The composition of the gut microbiota in patients with anorexia nervosa (AN), and the ability of this microbial community to influence the host, remains uncertain. To achieve a broader understanding of the role of the intestinal microbiota in patients with AN, we collected fecal samples before and following clinical treatment at two geographically distinct eating disorder units (Center of Excellence for Eating Disorders [UNC-CH] and ACUTE Center for Eating Disorders [Denver Health]). Gut microbiotas were characterized in patients with AN, before and after inpatient treatment, and in non-eating disorder (non-ED) controls using shotgun metagenomic sequencing. The impact of inpatient treatment on the AN gut microbiota was remarkably consistent between eating disorder units. Although weight in patients with AN showed improvements, AN microbiotas post-treatment remained distinct from non-ED controls. Additionally, AN gut microbiotas prior to treatment exhibited more fermentation pathways and a lower ability to degrade carbohydrates than non-ED controls. As the intestinal microbiota can influence nutrient metabolism, our data highlight the complex microbial communities in patients with AN as an element needing further attention post inpatient treatment. Additionally, this study defines the effects of renourishment on the AN gut microbiota and serves as a platform to develop precision nutrition approaches to potentially mitigate impediments to recovery.

Electric discharge machining drill (EDM-drill) is an efficient process for the fabrication of micro-diameter deep metal hole. As there is non-physical contact between tool (electrode) and workpiece, EDM-drill is widely used to machine the hard machining materials such as high strength steel, cemented carbide, titanium alloys. The electro-thermal energy forces the electrode to wear out together with the workpiece to be machined. The electrode wear occurs inside of a machining hole. and It causes hard to monitor the machining state, which leads the productivity and the quality to decrease. Thus, this study presents a methodology to estimated the electrode wear amount while two coefficients (scale factor and shape factor) of the logarithmic regression model are evaluated from the experiment result. To increase the accuracy of estimation model, the linear transformation method is adopted using the differences of initial electrode wear differences. The estimation model is verified through experiment. The experimental result shows that within minute error, the estimation model is able to predict accurately.