Miracle-dz/UniFAR-FTUs huggingface dataset:
Epithelial Galectin-3 Induced the Mitochondrial Complex Inhibition and Cell Cycle Arrest of CD8+ T Cells in Severe/Critical COVID-19

Previous research suggested that the dramatical decrease in CD8+ T cells is a contributing factor in the poor prognosis and disease progression of COVID-19 patients. However, the underlying mechanisms are not fully understood. In this study, we conducted Single-cell RNA sequencing (scRNA-seq) and single-cell T cell receptor sequencing (scTCR-seq) analysis, which revealed a proliferative-exhausted MCM+FASLGlow CD8+ T cell phenotype in severe/critical COVID-19 patients. These CD8+ T cells were characterized by G2/M cell cycle arrest, downregulation of respiratory chain complex genes, and inhibition of mitochondrial biogenesis. CellChat analysis of infected lung epithelial cells and CD8+ T cells found that the galectin signaling pathway played a crucial role in CD8+ T cell reduction and dysfunction. To further elucidate the mechanisms, we established SARS-CoV-2 ORF3a-transfected A549 cells, and co-cultured them with CD8+ T cells for ex vivo experiments. Our results showed that epithelial galectin-3 inhibited the transcription of the mitochondrial respiratory chain complex III/IV genes of CD8+ T cells by suppressing the nuclear translocation of nuclear respiratory factor 1 (NRF1). Further findings showed that the suppression of NRF1 translocation was associated with ERK-related and Akt-related signaling pathways. Importantly, the galectin-3 inhibitor, TD-139, promoted nuclear translocation of NRF1, thus enhancing the expression of the mitochondrial respiratory chain complex III/IV genes and the mitochondrial biogenesis of CD8+ T cells. Our study provided new insights into the immunopathogenesis of COVID-19 and identified potential therapeutic targets for the prevention and treatment of severe/critical COVID-19 patients.
3DSAINT Representation for 3D Point Clouds

This paper introduces a Sphere-based representation to model a 3D scene and show its performance on various tasks, including Structure from Motion (SfM) and 3D scene classification. A significant target application of this work is Mixed Reality, where 3D data can be efficiently represented, and synthetic and real data can be mixed for an immersive experience. Over the past few decades, 3D big data has garnered increased attention in computer vision. Acquiring, representing, reconstructing, querying, classifying, and visualizing 3D models for Mixed Reality has become crucial for many applications, such as medicine, architecture, entertainment, and bioinformatics. With the ever-increasing amount of data that the 3D scanners produce, storing, processing, and transmitting the data becomes challenging. Techniques that exploit the shape information need to be developed to model, classify and visualize the data. Our work offers a novel multi-scale surface representation based on spheres, with the ultimate goal of helping scientists to see and work with 3D data in Mixed Reality more effectively and efficiently.
Design and Development of a Remote Intelligent Inspection System

This study proposes a design and development plan for a remote intelligent inspection system to solve the two problems that the industry currently faces in inspections: 1) the data error of the instrument itself and 2) the error of artificially compiling data or meter reading. This system is composed of smart glasses, smart phone and server. It requires 5Mbps bandwidth to transmit 1080P video real-time streaming through 4G/5G mobile communications and provides inspection behavior detection, moving video surveillance, remote guidance, and automatically meter reading. The innovations of this study are as follows: (1)inspection behavior detection of arrival at or departure from an inspection station, (2)hybrid positioning for outdoor with GPS and indoor with AI scene recognition, reducing indoor maintenance and operation costs of QR-Code or RFID devices, (3)hands free on meter reading and logging, (4)automation of meter reading, through combining AI and image processing to compute meter scenes and then get values. The meter reading operation is thus automated and intelligent, and the data error of instrument from itself or an inspector can be solved.
Unsupervised Learning of Discrete Latent Representations with Data-Adaptive Dimensionality from Continuous Speech Streams

This work presents a novel deep generative model for unsupervised learning of sparse binary feature representations with data-adaptive dimensionality directly from continuous speech streams. Sharing the critical assumption of unbounded latent dimensionality with previously proposed Bayesian non-parametric approaches, our proposed model can capture the much richer, non-Markovian dependencies between its latent representations. The present work focuses on an investigation of our proposed model’s performance in learning linguistically meaningful representations under challenging, realistic scenarios. We train our model with highly speaker-imbalanced datasets and evaluate it on the ABX phone discriminability test. Our model achieves a promising, competitive performance to the state-of-the-art model, despite its huge disadvantage: limited or no access to speaker information during training.
Target Recognition in SAR Images Using Complex-Valued Network Guided with Sub-Aperture Decomposition

Synthetic aperture radar (SAR) images have special physical scattering characteristics owing to their unique imaging mechanism. Traditional deep learning algorithms usually extract features from real-valued SAR images in a purely data-driven manner, which may ignore some important physical scattering characteristics and sacrifice some useful target information in SAR images. This undoubtedly limits the improvement in performance for SAR target recognition. To take full advantage of the physical information contained in SAR images, a complex-valued network guided with sub-aperture decomposition (CGS-Net) for SAR target recognition is proposed. According to the fact that different targets have different physical scattering characteristics at different angles, the sub-aperture decomposition is used to improve accuracy with a multi-task learning strategy. Specifically, the proposed method includes main and auxiliary tasks, which can improve the performance of the main task by learning and sharing useful information from the auxiliary task. Here, the main task is the target recognition task, and the auxiliary task is the target reconstruction task. In addition, a complex-valued network is used to extract the features from the original complex-valued SAR images, which effectively utilizes the amplitude and phase information in SAR images. The experimental results obtained using the MSTAR dataset illustrate that the proposed CGS-Net achieved an accuracy of 99.59% (without transfer learning or data augmentation) for the ten-classes targets, which is superior to the other popular deep learning methods. Moreover, the proposed method has a lightweight network structure, which is suitable for SAR target recognition tasks because SAR images usually lack a large number of labeled data. Here, the experimental results obtained using the small dataset further demonstrate the excellent performance of the proposed CGS-Net.
Concurrent Topology Optimization of Multi-Scale Composite Structures Subjected to Dynamic Loads in the Time Domain

This paper presents a concurrent topology optimization of multi-scale composite structures subjected to general time-dependent loads for minimizing dynamic compliance. A three-field density-based method is adopted to implement the concurrent topological design, with macroscopic effective properties of the microstructure evaluated through energy-based homogenization method (EBHM). Transient response is obtained from the two-scale finite element analysis with the HHT-α approach as an implicit time integration procedure. Design sensitivities are formulated employing the adjoint variable method (AVM) based on two main philosophies: “discretize-then-differentiate” and “differentiate-then-discretize” approaches, respectively. The method of moving asymptotes is adopted to update the design variables at two scales. Several benchmark examples are presented to demonstrate that the “discretize-then-differentiate” AVM attains consistent sensitivities in an inherent manner such that the resulting optimal topology is more efficient when compared with the “differentiate-then-discretize” AVM. Moreover, the potential of the proposed method for concurrent dynamic topology optimization problems under general time-dependent loads is also highlighted.
The Effect of Long Chain n-3 Fatty Acid Supplementation on Muscle Strength in Older Adults: A Systematic Review and Meta-Analysis

The main objective of the current study was to perform a systematic literature review with the purpose of exploring the impact of long-chain n-3 polyunsaturated fatty acid (LCn-3 PUFA) relative to control oil supplementation on muscle strength, with secondary outcomes of muscle mass and physical function in older individuals under conditions of habitual physical activity/exercise. The review protocol was registered with PROSPERO (CRD42021267011) and followed the guidelines outlined in the Preferred Reporting Items for Systematic Review and Meta-Analysis (PRISMA) statement. The search for relevant studies was performed utilizing databases such as PubMed, EMBASE, CINAHL, Scopus, Web of Science, and the Cochrane Central Register of Controlled Trials (CENTRAL) up to June 2023. Randomized controlled trials (RCTs) in older adults comparing the effects of LCn-3 PUFA with a control oil supplement on muscle strength were included. Five studies involving a total of 488 participants (348 females and 140 males) were identified that met the specified inclusion criteria and were included. Upon analyzing the collective data from these studies, it was observed that supplementation with LCn-3 PUFA did not have a significant impact on grip strength (standardized mean difference (SMD) 0.61, 95% confidence interval [−0.05, 1.27]; p = 0.07) in comparison to the control group. However, there was a considerable level of heterogeneity among the studies (I2 = 90%; p < 0.001). As secondary outcomes were only measured in a few studies, with significant heterogeneity in methods, meta-analyses of muscle mass and functional abilities were not performed. Papers with measures of knee extensor muscle mass as an outcome (n = 3) found increases with LCn-3 PUFA supplementation, but studies measuring whole body lean/muscle mass (n = 2) and functional abilities (n = 4) reported mixed results. With a limited number of studies, our data indicate that LCn-3 PUFA supplementation has no effect on muscle strength or functional abilities in older adults but may increase muscle mass, although, with only a few studies and considerable heterogeneity, further work is needed to confirm these findings.
Quantifying the effects of temperature and concentration on variable-density flow in numerical modeling of groundwater systems: Implications for predictive uncertainty and data collection

OF THE DISSERTATION QUANTIFYING THE EFFECTS OF TEMPERATURE AND CONCENTRATION ON VARIABLE-DENSITY FLOW IN NUMERICAL MODELING OF GROUNDWATER SYSTEMS: IMPLICATIONS FOR PREDICTIVE UNCERTAINTY AND DATA COLLECTION by Alyssa Marie Dausman Florida International University, 2008 Miami, Florida Professor Michael C. Sukop, Major Professor Groundwater systems of different densities are often mathematically modeled to understand and predict environmental behavior such as seawater intrusion or submarine groundwater discharge. Additional data collection may be justified if it will costeffectively aid in reducing the uncertainty of a model's prediction. The collection of salinity, as well as, temperature data could aid in reducing predictive uncertainty in a variable-density model. However, before numerical models can be created, rigorous testing of the modeling code needs to be completed. This research documents the benchmark testing of a new modeling code, SEAWAT Version 4. The benchmark problems include various combinations of density-dependent flow resulting from variations in concentration and temperature. The verified code, SEAWAT, was then applied to two different hydrological analyses to explore the capacity of a variabledensity model to guide data collection.
Spatialization Quality Metric for Binaural Speech

In spatial-audio enabled systems, evaluating the quality of spatialization is an essential process. This paper proposes a new objective metric to measure the spatialization quality (SQ) be-tween any pair of binaural signals while being agnostic to speech content and signal duration. We formulate SQ as a metric learning problem and compute deep-feature distance on embeddings learned using triplet loss and multi-task learning with direction-of-arrival and binaural speech synthesis as auxiliary tasks. We show the robustness of our model on localization in (un)seen contexts, monotonicity with increasing angular distance, content in-variance and retrieval performance. Experiments show that our metric correlates well with publicly available subjective ratings, and it yields improvements when used as a differentiable loss in a binaural speech enhancement system.
The Overexpression of Peanut (Arachis hypogaea L.) AhALDH2B6 in Soybean Enhances Cold Resistance

Soybeans are the main source of oils and protein for humans and animals; however, cold stress jeopardizes their growth and limits the soybean planting area. Aldehyde dehydrogenases (ALDH) are conserved enzymes that catalyze aldehyde oxidation for detoxification in response to stress. Additionally, transgenic breeding is an efficient method for producing stress-resistant germplasms. In this study, the peanut ALDH gene AhALDH2B6 was heterologously expressed in soybean, and its function was tested. We performed RNA-seq using transgenic and wild-type soybeans with and without cold treatment to investigate the potential mechanism. Transgenic soybeans developed stronger cold tolerance, with longer roots and taller stems than P3 soybeans. Biochemically, the transgenic soybeans exhibited a decrease in malondialdehyde activity and an increase in peroxidase and catalase content, both of which are indicative of stress alleviation. They also possessed higher levels of ALDH enzyme activity. Two phenylpropanoid-related pathways were specifically enriched in up-regulated differentially expressed genes (DEGs), including the phenylpropanoid metabolic process and phenylpropanoid biosynthetic process. Our findings suggest that AhALDH2B6 specifically up-regulates genes involved in oxidoreductase-related functions such as peroxidase, oxidoreductase, monooxygenase, and antioxidant activity, which is partially consistent with our biochemical data. These findings established the function of AhALDH2B6, especially its role in cold stress processes, and provided a foundation for molecular plant breeding, especially plant-stress-resistance breeding.
Leveraging Label Information for Multimodal Emotion Recognition

Multimodal emotion recognition (MER) aims to detect the emotional status of a given expression by combining the speech and text information. Intuitively, label information should be capable of helping the model locate the salient tokens/frames relevant to the specific emotion, which finally facilitates the MER task. Inspired by this, we propose a novel approach for MER by leveraging label information. Specifically, we first obtain the representative label embeddings for both text and speech modalities, then learn the label-enhanced text/speech representations for each utterance via label-token and label-frame interactions. Finally, we devise a novel label-guided attentive fusion module to fuse the label-aware text and speech representations for emotion classification. Extensive experiments were conducted on the public IEMOCAP dataset, and experimental results demonstrate that our proposed approach outperforms existing baselines and achieves new state-of-the-art performance.
DEEPENING THE PRINCIPLES OF MEDIA EDUCATION BASED ON THE GENERAL SYSTEMS THEORY

The purpose of the article is to generalize and deepen the principles of media education (as a system-building basis for creating the theory and practice of media education) based on Yu. A. Urmantsev’s general systems theory. To achieve the goal, the following methodological tools are used: the philosophical principle of the unity of the world as the methodological basis of the research; the method of theoretical analysis of the problem field of the research; the general systems theory, which made it possible to generalize and deepen some principles of media education. In the article, the principles of the Concept of implementing media education in Ukraine were generalized and somewhat deepened, that is, they received a systemic meaning being based on certain meaningful elements of the general systems theory. This made it possible to carry out a systematic correlation of the principles of media education and some pedagogical phenomena on the basis of meaningful elements of the general systems theory. It is shown that the principles of media education, the elements of the model of a school as a social institution, and the elements of the model of a man as a subject of psychology are in system-correlation relations, which realizes the philosophical principle of the unity of the world and testifies to the fact that the procedure of systematic generalization of the principles of media education is adequate to the theoretical and methodological principles both the general systems theory, and the pedagogical teaching about the school as a social institution and about media education as its essential aspect. The table developed by the author "Systemic correlation of the principles of media education and some pedagogical phenomena being based on the meaningful elements of the general theory of systems" allows to deepen the formulation of such principle of media education as "aesthetic elevation in the context of media education". The task of systematic analysis and deepening each of the seven systemically presented principles of media education belongs to the promising areas of development of the principles of media education.
On the Optimal Assignment of Mirror Element in UAV and OIRS-Assisted OWC based Architecture

Optical intelligent reflective surface (OIRS) is one of the promising solutions to address the ‘skip-zone’ problem between the base stations and the mobile users. The performance of OIRS can be further complemented by mounting them on unmanned aerial vehicles (UAVs), which are known for their flexibility and maneuverability. The proposed work aims to utilize the OIRS-assisted UAV to improve the performance of optical wireless communication (OWC) systems. In this work, multiple mirror elements of the OIRS are grouped and assigned to the user. Specifically, a novel mirror element assignment scheme is proposed, namely a signal-to-noise ratio (SNR) based mirror assignment (SMA) to achieve maximum user throughput with proportional fairness for each user. The performance of the proposed SMA scheme is compared against the equal mirror assignment (EMA) scheme and well-known conventional bench-mark approaches, e.g., time-division multiple-access (TDMA) and non-orthogonal multiple access (NOMA). The results are reported in terms of average sum rate, fairness, and capacity outage probability. The obtained results show that the proposed SMA scheme outperforms EMA, TDMA, and NOMA schemes.
Authenticity and motivations towards film destination

ABSTRACT Based on a series of film motivations, this research aims to identify different dimensions associated with film tourism and analyse how each of these dimensions influences loyalty towards a cinematographic destination, following factorial analysis method. Also, confirm authenticity’s existential or constructive influence on loyalty towards a film destination through software that uses the partial least squares methodology to test the hypotheses raised around the motivational dimensions and authenticity regarding loyalty towards a film destiny. The main results identify four motivational dimensions, a positive influence of experiential motivations on loyalty to a film destination, and the predominance of existential authenticity over constructivist authenticity in generating the same impact on loyalty. This work will allow public authorities and private companies managing film destinations to establish strategies that dynamise the area where the tourist activity takes place and increase the income of both the companies and the local community.
The MASCFLICHT Corpus: Face Mask Type and Coverage Area Recognition from Speech

We present a novel speech dataset for face mask type and coverage area recognition collected with a smartphone. The dataset contains 2h 27m 55s of data from 30 German speakers (15f, 15m). The baseline results exploit the functionals of the eGeMAPS feature set, the Mel-spectrogram, and the spectrogram representations of the audio samples. To model the one-dimensional features, we investigate Support Vector Classi-fiers (SVC) and a neural network classifier. We extract salient information from the two-dimensional representations with Convolutional Neural Network (CNN) based encoders, coupled with a classification block. We use the Unweighted Average Re-call (UAR) as the evaluation metric. For the face mask type and the coverage area recognition tasks (3-class problems), the best models on the test partition score a UAR of 49.3% and 47.8%, respectively. For the face mask type and coverage area recognition task (5-class problem), the optimal model on the test partition obtains a UAR of 35.0%.
Deep Reinforcement Learning Aided Online Trajectory Optimization of Cellular-Connected UAVs with Offline Map Reconstruction

To reduce the outage of the connection between unmanned aerial vehicles (UAVs) and cellular networks in complex real-time channel state, and reduce the energy consumption of UAV during flight mission, an online trajectory optimization scheme of UAV based on outage probability knowledge map reconstruction is proposed. The outage probability knowledge map is a database that simulates the connection between UAV and the cellular network during real hovers. The UAV first samples sparsely from the target area and calculates the outage probability of the sampling point, and then uses the Kriging algorithm to reconstruct the outage probability knowledge map. Based on the reconstructed outage probability knowledge map, with the goal of minimizing the energy consumption of UAV task execution, the UAV trajectory optimization problem is established, and a trajectory optimization algorithm based on deep reinforcement learning (DRL) is proposed to solve it. Numerical results show that the proposed online trajectory optimization scheme based on outage probability knowledge map can obtain great returns in terms of maintaining connectivity, reducing task completion time and energy consumption.