scholarly journals Acoustic-Based Prediction of End-Product-Based Fibre Determinates within Standing Jack Pine Trees

Forests ◽  
2019 ◽  
Vol 10 (7) ◽  
pp. 605
Author(s):  
Peter F. Newton
Keyword(s):  
Wood Density ◽  
Pinus Banksiana ◽  
Goodness Of Fit ◽  
Prediction Models ◽  
Microfibril Angle ◽  
Model Performance ◽  
Predictive Ability ◽  
Analytical Framework ◽  
Jack Pine ◽  
Cross Sectional

The objective of this study was to specify, parameterize, and evaluate an acoustic-based inferential framework for estimating commercially-relevant wood attributes within standing jack pine (Pinus banksiana Lamb) trees. The analytical framework consisted of a suite of models for predicting the dynamic modulus of elasticity (me), microfibril angle (ma), oven-dried wood density (wd), tracheid wall thickness (wt), radial and tangential tracheid diameters (dr and dt, respectively), fibre coarseness (co), and specific surface area (sa), from dilatational stress wave velocity (vd). Data acquisition consisted of (1) in-forest collection of acoustic velocity measurements on 61 sample trees situated within 10 variable-sized plots that were established in four mature jack pine stands situated in boreal Canada followed by the removal of breast-height cross-sectional disk samples, and (2) given (1), in-laboratory extraction of radial-based transverse xylem samples from the 61 disks and subsequent attribute determination via Silviscan-3. Statistically, attribute-specific acoustic prediction models were specified, parameterized, and, subsequently, evaluated on their goodness-of-fit, lack-of-fit, and predictive ability. The results indicated that significant (p ≤ 0.05) and unbiased relationships could be established for all attributes but dt. The models explained 71%, 66%, 61%, 42%, 30%, 19%, and 13% of the variation in me, wt, sa, co, wd, ma, and dr, respectively. Simulated model performance when deploying an acoustic-based wood density estimate indicated that the expected magnitude of the error arising from predicting dt, co, sa, wt, me, and ma prediction would be in the order of ±8%, ±12%, ±12%, ±13%, ±20%, and ±39% of their true values, respectively. Assessment of the utility of predicting the prerequisite wd estimate using micro-drill resistance measures revealed that the amplitude-based wd estimate was inconsequentially more precise than that obtained from vd (≈ <2%). A discourse regarding the potential utility and limitations of the acoustic-based computational suite for forecasting jack pine end-product potential was also articulated.

THE SUPPRESSION OF GROWTH RINGS IN JACK PINE IN RELATION TO DEFOLIATION BY THE SWAINE JACK-PINE SAWFLY

1963 ◽  
Vol 41 (2) ◽  
pp. 227-235 ◽  
Author(s):  
L. C. O'Neil
Keyword(s):  
Pinus Banksiana ◽  
Radial Growth ◽  
Jack Pine ◽  
Growth Rings ◽  
Cross Sectional ◽  
Pine Sawfly ◽  
Young Stand ◽  
The Dead ◽  
Dense Stand

An investigation of the radial growth of jack pine (Pinus banksiana Lamb.) defoliated by the Swaine jack-pine sawfly (Neodiprion swainei Midd.) disclosed that growth rings were discontinuous and missing in cross-sectional disks from severely damaged trees. In young and open-grown trees with dead tops, the incidence of such deficiencies in radial growth was especially high in disks from upper regions of the stems, in the vicinity of the dead tops; radial growth was suspended for 1 year and subsequently resumed in disks from the lower regions of some stems. Cambial inactivity was more generalized in trees from an old and dense stand and it was detected in disks representing major portions of some of the stems sampled; the death of some trees followed 2 to 6 years of cambial inactivity in disks cut at various heights along their entire stems. Growth deficiencies in the young stand were clearly effects of severe sawfly defoliation. Data from the old, dense stand indicated that sawfly defoliation had perhaps merely hastened the gradual deterioration of the stand in which intertree competition was intense.

Abstract W MP37: Novel Prognostic Scores for Early Prediction of Outcome Following Aneurysmal Subarachnoid Hemorrhage

Stroke ◽  
2015 ◽  
Vol 46 (suppl_1) ◽  
Author(s):  
Blessing Jaja ◽  
Hester Lingsma ◽  
Ewout Steyerberg ◽  
R. Loch Macdonald ◽  
Keyword(s):  
Subarachnoid Hemorrhage ◽  
Cross Validation ◽  
Prediction Models ◽  
Aneurysmal Subarachnoid Hemorrhage ◽  
Model Performance ◽  
Predictor Variable ◽  
Predictive Ability ◽  
Prognostic Scores ◽  
Operating Characteristics ◽  
Fisher Grade

Background: Aneurysmal subarachnoid hemorrhage (SAH) is a cerebrovascular emergency. Currently, clinicians have limited tools to estimate outcomes early after hospitalization. We aimed to develop novel prognostic scores using large cohorts of patients reflecting experience from different settings. Methods: Logistic regression analysis was used to develop prediction models for mortality and unfavorable outcomes according to 3-month Glasgow outcome score after SAH based on readily obtained parameters at hospital admission. The development cohort was derived from 10 prospective studies involving 10936 patients in the Subarachnoid Hemorrhage International Trialists (SAHIT) repository. Model performance was assessed by bootstrap internal validation and by cross validation by omission of each of the 10 studies, using R2 statistic, Area under the receiver operating characteristics curve (AUC), and calibration plots. Prognostic scores were developed from the regression coefficients. Results: Predictor variable with the strongest prognostic strength was neurologic status (partial R2 = 12.03%), followed by age (1.91%), treatment modality (1.25%), Fisher grade of CT clot burden (0.65%), history of hypertension (0.37%), aneurysm size (0.12%) and aneurysm location (0.06%). These predictors were combined to develop 3 sets of hierarchical scores based on the coefficients of the regression models. The AUC at bootstrap validation was 0.79-0.80, and at cross validation was 0.64-0.85. Calibration plots demonstrated satisfactory agreement between predicted and observed probabilities of the outcomes. Conclusions: The novel prognostic scores have good predictive ability and potential for broad application as they have been developed from prospective cohorts reflecting experience from different centers globally.

scholarly journals Acoustic Velocity—Wood Fiber Attribute Relationships for Jack Pine Logs and Their Potential Utility

Forests ◽  
2018 ◽  
Vol 9 (12) ◽  
pp. 749 ◽  
Author(s):  
Peter Newton
Keyword(s):  
Decision Making ◽  
Boreal Forest ◽  
Wood Fiber ◽  
Predictive Ability ◽  
Jack Pine ◽  
Mixed Effects ◽  
Acoustic Velocity ◽  
Potential Utility ◽  
Cross Sectional ◽  
Validation Data

This study presents an acoustic-based predictive modeling framework for estimating a suite of wood fiber attributes within jack pine (Pinus banksiana Lamb.) logs for informing in-forest log-segregation decision-making. Specifically, the relationships between acoustic velocity (longitudinal stress wave velocity; vl) and the dynamic modulus of elasticity (me), wood density (wd), microfibril angle (ma), tracheid wall thickness (wt), tracheid radial and tangential diameters (dr and dt, respectively), fiber coarseness (co), and specific surface area (sa), were parameterized deploying hierarchical mixed-effects model specifications and subsequently evaluated on their resultant goodness-of-fit, lack-of-fit, and predictive precision. Procedurally, the data acquisition phase involved: (1) randomly selecting 61 semi-mature sample trees within ten variable-sized plots established in unthinned and thinned compartments of four natural-origin stands situated in the central portion the Canadian Boreal Forest Region; (2) felling and sectioning each sample tree into four equal-length logs and obtaining twice-replicate vl measurements at the bottom and top cross-sectional faces of each log (n = 4) from which a log-specific mean vl value was calculated; and (3) sectioning each log at its midpoint and obtaining a cross-sectional sample disk from which a 2 × 2 cm bark-to-pith radial xylem sample was extracted and subsequently processed via SilviScan-3 to derive annual-ring-specific attribute values. The analytical phase involved: (1) stratifying the resultant attribute—acoustic velocity observational pairs for the 243 sample logs into approximately equal-sized calibration and validation data subsets; (2) parameterizing the attribute—acoustic relationships employing mixed-effects hierarchical linear regression specifications using the calibration data subset; and (3) evaluating the resultant models using the validation data subset via the deployment of suite of statistical-based metrics pertinent to the evaluation of the underlying assumptions and predictive performance. The results indicated that apart from tracheid diameters (dr and dt), the regression models were significant (p ≤ 0.05) and unbiased predictors which adhered to the underlying parameterization assumptions. However, the relationships varied widely in terms of explanatory power (index-of-fit ranking: wt (0.53) > me > sa > co > wd >> ma (0.08)) and predictive ability (sa > wt > wd > co >> me >>> ma). Likewise, based on simulations where an acoustic-based wd estimate is used as a surrogate measure for a Silviscan-equivalent value for a newly sampled log, predictive ability also varied by attribute: 95% of all future predictions for sa, wt, co, me, and ma would be within ±12%, ±14%, ±15%, ±27%, and ±55% and of the true values, respectively. Both the limitations and potential utility of these predictive relationships for use in log-segregation decision-making, are discussed. Future research initiatives, consisting of identifying and controlling extraneous sources of variation on acoustic velocity and establishing attribute-specific end-product-based design specifications, would be conducive to advancing the acoustic approach in boreal forest management.

scholarly journals Prediction of Medical Costs in a Health Insurance Carrier according to Risk Profiles and Uses by its Affiliates

2018 ◽  
Vol 1 (1) ◽  
Author(s):  
Rodolfo Herrera Medina ◽  
Jaime Lee ◽  
Ferney Herrera Cruz
Keyword(s):  
Goodness Of Fit ◽  
Linear Models ◽  
Prediction Models ◽  
Age Groups ◽  
Simple Random Sampling ◽  
Medical Costs ◽  
Sampling Frame ◽  
Cross Sectional ◽  
Insurance Carrier ◽  
Statistical Criteria

Objective: To find a model of prediction of the medical cost of a Health Benefits Management Company (EAPB) with adequate statistical criteria. Methods: A Cross-sectional study with retrospective follow-up of the use of health services in an EAPB during a one-year period. The sampling frame consisted of a population of 1,529,188 affiliates who were assigned to a primary care IPS group. By simple random sampling size was estimated at 190,917 users. The dependent variable was the cost of the services used deflated to the year 2013. As independent variables besides the traditional sociodemographic variables chosen in this type of prediction models, variables of the insurance were added; Variables of risk management (inclusion or not in promotion and prevention program) and of comorbidities. Results: Simple Linear Regression modeling showed errors of inappropriate statistical criteria such as violating the principle of normality in cost errors. The Generalized Linear Models, proposed to estimate POS average costs, have an appropriate goodness of fit and evaluated with small Deviations and minimum Akaike criterion (AIC) compared to other models of the exponential family Conclusions: The appropriate statistical model to predict medical costs was the Generalized Linear Model with two parts segmented by age groups and gender. This research suggests that to estimate the benefit premium of any EAPB, besides socio-demographic variables, insurance variables, membership or not in promotion programs and risk prevention and/or management and the burden of disease of that population should be used.

scholarly journals Generalizability Challenges of Mortality Risk Prediction Models: A Retrospective Analysis on a Multi-center Database

2021 ◽  
Author(s):  
Harvineet Singh ◽  
Vishwali Mhasawade ◽  
Rumi Chunara
Keyword(s):  
Hospital Mortality ◽  
Risk Prediction ◽  
Performance Metrics ◽  
Prediction Models ◽  
Characteristic Curve ◽  
False Negative ◽  
Model Performance ◽  
Clinical Practices ◽  
Cross Sectional ◽  
Risk Prediction Models

Importance: Modern predictive models require large amounts of data for training and evaluation which can result in building models that are specific to certain locations, populations in them and clinical practices. Yet, best practices and guidelines for clinical risk prediction models have not yet considered such challenges to generalizability. Objectives: To investigate changes in measures of predictive discrimination, calibration, and algorithmic fairness when transferring models for predicting in-hospital mortality across ICUs in different populations. Also, to study the reasons for the lack of generalizability in these measures. Design, Setting, and Participants: In this multi-center cross-sectional study, electronic health records from 179 hospitals across the US with 70,126 hospitalizations were analyzed. Time of data collection ranged from 2014 to 2015. Main Outcomes and Measures: The main outcome is in-hospital mortality. Generalization gap, defined as difference between model performance metrics across hospitals, is computed for discrimination and calibration metrics, namely area under the receiver operating characteristic curve (AUC) and calibration slope. To assess model performance by race variable, we report differences in false negative rates across groups. Data were also analyzed using a causal discovery algorithm "Fast Causal Inference" (FCI) that infers paths of causal influence while identifying potential influences associated with unmeasured variables. Results: In-hospital mortality rates differed in the range of 3.9%-9.3% (1st-3rd quartile) across hospitals. When transferring models across hospitals, AUC at the test hospital ranged from 0.777 to 0.832 (1st to 3rd quartile; median 0.801); calibration slope from 0.725 to 0.983 (1st to 3rd quartile; median 0.853); and disparity in false negative rates from 0.046 to 0.168 (1st to 3rd quartile; median 0.092). When transferring models across geographies, AUC ranged from 0.795 to 0.813 (1st to 3rd quartile; median 0.804); calibration slope from 0.904 to 1.018 (1st to 3rd quartile; median 0.968); and disparity in false negative rates from 0.018 to 0.074 (1st to 3rd quartile; median 0.040). Distribution of all variable types (demography, vitals, and labs) differed significantly across hospitals and regions. Shifts in the race variable distribution and some clinical (vitals, labs and surgery) variables by hospital or region. Race variable also mediates differences in the relationship between clinical variables and mortality, by hospital/region. Conclusions and Relevance: Group-specific metrics should be assessed during generalizability checks to identify potential harms to the groups. In order to develop methods to improve and guarantee performance of prediction models in new environments for groups and individuals, better understanding and provenance of health processes as well as data generating processes by sub-group are needed to identify and mitigate sources of variation.

The use of multiple measurement techniques to refine estimates of conifer needle geometry

2003 ◽  
Vol 33 (1) ◽  
pp. 101-105 ◽  
Author(s):  
B Bond-Lamberty ◽  
C Wang ◽  
S T Gower
Keyword(s):  
Leaf Area ◽  
Pinus Banksiana ◽  
Black Spruce ◽  
Water Immersion ◽  
Measurement Techniques ◽  
Jack Pine ◽  
Cross Sectional ◽  
Axis Ratio ◽  
Optical Scanning ◽  
Simple Equations

Knowledge of foliar surface area is important in many fields, but estimating the area of nonflat conifer needles is difficult. The primary goal of this study was to use optical scanning and immersion methods to test and refine the standard cross-sectional geometries assumed for black spruce (Picea mariana (Mill.) BSP) and jack pine (Pinus banksiana Lamb.) needles. Projected leaf area (PLA, measured using a flatbed scanner), and hemisurface leaf area (HSLA, estimated from water immersion) were compared for conifer samples from a 37-year-old even-aged stand in northern Manitoba, Canada. The HSLA–PLA relationship was used to infer information about needle cross-sectional geometry after assuming a basic form (rhombus for black spruce and hemiellipse for jack pine). The cross section of black spruce needles was best approximated as a rhombus with a major/minor diagonal ratio of 1.35. Jack pine needles were best described by a hemiellipse with major/minor axis ratio of 1.30. Minor but incorrect assumptions of needle cross-sectional geometry resulted in foliar area errors of 6–8% using scanning methods and 1–2% using immersion methods. Simple equations are presented to calculate hemisurface needle area from volume or projected needle area based on these refined parameters.

Basic Density of Jack Pine Wood Influenced by Fertilization and Thinning

1982 ◽  
Vol 58 (1) ◽  
pp. 44-46 ◽  
Author(s):  
S. L. Scott ◽  
J. E. Barker ◽  
I. K. Morrison ◽  
N. W. Foster
Keyword(s):  
Wood Density ◽  
Pinus Banksiana ◽  
Basic Density ◽  
Jack Pine ◽  
Average Density ◽  
Low Density ◽  
Density Gradients ◽  
Pine Wood ◽  
Mature Type ◽  
Juvenile Type

Basic wood density was measured at eight bole positions within and below the green crown in a jack pine (Pinus banksiana, Lamb.) fertilization and thinning trial near Chapleau, Ontario. Analysis showed a 6% reduction of average density in wood laid down during the first 5 years following treatment. A significant height × fertilizer interaction was noted during the same period indicating that bole density gradients specific to fertilized trees should be used to calculate biomass gains from fertilization if substantial underestimates of response are to be avoided. The portion of the bole where the wood changed most rapidly from low density, juvenile-type to higher density mature-type wood appeared to be just beneath the base of the green crown.

scholarly journals A Mathematically Tractable Stem Profile Model for Jack Pine in Ontario

1999 ◽  
Vol 16 (3) ◽  
pp. 138-143 ◽  
Author(s):  
W. T. Zakrzewski
Keyword(s):  
Acer Saccharum ◽  
Pinus Banksiana ◽  
Sugar Maple ◽  
Stand Density ◽  
Tree Height ◽  
Jack Pine ◽  
Volume Data ◽  
Cross Sectional ◽  
New Model ◽  
Volume Estimates

Abstract A new model was derived to describe the inside bark cross-sectional area of tree stems. It is a rational function. The inputs required by the model are outside bark tree diameter at breast height (DBH) and total tree height (H). Knowledge of a species-specific bark thickness at 1.3 m expressed in terms of input variables is also needed. Defining the model involves estimating two regression coefficients using either nonlinear or linear regression (after linearization of the model). The formula is analytically integrable and thus provides analytical inside bark volume estimates for any stem section defined by height limits. The model is analytically solvable for a stem height location at any given inside bark diameter, so that stem sections can be defined by the required inside bark diameter limits. The new model can be calibrated using either section diameter or section volume data. It is suggested that involving the ratio H/DBH in the model accounts for the influence of stand density on stem profile. The formula was calibrated for jack pine (Pinus banksiana Lamb.) in Ontario. Wider applicability of the model is supported by results obtained for sugar maple (Acer saccharum Marsh.) in Ontario and Scots pine (Pinus silvestris L.) in Finland. Comparing volume estimates from the new model with those generated by Honer's formula confirms the advantages of the new model. North. J. Appl. For. 16(3):138-143.

scholarly journals Imputation of missing values for electronic health record laboratory data

2021 ◽  
Vol 4 (1) ◽  
Author(s):  
Jiang Li ◽  
Xiaowei S. Yan ◽  
Durgesh Chaudhary ◽  
Venkatesh Avula ◽  
Satish Mudiganti ◽  
...  
Keyword(s):  
Missing Values ◽  
Prediction Models ◽  
Laboratory Data ◽  
Model Performance ◽  
Cross Sectional ◽  
Health Records ◽  
Laboratory Variables ◽  
Multi Level ◽  
Electronic Health

AbstractLaboratory data from Electronic Health Records (EHR) are often used in prediction models where estimation bias and model performance from missingness can be mitigated using imputation methods. We demonstrate the utility of imputation in two real-world EHR-derived cohorts of ischemic stroke from Geisinger and of heart failure from Sutter Health to: (1) characterize the patterns of missingness in laboratory variables; (2) simulate two missing mechanisms, arbitrary and monotone; (3) compare cross-sectional and multi-level multivariate missing imputation algorithms applied to laboratory data; (4) assess whether incorporation of latent information, derived from comorbidity data, can improve the performance of the algorithms. The latter was based on a case study of hemoglobin A1c under a univariate missing imputation framework. Overall, the pattern of missingness in EHR laboratory variables was not at random and was highly associated with patients’ comorbidity data; and the multi-level imputation algorithm showed smaller imputation error than the cross-sectional method.

scholarly journals Mortality Prediction Using SaO2/FiO2 Ratio Based on eICU Database Analysis

2021 ◽  
Vol 2021 ◽  
pp. 1-9
Author(s):  
Sharad Patel ◽  
Gurkeerat Singh ◽  
Samson Zarbiv ◽  
Kia Ghiassi ◽  
Jean-Sebastien Rachoin
Keyword(s):  
Prediction Models ◽  
Model Performance ◽  
Predictive Ability ◽  
Mortality Prediction ◽  
Gradient Boosting ◽  
Admission Diagnosis ◽  
Research Database ◽  
Icu Mortality ◽  
Feature Importance ◽  
Partial Dependence

Purpose. PaO2 to FiO2 ratio (P/F) is used to assess the degree of hypoxemia adjusted for oxygen requirements. The Berlin definition of Acute Respiratory Distress Syndrome (ARDS) includes P/F as a diagnostic criterion. P/F is invasive and cost-prohibitive for resource-limited settings. SaO2/FiO2 (S/F) ratio has the advantages of being easy to calculate, noninvasive, continuous, cost-effective, and reliable, as well as lower infection exposure potential for staff, and avoids iatrogenic anemia. Previous work suggests that the SaO2/FiO2 ratio (S/F) correlates with P/F and can be used as a surrogate in ARDS. Quantitative correlation between S/F and P/F has been verified, but the data for the relative predictive ability for ICU mortality remains in question. We hypothesize that S/F is noninferior to P/F as a predictive feature for ICU mortality. Using a machine-learning approach, we hope to demonstrate the relative mortality predictive capacities of S/F and P/F. Methods. We extracted data from the eICU Collaborative Research Database. The features age, gender, SaO2, PaO2, FIO2, admission diagnosis, Apache IV, mechanical ventilation (MV), and ICU mortality were extracted. Mortality was the dependent variable for our prediction models. Exploratory data analysis was performed in Python. Missing data was imputed with Sklearn Iterative Imputer. Random assignment of all the encounters, 80% to the training (n = 26690) and 20% to testing (n = 6741), was stratified by positive and negative classes to ensure a balanced distribution. We scaled the data using the Sklearn Standard Scaler. Categorical values were encoded using Target Encoding. We used a gradient boosting decision tree algorithm variant called XGBoost as our model. Model hyperparameters were tuned using the Sklearn RandomizedSearchCV with tenfold cross-validation. We used AUC as our metric for model performance. Feature importance was assessed using SHAP, ELI5 (permutation importance), and a built-in XGBoost feature importance method. We constructed partial dependence plots to illustrate the relationship between mortality probability and S/F values. Results. The XGBoost hyperparameter optimized model had an AUC score of .85 on the test set. The hyperparameters selected to train the final models were as follows: colsample_bytree of 0.8, gamma of 1, max_depth of 3, subsample of 1, min_child_weight of 10, and scale_pos_weight of 3. The SHAP, ELI5, and XGBoost feature importance analysis demonstrates that the S/F ratio ranks as the strongest predictor for mortality amongst the physiologic variables. The partial dependence plots illustrate that mortality rises significantly above S/F values of 200. Conclusion. S/F was a stronger predictor of mortality than P/F based upon feature importance evaluation of our data. Our study is hypothesis-generating and a prospective evaluation is warranted. Take-Home Points. S/F ratio is a noninvasive continuous method of measuring hypoxemia as compared to P/F ratio. Our study shows that the S/F ratio is a better predictor of mortality than the more widely used P/F ratio to monitor and manage hypoxemia.

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