How Team Culture Affects NCAA Swimming and Diving Team Performance

This study applied Robert Quinn’s Competing Values Framework, one of the leading cultural assessment tools, to successful NCAA swimming and diving teams to identify cultural trends among some of the NCAA’s highest performing teams. Through this framework, teams were assessed to determine if cultural differences arose between the three NCAA divisions, if perennially successful teams differed culturally from those who were not, and if any one cultural element correlated with NCAA Championship placement. Notably, there were significant differences between Division I and III programs, where Division I programs reported higher “collaboration” scores, while Division III reported higher “control” scores. Additionally, programs that averaged a Top 15 NCAA finish over a 5-year span placed significantly more emphasis on competition results than those that did not. Finally, an emphasis on competition correlated with a performance boost in Division I, while higher collaboration correlated with a performance decrease in Division III

Irreversible oxidation of protein cysteine residues

Cysteine residues have a reduced thiol group that frequently exerts an influence on the function of proteins. Oxidation of the cysteine through S-thiolation, S-nitrosylation, or irreversible oxidation perturbs these functions. Sulfinic and sulfonic acids produced by irreversible oxidation are exceptionally damaging since these modifications are permanent in cells. In order to detect and quantitate both protein sulfinic and sulfonic acids, it was necessary to develop an assay using a novel amino acid analysis procedure. Purified creatine kinase and carbonic anhydrase III were used to establish the method, and the assay produced results that agreed with total cysteine irreversible oxidation determined by isoelectric focusing.;Glutathione has been hypothesized to protect cysteine residues from irreversible oxidation through protein S-glutathiolation. This concept was investigated both in vitro and in vivo using carbonic anhydrase III as a model protein. The two sulfhydryls of carbonic anhydrase III were irreversibly oxidized to sulfinic acid and sulfonic acids by hydrogen peroxide and peroxyl radicals, but when glutathione was approximately equimolar to protein thiols, these modifications were prevented through S-glutathiolation. Hypochlorous acid oxidation did not produce protein S-glutathiolation and generated irreversible cysteine oxidation even in the presence of glutathione. Protection of carbonic anhydrase III thiols by S-glutathiolation was highly dependent on glutathione concentration both in vitro and in cultured rat hepatocytes. When hepatocyte glutathione was depleted with diethyl maleate, carbonic anhydrase III was more prone to irreversible oxidation following menadione stimulated oxidation. Age-related depletion of glutathione might increase irreversible cysteine oxidation, and both total protein extracts and carbonic anhydrase III were significantly more irreversibly oxidized in the liver tissues of older rats.;Experiments using creatine kinase as a model protein also demonstrated that irreversible cysteine oxidation produced by hydrogen peroxide or peroxyl radicals could be prevented by glutathione through protein S-glutathiolation. Rat skeletal muscle and heart tissue showed an approximate two-fold age-related increase in total protein sulfinic acids, with a concurrent 30--40% decrease in soluble glutathione. Irreversible oxidation of creatine kinase in these tissues followed the same trends as total protein sulfinic acids. Thus, depletion of cellular glutathione is expected to accelerate cysteine irreversible oxidation

Generating realistic scaled complex networks

Research on generative models is a central project in the emerging field of network science, and it studies how statistical patterns found in real networks could be generated by formal rules. Output from these generative models is then the basis for designing and evaluating computational methods on networks, and for verification and simulation studies. During the last two decades, a variety of models has been proposed with an ultimate goal of achieving comprehensive realism for the generated networks. In this study, we (a) introduce a new generator, termed ReCoN; (b) explore how ReCoN and some existing models can be fitted to an original network to produce a structurally similar replica, (c) use ReCoN to produce networks much larger than the original exemplar, and finally (d) discuss open problems and promising research directions. In a comparative experimental study, we find that ReCoN is often superior to many other state-of-the-art network generation methods. We argue that ReCoN is a scalable and effective tool for modeling a given network while preserving important properties at both micro- and macroscopic scales, and for scaling the exemplar data by orders of magnitude in size.Comment: 26 pages, 13 figures, extended version, a preliminary version of the paper was presented at the 5th International Workshop on Complex Networks and their Application

Deterministic Parallel Hypergraph Partitioning

Balanced hypergraph partitioning is a classical NP-hard optimization problem with applications in various domains such as VLSI design, simulating quantum circuits, optimizing data placement in distributed databases or minimizing communication volume in high-performance computing. Engineering parallel heuristics for this problem is a topic of recent research. Most of them are non-deterministic though. In this work, we design and implement a highly scalable deterministic algorithm in the state-of-the-art parallel partitioning framework Mt-KaHyPar. On our extensive set of benchmark instances, it achieves similar partition quality and performance as a comparable but non-deterministic configuration of Mt-KaHyPar and outperforms the only other existing parallel deterministic algorithm BiPart regarding partition quality, running time and parallel speedups

Correspondence between Multilevel Graph Partitions and Tree Decompositions

We present amapping between rooted tree decompositions and node separator basedmultilevel graph partitions. Significant research into both tree decompositions and graph partitions exists. We hope that our result allows for an easier knowledge transfer between the two research avenues