Miracle-dz/UniFAR-FTUs huggingface dataset:
Effect of Thyme (Thymus vulgaris L.) Used in Diets with Extruded Flaxseed on the Antioxidant and Lipid Profile of the Blood and Tissues of Fattening Pigs

Thyme has strong antioxidant properties and, therefore, can reduce the intensity of oxidative processes taking place in the body. The study aimed to assess whether the addition of thyme to diets for fattening pigs containing extruded flaxseeds, a source of n-3 PUFAs, which are particularly susceptible to oxidation, would have a positive effect on redox status and lipid metabolism. The experiment was conducted using 120 weaners (WBP × Neckar crosses) of about 30 kg BW, which were kept until the end of fattening (about 110 kg BW) and divided into three groups of 40 pigs. The control group received a diet with 4% extruded flaxseed. In groups T1 and T3, 1% or 3% of thyme was added to the basal diet. The introduction of 3% thyme resulted in a decrease in the total cholesterol level in the blood and the loin muscle. Moreover, an increase in SOD and CAT activity and a decrease in FRAP and LOOH was noted. Following supplementation with 3% thyme, the n-3 PUFA content and n-3/n-6 ratio increased, while the SFA content was significantly reduced. The results of the studies indicate that thyme has a positive effect on the redox status and lipid profile of the blood and muscles.
Framewise Multiple Sound Source Localization and Counting Using Binaural Spatial Audio Signals

Sound source localization is the problem of estimating the positions of one or several sound sources. In terms of binaural audio, localization is a paramount perceptual characteristic which can be assessed subjectively or objectively. For objective evaluation of binaural sound localization, typical methods exploit binaural or monaural cues to predict directions of sound sources. Since multiple sound sources are often perceived simultaneously in daily sound scenes, an objective sound localization model which can detect temporally overlapping sources is required. In this paper, we propose a binaural multiple sound source localization network (BMSSLnet) model, which can predict framewise azimuths without prior knowledge of sound source number in a binaural audio signal. We implement multiple azimuth prediction as a multi-label classification task, and propose to use separated multi-label cross-entropy and mean square error as the loss function. Experimental results show that the proposed model obtains the average precision of 0.9 and 0.75 for spatial prediction on the anechoic dataset and reverberant dataset with up to three temporally overlapping sources, respectively. Framewise temporal prediction with average accuracy of 38.3 ms is achieved.
Improving Heart Rate and Heart Rate Variability Estimation from Video Through a HR-RR-Tuned Filter

This paper presents algorithms to improve the estimation of heart rate (HR) and heart rate variability (HRV) from smartphone video. The remote photoplethysmogram (rPPG) signals are first extracted from the videos recorded. Next, we proposed an rPPG filter adaptively tuned by HR and respiratory rate (RR) to better enhance source signal that modulates HR. Additionally, we also addressed a unique smartphone artifact—occasionally seen in smartphone videos—by introducing a threshold-based algorithm. HR and HRV accuracies are assessed on 22 subjects who were instructed to breath at seven different RRs. The mean absolute errors of HR and standard deviation of the NN intervals (SDNN) are found to be 1.13 ± 0.68 bpm and 18.30 ± 10.33 ms respectively. Finally, we also conduct a few experiments to highlight the accuracy improvements made by the proposed algorithms.
Hierarchical Clustering Based on a Bayesian Harmony Measure

Hierarchical clustering is an important data analysis technique. Traditional hierarchical clustering methods measure the similarity between two classes based on the Euclidean distance metric,and those methods can not deal with the overlapping between classes and the changes of the class density in range effectively. In this paper,a hierarchical clustering method based on a Bayesian harmony measure is presented. Instead of the Euclidean distance,the increase in the harmony degree is used to measure the similarity between two classes. The Bayesian harmony degree,introduced from the Bayesian Ying-Yang harmony learning theory,can measure the distribution of the entire dataset and guide the selection of the number of categories. The proposed method overcomes the drawbacks of the traditional methods. With the measure of Bayesian harmony degree,it becomes easier to select the threshold to terminate the merger of the hierarchical clustering and to generate the right number of categories. The experimental results on benchmark problems confirm the effectiveness of the proposed method.
Quantitative analysis and prevention of genetic algorithm premature convergence

To aim at the problem of genetic algorithm premature convergence,the new definition of premature convergence is given,and a new fuzzy index for measuring population maturity degree is presented on the basis of fuzzy system theory.Finally,the method of adaptively adjusting crossover probability and mutation probability with population maturity degree is proposed to prevent premature convergence,and its validity is verified through a simulation experiment.
Modeling Human Muscular Dystrophies in Zebrafish: Mutant Lines, Transgenic Fluorescent Biosensors, and Phenotyping Assays

Muscular dystrophies (MDs) are a heterogeneous group of myopathies characterized by progressive muscle weakness leading to death from heart or respiratory failure. MDs are caused by mutations in genes involved in both the development and organization of muscle fibers. Several animal models harboring mutations in MD-associated genes have been developed so far. Together with rodents, the zebrafish is one of the most popular animal models used to reproduce MDs because of the high level of sequence homology with the human genome and its genetic manipulability. This review describes the most important zebrafish mutant models of MD and the most advanced tools used to generate and characterize all these valuable transgenic lines. Zebrafish models of MDs have been generated by introducing mutations to muscle-specific genes with different genetic techniques, such as (i) N-ethyl-N-nitrosourea (ENU) treatment, (ii) the injection of specific morpholino, (iii) tol2-based transgenesis, (iv) TALEN, (v) and CRISPR/Cas9 technology. All these models are extensively used either to study muscle development and function or understand the pathogenetic mechanisms of MDs. Several tools have also been developed to characterize these zebrafish models by checking (i) motor behavior, (ii) muscle fiber structure, (iii) oxidative stress, and (iv) mitochondrial function and dynamics. Further, living biosensor models, based on the expression of fluorescent reporter proteins under the control of muscle-specific promoters or responsive elements, have been revealed to be powerful tools to follow molecular dynamics at the level of a single muscle fiber. Thus, zebrafish models of MDs can also be a powerful tool to search for new drugs or gene therapies able to block or slow down disease progression.
Multi-Scale Receptive Field Graph Model for Emotion Recognition in Conversations

Emotion recognition in conversations (ERC) has gained more attention, where contextual information modeling and multimodal fusion have been the focus and challenges in recent years. In this paper, we proposed a Multi-Scale Receptive Field Graph model (MSRFG) to tackle the challenges of ERC. Specifically, MSRFG constructs multi-scale perception graphs and learns contextual information via parallel multi-scale receptive field paths. To compensate for the deficiency of temporal information learning by the graph network, MSRFG injects temporal dependencies into the graph network to model the temporal relationships between utterances. Moreover, to achieve the effective fusion of multimodal information, MSRFG converges the multi-scale features of each modality separately and performs the learning of attention weights after the integration of converged features. We carried out experiments on IEMOCAP and MELD datasets to validate the effectiveness of the proposed method, and the results proved the superiority of our model over the existing SOTA methods. The code is available at https://github.com/Janie1996/MSRFG1.
Identifying Influential Nodes in Complex Networks Based on Information Entropy and Relationship Strength

Identifying influential nodes is a key research topic in complex networks, and there have been many studies based on complex networks to explore the influence of nodes. Graph neural networks (GNNs) have emerged as a prominent deep learning architecture, capable of efficiently aggregating node information and discerning node influence. However, existing graph neural networks often ignore the strength of the relationships between nodes when aggregating information about neighboring nodes. In complex networks, neighboring nodes often do not have the same influence on the target node, so the existing graph neural network methods are not effective. In addition, the diversity of complex networks also makes it difficult to adapt node features with a single attribute to different types of networks. To address the above problems, the paper constructs node input features using information entropy combined with the node degree value and the average degree of the neighbor, and proposes a simple and effective graph neural network model. The model obtains the strength of the relationships between nodes by considering the degree of neighborhood overlap, and uses this as the basis for message passing, thereby effectively aggregating information about nodes and their neighborhoods. Experiments are conducted on 12 real networks, using the SIR model to verify the effectiveness of the model with the benchmark method. The experimental results show that the model can identify the influence of nodes in complex networks more effectively.
A Robust Method for Block Adjustment of UAV SAR Images

To reduce the number of ground control points required for block adjustment of unmanned aerial vehicle (UAV) synthetic aperture radar (SAR) images, a robust block adjustment method is pro-posed. The proposed method aims to solve the problem of unstable adjustment solutions caused by the jittering of UAV SAR platforms. This method is based on the range Doppler model. By analyzing the correlation between the antenna phase center, velocity, and Doppler centroid of the seven orientation parameters, this method performs a block adjustment of UAV SAR images with three orientation parameters: the antenna phase center initial imaging moment, Doppler centroid, and proximity delay. UAV SAR images obtained from an area of $2\times $ 3 km in Dengfeng were used to conduct adjustment experiments. The results obtained using different orientation parameter set-tings verify the robustness and effectiveness of the proposed method. The adjustment results under various control-point layout plans indicate that a uniform layout plan can achieve an adjustment accuracy better than 1 m with a small number of control points.
Barriers to optimizing inflammatory bowel disease care in the United States

Significant progress in the management and modification of inflammatory bowel disease (IBD) has been made; however, significant barriers to the optimization of IBD care in the United States still exist. The majority of these barriers are constructed by insurance carriers and the integration of market pressures into healthcare decision-making. In this review, we highlight the barriers to IBD care optimization within the context of the US insurance system and review current and proposed solutions.
Clinical Assessment of Bone Quality at Implant Site using CBCT and Hounsfield Unit

Objectives: The current research was done to assess the bone quality at implant site using CBCT. Materials and Methods: The present study was conducted on 50 partially edentulous patients of both genders. All subjects had their chests scanned using a Kodac machine set to 120 kVp, 12 mA, and a 17-second exposure time. Using Hounsfield units, bone quality was classified as D1, D2, D3, D4, and D5 (HU). Result: Out of 50 patients, 27 were males and 23 were females. The average HU was 786.1 at the anterior maxilla, 1174.3 at the anterior mandible, 332.1 at the posterior maxilla, and 742.4 at the posterior mandible. The variation was considerable (P-0.01). Conclusion: The anterior mandible, anterior maxilla, posterior mandible, and posterior maxilla were found to have the highest densities. Based on Hounsfield units, CBCT is helpful in determining the bone density at the implant site.
Joint Estimation of DOA and Distance in Noisy Reverberant Conditions

Sound Source localization (SSL) using microphone arrays is an active research topic with many applications, but noise and reverberation make the direction-of-arrival (DOA) and distance estimation a challenging problem. In this work, we propose a novel method to jointly estimate the DOA and distance in noisy and reverberant environments. Our method exploits the linear phase structure across frequencies in a steering vector (SV). We convert the joint estimation issue into an optimization problem, which can be solved by Newton’s method augmented by a gradient ascent method. Our method does not depend on certain microphone array geometry, and it can also be extended to estimate the elevation angle. We conducted experimental evaluations in simulated noisy and reverberant acoustic conditions, which verified the superiority of our proposed method to several established methods in estimation accuracy and computation efficiency.
Long Period Fiber Grating for Refractive Index Sensing

Refractive index (RI) sensors are very valuable in first level detection of changes to the environment. In this project an optical fiber-based sensor is proposed to detect changes in surrounding RI. Most optical fiber sensors require tapering to be done to enhance the light interaction with the surrounding. This causes the fiber to become fragile and difficult to handle. In this research, optical fibers with long period gratings (LPG) are proposed to overcome this issue. Zinc Oxide (ZnO) nanomaterial was deposited over the LPG region using seeding method to enhance the performance of the sensor. The LPG fiber sensor was then used to investigate RI changes in the environment. A broadband laser source was used as the input and an optical spectrum analyser was used to observe the output light spectrum of the LPG sensor for different refractive index mediums. The ZnO coated LPG showed a sensitivity of 428.57 nm/RIU over an RI range of 1 – 1.3578.
Metabolomics Insights into the Differential Response of Breast Cancer Cells to the Phenolic Compounds Hydroxytyrosol and Luteolin

The aim of this study was to investigate the effects of two phenolic compounds found in extra virgin olive oil, hydroxytyrosol (HT) and luteolin (LUT), on the metabolism of breast cancer (BC) cells of different molecular subtypes. An untargeted metabolomics approach was used to characterize the metabolic responses of both triple-negative MDA-MB-231 cells and hormone-responsive MCF-7 cells to treatment with these phenols. Notably, while some effects were common across both cell types, others were dependent on the cell type, highlighting the importance of cellular metabolic phenotype. Common effects included stimulation of mitochondrial metabolism, acetate production, and formate overflow. On the other hand, glucose metabolism and lactate production were differentially modulated. HT and LUT appeared to inhibit glycolysis and promote the hexosamine biosynthetic pathway in MDA-MB-231 cells, while MCF-7 cells exhibited higher glycolytic flux when treated with phenolic compounds. Another significant difference was observed in lipid metabolism. Treated MDA-MB-231 cells displayed increased levels of neutral lipids (likely stored in cytosolic droplets), whereas treatment of MCF-7 cells with HT led to a decrease in triacylglycerols. Additionally, glutathione levels increased in MDA-MB-231 cells treated with HT or LUT, as well as in MCF-7 cells treated with LUT. In contrast, in HT-treated MCF-7 cells, glutathione levels decreased, indicating different modulation of cellular redox status. Overall, this work provides new insights into the metabolic impact of HT and LUT on different BC cell subtypes, paving the way for a better understanding of the nutritional relevance of these phenolic compounds in the context of BC prevention and management.
Accuracy in dynamic laser speckle: optimum size of speckles for temporal and frequency analyses

Abstract. The dynamic laser-speckle phenomenon has been used as a potential tool to monitor the activity of many biological and nonbiological samples; however, a key tailoring of the experimental configuration must be taken into account to avoid wrong measurements, since the general rules addressed to speckle as information cannot be directly adopted in dynamic laser-speckle monitoring. The speckle/pixel size ratio is provided by the f-number and by the magnification of the macro lens, and attention is mainly directed toward adjusting a speckle/pixel ratio higher than 1. However, a speckle/pixel ratio much higher than one does not mean an optimum adjustment. This work tested different apertures with fixed magnification yielding to monitor a drying paint process. The outcomes were evaluated in the time and frequency domains. The highest speckle/pixel size ratio was not the best to monitor the process using the dynamic laser speckle under frequency analysis. Tailoring of the devices must take into account the optimum speckle/pixel size ratio, which could vary depending on the application, and the known Nyquist theorem cannot be considered as a sufficient condition since the setup of the optical camera with its macro and iris must also be adjusted in accordance with the frequency response.
Designing Transformer Networks for Sparse Recovery of Sequential Data Using Deep Unfolding

Deep unfolding models are designed by unrolling an optimization algorithm into a deep learning network. These models have shown faster convergence and higher performance compared to the original optimization algorithms. Additionally, by incorporating domain knowledge from the optimization algorithm, they need much less training data to learn efficient representations. Current deep unfolding networks for sequential sparse recovery consist of recurrent neural networks (RNNs), which leverage the similarity between consecutive signals. We redesign the optimization problem to use correlations across the whole sequence, which unfolds into a Transformer architecture. Our model is used for the task of video frame reconstruction from low-dimensional measurements and is shown to outperform state-of-the-art deep unfolding RNN and Transformer models, as well as a traditional Vision Transformer on several video datasets.
Anti-Drug Antibodies in the Biological Therapy of Autoimmune Rheumatic Diseases

Autoimmune rheumatic diseases are a cluster of heterogeneous disorders that share some clinical symptoms such as pain, tissue damage, immune deregulation, and the presence of inflammatory mediators. Biologic disease-modifying antirheumatic drugs are some of the most effective treatments for rheumatic diseases. However, their molecular and pharmacological complexity makes them potentially immunogenic and capable of inducing the development of anti-drug antibodies. TNF inhibitors appear to be the main contributors to immunogenicity because they are widely used, especially in rheumatoid arthritis. Immunogenicity response on these treatments is crucial since the appearance of ADAs has consequences in terms of safety and efficacy. Therefore, this review proposes an overview of the immunogenicity of biological agents used in autoimmune rheumatic diseases highlighting the prevalence of anti-drug antibodies.
Effects of Aerosol Number Concentration and Updraft Velocity on Relative Dispersion during the Collision–Coalescence Growth Stage of Warm Clouds

Relative dispersion (ɛ) is a key expression used to parameterize various cloud processes in global circulation models (GCMs) and meteorological mesoscale models. Aerosols, updraft velocity (w), and different growth stages of warm clouds are known to affect relative dispersion. A two-dimensional detailed bin microphysical cloud model is used to investigate the combined impacts of aerosol number concentration (Na) and updraft velocity on relative dispersion in the collision–coalescence stage. In addition, the causes potentially controlling the changes in ɛ with updraft velocity are explored. There are three main influence regimes: the updraft velocity main influence regime, the aerosol main influence regime, and the joint influence regime. The cause of the variations in ɛ with updraft velocity is found to be different in the three main influence regimes. In the updraft velocity main influence regime, vigorous collision–coalescence due to stronger w results in a shift in the cloud droplet number concentration spectrum toward larger droplets, and the average cloud droplet radius increases, but the spectral width is less variable, so ε decreases. In the joint influence regime, stronger cloud droplet evaporation due to the stronger dragging effect of large cloud droplets widens the spectrum, mainly by reducing the cloud droplet number concentration (Nc) of 4–30 μm, and ε increases with the reduction in w. In the aerosol main influence regime, the strongest dragging effect reduces Nc at all radii with decreasing w, and the cloud droplet number concentration spectrum (CDNCS) narrows, which becomes the formation mechanism of the positive correlation between ε and w. Evaporation mainly causes a negative correlation between ε and Nc, but weak evaporation causes the correlation to become positive under the background of high aerosol concentration. At low aerosol concentrations, a strong collision–coalescence effect leads to a negative correlation between Nc and ε, but at high aerosol concentrations, the correlation is the opposite due to a weak collision–coalescence effect.
Application of multiple-layer SIFT to semantic concept detection

The Scale Invariant Feature Transform(SIFT) has been widely used in video concept detection.A lot of researches about SIFT have been dones,uch as PCA-SIFT,SURF and MESR.But there are few attentions about the influence of different down-sampling ratios to SIFT extraction.Based on the analysis of the influence of different down-sampling ratios to SIFT extraction,and a Multiple-Level SIFT(ML-SIFT) method for senmantic concept detection is proposed.Experiments on Caltech256 and Scene Class13 show that MAPs of ML-SIFT outperform MAPs of SIFT on Caltech256 and SceneClass13 by 15.7% and 5.1% respectively.In addition,when training the models using different ratios of positive and negative samples,the performances of ML-SIFT are stable.At the same time,the comparison of SIFT,SURF and ML-SIFT is given in the paper.From the experiments,the performances of SIFT and SURF are similar,but when comparing to ML-SIFT,their performances are worse than ML-SIFT.From above analysis,the ML-SIFT algorithm is effective.