Miracle-dz/UniFAR-FTUs huggingface dataset:
The effect of Phaseolus vulgaris pods extract on cytokines profile in the condition of alkali burn esophagus 2 degree

Introduction: As a result of alkaline substances entering the esophagus wall, necrosis occurs, as well as destruction of the mucous, submucosal and muscular layers of the esophagus. The cellular immune system plays an important role in regulating the various phases of the wound healing process, during which, depending on the time and through specific adhesion interactions, polymorphonuclear leukocytes, macrophages, and lymphocytes penetrate the site of the trauma and lead to an intensive proliferation of fibroblasts, endothelial cells, and keratinocytes. The substances of natural origins based on polyphenolic compounds are possible as a remedy for the normalization of physiological and biochemical parameters in a condition of various pathologies. Phaseolus vulgaris pods extract (PVPE) are an example of a rich source of bioactive compounds with proven human health benefits. Our results revealed imbalance of pro-inflammatory and anti-inflammatory cytokines in esophageal homogenates in rats with alkali burn of esophagus II degree, which can indicate a prolonged inflammation in the burned area. In the groups of animals with the alkaline burn of the esophagus, that were injected extract there can be traced a change of the indicators in the direction of normalization, indicating about the therapeutic effect of aqueous extract of pods of Phaseolus vulgaris. Objective: The cellular immune system plays an important role in regulating the various phases of the wound healing process, and can be used as a marker of pathological post-burn wound healing, Phaseolus vulgaris pods extract (PVPE) are an example of a rich source of bioactive compounds with proven human health benefits. The aim of the study was to investigate changes in cytokines levels in rat esophageal supernatants in the condition of alkali burn esophagus second degree (AEB 2) and treatment with Phaseolus vulgaris pods extract. Material and Methods: The animals were experimentally simulated with the alkali esophageal burn with 20% (grade II) solvent of NaOH. Levels of cytokines in esophageal tissue homogenates were done by ELISA. Results: In the present study, we investigate the imbalance of pro-inflammatory and anti-inflammatory cytokines in esophageal homogenates in rats with AEB 2, which can indicate a prolonged inflammation in the burned area. In the groups of animals with the AEB 2, that were injected extract there can be traced a change of the indicators in the direction of normalization. Conclusion: The administration of the extract, in conditions of alkaline burn of the esophagus, contributes change of the indicators in the direction of normalization indicating the therapeutic effect of aqueous extract of pods of Phaseolus vulgaris.
Low-level liquid cloud properties during ORACLES retrieved using airborne polarimetric measurements and a neural network algorithm

Abstract. In this study we developed a neural network (NN) that can be used to retrieve cloud microphysical properties from multiangular and multispectral polarimetric remote sensing observations. This effort builds upon our previous work, which explored the sensitivity of neural network input, architecture, and other design requirements for this type of remote sensing problem. In particular this work introduces a framework for appropriately weighting total and polarized reflectances, which have vastly different magnitudes and measurement uncertainties. The NN is trained using an artificial training set and applied to research scanning polarimeter (RSP) data obtained during the ORACLES field campaign (ObseRvations of Aerosols above CLouds and their intEractionS). The polarimetric RSP observations are unique in that they observe the same cloud from a very large number of angles within a variety of spectral bands, resulting in a large dataset that can be explored rapidly with a NN approach. The usefulness of applying a NN to a dataset such as this one stems from the possibility of rapidly obtaining a retrieval that could be subsequently applied as a first guess for slower but more rigorous physical-based retrieval algorithms. This approach could be particularly advantageous for more complicated atmospheric retrievals – such as when an aerosol layer lies above clouds like in ORACLES. For RSP observations obtained during ORACLES 2016, comparisons between the NN and standard parametric polarimetric (PP) cloud retrieval give reasonable results for droplet effective radius (re: R=0.756, RMSE=1.74 µm) and cloud optical thickness (τ: R=0.950, RMSE=1.82). This level of statistical agreement is shown to be similar to comparisons between the two most well-established cloud retrievals, namely, the polarimetric and the bispectral total reflectance cloud retrievals. The NN retrievals from the ORACLES 2017 dataset result in retrievals of re (R=0.54, RMSE=4.77 µm) and τ (R=0.785, RMSE=5.61) that behave much more poorly. In particular we found that our NN retrieval approach does not perform well for thin (τ<3), inhomogeneous, or broken clouds. We also found that correction for above-cloud atmospheric absorption improved the NN retrievals moderately – but retrievals without this correction still behaved similarly to existing cloud retrievals with a slight systematic offset.
A review of the effects of ozonation process on biological sludge reduction

The conventional activated sludge systems are commonly used in various types of wastewater treatment processes. Due to stringent environmental regulations on sludge disposal, and the high cost of conventional methods for the disposal of sludge, researches around the world have been looking for new methods to minimize sludge production in wastewater treatment plants. In this regard, the use of new processes that produce less sludge than the current processes have received much attention. This article focused on hopeful technologies, which aimed at investigating the on-site reduction of generated sludge in wastewater treatment plants. Recently, the combination of ozonation with the biological aerobic process such as activated sludge has been introduced as a strategy for reducing sludge production. However, the sludge management systems can reduce up to 60 of the total operating costs, and ozonation process reduces up to 90 of the sludge production. Finally, this review indicated that ozonation process is a promising and efficient process for zero sludge production in the wastewater treatment processes. © 2019 Desalination Publications.
Development and testing of an efficient data acquisition platform for machine learning of optical emission spectroscopy of plasmas in aqueous solution

This study presents the development of an efficient data acquisition platform and discusses machine learning of optical emission spectroscopy (OES) of plasma in aqueous solution to demonstrate the multivariate analysis of spectra. A specially designed platform for efficient acquisition of spectra emanated from plasmas in solutions is developed, and several machine learning algorithms are tested for plasma analysis. This platform enables acquiring up to 10k spectra solutions with various pH values under constant solution conductivity, or vice versa, within 15 min. This rapid acquisition scheme provides a sufficient dataset for testing machine learning algorithms. We test the OES of plasmas ignited in solutions with designated conductivities with pH of 2.2–5.2. A total 40k spectra are collected and tested with principal component analysis (PCA) and an artificial neural network (ANN) to predict the conductivity of the solution. In PCA, the results show that most data points are overlapped in the score plot constructed using principal components 1 and 2, implying that PCA cannot discriminate the conductivity based on the spectra. In an ANN, several network structures are constructed and tested. The results show that the deep ANN significantly improves the accuracy of conductivity prediction in terms of mean squared error by three orders of magnitude compared with the method of using single emission line. Regularization techniques including dropout and early stopping show promise for mitigating overfitting in a deep ANN. Such improvements suggest that a deep ANN considers the nonlinear behaviors of plasma and can handle datasets with high complexity. In addition, the application of a deep ANN with a large number of parameters makes using this experimental platform for efficient spectra acquisition highly desirable.
Aptamer-Based Lateral Flow Test Strip for the Simultaneous Detection of Salmonella typhimurium, Escherichia coli O157:H7 and Staphylococcus aureus

Abstract An aptamer-based lateral flow test strip was developed for the simultaneous detection of Salmonella Typhimurium, Escherichia coli O157:H7, and Staphylococcus aureus. This assay was based on the sandwich-type format using primary aptamer conjugated with gold nanoparticles (AuNPs) as the signal probe and a secondary aptamer-coated membrane as a capture probe. Several experimental conditions that influenced sensitivity were investigated, including the aptamer and NaCl concentrations and the loaded amounts of gold nanoparticle–aptamer conjugates and streptavidin–aptamer conjugates. Under the optimal experimental conditions, the visual limits of detection of the strip for S. Typhimurium, E. coli O157:H7 and S. aureus were 103 CFU/mL, 104 CFU/mL, and 104 CFU/mL, respectively. All measurements were achieved within 10 min, and no significant cross-reactivity with several foodborne pathogens was observed. This aptamer-based strip was successfully applied to the simultaneous detection of these foodborne pathogens in food samples. The aptamer-based lateral flow test strip was shown to provide a reliable, simple and accurate tool for the simultaneous detection of multiple foodborne pathogens.
Thermodynamic properties of iron, aluminum, boron and phosphorus in dilute silicon solutions by molecular interaction volume model

The thermodynamic properties of impurity components in silicon solutions play an important role in the chemical removal process to the metallurgical route. In this paper, the component activity coefficients and interaction parameters of dilute silicon solutions were estimated by the molecular interaction volume model (MIVM). The activity coefficients ( ) of component i in dilute binary Si-i and ternary Si-i-j solutions at 1687-1873 K were firstly calculated. The concentration dependences of the interaction parameter and activity coefficient were also obtained. The self-interaction parameters ( ) for Si-i system were obtained as , , and . At the same time, the interaction parameters among components B, Al, and Fe in dilute ternary Sii-j solutions were also derived as , and . Most important of all, the temperature dependences of the interaction parameters and activity coefficients in dilute Si-i and Si-i-j solutions with a certain i or j concentration were deduced. The results show that the predicted self-interaction parameters of B, Al, Fe, and P in binary silicon solutions reasonably agree with the experimental data. This further shows that MIVM is of reliability and can be expanded to a multi-component dilute silicon solution.
Different Approaches for Enhancement of Curcumin Aqueous Solubility and Dissolution rate

Curcumin (CUR) is a nature polyphenolic phytoingredient. CUR showed anti-inflammatory, anti-oxidant, anti-fungal and anti-cancer activities. The therapeutic efficacy of CUR was limited due to its poor aqueous solubility, poor oral bioavailability. Poor solubility of drugs is the major challenge associated with formulation development. Therefore, the aim of present work was to enhance CUR aqueous solubility and dissolution rate using Physical mixture and its solid dispersion. CUR solid dispersions were prepared by solvent evaporation and Freeze drying techniques using different polymers, such as cyclodextrins, polyvinyl pyrrolidone (PVP K30), polyethylene glycol 6000 and Pluronic®F-127. The prepared physical mixtures, solid dispersions were characterized using different techniques such as (DSC), (FTIR) and (XRD). Furthermore, the solubility and the dissolution rate of the drug in its different systems were explored. The results of physical characterization of the different systems show no interaction between them. Dissolution studies of CUR solid dispersions showed that the highest drug dissolution rate was achieved at CUR/ Pluronic®F-127 weight ratio of 1:3. Also, complete drug dissolution was obtained for CUR/Pluronic F-127 solid dispersion after 30 min compared to 35% dissolution for CUR alone after the same time. Also, CUR permeability coefficient through rat skin for Pluronic®F-127 micelles and solid dispersion were two times higher than that of the CUR alone. The obtained results concluded that, the preparation of solid dispersion of Pluronic®F-127 solid dispersions by freeze drying method is a promising one to overcome CUR shortcomings through enhancing its aqueous solubility, dissolution rate and skin permeability.
Integrated Biometric DNA Identification System

In this paper an integrated biometric identification system based on STR/DNA profiles database has been proposed, each profile composed of 20 autosomal loci and Amelogenin. The objective of this research paper is to design and implement a search engine in such a way that enhance the speediness of searching for matches for Target Profile (TP) regardless, the profile is partial or optimum. This is accomplished by utilizing the alleles distribution nature for the population of interest. In this research more than 15 million profiles were simulated to test the system reliability, accuracy, and speed. The result shows that the proposed system is improving the speed of matching about 72% and all tested profiles were identified successfully.
Effect of thymol and environmental factors on growth and biofilm formation by Listeria monocytogenes

Thymol (2-isopropyl-5-methylphenol) is an aromatic monoterpene found in essential oils extracted from plants belonging to the Lamiaceae family, such as Thymus, Ocimum, Origanum, Satureja, Thymbra and Monarda genera. Growth and biofilm formation by Listeria monocytogenes CLIP 74902 were evaluate using three carbon sources in the presence of thymol. Specific growth rate (h-1) values at 37o with glucose, trehalose and cellobiose with the addition of thymol (μg/mL) 0 (control) and 750, were respectively: 0.22, 0.07; 0.14, 0.04; 0.11, 0.04. Lag periods obtained under the same conditions were (h): 8.19, 13.2; 22.5, 27.5; 23.1, 28.1. A marked antibiofilm activity was observed against the exposure with 750 μg/mL of thymol, showing a high percentage of inhibition: glucose (99 %), trehalose (97 %) and cellobiose (98%), compared to the control. The results suggest that thymol could be used to inhibit the growth and production of biofilms by L. monocytogenes in the food industry.
Changes in thermal resources and limitations for Georgian viticulture

BACKGROUND AND AIMS: The growth and development of grapevine is constrained by temperature, affecting the bulk of physiological processes of the plant. The aim of this study was to evaluate the effects of climate change on thermal resources and limitations in the context of Georgia, one of the oldest viticultural areas in the world. METHODS AND RESULTS: The analysis on daily series of temperature for the time period 1974–2013 for the main viticultural areas of Georgia shows that an abrupt change to warmer temperature occurred in 1994, leading to a decline in low temperature stress and an increase in high temperature stress, associated with more frequent heat waves. The availability of thermal resources for grapevine growth remained unchanged. CONCLUSIONS: In Georgia, the shift to warmer temperature did not affect the availability of thermal resources for grapegrowing. Although some attention should be paid to possible damage due to an earlier budburst and increased frost risk. Furthermore, it is important to highlight the strong increase of summer heatwaves with potential negative impacts on grape composition and phenolic maturity, when combined with severe water stress. SIGNIFICANCE OF THE STUDY: Quantitative studies of the effects of climate change on Georgian viticulture are essential to develop adaptation strategies based on either germplasm (e.g. rootstocks and cultivars) and agronomical practices (e.g. pruning, trellis systems, soil and water management).
Structure Sensitivity of Silver-Catalyzed Ethylene Epoxidation

The influence of particle size (20–200 nm) of Ag/α-Al2O3 catalysts for epoxidation of ethylene to ethylene oxide (EO) under industrial conditions was investigated. Small silver particles up to ∼40 nm are predominantly monocrystalline and show a decreasing weight-normalized reaction rate with increasing particle size. Particles larger than 50 nm consist of multiple silver crystallites with a much smaller domain size between 25 and 30 nm. For these polycrystalline silver particles, the weight-normalized reaction rate is independent of particle size. The ethylene conversion rate normalized to the external surface area increases when the silver particles become larger. We attribute this to a specific role of the grain boundaries between silver crystallites in supplying oxygen atoms to the external surface. Oxygen is likely activated at defects of an otherwise low-reactivity silver surface (for oxygen adsorption) followed by diffusion along grain boundaries, dissolution in the bulk, and diffusion to the extern.
IoT on Heart Arrhythmia Real Time Monitoring

Monitoring jantung telah populer sejak 5 tahun terakhir. Hal ini ditandai dengan munculnya berbagai produk monitoring jantung berbasis wearable sensor. Umumnya komunikasi yang digunakan pada sistem tersebut adalah menggunakan radio telemetri dengan biaya opera- sional yang mahal. Beberapa riset mencoba menggunakan konsep internet of things (IoT) untuk mengatasi hal tersebut. Namun demikian, desain komunikasi IoT yang ada belum efisien. Ini disebabkan riset yang ada hanya berfokus pada bagaimana hasil baca sensor dapat dipantau secara realtime. Untuk mengatasi hal tersebut, riset ini mengusulkan sebuah arsitektur IoT berbasis cloud untuk memonitor aritmia, salah satu jenis penyakit jantung yang umum ditemukan. Deteksi aritmia yang diusulkan adalah pengembangan algoritma deteksi aritmia berbasis Tsipuras et al, dengan menggunakan deteksi fitur R. Sistem yang diusulkan pada paper ini telah diuji menggunakan dataset MIT-BIH dan menghasilkan akurasi 93.11% terhadap 3 kelas aritmia, yaitu PAC, PVC dan VT. Menariknya, dengan penerapan IoT, efisiensi algoritma deteksi fitur R meningkat 30% dibanding yang diusulkan oleh Pan dan Tompkins. Terbukti dengan rendahnya waktu rata-rata eksekusi tiap sampel data, yaitu sekitar 0.00749 ms.
Linear and Branched Fiber-like Micelles from the Crystallization-Driven Self-Assembly of Heterobimetallic Block Copolymer Polyelectrolyte/Surfactant Complexes

The solution self-assembly of a heterobimetallic diblock copolymer with a crystallizable poly(ferrocenyldimethylsilane) (PFS) core-forming block and a corona-forming segment featuring a poly(cobaltoceniumethylene) (PCE)-based polyelectrolyte/surfactant complex, PFS50-b-[PCE][AOT]50 (AOT = bis(2-ethylhexyl) sulfosuccinate), has been explored. Fiber-like micelles were formed in selective solvents for the Co-containing block, yielding a corona in which every repeat unit bears a charge. We found that key features of the one-dimensional (1D) morphologies were dependent on both the polarity of the solvent medium and temperature. In the most polar of the explored solvent media (iPrOH), seeded growth from 1D seeds of PFS50-b-P2VP739 (P2VP = poly(2-vinylpyridine)) yielded tapered B-A-B triblock comicelles, where the width of the terminal B region derived from PFS-b-[PCE][AOT]n reduced on moving from the interface with the seed to the micelle termini. In addition, ca. 30% of the micelles featured branching to yield.
Simplifying Deep Neural Networks for FPGA-Like Neuromorphic Systems

Deep learning using deep neural networks is taking machine intelligence to the next level in computer vision, speech recognition, natural language processing, etc. Brain-like hardware platforms for the brain-inspired computational models are being studied, but the maximum size of neural networks they can evaluate is often limited by the number of neurons and synapses equipped with the hardware. This paper presents two techniques, factorization and pruning, that not only compress the models but also maintain the form of the models for the execution on neuromorphic architectures. We also propose a novel method to combine the two techniques. The proposed method shows significant improvements in reducing the number of model parameters over standalone use of each method while maintaining the performance. Our experimental results show that the proposed method can achieve ${30\times }$ reduction rate within 1% budget of accuracy for the largest layer of AlexNet.
Characterization of fatty acid based nanostructured lipid carrier (NLC) and their sustained release properties

Nanostructured lipid carrier (NLC) is a second generation lipid nanoparticle formed by blends of solid and liquid lipids through hot homogenization technique. In this study, arachidic acid (C20) and erucic acid (C22:1) were used as solid lipids while oleic acid as liquid lipid in the preparation of NLC. Five types of NLC were prepared by varying the amount of oleic acid (C18:1) with respect to arachidic acid while maintaining the amount of erucic acid. Physical stability of the prepared NLC was characterized by its size and zeta potential for a period of 28 days. The results showed that the size of NLCs were between 200 to 260 nm with zeta potential of -55 to -40 mV. Differential scanning calorimetry (DSC) data showed that presence of oleic acid reduces the crystallinity of nanoparticle. Apart from that, depending on the compositions, the morphology of NLC examined under transmission electron microscopy’s (TEM) was round to elongate in shape. Then, active ingredients of ascorbic acid, caffeine and lidocaine with varied hydrophilicity were then loaded into the NLC. Lidocaine has an encapsulation efficiency of nearly 78% while caffeine reaches 43%. Surprisingly, ascorbic acid which is hydrophilic gave a comparative amount of encapsulation efficiency as caffeine at low concentration. This might relate to attraction between opposite charge of NLC and ascorbic acid. When the active ingredients loaded NLC was subjected to in vitro release, the active ingredients’ release profile suggested that NLC exhibit sustained release properties whereby the rate of release is concentration dependent.
Scale Adaptive Kernel Correlation Filter Tracking Algorithm Combined with Learning Rate Adjustment

Aiming at the problem that the traditional correlation filter tracking algorithm is prone to tracking failure under the target’s scale change and occlusion environment, we propose a scale-adaptive Kernel Correlation Filter (KCF) target tracking algorithm combined with the learning rate adjustment. Firstly, we use the KCF to obtain the initial position of the target, and then adopt a low-complexity scale estimation scheme to get the target's scale, which improves the ability of the proposed algorithm to adapt to the change of the target's scale, and the tracking speed is also ensured. Finally, we use the average difference between two adjacent images to analyze the change of the image, and adjust the learning rate of the target model in segments according to the average difference to solve the tracking failure problem when the target is severely obstructed. Compared the proposed algorithm with other five classic target tracking algorithms, the experimental results show that the proposed algorithm is well adapted to the complex environment such as target’s scale change, severe occlusion and background interference. At the same time, it has a real-time tracking speed of 231 frame/s.