Miracle-dz/UniFAR-FTUs huggingface dataset:
Effect of Short-Term Kinesiology Taping on Knee Proprioception and Quadriceps Performance in Healthy Individuals

Background: Kinesiology taping (KT) is well known measure for preventing musculoskeletal injuries. Our study aims to explore the actual effects of KT on healthy participants’ knee proprioception and quadriceps performance within 1 h. Methods: A total of 35 healthy male amateur runners were recruited in our study. Four taping sequences were randomly allocated to four different weeks, namely, no taping, placebo taping, KT with tension, and KT with no tension. A CON-TREX isokinetic dynamometer was used in assessing the participants’ knee proprioception and muscle strength of knee extension and flexion at 60°/s. The electromyography (EMG) signals of medial oblique muscle and vastus lateralis were collected using Myon EMG system synchronously. Two-way repeated measures ANOVA was used in exploring the difference between taping and time effects, and the significance was set to alpha <0.05. Results: Significant interaction effect was found between the taping groups and time effect [F (3.32) = 2.389, p = 0.029, η2 = 0.050] in the peak torque during the concentric contraction of quadriceps. No significant interaction and no significant differences between groups and time effects in knee proprioception and muscle activation. Conclusion: The effect of KT seems insufficiently large to impose a positive effect on healthy people within short periods. Health participants may not necessarily use KT to increase muscle activation and proprioception of knee.
Biosensors for Studies on Adhesion-Mediated Cellular Responses to Their Microenvironment

Cells interact with their microenvironment by constantly sensing mechanical and chemical cues converting them into biochemical signals. These processes allow cells to respond and adapt to changes in their environment, and are crucial for most cellular functions. Understanding the mechanism underlying this complex interplay at the cell-matrix interface is of fundamental value to decipher key biochemical and mechanical factors regulating cell fate. The combination of material science and surface chemistry aided in the creation of controllable environments to study cell mechanosensing and mechanotransduction. Biologically inspired materials tailored with specific bioactive molecules, desired physical properties and tunable topography have emerged as suitable tools to study cell behavior. Among these materials, synthetic cell interfaces with built-in sensing capabilities are highly advantageous to measure biophysical and biochemical interaction between cells and their environment. In this review, we discuss the design of micro and nanostructured biomaterials engineered not only to mimic the structure, properties, and function of the cellular microenvironment, but also to obtain quantitative information on how cells sense and probe specific adhesive cues from the extracellular domain. This type of responsive biointerfaces provides a readout of mechanics, biochemistry, and electrical activity in real time allowing observation of cellular processes with molecular specificity. Specifically designed sensors based on advanced optical and electrochemical readout are discussed. We further provide an insight into the emerging role of multifunctional micro and nanosensors to control and monitor cell functions by means of material design.
Overexpression Levels of LbDREB6 Differentially Affect Growth, Drought, and Disease Tolerance in Poplar

The application of drought stress-regulating transcription factors (TFs) offers a credible way to improve drought tolerance in plants. However, many drought resistant TFs always showed unintended adverse effects on plant growth or other traits. Few studies have been conducted in trees to evaluate and overcome the pleiotropic effects of drought tolerance TFs. Here, we report the dose-dependent effect of the Limonium bicolor LbDREB6 gene on its overexpression in Populus ussurensis. High- and moderate-level overexpression of LbDREB6 significantly increased drought tolerance in a dose-dependent manner. However, the OE18 plants showed stunted growth under normal conditions, but they were also more sensitive to Marssonina brunnea infection than wild type (WT) and OE14 plants. While, OE14 showed normal growth, the pathogen tolerance of them was not significantly different from WT. Many stress-responsive genes were up-regulated in OE18 and OE14 compared to WT, especially for OE18 plants. Meanwhile, more pathogen tolerance related genes were down-regulated in OE18 compared to OE14 and WT plants. We achieved improved drought tolerance by adjusting the increased levels of exogenous DREB genes to avoid the occurrence of growth reduction and reduced disease tolerance.
Intracellular A53T Mutant α-Synuclein Impairs Adult Hippocampal Newborn Neuron Integration

Dendritic dysfunction is an early event in α-synuclein (α-syn) mediated neurodegeneration. Altered postsynaptic potential and loss of dendritic spines have been observed in different in vitro and in vivo models of synucleinopathies. The integration of newborn neurons into the hippocampus offers the possibility to study dendrite and spine formation in an adult environment. Specifically, survival of hippocampal adult newborn neurons is regulated by synaptic input and was reduced in a mouse model transgenic for human A53T mutant α-syn. We thus hypothesized that dendritic integration of newborn neurons is impaired in the adult hippocampus of A53T mice. We analyzed dendritic morphology of adult hippocampal neurons 1 month after retroviral labeling. Dendrite length was unchanged in the dentate gyrus of A53T transgenic mice. However, spine density and mushroom spine density of newborn neurons were severely decreased. In this mouse model, transgenic α-syn was expressed both within newborn neurons and within their environment. To specifically determine the cell autonomous effects, we analyzed cell-intrinsic overexpression of A53T α-syn using a retrovirus. Since A53T α-syn overexpressing newborn neurons exhibited decreased spine density 1 month after labeling, we conclude that cell-intrinsic A53T α-syn impairs postsynaptic integration of adult hippocampal newborn neurons. Our findings further support the role of postsynaptic degeneration as an early feature in synucleinopathies and provide a model system to study underlying mechanisms.
Genetic Variation in Neisseria meningitidis Does Not Influence Disease Severity in Meningococcal Meningitis

Neisseria meningitidis causes sepsis and meningitis in humans. It has been suggested that pathogen genetic variation determines variance in disease severity. Here we report results of a genome-wide association study of 486 N. meningitidis genomes from meningococcal meningitis patients and their association with disease severity. Of 369 meningococcal meningitis patients for whom clinical data was available, 44 (12%) had unfavorable outcome and 24 (7%) died. To increase power, thrombocyte count was used as proxy marker for disease severity. Bacterial genetic variants were called as k-mers, SNPs, insertions and deletions and clusters of orthologous genes (COGs). Population-level meningococcal genetic variation did not explain variance in disease severity (unfavorable outcome or thrombocyte count) in this cohort (h2 = 0.0%; 95% confidence interval: 0.0–0.9). Genetic variants in the bacterial uppS gene represented the top signal associated with thrombocyte count (p-value = 9.96e-07) but this did not reach statistical significance. We did not find an association between previously published variants in lpxL1, fHbp, and tps genes and unfavorable outcome or thrombocyte count. A power analysis based on simulated phenotypes based on real genetic data from 880 N. meningitidis genomes showed that we would be able to detect a continuous phenotype with h2 > = 0.5 with the population size available in this study. This rules out a major contribution of pathogen genetic variation to disease severity in meningococcal meningitis, and shows that much larger sample sizes are required to find specific low-effect genetic variants modulating disease outcome in meningococcal meningitis.
Modeling artificial agents' actions in context - a deontic cognitive event ontology

Although there have been efforts to integrate Semantic Web technologies and artificial agents related AI research approaches, they remain relatively isolated from each other. Herein, we introduce a new ontology framework designed to support the knowledge representation of artificial agents’ actions within the context of the actions of other autonomous agents and inspired by standard cognitive architectures. The framework consists of four parts: 1) an event ontology for information pertaining to actions and events; 2) an epistemic ontology containing facts about knowledge, beliefs, perceptions and communication; 3) an ontology concerning future intentions, desires, and aversions; and, finally, 4) a deontic ontology for modeling obligations and prohibitions which limit agents’ actions. The architecture of the ontology framework is inspired by deontic cognitive event calculus as well as epistemic and deontic logic. We also describe a case study in which the proposed DCEO ontology supports autonomous vehicle navigation.
Modelling of Grey Wolf Optimization Algorithm Using 2D P Colonies

: The P colonies are a well-established version of the P systems, the computational device based on membrane computing. One branch of the research of the P colonies focuses on the possibility to consider the two-dimensional environment, in which the agents act, and the 2D P colonies were introduced. 2D P colonies showed to be suitable for the simulations of various (not only) multi-agent systems, and natural phenomena, like the ﬂash ﬂoods. However, the agents of the 2D P colony are able to communicate via the environment, by leaving some special symbols in it, this may not be sufﬁcient for simulating some more complex communities of the agents, for example the hunting pack of the grey wolves. In this paper, we introduce a formal model of the 2D P colonies with the blackboard as an extension of the 2D P colonies. We also allow the agents to use simple relational operations to compare real numbers that can be stored inside special symbols. We use this model to simulate the Grey wolf algorithm.
The BBC project - Body based classrooms

Despite growing strands of evidence indicate that learning processes can be fostered by making learners cognitively and bodily active, too often school considers learners as passive receivers of knowledge and tends to ignore the role of the body, thereby providing disembodied and too abstract teaching methods We present a project in which experimental evidence on embodied cognition and active learning is used to devise a training for learning from text in primary school children The evidence-based training, which exploits body and sensorimotor information, involves learning of information technology and can be easily incorporated into teaching settings and procedures Both learning in presence and distant learning in Covid era can benefit from bringing the body back into the learning process © 2020 CEUR-WS All rights reserved
Keypoints Selection Using Evolutionary Algorithms

: This contribution presents the use of neural networks trained by an evolutionary algorithm for a selection of visual keypoints. Visual keypoints play an important role in many computer vision tasks but many algorithms for keypoint detection produce many keypoints which are not useful for the target task. We aim to ﬁlter them in a data-driven way. Our model uses a neural network that ranks each keypoint by a relevancy score that we use to choose top-K keypoints with the highest rank. These key-points are then used for the target task, which is image classiﬁcation in our case. Because we use discrete operations in our model, we can not easily obtain gradients for weight updates. We, therefore, optimize the weights of the network by CMA-ES algorithm, which enables efﬁcient optimization of continuous parameters of black-box functions. In this article, we present our initial experiments with this method.
Abstractive Text Summarization using Transfer Learning

: Recently, abstractive text summarization has achieved success in switching from linear models via sparse and handcrafted features to nonlinear neural network models via dense inputs. This success comes from the application of deep learning models on natural language processing tasks where these models are capable of modeling intricate patterns in data without handcrafted features. In this work, the text summarization problem has been explored using Sequence-to-sequence recurrent neural networks and Transfer Learning with a Uniﬁed Text-to-Text Transformer approaches. Experimental results showed that the Transfer Learning-based model achieved considerable improvement for abstractive text summarization.
MTD: A Multimodal Dataset of Musical Themes for MIR Research

Musical themes are essential elements in Western classical music. In this paper, we present the Musical Theme Dataset (MTD), a multimodal dataset inspired by “A Dictionary of Musical Themes” by Barlow and Morgenstern from 1948. For a subset of 2067 themes of the printed book, we created several digital representations of the musical themes. Beyond graphical sheet music, we provide symbolic music encodings, audio snippets of music recordings, alignments between the symbolic and audio representations, as well as detailed metadata on the composer, work, recording, and musical characteristics of the themes. In addition to the data, we also make several parsers and web-based interfaces available to access and explore the different modalities and their relations through visualizations and sonifications. These interfaces also include computational tools, bridging the gap between the original dictionary and music information retrieval (MIR) research. The dataset is of relevance for various subfields and tasks in MIR, such as cross-modal music retrieval, music alignment, optical music recognition, music transcription, and computational musicology.
Descubrimiento de modelos de comportamiento de perfiles de jugadores en juegos serios(Discovering player profile behavior models in serious games)

Serious games facilitate the learning of skills in educational environments, due to their entertaining nature. Ad-hoc developed serious games ease aligning the ludic experience with speciﬁc learning goals, allowing the recording of player activity. However, the data generated can be too large to be analysed through manual techniques. In this paper we propose applying process mining techniques to analyse the data resulting from a serious game experience. Speciﬁcally, model discovery is applied to a serious game speciﬁcally developed to work skills on conceptual data modelling in a computer system. Detailed data about each game experience is recorded, including not only the ﬁnal outcome but also how the player reached it. Preliminary results in an actual case study
Orchestrating the Technology-Enhanced Embodied Learning Classroom via Learning Stations Rotation: A case study (extended abstract)

This work presents a case study of orchestrating a technology-enhanced embodied learning experience in an authentic classroom. As new technologies (e.g., motion-based technologies, natural user interfaces) and emerging pedagogies (e.g., embodied learning) find their way into the classroom it is extremely important to advance our knowledge of how to improve practice with respect to planning and orchestrating the classroom environment. This case study presents researchers and teachers’ efforts in orchestrating a technology-enhanced embodied learning classroom in elementary education demonstrating the strategies and methods adopted.
Time-Dependent Tourist Tour Planning with Adjustable Profits

Planning a tourist trip in a foreign city can be a complex undertaking: when selecting the attractions and choosing visit order and visit durations, opening hours as well as the public transit timetable need to be considered. Additionally, when planning trips for multiple days, it is desirable to avoid redundancy. Since the attractiveness of activities such as shopping or sightseeing depends on personal preferences, there is no one-size-fits-all solution to this problem. We propose several realistic extensions to the Time-Dependent Team Orienteering Problem with Time Windows (TDTOPTW) which are relevant in practice and present the first MILP representation of it. Furthermore, we propose a problem-specific preprocessing step which enables fast heuristic (iterated local search) and exact (mixed-integer linear programming) personalized trip-planning for tourists. Experimental results for the city of Berlin show that the approach is feasible in practice. 2012 ACM Subject Classification Mathematics of computing → Combinatorial optimization; Mathematics of computing → Graph algorithms; Theory of computation → Randomized local search
Memorization techniques: a literature review to verify the feasibility of implementing memorization techniques through tangible user interfaces

This work wishes to present some studies existing in the literature that refer to memorization techniques. After a short introduction, where cognitive processes involved in memory and learning (perception, attention, codification, and recovery) will be mentioned, we will proceed to an analysis of the scientific contributions that represent the scientific substratum of mnemonics. In particular, this work will consider existing studies that focus on the efficacy of the mnemonics “keyword method” in the study of foreign languages, which represent the vast majority of the studies currently existing in the literature. The purpose of this review is to identify possible memorization techniques and verify if, in the future, it will be possible to use these techniques by developing technological tools based on tangible user interfaces.
Brief review of robotics in low-functioning autism therapy

In the last decade, numerous research studies showed that robots can be suitable assistants in the care and treatment of children with Autism Spectrum Disorder (ASD). Still, more investigation is required to fully assess the introduction of robotics assistants, as the majority of the studies was limited in numbers of participants and scope, e.g. by considering stand-alone interventions, High Functioning Autism (HFA) individuals only, and provided limited objective results, i.e. usually the success is evaluated via qualitative analysis of videos recorded during the interaction. In this paper, we present a brief review of the experience on integrating robotassisted therapy also in the treatment of children with Low-Functioning Autism (LFA) which is the most common case (>70%). Studies described here investigated the integration of a robot-assisted intervention in the training, and the results encourage the use of a robotic assistant also in LFA. Based on this experience, we suggest that current robotic technology is still at an experimental stage and require to actively involve all stakeholders in design of new robotic systems that can successfully account for the peculiar characteristics of ASD individuals.
Cardiovascular genetics: the role of genetic testing in diagnosis and management of patients with hypertrophic cardiomyopathy

Genetic testing in hypertrophic cardiomyopathy (HCM) is a valuable tool to manage patients and their families. Genetic testing can help inform diagnosis and differentiate HCM from other disorders that also result in increased left ventricular wall thickness, thereby directly impacting treatment. Moreover, genetic testing can definitively identify at-risk relatives and focus family management. Pathogenic variants in sarcomere and sarcomere-related genes have been implicated in causing HCM, and targeted gene panel testing is recommended for patients once a clinical diagnosis has been established. If a pathogenic or likely pathogenic variant is identified in a patient with HCM, predictive genetic testing is recommended for their at-risk relatives to determine who is at risk and to guide longitudinal screening and risk stratification. However, there are important challenges and considerations to implementing genetic testing in clinical practice. Genetic testing results can have psychological and other implications for patients and their families, emphasising the importance of genetic counselling before and after genetic testing. Determining the clinical relevance of genetic testing results is also complex and requires expertise in understanding of human genetic variation and clinical manifestations of the disease. In this review, we discuss the genetics of HCM and how to integrate genetic testing in clinical practice.
Integrating ULTRA and Trip-Based Routing

We study a bi-modal journey planning scenario consisting of a public transit network and a transfer graph representing a secondary transportation mode (e.g., walking or cycling). Given a pair of source and target locations, the objective is to ﬁnd a Pareto set of journeys optimizing arrival time and the number of required transfers. For public transit networks with a restricted, transitively closed transfer graph, one of the fastest known algorithms solving this bi-criteria problem is Trip-Based Routing [26]. However, this algorithm cannot be trivially extended to unrestricted transfer graphs. In this work, we combine Trip-Based Routing with ULTRA [5], a preprocessing technique that allows any public transit algorithm that requires transitive transfers to handle an unrestricted transfer graph. Since both ULTRA and Trip-Based Routing precompute transfer shortcuts in a preprocessing phase, a naive combination of the two leads to a three-phase algorithm that performs redundant work and produces superﬂuous shortcuts. We therefore propose a new, integrated preprocessing phase that combines the advantages of both and reduces the number of computed shortcuts by up to a factor of 9 compared to a naive combination. The resulting query algorithm, ULTRA-Trip-Based is the fastest known algorithm for the considered problem setting, achieving a speedup of up to 4 compared to the fastest previously known approach, ULTRA-RAPTOR.
How can we engage people to map places suitable for the autistic population? A crowdsourced approach

The crowdsourcing paradigm applied to the urban environment (i.e., people while moving can provide data from different places) may play a fundamental role in transforming users in significant actors of the places in which they live. In the last years, several crowdsourcing services have been developed to allow citizens to collaborate, by collecting data about urban accessibility. This effort, however, focused mainly on physical disabilities. We aim to help people with Autism Spectrum Disorder (ASD) move across and live in urban environments by means of a crowdsourced personalized map. The map is populated with comments and reviews by people with ASD and caregivers, in order to highlight places, routes, and activities (e.g., less crowded routes, quiet places) to make ASD people’s daily lives more comfortable.
On Lindenmayer Systems and Autoencoders

: Lindenmayer Systems can serve to deep learning not only as a generator of simulated datasets. They could provide datasets of images generated from a very few parameters that enable us a better study of the latent space, which is crucial for the majority of deep neural networks. We process a dataset, generated by a parametric Lindenmayer system, with the convolutional autoencoder. We aim to recognize the values of the Lindenmayer system parameters by its encoder part. Finally, we partially turn a generator based on its decoder part to a neural network that generates images from the dataset upon the Lindenmayer system parameters.