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Zusammenfassung Hintergrund und Fragestellung Gutes Stereosehen scheint bei Hochleistungssportlern und im Alltag eine wichtige Rolle zu spielen. Wir untersuchten die Fragestellung, ob sich das Stereosehen bei Sportlern mit bereits guter Stereosehschärfe durch ein digitales Sehtraining noch verbessern kann. Studiendesign und Untersuchungsmethoden N = 31 männliche und weibliche Tennisspieler/innen absolvierten innerhalb 6 Wochen mindestens 6 Trainingseinheiten mit je 192 stereoskopischen Einzeltests mit variablem Schwierigkeitsgrad von 15 bis 300 Bogensekunden. Die Trainingsstimuli waren dynamischer, die Teststimuli statischer Natur. Zur Darstellung der Reaktionszeitverbesserung in Abhängigkeit des Schwierigkeitsgrades wurde der Parameter „ReSt“ (Reaktionszeitzuwachs pro Stereodisparitätsabnahme) bestimmt. Ergebnisse Die Reaktionszeit bei Stimuli von 15 Bogensekunden verkürzte sich durch das Sehtraining im Mittel signifikant von 3,9 s auf 1,6 s (59 %). Die Korrektheit der Versuche bei 30 Bogensekunden steigerte sich im Mittel signifikant um 23 %. Diskussion Die beobachtete Verbesserung der Reaktionszeit im Sehtraining führte nicht zur Abnahme der Korrektheit bei der Beantwortung der visuellen Fragen und stellt damit insgesamt eine Verbesserung der Stereosehfähigkeit dar. Schlussfolgerung Durch das digitale Sehtraining verbesserte sich das stereoskopische Sehen signifikant hinsichtlich Stereogrenzwinkel, Reaktionszeit und Korrektheit.

Postmenopausal bone loss often leads to osteoporosis and fragility fractures. Bone mass can be increased by the first 34 amino acids of human parathyroid hormone (PTH), parathyroid hormone-related protein (PTHrP), or by a monoclonal antibody against sclerostin (Scl-Ab). Here, we show that PTH and Scl-Ab reduce the expression of microRNA-19a and microRNA-19b (miR-19a/b) in bone. In bones from patients with lower bone mass and from osteoporotic mice, miR-19a/b expression is elevated, suggesting an inhibitory function in bone remodeling. Indeed, antagonizing miR-19a/b in vivo increased bone mass without overt cytotoxic effects. We identified TG-interacting factor 1 (Tgif1) as the target of miR-19a/b in osteoblasts and essential for the increase in bone mass following miR-19a/b inhibition. Furthermore, antagonizing miR-19a/b augments the gain in bone mass by PTH and restores bone loss in mouse models of osteoporosis in a dual mode of action by supporting bone formation and decreasing receptor activator of NF-κB ligand (RANKL)-dependent bone resorption. Thus, this study identifies novel mechanisms regulating bone remodeling, which opens opportunities for new therapeutic concepts to treat bone fragility.

Purpose The study aimed to evaluate stereopsis as a surrogate marker for post-concussion oculomotor function to develop an objective test that can reliably and quickly detect mild traumatic brain injuries (TBI). Methods The cohort of this prospective clinical study included 30 healthy subjects (mean age 25 ± 2 years) and 30 TBI patients (43 ± 22 years) comprising 11 patients with moderate TBI and 19 patients with mild TBI. The healthy subjects were examined once, whereas the TBI patients were examined immediately after hospitalization, at 1 week, and at 2 months. A virtual reality (VR) program displayed three-dimensional rendering of four rotating soccer balls over VR glasses in different gaze directions. The subjects were instructed to select the ball that appeared to be raised from the screen as quickly as possible via remote control. The response times and fusion abilities in different gaze directions were recorded. Results The correlation between stereopsis and TBI severity was significant. The response times of the moderate and mild TBI groups were significantly longer than those of the healthy reference group. The response times of the moderate TBI group were significantly longer than those of the mild TBI group. The response times at follow-up examinations were significantly shorter than those immediately after hospitalization. Fusion ability was primarily defective in the gaze direction to the right (90°) and left (270° and 315°). Conclusions TBI patients showed impaired stereopsis. Measuring stereopsis in different positions of the visual field using VR can be effective for rapid concussion assessment.

Strabismus is a visual disorder characterized by eye misalignment. The effect of Panum’s Fusional Area (PFA) compensates for small misalignments. However, prominent misalignments affect binocular vision and when present in childhood it may lead to amblyopia, a developmental disorder of the visual system. With the advent of Virtual Reality (VR) technology, possibilities for novel binocular treatments to amblyopia arise in which the measurement of strabismus is crucial to correctly compensate for it. Thus, VR yields great potential due to the ability of displaying content to each eye independently. Major research in VR addresses this topic using eye-tracking while there is a paucity of research on image-based assessment methods. In this work, we propose a VR application for measuring strabismus in nine lines of sight. We conducted a study with 14 healthy participants to evaluate the system under two conditions: no strabismus and an artificial deviation induced by prism lenses. Further, we evaluated the effect of PFA on the system by measuring its extent in horizontal and vertical lines of sight. Results show significant difference between the expected deviation induced by prism lenses and the measured deviation. The existing difference within the measurements can be explained with the recorded extent of the PFA.

IntroductionThe history of the use of telemedicine in maritime medicine dates back to the 1930s. In the early 2000s, the German Navy built up a telemedicine network which today is installed in all ships and provides a connection to the medical infrastructure ashore. The objective of this study was to optimize the implementation of telemedicine based on the experience gained in the German Navy. For this purpose, qualitative and quantitative methods were used to identify determinants which affect the use of telemedicine in order to determine whether there is any need for optimization.MethodsThe study was conducted using a mixed methods design. First, guideline-based interviews were conducted with Navy medical officers who had been recruited via various email distribution lists. The interviews were then transcribed and coded. In an analysis, deductive and inductive categories were derived from the codes. Hypotheses were deduced from the interviews, too, and used to develop a questionnaire.Besides the medical officers, other Navy medical personnel with experience in the field of telemedicine took part in the survey. The study was concluded by a descriptive analysis of the quantitative data.ResultsThe analysis of the interviews revealed that a regular use of telemedicine workstations increased the users’ confidence and, in their opinion, improved medical treatment. Technical and organizational problems posed obstacles, which increased the use of partly insecure alternatives. A proper technical support was regarded as a precondition for effectively using telemedicine.The results of the quantitative analysis showed that consultation was mainly sought for dermatological (46%), surgical (24%), and internal (22%) conditions.ConclusionThe study revealed determinants for the use of telemedicine in the German Navy. Factors improving the motivation of the users should be strengthened in order to optimize the use of telemedicine. Furthermore, it can be assumed that a successful implementation will be supported by reducing or eliminating obstacles. The findings on the main reasons for seeking medical advice could be taken into account in the further planning of specific training.

Gaussian RBF kernels are approximated to speed up SVM classifications.An upper bound for the relative approximation error is given.Error decreases with factorial growth if approximation quality is linearly increased.Experiments showed an average 18-fold speed-up without losing accuracy. In theory, kernel support vector machines (SVMs) can be reformulated to linear SVMs. This reformulation can speed up SVM classifications considerably, in particular, if the number of support vectors is high. For the widely-used Gaussian radial basis function (RBF) kernel, however, this theoretical fact is impracticable because the reproducing kernel Hilbert space (RKHS) of this kernel has infinite dimensionality. Therefore, we derive a finite-dimensional approximative feature map, based on an orthonormal basis of the kernels RKHS, to enable the reformulation of Gaussian RBF SVMs to linear SVMs. We show that the error of this approximative feature map decreases with factorial growth if the approximation quality is linearly increased. Experimental evaluations demonstrated that the approximative feature map achieves considerable speed-ups (about 18-fold on average), mostly without losing classification accuracy. Therefore, the proposed approximative feature map provides an efficient SVM evaluation method with minimal loss of precision.

Introduction: We investigated the effect of stimulation intensity (in percent of maximal tolerated stimulation current, mTSC), frequency, and impulse width on muscle fatigue. Methods: Using a randomized crossover design, 6 parameter combinations (80% mTSC, 80 Hz, 400 μs; 60% mTSC, 80 Hz, 400 μs; 80% mTSC, 20 Hz, 400 μs; 60% mTSC, 20 Hz, 400 μs; 80% mTSC, 80 Hz, 150 μs; 60% mTSC, 80 Hz, 150 μs) were tested in both legs of 13 athletic men (age 26 ± 2.3). The slope of the linear regression line over all tetani (FIS) and the number of tetani whose force was above 50% of the initial tetanus (FIN) were used to quantify fatigue. Results: FIS and FIN were significantly lower in high-frequency protocols. No effects on FIS and FIN were found for intensity and impulse width. Conclusions: Stimulation frequency, but not impulse width or intensity, affected fatigue kinetics. Muscle Nerve 53: 608–616, 2016

Branch-and-bound feature selection is performed in kernel space.Optimal feature subsets are found without the enormous effort of an exhaustive search.Classification accuracy is competitive to the popular wrapper approach. Branch-and-bound (B&B) feature selection finds optimal feature subsets without performing an exhaustive search. However, the classification accuracy achievable with optimal B&B feature subsets is often inferior compared to the accuracy achievable with other algorithms that guarantee optimality. We argue this is due to the existing criterion functions that define the optimal feature subset but may not conceive inherent nonlinear data structures. Therefore, we propose B&B feature selection in Reproducing Kernel Hilbert Space?(B&B-RKHS). This algorithm employs two existing criterion functions (Bhattacharyya distance, Kullback-Leibler divergence) and one new criterion function (mean class distance), however, all computed in RKHS. This enables B&B-RKHS to conceive inherent nonlinear data structures. The algorithm was experimentally compared to the popular wrapper approach that has to use an exhaustive search to guarantee optimality. The classification accuracy achieved with both methods was comparable. However, runtime of B&B-RKHS was superior using the two existing criterion functions and even completely out of reach using the new criterion function (about 60 times faster on average). Therefore, this paper proposes an efficient algorithm if feature subsets that guarantee optimality have to be selected in data sets with inherent nonlinear structures.

Smart embedded systems often run sophisticated pattern recognition algorithms and are found in many areas like automotive, sports and medicine. The developer of such a system is often confronted with the accuracy---cost conflict as the resulting system should be as accurate as possible while being able to run on resource constraint hardware. This article introduces a method to support the solution of this design conflict with accuracy---cost reports. These reports compare classification systems regarding their classification rate (accuracy) and the mathematical operations and parameters of the working phase (cost). Our method is used to deduce the specific cost of various popular pattern recognition algorithms and to derive the overall cost of a classification system. We also show how our analysis can be used to estimate the computational cost for specific hardware architectures. A software toolbox to create accuracy---cost reports was implemented to facilitate the automatic classification system comparison with the presented methodology. The software is available for download and as supplementary material. We performed different experiments on synthetic and real-world data to underline the value of this analysis. Accurate and computationally cheap classification systems were easily identified. We were even able to find a better implementation candidate in an existing embedded classification problem. This work is the first step towards a comprehensive support tool for the design of embedded classification systems.

Salivary markers have been proposed as noninvasive and easy-to-collect indicators of dehydrations during physical exercise. It has been demonstrated that threshold-based classifications can distinguish dehydrated from euhydrated subjects. However, considerable challenges were reported simultaneously, for example, high intersubject variabilities in these markers. Therefore, we propose a machine-learning approach to handle the intersubject variabilities and to advance from binary classifications to quantitative estimations of total body water (TBW) loss. For this purpose, salivary samples and reference values of TBW loss were collected from ten subjects during a 2-h running workout without fluid intake. The salivary samples were analyzed for previously investigated markers (osmolality, proteins) as well as additional unexplored markers (amylase, chloride, cortisol, cortisone, and potassium). Processing all these markers with a Gaussian process approach showed that quantitative TBW loss estimations are possible within an error of 0.34 l, roughly speaking, a glass of water. Furthermore, a data analysis illustrated that the salivary markers grow nonlinearly during progressive dehydration, which is in contrast to previously reported linear observations. This insight could help to develop more accurate physiological models for salivary markers and TBW loss. Such models, in turn, could facilitate even more precise TBW loss estimations in the future.

Smart heating applications promise to increase energy efficiency and comfort by collecting and processing room climate data. While it has been suspected that the sensed data may leak crucial personal information about the occupants, this belief has up until now not been supported by evidence.

Several research tools and projects require groups of similar code changes asinput. Examples are recommendation and bug finding tools that can providevaluable information to developers based on such data. With the help ofsimilar code changes they can simplify the application of bug fixes and codechanges to multiple locations in a project. But despite their benefit, thepractical value of existing tools is limited, as users need to manually specifythe input data, i.e., the groups of similar code changes.To overcome this drawback, this paper presents and evaluates two syntacticalsimilarity metrics, one of them is specifically designed to run fast, incombination with two carefully selected and self-tuning clustering algorithmsto automatically detect groups of similar code changes.We evaluate the combinations of metrics and clustering algorithms by applyingthem to several open source projects and also publish the detected groups ofsimilar code changes online as a reference dataset. The automatically detectedgroups of similar code changes work well when used as input for LASE, arecommendation system for code changes.

BACKGROUND Recovery from exercise and competition is important in sports medicine, particularly when rest periods are short. The objective is to determine the efficacy of cryo exposition (CRY) and manual lymphatic drainage (MLD) to hasten short term recovery of muscle performance after eccentric contractions. METHODS In a randomized controlled trial, 30 healthy sport students (21 males, 9 females; age: 25.7±2.8 years) performed 4×20 eccentric contractions of knee extensors, followed by 30 min MLD, CRY, or rest (RST) under controlled laboratory environment. Maximal voluntary contractions (MVC), electrically induced muscle fatigue (FI), and electrically induced tetani (EIT) at low (T2: 20 Hz) and high frequencies were tested. RESULTS Force decline and recovery kinetics regarding MVC, FI, and EIT did not differ significantly (p

The amount of total body water (TBW) can be estimated based on bioimpedance measurements of the human body. In sports, TBW estimations are of importance because mild water losses can impair muscular strength and aerobic endurance. Severe water losses can even be life threatening. TBW estimations based on bioimpedance, however, fail during sports because the increased body temperature corrupts bioimpedance measurements. Therefore, this paper proposes a machine learning method that eliminates the effects of increased temperature on bioimpedance and, consequently, reveals the changes in bioimpedance that are due to TBW loss. This is facilitated by utilizing changes in skin and core temperature. The method was evaluated in a study in which bioimpedance, temperature, and TBW loss were recorded every 15 min during a 2-h running workout. The evaluation demonstrated that the proposed method is able to reduce the error of TBW loss estimation by up to 71%, compared to the state of art. In the future, the proposed method in combination with portable bioimpedance devices might facilitate the development of wearable systems for continuous and noninvasive TBW loss monitoring during sports.

Quantitative estimation of water loss during physical exercise is of importance because dehydration can impair both muscular strength and aerobic endurance. A physiological indicator for deficit of total body water (TBW) might be the concentration of electrolytes in sweat. It has been shown that concentrations differ after physical exercise depending on whether water loss was replaced by fluid intake or not. However, to the best of our knowledge, this fact has not been examined for its potential to quantitatively estimate TBW loss. Therefore, we conducted a study in which sweat samples were collected continuously during two hours of physical exercise without fluid intake. A statistical analysis of these sweat samples revealed significant correlations between chloride concentration in sweat and TBW loss (r = 0.41, p < 0.01), and between sweat osmolality and TBW loss (r = 0.43, p < 0.01). A quantitative estimation of TBW loss resulted in a mean absolute error of 0.49 l per estimation. Although the precision has to be improved for practical applications, the present results suggest that TBW loss estimation could be realizable using sweat samples.

We investigated the effect of stimulation intensity (in percent of maximal tolerated stimulation current, mTSC), frequency, and impulse width on muscle fatigue.Using a randomized crossover design, 6 parameter combinations (80% mTSC, 80 Hz, 400 μs; 60% mTSC, 80 Hz, 400 μs; 80% mTSC, 20 Hz, 400 μs; 60% mTSC, 20 Hz, 400 μs; 80% mTSC, 80 Hz, 150 μs; 60% mTSC, 80 Hz, 150 μs) were tested in both legs of 13 athletic men (age 26 ± 2.3). The slope of the linear regression line over all tetani (FIS) and the number of tetani whose force was above 50% of the initial tetanus (FIN) were used to quantify fatigue.FIS and FIN were significantly lower in high-frequency protocols. No effects on FIS and FIN were found for intensity and impulse width.Stimulation frequency, but not impulse width or intensity, affected fatigue kinetics.

Bio impedance analysis (BIA) estimates the amount of total body water (TBW) in the human body. During sports, however, the increased skin temperature distorts bio impedance measurements and, thus, prevents the application of BIA. In this paper, we propose a two-stage regression that includes temperature information in order to correct the temperature-distorted bio impedance. In detail, the first regression stage corrects temperature-distored bio impedance using information of skin and core temperature. The second regression stage estimates TBW loss on basis of the corrected bio impedance. The two-stage regression was evaluated using data of an ongoing study. The results showed that estimations of TBW loss during sports can be considerably improved if temperature information is included. However, a remaining error was still observed. Therefore, additional measurements, e.g., skin blood flow, are discussed because they also influence bio impedance and could further reduce the error.

Voice scrambling is widely used to add privacy to the radio communication of various authorities — but is also used by criminals to evade prosecution. In this article, we consider various analog voice scrambling techniques such as fixed frequency inversion, splitband inversion and rolling code scramblers. We explain how to break them using automatically extracted measures and scoring algorithms, and evaluate the proposed system using simulated data. While the simple inversion can be easily broken, the more advanced techniques require additional work prior to unsupervised automatization; the presented user interface allows the user to refine the automatic results to obtain a high quality solution.