Dynamic changes in physicochemical property,biogenic amines content and microbial diversity during the fermentation of Sanchuan ham

Sanchuan ham is appreciated in Yunnan Province,China,for its characteristic flavor and taste,while the microbial community structure and biogenic amines content remain unclear during fermentation processes.In this study,we explored the physicochemical property,biogenic amines concentration and microbial diversity of external and internal Sanchuan ham by high-throughput sequencing during the processing of Sanchuan ham.Results showed that the nitrite remained at a stable level of 0.15 mg/kg which was significantly lower than the national health standard safety level of 20 mg/kg.In addition,compared with fresh hams,the content of total free amino acids in ripe Sanchuan ham has grown 14 folds;sour and bitter were the main tastes of Sanchuan ham.Notably,the concentration of cadaverine was the highest of all biogenic amines during the entire fermentation period.At the bacterial phyla level,Firmicutes and Actinobacteria were the two main phyla,while at the genus level,Staphylococcus was a significant strain throughout the whole fermentation.Moreover,the dry stage has a great impact on the succession change of microbial community structure.Simultaneously,the change trends and composition of bacteria in the interior have slight discrepancies with those of the exterior of Sanchuan ham.

Adversarial Active Learning for Named Entity Recognition in Cybersecurity

Owing to the continuous barrage of cyber threats,there is a massive amount of cyber threat intelligence.However,a great deal of cyber threat intelligence come from textual sources.For analysis of cyber threat intelligence,many security analysts rely on cumbersome and time-consuming manual efforts.Cybersecurity knowledge graph plays a significant role in automatics analysis of cyber threat intelligence.As the foundation for constructing cybersecurity knowledge graph,named entity recognition(NER)is required for identifying critical threat-related elements from textual cyber threat intelligence.Recently,deep neural network-based models have attained very good results in NER.However,the performance of these models relies heavily on the amount of labeled data.Since labeled data in cybersecurity is scarce,in this paper,we propose an adversarial active learning framework to effectively select the informative samples for further annotation.In addition,leveraging the long short-term memory(LSTM)network and the bidirectional LSTM(BiLSTM)network,we propose a novel NER model by introducing a dynamic attention mechanism into the BiLSTM-LSTM encoderdecoder.With the selected informative samples annotated,the proposed NER model is retrained.As a result,the performance of the NER model is incrementally enhanced with low labeling cost.Experimental results show the effectiveness of the proposed method.

A Hybrid Method of Coreference Resolution in Information Security

In the field of information security,a gap exists in the study of coreference resolution of entities.A hybrid method is proposed to solve the problem of coreference resolution in information security.The work consists of two parts:the first extracts all candidates(including noun phrases,pronouns,entities,and nested phrases)from a given document and classifies them;the second is coreference resolution of the selected candidates.In the first part,a method combining rules with a deep learning model(Dictionary BiLSTM-Attention-CRF,or DBAC)is proposed to extract all candidates in the text and classify them.In the DBAC model,the domain dictionary matching mechanism is introduced,and new features of words and their contexts are obtained according to the domain dictionary.In this way,full use can be made of the entities and entity-type information contained in the domain dictionary,which can help solve the recognition problem of both rare and long entities.In the second part,candidates are divided into pronoun candidates and noun phrase candidates according to the part of speech,and the coreference resolution of pronoun candidates is solved by making rules and coreference resolution of noun phrase candidates by machine learning.Finally,a dataset is created with which to evaluate our methods using information security data.The experimental results show that the proposed model exhibits better performance than the other baseline models.

Mechanistic investigation on Hg^(0) capture over MnO_(x) adsorbents:effects of the synthesis methods

This study demonstrated the impacts of the synthesis methods on the textural structures,chemical properties,and Hg^(0)capture capability of the MnO_(x)system.Compared with the samples synthesized using the precipitation(PR)and hydrothermal(HT)methods,the adsorbent prepared via the sol-gel(SG)technique gave the best performance.At 150℃,ca.90%Hg^(0)removal efficiency was reached after 7.5 h for MnO_(x)prepared by the SG method,ca.40%higher than that of the other two methods.The specific surface area of the adsorbent synthesized via the SG technique(23 m^(2)/g)was almost double that of the adsorbent prepared by the HT method(12 m^(2)/g)and three times that of the one prepared by the PR method(7 m^(2)/g).The presence of plentiful acid sites from the SG method facilitated the physisorption of Hg^(0),making more Hg^(0)available to be oxidized to HgO by the redox sites and thus giving the adsorbent prepared by the SG method the highest Hg^(0)removal efficiency.The strong oxidative ability accelerated the oxidation of the physically adsorbed Hg^(0)to HgO,which explained the higher Hg^(0)removal efficiency of the sample prepared using the HT method than that of the one synthesized by the PR technique.During the whole Hg^(0)removal cycles,chemisorption dominated,with the initial adsorption stage and the external mass-transfer process playing important roles.