Plastic pollution endangers all natural ecosystems and living creatures on earth. Excessive reliance on plastic products and excessive production of plastic packaging are extremely dangerous for humans because plastic waste has polluted almost the entire world, whether it is in the sea or on the land. This review introduces the examination of pollution brought by non-degradable plastics, the classification and application of degradable materials, and the current situation and strategy to address plastic pollution and plastic degradation by insects, which mainly include Galleria mellonella, Zophobas atratus, Tenebrio molitor, and other insects. The efficiency of plastic degradation by insects, biodegradation mechanism of plastic waste, and the structure and composition of degradable products are reviewed. The development direction of degradable plastics in the future and plastic degradation by insects are prospected. This review provides effective ways to solve plastic pollution.

Schizophrenia is a neuropsychiatric disease that affects the nonlinear dynamics of brain activity. The primary objective of this study was to explore the complexity of magnetoencephalograms (MEG) in patients with schizophrenia. We combined a multiscale method and weighted permutation entropy to characterize MEG signals from 19 schizophrenia patients and 16 healthy controls. When the scale was larger than 42, the MEG signals of schizophrenia patients were significantly more complex than those of healthy controls ($p<0.004$). The difference in complexity between patients with schizophrenia and the controls was strongest in the frontal and occipital areas ($p<0.001$), and there was almost no difference in the central area. In addition, the results showed that the dynamic range of MEG complexity is wider in healthy individuals than in people with schizophrenia. Overall, the multiscale weighted permutation entropy method reliably quantified the complexity of MEG from schizophrenia patients, contributing to the development of potential magnetoencephalographic biomarkers for schizophrenia.

We developed a novel network to probe pathological states with the aid of heartbeat time series, by regarding each vector in the embedding phase space as one node of the network and using a permutation-based measure to determine links between nodes. The entropy of the degree distribution of the network shows a general and significant reduction under pathological conditions, even when there are only ultra short-term heartbeats available. The reduction of is possibly a dynamic marker of cardiac disorders. Our results reveal that a comparatively strong "memory" should usually exist in the healthy cardiovascular system whereas it dramatically declines when a cardiac disease is arising. The proposed method shows great promise in screening cardiac diseases and monitoring dynamic changes of the autonomic nervous system. Besides, as a universal method for analyzing the time series, the proposed approach seems to be promising also for other research disciplines.