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This paper proposes a method in which an object tracking robot system is implemented on field programmable gate arrays (FPGAs). The OV7670 camera provides real-time object pictures to the system. To improve picture quality, images are put via the median filter phase. The item is distinguished from the backdrop based on color (red), after which it is subjected to a mathematical morphological approach of filtering to eliminate noise. To send the robot control signals, the object's (new) coordinates are found. In this method, the median filter, color separation, hardware IP cores, and morphological filter are all part of the embedded system on FPGA. Through the direct memory access (DMA) controller, these cores may communicate and perform high-speed pipeline computing at higher data rates. The entire system is executed in real-time on Xilinx's spartan-6 FPGA KIT. The results show practical and promise.

Wireless sensor networks (WSNs) facilitate many applications in different fields and are well-known in both academic and industrial areas. The networks mainly include sensors, networking parts and a data processing center to collect data for a variety of purposes. However, the energy consumption problem in the networks is always a critical problem that many researchers are looking for good solutions. In this paper, we propose a data collection method that uses mobile sensors attached with bigger energy storages to collect data from static sensors. In addition, we propose a hybrid energy harvesting scheme to harvest energy from ambient environments to support static sensors to prolong the network lifetime. Some algorithms are provided to support the networks either with data collection or harvesting energy. We outline various possibilities for powering the gadgets with solar and RF energy gathering. In particular, all the circuits that are constructed using mathematical equations are offered. All of the network possibilities are clarified using simulation and experimental results. The outcomes are encouraging and useful.

Recently, unmanned aerial vehicles (UAVs) enhance connectivity and accessibility for civilian and military applications. A group of UAVs with on-board cameras usually monitors or collects information about designated areas. The UAVs can build a distributed network to share/exchange and to process collected sensing data before sending to a data processing center. A huge data transmission among them may cause latency and high-energy consumption. This paper deploys artificial intelligent (AI) techniques to process the video data streaming among the UAVs. Thus, each distributed UAV only needs to send a certain required information to each other. Each UAV processes data utilizing AI and only sends the data that matters to the others. The UAVs, formed as a connected network, communicate within a short communication range and share their own data to each other. Convolution neural network (CNN) technique extracts feature from images automatically that the UAVs only send the moving objects instead of the whole frames. This significantly reduces redundant information for either each UAV or the whole network and saves a huge energy consumption for the network. The UAVs can also save energy for their motion in the sensing field. In addition, a flocking control algorithm is deployed to lead the group of UAVs in the working fields and to avoid obstacles if needed. Simulation and experimental results are provided to verify the proposed algorithms in either AI-based data processing or controlling the UAVs. The results show promising points to save energy for the networks.

In recent decades, there has been a constant increase in the use of unmanned aerial vehicles (UAVs). There has also been a huge growth in the number of control algorithms to support the many applications embodied by the vehicles, including challenges and open issues to develop. This paper focuses on three major classes of control methods applied to quadrotors in order to create an open-source model based on the Arduino Mega that allows for the derivation and design of quadrotor control strategies. We consider the perspective classes, including linear, nonlinear, and intelligent methods representing in details with applications in developing an open-source controller for the quadrotor using the Arduino Mega and the BNO055 9 DOF sensor. We propose Proportional Integral Derivative (PID), backstepping integrator, and model predictive control (MPC) to track a generated Lissajous curve for surveillance. Simulations in the Matlab–Simulink environment with 3D visualization of a developed quadrotor model using CAD software, with robustness and performance discussion, are provided. Our experimental work is developed with an extensive illustration of the hardware and algorithm design and by demonstrating the effectiveness of the proposed architectures. The results show promise in practical and in intelligent applications.

The COVID-19 pandemic has had a massive impact on the global aviation industry. As a result, the airline industry has been forced to embrace new technologies and procedures in order to provide a more secure and bio-safe travel. Currently, the role of smart technology in airport systems has expanded significantly as a result of the contemporary Industry 4.0 context. The article presents a novel construction of an intelligent mobile robot system to guide passengers to take the plane at the departure terminals at busy airports. The robot provides instructions to the customer through the interaction between the robot and the customer utilizing voice communications. The usage of the Google Cloud Speech-to-Text API combined with technical machine learning to analyze and understand the customer's requirements are deployed. In addition, we use a face detection technique based on Multi-task Cascaded Convolutional Networks (MTCNN) to predict the distance between the robot and passengers to perform the function. The robot can guide passengers to desired areas in the terminal. The results and evaluation of the implementation process are also mentioned in the article and show promise.

Wireless sensor networks (WSNs) have been deployed widely in many different application including in civil and military fields. The sensing data from the networks is very diverse and may cost huge energy consumption for transmission. Hence, mobile sensors with high capacity are deployed to support the static sensors dealing with longer range communicating distances and also supplying power wirelessly the sensors if possible. This paper focuses on an energy harvesting (EH) design for either mobile agents or sensor nodes in WSNs. A hybrid EH system can harvest energy from ambient environment around to power the mobile sensors and static sensors. In addition, these mobile sensors and transfer power wirelessly to the sensors if required. We provide different scenarios for harvesting energy from both RF and solar energy to support the devices. All the designed circuits based on mathematical equations are provided, specifically. Simulation and experimental results are addressed to clarify all the scenarios for the networks. The results show promise and practical.

Sensing networks have recently proven to be useful in a variety of disciplines. The use of mobile sensing robots and unmanned aerial vehicles (UAVs) is intended to lessen the burden of sensor node energy consumption. However, conserving energy in the networks remains a major concern. In this work, a design of a hybrid RF-Solar harvesting system to support sensing devices is investigated. The harvesting circuits may collect energy from both solar and radio frequency (RF) resources that are commonly available in ambient environments. This approach creates hybrid harvesting circuits for all elements in the combined sensor networks by combining RF and solar harvester circuits. The RF harvester circuit is a dual-band multi-stage harvester for the 2.4GHz Wi-Fi/WLAN spectrum. In the solo solar harvester circuit, a solar panel with a maximum power point tracking (MPPT) algorithm is employed. The hybrid system can offer a maximum power of up to 136.5W due to a boost current element in the charging system. Because each node has a rechargeable battery, the gathered power from the proposed circuits is used to charge all of the batteries. This strategy allows networks to run safely and continuously even when there is a scarcity of gathered electricity due to severe weather, as cloudy or rainy days. This work is both promising and practical.

Background In infants, distinct nasopharyngeal bacterial microbiotas differentially associate with the incidence and severity of acute respiratory tract infection and childhood asthma development. Objective We hypothesized that distinct nasal airway microbiota structures also exist in children with asthma and relate to clinical outcomes. Methods Nasal secretion samples (n = 3122) collected after randomization during the fall season from children with asthma (6-17 years, n = 413) enrolled in a trial of omalizumab (anti-IgE) underwent 16S rRNA profiling. Statistical analyses with exacerbation as the primary outcome and rhinovirus infection and respiratory illnesses as secondary outcomes were performed. Using A549 epithelial cells, we assessed nasal isolates of Moraxella, Staphylococcus, and Corynebacterium species for their capacity to induce epithelial damage and inflammatory responses. Results Six nasal airway microbiota assemblages, each dominated by Moraxella, Staphylococcus, Corynebacterium, Streptococcus, Alloiococcus, or Haemophilus species, were observed. Moraxella and Staphylococcus species–dominated microbiotas were most frequently detected and exhibited temporal stability. Nasal microbiotas dominated by Moraxella species were associated with increased exacerbation risk and eosinophil activation. Staphylococcus or Corynebacterium species–dominated microbiotas were associated with reduced respiratory illness and exacerbation events, whereas Streptococcus species–dominated assemblages increased the risk of rhinovirus infection. Nasal microbiota composition remained relatively stable despite viral infection or exacerbation; only a few taxa belonging to the dominant genera exhibited relative abundance fluctuations during these events. In vitro, Moraxella catarrhalis induced significantly greater epithelial damage and inflammatory cytokine expression (IL-33 and IL-8) compared with other dominant nasal bacterial isolates tested. Conclusion Distinct nasal airway microbiotas of children with asthma relate to the likelihood of exacerbation, rhinovirus infection, and respiratory illnesses during the fall season.

Type 2 diabetes mellitus (T2DM) patients require close glycemic control to decrease complications especially the incidence of hypoglycemia. T2DM patients with hypertension comorbidity may require extra monitoring to control this complication. This study aims to investigate the effect of hypertension comorbidity on the development of hypoglycemia in T2DM patients. A national representative survey of T2DM patients from 602 hospitals across Thailand during 2011–2012 was conducted. Binary logistic mixed effect regression was used to estimate the effect of hypertension along with other risk factors on the development of hypoglycemia. Of the 55,797 T2DM patient sample, 55.35 % were hypertensive. The prevalence of hypoglycemia in T2DM with hypertension comorbidity (4.87 %) was higher than that without hypertension (3.87 %). After adjusting for confounders, gender, age, health care coverage, body mass index, diabetes duration, diabetes medication, and microvascular complications were significantly associated with hypoglycemia. T2DM patients with a hypertension comorbidity had 1.12 times the odds of developing hypoglycemia than patients without hypertension (95 % confidence interval (CI) 1.00, 1.25; P < 0.05). Hypertension comorbidity is associated with hypoglycemia among patients with type 2 diabetes.

As an important aspect of computer-aided drug design, structure-based drug design brought a new horizon to pharmaceutical development. This in silico method permeates all aspects of drug discovery today, including lead identification, lead optimization, ADMET prediction and drug repurposing. Structure-based drug design has resulted in fruitful successes drug discovery targeting proteinligand and protein-protein interactions. Meanwhile, challenges, noted by low accuracy and combinatoric issues, may also cause failures. In this review, state-of-the-art techniques for protein modeling (e.g. structure prediction, modeling protein flexibility, etc.), hit identification/ optimization (e.g. molecular docking, focused library design, fragment-based design, molecular dynamic, etc.), and polypharmacology design will be discussed. We will explore how structure-based techniques can facilitate the drug discovery process and interplay with other experimental approaches.

This study examined the effects of endogenous overexpression of laminin-8 on angiogenesis and wound healing in primary human dermal microvascular endothelial cells (HDMECs). HDMECs expressed laminin-8 and laminin-10, but no other laminins, as determined by radioimmunoprecipitation assay using a panel of antibodies to individual laminin chains. To study laminin-8 function, full-length human laminin alpha4 cDNA was retrovirally transferred to HDMEC, and specific overexpression of laminin-8 was verified by Western blot. Laminin-8 overexpression promoted endothelial cell spreading and migration in scratch assays and accelerated angiogenic tubule formation in collagen gel overlay assays. Strong inhibitory effect of beta1 integrin and weak inhibition by alphavbeta3 integrin antibodies were observed in laminin-8-stimulated cell migration, but only beta1 integrin antibodies affected tubule formation. These studies suggest that laminin-8 overexpression may prove to be a useful method to engineer HDMECs to promote angiogenesis and wound repair.

Radii of gyration of denatured proteins vary with chain length and are insensitive to details of amino acid sequence. Observations of sequence independence in polymeric properties conflict with results from spectroscopic experiments, which suggest the presence of sequence-specific residual structure in denatured states. Can we reconcile the two apparently conflicting sets of observations? To answer this question, we need knowledge of the ensemble of conformations accessible to proteins in good solvents. The excluded-volume limit provides an ideal mimic of polymers in good solvents. Therefore, we attempt to solve the "reconciliation problem" by simulating conformational ensembles accessible to peptides and proteins in the excluded-volume limit. Analysis of these ensembles for a variety of polypeptide sequences leads to results that are consistent with experimental observations of sequence-specific conformational preferences in short peptides and the scaling behavior of polymeric quantities for denatured proteins. Reconciliation in the excluded-volume limit comes about due to a tug of war between two factors, namely, minimization of steric overlap and the competing effects of conformational entropy. Minimization of steric overlap promotes chain stretching and leads to experimentally observed sequence-dependent preferences for locally extended segments such as polyproline II helices, beta-strands, and very short stretches of alpha-helix. Conformational entropy opposes chain stretching, and the calculated persistence length for sequence-dependent conformational preferences is less than five amino acids. This estimate does not vary with amino acid sequence. The short persistence lengths lead directly to experimental observations of generic sequence-independent behavior of radii of gyration for denatured proteins.

Generalized atrophic benign epidermolysis bullosa, GABEB (OMIM# 226650), is a nonlethal variant of epidermolysis bullosa with autosomal recessive inheritance pattern. The pathogenesis of this disorder can be caused by mutations affecting two different gene/protein systems. Most of the mutations have been identified in the BPAG2/COL17A1 gene encoding a hemidesmosomal transmembrane protein, the 180 kDa bullous pemphigoid antigen (BP180), also known as type XVII collagen. The minority of the mutations are localized in the LAMB3 gene encoding the beta3 polypeptide of laminin 5. In In this study we describe a GABEB patient who showed absent expression of BP180 in the cultured keratinocytes as well as in the skin. The patient was a compound heterozygote for two different splice site mutations, 3053-1G--C and 3871+1G--C, affecting the extra-cellular domain of the protein. These mutations resulted in multiple aberrant splice variants, three of them causing premature termination codons for translation. This case, dealing with out-of-frame splice site mutations in BPAG2/COL17A1, attests to the molecular heterogeneity of GABEB.

Molecular docking is often performed with rigid receptors. This can be a serious limitation, since the receptor often differs between bound and unbound forms, or between bound forms with different ligands. We recently developed a normal-mode based docking method and showed that it is possible to obtain reasonable estimates of the complexed form of the pleckstrin homology (PH) domain of Akt, starting with the free form of the receptor. With in-ositol (1,3,4,5)-tetrakisphosphate (IP4) as the ligand the docked results agree with the known high-resolution X-ray crystal structure of the IP4-Akt PH domain complex. We also tested our methods with PH4, SC66, and PIT-1, several recently designed PH domain inhibitors. The results are shown to be consistent with available experimental data and previous modeling studies. The method we described can be used for molecular docking analysis even when only an approximation of the experimental structure or model is known.

Computational biology methods are now firmly entrenched in the drug discovery process. These methods focus on modeling and simulations of biological systems to complement and direct conventional experimental approaches. Two important branches of computational biology include protein homology modeling and the computational biophysics method of molecular dynamics. Protein modeling methods attempt to accurately predict three-dimensional (3D) structures of uncrystallized proteins for subsequent structure-based drug design applications. Molecular dynamics methods aim to elucidate the molecular motions of the static representations of crystallized protein structures. In this review we highlight recent novel methodologies in the field of homology modeling and molecular dynamics. Selected drug discovery applications using these methods conclude the review.

Intrinsically disordered proteins (IDPs) are functional proteins that do not fold into well-defined three-dimensional structures under physiological conditions. IDP sequences have low hydrophobicity, and hence, recent experiments have focused on quantitative studies of conformational ensembles of archetypal IDP sequences such as polyglutamine and glycine-serine block copolypeptides. Results from these experiments show that, despite the absence of hydrophobic residues, polar IDPs prefer ensembles of collapsed structures in aqueous milieus. Do these preferences originate in interactions that are unique to polar sidechains? The current study addresses this issue by analyzing conformational equilibria for polyglycine and a glycine-serine block copolypeptide in two environments, namely, water and 8 M urea. Polyglycine, a poly secondary-amide, has no sidechains and is a useful model system for generic polypeptide backbones. Results based on large-scale molecular dynamics simulations show that polyglycine forms compact, albeit disordered, globules in water and swollen, disordered coils in 8 M urea. There is minimal overlap between conformational ensembles in the two environments. Analysis of order parameters derived from theories for flexible polymers show that water at ambient temperatures is a poor solvent for generic polypeptide backbones. Therefore, the experimentally observed preferences for polyglutamine and glycine-serine block copolypeptides must originate, at least partially, in polypeptide backbones. A preliminary analysis of the driving forces that lead to distinct conformational preferences for polyglycine in two different environments is presented. Implications for describing conformational ensembles of generic IDP sequences are also discussed.

Our focus is on an appropriate theoretical framework for describing highly denatured proteins. In high concentrations of denaturants, proteins behave like polymers in a good solvent and ensembles for denatured proteins can be modeled by ignoring all interactions except excluded volume (EV) effects. To assay conformational preferences of highly denatured proteins, we quantify a variety of properties for EV-limit ensembles of 23 two-state proteins. We find that modeled denatured proteins can be best described as follows. Average shapes are consistent with prolate ellipsoids. Ensembles are characterized by large correlated fluctuations. Sequence-specific conformational preferences are restricted to local length scales that span five to nine residues. Beyond local length scales, chain properties follow well-defined power laws that are expected for generic polymers in the EV limit. The average available volume is filled inefficiently, and cavities of all sizes are found within the interiors of denatured proteins. All properties characterized from simulated ensembles match predictions from rigorous field theories. We use our results to resolve between conflicting proposals for structure in ensembles for highly denatured states.