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In many brain areas, neuronal activity is associated with a variety of behavioral and environmental variables. In particular, neuronal responses in the zebrafish hindbrain relate to oculomotor and swimming variables as well as sensory information. However, the precise functional organization of the neurons has been difficult to unravel because neuronal responses are heterogeneous. Here, we used dimensionality reduction methods on neuronal population data to reveal the role of the hindbrain in visually driven oculomotor behavior and swimming. We imaged neuronal activity in zebrafish expressing GCaMP6s in the nucleus of almost all neurons while monitoring the behavioral response to gratings that rotated with different speeds. We then used reduced-rank regression, a method that condenses the sensory and motor variables into a smaller number of "features," to predict the fluorescence traces of all ROIs (regions of interest). Despite the potential complexity of the visuo-motor transformation, our analysis revealed that a large fraction of the population activity can be explained by only two features. Based on the contribution of these features to each ROI's activity, ROIs formed three clusters. One cluster was related to vergent movements and swimming, whereas the other two clusters related to leftward and rightward rotation. Voxels corresponding to these clusters were segregated anatomically, with leftward and rightward rotation clusters located selectively to the left and right hemispheres, respectively. Just as described in many cortical areas, our analysis revealed that single-neuron complexity co-exists with a simpler population-level description, thereby providing insights into the organization of visuo-motor transformations in the hindbrain.
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http://dx.doi.org/10.1016/j.cub.2023.08.037 | DOI Listing |
Arq Gastroenterol
September 2025
The Japanese Society of Internal Medicine, Editorial Department, Tokyo, Japan.
Background: This study aims to analyze research trends and emerging insights into gut microbiota studies from 2015 to 2024 through bibliometric analysis techniques. By examining bibliographic data from the Web of Science (WoS) Core Collection, it seeks to identify key research topics, evolving themes, and significant shifts in gut microbiota research. The study employs co-occurrence analysis, principal component analysis (PCA), and burst detection analysis to uncover latent patterns and the development trajectory of this rapidly expanding field.
View Article and Find Full Text PDFPLoS One
September 2025
State Key Laboratory of Precision Blasting Engineering, Jianghan University, Wuhan, Hubei, PR China.
Numerous parameters influence the slotting performance of slotted cartridge, to facilitate rapid, efficient, and accurate predictions of the slitting performance, statistical analysis of PMMA blasting experiments with six different slitted cartridge parameters yielded 12 evaluation indicators. Subsequently, a principal component analysis (PCA) method was introduced to reduce the dimensionality of the data associated with these indicators, and three new comprehensive indicators were extracted for a comprehensive assessment of the slotting performance. The PCA scores ranked the influence of the six slotted cartridge parameters on slotting performance as follows: decoupling coefficient, slotting width, slotting angle, slotting tube thickness, slotting tube material, and charge amount.
View Article and Find Full Text PDFPLoS One
September 2025
Department of Computer Science, Osun State University, Osogbo, Nigeria.
Probabilistic Random Forest is an extension of the traditional Random Forest machine learning algorithm that is one of the frequently used machine learning algorithms employed for species distribution modeling. However, with the use of complex dataset for predicting the presence or absence of the species, It is essential that feature extraction is important to generate optimal prediction that can affect the model accuracy and AUC score of the model simulation. In this paper, we integrated the Genetic Algorithm Optimization technique, which is popular for its excellent feature extraction technique, to enhance the predictive performance of the PRF Model.
View Article and Find Full Text PDFAngew Chem Int Ed Engl
September 2025
Department of Chemistry, Inorganic Chemistry Laboratory, University of Oxford, South Parks Road, Oxford, OX1 3QR, UK.
Topochemical reduction of the n = 2 Ruddlesden-Popper oxide, LaSrCoRuO, yields LaSrCoRuO, a phase containing (Co/Ru)O squares which share corners to form 1D infinite double-chains. In contrast, fluorination of LaSrCoRuO yields the oxyfluoride LaSrCoRuOF, which can then be reduced to form LaSrCoRuOF. This reduced oxyfluoride is almost isoelectronic with LaSrCoRuO, but LaSrCoRuOF has a crystal structure in which the (Co/Ru)O squares are connected into 2D infinite sheets.
View Article and Find Full Text PDFPLoS One
September 2025
Department of Economics, Cornell University, Ithaca, United States of America.
In this paper, we study the impact of momentum, volume and investor sentiment on U.S. tech sector stock returns using Principal Component Analysis-Hidden Markov Model (PCA-HMM) methodology.
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