GP-SUM. Gaussian Processes Filtering of non-Gaussian Beliefs

This work studies the problem of stochastic dynamic filtering and state propagation with complex beliefs. The main contribution is GP-SUM, a filtering algorithm tailored to dynamic systems and observation models expressed as Gaussian Processes (GP), and to states represented as a weighted sum of Gaussians. The key attribute of GP-SUM is that it does not rely on linearizations of the dynamic or observation models, or on unimodal Gaussian approximations of the belief, hence enables tracking complex state distributions. The algorithm can be seen as a combination of a sampling-based filter with a probabilistic Bayes filter. On the one hand, GP-SUM operates by sampling the state distribution and propagating each sample through the dynamic system and observation models. On the other hand, it achieves effective sampling and accurate probabilistic propagation by relying on the GP form of the system, and the sum-of-Gaussian form of the belief. We show that GP-SUM outperforms several GP-Bayes and Particle Filters on a standard benchmark. We also demonstrate its use in a pushing task, predicting with experimental accuracy the naturally occurring non-Gaussian distributions.Comment: WAFR 2018, 16 pages, 7 figure

Optimal schedule of home care visits for a health care center

The provision of home health care services is becoming an important research area, mainly because in Portugal the population is ageing. Home care visits are organized taking into account the medical treatments and general support that elder/sick people need at home. This health service can be provided by nurse teams from Health Care Centers. Usually, the visits are manually planned and without computer support. The main goal of this work is to carry out the automatic schedule of home care visits, of one Portuguese Health Care Center, in order to minimize the time spent in all home care visits and, consequently, reduce the costs involved. The developed algorithms were coded in MatLab Software and the problem was efficiently solved, obtaining several schedule solutions of home care visits for the presented data. Solutions found by genetic and particle swarm algorithms lead to significant time reductions for both nurse teams and patients.This work has been supported by COMPETE: POCI-01-0145- FEDER-007043 and FCT - Fundru;ao para a Ciencia e Tecnologia within the Project Scope: UID/CEC/00319/2013.info:eu-repo/semantics/publishedVersio

Update to the study protocol, including statistical analysis plan for a randomized clinical trial comparing comprehensive cardiac rehabilitation after heart valve surgery with control: the CopenHeartVR trial

Comparative StudyRandomized Controlled TrialThis is the final version of the article. Available from BioMed Central via the DOI in this record.BACKGROUND: Heart valve diseases are common with an estimated prevalence of 2.5% in the Western world. The number is rising because of an ageing population. Once symptomatic, heart valve diseases are potentially lethal, and heavily influence daily living and quality of life. Surgical treatment, either valve replacement or repair, remains the treatment of choice. However, post-surgery, the transition to daily living may become a physical, mental and social challenge. We hypothesize that a comprehensive cardiac rehabilitation program can improve physical capacity and self-assessed mental health and reduce hospitalization and healthcare costs after heart valve surgery. METHODS: This randomized clinical trial, CopenHeartVR, aims to investigate whether cardiac rehabilitation in addition to usual care is superior to treatment as usual after heart valve surgery. The trial will randomly allocate 210 patients 1:1 to an intervention or a control group, using central randomization, and blinded outcome assessment and statistical analyses. The intervention consists of 12 weeks of physical exercise and a psycho-educational intervention comprising five consultations. The primary outcome is peak oxygen uptake (VO2 peak) measured by cardiopulmonary exercise testing with ventilatory gas analysis. The secondary outcome is self-assessed mental health measured by the standardized questionnaire Short Form-36. Long-term healthcare utilization and mortality as well as biochemistry, echocardiography and cost-benefit will be assessed. A mixed-method design will be used to evaluate qualitative and quantitative findings, encompassing a survey-based study before the trial and a qualitative pre- and post-intervention study. CONCLUSION: This randomized clinical trial will contribute with evidence of whether cardiac rehabilitation should be provided after heart valve surgery. The study is approved by the local regional Research Ethics Committee (H-1-2011-157), and the Danish Data Protection Agency (j.nr. 2007-58-0015). TRIAL REGISTRATION: Trial registered 16 March 2012; ClinicalTrials.gov ( NCT01558765 ).This work is supported by the Strategic Research Council, The Heart Centre Research Foundation Rigshospitalet, Familien Hede Nielsens Fond, The Regional Research Council of Region Sealand (Denmark), The National Institute of Public Health, and the University of Southern Denmark

Short-cut to new anomalies in gravity duals to logarithmic conformal field theories

Various massive gravity theories in three dimensions are conjecturally dual to logarithmic conformal field theories (LCFTs). We summarise the status of these conjectures. LCFTs are characterised by the values of the central charges and the so-called "new anomalies". We employ a short-cut to calculate these new anomalies in generalised massive gravity and in the recently proposed higher-derivative gravity theories with holographic c-theorem. Both cases permit LCFTs exhibiting intriguing features, like rank three Jordan cells or non-zero central charges. Finally, as an example we discuss in some detail the partially massless version of new massive gravity, a theory with several special properties that we call "partially massless gravity".Comment: 34 pages, 2 figures; v2: added references; v3: Several rewordings in the introduction and section 2, added references. Matches published versio

On RAF Sets and Autocatalytic Cycles in Random Reaction Networks

The emergence of autocatalytic sets of molecules seems to have played an important role in the origin of life context. Although the possibility to reproduce this emergence in laboratory has received considerable attention, this is still far from being achieved. In order to unravel some key properties enabling the emergence of structures potentially able to sustain their own existence and growth, in this work we investigate the probability to observe them in ensembles of random catalytic reaction networks characterized by different structural properties. From the point of view of network topology, an autocatalytic set have been defined either in term of strongly connected components (SCCs) or as reflexively autocatalytic and food-generated sets (RAFs). We observe that the average level of catalysis differently affects the probability to observe a SCC or a RAF, highlighting the existence of a region where the former can be observed, whereas the latter cannot. This parameter also affects the composition of the RAF, which can be further characterized into linear structures, autocatalysis or SCCs. Interestingly, we show that the different network topology (uniform as opposed to power-law catalysis systems) does not have a significantly divergent impact on SCCs and RAFs appearance, whereas the proportion between cleavages and condensations seems instead to play a role. A major factor that limits the probability of RAF appearance and that may explain some of the difficulties encountered in laboratory seems to be the presence of molecules which can accumulate without being substrate or catalyst of any reaction.Comment: pp 113-12

The value of EBV DNA in early detection of post-transplant lymphoproliferative disorders among solid organ and hematopoietic stem cell transplant recipients

PURPOSE: Emerging EBV DNAemia in plasma is considered an early sign of post-transplant lymphoproliferative disorder (PTLD). The aim of this study was to quantify the extent of benefit from screening for EBV DNAemia to detect emerging PTLD among solid organ (SOT) or hematopoietic stem cell transplant recipients (HSCT). METHODS: We used receiver operating characteristic (ROC) curves for assessing ability of models to predict PTLD. Among 2642 recipients transplanted between January 2004 and December 2014, 79 (3%) developed PTLD. RESULTS: EBV DNAemia was observed in 331/1784 recipients (18.6%, 95% CI 16.8-20.4) with measured EBV DNA. The area under the curve (AUC) of the ROC of EBV DNAemia to identify persons with subsequent PTLD was 72% (95% CI, 64-79%) among SOT and 59% (51-68%) among HSCT. Including clinical predictors such as age, gender, transplant year and type, high-risk EBV serostatus, and routine biochemistry in addition to EBV DNAemia increased AUC to 83% (75-90%) among SOT and 84% (79-89%) among HSCT. Among HSCT, including additional factors such as T-cell-depleting treatment, acute graft vs. host disease and donor match increased AUC to 85% (78-91%). CONCLUSIONS: We constructed a model to better predict PTLD compared to EBV DNA screening alone which could have clinical implications

Predictive modeling of die filling of the pharmaceutical granules using the flexible neural tree

In this work, a computational intelligence (CI) technique named flexible neural tree (FNT) was developed to predict die filling performance of pharmaceutical granules and to identify significant die filling process variables. FNT resembles feedforward neural network, which creates a tree-like structure by using genetic programming. To improve accuracy, FNT parameters were optimized by using differential evolution algorithm. The performance of the FNT-based CI model was evaluated and compared with other CI techniques: multilayer perceptron, Gaussian process regression, and reduced error pruning tree. The accuracy of the CI model was evaluated experimentally using die filling as a case study. The die filling experiments were performed using a model shoe system and three different grades of microcrystalline cellulose (MCC) powders (MCC PH 101, MCC PH 102, and MCC DG). The feed powders were roll-compacted and milled into granules. The granules were then sieved into samples of various size classes. The mass of granules deposited into the die at different shoe speeds was measured. From these experiments, a dataset consisting true density, mean diameter (d50), granule size, and shoe speed as the inputs and the deposited mass as the output was generated. Cross-validation (CV) methods such as 10FCV and 5x2FCV were applied to develop and to validate the predictive models. It was found that the FNT-based CI model (for both CV methods) performed much better than other CI models. Additionally, it was observed that process variables such as the granule size and the shoe speed had a higher impact on the predictability than that of the powder property such as d50. Furthermore, validation of model prediction with experimental data showed that the die filling behavior of coarse granules could be better predicted than that of fine granules

Risk Factors for Failure of Primary (Val)ganciclovir Prophylaxis Against Cytomegalovirus Infection and Disease in Solid Organ Transplant Recipients

Background: Rates and risk factors for cytomegalovirus (CMV) prophylaxis breakthrough and discontinuation were investigated, given uncertainty regarding optimal dosing for CMV primary (val)ganciclovir prophylaxis after solid organ transplantation (SOT). Methods: Recipients transplanted from 2012 to 2016 and initiated on primary prophylaxis were followed until 90 days post-transplantation. A (val)ganciclovir prophylaxis score for each patient per day was calculated during the follow-up time (FUT; score of 100 corresponding to manufacturers' recommended dose for a given estimated glomerular filtration rate [eGFR]). Cox models were used to estimate hazard ratios (HRs), adjusted for relevant risk factors. Results: Of 585 SOTs (311 kidney, 117 liver, 106 lung, 51 heart) included, 38/585 (6.5%) experienced prophylaxis breakthrough and 35/585 (6.0%) discontinued prophylaxis for other reasons. CMV IgG donor+/receipient- mismatch (adjusted HR [aHR], 5.37; 95% confidence interval [CI], 2.63 to 10.98; P < 0.001) and increasing % FUT with a prophylaxis score <90 (aHR, 1.16; 95% CI, 1.04 to 1.29; P = .01 per 10% longer FUT w/ score <90) were associated with an increased risk of breakthrough. Lung recipients were at a significantly increased risk of premature prophylaxis discontinuation (aHR, 20.2 vs kidney; 95% CI, 3.34 to 121.9; P = .001), mainly due to liver or myelotoxicity. Conclusions: Recipients of eGFR-adjusted prophylaxis doses below those recommended by manufacturers were at an increased risk of prophylaxis breakthrough, emphasizing the importance of accurate dose adjustment according to the latest eGFR and the need for novel, less toxic agents

Multidisciplinary team meetings and their impact on workflow in radiology and pathology departments

<p>Abstract</p> <p>Background</p> <p>The development of multidisciplinary team meetings (MDTMs) for radiology and pathology is a burgeoning area that increasingly impacts on work processes in both of these departments. The aim of this study was to examine work processes and quantify the time demands on radiologists and pathologists associated with MDTM practices at a large teaching hospital. The observations reported in this paper reflect a general trend affecting hospitals and our conclusions will have relevance for others implementing clinical practice guidelines.</p> <p>Methods</p> <p>For one month, all work related to clinical meetings between pathology and radiology with clinical staff was documented and later analysed.</p> <p>Results</p> <p>The number of meetings to which pathology and radiology contribute at a large university teaching hospital, ranges from two to eight per day, excluding grand rounds, and amounts to approximately 50 meetings per month for each department. For one month, over 300 h were spent by pathologists and radiologists on 81 meetings, where almost 1000 patients were discussed. For each meeting hour, there were, on average, 2.4 pathology hours and 2 radiology hours spent in preparation. Two to three meetings per week are conducted over a teleconferencing link. Average meeting time is 1 h. Preparation time per meeting ranges from 0.3 to 6 h for pathology, and 0.5 to 4 for radiology. The review process in preparation for meetings improves internal quality standards. Materials produced externally (for example imaging) can amount to almost 50% of the material to be reviewed on a single patient. The number of meetings per month has increased by 50% over the past two years. Further increase is expected in both the numbers and duration of meetings when scheduling issues are resolved. A changing trend in the management of referred patients with the development of MDTMs and the introduction of teleconferencing was noted.</p> <p>Conclusion</p> <p>Difficulties are being experienced by pathology and radiology departments participating fully in several multidisciplinary teams. Time spent at meetings, and in preparation for MDTMs is significant. Issues of timing and the coordination of materials to be reviewed are sometimes irreconcilable. The exchange of patient materials with outside institutions is a cause for concern when full data are not made available in a timely fashion. The process of preparation for meetings is having a positive influence on quality, but more resources are needed in pathology and radiology to realise the full benefits of multidisciplinary team working.</p

Cataract research using electronic health records

<p>Abstract</p> <p>Background</p> <p>The eMERGE (electronic MEdical Records and Genomics) network, funded by the National Human Genome Research Institute, is a national consortium formed to develop, disseminate, and apply approaches to research that combine DNA biorepositories with electronic health record (EHR) systems for large-scale, high-throughput genetic research. Marshfield Clinic is one of five sites in the eMERGE network and primarily studied: 1) age-related cataract and 2) HDL-cholesterol levels. The purpose of this paper is to describe the approach to electronic evaluation of the epidemiology of cataract using the EHR for a large biobank and to assess previously identified epidemiologic risk factors in cases identified by electronic algorithms.</p> <p>Methods</p> <p>Electronic algorithms were used to select individuals with cataracts in the Personalized Medicine Research Project database. These were analyzed for cataract prevalence, age at cataract, and previously identified risk factors.</p> <p>Results</p> <p>Cataract diagnoses and surgeries, though not type of cataract, were successfully identified using electronic algorithms. Age specific prevalence of both cataract (22% compared to 17.2%) and cataract surgery (11% compared to 5.1%) were higher when compared to the Eye Diseases Prevalence Research Group. The risk factors of age, gender, diabetes, and steroid use were confirmed.</p> <p>Conclusions</p> <p>Using electronic health records can be a viable and efficient tool to identify cataracts for research. However, using retrospective data from this source can be confounded by historical limits on data availability, differences in the utilization of healthcare, and changes in exposures over time.</p