Relative Contribution among Physical Fitness Factors Contributing to the Performance of Modern Pentathlon.

This study reveals the relationship between physical fitness factors and performance in modern pentathlon and identifies the contribution of each physical factor to overall performance. The physical fitness assessment data and the competition records collected by the Korean national team pentathletes for the years 2005 to 2019 were tracked. The correlation between the competition records and fitness factors was confirmed by correlation analysis. In addition, the physical factors affecting performance were identified through multiple regression analysis, and the average difference between national and international competitions was verified by t-test. The first result was that fencing, swimming, and horseback riding rankings were more relevant to the overall pentathlon performance score than the combined rankings in national competitions. In the international competitions, performance in the combined running and shooting event was more relevant than fencing, swimming, and horseback riding. Second, the basic fitness factors of grip strength and sergeant jump and the specific fitness factors of leg strength-left and right average flexor were correlated with overall pentathlon performance national competitions. However, in international competitions, sergeant jump, 20 m shuttle run, reaction time, lung capacity, and back strength were correlated (presented in high to low order). In terms of the specific fitness factors, relative (%BW) and absolute (Nm) leg strength-left and right average flexor, lower body anaerobic fatigue rate, half squat, relative (W/kg) and absolute (Watts) maximal lower body anaerobic power were correlated accordingly with overall pentathlon performance. Third, we analyzed the differences between average performance in national and international competitions. Only the combined running and shooting event out of the five modern pentathlon events showed a difference. Grip strength and relative leg strength-average extensor AP (%BW) appeared to be different among the physical fitness factors. Fourth, we examined the level of contribution of each of the fitness factors on overall performance. The model's goodness of fit was confirmed, and grip strength was found to have a significant contribution on overall performance. Furthermore, the level of contribution was higher in the following order: relative leg strength-left and right average flexor (%BW), bench press, half squat, relative leg strength-average extensor AP(%BW), GXT-time to exhaustion, relative lower body anaerobic average power (W/kg), and maximal lactic acid concentration. With the 2020 Tokyo Olympics just around the corner, combined running and shooting performance appeared to be a decisive factor in the final ranking in modern pentathlon according to the analysis of the basic and specific fitness factors of pentathletes. The basic fitness factors are critical in order of sergeant jump, grip strength, reaction time, lung capacity, side-step, back strength, 20m shuttle run, sit-and-reach, sit-ups, and single leg standing. With respect to the specific fitness factors, relative leg strength-left and right average flexor (%BW), bench press, half squat, relative leg strength-average extensor AP (%BW), GXT-time to exhaustion, relative lower body anaerobic average power (W/kg), and maximal lactic acid concentration showed their relevance accordingly.

Neuron anatomy structure reconstruction based on a sliding filter.

BACKGROUND: Reconstruction of neuron anatomy structure is a challenging and important task in neuroscience. However, few algorithms can automatically reconstruct the full structure well without manual assistance, making it essential to develop new methods for this task. METHODS: This paper introduces a new pipeline for reconstructing neuron anatomy structure from 3-D microscopy image stacks. This pipeline is initialized with a set of seeds that were detected by our proposed Sliding Volume Filter (SVF), given a non-circular cross-section of a neuron cell. Then, an improved open curve snake model combined with a SVF external force is applied to trace the full skeleton of the neuron cell. A radius estimation method based on a 2D sliding band filter is developed to fit the real edge of the cross-section of the neuron cell. Finally, a surface reconstruction method based on non-parallel curve networks is used to generate the neuron cell surface to finish this pipeline. RESULTS: The proposed pipeline has been evaluated using publicly available datasets. The results show that the proposed method achieves promising results in some datasets from the DIgital reconstruction of Axonal and DEndritic Morphology (DIADEM) challenge and new BigNeuron project. CONCLUSION: The new pipeline works well in neuron tracing and reconstruction. It can achieve higher efficiency, stability and robustness in neuron skeleton tracing. Furthermore, the proposed radius estimation method and applied surface reconstruction method can obtain more accurate neuron anatomy structures.

Bright field microscopic cells counting method for BEVS using nonlinear convergence index sliding band filter.

BACKGROUND: The Baculovirus Expression Vector System (BEVS) is a very popular expression vector system in gene engineering. An effective host cell line cultivation protocol can facilitate the baculovirus preparation and following experiments. However, the counting of the number of host cells in the protocol is usually performed by manual observation with microscopy, which is time consuming and labor intensive work, and prone to errors for one person or between different individuals. This study aims at giving a bright field insect cells counting protocol to help improve the efficient of BEVS. METHOD: To develop a reliable and accurate counting method for the host cells in the bright field, such as Sf9 insect cells, a novel method based on a nonlinear Transformed Sliding Band Filter (TSBF) was proposed. And 3 collaborators counted cells at the same time to produce the ground truth for evaluation. The performance of TSBF method was evaluated with the image datasets of Sf9 insect cells according to the different periods of cell cultivation on the cell density, error rate and growth curve. RESULTS: The average error rate of our TSBF method is 2.21% on average, ranging from 0.89% to 3.97%, which exhibited an excellent performance with its high accuracy in lower error rate compared with traditional methods and manual counting. And the growth curve was much the manual method well. CONCLUSION: Results suggest the proposed TSBF method can detect insect cells with low error rate, and it is suitable for the counting task in BEVS to take the place of manual counting by humans. Growth curve results can reflect the cells' growth manner, which was generated by our proposed TSBF method in this paper can reflected the similar manner with it's from the manual method. All of these proven that the proposed insect cell counting method can clearly improve the efficiency of BEVS.

A pipeline for neuron reconstruction based on spatial sliding volume filter seeding.

Neuron's shape and dendritic architecture are important for biosignal transduction in neuron networks. And the anatomy architecture reconstruction of neuron cell is one of the foremost challenges and important issues in neuroscience. Accurate reconstruction results can facilitate the subsequent neuron system simulation. With the development of confocal microscopy technology, researchers can scan neurons at submicron resolution for experiments. These make the reconstruction of complex dendritic trees become more feasible; however, it is still a tedious, time consuming, and labor intensity task. For decades, computer aided methods have been playing an important role in this task, but none of the prevalent algorithms can reconstruct full anatomy structure automatically. All of these make it essential for developing new method for reconstruction. This paper proposes a pipeline with a novel seeding method for reconstructing neuron structures from 3D microscopy images stacks. The pipeline is initialized with a set of seeds detected by sliding volume filter (SVF), and then the open curve snake is applied to the detected seeds for reconstructing the full structure of neuron cells. The experimental results demonstrate that the proposed pipeline exhibits excellent performance in terms of accuracy compared with traditional method, which is clearly a benefit for 3D neuron detection and reconstruction.