Crime Prediction Methods Based on Machine Learning:A Survey

The objective of crime prediction,one of the most important technologies in social computing,is to extract useful information from many existing criminal records to predict the next process-related crime.It can aid the police in obtaining criminal information and warn the public to be vigilant in certain areas.With the rapid growth of big data,the Internet of Things,and other technologies,as well as the increasing use of artificial intelligence in forecasting models,crime prediction models based on deep learning techniques are accelerating.Therefore,it is necessary to classify the existing crime prediction algorithms and compare in depth the attributes and conditions that play an essential role in the analysis of crime prediction algorithms.Existing crime prediction methods can be roughly divided into two categories:those based on conventional machine learning and those based on contemporary deep learning.This survey analyses the fundamental theories and procedures.The most frequently used data sets are then enumerated,and the fundamental procedures of various algorithms are also analyzed in this paper.In light of the insufficient scale of existing data in this field,the ambiguity of data types used to predict crimes,and the absence of public data sets that have a significant impact on the research of algorithm models,this survey proposes the construction of a machine learning-based big data research model to address these issues.Future researchers who will enter this field are provided with a guide to the direction of future research development.

Epidermal growth factor receptor stabilizes programmed death ligand 1 by glycosylation in colorectal cancer with microstatellite instability status

Programmed death ligand-1(PD-L1)is involved in inhibiting of T lymphocyte proliferation,producing cytokine,cytolytic activity,and suppressing of the immune response.Genes with molecular alterations involved in DNA mismatch repair promote cancer initiation and tumor progression.Clinical studies show that colorectal cancer(CRC)patients harboring microsatellite instability(MSI)have a higher anti-programmed cell death protein 1/PD-L1 immunotherapy response ratio compared with microsatellite stable subgroup patients.The underlying mechanism has however remained unclear.Here,we found that compared with microsatellite stable samples,PD-L1 was glycosylated and highly expressed both in MSI CRC cell lines and tissue samples.Specifically,PD-L1 was Nglycosylated at its N35,N192,N200,and N219 sites,and the four glycosylation sites were all responsible for PD-L1 degradation.Additionally,non-glycosylated PD-L1 underwent rapid degradation compared with glycosylated PD-L1 through the 26S proteasome pathway.The faster degradation of the non-glycosylated PD-L1 was ascribed to its binding to glycogen synthase kinase 3b via ubiquitination.This degradation phenotype was,however,not observed for glycosylated PD-L1.Significantly,glycosylated PD-L1 was up-regulated by activated epidermal growth factor receptor in MSI CRC cells.Together,our results indicate that epidermal growth factor receptor stabilized PD-L1 via glycosylation in MSI CRC cells,uncovering a novel role of PD-L1 in MSI CRC immunosuppression and disease progression.The study was approved by the Clinical Ethics Review Committee at the Six Affiliated Hospital of Sun Yat-sen University,China(Approval No.2019ZSLYEC-005).