Miracle-dz/UniFAR-FTUs huggingface dataset:
Isorhamnetin decreased the expression of HMG-CoA reductase and increased LDL receptors in HepG2 cells

Isorhamnetin is a flavonoid present in many plants. In a previous in vivo study, isorhamnetin lowered serum cholesterol of rats fed high-cholesterol diet. However, its mechanism of action was not investigated. In the present work, the mechanism of its hypocholesterolemic effect was studied. The expression of 3-hydroxy-3-methylglutaryl-CoA reductase (HMG-CoA reductase) and low-density lipoprotein receptor (LDLR) genes and proteins was studied in HepG2 liver cancer cell line by polymerase chain reaction, western blot, and indirect enzyme-linked immunosorbent assay as well as its antioxidant activity. sorhamnetin had an IC 50 of 100, 53, and 40 µM at 24, 48, and 72 hours, respectively, in HepG2 cells. Isorhamnetin downregulated HMG-CoA reductase gene expression significantly. Also, all tested doses of isorhamnetin downregulated LDLR expression and produced no change in membranous LDLR protein expression. In cell lysate, LDLR was increased by all studied concentrations of isorhamnetin. Isorhamnetin (100 µM) decreased intracellular HMG-CoA reductase compared to vehicle-treated control. Furthermore, isorhamnetin increased superoxide dismutase activity and reduced H 2 O 2 level, due to catalase activity. Isorhamnetin reduced HMG-CoA reductase gene expression and increased total LDLR and exerted pronounced antioxidant action.
Monitoring of Alarm Reactions of Red Deer (Cervus elaphus) in a Captive Population in Paneveggio Pale di San Martino Natural Park

Simple Summary After several years of inappropriate management, the pasture inside the enclosure for captive red deer in Paneveggio Pale di San Martino regional Park (TN, Italy) lost its nutritional value, due to the expansion of unpalatable tall grasses. Therefore, several measures to restore a suitable pasture composition were needed. The mowing activity represents a disturbance for the captive deer, which negatively affects the animals’ well-being. To establish the more appropriate times/days to perform activities inside the enclosure, we observed the alarm reactions and relative intensity of animals exposed to different visual stimuli presented inside and outside the enclosure. Some differences were highlighted between the males and the nursery (females and fawns) groups. Considering the deer biology and the studied location, the best months in which it would be possible to plan activities inside the enclosure are March, April (if the snow is not present) and August. Data elaboration suggests that the best day to perform activities inside the enclosure is Wednesday because the animals showed less sensitivity to disturbances; Tuesday and Thursday may also be considered additional suitable days. Abstract The study analyzes red deer responses to disturbances during the day and different exposures to tourists, to establish the more appropriate times to carry out activities inside the Paneveggio deer enclosure. The alarm reactions of red deer were observed after presenting different types of visual stimuli inside and outside the fence, in order to answer some questions: Which stimuli produce the strongest reactions from the animals? Do animals differently react to stimuli presented outside and inside the fence? On which days and times are the animals more sensitive to disturbances? Are there different reactions between the males and females? The results suggest that the red deer adversely react to the disturbance at different degrees of intensity in relation to day, sex, tourist and where the stimuli are presented. It was observed that during the days with the highest tourist presence, the animals were particularly alarmed; discomfort accumulation produced the highest number of alarm reactions on Monday. For these reasons, it would be opportune to manage the pasture on Tuesday, Wednesday and Thursday, scheduled at specific times of day, preferably far from the estimated presence of tourists.
Defining Computational Thinking as an Evident Tool in Problem-Solving: Comparative Research on Chinese and Canadian Mathematics Textbooks

Purpose To analyze mathematics problem-solving (PS) procedures in Chinese (CH) and Canadian (CA) elementary mathematics textbooks that leverage computational thinking (CT) as a cognitive tool, which have evidently existed and been implemented. Design/Approach/Methods In this study, an analysis framework was developed to investigate the characteristics of CT tools for three PS steps—understand the problem, devise and conduct plans, and look back into textbooks—in four contexts: data practices, modeling and simulation practices, computational tools practices, and systemic thinking practices. Findings Our results demonstrate the tools (CT) employed in the PS process in CH and CA mathematics textbooks. The strong connections between the “look back” stage and CT tools were explored. During the “look back” stage, both countries required students to transfer their knowledge and perform generalization. In addition, CT is regarded as a basic skill analysis for students in mathematics education and has received significant attention at every stage of the PS process. Originality/Value This study brings a new perspective to CT research in education by regarding CT as a cognitive tool for students in mathematics PS.
Simulation of Acquired LQT Syndrome Using Human Virtual Ventricular Cardiomyocyte Model

Acquired long QT syndrome is a cardiac channelopathy, usually manifested by prolonged QT intervals in the electrocardiogram, which can lead to arrhythmias and an increased risk of sudden death. However, there is a diversity of drugs that target LQT syndrome. In this study, we simulated acquired LQT syndrome on a model of human ventricular cardiomyocytes and tested the therapeutic effects of potassium supplements and the L-type calcium blocker nifedipine on this basis. The results showed that the L-type calcium blocker and potassium ion supplementation could effectively shorten the action potential and QT interval of the ECG in cardiomyocytes and shorten the effective nonresponse period. Taken together, this study provides data to support the use of calcium channel blockers and potassium supplementation as a new treatment for LQTS.
Far-Field Intracardiac Electrograms Removal Enables Highly Reliable Automatic Cycle Length Estimation During Atrial Arrhythmia

Electrograms (EGMs) from the intracardiac reference catheter placed in the coronary sinus (CS) vein are suscep-tible to ventricular noise. Most atrial tachycardia (AT) or fibrillation (AF) catheter ablation procedures use this lo-cation. Because of its proximity to ventricles, electrograms tend to include waves similar to the QRS complex (far-field activity) as seen on the electrocardiogram (ECG). These ventricular activities can, for instance, disturb the atrial cycle length estimation from those electrograms, or could fail its estimation. This study aims to determine whether the electrograms with ventricular far field (VFF) removed allow for higher cycle length estimation performance and reliable real-time diagnostic assistance. A method proposed in this paper separates and adaptively removes VFF while preserving useful atrial activity even when the two activities are su-perposed. This is achieved through generating a VFF blueprint from the time-frequency analysis using wavelet decomposition. Suppressing this blueprint in the wavelet domain and reconstructing the signal permits us to achieve artifact-free EGMs.
Hybrid Multilevel Thresholding Image Segmentation Approach for Brain MRI

A brain tumor is an abnormal growth of tissues inside the skull that can interfere with the normal functioning of the neurological system and the body, and it is responsible for the deaths of many individuals every year. Magnetic Resonance Imaging (MRI) techniques are widely used for detection of brain cancers. Segmentation of brain MRI is a foundational process with numerous clinical applications in neurology, including quantitative analysis, operational planning, and functional imaging. The segmentation process classifies the pixel values of the image into different groups based on the intensity levels of the pixels and a selected threshold value. The quality of the medical image segmentation extensively depends on the method which selects the threshold values of the image for the segmentation process. The traditional multilevel thresholding methods are computationally expensive since these methods thoroughly search for the best threshold values to maximize the accuracy of the segmentation process. Metaheuristic optimization algorithms are widely used for solving such problems. However, these algorithms suffer from the problem of local optima stagnation and slow convergence speed. In this work, the original Bald Eagle Search (BES) algorithm problems are resolved in the proposed Dynamic Opposite Bald Eagle Search (DOBES) algorithm by employing Dynamic Opposition Learning (DOL) at the initial, as well as exploitation, phases. Using the DOBES algorithm, a hybrid multilevel thresholding image segmentation approach has been developed for MRI image segmentation. The hybrid approach is divided into two phases. In the first phase, the proposed DOBES optimization algorithm is used for the multilevel thresholding. After the selection of the thresholds for the image segmentation, the morphological operations have been utilized in the second phase to remove the unwanted area present in the segmented image. The performance efficiency of the proposed DOBES based multilevel thresholding algorithm with respect to BES has been verified using the five benchmark images. The proposed DOBES based multilevel thresholding algorithm attains higher Peak Signal-to-Noise ratio (PSNR) and Structured Similarity Index Measure (SSIM) value in comparison to the BES algorithm for the benchmark images. Additionally, the proposed hybrid multilevel thresholding segmentation approach has been compared with the existing segmentation algorithms to validate its significance. The results show that the proposed algorithm performs better for tumor segmentation in MRI images as the SSIM value attained using the proposed hybrid segmentation approach is nearer to 1 when compared with ground truth images.
Predictive value of pre-procedural N-terminal pro-B-type natriuretic peptide level for atrial fibrillation recurrence after radiofrequency catheter ablation

Introduction N-terminal pro-B-type natriuretic peptide (NT-proBNP) has been demonstrated as a valuable risk marker for mortality and morbidity of cardiovascular disease. Recurrence after atrial fibrillation (AF) radiofrequency catheter ablation remains common. Aim We investigated the predictive value of the pre-procedural level of NT-proBNP to differentiate high-risk patients for post-ablation AF recurrence. Material and methods 326 individuals with nonvalvular AF and preserved systolic function after enduring an initial radiofrequency catheter ablation (RFCA) between March 2018 and December 2019 were categorized into a recurrent group and a non-AF recurrent group. The serum NT-proBNP levels were examined before the ablation procedure. The researchers used multivariate logistic regression to find the determinants of AF recurrence. Results During a 14-month (interquartile range (IQR): 12–16) median follow-up, AF recurred in 84 (25.8%) patients. Patients in the recurrence group had considerably greater pre-ablation NT-proBNP levels (389.4 vs. 141.7 pg/ml, p < 0.001 in non-paroxysmal AF and 348.0 vs. 99.4 pg/ml, p < 0.001 in paroxysmal AF) as well as a greater left atrium (40 vs. 36 mm, p = 0.01 in non-paroxysmal AF and 38 vs. 36 mm, p = 0.01 in paroxysmal AF) than the non-AF recurrence group. A cut-off value of NT-proBNP ≥ 168.05 pg/ml identified AF recurrence with a sensitivity of 78.6% and specificity of 53.7% (area under ROC curve 0.68, 95% confidence interval (CI) 0.62–0.74, p < 0.001). Kaplan-Meier examination revealed that the elevated NT-proBNP (≥ 168.05 pg/ml) group presented a considerably shorter period without an occurrence compared to the low-NT-proBNP group (18.4 vs. 22.2 months, log-rank p = 0.001). Multivariate cox regression investigation showed that a level of NT-proBNP ≥ 168.05 pg/ml (hazard ratio (HR): 2.89, 95% CI: 1.71-4.903, p < 0.001) was a reliable predictor of AF recurrence after RFCA. Conclusions A high pre-ablation NT-proBNP level was associated with AF recurrence, and it was also discovered to be a prognostic factor of recurrence of AF following RFCA.
THE CONSENSUS IN MULTI-AGENT SYSTEM WITH SPEED-OPTIMIZED NETWORK

In this paper, we mainly investigate the character of consensus convergence speed of Multi-Agent Systems (MAS) with small-world communication network and the method of devising a speed-optimized small-world communication network in consensus problem based on genetic-algorithm (GA). It is found that, for a small-world communication network, the time to reach a consensus changes rapidly with the change of the number of long-range communication links and the agents which the long-range links connect. The convergence speed of consensus of MAS increases rapidly with the number of long-range links increasing. As we construct a small-world communication network for MAS with a smaller network size and fixed long-range links, we can optimize the long-range link configuration using GA methodology to obtain a small-world communication network with faster consensus speed for MAS.
Particle simulations of dispersion using observed meandering and turbulence

A Lagrangian stochastic particle model driven by observed winds from a network of 13 sonic anemometers is used to simulate the transport of contaminates due to meandering of the mean wind vector and diffusion by turbulence. The turbulence and the meandering motions are extracted from the observed velocity variances using a variable averaging window width. Such partitioning enables determination of the separate contributions from turbulence and meandering to the total dispersion. The turbulence is described by a Markov Chain Monte Carlo process based on the Langevin equation using the observed turbulence variances.The meandering motions, not the turbulence, are primarily responsible for the 1-h averaged horizontal dispersion as measured by the travel time dependence of the particle position variances. As a result, the 1-h averaged horizontal concentration patterns are often characterized by streaks and multi-modal distributions. Time series of concentration at a fixed location are highly nonstationary even when the 1-h averaged spatial distribution is close to Gaussian. The results show that meandering dominates the travel-time dependence of the horizontal dispersion under all atmospheric conditions: weak and strong winds, and unstable and stable stratification.
Heart Rate Variability Parameters to Evaluate Autonomic Functions in Healthy Young Subjects during Short-Term “Dry” Immersion

There is a gap in the current knowledge on the immediate mechanisms of cardiovascular regulation in human subjects within short-term exposure to modeled microgravity using “dry” immersion. Aim. The purpose of the study was to evaluate cardiovascular responses in young healthy subjects during a 45 min session with the help of linear and nonlinear heart rate variability and hemodynamics parameters. The research voluntarily enrolled 33 subjects (18 men, 15 women) aged 19–23 years old. Results. The study showed that systolic and diastolic blood pressure was quite stable, some time-domain parameters of heart rate variability (SDNN, RMSSD, pNN50, etc.) and the frequency-domain (TP, HF, LF, but not VLF) have significantly increased within a 45 min “dry” immersion session. Of the non-linear parameters of heart rate variability, only ApEn significantly decreased during the “dry” immersion session. Conclusion. Our results suggest that a short-term 45 min DI session provokes in young healthy subjects neurogenic autonomic reaction based on the baroreceptor reflex. This provides stable hemodynamics in these subjects along the “dry” immersion session.
Direct and indirect effects of dominant plants on ecosystem multifunctionality

Biodiversity is essential for the provision of multiple ecosystem functions simultaneously (ecosystem multifunctionality EMF). Yet, it remains unclear whether and how dominant plant species impact EMF. Here, we aimed at disentangling the direct from indirect above- and belowground pathways by which dominant plant species influence EMF. We evaluated the effects of two dominant plant species (Dasiphora fruticosa, and the toxic perennial plant Ligularia virgaurea) with expected positive and negative impacts on the abiotic environment (soil water content and pH), surrounding biological communities (plant and nematode richness, biomass, and abundance in the vicinity), and on the EMF of alpine meadows, respectively. We found that the two dominant plants enhanced EMF, with a positive effect of L. virgaurea on EMF greater than that of D. fruticosa. We also observed that dominant plants impacted on EMF through changes in soil water content and pH (indirect abiotic effects), but not through changes in biodiversity of surrounding plants and nematodes (indirect biotic pathway). Our study suggests that dominant plants may play an important role in promoting EMF, thus expanding the pervasive mass-ratio hypothesis originally framed for individual functions, and could mitigate the negative impacts of vegetation changes on EMF in the alpine meadows.
Interpretable Deep Learning Framework for Land Use and Land Cover Classification in Remote Sensing Using SHAP

An interpretable deep learning framework for land use and land cover (LULC) classification in remote sensing using Shapley additive explanations (SHAPs) is introduced. It utilizes a compact convolutional neural network (CNN) model for the classification of satellite images and then feeds the results to a SHAP deep explainer so as to strengthen the classification results. The proposed framework is applied to Sentinel-2 satellite images containing 27000 images of pixel size $64 \times 64$ and operates on three-band combinations, reducing the model’s input data by 77% considering that 13 channels are available, while at the same time investigating on how different spectrum bands affect predictions on the dataset’s classes. Experimental results on the EuroSAT dataset demonstrate the CNN’s accurate classification with an overall accuracy of 94.72 %, whereas the classification accuracy on three-band combinations on each of the dataset’s classes highlights its improvement when compared to standard approaches with larger number of trainable parameters. The SHAP explainable results of the proposed framework shield the network’s predictions by showing correlation values that are relevant to the predicted class, thereby improving the classifications occurring in urban and rural areas with different land uses in the same scene.
Column crossing and lateral transport in numerical weather models

Transport in numerical weather models is typically restricted to advection by the resolved wind field, and representation of other flow processes relies on forcing or coupling to other models. A discussion of non‐advective transport in numerical weather models is presented and illustrated using two examples. These are the sea breeze and surface water flow. Simple models of these phenomena are represented in an adapted version of the Met Office Unified Model (UM) by means of modifying the existing cold‐pool scheme in the UM. This approach of incorporating different transport processes in one framework could facilitate a better representation of the Earth system, by increasing interaction between meteorological, surface and subsurface processes.
Modeling of Sequences of Wet and Dry Days by Binary Discrete Autoregressive Moving Average Processes

Abstract The Binary Discrete Autoregressive Moving Average (B-DARMA) process, which includes the Markov chain as a particular case, is used to describe the wet-dry day sequences that are obtained from daily precipitation time series. A three-step procedure, consisting of identification, estimation and model selection is developed and shown to be effective in model building. The identification step is based on the plot of the autocorrelation function, while the estimation of the parameters is done by fitting the autocorrelation function by a nonlinear least-squares method. The model selection uses the probability distributions of run lengths, which are defined and discussed in this paper. The persistences of wet and dry spells, which are important properties in the study of floods and droughts, are well reproduced through the preservation of the run length properties. The best model is chosen as the one with the run length distribution that has the minimum sum-of-squares error in estimating the actual run .
Healthcare Technology Evolution And Adoption Of Fog Computing In Healthcare: Review, Issue And Challenges

Health Information System (HIS) is a combination of hardware, software, process, infrastructure, people and protocol which is developed for management of healthcare data including collection, storage, analysis, decision making and transmission. It supports functional management of a hospital as well as provides an infrastructure that support policy decisions over healthcare data. HIS grown decade by decade starting from 1960 to present era and divided into four generation versions, starting from HIS. V.1. 0 to HIS V.4. 0. HIS V.4. 0 is dedicated for smart medical technology with fog-based computing system including disease-oriented sensing system, IoT, artificial intelligence, high speed internet and advanced internet protocols. Fog Computing is a distributed computing framework and provide an extension to cloud computing. It lies between medical sensor (IoT devices) and cloud datacenters. This paper presents various HIS generations and technologies used in HIS V.4. 0. Include review of cloud computing, IoT and Fog computing. Further this paper provides a systematic review of existing research in fog computing with various issue and challenges. Finally, paper describes comparison of fog computing with cloud and edge computing and present discussion, open challenges and recommendation in Fog-based HIS.
Data Secure De-Duplication and Recovery Based on Public Key Encryption With Keyword Search

In the current era of information explosion, users’ demand for data storage is increasing, and data on the cloud has become the first choice of users and enterprises. Cloud storage facilitates users to backup and share data, effectively reducing users’ storage expenses. As the duplicate data of different users are stored multiple times, leading to a sudden decrease in storage utilization of cloud servers. Data stored in plaintext form can directly remove duplicate data, while cloud servers are semi-trusted and usually need to store data after encryption to protect user privacy. In this paper, we focus on how to achieve secure de-duplication and recover data in ciphertext for different users, and determine whether the indexes of public key searchable encryption and the matching relationship of trapdoor are equal in ciphertext to achieve secure de-duplication. For the duplicate file, the data user’s re- encryption key about the file is appended to the ciphertext chain table of the stored copy. The cloud server uses the re- encryption key to generate the specified transformed ciphertext, and the data user decrypts the transformed ciphertext by its private key to recover the file. The proposed scheme is secure and efficient through security analysis and experimental simulation analysis.