Kinanthropometric Attributes of Young Male Combat Sports Athletes

Although there are enough studies concerning the kinanthropometric attributes of players of sports such as football, basketball, or volleyball in Turkey, there are not enough studies on the same for combat sports. Hence, our aim is to assess the kinanthropometric attributes of different combat sports like karate, taekwondo, judo and kickboxing. The present study included 48 national level male athletes from four different combat sports (age, 20.3 (3.19) years; number of years playing the sport, 8.33 (4.59); height, 174.3 (7.15) cm; weight, 67.35 (10.55 kg). Skinfold thickness was measured with a skinfold caliper (Holtain Ltd., UK), and Yuhazs formula was used to calculate the body fat percentage. Somatotype as- sessment was carried out with a computer program (Sweat Technology Trial Version, South Australia). Width measure- ments were obtained with a slide caliper (HLT-100, Holtain Ltd.), and girth measurements were obtained with a non- -flexible tape measure. The data obtained were analyzed with the computer program SPSS 17.0 in terms of the SD. The findings were as follows: body mass index (BMI), 22.00 (2.66) kg/m 2 ; body fat percentage, 12.20% (3.07%); endomorphic component, 2.9 (1.30); mesomorphic component, 4.25 (1.30); and ectomorphic component, 3.10 (1.30). The cormic index was 51.99% (1.88%); Monourier index, 92.39% (4.47%); Acromio-iliac index, 60.87% (6.61%); Martine index, 6.29% (0.70%); Biacromial index, 22.58% (0.99%); and hip index, 13.91% (0.86%). The mesomorphic component was found to be dominant in our study. Although BMIs were found to be normal, body fat percentages were low. According to body pro- portions, the athletes who participated in this study had wide shoulders, narrow hips, and medium-sized trunks

Harmonizing semantic annotations for computational models in biology

Life science researchers use computational models to articulate and test hypotheses about the behavior of biological systems. Semantic annotation is a critical component for enhancing the interoperability and reusability of such models as well as for the integration of the data needed for model parameterization and validation. Encoded as machine-readable links to knowledge resource terms, semantic annotations describe the computational or biological meaning of what models and data represent. These annotations help researchers find and repurpose models, accelerate model composition and enable knowledge integration across model repositories and experimental data stores. However, realizing the potential benefits of semantic annotation requires the development of model annotation standards that adhere to a community-based annotation protocol.Without such standards, tool developers must account for a variety of annotation formats and approaches, a situation that can become prohibitively cumbersome and which can defeat the purpose of linking model elements to controlled knowledge resource terms. Currently, no consensus protocol for semantic annotation exists among the larger biological modeling community. Here, we report on the landscape of current annotation practices among the Computational Modeling in BIology NEtwork community and provide a set of recommendations for building a consensus approach to semantic annotation

Relationship Between Fatigue Index and Number of Repetition Maxima with Sub-Maximal Loads in Biceps Curl

WOS: 000325555700018PubMed ID: 24235992The aim of this study was to investigate the relationship between the number of repetition maxima to volitional failure (RM) at 60%, 75%, 90% of 1RM and fatigue index (FI), a determinant of the muscular endurance level. Thirty four resistance trained male participants attended two testing sessions. The first session was conducted to assess 1RM load and RM at 60%, 75% and 90% of 1RM in the supine biceps curl (SBC) exercise. In the second session, a FI test protocol consisting of five sets of SBC with 90 s rest between sets was performed to determine FI values. Each set was performed to volitional failure using a sub-maximal load in the range of 15-20RM. Hypothetical high FI and low FI groups (17 participants with the highest and lowest FI values, respectively) were formed for statistical analyses. ANOVA results revealed that RM at 60%, 75%, 90% of 1RM were not significantly different between FI groups when controlled for mean repetition tempo (p=0.11, p=0.38, p=0.13, respectively). Pearson's correlation coefficients revealed that no significant relationship was present between FI values and RM at 60%, 75%, 90% of 1RM (p=0.40, p=0.46, p=0.14, respectively). In conclusion, the muscular endurance level of participants defined in terms of FI value was not an indicator of RM in SBC. Therefore, athletes with different muscular endurance levels can use similar percentages of 1RM in biceps curl exercise in their training programs when the aim is to elicit training adaptations related to specific RM zones

Building the Digital Patient: Graphical Models / Virtual Physiological Human (and Connecting the Dots with Clinical Medicine)

<p>My invited lecture at the 2nd ICCAS Digtital Operating Room Summer School (DORS 2015) held in Lepzig/Germany on 27 August 2015.</p

Relationship between fatigue index and number of repetition maxima with sub-maximal loads in Biceps Curl

The aim of this study was to investigate the relationship between the number of repetition maxima to volitional failure (RM) at 60%, 75%, 90% of 1RM and fatigue index (FI), a determinant of the muscular endurance level. Thirty four resistance trained male participants attended two testing sessions. The first session was conducted to assess 1RM load and RM at 60%, 75% and 90% of 1RM in the supine biceps curl (SBC) exercise. In the second session, a FI test protocol consisting of five sets of SBC with 90 s rest between sets was performed to determine FI values. Each set was performed to volitional failure using a sub-maximal load in the range of 15-20RM. Hypothetical high FI and low FI groups (17 participants with the highest and lowest FI values, respectively) were formed for statistical analyses. ANOVA results revealed that RM at 60%, 75%, 90% of 1RM were not significantly different between FI groups when controlled for mean repetition tempo (p=0.11, p=0.38, p=0.13, respectively). Pearson's correlation coefficients revealed that no significant relationship was present between FI values and RM at 60%, 75%, 90% of 1RM (p=0.40, p=0.46, p=0.14, respectively). In conclusion, the muscular endurance level of participants defined in terms of FI value was not an indicator of RM in SBC. Therefore, athletes with different muscular endurance levels can use similar percentages of 1RM in biceps curl exercise in their training programs when the aim is to elicit training adaptations related to specific RM zones. © Editorial Committee of Journal of Human Kinetics

Bringing Things Together and Linking to Health Information using openEHR

<p>Presentation at the Digital Patient Modeling and Clinical Decision Support workshop at Medinfo 2015 Conference</p> <p>Clinical decision making is non-trivial given the amounts of data and knowledge that need to be considered. Examination results that are stored in multiple formats and data types in clinical information systems build the basis for decision making. Beyond, medical knowledge and biological or molecular-biological processes influence the process. So far, medical knowledge, biological knowledge and patient data are separated from each other and need to be integrated mentally by a physician to form an overarching patient model. Digital patient modelling is addressing this problem by developing methods for integrating information and patient data related to specific medical conditions and making it available for various applications such as decision support. This workshop is devoted to digital patient modelling as basis for clinical decision support. This topic poses many challenges with respect to semantic linking of data, modelling of domain knowledge, structured representation and modelling of patient data as well as knowledge and information extraction from free textual sources. The workshop allows participants to learn about digital patient modelling and the required technologies and methods. More importantly, it will provide a platform to discuss current challenges and future steps towards new directions for data linking, information modelling and model-based clinical decision support.</p