Miracle-dz/UniFAR-FTUs huggingface dataset:
Advancements in Genetic Biomarkers and Exogenous Antioxidant Supplementation for Safeguarding Mammalian Cells against Heat-Induced Oxidative Stress and Apoptosis

Heat stress represents a pervasive global concern with far-reaching implications for the reproductive efficiency of both animal and human populations. An extensive body of published research on heat stress effects utilizes controlled experimental environments to expose cells and tissues to heat stress and its disruptive influence on the physiological aspects of reproductive phenotypic traits, encompassing parameters such as sperm quality, sperm motility, viability, and overall competence. Beyond these immediate effects, heat stress has been linked to embryo losses, compromised oocyte development, and even infertility across diverse species. One of the primary mechanisms underlying these adverse reproductive outcomes is the elevation of reactive oxygen species (ROS) levels precipitating oxidative stress and apoptosis within mammalian reproductive cells. Oxidative stress and apoptosis are recognized as pivotal biological factors through which heat stress exerts its disruptive impact on both male and female reproductive cells. In a concerted effort to mitigate the detrimental consequences of heat stress, supplementation with antioxidants, both in natural and synthetic forms, has been explored as a potential intervention strategy. Furthermore, reproductive cells possess inherent self-protective mechanisms that come into play during episodes of heat stress, aiding in their survival. This comprehensive review delves into the multifaceted effects of heat stress on reproductive phenotypic traits and elucidates the intricate molecular mechanisms underpinning oxidative stress and apoptosis in reproductive cells, which compromise their normal function. Additionally, we provide a succinct overview of potential antioxidant interventions and highlight the genetic biomarkers within reproductive cells that possess self-protective capabilities, collectively offering promising avenues for ameliorating the negative impact of heat stress by restraining apoptosis and oxidative stress.
Extracellular vesicles derived from mesenchymal stem cells: the wine in Hebe's hands to treat skin aging

Abstract Owing to its constant exposure to the external environment and various stimuli, skin ranks among the organs most vulnerable to manifestations of aging. Preventing and delaying skin aging has become one of the prominent research subjects in recent years. Mesenchymal stem cells (MSCs) are multipotent stem cells derived from mesoderm with high self-renewal ability and multilineage differentiation potential. MSC-derived extracellular vesicles (MSC-EVs) are nanoscale biological vesicles that facilitate intercellular communication and regulate biological behavior. Recent studies have shown that MSC-EVs have potential applications in anti-aging therapy due to their anti-inflammatory, anti-oxidative stress, and wound healing promoting abilities. This review presents the latest progress of MSC-EVs in delaying skin aging. It mainly includes the MSC-EVs promoting the proliferation and migration of keratinocytes and fibroblasts, reducing the expression of matrix metalloproteinases, resisting oxidative stress, and regulating inflammation. We then briefly discuss the recently discovered treatment methods of MSC-EVs in the field of skin anti-aging. Moreover, the advantages and limitations of EV-based treatments are also presented.
A Transfer Learning Approach Based on Radar Rainfall for River Water-Level Prediction

River water-level prediction is crucial for mitigating flood damage caused by torrential rainfall. In this paper, we attempt to predict river water levels using a deep learning model based on radar rainfall data instead of data from upstream hydrological stations. A prediction model incorporating a two-dimensional convolutional neural network (2D-CNN) and long short-term memory (LSTM) is constructed to exploit geographical and temporal features of radar rainfall data, and a transfer learning method using a newly defined flow–distance matrix is presented. The results of our evaluation of the Oyodo River basin in Japan show that the presented transfer learning model using radar rainfall instead of upstream measurements has a good prediction accuracy in the case of torrential rain, with a Nash–Sutcliffe efficiency (NSE) value of 0.86 and a Kling–Gupta efficiency (KGE) of 0.83 for 6-h-ahead forecast for the top-four peak water-level height cases, which is comparable to the conventional model using upstream measurements (NSE = 0.84 and KGE = 0.83). It is also confirmed that the transfer learning model maintains its performance even when the amount of training data for the prediction site is reduced; values of NSE = 0.82 and KGE = 0.82 were achieved when reducing the training torrential-rain-period data from 12 to 3 periods (with 105 periods of data from other rivers for transfer learning). The results demonstrate that radar rainfall data and a few torrential rain measurements at the prediction location potentially enable us to predict river water levels even if hydrological stations have not been installed at the prediction location.
Abrupt regime shifts in the North Atlantic atmospheric circulation over the last deglaciation

We analyze modeling results of the North Atlantic atmospheric winter circulation from a transient climate simulation over the last 21,000 years. In agreement with previous studies, we find that the midlatitude jet stream assumes a strong, stable, and zonal disposition so long as the North American ice sheets remain in their continent‐wide Last Glacial Maximum (LGM) configuration. However, when the Laurentide ice sheet (LIS) and Cordilleran ice sheet separate (∼14,000 years ago), the jet stream abruptly changes to a tilted circulation regime, similar to modern. The proposed explanation is that the dominant stationary wave source in the North Atlantic sector changes from the LIS to the Cordilleran mountain range during the saddle collapse. As long as the LIS dominates, the circulation retains the zonal LGM state characterized by prevalent stationary wave reflection in the subtropical North Atlantic. When the Cordillera takes over, the circulation acquires its modern disposition with a weak and meridionally tilted jet stream and storm track.
Effect of Potassium-Boron Content of Leaf on Copra Yield of Coconut (Cocos nucifera L.) in Terai Region of West Bengal, India

Since the last two decades, there has been a steady and consistent increase in the acreage under the cultivation of coconut (Cocos nucifera L.) in West Bengal. It is more so in the districts of North Bengal, where the palm is grown in homestead gardens, though the crop is gaining increasing importance of a plantation crop in the region. However, based on soils’ inherent capacity to meet the plant’s requirements, rational crop culture and nutrient management are very poorly practiced. Sandy soils, heavy rainfall, acidic nature of soil, nutrient removal by the crop, and leaching and percolation losses result in multiple nutrient deficiencies and makes the situation further complicated. Among the International Journal of Current Microbiology and Applied Sciences ISSN: 2319-7706 Volume 7 Number 02 (2018) Journal homepage: http://www.ijcmas. com
Risk-Quantification Method for Car-Following Behavior Considering Driving-Style Propensity

To systematically study the influence of a propensity for a particular driving style on car-following risk, a safety potential field risk-quantification method that considers driving style is proposed. First, we classify driving behaviors and construct a field-based safety potential car-following model via analogy with intermolecular interactions; second, we establish a risk-quantification model by considering driving style, risk exposure, and risk severity and classify the consequent risk into four levels, high risk, medium risk, low risk, and safe, using the fuzzy C-means algorithm. Finally, we predict the car-following risk using the LightGBM algorithm in real time. The experimental results show that the LightGBM algorithm can recognize up to 86% of medium–high risk levels compared to the Decision Tree and Random Forest Algorithms. It can achieve effective prediction of car-following risk, which provides sufficient warning information to drivers and helps improve the overall safety of vehicle operation.
Intelligent vehicle navigation systems and autonomous driving technology: A comprehensive analysis

This paper conducts a comprehensive study and analysis of intelligent vehicle navigation systems and autonomous driving technology. We review the historical development of autonomous driving technology, discuss key concepts such as perception, decision-making, and control, explore various types of autonomous vehicles, and examine various aspects of intelligent vehicle navigation systems. Additionally, we investigate safety, reliability, legal frameworks in the field of autonomous driving, as well as future trends and ethical considerations. Finally, we summarize the main findings of the research and provide recommendations for future studies.
Nanoparticle‑based antiviral strategies to combat the influenza virus (Review)

The rapid availability of effective antiviral treatments would be beneficial during the early phases of a pandemic, as they could reduce viral loads and control serious infections until antigenic vaccines become widely available. One promising alternative therapy to combat pandemics is nanotechnology, which has the potential to inhibit a wide variety of viruses, including the influenza virus. This review summarizes the recent progress using gold, copper, silver, silicone, zinc and selenium nanoparticles, since these materials have shown remarkable antiviral capacity against influenza A virus.
Investigating the Role of Gastrointestinal-Specific Anxiety and Perceived Disability in the Adjustment to Inflammatory Bowel Disease

In this study, an exploratory model was tested to investigate the mediating role of gastrointestinal-specific anxiety and perceived disability in the relation between disease activity and the outcomes of quality of life and depression in patients with inflammatory bowel disease (IBD). In a cross-sectional design, data from an online survey of patients with IBD (over the age of 16) recruited through a UK-based IBD charity were used to test a model of serial mediation. Measures targeted disease activity (self-reported), gastrointestinal-specific anxiety (Visceral Sensitivity Index), perceived disability (Perceived Disability Scale), IBD-specific quality of life (UK Inflammatory Bowel Disease Questionnaire), and depression (Depression subscale of the Depression, Anxiety, and Stress Scale). The theoretical model was investigated using model 6 of the PROCESS macro for SPSS. Bootstrapped 95% confidence intervals were constructed for each estimate A total of 320 participants provided valid responses to the survey. Gastrointestinal-specific anxiety and perceived disability were found to serially mediate the relationship between disease activity and quality of life (95%CI = −0.80 to −0.19) and disease activity and depression (95%CI = 0.13 to 0.59). Findings suggested that the effect of multiple psychosocial variables in the experience of people living with IBD and its respective outcomes should be taken into account when planning treatment. The theoretical and clinical implications of these findings are discussed.
Computer Vision Based Transfer Learning-Aided Transformer Model for Fall Detection and Prediction

Falls bring about significant risks to individuals’ well-being and independence, prompting widespread public health concerns. Swift detection and even predicting the risk of falls are crucial for implementing effective measures to alleviate the adverse consequences associated with such incidents. This study presents a new framework for identifying and forecasting fall risks. Our approach utilizes a novel transformer model trained on 2D poses extracted through an off-the-shelf pose extractor, incorporating transfer learning techniques. Initially, the transformer is trained on a large dataset containing 2D poses of general actions. Subsequently, we freeze the majority of its layers and fine-tune only the last few layers using relatively smaller datasets for fall detection and prediction tasks. Experimental results indicate that our proposed method outperforms traditional machine learning (e.g., SVM, Decision Tree, etc.) and deep learning approaches (e.g., LSTM, CNN, ST-GCN, PoseC3D, etc.) in both fall detection and prediction tasks across various datasets.
Monzonite suites: the innermost Cordilleran plutonism of Patagonia

ABSTRACT: In Patagonia a Triassic-Early Jurassic Cordilleran interior magmatic belt preceded the widespread eruption of Middle Jurassic syn-extensional rhyolites. Two plutons (La Calandria and La Leona) represent the easternmost plutonic rocks of this belt, > 750 km east of the present oceanic trench. They define a high-K calc-alkaline monzonite series in contrast with the main Andinotype arc magmatism of the Pacific margin: they are enriched in large ion lithophile elements (K, Rb, Ba, Sr and Th), LREE and P2O5 and depleted in HREE and Y, with low FeO*/MgO ratio. The range of observed compositions (56-76% SiO2) resulted from high-level fractionation of plagioclase, hornbleńde, biotite, K-feldspar and accessories (sphene, apatite and zircon). Initial 87Sr/86Sr ratios, average εNdt and mean depleted-mantle Nd model ages of the two plutons are 0·70487, -0·5 and 1050 Ma for La Calandria and 0·70509, -1·4 and 1125 Ma for La Leona, respectively. They are thus isotopically more primitive than the Middle Jurassic rhyolites, previously attributed to partial melting of Mesoproterozoic mafic lower crust. The preferred model for the origin of the monzonites is remelting of an amphibole- + garnet-bearing, plagioclase-poor, high-K mafic source (? underplating). This occurred in a distal sector of a dying oblique subduction regime, immediately preceding the extensional silicic volcanism.
Otimização dos parâmetros de monopolos planares de microfita para aplicações em sistemas de banda ultra larga

This work presents a theoretical and experimental investigation about the properties of microstrip antennas for ultra-wideband systems. Configurations of elliptic monopoles with different eccentricities and circular monopoles are considered. Two prototypes for each antenna configuration were built, one with the typical microstrip configuration and the other is similar to the first, except for a small aperture in the ground plane. Therefore, this work proposes to modify the configuration of the ground plane of the monopoles designed adding a rectangular stub, in order to optimize and improve the performance of such structures. The obtained results show that the introduction of that rectangular aperture in the ground plane allows an improvement of the frequency response for the considered antenna propotypes. It is observed a good agreement between the measured and simulated results. Finally, some proposals for future works are presented.
The determination of antioxidant and lead content of hemiparasite Dendrophthoe vitellina (F. Muell) Tiegh on Nutmeg

Mistletoe, a parasitic plant growing on nutmeg trees, is conventionally perceived as a parasitic entity with limited regard for its potential medicinal value. On the other hand, mistletoe has a remarkable potential source of valuable medicinal compounds, particularly in traditional healthcare, due to its secondary metabolites such as flavonoid, phenolic, ascorbic acid, and antioxidant activity. Nevertheless, both the host tree and the environment, like Pb, influence the adaptive responses of secondary metabolites. This study aimed to determine the secondary metabolites, such as flavonoids, phenolics, ascorbic acid, antioxidant activity, and Pb content in the leaves of D. vitellina and nutmeg ( M. fragrans ). The samples were obtained via maceration using ethanol. The spectrophotometric analysis method was used to measure several parameters, using particular reagents for phenolic compounds using Folin-Ciocalteu, flavonoids using AlCl 3 , and ascorbic acid using sulfosalicylic acid), antioxidant activity using DPPH, and Pb using the AAS method. The results indicated that D. vitellina possesses a higher concentration of flavonoid and phenolic compounds, followed by 3.36 ± 1.92 % (w/w) and 18.45 ± 2.35 % (w/w) respectively. Conversely, nutmeg had a significant ascorbic acid concentration of 3.99 ± 4.38 % (w/w). The significant presence of phenolics and flavonoids had a crucial role in the antioxidant activity seen in D. vitellina , which exhibited exceptionally potent antioxidant properties. All samples contain Pb, ranging from 0.09-2.10 µg/g, within the maximum allowable limits. Therefore, mistletoe is notable for being a reliable and encouraging plant species that can provide a natural supply of antioxidants and be safely used in traditional medicine.
RCF-ST: RICHER CONVOLUTIONAL FEATURES NETWORK WITH STRUCTURAL TUNING FOR THE EDGE DETECTION ON NATURAL IMAGES

Context. The problem of automating of the edge detection on natural images in intelligent systems is considered. The subject of the research is the deep learning convolutional neural networks for edge detection on natural images. Objective. The objective of the research is to improve the edge detection performance of natural images by structural tuning the richer convolutional features network architecture. Method. In general, the edge detection performance is influenced by a neural network architecture. To automate the design of the network structure in the paper a structural tuning of a neural network is applied. Computational costs of a structural tuning are incomparably less compared with neural architecture search, but a higher qualification of the researcher is required, and the resulting solution will be suboptimal. In this research it is successively applied first a destructive approach and then a constructive approach to structural tuning of the based architecture of the RCF neural network. The constructive approach starts with a simple architecture network. Hidden layers, nodes, and connections are added to expand the network. The destructive approach starts with a complex architecture network. Hidden layers, nodes, and connections are then deleted to contract the network. The structural tuning of the richer convolutional features network includes: (1) reducing the number of convolutional layers; (2) reducing the number of convolutions in convolutional layers; (3) removing at each stage the sigmoid activation function with subsequent calculation of the loss function; (4) addition of the batch normalization layers after convolutional layers; (5) including the ReLU activation functions after the added batch normalization layers. The obtained neural network is named RCF-ST. The initial color images were scaled to the specified size and then inputted in the neural network. The advisability of each of the proposed stages of network structural tuning was reseached by estimating the edge detection performance using the confusion matrix elements and Figure of Merit. The advisability of a structural tuning of the neural network as a whole was estimated by comparing it with methods known from the literature using the Optimal Dataset Scale and Optimal Image Scale. Results. The proposed convolutional neural network has been implemented in software and researched for solving the problem of edge detection on natural images. The structural tuning technique may be used for informed design of the neural network architectures for other artificial intelligence problems. Conclusions. The obtained RCF-ST network allows to improve the performance of edge detection on natural images. RCF-ST network is characterized by a significantly fewer parameters compared to the RCF network, which makes it possible to reduce the resource consumption of the network. Besides, RCF-ST network ensures the enhancing of the robustness of edge detection on texture background.
The Influence of e-learning on Students' Learning Motivation in Jiangsu University

Learning motivation refers to the basic form of learning activities that learners show in order to complete the corresponding learning tasks. The choice of learning motivation is not only influenced by subjective factors but also changed by the change of learning environment, which has a distinct era. The formation of the online learning environment has significantly changed the learning style of all college students, which profoundly impacts their way of thinking. It is of great theoretical and practical value to study college students' learning motivation systematically and deeply in the online environment by the method of literature research and investigation. Accordingly, the new changes and characteristics of college students in the online environment affected their learning motivations; thus, this paper analyzes the current situation of Jiangsu College using the questionnaire survey method. The results showed that students' learning motivation is concentrated and shallow, with high utility and fewer college students wanting to study independently or in-depth. Therefore, the author puts forward targeted improvement strategies for the current situation of learning motivation of college students in Jiangsu Province.
High prevalence of pre-treatment and acquired HIV-1 drug resistance mutations among non-citizens living with HIV in Botswana

Background Approximately 30,000 non-citizens are living with HIV in Botswana, all of whom as of 2020 are eligible to receive free antiretroviral treatment (ART) within the country. We assessed the prevalence of HIV-1 mutational profiles [pre-treatment drug resistance (PDR) and acquired drug resistance (ADR)] among treatment-experienced (TE) and treatment-naïve (TN) non-citizens living with HIV in Botswana. Methods A total of 152 non-citizens living with HIV were enrolled from a migrant HIV clinic at Independence Surgery, a private practice in Botswana from 2019–2021. Viral RNA isolated from plasma samples were genotyped for HIV drug resistance (HIVDR) using Sanger sequencing. Major known HIV drug resistance mutations (DRMs) in the pol region were determined using the Stanford HIV Drug Resistance Database. The proportions of HIV DRMs amongst TE and TN non-citizens were estimated with 95% confidence intervals (95% CI) and compared between the two groups. Results A total of 60/152 (39.5%) participants had a detectable viral load (VL) >40 copies/mL and these were included in the subsequent analyses. The median age at enrollment was 43 years (Q1, Q3: 38–48). Among individuals with VL > 40 copies/mL, 60% (36/60) were treatment-experienced with 53% (19/36) of them on Atripla. Genotyping had a 62% (37/60) success rate – 24 were TE, and 13 were TN. A total of 29 participants (78.4, 95% CI: 0.12–0.35) had major HIV DRMs, including at least one non-nucleoside reverse transcriptase inhibitor (NNRTI) associated DRM. In TE individuals, ADR to any antiretroviral drug was 83.3% (20/24), while for PDR was 69.2% (9/13). The most frequent DRMs were nucleoside reverse transcriptase inhibitors (NRTIs) M184V (62.1%, 18/29), NNRTIs V106M (41.4%, 12/29), and K103N (34.4%, 10/29). No integrase strand transfer inhibitor-associated DRMs were reported. Conclusion We report high rates of PDR and ADR in ART-experienced and ART-naïve non-citizens, respectively, in Botswana. Given the uncertainty of time of HIV acquisition and treatment adherence levels in this population, routine HIV-1C VL monitoring coupled with HIVDR genotyping is crucial for long-term ART success.