820 research outputs found
Sequential Wnt Agonist then Antagonist Treatment Accelerates Tissue Repair and Minimizes Fibrosis
Tissue fibrosis compromises organ function and occurs as a potential
long-term outcome in response to acute tissue injuries. Currently, lack of
mechanistic understanding prevents effective prevention and treatment of the
progression from acute injury to fibrosis. Here, we combined quantitative
experimental studies with a mouse kidney injury model and a computational
approach to determine how the physiological consequences are determined by the
severity of ischemia injury, and to identify how to manipulate Wnt signaling to
accelerate repair of ischemic tissue damage while minimizing fibrosis. The
study reveals that Wnt-mediated memory of prior injury contributes to fibrosis
progression, and ischemic preconditioning reduces the risk of death but
increases the risk of fibrosis. Furthermore, we validated the prediction that
sequential combination therapy of initial treatment with a Wnt agonist followed
by treatment with a Wnt antagonist can reduce both the risk of death and
fibrosis in response to acute injuries
XML Data Retrieval Model Based on Two-dimensional Table Datasets
Retrieval problems of XML-based representation of data have been researched in this paper. In order to solve the large time and space overhead problem in building content index, this paper establish a data retrieval model advantageous to xml representation using the system automatically build two-dimensional table datasets. Take crop diseases and insect pests data for an example, this paper first gives the architecture of retrieval system based on XML crop diseases and insect pests' data; it also discusses about how to construct the two-dimensional table dataset and achieve the retrieval process; then it describes the text segmentation technique and the XSL style sheet conversion technology. Finally, under the VS.NET platform, using MVC design pattern develop and implement a prototype
Spatial representativeness and uncertainty of eddy covariance carbon flux measurements for upscaling net ecosystem productivity to the grid scale
Eddy covariance (EC) measurements are often used to validate net ecosystem productivity (NEP) estimated from satellite remote sensing data and biogeochemical models. However, EC measurements represent an integrated flux over their footprint area, which usually differs from respective model grids or remote sensing pixels. Quantifying the uncertainties of scale mismatch associated with gridded flux estimates by upscaling single EC tower NEP measurements to the grid scale is an important but not yet fully investigated issue due to limited data availability as well as knowledge of flux variability at the grid scale. The Heihe Watershed Allied Telemetry Experimental Research (HiWATER) Multi-Scale Observation Experiment on Evapotranspiration (MUSOEXE) built a flux observation matrix that includes 17 EC towers within a 5 km × 5 km area in a heterogeneous agricultural landscape in northwestern China, providing an unprecedented opportunity to evaluate the uncertainty of upscaling due to spatial representative differences at the grid scale. Based on the HiWATER-MUSOEXE data, this study evaluated the spatial representativeness and uncertainty of EC CO2 flux measurements for upscaling to the grid scale using a scheme that combines a footprint model and a model-data fusion method. The results revealed the large spatial variability of gross primary productivity (GPP), ecosystem respiration (Re), and NEP within the study site during the growing season from 10 June to 14 September 2012. The variability of fluxes led to high variability in the representativeness of single EC towers for grid-scale NEP. The systematic underestimations of a single EC tower may reach 92(±11)%, 30(±11)%, and 165(±150)% and the overestimations may reach 25(±14)%, 20(±13)%, and 40(±33)% for GPP, Re, and NEP, respectively. This finding suggests that remotely sensed NEP at the global scale (e.g., MODIS products) should not be validated against single EC tower data in the case of heterogeneous surfaces. Any systematic bias should be addressed before upscaling EC data to grid scale. Otherwise, most of the systematic bias may be propagated to grid scale due to the scale dependence of model parameters. A systematic bias greater than 20% of the EC measurements can be corrected effectively using four indicators proposed in this study. These results will contribute to the understanding of spatial representativeness of EC towers within a heterogeneous landscape, to upscaling carbon fluxes from the footprint to the grid scale, to the selection of the location of EC towers, and to the reduction in the bias of NEP products by using an improved parameterization scheme of remote-sensing driven models, such as VPRM
A virus-like particle of the hepatitis B virus preS antigen elicits robust neutralizing antibodies and T cell responses in mice
The preS antigen of hepatitis B virus (HBV) corresponds to the N-terminal polypeptide in the large (L) antigen in addition to the small (S) antigen. The virus-like particle (VLP) of the S antigen is widely used as a vaccine to protect the population from HBV infection. The presence of the S antigen and its antibodies in patient blood has been used as markers to monitor hepatitis B. However, there is very limited knowledge about the preS antigen. We generated a preS VLP that is formed by a chimeric protein between preS and hemagglutinin (HA), and the matrix protein M1 of influenza virus. The HBV preS antigen is displayed on the surface of preS VLP. Asn112 and Ser98 of preS in VLP were found to be glycosylated and O-glycosylation of Ser98 has not been reported previously. The preS VLP shows a significantly higher immunogenicity than recombinant preS, eliciting robust anti-preS neutralizing antibodies. In addition, preS VLP is also capable of stimulating preS-specific CD8+ and CD4+ T cell responses in Balb/c mice and HBV transgenic mice. Furthermore, preS VLP immunization provided protection against hydrodynamic transfection of HBV DNA in mice. The data clearly suggest that this novel preS VLP could elicit robust immune responses to the HBV antigen, and can be potentially developed into prophylactic and therapeutic vaccines
NineRec: A Benchmark Dataset Suite for Evaluating Transferable Recommendation
Learning a recommender system model from an item's raw modality features
(such as image, text, audio, etc.), called MoRec, has attracted growing
interest recently. One key advantage of MoRec is that it can easily benefit
from advances in other fields, such as natural language processing (NLP) and
computer vision (CV). Moreover, it naturally supports transfer learning across
different systems through modality features, known as transferable recommender
systems, or TransRec.
However, so far, TransRec has made little progress, compared to
groundbreaking foundation models in the fields of NLP and CV. The lack of
large-scale, high-quality recommendation datasets poses a major obstacle. To
this end, we introduce NineRec, a TransRec dataset suite that includes a
large-scale source domain recommendation dataset and nine diverse target domain
recommendation datasets. Each item in NineRec is represented by a text
description and a high-resolution cover image. With NineRec, we can implement
TransRec models in an end-to-end training manner instead of using pre-extracted
invariant features. We conduct a benchmark study and empirical analysis of
TransRec using NineRec, and our findings provide several valuable insights. To
support further research, we make our code, datasets, benchmarks, and
leaderboards publicly available at https://github.com/westlake-repl/NineRec
Copolymer micelles function as pH-responsive nanocarriers to enhance the cytotoxicity of a HER2 aptamer in HER2-positive breast cancer cells
Trust Model in Cloud Computing Environment Based on Fuzzy Theory
Recent years have witnessed the development of cloud computing. However,there also come some security concerns in cloud computing environment, suchas emerging network attacks and intrusions, and instable cloud service provision dueto flexible cloud infrastructure and resources. To this end, we research on the trustedcomputing in cloud computing environment. Specifically, in this paper, we proposea trust model based on virtual machines, with two considerations. First, we introducetimeliness strategy to ensure the response time and also minimize the idle timeof servers. Second, we extend the linear trust chain by differentiating the trust ofthe platform domain and user domain. Besides, we develop a fuzzy theory basedmethod to calculate the trust value of cloud service providers. We also conduct someexperiments to evaluate our method
CRISPR-Cas technology opens a new era for the creation of novel maize germplasms
Maize (Zea mays) is one of the most important food crops in the world with the greatest global production, and contributes to satiating the demands for human food, animal feed, and biofuels. With population growth and deteriorating environment, efficient and innovative breeding strategies to develop maize varieties with high yield and stress resistance are urgently needed to augment global food security and sustainable agriculture. CRISPR-Cas-mediated genome-editing technology (clustered regularly interspaced short palindromic repeats (CRISPR)-Cas (CRISPR-associated)) has emerged as an effective and powerful tool for plant science and crop improvement, and is likely to accelerate crop breeding in ways dissimilar to crossbreeding and transgenic technologies. In this review, we summarize the current applications and prospects of CRISPR-Cas technology in maize gene-function studies and the generation of new germplasm for increased yield, specialty corns, plant architecture, stress response, haploid induction, and male sterility. Optimization of gene editing and genetic transformation systems for maize is also briefly reviewed. Lastly, the challenges and new opportunities that arise with the use of the CRISPR-Cas technology for maize genetic improvement are discussed
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