Miracle-dz/UniFAR-FTUs huggingface dataset:
Anti-Obesity and Anti-Dyslipidemic Effects of Salicornia arabica Decocted Extract in Tunisian Psammomys obesus Fed a High-Calorie Diet

Salicornia is a halophyte plant that has been used in traditional medicine for the treatment of scurvy, goiter, and hypertension. It is commercialized in Europe and Asia as fresh salads, pickled vegetables, green salt, or tea powder. This work is the first to assess the potential anti-obesity and anti-dyslipidemic effects of Salicornia arabica decocted extract (SADE). SADE was characterized by its significant in vitro radical scavenging activity (using DPPH and ABTS assays). The effect of SADE on food intake, weight loss, serum biochemical parameters, liver and kidney weights, adiposity index and on liver histology was investigated in the Tunisian gerbil Psammomys obesus (P. obesus), which is recognized as a relevant animal model of human obesity and diabetes. P. obesus animals were firstly randomly divided into two groups: the first received a natural low-calorie chow diet (LCD), and the second group received a high-calorie diet (HCD) over 12 weeks. On day 90, animals were divided into four groups receiving or not receiving SADE (LCD, LCD + SADE, HCD, and HCD + SADE). If compared to the HCD group, SADE oral administration (300 mg/kg per day during 4 weeks) in HCD + SADE group showed on day 120 a significant decrease in body weight (−34%), blood glucose (−47.85%), serum levels of total cholesterol (−54.92%), LDL cholesterol (−60%), triglycerides (−48.03%), and of the levels of hepatic enzymes: ASAT (−66.28%) and ALAT (−31.87%). Oral administration of SADE restored the relative liver weight and adiposity index and significantly limited HCD-induced hepatic injury in P. obesus. SADE seems to have promising in vivo anti-obesity and anti-dyslipidemic effects.
Deep Learning for the Design of Toroidal Metasurfaces

In recent years, the toroidal dipoles have had a profound impact on several fields including electromagnetism. However, the on-demand design of toroidal metasurfaces is still a very time-consuming process. In this paper, a method of neural network simulating the nonlinear relationship between the structural parameters of metasurfaces and its multipole scattered powers is proposed based on a deep learning algorithm. The forward network can quickly predict the scattered powers from input structural parameters, which can achieve an accuracy comparable to the electromagnetic simulations. In addition, with the required scattering spectrum as input, the appropriate parameters of the structure could be automatically calculated and then output by the inverse network which can achieve a low mean square error of 0.074 in training set and 0.18 in the test set. Compared with the conventional design process, the proposed deep learning model can guide the design of the toroidal dipole metasurface faster and pave the way for the rapid development of toroidal metasurfaces.
Scale formation in wet scrubbers and the current state of anti-scaling and softening methods for hard waters: A review

ABSTRACT Flue-gas desulphurization (FGDs) systems were developed primarily to remove sulphur dioxide (SO2) from industrial gas streams. The main techniques adopted involve wet scrubbing, particularly lime/limestone scrubbing. These techniques are relatively simple, cost less, and have thus, become applicable to other types of plants that emit SO2 such as refineries and smelters. In lime and limestone scrubbing, calcium compounds are added as slurry to the scrubber liquid, and this can result in the formation of solid calcium salts. As a result, the efficiency of such wet calcium-containing scrubbing systems is limited by the solubility of calcium salts in the slurry. As the concentrations of calcium ions build up in the water, the solubility limit of calcium sulphate (CaSO4) and many other compounds is often exceeded. In such systems, the formation of supersaturated CaSO4 causes scaling or deposition in the scrubber. The scaling problem further results in numerous plant downtimes due to the need to open lines and descale. The current review looks into existing and potential methods that have and can be developed to prevent or remove the formation of scaling in FGD systems such as scrubbers. A particular focus of this review is given to approaches such as chemical “softening” of hard waters using ion-exchange and precipitation using lime and soda ash. This review also looks into a contentious physical water treatment – magnetic water treatment (MWT) - as a potential alternative and environmentally conscious solution for the prevention of scale in scrubbers and hard waters in general.
Measuring motion blur in liquid-crystal displays

An experimentally simple method is proposed to to determine motion blur in Liquid crystal display (LCD), based on a luminance data acquisition system that accurately measures the temporal step response of various luminance transitions on the display. A fast-response photodiode captures the temporal luminance variations at a fixed position on the display. Its output is connected to the data-acquisition system via additional circuitry for signal amplification and low pass filtering. It is important that the spectral sensitivity of the sensor is matched to the spectral luminous-efficiency function for photopic vision to obtain luminance values from the photodiode. The temporal luminance variation is precisely acquired with a fast, eye sensitivity compensated photodiode in combination with a data-acquisition system. This method is very well simulated, and it is considered to be a good alternative to the smooth-pursuit camera system.
DcPCA: A Deep Learning Model for Contrastive Analytics of Nonlinear Data

Principal component analysis (PCA) is one of the most fundamental techniques for Big Data analytics, capable of extracting the most essential information from a single dataset. However, when it encounters multiple datasets, PCA cannot reveal the specific inherent data structure hidden in one dataset relative to the other(s), which we term contrastive analytics in this paper. Although a number of proposals such as contrastive or discriminative PCA have been advocated, they require fine-tuning of hyper-parameters or cannot effectively deal with nonlinear data. In this paper, we advocate deep contrastive (Dc) PCA for nonlinear contrastive analytics, which leverages deep neural networks to learn the hidden nonlinear relationships in the datasets and further extracts the desired contrastive features. An alternating minimization algorithm is developed for simultaneously seeking the best nonlinear transformations for the data as well as the associated contrastive projections, tantamount to performing an eigenvalue decomposition and a back-propagation step. Experiments using both synthetic and real world datasetsare performed, which corroborate the superior adaptivity of DcPCA in dealing with nonlinear data relative to a set of competing alternatives.
Pulmonary hypertension in patients with multiple myeloma: A comprehensive review

Abstract Multiple myeloma (MM) is a common hematological malignancy resulting from clonal proliferation of plasma cells and is defined by criteria set forth by the international myeloma working group. Pulmonary hypertension (PH) is defined by an elevated mean pulmonary artery pressure >20 mmHg measured during right heart catheterization. Echocardiography‐diagnosed PH is relatively common in patients with MM and has been associated with increased mortality, morbidity, and poor stem cell transplant outcomes. PH in patients with MM (PH‐MM) is usually multifactorial in origin. MM disease‐specific factors, host comorbidities, and treatment‐related adverse effects are the key factors for the development of PH‐MM. Pragmatically, patients with PH‐MM can be grouped into either (i) PH in patients with a new diagnosis of MM or (ii) PH that develops or worsens along the way of MM treatment. In the latter group, drug‐induced PH, venous thromboembolism, pulmonary veno occlusive disease, and cardiotoxicity should be considered as possible causes. PH‐MM should be evaluated and managed in a multidisciplinary setting. Select individuals with PH‐MM could be considered for pulmonary vasodilators at PH‐specialized centers.
Ischemic Tolerance—A Way to Reduce the Extent of Ischemia–Reperfusion Damage

Individual tissues have significantly different resistance to ischemia–reperfusion damage. There is still no adequate treatment for the consequences of ischemia–reperfusion damage. By utilizing ischemic tolerance, it is possible to achieve a significant reduction in the extent of the cell damage due to ischemia–reperfusion injury. Since ischemia–reperfusion damage usually occurs unexpectedly, the use of preconditioning is extremely limited. In contrast, postconditioning has wider possibilities for use in practice. In both cases, the activation of ischemic tolerance can also be achieved by the application of sublethal stress on a remote organ. Despite very encouraging and successful results in animal experiments, the clinical results have been disappointing so far. To avoid the factors that prevent the activation of ischemic tolerance, the solution has been to use blood plasma containing tolerance effectors. This plasma is taken from healthy donors in which, after exposure to two sublethal stresses within 48 h, effectors of ischemic tolerance occur in the plasma. Application of this activated plasma to recipient animals after the end of lethal ischemia prevents cell death and significantly reduces the consequences of ischemia–reperfusion damage. Until there is a clear chemical identification of the end products of ischemic tolerance, the simplest way of enhancing ischemic tolerance will be the preparation of activated plasma from young healthy donors with the possibility of its immediate use in recipients during the initial treatment.
Local linear LUT method for spatial colour-correction algorithm speed-up

There is a class of nonlinear filtering algorithms for digital colour enhancement, characterised by data-driven local effects and high computational cost. A new method called LLL (local linear look-up table (LUT)) is presented, which speeds up these filters without losing their local effect. Usually, classic LUT-based methods are global whereas the approach presented here uses the principles of LUT transformation in a local way. The main idea of this method is to apply the colour-enhancement algorithm to a small sub-sampled version of the input image and to use a modified look-up table technique to maintain the local filtering effect of the colour-enhancement algorithm. The method increases the speed of colour-filtering algorithms, reducing the number of pixels involved in the computation by sub-sampling the input image. To overcome possible loss of detail due to sub-sampling, an optional, additional stage to maintain high-frequency content is shown. LLL with two of these filters, the Brownian Retinex implementation and the automatic colour equalisation algorithm, are tested. Results, comparison and conclusions are presented.
A THREE-DIMENSIONAL AGGREGATE GENERATION AND PACKING ALGORITHM FOR MODELING ASPHALT MIXTURE WITH GRADED AGGREGATES

ABSTRACT A three-dimensional aggregate generation and packing algorithm applicable for modeling asphalt mix-ture with high content of graded aggregates is presented in this paper. In the algorithm, arbitrary-shaped polyhedra are used to model aggregates, so that the effect of aggregate shape on the mechanical perform-ance of asphalt mixture can be considered. The algorithm consists of two steps: Aggregate generation and packing. Polyhedra are created by extending triangular fundaments and treated as visualized aggre-gates after passing through convex control and sharpness judgment. After that, graded aggregates are taken out from the aggregate base and randomly packed in a given cylindrical or cubical region one by one equiprobably. Overlapping between nearby aggregates is avoided by the help of Boolean partition operation in ANSYS. Finally, some asphalt mixture samples with a given gradation are modeled as examples, and their effective elastic properties and creep behaviors under uniaxial compression are simu-lated.
Microbial Enrichment Techniques on Syngas and CO2 Targeting Production of Higher Acids and Alcohols

(1) Background: Microbial conversion of gaseous molecules, such as CO2, CO and H2, to valuable compounds, has come to the forefront since the beginning of the 21st century due to increasing environmental concerns and the necessity to develop alternative technologies that contribute to a fast transition to a more sustainable era. Research efforts so far have focused on C1–C2 molecules, i.e., ethanol and methane, while interest in molecules with higher carbon atoms has also started to emerge. Research efforts have already started to pay off, and industrial installments on ethanol production from steel-mill off-gases as well as methane production from the CO2 generated in biogas plants are a reality. (2) Methodology: The present study addresses C4–C6 acids and butanol as target molecules and responds to how the inherent metabolic potential of mixed microbial consortia could be revealed and exploited based on the application of different enrichment methods (3) Results and Conclusions: In most of the enrichment series, the yield of C4–C6 acids was enhanced with supplementation of acetic acid and ethanol together with the gas substrates, resulting in a maximum of 43 and 68% (e-mol basis) for butyric and caproic acid, respectively. Butanol formation was also enhanced, to a lesser degree though and up to 9% (e-mol basis). Furthermore, the microbial community exhibited significant shifts depending on the enrichment conditions applied, implying that a more profound microbial analysis on the species level taxonomy combined with the development of minimal co-cultures could set the basis for discovering new microbial co-cultures and/or co-culturing schemes.
Identification of Blood Transport Proteins to Carry Temoporfin: A Domino Approach from Virtual Screening to Synthesis and In Vitro PDT Testing

Temoporfin (mTHPC) is one of the most promising photosensitizers used in photodynamic therapy (PDT). Despite its clinical use, the lipophilic character of mTHPC still hampers the full exploitation of its potential. Low solubility in water, high tendency to aggregate, and low biocompatibility are the main limitations because they cause poor stability in physiological environments, dark toxicity, and ultimately reduce the generation of reactive oxygen species (ROS). Applying a reverse docking approach, here, we identified a number of blood transport proteins able to bind and disperse monomolecularly mTHPC, namely apohemoglobin, apomyoglobin, hemopexin, and afamin. We validated the computational results synthesizing the mTHPC-apomyoglobin complex (mTHPC@apoMb) and demonstrated that the protein monodisperses mTHPC in a physiological environment. The mTHPC@apoMb complex preserves the imaging properties of the molecule and improves its ability to produce ROS via both type I and type II mechanisms. The effectiveness of photodynamic treatment using the mTHPC@apoMb complex was then demonstrated in vitro. Blood transport proteins can be used as molecular “Trojan horses” in cancer cells by conferring mTHPC (i) water solubility, (ii) monodispersity, and (iii) biocompatibility, ultimately bypassing the current limitations of mTHPC.
Cross-frequency correlation of intensity fluctuations for a deeply modulated phase screen

We consider the intensity fluctuation patterns produced beyond a deeply modulated phase changing screen when two plane waves, each with a different frequency, are incident on the same screen. The spatial frequency spectrum corresponding to the cross-correlation between these two different intensity fluctuation patterns is derived. This spectrum has two distinct regions, one of low spatial frequencies which is independent of the incident wave frequencies, and another at high spatial frequencies which is wave frequency dependent. As the difference between the incident wave frequencies increases the upper cut-off spatial frequency of the spectrum decreases. The corresponding cross-scintillation index is also considered and it is shown that this is independent of the incident wave frequencies close to the phase screen and including the focal region where there is a peak in the index. In the far field the scintillation index approaches unity as the distance from the screen increases in the monochromatic case. However, for different incident wave frequencies the far-field scintillation index falls off both with distance from screen and with increasing wave frequency difference.
Performance Analysis and Experimental Validation of PD-NOMA using Constrained Optimization for Different Modulation Schemes

Non-orthogonal multiple access (NOMA) is a promising radio access technology for 5G cellular networks which outperforms the conventional multiple access technique to meet the requirement of maximizing spectral efficiency and minimizing latency through multiplexing users in the power domain. In this paper, a detailed performance analysis with respect to BER and Sum Capacity of downlink power domain NOMA is presented under various modulation schemes using fixed and constrained optimization-based power allocation techniques, in consideration with different fairness factor values. Also, it has been experimentally proven using software defined radio kits that an adaptive modulation scheme would be quite useful for a PD-NOMA system. The novelty of the work lies in the analysis of fixed and dynamic power allocation techniques, using constrained optimization mechanism for different modulation schemes, in MATLAB simulation and experimentally validating them using software defined radio kits, namely WARP v3 boards, acting as transceiving systems, based on which the respective conclusions have been made.
The Effects of Social Processing and Role Type on Attention Networks: Insights from Team Ball Athletes

(1) Background: Several findings have shown how social stimuli can influence attentional processes. Social attention is crucial in team ball sports, in which players have to react to dynamically changing, unpredictable, and externally paced environments. Our study aimed at demonstrating the influence of social processing on team ball sports athletes’ attentional abilities. (2) Methods: A total of 103 male players divided by sport (soccer, handball, and basketball) and by role (striker, midfielder, or defender) were tested through a modified version of the Attention Network Test (ANT) in which they were exposed to both social and non-social stimuli. (3) Results: Social stimuli positively impacted the athletes’ abilities to focus on target stimuli and ignore conflicting environmental requests (t = −2.600, p = 0.011 *). We also found that the athletes’ roles impacted their performance accuracy. Specifically, differences were found in the ability to maintain a general state of reactivity between athletes (strikers vs. midfielders: t = 3.303, p = 0.004 **; striker vs. defenders: t = −2.820, p = 0.017 *; midfielders vs. defenders: t = −5.876, p < 001 ***). (4) Conclusion: These findings revealed that social stimuli are crucial for performance enhancement in team ball sports athletes. Further, we suggest that it is possible to draw specific attentional profiles for athletes in different roles.
Improved component predictions of batting and pitching measures

Standard measures of batting performance such as batting average and on-base percentage can be decomposed into component rates such as strikeout rates and home run rates. The likelihood of hitting data for a group of players can be expressed as a product of likelihoods of the component probabilities and this motivates the use of Bayesian random effects models to estimate the groups of component rates. By combining the separate component rates, the aggregate predictions of batting performance for subsequent seasons improve upon standard shrinkage methods. This “separate and aggregate” approach is also illustrated for estimating on-base probabilities and fielding independent pitching (FIP) abilities of pitchers.
Therapeutic Inhalation of Hydrogen Gas for Alzheimer’s Disease Patients and Subsequent Long-Term Follow-Up as a Disease-Modifying Treatment: An Open Label Pilot Study

(1) Background: Alzheimer’s disease (AD) is a progressive and fatal neurodegenerative disorder. Hydrogen gas (H2) is a therapeutic medical gas with multiple functions such as anti-oxidant, anti-inflammation, anti-cell death, and the stimulation of energy metabolism. To develop a disease-modifying treatment for AD through multifactorial mechanisms, an open label pilot study on H2 treatment was conducted. (2) Methods: Eight patients with AD inhaled 3% H2 gas for one hour twice daily for 6 months and then followed for 1 year without inhaling H2 gas. The patients were clinically assessed using the Alzheimer’s Disease Assessment Scale-cognitive subscale (ADAS-cog). To objectively assess the neuron integrity, diffusion tensor imaging (DTI) with advanced magnetic resonance imaging (MRI) was applied to neuron bundles passing through the hippocampus. (3) Results: The mean individual ADAS-cog change showed significant improvement after 6 months of H2 treatment (−4.1) vs. untreated patients (+2.6). As assessed by DTI, H2 treatment significantly improved the integrity of neurons passing through the hippocampus vs. the initial stage. The improvement by ADAS-cog and DTI assessments were maintained during the follow-up after 6 months (significantly) or 1 year (non-significantly). (4) Conclusions: This study suggests that H2 treatment not only relieves temporary symptoms, but also has disease-modifying effects, despite its limitations.
Learning-based Near-Optimal Formation Control of Wheeled Mobile Robots with Input Saturations

This paper investigates the formation control problem of wheeled mobile robots (WMRs) in the case of actuator input saturations. A learning-based distributed near-optimal formation control algorithm for WMRs with the kinematic and dynamic model is proposed. In the algorithm, the kinematic controllers of WMRs in the formation are obtained by the distributed backstepping technique, which generates reference signals for the dynamic model of the WMRs. Then, the learning-based dynamic controllers of the WMRs in the formation are designed by the actor-critic-based reinforcement learning algorithm. To deal with the problem of the control input saturations, the performance index with a non-quadratic function is designed. Subsequently, neural networks are employed to approximate the near-optimal costate functions and control policies in the learning-based dynamic controllers of WMRs in the formation. Finally, various numerical simulations are performed to validate the effectiveness and feasibility of the learning-based formation control algorithm.
Comparison of Three Diagnostic Definitions of Metabolic Syndrome and Estimation of Its Prevalence in Mongolia

We sought to estimate the prevalence of metabolic syndrome (MS) in the urban population of Mongolia and suggest a preferred definition. This cross-sectional study comprised 2076 representative samples, which were randomly selected to provide blood samples. MS was defined by the National Cholesterol Education Program’s Adults Treatment Panel III (NCEP ATP III), the International Diabetes Federation (IDF), and the Joint Interim Statement (JIS). The Cohen’s kappa coefficient (κ) was analyzed to determine the agreement between the individual MS components using the three definitions. The prevalence of MS in the 2076 samples was 19.4% by NCEP ATP III, 23.6% by IDF, and 25.4% by JIS criteria. For men, moderate agreement was found between the NCEP ATP III and waist circumference (WC) (κ = 0.42), and between the JIS and fasting blood glucose (FBG) (κ = 0.44) and triglycerides (TG) (κ = 0.46). For women, moderate agreement was found between the NCEP ATP III and high-density lipoprotein cholesterol (HDL-C) (κ = 0.43), and between the JIS and HDL-C (κ = 0.43). MS is highly prevalent in the Mongolian urban population. The JIS definition is recommended as the provisional definition.