DeepRicci: Self-supervised Graph Structure-Feature Co-Refinement for Alleviating Over-squashing

Graph Neural Networks (GNNs) have shown great power for learning and mining on graphs, and Graph Structure Learning (GSL) plays an important role in boosting GNNs with a refined graph. In the literature, most GSL solutions either primarily focus on structure refinement with task-specific supervision (i.e., node classification), or overlook the inherent weakness of GNNs themselves (e.g., over-squashing), resulting in suboptimal performance despite sophisticated designs. In light of these limitations, we propose to study self-supervised graph structure-feature co-refinement for effectively alleviating the issue of over-squashing in typical GNNs. In this paper, we take a fundamentally different perspective of the Ricci curvature in Riemannian geometry, in which we encounter the challenges of modeling, utilizing and computing Ricci curvature. To tackle these challenges, we present a self-supervised Riemannian model, DeepRicci. Specifically, we introduce a latent Riemannian space of heterogeneous curvatures to model various Ricci curvatures, and propose a gyrovector feature mapping to utilize Ricci curvature for typical GNNs. Thereafter, we refine node features by geometric contrastive learning among different geometric views, and simultaneously refine graph structure by backward Ricci flow based on a novel formulation of differentiable Ricci curvature. Finally, extensive experiments on public datasets show the superiority of DeepRicci, and the connection between backward Ricci flow and over-squashing. Codes of our work are given in https://github.com/RiemanGraph/.Comment: Accepted by IEEE ICDM 2023, Full paper, 10 page

Rosiglitazone Inhibits Transforming Growth Factor-β1 Mediated Fibrogenesis in ADPKD Cyst-Lining Epithelial Cells

BACKGROUND: Interstitial fibrosis plays an important role in progressive renal dysfunction in autosomal dominant polycystic kidney disease (ADPKD). In our previous studies, we confirmed that PPAR-γ agonist, rosiglitazone could protect renal function and prolong the survival of a slowly progressive ADPKD animal model by reducing renal fibrosis. However, the mechanism remains unknown. METHODS: Primary culture epithelial cells pretreated with TGF-β1 were incubated with rosiglitazone. Extracellular matrix proteins were detected using real-time PCR and Western blotting. MAPK and Smad2 phosphorylation were measured with western blot. ERK1/2 pathway and P38 pathway were inhibited with the specific inhibitors PD98059 and SB203580. The Smad2 pathway was blocked with the siRNA. To address whether PPAR-γ agonist-mediated inhibition of TGF-β1-induced collagen type I expression was mediated through a PPAR-γ dependent mechanism, genetic and pharmaceutical approaches were used to block the activity of endogenous PPARγ. RESULTS: TGF-β1-stimulated collagen type I and fibronectin expression of ADPKD cyst-lining epithelia were inhibited by rosiglitazone in a dosage-dependent manner. Smad2, ERK1/2 and P38 pathways were activated in response to TGF-β1; however, TGF-β1 had little effect on JNK pathway. Rosiglitazone suppressed TGF-β1 induced Smad2 activation, while ERK1/2 and P38MAPK signals remained unaffected. Rosiglitazone could also attenuate TGF-β1-stimulated collagen type I and fibronectin expression in primary renal tubular epithelial cells, but had no effect on TGF-β1-induced activation of Smad2, ERK1/2 and P38 pathways. There was no crosstalk between the Smad2 and MAPK pathways in ADPKD cyst-lining epithelial cells. These inhibitory effects of rosiglitazone were reversed by the PPARγ specific antagonist GW9662 and PPARγ siRNA. CONCLUSION: ADPKD cyst-lining epithelial cells participate in TGF-β1 mediated fibrogenesis. Rosiglitazone could suppress TGF-β1-induced collagen type I and fibronectin expression in ADPKD cyst-lining epithelia through modulation of the Smad2 pathway. Our study may provide therapeutic basis for clinical applications of rosiglitazone in retarding the progression of ADPKD

A Semiparametric Gaussian Copula Regression Model for Predicting Financial Risks from Earnings Calls

Earnings call summarizes the financial performance of a company, and it is an important indicator of the future financial risks of the company. We quantitatively study how earnings calls are correlated with the financial risks, with a special fo-cus on the financial crisis of 2009. In par-ticular, we perform a text regression task: given the transcript of an earnings call, we predict the volatility of stock prices from the week after the call is made. We pro-pose the use of copula: a powerful statis-tical framework that separately models the uniform marginals and their complex mul-tivariate stochastic dependencies, while not requiring any prior assumptions on the distributions of the covariate and the de-pendent variable. By performing probabil-ity integral transform, our approach moves beyond the standard count-based bag-of-words models in NLP, and improves pre-vious work on text regression by incor-porating the correlation among local fea-tures in the form of semiparametric Gaus-sian copula. In experiments, we show that our model significantly outperforms strong linear and non-linear discriminative baselines on three datasets under various settings.

An Improved Lagrangian Relaxation Algorithm for the Robust Generation Self-Scheduling Problem

The robust generation self-scheduling problem under electricity price uncertainty is usually solved by the commercial solver, which is limited in computation time and memory requirement. This paper proposes an improved Lagrangian relaxation algorithm for the robust generation self-scheduling problem where the quadratic fuel cost and the time-dependent exponential startup cost are considered. By using the optimal duality theory, the robust generation self-scheduling problem, which has a max-min structure, is reformulated as a minimization mixed integer nonlinear programming (MINLP) problem. Upon the reformulation, the Lagrangian relaxation algorithm is developed. To obtain a solvable relaxed problem, the variable splitting technique is introduced before the relaxation. The obtained relaxed problem is decomposed into a linear programming-type subproblem and multiple single-unit subproblems. Each single-unit subproblem is solved optimally by a two-stage backward dynamic programming procedure. The special cases of the problem are discussed and a two-stage algorithm is proposed. The proposed algorithms are tested on test cases of different sizes and the numerical results show that the algorithms can find near-optimal solutions in a reasonable time

Cloning and Functional Analysis of Lignin Biosynthesis Genes Cf4CL and CfCCoAOMT in Cryptomeria fortunei

Cryptomeria fortunei, also known as the Chinese cedar, is an important timber species in southern China. The primary component of its woody tissues is lignin, mainly present in secondary cell walls. Therefore, continuous lignin synthesis is crucial for wood formation. In this study, we aimed to discover key genes involved in lignin synthesis expressed in the vascular cambium of C. fortunei. Through transcriptome sequencing, we detected expression of two genes, 4CL and CCoAOMT, known to be homologous to enzymes involved in the lignin synthesis pathway. We studied the function of these genes through bioinformatics analysis, cloning, vascular cambium expression analysis, and transgenic cross-species functional validation studies. Our results show that Cf4CL and CfCCoAOMT do indeed function in the pathway of lignin synthesis and likely perform this function in C. fortunei. They are prime candidates for future (gene-editing) studies aimed at optimizing C. fortunei wood production

Gastroprotective Effects of Ganoderma lucidum Polysaccharides with Different Molecular Weights on Ethanol-Induced Acute Gastric Injury in Rats

Ganoderma lucidum is known as a medicine food homology that can ameliorate gastrointestinal diseases. To evaluate the gastroprotective effects on different Ganoderma lucidum polysaccharides (GLPs), GLP was separated into three parts with different molecular weights using 100 kDa, 10 kDa, and 1 kDa membranes. The mitigation effects of different GLPs on ethanol-induced acute gastric injury were observed in rats. After pretreatment with different GLPs, especially GLP above 10 kDa, the symptoms of gastric mucosal congestion and bleeding were improved; serum myeloperoxidase, inflammatory factor, and histamine were decreased; and antioxidant activity and defense factors (NO and EGF) were increased. Results showed that GLP with different molecular weights had a dose-dependent effect in alleviating alcohol-induced gastric injury. The underlying mechanism might be related to regulating anti-oxidation, promoting the release of related defense factors, reducing inflammatory factors, and reducing the level of histamine in serum. The current work indicated that GLPs above 10 kDa could be applied as natural resources for producing new functional foods to prevent gastric injury induced by ethanol

Evaluation of inhibitory effect of rosiglitazone on TGF-β1 induced Smad2 (A), ERK1/2 (B) and p38MAPK(C) activation in ADPKD cyst-lining epithelial cells.

<p>Cells were pretreated with rosiglitazone for 1 h, and then incubated with rosiglitazone in the presence or absence of TGF-β1 (5 ng/mL) for another 1h. * P<0.05 vs. TGF-β1 alone.</p

Rosiglitazone inhibited TGF-β1-induced collagen type I expression in ADPKD cyst-lining epithelial cells through PPARγ.

<p>(A) Cells were incubated with GW9662 (1 µmol/L) and cultured for 48 h. Then cells were pretreated with 10 µmol/L rosiglitazone for 1 h and incubated with TGF-β1 (5 ng/mL) for 24 h. (B) Cells were transfected with a PPARγ siRNA for 48 h, then pretreated with 10 µmol/L rosiglitazone for 1 h and incubated with TGF-β1(5 ng/mL) for 24 h. (C) PPARγ mRNA was decreased to 36% using real-time RT–PCR in PPARγ siRNA-transfected ADPKD cyst-lining epithelial cells. The results were representative of three independent experiments. *P<0.05 vs. control; #P<0.05 vs. TGF-β1 alone Δ P<0.05 vs. rosiglitazone + TGF-β1.</p