Regularized Integrals on Riemann Surfaces and Modular Forms

We introduce a simple procedure to integrate differential forms with arbitrary holomorphic poles on Riemann surfaces. It gives rise to an intrinsic regularization of such singular integrals in terms of the underlying conformal geometry. Applied to products of Riemann surfaces, this regularization scheme establishes an analytic theory for integrals over configuration spaces, including Feynman graph integrals arising from two dimensional chiral quantum field theories. Specializing to elliptic curves, we show such regularized graph integrals are almost-holomorphic modular forms that geometrically provide modular completions of the corresponding ordered $A$-cycle integrals. This leads to a simple geometric proof of the mixed-weight quasi-modularity of ordered A-cycle integrals, as well as novel combinatorial formulae for all the components of different weights.Comment: 65 pages. Comments are welcom

Regularized Integrals on Elliptic Curves and Holomorphic Anomaly Equations

We derive residue formulas for the regularized integrals (introduced by Li-Zhou) on configuration spaces of elliptic curves. Based on these formulas, we prove that the regularized integrals satisfy holomorphic anomaly equations, providing a mathematical formulation of the so-called contact term singularities. We also discuss residue formulas for the ordered $A$-cycle integrals and establish their relations with those for the regularized integrals.Comment: Appendices added. To appear in Communications in Mathematical Physic

New mixed adaptive detection algorithm for moving target with big data

Aiming at the troubles (such as complex background, illumination changes, shadows and others on traditional methods) for detecting of a walking person, we put forward a new adaptive detection algorithm through mixing Gaussian Mixture Model (GMM), edge detection algorithm and continuous frame difference algorithm in this paper. In time domain, the new algorithm uses GMM to model and updates the background. In spatial domain, it uses the hybrid detection algorithm which mixes the edge detection algorithm, continuous frame difference algorithm and GMM to get the initial contour of moving target with big data, and gets the ultimate moving target with big data. This algorithm not only can adapt to the illumination gradients and background disturbance occurred on scene, but also can solve some problems such as inaccurate target detection, incomplete edge detection, cavitation and ghost which usually appears in traditional algorithm. As experimental result showing, this algorithm holds better real-time and robustness. It is not only easily implemented, but also can accurately detect the moving target with big data

Comprehensive analysis of ceRNA network composed of circRNA, miRNA, and mRNA in septic acute kidney injury patients based on RNA-seq

Background: Sepsis is a complex, life-threatening clinical syndrome that can cause other related diseases, such as acute kidney injury (AKI). Circular RNA (circRNA) is a type of non-coding RNA with a diverse range of functions, and it plays essential roles in miRNA sponge. CircRNA plays a huge part in the development of various diseases. CircRNA and the competing endogenous RNA (ceRNA) regulatory network are unknown factors in the onset and progression of septic AKI (SAKI). This study aimed to clarify the complex circRNA-associated regulatory mechanism of circRNAs in SAKI.Methods: We collected 40 samples of whole blood of adults, including 20 cases of SAKI and 20 cases of healthy controls. Moreover, five cases were each analyzed by RNA sequencing, and we identified differentially expressed circRNA, miRNA, and mRNA (DEcircRNAs, DEmiRNAs, and DEmRNAs, respectively). All samples were from SAKI patients with intraperitoneal infection.Results: As a result, we screened out 236 DEcircRNAs, 105 DEmiRNAs, and 4065 DEmRNAs. Then, we constructed two co-expression networks based on RNA–RNA interaction, including circRNA–miRNA and miRNA–mRNA co-expression networks. We finally created a circRNA–miRNA–mRNA regulation network by combining the two co-expression networks. Functional and pathway analyses indicated that DEmRNAs in ceRNA were mostly concentrated in T cell activation, neutrophils and their responses, and cytokines. The protein–protein interaction network was established to screen out the key genes participating in the regulatory network of SAKI. The hub genes identified as the top 10 nodes included the following: ZNF727, MDFIC, IFITM2, FOXD4L6, CIITA, KCNE1B, BAGE2, PPIAL4A, USP17L7, and PRSS2.Conclusion: To our knowledge, this research is the first study to describe changes in the expression profiles of circRNAs, miRNAs, and mRNAs in patients with SAKI. These findings provide a new treatment target for SAKI treatment and novel ideas for its pathogenesis

Solving Solar Neutrino Puzzle via LMA MSW Conversion

We analyze the existing solar neutrino experiment data and show the allowed regions. The result from SNO's salt phase itself restricts quite a lot the allowed region's area. Reactor neutrinos play an important role in determining oscillation parameters. KamLAND gives decisive conclusion on the solution to the solar neutrino puzzle, in particular, the spectral distortion in the 766.3 Ty KamLAND data gives another new improvement in the constraint of solar MSW-LMA solutions. We confirm that at 99.73% C.L. the high-LMA solution is excluded.Comment: 6 eps figure

MAP3K19 regulatory variation in populations with African ancestry may increase COVID-19 severity

To identify ancestry-linked genetic risk variants associated with COVID-19 hospitalization, we performed an integrative analysis of two genome-wide association studies and resolved four single nucleotide polymorphisms more frequent in COVID-19-hospitalized patients with non-European ancestry. Among them, the COVID-19 risk SNP rs16831827 shows the largest difference in minor allele frequency (MAF) between populations with African and European ancestry and also shows higher MAF in hospitalized COVID-19 patients among cohorts of mixed ancestry (odds ratio [OR] = 1.20, 95% CI: 1.10-1.30) and entirely African ancestry (OR = 1.30, 95% CI: 1.02-1.67). rs16831827 is an expression quantitative trait locus of MAP3K19. MAP3K19 expression is induced during ciliogenesis and most abundant in ciliated tissues including lungs. Single-cell RNA sequencing analyses revealed that MAP3K19 is highly expressed in multiple ciliated cell types. As rs16831827∗T is associated with reduced MAP3K19 expression, it may increase the risk of severe COVID-19 by reducing MAP3K19 expression

Language Prior Is Not the Only Shortcut: A Benchmark for Shortcut Learning in VQA

Visual Question Answering (VQA) models are prone to learn the shortcut solution formed by dataset biases rather than the intended solution. To evaluate the VQA models' reasoning ability beyond shortcut learning, the VQA-CP v2 dataset introduces a distribution shift between the training and test set given a question type. In this way, the model cannot use the training set shortcut (from question type to answer) to perform well on the test set. However, VQA-CP v2 only considers one type of shortcut and thus still cannot guarantee that the model relies on the intended solution rather than a solution specific to this shortcut. To overcome this limitation, we propose a new dataset that considers varying types of shortcuts by constructing different distribution shifts in multiple OOD test sets. In addition, we overcome the three troubling practices in the use of VQA-CP v2, e.g., selecting models using OOD test sets, and further standardize OOD evaluation procedure. Our benchmark provides a more rigorous and comprehensive testbed for shortcut learning in VQA. We benchmark recent methods and find that methods specifically designed for particular shortcuts fail to simultaneously generalize to our varying OOD test sets. We also systematically study the varying shortcuts and provide several valuable findings, which may promote the exploration of shortcut learning in VQA.Comment: Fingdings of EMNLP-202

UHPLC-QTOFMS-Based Metabolomic Analysis of the Hippocampus in Hypoxia Preconditioned Mouse

Background: Hypoxia appears in a number of extreme environments, including high altitudes, the deep sea, and during aviation, and occurs in cancer, cardiovascular disease, respiratory failures and neurological disorders. Though it is well recognized that hypoxic preconditioning (HPC) exerts endogenous neuroprotective effect against severe hypoxia, the mediators and underlying molecular mechanism for the protective effect are still not fully understood. This study established a hippocampus metabolomics approach to explore the alterations associated with HPC.Methods: In this study, an animal model of HPC was established by exposing the adult BALB/c mice to acute repetitive hypoxia four times. Ultra-high liquid chromatography-quadrupole time-of-flight mass spectrometry (UHPLC-QTOFMS) in combination with univariate and multivariate statistical analyses was employed to deciphering metabolic changes associated with HPC in hippocampus tissue. MetaboAnalyst 3.0 was used to construct HPC related metabolic pathways.Results: The significant metabolic differences in hippocampus between the HPC groups and control were observed, indicating that HPC mouse model was successfully established and HPC could caused significant metabolic changes. Several key metabolic pathways were found to be acutely perturbed, including phenylalanine, tyrosine and tryptophan biosynthesis, taurine and hypotaurine metabolism, phenylalanine metabolism, glutathione metabolism, alanine, aspartate and glutamate metabolism, tyrosine metabolism, tryptophan metabolism, purine metabolism, citrate cycle, and glycerophospholipid metabolism.Conclusion: The results of the present study provided novel insights into the mechanisms involved in the acclimatization of organisms to hypoxia, and demonstrated the neuroprotective mechanism of HPC