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Understanding urban heat exposure dynamics is critical for public health, urban management, and climate change resilience. Near real-time analysis of urban heat enables quick decision-making and timely resource allocation, thereby enhancing the well-being of urban residents, especially during heatwaves or electricity shortages. To serve this purpose, we develop a cyberGIS framework to analyze and visualize human sentiments of heat exposure dynamically based on near real-time location-based social media (LBSM) data. Large volumes and low-cost LBSM data, together with a content analysis algorithm based on natural language processing are used effectively to generate near real-time heat exposure maps from human sentiments on social media at both city and national scales with km spatial resolution and census tract spatial unit. We conducted a case study to visualize and analyze human sentiments of heat exposure in Chicago and the United States in September 2021. Enabled with high-performance computing, dynamic visualization of heat exposure is achieved with fine spatiotemporal scales while heat exposure detected from social media data can be used to understand heat exposure from a human perspective and allow timely responses to extreme heat. Near real-time and high spatial resolution mapping of human sentiments of heat exposure with Twitter data An integrated cyberGIS and machine learning framework for visualizing heat exposure with Twitter data Human sentiment of heat exposure mapping in the City of Chicago and the United States [ABSTRACT FROM AUTHOR]

The source vessel noise is a very common noise type in offshore seismic surveys. The state‐of‐art deep learning‐based methods provide an end‐to‐end framework for seismic data denoising. The denoising performance of a pretrained network is, however, highly dependent on the completeness of the training set. When training a denoising network with only field data, especially for attenuating erratic noise, it is hard to obtain a noise‐free data as the training target for the network. Transfer learning, by combining the synthetic and field data, is an alternative solution for improving the generalization capabilities of the network, although being able to model such erratic noise represents also a challenge. Although the denoising results by traditional methods are not accurate enough for creating a complete training set, the features in residual noise by subtracting the denoised data from noisy data are enough for the network to learn. Considering the aforementioned factors, we develop a deep learning‐based workflow for the attenuation of the erratic source vessel noise from ocean bottom node 4C data. Instead of using denoising results directly, we use the conventional methods to extract noise and add them to the high signal‐to‐ratio region of the field data. The created noisy dataset is different from the original noisy data in noise regions; thus, the pretrained network can also be used for predicting the same original data. The denoising results of synthetic and field data all show that even the network is trained on a noisy labelled dataset, we still can obtain high signal‐to‐noise ratio denoising result. Besides, when compare with the results by filtering‐based methods, our proposed method can attenuate the vessel noise more effectively and preserve the near offsets reflections. [ABSTRACT FROM AUTHOR]

Ocean‐bottom node exploration has developed rapidly in marine seismic exploration. However, due to no illumination for the seafloor, and the observation system of the 'less traces but more shots' mode, the conventional surface‐related multiples elimination meets challenges for the application in the ocean‐bottom node exploration. This paper develops a new three‐dimensional surface‐related multiples elimination technique for ocean‐bottom node data. First, a three‐dimensional Kirchhoff pre‐stack time mirror migration algorithm is proposed to realize the accurate imaging for the seabed. Second, the time domain Kirchhoff demigration method is used to construct the contribution traces for multiples prediction. Finally, the surface multiples can be predicted based on the equation derived in the paper. Model tests and field data processing prove that our method can accurately predict the surface‐related multiples for ocean‐bottom node data and be helpful to the multiple elimination, which shows that the proposed technique has potential in actual ocean‐bottom node data processing. [ABSTRACT FROM AUTHOR]

The origin of T cells in the teleost's brain is unclear. While viewing the central nervous system (CNS) as immune privileged has been widely accepted, previous studies suggest that T cells residing in the thymus but not in the spleen of the teleost play an essential role in communicating with the peripheral organs. Here, we identified nine T cell subpopulations in the thymus and spleen of orange-spotted grouper (Epinephelus coioices) through single-cell RNA-sequencing analysis. After viral CNS infection with red-spotted grouper nervous necrosis virus (RGNNV), the number of slc43a2+ T cells synchronously increased in the spleen and brain. During the infection tests in asplenic zebrafish (tlx1▲ zebrafish model), no increase in the number of slc43a2+ T cells was observed in the brain. Single-cell transcriptomic analysis indicated that slc43a2+ T cells mature and functionally differentiate within the spleen and then migrate into the brain to trigger an immune response. This study suggests a novel route for T cell migration from the spleen to the brain during viral infection in fish. [ABSTRACT FROM AUTHOR]

Summary: Science gateways have become a core part of the cyberinfrastructure ecosystem by increasing access to computational resources and providing community platforms for sharing and publishing education and research materials. While science gateways represent a promising solution for computational reproducibility, common methods for providing users with their user environments on gateways present challenges which are difficult to overcome. This article presents EasyScienceGateway: a new framework for providing user environments on science gateways to resolve these challenges, provides the technical details on implementing the framework on a science gateway based on Jupyter Notebook, and discusses our experience applying the framework to the CyberGIS‐Jupyter and CyberGIS‐Jupyter for Water gateways. [ABSTRACT FROM AUTHOR]

For a potential application of FK506 in the treatment of acute kidney failure only the FKBP12 binding capability of the compound is required, while the immunosuppressive activity via calcineurin binding is considered as a likely risk to the patients. The methoxy groups at C13 and C15 are thought to have significant influence on the immunosuppressive activity of the molecule. Consequently, FK506 analogs with different functionalities at C13 and C15 were generated by targeted CRISPR editing of the AT domains in module 7 and 8 of the biosynthetic assembly line in Streptomyces tsukubaensis. In addition, the corresponding FK520 (C21 ethyl derivative of FK506) analogs could be obtained by media adjustments. The compounds were tested for their bioactivity in regards to FKBP12 binding, BMP potentiation and calcineurin sparing. 15‐desmethoxy FK506 was superior to the other tested analogs as it did not inhibit calcineurin but retained high potency towards FKBP12 binding and BMP potentiation. [ABSTRACT FROM AUTHOR]

Blended acquisition, which allows multiple sources almost simultaneously fired, has become an effective way for accelerating seismic data acquisition. In order to use conventional processing methods for imaging, deblending is necessary for this special acquisition. Convolutional neural network‐based deblending methods provide a novel end‐to‐end framework for source separation. We proposed a field‐data‐based augmentation method that uses shuffled deblending noise as the features to be learned and take the inaccurate labels as the output of the network. Synthetic data experiments show that a network trained on data set with the proposed data augmentation method has higher accuracy for deblending even if the labelled data are noisy. Besides, 2D discrete wavelet transform, which has the advantage of multiscale decomposition and dimensionality reduction, is introduced to accelerate the computation of the network. The data augmentation method for data set generation and the computational speedup method for network training/predicting are also applied to field data. The results from synthetic and field data all confirm the performance of our methods. [ABSTRACT FROM AUTHOR]

Distracted behavior detection is an important task in computer-assisted driving. Although deep learning has made significant progress in this area, it is still difficult to meet the requirements of the real-time analysis and processing of massive data by relying solely on local computing power. To overcome these problems, this paper proposes a driving distraction detection method based on cloud–fog computing architecture, which introduces scalable modules and a model-driven optimization based on greedy pruning. Specifically, the proposed method makes full use of cloud–fog computing to process complex driving scene data, solves the problem of local computing resource limitations, and achieves the goal of detecting distracted driving behavior in real time. In terms of feature extraction, scalable modules are used to adapt to different levels of feature extraction to effectively capture the diversity of driving behaviors. Additionally, in order to improve the performance of the model, a model-driven optimization method based on greedy pruning is introduced to optimize the model structure to obtain a lighter and more efficient model. Through verification experiments on multiple driving scene datasets such as LDDB and Statefarm, the effectiveness of the proposed driving distraction detection method is proved. [ABSTRACT FROM AUTHOR]