Graph-structured data, where nodes exhibit either pair-wise or high-order relations, are ubiquitous and essential in graph learning. Despite the great achievement made by existing graph learning models, these models use the direct information (edges or hyperedges) from graphs and do not adopt the underlying indirect information (hidden pair-wise or high-order relations). To address this issue, in this paper, we propose a general framework named Simplicial Complex Neural (SCN) network, in which we construct a simplicial complex based on the direct and indirect graph information from a graph so that all information can be employed in the complex network learning. Specifically, we learn representations of simplices by aggregating and integrating information from all the simplices together via layer-by-layer simplicial complex propagation. In consequence, the representations of nodes, edges, and other high-order simplices are obtained simultaneously and can be used for learning purposes. By making use of block matrix properties, we derive the theoretical bound of the simplicial complex filter learnt by the propagation and establish the generalization error bound of the proposed simplicial complex network. We perform extensive experiments on node (0-simplex), edge (1-simplex), and triangle (2-simplex) classifications, and promising results demonstrate the performance of the proposed method is better than that of existing graph and hypergraph network approaches.

Graph neural networks have been shown to be very effective in utilizing pairwise relationships across samples. Recently, there have been several successful proposals to generalize graph neural networks to hypergraph neural networks to exploit more complex relationships. In particular, the hypergraph collaborative networks yield superior results compared to other hypergraph neural networks for various semi-supervised learning tasks. The collaborative network can provide high quality vertex embeddings and hyperedge embeddings together by formulating them as a joint optimization problem and by using their consistency in reconstructing the given hypergraph. In this paper, we aim to establish the algorithmic stability of the core layer of the collaborative network and provide generalization guarantees. The analysis sheds light on the design of hypergraph filters in collaborative networks, for instance, how the data and hypergraph filters should be scaled to achieve uniform stability of the learning process. Some experimental results on real-world datasets are presented to illustrate the theory.

The task of transfer learning using pretrained convolutional neural networks is considered. We propose a convolution-SVD layer to analyze the convolution operators with a singular value decomposition computed in the Fourier domain. Singular vectors extracted from the source domain are transferred to the target domain, whereas the singular values are fine-tuned with a target data set. In this way, dimension reduction is achieved to avoid overfitting, while some flexibility to fine-tune the convolution kernels is maintained. We extend an existing convolution kernel reconstruction algorithm to allow for a reconstruction from an arbitrary set of learned singular values. A generalization bound for a single convolution-SVD layer is devised to show the consistency between training and testing errors. We further introduce a notion of transfer learning gap. We prove that the testing error for a single convolution-SVD layer is bounded in terms of the gap, which motivates us to develop a regularization model with the gap as the regularizer. Numerical experiments are conducted to demonstrate the superiority of the proposed model in solving classification problems and the influence of various parameters. In particular, the regularization is shown to yield a significantly higher prediction accuracy.

Graph Convolution Networks (GCNs) have been shown to be very effective in utilizing pair-wise relationships across samples. They have been successfully applied to solve various machine learning problems in practice. In many applications, the construction of GCNs involves more than one layer. However, their generalization and stability analysis are limited. The main aim of this paper is to analyze GCNs with two layers. The formulation is based on transductive semi-supervised learning and the filtering is done in the eigen-domain. We show the uniform stability of the neural network and the convergence of the generalization gap to zero. The analysis of two-layer GCN is more involved than the single-layer case and requires some new estimates of the neural network’s quantities. The analysis confirms the usefulness of GCNs. It also sheds light on the design of the neural network, for instance, how the data should be scaled to achieve the uniform stability of the learning process. Some experimental results on benchmark datasets are presented to illustrate the theory.

We present an algorithmic trading strategy based upon a graph version of the dynamic mode decomposition (DMD) model. Unlike the traditional DMD model which tries to characterize a stock’s dynamics based on all other stocks in a universe, the proposed model characterizes a stock’s dynamics based only on stocks that are deemed relevant to the stock in question. The relevance between each pair of stocks in a universe is represented as a directed graph and is updated dynamically. The incorporation of a graph model into DMD effects a model reduction that avoids overfitting of data and improves the quality of the trend predictions. We show that, in a practical setting, the precision and recall rate of the proposed model are significantly better than the traditional DMD and the benchmarks. The proposed model yields portfolios that have more stable returns in most of the universes we backtested.

The class of multi-relational graph convolutional networks (MRGCNs) is a recent extension of standard graph convolutional networks (GCNs) to handle heterogenous graphs with multiple types of relationships. MRGCNs have been shown to yield results superior than traditional GCNs in various machine learning tasks. The key idea is to introduce a new kind of convolution operated on tensors that can effectively exploit correlations exhibited in multiple relationships. The main objective of this paper is to analyze the algorithmic stability and generalization guarantees of MRGCNs to confirm the usefulness of MRGCNs. Our contributions are of three folds. First, we develop a matrix representation of various tensor operations underneath MRGCNs to simplify the analysis significantly. Next, we prove the uniform stability of MRGCNs and deduce the convergence of the generalization gap to support the usefulness of MRGCNs. The analysis sheds lights on the design of MRGCNs, for instance, how the data should be scaled to achieve the uniform stability of the learning process. Finally, we provide experimental results to demonstrate the stability results.

Hypnozoites are the liver stage non-dividing form of the malaria parasite that are responsible for relapse and acts as a natural reservoir for human malaria Plasmodium vivax and P. ovale as well as a phylogenetically related simian malaria P. cynomolgi. Our understanding of hypnozoite biology remains limited due to the technical challenge of requiring the use of primary hepatocytes and the lack of robust and predictive in vitro models. In this study, we developed a malaria liver stage model using 3D spheroid-cultured primary hepatocytes. The infection of primary hepatocytes in suspension led to increased infectivity of both P. cynomolgi and P. vivax infections. We demonstrated that this hepatic spheroid model was capable of maintaining long term viability, hepatocyte specific functions and cell polarity which enhanced permissiveness and thus, permitting for the complete development of both P. cynomolgi and P. vivax liver stage parasites in the infected spheroids. The model described here was able to capture the full liver stage cycle starting with sporozoites and ending in the release of hepatic merozoites capable of invading simian erythrocytes in vitro. Finally, we showed that this system can be used for compound screening to discriminate between causal prophylactic and cidal antimalarials activity in vitro for relapsing malaria.

Plasmodium vivax hypnozoites persist in the liver, cause malaria relapse and represent a major challenge to malaria elimination. Our previous transcriptomic study provided a novel molecular framework to enhance our understanding of the hypnozoite biology (Voorberg-van der Wel A, et al., 2017). In this dataset, we identified and characterized the Liver-Specific Protein 2 (LISP2) protein as an early molecular marker of liver stage development. Immunofluorescence analysis of hepatocytes infected with relapsing malaria parasites, in vitro (P. cynomolgi) and in vivo (P. vivax), reveals that LISP2 expression discriminates between dormant hypnozoites and early developing parasites. We further demonstrate that prophylactic drugs selectively kill all LISP2-positive parasites, while LISP2-negative hypnozoites are only sensitive to anti-relapse drug tafenoquine. Our results provide novel biological insights in the initiation of liver stage schizogony and an early marker suitable for the development of drug discovery assays predictive of anti-relapse activity.

The ability to culture pathogenic organisms substantially enhances the quest for fundamental knowledge and the development of vaccines and drugs. Thus, the elaboration of a protocol for the in vitro cultivation of the erythrocytic stages of Plasmodium falciparum revolutionized research on this important parasite. However, for P. vivax , the most widely distributed and difficult to treat malaria parasite, a strict preference for reticulocytes thwarts efforts to maintain it in vitro. Cultivation of P. cynomolgi , a macaque-infecting species phylogenetically close to P. vivax , was briefly reported in the early 1980s, but not pursued further. Here, we define the conditions under which P. cynomolgi can be adapted to long term in vitro culture to yield parasites that share many of the morphological and phenotypic features of P. vivax . We further validate the potential of this culture system for high-throughput screening to prime and accelerate anti- P. vivax drug discovery efforts.

Background We aimed to test a mindfulness-based psychoeducation group (MBPEG), v. a conventional psychoeducation group (CPEG) v. treatment as usual (TAU), in patients with schizophrenia-spectrum disorders over a 24-month follow-up. Method This single-blind, multi-site, pragmatic randomized controlled trial was conducted in six community treatment facilities across three countries (Hong Kong, mainland China and Taiwan). Patients were randomly allocated to one of the treatment conditions, and underwent 6 months of treatment. The primary outcomes were changes in duration of re-hospitalizations and mental state (Positive and Negative Syndrome Scale; PANSS) between baseline and 1 week, and 6, 12 and 18 months post-treatment. Results A total of 300 patients in each country were assessed for eligibility between October 2013 and 30 April 2014, 38 patients per country ( n = 342) were assigned to each treatment group and included in the intention-to-treat analysis. There was a significant difference in the length of re-hospitalizations between the three groups over 24 months ( F 2,330 = 5.23, p = 0.005), with MBPEG participants having a shorter mean duration of re-hospitalizations than those in the other groups. The MBPEG and CPEG participants had significant differential changes in proportional odds ratios of complete remission (all individual PANSS items <3) over the 24-month follow-up (37 and 26%, respectively), as opposed to only 7.2% of the TAU group (χ 2 = 8.9 and 8.0, p = 0.001 and 0.003, relative risk = 3.5 and 3.1, 95% confidence interval 2.0–7.2 and 1.6–6.3). Conclusions Compared with TAU and CPEG, MBPEG improves remission and hospitalization rates of people with schizophrenia spectrum disorders over 24 months.

Background: Distance running is one of the most popular sports globally. Running economy in terms of oxygen consumption (VO2) is strongly associated with running performance. In distance running, respiratory muscles are important in maintaining sufficient alveolar ventilation and oxygen supply. Fatigue of respiratory muscles can increase work of breathing and oxygen demand by the respiratory muscles which further limit the total blood flow to peripheral muscles thus affect exercise performance. Inspiratory muscle training (IMT) has been reported to improve the strength and endurance of respiratory muscle by increasing the percentage of type I fibers and the size of type II fibers. This may reduce the energy requirement for ventilation and hence lessening the lactate accumulation. All these may eventually lead to performance enhancement. However, the association of these proposed mechanisms remains speculative.

ABSTRACT The four serotypes of dengue virus (DENV-1 to -4) represent the most prevalent mosquito-borne viral pathogens in humans. No clinically approved vaccine or antiviral is currently available for DENV. Here we report a spiropyrazolopyridone compound that potently inhibits DENV both in vitro and in vivo. The inhibitor was identified through screening of a 1.8-million-compound library by using a DENV-2 replicon assay. The compound selectively inhibits DENV-2 and -3 (50% effective concentration [EC50], 10 to 80 nM) but not DENV-1 and -4 (EC50, >20 μM). Resistance analysis showed that a mutation at amino acid 63 of DENV-2 NS4B (a nonenzymatic transmembrane protein and a component of the viral replication complex) could confer resistance to compound inhibition. Genetic studies demonstrate that variations at amino acid 63 of viral NS4B are responsible for the selective inhibition of DENV-2 and -3. Medicinal chemistry improved the physicochemical properties of the initial “hit” (compound 1), leading to compound 14a, which has good in vivo pharmacokinetics. Treatment of DENV-2-infected AG129 mice with compound 14a suppressed viremia, even when the treatment started after viral infection. The results have proven the concept that inhibitors of NS4B could potentially be developed for clinical treatment of DENV infection. Compound 14a represents a potential preclinical candidate for treatment of DENV-2- and -3-infected patients. IMPORTANCE Dengue virus (DENV) threatens up to 2.5 billion people and is now spreading in many regions in the world where it was not previously endemic. While there are several promising vaccine candidates in clinical trials, approved vaccines or antivirals are not yet available. Here we describe the identification and characterization of a spiropyrazolopyridone as a novel inhibitor of DENV by targeting the viral NS4B protein. The compound potently inhibits two of the four serotypes of DENV (DENV-2 and -3) both in vitro and in vivo. Our results validate, for the first time, that NS4B inhibitors could potentially be developed for antiviral therapy for treatment of DENV infection in humans.

Spiropyrazolopyridone 1 was identified, as a novel dengue virus (DENV) inhibitor, from a DENV serotype 2 (DENV-2) high-throughput phenotypic screen. As a general trend within this chemical class, chiral resolution of the racemate revealed that R enantiomer was significantly more potent than the S. Cell-based lead optimization of the spiropyrazolopyridones focusing on improving the physicochemical properties is described. As a result, an optimal compound 14a, with balanced in vitro potency and pharmacokinetic profile, achieved about 1.9 log viremia reduction at 3 × 50 mg/kg (bid) or 3 × 100 mg/kg (QD) oral doses in the dengue in vivo mouse efficacy model.

Since their introduction in a classic paper by Rudin, Osher, and Fatemi (Physica D 60:259–268, 1992), total variation minimizing models have become one of the most popular and successful methodologies for image restoration. New developments continue to expand the capability of the basic method in various aspects. Many faster numerical algorithms and more sophisticated applications have been proposed. This chapter reviews some of these recent developments.

Dengue virus (DENV) infection could lead to dengue fever (DF), dengue hemorrhagic fever (DHF) or dengue shock syndrome (DSS). The disease outcome is controlled by both viral and host factors. Inflammation mediators from DENV-infected cells could contribute to increased vascular permeability, leading to severe DHF/DSS. Therefore, suppression of inflammation could be a potential therapeutic approach for treatment of dengue patients. In this context, p38 MAPK (mitogen-activated protein kinase) is a key enzyme that modulates the initiation of stress and inflammatory responses. Here we show that SB203580, a p38 MAPK inhibitor, suppressed the over production of DENV-induced pro-inflammatory mediators such as TNF-α, IL-8, and RANTES from human PBMCs, monocytic THP-1, and granulocyte KU812 cell lines. Oral administration of SB203580 in DENV-infected AG129 mice prevented hematocrit rise and lymphopenia, limited the development of inflammation and pathology (including intestine leakage), and significantly improved survival. These results, for the first time, have provided experimental evidence to imply that a short term inhibition of p38 MAPK may be beneficial to reduce disease symptoms in dengue patients.

The dengue virus (DENV) is a mosquito-borne pathogen responsible for an estimated 100 million human infections annually. The viral genome encodes a two-component trypsin-like protease that contains the cofactor region from the nonstructural protein NS2B and the protease domain from NS3 (NS3pro). The NS2B-NS3pro complex plays a crucial role in viral maturation and has been identified as a potential drug target. Using a DENV protease construct containing NS2B covalently linked to NS3pro via a Gly4-Ser-Gly4 linker ("linked protease"), previous x-ray crystal structures show that the C-terminal fragment of NS2B is remote from NS3pro and exists in an open state in the absence of an inhibitor; however, in the presence of an inhibitor, NS2B complexes with NS3pro to form a closed state. This linked enzyme produced NMR spectra with severe signal overlap and line broadening. To obtain a protease construct with a resolved NMR spectrum, we expressed and purified an unlinked protease complex containing a 50-residue segment of the NS2B cofactor region and NS3pro without the glycine linker using a coexpression system. This unlinked protease complex was catalytically active at neutral pH in the absence of glycerol and produced dispersed cross-peaks in a (1)H-(15)N heteronuclear single quantum correlation spectrum that enabled us to conduct backbone assignments using conventional techniques. In addition, titration with an active-site peptide aldehyde inhibitor and paramagnetic relaxation enhancement studies demonstrated that the unlinked DENV protease exists predominantly in a closed conformation in solution. This protease complex can serve as a useful tool for drug discovery against DENV.

We propose a novel fast numerical method for denoising of 1D signals based on curvature minimization. Motivated by the primal-dual formulation for total variation minimization introduced by Chan, Golub, and Mulet, the proposed method makes use of some auxiliary variables to reformulate the stiff terms presented in the Euler-Lagrange equation which is a fourth-order differential equation. A direct application of Newton's method to the resulting system of equations often fails to converge. We propose a modified Newton's iteration which exhibits local superlinear convergence and global convergence in practical settings. The method is much faster than other existing methods for the model. Unlike all other existing methods, it also does not require tuning any additional parameter besides the model parameter. Numerical experiments are presented to demonstrate the effectiveness of the proposed method.

Dengue fever is an emerging mosquito-borne flaviviral disease that threatens 2.5 billion people worldwide. No clinically approved vaccine and antiviral therapy are currently available to prevent or treat dengue virus (DENV) infection. Vertebrate animals other than primates are not normally infectable with DENV; however, a small animal dengue infection model would greatly facilitate the development of a vaccine or an antiviral therapy. To this end, a rodent model for DENV infection has been established in IFN-α/β and IFN-γ receptor-deficient (AG129) mice. This chapter describes the protocol for the DENV infection model in AG129 mice and testing of antiviral compounds by oral gavage or parenteral injection.