Capacity Of Different Models Research Articles

Serum sodium concentration predicting mortality in patients with aneurysmal subarachnoid hemorrhage

BackgroundTo date, inconsistent evidence exists on the role of hypernatremia in patients with aneurysmal subarachnoid hemorrhage (aSAH) who underwent surgical clipping. We aimed to investigate the association between serum sodium and mortality in these patients. MethodsA cohort study was performed to include adult patients with aSAH who underwent surgical clipping in a university hospital. The primary outcome was follow-up mortality. Propensity score matching (PSM) was used for matching patients’ baseline characteristics. Net reclassification improvement (NRI), and integrated discrimination improvement (IDI) were calculated to assess and compare the reclassification and discrimination capacity of different models. Trends in serum sodium over time were detected by the ordinary least squares model. ResultsOf 618 aSAH patients with surgical clipping during the study period, normal serum sodium was observed in 467 patients (75.6 %), and admission hypernatremia was noted in 151 patients (24.4 %). After adjustment with multivariate regression analysis, patients with hypernatremia had significantly higher odds for follow-up mortality (aOR: 2.86, 95 % CI: 1.54 to 5.30; P = 0.001). PSM analysis observed similar results (aOR: 2.38, 95 % CI: 1.29 to 4.55; P = 0.009). The incorporation of serum sodium during hospitalization markedly enhanced the IDI (P < 0.001) and NRI (P < 0.001) for the prediction of mortality. ConclusionsIn conclusion, the findings from this cohort study of aSAH patients with surgical clipping indicated that serum sodium can be an independent predictive factor of all-cause mortality, and inferior sequelae in aSAH patients. These findings endorsed the importance of managing hypernatremia and monitoring serum sodium in patients with aSAH.

Novel multi-spatial receptive field (MSRF) XGBoost method for predicting geological cross-section based on sparse borehole data

Due to the complex spatial features of geological formation, it remains a significant challenge to accurately predict geological cross-sections from limited borehole data. This study develops an innovative multi-spatial receptive field (MSRF) XGBoost approach, which encompasses classification and identification modules to forecast geological cross-sections using sparse borehole data. The classification module exclusively employs sparse borehole data to train a series of MSRF XGBoost models for soil classification. The identification module leverages all the trained models to generate potential predictions of unknown soil strata, automatically pinpointing the optimal one via Gaussian filtering and boundary similarity algorithms. A new boundary accuracy criterion is proposed to assess the prediction capacity of different models. Following this, the developed MSRF XGBoost method is compared with an existing conventional XGBoost method using both linear and nonlinear cases. The findings illustrate that our proposed method enhances the prediction accuracy for both linear and nonlinear geological cross-sections. Furthermore, the developed method is employed to determine a geological cross-section in the Netherlands using open-source borehole data. The accuracy of the method in predicting soil layers in all in situ boreholes reaches an impressive 90%, validating its effectiveness in practical geotechnical engineering.

Hierarchical forecasting models of stink bug population dynamics for pest management

In recent decades, the intensification of agricultural production, accompanied by an increasing pressure from pests in various crops, has resulted in a substantial increase in the use of synthetic pesticides. Integrated pest management (IPM) provides a framework for the development and use of sustainable control strategies, which include the monitoring of the crop pest complex and the use of decision tools such as predictive models. In this study, an empirical modeling approach based on a hierarchical Bayesian model with a state-space structure was developed to perform stink bug (Pentatomidae) density forecasts to assist in deciding when to carry out pest control interventions, thus increasing the efficacy and efficiency of IPM. Using stink bug abundance and crop phenology data, along with meteorological data from eight different sites in Argentina, we made 1-week forecasts of population density, evaluated the predictive capacity of different models using Leave-One-Out-Cross-Validation, and analyzed how the uncertainty in the predictions vary as a function of the number of vertical beat sheet samples. The forecasts made with our best model showed a reasonable degree of accuracy. We found that i) the observation error of the vertical beat sheet method was much larger than expected, and ii) the uncertainty analysis suggested a sample size of 40 to obtain a good balance between precision and sampling effort, which is in stark contrast to the average sample size usually taken by advisors. Our approach provides advisors with a tool to make better-informed decisions about when and if to carry out pest control interventions.

The prediction of distant metastasis risk for male breast cancer patients based on an interpretable machine learning model

ObjectivesThis research was designed to compare the ability of different machine learning (ML) models and nomogram to predict distant metastasis in male breast cancer (MBC) patients and to interpret the optimal ML model by SHapley Additive exPlanations (SHAP) framework.MethodsFour powerful ML models were developed using data from male breast cancer (MBC) patients in the SEER database between 2010 and 2015 and MBC patients from our hospital between 2010 and 2020. The area under curve (AUC) and Brier score were used to assess the capacity of different models. The Delong test was applied to compare the performance of the models. Univariable and multivariable analysis were conducted using logistic regression.ResultsOf 2351 patients were analyzed; 168 (7.1%) had distant metastasis (M1); 117 (5.0%) had bone metastasis, and 71 (3.0%) had lung metastasis. The median age at diagnosis is 68.0 years old. Most patients did not receive radiotherapy (1723, 73.3%) or chemotherapy (1447, 61.5%). The XGB model was the best ML model for predicting M1 in MBC patients. It showed the largest AUC value in the tenfold cross validation (AUC:0.884; SD:0.02), training (AUC:0.907; 95% CI: 0.899—0.917), testing (AUC:0.827; 95% CI: 0.802—0.857) and external validation (AUC:0.754; 95% CI: 0.739—0.771) sets. It also showed powerful ability in the prediction of bone metastasis (AUC: 0.880, 95% CI: 0.856—0.903 in the training set; AUC: 0.823, 95% CI:0.790—0.848 in the test set; AUC: 0.747, 95% CI: 0.727—0.764 in the external validation set) and lung metastasis (AUC: 0.906, 95% CI: 0.877—0.928 in training set; AUC: 0.859, 95% CI: 0.816—0.891 in the test set; AUC: 0.756, 95% CI: 0.732—0.777 in the external validation set). The AUC value of the XGB model was larger than that of nomogram in the training (0.907 vs 0.802) and external validation (0.754 vs 0.706) sets.ConclusionsThe XGB model is a better predictor of distant metastasis among MBC patients than other ML models and nomogram; furthermore, the XGB model is a powerful model for predicting bone and lung metastasis. Combining with SHAP values, it could help doctors intuitively understand the impact of each variable on outcome.

Modeling and Optimization of Multi-Model Waste Vehicle Routing Problem Based on the Time Window

With the development of China's economy, the urban floating population is also increasing, resulting in a sharp increase in the amount of urban waste. How to recycle and dispose of municipal waste more efficiently has become the top concern of municipalities and other relevant departments. In this article, the above problem is transformed into the municipal waste collection vehicle routing problem (MWCVRP) to solve the problem with the minimum total waste transportation cost. Because the carrying capacity of different models is different, this article introduces a cost calculation criterion that combines the total mileage of different models of transport vehicles and the number of station services. A multi-model garbage truck path optimization model is established, and then a heuristic-based task dynamic assignment algorithm is designed to solve the problem. The Solomon dataset is used to verify the feasibility and effectiveness of the model and algorithm through experiments.

Clinically sufficient classification accuracy and key predictors of treatment failure in a randomized controlled trial of Internet-delivered Cognitive Behavior Therapy for Insomnia

BackgroundIn Adaptive Treatment Strategies, each patient's outcome is predicted early in treatment, and treatment is adapted for those at risk of failure. It is unclear what minimum accuracy is needed for a classifier to be clinically useful. This study aimed to establish a empirically supported benchmark accuracy for an Adaptive Treatment Strategy and explore the relative value of input predictors. MethodPredictions from 200 patients receiving Internet-delivered cognitive-behavioral therapy in an RCT was analyzed. Correlation and logistic regression was used to explore all included predictors and the predictive capacity of different models. ResultsThe classifier had a Balanced accuracy of 67 %. Eleven out of the 21 predictors correlated significantly with Failure. A model using all predictors explained 56 % of the outcome variance, and simpler models between 16 and 47 %. Important predictors were patient rated stress, treatment credibility, depression change, and insomnia symptoms at week 3 as well as clinician rated attitudes towards homework and sleep medication. ConclusionsThe accuracy (67 %) found in this study sets a minimum benchmark for when prediction accuracy could be clinically useful. Key predictive factors were mainly related to insomnia, depression or treatment involvement. Simpler predictive models showed some promise and should be developed further, possibly using machine learning methods.

Exploring quantum perceptron and quantum neural network structures with a teacher-student scheme

Near-term quantum devices can be used to build quantum machine learning models, such as quantum kernel methods and quantum neural networks (QNN), to perform classification tasks. There have been many proposals on how to use variational quantum circuits as quantum perceptrons or as QNNs. The aim of this work is to introduce a teacher-student scheme that could systematically compare any QNN architectures and evaluate their relative expressive power. Specifically, the teacher model generates the datasets mapping random inputs to outputs which then have to be learned by the student models. This way, we avoid training on arbitrary data sets and allow to compare the learning capacity of different models directly via the loss, the prediction map, the accuracy and the relative entropy between the prediction maps. Here, we focus particularly on a quantum perceptron model inspired by the recent work of Tacchino et al. (2019) and compare it to the data re-uploading scheme that was originally introduced by Pérez-Salinas et al. (2020). We discuss alterations of the perceptron model and the formation of deep QNN to better understand the role of hidden units and the non-linearities in these architectures.

Visualizing surrogate decision trees of convolutional neural networks

Interpreting the decision-making of black boxes in machine learning becomes urgent nowadays due to their lack of transparency. One effective way to interpret these models is to transform them into interpretable surrogate models such as decision trees and rule lists. Compared with other methods that open the black boxes, rule extraction is a universal method which can theoretically extend to any black boxes. However, in practice, it is not appropriate for deep learning models such as convolutional neural networks (CNNs), since the extracted rules or decision trees are too large to interpret and the rules are not at the semantic level. These two drawbacks limit the usability of rule extraction for deep learning models. In this paper, we adopt a new strategy to solve the problem. We first decompose a CNN into a feature extractor and a classifier. Then extract the decision tree only from the classifier. Then, we leverage lots of segmented labeled images to learn the concepts of each feature. This method can extract human-readable decision trees from CNNs. Finally, we build CNN2DT, a visual analysis system to enable users to explore the surrogate decision trees. Use cases show that CNN2DT provides global and local interpretations of the CNN decision process. Besides, users can easily find the misclassification reasons for single images and the discriminating capacity of different models. A user study has demonstrated the effectiveness of CNN2DT on AlexNet and VGG16 for image classification.

Nonlinear analysis of reinforced concrete frame under lateral load

This study aims to investigate the capacity of different models to reproduce the nonlinear behavior of reinforced concrete framed structures. To accomplish this goal, a combined experimental and analytical research program was carried out on a large scaled reinforced concrete frame. Analyses were performed by SAP2000 and compared to experimental and VecTor2 results. Models made in SAP2000 differ in the simulation of the plasticity and the type of the frame elements used to discretize the frame structure. The results obtained allow a better understanding of the characteristics of all numerical models, helping the users to choose the best approach to perform nonlinear analysis.

Correlations between some physico-chemical properties of sunflower oil

Abstract The major objective of this study is to report physico-chemical properties of sunflower oil samples collected from different stages of the technological process for sunflower oil refining for food industry. The samples of oil were crude oil, washed oil, bleached oil and deodorized oil. The physico-chemical properties of sunflower oil experimentally determined were density, saponification value (SV), iodine value (IV), and acid value (AV). It was found that the density of sunflower oil remains approximately constant over the different stages of the manufacturing flow of cooking oil, except the crude oil. The acid value significantly decreases from crude oil (2.588) to deodorized oil (0.366). The iodine value and saponification value of the different samples of the sunflower oil corresponding to different stages of oil processing varies slightly. The capacity of different models to accurately correlate and/or predict the density of vegetable oil was tested. The density of sunflower oil can be accurately estimated from its SV and IV or with an empirical equation, when density data are available.

NormaCurve: A SuperCurve-Based Method That Simultaneously Quantifies and Normalizes Reverse Phase Protein Array Data

MotivationReverse phase protein array (RPPA) is a powerful dot-blot technology that allows studying protein expression levels as well as post-translational modifications in a large number of samples simultaneously. Yet, correct interpretation of RPPA data has remained a major challenge for its broad-scale application and its translation into clinical research. Satisfying quantification tools are available to assess a relative protein expression level from a serial dilution curve. However, appropriate tools allowing the normalization of the data for external sources of variation are currently missing.ResultsHere we propose a new method, called NormaCurve, that allows simultaneous quantification and normalization of RPPA data. For this, we modified the quantification method SuperCurve in order to include normalization for (i) background fluorescence, (ii) variation in the total amount of spotted protein and (iii) spatial bias on the arrays. Using a spike-in design with a purified protein, we test the capacity of different models to properly estimate normalized relative expression levels. The best performing model, NormaCurve, takes into account a negative control array without primary antibody, an array stained with a total protein stain and spatial covariates. We show that this normalization is reproducible and we discuss the number of serial dilutions and the number of replicates that are required to obtain robust data. We thus provide a ready-to-use method for reliable and reproducible normalization of RPPA data, which should facilitate the interpretation and the development of this promising technology.AvailabilityThe raw data, the scripts and the NormaCurve package are available at the following web site: http://microarrays.curie.fr.

Regulation of Hemostasis and Thrombosis

HomeArteriosclerosis, Thrombosis, and Vascular BiologyVol. 27, No. 3Regulation of Hemostasis and Thrombosis Free AccessReview ArticlePDF/EPUBAboutView PDFView EPUBSections ToolsAdd to favoritesDownload citationsTrack citationsPermissions ShareShare onFacebookTwitterLinked InMendeleyReddit Jump toFree AccessReview ArticlePDF/EPUBRegulation of Hemostasis and ThrombosisInsights from Murine Models Daniel T. Eitzman Daniel T. EitzmanDaniel T. Eitzman From the Division of Cardiovascular Medicine, Department of Internal Medicine, University of Michigan Medical Center, Ann Arbor. Search for more papers by this author Originally published1 Mar 2007https://doi.org/10.1161/01.ATV.0000257138.15250.48Arteriosclerosis, Thrombosis, and Vascular Biology. 2007;27:453This issue of Arteriosclerosis, Thrombosis, and Vascular Biology contains the first of a series of articles describing the use of murine models to explore the regulation of hemostasis and thrombosis.See page 454Thrombotic complications of atherosclerosis, such as myocardial infarction and stroke, remain the leading cause of mortality in the Western world. Despite advances in therapies such as statins, which retard the progression of atherosclerosis and reduce vascular complications, myocardial infarction and stroke continue to dominate the healthcare system. Thus there is an urgent need to develop effective chronic therapeutic interventions for prevention of these thrombotic vascular complications. Current treatment is limited in part because of insufficient knowledge regarding the important regulatory factors in atherothrombosis and also because therapies targeting this process may also impair hemostasis leading to bleeding complications. Therapeutic interventions are similarly limited for prophylaxis of deep venous thrombosis and thrombi occurring in the atrial appendages. It is also likely that different factors affect the predisposition to thrombosis in different disease states and vascular beds, and that disease-specific antithrombotic treatments will be necessary.Because thrombosis appears to be affected by many factors, including circulating blood cells, the vessel wall, blood flow patterns, and many other unknown elements, an in vivo model may be required to accurately assess the impact of potential important regulatory factors. Because mice are particularly amenable to genetic manipulation, the mouse has become a valuable tool to study thrombosis. There are now several models of induced and spontaneous thrombosis available in the mouse. The use of mice to study thrombosis has steadily increased over the last 2 decades as depicted in the Figure. Download figureDownload PowerPointPubMed references over the years with “mouse” and “thrombosis” as search terms.Thi s series of brief reviews will highlight the potential of the mouse to advance our understanding of vascular thrombosis. The first review in this issue by Tollefsen will demonstrate the utility of mouse models toward characterizing the physiological relevance of a serpin with thrombin inhibitory activity, heparin cofactor II, and how these studies have enhanced our knowledge of the underlying antithrombotic mechanisms of this protein. Other reviews will demonstrate how mouse models have elucidated the physiological impact of other putative regulators of coagulation as well as platelet adhesion molecules. The availability of various thrombosis models and the capacity of different models to address disease-specific questions will also be addressed. In addition, molecular imaging of dynamic thrombus formation in mice will be covered as this technology may provide direct evidence of the sequence of events and spatial orientation of the various molecules and cell types involved in a forming thrombus. Another review will describe how mice are being used to explore the pathogenesis of a common complication of heparin therapy, heparin-induced thrombocytopenia. Although most investigations using mice have involved assessing the impact of a specific genetic alteration in mice, there may also be utility in determining the potential of cardiovascular risk factors on a thrombotic endpoint in mice. Because the mechanisms by which many factors increase MI and stroke are not clear, it is possible that some of these factors directly affect thrombosis. For example, animal models will be reviewed that demonstrate effects of obesity, diabetes, dietary interventions, inflammation, and elevated homocysteine on thrombosis. In addition to directly showing the impact of these complex perturbations on a thrombotic end point, these phenotypes will be amenable to molecular and genetic dissection using mice, and new therapeutic targets may be identified.Mouse models of thrombosis have already had a large impact toward our understanding of thrombosis regulation. The rate at which this area of research is progressing will likely translate into new therapeutic interventions for the prevention of arterial and venous thrombotic disease in the near future.FootnotesCorrespondence to Daniel T. Eitzman, MD, 1150 W. Medical Center Dr., 7301 MSRB III, Ann Arbor, MI 48109-0644. E-mail [email protected] Previous Back to top Next FiguresReferencesRelatedDetailsCited By Heather L, Hafstad A, Halade G, Harmancey R, Mellor K, Mishra P, Mulvihill E, Nabben M, Nakamura M, Rider O, Ruiz M, Wende A and Ussher J (2022) Guidelines on models of diabetic heart disease, American Journal of Physiology-Heart and Circulatory Physiology, 10.1152/ajpheart.00058.2022, 323:1, (H176-H200), Online publication date: 1-Jul-2022. March 2007Vol 27, Issue 3 Advertisement Article InformationMetrics https://doi.org/10.1161/01.ATV.0000257138.15250.48 Originally publishedMarch 1, 2007 PDF download Advertisement