AIMC Topic: Animals

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Deconvolution of subcellular protrusion heterogeneity and the underlying actin regulator dynamics from live cell imaging.

Nature communications
Cell protrusion is morphodynamically heterogeneous at the subcellular level. However, the mechanism of cell protrusion has been understood based on the ensemble average of actin regulator dynamics. Here, we establish a computational framework called ...

How Much Does Movement and Location Encoding Impact Prefrontal Cortex Activity? An Algorithmic Decoding Approach in Freely Moving Rats.

eNeuro
Specialized brain structures encode spatial locations and movements, yet there is growing evidence that this information is also represented in the rodent medial prefrontal cortex (mPFC). Disambiguating such information from the encoding of other typ...

Enhancing the Image Quality via Transferred Deep Residual Learning of Coarse PET Sinograms.

IEEE transactions on medical imaging
Increasing the image quality of positron emission tomography (PET) is an essential topic in the PET community. For instance, thin-pixelated crystals have been used to provide high spatial resolution images but at the cost of sensitivity and manufactu...

Computational Analysis of Cell Dynamics in Videos with Hierarchical-Pooled Deep-Convolutional Features.

Journal of computational biology : a journal of computational molecular cell biology
Computational analysis of cellular appearance and its dynamics is used to investigate physiological properties of cells in biomedical research. In consideration of the great success of deep learning in video analysis, we first introduce two-stream co...

A Dynamic Connectome Supports the Emergence of Stable Computational Function of Neural Circuits through Reward-Based Learning.

eNeuro
Synaptic connections between neurons in the brain are dynamic because of continuously ongoing spine dynamics, axonal sprouting, and other processes. In fact, it was recently shown that the spontaneous synapse-autonomous component of spine dynamics is...

Multiple Machine Learning Based-Chemoinformatics Models for Identification of Histone Acetyl Transferase Inhibitors.

Molecular informatics
The histone acetyl transferase (HAT) are involved in acetylation of histones that lead to transcription activation in numerous gene regulatory mechanisms. There are very few GCN5 HAT inhibitors reported despite of their role in cancer progression. In...

Supervised machine learning reveals introgressed loci in the genomes of Drosophila simulans and D. sechellia.

PLoS genetics
Hybridization and gene flow between species appears to be common. Even though it is clear that hybridization is widespread across all surveyed taxonomic groups, the magnitude and consequences of introgression are still largely unknown. Thus it is cru...

A machine learning based framework to identify and classify long terminal repeat retrotransposons.

PLoS computational biology
Transposable elements (TEs) are repetitive nucleotide sequences that make up a large portion of eukaryotic genomes. They can move and duplicate within a genome, increasing genome size and contributing to genetic diversity within and across species. A...

An evaluation of selected (Q)SARs/expert systems for predicting skin sensitisation potential.

SAR and QSAR in environmental research
Predictive testing to characterise substances for their skin sensitisation potential has historically been based on animal models such as the Local Lymph Node Assay (LLNA) and the Guinea Pig Maximisation Test (GPMT). In recent years, EU regulations, ...

Use of artificial neuronal networks for prediction of the control parameters in the process of anaerobic digestion with thermal pretreatment.

Journal of environmental science and health. Part A, Toxic/hazardous substances & environmental engineering
This article focuses on the analysis of the behavior patterns of the variables involved in the anaerobic digestion process. The objective is to predict the impact factor and the behavior pattern of the variables, i.e., temperature, pH, volatile solid...