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The objective of this study was to determine the occurrence of antimicrobial resistance among Staphylococcus aureus and to estimate the presence of methicillin-resistance in S. aureus (MRSA) isolates obtained by culture and polymerase chain reaction (PCR) methods. For this purposes, 100 Iranian white and feta cheese samples collected from different suppliers were initially evaluated for the occurrence of S. aureus by culturing methods. The obtained isolates were subjected to disc diffusion antimicrobial susceptibility tests and a PCR method to detect the mecA gene. Out of the 100 cheese samples examined, 25 (25%) samples were contaminated with S. aureus with a mean of 5.74 ± 5.67 log cfu/g. Out of the 25 isolates, 23 (92%) were found to be resistant to at least one antibiotic or more, tested by a disk diffusion method. The highest rate of antibiotic resistance was observed to penicillin G (92%) followed by ampicillin (73%) and cloxacillin (68%). None of the isolates was resistant to gentamycin and vancomycin. Eight (34.78%) of the 23 S. aureus isolates were genotypically confirmed as MRSA. The results indicate that the presence of antimicrobial resistant strains of S. aureus in Iranian cheese samples constitute a potential risk for human health. This calls for better control of the spread of antimicrobial resistant strains as well as cheese contamination sources. [ABSTRACT FROM AUTHOR]

Recently, there has been growing interest in deep spectral methods for image localization and segmentation, influenced by traditional spectral segmentation approaches. These methods reframe the image decomposition process as a graph partitioning task by extracting features using self-supervised learning and utilizing the Laplacian of the affinity matrix to obtain eigensegments. However, instance segmentation has received less attention than other tasks within the context of deep spectral methods. This paper addresses that not all channels of the feature map extracted from a self-supervised backbone contain sufficient information for instance segmentation purposes. Some channels are noisy and hinder the accuracy of the task. To overcome this issue, this paper proposes two channel reduction modules, Noise Channel Reduction (NCR) and Deviation-based Channel Reduction (DCR). The NCR retains channels with lower entropy, as they are less likely to be noisy, while DCR prunes channels with low standard deviation, as they lack sufficient information for effective instance segmentation. Furthermore, the paper demonstrates that the dot product, commonly used in deep spectral methods, is not suitable for instance segmentation due to its sensitivity to feature map values, potentially leading to incorrect instance segments. A novel similarity metric called Bray-curtis over Chebyshev (BoC) is proposed to address this issue. This metric considers the distribution of features in addition to their values, providing a more robust similarity measure for instance segmentation. Quantitative and qualitative results on the Youtube-VIS 2019 and OVIS datasets highlight the improvements achieved by the proposed channel reduction methods and using BoC instead of the conventional dot product for creating the affinity matrix. These improvements regarding mean Intersection over Union (mIoU) and extracted instance segments are observed, demonstrating enhanced instance segmentation performance. The code is available on: https://github.com/farnooshar/SpecUnIIS., Competing Interests: The authors have declared that no competing interests exist., (Copyright: © 2024 Arefi et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.)

Objectives: We evaluate the diagnostic performance of dried blood microsampling combined with a high-throughput microfluidic nano-immunoassay (NIA) for the identification of anti-SARS-CoV-2 Spike IgG seropositivity., Methods: We conducted a serological study among 192 individuals with documented prior SARS-CoV-2 infection and 44 SARS-CoV-2 negative individuals. Participants with prior SARS-CoV-2 infection had a long interval of 11 months since their qRT-PCR positive test. Serum was obtained after venipuncture and tested with an automated electrochemiluminescence anti-SARS-CoV-2 S total Ig reference assay, a commercial ELISA anti-S1 IgG assay, and the index test NIA. In addition, 109 participants from the positive cohort and 44 participants from the negative cohort participated in capillary blood collection using three microsampling devices: Mitra, repurposed glucose test strips, and HemaXis. Samples were dried, shipped by regular mail, extracted, and measured with NIA., Results: Using serum samples, we achieve a clinical sensitivity of 98·33% and specificity of 97·62% on NIA, affirming the high performance of NIA in participants 11 months post infection. Combining microsampling with NIA, we obtain a clinical sensitivity of 95·05% using Mitra, 61·11% using glucose test strips, 83·16% using HemaXis, and 91·49% for HemaXis after automated extraction, without any drop in specificity., Discussion: High sensitivity and specificity was demonstrated when testing micro-volume capillary dried blood samples using NIA, which is expected to facilitate its use in large-scale studies using home-based sampling or samples collected in the field., Competing Interests: The automated DBS extraction was performed using an instrument loaned free of charge by Gerstel AG. Gerstel AG did not take part in the decision to publish the study and did not have editorial control of the results presented. This does not alter our adherence to PLOS ONE policies on sharing data and materials., (Copyright: © 2023 Michielin et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.)

Objectives: To report a prospective epidemiological, virological and serological investigation of a SARS-CoV-2 outbreak in a primary school., Methods: As part of a longitudinal, prospective, school-based surveillance study, this investigation involved repeated testing of 73 pupils, 9 teachers, 13 non-teaching staff and 26 household members of participants who tested positive, with rapid antigen tests and/or RT-PCR (Day 0-2 and Day 5-7), serologies on dried capillary blood samples (Day 0-2 and Day 30), contact tracing interviews and SARS-CoV-2 whole genome sequencing., Results: We identified 20 children (aged 4 to 6 years from 4 school classes), 2 teachers and a total of 4 household members who were infected by the Alpha variant during this outbreak. Infection attack rates were between 11.8 and 62.0% among pupils from the 4 school classes, 22.2% among teachers and 0% among non-teaching staff. Secondary attack rate among household members was 15.4%. Symptoms were reported by 63% of infected children, 100% of teachers and 50% of household members. All analysed sequences but one showed 100% identity. Serological tests detected 8 seroconversions unidentified by SARS-CoV-2 virological tests., Conclusions: This study confirmed child-to-child and child-to-adult SARS-CoV-2 transmission and introduction into households. Effective measures to limit transmission in schools have the potential to reduce the overall community circulation., Competing Interests: The authors have declared that no competing interests exist.

In the current work by using statistical methods and available software, the wind energy assessment of prone regions for installation of wind turbines in, Qorveh, has been investigated. Information was obtained from weather stations of Baneh, Bijar, Zarina, Saqez, Sanandaj, Qorveh, and Marivan. The monthly average and maximum of wind speed were investigated between the years 2000-2010 and the related curves were drawn. The Golobad curve (direction and percentage of dominant wind and calm wind as monthly rate) between the years 1997-2000 was analyzed and drawn with plot software. The ten-minute speed (at 10, 30, and 60 m height) and direction (at 37.5 and 10 m height) wind data were collected from weather stations of Iranian new energy organization. The wind speed distribution during one year was evaluated by using Weibull probability density function (two-parametrical), and the Weibull curve histograms were drawn by MATLAB software. According to the average wind speed of stations and technical specifications of the types of turbines, the suitable wind turbine for the station was selected. Finally, the Divandareh and Qorveh sites with favorable potential were considered for installation of wind turbines and construction of wind farms.