Effect of Erosive Agents on Surface Characteristics of Nano-Fluorapatite Ceramic: An In-Vitro Study

Erosive beverages cause dissolution of natural teeth and intra-oral restorations, resulting in surface characteristic changes, particularly roughness and degradation. The purpose of this study was to evaluate the surface roughness and topography of a dental ceramic following immersion in locally available erosive solutions. A total of 160 disc specimens of a nano-fluorapatite type ceramic (12 mm diameter and 2 mm thickness) were fabricated and equally distributed into two groups (n = 80) and then evenly distributed among the following five testing groups (n = 16): lemon juice, citrate buffer solution, 4% acetic acid, soft cola drink, and distilled water which served as a control. The surface roughness (Ra) and topography were evaluated using a profilometer and scanning electron microscope at baseline, 24 h, 96 h, and 168 h respectively. Data were analyzed using ANOVA and Tukey’s multiple comparisons (p ≤ 0.05). Surface changes were observed upon exposure to all acidic beverages except distilled water. Amongst all immersion media, 4% acetic acid produced the most severe surface roughness across all time periods (i.e., baseline, 24 h, 96 h, and 168 h). A statistically significant difference in the surface roughness values between all immersion media and across all four time intervals was observed. Erosive agents had a negative effect on the surface roughness and topography of the tested ceramic. The surface roughness increased with increased storage time intervals.     

Fumigant Activity of Bacterial Volatile Organic Compounds against the Nematodes Caenorhabditis elegans and Meloidogyne incognita

Plant-parasitic nematodes infect a diversity of crops, resulting in severe economic losses in agriculture. Microbial volatile organic compounds (VOCs) are potential agents to control plant-parasitic nematodes and other pests. In this study, VOCs emitted by a dozen bacterial strains were analyzed using solid-phase microextraction followed by gas chromatography–mass spectrometry. Fumigant toxicity of selected VOCs, including dimethyl disulfide (DMDS), 2-butanone, 2-pentanone, 2-nonanone, 2-undecanone, anisole, 2,5-dimethylfuran, glyoxylic acid, and S-methyl thioacetate (MTA) was then tested against Caenorhabditis elegans. DMDS and MTA exhibited much stronger fumigant toxicity than the others. Probit analysis suggested that the values of LC₅₀ were 8.57 and 1.43 μg/cm³ air for DMDS and MTA, respectively. MTA also showed stronger fumigant toxicity than DMDS against the root-knot nematode Meloidogyne incognita, suggesting the application potential of MTA.     

Novel Design of RNA Aptamers as Cancer Inhibitors and Diagnosis Targeting the Tyrosine Kinase Domain of the NT-3 Growth Factor Receptor Using a Computational Sequence-Based Approach

Aptamers, the nucleic acid analogs of antibodies, bind to their target molecules with remarkable specificity and sensitivity, making them promising diagnostic and therapeutic tools. The systematic evolution of ligands by exponential enrichment (SELEX) is time-consuming and expensive. However, regardless of those issues, it is the most used in vitro method for selecting aptamers. Therefore, recent studies have used computational approaches to reduce the time and cost associated with the synthesis and selection of aptamers. In an effort to present the potential of computational techniques in aptamer selection, a simple sequence-based method was used to design a 69-nucleotide long aptamer (mod_09) with a relatively stable structure (with a minimum free energy of −32.2 kcal/mol) and investigate its binding properties to the tyrosine kinase domain of the NT-3 growth factor receptor, for the first time, by employing computational modeling and docking tools.     

Deep learning methods for fingerprint-based indoor positioning: a review

ABSTRACT

Outdoor positioning systems based on the Global Navigation Satellite System have several shortcomings that have deemed their use for indoor positioning impractical. Location fingerprinting, which utilizes machine learning, has emerged as a viable method and solution for indoor positioning due to its simple concept and accurate performance. In the past, shallow learning algorithms were traditionally used in location fingerprinting. Recently, the research community started utilizing deep learning methods for fingerprinting after witnessing the great success and superiority these methods have over traditional/shallow machine learning algorithms. This paper provides a comprehensive review of deep learning methods in indoor positioning. First, the advantages and disadvantages of various fingerprint types for indoor positioning are discussed. The solutions proposed in the literature are then analyzed, categorized, and compared against various performance evaluation metrics. Since data is key in fingerprinting, a detailed review of publicly available indoor positioning datasets is presented. While incorporating deep learning into fingerprinting has resulted in significant improvements, doing so, has also introduced new challenges. These challenges along with the common implementation pitfalls are discussed. Finally, the paper is concluded with some remarks as well as future research trends.     

Hybrid Paper–Plastic Microchip for Flexible and High‐Performance Point‐of‐Care Diagnostics

A low‐cost and easy‐to‐fabricate microchip remains a key challenge for the development of true point‐of‐care (POC) diagnostics. Cellulose paper and plastic are thin, light, flexible, and abundant raw materials, which make them excellent substrates for mass production of POC devices. Herein, a hybrid paper–plastic microchip (PPMC) is developed, which can be used for both single and multiplexed detection of different targets, providing flexibility in the design and fabrication of the microchip. The developed PPMC with printed electronics is evaluated for sensitive and reliable detection of a broad range of targets, such as liver and colon cancer protein biomarkers, intact Zika virus, and human papillomavirus nucleic acid amplicons. The presented approach allows a highly specific detection of the tested targets with detection limits as low as 10² ng mL^?1 for protein biomarkers, 10³ particle per milliliter for virus particles, and 10² copies per microliter for a target nucleic acid. This approach can potentially be considered for the development of inexpensive and stable POC microchip diagnostics and is suitable for the detection of a wide range of microbial infections and cancer biomarkers.     

A novel energy‐aware routing mechanism for SDN‐enabled WSAN

Energy consumption is one of the most important design constraints when building a wireless sensor and actuator network since each device in the network has a limited battery capacity, and prolonging the lifetime of the network depends on saving energy. Overcoming this challenge requires a smart and reconfigurable network energy management strategy. The Software‐Defined Networking (SDN) paradigm aims at building a flexible and dynamic network structure, especially in wireless sensor networks. In this study, we propose an SDN‐enabled wireless sensor and actuator network architecture that has a new routing discovery mechanism. To build a flexible and energy‐efficient network structure, a new routing decision approach that uses a fuzzy‐based Dijkstra's algorithm is developed in the study. The proposed architecture can change the existing path during data transmission, which is the key property of our model and is achieved through the adoption of the SDN approach. All the components and algorithms of the proposed system are modeled and simulated using the Riverbed Modeler software for more realistic performance evaluation. The results indicate that the proposed SDN‐enabled structure with fuzzy‐based Dijkstra's algorithm outperforms the one using the regular Dijkstra's and the ZigBee‐based counterpart, in terms of the energy consumption ratio, and the proposed architecture can provide an effective cluster routing while prolonging the network lifetime.     

Anti‐Oxygen Leaking LiCoO2

LiCoO₂ is a prime example of widely used cathodes that suffer from the structural/thermal instability issues that lead to the release of their lattice oxygen under nonequilibrium conditions and safety concerns in Li‐ion batteries. Here, it is shown that an atomically thin layer of reduced graphene oxide can suppress oxygen release from Li_xCoO₂ particles and improve their structural stability. Electrochemical cycling, differential electrochemical mass spectroscopy, differential scanning calorimetry, and in situ heating transmission electron microscopy are performed to characterize the effectiveness of the graphene‐coating on the abusive tolerance of Li_xCoO₂. Electrochemical cycling mass spectroscopy results suggest that oxygen release is hindered at high cutoff voltage cycling when the cathode is coated with reduced graphene oxide. Thermal analysis, in situ heating transmission electron microscopy, and electron energy loss spectroscopy results show that the reduction of Co species from the graphene‐coated samples is delayed when compared with bare cathodes. Finally, density functional theory and ab initio molecular dynamics calculations show that the rGO layers could suppress O₂ formation more effectively due to the strong C? O_cathode bond formation at the interface of rGO/LCO where low coordination oxygens exist. This investigation uncovers a reliable approach for hindering the oxygen release reaction and improving the thermal stability of battery cathodes.     

Unraveling the Reasons for Efficiency Loss in Perovskite Solar Cells

The effect of the presence of unreacted PbI₂ on the perovskite solar cells efficiency is reported. N,N‐Dimethylformamide vapor treatment is introduced to study the influence of complete conversion to a power conversion efficiency of the device. It is discovered that the optimized morphology of the PbI₂ under layer is essential to form a dense perovskite layer preventing recombination by direct contact between TiO₂ and a hole transporting layer, and to increase the charge collection efficiency. The present findings provide an insight into the morphology and growth mechanism of perovskite layer, the correlation between the device performance, and the film deposition process.     

Development,In-Vitro Characterization and Preclinical Evaluation of Esomeprazole-Encapsulated Proniosomal Formulation for the Enhancement of Anti-Ulcer Activity

Objective: The present study aimed to develop and optimize esomeprazole loaded proniosomes (EZL-PNs) to improve bioavailability and therapeutic efficacy. Method: EZL-PNs formulation was developed by slurry method and optimized by 33 box-Bhekhen statistical design software. Span 60 (surfactant), cholesterol, EZL concentration were taken as independent variables and their effects were evaluated on vesicle size (nm), entrapment efficiency (%, EE) and drug release (%, DR). Furthermore, optimized EZL-PNs (EZL-PNs-opt) formulation was evaluated for ex vivo permeation, pharmacokinetic and ulcer protection activity. Result: The EZL-PNs-opt formulation showed 616 ± 13.21 nm of vesicle size, and 81.21 ± 2.35% of EE. EZL-PNs-opt exhibited negative zeta potential and spherical confirmed scanning electron microscopy. EZL-PNs-opt showed sustained release of EZL (95.07 ± 2.10% in 12 h) than pure EZL dispersion. The ex-vivo gut permeation result exhibited a significantly (p < 0.05) enhanced flux than pure EZL. The in vivo results revealed 4.02-fold enhancement in bioavailability and 61.65% protection in ulcer than pure EZL dispersion (43.82%). Conclusion: Our findings revealed that EZL-PNs formulation could be an alternative delivery system of EZL to enhance oral bioavailability and antiulcer activity.     

Spin-correlation function of the fully frustrated Ising model and ± J Ising spin glass on the square lattice

In this work we study the correlation function of the ground state of two-dimensional fully frustrated Ising model as well as spin glass. The Pfaffian method is used to calculate free energy and entropy as well as correlation function. We estimate the exponent of spin correlation function for fully frustrated model and spin glass. In this paper an overview of the latest results on the spin correlation function is presented.