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Nutritionally Relevant Technological Advancements in Professional Cycling
Get PDFTechnological innovations can provide cyclists and their support team additional data. These data have potential to improve understanding of performance determinants and could be used to identify and tailor nutritional strategies to improve cycling performance. This potential, however, is dependent on the quality, interpretation and practical use of the data generated. In this review, several technologies which are used, or have some potential for use, in professional cycling are discussed. These include power meters, continuous glucose monitors, portable sweat and lactate analyzers, non-invasive estimation of muscle fiber typology, ultrasound for muscle glycogen concentrations and subcutaneous fat quantification, non-invasive core body temperature sensors and portable substrate metabolism analyzers. The evidence regarding the validity of these technologies is critically evaluated, alongside a discussion of the potential rationale (or lack thereof) for their use in guiding nutritional strategies. Some of these technologies have sufficient validity and reliability to provide data of sufficient quality, and combined with appropriate rationale, can inform some nutritional strategies (e.g., energy expenditure from power meters). In contrast, other technologies either have insufficient rationale to inform a nutritional strategy or currently lack the validity and/or reliability to provide data of sufficient quality to inform nutritional strategies. Practitioners working with athletes are recommended to consider whether there is any practical value in each metric, and if so, then consider the validity and reliability of a method to measure such a metric before implementation
AI and Inverse Methods for Building Digital Twins in Neuroscience
Get PDFDeep learning is revolutionizing Neuroscience and Healthcare. One driver for this change is the exponential growth in the volume of biomedical data generated by modern medical imaging technology and automated data acquisition systems, such as automated patch clamps. Another driver is the increasing reliance of clinical diagnosis on deep learning algorithms to detect abnormalities in MRI scans. By automating the analysis of MRI images, algorithms free clinical staff from repetitive time and consuming tasks while removing subjectivity in the diagnosis and classification of brain tumors. Deep learning algorithms routinely assist neurosurgeons during critical operations, for example by stimulating surrounding brain tissue during tumor resection. Deep learning algorithms are also increasingly capable of forecasting epileptic seizures and assisting researchers in understanding how language is coded in the brain. Digital twins of brain activity trained on electroencephalographic time series have predicted epileptic seizures and connectivity changes in the brain during language comprehension. Machine learning is also progressing neuroscience by modelling biocircuits at the single neuron level. Recursive neural networks trained on electrophysiological data are making accurate predictions of the voltage oscillations of central pattern generators. The dynamics of individual ionic currents is also inferred to a good degree of accuracy when additional information in the form of surrogate model is provided. This importantly suggests that the dynamics of the membrane voltage which is observed and the ionic current waveforms which cannot be directly measured may be reconstructed from the analysis of electrophysiological time series
Even redder than we knew: Color and AV evolution up to z = 2.5 from JWST/NIRCam photometry
Get PDFAims. JWST/NIRCam provides rest-frame near-IR photometry of galaxies up to z=-2.5 with exquisite depth and accuracy. This affords us an unprecedented view of the evolution of the UV/optical/near-IR color distribution and its interpretation in terms of the evolving dust attenuation, A V. Methods. We used the value-added data products (photometric redshift, stellar mass, rest-frame U-V and V-J colors, and A V) provided by the public DAWN JWST Archive. These data products derive from fitting the spectral energy distributions obtained from multiple NIRCam imaging surveys, augmented with preexisting HST imaging data. Our sample consists of a stellar-mass-complete sample of ≈28 000M ∗ > -10 9 M ⊙ galaxies in the redshift range 0.5 < z < -2.5. Results. The V-J color distribution of star-forming galaxies evolves strongly, in particular for high-mass galaxies (M ∗ > 3 × -10 10 M ⊙), which have a pronounced tail of very red galaxies reaching V-J > -2.5 at z > -1.5 that does not exist at z < -1. Such red V-J can only be explained by dust attenuation, with typical values for M ∗ ≈ 10 11 M ⊙ galaxies in the range A V ≈ 1.5-3.5 at z ≈ 2. This redshift evolution went largely unnoticed before. Today, however, photometric redshift estimates for the reddest (V-J > -2.5), most attenuated galaxies have markedly improved thanks to the new, precise photometry, which is in much better agreement with the 25 available spectroscopic redshifts for such galaxies. The reddest population readily stands out as the independently identified population of galaxies detected at submillimeter wavelengths. Despite the increased attenuation, U-V colors across the entire mass range are slightly bluer at higher z. A well-defined and tight color sequence exists at redshifts 0.5 < z < 2.5 for M ∗ > 3 × 10 10 M ⊙ quiescent galaxies, in both U-V and V-J, but in V-J it is bluer rather than redder compared to star-forming galaxies. In conclusion, whereas the rest-frame UV-optical color distribution evolves remarkably little from z = 0.5 to z=2.5, the rest-frame optical/near-IR color distribution evolves strongly, primarily due to a very substantial increase with redshift in dust attenuation for massive galaxies.</p
Dataset for "RetroSketch: A Retrospective Method for Measuring Emotions and Presence in Virtual Reality"
No full textThe aggregated data file containing 140 participants' data collected and analysed in the CHI 2025 paper "RetroSketch: A Retrospective Method for Measuring Emotions and Presence in Virtual Reality". Each participant completed two half-hour sessions and continuous measures for both sessions were aggregated in 60-second intervals, resulting in 31 rows per session and 62 rows per participant. The measures used in this study fall into three groups: - Pre-measures include demographic information, gaming experience and preferences, personality and gamer traits, baseline emotions and physiology. - Exposure measures include Retrospective method emotion and keypoint measures, experience sampling method (ESM) measures, and various physiological measures. - Post-measures include measures of flow state, intrinsic motivation, multimodal presence, and simulator sickness, and participants' qualitative evaluations of RetroSketch and ESM measures
Exploring the relationship between cultural capital and the use of school resources for parental involvement across countries:Evidence from PISA 2018
Get PDFThis study aims to explore the effect of cultural capital on the perception of parental involvement policies offered by schools in various countries. Using PISA (2018) data, a multilevel model is implemented for each participant country to adopt a comparative perspective. While the results suggest a complex effect, moderated by idiosyncratic country characteristics, the objectified component of cultural capital shows a positive trend pattern for most countries, with a higher effect size than institutionalised and embodied cultural capital, which show a somewhat more ambiguous pattern. Additionally, variables such as gender, immigrant status, and home language show a significant effect in some of the analysed countries, suggesting that they might act as moderators. The study discusses theoretical and practical implications, as well as future research
Dataset for "Analysing longitudinal wearable physical activity data using Non-stationary Time Series models"
No full textThis dataset contains secondary data from the Multidimensional Individualised Physical Activity (MIPACT) randomized controlled trial used for analysis in “Analysing longitudinal wearable physical activity data using Non-stationary Time Series models”. Physical activity data over the 12-week intervention for 80 participants (28 women) aged between 43 and 70 years old is presented in this dataset at hourly resolution
Harnessing virtues for educational success:introducing the Positive Development and Assessment Competencies Theory (PDAC)
Get PDFThis study investigates the role of positive psychology virtues in predicting educational competencies 5th and 6th-grade students. The evidence-based benefits of fostering virtues in educational settings, including academic success, emotional resilience, ethical behavior and social integration, are well documented. This research further emphasizes the contribution of virtues to the formation of well-rounded individuals, prepared for lifelong learning and personal growth. Using a quantitative, correlational, cross-sectional design, data were collected from 993 students through validated questionnaires. Random forest logistic regression analysis identified six as significant predictors of perceived competencies, with Transcendence standing out as a particularly strong and consistent predictor across multiple competencies. These findings emphasize the profound impact of virtues on student development. The study also introduces the Positive Development and Assessment Competencies Theory (PDAC), which advocates for the integration of virtue-based interventions and character strengths into educational programs. PDAC aims to enhance the assessment of subjective competencies, improve educational interventions, and promote student wellbeing.</p
The effects of academic oral presentations on English as a foreign language students’ oral communication strategy use:An intervention study
Get PDFThis study investigated how academic oral presentations impacted the use of oral communication strategies by Turkish students learning English to overcome speaking difficulties. The "Strategies for Coping with Speaking Problems" scale was used to measure the effectiveness of an oral presentation intervention. Two groups of first-year students: an intervention group and a control group took part in this study. Both groups were given a pre-test. The intervention group (n=35) engaged in giving academic oral presentations in English for 30 minutes, while the control group (n=34) attended conventional classes for 12 weeks. After the intervention, both groups were given a post-test. The results showed that academic oral presentations had a positive impact on the oral communication strategies used by the intervention group. The strategies employed were categorized into several types: fluency-oriented strategies, accuracy-oriented strategies, and nonverbal strategies. Addition-ally, 'total strategy use' refers to the integrated application of all these strategies. There was a significant improvement in most sub-dimensions of the scale. No significant difference was found in the pre-test and post-test results of the control group. Overall, the study suggests that academic oral presentations can help students improve their oral communication strategies and overcome speaking difficulties when learning a new language.</p
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