Subset Selection Research Articles

Unraveling autonomic cardiovascular control complexity during orthostatic stress: Insights from a mathematical model

Understanding cardiovascular control mediated by the autonomic system remains challenging due to its inherent complexity. Consequently, syndromes such as orthostatic intolerance continue to evoke debates regarding the underlying pathophysiological mechanisms. This study develops a comprehensive mathematical model simulating the control of the sympathetic branch of the cardiovascular system in individuals with normal and abnormal responses to the head-up-tilt test. We recruited four young women: one control, one with vasovagal syncope, one with orthostatic hypertension, and one with orthostatic hypotension, exposing them to an orthostatic head-up tilt test (HUTT) employing non-invasive methods to measure electrocardiography and continuous blood pressure.Our work encompasses a compartmental model formulated using a system of ordinary differential equations. Using heart rate as input, we predict blood pressure, flow, and volume in compartments representing the veins, arteries, heart, and the sympathetic branch of the baroreflex control system. The latter is modulated by high- and low-pressure baroreceptor afferents activated by changes in blood pressure induced by the HUTT. Sensitivity analysis, parameter subset selection, and optimization are employed to estimate patient-specific parameters associated with autonomic performance. The model has seven sensitive and identifiable parameters with significant physiological relevance that can serve as biomarkers for patient classification.Results show that the model can reproduce a spectrum of blood pressure responses successfully, fitting the trajectory displayed by the experimental data. The controller exhibits behavior that emulates the operation of the sympathetic system. These encouraging findings underscore the potential of computational methods in evaluating pathologies associated with autonomic nervous system control, warranting further exploration and novel approaches.

Multivariate Bayesian variable selection for multi-trait genetic fine mapping

Abstract Genome-wide association studies (GWAS) have identified thousands of single-nucleotide polymorphisms (SNPs) associated with complex traits, but determining the underlying causal variants remains challenging. Fine mapping aims to pinpoint the potentially causal variants from a large number of correlated SNPs possibly with group structure in GWAS-enriched genomic regions using variable selection approaches. In multi-trait fine mapping, we are interested in identifying the causal variants for multiple related traits. Existing multivariate variable selection methods for fine mapping select variables for all responses without considering the possible heterogeneity across different responses. Here, we develop a novel multivariate Bayesian variable selection method for multi-trait fine mapping to select causal variants from a large number of grouped SNPs that target at multiple correlated and possibly heterogeneous traits. Our new method is featured by its selection at multiple levels, incorporation of prior biological knowledge to guide selection and identification of best subset of traits the variants target at. We showed the advantage of our method over existing methods via comprehensive simulations that mimic typical fine-mapping settings and a real-world fine-mapping example in UK Biobank, where we identified critical causal variants potentially targeting at different subsets of addictive behaviours and risk factors.

Primary diffuse large B-cell lymphoma of bone in adults: A SEER population-based study.

Primary diffuse large B-cell lymphoma of the bone (PB-DLBCL) is an extremely rare type of extra-nodal lymphoma. The clinical characteristics, management, and survival outcomes of adult PB-DLBCL patients remain poorly defined. To explore the clinical manifestations, staging, therapeutic options, prognostic factors and outcomes of adult patients with PB-DLBCL and to create a model to predict survival outcomes. Data of adult PB-DLBCL patients were obtained from the Surveillance, Epidemiology, and End Results (SEER) Program 18 registries database from 2000 to 2018. The Kaplan-Meier survival analysis was conducted to calculate survival rates. Univariate Cox regression, best subset selection (BESS), and least absolute shrinkage and selection operator (LASSO), followed by backward stepwise multivariable Cox regression, were used to construct the nomogram. The nomograms were evaluated using the concordance index (C-index), calibration curves and decision curve analysis (DCA). Diffuse large B-cell lymphoma (DLBCL) (67.51%) was the most frequent type of primary bone lymphoma. The most involved sites were the spine and lower-limb long bones. For the whole cohort, the 3-, 5-, 10- and 15-year overall survival (OS) rates were 74.9%, 70.5%, 60.0%, and 49.9%, and corresponding disease-specific survival (DSS) rates were 79.7%, 77.8%, 75.1%, and 71.4%, respectively. For OS, age, Ann Arbor stage, primary site and therapy were confirmed as final factors to develop the nomogram in adult PB-DLBCL patients, whereas for DSS, Age, marital status, Ann Arbor stage, number of bone lesions, therapy and year of diagnosis were confirmed as final factors in developing the nomogram. The nomograms demonstrated good accuracy and clinical utility. Established nomograms can accurately predict the survival of patients with PB-DLBCL and help clinicians optimize treatment.

Uncertainty-informed selection of CMIP6 Earth system model subsets for use in multisectoral and impact models

Abstract. Earth system models (ESMs) and general circulation models (GCMs) are heavily used to provide inputs to sectoral impact and multisector dynamic models, which include representations of energy, water, land, economics, and their interactions. Therefore, representing the full range of model uncertainty, scenario uncertainty, and interannual variability that ensembles of these models capture is critical to the exploration of the future co-evolution of the integrated human–Earth system. The pre-eminent source of these ensembles has been the Coupled Model Intercomparison Project (CMIP). With more modeling centers participating in each new CMIP phase, the size of the model archive is rapidly increasing, which can be intractable for impact modelers to effectively utilize due to computational constraints and the challenges of analyzing large datasets. In this work, we present a method to select a subset of the latest phase, CMIP6, featuring models for use as inputs to a sectoral impact or multisector dynamics models, while prioritizing preservation of the range of model uncertainty, scenario uncertainty, and interannual variability in the full CMIP6 ensemble results. This method is intended to help impact modelers select climate information from the CMIP archive efficiently for use in downstream models that require global coverage of climate information. This is particularly critical for large-ensemble experiments of multisector dynamic models that may be varying additional features beyond climate inputs in a factorial design, thus putting constraints on the number of climate simulations that can be used. We focus on temperature and precipitation outputs of CMIP6 models, as these are two of the most used variables among impact models, and many other key input variables for impacts are at least correlated with one or both of temperature and precipitation (e.g., relative humidity). Besides preserving the multi-model ensemble variance characteristics, we prioritize selecting CMIP6 models in the subset that preserve the very likely distribution of equilibrium climate sensitivity values as assessed by the latest Intergovernmental Panel on Climate Change (IPCC) report. This approach could be applied to other output variables of climate models and, possibly when combined with emulators, offers a flexible framework for designing more efficient experiments on human-relevant climate impacts. It can also provide greater insight into the properties of existing CMIP6 models.

Assessing Alkaline Phosphatase Reference Intervals Using Gamma-Glutamyl Transferase as a Surrogate for Normal Liver Function

Abstract Limited studies assess reference intervals (RIs) for hepatic enzymes, particularly for alkaline phosphatase (ALP) and gamma-glutamyl transferase (GGT). In the US, ALP and GGT RIs were established on Roche assays using a European cohort of ≤ 450 patients that may not reflect population variance. Further, age- and sex-specific variation in ALP and GGT may warrant tiered RIs, but evidence to justify this approach is limited. We aimed to evaluate the manufacturer’s RIs by comparing them to indirectly estimated RIs (IRIs). 217,333 AST, ALT, ALP, and GGT results (Roche c702) performed between 1/1/2019-12/31/2022 on outpatients ≥21 years were extracted from the laboratory information system. The first ALP for each patient was identified, and the earliest AST, ALT, and GGT drawn within 7 days was included. This approach was repeated for GGT. Concordance and correlation were calculated using Weighted Cohen’s Kappa and Spearman’s rank correlation coefficient respectively. {refineR} was applied to patients with “normal” liver function (defined as normal AST, ALT for all IRIs, plus normal GGT for ALP IRIs, and ALP for GGT IRIs). IRIs were estimated for all males, all females, females ≤50, and females >50 years and compared to the method specific manufacturer RI (ALP: 40-129 Units/L for males and 35-104 Units/L for females; GGT: 8-61 Units/L for males and 5-36 Units/L for females). Of the 71,783 outpatients, 54.9% were female, 74% self-identified as white and 19% as black. The median age was 58 (IQR: 43-68 years), the median ALP concentration was 77 (62-97 Units/L), and the median GGT concentration was 28 (17-57 Units/L). A moderate agreement (Kappa = 0.48, 95% confidence interval: 0.46-0.51) and positive correlation (r = 0.51; p<0.0001) between ALP and GGT was observed. For ALP and GGT, no significant differences were observed between the lower limit of the IRIs and the current RI. The upper limit of estimated IRIs was 132 [95% confidence interval: 117-147 Units/L] for patients with normal AST/ALT/GGT (n = 1596). For males, the sex-specific upper limit was 120 [101-140 Units/L] and for females, was 123 [110-136 Units/L]. Age stratified upper limits for females were 120 [101-140 Units/L], and 109 [97-121 Units/L] for ≤50 and >50 years, respectively. The upper limit for all patients with normal AST/ALT/ALP (n = 2,446) was 62 [55-70 Units/L]. For males, all females, females ≤50 and >50 years was 58 [46-69 Units/L], 40 [39-51 Units/L], 31 [23-39 Units/L], and 56 [41-71 Units/L]. Manufacturer RIs agreed well with the IRIs for most subsets, but differed for ALP IRIs of all females with normal hepatobilliary function and in GGT for all females and females >50 years. Further investigation of the impact of preselecting patient populations on IRIs and a direct RI study on select patient subsets is worth investigation.

A fast block sparse Kaczmarz algorithm for sparse signal recovery

The randomized sparse Kaczmarz (RSK) method is an iterative algorithm for computing sparse solutions of linear systems. Recently, Tondji and Lorenz analyzed the parallel version of the RSK method and established its linear expected convergence by implementing a randomized control scheme for subset selection at each iteration. Expanding upon this groundwork, we explore a natural extension of the randomized control scheme: greedy strategies such as the Motzkin criteria. Specifically, we propose a fast block sparse Kaczmarz algorithm based on the Motzkin criterion. It is proved that the proposed method converges linearly to the sparse solutions of the linear systems. Additionally, we offer error estimates for linear systems with noisy right-hand sides, and show that the proposed method converges within an error threshold of the noise level. Numerical results substantiate the feasibility of our proposed method and highlight its superior convergence rate compared to the parallel version of the RSK method.

An Empirical Parameterization of the Subgrid‐Scale Distribution of Water Vapor in the UTLS for Atmospheric General Circulation Models

AbstractTemperature and water vapor are known to fluctuate on multiple scales. In this study 27 years of airborne measurements of temperature and relative humidity from In‐service Aircraft for a Global Observing System (IAGOS) are used to parameterize the distribution of water vapor in the upper troposphere and lower stratosphere. The parameterization is designed to simulate water vapor fluctuations within gridboxes of atmospheric general circulation models (AGCMs) with typical size of a few tens to a few hundred kilometers. The distributions currently used in such models are often not supported by observations at high altitude. More sophisticated distributions are key to represent ice supersaturation, a physical phenomenon that plays a major role in the formation of natural cirrus and contrail cirrus. Here the observed distributions are fitted with a beta law whose parameters are adjusted from the gridbox mean variables. More specifically the standard deviation and skewness of the distributions are expressed as empirical functions of the average temperature and specific humidity, two typical prognostic variables of AGCMs. Thus, the distribution of water vapor is fully parameterized for a use in these models. The new parameterization reproduces the observed distributions with a determination coefficient always greater than 0.917 and with a mean value of 0.997. The parameterization is robust to a selection of various geographical subsets of data and to gridbox sizes varying between 25 and 300 km.

Advanced mathematical modeling of mitigating security threats in smart grids through deep ensemble model

A smart grid (SG) is a cutting-edge electrical grid that utilizes digital communication technology and automation to effectively handle electricity consumption, distribution, and generation. It incorporates energy storage systems, smart meters, and renewable energy sources for bidirectional communication and enhanced energy flow between grid modules. Due to their cyberattack vulnerability, SGs need robust safety measures to protect sensitive data, ensure public safety, and maintain a reliable power supply. Robust safety measures, comprising intrusion detection systems (IDSs), are significant to protect against malicious manipulation, unauthorized access, and data breaches in grid operations, confirming the electricity supply chain’s integrity, resilience, and reliability. Deep learning (DL) improves intrusion recognition in SGs by effectually analyzing network data, recognizing complex attack patterns, and adjusting to dynamic threats in real-time, thereby strengthening the reliability and resilience of the grid against cyber-attacks. This study develops a novel Mountain Gazelle Optimization with Deep Ensemble Learning based intrusion detection (MGODEL-ID) technique on SG environment. The MGODEL-ID methodology exploits ensemble learning with metaheuristic approaches to identify intrusions in the SG environment. Primarily, the MGODEL-ID approach utilizes Z-score normalization to convert the input data into a uniform format. Besides, the MGODEL-ID approach employs the MGO model for feature subset selection. Meanwhile, the detection of intrusions is performed by an ensemble of three classifiers such as long short-term memory (LSTM), deep autoencoder (DAE), and extreme learning machine (ELM). Eventually, the dung beetle optimizer (DBO) is utilized to tune the hyperparameter tuning of the classifiers. A widespread simulation outcome is made to demonstrate the improved security outcomes of the MGODEL-ID model. The experimental values implied that the MGODEL-ID model performs better than other models.

A-093 Assessing Alkaline Phosphatase Reference Intervals Using Gamma-Glutamyl Transferase as a Surrogate for Normal Liver Function

Abstract Background There are limited published studies assessing reference intervals (RIs) for hepatic enzymes, particularly for alkaline phosphatase (ALP) and gamma-glutamyl transferase (GGT). In the US, ALP and GGT RIs were established on Roche assays using a European cohort of ≤ 450 patients that may not reflect population variance across practice settings. Further, age- and sex-specific variation in ALP and GGT may warrant tiered RIs, but evidence to justify this approach is limited. We aimed to evaluate the manufacturer’s RIs by comparing them to indirectly estimated RIs (IRIs) from routine clinical data. Methods 217,333 AST, ALT, ALP, and GGT results (Roche c702) performed between 1/1/2019-12/31/2022 on outpatients ≥21 years were extracted from the laboratory information system. The first ALP for each patient was identified, and the earliest AST, ALT, and GGT drawn within 7 days was included. This approach was repeated for GGT. Concordance and correlation were calculated using Weighted Cohen’s Kappa and Spearman’s rank correlation coefficient respectively. {refineR}, an algorithm for calculating IRIs, was applied to patients with “normal” liver function (defined as normal AST, ALT for all IRIs, plus normal GGT for ALP IRIs, and ALP for GGT IRIs). IRIs were estimated for all males, all females, females ≤50, and females >50 years and compared to the method specific manufacturer RI (ALP: 40-129 Units/L for males and 35-104 Units/L for females; GGT: 8-61 Units/L for males and 5-36 Units/L for females). Results Of the 71,783 outpatients, 54.9% were female, 74% self-identified as white and 19% as black. The median age was 58 (IQR: 43-68 years), the median ALP concentration was 77 (62-97 Units/L), and the median GGT concentration was 28 (17-57 Units/L). A moderate agreement between ALP and GGT was observed (Kappa = 0.48, 95% confidence interval: 0.46-0.51). A positive correlation between ALP and GGT was observed r = 0.51 (p<0.0001). For ALP, no significant differences were observed between the lower limit of the IRIs and the current RI. The upper limit of estimated IRIs was 132 [95% confidence interval: 117-147 Units/L] for patients with normal AST/ALT/GGT (n = 1596). For males, the sex-specific upper limit was 120 [101-140 Units/L] and for females, was 123 [110-136 Units/L]. Age stratified upper limits for females were 120 [101-140 Units/L], and 109 [97-121 Units/L] for ≤50 and >50 years, respectively. For GGT, no significant differences were observed for the lower limits. The upper limit for all patients with normal AST/ALT/ALP (n = 2,446) was 62 [55-70 Units/L]. For males, all females, females ≤50 and >50 years was 58 [46-69 Units/L], 40 [39-51 Units/L], 31 [23-39 Units/L], and 56 [41-71 Units/L]. Conclusions For the majority of patient subsets, the manufacturer RI agreed well with the IRIs. However, significant differences in IRIs were observed for the upper limit of ALP IRIs of all females with normal hepatobilliary function and in GGT for all females and females >50 years. Further investigation of the impact of preselecting patient populations on IRI estimation and a direct RI study on select patient subsets is worth investigation.

Post-Estimation Shrinkage in Full and Selected Linear Regression Models in Low-Dimensional Data Revisited.

The fit of a regression model to new data is often worse due to overfitting. Analysts use variable selection techniques to develop parsimonious regression models, which may introduce bias into regression estimates. Shrinkage methods have been proposed to mitigate overfitting and reduce bias in estimates. Post-estimation shrinkage is an alternative to penalized methods. This study evaluates effectiveness of post-estimation shrinkage in improving prediction performance of full and selected models. Through a simulation study, results were compared with ordinary least squares (OLS) and ridge in full models, and best subset selection (BSS) and lasso in selected models. We focused on prediction errors and the number of selected variables. Additionally, we proposed a modified version of the parameter-wise shrinkage (PWS) approach named non-negative PWS (NPWS) to address weaknesses of PWS. Results showed that no method was superior in all scenarios. In full models, NPWS outperformed global shrinkage, whereas PWS was inferior to OLS. In low correlation with moderate-to-high signal-to-noise ratio (SNR), NPWS outperformed ridge, but ridge performed best in small sample sizes, high correlation, and low SNR. In selected models, all post-estimation shrinkage performed similarly, with global shrinkage slightly inferior. Lasso outperformed BSS and post-estimation shrinkage in small sample sizes, low SNR, and high correlation but was inferior when the opposite was true. Our study suggests that, with sufficient information, NPWS is more effective than global shrinkage in improving prediction accuracy of models. However, in high correlation, small sample sizes, and low SNR, penalized methods generally outperform post-estimation shrinkage methods.

Development and validation of a risk prediction model for acute care use among patients with advanced cancer on clinical trials.

277 Background: Patients with advanced cancer on clinical trials are at high risk for unplanned Emergency Room visits (ER_visits) and hospital stays (HS). Identifying patients at highest risk could inform personalized intervention strategies. Methods: The data were from the SWOG Cancer Research Network. Medicare claims data were linked to data from advanced cancer clinical trials. The occurrence of any HS or ER visit within 1 year after enrollment was the primary endpoint. Patients must have had 6 months of continuous Medicare claims prior to registration to determine comorbid conditions prior to treatment, and 12 months of claims after registration (or until death) to detect outcomes. We examined 23 sociodemographic (age, sex, race, ethnicity, insurance status), geographic (rural or urban, area-level deprivation), clinical (disease severity, performance status), treatment (line of therapy, therapy type), and individual comorbidity factors to establish a predictive risk model. Model derivation was conducted in a 60% random sample and tested in the remaining 40%. Best subset selection with logistic regression was used with iterative application of 3-, 4-, and 5-fold cross validation repeated 10 times. Results: Among N=1397 total patients from 6 trials (lung, 3; prostate, 2; pancreas, 1), 33.9% were 75+ years, 20.3% were female, and 8.2% were Black. The overall proportion of patients with ≥1 HS/ER visit was 68.8%. In the training set (n=839), a best 5-variable risk was identified with adverse risk factors including cancer type (prostate vs other), performance status (PS; 0 vs. >=1), coronary artery disease (CAD; yes vs. no), hypertension (yes vs. no), and liver disease (yes vs. no). As cancer type was a study-level variable, we derived a risk model using PS, CAD, hypertension, and liver disease and adjusted for cancer type. There were no differences in adverse risk between those in with and without prostate cancer (interaction p-value=.44), justifying combining across cancer types. In the training set, patients with ≥2 risk factors (high risk; n=303, 36.1%) vs. 0/1 risk factor (low risk; n=536, 63.9%) had nearly a 3-fold increased odds of experiencing HS/ER visit (82.2% vs. 59.1%, OR=2.85, 95% CI, 2.01-4.03, p<.001). Results were successfully validated in the test set (OR=1.93, p=.002). In all patients, those in the highest risk quartile (3/4 factors) vs the lowest risk (0 factors) had nearly a fourfold increased odds of HS/ER visits (OR=3.92, 95% CI, 2.25-6.83, p<.001). Conclusions: Rates of HS_ER visits were high within the first year after enrolling on a trial for advanced cancer. We derived and validated a risk model that delineated strong differences in risk. With expansion of eligibility to include patients with more comorbid conditions, personalized interventions aimed at preventing acute care use could decrease the cost and improve the quality of cancer care.

A Novel Surrogate-Assisted Multi-Objective Well Control Parameter Optimization Method Based on Selective Ensembles

Multi-objective optimization algorithms are crucial for addressing real-world problems, particularly with regard to optimizing well control parameters, which are often computationally expensive due to their reliance on numerical simulations. Surrogate-assisted models help to reduce this computational burden, but their effectiveness depends on the quality of the surrogates, which can be affected by candidate dimension and noise. This study proposes a novel surrogate-assisted multi-objective optimization framework (MOO-SESA) that combines selective ensemble support-vector regression with NSGA-II. The framework’s uniqueness lies in its adaptive selection of a diverse subset of surrogates, established prior to iteration, to enhance accuracy, robustness, and computational efficiency. To our knowledge, this is the first instance in which selective ensemble techniques with multi-objective optimization have been applied to reservoir well control problems. Through employing an ensemble strategy for improving the quality of the surrogate model, MOO-SESA demonstrated superior well control scenarios and faster convergence compared to traditional surrogate-assisted models when applied to the SPE10 and Egg reservoir models.

Predicting creep life of CrMo pressure vessel steel using machine learning models with optimal feature subset selection

The data-driven approach for creep life prediction typically integrates numerous characteristics, including material compositions, manufacturing details, and service conditions, into machine learning models. In this study, a machine learning-based creep life prediction approach with optimal feature subset selection is established for 2.25Cr1Mo pressure vessel steel. Before model training and testing, six critical features that significantly impact the creep life of 2.25Cr1Mo steel are selected, specifically the applied stress, temperature, and chemical compositions consisting of Cr, Ni, Mn, and Mo. Various machine learning algorithms, along with the traditional L–M method, are utilized for model training and performance evaluation. Additionally, the developed models undergo validation using experimental data independent of the training and testing datasets to assess their generalization abilities. The results reveal that, among all tested models, the support vector regression (SVR) model, coupled with the optimal feature subset, demonstrates superior prediction accuracy and generalization capability. Finally, the creep life prediction model exhibiting optimal performance is deployed into a software application, leveraging the Python programming language. This predictor tool facilitates rapid and precise creep life predictions for 2.25Cr1Mo pressure vessel steel, relying solely on a limited amount of input information, and provides a clear and visual presentation of the prediction results.

Machine Learning based Lung Cancer Diagnostic System using Optimized Feature Subset Selection

Lung is a vital organ that plays a major role in respiration. Without breathing, one may not survive in this world. Hence lung is an important organ that acts as filter to absorb oxygen and supply it to heart where pumping takes place through blood vessel in the circulatory system .The pumped blood takes oxygen and other nutrients to every other parts of the body. Hence one must take care of lung. There are various diseases associated with lungs. Lung Cancer is a deadly disease that spread across the countries all over the world. An early detection of lung cancer has been proved to improve the survival rate of human life. There are various resources are available to detect the lung cancer disease. They are low dose CT-scans, X-rays, blood-based screening, pathology slide reading, biopsy’s test, survey data(clinical dataset) etc. helps to predict the disease well in advance. Our proposed work uses two clinical datasets that has various features to detect how likely the persons get affected from the lung disease. Dataset1 includes features such as age, gender, smoking, yellow fingers, anxiety, peer-pressure, chronic disease, fatigue, allergy, wheezing, alcohol, coughing, shortness of breath, swallowing difficulty, and chest pain. Also, the work has experimented with another dataset2 that represents causes of lung cancer due to exposure of pesticide. Our proposed diagnostic system consider all these features in total and perform feature selection to extract optimal feature subsets using cuckoo search algorithm then perform classification using machine learning algorithms such as Linear Support Vector Machine, Logistic Regression and Random Forest algorithm. It is observed that with the cuckoo search algorithm, dataset 1 achieves an accuracy of 100%, precision of 100%, recall of 100%, and F1-score of 100% by LR Classifier. The Linear SVC classifier achieves an accuracy of 90%, a precision of 88%, a recall of 86%, and an F1-score of 87%.The Random forest Classifier achieves an accuracy of 91%, precision of 86%, recall of 93%, and F1-score of 90%. For dataset 2, both the LR classifier and Linear SVC classifier outperform with an accuracy of 100%, precision of 100%, recall of 100%, and F1-score of 100%. Whereas Random Forest provides accuracy of 97%, precision of 97%, recall of 96%, and F1-score of 97%.

Pluto: Sample Selection for Robust Anomaly Detection on Polluted Log Data

Log anomaly detection, critical in identifying system failures and preempting security breaches, finds irregular patterns within large volumes of log data. Modern log anomaly detectors rely on training deep learning models on clean anomaly-free log data. However, such clean log data requires expensive and tedious human labeling. In this paper, we thus propose a robust log anomaly detection framework, PlutoNOSPACE, that automatically selects a clean representative sample subset of the polluted log sequence data to train a Transformer-based anomaly detection model. Pluto features three innovations. First, due to localized concentrations of anomalies inherent in the embedding space of log data, Pluto partitions the sequence embedding space generated by the model into regions that then allow it to identify and discard regions that are highly polluted by our pollution level estimation scheme, based on our pollution quantification via Gaussian mixture modeling. Second, for the remaining more slightly polluted regions, we select samples that maximally purify the eigenvector spectrum, which can be transformed into the NP-hard facility location problem; allowing us to leverage its greedy solution with a (1-(1/e)) approximation guarantee in optimality. Third, by iteratively alternating between the above subset selection, a model re-training on the latest subset, and a subset filtering using dynamic training artifacts generated by the latest model, the data selected is progressively refined. The final sample set is used to retrain the final anomaly detection model. Our experiments on four real-world log benchmark datasets demonstrate that by retaining 77.7% (BGL) to 96.6% (ThunderBird) of the normal sequences while effectively removing 90.3% (BGL) to 100.0% (ThunderBird, HDFS) of the anomalies, Pluto provides a significant absolute F-1 improvement up to 68.86% (2.16% → 71.02%) compared to the state-of-the-art sample selection methods. The implementation of this work is available at https://github.com/LeiMa0324/Pluto-SIGMOD25.

Analyzing Resampling Techniques for Addressing the Class Imbalance in NIDS using SVM with Random Forest Feature Selection

The purpose of Network Intrusion Detection Systems (NIDS) is to ensure and protect computer networks from harmful actions. A major concern in NIDS development is the class imbalance problem, i.e., normal traffic dominates the communication data plane more than intrusion attempts. Such a state of affairs can pose certain hazards to the effectiveness of detection algorithms, including those useful for detecting less frequent but still highly dangerous intrusions. This paper aims to utilize resampling techniques to tackle this problem of class imbalance in NIDS using a Support Vector Machine (SVM) classifier alongside utilizing features selected by Random Forest to improve the feature subset selection process. The analysis highlights the combativeness of each sampling method, offering insights into their efficiency and practicality for real-world applications. Four resampling techniques are analyzed. Such techniques include Synthetic Minority Over-sampling Technique (SMOTE), Random Under-sampling (RUS), Random Over-sampling (ROS) and SMOTE with two different combinations i.e., RUS SMOTE and RUS ROS. Feature selection was done using Random Forest, which was improved by Bayesian methods to create subsets of features with feature rankings determined by Cumulative Feature Importance Score (CFIS). The CIDDS-2017 dataset is used for the performance evaluation, and the metrics used include accuracy, precision, recall, F-measure and CPU time. The algorithm that performs best overall in the CFIS feature subsets is SMOTE, and the features that give the best result are selected at the 90% level with 25 features. This subset accomplishes a relative accuracy enhancement of 0.08% than the other approaches. The RUS+ROS technique is also fine but somehow slower than SMOTE. On the other hand, RUS+SMOTE shows relatively poor results although it consumes less time in terms of computational time compared to other methods, giving about 50% of the performance shown by the other methods. This paper's novelty is adapting the RUS method as a standalone test for screening new and potentially contaminated datasets. The standalone RUS method is more efficient in terms of computations; the algorithm returned the best result of 98.13% accuracy at 85% at the CFIS level of 34 features with a computation time of 137.812 s. It is also noted that SMOTE is considered to be proficient among all resampling techniques used for handling the problem of class imbalance in NIDS, vice 90% CFIS feature subset. Future research directions could include using these techniques in different data sets and other machine learning and deep learning methods together with ROC curve analysis to provide useful pointers to NIDS designers on how to select the right data mining tools and strategies for their projects.

Total Artificial Heart Implantation as a Bridge to Transplantation in Slovakia.

Although left ventricular assist device implantation represents the majority of durable mechanical circulatory support implants for patients with advanced heart failure, as many as 20 to 30% will subsequently have right heart failure requiring extended inotropic support or short-term mechanical circulatory support, and the total artificial heart is an established tool in the bridge to transplant armamentarium. The aim of this short report is to present our center's experience with the use of SynCardia total artificial heart. Between November 2017 and April 2021, 10 SynCardia total artificial heart devices were implanted. Of the 10 patients who underwent total artificial heart implantation, 6 (60%) were successfully bridged to transplant with a median time of 6.5 (interquartile range [IQR] 6-8) months, and 4 patients died on device support during the index hospitalization. The 30-day, 1-year, and 3-year survival rates after heart transplantation were the same at 66.7% (4/6). Despite the uncertain future of total artificial hearts, it remains a viable option for patients who require biventricular bridge to transplant or for a select subset of patients with advance heart failure who may not otherwise survive.

Baseline factors relating to depressive symptoms at one year postoperative in patients with diffuse glioma

Abstract Background Depressive symptoms are common in patients with diffuse glioma, potentially reducing their quality of life. Understanding baseline factors associated with the development of depressive symptoms is important for psychoeducation and early intervention. This study investigates the associations of baseline patient- and tumor-related characteristics and depressive symptoms 1 year after surgery. Methods We combined retrospective longitudinal datasets from Amsterdam UMC and Rigshospitalet Copenhagen. Several characteristics of patients and tumors were retrieved, in particular items of their mood and functioning status. Depression instruments were harmonized to the Patient-Reported Outcomes Measurement Information System Depression scale through previously developed item response theory. Functioning items were harmonized to the International Classification of Functioning, Disability, and Health (ICF) domains using linking methods published previously. We analyzed the associations of 25 baseline factors with depressive symptoms one year after surgery by multivariable stepwise backward linear regression models and verified model robustness using best subset selection. Results We included 118 patients with diffuse glioma with a mean age of 48 years and a glioblastoma in 29%. Baseline depressive symptoms, lower ICF energy, and impaired ICF language functioning were associated with more depressive symptoms at follow-up in the multivariable model (R-squared: 0.379). Conclusions We identified 3 key baseline factors associated with depressive symptoms one year after surgery. Clinically, our findings contribute to the comprehension of predictive factors for depressive symptoms, aiding healthcare providers and patients in understanding and possibly allowing for early intervention.

Assessing Variable Importance for Best Subset Selection.

One of the primary issues that arises in statistical modeling pertains to the assessment of the relative importance of each variable in the model. A variety of techniques have been proposed to quantify variable importance for regression models. However, in the context of best subset selection, fewer satisfactory methods are available. With this motivation, we here develop a variable importance measure expressly for this setting. We investigate and illustrate the properties of this measure, introduce algorithms for the efficient computation of its values, and propose a procedure for calculating p-values based on its sampling distributions. We present multiple simulation studies to examine the properties of the proposed methods, along with an application to demonstrate their practical utility.

Correction to: The COR criterion for optimal subset selection in distributed estimation

Correction to: The COR criterion for optimal subset selection in distributed estimation