Data Augmentation through Pseudolabels in Automatic Region Based Coronary Artery Segmentation for Disease Diagnosis

Coronary Artery Diseases(CADs) though preventable are one of the leading causes of death and disability. Diagnosis of these diseases is often difficult and resource intensive. Segmentation of arteries in angiographic images has evolved as a tool for assistance, helping clinicians in making accurate diagnosis. However, due to the limited amount of data and the difficulty in curating a dataset, the task of segmentation has proven challenging. In this study, we introduce the idea of using pseudolabels as a data augmentation technique to improve the performance of the baseline Yolo model. This method increases the F1 score of the baseline by 9% in the validation dataset and by 3% in the test dataset.Comment: arXiv admin note: text overlap with arXiv:2310.0474

Estimation of Runoff and Sediment Yield in Response to Temporal Land Cover Change in Kentucky, USA

Land cover change is prevalent in the eastern Kentucky Appalachian region, mainly due to increased surface mining activities. This study explored the potential change in land cover and its relationship with stream discharge and sediment yield in a watershed of the Cumberland River near Harlan, Kentucky, between 2001 and 2016, using the Soil and Water Assessment Tool (SWAT). Two land cover scenarios for the years 2001 and 2016 were used separately to simulate the surface runoff and sediment yield at the outlet of the Cumberland River near Harlan. Land cover datasets from the National Land Cover Database (NLCD) were used to reclassify the land cover type into the following classes: water, developed, forest, barren, shrubland, and pasture/grassland. Evaluation of the relationship between the land cover change on discharge and sediment was performed by comparing the average annual basin values of streamflow and sediment from each of the land cover scenarios. The SWAT model output was evaluated based on several statistical parameters, including the Nash–Sutcliffe efficiency coefficient (NSE), RMSE-observations standard deviation ratio (RSR), percent bias (PBIAS), and the coefficient of determination (R²). Moreover, P-factor and R-factor indices were used to measure prediction uncertainty. The model showed an acceptable range of agreement for both calibration and validation between observed and simulated values. The temporal land cover change showed a decrease in forest area by 2.42% and an increase in developed, barren, shrubland, and grassland by 0.11%, 0.34%, 0.53%, and 1.44%, respectively. The discharge increased from 92.34 mm/year to 104.7 mm/year, and sediment increased from 0.83 t/ha to 1.63 t/ha from 2001 to 2016, respectively. Based on results from the model, this study concluded that the conversion of forest land into other land types could contribute to increased surface runoff and sediment transport detached from the soil along with runoff water. The research provides a robust approach to evaluating the effect of temporal land cover change on Appalachian streams and rivers. Such information can be useful for designing land management practices to conserve water and control soil erosion in the Appalachian region of eastern Kentucky

Assessing the Effect of Land-Use and Land-Cover Changes on Discharge and Sediment Yield in a Rural Coal-Mine Dominated Watershed in Kentucky, USA

The Appalachian Mountain region of eastern Kentucky is unique and contains high proportions of forestland along with coal and natural gas depositaries. Landscape changes due to extreme mining activities can eventually threaten the downstream ecosystems, including soil and water quality, resulting in excessive runoff and sedimentation. The purpose of this study is to assess the impacts of land-use and land-cover (LULC) changes in streamflow and sediment yield in Yellow Creek Watershed, Kentucky, USA, between 1992 and 2016. LULC, digital elevation model, soil, and weather data were inputted into the Soil and Water Assessment Tool (SWAT) to simulate discharge and sediment yield. The model output was evaluated on several statistical parameters, such as the Nash-Sutcliffe efficiency coefficient (NSE), RMSE-observations standard deviation ratio (RSR), percent bias (PBIAS), and the coefficient of determination (R2). In addition, two indices, P-factor and R-factor, were used to measure the prediction uncertainty. The calibrated model showed an increase in surface runoff and sediment yield due to changes in LULC in the Yellow Creek Watershed. The results provided important insights for studying water management strategies to make more informed land management decisions and adaptive practices

Assessing the Effect of Land-Use and Land-Cover Changes on Discharge and Sediment Yield in a Rural Coal-Mine Dominated Watershed in Kentucky, USA

The Appalachian Mountain region of eastern Kentucky is unique and contains high proportions of forestland along with coal and natural gas depositaries. Landscape changes due to extreme mining activities can eventually threaten the downstream ecosystems, including soil and water quality, resulting in excessive runoff and sedimentation. The purpose of this study is to assess the impacts of land-use and land-cover (LULC) changes in streamflow and sediment yield in Yellow Creek Watershed, Kentucky, USA, between 1992 and 2016. LULC, digital elevation model, soil, and weather data were inputted into the Soil and Water Assessment Tool (SWAT) to simulate discharge and sediment yield. The model output was evaluated on several statistical parameters, such as the Nash-Sutcliffe efficiency coefficient (NSE), RMSE-observations standard deviation ratio (RSR), percent bias (PBIAS), and the coefficient of determination (R2). In addition, two indices, P-factor and R-factor, were used to measure the prediction uncertainty. The calibrated model showed an increase in surface runoff and sediment yield due to changes in LULC in the Yellow Creek Watershed. The results provided important insights for studying water management strategies to make more informed land management decisions and adaptive practices

Variations in demographic characteristics, comorbid conditions, and clinical outcomes among hospitalized individuals with encephalitis with comorbid psychotic disorders.

Variations in demographic characteristics, comorbid conditions, and clinical outcomes among hospitalized individuals with encephalitis with comorbid psychotic disorders.</p

Predictive factors indicating comorbid psychotic disorders among hospitalized individuals with encephalitis.

Predictive factors indicating comorbid psychotic disorders among hospitalized individuals with encephalitis.</p