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Building context-aware applications is an already widely researched topic. It is our belief that context awareness has the potential to supplement the Internet of Things, when a suitable methodology including supporting tools will ease the development of context-aware applications. We believe that a meta-model based approach can be key to achieving this goal. In this paper, we present our meta-model based methodology, which allows us to define and build application-specific context models and the integration of sensor data without any programming. We describe how that methodology is applied with the implementation of a relatively simple context-aware COVID-safe navigation app. The outcome showed that programmers with no experience in context-awareness were able to understand the concepts easily and were able to effectively use it after receiving a short training. Therefore, context-awareness is able to be implemented within a short amount of time. We conclude that this can also be the case for the development of other context-aware applications, which have the same context-awareness characteristics. We have also identified further optimization potential, which we will discuss at the conclusion of this article.
Cell migration is associated with the dynamic protrusion of a thin actin-based cytoskeletal extension at the cell front, which has been shown to consist of two different substructures, the leading lamellipodium and the subsequent lamellum. While the formation of the lamellipodium is increasingly well understood, organizational principles underlying the emergence of the lamellum are just beginning to be unraveled. We report here on a 1D mathematical model which describes the reaction-diffusion processes of a polarized actin network in steady state, and reproduces essential characteristics of the lamellipodium-lamellum system. We observe a steep gradient in filament lengths at the protruding edge, a local depolymerization maximum a few microns behind the edge, as well as a differential dominance of the network destabilizer ADF/cofilin and the stabilizer tropomyosin. We identify simple and robust organizational principles giving rise to the derived network characteristics, uncoupled from the specifics of any molecular implementation, and thus plausibly valid across cell types. An analysis of network length dependence on physico-chemical system parameters implies that to limit array treadmilling to cellular dimensions, network growth has to be truncated by mechanisms other than aging-induced depolymerization, e.g., by myosin-associated network dissociation at the transition to the cell body. Our work contributes to the analytical understanding of the cytoskeletal extension's bisection into lamellipodium and lamellum and sheds light on how cells organize their molecular machinery to achieve motility.
Sound and music are well-studied aspects of the quality of experience in restaurants; the role of the room acoustical conditions, their influence on the visitors’ soundscape evaluation and their impact on the overall customer satisfaction in restaurants, however, has received less scientific attention. The present field study therefore investigated whether sound pressure level, reverberation time, and soundscape pleasantness can predict factors associated with overall restaurant quality. In total, 142 persons visiting 12 restaurants in Berlin rated relevant acoustical and non-acoustical factors associated with restaurant quality. Simultaneously, the A-weighted sound pressure level (LA,eq,15) was measured, and the reverberation time in the occupied state (T20,occ) was obtained by measurements performed in the unoccupied room and a subsequent calculation of the occupied condition according to DIN 18041. Results from linear mixed-effects models revealed that both the LA,eq,15 and T20,occ had a significant influence on soundscape pleasantness and eventfulness, whereby the effect of T20,occ was meditated by the LA,eq,15. Also, the LA,eq,15 as well as soundscape pleasantness were significant predictors of overall restaurant quality. A comprehensive structural equation model including both acoustical and non-acoustical factors, however, indicates that the effect of soundscape pleasantness on overall restaurant quality is mediated by the restaurant’s atmosphere. Our results support and extend previous findings which suggest that the acoustical design of restaurants involves a trade-off between comfort and liveliness, depending on the desired character of the place.
We present a system that exploits mobile rotational tracking and photospherical imagery to allow users to share their environment with remotely connected peers “on the go.” We surveyed related interfaces and developed a unique groupware application that shares a mixed reality space with spatially-oriented live video feeds. Users can collaborate through realtime audio, video, and drawings in a virtual space. The developed system was tested in a preliminary user study, which confirmed an increase in spatial and situational awareness among viewers as well as reduction in cognitive workload. Believing that our system provides a novel style of collaboration in mixed reality environments, we discuss future applications and extensions of our prototype.
Kompetenzen auf dem Gebiet der Datenbanken gehören zum Pflichtbereich der Informatik. Das Angebot an Lehrbüchern, Vorlesungsformaten und Tools lässt sich jedoch für Lehrende oft nur eingeschränkt in die eigene Lehre integrieren. In diesem Aufsatz schildern wir unsere Erfahrungen in der Nutzung (frei) verfügbarer und der Entwicklung eigener digitaler Inhalte für grundlegende Datenbankveranstaltungen. Die Präferenzen der Studierenden werden mittels Nutzungsanalysen und Befragungen ermittelt. Wir stellen die Anforderungen auf, wie die nicht selten aufwendig herzustellenden digitalen Materialien von Lehrenden in ihre Lehr- und Lernumgebungen integriert werden können. Als konstruktive Antwort auf diese Herausforderung wird das Konzept EILD zur Entwicklung von Inhalten für die Lehre im Fach Datenbanken vorgestellt. Die Inhalte sollen in vielfältigen Lernszenarien eingesetzt werden können und mit einer Creative Commons (CC) Lizenzierung als OER (open educational resources) frei zur Verfügung stehen.
To power dynamic processes in cells, the actin and microtubule cytoskeletons organize into complex structures. Although it is known that cytoskeletal coordination is vital for cell function, the mechanisms by which cross-linking proteins coordinate actin and microtubule activities remain poorly understood. In particular, it is unknown how the distinct mechanical properties of different actin architectures modulate the outcome of actin-microtubule interactions. To address this question, we engineered the protein TipAct, which links growing microtubule ends via end-binding proteins to actin filaments. We show that growing microtubules can be captured and guided by stiff actin bundles, leading to global actin-microtubule alignment. Conversely, growing microtubule ends can transport, stretch and bundle individual actin filaments, thereby globally defining actin filament organization. Our results provide a physical basis to understand actin-microtubule cross-talk, and reveal that a simple cross-linker can enable a mechanical feedback between actin and microtubule organization that is relevant to diverse biological contexts.
Singing in different rooms: Common or individual adaptation patterns to the acoustic conditions?
(2020)
Mass spectrometry data is one of the key sources of information in many workflows in medicine and across the life sciences. Mass fragmentation spectra are generally considered to be characteristic signatures of the chemical compound they originate from, yet the chemical structure itself usually cannot be easily deduced from the spectrum. Often, spectral similarity measures are used as a proxy for structural similarity but this approach is strongly limited by a generally poor correlation between both metrics. Here, we propose MS2DeepScore: a novel Siamese neural network to predict the structural similarity between two chemical structures solely based on their MS/MS fragmentation spectra. Using a cleaned dataset of > 100,000 mass spectra of about 15,000 unique known compounds, we trained MS2DeepScore to predict structural similarity scores for spectrum pairs with high accuracy. In addition, sampling different model varieties through Monte-Carlo Dropout is used to further improve the predictions and assess the model's prediction uncertainty. On 3600 spectra of 500 unseen compounds, MS2DeepScore is able to identify highly-reliable structural matches and to predict Tanimoto scores for pairs of molecules based on their fragment spectra with a root mean squared error of about 0.15. Furthermore, the prediction uncertainty estimate can be used to select a subset of predictions with a root mean squared error of about 0.1. Furthermore, we demonstrate that MS2DeepScore outperforms classical spectral similarity measures in retrieving chemically related compound pairs from large mass spectral datasets, thereby illustrating its potential for spectral library matching. Finally, MS2DeepScore can also be used to create chemically meaningful mass spectral embeddings that could be used to cluster large numbers of spectra. Added to the recently introduced unsupervised Spec2Vec metric, we believe that machine learning-supported mass spectral similarity measures have great potential for a range of metabolomics data processing pipelines.
Despite their overwhelming complexity, living cells display a high degree of internal mechanical and functional organization which can largely be attributed to the intracellular biopolymer scaffold, the cytoskeleton. Being a very complex system far from thermodynamic equilibrium, the cytoskeleton's ability to organize is at the same time challenging and fascinating. The extensive amounts of frequently interacting cellular building blocks and their inherent multifunctionality permits highly adaptive behavior and obstructs a purely reductionist approach. Nevertheless (and despite the field's relative novelty), the physics approach has already proved to be extremely successful in revealing very fundamental concepts of cytoskeleton organization and behavior. This review aims at introducing the physics of the cytoskeleton ranging from single biopolymer filaments to multicellular organisms. Throughout this wide range of phenomena, the focus is set on the intertwined nature of the different physical scales (levels of complexity) that give rise to numerous emergent properties by means of self-organization or self-assembly.
Spec2Vec: Improved mass spectral similarity scoring through learning of structural relationships
(2021)
Spectral similarity is used as a proxy for structural similarity in many tandem mass spectrometry (MS/MS) based metabolomics analyses such as library matching and molecular networking. Although weaknesses in the relationship between spectral similarity scores and the true structural similarities have been described, little development of alternative scores has been undertaken. Here, we introduce Spec2Vec, a novel spectral similarity score inspired by a natural language processing algorithm-Word2Vec. Spec2Vec learns fragmental relationships within a large set of spectral data to derive abstract spectral embeddings that can be used to assess spectral similarities. Using data derived from GNPS MS/MS libraries including spectra for nearly 13,000 unique molecules, we show how Spec2Vec scores correlate better with structural similarity than cosine-based scores. We demonstrate the advantages of Spec2Vec in library matching and molecular networking. Spec2Vec is computationally more scalable allowing structural analogue searches in large databases within seconds.
Many different cell types are able to migrate by formation of a thin actin-based cytoskeletal extension. Recently, it became evident that this extension consists of two distinct substructures, designated lamellipodium and lamellum, which differ significantly in their kinetic and kinematic properties as well as their biochemical composition. We developed a stochastic two-dimensional computer simulation that includes chemical reaction kinetics, G-actin diffusion, and filament transport to investigate the formation of growing actin networks in migrating cells. Model parameters were chosen based on experimental data or theoretical considerations. In this work, we demonstrate the system's ability to form two distinct networks by self-organization. We found a characteristic transition in mean filament length as well as a distinct maximum in depolymerization flux, both within the first 1-2 microm. The separation into two distinct substructures was found to be extremely robust with respect to initial conditions and variation of model parameters. We quantitatively investigated the complex interplay between ADF/cofilin and tropomyosin and propose a plausible mechanism that leads to spatial separation of, respectively, ADF/cofilin- or tropomyosin-dominated compartments. Tropomyosin was found to play an important role in stabilizing the lamellar actin network. Furthermore, the influence of filament severing and annealing on the network properties is explored, and simulation data are compared to existing experimental data.
Despite its impressive complexity the cytoskeleton succeeds to persistently organize itself and thus the cells' interior. In contrast to classical man-made machines, much of the cellular organization originates from inherent self-assembly and self-organization allowing a high degree of autonomy for various functional units. Recent experimental and theoretical studies revealed numerous examples of cytoskeleton components that arrange and organize in a self-regulative way. In the present review we want to shortly summarize some of the principle mechanisms that are able to inherently trigger and regulate the cytoskeleton organization. Although taken individually most of these regulative principles are rather simple with intuitively predictable consequences, combinations of two or more of these mechanisms can quickly give rise to very complex, unexpected behavior and might even be able to explain the formation of different functional units out of a common pool of available building blocks.
Music listening has become a highly individualized activity with smartphones and music streaming services providing listeners with absolute freedom to listen to any kind of music in any situation. Until now, little has been written about the processes underlying the selection of music in daily life. The present study aimed to disentangle some of the complex processes among the listener, situation, and functions of music listening involved in music selection. Utilizing the experience sampling method, data were collected from 119 participants using a smartphone application. For 10 consecutive days, participants received 14 prompts using stratified-random sampling throughout the day and reported on their music-listening behavior. Statistical learning procedures on multilevel regression models and multilevel structural equation modeling were used to determine the most important predictors and analyze mediation processes between person, situation, functions of listening, and music selection. Results revealed that the features of music selected in daily life were predominantly determined by situational characteristics, whereas consistent individual differences were of minor importance. Functions of music listening were found to act as a mediator between characteristics of the situation and music-selection behavior. We further observed several significant random effects, which indicated that individuals differed in how situational variables affected their music selection behavior. Our findings suggest a need to shift the focus of music-listening research from individual differences to situational influences, including potential person-situation interactions.
The Sound of Success: Investigating Cognitive and Behavioral Effects of Motivational Music in Sports
(2017)
Listening to music before, during, or after sports is a common phenomenon, yet its functions and effects on performance, cognition, and behavior remain to be investigated. Listening to music before, during, or after sports is a common phenomenon, yet its functions and effects on performance, cognition, and behavior remain to be investigated. In this study we present a novel approach to the role of music in sports and exercise that focuses on the notion of musical self-enhancement (Elvers, 2016). We derived the following hypotheses from this framework: listening to motivational music will (i) enhance self-evaluative cognition, (ii) improve performance in a ball game, and (iii) evoke greater risk-taking behavior. To evaluate the hypotheses, we conducted a between-groups experiment (N = 150) testing the effectiveness of both an experimenter playlist and a participant-selected playlist in comparison to a no-music control condition. All participants performed a ball-throwing task developed by Decharms and Davé (1965), consisting of two parts: First, participants threw the ball from fixed distances into a funnel basket. During this task, performance was measured. In the second part, the participants themselves chose distances from the basket, which allowed their risk-taking behavior to be assessed. The results indicate that listening to motivational music led to greater risk taking but did not improve ball-throwing performance. This effect was more pronounced in male participants and among those who listened to their own playlists. Furthermore, self-selected music enhanced state self-esteem in participants who were performing well but not in those who were performing poorly. We also discuss further implications for the notion of musical self-enhancement. // Listening to music before, during, or after sports is a common phenomenon, yet its functions and effects on performance, cognition, and behavior remain to be investigated. In this study we present a novel approach to the role of music in sports and exercise that focuses on the notion of musical self-enhancement (Elvers, 2016). We derived the following hypotheses from this framework: listening to motivational music will (i) enhance self-evaluative cognition, (ii) improve performance in a ball game, and (iii) evoke greater risk-taking behavior. To evaluate the hypotheses, we conducted a between-groups experiment (N = 150) testing the effectiveness of both an experimenter playlist and a participant-selected playlist in comparison to a no-music control condition. All participants performed a ball-throwing task developed by Decharms and Davé (1965), consisting of two parts: First, participants threw the ball from fixed distances into a funnel basket. During this task, performance was measured. In the second part, the participants themselves chose distances from the basket, which allowed their risk-taking behavior to be assessed. The results indicate that listening to motivational music led to greater risk taking but did not improve ball-throwing performance. This effect was more pronounced in male participants and among those who listened to their own playlists. Furthermore, self-selected music enhanced state self-esteem in participants who were performing well but not in those who were performing poorly. We also discuss further implications for the notion of musical self-enhancement.
Music-related decision-making encompasses a wide range of behaviours including those associated with listening choices, composition and performance, and decisions involving music education and therapy. Although research programmes in psychology and economics have contributed to an improved understanding of music-related behaviour, historically, these disciplines have been unconnected. Recently, however, researchers have begun to bridge this gap by employing tools from behavioural economics. This article contributes to the literature by providing a discussion about the benefits of using behavioural economics in music-decision research. We achieve this in two ways. First, through a systematic review, we identify the current state of the literature within four key areas of behavioural economics-heuristics and biases, social decision-making, behavioural time preferences, and dual-process theory. Second, taking findings of the literature as a starting point, we demonstrate how behavioural economics can inform future research. Based on this, we propose the Behavioural Economics of Music (BEM), an integrated research programme that aims to break new ground by stimulating interdisciplinary research in the intersection between music, psychology, and economics.
The PSFC, or Pioneer Sound Field Controller, is a DSP-driven hemispherical loudspeaker array, installed at the University of Aizu Multimedia Center. The PSFC features realtime manipulation of the primary components of sound spatialization for each of two audio sources located in a virtual environment, including the content (apparent direction and distance) and context (room characteristics: reverberation level, room size and liveness). In an alternate mode, it can also direct the destination of the two separate input signals across 14 loudspeakers, manipulating the direction of the virtual sound sources with no control over apparent distance other than that afforded by source loudness (including no simulated environmental reflections or reverberation). The PSFC speaker dome is about 10 m in diameter, accommodating about fifty simultaneous users, including about twenty users comfortably standing or sitting near its ``sweet spot,'' the area in which the illusions of sound spatialization are most vivid. Collocated with a large screen rear-projection stereographic display, the PSFC is intended for advanced multimedia and virtual reality applications.
Actin droplet machine
(2019)
The actin droplet machine is a computer model of a three-dimensional network of actin bundles developed in a droplet of a physiological solution, which implements mappings of sets of binary strings. The actin bundle network is conductive to travelling excitations, i.e. impulses. The machine is interfaced with an arbitrary selected set of k electrodes through which stimuli, binary strings of length k represented by impulses generated on the electrodes, are applied and responses are recorded. The responses are recorded in a form of impulses and then converted to binary strings. The machine's state is a binary string of length k: if there is an impulse recorded on the ith electrode, there is a '1' in the ith position of the string, and '0' otherwise. We present a design of the machine and analyse its state transition graphs. We envisage that actin droplet machines could form an elementary processor of future massive parallel computers made from biopolymers.
Actin filaments are conductive to ionic currents, mechanical and voltage solitons. These travelling localisations can be utilised to generate computing circuits from actin networks. The propagation of localisations on a single actin filament is experimentally unfeasible to control. Therefore, we consider excitation waves propagating on bundles of actin filaments. In computational experiments with a two-dimensional slice of an actin bundle network we show that by using an arbitrary arrangement of electrodes, it is possible to implement two-inputs-one-output circuits.