miR-27b Suppresses Endothelial Cell Proliferation and Migration by Targeting Smad7 in Kawasaki Disease

Background/Aims: Increasing evidence indicates that microRNAs (miRNAs) play important roles in Kawasaki disease (KD). Our previous study demonstrated that hsa-miR-27b-3p (miR-27b) was up-regulated in KD serum. However, the specific role of miR-27b in KD remains unclear. We aimed to investigate that miR-27b could be a biomarker and therapeutic target for KD treatment. As well, the specific mechanism of miR-27b effecting endothelial cell functions was studied. Methods: The expression of miR-27b and Smad7 was measured by qRT-PCR. Gain-of-function strategy was used to observe the effect of miR-27b on human umbilical vein endothelial cells (HUVECs) proliferation and migration. Bioinformatics analyses were applied to predict miR-27b targets and then we verified Smad7 by a luciferase reporter assay. Western blot was performed to detect the protein expression of Smad7, PCNA, MMP9, MMP12 and TGF-β-related genes. Results: We confirmed that miR-27b was shown to be dramatically up-regulated in KD serum and KD serum-treated HUVECs and that elevated expression of miR-27b suppressed the proliferation and migration of HUVECs. Furthermore, our results verified that miR-27b mediated cell functions by affecting the TGF-β via targeting Smad7 in HUVECs. Conclusion: These results suggested that up-regulated miR-27b had a protective role in HUVECs proliferation and migration via targeting Smad7 and affecting TGF-β pathway. Therefore, miR-27b represented a potential biomarker for KD and may serve as a promising therapeutic target for KD treatment

Association of multiple blood metals with thyroid function in general adults: A cross−sectional study

IntroductionThyroid function has a large impact on humans’ metabolism and is affected by iodine levels, but there is a scarcity of studies that elucidate the association between thyroid function and other elements.MethodsWe performed a cross-sectional study on 1,067 adults to evaluate the associations of the common essential metals with thyroid function in adults living in an iodine-adequate area of China. Serum free thyroxine (FT4), free triiodothyronine (FT3), thyroid stimulating hormone (TSH), and blood metals (zinc, iron, copper, magnesium, manganese, and calcium) were measured. Further, the thyroid hormone sensitivity indexes, FT3:FT4 ratio, and thyrotropin T4 resistance index (TT4RI) were calculated. Linear regression, quantile g-computation, and Bayesian kernel machine regression methods were used to explore the association of metals with thyroid function.ResultsWe found that the TSH levels correlated with copper (negative) and zinc (positive). Iron and copper were positively associated with FT3 and FT4 levels, respectively. Iron (positive) and copper (negative) were correlated with the FT3:FT4 ratio. Furthermore, we found that manganese was inversely correlated with TT4RI, while zinc was positively correlated.DiscussionOur findings suggest that manganese, iron, copper, and zinc levels were strongly correlated with thyroid function, and patients with thyroid disorders are recommended to measure those metals levels

Industry/University Collaboration at the University of Michigan-Dearborn: A Focus on Relevant Technology

https://deepblue.lib.umich.edu/bitstream/2027.42/154106/1/kampfner1998.pd

Energy consumption analysis of a ground water-source heat pump for the plant factory based on TRNSYS simulation

The ground water-source heat pump system for the plant factory lacks a scientific operation strategy to solve the problem of high energy consumption in winter and summer. It is very difficult and uneconomical to change the operation conditions by experimental means to obtain actual operation data. The current study aims to build a TRNSYS simulation model of the ground water-source heat pump system of Shanghai Chongming Natural Light Plant Factory. For the heating season, the simulation of energy consumption was 2315 GJ. Compared with the actual energy consumption, the relative error is -0.98%, which indicates that the simulation results are accurate and the simulation model developed is appropriate and usable. Numerical simulations for the whole year on this basis yielded that the plant factory energy supply system operates from November to March with a heating energy consumption of 3530.84 GJ and from June to September with a cooling energy consumption of 1126.24 GJ. In most cases, the indoor temperature fluctuates within a reasonable range, but in the summer high-temperature season, the plant factory temperature will reach above 40℃, which seriously affects plant growth. After optimization, the plant factory stops production in July and August, and the system stops running, the results are that the optimized system can save 56% of the annual cooling energy consumption, totalling 767.48 GJ, which can reduce the costs by 160,318.05 RMB

An Urban Hot/Cold Spot Detection Method Based on the Page Rank Value of Spatial Interaction Networks Constructed from Human Communication Records

Applying spatial clustering algorithms on large-scale spatial interactive dataset to find urban hot/cold spots is a new idea to assist urban management. However, the research usually focuses on the dataset with spatio-temporal proximity, rather than remote dataset. This article proposes a spatial hot/cold spot detection method for human communication by auto-correlating the PageRank values of the spatial interaction networks constructed by records. Milan was selected as the study area, and the spatial interaction records reflected by telephone calls, the land-use dataset, and the POI dataset were used as experimental data. The results showed that the proposed method can be applied to long-distance spatial interactive recording data, and the hot/cold spot were clearly distinguished by the statistical distribution of the containing land-use dataset and the POI dataset. These differences were consistent with the actual situation in the study area, indicating the accuracy of the proposed method for detecting hot/cold areas

An Urban Hot/Cold Spot Detection Method Based on the Page Rank Value of Spatial Interaction Networks Constructed from Human Communication Records

Applying spatial clustering algorithms on large-scale spatial interactive dataset to find urban hot/cold spots is a new idea to assist urban management. However, the research usually focuses on the dataset with spatio-temporal proximity, rather than remote dataset. This article proposes a spatial hot/cold spot detection method for human communication by auto-correlating the PageRank values of the spatial interaction networks constructed by records. Milan was selected as the study area, and the spatial interaction records reflected by telephone calls, the land-use dataset, and the POI dataset were used as experimental data. The results showed that the proposed method can be applied to long-distance spatial interactive recording data, and the hot/cold spot were clearly distinguished by the statistical distribution of the containing land-use dataset and the POI dataset. These differences were consistent with the actual situation in the study area, indicating the accuracy of the proposed method for detecting hot/cold areas

Application Research of File Fingerprint Identification Detection Based on a Network Security Protection System

A DLP (data loss prevention) system usually arranges network monitors at the network boundary to perform network traffic capture, file parsing, and strategy matching procedures. Strategy matching is a key process to prevent corporate secret-related documents from leaking. This paper adopts the document fingerprint similarity detection method based on the SimHash principle and customizes the KbS (Keyword-based SimHash) fingerprint, PbS (Paragraph-based SimHash) fingerprint, and SoP (SimHash of Paragraph) fingerprint, three different feature extraction SimHash algorithms for strategy matching to detect. The parsed unstructured data is stored as a file type in.txt format, and then a file fingerprint is generated. Matching the established sensitive document library to calculate the Hamming distance between the fingerprints, the Hamming distance values under different modification degrees are summarized. The experimental results reveal that the hybrid algorithmic strategy matching rules with different levels and accuracy are established. This paper has a reference role for the leakage prevention research of enterprise sensitive data

Custom 3D fMRI Registration Template Construction Method Based on Time-Series Fusion

As the brain standard template for medical image registration has only been constructed with an MRI template, there is no three-dimensional fMRI standard template for use, and when the subject’s brain structure is quite different from the standard brain structure, the registration to the standard space will lead to large errors. Registration to an individual space can avoid this problem. However, in the current fMRI registration algorithm based on individual space, the reference image is often selected by researchers or randomly selected fMRI images at a certain time point. This makes the quality of the reference image very dependent on the experience and ability of the researchers and has great contingency. Whether the reference image is appropriate and reasonable affects the rationality and accuracy of the registration results to a great extent. Therefore, a method for constructing a 3D custom fMRI template is proposed. First, the data are preprocessed; second, by taking a group of two-dimensional slices corresponding to the same layer of the brain in three-dimensional fMRI images at multiple time points as image sequences, each group of slice sequences are registered and fused; and finally, a group of fused slices corresponding to different layers of the brain are obtained. In the process of registration, in order to make full use of the correlation information between the sequence data, the feature points of each two slices of adjacent time points in the sequence are matched, and then according to the transformation relationship between the adjacent images, they are recursively forwarded and mapped to the same space. Then, the fused slices are stacked in order to form a three-dimensional customized fMRI template with individual pertinence. Finally, in the classic registration algorithm, the difference in the registration accuracy between using a custom fMRI template and different standard spaces is compared, which proves that using a custom template can improve the registration effect to a certain extent

Application Research of Intelligent Classification Technology in Enterprise Data Classification and Gradation System

Classification and gradation system adopts different security protection schemes for different types of data by implementing classification and gradation management of data, which is an important pretechnical means for data security protection and prevention of data leakage. This paper introduces artificial intelligence classification, machine learning, and other means to learn and train enterprise documents according to the characteristics of enterprise sensitive data. The generated training model can intelligently identify and classify file streams, improving work efficiency and accuracy of classification and gradation. At the same time, the differences, advantages, and disadvantages of K-NN (K-Nearest Neighbors), DT (Decision Tree), and LinearSVC algorithms are compared. The experimental data shows that LinearSVC algorithm is applicable to high-dimensional data, with discrete, sparse data features and large number of features, which is more suitable for classification of sensitive data of enterprises