Miracle-dz/UniFAR-FTUs huggingface dataset:
Editorial: Can virtual reality be a solution for assessing and treating psychological symptoms caused by eating and weight disorders?

COPYRIGHT © 2023 Vázquez-de Sebastián, Anastasiadou, Cano Porras, Friedman, Castellano-Tejedor and Lusilla-Palacios. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms. Editorial: Can virtual reality be a solution for assessing and treating psychological symptoms caused by eating and weight disorders?
Articulatory speech measures can be related to the severity of multiple sclerosis

Background Dysarthria is one of the most frequent communication disorders in patients with Multiple Sclerosis (MS), with an estimated prevalence of around 50%. However, it is unclear if there is a relationship between dysarthria and the severity or duration of the disease. Objective Describe the speech pattern in MS, correlate with clinical data, and compare with controls. Methods A group of MS patients (n = 73) matched to healthy controls (n = 37) by sex and age. Individuals with neurological and/or systemic conditions that could interfere with speech were excluded. MS group clinical data were obtained through the analysis of medical records. The speech assessment consisted of auditory-perceptual and speech acoustic analysis, from recording the following speech tasks: phonation and breathing (sustained vowel/a/); prosody (sentences with different intonation patterns) and articulation (diadochokinesis; spontaneous speech; diphthong/iu/repeatedly). Results In MS, 72.6% of the individuals presented mild dysarthria, with alterations in speech subsystems: phonation, breathing, resonance, and articulation. In the acoustic analysis, individuals with MS were significantly worse than the control group (CG) in the variables: standard deviation of the fundamental frequency (p = 0.001) and maximum phonation time (p = 0.041). In diadochokinesis, individuals with MS had a lower number of syllables, duration, and phonation time, but larger pauses per seconds, and in spontaneous speech, a high number of pauses were evidenced as compared to CG. Correlations were found between phonation time in spontaneous speech and the Expanded Disability Status Scale (EDSS) (r = − 0.238, p = 0.043) and phonation ratio in spontaneous speech and EDSS (r = −0.265, p = 0.023), which indicates a correlation between the number of pauses during spontaneous speech and the severity of the disease. Conclusion The speech profile in MS patients was mild dysarthria, with a decline in the phonatory, respiratory, resonant, and articulatory subsystems of speech, respectively, in order of prevalence. The increased number of pauses during speech and lower rates of phonation ratio can reflect the severity of MS.
Development potential of extracellular matrix hydrogels as hemostatic materials

The entry of subcutaneous extracellular matrix proteins into the circulation is a key step in hemostasis initiation after vascular injury. However, in cases of severe trauma, extracellular matrix proteins are unable to cover the wound, making it difficult to effectively initiate hemostasis and resulting in a series of bleeding events. Acellular-treated extracellular matrix (ECM) hydrogels are widely used in regenerative medicine and can effectively promote tissue repair due to their high mimic nature and excellent biocompatibility. ECM hydrogels contain high concentrations of extracellular matrix proteins, including collagen, fibronectin, and laminin, which can simulate subcutaneous extracellular matrix components and participate in the hemostatic process. Therefore, it has unique advantages as a hemostatic material. This paper first reviewed the preparation, composition and structure of extracellular hydrogels, as well as their mechanical properties and safety, and then analyzed the hemostatic mechanism of the hydrogels to provide a reference for the application and research, and development of ECM hydrogels in the field of hemostasis.
Breaking the boundaries: the power of ligatures in visual-word recognition

Introduction Current neurobiological-inspired models of visual-word recognition propose that letter detectors in the word recognition system can tolerate some variations in the visual form of the letters. However, it is unclear whether this tolerance extends to novel ligatures, which combine two letters into a single glyph. Methods To investigate this, the present study utilized a masked priming experiment with a lexical decision task to examine whether primes containing novel ligatures are effective in activating their corresponding base word, relative to omitted-letter primes, in the initial stages of word processing. For each target word (e.g., VIRTUAL), were created an identity prime (virtual), a prime containing a novel ligature of two of the letters (e.g., virtual; “ir” in a single glyph), and an omitted-letter prime where one letter was removed (e.g., vrtual [omitted-vowel] in Experiment 1; vitual [omitted-consonant] in Experiment 2). Results Results showed that the presence of a novel ligature in the prime resulted in faster lexical decision times compared to a prime with an omitted vowel (Experiment 1), but not with an omitted consonant (Experiment 2). Furthermore, the performance with the primes containing the novel ligature was not different from that of the identity primes. Discussion These results suggest that the word recognition system can quickly enable separate letter detectors for novel ligatures. These findings have important implications for our understanding of the front-end of visual-word recognition.
Chronic pulmonary fibrosis alters the functioning of the respiratory neural network

Some patients with idiopathic pulmonary fibrosis present impaired ventilatory variables characterised by low forced vital capacity values associated with an increase in respiratory rate and a decrease in tidal volume which could be related to the increased pulmonary stiffness. The lung stiffness observed in pulmonary fibrosis may also have an effect on the functioning of the brainstem respiratory neural network, which could ultimately reinforce or accentuate ventilatory alterations. To this end, we sought to uncover the consequences of pulmonary fibrosis on ventilatory variables and how the modification of pulmonary rigidity could influence the functioning of the respiratory neuronal network. In a mouse model of pulmonary fibrosis obtained by 6 repeated intratracheal instillations of bleomycin (BLM), we first observed an increase in minute ventilation characterised by an increase in respiratory rate and tidal volume, a desaturation and a decrease in lung compliance. The changes in these ventilatory variables were correlated with the severity of the lung injury. The impact of lung fibrosis was also evaluated on the functioning of the medullary areas involved in the elaboration of the central respiratory drive. Thus, BLM-induced pulmonary fibrosis led to a change in the long-term activity of the medullary neuronal respiratory network, especially at the level of the nucleus of the solitary tract, the first central relay of the peripheral afferents, and the Pre-Bötzinger complex, the inspiratory rhythm generator. Our results showed that pulmonary fibrosis induced modifications not only of pulmonary architecture but also of central control of the respiratory neural network.
Using knowledge graphs to infer gene expression in plants

Introduction Climate change is already affecting ecosystems around the world and forcing us to adapt to meet societal needs. The speed with which climate change is progressing necessitates a massive scaling up of the number of species with understood genotype-environment-phenotype (G×E×P) dynamics in order to increase ecosystem and agriculture resilience. An important part of predicting phenotype is understanding the complex gene regulatory networks present in organisms. Previous work has demonstrated that knowledge about one species can be applied to another using ontologically-supported knowledge bases that exploit homologous structures and homologous genes. These types of structures that can apply knowledge about one species to another have the potential to enable the massive scaling up that is needed through in silico experimentation. Methods We developed one such structure, a knowledge graph (KG) using information from Planteome and the EMBL-EBI Expression Atlas that connects gene expression, molecular interactions, functions, and pathways to homology-based gene annotations. Our preliminary analysis uses data from gene expression studies in Arabidopsis thaliana and Populus trichocarpa plants exposed to drought conditions. Results A graph query identified 16 pairs of homologous genes in these two taxa, some of which show opposite patterns of gene expression in response to drought. As expected, analysis of the upstream cis-regulatory region of these genes revealed that homologs with similar expression behavior had conserved cis-regulatory regions and potential interaction with similar trans-elements, unlike homologs that changed their expression in opposite ways. Discussion This suggests that even though the homologous pairs share common ancestry and functional roles, predicting expression and phenotype through homology inference needs careful consideration of integrating cis and trans-regulatory components in the curated and inferred knowledge graph.
Two-stage learning-based prediction of bronchopulmonary dysplasia in very low birth weight infants: a nationwide cohort study

Introduction The aim of this study is to develop an enhanced machine learning-based prediction models for bronchopulmonary dysplasia (BPD) and its severity through a two-stage approach integrated with the duration of respiratory support (RSd) using prenatal and early postnatal variables from a nationwide very low birth weight (VLBW) infant cohort. Methods We included 16,384 VLBW infants admitted to the neonatal intensive care unit (NICU) of the Korean Neonatal Network (KNN), a nationwide VLBW infant registry (2013–2020). Overall, 45 prenatal and early perinatal clinical variables were selected. A multilayer perceptron (MLP)-based network analysis, which was recently introduced to predict diseases in preterm infants, was used for modeling and a stepwise approach. Additionally, we applied a complementary MLP network and established new BPD prediction models (PMbpd). The performances of the models were compared using the area under the receiver operating characteristic curve (AUROC) values. The Shapley method was used to determine the contribution of each variable. Results We included 11,177 VLBW infants (3,724 without BPD (BPD 0), 3,383 with mild BPD (BPD 1), 1,375 with moderate BPD (BPD 2), and 2,695 with severe BPD (BPD 3) cases). Compared to conventional machine learning (ML) models, our PMbpd and two-stage PMbpd with RSd (TS-PMbpd) model outperformed both binary (0 vs. 1,2,3; 0,1 vs. 2,3; 0,1,2 vs. 3) and each severity (0 vs. 1 vs. 2 vs. 3) prediction (AUROC = 0.895 and 0.897, 0.824 and 0.825, 0.828 and 0.823, 0.783, and 0.786, respectively). GA, birth weight, and patent ductus arteriosus (PDA) treatment were significant variables for the occurrence of BPD. Birth weight, low blood pressure, and intraventricular hemorrhage were significant for BPD ≥2, birth weight, low blood pressure, and PDA ligation for BPD ≥3. GA, birth weight, and pulmonary hypertension were the principal variables that predicted BPD severity in VLBW infants. Conclusions We developed a new two-stage ML model reflecting crucial BPD indicators (RSd) and found significant clinical variables for the early prediction of BPD and its severity with high predictive accuracy. Our model can be used as an adjunctive predictive model in the practical NICU field.
Effects of virtual reality and test environment on user experience, usability, and mental workload in the evaluation of a blood pressure monitor

User experience and user acceptance of a product are essential for the product’s success. Virtual reality (VR) technology has the potential to assess these parameters early in the development process. However, research is scarce on whether the evaluation of the user experience and user acceptance of prototypes in VR, as well as the simulation of the usage environment, lead to comparable results to reality. To investigate this, a digital twin of a blood pressure monitor (BPM) was created using VR. In a 2 × 2 factorial between-subjects design, 48 participants tested the real or VR BPM. The tests were performed either in a low-detail room at a desk or in a detailed operating room (OR) environment. Participants executed three use scenarios with the BPM and rated their user experience and acceptance with standardized questionnaires. A test leader evaluated the performance of the participants’ actions using a three-point scheme. The number of user interactions, task time, and perceived workload were assessed. The participants rated the user experience of the BPM significantly (p < .05) better in VR. User acceptance was significantly higher when the device was tested in VR and in a detailed OR environment. Participant performance and time on task did not significantly differ between VR and reality. However, there was significantly less interaction with the VR device (p < .001). Participants who tested the device in a detailed OR environment rated their performance significantly worse. In reality, the participants were able to haptically experience the device and thus better assess its quality. Overall, this study shows that user evaluations in VR should focus on objective criteria, such as user errors. Subjective criteria, such as user experience, are significantly biased by VR.
A TDOA Estimation Method of PRS in LTE Systems

We propose a novel Time Difference of Arrival (TDOA) estimation method exploiting the downlink Positioning Reference Signal (PRS) of the Long Term Evolution (LTE). The conventional correlation method can’t calculate the time difference less than one sampling interval. Therefore, we calculate the phase to estimate the TDOA by establishing the one-to-one relationship between the phase and the time delay. Theoretical analysis and simulation results show that the proposed method can estimate the time difference from 0 to 1 sampling interval accurately and is well suited for timing estimation purposes in LTE location-awareness services.
Things of Interest Recommendation with Multidimensional Context Embedding in the Internet of Things

— The emerging Internet of Things (IoT) makes users and things closely related together, and the interactions between users and things generate massive context data, where the preference information in time, space, and textual content is embedded. Traditional recommendation methods (e.g., movie, music, and location recommendations) are based on static intrinsic context information, which lacks consideration regarding real-time content and spatiotemporal features, failing to adapt to the personalized recommendation in IoT. Therefore, to meet users’ interests and needs in IoT, a novel effective and efficient recommendation method is urgently needed. The paper focuses on mining users’ things of interest in IoT via leveraging multidimensional context embedding. Specifically, to address the challenge from massive context data embedding different user preference information, the paper employs Convolutional Neural Networks (CNN) to mine the intrinsic content information of things and learn their represent. To solve the real-time recommendation problem, the paper proposes a real-time multimodal model embedded into location, time, and some instant content information to track the features of users and things. Furthermore, the paper proposes a matrix factorization-based framework using the regularization method to fuse real-time context embedding and intrinsic information embedding. The experimental results demonstrate the proposed method tailored to IoT is adaptable and flexible, and able to capture user personalized preference effectively.
DAPPER

Many applications utilize sensors in mobile devices and machine learning to provide novel services. However, various factors such as different users, devices, and environments impact the performance of such applications, thus making the domain shift (i.e., distributional shift between the training domain and the target domain) a critical issue in mobile sensing. Despite attempts in domain adaptation to solve this challenging problem, their performance is unreliable due to the complex interplay among diverse factors. In principle, the performance uncertainty can be identified and redeemed by performance validation with ground-truth labels. However, it is infeasible for every user to collect high-quality, sufficient labeled data. To address the issue, we present DAPPER (Domain AdaPtation Performance EstimatoR) that estimates the adaptation performance in a target domain with only unlabeled target data. Our key idea is to approximate the model performance based on the mutual information between the model inputs and corresponding outputs. Our evaluation with four real-world sensing datasets compared against six baselines shows that on average, DAPPER outperforms the state-of-the-art baseline by 39.8% in estimation accuracy. Moreover, our on-device experiment shows that DAPPER achieves up to 396x less computation overhead compared with the baselines.
TouchKey

Secure device pairing is important to wearables. Existing solutions either degrade usability due to the need of specific actions like shaking, or they lack universality due to the need of dedicated hardware like electrocardiogram sensors. This paper proposes TouchKey, a symmetric key generation scheme that exploits the skin electric potential (SEP) induced by powerline electromagnetic radiation. The SEP is ubiquitously accessible indoors with analog-to-digital converters widely available on Internet of Things devices. Our measurements show that the SEP has high randomness and the SEPs measured at two close locations on the same human body are similar. Extensive experiments show that TouchKey achieves a high key generation rate of 345 bit/s and an average success rate of 99.29%. Under a range of adversary models including active and passive attacks, TouchKey shows a low false acceptance rate of 0.86%, which outperforms existing solutions. Besides, the overall execution time and energy usage are 0.44 s and 2.716 mJ, which make it suitable for resource-constrained devices.
Structural response of high strength concrete beams using fiber reinforced polymers under reversed cyclic loading

: This paper investigates the response of high strength concrete (HSC) beams subjected to reversed cyclic loading using carbon fiber-reinforced polymer (CFRP), glass fiber-reinforced polymers (GFRP), and hybrid FRP/steel bars as bottom tensile reinforcement. Five HSC beams with a rectangular cross-section were prepared and poured using with a 28-day concrete compressive strength of 60 MPa (8.7 ksi). A displacement-controlled reversed cyclic loading has been applied to all the beams. The test setup has been designed to represent seismic effect on structures. Flexural capacity, concrete and reinforcement steel strains, cracking behavior, and ductility results were obtained. The hybrid steel/FRP has shown an improved performance in terms of flexural capacity, strains, and ductility. While the inclusion of FRP grids reduces the flexural capacity, this can be improved by adding more layers of FRP. Overall, the nominal moment of the hybrid sections were the highest between all beams. The ACI 318 code’s empirical equations showed close results in terms of the tensile strength of the FRP. The ACI 440R has shown overestimated nominal moment values compared to the experimental results.
Job Satisfaction Mediates the Effect of the Work Environment on Employee Productivity (Case Study on Kober Mie Setan RestaurantDenpasar

: Employee productivity is the ability to achieve certain tasks according to predetermined targets. In order for employees to have enthusiasm in carrying out their duties, companies need to pay attention to factors that affect Employee productivity including job satisfaction and work environment so that employees can increase their productivity. The purpose of this study was to determine the role of job satisfaction in mediating the influence of the work environment on Employee productivity. This research was conducted at Kober Mie Setan, Denpasar. The population used in this study was 42 people, with saturated sampling method. Data collection was carried out through interviews and questionnaires. The analysis technique used is path analysis. The results show, work environment has a positive and significant effect on Employee productivity; work environment has a positive and significant effect on job satisfaction; Job satisfaction has positive and significant effect on employee productivity; Job satisfaction mediates significantly the influence of the work environment on employee productivity .
The Application of Virtual Technology Based on Posture Recognition in Art Design Teaching

—With the development of virtual technology, posture recognition technology has been integrated into virtual technology. This new technology allows users to further understand and observe the activities carried out in life scenes based on their original observation of the external world. And it enables them to make intelligent decisions. Existing posture recognition cannot meet the requirements of precise positioning in virtual environments. Therefore, a two-stage three-dimensional pose recognition model is proposed. The experiment illustrates that the three-dimensional gesture recognition performance is excellent. In addition, under the ablation experiment, the error accuracy of the research model decreased by more than 5 mm, and the overall error accuracy decreased by 10%. In the P-R curve, the accuracy rate of the model reaches 0.741, and the recall rate reaches 0.65. When conducting empirical analysis, the virtual posture disassembly action is complete; the disassembly error is less than 5%, and the disassembly error accuracy is good. The fit degree of the leg bending amplitude reaches over 96%, and the fit degree of the arm bending amplitude reaches over 95%. When the model is applied to actual teaching, the overall satisfaction score of teachers and students reaches 94.6 points. This has effectively improved the teaching effect of art design and is of great significance to the development of education in China.
High-sensitivity extended-gate field-effect transistors as pH sensors with oxygen-modified reduced graphene oxide films coated on different reverse-pyramid silicon structures as sensing heads

A high-performance extended-gate field-effect transistor (EGFET) as pH sensor with its microstructured sensing head composed of an oxygen-modified reduced graphene oxide film (RGOF) on a reverse-pyramid (RP) Si structure was developed to achieve a high sensitivity of 57.5 mV/pH with an excellent linearity of 0.9929 in a wide pH sensing range of 1–13. These features were ascribed to the large amount of sensing sites and large sensing area. In contrast, the planar Si substrate with the oxygen-plasma-treated RGOF (OPT-RGOF) at the optimal bias power showed a sensitivity of 52.9 mV/pH compared with 45.0 mV/pH for that without plasma treatment. It reveals that oxygen plasma can produce oxygen-containing groups as sensing sites, enhancing proton sensing characteristics. However, oxygen plasma treatment at high bias powers would cause damage to the RGOFs, resulting in poor conducting and sensing properties. On the other hand, the use of the RP structures could increase the effective sensing area and further promote the sensing performance.