Miracle-dz/UniFAR-FTUs huggingface dataset:
Alteration of Collagen Content and Macrophage Distribution in White Adipose Tissue under the Influence of Maternal and Postnatal Diet in Male Rat Offspring

Background and Objectives: The extracellular matrix is important for adipose tissue growth, and numerous interactions between adipocytes and extracellular matrix components occur during adipose tissue development. The main objective of this study was to investigate the interaction and influence of maternal and postnatal diet on adipose tissue remodeling in Sprague Dawley offspring. Materials and Methods: 10 Sprague Dawley females were randomly divided into two groups at nine weeks of age and fed a standard laboratory diet or high-fat diet for six weeks. Then, they were mated, and after birth, their male rat offspring were divided into four subgroups according to diet. After euthanizing the offspring at 22 weeks of age, samples of subcutaneous, perirenal and epididymal adipose tissue were collected. Sections were stained with Mallory’s trichrome and analyzed by immunohistochemistry for CD68+ and CD163+ cells. Results: Staining of extracellular components showed higher collagen deposition in the perirenal and epididymal depot of offspring fed a high-fat diet. The number of CD163/CD68+ cells in the perirenal adipose tissue was lower in the CD-HFD group compared with other groups, and in the subcutaneous fat pad when the groups with modified diet were compared with those on non-modified diet. Conclusion: Morphological changes in adipose tissue, increased collagen deposition, and changes in macrophage polarization may be related to intergenerational changes in diet.
Nonnegative Block-Term Decomposition with the β-Divergence: Joint Data Fusion and Blind Spectral Unmixing

We present a new method for solving simultaneously hyperspectral super-resolution and spectral unmixing of the unknown super-resolution image. Our method relies on three key elements: (1) the nonnegative decomposition in rank-(Lr, Lr, 1) block-terms, (2) joint tensor factorization with multiplicative updates, and (3) the formulation of a family of optimization problems with β-divergences objective functions. We come up with a family of simple, robust and efficient algorithms, adaptable to various noise statistics. Experiments show that our approach competes favorably with state-of-the-art methods for solving both problems at hand for various noise statistics.
Deep Plug-and-Play for Tensor Robust Principal Component Analysis

Tensor Robust Principal Component Analysis (TRPCA) aims at recovering the low-rank and sparse components from target tensor, which has extensive applications in multi-dimensional data recovery. However, most of the existing methods only exploit the global low-rank of image data, which result in missing local details in the recovered data. To restore the data more accurately, we propose a new TRPCA method which simultaneously combines the model-based method and data-driven approaches to preserve the global structure and fine local information. Specially, we pick the tensor nuclear norm to characterize the global correlation and a convolutional neural network(CNN) denoiser which reserves the local detail. Then, a flexible alternating direction method of multipliers (ADMM) is designed to deal with the proposed optimization model. Extensive experiments on various types of tensor data illustrate that our model enhances performance compared to state-of-the-art methods.
EMIX: A Data Augmentation Method for Speech Emotion Recognition

In the last few years, many deep learning (DL) models have been developed to improve the accuracy of speech emotion recognition (SER). However, as SER datasets are generally small and insufficient due to their difficult and expensive collection, the DL models are prone to overfitting, so their performance is limited. In this paper, we introduce a novel data augmentation (DA) method for the SER problem, namely EMix, which is simple but effective. The method creates new data by mixing pairs of selective samples from the original data. The generated mixtures will be noisier or less ambiguous than their constructive ones. To verify the effectiveness of the proposed DA, we develop a transformer-based network for the SER task, and experiment with the two public datasets including IEMOCAP and Crema-D. The experimental results demonstrate the superiority of EMix over other DA methods. In comparison with state-of-the-art methods, our approach shows competitive performance.
Ensemble Graph Q-Learning for Large Scale Networks

The optimization of large-scale networks such as finding the optimal control strategies through cost minimization is challenged by large state spaces. For networks that can be modeled via Markov Decision Processes (MDP), a previously proposed graph reduction strategy is used in conjunction with a novel ensemble learning method based on Q-learning algorithm for policy optimization in unknown environments. By exploiting the structural properties of the network, several structurally related Markov chains are created and these multiple chains are sampled to learn multiple policies which are fused. The convergence of the learning approach is analyzed and the ensemble learning strategy is shown to inherit the properties of classical Q-learning. Numerical results show that the proposed algorithm achieves a reduction of 60% with respect to the policy error and 80% for the runtime versus other state-of-the-art Q-learning algorithms.
The effectiveness of a recreational sports program to enhance tolerance among students of Al-Qasim Green University in Babylon, Iraq

The current research aimed to identify the effectiveness of a recreational sports program to enhance tolerance among students of Al-Qasim Green University, and the researcher used the experimental approach to design the two groups (experimental and control), and the study sample consisted of (82) students, (41) students for the experimental group, (41) students for the experimental group. control
Channel Estimation in Massive MIMO with Heavy-Tailed Noise: Gaussian-Mixture Versus Cauchy Models

Impulsive noise can appear in communication links. In the literature, it was demonstrated that when the noise is impulsive, standard Gaussian receivers perform poorly because of the outliers in the noise. Therefore, appropriate receivers must be used when the noise is impulsive. In this paper, we compare two types of massive multiple- input multiple-output (MIMO) receivers, namely those based on a Gaussian-mixture assumption and those based on a Cauchy assumption, in terms of channel estimation quality, when the noise is impulsive. Symmetric α-stable (SαS) noises are used to model impulsive noises in the paper. In the numerical results, the Gaussian-mixture receiver outperforms the Cauchy-based receiver.
A Continental‐Scale Estimate of Soil Organic Carbon Change at NEON Sites and Their Environmental and Edaphic Controls

Current carbon cycle models focus on the effects of climate and land‐use change on primary productivity and microbial‐mineral dependent carbon turnover in the topsoil, while less attention has been paid to vertical soil processes and soil‐dependent response to land‐use change along the profile. In this study, a spatial‐temporal analysis was used to estimate soil organic carbon (SOC) change in topsoil/A horizon and subsoil/B horizon at National Ecological Observatory Network (NEON) sites, USA over 30 years. To separate the effects of land‐use, environmental, and edaphic factors on SOC change, space‐for‐time substitution was used in combination with the Continuous Change Detection and Classification algorithm and Structural Equation Modeling. Results showed that (a) under natural vegetation, Spodosols and Inceptisols found in the eastern NEON sites had substantial topsoil SOC accumulation (+0.4 to +1.2 Mg C ha−1 year−1), while Inceptisols and Andisols in the west had a comparable magnitude of topsoil SOC loss (−0.5 to −1.8 Mg C ha−1 year−1); (b) Mollisols and Alfisols in the Central Plains sites were susceptible to significant SOC loss under farming and grazing; (c) Runoff/erosion and leaching potential, vertical translocation, and mineral sorption were the most important factors controlling SOC variation across the NEON sites. Our work could be used to parameterize ecosystem models simulating SOC change.
On-Chip Nucleic Acid Purification Followed by ddPCR for SARS-CoV-2 Detection

We developed a microfluidic chip integrated with nucleic acid purification and droplet-based digital polymerase chain reaction (ddPCR) modules to realize a ‘sample-in, result-out’ infectious virus diagnosis. The whole process involved pulling magnetic beads through drops in an oil-enclosed environment. The purified nucleic acids were dispensed into microdroplets by a concentric-ring, oil–water-mixing, flow-focusing droplets generator driven under negative pressure conditions. Microdroplets were generated with good uniformity (CV = 5.8%), adjustable diameters (50–200 μm), and controllable flow rates (0–0.3 μL/s). Further verification was provided by quantitative detection of plasmids. We observed a linear correlation of R2 = 0.9998 in the concentration range from 10 to 105 copies/μL. Finally, this chip was applied to quantify the nucleic acid concentrations of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). The measured nucleic acid recovery rate of 75 ± 8.8% and detection limit of 10 copies/μL proved its on-chip purification and accurate detection abilities. This chip can potentially be a valuable tool in point-of-care testing.
Algorithms to define limits for wood property categorization.

Grading is the attempt to allocate objects to predefined categories in order to obtain groups that are homogeneous. These categories are only defined by their limits and therefore the definition of the category limits is a crucial factor in the establishment of grading rules. Log grading is the basis for a meaningful and cost-optimal use of wood and can be conducted by various methods depending on the specific utilization and end product. In this paper a new approach for the calculation of the limits between categories of wood properties is shown. Algorithms were developed based on the principles of the analysis of variance (ANOVA) and adopted according to the specific demands of the categorization procedure. Categorization was carried out for roundwood features, and it was shown that the algorithms fulfill the set of requirements. The method shown is promising for the support of the definition of limits in the development of norms and standards. In particular, existing grading rules could not be applied satisfactorily to the investigated data set and the application of the algorithm led to significantly better results. On a company level, the method developed can be applied to define limits between quality grades of raw materials based on quality measures for the final product.
Speech Emotion Recognition via Heterogeneous Feature Learning

Speech emotion recognition (SER) based on multi-view learning has made some progress on speaker-independent scenarios. How-ever, the existing SER methods always rely on excessive feature views and ignore the importance of heterogeneous feature learning. In this paper, we propose a novel multi-level attention method to effectively learn the heterogeneous information from the hand-crafted feature (MFCC) and the feature (W2V2) extracted from the pre-trained model. Specifically, we first design an Attention based Multi-scale Low-level Feature (A-MLF) extractor to extract scale-specific emotion-related regions from MFCC. Then, the Multi-Unit Attention (MUA) module is used to simultaneously learn discriminative features in three different dimensions. Finally, a two-stage feature fusion strategy is used for joint representation space learning. We demonstrate our method on two speaker-independent validation strategies and interpret the SOTA performance by visualizing the feature distribution.
Quantifying Safety in the Roadway Safety Management Process The Quantifying Safety in the Roadway Safety Management Process guide describes and illustrates opportunities

The Quantifying Safety in the Roadway Safety Management Process guide describes and illustrates opportunities to incorporate the latest tools and techniques to quantify safety in the roadway safety management process. The target audience includes safety program managers and safety analysts. The purpose of this guide is to help raise awareness of opportunities to apply crash modification factors (CMFs) in the roadway safety management process. The objectives are to 1) identify opportunities to apply CMFs in the various steps of the safety management process, 2) describe the process of applying CMFs to quantify safety, and 3) explain potential challenges related to the application of CMFs and opportunities to overcome those challenges. Readers will better understand the purpose of CMFs and how they can be applied in the roadway safety management process.
A Low-Power WLAN CMOS LNA for Wireless Sensor Network Wake-Up Receiver Applications

Wireless communication integration is related to many challenges such as reliability, quality of service, communication range, and energy consumption. As the overall performance of wireless sensor networks (WSN) will be improved if the capacity of each sensor node is optimized, several techniques are used to fine-tune the various circuits of each node. In recent works, the wake-up receiver nodes have been introduced to minimize latencies without increasing energy consumption. To overcome the sensitivity of wake-up receiver limitations, a design of a low-noise amplifier (LNA) with several design specifications is required. This article discusses the relevance of the wake-up receiver in WSN applications and provides a brief study of this component. An LNA design for WSN wake-up receiver applications is presented. The challenging task of the LNA design is to provide equitable trade-off performances such as noise figure, gain, power consumption, impedance matching, and linearity. The LNA circuit is designed for wireless personal area network (WLAN) standards utilizing RF-TSMC CMOS 0.18 μm. Two innovative techniques are applied to the LNA topology to improve its performance: forward body biasing is used to reduce power consumption by 11.43 mW, and substrate resistance is added to reduce noise by 1.8 dB. The developed LNA achieves a noise figure of 1.6 dB and a power gain of 21.7 dB at 5.2 GHz. At 0.6 V, the designed LNA dissipates 0.87 mW.
Approximation Error Back-Propagation for Q-Function in Scalable Reinforcement Learning with Tree Dependence Structure

This paper applies the exponential decay property of scalable RL theory to a specific scenario where the network structure is a tree, and use KL (Kullback-Leibler) divergence to analyze the propagation of approximation error along the structure over time, in order to quantify its backtracking result. We gain the insight that most of the approximation error originates from the inaccurate estimation of the state of the source nodes (root in Top-Down mode and leaves in Bottom-Up mode), which can be largely recovered by establishing the long-hop communication link12.
Concerns about cancer recurrence in Mexican women breast cancer survivors. Psychometric properties of the CARS scale

Fear of recurrence is one of the main affectations of cancer survivors. In Latin-American exist a lack of evidence on the subject and of valid instruments to measure it. The objective of this study was to determine the psychometric properties of the Concerns About Recurrence Scale in Mexican breast cancer survivors. An instrumental study was carried out with 200 women breast cancer survivors with a mean age of 53 years old and a mean survival of 52.5 months. A confirmatory factor analysis of the original version of the scale identified four factors that explained 77 % of the variance and satisfactory goodness of fit indicators: CFI = . 967, TLI = . 957, SRMR = . 035, RMSEA = . 071 as well as high internal consistency α = . 945. Significant associations were also identified between general fear subscale, anxiety subscale of the HADS and total HADS score (r = .638 and r = .607; p = .01). The CARS presented adequate validity, reliability and factorial structure. It is recommended for usage in clinical care and research with Mexican breast cancer survivors.
Diffuse interstellar medium organic polymers: Photoproduction of the 3.4, 6.85 and 7.25 μm features

We discuss the production of a hydrogenated amorphous carbon polymer (a-C:H) via the photolysis of methane at low temperature, resulting in a network made of CH2 and CH3 groups with some contribution of unsaturated bonds (C=C, C≡C). This solid provides an excellent fit to the Diffuse Interstellar Medium (DISM) 3.4, 6.85 and 7.25 µm features, as observed either in our Galaxy or towards external galaxies lines of sight. The photoproduction and thus photoresistance of this a-C:H made under conditions that resemble those of the DISM (low temperature, UV field) are relevant for astrophysical applications. The profiles of the absorption bands of this material are closer to the astrophysical ones than any previously reported carbonaceous analogs of the DISM organics.
Current Applications of Liposomes for the Delivery of Vitamins: A Systematic Review

Liposomes have been used for several decades for the encapsulation of drugs and bioactives in cosmetics and cosmeceuticals. On the other hand, the use of these phospholipid vesicles in food applications is more recent and is increasing significantly in the last ten years. Although in different stages of technological maturity—in the case of cosmetics, many products are on the market—processes to obtain liposomes suitable for the encapsulation and delivery of bioactives are highly expensive, especially those aiming at scaling up. Among the bioactives proposed for cosmetics and food applications, vitamins are the most frequently used. Despite the differences between the administration routes (oral for food and mainly dermal for cosmetics), some challenges are very similar (e.g., stability, bioactive load, average size, increase in drug bioaccessibility and bioavailability). In the present work, a systematic review of the technological advancements in the nanoencapsulation of vitamins using liposomes and related processes was performed; challenges and future perspectives were also discussed in order to underline the advantages of these drug-loaded biocompatible nanocarriers for cosmetics and food applications.
Vascularisation in Deep Endometriosis: A Systematic Review with Narrative Outcomes

Deep endometriosis (DE) is the most severe subtype of endometriosis, with the hallmark of lesions infiltrating adjacent tissue. Abnormal vascularisation has been implicated in contributing to endometriosis lesion development in general, and how vascularisation influences the pathogenesis of DE, in particular, is of interest. This systematic review followed the PRISMA guidelines to elucidate and examine the evidence for DE-specific vascularisation. A literature search was performed using MEDLINE, Embase, PubMed, Scopus, Cochrane CENTRAL Library and Europe PubMed Central databases. The databases were searched from inception to the 13 March 2023. A total of 15 studies with 1125 patients were included in the review. The DE lesions were highly vascularised, with a higher microvessel density (MVD) than other types of endometriotic lesions, eutopic endometrium from women with endometriosis and control tissue. Vascular endothelial growth factor, its major subtype (VEGF-A) and associated receptor (VEGFR-2) were significantly increased in the DE lesions compared to superficial endometriosis, eutopic endometrium and control tissue. Progestin therapy was associated with a significant decrease in the MVD of the DE lesions, explaining their therapeutic effect. This review comprehensively summarises the available literature, reporting abnormal vascularisation to be intimately related to the pathogenesis of DE and presents potentially preferential therapeutic targets for the medical management of DE.
Difference Guided VHR Remote Sensing Image Change Detection

VHR remote sensing images have abundant ground features and details, but it is a great challenge for machine understanding. The "same object with different spectral" problem caused by environment changes, such as seasonal alternation, bad weather and shadow, is the biggest challenge in multitemporal image change detection, which is more prominent in VHR images. For this problem, a novel difference guided VHR image change detection (DGCD) method is proposed in this paper. In the feature learning stage of DGCD model, difference features are used to guide the feature extraction to suit with the change detection task. In order to make the model focus on the change features, both of the spatial and channel attention mechanism are introduced. Finally, for the edge region of VHR image which is hard to be discriminated, a new edge enhanced loss function based on BCL loss is designed. Experiments on public datasets show the superiority of proposed DGCD method. It has good generalization ability in different classical challenging scenarios. Compared with the representative methods in recent years, the proposed DGCD method performs better on VHR image change detection.
ψ-Net: Point Structural Information Network for No-Reference Point Cloud Quality Assessment

The human vision system is highly adapted to extract structural information from the viewed scenes. The irregularity of point clouds makes the extraction of structural information containing both color and geometry an important challenge for point cloud quality assessment (PCQA). This paper proposes a point structural information (PSI) network (ψ-Net) for no-reference PCQA. Firstly, a PSI module is proposed to map the position vectors of neighboring points to weights for the calculation of color and geometric structure information. Secondly, a dual-stream network is presented to introduce distortion-related features for PCQA. Experimental results show the effectiveness of the proposed method.