OBJECTIVE: Lung transplant (LTx) saves lives in cystic fibrosis (CF). However, many potential candidates express uncertainty about LTx and die before receiving this treatment. CF guidelines recommend LTx education and clinical discussions well before the need for LTx arises, but limited patient resources exist. MATERIALS AND METHODS: We engaged people with CF and CF physicians in human-centered design of "Take On Transplant" (TOT), a web-based education tool to prepare patients for LTx discussions. Across 3 phases, needs assessment, design groups, and iterative user testing of TOT, we refined TOT from wireframe prototypes, to an interactive website, to a fully functional intervention ready for clinical trials. RESULTS: Fifty-five people with CF and 105 physicians identified information needs to prepare for LTx discussions. Design groups (n = 14 participants) then established core requirements: didactic education ("Resource Library"), patient narratives ("CF Stories"), frequently asked questions ("FAQ"), and self-assessment to tailor content ("My CF Stage"). Iterative usability testing (n = 39) optimized the design of CF Stories and prototype layout. We then developed the TOT website and demonstrated feasibility and preliminary efficacy of use through 2-week field testing (n = 9). DISCUSSION: Our human-centered design process provided guidance for educational tools to serve the evolving needs of potential LTx candidates. Our findings support the process of patient deliberation as a foundation for shared decision-making in CF, and inform educational tools that could potentially translate beyond LTx. CONCLUSION: TOT fills a critical gap in preparing people with CF for shared decision-making about LTx and may serve as a model for educational tools for other preference-sensitive decisions.

In this paper, we present our findings on the experiences of West Bengal Community Health Workers (CHWs) in transitioning from paper to tablet- and mobile-based data collection. Through qualitative interviews, usability testing and timed observations, we found that efficiency and quality of data collected were comparable between the use of tablet devices and traditional paper methods, but data collection performed on smaller mobile phone interfaces was less efficient compared to paper. There was no significant difference in the quality of data collected across all three modes. In terms of work practices, we found that while initial interactions with CHWs suggested positive feelings about switching to digital devices, in their actual practices they retained and preferred the use of paper, and had workarounds to circumvent the digital data collection process. While there were foreseeable challenges around individual user experience, such as device familiarity, and application interface flexibility, the more compelling challenge in transitioning CHWs to digital data collection was organizational. The agency of CHWs within organizations, the levels of training with both data practices and devices themselves, and the sense of comfort that the data collectors felt with the overall project emerge as important factors of attention for implementers of new data management practices.

High resolution remote sensing is a valuable tool for quantifying the distribution and density of trees with applications ranging from forest inventory, mapping urban parklands to understanding impacts on soil nutrient and carbon dynamics in farming land. The present study aims to compare the accuracy of different remote sensing techniques for delineating the tree cover in 50 cm resolution WorldView2 imagery of farmland. An image of farmland comprising pastures, remnant vegetation and woodland was initially classified into six classes, namely tree cover, bare soil, rock outcrop, natural pasture, degraded pasture and water body using different techniques. Pixel based classification based on all four available wavebands, were tested and an overall classification accuracy of 96.8% and 72.9 % were achieved for supervised and unsupervised techniques. Object based segmentation and subsequent classification yielded an improved overall classification accuracy of 98.3%. Addition of a fifth NDVI layer to the available wavebands did improve the accuracy but not significantly (98.1%, approx 1.3%). In addition to the improvements in overall classification accuracy, a visual inspections of results from the different methods indicated the object based method to yield a more `realistic' result, avoiding the `salt and pepper' effects apparent in the pixel-based methods. Overall, object based classification hence is considered more suitable for tree cover extraction from high resolution images.

As cameraphones become the dominant platform for consumer multimedia capture worldwide, multimedia researchers are faced both with the challenge of how to help users manage the billions of photographs they are collectively producing and the opportunity to leverage cameraphones' ability to automatically capture temporal, spatial, and social contextual metadata to help manage consumer multimedia content. In our Mobile Media Metadata 2 (MMM2) prototype, we apply collaborative filtering techniques to automatically gathered contextual metadata to infer the likely sharing recipients for photos captured on cameraphones. We show that while current cameraphone sharing interfaces are fraught with difficulty, it is possible to use a context-aware approach to make the sharing of cameraphone photos simpler and more satisfying for users. Based on our analysis of the relative contributions of different cameraphone sensors to predicting the likely recipients for photos, we discover for our user population that the temporal context of photo capture proved highly predictive of photo sharing behavior.

The number of digital photographs is growing beyond the abilities of individuals to easily manage and understand their own photo collections. Photo LOI (Level of Interest) is a technique that filters, aggregates, and visualizes photographs taken by multiple users who shared temporal, spatial, and/or social context at the point of photo capture. Photo LOI enables groups of photographers to see and manipulate visualizations of their photographic activities over time and social space in order to help cluster and select photos, and enables researchers to study contextual patterns in the phototaking habits of different users and groups of users. In this paper we give a brief overview of Photo LOI's features and describe some of its applications.