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Multiple Changes in Connectivity Between Buccal Ganglia Mechanoafferents and Motor Neurons with Different Functions After Learning That Food is Inedible in Aplysia
No full textChanges caused by learning that a food is inedible in Aplysia were examined for fast and slow synaptic connections from the buccal ganglia S1 cluster of mechanoafferents to five followers, in response to repeated stimulus trains. Learning affected only fast connections. For these, unique patterns of change were present in each follower, indicating that learning differentially affects the different branches of the mechanoafferents to their followers. In some followers, there were increases in either excitatory or inhibitory connections, and in others, there were decreases. Changes in connectivity resulted from changes in the amplitude of excitation or inhibition, or as a result of the number of connections, or of both. Some followers also exhibited changes in either within or between stimulus train plasticity as a result of learning. In one follower, changes differed from the different areas of the S1 cluster. The patterns of changes in connectivity were consistent with the behavioral changes produced by learning, in that they would produce an increase in the bias to reject or to release food, and a decrease in the likelihood to respond to food
Scaling of Buccal Mass Growth and Muscle Activation Determine the Duration of Feeding Behaviours in the Marine Mollusc Aplysia Californica
No full textThe mechanical forces experienced during movement and the time constants of muscle activation are important determinants of the durations of behaviours, which may both be affected by size-dependent scaling. The mechanics of slow movements in small animals are dominated by elastic forces and are thus quasistatic (i.e. always near mechanical equilibrium). Muscular forces producing movement and elastic forces resisting movement should scale identically (proportional to mass2/3), leaving the scaling of the time constant of muscle activation to play a critical role in determining behavioural duration. We tested this hypothesis by measuring the duration of feeding behaviours in the marine mollusc Aplysia californica whose body sizes spanned three orders of magnitude. The duration of muscle activation was determined by measuring the time it took for muscles to produce maximum force as A. californica attempted to feed on tethered inedible seaweed, which provided an in vivo approximation of an isometric contraction. The timing of muscle activation scaled with mass0.3. The total duration of biting behaviours scaled identically, with mass0.3, indicating a lack of additional mechanical effects. The duration of swallowing behaviour, however, exhibited a shallower scaling of mass0.17. We suggest that this was due to the allometric growth of the anterior retractor muscle during development, as measured by micro-computed tomography (micro-CT) scans of buccal masses. Consequently, larger A. californica did not need to activate their muscles as fully to produce equivalent forces. These results indicate that muscle activation may be an important determinant of the scaling of behavioural durations in quasistatic systems
Electronic Relaxation Pathways in Thio-Acridone and Thio-Coumarin: Two Heavy-Atom-Free Photosensitizers Absorbing Visible Light
Get PDFHeavy-atom-free photosensitizers (HAF-PSs) have emerged as a new class of photosensitizers aiming to broaden their applicability and versatility across various fields of the photodynamic therapy of cancers. The strategy involves replacing the exocyclic oxygen atoms of the carbonyl groups of established biocompatible organic fluorophores with sulfur, thereby bathochromically shifting their absorption spectra and enhancing their intersystem crossing efficiencies. Despite these advancements, the photophysical attributes and electronic relaxation mechanisms of many of these HAF-PSs remain inadequately elucidated. In this study, we investigate the excited state dynamics and photochemical properties of two promising HAF-PSs, thio-coumarin and thio-acridone. Employing a combination of steady-state and time-resolved techniques from femtoseconds to microseconds, coupled with quantum chemical calculations, we unravel the electronic relaxation mechanisms that give rise to the efficient population of long-lived and reactive triplet states in these HAF-PSs
A Simple and Fast Optical Clearing Method for Whole-Mount Fluorescence In Situ Hybridization (FISH) Imaging
Get PDFWe report a single-step optical clearing method that is compatible with RNA fluorescence in situ hybridization (FISH) imaging. We previously demonstrated microscopy imaging with immunohistochemistry and genetic reporters using a technique called lipid-preserving refractive index matching for prolonged imaging depth (LIMPID). Our protocol reliably produces high-resolution three-dimensional (3D) images with minimal aberrations using high magnification objectives, captures large field-of-view images of whole-mount tissues, and supports co-labeling with antibody and FISH probes. We also custom-designed FISH probes for quail embryos, demonstrating the ease of fabricating probes for use with less common animal models. Furthermore, we show high-quality 3D images using a conventional fluorescence microscope, without using more advanced depth sectioning instruments such as confocal or light-sheet microscopy. For broader adoption, we simplified and optimized 3D-LIMPID-FISH to minimize the barrier to entry, and we provide a detailed protocol to aid users with navigating the thick and thin of 3D microscopy
A General Materials Data Science Framework for Quantitative 2D Analysis of Particle Growth from Image Sequences
Get PDFPhase transformations are a challenging problem in materials science, which lead to changes in properties and may impact performance of material systems in various applications. We introduce a general framework for the analysis of particle growth kinetics by utilizing concepts from machine learning and graph theory. As a model system, we use image sequences of atomic force microscopy showing the crystallization of an amorphous fluoroelastomer film. To identify crystalline particles in an amorphous matrix and track the temporal evolution of the particle dispersion, we have developed quantitative methods of 2D analysis. 700 image sequences were analyzed using a neural network architecture, achieving 0.97 pixel-wise classification accuracy as a measure of the correctly classified pixels. The growth kinetics of isolated and impinged particles were tracked throughout time using these image sequences. The relationship between image sequences and spatiotemporal graph representations was explored to identify the proximity of crystallites from each other. The framework enables the analysis of all image sequences without the requirement of sampling for specific particles or timesteps for various materials systems
Exploring 2D X-ray Diffraction Phase Fraction Analysis with Convolutional Neural Networks: Insights from Kinematic-Diffraction Simulations
Get PDFDeep-learning models are effective for analyzing the complex information in 2D X-ray diffraction (XRD) patterns. Accurately collecting parameters of the material sample is crucial during model training, significantly impacting model performance. In this study, we employ a kinematic-diffraction simulator to generate simulated 2D XRD patterns for Ti–6Al–4V alloy, allowing precise control of sample parameters. These simulated patterns are used to train convolutional neural networks, predicting β-phase volume fractions. The training data set consists exclusively of 2D XRD patterns with pure α- or pure β-phase, while the testing set incorporates patterns with intermediate phase volume fraction. In particular, we investigate how the architectures of the model influence prediction reliability and computational performance. Experimental results reveal that, with appropriate training, the convolutional neural network accurately detects intermediate phase volume fractions even trained with only pure-phase patterns, achieving a mean square error accuracy of 9.4×10-4. Graphical abstract: (Figure presented.)
Integrating Multiscale Geospatial Analysis for Monitoring Crop Growth, Nutrient Distribution, and Hydrological Dynamics in Large-Scale Agricultural Systems
Get PDFMonitoring crop growth, soil conditions, and hydrological dynamics are imperative for sustainable agriculture and reduced environmental impacts. This interdisciplinary study integrates remote sensing, digital soil mapping, and hydrological data to elucidate intricate connections between these factors in the state of Ohio, USA. Advanced spatiotemporal analysis techniques were applied to key datasets, including the MODIS sensor satellite imagery, USDA crop data, soil datasets, Aster GDEM, and USGS stream gauge measurements. Vegetation indices derived from MODIS characterized crop-specific phenology and productivity patterns. Exploratory spatial data analysis show relationships of vegetation dynamics and soil properties, uncovering links between plant vigor, edaphic fertility, and nutrient distributions. Correlation analysis quantified these relationships and their seasonal evolution. Examination of stream gauge data revealed insights into spatiotemporal relationships of nutrient pollution and stream discharge. By synthesizing diverse geospatial data through cutting-edge data analytics, this work illuminated complex interactions between crop health, soil nutrients, and water quality in Ohio. The methodology and findings provide actionable perspectives to inform sustainable agricultural management and environmental policy. This study demonstrates the significant potential of open geospatial resources when integrated using a robust spatiotemporal framework. Integrating additional measurements and high-resolution data sources through advanced analytics and interactive visualizations could strengthen these insights
A Magnetic-Directed Micro-Particle with Near-Ir Light Triggered Guest-Release Property
No full textIn this study, guest carriers, composed of magnetic-directed hydrogel particles with near-infrared (NIR)-light-triggered guest-release properties, are prepared via the one-pot method. The gel particles contain photostable NIR-responsive polypyrrole nanoparticles with temperature-sensitive poly(N-isopropyl acrylamide), the combination of which can induce the accelerated release of encapsulated drugs by 337% upon 5 minutes of NIR light irradiation based on the release profile of NRMG-1 at 10–15 min. Additionally, with magnetic iron oxide particles crosslinking the polyvinyl alcohol frames, these gel particles can form a physical barrier that decreases the release rate by 20% under an external magnetic field over 24 h. The magnetic iron oxide particles also enable the gel particles to reach their target destination without extra drug losses
3D MR Fingerprinting for Dynamic Contrast-Enhanced Imaging of Whole Mouse Brain
Get PDFPurpose: Quantitative MRI enables direct quantification of contrast agent concentrations in contrast-enhanced scans. However, the lengthy scan times required by conventional methods are inadequate for tracking contrast agent transport dynamically in mouse brain. We developed a 3D MR fingerprinting (MRF) method for simultaneous T1 and T2 mapping across the whole mouse brain with 4.3-min temporal resolution. Method: We designed a 3D MRF sequence with variable acquisition segment lengths and magnetization preparations on a 9.4T preclinical MRI scanner. Model-based reconstruction approaches were employed to improve the accuracy and speed of MRF acquisition. The method\u27s accuracy for T1 and T2 measurements was validated in vitro, while its repeatability of T1 and T2 measurements was evaluated in vivo (n = 3). The utility of the 3D MRF sequence for dynamic tracking of intracisternally infused gadolinium-diethylenetriamine pentaacetic acid (Gd-DTPA) in the whole mouse brain was demonstrated (n = 5). Results: Phantom studies confirmed accurate T1 and T2 measurements by 3D MRF with an undersampling factor of up to 48. Dynamic contrast-enhanced MRF scans achieved a spatial resolution of 192 × 192 × 500 μm3 and a temporal resolution of 4.3 min, allowing for the analysis and comparison of dynamic changes in concentration and transport kinetics of intracisternally infused Gd-DTPA across brain regions. The sequence also enabled highly repeatable, high-resolution T1 and T2 mapping of the whole mouse brain (192 × 192 × 250 μm3) in 30 min. Conclusion: We present the first dynamic and multi-parametric approach for quantitatively tracking contrast agent transport in the mouse brain using 3D MRF
A Framework for Building Comprehensive Cancer Center’s Capacity for Bidirectional Engagement
Get PDFPurpose: Community engagement has benefits for cancer centers’ work and for its researchers. This study examined the experiences and perceptions of community engagement by members of the Case Comprehensive Cancer Center (Case CCC) to create and implement a framework to meet the needs of the entire cancer center. Methods: This study included three phases: 1) Semi-structured interviews with 12 researchers from a basic science program to identify needs and suggestions for the support of community engagement; 2) Preliminary interview results informed the development of a survey of 86 cancer center members’ about their awareness of and readiness to integrate community outreach and engagement into their research; and 3) The Case CCC Office of Community Outreach and Engagement reviewed the results from phases 1 and 2 to develop and then utilize a framework of engagement opportunities. Results: In the interviews and surveys, cancer center members recognized the importance of community engagement and expressed an interest in participating in COE-organized opportunities for bidirectional engagement. While participation barriers include communication issues, limited awareness of opportunities, and competing priorities, members were open to learning new skills, changing approaches, and utilizing services to facilitate engagement. The framework outlines engagement opportunities ranging from high touch, low reach to low touch, and high reach and was used to develop specific services. Conclusion: This study identified varying needs around community engagement using an approach aimed at understanding the perspectives of a community of scientists. Implementing the framework enables reaching scientists in different ways and facilitates scientists’ recognition of and engagement with opportunities
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