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Lab-On-A-Chip Devices

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Quantification of Mechanical Forces and Physiological Processes Involved in Pollen Tube Growth Using Microfluidics and Microrobotics.

Methods in molecular biology (Clifton, N.J.)
Pollen tubes face many obstacles on their way to the ovule. They have to decide whether to navigate around cells or penetrate the cell wall and grow through it or even within it. Besides chemical sensing, which directs the pollen tubes on their path ...

A ferrobotic system for automated microfluidic logistics.

Science robotics
Automated technologies that can perform massively parallelized and sequential fluidic operations at small length scales can resolve major bottlenecks encountered in various fields, including medical diagnostics, -omics, drug development, and chemical...

DeLTA: Automated cell segmentation, tracking, and lineage reconstruction using deep learning.

PLoS computational biology
Microscopy image analysis is a major bottleneck in quantification of single-cell microscopy data, typically requiring human oversight and curation, which limit both accuracy and throughput. To address this, we developed a deep learning-based image an...

Intelligent classification of platelet aggregates by agonist type.

eLife
Platelets are anucleate cells in blood whose principal function is to stop bleeding by forming aggregates for hemostatic reactions. In addition to their participation in physiological hemostasis, platelet aggregates are also involved in pathological ...

Synapse cell optimization and back-propagation algorithm implementation in a domain wall synapse based crossbar neural network for scalable on-chip learning.

Nanotechnology
On-chip learning in spin orbit torque driven domain wall synapse based crossbar fully connected neural network (FCNN) has been shown to be extremely efficient in terms of speed and energy, when compared to training on a conventional computing unit or...

Converging Multidimensional Sensor and Machine Learning Toward High-Throughput and Biorecognition Element-Free Multidetermination of Extracellular Vesicle Biomarkers.

ACS sensors
Extracellular vesicles (EVs) are a frontier class of circulating biomarkers for the diagnosis and prognosis of different diseases. These lipid structures afford various biomarkers such as the concentrations of the EVs () themselves and carried protei...

Quantifying the Brain Metastatic Tumor Micro-Environment using an Organ-On-A Chip 3D Model, Machine Learning, and Confocal Tomography.

Journal of visualized experiments : JoVE
Brain metastases are the most lethal cancer lesions; 10-30% of all cancers metastasize to the brain, with a median survival of only ~5-20 months, depending on the cancer type. To reduce the brain metastatic tumor burden, gaps in basic and translation...

Discriminating between sleep and exercise-induced fatigue using computer vision and behavioral genetics.

Journal of neurogenetics
Following prolonged swimming, cycle between active swimming bouts and inactive quiescent bouts. Swimming is exercise for and here we suggest that inactive bouts are a recovery state akin to fatigue. It is known that cGMP-dependent kinase (PKG) acti...

AI on a chip.

Lab on a chip
Artificial intelligence (AI) has dramatically changed the landscape of science, industry, defence, and medicine in the last several years. Supported by considerably enhanced computational power and cloud storage, the field of AI has shifted from most...

Droplet size prediction in a microfluidic flow focusing device using an adaptive network based fuzzy inference system.

Biomedical microdevices
Microfluidics has wide applications in different technologies such as biomedical engineering, chemistry engineering, and medicine. Generating droplets with desired size for special applications needs costly and time-consuming iterations due to the no...