Dynamic predicting by landmarking as an alternative for multi-state modeling: an application to acute lymphoid leukemia data

This paper considers the problem of obtaining a dynamic prediction for 5-year failure free survival after bone marrow transplantation in ALL patients using data from the EBMT, the European Group for Blood and Marrow Transplantation. The paper compares the new landmark methodology as developed by the first author and the established multi-state modeling as described in a recent Tutorial in Biostatistics in Statistics in Medicine by the second author and colleagues. As expected the two approaches give similar results. The landmark methodology does not need complex modeling and leads to easy prediction rules. On the other hand, it does not give the insight in the biological processes as obtained for the multi-state model

A Recurrent Neural Network Survival Model: Predicting Web User Return Time

The size of a website's active user base directly affects its value. Thus, it is important to monitor and influence a user's likelihood to return to a site. Essential to this is predicting when a user will return. Current state of the art approaches to solve this problem come in two flavors: (1) Recurrent Neural Network (RNN) based solutions and (2) survival analysis methods. We observe that both techniques are severely limited when applied to this problem. Survival models can only incorporate aggregate representations of users instead of automatically learning a representation directly from a raw time series of user actions. RNNs can automatically learn features, but can not be directly trained with examples of non-returning users who have no target value for their return time. We develop a novel RNN survival model that removes the limitations of the state of the art methods. We demonstrate that this model can successfully be applied to return time prediction on a large e-commerce dataset with a superior ability to discriminate between returning and non-returning users than either method applied in isolation.Comment: Accepted into ECML PKDD 2018; 8 figures and 1 tabl

Unsupervised Deformable Image Registration Using Cycle-Consistent CNN

Medical image registration is one of the key processing steps for biomedical image analysis such as cancer diagnosis. Recently, deep learning based supervised and unsupervised image registration methods have been extensively studied due to its excellent performance in spite of ultra-fast computational time compared to the classical approaches. In this paper, we present a novel unsupervised medical image registration method that trains deep neural network for deformable registration of 3D volumes using a cycle-consistency. Thanks to the cycle consistency, the proposed deep neural networks can take diverse pair of image data with severe deformation for accurate registration. Experimental results using multiphase liver CT images demonstrate that our method provides very precise 3D image registration within a few seconds, resulting in more accurate cancer size estimation.Comment: accepted for MICCAI 201

Entropy bounds in terms of the w parameter

In a pair of recent articles [PRL 105 (2010) 041302 - arXiv:1005.1132; JHEP 1103 (2011) 056 - arXiv:1012.2867] two of the current authors have developed an entropy bound for equilibrium uncollapsed matter using only classical general relativity, basic thermodynamics, and the Unruh effect. An odd feature of that bound, S <= A/2, was that the proportionality constant, 1/2, was weaker than that expected from black hole thermodynamics, 1/4. In the current article we strengthen the previous results by obtaining a bound involving the (suitably averaged) w parameter. Simple causality arguments restrict this averaged parameter to be <= 1. When equality holds, the entropy bound saturates at the value expected based on black hole thermodynamics. We also add some clarifying comments regarding the (net) positivity of the chemical potential. Overall, we find that even in the absence of any black hole region, we can nevertheless get arbitrarily close to the Bekenstein entropy.Comment: V1: 14 pages. V2: One reference added. V3: This version accepted for publication in JHE

A mixture model for the joint analysis of latent developmental trajectories and survival

A general joint modeling framework is proposed that includes a parametric stratified survival component for continuous time survival data, and a mixture multilevel item response component to model latent developmental trajectories given mixed discrete response data. The joint model is illustrated in a real data setting, where the utility of longitudinally measured cognitive function as a predictor for survival is investigated in a group of elderly persons. The object is partly to determine whether cognitive impairment is accompanied by a higher mortality rate. Time-dependent cognitive function is measured using the generalized partial credit model given occasion-specific mini-mental state examination response data. A parametric survival model is applied for the survival information, and cognitive function as a continuous latent variable is included as a time-dependent explanatory variable along with other explanatory information. A mixture model is defined, which incorporates the latent developmental trajectory and the survival component. The mixture model captures the heterogeneity in the developmental trajectories that could not be fully explained by the multilevel item response model and other explanatory variables. A Bayesian modeling approach is pursued, where a Markov chain Monte Carlo algorithm is developed for simultaneous estimation of the joint model parameters. Practical issues as model building and assessment are addressed using the DIC and various posterior predictive tests

Ageing memory and glassiness of a driven vortex system

Many systems in nature, glasses, interfaces and fractures being some examples, cannot equilibrate with their environment, which gives rise to novel and surprising behaviour such as memory effects, ageing and nonlinear dynamics. Unlike their equilibrated counterparts, the dynamics of out-of- equilibrium systems is generally too complex to be captured by simple macroscopic laws. Here we investigate a system that straddles the boundary between glass and crystal: a Bragg glass formed by vortices in a superconductor. We find that the response to an applied force evolves according to a stretched exponential, with the exponent reflecting the deviation from equilibrium. After the force is removed, the system ages with time and its subsequent response time scales linearly with its age (simple ageing), meaning that older systems are slower than younger ones. We show that simple ageing can occur naturally in the presence of sufficient quenched disorder. Moreover, the hierarchical distribution of timescales, arising when chunks of loose vortices cannot move before trapped ones become dislodged, leads to a stretched-exponential response.Comment: 16 pages, 5 figure

Diminished temperature and vegetation seasonality over northern high latitudes

Global temperature is increasing, especially over northern lands (>50° N), owing to positive feedbacks1. As this increase is most pronounced in winter, temperature seasonality (ST)—conventionally defined as the difference between summer and winter temperatures—is diminishing over time2, a phenomenon that is analogous to its equatorward decline at an annual scale. The initiation, termination and performance of vegetation photosynthetic activity are tied to threshold temperatures3. Trends in the timing of these thresholds and cumulative temperatures above them may alter vegetation productivity, or modify vegetation seasonality (SV), over time. The relationship between ST and SV is critically examined here with newly improved ground and satellite data sets. The observed diminishment of ST and SV is equivalent to 4° and 7° (5° and 6°) latitudinal shift equatorward during the past 30 years in the Arctic (boreal) region. Analysis of simulations from 17 state-of-the-art climate models4 indicates an additional STdiminishment equivalent to a 20° equatorward shift could occur this century. How SV will change in response to such large projected ST declines and the impact this will have on ecosystem services5 are not well understood. Hence the need for continued monitoring6 of northern lands as their seasonal temperature profiles evolve to resemble thosefurther south.Lopullinen vertaisarvioitu käsikirjoitu

Biomechanical factors and physical examination findings in osteoarthritis of the knee: associations with tissue abnormalities assessed by conventional radiography and high resolution 3.0 Tesla magnetic resonance imaging

INTRODUCTION: We aimed to explore the associations between knee osteoarthritis (OA)-related tissue abnormalities assessed by conventional radiography (CR) and by high-resolution 3.0 Tesla magnetic resonance imaging (MRI), as well as biomechanical factors and findings from physical examination in patients with knee OA. METHODS: This was an explorative cross-sectional study of 105 patients with knee OA. Index knees were imaged using CR and MRI. Multiple features from CR and MRI (cartilage, osteophytes, bone marrow lesions, effusion and synovitis) were related to biomechanical factors (quadriceps and hamstrings muscle strength, proprioceptive accuracy and varus-valgus laxity) and physical examination findings (bony tenderness, crepitus, bony enlargement and palpable warmth), using multivariable regression analyses. RESULTS: Quadriceps weakness was associated with cartilage integrity, effusion, synovitis (all detected by MRI) and CR-detected joint space narrowing. Knee joint laxity was associated with MRI-detected cartilage integrity, CR-detected joint space narrowing and osteophyte formation. Multiple tissue abnormalities including cartilage integrity, osteophytes and effusion, but only those detected by MRI, were found to be associated with physical examination findings such as crepitus. CONCLUSION: We observed clinically relevant findings, including a significant association between quadriceps weakness and both effusion and synovitis, detected by MRI. Inflammation was detected in over one-third of the participants, emphasizing the inflammatory component of OA and a possible important role for anti-inflammatory therapies in knee OA. In general, OA-related tissue abnormalities of the knee, even those detected by MRI, were found to be discordant with biomechanical and physical examination features