247 research outputs found

    CD-CNN: A Partially Supervised Cross-Domain Deep Learning Model for Urban Resident Recognition

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    Driven by the wave of urbanization in recent decades, the research topic about migrant behavior analysis draws great attention from both academia and the government. Nevertheless, subject to the cost of data collection and the lack of modeling methods, most of existing studies use only questionnaire surveys with sparse samples and non-individual level statistical data to achieve coarse-grained studies of migrant behaviors. In this paper, a partially supervised cross-domain deep learning model named CD-CNN is proposed for migrant/native recognition using mobile phone signaling data as behavioral features and questionnaire survey data as incomplete labels. Specifically, CD-CNN features in decomposing the mobile data into location domain and communication domain, and adopts a joint learning framework that combines two convolutional neural networks with a feature balancing scheme. Moreover, CD-CNN employs a three-step algorithm for training, in which the co-training step is of great value to partially supervised cross-domain learning. Comparative experiments on the city Wuxi demonstrate the high predictive power of CD-CNN. Two interesting applications further highlight the ability of CD-CNN for in-depth migrant behavioral analysis.Comment: 8 pages, 5 figures, conferenc

    TCMGIS-II based prediction of medicinal plant distribution for conservation planning: a case study of Rheum tanguticum

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    <p>Abstract</p> <p>Background</p> <p>Many medicinal plants are increasingly endangered due to overexploitation and habitat destruction. To provide reliable references for conservation planning and regional management, this study focuses on large-scale distribution prediction of <it>Rheum tanguticum </it>Maxim. ex Balf (<it>Dahuang</it>).</p> <p>Methods</p> <p>Native habitats were determined by specimen examination. An improved version of GIS-based program for the distribution prediction of traditional Chinese medicine (TCMGIS-II) was employed to integrate national geographic, climate and soil type databases of China. Grid-based distance analysis of climate factors was based on the Mikowski distance and the analysis of soil types was based on grade division. The database of resource survey was employed to assess the reliability of prediction result.</p> <p>Results</p> <p>A total of 660 counties of 17 provinces in China, covering a land area of 3.63 × 10<sup>6 </sup>km<sup>2</sup>, shared similar ecological factors with those of native habitats appropriate for <it>R. tanguticum </it>growth.</p> <p>Conclusion</p> <p>TCMGIS-II modeling found the potential habitats of target medicinal plants for their conservation planning. This technology is useful in conservation planning and regional management of medicinal plant resources.</p

    Research progress in the role of gut microbiota in the pathogenesis and treatment of IgA nephropathy

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    As the most common form of glomerulonephritis worldwide, IgA nephropathy (IgAN) is characterized by the diffuse deposition of immune complexes formed by glycosylation-deficient IgA1 (Gd-IgA1) and its specific antibodies (Gd-IgA1-IgG) in the glomerular mesangium. Although the mechanisms of Gd-IgA1 production are still unknown, there is accumulating evidence that Gd-IgA1-producing plasma cells are primarily derived from gut-associated lymphoid tissue, giving rise to the "gut-kidney axis" theory. Further research has discovered that gut microbiota may be involved in IgAN development and progression, and that several interventions to regulate gut microbiota, such as probiotics, fecal microbiota transplantation, and intestinal immunity modulation, may be used in the treatment of IgAN. In patients with IgAN, targeted-release formulation-budesonide has been shown to reduce urinary protein levels and delay kidney progression. Gut microbiota has promising potential as a preventive, diagnostic and therapeutic target for IgAN, and further research is needed

    New risk score for predicting progression of membranous nephropathy

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    Abstract Background Patients with Idiopathic membranous nephropathy (IMN) have various outcomes. The aim of this study is to construct a tool for clinicians to precisely predict outcome of IMN. Methods IMN patients diagnosed by renal biopsy from Shanghai Ruijin Hospital from 2009.01 to 2013.12 were enrolled in this study. Primary outcome was defined as a combination of renal function progression [defined as a reduction of estimated glomerular filtration rate (eGFR) equal to or over 30% comparing to baseline], ESRD or death. Risk models were established by Cox proportional hazard regression analysis and validated by bootstrap resampling analysis. ROC curve was applied to test the performance of risk score. Results Totally 439 patients were recruited in this study. The median follow-up time was 38.73 ± 19.35 months. The enrolled patients were 56 (15–83) years old with a male predominance (sex ratio: male vs female, 1:0.91). The median baseline serum albumin, eGFR-EPI and proteinuria were 23(8–43) g/l, 100.31(12.81–155.98) ml/min/1.73 m2 and 3.98(1.50–22.98) g/24 h, respectively. In total, there were 36 primary outcomes occurred. By Cox regression analysis, the best risk model included age [HR: 1.04(1.003–1.08), 95% CI from bootstrapping: 1.01–1.08), eGFR [HR: 0.97 (0.96–0.99), 95% CI from bootstrapping: 0.96–0.99) and proteinuria [HR: 1.09 (1.01–1.18), 95% CI from bootstrapping: 1.02–1.16). One unit increasing of the risk score based on the best model was associated with 2.57 (1.97–3.36) fold increased risk of combined outcome. The discrimination of this risk score was excellent in predicting combined outcome [C statistics: 0.83, 95% CI 0.76–0.90]. Conclusions Our study indicated that older IMN patients with lower eGFR and heavier proteinuria at the time of renal biopsy were at a higher risk for adverse outcomes. A risk score based on these three variables provides clinicians with an effective tool for risk stratification.https://deepblue.lib.umich.edu/bitstream/2027.42/147736/1/12967_2019_Article_1792.pd

    Electrospun polycaprolactone/silk fibroin nanofiber scaffold with aligned fiber orientation for articular chondrocyte regeneration

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    Objective: Electrospun nanofibers exhibit potential as scaffolds for articular cartilage tissue regeneration. This study aimed to fabricate electrospun polycaprolactone (PCL)/silk fibroin (SF) composite nanofiber scaffolds and to explore performance of the scaffolds for articular chondrocyte regeneration.Methods: By altering material composition and preparation methods, three types of nanofiber scaffolds were effectively fabricated, including randomly oriented PCL (RPCL) nanofiber scaffold, randomly oriented PCL/SF (RPCL/SF) nanofiber scaffold, and aligned PCL/SF (APCL/SF) nanofiber scaffold. Physiochemical analyses were performed to determine mechanical properties and surface hydrophilicity of the nanofiber scaffolds. In vitro studies were conducted to investigate performance of the scaffolds on articular chondrocyte proliferation, gene expression and glycosaminoglycan secretion. Cytoskeleton staining was used to observe the arrangement of chondrocytes along the direction of the fibers and their elongation along the fiber arrangement.Results: The physicochemical analysis demonstrated that the APCL/SF nanofiber scaffold exhibited improved mechanical properties and surface hydrophilicity compared to the RPCL and RPCL/SF nanofiber scaffolds. Furthermore, the in vitro cell culture studies confirmed that the APCL/SF nanofibers could significantly promote articular chondrocyte proliferation, type II collagen (COL-II) gene expression, and glycosaminoglycan secretion compared to the RPCL and RPCL/SF nanofiber scaffolds. Additionally, cytoskeletal staining displayed that the APCL/SF nanofiber scaffold promoted the elongation of articular chondrocytes in the direction of parallel fiber alignment.Conclusion: The APCL/SF nanofiber scaffold exhibited promising potential as a composite scaffold for articular cartilage regeneration

    Development of genomic phenotype and immunophenotype of acute respiratory distress syndrome using autophagy and metabolism-related genes.

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    BackgroundDistinguishing ARDS phenotypes is of great importance for its precise treatment. In the study, we attempted to ascertain its phenotypes based on metabolic and autophagy-related genes and infiltrated immune cells.MethodsTranscription datasets of ARDS patients were obtained from Gene expression omnibus (GEO), autophagy and metabolic-related genes were from the Human Autophagy Database and the GeneCards Database, respectively. Autophagy and metabolism-related differentially expressed genes (AMRDEGs) were further identified by machine learning and processed for constructing the nomogram and the risk prediction model. Functional enrichment analyses of differentially expressed genes were performed between high- and low-risk groups. According to the protein-protein interaction network, these hub genes closely linked to increased risk of ARDS were identified with CytoHubba. ssGSEA and CIBERSORT was applied to analyze the infiltration pattern of immune cells in ARDS. Afterwards, immunologically characterized and molecular phenotypes were constructed according to infiltrated immune cells and hub genes.ResultsA total of 26 AMRDEGs were obtained, and CTSB and EEF2 were identified as crucial AMRDEGs. The predictive capability of the risk score, calculated based on the expression levels of CTSB and EEF2, was robust for ARDS in both the discovery cohort (AUC = 1) and the validation cohort (AUC = 0.826). The mean risk score was determined to be 2.231332, and based on this score, patients were classified into high-risk and low-risk groups. 371 differential genes in high- and low-risk groups were analyzed. ITGAM, TYROBP, ITGB2, SPI1, PLEK, FGR, MPO, S100A12, HCK, and MYC were identified as hub genes. A total of 12 infiltrated immune cells were differentially expressed and have correlations with hub genes. According to hub genes and implanted immune cells, ARDS patients were divided into two different molecular phenotypes (Group 1: n = 38; Group 2: n = 19) and two immune phenotypes (Cluster1: n = 22; Cluster2: n = 35), respectively.ConclusionThis study picked up hub genes of ARDS related to autophagy and metabolism and clustered ARDS patients into different molecular phenotypes and immunophenotypes, providing insights into the precision medicine of treating patients with ARDS

    Structural transition, electric transport, and electronic structures in the compressed trilayer nickelate La4Ni3O10

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    Atomic structure and electronic band structure are fundamental properties for understanding the mechanism of superconductivity. Motivated by the discovery of pressure-induced high-temperature superconductivity at 80 K in the bilayer Ruddlesden-Popper nickelate La3Ni2O7, the atomic structure and electronic band structure of the trilayer nickelate La4Ni3O10 under pressure up to 44.3 GPa are investigated. A structural transition from the monoclinic P21/a space group to the tetragonal I4/mmm around 12.6-13.4 GPa is identified, accompanying with a drop of resistance below 7 K. Density functional theory calculations suggest that the bonding state of Ni 3dz2 orbital rises and crosses the Fermi level at high pressures, which may give rise to possible superconductivity observed in resistance under pressure in La4Ni3O10. The trilayer nickelate La4Ni3O10 shows some similarities with the bilayer La3Ni2O7 and has unique properties, providing a new platform to investigate the underlying mechanism of superconductivity in nickelates.Comment: 19 pages, 4 figure

    Case report: Abolishing primary resistance to PD-1 blockade by short-term treatment of lenvatinib in a patient with advanced metastatic renal cell carcinoma

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    Anti-PD-1 immunotherapy has been extensively used in treatment of patients with advanced metastatic renal cell carcinoma (mRCC). Several prospective clinical trials showed that the combined treatment of anti-PD-1 antibody plus lenvatinib, a potent receptor tyrosine kinase inhibitor (TKI), exhibited high response rate compared with single-agent sunitinib. However, whether the patients with primary resistance to PD-1 blockade could benefit from the addition of lenvatinib is still unclear. Herein, we reported a patient with mRCC who was primary resistant to pembrolizumab and achieved a durable complete response after a short-term treatment with lenvatinib. This case report indicates that the patients with primary resistance to anti-PD-1 therapy could benefit from the short-term lenvatinib in combination with anti-PD-1 therapy, and provides a useful paradigm worthy of establishing a clinical trial for mRCC patients with primary resistance to anti-PD-1 therapy
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