Cell deformation behavior in mechanically loaded rabbit articular cartilage 4 weeks after anterior cruciate ligament transection

SummaryObjectiveChondrocyte stresses and strains in articular cartilage are known to modulate tissue mechanobiology. Cell deformation behavior in cartilage under mechanical loading is not known at the earliest stages of osteoarthritis. Thus, the aim of this study was to investigate the effect of mechanical loading on volume and morphology of chondrocytes in the superficial tissue of osteoarthritic cartilage obtained from anterior cruciate ligament transected (ACLT) rabbit knee joints, 4 weeks after intervention.MethodsA unique custom-made microscopy indentation system with dual-photon microscope was used to apply controlled 2 MPa force-relaxation loading on patellar cartilage surfaces. Volume and morphology of chondrocytes were analyzed before and after loading. Also global and local tissue strains were calculated. Collagen content, collagen orientation and proteoglycan content were quantified with Fourier transform infrared microspectroscopy, polarized light microscopy and digital densitometry, respectively.ResultsFollowing the mechanical loading, the volume of chondrocytes in the superficial tissue increased significantly in ACLT cartilage by 24% (95% confidence interval (CI) 17.2–31.5, P < 0.001), while it reduced significantly in contralateral group tissue by −5.3% (95% CI −8.1 to −2.5, P = 0.003). Collagen content in ACLT and contralateral cartilage were similar. PG content was reduced and collagen orientation angle was increased in the superficial tissue of ACLT cartilage compared to the contralateral cartilage.ConclusionsWe found the novel result that chondrocyte deformation behavior in the superficial tissue of rabbit articular cartilage is altered already at 4 weeks after ACLT, likely because of changes in collagen fibril orientation and a reduction in PG content

Unsupervised clustering with spiking neurons by sparse temporal coding and multi-layer RBF networks

We demonstrate that spiking neural networks encoding information in spike times are capable of computing and learning clusters from realistic data. We show how a spiking neural network based on spike-time coding and Hebbian learning can successfully perform unsupervised clustering on real-world data, and we demonstrate how temporal synchrony in a multi-layer network induces hierarchical clustering. We develop a temporal encoding of continuously valued data to obtain adjustable clustering capacity and precision with an efficient use of neurons: input variables are encoded in a population code by neurons with graded and overlapping sensitivity profiles. We also discuss methods for enhancing scale-sensitivity of the network and show how induced synchronization of neurons within early RBF layers allows for the subsequent detection of complex clusters

Competitive market-based allocation of consumer attention space

The amount of attention space available for recommending suppliers to consumers on e-commerce sites is typically limited. We present a competitive distributed recommendation mechanism based on adaptive software agents for efficiently allocating the 'consumer attention space', or banners. In the example of an electronic shopping mall, the task is delegated to the individual shops, each of which evaluates the information that is available about the consumer and his or her interests (e.g. keywords, product queries, and available parts of a profile). Shops make a monetary bid in an auction where a limited amount of 'consumer attention space' for the arriving consumer is sold. Each shop is represented by a software agent that bids for each consumer. This allows shops to rapidly adapt their bidding strategy to focus on consumers interested in their offerings. For various basic and simple models for on-line consumers, shops, and profiles, we demonstrate the feasibility of our system by evolutionary simulations as in the field of agent-based computational economics (ACE). We also develop adaptive software agents that learn bidding-strategies, based on neural networks and strategy exploration heuristics. Furthermore, we address the commercial and technological advantages of this distributed market-based approach. The mechanism we describe is not limited to the example of the electronic shopping mall, but can easily be extended to other domains

Error-backpropagation in temporally encoded networks of spiking neurons

For a network of spiking neurons that encodes information in the timing of individual spike-times, we derive a supervised learning rule, emph{SpikeProp, akin to traditional error-backpropagation and show how to overcome the discontinuities introduced by thresholding. With this algorithm, we demonstrate how networks of spiking neurons with biologically reasonable action potentials can perform complex non-linear classification in fast temporal coding just as well as rate-coded networks. We perform experiments for the classical XOR-problem, when posed in a temporal setting, as well as for a number of other benchmark datasets. Comparing the (implicit) number of spiking neurons required for the encoding of the interpolated XOR problem, it is demonstrated that temporal coding requires significantly less neurons than instantaneous rate-coding

Learning from induced changes in opponent (re)actions in multi-agent games

Multi-agent learning is a growing area of research. An important topic is to formulate how an agent can learn a good policy in the face of adaptive, competitive opponents. Most research has focused on extensions of single agent learning techniques originally designed for agents in more static environments. These techniques however fail to incorporate a notion of the effect of own previous actions on the development of the policy of the other agents in the system. We argue that incorporation of this property is beneficial in competitive settings. In this paper, we present a novel algorithm to capture this notion, and present experimental results to validate our claim

Foresighted policy gradient reinforcement learning: solving large-scale social dilemmas with rational altruistic punishment

Many important and difficult problems can be modeled as “social dilemmas”, like Hardin's Tragedy of the Commons or the classic iterated Prisoner's Dilemma. It is well known that in these problems, it can be rational for self-interested agents to promote and sustain cooperation by altruistically dispensing costly punishment to other agents, thus maximizing their own long-term reward. However, self-interested agents using most current multi-agent reinforcement learning algorithms will not sustain cooperation in social dilemmas: the algorithms do not sufficiently capture the consequences on the agent's reward of the interactions that it has with other agents. Recent more foresighted algorithms specifically account for such expected consequences, and have been shown to work well for the small-scale Prisoner's Dilemma. However, this approach quickly becomes intractable for larger social dilemmas. Here, we advance on this work and develop a “teach/learn” stateless foresighted policy gradient reinforcement learning algorithm that applies to Social Dilemma's with negative, unilateral side-payments, in the from of costly punishment. In this setting, the algorithm allows agents to learn the most rewarding actions to take with respect to both the dilemma (Cooperate/Defect) and the “teaching” of other agent's behavior through the dispensing of punishment. Unlike other algorithms, we show that this approach scales well to large settings like the Tragedy of the Commons. We show for a variety of settings that large groups of self-interested agents using this algorithm will robustly find and sustain cooperation in social dilemmas where adaptive agents can punish the behavior of other similarly adaptive agents

Adaptive resource allocation for efficient patient scheduling

Objective Efficient scheduling of patient appointments on expensive resources is a complex and dynamic task. A resource is typically used by several patient groups. To service these groups, resource capacity is often allocated per group, explicitly or implicitly. Importantly, due to fluctuations in demand, for the most efficient use of resources this allocation must be flexible. Methods We present an adaptive approach to automatic optimization of resource calendars. In our approach, the allocation of capacity to different patient groups is flexible and adaptive to the current and expected future situation. We additionally present an approach to determine optimal resource openings hours on a larger time frame. Our model and its parameter values are based on extensive case analysis at the Academic Medical Hospital Amsterdam. Results and conclusion We have implemented a comprehensive computer simulation of the application case. Simulation experiments show that our approach of adaptive capacity allocation improves the performance of scheduling patients groups with different attributes and makes efficient use of resource capacity

Creating transparency in the Chinese real estate development industry : a case study

Thesis (S.M.)--Massachusetts Institute of Technology, Dept. of Architecture, 2005.This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.Includes bibliographical references (leaf 134).Transparency issue remains one of the top issues that have discouraged foreign investors to invest China's real estate market. This thesis establishes a framework for Chinese developers to create transparency for their development projects. It consists of the company transparency, the country-level, region-level, city-level, and project-level analyses around a project in Chongqing, China. Many special situations in China are discussed as well in order to acknowledge the existing transparency issue in China, especially in the real estate industry.by Feng Han.S.M