mean firing rate was close to pre-ictal levels also within this time frame, suggesting that the more protracted post-ictal state cannot be explained by persistent cellular neurophysiological dysfunction in either the penumbral or the core territories. These studies lay the foundation for future investigations of how these recordings may inform clinical practice. See Kimchi and Cash (doi:10.1093/awv264) for a scientific commentary on this article. PMID:26187332

Convex relaxations of spectral sparsity for robust super-resolution and line spectrum estimation

NASA Astrophysics Data System (ADS)

Chi, Yuejie

2017-08-01

We consider recovering the amplitudes and locations of spikes in a point source signal from its low-pass spectrum that may suffer from missing data and arbitrary outliers. We first review and provide a unified view of several recently proposed convex relaxations that characterize and capitalize the spectral sparsity of the point source signal without discretization under the framework of atomic norms. Next we propose a new algorithm when the spikes are known a priori to be positive, motivated by applications such as neural spike sorting and fluorescence microscopy imaging. Numerical experiments are provided to demonstrate the effectiveness of the proposed approach.

Closed-loop optical neural stimulation based on a 32-channel low-noise recording system with online spike sorting

NASA Astrophysics Data System (ADS)

Nguyen, T. K. T.; Navratilova, Z.; Cabral, H.; Wang, L.; Gielen, G.; Battaglia, F. P.; Bartic, C.

2014-08-01

Objective. Closed-loop operation of neuro-electronic systems is desirable for both scientific and clinical (neuroprosthesis) applications. Integrating optical stimulation with recording capability further enhances the selectivity of neural stimulation. We have developed a system enabling the local delivery of optical stimuli and the simultaneous electrical measuring of the neural activities in a closed-loop approach. Approach. The signal analysis is performed online through the implementation of a template matching algorithm. The system performance is demonstrated with the recorded data and in awake rats. Main results. Specifically, the neural activities are simultaneously recorded, detected, classified online (through spike sorting) from 32 channels, and used to trigger a light emitting diode light source using generated TTL signals. Significance. A total processing time of 8 ms is achieved, suitable for optogenetic studies of brain mechanisms online.

Method of analysis of local neuronal circuits in the vertebrate central nervous system.

PubMed

Reinis, S; Weiss, D S; McGaraughty, S; Tsoukatos, J

1992-06-01

Although a considerable amount of knowledge has been accumulated about the activity of individual nerve cells in the brain, little is known about their mutual interactions at the local level. The method presented in this paper allows the reconstruction of functional relations within a group of neurons as recorded by a single microelectrode. Data are sampled at 10 or 13 kHz. Prominent spikes produced by one or more single cells are selected and sorted by K-means cluster analysis. The activities of single cells are then related to the background firing of neurons in their vicinity. Auto-correlograms of the leading cells, auto-correlograms of the background cells (mass correlograms) and cross-correlograms between these two levels of firing are computed and evaluated. The statistical probability of mutual interactions is determined, and the statistically significant, most common interspike intervals are stored and attributed to real pairs of spikes in the original record. Selected pairs of spikes, characterized by statistically significant intervals between them, are then assembled into a working model of the system. This method has revealed substantial differences between the information processing in the visual cortex, the inferior colliculus, the rostral ventromedial medulla and the ventrobasal complex of the thalamus. Even short 1-s records of the multiple neuronal activity may provide meaningful and statistically significant results.

Real-time separation of multineuron recordings with a DSP32C signal processor.

PubMed

Gädicke, R; Albus, K

1995-04-01

We have developed a hardware and software package for real-time discrimination of multiple-unit activities recorded simultaneously from multiple microelectrodes using a VME-Bus system. Compared with other systems cited in literature or commercially available, our system has the following advantages. (1) Each electrode is served by its own preprocessor (DSP32C); (2) On-line spike discrimination is performed independently for each electrode. (3) The VME-bus allows processing of data received from 16 electrodes. The digitized (62.5 kHz) spike form is itself used as the model spike; the algorithm allows for comparing and sorting complete wave forms in real time into 8 different models per electrode.

Non-targeted analysis of unexpected food contaminants using LC-HRMS.

PubMed

Kunzelmann, Marco; Winter, Martin; Åberg, Magnus; Hellenäs, Karl-Erik; Rosén, Johan

2018-03-29

A non-target analysis method for unexpected contaminants in food is described. Many current methods referred to as "non-target" are capable of detecting hundreds or even thousands of contaminants. However, they will typically still miss all other possible contaminants. Instead, a metabolomics approach might be used to obtain "true non-target" analysis. In the present work, such a method was optimized for improved detection capability at low concentrations. The method was evaluated using 19 chemically diverse model compounds spiked into milk samples to mimic unknown contamination. Other milk samples were used as reference samples. All samples were analyzed with UHPLC-TOF-MS (ultra-high-performance liquid chromatography time-of-flight mass spectrometry), using reversed-phase chromatography and electrospray ionization in positive mode. Data evaluation was performed by the software TracMass 2. No target lists of specific compounds were used to search for the contaminants. Instead, the software was used to sort out all features only occurring in the spiked sample data, i.e., the workflow resembled a metabolomics approach. Procedures for chemical identification of peaks were outside the scope of the study. Method, study design, and settings in the software were optimized to minimize manual evaluation and faulty or irrelevant hits and to maximize hit rate of the spiked compounds. A practical detection limit was established at 25 μg/kg. At this concentration, most compounds (17 out of 19) were detected as intact precursor ions, as fragments or as adducts. Only 2 irrelevant hits, probably natural compounds, were obtained. Limitations and possible practical use of the approach are discussed.

Large-scale recording of neuronal ensembles.

PubMed

Buzsáki, György

2004-05-01

How does the brain orchestrate perceptions, thoughts and actions from the spiking activity of its neurons? Early single-neuron recording research treated spike pattern variability as noise that needed to be averaged out to reveal the brain's representation of invariant input. Another view is that variability of spikes is centrally coordinated and that this brain-generated ensemble pattern in cortical structures is itself a potential source of cognition. Large-scale recordings from neuronal ensembles now offer the opportunity to test these competing theoretical frameworks. Currently, wire and micro-machined silicon electrode arrays can record from large numbers of neurons and monitor local neural circuits at work. Achieving the full potential of massively parallel neuronal recordings, however, will require further development of the neuron-electrode interface, automated and efficient spike-sorting algorithms for effective isolation and identification of single neurons, and new mathematical insights for the analysis of network properties.

Minimizing data transfer with sustained performance in wireless brain-machine interfaces

NASA Astrophysics Data System (ADS)

Thor Thorbergsson, Palmi; Garwicz, Martin; Schouenborg, Jens; Johansson, Anders J.

2012-06-01

Brain-machine interfaces (BMIs) may be used to investigate neural mechanisms or to treat the symptoms of neurological disease and are hence powerful tools in research and clinical practice. Wireless BMIs add flexibility to both types of applications by reducing movement restrictions and risks associated with transcutaneous leads. However, since wireless implementations are typically limited in terms of transmission capacity and energy resources, the major challenge faced by their designers is to combine high performance with adaptations to limited resources. Here, we have identified three key steps in dealing with this challenge: (1) the purpose of the BMI should be clearly specified with regard to the type of information to be processed; (2) the amount of raw input data needed to fulfill the purpose should be determined, in order to avoid over- or under-dimensioning of the design; and (3) processing tasks should be allocated among the system parts such that all of them are utilized optimally with respect to computational power, wireless link capacity and raw input data requirements. We have focused on step (2) under the assumption that the purpose of the BMI (step 1) is to assess single- or multi-unit neuronal activity in the central nervous system with single-channel extracellular recordings. The reliability of this assessment depends on performance in detection and sorting of spikes. We have therefore performed absolute threshold spike detection and spike sorting with the principal component analysis and fuzzy c-means on a set of synthetic extracellular recordings, while varying the sampling rate and resolution, noise level and number of target units, and used the known ground truth to quantitatively estimate the performance. From the calculated performance curves, we have identified the sampling rate and resolution breakpoints, beyond which performance is not expected to increase by more than 1-5%. We have then estimated the performance of alternative algorithms for spike detection and spike sorting in order to examine the generalizability of our results to other algorithms. Our findings indicate that the minimization of recording noise is the primary factor to consider in the design process. In most cases, there are breakpoints for sampling rates and resolution that provide guidelines for BMI designers in terms of minimum amount raw input data that guarantees sustained performance. Such guidelines are essential during system dimensioning. Based on these findings we conclude by presenting a quantitative task-allocation scheme that can be followed to achieve optimal utilization of available resources.

Minimizing data transfer with sustained performance in wireless brain-machine interfaces.

PubMed

Thorbergsson, Palmi Thor; Garwicz, Martin; Schouenborg, Jens; Johansson, Anders J

2012-06-01

Brain-machine interfaces (BMIs) may be used to investigate neural mechanisms or to treat the symptoms of neurological disease and are hence powerful tools in research and clinical practice. Wireless BMIs add flexibility to both types of applications by reducing movement restrictions and risks associated with transcutaneous leads. However, since wireless implementations are typically limited in terms of transmission capacity and energy resources, the major challenge faced by their designers is to combine high performance with adaptations to limited resources. Here, we have identified three key steps in dealing with this challenge: (1) the purpose of the BMI should be clearly specified with regard to the type of information to be processed; (2) the amount of raw input data needed to fulfill the purpose should be determined, in order to avoid over- or under-dimensioning of the design; and (3) processing tasks should be allocated among the system parts such that all of them are utilized optimally with respect to computational power, wireless link capacity and raw input data requirements. We have focused on step (2) under the assumption that the purpose of the BMI (step 1) is to assess single- or multi-unit neuronal activity in the central nervous system with single-channel extracellular recordings. The reliability of this assessment depends on performance in detection and sorting of spikes. We have therefore performed absolute threshold spike detection and spike sorting with the principal component analysis and fuzzy c-means on a set of synthetic extracellular recordings, while varying the sampling rate and resolution, noise level and number of target units, and used the known ground truth to quantitatively estimate the performance. From the calculated performance curves, we have identified the sampling rate and resolution breakpoints, beyond which performance is not expected to increase by more than 1-5%. We have then estimated the performance of alternative algorithms for spike detection and spike sorting in order to examine the generalizability of our results to other algorithms. Our findings indicate that the minimization of recording noise is the primary factor to consider in the design process. In most cases, there are breakpoints for sampling rates and resolution that provide guidelines for BMI designers in terms of minimum amount raw input data that guarantees sustained performance. Such guidelines are essential during system dimensioning. Based on these findings we conclude by presenting a quantitative task-allocation scheme that can be followed to achieve optimal utilization of available resources.

Classification of EEG abnormalities in partial epilepsy with simultaneous EEG-fMRI recordings.

PubMed

Pedreira, C; Vaudano, A E; Thornton, R C; Chaudhary, U J; Vulliemoz, S; Laufs, H; Rodionov, R; Carmichael, D W; Lhatoo, S D; Guye, M; Quian Quiroga, R; Lemieux, L

2014-10-01

Scalp EEG recordings and the classification of interictal epileptiform discharges (IED) in patients with epilepsy provide valuable information about the epileptogenic network, particularly by defining the boundaries of the "irritative zone" (IZ), and hence are helpful during pre-surgical evaluation of patients with severe refractory epilepsies. The current detection and classification of epileptiform signals essentially rely on expert observers. This is a very time-consuming procedure, which also leads to inter-observer variability. Here, we propose a novel approach to automatically classify epileptic activity and show how this method provides critical and reliable information related to the IZ localization beyond the one provided by previous approaches. We applied Wave_clus, an automatic spike sorting algorithm, for the classification of IED visually identified from pre-surgical simultaneous Electroencephalogram-functional Magnetic Resonance Imagining (EEG-fMRI) recordings in 8 patients affected by refractory partial epilepsy candidate for surgery. For each patient, two fMRI analyses were performed: one based on the visual classification and one based on the algorithmic sorting. This novel approach successfully identified a total of 29 IED classes (compared to 26 for visual identification). The general concordance between methods was good, providing a full match of EEG patterns in 2 cases, additional EEG information in 2 other cases and, in general, covering EEG patterns of the same areas as expert classification in 7 of the 8 cases. Most notably, evaluation of the method with EEG-fMRI data analysis showed hemodynamic maps related to the majority of IED classes representing improved performance than the visual IED classification-based analysis (72% versus 50%). Furthermore, the IED-related BOLD changes revealed by using the algorithm were localized within the presumed IZ for a larger number of IED classes (9) in a greater number of patients than the expert classification (7 and 5, respectively). In contrast, in only one case presented the new algorithm resulted in fewer classes and activation areas. We propose that the use of automated spike sorting algorithms to classify IED provides an efficient tool for mapping IED-related fMRI changes and increases the EEG-fMRI clinical value for the pre-surgical assessment of patients with severe epilepsy. Copyright © 2014 Elsevier Inc. All rights reserved.

OpenElectrophy: An Electrophysiological Data- and Analysis-Sharing Framework

PubMed Central

Garcia, Samuel; Fourcaud-Trocmé, Nicolas

2008-01-01

Progress in experimental tools and design is allowing the acquisition of increasingly large datasets. Storage, manipulation and efficient analyses of such large amounts of data is now a primary issue. We present OpenElectrophy, an electrophysiological data- and analysis-sharing framework developed to fill this niche. It stores all experiment data and meta-data in a single central MySQL database, and provides a graphic user interface to visualize and explore the data, and a library of functions for user analysis scripting in Python. It implements multiple spike-sorting methods, and oscillation detection based on the ridge extraction methods due to Roux et al. (2007). OpenElectrophy is open source and is freely available for download at http://neuralensemble.org/trac/OpenElectrophy. PMID:19521545

Combined process automation for large-scale EEG analysis.

PubMed

Sfondouris, John L; Quebedeaux, Tabitha M; Holdgraf, Chris; Musto, Alberto E

2012-01-01

Epileptogenesis is a dynamic process producing increased seizure susceptibility. Electroencephalography (EEG) data provides information critical in understanding the evolution of epileptiform changes throughout epileptic foci. We designed an algorithm to facilitate efficient large-scale EEG analysis via linked automation of multiple data processing steps. Using EEG recordings obtained from electrical stimulation studies, the following steps of EEG analysis were automated: (1) alignment and isolation of pre- and post-stimulation intervals, (2) generation of user-defined band frequency waveforms, (3) spike-sorting, (4) quantification of spike and burst data and (5) power spectral density analysis. This algorithm allows for quicker, more efficient EEG analysis. Copyright © 2011 Elsevier Ltd. All rights reserved.

Computational solution of spike overlapping using data-based subtraction algorithms to resolve synchronous sympathetic nerve discharge

PubMed Central

Su, Chun-Kuei; Chiang, Chia-Hsun; Lee, Chia-Ming; Fan, Yu-Pei; Ho, Chiu-Ming; Shyu, Liang-Yu

2013-01-01

Sympathetic nerves conveying central commands to regulate visceral functions often display activities in synchronous bursts. To understand how individual fibers fire synchronously, we establish “oligofiber recording techniques” to record “several” nerve fiber activities simultaneously, using in vitro splanchnic sympathetic nerve–thoracic spinal cord preparations of neonatal rats as experimental models. While distinct spike potentials were easily recorded from collagenase-dissociated sympathetic fibers, a problem arising from synchronous nerve discharges is a higher incidence of complex waveforms resulted from spike overlapping. Because commercial softwares do not provide an explicit solution for spike overlapping, a series of custom-made LabVIEW programs incorporated with MATLAB scripts was therefore written for spike sorting. Spikes were represented as data points after waveform feature extraction and automatically grouped by k-means clustering followed by principal component analysis (PCA) to verify their waveform homogeneity. For dissimilar waveforms with exceeding Hotelling's T2 distances from the cluster centroids, a unique data-based subtraction algorithm (SA) was used to determine if they were the complex waveforms resulted from superimposing a spike pattern close to the cluster centroid with the other signals that could be observed in original recordings. In comparisons with commercial software, higher accuracy was achieved by analyses using our algorithms for the synthetic data that contained synchronous spiking and complex waveforms. Moreover, both T2-selected and SA-retrieved spikes were combined as unit activities. Quantitative analyses were performed to evaluate if unit activities truly originated from single fibers. We conclude that applications of our programs can help to resolve synchronous sympathetic nerve discharges (SND). PMID:24198782

Validating silicon polytrodes with paired juxtacellular recordings: method and dataset

PubMed Central

Lopes, Gonçalo; Frazão, João; Nogueira, Joana; Lacerda, Pedro; Baião, Pedro; Aarts, Arno; Andrei, Alexandru; Musa, Silke; Fortunato, Elvira; Barquinha, Pedro; Kampff, Adam R.

2016-01-01

Cross-validating new methods for recording neural activity is necessary to accurately interpret and compare the signals they measure. Here we describe a procedure for precisely aligning two probes for in vivo “paired-recordings” such that the spiking activity of a single neuron is monitored with both a dense extracellular silicon polytrode and a juxtacellular micropipette. Our new method allows for efficient, reliable, and automated guidance of both probes to the same neural structure with micrometer resolution. We also describe a new dataset of paired-recordings, which is available online. We propose that our novel targeting system, and ever expanding cross-validation dataset, will be vital to the development of new algorithms for automatically detecting/sorting single-units, characterizing new electrode materials/designs, and resolving nagging questions regarding the origin and nature of extracellular neural signals. PMID:27306671

The transfer function of neuron spike.

PubMed

Palmieri, Igor; Monteiro, Luiz H A; Miranda, Maria D

2015-08-01

The mathematical modeling of neuronal signals is a relevant problem in neuroscience. The complexity of the neuron behavior, however, makes this problem a particularly difficult task. Here, we propose a discrete-time linear time-invariant (LTI) model with a rational function in order to represent the neuronal spike detected by an electrode located in the surroundings of the nerve cell. The model is presented as a cascade association of two subsystems: one that generates an action potential from an input stimulus, and one that represents the medium between the cell and the electrode. The suggested approach employs system identification and signal processing concepts, and is dissociated from any considerations about the biophysical processes of the neuronal cell, providing a low-complexity alternative to model the neuronal spike. The model is validated by using in vivo experimental readings of intracellular and extracellular signals. A computational simulation of the model is presented in order to assess its proximity to the neuronal signal and to observe the variability of the estimated parameters. The implications of the results are discussed in the context of spike sorting. Copyright © 2015 Elsevier Ltd. All rights reserved.

Action Potential Waveform Variability Limits Multi-Unit Separation in Freely Behaving Rats

PubMed Central

Stratton, Peter; Cheung, Allen; Wiles, Janet; Kiyatkin, Eugene; Sah, Pankaj; Windels, François

2012-01-01

Extracellular multi-unit recording is a widely used technique to study spontaneous and evoked neuronal activity in awake behaving animals. These recordings are done using either single-wire or mulitwire electrodes such as tetrodes. In this study we have tested the ability of single-wire electrodes to discriminate activity from multiple neurons under conditions of varying noise and neuronal cell density. Using extracellular single-unit recording, coupled with iontophoresis to drive cell activity across a wide dynamic range, we studied spike waveform variability, and explored systematic differences in single-unit spike waveform within and between brain regions as well as the influence of signal-to-noise ratio (SNR) on the similarity of spike waveforms. We also modelled spike misclassification for a range of cell densities based on neuronal recordings obtained at different SNRs. Modelling predictions were confirmed by classifying spike waveforms from multiple cells with various SNRs using a leading commercial spike-sorting system. Our results show that for single-wire recordings, multiple units can only be reliably distinguished under conditions of high recording SNR (≥4) and low neuronal density (≈20,000/ mm3). Physiological and behavioural changes, as well as technical limitations typical of awake animal preparations, reduce the accuracy of single-channel spike classification, resulting in serious classification errors. For SNR <4, the probability of misclassifying spikes approaches 100% in many cases. Our results suggest that in studies where the SNR is low or neuronal density is high, separation of distinct units needs to be evaluated with great caution. PMID:22719894

A wireless neural recording system with a precision motorized microdrive for freely behaving animals

PubMed Central

Hasegawa, Taku; Fujimoto, Hisataka; Tashiro, Koichiro; Nonomura, Mayu; Tsuchiya, Akira; Watanabe, Dai

2015-01-01

The brain is composed of many different types of neurons. Therefore, analysis of brain activity with single-cell resolution could provide fundamental insights into brain mechanisms. However, the electrical signal of an individual neuron is very small, and precise isolation of single neuronal activity from moving subjects is still challenging. To measure single-unit signals in actively behaving states, establishment of technologies that enable fine control of electrode positioning and strict spike sorting is essential. To further apply such a single-cell recording approach to small brain areas in naturally behaving animals in large spaces or during social interaction, we developed a compact wireless recording system with a motorized microdrive. Wireless control of electrode placement facilitates the exploration of single neuronal activity without affecting animal behaviors. Because the system is equipped with a newly developed data-encoding program, the recorded data are readily compressed almost to theoretical limits and securely transmitted to a host computer. Brain activity can thereby be stably monitored in real time and further analyzed using online or offline spike sorting. Our wireless recording approach using a precision motorized microdrive will become a powerful tool for studying brain mechanisms underlying natural or social behaviors. PMID:25597933

Rare Cell Capture in Microfluidic Devices

PubMed Central

Pratt, Erica D.; Huang, Chao; Hawkins, Benjamin G.; Gleghorn, Jason P.; Kirby, Brian J.

2010-01-01

This article reviews existing methods for the isolation, fractionation, or capture of rare cells in microfluidic devices. Rare cell capture devices face the challenge of maintaining the efficiency standard of traditional bulk separation methods such as flow cytometers and immunomagnetic separators while requiring very high purity of the target cell population, which is typically already at very low starting concentrations. Two major classifications of rare cell capture approaches are covered: (1) non-electrokinetic methods (e.g., immobilization via antibody or aptamer chemistry, size-based sorting, and sheath flow and streamline sorting) are discussed for applications using blood cells, cancer cells, and other mammalian cells, and (2) electrokinetic (primarily dielectrophoretic) methods using both electrode-based and insulative geometries are presented with a view towards pathogen detection, blood fractionation, and cancer cell isolation. The included methods were evaluated based on performance criteria including cell type modeled and used, number of steps/stages, cell viability, and enrichment, efficiency, and/or purity. Major areas for improvement are increasing viability and capture efficiency/purity of directly processed biological samples, as a majority of current studies only process spiked cell lines or pre-diluted/lysed samples. Despite these current challenges, multiple advances have been made in the development of devices for rare cell capture and the subsequent elucidation of new biological phenomena; this article serves to highlight this progress as well as the electrokinetic and non-electrokinetic methods that can potentially be combined to improve performance in future studies. PMID:21532971

Validating silicon polytrodes with paired juxtacellular recordings: method and dataset.

PubMed

Neto, Joana P; Lopes, Gonçalo; Frazão, João; Nogueira, Joana; Lacerda, Pedro; Baião, Pedro; Aarts, Arno; Andrei, Alexandru; Musa, Silke; Fortunato, Elvira; Barquinha, Pedro; Kampff, Adam R

2016-08-01

Cross-validating new methods for recording neural activity is necessary to accurately interpret and compare the signals they measure. Here we describe a procedure for precisely aligning two probes for in vivo "paired-recordings" such that the spiking activity of a single neuron is monitored with both a dense extracellular silicon polytrode and a juxtacellular micropipette. Our new method allows for efficient, reliable, and automated guidance of both probes to the same neural structure with micrometer resolution. We also describe a new dataset of paired-recordings, which is available online. We propose that our novel targeting system, and ever expanding cross-validation dataset, will be vital to the development of new algorithms for automatically detecting/sorting single-units, characterizing new electrode materials/designs, and resolving nagging questions regarding the origin and nature of extracellular neural signals. Copyright © 2016 the American Physiological Society.

Real-time position reconstruction with hippocampal place cells.

PubMed

Guger, Christoph; Gener, Thomas; Pennartz, Cyriel M A; Brotons-Mas, Jorge R; Edlinger, Günter; Bermúdez I Badia, S; Verschure, Paul; Schaffelhofer, Stefan; Sanchez-Vives, Maria V

2011-01-01

Brain-computer interfaces (BCI) are using the electroencephalogram, the electrocorticogram and trains of action potentials as inputs to analyze brain activity for communication purposes and/or the control of external devices. Thus far it is not known whether a BCI system can be developed that utilizes the states of brain structures that are situated well below the cortical surface, such as the hippocampus. In order to address this question we used the activity of hippocampal place cells (PCs) to predict the position of an rodent in real-time. First, spike activity was recorded from the hippocampus during foraging and analyzed off-line to optimize the spike sorting and position reconstruction algorithm of rats. Then the spike activity was recorded and analyzed in real-time. The rat was running in a box of 80 cm × 80 cm and its locomotor movement was captured with a video tracking system. Data were acquired to calculate the rat's trajectories and to identify place fields. Then a Bayesian classifier was trained to predict the position of the rat given its neural activity. This information was used in subsequent trials to predict the rat's position in real-time. The real-time experiments were successfully performed and yielded an error between 12.2 and 17.4% using 5-6 neurons. It must be noted here that the encoding step was done with data recorded before the real-time experiment and comparable accuracies between off-line (mean error of 15.9% for three rats) and real-time experiments (mean error of 14.7%) were achieved. The experiment shows proof of principle that position reconstruction can be done in real-time, that PCs were stable and spike sorting was robust enough to generalize from the training run to the real-time reconstruction phase of the experiment. Real-time reconstruction may be used for a variety of purposes, including creating behavioral-neuronal feedback loops or for implementing neuroprosthetic control.

Real-Time Position Reconstruction with Hippocampal Place Cells

PubMed Central

Guger, Christoph; Gener, Thomas; Pennartz, Cyriel M. A.; Brotons-Mas, Jorge R.; Edlinger, Günter; Bermúdez i Badia, S.; Verschure, Paul; Schaffelhofer, Stefan; Sanchez-Vives, Maria V.

2011-01-01

Brain–computer interfaces (BCI) are using the electroencephalogram, the electrocorticogram and trains of action potentials as inputs to analyze brain activity for communication purposes and/or the control of external devices. Thus far it is not known whether a BCI system can be developed that utilizes the states of brain structures that are situated well below the cortical surface, such as the hippocampus. In order to address this question we used the activity of hippocampal place cells (PCs) to predict the position of an rodent in real-time. First, spike activity was recorded from the hippocampus during foraging and analyzed off-line to optimize the spike sorting and position reconstruction algorithm of rats. Then the spike activity was recorded and analyzed in real-time. The rat was running in a box of 80 cm × 80 cm and its locomotor movement was captured with a video tracking system. Data were acquired to calculate the rat's trajectories and to identify place fields. Then a Bayesian classifier was trained to predict the position of the rat given its neural activity. This information was used in subsequent trials to predict the rat's position in real-time. The real-time experiments were successfully performed and yielded an error between 12.2 and 17.4% using 5–6 neurons. It must be noted here that the encoding step was done with data recorded before the real-time experiment and comparable accuracies between off-line (mean error of 15.9% for three rats) and real-time experiments (mean error of 14.7%) were achieved. The experiment shows proof of principle that position reconstruction can be done in real-time, that PCs were stable and spike sorting was robust enough to generalize from the training run to the real-time reconstruction phase of the experiment. Real-time reconstruction may be used for a variety of purposes, including creating behavioral–neuronal feedback loops or for implementing neuroprosthetic control. PMID:21808603

Selectivity of Local Field Potentials in Macaque Inferior Temporal Cortex

DTIC Science & Technology

2004-09-01

Selectivity of Local Field Potentials in Macaque Inferior Temporal Cortex Gabriel Kreiman , Chou Hung, Tomaso Poggio and James DiCarlo AI Memo 2004...IT. Note: Gabriel Kreiman and Chou Hung contributed equally to this work This report describes research done within the Center for Biological...separate the neuronal components of the MUA by using spike sorting algorithms (Quiroga et al., 2004; Yu and Kreiman , 1999). It will be interesting to

Overexpression of cypin alters dendrite morphology, single neuron activity, and network properties via distinct mechanisms

NASA Astrophysics Data System (ADS)

Rodríguez, Ana R.; O'Neill, Kate M.; Swiatkowski, Przemyslaw; Patel, Mihir V.; Firestein, Bonnie L.

2018-02-01

Objective. This study investigates the effect that overexpression of cytosolic PSD-95 interactor (cypin), a regulator of synaptic PSD-95 protein localization and a core regulator of dendrite branching, exerts on the electrical activity of rat hippocampal neurons and networks. Approach. We cultured rat hippocampal neurons and used lipid-mediated transfection and lentiviral gene transfer to achieve high levels of cypin or cypin mutant (cypinΔPDZ PSD-95 non-binding) expression cellularly and network-wide, respectively. Main results. Our analysis revealed that although overexpression of cypin and cypinΔPDZ increase dendrite numbers and decrease spine density, cypin and cypinΔPDZ distinctly regulate neuronal activity. At the single cell level, cypin promotes decreases in bursting activity while cypinΔPDZ reduces sEPSC frequency and further decreases bursting compared to cypin. At the network level, by using the Fano factor as a measure of spike count variability, cypin overexpression results in an increase in variability of spike count, and this effect is abolished when cypin cannot bind PSD-95. This variability is also dependent on baseline activity levels and on mean spike rate over time. Finally, our spike sorting data show that overexpression of cypin results in a more complex distribution of spike waveforms and that binding to PSD-95 is essential for this complexity. Significance. Our data suggest that dendrite morphology does not play a major role in cypin action on electrical activity.

An online supervised learning method based on gradient descent for spiking neurons.

PubMed

Xu, Yan; Yang, Jing; Zhong, Shuiming

2017-09-01

The purpose of supervised learning with temporal encoding for spiking neurons is to make the neurons emit a specific spike train encoded by precise firing times of spikes. The gradient-descent-based (GDB) learning methods are widely used and verified in the current research. Although the existing GDB multi-spike learning (or spike sequence learning) methods have good performance, they work in an offline manner and still have some limitations. This paper proposes an online GDB spike sequence learning method for spiking neurons that is based on the online adjustment mechanism of real biological neuron synapses. The method constructs error function and calculates the adjustment of synaptic weights as soon as the neurons emit a spike during their running process. We analyze and synthesize desired and actual output spikes to select appropriate input spikes in the calculation of weight adjustment in this paper. The experimental results show that our method obviously improves learning performance compared with the offline learning manner and has certain advantage on learning accuracy compared with other learning methods. Stronger learning ability determines that the method has large pattern storage capacity. Copyright © 2017 Elsevier Ltd. All rights reserved.

Brainlab: A Python Toolkit to Aid in the Design, Simulation, and Analysis of Spiking Neural Networks with the NeoCortical Simulator.

PubMed

Drewes, Rich; Zou, Quan; Goodman, Philip H

2009-01-01

Neuroscience modeling experiments often involve multiple complex neural network and cell model variants, complex input stimuli and input protocols, followed by complex data analysis. Coordinating all this complexity becomes a central difficulty for the experimenter. The Python programming language, along with its extensive library packages, has emerged as a leading "glue" tool for managing all sorts of complex programmatic tasks. This paper describes a toolkit called Brainlab, written in Python, that leverages Python's strengths for the task of managing the general complexity of neuroscience modeling experiments. Brainlab was also designed to overcome the major difficulties of working with the NCS (NeoCortical Simulator) environment in particular. Brainlab is an integrated model-building, experimentation, and data analysis environment for the powerful parallel spiking neural network simulator system NCS.

Python for large-scale electrophysiology.

PubMed

Spacek, Martin; Blanche, Tim; Swindale, Nicholas

2008-01-01

Electrophysiology is increasingly moving towards highly parallel recording techniques which generate large data sets. We record extracellularly in vivo in cat and rat visual cortex with 54-channel silicon polytrodes, under time-locked visual stimulation, from localized neuronal populations within a cortical column. To help deal with the complexity of generating and analysing these data, we used the Python programming language to develop three software projects: one for temporally precise visual stimulus generation ("dimstim"); one for electrophysiological waveform visualization and spike sorting ("spyke"); and one for spike train and stimulus analysis ("neuropy"). All three are open source and available for download (http://swindale.ecc.ubc.ca/code). The requirements and solutions for these projects differed greatly, yet we found Python to be well suited for all three. Here we present our software as a showcase of the extensive capabilities of Python in neuroscience.

Brainlab: A Python Toolkit to Aid in the Design, Simulation, and Analysis of Spiking Neural Networks with the NeoCortical Simulator

PubMed Central

Drewes, Rich; Zou, Quan; Goodman, Philip H.

2008-01-01

Neuroscience modeling experiments often involve multiple complex neural network and cell model variants, complex input stimuli and input protocols, followed by complex data analysis. Coordinating all this complexity becomes a central difficulty for the experimenter. The Python programming language, along with its extensive library packages, has emerged as a leading “glue” tool for managing all sorts of complex programmatic tasks. This paper describes a toolkit called Brainlab, written in Python, that leverages Python's strengths for the task of managing the general complexity of neuroscience modeling experiments. Brainlab was also designed to overcome the major difficulties of working with the NCS (NeoCortical Simulator) environment in particular. Brainlab is an integrated model-building, experimentation, and data analysis environment for the powerful parallel spiking neural network simulator system NCS. PMID:19506707

Python for Large-Scale Electrophysiology

PubMed Central

Spacek, Martin; Blanche, Tim; Swindale, Nicholas

2008-01-01

Electrophysiology is increasingly moving towards highly parallel recording techniques which generate large data sets. We record extracellularly in vivo in cat and rat visual cortex with 54-channel silicon polytrodes, under time-locked visual stimulation, from localized neuronal populations within a cortical column. To help deal with the complexity of generating and analysing these data, we used the Python programming language to develop three software projects: one for temporally precise visual stimulus generation (“dimstim”); one for electrophysiological waveform visualization and spike sorting (“spyke”); and one for spike train and stimulus analysis (“neuropy”). All three are open source and available for download (http://swindale.ecc.ubc.ca/code). The requirements and solutions for these projects differed greatly, yet we found Python to be well suited for all three. Here we present our software as a showcase of the extensive capabilities of Python in neuroscience. PMID:19198646

High-dimensional cluster analysis with the Masked EM Algorithm

PubMed Central

Kadir, Shabnam N.; Goodman, Dan F. M.; Harris, Kenneth D.

2014-01-01

Cluster analysis faces two problems in high dimensions: first, the “curse of dimensionality” that can lead to overfitting and poor generalization performance; and second, the sheer time taken for conventional algorithms to process large amounts of high-dimensional data. We describe a solution to these problems, designed for the application of “spike sorting” for next-generation high channel-count neural probes. In this problem, only a small subset of features provide information about the cluster member-ship of any one data vector, but this informative feature subset is not the same for all data points, rendering classical feature selection ineffective. We introduce a “Masked EM” algorithm that allows accurate and time-efficient clustering of up to millions of points in thousands of dimensions. We demonstrate its applicability to synthetic data, and to real-world high-channel-count spike sorting data. PMID:25149694

Sparse Spectro-Temporal Receptive Fields Based on Multi-Unit and High-Gamma Responses in Human Auditory Cortex

PubMed Central

Jenison, Rick L.; Reale, Richard A.; Armstrong, Amanda L.; Oya, Hiroyuki; Kawasaki, Hiroto; Howard, Matthew A.

2015-01-01

Spectro-Temporal Receptive Fields (STRFs) were estimated from both multi-unit sorted clusters and high-gamma power responses in human auditory cortex. Intracranial electrophysiological recordings were used to measure responses to a random chord sequence of Gammatone stimuli. Traditional methods for estimating STRFs from single-unit recordings, such as spike-triggered-averages, tend to be noisy and are less robust to other response signals such as local field potentials. We present an extension to recently advanced methods for estimating STRFs from generalized linear models (GLM). A new variant of regression using regularization that penalizes non-zero coefficients is described, which results in a sparse solution. The frequency-time structure of the STRF tends toward grouping in different areas of frequency-time and we demonstrate that group sparsity-inducing penalties applied to GLM estimates of STRFs reduces the background noise while preserving the complex internal structure. The contribution of local spiking activity to the high-gamma power signal was factored out of the STRF using the GLM method, and this contribution was significant in 85 percent of the cases. Although the GLM methods have been used to estimate STRFs in animals, this study examines the detailed structure directly from auditory cortex in the awake human brain. We used this approach to identify an abrupt change in the best frequency of estimated STRFs along posteromedial-to-anterolateral recording locations along the long axis of Heschl’s gyrus. This change correlates well with a proposed transition from core to non-core auditory fields previously identified using the temporal response properties of Heschl’s gyrus recordings elicited by click-train stimuli. PMID:26367010

Extracting information in spike time patterns with wavelets and information theory.

PubMed

Lopes-dos-Santos, Vítor; Panzeri, Stefano; Kayser, Christoph; Diamond, Mathew E; Quian Quiroga, Rodrigo

2015-02-01

We present a new method to assess the information carried by temporal patterns in spike trains. The method first performs a wavelet decomposition of the spike trains, then uses Shannon information to select a subset of coefficients carrying information, and finally assesses timing information in terms of decoding performance: the ability to identify the presented stimuli from spike train patterns. We show that the method allows: 1) a robust assessment of the information carried by spike time patterns even when this is distributed across multiple time scales and time points; 2) an effective denoising of the raster plots that improves the estimate of stimulus tuning of spike trains; and 3) an assessment of the information carried by temporally coordinated spikes across neurons. Using simulated data, we demonstrate that the Wavelet-Information (WI) method performs better and is more robust to spike time-jitter, background noise, and sample size than well-established approaches, such as principal component analysis, direct estimates of information from digitized spike trains, or a metric-based method. Furthermore, when applied to real spike trains from monkey auditory cortex and from rat barrel cortex, the WI method allows extracting larger amounts of spike timing information. Importantly, the fact that the WI method incorporates multiple time scales makes it robust to the choice of partly arbitrary parameters such as temporal resolution, response window length, number of response features considered, and the number of available trials. These results highlight the potential of the proposed method for accurate and objective assessments of how spike timing encodes information. Copyright © 2015 the American Physiological Society.

Real-Time Neural Signals Decoding onto Off-the-Shelf DSP Processors for Neuroprosthetic Applications.

PubMed

Pani, Danilo; Barabino, Gianluca; Citi, Luca; Meloni, Paolo; Raspopovic, Stanisa; Micera, Silvestro; Raffo, Luigi

2016-09-01

The control of upper limb neuroprostheses through the peripheral nervous system (PNS) can allow restoring motor functions in amputees. At present, the important aspect of the real-time implementation of neural decoding algorithms on embedded systems has been often overlooked, notwithstanding the impact that limited hardware resources have on the efficiency/effectiveness of any given algorithm. Present study is addressing the optimization of a template matching based algorithm for PNS signals decoding that is a milestone for its real-time, full implementation onto a floating-point digital signal processor (DSP). The proposed optimized real-time algorithm achieves up to 96% of correct classification on real PNS signals acquired through LIFE electrodes on animals, and can correctly sort spikes of a synthetic cortical dataset with sufficiently uncorrelated spike morphologies (93% average correct classification) comparably to the results obtained with top spike sorter (94% on average on the same dataset). The power consumption enables more than 24 h processing at the maximum load, and latency model has been derived to enable a fair performance assessment. The final embodiment demonstrates the real-time performance onto a low-power off-the-shelf DSP, opening to experiments exploiting the efferent signals to control a motor neuroprosthesis.

Development of a novel cell sorting method that samples population diversity in flow cytometry.

PubMed

Osborne, Geoffrey W; Andersen, Stacey B; Battye, Francis L

2015-11-01

Flow cytometry based electrostatic cell sorting is an important tool in the separation of cell populations. Existing instruments can sort single cells into multi-well collection plates, and keep track of cell of origin and sorted well location. However currently single sorted cell results reflect the population distribution and fail to capture the population diversity. Software was designed that implements a novel sorting approach, "Slice and Dice Sorting," that links a graphical representation of a multi-well plate to logic that ensures that single cells are sampled and sorted from all areas defined by the sort region/s. Therefore the diversity of the total population is captured, and the more frequently occurring or rarer cell types are all sampled. The sorting approach was tested computationally, and using functional cell based assays. Computationally we demonstrate that conventional single cell sorting can sample as little as 50% of the population diversity dependant on the population distribution, and that Slice and Dice sorting samples much more of the variety present within a cell population. We then show by sorting single cells into wells using the Slice and Dice sorting method that there are cells sorted using this method that would be either rarely sorted, or not sorted at all using conventional single cell sorting approaches. The present study demonstrates a novel single cell sorting method that samples much more of the population diversity than current methods. It has implications in clonal selection, stem cell sorting, single cell sequencing and any areas where population heterogeneity is of importance. © 2015 International Society for Advancement of Cytometry.

Deciphering neuronal population codes for acute thermal pain

NASA Astrophysics Data System (ADS)

Chen, Zhe; Zhang, Qiaosheng; Phuong Sieu Tong, Ai; Manders, Toby R.; Wang, Jing

2017-06-01

Objective. Pain is defined as an unpleasant sensory and emotional experience associated with actual or potential tissue damage, or described in terms of such damage. Current pain research mostly focuses on molecular and synaptic changes at the spinal and peripheral levels. However, a complete understanding of pain mechanisms requires the physiological study of the neocortex. Our goal is to apply a neural decoding approach to read out the onset of acute thermal pain signals, which can be used for brain-machine interface. Approach. We used micro wire arrays to record ensemble neuronal activities from the primary somatosensory cortex (S1) and anterior cingulate cortex (ACC) in freely behaving rats. We further investigated neural codes for acute thermal pain at both single-cell and population levels. To detect the onset of acute thermal pain signals, we developed a novel latent state-space framework to decipher the sorted or unsorted S1 and ACC ensemble spike activities, which reveal information about the onset of pain signals. Main results. The state space analysis allows us to uncover a latent state process that drives the observed ensemble spike activity, and to further detect the ‘neuronal threshold’ for acute thermal pain on a single-trial basis. Our method achieved good detection performance in sensitivity and specificity. In addition, our results suggested that an optimal strategy for detecting the onset of acute thermal pain signals may be based on combined evidence from S1 and ACC population codes. Significance. Our study is the first to detect the onset of acute pain signals based on neuronal ensemble spike activity. It is important from a mechanistic viewpoint as it relates to the significance of S1 and ACC activities in the regulation of the acute pain onset.

Feasibility study for future implantable neural-silicon interface devices.

PubMed

Al-Armaghany, Allann; Yu, Bo; Mak, Terrence; Tong, Kin-Fai; Sun, Yihe

2011-01-01

The emerging neural-silicon interface devices bridge nerve systems with artificial systems and play a key role in neuro-prostheses and neuro-rehabilitation applications. Integrating neural signal collection, processing and transmission on a single device will make clinical applications more practical and feasible. This paper focuses on the wireless antenna part and real-time neural signal analysis part of implantable brain-machine interface (BMI) devices. We propose to use millimeter-wave for wireless connections between different areas of a brain. Various antenna, including microstrip patch, monopole antenna and substrate integrated waveguide antenna are considered for the intra-cortical proximity communication. A Hebbian eigenfilter based method is proposed for multi-channel neuronal spike sorting. Folding and parallel design techniques are employed to explore various structures and make a trade-off between area and power consumption. Field programmable logic arrays (FPGAs) are used to evaluate various structures.

A Binary Array Asynchronous Sorting Algorithm with Using Petri Nets

NASA Astrophysics Data System (ADS)

Voevoda, A. A.; Romannikov, D. O.

2017-01-01

Nowadays the tasks of computations speed-up and/or their optimization are actual. Among the approaches on how to solve these tasks, a method applying approaches of parallelization and asynchronization to a sorting algorithm is considered in the paper. The sorting methods are ones of elementary methods and they are used in a huge amount of different applications. In the paper, we offer a method of an array sorting that based on a division into a set of independent adjacent pairs of numbers and their parallel and asynchronous comparison. And this one distinguishes the offered method from the traditional sorting algorithms (like quick sorting, merge sorting, insertion sorting and others). The algorithm is implemented with the use of Petri nets, like the most suitable tool for an asynchronous systems description.

Neurophysiological analytics for all! Free open-source software tools for documenting, analyzing, visualizing, and sharing using electronic notebooks.

PubMed

Rosenberg, David M; Horn, Charles C

2016-08-01

Neurophysiology requires an extensive workflow of information analysis routines, which often includes incompatible proprietary software, introducing limitations based on financial costs, transfer of data between platforms, and the ability to share. An ecosystem of free open-source software exists to fill these gaps, including thousands of analysis and plotting packages written in Python and R, which can be implemented in a sharable and reproducible format, such as the Jupyter electronic notebook. This tool chain can largely replace current routines by importing data, producing analyses, and generating publication-quality graphics. An electronic notebook like Jupyter allows these analyses, along with documentation of procedures, to display locally or remotely in an internet browser, which can be saved as an HTML, PDF, or other file format for sharing with team members and the scientific community. The present report illustrates these methods using data from electrophysiological recordings of the musk shrew vagus-a model system to investigate gut-brain communication, for example, in cancer chemotherapy-induced emesis. We show methods for spike sorting (including statistical validation), spike train analysis, and analysis of compound action potentials in notebooks. Raw data and code are available from notebooks in data supplements or from an executable online version, which replicates all analyses without installing software-an implementation of reproducible research. This demonstrates the promise of combining disparate analyses into one platform, along with the ease of sharing this work. In an age of diverse, high-throughput computational workflows, this methodology can increase efficiency, transparency, and the collaborative potential of neurophysiological research. Copyright © 2016 the American Physiological Society.

Neurophysiological analytics for all! Free open-source software tools for documenting, analyzing, visualizing, and sharing using electronic notebooks

PubMed Central

2016-01-01

Neurophysiology requires an extensive workflow of information analysis routines, which often includes incompatible proprietary software, introducing limitations based on financial costs, transfer of data between platforms, and the ability to share. An ecosystem of free open-source software exists to fill these gaps, including thousands of analysis and plotting packages written in Python and R, which can be implemented in a sharable and reproducible format, such as the Jupyter electronic notebook. This tool chain can largely replace current routines by importing data, producing analyses, and generating publication-quality graphics. An electronic notebook like Jupyter allows these analyses, along with documentation of procedures, to display locally or remotely in an internet browser, which can be saved as an HTML, PDF, or other file format for sharing with team members and the scientific community. The present report illustrates these methods using data from electrophysiological recordings of the musk shrew vagus—a model system to investigate gut-brain communication, for example, in cancer chemotherapy-induced emesis. We show methods for spike sorting (including statistical validation), spike train analysis, and analysis of compound action potentials in notebooks. Raw data and code are available from notebooks in data supplements or from an executable online version, which replicates all analyses without installing software—an implementation of reproducible research. This demonstrates the promise of combining disparate analyses into one platform, along with the ease of sharing this work. In an age of diverse, high-throughput computational workflows, this methodology can increase efficiency, transparency, and the collaborative potential of neurophysiological research. PMID:27098025

CLUSTERING OF INTERICTAL SPIKES BY DYNAMIC TIME WARPING AND AFFINITY PROPAGATION

PubMed Central

Thomas, John; Jin, Jing; Dauwels, Justin; Cash, Sydney S.; Westover, M. Brandon

2018-01-01

Epilepsy is often associated with the presence of spikes in electroencephalograms (EEGs). The spike waveforms vary vastly among epilepsy patients, and also for the same patient across time. In order to develop semi-automated and automated methods for detecting spikes, it is crucial to obtain a better understanding of the various spike shapes. In this paper, we develop several approaches to extract exemplars of spikes. We generate spike exemplars by applying clustering algorithms to a database of spikes from 12 patients. As similarity measures for clustering, we consider the Euclidean distance and Dynamic Time Warping (DTW). We assess two clustering algorithms, namely, K-means clustering and affinity propagation. The clustering methods are compared based on the mean squared error, and the similarity measures are assessed based on the number of generated spike clusters. Affinity propagation with DTW is shown to be the best combination for clustering epileptic spikes, since it generates fewer spike templates and does not require to pre-specify the number of spike templates. PMID:29527130

Estimating Extracellular Spike Waveforms from CA1 Pyramidal Cells with Multichannel Electrodes

PubMed Central

Molden, Sturla; Moldestad, Olve; Storm, Johan F.

2013-01-01

Extracellular (EC) recordings of action potentials from the intact brain are embedded in background voltage fluctuations known as the “local field potential” (LFP). In order to use EC spike recordings for studying biophysical properties of neurons, the spike waveforms must be separated from the LFP. Linear low-pass and high-pass filters are usually insufficient to separate spike waveforms from LFP, because they have overlapping frequency bands. Broad-band recordings of LFP and spikes were obtained with a 16-channel laminar electrode array (silicone probe). We developed an algorithm whereby local LFP signals from spike-containing channel were modeled using locally weighted polynomial regression analysis of adjoining channels without spikes. The modeled LFP signal was subtracted from the recording to estimate the embedded spike waveforms. We tested the method both on defined spike waveforms added to LFP recordings, and on in vivo-recorded extracellular spikes from hippocampal CA1 pyramidal cells in anaesthetized mice. We show that the algorithm can correctly extract the spike waveforms embedded in the LFP. In contrast, traditional high-pass filters failed to recover correct spike shapes, albeit produceing smaller standard errors. We found that high-pass RC or 2-pole Butterworth filters with cut-off frequencies below 12.5 Hz, are required to retrieve waveforms comparable to our method. The method was also compared to spike-triggered averages of the broad-band signal, and yielded waveforms with smaller standard errors and less distortion before and after the spike. PMID:24391714

VLSI Implementation of a 2.8 Gevent/s Packet-Based AER Interface with Routing and Event Sorting Functionality

PubMed Central

Scholze, Stefan; Schiefer, Stefan; Partzsch, Johannes; Hartmann, Stephan; Mayr, Christian Georg; Höppner, Sebastian; Eisenreich, Holger; Henker, Stephan; Vogginger, Bernhard; Schüffny, Rene

2011-01-01

State-of-the-art large-scale neuromorphic systems require sophisticated spike event communication between units of the neural network. We present a high-speed communication infrastructure for a waferscale neuromorphic system, based on application-specific neuromorphic communication ICs in an field programmable gate arrays (FPGA)-maintained environment. The ICs implement configurable axonal delays, as required for certain types of dynamic processing or for emulating spike-based learning among distant cortical areas. Measurements are presented which show the efficacy of these delays in influencing behavior of neuromorphic benchmarks. The specialized, dedicated address-event-representation communication in most current systems requires separate, low-bandwidth configuration channels. In contrast, the configuration of the waferscale neuromorphic system is also handled by the digital packet-based pulse channel, which transmits configuration data at the full bandwidth otherwise used for pulse transmission. The overall so-called pulse communication subgroup (ICs and FPGA) delivers a factor 25–50 more event transmission rate than other current neuromorphic communication infrastructures. PMID:22016720

A two-dimensionally coincident second difference cosmic ray spike removal method for the fully automated processing of Raman spectra.

PubMed

Schulze, H Georg; Turner, Robin F B

2014-01-01

Charge-coupled device detectors are vulnerable to cosmic rays that can contaminate Raman spectra with positive going spikes. Because spikes can adversely affect spectral processing and data analyses, they must be removed. Although both hardware-based and software-based spike removal methods exist, they typically require parameter and threshold specification dependent on well-considered user input. Here, we present a fully automated spike removal algorithm that proceeds without requiring user input. It is minimally dependent on sample attributes, and those that are required (e.g., standard deviation of spectral noise) can be determined with other fully automated procedures. At the core of the method is the identification and location of spikes with coincident second derivatives along both the spectral and spatiotemporal dimensions of two-dimensional datasets. The method can be applied to spectra that are relatively inhomogeneous because it provides fairly effective and selective targeting of spikes resulting in minimal distortion of spectra. Relatively effective spike removal obtained with full automation could provide substantial benefits to users where large numbers of spectra must be processed.

Removing cosmic spikes using a hyperspectral upper-bound spectrum method

DOE PAGES

Anthony, Stephen Michael; Timlin, Jerilyn A.

2016-11-04

Cosmic ray spikes are especially problematic for hyperspectral imaging because of the large number of spikes often present and their negative effects upon subsequent chemometric analysis. Fortunately, while the large number of spectra acquired in a hyperspectral imaging data set increases the probability and number of cosmic spikes observed, the multitude of spectra can also aid in the effective recognition and removal of the cosmic spikes. Zhang and Ben-Amotz were perhaps the first to leverage the additional spatial dimension of hyperspectral data matrices (DM). They integrated principal component analysis (PCA) into the upper bound spectrum method (UBS), resulting in amore » hybrid method (UBS-DM) for hyperspectral images. Here, we expand upon their use of PCA, recognizing that principal components primarily present in only a few pixels most likely correspond to cosmic spikes. Eliminating the contribution of those principal components in those pixels improves the cosmic spike removal. Both simulated and experimental hyperspectral Raman image data sets are used to test the newly developed UBS-DM-hyperspectral (UBS-DM-HS) method which extends the UBS-DM method by leveraging characteristics of hyperspectral data sets. As a result, a comparison is provided between the performance of the UBS-DM-HS method and other methods suitable for despiking hyperspectral images, evaluating both their ability to remove cosmic ray spikes and the extent to which they introduce spectral bias.« less

Removing cosmic spikes using a hyperspectral upper-bound spectrum method

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

Anthony, Stephen Michael; Timlin, Jerilyn A.

Cosmic ray spikes are especially problematic for hyperspectral imaging because of the large number of spikes often present and their negative effects upon subsequent chemometric analysis. Fortunately, while the large number of spectra acquired in a hyperspectral imaging data set increases the probability and number of cosmic spikes observed, the multitude of spectra can also aid in the effective recognition and removal of the cosmic spikes. Zhang and Ben-Amotz were perhaps the first to leverage the additional spatial dimension of hyperspectral data matrices (DM). They integrated principal component analysis (PCA) into the upper bound spectrum method (UBS), resulting in amore » hybrid method (UBS-DM) for hyperspectral images. Here, we expand upon their use of PCA, recognizing that principal components primarily present in only a few pixels most likely correspond to cosmic spikes. Eliminating the contribution of those principal components in those pixels improves the cosmic spike removal. Both simulated and experimental hyperspectral Raman image data sets are used to test the newly developed UBS-DM-hyperspectral (UBS-DM-HS) method which extends the UBS-DM method by leveraging characteristics of hyperspectral data sets. As a result, a comparison is provided between the performance of the UBS-DM-HS method and other methods suitable for despiking hyperspectral images, evaluating both their ability to remove cosmic ray spikes and the extent to which they introduce spectral bias.« less

Removing Cosmic Spikes Using a Hyperspectral Upper-Bound Spectrum Method.

PubMed

Anthony, Stephen M; Timlin, Jerilyn A

2017-03-01

Cosmic ray spikes are especially problematic for hyperspectral imaging because of the large number of spikes often present and their negative effects upon subsequent chemometric analysis. Fortunately, while the large number of spectra acquired in a hyperspectral imaging data set increases the probability and number of cosmic spikes observed, the multitude of spectra can also aid in the effective recognition and removal of the cosmic spikes. Zhang and Ben-Amotz were perhaps the first to leverage the additional spatial dimension of hyperspectral data matrices (DM). They integrated principal component analysis (PCA) into the upper bound spectrum method (UBS), resulting in a hybrid method (UBS-DM) for hyperspectral images. Here, we expand upon their use of PCA, recognizing that principal components primarily present in only a few pixels most likely correspond to cosmic spikes. Eliminating the contribution of those principal components in those pixels improves the cosmic spike removal. Both simulated and experimental hyperspectral Raman image data sets are used to test the newly developed UBS-DM-hyperspectral (UBS-DM-HS) method which extends the UBS-DM method by leveraging characteristics of hyperspectral data sets. A comparison is provided between the performance of the UBS-DM-HS method and other methods suitable for despiking hyperspectral images, evaluating both their ability to remove cosmic ray spikes and the extent to which they introduce spectral bias.

Serial Spike Time Correlations Affect Probability Distribution of Joint Spike Events.

PubMed

Shahi, Mina; van Vreeswijk, Carl; Pipa, Gordon

2016-01-01

Detecting the existence of temporally coordinated spiking activity, and its role in information processing in the cortex, has remained a major challenge for neuroscience research. Different methods and approaches have been suggested to test whether the observed synchronized events are significantly different from those expected by chance. To analyze the simultaneous spike trains for precise spike correlation, these methods typically model the spike trains as a Poisson process implying that the generation of each spike is independent of all the other spikes. However, studies have shown that neural spike trains exhibit dependence among spike sequences, such as the absolute and relative refractory periods which govern the spike probability of the oncoming action potential based on the time of the last spike, or the bursting behavior, which is characterized by short epochs of rapid action potentials, followed by longer episodes of silence. Here we investigate non-renewal processes with the inter-spike interval distribution model that incorporates spike-history dependence of individual neurons. For that, we use the Monte Carlo method to estimate the full shape of the coincidence count distribution and to generate false positives for coincidence detection. The results show that compared to the distributions based on homogeneous Poisson processes, and also non-Poisson processes, the width of the distribution of joint spike events changes. Non-renewal processes can lead to both heavy tailed or narrow coincidence distribution. We conclude that small differences in the exact autostructure of the point process can cause large differences in the width of a coincidence distribution. Therefore, manipulations of the autostructure for the estimation of significance of joint spike events seem to be inadequate.

Serial Spike Time Correlations Affect Probability Distribution of Joint Spike Events

PubMed Central

Shahi, Mina; van Vreeswijk, Carl; Pipa, Gordon

2016-01-01

Detecting the existence of temporally coordinated spiking activity, and its role in information processing in the cortex, has remained a major challenge for neuroscience research. Different methods and approaches have been suggested to test whether the observed synchronized events are significantly different from those expected by chance. To analyze the simultaneous spike trains for precise spike correlation, these methods typically model the spike trains as a Poisson process implying that the generation of each spike is independent of all the other spikes. However, studies have shown that neural spike trains exhibit dependence among spike sequences, such as the absolute and relative refractory periods which govern the spike probability of the oncoming action potential based on the time of the last spike, or the bursting behavior, which is characterized by short epochs of rapid action potentials, followed by longer episodes of silence. Here we investigate non-renewal processes with the inter-spike interval distribution model that incorporates spike-history dependence of individual neurons. For that, we use the Monte Carlo method to estimate the full shape of the coincidence count distribution and to generate false positives for coincidence detection. The results show that compared to the distributions based on homogeneous Poisson processes, and also non-Poisson processes, the width of the distribution of joint spike events changes. Non-renewal processes can lead to both heavy tailed or narrow coincidence distribution. We conclude that small differences in the exact autostructure of the point process can cause large differences in the width of a coincidence distribution. Therefore, manipulations of the autostructure for the estimation of significance of joint spike events seem to be inadequate. PMID:28066225

On the Spike Train Variability Characterized by Variance-to-Mean Power Relationship.

PubMed

Koyama, Shinsuke

2015-07-01

We propose a statistical method for modeling the non-Poisson variability of spike trains observed in a wide range of brain regions. Central to our approach is the assumption that the variance and the mean of interspike intervals are related by a power function characterized by two parameters: the scale factor and exponent. It is shown that this single assumption allows the variability of spike trains to have an arbitrary scale and various dependencies on the firing rate in the spike count statistics, as well as in the interval statistics, depending on the two parameters of the power function. We also propose a statistical model for spike trains that exhibits the variance-to-mean power relationship. Based on this, a maximum likelihood method is developed for inferring the parameters from rate-modulated spike trains. The proposed method is illustrated on simulated and experimental spike trains.

A new supervised learning algorithm for spiking neurons.

PubMed

Xu, Yan; Zeng, Xiaoqin; Zhong, Shuiming

2013-06-01

The purpose of supervised learning with temporal encoding for spiking neurons is to make the neurons emit a specific spike train encoded by the precise firing times of spikes. If only running time is considered, the supervised learning for a spiking neuron is equivalent to distinguishing the times of desired output spikes and the other time during the running process of the neuron through adjusting synaptic weights, which can be regarded as a classification problem. Based on this idea, this letter proposes a new supervised learning method for spiking neurons with temporal encoding; it first transforms the supervised learning into a classification problem and then solves the problem by using the perceptron learning rule. The experiment results show that the proposed method has higher learning accuracy and efficiency over the existing learning methods, so it is more powerful for solving complex and real-time problems.

Generalized analog thresholding for spike acquisition at ultralow sampling rates

PubMed Central

He, Bryan D.; Wein, Alex; Varshney, Lav R.; Kusuma, Julius; Richardson, Andrew G.

2015-01-01

Efficient spike acquisition techniques are needed to bridge the divide from creating large multielectrode arrays (MEA) to achieving whole-cortex electrophysiology. In this paper, we introduce generalized analog thresholding (gAT), which achieves millisecond temporal resolution with sampling rates as low as 10 Hz. Consider the torrent of data from a single 1,000-channel MEA, which would generate more than 3 GB/min using standard 30-kHz Nyquist sampling. Recent neural signal processing methods based on compressive sensing still require Nyquist sampling as a first step and use iterative methods to reconstruct spikes. Analog thresholding (AT) remains the best existing alternative, where spike waveforms are passed through an analog comparator and sampled at 1 kHz, with instant spike reconstruction. By generalizing AT, the new method reduces sampling rates another order of magnitude, detects more than one spike per interval, and reconstructs spike width. Unlike compressive sensing, the new method reveals a simple closed-form solution to achieve instant (noniterative) spike reconstruction. The base method is already robust to hardware nonidealities, including realistic quantization error and integration noise. Because it achieves these considerable specifications using hardware-friendly components like integrators and comparators, generalized AT could translate large-scale MEAs into implantable devices for scientific investigation and medical technology. PMID:25904712

Analysis of pharmaceutical and other organic wastewater compounds in filtered and unfiltered water samples by gas chromatography/mass spectrometry

USGS Publications Warehouse

Zaugg, Steven D.; Phillips, Patrick J.; Smith, Steven G.

2014-01-01

Research on the effects of exposure of stream biota to complex mixtures of pharmaceuticals and other organic compounds associated with wastewater requires the development of additional analytical capabilities for these compounds in water samples. Two gas chromatography/mass spectrometry (GC/MS) analytical methods used at the U.S. Geological Survey National Water Quality Laboratory (NWQL) to analyze organic compounds associated with wastewater were adapted to include additional pharmaceutical and other organic compounds beginning in 2009. This report includes a description of method performance for 42 additional compounds for the filtered-water method (hereafter referred to as the filtered method) and 46 additional compounds for the unfiltered-water method (hereafter referred to as the unfiltered method). The method performance for the filtered method described in this report has been published for seven of these compounds; however, the addition of several other compounds to the filtered method and the addition of the compounds to the unfiltered method resulted in the need to document method performance for both of the modified methods. Most of these added compounds are pharmaceuticals or pharmaceutical degradates, although two nonpharmaceutical compounds are included in each method. The main pharmaceutical compound classes added to the two modified methods include muscle relaxants, opiates, analgesics, and sedatives. These types of compounds were added to the original filtered and unfiltered methods largely in response to the tentative identification of a wide range of pharmaceutical and other organic compounds in samples collected from wastewater-treatment plants. Filtered water samples are extracted by vacuum through disposable solid-phase cartridges that contain modified polystyrene-divinylbenzene resin. Unfiltered samples are extracted by using continuous liquid-liquid extraction with dichloromethane. The compounds of interest for filtered and unfiltered sample types were determined by use of the capillary-column gas chromatography/mass spectrometry. The performance of each method was assessed by using data on recoveries of compounds in fortified surface-water, wastewater, and reagent-water samples. These experiments (referred to as spike experiments) consist of fortifying (or spiking) samples with known amounts of target analytes. Surface-water-spike experiments were performed by using samples obtained from a stream in Colorado (unfiltered method) and a stream in New York (filtered method). Wastewater spike experiments for both the filtered and unfiltered methods were performed by using a treated wastewater obtained from a single wastewater treatment plant in New York. Surface water and wastewater spike experiments were fortified at both low and high concentrations and termed low- and high-level spikes, respectively. Reagent water spikes were assessed in three ways: (1) set spikes, (2) a low-concentration fortification experiment, and (3) a high-concentration fortification experiment. Set spike samples have been determined since 2009, and consist of analysis of fortified reagent water for target compounds included for each group of 10 to18 environmental samples analyzed at the NWQL. The low-concentration and high-concentration reagent spike experiments, by contrast, represent a one-time assessment of method performance. For each spike experiment, mean recoveries ranging from 60 to 130 percent indicate low bias, and relative standard deviations (RSDs) less than ( Of the compounds included in the filtered method, 21 had mean recoveries ranging from 63 to 129 percent for the low-level and high-level surface-water spikes, and had low ()132 percent]. For wastewater spikes, 24 of the compounds included in the filtered method had recoveries ranging from 61 to 130 percent for the low-level and high-level spikes. RSDs were 130 percent) or variable recoveries (RSDs >30 percent) for low-level wastewater spikes, or low recoveries ( Of the compounds included in the unfiltered method, 17 had mean spike recoveries ranging from 74 to 129 percent and RSDs ranging from 5 to 25 percent for low-level and high-level surface water spikes. The remaining compounds had poor mean recoveries (130 percent), or high RSDs (>29 percent) for these spikes. For wastewater, 14 of the compounds included in the unfiltered method had mean recoveries ranging from 62 to 127 percent and RSDs 130 percent), or low mean recoveries (33 percent) for the low-level wastewater spikes. Of the compounds found in wastewater, 24 had mean set spike recoveries ranging from 64 to 104 percent and RSDs Separate method detection limits (MDLs) were computed for surface water and wastewater for both the filtered and unfiltered methods. Filtered method MDLs ranged from 0.007 to 0.14 microgram per liter (μg/L) for the surface water matrix and from 0.004 to 0.62 μg/L for the wastewater matrix. Unfiltered method MDLs ranged from 0.014 to 0.33 μg/L for the surface water matrix and from 0.008 to 0.36 μg/L for the wastewater matrix.

Statistical technique for analysing functional connectivity of multiple spike trains.

PubMed

Masud, Mohammad Shahed; Borisyuk, Roman

2011-03-15

A new statistical technique, the Cox method, used for analysing functional connectivity of simultaneously recorded multiple spike trains is presented. This method is based on the theory of modulated renewal processes and it estimates a vector of influence strengths from multiple spike trains (called reference trains) to the selected (target) spike train. Selecting another target spike train and repeating the calculation of the influence strengths from the reference spike trains enables researchers to find all functional connections among multiple spike trains. In order to study functional connectivity an "influence function" is identified. This function recognises the specificity of neuronal interactions and reflects the dynamics of postsynaptic potential. In comparison to existing techniques, the Cox method has the following advantages: it does not use bins (binless method); it is applicable to cases where the sample size is small; it is sufficiently sensitive such that it estimates weak influences; it supports the simultaneous analysis of multiple influences; it is able to identify a correct connectivity scheme in difficult cases of "common source" or "indirect" connectivity. The Cox method has been thoroughly tested using multiple sets of data generated by the neural network model of the leaky integrate and fire neurons with a prescribed architecture of connections. The results suggest that this method is highly successful for analysing functional connectivity of simultaneously recorded multiple spike trains. Copyright © 2011 Elsevier B.V. All rights reserved.

EVALUATION OF SPIKING PROCEDURES FOR RECOVERY OF CRYPTOSPORIDIUM IN STREAM WATERS USING USEPA METHOD 1623

EPA Science Inventory

U.S. Environmental Protection Agency Method 1623 is widely used to monitor source waters and drinking water supplies for Cryptosporidium oocysts. Analyzing matrix spikes is an important component of Method 1623. Matrix spikes are used to determine the effect of the environmental...

Spike-train communities: finding groups of similar spike trains.

PubMed

Humphries, Mark D

2011-02-09

Identifying similar spike-train patterns is a key element in understanding neural coding and computation. For single neurons, similar spike patterns evoked by stimuli are evidence of common coding. Across multiple neurons, similar spike trains indicate potential cell assemblies. As recording technology advances, so does the urgent need for grouping methods to make sense of large-scale datasets of spike trains. Existing methods require specifying the number of groups in advance, limiting their use in exploratory analyses. I derive a new method from network theory that solves this key difficulty: it self-determines the maximum number of groups in any set of spike trains, and groups them to maximize intragroup similarity. This method brings us revealing new insights into the encoding of aversive stimuli by dopaminergic neurons, and the organization of spontaneous neural activity in cortex. I show that the characteristic pause response of a rat's dopaminergic neuron depends on the state of the superior colliculus: when it is inactive, aversive stimuli invoke a single pattern of dopaminergic neuron spiking; when active, multiple patterns occur, yet the spike timing in each is reliable. In spontaneous multineuron activity from the cortex of anesthetized cat, I show the existence of neural ensembles that evolve in membership and characteristic timescale of organization during global slow oscillations. I validate these findings by showing that the method both is remarkably reliable at detecting known groups and can detect large-scale organization of dynamics in a model of the striatum.

Automatic EEG spike detection.

PubMed

Harner, Richard

2009-10-01

Since the 1970s advances in science and technology during each succeeding decade have renewed the expectation of efficient, reliable automatic epileptiform spike detection (AESD). But even when reinforced with better, faster tools, clinically reliable unsupervised spike detection remains beyond our reach. Expert-selected spike parameters were the first and still most widely used for AESD. Thresholds for amplitude, duration, sharpness, rise-time, fall-time, after-coming slow waves, background frequency, and more have been used. It is still unclear which of these wave parameters are essential, beyond peak-peak amplitude and duration. Wavelet parameters are very appropriate to AESD but need to be combined with other parameters to achieve desired levels of spike detection efficiency. Artificial Neural Network (ANN) and expert-system methods may have reached peak efficiency. Support Vector Machine (SVM) technology focuses on outliers rather than centroids of spike and nonspike data clusters and should improve AESD efficiency. An exemplary spike/nonspike database is suggested as a tool for assessing parameters and methods for AESD and is available in CSV or Matlab formats from the author at brainvue@gmail.com. Exploratory Data Analysis (EDA) is presented as a graphic method for finding better spike parameters and for the step-wise evaluation of the spike detection process.

Adjustment of Pesticide Concentrations for Temporal Changes in Analytical Recovery, 1992-2006

USGS Publications Warehouse

Martin, Jeffrey D.; Stone, Wesley W.; Wydoski, Duane S.; Sandstrom, Mark W.

2009-01-01

Recovery is the proportion of a target analyte that is quantified by an analytical method and is a primary indicator of the analytical bias of a measurement. Recovery is measured by analysis of quality-control (QC) water samples that have known amounts of target analytes added ('spiked' QC samples). For pesticides, recovery is the measured amount of pesticide in the spiked QC sample expressed as percentage of the amount spiked, ideally 100 percent. Temporal changes in recovery have the potential to adversely affect time-trend analysis of pesticide concentrations by introducing trends in environmental concentrations that are caused by trends in performance of the analytical method rather than by trends in pesticide use or other environmental conditions. This report examines temporal changes in the recovery of 44 pesticides and 8 pesticide degradates (hereafter referred to as 'pesticides') that were selected for a national analysis of time trends in pesticide concentrations in streams. Water samples were analyzed for these pesticides from 1992 to 2006 by gas chromatography/mass spectrometry. Recovery was measured by analysis of pesticide-spiked QC water samples. Temporal changes in pesticide recovery were investigated by calculating robust, locally weighted scatterplot smooths (lowess smooths) for the time series of pesticide recoveries in 5,132 laboratory reagent spikes; 1,234 stream-water matrix spikes; and 863 groundwater matrix spikes. A 10-percent smoothing window was selected to show broad, 6- to 12-month time scale changes in recovery for most of the 52 pesticides. Temporal patterns in recovery were similar (in phase) for laboratory reagent spikes and for matrix spikes for most pesticides. In-phase temporal changes among spike types support the hypothesis that temporal change in method performance is the primary cause of temporal change in recovery. Although temporal patterns of recovery were in phase for most pesticides, recovery in matrix spikes was greater than recovery in reagent spikes for nearly every pesticide. Models of recovery based on matrix spikes are deemed more appropriate for adjusting concentrations of pesticides measured in groundwater and stream-water samples than models based on laboratory reagent spikes because (1) matrix spikes are expected to more closely match the matrix of environmental water samples than are reagent spikes and (2) method performance is often matrix dependent, as was shown by higher recovery in matrix spikes for most of the pesticides. Models of recovery, based on lowess smooths of matrix spikes, were developed separately for groundwater and stream-water samples. The models of recovery can be used to adjust concentrations of pesticides measured in groundwater or stream-water samples to 100 percent recovery to compensate for temporal changes in the performance (bias) of the analytical method.

Automated mesostructural analyses using GIS, Beta test: Paleozoic structures from the New Jersey Great Valley region

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

Herman, G.C.; French, M.A.; Monteverde, D.H.

1993-03-01

An automated method has been developed for representing outcrop data on geologic structures on maps. Using a MS-DOS custom database management system in conjunction with the ARC/INFO Geographic Information System (GIS), trends of geologic structures are plotted with user-specific symbols. The length of structural symbols can be frequency-weighted based on collective values from structural domains. The PC-based data manager is the NJGS Field data Management System (FMS) Version 2.0 which includes sort, output, and analysis functions for structural data input in either azimuth or quadrant form. Program options include lineament sorting, data output to other data management and analysis software,more » and a circular histogram (rose diagram) routine for trend frequency analysis. Trends can be displayed with either half-or full-rose diagrams using either 10[degree] sectors or one degree spikes for strike, trend, or dip azimuth readings. Scalar and vector statistics are both included. For the mesostructural analysis, ASCII files containing the station number, structural trend and inclination, and plot-symbol-length value are downloaded from FMS and uploaded into an ARC/INFO macro which sequentially plots the information. Plots can be generated in conjunction with any complimentary GIS coverage for various types of spatial analyses. Mesostructural plots can be used for regional tectonic analyses, for hydrogeologic analysis of fractured bedrock aquifers, or for ground-truthing data from fracture-trace or lineament analyses.« less

The interactive electrode localization utility: software for automatic sorting and labeling of intracranial subdural electrodes

PubMed Central

Tang, Wei; Peled, Noam; Vallejo, Deborah I.; Borzello, Mia; Dougherty, Darin D.; Eskandar, Emad N.; Widge, Alik S.; Cash, Sydney S.; Stufflebeam, Steven M.

2018-01-01

Purpose Existing methods for sorting, labeling, registering, and across-subject localization of electrodes in intracranial encephalography (iEEG) may involve laborious work requiring manual inspection of radiological images. Methods We describe a new open-source software package, the interactive electrode localization utility which presents a full pipeline for the registration, localization, and labeling of iEEG electrodes from CT and MR images. In addition, we describe a method to automatically sort and label electrodes from subdural grids of known geometry. Results We validated our software against manual inspection methods in twelve subjects undergoing iEEG for medically intractable epilepsy. Our algorithm for sorting and labeling performed correct identification on 96% of the electrodes. Conclusions The sorting and labeling methods we describe offer nearly perfect performance and the software package we have distributed may simplify the process of registering, sorting, labeling, and localizing subdural iEEG grid electrodes by manual inspection. PMID:27915398

Unbiased and robust quantification of synchronization between spikes and local field potential.

PubMed

Li, Zhaohui; Cui, Dong; Li, Xiaoli

2016-08-30

In neuroscience, relating the spiking activity of individual neurons to the local field potential (LFP) of neural ensembles is an increasingly useful approach for studying rhythmic neuronal synchronization. Many methods have been proposed to measure the strength of the association between spikes and rhythms in the LFP recordings, and most existing measures are dependent upon the total number of spikes. In the present work, we introduce a robust approach for quantifying spike-LFP synchronization which performs reliably for limited samples of data. The measure is termed as spike-triggered correlation matrix synchronization (SCMS), which takes LFP segments centered on each spike as multi-channel signals and calculates the index of spike-LFP synchronization by constructing a correlation matrix. The simulation based on artificial data shows that the SCMS output almost does not change with the sample size. This property is of crucial importance when making comparisons between different experimental conditions. When applied to actual neuronal data recorded from the monkey primary visual cortex, it is found that the spike-LFP synchronization strength shows orientation selectivity to drifting gratings. In comparison to another unbiased method, pairwise phase consistency (PPC), the proposed SCMS behaves better for noisy spike trains by means of numerical simulations. This study demonstrates the basic idea and calculating process of the SCMS method. Considering its unbiasedness and robustness, the measure is of great advantage to characterize the synchronization between spike trains and rhythms present in LFP. Copyright © 2016 Elsevier B.V. All rights reserved.

Suitability of DPPH spiking for antioxidant screening in natural products: the example of galloyl derivatives from red maple bark extract.

PubMed

Geoffroy, Thibaud R; Meda, Naamwin R; Stevanovic, Tatjana

2017-09-01

To investigate the antioxidant potential in natural products, radical scavenging tests (ABTS, DPPH, ORAC, etc.) are usually considered as the first approach. In addition to the standard colorimetric assays, methods using separation techniques (on-line and pre-column assays) have been developed in the past decades. Based on the peak area (PA) reductions of compounds monitored by HPLC, the pre-column spiking method allows rapid characterisation of natural matrices avoiding laborious isolation steps. However, available information about the significance of the results produced remains scarce. Here, we report, for the first time, a discussion of the potential of the pre-column DPPH spiking method to pinpoint antioxidant compounds using red maple bark extract (RMBE). First, DPPH spiking was conventionally applied to the galloyl compounds in the extract showing the inadequacy of assessing results by PA reductions. The method was then applied to pure galloyl derivatives, evaluating their molar amount reacted (MAR) for more significance. The comparison with the standard DPPH-HPLC/AE method directly monitoring DPPH • inhibition highlighted the inability to retrieve the respective antioxidant efficiencies (AE) of each compound by using DPPH spiking. Despite its limitations, the DPPH spiking method brought to light an autoxidation phenomenon and a matrix/mixture effect investigated through tertiary mixtures of galloyl compounds. Although restricted to the compounds from one natural matrix, this study questions the validity of the spiking method as usually performed and could serve as a basis for further investigations (explorations of other natural products, kinetics considerations). Graphical abstract Investigation of the pre-column DPPH spiking method through the case of galloyl derivatives.

Designing optimal stimuli to control neuronal spike timing

PubMed Central

Packer, Adam M.; Yuste, Rafael; Paninski, Liam

2011-01-01

Recent advances in experimental stimulation methods have raised the following important computational question: how can we choose a stimulus that will drive a neuron to output a target spike train with optimal precision, given physiological constraints? Here we adopt an approach based on models that describe how a stimulating agent (such as an injected electrical current or a laser light interacting with caged neurotransmitters or photosensitive ion channels) affects the spiking activity of neurons. Based on these models, we solve the reverse problem of finding the best time-dependent modulation of the input, subject to hardware limitations as well as physiologically inspired safety measures, that causes the neuron to emit a spike train that with highest probability will be close to a target spike train. We adopt fast convex constrained optimization methods to solve this problem. Our methods can potentially be implemented in real time and may also be generalized to the case of many cells, suitable for neural prosthesis applications. With the use of biologically sensible parameters and constraints, our method finds stimulation patterns that generate very precise spike trains in simulated experiments. We also tested the intracellular current injection method on pyramidal cells in mouse cortical slices, quantifying the dependence of spiking reliability and timing precision on constraints imposed on the applied currents. PMID:21511704

Identification of spikes associated with local sources in continuous time series of atmospheric CO, CO2 and CH4

NASA Astrophysics Data System (ADS)

El Yazidi, Abdelhadi; Ramonet, Michel; Ciais, Philippe; Broquet, Gregoire; Pison, Isabelle; Abbaris, Amara; Brunner, Dominik; Conil, Sebastien; Delmotte, Marc; Gheusi, Francois; Guerin, Frederic; Hazan, Lynn; Kachroudi, Nesrine; Kouvarakis, Giorgos; Mihalopoulos, Nikolaos; Rivier, Leonard; Serça, Dominique

2018-03-01

This study deals with the problem of identifying atmospheric data influenced by local emissions that can result in spikes in time series of greenhouse gases and long-lived tracer measurements. We considered three spike detection methods known as coefficient of variation (COV), robust extraction of baseline signal (REBS) and standard deviation of the background (SD) to detect and filter positive spikes in continuous greenhouse gas time series from four monitoring stations representative of the European ICOS (Integrated Carbon Observation System) Research Infrastructure network. The results of the different methods are compared to each other and against a manual detection performed by station managers. Four stations were selected as test cases to apply the spike detection methods: a continental rural tower of 100 m height in eastern France (OPE), a high-mountain observatory in the south-west of France (PDM), a regional marine background site in Crete (FKL) and a marine clean-air background site in the Southern Hemisphere on Amsterdam Island (AMS). This selection allows us to address spike detection problems in time series with different variability. Two years of continuous measurements of CO2, CH4 and CO were analysed. All methods were found to be able to detect short-term spikes (lasting from a few seconds to a few minutes) in the time series. Analysis of the results of each method leads us to exclude the COV method due to the requirement to arbitrarily specify an a priori percentage of rejected data in the time series, which may over- or underestimate the actual number of spikes. The two other methods freely determine the number of spikes for a given set of parameters, and the values of these parameters were calibrated to provide the best match with spikes known to reflect local emissions episodes that are well documented by the station managers. More than 96 % of the spikes manually identified by station managers were successfully detected both in the SD and the REBS methods after the best adjustment of parameter values. At PDM, measurements made by two analyzers located 200 m from each other allow us to confirm that the CH4 spikes identified in one of the time series but not in the other correspond to a local source from a sewage treatment facility in one of the observatory buildings. From this experiment, we also found that the REBS method underestimates the number of positive anomalies in the CH4 data caused by local sewage emissions. As a conclusion, we recommend the use of the SD method, which also appears to be the easiest one to implement in automatic data processing, used for the operational filtering of spikes in greenhouse gases time series at global and regional monitoring stations of networks like that of the ICOS atmosphere network.

Advanced correlation grid: Analysis and visualisation of functional connectivity among multiple spike trains.

PubMed

Masud, Mohammad Shahed; Borisyuk, Roman; Stuart, Liz

2017-07-15

This study analyses multiple spike trains (MST) data, defines its functional connectivity and subsequently visualises an accurate diagram of connections. This is a challenging problem. For example, it is difficult to distinguish the common input and the direct functional connection of two spike trains. The new method presented in this paper is based on the traditional pairwise cross-correlation function (CCF) and a new combination of statistical techniques. First, the CCF is used to create the Advanced Correlation Grid (ACG) correlation where both the significant peak of the CCF and the corresponding time delay are used for detailed analysis of connectivity. Second, these two features of functional connectivity are used to classify connections. Finally, the visualization technique is used to represent the topology of functional connections. Examples are presented in the paper to demonstrate the new Advanced Correlation Grid method and to show how it enables discrimination between (i) influence from one spike train to another through an intermediate spike train and (ii) influence from one common spike train to another pair of analysed spike trains. The ACG method enables scientists to automatically distinguish between direct connections from spurious connections such as common source connection and indirect connection whereas existing methods require in-depth analysis to identify such connections. The ACG is a new and effective method for studying functional connectivity of multiple spike trains. This method can identify accurately all the direct connections and can distinguish common source and indirect connections automatically. Copyright © 2017 Elsevier B.V. All rights reserved.

Comparison of Listeria monocytogenes recoveries from spiked mung bean sprouts by the enrichment methods of three regulatory agencies.

PubMed

Cauchon, Kaitlin E; Hitchins, Anthony D; Smiley, R Derike

2017-09-01

Three selective enrichment methods, the United States Food and Drug Administration's (FDA method), the United States Department of Agriculture Food Safety Inspection Service's (USDA method), and the EN ISO 11290-1 standard method, were assessed for their suitability for recovery of Listeria monocytogenes from spiked mung bean sprouts. Three parameters were evaluated; the enrichment L. monocytogenes population from singly-spiked sprouts, the enrichment L. monocytogenes population from doubly-spiked (L. monocytogenes and Listeria innocua) sprouts, and the population differential resulting from the enrichment of doubly-spiked sprouts. Considerable L. monocytogenes inter-strain variation was observed. The mean enrichment L. monocytogenes populations for singly-spiked sprouts were 6.1 ± 1.2, 4.9 ± 1.2, and 6.9 ± 2.3 log CFU/mL for the FDA, USDA, and EN ISO 11290-1 methods, respectively. The mean L. monocytogenes populations for doubly-spiked sprouts were 4.7 ± 1.1, 5.5 ± 1.3, and 4.6 ± 1.4 log CFU/mL for the FDA, USDA, and ISO 11290-1 enrichment methods, respectively. The corresponding mean population differentials were 2.8 ± 1.1, 3.3 ± 1.3, and 3.6 ± 1.4 Δlog CFU/mL for the same three enrichment methods, respectively. The presence of L. innocua and resident microorganisms on the sprouts negatively impacted final levels of L. monocytogenes with all three enrichment methods. Published by Elsevier Ltd.

A probability-based multi-cycle sorting method for 4D-MRI: A simulation study.

PubMed

Liang, Xiao; Yin, Fang-Fang; Liu, Yilin; Cai, Jing

2016-12-01

To develop a novel probability-based sorting method capable of generating multiple breathing cycles of 4D-MRI images and to evaluate performance of this new method by comparing with conventional phase-based methods in terms of image quality and tumor motion measurement. Based on previous findings that breathing motion probability density function (PDF) of a single breathing cycle is dramatically different from true stabilized PDF that resulted from many breathing cycles, it is expected that a probability-based sorting method capable of generating multiple breathing cycles of 4D images may capture breathing variation information missing from conventional single-cycle sorting methods. The overall idea is to identify a few main breathing cycles (and their corresponding weightings) that can best represent the main breathing patterns of the patient and then reconstruct a set of 4D images for each of the identified main breathing cycles. This method is implemented in three steps: (1) The breathing signal is decomposed into individual breathing cycles, characterized by amplitude, and period; (2) individual breathing cycles are grouped based on amplitude and period to determine the main breathing cycles. If a group contains more than 10% of all breathing cycles in a breathing signal, it is determined as a main breathing pattern group and is represented by the average of individual breathing cycles in the group; (3) for each main breathing cycle, a set of 4D images is reconstructed using a result-driven sorting method adapted from our previous study. The probability-based sorting method was first tested on 26 patients' breathing signals to evaluate its feasibility of improving target motion PDF. The new method was subsequently tested for a sequential image acquisition scheme on the 4D digital extended cardiac torso (XCAT) phantom. Performance of the probability-based and conventional sorting methods was evaluated in terms of target volume precision and accuracy as measured by the 4D images, and also the accuracy of average intensity projection (AIP) of 4D images. Probability-based sorting showed improved similarity of breathing motion PDF from 4D images to reference PDF compared to single cycle sorting, indicated by the significant increase in Dice similarity coefficient (DSC) (probability-based sorting, DSC = 0.89 ± 0.03, and single cycle sorting, DSC = 0.83 ± 0.05, p-value <0.001). Based on the simulation study on XCAT, the probability-based method outperforms the conventional phase-based methods in qualitative evaluation on motion artifacts and quantitative evaluation on tumor volume precision and accuracy and accuracy of AIP of the 4D images. In this paper the authors demonstrated the feasibility of a novel probability-based multicycle 4D image sorting method. The authors' preliminary results showed that the new method can improve the accuracy of tumor motion PDF and the AIP of 4D images, presenting potential advantages over the conventional phase-based sorting method for radiation therapy motion management.

Action potential propagation recorded from single axonal arbors using multi-electrode arrays.

PubMed

Tovar, Kenneth R; Bridges, Daniel C; Wu, Bian; Randall, Connor; Audouard, Morgane; Jang, Jiwon; Hansma, Paul K; Kosik, Kenneth S

2018-04-11

We report the presence of co-occurring extracellular action potentials (eAPs) from cultured mouse hippocampal neurons among groups of planar electrodes on multi-electrode arrays (MEAs). The invariant sequences of eAPs among co-active electrode groups, repeated co-occurrences and short inter-electrode latencies are consistent with action potential propagation in unmyelinated axons. Repeated eAP co-detection by multiple electrodes was widespread in all our data records. Co-detection of eAPs confirms they result from the same neuron and allows these eAPs to be isolated from all other spikes independently of spike sorting algorithms. We averaged co-occurring events and revealed additional electrodes with eAPs that would otherwise be below detection threshold. We used these eAP cohorts to explore the temperature sensitivity of action potential propagation and the relationship between voltage-gated sodium channel density and propagation velocity. The sequence of eAPs among co-active electrodes 'fingerprints' neurons giving rise to these events and identifies them within neuronal ensembles. We used this property and the non-invasive nature of extracellular recording to monitor changes in excitability at multiple points in single axonal arbors simultaneously over several hours, demonstrating independence of axonal segments. Over several weeks, we recorded changes in inter-electrode propagation latencies and ongoing changes in excitability in different regions of single axonal arbors. Our work illustrates how repeated eAP co-occurrences can be used to extract physiological data from single axons with low electrode density MEAs. However, repeated eAP co-occurrences leads to over-sampling spikes from single neurons and thus can confound traditional spike-train analysis.

DuPont Qualicon BAX System polymerase chain reaction assay. Performance Tested Method 100201.

PubMed

Tice, George; Andaloro, Bridget; Fallon, Dawn; Wallace, F Morgan

2009-01-01

A recent outbreak of Salmonella in peanut butter has highlighted the need for validation of rapid detection methods. A multilaboratory study for detecting Salmonella in peanut butter was conducted as part of the AOAC Research Institute Emergency Response Validation program for methods that detect outbreak threats to food safety. Three sites tested spiked samples from the same master mix according to the U.S. Food and Drug Administration's Bacteriological Analytical Manual (FDA-BAM) method and the BAX System method. Salmonella Typhimurium (ATCC 14028) was grown in brain heart infusion for 24 h at 37 degrees C, then diluted to appropriate levels for sample inoculation. Master samples of peanut butter were spiked at high and low target levels, mixed, and allowed to equilibrate at room temperature for 2 weeks. Spike levels were low [1.08 most probable number (MPN)/25 g]; high (11.5 MPN/25 g) and unspiked to serve as negative controls. Each master sample was divided into 25 g portions and coded to blind the samples. Twenty portions of each spiked master sample and five portions of the unspiked sample were tested at each site. At each testing site, samples were blended in 25 g portions with 225 mL prewarmed lactose broth until thoroughly homogenized, then allowed to remain at room temperature for 55-65 min. Samples were adjusted to a pH of 6.8 +/- 0.2, if necessary, and incubated for 22-26 h at 35 degrees C. Across the three reporting laboratories, the BAX System detected Salmonella in 10/60 low-spike samples and 58/60 high-spike samples. The reference FDA-BAM method yielded positive results for 11/60 low-spike and 58/60 high-spike samples. Neither method demonstrated positive results for any of the 15 unspiked samples.

A supervised learning rule for classification of spatiotemporal spike patterns.

PubMed

Lilin Guo; Zhenzhong Wang; Adjouadi, Malek

2016-08-01

This study introduces a novel supervised algorithm for spiking neurons that take into consideration synapse delays and axonal delays associated with weights. It can be utilized for both classification and association and uses several biologically influenced properties, such as axonal and synaptic delays. This algorithm also takes into consideration spike-timing-dependent plasticity as in Remote Supervised Method (ReSuMe). This paper focuses on the classification aspect alone. Spiked neurons trained according to this proposed learning rule are capable of classifying different categories by the associated sequences of precisely timed spikes. Simulation results have shown that the proposed learning method greatly improves classification accuracy when compared to the Spike Pattern Association Neuron (SPAN) and the Tempotron learning rule.

Spike Pattern Structure Influences Synaptic Efficacy Variability under STDP and Synaptic Homeostasis. II: Spike Shuffling Methods on LIF Networks

PubMed Central

Bi, Zedong; Zhou, Changsong

2016-01-01

Synapses may undergo variable changes during plasticity because of the variability of spike patterns such as temporal stochasticity and spatial randomness. Here, we call the variability of synaptic weight changes during plasticity to be efficacy variability. In this paper, we investigate how four aspects of spike pattern statistics (i.e., synchronous firing, burstiness/regularity, heterogeneity of rates and heterogeneity of cross-correlations) influence the efficacy variability under pair-wise additive spike-timing dependent plasticity (STDP) and synaptic homeostasis (the mean strength of plastic synapses into a neuron is bounded), by implementing spike shuffling methods onto spike patterns self-organized by a network of excitatory and inhibitory leaky integrate-and-fire (LIF) neurons. With the increase of the decay time scale of the inhibitory synaptic currents, the LIF network undergoes a transition from asynchronous state to weak synchronous state and then to synchronous bursting state. We first shuffle these spike patterns using a variety of methods, each designed to evidently change a specific pattern statistics; and then investigate the change of efficacy variability of the synapses under STDP and synaptic homeostasis, when the neurons in the network fire according to the spike patterns before and after being treated by a shuffling method. In this way, we can understand how the change of pattern statistics may cause the change of efficacy variability. Our results are consistent with those of our previous study which implements spike-generating models on converging motifs. We also find that burstiness/regularity is important to determine the efficacy variability under asynchronous states, while heterogeneity of cross-correlations is the main factor to cause efficacy variability when the network moves into synchronous bursting states (the states observed in epilepsy). PMID:27555816

Memory recall and spike-frequency adaptation

NASA Astrophysics Data System (ADS)

Roach, James P.; Sander, Leonard M.; Zochowski, Michal R.

2016-05-01

The brain can reproduce memories from partial data; this ability is critical for memory recall. The process of memory recall has been studied using autoassociative networks such as the Hopfield model. This kind of model reliably converges to stored patterns that contain the memory. However, it is unclear how the behavior is controlled by the brain so that after convergence to one configuration, it can proceed with recognition of another one. In the Hopfield model, this happens only through unrealistic changes of an effective global temperature that destabilizes all stored configurations. Here we show that spike-frequency adaptation (SFA), a common mechanism affecting neuron activation in the brain, can provide state-dependent control of pattern retrieval. We demonstrate this in a Hopfield network modified to include SFA, and also in a model network of biophysical neurons. In both cases, SFA allows for selective stabilization of attractors with different basins of attraction, and also for temporal dynamics of attractor switching that is not possible in standard autoassociative schemes. The dynamics of our models give a plausible account of different sorts of memory retrieval.

Spitzer Sees Water Loud and Clear

NASA Technical Reports Server (NTRS)

2007-01-01

This plot of infrared data, called a spectrum, shows the strong signature of water vapor deep within the core of an embryonic star system, called NGC 1333-IRAS 4B.

The data were captured by NASA's Spitzer Space Telescope using an instrument called a spectrograph. A spectrograph collects light and sorts it according to color, or wavelength. In this case, infrared light from NGC 1333-IRAS 4B was broken up into the wavelengths listed on the horizontal axis of the plot. The sharp spikes, called spectral lines, occur at wavelengths at which the stellar object is particularly bright. The signature of water vapor is revealed in the pattern of wavelengths at which the spikes appear.

By comparing the observed data to a model (lower curve), astronomers can also determine the physical and chemical details of the region. For example, astronomers say these data suggest that ice in a cocoon surrounding the forming star is falling inward. The ice then smacks supersonically into a dusty planet-forming disk surrounding the stellar embryo, heats up and vaporizes quickly, releasing the infrared light that Spitzer collected.

Standardized Methods to Generate Mock (Spiked) Clinical Specimens by Spiking Blood or Plasma with Cultured Pathogens

PubMed Central

Dong, Ming; Fisher, Carolyn; Añez, Germán; Rios, Maria; Nakhasi, Hira L.; Hobson, J. Peyton; Beanan, Maureen; Hockman, Donna; Grigorenko, Elena; Duncan, Robert

2016-01-01

Aims To demonstrate standardized methods for spiking pathogens into human matrices for evaluation and comparison among diagnostic platforms. Methods and Results This study presents detailed methods for spiking bacteria or protozoan parasites into whole blood and virus into plasma. Proper methods must start with a documented, reproducible pathogen source followed by steps that include standardized culture, preparation of cryopreserved aliquots, quantification of the aliquots by molecular methods, production of sufficient numbers of individual specimens and testing of the platform with multiple mock specimens. Results are presented following the described procedures that showed acceptable reproducibility comparing in-house real-time PCR assays to a commercially available multiplex molecular assay. Conclusions A step by step procedure has been described that can be followed by assay developers who are targeting low prevalence pathogens. Significance and Impact of Study The development of diagnostic platforms for detection of low prevalence pathogens such as biothreat or emerging agents is challenged by the lack of clinical specimens for performance evaluation. This deficit can be overcome using mock clinical specimens made by spiking cultured pathogens into human matrices. To facilitate evaluation and comparison among platforms, standardized methods must be followed in the preparation and application of spiked specimens. PMID:26835651

Cloning of Plasmodium falciparum by single-cell sorting

PubMed Central

Miao, Jun; Li, Xiaolian; Cui, Liwang

2010-01-01

Malaria parasite cloning is traditionally carried out mainly by using the limiting dilution method, which is laborious, imprecise, and unable to distinguish multiply-infected RBCs. In this study, we used a parasite engineered to express green fluorescent protein (GFP) to evaluate a single-cell sorting method for rapidly cloning Plasmodium falciparum. By dividing a two dimensional scattergram from a cell sorter into 17 gates, we determined the parameters for isolating singly-infected erythrocytes and sorted them into individual cultures. Pre-gating of the engineered parasites for GFP allowed the isolation of almost 100% GFP-positive clones. Compared with the limiting dilution method, the number of parasite clones obtained by single-cell sorting was much higher. Molecular analyses showed that parasite isolates obtained by single-cell sorting were highly homogenous. This highly efficient single-cell sorting method should prove very useful for cloning both P. falciparum laboratory populations from genetic manipulation experiments and clinical samples. PMID:20435038

Multivariate Autoregressive Modeling and Granger Causality Analysis of Multiple Spike Trains

PubMed Central

Krumin, Michael; Shoham, Shy

2010-01-01

Recent years have seen the emergence of microelectrode arrays and optical methods allowing simultaneous recording of spiking activity from populations of neurons in various parts of the nervous system. The analysis of multiple neural spike train data could benefit significantly from existing methods for multivariate time-series analysis which have proven to be very powerful in the modeling and analysis of continuous neural signals like EEG signals. However, those methods have not generally been well adapted to point processes. Here, we use our recent results on correlation distortions in multivariate Linear-Nonlinear-Poisson spiking neuron models to derive generalized Yule-Walker-type equations for fitting ‘‘hidden” Multivariate Autoregressive models. We use this new framework to perform Granger causality analysis in order to extract the directed information flow pattern in networks of simulated spiking neurons. We discuss the relative merits and limitations of the new method. PMID:20454705

[CD34(+)/CD123(+) cell sorting from the patients with leukemia by Midi MACS method].

PubMed

Wang, Guang-Ping; Cao, Xin-Yu; Xin, Hong-Ya; Li, Qun; Qi, Zhen-Hua; Chen, Fang-Ping

2006-10-01

The aim of this study was to sort the CD34(+)/CD123(+) cells from the bone marrow cells of patients with acute myeloid leukemia (AML) by Midi MACS method. Firstly, the bone marrow mononuclear cells (BMMNC) were isolated from the patients with AML with Ficoll Paque, CD34(+) cells were then isolated by Midi MACS method followed by the isolation of CD34(+)/CD123(+) cells from the fraction of CD34(+) cells. The enrichment and recovery of CD34(+) and CD34(+)/CD123(+) cells were assayed by FACS technique. The results showed that the enrichment of CD34(+) cells was up to 98.73%, its average enrichment was 95.6%, and the recovery of CD34(+) was 84.6%, its average recovery was 51% after the first round sorting, by the second round sorting, the enrichment of CD34(+)/CD123(+) cells was up to 99.23%, its average enrichment was 83%. With regard to BMMNCs before sorting, the recovery of CD34(+)/CD123(+) was 34%. But, on the CD34(+) cells obtained by the first round sorting, its recovery was 56%. In conclusion, these results confirmed that the method of Midi MACS sorting can be applied to sort CD34(+)/CD123(+) cells from the bone marrow cells of AML patients, which give rise to the similar enrichment and recovery of the sorted cells with that of literature reported by the method of FACS.

[A wavelet neural network algorithm of EEG signals data compression and spikes recognition].

PubMed

Zhang, Y; Liu, A; Yu, K

1999-06-01

A novel method of EEG signals compression representation and epileptiform spikes recognition based on wavelet neural network and its algorithm is presented. The wavelet network not only can compress data effectively but also can recover original signal. In addition, the characters of the spikes and the spike-slow rhythm are auto-detected from the time-frequency isoline of EEG signal. This method is well worth using in the field of the electrophysiological signal processing and time-frequency analyzing.

A probability-based multi-cycle sorting method for 4D-MRI: A simulation study

PubMed Central

Liang, Xiao; Yin, Fang-Fang; Liu, Yilin; Cai, Jing

2016-01-01

Purpose: To develop a novel probability-based sorting method capable of generating multiple breathing cycles of 4D-MRI images and to evaluate performance of this new method by comparing with conventional phase-based methods in terms of image quality and tumor motion measurement. Methods: Based on previous findings that breathing motion probability density function (PDF) of a single breathing cycle is dramatically different from true stabilized PDF that resulted from many breathing cycles, it is expected that a probability-based sorting method capable of generating multiple breathing cycles of 4D images may capture breathing variation information missing from conventional single-cycle sorting methods. The overall idea is to identify a few main breathing cycles (and their corresponding weightings) that can best represent the main breathing patterns of the patient and then reconstruct a set of 4D images for each of the identified main breathing cycles. This method is implemented in three steps: (1) The breathing signal is decomposed into individual breathing cycles, characterized by amplitude, and period; (2) individual breathing cycles are grouped based on amplitude and period to determine the main breathing cycles. If a group contains more than 10% of all breathing cycles in a breathing signal, it is determined as a main breathing pattern group and is represented by the average of individual breathing cycles in the group; (3) for each main breathing cycle, a set of 4D images is reconstructed using a result-driven sorting method adapted from our previous study. The probability-based sorting method was first tested on 26 patients’ breathing signals to evaluate its feasibility of improving target motion PDF. The new method was subsequently tested for a sequential image acquisition scheme on the 4D digital extended cardiac torso (XCAT) phantom. Performance of the probability-based and conventional sorting methods was evaluated in terms of target volume precision and accuracy as measured by the 4D images, and also the accuracy of average intensity projection (AIP) of 4D images. Results: Probability-based sorting showed improved similarity of breathing motion PDF from 4D images to reference PDF compared to single cycle sorting, indicated by the significant increase in Dice similarity coefficient (DSC) (probability-based sorting, DSC = 0.89 ± 0.03, and single cycle sorting, DSC = 0.83 ± 0.05, p-value <0.001). Based on the simulation study on XCAT, the probability-based method outperforms the conventional phase-based methods in qualitative evaluation on motion artifacts and quantitative evaluation on tumor volume precision and accuracy and accuracy of AIP of the 4D images. Conclusions: In this paper the authors demonstrated the feasibility of a novel probability-based multicycle 4D image sorting method. The authors’ preliminary results showed that the new method can improve the accuracy of tumor motion PDF and the AIP of 4D images, presenting potential advantages over the conventional phase-based sorting method for radiation therapy motion management. PMID:27908178

Detection and Evaluation of Spatio-Temporal Spike Patterns in Massively Parallel Spike Train Data with SPADE.

PubMed

Quaglio, Pietro; Yegenoglu, Alper; Torre, Emiliano; Endres, Dominik M; Grün, Sonja

2017-01-01

Repeated, precise sequences of spikes are largely considered a signature of activation of cell assemblies. These repeated sequences are commonly known under the name of spatio-temporal patterns (STPs). STPs are hypothesized to play a role in the communication of information in the computational process operated by the cerebral cortex. A variety of statistical methods for the detection of STPs have been developed and applied to electrophysiological recordings, but such methods scale poorly with the current size of available parallel spike train recordings (more than 100 neurons). In this work, we introduce a novel method capable of overcoming the computational and statistical limits of existing analysis techniques in detecting repeating STPs within massively parallel spike trains (MPST). We employ advanced data mining techniques to efficiently extract repeating sequences of spikes from the data. Then, we introduce and compare two alternative approaches to distinguish statistically significant patterns from chance sequences. The first approach uses a measure known as conceptual stability, of which we investigate a computationally cheap approximation for applications to such large data sets. The second approach is based on the evaluation of pattern statistical significance. In particular, we provide an extension to STPs of a method we recently introduced for the evaluation of statistical significance of synchronous spike patterns. The performance of the two approaches is evaluated in terms of computational load and statistical power on a variety of artificial data sets that replicate specific features of experimental data. Both methods provide an effective and robust procedure for detection of STPs in MPST data. The method based on significance evaluation shows the best overall performance, although at a higher computational cost. We name the novel procedure the spatio-temporal Spike PAttern Detection and Evaluation (SPADE) analysis.

Detection and Evaluation of Spatio-Temporal Spike Patterns in Massively Parallel Spike Train Data with SPADE

PubMed Central

Quaglio, Pietro; Yegenoglu, Alper; Torre, Emiliano; Endres, Dominik M.; Grün, Sonja

2017-01-01

Repeated, precise sequences of spikes are largely considered a signature of activation of cell assemblies. These repeated sequences are commonly known under the name of spatio-temporal patterns (STPs). STPs are hypothesized to play a role in the communication of information in the computational process operated by the cerebral cortex. A variety of statistical methods for the detection of STPs have been developed and applied to electrophysiological recordings, but such methods scale poorly with the current size of available parallel spike train recordings (more than 100 neurons). In this work, we introduce a novel method capable of overcoming the computational and statistical limits of existing analysis techniques in detecting repeating STPs within massively parallel spike trains (MPST). We employ advanced data mining techniques to efficiently extract repeating sequences of spikes from the data. Then, we introduce and compare two alternative approaches to distinguish statistically significant patterns from chance sequences. The first approach uses a measure known as conceptual stability, of which we investigate a computationally cheap approximation for applications to such large data sets. The second approach is based on the evaluation of pattern statistical significance. In particular, we provide an extension to STPs of a method we recently introduced for the evaluation of statistical significance of synchronous spike patterns. The performance of the two approaches is evaluated in terms of computational load and statistical power on a variety of artificial data sets that replicate specific features of experimental data. Both methods provide an effective and robust procedure for detection of STPs in MPST data. The method based on significance evaluation shows the best overall performance, although at a higher computational cost. We name the novel procedure the spatio-temporal Spike PAttern Detection and Evaluation (SPADE) analysis. PMID:28596729

Determination of (187)Os in molybdenite by ICP-MS with neutron-induced (186)Os and (188)Os spikes.

PubMed

Qu, W; Du, A; Zhao, D

2001-10-31

The article describes a method for the determination of (187)Os in molybdenite by isotope dilution inductively coupled plasma-mass spectrometry (ID-ICP-MS) with neutron-induced (186)Os and (188)Os spike. The spike used in the present work was prepared in line with the principle by which artificial nuclides are produced in a nuclear reaction. The concentration and isotopic composition of osmium in the prepared spike were evaluated accurately with the isotope dilution method, using negative thermal ion mass spectrometry (N-TIMS). The advantage of this method is that using (186)Os and (188)Os double spikes can effectively compensate for the mass discrimination effects of ICP-MS. Thus, the common correction practice for mass bias in the isotope dilution method with a single spike is unnecessary. In addition, the method enables one to reduce the determined error arising from instrumental instability. The precision for the (187)Os/((186)Os+(188)Os) ratio was approximately 2% (2sigma, RSD), but in the case of (187)Os/(186)Os, (187)Os/(188)Os and (186)Os/(188)Os, precision ranged from 2.0 to 8% (2sigma, RSD). The results for (187)Os concentration in a molybdenite sample determined with this method showed good agreement with reference values.

Reducing 4D CT artifacts using optimized sorting based on anatomic similarity.

PubMed

Johnston, Eric; Diehn, Maximilian; Murphy, James D; Loo, Billy W; Maxim, Peter G

2011-05-01

Four-dimensional (4D) computed tomography (CT) has been widely used as a tool to characterize respiratory motion in radiotherapy. The two most commonly used 4D CT algorithms sort images by the associated respiratory phase or displacement into a predefined number of bins, and are prone to image artifacts at transitions between bed positions. The purpose of this work is to demonstrate a method of reducing motion artifacts in 4D CT by incorporating anatomic similarity into phase or displacement based sorting protocols. Ten patient datasets were retrospectively sorted using both the displacement and phase based sorting algorithms. Conventional sorting methods allow selection of only the nearest-neighbor image in time or displacement within each bin. In our method, for each bed position either the displacement or the phase defines the center of a bin range about which several candidate images are selected. The two dimensional correlation coefficients between slices bordering the interface between adjacent couch positions are then calculated for all candidate pairings. Two slices have a high correlation if they are anatomically similar. Candidates from each bin are then selected to maximize the slice correlation over the entire data set using the Dijkstra's shortest path algorithm. To assess the reduction of artifacts, two thoracic radiation oncologists independently compared the resorted 4D datasets pairwise with conventionally sorted datasets, blinded to the sorting method, to choose which had the least motion artifacts. Agreement between reviewers was evaluated using the weighted kappa score. Anatomically based image selection resulted in 4D CT datasets with significantly reduced motion artifacts with both displacement (P = 0.0063) and phase sorting (P = 0.00022). There was good agreement between the two reviewers, with complete agreement 34 times and complete disagreement 6 times. Optimized sorting using anatomic similarity significantly reduces 4D CT motion artifacts compared to conventional phase or displacement based sorting. This improved sorting algorithm is a straightforward extension of the two most common 4D CT sorting algorithms.

High precision calcium isotope analysis using 42Ca-48Ca double-spike TIMS technique

NASA Astrophysics Data System (ADS)

Feng, L.; Zhou, L.; Gao, S.; Tong, S. Y.; Zhou, M. L.

2014-12-01

Double spike techniques are widely used for determining calcium isotopic compositions of natural samples. The most important factor controlling precision of the double spike technique is the choice of appropriate spike isotope pair, the composition of double spikes and the ratio of spike to sample（CSp/CN）. We propose an optimal 42Ca-48Ca double spike protocol which yields the best internal precision for calcium isotopic composition determinations among all kinds of spike pairs and various spike compositions and ratios of spike to sample, as predicted by linear error propagation method. It is suggested to use spike composition of 42Ca/(42Ca+48Ca) = 0.44 mol/mol and CSp/(CN+ CSp)= 0.12mol/mol because it takes both advantages of the largest mass dispersion between 42Ca and 48Ca (14%) and lowest spike cost. Spiked samples were purified by pass through homemade micro-column filled with Ca special resin. K, Ti and other interference elements were completely separated, while 100% calcium was recovered with negligible blank. Data collection includes integration time, idle time, focus and peakcenter frequency, which were all carefully designed for the highest internal precision and lowest analysis time. All beams were automatically measured in a sequence by Triton TIMS so as to eliminate difference of analytical conditions between samples and standards, and also to increase the analytical throughputs. The typical internal precision of 100 duty cycles for one beam is 0.012‒0.015 ‰ (2δSEM), which agrees well with the predicted internal precision of 0.0124 ‰ (2δSEM). Our methods improve internal precisions by a factor of 2‒10 compared to previous methods of determination of calcium isotopic compositions by double spike TIMS. We analyzed NIST SRM 915a, NIST SRM 915b and Pacific Seawater as well as interspersed geological samples during two months. The obtained average δ44/40Ca (all relative to NIST SRM 915a) is 0.02 ± 0.02 ‰ (n=28), 0.72±0.04 ‰ (n=10) and 1.93±0.03 ‰ (n=21) for NIST SRM 915a, NIST SRM 915b and Pacific Seawater, respectively. The long-term reproducibility is 0.10‰ (2 δSD), which is comparable to the best external precision of 0.04 ‰ (2 δSD) of previous methods, but our sample throughputs are doubled with significant reduction in amount of spike used for single samples.

Reconstruction of audio waveforms from spike trains of artificial cochlea models

PubMed Central

Zai, Anja T.; Bhargava, Saurabh; Mesgarani, Nima; Liu, Shih-Chii

2015-01-01

Spiking cochlea models describe the analog processing and spike generation process within the biological cochlea. Reconstructing the audio input from the artificial cochlea spikes is therefore useful for understanding the fidelity of the information preserved in the spikes. The reconstruction process is challenging particularly for spikes from the mixed signal (analog/digital) integrated circuit (IC) cochleas because of multiple non-linearities in the model and the additional variance caused by random transistor mismatch. This work proposes an offline method for reconstructing the audio input from spike responses of both a particular spike-based hardware model called the AEREAR2 cochlea and an equivalent software cochlea model. This method was previously used to reconstruct the auditory stimulus based on the peri-stimulus histogram of spike responses recorded in the ferret auditory cortex. The reconstructed audio from the hardware cochlea is evaluated against an analogous software model using objective measures of speech quality and intelligibility; and further tested in a word recognition task. The reconstructed audio under low signal-to-noise (SNR) conditions (SNR < –5 dB) gives a better classification performance than the original SNR input in this word recognition task. PMID:26528113

Feature Representations for Neuromorphic Audio Spike Streams.

PubMed

Anumula, Jithendar; Neil, Daniel; Delbruck, Tobi; Liu, Shih-Chii

2018-01-01

Event-driven neuromorphic spiking sensors such as the silicon retina and the silicon cochlea encode the external sensory stimuli as asynchronous streams of spikes across different channels or pixels. Combining state-of-art deep neural networks with the asynchronous outputs of these sensors has produced encouraging results on some datasets but remains challenging. While the lack of effective spiking networks to process the spike streams is one reason, the other reason is that the pre-processing methods required to convert the spike streams to frame-based features needed for the deep networks still require further investigation. This work investigates the effectiveness of synchronous and asynchronous frame-based features generated using spike count and constant event binning in combination with the use of a recurrent neural network for solving a classification task using N-TIDIGITS18 dataset. This spike-based dataset consists of recordings from the Dynamic Audio Sensor, a spiking silicon cochlea sensor, in response to the TIDIGITS audio dataset. We also propose a new pre-processing method which applies an exponential kernel on the output cochlea spikes so that the interspike timing information is better preserved. The results from the N-TIDIGITS18 dataset show that the exponential features perform better than the spike count features, with over 91% accuracy on the digit classification task. This accuracy corresponds to an improvement of at least 2.5% over the use of spike count features, establishing a new state of the art for this dataset.

Feature Representations for Neuromorphic Audio Spike Streams

PubMed Central

Anumula, Jithendar; Neil, Daniel; Delbruck, Tobi; Liu, Shih-Chii

2018-01-01

Event-driven neuromorphic spiking sensors such as the silicon retina and the silicon cochlea encode the external sensory stimuli as asynchronous streams of spikes across different channels or pixels. Combining state-of-art deep neural networks with the asynchronous outputs of these sensors has produced encouraging results on some datasets but remains challenging. While the lack of effective spiking networks to process the spike streams is one reason, the other reason is that the pre-processing methods required to convert the spike streams to frame-based features needed for the deep networks still require further investigation. This work investigates the effectiveness of synchronous and asynchronous frame-based features generated using spike count and constant event binning in combination with the use of a recurrent neural network for solving a classification task using N-TIDIGITS18 dataset. This spike-based dataset consists of recordings from the Dynamic Audio Sensor, a spiking silicon cochlea sensor, in response to the TIDIGITS audio dataset. We also propose a new pre-processing method which applies an exponential kernel on the output cochlea spikes so that the interspike timing information is better preserved. The results from the N-TIDIGITS18 dataset show that the exponential features perform better than the spike count features, with over 91% accuracy on the digit classification task. This accuracy corresponds to an improvement of at least 2.5% over the use of spike count features, establishing a new state of the art for this dataset. PMID:29479300

Detection of Bursts and Pauses in Spike Trains

PubMed Central

Ko, D.; Wilson, C. J.; Lobb, C. J.; Paladini, C. A.

2012-01-01

Midbrain dopaminergic neurons in vivo exhibit a wide range of firing patterns. They normally fire constantly at a low rate, and speed up, firing a phasic burst when reward exceeds prediction, or pause when an expected reward does not occur. Therefore, the detection of bursts and pauses from spike train data is a critical problem when studying the role of phasic dopamine (DA) in reward related learning, and other DA dependent behaviors. However, few statistical methods have been developed that can identify bursts and pauses simultaneously. We propose a new statistical method, the Robust Gaussian Surprise (RGS) method, which performs an exhaustive search of bursts and pauses in spike trains simultaneously. We found that the RGS method is adaptable to various patterns of spike trains recorded in vivo, and is not influenced by baseline firing rate, making it applicable to all in vivo spike trains where baseline firing rates vary over time. We compare the performance of the RGS method to other methods of detecting bursts, such as the Poisson Surprise (PS), Rank Surprise (RS), and Template methods. Analysis of data using the RGS method reveals potential mechanisms underlying how bursts and pauses are controlled in DA neurons. PMID:22939922

Efficient sampling of parsimonious inversion histories with application to genome rearrangement in Yersinia.

PubMed

Miklós, István; Darling, Aaron E

2009-06-22

Inversions are among the most common mutations acting on the order and orientation of genes in a genome, and polynomial-time algorithms exist to obtain a minimal length series of inversions that transform one genome arrangement to another. However, the minimum length series of inversions (the optimal sorting path) is often not unique as many such optimal sorting paths exist. If we assume that all optimal sorting paths are equally likely, then statistical inference on genome arrangement history must account for all such sorting paths and not just a single estimate. No deterministic polynomial algorithm is known to count the number of optimal sorting paths nor sample from the uniform distribution of optimal sorting paths. Here, we propose a stochastic method that uniformly samples the set of all optimal sorting paths. Our method uses a novel formulation of parallel Markov chain Monte Carlo. In practice, our method can quickly estimate the total number of optimal sorting paths. We introduce a variant of our approach in which short inversions are modeled to be more likely, and we show how the method can be used to estimate the distribution of inversion lengths and breakpoint usage in pathogenic Yersinia pestis. The proposed method has been implemented in a program called "MC4Inversion." We draw comparison of MC4Inversion to the sampler implemented in BADGER and a previously described importance sampling (IS) technique. We find that on high-divergence data sets, MC4Inversion finds more optimal sorting paths per second than BADGER and the IS technique and simultaneously avoids bias inherent in the IS technique.

A Cross-Correlated Delay Shift Supervised Learning Method for Spiking Neurons with Application to Interictal Spike Detection in Epilepsy.

PubMed

Guo, Lilin; Wang, Zhenzhong; Cabrerizo, Mercedes; Adjouadi, Malek

2017-05-01

This study introduces a novel learning algorithm for spiking neurons, called CCDS, which is able to learn and reproduce arbitrary spike patterns in a supervised fashion allowing the processing of spatiotemporal information encoded in the precise timing of spikes. Unlike the Remote Supervised Method (ReSuMe), synapse delays and axonal delays in CCDS are variants which are modulated together with weights during learning. The CCDS rule is both biologically plausible and computationally efficient. The properties of this learning rule are investigated extensively through experimental evaluations in terms of reliability, adaptive learning performance, generality to different neuron models, learning in the presence of noise, effects of its learning parameters and classification performance. Results presented show that the CCDS learning method achieves learning accuracy and learning speed comparable with ReSuMe, but improves classification accuracy when compared to both the Spike Pattern Association Neuron (SPAN) learning rule and the Tempotron learning rule. The merit of CCDS rule is further validated on a practical example involving the automated detection of interictal spikes in EEG records of patients with epilepsy. Results again show that with proper encoding, the CCDS rule achieves good recognition performance.

Fast EEG spike detection via eigenvalue analysis and clustering of spatial amplitude distribution

NASA Astrophysics Data System (ADS)

Fukami, Tadanori; Shimada, Takamasa; Ishikawa, Bunnoshin

2018-06-01

Objective. In the current study, we tested a proposed method for fast spike detection in electroencephalography (EEG). Approach. We performed eigenvalue analysis in two-dimensional space spanned by gradients calculated from two neighboring samples to detect high-amplitude negative peaks. We extracted the spike candidates by imposing restrictions on parameters regarding spike shape and eigenvalues reflecting detection characteristics of individual medical doctors. We subsequently performed clustering, classifying detected peaks by considering the amplitude distribution at 19 scalp electrodes. Clusters with a small number of candidates were excluded. We then defined a score for eliminating spike candidates for which the pattern of detected electrodes differed from the overall pattern in a cluster. Spikes were detected by setting the score threshold. Main results. Based on visual inspection by a psychiatrist experienced in EEG, we evaluated the proposed method using two statistical measures of precision and recall with respect to detection performance. We found that precision and recall exhibited a trade-off relationship. The average recall value was 0.708 in eight subjects with the score threshold that maximized the F-measure, with 58.6  ±  36.2 spikes per subject. Under this condition, the average precision was 0.390, corresponding to a false positive rate 2.09 times higher than the true positive rate. Analysis of the required processing time revealed that, using a general-purpose computer, our method could be used to perform spike detection in 12.1% of the recording time. The process of narrowing down spike candidates based on shape occupied most of the processing time. Significance. Although the average recall value was comparable with that of other studies, the proposed method significantly shortened the processing time.

Cloning of Plasmodium falciparum by single-cell sorting.

PubMed

Miao, Jun; Li, Xiaolian; Cui, Liwang

2010-10-01

Malaria parasite cloning is traditionally carried out mainly by using the limiting dilution method, which is laborious, imprecise, and unable to distinguish multiply-infected RBCs. In this study, we used a parasite engineered to express green fluorescent protein (GFP) to evaluate a single-cell sorting method for rapidly cloning Plasmodium falciparum. By dividing a two-dimensional scattergram from a cell sorter into 17 gates, we determined the parameters for isolating singly-infected erythrocytes and sorted them into individual cultures. Pre-gating of the engineered parasites for GFP allowed the isolation of almost 100% GFP-positive clones. Compared with the limiting dilution method, the number of parasite clones obtained by single-cell sorting was much higher. Molecular analyses showed that parasite isolates obtained by single-cell sorting were highly homogenous. This highly efficient single-cell sorting method should prove very useful for cloning both P. falciparum laboratory populations from genetic manipulation experiments and clinical samples. Copyright 2010 Elsevier Inc. All rights reserved.

Identification and genetic analysis of cancer cells with PCR-activated cell sorting

PubMed Central

Eastburn, Dennis J.; Sciambi, Adam; Abate, Adam R.

2014-01-01

Cell sorting is a central tool in life science research for analyzing cellular heterogeneity or enriching rare cells out of large populations. Although methods like FACS and FISH-FC can characterize and isolate cells from heterogeneous populations, they are limited by their reliance on antibodies, or the requirement to chemically fix cells. We introduce a new cell sorting technology that robustly sorts based on sequence-specific analysis of cellular nucleic acids. Our approach, PCR-activated cell sorting (PACS), uses TaqMan PCR to detect nucleic acids within single cells and trigger their sorting. With this method, we identified and sorted prostate cancer cells from a heterogeneous population by performing >132 000 simultaneous single-cell TaqMan RT-PCR reactions targeting vimentin mRNA. Following vimentin-positive droplet sorting and downstream analysis of recovered nucleic acids, we found that cancer-specific genomes and transcripts were significantly enriched. Additionally, we demonstrate that PACS can be used to sort and enrich cells via TaqMan PCR reactions targeting single-copy genomic DNA. PACS provides a general new technical capability that expands the application space of cell sorting by enabling sorting based on cellular information not amenable to existing approaches. PMID:25030902

A Hippocampal Cognitive Prosthesis: Multi-Input, Multi-Output Nonlinear Modeling and VLSI Implementation

PubMed Central

Berger, Theodore W.; Song, Dong; Chan, Rosa H. M.; Marmarelis, Vasilis Z.; LaCoss, Jeff; Wills, Jack; Hampson, Robert E.; Deadwyler, Sam A.; Granacki, John J.

2012-01-01

This paper describes the development of a cognitive prosthesis designed to restore the ability to form new long-term memories typically lost after damage to the hippocampus. The animal model used is delayed nonmatch-to-sample (DNMS) behavior in the rat, and the “core” of the prosthesis is a biomimetic multi-input/multi-output (MIMO) nonlinear model that provides the capability for predicting spatio-temporal spike train output of hippocampus (CA1) based on spatio-temporal spike train inputs recorded presynaptically to CA1 (e.g., CA3). We demonstrate the capability of the MIMO model for highly accurate predictions of CA1 coded memories that can be made on a single-trial basis and in real-time. When hippocampal CA1 function is blocked and long-term memory formation is lost, successful DNMS behavior also is abolished. However, when MIMO model predictions are used to reinstate CA1 memory-related activity by driving spatio-temporal electrical stimulation of hippocampal output to mimic the patterns of activity observed in control conditions, successful DNMS behavior is restored. We also outline the design in very-large-scale integration for a hardware implementation of a 16-input, 16-output MIMO model, along with spike sorting, amplification, and other functions necessary for a total system, when coupled together with electrode arrays to record extracellularly from populations of hippocampal neurons, that can serve as a cognitive prosthesis in behaving animals. PMID:22438335

Discriminative Learning of Receptive Fields from Responses to Non-Gaussian Stimulus Ensembles

PubMed Central

Meyer, Arne F.; Diepenbrock, Jan-Philipp; Happel, Max F. K.; Ohl, Frank W.; Anemüller, Jörn

2014-01-01

Analysis of sensory neurons' processing characteristics requires simultaneous measurement of presented stimuli and concurrent spike responses. The functional transformation from high-dimensional stimulus space to the binary space of spike and non-spike responses is commonly described with linear-nonlinear models, whose linear filter component describes the neuron's receptive field. From a machine learning perspective, this corresponds to the binary classification problem of discriminating spike-eliciting from non-spike-eliciting stimulus examples. The classification-based receptive field (CbRF) estimation method proposed here adapts a linear large-margin classifier to optimally predict experimental stimulus-response data and subsequently interprets learned classifier weights as the neuron's receptive field filter. Computational learning theory provides a theoretical framework for learning from data and guarantees optimality in the sense that the risk of erroneously assigning a spike-eliciting stimulus example to the non-spike class (and vice versa) is minimized. Efficacy of the CbRF method is validated with simulations and for auditory spectro-temporal receptive field (STRF) estimation from experimental recordings in the auditory midbrain of Mongolian gerbils. Acoustic stimulation is performed with frequency-modulated tone complexes that mimic properties of natural stimuli, specifically non-Gaussian amplitude distribution and higher-order correlations. Results demonstrate that the proposed approach successfully identifies correct underlying STRFs, even in cases where second-order methods based on the spike-triggered average (STA) do not. Applied to small data samples, the method is shown to converge on smaller amounts of experimental recordings and with lower estimation variance than the generalized linear model and recent information theoretic methods. Thus, CbRF estimation may prove useful for investigation of neuronal processes in response to natural stimuli and in settings where rapid adaptation is induced by experimental design. PMID:24699631

Discriminative learning of receptive fields from responses to non-Gaussian stimulus ensembles.

PubMed

Meyer, Arne F; Diepenbrock, Jan-Philipp; Happel, Max F K; Ohl, Frank W; Anemüller, Jörn

2014-01-01

Analysis of sensory neurons' processing characteristics requires simultaneous measurement of presented stimuli and concurrent spike responses. The functional transformation from high-dimensional stimulus space to the binary space of spike and non-spike responses is commonly described with linear-nonlinear models, whose linear filter component describes the neuron's receptive field. From a machine learning perspective, this corresponds to the binary classification problem of discriminating spike-eliciting from non-spike-eliciting stimulus examples. The classification-based receptive field (CbRF) estimation method proposed here adapts a linear large-margin classifier to optimally predict experimental stimulus-response data and subsequently interprets learned classifier weights as the neuron's receptive field filter. Computational learning theory provides a theoretical framework for learning from data and guarantees optimality in the sense that the risk of erroneously assigning a spike-eliciting stimulus example to the non-spike class (and vice versa) is minimized. Efficacy of the CbRF method is validated with simulations and for auditory spectro-temporal receptive field (STRF) estimation from experimental recordings in the auditory midbrain of Mongolian gerbils. Acoustic stimulation is performed with frequency-modulated tone complexes that mimic properties of natural stimuli, specifically non-Gaussian amplitude distribution and higher-order correlations. Results demonstrate that the proposed approach successfully identifies correct underlying STRFs, even in cases where second-order methods based on the spike-triggered average (STA) do not. Applied to small data samples, the method is shown to converge on smaller amounts of experimental recordings and with lower estimation variance than the generalized linear model and recent information theoretic methods. Thus, CbRF estimation may prove useful for investigation of neuronal processes in response to natural stimuli and in settings where rapid adaptation is induced by experimental design.

Dielectrophoretic Capture and Genetic Analysis of Single Neuroblastoma Tumor Cells

PubMed Central

Carpenter, Erica L.; Rader, JulieAnn; Ruden, Jacob; Rappaport, Eric F.; Hunter, Kristen N.; Hallberg, Paul L.; Krytska, Kate; O’Dwyer, Peter J.; Mosse, Yael P.

2014-01-01

Our understanding of the diversity of cells that escape the primary tumor and seed micrometastases remains rudimentary, and approaches for studying circulating and disseminated tumor cells have been limited by low throughput and sensitivity, reliance on single parameter sorting, and a focus on enumeration rather than phenotypic and genetic characterization. Here, we utilize a highly sensitive microfluidic and dielectrophoretic approach for the isolation and genetic analysis of individual tumor cells. We employed fluorescence labeling to isolate 208 single cells from spiking experiments conducted with 11 cell lines, including 8 neuroblastoma cell lines, and achieved a capture sensitivity of 1 tumor cell per 106 white blood cells (WBCs). Sample fixation or freezing had no detectable effect on cell capture. Point mutations were accurately detected in the whole genome amplification product of captured single tumor cells but not in negative control WBCs. We applied this approach to capture 144 single tumor cells from 10 bone marrow samples of patients suffering from neuroblastoma. In this pediatric malignancy, high-risk patients often exhibit wide-spread hematogenous metastasis, but access to primary tumor can be difficult or impossible. Here, we used flow-based sorting to pre-enrich samples with tumor involvement below 0.02%. For all patients for whom a mutation in the Anaplastic Lymphoma Kinase gene had already been detected in their primary tumor, the same mutation was detected in single cells from their marrow. These findings demonstrate a novel, non-invasive, and adaptable method for the capture and genetic analysis of single tumor cells from cancer patients. PMID:25133137

An on-chip imaging droplet-sorting system: a real-time shape recognition method to screen target cells in droplets with single cell resolution

NASA Astrophysics Data System (ADS)

Girault, Mathias; Kim, Hyonchol; Arakawa, Hisayuki; Matsuura, Kenji; Odaka, Masao; Hattori, Akihiro; Terazono, Hideyuki; Yasuda, Kenji

2017-01-01

A microfluidic on-chip imaging cell sorter has several advantages over conventional cell sorting methods, especially to identify cells with complex morphologies such as clusters. One of the remaining problems is how to efficiently discriminate targets at the species level without labelling. Hence, we developed a label-free microfluidic droplet-sorting system based on image recognition of cells in droplets. To test the applicability of this method, a mixture of two plankton species with different morphologies (Dunaliella tertiolecta and Phaeodactylum tricornutum) were successfully identified and discriminated at a rate of 10 Hz. We also examined the ability to detect the number of objects encapsulated in a droplet. Single cell droplets sorted into collection channels showed 91 ± 4.5% and 90 ± 3.8% accuracy for D. tertiolecta and P. tricornutum, respectively. Because we used image recognition to confirm single cell droplets, we achieved highly accurate single cell sorting. The results indicate that the integrated method of droplet imaging cell sorting can provide a complementary sorting approach capable of isolating single target cells from a mixture of cells with high accuracy without any staining.

An on-chip imaging droplet-sorting system: a real-time shape recognition method to screen target cells in droplets with single cell resolution.

PubMed

Girault, Mathias; Kim, Hyonchol; Arakawa, Hisayuki; Matsuura, Kenji; Odaka, Masao; Hattori, Akihiro; Terazono, Hideyuki; Yasuda, Kenji

2017-01-06

A microfluidic on-chip imaging cell sorter has several advantages over conventional cell sorting methods, especially to identify cells with complex morphologies such as clusters. One of the remaining problems is how to efficiently discriminate targets at the species level without labelling. Hence, we developed a label-free microfluidic droplet-sorting system based on image recognition of cells in droplets. To test the applicability of this method, a mixture of two plankton species with different morphologies (Dunaliella tertiolecta and Phaeodactylum tricornutum) were successfully identified and discriminated at a rate of 10 Hz. We also examined the ability to detect the number of objects encapsulated in a droplet. Single cell droplets sorted into collection channels showed 91 ± 4.5% and 90 ± 3.8% accuracy for D. tertiolecta and P. tricornutum, respectively. Because we used image recognition to confirm single cell droplets, we achieved highly accurate single cell sorting. The results indicate that the integrated method of droplet imaging cell sorting can provide a complementary sorting approach capable of isolating single target cells from a mixture of cells with high accuracy without any staining.

An on-chip imaging droplet-sorting system: a real-time shape recognition method to screen target cells in droplets with single cell resolution

PubMed Central

Girault, Mathias; Kim, Hyonchol; Arakawa, Hisayuki; Matsuura, Kenji; Odaka, Masao; Hattori, Akihiro; Terazono, Hideyuki; Yasuda, Kenji

2017-01-01

A microfluidic on-chip imaging cell sorter has several advantages over conventional cell sorting methods, especially to identify cells with complex morphologies such as clusters. One of the remaining problems is how to efficiently discriminate targets at the species level without labelling. Hence, we developed a label-free microfluidic droplet-sorting system based on image recognition of cells in droplets. To test the applicability of this method, a mixture of two plankton species with different morphologies (Dunaliella tertiolecta and Phaeodactylum tricornutum) were successfully identified and discriminated at a rate of 10 Hz. We also examined the ability to detect the number of objects encapsulated in a droplet. Single cell droplets sorted into collection channels showed 91 ± 4.5% and 90 ± 3.8% accuracy for D. tertiolecta and P. tricornutum, respectively. Because we used image recognition to confirm single cell droplets, we achieved highly accurate single cell sorting. The results indicate that the integrated method of droplet imaging cell sorting can provide a complementary sorting approach capable of isolating single target cells from a mixture of cells with high accuracy without any staining. PMID:28059147

Sorting Rotating Micromachines by Variations in Their Magnetic Properties

NASA Astrophysics Data System (ADS)

Howell, Taylor A.; Osting, Braxton; Abbott, Jake J.

2018-05-01

We consider sorting for the broad class of micromachines (also known as microswimmers, microrobots, micropropellers, etc.) propelled by rotating magnetic fields. We present a control policy that capitalizes on the variation in magnetic properties between otherwise-homogeneous micromachines to enable the sorting of a select fraction of a group from the remainder and prescribe its net relative movement, using a uniform magnetic field that is applied equally to all micromachines. The method enables us to accomplish this sorting task using open-loop control, without relying on a structured environment or localization information of individual micromachines. With our method, the control time to perform the sort is invariant to the number of micromachines. The method is verified through simulations and scaled experiments. Finally, we include an extended discussion about the limitations of the method and address open questions related to its practical application.

Multiscale analysis of neural spike trains.

PubMed

Ramezan, Reza; Marriott, Paul; Chenouri, Shojaeddin

2014-01-30

This paper studies the multiscale analysis of neural spike trains, through both graphical and Poisson process approaches. We introduce the interspike interval plot, which simultaneously visualizes characteristics of neural spiking activity at different time scales. Using an inhomogeneous Poisson process framework, we discuss multiscale estimates of the intensity functions of spike trains. We also introduce the windowing effect for two multiscale methods. Using quasi-likelihood, we develop bootstrap confidence intervals for the multiscale intensity function. We provide a cross-validation scheme, to choose the tuning parameters, and study its unbiasedness. Studying the relationship between the spike rate and the stimulus signal, we observe that adjusting for the first spike latency is important in cross-validation. We show, through examples, that the correlation between spike trains and spike count variability can be multiscale phenomena. Furthermore, we address the modeling of the periodicity of the spike trains caused by a stimulus signal or by brain rhythms. Within the multiscale framework, we introduce intensity functions for spike trains with multiplicative and additive periodic components. Analyzing a dataset from the retinogeniculate synapse, we compare the fit of these models with the Bayesian adaptive regression splines method and discuss the limitations of the methodology. Computational efficiency, which is usually a challenge in the analysis of spike trains, is one of the highlights of these new models. In an example, we show that the reconstruction quality of a complex intensity function demonstrates the ability of the multiscale methodology to crack the neural code. Copyright © 2013 John Wiley & Sons, Ltd.

Synchronous Spike Patterns in Macaque Motor Cortex during an Instructed-Delay Reach-to-Grasp Task

PubMed Central

Torre, Emiliano; Quaglio, Pietro; Denker, Michael; Brochier, Thomas; Riehle, Alexa

2016-01-01

The computational role of spike time synchronization at millisecond precision among neurons in the cerebral cortex is hotly debated. Studies performed on data of limited size provided experimental evidence that low-order correlations occur in relation to behavior. Advances in electrophysiological technology to record from hundreds of neurons simultaneously provide the opportunity to observe coordinated spiking activity of larger populations of cells. We recently published a method that combines data mining and statistical evaluation to search for significant patterns of synchronous spikes in massively parallel spike trains (Torre et al., 2013). The method solves the computational and multiple testing problems raised by the high dimensionality of the data. In the current study, we used our method on simultaneous recordings from two macaque monkeys engaged in an instructed-delay reach-to-grasp task to determine the emergence of spike synchronization in relation to behavior. We found a multitude of synchronous spike patterns aligned in both monkeys along a preferential mediolateral orientation in brain space. The occurrence of the patterns is highly specific to behavior, indicating that different behaviors are associated with the synchronization of different groups of neurons (“cell assemblies”). However, pooled patterns that overlap in neuronal composition exhibit no specificity, suggesting that exclusive cell assemblies become active during different behaviors, but can recruit partly identical neurons. These findings are consistent across multiple recording sessions analyzed across the two monkeys. SIGNIFICANCE STATEMENT Neurons in the brain communicate via electrical impulses called spikes. How spikes are coordinated to process information is still largely unknown. Synchronous spikes are effective in triggering a spike emission in receiving neurons and have been shown to occur in relation to behavior in a number of studies on simultaneous recordings of few neurons. We recently published a method to extend this type of investigation to larger data. Here, we apply it to simultaneous recordings of hundreds of neurons from the motor cortex of macaque monkeys performing a motor task. Our analysis reveals groups of neurons selectively synchronizing their activity in relation to behavior, which sheds new light on the role of synchrony in information processing in the cerebral cortex. PMID:27511007

Synchronous Spike Patterns in Macaque Motor Cortex during an Instructed-Delay Reach-to-Grasp Task.

PubMed

Torre, Emiliano; Quaglio, Pietro; Denker, Michael; Brochier, Thomas; Riehle, Alexa; Grün, Sonja

2016-08-10

The computational role of spike time synchronization at millisecond precision among neurons in the cerebral cortex is hotly debated. Studies performed on data of limited size provided experimental evidence that low-order correlations occur in relation to behavior. Advances in electrophysiological technology to record from hundreds of neurons simultaneously provide the opportunity to observe coordinated spiking activity of larger populations of cells. We recently published a method that combines data mining and statistical evaluation to search for significant patterns of synchronous spikes in massively parallel spike trains (Torre et al., 2013). The method solves the computational and multiple testing problems raised by the high dimensionality of the data. In the current study, we used our method on simultaneous recordings from two macaque monkeys engaged in an instructed-delay reach-to-grasp task to determine the emergence of spike synchronization in relation to behavior. We found a multitude of synchronous spike patterns aligned in both monkeys along a preferential mediolateral orientation in brain space. The occurrence of the patterns is highly specific to behavior, indicating that different behaviors are associated with the synchronization of different groups of neurons ("cell assemblies"). However, pooled patterns that overlap in neuronal composition exhibit no specificity, suggesting that exclusive cell assemblies become active during different behaviors, but can recruit partly identical neurons. These findings are consistent across multiple recording sessions analyzed across the two monkeys. Neurons in the brain communicate via electrical impulses called spikes. How spikes are coordinated to process information is still largely unknown. Synchronous spikes are effective in triggering a spike emission in receiving neurons and have been shown to occur in relation to behavior in a number of studies on simultaneous recordings of few neurons. We recently published a method to extend this type of investigation to larger data. Here, we apply it to simultaneous recordings of hundreds of neurons from the motor cortex of macaque monkeys performing a motor task. Our analysis reveals groups of neurons selectively synchronizing their activity in relation to behavior, which sheds new light on the role of synchrony in information processing in the cerebral cortex. Copyright © 2016 Torre, et al.

Knowledge extraction from evolving spiking neural networks with rank order population coding.

PubMed

Soltic, Snjezana; Kasabov, Nikola

2010-12-01

This paper demonstrates how knowledge can be extracted from evolving spiking neural networks with rank order population coding. Knowledge discovery is a very important feature of intelligent systems. Yet, a disproportionally small amount of research is centered on the issue of knowledge extraction from spiking neural networks which are considered to be the third generation of artificial neural networks. The lack of knowledge representation compatibility is becoming a major detriment to end users of these networks. We show that a high-level knowledge can be obtained from evolving spiking neural networks. More specifically, we propose a method for fuzzy rule extraction from an evolving spiking network with rank order population coding. The proposed method was used for knowledge discovery on two benchmark taste recognition problems where the knowledge learnt by an evolving spiking neural network was extracted in the form of zero-order Takagi-Sugeno fuzzy IF-THEN rules.

Character recognition from trajectory by recurrent spiking neural networks.

PubMed

Jiangrong Shen; Kang Lin; Yueming Wang; Gang Pan

2017-07-01

Spiking neural networks are biologically plausible and power-efficient on neuromorphic hardware, while recurrent neural networks have been proven to be efficient on time series data. However, how to use the recurrent property to improve the performance of spiking neural networks is still a problem. This paper proposes a recurrent spiking neural network for character recognition using trajectories. In the network, a new encoding method is designed, in which varying time ranges of input streams are used in different recurrent layers. This is able to improve the generalization ability of our model compared with general encoding methods. The experiments are conducted on four groups of the character data set from University of Edinburgh. The results show that our method can achieve a higher average recognition accuracy than existing methods.

Efficient Sampling of Parsimonious Inversion Histories with Application to Genome Rearrangement in Yersinia

PubMed Central

Darling, Aaron E.

2009-01-01

Inversions are among the most common mutations acting on the order and orientation of genes in a genome, and polynomial-time algorithms exist to obtain a minimal length series of inversions that transform one genome arrangement to another. However, the minimum length series of inversions (the optimal sorting path) is often not unique as many such optimal sorting paths exist. If we assume that all optimal sorting paths are equally likely, then statistical inference on genome arrangement history must account for all such sorting paths and not just a single estimate. No deterministic polynomial algorithm is known to count the number of optimal sorting paths nor sample from the uniform distribution of optimal sorting paths. Here, we propose a stochastic method that uniformly samples the set of all optimal sorting paths. Our method uses a novel formulation of parallel Markov chain Monte Carlo. In practice, our method can quickly estimate the total number of optimal sorting paths. We introduce a variant of our approach in which short inversions are modeled to be more likely, and we show how the method can be used to estimate the distribution of inversion lengths and breakpoint usage in pathogenic Yersinia pestis. The proposed method has been implemented in a program called “MC4Inversion.” We draw comparison of MC4Inversion to the sampler implemented in BADGER and a previously described importance sampling (IS) technique. We find that on high-divergence data sets, MC4Inversion finds more optimal sorting paths per second than BADGER and the IS technique and simultaneously avoids bias inherent in the IS technique. PMID:20333186

Detection of changes of high-frequency activity by statistical time-frequency analysis in epileptic spikes

PubMed Central

Kobayashi, Katsuhiro; Jacobs, Julia; Gotman, Jean

2013-01-01

Objective A novel type of statistical time-frequency analysis was developed to elucidate changes of high-frequency EEG activity associated with epileptic spikes. Methods The method uses the Gabor Transform and detects changes of power in comparison to background activity using t-statistics that are controlled by the false discovery rate (FDR) to correct type I error of multiple testing. The analysis was applied to EEGs recorded at 2000 Hz from three patients with mesial temporal lobe epilepsy. Results Spike-related increase of high-frequency oscillations (HFOs) was clearly shown in the FDR-controlled t-spectra: it was most dramatic in spikes recorded from the hippocampus when the hippocampus was the seizure onset zone (SOZ). Depression of fast activity was observed immediately after the spikes, especially consistently in the discharges from the hippocampal SOZ. It corresponded to the slow wave part in case of spike-and-slow-wave complexes, but it was noted even in spikes without apparent slow waves. In one patient, a gradual increase of power above 200 Hz preceded spikes. Conclusions FDR-controlled t-spectra clearly detected the spike-related changes of HFOs that were unclear in standard power spectra. Significance We developed a promising tool to study the HFOs that may be closely linked to the pathophysiology of epileptogenesis. PMID:19394892

Learning cellular sorting pathways using protein interactions and sequence motifs.

PubMed

Lin, Tien-Ho; Bar-Joseph, Ziv; Murphy, Robert F

2011-11-01

Proper subcellular localization is critical for proteins to perform their roles in cellular functions. Proteins are transported by different cellular sorting pathways, some of which take a protein through several intermediate locations until reaching its final destination. The pathway a protein is transported through is determined by carrier proteins that bind to specific sequence motifs. In this article, we present a new method that integrates protein interaction and sequence motif data to model how proteins are sorted through these sorting pathways. We use a hidden Markov model (HMM) to represent protein sorting pathways. The model is able to determine intermediate sorting states and to assign carrier proteins and motifs to the sorting pathways. In simulation studies, we show that the method can accurately recover an underlying sorting model. Using data for yeast, we show that our model leads to accurate prediction of subcellular localization. We also show that the pathways learned by our model recover many known sorting pathways and correctly assign proteins to the path they utilize. The learned model identified new pathways and their putative carriers and motifs and these may represent novel protein sorting mechanisms. Supplementary results and software implementation are available from http://murphylab.web.cmu.edu/software/2010_RECOMB_pathways/.

Correlations Decrease with Propagation of Spiking Activity in the Mouse Barrel Cortex

PubMed Central

Ranganathan, Gayathri Nattar; Koester, Helmut Joachim

2011-01-01

Propagation of suprathreshold spiking activity through neuronal populations is important for the function of the central nervous system. Neural correlations have an impact on cortical function particularly on the signaling of information and propagation of spiking activity. Therefore we measured the change in correlations as suprathreshold spiking activity propagated between recurrent neuronal networks of the mammalian cerebral cortex. Using optical methods we recorded spiking activity from large samples of neurons from two neural populations simultaneously. The results indicate that correlations decreased as spiking activity propagated from layer 4 to layer 2/3 in the rodent barrel cortex. PMID:21629764

Fast numerical methods for simulating large-scale integrate-and-fire neuronal networks.

PubMed

Rangan, Aaditya V; Cai, David

2007-02-01

We discuss numerical methods for simulating large-scale, integrate-and-fire (I&F) neuronal networks. Important elements in our numerical methods are (i) a neurophysiologically inspired integrating factor which casts the solution as a numerically tractable integral equation, and allows us to obtain stable and accurate individual neuronal trajectories (i.e., voltage and conductance time-courses) even when the I&F neuronal equations are stiff, such as in strongly fluctuating, high-conductance states; (ii) an iterated process of spike-spike corrections within groups of strongly coupled neurons to account for spike-spike interactions within a single large numerical time-step; and (iii) a clustering procedure of firing events in the network to take advantage of localized architectures, such as spatial scales of strong local interactions, which are often present in large-scale computational models-for example, those of the primary visual cortex. (We note that the spike-spike corrections in our methods are more involved than the correction of single neuron spike-time via a polynomial interpolation as in the modified Runge-Kutta methods commonly used in simulations of I&F neuronal networks.) Our methods can evolve networks with relatively strong local interactions in an asymptotically optimal way such that each neuron fires approximately once in [Formula: see text] operations, where N is the number of neurons in the system. We note that quantifications used in computational modeling are often statistical, since measurements in a real experiment to characterize physiological systems are typically statistical, such as firing rate, interspike interval distributions, and spike-triggered voltage distributions. We emphasize that it takes much less computational effort to resolve statistical properties of certain I&F neuronal networks than to fully resolve trajectories of each and every neuron within the system. For networks operating in realistic dynamical regimes, such as strongly fluctuating, high-conductance states, our methods are designed to achieve statistical accuracy when very large time-steps are used. Moreover, our methods can also achieve trajectory-wise accuracy when small time-steps are used.

Comparison of neuronal spike exchange methods on a Blue Gene/P supercomputer.

PubMed

Hines, Michael; Kumar, Sameer; Schürmann, Felix

2011-01-01

For neural network simulations on parallel machines, interprocessor spike communication can be a significant portion of the total simulation time. The performance of several spike exchange methods using a Blue Gene/P (BG/P) supercomputer has been tested with 8-128 K cores using randomly connected networks of up to 32 M cells with 1 k connections per cell and 4 M cells with 10 k connections per cell, i.e., on the order of 4·10(10) connections (K is 1024, M is 1024(2), and k is 1000). The spike exchange methods used are the standard Message Passing Interface (MPI) collective, MPI_Allgather, and several variants of the non-blocking Multisend method either implemented via non-blocking MPI_Isend, or exploiting the possibility of very low overhead direct memory access (DMA) communication available on the BG/P. In all cases, the worst performing method was that using MPI_Isend due to the high overhead of initiating a spike communication. The two best performing methods-the persistent Multisend method using the Record-Replay feature of the Deep Computing Messaging Framework DCMF_Multicast; and a two-phase multisend in which a DCMF_Multicast is used to first send to a subset of phase one destination cores, which then pass it on to their subset of phase two destination cores-had similar performance with very low overhead for the initiation of spike communication. Departure from ideal scaling for the Multisend methods is almost completely due to load imbalance caused by the large variation in number of cells that fire on each processor in the interval between synchronization. Spike exchange time itself is negligible since transmission overlaps with computation and is handled by a DMA controller. We conclude that ideal performance scaling will be ultimately limited by imbalance between incoming processor spikes between synchronization intervals. Thus, counterintuitively, maximization of load balance requires that the distribution of cells on processors should not reflect neural net architecture but be randomly distributed so that sets of cells which are burst firing together should be on different processors with their targets on as large a set of processors as possible.

Microfluidic cell sorting: a review of the advances in the separation of cells from debulking to rare cell isolation.

PubMed

Shields, C Wyatt; Reyes, Catherine D; López, Gabriel P

2015-03-07

Accurate and high throughput cell sorting is a critical enabling technology in molecular and cellular biology, biotechnology, and medicine. While conventional methods can provide high efficiency sorting in short timescales, advances in microfluidics have enabled the realization of miniaturized devices offering similar capabilities that exploit a variety of physical principles. We classify these technologies as either active or passive. Active systems generally use external fields (e.g., acoustic, electric, magnetic, and optical) to impose forces to displace cells for sorting, whereas passive systems use inertial forces, filters, and adhesion mechanisms to purify cell populations. Cell sorting on microchips provides numerous advantages over conventional methods by reducing the size of necessary equipment, eliminating potentially biohazardous aerosols, and simplifying the complex protocols commonly associated with cell sorting. Additionally, microchip devices are well suited for parallelization, enabling complete lab-on-a-chip devices for cellular isolation, analysis, and experimental processing. In this review, we examine the breadth of microfluidic cell sorting technologies, while focusing on those that offer the greatest potential for translation into clinical and industrial practice and that offer multiple, useful functions. We organize these sorting technologies by the type of cell preparation required (i.e., fluorescent label-based sorting, bead-based sorting, and label-free sorting) as well as by the physical principles underlying each sorting mechanism.

Microfluidic Cell Sorting: A Review of the Advances in the Separation of Cells from Debulking to Rare Cell Isolation

PubMed Central

Shields, C. Wyatt; Reyes, Catherine D.; López, Gabriel P.

2015-01-01

Accurate and high throughput cell sorting is a critical enabling technology in molecular and cellular biology, biotechnology, and medicine. While conventional methods can provide high efficiency sorting in short timescales, advances in microfluidics have enabled the realization of miniaturized devices offering similar capabilities that exploit a variety of physical principles. We classify these technologies as either active or passive. Active systems generally use external fields (e.g., acoustic, electric, magnetic, and optical) to impose forces to displace cells for sorting, whereas passive systems use inertial forces, filters, and adhesion mechanisms to purify cell populations. Cell sorting on microchips provides numerous advantages over conventional methods by reducing the size of necessary equipment, eliminating potentially biohazardous aerosols, and simplifying the complex protocols commonly associated with cell sorting. Additionally, microchip devices are well suited for parallelization, enabling complete lab-on-a-chip devices for cellular isolation, analysis, and experimental processing. In this review, we examine the breadth of microfluidic cell sorting technologies, while focusing on those that offer the greatest potential for translation into clinical and industrial practice and that offer multiple, useful functions. We organize these sorting technologies by the type of cell preparation required (i.e., fluorescent label-based sorting, bead-based sorting, and label-free sorting) as well as by the physical principles underlying each sorting mechanism. PMID:25598308

Evaluation of Offline Tandem and Online Solid-Phase Extraction with Liquid Chromatography/Electrospray Ionization-Mass Spectrometry for Analysis of Antibiotics in Ambient Water and Comparison to an Independent Method

USGS Publications Warehouse

Meyer, M.T.; Lee, E.A.; Ferrell, G.M.; Bumgarner, J.E.; Varns, Jerry

2007-01-01

This report describes the performance of an offline tandem solid-phase extraction (SPE) method and an online SPE method that use liquid chromatography/mass spectrometry for the analysis of 23 and 35 antibiotics, respectively, as used in several water-quality surveys conducted since 1999. In the offline tandem SPE method, normalized concentrations for the quinolone, macrolide, and sulfonamide antibiotics in spiked environmental samples averaged from 81 to 139 percent of the expected spiked concentrations. A modified standard-addition technique was developed to improve the quantitation of the tetracycline antibiotics, which had 'apparent' concentrations that ranged from 185 to 1,200 percent of their expected spiked concentrations in matrix-spiked samples. In the online SPE method, normalized concentrations for the quinolone, macrolide, sulfonamide, and tetracycline antibiotics in matrix-spiked samples averaged from 51 to 142 percent of their expected spiked concentrations, and the beta-lactam antibiotics in matrix-spiked samples averaged from 22 to 76 percent of their expected spiked concentration. Comparison of 44 samples analyzed by both the offline tandem SPE and online SPE methods showed 50 to 100 percent agreement in sample detection for overlapping analytes and 68 to 100 percent agreement in a presence-absence comparison for all analytes. The offline tandem and online SPE methods were compared to an independent method that contains two overlapping antibiotic compounds, sulfamethoxazole and trimethoprim, for 96 and 44 environmental samples, respectively. The offline tandem SPE showed 86 and 92 percent agreement in sample detection and 96 and 98 percent agreement in a presence-absence comparison for sulfamethoxazole and trimethoprim, respectively. The online SPE method showed 57 and 56 percent agreement in sample detection and 72 and 91 percent agreement in presence-absence comparison for sulfamethoxazole and trimethoprim, respectively. A linear regression with an R2 of 0.91 was obtained for trimethoprim concentrations, and an R2 of 0.35 was obtained for sulfamethoxazole concentrations determined from samples analyzed by the offline tandem SPE and online SPE methods. Linear regressions of trimethoprim and sulfamethoxazole concentrations determined from samples analyzed by the offline tandem SPE method and the independent M3 pharmaceutical method yielded R2 of 0.95 and 0.87, respectively. Regressed comparison of the offline tandem SPE method to the online SPE and M3 methods showed that the online SPE method gave higher concentrations for sulfamethoxazole and trimethoprim than were obtained from the offline tandem SPE or M3 methods.

Supervised Learning Based on Temporal Coding in Spiking Neural Networks.

PubMed

Mostafa, Hesham

2017-08-01

Gradient descent training techniques are remarkably successful in training analog-valued artificial neural networks (ANNs). Such training techniques, however, do not transfer easily to spiking networks due to the spike generation hard nonlinearity and the discrete nature of spike communication. We show that in a feedforward spiking network that uses a temporal coding scheme where information is encoded in spike times instead of spike rates, the network input-output relation is differentiable almost everywhere. Moreover, this relation is piecewise linear after a transformation of variables. Methods for training ANNs thus carry directly to the training of such spiking networks as we show when training on the permutation invariant MNIST task. In contrast to rate-based spiking networks that are often used to approximate the behavior of ANNs, the networks we present spike much more sparsely and their behavior cannot be directly approximated by conventional ANNs. Our results highlight a new approach for controlling the behavior of spiking networks with realistic temporal dynamics, opening up the potential for using these networks to process spike patterns with complex temporal information.

A method for decoding the neurophysiological spike-response transform.

PubMed

Stern, Estee; García-Crescioni, Keyla; Miller, Mark W; Peskin, Charles S; Brezina, Vladimir

2009-11-15

Many physiological responses elicited by neuronal spikes-intracellular calcium transients, synaptic potentials, muscle contractions-are built up of discrete, elementary responses to each spike. However, the spikes occur in trains of arbitrary temporal complexity, and each elementary response not only sums with previous ones, but can itself be modified by the previous history of the activity. A basic goal in system identification is to characterize the spike-response transform in terms of a small number of functions-the elementary response kernel and additional kernels or functions that describe the dependence on previous history-that will predict the response to any arbitrary spike train. Here we do this by developing further and generalizing the "synaptic decoding" approach of Sen et al. (1996). Given the spike times in a train and the observed overall response, we use least-squares minimization to construct the best estimated response and at the same time best estimates of the elementary response kernel and the other functions that characterize the spike-response transform. We avoid the need for any specific initial assumptions about these functions by using techniques of mathematical analysis and linear algebra that allow us to solve simultaneously for all of the numerical function values treated as independent parameters. The functions are such that they may be interpreted mechanistically. We examine the performance of the method as applied to synthetic data. We then use the method to decode real synaptic and muscle contraction transforms.

SPIKE: AI scheduling techniques for Hubble Space Telescope

NASA Astrophysics Data System (ADS)

Johnston, Mark D.

1991-09-01

AI (Artificial Intelligence) scheduling techniques for HST are presented in the form of the viewgraphs. The following subject areas are covered: domain; HST constraint timescales; HTS scheduling; SPIKE overview; SPIKE architecture; constraint representation and reasoning; use of suitability functions by scheduling agent; SPIKE screen example; advantages of suitability function framework; limiting search and constraint propagation; scheduling search; stochastic search; repair methods; implementation; and status.

A novel method for isolating podocytes using magnetic activated cell sorting.

PubMed

Murakami, Ayumi; Oshiro, Hisashi; Kanzaki, Seiichi; Yamaguchi, Akira; Yamanaka, Shoji; Furuya, Mitsuko; Miura, Satoshi; Kanno, Hiroshi; Nagashima, Yoji; Aoki, Ichiro; Nagahama, Kiyotaka

2010-12-01

A large body of accumulated data has now revealed that podocytes play a major role in the development of proteinuria. However, the mechanisms of podocyte injury, leading to foot process effacement and proteinuria, are still unclear partly due to the current lack of an appropriate strategy for preparing podocytes. In this study, we have developed a novel method of rapid isolation of podocytes from mice using magnetic activated cell sorting with an anti-nephrin antibody. After endothelial cell depletion using anti-CD31 antibody, nephrin-positive cells were prepared from mouse kidneys using magnetic activated cell sorting with polyclonal rabbit anti-nephrin antibody. Purity of the positively sorted cells was determined by confocal microscopy and fluorescence-activated cell sorting (FACS) analysis. Expression profiles of podocyte-specific molecules in the sorted fractions were characterized by qualitative PCR and immunoblot analysis. Nephrin-positive cells, isolated from mouse kidneys within 6 h, showed dual positivity for synaptopodin and rabbit IgG on confocal microscopy. FACS analysis revealed that the purity of the positively sorted fractions was ∼75%. The nephrin-positive cells sorted by this approach showed a significantly higher expression of podocyte-specific molecules compared with nephrin-negative fractions. These data strongly suggest that our novel method for isolating podocytes has great utility for various downstream applications such as genomic analysis, proteomics and transcriptomics to elucidate molecular profiling of podocyte biology in vivo compared with conventional methods as our approach requires only several hours to complete and no tissue culture.

Method for Coating a Tow with an Electrospun Nanofiber

NASA Technical Reports Server (NTRS)

Kohlman, Lee W. (Inventor); Roberts, Gary D. (Inventor)

2015-01-01

Method and apparatus for enhancing the durability as well as the strength and stiffness of prepreg fiber tows of the sort used in composite materials are disclosed. The method involves adhering electrospun fibers onto the surface of such composite materials as filament-wound composite objects and the surface of prepreg fiber tows of the sort that are subsequently used in the production of composite materials of the filament-wound, woven, and braided sorts. The apparatus performs the methods described herein.

Learning Cellular Sorting Pathways Using Protein Interactions and Sequence Motifs

PubMed Central

Lin, Tien-Ho; Bar-Joseph, Ziv

2011-01-01

Abstract Proper subcellular localization is critical for proteins to perform their roles in cellular functions. Proteins are transported by different cellular sorting pathways, some of which take a protein through several intermediate locations until reaching its final destination. The pathway a protein is transported through is determined by carrier proteins that bind to specific sequence motifs. In this article, we present a new method that integrates protein interaction and sequence motif data to model how proteins are sorted through these sorting pathways. We use a hidden Markov model (HMM) to represent protein sorting pathways. The model is able to determine intermediate sorting states and to assign carrier proteins and motifs to the sorting pathways. In simulation studies, we show that the method can accurately recover an underlying sorting model. Using data for yeast, we show that our model leads to accurate prediction of subcellular localization. We also show that the pathways learned by our model recover many known sorting pathways and correctly assign proteins to the path they utilize. The learned model identified new pathways and their putative carriers and motifs and these may represent novel protein sorting mechanisms. Supplementary results and software implementation are available from http://murphylab.web.cmu.edu/software/2010_RECOMB_pathways/. PMID:21999284

A fully automated non-external marker 4D-CT sorting algorithm using a serial cine scanning protocol.

PubMed

Carnes, Greg; Gaede, Stewart; Yu, Edward; Van Dyk, Jake; Battista, Jerry; Lee, Ting-Yim

2009-04-07

Current 4D-CT methods require external marker data to retrospectively sort image data and generate CT volumes. In this work we develop an automated 4D-CT sorting algorithm that performs without the aid of data collected from an external respiratory surrogate. The sorting algorithm requires an overlapping cine scan protocol. The overlapping protocol provides a spatial link between couch positions. Beginning with a starting scan position, images from the adjacent scan position (which spatial match the starting scan position) are selected by maximizing the normalized cross correlation (NCC) of the images at the overlapping slice position. The process was continued by 'daisy chaining' all couch positions using the selected images until an entire 3D volume was produced. The algorithm produced 16 phase volumes to complete a 4D-CT dataset. Additional 4D-CT datasets were also produced using external marker amplitude and phase angle sorting methods. The image quality of the volumes produced by the different methods was quantified by calculating the mean difference of the sorted overlapping slices from adjacent couch positions. The NCC sorted images showed a significant decrease in the mean difference (p < 0.01) for the five patients.

Estimation of parameters in Shot-Noise-Driven Doubly Stochastic Poisson processes using the EM algorithm--modeling of pre- and postsynaptic spike trains.

PubMed

Mino, H

2007-01-01

To estimate the parameters, the impulse response (IR) functions of some linear time-invariant systems generating intensity processes, in Shot-Noise-Driven Doubly Stochastic Poisson Process (SND-DSPP) in which multivariate presynaptic spike trains and postsynaptic spike trains can be assumed to be modeled by the SND-DSPPs. An explicit formula for estimating the IR functions from observations of multivariate input processes of the linear systems and the corresponding counting process (output process) is derived utilizing the expectation maximization (EM) algorithm. The validity of the estimation formula was verified through Monte Carlo simulations in which two presynaptic spike trains and one postsynaptic spike train were assumed to be observable. The IR functions estimated on the basis of the proposed identification method were close to the true IR functions. The proposed method will play an important role in identifying the input-output relationship of pre- and postsynaptic neural spike trains in practical situations.

Perfect Detection of Spikes in the Linear Sub-threshold Dynamics of Point Neurons

PubMed Central

Krishnan, Jeyashree; Porta Mana, PierGianLuca; Helias, Moritz; Diesmann, Markus; Di Napoli, Edoardo

2018-01-01

Spiking neuronal networks are usually simulated with one of three main schemes: the classical time-driven and event-driven schemes, and the more recent hybrid scheme. All three schemes evolve the state of a neuron through a series of checkpoints: equally spaced in the first scheme and determined neuron-wise by spike events in the latter two. The time-driven and the hybrid scheme determine whether the membrane potential of a neuron crosses a threshold at the end of the time interval between consecutive checkpoints. Threshold crossing can, however, occur within the interval even if this test is negative. Spikes can therefore be missed. The present work offers an alternative geometric point of view on neuronal dynamics, and derives, implements, and benchmarks a method for perfect retrospective spike detection. This method can be applied to neuron models with affine or linear subthreshold dynamics. The idea behind the method is to propagate the threshold with a time-inverted dynamics, testing whether the threshold crosses the neuron state to be evolved, rather than vice versa. Algebraically this translates into a set of inequalities necessary and sufficient for threshold crossing. This test is slower than the imperfect one, but can be optimized in several ways. Comparison confirms earlier results that the imperfect tests rarely miss spikes (less than a fraction 1/108 of missed spikes) in biologically relevant settings. PMID:29379430

Data-Driven Significance Estimation for Precise Spike Correlation

PubMed Central

Grün, Sonja

2009-01-01

The mechanisms underlying neuronal coding and, in particular, the role of temporal spike coordination are hotly debated. However, this debate is often confounded by an implicit discussion about the use of appropriate analysis methods. To avoid incorrect interpretation of data, the analysis of simultaneous spike trains for precise spike correlation needs to be properly adjusted to the features of the experimental spike trains. In particular, nonstationarity of the firing of individual neurons in time or across trials, a spike train structure deviating from Poisson, or a co-occurrence of such features in parallel spike trains are potent generators of false positives. Problems can be avoided by including these features in the null hypothesis of the significance test. In this context, the use of surrogate data becomes increasingly important, because the complexity of the data typically prevents analytical solutions. This review provides an overview of the potential obstacles in the correlation analysis of parallel spike data and possible routes to overcome them. The discussion is illustrated at every stage of the argument by referring to a specific analysis tool (the Unitary Events method). The conclusions, however, are of a general nature and hold for other analysis techniques. Thorough testing and calibration of analysis tools and the impact of potentially erroneous preprocessing stages are emphasized. PMID:19129298

Evaluation of motility, membrane status and DNA integrity of frozen-thawed bottlenose dolphin (Tursiops truncatus) spermatozoa after sex-sorting and recryopreservation.

PubMed

Montano, G A; Kraemer, D C; Love, C C; Robeck, T R; O'Brien, J K

2012-06-01

Artificial insemination (AI) with sex-sorted frozen-thawed spermatozoa has led to enhanced management of ex situ bottlenose dolphin populations. Extended distance of animals from the sorting facility can be overcome by the use of frozen-thawed, sorted and recryopreserved spermatozoa. Although one bottlenose dolphin calf had been born using sexed frozen-thawed spermatozoa derived from frozen semen, a critical evaluation of in vitro sperm quality is needed to justify the routine use of such samples in AI programs. Sperm motility parameters and plasma membrane integrity were influenced by stage of the sex-sorting process, sperm type (non-sorted and sorted) and freezing method (straw and directional) (P<0.05). After recryopreservation, sorted spermatozoa frozen with the directional freezing method maintained higher (P<0.05) motility parameters over a 24-h incubation period compared to spermatozoa frozen using straws. Quality of sperm DNA of non-sorted spermatozoa, as assessed by the sperm chromatin structure assay (SCSA), was high and remained unchanged throughout freeze-thawing and incubation processes. Though a possible interaction between Hoechst 33342 and the SCSA-derived acridine orange was observed in stained and sorted samples, the proportion of sex-sorted, recryopreserved spermatozoa exhibiting denatured DNA was low (6.6±4.1%) at 6 h after the second thawing step and remained unchanged (P>0.05) at 24 h. The viability of sorted spermatozoa was higher (P<0.05) than that of non-sorted spermatozoa across all time points after recryopreservation. Collective results indicate that bottlenose dolphin spermatozoa undergoing cryopreservation, sorting and recryopreservation are of adequate quality for use in AI.

Exact simulation of integrate-and-fire models with exponential currents.

PubMed

Brette, Romain

2007-10-01

Neural networks can be simulated exactly using event-driven strategies, in which the algorithm advances directly from one spike to the next spike. It applies to neuron models for which we have (1) an explicit expression for the evolution of the state variables between spikes and (2) an explicit test on the state variables that predicts whether and when a spike will be emitted. In a previous work, we proposed a method that allows exact simulation of an integrate-and-fire model with exponential conductances, with the constraint of a single synaptic time constant. In this note, we propose a method, based on polynomial root finding, that applies to integrate-and-fire models with exponential currents, with possibly many different synaptic time constants. Models can include biexponential synaptic currents and spike-triggered adaptation currents.

Classification of epileptiform and wicket spike of EEG pattern using backpropagation neural network

NASA Astrophysics Data System (ADS)

Puspita, Juni Wijayanti; Jaya, Agus Indra; Gunadharma, Suryani

2017-03-01

Epilepsy is characterized by recurrent seizures that is resulted by permanent brain abnormalities. One of tools to support the diagnosis of epilepsy is Electroencephalograph (EEG), which describes the recording of brain electrical activity. Abnormal EEG patterns in epilepsy patients consist of Spike and Sharp waves. While both waves, there is a normal pattern that sometimes misinterpreted as epileptiform by electroenchepalographer (EEGer), namely Wicket Spike. The main difference of the three waves are on the time duration that related to the frequency. In this study, we proposed a method to classify a EEG wave into Sharp wave, Spike wave or Wicket spike group using Backpropagation Neural Network based on the frequency and amplitude of each wave. The results show that the proposed method can classifies the three group of waves with good accuracy.

The value of magnetoencephalography for seizure-onset zone localization in magnetic resonance imaging-negative partial epilepsy

PubMed Central

Bouet, Romain; Delpuech, Claude; Ryvlin, Philippe; Isnard, Jean; Guenot, Marc; Bertrand, Olivier; Hammers, Alexander; Mauguière, François

2013-01-01

Surgical treatment of epilepsy is a challenge for patients with non-contributive brain magnetic resonance imaging. However, surgery is feasible if the seizure-onset zone is precisely delineated through intracranial electroencephalography recording. We recently described a method, volumetric imaging of epileptic spikes, to delineate the spiking volume of patients with focal epilepsy using magnetoencephalography. We postulated that the extent of the spiking volume delineated with volumetric imaging of epileptic spikes could predict the localizability of the seizure-onset zone by intracranial electroencephalography investigation and outcome of surgical treatment. Twenty-one patients with non-contributive magnetic resonance imaging findings were included. All patients underwent intracerebral electroencephalography investigation through stereotactically implanted depth electrodes (stereo-electroencephalography) and magnetoencephalography with delineation of the spiking volume using volumetric imaging of epileptic spikes. We evaluated the spatial congruence between the spiking volume determined by magnetoencephalography and the localization of the seizure-onset zone determined by stereo-electroencephalography. We also evaluated the outcome of stereo-electroencephalography and surgical treatment according to the extent of the spiking volume (focal, lateralized but non-focal or non-lateralized). For all patients, we found a spatial overlap between the seizure-onset zone and the spiking volume. For patients with a focal spiking volume, the seizure-onset zone defined by stereo-electroencephalography was clearly localized in all cases and most patients (6/7, 86%) had a good surgical outcome. Conversely, stereo-electroencephalography failed to delineate a seizure-onset zone in 57% of patients with a lateralized spiking volume, and in the two patients with bilateral spiking volume. Four of the 12 patients with non-focal spiking volumes were operated upon, none became seizure-free. As a whole, patients having focal magnetoencephalography results with volumetric imaging of epileptic spikes are good surgical candidates and the implantation strategy should incorporate volumetric imaging of epileptic spikes results. On the contrary, patients with non-focal magnetoencephalography results are less likely to have a localized seizure-onset zone and stereo electroencephalography is not advised unless clear localizing information is provided by other presurgical investigation methods. PMID:24014520

The value of magnetoencephalography for seizure-onset zone localization in magnetic resonance imaging-negative partial epilepsy.

PubMed

Jung, Julien; Bouet, Romain; Delpuech, Claude; Ryvlin, Philippe; Isnard, Jean; Guenot, Marc; Bertrand, Olivier; Hammers, Alexander; Mauguière, François

2013-10-01

Surgical treatment of epilepsy is a challenge for patients with non-contributive brain magnetic resonance imaging. However, surgery is feasible if the seizure-onset zone is precisely delineated through intracranial electroencephalography recording. We recently described a method, volumetric imaging of epileptic spikes, to delineate the spiking volume of patients with focal epilepsy using magnetoencephalography. We postulated that the extent of the spiking volume delineated with volumetric imaging of epileptic spikes could predict the localizability of the seizure-onset zone by intracranial electroencephalography investigation and outcome of surgical treatment. Twenty-one patients with non-contributive magnetic resonance imaging findings were included. All patients underwent intracerebral electroencephalography investigation through stereotactically implanted depth electrodes (stereo-electroencephalography) and magnetoencephalography with delineation of the spiking volume using volumetric imaging of epileptic spikes. We evaluated the spatial congruence between the spiking volume determined by magnetoencephalography and the localization of the seizure-onset zone determined by stereo-electroencephalography. We also evaluated the outcome of stereo-electroencephalography and surgical treatment according to the extent of the spiking volume (focal, lateralized but non-focal or non-lateralized). For all patients, we found a spatial overlap between the seizure-onset zone and the spiking volume. For patients with a focal spiking volume, the seizure-onset zone defined by stereo-electroencephalography was clearly localized in all cases and most patients (6/7, 86%) had a good surgical outcome. Conversely, stereo-electroencephalography failed to delineate a seizure-onset zone in 57% of patients with a lateralized spiking volume, and in the two patients with bilateral spiking volume. Four of the 12 patients with non-focal spiking volumes were operated upon, none became seizure-free. As a whole, patients having focal magnetoencephalography results with volumetric imaging of epileptic spikes are good surgical candidates and the implantation strategy should incorporate volumetric imaging of epileptic spikes results. On the contrary, patients with non-focal magnetoencephalography results are less likely to have a localized seizure-onset zone and stereo electroencephalography is not advised unless clear localizing information is provided by other presurgical investigation methods.

On the robustness of EC-PC spike detection method for online neural recording.

PubMed

Zhou, Yin; Wu, Tong; Rastegarnia, Amir; Guan, Cuntai; Keefer, Edward; Yang, Zhi

2014-09-30

Online spike detection is an important step to compress neural data and perform real-time neural information decoding. An unsupervised, automatic, yet robust signal processing is strongly desired, thus it can support a wide range of applications. We have developed a novel spike detection algorithm called "exponential component-polynomial component" (EC-PC) spike detection. We firstly evaluate the robustness of the EC-PC spike detector under different firing rates and SNRs. Secondly, we show that the detection Precision can be quantitatively derived without requiring additional user input parameters. We have realized the algorithm (including training) into a 0.13 μm CMOS chip, where an unsupervised, nonparametric operation has been demonstrated. Both simulated data and real data are used to evaluate the method under different firing rates (FRs), SNRs. The results show that the EC-PC spike detector is the most robust in comparison with some popular detectors. Moreover, the EC-PC detector can track changes in the background noise due to the ability to re-estimate the neural data distribution. Both real and synthesized data have been used for testing the proposed algorithm in comparison with other methods, including the absolute thresholding detector (AT), median absolute deviation detector (MAD), nonlinear energy operator detector (NEO), and continuous wavelet detector (CWD). Comparative testing results reveals that the EP-PC detection algorithm performs better than the other algorithms regardless of recording conditions. The EC-PC spike detector can be considered as an unsupervised and robust online spike detection. It is also suitable for hardware implementation. Copyright © 2014 Elsevier B.V. All rights reserved.

[Method of file sorting for mini- and microcomputers].

PubMed

Chau, N; Legras, B; Benamghar, L; Martin, J

1983-05-01

The authors describe a new sorting method of files which belongs to the class of direct-addressing sorting methods. It makes use of a variant of the classical technique of 'virtual memory'. It is particularly well suited to mini- and micro-computers which have a small core memory (32 K words, for example) and are fitted with a direct-access peripheral device, such as a disc unit. When the file to be sorted is medium-sized (some thousand records), the running of the program essentially occurs inside the core memory and consequently, the method becomes very fast. This is very important because most medical files handled in our laboratory are in this category. However, the method is also suitable for big computers and large files; its implementation is easy. It does not require any magnetic tape unit, and it seems to us to be one of the fastest methods available.

A Quality Sorting of Fruit Using a New Automatic Image Processing Method

NASA Astrophysics Data System (ADS)

Amenomori, Michihiro; Yokomizu, Nobuyuki

This paper presents an innovative approach for quality sorting of objects such as apples sorting in an agricultural factory, using an image processing algorithm. The objective of our approach are; firstly to sort the objects by their colors precisely; secondly to detect any irregularity of the colors surrounding the apples efficiently. An experiment has been conducted and the results have been obtained and compared with that has been preformed by human sorting process and by color sensor sorting devices. The results demonstrate that our approach is capable to sort the objects rapidly and the percentage of classification valid rate was 100 %.

Automated Epileptiform Spike Detection via Affinity Propagation-Based Template Matching

PubMed Central

Thomas, John; Jin, Jing; Dauwels, Justin; Cash, Sydney S.; Westover, M. Brandon

2018-01-01

Interictal epileptiform spikes are the key diagnostic biomarkers for epilepsy. The clinical gold standard of spike detection is visual inspection performed by neurologists. This is a tedious, time-consuming, and expert-centered process. The development of automated spike detection systems is necessary in order to provide a faster and more reliable diagnosis of epilepsy. In this paper, we propose an efficient template matching spike detector based on a combination of spike and background waveform templates. We generate a template library by clustering a collection of spikes and background waveforms extracted from a database of 50 patients with epilepsy. We benchmark the performance of five clustering techniques based on the receiver operating characteristic (ROC) curves. In addition, background templates are integrated with existing spike templates to improve the overall performance. The affinity propagation-based template matching system with a combination of spike and background templates is shown to outperform the other four conventional methods with the highest area-under-curve (AUC) of 0.953. PMID:29060543

Elaboration and validation of the method for the quantification of the emetic toxin of Bacillus cereus as described in EN-ISO 18465 - Microbiology of the food chain - Quantitative determination of emetic toxin (cereulide) using LC-MS/MS.

PubMed

In 't Veld, P H; van der Laak, L F J; van Zon, M; Biesta-Peters, E G

2018-04-12

A method for the quantification of the Bacillus cereus emetic toxin (cereulide) was developed and validated. The method principle is based on LC-MS as this is the most sensitive and specific method for cereulide. Therefore the study design is different from the microbiological methods validated under this mandate. As the method had to be developed a two stage validation study approach was used. The first stage (pre-study) focussed on the method applicability and the experience of the laboratories with the method. Based on the outcome of the pre-study and comments received during voting at CEN and ISO level a final method was agreed to be used for the second stage the (final) validation of the method. In the final (validation) study samples of cooked rice (both artificially contaminated with cereulide or contaminated with B. cereus for production of cereulide in the rice) and 6 other food matrices (fried rice dish, cream pastry with chocolate, hotdog sausage, mini pancakes, vanilla custard and infant formula) were used. All these samples were spiked by the participating laboratories using standard solutions of cereulide supplied by the organising laboratory. The results of the study indicate that the method is fit for purpose. Repeatability values were obtained of 0.6 μg/kg at low level spike (ca. 5 μg/kg) and 7 to 9.6 μg/kg at high level spike (ca. 75 μg/kg). Reproducibility at low spike level ranged from 0.6 to 0.9 μg/kg and from 8.7 to 14.5 μg/kg at high spike level. Recovery from the spiked samples ranged between 96.5% for mini-pancakes to 99.3% for fries rice dish. Copyright © 2018. Published by Elsevier B.V.

Regular Cycles of Forward and Backward Signal Propagation in Prefrontal Cortex and in Consciousness

PubMed Central

Werbos, Paul J.; Davis, Joshua J. J.

2016-01-01

This paper addresses two fundamental questions: (1) Is it possible to develop mathematical neural network models which can explain and replicate the way in which higher-order capabilities like intelligence, consciousness, optimization, and prediction emerge from the process of learning (Werbos, 1994, 2016a; National Science Foundation, 2008)? and (2) How can we use and test such models in a practical way, to track, to analyze and to model high-frequency (≥ 500 hz) many-channel data from recording the brain, just as econometrics sometimes uses models grounded in the theory of efficient markets to track real-world time-series data (Werbos, 1990)? This paper first reviews some of the prior work addressing question (1), and then reports new work performed in MATLAB analyzing spike-sorted and burst-sorted data on the prefrontal cortex from the Buzsaki lab (Fujisawa et al., 2008, 2015) which is consistent with a regular clock cycle of about 153.4 ms and with regular alternation between a forward pass of network calculations and a backwards pass, as in the general form of the backpropagation algorithm which one of us first developed in the period 1968–1974 (Werbos, 1994, 2006; Anderson and Rosenfeld, 1998). In business and finance, it is well known that adjustments for cycles of the year are essential to accurate prediction of time-series data (Box and Jenkins, 1970); in a similar way, methods for identifying and using regular clock cycles offer large new opportunities in neural time-series analysis. This paper demonstrates a few initial footprints on the large “continent” of this type of neural time-series analysis, and discusses a few of the many further possibilities opened up by this new approach to “decoding” the neural code (Heller et al., 1995). PMID:27965547

Regular Cycles of Forward and Backward Signal Propagation in Prefrontal Cortex and in Consciousness.

PubMed

Werbos, Paul J; Davis, Joshua J J

2016-01-01

This paper addresses two fundamental questions: (1) Is it possible to develop mathematical neural network models which can explain and replicate the way in which higher-order capabilities like intelligence, consciousness, optimization, and prediction emerge from the process of learning (Werbos, 1994, 2016a; National Science Foundation, 2008)? and (2) How can we use and test such models in a practical way, to track, to analyze and to model high-frequency (≥ 500 hz) many-channel data from recording the brain, just as econometrics sometimes uses models grounded in the theory of efficient markets to track real-world time-series data (Werbos, 1990)? This paper first reviews some of the prior work addressing question (1), and then reports new work performed in MATLAB analyzing spike-sorted and burst-sorted data on the prefrontal cortex from the Buzsaki lab (Fujisawa et al., 2008, 2015) which is consistent with a regular clock cycle of about 153.4 ms and with regular alternation between a forward pass of network calculations and a backwards pass, as in the general form of the backpropagation algorithm which one of us first developed in the period 1968-1974 (Werbos, 1994, 2006; Anderson and Rosenfeld, 1998). In business and finance, it is well known that adjustments for cycles of the year are essential to accurate prediction of time-series data (Box and Jenkins, 1970); in a similar way, methods for identifying and using regular clock cycles offer large new opportunities in neural time-series analysis. This paper demonstrates a few initial footprints on the large "continent" of this type of neural time-series analysis, and discusses a few of the many further possibilities opened up by this new approach to "decoding" the neural code (Heller et al., 1995).

Multineuron spike train analysis with R-convolution linear combination kernel.

PubMed

Tezuka, Taro

2018-06-01

A spike train kernel provides an effective way of decoding information represented by a spike train. Some spike train kernels have been extended to multineuron spike trains, which are simultaneously recorded spike trains obtained from multiple neurons. However, most of these multineuron extensions were carried out in a kernel-specific manner. In this paper, a general framework is proposed for extending any single-neuron spike train kernel to multineuron spike trains, based on the R-convolution kernel. Special subclasses of the proposed R-convolution linear combination kernel are explored. These subclasses have a smaller number of parameters and make optimization tractable when the size of data is limited. The proposed kernel was evaluated using Gaussian process regression for multineuron spike trains recorded from an animal brain. It was compared with the sum kernel and the population Spikernel, which are existing ways of decoding multineuron spike trains using kernels. The results showed that the proposed approach performs better than these kernels and also other commonly used neural decoding methods. Copyright © 2018 Elsevier Ltd. All rights reserved.

A new method for reporting and interpreting textural composition of spawning gravel.

Treesearch

Fredrick B. Lotspeich; Fred H. Everest

1981-01-01

A new method has been developed for collecting, sorting, and interpreting gravel quality. Samples are collected with a tri-tube freeze-core device and dry-sorted by using sieves based on the Wentworth scale. An index to the quality of gravel is obtained by dividing geometric mean particle size by the sorting coefficient (a measure of the distribution of grain sizes) of...

4D CT sorting based on patient internal anatomy

NASA Astrophysics Data System (ADS)

Li, Ruijiang; Lewis, John H.; Cerviño, Laura I.; Jiang, Steve B.

2009-08-01

Respiratory motion during free-breathing computed tomography (CT) scan may cause significant errors in target definition for tumors in the thorax and upper abdomen. A four-dimensional (4D) CT technique has been widely used for treatment simulation of thoracic and abdominal cancer radiotherapy. The current 4D CT techniques require retrospective sorting of the reconstructed CT slices oversampled at the same couch position. Most sorting methods depend on external surrogates of respiratory motion recorded by extra instruments. However, respiratory signals obtained from these external surrogates may not always accurately represent the internal target motion, especially when irregular breathing patterns occur. We have proposed a new sorting method based on multiple internal anatomical features for multi-slice CT scan acquired in the cine mode. Four features are analyzed in this study, including the air content, lung area, lung density and body area. We use a measure called spatial coherence to select the optimal internal feature at each couch position and to generate the respiratory signals for 4D CT sorting. The proposed method has been evaluated for ten cancer patients (eight with thoracic cancer and two with abdominal cancer). For nine patients, the respiratory signals generated from the combined internal features are well correlated to those from external surrogates recorded by the real-time position management (RPM) system (average correlation: 0.95 ± 0.02), which is better than any individual internal measures at 95% confidence level. For these nine patients, the 4D CT images sorted by the combined internal features are almost identical to those sorted by the RPM signal. For one patient with an irregular breathing pattern, the respiratory signals given by the combined internal features do not correlate well with those from RPM (correlation: 0.68 ± 0.42). In this case, the 4D CT image sorted by our method presents fewer artifacts than that from the RPM signal. Our 4D CT internal sorting method eliminates the need of externally recorded surrogates of respiratory motion. It is an automatic, accurate, robust, cost efficient and yet simple method and therefore can be readily implemented in clinical settings.

The effect of spiked boots on logger safety, productivity and workload.

PubMed

Kirk, P; Parker, R

1994-04-01

Analysis of 1657 lost-time logging accidents in the New Zealand logging industry (1985-1991) indicates that 17.5% were as a result of slips, trips and falls and a total of 2870 days were lost. Most (56%) of these slipping, tripping and falling accidents occurred in the felling and delimbing phase of the logging operation, where 37% of the workforce are employed. In an attempt to reduce the number of slipping injuries to loggers employed in felling and delimbing, a study of the effectiveness of spike-soled (caulk) boots was undertaken. Four loggers were intensively observed at work, by continuous time-study methods, while wearing their conventional rubber-soled boots and then spike-soled boots. The number of slips, work methods used, physiological workload and productivity were compared for loggers wearing the two footwear types. Results indicated that spike-soled boots were associated with a significant reduction in the frequency of slips and had no adverse effect on work methods, physiological workload or productivity. Spike-soled boots are now being promoted for use by loggers in New Zealand as a simple method to reduce slipping, tripping and falling accidents.

The Equivalence of Information-Theoretic and Likelihood-Based Methods for Neural Dimensionality Reduction

PubMed Central

Williamson, Ross S.; Sahani, Maneesh; Pillow, Jonathan W.

2015-01-01

Stimulus dimensionality-reduction methods in neuroscience seek to identify a low-dimensional space of stimulus features that affect a neuron’s probability of spiking. One popular method, known as maximally informative dimensions (MID), uses an information-theoretic quantity known as “single-spike information” to identify this space. Here we examine MID from a model-based perspective. We show that MID is a maximum-likelihood estimator for the parameters of a linear-nonlinear-Poisson (LNP) model, and that the empirical single-spike information corresponds to the normalized log-likelihood under a Poisson model. This equivalence implies that MID does not necessarily find maximally informative stimulus dimensions when spiking is not well described as Poisson. We provide several examples to illustrate this shortcoming, and derive a lower bound on the information lost when spiking is Bernoulli in discrete time bins. To overcome this limitation, we introduce model-based dimensionality reduction methods for neurons with non-Poisson firing statistics, and show that they can be framed equivalently in likelihood-based or information-theoretic terms. Finally, we show how to overcome practical limitations on the number of stimulus dimensions that MID can estimate by constraining the form of the non-parametric nonlinearity in an LNP model. We illustrate these methods with simulations and data from primate visual cortex. PMID:25831448

A single-cell resolution map of mouse hematopoietic stem and progenitor cell differentiation.

PubMed

Nestorowa, Sonia; Hamey, Fiona K; Pijuan Sala, Blanca; Diamanti, Evangelia; Shepherd, Mairi; Laurenti, Elisa; Wilson, Nicola K; Kent, David G; Göttgens, Berthold

2016-08-25

Maintenance of the blood system requires balanced cell fate decisions by hematopoietic stem and progenitor cells (HSPCs). Because cell fate choices are executed at the individual cell level, new single-cell profiling technologies offer exciting possibilities for mapping the dynamic molecular changes underlying HSPC differentiation. Here, we have used single-cell RNA sequencing to profile more than 1600 single HSPCs, and deep sequencing has enabled detection of an average of 6558 protein-coding genes per cell. Index sorting, in combination with broad sorting gates, allowed us to retrospectively assign cells to 12 commonly sorted HSPC phenotypes while also capturing intermediate cells typically excluded by conventional gating. We further show that independently generated single-cell data sets can be projected onto the single-cell resolution expression map to directly compare data from multiple groups and to build and refine new hypotheses. Reconstruction of differentiation trajectories reveals dynamic expression changes associated with early lymphoid, erythroid, and granulocyte-macrophage differentiation. The latter two trajectories were characterized by common upregulation of cell cycle and oxidative phosphorylation transcriptional programs. By using external spike-in controls, we estimate absolute messenger RNA (mRNA) levels per cell, showing for the first time that despite a general reduction in total mRNA, a subset of genes shows higher expression levels in immature stem cells consistent with active maintenance of the stem-cell state. Finally, we report the development of an intuitive Web interface as a new community resource to permit visualization of gene expression in HSPCs at single-cell resolution for any gene of choice. © 2016 by The American Society of Hematology.

Importance of Neutralizing Monoclonal Antibodies Targeting Multiple Antigenic Sites on the Middle East Respiratory Syndrome Coronavirus Spike Glycoprotein To Avoid Neutralization Escape

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

Wang, Lingshu; Shi, Wei; Chappell, James D.

ABSTRACT Middle East respiratory syndrome coronavirus (MERS-CoV) causes a highly lethal pulmonary infection with ~35% mortality. The potential for a future pandemic originating from animal reservoirs or health care-associated events is a major public health concern. There are no vaccines or therapeutic agents currently available for MERS-CoV. Using a probe-based single B cell cloning strategy, we have identified and characterized multiple neutralizing monoclonal antibodies (MAbs) specifically binding to the receptor-binding domain (RBD) or S1 (non-RBD) regions from a convalescent MERS-CoV-infected patient and from immunized rhesus macaques. RBD-specific MAbs tended to have greater neutralizing potency than non-RBD S1-specific MAbs. Six RBD-specificmore » and five S1-specific MAbs could be sorted into four RBD and three non-RBD distinct binding patterns, based on competition assays, mapping neutralization escape variants, and structural analysis. We determined cocrystal structures for two MAbs targeting the RBD from different angles and show they can bind the RBD only in the “out” position. We then showed that selected RBD-specific, non-RBD S1-specific, and S2-specific MAbs given prophylactically prevented MERS-CoV replication in lungs and protected mice from lethal challenge. Importantly, combining RBD- and non-RBD MAbs delayed the emergence of escape mutations in a cell-based virus escape assay. These studies identify MAbs targeting different antigenic sites on S that will be useful for defining mechanisms of MERS-CoV neutralization and for developing more effective interventions to prevent or treat MERS-CoV infections. IMPORTANCEMERS-CoV causes a highly lethal respiratory infection for which no vaccines or antiviral therapeutic options are currently available. Based on continuing exposure from established reservoirs in dromedary camels and bats, transmission of MERS-CoV into humans and future outbreaks are expected. Using structurally defined probes for the MERS-CoV spike glycoprotein (S), the target for neutralizing antibodies, single B cells were sorted from a convalescent human and immunized nonhuman primates (NHPs). MAbs produced from paired immunoglobulin gene sequences were mapped to multiple epitopes within and outside the receptor-binding domain (RBD) and protected against lethal MERS infection in a murine model following passive immunization. Importantly, combining MAbs targeting distinct epitopes prevented viral neutralization escape from RBD-directed MAbs. These data suggest that antibody responses to multiple domains on CoV spike protein may improve immunity and will guide future vaccine and therapeutic development efforts.« less

Fast fMRI provides high statistical power in the analysis of epileptic networks.

PubMed

Jacobs, Julia; Stich, Julia; Zahneisen, Benjamin; Assländer, Jakob; Ramantani, Georgia; Schulze-Bonhage, Andreas; Korinthenberg, Rudolph; Hennig, Jürgen; LeVan, Pierre

2014-03-01

EEG-fMRI is a unique method to combine the high temporal resolution of EEG with the high spatial resolution of MRI to study generators of intrinsic brain signals such as sleep grapho-elements or epileptic spikes. While the standard EPI sequence in fMRI experiments has a temporal resolution of around 2.5-3s a newly established fast fMRI sequence called MREG (Magnetic-Resonance-Encephalography) provides a temporal resolution of around 100ms. This technical novelty promises to improve statistics, facilitate correction of physiological artifacts and improve the understanding of epileptic networks in fMRI. The present study compares simultaneous EEG-EPI and EEG-MREG analyzing epileptic spikes to determine the yield of fast MRI in the analysis of intrinsic brain signals. Patients with frequent interictal spikes (>3/20min) underwent EEG-MREG and EEG-EPI (3T, 20min each, voxel size 3×3×3mm, EPI TR=2.61s, MREG TR=0.1s). Timings of the spikes were used in an event-related analysis to generate activation maps of t-statistics. (FMRISTAT, |t|>3.5, cluster size: 7 voxels, p<0.05 corrected). For both sequences, the amplitude and location of significant BOLD activations were compared with the spike topography. 13 patients were recorded and 33 different spike types could be analyzed. Peak T-values were significantly higher in MREG than in EPI (p<0.0001). Positive BOLD effects correlating with the spike topography were found in 8/29 spike types using the EPI and in 22/33 spikes types using the MREG sequence. Negative BOLD responses in the default mode network could be observed in 3/29 spike types with the EPI and in 19/33 with the MREG sequence. With the latter method, BOLD changes were observed even when few spikes occurred during the investigation. Simultaneous EEG-MREG thus is possible with good EEG quality and shows higher sensitivity in regard to the localization of spike-related BOLD responses than EEG-EPI. The development of new methods of analysis for this sequence such as modeling of physiological noise, temporal analysis of the BOLD signal and defining appropriate thresholds is required to fully profit from its high temporal resolution. © 2013.

Application of Quality Function Deployment (QFD) method and kano model to redesign fresh fruit bunches sorting tool

NASA Astrophysics Data System (ADS)

Anizar; Siregar, I.; Yahya, I.; Yesika, N.

2018-02-01

The activity of lowering fresh fruit bunches (FFB) from truck to sorting floor is performed manually by workers using a sorting tool. Previously, the sorting tool used is a pointed iron bar with a T-shaped handle. Changes made to the sorting tool causes several complaints on worker and affect the time to lower the fruit. The purpose of this article is to obtain the design of an FFB sorting tool that suits the needs of these workers by applying the Quality Function Deployment (QFD) and Kano Model methods. Both of the two methods will be integrated to find the design that matches workers’ image and psychological feeling. The main parameters are to obtain the customer requirements of the palm fruit loading workers, to find the most important technical characteristics and critical part affecting the quality of the FFB sorting tool. The customer requirements of the palm loading workers are the following : the color of the coating paint is gray, the bar material is made of stainless pipe, the main grip coating material is made of grip, the tip material is made of the spring steel, the additional grip is made of rubber and the handle is of triangular shape.

Training Deep Spiking Neural Networks Using Backpropagation.

PubMed

Lee, Jun Haeng; Delbruck, Tobi; Pfeiffer, Michael

2016-01-01

Deep spiking neural networks (SNNs) hold the potential for improving the latency and energy efficiency of deep neural networks through data-driven event-based computation. However, training such networks is difficult due to the non-differentiable nature of spike events. In this paper, we introduce a novel technique, which treats the membrane potentials of spiking neurons as differentiable signals, where discontinuities at spike times are considered as noise. This enables an error backpropagation mechanism for deep SNNs that follows the same principles as in conventional deep networks, but works directly on spike signals and membrane potentials. Compared with previous methods relying on indirect training and conversion, our technique has the potential to capture the statistics of spikes more precisely. We evaluate the proposed framework on artificially generated events from the original MNIST handwritten digit benchmark, and also on the N-MNIST benchmark recorded with an event-based dynamic vision sensor, in which the proposed method reduces the error rate by a factor of more than three compared to the best previous SNN, and also achieves a higher accuracy than a conventional convolutional neural network (CNN) trained and tested on the same data. We demonstrate in the context of the MNIST task that thanks to their event-driven operation, deep SNNs (both fully connected and convolutional) trained with our method achieve accuracy equivalent with conventional neural networks. In the N-MNIST example, equivalent accuracy is achieved with about five times fewer computational operations.

A method for decoding the neurophysiological spike-response transform

PubMed Central

Stern, Estee; García-Crescioni, Keyla; Miller, Mark W.; Peskin, Charles S.; Brezina, Vladimir

2009-01-01

Many physiological responses elicited by neuronal spikes—intracellular calcium transients, synaptic potentials, muscle contractions—are built up of discrete, elementary responses to each spike. However, the spikes occur in trains of arbitrary temporal complexity, and each elementary response not only sums with previous ones, but can itself be modified by the previous history of the activity. A basic goal in system identification is to characterize the spike-response transform in terms of a small number of functions—the elementary response kernel and additional kernels or functions that describe the dependence on previous history—that will predict the response to any arbitrary spike train. Here we do this by developing further and generalizing the “synaptic decoding” approach of Sen et al. (J Neurosci 16:6307-6318, 1996). Given the spike times in a train and the observed overall response, we use least-squares minimization to construct the best estimated response and at the same time best estimates of the elementary response kernel and the other functions that characterize the spike-response transform. We avoid the need for any specific initial assumptions about these functions by using techniques of mathematical analysis and linear algebra that allow us to solve simultaneously for all of the numerical function values treated as independent parameters. The functions are such that they may be interpreted mechanistically. We examine the performance of the method as applied to synthetic data. We then use the method to decode real synaptic and muscle contraction transforms. PMID:19695289

Model-based decoding, information estimation, and change-point detection techniques for multineuron spike trains.

PubMed

Pillow, Jonathan W; Ahmadian, Yashar; Paninski, Liam

2011-01-01

One of the central problems in systems neuroscience is to understand how neural spike trains convey sensory information. Decoding methods, which provide an explicit means for reading out the information contained in neural spike responses, offer a powerful set of tools for studying the neural coding problem. Here we develop several decoding methods based on point-process neural encoding models, or forward models that predict spike responses to stimuli. These models have concave log-likelihood functions, which allow efficient maximum-likelihood model fitting and stimulus decoding. We present several applications of the encoding model framework to the problem of decoding stimulus information from population spike responses: (1) a tractable algorithm for computing the maximum a posteriori (MAP) estimate of the stimulus, the most probable stimulus to have generated an observed single- or multiple-neuron spike train response, given some prior distribution over the stimulus; (2) a gaussian approximation to the posterior stimulus distribution that can be used to quantify the fidelity with which various stimulus features are encoded; (3) an efficient method for estimating the mutual information between the stimulus and the spike trains emitted by a neural population; and (4) a framework for the detection of change-point times (the time at which the stimulus undergoes a change in mean or variance) by marginalizing over the posterior stimulus distribution. We provide several examples illustrating the performance of these estimators with simulated and real neural data.

Analysis of Proportional Integral and Optimized Proportional Integral Controllers for Resistance Spot Welding System (RSWS) - A Performance Perspective

NASA Astrophysics Data System (ADS)

Rama Subbanna, S.; Suryakalavathi, M., Dr.

2017-08-01

This paper is an attempt to accomplish a performance analysis of the different control techniques on spikes reduction method applied on the medium frequency transformer based DC spot welding system. Spike reduction is an important factor to be considered while spot welding systems are concerned. During normal RSWS operation welding transformer’s magnetic core can become saturated due to the unbalanced resistances of both transformer secondary windings and different characteristics of output rectifier diodes, which causes current spikes and over-current protection switch-off of the entire system. The current control technique is a piecewise linear control technique that is inspired from the DC-DC converter control algorithms to register a novel spike reduction method in the MFDC spot welding applications. Two controllers that were used for the spike reduction portion of the overall applications involve the traditional PI controller and Optimized PI controller. Care is taken such that the current control technique would maintain a reduced spikes in the primary current of the transformer while it reduces the Total Harmonic Distortion. The performance parameter that is involved in the spikes reduction technique is the THD, Percentage of current spike reduction for both techniques. Matlab/SimulinkTM based simulation is carried out for the MFDC RSWS with KW and results are tabulated for the PI and Optimized PI controllers and a tradeoff analysis is carried out.

Frequency and time properties of decimeter narrowband spikes in solar flares

NASA Astrophysics Data System (ADS)

Wang, Shujuan

2013-07-01

In this paper, we focus to study the frequency and time properties of a group of spikes recorded by the 1.08-2.04 GHz spectrometer of NAOC on 27 October 2003. At the first we calculate the mean and minimum bandwidth of the spikes. We apply two different methods based on the wavelet analysis according to Messmer & Benz (2000). The first method determines the dominant spike bandwidth scale based on their scalegram, and the second method is a feature detection algorithm in the time-frequency plane. Secondly the time profile of each single spike was fitted and analyzed. In particular, we determined the e-folding rise and decay times corresponding to the ascending and decaying parts of the time profile, respectively. Several important correlations were studied and compared with the results in earlier literature, i.e. those between duration and frequency, e-folding rise time and decay time, e-folding decay time and duration, and e-folding decay time and peak flux. Finally some parameters of source region were estimated and the possible decaying mechanism was discussed.

ASSET: Analysis of Sequences of Synchronous Events in Massively Parallel Spike Trains

PubMed Central

Canova, Carlos; Denker, Michael; Gerstein, George; Helias, Moritz

2016-01-01

With the ability to observe the activity from large numbers of neurons simultaneously using modern recording technologies, the chance to identify sub-networks involved in coordinated processing increases. Sequences of synchronous spike events (SSEs) constitute one type of such coordinated spiking that propagates activity in a temporally precise manner. The synfire chain was proposed as one potential model for such network processing. Previous work introduced a method for visualization of SSEs in massively parallel spike trains, based on an intersection matrix that contains in each entry the degree of overlap of active neurons in two corresponding time bins. Repeated SSEs are reflected in the matrix as diagonal structures of high overlap values. The method as such, however, leaves the task of identifying these diagonal structures to visual inspection rather than to a quantitative analysis. Here we present ASSET (Analysis of Sequences of Synchronous EvenTs), an improved, fully automated method which determines diagonal structures in the intersection matrix by a robust mathematical procedure. The method consists of a sequence of steps that i) assess which entries in the matrix potentially belong to a diagonal structure, ii) cluster these entries into individual diagonal structures and iii) determine the neurons composing the associated SSEs. We employ parallel point processes generated by stochastic simulations as test data to demonstrate the performance of the method under a wide range of realistic scenarios, including different types of non-stationarity of the spiking activity and different correlation structures. Finally, the ability of the method to discover SSEs is demonstrated on complex data from large network simulations with embedded synfire chains. Thus, ASSET represents an effective and efficient tool to analyze massively parallel spike data for temporal sequences of synchronous activity. PMID:27420734

Developments in label-free microfluidic methods for single-cell analysis and sorting.

PubMed

Carey, Thomas R; Cotner, Kristen L; Li, Brian; Sohn, Lydia L

2018-04-24

Advancements in microfluidic technologies have led to the development of many new tools for both the characterization and sorting of single cells without the need for exogenous labels. Label-free microfluidics reduce the preparation time, reagents needed, and cost of conventional methods based on fluorescent or magnetic labels. Furthermore, these devices enable analysis of cell properties such as mechanical phenotype and dielectric parameters that cannot be characterized with traditional labels. Some of the most promising technologies for current and future development toward label-free, single-cell analysis and sorting include electronic sensors such as Coulter counters and electrical impedance cytometry; deformation analysis using optical traps and deformation cytometry; hydrodynamic sorting such as deterministic lateral displacement, inertial focusing, and microvortex trapping; and acoustic sorting using traveling or standing surface acoustic waves. These label-free microfluidic methods have been used to screen, sort, and analyze cells for a wide range of biomedical and clinical applications, including cell cycle monitoring, rapid complete blood counts, cancer diagnosis, metastatic progression monitoring, HIV and parasite detection, circulating tumor cell isolation, and point-of-care diagnostics. Because of the versatility of label-free methods for characterization and sorting, the low-cost nature of microfluidics, and the rapid prototyping capabilities of modern microfabrication, we expect this class of technology to continue to be an area of high research interest going forward. New developments in this field will contribute to the ongoing paradigm shift in cell analysis and sorting technologies toward label-free microfluidic devices, enabling new capabilities in biomedical research tools as well as clinical diagnostics. This article is categorized under: Diagnostic Tools > Biosensing Diagnostic Tools > Diagnostic Nanodevices. © 2018 Wiley Periodicals, Inc.

Solar flare hard X-ray spikes observed by RHESSI: a statistical study

NASA Astrophysics Data System (ADS)

Cheng, J. X.; Qiu, J.; Ding, M. D.; Wang, H.

2012-11-01

Context. Hard X-ray (HXR) spikes refer to fine time structures on timescales of seconds to milliseconds in high-energy HXR emission profiles during solar flare eruptions. Aims: We present a preliminary statistical investigation of temporal and spectral properties of HXR spikes. Methods: Using a three-sigma spike selection rule, we detected 184 spikes in 94 out of 322 flares with significant counts at given photon energies, which were detected from demodulated HXR light curves obtained by the Reuven Ramaty High Energy Solar Spectroscopic Imager (RHESSI). About one fifth of these spikes are also detected at photon energies higher than 100 keV. Results: The statistical properties of the spikes are as follows. (1) HXR spikes are produced in both impulsive flares and long-duration flares with nearly the same occurrence rates. Ninety percent of the spikes occur during the rise phase of the flares, and about 70% occur around the peak times of the flares. (2) The time durations of the spikes vary from 0.2 to 2 s, with the mean being 1.0 s, which is not dependent on photon energies. The spikes exhibit symmetric time profiles with no significant difference between rise and decay times. (3) Among the most energetic spikes, nearly all of them have harder count spectra than their underlying slow-varying components. There is also a weak indication that spikes exhibiting time lags in high-energy emissions tend to have harder spectra than spikes with time lags in low-energy emissions.

DL-ReSuMe: A Delay Learning-Based Remote Supervised Method for Spiking Neurons.

PubMed

Taherkhani, Aboozar; Belatreche, Ammar; Li, Yuhua; Maguire, Liam P

2015-12-01

Recent research has shown the potential capability of spiking neural networks (SNNs) to model complex information processing in the brain. There is biological evidence to prove the use of the precise timing of spikes for information coding. However, the exact learning mechanism in which the neuron is trained to fire at precise times remains an open problem. The majority of the existing learning methods for SNNs are based on weight adjustment. However, there is also biological evidence that the synaptic delay is not constant. In this paper, a learning method for spiking neurons, called delay learning remote supervised method (DL-ReSuMe), is proposed to merge the delay shift approach and ReSuMe-based weight adjustment to enhance the learning performance. DL-ReSuMe uses more biologically plausible properties, such as delay learning, and needs less weight adjustment than ReSuMe. Simulation results have shown that the proposed DL-ReSuMe approach achieves learning accuracy and learning speed improvements compared with ReSuMe.

A QR code identification technology in package auto-sorting system

NASA Astrophysics Data System (ADS)

di, Yi-Juan; Shi, Jian-Ping; Mao, Guo-Yong

2017-07-01

Traditional manual sorting operation is not suitable for the development of Chinese logistics. For better sorting packages, a QR code recognition technology is proposed to identify the QR code label on the packages in package auto-sorting system. The experimental results compared with other algorithms in literatures demonstrate that the proposed method is valid and its performance is superior to other algorithms.

Statistical evaluation of synchronous spike patterns extracted by frequent item set mining

PubMed Central

Torre, Emiliano; Picado-Muiño, David; Denker, Michael; Borgelt, Christian; Grün, Sonja

2013-01-01

We recently proposed frequent itemset mining (FIM) as a method to perform an optimized search for patterns of synchronous spikes (item sets) in massively parallel spike trains. This search outputs the occurrence count (support) of individual patterns that are not trivially explained by the counts of any superset (closed frequent item sets). The number of patterns found by FIM makes direct statistical tests infeasible due to severe multiple testing. To overcome this issue, we proposed to test the significance not of individual patterns, but instead of their signatures, defined as the pairs of pattern size z and support c. Here, we derive in detail a statistical test for the significance of the signatures under the null hypothesis of full independence (pattern spectrum filtering, PSF) by means of surrogate data. As a result, injected spike patterns that mimic assembly activity are well detected, yielding a low false negative rate. However, this approach is prone to additionally classify patterns resulting from chance overlap of real assembly activity and background spiking as significant. These patterns represent false positives with respect to the null hypothesis of having one assembly of given signature embedded in otherwise independent spiking activity. We propose the additional method of pattern set reduction (PSR) to remove these false positives by conditional filtering. By employing stochastic simulations of parallel spike trains with correlated activity in form of injected spike synchrony in subsets of the neurons, we demonstrate for a range of parameter settings that the analysis scheme composed of FIM, PSF and PSR allows to reliably detect active assemblies in massively parallel spike trains. PMID:24167487

Improved selenium recovery from tissue with modified sample decomposition

USGS Publications Warehouse

Brumbaugh, W. G.; Walther, M.J.

1991-01-01

The present paper describes a simple modification of a recently reported decomposition method for determination of selenium in biological tissue by hydride generation atomic absorption. The modified method yielded slightly higher selenium recoveries (3-4%) for selected reference tissues and fish tissue spiked with selenomethionine. Radiotracer experiments indicated that the addition of a small volume of hydrochloric acid to the wet digestate mixture reduced slight losses of selenium as the sample initially went to dryness before ashing. With the modified method, selenium spiked as selenomethionine behaved more like the selenium in reference tissues than did the inorganic spike forms when this digestion modification was used.

Rapid detection of Salmonella spp. in food by use of the ISO-GRID hydrophobic grid membrane filter.

PubMed Central

Entis, P; Brodsky, M H; Sharpe, A N; Jarvis, G A

1982-01-01

A rapid hydrophobic grid-membrane filter (HGMF) method was developed and compared with the Health Protection Branch cultural method for the detection of Salmonella spp. in 798 spiked samples and 265 naturally contaminated samples of food. With the HGMF method, Salmonella spp. were isolated from 618 of the spiked samples and 190 of the naturally contaminated samples. The conventional method recovered Salmonella spp. from 622 spiked samples and 204 unspiked samples. The isolation rates from Salmonella-positive samples for the two methods were not significantly different (94.6% overall for the HGMF method and 96.7% for the conventional approach), but the HGMF results were available in only 2 to 3 days after sample receipt compared with 3 to 4 days by the conventional method. Images PMID:7059168

Using Matrix and Tensor Factorizations for the Single-Trial Analysis of Population Spike Trains.

PubMed

Onken, Arno; Liu, Jian K; Karunasekara, P P Chamanthi R; Delis, Ioannis; Gollisch, Tim; Panzeri, Stefano

2016-11-01

Advances in neuronal recording techniques are leading to ever larger numbers of simultaneously monitored neurons. This poses the important analytical challenge of how to capture compactly all sensory information that neural population codes carry in their spatial dimension (differences in stimulus tuning across neurons at different locations), in their temporal dimension (temporal neural response variations), or in their combination (temporally coordinated neural population firing). Here we investigate the utility of tensor factorizations of population spike trains along space and time. These factorizations decompose a dataset of single-trial population spike trains into spatial firing patterns (combinations of neurons firing together), temporal firing patterns (temporal activation of these groups of neurons) and trial-dependent activation coefficients (strength of recruitment of such neural patterns on each trial). We validated various factorization methods on simulated data and on populations of ganglion cells simultaneously recorded in the salamander retina. We found that single-trial tensor space-by-time decompositions provided low-dimensional data-robust representations of spike trains that capture efficiently both their spatial and temporal information about sensory stimuli. Tensor decompositions with orthogonality constraints were the most efficient in extracting sensory information, whereas non-negative tensor decompositions worked well even on non-independent and overlapping spike patterns, and retrieved informative firing patterns expressed by the same population in response to novel stimuli. Our method showed that populations of retinal ganglion cells carried information in their spike timing on the ten-milliseconds-scale about spatial details of natural images. This information could not be recovered from the spike counts of these cells. First-spike latencies carried the majority of information provided by the whole spike train about fine-scale image features, and supplied almost as much information about coarse natural image features as firing rates. Together, these results highlight the importance of spike timing, and particularly of first-spike latencies, in retinal coding.

Using Matrix and Tensor Factorizations for the Single-Trial Analysis of Population Spike Trains

PubMed Central

Onken, Arno; Liu, Jian K.; Karunasekara, P. P. Chamanthi R.; Delis, Ioannis; Gollisch, Tim; Panzeri, Stefano

2016-01-01

Advances in neuronal recording techniques are leading to ever larger numbers of simultaneously monitored neurons. This poses the important analytical challenge of how to capture compactly all sensory information that neural population codes carry in their spatial dimension (differences in stimulus tuning across neurons at different locations), in their temporal dimension (temporal neural response variations), or in their combination (temporally coordinated neural population firing). Here we investigate the utility of tensor factorizations of population spike trains along space and time. These factorizations decompose a dataset of single-trial population spike trains into spatial firing patterns (combinations of neurons firing together), temporal firing patterns (temporal activation of these groups of neurons) and trial-dependent activation coefficients (strength of recruitment of such neural patterns on each trial). We validated various factorization methods on simulated data and on populations of ganglion cells simultaneously recorded in the salamander retina. We found that single-trial tensor space-by-time decompositions provided low-dimensional data-robust representations of spike trains that capture efficiently both their spatial and temporal information about sensory stimuli. Tensor decompositions with orthogonality constraints were the most efficient in extracting sensory information, whereas non-negative tensor decompositions worked well even on non-independent and overlapping spike patterns, and retrieved informative firing patterns expressed by the same population in response to novel stimuli. Our method showed that populations of retinal ganglion cells carried information in their spike timing on the ten-milliseconds-scale about spatial details of natural images. This information could not be recovered from the spike counts of these cells. First-spike latencies carried the majority of information provided by the whole spike train about fine-scale image features, and supplied almost as much information about coarse natural image features as firing rates. Together, these results highlight the importance of spike timing, and particularly of first-spike latencies, in retinal coding. PMID:27814363

Viable cell sorting of dinoflagellates by multi-parametric flow cytometry.

USDA-ARS?s Scientific Manuscript database

Electronic cell sorting for isolation and culture of dinoflagellates and other marine eukaryotic phytoplankton was compared to the traditional method of manually picking of cells using a micropipette. Trauma to electronically sorted cells was not a limiting factor as fragile dinoflagellates, such a...

Magnetoencephalographic Mapping of Epileptic Spike Population Using Distributed Source Analysis: Comparison With Intracranial Electroencephalographic Spikes.

PubMed

Tanaka, Naoaki; Papadelis, Christos; Tamilia, Eleonora; Madsen, Joseph R; Pearl, Phillip L; Stufflebeam, Steven M

2018-04-27

This study evaluates magnetoencephalographic (MEG) spike population as compared with intracranial electroencephalographic (IEEG) spikes using a quantitative method based on distributed source analysis. We retrospectively studied eight patients with medically intractable epilepsy who had an MEG and subsequent IEEG monitoring. Fifty MEG spikes were analyzed in each patient using minimum norm estimate. For individual spikes, each vertex in the source space was considered activated when its source amplitude at the peak latency was higher than a threshold, which was set at 50% of the maximum amplitude over all vertices. We mapped the total count of activation at each vertex. We also analyzed 50 IEEG spikes in the same manner over the intracranial electrodes and created the activation count map. The location of the electrodes was obtained in the MEG source space by coregistering postimplantation computed tomography to MRI. We estimated the MEG- and IEEG-active regions associated with the spike populations using the vertices/electrodes with a count over 25. The activation count maps of MEG spikes demonstrated the localization associated with the spike population by variable count values at each vertex. The MEG-active region overlapped with 65 to 85% of the IEEG-active region in our patient group. Mapping the MEG spike population is valid for demonstrating the trend of spikes clustering in patients with epilepsy. In addition, comparison of MEG and IEEG spikes quantitatively may be informative for understanding their relationship.

When the Ostrich-Algorithm Fails: Blanking Method Affects Spike Train Statistics.

PubMed

Joseph, Kevin; Mottaghi, Soheil; Christ, Olaf; Feuerstein, Thomas J; Hofmann, Ulrich G

2018-01-01

Modern electroceuticals are bound to employ the usage of electrical high frequency (130-180 Hz) stimulation carried out under closed loop control, most prominent in the case of movement disorders. However, particular challenges are faced when electrical recordings of neuronal tissue are carried out during high frequency electrical stimulation, both in-vivo and in-vitro . This stimulation produces undesired artifacts and can render the recorded signal only partially useful. The extent of these artifacts is often reduced by temporarily grounding the recording input during stimulation pulses. In the following study, we quantify the effects of this method, "blanking," on the spike count and spike train statistics. Starting from a theoretical standpoint, we calculate a loss in the absolute number of action potentials, depending on: width of the blanking window, frequency of stimulation, and intrinsic neuronal activity. These calculations were then corroborated by actual high signal to noise ratio (SNR) single cell recordings. We state that, for clinically relevant frequencies of 130 Hz (used for movement disorders) and realistic blanking windows of 2 ms, up to 27% of actual existing spikes are lost. We strongly advice cautioned use of the blanking method when spike rate quantification is attempted. Blanking (artifact removal by temporarily grounding input), depending on recording parameters, can lead to significant spike loss. Very careful use of blanking circuits is advised.

When the Ostrich-Algorithm Fails: Blanking Method Affects Spike Train Statistics

PubMed Central

Joseph, Kevin; Mottaghi, Soheil; Christ, Olaf; Feuerstein, Thomas J.; Hofmann, Ulrich G.

2018-01-01

Modern electroceuticals are bound to employ the usage of electrical high frequency (130–180 Hz) stimulation carried out under closed loop control, most prominent in the case of movement disorders. However, particular challenges are faced when electrical recordings of neuronal tissue are carried out during high frequency electrical stimulation, both in-vivo and in-vitro. This stimulation produces undesired artifacts and can render the recorded signal only partially useful. The extent of these artifacts is often reduced by temporarily grounding the recording input during stimulation pulses. In the following study, we quantify the effects of this method, “blanking,” on the spike count and spike train statistics. Starting from a theoretical standpoint, we calculate a loss in the absolute number of action potentials, depending on: width of the blanking window, frequency of stimulation, and intrinsic neuronal activity. These calculations were then corroborated by actual high signal to noise ratio (SNR) single cell recordings. We state that, for clinically relevant frequencies of 130 Hz (used for movement disorders) and realistic blanking windows of 2 ms, up to 27% of actual existing spikes are lost. We strongly advice cautioned use of the blanking method when spike rate quantification is attempted. Impact statement Blanking (artifact removal by temporarily grounding input), depending on recording parameters, can lead to significant spike loss. Very careful use of blanking circuits is advised. PMID:29780301

Enrichment of circulating tumor cells from a large blood volume using leukapheresis and elutriation: proof of concept.

PubMed

Eifler, Robert L; Lind, Judith; Falkenhagen, Dieter; Weber, Viktoria; Fischer, Michael B; Zeillinger, Robert

2011-03-01

The aim of this study was to determine the applicability of a sequential process using leukapheresis, elutriation, and fluorescence-activated cell sorting (FACS) to enrich and isolate circulating tumor cells from a large blood volume to allow further molecular analysis. Mononuclear cells were collected from 10 L of blood by leukapheresis, to which carboxyfluorescein succinimidyl ester prelabeled CaOV-3 tumor cells were spiked at a ratio of 26 to 10⁶ leukocytes. Elutriation separated the spiked leukapheresates primarily by cell size into distinct fractions, and leukocytes and tumor cells, characterized as carboxyfluorescein succinimidyl ester positive, EpCAM positive and CD45 negative events, were quantified by flow cytometry. Tumor cells were isolated from the last fraction using FACS or anti-EpCAM coupled immunomagnetic beads, and their recovery and purity determined by fluorescent microscopy and real-time PCR. Leukapheresis collected 13.5 x 10⁹ mononuclear cells with 87% efficiency. In total, 53 to 78% of spiked tumor cells were pre-enriched in the last elutriation fraction among 1.6 x 10⁹ monocytes. Flow cytometry predicted a circulating tumor cell purity of ~90% giving an enrichment of 100,000-fold following leukapheresis, elutriation, and FACS, where CaOV-3 cells were identified as EpCAM positive and CD45 negative events. FACS confirmed this purity. Alternatively, immunomagnetic bead adsorption recovered 10% of tumor cells with a median purity of 3.5%. This proof of concept study demonstrated that elutriation and FACS following leukapheresis are able to enrich and isolate tumor cells from a large blood volume for molecular characterization. Copyright © 2010 International Clinical Cytometry Society.

Stochastic modeling for neural spiking events based on fractional superstatistical Poisson process

NASA Astrophysics Data System (ADS)

Konno, Hidetoshi; Tamura, Yoshiyasu

2018-01-01

In neural spike counting experiments, it is known that there are two main features: (i) the counting number has a fractional power-law growth with time and (ii) the waiting time (i.e., the inter-spike-interval) distribution has a heavy tail. The method of superstatistical Poisson processes (SSPPs) is examined whether these main features are properly modeled. Although various mixed/compound Poisson processes are generated with selecting a suitable distribution of the birth-rate of spiking neurons, only the second feature (ii) can be modeled by the method of SSPPs. Namely, the first one (i) associated with the effect of long-memory cannot be modeled properly. Then, it is shown that the two main features can be modeled successfully by a class of fractional SSPP (FSSPP).

Detection limits of Legionella pneumophila in environmental samples after co-culture with Acanthamoeba polyphaga

PubMed Central

2013-01-01

Background The efficiency of recovery and the detection limit of Legionella after co-culture with Acanthamoeba polyphaga are not known and so far no investigations have been carried out to determine the efficiency of the recovery of Legionella spp. by co-culture and compare it with that of conventional culturing methods. This study aimed to assess the detection limits of co-culture compared to culture for Legionella pneumophila in compost and air samples. Compost and air samples were spiked with known concentrations of L. pneumophila. Direct culturing and co-culture with amoebae were used in parallel to isolate L. pneumophila and recovery standard curves for both methods were produced for each sample. Results The co-culture proved to be more sensitive than the reference method, detecting 102-103 L. pneumophila cells in 1 g of spiked compost or 1 m3 of spiked air, as compared to 105-106 cells in 1 g of spiked compost and 1 m3 of spiked air. Conclusions Co-culture with amoebae is a useful, sensitive and reliable technique to enrich L. pneumophila in environmental samples that contain only low amounts of bacterial cells. PMID:23442526

Efficiently passing messages in distributed spiking neural network simulation.

PubMed

Thibeault, Corey M; Minkovich, Kirill; O'Brien, Michael J; Harris, Frederick C; Srinivasa, Narayan

2013-01-01

Efficiently passing spiking messages in a neural model is an important aspect of high-performance simulation. As the scale of networks has increased so has the size of the computing systems required to simulate them. In addition, the information exchange of these resources has become more of an impediment to performance. In this paper we explore spike message passing using different mechanisms provided by the Message Passing Interface (MPI). A specific implementation, MVAPICH, designed for high-performance clusters with Infiniband hardware is employed. The focus is on providing information about these mechanisms for users of commodity high-performance spiking simulators. In addition, a novel hybrid method for spike exchange was implemented and benchmarked.

On the applicability of STDP-based learning mechanisms to spiking neuron network models

NASA Astrophysics Data System (ADS)

Sboev, A.; Vlasov, D.; Serenko, A.; Rybka, R.; Moloshnikov, I.

2016-11-01

The ways to creating practically effective method for spiking neuron networks learning, that would be appropriate for implementing in neuromorphic hardware and at the same time based on the biologically plausible plasticity rules, namely, on STDP, are discussed. The influence of the amount of correlation between input and output spike trains on the learnability by different STDP rules is evaluated. A usability of alternative combined learning schemes, involving artificial and spiking neuron models is demonstrated on the iris benchmark task and on the practical task of gender recognition.

Retinal ganglion cell maps in the brain: implications for visual processing.

PubMed

Dhande, Onkar S; Huberman, Andrew D

2014-02-01

Everything the brain knows about the content of the visual world is built from the spiking activity of retinal ganglion cells (RGCs). As the output neurons of the eye, RGCs include ∼20 different subtypes, each responding best to a specific feature in the visual scene. Here we discuss recent advances in identifying where different RGC subtypes route visual information in the brain, including which targets they connect to and how their organization within those targets influences visual processing. We also highlight examples where causal links have been established between specific RGC subtypes, their maps of central connections and defined aspects of light-mediated behavior and we suggest the use of techniques that stand to extend these sorts of analyses to circuits underlying visual perception. Copyright © 2013. Published by Elsevier Ltd.

Solving Constraint Satisfaction Problems with Networks of Spiking Neurons

PubMed Central

Jonke, Zeno; Habenschuss, Stefan; Maass, Wolfgang

2016-01-01

Network of neurons in the brain apply—unlike processors in our current generation of computer hardware—an event-based processing strategy, where short pulses (spikes) are emitted sparsely by neurons to signal the occurrence of an event at a particular point in time. Such spike-based computations promise to be substantially more power-efficient than traditional clocked processing schemes. However, it turns out to be surprisingly difficult to design networks of spiking neurons that can solve difficult computational problems on the level of single spikes, rather than rates of spikes. We present here a new method for designing networks of spiking neurons via an energy function. Furthermore, we show how the energy function of a network of stochastically firing neurons can be shaped in a transparent manner by composing the networks of simple stereotypical network motifs. We show that this design approach enables networks of spiking neurons to produce approximate solutions to difficult (NP-hard) constraint satisfaction problems from the domains of planning/optimization and verification/logical inference. The resulting networks employ noise as a computational resource. Nevertheless, the timing of spikes plays an essential role in their computations. Furthermore, networks of spiking neurons carry out for the Traveling Salesman Problem a more efficient stochastic search for good solutions compared with stochastic artificial neural networks (Boltzmann machines) and Gibbs sampling. PMID:27065785

Report on the analysis of common beverages spiked with gamma-hydroxybutyric acid (GHB) and gamma-butyrolactone (GBL) using NMR and the PURGE solvent-suppression technique.

PubMed

Lesar, Casey T; Decatur, John; Lukasiewicz, Elaan; Champeil, Elise

2011-10-10

In forensic evidence, the identification and quantitation of gamma-hydroxybutyric acid (GHB) in "spiked" beverages is challenging. In this report, we present the analysis of common alcoholic beverages found in clubs and bars spiked with gamma-hydroxybutyric acid (GHB) and gamma-butyrolactone (GBL). Our analysis of the spiked beverages consisted of using (1)H NMR with a water suppression method called Presaturation Utilizing Relaxation Gradients and Echoes (PURGE). The following beverages were analyzed: water, 10% ethanol in water, vodka-cranberry juice, rum and coke, gin and tonic, whisky and diet coke, white wine, red wine, and beer. The PURGE method allowed for the direct identification and quantitation of both compounds in all beverages except red and white wine where small interferences prevented accurate quantitation. The NMR method presented in this paper utilizes PURGE water suppression. Thanks to the use of a capillary internal standard, the method is fast, non-destructive, sensitive and requires no sample preparation which could disrupt the equilibrium between GHB and GBL. Published by Elsevier Ireland Ltd.

A Method for Evaluating Tuning Functions of Single Neurons based on Mutual Information Maximization

NASA Astrophysics Data System (ADS)

Brostek, Lukas; Eggert, Thomas; Ono, Seiji; Mustari, Michael J.; Büttner, Ulrich; Glasauer, Stefan

2011-03-01

We introduce a novel approach for evaluation of neuronal tuning functions, which can be expressed by the conditional probability of observing a spike given any combination of independent variables. This probability can be estimated out of experimentally available data. By maximizing the mutual information between the probability distribution of the spike occurrence and that of the variables, the dependence of the spike on the input variables is maximized as well. We used this method to analyze the dependence of neuronal activity in cortical area MSTd on signals related to movement of the eye and retinal image movement.

Methods of analysis by the U.S. Geological Survey National Water Quality Laboratory; determination of organophosphate pesticides in filtered water by gas chromatography with flame photometric detection

USGS Publications Warehouse

Jha, Virendra K.; Wydoski, Duane S.

2002-01-01

A method for the isolation of 20 parent organophosphate pesticides and 5 pesticide degradates from filtered natural-water samples is described. Seven of these compounds are reported permanently with an estimated concentration because of performance issues. Water samples are filtered to remove suspended particulate matter, and then 1 liter of filtrate is pumped through disposable solid-phase extraction columns that contain octadecyl-bonded porous silica to extract the compounds. The C-18 columns are dried with nitrogen gas, and method compounds are eluted from the columns with ethyl acetate. The extract is analyzed by dual capillary-column gas chromatography with flame photometric detection. Single-operator method detection limits in all three water-matrix samples ranged from 0.004 to 0.012 microgram per liter. Method performance was validated by spiking all compounds into three different matrices at three different concentrations. Eight replicates were analyzed at each concentration level in each matrix. Mean recoveries of method compounds spiked in surface-water samples ranged from 39 to 149 percent and those in ground-water samples ranged from 40 to 124 percent for all pesticides except dimethoate. Mean recoveries of method compounds spiked in reagent-water samples ranged from 41 to 119 percent for all pesticides except dimethoate. Dimethoate exhibited reduced recoveries (mean of 43 percent in low- and medium-concentration level spiked samples and 20 percent in high-concentration level spiked samples) in all matrices because of incomplete collection on the C-18 column. As a result, concen-trations of dimethoate and six other compounds (based on performance issues) in samples are reported in this method with an estimated remark code.

An Event-Driven Classifier for Spiking Neural Networks Fed with Synthetic or Dynamic Vision Sensor Data.

PubMed

Stromatias, Evangelos; Soto, Miguel; Serrano-Gotarredona, Teresa; Linares-Barranco, Bernabé

2017-01-01

This paper introduces a novel methodology for training an event-driven classifier within a Spiking Neural Network (SNN) System capable of yielding good classification results when using both synthetic input data and real data captured from Dynamic Vision Sensor (DVS) chips. The proposed supervised method uses the spiking activity provided by an arbitrary topology of prior SNN layers to build histograms and train the classifier in the frame domain using the stochastic gradient descent algorithm. In addition, this approach can cope with leaky integrate-and-fire neuron models within the SNN, a desirable feature for real-world SNN applications, where neural activation must fade away after some time in the absence of inputs. Consequently, this way of building histograms captures the dynamics of spikes immediately before the classifier. We tested our method on the MNIST data set using different synthetic encodings and real DVS sensory data sets such as N-MNIST, MNIST-DVS, and Poker-DVS using the same network topology and feature maps. We demonstrate the effectiveness of our approach by achieving the highest classification accuracy reported on the N-MNIST (97.77%) and Poker-DVS (100%) real DVS data sets to date with a spiking convolutional network. Moreover, by using the proposed method we were able to retrain the output layer of a previously reported spiking neural network and increase its performance by 2%, suggesting that the proposed classifier can be used as the output layer in works where features are extracted using unsupervised spike-based learning methods. In addition, we also analyze SNN performance figures such as total event activity and network latencies, which are relevant for eventual hardware implementations. In summary, the paper aggregates unsupervised-trained SNNs with a supervised-trained SNN classifier, combining and applying them to heterogeneous sets of benchmarks, both synthetic and from real DVS chips.

Microfluidic droplet sorting using integrated bilayer micro-valves

NASA Astrophysics Data System (ADS)

Chen, Yuncong; Tian, Yang; Xu, Zhen; Wang, Xinran; Yu, Sicong; Dong, Liang

2016-10-01

This paper reports on a microfluidic device capable of sorting microfluidic droplets utilizing conventional bilayer pneumatic micro-valves as sorting controllers. The device consists of two micro-valves placed symmetrically on two sides of a sorting area, each on top of a branching channel at an inclined angle with respect to the main channel. Changes in transmitted light intensity, induced by varying light absorbance by each droplet, are used to divert the droplet from the sorting area into one of the three outlet channels. When no valve is activated, the droplet flows into the outlet channel in the direction of the main channel. When one of the valves is triggered, the flexible membrane of valve will first be deflected. Once the droplet leaves the detection point, the deflected membrane will immediately return to its default flattened position, thereby exerting a drawing pressure on the droplet and deviating it from its original streamline to the outlet on the same side as the valve. This sorting method will be particularly suitable for numerous large-scale integrated microfluidic systems, where pneumatic micro-valves are already used. Only few structural modifications are needed to achieve droplet sorting capabilities in these systems. Due to the mechanical nature of diverting energy applied to droplets, the proposed sorting method may induce only minimal interference to biological species or microorganisms encapsulated inside the droplets that may accompany electrical, optical and magnetic-based techniques.

The Use of Binary Search Trees in External Distribution Sorting.

ERIC Educational Resources Information Center

Cooper, David; Lynch, Michael F.

1984-01-01

Suggests new method of external distribution called tree partitioning that involves use of binary tree to split incoming file into successively smaller partitions for internal sorting. Number of disc accesses during a tree-partitioning sort were calculated in simulation using files extracted from British National Bibliography catalog files. (19…

Efficient Training of Supervised Spiking Neural Network via Accurate Synaptic-Efficiency Adjustment Method.

PubMed

Xie, Xiurui; Qu, Hong; Yi, Zhang; Kurths, Jurgen

2017-06-01

The spiking neural network (SNN) is the third generation of neural networks and performs remarkably well in cognitive tasks, such as pattern recognition. The temporal neural encode mechanism found in biological hippocampus enables SNN to possess more powerful computation capability than networks with other encoding schemes. However, this temporal encoding approach requires neurons to process information serially on time, which reduces learning efficiency significantly. To keep the powerful computation capability of the temporal encoding mechanism and to overcome its low efficiency in the training of SNNs, a new training algorithm, the accurate synaptic-efficiency adjustment method is proposed in this paper. Inspired by the selective attention mechanism of the primate visual system, our algorithm selects only the target spike time as attention areas, and ignores voltage states of the untarget ones, resulting in a significant reduction of training time. Besides, our algorithm employs a cost function based on the voltage difference between the potential of the output neuron and the firing threshold of the SNN, instead of the traditional precise firing time distance. A normalized spike-timing-dependent-plasticity learning window is applied to assigning this error to different synapses for instructing their training. Comprehensive simulations are conducted to investigate the learning properties of our algorithm, with input neurons emitting both single spike and multiple spikes. Simulation results indicate that our algorithm possesses higher learning performance than the existing other methods and achieves the state-of-the-art efficiency in the training of SNN.

Methods of analysis by the U.S. Geological Survey National Water Quality Laboratory; processing, taxonomy, and quality control of benthic macroinvertebrate samples

USGS Publications Warehouse

Moulton, Stephen R.; Carter, James L.; Grotheer, Scott A.; Cuffney, Thomas F.; Short, Terry M.

2000-01-01

Qualitative and quantitative methods to process benthic macroinvertebrate (BMI) samples have been developed and tested by the U.S. Geological Survey?s National Water Quality Laboratory Biological Group. The qualitative processing method is based on visually sorting a sample for up to 2 hours. Sorting focuses on attaining organisms that are likely to result in taxonomic identifications to lower taxonomic levels (for example, Genus or Species). Immature and damaged organisms are also sorted when they are likely to result in unique determinations. The sorted sample remnant is scanned briefly by a second person to determine if obvious taxa were missed. The quantitative processing method is based on a fixed-count approach that targets some minimum count, such as 100 or 300 organisms. Organisms are sorted from randomly selected 5.1- by 5.1-centimeter parts of a gridded subsampling frame. The sorted remnant from each sample is resorted by a second individual for at least 10 percent of the original sort time. A large-rare organism search is performed on the unsorted remnant to sort BMI taxa that were not likely represented in the sorted grids. After either qualitatively or quantitatively sorting the sample, BMIs are identified by using one of three different types of taxonomic assessment. The Standard Taxonomic Assessment is comparable to the U.S. Environmental Protection Agency Rapid Bioassessment Protocol III and typically provides Genus- or Species-level taxonomic resolution. The Rapid Taxonomic Assessment is comparable to the U.S. Environmental Protection Agency Rapid Bioassessment Protocol II and provides Familylevel and higher taxonomic resolution. The Custom Taxonomic Assessment provides Species-level resolution whenever possible for groups identified to higher taxonomic levels by using the Standard Taxonomic Assessment. The consistent use of standardized designations and notes facilitates the interpretation of BMI data within and among water-quality studies. Taxonomic identifications are quality assured by verifying all referenced taxa and randomly reviewing 10 percent of the taxonomic identifications performed weekly by Biological Group taxonomists. Taxonomic errors discovered during this review are corrected. BMI data are reviewed for accuracy and completeness prior to release. BMI data are released phylogenetically in spreadsheet format and unprocessed abundances are corrected for laboratory and field subsampling when necessary.

Analysis of the effects of periodic forcing in the spike rate and spike correlation's in semiconductor lasers with optical feedback

NASA Astrophysics Data System (ADS)

Quintero-Quiroz, C.; Sorrentino, Taciano; Torrent, M. C.; Masoller, Cristina

2016-04-01

We study the dynamics of semiconductor lasers with optical feedback and direct current modulation, operating in the regime of low frequency fluctuations (LFFs). In the LFF regime the laser intensity displays abrupt spikes: the intensity drops to zero and then gradually recovers. We focus on the inter-spike-intervals (ISIs) and use a method of symbolic time-series analysis, which is based on computing the probabilities of symbolic patterns. We show that the variation of the probabilities of the symbols with the modulation frequency and with the intrinsic spike rate of the laser allows to identify different regimes of noisy locking. Simulations of the Lang-Kobayashi model are in good qualitative agreement with experimental observations.

Time-gated flow cytometry: an ultra-high selectivity method to recover ultra-rare-event μ-targets in high-background biosamples

NASA Astrophysics Data System (ADS)

Jin, Dayong; Piper, James A.; Leif, Robert C.; Yang, Sean; Ferrari, Belinda C.; Yuan, Jingli; Wang, Guilan; Vallarino, Lidia M.; Williams, John W.

2009-03-01

A fundamental problem for rare-event cell analysis is auto-fluorescence from nontarget particles and cells. Time-gated flow cytometry is based on the temporal-domain discrimination of long-lifetime (>1 μs) luminescence-stained cells and can render invisible all nontarget cell and particles. We aim to further evaluate the technique, focusing on detection of ultra-rare-event 5-μm calibration beads in environmental water dirt samples. Europium-labeled 5-μm calibration beads with improved luminescence homogeneity and reduced aggregation were evaluated using the prototype UV LED excited time-gated luminescence (TGL) flow cytometer (FCM). A BD FACSAria flow cytometer was used to sort accurately a very low number of beads (<100 events), which were then spiked into concentrated samples of environmental water. The use of europium-labeled beads permitted the demonstration of specific detection rates of 100%+/-30% and 91%+/-3% with 10 and 100 target beads, respectively, that were mixed with over one million nontarget autofluorescent background particles. Under the same conditions, a conventional FCM was unable to recover rare-event fluorescein isothiocyanate (FITC) calibration beads. Preliminary results on Giardia detection are also reported. We have demonstrated the scientific value of lanthanide-complex biolabels in flow cytometry. This approach may augment the current method that uses multifluorescence-channel flow cytometry gating.

Corner detection and sorting method based on improved Harris algorithm in camera calibration

NASA Astrophysics Data System (ADS)

Xiao, Ying; Wang, Yonghong; Dan, Xizuo; Huang, Anqi; Hu, Yue; Yang, Lianxiang

2016-11-01

In traditional Harris corner detection algorithm, the appropriate threshold which is used to eliminate false corners is selected manually. In order to detect corners automatically, an improved algorithm which combines Harris and circular boundary theory of corners is proposed in this paper. After detecting accurate corner coordinates by using Harris algorithm and Forstner algorithm, false corners within chessboard pattern of the calibration plate can be eliminated automatically by using circular boundary theory. Moreover, a corner sorting method based on an improved calibration plate is proposed to eliminate false background corners and sort remaining corners in order. Experiment results show that the proposed algorithms can eliminate all false corners and sort remaining corners correctly and automatically.

Fluence map optimization (FMO) with dose-volume constraints in IMRT using the geometric distance sorting method.

PubMed

Lan, Yihua; Li, Cunhua; Ren, Haozheng; Zhang, Yong; Min, Zhifang

2012-10-21

A new heuristic algorithm based on the so-called geometric distance sorting technique is proposed for solving the fluence map optimization with dose-volume constraints which is one of the most essential tasks for inverse planning in IMRT. The framework of the proposed method is basically an iterative process which begins with a simple linear constrained quadratic optimization model without considering any dose-volume constraints, and then the dose constraints for the voxels violating the dose-volume constraints are gradually added into the quadratic optimization model step by step until all the dose-volume constraints are satisfied. In each iteration step, an interior point method is adopted to solve each new linear constrained quadratic programming. For choosing the proper candidate voxels for the current dose constraint adding, a so-called geometric distance defined in the transformed standard quadratic form of the fluence map optimization model was used to guide the selection of the voxels. The new geometric distance sorting technique can mostly reduce the unexpected increase of the objective function value caused inevitably by the constraint adding. It can be regarded as an upgrading to the traditional dose sorting technique. The geometry explanation for the proposed method is also given and a proposition is proved to support our heuristic idea. In addition, a smart constraint adding/deleting strategy is designed to ensure a stable iteration convergence. The new algorithm is tested on four cases including head-neck, a prostate, a lung and an oropharyngeal, and compared with the algorithm based on the traditional dose sorting technique. Experimental results showed that the proposed method is more suitable for guiding the selection of new constraints than the traditional dose sorting method, especially for the cases whose target regions are in non-convex shapes. It is a more efficient optimization technique to some extent for choosing constraints than the dose sorting method. By integrating a smart constraint adding/deleting scheme within the iteration framework, the new technique builds up an improved algorithm for solving the fluence map optimization with dose-volume constraints.

Millisecond Microwave Spikes: Statistical Study and Application for Plasma Diagnostics

NASA Astrophysics Data System (ADS)

Rozhansky, I. V.; Fleishman, G. D.; Huang, G.-L.

2008-07-01

We analyze a dense cluster of solar radio spikes registered at 4.5-6 GHz by the Purple Mountain Observatory spectrometer (Nanjing, China), operating in the 4.5-7.5 GHz range with 5 ms temporal resolution. To handle the data from the spectrometer, we developed a new technique that uses a nonlinear multi-Gaussian spectral fit based on χ2 criteria to extract individual spikes from the originally recorded spectra. Applying this method to the experimental raw data, we eventually identified about 3000 spikes for this event, which allows us to make a detailed statistical analysis. Various statistical characteristics of the spikes have been evaluated, including the intensity distributions, the spectral bandwidth distributions, and the distribution of the spike mean frequencies. The most striking finding of this analysis is the distributions of the spike bandwidth, which are remarkably asymmetric. To reveal the underlaying microphysics, we explore the local-trap model with the renormalized theory of spectral profiles of the electron cyclotron maser (ECM) emission peak in a source with random magnetic irregularities. The distribution of the solar spike relative bandwidths calculated within the local-trap model represents an excellent fit to the experimental data. Accordingly, the developed technique may offer a new tool with which to study very low levels of magnetic turbulence in the spike sources, when the ECM mechanism of the spike cluster is confirmed.

Method and system rapid piece handling

DOEpatents

Spletzer, Barry L.

1996-01-01

The advent of high-speed fabric cutters has made necessary the development of automated techniques for the collection and sorting of garment pieces into collated piles of pieces ready for assembly. The present invention enables a new method for such handling and sorting of garment parts, and to apparatus capable of carrying out this new method. The common thread is the application of computer-controlled shuttling bins, capable of picking up a desired piece of fabric and dropping it in collated order for assembly. Such apparatus with appropriate computer control relieves the bottleneck now presented by the sorting and collation procedure, thus greatly increasing the overall rate at which garments can be assembled.

Using recurrence plot analysis for software execution interpretation and fault detection

NASA Astrophysics Data System (ADS)

Mosdorf, M.

2015-09-01

This paper shows a method targeted at software execution interpretation and fault detection using recurrence plot analysis. In in the proposed approach recurrence plot analysis is applied to software execution trace that contains executed assembly instructions. Results of this analysis are subject to further processing with PCA (Principal Component Analysis) method that simplifies number coefficients used for software execution classification. This method was used for the analysis of five algorithms: Bubble Sort, Quick Sort, Median Filter, FIR, SHA-1. Results show that some of the collected traces could be easily assigned to particular algorithms (logs from Bubble Sort and FIR algorithms) while others are more difficult to distinguish.

Surface acoustic wave actuated cell sorting (SAWACS).

PubMed

Franke, T; Braunmüller, S; Schmid, L; Wixforth, A; Weitz, D A

2010-03-21

We describe a novel microfluidic cell sorter which operates in continuous flow at high sorting rates. The device is based on a surface acoustic wave cell-sorting scheme and combines many advantages of fluorescence activated cell sorting (FACS) and fluorescence activated droplet sorting (FADS) in microfluidic channels. It is fully integrated on a PDMS device, and allows fast electronic control of cell diversion. We direct cells by acoustic streaming excited by a surface acoustic wave which deflects the fluid independently of the contrast in material properties of deflected objects and the continuous phase; thus the device underlying principle works without additional enhancement of the sorting by prior labelling of the cells with responsive markers such as magnetic or polarizable beads. Single cells are sorted directly from bulk media at rates as fast as several kHz without prior encapsulation into liquid droplet compartments as in traditional FACS. We have successfully directed HaCaT cells (human keratinocytes), fibroblasts from mice and MV3 melanoma cells. The low shear forces of this sorting method ensure that cells survive after sorting.

Noise-robust speech recognition through auditory feature detection and spike sequence decoding.

PubMed

Schafer, Phillip B; Jin, Dezhe Z

2014-03-01

Speech recognition in noisy conditions is a major challenge for computer systems, but the human brain performs it routinely and accurately. Automatic speech recognition (ASR) systems that are inspired by neuroscience can potentially bridge the performance gap between humans and machines. We present a system for noise-robust isolated word recognition that works by decoding sequences of spikes from a population of simulated auditory feature-detecting neurons. Each neuron is trained to respond selectively to a brief spectrotemporal pattern, or feature, drawn from the simulated auditory nerve response to speech. The neural population conveys the time-dependent structure of a sound by its sequence of spikes. We compare two methods for decoding the spike sequences--one using a hidden Markov model-based recognizer, the other using a novel template-based recognition scheme. In the latter case, words are recognized by comparing their spike sequences to template sequences obtained from clean training data, using a similarity measure based on the length of the longest common sub-sequence. Using isolated spoken digits from the AURORA-2 database, we show that our combined system outperforms a state-of-the-art robust speech recognizer at low signal-to-noise ratios. Both the spike-based encoding scheme and the template-based decoding offer gains in noise robustness over traditional speech recognition methods. Our system highlights potential advantages of spike-based acoustic coding and provides a biologically motivated framework for robust ASR development.

Dynamic spiking studies using the DNPH sampling train

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

Steger, J.L.; Knoll, J.E.

1996-12-31

The proposed aldehyde and ketone sampling method using aqueous 2,4-dinitrophenylhydrazine (DNPH) was evaluated in the laboratory and in the field. The sampling trains studied were based on the train described in SW 846 Method 0011. Nine compounds were evaluated: formaldehyde, acetaldehyde, quinone, acrolein, propionaldeyde, methyl isobutyl ketone, methyl ethyl ketone, acetophenone, and isophorone. In the laboratory, the trains were spiked both statistically and dynamically. Laboratory studies also investigated potential interferences to the method. Based on their potential to hydrolyze in acid solution to form formaldehyde, dimethylolurea, saligenin, s-trioxane, hexamethylenetetramine, and paraformaldehyde were investigated. Ten runs were performed using quadruplicate samplingmore » trains. Two of the four trains were dynamically spiked with the nine aldehydes and ketones. The test results were evaluated using the EPA method 301 criteria for method precision (< + pr - 50% relative standard deviation) and bias (correction factor of 1.00 + or - 0.30).« less

Laser penetration spike welding: a welding tool enabling novel process and design opportunities

NASA Astrophysics Data System (ADS)

Dijken, Durandus K.; Hoving, Willem; De Hosson, J. Th. M.

2002-06-01

A novel method for laser welding for sheet metal. is presented. This laser spike welding method is capable of bridging large gaps between sheet metal plates. Novel constructions can be designed and manufactured. Examples are light weight metal epoxy multi-layers and constructions having additional strength with respect to rigidity and impact resistance. Its capability to bridge large gaps allows higher dimensional tolerances in production. The required laser systems are commercially available and are easily implemented in existing production lines. The lasers are highly reliable, the resulting spike welds are quickly realized and the cost price per weld is very low.

Application of Savitzky-Golay differentiation filters and Fourier functions to simultaneous determination of cefepime and the co-administered drug, levofloxacin, in spiked human plasma

NASA Astrophysics Data System (ADS)

Abdel-Aziz, Omar; Abdel-Ghany, Maha F.; Nagi, Reham; Abdel-Fattah, Laila

2015-03-01

The present work is concerned with simultaneous determination of cefepime (CEF) and the co-administered drug, levofloxacin (LEV), in spiked human plasma by applying a new approach, Savitzky-Golay differentiation filters, and combined trigonometric Fourier functions to their ratio spectra. The different parameters associated with the calculation of Savitzky-Golay and Fourier coefficients were optimized. The proposed methods were validated and applied for determination of the two drugs in laboratory prepared mixtures and spiked human plasma. The results were statistically compared with reported HPLC methods and were found accurate and precise.

Robustness and versatility of a nonlinear interdependence method for directional coupling detection from spike trains.

PubMed

Malvestio, Irene; Kreuz, Thomas; Andrzejak, Ralph G

2017-08-01

The detection of directional couplings between dynamics based on measured spike trains is a crucial problem in the understanding of many different systems. In particular, in neuroscience it is important to assess the connectivity between neurons. One of the approaches that can estimate directional coupling from the analysis of point processes is the nonlinear interdependence measure L. Although its efficacy has already been demonstrated, it still needs to be tested under more challenging and realistic conditions prior to an application to real data. Thus, in this paper we use the Hindmarsh-Rose model system to test the method in the presence of noise and for different spiking regimes. We also examine the influence of different parameters and spike train distances. Our results show that the measure L is versatile and robust to various types of noise, and thus suitable for application to experimental data.

Robustness and versatility of a nonlinear interdependence method for directional coupling detection from spike trains

NASA Astrophysics Data System (ADS)

Malvestio, Irene; Kreuz, Thomas; Andrzejak, Ralph G.

2017-08-01

The detection of directional couplings between dynamics based on measured spike trains is a crucial problem in the understanding of many different systems. In particular, in neuroscience it is important to assess the connectivity between neurons. One of the approaches that can estimate directional coupling from the analysis of point processes is the nonlinear interdependence measure L . Although its efficacy has already been demonstrated, it still needs to be tested under more challenging and realistic conditions prior to an application to real data. Thus, in this paper we use the Hindmarsh-Rose model system to test the method in the presence of noise and for different spiking regimes. We also examine the influence of different parameters and spike train distances. Our results show that the measure L is versatile and robust to various types of noise, and thus suitable for application to experimental data.

Non-destructive, high-content analysis of wheat grain traits using X-ray micro computed tomography.

PubMed

Hughes, Nathan; Askew, Karen; Scotson, Callum P; Williams, Kevin; Sauze, Colin; Corke, Fiona; Doonan, John H; Nibau, Candida

2017-01-01

Wheat is one of the most widely grown crop in temperate climates for food and animal feed. In order to meet the demands of the predicted population increase in an ever-changing climate, wheat production needs to dramatically increase. Spike and grain traits are critical determinants of final yield and grain uniformity a commercially desired trait, but their analysis is laborious and often requires destructive harvest. One of the current challenges is to develop an accurate, non-destructive method for spike and grain trait analysis capable of handling large populations. In this study we describe the development of a robust method for the accurate extraction and measurement of spike and grain morphometric parameters from images acquired by X-ray micro-computed tomography (μCT). The image analysis pipeline developed automatically identifies plant material of interest in μCT images, performs image analysis, and extracts morphometric data. As a proof of principle, this integrated methodology was used to analyse the spikes from a population of wheat plants subjected to high temperatures under two different water regimes. Temperature has a negative effect on spike height and grain number with the middle of the spike being the most affected region. The data also confirmed that increased grain volume was correlated with the decrease in grain number under mild stress. Being able to quickly measure plant phenotypes in a non-destructive manner is crucial to advance our understanding of gene function and the effects of the environment. We report on the development of an image analysis pipeline capable of accurately and reliably extracting spike and grain traits from crops without the loss of positional information. This methodology was applied to the analysis of wheat spikes can be readily applied to other economically important crop species.

A round robin approach to the analysis of bisphenol a (BPA) in human blood samples

PubMed Central

2014-01-01

Background Human exposure to bisphenol A (BPA) is ubiquitous, yet there are concerns about whether BPA can be measured in human blood. This Round Robin was designed to address this concern through three goals: 1) to identify collection materials, reagents and detection apparatuses that do not contribute BPA to serum; 2) to identify sensitive and precise methods to accurately measure unconjugated BPA (uBPA) and BPA-glucuronide (BPA-G), a metabolite, in serum; and 3) to evaluate whether inadvertent hydrolysis of BPA-G occurs during sample handling and processing. Methods Four laboratories participated in this Round Robin. Laboratories screened materials to identify BPA contamination in collection and analysis materials. Serum was spiked with concentrations of uBPA and/or BPA-G ranging from 0.09-19.5 (uBPA) and 0.5-32 (BPA-G) ng/mL. Additional samples were preserved unspiked as ‘environmental’ samples. Blinded samples were provided to laboratories that used LC/MSMS to simultaneously quantify uBPA and BPA-G. To determine whether inadvertent hydrolysis of BPA metabolites occurred, samples spiked with only BPA-G were analyzed for the presence of uBPA. Finally, three laboratories compared direct and indirect methods of quantifying BPA-G. Results We identified collection materials and reagents that did not introduce BPA contamination. In the blinded spiked sample analysis, all laboratories were able to distinguish low from high values of uBPA and BPA-G, for the whole spiked sample range and for those samples spiked with the three lowest concentrations (0.5-3.1 ng/ml). By completion of the Round Robin, three laboratories had verified methods for the analysis of uBPA and two verified for the analysis of BPA-G (verification determined by: 4 of 5 samples within 20% of spiked concentrations). In the analysis of BPA-G only spiked samples, all laboratories reported BPA-G was the majority of BPA detected (92.2 – 100%). Finally, laboratories were more likely to be verified using direct methods than indirect ones using enzymatic hydrolysis. Conclusions Sensitive and accurate methods for the direct quantification of uBPA and BPA-G were developed in multiple laboratories and can be used for the analysis of human serum samples. BPA contamination can be controlled during sample collection and inadvertent hydrolysis of BPA conjugates can be avoided during sample handling. PMID:24690217

Label-free density difference amplification-based cell sorting.

PubMed

Song, Jihwan; Song, Minsun; Kang, Taewook; Kim, Dongchoul; Lee, Luke P

2014-11-01

The selective cell separation is a critical step in fundamental life sciences, translational medicine, biotechnology, and energy harvesting. Conventional cell separation methods are fluorescent activated cell sorting and magnetic-activated cell sorting based on fluorescent probes and magnetic particles on cell surfaces. Label-free cell separation methods such as Raman-activated cell sorting, electro-physiologically activated cell sorting, dielectric-activated cell sorting, or inertial microfluidic cell sorting are, however, limited when separating cells of the same kind or cells with similar sizes and dielectric properties, as well as similar electrophysiological phenotypes. Here we report a label-free density difference amplification-based cell sorting (dDACS) without using any external optical, magnetic, electrical forces, or fluidic activations. The conceptual microfluidic design consists of an inlet, hydraulic jump cavity, and multiple outlets. Incoming particles experience gravity, buoyancy, and drag forces in the separation chamber. The height and distance that each particle can reach in the chamber are different and depend on its density, thus allowing for the separation of particles into multiple outlets. The separation behavior of the particles, based on the ratio of the channel heights of the inlet and chamber and Reynolds number has been systematically studied. Numerical simulation reveals that the difference between the heights of only lighter particles with densities close to that of water increases with increasing the ratio of the channel heights, while decreasing Reynolds number can amplify the difference in the heights between the particles considered irrespective of their densities.

Nondestructive test method accurately sorts mixed bolts

NASA Technical Reports Server (NTRS)

Dezeih, C. J.

1966-01-01

Neutron activation analysis method sorts copper plated steel bolts from nickel plated steel bolts. Copper and nickel plated steel bolt specimens of the same configuration are irradiated with thermal neutrons in a test reactor for a short time. After thermal neutron irradiation, the bolts are analyzed using scintillation energy readout equipment.

A Low-Tech, Hands-On Approach To Teaching Sorting Algorithms to Working Students.

ERIC Educational Resources Information Center

Dios, R.; Geller, J.

1998-01-01

Focuses on identifying the educational effects of "activity oriented" instructional techniques. Examines which instructional methods produce enhanced learning and comprehension. Discusses the problem of learning "sorting algorithms," a major topic in every Computer Science curriculum. Presents a low-tech, hands-on teaching method for sorting…

Toxicity of nickel-spiked freshwater sediments to benthic invertebrates-Spiking methodology, species sensitivity, and nickel bioavailability

USGS Publications Warehouse

Besser, John M.; Brumbaugh, William G.; Kemble, Nile E.; Ivey, Chris D.; Kunz, James L.; Ingersoll, Christopher G.; Rudel, David

2011-01-01

This report summarizes data from studies of the toxicity and bioavailability of nickel in nickel-spiked freshwater sediments. The goal of these studies was to generate toxicity and chemistry data to support development of broadly applicable sediment quality guidelines for nickel. The studies were conducted as three tasks, which are presented here as three chapters: Task 1, Development of methods for preparation and toxicity testing of nickel-spiked freshwater sediments; Task 2, Sensitivity of benthic invertebrates to toxicity of nickel-spiked freshwater sediments; and Task 3, Effect of sediment characteristics on nickel bioavailability. Appendices with additional methodological details and raw chemistry and toxicity data for the three tasks are available online at http://pubs.usgs.gov/sir/2011/5225/downloads/.

Estimating botanical composition by the dry-weight-rank method in California's annual grasslands

Treesearch

Raymond D. Ratliff; William E. Frost

1990-01-01

The dry-weight-rank method of estimating botanical composition on California's annual grasslands is a viable alternative to harvesting and sorting or methods using points. Two data sets of sorted species weights were available. One spanned nine years with quadrats harvested at peak of production. The second spanned one growing season with 20 harvest dates. Two...

Production analysis of two tree-bucking and product-sorting methods for hardwoods

Treesearch

John E. Baumgras; Chris B. LeDoux

1989-01-01

This paper documents the results of a study to determine the cost and productivity of two tree-bucking and product-sorting methods used by West Virginia loggers harvesting three to four types of roundwood products. The methods include manual chainsaw bucking and bucking with a hydraulically powered chainsaw slasher. Results show that chain saw bucking of trees...

A General and Efficient Method for Incorporating Precise Spike Times in Globally Time-Driven Simulations

PubMed Central

Hanuschkin, Alexander; Kunkel, Susanne; Helias, Moritz; Morrison, Abigail; Diesmann, Markus

2010-01-01

Traditionally, event-driven simulations have been limited to the very restricted class of neuronal models for which the timing of future spikes can be expressed in closed form. Recently, the class of models that is amenable to event-driven simulation has been extended by the development of techniques to accurately calculate firing times for some integrate-and-fire neuron models that do not enable the prediction of future spikes in closed form. The motivation of this development is the general perception that time-driven simulations are imprecise. Here, we demonstrate that a globally time-driven scheme can calculate firing times that cannot be discriminated from those calculated by an event-driven implementation of the same model; moreover, the time-driven scheme incurs lower computational costs. The key insight is that time-driven methods are based on identifying a threshold crossing in the recent past, which can be implemented by a much simpler algorithm than the techniques for predicting future threshold crossings that are necessary for event-driven approaches. As run time is dominated by the cost of the operations performed at each incoming spike, which includes spike prediction in the case of event-driven simulation and retrospective detection in the case of time-driven simulation, the simple time-driven algorithm outperforms the event-driven approaches. Additionally, our method is generally applicable to all commonly used integrate-and-fire neuronal models; we show that a non-linear model employing a standard adaptive solver can reproduce a reference spike train with a high degree of precision. PMID:21031031

Ripples on spikes show increased phase-amplitude coupling in mesial temporal lobe epilepsy seizure onset zones

PubMed Central

Weiss, Shennan A; Orosz, Iren; Salamon, Noriko; Moy, Stephanie; Wei, Linqing; Van ’t Klooster, Maryse A; Knight, Robert T; Harper, Ronald M; Bragin, Anatol; Fried, Itzhak; Engel, Jerome; Staba, Richard J

2016-01-01

Objective Ripples (80–150 Hz) recorded from clinical macroelectrodes have been shown to be an accurate biomarker of epileptogenic brain tissue. We investigated coupling between epileptiform spike phase and ripple amplitude to better understand the mechanisms that generate this type of pathological ripple (pRipple) event. Methods We quantified phase amplitude coupling (PAC) between epileptiform EEG spike phase and ripple amplitude recorded from intracranial depth macroelectrodes during episodes of sleep in 12 patients with mesial temporal lobe epilepsy. PAC was determined by 1) a phasor transform that corresponds to the strength and rate of ripples coupled with spikes, and a 2) ripple-triggered average to measure the strength, morphology, and spectral frequency of the modulating and modulated signals. Coupling strength was evaluated in relation to recording sites within and outside the seizure onset zone (SOZ). Results Both the phasor transform and ripple-triggered averaging methods showed ripple amplitude was often robustly coupled with epileptiform EEG spike phase. Coupling was more regularly found inside than outside the SOZ, and coupling strength correlated with the likelihood a macroelectrode’s location was within the SOZ (p<0.01). The ratio of the rate of ripples coupled with EEG spikes inside the SOZ to rates of coupled ripples in non-SOZ was greater than the ratio of rates of ripples on spikes detected irrespective of coupling (p<0.05). Coupling strength correlated with an increase in mean normalized ripple amplitude (p<0.01), and a decrease in mean ripple spectral frequency (p<0.05). Significance Generation of low-frequency (80–150 Hz) pRipples in the SOZ involves coupling between epileptiform spike phase and ripple amplitude. The changes in excitability reflected as epileptiform spikes may also cause clusters of pathologically interconnected bursting neurons to grow and synchronize into aberrantly large neuronal assemblies. PMID:27723936

Surface mapping of spike potential fields: experienced EEGers vs. computerized analysis.

PubMed

Koszer, S; Moshé, S L; Legatt, A D; Shinnar, S; Goldensohn, E S

1996-03-01

An EEG epileptiform spike focus recorded with scalp electrodes is clinically localized by visual estimation of the point of maximal voltage and the distribution of its surrounding voltages. We compared such estimated voltage maps, drawn by experienced electroencephalographers (EEGers), with a computerized spline interpolation technique employed in the commercially available software package FOCUS. Twenty-two spikes were recorded from 15 patients during long-term continuous EEG monitoring. Maps of voltage distribution from the 28 electrodes surrounding the points of maximum change in slope (the spike maximum) were constructed by the EEGer. The same points of maximum spike and voltage distributions at the 29 electrodes were mapped by computerized spline interpolation and a comparison between the two methods was made. The findings indicate that the computerized spline mapping techniques employed in FOCUS construct voltage maps with similar maxima and distributions as the maps created by experienced EEGers. The dynamics of spike activity, including correlations, are better visualized using the computerized technique than by manual interpretation alone. Its use as a technique for spike localization is accurate and adds information of potential clinical value.

Automatic Fitting of Spiking Neuron Models to Electrophysiological Recordings

PubMed Central

Rossant, Cyrille; Goodman, Dan F. M.; Platkiewicz, Jonathan; Brette, Romain

2010-01-01

Spiking models can accurately predict the spike trains produced by cortical neurons in response to somatically injected currents. Since the specific characteristics of the model depend on the neuron, a computational method is required to fit models to electrophysiological recordings. The fitting procedure can be very time consuming both in terms of computer simulations and in terms of code writing. We present algorithms to fit spiking models to electrophysiological data (time-varying input and spike trains) that can run in parallel on graphics processing units (GPUs). The model fitting library is interfaced with Brian, a neural network simulator in Python. If a GPU is present it uses just-in-time compilation to translate model equations into optimized code. Arbitrary models can then be defined at script level and run on the graphics card. This tool can be used to obtain empirically validated spiking models of neurons in various systems. We demonstrate its use on public data from the INCF Quantitative Single-Neuron Modeling 2009 competition by comparing the performance of a number of neuron spiking models. PMID:20224819

Reprint of “Non-causal spike filtering improves decoding of movement intention for intracortical BCIs”☆

PubMed Central

Masse, Nicolas Y.; Jarosiewicz, Beata; Simeral, John D.; Bacher, Daniel; Stavisky, Sergey D.; Cash, Sydney S.; Oakley, Erin M.; Berhanu, Etsub; Eskandar, Emad; Friehs, Gerhard; Hochberg, Leigh R.; Donoghue, John P.

2015-01-01

Background Multiple types of neural signals are available for controlling assistive devices through brain–computer interfaces (BCIs). Intracortically recorded spiking neural signals are attractive for BCIs because they can in principle provide greater fidelity of encoded information compared to electrocorticographic (ECoG) signals and electroencephalograms (EEGs). Recent reports show that the information content of these spiking neural signals can be reliably extracted simply by causally band-pass filtering the recorded extracellular voltage signals and then applying a spike detection threshold, without relying on “sorting” action potentials. New method We show that replacing the causal filter with an equivalent non-causal filter increases the information content extracted from the extracellular spiking signal and improves decoding of intended movement direction. This method can be used for real-time BCI applications by using a 4 ms lag between recording and filtering neural signals. Results Across 18 sessions from two people with tetraplegia enrolled in the BrainGate2 pilot clinical trial, we found that threshold crossing events extracted using this non-causal filtering method were significantly more informative of each participant’s intended cursor kinematics compared to threshold crossing events derived from causally filtered signals. This new method decreased the mean angular error between the intended and decoded cursor direction by 9.7° for participant S3, who was implanted 5.4 years prior to this study, and by 3.5° for participant T2, who was implanted 3 months prior to this study. PMID:25681017

Parameter Estimation of a Spiking Silicon Neuron

PubMed Central

Russell, Alexander; Mazurek, Kevin; Mihalaş, Stefan; Niebur, Ernst; Etienne-Cummings, Ralph

2012-01-01

Spiking neuron models are used in a multitude of tasks ranging from understanding neural behavior at its most basic level to neuroprosthetics. Parameter estimation of a single neuron model, such that the model’s output matches that of a biological neuron is an extremely important task. Hand tuning of parameters to obtain such behaviors is a difficult and time consuming process. This is further complicated when the neuron is instantiated in silicon (an attractive medium in which to implement these models) as fabrication imperfections make the task of parameter configuration more complex. In this paper we show two methods to automate the configuration of a silicon (hardware) neuron’s parameters. First, we show how a Maximum Likelihood method can be applied to a leaky integrate and fire silicon neuron with spike induced currents to fit the neuron’s output to desired spike times. We then show how a distance based method which approximates the negative log likelihood of the lognormal distribution can also be used to tune the neuron’s parameters. We conclude that the distance based method is better suited for parameter configuration of silicon neurons due to its superior optimization speed. PMID:23852978

Genes with a spike expression are clustered in chromosome (sub)bands and spike (sub)bands have a powerful prognostic value in patients with multiple myeloma

PubMed Central

Kassambara, Alboukadel; Hose, Dirk; Moreaux, Jérôme; Walker, Brian A.; Protopopov, Alexei; Reme, Thierry; Pellestor, Franck; Pantesco, Véronique; Jauch, Anna; Morgan, Gareth; Goldschmidt, Hartmut; Klein, Bernard

2012-01-01

Background Genetic abnormalities are common in patients with multiple myeloma, and may deregulate gene products involved in tumor survival, proliferation, metabolism and drug resistance. In particular, translocations may result in a high expression of targeted genes (termed spike expression) in tumor cells. We identified spike genes in multiple myeloma cells of patients with newly-diagnosed myeloma and investigated their prognostic value. Design and Methods Genes with a spike expression in multiple myeloma cells were picked up using box plot probe set signal distribution and two selection filters. Results In a cohort of 206 newly diagnosed patients with multiple myeloma, 2587 genes/expressed sequence tags with a spike expression were identified. Some spike genes were associated with some transcription factors such as MAF or MMSET and with known recurrent translocations as expected. Spike genes were not associated with increased DNA copy number and for a majority of them, involved unknown mechanisms. Of spiked genes, 36.7% clustered significantly in 149 out of 862 documented chromosome (sub)bands, of which 53 had prognostic value (35 bad, 18 good). Their prognostic value was summarized with a spike band score that delineated 23.8% of patients with a poor median overall survival (27.4 months versus not reached, P<0.001) using the training cohort of 206 patients. The spike band score was independent of other gene expression profiling-based risk scores, t(4;14), or del17p in an independent validation cohort of 345 patients. Conclusions We present a new approach to identify spike genes and their relationship to patients’ survival. PMID:22102711

The Americleft Project: A Modification of Asher-McDade Method for Rating Nasolabial Esthetics in Patients With Unilateral Cleft Lip and Palate Using Q-sort.

PubMed

Stoutland, Alicia; Long, Ross E; Mercado, Ana; Daskalogiannakis, John; Hathaway, Ronald R; Russell, Kathleen A; Singer, Emily; Semb, Gunvor; Shaw, William C

2017-11-01

The purpose of this study was to investigate ways to improve rater reliability and satisfaction in nasolabial esthetic evaluations of patients with complete unilateral cleft lip and palate (UCLP), by modifying the Asher-McDade method with use of Q-sort methodology. Blinded ratings of cropped photographs of one hundred forty-nine 5- to 7-year-old consecutively treated patients with complete UCLP from 4 different centers were used in a rating of frontal and profile nasolabial esthetic outcomes by 6 judges involved in the Americleft Project's intercenter outcome comparisons. Four judges rated in previous studies using the original Asher-McDade approach. For the Q-sort modification, rather than projection of images, each judge had cards with frontal and profile photographs of each patient and rated them on a scale of 1 to 5 for vermillion border, nasolabial frontal, and profile, using the Q-sort method with placement of cards into categories 1 to 5. Inter- and intrarater reliabilities were calculated using the Weighted Kappa (95% confidence interval). For 4 raters, the reliabilities were compared with those in previous studies. There was no significant improvement in inter-rater reliabilities using the new method. Intrarater reliability consistently improved. All raters preferred the Q-sort method with rating cards rather than a PowerPoint of photos, which improved internal consistency in rating compared to previous studies using the original Asher-McDade method. All raters preferred this method because of the ability to continuously compare photos and adjust relative ratings between patients.

Mammalian Odor Information Recognition by Implanted Microsensor Array in vivo

NASA Astrophysics Data System (ADS)

Zhou, Jun; Dong, Qi; Zhuang, Liujing; Liu, Qingjun; Wang, Ping

2011-09-01

The mammalian olfactory system has an exquisite capacity to rapidly recognize and discriminate thousands of distinct odors in our environment. Our research group focus on reading information from olfactory bulb circuit of anethetized Sprague-Dawley rat and utilize artificial recognition system for odor discrimination. After being stimulated by three odors with concentration of 10 μM to rat nose, the response of mitral cells in olfactory bulb is recorded by eight channel microwire sensor array. In 20 sessions with 3 animals, we obtained 30 discriminated individual cells recordings. The average firing rates of the cells are Isoamyl acetate 26 Hz, Methoxybenzene 16 Hz, and Rose essential oil 11 Hz. By spike sorting, we detect peaks and analyze the interspike interval distribution. Further more, principal component analysis is applied to reduce the dimensionality of the data sets and classify the response.

Preparation and characterization of nickel-spiked freshwater sediments for toxicity tests: toward more environmentally realistic nickel partitioning

USGS Publications Warehouse

Brumbaugh, William G.; Besser, John M.; Ingersoll, Christopher G.; May, Thomas W.; Ivey, Chris D.; Schlekat, Christian E.; Garman, Emily R.

2013-01-01

Two spiking methods were compared and nickel (Ni) partitioning was evaluated during a series of toxicity tests with 8 different freshwater sediments having a range of physicochemical characteristics. A 2-step spiking approach with immediate pH adjustment by addition of NaOH at a 2:1 molar ratio to the spiked Ni was effective in producing consistent pH and other chemical characteristics across a range of Ni spiking levels. When Ni was spiked into sediment having a high acid-volatile sulfide and organic matter content, a total equilibration period of at least 10 wk was needed to stabilize Ni partitioning. However, highest spiking levels evidently exceeded sediment binding capacities; therefore, a 7-d equilibration in toxicity test chambers and 8 volume-additions/d of aerobic overlying water were used to avoid unrealistic Ni partitioning during toxicity testing. The 7-d pretest equilibration allowed excess spiked Ni and other ions from pH adjustment to diffuse from sediment porewater and promoted development of an environmentally relevant, 0.5- to 1-cm oxic/suboxic sediment layer in the test chambers. Among the 8 different spiked sediments, the logarithm of sediment/porewater distribution coefficient values (log Kd) for Ni during the toxicity tests ranged from 3.5 to 4.5. These Kd values closely match the range of values reported for various field Ni-contaminated sediments, indicating that testing conditions with our spiked sediments were environmentally realistic.

Volatile organic compound matrix spike recoveries for ground- and surface-water samples, 1997-2001

USGS Publications Warehouse

Rowe, Barbara L.; Delzer, Gregory C.; Bender, David A.; Zogorski, John S.

2005-01-01

The U.S. Geological Survey's National Water-Quality Assessment (NAWQA) Program used field matrix spikes (FMSs), field matrix spike replicates (FMSRs), laboratory matrix spikes (LMSs), and laboratory reagent spikes (LRSs), in part, to assess the quality of volatile organic compound (VOC) data from water samples collected and analyzed in more than 50 of the Nation's largest river basins and aquifers (Study Units). The data-quality objectives of the NAWQA Program include estimating the extent to which variability, degradation, and matrix effects, if any, may affect the interpretation of chemical analyses of ground- and surface-water samples. In order to help meet these objectives, a known mass of VOCs was added (spiked) to water samples collected in 25 Study Units. Data within this report include recoveries from 276 ground- and surface-water samples spiked with a 25-microliter syringe with a spike solution containing 85 VOCs to achieve a concentration of 0.5 microgram per liter. Combined recoveries for 85 VOCs from spiked ground- and surface-water samples and reagent water were used to broadly characterize the overall recovery of VOCs. Median recoveries for 149 FMSs, 107 FMSRs, 20 LMSs, and 152 LRSs were 79.9, 83.3, 113.1, and 103.5 percent, respectively. Spike recoveries for 85 VOCs also were calculated individually. With the exception of a few VOCs, the median percent recoveries determined from each spike type for individual VOCs followed the same pattern as for all VOC recoveries combined, that is, listed from least to greatest recovery-FMSs, FMSRs, LRSs, and LMSs. The median recoveries for individual VOCs ranged from 63.7 percent to 101.5 percent in FMSs; 63.1 percent to 101.4 percent in FMSRs; 101.7 percent to 135.0 percent in LMSs; and 91.0 percent to 118.7 percent in LRSs. Additionally, individual VOC recoveries were compared among paired spike types, and these recoveries were used to evaluate potential bias in the method. Variability associated with field spiking, field handling, transport, and analysis was assessed by comparing recoveries between 107 pairs of FMR and FMSR samples. For most VOCs, FMSR recoveries were greater than the paired FMS recoveries. This may result from routinely processing the FMS sample first, allowing a more fluid and efficient technique when processing the FMSR. Degradation was examined by comparing VOC recoveries between 20 pairs of FMS and LMS samples. For all VOCs, the LMS recoveries were greater than FMS recoveries. However, data presented in a previously published VOC stability study were interpreted, and recoveries indicated that VOC degradation should not affect the recovery for most VOCs monitored by the NAWQA Program. Matrix effects were examined by comparing VOC recoveries from 20 pairs of LMS and LRS samples. With the exception of two VOCs, individual recoveries were not significantly different between LMSs and LRSs, indicating that most VOC recoveries are not affected by matrix effects. Additionally, matrix effects should be negligible due to the analytical technique (purge and trap capillary column gas chromatography/mass spectrometry) used for VOC analysis at the U.S. Geological Survey National Water Quality Laboratory (NWQL). The reason for the lower VOC recoveries from FMSs and FMSRs than from LMSs and LRSs may be associated with differences in spiking technique and experience, and to varying environmental conditions at the time of spiking. However, for all spike types, 87 percent of the individual VOC recoveries were within the range of 60 to 140 percent, a range that is considered acceptable by the U.S. Environmental Protection Agency's established analytical method. Additionally, the median recovery for each spike type was within the range of 60 to 140 percent. The excellent VOC recoveries from LMSs and LRSs demonstrate that low VOC concentrations can routinely and accurately be measured by the analytical methods used by the NWQL.

A simple method for the enrichment of bisphenols using boron nitride.

PubMed

Fischnaller, Martin; Bakry, Rania; Bonn, Günther K

2016-03-01

A simple solid-phase extraction method for the enrichment of 5 bisphenol derivatives using hexagonal boron nitride (BN) was developed. BN was applied to concentrate bisphenol derivatives in spiked water samples and the compounds were analyzed using HPLC coupled to fluorescence detection. The effect of pH and organic solvents on the extraction efficiency was investigated. An enrichment factor up to 100 was achieved without evaporation and reconstitution. The developed method was applied for the determination of bisphenol A migrated from some polycarbonate plastic products. Furthermore, bisphenol derivatives were analyzed in spiked and non-spiked canned food and beverages. None of the analyzed samples exceeded the migration limit set by the European Union of 0.6mg/kg food. The method showed good recovery rates ranging from 80% to 110%. Validation of the method was performed in terms of accuracy and precision. The applied method is robust, fast, efficient and easily adaptable to different analytical problems. Copyright © 2015 Elsevier Ltd. All rights reserved.

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.

Functional Neuroimaging of Spike-Wave Seizures

PubMed Central

Motelow, Joshua E.; Blumenfeld, Hal

2013-01-01

Generalized spike-wave seizures are typically brief events associated with dynamic changes in brain physiology, metabolism, and behavior. Functional magnetic resonance imaging (fMRI) provides a relatively high spatio-temporal resolution method for imaging cortical-subcortical network activity during spike-wave seizures. Patients with spike-wave seizures often have episodes of staring and unresponsiveness which interfere with normal behavior. Results from human fMRI studies suggest that spike-wave seizures disrupt specific networks in the thalamus and fronto-parietal association cortex which are critical for normal attentive consciousness. However, the neuronal activity underlying imaging changes seen during fMRI is not well understood, particularly in abnormal conditions such as seizures. Animal models have begun to provide important fundamental insights into the neuronal basis for fMRI changes during spike-wave activity. Work from these models including both fMRI and direct neuronal recordings suggest that, like in humans, specific cortical-subcortical networks are involved in spike-wave, while other regions are spared. Regions showing fMRI increases demonstrate correlated increases in neuronal activity in animal models. The mechanisms of fMRI decreases in spike-wave will require further investigation. A better understanding of the specific brain regions involved in generating spike-wave seizures may help guide efforts to develop targeted therapies aimed at preventing or reversing abnormal excitability in these brain regions, ultimately leading to a cure for this disorder. PMID:18839093

An Empirical Derivation of the Run Time of the Bubble Sort Algorithm.

ERIC Educational Resources Information Center

Gonzales, Michael G.

1984-01-01

Suggests a moving pictorial tool to help teach principles in the bubble sort algorithm. Develops such a tool applied to an unsorted list of numbers and describes a method to derive the run time of the algorithm. The method can be modified to run the times of various other algorithms. (JN)

Decoding spike timing: the differential reverse correlation method

PubMed Central

Tkačik, Gašper; Magnasco, Marcelo O.

2009-01-01

It is widely acknowledged that detailed timing of action potentials is used to encode information, for example in auditory pathways; however the computational tools required to analyze encoding through timing are still in their infancy. We present a simple example of encoding, based on a recent model of time-frequency analysis, in which units fire action potentials when a certain condition is met, but the timing of the action potential depends also on other features of the stimulus. We show that, as a result, spike-triggered averages are smoothed so much they do not represent the true features of the encoding. Inspired by this example, we present a simple method, differential reverse correlations, that can separate an analysis of what causes a neuron to spike, and what controls its timing. We analyze with this method the leaky integrate-and-fire neuron and show the method accurately reconstructs the model's kernel. PMID:18597928

Localization of interictal epileptic spikes with MEG: optimization of an automated beamformer screening method (SAMepi) in a diverse epilepsy population

PubMed Central

Scott, Jonathan M.; Robinson, Stephen E.; Holroyd, Tom; Coppola, Richard; Sato, Susumu; Inati, Sara K.

2016-01-01

OBJECTIVE To describe and optimize an automated beamforming technique followed by identification of locations with excess kurtosis (g2) for efficient detection and localization of interictal spikes in medically refractory epilepsy patients. METHODS Synthetic Aperture Magnetometry with g2 averaged over a sliding time window (SAMepi) was performed in 7 focal epilepsy patients and 5 healthy volunteers. The effect of varied window lengths on detection of spiking activity was evaluated. RESULTS Sliding window lengths of 0.5–10 seconds performed similarly, with 0.5 and 1 second windows detecting spiking activity in one of the 3 virtual sensor locations with highest kurtosis. These locations were concordant with the region of eventual surgical resection in these 7 patients who remained seizure free at one year. Average g2 values increased with increasing sliding window length in all subjects. In healthy volunteers kurtosis values stabilized in datasets longer than two minutes. CONCLUSIONS SAMepi using g2 averaged over 1 second sliding time windows in datasets of at least 2 minutes duration reliably identified interictal spiking and the presumed seizure focus in these 7 patients. Screening the 5 locations with highest kurtosis values for spiking activity is an efficient and accurate technique for localizing interictal activity using MEG. SIGNIFICANCE SAMepi should be applied using the parameter values and procedure described for optimal detection and localization of interictal spikes. Use of this screening procedure could significantly improve the efficiency of MEG analysis if clinically validated. PMID:27760068

Lowering the quantification limit of the QubitTM RNA HS assay using RNA spike-in.

PubMed

Li, Xin; Ben-Dov, Iddo Z; Mauro, Maurizio; Williams, Zev

2015-05-06

RNA quantification is often a prerequisite for most RNA analyses such as RNA sequencing. However, the relatively low sensitivity and large sample consumption of traditional RNA quantification methods such as UV spectrophotometry and even the much more sensitive fluorescence-based RNA quantification assays, such as the Qubit™ RNA HS Assay, are often inadequate for measuring minute levels of RNA isolated from limited cell and tissue samples and biofluids. Thus, there is a pressing need for a more sensitive method to reliably and robustly detect trace levels of RNA without interference from DNA. To improve the quantification limit of the Qubit™ RNA HS Assay, we spiked-in a known quantity of RNA to achieve the minimum reading required by the assay. Samples containing trace amounts of RNA were then added to the spike-in and measured as a reading increase over RNA spike-in baseline. We determined the accuracy and precision of reading increases between 1 and 20 pg/μL as well as RNA-specificity in this range, and compared to those of RiboGreen(®), another sensitive fluorescence-based RNA quantification assay. We then applied Qubit™ Assay with RNA spike-in to quantify plasma RNA samples. RNA spike-in improved the quantification limit of the Qubit™ RNA HS Assay 5-fold, from 25 pg/μL down to 5 pg/μL while maintaining high specificity to RNA. This enabled quantification of RNA with original concentration as low as 55.6 pg/μL compared to 250 pg/μL for the standard assay and decreased sample consumption from 5 to 1 ng. Plasma RNA samples that were not measurable by the Qubit™ RNA HS Assay were measurable by our modified method. The Qubit™ RNA HS Assay with RNA spike-in is able to quantify RNA with high specificity at 5-fold lower concentration and uses 5-fold less sample quantity than the standard Qubit™ Assay.

A degradation-based sorting method for lithium-ion battery reuse.

PubMed

Chen, Hao; Shen, Julia

2017-01-01

In a world where millions of people are dependent on batteries to provide them with convenient and portable energy, battery recycling is of the utmost importance. In this paper, we developed a new method to sort 18650 Lithium-ion batteries in large quantities and in real time for harvesting used cells with enough capacity for battery reuse. Internal resistance and capacity tests were conducted as a basis for comparison with a novel degradation-based method based on X-ray radiographic scanning and digital image contrast computation. The test results indicate that the sorting accuracy of the test cells is about 79% and the execution time of our algorithm is at a level of 200 milliseconds, making our method a potential real-time solution for reusing the remaining capacity in good used cells.

Application of Savitzky-Golay differentiation filters and Fourier functions to simultaneous determination of cefepime and the co-administered drug, levofloxacin, in spiked human plasma.

PubMed

Abdel-Aziz, Omar; Abdel-Ghany, Maha F; Nagi, Reham; Abdel-Fattah, Laila

2015-03-15

The present work is concerned with simultaneous determination of cefepime (CEF) and the co-administered drug, levofloxacin (LEV), in spiked human plasma by applying a new approach, Savitzky-Golay differentiation filters, and combined trigonometric Fourier functions to their ratio spectra. The different parameters associated with the calculation of Savitzky-Golay and Fourier coefficients were optimized. The proposed methods were validated and applied for determination of the two drugs in laboratory prepared mixtures and spiked human plasma. The results were statistically compared with reported HPLC methods and were found accurate and precise. Copyright © 2014 Elsevier B.V. All rights reserved.

The Methods and Goals of Teaching Sorting Algorithms in Public Education

ERIC Educational Resources Information Center

Bernát, Péter

2014-01-01

The topic of sorting algorithms is a pleasant subject of informatics education. Not only is it so because the notion of sorting is well known from our everyday life, but also because as an algorithm task, whether we expect naive or practical solutions, it is easy to define and demonstrate. In my paper I will present some of the possible methods…

Hebbian based learning with winner-take-all for spiking neural networks

NASA Astrophysics Data System (ADS)

Gupta, Ankur; Long, Lyle

2009-03-01

Learning methods for spiking neural networks are not as well developed as the traditional neural networks that widely use back-propagation training. We propose and implement a Hebbian based learning method with winner-take-all competition for spiking neural networks. This approach is spike time dependent which makes it naturally well suited for a network of spiking neurons. Homeostasis with Hebbian learning is implemented which ensures stability and quicker learning. Homeostasis implies that the net sum of incoming weights associated with a neuron remains the same. Winner-take-all is also implemented for competitive learning between output neurons. We implemented this learning rule on a biologically based vision processing system that we are developing, and use layers of leaky integrate and fire neurons. The network when presented with 4 bars (or Gabor filters) of different orientation learns to recognize the bar orientations (or Gabor filters). After training, each output neuron learns to recognize a bar at specific orientation and responds by firing more vigorously to that bar and less vigorously to others. These neurons are found to have bell shaped tuning curves and are similar to the simple cells experimentally observed by Hubel and Wiesel in the striate cortex of cat and monkey.

Uncovering representations of sleep-associated hippocampal ensemble spike activity

NASA Astrophysics Data System (ADS)

Chen, Zhe; Grosmark, Andres D.; Penagos, Hector; Wilson, Matthew A.

2016-08-01

Pyramidal neurons in the rodent hippocampus exhibit spatial tuning during spatial navigation, and they are reactivated in specific temporal order during sharp-wave ripples observed in quiet wakefulness or slow wave sleep. However, analyzing representations of sleep-associated hippocampal ensemble spike activity remains a great challenge. In contrast to wake, during sleep there is a complete absence of animal behavior, and the ensemble spike activity is sparse (low occurrence) and fragmental in time. To examine important issues encountered in sleep data analysis, we constructed synthetic sleep-like hippocampal spike data (short epochs, sparse and sporadic firing, compressed timescale) for detailed investigations. Based upon two Bayesian population-decoding methods (one receptive field-based, and the other not), we systematically investigated their representation power and detection reliability. Notably, the receptive-field-free decoding method was found to be well-tuned for hippocampal ensemble spike data in slow wave sleep (SWS), even in the absence of prior behavioral measure or ground truth. Our results showed that in addition to the sample length, bin size, and firing rate, number of active hippocampal pyramidal neurons are critical for reliable representation of the space as well as for detection of spatiotemporal reactivated patterns in SWS or quiet wakefulness.

A cell sorting and trapping microfluidic device with an interdigital channel

NASA Astrophysics Data System (ADS)

Tu, Jing; Qiao, Yi; Xu, Minghua; Li, Junji; Liang, Fupeng; Duan, Mengqin; Ju, An; Lu, Zuhong

2016-12-01

The growing interest in cell sorting and trapping is driving the demand for high performance technologies. Using labeling techniques or external forces, cells can be identified by a series of methods. However, all of these methods require complicated systems with expensive devices. Based on inherent differences in cellular morphology, cells can be sorted by specific structures in microfluidic devices. The weir filter is a basic and efficient cell sorting and trapping structure. However, in some existing weir devices, because of cell deformability and high flow velocity in gaps, trapped cells may become stuck or even pass through the gaps. Here, we designed and fabricated a microfluidic device with interdigital channels for cell sorting and trapping. The chip consisted of a sheet of silicone elastomer polydimethylsiloxane and a sheet of glass. A square-wave-like weir was designed in the middle of the channel, comprising the interdigital channels. The square-wave pattern extended the weir length by three times with the channel width remaining constant. Compared with a straight weir, this structure exhibited a notably higher trapping capacity. Interdigital channels provided more space to slow down the rate of the pressure decrease, which prevented the cells from becoming stuck in the gaps. Sorting a mixture K562 and blood cells to trap cells demonstrated the efficiency of the chip with the interdigital channel to sort and trap large and less deformable cells. With stable and efficient cell sorting and trapping abilities, the chip with an interdigital channel may be widely applied in scientific research fields.

Neuroscience-inspired computational systems for speech recognition under noisy conditions

NASA Astrophysics Data System (ADS)

Schafer, Phillip B.

Humans routinely recognize speech in challenging acoustic environments with background music, engine sounds, competing talkers, and other acoustic noise. However, today's automatic speech recognition (ASR) systems perform poorly in such environments. In this dissertation, I present novel methods for ASR designed to approach human-level performance by emulating the brain's processing of sounds. I exploit recent advances in auditory neuroscience to compute neuron-based representations of speech, and design novel methods for decoding these representations to produce word transcriptions. I begin by considering speech representations modeled on the spectrotemporal receptive fields of auditory neurons. These representations can be tuned to optimize a variety of objective functions, which characterize the response properties of a neural population. I propose an objective function that explicitly optimizes the noise invariance of the neural responses, and find that it gives improved performance on an ASR task in noise compared to other objectives. The method as a whole, however, fails to significantly close the performance gap with humans. I next consider speech representations that make use of spiking model neurons. The neurons in this method are feature detectors that selectively respond to spectrotemporal patterns within short time windows in speech. I consider a number of methods for training the response properties of the neurons. In particular, I present a method using linear support vector machines (SVMs) and show that this method produces spikes that are robust to additive noise. I compute the spectrotemporal receptive fields of the neurons for comparison with previous physiological results. To decode the spike-based speech representations, I propose two methods designed to work on isolated word recordings. The first method uses a classical ASR technique based on the hidden Markov model. The second method is a novel template-based recognition scheme that takes advantage of the neural representation's invariance in noise. The scheme centers on a speech similarity measure based on the longest common subsequence between spike sequences. The combined encoding and decoding scheme outperforms a benchmark system in extremely noisy acoustic conditions. Finally, I consider methods for decoding spike representations of continuous speech. To help guide the alignment of templates to words, I design a syllable detection scheme that robustly marks the locations of syllabic nuclei. The scheme combines SVM-based training with a peak selection algorithm designed to improve noise tolerance. By incorporating syllable information into the ASR system, I obtain strong recognition results in noisy conditions, although the performance in noiseless conditions is below the state of the art. The work presented here constitutes a novel approach to the problem of ASR that can be applied in the many challenging acoustic environments in which we use computer technologies today. The proposed spike-based processing methods can potentially be exploited in effcient hardware implementations and could significantly reduce the computational costs of ASR. The work also provides a framework for understanding the advantages of spike-based acoustic coding in the human brain.

An empirical investigation of motion effects in eMRI of interictal epileptiform spikes.

PubMed

Sundaram, Padmavathi; Mulkern, Robert V; Wells, William M; Triantafyllou, Christina; Loddenkemper, Tobias; Bubrick, Ellen J; Orbach, Darren B

2011-12-01

We recently developed a functional neuroimaging technique called encephalographic magnetic resonance imaging (eMRI). Our method acquires rapid single-shot gradient-echo echo-planar MRI (repetition time=47 ms); it attempts to measure an MR signal more directly linked to neuronal electromagnetic activity than existing methods. To increase the likelihood of detecting such an MR signal, we recorded concurrent MRI and scalp electroencephalography (EEG) during fast (20-200 ms), localized, high-amplitude (>50 μV on EEG) cortical discharges in a cohort of focal epilepsy patients. Seen on EEG as interictal spikes, these discharges occur in between seizures and induced easily detectable MR magnitude and phase changes concurrent with the spikes with a lag of milliseconds to tens of milliseconds. Due to the time scale of the responses, localized changes in blood flow or hemoglobin oxygenation are unlikely to cause the MR signal changes that we observed. While the precise underlying mechanisms are unclear, in this study, we empirically investigate one potentially important confounding variable - motion. Head motion in the scanner affects both EEG and MR recording. It can produce brief "spike-like" artifacts on EEG and induce large MR signal changes similar to our interictal spike-related signal changes. In order to explore the possibility that interictal spikes were associated with head motions (although such an association had never been reported), we had previously tracked head position in epilepsy patients during interictal spikes and explicitly demonstrated a lack of associated head motion. However, that study was performed outside the MR scanner, and the root-mean-square error in the head position measurement was 0.7 mm. The large inaccuracy in this measurement therefore did not definitively rule out motion as a possible signal generator. In this study, we instructed healthy subjects to make deliberate brief (<500 ms) head motions inside the MR scanner and imaged these head motions with concurrent EEG and MRI. We compared these artifactual MR and EEG data to genuine interictal spikes. While per-voxel MR and per-electrode EEG time courses for the motion case can mimic the corresponding time courses associated with a genuine interictal spike, head motion can be unambiguously differentiated from interictal spikes via scalp EEG potential maps. Motion induces widespread changes in scalp potential, whereas interictal spikes are localized and have a regional fall-off in amplitude. These findings make bulk head motion an unlikely generator of the large spike-related MR signal changes that we had observed. Further work is required to precisely identify the underlying mechanisms. Copyright © 2011 Elsevier Inc. All rights reserved.

Predicting Spike Occurrence and Neuronal Responsiveness from LFPs in Primary Somatosensory Cortex

PubMed Central

Storchi, Riccardo; Zippo, Antonio G.; Caramenti, Gian Carlo; Valente, Maurizio; Biella, Gabriele E. M.

2012-01-01

Local Field Potentials (LFPs) integrate multiple neuronal events like synaptic inputs and intracellular potentials. LFP spatiotemporal features are particularly relevant in view of their applications both in research (e.g. for understanding brain rhythms, inter-areal neural communication and neronal coding) and in the clinics (e.g. for improving invasive Brain-Machine Interface devices). However the relation between LFPs and spikes is complex and not fully understood. As spikes represent the fundamental currency of neuronal communication this gap in knowledge strongly limits our comprehension of neuronal phenomena underlying LFPs. We investigated the LFP-spike relation during tactile stimulation in primary somatosensory (S-I) cortex in the rat. First we quantified how reliably LFPs and spikes code for a stimulus occurrence. Then we used the information obtained from our analyses to design a predictive model for spike occurrence based on LFP inputs. The model was endowed with a flexible meta-structure whose exact form, both in parameters and structure, was estimated by using a multi-objective optimization strategy. Our method provided a set of nonlinear simple equations that maximized the match between models and true neurons in terms of spike timings and Peri Stimulus Time Histograms. We found that both LFPs and spikes can code for stimulus occurrence with millisecond precision, showing, however, high variability. Spike patterns were predicted significantly above chance for 75% of the neurons analysed. Crucially, the level of prediction accuracy depended on the reliability in coding for the stimulus occurrence. The best predictions were obtained when both spikes and LFPs were highly responsive to the stimuli. Spike reliability is known to depend on neuron intrinsic properties (i.e. on channel noise) and on spontaneous local network fluctuations. Our results suggest that the latter, measured through the LFP response variability, play a dominant role. PMID:22586452

Predicting spike occurrence and neuronal responsiveness from LFPs in primary somatosensory cortex.

PubMed

Storchi, Riccardo; Zippo, Antonio G; Caramenti, Gian Carlo; Valente, Maurizio; Biella, Gabriele E M

2012-01-01

Local Field Potentials (LFPs) integrate multiple neuronal events like synaptic inputs and intracellular potentials. LFP spatiotemporal features are particularly relevant in view of their applications both in research (e.g. for understanding brain rhythms, inter-areal neural communication and neuronal coding) and in the clinics (e.g. for improving invasive Brain-Machine Interface devices). However the relation between LFPs and spikes is complex and not fully understood. As spikes represent the fundamental currency of neuronal communication this gap in knowledge strongly limits our comprehension of neuronal phenomena underlying LFPs. We investigated the LFP-spike relation during tactile stimulation in primary somatosensory (S-I) cortex in the rat. First we quantified how reliably LFPs and spikes code for a stimulus occurrence. Then we used the information obtained from our analyses to design a predictive model for spike occurrence based on LFP inputs. The model was endowed with a flexible meta-structure whose exact form, both in parameters and structure, was estimated by using a multi-objective optimization strategy. Our method provided a set of nonlinear simple equations that maximized the match between models and true neurons in terms of spike timings and Peri Stimulus Time Histograms. We found that both LFPs and spikes can code for stimulus occurrence with millisecond precision, showing, however, high variability. Spike patterns were predicted significantly above chance for 75% of the neurons analysed. Crucially, the level of prediction accuracy depended on the reliability in coding for the stimulus occurrence. The best predictions were obtained when both spikes and LFPs were highly responsive to the stimuli. Spike reliability is known to depend on neuron intrinsic properties (i.e. on channel noise) and on spontaneous local network fluctuations. Our results suggest that the latter, measured through the LFP response variability, play a dominant role.

Single gene-based distinction of individual microbial genomes from a mixed population of microbial cells.

PubMed

Tamminen, Manu V; Virta, Marko P J

2015-01-01

Recent progress in environmental microbiology has revealed vast populations of microbes in any given habitat that cannot be detected by conventional culturing strategies. The use of sensitive genetic detection methods such as CARD-FISH and in situ PCR have been limited by the cell wall permeabilization requirement that cannot be performed similarly on all cell types without lysing some and leaving some nonpermeabilized. Furthermore, the detection of low copy targets such as genes present in single copies in the microbial genomes, has remained problematic. We describe an emulsion-based procedure to trap individual microbial cells into picoliter-volume polyacrylamide droplets that provide a rigid support for genetic material and therefore allow complete degradation of cellular material to expose the individual genomes. The polyacrylamide droplets are subsequently converted into picoliter-scale reactors for genome amplification. The amplified genomes are labeled based on the presence of a target gene and differentiated from those that do not contain the gene by flow cytometry. Using the Escherichia coli strains XL1 and MC1061, which differ with respect to the presence (XL1), or absence (MC1061) of a single copy of a tetracycline resistance gene per genome, we demonstrate that XL1 genomes present at 0.1% of MC1061 genomes can be differentiated using this method. Using a spiked sediment microbial sample, we demonstrate that the method is applicable to highly complex environmental microbial communities as a target gene-based screen for individual microbes. The method provides a novel tool for enumerating functional cell populations in complex microbial communities. We envision that the method could be optimized for fluorescence-activated cell sorting to enrich genetic material of interest from complex environmental samples.

Monte Carlo point process estimation of electromyographic envelopes from motor cortical spikes for brain-machine interfaces

NASA Astrophysics Data System (ADS)

Liao, Yuxi; She, Xiwei; Wang, Yiwen; Zhang, Shaomin; Zhang, Qiaosheng; Zheng, Xiaoxiang; Principe, Jose C.

2015-12-01

Objective. Representation of movement in the motor cortex (M1) has been widely studied in brain-machine interfaces (BMIs). The electromyogram (EMG) has greater bandwidth than the conventional kinematic variables (such as position, velocity), and is functionally related to the discharge of cortical neurons. As the stochastic information of EMG is derived from the explicit spike time structure, point process (PP) methods will be a good solution for decoding EMG directly from neural spike trains. Previous studies usually assume linear or exponential tuning curves between neural firing and EMG, which may not be true. Approach. In our analysis, we estimate the tuning curves in a data-driven way and find both the traditional functional-excitatory and functional-inhibitory neurons, which are widely found across a rat’s motor cortex. To accurately decode EMG envelopes from M1 neural spike trains, the Monte Carlo point process (MCPP) method is implemented based on such nonlinear tuning properties. Main results. Better reconstruction of EMG signals is shown on baseline and extreme high peaks, as our method can better preserve the nonlinearity of the neural tuning during decoding. The MCPP improves the prediction accuracy (the normalized mean squared error) 57% and 66% on average compared with the adaptive point process filter using linear and exponential tuning curves respectively, for all 112 data segments across six rats. Compared to a Wiener filter using spike rates with an optimal window size of 50 ms, MCPP decoding EMG from a point process improves the normalized mean square error (NMSE) by 59% on average. Significance. These results suggest that neural tuning is constantly changing during task execution and therefore, the use of spike timing methodologies and estimation of appropriate tuning curves needs to be undertaken for better EMG decoding in motor BMIs.

Temporal Correlations and Neural Spike Train Entropy

NASA Astrophysics Data System (ADS)

Schultz, Simon R.; Panzeri, Stefano

2001-06-01

Sampling considerations limit the experimental conditions under which information theoretic analyses of neurophysiological data yield reliable results. We develop a procedure for computing the full temporal entropy and information of ensembles of neural spike trains, which performs reliably for limited samples of data. This approach also yields insight to the role of correlations between spikes in temporal coding mechanisms. The method, when applied to recordings from complex cells of the monkey primary visual cortex, results in lower rms error information estimates in comparison to a ``brute force'' approach.

The Q-Sort method: use in landscape assessment research and landscape planning

Treesearch

David G. Pitt; Ervin H. Zube

1979-01-01

The assessment of visual quality inherently involves the measurement of perceptual response to landscape. The Q-Sort Method is a psychometric technique which produces reliable and valid interval measurements of people's perceptions of landscape visual quality as depicted in photographs. It is readily understood by participants across a wide range of age groups and...

Microspherical photonics: Sorting resonant photonic atoms by using light

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

Maslov, Alexey V., E-mail: avmaslov@yandex.ru; Astratov, Vasily N., E-mail: astratov@uncc.edu

2014-09-22

A method of sorting microspheres by resonant light forces in vacuum, air, or liquid is proposed. Based on a two-dimensional model, it is shown that the sorting can be realized by allowing spherical particles to traverse a focused beam. Under resonance with the whispering gallery modes, the particles acquire significant velocity along the beam direction. This opens a unique way of large-volume sorting of nearly identical photonic atoms with 1/Q accuracy, where Q is the resonance quality factor. This is an enabling technology for developing super-low-loss coupled-cavity structures and devices.

Droplet electric separator microfluidic device for cell sorting

NASA Astrophysics Data System (ADS)

Guo, Feng; Ji, Xing-Hu; Liu, Kan; He, Rong-Xiang; Zhao, Li-Bo; Guo, Zhi-Xiao; Liu, Wei; Guo, Shi-Shang; Zhao, Xing-Zhong

2010-05-01

A simple and effective droplet electric separator microfluidic device was developed for cell sorting. The aqueous droplet without precharging operation was influenced to move a distance in the channel along the electric field direction by applying dc voltage on the electrodes beside the channel, which made the target droplet flowing to the collector. Single droplet can be isolated in a sorting rate of ˜100 Hz with microelectrodes under a required pulse. Single or multiple mammalian cell (HePG2) encapsulated in the surfactant free alginate droplet could be sorted out respectively. This method may be used for single cell operation or analysis.

Evaluation of microarray data normalization procedures using spike-in experiments

PubMed Central

Rydén, Patrik; Andersson, Henrik; Landfors, Mattias; Näslund, Linda; Hartmanová, Blanka; Noppa, Laila; Sjöstedt, Anders

2006-01-01

Background Recently, a large number of methods for the analysis of microarray data have been proposed but there are few comparisons of their relative performances. By using so-called spike-in experiments, it is possible to characterize the analyzed data and thereby enable comparisons of different analysis methods. Results A spike-in experiment using eight in-house produced arrays was used to evaluate established and novel methods for filtration, background adjustment, scanning, channel adjustment, and censoring. The S-plus package EDMA, a stand-alone tool providing characterization of analyzed cDNA-microarray data obtained from spike-in experiments, was developed and used to evaluate 252 normalization methods. For all analyses, the sensitivities at low false positive rates were observed together with estimates of the overall bias and the standard deviation. In general, there was a trade-off between the ability of the analyses to identify differentially expressed genes (i.e. the analyses' sensitivities) and their ability to provide unbiased estimators of the desired ratios. Virtually all analysis underestimated the magnitude of the regulations; often less than 50% of the true regulations were observed. Moreover, the bias depended on the underlying mRNA-concentration; low concentration resulted in high bias. Many of the analyses had relatively low sensitivities, but analyses that used either the constrained model (i.e. a procedure that combines data from several scans) or partial filtration (a novel method for treating data from so-called not-found spots) had with few exceptions high sensitivities. These methods gave considerable higher sensitivities than some commonly used analysis methods. Conclusion The use of spike-in experiments is a powerful approach for evaluating microarray preprocessing procedures. Analyzed data are characterized by properties of the observed log-ratios and the analysis' ability to detect differentially expressed genes. If bias is not a major problem; we recommend the use of either the CM-procedure or partial filtration. PMID:16774679

Analysis of noise-induced temporal correlations in neuronal spike sequences

NASA Astrophysics Data System (ADS)

Reinoso, José A.; Torrent, M. C.; Masoller, Cristina

2016-11-01

We investigate temporal correlations in sequences of noise-induced neuronal spikes, using a symbolic method of time-series analysis. We focus on the sequence of time-intervals between consecutive spikes (inter-spike-intervals, ISIs). The analysis method, known as ordinal analysis, transforms the ISI sequence into a sequence of ordinal patterns (OPs), which are defined in terms of the relative ordering of consecutive ISIs. The ISI sequences are obtained from extensive simulations of two neuron models (FitzHugh-Nagumo, FHN, and integrate-and-fire, IF), with correlated noise. We find that, as the noise strength increases, temporal order gradually emerges, revealed by the existence of more frequent ordinal patterns in the ISI sequence. While in the FHN model the most frequent OP depends on the noise strength, in the IF model it is independent of the noise strength. In both models, the correlation time of the noise affects the OP probabilities but does not modify the most probable pattern.

A combined emitter threat assessment method based on ICW-RCM

NASA Astrophysics Data System (ADS)

Zhang, Ying; Wang, Hongwei; Guo, Xiaotao; Wang, Yubing

2017-08-01

Considering that the tradition al emitter threat assessment methods are difficult to intuitively reflect the degree of target threaten and the deficiency of real-time and complexity, on the basis of radar chart method(RCM), an algorithm of emitter combined threat assessment based on ICW-RCM (improved combination weighting method, ICW) is proposed. The coarse sorting is integrated with fine sorting in emitter combined threat assessment, sequencing the emitter threat level roughly accordance to radar operation mode, and reducing task priority of the low-threat emitter; On the basis of ICW-RCM, sequencing the same radar operation mode emitter roughly, finally, obtain the results of emitter threat assessment through coarse and fine sorting. Simulation analyses show the correctness and effectiveness of this algorithm. Comparing with classical method of emitter threat assessment based on CW-RCM, the algorithm is visual in image and can work quickly with lower complexity.

Isolation and characterization of anti-SEB peptides using magnetic sorting and bacterial peptide display library technology

NASA Astrophysics Data System (ADS)

Pennington, Joseph M.; Kogot, Joshua M.; Sarkes, Deborah A.; Pellegrino, Paul M.; Stratis-Cullum, Dimitra N.

2012-06-01

Peptide display libraries offer an alternative method to existing antibody development methods enabling rapid isolation of highly stable reagents for detection of new and emerging biological threats. Bacterial display libraries are used to isolate new peptide reagents within 1 week, which is simpler and timelier than using competing display library technology based on phage or yeast. Using magnetic sorting methods, we have isolated peptide reagents with high affinity and specificity to staphylococcal enterotoxin B (SEB), a suspected food pathogen. Flow cytometry methods were used for on-cell characterization and the binding affinity (Kd) of this new peptide reagent was determined to be 56 nm with minimal cross-reactivity to other proteins. These results demonstrated that magnetic sorting for new reagents using bacterial display libraries is a rapid and effective method and has the potential for current and new and emerging food pathogen targets.

Brain-Based Devices for Neuromorphic Computer Systems

DTIC Science & Technology

2013-07-01

and Deco, G. (2012). Effective Visual Working Memory Capacity: An Emergent Effect from the Neural Dynamics in an Attractor Network. PLoS ONE 7, e42719...models, apply them to a recognition task, and to demonstrate a working memory . In the course of this work a new analytical method for spiking data was...4 3.4 Spiking Neural Model Simulation of Working Memory ..................................... 5 3.5 A Novel Method for Analysis

Measuring multiple spike train synchrony.

PubMed

Kreuz, Thomas; Chicharro, Daniel; Andrzejak, Ralph G; Haas, Julie S; Abarbanel, Henry D I

2009-10-15

Measures of multiple spike train synchrony are essential in order to study issues such as spike timing reliability, network synchronization, and neuronal coding. These measures can broadly be divided in multivariate measures and averages over bivariate measures. One of the most recent bivariate approaches, the ISI-distance, employs the ratio of instantaneous interspike intervals (ISIs). In this study we propose two extensions of the ISI-distance, the straightforward averaged bivariate ISI-distance and the multivariate ISI-diversity based on the coefficient of variation. Like the original measure these extensions combine many properties desirable in applications to real data. In particular, they are parameter-free, time scale independent, and easy to visualize in a time-resolved manner, as we illustrate with in vitro recordings from a cortical neuron. Using a simulated network of Hindemarsh-Rose neurons as a controlled configuration we compare the performance of our methods in distinguishing different levels of multi-neuron spike train synchrony to the performance of six other previously published measures. We show and explain why the averaged bivariate measures perform better than the multivariate ones and why the multivariate ISI-diversity is the best performer among the multivariate methods. Finally, in a comparison against standard methods that rely on moving window estimates, we use single-unit monkey data to demonstrate the advantages of the instantaneous nature of our methods.

A case for spiking neural network simulation based on configurable multiple-FPGA systems.

PubMed

Yang, Shufan; Wu, Qiang; Li, Renfa

2011-09-01

Recent neuropsychological research has begun to reveal that neurons encode information in the timing of spikes. Spiking neural network simulations are a flexible and powerful method for investigating the behaviour of neuronal systems. Simulation of the spiking neural networks in software is unable to rapidly generate output spikes in large-scale of neural network. An alternative approach, hardware implementation of such system, provides the possibility to generate independent spikes precisely and simultaneously output spike waves in real time, under the premise that spiking neural network can take full advantage of hardware inherent parallelism. We introduce a configurable FPGA-oriented hardware platform for spiking neural network simulation in this work. We aim to use this platform to combine the speed of dedicated hardware with the programmability of software so that it might allow neuroscientists to put together sophisticated computation experiments of their own model. A feed-forward hierarchy network is developed as a case study to describe the operation of biological neural systems (such as orientation selectivity of visual cortex) and computational models of such systems. This model demonstrates how a feed-forward neural network constructs the circuitry required for orientation selectivity and provides platform for reaching a deeper understanding of the primate visual system. In the future, larger scale models based on this framework can be used to replicate the actual architecture in visual cortex, leading to more detailed predictions and insights into visual perception phenomenon.

Spatiotemporal Spike Coding of Behavioral Adaptation in the Dorsal Anterior Cingulate Cortex

PubMed Central

Logiaco, Laureline; Quilodran, René; Procyk, Emmanuel; Arleo, Angelo

2015-01-01

The frontal cortex controls behavioral adaptation in environments governed by complex rules. Many studies have established the relevance of firing rate modulation after informative events signaling whether and how to update the behavioral policy. However, whether the spatiotemporal features of these neuronal activities contribute to encoding imminent behavioral updates remains unclear. We investigated this issue in the dorsal anterior cingulate cortex (dACC) of monkeys while they adapted their behavior based on their memory of feedback from past choices. We analyzed spike trains of both single units and pairs of simultaneously recorded neurons using an algorithm that emulates different biologically plausible decoding circuits. This method permits the assessment of the performance of both spike-count and spike-timing sensitive decoders. In response to the feedback, single neurons emitted stereotypical spike trains whose temporal structure identified informative events with higher accuracy than mere spike count. The optimal decoding time scale was in the range of 70–200 ms, which is significantly shorter than the memory time scale required by the behavioral task. Importantly, the temporal spiking patterns of single units were predictive of the monkeys’ behavioral response time. Furthermore, some features of these spiking patterns often varied between jointly recorded neurons. All together, our results suggest that dACC drives behavioral adaptation through complex spatiotemporal spike coding. They also indicate that downstream networks, which decode dACC feedback signals, are unlikely to act as mere neural integrators. PMID:26266537

Particle swarm optimization algorithm based parameters estimation and control of epileptiform spikes in a neural mass model

NASA Astrophysics Data System (ADS)

Shan, Bonan; Wang, Jiang; Deng, Bin; Wei, Xile; Yu, Haitao; Zhang, Zhen; Li, Huiyan

2016-07-01

This paper proposes an epilepsy detection and closed-loop control strategy based on Particle Swarm Optimization (PSO) algorithm. The proposed strategy can effectively suppress the epileptic spikes in neural mass models, where the epileptiform spikes are recognized as the biomarkers of transitions from the normal (interictal) activity to the seizure (ictal) activity. In addition, the PSO algorithm shows capabilities of accurate estimation for the time evolution of key model parameters and practical detection for all the epileptic spikes. The estimation effects of unmeasurable parameters are improved significantly compared with unscented Kalman filter. When the estimated excitatory-inhibitory ratio exceeds a threshold value, the epileptiform spikes can be inhibited immediately by adopting the proportion-integration controller. Besides, numerical simulations are carried out to illustrate the effectiveness of the proposed method as well as the potential value for the model-based early seizure detection and closed-loop control treatment design.

Asymptotic Linear Spectral Statistics for Spiked Hermitian Random Matrices

NASA Astrophysics Data System (ADS)

Passemier, Damien; McKay, Matthew R.; Chen, Yang

2015-07-01

Using the Coulomb Fluid method, this paper derives central limit theorems (CLTs) for linear spectral statistics of three "spiked" Hermitian random matrix ensembles. These include Johnstone's spiked model (i.e., central Wishart with spiked correlation), non-central Wishart with rank-one non-centrality, and a related class of non-central matrices. For a generic linear statistic, we derive simple and explicit CLT expressions as the matrix dimensions grow large. For all three ensembles under consideration, we find that the primary effect of the spike is to introduce an correction term to the asymptotic mean of the linear spectral statistic, which we characterize with simple formulas. The utility of our proposed framework is demonstrated through application to three different linear statistics problems: the classical likelihood ratio test for a population covariance, the capacity analysis of multi-antenna wireless communication systems with a line-of-sight transmission path, and a classical multiple sample significance testing problem.

On the use of video projectors for three-dimensional scanning

NASA Astrophysics Data System (ADS)

Juarez-Salazar, Rigoberto; Diaz-Ramirez, Victor H.; Robledo-Sanchez, Carlos; Diaz-Gonzalez, Gerardo

2017-08-01

Structured light projection is one of the most useful methods for accurate three-dimensional scanning. Video projectors are typically used as the illumination source. However, because video projectors are not designed for structured light systems, some considerations such as gamma calibration must be taken into account. In this work, we present a simple method for gamma calibration of video projectors. First, the experimental fringe patterns are normalized. Then, the samples of the fringe patterns are sorted in ascending order. The sample sorting leads to a simple three-parameter sine curve that is fitted using the Gauss-Newton algorithm. The novelty of this method is that the sorting process removes the effect of the unknown phase. Thus, the resulting gamma calibration algorithm is significantly simplified. The feasibility of the proposed method is illustrated in a three-dimensional scanning experiment.

A degradation-based sorting method for lithium-ion battery reuse

PubMed Central

Chen, Hao

2017-01-01

In a world where millions of people are dependent on batteries to provide them with convenient and portable energy, battery recycling is of the utmost importance. In this paper, we developed a new method to sort 18650 Lithium-ion batteries in large quantities and in real time for harvesting used cells with enough capacity for battery reuse. Internal resistance and capacity tests were conducted as a basis for comparison with a novel degradation-based method based on X-ray radiographic scanning and digital image contrast computation. The test results indicate that the sorting accuracy of the test cells is about 79% and the execution time of our algorithm is at a level of 200 milliseconds, making our method a potential real-time solution for reusing the remaining capacity in good used cells. PMID:29023485

Particle migration and sorting in microbubble streaming flows

PubMed Central

Thameem, Raqeeb; Hilgenfeldt, Sascha

2016-01-01

Ultrasonic driving of semicylindrical microbubbles generates strong streaming flows that are robust over a wide range of driving frequencies. We show that in microchannels, these streaming flow patterns can be combined with Poiseuille flows to achieve two distinctive, highly tunable methods for size-sensitive sorting and trapping of particles much smaller than the bubble itself. This method allows higher throughput than typical passive sorting techniques, since it does not require the inclusion of device features on the order of the particle size. We propose a simple mechanism, based on channel and flow geometry, which reliably describes and predicts the sorting behavior observed in experiment. It is also shown that an asymptotic theory that incorporates the device geometry and superimposed channel flow accurately models key flow features such as peak speeds and particle trajectories, provided it is appropriately modified to account for 3D effects caused by the axial confinement of the bubble. PMID:26958103

Revealing degree distribution of bursting neuron networks.

PubMed

Shen, Yu; Hou, Zhonghuai; Xin, Houwen

2010-03-01

We present a method to infer the degree distribution of a bursting neuron network from its dynamics. Burst synchronization (BS) of coupled Morris-Lecar neurons has been studied under the weak coupling condition. In the BS state, all the neurons start and end bursting almost simultaneously, while the spikes inside the burst are incoherent among the neurons. Interestingly, we find that the spike amplitude of a given neuron shows an excellent linear relationship with its degree, which makes it possible to estimate the degree distribution of the network by simple statistics of the spike amplitudes. We demonstrate the validity of this scheme on scale-free as well as small-world networks. The underlying mechanism of such a method is also briefly discussed.

ANALYSES OF FISH TISSUE BY VACUUM DISTILLATION/GAS CHROMATOGRAPHY/MASS SPECTROMETRY

EPA Science Inventory

The analyses of fish tissue using VD/GC/MS with surrogate-based matrix corrections is described. Techniques for equilibrating surrogate and analyte spikes with a tissue matrix are presented, and equilibrated spiked samples are used to document method performance. The removal of a...

Rapid discovery of peptide capture candidates with demonstrated specificity for structurally similar toxins

NASA Astrophysics Data System (ADS)

Sarkes, Deborah A.; Hurley, Margaret M.; Coppock, Matthew B.; Farrell, Mikella E.; Pellegrino, Paul M.; Stratis-Cullum, Dimitra N.

2016-05-01

Peptides have emerged as viable alternatives to antibodies for molecular-based sensing due to their similarity in recognition ability despite their relative structural simplicity. Various methods for peptide capture reagent discovery exist, including phage display, yeast display, and bacterial display. One of the primary advantages of peptide discovery by bacterial display technology is the speed to candidate peptide capture agent, due to both rapid growth of bacteria and direct utilization of the sorted cells displaying each individual peptide for the subsequent round of biopanning. We have previously isolated peptide affinity reagents towards protective antigen of Bacillus anthracis using a commercially available automated magnetic sorting platform with improved enrichment as compared to manual magnetic sorting. In this work, we focus on adapting our automated biopanning method to a more challenging sort, to demonstrate the specificity possible with peptide capture agents. This was achieved using non-toxic, recombinant variants of ricin and abrin, RiVax and abrax, respectively, which are structurally similar Type II ribosomal inactivating proteins with significant sequence homology. After only two rounds of biopanning, enrichment of peptide capture candidates binding abrax but not RiVax was achieved as demonstrated by Fluorescence Activated Cell Sorting (FACS) studies. Further sorting optimization included negative sorting against RiVax, proper selection of autoMACS programs for specific sorting rounds, and using freshly made buffer and freshly thawed protein target for each round of biopanning for continued enrichment over all four rounds. Most of the resulting candidates from biopanning for abrax binding peptides were able to bind abrax but not RiVax, demonstrating that short peptide sequences can be highly specific even at this early discovery stage.

A Spiking Neural Simulator Integrating Event-Driven and Time-Driven Computation Schemes Using Parallel CPU-GPU Co-Processing: A Case Study.

PubMed

Naveros, Francisco; Luque, Niceto R; Garrido, Jesús A; Carrillo, Richard R; Anguita, Mancia; Ros, Eduardo

2015-07-01

Time-driven simulation methods in traditional CPU architectures perform well and precisely when simulating small-scale spiking neural networks. Nevertheless, they still have drawbacks when simulating large-scale systems. Conversely, event-driven simulation methods in CPUs and time-driven simulation methods in graphic processing units (GPUs) can outperform CPU time-driven methods under certain conditions. With this performance improvement in mind, we have developed an event-and-time-driven spiking neural network simulator suitable for a hybrid CPU-GPU platform. Our neural simulator is able to efficiently simulate bio-inspired spiking neural networks consisting of different neural models, which can be distributed heterogeneously in both small layers and large layers or subsystems. For the sake of efficiency, the low-activity parts of the neural network can be simulated in CPU using event-driven methods while the high-activity subsystems can be simulated in either CPU (a few neurons) or GPU (thousands or millions of neurons) using time-driven methods. In this brief, we have undertaken a comparative study of these different simulation methods. For benchmarking the different simulation methods and platforms, we have used a cerebellar-inspired neural-network model consisting of a very dense granular layer and a Purkinje layer with a smaller number of cells (according to biological ratios). Thus, this cerebellar-like network includes a dense diverging neural layer (increasing the dimensionality of its internal representation and sparse coding) and a converging neural layer (integration) similar to many other biologically inspired and also artificial neural networks.

Adjustment of pesticide concentrations for temporal changes in analytical recovery, 1992–2010

USGS Publications Warehouse

Martin, Jeffrey D.; Eberle, Michael

2011-01-01

Recovery is the proportion of a target analyte that is quantified by an analytical method and is a primary indicator of the analytical bias of a measurement. Recovery is measured by analysis of quality-control (QC) water samples that have known amounts of target analytes added ("spiked" QC samples). For pesticides, recovery is the measured amount of pesticide in the spiked QC sample expressed as a percentage of the amount spiked, ideally 100 percent. Temporal changes in recovery have the potential to adversely affect time-trend analysis of pesticide concentrations by introducing trends in apparent environmental concentrations that are caused by trends in performance of the analytical method rather than by trends in pesticide use or other environmental conditions. This report presents data and models related to the recovery of 44 pesticides and 8 pesticide degradates (hereafter referred to as "pesticides") that were selected for a national analysis of time trends in pesticide concentrations in streams. Water samples were analyzed for these pesticides from 1992 through 2010 by gas chromatography/mass spectrometry. Recovery was measured by analysis of pesticide-spiked QC water samples. Models of recovery, based on robust, locally weighted scatterplot smooths (lowess smooths) of matrix spikes, were developed separately for groundwater and stream-water samples. The models of recovery can be used to adjust concentrations of pesticides measured in groundwater or stream-water samples to 100 percent recovery to compensate for temporal changes in the performance (bias) of the analytical method.

Methods of automated absence seizure detection, interference by stimulation, and possibilities for prediction in genetic absence models.

PubMed

van Luijtelaar, Gilles; Lüttjohann, Annika; Makarov, Vladimir V; Maksimenko, Vladimir A; Koronovskii, Alexei A; Hramov, Alexander E

2016-02-15

Genetic rat models for childhood absence epilepsy have become instrumental in developing theories on the origin of absence epilepsy, the evaluation of new and experimental treatments, as well as in developing new methods for automatic seizure detection, prediction, and/or interference of seizures. Various methods for automated off and on-line analyses of ECoG in rodent models are reviewed, as well as data on how to interfere with the spike-wave discharges by different types of invasive and non-invasive electrical, magnetic, and optical brain stimulation. Also a new method for seizure prediction is proposed. Many selective and specific methods for off- and on-line spike-wave discharge detection seem excellent, with possibilities to overcome the issue of individual differences. Moreover, electrical deep brain stimulation is rather effective in interrupting ongoing spike-wave discharges with low stimulation intensity. A network based method is proposed for absence seizures prediction with a high sensitivity but a low selectivity. Solutions that prevent false alarms, integrated in a closed loop brain stimulation system open the ways for experimental seizure control. The presence of preictal cursor activity detected with state of the art time frequency and network analyses shows that spike-wave discharges are not caused by sudden and abrupt transitions but that there are detectable dynamic events. Their changes in time-space-frequency characteristics might yield new options for seizure prediction and seizure control. Copyright © 2015 Elsevier B.V. All rights reserved.

Static optical sorting in a laser interference field

NASA Astrophysics Data System (ADS)

Jákl, Petr; Čižmár, Tomáš; Šerý, Mojmír; Zemánek, Pavel

2008-04-01

We present a unique technique for optical sorting of heterogeneous suspensions of microparticles, which does not require the flow of the immersion medium. The method employs the size-dependent response of suspended dielectric particles to the optical field of three intersecting beams that form a fringelike interference pattern. We experimentally demonstrate sorting of a polydisperse suspension of polystyrene beads of diameters 1, 2, and 5.2μm and living yeast cells.

Acoustic bubble sorting for ultrasound contrast agent enrichment.

PubMed

Segers, Tim; Versluis, Michel

2014-05-21

An ultrasound contrast agent (UCA) suspension contains encapsulated microbubbles with a wide size distribution, with radii ranging from 1 to 10 μm. Medical transducers typically operate at a single frequency, therefore only a small selection of bubbles will resonate to the driving ultrasound pulse. Thus, the sensitivity can be improved by narrowing down the size distribution. Here, we present a simple lab-on-a-chip method to sort the population of microbubbles on-chip using a traveling ultrasound wave. First, we explore the physical parameter space of acoustic bubble sorting using well-defined bubble sizes formed in a flow-focusing device, then we demonstrate successful acoustic sorting of a commercial UCA. This novel sorting strategy may lead to an overall improvement of the sensitivity of contrast ultrasound by more than 10 dB.

Klusters, NeuroScope, NDManager: a free software suite for neurophysiological data processing and visualization.

PubMed

Hazan, Lynn; Zugaro, Michaël; Buzsáki, György

2006-09-15

Recent technological advances now allow for simultaneous recording of large populations of anatomically distributed neurons in behaving animals. The free software package described here was designed to help neurophysiologists process and view recorded data in an efficient and user-friendly manner. This package consists of several well-integrated applications, including NeuroScope (http://neuroscope.sourceforce.net), an advanced viewer for electrophysiological and behavioral data with limited editing capabilities, Klusters (http://klusters.sourceforge.net), a graphical cluster cutting application for manual and semi-automatic spike sorting, NDManager (GPL,see http://www.gnu.org/licenses/gpl.html), an experimental parameter and data processing manager. All of these programs are distributed under the GNU General Public License (GPL, see ), which gives its users legal permission to copy, distribute and/or modify the software. Also included are extensive user manuals and sample data, as well as source code and documentation.

Frequency domain beamforming of magnetoencephalographic beta band activity in epilepsy patients with focal cortical dysplasia.

PubMed

Heers, Marcel; Hirschmann, Jan; Jacobs, Julia; Dümpelmann, Matthias; Butz, Markus; von Lehe, Marec; Elger, Christian E; Schnitzler, Alfons; Wellmer, Jörg

2014-09-01

Spike-based magnetoencephalography (MEG) source localization is an established method in the presurgical evaluation of epilepsy patients. Focal cortical dysplasias (FCDs) are associated with focal epileptic discharges of variable morphologies in the beta frequency band in addition to single epileptic spikes. Therefore, we investigated the potential diagnostic value of MEG-based localization of spike-independent beta band (12-30Hz) activity generated by epileptogenic lesions. Five patients with FCD IIB underwent MEG. In one patient, invasive EEG (iEEG) was recorded simultaneously with MEG. In two patients, iEEG succeeded MEG, and two patients had MEG only. MEG and iEEG were evaluated for epileptic spikes. Two minutes of iEEG data and MEG epochs with no spikes as well as MEG epochs with epileptic spikes were analyzed in the frequency domain. MEG oscillatory beta band activity was localized using Dynamic Imaging of Coherent Sources. Intralesional beta band activity was coherent between simultaneous MEG and iEEG recordings. Continuous 14Hz beta band power correlated with the rate of interictal epileptic discharges detected in iEEG. In cases where visual MEG evaluation revealed epileptic spikes, the sources of beta band activity localized within <2cm of the epileptogenic lesion as shown on magnetic resonance imaging. This result held even when visually marked epileptic spikes were deselected. When epileptic spikes were detectable in iEEG but not MEG, MEG beta band activity source localization failed. Source localization of beta band activity has the potential to contribute to the identification of epileptic foci in addition to source localization of visually marked epileptic spikes. Thus, this technique may assist in the localization of epileptic foci in patients with suspected FCD. Copyright © 2014 Elsevier B.V. All rights reserved.

A high performing brain-machine interface driven by low-frequency local field potentials alone and together with spikes

NASA Astrophysics Data System (ADS)

Stavisky, Sergey D.; Kao, Jonathan C.; Nuyujukian, Paul; Ryu, Stephen I.; Shenoy, Krishna V.

2015-06-01

Objective. Brain-machine interfaces (BMIs) seek to enable people with movement disabilities to directly control prosthetic systems with their neural activity. Current high performance BMIs are driven by action potentials (spikes), but access to this signal often diminishes as sensors degrade over time. Decoding local field potentials (LFPs) as an alternative or complementary BMI control signal may improve performance when there is a paucity of spike signals. To date only a small handful of LFP decoding methods have been tested online; there remains a need to test different LFP decoding approaches and improve LFP-driven performance. There has also not been a reported demonstration of a hybrid BMI that decodes kinematics from both LFP and spikes. Here we first evaluate a BMI driven by the local motor potential (LMP), a low-pass filtered time-domain LFP amplitude feature. We then combine decoding of both LMP and spikes to implement a hybrid BMI. Approach. Spikes and LFP were recorded from two macaques implanted with multielectrode arrays in primary and premotor cortex while they performed a reaching task. We then evaluated closed-loop BMI control using biomimetic decoders driven by LMP, spikes, or both signals together. Main results. LMP decoding enabled quick and accurate cursor control which surpassed previously reported LFP BMI performance. Hybrid decoding of both spikes and LMP improved performance when spikes signal quality was mediocre to poor. Significance. These findings show that LMP is an effective BMI control signal which requires minimal power to extract and can substitute for or augment impoverished spikes signals. Use of this signal may lengthen the useful lifespan of BMIs and is therefore an important step towards clinically viable BMIs.

Development of procedures for sex-sorting frozen-thawed bovine spermatozoa.

PubMed

Underwood, S L; Bathgate, R; Maxwell, W M C; Evans, G

2009-06-01

Dairy bull sperm may be sex-sorted, frozen and used to artificially inseminate heifers with acceptable fertility if the herd is well-managed. One drawback to the technology is that donor bulls must be located within a short distance of the sorting facility in order to collect semen, which limits the number of bulls from which sorted sperm are available. A successful method used to overcome this limitation in sheep is sex-sorting from frozen-thawed semen and refreezing for artificial insemination. This technique is attractive to the dairy industry, and therefore a series of three experiments was designed to investigate the optimal methods to prepare, sex-sort and re-freeze frozen-thawed bovine sperm. Sperm were prepared for sorting by density gradient separation in either PureSperm or BoviPure, followed by staining in one of three diluents (Androhep, Bovine Sheath Fluid + 0.3% BSA or TALP buffer). Sperm were sorted and collected into Test yolk buffer, and frozen in an extender containing 0, 0.25, 0.375 or 0.5% Equex STM Paste. Frozen-thawed sperm were better orientated (p = 0.006) and had fewer damaged membranes (8.7 +/- 0.6% vs 19.5 +/- 2.4%; p = 0.003) after centrifugation in PureSperm rather than BoviPure gradients. Sperm orientation (p < 0.05) and motility (69.9 +/- 3.0 vs 55.6 +/- 4.0; p < 0.001) were highest after staining in Androhep rather than in TALP buffer. Sperm were more motile (58.2 +/- 4.7 vs 38.7 +/- 3.5; p < 0.001) and had better acrosome integrity (74.3 +/- 2.9 vs 66.8 +/- 2.0; p < 0.001) after freezing in an extender containing 0.375% Equex STM Paste than in extender without Equex. Hence, a protocol has been developed to allow frozen-thawed bull sperm to be sex-sorted with high resolution between the sexes, then re-frozen and thawed with retention of motility and acrosome integrity.

Fecal electrolyte testing for evaluation of unexplained diarrhea: Validation of body fluid test accuracy in the absence of a reference method.

PubMed

Voskoboev, Nikolay V; Cambern, Sarah J; Hanley, Matthew M; Giesen, Callen D; Schilling, Jason J; Jannetto, Paul J; Lieske, John C; Block, Darci R

2015-11-01

Validation of tests performed on body fluids other than blood or urine can be challenging due to the lack of a reference method to confirm accuracy. The aim of this study was to evaluate alternate assessments of accuracy that laboratories can rely on to validate body fluid tests in the absence of a reference method using the example of sodium (Na(+)), potassium (K(+)), and magnesium (Mg(2+)) testing in stool fluid. Validations of fecal Na(+), K(+), and Mg(2+) were performed on the Roche cobas 6000 c501 (Roche Diagnostics) using residual stool specimens submitted for clinical testing. Spiked recovery, mixing studies, and serial dilutions were performed and % recovery of each analyte was calculated to assess accuracy. Results were confirmed by comparison to a reference method (ICP-OES, PerkinElmer). Mean recoveries for fecal electrolytes were Na(+) upon spiking=92%, mixing=104%, and dilution=105%; K(+) upon spiking=94%, mixing=96%, and dilution=100%; and Mg(2+) upon spiking=93%, mixing=98%, and dilution=100%. When autoanalyzer results were compared to reference ICP-OES results, Na(+) had a slope=0.94, intercept=4.1, and R(2)=0.99; K(+) had a slope=0.99, intercept=0.7, and R(2)=0.99; and Mg(2+) had a slope=0.91, intercept=-4.6, and R(2)=0.91. Calculated osmotic gap using both methods were highly correlated with slope=0.95, intercept=4.5, and R(2)=0.97. Acid pretreatment increased magnesium recovery from a subset of clinical specimens. A combination of mixing, spiking, and dilution recovery experiments are an acceptable surrogate for assessing accuracy in body fluid validations in the absence of a reference method. Copyright © 2015 The Canadian Society of Clinical Chemists. Published by Elsevier Inc. All rights reserved.

Application of a new convenience gender sorting method for mouse spermatozoa to mouse reproductive engineering technology.

PubMed

Hashimoto, Haruo; Eto, Tomoo; Suemizu, Hiroshi; Ito, Mamoru

2013-02-01

In this study, we attempted to apply new convenience gender sorting methods using sex-determining region Y (SRY) gene expression on Y spermatozoa to mice. Mouse spermatozoa labeled with Cy3-SRY antibody conjugate were used for intracytoplasmic sperm injection (ICSI). In addition, spermatozoa conjugated with SRY antibody were conjugated with magnetic beads (Mag) and were pulled to the bottom of the medium. The supernatant of the medium was used for in vitro fertilization (IVF). The rate of males reproduced by ICSI using the spermatozoa conjugated with Cy3-SRY antibody was 86.1%. The female proportion reproduced by IVF using the spermatozoa separated in the supernatant after Mag-SRY antibody conjugation was 67.3%. These gender sorting methods are effective for the reproduction of transgenic mice.

Spatiotemporal source analysis in scalp EEG vs. intracerebral EEG and SPECT: a case study in a 2-year-old child.

PubMed

Aarabi, A; Grebe, R; Berquin, P; Bourel Ponchel, E; Jalin, C; Fohlen, M; Bulteau, C; Delalande, O; Gondry, C; Héberlé, C; Moullart, V; Wallois, F

2012-06-01

This case study aims to demonstrate that spatiotemporal spike discrimination and source analysis are effective to monitor the development of sources of epileptic activity in time and space. Therefore, they can provide clinically useful information allowing a better understanding of the pathophysiology of individual seizures with time- and space-resolved characteristics of successive epileptic states, including interictal, preictal, postictal, and ictal states. High spatial resolution scalp EEGs (HR-EEG) were acquired from a 2-year-old girl with refractory central epilepsy and single-focus seizures as confirmed by intracerebral EEG recordings and ictal single-photon emission computed tomography (SPECT). Evaluation of HR-EEG consists of the following three global steps: (1) creation of the initial head model, (2) automatic spike and seizure detection, and finally (3) source localization. During the source localization phase, epileptic states are determined to allow state-based spike detection and localization of underlying sources for each spike. In a final cluster analysis, localization results are integrated to determine the possible sources of epileptic activity. The results were compared with the cerebral locations identified by intracerebral EEG recordings and SPECT. The results obtained with this approach were concordant with those of MRI, SPECT and distribution of intracerebral potentials. Dipole cluster centres found for spikes in interictal, preictal, ictal and postictal states were situated an average of 6.3mm from the intracerebral contacts with the highest voltage. Both amplitude and shape of spikes change between states. Dispersion of the dipoles was higher in the preictal state than in the postictal state. Two clusters of spikes were identified. The centres of these clusters changed position periodically during the various epileptic states. High-resolution surface EEG evaluated by an advanced algorithmic approach can be used to investigate the spatiotemporal characteristics of sources located in the epileptic focus. The results were validated by standard methods, ensuring good spatial resolution by MRI and SPECT and optimal temporal resolution by intracerebral EEG. Surface EEG can be used to identify different spike clusters and sources of the successive epileptic states. The method that was used in this study will provide physicians with a better understanding of the pathophysiological characteristics of epileptic activities. In particular, this method may be useful for more effective positioning of implantable intracerebral electrodes. Copyright © 2011 Elsevier Masson SAS. All rights reserved.

Microfluidic Blood Cell Preparation: Now and Beyond

PubMed Central

Yu, Zeta Tak For; Yong, Koh Meng Aw; Fu, Jianping

2014-01-01

Blood plays an important role in homeostatic regulation with each of its cellular components having important therapeutic and diagnostic uses. Therefore, separation and sorting of blood cells has been of a great interest to clinicians and researchers. However, while conventional methods of processing blood have been successful in generating relatively pure fractions, they are time consuming, labor intensive, and are not optimal for processing small volume blood samples. In recent years, microfluidics has garnered great interest from clinicians and researchers as a powerful technology for separating blood into different cell fractions. As microfluidics involves fluid manipulation at the microscale level, it has the potential for achieving high-resolution separation and sorting of blood cells down to a single-cell level, with an added benefit of integrating physical and biological methods for blood cell separation and analysis on the same single chip platform. This paper will first review the conventional methods of processing and sorting blood cells, followed by a discussion on how microfluidics is emerging as an efficient tool to rapidly change the field of blood cell sorting for blood-based therapeutic and diagnostic applications. PMID:24515899

Particle separation

NASA Technical Reports Server (NTRS)

Arnott, W. Patrick (Inventor); Chakrabarty, Rajan K. (Inventor); Moosmuller, Hans (Inventor)

2011-01-01

Embodiments of a method for selecting particles, such as based on their morphology, is disclosed. In a particular example, the particles are charged and acquire different amounts of charge, or have different charge distributions, based on their morphology. The particles are then sorted based on their flow properties. In a specific example, the particles are sorted using a differential mobility analyzer, which sorts particles, at least in part, based on their electrical mobility. Given a population of particles with similar electrical mobilities, the disclosed process can be used to sort particles based on the net charge carried by the particle, and thus, given the relationship between charge and morphology, separate the particles based on their morphology.

Particle separation

DOEpatents

Moosmuller, Hans [Reno, NV; Chakrabarty, Rajan K [Reno, NV; Arnott, W Patrick [Reno, NV

2011-04-26

Embodiments of a method for selecting particles, such as based on their morphology, is disclosed. In a particular example, the particles are charged and acquire different amounts of charge, or have different charge distributions, based on their morphology. The particles are then sorted based on their flow properties. In a specific example, the particles are sorted using a differential mobility analyzer, which sorts particles, at least in part, based on their electrical mobility. Given a population of particles with similar electrical mobilities, the disclosed process can be used to sort particles based on the net charge carried by the particle, and thus, given the relationship between charge and morphology, separate the particles based on their morphology.

Non-causal spike filtering improves decoding of movement intention for intracortical BCIs

PubMed Central

Masse, Nicolas Y.; Jarosiewicz, Beata; Simeral, John D.; Bacher, Daniel; Stavisky, Sergey D.; Cash, Sydney S.; Oakley, Erin M.; Berhanu, Etsub; Eskandar, Emad; Friehs, Gerhard; Hochberg, Leigh R.; Donoghue, John P.

2014-01-01

Background Multiple types of neural signals are available for controlling assistive devices through brain-computer interfaces (BCIs). Intracortically-recorded spiking neural signals are attractive for BCIs because they can in principle provide greater fidelity of encoded information compared to electrocorticographic (ECoG) signals and electroencephalograms (EEGs). Recent reports show that the information content of these spiking neural signals can be reliably extracted simply by causally band-pass filtering the recorded extracellular voltage signals and then applying a spike detection threshold, without relying on “sorting” action potentials. New method We show that replacing the causal filter with an equivalent non-causal filter increases the information content extracted from the extracellular spiking signal and improves decoding of intended movement direction. This method can be used for real-time BCI applications by using a 4 ms lag between recording and filtering neural signals. Results Across 18 sessions from two people with tetraplegia enrolled in the BrainGate2 pilot clinical trial, we found that threshold crossing events extracted using this non-causal filtering method were significantly more informative of each participant’s intended cursor kinematics compared to threshold crossing events derived from causally filtered signals. This new method decreased the mean angular error between the intended and decoded cursor direction by 9.7° for participant S3, who was implanted 5.4 years prior to this study, and by 3.5° for participant T2, who was implanted 3 months prior to this study. Conclusions Non-causally filtering neural signals prior to extracting threshold crossing events may be a simple yet effective way to condition intracortically recorded neural activity for direct control of external devices through BCIs. PMID:25128256

Parameter estimation of history-dependent leaky integrate-and-fire neurons using maximum-likelihood methods

PubMed Central

Dong, Yi; Mihalas, Stefan; Russell, Alexander; Etienne-Cummings, Ralph; Niebur, Ernst

2012-01-01

When a neuronal spike train is observed, what can we say about the properties of the neuron that generated it? A natural way to answer this question is to make an assumption about the type of neuron, select an appropriate model for this type, and then to choose the model parameters as those that are most likely to generate the observed spike train. This is the maximum likelihood method. If the neuron obeys simple integrate and fire dynamics, Paninski, Pillow, and Simoncelli (2004) showed that its negative log-likelihood function is convex and that its unique global minimum can thus be found by gradient descent techniques. The global minimum property requires independence of spike time intervals. Lack of history dependence is, however, an important constraint that is not fulfilled in many biological neurons which are known to generate a rich repertoire of spiking behaviors that are incompatible with history independence. Therefore, we expanded the integrate and fire model by including one additional variable, a variable threshold (Mihalas & Niebur, 2009) allowing for history-dependent firing patterns. This neuronal model produces a large number of spiking behaviors while still being linear. Linearity is important as it maintains the distribution of the random variables and still allows for maximum likelihood methods to be used. In this study we show that, although convexity of the negative log-likelihood is not guaranteed for this model, the minimum of the negative log-likelihood function yields a good estimate for the model parameters, in particular if the noise level is treated as a free parameter. Furthermore, we show that a nonlinear function minimization method (r-algorithm with space dilation) frequently reaches the global minimum. PMID:21851282

A Novel Nonparametric Approach for Neural Encoding and Decoding Models of Multimodal Receptive Fields.

PubMed

Agarwal, Rahul; Chen, Zhe; Kloosterman, Fabian; Wilson, Matthew A; Sarma, Sridevi V

2016-07-01

Pyramidal neurons recorded from the rat hippocampus and entorhinal cortex, such as place and grid cells, have diverse receptive fields, which are either unimodal or multimodal. Spiking activity from these cells encodes information about the spatial position of a freely foraging rat. At fine timescales, a neuron's spike activity also depends significantly on its own spike history. However, due to limitations of current parametric modeling approaches, it remains a challenge to estimate complex, multimodal neuronal receptive fields while incorporating spike history dependence. Furthermore, efforts to decode the rat's trajectory in one- or two-dimensional space from hippocampal ensemble spiking activity have mainly focused on spike history-independent neuronal encoding models. In this letter, we address these two important issues by extending a recently introduced nonparametric neural encoding framework that allows modeling both complex spatial receptive fields and spike history dependencies. Using this extended nonparametric approach, we develop novel algorithms for decoding a rat's trajectory based on recordings of hippocampal place cells and entorhinal grid cells. Results show that both encoding and decoding models derived from our new method performed significantly better than state-of-the-art encoding and decoding models on 6 minutes of test data. In addition, our model's performance remains invariant to the apparent modality of the neuron's receptive field.

A 16-Channel Nonparametric Spike Detection ASIC Based on EC-PC Decomposition.

PubMed

Wu, Tong; Xu, Jian; Lian, Yong; Khalili, Azam; Rastegarnia, Amir; Guan, Cuntai; Yang, Zhi

2016-02-01

In extracellular neural recording experiments, detecting neural spikes is an important step for reliable information decoding. A successful implementation in integrated circuits can achieve substantial data volume reduction, potentially enabling a wireless operation and closed-loop system. In this paper, we report a 16-channel neural spike detection chip based on a customized spike detection method named as exponential component-polynomial component (EC-PC) algorithm. This algorithm features a reliable prediction of spikes by applying a probability threshold. The chip takes raw data as input and outputs three data streams simultaneously: field potentials, band-pass filtered neural data, and spiking probability maps. The algorithm parameters are on-chip configured automatically based on input data, which avoids manual parameter tuning. The chip has been tested with both in vivo experiments for functional verification and bench-top experiments for quantitative performance assessment. The system has a total power consumption of 1.36 mW and occupies an area of 6.71 mm (2) for 16 channels. When tested on synthesized datasets with spikes and noise segments extracted from in vivo preparations and scaled according to required precisions, the chip outperforms other detectors. A credit card sized prototype board is developed to provide power and data management through a USB port.

Design of Spiking Central Pattern Generators for Multiple Locomotion Gaits in Hexapod Robots by Christiansen Grammar Evolution

PubMed Central

Espinal, Andres; Rostro-Gonzalez, Horacio; Carpio, Martin; Guerra-Hernandez, Erick I.; Ornelas-Rodriguez, Manuel; Sotelo-Figueroa, Marco

2016-01-01

This paper presents a method to design Spiking Central Pattern Generators (SCPGs) to achieve locomotion at different frequencies on legged robots. It is validated through embedding its designs into a Field-Programmable Gate Array (FPGA) and implemented on a real hexapod robot. The SCPGs are automatically designed by means of a Christiansen Grammar Evolution (CGE)-based methodology. The CGE performs a solution for the configuration (synaptic weights and connections) for each neuron in the SCPG. This is carried out through the indirect representation of candidate solutions that evolve to replicate a specific spike train according to a locomotion pattern (gait) by measuring the similarity between the spike trains and the SPIKE distance to lead the search to a correct configuration. By using this evolutionary approach, several SCPG design specifications can be explicitly added into the SPIKE distance-based fitness function, such as looking for Spiking Neural Networks (SNNs) with minimal connectivity or a Central Pattern Generator (CPG) able to generate different locomotion gaits only by changing the initial input stimuli. The SCPG designs have been successfully implemented on a Spartan 6 FPGA board and a real time validation on a 12 Degrees Of Freedom (DOFs) hexapod robot is presented. PMID:27516737

Synthetic spike-in standards for high-throughput 16S rRNA gene amplicon sequencing

PubMed Central

Tourlousse, Dieter M.; Yoshiike, Satowa; Ohashi, Akiko; Matsukura, Satoko; Noda, Naohiro

2017-01-01

Abstract High-throughput sequencing of 16S rRNA gene amplicons (16S-seq) has become a widely deployed method for profiling complex microbial communities but technical pitfalls related to data reliability and quantification remain to be fully addressed. In this work, we have developed and implemented a set of synthetic 16S rRNA genes to serve as universal spike-in standards for 16S-seq experiments. The spike-ins represent full-length 16S rRNA genes containing artificial variable regions with negligible identity to known nucleotide sequences, permitting unambiguous identification of spike-in sequences in 16S-seq read data from any microbiome sample. Using defined mock communities and environmental microbiota, we characterized the performance of the spike-in standards and demonstrated their utility for evaluating data quality on a per-sample basis. Further, we showed that staggered spike-in mixtures added at the point of DNA extraction enable concurrent estimation of absolute microbial abundances suitable for comparative analysis. Results also underscored that template-specific Illumina sequencing artifacts may lead to biases in the perceived abundance of certain taxa. Taken together, the spike-in standards represent a novel bioanalytical tool that can substantially improve 16S-seq-based microbiome studies by enabling comprehensive quality control along with absolute quantification. PMID:27980100

Robust Measurements of Phase Response Curves Realized via Multicycle Weighted Spike-Triggered Averages

NASA Astrophysics Data System (ADS)

Imai, Takashi; Ota, Kaiichiro; Aoyagi, Toshio

2017-02-01

Phase reduction has been extensively used to study rhythmic phenomena. As a result of phase reduction, the rhythm dynamics of a given system can be described using the phase response curve. Measuring this characteristic curve is an important step toward understanding a system's behavior. Recently, a basic idea for a new measurement method (called the multicycle weighted spike-triggered average method) was proposed. This paper confirms the validity of this method by providing an analytical proof and demonstrates its effectiveness in actual experimental systems by applying the method to an oscillating electric circuit. Some practical tips to use the method are also presented.

Method for the determination of catechin and epicatechin enantiomers in cocoa-based ingredients and products by high-performance liquid chromatography: single-laboratory validation.

PubMed

Machonis, Philip R; Jones, Matthew A; Schaneberg, Brian T; Kwik-Uribe, Catherine L

2012-01-01

A single-laboratory validation study was performed for an HPLC method to identify and quantify the flavanol enantiomers (+)- and (-)-epicatechin and (+)- and (-)-catechin in cocoa-based ingredients and products. These compounds were eluted isocratically with an ammonium acetate-methanol mobile phase applied to a modified beta-cyclodextrin chiral stationary phase and detected using fluorescence. Spike recovery experiments using appropriate matrix blanks, along with cocoa extract, cocoa powder, and dark chocolate, were used to evaluate accuracy, repeatability, specificity, LOD, LOQ, and linearity of the method as performed by a single analyst on multiple days. In all samples analyzed, (-)-epicatechin was the predominant flavanol and represented 68-91% of the total monomeric flavanols detected. For the cocoa-based products, within-day (intraday) precision for (-)-epicatechin was between 1.46-3.22%, for (+)-catechin between 3.66-6.90%, and for (-)-catechin between 1.69-6.89%; (+)-epicatechin was not detected in these samples. Recoveries for the three sample types investigated ranged from 82.2 to 102.1% at the 50% spiking level, 83.7 to 102.0% at the 100% spiking level, and 80.4 to 101.1% at the 200% spiking level. Based on performance results, this method may be suitable for routine laboratory use in analysis of cocoa-based ingredients and products.

One-step fabrication of 3D silver paste electrodes into microfluidic devices for enhanced droplet-based cell sorting

NASA Astrophysics Data System (ADS)

Rao, Lang; Cai, Bo; Yu, Xiao-Lei; Guo, Shi-Shang; Liu, Wei; Zhao, Xing-Zhong

2015-05-01

3D microelectrodes are one-step fabricated into a microfluidic droplet separator by filling conductive silver paste into PDMS microchambers. The advantages of 3D silver paste electrodes in promoting droplet sorting accuracy are systematically demonstrated by theoretical calculation, numerical simulation and experimental validation. The employment of 3D electrodes also helps to decrease the droplet sorting voltage, guaranteeing that cells encapsulated in droplets undergo chip-based sorting processes are at better metabolic status for further potential cellular assays. At last, target droplet containing single cell are selectively sorted out from others by an appropriate electric pulse. This method provides a simple and inexpensive alternative to fabricate 3D electrodes, and it is expected our 3D electrode-integrated microfluidic droplet separator platform can be widely used in single cell operation and analysis.

Determination method for nitromethane in workplace air.

PubMed

Takeuchi, Akito; Nishimura, Yasuki; Kaifuku, Yuichiro; Imanaka, Tsutoshi; Natsumeda, Shuichiro; Ota, Hirokazu; Yamada, Shu; Kurotani, Ichiro; Sumino, Kimiaki; Kanno, Seiichiro

2010-01-01

The purpose of this research was to develop a determination method for nitromethane (NM) in workplace air for risk assessment. A suitable sampler and appropriate desorption condition were selected by a recovery test in which a spiked sampler was used. The characteristics of the proposed method, such as recovery, detection limit, and reproducibility, and the storage stability of the sample were examined. A sampling tube containing bead-shaped activated carbon was chosen as the sampler. NM in the sampler was desorbed with acetone and analyzed by a gas chromatograph equipped with a flame ionization detector. The recoveries of NM from the spiked sampler were 81-97% and 80-98% for personal exposure monitoring and working environment measurement, respectively. On the first day of storage in a refrigerator, the recovery from the spiked samplers exceeded 90%; however, it decreased dramatically with increasing storage time. In particular, the decrease was more remarkable for the smaller spiked amounts. The overall LOQ was 2 microg/sample. The relative standard deviation, which represents the overall reproducibility, was 1.1-4.0%. The proposed method enables 4-hour personal exposure monitoring of NM at concentrations equaling 0.001-2 times the threshold limit value-time-weighted average (TLV-TWA: 20 ppm) proposed by the American Conference of Governmental Industrial Hygienists, as well as 10-minute working environment measurement at concentrations equaling 0.02-2 times TLV-TWA. Thus, the proposed method will be useful for estimating worker exposure to NM.

ERAASR: an algorithm for removing electrical stimulation artifacts from multielectrode array recordings

NASA Astrophysics Data System (ADS)

O'Shea, Daniel J.; Shenoy, Krishna V.

2018-04-01

Objective. Electrical stimulation is a widely used and effective tool in systems neuroscience, neural prosthetics, and clinical neurostimulation. However, electrical artifacts evoked by stimulation prevent the detection of spiking activity on nearby recording electrodes, which obscures the neural population response evoked by stimulation. We sought to develop a method to clean artifact-corrupted electrode signals recorded on multielectrode arrays in order to recover the underlying neural spiking activity. Approach. We created an algorithm, which performs estimation and removal of array artifacts via sequential principal components regression (ERAASR). This approach leverages the similar structure of artifact transients, but not spiking activity, across simultaneously recorded channels on the array, across pulses within a train, and across trials. The ERAASR algorithm requires no special hardware, imposes no requirements on the shape of the artifact or the multielectrode array geometry, and comprises sequential application of straightforward linear methods with intuitive parameters. The approach should be readily applicable to most datasets where stimulation does not saturate the recording amplifier. Main results. The effectiveness of the algorithm is demonstrated in macaque dorsal premotor cortex using acute linear multielectrode array recordings and single electrode stimulation. Large electrical artifacts appeared on all channels during stimulation. After application of ERAASR, the cleaned signals were quiescent on channels with no spontaneous spiking activity, whereas spontaneously active channels exhibited evoked spikes which closely resembled spontaneously occurring spiking waveforms. Significance. We hope that enabling simultaneous electrical stimulation and multielectrode array recording will help elucidate the causal links between neural activity and cognition and facilitate naturalistic sensory protheses.

Sperm sex-sorting and preservation for managing the sex ratio and genetic diversity of the southern white rhinoceros (Ceratotherium simum simum).

PubMed

O'Brien, J K; Roth, T L; Stoops, M A; Ball, R L; Steinman, K J; Montano, G A; Love, C C; Robeck, T R

2015-01-01

White rhinoceros ejaculates (n=9) collected by electroejaculation from four males were shipped (10°C, 12h) to develop procedures for the production of chilled and frozen-thawed sex-sorted spermatozoa of adequate quality for artificial insemination (AI). Of all electroejaculate fractions, 39.7% (31/78) exhibited high quality post-collection (≥70% total motility and membrane integrity) and of those, 54.8% (17/31) presented reduced in vitro quality after transport and were retrospectively determined to exhibit urine-contamination (≥21.0μg creatinine/ml). Of fractions analyzed for creatinine concentration, 69% (44/64) were classified as urine-contaminated. For high quality non-contaminated fractions, in vitro parameters (motility, velocity, membrane, acrosome and DNA integrity) of chilled non-sorted and sorted spermatozoa were well-maintained at 5°C up to 54h post-collection, whereby >70% of post-transport (non-sorted) or post-sort (sorted) values were retained. By 54h post-collection, some motility parameters were higher (P<0.05) for non-sorted spermatozoa (total motility, rapid velocity, average path velocity) whereas all remaining motion parameters as well as membrane, acrosome and DNA integrity were similar between sperm types. In comparison with a straw method, directional freezing resulted in enhanced (P<0.05) motility and velocity of non-sorted and sorted spermatozoa, with comparable overall post-thaw quality between sperm types. High purity enrichment of X-bearing (89±6%) or Y-bearing (86±3%) spermatozoa was achieved using moderate sorting rates (2540±498X-spermatozoa/s; 1800±557Y-spermatozoa/s). Collective in vitro characteristics of sorted-chilled or sorted-frozen-thawed spermatozoa derived from high quality electroejaculates indicate acceptable fertility potential for use in AI. Copyright © 2014 Elsevier B.V. All rights reserved.

Encapsulation of sex sorted boar semen: sperm membrane status and oocyte penetration parameters.

PubMed

Spinaci, Marcella; Chlapanidas, Theodora; Bucci, Diego; Vallorani, Claudia; Perteghella, Sara; Lucconi, Giulia; Communod, Ricardo; Vigo, Daniele; Galeati, Giovanna; Faustini, Massimo; Torre, Maria Luisa

2013-03-01

Although sorted semen is experimentally used for artificial, intrauterine, and intratubal insemination and in vitro fertilization, its commercial application in swine species is still far from a reality. This is because of the low sort rate and the large number of sperm required for routine artificial insemination in the pig, compared with other production animals, and the greater susceptibility of porcine spermatozoa to stress induced by the different sex sorting steps and the postsorting handling protocols. The encapsulation technology could overcome this limitation in vivo, protecting and allowing the slow release of low-dose sorted semen. The aim of this work was to evaluate the impact of the encapsulation process on viability, acrosome integrity, and on the in vitro fertilizing potential of sorted boar semen. Our results indicate that the encapsulation technique does not damage boar sorted semen; in fact, during a 72-hour storage, no differences were observed between liquid-stored sorted semen and encapsulated sorted semen in terms of plasma membrane (39.98 ± 14.38% vs. 44.32 ± 11.72%, respectively) and acrosome integrity (74.32 ± 12.17% vs. 66.07 ± 10.83%, respectively). Encapsulated sorted spermatozoa presented a lower penetration potential than nonencapsulated ones (47.02% vs. 24.57%, respectively, P < 0.0001), and a significant reduction of polyspermic fertilization (60.76% vs. 36.43%, respectively, polyspermic ova/total ova; P < 0.0001). However, no difference (P > 0.05) was observed in terms of total efficiency of fertilization expressed as normospermic oocytes/total oocytes (18.45% vs. 15.43% for sorted diluted and sorted encapsulated semen, respectively). The encapsulation could be an alternative method of storing of pig sex sorted spermatozoa and is potentially a promising technique in order to optimize the use of low dose of sexed spermatozoa in vivo. Copyright © 2013 Elsevier Inc. All rights reserved.

Emergence of spike correlations in periodically forced excitable systems

NASA Astrophysics Data System (ADS)

Reinoso, José A.; Torrent, M. C.; Masoller, Cristina

2016-09-01

In sensory neurons the presence of noise can facilitate the detection of weak information-carrying signals, which are encoded and transmitted via correlated sequences of spikes. Here we investigate the relative temporal order in spike sequences induced by a subthreshold periodic input in the presence of white Gaussian noise. To simulate the spikes, we use the FitzHugh-Nagumo model and to investigate the output sequence of interspike intervals (ISIs), we use the symbolic method of ordinal analysis. We find different types of relative temporal order in the form of preferred ordinal patterns that depend on both the strength of the noise and the period of the input signal. We also demonstrate a resonancelike behavior, as certain periods and noise levels enhance temporal ordering in the ISI sequence, maximizing the probability of the preferred patterns. Our findings could be relevant for understanding the mechanisms underlying temporal coding, by which single sensory neurons represent in spike sequences the information about weak periodic stimuli.

A mathematical modeling approach to resource allocation for railroad-highway crossing safety upgrades.

PubMed

Konur, Dinçer; Golias, Mihalis M; Darks, Brandon

2013-03-01

State Departments of Transportation (S-DOT's) periodically allocate budget for safety upgrades at railroad-highway crossings. Efficient resource allocation is crucial for reducing accidents at railroad-highway crossings and increasing railroad as well as highway transportation safety. While a specific method is not restricted to S-DOT's, sorting type of procedures are recommended by the Federal Railroad Administration (FRA), United States Department of Transportation for the resource allocation problem. In this study, a generic mathematical model is proposed for the resource allocation problem for railroad-highway crossing safety upgrades. The proposed approach is compared to sorting based methods for safety upgrades of public at-grade railroad-highway crossings in Tennessee. The comparison shows that the proposed mathematical modeling approach is more efficient than sorting methods in reducing accidents and severity. Copyright © 2012 Elsevier Ltd. All rights reserved.

Reconstruction of neuronal input through modeling single-neuron dynamics and computations

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

Qin, Qing; Wang, Jiang; Yu, Haitao

Mathematical models provide a mathematical description of neuron activity, which can better understand and quantify neural computations and corresponding biophysical mechanisms evoked by stimulus. In this paper, based on the output spike train evoked by the acupuncture mechanical stimulus, we present two different levels of models to describe the input-output system to achieve the reconstruction of neuronal input. The reconstruction process is divided into two steps: First, considering the neuronal spiking event as a Gamma stochastic process. The scale parameter and the shape parameter of Gamma process are, respectively, defined as two spiking characteristics, which are estimated by a state-spacemore » method. Then, leaky integrate-and-fire (LIF) model is used to mimic the response system and the estimated spiking characteristics are transformed into two temporal input parameters of LIF model, through two conversion formulas. We test this reconstruction method by three different groups of simulation data. All three groups of estimates reconstruct input parameters with fairly high accuracy. We then use this reconstruction method to estimate the non-measurable acupuncture input parameters. Results show that under three different frequencies of acupuncture stimulus conditions, estimated input parameters have an obvious difference. The higher the frequency of the acupuncture stimulus is, the higher the accuracy of reconstruction is.« less

Reconstruction of neuronal input through modeling single-neuron dynamics and computations

NASA Astrophysics Data System (ADS)

Qin, Qing; Wang, Jiang; Yu, Haitao; Deng, Bin; Chan, Wai-lok

2016-06-01

Mathematical models provide a mathematical description of neuron activity, which can better understand and quantify neural computations and corresponding biophysical mechanisms evoked by stimulus. In this paper, based on the output spike train evoked by the acupuncture mechanical stimulus, we present two different levels of models to describe the input-output system to achieve the reconstruction of neuronal input. The reconstruction process is divided into two steps: First, considering the neuronal spiking event as a Gamma stochastic process. The scale parameter and the shape parameter of Gamma process are, respectively, defined as two spiking characteristics, which are estimated by a state-space method. Then, leaky integrate-and-fire (LIF) model is used to mimic the response system and the estimated spiking characteristics are transformed into two temporal input parameters of LIF model, through two conversion formulas. We test this reconstruction method by three different groups of simulation data. All three groups of estimates reconstruct input parameters with fairly high accuracy. We then use this reconstruction method to estimate the non-measurable acupuncture input parameters. Results show that under three different frequencies of acupuncture stimulus conditions, estimated input parameters have an obvious difference. The higher the frequency of the acupuncture stimulus is, the higher the accuracy of reconstruction is.

Preparation of cherry-picked combinatorial libraries by string synthesis.

PubMed

Furka, Arpád; Dibó, Gábor; Gombosuren, Naran

2005-03-01

String synthesis [1-3] is an efficient and cheap manual method for preparation of combinatorial libraries by using macroscopic solid support units. Sorting the units between two synthetic steps is an important operation of the procedure. The software developed to guide sorting can be used only when complete combinatorial libraries are prepared. Since very often only selected components of the full libraries are needed, new software was constructed that guides sorting in preparation of non-complete combinatorial libraries. Application of the software is described in details.

Functional single-cell hybridoma screening using droplet-based microfluidics.

PubMed

El Debs, Bachir; Utharala, Ramesh; Balyasnikova, Irina V; Griffiths, Andrew D; Merten, Christoph A

2012-07-17

Monoclonal antibodies can specifically bind or even inhibit drug targets and have hence become the fastest growing class of human therapeutics. Although they can be screened for binding affinities at very high throughput using systems such as phage display, screening for functional properties (e.g., the inhibition of a drug target) is much more challenging. Typically these screens require the generation of immortalized hybridoma cells, as well as clonal expansion in microtiter plates over several weeks, and the number of clones that can be assayed is typically no more than a few thousand. We present here a microfluidic platform allowing the functional screening of up to 300,000 individual hybridoma cell clones within less than a day. This approach should also be applicable to nonimmortalized primary B-cells, as no cell proliferation is required: Individual cells are encapsulated into aqueous microdroplets and assayed directly for the release of antibodies inhibiting a drug target based on fluorescence. We used this system to perform a model screen for antibodies that inhibit angiotensin converting enzyme 1, a target for hypertension and congestive heart failure drugs. When cells expressing these antibodies were spiked into an unrelated hybridoma cell population in a ratio of 1:10,000 we observed a 9,400-fold enrichment after fluorescence activated droplet sorting. A wide variance in antibody expression levels at the single-cell level within a single hybridoma line was observed and high expressors could be successfully sorted and recultivated.

Total phenolic content, antioxidant activity, and cross-cultural consumer rejection threshold in white and red wines functionally enhanced with catechin-rich extracts.

PubMed

Yoo, Yung J; Saliba, Anthony J; Prenzler, Paul D; Ryan, Danielle

2012-01-11

White and red wines spiked with catechin-rich green tea extract and grape seed extract were assessed for phenolic content, antioxidant activity, and cross-cultural consumer rejection thresholds in relation to wine as a functional food. Health functionality is an important factor in functional foods, and spiking pure compounds or plant extracts is an effective method to increase or control functionality. The total phenolic content and antioxidant activity were measured in wines spiked to different extract concentrations, namely, control and 50, 100, 200, 400, and 800 mg/L, to confirm the dose-response curves in both white and red wines. Consumer rejection thresholds (CRTs) were established for spiked wines in a Korean and in an Australian population. Our results showed that the green tea extract and grape seed extract increased the antioxidant activity dose dependently, and the CRTs varied considerably between the Korean and the Australian groups, with Koreans preferring wines spiked with green tea extract and Australians showing a preference for wines spiked with grape seed extract. These results have implications for producing wine products that are enhanced in phenolic compounds and targeted to different cultural groups.

Millimeter-scale epileptiform spike propagation patterns and their relationship to seizures

PubMed Central

Vanleer, Ann C; Blanco, Justin A; Wagenaar, Joost B; Viventi, Jonathan; Contreras, Diego; Litt, Brian

2016-01-01

Objective Current mapping of epileptic networks in patients prior to epilepsy surgery utilizes electrode arrays with sparse spatial sampling (∼1.0 cm inter-electrode spacing). Recent research demonstrates that sub-millimeter, cortical-column-scale domains have a role in seizure generation that may be clinically significant. We use high-resolution, active, flexible surface electrode arrays with 500 μm inter-electrode spacing to explore epileptiform local field potential spike propagation patterns in two dimensions recorded from subdural micro-electrocorticographic signals in vivo in cat. In this study, we aimed to develop methods to quantitatively characterize the spatiotemporal dynamics of epileptiform activity at high-resolution. Approach We topically administered a GABA-antagonist, picrotoxin, to induce acute neocortical epileptiform activity leading up to discrete electrographic seizures. We extracted features from local field potential spikes to characterize spatiotemporal patterns in these events. We then tested the hypothesis that two dimensional spike patterns during seizures were different from those between seizures. Main results We showed that spatially correlated events can be used to distinguish ictal versus interictal spikes. Significance We conclude that sub-millimeter-scale spatiotemporal spike patterns reveal network dynamics that are invisible to standard clinical recordings and contain information related to seizure-state. PMID:26859260

Millimeter-scale epileptiform spike propagation patterns and their relationship to seizures

NASA Astrophysics Data System (ADS)

Vanleer, Ann C.; Blanco, Justin A.; Wagenaar, Joost B.; Viventi, Jonathan; Contreras, Diego; Litt, Brian

2016-04-01

Objective. Current mapping of epileptic networks in patients prior to epilepsy surgery utilizes electrode arrays with sparse spatial sampling (∼1.0 cm inter-electrode spacing). Recent research demonstrates that sub-millimeter, cortical-column-scale domains have a role in seizure generation that may be clinically significant. We use high-resolution, active, flexible surface electrode arrays with 500 μm inter-electrode spacing to explore epileptiform local field potential (LFP) spike propagation patterns in two dimensions recorded from subdural micro-electrocorticographic signals in vivo in cat. In this study, we aimed to develop methods to quantitatively characterize the spatiotemporal dynamics of epileptiform activity at high-resolution. Approach. We topically administered a GABA-antagonist, picrotoxin, to induce acute neocortical epileptiform activity leading up to discrete electrographic seizures. We extracted features from LFP spikes to characterize spatiotemporal patterns in these events. We then tested the hypothesis that two-dimensional spike patterns during seizures were different from those between seizures. Main results. We showed that spatially correlated events can be used to distinguish ictal versus interictal spikes. Significance. We conclude that sub-millimeter-scale spatiotemporal spike patterns reveal network dynamics that are invisible to standard clinical recordings and contain information related to seizure-state.

Chemically programmed self-sorting of gelator networks.

PubMed

Morris, Kyle L; Chen, Lin; Raeburn, Jaclyn; Sellick, Owen R; Cotanda, Pepa; Paul, Alison; Griffiths, Peter C; King, Stephen M; O'Reilly, Rachel K; Serpell, Louise C; Adams, Dave J

2013-01-01

Controlling the order and spatial distribution of self-assembly in multicomponent supramolecular systems could underpin exciting new functional materials, but it is extremely challenging. When a solution of different components self-assembles, the molecules can either coassemble, or self-sort, where a preference for like-like intermolecular interactions results in coexisting, homomolecular assemblies. A challenge is to produce generic and controlled 'one-pot' fabrication methods to form separate ordered assemblies from 'cocktails' of two or more self-assembling species, which might have relatively similar molecular structures and chemistry. Self-sorting in supramolecular gel phases is hence rare. Here we report the first example of the pH-controlled self-sorting of gelators to form self-assembled networks in water. Uniquely, the order of assembly can be predefined. The assembly of each component is preprogrammed by the pK(a) of the gelator. This pH-programming method will enable higher level, complex structures to be formed that cannot be accessed by simple thermal gelation.

Rare cancer cell analyzer for whole blood applications: microcytometer cell counting and sorting subcircuits.

PubMed

Lancaster, C; Kokoris, M; Nabavi, M; Clemmens, J; Maloney, P; Capadanno, J; Gerdes, J; Battrell, C F

2005-09-01

We demonstrate sorting of rare cancer cells from blood using a thin ribbon monolayer of cells within a credit-card sized, microfluidic laboratory-on-a-card ("lab card") structure. This enables higher cell throughput per minute thereby speeding up cell interrogation. In this approach, multiple cells are viewed and sorted, not individually, but as a whole cell row or section of the ribbon at a time. Gated selection of only the cell rows containing a tagged rare cell provides enrichment of the rare cell relative to background blood cells. We also designed the cell injector for laminar flow antibody labeling within 20s. The approach combines rapid laminar flow cell labeling with monolayer cell sorting thereby enabling rare cell target detection at sensitivity levels 1000 to 10,000 times that of existing flow cytometers. Using this method, total cell labeling and data acquisition time on card may be reduced to a few minutes compared to 30-60 min for standard flow methods.

Functional analysis of ultra high information rates conveyed by rat vibrissal primary afferents

PubMed Central

Chagas, André M.; Theis, Lucas; Sengupta, Biswa; Stüttgen, Maik C.; Bethge, Matthias; Schwarz, Cornelius

2013-01-01

Sensory receptors determine the type and the quantity of information available for perception. Here, we quantified and characterized the information transferred by primary afferents in the rat whisker system using neural system identification. Quantification of “how much” information is conveyed by primary afferents, using the direct method (DM), a classical information theoretic tool, revealed that primary afferents transfer huge amounts of information (up to 529 bits/s). Information theoretic analysis of instantaneous spike-triggered kinematic stimulus features was used to gain functional insight on “what” is coded by primary afferents. Amongst the kinematic variables tested—position, velocity, and acceleration—primary afferent spikes encoded velocity best. The other two variables contributed to information transfer, but only if combined with velocity. We further revealed three additional characteristics that play a role in information transfer by primary afferents. Firstly, primary afferent spikes show preference for well separated multiple stimuli (i.e., well separated sets of combinations of the three instantaneous kinematic variables). Secondly, neurons are sensitive to short strips of the stimulus trajectory (up to 10 ms pre-spike time), and thirdly, they show spike patterns (precise doublet and triplet spiking). In order to deal with these complexities, we used a flexible probabilistic neuron model fitting mixtures of Gaussians to the spike triggered stimulus distributions, which quantitatively captured the contribution of the mentioned features and allowed us to achieve a full functional analysis of the total information rate indicated by the DM. We found that instantaneous position, velocity, and acceleration explained about 50% of the total information rate. Adding a 10 ms pre-spike interval of stimulus trajectory achieved 80–90%. The final 10–20% were found to be due to non-linear coding by spike bursts. PMID:24367295

A “Spike-Based” Grammar Underlies Directional Modification in Network Connectivity: Effect on Bursting Activity and Implications for Bio-Hybrids Systems

PubMed Central

Zullo, Letizia; Chiappalone, Michela; Martinoia, Sergio; Benfenati, Fabio

2012-01-01

Developed biological systems are endowed with the ability of interacting with the environment; they sense the external state and react to it by changing their own internal state. Many attempts have been made to build ‘hybrids’ with the ability of perceiving, modifying and reacting to external modifications. Investigation of the rules that govern network changes in a hybrid system may lead to finding effective methods for ‘programming’ the neural tissue toward a desired task. Here we show a new perspective in the use of cortical neuronal cultures from embryonic mouse as a working platform to study targeted synaptic modifications. Differently from the common timing-based methods applied in bio-hybrids robotics, here we evaluated the importance of endogenous spike timing in the information processing. We characterized the influence of a spike-patterned stimulus in determining changes in neuronal synchronization (connectivity strength and precision) of the evoked spiking and bursting activity in the network. We show that tailoring the stimulation pattern upon a neuronal spike timing induces the network to respond stronger and more precisely to the stimulation. Interestingly, the induced modifications are conveyed more consistently in the burst timing. This increase in strength and precision may be a key in the interaction of the network with the external world and may be used to induce directional changes in bio-hybrid systems. PMID:23145147

An analysis of neural receptive field plasticity by point process adaptive filtering

PubMed Central

Brown, Emery N.; Nguyen, David P.; Frank, Loren M.; Wilson, Matthew A.; Solo, Victor

2001-01-01

Neural receptive fields are plastic: with experience, neurons in many brain regions change their spiking responses to relevant stimuli. Analysis of receptive field plasticity from experimental measurements is crucial for understanding how neural systems adapt their representations of relevant biological information. Current analysis methods using histogram estimates of spike rate functions in nonoverlapping temporal windows do not track the evolution of receptive field plasticity on a fine time scale. Adaptive signal processing is an established engineering paradigm for estimating time-varying system parameters from experimental measurements. We present an adaptive filter algorithm for tracking neural receptive field plasticity based on point process models of spike train activity. We derive an instantaneous steepest descent algorithm by using as the criterion function the instantaneous log likelihood of a point process spike train model. We apply the point process adaptive filter algorithm in a study of spatial (place) receptive field properties of simulated and actual spike train data from rat CA1 hippocampal neurons. A stability analysis of the algorithm is sketched in the Appendix. The adaptive algorithm can update the place field parameter estimates on a millisecond time scale. It reliably tracked the migration, changes in scale, and changes in maximum firing rate characteristic of hippocampal place fields in a rat running on a linear track. Point process adaptive filtering offers an analytic method for studying the dynamics of neural receptive fields. PMID:11593043

Evoking prescribed spike times in stochastic neurons

NASA Astrophysics Data System (ADS)

Doose, Jens; Lindner, Benjamin

2017-09-01

Single cell stimulation in vivo is a powerful tool to investigate the properties of single neurons and their functionality in neural networks. We present a method to determine a cell-specific stimulus that reliably evokes a prescribed spike train with high temporal precision of action potentials. We test the performance of this stimulus in simulations for two different stochastic neuron models. For a broad range of parameters and a neuron firing with intermediate firing rates (20-40 Hz) the reliability in evoking the prescribed spike train is close to its theoretical maximum that is mainly determined by the level of intrinsic noise.

From individual spiking neurons to population behavior: Systematic elimination of short-wavelength spatial modes

NASA Astrophysics Data System (ADS)

Steyn-Ross, Moira L.; Steyn-Ross, D. A.

2016-02-01

Mean-field models of the brain approximate spiking dynamics by assuming that each neuron responds to its neighbors via a naive spatial average that neglects local fluctuations and correlations in firing activity. In this paper we address this issue by introducing a rigorous formalism to enable spatial coarse-graining of spiking dynamics, scaling from the microscopic level of a single type 1 (integrator) neuron to a macroscopic assembly of spiking neurons that are interconnected by chemical synapses and nearest-neighbor gap junctions. Spiking behavior at the single-neuron scale ℓ ≈10 μ m is described by Wilson's two-variable conductance-based equations [H. R. Wilson, J. Theor. Biol. 200, 375 (1999), 10.1006/jtbi.1999.1002], driven by fields of incoming neural activity from neighboring neurons. We map these equations to a coarser spatial resolution of grid length B ℓ , with B ≫1 being the blocking ratio linking micro and macro scales. Our method systematically eliminates high-frequency (short-wavelength) spatial modes q ⃗ in favor of low-frequency spatial modes Q ⃗ using an adiabatic elimination procedure that has been shown to be equivalent to the path-integral coarse graining applied to renormalization group theory of critical phenomena. This bottom-up neural regridding allows us to track the percolation of synaptic and ion-channel noise from the single neuron up to the scale of macroscopic population-average variables. Anticipated applications of neural regridding include extraction of the current-to-firing-rate transfer function, investigation of fluctuation criticality near phase-transition tipping points, determination of spatial scaling laws for avalanche events, and prediction of the spatial extent of self-organized macrocolumnar structures. As a first-order exemplar of the method, we recover nonlinear corrections for a coarse-grained Wilson spiking neuron embedded in a network of identical diffusively coupled neurons whose chemical synapses have been disabled. Intriguingly, we find that reblocking transforms the original type 1 Wilson integrator into a type 2 resonator whose spike-rate transfer function exhibits abrupt spiking onset with near-vertical takeoff and chaotic dynamics just above threshold.

After site selection and before data analysis: sampling, sorting, and laboratory procedures used in stream benthic macroinvertebrate monitoring programs by USA state agencies

USGS Publications Warehouse

Carter, James L.; Resh, Vincent H.

2001-01-01

A survey of methods used by US state agencies for collecting and processing benthic macroinvertebrate samples from streams was conducted by questionnaire; 90 responses were received and used to describe trends in methods. The responses represented an estimated 13,000-15,000 samples collected and processed per year. Kicknet devices were used in 64.5% of the methods; other sampling devices included fixed-area samplers (Surber and Hess), artificial substrates (Hester-Dendy and rock baskets), grabs, and dipnets. Regional differences existed, e.g., the 1-m kicknet was used more often in the eastern US than in the western US. Mesh sizes varied among programs but 80.2% of the methods used a mesh size between 500 and 600 (mu or u)m. Mesh size variations within US Environmental Protection Agency regions were large, with size differences ranging from 100 to 700 (mu or u)m. Most samples collected were composites; the mean area sampled was 1.7 m2. Samples rarely were collected using a random method (4.7%); most samples (70.6%) were collected using "expert opinion", which may make data obtained operator-specific. Only 26.3% of the methods sorted all the organisms from a sample; the remainder subsampled in the laboratory. The most common method of subsampling was to remove 100 organisms (range = 100-550). The magnification used for sorting ranged from 1 (sorting by eye) to 30x, which results in inconsistent separation of macroinvertebrates from detritus. In addition to subsampling, 53% of the methods sorted large/rare organisms from a sample. The taxonomic level used for identifying organisms varied among taxa; Ephemeroptera, Plecoptera, and Trichoptera were generally identified to a finer taxonomic resolution (genus and species) than other taxa. Because there currently exists a large range of field and laboratory methods used by state programs, calibration among all programs to increase data comparability would be exceptionally challenging. However, because many techniques are shared among methods, limited testing could be designed to evaluate whether procedural differences affect the ability to determine levels of environmental impairment using benthic macroinvertebrate communities.

Synthetic spike-in standards for high-throughput 16S rRNA gene amplicon sequencing.

PubMed

Tourlousse, Dieter M; Yoshiike, Satowa; Ohashi, Akiko; Matsukura, Satoko; Noda, Naohiro; Sekiguchi, Yuji

2017-02-28

High-throughput sequencing of 16S rRNA gene amplicons (16S-seq) has become a widely deployed method for profiling complex microbial communities but technical pitfalls related to data reliability and quantification remain to be fully addressed. In this work, we have developed and implemented a set of synthetic 16S rRNA genes to serve as universal spike-in standards for 16S-seq experiments. The spike-ins represent full-length 16S rRNA genes containing artificial variable regions with negligible identity to known nucleotide sequences, permitting unambiguous identification of spike-in sequences in 16S-seq read data from any microbiome sample. Using defined mock communities and environmental microbiota, we characterized the performance of the spike-in standards and demonstrated their utility for evaluating data quality on a per-sample basis. Further, we showed that staggered spike-in mixtures added at the point of DNA extraction enable concurrent estimation of absolute microbial abundances suitable for comparative analysis. Results also underscored that template-specific Illumina sequencing artifacts may lead to biases in the perceived abundance of certain taxa. Taken together, the spike-in standards represent a novel bioanalytical tool that can substantially improve 16S-seq-based microbiome studies by enabling comprehensive quality control along with absolute quantification. © The Author(s) 2016. Published by Oxford University Press on behalf of Nucleic Acids Research.

A technique for generating phase-space-based Monte Carlo beamlets in radiotherapy applications.

PubMed

Bush, K; Popescu, I A; Zavgorodni, S

2008-09-21

As radiotherapy treatment planning moves toward Monte Carlo (MC) based dose calculation methods, the MC beamlet is becoming an increasingly common optimization entity. At present, methods used to produce MC beamlets have utilized a particle source model (PSM) approach. In this work we outline the implementation of a phase-space-based approach to MC beamlet generation that is expected to provide greater accuracy in beamlet dose distributions. In this approach a standard BEAMnrc phase space is sorted and divided into beamlets with particles labeled using the inheritable particle history variable. This is achieved with the use of an efficient sorting algorithm, capable of sorting a phase space of any size into the required number of beamlets in only two passes. Sorting a phase space of five million particles can be achieved in less than 8 s on a single-core 2.2 GHz CPU. The beamlets can then be transported separately into a patient CT dataset, producing separate dose distributions (doselets). Methods for doselet normalization and conversion of dose to absolute units of Gy for use in intensity modulated radiation therapy (IMRT) plan optimization are also described.

Solid-phase extraction of the alcohol abuse biomarker phosphatidylethanol using newly synthesized polymeric sorbent materials containing quaternary heterocyclic groups.

PubMed

Duarte, Mariana; Jagadeesan, Kishore Kumar; Billing, Johan; Yilmaz, Ecevit; Laurell, Thomas; Ekström, Simon

2017-10-13

Phosphatidylethanol (PEth) is an interesting biomarker finding increased use for detecting long term alcohol abuse with high specificity and sensitivity. Prior to detection, sample preparation is an unavoidable step in the work-flow of PEth analysis and new protocols may facilitate it. Solid-phase extraction (SPE) is a versatile sample preparation method widely spread in biomedical laboratories due to its simplicity of use and the possibility of automation. In this work, SPE was used for the first time to directly extract PEth from spiked human plasma and spiked human blood. A library of polymeric SPE materials with different surface functionalities was screened for PEth extraction in order to identify the surface characteristics that control PEth retention and recovery. The plasma samples were diluted 1:10 (v/v) in water and spiked at different concentrations ranging from 0.3 to 5μM. The library of SPE materials was then evaluated using the proposed SPE method and detection was done by LC-MS/MS. One SPE material efficiently retained and recovered PEth from spiked human plasma. With this insight, four new SPE materials were formulated and synthesized based on the surface characteristics of the best SPE material found in the first screening. These new materials were tested with spiked human blood, to better mimic a real clinical sample. All the newly synthetized materials outperformed the pre-existing commercially available materials. Recovery values for the new SPE materials were found between 29.5% and 48.6% for the extraction of PEth in spiked blood. A material based on quaternized 1-vinylimidazole with a poly(trimethylolpropane trimethacrylate) backbone was found suitable for PEth extraction in spiked blood showing the highest analyte recovery in this experiment, 48.6%±6.4%. Copyright © 2017 Elsevier B.V. All rights reserved.

Estimating the Information Extracted by a Single Spiking Neuron from a Continuous Input Time Series.

PubMed

Zeldenrust, Fleur; de Knecht, Sicco; Wadman, Wytse J; Denève, Sophie; Gutkin, Boris

2017-01-01

Understanding the relation between (sensory) stimuli and the activity of neurons (i.e., "the neural code") lies at heart of understanding the computational properties of the brain. However, quantifying the information between a stimulus and a spike train has proven to be challenging. We propose a new ( in vitro ) method to measure how much information a single neuron transfers from the input it receives to its output spike train. The input is generated by an artificial neural network that responds to a randomly appearing and disappearing "sensory stimulus": the hidden state. The sum of this network activity is injected as current input into the neuron under investigation. The mutual information between the hidden state on the one hand and spike trains of the artificial network or the recorded spike train on the other hand can easily be estimated due to the binary shape of the hidden state. The characteristics of the input current, such as the time constant as a result of the (dis)appearance rate of the hidden state or the amplitude of the input current (the firing frequency of the neurons in the artificial network), can independently be varied. As an example, we apply this method to pyramidal neurons in the CA1 of mouse hippocampi and compare the recorded spike trains to the optimal response of the "Bayesian neuron" (BN). We conclude that like in the BN, information transfer in hippocampal pyramidal cells is non-linear and amplifying: the information loss between the artificial input and the output spike train is high if the input to the neuron (the firing of the artificial network) is not very informative about the hidden state. If the input to the neuron does contain a lot of information about the hidden state, the information loss is low. Moreover, neurons increase their firing rates in case the (dis)appearance rate is high, so that the (relative) amount of transferred information stays constant.

Determination of Trace Level Iodine in Biological and Botanical Reference Materials by Isotope Dilution Mass Spectrometry

PubMed Central

Gramlich, John W.; Murphy, Thomas J.

1989-01-01

A method has been developed for the determination of trace level iodine in biological and botanical materials. The method consists of spiking a sample with 129I, equilibration of the spike with the natural iodine, wet ashing under carefully controlled conditions, and separation of the iodine by co-precipitation with silver chloride. Measurement of the 129I/127I ratio is accomplished by negative thermal ionization mass spectrometry using LaB6 for ionization enhancement. The application of the method to the certification of trace iodine in two Standard Reference Materials is described. PMID:28053411

A real-time traffic control method for the intersection with pre-signals under the phase swap sorting strategy

PubMed Central

Bie, Yiming; Wang, Yinhai

2017-01-01

To deal with the conflicts between left-turn and through traffic streams and increase the discharge capacity, this paper addresses the pre-signal which is implemented at a signalized intersection. Such an intersection with pre-signal is termed as a tandem intersection. For the tandem intersection, phase swap sorting strategy is deemed as the most effective phasing scheme in view of some exclusive merits, such as easier compliance of drivers, and shorter sorting area. However, a major limitation of the phase swap sorting strategy is not considered in previous studies: if one or more vehicle is left at the sorting area after the signal light turns to red, the capacity of the approach would be dramatically dropped. Besides, previous signal control studies deal with a fixed timing plan that is not adaptive with the fluctuation of traffic flows. Therefore, to cope with these two gaps, this paper firstly takes an in-depth analysis of the traffic flow operations at the tandem intersection. Secondly, three groups of loop detectors are placed to obtain the real-time vehicle information for adaptive signalization. The lane selection behavior in the sorting area is considered to set the green time for intersection signals. With the objective of minimizing the vehicle delay, the signal control parameters are then optimized based on a dynamic programming method. Finally, numerical experiments show that average vehicle delay and maximum queue length can be reduced under all scenarios. PMID:28531198

A real-time traffic control method for the intersection with pre-signals under the phase swap sorting strategy.

PubMed

Bie, Yiming; Liu, Zhiyuan; Wang, Yinhai

2017-01-01

To deal with the conflicts between left-turn and through traffic streams and increase the discharge capacity, this paper addresses the pre-signal which is implemented at a signalized intersection. Such an intersection with pre-signal is termed as a tandem intersection. For the tandem intersection, phase swap sorting strategy is deemed as the most effective phasing scheme in view of some exclusive merits, such as easier compliance of drivers, and shorter sorting area. However, a major limitation of the phase swap sorting strategy is not considered in previous studies: if one or more vehicle is left at the sorting area after the signal light turns to red, the capacity of the approach would be dramatically dropped. Besides, previous signal control studies deal with a fixed timing plan that is not adaptive with the fluctuation of traffic flows. Therefore, to cope with these two gaps, this paper firstly takes an in-depth analysis of the traffic flow operations at the tandem intersection. Secondly, three groups of loop detectors are placed to obtain the real-time vehicle information for adaptive signalization. The lane selection behavior in the sorting area is considered to set the green time for intersection signals. With the objective of minimizing the vehicle delay, the signal control parameters are then optimized based on a dynamic programming method. Finally, numerical experiments show that average vehicle delay and maximum queue length can be reduced under all scenarios.

Comparative study of six sequential spectrophotometric methods for quantification and separation of ribavirin, sofosbuvir and daclatasvir: An application on Laboratory prepared mixture, pharmaceutical preparations, spiked human urine, spiked human plasma, and dissolution test.

PubMed

Hassan, Wafaa S; Elmasry, Manal S; Elsayed, Heba M; Zidan, Dalia W

2018-09-05

In accordance with International Conference on Harmonisation of Technical Requirements for Registration of Pharmaceuticals for Human Use (ICH) guidelines, six novel, simple and precise sequential spectrophotometric methods were developed and validated for the simultaneous analysis of Ribavirin (RIB), Sofosbuvir (SOF), and Daclatasvir (DAC) in their mixture without prior separation steps. These drugs are described as co-administered for treatment of Hepatitis C virus (HCV). HCV is the cause of hepatitis C and some cancers such as liver cancer (hepatocellular carcinoma) and lymphomas in humans. These techniques consisted of several sequential steps using zero, ratio and/or derivative spectra. DAC was first determined through direct spectrophotometry at 313.7 nm without any interference of the other two drugs while RIB and SOF can be determined after ratio subtraction through five methods; Ratio difference spectrophotometric method, successive derivative ratio method, constant center, isoabsorptive method at 238.8 nm, and mean centering of the ratio spectra (MCR) at 224 nm and 258 nm for RIB and SOF, respectively. The calibration curve is linear over the concentration ranges of (6-42), (10-70) and (4-16) μg/mL for RIB, SOF, and DAC, respectively. This method was successfully applied to commercial pharmaceutical preparation of the drugs, spiked human urine, and spiked human plasma. The above methods are very simple methods that were developed for the simultaneous determination of binary and ternary mixtures and so enhance signal-to-noise ratio. The method has been successfully applied to the simultaneous analysis of RIB, SOF, and DAC in laboratory prepared mixtures. The obtained results are statistically compared with those obtained by the official or reported methods, showing no significant difference with respect to accuracy and precision at p = 0.05. Copyright © 2018 Elsevier B.V. All rights reserved.

The Analysis and Suppression of the spike noise in vibrator record

NASA Astrophysics Data System (ADS)

Jia, H.; Jiang, T.; Xu, X.; Ge, L.; Lin, J.; Yang, Z.

2013-12-01

During the seismic exploration with vibrator, seismic recording systems have often been affected by random spike noise in the background, which leads to strong data distortions as a result of the cross-correlation processing of the vibrator method. Partial or total loss of the desired seismic information is possible if no automatic spike reduction is available in the field prior to correlation of the field record. Generally speaking, original record of vibrator is uncorrelated data, in which the signal is non-wavelet form. In order to obtain the seismic record similar to explosive source, the signal of uncorrelated data needs to use the correlation algorithm to compress into wavelet form. The correlation process results in that the interference of spike in correlated data is not only being suppressed, but also being expanded. So the spike noise suppression of vibrator is indispensable. According to numerical simulation results, the effect of spike in the vibrator record is mainly affected by the amplitude and proportional points in the uncorrelated record. When the spike noise ratio in uncorrelated record reaches 1.5% and the average amplitude exceeds 200, it will make the SNR(signal-to-noise ratio) of the correlated record lower than 0dB, so that it is difficult to separate the signal. While the amplitude and ratio is determined by the intensity of background noise. Therefore, when the noise level is strong, in order to improve SNR of the seismic data, the uncorrelated record of vibrator need to take necessary steps to suppress spike noise. For the sake of reducing the influence of the spike noise, we need to make the detection and suppression of spike noise process for the uncorrelated record. Because vibrator works by inputting sweep signal into the underground long time, ideally, the peak and valley values of each trace have little change. On the basis of the peak and valley values, we can get a reference amplitude value. Then the spike can be detected and suppressed. After this process, it can reduce the effection of spike noise in the uncorrelated record to improve the SNR. At present, because the memory space of vibrator uncorrelated data is always very large, in order to reduce acquisition costs, we usually record correlated data directly. It's reasonable if there is no strong spike sneaking into uncorrelated record. However, due to the fact that the random spike in the background is not avoidable in the acquisition process, and the instantaneous input energy of the vibrator is probably smaller than spike noise, which makes the uncorrelated data contain a certain amount of spike noise, it severely reduces the acquisition quality of vibrator if there is no noise suppression module beforehand. Of course, the suppressing process of spike noise can be carried out in the field acquisition or data processing stage. In the field of vibrator acquisition system, we can use the spike noise suppression before the correlated module, so that it can directly record correlated data without the spike affection. If in the stage of data processing, it is necessary to record uncorrelated data.

Adaptive exponential integrate-and-fire model as an effective description of neuronal activity.

PubMed

Brette, Romain; Gerstner, Wulfram

2005-11-01

We introduce a two-dimensional integrate-and-fire model that combines an exponential spike mechanism with an adaptation equation, based on recent theoretical findings. We describe a systematic method to estimate its parameters with simple electrophysiological protocols (current-clamp injection of pulses and ramps) and apply it to a detailed conductance-based model of a regular spiking neuron. Our simple model predicts correctly the timing of 96% of the spikes (+/-2 ms) of the detailed model in response to injection of noisy synaptic conductances. The model is especially reliable in high-conductance states, typical of cortical activity in vivo, in which intrinsic conductances were found to have a reduced role in shaping spike trains. These results are promising because this simple model has enough expressive power to reproduce qualitatively several electrophysiological classes described in vitro.

Effect of different spiking procedures on the distribution and toxicity of ZnO nanoparticles in soil.

PubMed

Waalewijn-Kool, Pauline L; Diez Ortiz, Maria; van Gestel, Cornelis A M

2012-10-01

Due to the difficulty in dispersing some engineered nanomaterials in exposure media, realizing homogeneous distributions of nanoparticles (NP) in soil may pose major challenges. The present study investigated the distribution of zinc oxide (ZnO) NP (30 nm) and non-nano ZnO (200 nm) in natural soil using two different spiking procedures, i.e. as dry powder and as suspension in soil extract. Both spiking procedures showed a good recovery (>85 %) of zinc and based on total zinc concentrations no difference was found between the two spiking methods. Both spiking procedures resulted in a fairly homogeneous distribution of the ZnO particles in soil, as evidenced by the low variation in total zinc concentration between replicate samples (<12 % in most cases). Survival of Folsomia candida in soil spiked at concentrations up to 6,400 mg Zn kg(-1) d.w. was not affected for both compounds. Reproduction was reduced in a concentration-dependent manner with EC50 values of 3,159 and 2,914 mg Zn kg(-1) d.w. for 30 and 200 nm ZnO spiked as dry powder and 3,593 and 5,633 mg Zn kg(-1) d.w. introduced as suspension. Toxicity of ZnO at 30 and 200 nm did not differ. We conclude that the ZnO particle toxicity is not size related and that the spiking of the soil with ZnO as dry powder or as a suspension in soil extract does not affect its toxicity to F. candida.

Size-based cell sorting with a resistive pulse sensor and an electromagnetic pump in a microfluidic chip.

PubMed

Song, Yongxin; Li, Mengqi; Pan, Xinxiang; Wang, Qi; Li, Dongqing

2015-02-01

An electrokinetic microfluidic chip is developed to detect and sort target cells by size from human blood samples. Target-cell detection is achieved by a differential resistive pulse sensor (RPS) based on the size difference between the target cell and other cells. Once a target cell is detected, the detected RPS signal will automatically actuate an electromagnetic pump built in a microchannel to push the target cell into a collecting channel. This method was applied to automatically detect and sort A549 cells and T-lymphocytes from a peripheral fingertip blood sample. The viability of A549 cells sorted in the collecting well was verified by Hoechst33342 and propidium iodide staining. The results show that as many as 100 target cells per minute can be sorted out from the sample solution and thus is particularly suitable for sorting very rare target cells, such as circulating tumor cells. The actuation of the electromagnetic valve has no influence on RPS cell detection and the consequent cell-sorting process. The viability of the collected A549 cell is not impacted by the applied electric field when the cell passes the RPS detection area. The device described in this article is simple, automatic, and label-free and has wide applications in size-based rare target cell sorting for medical diagnostics. © 2014 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

Quantitating Changes in Jitter and Spike Number Using Concentric Needle Electrodes in Amyotrophic Lateral Sclerosis Patients

PubMed Central

Liu, Ming-Sheng; Niu, Jing-Wen; Li, Yi; Guan, Yu-Zhou; Cui, Li-Ying

2016-01-01

Background: Single-fiber electromyography (SFEMG) has been suggested as a quantitative method for supporting chronic partial denervation in amyotrophic lateral sclerosis (ALS) by the revised EI Escorial criteria. Although concentric needle (CN) electrodes have been used to assess jitter in myasthenia gravis patients and healthy controls, there are few reports using CN electrodes to assess motor unit instability and denervation in neurogenic diseases. The aim of this study was to determine whether quantitative changes in jitter and spike number using CN electrodes could be used for ALS studies. Methods: Twenty-seven healthy controls and 23 ALS patients were studied using both CN and single-fiber needle (SFN) electrodes on the extensor digitorum communis muscle with an SFEMG program. The SFN-jitter and SFN-fiber density data were measured using SFN electrodes. The CN-jitter and spike number were measured using CN electrodes. Results: The mean CN-jitter was significantly increased in ALS patients (47.3 ± 17.0 μs) than in healthy controls (27.4 ± 3.3 μs) (P < 0.001). Besides, the mean spike number was significantly increased in ALS patients (2.5 ± 0.5) than in healthy controls (1.7 ± 0.3) (P < 0.001). The sensitivity and specificity in the diagnosis of ALS were 82.6% and 92.6% for CN-jitter (cut-off value: 32 μs), and 91.3% and 96.3% for the spike number (cut-off value: 2.0), respectively. There was no significant difference between the SFN-jitter and CN-jitter in ALS patients; meanwhile, there was no significant difference between the SFN-jitter and CN-jitter in healthy controls. Conclusion: CN-jitter and spike number could be used to quantitatively evaluate changes due to denervation-reinnervation in ALS. PMID:27098787

A closed-loop compressive-sensing-based neural recording system.

PubMed

Zhang, Jie; Mitra, Srinjoy; Suo, Yuanming; Cheng, Andrew; Xiong, Tao; Michon, Frederic; Welkenhuysen, Marleen; Kloosterman, Fabian; Chin, Peter S; Hsiao, Steven; Tran, Trac D; Yazicioglu, Firat; Etienne-Cummings, Ralph

2015-06-01

This paper describes a low power closed-loop compressive sensing (CS) based neural recording system. This system provides an efficient method to reduce data transmission bandwidth for implantable neural recording devices. By doing so, this technique reduces a majority of system power consumption which is dissipated at data readout interface. The design of the system is scalable and is a viable option for large scale integration of electrodes or recording sites onto a single device. The entire system consists of an application-specific integrated circuit (ASIC) with 4 recording readout channels with CS circuits, a real time off-chip CS recovery block and a recovery quality evaluation block that provides a closed feedback to adaptively adjust compression rate. Since CS performance is strongly signal dependent, the ASIC has been tested in vivo and with standard public neural databases. Implemented using efficient digital circuit, this system is able to achieve >10 times data compression on the entire neural spike band (500-6KHz) while consuming only 0.83uW (0.53 V voltage supply) additional digital power per electrode. When only the spikes are desired, the system is able to further compress the detected spikes by around 16 times. Unlike other similar systems, the characteristic spikes and inter-spike data can both be recovered which guarantes a >95% spike classification success rate. The compression circuit occupied 0.11mm(2)/electrode in a 180nm CMOS process. The complete signal processing circuit consumes <16uW/electrode. Power and area efficiency demonstrated by the system make it an ideal candidate for integration into large recording arrays containing thousands of electrode. Closed-loop recording and reconstruction performance evaluation further improves the robustness of the compression method, thus making the system more practical for long term recording.

Modeling continuous covariates with a "spike" at zero: Bivariate approaches.

PubMed

Jenkner, Carolin; Lorenz, Eva; Becher, Heiko; Sauerbrei, Willi

2016-07-01

In epidemiology and clinical research, predictors often take value zero for a large amount of observations while the distribution of the remaining observations is continuous. These predictors are called variables with a spike at zero. Examples include smoking or alcohol consumption. Recently, an extension of the fractional polynomial (FP) procedure, a technique for modeling nonlinear relationships, was proposed to deal with such situations. To indicate whether or not a value is zero, a binary variable is added to the model. In a two stage procedure, called FP-spike, the necessity of the binary variable and/or the continuous FP function for the positive part are assessed for a suitable fit. In univariate analyses, the FP-spike procedure usually leads to functional relationships that are easy to interpret. This paper introduces four approaches for dealing with two variables with a spike at zero (SAZ). The methods depend on the bivariate distribution of zero and nonzero values. Bi-Sep is the simplest of the four bivariate approaches. It uses the univariate FP-spike procedure separately for the two SAZ variables. In Bi-D3, Bi-D1, and Bi-Sub, proportions of zeros in both variables are considered simultaneously in the binary indicators. Therefore, these strategies can account for correlated variables. The methods can be used for arbitrary distributions of the covariates. For illustration and comparison of results, data from a case-control study on laryngeal cancer, with smoking and alcohol intake as two SAZ variables, is considered. In addition, a possible extension to three or more SAZ variables is outlined. A combination of log-linear models for the analysis of the correlation in combination with the bivariate approaches is proposed. © 2016 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

Cylindrical effects on Richtmyer-Meshkov instability for arbitrary Atwood numbers in weakly nonlinear regime

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

Liu, W. H.; He, X. T.; LCP, Institute of Applied Physics and Computational Mathematics, Beijing 100088

2012-07-15

When an incident shock collides with a corrugated interface separating two fluids of different densities, the interface is prone to Richtmyer-Meshkov instability (RMI). Based on the formal perturbation expansion method as well as the potential flow theory, we present a simple method to investigate the cylindrical effects in weakly nonlinear RMI with the transmitted and reflected cylindrical shocks by considering the nonlinear corrections up to fourth order. The cylindrical results associated with the material interface show that the interface expression consists of two parts: the result in the planar system and that from the cylindrical effects. In the limit ofmore » the cylindrical radius tending to infinity, the cylindrical results can be reduced to those in the planar system. Our explicit results show that the cylindrical effects exert an inward velocity on the whole perturbed interface, regardless of bubbles or spikes of the interface. On the one hand, outgoing bubbles are constrained and ingoing spikes are accelerated for different Atwood numbers (A) and mode numbers k'. On the other hand, for ingoing bubbles, when |A|k'{sup 3/2} Less-Than-Or-Equivalent-To 1, bubbles are considerably accelerated especially at the small |A| and k'; otherwise, bubbles are decelerated. For outgoing spikes, when |A|k' Greater-Than-Or-Equivalent-To 1, spikes are dramatically accelerated especially at large |A| and k'; otherwise, spikes are decelerated. Furthermore, the cylindrical effects have a significant influence on the amplitudes of the ingoing spike and bubble for large k'. Thus, it should be included in applications where the cylindrical effects play a role, such as inertial confinement fusion ignition target design.« less

IB-LBM simulation on blood cell sorting with a micro-fence structure.

PubMed

Wei, Qiang; Xu, Yuan-Qing; Tian, Fang-bao; Gao, Tian-xin; Tang, Xiao-ying; Zu, Wen-Hong

2014-01-01

A size-based blood cell sorting model with a micro-fence structure is proposed in the frame of immersed boundary and lattice Boltzmann method (IB-LBM). The fluid dynamics is obtained by solving the discrete lattice Boltzmann equation, and the cells motion and deformation are handled by the immersed boundary method. A micro-fence consists of two parallel slope post rows which are adopted to separate red blood cells (RBCs) from white blood cells (WBCs), in which the cells to be separated are transported one after another by the flow into the passageway between the two post rows. Effected by the cross flow, RBCs are schemed to get through the pores of the nether post row since they are smaller and more deformable compared with WBCs. WBCs are required to move along the nether post row till they get out the micro-fence. Simulation results indicate that for a fix width of pores, the slope angle of the post row plays an important role in cell sorting. The cells mixture can not be separated properly in a small slope angle, while obvious blockages by WBCs will take place to disturb the continuous cell sorting in a big slope angle. As an optimal result, an adaptive slope angle is found to sort RBCs form WBCs correctly and continuously.

The effect of self-sorting and co-assembly on the mechanical properties of low molecular weight hydrogels

NASA Astrophysics Data System (ADS)

Colquhoun, Catherine; Draper, Emily R.; Eden, Edward G. B.; Cattoz, Beatrice N.; Morris, Kyle L.; Chen, Lin; McDonald, Tom O.; Terry, Ann E.; Griffiths, Peter C.; Serpell, Louise C.; Adams, Dave J.

2014-10-01

Self-sorting in low molecular weight hydrogels can be achieved using a pH triggered approach. We show here that this method can be used to prepare gels with different types of mechanical properties. Cooperative, disruptive or orthogonal assembled systems can be produced. Gels with interesting behaviour can be also prepared, for example self-sorted gels where delayed switch-on of gelation occurs. By careful choice of gelator, co-assembled structures can also be generated, which leads to synergistic strengthening of the mechanical properties.Self-sorting in low molecular weight hydrogels can be achieved using a pH triggered approach. We show here that this method can be used to prepare gels with different types of mechanical properties. Cooperative, disruptive or orthogonal assembled systems can be produced. Gels with interesting behaviour can be also prepared, for example self-sorted gels where delayed switch-on of gelation occurs. By careful choice of gelator, co-assembled structures can also be generated, which leads to synergistic strengthening of the mechanical properties. Electronic supplementary information (ESI) available: Full experimental and synthetic details for the dipeptides, full experimental descriptions, further NMR, single crystal diffraction data, fXRD data and SANS data. See DOI: 10.1039/c4nr04039b

A double-spike method for K-Ar measurement: A technique for high precision in situ dating on Mars and other planetary surfaces

NASA Astrophysics Data System (ADS)

Farley, K. A.; Hurowitz, J. A.; Asimow, P. D.; Jacobson, N. S.; Cartwright, J. A.

2013-06-01

A new method for K-Ar dating using a double isotope dilution technique is proposed and demonstrated. The method is designed to eliminate known difficulties facing in situ dating on planetary surfaces, especially instrument complexity and power availability. It may also have applicability in some terrestrial dating applications. Key to the method is the use of a solid tracer spike enriched in both 39Ar and 41K. When mixed with lithium borate flux in a Knudsen effusion cell, this tracer spike and a sample to be dated can be successfully fused and degassed of Ar at <1000 °C. The evolved 40Ar∗/39Ar ratio can be measured to high precision using noble gas mass spectrometry. After argon measurement the sample melt is heated to a slightly higher temperature (˜1030 °C) to volatilize potassium, and the evolved 39K/41K ratio measured by Knudsen effusion mass spectrometry. Combined with the known composition of the tracer spike, these two ratios define the K-Ar age using a single sample aliquot and without the need for extreme temperature or a mass determination. In principle the method can be implemented using a single mass spectrometer. Experiments indicate that quantitative extraction of argon from a basalt sample occurs at a sufficiently low temperature that potassium loss in this step is unimportant. Similarly, potassium isotope ratios measured in the Knudsen apparatus indicate good sample-spike equilibration and acceptably small isotopic fractionation. When applied to a flood basalt from the Viluy Traps, Siberia, a K-Ar age of 351 ± 19 Ma was obtained, a result within 1% of the independently known age. For practical reasons this measurement was made on two separate mass spectrometers, but a scheme for combining the measurements in a single analytical instrument is described. Because both parent and daughter are determined by isotope dilution, the precision on K-Ar ages obtained by the double isotope dilution method should routinely approach that of a pair of isotope ratio determinations, likely better than ±5%.

Stochastic Order Redshift Technique (SORT): a simple, efficient and robust method to improve cosmological redshift measurements

NASA Astrophysics Data System (ADS)

Tejos, Nicolas; Rodríguez-Puebla, Aldo; Primack, Joel R.

2018-01-01

We present a simple, efficient and robust approach to improve cosmological redshift measurements. The method is based on the presence of a reference sample for which a precise redshift number distribution (dN/dz) can be obtained for different pencil-beam-like sub-volumes within the original survey. For each sub-volume we then impose that: (i) the redshift number distribution of the uncertain redshift measurements matches the reference dN/dz corrected by their selection functions and (ii) the rank order in redshift of the original ensemble of uncertain measurements is preserved. The latter step is motivated by the fact that random variables drawn from Gaussian probability density functions (PDFs) of different means and arbitrarily large standard deviations satisfy stochastic ordering. We then repeat this simple algorithm for multiple arbitrary pencil-beam-like overlapping sub-volumes; in this manner, each uncertain measurement has multiple (non-independent) 'recovered' redshifts which can be used to estimate a new redshift PDF. We refer to this method as the Stochastic Order Redshift Technique (SORT). We have used a state-of-the-art N-body simulation to test the performance of SORT under simple assumptions and found that it can improve the quality of cosmological redshifts in a robust and efficient manner. Particularly, SORT redshifts (zsort) are able to recover the distinctive features of the so-called 'cosmic web' and can provide unbiased measurement of the two-point correlation function on scales ≳4 h-1Mpc. Given its simplicity, we envision that a method like SORT can be incorporated into more sophisticated algorithms aimed to exploit the full potential of large extragalactic photometric surveys.

Effect of fungal mycelia on the HPLC-UV and UV-vis spectrophotometric assessment of mycelium-bound epoxide hydrolase using glycidyl phenyl ether.

PubMed

Dolcet, Marta M; Torres, Mercè; Canela, Ramon

2016-06-25

The use of mycelia as biocatalysts has technical and economic advantages. However, there are several difficulties in obtaining accurate results in mycelium-catalysed reactions. Firstly, sample extraction, indispensable because of the presence of mycelia, can bring into the extract components with a similar structure to that of the analyte of interest; secondly, mycelia can influence the recovery of the analyte. We prepared calibration standards of 3-phenoxy-1,2-propanediol (PPD) in the pure solvent and in the presence of mycelia (spiked before or after extraction) from five fungi (Aspergillus niger, Aspergillus tubingensis, Penicillium aurantiogriseum, Penicillium sp. and Aspergillus terreus). The quantification of PPD was carried out by HPLC-UV and UV-vis spectrophotometry. The manuscript shows that the last method is as accurate as the HPLC method. However, the colorimetric method led to a higher data throughput, which allowed the study of more samples in a shorter time. Matrix effects were evaluated visually from the plotted calibration data and statistically by simultaneously comparing the intercept and slope of calibration curves performed with solvent, post-extraction spiked standards and pre-extraction spiked standards. Significant differences were found between the post- and pre-extraction spiked matrix-matched functions. Pre-extraction spiked matrix-matched functions based on A. tubingensis mycelia, selected as the reference, were validated and used to compensate for low recoveries. These validated functions were successfully applied to the quantification of PPD achieved during the hydrolysis of glycidyl phenyl ether by mycelium-bound epoxide hydrolases and equivalent hydrolysis yields were determined by HPLC-UV and UV-vis spectrophotometry. This study may serve as starting point to implement matrix effects evaluation when mycelium-bound epoxide hydrolases are studied. Copyright © 2016 Elsevier B.V. All rights reserved.

High-throughput, low-loss, low-cost, and label-free cell separation using electrophysiology-activated cell enrichment.

PubMed

Faraghat, Shabnam A; Hoettges, Kai F; Steinbach, Max K; van der Veen, Daan R; Brackenbury, William J; Henslee, Erin A; Labeed, Fatima H; Hughes, Michael P

2017-05-02

Currently, cell separation occurs almost exclusively by density gradient methods and by fluorescence- and magnetic-activated cell sorting (FACS/MACS). These variously suffer from lack of specificity, high cell loss, use of labels, and high capital/operating cost. We present a dielectrophoresis (DEP)-based cell-separation method, using 3D electrodes on a low-cost disposable chip; one cell type is allowed to pass through the chip whereas the other is retained and subsequently recovered. The method advances usability and throughput of DEP separation by orders of magnitude in throughput, efficiency, purity, recovery (cells arriving in the correct output fraction), cell losses (those which are unaccounted for at the end of the separation), and cost. The system was evaluated using three example separations: live and dead yeast; human cancer cells/red blood cells; and rodent fibroblasts/red blood cells. A single-pass protocol can enrich cells with cell recovery of up to 91.3% at over 300,000 cells per second with >3% cell loss. A two-pass protocol can process 300,000,000 cells in under 30 min, with cell recovery of up to 96.4% and cell losses below 5%, an effective processing rate >160,000 cells per second. A three-step protocol is shown to be effective for removal of 99.1% of RBCs spiked with 1% cancer cells while maintaining a processing rate of ∼170,000 cells per second. Furthermore, the self-contained and low-cost nature of the separator device means that it has potential application in low-contamination applications such as cell therapies, where good manufacturing practice compatibility is of paramount importance.

Estimating Temporal Causal Interaction between Spike Trains with Permutation and Transfer Entropy

PubMed Central

Li, Zhaohui; Li, Xiaoli

2013-01-01

Estimating the causal interaction between neurons is very important for better understanding the functional connectivity in neuronal networks. We propose a method called normalized permutation transfer entropy (NPTE) to evaluate the temporal causal interaction between spike trains, which quantifies the fraction of ordinal information in a neuron that has presented in another one. The performance of this method is evaluated with the spike trains generated by an Izhikevich’s neuronal model. Results show that the NPTE method can effectively estimate the causal interaction between two neurons without influence of data length. Considering both the precision of time delay estimated and the robustness of information flow estimated against neuronal firing rate, the NPTE method is superior to other information theoretic method including normalized transfer entropy, symbolic transfer entropy and permutation conditional mutual information. To test the performance of NPTE on analyzing simulated biophysically realistic synapses, an Izhikevich’s cortical network that based on the neuronal model is employed. It is found that the NPTE method is able to characterize mutual interactions and identify spurious causality in a network of three neurons exactly. We conclude that the proposed method can obtain more reliable comparison of interactions between different pairs of neurons and is a promising tool to uncover more details on the neural coding. PMID:23940662

Sparse spikes super-resolution on thin grids II: the continuous basis pursuit

NASA Astrophysics Data System (ADS)

Duval, Vincent; Peyré, Gabriel

2017-09-01

This article analyzes the performance of the continuous basis pursuit (C-BP) method for sparse super-resolution. The C-BP has been recently proposed by Ekanadham, Tranchina and Simoncelli as a refined discretization scheme for the recovery of spikes in inverse problems regularization. One of the most well known discretization scheme, the basis pursuit (BP, also known as \

Separating Spike Count Correlation from Firing Rate Correlation

PubMed Central

Vinci, Giuseppe; Ventura, Valérie; Smith, Matthew A.; Kass, Robert E.

2016-01-01

Populations of cortical neurons exhibit shared fluctuations in spiking activity over time. When measured for a pair of neurons over multiple repetitions of an identical stimulus, this phenomenon emerges as correlated trial-to-trial response variability via spike count correlation (SCC). However, spike counts can be viewed as noisy versions of firing rates, which can vary from trial to trial. From this perspective, the SCC for a pair of neurons becomes a noisy version of the corresponding firing-rate correlation (FRC). Furthermore, the magnitude of the SCC is generally smaller than that of the FRC, and is likely to be less sensitive to experimental manipulation. We provide statistical methods for disambiguating time-averaged drive from within-trial noise, thereby separating FRC from SCC. We study these methods to document their reliability, and we apply them to neurons recorded in vivo from area V4, in an alert animal. We show how the various effects we describe are reflected in the data: within-trial effects are largely negligible, while attenuation due to trial-to-trial variation dominates, and frequently produces comparisons in SCC that, because of noise, do not accurately reflect those based on the underlying FRC. PMID:26942746

How the modified method of orbit quality assessment works for Oort spike comets?

NASA Astrophysics Data System (ADS)

Królikowska, Małgorzata; Dybczyński, Piotr A.

2018-06-01

We present a brief overview of the effectiveness of the modified method of a quality of orbit estimation proposed by us a few years ago. Having now a complete sample of 100 Oort spike comets with large perihelion distances, we show that it was justified to introduce more restricted conditions separating the individual quality classes as well as introducing a new quality class containing orbits of the excellent quality, marked by us as 1a+. To enrich the perception, we provided a complete collection of visual time distributions of positional data sets used by us for an orbit determination (see the Appendix). We show that modern positional measurements of large-perihelion Oort spike comets should be carried out for at least 3 yr around perihelion (three-four oppositions) to be almost certain that the derived orbit will be of the highest quality (1a+ class). Our results strongly support an expectation that in near future it will be possible to study the shape of 1/aori-distribution of the Oort spike comets in great detail basing only on the highest quality orbits, having 1/aori-uncertainties well below 5 × 10-6 au-1.

SPICODYN: A Toolbox for the Analysis of Neuronal Network Dynamics and Connectivity from Multi-Site Spike Signal Recordings.

PubMed

Pastore, Vito Paolo; Godjoski, Aleksandar; Martinoia, Sergio; Massobrio, Paolo

2018-01-01

We implemented an automated and efficient open-source software for the analysis of multi-site neuronal spike signals. The software package, named SPICODYN, has been developed as a standalone windows GUI application, using C# programming language with Microsoft Visual Studio based on .NET framework 4.5 development environment. Accepted input data formats are HDF5, level 5 MAT and text files, containing recorded or generated time series spike signals data. SPICODYN processes such electrophysiological signals focusing on: spiking and bursting dynamics and functional-effective connectivity analysis. In particular, for inferring network connectivity, a new implementation of the transfer entropy method is presented dealing with multiple time delays (temporal extension) and with multiple binary patterns (high order extension). SPICODYN is specifically tailored to process data coming from different Multi-Electrode Arrays setups, guarantying, in those specific cases, automated processing. The optimized implementation of the Delayed Transfer Entropy and the High-Order Transfer Entropy algorithms, allows performing accurate and rapid analysis on multiple spike trains from thousands of electrodes.

Wiener Filtering of Surface EMG with a priori SNR Estimation Toward Myoelectric Control for Neurological Injury Patients

PubMed Central

Liu, Jie; Ying, Dongwen; Zhou, Ping

2014-01-01

Voluntary surface electromyogram (EMG) signals from neurological injury patients are often corrupted by involuntary background interference or spikes, imposing difficulties for myoelectric control. We present a novel framework to suppress involuntary background spikes during voluntary surface EMG recordings. The framework applies a Wiener filter to restore voluntary surface EMG signals based on tracking a priori signal to noise ratio (SNR) by using the decision-directed method. Semi-synthetic surface EMG signals contaminated by different levels of involuntary background spikes were constructed from a database of surface EMG recordings in a group of spinal cord injury subjects. After the processing, the onset detection of voluntary muscle activity was significantly improved against involuntary background spikes. The magnitude of voluntary surface EMG signals can also be reliably estimated for myoelectric control purpose. Compared with the previous sample entropy analysis for suppressing involuntary background spikes, the proposed framework is characterized by quick and simple implementation, making it more suitable for application in a myoelectric control system toward neurological injury rehabilitation. PMID:25443536

Solid matrix transformation and tracer addition using molten ammonium bifluoride salt as a sample preparation method for laser ablation inductively coupled plasma mass spectrometry.

PubMed

Grate, Jay W; Gonzalez, Jhanis J; O'Hara, Matthew J; Kellogg, Cynthia M; Morrison, Samuel S; Koppenaal, David W; Chan, George C-Y; Mao, Xianglei; Zorba, Vassilia; Russo, Richard E

2017-09-08

Solid sampling and analysis methods, such as laser ablation inductively coupled plasma mass spectrometry (LA-ICP-MS), are challenged by matrix effects and calibration difficulties. Matrix-matched standards for external calibration are seldom available and it is difficult to distribute spikes evenly into a solid matrix as internal standards. While isotopic ratios of the same element can be measured to high precision, matrix-dependent effects in the sampling and analysis process frustrate accurate quantification and elemental ratio determinations. Here we introduce a potentially general solid matrix transformation approach entailing chemical reactions in molten ammonium bifluoride (ABF) salt that enables the introduction of spikes as tracers or internal standards. Proof of principle experiments show that the decomposition of uranium ore in sealed PFA fluoropolymer vials at 230 °C yields, after cooling, new solids suitable for direct solid sampling by LA. When spikes are included in the molten salt reaction, subsequent LA-ICP-MS sampling at several spots indicate that the spikes are evenly distributed, and that U-235 tracer dramatically improves reproducibility in U-238 analysis. Precisions improved from 17% relative standard deviation for U-238 signals to 0.1% for the ratio of sample U-238 to spiked U-235, a factor of over two orders of magnitude. These results introduce the concept of solid matrix transformation (SMT) using ABF, and provide proof of principle for a new method of incorporating internal standards into a solid for LA-ICP-MS. This new approach, SMT-LA-ICP-MS, provides opportunities to improve calibration and quantification in solids based analysis. Looking forward, tracer addition to transformed solids opens up LA-based methods to analytical methodologies such as standard addition, isotope dilution, preparation of matrix-matched solid standards, external calibration, and monitoring instrument drift against external calibration standards.

Evaluation of sampling and analytical methods for nicotine and polynuclear aromatic hydrocarbon in indoor air. Final report, 1 February 1987-30 March 1987

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

Chuang, J.C.; Kuhlman, M.R.; Hannan, S.W.

1987-11-01

The objective of this project was to evaluate a potential collection medium, XAD-4 resin, for collecting nicotine and polynuclear aromatic hydrocarbon (PAH) and to determine whether one collection system and one analytical method will allow quantification of both compound classes in air. The extraction efficiency study was to determine the extraction method to quantitatively remove nicotine and PAH from XAD-4 resin. The results showed that a two-step Soxhlet extraction consisting of dichloromethane followed by ethyl acetate resulted in the best recoveries for both nicotine and PAH. In the sampling efficiency study, XAD-2 and XAD-4 resin were compared, in parallel, formore » collection of PAH and nicotine. Quartz fiber filters were placed upstream of both adsorbents to collect particles. Prior to sampling, both XAD-2 and XAD-4 traps were spiked with known amounts (2 microgram) of perdeuterated PAH and D3-nicotine. The experiments were performed with cigarette smoking and nonsmoking conditions. The spiked PAH were retained well in both adsorbents after exposure to more than 300 cu. m. of indoor air. The spiked XAD-4 resin gave higher recoveries for D3-nicotine than did the spiked XAD-2 resin. The collection efficiency for PAH for both adsorbents is very similar but higher levels of nicotine were collected on XAD-4 resin.« less

Population decoding of motor cortical activity using a generalized linear model with hidden states.

PubMed

Lawhern, Vernon; Wu, Wei; Hatsopoulos, Nicholas; Paninski, Liam

2010-06-15

Generalized linear models (GLMs) have been developed for modeling and decoding population neuronal spiking activity in the motor cortex. These models provide reasonable characterizations between neural activity and motor behavior. However, they lack a description of movement-related terms which are not observed directly in these experiments, such as muscular activation, the subject's level of attention, and other internal or external states. Here we propose to include a multi-dimensional hidden state to address these states in a GLM framework where the spike count at each time is described as a function of the hand state (position, velocity, and acceleration), truncated spike history, and the hidden state. The model can be identified by an Expectation-Maximization algorithm. We tested this new method in two datasets where spikes were simultaneously recorded using a multi-electrode array in the primary motor cortex of two monkeys. It was found that this method significantly improves the model-fitting over the classical GLM, for hidden dimensions varying from 1 to 4. This method also provides more accurate decoding of hand state (reducing the mean square error by up to 29% in some cases), while retaining real-time computational efficiency. These improvements on representation and decoding over the classical GLM model suggest that this new approach could contribute as a useful tool to motor cortical decoding and prosthetic applications. Copyright (c) 2010 Elsevier B.V. All rights reserved.

Population Decoding of Motor Cortical Activity using a Generalized Linear Model with Hidden States

PubMed Central

Lawhern, Vernon; Wu, Wei; Hatsopoulos, Nicholas G.; Paninski, Liam

2010-01-01

Generalized linear models (GLMs) have been developed for modeling and decoding population neuronal spiking activity in the motor cortex. These models provide reasonable characterizations between neural activity and motor behavior. However, they lack a description of movement-related terms which are not observed directly in these experiments, such as muscular activation, the subject's level of attention, and other internal or external states. Here we propose to include a multi-dimensional hidden state to address these states in a GLM framework where the spike count at each time is described as a function of the hand state (position, velocity, and acceleration), truncated spike history, and the hidden state. The model can be identified by an Expectation-Maximization algorithm. We tested this new method in two datasets where spikes were simultaneously recorded using a multi-electrode array in the primary motor cortex of two monkeys. It was found that this method significantly improves the model-fitting over the classical GLM, for hidden dimensions varying from 1 to 4. This method also provides more accurate decoding of hand state (lowering the Mean Square Error by up to 29% in some cases), while retaining real-time computational efficiency. These improvements on representation and decoding over the classical GLM model suggest that this new approach could contribute as a useful tool to motor cortical decoding and prosthetic applications. PMID:20359500

Decoding intravesical pressure from local field potentials in rat lumbosacral spinal cord

NASA Astrophysics Data System (ADS)

Im, Changkyun; Park, Hae Yong; Koh, Chin Su; Ryu, Sang Baek; Seo, In Seok; Kim, Yong Jung; Kim, Kyung Hwan; Shin, Hyung-Cheul

2016-10-01

Chronic monitoring of intravesical pressure is required to detect the onset of intravesical hypertension and the progression of a more severe condition. Recent reports demonstrate the bladder state can be monitored from the spiking activity of the dorsal root ganglia or lumbosacral spinal cord. However, one of the most serious challenges for these methods is the difficulty of sustained spike signal acquisition due to the high-electrode-location-sensitivity of spikes or neuro-degeneration. Alternatively, it has been demonstrated that local field potential recordings are less affected by encapsulation reactions or electrode location changes. Here, we hypothesized that local field potential (LFP) from the lumbosacral dorsal horn may provide information concerning the intravesical pressure. LFP and spike activities were simultaneously recorded from the lumbosacral spinal cord of anesthetized rats during bladder filling. The results show that the LFP activities carry significant information about intravesical pressure along with spiking activities. Importantly, the intravesical pressure is decoded from the power in high-frequency bands (83.9-256 Hz) with a substantial performance similar to that of the spike train decoding. These findings demonstrate that high-frequency LFP activity can be an alternative intravesical pressure monitoring signal, which could lead to a proper closed loop system for urinary control.

Method and apparatus for electrostatically sorting biological cells

DOEpatents

Merrill, John T.

1982-01-01

An improved method of sorting biological cells in a conventional cell sorter apparatus includes generating a fluid jet containing cells to be sorted, measuring the distance between the centers of adjacent droplets in a zone thereof defined at the point where the fluid jet separates into descrete droplets, setting the distance between the center of a droplet in said separation zone and the position along said fluid jet at which the cell is optically sensed for specific characteristics to be an integral multiple of said center-to-center distance, and disabling a charger from electrically charging a specific droplet if a cell is detected by the optical sensor in a position wherein it will be in the neck area between droplets during droplet formation rather than within a predetermined distance from the droplet center.

Comparison of spike and aerosol challenge tests for the recovery of viable influenza virus from non-woven fabrics.

PubMed

Zuo, Zhili; de Abin, Martha; Chander, Yogesh; Kuehn, Thomas H; Goyal, Sagar M; Pui, David Y H

2013-09-01

To experimentally determine the survival kinetics of influenza virus on personal protective equipment (PPE) and to evaluate the risk of virus transfer from PPE, it is important to compare the effects on virus recovery of the method used to contaminate the PPE with virus and the type of eluent used to recover it. Avian influenza virus (AIV) was applied as a liquid suspension (spike test) and as an aerosol to three types of non-woven fabrics [polypropylene (PP), polyester (PET), and polyamide (Nylon)] that are commonly used in the manufacture of PPE. This was followed by virus recovery using eight different eluents (phosphate-buffered saline, minimum essential medium, and 1.5% or 3.0% beef extract at pH 7, 8, or 9). For spike tests, no statistically significant difference was found in virus recovery using any of the eluents tested. Hydrophobic surfaces (PP and PET) yielded higher spiked virus recovery than hydrophilic Nylon. From all materials, the virus recovery was much lower in aerosol challenge tests than in spike tests. Significant differences were found in the recovery of viable AIV from non-woven fabrics between spike and aerosol challenge tests. The findings of this study demonstrate the need for realistic aerosol challenge tests rather than liquid spike tests in studies of virus survival on surfaces where airborne transmission of influenza virus may get involved. © 2013 John Wiley & Sons Ltd.

Blood flow velocity measurements in chicken embryo vascular network via PIV approach

NASA Astrophysics Data System (ADS)

Kurochkin, Maxim A.; Stiukhina, Elena S.; Fedosov, Ivan V.; Tuchin, Valery V.

2018-04-01

A method for measuring of blood velocity in the native vasculature of a chick embryo by the method of micro anemometry from particle images (μPIV) is improved. A method for interrogation regions sorting by the mask of the vasculature is proposed. A method for sorting of the velocity field of capillary blood flow is implemented. The in vitro method was evaluated for accuracy in a glass phantom of a blood vessel with a diameter of 50 μm and in vivo on the bloodstream of a chicken embryo, by comparing the transverse profile of the blood velocity obtained by the PIV method with the theoretical Poiseuille laminar flow profile.

Spike shape analysis of electromyography for parkinsonian tremor evaluation.

PubMed

Marusiak, Jarosław; Andrzejewska, Renata; Świercz, Dominika; Kisiel-Sajewicz, Katarzyna; Jaskólska, Anna; Jaskólski, Artur

2015-12-01

Standard electromyography (EMG) parameters have limited utility for evaluation of Parkinson disease (PD) tremor. Spike shape analysis (SSA) EMG parameters are more sensitive than standard EMG parameters for studying motor control mechanisms in healthy subjects. SSA of EMG has not been used to assess parkinsonian tremor. This study assessed the utility of SSA and standard time and frequency analysis for electromyographic evaluation of PD-related resting tremor. We analyzed 1-s periods of EMG recordings to detect nontremor and tremor signals in relaxed biceps brachii muscle of seven mild to moderate PD patients. SSA revealed higher mean spike amplitude, duration, and slope and lower mean spike frequency in tremor signals than in nontremor signals. Standard EMG parameters (root mean square, median, and mean frequency) did not show differences between the tremor and nontremor signals. SSA of EMG data is a sensitive method for parkinsonian tremor evaluation. © 2015 Wiley Periodicals, Inc.

Development and Validation of a Spike Detection and Classification Algorithm Aimed at Implementation on Hardware Devices

PubMed Central

Biffi, E.; Ghezzi, D.; Pedrocchi, A.; Ferrigno, G.

2010-01-01

Neurons cultured in vitro on MicroElectrode Array (MEA) devices connect to each other, forming a network. To study electrophysiological activity and long term plasticity effects, long period recording and spike sorter methods are needed. Therefore, on-line and real time analysis, optimization of memory use and data transmission rate improvement become necessary. We developed an algorithm for amplitude-threshold spikes detection, whose performances were verified with (a) statistical analysis on both simulated and real signal and (b) Big O Notation. Moreover, we developed a PCA-hierarchical classifier, evaluated on simulated and real signal. Finally we proposed a spike detection hardware design on FPGA, whose feasibility was verified in terms of CLBs number, memory occupation and temporal requirements; once realized, it will be able to execute on-line detection and real time waveform analysis, reducing data storage problems. PMID:20300592

Multiclass Classification by Adaptive Network of Dendritic Neurons with Binary Synapses Using Structural Plasticity

PubMed Central

Hussain, Shaista; Basu, Arindam

2016-01-01

The development of power-efficient neuromorphic devices presents the challenge of designing spike pattern classification algorithms which can be implemented on low-precision hardware and can also achieve state-of-the-art performance. In our pursuit of meeting this challenge, we present a pattern classification model which uses a sparse connection matrix and exploits the mechanism of nonlinear dendritic processing to achieve high classification accuracy. A rate-based structural learning rule for multiclass classification is proposed which modifies a connectivity matrix of binary synaptic connections by choosing the best “k” out of “d” inputs to make connections on every dendritic branch (k < < d). Because learning only modifies connectivity, the model is well suited for implementation in neuromorphic systems using address-event representation (AER). We develop an ensemble method which combines several dendritic classifiers to achieve enhanced generalization over individual classifiers. We have two major findings: (1) Our results demonstrate that an ensemble created with classifiers comprising moderate number of dendrites performs better than both ensembles of perceptrons and of complex dendritic trees. (2) In order to determine the moderate number of dendrites required for a specific classification problem, a two-step solution is proposed. First, an adaptive approach is proposed which scales the relative size of the dendritic trees of neurons for each class. It works by progressively adding dendrites with fixed number of synapses to the network, thereby allocating synaptic resources as per the complexity of the given problem. As a second step, theoretical capacity calculations are used to convert each neuronal dendritic tree to its optimal topology where dendrites of each class are assigned different number of synapses. The performance of the model is evaluated on classification of handwritten digits from the benchmark MNIST dataset and compared with other spike classifiers. We show that our system can achieve classification accuracy within 1 − 2% of other reported spike-based classifiers while using much less synaptic resources (only 7%) compared to that used by other methods. Further, an ensemble classifier created with adaptively learned sizes can attain accuracy of 96.4% which is at par with the best reported performance of spike-based classifiers. Moreover, the proposed method achieves this by using about 20% of the synapses used by other spike algorithms. We also present results of applying our algorithm to classify the MNIST-DVS dataset collected from a real spike-based image sensor and show results comparable to the best reported ones (88.1% accuracy). For VLSI implementations, we show that the reduced synaptic memory can save upto 4X area compared to conventional crossbar topologies. Finally, we also present a biologically realistic spike-based version for calculating the correlations required by the structural learning rule and demonstrate the correspondence between the rate-based and spike-based methods of learning. PMID:27065782

TT : a program that implements predictor sort design and analysis

Treesearch

S. P. Verrill; D. W. Green; V. L. Herian

1997-01-01

In studies on wood strength, researchers sometimes replace experimental unit allocation via random sampling with allocation via sorts based on nondestructive measurements of strength predictors such as modulus of elasticity and specific gravity. This report documents TT, a computer program that implements recently published methods to increase the sensitivity of such...

High-Throughput, Motility-Based Sorter for Microswimmers such as C. elegans

PubMed Central

Yuan, Jinzhou; Zhou, Jessie; Raizen, David M.; Bau, Haim H.

2015-01-01

Animal motility varies with genotype, disease, aging, and environmental conditions. In many studies, it is desirable to carry out high throughput motility-based sorting to isolate rare animals for, among other things, forward genetic screens to identify genetic pathways that regulate phenotypes of interest. Many commonly used screening processes are labor-intensive, lack sensitivity, and require extensive investigator training. Here, we describe a sensitive, high throughput, automated, motility-based method for sorting nematodes. Our method is implemented in a simple microfluidic device capable of sorting thousands of animals per hour per module, and is amenable to parallelism. The device successfully enriches for known C. elegans motility mutants. Furthermore, using this device, we isolate low-abundance mutants capable of suppressing the somnogenic effects of the flp-13 gene, which regulates C. elegans sleep. By performing genetic complementation tests, we demonstrate that our motility-based sorting device efficiently isolates mutants for the same gene identified by tedious visual inspection of behavior on an agar surface. Therefore, our motility-based sorter is capable of performing high throughput gene discovery approaches to investigate fundamental biological processes. PMID:26008643

An Efficient Supervised Training Algorithm for Multilayer Spiking Neural Networks

PubMed Central

Xie, Xiurui; Qu, Hong; Liu, Guisong; Zhang, Malu; Kurths, Jürgen

2016-01-01

The spiking neural networks (SNNs) are the third generation of neural networks and perform remarkably well in cognitive tasks such as pattern recognition. The spike emitting and information processing mechanisms found in biological cognitive systems motivate the application of the hierarchical structure and temporal encoding mechanism in spiking neural networks, which have exhibited strong computational capability. However, the hierarchical structure and temporal encoding approach require neurons to process information serially in space and time respectively, which reduce the training efficiency significantly. For training the hierarchical SNNs, most existing methods are based on the traditional back-propagation algorithm, inheriting its drawbacks of the gradient diffusion and the sensitivity on parameters. To keep the powerful computation capability of the hierarchical structure and temporal encoding mechanism, but to overcome the low efficiency of the existing algorithms, a new training algorithm, the Normalized Spiking Error Back Propagation (NSEBP) is proposed in this paper. In the feedforward calculation, the output spike times are calculated by solving the quadratic function in the spike response model instead of detecting postsynaptic voltage states at all time points in traditional algorithms. Besides, in the feedback weight modification, the computational error is propagated to previous layers by the presynaptic spike jitter instead of the gradient decent rule, which realizes the layer-wised training. Furthermore, our algorithm investigates the mathematical relation between the weight variation and voltage error change, which makes the normalization in the weight modification applicable. Adopting these strategies, our algorithm outperforms the traditional SNN multi-layer algorithms in terms of learning efficiency and parameter sensitivity, that are also demonstrated by the comprehensive experimental results in this paper. PMID:27044001

Predicting and interpreting identification errors in military vehicle training using multidimensional scaling.

PubMed

Bohil, Corey J; Higgins, Nicholas A; Keebler, Joseph R

2014-01-01

We compared methods for predicting and understanding the source of confusion errors during military vehicle identification training. Participants completed training to identify main battle tanks. They also completed card-sorting and similarity-rating tasks to express their mental representation of resemblance across the set of training items. We expected participants to selectively attend to a subset of vehicle features during these tasks, and we hypothesised that we could predict identification confusion errors based on the outcomes of the card-sort and similarity-rating tasks. Based on card-sorting results, we were able to predict about 45% of observed identification confusions. Based on multidimensional scaling of the similarity-rating data, we could predict more than 80% of identification confusions. These methods also enabled us to infer the dimensions receiving significant attention from each participant. This understanding of mental representation may be crucial in creating personalised training that directs attention to features that are critical for accurate identification. Participants completed military vehicle identification training and testing, along with card-sorting and similarity-rating tasks. The data enabled us to predict up to 84% of identification confusion errors and to understand the mental representation underlying these errors. These methods have potential to improve training and reduce identification errors leading to fratricide.

Point process modeling and estimation: Advances in the analysis of dynamic neural spiking data

NASA Astrophysics Data System (ADS)

Deng, Xinyi

2016-08-01

A common interest of scientists in many fields is to understand the relationship between the dynamics of a physical system and the occurrences of discrete events within such physical system. Seismologists study the connection between mechanical vibrations of the Earth and the occurrences of earthquakes so that future earthquakes can be better predicted. Astrophysicists study the association between the oscillating energy of celestial regions and the emission of photons to learn the Universe's various objects and their interactions. Neuroscientists study the link between behavior and the millisecond-timescale spike patterns of neurons to understand higher brain functions. Such relationships can often be formulated within the framework of state-space models with point process observations. The basic idea is that the dynamics of the physical systems are driven by the dynamics of some stochastic state variables and the discrete events we observe in an interval are noisy observations with distributions determined by the state variables. This thesis proposes several new methodological developments that advance the framework of state-space models with point process observations at the intersection of statistics and neuroscience. In particular, we develop new methods 1) to characterize the rhythmic spiking activity using history-dependent structure, 2) to model population spike activity using marked point process models, 3) to allow for real-time decision making, and 4) to take into account the need for dimensionality reduction for high-dimensional state and observation processes. We applied these methods to a novel problem of tracking rhythmic dynamics in the spiking of neurons in the subthalamic nucleus of Parkinson's patients with the goal of optimizing placement of deep brain stimulation electrodes. We developed a decoding algorithm that can make decision in real-time (for example, to stimulate the neurons or not) based on various sources of information present in population spiking data. Lastly, we proposed a general three-step paradigm that allows us to relate behavioral outcomes of various tasks to simultaneously recorded neural activity across multiple brain areas, which is a step towards closed-loop therapies for psychological diseases using real-time neural stimulation. These methods are suitable for real-time implementation for content-based feedback experiments.

Protein Sorting Prediction.

PubMed

Nielsen, Henrik

2017-01-01

Many computational methods are available for predicting protein sorting in bacteria. When comparing them, it is important to know that they can be grouped into three fundamentally different approaches: signal-based, global-property-based and homology-based prediction. In this chapter, the strengths and drawbacks of each of these approaches is described through many examples of methods that predict secretion, integration into membranes, or subcellular locations in general. The aim of this chapter is to provide a user-level introduction to the field with a minimum of computational theory.

Method for stationarity-segmentation of spike train data with application to the Pearson cross-correlation.

PubMed

Quiroga-Lombard, Claudio S; Hass, Joachim; Durstewitz, Daniel

2013-07-01

Correlations among neurons are supposed to play an important role in computation and information coding in the nervous system. Empirically, functional interactions between neurons are most commonly assessed by cross-correlation functions. Recent studies have suggested that pairwise correlations may indeed be sufficient to capture most of the information present in neural interactions. Many applications of correlation functions, however, implicitly tend to assume that the underlying processes are stationary. This assumption will usually fail for real neurons recorded in vivo since their activity during behavioral tasks is heavily influenced by stimulus-, movement-, or cognition-related processes as well as by more general processes like slow oscillations or changes in state of alertness. To address the problem of nonstationarity, we introduce a method for assessing stationarity empirically and then "slicing" spike trains into stationary segments according to the statistical definition of weak-sense stationarity. We examine pairwise Pearson cross-correlations (PCCs) under both stationary and nonstationary conditions and identify another source of covariance that can be differentiated from the covariance of the spike times and emerges as a consequence of residual nonstationarities after the slicing process: the covariance of the firing rates defined on each segment. Based on this, a correction of the PCC is introduced that accounts for the effect of segmentation. We probe these methods both on simulated data sets and on in vivo recordings from the prefrontal cortex of behaving rats. Rather than for removing nonstationarities, the present method may also be used for detecting significant events in spike trains.

A quantitative estimate of schema abnormality in socially anxious and non-anxious individuals.

PubMed

Wenzel, Amy; Brendle, Jennifer R; Kerr, Patrick L; Purath, Donna; Ferraro, F Richard

2007-01-01

Although cognitive theories of anxiety suggest that anxious individuals are characterized by abnormal threat-relevant schemas, few empirical studies have estimated the nature of these cognitive structures using quantitative methods that lend themselves to inferential statistical analysis. In the present study, socially anxious (n = 55) and non-anxious (n = 62) participants completed 3 Q-Sort tasks to assess their knowledge of events that commonly occur in social or evaluative scenarios. Participants either sorted events according to how commonly they personally believe the events occur (i.e. "self" condition), or to how commonly they estimate that most people believe they occur (i.e. "other" condition). Participants' individual Q-Sorts were correlated with mean sorts obtained from a normative sample to obtain an estimate of schema abnormality, with lower correlations representing greater levels of abnormality. Relative to non-anxious participants, socially anxious participants' sorts were less strongly associated with sorts of the normative sample, particularly in the "self" condition, although secondary analyses suggest that some significant results might be explained, in part, by depression and experience with the scenarios. These results provide empirical support for the theoretical notion that threat-relevant self-schemas of anxious individuals are characterized by some degree of abnormality.

Support for designing waste sorting systems: A mini review.

PubMed

Rousta, Kamran; Ordoñez, Isabel; Bolton, Kim; Dahlén, Lisa

2017-11-01

This article presents a mini review of research aimed at understanding material recovery from municipal solid waste. It focuses on two areas, waste sorting behaviour and collection systems, so that research on the link between these areas could be identified and evaluated. The main results presented and the methods used in the articles are categorised and appraised. The mini review reveals that most of the work that offered design guidelines for waste management systems was based on optimising technical aspects only. In contrast, most of the work that focused on user involvement did not consider developing the technical aspects of the system, but was limited to studies of user behaviour. The only clear consensus among the articles that link user involvement with the technical system is that convenient waste collection infrastructure is crucial for supporting source separation. This mini review reveals that even though the connection between sorting behaviour and technical infrastructure has been explored and described in some articles, there is still a gap when using this knowledge to design waste sorting systems. Future research in this field would benefit from being multidisciplinary and from using complementary methods, so that holistic solutions for material recirculation can be identified. It would be beneficial to actively involve users when developing sorting infrastructures, to be sure to provide a waste management system that will be properly used by them.

Benchmarking Spike-Based Visual Recognition: A Dataset and Evaluation

PubMed Central

Liu, Qian; Pineda-García, Garibaldi; Stromatias, Evangelos; Serrano-Gotarredona, Teresa; Furber, Steve B.

2016-01-01

Today, increasing attention is being paid to research into spike-based neural computation both to gain a better understanding of the brain and to explore biologically-inspired computation. Within this field, the primate visual pathway and its hierarchical organization have been extensively studied. Spiking Neural Networks (SNNs), inspired by the understanding of observed biological structure and function, have been successfully applied to visual recognition and classification tasks. In addition, implementations on neuromorphic hardware have enabled large-scale networks to run in (or even faster than) real time, making spike-based neural vision processing accessible on mobile robots. Neuromorphic sensors such as silicon retinas are able to feed such mobile systems with real-time visual stimuli. A new set of vision benchmarks for spike-based neural processing are now needed to measure progress quantitatively within this rapidly advancing field. We propose that a large dataset of spike-based visual stimuli is needed to provide meaningful comparisons between different systems, and a corresponding evaluation methodology is also required to measure the performance of SNN models and their hardware implementations. In this paper we first propose an initial NE (Neuromorphic Engineering) dataset based on standard computer vision benchmarksand that uses digits from the MNIST database. This dataset is compatible with the state of current research on spike-based image recognition. The corresponding spike trains are produced using a range of techniques: rate-based Poisson spike generation, rank order encoding, and recorded output from a silicon retina with both flashing and oscillating input stimuli. In addition, a complementary evaluation methodology is presented to assess both model-level and hardware-level performance. Finally, we demonstrate the use of the dataset and the evaluation methodology using two SNN models to validate the performance of the models and their hardware implementations. With this dataset we hope to (1) promote meaningful comparison between algorithms in the field of neural computation, (2) allow comparison with conventional image recognition methods, (3) provide an assessment of the state of the art in spike-based visual recognition, and (4) help researchers identify future directions and advance the field. PMID:27853419

Benchmarking Spike-Based Visual Recognition: A Dataset and Evaluation.

PubMed

Liu, Qian; Pineda-García, Garibaldi; Stromatias, Evangelos; Serrano-Gotarredona, Teresa; Furber, Steve B

2016-01-01

Today, increasing attention is being paid to research into spike-based neural computation both to gain a better understanding of the brain and to explore biologically-inspired computation. Within this field, the primate visual pathway and its hierarchical organization have been extensively studied. Spiking Neural Networks (SNNs), inspired by the understanding of observed biological structure and function, have been successfully applied to visual recognition and classification tasks. In addition, implementations on neuromorphic hardware have enabled large-scale networks to run in (or even faster than) real time, making spike-based neural vision processing accessible on mobile robots. Neuromorphic sensors such as silicon retinas are able to feed such mobile systems with real-time visual stimuli. A new set of vision benchmarks for spike-based neural processing are now needed to measure progress quantitatively within this rapidly advancing field. We propose that a large dataset of spike-based visual stimuli is needed to provide meaningful comparisons between different systems, and a corresponding evaluation methodology is also required to measure the performance of SNN models and their hardware implementations. In this paper we first propose an initial NE (Neuromorphic Engineering) dataset based on standard computer vision benchmarksand that uses digits from the MNIST database. This dataset is compatible with the state of current research on spike-based image recognition. The corresponding spike trains are produced using a range of techniques: rate-based Poisson spike generation, rank order encoding, and recorded output from a silicon retina with both flashing and oscillating input stimuli. In addition, a complementary evaluation methodology is presented to assess both model-level and hardware-level performance. Finally, we demonstrate the use of the dataset and the evaluation methodology using two SNN models to validate the performance of the models and their hardware implementations. With this dataset we hope to (1) promote meaningful comparison between algorithms in the field of neural computation, (2) allow comparison with conventional image recognition methods, (3) provide an assessment of the state of the art in spike-based visual recognition, and (4) help researchers identify future directions and advance the field.

A systems approach for data compression and latency reduction in cortically controlled brain machine interfaces.

PubMed

Oweiss, Karim G

2006-07-01

This paper suggests a new approach for data compression during extracutaneous transmission of neural signals recorded by high-density microelectrode array in the cortex. The approach is based on exploiting the temporal and spatial characteristics of the neural recordings in order to strip the redundancy and infer the useful information early in the data stream. The proposed signal processing algorithms augment current filtering and amplification capability and may be a viable replacement to on chip spike detection and sorting currently employed to remedy the bandwidth limitations. Temporal processing is devised by exploiting the sparseness capabilities of the discrete wavelet transform, while spatial processing exploits the reduction in the number of physical channels through quasi-periodic eigendecomposition of the data covariance matrix. Our results demonstrate that substantial improvements are obtained in terms of lower transmission bandwidth, reduced latency and optimized processor utilization. We also demonstrate the improvements qualitatively in terms of superior denoising capabilities and higher fidelity of the obtained signals.

High Density Shielded MEA / Optrode Arrays

NASA Astrophysics Data System (ADS)

Naughton, Jeff; Varela, Juan M.; Christianson, John P.; Chiles, Thomas C.; Burns, Michael J.; Naughton, Michael J.

We report on the development of a novel, high density, locally-shielded neuroelectronic / optoelectronic array architecture, useful for bioelectronics and neurophysiology. The device has been used in real time to noninvasively couple to leech neurons, allowing for extracellular recording of synaptic activity in the form of spontaneous synapse firing in pre- and post-synaptic somata. In addition, we show by subtly altering the architecture the ability for optical integration with the device - that is, it can function as both a local light delivery conduit and a recording electrode. We utilized this novel device to optically elicit and electrically record membrane currents in HEK293 cells transfected with plasmids encoding ChR2-YFP (i.e. optogenetics). Finally, we show that the local (Faraday) shield is effective in isolating the sensing area, so as to record only from cells in immediate proximity. This effective isolation or cross-talk suppression is important for moving closer to ``ground truth'' measurements of neurons, critical to the development of valid spike sorting algorithms.

Research and development of a field-ready protocol for sampling of phosgene from stationary source emissions: Diethylamine reagent studies. Research report, 11 July 1995--30 September 1996

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

Steger, J.L.; Bursey, J.T.; Merrill, R.G.

1999-03-01

This report presents the results of laboratory studies to develop and evaluate a method for the sampling and analysis of phosgene from stationary sources of air emissions using diethylamine (DEA) in toluene as the collection media. The method extracts stack gas from emission sources and stabilizes the reactive gas for subsequent analysis. DEA was evaluated both in a benchtop study and in a laboratory train spiking study. This report includes results for both the benchtop study and the train spiking study. Benchtop studies to evaluate the suitability of DEA for collecting and analyzing phosgene investigated five variables: storage time, DEAmore » concentration, moisture/pH, phosgene concentration, and sample storage temperature. Prototype sampling train studies were performed to determine if the benchtop chemical studies were transferable to a Modified Method 5 sampling train collecting phosgene in the presence of clean air mixed with typical stack gas components. Four conditions, which varied the moisture and phosgene spike were evaluated in triplicate. In addition to research results, the report includes a detailed draft method for sampling and analysis of phosgene from stationary source emissions.« less

Optimization of flow cytometric detection and cell sorting of transgenic Plasmodium parasites using interchangeable optical filters

PubMed Central

2012-01-01

Background Malaria remains a major cause of morbidity and mortality worldwide. Flow cytometry-based assays that take advantage of fluorescent protein (FP)-expressing malaria parasites have proven to be valuable tools for quantification and sorting of specific subpopulations of parasite-infected red blood cells. However, identification of rare subpopulations of parasites using green fluorescent protein (GFP) labelling is complicated by autofluorescence (AF) of red blood cells and low signal from transgenic parasites. It has been suggested that cell sorting yield could be improved by using filters that precisely match the emission spectrum of GFP. Methods Detection of transgenic Plasmodium falciparum parasites expressing either tdTomato or GFP was performed using a flow cytometer with interchangeable optical filters. Parasitaemia was evaluated using different optical filters and, after optimization of optics, the GFP-expressing parasites were sorted and analysed by microscopy after cytospin preparation and by imaging cytometry. Results A new approach to evaluate filter performance in flow cytometry using two-dimensional dot blot was developed. By selecting optical filters with narrow bandpass (BP) and maximum position of filter emission close to GFP maximum emission in the FL1 channel (510/20, 512/20 and 517/20; dichroics 502LP and 466LP), AF was markedly decreased and signal-background improve dramatically. Sorting of GFP-expressing parasite populations in infected red blood cells at 90 or 95% purity with these filters resulted in 50-150% increased yield when compared to the standard filter set-up. The purity of the sorted population was confirmed using imaging cytometry and microscopy of cytospin preparations of sorted red blood cells infected with transgenic malaria parasites. Discussion Filter optimization is particularly important for applications where the FP signal and percentage of positive events are relatively low, such as analysis of parasite-infected samples with in the intention of gene-expression profiling and analysis. The approach outlined here results in substantially improved yield of GFP-expressing parasites, and requires decreased sorting time in comparison to standard methods. It is anticipated that this protocol will be useful for a wide range of applications involving rare events. PMID:22950515

Check-All-That-Apply (CATA), Sorting, and Polarized Sensory Positioning (PSP) with Astringent Stimuli

PubMed Central

Fleming, Erin E.; Ziegler, Gregory R.; Hayes, John E.

2015-01-01

Multiple rapid sensory profiling techniques have been developed as more efficient alternatives to traditional sensory descriptive analysis. Here, we compare the results of three rapid sensory profiling techniques – check-all-that-apply (CATA), sorting, and polarized sensory positioning (PSP) – using a diverse range of astringent stimuli. These rapid methods differ in their theoretical basis, implementation, and data analyses, and the relative advantages and limitations are largely unexplored. Additionally, we were interested in using these methods to compare varied astringent stimuli, as these compounds are difficult to characterize using traditional descriptive analysis due to high fatigue and potential carry-over. In the CATA experiment, subjects (n=41) were asked to rate the overall intensity of each stimulus as well as to endorse any relevant terms (from a list of 13) which characterized the sample. In the sorting experiment, subjects (n=30) assigned intensity-matched stimuli into groups 1-on-1 with the experimenter. In the PSP experiment, (n=41) subjects first sampled and took notes on three blind references (‘poles’) before rating each stimulus for its similarity to each of the 3 poles. Two-dimensional perceptual maps from correspondence analysis (CATA), multidimensional scaling (sorting), and multiple factor analysis (PSP) were remarkably similar, with normalized RV coefficients indicating significantly similar plots, regardless of method. Agglomerative hierarchical clustering of all data sets using Ward’s minimum variance as the linkage criteria showed the clusters of astringent stimuli were approximately based on the respective class of astringent agent. Based on the descriptive CATA data, it appears these differences may be due to the presence of side tastes such as bitterness and sourness, rather than astringent sub-qualities per se. Although all three methods are considered ‘rapid,’ our prior experience with sorting suggests it is best performed 1:1 with the experimenter, which makes sorting relatively less efficient than CATA or PSP. Based on the evaluation criteria used here, the choice of method depends on the time constraints of the experimenter and the need for descriptive terms to understand the sensory space of the samples. Accordingly, we recommend a mixed approach that combines CATA with a subsequent PSP task so that the product space can be well characterized before choosing poles for PSP. PMID:26113771

Suitability of TBA method for the evaluation of the oxidative effect of non-water-soluble and water-soluble rosemary extracts.

PubMed

Wada, Mitsuhiro; Nagano, Minori; Kido, Hirotsugu; Ikeda, Rie; Kuroda, Naotaka; Nakashima, Kenichiro

2011-01-01

The antioxidative effects of rosemary and grape-seed extracts spiked in human plasma were examined using the thiobarbituric acid (TBA) method. The TBA values of plasma spiked with reagents to generate reactive oxygen species, such as singlet oxygen ((1)O(2)), hydroxyl radicals ((·)OH), peroxynitrite (ONOO(-)), and superoxide anions (O(2)(·-)), were measured by a flow injection analysis method with fluorescence (FL) detection. TBA values obtained by the addition of 50 mg/mL of rosemary extracts for (1)O(2), (·)OH, ONOO(-), and O(2)(·-) increased to 964 ± 65%, 1063 ± 61%, 758 ± 78%, and 698 ± 41%, respectively (n = 3, P < 0.01), whereas the values with 1 mg/mL of grape-seed extracts or tocopherol decreased (40.2 - 66.3%). Furthermore, the antioxidative effects of rosemary extract in rat plasma, spiked with reagents to generate (·)OH, were examined by high-performance liquid chromatography with FL detection. No peak, other than TBA-malondialdehyde, could be detected using wavelengths of 532 (λ(ex)) and 553 nm (λ(em)).

Influence of baking time and matrix effects on the detection of milk allergens in cookie model food system by ELISA.

PubMed

Monaci, Linda; Brohée, Marcel; Tregoat, Virginie; van Hengel, Arjon

2011-07-15

Milk allergens are common allergens occurring in foods, therefore raising concern in allergic consumers. Enzyme-linked immunosorbent assay (ELISA) is, to date, the method of choice for the detection of food allergens by the food industry although, the performance of ELISA might be compromised when severe food processing techniques are applied to allergen-containing foods. In this paper we investigated the influence of baking time on the detection of milk allergens by using commercial ELISA kits. Baked cookies were chosen as a model food system and experiments were set up to study the impact of spiking a matrix food either before, or after the baking process. Results revealed clear analytical differences between both spiking methods, which stress the importance of choosing appropriate spiking methodologies for method validation purposes. Finally, since the narrow dynamic range of quantification of ELISA implies that dilution of samples is required, the impact of sample dilution on the quantitative results was investigated. All parameters investigated were shown to impact milk allergen detection by means of ELISA. Copyright © 2011 Elsevier Ltd. All rights reserved.

Simultaneous deblending and interpolation using structure-oriented filters

NASA Astrophysics Data System (ADS)

Zhou, Yatong; Li, Song

2018-03-01

Simultaneous source shooting is a modern marine acquisition technology that accelerates field acquisition tremendously. However, we need to carefully remove the spike-like noise in the recorded seismic data, the process of which is called deblending. Considering the field obstacles, the recorded data may also contain missing traces. In this paper, we propose a very efficient way to simultaneously remove the spike-like noise to separate simultaneous sources and fill the data gaps in the recorded data. We propose to apply structure-oriented median and mean filters to reject the spike-like noise and restore the missing data. The commonly used median and mean filters guarantee the efficiency and convenience of the proposed algorithm framework. We use a robust slope estimation method to calculate the local slope of the structure patterns in the seismic data. Both synthetic and field data examples demonstrate the successful performance of the proposed algorithm. When compared with the state-of-the-art FK transform based projection onto convex sets (POCS) method, the presented method can obtain better performance with much less computational cost.

Improved method and apparatus for electrostatically sorting biological cells. [DOE patent application

DOEpatents

Merrill, J.T.

An improved method of sorting biological cells in a conventional cell sorter apparatus includes generating a fluid jet containing cells to be sorted, measuring the distance between the centers of adjacent droplets in a zone thereof defined at the point where the fluid jet separates into descrete droplets, setting the distance between the center of a droplet in said separation zone and the position along said fluid jet at which the cell is optically sensed for specific characteristics to be an integral multiple of said center-to-center distance, and disabling a charger from electrically charging a specific droplet if a cell is detected by the optical sensor in a position wherein it will be in the neck area between droplets during droplet formation rather than within a predetermined distance from the droplet center.

Microfluidic Device

NASA Technical Reports Server (NTRS)

Tai, Yu-Chong (Inventor); Kasdan, Harvey L. (Inventor); Zheng, Siyang (Inventor); Lin, Jeffrey Chun-Hui (Inventor)

2016-01-01

Described herein are particular embodiments relating to a microfluidic device that may be utilized for cell sensing, counting, and/or sorting. Particular aspects relate to a microfabricated device that is capable of differentiating single cell types from dense cell populations. One particular embodiment relates a device and methods of using the same for sensing, counting, and/or sorting leukocytes from whole, undiluted blood samples.

Microfluidic Device

NASA Technical Reports Server (NTRS)

Zheng, Siyang (Inventor); Lin, Jeffrey Chun-Hui (Inventor); Kasdan, Harvey (Inventor); Tai, Yu-Chong (Inventor)

2015-01-01

Described herein are particular embodiments relating to a microfluidic device that may be utilized for cell sensing, counting, and/or sorting. Particular aspects relate to a microfabricated device that is capable of differentiating single cell types from dense cell populations. One particular embodiment relates a device and methods of using the same for sensing, counting, and/or sorting leukocytes from whole, undiluted blood samples.

Microfluidic Device

NASA Technical Reports Server (NTRS)

Tai, Yu-Chong (Inventor); Zheng, Siyang (Inventor); Lin, Jeffrey Chun-Hui (Inventor); Kasdan, Harvey L. (Inventor)

2017-01-01

Described herein are particular embodiments relating to a microfluidic device that may be utilized for cell sensing, counting, and/or sorting. Particular aspects relate to a microfabricated device that is capable of differentiating single cell types from dense cell populations. One particular embodiment relates a device and methods of using the same for sensing, counting, and/or sorting leukocytes from whole, undiluted blood samples.

Conceptual Abilities of Children with Mild Intellectual Disability: Analysis of Wisconsin Card Sorting Test Performance

ERIC Educational Resources Information Center

Gligorovic, Milica; Buha, Natasa

2013-01-01

Background: The ability to generate and flexibly change concepts is of great importance for the development of academic and adaptive skills. This paper analyses the conceptual reasoning ability of children with mild intellectual disability (MID) by their achievements on the Wisconsin Card Sorting Test (WCST). Method: The sample consisted of 95…

A Spiking Strategy for ChIP-chip Data Normalization in S. cerevisiae.

PubMed

Jeronimo, Célia; Robert, François

2017-01-01

Chromatin immunoprecipitation coupled to DNA microarrays (ChIP-chip) is widely used in the chromatin field, notably to map the position of histone variants or histone modifications along the genome. Often, the position and the occupancy of these epigenetic marks are to be compared between different experiments. It is now increasingly recognized that such cross-sample comparison is better done using externally added exogenous controls for normalization but no such method has been described for ChIP-chip. Here we describe a spiking normalization strategy that makes use of phiX174 phage DNA as a spiked control for normalization of ChIP-chip signals across different experiments.

Bacterial Microcolonies in Gel Beads for High-Throughput Screening of Libraries in Synthetic Biology.

PubMed

Duarte, José M; Barbier, Içvara; Schaerli, Yolanda

2017-11-17

Synthetic biologists increasingly rely on directed evolution to optimize engineered biological systems. Applying an appropriate screening or selection method for identifying the potentially rare library members with the desired properties is a crucial step for success in these experiments. Special challenges include substantial cell-to-cell variability and the requirement to check multiple states (e.g., being ON or OFF depending on the input). Here, we present a high-throughput screening method that addresses these challenges. First, we encapsulate single bacteria into microfluidic agarose gel beads. After incubation, they harbor monoclonal bacterial microcolonies (e.g., expressing a synthetic construct) and can be sorted according their fluorescence by fluorescence activated cell sorting (FACS). We determine enrichment rates and demonstrate that we can measure the average fluorescent signals of microcolonies containing phenotypically heterogeneous cells, obviating the problem of cell-to-cell variability. Finally, we apply this method to sort a pBAD promoter library at ON and OFF states.

Validated spectrofluorimetric method for determination of two phosphodiesterase inhibitors tadalafil and vardenafil in pharmaceutical preparations and spiked human plasma.

PubMed

Abu El-Enin, Mohammed Abu Bakr; Al-Ghaffar Hammouda, Mohammed El-Sayed Abd; El-Sherbiny, Dina Tawfik; El-Wasseef, Dalia Rashad; El-Ashry, Saadia Mahmoud

2016-02-01

A valid, sensitive and rapid spectrofluorimetric method has been developed and validated for determination of both tadalafil (TAD) and vardenafil (VAR) either in their pure form, in their tablet dosage forms or spiked in human plasma. This method is based on measurement of the native fluorescence of both drugs in acetonitrile at λem 330 and 470 nm after excitation at 280 and 275 nm for tadalafil and vardenafil, respectively. Linear relationships were obtained over the concentration range 4-40 and 10-250 ng/mL with a minimum detection of 1 and 3 ng/mL for tadalafil and vardenafil, respectively. Various experimental parameters affecting the fluorescence intensity were carefully studied and optimized. The developed method was applied successfully for the determination of tadalafil and vardenafil in bulk drugs and tablet dosage forms. Moreover, the high sensitivity of the proposed method permitted their determination in spiked human plasma. The developed method was validated in terms of specificity, linearity, lower limit of quantification (LOQ), lower limit of detection (LOD), precision and accuracy. The mean recoveries of the analytes in pharmaceutical preparations were in agreement with those obtained from the comparison methods, as revealed by statistical analysis of the obtained results using Student's t-test and the variance ratio F-test. Copyright © 2015 John Wiley & Sons, Ltd.

Method for determination of aflatoxin M₁ in cheese and butter by HPLC using an immunoaffinity column.

PubMed

Sakuma, Hisako; Kamata, Yoichi; Sugita-Konishi, Yoshiko; Kawakami, Hiroshi

2011-01-01

A rapid, sensitive convenient method for determination of aflatoxin M₁ (AFM₁) in cheese and butter by HPLC was developed and validated. The method employs a safe extraction solution (mixture of acetonitrile, methanol and water) and an immunoaffinity column (IAC) for clean-up. Compared with the widely used method employing chloroform and a Florisil column, the IAC method has a short analytical time and there are no interference peaks. The limits of quantification (LOQ) of the IAC method were 0.12 and 0.14 µg/kg, while those of the Florisil column method were 0.47 and 0.23 µg/kg in cheese and buffer, respectively. The recovery and relative standard deviation (RSD) for cheese (spiked at 0.5 µg/kg) in the IAC method were 92% and 7%, respectively, while for the Florisil column method the corresponding values were 76% and 10%. The recovery and RSD for butter (spiked at 0.5 µg/kg) in the IAC method were 97% and 9%, and those in the Florisil method were 74% and 9%, respectively. In the IAC method, the values of in-house precision (n=2, day=5) of cheese and butter (spiked at 0.5 µg/kg) were 9% and 13%, respectively. The IAC method is superior to the Florisil column method in terms of safety, ease of handling, sensitivity and reliability. A survey of AFM₁ contamination in imported cheese and butter in Japan was conducted by the IAC method. AFM₁ was not detected in 60 samples of cheese and 30 samples of butter.

Label-free capture of breast cancer cells spiked in buffy coats using carbon nanotube antibody micro-arrays

NASA Astrophysics Data System (ADS)

Khosravi, Farhad; Trainor, Patrick; Rai, Shesh N.; Kloecker, Goetz; Wickstrom, Eric; Panchapakesan, Balaji

2016-04-01

We demonstrate the rapid and label-free capture of breast cancer cells spiked in buffy coats using nanotube-antibody micro-arrays. Single wall carbon nanotube arrays were manufactured using photo-lithography, metal deposition, and etching techniques. Anti-epithelial cell adhesion molecule (EpCAM) antibodies were functionalized to the surface of the nanotube devices using 1-pyrene-butanoic acid succinimidyl ester functionalization method. Following functionalization, plain buffy coat and MCF7 cell spiked buffy coats were adsorbed on to the nanotube device and electrical signatures were recorded for differences in interaction between samples. A statistical classifier for the ‘liquid biopsy’ was developed to create a predictive model based on dynamic time warping to classify device electrical signals that corresponded to plain (control) or spiked buffy coats (case). In training test, the device electrical signals originating from buffy versus spiked buffy samples were classified with ˜100% sensitivity, ˜91% specificity and ˜96% accuracy. In the blinded test, the signals were classified with ˜91% sensitivity, ˜82% specificity and ˜86% accuracy. A heatmap was generated to visually capture the relationship between electrical signatures and the sample condition. Confocal microscopic analysis of devices that were classified as spiked buffy coats based on their electrical signatures confirmed the presence of cancer cells, their attachment to the device and overexpression of EpCAM receptors. The cell numbers were counted to be ˜1-17 cells per 5 μl per device suggesting single cell sensitivity in spiked buffy coats that is scalable to higher volumes using the micro-arrays.

Neural Dynamics as Sampling: A Model for Stochastic Computation in Recurrent Networks of Spiking Neurons

PubMed Central

Buesing, Lars; Bill, Johannes; Nessler, Bernhard; Maass, Wolfgang

2011-01-01

The organization of computations in networks of spiking neurons in the brain is still largely unknown, in particular in view of the inherently stochastic features of their firing activity and the experimentally observed trial-to-trial variability of neural systems in the brain. In principle there exists a powerful computational framework for stochastic computations, probabilistic inference by sampling, which can explain a large number of macroscopic experimental data in neuroscience and cognitive science. But it has turned out to be surprisingly difficult to create a link between these abstract models for stochastic computations and more detailed models of the dynamics of networks of spiking neurons. Here we create such a link and show that under some conditions the stochastic firing activity of networks of spiking neurons can be interpreted as probabilistic inference via Markov chain Monte Carlo (MCMC) sampling. Since common methods for MCMC sampling in distributed systems, such as Gibbs sampling, are inconsistent with the dynamics of spiking neurons, we introduce a different approach based on non-reversible Markov chains that is able to reflect inherent temporal processes of spiking neuronal activity through a suitable choice of random variables. We propose a neural network model and show by a rigorous theoretical analysis that its neural activity implements MCMC sampling of a given distribution, both for the case of discrete and continuous time. This provides a step towards closing the gap between abstract functional models of cortical computation and more detailed models of networks of spiking neurons. PMID:22096452

Spike timing precision of neuronal circuits.

PubMed

Kilinc, Deniz; Demir, Alper

2018-06-01

Spike timing is believed to be a key factor in sensory information encoding and computations performed by the neurons and neuronal circuits. However, the considerable noise and variability, arising from the inherently stochastic mechanisms that exist in the neurons and the synapses, degrade spike timing precision. Computational modeling can help decipher the mechanisms utilized by the neuronal circuits in order to regulate timing precision. In this paper, we utilize semi-analytical techniques, which were adapted from previously developed methods for electronic circuits, for the stochastic characterization of neuronal circuits. These techniques, which are orders of magnitude faster than traditional Monte Carlo type simulations, can be used to directly compute the spike timing jitter variance, power spectral densities, correlation functions, and other stochastic characterizations of neuronal circuit operation. We consider three distinct neuronal circuit motifs: Feedback inhibition, synaptic integration, and synaptic coupling. First, we show that both the spike timing precision and the energy efficiency of a spiking neuron are improved with feedback inhibition. We unveil the underlying mechanism through which this is achieved. Then, we demonstrate that a neuron can improve on the timing precision of its synaptic inputs, coming from multiple sources, via synaptic integration: The phase of the output spikes of the integrator neuron has the same variance as that of the sample average of the phases of its inputs. Finally, we reveal that weak synaptic coupling among neurons, in a fully connected network, enables them to behave like a single neuron with a larger membrane area, resulting in an improvement in the timing precision through cooperation.

A novel scheme for the validation of an automated classification method for epileptic spikes by comparison with multiple observers.

PubMed

Sharma, Niraj K; Pedreira, Carlos; Centeno, Maria; Chaudhary, Umair J; Wehner, Tim; França, Lucas G S; Yadee, Tinonkorn; Murta, Teresa; Leite, Marco; Vos, Sjoerd B; Ourselin, Sebastien; Diehl, Beate; Lemieux, Louis

2017-07-01

To validate the application of an automated neuronal spike classification algorithm, Wave_clus (WC), on interictal epileptiform discharges (IED) obtained from human intracranial EEG (icEEG) data. Five 10-min segments of icEEG recorded in 5 patients were used. WC and three expert EEG reviewers independently classified one hundred IED events into IED classes or non-IEDs. First, we determined whether WC-human agreement variability falls within inter-reviewer agreement variability by calculating the variation of information for each classifier pair and quantifying the overlap between all WC-reviewer and all reviewer-reviewer pairs. Second, we compared WC and EEG reviewers' spike identification and individual spike class labels visually and quantitatively. The overlap between all WC-human pairs and all human pairs was >80% for 3/5 patients and >58% for the other 2 patients demonstrating WC falling within inter-human variation. The average sensitivity of spike marking for WC was 91% and >87% for all three EEG reviewers. Finally, there was a strong visual and quantitative similarity between WC and EEG reviewers. WC performance is indistinguishable to that of EEG reviewers' suggesting it could be a valid clinical tool for the assessment of IEDs. WC can be used to provide quantitative analysis of epileptic spikes. Copyright © 2017 International Federation of Clinical Neurophysiology. Published by Elsevier B.V. All rights reserved.

Characterizing the complexity of spontaneous motor unit patterns of amyotrophic lateral sclerosis using approximate entropy

NASA Astrophysics Data System (ADS)

Zhou, Ping; Barkhaus, Paul E.; Zhang, Xu; Zev Rymer, William

2011-10-01

This paper presents a novel application of the approximate entropy (ApEn) measurement for characterizing spontaneous motor unit activity of amyotrophic lateral sclerosis (ALS) patients. High-density surface electromyography (EMG) was used to record spontaneous motor unit activity bilaterally from the thenar muscles of nine ALS subjects. Three distinct patterns of spontaneous motor unit activity (sporadic spikes, tonic spikes and high-frequency repetitive spikes) were observed. For each pattern, complexity was characterized by calculating the ApEn values of the representative signal segments. A sliding window over each segment was also introduced to quantify the dynamic changes in complexity for the different spontaneous motor unit patterns. We found that the ApEn values for the sporadic spikes were the highest, while those of the high-frequency repetitive spikes were the lowest. There is a significant difference in mean ApEn values between two arbitrary groups of the three spontaneous motor unit patterns (P < 0.001). The dynamic ApEn curve from the sliding window analysis is capable of tracking variations in EMG activity, thus providing a vivid, distinctive description for different patterns of spontaneous motor unit action potentials in terms of their complexity. These findings expand the existing knowledge of spontaneous motor unit activity in ALS beyond what was previously obtained using conventional linear methods such as firing rate or inter-spike interval statistics.

Quantitative mass spectrometric analysis of ropivacaine and bupivacaine in authentic, pharmaceutical and spiked human plasma without chromatographic separation.

PubMed

Salama, Nahla N; Wang, Shudong

2008-05-28

The present study employs time of flight mass and bupivacaine in authentic, pharmaceutical and spiked human plasma as well as in the presence of their impurities 2,6-dimethylaniline and alkaline degradation product. The method is based on time of flight electron spray ionization mass spectrometry technique without preliminary chromatographic separation and makes use of bupivacaine as internal standard for ropivacaine, which is used as internal standard for bupivacaine. A linear relationship between drug concentrations and the peak intensity ratio of ions of the analyzed substances is established. The method is linear from 23.8 to 2380.0 ng mL(-1) for both drugs. The correlation coefficient was >or=0.996 in authentic and spiked human plasma. The average percentage recoveries in the ranges of 95.39%-102.75% was obtained. The method is accurate (% RE < 5%) and reproducible with intra- and inter-assay precision (RSD% < 8.0%). The quantification limit is 23.8 ng mL(-1) for both drugs. The method is not only highly sensitive and selective, but also simple and effective for determination or identification of both drugs in authentic and biological fluids. The method can be applied in purity testing, quality control and stability monitoring for the studied drugs.

Quantitative Mass Spectrometric Analysis of Ropivacaine and Bupivacaine in Authentic, Pharmaceutical and Spiked Human Plasma without Chromatographic Separation

PubMed Central

Salama, Nahla N.; Wang, Shudong

2009-01-01

The present study employs time of flight mass and bupivacaine in authentic, pharmaceutical and spiked human plasma as well as in the presence of their impurities 2,6-dimethylaniline and alkaline degradation product. The method is based on time of flight electron spray ionization mass spectrometry technique without preliminary chromatographic separation and makes use of bupivacaine as internal standard for ropivacaine, which is used as internal standard for bupivacaine. A linear relationship between drug concentrations and the peak intensity ratio of ions of the analyzed substances is established. The method is linear from 23.8 to 2380.0 ng mL−1 for both drugs. The correlation coefficient was ≥0.996 in authentic and spiked human plasma. The average percentage recoveries in the ranges of 95.39%–102.75% was obtained. The method is accurate (% RE < 5%) and reproducible with intra- and inter-assay precision (RSD% < 8.0%). The quantification limit is 23.8 ng mL−1 for both drugs. The method is not only highly sensitive and selective, but also simple and effective for determination or identification of both drugs in authentic and biological fluids. The method can be applied in purity testing, quality control and stability monitoring for the studied drugs. PMID:19652756

In-Line Sorting of Harumanis Mango Based on External Quality Using Visible Imaging

PubMed Central

Ibrahim, Mohd Firdaus; Ahmad Sa’ad, Fathinul Syahir; Zakaria, Ammar; Md Shakaff, Ali Yeon

2016-01-01

The conventional method of grading Harumanis mango is time-consuming, costly and affected by human bias. In this research, an in-line system was developed to classify Harumanis mango using computer vision. The system was able to identify the irregularity of mango shape and its estimated mass. A group of images of mangoes of different size and shape was used as database set. Some important features such as length, height, centroid and parameter were extracted from each image. Fourier descriptor and size-shape parameters were used to describe the mango shape while the disk method was used to estimate the mass of the mango. Four features have been selected by stepwise discriminant analysis which was effective in sorting regular and misshapen mango. The volume from water displacement method was compared with the volume estimated by image processing using paired t-test and Bland-Altman method. The result between both measurements was not significantly different (P > 0.05). The average correct classification for shape classification was 98% for a training set composed of 180 mangoes. The data was validated with another testing set consist of 140 mangoes which have the success rate of 92%. The same set was used for evaluating the performance of mass estimation. The average success rate of the classification for grading based on its mass was 94%. The results indicate that the in-line sorting system using machine vision has a great potential in automatic fruit sorting according to its shape and mass. PMID:27801799

In-Line Sorting of Harumanis Mango Based on External Quality Using Visible Imaging.

PubMed

Ibrahim, Mohd Firdaus; Ahmad Sa'ad, Fathinul Syahir; Zakaria, Ammar; Md Shakaff, Ali Yeon

2016-10-27

The conventional method of grading Harumanis mango is time-consuming, costly and affected by human bias. In this research, an in-line system was developed to classify Harumanis mango using computer vision. The system was able to identify the irregularity of mango shape and its estimated mass. A group of images of mangoes of different size and shape was used as database set. Some important features such as length, height, centroid and parameter were extracted from each image. Fourier descriptor and size-shape parameters were used to describe the mango shape while the disk method was used to estimate the mass of the mango. Four features have been selected by stepwise discriminant analysis which was effective in sorting regular and misshapen mango. The volume from water displacement method was compared with the volume estimated by image processing using paired t -test and Bland-Altman method. The result between both measurements was not significantly different (P > 0.05). The average correct classification for shape classification was 98% for a training set composed of 180 mangoes. The data was validated with another testing set consist of 140 mangoes which have the success rate of 92%. The same set was used for evaluating the performance of mass estimation. The average success rate of the classification for grading based on its mass was 94%. The results indicate that the in-line sorting system using machine vision has a great potential in automatic fruit sorting according to its shape and mass.

Flow cytometric sorting of fresh and frozen-thawed spermatozoa in the western lowland gorilla (Gorilla gorilla gorilla).

PubMed

O'Brien, J K; Stojanov, T; Crichton, E G; Evans, K M; Leigh, D; Maxwell, W M C; Evans, G; Loskutoff, N M

2005-08-01

We adapted flow cytometry technology for high-purity sorting of X chromosome-bearing spermatozoa in the western lowland gorilla (Gorilla gorilla gorilla). Our objectives were to develop methodologies for liquid storage of semen prior to sorting, sorting of liquid-stored and frozen-thawed spermatozoa, and assessment of sorting accuracy. In study 1, the in vitro sperm characteristics of gorilla ejaculates from one male were unchanged (P > 0.05) after 8 hr of liquid storage at 15 degrees C in a non-egg yolk diluent (HEPES-buffered modified Tyrode's medium). In study 2, we examined the efficacy of sorting fresh and frozen-thawed spermatozoa using human spermatozoa as a model for gorilla spermatozoa. Ejaculates from one male were split into fresh and frozen aliquots. X-enriched samples derived from both fresh and frozen-thawed human semen were of high purity, as determined by fluorescence in situ hybridization (FISH; 90.7%+/-2.3%, overall), and contained a high proportion of morphologically normal spermatozoa (86.0%+/-1.0%, overall). In study 3, we processed liquid-stored semen from two gorillas for sorting using a modification of methods for human spermatozoa. The sort rate for enrichment of X-bearing spermatozoa was 7.3+/-2.5 spermatozoa per second. The X-enriched samples were of high purity (single-sperm PCR: 83.7%) and normal morphology (79.0%+/-3.9%). In study 4 we examined frozen-thawed gorilla semen, and the sort rate (8.3+/-2.9 X-bearing sperm/sec), purity (89.7%), and normal morphology (81.4%+/-3.4%) were comparable to those of liquid-stored semen. Depending on the male and the type of sample used (fresh or frozen-thawed), 0.8-2.2% of gorilla spermatozoa in the processed ejaculate were present in the X-enriched sample. These results demonstrate that fresh or frozen-thawed gorilla spermatozoa can be flow cytometrically sorted into samples enriched for X-bearing spermatozoa. Copyright 2005 Wiley-Liss, Inc.

Amplitude sorting of oscillatory burst signals by sampling

DOEpatents

Davis, Thomas J.

1977-01-01

A method and apparatus for amplitude sorting of oscillatory burst signals is described in which the burst signal is detected to produce a burst envelope signal and an intermediate or midportion of such envelope signal is sampled to provide a sample pulse output. The height of the sample pulse is proportional to the amplitude of the envelope signal and to the maximum burst signal amplitude. The sample pulses are fed to a pulse height analyzer for sorting. The present invention is used in an acoustic emission testing system to convert the amplitude of the acoustic emission burst signals into sample pulse heights which are measured by a pulse height analyzer for sorting the pulses in groups according to their height in order to identify the material anomalies in the test material which emit the acoustic signals.

Spiking neural network simulation: memory-optimal synaptic event scheduling.

PubMed

Stewart, Robert D; Gurney, Kevin N

2011-06-01

Spiking neural network simulations incorporating variable transmission delays require synaptic events to be scheduled prior to delivery. Conventional methods have memory requirements that scale with the total number of synapses in a network. We introduce novel scheduling algorithms for both discrete and continuous event delivery, where the memory requirement scales instead with the number of neurons. Superior algorithmic performance is demonstrated using large-scale, benchmarking network simulations.

Vortex assisted solid-phase extraction of lead(II) using orthorhombic nanosized Bi2WO6 as a sorbent.

PubMed

Baghban, Neda; Yilmaz, Erkan; Soylak, Mustafa

2017-12-07

Nanosized single crystal orthorhombic Bi 2 WO 6 was synthesized by a hydrothermal method and used as a sorbent for vortex assisted solid phase extraction of lead(II). The crystal and molecular structure of the sorbent was examined using XRD, Raman, SEM and SEM-EDX analysis. Various parameters affecting extraction efficiency were optimized by using multivariate design. The effect of diverse ions on the extraction also was studied. Lead was quantified by flame atomic absorption spectrometry (FAAS). The recoveries of lead(II) from spiked samples (at a typical spiking level of 200-400 ng·mL -1 ) are >95%. Other figures of merit includes (a) a detection limit of 6 ng·mL -1 , (b) a preconcentration factor of 50, (c) a relative standard deviation of 1.6%, and (d) and adsorption capacity of 6.6 mg·g -1 . The procedure was successfully applied to accurate determination of lead in (spiked) pomegranate and water samples. Graphical abstract Nanosized single crystal orthorhombic Bi 2 WO 6 was synthesized and characterized by a hydrothermal method and used as a sorbent for vortex assisted solid phase extraction of lead(II). The procedure was successfully applied to accurate determination of lead in (spiked) pomegranate and water samples.

Purified coronavirus Spike protein nanoparticles induce coronavirus neutralizing antibodies in mice

PubMed Central

Mu, Haiyan; Taylor, Justin K; Massare, Michael; Flyer, David C

2014-01-01

Development of vaccination strategies for emerging pathogens are particularly challenging because of the sudden nature of the emergence of these viruses and the long process needed for traditional vaccine development. Therefore, there is a need for development of a rapid method of vaccine development that can respond to emerging pathogens in a short time frame. The emergence of severe acute respiratory syndrome coronavirus (SARS-CoV) in 2003 and Middle East respiratory syndrome (MERS-CoV) in late 2012 demonstrate the importance of coronaviruses as emerging pathogens. The spike glycoproteins of coronaviruses reside on the surface of the virion and are responsible for virus entry. The spike glycoprotein is the major immunodominant antigen of coronaviruses and has proven to be an excellent target for vaccine designs that seek to block coronavirus entry and promote antibody targeting of infected cells. Vaccination strategies for coronaviruses have involved live attenuated virus, recombinant viruses, non-replicative virus-like particles expressing coronavirus proteins or DNA plasmids expressing coronavirus genes. None of these strategies has progressed to an approved human coronavirus vaccine in the ten years since SARS-CoV emerged. Here we describe a novel method for generating MERS-CoV and SARS-CoV full-length spike nanoparticles, which in combination with adjuvants are able to produce high titer antibodies in mice. PMID:24736006

Parameter estimation in spiking neural networks: a reverse-engineering approach.

PubMed

Rostro-Gonzalez, H; Cessac, B; Vieville, T

2012-04-01

This paper presents a reverse engineering approach for parameter estimation in spiking neural networks (SNNs). We consider the deterministic evolution of a time-discretized network with spiking neurons, where synaptic transmission has delays, modeled as a neural network of the generalized integrate and fire type. Our approach aims at by-passing the fact that the parameter estimation in SNN results in a non-deterministic polynomial-time hard problem when delays are to be considered. Here, this assumption has been reformulated as a linear programming (LP) problem in order to perform the solution in a polynomial time. Besides, the LP problem formulation makes the fact that the reverse engineering of a neural network can be performed from the observation of the spike times explicit. Furthermore, we point out how the LP adjustment mechanism is local to each neuron and has the same structure as a 'Hebbian' rule. Finally, we present a generalization of this approach to the design of input-output (I/O) transformations as a practical method to 'program' a spiking network, i.e. find a set of parameters allowing us to exactly reproduce the network output, given an input. Numerical verifications and illustrations are provided.

Input-output mapping reconstruction of spike trains at dorsal horn evoked by manual acupuncture

NASA Astrophysics Data System (ADS)

Wei, Xile; Shi, Dingtian; Yu, Haitao; Deng, Bin; Lu, Meili; Han, Chunxiao; Wang, Jiang

2016-12-01

In this study, a generalized linear model (GLM) is used to reconstruct mapping from acupuncture stimulation to spike trains driven by action potential data. The electrical signals are recorded in spinal dorsal horn after manual acupuncture (MA) manipulations with different frequencies being taken at the “Zusanli” point of experiment rats. Maximum-likelihood method is adopted to estimate the parameters of GLM and the quantified value of assumed model input. Through validating the accuracy of firings generated from the established GLM, it is found that the input-output mapping of spike trains evoked by acupuncture can be successfully reconstructed for different frequencies. Furthermore, via comparing the performance of several GLMs based on distinct inputs, it suggests that input with the form of half-sine with noise can well describe the generator potential induced by acupuncture mechanical action. Particularly, the comparison of reproducing the experiment spikes for five selected inputs is in accordance with the phenomenon found in Hudgkin-Huxley (H-H) model simulation, which indicates the mapping from half-sine with noise input to experiment spikes meets the real encoding scheme to some extent. These studies provide us a new insight into coding processes and information transfer of acupuncture.

Two important limitations relating to the spiking of environmental samples with contaminants of emerging concern: How close to the real analyte concentrations are the reported recovered values?

PubMed

Michael, Costas; Bayona, Josep Maria; Lambropoulou, Dimitra; Agüera, Ana; Fatta-Kassinos, Despo

2017-06-01

Occurrence and effects of contaminants of emerging concern pose a special challenge to environmental scientists. The investigation of these effects requires reliable, valid, and comparable analytical data. To this effect, two critical aspects are raised herein, concerning the limitations of the produced analytical data. The first relates to the inherent difficulty that exists in the analysis of environmental samples, which is related to the lack of knowledge (information), in many cases, of the form(s) of the contaminant in which is present in the sample. Thus, the produced analytical data can only refer to the amount of the free contaminant ignoring the amount in which it may be present in other forms; e.g., as in chelated and conjugated form. The other important aspect refers to the way with which the spiking procedure is generally performed to determine the recovery of the analytical method. Spiking environmental samples, in particular solid samples, with standard solution followed by immediate extraction, as is the common practice, can lead to an overestimation of the recovery. This is so, because no time is given to the system to establish possible equilibria between the solid matter-inorganic and/or organic-and the contaminant. Therefore, the spiking procedure need to be reconsidered by including a study of the extractable amount of the contaminant versus the time elapsed between spiking and the extraction of the sample. This study can become an element of the validation package of the method.

Visually Evoked Spiking Evolves While Spontaneous Ongoing Dynamics Persist

PubMed Central

Huys, Raoul; Jirsa, Viktor K.; Darokhan, Ziauddin; Valentiniene, Sonata; Roland, Per E.

2016-01-01

Neurons in the primary visual cortex spontaneously spike even when there are no visual stimuli. It is unknown whether the spiking evoked by visual stimuli is just a modification of the spontaneous ongoing cortical spiking dynamics or whether the spontaneous spiking state disappears and is replaced by evoked spiking. This study of laminar recordings of spontaneous spiking and visually evoked spiking of neurons in the ferret primary visual cortex shows that the spiking dynamics does not change: the spontaneous spiking as well as evoked spiking is controlled by a stable and persisting fixed point attractor. Its existence guarantees that evoked spiking return to the spontaneous state. However, the spontaneous ongoing spiking state and the visual evoked spiking states are qualitatively different and are separated by a threshold (separatrix). The functional advantage of this organization is that it avoids the need for a system reorganization following visual stimulation, and impedes the transition of spontaneous spiking to evoked spiking and the propagation of spontaneous spiking from layer 4 to layers 2–3. PMID:26778982

Uncovering Neuronal Networks Defined by Consistent Between-Neuron Spike Timing from Neuronal Spike Recordings

PubMed Central

2018-01-01

Abstract It is widely assumed that distributed neuronal networks are fundamental to the functioning of the brain. Consistent spike timing between neurons is thought to be one of the key principles for the formation of these networks. This can involve synchronous spiking or spiking with time delays, forming spike sequences when the order of spiking is consistent. Finding networks defined by their sequence of time-shifted spikes, denoted here as spike timing networks, is a tremendous challenge. As neurons can participate in multiple spike sequences at multiple between-spike time delays, the possible complexity of networks is prohibitively large. We present a novel approach that is capable of (1) extracting spike timing networks regardless of their sequence complexity, and (2) that describes their spiking sequences with high temporal precision. We achieve this by decomposing frequency-transformed neuronal spiking into separate networks, characterizing each network’s spike sequence by a time delay per neuron, forming a spike sequence timeline. These networks provide a detailed template for an investigation of the experimental relevance of their spike sequences. Using simulated spike timing networks, we show network extraction is robust to spiking noise, spike timing jitter, and partial occurrences of the involved spike sequences. Using rat multineuron recordings, we demonstrate the approach is capable of revealing real spike timing networks with sub-millisecond temporal precision. By uncovering spike timing networks, the prevalence, structure, and function of complex spike sequences can be investigated in greater detail, allowing us to gain a better understanding of their role in neuronal functioning. PMID:29789811

Motion artifact detection and correction in functional near-infrared spectroscopy: a new hybrid method based on spline interpolation method and Savitzky-Golay filtering.

PubMed

Jahani, Sahar; Setarehdan, Seyed K; Boas, David A; Yücel, Meryem A

2018-01-01

Motion artifact contamination in near-infrared spectroscopy (NIRS) data has become an important challenge in realizing the full potential of NIRS for real-life applications. Various motion correction algorithms have been used to alleviate the effect of motion artifacts on the estimation of the hemodynamic response function. While smoothing methods, such as wavelet filtering, are excellent in removing motion-induced sharp spikes, the baseline shifts in the signal remain after this type of filtering. Methods, such as spline interpolation, on the other hand, can properly correct baseline shifts; however, they leave residual high-frequency spikes. We propose a hybrid method that takes advantage of different correction algorithms. This method first identifies the baseline shifts and corrects them using a spline interpolation method or targeted principal component analysis. The remaining spikes, on the other hand, are corrected by smoothing methods: Savitzky-Golay (SG) filtering or robust locally weighted regression and smoothing. We have compared our new approach with the existing correction algorithms in terms of hemodynamic response function estimation using the following metrics: mean-squared error, peak-to-peak error ([Formula: see text]), Pearson's correlation ([Formula: see text]), and the area under the receiver operator characteristic curve. We found that spline-SG hybrid method provides reasonable improvements in all these metrics with a relatively short computational time. The dataset and the code used in this study are made available online for the use of all interested researchers.

Optimized distributed systems achieve significant performance improvement on sorted merging of massive VCF files.

PubMed

Sun, Xiaobo; Gao, Jingjing; Jin, Peng; Eng, Celeste; Burchard, Esteban G; Beaty, Terri H; Ruczinski, Ingo; Mathias, Rasika A; Barnes, Kathleen; Wang, Fusheng; Qin, Zhaohui S

2018-06-01

Sorted merging of genomic data is a common data operation necessary in many sequencing-based studies. It involves sorting and merging genomic data from different subjects by their genomic locations. In particular, merging a large number of variant call format (VCF) files is frequently required in large-scale whole-genome sequencing or whole-exome sequencing projects. Traditional single-machine based methods become increasingly inefficient when processing large numbers of files due to the excessive computation time and Input/Output bottleneck. Distributed systems and more recent cloud-based systems offer an attractive solution. However, carefully designed and optimized workflow patterns and execution plans (schemas) are required to take full advantage of the increased computing power while overcoming bottlenecks to achieve high performance. In this study, we custom-design optimized schemas for three Apache big data platforms, Hadoop (MapReduce), HBase, and Spark, to perform sorted merging of a large number of VCF files. These schemas all adopt the divide-and-conquer strategy to split the merging job into sequential phases/stages consisting of subtasks that are conquered in an ordered, parallel, and bottleneck-free way. In two illustrating examples, we test the performance of our schemas on merging multiple VCF files into either a single TPED or a single VCF file, which are benchmarked with the traditional single/parallel multiway-merge methods, message passing interface (MPI)-based high-performance computing (HPC) implementation, and the popular VCFTools. Our experiments suggest all three schemas either deliver a significant improvement in efficiency or render much better strong and weak scalabilities over traditional methods. Our findings provide generalized scalable schemas for performing sorted merging on genetics and genomics data using these Apache distributed systems.

Optimized distributed systems achieve significant performance improvement on sorted merging of massive VCF files

PubMed Central

Gao, Jingjing; Jin, Peng; Eng, Celeste; Burchard, Esteban G; Beaty, Terri H; Ruczinski, Ingo; Mathias, Rasika A; Barnes, Kathleen; Wang, Fusheng

2018-01-01

Abstract Background Sorted merging of genomic data is a common data operation necessary in many sequencing-based studies. It involves sorting and merging genomic data from different subjects by their genomic locations. In particular, merging a large number of variant call format (VCF) files is frequently required in large-scale whole-genome sequencing or whole-exome sequencing projects. Traditional single-machine based methods become increasingly inefficient when processing large numbers of files due to the excessive computation time and Input/Output bottleneck. Distributed systems and more recent cloud-based systems offer an attractive solution. However, carefully designed and optimized workflow patterns and execution plans (schemas) are required to take full advantage of the increased computing power while overcoming bottlenecks to achieve high performance. Findings In this study, we custom-design optimized schemas for three Apache big data platforms, Hadoop (MapReduce), HBase, and Spark, to perform sorted merging of a large number of VCF files. These schemas all adopt the divide-and-conquer strategy to split the merging job into sequential phases/stages consisting of subtasks that are conquered in an ordered, parallel, and bottleneck-free way. In two illustrating examples, we test the performance of our schemas on merging multiple VCF files into either a single TPED or a single VCF file, which are benchmarked with the traditional single/parallel multiway-merge methods, message passing interface (MPI)–based high-performance computing (HPC) implementation, and the popular VCFTools. Conclusions Our experiments suggest all three schemas either deliver a significant improvement in efficiency or render much better strong and weak scalabilities over traditional methods. Our findings provide generalized scalable schemas for performing sorted merging on genetics and genomics data using these Apache distributed systems. PMID:29762754

Robust Brain-Machine Interface Design Using Optimal Feedback Control Modeling and Adaptive Point Process Filtering

PubMed Central

Carmena, Jose M.

2016-01-01

Much progress has been made in brain-machine interfaces (BMI) using decoders such as Kalman filters and finding their parameters with closed-loop decoder adaptation (CLDA). However, current decoders do not model the spikes directly, and hence may limit the processing time-scale of BMI control and adaptation. Moreover, while specialized CLDA techniques for intention estimation and assisted training exist, a unified and systematic CLDA framework that generalizes across different setups is lacking. Here we develop a novel closed-loop BMI training architecture that allows for processing, control, and adaptation using spike events, enables robust control and extends to various tasks. Moreover, we develop a unified control-theoretic CLDA framework within which intention estimation, assisted training, and adaptation are performed. The architecture incorporates an infinite-horizon optimal feedback-control (OFC) model of the brain’s behavior in closed-loop BMI control, and a point process model of spikes. The OFC model infers the user’s motor intention during CLDA—a process termed intention estimation. OFC is also used to design an autonomous and dynamic assisted training technique. The point process model allows for neural processing, control and decoder adaptation with every spike event and at a faster time-scale than current decoders; it also enables dynamic spike-event-based parameter adaptation unlike current CLDA methods that use batch-based adaptation on much slower adaptation time-scales. We conducted closed-loop experiments in a non-human primate over tens of days to dissociate the effects of these novel CLDA components. The OFC intention estimation improved BMI performance compared with current intention estimation techniques. OFC assisted training allowed the subject to consistently achieve proficient control. Spike-event-based adaptation resulted in faster and more consistent performance convergence compared with batch-based methods, and was robust to parameter initialization. Finally, the architecture extended control to tasks beyond those used for CLDA training. These results have significant implications towards the development of clinically-viable neuroprosthetics. PMID:27035820

Supervised Learning in Spiking Neural Networks for Precise Temporal Encoding

PubMed Central

Gardner, Brian; Grüning, André

2016-01-01

Precise spike timing as a means to encode information in neural networks is biologically supported, and is advantageous over frequency-based codes by processing input features on a much shorter time-scale. For these reasons, much recent attention has been focused on the development of supervised learning rules for spiking neural networks that utilise a temporal coding scheme. However, despite significant progress in this area, there still lack rules that have a theoretical basis, and yet can be considered biologically relevant. Here we examine the general conditions under which synaptic plasticity most effectively takes place to support the supervised learning of a precise temporal code. As part of our analysis we examine two spike-based learning methods: one of which relies on an instantaneous error signal to modify synaptic weights in a network (INST rule), and the other one relying on a filtered error signal for smoother synaptic weight modifications (FILT rule). We test the accuracy of the solutions provided by each rule with respect to their temporal encoding precision, and then measure the maximum number of input patterns they can learn to memorise using the precise timings of individual spikes as an indication of their storage capacity. Our results demonstrate the high performance of the FILT rule in most cases, underpinned by the rule’s error-filtering mechanism, which is predicted to provide smooth convergence towards a desired solution during learning. We also find the FILT rule to be most efficient at performing input pattern memorisations, and most noticeably when patterns are identified using spikes with sub-millisecond temporal precision. In comparison with existing work, we determine the performance of the FILT rule to be consistent with that of the highly efficient E-learning Chronotron rule, but with the distinct advantage that our FILT rule is also implementable as an online method for increased biological realism. PMID:27532262

Extending Transfer Entropy Improves Identification of Effective Connectivity in a Spiking Cortical Network Model

PubMed Central

Ito, Shinya; Hansen, Michael E.; Heiland, Randy; Lumsdaine, Andrew; Litke, Alan M.; Beggs, John M.

2011-01-01

Transfer entropy (TE) is an information-theoretic measure which has received recent attention in neuroscience for its potential to identify effective connectivity between neurons. Calculating TE for large ensembles of spiking neurons is computationally intensive, and has caused most investigators to probe neural interactions at only a single time delay and at a message length of only a single time bin. This is problematic, as synaptic delays between cortical neurons, for example, range from one to tens of milliseconds. In addition, neurons produce bursts of spikes spanning multiple time bins. To address these issues, here we introduce a free software package that allows TE to be measured at multiple delays and message lengths. To assess performance, we applied these extensions of TE to a spiking cortical network model (Izhikevich, 2006) with known connectivity and a range of synaptic delays. For comparison, we also investigated single-delay TE, at a message length of one bin (D1TE), and cross-correlation (CC) methods. We found that D1TE could identify 36% of true connections when evaluated at a false positive rate of 1%. For extended versions of TE, this dramatically improved to 73% of true connections. In addition, the connections correctly identified by extended versions of TE accounted for 85% of the total synaptic weight in the network. Cross correlation methods generally performed more poorly than extended TE, but were useful when data length was short. A computational performance analysis demonstrated that the algorithm for extended TE, when used on currently available desktop computers, could extract effective connectivity from 1 hr recordings containing 200 neurons in ∼5 min. We conclude that extending TE to multiple delays and message lengths improves its ability to assess effective connectivity between spiking neurons. These extensions to TE soon could become practical tools for experimentalists who record hundreds of spiking neurons. PMID:22102894

Supervised Learning in Spiking Neural Networks for Precise Temporal Encoding.

PubMed

Gardner, Brian; Grüning, André

2016-01-01

Precise spike timing as a means to encode information in neural networks is biologically supported, and is advantageous over frequency-based codes by processing input features on a much shorter time-scale. For these reasons, much recent attention has been focused on the development of supervised learning rules for spiking neural networks that utilise a temporal coding scheme. However, despite significant progress in this area, there still lack rules that have a theoretical basis, and yet can be considered biologically relevant. Here we examine the general conditions under which synaptic plasticity most effectively takes place to support the supervised learning of a precise temporal code. As part of our analysis we examine two spike-based learning methods: one of which relies on an instantaneous error signal to modify synaptic weights in a network (INST rule), and the other one relying on a filtered error signal for smoother synaptic weight modifications (FILT rule). We test the accuracy of the solutions provided by each rule with respect to their temporal encoding precision, and then measure the maximum number of input patterns they can learn to memorise using the precise timings of individual spikes as an indication of their storage capacity. Our results demonstrate the high performance of the FILT rule in most cases, underpinned by the rule's error-filtering mechanism, which is predicted to provide smooth convergence towards a desired solution during learning. We also find the FILT rule to be most efficient at performing input pattern memorisations, and most noticeably when patterns are identified using spikes with sub-millisecond temporal precision. In comparison with existing work, we determine the performance of the FILT rule to be consistent with that of the highly efficient E-learning Chronotron rule, but with the distinct advantage that our FILT rule is also implementable as an online method for increased biological realism.

Assessment of a novel multi-array normalization method based on spike-in control probes suitable for microRNA datasets with global decreases in expression.

PubMed

Sewer, Alain; Gubian, Sylvain; Kogel, Ulrike; Veljkovic, Emilija; Han, Wanjiang; Hengstermann, Arnd; Peitsch, Manuel C; Hoeng, Julia

2014-05-17

High-quality expression data are required to investigate the biological effects of microRNAs (miRNAs). The goal of this study was, first, to assess the quality of miRNA expression data based on microarray technologies and, second, to consolidate it by applying a novel normalization method. Indeed, because of significant differences in platform designs, miRNA raw data cannot be normalized blindly with standard methods developed for gene expression. This fundamental observation motivated the development of a novel multi-array normalization method based on controllable assumptions, which uses the spike-in control probes to adjust the measured intensities across arrays. Raw expression data were obtained with the Exiqon dual-channel miRCURY LNA™ platform in the "common reference design" and processed as "pseudo-single-channel". They were used to apply several quality metrics based on the coefficient of variation and to test the novel spike-in controls based normalization method. Most of the considerations presented here could be applied to raw data obtained with other platforms. To assess the normalization method, it was compared with 13 other available approaches from both data quality and biological outcome perspectives. The results showed that the novel multi-array normalization method reduced the data variability in the most consistent way. Further, the reliability of the obtained differential expression values was confirmed based on a quantitative reverse transcription-polymerase chain reaction experiment performed for a subset of miRNAs. The results reported here support the applicability of the novel normalization method, in particular to datasets that display global decreases in miRNA expression similarly to the cigarette smoke-exposed mouse lung dataset considered in this study. Quality metrics to assess between-array variability were used to confirm that the novel spike-in controls based normalization method provided high-quality miRNA expression data suitable for reliable downstream analysis. The multi-array miRNA raw data normalization method was implemented in an R software package called ExiMiR and deposited in the Bioconductor repository.

Assessment of a novel multi-array normalization method based on spike-in control probes suitable for microRNA datasets with global decreases in expression

PubMed Central

2014-01-01

Background High-quality expression data are required to investigate the biological effects of microRNAs (miRNAs). The goal of this study was, first, to assess the quality of miRNA expression data based on microarray technologies and, second, to consolidate it by applying a novel normalization method. Indeed, because of significant differences in platform designs, miRNA raw data cannot be normalized blindly with standard methods developed for gene expression. This fundamental observation motivated the development of a novel multi-array normalization method based on controllable assumptions, which uses the spike-in control probes to adjust the measured intensities across arrays. Results Raw expression data were obtained with the Exiqon dual-channel miRCURY LNA™ platform in the “common reference design” and processed as “pseudo-single-channel”. They were used to apply several quality metrics based on the coefficient of variation and to test the novel spike-in controls based normalization method. Most of the considerations presented here could be applied to raw data obtained with other platforms. To assess the normalization method, it was compared with 13 other available approaches from both data quality and biological outcome perspectives. The results showed that the novel multi-array normalization method reduced the data variability in the most consistent way. Further, the reliability of the obtained differential expression values was confirmed based on a quantitative reverse transcription–polymerase chain reaction experiment performed for a subset of miRNAs. The results reported here support the applicability of the novel normalization method, in particular to datasets that display global decreases in miRNA expression similarly to the cigarette smoke-exposed mouse lung dataset considered in this study. Conclusions Quality metrics to assess between-array variability were used to confirm that the novel spike-in controls based normalization method provided high-quality miRNA expression data suitable for reliable downstream analysis. The multi-array miRNA raw data normalization method was implemented in an R software package called ExiMiR and deposited in the Bioconductor repository. PMID:24886675

Fluorescence Spectrometric Determination of Drugs Containing α-Methylene Sulfone/Sulfonamide Functional Groups Using N-Methylnicotinamide Chloride as a Fluorogenic Agent.

PubMed

Elokely, Khaled M; Eldawy, Mohamed A; Elkersh, Mohamed A; El-Moselhy, Tarek F

2011-01-01

A simple spectrofluorometric method has been developed, adapted, and validated for the quantitative estimation of drugs containing α-methylene sulfone/sulfonamide functional groups using N(1)-methylnicotinamide chloride (NMNCl) as fluorogenic agent. The proposed method has been applied successfully to the determination of methyl sulfonyl methane (MSM) (1), tinidazole (2), rofecoxib (3), and nimesulide (4) in pure forms, laboratory-prepared mixtures, pharmaceutical dosage forms, spiked human plasma samples, and in volunteer's blood. The method showed linearity over concentration ranging from 1 to 150 μg/mL, 10 to 1000 ng/mL, 1 to 1800 ng/mL, and 30 to 2100 ng/mL for standard solutions of 1, 2, 3, and 4, respectively, and over concentration ranging from 5 to 150 μg/mL, 10 to 1000 ng/mL, 10 to 1700 ng/mL, and 30 to 2350 ng/mL in spiked human plasma samples of 1, 2, 3, and 4, respectively. The method showed good accuracy, specificity, and precision in both laboratory-prepared mixtures and in spiked human plasma samples. The proposed method is simple, does not need sophisticated instruments, and is suitable for quality control application, bioavailability, and bioequivalency studies. Besides, its detection limits are comparable to other sophisticated chromatographic methods.

Integral equation methods for computing likelihoods and their derivatives in the stochastic integrate-and-fire model.

PubMed

Paninski, Liam; Haith, Adrian; Szirtes, Gabor

2008-02-01

We recently introduced likelihood-based methods for fitting stochastic integrate-and-fire models to spike train data. The key component of this method involves the likelihood that the model will emit a spike at a given time t. Computing this likelihood is equivalent to computing a Markov first passage time density (the probability that the model voltage crosses threshold for the first time at time t). Here we detail an improved method for computing this likelihood, based on solving a certain integral equation. This integral equation method has several advantages over the techniques discussed in our previous work: in particular, the new method has fewer free parameters and is easily differentiable (for gradient computations). The new method is also easily adaptable for the case in which the model conductance, not just the input current, is time-varying. Finally, we describe how to incorporate large deviations approximations to very small likelihoods.

Hydroxypropyl-beta-cyclodextrin as non-exhaustive extractant for organochlorine pesticides and polychlorinated biphenyls in muck soil.

PubMed

Wong, Fiona; Bidleman, Terry F

2010-05-01

Hydroxypropyl-beta-cyclodextrin (HPCD) was used as a non-exhaustive extractant for organochlorine pesticides (OCs) and polychlorinated biphenyls (PCBs) in muck soil. An optimized extraction method was developed which involved using a HPCD to soil mass ratio of 5.8 with a single extraction period of 20 h. An aging experiment was performed by spiking a muck soil with (13)C-labeled OCs and non-labeled PCBs. The soil was extracted with the optimized HPCD method and Soxhlet apparatus with dichloromethane over 550 d periodically. The HPCD extractability of the spiked OCs was greater than of the native OCs. A decreased in HPCD extractability was observed for the spiked OCs after 550 d of aging and their extractability approached those of the natives. The partition coefficient between HPCD and soil (logK(CD-Soil)) was negatively correlated with the octanol-water partition coefficient (logK(OW)) with r(2)=0.67 and p<0.05. Crown Copyright 2010. Published by Elsevier Ltd. All rights reserved.

Estimating linear-nonlinear models using Rényi divergences

PubMed Central

Kouh, Minjoon; Sharpee, Tatyana O.

2009-01-01

This paper compares a family of methods for characterizing neural feature selectivity using natural stimuli in the framework of the linear-nonlinear model. In this model, the spike probability depends in a nonlinear way on a small number of stimulus dimensions. The relevant stimulus dimensions can be found by optimizing a Rényi divergence that quantifies a change in the stimulus distribution associated with the arrival of single spikes. Generally, good reconstructions can be obtained based on optimization of Rényi divergence of any order, even in the limit of small numbers of spikes. However, the smallest error is obtained when the Rényi divergence of order 1 is optimized. This type of optimization is equivalent to information maximization, and is shown to saturate the Cramér-Rao bound describing the smallest error allowed for any unbiased method. We also discuss conditions under which information maximization provides a convenient way to perform maximum likelihood estimation of linear-nonlinear models from neural data. PMID:19568981

Estimating linear-nonlinear models using Renyi divergences.

PubMed

Kouh, Minjoon; Sharpee, Tatyana O

2009-01-01

This article compares a family of methods for characterizing neural feature selectivity using natural stimuli in the framework of the linear-nonlinear model. In this model, the spike probability depends in a nonlinear way on a small number of stimulus dimensions. The relevant stimulus dimensions can be found by optimizing a Rényi divergence that quantifies a change in the stimulus distribution associated with the arrival of single spikes. Generally, good reconstructions can be obtained based on optimization of Rényi divergence of any order, even in the limit of small numbers of spikes. However, the smallest error is obtained when the Rényi divergence of order 1 is optimized. This type of optimization is equivalent to information maximization, and is shown to saturate the Cramer-Rao bound describing the smallest error allowed for any unbiased method. We also discuss conditions under which information maximization provides a convenient way to perform maximum likelihood estimation of linear-nonlinear models from neural data.

NeuroGrid: recording action potentials from the surface of the brain.

PubMed

Khodagholy, Dion; Gelinas, Jennifer N; Thesen, Thomas; Doyle, Werner; Devinsky, Orrin; Malliaras, George G; Buzsáki, György

2015-02-01

Recording from neural networks at the resolution of action potentials is critical for understanding how information is processed in the brain. Here, we address this challenge by developing an organic material-based, ultraconformable, biocompatible and scalable neural interface array (the 'NeuroGrid') that can record both local field potentials(LFPs) and action potentials from superficial cortical neurons without penetrating the brain surface. Spikes with features of interneurons and pyramidal cells were simultaneously acquired by multiple neighboring electrodes of the NeuroGrid, allowing for the isolation of putative single neurons in rats. Spiking activity demonstrated consistent phase modulation by ongoing brain oscillations and was stable in recordings exceeding 1 week's duration. We also recorded LFP-modulated spiking activity intraoperatively in patients undergoing epilepsy surgery. The NeuroGrid constitutes an effective method for large-scale, stable recording of neuronal spikes in concert with local population synaptic activity, enhancing comprehension of neural processes across spatiotemporal scales and potentially facilitating diagnosis and therapy for brain disorders.

Flight Test Results on the Stability and Control of the F-15B Quiet Spike Aircraft

NASA Technical Reports Server (NTRS)

Moua, Cheng; McWherter, Shaun H.; Cox, Timothy H.; Gera, Joseph

2007-01-01

The Quiet Spike (QS) flight research program was an aerodynamic and structural proof-of-concept of a telescoping sonic-boom suppressing nose boom on an F-15 B aircraft. The program goal was to collect flight data for model validation up to 1.8 Mach. The primary test philosophy was maintaining safety of flight. In the area of stability and controls the primary concerns were to assess the potential destabilizing effect of the spike on the stability, controllability, and handling qualities of the aircraft and to ensure adequate stability margins across the entire QS flight envelop. This paper reports on the stability and control methods used for flight envelope clearance and flight test results of the F-15B Quiet Spike. Also discussed are the flight test approach, the criteria to proceed to the next flight condition, brief pilot commentary on typical piloting tasks, approach and landing, and refueling task, and air data sensitivity to the flight control system.

Analysis of Neuronal Spike Trains, Deconstructed

PubMed Central

Aljadeff, Johnatan; Lansdell, Benjamin J.; Fairhall, Adrienne L.; Kleinfeld, David

2016-01-01

As information flows through the brain, neuronal firing progresses from encoding the world as sensed by the animal to driving the motor output of subsequent behavior. One of the more tractable goals of quantitative neuroscience is to develop predictive models that relate the sensory or motor streams with neuronal firing. Here we review and contrast analytical tools used to accomplish this task. We focus on classes of models in which the external variable is compared with one or more feature vectors to extract a low-dimensional representation, the history of spiking and other variables are potentially incorporated, and these factors are nonlinearly transformed to predict the occurrences of spikes. We illustrate these techniques in application to datasets of different degrees of complexity. In particular, we address the fitting of models in the presence of strong correlations in the external variable, as occurs in natural sensory stimuli and in movement. Spectral correlation between predicted and measured spike trains is introduced to contrast the relative success of different methods. PMID:27477016

Simultaneous determination of creatinine and creatine in human serum by double-spike isotope dilution liquid chromatography-tandem mass spectrometry (LC-MS/MS) and gas chromatography-mass spectrometry (GC-MS).

PubMed

Fernández-Fernández, Mario; Rodríguez-González, Pablo; Añón Álvarez, M Elena; Rodríguez, Felix; Menéndez, Francisco V Álvarez; García Alonso, J Ignacio

2015-04-07

This work describes the first multiple spiking isotope dilution procedure for organic compounds using (13)C labeling. A double-spiking isotope dilution method capable of correcting and quantifying the creatine-creatinine interconversion occurring during the analytical determination of both compounds in human serum is presented. The determination of serum creatinine may be affected by the interconversion between creatine and creatinine during sample preparation or by inefficient chemical separation of those compounds by solid phase extraction (SPE). The methodology is based on the use differently labeled (13)C analogues ((13)C1-creatinine and (13)C2-creatine), the measurement of the isotopic distribution of creatine and creatinine by liquid chromatography-tandem mass spectrometry (LC-MS/MS) and the application of multiple linear regression. Five different lyophilized serum-based controls and two certified human serum reference materials (ERM-DA252a and ERM-DA253a) were analyzed to evaluate the accuracy and precision of the proposed double-spike LC-MS/MS method. The methodology was applied to study the creatine-creatinine interconversion during LC-MS/MS and gas chromatography-mass spectrometry (GC-MS) analyses and the separation efficiency of the SPE step required in the traditional gas chromatography-isotope dilution mass spectrometry (GC-IDMS) reference methods employed for the determination of serum creatinine. The analysis of real serum samples by GC-MS showed that creatine-creatinine separation by SPE can be a nonquantitative step that may induce creatinine overestimations up to 28% in samples containing high amounts of creatine. Also, a detectable conversion of creatine into creatinine was observed during sample preparation for LC-MS/MS. The developed double-spike LC-MS/MS improves the current state of the art for the determination of creatinine in human serum by isotope dilution mass spectrometry (IDMS), because corrections are made for all the possible errors derived from the sample preparation step.

Inner- and outer-wall sorting of double-walled carbon nanotubes

NASA Astrophysics Data System (ADS)

Li, Han; Gordeev, Georgy; Wasserroth, Sören; Chakravadhanula, Venkata Sai Kiran; Neelakandhan, Shyam Kumar Chethala; Hennrich, Frank; Jorio, Ado; Reich, Stephanie; Krupke, Ralph; Flavel, Benjamin Scott

2017-12-01

Double-walled carbon nanotubes (DWCNTs) consist of two coaxially aligned single-walled carbon nanotubes (SWCNTs), and previous sorting methods only achieved outer-wall electronic-type selectivity. Here, a separation technique capable of sorting DWCNTs by semiconducting (S) or metallic (M) inner- and outer-wall electronic type is presented. Electronic coupling between the inner and outer wall is used to alter the surfactant coating around each of the DWCNT types, and aqueous gel permeation is used to separate them. Aqueous methods are used to remove SWCNT species from the raw material and prepare enriched DWCNT fractions. The enriched DWCNT fractions are then transferred into either chlorobenzene or toluene using the copolymer PFO-BPy to yield the four inner@outer combinations of M@M, M@S, S@M and S@S. The high purity of the resulting fractions is verified by absorption measurements, transmission electron microscopy, atomic force microscopy, resonance Raman mapping and high-density field-effect transistor devices.

Encapsulation of single cells on a microfluidic device integrating droplet generation with fluorescence-activated droplet sorting.

PubMed

Wu, Liang; Chen, Pu; Dong, Yingsong; Feng, Xiaojun; Liu, Bi-Feng

2013-06-01

Encapsulation of single cells is a challenging task in droplet microfluidics due to the random compartmentalization of cells dictated by Poisson statistics. In this paper, a microfluidic device was developed to improve the single-cell encapsulation rate by integrating droplet generation with fluorescence-activated droplet sorting. After cells were loaded into aqueous droplets by hydrodynamic focusing, an on-flight fluorescence-activated sorting process was conducted to isolate droplets containing one cell. Encapsulation of fluorescent polystyrene beads was investigated to evaluate the developed method. A single-bead encapsulation rate of more than 98 % was achieved under the optimized conditions. Application to encapsulate single HeLa cells was further demonstrated with a single-cell encapsulation rate of 94.1 %, which is about 200 % higher than those obtained by random compartmentalization. We expect this new method to provide a useful platform for encapsulating single cells, facilitating the development of high-throughput cell-based assays.

Non-invasive sex assessment in bovine semen by Raman spectroscopy

NASA Astrophysics Data System (ADS)

De Luca, A. C.; Managó, S.; Ferrara, M. A.; Rendina, I.; Sirleto, L.; Puglisi, R.; Balduzzi, D.; Galli, A.; Ferraro, P.; Coppola, G.

2014-05-01

X- and Y-chromosome-bearing sperm cell sorting is of great interest, especially for animal production management systems and genetic improvement programs. Here, we demonstrate an optical method based on Raman spectroscopy to separate X- and Y-chromosome-bearing sperm cells, overcoming many of the limitations associated with current sex-sorting protocols. A priori Raman imaging of bull spermatozoa was utilized to select the sampling points (head-neck region), which were then used to discriminate cells based on a spectral classification model. Main variations of Raman peaks associated with the DNA content were observed together with a variation due to the sex membrane proteins. Next, we used principal component analysis to determine the efficiency of our device as a cell sorting method. The results (>90% accuracy) demonstrated that Raman spectroscopy is a powerful candidate for the development of a highly efficient, non-invasive, and non-destructive tool for sperm sexing.

Using Single Free Sorting and Multivariate Exploratory Methods to Design a New Coffee Taster's Flavor Wheel

PubMed Central

Sage, Emma; Velez, Martin; Guinard, Jean‐Xavier

2016-01-01

Abstract The original Coffee Taster's Flavor Wheel was developed by the Specialty Coffee Assn. of America over 20 y ago, and needed an innovative revision. This study used a novel application of traditional sensory and statistical methods in order to reorganize the new coffee Sensory Lexicon developed by World Coffee Research and Kansas State Univ. into scientifically valid clusters and levels to prepare a new, updated flavor wheel. Seventy‐two experts participated in a modified online rapid free sorting activity (no tasting) to sort flavor attributes of the lexicon. The data from all participants were compiled and agglomeration hierarchical clustering was used to determine the clusters and levels of the flavor attributes, while multidimensional scaling was used to determine the positioning of the clusters around the Coffee Taster's Flavor Wheel. This resulted in a new flavor wheel for the coffee industry. PMID:27861864

Inner- and outer-wall sorting of double-walled carbon nanotubes.

PubMed

Li, Han; Gordeev, Georgy; Wasserroth, Sören; Chakravadhanula, Venkata Sai Kiran; Neelakandhan, Shyam Kumar Chethala; Hennrich, Frank; Jorio, Ado; Reich, Stephanie; Krupke, Ralph; Flavel, Benjamin Scott

2017-12-01

Double-walled carbon nanotubes (DWCNTs) consist of two coaxially aligned single-walled carbon nanotubes (SWCNTs), and previous sorting methods only achieved outer-wall electronic-type selectivity. Here, a separation technique capable of sorting DWCNTs by semiconducting (S) or metallic (M) inner- and outer-wall electronic type is presented. Electronic coupling between the inner and outer wall is used to alter the surfactant coating around each of the DWCNT types, and aqueous gel permeation is used to separate them. Aqueous methods are used to remove SWCNT species from the raw material and prepare enriched DWCNT fractions. The enriched DWCNT fractions are then transferred into either chlorobenzene or toluene using the copolymer PFO-BPy to yield the four inner@outer combinations of M@M, M@S, S@M and S@S. The high purity of the resulting fractions is verified by absorption measurements, transmission electron microscopy, atomic force microscopy, resonance Raman mapping and high-density field-effect transistor devices.

Review: Semen sexing - current state of the art with emphasis on bovine species.

PubMed

Vishwanath, R; Moreno, J F

2018-06-01

It is approaching three decades since the first public evidence of sex-sorting of semen. The technology has progressed considerably since then with a number of institutions and researchers collaborating to eventually bring this to application. The technical challenges have been quite substantial and in the early years the application was limited to only heifer inseminations. Comparable fertility of sex-sorted semen with conventional semen has been an aspirational benchmark for the industry for many years. Significant investment in research in the primary biology of sex-sorted sperm and associated sorting equipment ensured steady progress over the years and current methods particularly the new SexedULTRA-4M™ seems to have now mostly bridged this fertility gap. The dairy and beef industry have adopted this technology quite rapidly. Other animal industries are progressively testing it for application in their specific niches and environments. The current state of the art in the fundamentals of sex-sorting, the biology of the process as well as new developments in machinery are described in this review.

Reduction of irregular breathing artifacts in respiration-correlated CT images using a respiratory motion model.

PubMed

Hertanto, Agung; Zhang, Qinghui; Hu, Yu-Chi; Dzyubak, Oleksandr; Rimner, Andreas; Mageras, Gig S

2012-06-01

Respiration-correlated CT (RCCT) images produced with commonly used phase-based sorting of CT slices often exhibit discontinuity artifacts between CT slices, caused by cycle-to-cycle amplitude variations in respiration. Sorting based on the displacement of the respiratory signal yields slices at more consistent respiratory motion states and hence reduces artifacts, but missing image data (gaps) may occur. The authors report on the application of a respiratory motion model to produce an RCCT image set with reduced artifacts and without missing data. Input data consist of CT slices from a cine CT scan acquired while recording respiration by monitoring abdominal displacement. The model-based generation of RCCT images consists of four processing steps: (1) displacement-based sorting of CT slices to form volume images at 10 motion states over the cycle; (2) selection of a reference image without gaps and deformable registration between the reference image and each of the remaining images; (3) generation of the motion model by applying a principal component analysis to establish a relationship between displacement field and respiration signal at each motion state; (4) application of the motion model to deform the reference image into images at the 9 other motion states. Deformable image registration uses a modified fast free-form algorithm that excludes zero-intensity voxels, caused by missing data, from the image similarity term in the minimization function. In each iteration of the minimization, the displacement field in the gap regions is linearly interpolated from nearest neighbor nonzero intensity slices. Evaluation of the model-based RCCT examines three types of image sets: cine scans of a physical phantom programmed to move according to a patient respiratory signal, NURBS-based cardiac torso (NCAT) software phantom, and patient thoracic scans. Comparison in physical motion phantom shows that object distortion caused by variable motion amplitude in phase-based sorting is visibly reduced with model-based RCCT. Comparison of model-based RCCT to original NCAT images as ground truth shows best agreement at motion states whose displacement-sorted images have no missing slices, with mean and maximum discrepancies in lung of 1 and 3 mm, respectively. Larger discrepancies correlate with motion states having a larger number of missing slices in the displacement-sorted images. Artifacts in patient images at different motion states are also reduced. Comparison with displacement-sorted patient images as a ground truth shows that the model-based images closely reproduce the ground truth geometry at different motion states. Results in phantom and patient images indicate that the proposed method can produce RCCT image sets with reduced artifacts relative to phase-sorted images, without the gaps inherent in displacement-sorted images. The method requires a reference image at one motion state that has no missing data. Highly irregular breathing patterns can affect the method's performance, by introducing artifacts in the reference image (although reduced relative to phase-sorted images), or in decreased accuracy in the image prediction of motion states containing large regions of missing data. © 2012 American Association of Physicists in Medicine.

Simultaneous determination of vitamins D2 and D3 by electrospray ionization LC/MS/MS in infant formula and adult nutritionals: First Action 2012.11.

PubMed

Gilliland, Donald L; Black, Charles K; Denison, James E; Seipelt, Charles T; Baugh, Steve

2013-01-01

A method was developed for the analysis of vitamins D2 and D3 in a variety of nutritional products. To extract vitamins D2 and D3 from products containing substantial amounts of fat, a saponification with alcoholic potassium hydroxide is required to release the vitamin D. Trideuterium-labeled vitamin D is added to the sample prior to saponification, and quantitation is achieved using linear regression of the ratio of peak response for 2H3-D and vitamin D. Acceptable linearity was achieved between 0.6 and 27 microg/100 g with a correlation requirement of >0.999. The method detection limit of 0.02 microg/100 g was verified by spiking placebo products carried through the saponification and extraction steps of the method. At the quantitation limit (0.12 microg/100 g), the signal was easily distinguished from the background. Vitamin D3 spike recoveries ranged from 107 to 119% at the low level and 104 to 116% at the high-level spike. Vitamin D2 recoveries were 105 to 116% and 91 to 110% for the low- and high-level spikes, respectively. SRM 1849a has a certified concentration of 11.1 +/- 1.7 microg/100 g; using this standard reference material, the range of 9.4 to 12.8 microg/100 g was met on each of the 6 days. Method repeatability, determined in 12 vitamin D3 product matrixes over 6 days, ranged from 3.9 to 48%. The adult nutrition-milk protein sample was the most notable; it failed within-day, as well as day-to-day, precision requirements. There was no attempt to optimize the sample preparation to accommodate any problem matrix.

HLA-targeted flow cytometric sorting of blood cells allows separation of pure and viable microchimeric cell populations.

PubMed

Drabbels, Jos J M; van de Keur, Carin; Kemps, Berit M; Mulder, Arend; Scherjon, Sicco A; Claas, Frans H J; Eikmans, Michael

2011-11-10

Microchimerism is defined by the presence of low levels of nonhost cells in a person. We developed a reliable method for separating viable microchimeric cells from the host environment. For flow cytometric cell sorting, HLA antigens were targeted with human monoclonal HLA antibodies (mAbs). Optimal separation of microchimeric cells (present at a proportion as low as 0.01% in artificial mixtures) was obtained with 2 different HLA mAbs, one targeting the chimeric cells and the other the background cells. To verify purity of separated cell populations, flow-sorted fractions of 1000 cells were processed for DNA analysis by HLA-allele-specific and Y-chromosome-directed real-time quantitative PCR assays. After sorting, PCR signals of chimeric DNA markers in the positive fractions were significantly enhanced compared with those in the presort samples, and they were similar to those in 100% chimeric control samples. Next, we demonstrate applicability of HLA-targeted FACS sorting after pregnancy by separating chimeric maternal cells from child umbilical cord mononuclear cells. Targeting allelic differences with anti-HLA mAbs with FACS sorting allows maximal enrichment of viable microchimeric cells from a background cell population. The current methodology enables reliable microchimeric cell detection and separation in clinical specimens.

Interaction sorting method for molecular dynamics on multi-core SIMD CPU architecture.

PubMed

Matvienko, Sergey; Alemasov, Nikolay; Fomin, Eduard

2015-02-01

Molecular dynamics (MD) is widely used in computational biology for studying binding mechanisms of molecules, molecular transport, conformational transitions, protein folding, etc. The method is computationally expensive; thus, the demand for the development of novel, much more efficient algorithms is still high. Therefore, the new algorithm designed in 2007 and called interaction sorting (IS) clearly attracted interest, as it outperformed the most efficient MD algorithms. In this work, a new IS modification is proposed which allows the algorithm to utilize SIMD processor instructions. This paper shows that the improvement provides an additional gain in performance, 9% to 45% in comparison to the original IS method.

Viterbi sparse spike detection and a compositional origin to ultralow-velocity zones

NASA Astrophysics Data System (ADS)

Brown, Samuel Paul

Accurate interpretation of seismic travel times and amplitudes in both the exploration and global scales is complicated by the band-limited nature of seismic data. We present a stochastic method, Viterbi sparse spike detection (VSSD), to reduce a seismic waveform into a most probable constituent spike train. Model waveforms are constructed from a set of candidate spike trains convolved with a source wavelet estimate. For each model waveform, a profile hidden Markov model (HMM) is constructed to represent the waveform as a stochastic generative model with a linear topology corresponding to a sequence of samples. The Viterbi algorithm is employed to simultaneously find the optimal nonlinear alignment between a model waveform and the seismic data, and to assign a score to each candidate spike train. The most probable travel times and amplitudes are inferred from the alignments of the highest scoring models. Our analyses show that the method can resolve closely spaced arrivals below traditional resolution limits and that travel time estimates are robust in the presence of random noise and source wavelet errors. We applied the VSSD method to constrain the elastic properties of a ultralow- velocity zone (ULVZ) at the core-mantle boundary beneath the Coral Sea. We analyzed vertical component short period ScP waveforms for 16 earthquakes occurring in the Tonga-Fiji trench recorded at the Alice Springs Array (ASAR) in central Australia. These waveforms show strong pre and postcursory seismic arrivals consistent with ULVZ layering. We used the VSSD method to measure differential travel-times and amplitudes of the post-cursor arrival ScSP and the precursor arrival SPcP relative to ScP. We compare our measurements to a database of approximately 340,000 synthetic seismograms finding that these data are best fit by a ULVZ model with an S-wave velocity reduction of 24%, a P-wave velocity reduction of 23%, a thickness of 8.5 km, and a density increase of 6%. We simultaneously constrain both P- and S-wave velocity reductions as a 1:1 ratio inside this ULVZ. This 1:1 ratio is not consistent with a partial melt origin to ULVZs. Rather, we demonstrate that a compositional origin is more likely.