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BackgroundClostridioides difficile Infection (CDI) is a healthcare-associated diarrheal disease prevalent worldwide. A common diagnostic algorithm relies on a two-step protocol that employs stool enzyme immunoassays (EIAs) to detect the pathogen, and its toxins, respectively. Active CDI is deemed less likely when the Toxin EIA result is negative, even if the pathogen-specific EIA is positive for C. difficile. We recently reported, however, that low-toxin-producing C. difficile strains recovered from Toxin-negative (‘discrepant’) clinical stool specimens can be fully pathogenic, and cause lethality in a rodent CDI model. To document frequency of discrepant CDI specimens, and evaluate C. difficile strain diversity, we performed longitudinal surveillance at a Southern Arizona tertiary-care hospital.MethodsDiarrheic stool specimens from patients with clinical suspicion of CDI were obtained over an eight-year period (2015–2022) from all inpatient and outpatient Units of a > 600-bed Medical Center in Southern Arizona. Clinical laboratory EIA testing identified C. difficile-containing specimens, and classified them as Toxin-positive or Toxin-negative. C. difficile isolates recovered from the stool specimens were DNA fingerprinted using an international phylogenetic lineage assignment system (“ribotyping”). For select isolates, toxin abundance in stationary phase supernatants of pure cultures was quantified via EIA.ResultsOf 8,910 diarrheic specimens that underwent diagnostic testing, 1733 (19.4%) harbored C. difficile. Our major findings were that: (1) C. difficile prevalence and phylogenetic diversity was stable over the 8-year period; (2) toxigenic C. difficile was recovered from 69% of clinically Tox-neg (‘discrepant’) specimens; (3) the six most prevalent USA ribotypes were recovered in significant proportions (>60%) from Tox-neg specimens; and (4) toxin–producing C. difficile recovered from discrepant specimens produced less toxin than strains of the same ribotype isolated from non-discrepant specimens.ConclusionOur study highlights the dominance of Toxin EIA-negative CDI specimens in a clinical setting and the high frequency of known virulent ribotypes in these specimens. Therefore, a careful reevaluation of the clinical relevance of diagnostically-discrepant specimens particularly in the context of missed CDI diagnoses and C. difficile persistence, is warranted.

Abstract Clostridioides difficile infection (CDI) is a major healthcare-associated diarrheal disease. Consistent with trends across the United States, C. difficile RT106 was the second-most prevalent molecular type in our surveillance in Arizona from 2015 to 2018. A representative RT106 strain displayed robust virulence and 100% lethality in the hamster model of acute CDI. We identified a unique 46 KB genomic island (GI1) in all RT106 strains sequenced to date, including those in public databases. GI1 was not found in its entirety in any other C. difficile clade, or indeed, in any other microbial genome; however, smaller segments were detected in Enterococcus faecium strains. Molecular clock analyses suggested that GI1 was horizontally acquired and sequentially assembled over time. GI1 encodes homologs of VanZ and a SrtB-anchored collagen-binding adhesin, and correspondingly, all tested RT106 strains had increased teicoplanin resistance, and a majority displayed collagen-dependent biofilm formation. Two additional genomic islands (GI2 and GI3) were also present in a subset of RT106 strains. All three islands are predicted to encode mobile genetic elements as well as virulence factors. Emergent phenotypes associated with these genetic islands may have contributed to the relatively rapid expansion of RT106 in US healthcare and community settings.

In this paper, we present the data set of surface electromyography (sEMG) and an Inertial Measurement Unit (IMU) against human muscle activity during routine activities. The Myo Thalamic Armband is used to acquire the signals from muscles below the elbow. The dataset comprises of raw sEMG, accelerometer, gyro and derived orientation signals for four different activities. The four activities, which are selected for this dataset acquisition, are resting, typing, push up exercise and lifting a heavy object. Therefore, there are five associated files against each activity. The IMU data can be fused with the sEMG data for better classification of activities especially to separate aggressive and normal activities. The data is valuable for researchers working on assistive computer aided support systems for subjects with disabilities due to physical or mental disorder.

Human beings tend to incrementally learn from the rapidly changing environment without comprising or forgetting the already learned representations. Although deep learning also has the potential to mimic such human behaviors to some extent, it suffers from catastrophic forgetting due to which its performance on already learned tasks drastically decreases while learning about newer knowledge. Many researchers have proposed promising solutions to eliminate such catastrophic forgetting during the knowledge distillation process. However, to our best knowledge, there is no literature available to date that exploits the complex relationships between these solutions and utilizes them for the effective learning that spans over multiple datasets and even multiple domains. In this paper, we propose a continual learning objective that encompasses mutual distillation loss to understand such complex relationships and allows deep learning models to effectively retain the prior knowledge while adapting to the new classes, new datasets, and even new applications. The proposed objective was rigorously tested on nine publicly available, multi-vendor, and multimodal datasets that span over three applications, and it achieved the top-1 accuracy of 0.9863% and an F1-score of 0.9930.

Background & Aims: The diarrheagenic pathogen, enteropathogenic Escherichia coli (EPEC), uses a type III secretion system to deliver effector molecules into intestinal epithelial cells (IECs). While exploring the basis for the lateral membrane separation of EPEC-infected IECs, we observed infection-induced loss of the desmosomal cadherin desmoglein-2 (DSG2). We sought to identify the molecule(s) involved in, and delineate the mechanisms and consequences of, EPEC-induced DSG2 loss. Methods: DSG2 abundance and localization was monitored via immunoblotting and immunofluorescence, respectively. Junctional perturbations were visualized by electron microscopy, and cell–cell adhesion was assessed using dispase assays. EspH alanine-scan mutants as well as pharmacologic agents were used to evaluate impacts on desmosomal alterations. EPEC-mediated DSG2 loss, and its impact on bacterial colonization in vivo, was assessed using a murine model. Results: The secreted virulence protein EspH mediates EPEC-induced DSG2 degradation, and contributes to desmosomal perturbation, loss of cell junction integrity, and barrier disruption in infected IECs. EspH sequesters Rho guanine nucleotide exchange factors and inhibits Rho guanosine triphosphatase signaling; EspH mutants impaired for Rho guanine nucleotide exchange factor interaction failed to inhibit RhoA or deplete DSG2. Cytotoxic necrotizing factor 1, which locks Rho guanosine triphosphatase in the active state, jasplakinolide, a molecule that promotes actin polymerization, and the lysosomal inhibitor bafilomycin A, respectively, rescued infected cells from EPEC-induced DSG2 loss. Wild-type EPEC, but not an espH-deficient strain, colonizes mouse intestines robustly, widens paracellular junctions, and induces DSG2 re-localization in vivo. Conclusions: Our studies define the mechanism and consequences of EPEC-induced desmosomal alterations in IECs. These perturbations contribute to the colonization and virulence of EPEC, and likely related pathogens. Keywords: EPEC, Desmoglein, DSG2, Host–Pathogen Interaction

Morbidity and mortality attributed to Clostridium difficileinfection (CDI) have increased over the past 20 years. Currently, antibiotics are the only US FDA-approved treatment for primary C. difficile infection, and these are, ironically, associated with disease relapse and the threat of burgeoning drug resistance. We previously showed that non-toxin virulence factors play key roles in CDI, and that colonization factors are critical for disease. Specifically, a C. difficile adhesin, Surface Layer Protein A (SlpA) is a major contributor to host cell attachment. In this work, we engineered Syn-LAB 2.0 and Syn-LAB 2.1, two synthetic biologic agents derived from lactic acid bacteria, to stably and constitutively express a host-cell binding fragment of the C. difficile adhesin SlpA on their cell-surface. Both agents harbor conditional suicide plasmids expressing a codon-optimized chimera of the lactic acid bacterium’s cell-wall anchoring surface-protein domain, fused to the conserved, highly adherent, host-cell-binding domain of C. difficile SlpA. Both agents also incorporate engineered biocontrol, obviating the need for any antibiotic selection. Syn-LAB 2.0 and Syn-LAB 2.1 possess positive biophysical and in vivo properties compared with their parental antecedents in that they robustly and constitutively display the SlpA chimera on their cell surface, potentiate human intestinal epithelial barrier function in vitro, are safe, tolerable and palatable to Golden Syrian hamsters and neonatal piglets at high daily doses, and are detectable in animal feces within 24 h of dosing, confirming robust colonization. In combination, the engineered strains also delay (in fixed doses) or prevent (when continuously administered) death of infected hamsters upon challenge with high doses of virulent C. difficile. Finally, fixed-dose Syn-LAB ameliorates diarrhea in a non-lethal model of neonatal piglet enteritis. Taken together, our findings suggest that the two synthetic biologics may be effectively employed as non-antibiotic interventions for CDI.

Data mining and machine learning algorithms deal with large amount of data, which with the invention of cost-efficient devices has increased by massive amounts. Many algorithms of these domains are not part of real-time systems because of their computational complexity and large data on which they need to work. A lot of algorithms are being implemented on parallel processing systems like GPUs and FPGAs to achieve the desired speed. The purpose of this article is to provide parallel processing model of mean shift clustering algorithm, targeted to run on FPGA. The general model consists of multiple homogeneous processing entities (PEs) connected through a bus. These PEs work in collaborative working environment with each PE working independently and also communicating with its peers according to the requirements of algorithms. The proposed architecture is implemented on FPGA for one-dimensional data. The algorithm is tested on 99 images from segmentation evaluation database for different number of PEs and different number of fractional bits used to represent mean. The simplicity of algorithm resulted in utilizing only 10.31% of total device slice registers and 33% of total slice LUTs of Spartan 6 FPGA. The processing requirements for the proposed algorithm show that it can be used in real-time systems. [ABSTRACT FROM AUTHOR]