Application of prophage sequence analysis to investigate a disease outbreak involving Salmonella Adjame, a rare serovar and implications for the population structure

Introduction

Outbreak investigation of foodborne salmonellosis is hindered when the food source is contaminated by multiple strains of Salmonella, creating difficulties matching an incriminated organism recovered from patients with the specific strain in the suspect food. An outbreak of the rare Salmonella Adjame was caused by multiple strains of the organism as revealed by single-nucleotide polymorphism (SNP) variation. The use of highly discriminatory prophage analysis to characterize strains of Salmonella should enable a more precise strain characterization and aid the investigation of foodborne salmonellosis.

Methods

We have carried out genomic analysis of S. Adjame strains recovered during the course of a recent outbreak and compared them with other strains of the organism (n = 38 strains), using SNPs to evaluate strain differences present in the core genome, and prophage sequence typing (PST) to evaluate the accessory genome. Phylogenetic analyses were performed using both total prophage content and conserved prophages.

Results

The PST analysis of the S. Adjame isolates showed a high degree of strain heterogeneity. We observed small clusters made up of 2-6 isolates (n = 27) and singletons (n = 11) in stark contrast with the three clusters observed by SNP analysis. In total, we detected 24 prophages of which only four were highly prevalent, namely: Entero_p88 (36/38 strains), Salmon_SEN34 (35/38 strains), Burkho_phiE255 (33/38 strains) and Edward_GF (28/38 strains). Despite the marked strain diversity seen with prophage analysis, the distribution of the four most common prophages matched the clustering observed using core genome.

Discussion

Mutations in the core and accessory genomes of S. Adjame have shed light on the evolutionary relationships among the Adjame strains and demonstrated a convergence of the variations observed in both fractions of the genome. We conclude that core and accessory genomes analyses should be adopted in foodborne bacteria outbreak investigations to provide a more accurate strain description and facilitate reliable matching of isolates from patients and incriminated food sources. The outcomes should translate to a better understanding of the microbial population structure and an 46 improved source attribution in foodborne illnesses.

Sensitivity of Field Tests for Assessment of Wrestlers Specific Fitness

The aim of this study was to determine the differences in performance, cardiac, and metabolic variables of combat-specific fitness of wrestlers from varying sports levels. The sample consisted of 61 active and highly trained adult wrestlers of national and international levels from Serbia, from both Greco-Roman and freestyle forms of wrestling, allocated into three groups: national team wrestlers (N = 15), first league competitive wrestlers (N = 20), and second league competitive wrestlers (N = 26). Participants performed two tests: a Specific Wrestling Fitness Test (SWFT) and a Specific Wrestling Performance Test (SWPT). Between-group differences were determined using multiple analysis of variance (MANOVA). Significant differences were obtained between wrestlers of different sports levels in Tn^Throws (F = 77.491, p < 0.001), SJF^Index (F = 49.710, p < 0.001), and SWP^Index (F = 31.205, p < 0.001) in the SWFT and in Tn^Throws (F = 71.051, p < 0.001), SJF^Index F = 45.343, p < 0.001), and SWP^Index (F = 26.820, p < 0.001) in the SWPT. Based on the results, it could be concluded that the SWFT and the SWPT provide very good sensitivity in the evaluation of the wrestlers specific fitness of wrestlers.

Discriminative Ridge Machine: A Classifier for High-Dimensional Data or Imbalanced Data

In this article, we introduce a discriminative ridge regression approach to supervised classification. It estimates a representation model while accounting for discriminativeness between classes, thereby enabling accurate derivation of categorical information. This new type of regression model extends the existing models, such as ridge, lasso, and group lasso, by explicitly incorporating discriminative information. As a special case, we focus on a quadratic model that admits a closed-form analytical solution. The corresponding classifier is called the discriminative ridge machine (DRM). Three iterative algorithms are further established for the DRM to enhance the efficiency and scalability for real applications. Our approach and the algorithms are applicable to general types of data including images, high-dimensional data, and imbalanced data. We compare the DRM with current state-of-the-art classifiers. Our extensive experimental results show the superior performance of the DRM and confirm the effectiveness of the proposed approach.

Diagnostic Value of Detecting Feline Coronavirus RNA and Spike Gene Mutations in Cerebrospinal Fluid to Confirm Feline Infectious Peritonitis

BACKGROUND

Cats with neurologic feline infectious peritonitis (FIP) are difficult to diagnose. Aim of this study was to evaluate the diagnostic value of detecting feline coronavirus (FCoV) RNA and spike (S) gene mutations in cerebrospinal fluid (CSF).

METHODS

The study included 30 cats with confirmed FIP (six with neurological signs) and 29 control cats (eleven with neurological signs) with other diseases resulting in similar clinical signs. CSF was tested for FCoV RNA by 7b-RT-qPCR in all cats. In RT-qPCR-positive cases, S-RT-qPCR was additionally performed to identify spike gene mutations.

RESULTS

Nine cats with FIP (9/30, 30%), but none of the control cats were positive for FCoV RNA in CSF. Sensitivity of 7b-RT-qPCR in CSF was higher for cats with neurological FIP (83.3%; 95% confidence interval (95% CI) 41.8-98.9) than for cats with non-neurological FIP (16.7%; 95% CI 6.1-36.5). Spike gene mutations were rarely detected.

CONCLUSIONS

FCoV RNA was frequently present in CSF of cats with neurological FIP, but only rarely in cats with non-neurological FIP. Screening for spike gene mutations did not enhance specificity in this patient group. Larger populations of cats with neurological FIP should be explored in future studies.

Multivariate Analysis of Structural and Functional Neuroimaging Can Inform Psychiatric Differential Diagnosis

Traditional psychiatric diagnosis has been overly reliant on either self-reported measures (introspection) or clinical rating scales (interviews). This produced the so-called explanatory gap with the bio-medical disciplines, such as neuroscience, which are supposed to deliver biological explanations of disease. In that context the neuro-biological and clinical assessment in psychiatry remained discrepant and incommensurable under conventional statistical frameworks. The emerging field of translational neuroimaging attempted to bridge the explanatory gap by means of simultaneous application of clinical assessment tools and functional magnetic resonance imaging, which also turned out to be problematic when analyzed with standard statistical methods. In order to overcome this problem our group designed a novel machine learning technique, multivariate linear method (MLM) which can capture convergent data from voxel-based morphometry, functional resting state and task-related neuroimaging and the relevant clinical measures. In this paper we report results from convergent cross-validation of biological signatures of disease in a sample of patients with schizophrenia as compared to depression. Our model provides evidence that the combination of the neuroimaging and clinical data in MLM analysis can inform the differential diagnosis in terms of incremental validity.