Robust estimation of bacterial cell count from optical density

Optical density (OD) is widely used to estimate the density of cells in liquid culture, but cannot be compared between instruments without a standardized calibration protocol and is challenging to relate to actual cell count. We address this with an interlaboratory study comparing three simple, low-cost, and highly accessible OD calibration protocols across 244 laboratories, applied to eight strains of constitutive GFP-expressing E. coli. Based on our results, we recommend calibrating OD to estimated cell count using serial dilution of silica microspheres, which produces highly precise calibration (95.5% of residuals <1.2-fold), is easily assessed for quality control, also assesses instrument effective linear range, and can be combined with fluorescence calibration to obtain units of Molecules of Equivalent Fluorescein (MEFL) per cell, allowing direct comparison and data fusion with flow cytometry measurements: in our study, fluorescence per cell measurements showed only a 1.07-fold mean difference between plate reader and flow cytometry data

Home Recovery In New Orleans After Hurricane Katrina

Hurricane Katrina, the costliest hurricane in U.S. history, hit the New Orleans metropolitan area in 2005. Many studies have examined differences in both damage and recovery with respect to more socially vulnerable groups, and have identified lack of access to financial assistance as a key explanatory factor. But studies to date have focused only on differences at the community level and have concentrated exclusively on Orleans Parish. This dissertation investigates recovery prevalence and speed at the individual homeowner level and to broadens to the New Orleans metropolitan area. I focus on three research questions. First, among socially vulnerable demographic groups identified in the literature (including Black, Hispanic, female heads of household, people ≥ age 65), which were most likely to suffer housing damage at the homeowner level? Second, among those suffering the most housing damage, how did their access to financial assistance differ from other homeowners? Finally, what role did these differences play in relative prevalence and speed of recovery for those suffering the most housing damage? Data from the 2004 and 2009 American Housing Surveys in the New Orleans Metropolitan Statistical Area are used to model home damage by a series of nested logistic regressions, and to model home recovery by both logistic and Cox regressions. Analyses suggest the following. First, among the socially vulnerable groups, Black homeowners were most vulnerable to housing damage. Vulnerability was partially due to their older homes, which was strongly associated with damage from Katrina. Second, Black homeowners were less likely than others to receive private financial assistance and more likely to receive public financial assistance. They were also more likely to perceive financial gaps impeding their recovery process. Third, private financial assistance positively contributed to prevalence and speed of recovery whereas reliance on public financial assistance slowed speed of recovery. While prevalence of home recovery was similar between Black and non-Blacks, Black homeowners took much longer to start and complete recovery than non-Black homeowners. Delays were partially due to Blacks’ relative lower incomes, higher number of replacements/additions, lack of private financial assistance, and financial gaps they perceived after the disaster

SNS user classification and its application to obscure POI discovery

Technologies are increasingly taking advantage of the explosion of social media (e.g., web searches, ad targeting, personalized geo-social recommendations, urban computing). Estimating the characteristics of users, or user profiling, is one of the key challenges for such technologies. This paper focuses on the important problem of automatically estimating social networking service (SNS) user authority with a given city, which can significantly improve location-based services and systems. The “authority” in our work measures a user’s familiarity with a particular city. By analyzing users’ social, temporal, and spatial behavior, we respectively propose and compare three models for user authority: a social-network-driven model, time-driven model, and location-driven model. Furthermore, we discuss the integration of these three models. Finally, by using these user-profiling models, we propose a new application for geo-social recommendations. In contrast to related studies, which focus on popular and famous points of interests (POIs), our models help discover obscure POIs that are not well known. Experimental evaluations and analysis on a real dataset collected from three cities demonstrate the performance of the proposed user-profiling models. To verify the effect of discovering obscure POIs, the proposed application was implemented to discover and explore obscure POIs in Kyoto, Japan

Location familiarity based flickr photographer classification for POI mining

In this paper, we propose and compare three ways of modeling photographers' location familiarity: a social network driven model, a time driven model and a location driven model. Then, the integration of the three models is further discussed. Experimental evaluations and analysis on a real data set consisting of 14, 112 images collected from three cities well demonstrate the performance of the proposed classification methods. Many applications could benefit from information about the location familiarity, such as personalized geo-social recommendation, epidemic dispersion, urban computing, and so on

Discovering obscure sightseeing spots by analysis of geo-tagged social images

In contrast to conventional studies of discovering hot spots, by analyzing geo-tagged images on Flickr, we introduce novel methods to discover obscure sightseeing spots that are less well-known while still worth visiting. To this end, we face two new challenges that the classical authority analysis based methods do not encounter: how to discover and rank spots on the basis of 1) popularity (obscurity level) and 2) scenery quality. For the first challenge, we estimate the obscurity level of a spot in accordance with the visiting asymmetry between photographers who are familiar with a target city and those who are not. For the second challenge, the behavior of both viewers who browsed the images and photographers are analyzed per each spot. We also develop an application system to help users to explore sightseeing spots with different geographical granularities. Experimental evaluations and analysis on a real dataset well demonstrate the effectiveness of the proposed methods

Sightseeing value estimation by analyzing geosocial images

Recommending points of interests (POIs) is drawing more attention to meet the growing demand for tours. A POI's quality (sightseeing value) estimation is one of the important challenges. In contrast to conventional studies of ranking POIs based on user behavior analysis, in this paper, we propose methods of quality estimation by analyzing geosocial images. Our approach estimates the sightseeing value from two aspects: (1) nature value, and (2) culture value. For the nature value, we extract image features that are related to favorable human perception to verify whether a POI would meet tourists' psychological requirements. Three criteria, coherence, imageability and visual-scale, are defined accordingly. For the culture value, we recognize the cultural elements (i.e., architectures) included in a POI. In the experiments, by applying our methods on the real discovered POIs, we present the effect of our approach