Model of ‘implant-host’ neural circuits in a microfluidic chip in vitro

Abstract In this study, we developed a new model of neuronal cells plating into a developed neural network to study functional integration using microfluidic methods. The integration was modeled in a three-chamber microfluidic chip by growing two weakly coupled neuronal networks and enhancing its connectivity by plating new dissociated cells. The direction of connections was formed by the asymmetric design of the chip. Such technology can be used to develop a new type of scaffold to recover the modular structure of the network.

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Structuring a multi-nodal neural network in vitro within a novel design microfluidic chip

Biomedical Microdevices ◽

10.1007/s10544-017-0254-4 ◽

2018 ◽

Vol 20 (1) ◽

Cited By ~ 12

Author(s):

Rosanne van de Wijdeven ◽

Ola Huse Ramstad ◽

Ulrich Stefan Bauer ◽

Øyvind Halaas ◽

Axel Sandvig ◽

...

Keyword(s):

Neural Network ◽

Microfluidic Chip ◽

Novel Design

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Photoinhibition in vivo and in vitro Involves Weakly Coupled Chlorophyll–Protein Complexes‡¶

Photochemistry and Photobiology ◽

10.1562/0031-8655(2002)075<0613:pivaiv>2.0.co;2 ◽

2002 ◽

Vol 75 (6) ◽

pp. 613 ◽

Cited By ~ 31

Author(s):

Stefano Santabarbara ◽

Ilaria Cazzalini ◽

Andrea Rivadossi ◽

Flavio M. Garlaschi ◽

Giuseppe Zucchelli ◽

...

Keyword(s):

Protein Complexes ◽

Weakly Coupled ◽

Chlorophyll Protein Complexes ◽

Chlorophyll Protein

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LENS CLASSIFICATION ACCORDING TO THE TYPE OF LIGHT SPOT USING A NEURAL NETWORK

Automation and modeling in design and management of ◽

10.30987/2658-6436-2020-4-4-14 ◽

2020 ◽

Vol 2020 (4) ◽

pp. 4-14

Author(s):

Vladimir Budak ◽

Ekaterina Ilyina

Keyword(s):

Neural Network ◽

Light Intensity ◽

Convolutional Neural Network ◽

Deep Convolutional Neural Network ◽

Beam Angle ◽

New Model ◽

Technical Parameters ◽

Transfer Training ◽

Trained Network

The article proposes the classification of lenses with different symmetrical beam angles and offers a scale as a spot-light’s palette. A collection of spotlight’s images was created and classified according to the proposed scale. The analysis of 788 pcs of existing lenses and reflectors with different LEDs and COBs carried out, and the dependence of the axial light intensity from beam angle was obtained. A transfer training of new deep convolutional neural network (CNN) based on the pre-trained GoogleNet was performed using this collection. GradCAM analysis showed that the trained network correctly identifies the features of objects. This work allows us to classify arbitrary spotlights with an accuracy of about 80 %. Thus, light designer can determine the class of spotlight and corresponding type of lens with its technical parameters using this new model based on CCN.

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CirBiTree: Citrullination Site Inference Based on a Fuzzy Neural Network and Flexible Neural Tree

Scientific Programming ◽

10.1155/2020/8847694 ◽

2020 ◽

Vol 2020 ◽

pp. 1-8

Author(s):

Chuandong Song ◽

Haifeng Wang

Keyword(s):

Neural Network ◽

Fuzzy Neural Network ◽

Classification Model ◽

Peptide Sequence ◽

Sequence Information ◽

Post Translational Modification ◽

Fuzzy Neural ◽

Human Complex ◽

Better Than

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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New model to study cholesterol uptake in the human intestine in vitro.

The Journal of Lipid Research ◽

10.1016/s0022-2275(20)40760-6 ◽

1993 ◽

Vol 34 (2) ◽

pp. 331-339

Author(s):

D.D. Sviridov ◽

I.G. Safonova ◽

J.L. Nano ◽

M.Y. Pavlov ◽

P Rampal ◽

...

Keyword(s):

Human Intestine ◽

New Model ◽

Cholesterol Uptake

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PLATELET ACTIVATION BY HUMAN CANCER CELLS GROWN “IN VITRO” OR DISSOCIATED FROM TUMOUR TISSUES

10.1055/s-0038-1643200 ◽

1987 ◽

Author(s):

G Grignani ◽

L Pacchiarini ◽

M Zucchella ◽

L Dezza ◽

S C Rizzo

Keyword(s):

Platelet Aggregation ◽

Platelet Activation ◽

Human Cancer ◽

Iodoacetic Acid ◽

Tumour Cells ◽

Human Cancer Cells ◽

Human Tumour Cells ◽

Dissociated Cells ◽

Lymphoblastic Cells

The mechanisms of platelet activation by human tumour cells grown “in vitro” or freshly dissociated from tumour tissues have been investigated.MoCCL human T-lymphoblastic cells cultured “in vitro” induced platelet aggregation through the production of ADP, as evidenced by inhibition of the effect by apyrase. The maximum of ADP production by tumour cells was reached after 1 hour and was 225 p moles/106 cells.On the contrary, platelet aggregation induced by 5637 human bladder carcinoma cells was not inhibited by apyrase, but was abolished by hirudin, indicating the important role of thrombin in this effect.Tumour cells dissociated from 3 breast carcinomas showed a very high platelet aggregating activity, which was not inhibited by hirudin or apyrase, but was abolished by iodoacetic acid, suggesting a role for a cystein-protease in platelet activation.These results confirm that platelets can be activated by tumour cells through different mechanisms; they also suggest that the methods employed to obtain the tumour cells can influence the results, probably because of the different cell populations which are present in the dissociated tumour tissues.Informations obtained with freshly dissociated cells are interesting, because this method has been used seldom so far and because it provides a more physiological approach to the study of the interactions of tumours and platelets.

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Studying the Potential of Graphcore® IPUs for Applications in Particle Physics

Computing and Software for Big Science ◽

10.1007/s41781-021-00057-z ◽

2021 ◽

Vol 5 (1) ◽

Author(s):

Samuel Maddrell-Mander ◽

Lakshan Ram Madhan Mohan ◽

Alexander Marshall ◽

Daniel O’Hanlon ◽

Konstantinos Petridis ◽

...

Keyword(s):

Neural Network ◽

Machine Learning ◽

Kalman Filter ◽

Particle Physics ◽

Processing Unit ◽

Network Architectures ◽

Track Reconstruction ◽

Event Simulation ◽

Scattering Correction ◽

New Type

AbstractThis paper presents the first study of Graphcore’s Intelligence Processing Unit (IPU) in the context of particle physics applications. The IPU is a new type of processor optimised for machine learning. Comparisons are made for neural-network-based event simulation, multiple-scattering correction, and flavour tagging, implemented on IPUs, GPUs and CPUs, using a variety of neural network architectures and hyperparameters. Additionally, a Kálmán filter for track reconstruction is implemented on IPUs and GPUs. The results indicate that IPUs hold considerable promise in addressing the rapidly increasing compute needs in particle physics.

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Using Wearable Sensors and a Convolutional Neural Network for Catch Detection in American Football

Sensors ◽

10.3390/s20236722 ◽

2020 ◽

Vol 20 (23) ◽

pp. 6722

Author(s):

Bernhard Hollaus ◽

Sebastian Stabinger ◽

Andreas Mehrle ◽

Christian Raschner

Keyword(s):

Neural Network ◽

Degrees Of Freedom ◽

Wearable Sensors ◽

Professional Sports ◽

American Football ◽

Data Logging ◽

Sensor Platform ◽

Magnetometer Data ◽

Audio Data ◽

New Type

Highly efficient training is a must in professional sports. Presently, this means doing exercises in high number and quality with some sort of data logging. In American football many things are logged, but there is no wearable sensor that logs a catch or a drop. Therefore, the goal of this paper was to develop and verify a sensor that is able to do exactly that. In a first step a sensor platform was used to gather nine degrees of freedom motion and audio data of both hands in 759 attempts to catch a pass. After preprocessing, the gathered data was used to train a neural network to classify all attempts, resulting in a classification accuracy of 93%. Additionally, the significance of each sensor signal was analysed. It turned out that the network relies most on acceleration and magnetometer data, neglecting most of the audio and gyroscope data. Besides the results, the paper introduces a new type of dataset and the possibility of autonomous training in American football to the research community.

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