The Physiological Demands of Youth Artistic Gymnastics; Applications to Strength and Conditioning

The sport of artistic gymnastics involves a series of complex events that can expose young gymnasts to relatively high forces. The sport is recognized as attracting early specialization, in which young children are exposed to a high volume of sports-specific training. Leading world authorities advocate that young athletes should participate in strength and conditioning related activities in order to increase athlete robustness and reduce the relative risk of injury. The purpose of this commentary is to provide a needs analysis of artistic gymnastics, and to highlight key issues surrounding training that practitioners should consider when working with this unique population

Diode-laser Based Photo-acoustic Spectroscopy In Atmospheric No­2 Detection

We have developed a simple, low cost, and compact NO2 detection system. It\u27s based on photoacoustic spectroscopy (PAS) method uses a diode laser as a source of radiation. The PAS system has a detection limit of 10 ppbv for NO2. With this set-up we were able to detect the NO2 concentration from urban air near our campus. We have also investigated the NO2 dissociation effect on the PAS system via NO measurements using a direct absorption spectroscopy method on quantum cascade laser (QCL) system. Keywords: photoacoustic spectroscop

Lower limb stiffness and maximal sprint speed in 11-16-year-old boys

The purpose of the study was to examine the relationship between vertical stiffness, leg stiffness and maximal sprint speed in a large cohort of 11-16-year-old boys. Three-hundred and thirty-six boys undertook a 30 m sprint test using a floor-level optical measurement system, positioned in the final 15 m section. Measures of speed, step length, step frequency, contact time and flight time were directly measured whilst force, displacement, vertical stiffness and leg stiffness, were modeled from contact and flight times, from the two fastest consecutive steps for each participant over two trials. All force, displacement and stiffness variables were significantly correlated with maximal sprint speed (p 0.7) relationship with sprint speed, while vertical center of mass displacement, absolute vertical stiffness, relative peak force, and maximal leg spring displacement had large (r > 0.5) relationships. Relative vertical stiffness and relative peak force did not significantly change with advancing age (p > 0.05), but together with maximal leg spring displacement accounted for 96% of the variance in maximal speed. It appears that relative vertical stiffness and relative peak force are important determinants of sprint speed in boys aged 11-16 years, but are qualities that may need to be trained due to no apparent increases from natural development. Practitioners may wish to utilize training modalities such as plyometrics and resistance training to enable adaptation to these qualities due to their importance as predictors of speed in youth

Visual category representations in the infant brain

Visual categorization is a human core cognitive capacity1,2 that depends on the development of visual category representations in the infant brain.3,4,5,6,7 However, the exact nature of infant visual category representations and their relationship to the corresponding adult form remains unknown.8 Our results clarify the nature of visual category representations from electroencephalography (EEG) data in 6- to 8-month-old infants and their developmental trajectory toward adult maturity in the key characteristics of temporal dynamics,2,9 representational format,10,11,12 and spectral properties.13,14 Temporal dynamics change from slowly emerging, developing representations in infants to quickly emerging, complex representations in adults. Despite those differences, infants and adults already partly share visual category representations. The format of infants' representations is visual features of low to intermediate complexity, whereas adults' representations also encode high-complexity features. Theta band activity contributes to visual category representations in infants, and these representations are shifted to the alpha/beta band in adults. Together, we reveal the developmental neural basis of visual categorization in humans, show how information transmission channels change in development, and demonstrate the power of advanced multivariate analysis techniques in infant EEG research for theory building in developmental cognitive science

The influence of biological maturity on dynamic force–time variables and vaulting performance in young female gymnasts

Purpose: This cross-sectional study investigated dynamic force–time variables and vaulting performance in young female gymnasts of different maturity status. Methods: 120 gymnasts aged 5–14 years were sub-divided into maturity groupings using percent of predicted adult height (%PAH) attained. Participants performed three jumping protocols, the squat jump (SJ), countermovement jump (CMJ) and drop jump (DJ), before completing straight jump vaults that were recorded using two-dimensional video. Results: Jumping performance improved with biological maturity evidenced by the most mature gymnasts’ producing significantly more absolute force (P \u3c 0.05; all d \u3e 0.78), impulse (P \u3c 0.05; all d \u3e 0.75) and power (P \u3c 0.05; all d \u3e 0.91) than the least mature group, resulting in the greater jump heights (P \u3c 0.05; all d \u3e 0.70). While, no significant differences were observed in relative peak force across multiple tests, measures of relative peak power did significantly increase with maturity. Based upon regression analyses, maturation was found to influence vertical take-off velocity during vaulting, explaining 41% of the variance in each jumping protocol. Across all tests, the DJ was found to have the highest predictive ability of vaulting vertical take-off velocity, explaining 55% of the total variance. Conclusion: Biological maturation impacts jump height and underpinning mechanical variables in young female gymnasts. Vaulting vertical take-off velocity appears to be influenced by maturation and various dynamic force–time variables, particularly those during DJ, which had the highest explained total variance

OK-Net Arable online knowledge platform

The complexity of organic farming requires farmers to have a very high level of knowledge and skills, but exchange on organic farming management techniques remains limited. The thematic network OK-Net Arable under Horizon 2020 has the aim to improve the exchange of innovative and traditional knowledge among farmers, farm advisers and scientists to increase productivity and quality in organic arable cropping in Europe. An online platform for knowledge exchange has been created, offering innovative education and end-user material as well as communication opportunities between actors. A number of specific tools – providing information about how to put existing knowledge from research and practice into use – have been chosen. They are presented on the platform with the possibility to find solutions, evaluate them, comment and discuss them or ask questions about them and to suggest new tools to be shown on the platfor

Replacement of Contentious Inputs in Organic Farming Systems (RELACS) – a comprehensive Horizon 2020 project

Organic farmers adhere to high standards in producing quality food while protecting the environment. However, organic farming needs to improve continuously to keep meeting its ambitious objectives. The project ‘Replacement of Contentious Inputs in Organic Farming Systems’ (RELACS) will foster the development and adoption of cost-efficient and environmentally safe tools and technologies to further reduce the use of external inputs on organic farms across Europe as well as in Non EU Mediterranean countries. Project partners will provide scientific support to develop fair and implementable EU rules to improve current practices in organic farming. Farm advisory networks in 11 European countries will reach out to farmers to ensure effective dissemination and adoption of the tools and techniques

Deep learning body-composition analysis of clinically acquired CT-scans estimates creatinine excretion with high accuracy in patients and healthy individuals

Assessment of daily creatinine production and excretion plays a crucial role in the estimation of renal function. Creatinine excretion is estimated by creatinine excretion equations and implicitly in eGFR equations like MDRD and CKD-EPI. These equations are however unreliable in patients with aberrant body composition. In this study we developed and validated equations estimating creatinine production using deep learning body-composition analysis of clinically acquired CT-scans. We retrospectively included patients in our center that received any CT-scan including the abdomen and had a 24-h urine collection within 2 weeks of the scan (n = 636). To validate the equations in healthy individuals, we included a kidney donor dataset (n = 287). We used a deep learning algorithm to segment muscle and fat at the 3rd lumbar vertebra, calculate surface areas and extract radiomics parameters. Two equations for CT-based estimate of RenAl FuncTion (CRAFT 1 including CT parameters, age, weight, and stature and CRAFT 2 excluding weight and stature) were developed and compared to the Cockcroft-Gault and the Ix equations. CRAFT1 and CRAFT 2 were both unbiased (MPE = 0.18 and 0.16 mmol/day, respectively) and accurate (RMSE = 2.68 and 2.78 mmol/day, respectively) in the patient dataset and were more accurate than the Ix (RMSE = 3.46 mmol/day) and Cockcroft-Gault equation (RMSE = 3.52 mmol/day). In healthy kidney donors, CRAFT 1 and CRAFT 2 remained unbiased (MPE = − 0.71 and − 0.73 mmol/day respectively) and accurate (RMSE = 1.86 and 1.97 mmol/day, respectively). Deep learning-based extraction of body-composition parameters from abdominal CT-scans can be used to reliably estimate creatinine production in both patients as well as healthy individuals. The presented algorithm can improve the estimation of renal function in patients who have recently had a CT scan. The proposed methods provide an improved estimation of renal function that is fully automatic and can be readily implemented in routine clinical practice

Towards personalised contrast injection: Artificial-intelligence-derived body composition and liver enhancement in computed tomography

In contrast-enhanced computed tomography, total body weight adapted contrast injection protocols have proven successful in achieving a homogeneous enhancement of vascular structures and liver parenchyma. However, because solid organs have greater perfusion than adipose tissue, the lean body weight (fat-free mass) rather than the total body weight is theorised to cause even more homogeneous enhancement. We included 102 consecutive patients who underwent a multiphase abdominal computed tomography between March 2016 and October 2019. Patients received contrast media (300 mgI/mL) according to bodyweight categories. Using regions of interest, we measured the Hounsfield unit (HU) increase in liver attenuation from unenhanced to contrast-enhanced computed tomography. Furthermore, subjective image quality was graded using a four-point Likert scale. An artificial intelligence algorithm automatically segmented and determined the body compositions and calculated the percentages of lean body weight. The hepatic enhancements were adjusted for iodine dose and iodine dose per total body weight, as well as percentage lean body weight. The associations between enhancement and total body weight, body mass index, and lean body weight were analysed using linear regression. Patients had a median age of 68 years (IQR: 58–74), a total body weight of 81 kg (IQR: 73 – 90), a body mass index of 26 kg/m2 (SD: ±4.2), and a lean body weight percentage of 50% (IQR: 36 – 55). Mean liver enhancements in the portal venous phase were 61 ± 12 HU (≤ 70 kg), 53 ± 10 HU (70 – 90 kg), and 53 ± 7 HU (≥ 90 kg). The majority (93%) of scans were rated as good or excellent. Regression analysis showed significant correlations between liver enhancement corrected for injected total iodine and total body weight (r = 0.53; p < 0.001) and between liver enhancement corrected for lean body weight and the percentage of lean body weight (r = 0.73; p < 0.001). Most benefits from personalising iodine injection using %LBW additive to total body weight would be achieved in patients under 90 kg. Liver enhancement is more strongly associated with the percentage of lean body weight than with the total body weight or body mass index. The observed variation in liver enhancement might be reduced by a personalised injection based on the artificial-intelligence-determined percentage of lean body weight

Use of automated assessment for determining associations of low muscle mass and muscle loss with overall survival in patients with colorectal cancer – A validation study

Background: Low muscle mass and skeletal muscle mass (SMM) loss are associated with adverse patient outcomes, but the time-consuming nature of manual SMM quantification prohibits implementation of this metric in clinical practice. Therefore, we assessed the feasibility of automated SMM quantification compared to manual quantification. We evaluated both diagnostic accuracy for low muscle mass and associations of SMM (change) with survival in colorectal cancer (CRC) patients. Methods: Computed tomography (CT) images from CRC patients enrolled in two clinical studies were analyzed. We compared i) manual vs. automated segmentation of preselected slices at the third lumbar [L3] vertebra (“semi-automated”), and ii) manual L3-slice-selection + manual segmentation vs. automated L3-slice-selection + automated segmentation (“fully-automated”). Automated L3-selection and automated segmentation was performed with Quantib Body Composition v0.2.1. Bland–Altman analyses, within-subject coefficients of variation (WSCVs) and Intraclass Correlation Coefficients (ICCs) were used to evaluate the agreement between manual and automatic segmentation. Diagnostic accuracy for low muscle mass (defined by an established sarcopenia cut-off) was calculated with manual assessment as the “gold standard”. Using either manual or automated assessment, Cox proportional hazard ratios (HRs) were used to study the association between changes in SMM (>5% decrease yes/no) during first-line metastatic CRC treatment and mortality adjusted for prognostic factors. SMM change was also assessed separately in weight-stable (<5%, i.e. occult SMM loss) patients. Results: In total, 1580 CT scans were analyzed, while a subset of 307 scans were analyzed in the fully-automated comparison. Included patients (n = 553) had a mean age of 63 ± 9 years and 39% were female. The semi-automated comparison revealed a bias of −2.41 cm2, 95% limits of agreement [-9.02 to 4.20], a WSCV of 2.25%, and an ICC of 0.99 (95% confidence intervals (CI) 0.97 to 1.00). The fully-automated comparison method revealed a bias of −0.08 cm2 [-10.91 to 10.75], a WSCV of 2.85% and an ICC of 0.98 (95% CI 0.98 to 0.99). Sensitivity and specificity for low muscle mass were 0.99 and 0.89 for the semi-automated comparison and 0.96 and 0.90 for the fully-automated comparison. SMM decrease was associated with shorter survival in both manual and automated assessment (n = 78/280, HR 1.36 [95% CI 1.03 to 1.80] and n = 89/280, HR 1.38 [95% CI 1.05 to 1.81]). Occult SMM loss was associated with shorter survival in manual assessment, but not significantly in automated assessment (n = 44/263, HR 1.43 [95% CI 1.01 to 2.03] and n = 51/2639, HR 1.23 [95% CI 0.87 to 1.74]). Conclusion: Deep-learning based assessment of SMM at L3 shows reliable performance, enabling the use of CT measures to guide clinical decision making. Implementation in clinical practice helps to identify patients with low muscle mass or (occult) SMM loss who may benefit from lifestyle interventions