C1q/TNF-related protein 14 (CTRP14), also known as C1q-like 1 (C1QL1), is a synaptic protein predominantly expressed in the brain. It plays a critical role in the formation and maintenance of the climbing fiber-Purkinje cell synapses, ensuring that only one single winning climbing fiber from the inferior olivary neuron synapses with the proximal dendrites of Purkinje cells during the early postnatal period. Loss of CTRP14/C1QL1 results in incomplete elimination of supernumerary climbing fibers, leading to multiple persistent climbing fibers synapsing with the Purkinje cells. While this deficit impairs oculomotor learning in adult mice, the impact of CTRP14 deficiency on motor function throughout adulthood has not been examined. Here, we conduct behavioral tests on a constitutive Ctrp14 knockout (KO) mouse model to determine whether CTRP14 is required for motor learning and function in mice across the lifespan. We show that CTRP14 deficiency does not affect grip strength, nor sprint and endurance running, in young and old mice of either sex. We performed accelerated rotarod tests on mice at 6, 12, and 18 months old to assess motor coordination and learning. No significant differences were observed between WT and Ctrp14-KO mice of either sex across the lifespan. Lastly, we performed complex running wheel tests to detect latent motor deficits and found that aged Ctrp14-KO mice have intact motor skills. Despite some limitations of the study, our data suggest that CTRP14 is dispensable for gross motor skills, coordination, and learning throughout adulthood based on the specific tests performed.
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http://dx.doi.org/10.1016/j.physbeh.2025.114799 | DOI Listing |
Neural Netw
December 2024
CAS Key Laboratory of GIPAS, University of Science and Technology of China, Hefei, China; Institute of Artificial Intelligence, Hefei Comprehensive National Science Center, Hefei, China. Electronic address:
In MARL (Multi-Agent Reinforcement Learning), the trial-and-error learning paradigm based on multiple agents requires massive interactions to produce training samples, significantly increasing both the training cost and difficulty. Therefore, enhancing data efficiency is a core issue in MARL. However, in the context of MARL, agent partially observed information leads to a lack of consideration for agent interactions and coordination from an ego perspective under the world model, which becomes the main obstacle to improving the data efficiency of current proposed MARL methods.
View Article and Find Full Text PDFCurr Biol
January 2025
Department of Neurobiology, School of Biological Sciences, University of California, San Diego, La Jolla, CA 92093, USA. Electronic address:
As soon as they can move, animals must learn to avoid potential predators. New research demonstrates that larval zebrafish can quickly learn to avoid specific predator-like robots in their first week of life, through coordinated noradrenergic and forebrain activity.
View Article and Find Full Text PDFEClinicalMedicine
August 2024
Department of Psychology, University of Cambridge, Cambridge, CB2 3EB, United Kingdom.
Background: Predicting dementia early has major implications for clinical management and patient outcomes. Yet, we still lack sensitive tools for stratifying patients early, resulting in patients being undiagnosed or wrongly diagnosed. Despite rapid expansion in machine learning models for dementia prediction, limited model interpretability and generalizability impede translation to the clinic.
View Article and Find Full Text PDFJ Gen Intern Med
January 2025
VA Palo Alto Cooperative Studies Program Coordinating Center, Palo Alto, CA, USA.
Background: Advances in artificial intelligence and machine learning have facilitated the creation of mortality prediction models which are increasingly used to assess quality of care and inform clinical practice. One open question is whether a hospital should utilize a mortality model trained from a diverse nationwide dataset or use a model developed primarily from their local hospital data.
Objective: To compare performance of a single-hospital, 30-day all-cause mortality model against an established national benchmark on the task of mortality prediction.
Sci Rep
January 2025
Scientific Affairs Department, Al-Mustaqbal University, Babylon, 51001, Iraq.
This study investigates the application of various neural network-based models for predicting temperature distribution in freeze drying process of biopharmaceuticals. For heat-sensitive biopharmaceutical products, freeze drying is preferred to prevent degradation of pharmaceutical compounds. The modeling framework is based on CFD (Computational Fluid Dynamics) and machine learning (ML).
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