MOTIVATION: RNA-protein interactions play essential roles in every living organism, with RNA transcription, processing, and translation being just a few examples. Therefore, determining the set of RNAs that are bound by individual RNA-binding proteins, as well as the precise location of the interaction, is crucial for biological understanding. CLIP (UV-cross-linking and immunoprecipitation) is a method developed to study these interactions. Several variations of the CLIP protocol have been developed, e.g. iCLIP (individual-nucleotide resolution CLIP), which offers nucleotide-precise resolution of the cross-linking event. RESULTS: PARANOiD is a versatile software for fully automated analysis of iCLIP and iCLIP2 data. It contains all steps necessary for preprocessing, the determination of cross-link locations, and several additional steps, which can be used to detect specific characteristics, e.g. definite distances between cross-link events or identify binding motifs. Additionally, results are visualized as statistical plots for a quick overview and as standardized bioinformatics file formats, which can be used for further analysis steps. AVAILABILITY AND IMPLEMENTATION: PARANOiD is published under the MIT license and is available from https://github.com/patrick-barth/PARANOiD. The documentation is available at https://paranoid.readthedocs.io/en/latest/index.html.

Scientific data management is at a critical juncture, driven by exponential data growth, increasing cross-domain dependencies, and a severe reproducibility crisis in modern research. Traditional centralized data management approaches are not only struggling with data volume but also fail to address the fragmentation of research results across domains. This hinders scientific reproducibility and cross-domain collaboration and increases concerns about data sovereignty and governance. This article proposes FAIR and federated Data Ecosystems as an improved architectural pattern for future research data ecosystems. It tries to incorporate the latest advancements in decentralized, distributed systems into existing research infrastructure to promote cross-domain collaboration. Based on established patterns from Data Commons, Data Meshes, and Data Spaces, our approach focuses on a layered architecture that consists of governance, data, service, and application layers. With this, it could be possible to preserve domain-specific expertise and control while facilitating data integration through standardized interfaces and semantic enrichment. Key requirements include adaptive metadata management, simplified user interaction, robust security, and transparent data transactions. Our architecture supports compute-to-data as well as data-to-compute paradigms, implementing a decentralized peer-to-peer network that scales horizontally. This article aims to provide both an impulse for the technical architecture as well as concepts for a governance framework so that FAIR and federated Data Ecosystems could enable researchers to build on existing work while maintaining control over their data and computing resources. This could provide a practical path towards an integrated research infrastructure that respects domain autonomy as well as interoperability requirements.

In an interdisciplinary and evolving research field like human-robot interaction, clear and precise results reporting is essential for study comparability and replicability. To address the lack of a standard for such reporting and, at the same time, provide guidance for novices in the field, we have developed a web-based reporting form to capture human-robot interaction studies, serving as a model for how conferences could adopt it into the submission pipeline. In this work, we present a formative evaluation of this form regarding its level of detail, format and clarity, and the perceived benefits for authors, reviewers, and the community as a whole. We report the expert review of nine researchers who highlight the substantial value of this tool. In addition, these experts also provide suggestions for improvements to its form and the addition of details surrounding qualitative reporting.

MOTIVATION AND RESULTS: Crypt4GH-JS is a browser-ready implementation of the Crypt4GH file encryption standard written in JavaScript. While having minimal to no impact on data upload and download throughput this library enables on-the-fly encryption of arbitrary data in web applications, regardless of whether on the client or server side. As development moves more and more toward cloud-native applications, this library represents a significant step forward for flexible data security in the context of opaque cloud storage systems. AVAILABILITY AND IMPLEMENTATION: Crypt4GH-JS can be installed via Node Package Manager (https://www.npmjs.com/package/crypt4gh_js) or through its public GitHub Repository (https://github.com/fathelen/crypt4ghJS), where the source code is available. Crypt4GH-JS can be tested in the browser using our demonstration website, which can be found at: https://fathelen.github.io/crypt4ghJS/.

Loneliness is rising amongst several age groups in several countries with older UK adults being one of the affected populations. Early work from South Korea on the use of a network of social robots exchanging activity information between different households, so-called ‘living noise’, yielded positive results in incentivizing communication between young professionals, thereby reducing loneliness. In this short article, we report the results of a co-design workshop held with lonely, older UK adults that aimed to determine the potential of adoption of living noise transmission amongst this population. The results were encouraging and will feed into future work involving the deployment of robots and virtual avatars in people’s homes.

Trust plays a major role when introducing interactive robots into people’s personal spaces, which, in large part, depends on how they perceive the robot. This paper presents the initial results of an investigation into the perception of robot agency as a potential factor influencing trust. We manipulated a robot’s agency to see how trust would change as a result. Our preliminary results indicate age as a confounding factor while we did not find differences when priming robot autonomy.

Fluidity is a key quality of human-human and more natural human-agent interaction (HAI). The concept of fluidity is difficult to define formally, however, interaction partners and users perceive the difference between more and less fluid interaction. As an initial informal definition, fluidity in interaction can be considered the abilities to seamlessly transition in turn-taking, to allow appropriate overlap of turns between agents, including multimodally, and to allow action using prediction. The purpose of this workshop is to bring agent designers together to attempt to define fluidity in interaction more precisely and propose ways in which we can make HAI more fluid.

Achieving truly fluid interaction with robots with speech interfaces remains a hard problem. Despite technical advances in sensors, processors and actuators, the experience of Human-Robot Interaction (HRI) remains laboured and frustrating. Some of the barriers to this stem from a lack of a suitable development platform for HRI to improve the interaction, particularly for mobile manipulator robots. In this paper we briefly overview some existing systems and propose a high-fidelity Virtual Reality (VR) HRI simulation environment with Wizard-of-Oz (WoZ) cabability applicable to multiple robots including mobile manipulators and social robots.

This is an open access article distributed under the Creative Commons Attribution License, to view a copy of the license, see: https://creativecommons.org/licenses/by-nc-nd/4.0/

Motor resonance, the activation of an observer’s motor control system by another actor’s movements, has been claimed to be an indicator for quality of interaction. Motor interference as one of the consequences of the presence of resonance can be detected by analyzing an actor’s spatial movements. It has therefore been used as an indicator for the presence of motor resonance. Unfortunately, the experimental paradigm in which motor interference has been shown to be detectable is ecologically implausible both in terms of the types of movements employed and the number of repetitions required. In the presented experiment, we tested whether some of these experimental constraints can be relaxed or modified toward a more naturalistic behavior without losing the ability to detect the interference effect. In the literature, spatial variance has been analytically quantified in many different ways. This study found these analytical variations to be nonequivalent by implementing them. Back-and-forth transitive movements were tested for motor interference; the effect was found to be more robust than with left-right movements, although the direction of interference was opposite to that reported in the literature. We conclude that motor interference, when measured by spatial variation, lacks promise for embedding in naturalistic interaction scenarios because the effect sizes were small.

“No” is one of the first ten words used by children and embodies the first form of linguistic negation. Despite its early occurrence, the details of its acquisition remain largely unknown. The circumstance that “no” cannot be construed as a label for perceptible objects or events puts it outside the scope of most modern accounts of language acquisition. Moreover, most symbol grounding architectures will struggle to ground the word due to its non-referential character. The presented work extends symbol grounding to encompass affect and motivation. In a study involving the child-like robot iCub, we attempt to illuminate the acquisition process of negation words. The robot is deployed in speech-wise unconstrained interaction with participants acting as its language teachers. The results corroborate the hypothesis that affect or volition plays a pivotal role in the acquisition process. Negation words are prosodically salient within prohibitive utterances and negative intent interpretations such that they can be easily isolated from the teacher’s speech signal. These words subsequently may be grounded in negative affective states. However, observations of the nature of prohibition and the temporal relationships between its linguistic and extra-linguistic components raise questions over the suitability of Hebbian-type algorithms for certain types of language grounding.

MOTIVATION: Today, the base code of DNA is mostly determined through sequencing by synthesis as provided by the Illumina sequencers. Although highly accurate, resulting reads are short, making their analyses challenging. Recently, a new technology, single molecule real-time (SMRT) sequencing, was developed that could address these challenges, as it generates reads of several thousand bases. But, their broad application has been hampered by a high error rate. Therefore, hybrid approaches that use high-quality short reads to correct erroneous SMRT long reads have been developed. Still, current implementations have great demands on hardware, work only in well-defined computing infrastructures and reject a substantial amount of reads. This limits their usability considerably, especially in the case of large sequencing projects. RESULTS: Here we present proovread, a hybrid correction pipeline for SMRT reads, which can be flexibly adapted on existing hardware and infrastructure from a laptop to a high-performance computing cluster. On genomic and transcriptomic test cases covering Escherichia coli, Arabidopsis thaliana and human, proovread achieved accuracies up to 99.9% and outperformed the existing hybrid correction programs. Furthermore, proovread-corrected sequences were longer and the throughput was higher. Thus, proovread combines the most accurate correction results with an excellent adaptability to the available hardware. It will therefore increase the applicability and value of SMRT sequencing. AVAILABILITY AND IMPLEMENTATION: proovread is available at the following URL: http://proovread.bioapps.biozentrum.uni-wuerzburg.de.

We overview how sensorimotor experience can be operationalized for interaction scenarios in which humanoid robots acquire skills and linguistic behaviours via enacting a “form-of-life” in interaction games (following Wittgenstein) with humans. The enactive paradigm is introduced which provides a powerful framework for the construction of complex adaptive systems, based on interaction, habit, and experience. Enactive cognitive architectures (following insights of Varela, Thompson and Rosch) that we have developed support social learning and robot ontogeny by harnessing information-theoretic methods and raw uninterpreted sensorimotor experience to scaffold the acquisition of behaviours. The success criterion here is validation by the robot engaging in ongoing human-robot interaction with naive participants who, over the course of iterated interactions, shape the robot's behavioural and linguistic development. Engagement in such interaction exhibiting aspects of purposeful, habitual recurring structure evidences the developed capability of the humanoid to enact language and interaction games as a successful participant.

It is thought that meaning may be grounded in early childhood language learning via the physical and social interaction of the infant with those around him or her, and that the capacity to use words, phrases and their meaning are acquired through shared referential dasiainferencepsila in pragmatic interactions. In order to create appropriate conditions for language learning by a humanoid robot, it would therefore be necessary to expose the robot to similar physical and social contexts. However in the early stages of language learning it is estimated that a 2-year-old child can be exposed to as many as 7,000 utterances per day in varied contextual situations. In this paper we report on the issues behind and the design of our currently ongoing and forthcoming experiments aimed to allow a robot to carry out language learning in a manner analogous to that in early child development and which effectively dasiashort cutspsila holophrase learning. Two approaches are used: (1) simulated babbling through mechanisms which will yield basic word or holophrase structures and (2) a scenario for interaction between a human and the humanoid robot where shared dasiaintentionalpsila referencing and the associations between physical, visual and speech modalities can be experienced by the robot. The output of these experiments, combined to yield word or holophrase structures grounded in the robot's own actions and modalities, would provide scaffolding for further proto-grammatical usage-based learning. This requires interaction with the physical and social environment involving human feedback to bootstrap developing linguistic competencies. These structures would then form the basis for further studies on language acquisition, including the emergence of negation and more complex grammar.