Recent Advances in Bronchopulmonary Dysplasia Protection and Therapy

For preterm infants, bronchopulmonary dysplasia (BPD) is usually caused by abnormal lung development due to various factors during prenatal and postnatal process. One of the reasons for death and bad prognosis of preterm infants is to have BPD. Up to now, there are no unified strategies or drugs to treat BPD. In clinical, many intervention treatments have been applied to achieve BPD therapy, mainly including preterm protection, protective ventilation strategies, and delivery of corticosteroids, pulmonary vasodilators, and antioxidants. This review summarizes the current advances in BPD protection and treatment, and notes that gut microbiota and mesenchymal stem cells (MSCs) can be the promising strategy for protecting and treating BPD in the future.

Ensemble Classifier Design Based on Perturbation Binary Salp Swarm Algorithm for Classification

Multiple classifier system exhibits strong classification capacity compared with single classifiers,but they require significant computational resources.Selective ensemble system aims to attain equivalent or better classification accuracy with fewer classifiers.However,current methods fail to identify precise solutions for constructing an ensemble classifier.In this study,we propose an ensemble classifier design technique based on the perturbation binary salp swarm algorithm(ECDPB).Considering that extreme learning machines(ELMs)have rapid learning rates and good generalization ability,they can serve as the basic classifier for creating multiple candidates while using fewer computational resources.Meanwhile,we introduce a combined diversity measure by taking the complementarity and accuracy of ELMs into account;it is used to identify the ELMs that have good diversity and low error.In addition,we propose an ECDPB with powerful optimizing ability;it is employed to find the optimal subset of ELMs.The selected ELMs can then be used to forman ensemble classifier.Experiments on 10 benchmark datasets have been conducted,and the results demonstrate that the proposed ECDPB delivers superior classification capacity when compared with alternative methods.

Differential privacy histogram publishing method based on dynamic sliding window

Differential privacy has recently become a widely recognized strict privacy protection model of data release.Differential privacy histogram publishing can directly show the statistical data distribution under the premise of ensuring user privacy for data query,sharing,and analysis.The dynamic data release is a study with a wide range of current industry needs.However,the amount of data varies considerably over different periods.Unreasonable data processing will result in the risk of users’information leakage and unavailability of the data.Therefore,we designed a differential privacy histogram publishing method based on the dynamic sliding window of LSTM(DPHP-DL),which can improve data availability on the premise of guaranteeing data privacy.DPHP-DL is integrated by DSW-LSTM and DPHK+.DSW-LSTM updates the size of sliding windows based on data value prediction via long shortterm memory(LSTM)networks,which evenly divides the data stream into several windows.DPHK+heuristically publishes non-isometric histograms based on k-mean++clustering of automatically obtaining the optimal K,so as to achieve differential privacy histogram publishing of dynamic data.Extensive experiments on real-world dynamic datasets demonstrate the superior performance of the DPHP-DL.