Data-augmented sequential deep learning for wind power forecasting

Accurate wind power forecasting plays a critical role in the operation of wind parks and the dispatch of wind energy into the power grid. With excellent automatic pattern recognition and nonlinear mapping ability for big data, deep learning is increasingly employed in wind power forecasting. However, salient realities are that in-situ measured wind data are relatively expensive and inaccessible and correlation between steps is omitted in most multistep wind power forecasts. This paper is the first time that data augmentation is applied to wind power forecasting by systematically summarizing and proposing both physics-oriented and data-oriented time-series wind data augmentation approaches to considerably enlarge primary datasets, and develops deep encoder-decoder long short-term memory networks that enable sequential input and sequential output for wind power forecasting. The proposed augmentation techniques and forecasting algorithm are deployed on five turbines with diverse topographies in an Arctic wind park, and the outcomes are evaluated against benchmark models and different augmentations. The main findings reveal that on one side, the average improvement in RMSE of the proposed forecasting model over the benchmarks is 33.89%, 10.60%, 7.12%, and 4.27% before data augmentations, and increases to 40.63%, 17.67%, 11.74%, and 7.06%, respectively, after augmentations. The other side unveils that the effect of data augmentations on prediction is intricately varying, but for the proposed model with and without augmentations, all augmentation approaches boost the model outperformance from 7.87% to 13.36% in RMSE, 5.24% to 8.97% in MAE, and similarly over 12% in QR90. Finally, data-oriented augmentations, in general, are slightly better than physics-driven ones

A southern, middle, and northern Norwegian offshore wind energy resources analysis by a transfer learning method for Energy Internet

As renewable energy sources offshore wind energy develop quickly, countries like Norway with long coastlines are exploring their potential. However, the diverse wind resources across different regions of Norway present challenges for study for effective utilization of offshore wind energy. This study proposes a novel method that utilizes transfer learning techniques to analyse the resource differences between these areas for optimum energy generation. The suggested approach is tested using real-world wind data from Norway’s southern, middle, and northern regions. The results show that transfer learning successfully bridges resource discrimination, boosting wind resource prediction precision in the target domains. The work can contribute to optimizing offshore wind energy utilization in Norway by addressing the resource disparities and forecasting between the different regions

Infer Implicit Contexts in Real-time Online-to-Offline Recommendation

Understanding users' context is essential for successful recommendations, especially for Online-to-Offline (O2O) recommendation, such as Yelp, Groupon, and Koubei. Different from traditional recommendation where individual preference is mostly static, O2O recommendation should be dynamic to capture variation of users' purposes across time and location. However, precisely inferring users' real-time contexts information, especially those implicit ones, is extremely difficult, and it is a central challenge for O2O recommendation. In this paper, we propose a new approach, called Mixture Attentional Constrained Denoise AutoEncoder (MACDAE), to infer implicit contexts and consequently, to improve the quality of real-time O2O recommendation. In MACDAE, we first leverage the interaction among users, items, and explicit contexts to infer users' implicit contexts, then combine the learned implicit-context representation into an end-to-end model to make the recommendation. MACDAE works quite well in the real system. We conducted both offline and online evaluations of the proposed approach. Experiments on several real-world datasets (Yelp, Dianping, and Koubei) show our approach could achieve significant improvements over state-of-the-arts. Furthermore, online A/B test suggests a 2.9% increase for click-through rate and 5.6% improvement for conversion rate in real-world traffic. Our model has been deployed in the product of "Guess You Like" recommendation in Koubei.Comment: 9 pages,KDD,KDD201

A Common Variant in CLDN14 is Associated with Primary Biliary Cirrhosis and Bone Mineral Density.

Primary biliary cirrhosis (PBC), a chronic autoimmune liver disease, has been associated with increased incidence of osteoporosis. Intriguingly, two PBC susceptibility loci identified through genome-wide association studies are also involved in bone mineral density (BMD). These observations led us to investigate the genetic variants shared between PBC and BMD. We evaluated 72 genome-wide significant BMD SNPs for association with PBC using two European GWAS data sets (n = 8392), with replication of significant findings in a Chinese cohort (685 cases, 1152 controls). Our analysis identified a novel variant in the intron of the CLDN14 gene (rs170183, Pfdr = 0.015) after multiple testing correction. The three associated variants were followed-up in the Chinese cohort; one SNP rs170183 demonstrated consistent evidence of association in diverse ethnic populations (Pcombined = 2.43 × 10(-5)). Notably, expression quantitative trait loci (eQTL) data revealed that rs170183 was correlated with a decline in CLDN14 expression in both lymphoblastoid cell lines and T cells (Padj = 0.003 and 0.016, respectively). In conclusion, our study identified a novel PBC susceptibility variant that has been shown to be strongly associated with BMD, highlighting the potential of pleiotropy to improve gene discovery

Architecture of Heptagonal Metallo-macrocycles via Embedding Metal Nodes Into Its Rigid Backbone

Metal-organic macrocycles have received increasing attention not only due to their versatile applications such as molecular recognition, compounds encapsulation, anti-bacteria and others, but also for their important role in the study of structure-property relationship at nano scale. However, most of the constructions utilize benzene ring as the backbone, which restricts the ligand arm angle in the range of 60, 120 and 180 degrees. Thus, the topologies of most metallo-macrocycles are limited as triangles and hexagons, and explorations of using other backbones with large angles and the construction of metallo-macrocycles with more than six edges are very rare. In this study, we present a novel strategy for self-assembly two giant heptagonal metallo-macrocycles with an inner diameter of 5 nm, by embedding metal nodes into the ligand backbone and regulating the ligand arm angle. By complexing with metal ions, the angle between two arms at the 4,4” position of the central terpyridine (tpy) was extended, resulting in ring expansion of the metallo-macrocycle. This approach enabled the construction of giant and more complex metallo- macrocycles that could not be achieved with traditional benzene ring backbones. The characterization of complex molecules often requires the use of multiple techniques, such as multi-dimensional and multinuclear NMR and multidimensional mass spectrometry analysis. Here, we also utilized transmission electron microscopy (TEM) and ultra-high vacuum (∼E-10 torr) low-temperature (∼77 K) scanning tunneling microscopy (UHV-LT-STM) to characterize complex supramolecules. The resulting metallo-macrocycles formed hierarchical self-assembled nanotube structures at larger densities, which is observed by TEM, while UHV-LT-STM was used for direct visualization of individual complex supramolecules deposited on an Au(111) substrate. Our findings indicate that UHV-LT-STM is an effective methodology for characterizing supramolecules at a single molecule level, providing more details of the molecular structure that is difficult to resolve by the resolution of TEM.https://digitalcommons.odu.edu/gradposters2023_sciences/1005/thumbnail.jp

Mucosal-Associated Invariant T Cells Improve Nonalcoholic Fatty Liver Disease Through Regulating Macrophage Polarization

Mucosal-associated invariant T (MAIT) cells, a novel population of innate-like lymphocytes, have been involved in various inflammatory and autoimmune diseases. However, their role in the development of nonalcoholic fatty liver disease (NAFLD) remains unclear. In this study, we investigated the alterations of phenotype and immunological function of MAIT cells in NAFLD. Analysis of PBMCs in 60 patients with NAFLD and 48 healthy controls (HC) revealed that circulating MAIT cell frequency decreased in NAFLD, especially in the patients with higher serum levels of γ-glutamyl transferase or total triglyceride. Functional alterations of circulating MAIT cells were also detected in NAFLD patients, such as the increased production of IL-4 whereas the decreased production of IFN-γ and TNF-α. Furthermore, elevated expression of CXCR6 was observed in circulating MAIT cells of patients. Meanwhile, we found an increased number of MAIT cells in the livers of NAFLD, and the number was even greater in patients with higher NAFLD activity score. Moreover, activated MAIT cells induced monocytes/macrophages differentiation into M2 phenotype in vitro. Additionally, MAIT cells were enriched and displayed Th2 type cytokines profile in livers of wild type mice fed with methionine and choline deficient diet (MCD). Notably, mice deficient of MAIT cells exhibited more severe hepatic steatosis and inflammation upon MCD, accompanied with more CD11c+ proinflammatory macrophages (M1) and less CD206+ anti-inflammatory macrophages (M2) in livers. Our results indicate that MAIT cells protect against inflammation in NAFLD through producing regulatory cytokines and inducing anti-inflammatory macrophage polarization, which may provide novel therapeutic strategies for NAFLD

Alterations to the Lung Microbiome in Idiopathic Pulmonary Fibrosis Patients

Lung microbiome ecosystem homeostasis in idiopathic pulmonary fibrosis (IPF) remains uncharacterized. The aims of this study were to identify unique microbial signatures of the lung microbiome and analyze microbial gene function in IPF patients. DNA isolated from BALF samples was obtained for high-throughput gene sequencing. Microbial metagenomic data were used for principal component analysis (PCA) and analyzed at different taxonomic levels. Shotgun metagenomic data were annotated using the KEGG database and were analyzed for functional and metabolic pathways. In this study, 17 IPF patients and 38 healthy subjects (smokers and non-smokers) were recruited. For the PCA, the first and the second principal component explained 16.3 and 13.4% of the overall variability, respectively. The β diversity of microbiome was reduced in the IPF group. Signature of IPF's microbes was enriched of Streptococcus, Pseudobutyrivibrio, and Anaerorhabdus. The translocation of lung microbiome was shown that 32.84% of them were from oral. After analysis of gene function, ABC transporter systems, biofilm formation, and two-component regulatory system were enriched in IPF patients' microbiome. Here we shown the microbiology characteristics in IPF patients. The microbiome may participate in altering internal conditions and involving in generating antibiotic resistance in IPF patients

Interleukin-17 Contributes to the Pathogenesis of Autoimmune Hepatitis through Inducing Hepatic Interleukin-6 Expression

T helper cells that produce IL-17 (Th17 cells) have recently been identified as the third distinct subset of effector T cells. Emerging data suggests that Th17 cells play an important role in the pathogenesis of many liver diseases by regulating innate immunity, adaptive immunity, and autoimmunity. In this study, we examine the role and mechanism of Th17 cells in the pathogenesis of autoimmune hepatitis (AIH). The serum levels of IL-17 and IL-23, as well as the frequency of IL-17+ cells in the liver, were significantly elevated in patients with AIH, compared to other chronic hepatitis and healthy controls. The hepatic expressions of IL-17, IL-23, ROR-γt, IL-6 and IL-1β in patients with AIH were also significantly increased and were associated with increased inflammation and fibrosis. IL-17 induces IL-6 expression via the MAPK signaling pathway in hepatocytes, which, in turn, may further stimulate Th17 cells and forms a positive feedback loop. In conclusion, Th17 cells are key effector T cells that regulate the pathogenesis of AIH, via induction of MAPK dependent hepatic IL-6 expression. Blocking the signaling pathway and interrupting the positive feedback loop are potential therapeutic targets for autoimmune hepatitis