Experimental arthritis : in vitro and in vivo models

As the primary cause of disability for people over the age of 45, arthritis actually consists of more than hundred different conditions. Osteoarthritis (OA) is the most common form of arthritis followed by rheumatoid arthritis (RA). OA is characterized by progressive articular cartilage loss and destruction, osteophyte formation, subchondral bone changes and synovial inflammation. The pathophysiology of OA is not yet completely understood, but mechanical influences, effects of aging, and genetic factors play a vital role in OA initiation and progression. Arthritis is a complex disease for two major reasons: the large number of contributing factors both in disease initiation and propagation; unknown mechanisms behind the disease development involving unknown interactions between the aforementioned factors. Studies to elucidate the pathogenesis of OA are further deterred by the relatively long dormant period where critical changes develop in both the bone and cartilage tissue with little to no outward symptoms. In order to properly address the problem of OA with effective therapeutic and preventative interventions, the mechanisms for its pathogenesis must be more clearly understood. However, as a disease not usually detected in patients until its last stages, OA proved to be a difficult subject of study. As such, both in vivo and in vitro models are employed as powerful tools for the research of OA, each with different strengths and limitations. The in vivo models address the complex and interacting mechanisms and factors for disease initiation and propagation, allowing for the study of natural disease progression over time. On the other hand, in vitro studies are better suited for the isolation of specific factors and the analysis of their contribution to the overall disease progression. By isolating a particular factor in vitro, these models have the advantage over their in vivo counterparts as a cost-effective and high throughput solution without the problem of variability between animals. The selection of an appropriate study model is important; each model introduces unique experimental conditions affects the results and provides unique insights in understanding the disease, and the results from different studies are therefore often complementary. The aim of this thesis is to combine a number of in vivo and in vitro models to gain better insights in the progression of OA, specifically focusing on the interactions between bone adaptation and cartilage degradation. Experimental Arthritis: in vitro and in vivo Models Chapter 1 reviews the current status of arthritis research and the various models currently employed in the study of OA and RA. Chapter 2 explores the subchondral bone microarchitecture changes in animal models of OA and RA using high resolution micro-computed tomography (micro CT) technique. The author had developed several in vitro arthritis models over the years, namely monolayer, multi-layered, and pellet culture using primary chondrocytes. In addition, the author also employed a co-culture model of chondrocytes, osteoblasts, and synovial cells. The best in vitro model was found to be the tissue engineered cartilage that resulted from a closed-chamber bioreactor. The resultant tissue engineered cartilage can be either non-scaffold or scaffold. Chapter 3 presents a study on the development of biphasic implants that consist of the aforementioned tissue engineered cartilage with or without various underlying biodegradable osteoconductive support materials. RA is a systemic autoimmune disease characterized by chronic joint inflammation and various degrees of bone and cartilage erosion. Study of RA animal models provides an understanding of the bone damage and its treatment. Chapter 4 presents a study utilizing a cell wall antigen induced arthritis model in rats. The aim of the study is to 1. Evaluate subchondral bone micro architecture change and 2. Investigate the efficacy of N-butyryl glucosamine (GlcNBu). The results show that GlcNBu inhibits inflammatory ankle swelling and preserves bone mineral density and bone connectivity, thus preventing further bone loss in this rat model of chronic arthritis. Subchondral bone change is hypothesized to play a significant role in the initiation and/or development of OA. Chapter 5 examines the periarticular subchondral bone changes, including bone mineral density, subchondral trabecular bone turnover, architecture, and connectivity, as well as subchondral plate thickness and mineralization using a rabbit anterior cruciate ligament transection model of osteoarthritis. Results show that orally administered Glucosamine HCl presents protective effects in subchondral bone changes in the abovementioned experimental OA model. The complexity in the development and progression of OA can be attributed to the close relationship between cartilage, subchondral bone, and neighboring tissues. Due to the complicated nature of OA progression, it is difficult to predict exactly when and how it is initiated. Numerous animal models were developed and their use has become indispensable in this field of study. To bring further clarity to the many unanswered questions concerning the role and importance of the subchondral bone in OA development, this thesis approaches the problem from two primary directions. First, we examine the minute changes of subchondral bone and cartilage to elucidate their relationship and impact on OA progression. Chapter 6 presents a study using three dimensional micro CT analyses combined with stereological histology assessment of cartilage changes in spontaneous knee osteoarthritis of two strains of guinea pig. A connection between bone remodeling and cartilage destruction is established by correlating three dimensional cartilage changes with bone remodeling. The second direction taken by this thesis is to study the OA development in a time course experiment using a slow progressive OA model. Chapter 7 examines OA progression in detail over time on both surgical induced OA (mimic secondary OA) and spontaneous OA (mimic primary OA) in guinea pigs, with special emphasis on the early stage of disease development. The progressive changes of subchondral bone over a 6 month time period is described in details for this experimental guinea pig OA model. It is now clear that increased subchondral bone turnover is a crucial step in the progression of OA and that the presence of cartilage lesion is always matched with significant bone remodeling directly below. This discovery has significant implications in both the understanding and treatment of OA. Having recognized the role of the subchondral bone in the OA progression, we hypothesize that the reduction of cartilage degeneration by suppressing subchondral bone turnover is highly achievable. Chapter 8 investigates the effect of Alendronate, a drug that prohibits bone resorption, in the aforementioned guinea pig OA model. This study demonstrates that by suppressing bone turnover, Alendronate exhibits positive effects on articular surface erosion, cartilage degradation and subchondral bone structure and mineralization; it also protected collagen and proteoglycan content of the articular cartilage. We conclude that anti-resorptive treatments have positive effects on both cartilage and bone degradation. Taken together, the thesis shows that cartilage and bone are tightly coupled together as a whole organ system. The two tissues cannot be considered separately in the study of arthritis pathogenesis; the interaction between subchondral bone and cartilage is one of the most important factors in OA progression. By suppressing subchondral bone turnover we have achieved cartilage protection in the guinea pig model of OA. This proves that increased subchondral bone turnover is a causal factor in OA progression. The combination of in vitro and in vivo models in this thesis has contributed to a better understanding of the etiology. In particular, in vitro models based on tissue engineered cartilage have been important for studying changes to the cartilage surface, and for screening of potential medication. For the study of progression of OA in the long term, the guinea pig model is very useful. This model simulates many aspects of normal development of OA in humans and can be used to evaluate treatments of OA in vivo

Unbiased Offline Evaluation of Contextual-bandit-based News Article Recommendation Algorithms

Contextual bandit algorithms have become popular for online recommendation systems such as Digg, Yahoo! Buzz, and news recommendation in general. \emph{Offline} evaluation of the effectiveness of new algorithms in these applications is critical for protecting online user experiences but very challenging due to their "partial-label" nature. Common practice is to create a simulator which simulates the online environment for the problem at hand and then run an algorithm against this simulator. However, creating simulator itself is often difficult and modeling bias is usually unavoidably introduced. In this paper, we introduce a \emph{replay} methodology for contextual bandit algorithm evaluation. Different from simulator-based approaches, our method is completely data-driven and very easy to adapt to different applications. More importantly, our method can provide provably unbiased evaluations. Our empirical results on a large-scale news article recommendation dataset collected from Yahoo! Front Page conform well with our theoretical results. Furthermore, comparisons between our offline replay and online bucket evaluation of several contextual bandit algorithms show accuracy and effectiveness of our offline evaluation method.Comment: 10 pages, 7 figures, revised from the published version at the WSDM 2011 conferenc

DDMT: Denoising Diffusion Mask Transformer Models for Multivariate Time Series Anomaly Detection

Anomaly detection in multivariate time series has emerged as a crucial challenge in time series research, with significant research implications in various fields such as fraud detection, fault diagnosis, and system state estimation. Reconstruction-based models have shown promising potential in recent years for detecting anomalies in time series data. However, due to the rapid increase in data scale and dimensionality, the issues of noise and Weak Identity Mapping (WIM) during time series reconstruction have become increasingly pronounced. To address this, we introduce a novel Adaptive Dynamic Neighbor Mask (ADNM) mechanism and integrate it with the Transformer and Denoising Diffusion Model, creating a new framework for multivariate time series anomaly detection, named Denoising Diffusion Mask Transformer (DDMT). The ADNM module is introduced to mitigate information leakage between input and output features during data reconstruction, thereby alleviating the problem of WIM during reconstruction. The Denoising Diffusion Transformer (DDT) employs the Transformer as an internal neural network structure for Denoising Diffusion Model. It learns the stepwise generation process of time series data to model the probability distribution of the data, capturing normal data patterns and progressively restoring time series data by removing noise, resulting in a clear recovery of anomalies. To the best of our knowledge, this is the first model that combines Denoising Diffusion Model and the Transformer for multivariate time series anomaly detection. Experimental evaluations were conducted on five publicly available multivariate time series anomaly detection datasets. The results demonstrate that the model effectively identifies anomalies in time series data, achieving state-of-the-art performance in anomaly detection.Comment: 16 pages, 9 figure

LambdaLoss: Metric-Driven Loss for Learning-to Rank

How to directly optimize ranking metrics such as Normalized Dis­counted Cumulative Gain (NDCG) is an interesting but challenging problem, because ranking metrics are either flat or discontinuous ev­erywhere. Among existing approaches, LambdaRank is a novel algo­rithm that incorporates metrics into its learning procedure. Though empirically effective, it still lacks theoretical justification. For ex­ample, what is the underlying loss that LambdaRank optimizes for? Due to this, it is unclear whether LambdaRank will always converge. In this paper, we present a well-defined loss for LambdaRank in a probabilistic framework and show that LambdaRank is a special configuration in our framework. This framework, which we call LambdaLoss, provides theoretical justification for Lamb-daRank. Furthermore, we propose a few more metric-driven loss functions in our LambdaLoss framework. Our loss functions have clear connection to ranking metrics and can be optimized in our framework efficiently. Experiments on three publicly available data sets show that our methods significantly outperform the state-of-the-art learning-to-rank algorithms. This confirms both the theo­retical soundness and the practical effectiveness of the LambdaLoss framework

TRAINING A RANKING MODEL

Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for training a ranking machine learning model. In one aspect, a method includes the actions of receiving training data for a ranking machine learning model, the training data including training examples, and each training example including data identifying: a search query, result documents from a result list for the search query, and a result document that was selected by a user from the result list, receiving position data for each training example in the training data, the position data identifying a respective position of the selected result document in the result list for the search query in the training example; determining, for each training example in the training data, a respective selection bias value; and determining a respective importance value for each training example from the selection bias value for the training example, the importance value