Prevention, prediction, control, and handling of bacterial foodborne diseases - an ongoing, serious, and costly concern worldwide - are continually facing a wide array of difficulties. Not the least ...due to that food matrices, highly variable and complex, can impact virulence expression in diverse and unpredictable ways. This review aims to present a comprehensive overview of challenges related to the presence of enterotoxigenic
in the food production chain. It focuses on characteristics, expression, and regulation of the highly stable staphylococcal enterotoxins and in particular staphylococcal enterotoxin A (SEA). Together with the robustness of the pathogen under diverse environmental conditions and the range of possible entry routes into the food chain, this poses some of the biggest challenges in the control of SFP. Furthermore, the emergence of new enterotoxins, found to be connected with SFP, brings new questions around their regulatory mechanisms and expression in different food environments. The appearance of increasing amounts of antibiotic resistant strains found in food is also highlighted. Finally, potentials and limitations of implementing existing risk assessment models are discussed. Various quantitative microbial risk assessment approaches have attempted to quantify the growth of the bacterium and production of disease causing levels of toxin under various food chain and domestic food handling scenarios. This requires employment of predictive modeling tools, quantifying the spatiotemporal population dynamics of
in response to intrinsic and extrinsic food properties. In this context, the armory of predictive modeling employs both kinetic and probabilistic models to estimate the levels that potentiate toxin production, the time needed to reach that levels, and overall, the likelihood of toxin production. Following risk assessment, the main challenge to mitigate the risk of
intoxication is first to prevent growth of the organism and then to hamper the production of enterotoxins, or at least prevent the accumulation of high levels (e.g., >10-20 ng) in food. The necessity for continued studies indeed becomes apparent based on the challenges to understand, control, and predict enterotoxin production in relation to the food environment. Different types of food, preservatives, processing, and packaging conditions; regulatory networks; and different staphylococcal enterotoxin-producing
strains need to be further explored to obtain more complete knowledge about the virulence of this intriguing pathogen.
In the terrestrial ecosystems, perennial challenges of increased frequency and intensity of wildfires are exacerbated by climate change and unplanned human activities. Development of robust ...management and suppression plans requires accurate estimates of future burn probabilities. This study describes the development and validation of two hybrid intelligence predictive models that rely on an adaptive neuro-fuzzy inference system (ANFIS) and two metaheuristic optimization algorithms, i.e., genetic algorithm (GA) and firefly algorithm (FA), for the spatially explicit prediction of wildfire probabilities. A suite of ten explanatory variables (altitude, slope, aspect, land use, rainfall, soil order, temperature, wind effect, and distance to roads and human settlements) was investigated and a spatial database constructed using 32 fire events from the Zagros ecoregion (Iran). The frequency ratio model was used to assign weights to each class of variables that depended on the strength of the spatial association between each class and the probability of wildfire occurrence. The weights were then used for training the ANFIS-GA and ANFIS-FA hybrid models. The models were validated using the ROC-AUC method that indicated that the ANFIS-GA model performed better (AUCsuccessrate = 0.92; AUCpredictionrate = 0.91) than the ANFIS-FA model (AUCsuccessrate = 0.89; AUCpredictionrate = 0.88). The efficiency of these models was compared to a single ANFIS model and statistical analyses of paired comparisons revealed that the two meta-optimized predictive models significantly improved wildfire prediction accuracy compared to the single ANFIS model (AUCsuccessrate = 0.82; AUCpredictionrate = 0.78). We concluded that such predictive models may become valuable toolkits to effectively guide fire management plans and on-the-ground decisions on firefighting strategies.
•Fine-tuning of ANFIS parameters using genetic and firefly optimization algorithms.•Overcoming the potential bias inherent in the over-fitted single ANFIS model.•Proving AUC>0.88 for wildfire prediction using the hybrid intelligence models.
Predictive modeling of microbial behavior in food is a critical tool for assessing and mitigating potential risks in the food industry. Such models are developed based on mathematical algorithms and ...empirical data, providing valuable insights into the behavior of microorganisms in various food products and processing conditions. These models must be rigorously validated to ensure their accuracy and applicability to specific cases. The integration of predictive modeling into food safety and hazard analysis offers several advantages, including the ability to forecast microbial growth, identify critical control points, and optimize preventive measures. By leveraging these models, the food industry can proactively manage and reduce the risk of foodborne illnesses, ensuring the safety of consumers. Moreover, predictive modeling aligns with the principles of Hazard Analysis and Critical Control Points (HACCP), contributing to a systematic and science-based approach to food safety. This comprehensive review delves into multiple facets of microbial influence in the food industry. It begins by emphasizing the pivotal role of microbes in food products. It then explores the application of predictive modeling to assess microbial growth and predict microbial behavior. The review does not shy away from discussing the drawbacks associated with predictive modeling in the context of food safety and hazard analysis. Furthermore, the review underscores the significance of developing and implementing predictive modeling to enhance the quality and safety of food products. It provides a comprehensive overview of how predictive modeling can be utilized as part of a systematic approach to ensure food safety.
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•Integration of predictive modelling optimize preventive measures in food safety.•Food and health safety agencies formulate food safety laws.•The hazard analysis-derived safe target must be met adequately and consistently.•Predictive mathematical modelling comprehends the behaviour of food microorganisms.
The ventral visual stream underlies key human visual object recognition abilities. However, neural encoding in the higher areas of the ventral stream remains poorly understood. Here, we describe a ...modeling approach that yields a quantitatively accurate model of inferior temporal (IT) cortex, the highest ventral cortical area. Using high-throughput computational techniques, we discovered that, within a class of biologically plausible hierarchical neural network models, there is a strong correlation between a model's categorization performance and its ability to predict individual IT neural unit response data. To pursue this idea, we then identified a high-performing neural network that matches human performance on a range of recognition tasks. Critically, even though we did not constrain this model to match neural data, its top output layer turns out to be highly predictive of IT spiking responses to complex naturalistic images at both the single site and population levels. Moreover, the model's intermediate layers are highly predictive of neural responses in the V4 cortex, a midlevel visual area that provides the dominant cortical input to IT. These results show that performance optimization—applied in a biologically appropriate model class— can be used to build quantitative predictive models of neural processing.
Alzheimer's disease (AD) is a neurodegenerative disease characterized by cognitive decline. Sex differences in the progression of AD exist, but the neural mechanisms are not well understood. The ...purpose of the current study was to explore sex differences in brain functional connectivity (FC) at different stages of AD and their predictive ability on Montreal Cognitive Assessment (MoCA) scores using connectome‐based predictive modeling (CPM). Resting‐state functional magnetic resonance imaging was collected from 81 AD patients (44 females), 78 amnestic mild cognitive impairment patients (44 females), and 92 healthy controls (50 females). The FC analysis was conducted and the interaction effect between sex and group was investigated using two‐factor variance analysis. The CPM was used to predict MoCA scores. There were sex‐by‐group interaction effects on FC between the left dorsolateral superior frontal gyrus and left middle temporal gyrus, left precuneus and right calcarine fissure surrounding cortex, left precuneus and left middle occipital gyrus, left middle temporal gyrus and left precentral gyrus, and between the left middle temporal gyrus and right cuneus. In the CPM, the positive network predictive model significantly predicted MoCA scores in both males and females. There were significant sex‐by‐group interaction effects on FC between the left precuneus and left middle occipital gyrus, and between the left middle temporal gyrus and right cuneus could predict MoCA scores in female patients. Our results suggest that there are sex differences in FC at different stages of AD. The sex‐specific FC can further predict MoCA scores at individual level.
Functional connectivity with sex specificity predicts cognitive scores.
Wind energy increasingly attracts investment from many countries as a clean and renewable energy source. Since wind energy investment cost is high, the efficiency of a potential wind power plant ...should be determined using wind power prediction models and wind speed data before installation. Accurate wind power estimation is crucial to set up comprehensive strategies for wind power generation. This study estimated the power produced in a wind turbine using six different regression algorithms based on machine learning using temperature, humidity, pressure, air density, and wind speed data. The proposed estimation model was evaluated on the data received between 2011 and 2020 at station 17,112 in Çanakkale, Turkey. XGBoost, Random Forest, LightGBM, CatBoost, AdaBoost, and M5-Prime algorithms were used to create predictive models. Furthermore, model explanations were presented using the SHAP methodology. Among the regression algorithms evaluated according to the R2 performance metric, the best performance was obtained from the XGBoost algorithm. Regarding computational speed, the LightGBM model emerged as the most efficient model. The wind speed was shown to be the input feature with the SHAP algorithm's most significant impact on the model predictions.
Coronavirus disease 2019 (COVID-19) is sweeping the globe. Despite multiple case-series, actionable knowledge to tailor decision-making proactively is missing.
Can a statistical model accurately ...predict infection with COVID-19?
We developed a prospective registry of all patients tested for COVID-19 in Cleveland Clinic to create individualized risk prediction models. We focus here on the likelihood of a positive nasal or oropharyngeal COVID-19 test. A least absolute shrinkage and selection operator logistic regression algorithm was constructed that removed variables that were not contributing to the model's cross-validated concordance index. After external validation in a temporally and geographically distinct cohort, the statistical prediction model was illustrated as a nomogram and deployed in an online risk calculator.
In the development cohort, 11,672 patients fulfilled study criteria, including 818 patients (7.0%) who tested positive for COVID-19; in the validation cohort, 2295 patients fulfilled criteria, including 290 patients who tested positive for COVID-19. Male, African American, older patients, and those with known COVID-19 exposure were at higher risk of being positive for COVID-19. Risk was reduced in those who had pneumococcal polysaccharide or influenza vaccine or who were on melatonin, paroxetine, or carvedilol. Our model had favorable discrimination (c-statistic = 0.863 in the development cohort and 0.840 in the validation cohort) and calibration. We present sensitivity, specificity, negative predictive value, and positive predictive value at different prediction cutoff points. The calculator is freely available at https://riskcalc.org/COVID19.
Prediction of a COVID-19 positive test is possible and could help direct health-care resources. We demonstrate relevance of age, race, sex, and socioeconomic characteristics in COVID-19 susceptibility and suggest a potential modifying role of certain common vaccinations and drugs that have been identified in drug-repurposing studies.
Narcissism is one of the most fundamental personality traits in which individuals in general population exhibit a large heterogeneity. Despite a surge of interest in examining behavioral ...characteristics of narcissism in the past decades, the neurobiological substrates underlying narcissism remain poorly understood. Here, we addressed this issue by applying a machine learning approach to decode trait narcissism from whole‐brain resting‐state functional connectivity (RSFC). Resting‐state functional MRI (fMRI) data were acquired for a large sample comprising 155 healthy adults, each of whom was assessed for trait narcissism. Using a linear prediction model, we examined the relationship between whole‐brain RSFC and trait narcissism. We demonstrated that the machine‐learning model was able to decode individual trait narcissism from RSFC across multiple neural systems, including functional connectivity between and within limbic and prefrontal systems as well as their connectivity with other networks. Key nodes that contributed to the prediction model included the amygdala, prefrontal and anterior cingulate regions that have been linked to trait narcissism. These findings remained robust using different validation procedures. Our findings thus demonstrate that RSFC among multiple neural systems predicts trait narcissism at the individual level.
Black persons bear a disproportionate burden of peripheral artery disease (PAD) and experience higher rates of endovascular revascularization failure (ERF) when compared with non-Hispanic White ...persons. We aimed to identify predictors of ERF in Black persons using predictive modeling.
This retrospective study included all persons identifying as Black who underwent an initial endovascular revascularization procedure for PAD between 2011 and 2018 at a midwestern tertiary care center. Three predictive models were developed using (1) logistic regression, (2) penalized logistic regression (least absolute shrinkage and selection operator LASSO), and (3) random forest (RF). Predictive performance was evaluated under repeated cross-validation.
Of the 163 individuals included in the study, 113 (63.1%) experienced ERF at 1 y. Those with ERF had significant differences in symptom status (P < 0.001), lesion location (P < 0.001), diabetes status (P = 0.037), and annual procedural volume of the attending surgeon (P < 0.001). Logistic regression and LASSO models identified tissue loss, smoking, femoro-popliteal lesion location, and diabetes control as risk factors for ERF. The RF model identified annual procedural volume, age, PAD symptoms, number of comorbidities, and lesion location as most predictive variables. LASSO and RF models were more sensitive than logistic regression but less specific, although all three methods had an overall accuracy of ≥75%.
Black persons undergoing endovascular revascularization for PAD are at high risk of ERF, necessitating need for targeted intervention. Predictive models may be clinically useful for identifying high-risk patients, although individual predictors of ERF varied by model. Further exploration into these models may improve limb salvage for this population.
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