Influence of prior activity (warm-up) and inspiratory muscle training upon between- and within-day reliability of maximal inspiratory pressure measurement

This is the post-print version of the article. The official published version can be obtained from the link below.BACKGROUND: A specific inspiratory muscle ‘warm-up’ (IWU) prior to assessment of maximal inspiratory mouth pressure (PImax) may reduce the number of measurements required to obtain reproducible, representative estimates of PImax. The influence of inspiratory muscle training (IMT) upon this phenomenon is unknown. OBJECTIVE: Compare the impact of an IWU on the between- and within-day reliability of PImax before and after IMT. METHOD: Eight participants were assessed on 4 separate occasions: 2 trials preceded IMT and 2 followed it. At each assessment, the highest of 3 initial efforts was recorded as the pre-IWU value (PI). The highest of 9 subsequent efforts that followed 2 sets of 30 breaths at 40% PI was recorded as PImax. Following 4 weeks of IMT, the trials were repeated. RESULTS: IWU increased PI by 11–17% (p ≤ 0.01), irrespective of IMT status. After IWU, 5–6 efforts were required to determine PImax, irrespective of IMT status. PImax was similar between the 2 trials before IMT and the 2 trials after IMT (p ≥ 0.05), and was 21% higher after IMT (p ≤ 0.01). The coefficient of variation was excellent before and after IWU, both before (1.9 and 0.6%, respectively) and after IMT (1.1 and 0.3%, respectively). Limits of agreement and sample sizes for effect sizes ≤10% were substantially smaller after IWU in all trials. CONCLUSIONS: (1) IWU enhances the between-day reliability of PImax measurement, and this is unaffected by IMT, and (2) judgements regarding acceptability in relation to PImax reliability should be made in relation to analytical goals and we present data to facilitate this

Black Effect: Black Students\u27 Experiences in CACREP Master\u27s Programs at HBCUs

African American students’ experiences in graduate programs at predominately White institutions (PWIs) have been explored in previous research. Said experiences were described using the following constructs: underrepresentation, invisibility, isolation, lack of support, and silence. There is minimal research that focuses on Black students’ experiences at Historically Black Colleges and Universities (HBCUs), where the environment was reported to be welcoming, nurturing, and supportive. This study adds to the literature by exploring the lived experiences of African American students in CACREP-accredited master’s counseling programs at HBCUs, with critical race theory (CRT) as the theoretical framework. After being identified as eligible based on a screening survey, semi-structured interviews and photovoice were implemented to allow space for the six participants to share their experiences. The following five themes and subthemes emerged from an analysis of their responses: relationship dynamics with the subthemes of representation, faculty, peers, and isolation; climate with the subthemes of program, HBCUs, and challenges; being Black; scholarship with the subthemes of CACREP and academic transactions; and COVID. Participants shared mostly positive experiences, noting the family feel of their programs, the pleasant relationships with their faculty, connectedness to their peers, and a sense of empowerment because of their Blackness. Implications for counselor educators suggest making the conscious effort to communicate in a timely manner, make students feel welcome, and teach from a multicultural lens

An electromyographic evaluation of dual role breathing and upper body muscles in response to front crawl swimming

The upper body trunk musculature is key in supporting breathing, propulsion, and stabilization during front crawl swimming. The aim of this study was to determine if the latissimus dorsi, pectoralis major, and serratus anterior contributed to the development of inspiratory muscle fatigue observed following front crawl swimming. Fourteen trained swimmers completed a 200-m front crawl swim at 90% of race pace. Maximal inspiratory and expiratory mouth pressures (PImax and PEmax) were assessed before (baseline) and after each swim, and electromyography was recorded from the three muscles. Post-swim PImax fell by 11% (P < 0.001, d = 0.57) and the median frequency (MDF: a measure of fatigue) of the latissimus dorsi, pectoralis major, and serratus anterior fell to 90% (P = 0.001, d = 1.57), 87% (P = 0.001, r = −0.60) and 89% (P = 0.018, d = 1.04) of baseline, respectively. The fall in serratus anterior MDF was correlated with breathing frequency (r = 0.675, P = 0.008) and stroke rate (r = 0.639, P = 0.014). The results suggest that the occurrence of inspiratory muscle fatigue was partly caused by fatigue of these muscles, and that breathing frequency and stroke rate particularly affect the serratus anterior

A classification for English primary schools using open data

England has statutory regulations in place that ensure state funded schools deliver broadly the same curriculum. However there still exists a wide range of contexts in which this education takes place, including: the management of schools; how the schools chose to spend their budgets; individual policies in regards to staffing, behaviour and attendance, and perhaps most importantly, the composition of the pupil population in the school. Given these contexts, one outcome of interest is the attainment profile of schools, and it is important that this performance is judged in context, for the benefits of pupils, parents and schools. To this end, this study develops a classification using contemporary data for English primary schools. The open data used captures aspects of the gender, ethnic, language, staffing and affluence makeup of each school. The nature of these derived groupings is described and made available as a mapping resource. These groupings allow the identification of “families of schools”, to act as a resource to foster better collaboration between schools and more nuanced benchmarking

Impact of Weekly Swimming Training Distance on the Ergogenicity of Inspiratory Muscle Training in Well-Trained Youth Swimmers

The aim of this study was to examine the impact of weekly swimming training distance upon the ergogenicity of inspiratory muscle training (IMT). Thirty-three youth swimmers were recruited and separated into a LOW and HIGH group based on weekly training distance ( 41 km.wk-1, respectively). The LOW and HIGH groups were further subdivided into control and IMT groups for a 6-week IMT intervention giving a total of four groups: LOWcon, LOWIMT, HIGHcon, HIGHIMT. Before and after the intervention period, swimmers completed maximal effort 100 m and 200 m front crawl swims, with maximal inspiratory and expiratory mouth pressures (PImax and PEmax, respectively) assessed before and after each swim. IMT increased PImax (but not PEmax) by 36% in LOWIMT and HIGHIMT groups (P < 0.05) but 100 m and 200 m swims were faster only in the LOWIMT group (3% and 7% respectively, P < 0.05). Performance benefits only occurred in those training up to 31 km.wk-1 and indicate that the ergogenicity of IMT is affected by weekly training distance. Consequently, training distances are important considerations, among others, when deciding whether or not to supplement swimming training with IMT

An experimental evaluation of the use of Δ13C as a proxy for palaeoatmospheric CO2

Understanding changes in atmospheric CO2 over geological time via the development of well constrained and tested proxies is of increasing importance within the Earth sciences. Recently a new proxy (identified as the C3 proxy) has been proposed that is based on the relationship between CO2 and carbon isotope discrimination (Δ13C) of plant leaf tissue. Initial work suggests that this proxy has the capacity to deliver accurate and potentially precise palaeo-CO2 reconstructions through geological time since the origins of vascular plants (∼450 Mya). However, the proposed model has yet to be fully validated through independent experiments. Using the model plant Arabidopsis thaliana exposed to different watering regimes and grown over a wide range of CO2 concentrations (380, 400, 760, 1000, 1200, 1500, 2000 and 3000ppm) relevant to plant evolution we provide an experimental framework that allows for such validation. Our experiments show that a wide variation in Δ13C as a function of water availability is independent of CO2 treatment. Validation of the C3 proxy was undertaken by comparing growth CO2 to estimates of CO2 derived from Δ13C. Our results show significant differences between predicted and observed CO2 across all CO2 treatments and water availabilities, with a strong under prediction of CO2 in experiments designed to simulate Cenozoic and Mesozoic atmospheric conditions (≥1500ppm). Further assessment of Δ13C to predict CO2 was undertaken using Monte Carlo error propagation. This suite of analysis revealed a lack of convergence between predicted and observed CO2. Collectively these findings suggest that the relationship between Δ13C and CO2 is poorly constrained. Consequently the use of Δ13C as a proxy to reconstruct palaeoatmospheric CO2 is of limited use as the estimates of CO2 are not accurate when compared to known growth conditions

Modelling the structure of star clusters with fractional Brownian motion

The degree of fractal substructure in molecular clouds can be quantified by comparing them with Fractional Brownian Motion (FBM) surfaces or volumes. These fields are self-similar over all length scales and characterised by a drift exponent $H$, which describes the structural roughness. Given that the structure of molecular clouds and the initial structure of star clusters are almost certainly linked, it would be advantageous to also apply this analysis to clusters. Currently, the structure of star clusters is often quantified by applying $\mathcal{Q}$ analysis. $\mathcal{Q}$ values from observed targets are interpreted by comparing them with those from artificial clusters. These are typically generated using a Box-Fractal (BF) or Radial Density Profile (RDP) model. We present a single cluster model, based on FBM, as an alternative to these models. Here, the structure is parameterised by $H$, and the standard deviation of the log-surface/volume density $\sigma$. The FBM model is able to reproduce both centrally concentrated and substructured clusters, and is able to provide a much better match to observations than the BF model. We show that $\mathcal{Q}$ analysis is unable to estimate FBM parameters. Therefore, we develop and train a machine learning algorithm which can estimate values of $H$ and $\sigma$, with uncertainties. This provides us with a powerful method for quantifying the structure of star clusters in terms which relate to the structure of molecular clouds. We use the algorithm to estimate the $H$ and $\sigma$ for several young star clusters, some of which have no measurable BF or RDP analogue.Comment: 11 Pages, accepted by MNRA