Salmonella contamination of pork products is a significant public health concern. Temperature abuse scenarios, such as inadequate refrigeration or prolonged exposure to room temperature, can enhance ...Salmonella proliferation. This study aimed to develop and validate models for Salmonella growth considering competition with background microbiota in raw ground pork, under isothermal and dynamic conditions of temperature abuse between 10 and 40 °C. The maximum specific growth rate (μmax) and maximum population density (MPD) were estimated to quantitatively describe the growth behavior of Salmonella. To reflect more realistic microbial interactions in Salmonella-contaminated product, our model considered competition with the background microbiota, measured as mesophilic aerobic plate counts (APC). Notably, the μmax of Salmonella in low-fat samples (∼5 %) was significantly higher (p < 0.05) than that in high-fat samples (∼25 %) at 10, 20, and 30 °C. The average doubling time of Salmonella was 26, 4, 2, 1.5, 0.8, and 1.1 h at 10, 15, 20, 25, 30, and 40 °C, respectively. The initial concentration of Salmonella minimally impacted its growth in ground pork at any temperature. The MPD of APC consistently exceeded that of Salmonella, indicating the growth of APC without competition from Salmonella. The competition model exhibited excellent fit with the experimental data, as 95 % (627/660) of residual errors fell within the desired acceptable prediction zone (pAPZ >0.70). The theoretical minimum and optimum growth temperatures for Salmonella ranged from 5 to 6 °C and 35 to 36 °C, respectively. The dynamic model displayed strong predictive performance, with 90 % (57/63) of residual errors falling within the APZ. Dynamic models could be valuable tools for validating and refining simpler static or isothermal models, ultimately improving their predictive capabilities to enhance food safety.
•Salmonella and APC showed growth without a lag phase in ground pork.•Salmonella growth is inhibited by APC, but not vice versa.•Significant impact of fat content was observed on Salmonella growth.•Prediction of dynamic models considering competitive effects is reasonably accurate.
The rate of unplanned hospital readmissions following total hip arthroplasty (THA) varies from 3 to 10%, representing a major economic burden. However, it is unknown if specific factors are ...associated with different types of complications (ie, medical or orthopaedic-related) that lead to readmissions. Therefore, this study aimed to: (1) determine the overall, medical-related, and orthopaedic-related 90-day readmission rate; and (2) develop a predictive model for risk factors affecting overall, medical-related, and orthopaedic-related 90-day readmissions following THA.
A prospective cohort of primary unilateral THAs performed at a large tertiary academic center in the United States from 2016 to 2020 was included (n = 8,893 patients) using a validated institutional data collection system. Orthopaedic-related readmissions were specific complications affecting the prosthesis, joint, and surgical wound. Medical readmissions were due to any other cause requiring medical management. Multivariable logistic regression models were used to investigate associations between prespecified risk factors and 90-day readmissions, as well as medical and orthopaedic-related readmissions independently.
Overall, the rate of 90-day readmissions was 5.6%. Medical readmissions (4.2%) were found to be more prevalent than orthopaedic-related readmissions (1.4%). The area under the curve for the 90-day readmission model was 0.71 (95% confidence interval: 0.69 to 0.74). Factors significantly associated with medical-related readmissions were advanced age, Black race, education, Charlson Comorbidity Index, surgical approach, opioid overdose risk score, and nonhome discharge. In contrast, risk factors linked to orthopaedic-related readmissions encompassed body mass index, patient-reported outcome measure phenotype, nonosteoarthritis indication, opioid overdose risk, and nonhome discharge.
Of the overall 90-day readmissions following primary THA, 75% were due to medical-related complications. Our successful predictive model for complication-specific 90-day readmissions highlights how different risk factors may disproportionately influence medical versus orthopaedic-related readmissions, suggesting that patient-specific, tailored preventive measures could reduce postoperative readmissions in the current value-based health care setting.
Remote sensing based on Remote Piloted Aircraft Systems (RPAS) has proved valuable for monitoring agronomic parameters in precision agriculture. This research aimed to develop predictive models based ...on machine learning to estimate indirect nitrogen levels (Narea) and grain yield in irrigated rice. During the five phenological stages of cultivation, a Sequoia® camera aboard the Phantom 4® Pro platform acquired the multispectral images. In addition to the spectral bands, 11 vegetation indices were taken as predictors of the response variables (Narea and grain yield). Spearman's correlation coefficient (p) analyzed the ideal monitoring window and selected the model variables. The Multi-Layer Perceptron (MLP) algorithm adjusted the predictive models that had their performance evaluated in training and testing. The results obtained by the Spearman correlation indicate that the ideal window for monitoring rice by RPAS, for both response variables, occurs at the beginning of the reproduction phase (R1). MLP generated a more accurate model for Narea, demonstrated by Pearson's correlation between predicted and observed values (0.82 and 0.71) and mean absolute error (MAE) of 9.47 and 10.89. Grain yield models show good MLP at all stages and excellent accuracy. In this way, our results reinforce the excellent efficiency of the combination of remote sensing via RPAS and machine learning in applications aimed at precision agriculture, serving as a useful tool for managing production and evaluating grain yield in irrigated rice fields.
Although trait and state rumination play a central role in the exacerbation of negative affect, evidence suggests that they are weakly correlated and exert distinct influences on emotional reactivity ...to stressors. Whether trait and state rumination share a common or distinct neural substrate remains unclear. In this study, we utilized functional near-infrared spectroscopy (fNIRS) combined with connectome-based predictive modeling (CPM) to identify neural fingerprints associated with trait and state rumination. CPM identified distinctive functional connectivity (FC) profiles that contribute to the prediction of trait rumination, primarily involving FC within the default mode network (DMN) and the dorsal attention network (DAN) as well as FC between the DMN, control network (CN), DAN, and salience network (SN). Conversely, state rumination was predominantly associated with FC between the DMN and CN. Furthermore, the predictive features of trait rumination can be robustly generalized to predict state rumination, and vice versa. In conclusion, this study illuminates the importance of both DMN and non-DMN systems in the emergence and persistence of rumination. While trait rumination was associated with stronger and broader FC than state rumination, the generalizability of the predictive features underscores the presence of shared neural mechanisms between the two forms of rumination. These identified connectivity fingerprints may hold promise as targets for innovative therapeutic interventions aimed at mitigating rumination-related negative affect.
•Trait and state rumination exhibit distinctive brain connectivity features•The FC model developed for one type of rumination predict the other•Trait and state rumination share common connectivity fingerprints
Grain boundary properties of elemental metals Zheng, Hui; Li, Xiang-Guo; Tran, Richard ...
Acta materialia,
March 2020, 2020-03-00, 2020-03-01, Volume:
186, Issue:
C
Journal Article
Peer reviewed
Open access
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The structure and energy of grain boundaries (GBs) are essential for predicting the properties of polycrystalline materials. In this work, we use high-throughput density functional ...theory calculations workflow to construct the Grain Boundary Database (GBDB), the largest database of DFT-computed grain boundary properties to date. The database currently encompasses 327 GBs of 58 elemental metals, including 10 common twist or symmetric tilt GBs for body-centered cubic (bcc) and face-centered cubic (fcc) systems and the Σ7 0001 twist GB for hexagonal close-packed (hcp) systems. In particular, we demonstrate a novel scaled-structural template approach for HT GB calculations, which reduces the computational cost of converging GB structures by a factor of ~ 3–6. The grain boundary energies and work of separation are rigorously validated against previous experimental and computational data. Using this large GB dataset, we develop an improved predictive model for the GB energy of different elements based on the cohesive energy and shear modulus. The open GBDB represents a significant step forward in the availability of first principles GB properties, which we believe would help guide the future design of polycrystalline materials.
Proliferative lupus nephritis (PLN) is a serious organ-threatening manifestation of systemic lupus erythematosus (SLE) that is associated with high mortality and renal failure. Here, we analyzed data ...from 1287 SLE patients with renal manifestations, including 780 of which were confirmed as proliferative or non-proliferative LN patients by renal biopsy, divided into a training cohort (547 patients) and a validation cohort (233 patients). By applying a least absolute shrinkage and selection operator (LASSO) regression approach combined with multivariate logistic regression analysis to build a nomogram for prediction of PLN that was then assessed by receiver operating characteristic (ROC) curves, calibration curves, and clinical decision curves (DCA) in both the training and validation cohorts. The area under the ROC curve (AUC) of the model in the training cohort was 0.921 (95% confidence interval (CI): 0.895–0.946), the AUC of internal validation in the training cohort was 0.909 and the AUC of external validation was 0.848 (95% CI: 0.796–0.900). The nomogram showed good performance as evaluated using calibration and DCA curves. Taken together, our results indicate that our nomogram that comprises 12 significantly relevant variables could be clinically valuable to prognosticate on the risk of PLN in SLE, so as to improve patient prognoses.
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•Renal biopsy is the gold standard for diagnosing Proliferative Lupus Nephritis (PLN).•We present a non-surgical biopsy approach to predicting and differentiating PLN.•We have identified 12 variables that could, in combination, reliably predict PLN.
The advance rate (AR) of a tunnel boring machine (TBM) under hard rock conditions is a key parameter in the successful implementation of tunneling engineering. In this study, we improved the accuracy ...of prediction models by employing a hybrid model of extreme gradient boosting (XGBoost) with Bayesian optimization (BO) to model the TBM AR. To develop the proposed models, 1286 sets of data were collected from the Peng Selangor Raw Water Transfer tunnel project in Malaysia. The database consists of rock mass and intact rock features, including rock mass rating, rock quality designation, weathered zone, uniaxial compressive strength, and Brazilian tensile strength. Machine specifications, including revolution per minute and thrust force, were considered to predict the TBM AR. The accuracies of the predictive models were examined using the root mean squares error (RMSE) and the coefficient of determination (R2) between the observed and predicted yield by employing a five-fold cross-validation procedure. Results showed that the BO algorithm can capture better hyper-parameters for the XGBoost prediction model than can the default XGBoost model. The robustness and generalization of the BO-XGBoost model yielded prominent results with RMSE and R2 values of 0.0967 and 0.9806 (for the testing phase), respectively. The results demonstrated the merits of the proposed BO-XGBoost model. In addition, variable importance through mutual information tests was applied to interpret the XGBoost model and demonstrated that machine parameters have the greatest impact as compared to rock mass and material properties.
A recent wave of research has attempted to define fairness quantitatively. In particular, this work has explored what fairness might mean in the context of decisions based on the predictions of ...statistical and machine learning models. The rapid growth of this new field has led to wildly inconsistent motivations, terminology, and notation, presenting a serious challenge for cataloging and comparing definitions. This article attempts to bring much-needed order. First, we explicate the various choices and assumptions made-often implicitly-to justify the use of prediction-based decision-making. Next, we show how such choices and assumptions can raise fairness concerns and we present a notationally consistent catalog of fairness definitions from the literature. In doing so, we offer a concise reference for thinking through the choices, assumptions, and fairness considerations of prediction-based decision-making.
Predictive models are central to many scientific disciplines and vital for informing management in a rapidly changing world. However, limited understanding of the accuracy and precision of models ...transferred to novel conditions (their ‘transferability’) undermines confidence in their predictions. Here, 50 experts identified priority knowledge gaps which, if filled, will most improve model transfers. These are summarized into six technical and six fundamental challenges, which underlie the combined need to intensify research on the determinants of ecological predictability, including species traits and data quality, and develop best practices for transferring models. Of high importance is the identification of a widely applicable set of transferability metrics, with appropriate tools to quantify the sources and impacts of prediction uncertainty under novel conditions.
Models transferred to novel conditions could provide predictions in data-poor scenarios, contributing to more informed management decisions.
The determinants of ecological predictability are, however, still insufficiently understood.
Predictions from transferred ecological models are affected by species’ traits, sampling biases, biotic interactions, nonstationarity, and the degree of environmental dissimilarity between reference and target systems.
We synthesize six technical and six fundamental challenges that, if resolved, will catalyze practical and conceptual advances in model transfers.
We propose that the most immediate obstacle to improving understanding lies in the absence of a widely applicable set of metrics for assessing transferability, and that encouraging the development of models grounded in well-established mechanisms offers the most immediate way of improving transferability.
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.
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