Twenty amino acids at the C-terminus of PA-X are associated with increased influenza A virus replication and pathogenicity

The PA-X protein, arising from ribosomal frameshift during PA translation, was recently discovered in influenza A virus (IAV). The C-terminal domain ‘X’ of PA-X proteins in IAVs can be classified as full-length (61 aa) or truncated (41 aa). In the main, avian influenza viruses express full-length PA-X proteins, whilst 2009 pandemic H1N1 (pH1N1) influenza viruses harbour truncated PA proteins. The truncated form lacks aa 232–252 of the full-length PA-X protein. The significance of PA-X length in virus function remains unclear. To address this issue, we constructed a set of contemporary influenza viruses (pH1N1, avian H5N1 and H9N2) with full and truncated PA-X by reverse genetics to compare their replication and host pathogenicity. All full-length PA-X viruses in human A549 cells conferred 10- to 100-fold increase in viral replication and 5–8 % increase in apoptosis relative to corresponding truncated PA-X viruses. Full-length PA-X viruses were more virulent and caused more severe inflammatory responses in mice. Furthermore, aa 233–252 at the C terminus of PA-X strongly suppressed co-transfected gene expression by ∼50 %, suggesting that these terminal 20 aa could play a role in enhancing viral replication and contribute to virulence

Evaluation of Sustainable Water Resource Use in the Tarim River Basin Based on Water Footprint

Quantifying water use for agricultural production and accurate evaluation is important for achieving a balance between water supply and demand and sustainable use, especially in arid regions. This study quantifies the water footprint of food production in the Tarim River Basin (TRB) from 2000 to 2019 by conducting a sustainability evaluation using both the water footprint and DPSIR model as a theoretical framework, and by analyzing spatial and temporal changes. The results show that the water footprint of the TRB increased from 2.15 m3/kg to 2.86 m3/kg per unit during the study period. The average annual weighted water footprint of the basin is 2.59 m3/kg, of which 2.41 m3/kg is blue water and 0.18 m3/kg is green water. Blue water inputs contribute more than 94% to food production annually. Furthermore, although the level of sustainable water use increased, its score is low, with the most prominent stress assessment value indicating poor regional water use. Prior to 2010, the Tarim River Basin region’s sustainability was less than 0.4, indicating that water resources were at or below the level of basic unsustainability. By 2019, however, the sustainability of areas with better water use was greater than 0.4., and the sustainability of 80% of the region was above 0.2. In the future, we need to reduce the crop water footprint and improve water use efficiency to ensure the sustainable use of water resources and avoid further pressure on water use

A Fast and Reliable Matching Method for Automated Georeferencing of Remotely-Sensed Imagery

Due to the limited accuracy of exterior orientation parameters, ground control points (GCPs) are commonly required to correct the geometric biases of remotely-sensed (RS) images. This paper focuses on an automatic matching technique for the specific task of georeferencing RS images and presents a technical frame to match large RS images efficiently using the prior geometric information of the images. In addition, a novel matching approach using online aerial images, e.g., Google satellite images, Bing aerial maps, etc., is introduced based on the technical frame. Experimental results show that the proposed method can collect a sufficient number of well-distributed and reliable GCPs in tens of seconds for different kinds of large-sized RS images, whose spatial resolutions vary from 30 m to 2 m. It provides a convenient and efficient way to automatically georeference RS images, as there is no need to manually prepare reference images according to the location and spatial resolution of sensed images

Load-Bearing Performance and Safety Assessment of Grid Pile Foundation

Group piles with cushion caps are a common structural form for deep-water bridge foundations. However, their application is limited by the challenges of complex construction, difficult recovery of the supporting large-scale temporary structure, and high engineering expenses. Therefore, we propose a new foundation form—grid pile foundation (GPF)—to improve the sustainability and reliability of foundations. In this study, the finite element software ABAQUS was used to investigate the mechanical properties and dimensional effects of the GPF. Subsequently, the Monte Carlo method was adopted to evaluate the safety under different geological conditions. The results demonstrated that along the depth, the inner frictional resistance of the GPF exhibits an exponential distribution, whereas the outer frictional resistance exhibits an approximate triangular distribution. In addition, the change in pile size has a non-negligible effect on the load-bearing capacity of the GPF. For the same work amount, the smaller pile and side lengths promoted the inner frictional resistance exertion of the GPF. Furthermore, the safety and reliability analysis suggested that the GPF proposed in this study can be used safely under complex geological conditions

Tr-Predictior: An Ensemble Transfer Learning Model for Small-Sample Cloud Workload Prediction

Accurate workload prediction plays a key role in intelligent scheduling decisions on cloud platforms. There are massive amounts of short-workload sequences in the cloud platform, and the small amount of data and the presence of outliers make accurate workload sequence prediction a challenge. For the above issues, this paper proposes an ensemble learning method based on sample weight transfer and long short-term memory (LSTM), termed as Tr-Predictor. Specifically, a selection method of similar sequences combining time warp edit distance (TWED) and transfer entropy (TE) is proposed to select a source domain dataset with higher similarity for the target workload sequence. Then, we upgrade the basic learner of the ensemble model two-stage TrAdaBoost.R2 to LSTM in the deep model and enhance the ability of the ensemble model to extract sequence features. To optimize the weight adjustment strategy, we adopt a two-stage weight adjustment strategy and select the best weight for the learner according to the sample error and model error. Finally, the above process determines the parameters of the target model and uses the target model to predict the short-task sequences. In the experimental validation, we arbitrarily select nine sets of short-workload data from the Google dataset and three sets of short-workload data from the Alibaba cluster to verify the prediction effectiveness of the proposed algorithm. The experimental results show that compared with the commonly used cloud workload prediction methods Tr-Predictor has higher prediction accuracy on the small-sample workload. The prediction indicators of the ablation experiments show the performance gain of each part in the proposed method

Eliminating the Effect of Image Border with Image Periodic Decomposition for Phase Correlation Based Remote Sensing Image Registration

In the remote sensing community, accurate image registration is the prerequisite of the subsequent application of remote sensing images. Phase correlation based image registration has drawn extensive attention due to its high accuracy and high efficiency. However, when the Discrete Fourier Transform (DFT) of an image is computed, the image is implicitly assumed to be periodic. In practical application, it is impossible to meet the periodic condition that opposite borders of an image are alike, and image always shows strong discontinuities across the frame border. The discontinuities cause a severe artifact in the Fourier Transform, namely the known cross structure composed of high energy coefficients along the axes. Here, this phenomenon was referred to as effect of image border. Even worse, the effect of image border corrupted its registration accuracy and success rate. Currently, the main solution is blurring out the border of the image by weighting window function on the reference and sensed image. However, the approach also inevitably filters out non-border information of an image. The existing understanding is that the design of window function should filter as little information as possible, which can improve the registration success rate and accuracy of methods based on phase correlation. In this paper, another approach of eliminating the effect of image border is proposed, namely decomposing the image into two images: one being the periodic image and the other the smooth image. Replacing the original image by the periodic one does not suffer from the effect on the image border when applying Fourier Transform. The smooth image is analogous to an error image, which has little information except at the border. Extensive experiments were carried out and showed that the novel algorithm of eliminating the image border can improve the success rate and accuracy of phase correlation based image registration in some certain cases. Additionally, we obtained a new understanding of the role of window function in eliminating the effect of image border, which is helpful for researchers to select the optimal method of eliminating the effect of image border to improve the registration success rate and accuracy

A low-inflammatory diet is associated with a lower incidence of diabetes: role of diabetes-related genetic risk

BACKGROUND: Whether a low-inflammatory diet relates to type 2 diabetes risk remains unclear. We examined the association between a low-inflammatory diet and risk of type 2 diabetes among normoglycemic and prediabetic participants. We also explored whether a low-inflammatory diet modifies genetic risk for type 2 diabetes. METHODS: Among 142,271 diabetes-free UK Biobank participants (aged 39–72 years), 126,203 were normoglycemic and 16,068 were prediabetic at baseline. Participants were followed for up to 15 years to detect incident type 2 diabetes. At baseline, dietary intake was assessed with a 24-h dietary record. An inflammatory diet index (IDI) was generated based on high-sensitivity C-reactive protein levels and was a weighted sum of 34 food groups (16 anti-inflammatory and 18 pro-inflammatory). Participants were grouped into tertiles corresponding to inflammatory level (low, moderate, and high) based on IDI scores. Prediabetes at baseline was defined as HbA1c 5.7–6.4% in diabetes-free participants. Incident type 2 diabetes and age of onset were ascertained according to the earliest recorded date of type 2 diabetes in the Primary Care and Hospital inpatient data. A diabetes-related genetic risk score (GRS) was calculated using 424 single-nucleotide polymorphisms. Data were analyzed using Cox regression and Laplace regression. RESULTS: During follow-up (median 8.40 years, interquartile range 6.89 to 11.02 years), 3348 (2.4%) participants in the normoglycemia group and 2496 (15.5%) in the prediabetes group developed type 2 diabetes. Type 2 diabetes risk was lower in normoglycemic (hazard ratio [HR] = 0.71, 95% confidence interval [CI] 0.65, 0.78) and prediabetic (HR = 0.81, 95% CI 0.73, 0.89) participants with low IDI scores compared to those with high IDI scores. A low-inflammatory diet may prolong type 2 diabetes onset by 2.20 (95% CI 1.67, 2.72) years among participants with normoglycemia and 1.11 (95% CI 0.59, 1.63) years among participants with prediabetes. In joint effect analyses, normoglycemic or prediabetes participants with low genetic predisposition to type 2 diabetes and low IDI scores had a significant 74% (HR = 0.26, 95% CI 0.21, 0.32) or 51% (HR = 0.49, 95% CI 0.40, 0.59) reduction in type 2 diabetes risk compared to those with high genetic risk plus high IDI scores. There were significant additive and multiplicative interactions between IDI and GRS in relation to type 2 diabetes risk in the normoglycemia group. CONCLUSIONS: A low-inflammatory diet is associated with a decreased risk of type 2 diabetes and may delay type 2 diabetes onset among participants with normal blood glucose or prediabetes. A low-inflammatory diet might significantly mitigate the risk of genetic factors on type 2 diabetes development

Expression Profiles and Ontology Analysis of Circular RNAs in a Mouse Model of Myocardial Ischemia/Reperfusion Injury

Circular RNAs (circRNAs) play important roles in cellular physiology. The association between circRNAs and myocardial ischemia/reperfusion (I/R) injury remains largely unknown. The aim of this study was to test the effects of myocardial I/R circRNA expression and explore the potential roles of these circRNAs. CircRNAs were screened by high-throughput sequencing, and the expression of dysregulated circRNAs was further validated using quantitative real-time polymerase chain reaction. Nineteen upregulated and 20 downregulated circRNAs were identified. Gene Ontology analysis indicated that the dysregulated transcripts were associated with fundamental pathophysiologic processes. Kyoto Encyclopedia of Genes and Genomes pathway analysis showed significant changes in adherens junction, the HIF-1 signaling pathway, the cell cycle, and the FoxO signaling pathway which have a close relationship with myocardial I/R injury. The circRNA-miRNA analysis demonstrated the broad potential of the differentially expressed circRNAs to regulate target genes by acting on the miRNAs. This study provides a foundation for understanding the roles and mechanisms of circRNAs in myocardial I/R injury