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Recent advances in single particle tracking and supercomputing techniques demonstrate the emergence of normal or anomalous, viscoelastic diffusion in conjunction with non-Gaussian distributions in soft, biological, and active matter systems. We here formulate a stochastic model based on a generalised Langevin equation in which non-Gaussian shapes of the probability density function and normal or anomalous diffusion have a common origin, namely a random parametrisation of the stochastic force. We perform a detailed analysis demonstrating how various types of parameter distributions for the memory kernel result in exponential, power law, or power-log law tails of the memory functions. The studied system is also shown to exhibit a further unusual property: the velocity has a Gaussian one point probability density but non-Gaussian joint distributions. This behaviour is reflected in the relaxation from a Gaussian to a non-Gaussian distribution observed for the position variable. We show that our theoretical results are in excellent agreement with stochastic simulations.
We show that the codifference is a useful tool in studying the ergodicity breaking and non-Gaussianity properties of stochastic time series. While the codifference is a measure of dependence that was previously studied mainly in the context of stable processes, we here extend its range of applicability to random-parameter and diffusing-diffusivity models which are important in contemporary physics, biology and financial engineering. We prove that the codifference detects forms of dependence and ergodicity breaking which are not visible from analysing the covariance and correlation functions. We also discuss a related measure of dispersion, which is a nonlinear analogue of the mean squared displacement.
Many studies on biological and soft matter systems report the joint presence of a linear mean-squared displacement and a non-Gaussian probability density exhibiting, for instance, exponential or stretched-Gaussian tails. This phenomenon is ascribed to the heterogeneity of the medium and is captured by random parameter models such as ‘superstatistics’ or ‘diffusing diffusivity’. Independently, scientists working in the area of time series analysis and statistics have studied a class of discrete-time processes with similar properties, namely, random coefficient autoregressive models. In this work we try to reconcile these two approaches and thus provide a bridge between physical stochastic processes and autoregressive models.Westart from the basic Langevin equation of motion with time-varying damping or diffusion coefficients and establish the link to random coefficient autoregressive processes. By exploring that link we gain access to efficient statistical methods which can help to identify data exhibiting Brownian yet non-Gaussian diffusion.
The increasing development of antibiotic resistance in bacteria has been a major problem for years, both in human and veterinary medicine. Prophylactic measures, such as the use of vaccines, are of great importance in reducing the use of antibiotics in livestock. These vaccines are mainly produced based on formaldehyde inactivation. However, the latter damages the recognition elements of the bacterial proteins and thus could reduce the immune response in the animal. An alternative inactivation method developed in this work is based on gentle photodynamic inactivation using carbon nanodots (CNDs) at excitation wavelengths λex > 290 nm. The photodynamic inactivation was characterized on the nonvirulent laboratory strain Escherichia coli K12 using synthesized CNDs. For a gentle inactivation, the CNDs must be absorbed into the cytoplasm of the E. coli cell. Thus, the inactivation through photoinduced formation of reactive oxygen species only takes place inside the bacterium, which means that the outer membrane is neither damaged nor altered. The loading of the CNDs into E. coli was examined using fluorescence microscopy. Complete loading of the bacterial cells could be achieved in less than 10 min. These studies revealed a reversible uptake process allowing the recovery and reuse of the CNDs after irradiation and before the administration of the vaccine. The success of photodynamic inactivation was verified by viability assays on agar. In a homemade flow photoreactor, the fastest successful irradiation of the bacteria could be carried out in 34 s. Therefore, the photodynamic inactivation based on CNDs is very effective. The membrane integrity of the bacteria after irradiation was verified by slide agglutination and atomic force microscopy. The method developed for the laboratory strain E. coli K12 could then be successfully applied to the important avian pathogens Bordetella avium and Ornithobacterium rhinotracheale to aid the development of novel vaccines.
Models are useful tools for understanding and predicting ecological patterns and processes. Under ongoing climate and biodiversity change, they can greatly facilitate decision-making in conservation and restoration and help designing adequate management strategies for an uncertain future. Here, we review the use of spatially explicit models for decision support and to identify key gaps in current modelling in conservation and restoration. Of 650 reviewed publications, 217 publications had a clear management application and were included in our quantitative analyses. Overall, modelling studies were biased towards static models (79%), towards the species and population level (80%) and towards conservation (rather than restoration) applications (71%). Correlative niche models were the most widely used model type. Dynamic models as well as the gene-to-individual level and the community-to-ecosystem level were underrepresented, and explicit cost optimisation approaches were only used in 10% of the studies. We present a new model typology for selecting models for animal conservation and restoration, characterising model types according to organisational levels, biological processes of interest and desired management applications. This typology will help to more closely link models to management goals. Additionally, future efforts need to overcome important challenges related to data integration, model integration and decision-making. We conclude with five key recommendations, suggesting that wider usage of spatially explicit models for decision support can be achieved by 1) developing a toolbox with multiple, easier-to-use methods, 2) improving calibration and validation of dynamic modelling approaches and 3) developing best-practise guidelines for applying these models. Further, more robust decision-making can be achieved by 4) combining multiple modelling approaches to assess uncertainty, and 5) placing models at the core of adaptive management. These efforts must be accompanied by long-term funding for modelling and monitoring, and improved communication between research and practise to ensure optimal conservation and restoration outcomes.
Objective: We investigated the effects of combined balance and strength training on measures of balance and muscle strength in older women with a history of falls.
Methods: Twenty-seven older women aged 70.4 ± 4.1 years (age range: 65 to 75 years) were randomly allocated to either an intervention (IG, n = 12) or an active control (CG, n = 15) group. The IG completed 8 weeks combined balance and strength training program with three sessions per week including visual biofeedback using force plates. The CG received physical therapy and gait training at a rehabilitation center. Training volumes were similar between the groups. Pre and post training, tests were applied for the assessment of muscle strength (weight-bearing squat [WBS] by measuring the percentage of body mass borne by each leg at different knee flexions [0°, 30°, 60°, and 90°], sit-to-stand test [STS]), and balance. Balance tests used the modified clinical test of sensory interaction (mCTSIB) with eyes closed (EC) and opened (EO), on stable (firm) and unstable (foam) surfaces as well as spatial parameters of gait such as step width and length (cm) and walking speed (cm/s).
Results: Significant group × time interactions were found for different degrees of knee flexion during WBS (0.0001 < p < 0.013, 0.441 < d < 0.762). Post hoc tests revealed significant pre-to-post improvements for both legs and for all degrees of flexion (0.0001 < p < 0.002, 0.697 < d < 1.875) for IG compared to CG. Significant group × time interactions were found for firm EO, foam EO, firm EC, and foam EC (0.006 < p < 0.029; 0.302 < d < 0.518). Post hoc tests showed significant pre-to-post improvements for both legs and for all degrees of oscillations (0.0001 < p < 0.004, 0.753 < d < 2.097) for IG compared to CG. This study indicates that combined balance and strength training improved percentage distribution of body weight between legs at different conditions of knee flexion (0°, 30°, 60°, and 90°) and also decreased the sway oscillation on a firm surface with eyes closed, and on foam surface (with eyes opened or closed) in the IG.
Conclusion: The higher positive effects of training seen in standing balance tests, compared with dynamic tests, suggests that balance training exercises including lateral, forward, and backward exercises improved static balance to a greater extent in older women.
Background: Agility in general and change-of-direction speed (CoD) in particular represent important performance determinants in elite soccer.
Objectives: The objectives of this study were to determine the effects of a 6-week neuromuscular training program on agility performance, and to determine differences in movement times between the slower and faster turning directions in elite soccer players. Materials and Methods: Twenty male elite soccer players from the Stade Rennais Football Club (Ligue 1, France) participated in this study. The players were randomly assigned to a neuromuscular training group (NTG, n = 10) or an active control (CG, n = 10) according to their playing position. NTG participated in a 6-week, twice per week neuromuscular training program that included CoD, plyometric and dynamic stability exercises. Neuromuscular training replaced the regular warm-up program. Each training session lasted 30 min. CG continued their regular training program. Training volume was similar between groups. Before and after the intervention, the two groups performed a reactive agility test that included 180° left and right body rotations followed by a 5-m linear sprint. The weak side was defined as the left/right turning direction that produced slower overall movement times (MT). Reaction time (RT) was assessed and defined as the time from the first appearance of a visual stimulus until the athlete’s first movement. MT corresponded to the time from the first movement until the athlete reached the arrival gate (5 m distance).
Results: No significant between-group baseline differences were observed for RT or MT. Significant group x time interactions were found for MT (p = 0.012, effect size = 0.332, small) for the slower and faster directions (p = 0.011, effect size = 0.627, moderate). Significant pre-to post improvements in MT were observed for NTG but not CG (p = 0.011, effect size = 0.877, moderate). For NTG, post hoc analyses revealed significant MT improvements for the slower (p = 0.012, effect size = 0.897, moderate) and faster directions (p = 0.017, effect size = 0.968, moderate).
Conclusion: Our results illustrate that 6 weeks of neuromuscular training with two sessions per week included in the warm-up program, significantly enhanced agility performance in elite soccer players. Moreover, improvements were found on both sides during body rotations. Thus, practitioners are advised to focus their training programs on both turning directions.
Objective: To determine immediate performance measures for short-term, multicomponent cardiac rehabilitation (CR) in clinical routine in patients of working age, taking into
account cardiovascular risk factors, physical performance, social medicine, and subjective health parameters and to explore the underlying dimensionality.
Design: Prospective observational multicenter register study in 12 rehabilitation centers throughout Germany.
Setting: Comprehensive 3-week CR.
In canoe sprint, the trunk muscles play an important role in stabilizing the body in an unstable environment (boat) and in generating forces that are transmitted through the shoulders and arms to the paddle for propulsion of the boat. Isokinetic training is well suited for sports in which propulsion is generated through water resistance due to similarities in the resistive mode. Thus, the purpose of this study was to determine the effects of isokinetic training in addition to regular sport-specific training on trunk muscular fitness and body composition in world-class canoeists and to evaluate associations between trunk muscular fitness and canoe-specific performance. Nine world-class canoeists (age: 25.6 ± 3.3 years; three females; four world champions; three Olympic gold medalists) participated in an 8-week progressive isokinetic training with a 6-week block “muscle hypertrophy” and a 2-week block “muscle power.” Pre- and post-tests included the assessment of peak isokinetic torque at different velocities in concentric (30 and 140∘s-1) and eccentric (30 and 90∘s-1) mode, trunk muscle endurance, and body composition (e.g., body fat, segmental lean mass). Additionally, peak paddle force was assessed in the flume at a water current of 3.4 m/s. Significant pre-to-post increases were found for peak torque of the trunk rotators at 30∘s-1 (p = 0.047; d = 0.4) and 140∘s-1 (p = 0.014; d = 0.7) in concentric mode. No significant pre-to-post changes were detected for eccentric trunk rotator torque, trunk muscle endurance, and body composition (p > 0.148). Significant medium-to-large correlations were observed between concentric trunk rotator torque but not trunk muscle endurance and peak paddle force, irrespective of the isokinetic movement velocity (all r ≥ 0.886; p ≤ 0.008). Isokinetic trunk rotator training is effective in improving concentric trunk rotator strength in world-class canoe sprinters. It is recommended to progressively increase angular velocity from 30∘s-1 to 140∘s-1 over the course of the training period.
Privacy regulations and the physical distribution of heterogeneous data are often primary concerns for the development of deep learning models in a medical context. This paper evaluates the feasibility of differentially private federated learning for chest X-ray classification as a defense against data privacy attacks. To the best of our knowledge, we are the first to directly compare the impact of differentially private training on two different neural network architectures, DenseNet121 and ResNet50. Extending the federated learning environments previously analyzed in terms of privacy, we simulated a heterogeneous and imbalanced federated setting by distributing images from the public CheXpert and Mendeley chest X-ray datasets unevenly among 36 clients. Both non-private baseline models achieved an area under the receiver operating characteristic curve (AUC) of 0.940.94 on the binary classification task of detecting the presence of a medical finding. We demonstrate that both model architectures are vulnerable to privacy violation by applying image reconstruction attacks to local model updates from individual clients. The attack was particularly successful during later training stages. To mitigate the risk of a privacy breach, we integrated Rényi differential privacy with a Gaussian noise mechanism into local model training. We evaluate model performance and attack vulnerability for privacy budgets ε∈{1,3,6,10}�∈{1,3,6,10}. The DenseNet121 achieved the best utility-privacy trade-off with an AUC of 0.940.94 for ε=6�=6. Model performance deteriorated slightly for individual clients compared to the non-private baseline. The ResNet50 only reached an AUC of 0.760.76 in the same privacy setting. Its performance was inferior to that of the DenseNet121 for all considered privacy constraints, suggesting that the DenseNet121 architecture is more robust to differentially private training.