Search

BackgroundIschemic stroke (IS) is the second leading cause of mortality worldwide, continuing to be a serious health concern. It is well known that oxidative stress and neutrophil response play vital roles in the pathophysiology of early IS. However, the complex interactions and critical genes associated with them have not been fully understood.MethodsTwo datasets (GSE37587 and GSE16561) from the Gene Expression Omnibus database were extracted and integrated as the discovery dataset. Subsequent GSVA and WGCNA approaches were used to investigate IS-specific oxidative stress-related genes (ISOSGS). Then, we explored IS-specific neutrophil-associated genes (ISNGS) using CIBERSORT analysis. Next, the protein-protein interaction network was established to ascertain candidate critical genes related with oxidative stress and neutrophil response. Furthermore, these candidate genes were validated using GSE58294 dataset and our clinical samples by RT-qPCR method. Finally, functional annotation, diagnostic capability evaluation and drug-gene interactions were performed by using GSEA analysis, ROC curves and DGIDB database.ResultIn our analysis of discovery dataset, 155 genes were determined as ISOSGS and 559 genes were defined as ISNGS. Afterward, 9 candidate genes were identified through the intersection of ISOSGS and ISNGS, PPI network construction, and filtration by degree algorithm. Then, six real critical genes, including STAT3, MMP9, AQP9, SELL, FPR1, and IRAK3, passed the validation using the GSE58294 dataset and our clinical samples. Further functional annotation analysis indicated these critical genes were associated with neutrophil response, especially neutrophil extracellular trap. Meanwhile, they had a good diagnostic performance. Lastly, 53 potential drugs targeting these genes were predicted by DGIDB database.ConclusionWe identified 6 critical genes, STAT3, FPR1, AQP9, SELL, MMP9 and IRAK3, related to oxidative stress and neutrophil response in early IS, which may provide new insights into understanding the pathophysiological mechanism of IS. We hope our analysis could help develop novel diagnostic biomarkers and therapeutic strategies for IS.

Isoprene is the most abundant biogenic volatile organic compound in the atmosphere. Till now, chromatography is the main detection method for isoprene, dependent on sophisticated instruments and complex operations. An UV-Vis spectrophotometric method based on the Diels–Alder (D-A) reaction was developed to detect isoprene in the atmosphere. The fast D-A reaction between the probe 4-phenyl−1,2,4-triazolin−3,5-dione (PTAD) and isoprene may cause absorbance decrease, which enable it to detect isoprene in solution at 118 nmol·L−1 level. Further, isoprene in air at the level of ppbv was detected using this method. Compared with chromatography this method has the advantages of low cost, fast and less dependence on instrument.

Additional file 3: Supplementary Table S1. CCHE1-interesting proteins>1.50 and p

The number of atomic layers confined in a two-dimensional structure is crucial for the electronic and magnetic properties. Single-layer and bilayer $J_{\rm eff} = 1/2$ square lattices are well-known examples where the presence of the extra layer turns the XY-anisotropy to the $c$-axis anisotropy. We report on experimental realization of a hybrid SrIrO$_3$/SrTiO$_3$ superlattice that integrates monolayer and bilayer square lattices in one layered structure. By synchrotron x-ray diffraction, resonant x-ray magnetic scattering, magnetization, and resistivity measurements, we found that the hybrid superlattice exhibits properties that are distinct from both the single-layer and bilayer systems and cannot be explained by a simple addition of them. In particular, the entire hybrid superlattice orders simultaneously through a single antiferromagnetic transition at temperatures similar to the bilayer system but with all the $J_{\rm eff} = 1/2$ moments mainly pointing in the $ab$-plane similar to the single-layer system. The results show that bringing monolayer and bilayer with orthogonal properties in proximity to each other in a hybrid superlattice structure is a powerful way to stabilize a unique state not obtainable in a uniform structure.

We investigate an experimental toy-model system of a pseudospin-half square-lattice Hubbard Hamiltonian in [(SrIrO3)1/(CaTiO3)1] to include both nontrivial complex hopping and moderate electronic correlation. While the former induces electronic Berry phases as anticipated from the weak-coupling limit, the later stabilizes an antiferromagnetic (AFM) Mott insulator ground state in analogous to the strong-coupling limit. Their combined results in the real system are found to be an anomalous Hall effect with a non-monotonic temperature dependence due to the self-competition of the electron-hole pairing in the Mott state, and an exceptionally large Ising anisotropy that is captured as a giant magnon gap beyond the superexchange approach. The unusual phenomena highlight the rich interplay of electronic topology and electronic correlation in the intermediate-coupling regime that is largely unexplored and challenging in theoretical modelling., Comment: Accepted by Phys. Rev. X

With the rapid development of satellite communication technologies, the space-based access network has been envisioned as a promising complementary part of the future 6G network. Aside from terrestrial base stations, satellite nodes, especially the low-earth-orbit (LEO) satellites, can also serve as base stations for Internet access, and constitute the LEO-satellite-based access network (LEO-SAN). LEO-SAN is expected to provide seamless massive access and extended coverage with high signal quality. However, its practical implementation still faces significant technical challenges, e.g., high mobility and limited budget for communication payloads of LEO satellite nodes. This paper aims at revealing the main technical issues that have not been fully addressed by the existing LEO-SAN designs, from three aspects namely random access, beam management and Doppler-resistant transmission technologies. More specifically, the critical issues of random access in LEO-SAN are discussed regarding low flexibility, long transmission delay, and inefficient handshakes. Then the beam management for LEO-SAN is investigated in complex propagation environments under the constraints of high mobility and limited payload budget. Furthermore, the influence of Doppler shifts on LEO-SAN is explored. Correspondingly, promising technologies to address these challenges are also discussed, respectively. Finally, the future research directions are envisioned.

We report on the epitaxial strain-driven electronic and antiferromagnetic modulations of a pseudospin-half square-lattice realized in superlattices of (SrIrO_{3})_{1}/(SrTiO_{3})_{1}. With increasing compressive strain, we find the low-temperature insulating behavior to be strongly suppressed with a corresponding systematic reduction of both the Néel temperature and the staggered moment. However, despite such a suppression, the system remains weakly insulating above the Néel transition. The emergence of metallicity is observed under large compressive strain but only at temperatures far above the Néel transition. These behaviors are characteristics of the Slater-Mott crossover regime, providing a unique experimental model system of the spin-half Hubbard Hamiltonian with a tunable intermediate coupling strength.

The main task of investment forecasting on engineering project is to make a reasonable cost estimate of power transmission and transformation project. As a significant part of electric power construction, the accuracy of investment forecasting for projects will directly affect the economy of project construction and the efficiency of resource allocation. Regression analysis and BP neural network model are used to forecast the investment, and the two models are weighted according to the error to obtain the final forecasting results. Under the premise of comprehensive consideration of line length, construction fee, basic steel quantity, wire price, steel price, topographical distribution, geological conditions, etc., an empirical analysis is conducted to take a certain area as an example. The experimental results fully prove that the proposed forecasting model can obtain more accurate results in the investment forecasting of power transmission and transformation project.

While the depth of convolutional neural networks has attracted substantial attention in the deep learning research, the width of these networks has recently received greater interest. The width of networks, defined as the size of the receptive fields and the density of the channels, has demonstrated crucial importance in low-level vision tasks such as image denoising and restoration. However, the limited generalization ability, due to the increased width of networks, creates a bottleneck in designing wider networks. In this paper, we propose the Deep Regulated Convolutional Network (RC-Net), a deep network composed of regulated sub-network blocks cascaded by skip-connections, to overcome this bottleneck. Specifically, the Regulated Convolution block (RC-block), featured by a combination of large and small convolution filters, balances the effectiveness of prominent feature extraction and the generalization ability of the network. RC-Nets have several compelling advantages: they embrace diversified features through large-small filter combinations, alleviate the hazy boundary and blurred details in image denoising and super-resolution problems, and stabilize the learning process. Our proposed RC-Nets outperform state-of-the-art approaches with significant performance gains in various image restoration tasks while demonstrating promising generalization ability. The code is available at https://github.com/cswin/RC-Nets.

Aimed at the characteristics of infrared images for moving target, such as complicated background, low signal-to-noise ratio and low contrast, a new method for complicated background suppression is proposed based on Large Field (LF) and Small Field (SF) system in the paper. Firstly, the CCD sensors are used to obtain infrared video data for moving target. Then, after analyzing the visual motion detection mechanism of insect compound eye, the paper establishes background suppression model based on LF&SF. In this model, LF is used to suppress noise of edge detection while SF is used to do nonlinear enhancement of target features and estimation of target movement direction. Finally, the paper uses the primary motion detector to integrate LF with SF. Real scene experiment shows that this proposed method has obvious advantages in suppressing the cloud cover, grass lands and other noise and enhancing target.

16S rDNA sequencing method is one of the effectively used culture-independent techniques in recent years. In this study, 16S rDNA sequencing method was used to investigate the bacterial diversity in deep-sea sediment from northeastern Pacific polymetallic nodule province. Total DNAs were extracted by using 2 different methods (chemical method and DNA extracting kit method). After purification, genomic DNA was amplified by using 2 universal primers (27F and 1492R). Clones were selected and sequenced randomly. After the sequences were checked by using the Chimera Check Program of the RDP database, a bacterial 16S rRNA gene library of 79 clones was established. Phylogenetic analysis indicated that 79 clones could be divided into 11 phylotypes. Gamma Proteobacteria (22.8%) and alpha Proteobacteria (16.5%) were the dominant components of the sediment bacterial community, followed by Planctomycetacia (7.6%), delta Proteobacteria (6.3%), Nitrospira (6.3%), Actinobacteria (6.3%), beta Proteobacteria (5%), Acidobacteria (5.1%), Sphingobacteria (3.8%), Firmicutes (2.5%) and uncultured bacteria (17.7%). Gamma Proteobacteria also dominated at slices 0–2 cm and 4–6 cm. Different slices had different types of bacteria, alpha Proteobacteria, gamma Proteobacteria, delta Proteobacteria, Planctomycetacia, Nitrospira, Actinobacteria and Acidobacteria, however, appeared in all slices. Pseudomonas is common in many different deep-sea environments. In this study, it accounted for 22.2% of the total gamma Proteobacteria.

Four seasons of investigations on macrofauna in Yueqing Bay, China were carried out from August 2002 to May 2003. One hundred and twenty four species of macrofauna were identified, including 41 species of polychaeta, 37 species of mollusc, 22 species of crustacea, 10 species of echinoderm and 14 others. The average biomass was 41.95 g·m−2 and the average density was 85 ind·m−2. The echinoderm contributed the highest proportion (about 60%) to the total biomass, and the mollusc contributed the maximum proportion (about 35%) to the total density. Biomass and density in spring were higher than those in other seasons. This article analyzes the macrofauna distribution, compares the study results with the history data of Yueqing Bay and adjacent bays, and discusses the response of macrofauna community to the aquiculture in Yueqing Bay.

Using Laplace pyramid algorithm and pulse coupled neural network (PCNN), this paper proposed an effective fusion algorithm of multi-focus image. First, the paper uses Laplace pyramid for multi-scale decomposition of the image, and by using the PCNN to process the decomposition image, this obtains corresponding neuron ignition frequency map. Then, the paper calculates local entropy for every pixel's ignition frequency map, and takes local entropy as a measure of the quality of the pixels for the source image fusion. Finally, the paper uses the Laplace pyramid reconstruction algorithm to generate the fused image. Experimental results indicate that proposed method is effective and better than other traditional fusion algorithms.

Underwater imaging is an important method to discover and study deep sea life. However, the information obtained from the 2-dimensional imaging materials acquired by the current technological method is too limited to meet the requirements of scientific research. In this article, a package of underwater lighting and stereo vision measurement hardware system is designed that could be carried by underwater platform. The feasibility and accuracy of the proposed methods is proved by the pool principle experiment. This system could provide technological support for the deep sea biology study and the exploitation of deep sea biological resources, and also be used for the 3D reconstruction and measurement of underwater micro topography and targets.

Research of the marine meiobenthos is of great value in the marine ecology and the marine mineralogy. At present, picking up meiobenthos from the sediments manually under microscope is still prevalent in spite of the large consumption of human power and time. So that in this paper, an automatic separation system was established. The excellent performance in separating marine meiobenthos was demonstrated by the precision of above 95%

In order to probe and improve temperature field during electric current sintering, high strength ceramic and graphite were used respectively as female die during sintering. Experiments and finite element method (FEM) were conducted to characterize the electric current density, resistance heating and temperature distribution in the specimen/die/punch. Obviously higher heating rate, higher final sintering temperature and higher gradient temperature were found in specimens when high strength ceramics was used as female die, compared with graphite female die under the conditions of the same die dimension and sintering parameters. A composite die was proposed to reduce and even eliminate the differences as well as to improve heating rate of the specimen. The experimental results are in good agreement with calculated values by FEM, which indicates that FEM may be an effective analysis method to simulate temperature field of electric current sintering.

The law of sintering temperature' changing during electric field assisted sintering (EFAST) is the very important technology information. Commonly very long time test, which could damage the test instrument, must be done in order to find the law. Grey system theory is suitable for systems in which some information is clear and some is blurry, so it is feasible to predict poor messages system's technology parameters. In this paper, the Grey system theory is adopted to forecasting the unknown sintering temperature during EFAST, but it is discovered that the predict results has some problems, for example the forecasting sintering temperature is monotonic rising during EFAST, but in fact the sintering temperature has a maximum value or peak value if the pulse current keep unchangeable during EFAST , so we modified the standard grey model by adding the temperature rising series information to the grey model. The result showed the temperature peak can be found and the forecasting precision is improved and the prediction error is decreased. The average relative error of modified grey model is less than 3%.