Miracle-dz/UniFAR-FTUs huggingface dataset:
Validation of the Slovenian Version of the Movement Imagery Questionnaire for Children (MIQ-C): A Measurement Tool to Assess the Imagery Ability of Motor Tasks in Children

Abstract Purpose The ability to perform motor imagery has been shown to influence individual athletic performance and rehabilitation. Recent evidence supports its potential as a training tool to improve motor skills in children. Although there is a standardized assessment of the imagery abilities in Slovenian-speaking adults, there is currently no validated instrument for use with Slovenian children. Therefore, the aim of the present study was to conduct a linguistic validation study of the movement imagery questionnaire for children (MIQ-C). Methods A total of 100 healthy children (mean age 10.3±1.3 years; 50 female) were assessed with a Slovenian version of the MIQ-C at Day 1 and Day 8. Inter-day agreement was examined using intraclass correlation coefficients (ICC). Construct validity and internal consistency were assessed using a Cronbach’s alpha coefficient and exploratory – confirmatory factor analysis, respectively. Results The test-retest ICC were very high for all three scales examined (ICCKI=0.90; ICCIVI=0.92; ICCEVI=0.90). Excellent internal consistency (up to 0.90) was found for kinaesthetic and both visual imageries. Confirmatory analysis confirmed a three-factorial structure of the MIQ-C. Conclusions The Slovenian version of the MIQ-C proved to be highly reliable and valid in assessing children’s motor imagery abilities, and as such for use with Slovene-speaking children. Moreover, this standardized instrument can be a helpful tool in training and rehabilitation practice with children aged 7–12 years.
Legal affordances in global wealth chains: How platform firms use legal and spatial scaling

Firms can use legal and spatial scaling to increase their control and capacity to exploit assets. Here we examine how platform firms, like AirBnB, Uber, and Bird, scale their operations through global wealth chains. Their use of law is to maximize wealth creation and protection, while their services use local spaces to extract value from established property, labor, and public thoroughfares. We examine how such ‘networked accumulation’ platform firms use legal and spatial scaling through legal affordances. This includes opportunities for absences, ambiguities and arbitrage that are realized via multi and inter-scalar strategies and produce variegation. Our analysis draws on legal documents, as well as interviews, from Barcelona and San Francisco. The article contributes with a model of how platform firms use legal and spatial scaling, as well as how activists can challenge their operations.
GlotBot: Hybrid Language Translator for Secondary Level Mathematics Classrooms

As the place where so many individuals make lifelong friends, learn how to navigate the world, and grow into themselves, compassion must start in the classroom. However, current American classrooms sometimes lack the very principles of equality and inclusion which underlie compassionate communities. One such case can be seen in the systemic lingual ostracization of students who are not native English speakers (English language learners). As our student design partners indicated, students’ and teachers’ mutual understanding despite differences in language, contributes to a feeling of “love and comfort in the classroom”. Inspired by our student design partners’ proposal of the Robo-Assistant, we decided to answer the Interaction Design and Children (IDC) 2023 research and design challenge with GlotBot! GlotBot is a mobile application to be used by teachers while delivering classroom instruction. GlotBot is designed for the secondary mathematics classroom, a prime setting in which students’ English language proficiencies are unfairly conflated with their mathematical abilities. GlotBot translanguages; it generates hybrid translations of teacher speech in real-time by translating non-technical terms into Spanish, but keeping technical terms (“coordinate plane”, “graph”) in English. The teacher can choose to repeat this hybrid translation out loud, to create more access points to the content for English language learners. The main objective of GlotBot is to assist teachers in delivering an equitable pedagogy to create an inclusive and compassionate classroom space that celebrates our nation’s lingual diversity.
Using fNIRS To Understand Adults’ Empathy for Children in AI and Cybersecurity Scenarios

Empathy for children is critical for designing AI technologies that may affect children. This paper presents the work in progress of a study on the feasibility of a new method to provide objective understanding of people’s empathy for children based on functional near infrared spectroscopy (fNIRS). Adult participants (n=13) were presented with benign or concerning scenarios involving children interacting with AI technologies. Their brain activation patterns were recorded and analyzed. Preliminary data analysis revealed distinctive patterns in the mPFC region, which justifies future work to fully realize the potential of this method.
Analysis of vertical profiles of mean velocity, shear stress and turbulent viscosity in a wave-current environment through RANS numerical simulations

The nearshore hydrodynamics and coastal circulation result from the contribution of a variety of phenomena (large scale circulation currents, tidal effects, waves, wind action, etc.) which have complex physical interactions with different scales. Among these interactions, we focus here on the interaction between waves and currents, especially when the current presents a strong shear over the water depth. In the present work, the evaluation and analysis of wave-current interactions is made through numerical simulations based on Reynolds Averaged Navier-Stokes (RANS) equations, applied to the modeling of the complete flow motion, namely waves and currents simultaneously (i.e. without decoupling the two phenomena). The advanced CFD solver code_Saturne (ARCHAMBEAU et al., 2004) is used for this purpose. Numerical results are compared with experimental data from UMEYAMA (2005). Four different wave heights and wave periods for each case of (i) only waves, (ii) waves following currents and (iii) waves opposing currents are tested. A detailed study of the mean horizontal velocities, shear stresses and turbulent viscosity vertical profile changes when waves and currents interact is presented.
Coil motion effects in watt balances: a theoretical check

A watt balance is a precision apparatus for the measurement of the Planck constant that has been proposed as a primary method for realizing the unit of mass in a revised International System of Units. In contrast to an ampere balance, which was historically used to realize the unit of current in terms of the kilogram, the watt balance relates electrical and mechanical units through a virtual power measurement and has far greater precision. However, because the virtual power measurement requires the execution of a prescribed motion of a coil in a fixed magnetic field, systematic errors introduced by horizontal and rotational deviations of the coil from its prescribed path will compromise the accuracy. We model these potential errors using an analysis that accounts for the fringing field in the magnet, creating a framework for assessing the impact of this class of errors on the uncertainty of watt balance results.
Sequential Scheduling of Dataflow Graphs for Memory Peak Minimization

Many computing systems are constrained by their fixed amount of shared memory. Modeling applications with task or Synchronous DataFlow (SDF) graphs makes it possible to analyze and optimize their memory peak. The problem studied by this paper is the memory peak minimization of such graphs when scheduled sequentially. Regarding task graphs, former work has focused on the Series-Parallel Directed Acyclic Graph (SP-DAG) subclass and proposed techniques to find the optimal sequential algorithm w.r. t. memory peak. In this paper, we propose task graph transformations and an optimized branch and bound algorithm to solve the problem on a larger class of task graphs. The approach also applies to SDF graphs after converting them to task graphs. However, since that conversion may produce very large graphs, we also propose a new suboptimal method, similar to Partial Expansion Graphs, to reduce the problem size. We evaluate our approach on classic benchmarks, on which we always outperform the state-of-the-art.
Five Design Recommendations for Teaching Teenagers’ about Artificial Intelligence and Machine Learning

Technologies based on AI/ML are playing an increasingly prominent role in teenagers’ everyday lives. Mirroring this trend is a concomitant interest in teaching young people about intelligent technologies. Whereas previous research in the field of Child–Computer Interaction has proposed curriculum and learning activities that describe what teenagers need to learn about AI/ML, there is still a shortage of studies which specifically address teenager-centered perspectives in the teaching of AI/ML. This paper presents a study of teenagers’ everyday understanding of AI/ML technologies. Using a thematic analysis of the teenagers’ own explanations during a series of workshops, we present a conceptual map of the teenagers’ understandings of these technologies. We go on to propose five general recommendations for the teaching of AI/ML to teenagers through the lens of Computational Empowerment. Taken together, these recommendations serve as a teenage-centered starting point for teaching young people about intelligent technologies, an approach that can be implemented in future research interventions with similar objectives.
Designing Multi Sensory Environments for Children’s Learning: An Analysis of Teachers’ and Researchers’ Perspectives

Embodied learning offers new opportunities to enhance learning effectively, and engage children with stimulating educational experiences. Multi Sensory Environments (MSEs) are spaces that allow for several interaction modalities that stimulate users’ senses and allow the collection of multimodal data. In educational contexts, they provide opportunities to support children’s learning in a playful manner. The use of MSEs is usually carried out with the collaboration of teachers; their perspectives and responsibilities are crucial for the children’s experience. The goal of our research is to uncover evidence-based challenges and opportunities, while considering teachers’ experiences. We conducted fourteen semi-structured interviews with teachers (n = 6) and researchers (n = 8) experienced using MSEs’, and analysed the identified challenges and considerations during a workshop with four Child-Computer Interaction (CCI) experts. We offer a series of implications for consideration when designing and/or using MSEs to support children’s learning.
Estimation of Leverage Effect: Kernel Function and Efficiency

Abstract This article proposes more efficient estimators for the leverage effect than the existing ones. The idea is to allow for nonuniform kernel functions in the spot volatility estimates or the aggregated returns. This finding highlights a critical difference between the leverage effect and integrated volatility functionals, where the uniform kernel is optimal. Another distinction between these two cases is that the overlapping estimators of the leverage effect are more efficient than the nonoverlapping ones. We offer two perspectives to explain these differences: one is based on the “effective kernel” and the other on the correlation structure of the nonoverlapping estimators. The simulation study shows that the proposed estimator with a nonuniform kernel substantially increases the estimation efficiency and testing power relative to the existing ones.
Single-cell analysis reveals the stromal dynamics and tumor-specific characteristics in the microenvironment of ovarian cancer

High-grade serous ovarian carcinoma (HGSOC) is a heterogeneous disease, and a high stromal/desmoplastic tumor microenvironment (TME) is associated with a poor outcome. Stromal cell subtypes, including fibroblasts, myofibroblasts, and cancer-associated mesenchymal stem cells, establish a complex network of paracrine signaling pathways with tumor-infiltrating immune cells that drive effector cell tumor immune exclusion and inhibit the antitumor immune response. Single-cell transcriptomics of the HGSOC TME from public and in-house datasets revealed a distinct transcriptomic landscape for immune and non-immune cells in high-stromal vs. low-stromal tumors. High-stromal tumors had a lower fraction of certain T cells, natural killer (NK) cells, and macrophages and increased expression of CXCL12 in epithelial cancer cells and cancer-associated mesenchymal stem cells (CA-MSCs). Analysis of cell-cell communication indicated that epithelial cancer cells and CA-MSCs secreted CXCL12 that interacted with the CXCR4 receptor, which was overexpressed on NK and CD8+ T cells. CXCL12 and/or CXCR4 antibodies confirmed the immunosuppressive role of CXCL12-CXCR4 in high-stromal tumors.
Aberrant landscapes of maternal meiotic crossovers contribute to aneuploidies in human embryos

Meiotic recombination is crucial for human genetic diversity and chromosome segregation accuracy. Understanding its variation across individuals and the processes by which it goes awry are long-standing goals in human genetics. Current approaches for inferring recombination landscapes either rely on population genetic patterns of linkage disequilibrium (LD)—capturing a time-averaged view—or direct detection of crossovers in gametes or multi-generation pedigrees, which limits dataset scale and availability. Here, we introduce an approach for inferring sex-specific recombination landscapes using data from preimplantation genetic testing for aneuploidy (PGT-A). This method relies on low-coverage (<0.05×) whole-genome sequencing of in vitro fertilized (IVF) embryo biopsies. To overcome the data sparsity, our method exploits its inherent relatedness structure, knowledge of haplotypes from external population reference panels, as well as the frequent occurrence of monosomies in embryos, whereby the remaining chromosome is phased by default. Extensive simulations demonstrate our method’s high accuracy, even at coverages as low as 0.02×. Applying this method to PGT-A data from 18,967 embryos, we mapped 70,660 recombination events with ∼150 kbp resolution, replicating established sex-specific recombination patterns. We observed a reduced total length of the female genetic map in trisomies compared to disomies, as well as chromosome-specific alterations in crossover distributions. Based on haplotype configurations in pericentromeric regions, our data indicate chromosome-specific propensities for different mechanisms of meiotic error. Our results provide a comprehensive view of the role of aberrant meiotic recombination in the origins of human aneuploidies and offer a versatile tool for mapping crossovers in low-coverage sequencing data from multiple siblings.
Highly degenerate quasilinear parabolic systems

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Genetic circuits for feedback control of gamma-aminobutyric acid biosynthesis in probiotic Escherichia coli Nissle 1917

Engineered microorganisms such as the probiotic strain Escherichia coli Nissle 1917 (EcN) offer a strategy to sense and modulate the concentration of metabolites or therapeutics in the gastrointestinal tract. Here, we present an approach to regulate production of the depression-associated metabolite gamma-aminobutyric acid (GABA) in EcN using genetic circuits that implement negative feedback. We engineered EcN to produce GABA by overexpressing glutamate decarboxylase (GadB) from E. coli and applied an intracellular GABA biosensor to identify growth conditions that improve GABA biosynthesis. We next employed characterized genetically-encoded NOT gates to construct genetic circuits with layered feedback to control the rate of GABA biosynthesis and the concentration of GABA produced. Looking ahead, this approach may be utilized to design feedback control of microbial metabolite biosynthesis to achieve designable smart microbes that act as living therapeutics.
Unifying sparse coding, predictive coding, and divisive normalization

Sparse coding, predictive coding and divisive normalization have each been found to be principles that underlie the function of neural circuits in many parts of the brain, supported by substantial experimental evidence. However, the connections between these related principles are still poorly understood. In this paper, we show how sparse coding, predictive coding, and divisive normalization can be described within a unified framework, and illustrate this explicitly within the context of a two-layer neural model. This two-layer model is constructed in a way that implements sparse coding with a network structure that is constructed by implementing predictive coding. The results demonstrate how a homeostatic function that regulates neural responses in the model can shape the nonlinearity of neural responses in a way that replicates different forms of divisive normalization. The equivalence of the model to divisive normalization in a single-neuron case is shown analytically. Simulations show that the model can learn simple cells in the primary visual cortex with the property of contrast saturation, which has previously been explained by divisive normalization. In summary, the study demonstrates that the three principles of sparse coding, predictive coding, and divisive normalization can be unified to provide a learning framework based on biophysical properties, such as Hebbian learning and homeostasis, and this framework incorporates both learning and more diverse response nonlinearities observed experimentally. This framework has the potential to also be used to explain how the brain learns to integrate input from different sensory modalities.
Astrocyte store-operated calcium entry is required for centrally mediated neuropathic pain

Central sensitization is a critical step in chronic neuropathic pain formation following acute nerve injury. Central sensitization is defined by nociceptive and somatosensory circuitry changes in the spinal cord leading to dysfunction of antinociceptive gamma-aminobutyric acid (GABA)ergic cells (Li et al., 2019), amplification of ascending nociceptive signals, and hypersensitivity (Woolf, 2011). Astrocytes are key mediators of the neurocircuitry changes that underlie central sensitization and neuropathic pain, and astrocytes respond to and regulate neuronal function through complex Ca2+ signaling mechanisms. Clear definition of the astrocyte Ca2+ signaling mechanisms involved in central sensitization may lead to new therapeutic targets for treatment of chronic neuropathic pain, as well as enhance our understanding of the complex central nervous system (CNS) adaptions that occur following nerve injury. Ca2+ release from astrocyte endoplasmic reticulum (ER) Ca2+ stores via the inositol 1,4,5-trisphosphate receptor (IP3R) is required for centrally mediated neuropathic pain (Kim et al, 2016); however recent evidence suggests the involvement of additional astrocyte Ca2+ signaling mechanisms. We therefore investigated the role of astrocyte store-operated Ca2+ entry (SOCE), which mediates Ca2+ influx in response to ER Ca2+ store depletion. Using an adult Drosophila melanogaster model of central sensitization based on thermal allodynia in response to leg amputation nerve injury (Khuong et al., 2019), we show that astrocytes exhibit SOCE-dependent Ca2+ signaling events three to four days following nerve injury. Astrocyte-specific suppression of Stim and Orai, the key mediators of SOCE Ca2+ influx, completely inhibited the development of thermal allodynia seven days following injury, and also inhibited the loss of ventral nerve cord (VNC) GABAergic neurons that is required for central sensitization in flies. We lastly show that constitutive SOCE in astrocytes results in thermal allodynia even in the absence of nerve injury. Our results collectively demonstrate that astrocyte SOCE is necessary and sufficient for central sensitization and development of hypersensitivity in Drosophila, adding key new understanding to the astrocyte Ca2+ signaling mechanisms involved in chronic pain.
Timbral effects on consonance illuminate psychoacoustics of music evolution

The phenomenon of musical ‘consonance’ is an essential feature in diverse musical styles. The traditional belief, supported by centuries of Western music theory and psychological studies, is that consonance derives from simple (‘harmonic’) frequency ratios between tones. Here we show through massive large-scale behavioral experiments (235,440 judgments) that harmonic consonance preferences can be deeply reshaped by timbral manipulations, even as far as to induce preferences for inharmonic intervals. We show how such effects may have shaped the cultural evolution of diverse scale systems ranging from the gamelan’s ‘slendro’ scale to the tuning of Western ‘mean-tone’ and ‘equal-tempered’ scales. Through computational modeling we show that these timbral manipulations elegantly dissociate competing psychoacoustic mechanisms underlying consonance, and we derive an updated computational model combining liking of harmonicity, disliking of fast beats (roughness), and liking of slow beats. Altogether, this work showcases how large-scale behavioral experiments can inform classical questions in auditory perception. Author summary “We reveal effects of timbre on consonance perception that motivate a new understanding of the evolution of musical scales. ”
CViewer: A Java-based statistical framework for integration of shotgun metagenomics with other omics datasets

We have developed CViewer, a java-based framework to consolidate, visualize, and explore enormous amount of information recovered from shotgun sequencing experiments. This information includes and integrates all levels of gene products, mRNA, protein, metabolites, as well as their interactions in a single platform. The software provides a single platform to give statistical inference, and employs algorithms, some borrowed from numerical ecology literature to allow exploratory as well as hypothesis driven analyses. The end product is a highly interactive toolkit with multiple document interface, that makes it easier for a person without specialized knowledge to perform analysis of multiomics datasets and unravel biologically relevant hypotheses. As a proof-of-concept, we have used CViewer to explore two distinct metagenomics datasets: a dietary intervention study to understand Crohn’s disease changes during a dietary treatment to include remission, as well as a gut microbiome profile for an obesity dataset comparing subjects who suffer from obesity of different aetiologies and against controls who were lean.