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لقاء في القرية العالمية = An encounter in the global village : قصص مختارة من المؤتمر الدولي الرابع عشر للقصة القصيرة
هذا الكتاب يحتوي على قصص مختارة من المؤتمر الدولي الرابع عشر للقصة القصيرة وهذا اللقاء الذي نظم من قبل جمعية دراسة القصص القصيرة الإنجليزية (أس أس أس أس إي) وهي جمعية عالمية أنشئت في الولايات المتحدة عام 1992 وينعقد كل عامين ويعتبر اللقاء العالمي الوحيد الذي يركز بشكل خاص على دراسات القصة القصيرة أما القصص المشاركة في اللقاء فهي مكتوبة من قبل 29 كاتبا ينتمون إلى عشرة دول هي الصين وتايوان والهند والولايات المتحدة وكندا ونيوزلندا وفرنسا وإيرلندا والنمسا وسنغافورا وجامايكا.
BOOK
The gut microbiome and liver cancer: mechanisms and clinical translation
Key Points
Intestinal dysbiosis and increased bacterial translocation contribute to the pathophysiology of chronic liver disease (CLD) and hepatocarcinogenesis
A large body of literature has demonstrated that targeting the gut-microbiota–liver axis can inhibit the development of hepatocellular carcinoma (HCC) in mice and rats
Promising findings from these preclinical studies in mice and rats have not yet been translated to clinical settings, presenting therapeutic opportunities
Targeting the gut–liver axis by nonabsorbable antibiotics such as rifaximin might not only prevent the development of HCC in patients with CLD, but additionally reduce other complications and improve survival
Increasing evidence suggests that the gut microbiota are important modulators of chronic liver disease progression and the development of hepatocellular carcinoma. In this Review, Yu and Schwabe discuss the mechanisms by which the gut microbiota promote hepatocarcinogenesis, and explore therapeutic interventions with clinical potential.
Hepatocellular carcinoma (HCC) is the third leading cause of worldwide cancer mortality. HCC almost exclusively develops in patients with chronic liver disease, driven by a vicious cycle of liver injury, inflammation and regeneration that typically spans decades. Increasing evidence points towards a key role of the bacterial microbiome in promoting the progression of liver disease and the development of HCC. Here, we will review mechanisms by which the gut microbiota promotes hepatocarcinogenesis, focusing on the leaky gut, bacterial dysbiosis, microbe-associated molecular patterns and bacterial metabolites as key pathways that drive cancer-promoting liver inflammation, fibrosis and genotoxicity. On the basis of accumulating evidence from preclinical studies, we propose the intestinal-microbiota–liver axis as a promising target for the simultaneous prevention of chronic liver disease progression and HCC development in patients with advanced liver disease. We will review in detail therapeutic modalities and discuss clinical settings in which targeting the gut-microbiota–liver axis for the prevention of disease progression and HCC development seems promising.
Journal Article
Progress and Trends in the Application of Google Earth and Google Earth Engine
Earth system science has changed rapidly due to global environmental changes and the advent of Earth observation technology. Therefore, new tools are required to monitor, measure, analyze, evaluate, and model Earth observation data. Google Earth (GE) was officially launched by Google in 2005 as a ”geobrowser”, and Google Earth Engine (GEE) was released in 2010 as a cloud computing platform with substantial computational capabilities. The use of these two tools or platforms in various applications, particularly as used by the remote sensing community, has developed rapidly. In this paper, we reviewed the applications and trends in the use of GE and GEE by analyzing peer-reviewed articles, dating up to January 2021, in the Web of Science (WoS) core collection using scientometric analysis (i.e., by using CiteSpace) and meta-analysis. We found the following: (1) the number of articles describing the use of GE or GEE increased substantially from two in 2006 to 530 in 2020. The number of GEE articles increased much faster than those concerned with the use of GE. (2) Both GE and GEE were extensively used by the remote sensing community as multidisciplinary tools. GE articles covered a broader range of research areas (e.g., biology, education, disease and health, economic, and information science) and appeared in a broader range of journals than those concerned with the use of GEE. (3) GE and GEE shared similar keywords (e.g., “land cover”, “water”, “model”, “vegetation”, and “forest”), which indicates that their application is of great importance in certain research areas. The main difference was that articles describing the use of GE emphasized its use as a visual display platform, while those concerned with GEE placed more emphasis on big data and time-series analysis. (4) Most applications of GE and GEE were undertaken in countries, such as the United States, China, and the United Kingdom. (5) GEE is an important tool for analysis, whereas GE is used as an auxiliary tool for visualization. Finally, in this paper, the merits and limitations of GE and GEE, and recommendations for further improvements, are summarized from an Earth system science perspective.
Journal Article
Biomineralization of Collagen-Based Materials for Hard Tissue Repair
Hydroxyapatite (HA) reinforced collagen fibrils serve as the basic building blocks of natural bone and dentin. Mineralization of collagen fibrils play an essential role in ensuring the structural and mechanical functionalities of hard tissues such as bone and dentin. Biomineralization of collagen can be divided into intrafibrillar and extrafibrillar mineralization in terms of HA distribution relative to collagen fibrils. Intrafibrillar mineralization is termed when HA minerals are incorporated within the gap zone of collagen fibrils, while extrafibrillar mineralization refers to the minerals that are formed on the surface of collagen fibrils. However, the mechanisms resulting in these two types of mineralization still remain debatable. In this review, the evolution of both classical and non-classical biomineralization theories is summarized. Different intrafibrillar mineralization mechanisms, including polymer induced liquid precursor (PILP), capillary action, electrostatic attraction, size exclusion, Gibbs-Donnan equilibrium, and interfacial energy guided theories, are discussed. Exemplary strategies to induce biomimetic intrafibrillar mineralization using non-collagenous proteins (NCPs), polymer analogs, small molecules, and fluidic shear stress are discussed, and recent applications of mineralized collagen fibers for bone regeneration and dentin repair are included. Finally, conclusions are drawn on these proposed mechanisms, and the future trend of collagen-based materials for bone regeneration and tooth repair is speculated.
Journal Article
Cytosolic DNA sensing by cGAS: regulation, function, and human diseases
Sensing invasive cytosolic DNA is an integral component of innate immunity. cGAS was identified in 2013 as the major cytosolic DNA sensor that binds dsDNA to catalyze the synthesis of a special asymmetric cyclic-dinucleotide, 2′3′-cGAMP, as the secondary messenger to bind and activate STING for subsequent production of type I interferons and other immune-modulatory genes. Hyperactivation of cGAS signaling contributes to autoimmune diseases but serves as an adjuvant for anticancer immune therapy. On the other hand, inactivation of cGAS signaling causes deficiency to sense and clear the viral and bacterial infection and creates a tumor-prone immune microenvironment to facilitate tumor evasion of immune surveillance. Thus, cGAS activation is tightly controlled. In this review, we summarize up-to-date multilayers of regulatory mechanisms governing cGAS activation, including cGAS pre- and post-translational regulations, cGAS-binding proteins, and additional cGAS regulators such as ions and small molecules. We will also reveal the pathophysiological function of cGAS and its product cGAMP in human diseases. We hope to provide an up-to-date review for recent research advances of cGAS biology and cGAS-targeted therapies for human diseases.
Journal Article
Large-Scale Oil Palm Tree Detection from High-Resolution Satellite Images Using Two-Stage Convolutional Neural Networks
Being an important economic crop that contributes 35% of the total consumption of vegetable oil, remote sensing-based quantitative detection of oil palm trees has long been a key research direction for both agriculture and environmental purposes. While existing methods already demonstrate satisfactory effectiveness for small regions, performing the detection for a large region with satisfactory accuracy is still challenging. In this study, we proposed a two-stage convolutional neural network (TS-CNN)-based oil palm detection method using high-resolution satellite images (i.e. Quickbird) in a large-scale study area of Malaysia. The TS-CNN consists of one CNN for land cover classification and one CNN for object classification. The two CNNs were trained and optimized independently based on 20,000 samples collected through human interpretation. For the large-scale oil palm detection for an area of 55 km2, we proposed an effective workflow that consists of an overlapping partitioning method for large-scale image division, a multi-scale sliding window method for oil palm coordinate prediction, and a minimum distance filter method for post-processing. Our proposed approach achieves a much higher average F1-score of 94.99% in our study area compared with existing oil palm detection methods (87.95%, 81.80%, 80.61%, and 78.35% for single-stage CNN, Support Vector Machine (SVM), Random Forest (RF), and Artificial Neural Network (ANN), respectively), and much fewer confusions with other vegetation and buildings in the whole image detection results.
Journal Article
Porous molybdenum carbide nano-octahedrons synthesized via confined carburization in metal-organic frameworks for efficient hydrogen production
Electrochemical water splitting has been considered as a promising approach to produce clean and sustainable hydrogen fuel. However, the lack of high-performance and low-cost electrocatalysts for hydrogen evolution reaction hinders the large-scale application. As a new class of porous materials with tunable structure and composition, metal-organic frameworks have been considered as promising candidates to synthesize various functional materials. Here we demonstrate a metal-organic frameworks-assisted strategy for synthesizing nanostructured transition metal carbides based on the confined carburization in metal-organic frameworks matrix. Starting from a compound consisting of copper-based metal-organic frameworks host and molybdenum-based polyoxometalates guest, mesoporous molybdenum carbide nano-octahedrons composed of ultrafine nanocrystallites are successfully prepared as a proof of concept, which exhibit remarkable electrocatalytic performance for hydrogen production from both acidic and basic solutions. The present study provides some guidelines for the design and synthesis of nanostructured electrocatalysts.
There is extensive research into non-platinum electrocatalysts for hydrogen evolution. Here, the authors report a molybdenum carbide catalyst, prepared via the carburization of a copper metal-organic framework host/molybdenum-based polyoxometalates guest system, and demonstrate its catalytic activity.
Journal Article
Nuclear pores as versatile reference standards for quantitative superresolution microscopy
Quantitative fluorescence and superresolution microscopy are often limited by insufficient data quality or artifacts. In this context, it is essential to have biologically relevant control samples to benchmark and optimize the quality of microscopes, labels and imaging conditions. Here, we exploit the stereotypic arrangement of proteins in the nuclear pore complex as in situ reference structures to characterize the performance of a variety of microscopy modalities. We created four genome edited cell lines in which we endogenously labeled the nucleoporin Nup96 with mEGFP, SNAP-tag, HaloTag or the photoconvertible fluorescent protein mMaple. We demonstrate their use (1) as three-dimensional resolution standards for calibration and quality control, (2) to quantify absolute labeling efficiencies and (3) as precise reference standards for molecular counting. These cell lines will enable the broader community to assess the quality of their microscopes and labels, and to perform quantitative, absolute measurements.
Journal Article
An Overview of the Applications of Earth Observation Satellite Data: Impacts and Future Trends
As satellite observation technology develops and the number of Earth observation (EO) satellites increases, satellite observations have become essential to developments in the understanding of the Earth and its environment. However, the current impacts to the remote sensing community of different EO satellite data and possible future trends of EO satellite data applications have not been systematically examined. In this paper, we review the impacts of and future trends in the use of EO satellite data based on an analysis of data from 15 EO satellites whose data are widely used. Articles that reference EO satellite missions included in the Web of Science core collection for 2020 were analyzed using scientometric analysis and meta-analysis. We found the following: (1) the number of publications and citations referencing EO satellites is increasing exponentially; however, the number of articles referencing AVHRR, SPOT, and TerraSAR is tending to decrease; (2) papers related to EO satellites are concentrated in a small number of journals: 43.79% of the articles that were reviewed were published in only 13 journals; and (3) remote sensing impact factor (RSIF), a new impact index, was constructed to measure the impacts of EO satellites and to predict future trends in applications of their data. Landsat, Sentinel, MODIS, Gaofen, and WorldView were found to be the most significant current EO satellite missions and MODIS data to have the widest range of applications. Over the next five years (2021–2025), it is expected that Sentinel will become the satellite mission with the greatest influence.
Journal Article
Semantic Segmentation-Based Building Footprint Extraction Using Very High-Resolution Satellite Images and Multi-Source GIS Data
Automatic extraction of building footprints from high-resolution satellite imagery has become an important and challenging research issue receiving greater attention. Many recent studies have explored different deep learning-based semantic segmentation methods for improving the accuracy of building extraction. Although they record substantial land cover and land use information (e.g., buildings, roads, water, etc.), public geographic information system (GIS) map datasets have rarely been utilized to improve building extraction results in existing studies. In this research, we propose a U-Net-based semantic segmentation method for the extraction of building footprints from high-resolution multispectral satellite images using the SpaceNet building dataset provided in the DeepGlobe Satellite Challenge of IEEE Conference on Computer Vision and Pattern Recognition 2018 (CVPR 2018). We explore the potential of multiple public GIS map datasets (OpenStreetMap, Google Maps, and MapWorld) through integration with the WorldView-3 satellite datasets in four cities (Las Vegas, Paris, Shanghai, and Khartoum). Several strategies are designed and combined with the U-Net–based semantic segmentation model, including data augmentation, post-processing, and integration of the GIS map data and satellite images. The proposed method achieves a total F1-score of 0.704, which is an improvement of 1.1% to 12.5% compared with the top three solutions in the SpaceNet Building Detection Competition and 3.0% to 9.2% compared with the standard U-Net–based method. Moreover, the effect of each proposed strategy and the possible reasons for the building footprint extraction results are analyzed substantially considering the actual situation of the four cities.
Journal Article