Meteorological Data Needs for Modeling Air Quality Uncertainties

Necrotizing enterocolitis (NEC) is a major cause of neonatal morbidity and mortality. Preventive and therapeutic research, surveillance, and quality improvement initiatives are hindered by variations in case definitions. To develop a gestational age (GA)-specific case definition for NEC. We conducted a prospective 34-month population study using clinician-recorded findings from the UK National Neonatal Research Database between December 2011 and September 2014 across all 163 neonatal units in England. We split study data into model development and validation data sets and categorized GA into groups (group 1, less than 26 weeks' GA; group 2, 26 to less than 30 weeks' GA; group 3, 30 to less than 37 weeks' GA; group 4, 37 or more weeks' GA). We entered GA, birth weight z score, and clinical and abdominal radiography findings as candidate variables in a logistic regression model, performed model fitting 1000 times, averaged the predictions, and used estimates from the fitted model to develop an ordinal NEC score and cut points to develop a dichotomous case definition based on the highest area under the receiver operating characteristic curves [AUCs] and positive predictive values [PPVs]. Abdominal radiography performed to investigate clinical concerns. Ordinal NEC likelihood score, dichotomous case definition, and GA-specific probability plots. Of the 3866 infants, the mean (SD) birth weight was 2049.1 (1941.7) g and mean (SD) GA was 32 (5) weeks; 2032 of 3663 (55.5%) were male. The total included 2978 infants (77.0%) without NEC and 888 (23.0%) with NEC. Infants with NEC in group 1 were less likely to present with pneumatosis (31.1% vs 47.2%; P = .01), blood in stool (11.8% vs 29.6%; P < .001), or mucus in stool (2.1% vs 5.6%; P = .048) but more likely to present with gasless abdominal radiography findings (6.3% vs 0.9%; P = .009) compared with infants with NEC in group 3. In the ordinal NEC score analysis, we allocated 3 points to pneumatosis, 2 points to blood in stool, and 1 point each to abdominal tenderness and abdominal discoloration; 1 point was assigned if 1 or more of pneumoperitoneum, fixed loop, and portal venous gas were present, and 1 point was assigned if both increased and/or bilious aspirates and abdominal distension were present. The cutoff scores for the dichotomous GA-specific case definition were 2 or greater for infants in groups 1 and 2, 3 or greater for infants in group 3, and 4 or greater for infants in group 4. The ordinal NEC score and dichotomous case definition discriminated well between infants with (AUC, 87%) and without (AUC, 80%) NEC. The case definition has a sensitivity of 66.2% (95% CI, 63.0-69.4), a specificity of 94.4% (95% CI, 93.2-95.4), an AUC of 80.0% (95% CI, 79-82), and a PPV of 85.5% (95% CI, 82.6-88.1). Applying the cut points to the 431 infants who underwent a laparotomy yielded a sensitivity of 76.5% (95% CI, 70.0-82.1), a specificity of 74.4% (95% CI, 68.3-80.0), an AUC of 75.0% (95% CI, 71.0- 80.0), and a PPV of 72.9% (95% CI, 66.4-78.7). The risk of NEC and clinical presentation are associated with GA. Adoption of a consistent GA-specific case definition would strengthen global efforts to reduce the population burden of this devastating neonatal disease.

Eulerian air quality models that require gridded meteorological inputs have to adapt to recent advances in meteorological models for fully compressible atmosphere. When the input meteorological data are recast with a robust fully compressible governing set of equations, chemistry-transport models can follow the dynamic and thermodynamic descriptions of the meteorological data closely. For evaluating mass consistency in meteorological data, one may take advantage of the characteristics of the governing set of equations applicable for a specific vertical coordinate system. This paper discusses how the data from meteorological models should be used in air quality simulations. It proposes a general methodology to conserve mass of trace species in air quality models by maintaining consistency in the wind and air density fields. Limitations of several simplifying assumptions on atmospheric dynamics are also discussed. In summary, it attempts to bridge the information gap between dynamic meteorologists ...

BackgroundBleeding complications in patients with acute ST-segment elevation myocardial infarction (STEMI) have been associated with increased risk of subsequent adverse consequences.ObjectiveThe objective of our study was to develop and externally validate a diagnostic model of in-hospital bleeding.MethodsWe performed multivariate logistic regression of a cohort for hospitalized patients with acute STEMI in the emergency department of a university hospital. Participants: The model development data set was obtained from 4262 hospitalized patients with acute STEMI from January 2002 to December 2013. A set of 6015 hospitalized patients with acute STEMI from January 2014 to August 2019 were used for external validation. We used logistic regression analysis to analyze the risk factors of in-hospital bleeding in the development data set. We developed a diagnostic model of in-hospital bleeding and constructed a nomogram. We assessed the predictive performance of the diagnostic model in the validation data sets by examining measures of discrimination, calibration, and decision curve analysis (DCA).ResultsIn-hospital bleeding occurred in 112 of 4262 participants (2.6%) in the development data set. The strongest predictors of in-hospital bleeding were advanced age and high Killip classification. Logistic regression analysis showed differences between the groups with and without in-hospital bleeding in age (odds ratio [OR] 1.047, 95% CI 1.029-1.066; P<.001), Killip III (OR 3.265, 95% CI 2.008-5.31; P<.001), and Killip IV (OR 5.133, 95% CI 3.196-8.242; P<.001). We developed a diagnostic model of in-hospital bleeding. The area under the receiver operating characteristic curve (AUC) was 0.777 (SD 0.021, 95% CI 0.73576-0.81823). We constructed a nomogram based on age and Killip classification. In-hospital bleeding occurred in 117 of 6015 participants (1.9%) in the validation data set. The AUC was 0.7234 (SD 0.0252, 95% CI 0.67392-0.77289).ConclusionsWe developed and externally validated a diagnostic model of in-hospital bleeding in patients with acute STEMI. The discrimination, calibration, and DCA of the model were found to be satisfactory.Trial RegistrationChiCTR.org ChiCTR1900027578; http://www.chictr.org.cn/showprojen.aspx?proj=45926

Deep learning (DL) is a sub-field of machine learning that has made huge improvements in recent years, utilizing the high computational power of state-of-the-art computer hardware and the availability of massive amounts of digital data, achieving equal and even superior performance to humans in task-specific applications [1][2][3][4] . Typically, traditional machine learning systems consist of one or two transformation layers [5][6][7] . DL emerged from the idea of stacking multiple layers in a ML system, with "deep" describing the number of layers as the depth of a system [8][9][10] , that has shown great results in many areas (e.g. face recognition 11 , speech recognition 12 , natural language processing 13 , self driving cars 14 , playing AlphaGo 4 , and many more). Furthermore, DL was successfully developed and applied in several medical applications, such as medical imaging and diagnostic, risk management, or virtual assistants, as well as medical image segmentation tasks, with U-Nets and U-Net-derived networks being prominent models [15][16][17][18][19] . These networks were successfully applied for brain tumor segmentation 20 , lung nodule segmentation 21 , whole heart segmentation 22,23 , and others. Recently, the success of deep learning has led to a vast amount of proof-of-principle studies in various clinical application areas [24][25][26] . However, often real world applicability of the proposed systems was not demonstrated due to a lack in sufficiently large, diverse and independent data sets for model development and validation. Hence, before clinical introduction can be considered, generalizability of these systems needs to be demonstrated as they need to perform reliably across multiple clinical scenarios, and work robustly on data recorded with various settings across institutions.

To establish a nomogram model to predict early cognitive impairment after supratentorial spontaneous intracranial hematoma in adult patients.A retrospective cohort study was held between January 2016 and October 2018. One hundred twenty seven out of 170 consecutive patients with supratentorial spontaneous intracranial hematoma were enrolled in this study. They were divided into development (n = 92) and validation (n = 35) dataset according to their admission time. Mini-mental State Examination (MMSE) was conducted between the third and the sixth month after the onset of stroke. MMSE ≤ 24 was considered as cognitive impairment. Univariate and multivariate logistic regression was used to screen for independent risk factors which correlate with cognitive impairment on the development dataset. A nomogram was built based on Akaike Information Criterion (AIC). Receiver operating characteristic (ROC) curve and calibration curve on development and validation dataset was drawn with each area under the curves (AUC) calculated. The decision curve analysis was also conducted with the development dataset.The bleeding volume, Glasgow Coma Scale (GCS), and intraventricular hemorrhage (IVH) are the most significant risk factors which may cause cognitive impairment both in the univariate and multivariate analysis. The finial model performed good discrimination ability on both development and validation dataset with AUC 0.911 and 0.919. Most patients would benefit from the model according to the decision curve analysis.A nomogram, constructed based on bleeding volume, GCS, and IVH can provide a feasible tool to evaluate cognitive impairment after supratentorial spontaneous intracranial hematoma in adult patients.

The aim was to develop a multivariable risk prediction model for 30-day mortality following elective abdominal aortic aneurysm (AAA) repair. Data collected prospectively on 2765 consecutive patients undergoing elective open and endovascular AAA repair from September 1999 to October 2009 in the North West of England were split randomly into development (1936 patients) and validation (829) data sets. Logistic regression analysis was undertaken to identify risk factors for 30-day mortality. Ninety-eight deaths (5·1 per cent) were recorded in the development data set. Variables associated with 30-day mortality included: increasing age (P = 0·005), female sex (P = 0·002), diabetes (P = 0·029), raised serum creatinine level (P = 0·006), respiratory disease (P = 0·031), antiplatelet medication (P < 0·001) and open surgery (P = 0·002). The area under the receiver operating characteristic (ROC) curve for predicted probability of 30-day mortality in the development and validation data sets was 0·73 and 0·70 respectively. Observed versus expected 30-day mortality was 3·2 versus 2·0 per cent (P = 0·272) in low-risk, 6·1 versus 5·1 per cent (P = 0·671) in medium-risk and 11·1 versus 10·7 per cent (P = 0·879) in high-risk patients. This multivariable model for predicting 30-day mortality following elective AAA repair can be used clinically to calculate patient-specific risk and is useful for case-mix adjustment. The model predicted well across all risk groups in the validation data set.

Assuming that state-of-the-art air quality models are accurate, then the precision and accuracy of their results directly depend on the precision and accuracy of their geographical, meteorological and emission input data. There are important applications, such as open pit mining, in which emission data are the main source of uncertainty. In such cases, historical air quality experimental data are typically available. The present work proposes a backward air quality simulation approach to assess the accuracy of emission inventories for these applications, with the goal of identifying sources that are over or underestimated. This approach consists of finding constants of the linear combination of the estimated emission that maximize R 2 and make the slope equal to one in the linear correlation analysis when the results from the air quality model are compared to the experimental measurements of air quality. This methodology was applied to the case of the mining region in northern Colombia. As one of the largest open pit coal mining regions in the world, this region consists of seven independent mines with no relevant additional sources of emission. Use of the proposed methodology allowed quantification of the amount by which companies over or underestimated their emission, as well as quantification of uncertainties due to sources not considered in the model but that locally affect each monitoring station.

Applicability of day-to-day variation in behavior for the automated detection of lameness in dairy cows

Delta-Radiomics Approach Using Contrast-Enhanced and Noncontrast-Enhanced Computed Tomography Images for Predicting Distant Metastasis in Patients With Borderline Resectable Pancreatic Carcinoma

Under the direction of the AMS Steering Committee for the EPA Cooperative Agreement on Air Quality Modeling, a small group of scientists convened to consider the question of uncertainty in air quality modeling. Because the group was particularly concerned with the regulatory use of models, its discussion focused on modeling tall stack, point source emissions. The group agreed that air quality model results should be viewed as containing both reducible error and inherent uncertainty. Reducible error results from improper or inadequate meteorological and air quality data inputs, and from inadequacies in the models. Inherent uncertainty results from the basic stochastic nature of the turbulent atmospheric motions that are responsible for transport and diffusion of released materials. Modelers should acknowledge that all their predictions to date contain some associated uncertainty and strive also to quantify uncertainty. How can the uncertainty be quantified? There was no consensus from the group as to precisely how uncertainty should be calculated. One subgroup, which addressed statistical procedures, suggested that uncertainty information could be obtained from comparisons of observations and predictions. Following recommendations from a previous AMS workshop on performance evaluation (Fox, 1981), the subgroup suggested construction of probability distribution functions from the differences between observations and predictions. Further, they recommended that relatively new computer-intensive statistical procedures be considered to improve the quality of uncertainty estimates for the extreme value statistics of interest in regulatory applications. A second subgroup, which addressed the basic nature of uncertainty in a stochastic system, also recommended that uncertainty be quantified by consideration of the differences between observations and predictions. They suggested that the average of the difference squared was appropriate to isolate the inherent uncertainty that arises because individual realizations of the concentrations are different from the average concentrations. The average square difference allows quantification of this fact. Viewed in this framework, uncertainty is related to the variance of concentration fluctuations and the integral time scale of the turbulent flow. How can the uncertainty be communicated to decision makers? There was concern expressed by a third subgroup that non-technical people would have little understanding of quantified uncertainty. This places an increased burden on modelers to ensure that their efforts are useful. Similarly, decision makers will need to educate themselves and accept the challenge of decision making with quantified uncertainty.

BackgroundBroncho-pleural fistula (BPF) and respiratory failure (RF) are life-threatening complications after lung cancer surgery and can result in long-term hospitalization and decreased quality of life. Risk assessments for BPF and RF in addition to mortality and major morbidities are indispensable in surgical decision-making and perioperative care.MethodsThe characteristics and operative data of 80,095 patients who had undergone lung cancer surgery were derived from the 2014 and 2015 National Clinical Database (NCD) of Japan datasets. After excluding 1501 patients, risk models were developed from these data and validated by another dataset for 42,352 patients derived from the 2016 NCD dataset. Receiver operating characteristic curves were generated for postoperative BPF and RF development. The concordance-index was used to assess the discriminatory ability and validity of the model.ResultsBPF and RF occurred in 259 (0.3%) and 420 patients (0.5%), respectively, in the model development dataset and in 129 (0.3%) and 198 patients (0.5%), respectively, in the model validation dataset. Characteristic variables including types of surgery and comorbidities were identified as risk factors for BPF and RF, respectively. The concordance indexes of assessments for BPF and RF were 0.847 (p < 0.001) and 0.848 (p < 0.001), respectively, for the development dataset and 0.850 (p < 0.001) and 0.844 (p < 0.001), respectively, for the validation dataset.ConclusionsThese models are satisfactory for predicting BPF and RF after lung cancer surgery in Japan and could guide preoperative assessment and optimal measures for preventing BPF and RF.

The air quality issues that we face today and will face in the future are becoming increasingly more complex and require an improved understanding of human exposure to be effectively addressed. The objectives of this paper are (1) to discuss how concepts of human exposure and exposure science and should be applied to improve air quality management practices, and (2) to show how air quality modeling tools can be used to improve exposures estimates used for understanding associations between air quality and human health. Data from a large human exposure monitoring study is presented to demonstrate the value of exposure in understanding important air quality issues, such as health effects associated with exposure to components of particulate matter (PM), to PM of different size fractions (coarse and ultrafine), and to air pollution in near roadway environments. Various approaches for improving estimates of exposure via application of air quality modeling are discussed and results from example modeling applications are presented. These air quality modeling approaches include: the integration of regional scale eulerian air quality models with local scale gaussian dispersion models; the fusion of modeled estimates with air quality observations; the integration of air quality and human exposure modeling tools; and the use of exposure factors, such as housing ventilation, to adjust modeled estimates of ambient air quality.

Asthma is a common respiratory disease that is affected by air pollutants and meteorological factors. In this study, we developed models that predict the daily number of patients receiving treatment for asthma using air pollution and meteorological data. A neural network with long short-term memory (LSTM) and fully connected (FC) layers was used. The daily number of asthma patients in the city of Seoul, the capital of South Korea, was collected from the National Health Insurance Service. The data from 2015 to 2018 were used as the training and validation datasets for model development. Unseen data from 2019 were used for testing. The daily number of asthma patients per 100,000 inhabitants was predicted. The LSTM-FC neural network model achieved a Pearson correlation coefficient of 0.984 (P < 0.001) and root mean square error of 3.472 between the predicted and original values on the unseen testing dataset. The factors that impacted the prediction were the number of asthma patients in the previous time step before the predicted date, type of day (regular day and day after a holiday), minimum temperature, SO2, daily changes in the amount of cloud, and daily changes in diurnal temperature range. We successfully developed a neural network that predicts the onset and exacerbation of asthma, and we identified the crucial influencing air pollutants and meteorological factors. This study will help us to establish appropriate measures according to the daily predicted number of asthma patients and reduce the daily onset and exacerbation of asthma in the susceptible population.

Taurus cedar (Cedrus libani A. Rich.) is one of the most important tree speciesin Turkey. Therefore, the information is necessary about growth and yield ofthe species for developing future management and planning strategies. The one of the essential building blocks in forestgrowth and yield prediction models is the equations for estimating individualtree volume. In thisstudy, tree volume equations were developed for Taurus cedar stands in WestMediterranean Region. The tested models were compared using six performancecriteria (Fit index, average bias, average absolute residuals, absolute maximumbias, and Akaike Information criteria) for model development and validationdataset. According to relative ranks of models, the best volume equations areTakata (1958) and Schumacher-Hall (1933) for Taurus cedar. As a result, treevolume can be estimated with high precision using Schumacher-Hall (1933)’sequation for natural cedar stands in this region.

MRI-based preoperative markers combined with narrow-margin hepatectomy result in higher early recurrence