Genetic variation in blue whales in the eastern pacific: implication for taxonomy and use of common wintering grounds.

Many aspects of blue whale biology are poorly understood. Some of the gaps in our knowledge, such as those regarding their basic taxonomy and seasonal movements, directly affect our ability to monitor and manage blue whale populations. As a step towards filling in some of these gaps, microsatellite and mtDNA sequence analyses were conducted on blue whale samples from the Southern Hemisphere, the eastern tropical Pacific (ETP) and the northeast Pacific. The results indicate that the ETP is differentially used by blue whales from the northern and southern eastern Pacific, with the former showing stronger affinity to the region off Central America known as the Costa Rican Dome, and the latter favouring the waters of Peru and Ecuador. Although the pattern of genetic variation throughout the Southern Hemisphere is compatible with the recently proposed subspecies status of Chilean blue whales, some discrepancies remain between catch lengths and lengths from aerial photography, and not all blue whales in Chilean waters can be assumed to be of this type. Also, the range of the proposed Chilean subspecies, which extends to the Galapagos region of the ETP, at least seasonally, perhaps should include the Costa Rican Dome and the eastern North Pacific as well.

Effect of ethnicity on care pathway and outcomes in patients hospitalized with influenza A(H1N1)pdm09 in the UK.

Data were extracted from the case records of UK patients admitted with laboratory-confirmed influenza A(H1N1)pdm09. White and non-White patients were characterized by age, sex, socioeconomic status, pandemic wave and indicators of pre-morbid health status. Logistic regression examined differences by ethnicity in patient characteristics, care pathway and clinical outcomes; multivariable models controlled for potential confounders. Whites (n = 630) and non-Whites (n = 510) differed by age, socioeconomic status, pandemic wave of admission, pregnancy, recorded obesity, previous and current smoking, and presence of chronic obstructive pulmonary disease. After adjustment for a priori confounders non-Whites were less likely to have received pre-admission antibiotics [adjusted odds ratio (aOR) 0·43, 95% confidence interval (CI) 0·28-0·68, P < 0·001) but more likely to receive antiviral drugs as in-patients (aOR 1·53, 95% CI 1·08-2·18, P = 0·018). However, there were no significant differences by ethnicity in delayed admission, severity at presentation for admission, or likelihood of severe outcome.

Risk factors for hospitalisation and poor outcome with pandemic A/H1N1 influenza: United Kingdom first wave (May-September 2009).

BACKGROUND: During the first wave of pandemic H1N1 influenza in 2009, most cases outside North America occurred in the UK. The clinical characteristics of UK patients hospitalised with pandemic H1N1 infection and risk factors for severe outcome are described. METHODS: A case note-based investigation was performed of patients admitted with confirmed pandemic H1N1 infection. RESULTS: From 27 April to 30 September 2009, 631 cases from 55 hospitals were investigated. 13% were admitted to a high dependency or intensive care unit and 5% died; 36% were aged <16 years and 5% were aged > or = 65 years. Non-white and pregnant patients were over-represented. 45% of patients had at least one underlying condition, mainly asthma, and 13% received antiviral drugs before admission. Of 349 with documented chest x-rays on admission, 29% had evidence of pneumonia, but bacterial co-infection was uncommon. Multivariate analyses showed that physician-recorded obesity on admission and pulmonary conditions other than asthma or chronic obstructive pulmonary disease (COPD) were associated with a severe outcome, as were radiologically-confirmed pneumonia and a raised C-reactive protein (CRP) level (> or = 100 mg/l). 59% of all in-hospital deaths occurred in previously healthy people. CONCLUSIONS: Pandemic H1N1 infection causes disease requiring hospitalisation of previously fit individuals as well as those with underlying conditions. An abnormal chest x-ray or a raised CRP level, especially in patients who are recorded as obese or who have pulmonary conditions other than asthma or COPD, indicate a potentially serious outcome. These findings support the use of pandemic vaccine in pregnant women, children <5 years of age and those with chronic lung disease.

The Swine Flu Triage (SwiFT) study: development and ongoing refinement of a triage tool to provide regular information to guide immediate policy and practice for the use of critical care services during the H1N1 swine influenza pandemic.

OBJECTIVES: To use, existing critical care and early pandemic, data to inform care during the pandemic influenza A 2009 (H1N1) pandemic (with a possible use for triage - if the demand for critical care seriously exceeded supply). To monitor the impact of the H1N1 pandemic on critical care services, in real time, with regular feedback to critical care clinicians and other relevant jurisdictions to inform ongoing policy and practice. DESIGN: Modelling of data and cohort study. SETTING: Modelling - 148 adult, general critical care units in England, Wales and Northern Ireland in the Intensive Care National Audit & Research Centre Case Mix Programme. Cohort study - 192 acute hospitals in England, Wales, Northern Ireland, Scotland and the Republic of Ireland. PARTICIPANTS: Modelling - 105,397 admissions to adult, general critical care units. Cohort study - 1728 H1N1 pandemic-related admissions referred and assessed as requiring critical care. MAIN OUTCOME MEASURES: Modelling - requirement for organ support and acute hospital mortality. Cohort study - survival to the end of critical care. RESULTS: Modelling - cancelled or postponed, elective or scheduled surgery resulted in savings in calendar days of critical, Level 3 and advanced respiratory care of 17, 11 and 10%, respectively. These savings varied across units. Using routine, physiological variables, the best triage models, for all and for acute respiratory admissions, achieved only satisfactory concordance of 0.79 and 0.75, respectively. Application of the best model on all admissions indicated that approximately 12.5% of calendar days of critical care could be saved. Cohort study - research governance approvals were achieved for 192 acute hospitals, for 91 within 1 day of central research and development approval across the five countries. A total of 1725 cases (562 confirmed) were reported. Confirmed cases were young (mean age of 40 years), had low severity of acute illness on presentation [61% CURB-65 (confusion, urea, respiratory rate, blood pressure, age over 65 years) 0-1], but had long stays in critical care (median 8.5 days) and were likely to be ventilated (77% for median 9 days). Risk factors for acute hospital death were similar to those for general critical care admissions. CONCLUSIONS: SwiFT was rapidly established. Models based on routine physiology suggested limited value for triage. More data and further modelling are warranted. The magnitude of the pandemic did not approach the worst-case scenario modelling, and UK-confirmed H1N1 cases appeared similar to those reported internationally. FUNDING: The National Institute for Health Research Health Technology Assessment programme.

Modelling the impact of an influenza A/H1N1 pandemic on critical care demand from early pathogenicity data: the case for sentinel reporting.

Projected critical care demand for pandemic influenza H1N1 in England was estimated in this study. The effect of varying hospital admission rates under statistical uncertainty was examined. Early in a pandemic, uncertainty in epidemiological parameters leads to a wide range of credible scenarios, with projected demand ranging from insignificant to overwhelming. However, even small changes to input assumptions make the major incident scenario increasingly likely. Before any cases are admitted to hospital, 95% confidence limit on admission rates led to a range in predicted peak critical care bed occupancy of between 0% and 37% of total critical care bed capacity, half of these cases requiring ventilatory support. For hospital admission rates above 0.25%, critical care bed availability would be exceeded. Further, only 10% of critical care beds in England are in specialist paediatric units, but best estimates suggest that 30% of patients requiring critical care will be children. Paediatric intensive care facilities are likely to be quickly exhausted and suggest that older children should be managed in adult critical care units to allow resource optimisation. Crucially this study highlights the need for sentinel reporting and real-time modelling to guide rational decision making.

Modelling the impact of an influenza pandemic on critical care services in England.

The UK Influenza Pandemic Contingency Plan does not consider the impact of a pandemic on critical care services. We modelled the demand for critical care beds in England with software developed by the Centers for Disease Control (Flusurge 1.0), using a range of attack rates and pandemic durations. Using inputs that have been employed in UK Department of Health scenarios (25% attack rate and 8-week pandemic duration) resulted in a demand for ventilatory support that exceeded 200% of present capacity. Demand remained unsustainably high even when more favourable scenarios were considered. Current critical care bed capacity in England would be unable to cope with the increased demand provided by an influenza pandemic. Appropriate contingency planning is essential.

The impact of low-risk intensive care unit admissions on mortality probabilities by SAPS II, APACHE II and APACHE III.

A large proportion of intensive care unit patients are low-risk admissions. Mortality probabilities generated by predictive systems may not accurately reflect the mortality experienced by subpopulations of critically ill patients. We prospectively assessed the impact of low-risk admissions (mortality risk < 10%) on the mortality estimates generated by three prognostic models. We studied 1497 consecutive admissions to a general intensive care unit. The performance of the three models for subgroups and the whole population was analysed. The proportions of patients designated as low risk varied with the model and differences in model performance were most pronounced for these patients. The APACHE II mortality ratios (1.32 vs. 1.19) did not differ for low- and higher risk patients, but mortality ratios generated by APACHE III (2.38 vs. 1.23) and SAPS II (2.19 vs. 1.16) were nearly two-fold greater. Calibration for higher risk patients was similar for all three models but the APACHE III system calibrated worse than the other models for low-risk patients. This may have contributed to the poorer overall calibration of the APACHE III system (Hosmer-Lemeshow C-test: APACHE III chi(2) = 329; APACHE II chi(2) = 42; SAPS II chi(2) = 62). Imperfect characterisation of the large proportion of low-risk intensive care unit admissions may contribute to the deterioration of the models' predictive accuracies for the intensive care population as a whole.

The FAD-PAS domain as a sensor for behavioral responses in Escherichia coli.

Aer, the aerotaxis receptor in Escherichia coli, is a member of a novel class of flavoproteins that act as redox sensors. The internal energy of the cell is coupled to the redox state of the electron transport system, and this status is sensed by Aer(FAD). This is a more versatile sensory response system than if E. coli sensed oxygen per se. Energy-depleting conditions that decrease electron transport also alter the redox state of the electron transport system. Aer responds by sending a signal to the flagellar motor to change direction. The output of other sensory systems that utilize redox sensors is more commonly transcriptional regulation than a behavioral response. Analysis in silico showed Aer to be part of a superfamily of PAS domain proteins that sense the intracellular environment. In Aer, FAD binds to the PAS domain. By using site-specific mutagenesis, residues critical for FAD binding and sensory transduction were identified in the PAS domain. The PAS domain appears to interact with a linker region in the C-terminus. The linker region is a member of a HAMP domain family, which has signal transduction roles in other systems.

Sports-related injuries in children.

OBJECTIVE: To describe the demographics and types of sports-related injuries (SRIs) in children. METHODS: The authors performed a retrospective chart review of children 5-18 years of age diagnosed as having an SRI in a pediatric emergency department (ED) during a two-year period. Patients were identified by ICD-9 codes. Data collected were age, sex, sport, ED interventions, consultations, mechanism, location, and injury type. Pairwise comparisons were reported as odds ratios with 95% confidence intervals. RESULTS: Six hundred seventy-seven SRIs fit the inclusion criteria; 480 of the patients were male (71%). The mean ages of the males and females were 13.0 years (SD +/- 3.0 yr) and 12.4 years (SD +/- 2.9 yr), respectively. The six most common sports implicated were basketball (19.5%), football (17.1%), baseball/softball (14.9%), soccer (14.2%), in-line skating (Rollerblading)/skating (5.7%), and hockey (4.6%). Sprains/strains (32.0%), fractures (29.4%), contusions/abrasions (19. 3%), and lacerations (9.7%) accounted for 90% of injury types. Pairwise comparison of the four injury types in the six sports listed showed significant associations for contusions/abrasions in baseball, sprains/strains in basketball, fractures in Rollerblading/skating, and lacerations in hockey. Age variance, including all sports, of the younger group (5-11 yr) in fractures and the older group (12-18 yr) in sprains was significant. The most common injury location was wrist/hand (28%), followed by head/face (22%) and ankle/foot (18%). Each had significant sport-specific predilections. Contact with person or object was the mechanism for >50% of the SRIs. Sport-specific mechanisms followed lines drawn from the sport-specific injury types and locations. CONCLUSIONS: The pediatric age group incurs a variety of injuries in numerous sports with diverse sex, age, mechanism, location, injury type, and sport-specific differences.

PAS domain residues involved in signal transduction by the Aer redox sensor of Escherichia coli.

PAS domains sense oxygen, redox potential and light, and are implicated in behaviour, circadian rhythmicity, development and metabolic regulation. Although PAS domains are widespread in archaea, bacteria and eukaryota, the mechanism of signal transduction has been elucidated only for the bacterial photo sensor PYP and oxygen sensor FixL. We investigated the signalling mechanism in the PAS domain of Aer, the redox potential sensor and aerotaxis transducer in Escherichia coli. Forty-two residues in Aer were substituted using cysteine-replacement mutagenesis. Eight mutations resulted in a null phenotype for aerotaxis, the behavioural response to oxygen. Four of them also led to the loss of the non-covalently bound FAD cofactor. Three mutant Aer proteins, N34C, F66C and N85C, transmitted a constant signal-on bias. One mutation, Y111C, inverted signalling by the transducer so that positive stimuli produced negative signals and vice versa. Residues critical for signalling were mapped onto a three-dimensional model of the Aer PAS domain, and an FAD-binding site and 'active site' for signal transduction are proposed.

Aerotaxis and other energy-sensing behavior in bacteria.

Energy taxis is widespread in motile bacteria and in some species is the only known behavioral response. The bacteria monitor their cellular energy levels and respond to a decrease in energy by swimming to a microenvironment that reenergizes the cells. This is in contrast to classical Escherichia coli chemotaxis in which sensing of stimuli is independent of cellular metabolism. Energy taxis encompasses aerotaxis (taxis to oxygen), phototaxis, redox taxis, taxis to alternative electron acceptors, and chemotaxis to a carbon source. All of these responses share a common signal transduction pathway. An environmental stimulus, such as oxygen concentration or light intensity, modulates the flow of reducing equivalents through the electron transport system. A transducer senses the change in electron transport, or possibly a related parameter such as proton motive force, and initiates a signal that alters the direction of swimming. The Aer and Tsr proteins in E. coli are newly recognized transducers for energy taxis. Aer is homologous to E. coli chemoreceptors but unique in having a PAS domain and a flavin-adenine dinucleotide cofactor that is postulated to interact with a component of the electron transport system. PAS domains are energy-sensing modules that are found in proteins from archaea to humans. Tsr, the serine chemoreceptor, is an independent transducer for energy taxis, but its sensory mechanism is unknown. Energy taxis has a significant ecological role in vertical stratification of microorganisms in microbial mats and water columns. It plays a central role in the behavior of magnetotactic bacteria and also appears to be important in plant-microbe interactions.

Loss of cytochrome c oxidase activity and acquisition of resistance to quinone analogs in a laccase-positive variant of Azospirillum lipoferum.

Laccase, a p-diphenol oxidase typical of plants and fungi, has been found recently in a proteobacterium, Azospirillum lipoferum. Laccase activity was detected in both a natural isolate and an in vitro-obtained phase variant that originated from the laccase-negative wild type. In this study, the electron transport systems of the laccase-positive variant and its parental laccase-negative forms were compared. During exponential (but not stationary) growth under fully aerobic (but not under microaerobic) conditions, the laccase-positive variant lost a respiratory branch that is terminated in a cytochrome c oxidase of the aa(3) type; this was most likely due to a defect in the biosynthesis of a heme component essential for the oxidase. The laccase-positive variant was significantly less sensitive to the inhibitory action of quinone analogs and fully resistant to inhibitors of the bc(1) complex, apparently due to the rearrangements of its respiratory system. We propose that the loss of the cytochrome c oxidase-containing branch in the variant is an adaptive strategy to the presence of intracellular oxidized quinones, the products of laccase activity.

PAS domains: internal sensors of oxygen, redox potential, and light.

PAS domains are newly recognized signaling domains that are widely distributed in proteins from members of the Archaea and Bacteria and from fungi, plants, insects, and vertebrates. They function as input modules in proteins that sense oxygen, redox potential, light, and some other stimuli. Specificity in sensing arises, in part, from different cofactors that may be associated with the PAS fold. Transduction of redox signals may be a common mechanistic theme in many different PAS domains. PAS proteins are always located intracellularly but may monitor the external as well as the internal environment. One way in which prokaryotic PAS proteins sense the environment is by detecting changes in the electron transport system. This serves as an early warning system for any reduction in cellular energy levels. Human PAS proteins include hypoxia-inducible factors and voltage-sensitive ion channels; other PAS proteins are integral components of circadian clocks. Although PAS domains were only recently identified, the signaling functions with which they are associated have long been recognized as fundamental properties of living cells.

First policy then science: why a management unit based solely on genetic criteria cannot work.

In contrast to the goals of the symposium from which this series of papers originated, we argue that attempts to apply unambiguously defined and general management unit criteria based solely on genetic parameters can easily lead to incorrect management decisions. We maintain that conservation genetics is best served by altering the perspective of data analysis so that decision making is optimally facilitated. To do so requires accounting for policy objectives early in the design and execution of the science. This contrasts with typical hypothesis testing approaches to analysing genetic data for determining population structure, which often aspire to objectivity by considering management objectives only after the analysis is complete. The null hypothesis is generally taken as panmixia with a strong predilection towards avoiding false acceptance of the alternative hypothesis (the existence of population structure). We show by example how defining management units using genetic data and standard scientific analyses that do not consider either the specific management objectives or the anthropogenic risks facing the populations being studied can easily result in a management failure by losing local populations. We then use the same example to show how an 'applied' approach driven by specific objectives and knowledge of abundance and mortality results in appropriate analyses and better decisions. Because management objectives stem from public policy, which differs among countries and among species groups, criteria for defining management units must be specific, not general. Therefore, we conclude that the most productive way to define management units is on a case-by-case basis. We also suggest that creating analytical tools designed specifically to address decision making in a management context, rather than re-tooling academic tools designed for other purposes, will increase and improve the use of genetics in conservation.

In search of higher energy: metabolism-dependent behaviour in bacteria.

Bacteria use different strategies to navigate to niches where environmental factors are favourable for growth. Chemotaxis is a behavioural response mediated by specific receptors that sense the concentration of chemicals in the environment. Recently, a new type of sensor has been described in Escherichia coli that responds to changes in cellular energy (redox) levels. This sensor, Aer, guides the bacteria to environments that support maximal energy levels in the cells. A variety of stimuli, such as oxygen, alternative electron acceptors, light, redox carriers that interact with the electron transport system and metabolized carbon sources, effect changes in the cellular energy (redox) levels. These changes are detected by Aer and by the serine chemotaxis receptor Tsr and are transduced into signals that elicit appropriate behavioural responses. Diverse environmental signals from Aer and chemotaxis receptors converge and integrate at the level of the CheA histidine kinase. Energy sensing is widespread in bacteria, and it is now evident that a variety of signal transduction strategies are used for the metabolism-dependent behaviours. The occurrence of putative energy-sensing domains in proteins from cells ranging from Archaea to humans indicates the importance of this function for all living systems.