Prediction of progression in idiopathic pulmonary fibrosis using CT scans atbaseline: A quantum particle swarm optimization - Random forest approach

Idiopathic pulmonary fibrosis (IPF) is a fatal lung disease characterized by an unpredictable progressive declinein lung function. Natural history of IPF is unknown and the prediction of disease progression at the time ofdiagnosis is notoriously difficult. High resolution computed tomography (HRCT) has been used for the diagnosisof IPF, but not generally for monitoring purpose. The objective of this work is to develop a novel predictivemodel for the radiological progression pattern at voxel-wise level using only baseline HRCT scans. Mainly, thereare two challenges: (a) obtaining a data set of features for region of interest (ROI) on baseline HRCT scans andtheir follow-up status; and (b) simultaneously selecting important features from high-dimensional space, andoptimizing the prediction performance. We resolved the first challenge by implementing a study design andhaving an expert radiologist contour ROIs at baseline scans, depending on its progression status in follow-upvisits. For the second challenge, we integrated the feature selection with prediction by developing an algorithmusing a wrapper method that combines quantum particle swarm optimization to select a small number of featureswith random forest to classify early patterns of progression. We applied our proposed algorithm to analyzeanonymized HRCT images from 50 IPF subjects from a multi-center clinical trial. We showed that it yields aparsimonious model with 81.8% sensitivity, 82.2% specificity and an overall accuracy rate of 82.1% at the ROIlevel. These results are superior to other popular feature selections and classification methods, in that ourmethod produces higher accuracy in prediction of progression and more balanced sensitivity and specificity witha smaller number of selected features. Our work is the first approach to show that it is possible to use onlybaseline HRCT scans to predict progressive ROIs at 6 months to 1year follow-ups using artificial intelligence

A Deep Reinforcement Learning Approach to Rare Event Estimation

An important step in the design of autonomous systems is to evaluate the probability that a failure will occur. In safety-critical domains, the failure probability is extremely small so that the evaluation of a policy through Monte Carlo sampling is inefficient. Adaptive importance sampling approaches have been developed for rare event estimation but do not scale well to sequential systems with long horizons. In this work, we develop two adaptive importance sampling algorithms that can efficiently estimate the probability of rare events for sequential decision making systems. The basis for these algorithms is the minimization of the Kullback-Leibler divergence between a state-dependent proposal distribution and a target distribution over trajectories, but the resulting algorithms resemble policy gradient and value-based reinforcement learning. We apply multiple importance sampling to reduce the variance of our estimate and to address the issue of multi-modality in the optimal proposal distribution. We demonstrate our approach on a control task with both continuous and discrete actions spaces and show accuracy improvements over several baselines

Novel lung imaging biomarkers and skin gene expression subsetting in dasatinib treatment of systemic sclerosis-associated interstitial lung disease.

BackgroundThere are no effective treatments or validated clinical response markers in systemic sclerosis (SSc). We assessed imaging biomarkers and performed gene expression profiling in a single-arm open-label clinical trial of tyrosine kinase inhibitor dasatinib in patients with SSc-associated interstitial lung disease (SSc-ILD).MethodsPrimary objectives were safety and pharmacokinetics. Secondary outcomes included clinical assessments, quantitative high-resolution computed tomography (HRCT) of the chest, serum biomarker assays and skin biopsy-based gene expression subset assignments. Clinical response was defined as decrease of >5 or >20% from baseline in the modified Rodnan Skin Score (MRSS). Pulmonary function was assessed at baseline and day 169.ResultsDasatinib was well-tolerated in 31 patients receiving drug for a median of nine months. No significant changes in clinical assessments or serum biomarkers were seen at six months. By quantitative HRCT, 65% of patients showed no progression of lung fibrosis, and 39% showed no progression of total ILD. Among 12 subjects with available baseline and post-treatment skin biopsies, three were improvers and nine were non-improvers. Improvers mapped to the fibroproliferative or normal-like subsets, while seven out of nine non-improvers were in the inflammatory subset (p = 0.0455). Improvers showed stability in forced vital capacity (FVC) and diffusing capacity for carbon monoxide (DLCO), while both measures showed a decline in non-improvers (p = 0.1289 and p = 0.0195, respectively). Inflammatory gene expression subset was associated with higher baseline HRCT score (p = 0.0556). Non-improvers showed significant increase in lung fibrosis (p = 0.0313).ConclusionsIn patients with SSc-ILD dasatinib treatment was associated with acceptable safety profile but no significant clinical efficacy. Patients in the inflammatory gene expression subset showed increase in skin fibrosis, decreasing pulmonary function and worsening lung fibrosis during the study. These findings suggest that target tissue-specific gene expression analyses can help match patients and therapeutic interventions in heterogeneous diseases such as SSc, and quantitative HRCT is useful for assessing clinical outcomes.Trial registrationClinicaltrials.gov NCT00764309

Monitoring Observations of the Jupiter-Family Comet 17P/Holmes during 2014 Perihelion Passage

We performed a monitoring observation of a Jupiter-Family comet, 17P/Holmes, during its 2014 perihelion passage to investigate its secular change in activity. The comet has drawn the attention of astronomers since its historic outburst in 2007, and this occasion was its first perihelion passage since then. We analyzed the obtained data using aperture photometry package and derived the Afrho parameter, a proxy for the dust production rate. We found that Afrho showed asymmetric properties with respect to the perihelion passage: it increased moderately from 100 cm at the heliocentric distance r_h=2.6-3.1 AU to a maximal value of 185 cm at r_h = 2.2 AU (near the perihelion) during the inbound orbit, while dropping rapidly to 35 cm at r_h = 3.2 AU during the outbound orbit. We applied a model for characterizing dust production rates as a function of r_h and found that the fractional active area of the cometary nucleus had dropped from 20%-40% in 2008-2011 (around the aphelion) to 0.1%-0.3% in 2014-2015 (around the perihelion). This result suggests that a dust mantle would have developed rapidly in only one orbital revolution around the sun. Although a minor eruption was observed on UT 2015 January 26 at r_h = 3.0 AU, the areas excavated by the 2007 outburst would be covered with a layer of dust (<~ 10 cm depth) which would be enough to insulate the subsurface ice and to keep the nucleus in a state of low activity.Comment: 25 pages, 6 figures, 2 tables, ApJ accepted on December 29, 201

Identification of antiviral roles for the exon-junction complex and nonsense-mediated decay in flaviviral infection.

West Nile virus (WNV) is an emerging mosquito-borne flavivirus, related to dengue virus and Zika virus. To gain insight into host pathways involved in WNV infection, we performed a systematic affinity-tag purification mass spectrometry (APMS) study to identify 259 WNV-interacting human proteins. RNA interference screening revealed 26 genes that both interact with WNV proteins and influence WNV infection. We found that WNV, dengue and Zika virus capsids interact with a conserved subset of proteins that impact infection. These include the exon-junction complex (EJC) recycling factor PYM1, which is antiviral against all three viruses. The EJC has roles in nonsense-mediated decay (NMD), and we found that both the EJC and NMD are antiviral and the EJC protein RBM8A directly binds WNV RNA. To counteract this, flavivirus infection inhibits NMD and the capsid-PYM1 interaction interferes with EJC protein function and localization. Depletion of PYM1 attenuates RBM8A binding to viral RNA, suggesting that WNV sequesters PYM1 to protect viral RNA from decay. Together, these data suggest a complex interplay between the virus and host in regulating NMD and the EJC

Comparison of Hyperthermal Ground Laboratory Atomic Oxygen Erosion Yields With Those in Low Earth Orbit

The atomic oxygen erosion yields of 26 materials (all polymers except for pyrolytic graphite) were measured in two directed hyperthermal radio frequency (RF) plasma ashers operating at 30 or 35 kHz with air. The hyperthermal asher results were compared with thermal energy asher results and low Earth orbital (LEO) results from the Materials International Space Station Experiment 2 and 7 (MISSE 2 and 7) flight experiments. The hyperthermal testing was conducted to a significant portion of the atomic oxygen fluence similar polymers were exposed to during the MISSE 2 and 7 missions. Comparison of the hyperthermal asher prediction of LEO erosion yields with thermal energy asher erosion yields indicates that except for the fluorocarbon polymers of PTFE and FEP, the hyperthermal energy ashers are a much more reliable predictor of LEO erosion yield than thermal energy asher testing, by a factor of four

Combining Clinical and Biological Data to Predict Progressive Pulmonary Fibrosis in Patients With Systemic Sclerosis Despite Immunomodulatory Therapy

OBJECTIVE: Progressive pulmonary fibrosis (PPF) is the leading cause of death in systemic sclerosis (SSc). This study aimed to develop a clinical prediction nomogram using clinical and biological data to assess risk of PPF among patients receiving treatment of SSc-related interstitial lung disease (SSc-ILD). METHODS: Patients with SSc-ILD who participated in the Scleroderma Lung Study II (SLS II) were randomized to treatment with either mycophenolate mofetil (MMF) or cyclophosphamide (CYC). Clinical and biological parameters were analyzed using univariable and multivariable logistic regression, and a nomogram was created to assess the risk of PPF and validated by bootstrap resampling. RESULTS: Among 112 participants with follow-up data, 22 (19.6%) met criteria for PPF between 12 and 24 months. An equal proportion of patients randomized to CYC (n = 11 of 56) and mycophenolate mofetil (n = 11 of 56) developed PPF. The baseline severity of ILD was similar for patients who did, compared to those who did not, experience PPF in terms of their baseline forced vital capacity percent predicted, diffusing capacity for carbon monoxide percent predicted, and quantitative radiological extent of ILD. Predictors in the nomogram included sex, baseline CXCL4 level, and baseline gastrointestinal reflux score. The nomogram demonstrated moderate discrimination in estimating the risk of PPF, with a C-index of 0.72 (95% confidence interval 0.60-0.84). CONCLUSION: The SLS II data set provided a unique opportunity to investigate predictors of PPF and develop a nomogram to help clinicians identify patients with SSc-ILD who require closer monitoring while on therapy and potentially an alternative treatment approach. This nomogram warrants external validation in other SSc-ILD cohorts to confirm its predictive power

Treatment With Mycophenolate and Cyclophosphamide Leads to Clinically Meaningful Improvements in Patient‐Reported Outcomes in Scleroderma Lung Disease: Results of Scleroderma Lung Study II

Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/156000/1/acr211125.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/156000/2/acr211125_am.pd