Research Progress on the <i>Actinomyces arthrobacter</i>

The genus Arthrobacter was established in 1947 by Conn & Dimmick. So far, more than 70 recognized?species of the genus Arthrobacter have been certified. Its special functions have been widely known by researchers, such as, in agricultural, in medical, in industrial, and in environmental areas, etc. What deserves to be mentioned is that some species of genus Arthrobacter have showed the function of degrading pesticides, fixing nitrogen, producing beneficial enzyme, treating sewage, and so on. Recently, the applications of the genus Arthrobacter, especially the VBNC?(viable but non-culturable) bacteria of this genus in the field of contaminated environment repair attract people’s attention and some related research results have also been obtained. The functions that we have known are waiting for us to study about mechanism, deeply. And, we can look forward to discovering more potential functions and applications of this genus in ecological environment. In the meantime, these discoveries must bring more new changes and knowledge.

NDFuzz:a non-intrusive coverage-guided fuzzing framework for virtualized network devices

Network function virtualization provides programmable in-network middlewares by leveraging virtualization tech-nologies and commodity hardware and has gained popularity among all mainstream network device manufacturers.Yet it is challenging to apply coverage-guided fuzzing,one of the state-of-the-art vulnerability discovery approaches,to those virtualized network devices,due to inevitable integrity protection adopted by those devices.In this paper,we propose a coverage-guided fuzzing framework NDFuzz for virtualized network devices with a novel integrity protec-tion bypassing method,which is able to distinguish processes of virtualized network devices from hypervisors with a carefully designed non-intrusive page global directory inference technique.We implement NDFuzz atop of two black-box fuzzers and evaluate NDFuzz with three representative network protocols,SNMP,DHCP and NTP,on nine popular virtualized network devices.NDFuzz obtains an average 36%coverage improvement in comparison with its black-box counterparts.NDFuzz discovers 2 O-Day vulnerabilities and 11-Day vulnerability with coverage guidance while the black-box fuzzer can find only one of them.All discovered vulnerabilities are confirmed by corresponding vendors.

PosFuzz:augmenting greybox fuzzing with effective position distribution

Mutation-based greybox fuzzing has been one of the most prevalent techniques for security vulnerability discovery and a great deal of research work has been proposed to improve both its efficiency and effectiveness.Mutation-based greybox fuzzing generates input cases by mutating the input seed,i.e.,applying a sequence of mutation operators to randomly selected mutation positions of the seed.However,existing fruitful research work focuses on scheduling mutation operators,leaving the schedule of mutation positions as an overlooked aspect of fuzzing efficiency.This paper proposes a novel greybox fuzzing method,PosFuzz,that statistically schedules mutation positions based on their historical performance.PosFuzz makes use of a concept of effective position distribution to represent the semantics of the input and to guide the mutations.PosFuzz first utilizes Good-Turing frequency estimation to calculate an effective position distribution for each mutation operator.It then leverages two sampling methods in different mutating stages to select the positions from the distribution.We have implemented PosFuzz on top of AFL,AFLFast and MOPT,called Pos-AFL,-AFLFast and-MOPT respectively,and evaluated them on the UNIFUZZ benchmark(20 widely used open source programs)and LAVA-M dataset.The result shows that,under the same testing time budget,the Pos-AFL,-AFLFast and-MOPT outperform their counterparts in code coverage and vulnerability discovery ability.Compared with AFL,AFLFast,and MOPT,PosFuzz gets 21%more edge coverage and finds 133%more paths on average.It also triggers 275%more unique bugs on average.

Large eddy simulation of room fire spread using a medium scale compartment made of medium density fibreboard (MDF) panels

At present,there is a shortage of experimental and simulation studies on fire spread in medium-and large-scale compartments while the existing models of the fire spread are limited for typical engineering applications.This paper proposes a new model for large-scale fire spread on medium density fibreboard(MDF)panels.Validating the model with single burning item(SBI)experiments,it is found that the numerical simulation closely predicts the experimental heat release rate(HRR)with some error near the peak.The predicted heat flux and distance of lateral flame spread are consistent with the experiments and an existing model.The effects of kinetic properties and heat of combustion are identified through a sensitivity analysis.The decrease of activation energy and increase of pre-exponential factor make the MDF easier to pyrolyze and the increase of heat of combustion enhances the flame temperature and thus provide more heat feedback to the sample surface.The low activation energy(71.9 kJ/mol)and high heat of combustion(46.5 MJ/kg)of the model ensure the occurrence of flame spread.Furthermore,the model was validated using medium-scale compartment fire experiments and the results showed that the model can accurately predict the HRR after flashover(the error is within 7%).While the burner is ignited,the predictions of in-compartment gas temperature and heat flux are more accurate.However,when the burner is extinguished,the modelled in-compartment gas temperature is lower than the experimental values,resulting in a lower heat flux prediction.The model leads to easier flame spread;therefore,the modelled flame spreads faster in the compartment compared to the experiment,and thus the HRR increases more rapidly.

ESRFuzzer:an enhanced fuzzing framework for physical SOHO router devices to discover multi-Type vulnerabilities

SOHO(small office/home office)routers provide services for end devices to connect to the Internet,playing an important role in cyberspace.Unfortunately,security vulnerabilities pervasively exist in these routers,especially in the web server modules,greatly endangering end users.To discover these vulnerabilities,fuzzing web server modules of SOHO routers is the most popular solution.However,its effectiveness is limited due to the lack of input specification,lack of routers’internal running states,and lack of testing environment recovery mechanisms.Moreover,existing works for device fuzzing are more likely to detect memory corruption vulnerabilities.In this paper,we propose a solution ESRFuzzer to address these issues.It is a fully automated fuzzing framework for testing physical SOHO devices.It continuously and effectively generates test cases by leveraging two input semantic models,i.e.,KEY-VALUE data model and CONF-READ communication model,and automatically recovers the testing environment with power management.It also coordinates diversified mutation rules with multiple monitoring mechanisms to trigger multi-type vulnerabilities.With the guidance of the two semantic models,ESRFuzzer can work in two ways:general mode fuzzing and D-CONF mode fuzzing.General mode fuzzing can discover both issues which occur in the CONF and READ operation,while D-CONF mode fuzzing focus on the READ-op issues especially missed by general mode fuzzing.We ran ESRFuzzer on 10 popular routers across five vendors.In total,it discovered 136 unique issues,120 of which have been confirmed as 0-day vulnerabilities we found.As an improvement of SRFuzzer,ESRFuzzer have discovered 35 previous undiscovered READ-op issues that belong to three vulnerability types,and 23 of them have been confirmed as 0-day vulnerabilities by vendors.The experimental results show that ESRFuzzer outperforms state-of-the-art solutions in terms of types and number of vulnerabilities found.

Reprogramming Mycobacterium tuberculosis CRISPR System for Gene Editing and Genomewide RNA Interference Screening

Mycobacterium tuberculosis is the causative agent of tuberculosis(TB), which is still the leading cause of mortality from a single infectious disease worldwide. The development of novel anti-TB drugs and vaccines is severely hampered by the complicated and time-consuming genetic manipulation techniques for M. tuberculosis. Here, we harnessed an endogenous type Ⅲ-A CRISPR/Cas10 system of M. tuberculosis for efficient gene editing and RNA interference(RNAi).This simple and easy method only needs to transform a single mini-CRISPR array plasmid, thus avoiding the introduction of exogenous protein and minimizing proteotoxicity. We demonstrated that M. tuberculosis genes can be efficiently and specifically knocked in/out by this system as confirmed by DNA high-throughput sequencing. This system was further applied to single-and multiple-gene RNAi. Moreover, we successfully performed genome-wide RNAi screening to identify M. tuberculosis genes regulating in vitro and intracellular growth. This system can be extensively used for exploring the functional genomics of M. tuberculosis and facilitate the development of novel anti-TB drugs and vaccines.