Nonrandom selection in one-sample Mendelian Randomization (MR) results in biased estimates and inflated type I error rates only when the selection effects are sufficiently large.In two-sample MR, the different selection mechanisms in two samples may more seriously affect the causal effect estimation.Firstly, we propose sufficient conditions for cau
Integration of UH SUH, HEC-RAS, and GIS in Flood Mitigation with Flood Forecasting and Early Warning System for Gilireng Watershed, Indonesia
A flood forecasting and early warning system is critical for rivers that have a large flood potential, one of which is Guide Rod Plug the Gilireng watershed, which floods every year and causes many losses in Wajo Regency, Indonesia.This research also introduces an integration model between UH SUH and HEC-RAS in flood impact analysis, as a reference
Effect of pre-etching on sealing ability of two current self-etching adhesives
Background: We evaluated the effect of phosphoric INCENSE LILY acid etching on microleakage of two current self-etching adhesives on enamel margins in comparison to a conventional total- etch system. Methods: Sixty buccal class V cavities were made at the cemento-enamel junction with beveled enamel margins of extracted human premolar teeth and
Is the renal kallikrein-kinin system a factor that modulates hypercalciuria?
Renal tubular calcium reabsorption is one of the principal Fridge Start Device factors that determine serum calcium concentration and calcium excretion.Calcium excretion is regulated by the distal convoluted tubule and connecting tubule, where the epithelial calcium channel TRPV5 can be found, which limits the rate of transcellular calcium transpor
Visual Perceptual Quality Assessment Based on Blind Machine Learning Techniques
This paper presents the construction of a new objective method for estimation of visual perceiving quality.The proposal provides an assessment of image quality without the need for a reference image or a specific distortion assumption.Two main processes have been used to build our models: The first one uses deep learning with a convolutional neural