The objective of this study was to determine if survivor curves for heat-inactivated Escherichia coli O157:H7 were affected by the physiological state of the cells relative to growth conditions and pH of the heating menstruum. A comparison was made between the log-linear model and non-log-linear Weibull approach. Cells were grown statically in aerobic culture tubes or in an aerobic chemostat in tryptic soy broth (pH 7.2). The heating menstruum was unbuffered peptone or phosphate buffer (pH 7.0). Thermal inactivation was carried out at 58, 59, 60, and 61°C, and recovery was on a nonselective medium. Longer inactivation times for statically grown cells indicated potential stress adaptation. This was more prevalent at 58°C. Shape response was also significantly different, with statically grown cells exhibiting decreasing thermal resistance over time and chemostat cells showing the opposite effect. Buffering the heating menstruum to ca. pH 7 resulted in inactivation curves that showed less variability or scatter of data points. Time to specific log reduction values (t(d)) for the Weibull model were conservative relative to the log-linear model depending upon the stage of reduction. The Weibull model offered the most accurate fit of the data in all cases, especially considering the log-linear model is equivalent to the Weibull model with a fixed shape factor of 1. The determination of z-value for the log-linear model showed a strong correlation between log D-value and process temperature. Correlations for the Weibull model parameters (log δ and log p) versus process temperature were not statistically significant.
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http://dx.doi.org/10.4315/0362-028x-73.11.2018 | DOI Listing |
PLoS One
January 2025
Faculty of Science and Engineering, School of Computer Science, University of Hull, Hull, United Kingdom.
Mold defects pose a significant risk to the preservation of valuable fine art paintings, typically arising from fungal growth in humid environments. This paper presents a novel approach for detecting and categorizing mold defects in fine art paintings. The technique leverages a feature extraction method called Derivative Level Thresholding to pinpoint suspicious regions within an image.
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January 2025
Faculty of Engineering, Free University of Bozen-Bolzano, Bolzano, South Tyrol, Italy.
Appraisal models, such as the Scherer's Component Process Model (CPM), represent an elegant framework for the interpretation of emotion processes, advocating for computational models that capture emotion dynamics. Today's emotion recognition research, however, typically classifies discrete qualities or categorised dimensions, neglecting the dynamic nature of emotional processes and thus limiting interpretability based on appraisal theory. In our research, we estimate emotion intensity from multiple physiological features associated to the CPM's neurophysiological component using dynamical models with the aim of bringing insights into the relationship between physiological dynamics and perceived emotion intensity.
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January 2025
Instituto de Investigaciones en Ecosistemas y Sustentabilidad, Universidad Nacional Autónoma de México, Morelia, Michoacán, México.
Land use change from wildlands to urban and productive environments can dramatically transform ecosystem structure and processes. Despite their structural and functional differences from wildlands, human-modified environments offer unique habitat elements for wildlife. In this study, we examined how migratory birds use urban, productive, and wildland environments of a highly anthropized region of Western Mexico known as "El Bajío".
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January 2025
College of Automation Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing, RP China.
This study develops an innovative method for analyzing and clustering tonal trends in Chinese Yue Opera to identify different vocal styles accurately. Linear interpolation is applied to process the time series data of vocal melodies, addressing inconsistent feature dimensions. The second-order difference method extracts tonal trend features.
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January 2025
Department of Environmental Health Sciences, Columbia University, New York, NY, United States of America.
Previous research indicates that the COVID-19 pandemic catalyzed alterations in behaviors that may impact exposures to environmental endocrine-disrupting chemicals. This includes changes in the use of chemicals found in consumer products, food packaging, and exposure to air pollutants. Within the Environmental influences on Child Health Outcomes (ECHO) program, a national consortium initiated to understand the effects of environmental exposures on child health and development, our objective was to assess whether urinary concentrations of a wide range of potential endocrine-disrupting chemicals varied before and during the pandemic.
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