Gaussian process regression to determine water content of methane: Application to methane transport modeling.

The uncontrolled release of methane from natural gas wells may pose risks to shallow groundwater resources. Numerical modeling of methane migration from deep hydrocarbon formations towards shallow systems requires knowledge of phase behavior of the water-methane system, usually calculated by classic thermodynamic approaches. This study presents a Gaussian process regression (GPR) model to estimate water content of methane gas using pressure and temperature as input parameters. Bayesian optimization algorithm was implemented to tune hyper-parameters of the GPR model. The GPR predictions were evaluated with experimental data as well as four thermodynamic models. The results revealed that the predictions of the GPR are in good correspondence with experimental data having a MSE value of 3.127 × 10-7 and R2 of 0.981. Furthermore, the analysis showed that the GPR model exhibits an acceptable performance comparing with the well-known thermodynamic models. The GPR predicts the water content of methane over widespread ranges of pressure and temperature with a degree of accuracy needed for typical subsurface engineering applications.

Modeling porosity loss in Fe0-based permeable reactive barriers with Faraday's law.

Solid iron corrosion products (FeCPs), continuously generated from iron corrosion in Fe0-based permeable reactive barriers (PRB) at pH > 4.5, can lead to significant porosity loss and possibility of system's failure. To avoid such failure and to estimate the long-term performance of PRBs, reliable models are required. In this study, a mathematical model is presented to describe the porosity change of a hypothetical Fe0-based PRB through-flowed by deionized water. The porosity loss is solely caused by iron corrosion process. The new model is based on Faraday's Law and considers the iron surface passivation. Experimental results from literature were used to calibrate the parameters of the model. The derived iron corrosion rates (2.60 mmol/(kg day), 2.07 mmol/(kg day) and 1.77 mmol/(kg day)) are significantly larger than the corrosion rate used in previous modeling studies (0.4 mmol/(kg day)). This suggests that the previous models have underestimated the impact of in-situ generated FeCPs on the porosity loss. The model results show that the assumptions for the iron corrosion rates on basis of a first-order dependency on iron surface area are only valid when no iron surface passivation is considered. The simulations demonstrate that volume-expansion by Fe0 corrosion products alone can cause a great extent of porosity loss and suggests careful evaluation of the iron corrosion process in individual Fe0-based PRB.

FOXM1 is required for small cell lung cancer tumorigenesis and associated with poor clinical prognosis.

Small cell lung cancer (SCLC) continues to cause poor clinical outcomes due to limited advances in sustained treatments for rapid cancer cell proliferation and progression. The transcriptional factor Forkhead Box M1 (FOXM1) regulates cell proliferation, tumor initiation, and progression in multiple cancer types. However, its biological function and clinical significance in SCLC remain unestablished. Analysis of the Cancer Cell Line Encyclopedia and SCLC datasets in the present study disclosed significant upregulation of FOXM1 mRNA in SCLC cell lines and tissues. Gene set enrichment analysis (GSEA) revealed that FOXM1 is positively correlated with pathways regulating cell proliferation and DNA damage repair, as evident from sensitization of FOXM1-depleted SCLC cells to chemotherapy. Furthermore, Foxm1 knockout inhibited SCLC formation in the Rb1fl/flTrp53fl/flMycLSL/LSL (RPM) mouse model associated with increased levels of neuroendocrine markers, Ascl1 and Cgrp, and decrease in Yap1. Consistently, FOXM1 depletion in NCI-H1688 SCLC cells reduced migration and enhanced apoptosis and sensitivity to cisplatin and etoposide. SCLC with high FOXM1 expression (N = 30, 57.7%) was significantly correlated with advanced clinical stage, extrathoracic metastases, and decrease in overall survival (OS), compared with the low-FOXM1 group (7.90 vs. 12.46 months). Moreover, the high-FOXM1 group showed shorter progression-free survival after standard chemotherapy, compared with the low-FOXM1 group (3.90 vs. 8.69 months). Our collective findings support the utility of FOXM1 as a prognostic biomarker and potential molecular target for SCLC.

Predicting methane solubility in water and seawater by machine learning algorithms: Application to methane transport modeling.

The upward migration of methane from natural gas wells associated with fracking operations may lead to contamination of groundwater resources and surface leakage. Numerical simulations of methane transport in the subsurface environment require knowledge of methane solubility in the aqueous phase. This study employs machine learning (ML) algorithms to predict methane solubility in aquatic systems for temperatures ranging from 273.15 to 518.3 K and pressures ranging from 1 to 1570 bar. Four regression algorithms including regression tree (RT), boosted regression tree (BRT), least square support vector machine (LSSVM) and Gaussian process regression (GPR) were utilized for predicting methane solubility in pure water and mixed aquatic systems containing Na+, K+, Ca2+, Mg2+, Cl- and SO4-2. The experimental data collected from the literature were used to implement the models. We used Grid search (GS), Random search (RS) and Bayesian optimization (BO) for tuning hyper-parameters of the ML models. Moreover, the predicted values of methane solubility were compared against Spivey et al. (2004) and Duan and Mao (2006) equations of state. The results show that the BRT-BO model is the most rigorous model for the prediction task. The coefficient of determination (R2) between experimental and predicted values is 0.99 and the mean squared error (MSE) is 1.19 × 10-7. The performance of the BRT-BO model is satisfactory, showing an acceptable agreement with experimental data. The comparison results demonstrated the superior performance of the BRT-BO model for predicting methane solubility in aquatic systems over a span of temperature, pressure and ionic strength that occurs in deep marine environments.

Exploring Volatile Organic Compounds in Breath for High-Accuracy Prediction of Lung Cancer.

(1) Background: Lung cancer is silent in its early stages and fatal in its advanced stages. The current examinations for lung cancer are usually based on imaging. Conventional chest X-rays lack accuracy, and chest computed tomography (CT) is associated with radiation exposure and cost, limiting screening effectiveness. Breathomics, a noninvasive strategy, has recently been studied extensively. Volatile organic compounds (VOCs) derived from human breath can reflect metabolic changes caused by diseases and possibly serve as biomarkers of lung cancer. (2) Methods: The selected ion flow tube mass spectrometry (SIFT-MS) technique was used to quantitatively analyze 116 VOCs in breath samples from 148 patients with histologically confirmed lung cancers and 168 healthy volunteers. We used eXtreme Gradient Boosting (XGBoost), a machine learning method, to build a model for predicting lung cancer occurrence based on quantitative VOC measurements. (3) Results: The proposed prediction model achieved better performance than other previous approaches, with an accuracy, sensitivity, specificity, and area under the curve (AUC) of 0.89, 0.82, 0.94, and 0.95, respectively. When we further adjusted the confounding effect of environmental VOCs on the relationship between participants' exhaled VOCs and lung cancer occurrence, our model was improved to reach 0.92 accuracy, 0.96 sensitivity, 0.88 specificity, and 0.98 AUC. (4) Conclusion: A quantitative VOCs databank integrated with the application of an XGBoost classifier provides a persuasive platform for lung cancer prediction.

HPCGCN: A Predictive Framework on High Performance Computing Cluster Log Data Using Graph Convolutional Networks.

This paper presents a novel use case of Graph Convolutional Network (GCN) learning representations for predictive data mining, specifically from user/task data in the domain of high-performance computing (HPC). It outlines an approach based on a coalesced data set: logs from the Slurm workload manager, joined with user experience survey data from computational cluster users. We introduce a new method of constructing a heterogeneous unweighted HPC graph consisting of multiple typed nodes after revealing the manifold relations between the nodes. The GCN structure used here supports two tasks: i) determining whether a job will complete or fail and ii) predicting memory and CPU requirements by training the GCN semi-supervised classification model and regression models on the generated graph. The graph is partitioned into partitions using graph clustering. We conducted classification and regression experiments using the proposed framework on our HPC log dataset and evaluated predictions by our trained models against baselines using test_score, F1-score, precision, recall for classification, and R1 score for regression, showing that our framework achieves significant improvements.

Ensemble Prediction of Job Resources to Improve System Performance for Slurm-Based HPC Systems.

In this paper, we present a novel methodology for predicting job resources (memory and time) for submitted jobs on HPC systems. Our methodology based on historical jobs data (saccount data) provided from the Slurm workload manager using supervised machine learning. This Machine Learning (ML) prediction model is effective and useful for both HPC administrators and HPC users. Moreover, our ML model increases the efficiency and utilization for HPC systems, thus reduce power consumption as well. Our model involves using Several supervised machine learning discriminative models from the scikit-learn machine learning library and LightGBM applied on historical data from Slurm. Our model helps HPC users to determine the required amount of resources for their submitted jobs and make it easier for them to use HPC resources efficiently. This work provides the second step towards implementing our general open source tool towards HPC service providers. For this work, our Machine learning model has been implemented and tested using two HPC providers, an XSEDE service provider (University of Colorado-Boulder (RMACC Summit) and Kansas State University (Beocat)). We used more than two hundred thousand jobs: one-hundred thousand jobs from SUMMIT and one-hundred thousand jobs from Beocat, to model and assess our ML model performance. In particular we measured the improvement of running time, turnaround time, average waiting time for the submitted jobs; and measured utilization of the HPC clusters. Our model achieved up to 86% accuracy in predicting the amount of time and the amount of memory for both SUMMIT and Beocat HPC resources. Our results show that our model helps dramatically reduce computational average waiting time (from 380 to 4 hours in RMACC Summit and from 662 hours to 28 hours in Beocat); reduced turnaround time (from 403 to 6 hours in RMACC Summit and from 673 hours to 35 hours in Beocat); and acheived up to 100% utilization for both HPC resources.

Rapid purification of lung cancer cells in pleural effusion through spiral microfluidic channels for diagnosis improvement.

Lung cancer is one of the leading causes of death worldwide. Fifteen percent of lung cancer patients will present with malignant pleural effusion initially, and up to 50% will have malignant pleural effusion throughout the course of the disease. In this study, we developed a spiral microfluidic device that can rapidly isolate cancer cells and improve their purity through fluid dynamics. This label-free, high-throughput device continuously isolates cancer cells and other unrelated molecules from pleural effusion. Most of the background cells that affect interpretation are flushed to outlets 1 to 3, and cancer cells are hydrodynamically concentrated to outlet 4, with 90% of lung cancer cells flowing to this outlet. After processing, the purity of cancer cells identified in pleural effusion by CD45 and epithelial cell adhesion molecule (EpCAM) antibodies in flow cytometry will be increased by 6 to 24 times. The microfluidic device presented here has the advantages of rapid processing and low cost, which are conducive to rapid and accurate clinical diagnosis.

Improving HPC System Performance by Predicting Job Resources via Supervised Machine Learning.

High-Performance Computing (HPC) systems are resources utilized for data capture, sharing, and analysis. The majority of our HPC users come from other disciplines than Computer Science. HPC users including computer scientists have difficulties and do not feel proficient enough to decide the required amount of resources for their submitted jobs on the cluster. Consequently, users are encouraged to over-estimate resources for their submitted jobs, so their jobs will not be killing due insufficient resources. This process will waste and devour HPC resources; hence, this will lead to inefficient cluster utilization. We created a supervised machine learning model and integrated it into the Slurm resource manager simulator to predict the amount of required memory resources (Memory) and the required amount of time to run the computation. Our model involves using different machine learning algorithms. Our goal is to integrate and test the proposed supervised machine learning model on Slurm. We used over 10000 tasks selected from our HPC log files to evaluate the performance and the accuracy of our integrated model. The purpose of our work is to increase the performance of the Slurm by predicting the amount of require jobs memory resources and the time required for each particular job in order to improve the utilization of the HPC system using our integrated supervised machine learning model. Our results indicate that for larger jobs our model helps dramatically reduce computational turnaround time (from five days to ten hours for large jobs), substantially increased utilization of the HPC system, and decreased the average waiting time for the submitted jobs.

[Behavioral responses of an older woman receiving tocolysis after repeated failed pregnancies].

The purpose of this study was to explore the behavioral responses to tocolysis of an older woman who has experienced two failed pregnancies. As a participant observer the primary researcher simultaneously provided care and recorded the woman's verbal and nonverbal behaviors. After repeated data analysis, it was found that the woman's stress mainly resulted from her un certainty about a safe passage through pregnancy and about the health of her fetus. In her effort to ensure a successful pregnancy and good fetal health, she exhibited the following behaviors: worrying about the status of her pregnancy, adopting effective strategies to ensure the success of the tocolysis, complying with medical procedures and nursing instructions, establishing the time marks of the pregnancy for the purpose of self-encouragement, and attaching importance to oral intake for the sake of good fetal health. The article concludes that medical personnel should actively identify the needs of pregnant women and provide family-centered nursing care to diminish the impact of preterm premature rupture of membrane and maximize the positive results of tocolysis.