Analyse molekularer Mechanismen trainingsbedingter Skelettmuskeladaptation

Abstract: Myostatin (GDF8) is a negative regulator of skeletel muscle mass but it´s role in human skeletal muscle is insufficiently desribed. The aim of this study was to examine the effects of strength and endurance training on myostatin mRNA in the vastus lateralis muscle of healthy and physically active humans. 33 healthy and physically active sports students (static and dynamic knee extensor strength 33 ± 4.5 N/kgBW; 1 185 ± 170 W, respectively; maximum oxygen uptake 52.5 ± 8 ml/kgBW/min) were recruited and randomly assigned to a moderate endurance training group (n=11), a strength training group (n=11) and a control group (n=11). Muscle biopsies were taken from the vastus lateralis muscle 2-3 days before the start as well as at the end of the 12 weeks' training period. Exercise-specific functional improvements after moderate endurance training and strength training were measured for submaximal endurance and for static and dynamic strength of the knee extensor muscles. None of the myostatin mRNA values showed significant pre-post differences or group-specific differences. These results are in contrast to data with sedentary subjects, suggesting that myostatin is necessary for adaptations of skeletal muscle to exercise stress. We conclude that functional improvements after moderate endurance training and strength training can occur without alterations in myostatin mRNA in physically active humans. Zusammenfassung: Myostatin oder auch GDF8 ist ein negativer Regulator der Skelettmuskel-Masse. Allerdings ist dessen Expression als Reaktion auf Training bisher nur unzureichend beschrieben worden. Das Ziel dieser Studie war es den Effekt von Kraft- und Ausdauertraining auf die Regulation der Myostatin mRNA bei gesunden und aktiven Männern zu untersuchen. Dazu wurden 33 männliche Sportstudenten (statische und dynamische Knie-Extensor Kraft: 33 ± 4.5 N/kgBW; 1185 ± 170 W; Maximale Sauerstoffaufnahme: 52.5 ± 8 ml/kgBW/min) randomisiert in eine Ausdauergruppe (n=11), Kraftgruppe (n=11) oder Kontrollgruppe (n=11) eingeteilt! Muskelbiopsien wurden 2-3 Tage vor und nach der zwölf-wöchigen Trainingsintervention am M. vastus lateralis entnommen. Funktionelle Steigerungen der Maximalkraft und des Ausdauerleistungsvermögens konnten in den jeweiligen Trainingsgruppen nachgewiesen werden (p<0,01). Die mRNA Analysen aller Gruppen ergaben keine signifikanten Veränderungen im prae-post Vergleich. Diese Ergebnisse unterscheiden sich von denen anderer Arbeitsgruppen, bei denen eher unsportliche und ältere Probanden auf eine Myostatin-Regulation untersucht worden sind. Zusammenfassend ist festzuhalten, dass funktionelle Steigerungen der Kraft- und Ausdauerfähigkeiten als Reaktion auf ein Training nicht in einem signifikanten Zusammenhang mit einer Myostatin-Regulation der mRNA bei gesunden sportlichen Männern stehen

Revisiting Robustness in Graph Machine Learning

Many works show that node-level predictions of Graph Neural Networks (GNNs) are unrobust to small, often termed adversarial, changes to the graph structure. However, because manual inspection of a graph is difficult, it is unclear if the studied perturbations always preserve a core assumption of adversarial examples: that of unchanged semantic content. To address this problem, we introduce a more principled notion of an adversarial graph, which is aware of semantic content change. Using Contextual Stochastic Block Models (CSBMs) and real-world graphs, our results uncover: $i)$ for a majority of nodes the prevalent perturbation models include a large fraction of perturbed graphs violating the unchanged semantics assumption; $ii)$ surprisingly, all assessed GNNs show over-robustness - that is robustness beyond the point of semantic change. We find this to be a complementary phenomenon to adversarial examples and show that including the label-structure of the training graph into the inference process of GNNs significantly reduces over-robustness, while having a positive effect on test accuracy and adversarial robustness. Theoretically, leveraging our new semantics-aware notion of robustness, we prove that there is no robustness-accuracy tradeoff for inductively classifying a newly added node.Comment: Published as a conference paper at ICLR 2023. Preliminary version accepted as an oral at the NeurIPS 2022 TSRML workshop and at the NeurIPS 2022 ML safety worksho

Topology-Matching Normalizing Flows for Out-of-Distribution Detection in Robot Learning

To facilitate reliable deployments of autonomous robots in the real world, Out-of-Distribution (OOD) detection capabilities are often required. A powerful approach for OOD detection is based on density estimation with Normalizing Flows (NFs). However, we find that prior work with NFs attempts to match the complex target distribution topologically with naive base distributions leading to adverse implications. In this work, we circumvent this topological mismatch using an expressive class-conditional base distribution trained with an information-theoretic objective to match the required topology. The proposed method enjoys the merits of wide compatibility with existing learned models without any performance degradation and minimum computation overhead while enhancing OOD detection capabilities. We demonstrate superior results in density estimation and 2D object detection benchmarks in comparison with extensive baselines. Moreover, we showcase the applicability of the method with a real-robot deployment.Comment: Accepted on CoRL202

Topology-Matching Normalizing Flows for Out-of-Distribution Detection in Robot Learning

To facilitate reliable deployments of autonomous robots in the real world, Out-of-Distribution (OOD) detection capabilities are often required. A powerful approach for OOD detection is based on density estimation with Normalizing Flows (NFs). However, we find that prior work with NFs attempts to match the complex target distribution topologically with naive base distributions leading to adverse implications. In this work, we circumvent this topological mismatch using an expressive class-conditional base distribution trained with an information-theoretic objective to match the required topology. The proposed method enjoys the merits of wide compatibility with existing learned models without any performance degradation and minimum computation overhead while enhancing OOD detection capabilities. We demonstrate superior results in density estimation and 2D object detection benchmarks in comparison with extensive baselines. Moreover, we showcase the applicability of the method with a real-robot deployment

Three Weeks of Detraining Does Not Decrease Muscle Thickness, Strength or Sport Performance in Adolescent Athletes

International Journal of Exercise Science 13(6): 633-644, 2020. The purpose of this study was to examine the effects of detraining following a block (BLOCK) or daily undulating periodized (DUP) resistance training (RT) on hypertrophy, strength, and athletic performance in adolescent athletes. Twenty-one males (age = 16 ± 0.7 years; range 15-18 years) were randomly assigned to one of two 12-week intervention groups (three full-body RT sessions per week): BLOCK (n = 9); DUP (n = 12). Subsequently a three-week detraining period was applied. Body mass, fat mass (FM), fat-free mass (FFM), muscle mass, muscle thickness (rectus femoris, vastus lateralis and triceps brachii), one-repetition maximum squat and bench press, countermovement jump (CMJ), peak power calculated from CMJ (Ppeak), medicine ball put distance, and 36.58m sprint were recorded before and after RT as well as after detraining. BLOCK and DUP were equally effective for improvements of athletic performance in young athletes. Both groups displayed significantly (ρ ≤ 0.05) higher values of all measures after RT except FM, which was unchanged. Only FM increased (p = 0.010; ES = 0.14) and FFM decreased (p = 0.018; ES = -0.18) after detraining. All other measurements were unaffected by the complete cessation of training. Values were still elevated compared to pre-training. Linear regression showed a strong correlation between the percentage change by resistance training and the decrease during detraining for CMJ (R² = 0.472) and MBP (R² = 0.629). BLOCK and DUP RT seem to be equally effective in adolescent athletes for increasing strength, muscle mass, and sport performance. In addition, three weeks of detraining did not affect muscle thickness, strength, or sport performance in adolescent athletes independent of previous resistance training periodization model used

Transformers Meet Directed Graphs

Transformers were originally proposed as a sequence-to-sequence model for text but have become vital for a wide range of modalities, including images, audio, video, and undirected graphs. However, transformers for directed graphs are a surprisingly underexplored topic, despite their applicability to ubiquitous domains, including source code and logic circuits. In this work, we propose two direction- and structure-aware positional encodings for directed graphs: (1) the eigenvectors of the Magnetic Laplacian - a direction-aware generalization of the combinatorial Laplacian; (2) directional random walk encodings. Empirically, we show that the extra directionality information is useful in various downstream tasks, including correctness testing of sorting networks and source code understanding. Together with a data-flow-centric graph construction, our model outperforms the prior state of the art on the Open Graph Benchmark Code2 relatively by 14.7%.Comment: 29 page

Are Defenses for Graph Neural Networks Robust?

A cursory reading of the literature suggests that we have made a lot of progress in designing effective adversarial defenses for Graph Neural Networks (GNNs). Yet, the standard methodology has a serious flaw – virtually all of the defenses are evaluated against non-adaptive attacks leading to overly optimistic robustness estimates. We perform a thorough robustness analysis of 7 of the most popular defenses spanning the entire spectrum of strategies, i.e., aimed at improving the graph, the architecture, or the training. The results are sobering – most defenses show no or only marginal improvement compared to an undefended baseline. We advocate using custom adaptive attacks as a gold standard and we outline the lessons we learned from successfully designing such attacks. Moreover, our diverse collection of perturbed graphs forms a (black-box) unit test offering a first glance at a model's robustness

Thermal history of Northwest Africa 5073--A coarse-grained Stannern-trend eucrite containing cm-sized pyroxenes and large zircon grains

International audienceWe report on the bulk chemical composition, petrology, oxygen isotopic composition, trace element composition of silicates, and degree of self-irradiation damage on zircon grains of the eucrite Northwest Africa (NWA) 5073 to constrain its formation and postcrystallization thermal history, and to discuss their implications for the geologic history of its parent body. This unequilibrated and unbrecciated meteorite is a new member of the rare Stannern-trend eucrites. It is mainly composed of elongated, zoned pyroxene phenocrysts up to 1.2 cm, plagioclase laths up to 0.3 cm in length, and is rich in mesostasis. The latter contains zircon grains up to 30 μm in diameter, metal, sulfide, tridymite, and Ca-phosphates. Textural observations and silicate compositions, coupled with the occurrence of extraordinary Fe-rich olivine veins that are restricted to large pyroxene laths, indicate that NWA 5073 underwent a complex thermal history. This is also supported by the annealed state of zircon grains inferred from μ-Raman spectroscopic measurements along with U and Th data obtained by electron probe microanalyses

Generalization of Neural Combinatorial Solvers Through the Lens of Adversarial Robustness

End-to-end (geometric) deep learning has seen first successes in approximating the solution of combinatorial optimization problems. However, generating data in the realm of NP-hard/-complete tasks brings practical and theoretical challenges, resulting in evaluation protocols that are too optimistic. Specifically, most datasets only capture a simpler subproblem and likely suffer from spurious features. We investigate these effects by studying adversarial robustness -a local generalization property- to reveal hard, model-specific instances and spurious features. For this purpose, we derive perturbation models for SAT and TSP. Unlike in other applications, where perturbation models are designed around subjective notions of imperceptibility, our perturbation models are efficient and sound, allowing us to determine the true label of perturbed samples without a solver. Surprisingly, with such perturbations, a sufficiently expressive neural solver does not suffer from the limitations of the accuracy-robustness trade-off common in supervised learning. Although such robust solvers exist, we show empirically that the assessed neural solvers do not generalize well w.r.t. small perturbations of the problem instance