Nodular and granulomatous form of periarteritis nodosa caused by the hepatitis B virus

Human herpesvirus type 6 DNA derived from human breath was discovered to contaminate PCR reactions during routine reaction preparation. Parallel PCR experiments were conducted in which expiratory secretions were blocked by a surgical mask, while others were performed without any attempt to circumvent respiratory contamination. The experimenter was previously determined to harbor HHV-6 DNA in the saliva. All reactions in which expiration was obstructed were negative for HHV-6 DNA via PCR. Reactions in which there was no attempt to obstruct respiratory secretions were positive for HHV-6 DNA. These data suggest that PCR assays investigating the presence of HHV-6 may be highly susceptible to contamination from the experimenter leading to false positive results.     

Towards Sustainable Agricultural Systems: A Lightweight Deep Learning Model for Plant Disease Detection

A country’s economy heavily depends on agricultural development. However, due to several plant diseases, crop growth rate and quality are highly suffered. Accurate identification of these diseases via a manual procedure is very challenging and time-consuming because of the deficiency of domain experts and low-contrast information. Therefore, the agricultural management system is searching for an automatic early disease detection technique. To this end, an efficient and lightweight Deep Learning (DL)-based framework (E-GreenNet) is proposed to overcome these problems and precisely classify the various diseases. In the end-to-end architecture, a MobileNetV3Small model is utilized as a backbone that generates refined, discriminative, and prominent features. Moreover, the proposed model is trained over the PlantVillage (PV), Data Repository of Leaf Images (DRLI), and a new Plant Composite (PC) dataset individually, and later on test samples, its actual performance is evaluated. After extensive experimental analysis, the proposed model obtained 1.00%, 0.96% and 0.99% accuracies on all three included datasets. Moreover, the proposed method achieves better inference speed when compared with other State-Of-The-Art (SOTA) approaches. In addition, a comparative analysis is conducted where the proposed strategy shows tremendous discriminative scores as compared to the various pre-trained models and other Machine Learning (ML) and DL methods.     

A sliding mode observer for uncertain nonlinear systems based on multiple models approach

This paper presents a method of state estimation for uncertain nonlinear systems described by multiple models approach. The uncertainties, supposed as norm bounded type, are caused by some parameters’ variations of the nonlinear system. Linear matrix inequalities (LMIs) have been established in order to ensure the stability conditions of the multiple observer which lead to determine the estimation gains. A sliding mode gain has been added in order to compensate the uncertainties. Numerical simulations through a state space model of a real process have been realized to show the robustness of the synthesized observer.     

On trajectory tracking control for nonholonomic mobile manipulators with dynamic uncertainties and external torque disturbances

This paper studies the trajectory tracking control problem of mobile manipulators subject to nonholonomic constraints, operating in task space, with the presence of external torque disturbances and dynamic uncertainties. The proposed controls are robust to external torque disturbances and can overcome the effects of the unknown dynamic parameters. The stability of the closed-loop system and the asymptotic convergences of tracking errors are proved using Lyapunov synthesis. The proposed control strategies have been designed to drive the system motion converges to the desired manifold and, at the same time, guarantees the boundedness of all the closed-loop signals. Simulation results validate that the system trajectory converge to the desired one.     

Adaptive motion/force control of uncertain nonholonomic mobile manipulator with estimation of unknown external force

This paper describes a stable adaptive motion/force control of uncertain nonholonomic mobile manipulator with the consideration of external force. As it is well known, unexpected external force makes the motion of the system unstable since there are no fixed points in the stationary coordinate. Here, a novel adaptive control scheme is utilized to estimate and compensate the unknown external force exerted to the end-effector even if the parameters of the system are uncertain. The important advantages of this approach are to achieve estimation without the requirement of force-sensing feedback and the knowledge of the system dynamic model. The update laws for the force and the parameters are derived from a Lyapunov function to guarantee the control system stability. Furthermore, a unified operational space dynamic formulation is presented to solve the problem of redundancy. As a result, the desired end-effector and platform trajectories are simultaneously tracked with a perfect coordination between the two subsystems. Therefore, the proposed controller proves that it can not only guarantee the stability, but also the tracking performance of the system in the task space. The effectiveness of the proposed algorithm is evaluated through extensive simulations and they demonstrate the stability, tracking trajectories and feasibility in estimating the external force and the dynamic uncertainties.     

Evaluation of a cocktail of three bacteriophages for the biocontrol of Salmonella of wastewater

Salmonella serovars are increasing in importance as significant pathogens of both human and animals. Although water and wastewater are treated to eliminate pathogenic microorganisms, they still play an important role in the transmission of Salmonella spp. In this study, bacteriophages infecting Salmonella spp. were isolated from wastewater and evaluated; for their potential to lyse environmental Salmonella strains in vitro at different MOIs and temperatures; and to control the wastewater bacterial community. Three distinct phages designated sww65, sww275, and sww297; as defined by plaque morphology, electron microscopy and host range; were obtained from wastewater. Challenge tests were performed at 37, and 30 °C with the infection of the Salmonella cultures with individual phage, a mixture of two phages, and cocktail of three phages at MOIs of 10⁰, 10², and 10⁴ PFU/CFU. At 30, and 37 °C, a cocktail of three phages reduced all of the Salmonella cultures tested. These results required a high multiplicity of infection. However, when infected with only one phage or a mixture of two phages at MOIs of 10⁰ or 10 ² PFU/CFU, an emergence of bacterial resistance was observed. The dynamic monitoring of wastewater enterobacterial community was conducted using Enterobacterial Repetitive Intergenic Consensus-PCR (ERIC-PCR). The number of bands decreased gradually with the use of individual phage or phage cocktails. Moreover, the dynamic monitoring of Salmonella community during wastewater treatment was performed using PCR detection of virulence gene invA. The results correlated with the ERIC-PCR fingerprints, and suggested that Salmonella community was affected by the phage treatment. Indeed, in wastewater, bacteriophages are reducing Salmonella and other members of the Enterobacteriaceae. These results indicated that dynamic changes are closely related with the process of treatment. The introduction of wide host range bacteriophages in wastewater can have a potential impact on the dynamics of the microbial communities, manifested by the reduction or the elimination of microbial species.