An interior-point method for mpecs based on strictly feasible relaxations.

An interior-point method for solving mathematical programs with equilibrium constraints (MPECs) is proposed. At each iteration of the algorithm, a single primaldual step is computed from each subproblem of a sequence. Each subproblem is defined as a relaxation of the MPEC with a nonempty strictly feasible region. In contrast to previous approaches, the proposed relaxation scheme preserves the nonempty strict feasibility of each subproblem even in the limit. Local and superlinear convergence of the algorithm is proved even with a less restrictive strict complementarity condition than the standard one. Moreover, mechanisms for inducing global convergence in practice are proposed. Numerical results on the MacMPEC test problem set demonstrate the fast-local convergence properties of the algorithm

AN INTERIOR-POINT METHOD FOR MPECs BASED ON STRICTLY FEASIBLE RELAXATIONS.

An interior-point method for solving mathematical programs with equilibrium constraints (MPECs) is proposed. At each iteration of the algorithm, a single primaldual step is computed from each subproblem of a sequence. Each subproblem is defined as a relaxation of the MPEC with a nonempty strictly feasible region. In contrast to previous approaches, the proposed relaxation scheme preserves the nonempty strict feasibility of each subproblem even in the limit. Local and superlinear convergence of the algorithm is proved even with a less restrictive strict complementarity condition than the standard one. Moreover, mechanisms for inducing global convergence in practice are proposed. Numerical results on the MacMPEC test problem set demonstrate the fast-local convergence properties of the algorithm.

Variables associated with performance of an active limb movement following within-session instruction in people with and people without low back pain

Modification of a movement pattern can be beneficial in decreasing low back pain (LBP) symptoms. There is variability, however, in how well people are able to modify performance of a movement. What has not been identified is the factors that may affect a person’s ability to modify performance of a movement. We examined factors related to performance of active hip lateral rotation (HLR) following standardized instructions in people with and people without LBP. Data were collected during performance of HLR under 3 conditions: passive, active, and active instructed. In people with LBP, motion demonstrated during the passive condition (r=0.873, P<0.001), motion demonstrated during the active condition (r=0.654, P=0.008), and gender (r=0.570, P=0.027) were related to motion demonstrated during the active-instructed condition. Motion demonstrated during the passive condition explained 76% (P<0.001) of the variance in motion demonstrated during the active-instructed condition. A similar relationship did not exist in people without LBP. The findings of the study suggest that it may be important to assess motion demonstrated during passive HLR to determine how difficult it will be for someone with LBP to modify the performance of HLR. Prognosis should be worst for those who display similar movement patterns during passive HLR and active-instructed HLR

Immunosurveillance of lung melanoma metastasis in EBI-3-deficient mice mediated by CD8+ T cells.

EBV-induced gene 3 (EBI-3) codes for a soluble type I receptor homologous to the p40 subunit of IL-12 that is expressed by APCs following activation. In this study, we assessed the role of EBI-3 in a model of lung melanoma metastasis. Intravenous injection of the B16-F10 cell line resulted in a significant reduction of lung tumor metastasis in EBI-3(-/-) recipient mice compared with wild-type mice. The immunological finding accompanying this effect was the expansion of a newly described cell subset called IFN-gamma producing killer dendritic cells associated with CD8(+) T cell responses in the lung of EBI-3(-/-) mice including IFN-gamma release and TNF-alpha-induced programmed tumor cell death. Depletion of CD8(+) T cells as well as targeting T-bet abrogated the protective effects of EBI-3 deficiency on lung melanoma metastases. Finally, adoptive transfer of EBI-3(-/-) CD8(+) T cells into tumor bearing wild-type mice inhibited lung metastasis in recipient mice. Taken together, these data demonstrate that targeting EBI-3 leads to a T-bet-mediated antitumor CD8(+) T cell responses in the lung

From Relational Data to Graphs: Inferring Significant Links using Generalized Hypergeometric Ensembles

The inference of network topologies from relational data is an important problem in data analysis. Exemplary applications include the reconstruction of social ties from data on human interactions, the inference of gene co-expression networks from DNA microarray data, or the learning of semantic relationships based on co-occurrences of words in documents. Solving these problems requires techniques to infer significant links in noisy relational data. In this short paper, we propose a new statistical modeling framework to address this challenge. It builds on generalized hypergeometric ensembles, a class of generative stochastic models that give rise to analytically tractable probability spaces of directed, multi-edge graphs. We show how this framework can be used to assess the significance of links in noisy relational data. We illustrate our method in two data sets capturing spatio-temporal proximity relations between actors in a social system. The results show that our analytical framework provides a new approach to infer significant links from relational data, with interesting perspectives for the mining of data on social systems.Comment: 10 pages, 8 figures, accepted at SocInfo201

Sampling of temporal networks: methods and biases

Temporal networks have been increasingly used to model a diversity of systems that evolve in time; for example, human contact structures over which dynamic processes such as epidemics take place. A fundamental aspect of real-life networks is that they are sampled within temporal and spatial frames. Furthermore, one might wish to subsample networks to reduce their size for better visualization or to perform computationally intensive simulations. The sampling method may affect the network structure and thus caution is necessary to generalize results based on samples. In this paper, we study four sampling strategies applied to a variety of real-life temporal networks. We quantify the biases generated by each sampling strategy on a number of relevant statistics such as link activity, temporal paths and epidemic spread. We find that some biases are common in a variety of networks and statistics, but one strategy, uniform sampling of nodes, shows improved performance in most scenarios. Given the particularities of temporal network data and the variety of network structures, we recommend that the choice of sampling methods be problem oriented to minimize the potential biases for the specific research questions on hand. Our results help researchers to better design network data collection protocols and to understand the limitations of sampled temporal network data

Pore-scale Modeling of Viscous Flow and Induced Forces in Dense Sphere Packings

We propose a method for effectively upscaling incompressible viscous flow in large random polydispersed sphere packings: the emphasis of this method is on the determination of the forces applied on the solid particles by the fluid. Pore bodies and their connections are defined locally through a regular Delaunay triangulation of the packings. Viscous flow equations are upscaled at the pore level, and approximated with a finite volume numerical scheme. We compare numerical simulations of the proposed method to detailed finite element (FEM) simulations of the Stokes equations for assemblies of 8 to 200 spheres. A good agreement is found both in terms of forces exerted on the solid particles and effective permeability coefficients

Role of bladder cancer metabolic reprogramming in the effectiveness of immunotherapy

Metabolic reprogramming (MR) is an upregulation of biosynthetic and bioenergetic pathways to satisfy increased energy and metabolic building block demands of tumors. This includes glycolytic activity, which deprives the tumor microenvironment (TME) of nutrients while increasing extracellular lactic acid. This inhibits cytotoxic immune activity either via direct metabolic competition between cancer cells and cytotoxic host cells or by the production of immune-suppressive metabolites such as lactate or kynurenine. Since immunotherapy is a major treatment option in patients with metastatic urothelial carcinoma (UC), MR may have profound implications for the success of such therapy. Here, we review how MR impacts host immune response to UC and the impact on immunotherapy response (including checkpoint inhibitors, adaptive T cell therapy, T cell activation, antigen presentation, and changes in the tumor microenvironm

Probing empirical contact networks by simulation of spreading dynamics

Disease, opinions, ideas, gossip, etc. all spread on social networks. How these networks are connected (the network structure) influences the dynamics of the spreading processes. By investigating these relationships one gains understanding both of the spreading itself and the structure and function of the contact network. In this chapter, we will summarize the recent literature using simulation of spreading processes on top of empirical contact data. We will mostly focus on disease simulations on temporal proximity networks -- networks recording who is close to whom, at what time -- but also cover other types of networks and spreading processes. We analyze 29 empirical networks to illustrate the methods

Random walk centrality for temporal networks

Nodes can be ranked according to their relative importance within a network. Ranking algorithms based on random walks are particularly useful because they connect topological and diffusive properties of the network. Previous methods based on random walks, for example the PageRank, have focused on static structures. However, several realistic networks are indeed dynamic, meaning that their structure changes in time. In this paper, we propose a centrality measure for temporal networks based on random walks under periodic boundary conditions that we call TempoRank. It is known that, in static networks, the stationary density of the random walk is proportional to the degree or the strength of a node. In contrast, we find that, in temporal networks, the stationary density is proportional to the in-strength of the so-called effective network, a weighted and directed network explicitly constructed from the original sequence of transition matrices. The stationary density also depends on the sojourn probability q, which regulates the tendency of the walker to stay in the node, and on the temporal resolution of the data. We apply our method to human interaction networks and show that although it is important for a node to be connected to another node with many random walkers (one of the principles of the PageRank) at the right moment, this effect is negligible in practice when the time order of link activation is included