Massive Thirring Model: Inverse Scattering and Soliton Resolution

In this paper the long-time dynamics of the massive Thirring model is investigated. Firstly the nonlinear steepest descent method for Riemann-Hilbert problem is explored to obtain the soliton resolution of the solutions to the massive Thirring model whose initial data belong to some weighted-Sobolev spaces. Secondly, the asymptotic stability of multi-solitons follow as a corollary. The main difficulty in studying the massive Thirring model through inverse scattering is that the corresponding Lax pair has singularities at the origin and infinity. We overcome this difficulty by making use of two transforms that separate the singularities.Comment: arXiv admin note: text overlap with arXiv:2009.04260, arXiv:1907.0711

Fault diagnosis for PV arrays considering dust impact based on transformed graphical feature of characteristic curves and convolutional neural network with CBAM modules

Various faults can occur during the operation of PV arrays, and both the dust-affected operating conditions and various diode configurations make the faults more complicated. However, current methods for fault diagnosis based on I-V characteristic curves only utilize partial feature information and often rely on calibrating the field characteristic curves to standard test conditions (STC). It is difficult to apply it in practice and to accurately identify multiple complex faults with similarities in different blocking diodes configurations of PV arrays under the influence of dust. Therefore, a novel fault diagnosis method for PV arrays considering dust impact is proposed. In the preprocessing stage, the Isc-Voc normalized Gramian angular difference field (GADF) method is presented, which normalizes and transforms the resampled PV array characteristic curves from the field including I-V and P-V to obtain the transformed graphical feature matrices. Then, in the fault diagnosis stage, the model of convolutional neural network (CNN) with convolutional block attention modules (CBAM) is designed to extract fault differentiation information from the transformed graphical matrices containing full feature information and to classify faults. And different graphical feature transformation methods are compared through simulation cases, and different CNN-based classification methods are also analyzed. The results indicate that the developed method for PV arrays with different blocking diodes configurations under various operating conditions has high fault diagnosis accuracy and reliability

Analysis of 22 Elements in Milk, Feed, and Water of Dairy Cow, Goat, and Buffalo from Different Regions of China

The objectives of this study were to measure the concentrations of elements in raw milk by inductively coupled plasma-mass spectrometry (ICP-MS) and evaluate differences in element concentrations among animal species and regions of China. Furthermore, drinking water and feed samples were analyzed to investigate whether the element concentrations in raw milk are correlated with those in water and feed. All samples were analyzed by ICP-MS following microwave assisted acid digestion. The mean recovery of the elements was 98.7 % from milk, 103.7 % from water, and 93.3 % from a certified reference material (cabbage). Principal component analysis results revealed that element concentrations differed among animal species and regions. Correlation analysis showed that trace elements Mn, Fe, Ni, Ga, Se, Sr, Cs, U in water and Co, Ni, Cu, Se, U in feed were significantly correlated with those in milk (p < 0.05). Toxic and potential toxic elements Cr, As, Cd, Tl, Pb in water and Al, Cr, As, Hg, Tl in feed were significantly correlated with those in milk (p < 0.05). Results of correlation analysis revealed that elements in water and feed might contribute to the elements in milk

Multi-omics profiles refine L-dopa decarboxylase (DDC) as a reliable biomarker for prognosis and immune microenvironment of clear cell renal cell carcinoma

BackgroundIncreasing evidence indicates that L-dopa decarboxylase (DDC), which mediates aberrant amino acid metabolism, is significantly associated with tumor progression. However, the impacts of DDC are not elucidated clearly in clear cell renal cell carcinoma (ccRCC). This study aimed to evaluate DDC prognostic value and potential mechanisms for ccRCC patients.MethodsTranscriptomic and proteomic expressions of and clinical data including 532 patients with ccRCC (The Cancer Genome Atlas RNA-seq data), 226 ccRCC samples (Gene Expression Omnibus), 101 ccRCC patients from the E-MTAB-1980 cohort, and 232 patients with ccRCC with proteogenomic data (Fudan University Shanghai Cancer Center) were downloaded and analyzed to investigate the prognostic implications of DDC expression. Cox regression analyses were implemented to explore the effect of DDC expression on the prognosis of pan-cancer. The "limma" package identified the differentially expressed genes (DEGs) between high DDC subgroups and low DDC groups. Functional enrichments were performed based DEGs between DDC subgroups. The differences of immune cell infiltrations and immune checkpoint genes between DDC subgroups were analyzed to identify potential influence on immune microenvironment.ResultsWe found significantly decreased DDC expression in ccRCC tissues compared with normal tissues from multiple independent cohorts based on multi-omics data. We also found that DDC expression was correlated with tumor grades and stages.The following findings revealed that lower DDC expression levels significantly correlated with shorter overall survival (P &lt;0.001) of patients with ccRCC. Moreover, we found that DDC expression significantly correlated with an immunosuppressive tumor microenvironment, higher intra-tumoral heterogeneity, elevated expression of immune checkpoint CD274, and possibly mediated malignant behaviors of ccRCC cells via the PI3k/Akt signaling pathway.ConclusionThe present study is the first to our knowledge to indicate that decreased DDC expression is significantly associated with poor survival and an immune-suppressive tumor microenvironment in ccRCC. These findings suggest that DDC could serve as a biomarker for guiding molecular diagnosis and facilitating the development of novel individual therapeutic strategies for patients with advanced ccRCC

MicroRNA Quantitation During Dendritic Cell Endocytosis Using Imaging Flow Cytometry: Key Factors and Requirements

Background/Aims: MicroRNA (miRNA)-induced suppression of dendritic cells (DCs) has been implicated in many diseases. Therefore, accurate monitoring of miRNA endocytosis by DCs is important for understanding the role of miRNAs in many diseases. Recently, a method for measuring the co-localization of Argonaute 2 (AGO2)-associated miRNAs on laser-scanning confocal microscopy method was proposed to localize the miRNAs. But its definition was limited by the number of observed cells through its accuracy. Methods: In this study, a method based on imaging flow cytometry was developed to localize miR-590-5p with fluorescent probes in DCs. miR-590-5p proven to play an important role in tumor immunity. This method enabled the quantification, visualization and localization of the fluorescence intensity in 30,000 individual cells. Results: Using this method, the DCs with different endocytotic ability were distinguished. The behaviour of miR-590-5p during endocytosis under the stimulation of tumor antigen in DCs was observed, binding to its cognate target mRNA and degradation in DCs. Conclusion: This method based on imaging flow cytometry provide an additional method to study miRNA processing in DCs, which makes it a valuable addition to existing miRNA research techniques

Characterizing and Understanding Development of Social Computing Through DBLP : A Data-Driven Analysis

During the past decades, the term 'social computing' has become a promising interdisciplinary area in the intersection of computer science and social science. In this work, we conduct a data-driven study to understand the development of social computing using the data collected from Digital Bibliography and Library Project (DBLP), a representative computer science bibliography website. We have observed a series of trends in the development of social computing, including the evolution of the number of publications, popular keywords, top venues, international collaborations, and research topics. Our findings will be helpful for researchers and practitioners working in relevant fields.publishedVersionPeer reviewe

Advances in the research, diagnosis and treatment of renal cell carcinoma in 2022

Renal cell carcinoma (RCC) is one of the three major urinary system tumors. With the changes of lifestyle and the rise of obesity, hypertension and other diseases, the incidence of RCC is increasing. The onset of RCC is hidden, and RCC has strong heterogeneity. Most RCC patients are found accidentally by imaging examination, so many patients were diagnosed in the advanced stage. Although the emergence of targeted therapy and immunotherapy has greatly prolonged the survival time of patients with advanced RCC, due to many pathological types of RCC, it is still difficult for many patients to benefit from the systematic treatment. Many basic and clinical studies are devoted to the development of new targets or drugs to prolong the survival time of patients. This article reviewed the advances in the research, diagnosis and treatment of RCC in 2022

Topological optimization of an offshore-wind-farm power collection system based on a hybrid optimization methodology

This paper proposes a hybrid optimization method to optimize the topological structure of an offshore-wind-farm power collection system, in which the cable connection, cable selection and substation location are optimally designed. Firstly, the optimization model was formulated, which integrates cable investment, energy loss and line construction. Then, the Prim algorithm was used to initialize the population. A novel hybrid optimization, named PSAO, based on the merits of the particle swarm optimization (PSO) and aquila optimization (AO) algorithms, was presented for topological structure optimization, in which the searching characteristics between PSO and AO are exploited to intensify the searching capability. Lastly, the proposed PSAO method was validated with a real case. The results showed that compared with GA, AO and PSO algorithms, the PSAO algorithm reduced the total cost by 4.8%, 3.3% and 2.6%, respectively, while achieving better optimization efficiency.Web of Science112art. no. 27

High-density lipoprotein cholesterol to apolipoprotein A-1 ratio is an important indicator predicting in-hospital death in patients with acute coronary syndrome

Background: Dyslipidemia plays a pivotal role in the pathogenesis of acute coronary syndrome (ACS). This study aims to investigate the value of two indices associated with lipid metabolism, low-density lipoprotein cholesterol to apolipoprotein B ratio (LBR) and high-density lipoprotein cholesterol to apolipoprotein A-1 ratio (HAR), to predict in-hospital death in patients with ACS. Methods: This single-center, retrospective, observational study included 3,366 consecutive ACS patients in Zhongda Hospital, Southeast University from July 2013 to January 2018. The clinical and laboratory data were extracted, and the in-hospital death and hospitalization days were also recorded. Results: All patients were equally divided into four groups according to quartiles of HAR: Q1 (HAR &lt; 1.0283), Q2 (1.0283 ≤ HAR &lt; 1.0860), Q3 (1.0860 ≤ HAR &lt; 1.1798), and Q4 (HAR ≥ 1.1798). Overall, HAR was positively associated with the counts of neutrophils and monocytes, whereas negatively correlated to lymphocyte counts. HAR was negatively correlated to left ventricular ejection fraction (LVEF). Compared to other three groups, in-hospital mortality (vs. Q1, Q2, and Q3, p &lt; 0.001) and hospitalization length (vs. Q1, Q2, and Q3, p &lt; 0.001) were significantly higher in the Q4 group. When grouped by LBR, however, there was no significant difference in LVEF, in-hospital mortality, and hospitalization length among groups. After adjusting potential impact from age, systolic blood pressure, creatine, lactate dehydrogenase, albumin, glucose, and uric acid, multivariate analysis indicated that HAR was an independent factor predicting in-hospital death among ACS patients. Conclusions: HAR had good predictive value for patients’ in-hospital death after the occurrence of acute coronary events, but LBR was not related to in-hospital adverse events