| 1. |
- Shen, Qian, et al.
(författare)
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The Genome of Artemisia annua Provides Insight into the Evolution of Asteraceae Family and Artemisinin Biosynthesis
- 2018
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Ingår i: Molecular Plant. - : Cell Press. - 1674-2052 .- 1752-9867. ; 11:6, s. 776-788
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Tidskriftsartikel (refereegranskat)abstract
- Artemisia annua, commonly known as sweet wormwood or Qinghao, is a shrub native to China and has long been used for medicinal purposes. A. annua is now cultivated globally as the only natural source of a potent anti-malarial compound, artemisinin. Here, we report a high-quality draft assembly of the 1.74-gigabase genome of A. annua, which is highly heterozygous, rich in repetitive sequences, and contains 63 226 protein-coding genes, one of the largest numbers among the sequenced plant species. We found that, as one of a few sequenced genomes in the Asteraceae, the A. annua genome contains a large number of genes specific to this large angiosperm clade. Notably, the expansion and functional diversification of genes encoding enzymes involved in terpene biosynthesis are consistent with the evolution of the artemisinin biosynthetic pathway. We further revealed by transcriptome profiling that A. annua has evolved the sophisticated transcriptional regulatory networks underlying artemisinin biosynthesis. Based on comprehensive genomic and transcriptomic analyses we generated transgenic A. annua lines producing high levels of artemisinin, which are now ready for large-scale production and thereby will help meet the challenge of increasing global demand of artemisinin.
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| 2. |
- Yu, Wenjin, et al.
(författare)
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Deep Learning-Based Classification of Cancer Cell in Leptomeningeal Metastasis on Cytomorphologic Features of Cerebrospinal Fluid
- 2022
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Ingår i: Frontiers in Oncology. - : Frontiers Media SA. - 2234-943X. ; 12, s. 1-11
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Tidskriftsartikel (refereegranskat)abstract
- Background: It is a critical challenge to diagnose leptomeningeal metastasis (LM), given its technical difficulty and the lack of typical symptoms. The existing gold standard of diagnosing LM is to use positive cerebrospinal fluid (CSF) cytology, which consumes significantly more time to classify cells under a microscope.Objective: This study aims to establish a deep learning model to classify cancer cells in CSF, thus facilitating doctors to achieve an accurate and fast diagnosis of LM in an early stage.Method: The cerebrospinal fluid laboratory of Xijing Hospital provides 53,255 cells from 90 LM patients in the research. We used two deep convolutional neural networks (CNN) models to classify cells in the CSF. A five-way cell classification model (CNN1) consists of lymphocytes, monocytes, neutrophils, erythrocytes, and cancer cells. A four-way cancer cell classification model (CNN2) consists of lung cancer cells, gastric cancer cells, breast cancer cells, and pancreatic cancer cells. Here, the CNN models were constructed by Resnet-inception-V2. We evaluated the performance of the proposed models on two external datasets and compared them with the results from 42 doctors of various levels of experience in the human-machine tests. Furthermore, we develop a computer-aided diagnosis (CAD) software to generate cytology diagnosis reports in the research rapidly.Results: With respect to the validation set, the mean average precision (mAP) of CNN1 is over 95% and that of CNN2 is close to 80%. Hence, the proposed deep learning model effectively classifies cells in CSF to facilitate the screening of cancer cells. In the human-machine tests, the accuracy of CNN1 is similar to the results from experts, with higher accuracy than doctors in other levels. Moreover, the overall accuracy of CNN2 is 10% higher than that of experts, with a time consumption of only one-third of that consumed by an expert. Using the CAD software saves 90% working time of cytologists.Conclusion: A deep learning method has been developed to assist the LM diagnosis with high accuracy and low time consumption effectively. Thanks to labeled data and step-by-step training, our proposed method can successfully classify cancer cells in the CSF to assist LM diagnosis early. In addition, this unique research can predict cancer’s primary source of LM, which relies on cytomorphologic features without immunohistochemistry. Our results show that deep learning can be widely used in medical images to classify cerebrospinal fluid cells. For complex cancer classification tasks, the accuracy of the proposed method is significantly higher than that of specialist doctors, and its performance is better than that of junior doctors and interns. The application of CNNs and CAD software may ultimately aid in expediting the diagnosis and overcoming the shortage of experienced cytologists, thereby facilitating earlier treatment and improving the prognosis of LM.
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| 3. |
- Chen, Liangkai, et al.
(författare)
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Physical frailty, adherence to ideal cardiovascular health and risk of cardiovascular disease : a prospective cohort study
- 2023
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Ingår i: Age and Ageing. - : Oxford University Press. - 0002-0729 .- 1468-2834. ; 52:1
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Tidskriftsartikel (refereegranskat)abstract
- Background: longitudinal evidence concerning frailty phenotype and the risk of cardiovascular disease (CVD) remained insufficient, and whether CVD preventive strategies exert low CVD risk on frail adults is unclear.Objectives: we aimed to prospectively evaluate the association of frailty phenotype, adherence to ideal cardiovascular health (CVH) and their joint associations with the risk of CVD.Methods: a total of 314,093 participants from the UK Biobank were included. Frailty phenotype was assessed according to the five criteria of Fried et al.: weight loss, exhaustion, low physical activity, slow gait speed and low grip strength. CVH included four core health behaviours (smoking, physical activity and diet) and three health factors (weight, cholesterol, blood pressure and glycaemic control). The outcome of interest was incident CVD, including coronary heart disease, heart failure and stroke.Results: compared with the non-frail people whose incident rate of overall CVD was 6.54 per 1,000 person-years, the absolute rate difference per 1,000 person-years was 1.67 (95% confidence interval, CI: 1.33, 2.02) for pre-frail and 5.00 (95% CI: 4.03, 5.97) for frail. The ideal CVH was significantly associated with a lower risk of all CVD outcomes. For the joint association of frailty and CVH level with incident CVD, the highest risk was observed among frailty accompanied by poor CVH with an HR of 2.92 (95% CI: 2.68, 3.18).Conclusions: our findings indicate that physical frailty is associated with CVD incidence. Improving CVH was significantly associated with a considerable decrease in CVD risk, and such cardiovascular benefits remain for the frailty population.
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| 4. |
- Deng, Min, et al.
(författare)
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Genome-wide association analyses in Han Chinese identify two new susceptibility loci for amyotrophic lateral sclerosis
- 2013
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Ingår i: Nature Genetics. - : Nature Publishing Group. - 1061-4036 .- 1546-1718. ; 45:6, s. 697-
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Tidskriftsartikel (refereegranskat)abstract
- To identify susceptibility genes for amyotrophic lateral sclerosis (ALS), we conducted a genome-wide association study (GWAS) in 506 individuals with sporadic ALS and 1,859 controls of Han Chinese ancestry. Ninety top SNPs suggested by the current GWAS and 6 SNPs identified by previous GWAS were analyzed in an independent cohort of 706 individuals with ALS and 1,777 controls of Han Chinese ancestry. We discovered two new susceptibility loci for ALS at 1q32 (CAMK1G, rs6703183, P-combined = 2.92 x 10(-8), odds ratio (OR) = 1.31) and 22p11 (CABIN1 and SUSD2, rs8141797, P-combined = 2.35 x 10(-9), OR = 1.52). These two loci explain 12.48% of the overall variance in disease risk in the Han Chinese population. We found no association evidence for the previously reported loci in the Han Chinese population, suggesting genetic heterogeneity of disease susceptibility for ALS between ancestry groups. Our study identifies two new susceptibility loci and suggests new pathogenic mechanisms of ALS.
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| 5. |
- Dong, Xinyang, et al.
(författare)
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Flexible Mixed-Criticality Scheduling with Dynamic Slack Management
- 2021
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Ingår i: Journal of Circuits, Systems and Computers. - : World Scientific. - 0218-1266. ; 30:10
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Tidskriftsartikel (refereegranskat)abstract
- Mixed-criticality (MC) system has attracted a lot of research attention in the past few years for its resource efficiency. Recent work attempted to provide a new MC model, the so-called Flexible Mixed-Criticality (FMC) task model, to relax the pessimistic assumptions in classic MC scheduling. However, in FMC, the behavior of MC tasks is still analyzed in offline stage. The run-time behavior such as dynamic slack has not yet been studied in FMC scheduling framework. In this paper, we present a utilization-based slack scheduling framework for FMC tasks. In particular, we monitor task execution on run time and collect dynamic slacks generated by task early completion. And these slacks can then be used either by high-criticality tasks to reduce mode-switches, or by low-criticality tasks so that less suspensions are triggered with more execution time, and thus quality of service is improved. We evaluate our approach with extensive simulations, and experiment results demonstrate the effectiveness of the proposed approaches.
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| 6. |
- Li, Dong-gang, et al.
(författare)
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Diffusion layer growth at Zn/Cu interface under uniform and gradient high magnetic fields
- 2008
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Ingår i: Materials Science & Engineering. - : Elsevier BV. - 0921-5093 .- 1873-4936. ; 495:1-2, s. 244-248
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Tidskriftsartikel (refereegranskat)abstract
- As a common phenomenon occurring in many material processes, diffusion may induce significant changes in composition and microstructure near the interface. In the present study, liquid/solid (Zn/Cu) interface diffusion experiments in high magnetic fields (up to 12 T) were conducted and the thickness changes of diffusion layer under different magnetic field conditions were examined. It was found that there were no noticeable effects of high magnetic fields on the formation of intermetallic phases at the interface. However, the magnetic flux density exerted a non-linear influence on the diffusion layer thickness. This phenomenon should be attributed to the effect of magnetic fields suppressing natural convection and inducing thermo-electromagnetic convection. In addition, the diffusion of Zn into Cu could be retarded by a magnetic field gradient. These results indicate that both the strength and the gradient of high magnetic fields can be used to control the diffusion behavior.
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| 7. |
- Ma, Ye, et al.
(författare)
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Efficient and Effective Dimension Control in Automotive Applications
- 2021
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Ingår i: IEEE Transactions on Industrial Informatics. - : Institute of Electrical and Electronics Engineers (IEEE). - 1551-3203 .- 1941-0050. ; 17:3, s. 1583-1591
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Tidskriftsartikel (refereegranskat)abstract
- In automotive industry, the production line for assembling mechanical parts of vehicles must place and weld hundreds of components on the right positions of the platform. The accuracy of deploying the components has great impact on the quality and performance of the produced vehicle. To ensure the assembly accuracy, a critical task in the production process is the so-called dimension quality control. The current state of practice in automotive industries is mainly based on a manual process where experienced engineers use production data to identify accuracy problems and suggest solutions for corrections on fixture adjustment in the assembly line. It is an extremely inefficient process, which typically takes the engineers around ten days for one batch of vehicles and a year to achieve the required assembly accuracy for final production. In this article, we present an automatic technique for dimension control. We formulate the dimension control problem as a constraint programming problem and present a refinement method to prune the exploration space. Our technique can not only identify the wrongly deployed parts leading to dimensional defects, but also provide high-quality fixture adjustment decisions. Experiments conducted on industrial production data from BMW Brilliance Automotive demonstrate the significantly improved efficiency and effectiveness of dimension control in automotive industries with our approach.
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| 8. |
- Qiao, Sibo, et al.
(författare)
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A Pseudo-Siamese Feature Fusion Generative Adversarial Network for Synthesizing High-quality Fetal Four-chamber Views
- 2023
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Ingår i: IEEE journal of biomedical and health informatics. - : Institute of Electrical and Electronics Engineers (IEEE). - 2168-2194 .- 2168-2208. ; 27:3, s. 1193-1204
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Tidskriftsartikel (refereegranskat)abstract
- Four-chamber (FC) views are the primary ultrasound (US) images that cardiologists diagnose whether the fetus has congenital heart disease (CHD) in prenatal diagnosis and screening. FC views intuitively depict the developmental morphology of the fetal heart. Early diagnosis of fetal CHD has always been the focus and difficulty of prenatal screening. Furthermore, deep learning technology has achieved great success in medical image analysis. Hence, applying deep learning technology in the early screening of fetal CHD helps improve diagnostic accuracy. However, the lack of large-scale and high-quality fetal FC views brings incredible difficulties to deep learning models or cardiologists. Hence, we propose a Pseudo-Siamese Feature Fusion Generative Adversarial Network (PSFFGAN), synthesizing high-quality fetal FC views using FC sketch images. In addition, we propose a novel Triplet Generative Adversarial Loss Function (TGALF), which optimizes PSFFGAN to fully extract the cardiac anatomical structure information provided by FC sketch images to synthesize the corresponding fetal FC views with speckle noises, artifacts, and other ultrasonic characteristics. The experimental results show that the fetal FC views synthesized by our proposed PSFFGAN have the best objective evaluation values: SSIM of 0.4627, MS-SSIM of 0.6224, and FID of 83.92, respectively. More importantly, two professional cardiologists evaluate healthy FC views and CHD FC views synthesized by our PSFFGAN, giving a subjective score that the average qualified rate is 82% and 79%, respectively, which further proves the effectiveness of the PSFFGAN.
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| 9. |
- Qiao, Sibo, et al.
(författare)
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DPC-MSGATNet : dual-path chain multi-scale gated axial-transformer network for four-chamber view segmentation in fetal echocardiography
- 2023
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Ingår i: Complex & Intelligent Systems. - : Springer Nature. - 2199-4536 .- 2198-6053. ; :9, s. 4503-4519
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Tidskriftsartikel (refereegranskat)abstract
- Echocardiography is essential in evaluating fetal cardiac anatomical structures and functions when clinicians conduct early treatment and screening for congenital heart defects, a common and intricate fetal malformation. Nevertheless, the prenatal detection rate of fetal CHD remains low since the peculiarities of fetal cardiac structures and the variousness of fetal CHD. Precisely segmenting four cardiac chambers can assist clinicians in analyzing cardiac morphology and further facilitate CHD diagnosis. Hence, we design a dual-path chain multi-scale gated axial-transformer network (DPC-MSGATNet) that simultaneously models global dependencies and local visual cues for fetal ultrasound (US) four-chamber (FC) views and further accurately segments four chambers. Our DPC-MSGATNet includes a global and a local branch that simultaneously operates on an entire FC view and image patches to learn multi-scale representations. We design a plug-and-play module, Interactive dual-path chain gated axial-transformer (IDPCGAT), to enhance the interactions between global and local branches. In IDPCGAT, the multi-scale representations from the two branches can complement each other, capturing the same region’s salient features and suppressing feature responses to maintain only the activations associated with specific targets. Extensive experiments demonstrate that the DPC-MSGATNet exceeds seven state-of-the-art convolution- and transformer-based methods by a large margin in terms of F1 and IoU scores on our fetal FC view dataset, achieving a F1 score of 96.87% and an IoU score of 93.99%. The codes and datasets can be available at https://github.comQiaoSiBo/DPC-MSGATNet.
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| 10. |
- Qiao, Sibo, et al.
(författare)
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SPReCHD : Four-Chamber Semantic Parsing Network for Recognizing Fetal Congenital Heart Disease in Medical Metaverse
- 2024
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Ingår i: IEEE journal of biomedical and health informatics. - : Institute of Electrical and Electronics Engineers (IEEE). - 2168-2194 .- 2168-2208. ; 28:6, s. 3672-3682
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Tidskriftsartikel (refereegranskat)abstract
- Echocardiography is essential for evaluating cardiac anatomy and function during early recognition and screening for congenital heart disease (CHD), a widespread and complex congenital malformation. However, fetal CHD recognition still faces many difficulties due to instinctive fetal movements, artifacts in ultrasound images, and distinctive fetal cardiac structures. These factors hinder capturing robust and discriminative representations from ultrasound images, resulting in CHD's low prenatal detection rate. Hence, we propose a multi-scale gated axial-transformer network (MSGATNet) to capture fetal four-chamber semantic information. Then, we propose a SPReCHD: four-chamber semantic parsing network for recognizing fetal CHD in the clinical treatment of the medical metaverse, integrating MSGATNet to segment and locate four-chamber arbitrary contours, further capturing distinguished representations for the fetal heart. Comprehensive experiments indicate that our SPReCHD is sufficient in recognizing fetal CHD, achieving a precision of 95.92%, a recall of 94%, an accuracy of 95%, and a F1 score of 94.95% on the test set, dramatically improving the fetal CHD's prenatal detection rate.
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