Notes

Glyoxalase I-4 characteristics downstream regarding NAC72 in order to regulate downy mould level of resistance within grape vine.
Recommendation algorithms have been leveraged in various ways within visualization systems to assist users as they perform of a range of information tasks. One common focus for these techniques has been the recommendation of content, rather than visual form, as a means to assist users in the identification of information that is relevant to their task context. A wide variety of techniques have been proposed to address this general problem, with a range of design choices in how these solutions surface relevant information to users. This paper reviews the state-of-the-art in how visualization systems surface recommended content to users during users' visual analysis; introduces a four-dimensional design space for visual content recommendation based on a characterization of prior work; and discusses key observations regarding common patterns and future research opportunities.Multiclass contour visualization is often used to interpret complex data attributes in such fields as weather forecasting, computational fluid dynamics, and artificial intelligence. However, effective and accurate representations of underlying data patterns and correlations can be challenging in multiclass contour visualization, primarily due to the inevitable visual cluttering and occlusions when the number of classes is significant. To address this issue, visualization design must carefully choose design parameters to make visualization more comprehensible. With this goal in mind, we proposed a framework for multiclass contour visualization. The framework has two components a set of four visualization design parameters, which are developed based on an extensive review of literature on contour visualization, and a declarative domain-specific language (DSL) for creating multiclass contour rendering, which enables a fast exploration of those design parameters. A task-oriented user study was conducted to assess how those design parameters affect users' interpretations of real-world data. The study results offered some suggestions on the value choices of design parameters in multiclass contour visualization.Label-efficient scene segmentation aims to achieve effective per-pixel classification with reduced labeling effort. Recent approaches for this task focus on leveraging unlabelled images by formulating consistency regularization or pseudo labels for individual pixels. Yet most of these methods ignore the 3D geometric structures naturally conveyed by image scenes, which is free for enhancing training segmentation models with better discrimination of image details. In this work, we present a novel Geometric Structure Refinement (GSR) framework to explicitly exploit the geometric structures of image scenes to enhance the semi-supervised training of segmentation models. In the training phase, we generate initial dense pseudo labels based on fast and coarse annotations, and then utilize the free unsupervised 3D reconstruction of the image scene to calibrate the dense pseudo labels with more reliable details. With the calibrated pseudo groundtruth, we are able to conveniently train any existing image segmentation models without increasing the costs of annotations or modifying the models' architectures. Moreover, we explore different strategies for allocating labeling effort in semi-supervised scene segmentation, and find that a combination of finely-labeled samples and coarsely-labeled samples performs better than the traditional dense-fine only annotations. Extensive experiments on datasets including Cityscapes and KITTI are conducted to evaluate our proposed methods. The results demonstrate that GSR can be easily applied to boost the performance of existing models like PSPNet, DeepLabv3+, etc with reduced annotations. With half of the annotation effort, GSR achieves 99% of the accuracy of its fully supervised state-of-the-art counterparts.Background Prenatal alcohol exposure (PAE) causes behavioral deficits and increases risk of metabolic diseases. Alzheimer's Disease (AD) is a neurodegenerative disease that has a higher risk in adults with metabolic diseases. Both present with persistent neuroinflammation.Objectives We tested whether PAE exacerbates AD-related cognitive decline in a mouse model (3xTg-AD; presenilin/amyloid precursor protein/tau), and assessed associations among cognition, metabolic impairment, and microglial reactivity.Methods Alcohol-exposed (ALC) pregnant 3xTg-AD mice received 3 g/kg alcohol from embryonic day 8.5-17.5. We evaluated recognition memory and associative memory (fear conditioning) in 8-10 males and females per group at 3 months of age (3mo), 7mo, and 11mo, then assessed glucose tolerance, body composition, and hippocampal microglial activation at 12mo.Results ALC females had higher body weights than controls from 5mo (p less then .0001). Controls showed improved recognition memory at 11mo compared with 3mo (p = .007); this was not seen in ALC mice. Older animals froze more during fear conditioning than younger, and ALC mice were hyper-responsive to the fear-related cue (p = .017). Fasting blood glucose was lower in ALC males and higher in ALC females than controls. Positive associations occurred between glucose and fear-related context (p = .04) and adiposity and fear-related cue (p = .0002) in ALC animals. Hippocampal microglial activation was higher in ALC than controls (p less then .0001); this trended to correlate with recognition memory.Conclusions ALC animals showed age-related cognitive impairments that did not interact with AD risk but did correlate with metabolic dysfunction and somewhat with microglial activation. Thus, metabolic disorders may be a therapeutic target for people with FASDs.Ten novel small-molecule fluorophores containing two electron-accepting imidazo[1,2-a]pyridine (ImPy) units are presented. Each ImPy core is functionalized at its C6 position with groups featuring either electron accepting (A) or donating (D) properties, thus providing emitters with general structure X-ImPy-Y-ImPy-X (X=either A or D; Y=phenyl or pyridine). The molecules bear either a phenyl (series 4) or a pyridine (series 5) π bridge that connects the two ImPys via meta (phenyl) or 2,6- (pyridine) positions, yielding an overall V-shaped architecture. The final compounds are synthetized straightforwardly by condensation between substituted 2-aminopyridines and α-halocarbonyl derivatives. All the compounds display intense photoluminescence with quantum yield (PLQY) in the range of 0.17-0.51. Remarkably, substituent effect enables tuning the emission from near-UV to (deep-)blue region while keeping Commission Internationale de l'Éclairage (CIE) y coordinate ≤0.07. The emitting excited state is characterized by a few nanoseconds lifetime and high radiative rate constant, and its nature is modulated from pure π-π* to intramolecular charge transfer (ICT) by the electronic properties of the peripheral X substituent. This is further corroborated by the nature of the frontier orbitals and vertical electronic excitations computed at (time-dependent) density functional level of theory (TD-)DFT. Finally, this study enlarges the palette of bright deep-blue emitters based on the interesting ImPy scaffolds in view of their potential application as photo-functional materials in optoelectronics.Wearable strain sensors have huge potential for applications in healthcare, human-machine interfacing, and augmented reality systems. However, the nonlinear response of the resistance signal to strain has caused considerable difficulty and complexity in data processing and signal transformation, thus impeding their practical applications severely. Herein, we propose a simple way to achieve linear and reproducible resistive signals responding to strain in a relatively wide strain range for flexible strain sensors, which is achieved via the fabrication of Janus and heteromodulus elastomeric fiber mats with micropatterns using microimprinting second processing technology. In detail, both isotropic and anisotropic fiber mats can turn into Janus fiber mats with periodical and heteromodulus micropatterns via controlling the fiber fusion and the diffusion of local macromolecular chains of thermoplastic elastomers. https://www.selleckchem.com/products/hdm201.html The Janus heterogeneous microstructure allows for stress redistribution upon stretching, thus leading to lower strain hysteresis and improved linearity of resistive signal. Moreover, tunable sensing performance can be achieved by tailoring the size of the micropatterns on the fiber mat surface and the fiber anisotropy. The Janus mat strain sensors with high signal linearity and good reproducibility have a very low strain detection limit, enabling potential applications in human-machine interfacing and intelligent control fields if combined with a wireless communication module.In this article, we present a digital platform for unmanned traffic management, UTM City, for research on visualization, simulation, and management of autonomous urban vehicle traffic. Such vehicles orient themselves automatically and provide services ranging from transport to remote presence and surveillance, and new regulations and standards for authorization and monitoring are currently being developed to accommodate for such services. Our system has been developed in close collaboration with domain experts that have contributed with scenarios and participated in numerous workshops to explore the use of visualization in airborne drone traffic monitoring, management, and development of the air space. We share here our experiences with this system and explore the need for visualization in future scenarios to ensure safe, free, and efficient air spaces.Educational Data Virtual Lab (EDVL) is an open-source platform for data exploration and analysis that combines the power of a coding environment, the convenience of an interactive visualization engine, and the infrastructure needed to handle the complete data lifecycle. Based on the building blocks of the FIWARE European platform and Apache Zeppelin, this tool allows domain experts to become acquainted with data science methods using the data available within their own organization, ensuring that the skills they acquire are relevant to their field and driven by their own professional goals. We used EDVL in a pilot study in which we carried out a focus group within a multinational company to gain insight into potential users' perceptions of EDVL, both from the educational and operational points of view. The results of our evaluation suggest that EDVL holds a great potential to train the workforce in data science skills and to enable collaboration among professionals with different levels of expertise.Layered manufacturing, the underlying technology of 3-D printing, has made rapid strides over the last 30 years. We discuss layered manufacturing from the artist's perspective, especially for intricate ceramic pottery. We contend that opportunities exist for applying visualization to the foremost problems plaguing layered manufacturing. Virtual pottery involves meeting two conflicting constraints rapid visualization during modeling and accurate rapid prototyping during manufacturing. Artists simultaneously need both low polygon shape representation for interactive visualization and adequate representation for generating accurate printable models. Artists also face the additional complexities of adding surface details that cannot be achieved by hand and handling materials like clay used in the manufacturing of real pottery. Illustrated by a system we have developed that uses sound resonance patterns to create volumetric textures for virtual pottery, we show how visualization helps address both these problem areas.
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