RNA-Seq of early follicle cells – EGFRact Rep2 Read2

Paired-end RNA-Sequencing data from early follicle cells Genotype 109-30-Gal4, UAS-mCD8::GFP, UAS-EGFR[lambda]top Replicate #2, Read

RNA-Seq of early follicle cells – EGFRact Rep1 Read1

Paired-end RNA-Sequencing data from early follicle cells Genotype 109-30-Gal4, UAS-mCD8::GFP, UAS-EGFR[lambda]top Replicate #1, Read

Data decomposition for code parallelization in practice: what do the experts need?

Parallelizing serial software systems in order to run in a High Performance Computing (HPC) environment presents many challenges to developers. In particular, the extant literature suggests the task of decomposing large-scale data applications is particularly complex and time-consuming. In order to take stock of the state of practice of data decomposition in HPC, we conducted a two-phased study. Firstly, using focus group methodology we conducted an exploratory study at a software laboratory with an established track record in HPC. Based on the findings of this first phase, we designed a survey to assess the state of practice among experts in this field around the world. Our study shows that approximately 75% of parallelized applications use some form of data decomposition. Furthermore, data decomposition was found to be the most challenging phase in the parallelization process, consuming approximately 40% of the total time. A key finding of our study is that experts do not use any of the available tools and formal representations, and in fact, are not aware of them. We discuss why existing tools have not been adopted in industry and based on our findings, provide a number of recommendations for future tool support

The muscle morphology of elite sprint running

The influence of muscle morphology and strength characteristics on sprint running performance, especially at elite level, is unclear. PURPOSE: This study aimed to investigate the differences in muscle volumes and strength between male elite sprinters, sub-elite sprinters, and untrained controls; and assess the relationships of muscle volumes and strength with sprint performance. METHODS: Five elite sprinters (100 m seasons best [SBE100]: 10.10 ± 0.07 s), 26 sub-elite sprinters (SBE100: 10.80 ± 0.30s) and 11 untrained control participants underwent: 3T magnetic resonance imaging scans to determine the volume of 23 individual lower limb muscles/compartments and 5 functional muscle groups; and isometric strength assessment of lower body muscle groups. RESULTS: Total lower body muscularity was distinct between the groups (controls < sub-elite +20% < elite +48%). The hip extensors exhibited the largest muscle group differences/relationships (elite, +32% absolute and +15% relative [per kg] volume vs sub-elite; explaining 31-48% of the variability in SBE100), whereas the plantarflexors showed no differences between sprint groups. Individual muscle differences showed pronounced anatomical specificity (elite vs sub-elite, absolute volume range +57% to -9%). Three hip muscles were consistently larger in elite vs. sub-elite (TFL, sartorius, gluteus maximus; absolute +45-57% and relative volume +25-37%), and gluteus maximus volume alone explained 34-44% of the variance in SBE100. Isometric strength of several muscle groups was greater in both sprint groups than controls, but similar for the sprint groups and not related to SBE100. CONCLUSIONS: These findings demonstrate the pronounced inhomogeneity and anatomically specific muscularity required for fast sprinting, and provides novel, robust evidence that greater hip extensor and gluteus maximus volumes discriminate between elite and sub-elite sprinters and are strongly associated with sprinting performance

The muscle morphology of elite female sprint running

Introduction: A paucity of research exists examining the importance of muscle morphological and functional characteristics for elite female sprint performance. Purpose: This study aimed to compare lower body muscle volumes and vertical jumping power between elite and subelite female sprinters and assess the relationships of these characteristics with sprint race and acceleration performance. Methods: Five elite (100 m seasons best [SBE100], 11.16 ± 0.06 s) and 17 subelite (SBE100, 11.84 ± 0.42 s) female sprinters underwent: 3T magnetic resonance imaging to determine the volume of 23 individual leg muscles/compartments and five functional muscle groups; countermovement jump and 30 m acceleration tests. Results: Total absolute lower body muscle volume was higher in elite versus subelite sprinters (+15%). Elite females exhibited greater muscle volume of the hip flexors (absolute, +28%; relative [to body mass], +19%), hip extensors (absolute, +22%; relative, +14%), and knee extensors (absolute, +21%), demonstrating pronounced anatomically specific muscularity, with relative hip flexor volume alone explaining 48% of sprint performance variability. The relative volume of five individual muscles (sartorius, gluteus maximus, adductor magnus, vastus lateralis, illiopsoas) were both distinct between groups (elite > subelite) and related to SBE100 (r = 0.553-0.639), with the combination of the sartorius (41%) and the adductor magnus (17%) explaining 58% of the variance in SBE100. Elite female sprinters demonstrated greater absolute countermovement jump power versus subelite, and absolute and relative power were related to both SBE100 (r = -0.520 to -0.741) and acceleration performance (r = 0.569 to 0.808). Conclusions: This investigation illustrates the distinctive, anatomically specific muscle volume distribution that facilitates elite sprint running in females, and emphasizes the importance of hip flexor and extensor relative muscle volume.</p

Interview duration and median number of turns per survey question.

<p>These timelines display the median duration of question-answer sequences with the median number of turns after each question.</p

Rounding effects for each question.

<p>Rounding effects for each question.</p

Participation, response rates and break-off rates.

<p>^aThe response rate (known as AAPOR RR1 [<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0128337#pone.0128337.ref029" target="_blank">29</a>]) is calculated as the number of complete interviews divided by the number of invitations.</p><p>^bThe break-off rate is calculated as the number of people who dropped off during the survey divided by the number of people who started.</p><p>Participation, response rates and break-off rates.</p

Multitasking across the four modes.

<p>Multitasking across the four modes.</p

Procedures.

<p>Procedures.</p