Recent studies have suggested potential roles of the microbiome in cervicovaginal diseases. However, there has been no report on the cervical microbiome in cervical intraepithelial neoplasia (CIN). We aimed to identify the cervical microbiota of Korean women and assess the association between the cervical microbiota and CIN, and to determine the combined effect of the microbiota and human papillomavirus (HPV) on the risk of CIN. The cervical microbiota of 70 women with CIN and 50 control women was analysed using pyrosequencing based on the 16S rRNA gene. The associations between specific microbial patterns or abundance of specific microbiota and CIN risk were assessed using multivariate logistic regression, and the relative excess risk due to interaction (RERI) and the synergy index (S) were calculated. The phyla Firmicutes, Actinobacteria, Bacteroidetes, Proteobacteria, Tenericutes, Fusobacteria and TM7 were predominant in the microbiota and four distinct community types were observed in all women. A high score of the pattern characterized by predominance of Atopobium vaginae, Gardnerella vaginalis and Lactobacillus iners with a minority of Lactobacillus crispatus had a higher CIN risk (OR 5.80, 95% CI 1.73-19.4) and abundance of A. vaginae had a higher CIN risk (OR 6.63, 95% CI 1.61-27.2). The synergistic effect of a high score of this microbial pattern and oncogenic HPV was observed (OR 34.1, 95% CI 4.95-284.5; RERI/S, 15.9/1.93). A predominance of A. vaginae, G. vaginalis and L. iners with a concomitant paucity of L. crispatus in the cervical microbiota was associated with CIN risk, suggesting that bacterial dysbiosis and its combination with oncogenic HPV may be a risk factor for cervical neoplasia.

How do you place a value on a perspective? Well, that depends on what you're seeking to accomplish. During this Pecha Kucha I journey of our current paradigm of Value to explore the role of the ethnographer in mediating business interests and human + planetary wellness. Outside of the metropolitan areas where can't afford to use an app to have someone come do their laundry, there lies an entire universe of perspectives that often go ignored, undervalued. What are the worldly consequences of excluding these perspectives when conducting business ethnography? Photo by Jen Byers Taylor Ferrari, is an applied anthropologist and systems thinker who has conducted UX Research for companies ranging from early stage startups, to Fortune 500. Deeply interested in the relationship between Structure and Agency, Taylor seeks to illuminate the ways in which organizations or entities impact humanity, and likewise how humanity feeds the existence of organizations.

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This paper presents landslide-susceptibility mapping using an adaptive neuro-fuzzy inference system (ANFIS) using a geographic information system (GIS) environment. In the first stage, landslide locations from the study area were identified by interpreting aerial photographs and supported by an extensive field survey. In the second stage, landslide-related conditioning factors such as altitude, slope angle, plan curvature, distance to drainage, distance to road, soil texture and stream power index (SPI) were extracted from the topographic and soil maps. Then, landslide-susceptible areas were analyzed by the ANFIS approach and mapped using landslide-conditioning factors. In particular, various membership functions (MFs) were applied for the landslide-susceptibility mapping and their results were compared with the field-verified landslide locations. Additionally, the receiver operating characteristics (ROC) curve for all landslide susceptibility maps were drawn and the areas under curve values were calculated. The ROC curve technique is based on the plotting of model sensitivitytrue positive fraction values calculated for different threshold values, versus model specificitytrue negative fraction values, on a graph. Landslide test locations that were not used during the ANFIS modeling purpose were used to validate the landslide susceptibility maps. The validation results revealed that the susceptibility maps constructed by the ANFIS predictive models using triangular, trapezoidal, generalized bell and polynomial MFs produced reasonable results (84.39%), which can be used for preliminary land-use planning. Finally, the authors concluded that ANFIS is a very useful and an effective tool in regional landslide susceptibility assessment.

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