Latest advances in the regulatory genes of adipocyte thermogenesis

An energy imbalance cause obesity: more energy intake or less energy expenditure, or both. Obesity could be the origin of many metabolic disorders, such as type 2 diabetes and cardiovascular disease. UCP1 (uncoupling protein1), which is highly and exclusively expressed in the thermogenic adipocytes, including beige and brown adipocytes, can dissipate proton motive force into heat without producing ATP to increase energy expenditure. It is an attractive strategy to combat obesity and its related metabolic disorders by increasing non-shivering adipocyte thermogenesis. Adipocyte thermogenesis has recently been reported to be regulated by several new genes. This work provided novel and potential targets to activate adipocyte thermogenesis and resist obesity, such as secreted proteins ADISSP and EMC10, enzyme SSU72, etc. In this review, we have summarized the latest research on adipocyte thermogenesis regulation to shed more light on this topic

Absence of Appl2 sensitizes endotoxin shock through activation of PI3K/Akt pathway

BACKGROUND: The adapter proteins Appl1 (adaptor protein containing pleckstrin homology domain, phosphotyrosine domain, and leucine zipper motif 1) and Appl2 are highly homologous and involved in several signaling pathways. While previous studies have shown that Appl1 plays a pivotal role in adiponectin signaling and insulin secretion, the physiological functions of Appl2 are largely unknown. RESULTS: In the present study, the role of Appl2 in sepsis shock was investigated by using Appl2 knockout (KO) mice. When challenged with lipopolysaccharides (LPS), Appl2 KO mice exhibited more severe symptoms of endotoxin shock, accompanied by increased production of proinflammatory cytokines. In comparison with the wild-type control, deletion of Appl2 led to higher levels of TNF-α and IL-1β in primary macrophages. In addition, phosphorylation of Akt and its downstream effector NF-κB was significantly enhanced. By co-immunoprecipitation, we found that Appl2 and Appl1 interacted with each other and formed a complex with PI3K regulatory subunit p85α, which is an upstream regulator of Akt. Consistent with these results, deletion of Appl1 in macrophages exhibited characteristics of reduced Akt activation and decreased the production of TNFα and IL-1β when challenged by LPS. CONCLUSIONS: Results of the present study demonstrated that Appl2 is a critical negative regulator of innate immune response via inhibition of PI3K/Akt/NF-κB signaling pathway by forming a complex with Appl1 and PI3K.published_or_final_versio

Harmine Induces Adipocyte Thermogenesis through RAC1-MEK-ERK-CHD4 Axis

© The Author(s) 2016.Harmine is a natural compound possessing insulin-sensitizing effect in db/db diabetic mice. However its effect on adipose tissue browning is unknown. Here we reveal that harmine antagonizes high fat diet-induced adiposity. Harmine-treated mice gained less weight on a high fat diet and displayed increased energy expenditure and adipose tissue thermogenesis. In vitro, harmine potently induced the expression of thermogenic genes in both brown and white adipocytes, which was largely abolished by inhibition of RAC1/MEK/ERK pathway. Post-transcriptional modification analysis revealed that chromodomain helicase DNA binding protein 4 (CHD4) is a potential downstream target of harmine-mediated ERK activation. CHD4 directly binds the proximal promoter region of Ucp1, which is displaced upon treatment of harmine, thereby serving as a negative modulator of Ucp1. Thus, here we reveal a new application of harmine in combating obesity via this off-target effect in adipocytes.published_or_final_versio

GNER: A Generative Model for Geological Named Entity Recognition Without Labeled Data Using Deep Learning

Abstract A variety of detailed data about geological topics and geoscience knowledge are buried in the geoscience literature and rarely used. Named entity recognition (NER) provides both opportunities and challenges to leverage this wealth of data in the geoscience literature for data analysis and further information extraction. Existing NER models and techniques are mainly based on rule‐based and supervised approaches, and developing such systems requires a costly manual effort. In this paper, we first design a generic stepwise framework for domain‐specific NER. Following this framework, domain‐specific entities and domain‐general words are collected and selected as seed terms. Normalization and grouping processes are then applied to these seed terms for further analysis. A random extraction algorithm based on a unigram language model is used to generate a large‐scale training data set consisting of probabilistically labeled pseudosentences. Each generated sentence is then used as input to the self‐training and learning algorithm. Experimental results on two constructed data sets demonstrate that the proposed model effectively recognizes and identifies geological named entities

An Urban Built Environment Analysis Approach for Street View Images Based on Graph Convolutional Neural Networks

Traditionally, research in the field of traffic safety has predominantly focused on two key areas—the identification of traffic black spots and the analysis of accident causation. However, such research heavily relies on historical accident records obtained from the traffic management department, which often suffer from missing or incomplete information. Moreover, these records typically offer limited insight into the various attributes associated with accidents, thereby posing challenges to comprehensive analyses. Furthermore, the collection and management of such data incur substantial costs. Consequently, there is a pressing need to explore how the features of the urban built environment can effectively facilitate the accurate identification and analysis of traffic black spots, enabling the formulation of effective management strategies to support urban development. In this study, we research the Kowloon Peninsula in Hong Kong, with a specific focus on road intersections as the fundamental unit of our analysis. We propose leveraging street view images as a valuable source of data, enabling us to depict the urban built environment comprehensively. Through the utilization of models such as random forest approaches, we conduct research on traffic black spot identification, attaining an impressive accuracy rate of 87%. To account for the impact of the built environment surrounding adjacent road intersections on traffic black spot identification outcomes, we adopt a node-based approach, treating road intersections as nodes and establishing spatial relationships between them as edges. The features characterizing the built environment at these road intersections serve as node attributes, facilitating the construction of a graph structure representation. By employing a graph-based convolutional neural network, we enhance the traffic black spot identification methodology, resulting in an improved accuracy rate of 90%. Furthermore, based on the distinctive attributes of the urban built environment, we analyze the underlying causes of traffic black spots. Our findings highlight the significant influence of buildings, sky conditions, green spaces, and billboards on the formation of traffic black spots. Remarkably, we observe a clear negative correlation between buildings, sky conditions, and green spaces, while billboards and human presence exhibit a distinct positive correlation

Indoor 3D Point Cloud Segmentation Based on Multi-Constraint Graph Clustering

Indoor scene point cloud segmentation plays an essential role in 3D reconstruction and scene classification. This paper proposes a multi-constraint graph clustering method (MCGC) for indoor scene segmentation. The MCGC method considers multi-constraints, including extracted structural planes, local surface convexity, and color information of objects for indoor segmentation. Firstly, the raw point cloud is partitioned into surface patches, and we propose a robust plane extraction method to extract the main structural planes of the indoor scene. Then, the match between the surface patches and the structural planes is achieved by global energy optimization. Next, we closely integrate multiple constraints mentioned above to design a graph clustering algorithm to partition cluttered indoor scenes into object parts. Finally, we present a post-refinement step to filter outliers. We conducted experiments on a benchmark RGB-D dataset and a real indoor laser-scanned dataset to perform numerous qualitative and quantitative evaluation experiments, the results of which have verified the effectiveness of the MCGC method. Compared with state-of-the-art methods, MCGC can deal with the segmentation of indoor scenes more efficiently and restore more details of indoor structures. The segment precision and the segment recall of experimental results reach 70% on average. In addition, a great advantage of the MCGC method is that the speed of processing point clouds is very fast; it takes about 1.38 s to segment scene data of 1 million points. It significantly reduces the computation overhead of scene point cloud data and achieves real-time scene segmentation

TE-SAGAN: An Improved Generative Adversarial Network for Remote Sensing Super-Resolution Images

Resolution is a comprehensive reflection and evaluation index for the visual quality of remote sensing images. Super-resolution processing has been widely applied for extracting information from remote sensing images. Recently, deep learning methods have found increasing application in the super-resolution processing of remote sensing images. However, issues such as blurry object edges and existing artifacts persist. To overcome these issues, this study proposes an improved generative adversarial network with self-attention and texture enhancement (TE-SAGAN) for remote sensing super-resolution images. We first designed an improved generator based on the residual dense block with a self-attention mechanism and weight normalization. The generator gains the feature extraction capability and enhances the training model stability to improve edge contour and texture. Subsequently, a joint loss, which is a combination of L1-norm, perceptual, and texture losses, is designed to optimize the training process and remove artifacts. The L1-norm loss is designed to ensure the consistency of low-frequency pixels; perceptual loss is used to entrench medium- and high-frequency details; and texture loss provides the local features for the super-resolution process. The results of experiments using a publicly available dataset (UC Merced Land Use dataset) and our dataset show that the proposed TE-SAGAN yields clear edges and textures in the super-resolution reconstruction of remote sensing images

Geographic Named Entity Recognition by Employing Natural Language Processing and an Improved BERT Model

Toponym recognition, or the challenge of detecting place names that have a similar referent, is involved in a number of activities connected to geographical information retrieval and geographical information sciences. This research focuses on recognizing Chinese toponyms from social media communications. While broad named entity recognition methods are frequently used to locate places, their accuracy is hampered by the many linguistic abnormalities seen in social media posts, such as informal sentence constructions, name abbreviations, and misspellings. In this study, we describe a Chinese toponym identification model based on a hybrid neural network that was created with these linguistic inconsistencies in mind. Our method adds a number of improvements to a standard bidirectional recurrent neural network model to help with location detection in social media messages. We demonstrate the results of a wide-ranging evaluation of the performance of different supervised machine learning methods, which have the natural advantage of avoiding human design features. A set of controlled experiments with four test datasets (one constructed and three public datasets) demonstrates the performance of supervised machine learning that can achieve good results on the task, significantly outperforming seven baseline models

Construction and application of a multilevel geohazard domain ontology: A case study of landslide geohazards

The occurrence of geohazards entails sudden, unpredictable, and cascading effects, with numerous conceptual frameworks and intricate spatiotemporal relationships existing between hazard events. Presently, the absence of a unified mechanism for describing and expressing geohazard knowledge poses substantial challenges in terms of sharing and reusing domain-specific knowledge pertaining to geohazards. Therefore, it is imperative to address the issue of constructing a cohesive descriptive model that facilitates the sharing and reuse of geohazard knowledge. In this study, we propose a multilayered ontology construction method tailored specifically for the domain of landslide geological hazards. By comparing existing methods, we establish a hierarchical structure and expression framework for the geological hazard ontology. Notably, our approach seamlessly integrates the conceptual and semantic layers in the relationship description at each level, enabling association representation of hazard data across multiple tiers. We define essential concepts and attributes related to landslide geological hazards, along with their respective interrelationships. To achieve effective knowledge sharing and reuse, we model the ontology of the landslide geological disaster domain using the Web Ontology Language (OWL). This modeling approach serves as a powerful tool that facilitates the sharing and reuse of disaster-related knowledge. Finally, we verify the method's validity and reliability by employing illustrative case studies. The results demonstrate that the proposed approach imposes an affordable workload on human resources. Additionally, the foundational domain ontology significantly enhances information retrieval performance, thereby yielding satisfactory outcomes

Location Optimization of Urban Fire Stations Considering the Backup Coverage

Urban fires threaten the economic stability and safety of urban residents. Therefore, the limited number of fire stations should cover as many places as possible. Moreover, places with high fire risk should be covered by more fire stations. To optimize the location of urban fire stations, we construct a multi-objective optimization model for fire station planning based on the backup coverage model. The improved value of environment and ecosystem (SAVEE) model is introduced to quantify the spatial heterogeneity of urban fires. The main city zone of Wuhan is used as the study area to validate the proposed method. The results show that, considering the existing fire stations (85 facilities), the proposed model achieves a significant 38.56% in high-risk areas that can be covered by more than one fire station. If the existing fire stations are not considered when building 95 fire stations, the proposed model can achieve coverage of 50.07% in high-risk areas by utilizing more than one fire station. As a result, the proposed backup coverage model would perform better if the protection of high-risk areas is improved with as few fire stations as possible to guarantee more places covered