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  1. Article ; Online: A Generalized Transformer-Based Pulse Detection Algorithm.

    Dematties, Dario / Wen, Chenyu / Zhang, Shi-Li

    ACS sensors

    2022  Volume 7, Issue 9, Page(s) 2710–2720

    Abstract: Pulse-like signals are ubiquitous in the field of single molecule analysis, ...

    Abstract Pulse-like signals are ubiquitous in the field of single molecule analysis,
    MeSH term(s) Algorithms ; Nanopores
    Language English
    Publishing date 2022-08-30
    Publishing country United States
    Document type Journal Article ; Research Support, Non-U.S. Gov't
    ISSN 2379-3694
    ISSN (online) 2379-3694
    DOI 10.1021/acssensors.2c01218
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  2. Article ; Online: A Guide to Signal Processing Algorithms for Nanopore Sensors.

    Wen, Chenyu / Dematties, Dario / Zhang, Shi-Li

    ACS sensors

    2021  Volume 6, Issue 10, Page(s) 3536–3555

    Abstract: Nanopore technology holds great promise for a wide range of applications such as biomedical sensing, chemical detection, desalination, and energy conversion. For sensing performed in electrolytes in particular, abundant information about the ... ...

    Abstract Nanopore technology holds great promise for a wide range of applications such as biomedical sensing, chemical detection, desalination, and energy conversion. For sensing performed in electrolytes in particular, abundant information about the translocating analytes is hidden in the fluctuating monitoring ionic current contributed from interactions between the analytes and the nanopore. Such ionic currents are inevitably affected by noise; hence, signal processing is an inseparable component of sensing in order to identify the hidden features in the signals and to analyze them. This Guide starts from untangling the signal processing flow and categorizing the various algorithms developed to extracting the useful information. By sorting the algorithms under Machine Learning (ML)-based versus non-ML-based, their underlying architectures and properties are systematically evaluated. For each category, the development tactics and features of the algorithms with implementation examples are discussed by referring to their common signal processing flow graphically summarized in a chart and by highlighting their key issues tabulated for clear comparison. How to get started with building up an ML-based algorithm is subsequently presented. The specific properties of the ML-based algorithms are then discussed in terms of learning strategy, performance evaluation, experimental repeatability and reliability, data preparation, and data utilization strategy. This Guide is concluded by outlining strategies and considerations for prospect algorithms.
    MeSH term(s) Algorithms ; Machine Learning ; Nanopores ; Reproducibility of Results
    Language English
    Publishing date 2021-10-04
    Publishing country United States
    Document type Journal Article ; Research Support, Non-U.S. Gov't
    ISSN 2379-3694
    ISSN (online) 2379-3694
    DOI 10.1021/acssensors.1c01618
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  3. Article ; Online: A Generalized Transformer-Based Pulse Detection Algorithm

    Dematties, Dario / Wen, Chenyu / Zhang, Shi Li

    ACS Sensors

    2022  Volume 7, Issue 9

    Abstract: Pulse-like signals are ubiquitous in the field of single molecule analysis, e.g., electrical or optical pulses caused by analyte translocations in nanopores. The primary challenge in processing pulse-like signals is to capture the pulses in noisy ... ...

    Abstract Pulse-like signals are ubiquitous in the field of single molecule analysis, e.g., electrical or optical pulses caused by analyte translocations in nanopores. The primary challenge in processing pulse-like signals is to capture the pulses in noisy backgrounds, but current methods are subjectively based on a user-defined threshold for pulse recognition. Here, we propose a generalized machine-learning based method, named pulse detection transformer (PETR), for pulse detection. PETR determines the start and end time points of individual pulses, thereby singling out pulse segments in a time-sequential trace. It is objective without needing to specify any threshold. It provides a generalized interface for downstream algorithms for specific application scenarios. PETR is validated using both simulated and experimental nanopore translocation data. It returns a competitive performance in detecting pulses through assessing them with several standard metrics. Finally, the generalization nature of the PETR output is demonstrated using two representative algorithms for feature extraction.
    Keywords artificial neural network ; generalized algorithm ; machine learning ; nanopore sensing ; spike recognition ; transformer
    Subject code 006
    Language English
    Publishing country nl
    Document type Article ; Online
    ISSN 2379-3694
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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  4. Article ; Online: Deep Learning of Nanopore Sensing Signals Using a Bi-Path Network.

    Dematties, Dario / Wen, Chenyu / Pérez, Mauricio David / Zhou, Dian / Zhang, Shi-Li

    ACS nano

    2021  Volume 15, Issue 9, Page(s) 14419–14429

    Abstract: Temporal changes in electrical resistance of a nanopore sensor caused by translocating target analytes are recorded as a sequence of pulses on current traces. Prevalent algorithms for feature extraction in pulse-like signals lack objectivity because ... ...

    Abstract Temporal changes in electrical resistance of a nanopore sensor caused by translocating target analytes are recorded as a sequence of pulses on current traces. Prevalent algorithms for feature extraction in pulse-like signals lack objectivity because empirical amplitude thresholds are user-defined to single out the pulses from the noisy background. Here, we use deep learning for feature extraction based on a bi-path network (B-Net). After training, the B-Net acquires the prototypical pulses and the ability of both pulse recognition and feature extraction without
    MeSH term(s) Deep Learning ; Nanopores
    Language English
    Publishing date 2021-08-17
    Publishing country United States
    Document type Journal Article ; Research Support, Non-U.S. Gov't
    ISSN 1936-086X
    ISSN (online) 1936-086X
    DOI 10.1021/acsnano.1c03842
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  5. Article ; Online: A Computational Theory for the Emergence of Grammatical Categories in Cortical Dynamics.

    Dematties, Dario / Rizzi, Silvio / Thiruvathukal, George K / Pérez, Mauricio David / Wainselboim, Alejandro / Zanutto, B Silvano

    Frontiers in neural circuits

    2020  Volume 14, Page(s) 12

    Abstract: A general agreement in psycholinguistics claims that syntax and meaning are unified precisely and very quickly during online sentence processing. Although several theories have advanced arguments regarding the neurocomputational bases of this phenomenon, ...

    Abstract A general agreement in psycholinguistics claims that syntax and meaning are unified precisely and very quickly during online sentence processing. Although several theories have advanced arguments regarding the neurocomputational bases of this phenomenon, we argue that these theories could potentially benefit by including neurophysiological data concerning cortical dynamics constraints in brain tissue. In addition, some theories promote the integration of complex optimization methods in neural tissue. In this paper we attempt to fill these gaps introducing a computational model inspired in the dynamics of cortical tissue. In our modeling approach, proximal afferent dendrites produce stochastic cellular activations, while distal dendritic branches-on the other hand-contribute independently to somatic depolarization by means of dendritic spikes, and finally, prediction failures produce massive firing events preventing formation of sparse distributed representations. The model presented in this paper combines semantic and coarse-grained syntactic constraints for each word in a sentence context until grammatically related word function discrimination emerges spontaneously by the sole correlation of lexical information from different sources without applying complex optimization methods. By means of support vector machine techniques, we show that the sparse activation features returned by our approach are well suited-bootstrapping from the features returned by Word Embedding mechanisms-to accomplish grammatical function classification of individual words in a sentence. In this way we develop a biologically guided computational explanation for linguistically relevant unification processes in cortex which connects psycholinguistics to neurobiological accounts of language. We also claim that the computational hypotheses established in this research could foster future work on biologically-inspired learning algorithms for natural language processing applications.
    MeSH term(s) Afferent Pathways/physiology ; Computer Simulation ; Dendrites/physiology ; Humans ; Linguistics/methods ; Neocortex/physiology ; Nerve Net/physiology ; Speech Perception/physiology
    Language English
    Publishing date 2020-04-16
    Publishing country Switzerland
    Document type Journal Article ; Research Support, Non-U.S. Gov't
    ZDB-ID 2452968-0
    ISSN 1662-5110 ; 1662-5110
    ISSN (online) 1662-5110
    ISSN 1662-5110
    DOI 10.3389/fncir.2020.00012
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  6. Book ; Online: Deep learning of nanopore sensing signals using a bi-path network

    Dematties, Dario / Wen, Chenyu / Pérez, Mauricio David / Zhou, Dian / Zhang, Shi-Li

    2021  

    Abstract: Temporary changes in electrical resistance of a nanopore sensor caused by translocating target analytes are recorded as a sequence of pulses on current traces. Prevalent algorithms for feature extraction in pulse-like signals lack objectivity because ... ...

    Abstract Temporary changes in electrical resistance of a nanopore sensor caused by translocating target analytes are recorded as a sequence of pulses on current traces. Prevalent algorithms for feature extraction in pulse-like signals lack objectivity because empirical amplitude thresholds are user-defined to single out the pulses from the noisy background. Here, we use deep learning for feature extraction based on a bi-path network (B-Net). After training, the B-Net acquires the prototypical pulses and the ability of both pulse recognition and feature extraction without a priori assigned parameters. The B-Net performance is evaluated on generated datasets and further applied to experimental data of DNA and protein translocation. The B-Net results show remarkably small relative errors and stable trends. The B-Net is further shown capable of processing data with a signal-to-noise ratio equal to one, an impossibility for threshold-based algorithms. The developed B-Net is generic for pulse-like signals beyond pulsed nanopore currents.
    Keywords Electrical Engineering and Systems Science - Signal Processing ; Computer Science - Machine Learning ; Physics - Biological Physics
    Subject code 004
    Publishing date 2021-05-08
    Publishing country us
    Document type Book ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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  7. Book ; Online: NANOPORE_TRANSLOCATIONS

    Dematties, Dario / Wen, Chenyu / Pérez, Mauricio / Zhou, Dian / Zhang, Shi Li

    Nanopore Translocation (Pulse) Detector : Nanopore Translocations for translocations count and feature extraction

    2021  

    Abstract: The software released here can be used for training, validating and testing different ResNet architectures for counting translocation events and extracting statistical features from nanopore translocation signals (i.e. average translocation duration and ... ...

    Abstract The software released here can be used for training, validating and testing different ResNet architectures for counting translocation events and extracting statistical features from nanopore translocation signals (i.e. average translocation duration and amplitude in traces temporal chunks). The models are used in a supervised learning fashion and can be tested on experimental traces obtained in the lab.
    Keywords Life Science
    Language English
    Publisher Zenodo
    Publishing country nl
    Document type Book ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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  8. Article ; Online: Phonetic acquisition in cortical dynamics, a computational approach.

    Dematties, Dario / Rizzi, Silvio / Thiruvathukal, George K / Wainselboim, Alejandro / Zanutto, B Silvano

    PloS one

    2019  Volume 14, Issue 6, Page(s) e0217966

    Abstract: Many computational theories have been developed to improve artificial phonetic classification performance from linguistic auditory streams. However, less attention has been given to psycholinguistic data and neurophysiological features recently found in ... ...

    Abstract Many computational theories have been developed to improve artificial phonetic classification performance from linguistic auditory streams. However, less attention has been given to psycholinguistic data and neurophysiological features recently found in cortical tissue. We focus on a context in which basic linguistic units-such as phonemes-are extracted and robustly classified by humans and other animals from complex acoustic streams in speech data. We are especially motivated by the fact that 8-month-old human infants can accomplish segmentation of words from fluent audio streams based exclusively on the statistical relationships between neighboring speech sounds without any kind of supervision. In this paper, we introduce a biologically inspired and fully unsupervised neurocomputational approach that incorporates key neurophysiological and anatomical cortical properties, including columnar organization, spontaneous micro-columnar formation, adaptation to contextual activations and Sparse Distributed Representations (SDRs) produced by means of partial N-Methyl-D-aspartic acid (NMDA) depolarization. Its feature abstraction capabilities show promising phonetic invariance and generalization attributes. Our model improves the performance of a Support Vector Machine (SVM) classifier for monosyllabic, disyllabic and trisyllabic word classification tasks in the presence of environmental disturbances such as white noise, reverberation, and pitch and voice variations. Furthermore, our approach emphasizes potential self-organizing cortical principles achieving improvement without any kind of optimization guidance which could minimize hypothetical loss functions by means of-for example-backpropagation. Thus, our computational model outperforms multiresolution spectro-temporal auditory feature representations using only the statistical sequential structure immerse in the phonotactic rules of the input stream.
    MeSH term(s) Acoustic Stimulation/methods ; Acoustics ; Animals ; Attention/physiology ; Auditory Cortex/physiology ; Auditory Pathways/physiology ; Auditory Perception/physiology ; Computer Simulation ; Humans ; Infant ; Language ; Phonetics ; Speech/physiology ; Speech Perception/physiology
    Language English
    Publishing date 2019-06-07
    Publishing country United States
    Document type Journal Article ; Research Support, Non-U.S. Gov't ; Research Support, U.S. Gov't, Non-P.H.S.
    ISSN 1932-6203
    ISSN (online) 1932-6203
    DOI 10.1371/journal.pone.0217966
    Database MEDical Literature Analysis and Retrieval System OnLINE

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