分类： 生物学 >> 生物物理学 提交时间： 2016-05-11
摘要： Microgravity is a major stress factor that astronauts have to face in space. In the past, the effects of microgravity on genomic DNA damage were studied, and it seems that the effect on genomic DNA depends on cell types and the length of exposure time to microgravity or simulated microgravity (SMG). In this study we used mouse embryonic stem (MES) and mouse embryonic fibroblast (MEF) cells to assess the effects of SMG on DNA lesions. To acquire the insight into potential mechanisms by which cells resist and/or adapt to SMG, we also included Rad9-deleted MES and Mdc1-deleted MEF cells in addition to wild type cells in this study. We observed significant SMG-induced DNA double strand breaks (DSBs) in Rad9(-/-) MES and Mdc1(-/-) MEF cells but not in their corresponding wild type cells. A similar pattern of DNA single strand break or modifications was also observed in Rad9(-/-) MES. As the exposure to SMG was prolonged, Rad9(-/-) MES cells adapted to the SMG disturbance by reducing the induced DNA lesions. The induced DNA lesions in Rad9(-/-) MES were due to SMG-induced reactive oxygen species (ROS). Interestingly, Mdc1(-/-) MEF cells were only partially adapted to the SMG disturbance. That is, the induced DNA lesions were reduced over time, but did not return to the control level while ROS returned to a control level. In addition, ROS was only partially responsible for the induced DNA lesions in Mdc1(-/-) MEF cells. Taken together, these data suggest that SMG is a weak genomic DNA stress and can aggravate genomic instability in cells with DNA damage response (DDR) defects.
分类： 天文学 >> 天文学 提交时间： 2023-02-19
摘要： Quasars can be used to measure baryon acoustic oscillations at high redshift, which are considered as direct tracers of the most distant large-scale structures in the Universe. It is fundamental to select quasars from observations before implementing the above research. This work focuses on creating a catalog of quasar candidates based on photometric data to provide primary priors for further object classification with spectroscopic data in the future, such as The Dark Energy Spectroscopic Instrument (DESI) Survey. We adopt a machine learning algorithm (Random Forest, RF) for quasar identification. The training set includes $651,073$ positives and $1,227,172$ negatives, in which the photometric information are from DESI Legacy Imaging Surveys (DESI-LIS) \& Wide-field Infrared Survey Explore (WISE), and the labels are from a database of spectroscopically confirmed quasars based on Sloan Digital Sky Survey (SDSS) and the Set of Identifications \& Measurements and Bibliography for Astronomical Data (SIMBAD). The trained RF model is applied to point-like sources in DESI-LIS Data Release 9. To quantify the classifier's performance, we also inject a testing set into the to-be-applied data. Eventually, we obtained $1,953,932$ Grade-A quasar candidates and $22,486, 884$ Grade-B quasar candidates out of $425,540,269$ sources ($\sim 5.7\%$). The catalog covers $\sim 99\%$ of quasars in the to-be-applied data by evaluating the completeness of the classification on the testing set. The statistical properties of the candidates agree with that given by the method of color-cut selection. Our catalog can intensely decrease the workload for confirming quasars with the upcoming DESI data by eliminating enormous non-quasars but remaining high completeness. All data in this paper is publicly available online.
分类： 核科学技术 >> 核材料与工艺技术 提交时间： 2023-06-18 合作期刊: 《Nuclear Science and Techniques》
摘要： Hydroxyethylidene diphosphonate (HEDP) was prepared and labeled with rhenium-188. Its lyophilized kit was developed for instant preparing of 188Re-HEDP. 188Re-HEDP showed high focile uptake in normal mice (>30% ID/g at carrier co-injection) in BABLC/SPF mice. High quality single photon emission computed tomography (SPECT) image of New Zealand rabbit was obtained at 4 h after intravenous injection of 74 MBq radiotracer. The lyophilized HEDP kit affords the new opportunity for routine clinical application in bone metastases therapy.
分类： 地球科学 >> 地理学 提交时间： 2022-01-30 合作期刊: 《干旱区科学》
摘要： As an important natural resource, forest land plays a key role in the maintenance of ecological security. However, variations of forest land in the agropastoral ecotone of northern China (AENC) have attracted little attention. Taking the AENC as an example and based on remote-sensing images from 2000, 2010 to 2020, we explored the spatiotemporal variation of forest land and its driving factors using the land-use transfer matrix, spatial autocorrelation analysis and spatial error model. The results showed that from 2000 to 2020, the total area of forest land in the AENC increased from 75,547.52 to 77,359.96 km2 and the changes were dominated by the transformations among forest land, grassland and cropland, which occurred mainly in areas with the elevation of 5002000 m and slope of 1525. There was obvious spatial agglomeration of forest land in the AENC from 2000 to 2020, with hot spots of forest land gathered in the southern marginal areas of the Yanshan Mountains and the low mountainous and hilly areas of the Loess Plateau. The sub-hot spots around hot spots moved southward, the sub-cold spots spread to the surrounding areas and the cold spots disappeared. The spatiotemporal variation of forest land resulted from the interactions of natural environment, socioeconomic and policy factors from 2000 to 2020. The variables of average annual precipitation, slope, terrain relief, ecological conversion program and afforestation policy for barren mountains affected the spatial pattern of forest land positively, while those of annual average temperature, slope and road network density influenced it negatively.
分类： 计算机科学 >> 计算机应用技术 提交时间： 2019-11-23
摘要： Controlling weeds with reduced reliance on herbicides is one of the main challenges to move toward a more sustainable agriculture. Robotic weeding is a thought to be a viable way to reduce the environmental loading of agrochemicals while keeping the operation efficiency high. One of the key technologies for performing robotic weeding is automatic detection of crops and weeds in fields. This paper presents an overview on various methods for detecting plants based on machine vision, mainly concentrating on two main challenges: dealing with changing light and crop/weed discrimination. To overcome the first challenge, both physical and algorithmic methods have been proposed. Physical methods can result in a more cumbersome machine while algorithmic methods are less robust. For crop/weed discrimination, deep-learning-based methods have shown obvious advantages over traditional methods based on hand-crafted features. However, traditional methods still hold some merits that can be leveraged to deep-learning-based methods. With the fast development of hardware technologies, researchers should take full advantage of advanced hardware to ease the algorithm design. In the future, the identification of crops and weeds can be more accurate and fine-grained with the support of online databases and computing resources based on the advances in artificial intelligence and communication technologies.
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