The Effect of Short and Long Recovery Periods on the Contribution of Oxidative Processes to Energy Expenditure During Multiple Bouts of Supramaximal Exercise

The Effect of Short and Long Recovery Periods on the Contribution of Oxidative Processes to Energy Expenditure During Multiple Bouts of Supramaximal Exercise Olson, E. (undergraduate), Christensen, K., Jajtner, A., Copeland, J., Unthank, M., and Mitchell, J. Exercise Physiology Lab, Texas Christian University, Ft. Worth, TX. The contribution of oxidative energy production to multiple sprint exercises is of interest due to implications for the training needs of people engaging in anaerobic activities. PURPOSE: The purpose of this study was to examine the effect of short and long active recovery durations on oxidative and anaerobic contributions to energy output during maximal intensity cycle ergometry. METHODS: Six male subjects, including well-trained endurance athletes and well-trained strength athletes, completed the study. After a VO2max test on the bicycle ergometer, each subject completed two conditions: a short recovery condition (SRC) and a long recovery condition (LRC). The SRC consisted of 10, 10-sec. supramaximal sprints with 30-sec. recovery periods. The LRC consisted of 10, 10-sec supramaximal sprints with 3-min. recovery periods. The load applied to the ergometer was 1.2 g/kg and the RPM during the sprints varied based on the maximal output. During recovery, no load was applied and subjects maintained a cadence of 80 RPM. Oxygen uptake was measured during the entirety of both conditions and peak power and total work were calculated from two, 5-sec RPM averages generated during the sprints. Blood samples were taken pre-exercise, after sprints 4, 7, and 10, and 3 minutes post-exercise. RESULTS: Peak power and total work were significantly greater (p \u3c 0.05) in the LRC (1091.3 + 88.7 W and 1363.6 + 34.6 kg-m) compared to the SRC (915.3 + 109.2 W and 1161.6 + 33.9 kg-m). In addition, peak power decayed by 21.7% over the 10 sprints in the SRC compared to no decay in the LRC. Oxygen uptake averaged 28.3 + 0.9 ml/kg/min for the entirety of the LRC; whereas, in the SRC there was a large increase in oxygen uptake during the second sprint that remained elevated and averaged 47 + 1.5 ml/kg/min for the remaining 8 sprints. There was no difference in blood lactate concentrations between conditions. CONCLUSION: The heightened aerobic response and the lower work and power outputs seen in the SRC are suggestive of a decrement in both anaerobic glycolysis and phosphocreatine (PCr) activity as successive sprints were completed. After repeated bouts of explosive exercise with short rest periods, oxidative processes play a more important role in energy production, most likely due to fatigue occurring in the anaerobic energy producing systems. These findings point to the need for enhancing the aerobic capacity of athletes engaging in consecutive high intensity bouts of exercise when rest intervals are short

Genetic characterization of clinical and agri-food isolates of multi drug resistant Salmonella enterica serovar Heidelberg from Canada

<p>Abstract</p> <p>Background</p> <p><it>Salmonella enterica </it>serovar Heidelberg ranks amongst the most prevalent causes of human salmonellosis in Canada and an increase in resistance to extended spectrum cephalosporins (ESC) has been observed by the Canadian Integrated Program for Antimicrobial Resistance Surveillance. This study examined the genetic relationship between <it>S</it>. Heidelberg isolates from livestock, abattoir, retail meat, and clinical human specimens to determine whether there was a link between the emergence of MDR <it>S</it>. Heidelberg in chicken agri-food sources and the simultaneous increase of MDR <it>S</it>. Heidelberg in human clinical samples.</p> <p>Results</p> <p>Chromosomal genetic homogeneity was observed by pulsed-field gel electrophoresis (PFGE), DNA sequence-based typing (SBT) and DNA microarray-based comparative genomic hybridization (CGH). Sixty one percent of isolates were indistinguishable by PFGE conducted using <it>Xba</it>I and <it>Bln</it>I restriction enzymes. An additional 15% of isolates had PFGE patterns that were closely related to the main cluster. SBT did not identify DNA polymorphisms and CGH revealed only genetic differences between the reference <it>S</it>. Typhimurium strain and <it>S</it>. Heidelberg isolates. Genetic variation observed by CGH between <it>S</it>. Heidelberg isolates could be attributed to experimental variation. Alternatively, plasmid content was responsible for differences in antimicrobial susceptibility, and restriction fragment length polymorphism (RFLP) analyses followed by replicon typing identified two divergent plasmid types responsible for ESC resistance.</p> <p>Conclusion</p> <p>Due to the overall limited genetic diversity among the isolates, it was not possible to identify variable traits that would be suitable for source tracking between human and agri-food isolates of <it>S</it>. Heidelberg in Canada.</p

Limited genetic diversity in Salmonella enterica Serovar Enteritidis PT13

<p>Abstract</p> <p>Background</p> <p><it>Salmonella enterica </it>serovar Enteritidis has emerged as a significant foodborne pathogen throughout the world and is commonly characterized by phage typing. In Canada phage types (PT) 4, 8 and 13 predominate and in 2005 a large foodborne PT13 outbreak occurred in the province of Ontario. The ability to link strains during this outbreak was difficult due to the apparent clonality of PT13 isolates in Canada, as there was a single dominant pulsed-field gel electrophoresis (PFGE) profile amongst epidemiologically linked human and food isolates as well as concurrent sporadic strains. The aim of this study was to perform comparative genomic hybridization (CGH), DNA sequence-based typing (SBT) genomic analyses, plasmid analyses, and automated repetitive sequence-based PCR (rep-PCR) to identify epidemiologically significant traits capable of subtyping <it>S</it>. Enteritidis PT13.</p> <p>Results</p> <p>CGH using an oligonucleotide array based upon chromosomal coding sequences of <it>S. enterica </it>serovar Typhimurium strain LT2 and the <it>Salmonella </it>genomic island 1 successfully determined major genetic differences between <it>S</it>. Typhimurium and <it>S</it>. Enteritidis PT13, but no significant strain-to-strain differences were observed between <it>S</it>. Enteritidis PT13 isolates. Individual loci (<it>safA </it>and <it>fliC</it>) that were identified as potentially divergent in the CGH data set were sequenced in a panel of <it>S</it>. Enteritidis strains, and no differences were detected between the PT13 strains. Additional sequence-based typing was performed at the <it>fimA</it>, <it>mdh</it>, <it>manB</it>, <it>cyaA</it>, <it>citT</it>, <it>caiC</it>, <it>dmsA</it>, <it>ratA </it>and STM0660 loci. Similarly, no diversity was observed amongst PT13 strains. Variation in plasmid content between PT13 strains was observed, but macrorestriction with B<it>gl</it>II did not identify further differences. Automated rep-PCR patterns were variable between serovars, but <it>S</it>. Enteritidis PT13 strains could not be differentiated.</p> <p>Conclusion</p> <p>None of the methods identified any significant variation between PT13 strains. Greater than 11,300 base pairs of sequence for each of seven <it>S</it>. Enteritidis PT13 strains were analyzed without detecting a single polymorphic site, although diversity between different phage types of <it>S</it>. Enteritidis was observed. These data suggest that Canadian <it>S</it>. Enteritidis PT13 strains are highly related genetically.</p

A New Algorithm for Supernova Neutrino Transport and Some Applications

We have developed an implicit, multi-group, time-dependent, spherical neutrino transport code based on the Feautrier variables, the tangent-ray method, and accelerated ${\bf \Lambda}$ iteration. The code achieves high angular resolution, is good to O($v/c$), is equivalent to a Boltzmann solver (without gravitational redshifts), and solves the transport equation at all optical depths with precision. In this paper, we present our formulation of the relevant numerics and microphysics and explore protoneutron star atmospheres for snapshot post-bounce models. Our major focus is on spectra, neutrino-matter heating rates, Eddington factors, angular distributions, and phase-space occupancies. In addition, we investigate the influence on neutrino spectra and heating of final-state electron blocking, stimulated absorption, velocity terms in the transport equation, neutrino-nucleon scattering asymmetry, and weak magnetism and recoil effects. Furthermore, we compare the emergent spectra and heating rates obtained using full transport with those obtained using representative flux-limited transport formulations to gauge their accuracy and viability. Finally, we derive useful formulae for the neutrino source strength due to nucleon-nucleon bremsstrahlung and determine bremsstrahlung's influence on the emergent $\nu_{\mu}$ and $\nu_{\tau}$ neutrino spectra.Comment: 58 pages, single-spaced LaTeX, 23 figures, revised title, also available at http://jupiter.as.arizona.edu/~burrows/papers, accepted for publication in the Ap.

Calibration of myocardial T2 and T1 against iron concentration.

BACKGROUND: The assessment of myocardial iron using T2* cardiovascular magnetic resonance (CMR) has been validated and calibrated, and is in clinical use. However, there is very limited data assessing the relaxation parameters T1 and T2 for measurement of human myocardial iron. METHODS: Twelve hearts were examined from transfusion-dependent patients: 11 with end-stage heart failure, either following death (n=7) or cardiac transplantation (n=4), and 1 heart from a patient who died from a stroke with no cardiac iron loading. Ex-vivo R1 and R2 measurements (R1=1/T1 and R2=1/T2) at 1.5 Tesla were compared with myocardial iron concentration measured using inductively coupled plasma atomic emission spectroscopy. RESULTS: From a single myocardial slice in formalin which was repeatedly examined, a modest decrease in T2 was observed with time, from mean (± SD) 23.7 ± 0.93 ms at baseline (13 days after death and formalin fixation) to 18.5 ± 1.41 ms at day 566 (p<0.001). Raw T2 values were therefore adjusted to correct for this fall over time. Myocardial R2 was correlated with iron concentration [Fe] (R2 0.566, p<0.001), but the correlation was stronger between LnR2 and Ln[Fe] (R2 0.790, p<0.001). The relation was [Fe] = 5081•(T2)-2.22 between T2 (ms) and myocardial iron (mg/g dry weight). Analysis of T1 proved challenging with a dichotomous distribution of T1, with very short T1 (mean 72.3 ± 25.8 ms) that was independent of iron concentration in all hearts stored in formalin for greater than 12 months. In the remaining hearts stored for <10 weeks prior to scanning, LnR1 and iron concentration were correlated but with marked scatter (R2 0.517, p<0.001). A linear relationship was present between T1 and T2 in the hearts stored for a short period (R2 0.657, p<0.001). CONCLUSION: Myocardial T2 correlates well with myocardial iron concentration, which raises the possibility that T2 may provide additive information to T2* for patients with myocardial siderosis. However, ex-vivo T1 measurements are less reliable due to the severe chemical effects of formalin on T1 shortening, and therefore T1 calibration may only be practical from in-vivo human studies

Another exact inflationary solution

A new closed-form inflationary solution is given for a hyperbolic interaction potential. The method used to arrive at this solution is outlined as it appears possible to generate additional sets of equations which satisfy the model. In addition a new form of decaying cosmological constant is presented.Comment: 10 pages, 0 figure

Science and Ideology in Economic, Political, and Social Thought

This paper has two sources: One is my own research in three broad areas: business cycles, economic measurement and social choice. In all of these fields I attempted to apply the basic precepts of the scientific method as it is understood in the natural sciences. I found that my effort at using natural science methods in economics was met with little understanding and often considerable hostility. I found economics to be driven less by common sense and empirical evidence, then by various ideologies that exhibited either a political or a methodological bias, or both. This brings me to the second source: Several books have appeared recently that describe in historical terms the ideological forces that have shaped either the direct areas in which I worked, or a broader background. These books taught me that the ideological forces in the social sciences are even stronger than I imagined on the basis of my own experiences. The scientific method is the antipode to ideology. I feel that the scientific work that I have done on specific, long standing and fundamental problems in economics and political science have given me additional insights into the destructive role of ideology beyond the history of thought orientation of the works I will be discussing

Reframing gene essentiality in terms of adaptive flexibility

Abstract Background Essentiality assays are important tools commonly utilized for the discovery of gene functions. Growth/no growth screens of single gene knockout strain collections are also often utilized to test the predictive power of genome-scale models. False positive predictions occur when computational analysis predicts a gene to be non-essential, however experimental screens deem the gene to be essential. One explanation for this inconsistency is that the model contains the wrong information, possibly an incorrectly annotated alternative pathway or isozyme reaction. Inconsistencies could also be attributed to experimental limitations, such as growth tests with arbitrary time cut-offs. The focus of this study was to resolve such inconsistencies to better understand isozyme activities and gene essentiality. Results In this study, we explored the definition of conditional essentiality from a phenotypic and genomic perspective. Gene-deletion strains associated with false positive predictions of gene essentiality on defined minimal medium for Escherichia coli were targeted for extended growth tests followed by population sequencing and transcriptome analysis. Of the twenty false positive strains available and confirmed from the Keio single gene knock-out collection, 11 strains were shown to grow with longer incubation periods making these actual true positives. These strains grew reproducibly with a diverse range of growth phenotypes. The lag phase observed for these strains ranged from less than one day to more than 7 days. It was found that 9 out of 11 of the false positive strains that grew acquired mutations in at least one replicate experiment and the types of mutations ranged from SNPs and small indels associated with regulatory or metabolic elements to large regions of genome duplication. Comparison of the detected adaptive mutations, modeling predictions of alternate pathways and isozymes, and transcriptome analysis of KO strains suggested agreement for the observed growth phenotype for 6 out of the 9 cases where mutations were observed. Conclusions Longer-term growth experiments followed by whole genome sequencing and transcriptome analysis can provide a better understanding of conditional gene essentiality and mechanisms of adaptation to such perturbations. Compensatory mutations are largely reproducible mechanisms and are in agreement with genome-scale modeling predictions to loss of function gene deletion events

Sequence-based typing of genetic targets encoded outside of the O-antigen gene cluster is indicative of Shiga toxin-producing Escherichia coli serogroup lineages

Serogroup classifications based upon the O-somatic antigen of Shiga toxin-producing Escherichia coli (STEC) provide significant epidemiological information on clinical isolates. Each O-antigen determinant is encoded by a unique cluster of genes present between the gnd and galF chromosomal genes. Alternatively, serogroup-specific polymorphisms might be encoded in loci that are encoded outside of the O-antigen gene cluster. Segments of the core bacterial loci mdh, gnd, gcl, ppk, metA, ftsZ, relA and metG for 30 O26 STEC strains have previously been sequenced, and comparative analyses to O157 distinguished these two serogroups. To screen these loci for serogroup-specific traits within a broader range of clinically significant serogroups, DNA sequences were obtained for 19 strains of 10 additional STEC serogroups. Unique alleles were observed at the gnd locus for each examined STEC serogroup, and this correlation persisted when comparative analyses were extended to 144 gnd sequences from 26 O-serogroups (comprising 42 O : H-serotypes). These included O157, O121, O103, O26, O5 : non-motile (NM), O145 : NM, O113 : H21, O111 : NM and O117 : H7 STEC; and furthermore, non-toxin encoding O157, O26, O55, O6 and O117 strains encoded distinct gnd alleles compared to STEC strains of the same serogroup. DNA sequencing of a 643 bp region of gnd was, therefore, sufficient to minimally determine the O-antigen of STEC through molecular means, and the location of gnd next to the O-antigen gene cluster offered additional support for the co-inheritance of these determinants. The gnd DNA sequence-based serogrouping method could improve the typing capabilities for STEC in clinical laboratories, and was used successfully to characterize O121 : H19, O26 : H11 and O177 : NM clinical isolates prior to serological confirmation during outbreak investigations

Canvass: a crowd-sourced, natural-product screening library for exploring biological space

NCATS thanks Dingyin Tao for assistance with compound characterization. This research was supported by the Intramural Research Program of the National Center for Advancing Translational Sciences, National Institutes of Health (NIH). R.B.A. acknowledges support from NSF (CHE-1665145) and NIH (GM126221). M.K.B. acknowledges support from NIH (5R01GM110131). N.Z.B. thanks support from NIGMS, NIH (R01GM114061). J.K.C. acknowledges support from NSF (CHE-1665331). J.C. acknowledges support from the Fogarty International Center, NIH (TW009872). P.A.C. acknowledges support from the National Cancer Institute (NCI), NIH (R01 CA158275), and the NIH/National Institute of Aging (P01 AG012411). N.K.G. acknowledges support from NSF (CHE-1464898). B.C.G. thanks the support of NSF (RUI: 213569), the Camille and Henry Dreyfus Foundation, and the Arnold and Mabel Beckman Foundation. C.C.H. thanks the start-up funds from the Scripps Institution of Oceanography for support. J.N.J. acknowledges support from NIH (GM 063557, GM 084333). A.D.K. thanks the support from NCI, NIH (P01CA125066). D.G.I.K. acknowledges support from the National Center for Complementary and Integrative Health (1 R01 AT008088) and the Fogarty International Center, NIH (U01 TW00313), and gratefully acknowledges courtesies extended by the Government of Madagascar (Ministere des Eaux et Forets). O.K. thanks NIH (R01GM071779) for financial support. T.J.M. acknowledges support from NIH (GM116952). S.M. acknowledges support from NIH (DA045884-01, DA046487-01, AA026949-01), the Office of the Assistant Secretary of Defense for Health Affairs through the Peer Reviewed Medical Research Program (W81XWH-17-1-0256), and NCI, NIH, through a Cancer Center Support Grant (P30 CA008748). K.N.M. thanks the California Department of Food and Agriculture Pierce's Disease and Glassy Winged Sharpshooter Board for support. B.T.M. thanks Michael Mullowney for his contribution in the isolation, elucidation, and submission of the compounds in this work. P.N. acknowledges support from NIH (R01 GM111476). L.E.O. acknowledges support from NIH (R01-HL25854, R01-GM30859, R0-1-NS-12389). L.E.B., J.K.S., and J.A.P. thank the NIH (R35 GM-118173, R24 GM-111625) for research support. F.R. thanks the American Lebanese Syrian Associated Charities (ALSAC) for financial support. I.S. thanks the University of Oklahoma Startup funds for support. J.T.S. acknowledges support from ACS PRF (53767-ND1) and NSF (CHE-1414298), and thanks Drs. Kellan N. Lamb and Michael J. Di Maso for their synthetic contribution. B.S. acknowledges support from NIH (CA78747, CA106150, GM114353, GM115575). W.S. acknowledges support from NIGMS, NIH (R15GM116032, P30 GM103450), and thanks the University of Arkansas for startup funds and the Arkansas Biosciences Institute (ABI) for seed money. C.R.J.S. acknowledges support from NIH (R01GM121656). D.S.T. thanks the support of NIH (T32 CA062948-Gudas) and PhRMA Foundation to A.L.V., NIH (P41 GM076267) to D.S.T., and CCSG NIH (P30 CA008748) to C.B. Thompson. R.E.T. acknowledges support from NIGMS, NIH (GM129465). R.J.T. thanks the American Cancer Society (RSG-12-253-01-CDD) and NSF (CHE1361173) for support. D.A.V. thanks the Camille and Henry Dreyfus Foundation, the National Science Foundation (CHE-0353662, CHE-1005253, and CHE-1725142), the Beckman Foundation, the Sherman Fairchild Foundation, the John Stauffer Charitable Trust, and the Christian Scholars Foundation for support. J.W. acknowledges support from the American Cancer Society through the Research Scholar Grant (RSG-13-011-01-CDD). W.M.W.acknowledges support from NIGMS, NIH (GM119426), and NSF (CHE1755698). A.Z. acknowledges support from NSF (CHE-1463819). (Intramural Research Program of the National Center for Advancing Translational Sciences, National Institutes of Health (NIH); CHE-1665145 - NSF; CHE-1665331 - NSF; CHE-1464898 - NSF; RUI: 213569 - NSF; CHE-1414298 - NSF; CHE1361173 - NSF; CHE1755698 - NSF; CHE-1463819 - NSF; GM126221 - NIH; 5R01GM110131 - NIH; GM 063557 - NIH; GM 084333 - NIH; R01GM071779 - NIH; GM116952 - NIH; DA045884-01 - NIH; DA046487-01 - NIH; AA026949-01 - NIH; R01 GM111476 - NIH; R01-HL25854 - NIH; R01-GM30859 - NIH; R0-1-NS-12389 - NIH; R35 GM-118173 - NIH; R24 GM-111625 - NIH; CA78747 - NIH; CA106150 - NIH; GM114353 - NIH; GM115575 - NIH; R01GM121656 - NIH; T32 CA062948-Gudas - NIH; P41 GM076267 - NIH; R01GM114061 - NIGMS, NIH; R15GM116032 - NIGMS, NIH; P30 GM103450 - NIGMS, NIH; GM129465 - NIGMS, NIH; GM119426 - NIGMS, NIH; TW009872 - Fogarty International Center, NIH; U01 TW00313 - Fogarty International Center, NIH; R01 CA158275 - National Cancer Institute (NCI), NIH; P01 AG012411 - NIH/National Institute of Aging; Camille and Henry Dreyfus Foundation; Arnold and Mabel Beckman Foundation; Scripps Institution of Oceanography; P01CA125066 - NCI, NIH; 1 R01 AT008088 - National Center for Complementary and Integrative Health; W81XWH-17-1-0256 - Office of the Assistant Secretary of Defense for Health Affairs through the Peer Reviewed Medical Research Program; P30 CA008748 - NCI, NIH, through a Cancer Center Support Grant; California Department of Food and Agriculture Pierce's Disease and Glassy Winged Sharpshooter Board; American Lebanese Syrian Associated Charities (ALSAC); University of Oklahoma Startup funds; 53767-ND1 - ACS PRF; PhRMA Foundation; P30 CA008748 - CCSG NIH; RSG-12-253-01-CDD - American Cancer Society; RSG-13-011-01-CDD - American Cancer Society; CHE-0353662 - National Science Foundation; CHE-1005253 - National Science Foundation; CHE-1725142 - National Science Foundation; Beckman Foundation; Sherman Fairchild Foundation; John Stauffer Charitable Trust; Christian Scholars Foundation)Published versionSupporting documentatio