Detecting and monitoring of water inrush in tunnels and coal mines using direct current resistivity method:A review

Detecting, real-time monitoring and early warning of underground water-bearing structures are critically important issues in prevention and mitigation of water inrush hazards in underground engineering. Direct current （DC） resistivity method is a widely used method for routine detection, advanced detection and real-time monitoring of water-bearing structures, due to its high sensitivity to groundwater. In this study, the DC resistivity method applied to underground engineering is reviewed and discussed, including the observation mode, multiple inversions, and real-time monitoring. It is shown that a priori information constrained inversion is desirable to reduce the non-uniqueness of inversion, with which the accuracy of detection can be significantly improved. The focused resistivity method is prospective for advanced detection;with this method, the flanking interference can be reduced and the detection dis-tance is increased subsequently. The time-lapse resistivity inversion method is suitable for the regions with continuous conductivity changes, and it can be used to monitor water inrush in those regions. Based on above-mentioned features of various methods in terms of benefits and limitations, we propose a three-dimensional （3D） induced polarization method characterized with multi-electrode array, and introduce it into tunnels and mines combining with real-time monitoring with time-lapse inversion and cross-hole resistivity method. At last, the prospective applications of DC resistivity method are discussed as follows： （1） available advanced detection technology and instrument in tunnel excavated by tunnel boring machine （TBM）, （2） high-resolution detection method in holes, （3） four-dimensional （4D） monitoring technology for water inrush sources, and （4） estimation of water volume in water-bearing structures.

Current strategies for improving limitations of proteolysis targeting chimeras

Proteolysis targeting chimeras(PROTACs)are bifunctional degrader molecules via hijacking the ubiquitinproteasome system(UPS)to specifically eliminate targeted proteins.PROTACs have gained momentum as a new modality of attractive technologies in the drug discovery landscape,since it allows to degrade disease-related proteins effectively.Although some PROTACs drugs reached the clinical research,they are still facing some bottlenecks and challenges that should not be neglected,such as poor oral bioavailability and potential toxic side effects.To overcome these limitations,herein,we provide an overview of recent strategies for improving the durability of PROTACs by enhancing cell permeability and reducing toxic side effects.Meanwhile,the impact of these strategies on improving oral bioavailability as well as their advantages and drawbacks will also be discussed.This review will give a useful reference toolbox for PROTACs design and further promote its clinical application.

A dog carrying mutations in AVP-NPII exhibits key features of central diabetes insipidus

Central diabetes insipidus(CDI)is a rare disease characterized by the excretion of copious amounts of diluted urine(polyuria),excess water intake(polydipsia),and a rise in serum sodium concentration(hypernatremia)(Christ-Crain et al.,2019).The neuropeptide arginine-vasopressin(AVP)is synthesized as a preprohormone along with its carrier protein neurophysin II(NPII)in hypothalamic supraoptic(SON)and paraventricular(PVN)magnocellular neurons,stored in the posterior pituitary,and secreted into the circulation.

Research on Artificial Lateral Line Perception of Flow Field based on Pressure Difference Matrix

In nature,with the help of lateral lines,fish is capable of sensing the state of the flow field and recognizing the surrounding near-fleld hydrodynamic environment in the condition of weak light or even complete darkness.In order to study the application of lateral lines,an improved pressure distribution model was proposed in this paper,and the pressure distributions of the lateral line carrier under different working conditions were obtained using hydrodynamic simulations.Subsequently,a visualized pressure difference matrix was constructed to identify the flow fields under different working conditions.The role of the lateral lines was investigated from a visual image perspective.Instinct features of different flow velocities,flow angles and obstacle offset distances were mapped into the pressure difference matrix.Lastly,a four-layer Convolutional Neural Network(CNN)model was built as a recognition tool to evaluate the effectiveness of the pressure difference matrix method.The recognition results demonstrate that the CNN can identify the flow field state with 2 s earlier than the current time.Hence,the proposed method provides a new way to identify flow field information in engineering applications.