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Biology subjects

Hu, R. K.

Publications and source records attributed to Hu, R. K..

4 recordsLinked to original sources

Tachykinin1-expressing neurons in the parasubthalamic nucleus control active avoidance learning

Active avoidance is a type of instrumental behavior that requires an organism actively to engage in specific actions to avoid or escape from a potentially aversive stimulus and is crucial for the survival and well-being of organisms. It requires a widely distributed, hard-wired neural circuits spanning multiple brain regions, including the amygdala and thalamus. However, less is known about whether and how the hypothalamus encodes and controls active avoidance learning. Here we identify a previously unknown role for the parasubthalamic nucleus (PSTN), located in the lateral subdivision of the posterior hypothalamus, in the encoding and control of active avoidance learning. Fiber photometry calcium imaging shows that the activity of tachykinin1-expressing PSTN (PSTNTac1) neurons progressively increases during this learning. Cell-type specific ablation and optogenetic inhibition of PSTNTac1 neurons attenuates active avoidance learning, whereas optogenetic activation of these cells promotes this learning via a negative motivational drive. Moreover, the PSTN mediates this learning differentially through its downstream targets. Together, this study identifies the PSTN as a new member of the neural networks involved in active avoidance learning and offers us potential implications for therapeutic interventions targeting anxiety disorders and other conditions involving maladaptive avoidance learning.

neuroscience↗

Global Genotype by Environment Prediction Competition Reveals That Diverse Modeling Strategies Can Deliver Satisfactory Maize Yield Estimates

Predicting phenotypes from a combination of genetic and environmental factors is a grand challenge of modern biology. Slight improvements in this area have the potential to save lives, improve food and fuel security, permit better care of the planet, and create other positive outcomes. In 2022 and 2023 the first open-to-the-public Genomes to Fields (G2F) initiative Genotype by Environment (GxE) prediction competition was held using a large dataset including genomic variation, phenotype and weather measurements and field management notes, gathered by the project over nine years. The competition attracted registrants from around the world with representation from academic, government, industry, and non-profit institutions as well as unaffiliated. These participants came from diverse disciplines include plant science, animal science, breeding, statistics, computational biology and others. Some participants had no formal genetics or plant-related training, and some were just beginning their graduate education. The teams applied varied methods and strategies, providing a wealth of modeling knowledge based on a common dataset. The winners strategy involved two models combining machine learning and traditional breeding tools: one model emphasized environment using features extracted by Random Forest, Ridge Regression and Least-squares, and one focused on genetics. Other high-performing teams methods included quantitative genetics, classical machine learning/deep learning, mechanistic models, and model ensembles. The dataset factors used, such as genetics; weather; and management data, were also diverse, demonstrating that no single model or strategy is far superior to all others within the context of this competition.

genetics↗

Improved Version of ChETA Promotes Aggression in the Medial Amygdala

The development of optogenetic tools has significantly advanced our understanding of neural circuits and behavior. The medial amygdala, posterior dorsal subdivision (MeApd) is part of a distributed network controlling social behaviors such as mating and aggression. Previous work showed that activation of GABAergic neurons in mouse MeApd using channelrodopsin-2 (ChR2H134R) promoted aggression. In a recent study, Baleisyte et al. (2022) confirmed these findings using the same reagents (i.e. ChR2H134R), but also reported that a different ChR2 variant with faster kinetics--ChETA--inhibited rather than promoted aggression when high laser power, long duration photostimulation conditions were used. As ChETA is known to have a substantially lower photocurrent than ChR2 and other opsins, an improved version of ChETA (i.e. ChR2E123T/T159C; ChETATC) was subsequently developed. ChETATC has larger photocurrents than the original ChETA while maintaining fast kinetics and low plateau depolarization. Here we show that activating MeApd GABAergic neurons using the improved ChETATC promotes aggression, similar to ChR2H134R, suggesting that the results obtained using the original ChETA are not due to a difference in channel kinetics. Furthermore, we found that ChETATC is capable of driving a rapid onset of aggression within 200-300 milliseconds of stimulation, suggesting that this effect reflects direct activation of MeApd GABAergic neurons. We conclude that the different behavioral phenotypes observed using the original ChETA vs. ChETATC and ChR2 likely reflects the weaker photocurrents in ChETA vs. other opsins, and/or the long duration/high power photostimulation conditions used with ChETA. Consistent with this conclusion, the results obtained using ChR2 or ChETATC are complementary to findings from loss-of-functions experiments using optogenetic inhibition, chemogenetic inhibition, and neuronal ablation. These data support a positive-acting role of MeApd Vgat+ neurons in aggression. Our findings, in conjunction with studies of Berndt et al. (2011), suggest that the improved ChETATC should be used when faster kinetics than ChR2 offers are required.

neuroscience↗

Cell Layers: Uncovering clustering structure and knowledge in unsupervised single-cell transcriptomic analysis

MotivationUnsupervised clustering of single-cell transcriptomics is a powerful method for identifying cell populations. Static visualization techniques for single-cell clustering only display results for a single resolution parameter. Analysts will often evaluate more than one resolution parameter, but then only report one. ResultsWe developed Cell Layers, an interactive Sankey tool for the quantitative investigation of gene expression, coexpression, biological processes, and cluster integrity across clustering resolutions. Cell Layers enhances the interpretability of single-cell clustering by linking molecular data and cluster evaluation metrics, to provide novel insight into cell populations. Availability and implementationUpon request

bioinformatics↗