Biology subjects

Huang, Y.-L.

Publications and source records attributed to Huang, Y.-L..

6 recordsLinked to original sources

bioRxiv2021

Fuzzy Integral With Particle Swarm Optimization for CNN-Based Motor-Imagery EEG Classification

Recently, decoding human electroencephalographic (EEG) data using convolutional neural network (CNN) has driven the state-of-the-art recognition of motor-imagery EEG patterns for brain-computer interfacing (BCI). While a variety of CNN models have been used to classify motor-imagery EEG data, it is unclear if aggregating an ensemble of heterogeneous CNN models could further enhance the classification performance. To integrate the outputs of ensemble classifiers, this work utilizes fuzzy integral with particle swarm optimization (PSO) to estimate optimal confidence levels assigned to classifiers. The proposed frame-work aggregates CNN classifiers and fuzzy integral with PSO, achieving robust performance in single-trial classification of motor-imagery EEG data across various CNN model training schemes depending on the scenarios of BCI usage. This proof-of-concept study demonstrates the feasibility of applying fuzzy fusion techniques to enhance CNN-based EEG decoding and benefit practical applications of BCI.

bioengineering↗

bioRxiv2021

The openCARP Simulation Environment for Cardiac Electrophysiology

Background and ObjectiveCardiac electrophysiology is a medical specialty with a long and rich tradition of computational modeling. Nevertheless, no community standard for cardiac electrophysiology simulation software has evolved yet. Here, we present the openCARP simulation environment as one solution that could foster the needs of large parts of this community. Methods and ResultsopenCARP and the Python-based carputils framework allow developing and sharing simulation pipelines which automate in silico experiments including all modeling and simulation steps to increase reproducibility and productivity. The continuously expanding openCARP user community is supported by tailored infrastructure. Documentation and training material facilitate access to this complementary research tool for new users. After a brief historic review, this paper summarizes requirements for a high-usability electrophysiology simulator and describes how openCARP fulfills them. We introduce the openCARP modeling workflow in a multi-scale example of atrial fibrillation simulations on single cell, tissue, organ and body level and finally outline future development potential. ConclusionAs an open simulator, openCARP can advance the computational cardiac electrophysiology field by making state-of-the-art simulations accessible. In combination with the carputils framework, it offers a tailored software solution for the scientific community and contributes towards increasing use, transparency, standardization and reproducibility of in silico experiments.

bioinformatics↗

bioRxiv2020

Sequence- and structure-specific cytosine-5 mRNA methylation by NSUN6

Methylation is the most common internal modification in mRNA. While the highly abundant N6-methyladonsine (m6A) modification affects most aspects of mRNA function, the precise functions of the rarer 5-methylcytosine (m5C) remains largely unknown. Here, we map m5C in the human transcriptome using methylation-dependent individual-nucleotide resolution cross-linking and immunoprecipitation (miCLIP) combined with RNA bisulfite sequencing. We identify NSUN6 as a methyltransferase with strong substrate specificity towards mRNA. NSUN6 primarily targeted three prime untranslated regions (3UTR) at the consensus sequence motif CTCCA, located in loops of hairpin structures. Knockout and rescue experiments revealed that only mRNA methylation sites containing the consensus motif depended on the presence of NSUN6. Furthermore, ribosome profiling demonstrated that NSUN6-specific consensus motifs marked translation termination. However, even though NSUN6-methylated mRNAs were reduced in NSUN6 knockout cells, NSUN6 was dispensable for mouse embryonic development. Thus, our study identifies NSUN6 as methyltransferase targeting mRNA in a sequence- and structure-specific manner.

molecular biology↗

bioRxiv2019

EMULSION: transparent and flexible multiscale stochastic models in epidemiology

Stochastic mechanistic models are essential to predictive epidemiology, to better explore biological assumptions and anticipate effects of control measures on pathogen spread. Their development is usually long and iterative, involving multi-disciplinary knowledge integration. However, knowledge often becomes lost in model code, hindering late changes in assumptions and often making models appearing as black boxes to end-users. We introduce here EMULSION, an artificial intelligence-based software intended to help modellers focus on model design rather than programming. EMULSION defines a domain-specific language to make all components of an epidemiological model (structure, processes, parameters...) explicit as a structured text file. This file is readable by scientists from other fields (epidemiologists, biologists, economists), who can contribute to validate or revise assumptions at any stage of model development. It is then automatically processed by EMULSION generic simulation engine, preventing any discrepancy between model description and implementation. The modelling language and simulation architecture both rely on the combination of advanced artificial intelligence methods (knowledge representation and multi-level agent-based simulation), allowing several modelling paradigms (from compartment- to individual-based models) at several scales (up to metapopulations). The flexibility of EMULSION and its capability to support iterative modelling are illustrated here through examples of progressive complexity, including late revisions of core model assumptions. EMULSION is also currently used to model the spread of several diseases in real pathosystems (zoonoses such as Q fever or the vector-borne Rift Valley fever; or bovine respiratory diseases, with a focus on detection and treatment protocols). EMULSION provides a command-line tool for checking models, producing model diagrams, running simulations, and plotting outputs. Implemented in Python 3, EMULSION runs on Linux, MacOS, and soon Windows. It is released under Apache-2.0 license. A comprehensive documentation with installation instructions, tutorial and examples is available from: https://sourcesup.renater.fr/emulsion-public.

epidemiology↗

bioRxiv2018

A generic multi-level stochastic modelling framework in computational epidemiology

There is currently an overwhelming increased interest in predictive biology and computational modelling. The development of reliable, reproducible and revisable simulation models in computational life sciences is often pointed out as a challenging issue. Population dynamics, including epidemiology, has not yet developed a language to formalize complex models in a univocal and automatable way, hence hindering the capability to implement in short time reliable, revisable and expert-friendly models intended for realistic mechanistic simulations. In epidemiology specifically, models aim not only at understanding pathogen spread but also at assessing control measures at several scales. To achieve this goal efficiently, best software practices should be supported by Artificial Intelligence methods to handle experts knowledge. The framework EMULSION presented here intends to both tackle multiple modelling paradigms in epidemiology and facilitate the automation of model design. We therefore built both a domain-specific language (DSL) for the modular description of complex epidemiological models, and a generic simulation engine designed to embed existing modelling paradigms within a homogeneous architecture based on adaptive software agents. The diversity of concerns (biology, economics, human activities) involved in real pathosystems requires an explicit, comprehensive and intelligible way to describe epidemiological models, to involve experts without computer science skills throughout the modelling, simulation and output analysis steps. This approach was applied to compare hypotheses in modelling a zoonosis (Q fever), to study its transmission dynamics within and between cattle herds at a regional scale, and to assess the contribution of transmission pathways. Separating model description from the simulation engine allowed epidemiologists to be involved in assumption revision, while guaranteeing very few code modifications. We assessed the added value of EMULSION by applying the DSL and the simulation engine to a concrete disease. Future extensions of EMULSION towards a broader range of epidemiological concerns will reduce significantly the time required to design and assess models and control measures against endemic and epidemic diseases. Ultimately, we believe this effort is a major lever to increase scientists preparedness to face emerging threats for public health and provide rapid, reliable, and reasoned assessments of control measures.

epidemiology↗

bioRxiv2018

Geometry-dependent instabilities in electrically excitable tissues

Little is known about how individual cells sense the macroscopic geometry of their tissue environment. Here we explore whether long-range electrical signaling can convey information on tissue geometry to influence electrical dynamics of individual cells. First, we studied an engineered electrically excitable cell line where all voltage-gated channels were well characterized. Cells grown in patterned islands of different shapes showed remarkably diverse firing patterns under otherwise identical conditions, including regular spiking, period-doubling alternans, and arrhythmic firing. A Hodgkin-Huxley numerical model quantitatively reproduced these effects, showing how the macroscopic geometry affected the single-cell electrophysiology via the influence of gap junction-mediated electrical coupling. Qualitatively similar geometry dependent dynamics were experimentally observed in human induced pluripotent stem cell (iPSC)-derived cardiomyocytes. The cardiac results urge caution in translating observations of arrhythmia in vitro to predictions in vivo where the tissue geometry is very different. We present simulation results and scaling arguments which explore how to extrapolate electrophysiological measurements between tissues with different geometries and different gap junction couplings.

biophysics↗