Recent advances in endogenous and exogenous stimuli-responsive nanoplatforms for bacterial infection treatment

As drug-resistant bacterial infections escalate and antimicrobial resources become insufficient,new alternative therapies are critical.The emergence of nano drug delivery system,in addition to giving drugs sustained,targeted or longer half-life characteristics,also plays an important role in improving the therapeutic effect and reducing the toxic side effects of conventional drugs.Despite its potential benefits,the traditional nanomedical drug delivery system has some practical limitations,including incomplete and slow drug release,as well as insufficient accumulation at infection sites.Stimuli responsive nanoplatforms are hence developed to overcome the disadvantages of conventional nanoparticles,which can provide several advantages like:enhancing the pharmacokinetics and biodistribution of antimicrobial drugs,increasing their effective bioavailability,reducing their dosage frequency,and improving their antimicrobial efficacy against biofilm-related infections,while slowing down the development of antimicrobial resistance,which is expected to trigger a medical revolution in the field of human health,thus bringing huge clinical benefits.In this review,we provide an extensive review of the recent progress of endogenous and exogenous stimuli-responsive nanoplatforms in the antibacterial area.Using specific infectious microenvironments(pH,enzymes,reactive oxygen species and toxins),this review systematically presents the design principles of nano delivery systems and the mechanisms by which endogenous stimuli induce changes in the morphology or properties of delivery systems to achieve programmed drug release.Furthermore,exogenous stimuli such as light,heat,and magnetic fields can also control the release of drugs.Last but not least,we discussed the challenges and opportunities for future clinical translation of stimuli-responsive nanoplatforms in bacterial infections.

A convolutional neural network based approach to sea clutter suppression for small boat detection

Current methods for radar target detection usually work on the basis of high signal-to-clutter ratios.In this paper we propose a novel convolutional neural network based dual-activated clutter suppression algorithm,to solve the problem caused by low signal-to-clutter ratios in actual situations on the sea surface.Dual activation has two steps.First,we multiply the activated weights of the last dense layer with the activated feature maps from the upsample layer.Through this,we can obtain the class activation maps(CAMs),which correspond to the positive region of the sea clutter.Second,we obtain the suppression coefficients by mapping the CAM inversely to the sea clutter spectrum.Then,we obtain the activated range-Doppler maps by multiplying the coefficients with the raw range-Doppler maps.In addition,we propose a sampling-based data augmentation method and an effective multiclass coding method to improve the prediction accuracy.Measurement on real datasets verified the effectiveness of the proposed method.

A novel algorithm to counter cross-eye jamming based on a multi-target model

Cross-eye jamming is an electronic attack technique that induces an angular error in the monopulse radar by artificially creating a false target and deceiving the radar into detecting and tracking it.Presently,there is no effective anti-jamming method to counteract cross-eye jamming.In our study,through detailed analysis of the jamming mechanism,a multi-target model for a cross-eye jamming scenario is established within a random finite set framework.A novel anti-jamming method based on multitarget tracking using probability hypothesis density filters is subsequently developed by combining the characteristic differences between target and jamming with the releasing process of jamming.The characteristic differences between target and jamming and the releasing process of jamming are used to optimize particle partitioning.Particle identity labels that represent the properties of target and jamming are introduced into the detection and tracking processes.The release of cross-eye jamming is detected by estimating the number of targets in the beam,and the distinction between true targets and false jamming is realized through correlation and transmission between labels and estimated states.Thus,accurate tracking of the true targets is achieved under severe jamming conditions.Simulation results showed that the proposed method achieves a minimum delay in detection of cross-eye jamming and an accurate estimation of the target state.