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During bone growth, development, and remodeling, angiogenesis as well as osteogenesis are closely associated processes, sharing some essential mediators. Vascular endothelial growth factor (VEGF) was initially recognized as the best-characterized endothelial-specific growth factor, which increased vascular permeability and angiogenesis, and it is now apparent that this cytokine regulates multiple biological functions in the endochondral ossification of mandibular condylar growth, as well as long bone formation. The complexity of VEGF biology is paralleled by the emerging complexity of interactions between VEGF ligands and their receptors. This narrative review summarizes the family of VEGF-related molecules, including 7 mammalian members, namely, VEGF, placenta growth factor (PLGF), and VEGF-B, -C, -D, -E, and -F. The biological functions of VEGF are mediated by at least 3 corresponding receptors: VEGFR-1/Flt-1, VEGFR-2/Flk-1, VEGFR-3/Flt-4 and 2 co-receptors of neuropilin (NRP) and heparan sulfate proteoglycans (HSPGs). Current findings on endochondral ossification are also discussed, with emphasis on VEGF-A action in osteoblasts, chondroblasts, and chondroclasts/osteoclasts and regulatory mechanisms involving oxygen tension, and some growth factors and hormones. Furthermore, the therapeutic implications of recombinant VEGF-A protein therapy and VEGF-A gene therapy are evaluated. Abbreviations used: VEGF, Vascular endothelial growth factor; PLGF, placenta growth factor; NRP, neuropilin; HSPGs, heparan sulfate proteoglycans; FGF, fibroblast growth factor; TGF, transforming growth factor; HGF, hepatocyte growth factor; TNF, tumor necrosis factor; ECM, extracellular matrix; RTKs, receptor tyrosine kinases; ERK, extracellular signal kinases; HIF, hypoxia-inducible factor

In the present work, an empirical approach based on a computational analysis is performed to study the shielding properties of epoxy/carbon fiber composites and epoxy/glass fiber composites incorporating lead nanoparticle (PbNPs) additives in the epoxy matrix. For this analysis, an MCNP5 model is developed for calculating the mass attenuation coefficients of the two fiber reinforced polymer (FRP) composites incorporating lead nanoparticles of different weight fractions. The model is verified and validated for different materials and different particle additives. Empirical correlations of the mass attenuation coefficient as a function of PbNPs weight fraction are developed and statistically analyzed. The results show that the mass attenuation coefficient increases as the weight fraction of lead nanoparticles increases up to a certain threshold (~15 wt%) beyond which the enhancement in the mass attenuation coefficient becomes negligible. Furthermore, statistical parameters of the developed correlations indicate that the correlations can accurately capture the behavior portrayed by the simulation data with acceptable root mean square error (RMSE) values.

With the advances in sensing technologies, sensor networks became the core of several different networks, including the Internet of Things (IoT) and drone networks. This led to the use of sensor networks in many critical applications including military, health care, and commercial applications. In addition, sensors might be mobile or stationary. Stationary sensors, once deployed, will not move; however, mobile nodes can move from one place to another. In most current applications, mobile sensors are used to collect data from stationary sensors. This raises many energy consumption challenges, including sensor networks’ energy consumption, urgent messages transfer for real-time analysis, and path planning. Moreover, sensors in sensor networks are usually exposed to environmental parameters and left unattended. These issues, up to our knowledge, are not deeply covered in the current research. This paper develops a complete framework to solve these challenges. It introduces novel path planning techniques considering areas’ priority, environmental parameters, and urgent messages. Consequently, a novel energy-efficient and reliable clustering algorithm is proposed considering the residual energy of the sensor nodes, the quality of wireless links, and the distance parameter representing the average intra-cluster distance. Moreover, it proposes a real-time, energy-efficient, reliable and environment-aware routing, taking into account the environmental data, link quality, delay, hop count, nodes’ residual energy, and load balancing. Furthermore, for the benefit of the sensor networks research community, all proposed algorithms are formed in integer linear programming (ILP) for optimal solutions. All proposed techniques are evaluated and compared to six recent algorithms. The results showed that the proposed framework outperforms the recent algorithms.

The world is experiencing the new development of smart cities. Smart cities’ infrastructure in its core is based on wireless sensor networks (WSNs) and the internet of things (IoT). WSNs consist of tiny smart devices (Motes) that are restricted in terms of memory, storage, processing capabilities, and sensing and communication ranges. Those limitations pose many security issues where regular cryptography algorithms are not suitable to be used. Besides, such capabilities might be degraded in case cheap sensors are deployed with very large numbers in applications, such as smart cities. One of the major security issues in WSNs that affect the overall operation, up to network interruption, in smart cities is the sinkhole routing attack. The paper has three-fold contributions: (1) it utilizes the concept of clustering for energy saving in WSNs, (2) proposing two light and simple algorithms for intrusion detection and prevention in smart cities—threshold-based intrusion detection system (TBIDS) and multipath-based intrusion detection system (MBIDS), and (3) utilizing the cross-layer technique between the application layer and network layer for the purpose of intrusion detection. The proposed methods are evaluated against recent algorithms—S-LEACH, MS-LEACH, and ABC algorithms.

Flying Ad Hoc Network (FANET) or drones’ technologies have gained much attraction in the last few years due to their critical applications. Therefore, various studies have been conducted on facilitating FANET applications in different fields. In fact, civil airspaces have gradually adopted FANET technology in their systems. However, FANET’s special roles made it complex to support emerging security threats, especially intrusion detection. This paper is a step forward towards the advances in FANET intrusion detection techniques. It investigates FANET intrusion detection threats by introducing a real-time data analytics framework based on deep learning. The framework consists of Recurrent Neural Networks (RNN) as a base. It also involves collecting data from the network and analyzing it using big data analytics for anomaly detection. The data collection is performed through an agent working inside each FANET. The agent is assumed to log the FANET real-time information. In addition, it involves a stream processing module that collects the drones’ communication information, including intrusion detection-related information. This information is fed into two RNN modules for data analysis, trained for this purpose. One of the RNN modules resides inside the FANET itself, and the second module resides at the base station. An extensive set of experiments were conducted based on various datasets to examine the efficiency of the proposed framework. The results showed that the proposed framework is superior to other recent approaches.

A new preservation approach is presented in this article to prolong the lifetime of raw chicken meat and enhance its quality at 4 °C via coating with highly soluble kidney bean protein hydrolysate. The hydrolysates of the black, red, and white kidney protein (BKH, RKH, and WKH) were obtained after 30 min enzymatic hydrolysis with Alcalase (E/S ratio of 1:100, hydrolysis degree 25–29%). The different phaseolin subunits (8S) appeared in SDS-PAGE in 35–45 kD molecular weight range while vicilin appeared in the molecular weight range of 55–75 kD. The kidney bean protein hydrolysates have considerable antioxidant activity as evidenced by the DPPH-scavenging activity and β-carotine-linolenic assay, as well as antimicrobial activity evaluated by disc diffusion assay. BKH followed by RKH (800 µg/mL) significantly (p ≤ 0.05) scavenged 95, 91% of DPPH and inhibited 82–88% of linoleic oxidation. The three studied hydrolysates significantly inhibited the growth of bacteria, yeast, and fungi, where BKH was the most performing. Kidney bean protein hydrolysates could shield the chicken meat because of their amphoteric nature and many functional properties (water and oil-absorbing capacity and foaming stability). The quality of chicken meat was assessed by tracing the fluctuations in the chemical parameters (pH, met-myoglobin, lipid oxidation, and TVBN), bacterial load (total bacterial count, and psychrophilic count), color parameters and sensorial traits during cold preservation (4 °C). The hydrolysates (800 µg/g) significantly p ≤ 0.05 reduced the increment in meat pH and TVBN values, inhibited 59–70% of lipid oxidation as compared to control during 30 days of cold storage via eliminating 50% of bacterial load and maintained secured storage for 30 days. RKH and WKH significantly (p ≤ 0.05) enhanced L*, a* values, thus augmented the meat whiteness and redness, while, BKH increased b* values, declining all color parameters during meat storage. RKH and WKH (800 µg/g) (p ≤ 0.05) maintained 50–71% and 69–75% of meat color and odor, respectively, increased the meat juiciness after 30 days of cold storage. BKH, RKH and WKH can be safely incorporated into novel foods.

Current research estimates hundreds of thousands of turbine-related bat fatalities in North America annually. In an effort to reduce impacts of wind energy production on bat populations, many facilities implement operational curtailment strategies that limit turbine blade rotation during conditions when nighttime wind speeds are low. Incorporating real-time bat activity data into wind speed-only curtailment (WOC) strategies may increase operational flexibility by allowing turbines to operate normally when bats are not present near turbines. We evaluated costs and benefits of implementing the Turbine Integrated Mortality Reduction (TIMR) system, an approach that informs a curtailment-triggering algorithm based on wind speed and real-time bat acoustic data, compared to a WOC strategy in which turbines were curtailed below 4.5 meters per second (m/s) at a wind energy facility in Fond Du Lac County, Wisconsin. TIMR is a proprietary system and we had no access to the acoustic data or bat call analysis software. Operational parameters for the TIMR system were set to allow curtailment at all wind speeds below 8.0 m/s during the study period when bats were acoustically detected. Overall, the TIMR system reduced fatalities by 75% compared to control turbines, while the WOC strategy reduced fatalities by 47%. An earlier analysis of the same TIMR data neglected to account for carcasses occurring outside the plot boundary and estimated an 84.5% fatality reduction due to the TIMR system. Over the study period, bat activity led to curtailment of TIMR turbines during 39.4% of nighttime hours compared to 31.0% of nighttime hours for WOC turbines, and revenue losses were approximately 280% as great for TIMR turbines as for turbines operated under the WOC strategy. The large cost difference between WOC and TIMR was driven by the 4.5 m/s versus 8.0 m/s wind speed thresholds for curtailment, but our study site has a relatively low average wind speed, which may also have contributed; other wind operators considering the TIMR system will need to consider their ability to absorb production losses in relation to their need to reduce bat fatality rates.

At present, the field of the Internet of Things (IoT) is one of the fastest-growing areas in terms of Artificial Intelligence (AI) and Machine Learning (ML) techniques [...].At present, the field of the Internet of Things (IoT) is one of the fastest-growing areas in terms of Artificial Intelligence (AI) and Machine Learning (ML) techniques [...].

Introduction Since 1970, monkey-pox, the last outbreak of smallpox, coronavirus was outbreak in the world for more than 50 years. To find if the outbreak dates could be predicted by their one-dimension protein sequence, the mathematical model was needed to establish between them. Methods (A) collecting the outbreak dates of monkey-pox, smallpox, and coronavirus, determine the outbreak time interval between the pathogen strain and the reference strain SARS-CoV-2 D614, z. (B) detecting the one-dimension antigenic amino acid sequence of the pathogen strain to determine the super-antigens. (C) calculating the super-antigen precision, determining the increase amount in antigen precision between the pathogen strain and the reference strain, x; y represents the number of tryptophan (W) in the super-antigen. (D) Determine the correlation among the outbreak time interval z, the increase amount in antigen precision, x, and the number of W the super-antigen contains, y. Results The regression equation is z = 13.762x 2  − 109.376x− 63.290y + 221.197, with a correlation coefficient of R = 1.0000000. After statistical testing, the probability of class I errors occurring is P  = 0.008. Conclusions The method can predict the outbreak dates by one-dimension protein sequence, such as monkey-pox, smallpox, and coronavirus.

Pyrimidines play an important role in modern medical fields. They have a wide spectrum of biological activities such as antimicrobial, anticancer, anti-allergic, anti-leishmanial, antioxidant agents and others. Moreover, in recent years, 3,4-dihydropyrimidin-2(1H)ones have attracted researchers to synthesize them via Biginelli reaction and evaluate their antihypertensive activities as bioisosters of Nifedipine, which is a famous calcium channel blocker. Our new target compounds were prepared through one-pot reaction of thiourea 1, ethyl acetoacetate 2 and/or 1H-indole-2-carbaldehyde, 2-chloroquinoline-3-carbaldehyde, 1,3-diphenyl-1H-pyrazole-4-carbaldehyde, 3a–c in acid medium (HCl) yielding pyrimidines 4a–c, which in turn were hydrolyzed to carboxylic acid derivatives 5a–c which were chlorinated by SOCl2 to give acyl chlorides 6a–c. Finally, the latter were reacted with some selected aromatic amines, namely, aniline, p-toluidine and p-nitroaniline, producing amides 7a–c, 8a–c, and 9a–c. The purity of the prepared compounds was examined via TLC monitoring, and structures were confirmed by different spectroscopic techniques such as IR, 1HNMR, 13CNMR, and mass spectroscopy. The in vivo evaluation of the antihypertensive activity revealed that compounds 4c, 7a, 7c, 8c, 9b and 9c had comparable antihypertensive properties with Nifedipine. On the other hand, the in vitro calcium channel blocking activity was evaluated by IC50 measurement and results revealed that compounds 4c, 7a, 7b, 7c, 8c, 9a, 9b, and 9c had comparable calcium channel blocking activity with the reference Nifedipine. Based on the aforementioned biological results, we selected compounds 8c and 9c to be docked onto Ryanodine and dihydropyridine receptors. Furthermore, we developed a structure–activity relationship. The designed compounds in this study show promising activity profiles in reducing blood pressure and as calcium channel blockers, and could be considered as new potential antihypertensive and/or antianginal agents.