RPCA-based techniques for pattern extraction, hotspot identification and signal correction using data from a dense network of low-cost NO2 sensors in London.

High-density low-cost air quality sensor networks are a promising technology to monitor air quality at high temporal and spatial resolution. However the collected data is high-dimensional and it is not always clear how to best leverage this information, particularly given the lower data quality coming from the sensors. Here we report on the use of robust Principal Component Analysis (RPCA) using nitrogen dioxide data obtained from a recently deployed dense network of 225 air pollution monitoring nodes based on low-cost sensors in the Borough of Camden in London. RPCA addresses the brittleness of singular value decomposition towards outliers by using a decomposition of the data into low-rank and sparse contributions, with the latter containing outliers. The modal decomposition enabled by RPCA identifies major periodic patterns including spatial and temporal bias, dominant spatial variance, and north-south bias. The five most descriptive components capture 98 % of the data's variance, achieving a compression by a factor of 1500. We present a new technique that uses the sparse part of the data to identify hotspots. The data indicates that at the locations of the top 15 % most susceptible nodes in the network, the model identifies 23 % more hotspots than in all other locations combined. Moreover, the median hotspot event at these at-risk locations exceeds the mean NO2concentration by 33µg/m3. We show the potential of RPCA for signal correction; it corrects random errors yielding a reference signal with R2>0.8. Moreover, RPCA successfully reconstructs missing data from a sensor with R2=0.72 from the rest of the sensor network, an improvement upon PCA of around 50 %, allowing air quality estimations even if a sensor is out of use temporarily.

Subgraph Isomorphic Decision Tree to Predict Radical Thermochemistry with Bounded Uncertainty Estimation.

Detailed chemical kinetic models offer valuable mechanistic insights into industrial applications. Automatic generation of reliable kinetic models requires fast and accurate radical thermochemistry estimation. Kineticists often prefer hydrogen bond increment (HBI) corrections from a closed-shell molecule to the corresponding radical for their interpretability, physical meaning, and facilitation of error cancellation as a relative quantity. Tree estimators, used due to limited data, currently rely on expert knowledge and manual construction, posing challenges in maintenance and improvement. In this work, we extend the subgraph isomorphic decision tree (SIDT) algorithm originally developed for rate estimation to estimate HBI corrections. We introduce a physics-aware splitting criterion, explore a bounded weighted uncertainty estimation method, and evaluate aleatoric uncertainty-based and model variance reduction-based prepruning methods. Moreover, we compile a data set of thermochemical parameters for 2210 radicals involving C, O, N, and H based on quantum chemical calculations from recently published works. We leverage the collected data set to train the SIDT model. Compared to existing empirical tree estimators, the SIDT model (1) offers an automatic approach to generating and extending the tree estimator for thermochemistry, (2) has better accuracy and R2, (3) provides significantly more realistic uncertainty estimates, and (4) has a tree structure much more advantageous in descent speed. Overall, the SIDT estimator marks a great leap in kinetic modeling, offering more precise, reliable, and scalable predictions for radical thermochemistry.

Predictors for Early Liver Cancer Survival After Ablation and Surgical Resection: A Surveillance, Epidemiology, and End Results Program-Medicare Study.

OBJECTIVE: To identify independent predictors of all-cause and cancer-specific mortality after ablation or surgical resection (SR) for small hepatocellular carcinomas (HCCs), after adjusting for key confounders. METHODS: Using Surveillance, Epidemiology, and End Results Program-Medicare, HCCs less than 5 cm treated with ablation or SR in 2009 to 2016 (n = 956) were identified. Univariate and multivariable Cox regression models for all-cause and cancer-specific mortality were performed including demographics, clinical factors (tumor size, medical comorbidities, and liver disease factors), social determinants of health, and treatment characteristics. We also determined the most influential predictors of survival using a random forest analysis. RESULTS: Larger tumor size (3-5 cm) is predictive of all-cause (hazard ratio [HR] 1.31, P = .002) and cancer-specific mortality (HR 1.59, P < .001). Furthermore, chronic kidney disease is predictive of all-cause mortality (HR 1.43, P = .013), though it is not predictive of cancer-specific death. Multiple liver disease factors are predictive of all-cause and cancer-specific mortality including portal hypertension and esophageal varices (HRs > 1, P < .05). Though Asian race is protective in univariate models, in fully adjusted, multivariable models, Asian race is not a significant protective factor. Likewise, other social determinants of health are not significantly predictive of all-cause or cancer-specific mortality. Finally, treatment with SR, in later procedure years or at high-volume centers, is protective for all-cause and cancer-specific mortality. In machine learning models, year procedure was performed, ascites, portal hypertension, and treatment choice were the most influential factors. DISCUSSION: Treatment characteristics, liver disease factors, and tumor size are more important predictors of all-cause and cancer-specific death than social determinants of health for small HCCs.

Beyond Survival: Adverse Events and Care Delivery Outcomes after Early Liver Cancer Treatment in a Nationally Representative Cohort.

PURPOSE: To compare secondary outcomes after ablation (AB), surgical resection (SR), and liver transplant (LT) for small hepatocellular carcinomas (HCCs), including resource utilization and adverse event (AE) rates. MATERIALS AND METHODS: Using Surveillance, Epidemiology, and End Results Program (SEER)-Medicare, HCCs <5 cm that were treated with AB, SR, or LT in 2009-2016 (n = 1,067) were identified using Healthcare Common Procedure Coding System codes through Medicare claims. Index procedure length of stay, need for intensive care unit (ICU) level care, readmission rates, and AE rates at 30 and 90 days were compared using chi-square tests or Fisher exact tests. Examined AEs included hemorrhage, abscess formation, biliary injury, pneumonia, sepsis, liver disease-related AEs, liver failure, and anesthesia-related AEs, identified by International Classification of Diseases, Ninth/10th Revision, codes. RESULTS: The median length of stay for initial treatment was 1 day, 6 days, and 7 days for AB, SR, and LT, respectively (P < .001). During initial hospital stay, 5.0%, 40.8%, and 63.4% of AB, SR, and LT cohorts, respectively, received ICU-level care (P < .001). By 30 and 90 days, there were significant differences among the AB, SR, and LT cohorts in the rate of postprocedural hemorrhage, abscess formation, biliary injury, pneumonia, sepsis, liver disease-related AEs, and anesthesia-related AEs (P < .05). By 90 days, the readmission rates after AB, SR, and LT were 18.6%, 28.2%, and 40.6% (P < .001), respectively. CONCLUSIONS: AB results in significantly less healthcare utilization during the initial 90 days after procedure compared with that after SR and LT due to shorter length of stay, lower intensity care, fewer readmissions, and fewer AEs.

Carbon uptake in Eurasian boreal forests dominates the high-latitude net ecosystem carbon budget.

Arctic-boreal landscapes are experiencing profound warming, along with changes in ecosystem moisture status and disturbance from fire. This region is of global importance in terms of carbon feedbacks to climate, yet the sign (sink or source) and magnitude of the Arctic-boreal carbon budget within recent years remains highly uncertain. Here, we provide new estimates of recent (2003-2015) vegetation gross primary productivity (GPP), ecosystem respiration (Reco ), net ecosystem CO2 exchange (NEE; Reco - GPP), and terrestrial methane (CH4 ) emissions for the Arctic-boreal zone using a satellite data-driven process-model for northern ecosystems (TCFM-Arctic), calibrated and evaluated using measurements from >60 tower eddy covariance (EC) sites. We used TCFM-Arctic to obtain daily 1-km2 flux estimates and annual carbon budgets for the pan-Arctic-boreal region. Across the domain, the model indicated an overall average NEE sink of -850 Tg CO2 -C year-1 . Eurasian boreal zones, especially those in Siberia, contributed to a majority of the net sink. In contrast, the tundra biome was relatively carbon neutral (ranging from small sink to source). Regional CH4 emissions from tundra and boreal wetlands (not accounting for aquatic CH4 ) were estimated at 35 Tg CH4 -C year-1 . Accounting for additional emissions from open water aquatic bodies and from fire, using available estimates from the literature, reduced the total regional NEE sink by 21% and shifted many far northern tundra landscapes, and some boreal forests, to a net carbon source. This assessment, based on in situ observations and models, improves our understanding of the high-latitude carbon status and also indicates a continued need for integrated site-to-regional assessments to monitor the vulnerability of these ecosystems to climate change.

Particulate air pollution in the Copenhagen metro part 2: Low-cost sensors and micro-environment classification.

In this study fine particulate matter (PM2.5) levels throughout the Copenhagen metro system are measured for the first time and found to be ∼10 times the roadside levels in Copenhagen. In this Part 2 article, low-cost sensor (LCS) nodes designed for personal-exposure monitoring are tested against a conventional mid-range device (TSI DustTrak), and gravimetric methods. The nodes were found to be effective for personal exposure measurements inside the metro system, with R2 values of > 0.8 at 1-min and > 0.9 at 5-min time-resolution, with an average slope of 1.01 in both cases, in comparison to the reference, which is impressive for this dynamic environment. Micro-environment (ME) classification techniques are also developed and tested, involving the use of auxiliary sensors, measuring light, carbon dioxide, humidity, temperature and motion. The output from these sensors is used to distinguish between specific MEs, namely, being aboard trains travelling above- or under- ground, with 83 % accuracy, and determining whether sensors were aboard a train or stationary at a platform with 92 % accuracy. This information was used to show a 143 % increase in mean PM2.5 concentration for underground sections relative to overground, and 22 % increase for train vs. platform measurements. The ME classification method can also be used to improve calibration models, assist in accurate exposure assessment based on detailed time-activity patterns, and facilitate field studies that do not require personnel to record time-activity diaries.

Long-Term Survival After Surgery Versus Ablation for Early Liver Cancer in a Large, Nationally Representative Cohort.

OBJECTIVE: To compare survival outcomes (all-cause, cancer-specific, and disease-free) for small hepatocellular carcinomas (HCCs), less than or equal to 5 cm, after ablation (AB) and surgical resection (SR) after adjusting for key confounders. Secondarily, to understand differential survival outcomes of liver transplant (TR) compared with SR and AB. METHODS: Using Surveillance, Epidemiology, and End Results Program-Medicare, HCCs less than 5 cm that were treated with AB, SR, or TR in 2009 to 2016 (n = 1,215) were identified using Healthcare Common Procedure Coding System codes through Medicare claims. The TR group was subdivided into two groups: TR with prior treatment and TR without prior treatment. All-cause survival, cancer-specific survival, and disease-free survival were analyzed using Kaplan-Meier curves and compared between groups using log-rank tests and Cox regression analyses. Propensity score-matched comparison of AB and SR groups was performed, with groups matched on demographics, social determinants of health, medical comorbidities, and liver disease severity prognostic indicators. RESULTS: Median study follow-up time was 2.71 years (interquartile range 1.25-3.83). Unadjusted 1-, 3-, and 5-year cancer-specific survivals were 85.9%, 67.6%, and 56.3% for the AB group; 91.7%, 82.6%, and 81.7% for the SR group; 93.5%, 88.7%, and 79.4% for TR without prior treatment group; and 96.4%, 93.2%, and 93.2% for TR with prior treatment group (P < .0001). With SR as the reference group, the propensity-matched hazard ratios for AB were 2.04 (95% confidence interval: 1.51-2.77) for all-cause mortality, 2.44 (95% confidence interval: 1.56-3.80) for cancer-specific mortality, and 2.12 (95% confidence interval: 1.61-2.78) for disease recurrence. DISCUSSION: SR is superior to AB for small HCCs in a large, nationally representative, modern cohort, and in secondary analysis TR was superior to both.

Who We Are and What We Can Become: An Analysis of Professional Identity Formation in IR.

PURPOSE: To characterize the unique experiences, values, and perspectives of interventional radiology (IR) fellows. MATERIALS AND METHODS: Sixteen fellows from 4 US vascular and IR programs were interviewed within 2 months of beginning and 2-3 months following their 2015-2016 fellowships about patient interactions, training experiences, and views of IR and other specialties. Interviews were systematically analyzed for dominant themes by using constructivist grounded theory. Four interviews with 2015-2016 interventional cardiology fellows, 16 interviews with IR attending physicians, and online descriptions of IR were also analyzed for context. Themes were compared qualitatively and quantitatively. RESULTS: Interobserver agreement was good for interview themes (κ = 0.70; P < .0001). IR fellows' professional identity emerged primarily from radiologic and surgical interests, with distinct emphasis on being "innovators," "thinking differently," and "needing to adapt and advertise abilities to survive." Fellows' descriptions of patient care were more clinically focused than past interviews with attending physicians (P = .05), but clinical interests common in medical specialties were limited, and descriptions of "nonprocedural patient care" were primarily periprocedural (81%). Descriptions of the future of the field conveyed competing pressures, loose role definition, and disconnect between academic and private-practice IR. CONCLUSIONS: IR fellows share professional interests, views of their field and others, and descriptions of patient care, but there is uncertainty regarding future roles of the specialty and a need for more specific and unified definitions of nonprocedural patient care in IR.

Randomized government safety inspections reduce worker injuries with no detectable job loss.

Controversy surrounds occupational health and safety regulators, with some observers claiming that workplace regulations damage firms' competitiveness and destroy jobs and others arguing that they make workplaces safer at little cost to employers and employees. We analyzed a natural field experiment to examine how workplace safety inspections affected injury rates and other outcomes. We compared 409 randomly inspected establishments in California with 409 matched-control establishments that were eligible, but not chosen, for inspection. Compared with controls, randomly inspected employers experienced a 9.4% decline in injury rates (95% confidence interval = -0.177 to -0.021) and a 26% reduction in injury cost (95% confidence interval = -0.513 to -0.083). We find no evidence that these improvements came at the expense of employment, sales, credit ratings, or firm survival.

Responsible conduct of radiology research. Part V. The Health Insurance Portability and Accountability Act and research.

For the past 5 years, the regulatory environment for research involving humans has been turbulent, with criticism coming from the federal government, the academic community, and the press. The purpose of this series of articles is to explain the ethical and legal bases for responsible conduct of radiology research and the rules that an investigator must follow. The purpose of this fifth part of the series is to explain the requirements of the Privacy Rule, which is a component of the Health Insurance Portability and Accountability Act (HIPAA), as they relate to human research. Under the HIPAA Privacy Rule, researchers within covered entities must follow appropriate methods as they use or disclose protected health information (PHI). Investigators should know the conditions under which PHI may be accessed for research purposes (ie, with authorization or waiver of authorization, when only a limited data set is evaluated, if data have been de-identified, or in reviews preparatory to research). Furthermore, researchers should know which information, such as the Notice of Privacy Practices and the Accounting of Disclosures, must be provided to potential subjects, when appropriate. At the conclusion of this article, several scenarios related to various types of radiology research and related regulatory requirements are presented.

Physician assistants in interventional radiology practice.

Interventional radiology (IR) is a clinical subspecialty; as such, there is a large amount of direct patient care. However, until recently, this topic has not been a major focus in radiology training programs. Additionally, as interventional radiologists develop busier and busier practices, there is less time to spend with individual patients. Physician extenders such as physician assistants (PAs) represent an excellent way to improve clinical patient care. This article describes what PAs are and how they work together with physicians. It illustrates differences between PAs and other physician extenders and describes the duties that may be delegated to PAs in the IR setting.