Bounded Model Checking for Probabilistic Programs

In this paper we investigate the applicability of standard model checking approaches to verifying properties in probabilistic programming. As the operational model for a standard probabilistic program is a potentially infinite parametric Markov decision process, no direct adaption of existing techniques is possible. Therefore, we propose an on-the-fly approach where the operational model is successively created and verified via a step-wise execution of the program. This approach enables to take key features of many probabilistic programs into account: nondeterminism and conditioning. We discuss the restrictions and demonstrate the scalability on several benchmarks

A deep learning methodology for the automated detection of end-diastolic frames in intravascular ultrasound images.

Coronary luminal dimensions change during the cardiac cycle. However, contemporary volumetric intravascular ultrasound (IVUS) analysis is performed in non-gated images as existing methods to acquire gated or to retrospectively gate IVUS images have failed to dominate in research. We developed a novel deep learning (DL)-methodology for end-diastolic frame detection in IVUS and compared its efficacy against expert analysts and a previously established methodology using electrocardiographic (ECG)-estimations as reference standard. Near-infrared spectroscopy-IVUS (NIRS-IVUS) data were prospectively acquired from 20 coronary arteries and co-registered with the concurrent ECG-signal to identify end-diastolic frames. A DL-methodology which takes advantage of changes in intensity of corresponding pixels in consecutive NIRS-IVUS frames and consists of a network model designed in a bidirectional gated-recurrent-unit (Bi-GRU) structure was trained to detect end-diastolic frames. The efficacy of the DL-methodology in identifying end-diastolic frames was compared with two expert analysts and a conventional image-based (CIB)-methodology that relies on detecting vessel movement to estimate phases of the cardiac cycle. A window of ± 100 ms from the ECG estimations was used to define accurate end-diastolic frames detection. The ECG-signal identified 3,167 end-diastolic frames. The mean difference between DL and ECG estimations was 3 ± 112 ms while the mean differences between the 1st-analyst and ECG, 2nd-analyst and ECG and CIB-methodology and ECG were 86 ± 192 ms, 78 ± 183 ms and 59 ± 207 ms, respectively. The DL-methodology was able to accurately detect 80.4%, while the two analysts and the CIB-methodology detected 39.0%, 43.4% and 42.8% of end-diastolic frames, respectively (P < 0.05). The DL-methodology can identify NIRS-IVUS end-diastolic frames accurately and should be preferred over expert analysts and CIB-methodologies, which have limited efficacy

Barriers and motivators to gaining access to smoking cessation services amongst deprived smokers – a qualitative study

BACKGROUND: Smoking is strongly associated with disadvantage and is an important contributor to inequalities in health. Smoking cessation services have been implemented in the UK targeting disadvantaged smokers, but there is little evidence available on how to design services to attract this priority group. METHODS: We conducted focus groups with 39 smokers aged 21–75 from the most socio-economically deprived areas of Nottingham UK who had made an unsuccessful attempt to quit within the last year without using smoking cessation services, to identify specific barriers or motivators to gaining access to these services. RESULTS: Barriers to use of existing services related to fear of being judged, fear of failure, a perceived lack of knowledge about existing services, a perception that available interventions – particularly Nicotine Replacement Therapy – are expensive and ineffective, and negative media publicity about bupropion. Participants expressed a preference for a personalised, non-judgemental approach combining counselling with affordable, accessible and effective pharmacological therapies; convenient and flexible timing of service delivery, and the possibility of subsidised complementary therapies. CONCLUSION: We conclude that smokers from these deprived areas generally had low awareness of the services available to help them, and misconceptions about their availability and effectiveness. A more personalised approach to promoting services that are non-judgemental, and with free pharmacotherapy and flexible support may encourage more deprived smokers to quit smoking

Impact of analyzing less image frames per segment for radiofrequency-based volumetric intravascular ultrasound measurements in mild-to-moderate coronary atherosclerosis

Volumetric radiofrequency-based intravascular ultrasound (RF–IVUS) data of coronary segments are increasingly used as endpoints in serial trials of novel anti-atherosclerotic therapies. In a relatively time-consuming process, vessel and lumen contours are defined; these contours are first automatically detected, then visually checked, and finally (in most cases) manually edited to generate reliable volumetric data of vessel geometry and plaque composition. Reduction in number of cross-sectional images for volumetric analysis could save analysis time but may also increase measurement variability of volumetric data. To assess whether a 50% reduction in number of frames per segment (every second frame) alters the reproducibility of volumetric measurements, we performed repeated RF–IVUS analyses of 15 coronary segments with mild-to-moderate atherosclerosis (20.2 ± 0.2 mm-long segments with 46 ± 13% plaque burden). Volumes were calculated based on a total of 731 image frames. Reducing the number of cross-sectional image frames for volumetric measurements saved analysis time (38 ± 9 vs. 68 ± 17 min/segment; P < 0.0001) and resulted for only a few parameters in (borderline) significant but mild differences versus measurements based on all frames (fibrous volume, P < 0.05; necrotic-core volume, P = 0.07). Compared to the intra-observer variability, there was a mild increase in measurement variability for most geometrical and compositional volumetric RF–IVUS parameters. In RF–IVUS studies of mild-to-moderate coronary disease, analyzing less image frames saved analysis time, left most volumetric parameters greatly unaffected, and resulted in a no more than mild increase in measurement variability of volumetric data

Collaboration with general practitioners: preferences of medical specialists – a qualitative study

BACKGROUND: Collaboration between general practitioners (GPs) and specialists has been the focus of many collaborative care projects during the past decade. Unfortunately, quite a number of these projects failed. This raises the question of what motivates medical specialists to initiate and continue participating with GPs in new collaborative care models. The following question is addressed in this study: What motivates medical specialists to initiate and sustain new models for collaborating with GPs? METHODS: We conducted semi-structured interviews with eighteen medical specialists in the province of Groningen, in the North of The Netherlands. The sampling criteria were age, gender, type of hospital in which they were practicing, and specialty. The interviews were recorded, fully transcribed, and analysed by three researchers working independently. The resulting motivational factors were grouped into categories. RESULTS: 'Teaching GPs' and 'regulating patient flow' (referrals) appeared to dominate when the motivational factors were considered. In addition, specialists want to develop relationships with the GPs on a more personal level. Most specialists believe that there is not much they can learn from GPs. 'Lack of time', 'no financial compensation', and 'no support from colleagues' were considered to be the main concerns to establishing collaborative care practices. Additionally, projects were often experienced as too complex and time consuming whereas guidelines were experienced as too restrictive. CONCLUSION: Specialists are particularly interested in collaborating because the GP is the gatekeeper for access to secondary health care resources. Specialists feel that they are able to teach the GPs something, but they do not feel that they have anything to learn from the GPs. With respect to professional expertise, therefore, specialists do not consider GPs as equals. Once personal relationships with the GPs have been established, an informal network with incidental professional contact seems to be sufficient to satisfy the collaborative needs of the specialist. The concerns seem to outweigh any positive motivational forces to developing new models of collaborative practice

Nitrogen limitation constrains sustainability of ecosystem response to CO2

Enhanced plant biomass accumulation in response to elevated atmospheric CO2 concentration could dampen the future rate of increase in CO2 levels and associated climate warming. However, it is unknown whether CO2-induced stimulation of plant growth and biomass accumulation will be sustained or whether limited nitrogen (N) availability constrains greater plant growth in a CO2-enriched world(1-9). Here we show, after a six-year field study of perennial grassland species grown under ambient and elevated levels of CO2 and N, that low availability of N progressively suppresses the positive response of plant biomass to elevated CO2. Initially, the stimulation of total plant biomass by elevated CO2 was no greater at enriched than at ambient N supply. After four to six years, however, elevated CO2 stimulated plant biomass much less under ambient than enriched N supply. This response was consistent with the temporally divergent effects of elevated CO2 on soil and plant N dynamics at differing levels of N supply. Our results indicate that variability in availability of soil N and deposition of atmospheric N are both likely to influence the response of plant biomass accumulation to elevated atmospheric CO2. Given that limitations to productivity resulting from the insufficient availability of N are widespread in both unmanaged and managed vegetation(5,7-9), soil N supply is probably an important constraint on global terrestrial responses to elevated CO2.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/62769/1/nature04486.pd

Methane production in ruminant animals

Agriculture is a significant source of GHGs globally and ruminant livestock animals are one of the largest contributors to these emissions, responsible for an estimated 14% of GHGs (CH4 and N2O combined) worldwide. A large portion of GHG fluxes from agricultural activities is related to CH4 emissions from ruminants. Both direct and indirect methods are available. Direct methods include enclosure techniques, artificial (e.g. SF6) or natural (e.g. CO2) tracer techniques, and micrometeorological methods using open-path lasers. Under the indirect methods, emission mechanisms are understood, where the CH4 emission potential is estimated based on the substrate characteristics and the digestibility (i.e. from volatile fatty acids). These approximate methods are useful if no direct measurement is possible. The different systems used to quantify these emission potentials are presented in this chapter. Also, CH4 from animal waste (slurry, urine, dung) is an important source: methods pertaining to measuring GHG potential from these sources are included

Reproducible Cancer Biomarker Discovery in SELDI-TOF MS Using Different Pre-Processing Algorithms

BACKGROUND: There has been much interest in differentiating diseased and normal samples using biomarkers derived from mass spectrometry (MS) studies. However, biomarker identification for specific diseases has been hindered by irreproducibility. Specifically, a peak profile extracted from a dataset for biomarker identification depends on a data pre-processing algorithm. Until now, no widely accepted agreement has been reached. RESULTS: In this paper, we investigated the consistency of biomarker identification using differentially expressed (DE) peaks from peak profiles produced by three widely used average spectrum-dependent pre-processing algorithms based on SELDI-TOF MS data for prostate and breast cancers. Our results revealed two important factors that affect the consistency of DE peak identification using different algorithms. One factor is that some DE peaks selected from one peak profile were not detected as peaks in other profiles, and the second factor is that the statistical power of identifying DE peaks in large peak profiles with many peaks may be low due to the large scale of the tests and small number of samples. Furthermore, we demonstrated that the DE peak detection power in large profiles could be improved by the stratified false discovery rate (FDR) control approach and that the reproducibility of DE peak detection could thereby be increased. CONCLUSIONS: Comparing and evaluating pre-processing algorithms in terms of reproducibility can elucidate the relationship among different algorithms and also help in selecting a pre-processing algorithm. The DE peaks selected from small peak profiles with few peaks for a dataset tend to be reproducibly detected in large peak profiles, which suggests that a suitable pre-processing algorithm should be able to produce peaks sufficient for identifying useful and reproducible biomarkers