Miracle-dz/UniFAR-FTUs huggingface dataset:
Effects of Long-Term Supplementation of Bovine Colostrum on Iron Homeostasis, Oxidative Stress, and Inflammation in Female Athletes: A Placebo-Controlled Clinical Trial

Bovine colostrum supplementation has been suggested as a potential factor in reducing oxidative stress and inflammation. The primary objective of this study was to evaluate the effects of six months of bovine colostrum supplement intake (3.2 g; four capsules/day) in highly trained female athletes on changes in oxidative stress level, inflammation, and iron metabolism biomarkers after intense exercise. In this study, 20 trained female athletes were recruited. Participants were divided into two groups: 11 in the bovine colostrum (6-month supplementation) and 9 in the placebo group (6-month placebo supplementation). All participants completed an intense exercise test at the beginning of the experiment and after six months post-treatment. Blood samples were taken before, following exercise, and after 3 h recovery. Compared to the placebo group, the colostrum group showed a significant decrease in TBARS level (p< 0.01) at all time points, whereas a marked increase was observed in IL-6 (p < 0.01; pre-exercise) and SOD activity (p < 0.01), and transferrin (p < 0.01; rest period) and lactoferrin (p < 0.05; post-exercise) levels. The results suggested that 6-months of bovine colostrum supplementation is beneficial in the reduction of the harmful effects produced by free radicals (ROS), oxidative stress, and inflammation. In consequence, alleviation of the inflammatory response by bovine colostrum supplementation may also cause positive action on iron homeostasis in female athletes.
Exploration of Flavonoids as Lead Compounds against Ewing Sarcoma through Molecular Docking, Pharmacogenomics Analysis, and Molecular Dynamics Simulations

Ewing sarcoma (ES) is a highly malignant carcinoma prevalent in children and most frequent in the second decade of life. It mostly occurs due to t(11;22) (q24;q12) translocation. This translocation encodes the oncogenic fusion protein EWS/FLI (Friend leukemia integration 1 transcription factor), which acts as an aberrant transcription factor to deregulate target genes essential for cancer. Traditionally, flavonoids from plants have been investigated against viral and cancerous diseases and have shown some promising results to combat these disorders. In the current study, representative flavonoid compounds from various subclasses are selected and used to disrupt the RNA-binding motif of EWS, which is required for EWS/FLI fusion. By blocking the RNA-binding motif of EWS, it might be possible to combat ES. Therefore, molecular docking experiments validated the binding interaction patterns and structural behaviors of screened flavonoid compounds within the active region of the Ewing sarcoma protein (EWS). Furthermore, pharmacogenomics analysis was used to investigate potential drug interactions with Ewing sarcoma-associated genes. Finally, molecular dynamics simulations were used to investigate the stability of the best selected docked complexes. Taken together, daidzein, kaempferol, and genistein exhibited a result comparable to ifosfamide in the proposed in silico study and can be further analyzed as possible candidate compounds in biological in vitro studies against ES.
A Comprehensive Technical Review of the Friction Stir Welding of Metal-to-Polymer Hybrid Structures

Metal–polymer hybrid structures are becoming desirable due to their wide range of applications in the automotive, aerospace, biomedical and construction industries. Properties such as a light weight, high specific strength, and design flexibility along with the low manufacturing costs of metal–polymer hybrid structures make them widely attractive in several applications. One of the main challenges that hinders the widespread utilization of metal–polymer hybrid structures is the challenging dissimilar joining of metals to polymers. Friction stir welding (FSW) shows a promising potential in overcoming most of the issues and limitations faced in the conventional joining methods of such structures. Several works in the literature have explored the FSW of different metal-to-polymer combinations. In some of the works, the joints are examined based on processing parameter optimization, microstructural characteristics, and mechanical performances. It is, therefore, important to summarize the findings of these works as a means of providing a reference to researchers to facilitate further research on the utilization of FSW in joining metals to polymers. Thus, this work aims to present a comprehensive technical review on the FSW technique for joining metals to polymers by reviewing the reported literature findings on the impact of materials, tools, process parameters, and defects on the strength and microstructure of the produced joints. In addition, this work reviews and presents the latest practices aiming to enhance the metal–polymer joint quality that have been reported in the literature.
Acceleration of the convergence speed of evolutionary algorithms using multi-layer neural networks

Despite the global optimization capability and low sensitivity to initial parameter estimates, evolutionary algorithms suffer from heavy computational loads especially when the fitness evaluation is time-consuming. The proposed acceleration method implements an online multi-layer neural network approximating the fitness calculation, which greatly decreases the computation time because the time-consuming fitness calculation can be replaced by the simple network output. The acceleration is achieved as the number of individuals used for the network training gradually decreases according to an adaptive scheme. A convergence theorem guarantees convergence to the optimal solution as well as ensuring the network stability. The proposed method is verified by a numerical example.
Enhancing the Production of Hydrolytic Enzymes in Elicited Tamarillo (Solanum betaceum Cav.) Cell Suspension Cultures

Plant cell suspension cultures are widely used as a tool for analyzing cellular and molecular processes, metabolite synthesis, and differentiation, bypassing the structural complexity of plants. Within the range of approaches used to increase the production of metabolites by plant cells, one of the most recurrent is applying elicitors capable of stimulating metabolic pathways related to defense mechanisms. Previous proteomics analysis of tamarillo cell lines and cell suspension cultures have been used to further characterize and optimize the growth and stress-related metabolite production under in vitro controlled conditions. The main objective of this work was to develop a novel plant-based bioreactor system to produce hydrolytic enzymes using an elicitation approach. Based on effective protocols for tamarillo micropropagation and plant cell suspension culture establishment from induced callus lines, cell growth has been optimized, and enzymatic activity profiles under in vitro controlled conditions characterized. By testing different sucrose concentrations and the effects of two types of biotic elicitors, it was found that 3% (w/v) sucrose concentration in the liquid medium enhanced the production of hydrolytic enzymes. Moreover, casein hydrolysate at 0.5 and 1.5 g/L promoted protein production, whereas yeast extract (0.5 g/L) enhanced glycosidase activity. Meanwhile, chitosan (0.05 and 0.1 g/L) enhanced glycosidases, alkaline phosphates, and protease activities.
Identification of Kynurenic Acid-Induced Apoptotic Biomarkers in Gastric Cancer-Derived AGS Cells through Next-Generation Transcriptome Sequencing Analysis

Understanding the triggers and therapeutic targets for gastric cancer, one of the most common cancers worldwide, can provide helpful information for the development of therapeutics. RNA sequencing technology can be utilized to identify complex disease targets and therapeutic applications. In the present study, we aimed to establish the pharmacological target of Kynurenic acid (KYNA) for gastric cancer AGS cells and to identify the biological network. RNA sequencing identified differentially expressed genes (DEGs) between KYNA-treated and untreated cells. A total of 278 genes were differentially expressed, of which 120 genes were up-regulated, and 158 genes were down-regulated. Gene ontology results confirmed that KYNA had effects such as a reduction in genes related to DNA replication and nucleosome organization on AGS cells. Protein–protein interaction was confirmed through STRING analysis, and it was confirmed that cancer cell growth and proliferation were inhibited through KEGG, Reactome, and Wiki pathway analysis, and various signaling pathways related to cancer cell death were induced. It was confirmed that KYNA treatment reduced the gene expression of cancer-causing AP-1 factors (Fos, Jun, ATF, and JDP) in AGS cell lines derived from gastric cancer. Overall, using next-generation transcriptome sequencing data and bioinformatics tools, we confirmed that KYNA had an apoptosis effect by inducing changes in various genes, including factor AP-1, in gastric cancer AGS cells. This study can identify pharmacological targets for gastric cancer treatment and provide a valuable resource for drug development.
Precision-Driven Multi-Target Path Planning and Fine Position Error Estimation on a Dual-Movement-Mode Mobile Robot Using a Three-Parameter Error Model

The multi-target path planning problem is a universal problem to mobile robots and mobile manipulators. The two movement modes of forward movement and rotation are universally implemented in integrated, commercially accessible mobile platforms used in logistics robots, construction robots, etc. Localization error in multi-target path tracking is one of the crucial measures in mobile robot applications. In this article, a precision-driven multi-target path planning is first proposed. According to the path’s odometry error evaluation function, the precision-optimized path can be discovered. Then, a three-parameter odometry error model is proposed based on the dual movement mode. The error model describes localization errors in terms of the theoretical motion command values issued to the mobile robot, the forward moving distances, and the rotation angles. It appears that the three error parameters follow the normal distribution. The error model is finally validated using a mobile robot prototype. The error parameters can be identified by analyzing the actual moving trajectory of arbitrary movements. The experimental localization error is compared to the simulated localization error in order to validate the proposed error model and the precision-driven path planning method. The OptiTrack motion capture device was used to capture the prototype mobile robot’s pose and position data.
The Potential of Rhizobacteria to Mitigate Abiotic Stress in Lessertia frutescens

Lessertia frutescens is a multipurpose medicinal plant indigenous to South Africa. The curative ability of the medicinal plant is attributed to its rich phytochemical composition, including amino acids, triterpenoids, and flavonoids. A literature review of some of the phytochemical compounds, particularly amino acids, in L. frutescens shows a steady decrease in concentration over the years. The reduction of the phytochemical compounds and diminishing biological activities may be attributed to drought and salt stress, which South Africa has been grappling with over the years. Canavanine, a phytochemical which is associated with the anticancer activity of L. frutescens, reduced slightly when the plant was subjected to salt stress. Like other legumes, L. frutescens forms a symbiotic relationship with plant-growth-promoting rhizobacteria, which facilitate plant growth and development. Studies employing commercial plant-growth-promoting rhizobacteria to enhance growth and biological activities in L. frutescens have been successfully carried out. Furthermore, alleviation of drought and salt stress in medicinal plants through inoculation with plant growth-promoting-rhizobacteria is well documented and effective. Therefore, this review seeks to highlight the potential of plant-growth-promoting rhizobacteria to alleviate the effect of salt and drought in Lessertia frutescens.
Towards Context-Aware Facial Emotion Reaction Database for Dyadic Interaction Settings

Emotion recognition is a significant issue in many sectors that use human emotion reactions as communication for marketing, technological equipment, or human–robot interaction. The realistic facial behavior of social robots and artificial agents is still a challenge, limiting their emotional credibility in dyadic face-to-face situations with humans. One obstacle is the lack of appropriate training data on how humans typically interact in such settings. This article focused on collecting the facial behavior of 60 participants to create a new type of dyadic emotion reaction database. For this purpose, we propose a methodology that automatically captures the facial expressions of participants via webcam while they are engaged with other people (facial videos) in emotionally primed contexts. The data were then analyzed using three different Facial Expression Analysis (FEA) tools: iMotions, the Mini-Xception model, and the Py-Feat FEA toolkit. Although the emotion reactions were reported as genuine, the comparative analysis between the aforementioned models could not agree with a single emotion reaction prediction. Based on this result, a more-robust and -effective model for emotion reaction prediction is needed. The relevance of this work for human–computer interaction studies lies in its novel approach to developing adaptive behaviors for synthetic human-like beings (virtual or robotic), allowing them to simulate human facial interaction behavior in contextually varying dyadic situations with humans. This article should be useful for researchers using human emotion analysis while deciding on a suitable methodology to collect facial expression reactions in a dyadic setting.
Variation in the Early Life and Adult Intestinal Microbiome of Intra-Uterine Growth Restricted Rat Offspring Exposed to a High Fat and Fructose Diet

Intra-Uterine Growth Restriction (IUGR) is a risk factor for many adult-onset chronic diseases, such as diabetes and obesity. These diseases are associated with intestinal microbiome perturbations (dysbiosis). The establishment of an intestinal microbiome begins in utero and continues postnatally (PN). Hypercaloric diet-induced dysbiosis is a major driver of childhood obesity. We hypothesized that different postnatal diets superimposed on IUGR will alter the postnatal intestinal microbiome. We compared four experimental rat groups: (1) Ad lib fed regular chow diet pre- and postnatally (CON), (2–3) IUGR induced by maternal caloric restriction prenatally followed postnatally (PN) by either (2) the control diet (IUGR-RC) or (3) High-Fat-high-fructose (IUGR-HFhf) diet, and lastly (4) HFhf ad lib pre- and postnatally (HFhf). Fecal samples were collected from dams and male and female rat offspring at postnatal day 2, 21, and adult day 180 for 16S rRNA gene sequencing. Maternal diet induced IUGR led to dysbiosis of the intestinal microbiome at PN21. Postnatal HFhf diet significantly reduced microbial diversity and worsened dysbiosis reflected by an increased Gammaproteobacteria/Clostridia ratio. Dysbiosis arising from a mismatch between IUGR and a postnatal HFhf diet may contribute to increased risk of the IUGR offspring for subsequent detrimental health problems.
Laser wakefield acceleration: application to Betatron x-ray radiation production and x-ray imaging

High intensity femtosecond laser pulses can be used to generate X-ray radiation. In the laser wakefield process, when a high intensity laser pulse (<1018 W/cm2) is focused onto a gas jet target, it interacts with the instantaneously created under-dense plasma and excites a wakefield wave. In the wakefield electrons are trapped and accelerated to high energies in short distances. The electrons trapped in the wakefield can perform Betatron oscillations across the propagation axis and emit X-ray photons. The Betatron X-ray beam is broadband as the radiation emission has a synchrotron distribution. The X-ray beam is collimated and its pulse duration is femtosecond. For high resolution and phase contrast X-ray imaging applications, the important feature of the X-ray Betatron beam is the μm source size. Using ALLS 100 TW class laser system we demonstrate that the Betatron X-ray beam is both energetic and bright enough to produce single laser shot phase contrast imaging of complex objects located in air.
The Surprising Dynamics of Electrochemical Coupling at Membrane Sandwiches in Plants

Transport processes across membranes play central roles in any biological system. They are essential for homeostasis, cell nutrition, and signaling. Fluxes across membranes are governed by fundamental thermodynamic rules and are influenced by electrical potentials and concentration gradients. Transmembrane transport processes have been largely studied on single membranes. However, several important cellular or subcellular structures consist of two closely spaced membranes that form a membrane sandwich. Such a dual membrane structure results in remarkable properties for the transport processes that are not present in isolated membranes. At the core of membrane sandwich properties, a small intermembrane volume is responsible for efficient coupling between the transport systems at the two otherwise independent membranes. Here, we present the physicochemical principles of transport coupling at two adjacent membranes and illustrate this concept with three examples. In the supplementary material, we provide animated PowerPoint presentations that visualize the relationships. They could be used for teaching purposes, as has already been completed successfully at the University of Talca.
Graph Learning-Based Blockchain Phishing Account Detection with a Heterogeneous Transaction Graph

Recently, cybercrimes that exploit the anonymity of blockchain are increasing. They steal blockchain users’ assets, threaten the network’s reliability, and destabilize the blockchain network. Therefore, it is necessary to detect blockchain cybercriminal accounts to protect users’ assets and sustain the blockchain ecosystem. Many studies have been conducted to detect cybercriminal accounts in the blockchain network. They represented blockchain transaction records as homogeneous transaction graphs that have a multi-edge. They also adopted graph learning algorithms to analyze transaction graphs. However, most graph learning algorithms are not efficient in multi-edge graphs, and homogeneous graphs ignore the heterogeneity of the blockchain network. In this paper, we propose a novel heterogeneous graph structure called an account-transaction graph, ATGraph. ATGraph represents a multi-edge as single edges by considering transactions as nodes. It allows graph learning more efficiently by eliminating multi-edges. Moreover, we compare the performance of ATGraph with homogeneous transaction graphs in various graph learning algorithms. The experimental results demonstrate that the detection performance using ATGraph as input outperforms that using homogeneous graphs as the input by up to 0.2 AUROC.
The Acute Effect of Multi-Ingredient Antioxidant Supplementation following Ionizing Radiation

Radiation exposure is an undeniable health threat encountered in various occupations and procedures. High energy waves in ionizing radiation cause DNA damage and induce reactive oxygen species (ROS) production, which further exacerbate DNA, protein, and lipid damage, increasing risk of mutations. Although endogenous antioxidants such as superoxide dismutase have evolved to upregulate and neutralize ROS, exogenous dietary antioxidants also have the potential to combat ionizing radiation (IR)-induced ROS production. We evaluated a cocktail of ingredients (AOX) purported to have antioxidant and mitochondrial protective properties on the acute effects of IR. We show that IR stimulates DNA damage through phosphorylation of DNA repair proteins in the heart, brain, and liver of mice. AOX showed partial protection in brain and liver, through a lack of significant activation in given repair proteins. In addition, AOX attenuated the IR-induced increase in NF-kβ mRNA and protein expression in brain and liver. Lastly, cytochrome c oxidase complex transcripts were significantly higher in heart and brain following radiation, which was also diminished by prior ingestion of AOX. Together, our findings suggest that a multi-ingredient AOX supplement may attenuate the IR-induced cellular damage response and represents a feasible and cost-effective preventative supplement for at-risk populations of radiation exposure.
The Influence of Zn Substitution on Physical Properties of CoFe2O4 Nanoparticles

Co1−xZnxFe2O4 nanoparticles (0 ≤ x ≤ 1) have been synthesized via a green sol–gel combustion method. The prepared samples were studied using X-ray diffraction measurements (XRD), transmission electron microscopy (TEM), Raman, and magnetic measurements. All samples were found to be single phases and have a cubic Fd-3m structure. EDS analysis confirmed the presence of cobalt, zinc, iron, and oxygen in all studied samples. Raman spectra clearly show that Zn ions are preferentially located in T sites for low Zn concentrations. Due to their high crystallinity, the nanoparticles show high values of the magnetization, which increases with the Zn content for x < 0.5. The magnetic properties are discussed based on Raman results. Co ferrite doped with 30% of Zn produced the largest SAR values, which increase linearly from 148 to 840 W/gMNPs as the H is increased from 20 to 60 kA/m.
Design of U-Shaped Frequency Tunable Microwave Filters in MEMS Technology

U-shaped microwave resonators implemented by RF MEMS switches can be considered the result of a novel design approach for obtaining small-footprint tunable resonators, owing to the bent shape of the resonator and the microsystem solution for changing the frequency of resonance. In this paper, we discuss the design approach for potential configurations of U-shaped structures combined with ohmic RF MEMS switches. Owing to their prospective application in RADAR and satellite systems, the devices were assessed for K-Band operation, specifically for 15 GHz, 20 GHz, and 26 GHz. The ON-OFF states determined by an electrostatic actuation of metal beams composing the RF MEMS ohmic switches allow for selecting different path lengths corresponding to different frequencies. In this contribution, initial configurations were designed and manufactured as a proof-of-concept. The advantages and critical aspects of the designs are discussed in detail.
MALS-Net: A Multi-Head Attention-Based LSTM Sequence-to-Sequence Network for Socio-Temporal Interaction Modelling and Trajectory Prediction

Predicting the trajectories of surrounding vehicles is an essential task in autonomous driving, especially in a highway setting, where minor deviations in motion can cause serious road accidents. The future trajectory prediction is often not only based on historical trajectories but also on a representation of the interaction between neighbouring vehicles. Current state-of-the-art methods have extensively utilized RNNs, CNNs and GNNs to model this interaction and predict future trajectories, relying on a very popular dataset known as NGSIM, which, however, has been criticized for being noisy and prone to overfitting issues. Moreover, transformers, which gained popularity from their benchmark performance in various NLP tasks, have hardly been explored in this problem, presumably due to the accumulative errors in their autoregressive decoding nature of time-series forecasting. Therefore, we propose MALS-Net, a Multi-Head Attention-based LSTM Sequence-to-Sequence model that makes use of the transformer’s mechanism without suffering from accumulative errors by utilizing an attention-based LSTM encoder-decoder architecture. The proposed model was then evaluated in BLVD, a more practical dataset without the overfitting issue of NGSIM. Compared to other relevant approaches, our model exhibits state-of-the-art performance for both short and long-term prediction.