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Diarrhoea remains a major cause of childhood morbidity and mortality worldwide. This study aimed to monitor the aetiology of acute diarrhoea in children in Shanghai. Paediatric outpatients with acute diarrhoea were enrolled in the study from Jan 2015 to Dec 2018. Faecal samples were collected for testing. Enteric bacteria were identified and typed by culture and serotyping, respectively. Enteric viruses were identified by real-time PCR. Enteric pathogens were identified in 1572 (58.4%) of the 2692 enrolled children with acute diarrhoea. Viruses were detected more frequently than bacteria (41.3% versus 25.0%). Nontyphoidal Salmonella spp. (NTS) was the most common (10.3%) bacteria isolated, followed by enteropathogenic Escherichia coli (EPEC) (6.5%), enteroaggregative Escherichia coli (EAEC) (6.2%), Campylobacter spp. (3.6%), enterotoxigenic Escherichia coli (ETEC) (1.1%), Shigella spp. (0.2%), and enterohemorrhagic Escherichia coli (EHEC) (0.1%). Rotavirus was the most common (16.0%) virus detected, followed by norovirus (15.5%), adenovirus (7.2%), sapovirus (3.0%) and astrovirus (2.7%). Rotavirus, norovirus and NTS were the major pathogens responsible for diarrhoea in Shanghainese children. Improving uptake of the rotavirus vaccine and strengthening foodborne-pathogen prevention will aid in reducing the burden of diarrhoeal disease in children in Shanghai.

We sequenced 25 isolates of phenotypically multidrug-resistant Salmonella Indiana (n = 11), Typhimurium (n = 8), and Enteritidis (n = 6) using both MinION long-read [SQK-LSK109 and flow cell (R9.4.1)] and MiSeq short-read (Nextera XT and MiSeq Reagent Kit v2) sequencing technologies to determine the advantages of each approach in terms of the characteristics of genome structure, antimicrobial resistance (AMR), virulence potential, whole-genome phylogeny, and pan-genome. The MinION reads were base-called in real-time using MinKnow 3.4.8 integrated with Guppy 3.0.7. The long-read-only assembly, Illumina-only assembly, and hybrid assembly pipelines of Unicycler 0.4.8 were used to generate the MinION, MiSeq, and hybrid assemblies, respectively. The MinION assemblies were highly contiguous compared to the MiSeq assemblies but lacked accuracy, a deficiency that was mitigated by adding the MiSeq short reads through the Unicycler hybrid assembly which corrected erroneous single nucleotide polymorphisms (SNPs). The MinION assemblies provided similar predictions of AMR and virulence potential compared to the MiSeq and hybrid assemblies, although they produced more total false negatives of AMR genotypes, primarily due to failure in identifying tetracycline resistance genes in 11 of the 19 MinION assemblies of tetracycline-resistant isolates. The MinION assemblies displayed a large genetic distance from their corresponding MiSeq and hybrid assemblies on the whole-genome phylogenetic tree, indicating that the lower read accuracy of MinION sequencing caused incorrect clustering. The pan-genome of the MinION assemblies contained significantly more accessory genes and less core genes compared to the MiSeq and hybrid assemblies, suggesting that although these assemblies were more contiguous, their sequencing errors reduced accurate genome annotations. Our research demonstrates that MinION sequencing by itself provides an efficient assessment of the genome structure, antimicrobial resistance, and virulence potential of Salmonella; however, it is not sufficient for whole-genome phylogenetic and pan-genome analyses. MinION in combination with MiSeq facilitated the most accurate genomic analyses.

An extreme learning machine (ELM) is a novel training method for single-hidden layer feedforward neural networks (SLFNs) in which the hidden nodes are randomly assigned and fixed without iterative tuning. ELMs have earned widespread global interest due to their fast learning speed, satisfactory generalization ability and ease of implementation. In this paper, we extend this theory to hypercomplex space and attempt to simultaneously consider multisource information using a hypercomplex representation. To illustrate the performance of the proposed hypercomplex extreme learning machine (HELM), we have applied this scheme to the task of multispectral palmprint recognition. Images from different spectral bands are utilized to construct the hypercomplex space. Extensive experiments conducted on the PolyU and CASIA multispectral databases demonstrate that the HELM scheme can achieve competitive results. The source code together with datasets involved in this paper can be available for free download at https://figshare.com/s/01aef7d48840afab9d6d.

Multispectral palmprint recognition has shown broad prospects for personal identification due to its high accuracy and great stability. In this paper, we develop a novel illumination-invariant multispectral palmprint recognition method. To combine the information from multiple spectral bands, an image-level fusion framework is completed based on a fast and adaptive bidimensional empirical mode decomposition (FABEMD) and a weighted Fisher criterion. The FABEMD technique decomposes the multispectral images into their bidimensional intrinsic mode functions (BIMFs), on which an illumination compensation operation is performed. The weighted Fisher criterion is to construct the fusion coefficients at the decomposition level, making the images be separated correctly in the fusion space. The image fusion framework has shown strong robustness against illumination variation. In addition, a tensor-based extreme learning machine (TELM) mechanism is presented for feature extraction and classification of two-dimensional (2D) images. In general, this method has fast learning speed and satisfying recognition accuracy. Comprehensive experiments conducted on the PolyU multispectral palmprint database illustrate that the proposed method can achieve favorable results. For the testing under ideal illumination, the recognition accuracy is as high as 99.93%, and the result is 99.50% when the lighting condition is unsatisfied.

Salmonella enterica infections continue to be a significant burden on public health worldwide. The ability of S. enterica to produce hydrogen sulfide (H2S) is an important phenotypic characteristic used to screen and identify Salmonella with selective medium; however, H2S-negative Salmonella have recently emerged. In this study, the H2S phenotype of Salmonella isolates was confirmed, and the selected isolates were subjected to antimicrobial susceptibility testing and molecular identification by multilocus sequence typing, pulsed-field gel electrophoresis, and clustered regularly interspaced short palindromic repeat (CRISPR) analysis. The phs genetic operon was also analyzed. A total of 160 S. enterica serovar Aberdeen isolates were detected between 2005 and 2013 in China. Of them, seven non-H2S-producing isolates were detected. Notably, four samples yielded four pairs of isolates with different H2S phenotypes, simultaneously. The data demonstrated that H2S-negative isolates were genetically closely related to H2S-positive isolates. Three new spacers (Abe1, Abe2, and Abe3) were identified in CRISPR locus 1 in four pairs of isolates with different H2S phenotypes from the same samples. Sequence analysis revealed a new nonsense mutation at position 208 in the phsA gene of all non-H2S-producing isolates. Additionally, we describe a new screening procedure to avoid H2S-negative Salmonella, which would normally be overlooked during laboratory and hospital screening. The prevalence of this pathogen may be underestimated; therefore, it is important to focus on improving surveillance of this organism to control its spread.