In a previous study, proteomics procedures identified blood proteins as potential overreaching and overtraining biomarkers, and a targeted proteomics panel of 21 proteins was developed.PurposeTo measure targeted blood protein changes in an ultraendurance cyclist competing in RAAM.MethodsThe athlete underwent testing 4-week pre-RAAM and 4-day post-RAAM to determine body composition and aerobic capacity. During RAAM training distress score (TDS) and body mass were measured daily. Power output and heart rate (HR) were measured during cycling. Blood sampling for proteomic analysis occurred 4 weeks, 24, and 2 h before the start, twice per day of the race, and after 1 and 4 days recovery.ResultsThe athlete completed the 4941 km race in 10.1 days at a speed of 24.5 km/h with 20 total hours of sleep. TDS was very low, 1, pre-RAAM and increased to very high, 47, at the finish. Post-RAAM maximal aerobic capacity and HR were 6.3 and 5.7% lower (61.6 vs. 57.5 mL.kg–1.min–1 and 192 bpm vs. 181 bpm). Body composition did not change. The change in blood proteins was calculated using pre-race samples and samples collected on days 8, 9, and recovery day 1. The blood proteins with the largest increase were complement component C7 (359%), complement C4-B (231%), serum amyloid A-4 protein (210%), inter-alpha-trypsin inhibitor heavy chain H4 (191%), and alpha-1-antitrypsin (188%).ConclusionThe RAAM athlete exhibited non-functional overreaching symptoms including increased training distress and decreased work capacity. Proteomic analysis indicated large increases for immune-related proteins involved with complement activation and the acute phase response, which could be useful biomarkers for non-functional overreaching.

Distributed information systems are increasing in prevalence and complexity as we see an increase in the number of both information consumers and information providers. Applications often need to integrate information from several different information providers. Current approaches for securing this process of integration do not scale well to handle complex trust relationships between consumer applications and providers. Trust mediatioll is a technique we introduce to address this problem by incorporating a model for representing trust into a framework for retrieving information in a distributed system. Our model for representing trust uses a type system by which data from a source is labeled with a trust type based on qualities of the data itself or the information source(s) providing the data. With this model we develop algorithms to perform static analysis of data queries to infer how the result of the data query can be trusted. We describe an enhanced mediation framework using this inference technique that enables the mediator to govern the flow of information to match intended trust policies in large distributed information systems, even when information may originate from many heterogeneous sources.

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Decision-makers in critical fields such as medicine and finance make use of a wide range of information available over the Internet. Mediation, a data integration technique for distributed, heterogeneous data sources, manages the complexity and diversity of the information schemas on behalf of clients. We raise here the issue of trust: is the information so obtained trustworthy? Each client can have different perspectives on the desired trustworthiness the information he or she needs. We consider here the scaling problem that arises from a very large number of users accessing information from many different sources. A mediator cannot be expected to manage the potentially quadratic scaling of trust relationships clients can have with information sources. Furthermore, the possibility of using untrustworthy data increases the risk that the resulting data will be unacceptable: a mediator might evaluate a complex query for a client, only to have the answer rejected because the client does not trust the sources of the information. To help address these issues, we introduce a general static trust-typing model, which can infer the trust ratings of query plans, based on trust meta-data about the input data to the query, even before executing the query. We also define essential properties of such a trust-typing model, namely correctness, precision and completeness. We present an example of a trust-typing model and describe some algorithmic frameworks for the use of such trust-typing models in mediator-based query evaluation.

Distributed, heterogeneous systems axe becoming very common, as global;zeal organizations integrate applications running on different platforms, possibly written in different languages. Certain requirements for such features as security, QoS, and flexible administration are specially critical to distributed heterogeneous systems. Unfortunately, such requirements axe often formulated late, since they depend upon a particular installation, and/or change rapidly with business and political climate. Distributed, heterogeneous systems axe particularly difficult to evolve, since the elements are written in different languages, and the operational environment is hetm-ogenvous and distributed.We would like to address this problem with solutions that are animated by practical software engineering goals: ~ype

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