Effects on muscle tissue remodeling and lipid metabolism in muscle tissue from adult patients with polymyositis or dermatomyositis treated with immunosuppressive agents.

BACKGROUND: Polymyositis (PM) and dermatomyositis (DM) are autoimmune muscle diseases, conventionally treated with high doses of glucocorticoids in combination with immunosuppressive drugs. Treatment is often dissatisfying, with persisting muscle impairment. We aimed to investigate molecular mechanisms that might contribute to the persisting muscle impairment despite immunosuppressive treatment in adult patients with PM or DM using gene expression profiling of repeated muscle biopsies. METHODS: Paired skeletal muscle biopsies from six newly diagnosed adult patients with DM or PM taken before and after conventional immunosuppressive treatment were examined by gene expression microarray analysis. Selected genes that displayed changes in expression were analyzed by Western blot. Muscle biopsy sections were evaluated for inflammation, T lymphocytes (CD3), macrophages (CD68), major histocompatibility complex (MHC) class I expression and fiber type composition. RESULTS: After treatment, genes related to immune response and inflammation, including inflammasome pathways and interferon, were downregulated. This was confirmed at the protein level for AIM-2 and caspase-1 in the inflammasome pathway. Changes in genes involved in muscle tissue remodeling suggested a negative effect on muscle regeneration and growth. Gene markers for fast type II fibers were upregulated and fiber composition was switched towards type II fibers in response to treatment. The expression of genes involved in lipid metabolism was altered, suggesting a potential lipotoxic effect on muscles of the immunosuppressive treatment. CONCLUSION: The anti-inflammatory effect of immunosuppressive treatment was combined with negative effects on genes involved in muscle tissue remodeling and lipid metabolism, suggesting a negative effect on recovery of muscle performance which may contribute to persisting muscle impairment in adult patients with DM and PM

Conditional over-expression of PITX1 causes skeletal muscle dystrophy in mice

Paired-like homeodomain transcription factor 1 (PITX1) was specifically up-regulated in patients with facioscapulohumeral muscular dystrophy (FSHD) by comparing the genome-wide mRNA expression profiles of 12 neuromuscular disorders. In addition, it is the only known direct transcriptional target of the double homeobox protein 4 (DUX4) of which aberrant expression has been shown to be the cause of FSHD. To test the hypothesis that up-regulation of PITX1 contributes to the skeletal muscle atrophy seen in patients with FSHD, we generated a tet-repressible muscle-specific Pitx1 transgenic mouse model in which expression of PITX1 in skeletal muscle can be controlled by oral administration of doxycycline. After PITX1 was over-expressed in the skeletal muscle for 5 weeks, the mice exhibited significant loss of body weight and muscle mass, decreased muscle strength, and reduction of muscle fiber diameters. Among the muscles examined, the tibialis anterior, gastrocnemius, quadricep, bicep, tricep and deltoid showed significant reduction of muscle mass, while the soleus, masseter and diaphragm muscles were not affected. The most prominent pathological change was the development of atrophic muscle fibers with mild necrosis and inflammatory infiltration. The affected myofibers stained heavily with NADH-TR with the strongest staining in angular-shaped atrophic fibers. Some of the atrophic fibers were also positive for embryonic myosin heavy chain using immunohistochemistry. Immunoblotting showed that the p53 was up-regulated in the muscles over-expressing PITX1. The results suggest that the up-regulation of PITX1 followed by activation of p53-dependent pathways may play a major role in the muscle atrophy developed in the mouse model

Optimizing Passenger On-Vehicle Experience through Simulation and Multi-Agent Multi-Criteria Mobility Planning

The rapid growth in urban population poses significant challenges to moving city dwellers in a fast and convenient manner. This thesis contributes to solving the challenges from the viewpoint of public transit passengers by improving their on-vehicle experience. Traditional transportation research focuses on pursuing minimal travel time of vehicles on the road network, paying no attention to people inside the vehicles. In contrast, the research in this thesis is passenger-driven, concerning the role of the on-vehicle experience in mobility planning through the public transit systems. The primary goal of the thesis is to address the following problem: Given an urban public transit network, how can we plan for the optimal experience of passengers in terms of their service preference? There are several challenges we have to address to meet this goal. First, a model or a simulator that captures not only the road traffic, but also the behaviors of passengers and other relevant factors is a prerequisite for this research but has seldom been developed previously. Second, to plan for passengers' mobility concerning the influence among passengers as well as multiple service preferences is computationally intensive, especially on a city scale. To achieve the research goal and overcome the challenges, this thesis develops a joint traffic-passenger simulator, which simulates the road traffic, behaviors of passengers and on-vehicle environment dynamics. Specifically, the simulator combines the urban road traffic, the interactions among the passengers and the infrastructures that support certain on-vehicle services, such as on-vehicle Wi-Fi, to provide a passenger-level simulation. A separate passenger behavior model and on-vehicle Wi-Fi service model are designed to run jointly with SUMO, a mature traffic simulator, for simulating the passenger behaviors and on-vehicle travel experience. A joint simulator for the bus transit system in the city of Porto, Portugal has been implemented and tested by comparing the simulation to the real passenger data. To configure the background passenger flow in the simulation, real passenger data are used. The data were collected by an entry-only system and the destination information was missing. This thesis contributes a machine learning algorithm, called semi-supervised self-training, to infer the missing destinations with a high inference confidence level. Given the simulation platform, the passenger mobility planning problem can be formalized as a multi-agent path planning (MAPP) problem, where multiple passengers may interfere with each other when contending for service resources. The mobility planning operates on the client passengers (i.e., a subset of the overall passengers who request the planning service from our planner). State-of-the-art MAPP solvers, such as M*, do not scale well to such a MAPP problem. This thesis proposes the soft-collision-free M* (SC-M*), a generalized version of M*, to efficiently handle the MAPP task under complex urban environments (i.e., with a large client passenger size and multiple types of client passengers requesting multiple types of service resources). We evaluate the performance of the SC-M* through a case study of the bus transit system in Porto, Portugal and the experimental results show the advantages of the SC-M* in terms of path cost, collision-free constraint, and the scalability in run time and success rate

Optimizing Passenger On-Vehicle Experience through Simulation and Multi-Agent Multi-Criteria Mobility Planning

The rapid growth in urban population poses significant challenges to moving city dwellers in a fast and convenient manner. This thesis contributes to solving the challenges from the viewpoint of public transit passengers by improving their on-vehicle experience. Traditional transportationresearch focuses on pursuing minimal travel time of vehicles on the road network, paying no attention to people inside the vehicles. In contrast, the research in this thesis is passenger-driven, concerning the role of the on-vehicle experience in mobility planning through the public transitsystems. The primary goal of the thesis is to address the following problem: Given an urban public transit network, how can we plan for the optimal experience of passengers in terms of their service preference? There are several challenges we have to address to meet this goal. First, a model or a simulator that captures not only the road traffic, but also the behaviors of passengers and other relevantfactors is a prerequisite for this research but has seldom been developed previously. Second, to plan for passengers’ mobility concerning the influence among passengers as well as multiple service preferences is computationally intensive, especially on a city scale. To achieve the research goal and overcome the challenges, this thesis develops a joint traffic passenger simulator, which simulates the road traffic, behaviors of passengers and on-vehicle environment dynamics. Specifically, the simulator combines the urban road traffic, the interactions among the passengers and the infrastructures that support certain on-vehicle services,such as on-vehicle Wi-Fi, to provide a passenger-level simulation. A separate passenger behavior model and on-vehicle Wi-Fi service model are designed to run jointly with SUMO, a mature traffic simulator, for simulating the passenger behaviors and on-vehicle travel experience. Ajoint simulator for the bus transit system in the city of Porto, Portugal has been implemented and tested by comparing the simulation to the real passenger data. To configure the background passenger flow in the simulation, real passenger data are used. The data were collected by an entry-only system and the destination information was missing.This thesis contributes a machine learning algorithm, called semi-supervised self-training, to infer the missing destinations with a high inference confidence level.Given the simulation platform, the passenger mobility planning problem can be formalized as a multi-agent path planning (MAPP) problem, where multiple passengers may interfere with each other when contending for service resources. The mobility planning operates on the client passengers (i.e., a subset of the overall passengers who request the planning service from our planner). State-of-the-art MAPP solvers, such as M*, do not scale well to such a MAPP problem. This thesis proposes the soft-collision-free M* (SC-M*), a generalized version of M*, to efficientlyhandle the MAPP task under complex urban environments (i.e., with a large client passenger size and multiple types of client passengers requesting multiple types of service resources). We evaluate the performance of the SC-M* through a case study of the bus transit system in Porto,Portugal and the experimental results show the advantages of the SC-M* in terms of path cost, collision-free constraint, and the scalability in run time and success rate. <br

Robust Data Sampling in Machine Learning: A Game-Theoretic Framework for Training and Validation Data Selection

How to sample training/validation data is an important question for machine learning models, especially when the dataset is heterogeneous and skewed. In this paper, we propose a data sampling method that robustly selects training/validation data. We formulate the training/validation data sampling process as a two-player game: a trainer aims to sample training data so as to minimize the test error, while a validator adversarially samples validation data that can increase the test error. Robust sampling is achieved at the game equilibrium. To accelerate the searching process, we adopt reinforcement learning aided Monte Carlo trees search (MCTS). We apply our method to a car-following modeling problem, a complicated scenario with heterogeneous and random human driving behavior. Real-world data, the Next Generation SIMulation (NGSIM), is used to validate this method, and experiment results demonstrate the sampling robustness and thereby the model out-of-sample performance