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Abstract Infectious disease outbreaks transcend the medical and public health realms, triggering widespread panic and impeding socio-economic development. Considering that self-limiting diarrhoea of sporadic cases is usually underreported, the Salmonella outbreak (SO) study offers a unique opportunity for source tracing, spatiotemporal correlation, and outbreak prediction. To summarize the pattern of SO and estimate observational epidemiological indicators, 1,134 qualitative reports screened from 1949 to 2023 were included in the systematic review dataset, which contained a 506-study meta-analysis dataset. In addition to the dataset comprising over 50 columns with a total of 46,494 entries eligible for inclusion in systematic reviews or input into prediction models, we also provide initial literature collection datasets and datasets containing socio-economic and climate information for relevant regions. This study has a broad impact on advancing knowledge regarding epidemic trends and prevention priorities in diverse salmonellosis outbreaks and guiding rational policy-making or predictive modeling to mitigate the infringement upon the right to life imposed by significant epidemics.

ABSTRACT Typhoid fever, caused by Salmonella Typhi (S. Typhi), is a life-threatening disease, usually food-borne and commonly associated with international travel. The disease transmission remains endemic in many low- and middle-income countries, representing further hotspots for seeding new global outbreaks. China has historically been affected by typhoid fever, but the respective roles of local transmission and importation remain unknown. Here, we generated a nationwide map of the typhoid burden in China and investigated the associations between typhoid disease, climate and various socioeconomic parameters. To assess transmission dynamics, we sub-sampled S. Typhi isolated within China over five decades and sequenced their genomes. The resulting 705 new genomes, placed in context with 5,190 global isolates from 87 countries on six continents, led to the discovery of several predominant inland Chinese clones belonging to the clades 2.1/2.3/3.2/4.3. These clones were associated with multiple introductions from overseas, followed by local expansion. Notably, 4.3.1 isolates from eastern China were not genetically close to those from northwestern China but to the international isolates, indicating their association with international travel. Additional in vitro assays showed that 4.3.1 elaborated better intracellular survival, acid tolerance, and desiccation tolerance than other lineages, partially explaining its success. For the first time, we have probed typhoid transmission in China, finding local transmission and importation, which could guide the policy for typhoid control. IMPORTANCE Typhoid fever is a life-threatening disease caused by Salmonella enterica serovar Typhi, resulting in a significant disease burden across developing countries. Historically, China was very much close to the global epicenter of typhoid, but the role of typhoid transmission within China and among epicenter remains overlooked in previous investigations. By using newly produced genomics on a national scale, we clarify the complex local and global transmission history of such a notorious disease agent in China spanning the most recent five decades, which largely undermines the global public health network.

Summary: Exponential accumulation of single-cell transcriptomes poses great challenge for efficient assimilation. Here, we present an approach entitled generative pretraining from transcriptomes (tGPT) for learning feature representation of transcriptomes. tGPT is conceptually simple in that it autoregressive models the ranking of a gene in the context of its preceding neighbors. We developed tGPT with 22.3 million single-cell transcriptomes and used four single-cell datasets to evalutate its performance on single-cell analysis tasks. In addition, we examine its applications on bulk tissues. The single-cell clusters and cell lineage trajectories derived from tGPT are highly aligned with known cell labels and states. The feature patterns of tumor bulk tissues learned by tGPT are associated with a wide range of genomic alteration events, prognosis, and treatment outcome of immunotherapy. tGPT represents a new analytical paradigm for integrating and deciphering massive amounts of transcriptome data and it will facilitate the interpretation and clinical translation of single-cell transcriptomes.

Listeria species are exposed to various stressors throughout the food chain, which are crucial for microbe mitigation strategy in the food industry. However, the survival capabilities and development of antimicrobial resistance by Listeria spp. under different food processing environments (FPEs) stressors are not yet well understood. Hence, this study aims to determine the difference in survivability and antimicrobial susceptibility of L. monocytogenes (Lm) and other Listeria species (non-Lm) strains exposed to different FPEs stressors, including heat, acidic and alkaline pH, UV irradiation, and osmotic stress. For this, a collection of 11 Lm and 10 non-Lm strains were used to conduct experiments. This study showed that Lm strains were relatively more tolerant to environmental stresses than non-Lm strains (p > 0.05). Additionally, the evaluation of stress-induced resistance toward antimicrobials showed that anaerobic incubation, after exposition to environmental stresses, rendered Lm and non-Lm more resistant to antimicrobial agents than aerobic incubation. Furthermore, the study observed that different stressors induced an increase in minimum inhibitory concentrations (MICs) of certain antimicrobials. Specifically, heat stress persuaded an increase in MICs of tetracycline under aerobic incubation, and gentamicin and ciprofloxacin under anaerobic incubation. Acidic/alkaline pH induced an increase in MICs of gentamicin, ciprofloxacin, and trimethoprim-sulfamethoxazole, especially under anaerobic incubation. However, UV stress induced increase in MICs of tetracycline and trimethoprim-sulfamethoxazole under aerobic incubation and gentamicin, ciprofloxacin, and trimethoprim-sulfamethoxazole under anaerobic incubation. Additionally, osmotic stress induced an increase in MICs of tetracycline and ampicillin under aerobic incubation and gentamicin, tetracycline, and trimethoprim-sulfamethoxazole under anaerobic incubation. Collectively, this study highlights that stress tolerance may contribute to the predominance of Listeria species among FPEs and induce the development of antimicrobial resistance even without antibiotic selection pressure. The findings of this study may guide updated strategies to mitigate Listeria species in the food industry.

Gastric cancer is the fifth most common type of human cancer and the third leading cause of cancer-related death. The purpose of this study is to investigate the immune infiltration signatures of gastric cancer and their relation to prognosis. We identified two distinct subtypes of gastric cancer (C1/C2) characterized by different immune infiltration signatures. C1 is featured by immune resting, epithelial–mesenchymal transition, and angiogenesis pathways, while C2 is featured by enrichment of the MYC target, oxidative phosphorylation, and E2F target pathways. The C2 subtype has a better prognosis than the C1 subtype (HR = 0.61, 95% CI: 0.44–0.85; log-rank test, p = 0.0029). The association of C1/C2 with prognosis remained statistically significant (HR = 0.62, 95% CI: 0.44–0.87; p = 0.006) after controlling for age, gender, and stage. The prognosis prediction of C1/C2 was verified in four independent cohorts (including an internal cohort). In summary, our study is helpful for better understanding of the association between immune infiltration and the prognosis of gastric cancer.

Summary: We developed Miscell, a self-supervised learning approach with deep neural network as latent feature encoder for mining information from single-cell transcriptomes. We demonstrated the capability of Miscell with canonical single-cell analysis tasks including delineation of single-cell clusters and identification of cluster-specific marker genes. We evaluated Miscell along with three state-of-the-art methods on three heterogeneous datasets. Miscell achieved at least comparable or better performance than the other methods by significant margin on a variety of clustering metrics such as adjusted rand index, normalized mutual information, and V-measure score. Miscell can identify cell-type specific markers by quantifying the influence of genes on cell clusters via deep learning approach.

BackgroundBrain tumor ranks as the most devastating cancer type. The complex tumor immune microenvironment prevents brain tumor from receiving therapeutic benefits. The purpose of this study was to stratify brain tumors based on their distinct immune infiltration signatures to facilitate better clinical decision making and prognosis prediction.MethodsWe developed a deep learning model to characterize immune infiltration from transcriptome. The developed model was applied to distill expression signatures of transcriptome of brain tumor samples. We performed molecular subtyping with the extracted expression signatures to unveil brain tumor subtypes. Computational methods, including gene set enrichment analysis, Kaplan-Meier survival and multivariate Cox regression analyses, were employed.ResultsWe identified two distinctive subtypes (i.e. C1/2) of brain tumor featured by distinct immune infiltration signatures. The C1 subtype is characterized by protective immune infiltration signatures, including high infiltration of CD8+ T cells and activation of CX3CL1. The C2 subtype has an extensive infiltration of tumor-associated macrophages and microglia, and was enriched with immune suppressive, wound-healing, and angiogenic signatures. The C1 subtype had significantly better prognosis as compared with C2 (Log-rank test, HR: 2.5, 95% CI: 2.2 – 2.7; P = 8.2e-78). This difference remained statistically significant (multivariate Cox model, HR: 2.2, 95% CI: 1.7 – 2.9; P = 3.7e-10) by taking into account age, gender, recurrent/secondary status at sampling time, tumor grade, histology, radio-chemotherapy, IDH mutation, MGMT methylation, and co-deletion of 1p and 19q. This finding was validated in six datasets. The C2 subtype of glioblastoma patients with IDH mutation has poor survival analogous to those without IDH mutation (Log-rank test, adjusted P = 0.8), while C1 has favorable prognosis as compared with glioblastoma of C2 subtype with IDH mutation (Log-rank test, adjusted P = 1.2e-3) or without IDH mutation (Log-rank test, adjusted P = 1.3e-6).ConclusionsWe identified two distinctive subtypes of brain tumor with different immune infiltration signatures, which might be helpful as an independent prognosticator for brain tumor.

Salmonella is a group of bacteria that constitutes the leading cause of diarrheal diseases, posing a great disease burden worldwide. There are numerous pathways for zoonotic Salmonella transmission to humans; however, the role of companion animals in spreading these bacteria is largely underestimated in China. We aimed to investigate the prevalence of Salmonella in pet dogs and cats in Hangzhou, China, and characterize the antimicrobial resistance profile and genetic features of these pet-derived pathogens. In total, 137 fecal samples of pets were collected from an animal hospital in Hangzhou in 2018. The prevalence of Salmonella was 5.8% (8/137) in pets, with 9.3% (5/54) of cats and 3.6% (3/83) of dogs being Salmonella positive. By whole-genome sequencing (WGS), in silico serotyping, and multilocus sequence typing (MLST), 26 pet-derived Salmonella isolates were identified as Salmonella Dublin (ST10, n = 22) and Salmonella Typhimurium (ST19, n = 4). All of the isolates were identified as being multidrug-resistant (MDR), by conducting antimicrobial susceptibility testing under both aerobic and anaerobic conditions. The antibiotics of the most prevalent resistance were streptomycin (100%), cotrimoxazole (100%), tetracycline (96.20%), and ceftriaxone (92.30%). Versatile antimicrobial-resistant genes were identified, including floR (phenicol-resistant gene), blaCTX-M-15, and blaCTX-M-55 (extended-spectrum beta-lactamase genes). A total of 11 incompatible (Inc) plasmids were identified, with IncA/C2, IncFII(S), and IncX1 being the most predominant among Salmonella Dublin, and IncFIB(S), IncFII(S), IncI1, and IncQ1 being the most prevailing among Salmonella Typhimurium. Our study applied WGS to characterize pet-derived Salmonella in China, showing the presence of MDR Salmonella in pet dogs and cats with a high diversity of ARGs and plasmids. These data indicate a necessity for the regular surveillance of pet-derived pathogens to mitigate zoonotic diseases.

Currently, haze removal of images captured at night for foggy scenes rely on the traditional, prior-based methods, but these methods are frequently ineffective at dealing with night hazy images. In addition, the light sources at night are complicated and there is a problem of inconsistent brightness. This makes the estimation of the transmission map complicated in the night scene. Based on the above analysis, we propose an autoencoder method to solve the problem of overestimation or underestimation of transmission captured by the traditional, prior-based methods. For nighttime hazy images, we first remove the color effect of the haze image with an edge-preserving maximum reflectance prior (MRP) method. Then, the hazy image without color influence is input into the self-encoder network with skip connections to obtain the transmission map. Moreover, instead of using the local maximum method, we estimate the ambient illumination through a guiding image filtering. In order to highlight the effectiveness of our experiments, a large number of comparison experiments were conducted between our method and the state-of-the-art methods. The results show that our method can effectively suppress the halo effect and reduce the effectiveness of glow. In the experimental part, we calculate that the average Peak Signal to Noise Ratio (PSNR) is 21.0968 and the average Structural Similarity (SSIM) is 0.6802.