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Abstract Small molecule can be utilized to restore the effectiveness of existing major classes of antibiotics against antibiotic‐resistant bacteria. In this study, it is demonstrated that celastrol, a natural compound, can modify the bacterial cell wall and subsequently render bacteria more suceptible to β‐lactam antibiotics. It is shown that celastrol leads to incomplete cell wall crosslinking by modulating levels of c‐di‐AMP, a secondary messenger, in methicillin‐resistant Staphylococcus aureus (MRSA). This mechanism enables celastrol to act as a potentiator, effectively rendering MRSA susceptible to a range of penicillins and cephalosporins. Restoration of in vivo susceptibility of MRSA to methicillin is also demonstrated using a sepsis animal model by co‐administering methicillin along with celastrol at a much lower amount than that of methicillin. The results suggest a novel approach for developing potentiators for major classes of antibiotics by exploring molecules that re‐program metabolic pathways to reverse β‐lactam‐resistant strains to susceptible strains.

Abstract Infections caused by Staphylococcus aureus, notably methicillin‐resistant S. aureus (MRSA), pose treatment challenges due to its ability to tolerate antibiotics and develop antibiotic resistance. The former, a mechanism independent of genetic changes, allows bacteria to withstand antibiotics by altering metabolic processes. Here, a potent methylazanediyl bisacetamide derivative, MB6, is described, which selectively targets MRSA membranes over mammalian membranes without observable resistance development. Although MB6 is effective against growing MRSA cells, its antimicrobial activity against MRSA persisters is limited. Nevertheless, MB6 significantly potentiates the bactericidal activity of gentamicin against MRSA persisters by facilitating gentamicin uptake. In addition, MB6 in combination with daptomycin exhibits enhanced anti‐persister activity through mutual reinforcement of their membrane‐disrupting activities. Crucially, the “triple” combination of MB6, gentamicin, and daptomycin exhibits a marked enhancement in the killing of MRSA persisters compared to individual components or any double combinations. These findings underscore the potential of MB6 to function as a potent and selective membrane‐active antimicrobial adjuvant to enhance the efficacy of existing antibiotics against persister cells. The molecular mechanisms of MB6 elucidated in this study provide valuable insights for designing anti‐persister adjuvants and for developing new antimicrobial combination strategies to overcome the current limitations of antibiotic treatments.

In the fields of body-worn sensors and computer vision, current research is being done to track and detect falls and activities of daily living using the automatic recognition of human actions. In the area of human–machine communication, different combinations of sensors and communication technologies are often used to capture human action. Many researchers have also worked with artificial intelligent systems to detect actions, understand scenes, and implement systems that are more efficient in human action recognition. Although effective approaches are needed to detect outdoor activities with the combination of human actions, feature extraction can be quite a complicated task in a human activity recognition system development. Thus, this paper proposed a solution to detect human activities via hybrid descriptors based on robust features and accurate results. In this study, complex backgrounds, including multiple humans in video frames, were detected. First, inertial signal and video frames are pre-processed using denoising techniques, after which the frames are used to remove the background by detecting human motions and extracting the silhouettes. Then, these silhouettes are further used to extract the human body key points to make the human skeleton. Then the time and frequency domain features are extracted for inertial signals, and geometric features are extracted for the skeleton body points. Finally, multiple feature sets are combined and fed into a zero order optimization model, after which logistic regression is utilized to recognize each action. The proposed system has been evaluated on three benchmark datasets, including, the UP Fall dataset, the University of Rzeszow Fall dataset, and the SisFall dataset and proved its significance by achieving accuracy of 91.51%, 92.98%, and 90.23%, on the aforementioned datasets respectively.

The maritime sector confronts an escalating challenge with the emergence of onboard fires aboard in ships, evidenced by a pronounced uptick in incidents in recent years. The ramifications of such fires transcend immediate safety apprehensions, precipitating repercussions that resonate on a global scale. This study underscores the paramount importance of ship fire detection as a proactive measure to mitigate risks and fortify maritime safety comprehensively. Initially, we created and labeled a custom ship dataset. The collected images are varied in their size, like having high- and low-resolution images in the dataset. Then, by leveraging the YOLO (You Only Look Once) object detection algorithm we developed an efficacious and accurate ship fire detection model for discerning the presence of fires aboard vessels navigating marine routes. The ship fire detection model was trained on 50 epochs with more than 25,000 images. The histogram equalization (HE) technique was also applied to avoid destruction from water vapor and to increase object detection. After training, images of ships were input into the inference model after HE, to be categorized into two classes. Empirical findings gleaned from the proposed methodology attest to the model’s exceptional efficacy, with the highest detection accuracy attaining a noteworthy 0.99% across both fire-afflicted and non-fire scenarios.

In the evolving landscape of Internet of Things (IoT) and Industrial IoT (IIoT) security, novel and efficient intrusion detection systems (IDSs) are paramount. In this article, we present a groundbreaking approach to intrusion detection for IoT-based electric vehicle charging stations (EVCS), integrating the robust capabilities of convolutional neural network (CNN), long short-term memory (LSTM), and gated recurrent unit (GRU) models. The proposed framework leverages a comprehensive real-world cybersecurity dataset, specifically tailored for IoT and IIoT applications, to address the intricate challenges faced by IoT-based EVCS. We conducted extensive testing in both binary and multiclass scenarios. The results are remarkable, demonstrating a perfect 100% accuracy in binary classification, an impressive 97.44% accuracy in six-class classification, and 96.90% accuracy in fifteen-class classification, setting new benchmarks in the field. These achievements underscore the efficacy of the CNN-LSTM-GRU ensemble architecture in creating a resilient and adaptive IDS for IoT infrastructures. The ensemble algorithm, accessible via GitHub, represents a significant stride in fortifying IoT-based EVCS against a diverse array of cybersecurity threats.

Abstract Pseudomonas aeruginosa (P. aeruginosa) is a major pathogen that causes nosocomial infections and often exhibits antibiotic resistance. Therefore, the development of an accurate method for detecting P. aeruginosa is required to control P. aeruginosa-related outbreaks. In this study, we established an enzyme-linked immunosorbent assay method for the sensitive detection of three P. aeruginosa strains, UCBPP PA14, ATCC 27853, and multidrug-resistant ATCC BAA-2108. We produced a recombinant antibody (rAb) against P. aeruginosa V‐antigen (PcrV), which is a needle tip protein of the type III secretion system of P. aeruginosa using mammalian cells with high yield and purity, and confirmed its P. aeruginosa binding efficiency. The rAb was paired with commercial anti-P. aeruginosa Ab for a sandwich ELISA, resulting in an antigen-concentration-dependent response with a limit of detection value of 230 CFU/mL. These results suggest that the rAb produced herein can be used for the sensitive detection of P. aeruginosa with a wide range of applications in clinical diagnosis and point-of-care testing.

This article is devoted to the formulation and numerical solution of boundary-value problems in the theory of elasticity with respect to deformations. Similar to the well-known Beltrami–Michell stress equations, the Saint-Venant compatibility conditions are written in the form of differential equations for strains. A new version of plane boundary-value problems in strains is formulated. It is shown that for the correctness of plane boundary value problems, in addition to the usual conditions, one more special boundary condition is required using the equilibrium equation. To discretize additional boundary conditions and differential equations, it is convenient to use the finite difference method. By resolving grid equations and additional boundary conditions with respect to the desired quantities at the diagonal nodal points, we obtained convergent iterative relations for the internal and boundary nodes. To solve grid equations, the elimination method was also used. By comparing with the Timoshenko–Goodyear solution on the tension of a rectangular plate with a parabolic load, the validity of the formulated boundary value problems in strains and the reliability of the numerical results are shown. The accuracy of the results has been increased by an average of 15%.

Since the WHO’s 2021 aging redefinition emphasizes “healthy aging” by focusing on the elderly’s ability to perform daily activities, sarcopenia, which is defined as the loss of skeletal muscle mass, is now becoming a critical health concern, especially in South Korea with a rapidly aging population. Therefore, we develop a prediction model for sarcopenia by using machine learning (ML) techniques based on the Korea National Health and Nutrition Examination Survey (KNHANES) data 2008–2011, in which we focus on the role of socioeconomic status (SES), social infrastructure, and quality of life (QoL) in the prevalence of sarcopenia. We successfully identify sarcopenia with approximately 80% accuracy by using random forest (RF) and LightGBM (LGB), CatBoost (CAT), and a deep neural network (DNN). For prediction reliability, we achieve area under curve (AUC) values of 0.831, 0.868, and 0.773 for both genders, males, and females, respectively. Especially when using only male data, all the models consistently exhibit better performance overall. Furthermore, using the SHapley Additive exPlanations (SHAP) analysis, we find several common key features, which mainly contribute to model building. These include SES features, such as monthly household income, housing type, marriage status, and social infrastructure accessibility. Furthermore, the causal relationships of household income, per capita neighborhood sports facility area, and life satisfaction are analyzed to establish an effective prediction model for sarcopenia management in an aging population.

The growing significance of ubiquitous 6G connectivity within the maritime sector is a consequence of its evolution into an era characterized by the adoption of autonomous ships. This evolution necessitates the development of adaptable communication capabilities, even in the face of increasing heterogeneity in Radio Access Networks (RANs). This heterogeneity is a consequence of the extended lifespans of maritime communication technologies used by both legacy and emerging ships at sea, in contrast to the generational shift seen in terrestrial communication technologies. This paper undertakes a comprehensive examination and provides an insightful overview of communication technologies within the framework of 3rd Generation Partnership Project (3GPP) standards with the aim of preparing for the forthcoming 6G standardization to enable ubiquitous 6G connectivity in the maritime domain. The primary focus of this paper is the mobile RAN entities (e.g., satellites and uncrewed aerial vehicles (UAVs)) positioned for integration into the 5G and beyond systems. These entities are distinguished by their differences from conventional terrestrial RAN entities, which are typically stationary on land. This integration enables User Equipment (UE) to connect to various RAN entities, including mobile RANs, interconnected with core networks, thereby granting UE secure access to external internets through 5G and beyond systems, enabling them to enjoy a diverse range of application services, even in areas beyond terrestrial coverage, such as at sea. This paper further conducts an in-depth analysis of a transport layer-level solution known as the Access Traffic Steering, Switching, and Splitting (ATSSS) feature enabling a concurrent connection to multiple RANs for data-traffic delivery. Furthermore, this paper explores opportunities and challenges for future research in the realm of forthcoming 6G standardization within 3GPP, especially when combined with the ATSSS features for the success of autonomous ships within the maritime sector. These considerations encompass the concept of autonomous ships as mobile RAN entities, the integration of legacy maritime communications into the 6G framework, and the variability in maritime channel measurements, generally employed as one of the criteria for selecting an appropriate RAN among multiple options, influenced by uncontrollable factors such as climate change.

To ensure safe, effective, and efficient marine operations, the optimization of maritime communication workflows with a task-oriented scheduling framework is of the utmost importance. Navigation, vessel traffic management, emergency response, and cargo operations are all made possible by maritime communication, which necessitates seamless information sharing between ships, ports, coast guards, and regulatory bodies. However, traditional communication methods face challenges in adapting to the dynamic and distributed nature of maritime activities. This study suggests a novel approach for overcoming these difficulties that combines task-oriented scheduling and resource-aware cloud environments to enhance marine communication operations. Utilizing cloud computing offers a scalable, adaptable infrastructure that can manage various computational and communication needs. Even during busy times, effective data processing, improved decision making, and improved communication are made possible by utilizing the cloud. The intelligent allocation and prioritization of communication activities using a task-oriented scheduling framework ensures that urgent messages receive prompt attention while maximizing resource utilization. The proposed approach attempts to improve marine communication workflows’ task prioritization, scalability, and resource optimization. In order to show the effectiveness of the proposed approach, simulations were performed in CloudSim. The performance evaluation parameters, i.e., throughput, latency, execution cost, and energy consumption, have been evaluated. Simulation results reflect the efficacy and practical usability of the framework in various maritime communication configurations. By making marine communication methods more durable, dependable, and adaptable to the changing needs of the maritime industry, this study advances maritime communication techniques. The findings of this research have the potential to revolutionize maritime communication, leading to safer, more efficient, and more resilient maritime operations on a large scale.

This paper investigates a cellular network assisted by unmanned aerial vehicles (UAVs) in the presence of a fluctuating 3-dimensional (3D) antenna beamwidth. The primary objective is to perform an analysis of typical user equipment (T-UE) performance with a specific focus on coverage probability and spectral efficiency (SE) in the presence of fluctuations of 3D antenna beamwidth. Within this analytical framework, the macro base stations (MBSs) are meticulously characterized through the application of an independent 2D homogeneous Poisson point process (PPP), while the low-altitude platforms (LAPs) are described using an independent 3D PPP. The study entails the derivation of association probabilities, determining the likelihood of the T-UE associating with MBSs, line-of-sight LAPs, and non-line-of-sight LAPs. Through rigorous mathematical analysis, the paper formulates precise analytical expressions that encapsulate the association and coverage probabilities, taking into account the inherent variability in the UAV antenna beamwidth. This research focuses on a thorough performance evaluation of the T-UE across diverse network configurations, encompassing LAP density, the transmission power of LAPs, and the critical signal-to-interference ratio threshold. The outcomes of this study distinctly underscore the substantial disruptive impact resulting from fluctuating beamwidth on the performance of the T-UE within the UAV-assisted cellular network. Additionally, this performance is further impacted by larger densities and transmission power of the LAP. Hence, it is imperative to take into account the influence of these fluctuations on network association, coverage, and SE whenever contemplating a UAV-assisted cellular network.

The automobile industry has been evolving with rapid growth over the last two decades. Conventionally, the narrow band dedicated short-range communications is used for the wireless connectivity of vehicular networks in the automobile industry. However, due to its shorter wireless range, higher latency, and lower capacity; cellular-assisted vehicular communications can be adopted to improve networks performance by disrupting the nearby infrastructure. The performance of cellular-assisted vehicular communications is typically compromised if jammers are present in their vicinity. Thus, it is important to characterize the interference in cellular-assisted vehicular communications with jammers. In this paper, we investigate jamming interference due to clustered-jammers for vehicular networks by considering that vehicles, road-side units, and pedestrians are modeled using an independent one-dimensional Poisson line process while cellular base stations are modeled using an independent two-dimensional homogeneous Poisson point process. The results show that jamming disrupts the performance of the vehicular networks and that this performance is severely compromised by increasing the density of jamming clusters and their transmission power. Therefore, the prime focus of the system designers should be to introduce anti-jamming scenario, when the transmission power and the number of jamming clusters in a network increase.

This extensive review examines sarcopenia, a condition characterized by a loss of muscle mass, stamina, and physical performance, with a particular emphasis on its detection and management using contemporary technologies. It highlights the lack of global agreement or standardization regarding the definition of sarcopenia and the various techniques used to measure muscle mass, stamina, and physical performance. The distinctive criteria employed by the European Working Group on Sarcopenia in Older People (EWGSOP) and the Asian Working Group for Sarcopenia (AWGSOP) for diagnosing sarcopenia are examined, emphasizing potential obstacles in comparing research results across studies. The paper delves into the use of machine learning techniques in sarcopenia detection and diagnosis, noting challenges such as data accessibility, data imbalance, and feature selection. It suggests that wearable devices, like activity trackers and smartwatches, could offer valuable insights into sarcopenia progression and aid individuals in monitoring and managing their condition. Additionally, the paper investigates the potential of blockchain technology and edge computing in healthcare data storage, discussing models and systems that leverage these technologies to secure patient data privacy and enhance personal health information management. However, it acknowledges the limitations of these models and systems, including inefficiencies in handling large volumes of medical data and the lack of dynamic selection capability. In conclusion, the paper provides a comprehensive summary of current sarcopenia research, emphasizing the potential of modern technologies in enhancing the detection and management of the condition while also highlighting the need for further research to address challenges in standardization, data management, and effective technology use.

This study presents a comprehensive exploration of the hyperparameter optimization in one-dimensional (1D) convolutional neural networks (CNNs) for network intrusion detection. The increasing frequency and complexity of cyberattacks have prompted an urgent need for effective intrusion-detection systems (IDSs). Herein, we focus on optimizing nine hyperparameters within a 1D-CNN model, using two well-established evolutionary computation methods—genetic algorithm (GA) and particle swarm optimization (PSO). The performances of these methods are assessed using three major datasets—UNSW-NB15, CIC-IDS2017, and NSL-KDD. The key performance metrics considered in this study include the accuracy, loss, precision, recall, and F1-score. The results demonstrate considerable improvements in all metrics across all datasets, for both GA- and PSO-optimized models, when compared to those of the original nonoptimized 1D-CNN model. For instance, on the UNSW-NB15 dataset, GA and PSO achieve accuracies of 99.31 and 99.28%, respectively. Both algorithms yield equivalent results in terms of the precision, recall, and F1-score. Similarly, the performances of GA and PSO vary on the CIC-IDS2017 and NSL-KDD datasets, indicating that the efficacy of the optimization algorithm is context-specific and dependent on the nature of the dataset. The findings of this study demonstrate the importance and effects of efficient hyperparameter optimization, greatly contributing to the field of network security. This study serves as a crucial step toward developing advanced, robust, and adaptable IDSs capable of addressing the evolving landscape of cyber threats.

Jammers emit strong intentional jamming signals aiming to limit or block legitimate communications. The distribution of jammers, whether in non-clustered or clustered form, significantly influences the performance of vehicle-to-everything (V2X) networks. In addition, the fluctuations in the three-dimensional (3D) antenna beam width of unmanned aerial vehicles (UAVs) can exert a substantial impact on the network’s overall performance. This paper introduces a model for UAV-V2X (U-V2X) communications in mm-Wave bands, considering non-clustered and clustered jammers, as well as the varying 3D antenna beam width. The roads are modeled using a Poisson line process, vehicular nodes (VNs) are modeled using a 1D Poisson point process (PPP), and UAVs are modeled using a 3D PPP. The jammers are distributed in two ways: non-clustered and clustered distributions. Moreover, the fluctuations in the 3D antenna beam width follow a normal distribution. To this end, a typical node’s performance in U-V2X communications is evaluated for various network configurations, including the number of UAVs, VNs, roads, jammers, and jammer’s transmission power. The analytical expressions for the outage probability (OP) of VN to VN connection (i.e., V2V), VN to UAV connection (i.e., V2U2V), and an overall connection (i.e., U-V2X), under non-clustered and clustered jamming, along with the fluctuating antenna beam width, are derived. The results revealed that the performance of the U-V2X communications utilizing mm-Waves is significantly degraded with the non-clustered jamming in comparison with the clustered jamming. The fluctuations in the 3D beam width of the UAV antennas further compromise the network’s performance. Thus, accurate modeling of these fluctuations is crucial, particularly in the presence of non-clustered jammers. Furthermore, the system designers should focus on implementing additional anti-jamming countermeasures specifically targeting non-clustered jammers in U-V2X communications.

Deep-sea object localization by underwater acoustic sensor networks is a current research topic in the field of underwater communication and navigation. To find a deep-sea object using underwater wireless sensor networks (UWSNs), the sensors must first detect the signals sent by the object. The sensor readings are then used to approximate the object’s position. A lot of parameters influence localization accuracy, including the number and location of sensors, the quality of received signals, and the algorithm used for localization. To determine position, the angle of arrival (AOA), time difference of arrival (TDoA), and received signal strength indicator (RSSI) are used. The UWSN requires precise and efficient localization algorithms because of the changing underwater environment. Time and position are required for sensor data, especially if the sensor is aware of its surroundings. This study describes a critical localization strategy for accomplishing this goal. Using beacon nodes, arrival distance validates sensor localization. We account for the fact that sensor nodes are not in perfect temporal sync and that sound speed changes based on the medium (water, air, etc.) in this section. Our simulations show that our system can achieve high localization accuracy by accounting for temporal synchronisation, measuring mean localization errors, and forecasting their variation. The suggested system localization has a lower mean estimation error (MEE) while using RSSI. This suggests that measurements based on RSSI provide more precision and accuracy during localization.

Locomotion prediction for human welfare has gained tremendous interest in the past few years. Multimodal locomotion prediction is composed of small activities of daily living and an efficient approach to providing support for healthcare, but the complexities of motion signals along with video processing make it challenging for researchers in terms of achieving a good accuracy rate. The multimodal internet of things (IoT)-based locomotion classification has helped in solving these challenges. In this paper, we proposed a novel multimodal IoT-based locomotion classification technique using three benchmarked datasets. These datasets contain at least three types of data, such as data from physical motion, ambient, and vision-based sensors. The raw data has been filtered through different techniques for each sensor type. Then, the ambient and physical motion-based sensor data have been windowed, and a skeleton model has been retrieved from the vision-based data. Further, the features have been extracted and optimized using state-of-the-art methodologies. Lastly, experiments performed verified that the proposed locomotion classification system is superior when compared to other conventional approaches, particularly when considering multimodal data. The novel multimodal IoT-based locomotion classification system has achieved an accuracy rate of 87.67% and 86.71% over the HWU-USP and Opportunity++ datasets, respectively. The mean accuracy rate of 87.0% is higher than the traditional methods proposed in the literature.

Sarcopenia is a well-known age-related disease that can lead to musculoskeletal disorders and chronic metabolic syndromes, such as sarcopenic obesity. Numerous studies have researched the relationship between sarcopenia and various risk factors, leading to the development of predictive models based on these factors. In this study, we explored the impact of physical activity (PA) in daily life and obesity on sarcopenia prediction. PA is easier to measure using personal devices, such as smartphones and watches, or lifelogs, than using other factors that require medical equipment and examination. To demonstrate the feasibility of sarcopenia prediction using PA, we trained various machine learning models, including gradient boosting machine (GBM), xgboost (XGB), lightgbm (LGB), catboost (CAT), logistic regression, support vector classifier, k-nearest neighbors, random forest (RF), multi-layer perceptron, and deep neural network (DNN), using data samples from the Korea National Health and Nutrition Examination Survey. Among the models, the DNN achieved the most precise accuracy on average, 81%, with PA features across all data combinations, and the accuracy increased up to 90% with the addition of obesity information, such as total fat mass and fat percentage. Considering the difficulty of measuring the obesity feature, when adding waist circumference to the PA features, the DNN recorded the highest accuracy of 84%. This model accuracy could be improved by using separate training sets according to gender. As a result of measurement with various metrics for accurate evaluation of models, GBM, XGB, LGB, CAT, RF, and DNN demonstrated significant predictive performance using only PA features including waist circumference, with AUC values at least around 0.85 and often approaching or exceeding 0.9. We also found the key features for a highly performing model such as the quantified PA value and metabolic equivalent score in addition to a simple obesity measure such as body mass index (BMI) and waist circumference using SHAP analysis.

Blockchain has introduced a new era for online payment services and its economy with tamper-proof cryptocurrencies. However, blockchain, which is based on global peer-to-peer networks, has its limitations due to payment delays from global consensus and transaction costs for maintenance. Thus, payment channel networks (PCN) have been proposed as one of the most promising off-chain solutions, allowing users to pay directly through payment channels (PC), with minimal blockchain involvement. However, payment delays and cost problems still exist, especially given the large size of the PCN. This study proposes a multiparty payment channel (MPC) that enables multiple users to join the same PC and exchange payment transactions, compared to the legacy PC. To avoid a consensus procedure among users in the PC, we introduce sequential and parallel updates for the PC status. Since increasing the MPC size limits the advantages in terms of the delay and cost, we propose a distributed coalition formation algorithm to form the MPC group, in which each user has the choice to join or leave the group. Simulations show that the proposed algorithm establishes MPCs successfully, considering the trade-off between the payoff gain and the MPC delay cost.

The opportunistic human pathogen Staphylococcus aureus can evade antibiotics by acquiring antibiotic resistance genes or by entering into a non-growing dormant state. Moreover, the particular circumstances of a specific infection site, such as acidity or anaerobicity, often weaken antibiotic potency. Decreased bacterial susceptibility combined with diminished antibiotic potency is responsible for high failure rates when treating S. aureus infections. Here, we report that the membrane-active antimicrobial agent nTZDpa does not only exhibit enhanced antibiotic activity against multidrug-resistant Gram-positive pathogens in acidic pH, but also retains antimicrobial potency under anaerobic conditions. This agent completely eradicated highly antibiotic-tolerant cells and biofilms formed by methicillin-resistant S. aureus at pH 5.5 at concentrations at which it was not potent at pH 7.4. Furthermore, nTZDpa was more potent at synergistically potentiating gentamicin killing against antibiotic-tolerant MRSA cells at low pH than at high pH. All-atom molecular dynamics simulations combined with membrane-permeabilization assays revealed that the neutral form of nTZDpa, which contains carboxylic acid, is more effective than the deprotonated form at penetrating the bacterial membrane and plays an essential role in membrane activity. An acidic pH increases the proportion of the neutrally charged nTZDpa, which results in antimicrobial enhancement. Our results provide key insights into rational design of pH-sensitive membrane-active antimicrobials and antibiotic adjuvants that are effective in an infection environment. These findings demonstrate that nTZDpa is a promising lead compound for developing new therapeutics against hard-to-cure infections caused by drug-resistant and -tolerant S. aureus.

Abstract Background In patients with atopic dermatitis (AD), Staphylococcus aureus frequently colonizes lesions and is hypothesized to be linked to disease severity and progression. Treatments that reduce S. aureus colonization without significantly affecting the skin commensal microbiota are needed. Methods and findings In this study, we tested ATx201 (niclosamide), a small molecule, on its efficacy to reduce S. aureus and propensity to evolve resistance in vitro. Various cutaneous formulations were then tested in a superficial skin infection model. Finally, a Phase 2 randomized, double‐blind and placebo‐controlled trial was performed to investigate the impact of ATx201 OINTMENT 2% on S. aureus colonization and skin microbiome composition in patients with mild‐to‐severe AD (EudraCT:2016‐003501‐33). ATx201 has a narrow minimal inhibitory concentration distribution (.125–.5 μg/ml) consistent with its mode of action – targeting the proton motive force effectively stopping cell growth. In murine models, ATx201 can effectively treat superficial skin infections of methicillin‐resistant S. aureus. In a Phase 2 trial in patients with mild‐to‐severe AD (N = 36), twice‐daily treatment with ATx201 OINTMENT 2% effectively reduces S. aureus colonization in quantitative colony forming unit (CFU) analysis (primary endpoint: 94.4% active vs. 38.9% vehicle success rate, p = .0016) and increases the Shannon diversity of the skin microbiome at day 7 significantly compared to vehicle. Conclusion These results suggest that ATx201 could become a new treatment modality as a decolonizing agent.

The health and productivity of animals, as well as farmers’ financial well-being, can be significantly impacted by cattle illnesses. Accurate and timely diagnosis is therefore essential for effective disease management and control. In this study, we consider the development of models and algorithms for diagnosing diseases in cattle based on Sugeno’s fuzzy inference. To achieve this goal, an analytical review of mathematical methods for diagnosing animal diseases and soft computing methods for solving classification problems was performed. Based on the clinical signs of diseases, an algorithm was proposed to build a knowledge base to diagnose diseases in cattle. This algorithm serves to increase the reliability of informative features. Based on the proposed algorithm, a program for diagnosing diseases in cattle was developed. Afterward, a computational experiment was performed. The results of the computational experiment are additional tools for decision-making on the diagnosis of a disease in cattle. Using the developed program, a Sugeno fuzzy logic model was built for diagnosing diseases in cattle. The analysis of the adequacy of the results obtained from the Sugeno fuzzy logic model was performed. The processes of solving several existing (model) classification and evaluation problems and comparing the results with several existing algorithms are considered. The results obtained enable it to be possible to promptly diagnose and perform certain therapeutic measures as well as reduce the time of data analysis and increase the efficiency of diagnosing cattle. The scientific novelty of this study is the creation of an algorithm for building a knowledge base and improving the algorithm for constructing the Sugeno fuzzy logic model for diagnosing diseases in cattle. The findings of this study can be widely used in veterinary medicine in solving the problems of diagnosing diseases in cattle and substantiating decision-making in intelligent systems.

This study presents the architectural design and implementation of a multi-RAT gateway (MRGW) supporting dual satellite and terrestrial connectivity that enables moving maritime vessels, such as autonomous surface ships, to be connected to multiple radio access networks in the maritime communication environment. We developed an MRGW combining LTE and very-small-aperture terminal (VSAT) access networks to realize access traffic steering, switching, and splitting functionalities between them. In addition, we developed communication interfaces between the MRGW and end-devices connecting to their corresponding radio access networks, as well as between the MRGW and the digital bridge system of an autonomous surface ship, enabling the MRGW to collect wireless channel information from each RAT end-device and provide the collected data to the digital bridge system to determine the optimal navigation route for the autonomous surface ship. Experiments on the MRGW with LTE and VSAT end-devices are conducted at sea near Ulsan city and the Kumsan satellite service center in Korea. Through validation experiments on a real maritime communication testbed, we demonstrate the feasibility of future maritime communication technologies capable of providing the minimum performance necessary for autonomous surface ships or digitized aids to navigation (A to N) systems.

The increasing use of cryptocurrencies has raised scalability and performance issues in blockchain systems that require global consensus and security assurance for real-time payments. The payment channel network (PCN) is a promising off-chain solution for cryptocurrencies; in these networks, transactions are offloaded from the blockchain and are handled directly using a payment channel with minimum involvement of the blockchain. Routing protocols for PCNs are critical for finding a path-based transaction (PBT) path with low latency and high throughput, and several routing protocols have been proposed to address decentralization, concurrency, and privacy issues. However, incentive mechanisms for the payment service provider (PSP), which are critical for successful path-based transactions, have not been sufficiently studied. Previous routing protocols are proposed only to find a best path in the given budget. In this study, we propose the payGo routing protocol, which not only discovers a feasible path but also derives the optimal incentive for the PSP using contract theory. Furthermore, payGo induces the PSP to make a contract with a counterparty to guarantee payment latency and throughput with a penalty. We implement the payGo protocol by extending the Raiden network and evaluate its performance on a testbed. The results indicate that, in comparison with the conventional linear pricing for latency, payGo can improve about 90% of payer's utility by optimizing latency and incentive.

Methicillin-resistant Staphylococcus aureus (MRSA), one of the most well-known human pathogens, houses many virulence factors and regulatory proteins that confer resistance to diverse antibiotics. Although they have been investigated intensively, the correlations among virulence factors, regulatory proteins and antibiotic resistance are still elusive. We aimed to identify the most significant global MRSA regulator by concurrently analyzing protein-binding and several promoters under same conditions and at the same time point. DNA affinity capture assay (DACA) was performed with the promoters of mecA, sarA, and sarR, all of which significantly impact survival of MRSA. Here, we show that SarA protein binds to all three promoters. Consistent with the previous reports, ΔsarA mutant exhibited weakened antibiotic resistance to oxacillin and reduced biofilm formation. Additionally, production and activity of many virulence factors such as phenol-soluble modulins (PSM), α-hemolysin, motility, staphyloxanthin, and other related proteins were decreased. Comparing the sequence of SarA with that of clinical strains of various lineages showed that all sequences were highly conserved, in contrast to that observed for AgrA, another major regulator of virulence and resistance in MRSA. We have demonstrated that SarA regulates antibiotic resistance and the expression of various virulence factors. Our results warrant that SarA could be a leading target for developing therapeutic agents against MRSA infections.

Federated learning is a type of privacy-preserving, collaborative machine learning. Instead of sharing raw data, the federated learning process cooperatively exchanges the model parameters and aggregates them in a decentralized manner through multiple users. In this study, we designed and implemented a hierarchical blockchain system using a public blockchain for a federated learning process without a trusted curator. This prevents model-poisoning attacks and provides secure updates of a global model. We conducted a comprehensive empirical study to characterize the performance of federated learning in our testbed and identify potential performance bottlenecks, thereby gaining a better understanding of the system.

Bacteria can evade antibiotics by acquiring resistance genes, as well as switching to a non-growing dormant state without accompanying genetic modification. Bacteria in this quiescent state are called persisters, and this non-inheritable ability to withstand multiple antibiotics is referred to as antibiotic tolerance. Although all bacteria are considered to be able to form antibiotic-tolerant persisters, the antibiotic tolerance of extremophilic bacteria is poorly understood. Previously, we identified the psychrotolerant bacterium Pseudomonas sp. B14-6 from the glacier foreland of Midtre Lovénbreen in High Arctic Svalbard. Herein, we investigated the resistance and tolerance of Pseudomonas sp. B14-6 against aminoglycosides at various temperatures. This bacterium was resistant to streptomycin and susceptible to apramycin, gentamicin, kanamycin, and tobramycin. The two putative aminoglycoside phosphotransferase genes aph1 and aph2 were the most likely contributors to streptomycin resistance. Notably, unlike the mesophilic Pseudomonas aeruginosa PA14, this cold-adapted bacterium demonstrated reduced susceptibility to all tested aminoglycosides in a temperature-dependent manner. Pseudomonas sp. B14-6 at a lower temperature formed the persister cells that shows tolerance to the 100-fold minimum inhibitory concentration (MIC) of gentamicin, as well as the partially tolerant cells that withstand 25-fold MIC gentamicin. The temperature-dependent gentamicin tolerance appears to result from reduced metabolic activity. Lastly, the partially tolerant Pseudomonas sp. B14-6 cells could slowly proliferate under the bactericidal concentrations of aminoglycosides. Our results demonstrate that Pseudomonas sp. B14-6 has a characteristic ability to form cells with a range of tolerance, which appears to be inversely proportional to its growth rate.

Staphylococcus aureus and Candida spp. are commonly linked with topical biofilm-associated infections such as those found on chronic wounds. These biofilms are notoriously difficult to treat, highlighting the grave need to discover and study new broad-spectrum agents to combat associated infections. Here we report that the kinase inhibitor Bay 11-7085 exhibited bactericidal activity against multidrug-resistant S. aureus with a minimum inhibitory concentration (MIC) of 4 μg/ml. In addition, S. aureus strain MW2 did not acquire resistance to antibiotic pressure. Furthermore, Bay 11-7085 exhibited potency against Candida albicans and the emerging pathogen Candida auris with a MIC of 0.5–1 μg/ml. Bay 11-7085 partially inhibited and eradicated biofilm formation of various pathogens, such as VRSA (vancomycin-resistant S. aureus), as well as antifungal-resistant Candida spp. isolates. Notably, Bay 11-7085 partially inhibited initial cell attachment and formation of a VRSA-C. albicans polymicrobial biofilm in vitro. In contrast to C. albicans, inhibition of VRSA biofilm was linked to initial cell attachment independent of its bactericidal activity. Finally, Bay 11-7085 was effective in vivo at increasing the lifespan of C. elegans during an S. aureus and a C. albicans infection. Our work proposes kinase inhibitor Bay 11-7085 as a potential compound capable of combating biofilms associated with primary multidrug-resistant bacteria and yeast pathogens associated with wound infections.

Methicillin-resistant Staphylococcus aureus (MRSA) is a pathogenic bacterium that causes severe diseases in humans. For decades, MRSA has acquired substantial resistance against conventional antibiotics through regulatory adaptation, thereby posing a challenge for treating MRSA infection. One of the emerging strategies to combat MRSA is the combinatory use of antibacterial agents. Based on the dramatic change in phospholipid fatty acid (PLFA) composition of MRSA in previous results, this study investigated branched-chain amino acid derivatives (precursors of fatty acid synthesis of cell membrane) and discovered the antimicrobial potency of D-norvaline. The compound, which can act synergistically with oxacillin, is among the three leucine-tRNA synthetase inhibitors with high potency to inhibit MRSA cell growth and biofilm formation. PLFA analysis and membrane properties revealed that D-norvaline decreased the overall amount of PLFA, increasing the fluidity and decreasing the hydrophobicity of the bacterial cell membrane. Additionally, we observed genetic differences to explore the response to D-norvaline. Furthermore, deletion mutants and clinically isolated MRSA strains were treated with D-norvaline. The study revealed that D-norvaline, with low concentrations of oxacillin, was effective in killing several MRSA strains. In summary, our findings provide a new combination of aminoacyl-tRNA synthetase inhibitor D-norvaline and oxacillin, which is effective against MRSA.

The seamless operation of inter-connected smart devices in Internet of Things (IoT) wireless sensor networks (WSNs) requires consistently available end-to-end routes. However, the sensor nodes that rely on a very limited power source tend to cause disconnection in multi-hop routes due to power shortages in the WSNs, which eventually results in the inefficiency of the overall IoT network. In addition, the density of the available sensor nodes affects the existence of feasible routes and the level of path multiplicity in the WSNs. Therefore, an efficient routing mechanism is expected to extend the lifetime of the WSNs by adaptively selecting the best routes for the data transfer between interconnected IoT devices. In this work, we propose a novel routing mechanism to balance the energy consumption among all the nodes and elongate the WSN lifetime, which introduces a score value assigned to each node along a path as the combination of evaluation metrics. Specifically, the scoring scheme considers the information of the node density at a certain area and the node energy levels in order to represent the importance of individual nodes in the routes. Furthermore, our routing mechanism allows for incorporating non-cooperative nodes. The simulation results show that the proposed work gives comparatively better results than some other experimented protocols.

ABSTRACT Resistance or tolerance to traditional antibiotics is a challenging issue in antimicrobial chemotherapy. Moreover, traditional bactericidal antibiotics kill only actively growing bacterial cells, whereas nongrowing metabolically inactive cells are tolerant to and therefore “persist” in the presence of legacy antibiotics. Here, we report that the diarylurea derivative PQ401, previously characterized as an inhibitor of the insulin-like growth factor I receptor, kills both antibiotic-resistant and nongrowing antibiotic-tolerant methicillin-resistant Staphylococcus aureus (MRSA) by lipid bilayer disruption. PQ401 showed several beneficial properties as an antimicrobial lead compound, including rapid killing kinetics, low probability for resistance development, high selectivity to bacterial membranes compared to mammalian membranes, and synergism with gentamicin. In contrast to well-studied membrane-disrupting cationic antimicrobial low-molecular-weight compounds and peptides, molecular dynamic simulations supported by efficacy data demonstrate that the neutral form of PQ401 penetrates and subsequently embeds into bacterial lipid bilayers more effectively than the cationic form. Lastly, PQ401 showed efficacy in both the Caenorhabditis elegans and Galleria mellonella models of MRSA infection. These data suggest that PQ401 may be a lead candidate for repurposing as a membrane-active antimicrobial and has potential for further development as a human antibacterial therapeutic for difficult-to-treat infections caused by both drug-resistant and -tolerant S. aureus. IMPORTANCE Membrane-damaging antimicrobial agents have great potential to treat multidrug-resistant or multidrug-tolerant bacteria against which conventional antibiotics are not effective. However, their therapeutic applications are often hampered due to their low selectivity to bacterial over mammalian membranes or their potential for cross-resistance to a broad spectrum of cationic membrane-active antimicrobial agents. We discovered that the diarylurea derivative compound PQ401 has antimicrobial potency against multidrug-resistant and multidrug-tolerant Staphylococcus aureus. PQ401 selectively disrupts bacterial membrane lipid bilayers in comparison to mammalian membranes. Unlike cationic membrane-active antimicrobials, the neutral form of PQ401 rather than its cationic form exhibits maximum membrane activity. Overall, our results demonstrate that PQ401 could be a promising lead compound that overcomes the current limitations of membrane selectivity and cross-resistance. Also, this work provides deeper insight into the design and development of new noncharged membrane-targeting therapeutics to combat hard-to-cure bacterial infections.

Wireless networking using GHz or THz spectra has encouraged mobile service providers to deploy small cells to improve link quality and cell capacity using mmWave backhaul links. As green networking for less CO2 emission is mandatory to confront global climate change, we need energy efficient network management for such denser small-cell heterogeneous networks (HetNets) that already suffer from observable power consumption. We establish a dual-objective optimization model that minimizes energy consumption by switching off unused small cells while maximizing user throughput, which is a mixed integer linear problem (MILP). Recently, the deep reinforcement learning (DRL) algorithm has been applied to many NP-hard problems of the wireless networking field, such as radio resource allocation, association and power saving, which can induce a near-optimal solution with fast inference time as an online solution. In this paper, we investigate the feasibility of the DRL algorithm for a dual-objective problem, energy efficient routing and throughput maximization, which has not been explored before. We propose a proximal policy (PPO)-based multi-objective algorithm using the actor-critic model that is realized as an optimistic linear support framework in which the PPO algorithm searches for feasible solutions iteratively. Experimental results show that our algorithm can achieve throughput and energy savings comparable to the CPLEX.

Sphingosine-1-phophate (S1P) is a sphingolipid-derived signaling molecule that controls diverse cellular functions including cell growth, homeostasis, and stress responses. In a variety of metazoans, cytosolic S1P is transported into the extracellular space where it activates S1P receptors in a concentration-dependent manner. In the free-living nematode Caenorhabditis elegans, the spin-2 gene, which encodes a S1P transporter, is activated during Gram-positive or Gram-negative bacterial infection of the intestine. However, the role during infection of spin-2 and three additional genes in the C. elegans genome encoding other putative S1P transporters has not been elucidated. Here, we report an evolutionally conserved function for S1P and a non-canonical role for S1P transporters in the C. elegans immune response to bacterial pathogens. We found that mutations in the sphingosine kinase gene (sphk-1) or in the S1P transporter genes spin-2 or spin-3 decreased nematode survival after infection with Pseudomonas aeruginosa or Enterococcus faecalis. In contrast to spin-2 and spin-3, mutating spin-1 leads to an increase in resistance to P. aeruginosa. Consistent with these results, when wild-type C. elegans were supplemented with extracellular S1P, we found an increase in their lifespan when challenged with P. aeruginosa and E. faecalis. In comparison, spin-2 and spin-3 mutations suppressed the ability of S1P to rescue the worms from pathogen-mediated killing, whereas the spin-1 mutation had no effect on the immune-enhancing activity of S1P. S1P demonstrated no antimicrobial activity toward P. aeruginosa and Escherichia coli and only minimal activity against E. faecalis MMH594 (40 µM). These data suggest that spin-2 and spin-3, on the one hand, and spin-1, on the other hand, transport S1P across cellular membranes in opposite directions. Finally, the immune modulatory effect of S1P was diminished in C. eleganssek-1 and pmk-1 mutants, suggesting that the immunomodulatory effects of S1P are mediated by the p38 MAPK signaling pathway.

The overuse of antibiotics has led to the emergence of multidrug-resistant bacteria, such as methicillin-resistant Staphylococcus aureus (MRSA). MRSA is difficult to kill with a single antibiotic because it has evolved to be resistant to various antibiotics by increasing the PBP2a (mecA) expression level, building up biofilm, introducing SCCmec for multidrug resistance, and changing its membrane properties. Therefore, to overcome antibiotic resistance and decrease possible genetic mutations that can lead to the acquisition of higher antibiotic resistance, drug combination therapy was applied based on previous results indicating that MRSA shows increased susceptibility to free fatty acids and surfactants. The optimal ratio of three components and the synergistic effects of possible combinations were investigated. The combinations were directly applied to clinically isolated strains, and the combination containing 15 μg/mL of oxacillin was able to control SCCmec type III and IV isolates having an oxacillin minimum inhibitory concentration (MIC) up to 1024 μg/mL; moreover, the combination with a slightly increased oxacillin concentration was able to kill SCCmec type II. Phospholipid analysis revealed that clinical strains with higher resistance contained a high portion of 12-methyltetradecanoic acid (anteiso-C15:0) and 14-methylhexadecanoic acid (anteiso-C17:0), although individual strains showed different patterns. In summary, we showed that combinatorial therapy with a low concentration of oxacillin controlled different laboratory and highly diversified clinical MRSA strains.

Multidrug-resistant pathogens pose a serious threat to human health. For decades, the antibiotic vancomycin has been a potent option when treating Gram-positive multidrug-resistant infections. Nonetheless, in recent decades, we have begun to see an increase in vancomycin-resistant bacteria. Here, we show that the nuclear factor-kappa B (NF-κB) inhibitor N-[3,5-Bis(trifluoromethyl)phenyl]-5-chloro-2-hydroxybenzamide (IMD0354) was identified as a positive hit through a Caenorhabditis elegans–methicillin-resistant Staphylococcus aureus (MRSA) infection screen. IMD0354 was a potent bacteriostatic drug capable of working at a minimal inhibitory concentration (MIC) as low as 0.06 µg/mL against various vancomycin-resistant strains. Interestingly, IMD0354 showed no hemolytic activity at concentrations as high as 16 µg/mL and is minimally toxic to C. elegans in vivo with 90% survival up to 64 µg/mL. In addition, we demonstrated that IMD0354′s mechanism of action at high concentrations is membrane permeabilization. Lastly, we found that IMD0354 is able to inhibit vancomycin-resistant Staphylococcus aureus (VRSA) initial cell attachment and biofilm formation at sub-MIC levels and above. Our work highlights that the NF-κB inhibitor IMD0354 has promising potential as a lead compound and an antimicrobial therapeutic candidate capable of combating multidrug-resistant bacteria.

Although celecoxib is quite effective in the management of inflammation-related diseases, especially arthritis, its use is limited by concerns including low bioavailability (BA), non-linear pharmacokinetic (PK) profile, and peak concentration-related toxicity. To overcome these issues, we designed and prepared hydrophilic celecoxib prodrugs, namely N-glycyl-aspart-1yl celecoxib (N-GA1C), glutam-1-yl celecoxib (G1C), and aspart-1yl celecoxib (A1C), for the sustained release of celecoxib in the intestine with limited systemic absorption. The celecoxib derivatives were converted to celecoxib in the intestinal contents. The conversion rates were in order of N-GA1C > G1C > A1C. Oral administration of the celecoxib derivatives (oral celecoxib derivatives) sustained the plasma concentration of celecoxib for 24 h, improving the BA and linearity of the PK profile of celecoxib. The peak concentrations (Cmax) of celecoxib after oral celecoxib derivatives were lower than that after oral celecoxib. In a carrageenan-induced rat paw edema model, oral N-GA1C exhibited greater anti-inflammatory activity for a longer duration compared with oral celecoxib. The order of efficacy of the celecoxib derivatives was N-GA1C > G1C > A1C. Taken together, the prodrug approach is a feasible strategy to improve the PK and therapeutic properties of celecoxib, and among the celecoxib derivatives, N-GA1C may be the most promising prodrug of celecoxib.

Phenol-soluble modulins (PSMs) are major determinants of Staphylococcus aureus virulence and their increased production in community-associated methicillin-resistant S. aureus (CA-MRSA) likely contributes to the enhanced virulence of MRSA strains. Here, we analyzed the differences in bacterial cell aggregation according to PSM presence in the specific human cerebrospinal fluid (CSF) environment. CSF samples from the intraventricular or lumbar intrathecal area of each patient and tryptic soy broth media were mixed at a 1:1 ratio, inoculated with WT and PSM-deleted mutants (Δpsm) of the CA-MRSA strain, USA300 LAC, and incubated overnight. Cell aggregation images were acquired after culture and image analysis was performed. The cell aggregation ratio in WT samples differed significantly between the two sampling sites (intraventricular: 0.2% vs. lumbar intrathecal: 6.7%, p < 0.001). The cell aggregation ratio in Δpsm samples also differed significantly between the two sampling sites (intraventricular: 0.0% vs. lumbar intrathecal: 1.2%, p < 0.001). Division of the study cases into two groups according to the aggregated area ratio (WT/Δpsm; group A: ratio of ≥ 2, group B: ratio of < 2) showed that the median aggregation ratio value differed significantly between groups A and B (5.5 and 0, respectively, p < 0.001). The differences in CSF distribution and PSM presence within the specific CSF environment are significant factors affecting bacterial cell aggregation.

Amisulpride (ASP), an anti-psychotic agent, is a pharmacologically equivalent to sulpiride (SP). Because SP demonstrates anti-ulcer and anti-colitic activities, ASP with an aniline moiety was azo-coupled to salicylic acid to generate 5-(aminoethanoylsulfamoyl)-N-[(1-ethylpyrrolidin-2-yl)methyl]-2-methoxybenzamide (ASP-azo-ASA), with the expectation that it would act as a colon-specific mutual prodrug against colitis. Following a 24 h incubation, approximately 80% of ASP-azo-ASA was cleaved to form ASP and 5-aminosalicylic acid (5-ASA) in the cecal contents, whereas it remained stable in the small intestinal contents. Oral gavage of ASP-azo-ASA (oral ASP-azo-ASA) delivered 5-ASA to the cecum to levels comparable with those observed for sulfasalazine (SSZ; clinical colon-specific prodrug of 5-ASA) and without detectable concentrations of ASP in the blood, indicating efficient colonic delivery. Oral ASP-azo-ASA ameliorated 2, 4-dinitrobenzenesulfonic acid hydrate (DNBS)-induced colitis in rats more effectively than oral SSZ. Additionally, oral ASP-azo-ASA lowered the levels of inflammatory mediators in the inflamed distal colon more effectively than oral SSZ. Combined treatment with 5-ASA and ASP via the rectal route more effectively reversed colonic damage and inflammation than treatment with 5-ASA or ASP alone, confirming the mutual anti-colitic actions of 5-ASA and ASP. In conclusion, ASP-azo-ASA is an orally active mutual prodrug against rat colitis with limited systemic absorption of ASP.

Millimeter wave (mmWave) vehicle-to-vehicle (V2V) communications has received significant attention as one of the key applications in 5G technology, which is called as Giga-V2V (GiV2V). The ultra-wide band of the GiV2V allows vehicles to transfer gigabit data within a few seconds, which can achieve platooning of autonomous vehicles. The platooning process requires the rich data of a 4K dash-camera and LiDAR sensors for accurate vehicle control. To achieve this, 3GPP, a global organization of standards that provides specifications for the 5G mobile technology, is developing a new standard for GiV2V technology by extending the existing specification for device-to-device (D2D) communication. Meanwhile, in the last decade, the mmWave spectrum has been used in the wireless local area network (WLAN) for indoor devices, such as home appliances, based on the IEEE 802.11ad (also known as Wireless Gigabit Alliance (WiGig)) technology, which supports gigabit wireless connectivity of approximately 10 m distance in the 60-GHz frequency spectrum. The WiGig technology has been commercialized and used for various applications ranging from Internet access points to set-top boxes for televisions. In this study, we investigated the applicability of the WiGig technology to the GiV2V communications through experiments on a real vehicular testbed. To achieve this, we built a testbed using commercial off-the-shelf WiGig devices and performed experiments to measure inter-vehicle connectivity on a campus and on city roads with different permitted vehicle speeds. The experimental results demonstrate that disconnections occurred frequently due to the short radio range and the connectivity varied with the vehicle speed. However, the instantaneous throughput was sufficient to exchange large data between moving vehicles in different road environments.

As massive distributed sensor devices are integrated into Internet for Internet of things (IoT) and generate tremendous data from simple measurement to rich multimedia information, wireless cellular networks like LTE are enforced to deploy more small cells to accommodate data from the countless IoT devices. In 3GPP Rel-12 specification, dual connectivity helps deploying the small cell eNBs by separating a control and data plane to a macro and small cell, respectively. The dual connectivity also improves per-user throughput and mobility robustness. Meanwhile, dynamic TDD configuration in the Rel-12 can enhance radio resource utilization of TDD-based small cells even though intercell interference can be worse than legacy static configuration within a small cell cluster. In this paper, we propose a heterogeneous cellular IoT network architecture using the aforementioned two small cell features, as well as scheduling algorithms for load balancing in the dual connectivity and for dynamic TDD configuration to mitigate interference in the small cell cluster. We evaluate proposed algorithms using LTE system level simulator and show that our approach improves network throughput.

The topical application of minoxidil may achieve millimolar concentrations in the skin. We investigated whether millimolar minoxidil could induce vascular endothelial growth factor (VEGF), a possible effector for minoxidil-mediated hair growth, and how it occurred at the molecular level. Cell-based experiments were performed to investigate a molecular mechanism underlying the millimolar minoxidil induction of VEGF. The inhibitory effect of minoxidil on hypoxia-inducible factor (HIF) prolyl hydroxylase-2 (PHD-2) was tested by an in vitro von Hippel–Lindau protein (VHL) binding assay. To examine the angiogenic potential of millimolar minoxidil, a chorioallantoic membrane (CAM) assay was used. In human keratinocytes and dermal papilla cells, millimolar minoxidil increased the secretion of VEGF, which was not attenuated by a specific adenosine receptor antagonist that inhibits the micromolar minoxidil induction of VEGF. Millimolar minoxidil induced hypoxia-inducible factor-1α (HIF-1α), and the induction of VEGF was dependent on HIF-1. Moreover, minoxidil applied to the dorsal area of mice increased HIF-1α and VEGF in the skin. In an in vitro VHL binding assay, minoxidil directly inhibited PHD-2, thus preventing the hydroxylation of cellular HIF-1α and VHL-dependent proteasome degradation and resulting in the stabilization of HIF-1α protein. Minoxidil inhibition of PHD-2 was reversed by ascorbate, a cofactor of PHD-2, and the minoxidil induction of cellular HIF-1α was abrogated by the cofactor. Millimolar minoxidil promoted angiogenesis in the CAM assay, an in vivo angiogenic test, and this was nullified by the specific inhibition of VEGF. Our data demonstrate that PHD may be the molecular target for millimolar minoxidil-mediated VEGF induction via HIF-1.

Staphylococcus aureus is a Gram-positive bacterium that has become the leading cause of hospital acquired infections in the US. Repurposing Food and Drug Administration (FDA) approved drugs for antimicrobial therapy involves lower risks and costs compared to de novo development of novel antimicrobial agents. In this study, we examined the antimicrobial properties of two commercially available anthelmintic drugs. The FDA approved drug niclosamide and the veterinary drug oxyclozanide displayed strong in vivo and in vitro activity against methicillin resistant S. aureus (minimum inhibitory concentration (MIC): 0.125 and 0.5 μg/ml respectively; minimum effective concentration: ≤ 0.78 μg/ml for both drugs). The two drugs were also effective against another Gram-positive bacteria Enterococcus faecium (MIC 0.25 and 2 μg/ml respectively), but not against the Gram-negative species Klebsiella pneumoniae, Acinetobacter baumannii, Pseudomonas aeruginosa and Enterobacter aerogenes. The in vitro antimicrobial activity of niclosamide and oxyclozanide were determined against methicillin, vancomycin, linezolid or daptomycin resistant S. aureus clinical isolates, with MICs at 0.0625-0.5 and 0.125-2 μg/ml for niclosamide and oxyclozanide respectively. A time-kill study demonstrated that niclosamide is bacteriostatic, whereas oxyclozanide is bactericidal. Interestingly, oxyclozanide permeabilized the bacterial membrane but neither of the anthelmintic drugs exhibited demonstrable toxicity to sheep erythrocytes. Oxyclozanide was non-toxic to HepG2 human liver carcinoma cells within the range of its in vitro MICs but niclosamide displayed toxicity even at low concentrations. These data show that the salicylanilide anthelmintic drugs niclosamide and oxyclozanide are suitable candidates for mechanism of action studies and further clinical evaluation for treatment of staphylococcal infections.

Persisters are a subpopulation of normal bacterial cells that show tolerance to conventional antibiotics. Persister cells are responsible for recalcitrant chronic infections and new antibiotics effective against persisters would be a major development in the treatment of these infections. Using the reporter dye SYTOX Green that only stains cells with permeabilized membranes, we developed a fluorescence-based screening assay in a 384-well format for identifying compounds that can kill methicillin-resistant Staphylococcus aureus (MRSA) persisters. The assay proved robust and suitable for high throughput screening (Z`-factor: >0.7). In screening a library of hits from a previous screen, which identified compounds that had the ability to block killing of the nematode Caenorhabditis by MRSA, we discovered that the low molecular weight compound NH125, a bacterial histidine kinase inhibitor, kills MRSA persisters by causing cell membrane permeabilization, and that 5 μg/mL of the compound can kill all cells to the limit of detection in a 108 CFU/mL culture of MRSA persisters within 3h. Furthermore, NH125 disrupts 50% of established MRSA biofilms at 20 μg/mL and completely eradicates biofilms at 160 μg/mL. Our results suggest that the SYTOX Green screening assay is suitable for large-scale projects to identify small molecules effective against MRSA persisters and should be easily adaptable to a broad range of pathogens that form persisters. Since NH125 has strong bactericidal properties against MRSA persisters and high selectivity to bacteria, we believe NH125 is a good anti-MRSA candidate drug that should be further evaluated.

The red flour beetle Tribolium castaneum is a common insect pest and has been established as a model beetle to study insect development and immunity. This study demonstrates that defensin 1 from T. castaneum displays in vitro and in vivo antimicrobial activity against drug resistant Staphylococcus aureus strains. The minimum inhibitory concentration (MIC) of defensin 1 against 11 reference and clinical staphylococcal isolates was between 16-64 μg/ml. The putative mode of action of the defensin peptide is disruption of the bacterial cell membrane. The antibacterial activity of defensin 1 was attenuated by salt concentrations of 1.56 mM and 25 mM for NaCl and CaCl2 respectively. Treatment of defensin 1 with the reducing agent dithiothreitol (DTT) at concentrations 1.56 to 3.13 mM abolished the antimicrobial activity of the peptide. In the presence of subinhibitory concentrations of antibiotics that also target the bacterial cell envelope such as telavancin and daptomycin, the MIC of the peptide was as low as 1 μg/ml. Moreover, when tested against an S. aureus strain that was defective in D-alanylation of the cell wall, the MIC of the peptide was 0.5 μg/ml. Defensin 1 exhibited no toxicity against human erythrocytes even at 400 μg/ml. The in vivo activity of the peptide was validated in a Caenorhabditis elegans-MRSA liquid infection assay. These results suggest that defensin 1 behaves similarly to other cationic AMPs in its mode of action against S. aureus and that the activity of the peptide can be enhanced in combination with other antibiotics with similar modes of action or with compounds that have the ability to decrease D-alanylation of the bacterial cell wall.

Understanding the effects of spaceflight on microbial communities is crucial for the success of long-term, manned space missions. Surface-associated bacterial communities, known as biofilms, were abundant on the Mir space station and continue to be a challenge on the International Space Station. The health and safety hazards linked to the development of biofilms are of particular concern due to the suppression of immune function observed during spaceflight. While planktonic cultures of microbes have indicated that spaceflight can lead to increases in growth and virulence, the effects of spaceflight on biofilm development and physiology remain unclear. To address this issue, Pseudomonas aeruginosa was cultured during two Space Shuttle Atlantis missions: STS-132 and STS-135, and the biofilms formed during spaceflight were characterized. Spaceflight was observed to increase the number of viable cells, biofilm biomass, and thickness relative to normal gravity controls. Moreover, the biofilms formed during spaceflight exhibited a column-and-canopy structure that has not been observed on Earth. The increase in the amount of biofilms and the formation of the novel architecture during spaceflight were observed to be independent of carbon source and phosphate concentrations in the media. However, flagella-driven motility was shown to be essential for the formation of this biofilm architecture during spaceflight. These findings represent the first evidence that spaceflight affects community-level behaviors of bacteria and highlight the importance of understanding how both harmful and beneficial human-microbe interactions may be altered during spaceflight.