De novo learning versus adaptation of continuous control in a manual tracking task.
Ontology highlight
ABSTRACT:
SUBMITTER: Yang CS
PROVIDER: S-EPMC8266385 | biostudies-literature |
REPOSITORIES: biostudies-literature
Similar Datasets
Project description:Corticostriatal regions play a pivotal role in visuomotor learning. However, less research has been done on how fMRI activity in their subregions is related to task performance, which is provided as visual feedback during motor learning. To address this, we conducted an fMRI experiment in which participants acquired a complex de novo motor skill using continuous or binary visual feedback related to performance. We found a highly selective response related to performance in the entire striatum in both conditions and a relatively higher response in the caudate nucleus for the binary feedback condition. However, the ventromedial prefrontal cortex (vmPFC) response was significant only for the continuous feedback condition. Furthermore, we also found functional distinction of the striatal subregions in random versus goal-directed motor control. These findings underscore the substantial effects of the visual feedback indicating performance on distinct corticostriatal responses, thereby elucidating its significance in reinforcement-based motor learning.
| S-EPMC10867026 | biostudies-literature
Project description:Active exploration of novel spatial environments enhances memory for subsequently presented explicit, declarative information in humans. These effects have been attributed to novelty promoting dopamine release via mesolimbic dopaminergic pathways in the brain. As procedural motor learning has been linked to dopamine as well, we predict that novelty effects extend to this domain. To test this hypothesis, the present study examined whether spatial novelty exploration benefits subsequent sensorimotor adaptation. Participants explored either two different virtual environments (i.e., novelty condition; n = 210) or two identical environments (i.e., familiar condition; n = 253). They then performed a manual adaptation task in which they had to adapt joystick movements to a visual perturbation. We assessed the rate of adaptation following the introduction of this perturbation, and the rate of deadaptation following its removal. While results showed reliable adaptation patterns and similar adaptation rates across both conditions, individuals in the novelty condition showed slower deadaptation. This suggests that exposure to spatial novelty induced stronger sensorimotor representations during adaptation, potentially through novelty-induced dopaminergic effects in mesocortical and/or nigrostriatal pathways. Novelty exposure may be employed to promote motor learning on tasks that require precision movements in altered sensory contexts, for example, in astronauts moving in microgravity or patients with impaired motor processing.
| S-EPMC9305111 | biostudies-literature
Project description:Illumina sequencing has revolutionized yeast genomics, with prices for commercial draft genome sequencing now below $200. The popular SPAdes assembler makes it simple to generate a de novo genome assembly for any yeast species. However, whereas making genome assemblies has become routine, understanding what they contain is still challenging. Here, we show how graphing the information that SPAdes provides about the length and coverage of each scaffold can be used to investigate the nature of an assembly, and to diagnose possible problems. Scaffolds derived from mitochondrial DNA, ribosomal DNA, and yeast plasmids can be identified by their high coverage. Contaminating data, such as cross-contamination from other samples in a multiplex sequencing run, can be identified by its low coverage. Scaffolds derived from the bacteriophage PhiX174 and Lambda DNAs that are frequently used as molecular standards in Illumina protocols can also be detected. Assemblies of yeast genomes with high heterozygosity, such as interspecies hybrids, often contain two types of scaffold: regions of the genome where the two alleles assembled into two separate scaffolds and each has a coverage level C, and regions where the two alleles co-assembled (collapsed) into a single scaffold that has a coverage level 2C Visualizing the data with Coverage-vs.-Length (CVL) plots, which can be done using Microsoft Excel or Google Sheets, provides a simple method to understand the structure of a genome assembly and detect aberrant scaffolds or contigs. We provide a Python script that allows assemblies to be filtered to remove contaminants identified in CVL plots.
| S-EPMC6404606 | biostudies-other
Project description:Time-lapse microscopy is routinely used to follow cells within organoids, allowing direct study of division and differentiation patterns. There is an increasing interest in cell tracking in organoids, which makes it possible to study their growth and homeostasis at the single-cell level. As tracking these cells by hand is prohibitively time consuming, automation using a computer program is required. Unfortunately, organoids have a high cell density and fast cell movement, which makes automated cell tracking difficult. In this work, a semi-automated cell tracker has been developed. To detect the nuclei, we use a machine learning approach based on a convolutional neural network. To form cell trajectories, we link detections at different time points together using a min-cost flow solver. The tracker raises warnings for situations with likely errors. Rapid changes in nucleus volume and position are reported for manual review, as well as cases where nuclei divide, appear and disappear. When the warning system is adjusted such that virtually error-free lineage trees can be obtained, still less than 2% of all detected nuclei positions are marked for manual analysis. This provides an enormous speed boost over manual cell tracking, while still providing tracking data of the same quality as manual tracking.
| S-EPMC7580893 | biostudies-literature
Project description:De novo peptide sequencing from tandem MS data is the key technology in proteomics for the characterization of proteins, especially for new sequences, such as mAbs. In this study, we propose a deep neural network model, DeepNovo, for de novo peptide sequencing. DeepNovo architecture combines recent advances in convolutional neural networks and recurrent neural networks to learn features of tandem mass spectra, fragment ions, and sequence patterns of peptides. The networks are further integrated with local dynamic programming to solve the complex optimization task of de novo sequencing. We evaluated the method on a wide variety of species and found that DeepNovo considerably outperformed state of the art methods, achieving 7.7-22.9% higher accuracy at the amino acid level and 38.1-64.0% higher accuracy at the peptide level. We further used DeepNovo to automatically reconstruct the complete sequences of antibody light and heavy chains of mouse, achieving 97.5-100% coverage and 97.2-99.5% accuracy, without assisting databases. Moreover, DeepNovo is retrainable to adapt to any sources of data and provides a complete end-to-end training and prediction solution to the de novo sequencing problem. Not only does our study extend the deep learning revolution to a new field, but it also shows an innovative approach in solving optimization problems by using deep learning and dynamic programming.
| S-EPMC5547637 | biostudies-literature
Project description:The visual system optimizes its functioning for a given environment through processes collectively called adaptation. It is currently unknown, however, whether adaptation is affected by the particular task the observer performs within that environment. Two experiments tested whether this is the case. Observers adapted to high contrast grating patterns, and the decay of adaptation was measured using a version of the tilt-aftereffect, while they performed two different secondary tasks. One task involved judging the luminance of a small circular spot at fixation, and was expected to be unaffected by adaptation. The other secondary task involved judging a low contrast grating, and adaptation was expected to make this task difficult by reducing the visibility of the grating. Identical displays containing both a fixation spot and a grating were used for both tasks. Tilt-aftereffects were smaller when subjects concurrently performed the grating task than when they performed the fixation task. These results suggest that the control of adaptation, in this case its decay, is sensitive to the nature of the task the observer is performing. Adaptation may attempt to optimize vision with respect to many different criteria simultaneously; task is likely one of the criteria included in this process.
| S-EPMC7046198 | biostudies-literature
Project description:The majority of subjects who attempt to learn control of a brain-computer interface (BCI) can do so with adequate training. Much like when one learns to type or ride a bicycle, BCI users report transitioning from a deliberate, cognitively focused mindset to near automatic control as training progresses. What are the neural correlates of this process of BCI skill acquisition? Seven subjects were implanted with electrocorticography (ECoG) electrodes and had multiple opportunities to practice a 1D BCI task. As subjects became proficient, strong initial task-related activation was followed by lessening of activation in prefrontal cortex, premotor cortex, and posterior parietal cortex, areas that have previously been implicated in the cognitive phase of motor sequence learning and abstract task learning. These results demonstrate that, although the use of a BCI only requires modulation of a local population of neurons, a distributed network of cortical areas is involved in the acquisition of BCI proficiency.
| S-EPMC3696802 | biostudies-other
Project description:This work introduces a method to tune a sequence-based generative model for molecular de novo design that through augmented episodic likelihood can learn to generate structures with certain specified desirable properties. We demonstrate how this model can execute a range of tasks such as generating analogues to a query structure and generating compounds predicted to be active against a biological target. As a proof of principle, the model is first trained to generate molecules that do not contain sulphur. As a second example, the model is trained to generate analogues to the drug Celecoxib, a technique that could be used for scaffold hopping or library expansion starting from a single molecule. Finally, when tuning the model towards generating compounds predicted to be active against the dopamine receptor type 2, the model generates structures of which more than 95% are predicted to be active, including experimentally confirmed actives that have not been included in either the generative model nor the activity prediction model. Graphical abstract .
| S-EPMC5583141 | biostudies-literature
Project description:We have devised and implemented a novel computational strategy for de novo design of molecules with desired properties termed ReLeaSE (Reinforcement Learning for Structural Evolution). On the basis of deep and reinforcement learning (RL) approaches, ReLeaSE integrates two deep neural networks-generative and predictive-that are trained separately but are used jointly to generate novel targeted chemical libraries. ReLeaSE uses simple representation of molecules by their simplified molecular-input line-entry system (SMILES) strings only. Generative models are trained with a stack-augmented memory network to produce chemically feasible SMILES strings, and predictive models are derived to forecast the desired properties of the de novo-generated compounds. In the first phase of the method, generative and predictive models are trained separately with a supervised learning algorithm. In the second phase, both models are trained jointly with the RL approach to bias the generation of new chemical structures toward those with the desired physical and/or biological properties. In the proof-of-concept study, we have used the ReLeaSE method to design chemical libraries with a bias toward structural complexity or toward compounds with maximal, minimal, or specific range of physical properties, such as melting point or hydrophobicity, or toward compounds with inhibitory activity against Janus protein kinase 2. The approach proposed herein can find a general use for generating targeted chemical libraries of novel compounds optimized for either a single desired property or multiple properties.
| S-EPMC6059760 | biostudies-literature
Project description:De novo enzyme design has sought to introduce active sites and substrate-binding pockets that are predicted to catalyse a reaction of interest into geometrically compatible native scaffolds1,2, but has been limited by a lack of suitable protein structures and the complexity of native protein sequence-structure relationships. Here we describe a deep-learning-based 'family-wide hallucination' approach that generates large numbers of idealized protein structures containing diverse pocket shapes and designed sequences that encode them. We use these scaffolds to design artificial luciferases that selectively catalyse the oxidative chemiluminescence of the synthetic luciferin substrates diphenylterazine3 and 2-deoxycoelenterazine. The designed active sites position an arginine guanidinium group adjacent to an anion that develops during the reaction in a binding pocket with high shape complementarity. For both luciferin substrates, we obtain designed luciferases with high selectivity; the most active of these is a small (13.9 kDa) and thermostable (with a melting temperature higher than 95 °C) enzyme that has a catalytic efficiency on diphenylterazine (kcat/Km = 106 M-1 s-1) comparable to that of native luciferases, but a much higher substrate specificity. The creation of highly active and specific biocatalysts from scratch with broad applications in biomedicine is a key milestone for computational enzyme design, and our approach should enable generation of a wide range of luciferases and other enzymes.
| S-EPMC9946828 | biostudies-literature