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Quantifying influence of human choice on the automated detection of Drosophila behavior by a supervised machine learning algorithm.

PloS one
Automated quantification of behavior is increasingly prevalent in neuroscience research. Human judgments can influence machine-learning-based behavior classification at multiple steps in the process, for both supervised and unsupervised approaches. S...

Decentralized control of insect walking: A simple neural network explains a wide range of behavioral and neurophysiological results.

PLoS computational biology
Controlling the six legs of an insect walking in an unpredictable environment is a challenging task, as many degrees of freedom have to be coordinated. Solutions proposed to deal with this task are usually based on the highly influential concept that...

Deep learning for automated detection of Drosophila suzukii: potential for UAV-based monitoring.

Pest management science
BACKGROUND: The fruit fly Drosophila suzukii, or spotted wing drosophila (SWD), is a serious pest worldwide, attacking many soft-skinned fruits. An efficient monitoring system that identifies and counts SWD in crops and their surroundings is therefor...

Fully Automated Leg Tracking in Freely Moving Insects using Feature Learning Leg Segmentation and Tracking (FLLIT).

Journal of visualized experiments : JoVE
The Drosophila model has been invaluable for the study of neurological function and for understanding the molecular and cellular mechanisms that underlie neurodegeneration. While fly techniques for the manipulation and study of neuronal subsets have ...

Intelligent image-based deformation-assisted cell sorting with molecular specificity.

Nature methods
Although label-free cell sorting is desirable for providing pristine cells for further analysis or use, current approaches lack molecular specificity and speed. Here, we combine real-time fluorescence and deformability cytometry with sorting based on...

Learning Retention Mechanisms and Evolutionary Parameters of Duplicate Genes from Their Expression Data.

Molecular biology and evolution
Learning about the roles that duplicate genes play in the origins of novel phenotypes requires an understanding of how their functions evolve. A previous method for achieving this goal, CDROM, employs gene expression distances as proxies for function...

Predicting individual neuron responses with anatomically constrained task optimization.

Current biology : CB
Artificial neural networks trained to solve sensory tasks can develop statistical representations that match those in biological circuits. However, it remains unclear whether they can reproduce properties of individual neurons. Here, we investigated ...

Deep-learning on-chip light-sheet microscopy enabling video-rate volumetric imaging of dynamic biological specimens.

Lab on a chip
Volumetric imaging of dynamic signals in a large, moving, and light-scattering specimen is extremely challenging, owing to the requirement on high spatiotemporal resolution and difficulty in obtaining high-contrast signals. Here we report that throug...

Deciphering enhancer sequence using thermodynamics-based models and convolutional neural networks.

Nucleic acids research
Deciphering the sequence-function relationship encoded in enhancers holds the key to interpreting non-coding variants and understanding mechanisms of transcriptomic variation. Several quantitative models exist for predicting enhancer function and und...