Athletic Performance and Recovery-Stress Factors in Cycling: An Ever Changing Balance

We sought to examine whether the relationship between recovery-stress factors and performance would differ at the beginning (Stage 1) and the end (Final Stage) of a multi-stage cycling competition. Sixty-seven cyclists with a mean age of 21.90 years (SD = 1.60) and extensive international experience participated in the study. The cyclists responded to the Recovery-Stress Questionnaire for Athletes (RESTQ-Sport) and rated their performance (1 = extremely poor to 10 = excellent) in respect to the first and last stage. Two step-down multiple regression models were used to estimate the relationship among recovery (nine factors; e.g., Physical Recovery, Sleep Quality) and stress factors (10 factors; e.g., Lack of Energy, Physical Complaints), as assessed by the RESTQ and in relation to performance. Model-1 pertained to Stage 1, whereas Model-2 used data from the Final Stage. The final Model-1 revealed that Physical Recovery (β = .46, p = .01), Injury (β = -.31, p = .01) and General Well-being (β = -.26, p = .04) predicted performance in Stage 1 (R2 = .21). The final Model-2 revealed a different relationship between recovery-stress factors and performance. Specifically, being a climber (β = .28, p = .01), Conflicts/Pressure (β = .33, p = .01), and Lack of Energy (β = -.37, p = .01) were associated with performance at the Final Stage (R2 = .19). Collectively, these results suggest that the relationship among recovery and stress factors changes greatly over a relatively short period of time, and dynamically influences performance in multi-stage competitions

Individual Patterns in Blood-Borne Indicators of Fatigue - Trait or Chance

© 2016 National Strength and Conditioning Association. Julian, R, Meyer, T, Fullagar, HHK, Skorski, S, Pfeiffer, M, Kellmann, M, Ferrauti, A, and Hecksteden, A. Individual patterns in blood-borne indicators of fatigue - trait or chance. J Strength Cond Res 31(3): 608-619, 2017 - Blood-borne markers of fatigue such as creatine kinase (CK) and urea (U) are widely used to fine-tune training recommendations. However, predictive accuracy is low. A possible explanation for this dissatisfactory characteristic is the propensity of athletes to react to different patterns of fatigue indicators (e.g., predominantly muscular [CK] or metabolic [U]). The aim of the present trial was to explore this hypothesis by using repetitive fatigue-recovery cycles. A total of 22 elite junior swimmers and triathletes (18 ± 3 years) were monitored for 9 weeks throughout 2 training phases (low-intensity, high-volume [LIHV] and high-intensity, low-volume [HILV] phases). Blood samples were collected each Monday (recovered) and Friday (fatigued) morning. From measured values of CK, U, free-testosterone (FT), and cortisol (C) as determined in the rested and fatigued state, respectively, Monday-Friday differences (Δ) were calculated and classified by magnitude before calculation of ratios (ΔCK/ΔU and ΔFT/ΔC). Coefficient of variation (CV) was calculated as group-based estimates of reproducibility. Linear mixed modeling was used to differentiate inter- and intraindividual variability. Consistency of patterns was analyzed by comparing with threshold values (1.1 for all weeks). Reproducibility was very low for fatigue-induced changes (CV ≥ 100%) with interindividual variation accounting for 45-60% of overall variability. Case-wise analysis indicated consistent ΔCK/ΔU patterns for 7 individuals in LIHV and 7 in HILV; 5 responded consistently throughout. For ΔFT/ΔC the number of consistent patterns was 2 in LIHV and 3 in HILV. These findings highlight the potential value of an individualized and multivariate approach in the assessment of fatigue

Convergent Validity of the Short Recovery and Stress Scale in Collegiate Weightlifters

International Journal of Exercise Science 15(6): 1457-1471, 2022. The purpose of this study was to determine whether changes in collegiate weightlifters’ external training load, biochemical markers, and jumping performance correlate to changes in items of the Short Recovery and Stress Scale (SRSS) throughout four microcycles. Twelve well-trained weightlifters (8 males, 4 females; age 24.30 ± 4.36 yr; height 170.28 ± 7.09 cm; body mass 81.73 ± 17.00 kg) with at least one year of competition experience participated in the study. Measurements included hydration, SRSS, biochemical analysis of blood (cortisol [C], creatine kinase [CK]), and unloaded and loaded squat jumps (SJ), and volume-load displacement. Pearson correlation coefficients were calculated between the changes in SRSS items and all other variables. The alpha criterion for all analyses was set at p ≤ 0.05. Negative relationships were observed between changes in SRSS recovery items and C (r = -0.608 to -0.723), and unloaded and loaded SJ height and peak power (r = -0.587 to -0.636). Positive relationships were observed between changes in several SRSS stress items and C (r = 0.609 to 0.723), CK (r = 0.922), and unloaded and loaded SJ height and peak power (r = 0.583 to 0.839). Relationships between changes in some SRSS items and cortisol agree with previous findings highlighting C as an indicator of training stress. Nonetheless, the non-significant relationships between changes in SRSS items, training volume and biochemical markers disagree with previous findings. This may partly be explained by the smaller undulations in training volume in the current study, which were characteristic of typical training. Further, relationships between changes in some SRSS items and jumping performance were opposite of what was expected indicating athletes’ perception of their stress and recovery state does not always correspond with their ability to perform

HOCl chemistry in the Antarctic stratospheric vortex 2002, as observed with the Michelson interferometer for passive atmospheric sounding (MIPAS)

In the 2002 Antarctic polar vortex enhanced HOCl mixing ratios were detected by the Michelson Interferometer for Passive Atmospheric Sounding both at altitudes of around 35 km (1000K potential temperature), where HOCl abundances are ruled by gas phase chemistry and at around 18–24 km (475–625 K), which belongs to the altitude domain where heterogeneous chlorine chemistry is relevant. At altitudes of 33 to 40 km polar vortex HOCl mixing ratios were found to be around 0.14 ppbv as long as the polar vortex was intact, centered at the pole, and thus received relatively little sunlight. This is the altitude region where in midlatitudinal and tropic atmospheres peak HOCl mixing ratios significantly above 0.2 ppbv (in terms of daily mean values) are observed. After deformation and displacement of the polar vortex in the course of a major warming, ClO-rich vortex air was more exposed to sunlight, where enhanced HOx abundances led to largely increased HOCl mixing ratios (up to 0.3 ppbv), exceeding typical midlatitudinal and tropical amounts significantly. The HOCl increase was preceded by an increase of ClO. Model runs could reproduce these measurements only when the Stimpfle et al. (1979) rate constant for the reaction ClO+HO2→HOCl+O2 was used but not with the current JPL recommendation. At an altitude of 24 km, HOCl mixing ratios of up to 0.15 ppbv were detected. This HOCl enhancement, which is already visible in 18 September data, is attributed to heterogeneous chemistry, which is in agreement with observations of polar stratospheric clouds. The measurements were compared to a model run where no polar stratospheric clouds appeared during the observation period. The fact that HOCl still was produced in the model run suggests that a significant part of HOCl was generated from ClO rather than directly via heterogeneous reaction. Excess ClO, lower ClONO2 and earlier loss of HOCl in the measurements are attributed to ongoing heterogeneous chemistry which is not reproduced by the model. On 11 October, polar vortex mean daytime mixing ratios were only 0.03 ppbv

Global stratospheric hydrogen peroxide distribution from MIPAS-Envisat full resolution spectra compared to KASIMA model results

MIPAS-ENVISAT full resolution spectra were analyzed to obtain a global distribution of hydrogen peroxide (H<sub>2</sub>O<sub>2</sub>) in the stratosphere. H<sub>2</sub>O<sub>2</sub> acts as reservoir gas for the HO<sub>x</sub> family (= H+OH+HO<sub>2</sub>) and thus, observations of H<sub>2</sub>O<sub>2</sub> provide a better understanding of the HO<sub>x</sub> chemistry in the atmosphere. A retrieval approach based on constrained least squares fitting was developed and applied to small dedicated spectral analysis windows with maximum H<sub>2</sub>O<sub>2</sub> information and minimum contribution of interfering gases. Due to a low signal to noise ratio in the measured spectra single profiles cannot be used for scientific interpretation and about 100 profiles have to be averaged temporally or spatially. Our retrievals of H<sub>2</sub>O<sub>2</sub> from MIPAS measurements provide meaningful results between approximately 20 and 60 km. A possible impact by the high uncertainty of the reaction rate constant for HO<sub>2</sub> + HO<sub>2</sub>→H<sub>2</sub>O<sub>2</sub> + O<sub>2</sub> in our 3D-CTM KASIMA is discussed. We find best agreement between model and observations for applying rate constants according to Christensen et al. (2002) however, a mismatch in vertical profile shape remains. The observations were compared to the model results of KASIMA focusing on low to mid latitudes. Good agreement in spatial distribution and in temporal evolution was found. Highest vmr of H<sub>2</sub>O<sub>2</sub> in the stratosphere were observed and modeled in low latitudes shortly after equinox at about 30 km. The modelled diurnal cycle with lowest vmr shortly after sunrise and highest vmr in the afternoon is confirmed by the MIPAS observations

A reassessment of the discrepancies in the annual variation of δD-H₂O in the tropical lower stratosphere between the MIPAS and ACE-FTS satellite data sets

The annual variation of δD in the tropical lower stratosphere is a critical indicator for the relative importance of different processes contributing to the transport of water vapour through the cold tropical tropopause region into the stratosphere. Distinct observational discrepancies of the δD annual variation were visible in the works of Steinwagner et al. (2010) and Randel et al. (2012). Steinwagner et al. (2010) analysed MIPAS (Michelson Interferometer for Passive Atmospheric Sounding) observations retrieved with the IMK/IAA (Institut für Meteorologie und Klimaforschung in Karlsruhe, Germany, in collaboration with the Instituto de Astrofísica de Andalucía in Granada, Spain) processor, while Randel et al. (2012) focused on ACE-FTS (Atmospheric Chemistry Experiment Fourier Transform Spectrometer) observations. Here we reassess the discrepancies based on newer MIPAS (IMK/IAA) and ACE-FTS data sets, also showing for completeness results from SMR (Sub-Millimetre Radiometer) observations and a ECHAM/MESSy (European Centre for Medium-Range Weather Forecasts Hamburg and Modular Earth Submodel System) Atmospheric Chemistry (EMAC) simulation (Eichinger et al., 2015b). Similar to the old analyses, the MIPAS data set yields a pronounced annual variation (maximum about 75 ‰), while that derived from the ACE-FTS data set is rather weak (maximum about 25 ‰). While all data sets exhibit the phase progression typical for the tape recorder, the annual maximum in the ACE-FTS data set precedes that in the MIPAS data set by 2 to 3 months. We critically consider several possible reasons for the observed discrepancies, focusing primarily on the MIPAS data set. We show that the δD annual variation in the MIPAS data up to an altitude of 40 hPa is substantially impacted by a “start altitude effect”, i.e. dependency between the lowermost altitude where MIPAS retrievals are possible and retrieved data at higher altitudes. In itself this effect does not explain the differences with the ACE-FTS data. In addition, there is a mismatch in the vertical resolution of the MIPAS HDO and H2O data (being consistently better for HDO), which actually results in an artificial tape-recorder-like signal in δD. Considering these MIPAS characteristics largely removes any discrepancies between the MIPAS and ACE-FTS data sets and shows that the MIPAS data are consistent with a δD tape recorder signal with an amplitude of about 25 ‰ in the lowermost stratosphere

Recovery and performance in sport: Consensus statement

© 2018 Human Kinetics, Inc. The relationship between recovery and fatigue and its impact on performance has attracted the interest of sport science for many years. An adequate balance between stress (training and competition load, other life demands) and recovery is essential for athletes to achieve continuous high-level performance. Research has focused on the examination of physiological and psychological recovery strategies to compensate external and internal training and competition loads. A systematic monitoring of recovery and the subsequent implementation of recovery routines aims at maximizing performance and preventing negative developments such as underrecovery, nonfunctional overreaching, the overtraining syndrome, injuries, or illnesses. Due to the inter- and intraindividual variability of responses to training, competition, and recovery strategies, a diverse set of expertise is required to address the multifaceted phenomena of recovery, performance, and their interactions to transfer knowledge from sport science to sport practice. For this purpose, a symposium on Recovery and Performance was organized at the Technical University Munich Science and Study Center Raitenhaslach (Germany) in September 2016. Various international experts from many disciplines and research areas gathered to discuss and share their knowledge of recovery for performance enhancement in a variety of settings. The results of this meeting are outlined in this consensus statement that provides central definitions, theoretical frameworks, and practical implications as a synopsis of the current knowledge of recovery and performance. While our understanding of the complex relationship between recovery and performance has significantly increased through research, some important issues for future investigations are also elaborated

Discovery of Nuclear-Encoded Genes for the Neurotoxin Saxitoxin in Dinoflagellates

Saxitoxin is a potent neurotoxin that occurs in aquatic environments worldwide. Ingestion of vector species can lead to paralytic shellfish poisoning, a severe human illness that may lead to paralysis and death. In freshwaters, the toxin is produced by prokaryotic cyanobacteria; in marine waters, it is associated with eukaryotic dinoflagellates. However, several studies suggest that saxitoxin is not produced by dinoflagellates themselves, but by co-cultured bacteria. Here, we show that genes required for saxitoxin synthesis are encoded in the nuclear genomes of dinoflagellates. We sequenced >1.2×106 mRNA transcripts from the two saxitoxin-producing dinoflagellate strains Alexandrium fundyense CCMP1719 and A. minutum CCMP113 using high-throughput sequencing technology. In addition, we used in silico transcriptome analyses, RACE, qPCR and conventional PCR coupled with Sanger sequencing. These approaches successfully identified genes required for saxitoxin-synthesis in the two transcriptomes. We focused on sxtA, the unique starting gene of saxitoxin synthesis, and show that the dinoflagellate transcripts of sxtA have the same domain structure as the cyanobacterial sxtA genes. But, in contrast to the bacterial homologs, the dinoflagellate transcripts are monocistronic, have a higher GC content, occur in multiple copies, contain typical dinoflagellate spliced-leader sequences and eukaryotic polyA-tails. Further, we investigated 28 saxitoxin-producing and non-producing dinoflagellate strains from six different genera for the presence of genomic sxtA homologs. Our results show very good agreement between the presence of sxtA and saxitoxin-synthesis, except in three strains of A. tamarense, for which we amplified sxtA, but did not detect the toxin. Our work opens for possibilities to develop molecular tools to detect saxitoxin-producing dinoflagellates in the environment

Origin of Saxitoxin Biosynthetic Genes in Cyanobacteria

BACKGROUND:Paralytic shellfish poisoning (PSP) is a potentially fatal syndrome associated with the consumption of shellfish that have accumulated saxitoxin (STX). STX is produced by microscopic marine dinoflagellate algae. Little is known about the origin and spread of saxitoxin genes in these under-studied eukaryotes. Fortuitously, some freshwater cyanobacteria also produce STX, providing an ideal model for studying its biosynthesis. Here we focus on saxitoxin-producing cyanobacteria and their non-toxic sisters to elucidate the origin of genes involved in the putative STX biosynthetic pathway. METHODOLOGY/PRINCIPAL FINDINGS:We generated a draft genome assembly of the saxitoxin-producing (STX+) cyanobacterium Anabaena circinalis ACBU02 and searched for 26 candidate saxitoxin-genes (named sxtA to sxtZ) that were recently identified in the toxic strain Cylindrospermopsis raciborskii T3. We also generated a draft assembly of the non-toxic (STX-) sister Anabaena circinalis ACFR02 to aid the identification of saxitoxin-specific genes. Comparative phylogenomic analyses revealed that nine putative STX genes were horizontally transferred from non-cyanobacterial sources, whereas one key gene (sxtA) originated in STX+ cyanobacteria via two independent horizontal transfers followed by fusion. In total, of the 26 candidate saxitoxin-genes, 13 are of cyanobacterial provenance and are monophyletic among the STX+ taxa, four are shared amongst STX+ and STX-cyanobacteria, and the remaining nine genes are specific to STX+ cyanobacteria. CONCLUSIONS/SIGNIFICANCE:Our results provide evidence that the assembly of STX genes in ACBU02 involved multiple HGT events from different sources followed presumably by coordination of the expression of foreign and native genes in the common ancestor of STX+ cyanobacteria. The ability to produce saxitoxin was subsequently lost multiple independent times resulting in a nested relationship of STX+ and STX- strains among Anabaena circinalis strains