Non-Employment Activity Type Imputation from Points of Interest and Mobility Data at an Individual Level: How Accurate Can We Get?

Human activity type inference has long been the focus for applications ranging from managing transportation demand to monitoring changes in land use patterns. Today’s ever increasing volume of mobility data allow researchers to explore a wide range of methodological approaches for this task. Such data, however, lack reference observations that would allow the validation of methodological approaches. This research proposes a methodological framework for urban activity type inference using a Dirichlet multinomial dynamic Bayesian network with an empirical Bayes prior that can be applied to mobility data of low spatiotemporal resolution. The method was validated using open source Foursquare data under different isochrone configurations. The results provide evidence of the limits of activity detection accuracy using such data as determined by the Area Under Receiving Operating Curve (AUROC), log-loss, and accuracy metrics. At the same time, results demonstrate that a hierarchical modeling framework can provide some flexibility against the challenges related to the nature of unsupervised activity classification using trajectory variables and POIs as input

Massive hematuria due to a congenital renal arteriovenous malformation mimicking a renal pelvis tumor: a case report

<p>Abstract</p> <p>Introduction</p> <p>Congenital renal arteriovenous malformations (AVMs) are very rare benign lesions. They are more common in women and rarely manifest in elderly people. In some cases they present with massive hematuria. Contemporary treatment consists of transcatheter selective arterial embolization which leads to resolution of the hematuria whilst preserving renal parenchyma.</p> <p>Case presentation</p> <p>A 72-year-old man, who was heavy smoker, presented with massive hematuria and flank pain. CT scan revealed a filling defect caused by a soft tissue mass in the renal pelvis, which initially led to the suspicion of a transitional cell carcinoma (TCC) of the upper tract, in view of the patient's age and smoking habits. However a subsequent retrograde study could not depict any filling defect in the renal pelvis. Selective right renal arteriography confirmed the presence of a renal AVM by demonstrating abnormal arterial communication with a vein with early visualization of the venous system. At the same time successful selective transcatheter embolization of the lesion was performed.</p> <p>Conclusion</p> <p>This case highlights the importance of careful diagnostic work-up in the evaluation of upper tract hematuria. In the case presented, a congenital renal AVM proved to be the cause of massive upper tract hematuria and flank pain in spite of the initial evidence indicating the likely diagnosis of a renal pelvis tumor.</p

The Use Of "Big Data" To Evaluate Accessibility Measures For Wheelchair And Mobility Scooter Users: The Case Of London Bus Network

As recognized by the Social Exclusion Unit poor transport access can contribute to the causes of social exclusion. People who require the use of a powered wheelchair or a mobility scooter in order to be able to carry out their daily activities are most likely to experience transport disadvantage, primarily due to architectural barriers present within the network that limit accessibility. Formulation of new policies is of primary importance to increase the transport network accessibility. Policy-making and evaluation are often based mainly on qualitative approaches. Although this can give a good appreciation of the users prospective, it fails to consider the more global impact that new and existing policies can have. The use of big datasets from automated fare collection systems could improve this aspect, allowing for a more quantitative approach to measuring accessibility measures. Furthermore these datasets could benefit from a more disaggregated classification to help categorise travel patterns and behaviour specific to wheelchairs and mobility scooter users

Bayesian Bootstrap Inference for the ROC Surface

Accurate diagnosis of disease is of great importance in clinical practice and medical research. The receiver operating characteristic (ROC) surface is a popular tool for evaluating the discriminatory ability of continuous diagnostic test outcomes when there exist three ordered disease classes (e.g., no disease, mild disease, advanced disease). We propose the Bayesian bootstrap, a fully nonparametric method, for conducting inference about the ROC surface and its functionals, such as the volume under the surface. The proposed method is based on a simple, yet interesting, representation of the ROC surface in terms of placement variables. Results from a simulation study demonstrate the ability of our method to successfully recover the true ROC surface and to produce valid inferences in a variety of complex scenarios. An application to data from the Trail Making Test to assess cognitive impairment in Parkinson's disease patients is provided

Cancer Biomarker Discovery: The Entropic Hallmark

Background: It is a commonly accepted belief that cancer cells modify their transcriptional state during the progression of the disease. We propose that the progression of cancer cells towards malignant phenotypes can be efficiently tracked using high-throughput technologies that follow the gradual changes observed in the gene expression profiles by employing Shannon's mathematical theory of communication. Methods based on Information Theory can then quantify the divergence of cancer cells' transcriptional profiles from those of normally appearing cells of the originating tissues. The relevance of the proposed methods can be evaluated using microarray datasets available in the public domain but the method is in principle applicable to other high-throughput methods. Methodology/Principal Findings: Using melanoma and prostate cancer datasets we illustrate how it is possible to employ Shannon Entropy and the Jensen-Shannon divergence to trace the transcriptional changes progression of the disease. We establish how the variations of these two measures correlate with established biomarkers of cancer progression. The Information Theory measures allow us to identify novel biomarkers for both progressive and relatively more sudden transcriptional changes leading to malignant phenotypes. At the same time, the methodology was able to validate a large number of genes and processes that seem to be implicated in the progression of melanoma and prostate cancer. Conclusions/Significance: We thus present a quantitative guiding rule, a new unifying hallmark of cancer: the cancer cell's transcriptome changes lead to measurable observed transitions of Normalized Shannon Entropy values (as measured by high-throughput technologies). At the same time, tumor cells increment their divergence from the normal tissue profile increasing their disorder via creation of states that we might not directly measure. This unifying hallmark allows, via the the Jensen-Shannon divergence, to identify the arrow of time of the processes from the gene expression profiles, and helps to map the phenotypical and molecular hallmarks of specific cancer subtypes. The deep mathematical basis of the approach allows us to suggest that this principle is, hopefully, of general applicability for other diseases

Aerostat Photogrammetry for Large Scale Hydrological Modeling with Special Focus on Energy Balance Terms

Knowledge of temperature distributions on streams and lakes is considered to be a valuable source of information for a wide range of disciplines such as ecologists, hydrologists and geochemists, as it can provide insights into the dynamics of these water bodies (Westhoff, 2006). Modeling of surface water temperature on the other hand is a complex process requiring coupling of spatial and hydrological data (Boyd, Kasper, 2003). At local scales, all influences of the landscape to the water temperature are considered important, even those which are too difficult to quantify. High resolution terrain data can compensate landscape influences by providing insight in the thermal effects of direct solar radiation (by shadow modeling) and longwave radiation (by modeling of ‘Sky View Coefficient’, SVC). Usually, the demand on high resolution terrain data is translated into increased costs during acquisition. As a result, scientists interested in temperature distribution along streams are forced to make a compromise between costs and more detailed temperature modeling. Photogrammetry employed from an aerostat platform is proposed as an inexpensive technique, able at providing terrain data of centimeter level accuracy and resolution. The applicability of the proposed method was tested on a first order stream located in Maisbich subcatchment in central Luxembourg, where temperature modeling experiments are taking place. A 10 x 10 cm digital elevation model (DEM) was extracted using photogrammetric principles for the upstream and downstream part of the subcatchment. The accuracy of the derived DEM was assessed using ground truth points measured by a total station and points collected using the floating mark principle. The resulted height root mean squared error was found 7cm for the upstream and 6.44 cm for the downstream part having the ground truth points as reference, and 8.34 cm and 23.14 cm having the floating mark points as reference. The DEM served as a basis for information extraction relevant to the temperature distribution model. The influence of shadow in the stream temperature was modeled using hillshade and viewshed algorithms. The SVC was modeled by using upward looking viewshed algorithms. The resulted data were imported in the temperature model. An improvement of 0.0727°C was observed when compared to the temperature output using data from a coarser DEM (5 x 5 meters).Optical and Laser Remote SensingAerospace Engineerin

Assessing transport related social exclusion using a capabilities approach to accessibility framework: A dynamic Bayesian network approach

Accessibility is considered to be a valuable concept that can be used to generate insights on issues related to social exclusion due to limited access to transport options. Recently, researchers have attempted to link accessibility with popular theories of social justice such as Amartya Sen's Capabilities Approach (CA). Such studies have set the theoretical foundations on the way accessibility can be expressed through the CA, however, attempts to operationalise this approach remain fragmented and predominantly qualitative in nature. In this study, a novel framework of expressing accessibility at the level of an individual is proposed, based on the basic elements of the CA. In particular, dynamic Bayesian networks are used to express the causal relationship between capabilities, functionings, personal and environmental characteristics. This is done by introducing informative Dirichlet prior distributions constructed using data from traditional mobility surveys, modelling the transition probabilities with data related to place based characteristics and defining an observation model from unlabelled mobility data and places of interest (POI). We demonstrate the usefulness of the proposed framework by assessing the equality levels and their link to transport related social exclusion of different population groups in London, using unlabelled, service provider generated mobility data