No Change in Perceptual or Chronotropic Outcome When Altering Preferred Step Frequency for a Short Duration

IIntroduction: Millions of individuals incorporate jogging into their physical activity routines as a leisurely pursuit and as a way to achieve positive health outcomes. People appear to choose jogging speed and the associated step frequency on pure, natural preference. Understandably, kinesthetics are important, but another important underlying factor is metabolic cost. The purpose of this work was to investigate if preferred step frequency (at a preferred jogging pace) also minimizes perceived effort (Borg Rating of Perceived Exertion, 6-20; RPE) and chronotropic stress (heart rate; HR) during a ten-minute activity bout when compared with step frequencies altered by 5%. Methods: Recreationally-trained male subjects underwent two testing visits. The first visit was used to establish RPE and HR responses during a 10-minute jogging activity at preferred speed and step frequency. On a subsequent visit, between two and four days later, with preferred speed maintained, subjects were guided by metronome to strike at either 95% or 105% of their preferred step frequency. The 10-minute runs were randomized, crossed-over, and separated by 20 minutes. RPE and HR were analyzed by repeated measures ANOVA. Results: Fourteen subjects (age: 21.1 ± 0.95; body mass index: 23.2 ± 2.5) enrolled. Preferred jogging speed (speed. 6.4 ± 1.0 miles per hour; 10.2 ± 1.6 kilometers per hour) and step frequency (steps. 161.2 ± 10.3 steps/minute) were determined at the first visit, along with RPE (11.3 ± 1.7) and HR (166.4 ± 12.7). At the second visit, preferred speed was maintained while the frequency of foot-strike was altered. Neither differences in RPE (p = 0.252; 11.3 ± 1.7, 11.6 ± 1.9, 11.8 ± 1.5) nor HR (p = 0.547; 166.4 ± 12.7, 164.7 ± 14.9, 165.2 ± 15.3) were different when comparing the preferred, 95%, and 105% step frequency trials, respectively. Although anecdotal, some subjects verbalized displeasure with the change in pace and most all appeared to markedly alter the initial foot strike phase of the gait to meet the directed foot strike tempo. Discussion: Our data must be interpreted cautiously. While altering step frequency by 5% for a short duration does not appear to alter an individual’s RPE or HR appreciably, the result during longer duration activity may not be the same. In addition, the implications for biomechanical loading and metabolic cost were not presently investigated

Identifying Replicable Subgroups in Neurodevelopmental Conditions Using Resting-State Functional Magnetic Resonance Imaging Data

IMPORTANCE Neurodevelopmental conditions, such as autism spectrum disorder (ASD), attention-deficit/hyperactivity disorder (ADHD), and obsessive-compulsive disorder (OCD), have highly heterogeneous and overlapping phenotypes and neurobiology. Data-driven approaches are beginning to identify homogeneous transdiagnostic subgroups of children; however, findings have yet to be replicated in independently collected data sets, a necessity for translation into clinical settings. OBJECTIVE To identify subgroups of children with and without neurodevelopmental conditions with shared functional brain characteristics using data from 2 large, independent data sets. DESIGN, SETTING, AND PARTICIPANTS This case-control study used data from the Province of Ontario Neurodevelopmental (POND) network (study recruitment began June 2012 and is ongoing; data were extracted April 2021) and the Healthy Brain Network (HBN; study recruitment began May 2015 and is ongoing; datawere extracted November 2020). POND and HBN data are collected from institutions across Ontario and New York, respectively. Participants who had diagnoses of ASD, ADHD, and OCD or were typically developing (TD); were aged between 5 and 19 years; and successfully completed the resting-state and anatomical neuroimaging protocol were included in the current study. MAIN OUTCOMES AND MEASURES The analyses consisted of a data-driven clustering procedure on measures derived from each participant's resting-state functional connectome, performed independently on each data set. Differences between each pair of leaves in the resulting clustering decision trees in the demographic and clinical characteristics were tested. RESULTS Overall, 551 children and adolescents were included from each data set. POND included 164 participants with ADHD; 217 with ASD; 60 with OCD; and 110 with TD (median [IQR] age, 11.87 [9.51-14.76] years; 393 [71.2%] male participants; 20 [3.6%] Black, 28 [5.1%] Latino, and 299 [54.2%] White participants) and HBN included 374 participants with ADHD; 66 with ASD; 11 with OCD; and 100 with TD (median [IQR] age, 11.50 [9.22-14.20] years; 390 [70.8%] male participants; 82 [14.9%] Black, 57 [10.3%] Hispanic, and 257 [46.6%] White participants). In both data sets, subgroups with similar biology that differed significantly in intelligence as well as hyperactivity and impulsivity problems were identified, yet these groups showed no consistent alignment with current diagnostic categories. For example, there was a significant difference in Strengths andWeaknesses ADHD Symptoms and Normal Behavior Hyperactivity/Impulsivity subscale (SWAN-HI) between 2 subgroups in the POND data (C and D), with subgroup D having increased hyperactivity and impulsivity traits compared with subgroup C (median [IQR], 2.50 [0.00-7.00] vs 1.00 [0.00-5.00]; U = 1.19 × 104; P = .01; η2 = 0.02). A significant difference in SWAN-HI scores between subgroups g and d in the HBN data was also observed (median [IQR], 1.00 [0.00-4.00] vs 0.00 [0.00-2.00]; corrected P = .02). There were no differences in the proportion of each diagnosis between the subgroups in either data set. CONCLUSIONS AND RELEVANCE The findings of this study suggest that homogeneity in the neurobiology of neurodevelopmental conditions transcends diagnostic boundaries and is instead associated with behavioral characteristics. This work takes an important step toward translating neurobiological subgroups into clinical settings by being the first to replicate our findings in independently collected data sets

Comparing the effectiveness of Henderson instructions and expert testimony: Which safeguard improves jurors’ evaluations of eyewitness evidence?

Objectives The New Jersey Supreme Court recently determined that jurors may not be able to effectively evaluate eyewitness evidence on their own. As a result, the Court proposed the use of judicial instructions to assist jurors (called Henderson instructions) and suggested the implementation of these instructions would reduce the need for expert testimony. We tested the efficacy of these instructions compared to alternative instructions and expert testimony. Methods We utilized a mock trial paradigm, randomly assigning 452 participants to 1 of 20 videotaped trial conditions that varied the quality of eyewitness evidence (both witnessing and identification conditions) and the type of safeguard presented during the mock trial. Results Jurors were sensitive to the quality of identification conditions on their own. Jurors were more likely to convict when identification conditions were good and less likely when identification conditions were poor. This relationship was mediated by eyewitness credibility ratings. Expert testimony resulted in skepticism by reducing the likelihood that jurors would convict regardless of the quality of witnessing and identification conditions. No variation of the instructions influenced verdicts. Conclusions While jurors were sensitive to the quality of identification conditions on their own, we observed no such effect for the quality of witnessing conditions, even with the aid of instructions and/or expert testimony. Both Henderson instructions and expert testimony may be insufficient for assisting jurors to effectively evaluate problematic witnessing conditions. Future research should examine the use of alternative safeguards

Retrotransposons Are the Major Contributors to the Expansion of the <i>Drosophila ananassae</i> Muller F Element.

The discordance between genome size and the complexity of eukaryotes can partly be attributed to differences in repeat density. The Muller F element (∼5.2 Mb) is the smallest chromosome in Drosophila melanogaster, but it is substantially larger (>18.7 Mb) in D. ananassae To identify the major contributors to the expansion of the F element and to assess their impact, we improved the genome sequence and annotated the genes in a 1.4-Mb region of the D. ananassae F element, and a 1.7-Mb region from the D element for comparison. We find that transposons (particularly LTR and LINE retrotransposons) are major contributors to this expansion (78.6%), while Wolbachia sequences integrated into the D. ananassae genome are minor contributors (0.02%). Both D. melanogaster and D. ananassae F-element genes exhibit distinct characteristics compared to D-element genes (e.g., larger coding spans, larger introns, more coding exons, and lower codon bias), but these differences are exaggerated in D. ananassae Compared to D. melanogaster, the codon bias observed in D. ananassae F-element genes can primarily be attributed to mutational biases instead of selection. The 5' ends of F-element genes in both species are enriched in dimethylation of lysine 4 on histone 3 (H3K4me2), while the coding spans are enriched in H3K9me2. Despite differences in repeat density and gene characteristics, D. ananassae F-element genes show a similar range of expression levels compared to genes in euchromatic domains. This study improves our understanding of how transposons can affect genome size and how genes can function within highly repetitive domains

Retrotransposons Are the Major Contributors to the Expansion of the Drosophila ananassae Muller F Element

The discordance between genome size and the complexity of eukaryotes can partly be attributed to differences in repeat density. The Muller F element (∼5.2 Mb) is the smallest chromosome in Drosophila melanogaster, but it is substantially larger (>18.7 Mb) in D. ananassae. To identify the major contributors to the expansion of the F element and to assess their impact, we improved the genome sequence and annotated the genes in a 1.4-Mb region of the D. ananassae F element, and a 1.7-Mb region from the D element for comparison. We find that transposons (particularly LTR and LINE retrotransposons) are major contributors to this expansion (78.6%), while Wolbachia sequences integrated into the D. ananassae genome are minor contributors (0.02%). Both D. melanogaster and D. ananassae F-element genes exhibit distinct characteristics compared to D-element genes (e.g., larger coding spans, larger introns, more coding exons, and lower codon bias), but these differences are exaggerated in D. ananassae. Compared to D. melanogaster, the codon bias observed in D. ananassae F-element genes can primarily be attributed to mutational biases instead of selection. The 5′ ends of F-element genes in both species are enriched in dimethylation of lysine 4 on histone 3 (H3K4me2), while the coding spans are enriched in H3K9me2. Despite differences in repeat density and gene characteristics, D. ananassae F-element genes show a similar range of expression levels compared to genes in euchromatic domains. This study improves our understanding of how transposons can affect genome size and how genes can function within highly repetitive domains