Bayesian time series analysis of terrestrial impact cratering

Giant impacts by comets and asteroids have probably had an important influence on terrestrial biological evolution. We know of around 180 high velocity impact craters on the Earth with ages up to 2400Myr and diameters up to 300km. Some studies have identified a periodicity in their age distribution, with periods ranging from 13 to 50Myr. It has further been claimed that such periods may be causally linked to a periodic motion of the solar system through the Galactic plane. However, many of these studies suffer from methodological problems, for example misinterpretation of p-values, overestimation of significance in the periodogram or a failure to consider plausible alternative models. Here I develop a Bayesian method for this problem in which impacts are treated as a stochastic phenomenon. Models for the time variation of the impact probability are defined and the evidence for them in the geological record is compared using Bayes factors. This probabilistic approach obviates the need for ad hoc statistics, and also makes explicit use of the age uncertainties. I find strong evidence for a monotonic decrease in the recorded impact rate going back in time over the past 250Myr for craters larger than 5km. The same is found for the past 150Myr when craters with upper age limits are included. This is consistent with a crater preservation/discovery bias modulating an otherwise constant impact rate. The set of craters larger than 35km (so less affected by erosion and infilling) and younger than 400Myr are best explained by a constant impact probability model. A periodic variation in the cratering rate is strongly disfavoured in all data sets. There is also no evidence for a periodicity superimposed on a constant rate or trend, although this more complex signal would be harder to distinguish.Comment: Minor typos corrected in arXiv v2. Erratum (minor notation corrections) corrected in arXiv v3. (Erratum available from http://www.mpia-hd.mpg.de/~calj/craterTS_erratum.pdf

Rapid Qualitative Urinary Tract Infection Pathogen Identification by SeptiFast® Real-Time PCR

Background Urinary tract infections (UTI) are frequent in outpatients. Fast pathogen identification is mandatory for shortening the time of discomfort and preventing serious complications. Urine culture needs up to 48 hours until pathogen identification. Consequently, the initial antibiotic regimen is empirical. Aim To evaluate the feasibility of qualitative urine pathogen identification by a commercially available real-time PCR blood pathogen test (SeptiFast®) and to compare the results with dipslide and microbiological culture. Design of study Pilot study with prospectively collected urine samples. Setting University hospital. Methods 82 prospectively collected urine samples from 81 patients with suspected UTI were included. Dipslide urine culture was followed by microbiological pathogen identification in dipslide positive samples. In parallel, qualitative DNA based pathogen identification (SeptiFast®) was performed in all samples. Results 61 samples were SeptiFast® positive, whereas 67 samples were dipslide culture positive. The inter-methodological concordance of positive and negative findings in the gram+, gram- and fungi sector was 371/410 (90%), 477/492 (97%) and 238/246 (97%), respectively. Sensitivity and specificity of the SeptiFast® test for the detection of an infection was 0.82 and 0.60, respectively. SeptiFast® pathogen identifications were available at least 43 hours prior to culture results. Conclusion The SeptiFast® platform identified bacterial DNA in urine specimens considerably faster compared to conventional culture. For UTI diagnosis sensitivity and specificity is limited by its present qualitative setup which does not allow pathogen quantification. Future quantitative assays may hold promise for PCR based UTI pathogen identification as a supplementation of conventional culture methods

Oscillations in the dark energy EoS: new MCMC lessons

We study the possibility of detecting oscillating patterns in the equation of state (EoS) of the dark energy using different cosmological datasets. We follow a phenomenological approach and study three different oscillating models for the EoS, one of them periodic and the other two damped (proposed here for the first time). All the models are characterised by the amplitude value, the centre and the frequency of oscillations. In contrast to previous works in the literature, we do not fix the value of the frequency to a fiducial value related to the time extension of chosen datasets, but consider a discrete set of values, so to avoid arbitrariness and try and detect any possible time period in the EoS. We test the models using a recent collection of SNeIa, direct Hubble data and Gamma Ray Bursts data. Main results are: I. even if constraints on the amplitude are not too strong, we detect a trend of it versus the frequency, i.e. decreasing (and even negatives) amplitudes for higher frequencies; II. the centre of oscillation (which corresponds to the present value of the EoS parameter) is very well constrained, phantom behaviour is excluded at $1\sigma$ level and trend which is in agreement with the one for the amplitude appears; III. the frequency is hard to constrain, showing similar statistical validity for all the values of the discrete set chosen, but the best fit of all the scenarios considered is associated with a period which is in the redshift range depicted by our cosmological data. The "best" oscillating models are compared with $\Lambda$CDM using dimensionally consistent a Bayesian approach based information criterion and the conclusion reached is the non existence of significant evidence against dark energy oscillations.Comment: 12 papers, mn2e, 8 figure

End-to-End Trainable Deep Active Contour Models for Automated Image Segmentation: Delineating Buildings in Aerial Imagery

The automated segmentation of buildings in remote sensing imagery is a challenging task that requires the accurate delineation of multiple building instances over typically large image areas. Manual methods are often laborious and current deep-learning-based approaches fail to delineate all building instances and do so with adequate accuracy. As a solution, we present Trainable Deep Active Contours (TDACs), an automatic image segmentation framework that intimately unites Convolutional Neural Networks (CNNs) and Active Contour Models (ACMs). The Eulerian energy functional of the ACM component includes per-pixel parameter maps that are predicted by the backbone CNN, which also initializes the ACM. Importantly, both the ACM and CNN components are fully implemented in TensorFlow and the entire TDAC architecture is end-to-end automatically differentiable and backpropagation trainable without user intervention. TDAC yields fast, accurate, and fully automatic simultaneous delineation of arbitrarily many buildings in the image. We validate the model on two publicly available aerial image datasets for building segmentation, and our results demonstrate that TDAC establishes a new state-of-the-art performance.Comment: Accepted to European Conference on Computer Vision (ECCV) 202

Towards Smart Homes Using Low Level Sensory Data

Ubiquitous Life Care (u-Life care) is receiving attention because it provides high quality and low cost care services. To provide spontaneous and robust healthcare services, knowledge of a patient’s real-time daily life activities is required. Context information with real-time daily life activities can help to provide better services and to improve healthcare delivery. The performance and accuracy of existing life care systems is not reliable, even with a limited number of services. This paper presents a Human Activity Recognition Engine (HARE) that monitors human health as well as activities using heterogeneous sensor technology and processes these activities intelligently on a Cloud platform for providing improved care at low cost. We focus on activity recognition using video-based, wearable sensor-based, and location-based activity recognition engines and then use intelligent processing to analyze the context of the activities performed. The experimental results of all the components showed good accuracy against existing techniques. The system is deployed on Cloud for Alzheimer’s disease patients (as a case study) with four activity recognition engines to identify low level activity from the raw data captured by sensors. These are then manipulated using ontology to infer higher level activities and make decisions about a patient’s activity using patient profile information and customized rules

Implementing health research through academic and clinical partnerships : a realistic evaluation of the Collaborations for Leadership in Applied Health Research and Care (CLAHRC)

Background: The English National Health Service has made a major investment in nine partnerships between higher education institutions and local health services called Collaborations for Leadership in Applied Health Research and Care (CLAHRC). They have been funded to increase capacity and capability to produce and implement research through sustained interactions between academics and health services. CLAHRCs provide a natural ‘test bed’ for exploring questions about research implementation within a partnership model of delivery. This protocol describes an externally funded evaluation that focuses on implementation mechanisms and processes within three CLAHRCs. It seeks to uncover what works, for whom, how, and in what circumstances. Design and methods: This study is a longitudinal three-phase, multi-method realistic evaluation, which deliberately aims to explore the boundaries around knowledge use in context. The evaluation funder wishes to see it conducted for the process of learning, not for judging performance. The study is underpinned by a conceptual framework that combines the Promoting Action on Research Implementation in Health Services and Knowledge to Action frameworks to reflect the complexities of implementation. Three participating CLARHCS will provide indepth comparative case studies of research implementation using multiple data collection methods including interviews, observation, documents, and publicly available data to test and refine hypotheses over four rounds of data collection. We will test the wider applicability of emerging findings with a wider community using an interpretative forum. Discussion: The idea that collaboration between academics and services might lead to more applicable health research that is actually used in practice is theoretically and intuitively appealing; however the evidence for it is limited. Our evaluation is designed to capture the processes and impacts of collaborative approaches for implementing research, and therefore should contribute to the evidence base about an increasingly popular (e.g., Mode two, integrated knowledge transfer, interactive research), but poorly understood approach to knowledge translation. Additionally we hope to develop approaches for evaluating implementation processes and impacts particularly with respect to integrated stakeholder involvement

Recombinant Human Adenovirus with Rat MIP-2 Gene Insertion Causes Prolonged PMN Recruitment to the Murine Brain

Single injections of recombinant cytokines/chemokines into tissue have provided insights into their possible roles during the inflammatory response. Adenoviral technology may allow us to mimic the in vivo situation more closely, with protein generated in a continuous but transient fashion. Replication-deficient human type 5 adenovirus containing a rat macrophage inflammatory protein-2 ( MIP-2 ) gene insertion and cytomegalovirus promoter was injected into the mouse brain to investigate the inflammatory response to continuous overproduction of MIP-2. Adenovirus with a LacZ gene insertion expressing Β-galactosidase was used as a control. At doses of 10 4 to 10 7 plaque-forming units, a minimal inflammatory response was detected to the LacZ virus, with leukocyte recruitment that was restricted to the injection site. A dose of 10 7 plaque-forming units of both the LacZ and the MIP-2 vector produced extensive transgene product expression that persisted for at least 7 days. Astrocytes, recognized by their morphology, were the predominant cell type expressing MIP-2 and Β-galactosidase. A dose of 10 7 plaque-forming units of MIP-2 vector caused dramatic polymorphonuclear leukocyte (PMN) recruitment to the brain parenchyma after 2 days. PMN recruitment was still observed after 4 and 7 days, but had become more localized to the injection site and was associated with numerous foam-like macrophages. At both 2 and 7 days the blood-brain barrier was breached in the region of leukocyte recruitment. Despite the extent of leukocyte recruitment there were no overt signs of neuronal degeneration or demyelination. Our findings demonstrate that continuous production of MIP-2 in the CNS results in persistent PMN recruitment to the brain parenchyma with no evidence of tachyphylaxis. The lack of PMN recruitment to the brain parenchyma following CNS injury may be a result of deficient production of PMN chemoattractants.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/75633/1/j.1460-9568.1996.tb01324.x.pd

A data mining approach in home healthcare: outcomes and service use

BACKGROUND: The purpose of this research is to understand the performance of home healthcare practice in the US. The relationships between home healthcare patient factors and agency characteristics are not well understood. In particular, discharge destination and length of stay have not been studied using a data mining approach which may provide insights not obtained through traditional statistical analyses. METHODS: The data were obtained from the 2000 National Home and Hospice Care Survey data for three specific conditions (chronic obstructive pulmonary disease, heart failure and hip replacement), representing nearly 580 patients from across the US. The data mining approach used was CART (Classification and Regression Trees). Our aim was twofold: 1) determining the drivers of home healthcare service outcomes (discharge destination and length of stay) and 2) examining the applicability of induction through data mining to home healthcare data. RESULTS: Patient age (85 and older) was a driving force in discharge destination and length of stay for all three conditions. There were also impacts from the type of agency, type of payment, and ethnicity. CONCLUSION: Patients over 85 years of age experience differential outcomes depending on the condition. There are also differential effects related to agency type by condition although length of stay was generally lower for hospital-based agencies. The CART procedure was sufficiently accurate in correctly classifying patients in all three conditions which suggests continuing utility in home health care

CPR in medical schools: learning by teaching BLS to sudden cardiac death survivors – a promising strategy for medical students?

BACKGROUND: Cardiopulmonary resuscitation (CPR) training is gaining more importance for medical students. There were many attempts to improve the basic life support (BLS) skills in medical students, some being rather successful, some less. We developed a new problem based learning curriculum, where students had to teach CPR to cardiac arrest survivors in order to improve the knowledge about life support skills of trainers and trainees. METHODS: Medical students who enrolled in our curriculum had to pass a 2 semester problem based learning session about the principles of cardiac arrest, CPR, BLS and defibrillation (CPR-D). Then the students taught cardiac arrest survivors who were randomly chosen out of a cardiac arrest database of our emergency department. Both, the student and the Sudden Cardiac Death (SCD) survivor were asked about their skills and knowledge via questionnaires immediately after the course. The questionnaires were then used to evaluate if this new teaching strategy is useful for learning CPR via a problem-based-learning course. The survey was grouped into three categories, namely "Use of AED", "CPR-D" and "Training". In addition, there was space for free answers where the participants could state their opinion in their own words, which provided some useful hints for upcoming programs. RESULTS: This new learning-by-teaching strategy was highly accepted by all participants, the students and the SCD survivors. Most SCD survivors would use their skills in case one of their relatives goes into cardiac arrest (96%). Furthermore, 86% of the trainees were able to deal with failures and/or disturbances by themselves. On the trainer's side, 96% of the students felt to be well prepared for the course and were considered to be competent by 96% of their trainees. CONCLUSION: We could prove that learning by teaching CPR is possible and is highly accepted by the students. By offering a compelling appreciation of what CPR can achieve in using survivors from SCD as trainees made them go deeper into the subject of resuscitation, what also might result in a longer lasting benefit than regular lecture courses in CPR