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Pickled ready-to-eat meat (PRTEM) is a meat product that is treated with various seasonings and then cooked. PRTEM is a popular food consumed mostly in China and some Asian countries. Since this food is considered ‘ready to eat’, once it is contaminated by foodborne pathogens such as Salmonella, the prospect for significant morbidity, mortality, and immeasurable economic losses can occur. Here we investigated the prevalence and concentration of Salmonella in 107 PRTEM samples collected from Shaanxi, China during 2015–2016. Furthermore, we analyzed the serotype, antibiotic susceptibility, and presence of antibiotic resistance genes and amino acid mutations in 219 Salmonella isolates, followed by subtyping of 115 representative isolates. The average detection rate of Salmonella-positive PRTEM was 58.9%, and the average most probable number (MPN) of Salmonella in positive samples was 2.27 logMPN per gram of sample (range: 2.10–2.43). Ten serotypes were identified from the 219 Salmonella isolates, with S. Thompson (37.9%) and S. Indiana (20.5%) being predominant. The remaining serotypes were S. Typhi (7.8%), S. Typhimurium (7.3%), S. Mbandaka (6.9%), S. Albany (6.4%), S. Blockley (5.5%), S. Infantis (4.1%), S. Escanaba (3.2%), and S. Dusseldorf (0.5%). All isolates were resistant to ceftiofur (100%), while most of them were resistant to ciprofloxacin (99.1%), amoxicillin-clavulanic acid (97.7%), trimethoprim-sulfamethoxazole (96.4%), ampicillin (92.3%), sulfisoxazole (92.2%), tetracyclines (90.4%), and nalidixic acid (90.4%), respectively. A single mutation of Ser83Phe (27.1%) and double mutations of Ser83Phe-Asp87Gly (25.9%) in GyrA were detected in 85 isolates, whereas mutations of Thr57Ser (63.9%) and Ser80Arg (36.1%) in ParC were detected in 122 isolates. qnrB, oqxAB, aac(6′)-Ib, and qnrA were present in 50 (22.8%), 48 (21.9%), 26 (11.9%), and 1 (0.5%) isolate(s), respectively. Pulse field gel electrophoresis results revealed that those isolates recovered from the same type of PRTEM or the same sampling place shared identical or similar DNA profiles, antibiotic resistance phenotypes, and even plasmid-mediated quinolone resistance encoding genes. The findings indicate that Salmonella is commonly prevalent in PRTEMs at high concentrations in Shaanxi, China. More attention should be paid to the processing and storage of this ready-to-eat food to prevent bacterial contamination and foodborne outbreaks.

Social media has become an essential channel for posting disaster-related information, which provides governments and relief agencies real-time data for better disaster management. However, research in this field has not received sufficient attention, and extracting useful information is still challenging. This paper aims to improve disaster relief efficiency via mining and analyzing social media data like public attitudes toward disaster response and public demands for targeted relief supplies during different types of disasters. We focus on different natural disasters based on properties such as types, durations, and damages, which contains a total of 41,993 tweets. In this paper, public perception is assessed qualitatively by manually classified tweets, which contain information like the demand for targeted relief supplies, satisfactions of disaster response, and public fear. Public attitudes to natural disasters are studied via a quantitative analysis using eight machine learning models. To better provide decision-makers with the appropriate model, the comparison of machine learning models based on computational time and prediction accuracy is conducted. The change of public opinion during different natural disasters and the evolution of peoples' behavior of using social media for disaster relief in the face of the identical type of natural disasters as Twitter continues to evolve are studied. The results in this paper demonstrate the feasibility and validation of the proposed research approach and provide relief agencies with insights into better disaster management.

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