Carbonate apatite (CO3Ap) is a bioceramic material with excellent properties for bone and dentin regeneration. To enhance its mechanical strength and bioactivity, silica calcium phosphate composites (Si-CaP) and calcium hydroxide (Ca(OH)2) were added to CO3Ap cement. The aim of this study was to investigate the effect of Si-CaP and Ca(OH)2 on the mechanical properties in terms of the compressive strength and biological characteristics of CO3Ap cement, specifically the formation of an apatite layer and the exchange of Ca, P, and Si elements. Five groups were prepared by mixing CO3Ap powder consisting of dicalcium phosphate anhydrous and vaterite powder added by varying ratios of Si-CaP and Ca(OH)2 and 0.2 mol/L Na2HPO4 as a liquid. All groups underwent compressive strength testing, and the group with the highest strength was evaluated for bioactivity by soaking it in simulated body fluid (SBF) for one, seven, 14, and 21 days. The group that added 3% Si-CaP and 7% Ca(OH)2 had the highest compressive strength among the groups. SEM analysis revealed the formation of needle-like apatite crystals from the first day of SBF soaking, and EDS analysis indicated an increase in Ca, P, and Si elements. XRD and FTIR analyses confirmed the presence of apatite. This combination of additives improved the compressive strength and showed the good bioactivity performance of CO3Ap cement, making it a potential biomaterial for bone and dental engineering applications.
Myocardial remodelling is a molecular, cellular, and interstitial adaptation of the heart in response to altered environmental demands. The heart undergoes reversible physiological remodelling in response to changes in mechanical loading or irreversible pathological remodelling induced by neurohumoral factors and chronic stress, leading to heart failure. Adenosine triphosphate (ATP) is one of the potent mediators in cardiovascular signalling that act on the ligand-gated (P2X) and G-protein-coupled (P2Y) purinoceptors via the autocrine or paracrine manners. These activations mediate numerous intracellular communications by modulating the production of other messengers, including calcium, growth factors, cytokines, and nitric oxide. ATP is known to play a pleiotropic role in cardiovascular pathophysiology, making it a reliable biomarker for cardiac protection. This review outlines the sources of ATP released under physiological and pathological stress and its cell-specific mechanism of action. We further highlight a series of cardiovascular cell-to-cell communications of extracellular ATP signalling cascades in cardiac remodelling, which can be seen in hypertension, ischemia/reperfusion injury, fibrosis, hypertrophy, and atrophy. Finally, we summarize current pharmacological intervention using the ATP network as a target for cardiac protection. A better understanding of ATP communication in myocardial remodelling could be worthwhile for future drug development and repurposing and the management of cardiovascular diseases.
MeSH terms: Cell Communication; Myocardium/metabolism; Signal Transduction
Ibuprofen (Ibf) is a biologically active drug (BADs) and an emerging contaminant of concern (CECs) in aqueous streams. Due to its adverse effects upon aquatic organisms and humans, the removal and recovery of Ibf are essential. Usually, conventional solvents are employed for the separation and recovery of ibuprofen. Due to environmental limitations, alternative green extracting agents need to be explored. Ionic liquids (ILs), emerging and greener alternatives, can also serve this purpose. It is essential to explore ILs that are effective for recovering ibuprofen, among millions of ILs. The conductor-like screening model for real solvents (COSMO-RS) is an efficient tool that can be used to screen ILs specifically for ibuprofen extraction. The main objective of this work was to identify the best IL for the extraction of ibuprofen. A total of 152 different cation-anion combinations consisting of eight aromatic and non-aromatic cations and nineteen anions were screened. The evaluation was based upon activity coefficients, capacity, and selectivity values. Furthermore, the effect of alkyl chain length was studied. The results suggest that quaternary ammonium (cation) and sulfate (anion) have better extraction ability for ibuprofen than the other combinations tested. An ionic liquid-based green emulsion liquid membrane (ILGELM) was developed using the selected ionic liquid as the extractant, sunflower oil as the diluent, Span 80 as the surfactant, and NaOH as the stripping agent. Experimental verification was carried out using the ILGELM. The experimental results indicated that the predicted COSMO-RS and the experimental results were in good agreement. The proposed IL-based GELM is highly effective for the removal and recovery of ibuprofen.
Graphene is a two-dimensional (2D) material with a single atomic crystal structure of carbon that has the potential to create next-generation devices for photonic, optoelectronic, thermoelectric, sensing, wearable electronics, etc., owing to its excellent electron mobility, large surface-to-volume ratio, adjustable optics, and high mechanical strength. In contrast, owing to their light-induced conformations, fast response, photochemical stability, and surface-relief structures, azobenzene (AZO) polymers have been used as temperature sensors and photo-switchable molecules and are recognized as excellent candidates for a new generation of light-controllable molecular electronics. They can withstand trans-cis isomerization by conducting light irradiation or heating but have poor photon lifetime and energy density and are prone to agglomeration even at mild doping levels, reducing their optical sensitivity. Graphene derivatives, including graphene oxide (GO) and reduced graphene oxide (RGO), are an excellent platform that, combined with AZO-based polymers, could generate a new type of hybrid structure with interesting properties of ordered molecules. AZO derivatives may modify the energy density, optical responsiveness, and photon storage capacity, potentially preventing aggregation and strengthening the AZO complexes. They are potential candidates for sensors, photocatalysts, photodetectors, photocurrent switching, and other optical applications. This review aimed to provide an overview of the recent progress in graphene-related 2D materials (Gr2MS) and AZO polymer AZO-GO/RGO hybrid structures and their synthesis and applications. The review concludes with remarks based on the findings of this study.
Flexible sensors have been extensively employed in wearable technologies for physiological monitoring given the technological advancement in recent years. Conventional sensors made of silicon or glass substrates may be limited by their rigid structures, bulkiness, and incapability for continuous monitoring of vital signs, such as blood pressure (BP). Two-dimensional (2D) nanomaterials have received considerable attention in the fabrication of flexible sensors due to their large surface-area-to-volume ratio, high electrical conductivity, cost effectiveness, flexibility, and light weight. This review discusses the transduction mechanisms, namely, piezoelectric, capacitive, piezoresistive, and triboelectric, of flexible sensors. Several 2D nanomaterials used as sensing elements for flexible BP sensors are reviewed in terms of their mechanisms, materials, and sensing performance. Previous works on wearable BP sensors are presented, including epidermal patches, electronic tattoos, and commercialized BP patches. Finally, the challenges and future outlook of this emerging technology are addressed for non-invasive and continuous BP monitoring.
In this study, α-LiAlO2 was investigated for the first time as a Li-capturing positive electrode material to recover Li from aqueous Li resources. The material was synthesized using hydrothermal synthesis and air annealing, which is a low-cost and low-energy fabrication process. The physical characterization showed that the material formed an α-LiAlO2 phase, and electrochemical activation revealed the presence of AlO2* as a Li deficient form that can intercalate Li+. The AlO2*/activated carbon electrode pair showed selective capture of Li+ ions when the concentrations were between 100 mM and 25 mM. In mono salt solution comprising 25 mM LiCl, the adsorption capacity was 8.25 mg g-1, and the energy consumption was 27.98 Wh mol Li-1. The system can also handle complex solutions such as first-pass seawater reverse osmosis brine, which has a slightly higher concentration of Li than seawater at 0.34 ppm.
Rigidoporus microporus, which causes white root rot disease (WRD) in Hevea brasiliensis, is a looming threat to rubber plantation in Malaysia. The current study was conducted to determine and evaluate the efficiency of fungal antagonists (Ascomycota) against R. microporus in rubber trees under laboratory and nursery conditions. A total of 35 fungal isolates established from the rubber tree rhizosphere soil were assessed for their antagonism against R. microporus by the dual culture technique. Trichoderma isolates can inhibit the radial growth of R. microporus by 75% or more in the dual culture test. Strains of T. asperellum, T. koningiopsis, T. spirale, and T. reesei were selected to assess the metabolites involved in their antifungal activity. Results indicated that T. asperellum exhibited an inhibitory effect against R. microporus in both volatile and non-volatile metabolite tests. All Trichoderma isolates were then tested for their ability in producing hydrolytic enzymes such as chitinase, cellulase and glucanase, indole acetic acid (IAA), siderophores production, and phosphate solubilization. From the positive results of the biochemical assays, T. asperellum and T. spirale were selected as the biocontrol candidates to be further tested in vivo against R. microporus. The nursery assessments revealed that rubber tree clone RRIM600 pretreated with only T. asperellum or with the combination of T. asperellum and T. spirale was able to reduce the disease severity index (DSI) and exert higher suppression of R. microporus compared to other pretreated samples, with the average DSI below 30%. Collectively, the present study demonstrates that T. asperellum represents a potential biocontrol agent that should be further explored to control R. microporus infection on rubber trees.
Lignin is a natural biopolymer with a complex three-dimensional network and it is rich in phenol, making it a good candidate for the production of bio-based polyphenol material. This study attempts to characterize the properties of green phenol-formaldehyde (PF) resins produced through phenol substitution by the phenolated lignin (PL) and bio-oil (BO), extracted from oil palm empty fruit bunch black liquor. Mixtures of PF with varied substitution rates of PL and BO were prepared by heating a mixture of phenol-phenol substitute with 30 wt.% NaOH and 80% formaldehyde solution at 94 °C for 15 min. After that, the temperature was reduced to 80 °C before the remaining 20% formaldehyde solution was added. The reaction was carried out by heating the mixture to 94 °C once more, holding it for 25 min, and then rapidly lowering the temperature to 60 °C, to produce the PL-PF or BO-PF resins. The modified resins were then tested for pH, viscosity, solid content, FTIR, and TGA. Results revealed that the substitution of 5% PL into PF resins is enough to improve its physical properties. The PL-PF resin production process was also deemed environmentally beneficial, as it met 7 of the 8 Green Chemistry Principle evaluation criteria.
In this work, the performance of anion exchange membrane (AEM) electrolysis is evaluated. A parametric study is conducted, focusing on the effects of various operating parameters on the AEM efficiency. The following parameters-potassium hydroxide (KOH electrolyte concentration (0.5-2.0 M), electrolyte flow rate (1-9 mL/min), and operating temperature (30-60 °C)-were varied to understand their relationship to AEM performance. The performance of the electrolysis unit is measured by its hydrogen production and energy efficiency using the AEM electrolysis unit. Based on the findings, the operating parameters greatly influence the performance of AEM electrolysis. The highest hydrogen production was achieved with the operational parameters of 2.0 M electrolyte concentration, 60 °C operating temperature, and 9 mL/min electrolyte flow at 2.38 V applied voltage. Hydrogen production of 61.13 mL/min was achieved with an energy consumption of 48.25 kW·h/kg and an energy efficiency of 69.64%.
One of the most significant environmental problems in the world is the massive release of dye wastewater from the dyeing industry. Therefore, the treatment of dyes effluents has received significant attention from researchers in recent years. Calcium peroxide (CP) from the group of alkaline earth metal peroxides acts as an oxidizing agent for the degradation of organic dyes in water. It is known that the commercially available CP has a relatively large particle size, which makes the reaction rate for pollution degradation relatively slow. Therefore, in this study, starch, a non-toxic, biodegradable and biocompatible biopolymer, was used as a stabilizer for synthesizing calcium peroxide nanoparticles (Starch@CPnps). The Starch@CPnps were characterized by Fourier transform infrared spectroscopy (FTIR), X-ray diffraction (XRD), Brunauer-Emmet-Teller (BET), dynamic light scattering (DLS), thermogravimetric analysis (TGA), energy dispersive X-ray analysis (EDX) and scanning electron microscopy (SEM). The degradation of organic dyes, methylene blue (MB), using Starch@CPnps as a novel oxidant was studied using three different parameters: initial pH of the MB solution, calcium peroxide initial dosage and contact time. The degradation of the MB dye was carried out via a Fenton reaction, and the degradation efficiency of Starch@CPnps was successfully achieved up to 99%. This study shows that the potential application of starch as a stabilizer can reduce the size of the nanoparticles as it prevents the agglomeration of the nanoparticles during synthesis.
Gallium nitride (GaN), widely known as a wide bandgap semiconductor material, has been mostly employed in high power devices, light emitting diodes (LED), and optoelectronic applications. However, it could be exploited differently due to its piezoelectric properties, such as its higher SAW velocity and strong electromechanical coupling. In this study, we investigated the affect of the presence of a guiding layer made from titanium/gold on the surface acoustic wave propagation of the GaN/sapphire substrate. By fixing the minimum thickness of the guiding layer at 200 nm, we could observe a slight frequency shift compared to the sample without a guiding layer, with the presence of different types of surface mode waves (Rayleigh and Sezawa). This thin guiding layer could be efficient in transforming the propagation modes, acting as a sensing layer for the binding of biomolecules to the gold layer, and influencing the output signal in terms of frequency or velocity. The proposed GaN/sapphire device integrated with a guiding layer could possibly be used as a biosensor and in wireless telecommunication applications.
Federated learning (FL) is a technique that allows multiple clients to collaboratively train a global model without sharing their sensitive and bandwidth-hungry data. This paper presents a joint early client termination and local epoch adjustment for FL. We consider the challenges of heterogeneous Internet of Things (IoT) environments including non-independent and identically distributed (non-IID) data as well as diverse computing and communication capabilities. The goal is to strike the best tradeoff among three conflicting objectives, namely global model accuracy, training latency and communication cost. We first leverage the balanced-MixUp technique to mitigate the influence of non-IID data on the FL convergence rate. A weighted sum optimization problem is then formulated and solved via our proposed FL double deep reinforcement learning (FedDdrl) framework, which outputs a dual action. The former indicates whether a participating FL client is dropped, whereas the latter specifies how long each remaining client needs to complete its local training task. Simulation results show that FedDdrl outperforms the existing FL scheme in terms of overall tradeoff. Specifically, FedDdrl achieves higher model accuracy by about 4% while incurring 30% less latency and communication costs.
With continuous advancements in Internet technology and the increased use of cryptographic techniques, the cloud has become the obvious choice for data sharing. Generally, the data are outsourced to cloud storage servers in encrypted form. Access control methods can be used on encrypted outsourced data to facilitate and regulate access. Multi-authority attribute-based encryption is a propitious technique to control who can access encrypted data in inter-domain applications such as sharing data between organizations, sharing data in healthcare, etc. The data owner may require the flexibility to share the data with known and unknown users. The known or closed-domain users may be internal employees of the organization, and unknown or open-domain users may be outside agencies, third-party users, etc. In the case of closed-domain users, the data owner becomes the key issuing authority, and in the case of open-domain users, various established attribute authorities perform the task of key issuance. Privacy preservation is also a crucial requirement in cloud-based data-sharing systems. This work proposes the SP-MAACS scheme, a secure and privacy-preserving multi-authority access control system for cloud-based healthcare data sharing. Both open and closed domain users are considered, and policy privacy is ensured by only disclosing the names of policy attributes. The values of the attributes are kept hidden. Characteristic comparison with similar existing schemes shows that our scheme simultaneously provides features such as multi-authority setting, expressive and flexible access policy structure, privacy preservation, and scalability. The performance analysis carried out by us shows that the decryption cost is reasonable enough. Furthermore, the scheme is demonstrated to be adaptively secure under the standard model.
MeSH terms: Cloud Computing; Confidentiality*; Delivery of Health Care; Humans; Computer Security; Privacy*; Information Dissemination
As a fundamental but difficult topic in computer vision, 3D object segmentation has various applications in medical image analysis, autonomous vehicles, robotics, virtual reality, lithium battery image analysis, etc. In the past, 3D segmentation was performed using hand-made features and design techniques, but these techniques could not generalize to vast amounts of data or reach acceptable accuracy. Deep learning techniques have lately emerged as the preferred method for 3D segmentation jobs as a result of their extraordinary performance in 2D computer vision. Our proposed method used a CNN-based architecture called 3D UNET, which is inspired by the famous 2D UNET that has been used to segment volumetric image data. To see the internal changes of composite materials, for instance, in a lithium battery image, it is necessary to see the flow of different materials and follow the directions analyzing the inside properties. In this paper, a combination of 3D UNET and VGG19 has been used to conduct a multiclass segmentation of publicly available sandstone datasets to analyze their microstructures using image data based on four different objects in the samples of volumetric data. In our image sample, there are a total of 448 2D images, which are then aggregated as one 3D volume to examine the 3D volumetric data. The solution involves the segmentation of each object in the volume data and further analysis of each object to find its average size, area percentage, total area, etc. The open-source image processing package IMAGEJ is used for further analysis of individual particles. In this study, it was demonstrated that convolutional neural networks can be trained to recognize sandstone microstructure traits with an accuracy of 96.78% and an IOU of 91.12%. According to our knowledge, many prior works have applied 3D UNET for segmentation, but very few papers extend it further to show the details of particles in the sample. The proposed solution offers a computational insight for real-time implementation and is discovered to be superior to the current state-of-the-art methods. The result has importance for the creation of an approximately similar model for the microstructural analysis of volumetric data.
Weld site inspection is a research area of interest in the manufacturing industry. In this study, a digital twin system for welding robots to examine various weld flaws that might happen during welding using the acoustics of the weld site is presented. Additionally, a wavelet filtering technique is implemented to remove the acoustic signal originating from machine noise. Then, an SeCNN-LSTM model is applied to recognize and categorize weld acoustic signals according to the traits of strong acoustic signal time sequences. The model verification accuracy was found to be 91%. In addition, using numerous indicators, the model was compared with seven other models, namely, CNN-SVM, CNN-LSTM, CNN-GRU, BiLSTM, GRU, CNN-BiLSTM, and LSTM. A deep learning model, and acoustic signal filtering and preprocessing techniques are integrated into the proposed digital twin system. The goal of this work was to propose a systematic on-site weld flaw detection approach encompassing data processing, system modeling, and identification methods. In addition, our proposed method could serve as a resource for pertinent research.
Wireless Local Area Networks (WLANs) have become an increasingly popular mode of communication and networking, with a wide range of applications in various fields. However, the increasing popularity of WLANs has also led to an increase in security threats, including denial of service (DoS) attacks. In this study, management-frames-based DoS attacks, in which the attacker floods the network with management frames, are particularly concerning as they can cause widespread disruptions in the network. Attacks known as denial of service (DoS) can target wireless LANs. None of the wireless security mechanisms in use today contemplate defence against them. At the MAC layer, there are multiple vulnerabilities that can be exploited to launch DoS attacks. This paper focuses on designing and developing an artificial neural network (NN) scheme for detecting management-frames-based DoS attacks. The proposed scheme aims to effectively detect fake de-authentication/disassociation frames and improve network performance by avoiding communication interruption caused by such attacks. The proposed NN scheme leverages machine learning techniques to analyse patterns and features in the management frames exchanged between wireless devices. By training the NN, the system can learn to accurately detect potential DoS attacks. This approach offers a more sophisticated and effective solution to the problem of DoS attacks in wireless LANs and has the potential to significantly enhance the security and reliability of these networks. According to the experimental results, the proposed technique exhibits higher effectiveness in detection compared to existing methods, as evidenced by a significantly increased true positive rate and a decreased false positive rate.
Human action recognition (HAR) is one of the most active research topics in the field of computer vision. Even though this area is well-researched, HAR algorithms such as 3D Convolution Neural Networks (CNN), Two-stream Networks, and CNN-LSTM (Long Short-Term Memory) suffer from highly complex models. These algorithms involve a huge number of weights adjustments during the training phase, and as a consequence, require high-end configuration machines for real-time HAR applications. Therefore, this paper presents an extraneous frame scrapping technique that employs 2D skeleton features with a Fine-KNN classifier-based HAR system to overcome the dimensionality problems.To illustrate the efficacy of our proposed method, two contemporary datasets i.e., Multi-Camera Action Dataset (MCAD) and INRIA Xmas Motion Acquisition Sequences (IXMAS) dataset was used in experiment. We used the OpenPose technique to extract the 2D information, The proposed method was compared with CNN-LSTM, and other State of the art methods. Results obtained confirm the potential of our technique. The proposed OpenPose-FineKNN with Extraneous Frame Scrapping Technique achieved an accuracy of 89.75% on MCAD dataset and 90.97% on IXMAS dataset better than existing technique.
MeSH terms: Algorithms; Humans; Human Activities; Skeleton; Neural Networks (Computer)*
Streptozotocin (STZ) is a broad-spectrum antibiotic that is toxic to the insulin-producing beta cells of the pancreatic islets. STZ is currently used clinically for the treatment of metastatic islet cell carcinoma of the pancreas and the induction of diabetes mellitus (DM) in rodents. So far, there has been no previous research to show that STZ injection in rodents causes insulin resistance in type 2 diabetes mellitus (T2DM). The purpose of this study was to determine if rats (Sprague-Dawley) developed type 2 diabetes mellitus (insulin resistance) after 72 h of intraperitoneal administration of 50 mg/kg STZ. Rats with fasting blood glucose levels above 11.0 mM, 72 h post-STZ induction, were used. The body weight and plasma glucose levels were measured every week throughout the 60-day treatment period. The plasma, liver, kidney, pancreas, and smooth muscle cells were harvested for antioxidant, biochemical analysis, histology, and gene expression studies. The results revealed that STZ was able to destroy the pancreatic insulin-producing beta cell, as evidenced by an increase in plasma glucose level, insulin resistance, and oxidative stress. Biochemical investigation indicates that STZ can generate diabetes complications through hepatocellular damage, elevated HbA1c, kidney damage, hyperlipidemia, cardiovascular damage, and impairment of the insulin-signaling pathway.
Protein kinases modulate the structure and function of proteins by adding phosphate groups to threonine, tyrosine, and serine residues. The phosphorylation process mediated by the kinases regulates several physiological processes, while their overexpression results in the development of chronic diseases, including cancer. Targeting of receptor tyrosine kinase pathways results in the inhibition of angiogenesis and cell proliferation that validates kinases as a key target in the management of aggressive cancers. As such, the identification of protein kinase inhibitors revolutionized the contemporary anticancer therapy by inducing a paradigm shift in the management of disease pathogenesis. Contemporary drug design programs focus on a broad range of kinase targets for the development of novel pharmacophores to manage the overexpression of kinases and their pathophysiology in cancer pathogenesis. In this review, we present the emerging trends in the development of rationally designed molecular inhibitors of kinases over the last five years (2016-2021) and their incipient role in the development of impending anticancer pharmaceuticals.
MeSH terms: Humans; Phosphorylation; Protein Kinases/metabolism; Receptor Protein-Tyrosine Kinases; Protein Kinase Inhibitors/therapeutic use
Morphological processing in visual word recognition has been extensively studied in a few languages, but other languages with interesting morphological systems have received little attention. Here, we examined Malay, an Austronesian language that is agglutinative. Agglutinative languages typically have a large number of morphemes per word. Our primary aim was to facilitate research on morphological processing in Malay by augmenting the Malay Lexicon Project (a database containing lexical information for almost 10,000 words) to include a breakdown of the words into morphemes as well as morphological properties for those morphemes. A secondary goal was to determine which morphological variables influence Malay word recognition. We collected lexical decision data for Malay words that had one prefix and one suffix, and first examined the predictive power of 15 morphological and four lexical variables on response times (RT). Of these variables, two lexical and three morphological variables emerged as strong predictors of RT. In GAMM models, we found a facilitatory effect of root family size, and inhibitory effects of prefix length and prefix percentage of more frequent words (PFMF) on RT. Next, we explored the interactions between overall word frequency and several of these predictors. Of particular interest, there was a significant word frequency by root family size interaction in which the effect of root family size is stronger for low-frequency words. We hope that this initial work on morphological processing in Malay inspires further research in this and other understudied languages, with the goal of developing a universal theory of morphological processing.
MeSH terms: Humans; Language*; Malaysia; Reaction Time