To construct a saliva-based caries risk assessment model, saliva samples from 176 severe early childhood caries (S-ECC) children and 178 healthy (H) children were screened by real-time PCR-based quantification of the selected species, including Streptococcus mutans, Prevotella pallens, Prevotella denticola and Lactobacillus fermentum. Host factors including caries status, dmft indices, age, gender, and geographic origin were assessed in their influence on abundance of the targeted species, which revealed host caries status as the dominant factor, followed by dmft indices (both P < 0.01). Moreover, levels of S. mutans and P. denticola in the S-ECC group were significantly higher than those in the healthy group (P < 0.001 for S. mutans and p < 0.01 for P. denticola). Interestingly, the co-occurrence network of these targeted species in the S-ECC group differed from that from the healthy group. Finally, based on the combined change pattern of S. mutans and P. pallens, we constructed an S-ECC diagnosis model with an accuracy of 72%. This saliva-based caries diagnosis model is of potential value for circumstances where sampling dental plague is difficult. Early childhood caries (ECC) is defined as the presence of one or more decayed, missing, or filled tooth surfaces in the primary dentition in children of 71 months or younger 1. Severe early childhood caries (S-ECC), an extraordinary form of ECC, is defined as the presence of decayed, missing, or filled score surfaces of either ≥4 (age 3 years), ≥5 (age 4 years), or ≥6 (age 5 years) 2. In USA, 23% of children between the ages of 2 and 5 are affected by ECC 3. In China, fresh reports from the Fourth National Oral Health Survey showed that over 70% of 5-year-old children carry dental caries in primary teeth 4. Unfortunately, childhood caries are wide-ranging, rapid-progressing and irreversible 5. Besides, severe caries can cause pulpal infection, as well as varieties of adverse physical and psychological effects, thus it affects children's development while posing a substantial economic burden on both families and society 6-8. Therefore, preventive measures and early diagnosis of ECC or S-ECC are of vital clinical and social importance. Many studies have shown that caries is a multifactorial disease 9,10 and pathogenic bacteria are the main cause of disease occurrence and progression 11. Streptococcus mutans (S. mutans) has been considered as a cariogenic bacterial agent in children 12-15 , due to its aciduric and acidogenic properties 16. Apart from this, Lactobacillus spp. was also linked to caries development and progression 16-18. Positive associations between certain Lactobacillus spp. (especially Lactobacillus fermentum) and the hard tissue changes were revealed in the process of caries progression 19,20. In addition, our past pyrosequencing of oral and plaque microbiota unveiled Prevotella spp's close relationship with caries, in both cross-sectional and longitudinal studies 21,22. Specifically, we proposed a caries

Aspergillus flavus is an opportunistic fungal pathogen that colonizes agriculture crops with aflatoxin contamination. We found that Perillaldehyde (PAE) effectively inhibited A. flavus viability and aflatoxin production by inducing excess reactive oxygen species (ROS). Transcriptome analysis indicated that the Gα protein FadA was significantly induced by PAE. Functional characterization of FadA showed it is important for asexual development and aflatoxin biosynthesis by regulation of cAMP-PKA signalling. The ΔfadA mutant was more sensitive to PAE, while ΔpdeL and ΔpdeH mutants can tolerate excess PAE compared to wild-type A. flavus. Further RNAsequence analysis showed that fadA was important for expression of genes involved in oxidationreduction and cellular metabolism. The flow cytometry and fluorescence microscopy demonstrated that ΔfadA accumulated more concentration of ROS in cells, and the transcriptome data indicated that genes involved in ROS scavenging were downregulated in ΔfadA mutant. We further found that FadA participated in regulating response to extracellular environmental stresses by increasing phosphorylation levels of MAPK Kinase Slt2 and Hog1. Overall, our results indicated that FadA signalling engages in mycotoxin production and A. flavus resistance to antimicrobial PAE, which provide valuable information for controlling this fungus and AF biosynthesis in pre-and postharvest of agricultural crops.

IntroductionSalmonellosis is a zoonotic disease, and Salmonella spp. can sometimes be found in dogs and cats, posing a risk to human health. In this study, the prevalence and antimicrobial susceptibility of faecal Salmonella were investigated in pet dogs and cats in Xuzhou, Jiangsu Province, China.Material and MethodsFaecal samples from 243 dogs and 113 cats, at seven pet clinics, were tested between March 2018 and May 2019. Each Salmonella isolate was characterised using serotyping and antimicrobial susceptibility tests.ResultsThe prevalence of Salmonella was 9.47% in dogs and 1.77% in cats. Among the 25 isolates, eight serotypes of Salmonella enterica subsp. enterica were detected, S. Kentucky (n = 11), S. Indiana (n = 5), and S. Typhimurium (n = 4) predominating. S. Derby, S. Toucra, S. Sandiego, S. Newport, and S. Saintpaul all occurred singly. The 23 Salmonella strains found in dogs were from seven different serovars, while the two strains in cats were from two. The highest resistance rates were found for tetracycline (92%), azithromycin (88%), cefazolin (84%), nalidixic acid (80%), ampicillin (80%), ceftriaxone (80%), and streptomycin (76%). Resistance to three or more antimicrobial agents was detected in 24 (96%) isolates. Most of the S. Kentucky and S. Indiana isolates were multi-drug resistant to more than 11 agents.ConclusionThe carriage rate was far higher in dogs than in cats from Xuzhou. Some isolated strains were highly resistant to antimicrobials used to treat infections in humans and pets, which may raise the risk of humans being infected with multi-drug resistant Salmonella via close contact with pets.

With the rapid development of the Internet and the growing complexity of the network topology, network anomaly has become more diverse. In this paper, we propose an algorithm named Deep Adaptive Feature Learning (DAFL) for traffic anomaly detection based on deep learning model. By setting proper feature parameters θ on the neural network structure, DAFL can effectively generate low-dimensional new abstract features. Experimental results show the DAFL algorithm has good adaptability and robustness, which can effectively improve the detection accuracy and significantly reduce the detection time.