Transparent Multi-Suction Electrode Arrays for in vitro Neural Network Investigations

Emerging evidence demonstrates that post-translational modification plays an important role in several human complex diseases. Nevertheless, considering the inherent high cost and time consumption of classical and typical in vitro experiments, an increasing attention has been paid to the development of efficient and available computational tools to identify the potential modification sites in the level of protein. In this work, we propose a machine learning-based model called CirBiTree for identification the potential citrullination sites. More specifically, we initially utilize the biprofile Bayesian to extract peptide sequence information. Then, a flexible neural tree and fuzzy neural network are employed as the classification model. Finally, the most available length of identified peptides has been selected in this model. To evaluate the performance of the proposed methods, some state-of-the-art methods have been employed for comparison. The experimental results demonstrate that the proposed method is better than other methods. CirBiTree can achieve 83.07% in sn%, 80.50% in sp, 0.8201 in F1, and 0.6359 in MCC, respectively.

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A hybrid model based on general regression neural network and fruit fly optimization algorithm for forecasting and optimizing paclitaxel biosynthesis in Corylus avellana cell culture

Plant Methods ◽

10.1186/s13007-021-00714-9 ◽

2021 ◽

Vol 17 (1) ◽

Author(s):

Mina Salehi ◽

Siamak Farhadi ◽

Ahmad Moieni ◽

Naser Safaie ◽

Mohsen Hesami

Keyword(s):

Neural Network ◽

Fruit Fly ◽

Corylus Avellana ◽

General Regression Neural Network ◽

Fruit Fly Optimization Algorithm ◽

Harvesting Time ◽

Fruit Fly Optimization ◽

General Regression ◽

Concentration Levels

Abstract Background Paclitaxel is a well-known chemotherapeutic agent widely applied as a therapy for various types of cancers. In vitro culture of Corylus avellana has been named as a promising and low-cost strategy for paclitaxel production. Fungal elicitors have been reported as an impressive strategy for improving paclitaxel biosynthesis in cell suspension culture (CSC) of C. avellana. The objectives of this research were to forecast and optimize growth and paclitaxel biosynthesis based on four input variables including cell extract (CE) and culture filtrate (CF) concentration levels, elicitor adding day and CSC harvesting time in C. avellana cell culture, as a case study, using general regression neural network-fruit fly optimization algorithm (GRNN-FOA) via data mining approach for the first time. Results GRNN-FOA models (0.88–0.97) showed the superior prediction performances as compared to regression models (0.57–0.86). Comparative analysis of multilayer perceptron-genetic algorithm (MLP-GA) and GRNN-FOA showed very slight difference between two models for dry weight (DW), intracellular and extracellular paclitaxel in testing subset, the unseen data. However, MLP-GA was slightly more accurate as compared to GRNN-FOA for total paclitaxel and extracellular paclitaxel portion in testing subset. The slight difference was observed in maximum growth and paclitaxel biosynthesis optimized by FOA and GA. The optimization analysis using FOA on developed GRNN-FOA models showed that optimal CE [4.29% (v/v)] and CF [5.38% (v/v)] concentration levels, elicitor adding day (17) and harvesting time (88 h and 19 min) can lead to highest paclitaxel biosynthesis (372.89 µg l−1). Conclusions Great accordance between the predicted and observed values of DW, intracellular, extracellular and total yield of paclitaxel, and also extracellular paclitaxel portion support excellent performance of developed GRNN-FOA models. Overall, GRNN-FOA as new mathematical tool may pave the way for forecasting and optimizing secondary metabolite production in plant in vitro culture.

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Modeling and Optimizing a New Culture Medium for In Vitro Rooting of G×N15 Prunus Rootstock using Artificial Neural Network-Genetic Algorithm

Scientific Reports ◽

10.1038/s41598-018-27858-4 ◽

2018 ◽

Vol 8 (1) ◽

Cited By ~ 16

Author(s):

Mohammad Mehdi Arab ◽

Abbas Yadollahi ◽

Maliheh Eftekhari ◽

Hamed Ahmadi ◽

Mohammad Akbari ◽

...

Keyword(s):

Neural Network ◽

Genetic Algorithm ◽

Artificial Neural Network ◽

Culture Medium ◽

In Vitro Rooting ◽

Artificial Neural

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In Vitro Method for Predicting Glycemic Index of Foods Using Simulated Digestion and an Artificial Neural Network

Cereal Chemistry ◽

10.1094/cchem-87-4-0363 ◽

2010 ◽

Vol 87 (4) ◽

pp. 363-369 ◽

Cited By ~ 12

Author(s):

Robert L. Magaletta ◽

Suzanne N. DiCataldo ◽

Dong Liu ◽

Hong Laura Li ◽

Rajendra P. Borwankar ◽

...

Keyword(s):

Neural Network ◽

Artificial Neural Network ◽

Glycemic Index ◽

In Vitro Method ◽

Simulated Digestion ◽

Artificial Neural

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Development of an in vitro cell-based bioassay for the detection of Clostridium botulinum neurotoxins by using mESC-derived neurons grown on multi electrode arrays

Toxicon ◽

10.1016/j.toxicon.2016.11.125 ◽

2016 ◽

Vol 123 ◽

pp. S44

Author(s):

Stephen P. Jenkinson ◽

Denis Grandgirard ◽

Martina Heidemann ◽

Anne Tscherter ◽

Marc-André Avondet ◽

...

Keyword(s):

Clostridium Botulinum ◽

Electrode Arrays ◽

Botulinum Neurotoxins

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Magnetic Steering of Robotically Inserted Lateral-wall Cochlear-implant Electrode Arrays Reduces Forces on the Basilar Membrane In Vitro

Otology & Neurotology ◽

10.1097/mao.0000000000003129 ◽

2021 ◽

Vol Publish Ahead of Print ◽

Author(s):

Cameron M. Hendricks ◽

Matt S. Cavilla ◽

David E. Usevitch ◽

Trevor L. Bruns ◽

Katherine E. Riojas ◽

...

Keyword(s):

Cochlear Implant ◽

Basilar Membrane ◽

Lateral Wall ◽

Electrode Arrays ◽

Magnetic Steering

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Single-unit recordings of arterial chemoreceptors from mouse petrosal ganglia in vitro

Journal of Applied Physiology ◽

10.1152/jappl.2000.88.4.1489 ◽

2000 ◽

Vol 88 (4) ◽

pp. 1489-1495 ◽

Cited By ~ 23

Author(s):

David F. Donnelly ◽

Ricardo Rigual

Keyword(s):

Mouse Model ◽

Carotid Body ◽

Unit Activity ◽

Action Potentials ◽

Single Unit ◽

Gene Deletions ◽

Suction Electrode ◽

Single Unit Recordings ◽

Unit Action

A preparation was developed that allows for the recording of single-unit chemoreceptor activity from mouse carotid body in vitro. An anesthetized mouse was decapitated, and each carotid body was harvested, along with the sinus nerve, glossopharyngeal nerve, and petrosal ganglia. After exposure to collagenase/trypsin, the cleaned complex was transferred to a recording chamber where it was superfused with oxygenated saline. The ganglia was searched for evoked or spontaneous unit activity by using a glass suction electrode. Single-unit action potentials were 57 ± 10 (SE) ( n = 16) standard deviations above the recording noise, and spontaneous spikes were generated as a random process. Decreasing superfusate[Formula: see text] to near 20 Torr caused an increase in spiking activity from 1.3 ± 0.4 to 14.1 ± 1.9 Hz ( n = 16). The use of mice for chemoreceptor studies may be advantageous because targeted gene deletions are well developed in the mouse model and may be useful in addressing unresolved questions regarding the mechanism of chemotransduction.

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Pattern recognition and bionic manipulator driving by surface electromyography signals using convolutional neural network

International Journal of Advanced Robotic Systems ◽

10.1177/1729881418802138 ◽

2018 ◽

Vol 15 (5) ◽

pp. 172988141880213 ◽

Cited By ~ 3

Author(s):

Yuanfang Wan ◽

Zishan Han ◽

Jun Zhong ◽

Guohua Chen

Keyword(s):

Neural Network ◽

Convolutional Neural Network ◽

Surface Electromyography ◽

Hot Spot ◽

Hand Movement ◽

Ease Of Use ◽

Electrode Arrays ◽

Small Surface ◽

Movement Data ◽

Electromyography Signals

With the development of robotics, intelligent neuroprosthesis for amputees is more concerned. Research of robot controlling based on electrocardiogram, electromyography, and electroencephalogram is a hot spot. In medical research, electrode arrays are commonly used as sensors for surface electromyograms. Although these sensors collect more accurate data and sampling at higher frequencies, they have no advantage in terms of portability and ease of use. In recent years, there are also some small surface electromyography sensors for research. The portability of the sensor and the calculation speed of the calculation method directly affect the development of the bionic prosthesis. A consumer-grade surface electromyography device is selected as surface electromyography sensor in this study. We first proposed a data structure to convert raw surface electromyography signals from an array structure into a matrix structure (we called it surface electromyography graph). Then, a convolutional neural network was used to classify it. Discrete surface electromyography signals recorded from three persons 14 gestures (widely used in other research to evaluate the performance of classifier) have been applied to train the classifier and we get an accuracy of 97.27%. The impacts of different components used in convolutional neural network were tested with this data, and subsequently, the best results were selected to build the classifier used in this article. The NinaPro database 5 (one of the biggest surface electromyography data sets) was also used to evaluate our method, which comprises of hand movement data of 10 intact subjects with two myo armbands as sensors, and the classification accuracy increased by 13.76% on average when using double myo armbands and increased by 18.92% on average when using single myo armband. In order to driving the robot hand (bionic manipulator), a group of continuous surface electromyography signals was recorded to train the classifier, and an accuracy of 91.72% was acquired. We also used the same method to collect a set of surface electromyography data from a disabled with hand lost, then classified it using the abovementioned network and achieved an accuracy of 89.37%. Finally, the classifier was deployed to the microcontroller to drive the bionic manipulator, and the full video URL is given in the conclusion, with both the healthy man and the disabled tested with the bionic manipulator. The abovementioned results suggest that this method will help to facilitate the development and application of surface electromyography neuroprosthesis.

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Establishment of a Human Induced Pluripotent Stem Cell-Derived Neuromuscular Co-Culture Under Optogenetic Control

10.1101/2020.04.10.036400 ◽

2020 ◽

Cited By ~ 1

Author(s):

Elliot W. Swartz ◽

Greg Shintani ◽

Jijun Wan ◽

Joseph S. Maffei ◽

Sarah H. Wang ◽

...

Keyword(s):

Motor Neurons ◽

Induced Pluripotent Stem Cell ◽

Cell Types ◽

Calcium Flux ◽

Electrode Arrays ◽

Spinal Motor Neurons ◽

Culture Model ◽

Skeletal Myotubes ◽

Induced Pluripotent

SummaryThe failure of the neuromuscular junction (NMJ) is a key component of degenerative neuromuscular disease, yet how NMJs degenerate in disease is unclear. Human induced pluripotent stem cells (hiPSCs) offer the ability to model disease via differentiation toward affected cell types, however, the re-creation of an in vitro neuromuscular system has proven challenging. Here we present a scalable, all-hiPSC-derived co-culture system composed of independently derived spinal motor neurons (MNs) and skeletal myotubes (sKM). In a model of C9orf72-associated disease, co-cultures form functional NMJs that can be manipulated through optical stimulation, eliciting muscle contraction and measurable calcium flux in innervated sKM. Furthermore, co-cultures grown on multi-electrode arrays (MEAs) permit the pharmacological interrogation of neuromuscular physiology. Utilization of this co-culture model as a tunable, patient-derived system may offer significant insights into NMJ formation, maturation, repair, or pathogenic mechanisms that underlie NMJ dysfunction in disease.

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Deep Learning Based Proarrhythmia Analysis Using Field Potentials Recorded from Human Pluripotent Stem Cells Derived Cardiomyocytes

10.1101/244442 ◽

2018 ◽

Cited By ~ 1

Author(s):

Zeinab Golgooni ◽

Sara Mirsadeghi ◽

Mahdieh Soleymani Baghshah ◽

Pedram Ataee ◽

Hossein Baharvand ◽

...

Keyword(s):

Neural Network ◽

Stem Cells ◽

Deep Learning ◽

Pluripotent Stem Cells ◽

Field Potential ◽

Input Sequence ◽

Patient Specific ◽

Field Potentials ◽

Drug Induced

AbstractAimAn early characterization of drug-induced cardiotoxicity may be possible by combining comprehensive in vitro pro-arrhythmia assay and deep learning techniques. The goal of this study was to develop a deep learning method to automatically detect irregular beating rhythm as well as abnormal waveforms of field potentials in an in vitro cardiotoxicity assay using human pluripotent stem cell (hPSC) derived cardiomyocytes and multi-electrode array (MEA) system.Methods and ResultsWe included field potential waveforms from 380 experiments which obtained by application of some cardioactive drugs on healthy and/or patient-specific induced pluripotent stem cells derived cardiomyocytes (iPSC-CM). We employed convolutional and recurrent neural networks, in order to develop a new method for automatic classification of field potential recordings without using any hand-engineered features. In the proposed method, a preparation phase was initially applied to split 60-second long recordings into a series of 5-second long windows. Thereafter, the classification phase comprising of two main steps was designed. In the first step, 5-second long windows were classified using a designated convolutional neural network (CNN). In the second step, the results of 5-second long window assessments were used as the input sequence to a recurrent neural network (RNN). The output was then compared to electrophysiologist-level arrhythmia (irregularity or abnormal waveforms) detection, resulting in 0.84 accuracy, 0.84 sensitivity, 0.85 specificity, and 0.88 precision.ConclusionA novel deep learning approach based on a two-step CNN-RNN method can be used for automated analysis of “irregularity or abnormal waveforms” in an in vitro model of cardiotoxicity experiments.

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