Assessing Predictive Ability of Dynamic Time Warping Functional Connectivity for ASD Classification.

Functional connectivity MRI (fcMRI) is a technique used to study the functional connectedness of distinct regions of the brain by measuring the temporal correlation between their blood oxygen level-dependent (BOLD) signals. fcMRI is typically measured with the Pearson correlation (PC), which assumes that there is no lag between time series. Dynamic time warping (DTW) is an alternative measure of similarity between time series that is robust to such time lags. We used PC fcMRI data and DTW fcMRI data as predictors in machine learning models for classifying autism spectrum disorder (ASD). When combined with dimension reduction techniques, such as principal component analysis, functional connectivity estimated with DTW showed greater predictive ability than functional connectivity estimated with PC. Our results suggest that DTW fcMRI can be a suitable alternative measure that may be characterizing fcMRI in a different, but complementary, way to PC fcMRI that is worth continued investigation. In studying different variants of cross validation (CV), our results suggest that, when it is necessary to tune model hyperparameters and assess model performance at the same time, a K-fold CV nested within leave-one-out CV may be a competitive contender in terms of performance and computational speed, especially when sample size is not large.

Epigenome-wide gene-age interaction study reveals reversed effects of MORN1 DNA methylation on survival between young and elderly oral squamous cell carcinoma patients.

DNA methylation serves as a reversible and prognostic biomarker for oral squamous cell carcinoma (OSCC) patients. It is unclear whether the effect of DNA methylation on OSCC overall survival varies with age. As a result, we performed a two-phase gene-age interaction study of OSCC prognosis on an epigenome-wide scale using the Cox proportional hazards model. We identified one CpG probe, cg11676291 MORN1 , whose effect was significantly modified by age (HRdiscovery = 1.018, p = 4.07 × 10-07, FDR-q = 3.67 × 10-02; HRvalidation = 1.058, p = 8.09 × 10-03; HR combined = 1.019, p = 7.36 × 10-10). Moreover, there was an antagonistic interaction between hypomethylation of cg11676291 MORN1 and age (HRinteraction = 0.284; 95% CI, 0.135-0.597; p = 9.04 × 10-04). The prognosis of OSCC patients was well discriminated by the prognostic score incorporating cg11676291 MORN1 -age interaction (HR high vs. low = 3.66, 95% CI: 2.40-5.60, p = 1.93 × 10-09). By adding 24 significant gene-age interactions using a looser criterion, we significantly improved the area under the receiver operating characteristic curve (AUC) of the model at 3- and 5-year prognostic prediction (AUC3-year = 0.80, AUC5-year = 0.79, C-index = 0.75). Our study identified a significant interaction between cg11676291 MORN1 and age on OSCC survival, providing a potential therapeutic target for OSCC patients.

APOLLO: An accurate and independently validated prediction model of lower-grade gliomas overall survival and a comparative study of model performance.

BACKGROUND: Virtually few accurate and robust prediction models of lower-grade gliomas (LGG) survival exist that may aid physicians in making clinical decisions. We aimed to develop a prognostic prediction model of LGG by incorporating demographic, clinical and transcriptional biomarkers with either main effects or gene-gene interactions. METHODS: Based on gene expression profiles of 1,420 LGG patients from six independent cohorts comprising both European and Asian populations, we proposed a 3-D analysis strategy to develop and validate an Accurate Prediction mOdel of Lower-grade gLiomas Overall survival (APOLLO). We further conducted decision curve analysis to assess the net benefit (NB) of identifying true positives and the net reduction (NR) of unnecessary interventions. Finally, we compared the performance of APOLLO and the existing prediction models by the first systematic review. FINDINGS: APOLLO possessed an excellent discriminative ability to identify patients at high mortality risk. Compared to those with less than the 20th percentile of APOLLO risk score, patients with more than the 90th percentile of APOLLO risk score had significantly worse overall survival (HR=54·18, 95% CI: 34·73-84·52, P=2·66 × 10-69). Further, APOLLO can accurately predict both 36- and 60-month survival in six independent cohorts with a pooled AUC36-month=0·901 (95% CI: 0·879-0·923), AUC60-month=0·843 (95% CI: 0·815-0·871) and C-index=0·818 (95% CI: 0·800-0·835). Moreover, APOLLO offered an effective screening strategy for detecting LGG patients susceptible to death (NB36-month=0·166, NR36-month=40·1% and NB60-month=0·258, NR60-month=19·2%). The systematic comparisons revealed APOLLO outperformed the existing models in accuracy and robustness. INTERPRETATION: APOLLO has the demonstrated feasibility and utility of predicting LGG survival (http://bigdata.njmu.edu.cn/APOLLO). FUNDING: National Key Research and Development Program of China (2016YFE0204900); Natural Science Foundation of Jiangsu Province (BK20191354); National Natural Science Foundation of China (81973142 and 82103946); China Postdoctoral Science Foundation (2020M681671); National Institutes of Health (CA209414, CA249096, CA092824 and ES000002).

A Combinatorial Optimization Framework for Scoring Students in University Admissions.

BACKGROUND AND OBJECTIVES: Selecting applications for college admission is critical for university operation and development. This paper leverages machine learning techniques to support enrollment management teams through data-informed decision-making in this otherwise laborious admissions processing. RESEARCH DESIGN AND MEASURES: Two aspects of university admissions are considered. An ensemble learning approach, through the SuperLearner algorithm, is used to predict student show (yield) rate. The goal is to improve prediction accuracy to minimize over- or under-enrollment. A combinatorial optimization framework is proposed to weigh academic performance and experiential factors for ranking and selecting students for admission. This framework uses simulated annealing, and an efficacy study is presented to evaluate performance. RESULTS: The proposed framework is illustrated for selecting an incoming class by optimizing predicted graduation rate and by developing an eligibility index. Each example presents a selection process under potential academic performance and experiential factor targets a university may place on an admitted class. R code is provided for higher education researchers and practitioners to apply the proposed methods in their own settings.

A Nanostructured Moisture-Absorbing Gel for Fast and Large-Scale Passive Dehumidification.

Dehumidification is significant for environmental sustainability and human health. Traditional dehumidification methods involve significant energy consumption and have negative impact on the environment. The core challenge is to expose hygroscopic surfaces to the air, and appropriately store the captured water and avoid surface inactivation. Here, a nanostructured moisture-absorbing gel (N-MAG) for passive dehumidification, which consists of a hydrophilic nanocellulose network functionalized by hygroscopic lithium chloride, is reported. The interconnected nanocellulose can transfer the captured water to the internal space of the bulky N-MAG, eliminating water accumulation near the surfaces and hence enabling high-rate moisture absorption. The N-MAG can reduce the relative humidity from 96.7% to 28.7% in 6 h, even if the space is over 2 × 104 times of its own volume. The condensed water can be completely confined in the N-MAG, overcoming the problem of environmental pollution. This research brings a new perspective for sustainable humidity management without energy consumption and with positive environmental footprint.

Epigenome-wide three-way interaction study identifies a complex pattern between TRIM27, KIAA0226, and smoking associated with overall survival of early-stage NSCLC.

The interaction between DNA methylation of tripartite motif containing 27 (cg05293407TRIM27 ) and smoking has previously been identified to reveal histologically heterogeneous effects of TRIM27 DNA methylation on early-stage non-small-cell lung cancer (NSCLC) survival. However, to understand the complex mechanisms underlying NSCLC progression, we searched three-way interactions. A two-phase study was adopted to identify three-way interactions in the form of pack-year of smoking (number of cigarettes smoked per day × number of years smoked) × cg05293407TRIM27 × epigenome-wide DNA methylation CpG probe. Two CpG probes were identified with FDR-q ≤ 0.05 in the discovery phase and P ≤ 0.05 in the validation phase: cg00060500KIAA0226 and cg17479956EXT2 . Compared to a prediction model with only clinical information, the model added 42 significant three-way interactions using a looser criterion (discovery: FDR-q ≤ 0.10, validation: P ≤ 0.05) had substantially improved the area under the receiver operating characteristic curve (AUC) of the prognostic prediction model for both 3-year and 5-year survival. Our research identified the complex interaction effects among multiple environment and epigenetic factors, and provided therapeutic target for NSCLC patients.

LncRNA SNHG7 Regulates Mesenchymal Stem Cell Through the Notch1/Jagged1/Hes-1 Signaling Pathway and Influences Folfirinox Resistance in Pancreatic Cancer.

Pancreatic cancer (PC) is one of the deadliest gastrointestinal cancers, accounting for the fourth highest number of cancer-related fatalities. Increasing data suggests that mesenchymal stem cells (MSCs) might influence the drug resistance of GC cells in the tumor microenvironment and play essential roles in drug resistance development. However, the precise underlying process remains a mystery. The purpose of this study was to look at the control of MSC-induced SNHG7 in pancreatic cancer. In vitro and in vivo sphere formation, colony formation, and flow cytometry investigations revealed the stemness and Folfirinox resistance in pancreatic cancer cells. To confirm the direct connections between SNHG7 and other related targets, RNA pulldown and immunoprecipitation tests were performed. MSC co-culture enhanced the stemness and Folfirinox resistance in pancreatic cancer cells according to the findings. MSC co-culture increased SNHG7 expression in pancreatic cancer cells, contributing to the stemness and Folfirinox resistance. We demonstrated that Notch1 interacted with SNHG7 and could reverse the facilitative effect of SNHG7 on the stemness and Folfirinox resistance in pancreatic cancer cells. Finally, our findings showed that MSCs increased SNHG7 expression in pancreatic cancer cells, promoting the stemness and Folfirinox resistance via the Notch1/Jagged1/Hes-1 signaling pathway. These findings could provide a novel approach and therapeutic target for pancreatic cancer patients.

Repeated measures random forests (RMRF): Identifying factors associated with nocturnal hypoglycemia.

Nocturnal hypoglycemia is a common phenomenon among patients with diabetes and can lead to a broad range of adverse events and complications. Identifying factors associated with hypoglycemia can improve glucose control and patient care. We propose a repeated measures random forest (RMRF) algorithm that can handle nonlinear relationships and interactions and the correlated responses from patients evaluated over several nights. Simulation results show that our proposed algorithm captures the informative variable more often than naïvely assuming independence. RMRF also outperforms standard random forest and extremely randomized trees algorithms. We demonstrate scenarios where RMRF attains greater prediction accuracy than generalized linear models. We apply the RMRF algorithm to analyze a diabetes study with 2524 nights from 127 patients with type 1 diabetes. We find that nocturnal hypoglycemia is associated with HbA1c, bedtime blood glucose (BG), insulin on board, time system activated, exercise intensity, and daytime hypoglycemia. The RMRF can accurately classify nights at high risk of nocturnal hypoglycemia.

Epigenetic-smoking interaction reveals histologically heterogeneous effects of TRIM27 DNA methylation on overall survival among early-stage NSCLC patients.

Tripartite motif containing 27 (TRIM27) is highly expressed in lung cancer, including non-small-cell lung cancer (NSCLC). Here, we profiled DNA methylation of lung adenocarcinoma (LUAD) and lung squamous cell carcinoma (LUSC) tumours from 613 early-stage NSCLC patients and evaluated associations between CpG methylation of TRIM27 and overall survival. Significant CpG probes were confirmed in 617 samples from The Cancer Genome Atlas. The methylation of the CpG probe cg05293407TRIM27 was significantly associated with overall survival in patients with LUSC (HR = 1.65, 95% CI: 1.30-2.09, P = 4.52 × 10-5), but not in patients with LUAD (HR = 1.08, 95% CI: 0.87-1.33, P = 0.493). As incidence of LUSC is associated with higher smoking intensity compared to LUAD, we investigated whether smoking intensity impacted on the prognostic effect of cg05293407TRIM27 methylation in NSCLC. LUSC patients had a higher average pack-year of smoking (37.49LUAD vs 54.79LUSC, P = 1.03 × 10-19) and included a higher proportion of current smokers than LUAD patients (28.24%LUAD vs 34.09%LUSC, P = 0.037). cg05293407TRIM27 was significantly associated with overall survival only in NSCLC patients with medium-high pack-year of smoking (HR = 1.58, 95% CI: 1.26-1.96, P = 5.25 × 10-5). We conclude that cg05293407TRIM27 methylation is a potential predictor of LUSC prognosis, and smoking intensity may impact on its prognostic value across the various types of NSCLC.

Postmarket surveillance of arthroplasty device components using machine learning methods.

PURPOSE: While joint arthroplasty is generally a safe and effective procedure, there are concerns that some devices are at increased risk of failure. Early identification of total hip arthroplasty devices with increased risk of failure can be challenging because devices consist of multiple components, hundreds of distinct components are currently used in surgery, and any estimated effect needs to address confounding due to device and patient factors. The purpose of this study was to assess the effectiveness of machine learning approaches at identifying recalled components listed by the US Food and Drug Administration using data from a US total joint arthroplasty registry. METHODS: An open cohort study was conducted using data (January 1, 2001, to December 31, 2015) from 74 520 implantations and 348 unique components in the Kaiser Permanente Total Joint Replacement Registry. Exposures of interest were device components used in elective primary total hip arthroplasty. The outcome was time to first revision surgery, defined as exchange, removal, or addition of any component. Machine learning methods included regularized/unregularized Cox models and random survival forest. RESULTS: Among the recalled components detected were ASR acetabular shell/large femoral head, Durom acetabular shell/Metasul large femoral head, and Rejuvenate modular neck stem. The three components not identified were characterized by small numbers of devices recorded in the registry. CONCLUSIONS: The novel approaches to signal detection may improve postmarket surveillance of frequently used arthroplasty devices, which in turn will improve public health.

LncRNA SNHG7 promotes pancreatic cancer proliferation through ID4 by sponging miR-342-3p.

BACKGROUND: Small nucleolar RNA host gene 7 (SNHG7) is a novel identified oncogenic gene in tumorigenesis. However, the role that SNHG7 plays in pancreatic cancer (PC) remains unclear. In this study, we aimed to investigate the functional effects of SNHG7 on PC and the possible mechanism. METHODS: The expression levels of SNHG7 in tissues and cell lines were measured by RT-qPCR. Cell viability, apoptosis, migration and invasion were examined to explore the function of SNHG7 on PC. Bioinformatics methods were used to predict the target genes. The mechanism was further investigated by transfection with specific si-RNA, miRNA mimics or miRNA inhibitor. Tumor xenograft was carried out to verify the effects of SNHG7 in vivo. RESULTS: We found that SNHG7 was overexpressed in both PC tissues and cell lines. High expression level of SNHG7 was correlated with the poor prognosis. SNHG7 knockdown inhibited the proliferation, migration and invasion of PC cells. Moreover, SNHG7 was found to regulate the expression of ID4 via sponging miR-342-3p. Additionally, this finding was supported by in vivo experiments. CONCLUSIONS: LncRNA SNHG7 was overexpressed in PC tissues, and knockdown of SNHG7 suppressed PC cell proliferation, migration and invasion via miR-342-3p/ID4 axis. The results indicated that SNHG7 as a potential target for clinical treatment of PC.

Between-within effects in survival models with cross-classified clustering: Application to the evaluation of the effectiveness of medical devices.

In many medical applications involving observational survival data there will be a cross-classification of doctors and hospitals, as well as an interest in controlling for potentially confounding doctor and hospital effects when evaluating the effectiveness of a medical intervention. In this paper, we propose the use of a between-within model with cross-classified random effects and show through simulation that it performs better than alternative models. A real data example illustrates the application of the proposed model in a study of the survival of hip implants. The proposed model has broad utility in determining the effectiveness of medical interventions.

Distributed Intrinsic Functional Connectivity Patterns Predict Diagnostic Status in Large Autism Cohort.

Diagnosis of autism spectrum disorder (ASD) currently relies on behavioral observations because brain markers are unknown. Machine learning approaches can identify patterns in imaging data that predict diagnostic status, but most studies using functional connectivity MRI (fcMRI) data achieved only modest accuracies of 60-80%. We used conditional random forest (CRF), an ensemble learning technique protected against bias from feature correlation (which exists in fcMRI matrices). We selected 252 low-motion resting-state functional MRI scans from the Autism Brain Imaging Data Exchange, including 126 typically developing (TD) and 126 ASD participants, matched for age, nonverbal IQ, and head motion. A matrix of functional connectivities between 220 functionally defined regions of interest was used for diagnostic classification. In several runs, we achieved accuracies of 92-99% for classifiers with >300 features (most informative connections). Features, including pericentral somatosensory and motor regions, were disproportionately informative. Findings differed partially from a previous study in the same sample that used feature selection with random forest (which is biased by feature correlations). External validation in a smaller in-house data set, however, achieved only 67-71% accuracy. The large number of features in optimal models can be attributed to etiological heterogeneity under the clinical ASD umbrella. Lower accuracy in external validation is expected due to differences in unknown composition of ASD variants across samples. High accuracy in the main data set is unlikely due to noise overfitting, but rather indicates optimized characterization of a given cohort.

Sparse estimation of Cox proportional hazards models via approximated information criteria.

We propose a new sparse estimation method for Cox (1972) proportional hazards models by optimizing an approximated information criterion. The main idea involves approximation of the ℓ0 norm with a continuous or smooth unit dent function. The proposed method bridges the best subset selection and regularization by borrowing strength from both. It mimics the best subset selection using a penalized likelihood approach yet with no need of a tuning parameter. We further reformulate the problem with a reparameterization step so that it reduces to one unconstrained nonconvex yet smooth programming problem, which can be solved efficiently as in computing the maximum partial likelihood estimator (MPLE). Furthermore, the reparameterization tactic yields an additional advantage in terms of circumventing postselection inference. The oracle property of the proposed method is established. Both simulated experiments and empirical examples are provided for assessment and illustration.

Propensity score and proximity matching using random forest.

In order to derive unbiased inference from observational data, matching methods are often applied to produce balanced treatment and control groups in terms of all background variables. Propensity score has been a key component in this research area. However, propensity score based matching methods in the literature have several limitations, such as model mis-specifications, categorical variables with more than two levels, difficulties in handling missing data, and nonlinear relationships. Random forest, averaging outcomes from many decision trees, is nonparametric in nature, straightforward to use, and capable of solving these issues. More importantly, the precision afforded by random forest (Caruana et al., 2008) may provide us with a more accurate and less model dependent estimate of the propensity score. In addition, the proximity matrix, a by-product of the random forest, may naturally serve as a distance measure between observations that can be used in matching. The proposed random forest based matching methods are applied to data from the National Health and Nutrition Examination Survey (NHANES). Our results show that the proposed methods can produce well balanced treatment and control groups. An illustration is also provided that the methods can effectively deal with missing data in covariates.

[Development of a Laboratory Cement Quality Analysis Apparatus Based on Laser-Induced Breakdown Spectroscopy].

A laboratory cement quality analysis apparatus based on laser-induced breakdown spectroscopy (LIBS) has been developed for rapid analysis of cement composition and ratio values. In this paper, the overall structure, the optical system, the sample preparation process, as well as the spectral data analysis methods are introduced. The calibration model is established with internal standard method. A comparison as to the measurement results between LIBS and X-ray fluorescence spectrometry (XRF) has been made and being analyzed. It shows that by using the LIBS apparatus, the mean absolute error of CaO, SiO2, Al2O3, and Fe2O3 in cement raw materials is 0.46%, 0.25%, 0.13%, and 0.05%, respectively, while the mean absolute error of the ratio value such as KH, SM, and IM in cement clinker is 0.02, 0.05, and 0.04, respectively. The generated cement plasmas are verified to be in the local thermal equilibrium (LTE) condition by calculating both the plasma temperature and the electron density.

[Surface Modification and Applications of Cardiovascular Stent].

Cardiovascular disease is one of the most common causes of death.Coronary artery stent implantation has been the most important method to cure coronary disease and inhibit angiostegnosis.However,restenosis and thrombus at the site of implanting cardiovascular devices remains a significant problem in the practice of interventional cardiology.Recently,lots of studies have revealed that endothelial impairment is considered as one of the most important mechanisms contributing to restenosis.As a result,the method of accelerating endothelial regeneration at the injury site could prevent restenosis and thrombus.Considering the surface modification of cardiovascular stent implantation,this paper summarizes the progress on this direction,especially for the prevention of cardiovascular restenosis.Furthermore,this paper also proposes the methods and the future developing prospects for accelerating in vivo re-endothelialization at the site of intravascular stent with different biological molecules.

Predictors of new-onset distal neuropathic pain in HIV-infected individuals in the era of combination antiretroviral therapy.

Despite modern combination antiretroviral therapy, distal neuropathic pain (DNP) continues to affect many individuals with HIV infection. We evaluated risk factors for new-onset DNP in the CNS Antiretroviral Therapy Effects Research (CHARTER) study, an observational cohort. Standardized, semiannual clinical evaluations were administered at 6 US sites. Distal neuropathic pain was defined by using a clinician-administered instrument standardized across sites. All participants analyzed were free of DNP at study entry. New-onset DNP was recorded at the first follow-up visit at which it was reported. Mixed-effects logistic regression was used to evaluate potential predictors including HIV disease and treatment factors, demographics, medical comorbidities, and neuropsychiatric factors. Among 493 participants, 131 (27%) reported new DNP over 2306 visits during a median follow-up of 24 months (interquartile range 12-42). In multivariable regression, after adjusting for other covariates, significant entry predictors of new DNP were older age, female sex, current and past antiretroviral treatment, lack of virologic suppression, and lifetime history of opioid use disorder. During follow-up, more severe depression symptoms conferred a significantly elevated risk. The associations with opioid use disorders and depression reinforce the view that the clinical expression of neuropathic pain with peripheral nerve disease is strongly influenced by neuropsychiatric factors. Delineating such risk factors might help target emerging preventive strategies, for example, to individuals with a history of opioid use disorder, or might lead to new treatment approaches such as the use of tools to ameliorate depressed mood.

[Research on locking of the output power of pulsed laser in laser-induced breakdown spectroscopy].

Laser-induced breakdown spectroscopy (LIBS) as a rapid spectral analysis technology shows the outstanding application foreground and research value in coal quality on-line monitoring. In the practical application of this technology, the pulsed laser power fluctuation leads to the worse performance of long term stability, so a closed-loop feedback pulsed laser power locking device is set up, using laser power feedback signal to control and lock Nd:YAG laser output power. The laser power locking experiments are investigated in the same pre-set value with different splitting ratios, the different laser output power with the same proportion and the long time running modes. The results show that the beam split ration has little impact to the stability of the laser power, and the smaller split ration leads to the faster stabilization. This device can keep the output power of the pulsed laser being locked in the pre-set range for a long-term running, RSD values decrease from 2.4% of free-running to 1.1%.