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

The Australian bushfires of February 2009: MIPAS observations and GEM-AQ model results

Starting on 7 February 2009, southeast Australia was devastated by large bushfires, which burned an area of about 3000 km<sup>2</sup> on this day alone. This event was extraordinary, because a large number of combustion products were transported into the uppermost troposphere and lower stratosphere within a few days. Various biomass burning products released by the fire were observed by the Michelson Interferometer for Passive Atmospheric Sounding (MIPAS) on the Envisat satellite. We tracked the plume using MIPAS C<sub>2</sub>H<sub>2</sub>, HCN and HCOOH single-scan measurements on a day-to-day basis. The measurements were compared with a high-resolution model run of the Global Environmental Multiscale Air Quality (GEM-AQ) model. Generally there is good agreement between the spatial distribution of measured and modelled pollutants. Both MIPAS and GEM-AQ show a fast southeastward transport of the pollutants to New Zealand within one day. During the following 3–4 days, the plume remained northeastward of New Zealand and was located at altitudes of 15 to 18 km. Thereafter its lower part was transported eastward, followed by westward transport of its upper part. On 17 February the eastern part had reached southern South America and on 20 February the central South Atlantic. On the latter day a second relic of the plume was observed moving eastward above the South Pacific. Between 20 February and the first week of March, the upper part of the plume was transported westward over Australia and the Indian Ocean towards southern Africa. First evidence for entry of the pollutants into the stratosphere was found in MIPAS data of 11 February, followed by larger amounts on 17 February and the days thereafter. From MIPAS data, C<sub>2</sub>H<sub>2</sub>/HCN and HCOOH/HCN enhancement ratios of 0.76 and 2.16 were calculated for the first days after the outbreak of the fires, which are considerably higher than the emission ratios assumed for the model run and at the upper end of values found in literature. From the temporal decrease of the enhancement ratios, mean lifetimes of 16–20 days and of 8–9 days were calculated for measured C<sub>2</sub>H<sub>2</sub> and HCOOH. The respective lifetimes calculated from the model data are 18 and 12 days

Validation of revised methane and nitrous oxide profiles from MIPAS-ENVISAT

Improved versions of CH4 and N2O profiles derived at the Institute of Meteorology and Climate Research and Instituto de Astrofísica de Andalucía (CSIC) from spectra measured by the Michelson Interferometer for Passive Atmospheric Sounding (MIPAS) have become available. For the MIPAS full resolution period (2002–2004) these are V5H_CH4_21 and V5H_N2O_21 and for the reduced resolution period (2005–2012) these are V5R_CH4_224, V5R_CH4_225, V5R_N2O_224 and V5R_N2O_225. Here, we compare CH4 profiles to those measured by the Fourier Transform Spectrometer on board of the Atmospheric Chemistry Experiment (ACE-FTS), the HALogen Occultation Experiment (HALOE) and the Scanning Imaging Absorption Spectrometer for Atmospheric CHartographY (SCIAMACHY) and to the Global Cooperative Air Sampling Network (GCASN) surface data. We find the MIPAS CH4 profiles below 25 km to be typically higher in the order of 0.1 ppmv for both measurement periods. N2O profiles are compared to those measured by ACE-FTS, the Microwave Limb Sounder on board of the Aura satellite (Aura-MLS) and the Sub-millimetre Radiometer on board of the Odin satellite (Odin-SMR) as well as to the Halocarbons and other Atmospheric Trace Species Group (HATS) surface data. The mixing ratios from the satellite instruments agree well for the full resolution period. For the reduced resolution period, MIPAS produces similar values as Odin-SMR, but higher values than ACE-FTS and HATS. Below 27 km, the MIPAS profiles show higher mixing ratios than Aura-MLS, and lower values between 27 and 41 km. Cross comparisons between the two MIPAS measurement periods show that they generally agree quite well, but, especially for CH4, the reduced resolution period seems to produce slightly higher mixing ratios than the full resolution data

MIPAS reduced spectral resolution UTLS-1 mode measurements of temperature, O₃, HNO₃, N₂O, H₂O and relative humidity over ice: retrievals and comparison to MLS

During several periods since 2005 the Michelson Interferometer for Passive Atmospheric Sounding (MIPAS) on Envisat has performed observations dedicated to the region of the upper troposphere/lower stratosphere (UTLS). For the duration of November/December 2005 global distributions of temperature and several trace gases from MIPAS UTLS-1 mode measurements have been retrieved using the IMK/IAA (Institut für Meteorologie und Klimaforschung/ Instituto de Astrof´ısica de Andalucía) scientific processor. In the UTLS region a vertical resolution of 3 km for temperaure, 3 to 4 km for H2O, 2.5 to 3 km for O3, 3.5 km for HNO3 and 3.5 to 2.5 km for N2O has been achieved. The retrieved temperature, H2O, O3, HNO3, N2O, and relative humidity over ice are intercompared with the Microwave Limb Sounder (MLS/Aura) v2.2 data in the pressure range 316 to 0.68 hPa, 316 to 0.68 hPa, 215 to 0.68 hPa, 215 to 3.16 hPa, 100 to 1 hPa and 316 to 10 hPa, respectively. In general, MIPAS and MLS temperatures are biased within ±4K over the whole pressure and latitude range. Systematic, latitude-independent differences of −2 to −4K (MIPASMLS) at 121 hPa are explained by previously observed biases in the MLS v2.2 temperature retrievals. Temperature differences of −4K up to 12K above 10.0 hPa are present both in MIPAS and MLS with respect to ECMWF (European Centre for Medium-Range Weather Forecasts) and are likely due to deficiencies of the ECMWF analysis data. MIPAS and MLS stratospheric volume mixing ratios (vmr) of H2O are biased within ±1 ppmv, with indication of oscillations between 146 and 26 hPa in the MLS dataset. Tropical upper tropospheric values of relative humidity over ice measured by the two instruments differ by ±20% in the pressure range ~146 to 68 hPa. These differences are mainly caused by the MLS temperature biases. Ozone mixing ratios agree within 0.5 ppmv (10 to 20%) between 68 and 14 hPa. At pressures smaller than 10 hPa, MIPAS O3 vmr are higher than MLS by an average of 0.5 ppmv (10%). General agreement between MIPAS and MLS HNO3 is within the range of −1.0 (−10%) to 1.0 ppbv (20%). MIPAS HNO3 is 1.0 ppbv (10%) higher compared to MLS between 46 hPa and 10 hPa over the Northern Hemisphere. Over the tropics at 31.6 hPaMLS shows a low bias of more than 1 ppbv (>50%). In general, MIPAS and MLS N2Ovmr agree within 20 to 40 ppbv (20 to 40%). Differences in the range between 100 to 21 hPa are attributed to a known 20% positive bias in MIPAS N2O data

MIPAS IMK/IAA CFC-11 (CCl₃F) and CFC-12 (CCl₂F₂) measurements: accuracy, precision and long-term stability

Profiles of CFC-11 (CCl3F) and CFC-12 (CCl2F2) of the Michelson Interferometer for Passive Atmospheric Sounding (MIPAS) abord the European satellite Envisat have been retrieved from versions MIPAS/4.61–MIPAS/4.62 and MIPAS/5.02–MIPAS/5.06 level-1b data using the scientific level-2 processor run by Karlsruhe Institute of Technology (KIT), Institute of Meteorology and Climate Research (IMK) and Consejo Superior de Investigaciones Científicas (CSIC), Instituto de Astrofísica de Andalucía (IAA). These profiles have been compared to measurements taken by the balloon borne Cryosampler, Mark IV (MkIV) and MIPAS-Balloon (MIPAS-B), the airborne MIPAS stratospheric aircraft (MIPAS-STR), the satellite borne Atmospheric Chemistry Experiment Fourier transform spectrometer (ACE-FTS) and the High Resolution Dynamic Limb Sounder (HIRDLS) as well as the ground based Halocarbon and other Atmospheric Trace Species (HATS) network for the reduced spectral resolution period (RR: January 2005–April 2012) of MIPAS Envisat. ACE-FTS, MkIV and HATS also provide measurements during the high spectral resolution period (FR: July 2002–March 2004) and were used to validate MIPAS Envisat CFC-11 and CFC-12 products during that time, as well as ILAS-II profiles. In general, we find that MIPAS Envisat shows slightly higher values for CFC-11 at the lower end of the profiles (below ~ 15 km) and in a comparison of HATS ground-based data and MIPAS Envisat measurements at 3 km below the tropopause. Differences range from approximately 10–50 pptv (~ 5–20 %) during the RR period. In general, differences are slightly smaller for the FR period. An indication of a slight high-bias at the lower end of the profile exists for CFC-12 as well, but this bias is far less pronounced than for CFC-11, so that differences at the lower end of the profile (below ~ 15 km) and in the comparison of HATS and MIPAS Envisat measurements taken at 3 km below the tropopause mainly stay within 10–50 pptv (~ 2–10 %) for the RR and the FR period. Above approximately 15 km, most comparisons are close to excellent, apart from ILAS-II, which shows large differences above ~ 17 km. Overall, percentage differences are usually smaller for CFC-12 than for CFC-11. For both species – CFC-11 and CFC-12 – we find that differences at the lower end of the profile tend to be larger at higher latitudes than in tropical and subtropical regions. In addition, MIPAS Envisat profiles have a maximum in the mixing ratio around the tropopause, which is most obvious in tropical mean profiles. Estimated measurement noise alone can, in most cases, not explain the standard deviation of the differences. This is attributed to error components not considered in the error estimate and also to natural variability which always plays a role when the compared instruments do not measure exactly the same air mass. Investigations concerning the temporal stability show very small negative drifts in MIPAS Envisat CFC-11 measurements. These drifts vary between ~ 1–3 % decade−1. For CFC-12, the drifts are also negative and close to zero up to ~ 30 km. Above that altitude larger drifts of up to ~ 50 % decade−1 appear which are negative up to ~ 35 km and positive, but of a similar magnitude, above

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

Psychological determinants of whole-body endurance performance

Background: No literature reviews have systematically identified and evaluated research on the psychological determinants of endurance performance, and sport psychology performance-enhancement guidelines for endurance sports are not founded on a systematic appraisal of endurance-specific research. Objective: A systematic literature review was conducted to identify practical psychological interventions that improve endurance performance and to identify additional psychological factors that affect endurance performance. Additional objectives were to evaluate the research practices of included studies, to suggest theoretical and applied implications, and to guide future research. Methods: Electronic databases, forward-citation searches, and manual searches of reference lists were used to locate relevant studies. Peer-reviewed studies were included when they chose an experimental or quasi-experimental research design, a psychological manipulation, endurance performance as the dependent variable, and athletes or physically-active, healthy adults as participants. Results: Consistent support was found for using imagery, self-talk, and goal setting to improve endurance performance, but it is unclear whether learning multiple psychological skills is more beneficial than learning one psychological skill. The results also demonstrated that mental fatigue undermines endurance performance, and verbal encouragement and head-to-head competition can have a beneficial effect. Interventions that influenced perception of effort consistently affected endurance performance. Conclusions: Psychological skills training could benefit an endurance athlete. Researchers are encouraged to compare different practical psychological interventions, to examine the effects of these interventions for athletes in competition, and to include a placebo control condition or an alternative control treatment. Researchers are also encouraged to explore additional psychological factors that could have a negative effect on endurance performance. Future research should include psychological mediating variables and moderating variables. Implications for theoretical explanations of endurance performance and evidence-based practice are described