Relative Operating Characteristic Research Articles

The Association Between Preoperative Neutrophil-Lymphocyte Ratio and Febrile Urinary Tract Infection in Patients Treated by Percutaneous Nephrolithotomy.

This study aimed to assess the association between preoperative neutrophil-lymphocyte ratio (NLR) and febrile urinary tract infection (fUTI) in patients who underwent percutaneous nephrolithotomy (PCNL). The clinical data of patients who underwent PCNL at our hospital between March 2020 and May 2023 were retrospectively analyzed. According to whether fUTI occurs after operation, collected data were divided to the patients into fUTI and non-fUTI. Univariate analysis, relative operating characteristic (ROC) curves and multivariate logistic regression analysis were performed on the data. A total of 405 patients were enrolled in the study. The procedure was successfully performed in all patients, and the incidence of fUTI was 8.4% (34/405). Eight patients eventually developed sepsis, and the incidence of sepsis in fUTI patients was 23.5%. The result shows a significant correlation between NLR and fUTI (95% confidence interval [CI],1.21-1.83, Odds ratio [OR]=1.49, p<0.001). The predictive ability of NLR on the occurrence of fUTI was assessed by plotting relative operating characteristic (ROC) curves. The area under the curve (AUC) in the ROC curve for NLR was 0.718 according to the Youden index, and the best cut-off value of NLR was 2.71. Furthermore, logistic multiple regression model adjustment was carried out to further confirm the robustness of the relationship between NLR and fUTI. The results indicated robustness regardless of whether NLR was a continuous variable or a categorical variable. NLR can be used as a simple and effective preoperative indicator for the prediction of fUTI in patients undergoing PCNL.

Identification of Urban Expansion Driving Factors using CA-Markov model: a Case Study Demak and Jepara Regency, Indonesia

Land cover changes are a critical concern in a developing country where rapid urbanization and population growth intersect with environmental dynamics. Understanding the driving forces behind these changes is essential for sustainable development and effective land management. This study analyzes land cover changes in Demak-Jepara regency, Indonesia, over a 20-year period using Landsat data. The objective is to identify the dominant factors driving the increase in built-up areas, considering both natural and human-induced factors. Factors such as road distance, existing build area, and natural features are evaluated. Using ArcGIS and Idrisi Selva's Land Change Modeler, land cover data is processed, and Cellular Automata-Markov analysis is conducted. The study considers a cell size of 30 x 30 meters and a time step of 5 years from 2001 to 2009. Transition persistence analysis identifies significant factors, validated using 2020 land cover data through AUC (Area Under the Curve) and ROC (Relative Operating Characteristic) analysis. The combination of natural and human-induced factors (scenario-C) shows the highest AUC value of 0.9406, indicating better conformity with 2020 land cover. Dominant factors in scenario C include roads, existing built-up areas, river order, and slope gradient. Results reveal that road development and proximity to existing settlements are the primary drivers of land cover changes. Natural factors like river order and slope gradient have a lesser impact, while the coastline has minimal influence. These findings highlight the importance of considering both natural and human-induced factors in land use planning. They provide valuable insights for policymakers and land managers in making informed decisions for sustainable development and land use strategies. Keywords: Land Cover Changes; Driving Factors; Natural Factors; CA-Markov; Sustainable Development

Predicting malaria hyper endemic zones in West Africa using a regional scale dynamical malaria model

Due to the continuing large number of malaria-related deaths in tropical Africa, the need to develop a robust Malaria Early Warning System (MEWS) for effective action is growing to guide cost-effective implementation of interventions. This study employs a two-stage hierarchical evaluation technique to evaluate the ability of the VECTRI malaria model to simulate malaria dynamics at seasonal time scale (1 - 7 months) over Nigeria and West Africa. Two sets of malaria simulations are considered. The first set is based on VECTRI simulations driven by observed rainfall and temperature datasets (hereafter referred to as control run). The second is based on malaria simulations driven by the European Centre for Medium-Range Weather Forecasting (ECMWF) System5 ensemble seasonal forecasting system (hereafter referred to as Forecast run). Different metrics are employed to assess the skill of the VECTRI malaria model. Results based on the control run indicate that the model can reproduce hyper-endemic zones and the evolution of malaria cases, particularly the observed increase in cases with decreasing population density. Despite having significant biases and low correlation, the model successfully predicts annual anomalies in malaria cases across Nigeria, particularly in the savannah region that experience large malaria burden. Annual correlations between the VECTRI Forecast run and the VECTRI Control run are relatively low at all lead times (LT) and for each start date (SD) across West Africa, although correlation generally increases from the Gulf of Guinea to the Sahel. Despite low correlations, the Rank Probability Skill Score (RPSS) reveals that the model has a statistically significant skill in predicting malaria occurrences across all categories of malaria cases, regardless of start date or lead time. While the Guinea Forest has the strongest RPSS, the increase or decrease in skill from the first to seventh lead time varies significantly across the region. In addition, the VECTRI malaria model has a good ability to discriminate variability in malaria cases across all regions, with an average Area Under the Relative Operating Characteristics (ROC) Curve (AUC) of approximately 0.62. Our findings suggest that the VECTRI malaria model could be used as a reliable Malaria Early Warning System (MEWS), particularly for identifying malaria hyper-endemic zones in West Africa at seasonal time scale.

An Empirical Model of the Occurrence Rate of Low Latitude Post‐Sunset Plasma Irregularities Derived From CHAMP and Swarm Magnetic Observations

AbstractThe prediction of post‐sunset equatorial plasma depletions (EPDs), often called ionospheric plasma bubbles, has remained a challenge for decades. In this study, we introduce the Ionospheric Bubble Probability (IBP) model, an empirical model to predict the occurrence probability of EPDs derived from 9 years of CHAMP and 9 years of Swarm magnetic field measurements. The model predicts the occurrence probability of EPDs for a given longitude, day of year, local time and solar activity, for the altitude range of about 350–510 km, and low geographic latitudes of ±45°. IBP has been found to successfully reconstruct the distribution of EPDs as reported in previous studies from independent data. IBP has been further evaluated using 1‐year of untrained data of the Ionospheric Bubble Index (IBI). IBI is a Level 2 product of the Swarm satellite mission used for EPD identification. The relative operating characteristics (ROC) curve shows positive excursion above the no‐skill line with Hanssen and Kuiper's Discriminant (H&amp;KSS) score of 0.52, 0.51, and 0.55 at threshold model output of 0.16 for Swarm A, B, and C satellites. Additionally, the reliability plots show proximity to the diagonal line with a decent Brier Skill Score (BSS) of 0.249, 0.210, and 0.267 for Swarm A, B, and C respectively at 15% climatological occurrence rate. These tests indicate that the model performs significantly better than a no‐skill forecast. The IBP model offers compelling glimpses into the future of EPD forecasting, thus demonstrating its potential to reliably predict EPD occurrences. The IBP model is publicly available.

The First Operational Version of Taiwan Central Weather Bureau’s One-Tier Global Atmosphere–Ocean Coupled Forecast System for Seasonal Prediction

Abstract The first version of the Taiwan Central Weather Bureau one-tier (TCWB1T) fully coupled global atmospheric and oceanic modeling forecast system had been developed and implemented as a routine operation for seasonal prediction at Central Weather Bureau (CWB) in 2017, with a minor revision in 2020. Based on NCEP CFSv1, the global atmospheric model in NCEP CFSv1 was replaced by CWB’s atmospheric global spectral model (GSM) and coupled with the GFDL MOM3. Several parameters have been tested and tuned in the CWB atmospheric GSM, achieving an optimal configuration with better sea surface temperature (SST) predictions for integration more than one year. Using NCEP CFSR as the initial condition, TCWB1T conducted hindcasts from 1982 to 2011 and forecasts from 2012 to 2019 to analyze its performance. The results of these hindcasts and forecasts show that the TCWB1T can make useful predictions as verified against the observations of OISST, ERSST, CFSR, and GPCP based on the methods of EOF, RMSE, anomaly correlation, ranked probability skill score (RPSS), reliability diagram (RD), and relative operating characteristics (ROCs). TCWB1T also has the same level of skill scores as NCEP CFSv2 and/or the ECMWF fifth-generation seasonal forecast system (SEAS5), based on EOF, anomaly pattern correlation, climatological bias, RMSE, temporal correlation, and anomaly correlation percentage of forecast skill. TCWB1T shows forecast skill that is better in winter than in summer. Overall, it indicates that TCWB1T can be used for seasonal ENSO predictions.

Using Calibrated Rainfall Forecasts and Observed Rainfall to Produce Probabilistic Meteorological Drought Forecasts

Most existing drought forecast systems rely only on observed or forecast rainfall, losing valuable context gained from considering both. The lack of a direct link between observed and forecast rainfall reduces the physical consistency of a system, motivating the development of a methodology that can directly link the two. The methodology developed in this study allows the comparison of the calibrated ensemble forecasts of rainfall totals from a dynamical climate model to observed rainfall deficiencies from a gridded rainfall analysis. The methodology is used to create a probabilistic product that forecasts the chance of entering meteorological drought, with lead times of one month (monthly forecast) and three months (seasonal forecast). Existing deficiency areas are included to facilitate analysis of how these areas are forecast to change. The performance of the developed methodology was verified using Percent Correct (PC), Brier Score (BS), and Relative Operating Characteristic (ROC) statistics. Analysis of the forecast plots was also completed visually. Forecast performance for areas with existing deficiencies as well as for non-deficiency areas was promising (PC rates of &gt;79% and &gt;97%, respectively). Although PC rates for observed deficiencies were low across most months, the mean forecast probability for these areas was 36%, indicating the system had value and outperformed climatology. A calibrated, coupled product like the one scoped in this study has not been explored and we note that it could be an invaluable tool for quantifying meteorological drought onset and persistence in Australia.

Integrating Sigmoid Calibration Function into Entropy Thresholding Segmentation for Enhanced Recognition of Potholes Imaged Using a UAV Multispectral Sensor

This study was aimed at enhancing pothole detection by combining sigmoid calibration function and entropy thresholding segmentation on UAV multispectral imagery. UAV imagery was acquired via the flying of the DJI Matrice 600 (M600) UAV system, with the MicaSense RedEdge imaging sensor mounted on its fixed wing. An endmember spectral pixel denoting pothole feature was selected and used as the base from which spectral radiance patterns of a pothole were analyzed. A field survey was carried out to measure pothole diameters, which were used as the base on which the pothole area was determined. Entropy thresholding segmentation was employed to classify potholes. The sigmoid calibration function was used to reconfigure spectral radiance properties of the UAV spectral bands to pothole features. The descriptive statistics was computed to determine radiance threshold values to be used in demarcating potholes from the reconfigured or calibrated spectral bands. The performance of the sigmoid calibration function was evaluated by analyzing the area under curve (AUC) results generated using the Relative Operating Characteristic (ROC) technique. Spectral radiance pattern analysis of the pothole surface revealed high radiance values in the red channel and low radiance values in the near-infrared (NIR) channels of the spectrum. The sigmoid calibration function radiometrically reconfigured UAV spectral bands based on a total of 500 sampled pixels of pothole surface obtained from all the spectral channels. Upon successful calibration of UAV radiometric properties to pothole surface, the reconfigured mean radiance values for pothole surface were noted to be 0.868, 0.886, 0.944, 0.211 and 0.863 for blue, green, red, NIR and red edge, respectively. The area under curve (AUC) results revealed the r2 values of 0.53, 0.35, 0.71, 0.19 and 0.35 for blue, green, red, NIR and red edge spectral channels, respectively. Overestimation of pothole 1 by both original and calibrated spectral channels was noted and can be attributed to the presence of soils adjacent to the pothole. However, calibrated red channel estimated pothole 2 and pothole 3 accurately, with a slight area deviation from the measured potholes. The results of this study emphasize the significance of reconfiguring radiometric properties of the UAV imagery for improved recognition of potholes.

Relative economic value of global ensemble prediction system of NCMRWF, India, for extreme weather events

AbstractAn ensemble prediction system quantifies the uncertainty in the forecast by a numerical weather prediction model and helps in decision‐making. The global ensemble prediction system (NEPS‐G) of NCMRWF, India, has 1 control and 22 perturbed ensemble members. In the present study, the relative economic value of NEPS‐G forecast has been investigated based on a simple decision‐analytic model. Temperature at 850 hPa (T850), maximum surface temperature (Tmax) and precipitation (rf) over the Indian domain have been studied. Ensemble mean forecast offers a larger value to the users than the control forecast. For rare extreme events, the area under the curve (T‐AUC) of a relative operating characteristic (ROC) obtained using the trapezoidal approximation for 22‐member NEPS‐G (NEPS‐22) is significantly smaller than the area under the curve (Z‐AUC) of the full ROC obtained using binormal model and Z‐transformation. As a result, the discrimination ability and relative economic value of NEPS‐G are underestimated. A method (SUB‐EM) which subdivides the lowest probability category into several subcategories and uses ensemble mean forecast as the secondary decision variable to enhance T‐AUC and improve relative economic value of NEPS‐G has been investigated. Tmax &gt; 45°C and rf &gt; 65 mm·day−1 have been considered as extreme events. The impact of increasing the number of perturbed ensemble members from 22 to 33 (NEPS‐33) by running additional 11 perturbed members from 0000 UTC initial condition has also been investigated and compared with the results obtained using SUB‐EM. SUB‐EM offers larger economic value to the users with small cost–loss ratio (α) without engaging any extra computational resources. An experiment is also carried out to study the impact of using different ensemble summary statistics as a secondary decision variable. Ensemble Maximum provides maximum value and benefits the largest range of users.

Probabilistic skill of statistically downscaled ECMWF S2S forecasts of maximum and minimum temperatures for weeks 1–4 over South Africa

AbstractThe probabilistic forecast skill level of statistically downscaled European Centre for Medium‐Range Weather Forecasts (ECMWF) subseasonal‐to‐seasonal (S2S) forecasts is determined in predicting maximum and minimum temperatures for weeks 1–4 lead times during 20‐year December–January–February (DJF) seasons from 2001 to 2020 over South Africa. Skilful S2S forecasts are vital in assisting decision‐makers in the development of contingency planning for any eventualities that may arise because of weather and climate phenomena. Extreme high‐ and low‐temperature events over a prolonged period can lead to hyperthermia and hypothermia, respectively, and can lead to loss of life. The results from the relative operating characteristic (ROC) and reliability diagrams indicate that the ECMWF S2S model has skill in predicting maximum temperature up to week 3 ahead, particularly over the central and eastern parts of South Africa. The ROC scores indicate that the model has skill in predicting minimum temperature up to week 4 ahead for the above‐normal category, particularly over the central and eastern parts of South Africa. Reliability diagrams indicate that the model has a tendency of overestimating the below‐normal category when predicting both maximum and minimum temperatures for weeks 1–4 lead times over South Africa. Furthermore, canonical correlation analysis (CCA) pattern analysis suggests that when there are anomalously positive and negative predicted 850‐hPa geopotential heights located over South Africa, there are anomalously hot and cold conditions during the DJF seasons over most parts of South Africa, respectively. These results suggests that statistical downscaling of model forecasts can improve forecast skill. Moreover, the results suggest that there is potential for S2S predictions in South Africa, and as such, S2S prediction system for maximum and minimum temperatures can be developed.

LUM as a novel prognostic marker and its correlation with immune infiltration in gastric cancer: a study based on immunohistochemical analysis and bioinformatics

BackgroundGastric cancer (GC) is considered the sixth highly prevailing malignant neoplasm and is ranked third in terms of cancer mortality rates. To enable an early and efficient diagnosis of GC, it is important to detect the fundamental processes involved in the oncogenesis and progression of gastric malignancy. The understanding of molecular signaling pathways can facilitate the development of more effective therapeutic strategies for GC patients.MethodsThe screening of genes that exhibited differential expression in early and advanced GC was performed utilizing the Gene Expression Omnibus databases (GSE3438). Based on this, the protein and protein interaction network was constructed to screen for hub genes. The resulting list of hub genes was evaluated with bioinformatic analysis and selected genes were validated the protein expression by immunohistochemistry (IHC). Finally, a competing endogenous RNA network of GC was constructed.ResultsThe three genes (ITGB1, LUM, and COL5A2) overexpressed in both early and advanced GC were identified for the first time. Their upregulation has been linked with worse overall survival (OS) time in patients with GC. Only LUM was identified as an independent risk factor for OS among GC patients by means of additional analysis. IHC results demonstrated that the expression of LUM protein was increased in GC tissue, and was positively associated with the pathological T stage. LUM expression can effectively differentiate tumorous tissue from normal tissue (area under the curve = 0.743). The area under 1-, 3-, and 5-year survival relative operating characteristics were greater than 0.6. Biological function enrichment analyses suggested that the genes related to LUM expression were involved in extracellular matrix development-related pathways and enriched in several cancer-related pathways. LUM affects the infiltration degree of cells linked to the immune system in the tumor microenvironment. In GC progression, the AC117386.2/hsa-miR-378c/LUM regulatory axis was also identified.ConclusionCollectively, a thorough bioinformatics analysis was carried out and an AC117386.2/hsa-miR-378c/LUM regulatory axis in the stomach adenocarcinoma dataset was detected. These findings should serve as a guide for future experimental investigations and warrant confirmation from larger studies.

Adaptive selection of members for convective-permitting regional ensemble prediction over the western Maritime Continent

A common issue faced by the downscaled regional ensemble prediction systems is the under-dispersiveness of the ensemble forecasts, often attributed to the lack of spread under the initial conditions from the global ensemble. In this study, a novel method that adopts an adaptive approach to selecting global ensemble members for regional downscaling has been developed. Instead of using a fixed set of pre-selected global ensemble members, the adaptive selection performs a sampling algorithm and selects the global ensemble members, which maximizes a fractions skill score (FSS)-based displacement between ensemble members. The method is applied to a convective-permitting ensemble prediction system over the western Maritime Continent, referred to as SINGV-EPS. SINGV-EPS has a grid spacing of 4.5 km and is a 12-member ensemble that is driven by the European Centre for Medium-Range Weather Forecasts (ECMWF) 51-member global ensemble. Month-long trials were conducted in June 2020 to assess the impact of adaptive selection on the ensemble forecast spread and rainfall verification scores. In both fixed pre-selection and adaptive selection experiments, SINGV-EPS was still under-dispersive. However, adaptive selection improved the ensemble spread and reduced the root-mean-square error (RMSE) of the ensemble mean in wind, temperature, and precipitation fields. Further verification of the rainfall forecasts showed that there was a reduction in the Brier score and a higher hit rate in the relative operating characteristic (ROC) curve for all rainfall thresholds when adaptive selection was applied. Additionally, the ensemble mean forecasts from adaptive selection experiments are more accurate beyond 24 h, with a higher FSS for all rainfall thresholds and neighborhood lengths. These results suggest that the adaptive selection is superior to the fixed pre-selection of global ensemble members for downscaled regional ensemble prediction.

Evaluation of Probabilistic Forecasts of Extreme Cold Events in S2S Models

The probabilistic prediction skill of the weekly forecasts of extreme cold events (ECE) is illustrated and measured in the form of the Brier Skill Score (BSS) and the area under Relative Operating Characteristics (ROC) curves based on the subseasonal-to-seasonal (S2S) prediction project database. The ROC scores show that six S2S models have the good potential predictability skill required for use in ECE probabilistic forecasts, and they were more useful than climatologic probabilistic models in creating forecasts of about 3–4 weeks in length. However, the BSS results show that the actual prediction skill of six models used in ECE probabilistic forecasts are different. The ECMWF model has a good performance, and its actual probabilistic prediction skill of ECE for forecasts of about 3–4 weeks in length was higher than those of climatology, which operates close to its potential predictability. The actual probabilistic prediction skill of the NCEP model for ECE was only about 2 weeks over the extra-tropics, and no skill was recorded over the tropics given its bad reliability, especially over the tropics. BoM, JMA, and CNRM models only have a 1-week actual prediction skill over the Northern Hemisphere extra-tropics, and they have no skill over the rest of the world’s land area. The CNR-ISAC model has a 1-week actual prediction skill over the extra-tropics and about 4 weeks over the tropics. There is still much room for improvement in the prediction ability of models used for ECE. MJO in tropical regions has an important influence on the probabilistic prediction skill of ECE required at middle and high latitudes. When there is an MJO in the initial conditions, the potential predictability and actual prediction skill of ECE probabilistic forecasts over North America in the 3rd week and over Europe in the 3rd–4th weeks are higher than those without MJO.

A novel approach for flood hazard assessment using hybridized ensemble models and feature selection algorithms

Identifying flood-prone regions is critical for effective management of flood hazards as floods are among the most devastating natural disasters globally. However, accurate modeling and prediction of floods are challenging due to their complexity. The current research has proposed a novel approach for Flood Hazard (FH) prediction using hybrid Machine Learning (ML) models that integrate ensemble ML models with several Feature Selection (FS) algorithms. An optimum set of Flood Influential Factors (FIFs) was determined using the Simulated Annealing (SA) and Information Gain (IG) FS algorithms. The ensemble ML models employed include AdaboostM1 (ABM), Boosted Generalized Linear Model (BGLM), and Stochastic Gradient Boosting (SGB) algorithms. In addition, the hyper-parameters of the hybrid models were optimized using the random search (RS) method and repeated cross-validation technique. The proposed hybrid models were trained using flood inventory map and FIFs obtained from a spatial database. The results verified that the SA and IG algorithms detect 9 and 13 factors as FIFs in the FH assessment, respectively. Moreover, rainfall, distance to river, altitude, and lithology FIFs have a greater impact than the other factors in the Sardabroud watershed, Mazandaran Province, Iran. Several robust indicators, such as the area under curve (AUC) in relative operating characteristic (ROC) curves and statistical measurements, were employed to assess the robustness of hybrid models. SA-ABM model had the highest AUC value (0.983), while IG-ABM, SA-BGLM, SA-SGB, IG-BGLM, and IG-SGB had lower values (0.952 to 0.973). Finally, the SA-ABM hybrid model classified 27% of the study area as having a high hazard of floods.

Probabilistic vs deterministic forecasts – interpreting skill statistics for the benefit of users

Owing to probabilistic uncertainties associated with seasonal forecasts, especially over areas such as southern Africa where forecast skill is limited, non-climatologists and users of such forecasts frequently prefer them to be presented or distributed in terms of the likelihood (expressed as a probability) of certain categories occurring or thresholds being exceeded. Probabilistic forecast verification is needed to verify such forecasts. Whilst the resulting verification statistics can provide clear insights into forecast attributes, they are often difficult to understand, which might hinder forecast uptake and use. This problem can be addressed by issuing forecasts with some understandable evidence of skill, with the purpose of reflecting how similar forecasts may have performed in the past. In this paper, we present a range of different probabilistic forecast verification scores, and determine if these statistics can be readily compared to more commonly known and understood ‘ordinary’ correlations between forecasts and their associated observations – assuming that ordinary correlations are more intuitively understood and informative to seasonal forecast users. Of the range of scores considered, the relative operating characteristics (ROC) was found to be the most intrinsically similar to correlation.

Utility of the Brief Young Adult Alcohol Consequences Questionnaire to Identify College Students At-Risk for Alcohol Related Problems: Relative Operating Characteristics across Seven Countries

Background: It is important to identify students who would benefit from early interventions to reduce harmful drinking patterns and associated consequences. the Brief Young Adult Alcohol Consequences Questionnaire (B-YAACQ) could be particularly useful as a screening tool in university settings. Objectives. The present study examined the utility of the B-YAACQ to distinguish among students at-risk for problematic alcohol use as measured by the AUDIT. Objectives: The present study examined the utility of the B-YAACQ to distinguish among students at-risk for problematic alcohol use as measured by the AUDIT. Methods: A sample of 6382 students (mean age=20.28, SD=3.75, 72.2% females) from seven countries (i.e., U.S., Canada, South-Africa, Spain, Argentina, Uruguay, England) completed the B-YAACQ, the AUDIT and different measures of alcohol use. Results: ROC analyses suggested that a cutoff score of 5 maximized the YAACQ’s discrimination utility to differentiate between students at low versus moderate/high risk in the total sample and across countries (except in Canada, where the cutoff was 4). In addition, a cutoff of 7 differentiated between students at low/moderate versus high risk in the total sample, while cutoffs of 10, 9, 8 and 7 differentiate between students at low/moderate versus high risk in Uruguay, U.S and Spain (10), Argentina (9), England (8), and Canada and South-Africa (7), respectively. Students classified at the three risk levels (i.e., low, moderate and high) differed in age (i.e., a younger age was associated with higher risk) and drinking patters (i.e., higher drinking frequency, quantity, binge drinking and AUDIT and B-YAACQ scores in the higher risk groups). Conclusions: This study suggest that the B-YAACQ is a useful tool to identify college students at-risk for experiencing problematic patterns of alcohol use.

FOREST FIRE SUSCEPTIBILITY MAPPING OF BHUTAN USING LOGISTIC REGRESSION AND FREQUENCY RATIO MODEL

Forest fire is not only observed as one of the most significant sources of forest degradation in Bhutan but also a serious danger to national conservation efforts. As a result, forest fire susceptibility analysis is recognised as an important part of Bhutan's forest fire management strategy. The study's major goal is to create a forest fire susceptibility map for Bhutan using logistic regression (LR) and frequency ratio (FR) models. The study gathered number of fire influencing factors, evaluated them, and created susceptibility maps. Using the relative operating characteristics technique, the efficiency of each of the two models was analysed and compared to select the best model. The Receiver Operating Characteristics (ROC) curves with the area under the curve (AUC) was used to check the correctness of the maps produced by the modelling procedure. The prediction and success rates of the LR model were 88.8% and 87.5%, while for the FR model they were 85.4% and 85.1%, respectively. The results showed that both models are good predictors of forest fire with the LR model performing fairly better than the FR model. So, the LR model was chosen as an optimum model for forest fire susceptibility mapping. The susceptibility map obtained from the optimum LR model was classified into five categories such as; very low, low, moderate, high, and very high.. The findings of this study give useful spatial information for implementing forest management techniques.

Clinical significance of prognostic nutritional index (PNI)-monocyte-to-lymphocyte ratio (MLR)-platelet (PLT) score on postoperative outcomes in non-metastatic clear cell renal cell carcinoma

BackgroundPrognositic nutritional index (PNI), monocyte-to-lymphocyte ratio (MLR) and platelet (PLT) are associated with tumor survival in many human malignancies. Whereas, no study combined PNI-MLR-PLT score and indicated its predictive significance on the prognosis of patients with non-metastatic clear cell renal cell carcinoma (ccRCC).MethodsIn this study, we retrospectively collected the clinicopathological characteristics and prognostic data from 164 cases of non-metastatic ccRCC and aimed to determine the clinical significance of PNI-MLR-PLT score on patients’ outcomes after surgery. The optimal cut-off values of PNI (PNI > 47.40 vs PNI < 47.40), MLR (MLR > 0.31 vs MLR < 0.31) and PLT (PLT > 245 vs PLT < 245) were identified with relative operating characteristic (ROC) curve analysis. The PNI-MLR-PLT score system was established by the value of three indexes, each indication was assigned a score of 0 or 1. Overall survival (OS) and metastasis-free survival (MFS) were analyzed using Kaplan–Meier estimate and Cox regression models.ResultsThe mean follow-up period was 85.67 months. Eight (5.0%) patients died, 4 (2.0%) relapsed, and 7 (4.0%) developed metastasis after surgery. The 3-year OS and MFS rates were 98.2% and 97.6%, and the 5-year OS and MFS rates were both 90.2%. Our results suggested that PNI-MLR-PLT score negatively correlated with pathological T stage and tumor grade. Survival outcomes revealed that lower PNI-MLR-PLT score is associated with inferior OS (P < 0.001) and MFS (P < 0.001) after surgery. Subgroup analysis regarding pathological T stage, tumor grade and surgical modalities obtained consistent results. univariable and multivariable Cox analysis showed that high PNI-MLR-PLT score was the independent protective factor of tumor survival in non-metastatic ccRCC patients.ConclusionsOur data suggested that PNI-MLR-PLT score could serve as a promising independent prognostic factor in patients with non-metastatic ccRCC.

How to assess severe burnout? Cutoff points for the Burnout Assessment Tool (BAT) based on three European samples

Despite decades of burnout research, clinical validated cut-off scores that discriminate between those who suffer from burnout and those who don't are still lacking. To establish such cut-off scores, the current study uses a newly developed questionnaire, the Burnout Assessment Tool (BAT) that consists of four subscales (exhaustion, mental distancing, and emotional and cognitive impairment). Separate cut-offs were computed for those at risk for burnout and those suffering from severe burnout for the original BAT-23 as well as for the shortened BAT-12. Relative operating characteristic (ROC) analyses were carried out using representative samples of healthy employees from The Netherlands (N=1370), Belgium (Flanders; N=1403) and Finland (N=1350). In addition, samples of employees who received a burnout diagnosis were used (N=335, 158 and 50, respectively). The diagnostic accuracy of the BAT (area under the curve) ranges from good to excellent with the exception of mental distancing, which is fair. The country-specific cut-off values as well as their specificity and sensitivity are comparable to those of the pooled sample. In addition to country-specific cut-offs, general cut-offs can be used tentatively in other similar countries, pending future replication studies. Caution is warranted for using cut-offs for mental distance as the sensitivity and specificity of this subscale is relatively poor. It is concluded that the BAT can be used in organizational surveys for identifying employees at risk for burnout and, in clinical treatment settings, for identifying those with severe burnout, keeping in mind the tentativeness of the present cut-offs.

Diagnosis values of three-dimensional power Doppler ultrasonography in polycystic ovary syndrome and its relationship with serum sex hormone levels

BackgroundThe diagnosis of polycystic ovary syndrome (PCOS) is of great significance for early detection, timely standardized treatment and prevention of long-term complications. This study aimed to observe and measure the intraovarian blood flow distribution and blood flow indexes in patients with PCOS by three-dimensional power Doppler ultrasonography, and to evaluate its diagnostic value for PCOS and its correlation with serum sex hormone levels. Experimental designPatients who were diagnosed with PCOS and met the inclusion and exclusion criteria were recruited as the PCOS group, and 85 age-matched healthy women were arranged in healthy control (HC) group. Participants underwent three-dimensional power Doppler ultrasound. Serum sex hormone was analyzed by chemiluminescence. The diagnostic value was analyzed by relative operating characteristic (ROC) analysis. Spearman correlation coefficient analysis was applied to determine the correlation of vascularization index with serum sex hormone levels. ResultsThe vascularization index (VI), flow index (FI), and vascularization flow index (VFI) values in PCOS patients were all statistically higher than those of HC. The combined index (0.445 * VI + 0.1 * FI + 1.057 * VFI) has a higher diagnostic value of PCOS compared with the three indexes alone. ConclusionThree-dimensional power Doppler ultrasound could observe and quantitatively evaluate the distribution of blood flow in the ovary, which has a high diagnostic value and might provide a reference in clinical use.

Forecasting land use and land cover dynamics using combined remote sensing, machine learning algorithm and local perception in the Agoènyivé Plateau, Togo

Knowledge of land use and land cover (LULC) dynamics helps policymakers set sustainable land management plans. Population growth, migration, and rural exodus have led to spatial expansion in many cities worldwide, especially in coastal urban settings. This research aims at assessing and predicting LULC over the Agoènyivé plateau (AP) in Togo, using remote sensing and Markov Chain along with local perceptions on LULC change, thus considering four scenarios (i.e., Business-as Usual, afforestation, wetland policy, and building policy). LULC was classified based on the maximum likelihood algorithm using Landsat images. The future scenarios maps were produced using the Multilayer Perceptron (MLP) neural networks-Markov chain modelling approach by considering five driver variables considered to affect future development. The historical change analysis was performed for the periods of 1986–2001, 2001–2011, and 2011–2020, while 1986–2020 was used for model validation and change prediction. Image accuracy assessment was performed using the error or confusion matrices, kappa coefficients (Kscores), Relative Operating Characteristics (ROC), and accuracy scores, with an overall accuracy (Kappa coefficient) of 87% (0.81), 88% (0.81), 90% (0.82), and 92% (0.84) for the years 1986, 2001, 2011, and 2020, respectively. The results showed an increase in built-up areas (38.71% land gain), while a decrease was observed in mixed vegetation/savannah (22.69% land loss), croplands/bare surfaces (22.89%), and wetlands (3.36% land loss) over the period of 1986–2020. An increase in built-up areas (7.79% and 19.79% land gain by 2030 and 2050, respectively) and a decrease in mixed vegetation (1.13% and 2.54% land loss by 2030 and 2050, respectively), croplands and bare surfaces (6.58% and 16.98% loss by 2030 and 2050, respectively), and wetlands (0.13% and 0.31% loss by 2030 and 2050, respectively), are expected under the BAU scenario. Contrary to the wetland policy, which induces change only in the wetlands (0.02% and 0.047% land loss by 2030 and 2050, respectively), the afforestation scenario results in a gain in forest/savannah (0.11% and 0.73% land gain by 2030 and 2050 compared to the BAU scenario), but also a decrease in croplands/bare surfaces (0.64% by 2050 compared to the BAU scenario), whereas the building policy scenario results in a long-term increase in croplands/bare surfaces (2.88% by 2050, compared to the BAU), and wetlands (0.027 and 2.88% by 2050) in addition to the mixed vegetation/savannah. LULC dynamics (including future urbanization trends) for the AP under various scenarios is a step ahead of time, highlighting the importance of the implementation of existing laws and policies, but also providing information to deal with the observed inharmonious spatial development of urban centres, and serving as a dataset source for LULC change impact assessments on related resources.