THE INFLUENCE OF PASSIVE HIP EXTENSION ON RUNNING BIOMECHANICS

J. Stoewer1, E. Foch2, M.B Pohl1 1University of Puget Sound, Tacoma, WA; 2Central Washington University, Ellensburg, WA Restricted passive range of motion (PROM) of hip extension has been anecdotally linked with low back pain. A potential mechanism for this may be that restrictions in passive hip extension prevents the hip from fully extending during running. As a consequence, the pelvis may undergo anterior tilt to allow the thigh to extend, thus, resulting in greater loading of the lumbar spine. However, it is currently unclear whether restricted passive hip extension has any bearing on hip and pelvis biomechanics during running. PURPOSE: To determine whether runners who differ in passive hip extension also demonstrate differences in hip extension and anterior pelvic tilt during running. METHODS: Participants included 9 healthy runners (3 males, 6 females) between the ages of 18-28. Passive hip extension was measured using the Thomas Test. Kinematic data during running was collected using a 3D motion capture system. Subjects were split into three groups (tight, normal, & flexible) using tertiles based on their Thomas Test score. Both hip extension and anterior pelvic tilt during running were then compared between groups using Cohen’s effect sizes (ES). RESULTS: The tight group exhibited the least amount of hip extension during running with a large effect size (ES=0.84) when compared to the flexible group (Table 1). The tight group exhibited the greatest amount of anterior pelvic tilt with large effect sizes when compared to both the normal (ES=0.80) and flexible (ES=2.34) groups. CONCLUSION: Limited passive hip extension was linked with alterations in running biomechanics including reduced hip extension and greater anterior pelvic tilt. These kinematic alterations could potentially place greater loading the lumbar spine

Berechnung der Strukturintensität von Fahrzeugstrukturen

Zur weiteren Verbesserung des Schwingungs- und Akustikverhaltens in modernen Fahrzeugen ist es wichtig, die Wirkkette von der Strukturanregung bis zum Schalldruck an den Ohren der Insassen genau zu verstehen. Während in der Luftschallakustik mit der Schallintensität eine Mess- und Simulationsgröße besteht, durch die der Schallenergiefluss grafisch dargestellt werden kann, sind Messungen der Körperschallausbreitung mit hohem Aufwand verbunden. Die Größe der Strukturintensität ermöglicht eine detaillierte, simulationsbasierte Analyse der Dynamik von Strukturen. Basierend auf einer Erweiterung bestehender Finite-Elemente-Programme lässt sich mit dieser Methode der Körperschallenergiefluss in dünnwandigen Strukturen lokalisieren und in seinen Anteilen visualisieren. Die Unterscheidung zwischen In-Plane- und Out-of-Plane-Wellen sowie aktivem und reaktivem Anteil des Energieflusses erlaubt einen gezielten konstruktiven Eingriff mit dem Ziel der Strukturverbesserung. In dieser Arbeit werden die Möglichkeiten zur Beeinflussung des Energieflusses systematisch von einfachen Plattenstrukturen im Frequenz- wie auch für transiente Anregungen im Zeitbe-reich hergeleitet und messtechnisch verifiziert. Im Frequenzbereich werden die Beeinflus-sungsmöglichkeiten sowohl für eine Einzelfrequenz als auch für ein Frequenzband dargestellt. Zusätzlich wird die Berechnung um die äquivalente abgestrahlte Schallleistung und die Schwingschnellen erweitert, um die Wirkkette für den Körperschall durchgängig zu beschreiben und eine Korrelation der beiden Größen mit der Strukturintensität zu untersuchen. Auf-bauend auf diesen Ergebnissen wird die Strukturintensität für reale Fahrzeugstrukturen be-rechnet, und Strukturverbesserungen werden für verschiedene Einsatzzwecke ausgewählt und anhand numerischer Simulationen bewertet. Es wird gezeigt, dass die aus der Berechnung der Strukturintensität gewonnenen Erkenntnisse wertvoll für eine effizientere Strukturauslegung sind. Die Berechnung der Strukturintensität für eine gesamte Rohkarosserie und für Struktu-ren aus faserverstärkten Kunststoffen zeigt, dass die Methode auch zur Analyse sehr umfangreicher, komplexer sowie anisotroper Strukturen genutzt werden kann. In der Arbeit wird somit nachgewiesen, dass sich die Strukturintensität für den zukünftigen serienmäßigen Ein-satz in der Fahrzeugstrukturberechnung eignet und dabei hilft, deutlich verbessertes Schwingungs- und Akustikverhalten in zukünftigen Fahrzeugen zu realisieren

Word class representations spontaneously emerge in a deep neural network trained on next word prediction

How do humans learn language, and can the first language be learned at all? These fundamental questions are still hotly debated. In contemporary linguistics, there are two major schools of thought that give completely opposite answers. According to Chomsky's theory of universal grammar, language cannot be learned because children are not exposed to sufficient data in their linguistic environment. In contrast, usage-based models of language assume a profound relationship between language structure and language use. In particular, contextual mental processing and mental representations are assumed to have the cognitive capacity to capture the complexity of actual language use at all levels. The prime example is syntax, i.e., the rules by which words are assembled into larger units such as sentences. Typically, syntactic rules are expressed as sequences of word classes. However, it remains unclear whether word classes are innate, as implied by universal grammar, or whether they emerge during language acquisition, as suggested by usage-based approaches. Here, we address this issue from a machine learning and natural language processing perspective. In particular, we trained an artificial deep neural network on predicting the next word, provided sequences of consecutive words as input. Subsequently, we analyzed the emerging activation patterns in the hidden layers of the neural network. Strikingly, we find that the internal representations of nine-word input sequences cluster according to the word class of the tenth word to be predicted as output, even though the neural network did not receive any explicit information about syntactic rules or word classes during training. This surprising result suggests, that also in the human brain, abstract representational categories such as word classes may naturally emerge as a consequence of predictive coding and processing during language acquisition.Comment: arXiv admin note: text overlap with arXiv:2301.0675

A brief intervention to improve exercising in patients with schizophrenia: a controlled pilot study with mental contrasting and implementation intentions (MCII)

BackgroundRegular exercise can have positive effects on both the physical and mental health of individuals with schizophrenia. However, deficits in cognition, perception, affect, and volition make it especially difficult for people with schizophrenia to plan and follow through with their exercising intentions, as indicated by poor attendance and high drop-out rates in prior studies. Mental Contrasting and Implementation Intentions (MCII) is a well-established strategy to support the enactment of intended actions. This pilot study tests whether MCII helps people with schizophrenia in highly structured or autonomy-focused clinical hospital settings to translate their exercising intentions into action.MethodsThirty-six inpatients (eleven women) with a mean age of 30.89 years (SD = 11.41) diagnosed with schizophrenia spectrum disorders from specialized highly structured or autonomy-focused wards were randomly assigned to two intervention groups. In the equal contact goal intention control condition, patients read an informative text about physical activity; they then set and wrote down the goal to attend jogging sessions. In the MCII experimental condition, patients read the same informative text and then worked through the MCII strategy. We hypothesized that MCII would increase attendance and persistence relative to the control condition over the course of four weeks and this will be especially be the case when applied in an autonomy-focused setting compared to when applied in a highly structured setting.ResultsWhen applied in autonomy-focused settings, MCII increased attendance and persistence in jogging group sessions relative to the control condition. In the highly structured setting, no differences between conditions were found, most likely due to a ceiling effect. These results remained even when adjusting for group differences in the pre-intervention scores for the control variables depression (BDI), physical activity (IPAQ), weight (BMI), age, and education. Whereas commitment and physical activity apart from the jogging sessions remained stable over the course of the treatment, depression and negative symptoms were reduced. There were no differences in pre-post treatment changes between intervention groups.ConclusionsThe intervention in the present study provides initial support for the hypothesis that MCII helps patients to translate their exercising intentions into real-life behavior even in autonomously-focused settings without social control.publishe

Data management routines for reproducible research using the G-Node Python Client library

Structured, efficient, and secure storage of experimental data and associated meta-information constitutes one of the most pressing technical challenges in modern neuroscience, and does so particularly in electrophysiology. The German INCF Node aims to provide open-source solutions for this domain that support the scientific data management and analysis workflow, and thus facilitate future data access and reproducible research. G-Node provides a data management system, accessible through an application interface, that is based on a combination of standardized data representation and flexible data annotation to account for the variety of experimental paradigms in electrophysiology. The G-Node Python Library exposes these services to the Python environment, enabling researchers to organize and access their experimental data using their familiar tools while gaining the advantages that a centralized storage entails. The library provides powerful query features, including data slicing and selection by metadata, as well as fine-grained permission control for collaboration and data sharing. Here we demonstrate key actions in working with experimental neuroscience data, such as building a metadata structure, organizing recorded data in datasets, annotating data, or selecting data regions of interest, that can be automated to large degree using the library. Compliant with existing de-facto standards, the G-Node Python Library is compatible with many Python tools in the field of neurophysiology and thus enables seamless integration of data organization into the scientific data workflow

Neural network based formation of cognitive maps of semantic spaces and the putative emergence of abstract concepts

AbstractHow do we make sense of the input from our sensory organs, and put the perceived information into context of our past experiences? The hippocampal-entorhinal complex plays a major role in the organization of memory and thought. The formation of and navigation in cognitive maps of arbitrary mental spaces via place and grid cells can serve as a representation of memories and experiences and their relations to each other. The multi-scale successor representation is proposed to be the mathematical principle underlying place and grid cell computations. Here, we present a neural network, which learns a cognitive map of a semantic space based on 32 different animal species encoded as feature vectors. The neural network successfully learns the similarities between different animal species, and constructs a cognitive map of ‘animal space’ based on the principle of successor representations with an accuracy of around 30% which is near to the theoretical maximum regarding the fact that all animal species have more than one possible successor, i.e. nearest neighbor in feature space. Furthermore, a hierarchical structure, i.e. different scales of cognitive maps, can be modeled based on multi-scale successor representations. We find that, in fine-grained cognitive maps, the animal vectors are evenly distributed in feature space. In contrast, in coarse-grained maps, animal vectors are highly clustered according to their biological class, i.e. amphibians, mammals and insects. This could be a putative mechanism enabling the emergence of new, abstract semantic concepts. Finally, even completely new or incomplete input can be represented by interpolation of the representations from the cognitive map with remarkable high accuracy of up to 95%. We conclude that the successor representation can serve as a weighted pointer to past memories and experiences, and may therefore be a crucial building block to include prior knowledge, and to derive context knowledge from novel input. Thus, our model provides a new tool to complement contemporary deep learning approaches on the road towards artificial general intelligence.</jats:p

Trajectories of charged particles trapped in Earth's magnetic field

I outline the theory of relativistic charged-particle motion in the magnetosphere in a way suitable for undergraduate courses. I discuss particle and guiding center motion, derive the three adiabatic invariants associated with them, and present particle trajectories in a dipolar field. I provide twelve computational exercises that can be used as classroom assignments or for self-study. Two of the exercises, drift-shell bifurcation and Speiser orbits, are adapted from active magnetospheric research. The Python code provided in the supplement can be used to replicate the trajectories and can be easily extended for different field geometries.Comment: 10 pages, 7 figures. Submitted to American Journal of Physic

COVID Restrictions Did Not Decrease Physical Activity in Community-Dwelling Older Adults

Background Understanding the long-term impacts of COVID-19-related stay-at-home orders on physical activity can help clinicians prepare for consequences that may impact their patient populations. Purpose This study examined effects of the 2020 COVID-19 stay-at-home orders on physical activity levels in community-dwelling older adults including the number of hours they spent walking outside of the home and working/volunteering in the community. Methods Eighty-nine participants completed a monthly Physical Activity Scale for the Elderly (PASE) for 10 months. One-way repeated measures ANOVAs with post hoc analyses were calculated to determine differences among PASE scores, PASE item 2 scores, and work/volunteer hours at baseline and for seven months following the implementation of COVID restrictions. Paired t-tests were calculated to determine differences in outcomes in the months prior to and after COVID restrictions. Results The mean baseline PASE score and PASE item 2 score were 131.96+56.49 and 23.39+21.10, respectively. Participants worked or volunteered 3.10+5.76 hours per week. There were no differences among monthly PASE scores (F=2.98, p=.018) except scores at baseline score and in August (107.26+60.19, p=.034). There were no differences in PASE item 2 scores or work/volunteer hours (F=1.03, p=.424; F=1.35, p=.246, respectively). No differences were found between pre- and post-restriction PASE scores, PASE item 2 scores, or work/volunteer hours (p=.732, .391, and .711, respectively). Conclusion Pre-COVID PASE scores did not differ from scores during seven months of COVID-19 restrictions. Participants maintained a similar amount of time walking in their communities during the pandemic. The number of work/volunteer hours did not change during the COVID-19 restrictions

Neural network based successor representations to form cognitive maps of space and language

How does the mind organize thoughts? The hippocampal-entorhinal complex is thought to support domain-general representation and processing of structural knowledge of arbitrary state, feature and concept spaces. In particular, it enables the formation of cognitive maps, and navigation on these maps, thereby broadly contributing to cognition. It has been proposed that the concept of multi-scale successor representations provides an explanation of the underlying computations performed by place and grid cells. Here, we present a neural network based approach to learn such representations, and its application to different scenarios: a spatial exploration task based on supervised learning, a spatial navigation task based on reinforcement learning, and a non-spatial task where linguistic constructions have to be inferred by observing sample sentences. In all scenarios, the neural network correctly learns and approximates the underlying structure by building successor representations. Furthermore, the resulting neural firing patterns are strikingly similar to experimentally observed place and grid cell firing patterns. We conclude that cognitive maps and neural network-based successor representations of structured knowledge provide a promising way to overcome some of the short comings of deep learning towards artificial general intelligence