CDO pricing using single factor MG-NIG copula model with stochastic correlation and random factor loading

AbstractWe consider the valuation of CDO tranches with single factor MG-NIG copula model, where the involved distributions are mixtures of Gaussian distribution and NIG distribution. In addition, we consider two cases for stochastic correlation and random factor loadings instead of constant factor loadings. We analyze the unconditional characteristic function of accumulated loss of the reference portfolio, and derive the loss distribution through the fast Fourier transform. Moreover, using the loss distribution and semi-analytic approach, we can get the CDO tranches spreads

Riemannian Natural Gradient Methods

This paper studies large-scale optimization problems on Riemannian manifolds whose objective function is a finite sum of negative log-probability losses. Such problems arise in various machine learning and signal processing applications. By introducing the notion of Fisher information matrix in the manifold setting, we propose a novel Riemannian natural gradient method, which can be viewed as a natural extension of the natural gradient method from the Euclidean setting to the manifold setting. We establish the almost-sure global convergence of our proposed method under standard assumptions. Moreover, we show that if the loss function satisfies certain convexity and smoothness conditions and the input-output map satisfies a Riemannian Jacobian stability condition, then our proposed method enjoys a local linear -- or, under the Lipschitz continuity of the Riemannian Jacobian of the input-output map, even quadratic -- rate of convergence. We then prove that the Riemannian Jacobian stability condition will be satisfied by a two-layer fully connected neural network with batch normalization with high probability, provided that the width of the network is sufficiently large. This demonstrates the practical relevance of our convergence rate result. Numerical experiments on applications arising from machine learning demonstrate the advantages of the proposed method over state-of-the-art ones

MIPI 2022 Challenge on RGBW Sensor Re-mosaic: Dataset and Report

Developing and integrating advanced image sensors with novel algorithms in camera systems are prevalent with the increasing demand for computational photography and imaging on mobile platforms. However, the lack of high-quality data for research and the rare opportunity for in-depth exchange of views from industry and academia constrain the development of mobile intelligent photography and imaging (MIPI). To bridge the gap, we introduce the first MIPI challenge including five tracks focusing on novel image sensors and imaging algorithms. In this paper, RGBW Joint Remosaic and Denoise, one of the five tracks, working on the interpolation of RGBW CFA to Bayer at full resolution, is introduced. The participants were provided with a new dataset including 70 (training) and 15 (validation) scenes of high-quality RGBW and Bayer pairs. In addition, for each scene, RGBW of different noise levels was provided at 0dB, 24dB, and 42dB. All the data were captured using an RGBW sensor in both outdoor and indoor conditions. The final results are evaluated using objective metrics including PSNR, SSIM, LPIPS, and KLD. A detailed description of all models developed in this challenge is provided in this paper. More details of this challenge and the link to the dataset can be found at https://github.com/mipi-challenge/MIPI2022.Comment: ECCV 2022 Mobile Intelligent Photography and Imaging (MIPI) Workshop--RGBW Sensor Re-mosaic Challenge Report. MIPI workshop website: http://mipi-challenge.org/. arXiv admin note: substantial text overlap with arXiv:2209.07060, arXiv:2209.07530, arXiv:2209.0705

A Dominant EV71-Specific CD4+ T Cell Epitope is Highly Conserved Among Human Enteroviruses

CD4+ T cell-mediated immunity plays a central role in determining the immunopathogenesis of viral infections. However, the role of CD4+ T cells in EV71 infection, which causes hand, foot and mouth disease (HFMD), has yet to be elucidated. We applied a sophisticated method to identify promiscuous CD4+ T cell epitopes contained within the sequence of the EV71 polyprotein. Fifteen epitopes were identified, and three of them are dominant ones. The most dominant epitope is highly conserved among enterovirus species, including HFMD-related coxsackieviruses, HFMD-unrelated echoviruses and polioviruses. Furthermore, the CD4+ T cells specific to the epitope indeed cross-reacted with the homolog of poliovirus 3 Sabin. Our findings imply that CD4+ T cell responses to poliovirus following vaccination, or to other enteroviruses to which individuals may be exposed in early childhood, may have a modulating effect on subsequent CD4+ T cell response to EV71 infection or vaccine

MIPI 2023 Challenge on RGBW Remosaic: Methods and Results

Developing and integrating advanced image sensors with novel algorithms in camera systems are prevalent with the increasing demand for computational photography and imaging on mobile platforms. However, the lack of high-quality data for research and the rare opportunity for an in-depth exchange of views from industry and academia constrain the development of mobile intelligent photography and imaging (MIPI). With the success of the 1st MIPI Workshop@ECCV 2022, we introduce the second MIPI challenge, including four tracks focusing on novel image sensors and imaging algorithms. This paper summarizes and reviews the RGBW Joint Remosaic and Denoise track on MIPI 2023. In total, 81 participants were successfully registered, and 4 teams submitted results in the final testing phase. The final results are evaluated using objective metrics, including PSNR, SSIM, LPIPS, and KLD. A detailed description of the top three models developed in this challenge is provided in this paper. More details of this challenge and the link to the dataset can be found at https://mipi-challenge.org/MIPI2023/.Comment: CVPR 2023 Mobile Intelligent Photography and Imaging (MIPI) Workshop--RGBW Sensor Remosaic Challenge Report. Website: https://mipi-challenge.org/MIPI2023/. arXiv admin note: substantial text overlap with arXiv:2209.08471, arXiv:2209.07060, arXiv:2209.07530, arXiv:2304.1008

Transactivated Epidermal Growth Factor Receptor Recruitment of α-actinin-4 From F-actin Contributes to Invasion of Brain Microvascular Endothelial Cells by Meningitic Escherichia coli

Bacterial penetration of the blood-brain barrier requires its successful invasion of brain microvascular endothelial cells (BMECs), and host actin cytoskeleton rearrangement in these cells is a key prerequisite for this process. We have reported previously that meningitic Escherichia coli can induce the activation of host's epidermal growth factor receptor (EGFR) to facilitate its invasion of BMECs. However, it is unknown how EGFR specifically functions during this invasion process. Here, we identified an important EGFR-interacting protein, α-actinin-4 (ACTN4), which is involved in maintaining and regulating the actin cytoskeleton. We observed that transactivated-EGFR competitively recruited ACTN4 from intracellular F-actin fibers to disrupt the cytoskeleton, thus facilitating bacterial invasion of BMECs. Strikingly, this mechanism operated not only for meningitic E. coli, but also for infections with Streptococcus suis, a Gram-positive meningitis-causing bacterial pathogen, thus revealing a common mechanism hijacked by these meningitic pathogens where EGFR competitively recruits ACTN4. Ever rising levels of antibiotic-resistant bacteria and the emergence of their extended-spectrum antimicrobial-resistant counterparts remind us that EGFR could act as an alternative non-antibiotic target to better prevent and control bacterial meningitis

Genetically predicted adiponectin causally reduces the risk of chronic kidney disease, a bilateral and multivariable mendelian randomization study

Background: It is not clarified whether the elevation of adiponectin is the results of kidney damage, or the cause of kidney function injury. To explore the causal association of adiponectin on estimated glomerular filtration rate (eGFR) and chronic kidney disease (CKD), this study was performed.Materials and methods: The genetic association of adiponectin were retrieved from one genome-wide association studies with 39,883 participants. The summary-level statistics regarding the eGFR (133,413 participants) and CKD (12,385 CKD cases and 104,780 controls) were retrieved from the CKDGen consortium in the European ancestry. Single-variable Mendelian randomization (MR), bilateral and multivariable MR analyses were used to verify the causal association between adiponectin, eGFR, and CKD.Results: Genetically predicted adiponectin reduces the risk of CKD (OR = 0.71, 95% CI = 0.57–0.89, p = 0.002) and increases the eGFR (β = 0.014, 95% CI = 0.001–0.026, p = 0.034) by the inverse variance weighting (IVW) estimator. These findings remain consistent in the sensitivity analyses. No heterogeneity and pleiotropy were detected in this study (P for MR-Egger 0.617, P for global test > 0.05, and P for Cochran’s Q statistics = 0.617). The bilateral MR identified no causal association of CKD on adiponectin (OR = 1.01, 95% CI = 0.96–1.07, p = 0.658), nor did it support the association of eGFR on adiponectin (OR = 0.86, 95% CI = 0.68–1.09, p = 0.207) by the IVW estimator. All the sensitivity analyses reported similar findings (p > 0.05). Additionally, after adjusting for cigarette consumption, alcohol consumption, body mass index, low density lipoprotein, and total cholesterol, the ORs for CKD are 0.70 (95% CI = 0.55–0.90, p = 0.005), 0.75 (95% CI = 0.58–0.97, p = 0.027), 0.82 (95% CI = 0.68–0.99, p = 0.039), 0.74 (95% CI = 0.59–0.93, p = 0.011), and 0.79 (95% CI = 0.61–0.95, p = 0.018), respectively.Conclusion: Using genetic data, this study provides novel causal evidence that adiponectin can protect the kidney function and further reduce the risk of CKD

Detecting Falsified Financial Statements Using a Hybrid SM-UTADIS Approach : Empirical Analysis of Listed Traditional Chinese Medicine Companies in China

By combining the similarity matching (SM) method with the utilities additives discriminates (UTADIS) method, we propose a hybrid SM-UTADIS approach to detect falsified financial statements (FFS) of listed companies. To evaluate the performance of this hybrid approach, we conduct experiments using the annual financial ratios of listed traditional Chinese medicine (TCM) companies in China. There are three stages in the detection procedure. First, we use the cosine similarity matching method to select matched companies for each considered company, derive the deviation data of each considered company as a sample dataset to capture the intrinsic law of the financial data, and further divide these into training and testing datasets for the next two stages. Second, we put the training dataset into the UTADIS to train the SM-UTADIS model. Finally, we use the trained SM-UTADIS model to classify the testing dataset and evaluate the performance of the proposed method. Furthermore, we use other approaches, such as single UTADIS and logistic and SM-logistic regression models, to detect FFS. By comparing these results to those of the hybrid SM-UTADIS approach, we find that the proposed hybrid approach greatly improves the accuracy of FFS detection

Emerging role of non-coding RNAs in neuroinflammation mediated by microglia and astrocytes

Abstract Neuroinflammation has been implicated in the initiation and progression of several central nervous system (CNS) disorders, including Alzheimer’s disease, Parkinson’s disease, amyotrophic lateral sclerosis, multiple sclerosis, ischemic stroke, traumatic brain injury, spinal cord injury, viral encephalitis, and bacterial encephalitis. Microglia and astrocytes are essential in neural development, maintenance of synaptic connections, and homeostasis in a healthy brain. The activation of astrocytes and microglia is a defense mechanism of the brain against damaged tissues and harmful pathogens. However, their activation triggers neuroinflammation, which can exacerbate or induce CNS injury. Non-coding RNAs (ncRNAs) are functional RNA molecules that lack coding capabilities but can actively regulate mRNA expression and function through various mechanisms. ncRNAs are highly expressed in astrocytes and microglia and are potential mediators of neuroinflammation. We reviewed the recent research progress on the role of miRNAs, lncRNAs, and circRNAs in regulating neuroinflammation in various CNS diseases. Understanding how these ncRNAs affect neuroinflammation will provide important therapeutic insights for preventing and managing CNS dysfunction

Unveiling the Hidden Regulators: The Impact of lncRNAs on Zoonoses

Zoonoses are diseases and infections naturally transmitted between humans and vertebrate animals. They form the dominant group of diseases among emerging infectious diseases and represent critical threats to global health security. This dilemma is largely attributed to our insufficient knowledge of the pathogenesis regarding zoonotic spillover. Long non-coding RNAs (lncRNAs) are transcripts with limited coding capacity. Recent technological advancements have enabled the identification of numerous lncRNAs in humans, animals, and even pathogens. An increasing body of literature suggests that lncRNAs function as key regulators in zoonotic infection. They regulate immune-related epigenetic, transcriptional, and post-transcriptional events across a broad range of organisms. In this review, we discuss the recent research progress on the roles of lncRNAs in zoonoses. We address the classification and regulatory mechanisms of lncRNAs in the interaction between host and zoonotic pathogens. Additionally, we explore the surprising function of pathogen-derived lncRNAs in mediating the pathogenicity and life cycle of zoonotic bacteria, viruses, and parasites. Understanding how these lncRNAs influence the zoonotic pathogenesis will provide important therapeutic insights to the prevention and control of zoonoses