Resistance to extended-spectrum cephalosporins (ESC) among members of the family Enterobacteriaceae occurs worldwide; however, little is known about ESC resistance in Escherichia coli strains from companion animals. Clinical isolates of E. coli were collected from veterinary diagnostic laboratories throughout the United States from 2008 to 2009. E. coli isolates (n = 54) with reduced susceptibility to ceftazidime or cefotaxime (MIC ≥ 16 μg/ml) and extended-spectrum-β-lactamase (ESBL) phenotypes were analyzed. PCR and sequencing were used to detect mutations in ESBL-encoding genes and the regulatory region of the chromosomal gene ampC. Conjugation experiments and plasmid identification were conducted to examine the transferability of resistance to ESCs. All isolates carried the bla(CTX-M-1)-group β-lactamase genes in addition to one or more of the following β-lactamase genes: bla(TEM), bla(SHV-3), bla(CMY-2), bla(CTX-M-14-like), and bla(OXA-1.) Different bla(TEM) sequence variants were detected in some isolates (n = 40). Three isolates harbored a bla(TEM-181) gene with a novel mutation resulting in an Ala184Val substitution. Approximately 78% of the isolates had mutations in promoter/attenuator regions of the chromosomal gene ampC, one of which was a novel insertion of adenine between bases -28 and -29. Plasmids ranging in size from 11 to 233 kbp were detected in the isolates, with a common plasmid size of 93 kbp identified in 60% of isolates. Plasmid-mediated transfer of β-lactamase genes increased the MICs (≥ 16-fold) of ESCs for transconjugants. Replicon typing among isolates revealed the predominance of IncI and IncFIA plasmids, followed by IncFIB plasmids. This study shows the emergence of conjugative plasmid-borne ESBLs among E. coli strains from companion animals in the United States, which may compromise the effective therapeutic use of ESCs in veterinary medicine.
Robust, specific, and rapid identification of toxic strains of bacteria and viruses, to guide the mitigation of their adverse health effects and optimum implementation of other response actions, remains a major analytical challenge. This need has driven the development of methods for classification of microorganisms using mass spectrometry, particularly matrix-assisted laser desorption/ionization mass spectrometry (MALDI-MS), that allows high-throughput analyses with minimum sample preparation. We describe a novel approach to cell typing based on pattern recognition of MALDI mass spectra, which involves charge-state deconvolution in conjunction with a new correlation analysis procedure. The method is applicable to both prokaryotic and eukaryotic cells. Charge-state deconvolution improves the quantitative reproducibility of spectra because multiply charged ions resulting from the same biomarker attaching a different number of protons are recognized and their abundances are combined. This allows a clearer distinction of bacterial strains or of cancerous and normal liver cells. Improved class distinction provided by charge-state deconvolution was demonstrated by cluster spacing on canonical variate score charts and by correlation analyses. Deconvolution may enhance detection of early disease state or therapy progress markers in various tissues analyzed by MALDI-MS.
Standard methods to detect Escherichia coli contamination in food use the polymerase chain reaction (PCR) and agar culture plates. These methods require multiple incubation steps and take a long time to results. An improved rapid flow-cytometry based detection method was developed, using a fluorescence-labeled oligonucleotide probe specifically binding a16S rRNA sequence. The method positively detected 51 E. coli isolates as well as 4 Shigella species. All 27 non-E. coli strains tested gave negative results. Comparison of the new genetic assay with a total plate count (TPC) assay and agar plate counting indicated similar sensitivity, agreement between cytometry cell and colony counts. This method can detect a small number of E.coli cells in the presence of large numbers of other bacteria. This method can be used for rapid, economical, and stable detection of E. coli and Shigella contamination in the food industry and other contexts.
Laser Induced Breakdown Spectroscopy (LIBS) is a multi-elemental analysis technique with various advantages and has the ability to detect any element in real time. This technique holds a potential for environmental monitoring and various such analysis has been done in soil, glass, paint, water, plastic etc confirms the robustness of this technique for such applications. Compared to the currently available water quality monitoring methods and techniques, LIBS has several advantages, viz. no need for sample preparation, fast and easy operation, and chemical free during the process. In LIBS, powerful pulsed laser generates plasma which is then analyzed to get quantitative and qualitative details of the elements present in the sample. Another main advantage of LIBS technique is that it can perform in standoff mode for real time analysis. Water samples from industries and agricultural strata tend to have a lot of pollutants making it harmful for consumption. The emphasis of this project is to determine such harmful pollutants present in trace amounts in industrial and agricultural wastewater. When high intensity laser is made incident on the sample, a plasma is generated which gives a multielemental emission spectra. LIBS analysis has shown outstanding success for solids samples. For liquid samples, the analysis is challenging as the liquid sample has the chances of splashing due to the high energy of laser and thus making it difficult to generate plasma. This project also deals with determining the most efficient method for testing of water sample for qualitative as well as quantitative analysis using LIBS.
The aim of this longitudinal study was to determine and compare the prevalences and genotypic profiles of antimicrobial-resistant (AR) Salmonella isolates from pigs reared in antimicrobial-free (ABF) and conventional production systems at farm, at slaughter, and in their environment. We collected 2,889 pig fecal and 2,122 environmental (feed, water, soil, lagoon, truck, and floor swabs) samples from 10 conventional and eight ABF longitudinal cohorts at different stages of production (farrowing, nursery, finishing) and slaughter (postevisceration, postchill, and mesenteric lymph nodes [MLN]). In addition, we collected 1,363 carcass swabs and 205 lairage and truck samples at slaughter. A total of 1,090 Salmonella isolates were recovered from the samples; these were isolated with a significantly higher prevalence in conventionally reared pigs (4.0%; n = 66) and their environment (11.7%; n = 156) than in ABF pigs (0.2%; n = 2) and their environment (0.6%; n = 5) (P < 0.001). Salmonella was isolated from all stages at slaughter, including the postchill step, in the two production systems. Salmonella prevalence was significantly higher in MLN extracted from conventional carcasses than those extracted from ABF carcasses (P < 0.001). We identified a total of 24 different serotypes, with Salmonella enterica serovar Typhimurium, Salmonella enterica serovar Anatum, Salmonella enterica serovar Infantis, and Salmonella enterica serovar Derby being predominant. The highest frequencies of antimicrobial resistance (AR) were exhibited to tetracycline (71%), sulfisoxazole (42%), and streptomycin (17%). Multidrug resistance (resistance to ≥ 3 antimicrobials; MDR) was detected in 27% (n = 254) of the Salmonella isolates from the conventional system. Our study reports a low prevalence of Salmonella in both production systems in pigs on farms, while a higher prevalence was detected among the carcasses at slaughter. The dynamics of Salmonella prevalence in pigs and carcasses were reciprocated in the farm and slaughter environment, clearly indicating an exchange of this pathogen between the pigs and their surroundings. Furthermore, the phenotypic and genotypic fingerprint profile results underscore the potential role played by environmental factors in dissemination of AR Salmonella to pigs.
The identification of unusual power usage in buildings is crucial for improving energy efficiency. Using an electrical consumption monitoring system can help with energy conservation by identifying unusual energy consumption patterns. This paper suggests a micro-moment-based methodology for detecting abnormal power use. This study makes use of a benchmark dataset called SimDataset, which is used in most of the micro-moment classification-related works. On the images created from the dataset labeled with two classes and five classes, binary and multi-class classifications have both been used. Transfer learning is used by employing pre-trained CNN models, namely DenseNet121, ResNet50V2, and Xception model. The results depicted that the DenseNet121 model has outperformed all other models by giving the best accuracy of 99% and F1-score of 0.984.
1. A comprehensive ecological survey was conducted from April 1997 to June 1999 on 4 turkey flocks (F1 to F4) to identify key pre-harvest sources/vectors of Salmonella colonisation. 2. Turkey caecal and crop content, litter, drinker, air, feed, feeder and environmental swab samples were collected. Conventional microbiological and serological procedures were used to isolate, identify, and confirm the presence or absence of Salmonella. 3. Salmonella was isolated from 13% of litter, 11% of turkey caeca, 10% of drinker, 5% of environmental swab, 3% of feed and 1% of feeder samples. Salmonella heidelberg (65%), S. senftenberg (19%), S. muenster (10%), S. anatum (3%), and S. worthington (3%) were identified. 4. Identifying environmental sources associated with Salmonella colonisation and characterising serotypes would assist in designing pre-harvest controls for this poultry-borne pathogen. Integrators and poultry producers may be able to design hazard analysis and critical control point (HACCP) protocols to reduce the incidence of Salmonella arriving at the processing plant.
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