EARLY RECOGNITION OF INTERNET TRAFFIC BASED ON SIGNATURE INSPECTION

The accurate and efficient classification of Internet traffic is the first and key step to ac-curate traffic management,network security and traffic analysis. The classic ways to identify flows is either inaccurate or inefficient,which are not suitable to be applied to real-time online classification. In this paper,we originally presented an early recognition method named Early Recognition Based on Deep Packet Inspection (ERBDPI) based on deep packet inspection,after analyzing the distribution of payload signature between packets of a flow in detail. The basic concept of ERBDPI is classifying flows based on the payload signature of their first some packets,so that we can identify traffic at the be-ginning of a flow connection. We compared the performance of ERBDPI with that of traditional sampling methods both synthetically and using real-world traffic traces. The result shows that ERBDPI can get a higher classification accuracy with a lower packet sampling rate,which makes it suitable to be applied to accurate real-time classification in high-speed links.

Early Identification and Diagnosis of Adrenal Crisis after Retroperitoneal Laparoscopic Unilateral Adrenalectomy

The occurrence of adrenal crisis after retroperitoneal laparoscopic unilateral adrenalectomy is usually concealed.If not timely diagnosis and treatment,it may cause shock,and even lead to death.It is very difficult to distinguish the clinical manifestations of adrenal crisis from nausea,vomiting,fatigue,gas separation from the lower diaphragm,abdominal pain,hypotension,hypertension,fever and hypothermia after operation.This makes it very difficult to identify and diagnose adrenal crisis early.This article mainly discusses the early recognition,diagnosis and treatment of adrenal crisis after unilateral adrenalectomy by retroperitoneoscope.

Early Diagnosis of Bipolar Disorder Coming Soon:Application of an Oxidative Stress Injury Biomarker(BIOS)Model

Early distinction of bipolar disorder(BD)from major depressive disorder(MDD)is difficult since no tools are available to estimate the risk of BD.In this study,we aimed to develop and validate a model of oxidative stress injury for predicting BD.Data were collected from 1252 BD and 1359 MDD patients,including 64 MDD patients identified as converting to BD from 2009 through 2018.30 variables from a randomly-selected subsample of 1827(70%)patients were used to develop the model,including age,sex,oxidative stress markers(uric acid,bilirubin,albumin,and prealbumin),sex hormones,cytokines,thyroid and liver function,and glycolipid metabolism.Univariate analyses and the Least Absolute Shrinkage and Selection Operator were applied for data dimension reduction and variable selection.Multivariable logistic regression was used to construct a model for predicting bipolar disorder by oxidative stress biomarkers(BIOS)on a nomogram.Internal validation was assessed in the remaining 784 patients(30%),and independent external validation was done with data from 3797 matched patients from five other hospitals in China.10 predictors,mainly oxidative stress markers,were shown on the nomogram.The BIOS model showed good discrimination in the training sample,with an AUC of 75.1%(95%CI:72.9%–77.3%),sensitivity of 0.66,and specificity of 0.73.The discrimination was good both in internal validation(AUC 72.1%,68.6%–75.6%)and external validation(AUC 65.7%,63.9%–67.5%).In this study,we developed a nomogram centered on oxidative stress injury,which could help in the individualized prediction of BD.For better real-world practice,a set of measurements,especially on oxidative stress markers,should be emphasized using big data in psychiatry.

Application of the Modified Early Obstetric Warning System in Three Patients with Obstetric Sepsis Through a Telemedicine System

To editor:Maternal sepsis represents the third cause of maternal mortality worldwide and the diagnosis delay portrays a great contribution due to its high lethality.The Modified Early Obstetric Warning System is an early detection tool validated in maternal sepsis scenarios.