Instances and connectors : issues for a second generation process language

This work is supported by UK EPSRC grants GR/L34433 and GR/L32699Over the past decade a variety of process languages have been defined, used and evaluated. It is now possible to consider second generation languages based on this experience. Rather than develop a second generation wish list this position paper explores two issues: instances and connectors. Instances relate to the relationship between a process model as a description and the, possibly multiple, enacting instances which are created from it. Connectors refers to the issue of concurrency control and achieving a higher level of abstraction in how parts of a model interact. We believe that these issues are key to developing systems which can effectively support business processes, and that they have not received sufficient attention within the process modelling community. Through exploring these issues we also illustrate our approach to designing a second generation process language.Postprin

A hidden crisis: strengthening the evidence base on the current failures of rural groundwater supplies

New ambitious international goals for universal access to safe drinking water depend critically on the ability of development partners to accelerate and sustain access to groundwater. However, available evidence (albeit fragmented and methodologically unclear) indicates >30% of new groundwater-based supplies are non-functional within a few years of construction. Critically, in the absence of a significant systematic evidence base or analysis on supply failures, there is little opportunity to learn from past mistakes, to ensure more sustainable services can be developed in the future. This work presents a new and robust methodology for investigating the causes of non-functionality, developed by an interdisciplinary team as part of an UPGro catalyst grant. The approach was successfully piloted within a test study in NE Uganda, and forms a basis for future research to develop a statistically significant systematic evidence base to unravel the underlying causes of failure

Altered monocyte and fibrocyte phenotype and function in scleroderma interstitial lung disease: reversal by caveolin-1 scaffolding domain peptide

Interstitial lung disease (ILD) is a major cause of morbidity and mortality in scleroderma (systemic sclerosis, or SSc). Fibrocytes are a monocyte-derived cell population implicated in the pathogenesis of fibrosing disorders. Given the recently recognized importance of caveolin-1 in regulating function and signaling in SSc monocytes, in the present study we examined the role of caveolin-1 in the migration and/or trafficking and phenotype of monocytes and fibrocytes in fibrotic lung disease in human patients and an animal model. These studies fill a gap in our understanding of how monocytes and fibrocytes contribute to SSc-ILD pathology. We found that C-X-C chemokine receptor type 4-positive (CXCR4+)/collagen I-positive (ColI+), CD34+/ColI+ and CD45+/ColI+ cells are present in SSc-ILD lungs, but not in control lungs, with CXCR4+ cells being most prevalent. Expression of CXCR4 and its ligand, stromal cell-derived factor 1 (CXCL12), are also highly upregulated in SSc-ILD lung tissue. SSc monocytes, which lack caveolin-1 and therefore overexpress CXCR4, exhibit almost sevenfold increased migration toward CXCL12 compared to control monocytes. Restoration of caveolin-1 function by administering the caveolin scaffolding domain (CSD) peptide reverses this hypermigration. Similarly, transforming growth factor β-treated normal monocytes lose caveolin-1, overexpress CXCR4 and exhibit 15-fold increased monocyte migration that is CSD peptide-sensitive. SSc monocytes exhibit a different phenotype than normal monocytes, expressing high levels of ColI, CD14 and CD34. Because ColI+/CD14+ cells are prevalent in SSc blood, we looked for such cells in lung tissue and confirmed their presence in SSc-ILD lungs but not in normal lungs. Finally, in the bleomycin model of lung fibrosis, we show that CSD peptide diminishes fibrocyte accumulation in the lungs. Our results suggest that low caveolin-1 in SSc monocytes contributes to ILD via effects on cell migration and phenotype and that the hyperaccumulation of fibrocytes in SSc-ILD may result from the altered phenotype and migratory activity of their monocyte precursors

Dynamic susceptibility-contrast magnetic resonance imaging with contrast agent leakage correction aids in predicting grade in pediatric brain tumours: a multicenter study

Background: Relative cerebral blood volume (rCBV) measured using dynamic susceptibility-contrast MRI can differentiate between low- and high-grade pediatric brain tumors. Multicenter studies are required for translation into clinical practice. Objective: We compared leakage-corrected dynamic susceptibility-contrast MRI perfusion parameters acquired at multiple centers in low- and high-grade pediatric brain tumors. Materials and methods: Eighty-five pediatric patients underwent pre-treatment dynamic susceptibility-contrast MRI scans at four centers. MRI protocols were variable. We analyzed data using the Boxerman leakage-correction method producing pixel-by-pixel estimates of leakage-uncorrected (rCBV uncorr) and corrected (rCBV corr) relative cerebral blood volume, and the leakage parameter, K 2. Histological diagnoses were obtained. Tumors were classified by high-grade tumor. We compared whole-tumor median perfusion parameters between low- and high-grade tumors and across tumor types. Results: Forty tumors were classified as low grade, 45 as high grade. Mean whole-tumor median rCBV uncorr was higher in high-grade tumors than low-grade tumors (mean ± standard deviation [SD] = 2.37±2.61 vs. –0.14±5.55; P<0.01). Average median rCBV increased following leakage correction (2.54±1.63 vs. 1.68±1.36; P=0.010), remaining higher in high-grade tumors than low grade-tumors. Low-grade tumors, particularly pilocytic astrocytomas, showed T1-dominant leakage effects; high-grade tumors showed T2*-dominance (mean K 2=0.017±0.049 vs. 0.002±0.017). Parameters varied with tumor type but not center. Median rCBV uncorr was higher (mean = 1.49 vs. 0.49; P=0.015) and K 2 lower (mean = 0.005 vs. 0.016; P=0.013) in children who received a pre-bolus of contrast agent compared to those who did not. Leakage correction removed the difference. Conclusion: Dynamic susceptibility-contrast MRI acquired at multiple centers helped distinguish between children’s brain tumors. Relative cerebral blood volume was significantly higher in high-grade compared to low-grade tumors and differed among common tumor types. Vessel leakage correction is required to provide accurate rCBV, particularly in low-grade enhancing tumors

IMG-06. PREDICTING SURVIVAL FROM PERFUSION AND DIFFUSION MRI BY MACHINE LEARNING

INTRODUCTION Magnetic Resonance Imaging (MRI) is routinely used in the assessment of children’s brain tumours. Reduced diffusion and increased perfusion on MRI are commonly associated with higher grade but there is a lack of quantitative data linking these parameters to survival. Machine learning is increasingly being used to develop diagnostic tools but its use in survival analysis is rare. In this study we combine quantitative parameters from diffusion and perfusion MRI with machine learning to develop a model of survival for paediatric brain tumours. METHOD: 69 children from 4 centres (Birmingham, Liverpool, Nottingham, Newcastle) underwent MRI with diffusion and perfusion (dynamic susceptibility contrast) at diagnosis. Images were processed to form ADC, cerebral blood volume (CBV) and vessel leakage correction (K2) parameter maps. Parameter mean, standard deviation and heterogeneity measures (skewness and kurtosis) were calculated from tumour and whole brain and used in iterative Bayesian survival analysis. The features selected were used for k-means clustering and differences in survival between clusters assessed by Kaplan-Meier and Cox-regression. RESULTS Bayesian analysis revealed the 5 top features determining survival to be tumour volume, ADC kurtosis, CBV mean, K2 mean and whole brain CBV mean. K-means clustering using these features showed two distinct clusters (high- and low-risk) which bore significantly different survival characteristics (Hazard Ratio = 5.6). DISCUSSION AND CONCLUSION Diffusion and perfusion MRI can be used to aid the prediction of survival in children’s brain tumours. Tumour perfusion played a particularly important role in predicting survival despite being less routinely measured than diffusion

Assisted reproductive technologies are associated with limited epigenetic variation at birth that largely resolves by adulthood

More than 7 million individuals have been conceived by Assisted Reproductive Technologies (ART) and there is clear evidence that ART is associated with a range of adverse early life outcomes, including rare imprinting disorders. The periconception period and early embryogenesis are associated with widespread epigenetic remodeling, which can be influenced by ART, with effects on the developmental trajectory in utero, and potentially on health throughout life. Here we profile genome-wide DNA methylation in blood collected in the newborn period and in adulthood (age 22-35 years) from a unique longitudinal cohort of ART-conceived individuals, previously shown to have no differences in health outcomes in early adulthood compared with non-ART-conceived individuals. We show evidence for specific ART-associated variation in methylation around birth, most of which occurred independently of embryo culturing. Importantly, ART-associated epigenetic variation at birth largely resolves by adulthood with no direct evidence that it impacts on development and health

Phylogenetic Relationships among the Colobine Monkeys Revisited: New Insights from Analyses of Complete mt Genomes and 44 Nuclear Non-Coding Markers

Background: Phylogenetic relationships among Asian and African colobine genera have been disputed and are not yet well established. In the present study, we revisit the contentious relationships within the Asian and African Colobinae by analyzing 44 nuclear non-coding genes (.23 kb) and mitochondrial (mt) genome sequences from 14 colobine and 4 noncolobine primates. Principal Findings: The combined nuclear gene and the mt genome as well as the combined nuclear and mt gene analyses yielded different phylogenetic relationships among colobine genera with the exception of a monophyletic ‘odd-nosed’ group consisting of Rhinopithecus, Pygathrix and Nasalis, and a monophyletic African group consisting of Colobus and Piliocolobus. The combined nuclear data analyses supported a sister-grouping between Semnopithecus and Trachypithecus, and between Presbytis and the odd-nosed monkey group, as well as a sister-taxon association of Pygathrix and Rhinopithecus within the odd-nosed monkey group. In contrast, mt genome data analyses revealed that Semnopithecus diverged earliest among the Asian colobines and that the odd-nosed monkey group is sister to a Presbytis and Trachypithecus clade, as well as a close association of Pygathrix with Nasalis. The relationships among these genera inferred from the analyses of combined nuclear and mt genes, however, varied with the tree-building methods used. Another remarkable finding of the present study is that all of our analyses rejected the recently proposed African colobine paraphyl

Combining multi-site magnetic resonance imaging with machine learning predicts survival in pediatric brain tumors

Brain tumors represent the highest cause of mortality in the pediatric oncological population. Diagnosis is commonly performed with magnetic resonance imaging. Survival biomarkers are challenging to identify due to the relatively low numbers of individual tumor types. 69 children with biopsy-confirmed brain tumors were recruited into this study. All participants had perfusion and diffusion weighted imaging performed at diagnosis. Imaging data were processed using conventional methods, and a Bayesian survival analysis performed. Unsupervised and supervised machine learning were performed with the survival features, to determine novel sub-groups related to survival. Sub-group analysis was undertaken to understand differences in imaging features. Survival analysis showed that a combination of diffusion and perfusion imaging were able to determine two novel sub-groups of brain tumors with different survival characteristics (p < 0.01), which were subsequently classified with high accuracy (98%) by a neural network. Analysis of high-grade tumors showed a marked difference in survival (p = 0.029) between the two clusters with high risk and low risk imaging features. This study has developed a novel model of survival for pediatric brain tumors. Tumor perfusion plays a key role in determining survival and should be considered as a high priority for future imaging protocols