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Characterize Endogenous Expression Patterns of Ghrelin Receptor in the Brain of Novel Reporter Mouse Line
No full textGhrelin, acting through its receptor growth hormone secretagogue receptor (GHS-R), is an important energy sensor and metabolic regulator. However, the regulatory mechanisms of ghrelin signaling are largely unknown due to the limited knowledge in the sites of expression of GHS-R. Due to the absence of a specific antibody for GHS-R, the study of GHS-R expression has been limited to RNA level by in situ and transgenic reporter. In this research, GHS-R expression is investigated using GFP-Ghsr reporter mice, where GFP reporter is integrated into endogenous Ghsr gene; thus, GFP expression precisely correlates with endogenous GHS-R expression. Immunohistochemistry and immunofluorescence staining is used to identify expression sites of GHS-R. Images were obtained using light microscopy and confocal microscopy, and detailed image analysis was performed. These approaches enabled us to map the precise expression patterns of endogenous GHS-R, which helped shed light on the sites of action of ghrelin. Understanding the expression pattern of GHS-R can help researchers expand their research on the role of ghrelin in obesity and insulin resistance
Fitting Multilayer Perceptrons to Hill's Muscle Models
Get PDFThe accurate modeling of muscle forces is of critical importance in biomechanics, impacting fields ranging from sports science to animation and robotics. Traditional and widely used models, such as Hill's Muscle Model (HMM), while foundational, can be computationally expensive when representing the complex dynamics of muscle forces. In some cases, this gap may hinder the usage of muscle function in realtime applications. As such, enhancing the efficiency of muscle force models��� computation is effective for advancing practical applications in biomechanics and related disciplines. This research introduces a new approach by integrating a machine learning model called Multilayer Perceptrons (MLPs) with HMM to fit each of its key component curves and their first derivate: Passive Force, Active Force, Force Velocity, and Tendon Force. This dual strategy leverages the capabilities of machine learning to achieve a higher level of computational efficiency in modeling muscle forces, highlighting a new application of MLPs in biomechanical analysis. The methodology involves the use of various MLP architectures, optimized through extensive hyperparameter tuning, to model the specific dynamics of muscle forces efficiently while maintaining its accuracy. The findings showcase different combinations of hyperparameters that demonstrate varying levels of accuracy, depending on whether the priority is a good representation of the function f or its derivative f'. This analysis underscores the nuanced trade-offs in model performance based on different objectives, and the study demonstrates the effectiveness of integrating machine learning techniques with biomechanical modeling, suggesting a promising direction for enhancing muscle force models
Land Cover Data Set
No full textThis data set is developed by the Institute of Geographical Sciences and Natural Resources Research (IGSNRR, http://www.igsnrr.ac.cn) of the Chinese Academy of Sciences (CAS). It includes land cover raster maps of China produced based on the digital images of AVHRR and the land use raster map of China with the Landsat TM/ETM digital images as the main data sources (see reference literature below). Access to the released data will be restricted to TAMU employees, as well as official visiting scholars on campus, using a computer with TAMU IP address only. This data can also be purchased directly from IGSNRR; those interested should contact: Professor Jiyuan LIU, Director General, Institute of Geographical Sciences and Natural Resources Research, Chinese Academy of Sciences Building 917, Datun Road, Anwai, Beijing 100101, P.R.China. Tel:+86 10 6488 9281 Fax:+86 10 6485 1844. Email: [email protected] Land Use and Land Cover Change data is accessible only from the campus network of Texas A&M University.Provider of LUCC dataset: Liu, J., Liu M., Deng X., Zhuang D., Zhang Z., and Luo D. 2002. The Land-use and land-cover change database and its relative studies in China. Journal of Geographical Sciences 12(3): 275-282.Provider of LUCC metadata: Liu, J., Liu M., Zhuang D., Zhang Z., and Deng X. 2003. Study on spatial pattern of land-use change in China during 1995-2000. Science in China (D) 46(4): 373-384.Provider of LUCC dataset: Liu, J., Zhuang D., Luo D., and Xiao, X. 2003. Land-cover Classification of China: Integrated Analysis of AVHRR Imagery and Geo-physical Data. International Journal of Remote Sensing 24(12): 2485-2500
The Coupled Effect of the Pore Angularity and Wettability on the Dynamics of Immiscible Displacement
Get PDFIn many natural and engineering applications, the dynamics of immiscible displacement in porous media plays a critical role, including infiltration of rainwater into the soils, CO2 geosequestration, enhanced oil recovery, fuel cells, microfluidics, and printing. While the impact of wettability on immiscible displacement is well understood, the interplay of wettability, geometry, and disorder of the porous matrix in the displacement process remains elusive. In this research, the focus will be investigating the pore angularity and wettability's coupled effect on the pore���scale dynamics of immiscible displacement using a computational fluid dynamic modeling approach. The numerical simulations were performed using OpenFoam on several porous media (designed using CAD software) of different pore angularity and viscosity ratio with a wide range of wetting contact angles. Our investigation improves the fundamental understanding of multiphase flow through porous media and paves the way for the interpretation and further investigation of wettability control on multiphase flow in natural and engineered porous media, which often exhibit spatial heterogeneity in pore angularity
Black Fire authors
No full textReferenced in Chapter 4 of the book "Digital Literary Redlining: African American Anthologies, Digital Humanities, and the Canon.
Study on the Embedded Electrotactile Feedback System Augmenting High Cognitive-load and Complex Motor Learning
No full textMotor learning acquires new motor skills through repeated training trials. Intrinsic sensory feedback, such as visual, auditory, and somatosensory, can promote motor learning, but it is often limited by modality mismatches. Augmented visual feedback (AVF) or augmented electrotactile feedback (AEF) can compensate for these limitations by providing knowledge of performance (KP) in real time.
However, AVF would be cognitively demanding to associate with the target motor task, would be prone to an intrinsic visual-proprioceptive mismatch, and would not be available at all times because AVF is often occupied by the motor task itself.
AEF has been shown to be effective in delivering KP and assisting motor learning in a variety of tasks, such as telerobotic pitch, basic piano skills, pseudorandom trajectory tracking, and fine control of grasping. However, it is still unclear if AEF can improve performance even when increasing the difficulties of motor tasks, and whether AEF outperforms AVF in delivering KP.
In this dissertation, a safety-enhanced embedded system is developed to generate AEF, and its efficacy is investigated in high cognitive-load motor tasks, such as bending an elbow accurately and hitting a ball in sports games. In the elbow bending task, AEF reduced angular deviation from the target angle by 11.5��, compared to a reduction of only 3.2�� without AEF. In the ball hitting task, AEF increased the hitting point localization rate from 0.31�� to 0.8��, while AVF showed no difference