Objective. Neural decoding is an important tool in neural engineering and neural data analysis. Of various machine learning algorithms adopted for neural decoding, the recently introduced deep learning is promising to excel. Therefore, we sought to apply deep learning to decode movement trajectories from the activity of motor cortical neurons. Approach. In this paper, we assessed the performance of deep learning methods in three different decoding schemes, concurrent, time-delay, and spatiotemporal. In the concurrent decoding scheme where the input to the network is the neural activity coincidental to the movement, deep learning networks including artificial neural network (ANN) and long-short term memory (LSTM) were applied to decode movement and compared with traditional machine learning algorithms. Both ANN and LSTM were further evaluated in the time-delay decoding scheme in which temporal delays are allowed between neural signals and movements. Lastly, in the spatiotemporal decoding scheme, we trained convolutional neural network (CNN) to extract movement information from images representing the spatial arrangement of neurons, their activity, and connectomes (i.e. the relative strengths of connectivity between neurons) and combined CNN and ANN to develop a hybrid spatiotemporal network. To reveal the input features of the CNN in the hybrid network that deep learning discovered for movement decoding, we performed a sensitivity analysis and identified specific regions in the spatial domain. Main results. Deep learning networks (ANN and LSTM) outperformed traditional machine learning algorithms in the concurrent decoding scheme. The results of ANN and LSTM in the time-delay decoding scheme showed that including neural data from time points preceding movement enabled decoders to perform more robustly when the temporal relationship between the neural activity and movement dynamically changes over time. In the spatiotemporal decoding scheme, the hybrid spatiotemporal network containing the concurrent ANN decoder outperformed single-network concurrent decoders. Significance. Taken together, our study demonstrates that deep learning could become a robust and effective method for the neural decoding of behavior.
Accurate manipulation of fluids in microfluidic devices is an important factor affecting their functions. Since the emergence of microfluidic technology to transport fluids in microchannels, the electric field has been utilized as an effective dynamic pumping mechanism. This review attempts to provide a fundamental insight of the various electric-driven flows in microchannels and their working mechanisms as micropumps for microfluidic devices.Different electrokinetic mechanisms implemented in electrohydrodynamic-, electroosmosis-, electrothermal, and dielectrophoresis-based micropumps are discussed. A detailed description of different mechanisms is presented to provide a comprehensive overview on the key parameters used in electric micropumps. Furthermore, electrode configurations and their shapes in different micropumps are explored and categorized to provide conclusive information for the selection of efficient, simple, and affordable strategies to transport fluids in microfluidic devices. In this paper, recent theoretical, numerical and experimental investigations are covered to provide a better insight both on the operational mechanisms and strategies for lab-on-chip applications.
FOXA factors are critical members of the developmental gene regulatory network (GRN) composed of master transcription factors (TF) which regulate murine cell fate and metabolism in the gut and liver. How FOXA factors dictate human liver cell fate, differentiation, and simultaneously regulate metabolic pathways is poorly understood. Here, we aimed to determine the role of FOXA2 (and FOXA1 which is believed to compensate for FOXA2) in controlling hepatic differentiation and cell metabolism in a human hepatic cell line (HepG2). siRNA mediated knockdown of FOXA1/2 in HepG2 cells significantly downregulated albumin (p < .05) and GRN TF gene expression (HNF4α, HEX, HNF1ß, TBX3) (p < .05) and significantly upregulated endoderm/gut/hepatic endoderm markers (goosecoid [GSC], FOXA3, and GATA4), gut TF (CDX2), pluripotent TF (NANOG), and neuroectodermal TF (PAX6) (p < .05), all consistent with partial/transient reprograming. shFOXA1/2 targeting resulted in similar findings and demonstrated evidence of reversibility of phenotype. RNA‐seq followed by bioinformatic analysis of shFOXA1/2 knockdown HepG2 cells demonstrated 235 significant downregulated genes and 448 upregulated genes, including upregulation of markers for alternate germ layers lineages (cardiac, endothelial, muscle) and neurectoderm (eye, neural). We found widespread downregulation of glycolysis, citric acid cycle, mitochondrial genes, and alterations in lipid metabolism, pentose phosphate pathway, and ketogenesis. Functional metabolic analysis agreed with these findings, demonstrating significantly diminished glycolysis and mitochondrial respiration, with concomitant accumulation of lipid droplets. We hypothesized that FOXA1/2 inhibit the initiation of human liver differentiation in vitro. During human pluripotent stem cells (hPSC)‐hepatic differentiation, siRNA knockdown demonstrated de‐differentiation and unexpectedly, activation of pluripotency factors and neuroectoderm. shRNA knockdown demonstrated similar results and activation of SOX9 (hepatobiliary). These results demonstrate that FOXA1/2 controls hepatic and developmental GRN, and their knockdown leads to reprogramming of both differentiation and metabolism, with applications in studies of cancer, differentiation, and organogenesis.
The inference of neuronal connectome from large-scale neuronal activity recordings, such as two-photon Calcium imaging, represents an active area of research in computational neuroscience. In this work, we developed FARCI (Fast and Robust Connectome Inference), a MATLAB package for neuronal connectome inference from high-dimensional two-photon Calcium fluorescence data. We employed partial correlations as a measure of the functional association strength between pairs of neurons to reconstruct a neuronal connectome. We demonstrated using in silico datasets from the Neural Connectomics Challenge (NCC) and those generated using the state-of-the-art simulator of Neural Anatomy and Optimal Microscopy (NAOMi) that FARCI provides an accurate connectome and its performance is robust to network sizes, missing neurons, and noise levels. Moreover, FARCI is computationally efficient and highly scalable to large networks. In comparison with the best performing connectome inference algorithm in the NCC, Generalized Transfer Entropy (GTE), and Fluorescence Single Neuron and Network Analysis Package (FluoroSNNAP), FARCI produces more accurate networks over different network sizes, while providing significantly better computational speed and scaling.
To date, a comprehensive systematic optimization framework, capable of accurately predicting an efficient electrode geometry, is not available. Here, different geometries, including 3D step electrodes, have been designed in order to fabricate AC electroosmosis micropumps. It is essential to optimize both geometrical parameters of electrode, such as width and height of steps on each base electrode and their location in one pair, the size of each base electrode (symmetric or the literature by improving the fluid velocity by 32%, with 80% less electrodes per unit length, and whereas the channel length is ∼80% shorter.
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