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The National Collegiate Athletic Association (NCAA) Summit on Gender Identity and Student-Athlete Participation was convened to identify institutional/athletic department strategies that may support the well-being of trans and gender nonconforming (TGNC) collegiate student-athletes in the USA. The Summit's purview did not include policy-level changes to eligibility rules. A modified Delphi consensus process was used to identify strategies for supporting collegiate TGNC student-athlete well-being. Key steps included an exploration phase (learning, generating ideas), and an evaluation phase (rating ideas in terms of their utility and feasibility). Summit participants (n=60) included individuals meeting at least one of the following criteria: current or former TGNC athlete, academic or healthcare professional with topical expertise, collegiate athletics stakeholder who would be involved in implementing potential strategies, representative from leading sports medicine organisation, or representative from relevant NCAA membership committee. Summit participants identified strategies in the following domains: healthcare practices (patient-centred care and culturally sensitive care); education for all stakeholders involved in athletics; and administration (inclusive language, quality improvement processes). Summit participants also proposed ways that the NCAA, through its existing committee and governance structures, could help support the well-being of TGNC athletes. NCAA-focused concepts were in the following domains: policy making processes; eligibility and transfer processes; resource development and dissemination; and visibility and support for TGNC athletes. The strategies developed represent important and relevant approaches that member institutions, athletic departments, NCAA committees, governance bodies and other stakeholders might consider in their efforts to support TGNC student-athlete well-being.

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Determining optimal surveillance networks for an emerging pathogen is difficult since it is not known beforehand what the characteristics of a pathogen will be or where it will emerge. The resources for surveillance of infectious diseases in animals and wildlife are often limited and mathematical modeling can play a supporting role in examining a wide range of scenarios of pathogen spread. We demonstrate how a hierarchy of mathematical and statistical tools can be used in surveillance planning help guide successful surveillance and mitigation policies for a wide range of zoonotic pathogens. The model forecasts can help clarify the complexities of potential scenarios, and optimize biosurveillance programs for rapidly detecting infectious diseases. Using the highly pathogenic zoonotic H5N1 avian influenza 2006-2007 epidemic in Nigeria as an example, we determined the risk for infection for localized areas in an outbreak and designed biosurveillance stations that are effective for different pathogen strains and a range of possible outbreak locations. We created a general multi-scale, multi-host stochastic SEIR epidemiological network model, with both short and long-range movement, to simulate the spread of an infectious disease through Nigerian human, poultry, backyard duck, and wild bird populations. We chose parameter ranges specific to avian influenza (but not to a particular strain) and used a Latin hypercube sample experimental design to investigate epidemic predictions in a thousand simulations. We ranked the risk of local regions by the number of times they became infected in the ensemble of simulations. These spatial statistics were then complied into a potential risk map of infection. Finally, we validated the results with a known outbreak, using spatial analysis of all the simulation runs to show the progression matched closely with the observed location of the farms infected in the 2006-2007 epidemic.

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The National Strategy for Biosurveillance defines biosurveillance as "the process of gathering, integrating, interpreting, and communicating essential information related to all-hazards threats or disease activity affecting human, animal, or plant health to achieve early detection and warning, contribute to overall situational awareness of the health aspects of an incident, and to enable better decision-making at all levels." However, the strategy does not specify how "essential information" is to be identified and integrated into the current biosurveillance enterprise, or what the metrics qualify information as being "essential". The question of data stream identification and selection requires a structured methodology that can systematically evaluate the tradeoffs between the many criteria that need to be taken in account. Multi-Attribute Utility Theory, a type of multi-criteria decision analysis, can provide a well-defined, structured approach that can offer solutions to this problem. While the use of Multi-Attribute Utility Theoryas a practical method to apply formal scientific decision theoretical approaches to complex, multi-criteria problems has been demonstrated in a variety of fields, this method has never been applied to decision support in biosurveillance.We have developed a formalized decision support analytic framework that can facilitate identification of "essential information" for use in biosurveillance systems or processes and we offer this framework to the global BSV community as a tool for optimizing the BSV enterprise. To demonstrate utility, we applied the framework to the problem of evaluating data streams for use in an integrated global infectious disease surveillance system.

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In recent years, biosurveillance has become the buzzword under which a diverse set of ideas and activities regarding detecting and mitigating biological threats are incorporated depending on context and perspective. Increasingly, biosurveillance practice has become global and interdisciplinary, requiring information and resources across public health, One Health, and biothreat domains. Even within the scope of infectious disease surveillance, multiple systems, data sources, and tools are used with varying and often unknown effectiveness. Evaluating the impact and utility of state-of-the-art biosurveillance is, in part, confounded by the complexity of the systems and the information derived from them. We present a novel approach conceptualizing biosurveillance from the perspective of the fundamental data streams that have been or could be used for biosurveillance and to systematically structure a framework that can be universally applicable for use in evaluating and understanding a wide range of biosurveillance activities. Moreover, the Biosurveillance Data Stream Framework and associated definitions are proposed as a starting point to facilitate the development of a standardized lexicon for biosurveillance and characterization of currently used and newly emerging data streams. Criteria for building the data stream framework were developed from an examination of the literature, analysis of information on operational infectious disease biosurveillance systems, and consultation with experts in the area of biosurveillance. To demonstrate utility, the framework and definitions were used as the basis for a schema of a relational database for biosurveillance resources and in the development and use of a decision support tool for data stream evaluation.

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For the past decade, the Food and Agriculture Organization of the United Nations has been working toward eradicating rinderpest through vaccination and intense surveillance by 2012. Because of the potential severity of a rinderpest epidemic, it is prudent to prepare for an unexpected outbreak in animal populations. There is no immunity to the disease among the livestock or wildlife in the United States (US). If rinderpest were to emerge in the US, the loss in livestock could be devastating. We predict the potential spread of rinderpest using a two-stage model for the spread of a multi-host infectious disease among agricultural animals in the US. The model incorporates large-scale interactions among US counties and the small-scale dynamics of disease spread within a county. The model epidemic was seeded in 16 locations and there was a strong dependence of the overall epidemic size on the starting location. The epidemics were classified according to overall size into small epidemics of 100 to 300 animals (failed epidemics), epidemics infecting 3,000 to 30,000 animals (medium epidemics), and the large epidemics infecting around one million beef cattle. The size of the rinderpest epidemics were directly related to the origin of the disease and whether or not the disease moved into certain key counties in high-livestock-density areas of the US. The epidemic size also depended upon response time and effectiveness of movement controls.

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Resonance Raman spectroelectrochemistry (RR-SEC) at -20 degrees C has been performed on the pyrazine-bridged dimer of mu-oxo-centered trinuclear ruthenium-acetate "clusters"--[(dmap)(CO)(mu-OAc)6(mu3-O)Ru3(mu-L(b))Ru3(mu3-O)(mu-OAc)6(CO)(dmap)]n (where dmap = 4-(dimethylamino)pyridine and L(b) = pyrazine-h4 and pyrazine-d4)-in three oxidation states: n = 0, -1, and -2. In the one-electron reduced, "mixed-valent" state (overall -1 charge and a single odd electron; formal oxidation states [II, II, III]-[III, III, II] on the metal centers), the Raman excitation at 800 nm is resonant with a cluster-to-cluster intervalence charge-transfer (IVCT) band. Under these conditions, scattering enhancement is observed for all four totally symmetric vibrational modes of the bridging pyrazine ligand (nu8a, nu9a, nu1, and nu6a) in the investigated spectral range (100-2000 cm(-1)), and there is no evidence of activity in non-totally symmetric vibrations. Resonantly enhanced Raman peaks related to peripheral pyridyl (dmap) ligand modes and low-frequency features arising from the trigonal Ru3O cluster core and the cluster[Ru]-[N]ligand vibrations were also observed in the spectra of the intermediate-valence (n = -1) cluster dimer. The vibrational assignments and interpretations proposed in this work were reinforced by observation of characteristic isotopic frequency shifts accompanying deuteration of the bridging pyrazine. The results reveal that the fully symmetric (A(g)) vibrational motions of the organic bridge are coupled to the nominally metal cluster-to-metal cluster fast intramolecular electron transfer (ET) and provide validation of the near-delocalized description according to a predicted three-site/three-state (e.g., metal-bridge-metal) vibronic coupling model, in which the important role of the bridging ligand in mediating electronic communication and delocalization between charge centers is explicitly considered. Further compelling evidence supporting an extended five-state model, which incorporates the peripheral cluster-bound pyridyl ligands, is also presented.

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Resonance Raman spectroscopy, performed using spectroelectrochemistry and with excitation in the intervalence bands of three pyrazine-bridged, mixed-valence dimers of trinuclear ruthenium clusters, shows resonant enhancement of symmetric bridging ligand modes. The resonant enhancements and frequency shifts of these bridging ligand modes are observed as a function of varying electronic communication between charge sites, and they show that a three-state vibronic model which explicitly includes the participation of the bridging ligand is needed to explain the spectroscopic behavior of these near-delocalized complexes.

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The novel charge-transfer ground state found in alpha,alpha'-diimine adducts of ytterbocene (C(5)Me(5))(2)Yb(L) [L = 2,2'-bipyridine (bpy) and 1,10-phenanthroline (phen)] in which an electron is spontaneously transferred from the f(14) metal center into the lowest unoccupied (pi*) molecular orbital (LUMO) of the diimine ligand to give an f(13)-L(*)(-) ground-state electronic configuration has been characterized by cyclic voltammetry, UV-vis-near-IR electronic absorption, and resonance Raman spectroscopies. The voltammetric data demonstrate that the diimine ligand LUMO is stabilized and the metal f orbital is destabilized by approximately 1.0 V each upon complexation for both bpy and phen adducts. The separation between the ligand-based oxidation wave (L(0/-)) and the metal-based reduction wave (Yb(3+/2+)) in the ytterbocene adducts is 0.79 V for both bpy and phen complexes. The UV-vis-near-IR absorption spectroscopic data for both the neutral adducts and the one-electron-oxidized complexes are consistent with those reported recently, but previously unreported bands in the near-IR have been recorded and assigned to ligand (pi*)-to-metal (f orbital) charge-transfer (LMCT) transitions. These optical electronic excited states are the converse of the ground-state charge-transfer process (e.g., f(13)-L(*-) <--> f(14)-L(0)). These new bands occur at approximately 5000 cm(-1) in both adducts, consistent with predictions from electrochemical data, and the spacings of the resolved vibronic bands in these transitions are consistent with the removal of an electron from the ligand pi* orbital. The unusually large intensity observed in the f --> f intraconfiguration transitions for the neutral phenanthroline adduct is discussed in terms of an intensity-borrowing mechanism involving the low-energy LMCT states. Raman vibrational data clearly reveal resonance enhancement for excitation into the low-lying pi* --> pi* ligand-localized excited states, and comparison of the vibrational energies with those reported for alkali-metal-reduced diimine ligands confirms that the ligands in the adducts are reduced radical anions. Differences in the resonance enhancement pattern for the modes in the bipyridine adduct with excitation into different pi* --> pi* levels illustrate the different nodal structures that exist in the various low-lying pi* orbitals.