Cutting edge: novel RNA ligands able to bind CD4 antigen and inhibit CD4+ T lymphocyte function

The value of high affinity-specific reagents in immunology is exemplified by the use of mAbs. Recent in vitro selection methods suggested that oligonucleotides may provide a useful alternative, especially where Abs have been insufficient thus far. We used a systematic evolution of ligands by exponential enrichment (SELEX) procedure to derive high affinity oligonucleotide ligands (aptamers) recognizing CD4. These RNase-resistant aptamers bound with high affinity and specificity as demonstrated using BIAcore (Stevenage, U.K.) technology. They also bound native CD4 on rat lymphocytes and specifically interfered with labeling by high affinity mAbs. All aptamers recognized the same binding site in the CDR2-like region in domain 1 of CD4. The applicability of these aptamers for immunologic studies was clearly demonstrated by their ability to block a fully allogeneic MLR in a CD4-specific manner. The high affinity and stability of aptamers point to their value in the analysis and functional manipulation of the immune system.     

Investigation of the energy flows for transparent and nontransparent building facade

Based on extensive measurements of energy usage for a variety of building facades, a computer model has been developed for calculating in advance the heating and cooling requirements of test rooms equipped with arbitrary facades. To simplify the predictive calculation, the three-dimensional physical model was transformed to a one-dimensional, linear, computational model. The computer model uses the results of an earlier sensitivity analysis. This permits the precise numerical modeling of the measured results and gives special value to the adaptation of this model for describing the geometric and thermal relations investigated experimentally. The results of the numerical calculations are found to be in good agreement with the measurements for six test rooms. This agreement justifies the linear numerical model and the computer program for predicting the energy flows in different room structures. Further, the computer model not only provides results which follow the course of weather conditions for the test year but yields results as well for a Standard Reference Year for the region.     

Extracellular Vesicles as Biological Indicators and Potential Sources of Autologous Therapeutics in Osteoarthritis

Along with cytokines, extracellular vesicles (EVs) released by immune cells in the joint contribute to osteoarthritis (OA) pathogenesis. By high-resolution flow cytometry, we characterized 18 surface markers and 4 proinflammatory cytokines carried by EVs of various sizes in plasma and synovial fluid (SF) from individuals with knee OA, with a primary focus on immune cells that play a major role in OA pathogenesis. By multiplex immunoassay, we also measured concentrations of cytokines within (endo) and outside (exo) EVs. EVs carrying HLA-DR, -DP and -DQ were the most enriched subpopulations in SF relative to plasma (25–50-fold higher depending on size), suggesting a major contribution to the SF EV pool from infiltrating immune cells in OA joints. In contrast, the CD34⁺ medium and small EVs, reflecting hematopoietic stem cells, progenitor cells, and endothelial cells, were the most significantly enriched subpopulations in plasma relative to SF (7.3- and 7.7-fold higher). Ratios of EVs derived from neutrophils and lymphocytes were highly correlated between SF and plasma, indicating that plasma EVs could reflect OA severity and serve as systemic biomarkers of OA joint pathogenesis. Select subsets of plasma EVs might also provide next generation autologous biological products for intra-articular therapy of OA joints.     

Temporal Reasoning for a Collaborative Planning Agent in a Dynamic Environment

We present a temporal reasoning mechanism for an individual agent situated in a dynamic environment such as the web and collaborating with other agents while interleaving planning and acting. Building a collaborative agent that can flexibly achieve its goals in changing environments requires a blending of real-time computing and AI technologies. Therefore, our mechanism consists of an Artificial Intelligence (AI) planning subsystem and a Real-Time (RT) scheduling subsystem. The AI planning subsystem is based on a model for collaborative planning. The AI planning subsystem generates a partial order plan dynamically. During the planning it sends the RT scheduling subsystem basic actions and time constraints. The RT scheduling subsystem receives the dynamic basic actions set with associated temporal constraints and inserts these actions into the agent's schedule of activities in such a way that the resulting schedule is feasible and satisfies the temporal constraints. Our mechanism allows the agent to construct its individual schedule independently. The mechanism handles various types of temporal constraints arising from individual activities and its collaborators. In contrast to other works on scheduling in planning systems which are either not appropriate for uncertain and dynamic environments or cannot be expanded for use in multi-agent systems, our mechanism enables the individual agent to determine the time of its activities in uncertain situations and to easily integrate its activities with the activities of other agents. We have proved that under certain conditions temporal reasoning mechanism of the AI planning subsystem is sound and complete. We show the results of several experiments on the system. The results demonstrate that interleave planning and acting in our environment is crucial.     

Negotiating with bounded rational agents in environments with incomplete information using an automated agent

Many tasks in day-to-day life involve interactions among several people. Many of these interactions involve negotiating over a desired outcome. Negotiation in and of itself is not an easy task, and it becomes more complex under conditions of incomplete information. For example, the parties do not know in advance the exact tradeoff of their counterparts between different outcomes. Furthermore information regarding the preferences of counterparts might only be elicited during the negotiation process itself. In this paper we propose a model for an automated negotiation agent capable of negotiating with bounded rational agents under conditions of incomplete information. We test this agent against people in two distinct domains, in order to verify that its model is generic, and thus can be adapted to any domain as long as the negotiators' preferences can be expressed in additive utilities. Our results indicate that the automated agent reaches more agreements and plays more effectively than its human counterparts. Moreover, in most of the cases, the automated agent achieves significantly better agreements, in terms of individual utility, than the human counterparts playing the same role.     

Algebraic Multigrid Based on Computational Molecules, 1: Scalar Elliptic Problems

We consider the problem of splitting a symmetric positive definite (SPD) stiffness matrix A arising from finite element discretization into a sum of edge matrices thereby assuming that A is given as the sum of symmetric positive semidefinite (SPSD) element matrices. We give necessary and sufficient conditions for the existence of an exact splitting into SPSD edge matrices and address the problem of best positive (nonnegative) approximation. Based on this disassembling process we present a new concept of ``strong' and ``weak' connections (edges), which provides a basis for selecting the coarse-grid nodes in algebraic multigrid methods. Furthermore, we examine the utilization of computational molecules (small collections of edge matrices) for deriving interpolation rules. The reproduction of edge matrices on coarse levels offers the opportunity to combine classical coarsening algorithms with effective (energy minimizing) interpolation principles yielding a flexible and robust new variant of AMG.     

Combining multiple knowledge bases

Combining knowledge present in multiple knowledge base systems into a single knowledge base is discussed. A knowledge based system can be considered an extension of a deductive database in that it permits function symbols as part of the theory. Alternative knowledge bases that deal with the same subject matter are considered. The authors define the concept of combining knowledge present in a set of knowledge bases and present algorithms to maximally combine them so that the combination is consistent with respect to the integrity constraints associated with the knowledge bases. For this, the authors define the concept of maximality and prove that the algorithms presented combine the knowledge bases to generate a maximal theory. The authors also discuss the relationships between combining multiple knowledge bases and the view update problem     

Beliefs, time and incomplete information in multiple encounter negotiations among autonomous agents

In negotiations among autonomous agents over resource allocation, beliefs about opponents, and about opponents’ beliefs, become particularly important when there is incomplete information. This paper considers interactions among self‐motivated, rational, and autonomous agents, each with its own utility function, and each seeking to maximize its expected utility. The paper expands upon previous work and focuses on incomplete information and multiple encounters among the agents. It presents a strategic model that takes into consideration the passage of time during the negotiation and also includes belief systems. The paper provides strategies for a wide range of situations. The framework satisfies the following criteria: symmetrical distribution, simplicity, instantaneously, efficiency and stability. This revised version was published online in June 2006 with corrections to the Cover Date.     

The Value of Temptation

There is an implicit assumption in electronic commerce that induces the buyers to believe that their deals will be handled appropriately. However, after a seller has already committed to a buyer, he may be tempted by several requests though he will not be able to supply them all. We analyze markets in which a finite set of automated buyers interacts repeatedly with a finite set of automated sellers. These sellers can satisfy one buyer at a time, and they can be tempted to break a commitment they already have. We have found the perfect equilibria that exist in markets with a finite horizon, and with an unrestricted horizon. A significant result stemming from our study reveals that sellers are almost always tempted to breach their commitments. However, we also show that if markets' designers implement an external mechanism that restricts the automated buyers actions, then sellers will keep their commitments.