LATTE: Label-efficient incident phenotyping from longitudinal electronic health records.

Electronic health record (EHR) data are increasingly used to support real-world evidence studies but are limited by the lack of precise timings of clinical events. Here, we propose a label-efficient incident phenotyping (LATTE) algorithm to accurately annotate the timing of clinical events from longitudinal EHR data. By leveraging the pre-trained semantic embeddings, LATTE selects predictive features and compresses their information into longitudinal visit embeddings through visit attention learning. LATTE models the sequential dependency between the target event and visit embeddings to derive the timings. To improve label efficiency, LATTE constructs longitudinal silver-standard labels from unlabeled patients to perform semi-supervised training. LATTE is evaluated on the onset of type 2 diabetes, heart failure, and relapses of multiple sclerosis. LATTE consistently achieves substantial improvements over benchmark methods while providing high prediction interpretability. The event timings are shown to help discover risk factors of heart failure among patients with rheumatoid arthritis.

Characteristics and management of tumor treating fields-related dermatological complications in patients with glioblastoma.

Tumor treating fields (TTFields) is a novel approved modality for the treatment of glioblastoma (GBM) exhibiting a satisfactory effect. Although TTFields has shown considerable safety for the normal brain, dermatological adverse events (DAEs) often occur during therapy. However, studies focused on the identification and management of DAEs are rare. The clinical data and photos of skin lesions from 9 patients with GBM were retrospectively analyzed, and the types and grades of individual scalp dermatitis were evaluated based on the Common Terminology Criteria for Adverse Events version 5.0 (CTCAE v 5.0). Adherence and safety were also evaluated on the basis of the device monitoring data. Eight patients (88.9%) exhibited grade 1 or grade 2 CTCAE DAEs, all of whom were cured after interventions. The adherence was >90%, with no relevant safety events reported. Finally, a guideline for preventing DAEs in patients with GBM was proposed. The identification and management of TTFields-related DAEs is necessary and urgent in patients with GBM. Timely interventions of DAEs will help to improve the adherence and quality of life of patients, which ultimately improves prognosis. The proposed guideline for preventing DAEs in patients with GBM assists in the management of healthcare providers and may avoid dermatologic complications.

ARCH: Large-scale Knowledge Graph via Aggregated Narrative Codified Health Records Analysis.

Objective: Electronic health record (EHR) systems contain a wealth of clinical data stored as both codified data and free-text narrative notes, covering hundreds of thousands of clinical concepts available for research and clinical care. The complex, massive, heterogeneous, and noisy nature of EHR data imposes significant challenges for feature representation, information extraction, and uncertainty quantification. To address these challenges, we proposed an efficient Aggregated naRrative Codified Health (ARCH) records analysis to generate a large-scale knowledge graph (KG) for a comprehensive set of EHR codified and narrative features. Methods: The ARCH algorithm first derives embedding vectors from a co-occurrence matrix of all EHR concepts and then generates cosine similarities along with associated p-values to measure the strength of relatedness between clinical features with statistical certainty quantification. In the final step, ARCH performs a sparse embedding regression to remove indirect linkage between entity pairs. We validated the clinical utility of the ARCH knowledge graph, generated from 12.5 million patients in the Veterans Affairs (VA) healthcare system, through downstream tasks including detecting known relationships between entity pairs, predicting drug side effects, disease phenotyping, as well as sub-typing Alzheimer's disease patients. Results: ARCH produces high-quality clinical embeddings and KG for over 60,000 EHR concepts, as visualized in the R-shiny powered web-API (https://celehs.hms.harvard.edu/ARCH/). The ARCH embeddings attained an average area under the ROC curve (AUC) of 0.926 and 0.861 for detecting pairs of similar EHR concepts when the concepts are mapped to codified data and to NLP data; and 0.810 (codified) and 0.843 (NLP) for detecting related pairs. Based on the p-values computed by ARCH, the sensitivity of detecting similar and related entity pairs are 0.906 and 0.888 under false discovery rate (FDR) control of 5%. For detecting drug side effects, the cosine similarity based on the ARCH semantic representations achieved an AUC of 0.723 while the AUC improved to 0.826 after few-shot training via minimizing the loss function on the training data set. Incorporating NLP data substantially improved the ability to detect side effects in the EHR. For example, based on unsupervised ARCH embeddings, the power of detecting drug-side effects pairs when using codified data only was 0.15, much lower than the power of 0.51 when using both codified and NLP concepts. Compared to existing large-scale representation learning methods including PubmedBERT, BioBERT and SAPBERT, ARCH attains the most robust performance and substantially higher accuracy in detecting these relationships. Incorporating ARCH selected features in weakly supervised phenotyping algorithms can improve the robustness of algorithm performance, especially for diseases that benefit from NLP features as supporting evidence. For example, the phenotyping algorithm for depression attained an AUC of 0.927 when using ARCH selected features but only 0.857 when using codified features selected via the KESER network[1]. In addition, embeddings and knowledge graphs generated from the ARCH network were able to cluster AD patients into two subgroups, where the fast progression subgroup had a much higher mortality rate. Conclusions: The proposed ARCH algorithm generates large-scale high-quality semantic representations and knowledge graph for both codified and NLP EHR features, useful for a wide range of predictive modeling tasks.

Multimodal representation learning for predicting molecule-disease relations.

MOTIVATION: Predicting molecule-disease indications and side effects is important for drug development and pharmacovigilance. Comprehensively mining molecule-molecule, molecule-disease and disease-disease semantic dependencies can potentially improve prediction performance. METHODS: We introduce a Multi-Modal REpresentation Mapping Approach to Predicting molecular-disease relations (M2REMAP) by incorporating clinical semantics learned from electronic health records (EHR) of 12.6 million patients. Specifically, M2REMAP first learns a multimodal molecule representation that synthesizes chemical property and clinical semantic information by mapping molecule chemicals via a deep neural network onto the clinical semantic embedding space shared by drugs, diseases and other common clinical concepts. To infer molecule-disease relations, M2REMAP combines multimodal molecule representation and disease semantic embedding to jointly infer indications and side effects. RESULTS: We extensively evaluate M2REMAP on molecule indications, side effects and interactions. Results show that incorporating EHR embeddings improves performance significantly, for example, attaining an improvement over the baseline models by 23.6% in PRC-AUC on indications and 23.9% on side effects. Further, M2REMAP overcomes the limitation of existing methods and effectively predicts drugs for novel diseases and emerging pathogens. AVAILABILITY AND IMPLEMENTATION: The code is available at https://github.com/celehs/M2REMAP, and prediction results are provided at https://shiny.parse-health.org/drugs-diseases-dev/. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Prediction Model for Tumor Volume Nadir in EGFR -mutant NSCLC Patients Treated With EGFR Tyrosine Kinase Inhibitors.

PURPOSE: In patients with advanced non-small cell lung cancer (NSCLC) and oncogenic driver mutations treated with effective targeted therapy, a characteristic pattern of tumor volume dynamics with an initial regression, nadir, and subsequent regrowth is observed on serial computed tomography (CT) scans. We developed and validated a linear model to predict the tumor volume nadir in EGFR -mutant advanced NSCLC patients treated with EGFR tyrosine kinase inhibitors (TKI). MATERIALS AND METHODS: Patients with EGFR -mutant advanced NSCLC treated with EGFR-TKI as their first EGFR-directed therapy were studied for CT tumor volume kinetics during therapy, using a previously validated CT tumor measurement technique. A linear regression model was built to predict tumor volume nadir in a training cohort of 34 patients, and then was validated in an independent cohort of 84 patients. RESULTS: The linear model for tumor nadir prediction was obtained in the training cohort of 34 patients, which utilizes the baseline tumor volume before initiating therapy (V 0 ) to predict the volume decrease (mm 3 ) when the nadir volume (V p ) was reached: V 0 -V p =0.717×V 0 -1347 ( P =2×10 -16 ; R2 =0.916). The model was tested in the validation cohort, resulting in the R2 value of 0.953, indicating that the prediction model generalizes well to another cohort of EGFR -mutant patients treated with EGFR-TKI. Clinical variables were not significant predictors of tumor volume nadir. CONCLUSION: The linear model was built to predict the tumor volume nadir in EGFR -mutant advanced NSCLC patients treated with EGFR-TKIs, which provide an important metrics in treatment monitoring and therapeutic decisions at nadir such as additional local abrasive therapy.

Penalized estimation of frailty-based illness-death models for semi-competing risks.

Semi-competing risks refer to the time-to-event analysis setting, where the occurrence of a non-terminal event is subject to whether a terminal event has occurred, but not vice versa. Semi-competing risks arise in a broad range of clinical contexts, including studies of preeclampsia, a condition that may arise during pregnancy and for which delivery is a terminal event. Models that acknowledge semi-competing risks enable investigation of relationships between covariates and the joint timing of the outcomes, but methods for model selection and prediction of semi-competing risks in high dimensions are lacking. Moreover, in such settings researchers commonly analyze only a single or composite outcome, losing valuable information and limiting clinical utility-in the obstetric setting, this means ignoring valuable insight into timing of delivery after preeclampsia has onset. To address this gap, we propose a novel penalized estimation framework for frailty-based illness-death multi-state modeling of semi-competing risks. Our approach combines non-convex and structured fusion penalization, inducing global sparsity as well as parsimony across submodels. We perform estimation and model selection via a pathwise routine for non-convex optimization, and prove statistical error rate results in this setting. We present a simulation study investigating estimation error and model selection performance, and a comprehensive application of the method to joint risk modeling of preeclampsia and timing of delivery using pregnancy data from an electronic health record.

Finite-time synchronization of reaction-diffusion memristive neural networks: A gain-scheduled integral sliding mode control scheme.

The finite-time synchronization issue of reaction-diffusion memristive neural networks (RDMNNs) is studied in this paper. To better synchronize the parameter-varying drive and response systems, an innovative gain-scheduled integral sliding mode control scheme is proposed, where the 2n controller gains can be scheduled and an integral switching surface function that contains a discontinuous term is involved. Moreover, by constructing a novel Lyapunov-Krasovskii functional and combining reciprocally convex combination (RCC) method, a less conservative finite-time synchronization criterion for RDMNNs is derived in the form of linear matrix inequalities (LMIs). Finally, three numerical simulations are exploited to illustrate the effectiveness, superiority and practicability of this paper.

Multiview Incomplete Knowledge Graph Integration with application to cross-institutional EHR data harmonization.

OBJECTIVE: The growing availability of electronic health records (EHR) data opens opportunities for integrative analysis of multi-institutional EHR to produce generalizable knowledge. A key barrier to such integrative analyses is the lack of semantic interoperability across different institutions due to coding differences. We propose a Multiview Incomplete Knowledge Graph Integration (MIKGI) algorithm to integrate information from multiple sources with partially overlapping EHR concept codes to enable translations between healthcare systems. METHODS: The MIKGI algorithm combines knowledge graph information from (i) embeddings trained from the co-occurrence patterns of medical codes within each EHR system and (ii) semantic embeddings of the textual strings of all medical codes obtained from the Self-Aligning Pretrained BERT (SAPBERT) algorithm. Due to the heterogeneity in the coding across healthcare systems, each EHR source provides partial coverage of the available codes. MIKGI synthesizes the incomplete knowledge graphs derived from these multi-source embeddings by minimizing a spherical loss function that combines the pairwise directional similarities of embeddings computed from all available sources. MIKGI outputs harmonized semantic embedding vectors for all EHR codes, which improves the quality of the embeddings and enables direct assessment of both similarity and relatedness between any pair of codes from multiple healthcare systems. RESULTS: With EHR co-occurrence data from Veteran Affairs (VA) healthcare and Mass General Brigham (MGB), MIKGI algorithm produces high quality embeddings for a variety of downstream tasks including detecting known similar or related entity pairs and mapping VA local codes to the relevant EHR codes used at MGB. Based on the cosine similarity of the MIKGI trained embeddings, the AUC was 0.918 for detecting similar entity pairs and 0.809 for detecting related pairs. For cross-institutional medical code mapping, the top 1 and top 5 accuracy were 91.0% and 97.5% when mapping medication codes at VA to RxNorm medication codes at MGB; 59.1% and 75.8% when mapping VA local laboratory codes to LOINC hierarchy. When trained with 500 labels, the lab code mapping attained top 1 and 5 accuracy at 77.7% and 87.9%. MIKGI also attained best performance in selecting VA local lab codes for desired laboratory tests and COVID-19 related features for COVID EHR studies. Compared to existing methods, MIKGI attained the most robust performance with accuracy the highest or near the highest across all tasks. CONCLUSIONS: The proposed MIKGI algorithm can effectively integrate incomplete summary data from biomedical text and EHR data to generate harmonized embeddings for EHR codes for knowledge graph modeling and cross-institutional translation of EHR codes.

Clinical knowledge extraction via sparse embedding regression (KESER) with multi-center large scale electronic health record data.

The increasing availability of electronic health record (EHR) systems has created enormous potential for translational research. However, it is difficult to know all the relevant codes related to a phenotype due to the large number of codes available. Traditional data mining approaches often require the use of patient-level data, which hinders the ability to share data across institutions. In this project, we demonstrate that multi-center large-scale code embeddings can be used to efficiently identify relevant features related to a disease of interest. We constructed large-scale code embeddings for a wide range of codified concepts from EHRs from two large medical centers. We developed knowledge extraction via sparse embedding regression (KESER) for feature selection and integrative network analysis. We evaluated the quality of the code embeddings and assessed the performance of KESER in feature selection for eight diseases. Besides, we developed an integrated clinical knowledge map combining embedding data from both institutions. The features selected by KESER were comprehensive compared to lists of codified data generated by domain experts. Features identified via KESER resulted in comparable performance to those built upon features selected manually or with patient-level data. The knowledge map created using an integrative analysis identified disease-disease and disease-drug pairs more accurately compared to those identified using single institution data. Analysis of code embeddings via KESER can effectively reveal clinical knowledge and infer relatedness among codified concepts. KESER bypasses the need for patient-level data in individual analyses providing a significant advance in enabling multi-center studies using EHR data.

Progression of traction bronchiectasis/bronchiolectasis in interstitial lung abnormalities is associated with increased all-cause mortality: Age Gene/Environment Susceptibility-Reykjavik Study.

PURPOSE: The aim of this study is to assess the role of traction bronchiectasis/bronchiolectasis and its progression as a predictor for early fibrosis in interstitial lung abnormalities (ILA). METHODS: Three hundred twenty-seven ILA participants out of 5764 in the Age, Gene/Environment Susceptibility (AGES)-Reykjavik Study who had undergone chest CT twice with an interval of approximately five-years were enrolled in this study. Traction bronchiectasis/bronchiolectasis index (TBI) was classified on a four-point scale: 0, ILA without traction bronchiectasis/bronchiolectasis; 1, ILA with bronchiolectasis but without bronchiectasis or architectural distortion; 2, ILA with mild to moderate traction bronchiectasis; 3, ILA and severe traction bronchiectasis and/or honeycombing. Traction bronchiectasis (TB) progression was classified on a five-point scale: 1, Improved; 2, Probably improved; 3, No change; 4, Probably progressed; 5, Progressed. Overall survival (OS) among participants with different TB Progression Score and between the TB progression group and No TB progression group was also investigated. Hazard radio (HR) was estimated with Cox proportional hazards model. RESULTS: The higher the TBI at baseline, the higher TB Progression Score (P < 0.001). All five participants with TBI = 3 at baseline progressed; 46 (90 %) of 51 participants with TBI = 2 progressed. TB progression was also associated with shorter OS with statistically significant difference (adjusted HR = 1.68, P < 0.001). CONCLUSION: TB progression was visualized on chest CT frequently and clearly. It has the potential to be the predictor for poorer prognosis of ILA.

Role of the long non-coding RNA HOTAIR/miR-126 axis in an in vitro psoriasis model.

Psoriasis is a T-cell-mediated inflammatory skin disease that is characterized by excessive keratinocyte proliferation and persistent skin inflammation. Accumulating evidence suggests that long non-coding RNAs (lncRNAs) are dysregulated in a number of inflammatory conditions. In the present study, an in vitro psoriasis cell model was established. Human HaCaT keratinocytes were activated using the inflammatory factor IL-22. Briefly, HaCaT cells were starved in serum-free DMEM for 24 h and then stimulated with 100 ng/ml IL-22 in serum-free DMEM for 24 h. Previous research indicated that HOX transcript antisense RNA (HOTAIR) may participate in the development of psoriasis. First, reverse transcription-quantitative PCR (RT-qPCR) analysis was performed to detect HOTAIR expression. The results indicated that HOTAIR expression was reduced in IL-22-stimulated HaCaT cells. Subsequently, a dual-luciferase reporter assay was performed to verify the binding site between HOTAIR and microRNA (miR)-126. The RT-qPCR results indicated that miR-126 expression was increased in IL-22-stimulated HaCaT cells. Moreover, the effects of HOTAIR and miR-126 on IL-22-stimulated HaCaT cell proliferation and apoptosis were assessed. HaCaT cells were transfected with control-plasmid, HOTAIR-plasmid, HOTAIR-plasmid + mimic control or HOTAIR-plasmid + miR-126 mimic for 24 h. At 24 h post-transfection, the cells were stimulated with 100 ng/ml IL-22 for 24 h and experiments were conducted. IL-22 induced cell proliferation and suppressed apoptosis. However, HOTAIR-plasmid inhibited cell viability and induced apoptosis in IL-22-stimulated HaCaT cells. In addition, the western blotting results indicated that HOTAIR-plasmid increased cleaved caspase-3 expression and the cleaved caspase-3/caspase-3 ratio, whereas the HOTAIR-plasmid-mediated effects were reversed by miR-126 mimic. Collectively, the results of the present study demonstrated that the lncRNA-HOTAIR/miR-126 axis may be implicated in the regulation of psoriasis progression and may serve as a potential therapeutic target for psoriasis.

Interstitial lung abnormalities in patients with stage I non-small cell lung cancer are associated with shorter overall survival: the Boston lung cancer study.

BACKGROUND: Interstitial lung abnormalities (ILA) can be detected on computed tomography (CT) in lung cancer patients and have an association with mortality in advanced non-small cell lung cancer (NSCLC) patients. The aim of this study is to demonstrate the significance of ILA for mortality in patients with stage I NSCLC using Boston Lung Cancer Study cohort. METHODS: Two hundred and thirty-one patients with stage I NSCLC from 2000 to 2011 were investigated in this retrospective study (median age, 69 years; 93 males, 138 females). ILA was scored on baseline CT scans prior to treatment using a 3-point scale (0 = no evidence of ILA, 1 = equivocal for ILA, 2 = ILA) by a sequential reading method. ILA score 2 was considered the presence of ILA. The difference of overall survival (OS) for patients with different ILA scores were tested via log-rank test and multivariate Cox proportional hazards models were used to estimate hazard ratios (HRs) including ILA score, age, sex, smoking status, and treatment as the confounding variables. RESULTS: ILA was present in 22 out of 231 patients (9.5%) with stage I NSCLC. The presence of ILA was associated with shorter OS (patients with ILA score 2, median 3.85 years [95% confidence interval (CI): 3.36 - not reached (NR)]; patients with ILA score 0 or 1, median 10.16 years [95%CI: 8.65 - NR]; P <  0.0001). In a Cox proportional hazards model, the presence of ILA remained significant for increased risk for death (HR = 2.88, P = 0.005) after adjusting for age, sex, smoking and treatment. CONCLUSIONS: ILA was detected on CT in 9.5% of patients with stage I NSCLC. The presence of ILA was significantly associated with a shorter OS and could be an imaging marker of shorter survival in stage I NSCLC.

Estimating and inferring the maximum degree of stimulus-locked time-varying brain connectivity networks.

Neuroscientists have enjoyed much success in understanding brain functions by constructing brain connectivity networks using data collected under highly controlled experimental settings. However, these experimental settings bear little resemblance to our real-life experience in day-to-day interactions with the surroundings. To address this issue, neuroscientists have been measuring brain activity under natural viewing experiments in which the subjects are given continuous stimuli, such as watching a movie or listening to a story. The main challenge with this approach is that the measured signal consists of both the stimulus-induced signal, as well as intrinsic-neural and nonneuronal signals. By exploiting the experimental design, we propose to estimate stimulus-locked brain networks by treating nonstimulus-induced signals as nuisance parameters. In many neuroscience applications, it is often important to identify brain regions that are connected to many other brain regions during cognitive process. We propose an inferential method to test whether the maximum degree of the estimated network is larger than a prespecific number. We prove that the type I error can be controlled and that the power increases to one asymptotically. Simulation studies are conducted to assess the performance of our method. Finally, we analyze a functional magnetic resonance imaging dataset obtained under the Sherlock Holmes movie stimuli.

Tumor Growth Rate After Nadir Is Associated With Survival in Patients With EGFR-Mutant Non-Small-Cell Lung Cancer Treated With Epidermal Growth Factor Receptor Tyrosine Kinase Inhibitor.

PURPOSE: To investigate the association between tumor volume growth rate after the nadir and survival in patients with EGFR-mutant advanced non-small-cell lung cancer (NSCLC) treated with erlotinib. MATERIALS AND METHODS: Seventy-one patients with EGFR-mutant advanced NSCLC treated with erlotinib were studied for computed tomography tumor volume kinetics during therapy. The tumor growth rate after nadir was obtained using a previously published analytic module for longitudinal volume tracking to study its relationship with overall survival (OS). RESULTS: The median tumor volume for the cohort was 19,842 mm3 at baseline and 4,083 mm3 at nadir. The median time to nadir was 6.2 months. The tumor growth rate after nadir for logeV (the natural logarithm of tumor volume measured in mm3) was 0.11/mo on average for the cohort (SE: 0.014), which was very similar to the previously validated reference value of 0.12/mo to define slow and fast tumor growth. The OS of 48 patients with slow tumor growth (≤ 0.12/mo) was significantly longer compared with 23 patients with fast tumor growth (> 0.12/mo; median OS: 37.8 v 25.0 months; P = .0012). In Cox models, tumor growth rate was also associated with survival (regression coefficient: 3.9903; P = .0024; faster rate leads to increased hazards), after adjusting for time to nadir (regression coefficient: -0.0863; P = .0008; longer time to nadir leads to decreased hazards) and smoking history. CONCLUSION: In patients with EGFR-mutant advanced NSCLC treated with erlotinib, slower tumor growth rates after nadir were associated with longer OS, providing a rationale for using tumor growth rates to guide precision therapy for lung cancer.

Inter-Subject Analysis: A Partial Gaussian Graphical Model Approach.

Different from traditional intra-subject analysis, the goal of inter-subject analysis (ISA) is to explore the dependency structure between different subjects with the intra-subject dependency as nuisance. ISA has important applications in neuroscience to study the functional connectivity between brain regions under natural stimuli. We propose a modeling framework for ISA that is based on Gaussian graphical models, under which ISA can be converted to the problem of estimation and inference of a partial Gaussian graphical model. The main statistical challenge is that we do not impose sparsity constraints on the whole precision matrix and we only assume the inter-subject part is sparse. For estimation, we propose to estimate an alternative parameter to get around the nonsparse issue and it can achieve asymptotic consistency even if the intra-subject dependency is dense. For inference, we propose an "untangle and chord" procedure to de-bias our estimator. It is valid without the sparsity assumption on the inverse Hessian of the log-likelihood function. This inferential method is general and can be applied to many other statistical problems, thus it is of independent theoretical interest. Numerical experiments on both simulated and brain imaging data validate our methods and theory. Supplementary materials for this article are available online.

Traction Bronchiectasis/Bronchiolectasis is Associated with Interstitial Lung Abnormality Mortality.

PURPOSE: To investigate if the presence and severity of traction bronchiectasis/bronchiolectasis are associated with poorer survival in subjects with ILA. METHOD: The study included 3,594 subjects (378 subjects with ILA and 3,216 subjects without ILA) in AGES-Reykjavik Study. Chest CT scans of 378 subjects with ILA were evaluated for traction bronchiectasis/bronchiolectasis, defined as dilatation of bronchi/bronchioles within areas demonstrating ILA. Traction bronchiectasis/bronchiolectasis Index (TBI) was assigned as: TBI = 0, ILA without traction bronchiectasis/bronchiolectasis: TBI = 1, ILA with bronchiolectasis but without bronchiectasis or architectural distortion: TBI = 2, ILA with mild to moderate traction bronchiectasis: TBI = 3, ILA and severe traction bronchiectasis and/or honeycombing. Overall survival (OS) was compared among the subjects in different TBI groups and those without ILA. RESULTS: The median OS was 12.93 years (95%CI; 12.67 - 13.43) in the subjects without ILA; 11.95 years (10.03 - not reached) in TBI-0 group; 8.52 years (7.57 - 9.30) in TBI-1 group; 7.63 years (6.09 - 9.10) in TBI-2 group; 5.40 years (1.85 - 5.98) in TBI-3 group. The multivariable Cox models demonstrated significantly shorter OS of TBI-1, TBI-2, and TBI-3 groups compared to subjects without ILA (P < 0.0001), whereas TBI-0 group had no significant OS difference compared to subjects without ILA, after adjusting for age, sex, and smoking status. CONCLUSIONS: The presence and severity of traction bronchiectasis/bronchiolectasis are associated with shorter survival. The traction bronchiectasis/bronchiolectasis is an important contributor to increased mortality among subjects with ILA.

DISTRIBUTED TESTING AND ESTIMATION UNDER SPARSE HIGH DIMENSIONAL MODELS.

This paper studies hypothesis testing and parameter estimation in the context of the divide-and-conquer algorithm. In a unified likelihood based framework, we propose new test statistics and point estimators obtained by aggregating various statistics from k subsamples of size n/k, where n is the sample size. In both low dimensional and sparse high dimensional settings, we address the important question of how large k can be, as n grows large, such that the loss of efficiency due to the divide-and-conquer algorithm is negligible. In other words, the resulting estimators have the same inferential efficiencies and estimation rates as an oracle with access to the full sample. Thorough numerical results are provided to back up the theory.

H∞ control for time-delay systems with randomly occurring nonlinearities subject to sensor saturations, missing measurements and channel fadings.

The H∞ control problem for a class of time-delay systems with randomly occurring nonlinearities (RONs) is addressed in this paper. Sensor saturations, missing measurements and channel fadings are governed by random variables obeying the Bernoulli distributions. The measurement output is subject to both data missing and randomly occurring sensor saturations (ROSSs) described by sector-nonlinearities as well as the channel fadings caused typically in wireless communication. The aim of the addressed problem is to design a full-order dynamic output-feedback controller such that the closed-loop system is exponentially mean-square stable and satisfies the prescribed H∞ performance constraint. Sufficient conditions are presented by resorting to intensive stochastic analysis and matrix inequality techniques, which not only guarantee the existence of the desired controller for all possible time-delays, RONs, missing measurements and ROSSs but also lead to the explicit expressions of such controllers. Finally, a numerical example is given to demonstrate the applicability of the proposed control scheme.

Adaptive Synchronization of Fractional-Order Complex-Valued Neural Networks with Discrete and Distributed Delays.

In this paper, the synchronization problem of fractional-order complex-valued neural networks with discrete and distributed delays is investigated. Based on the adaptive control and Lyapunov function theory, some sufficient conditions are derived to ensure the states of two fractional-order complex-valued neural networks with discrete and distributed delays achieve complete synchronization rapidly. Finally, numerical simulations are given to illustrate the effectiveness and feasibility of the theoretical results.

Multi-proxy temperature reconstruction from the West Qinling Mountains, China for the past 500 years.

A total of 290 tree-ring samples, collected from six sites in the West Qinling Mountains of China, were used to develop six new standard tree-ring chronologies. In addition, 73 proxy records were assembled in collaboration with Chinese and international scholars, from 27 publically available proxy records and 40 tree-ring chronologies that are not available in public datasets. These records were used to reconstruct annual mean temperature variability in the West Qinling Mountains over the past 500 years (AD 1500-1995), using a modified point-by-point regression (hybrid PPR) method. The results demonstrate that the hybrid PPR method successfully integrates the temperature signals from different types of proxies, and that the method preserves a high degree of low-frequency variability. The reconstruction shows greater temperature variability in the West Qinling Mountains than has been found in previous studies. Our temperature reconstruction for this region shows: 1) five distinct cold periods, at approximately AD 1520-1535, AD 1560-1575, AD 1610-1620, AD 1850-1875 and AD 1965-1985, and four warm periods, at approximately AD 1645-1660, AD 1705-1725, AD 1785-1795 and AD 1920-1945; 2) that in this region, the 20(th) century was not the warmest period of the past 500 years; and 3) that a dominant and persistent oscillation of ca. 64 years is significantly identified in the 1640-1790 period.