scholarly journals SPECIES DISTRIBUTION MODELING IN FOREST PLANNING OF ANNUAL PRODUCTION UNITS IN THE SOUTHWEST AMAZONIA

2021 ◽  
Vol 45 ◽  
Author(s):  
Alexandra Bezerra de Menezes ◽  
Symone Maria de Melo Figueiredo
Keyword(s):  
Vegetation Index ◽  
Area Under The Curve ◽  
Predictor Variable ◽  
Annual Production ◽  
Forest Species ◽  
Predictive Variables ◽  
Forest Inventories ◽  
Management Area ◽  
Individual Bands ◽  
Sampling Systems

ABSTRACT The generally limited resources for forest management and the growing need of forest production regulation requires the optimization of planning approaches for the spatialization of annual production units (APU). An APU planning methodology for forest species of high wood value (Amburana acreana (Ducke) ACSm., Apuleia leiocarpa (Vogel) JF Macbr. and Castilla ulei Warb.) in management area was proposed, using prediction of potential distribution of these species with data from the occurrence of a census forest inventory. It was used sample inventory data simulated in three sampling systems (random, conglomerate systematic, and systematic) and sample intensities (0.5% and 0.8%). As predictive variables, it was used the altitude, vertical distance to the nearest drain, individual bands of the TM sensor on board the Landsat 5, and vegetation index by normalized difference. Eighteen models were obtained, six per species. The test area under the curve (AUC) of the models ranged from 0.517 to 0.804. For all species, the best predictive model was considered the conglomerate system with a sample intensity of 0.8%. Altitude was the predictor variable that most contributed to the models. The AUC values for the Amburana acreana models were significantly different from Apuleia leiocarpa and Castilla ulei (p = 0.0138). For species of lower density, it is recommended greater sampling intensity and sampling systems that provide better spatialization of occurrence records. The use of data from sampling forest inventories in different sampling systems is capable of predicting environmental suitability for forest species and helps to define APUs. Thus, it is possible to strenghten the exploration strategies and management planning of management areas and to contribute to the perpetuation of the activity in the unequal forests of the Amazon region.

scholarly journals A diabetes risk score for Qatar utilizing a novel mathematical modeling approach to identify individuals at high risk for diabetes

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Susanne F. Awad ◽  
Soha R. Dargham ◽  
Amine A. Toumi ◽  
Elsy M. Dumit ◽  
Katie G. El-Nahas ◽  
...  
Keyword(s):  
High Risk ◽  
Sensitivity And Specificity ◽  
Risk Score ◽  
Area Under The Curve ◽  
Diabetes Risk ◽  
Predictive Values ◽  
Predictive Variables ◽  
Comparable Performance ◽  
Representative Random Sample ◽  
Diabetes Risk Score

AbstractWe developed a diabetes risk score using a novel analytical approach and tested its diagnostic performance to detect individuals at high risk of diabetes, by applying it to the Qatari population. A representative random sample of 5,000 Qataris selected at different time points was simulated using a diabetes mathematical model. Logistic regression was used to derive the score using age, sex, obesity, smoking, and physical inactivity as predictive variables. Performance diagnostics, validity, and potential yields of a diabetes testing program were evaluated. In 2020, the area under the curve (AUC) was 0.79 and sensitivity and specificity were 79.0% and 66.8%, respectively. Positive and negative predictive values (PPV and NPV) were 36.1% and 93.0%, with 42.0% of Qataris being at high diabetes risk. In 2030, projected AUC was 0.78 and sensitivity and specificity were 77.5% and 65.8%. PPV and NPV were 36.8% and 92.0%, with 43.0% of Qataris being at high diabetes risk. In 2050, AUC was 0.76 and sensitivity and specificity were 74.4% and 64.5%. PPV and NPV were 40.4% and 88.7%, with 45.0% of Qataris being at high diabetes risk. This model-based score demonstrated comparable performance to a data-derived score. The derived self-complete risk score provides an effective tool for initial diabetes screening, and for targeted lifestyle counselling and prevention programs.

scholarly journals COVID-IRS: A novel predictive score for risk of invasive mechanical ventilation in patients with COVID-19

PLoS ONE ◽  
2021 ◽  
Vol 16 (4) ◽  
pp. e0248357
Author(s):  
José Antonio Garcia-Gordillo ◽  
Antonio Camiro-Zúñiga ◽  
Mercedes Aguilar-Soto ◽  
Dalia Cuenca ◽  
Arturo Cadena-Fernández ◽  
...  
Keyword(s):  
Mechanical Ventilation ◽  
Systemic Disease ◽  
Invasive Mechanical Ventilation ◽  
Area Under The Curve ◽  
Lactic Dehydrogenase ◽  
Total Sample ◽  
Adverse Outcomes ◽  
Predictive Score ◽  
Neutrophil Lymphocyte Ratio ◽  
Predictive Variables

Background Coronavirus disease 2019 (COVID-19) is a systemic disease that can rapidly progress into acute respiratory failure and death. Timely identification of these patients is crucial for a proper administration of health-care resources. Objective To develop a predictive score that estimates the risk of invasive mechanical ventilation (IMV) among patients with COVID-19. Study design Retrospective cohort study of 401 COVID-19 patients diagnosed from March 12, to August 10, 2020. The score development cohort comprised 211 patients (52.62% of total sample) whereas the validation cohort included 190 patients (47.38% of total sample). We divided participants according to the need of invasive mechanical ventilation (IMV) and looked for potential predictive variables. Results We developed two predictive scores, one based on Interleukin-6 (IL-6) and the other one on the Neutrophil/Lymphocyte ratio (NLR), using the following variables: respiratory rate, SpO2/FiO2 ratio and lactic dehydrogenase (LDH). The area under the curve (AUC) in the development cohort was 0.877 (0.823–0.931) using the NLR based score and 0.891 (0.843–0.939) using the IL-6 based score. When compared with other similar scores developed for the prediction of adverse outcomes in COVID-19, the COVID-IRS scores proved to be superior in the prediction of IMV. Conclusion The COVID-IRS scores accurately predict the need for mechanical ventilation in COVID-19 patients using readily available variables taken upon admission. More studies testing the applicability of COVID-IRS in other centers and populations, as well as its performance as a triage tool for COVID-19 patients are needed.

scholarly journals Galectin-3, osteopontin and successful aging

2016 ◽  
Vol 54 (5) ◽  
Author(s):  
Fabian Sanchis-Gomar ◽  
Alejandro Santos-Lozano ◽  
Helios Pareja-Galeano ◽  
Nuria Garatachea ◽  
Rafael Alis ◽  
...  
Keyword(s):  
Successful Aging ◽  
Area Under The Curve ◽  
Predictor Variable ◽  
Serum Levels ◽  
Healthy Controls ◽  
Cross Sectional ◽  
Galectin 3 ◽  
Medical Interest ◽  
Potential Use ◽  
Potential Biomarkers

AbstractBackground:Individuals who reach exceptional longevity (100+ years of age) free of common chronic age diseases (i.e. ‘dodgers’) arguably represent the paradigm of successful aging in humans. As such, identification of potential biomarkers associated with this phenomenon is of medical interest.Methods:We measured serum levels of galectin-3 and osteopontin, both of which have been shown to be linked with major chronic or aging-related disorders in younger populations, in centenarian ‘dodgers’ (n=81; 40 men; 100–104 years) and healthy controls (n=41; 24 men, 70–80 years).Results:Both biomarkers showed significantly lower values (p<0.001) in the former (galectin-3: 2.4±1.7 vs. 4.8±2.8 ng/mL; osteopontin: 38.1±27.7 vs. 72.6±33.1 μg/mL). Logistic regression analysis identified the combination of these two biomarkers as a significant predictor variable associated with successful aging regardless of sex (p<0.001). The area under the curve (AUC) classified the ability of galectin-3 and osteopontin to predict the likelihood of successful aging as ‘fair’ (AUC=0.75) and ‘good’ (AUC=0.80), respectively. Particularly, the combination of the two biomarkers showed good discriminatory power for successful aging (AUC=0.86), with sensitivity=83% and specificity=74%.Conclusions:Lower levels of both galectin-3 and osteopontin are associated with successful aging, representing potential biomarkers of this condition. Our cross-sectional data must be however approached with caution. Further research is necessary to replicate the present preliminary results in other cohorts and to identify the potential use of galectin-3 and osteopontin as potential targets (or at least predictors) in future personalized anti-aging therapies.

scholarly journals Baseline Interleukin-6 Level Predicts Risk of Severe COVID-19: A Two-Center, Retrospective Study

2020 ◽  
Author(s):  
Weigang Zhu ◽  
Yingping Wu ◽  
Yueping Liu ◽  
Bin Li ◽  
Pian Xiong ◽  
...  
Keyword(s):  
Interleukin 6 ◽  
Characteristic Curve ◽  
Severe Disease ◽  
Multivariate Logistic Regression Analysis ◽  
Area Under The Curve ◽  
Severe Illness ◽  
Mild Disease ◽  
Predictive Variables ◽  
Initial Symptoms ◽  
Quartile Group

Abstract Background:The severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) pandemic has affected almost every country. Interleukin-6 (IL-6), a cytokine secreted by CD4+ T cell, has been shown to be a reliable marker of disease severity and a useful parameter for monitoring progression of coronavirus disease-2019 (COVID-19). However its value as a predictor of severe disease has not been assessed.Methods:A total of 160 laboratory-confirmed COVID-19 patients admitted to two hospitals were enrolledand separated into two groups according to whether or not they progressed to develop severe illness. Demographic and clinical characteristics at admission were compared between the groups.Results: Patients who developed severe COVID-19 had significantly higher baseline IL-6 levels than patients who had mild disease course in hospital (P< 0.001). Patients were further grouped according to quartiles of IL-6 level. The cumulative incidence of severe illnesswas significantly higher in the third and fourth quartiles groups than in the first quartile group (55% vs. 15% and 80% vs. 15%, respectively;bothP< 0.001). In multivariate logistic regression analysis, the risk for developing severe disease was markedly higher in the highest IL-6 quartile than in the lowest quartile (odds ratio: 14.95; 95% confidence interval: 3.65–61.30; P< 0.001). Receiver operating characteristic curve analysis of potential predictive variables showed the area under the curve to be largest for baseline IL-6, with the value of 5.20 pg/mL having the best balance of sensitivity and specificity for predicting risk of severe COVID-19.Conclusion: Serum baseline IL-6 appears to be a reliable predictor of risk of severe COVID-19. Early intervention may be advisable in patients with serum IL-6 levels >5.20 pg/mL, even if initial symptoms are mild.

A Simple Approach to Adjust for Case-Mix when Comparing Institutional Cesarean Birth Rates

2019 ◽  
Author(s):  
James Jeffry Howbert ◽  
Ellen Kauffman ◽  
Kristin Sitcov ◽  
Vivienne Souter
Keyword(s):  
External Validation ◽  
Area Under The Curve ◽  
Case Mix ◽  
Maternal Weight ◽  
Internal Validation ◽  
Training Cohort ◽  
Cesarean Birth ◽  
Predictive Variables ◽  
Nulliparous Women ◽  
And Gender

Abstract Objective To develop a validated model to predict intrapartum cesarean in nulliparous women and to use it to adjust for case-mix when comparing institutional laboring cesarean birth (CB) rates. Study Design This multicenter retrospective study used chart-abstracted data on nulliparous, singleton, term births over a 7-year period. Prelabor cesareans were excluded. Logistic regression was used to predict the probability of CB for individual pregnancies. Thirty-five potential predictive variables were evaluated including maternal demographics, prepregnancy health, pregnancy characteristics, and newborn weight and gender. Models were trained on 21,017 births during 2011 to 2015 (training cohort), and accuracy assessed by prediction on 15,045 births during 2016 to 2017 (test cohort). Results Six variables delivered predictive success equivalent to the full set of 35 variables: maternal weight, height, and age, gestation at birth, medically-indicated induction, and birth weight. Internal validation within the training cohort gave a receiver operator curve with area under the curve (ROC-AUC) of 0.722. External validation using the test cohort gave ROC-AUC of 0.722 (0.713–0.731 confidence interval). When comparing observed and predicted CB rates at 16 institutions in the test cohort, five had significantly lower than predicted rates and three had significantly higher than predicted rates. Conclusion Six routine clinical variables used to adjust for case-mix can identify outliers when comparing institutional CB rates.

Determinants of financial performance in Albanian economic entities, case of construction industry in Albania

2022 ◽  
Vol 19 ◽  
pp. 453-461
Author(s):  
Albana Gjoni (Karameta) ◽  
Shpresa Çela ◽  
Ahmad Mlouk ◽  
Griselda Marku
Keyword(s):  
Performance Measurement ◽  
Financial Performance ◽  
Economic Value ◽  
Predictor Variable ◽  
Value Added ◽  
Financial Ratios ◽  
Construction Sector ◽  
Leverage Ratio ◽  
Construction Companies ◽  
Predictive Variables

Financial performance mainly reflects the overall financial health of the business sector over a period of time. It shows how well an entity is using its resources to maximize shareholder’s wealth. Although a thorough assessment of a firm's financial performance takes into account many other measures, the most common performance measurement used in the area of finance are financial ratios. This paper provides a comprehensive study of the financial performance measurement literature related to the construction sector in Albania. The literature covers studies from Albania, Iran, India and Pakistan, but some international evidence has also been presented. The construction sector is chosen because of its impact on economic growth in Albania, it represents the second main sector according to its share effect on Albanian GDP. The financial ratios used to measure the financial performance of the construction sector are the debt ratio, the liquidity ratio and the profitability ratio from the period 2018-2020 for 100 construction companies in Albania. Return on Assets (ROA) is taken as the predictor variable and three financial ratios are taken as the predictive variables. This research reveals that the financial ratios have positive correlation with the dependent variable whereas the leverage ratio has negative correlation. To overcome the limitations of the forthcoming studies, the considered number of years need to be increased and other models such as Market Value Added, Capital Asset Pricing Model and Economic Value Added can be used to be tested for research to analyze other factors that may affect financial performance.

scholarly journals Predicting the Habitat Suitability of a Potential Invasive Fern, Cyclosorus afer in Lafia, Nigeria using Species Distribution Modelling

2019 ◽  
Vol 12 ◽  
pp. 1-10
Author(s):  
GBENGA FESTUS AKOMOLAFE ◽  
ZAKARIA BIN RAHMAD
Keyword(s):  
Test Data ◽  
Habitat Suitability ◽  
Area Under The Curve ◽  
Predictor Variable ◽  
Species Distribution Modelling ◽  
Environmental Predictors ◽  
Operating Characteristics ◽  
Jackknife Test ◽  
Presence Data ◽  
Precipitation Seasonality

The vast colonisation of some wetlands by Cyclosorus afer in Lafia, Nigeria has been a serious concern to ecologists and indigenous dwellers. In this study, we used the Maximum Entropy (Maxent) modelling technique to predict the habitat suitability of this fern in Lafia, Nigeria. We obtained the presence data of this fern in three already invaded wetlands of size 500 x 500m2 each using multiple 200m transect. Bioclimatic and elevation variables which were obtained from different databases were imputed into the model as predictor variables of C. afer. After that, the Maxent model was run with 70% of the presence data as training and 30% as test data. Our model result revealed that the area under the curve for receiver operating characteristics of training is 0.847 while and test data is 0.970. The model’s sensitivity was observed to be 100%. The model was assessed based on a jackknife test of individual contributions of each predictor variable to the model. Therefore, the environmental predictors of the occurrence of C. afer in this study area include precipitation seasonality, Precipitation of driest quarter, precipitation of coldest quarter and elevation. This model could be described as accurate, and the occurrence of C. afer in Lafia, Nigeria, is influenced by limiting environmental factors

scholarly journals Random forest variable selection in spatial malaria transmission modelling in Mpumalanga Province, South Africa

2016 ◽  
Vol 11 (3) ◽  
Author(s):  
Thandi Kapwata ◽  
Michael T. Gebreslasie
Keyword(s):  
South Africa ◽  
Random Forest ◽  
Malaria Transmission ◽  
Environmental Variables ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Mean Squared Error ◽  
Predictor Variable ◽  
Variable Importance ◽  
Mpumalanga Province

Malaria is an environmentally driven disease. In order to quantify the spatial variability of malaria transmission, it is imperative to understand the interactions between environmental variables and malaria epidemiology at a micro-geographic level using a novel statistical approach. The random forest (RF) statistical learning method, a relatively new variable-importance ranking method, measures the variable importance of potentially influential parameters through the percent increase of the mean squared error. As this value increases, so does the relative importance of the associated variable. The principal aim of this study was to create predictive malaria maps generated using the selected variables based on the RF algorithm in the Ehlanzeni District of Mpumalanga Province, South Africa. From the seven environmental variables used [temperature, lag temperature, rainfall, lag rainfall, humidity, altitude, and the normalized difference vegetation index (NDVI)], altitude was identified as the most influential predictor variable due its high selection frequency. It was selected as the top predictor for 4 out of 12 months of the year, followed by NDVI, temperature and lag rainfall, which were each selected twice. The combination of climatic variables that produced the highest prediction accuracy was altitude, NDVI, and temperature. This suggests that these three variables have high predictive capabilities in relation to malaria transmission. Furthermore, it is anticipated that the predictive maps generated from predictions made by the RF algorithm could be used to monitor the progression of malaria and assist in intervention and prevention efforts with respect to malaria.

scholarly journals Detecting an Invasive Shrub in Deciduous Forest Understories Using Remote Sensing

Weed Science ◽  
2009 ◽  
Vol 57 (5) ◽  
pp. 512-520 ◽  
Author(s):  
Bryan N. Wilfong ◽  
David L. Gorchov ◽  
Mary C. Henry
Keyword(s):  
Remote Sensing ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Deciduous Forest ◽  
Predictor Variable ◽  
Quadratic Function ◽  
Woody Species ◽  
Forest Understory ◽  
Native Forest ◽  
Amur Honeysuckle

Remote sensing has been used to directly detect and map invasive plants, but has not been used for forest understory invaders because they are obscured by a canopy. However, if the invasive species has a leaf phenology distinct from native forest species, then temporal opportunities exist to detect the invasive. Amur honeysuckle, an Asian shrub that invades North American forests, expands leaves earlier and retains leaves later than native woody species. This research project explored whether Landsat 5 TM and Landsat 7 ETM+ imagery could predict Amur honeysuckle cover in woodlots across Darke and Preble Counties in southwestern Ohio and Wayne County in adjacent eastern Indiana. The predictive abilities of six spectral vegetation indices and six reflectance bands were evaluated to determine the best predictor or predictors of Amur honeysuckle cover. The use of image differencing in which a January 2001 image was subtracted from a November 2005 image provided better prediction of Amur honeysuckle cover than the use of the single November 2005 image. The Normalized Difference Vegetation Index (NDVI) was the best-performing predictor variable, compared to other spectral indices, with a quadratic function providing a better fit (R2 = 0.75) than a linear function (R2 = 0.65). This predictive model was verified with 15 other woodlots (R2 = 0.77). With refinement, this approach could map current and past understory invasion by Amur honeysuckle.

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