Water bathing alters the speed-accuracy trade-off of escape flights in European starlings

Birds of most species regularly bathe in water, but the function of this behaviour is unknown. We tested the hypothesis that water bathing is important in feather maintenance, and hence should enhance flight performance. We manipulated European starlings', Sturnus vulgaris, access to bathing water in a 2 × 2 design: birds were housed in aviaries either with or without water baths for a minimum of 3 days (long-term access) before being caught and placed in individual cages either with or without water baths for a further 24 h (short-term access). We subsequently assessed the speed and accuracy of escape flights through an obstacle course of vertical strings. Birds that had bathed in the short-term flew more slowly and hit fewer strings than birds that were deprived of bathing water in the short term, whereas long-term access to bathing water had no significant effect on flight performance. Thus recent access to bathing water alters flight performance by altering the trade-off between escape flight speed and accuracy. We hypothesize that lack of bathing water provision could increase anxiety in captive starlings because of an increase in their perceived vulnerability to predation. This study therefore potentially provides an important functional link between the expression of natural behaviours in captivity and welfare considerations. © 2009 The Association for the Study of Animal Behaviour

An individualised approach to monitoring and prescribing training in elite youth football players

The concept of how training load affects performance is founded in the notion that training contributes to two specific outcomes, these are developed simultaneously by repeated bouts of training and act in conflict of each other; fitness and fatigue (Banister et al., 1975). The ability to understand these two components and how they interact with training load is commonly termed the “dose-response relationship” (Banister, 1991). The accurate quantification of training load, fitness and fatigue are therefore of paramount importance to coaches and practitioners looking to examine this relationship. In recent years, the advancement in technology has seen a rise in the number of methodologies used to assess training load and specific training outcomes. However, there is a general lack of evidence regarding the reliability, sensitivity and usefulness of these methods to help inform the training process. The aim of this thesis was therefore to improve the current understanding around the monitoring and prescription of training, with special reference to the relationship between training load, fitness and fatigue. Chapter 4 of this thesis looked to establish test re-test reliability. Variables selected for investigation were measures of subjective wellness; fatigue, muscle soreness, sleep quality, stress levels and mood state, assessments of physical performance; countermovement jump (CMJ), squat jump (SJ) and drop jump (DJ) and the assessment of tri-axial accelerometer data; PlayerLoadTM and individual component planes anterior-posterior (PLAP), mediolateral (PLML), and vertical (PLV), were collected during a sub-maximal shuttle run. The results from this investigation suggest that a short three minute sub-maximal shuttle run can be used as a reliable method to collect accelerometer data. Additionally, assessments of CMJ height, SJ height, DJ contact time (DJ-CT) and DJ reactive strength index (DJ-RSI) were all deemed to have good reliability. In contrast, this chapter highlighted the poor test re-test reliability of the subjective wellness questionnaire. Importantly, the minimum detectable change (MDC) was also calculated for all measures within this study to provide an estimate of measurement error and a threshold for changes that can be considered ‘real’. Chapter 5 assessed the sensitivity and reproducibility of these measures following a standardised training session. To assess sensitivity, the signal-to-noise (S: N) ratio was calculated by using the post training fatigue response (signal) and the MDC derived from Chapter 4 (noise). The fatigue response was considered reproducible if the S: N ratio was greater than one following two standardised training sessions. Three measures met the criteria to be considered both sensitive and reproducible; DJ-RSI, PLML and %PLV. All other measures did not meet the criteria. Subjective ratings of fatigue, muscle soreness and sleep quality did show a sensitive response on one occasion, however, this was not reproducible. This might be due to the categorical nature of the data, making detectable group changes hard to accomplish. The subjective wellness questionnaire was subsequently adapted to include three items; subjective fatigue, muscle soreness and sleep quality on a 10-point scale. The test re-test reliability of these three questions was established in Chapter 6, demonstrating that subjective fatigue and muscle soreness have good test re-test reliability. Chapter 6 was comprised of two studies looking to simultaneously establish the dose-response relationship between training load, measures of fatigue (Part I) and measures of fitness (Part II). In Part I training load was strategically altered on three occasions during a standardised training session in a randomised crossover design. In Part II training and match load was monitored over a 6-week training period with maximal aerobic speed (MAS) assessed pre and post. A key objective for both studies was to assess differences in the training load-fitness-fatigue relationship when using various training load measures, in particular differences between arbitrary and individualised speed thresholds. Results from Part I showed a large to very large relationship between training load and subjective fatigue, muscle soreness and DJ-RSI performance. No differences were found between arbitrary and individualised thresholds. In Part II however, individual external training load, assessed via time above MAS (t>MAS), showed a very large relationship with changes in aerobic fitness. This was in contrast to the unclear relationships with arbitrary thresholds. Taking the results from both studies into consideration it was concluded that t>MAS is a key measure of training load if the objective is to assess the relationship with both fitness and fatigue concurrently with one measure. Chapter 7 subsequently looked to validate the training load-fitness-fatigue relationships established in Chapter 6 via an intervention study. The aim was to develop a novel intervention that prescribed t>MAS, in order to improve aerobic fitness, based on the findings from Chapter 6. Additionally, the fatigue response following a standardised training session was assessed pre and post intervention to evaluate the effect the predicted improvements in aerobic fitness would have on measures of fatigue. Results from Chapter 7 indicate a highly predictable improvement in aerobic fitness from the training load completed during the study, validating the use of t>MAS as a monitoring and intervention tool. Furthermore, this improvement in aerobic fitness attenuated the fatigue response following a standardised training session. The final key finding was the very strong relationship between improvements in aerobic fitness and reductions in fatigue response. This further highlights the relationship between t>MAS, fitness and fatigue. In summary, this thesis has helped further current understanding on the monitoring and prescription of training load, with reference to fitness and fatigue. Firstly, a rigorous approach was used to identify fatigue monitoring measures that are reliable, sensitive and reproducible. Secondly, the relationship between training load, fatigue and fitness was clearly established. And finally, it has contributed new knowledge to the existing literature by establishing the efficacy of a novel MAS intervention to improve aerobic fitness and attenuate a fatigue response in elite youth football players

Quantification of abnormal repetitive behaviour in captive European starlings (Sturnus vulgaris).

Stereotypies are repetitive, unvarying and goalless behaviour patterns that are often considered indicative of poor welfare in captive animals. Quantifying stereotypies can be difficult, particularly during the early stages of their development when behaviour is still flexible. We compared two methods for objectively quantifying the development of route-tracing stereotypies in caged starlings. We used Markov chains and T-pattern analysis (implemented by the software package, Theme) to identify patterns in the sequence of locations a bird occupied within its cage. Pattern metrics produced by both methods correlated with the frequency of established measures of stereotypic behaviour and abnormal behaviour patterns counted from video recordings, suggesting that both methods could be useful for identifying stereotypic individuals and quantifying stereotypic behaviour. We discuss the relative benefits and disadvantages of the two approaches

Translational and Regulatory Challenges for Exon Skipping Therapies

Several translational challenges are currently impeding the therapeutic development of antisense-mediated exon skipping approaches for rare diseases. Some of these are inherent to developing therapies for rare diseases, such as small patient numbers and limited information on natural history and interpretation of appropriate clinical outcome measures. Others are inherent to the antisense oligonucleotide (AON)-mediated exon skipping approach, which employs small modified DNA or RNA molecules to manipulate the splicing process. This is a new approach and only limited information is available on long-term safety and toxicity for most AON chemistries. Furthermore, AONs often act in a mutation-specific manner, in which case multiple AONs have to be developed for a single disease. A workshop focusing on preclinical development, trial design, outcome measures, and different forms of marketing authorization was organized by the regulatory models and biochemical outcome measures working groups of Cooperation of Science and Technology Action: "Networking towards clinical application of antisense-mediated exon skipping for rare diseases." The workshop included participants from patient organizations, academia, and members of staff from the European Medicine Agency and Medicine Evaluation Board (the Netherlands). This statement article contains the key outcomes of this meeting.status: publishe

Workplace bullying: measurements and metrics to use in the NHS. Final Report for NHS Employers.

The aim of this report is to identify how workplace bullying can be tracked over time, to indicate what measures and metrics can be used to identify change, and to provide comparators for other sectors in the UK and internationally. Bullying can encompass a range of different behaviours. Deciding on a definition of workplace bullying can clarify what is regarded as bullying, but it may also narrow the focus and exclude relevant issues of concern. For example, bullying definitions typically state that negative behaviours should be experienced persistently over a period of time. The threshold for behaviours to be defined as ‘bullying’ could be set to include one or two negative acts per month over the previous six months; or more stringently to include only behaviours that occur at least weekly over the previous twelve months. Choosing an appropriate threshold for frequency and duration of behaviours raises several questions: should occasional negative behaviours be regarded as bullying? Would one or two serious episodes of negative behaviour be regarded as bullying? Some researchers use the criteria of weekly negative behaviours over six months to identify bullying, but others argue that occasional exposure to negative acts can act as a significant stressor at work (Zapf et al., 2011). We have identified a range of tools and metrics that can be used to track change over time. However, there are a number of important issues to consider when measuring bullying which may affect the interpretation of the results. In particular, bullying prevalence rates vary considerably depending on the type of metric and definition of bullying used. For example, one international review found prevalence rates ranging from less than 1% for weekly bullying in the last six months up to 87% for occasional bullying over a whole career (Zapf et al., 2011). There are three main types of direct measures of bullying: self-labelling without a definition, self-labelling with a definition, and the behavioural experience method. Self-labelling metrics typically ask a respondent to identify themselves as a target of bullying (e.g., “Have you been bullied at work?” with a yes/no response, or “How often have you been bullied at work?” with a frequency scale such as never/occasionally/monthly/weekly/daily). This approach is quick and easy to administer, but is more subjective as responses will be based on the respondent’s interpretation of bullying. This approach can be improved with the provision of a definition of bullying, and a request to use the definition when responding. However, following pilot work, Fevre et al. (2011) argued that respondents tended not to read and digest bullying definitions as they had already decided what bullying meant to them. The behavioural experience method offers a more objective approach, but is typically longer and more time consuming. This method involves respondents rating the frequency with which they have experienced different negative behaviours (e.g., “How often has someone humiliated or belittled you in front of others?” with a frequency scale such as never/now and then/monthly/weekly/daily). These behavioural inventories may not mention bullying, but capture the prevalence of specific negative acts, and a total score may be calculated. The threshold for the frequency and number of negative acts, or a total score, required for an experience to be regarded as bullying can be chosen by the researcher. Although this enhances the objectivity of the measure, it may be that the respondent themselves may not regard their experience as bullying. In a meta-analysis of bullying studies conducted across 24 countries, Nielsen et al. (2010) found an overall prevalence rate of 18.1% for self-labelling with no definition, 11.3% for self-labelling with a definition, and 14.8% using a behavioural experience checklist. For best practice, it is recommended that both the self-labelling with a definition and the behavioural experience method are used in bullying research (Zapf et al., 2011). It is also important to be specific about the type of bullying being measured. In particular, if the measure is designed to capture bullying at work between co-workers this should be explicitly stated, so that bullying from patients and their relatives is excluded. Interpretation of the results may also be somewhat complex. Although increases in bullying prevalence should undoubtedly be addressed, we need to be mindful that an increase in reported bullying may reflect a change in culture: changing expectations of the behaviour of colleagues and managers, or a move towards greater openness and willingness to address concerns that were previously ignored or condoned. A measure of employees’ trust in the organisation to respond appropriately to such allegations may act as a positive indicator. The perceived and actual anonymity of responses is a critical factor. Employees are understandably wary about providing sensitive information on bullying and have voiced concerns regarding being identified and the potential repercussions of reporting bullying (Carter et al., 2013). There is a considerable discrepancy between the prevalence of bullying as captured in anonymous questionnaires and direct reports of bullying made to the organisation (e.g., to managers or HR; Scott, Blanshard & Child, 2008). Protecting the anonymity of respondents, and ensuring that individuals cannot be identified, will be important factors in the administration of a bullying measure. Some metrics are already routinely collected by the NHS, and if examined closely could provide useful indicators of change. Direct indicators include complaints about bullying and responses to ongoing NHS staff surveys. Indirect metrics can be used to capture factors that are associated with bullying, such as psychological wellbeing (including stress, anxiety and depression), sickness rates, job satisfaction and organisational commitment. However, factors other than bullying will affect these measures. The prevalence of witnessed bullying could also be considered as an important metric. A large proportion of NHS staff report that they have witnessed bullying between staff, and this is associated with negative outcomes for individuals and teams (Carter et al., 2013). Comparing the NHS prevalence rates with other sectors in the UK and internationally is complex. Ideally comparators would have used the same definition, measurement method and reporting period, but the definitions and metrics often differ. Total populations are the ideal, but are rarely provided. Single site studies are less generalisable than multi-site studies, and total samples are preferred over open invitations to unknown populations which may be more likely to attract responses from those who have experienced bullying. This report begins with several definitions of bullying, describes direct and indirect measures of bullying, and compares the prevalence of bullying in the NHS to other sectors in the UK, and to the healthcare sector internationally

Town of Newcastle Maine Ordinances

Ordinances Cover: E911 Addressing; FEMA Flood Insurance; Finance Committee; Fireworks; Harbor Management; Joint Shellfish Conservation; Land Use; Parking; Purchasing and Bid; Recall of Elected Officials; Sign; Single Use Plastic Carryout Bags; Transient Seller and Lunch Wagon; Wind Energ