Gene expression in normal Urothelium depends on location within the bladder:A possible link to bladder carcinogenesis

Objectives: Clinical studies have shown that more than 70% of primary bladder tumours arise in the area around the ureteric orifice. In this study a genomic approach was taken to explore the molecular mechanisms that may influence this phenomenon.

Methods: RNA was isolated from each individual normal ureteric orifice and the dome biopsy from 33 male patients. Equal amounts of the pooled ureteric orifice and dome mRNAs were labelled with Cy3 and Cy5, respectively before hybridising to the gene chip (UniGEM 2.0, Incyte Genomics Inc., Wilmington, Delaware, USA). Results: Significant changes (more than a twofold difference) in gene expression were observed in 3.1% (312) of the 10,176 gene array: 211 genes upregulated and 101 downregulated. Analysis of Cdc25B, TK1, PKM, and PDGFra with RT-PCR supported the reliability of the microarray result. Seladin-1 was the most upregulated gene in the ureteric orifice: 8.3-fold on the microarray and 11.4-fold by real time PCR.

Conclusions: Overall, this study suggests significant altered gene expression between these two anatomically distinct areas of the normal human bladder. Of particular note is Seladin-1, whose significance in cancer is yet to be clarified. Further studies of the genes discovered by this work will help clarify which of these differences influence primary bladder carcinogenesis. (c) 2006 European Association of Urology. Published by Elsevier B.V. All rights reserved.

Location-Awareness for Radios and Networks: Part I

The papers in this special issue focus on the topic of location awareness for radio and networks. Localization-awareness using radio signals stands to revolutionize the fields of navigation and communication engineering. It can be utilized to great effect in the next generation of cellular networks, mining applications, health-care monitoring, transportation and intelligent highways, multi-robot applications, first responders operations, military applications, factory automation, building and environmental controls, cognitive wireless networks, commercial and social network applications, and smart spaces. A multitude of technologies can be used in location-aware radios and networks, including GNSS, RFID, cellular, UWB, WLAN, Bluetooth, cooperative localization, indoor GPS, device-free localization, IR, Radar, and UHF. The performances of these technologies are measured by their accuracy, precision, complexity, robustness, scalability, and cost. Given the many application scenarios across different disciplines, there is a clear need for a broad, up-to-date and cogent treatment of radio-based location awareness. This special issue aims to provide a comprehensive overview of the state-of-the-art in technology, regulation, and theory. It also presents a holistic view of research challenges and opportunities in the emerging areas of localization.

A survey of location inference techniques on Twitter

The increasing popularity of the social networking service, Twitter, has made it more involved in day-to-day communications, strengthening social relationships and information dissemination. Conversations on Twitter are now being explored as indicators within early warning systems to alert of imminent natural disasters such earthquakes and aid prompt emergency responses to crime. Producers are privileged to have limitless access to market perception from consumer comments on social media and microblogs. Targeted advertising can be made more effective based on user profile information such as demography, interests and location. While these applications have proven beneficial, the ability to effectively infer the location of Twitter users has even more immense value. However, accurately identifying where a message originated from or author’s location remains a challenge thus essentially driving research in that regard. In this paper, we survey a range of techniques applied to infer the location of Twitter users from inception to state-of-the-art. We find significant improvements over time in the granularity levels and better accuracy with results driven by refinements to algorithms and inclusion of more spatial features.

Supervisor Continuity or Co-Location: Which Matters in Residency Education? Findings From a Qualitative Study of Remote Supervisor Family Physicians in Australia and Canada

Purpose: Changes to health care systems andworking hours have fragmentedresidents’ clinical experiences withpotentially negative effects ontheir development as professionals.Investigation of off-site supervision,which has been implemented in isolatedrural practice, could reveal importantbut less overt components of residencyeducation. 

Method: Insights from sociocultural learningtheory and work-based learning provideda theoretical framework. In 2011–2012,16 family physicians in Australia andCanada were asked in-depth how theyremotely supervised residents’ workand learning, and for their reflectionson this experience. The verbatiminterview transcripts and researchers’memos formed the data set. Templateanalysis produced a description andinterpretation of remote supervision. 

Results: Thirteen Australian family physiciansfrom five states and one territory, andthree Canadians from one province,participated. The main themes werehow remoteness changed the dynamicsof care and supervision; the importanceof ongoing, holistic, nonhierarchical,supportive supervisory relationships; andthat residents learned “clinical courage”through responsibility for patients’ careover time. Distance required supervisorsto articulate and pass on their expertiseto residents but made monitoringdifficult. Supervisory continuityencouraged residents to build on pastexperiences and confront deficiencies. 

Conclusions: Remote supervision enabled residents todevelop as clinicians and professionals.This questions the supremacy of co-locationas an organizing principle forresidency education. Future specialists maybenefit from programs that give themongoing and increasing responsibilityfor a group of patients and supportive.

Inferring and Exploiting Categories for Next Location Prediction

Predicting the next location of a user based on their previous visiting pattern is one of the primary tasks over data from location based social networks (LBSNs) such as Foursquare. Many different aspects of these so-called “check-in” profiles of a user have been made use of in this task, including spatial and temporal information of check-ins as well as the social network information of the user. Building more sophisticated prediction models by enriching these check-in data by combining them with information from other sources is challenging due to the limited data that these LBSNs expose due to privacy concerns. In this paper, we propose a framework to use the location data from LBSNs, combine it with the data from maps for associating a set of venue categories with these locations. For example, if the user is found to be checking in at a mall that has cafes, cinemas and restaurants according to the map, all these information is associated. This category information is then leveraged to predict the next checkin location by the user. Our experiments with publicly available check-in dataset show that this approach improves on the state-of-the-art methods for location prediction.