Formally Defining and Iterating Infinite Models

International audienceThe wide adoption of MDE raises new situations where we need to manipulate very large models or even infinite model streams gathered at runtime. These new uses cases for MDE raise challenges that had been unforeseen by the time standard modeling framework were designed. This paper proposes a formal definition of an infinite model, as well as a formal framework to reason on queries over infinite models. This formal query definition aims at supporting the design and verification of operations that manipulate infinite models. First, we precisely identify the MOF parts which must be refined to support infinite structure. Then, we provide a formal coinductive definition dealing with unbounded and potentially infinite graph-based structure

Independent components of human brain morphology

Quantification of brain morphology has become an important cornerstone in understanding brain structure. Measures of cortical morphology such as thickness and surface area are frequently used to compare groups of subjects or characterise longitudinal changes. However, such measures are often treated as independent from each other. A recently described scaling law, derived from a statistical physics model of cortical folding, demonstrates that there is a tight covariance between three commonly used cortical morphology measures: cortical thickness, total surface area, and exposed surface area. We show that assuming the independence of cortical morphology measures can hide features and potentially lead to misinterpretations. Using the scaling law, we account for the covariance between cortical morphology measures and derive novel independent measures of cortical morphology. By applying these new measures, we show that new information can be gained; in our example we show that distinct morphological alterations underlie healthy ageing compared to temporal lobe epilepsy, even on the coarse level of a whole hemisphere. We thus provide a conceptual framework for characterising cortical morphology in a statistically valid and interpretable manner, based on theoretical reasoning about the shape of the cortex

Distributed Model-to-Model Transformation with ATL on MapReduce

International audienceEfficient processing of very large models is a key requirement for the adoption of Model-Driven Engineering (MDE) in some industrial contexts. One of the central operations in MDE is rule-based model transformation (MT). It is used to specify manipulation operations over structured data coming in the form of model graphs. However, being based on com-putationally expensive operations like subgraph isomorphism, MT tools are facing issues on both memory occupancy and execution time while dealing with the increasing model size and complexity. One way to overcome these issues is to exploit the wide availability of distributed clusters in the Cloud for the distributed execution of MT. In this paper, we propose an approach to automatically distribute the execution of model transformations written in a popular MT language, ATL, on top of a well-known distributed programming model, MapReduce. We show how the execution semantics of ATL can be aligned with the MapReduce computation model. We describe the extensions to the ATL transformation engine to enable distribution, and we experimentally demonstrate the scalability of this solution in a reverse-engineering scenario

Phase II of oral gimatecan in patients with recurrent epithelial ovarian, fallopian tube or peritoneal cancer, previously treated with platinum and taxanes

Background: A prospective phase II study was conducted to evaluate the efficacy and toxicity of oral gimatecan in patients with recurrent epithelial ovarian, fallopian tube or peritoneal cancer

Modifiable and non-modifiable factors related to HPV infection and cervical abnormalities in women at high risk: a cross-sectional analysis from the Valhidate Study

Abstract: Migrant women, and women infected with HIV, are at enhanced risk of cervical HPV infection and HPV-related cancers. We investigated factors that can reduce these risks through public health preventive and screening interventions. We compared prevalence and risk factors for cervical HPV infection/lesions in women with HIV-infection (HIW) and migrant women (RMW) with a control group of resident women (SPW) who were enrolled in the study for the eVALuation and monitoring of HPV Infections and relATEd cervical diseases in high-risk women (VALHIDATE). Among 3093 evaluable women, age-standardized HPV prevalence was 36.3% (95%CI: 28.1\u201344.4) in HIW, 21.6% (95%CI: 15.7\u201327.5) in RMW, and 14.3% (95%CI: 12.5\u201316.1) in SPW. Adjusted prevalence of HPV infection was 2.07 times higher among HIW (95%CI: 1.75\u20132.45), and 1.45 times higher among RMW (95%CI: 1.17\u20131.80) than in SPW. Prevalence-ratios of SIL and HG-SIL were 2.67 (95%CI: 2.06\u20133.45) and 2.82 (95%CI: 1.28\u20136.20), respectively, in HIW compared to controls. A multivariate log-binomial regression model showed modifiable risk factors associated with HPV infection/lesion to have different patterns among groups. Specific public-health intervention, including health and sexual-health education, safe-sex procedures, and improvements to screening programmes, could favorably affect these highly vulnerable women

The Impact of Temporal Lobe Epilepsy Surgery on Picture Naming and its Relationship to Network Metric Change

Background: Anterior temporal lobe resection (ATLR) is a successful treatment for medically-refractory temporal lobe epilepsy (TLE). In the language-dominant hemisphere, 30%- 50% of individuals experience a naming decline which can impact upon daily life. Measures of structural networks are associated with language performance pre-operatively. It is unclear if analysis of network measures may predict post-operative decline. Methods: White matter fibre tractography was performed on preoperative diffusion MRI of 44 left lateralised and left resection individuals with TLE to reconstruct the preoperative structural network. Resection masks, drawn on co-registered pre- and post-operative T1-weighted MRI scans, were used as exclusion regions on pre-operative tractography to estimate the post-operative network. Changes in graph theory metrics, cortical strength, betweenness centrality, and clustering coefficient were generated by comparing the estimated pre- and post-operative networks. These were thresholded based on the presence of the connection in each patient, ranging from 75% to 100% in steps of 5%. The average graph theory metric across thresholds was taken. We incorporated leave-one-out cross-validation with smoothly clipped absolute deviation (SCAD) least absolute shrinkage and selection operator (LASSO) feature selection and a support vector classifier to assess graph theory metrics on picture naming decline. Picture naming was assessed via the Graded Naming Test preoperatively and at 3 and 12 months post-operatively and the outcome was classified using the reliable change index (RCI) to identify clinically significant decline. The best feature combination and model was selected using the area under the curve (AUC). The sensitivity, specificity and F1-score were also reported. Permutation testing was performed to assess the machine learning model and selected regions difference significance. Results: A combination of clinical and graph theory metrics were able to classify outcome of picture naming at 3 months with an AUC of 0.84. At 12 months, change in strength to cortical regions was best able to correctly classify outcome with an AUC of 0.86. Longitudinal analysis revealed that betweenness centrality was the best metric to identify patients who declined at 3 months, who will then continue to experience decline from 3-12 months. Both models were significantly higher AUC values than a random classifier. Conclusion: Our results suggest that inferred changes of network integrity were able to correctly classify picture naming decline after ATLR. These measures may be used to prospectively to identify patients who are at risk of picture naming decline after surgery and could potentially be utilised to assist tailoring the resection in order to prevent this decline

Infections in patients with lymphoproliferative diseases treated with targeted agents: SEIFEM multicentric retrospective study

We describe the opportunistic infections occurring in 362 patients with lymphoproliferative disorders treated with ibrutinib and idelalisib in clinical practice. Overall, 108 of 362 patients (29·8%) developed infections, for a total of 152 events. Clinically defined infections (CDI) were 49·3% (75/152) and microbiologically defined infections (MDI) were 50·7% (77/152). Among 250 patients treated with ibrutinib, 28·8% (72/250) experienced one or more infections, for a total of 104 episodes. MDI were 49% (51/104). Bacterial infections were 66·7% (34/51), viral 19·6% (10/51) and invasive fungal diseases (IFD) 13·7% (7/51). Among the 112 patients treated with idelalisib, 32·1% (36/112) experienced one or more infections, for a total of 48 episodes. MDI were 54·2% (26/48). Bacterial infections were 34·6% (9/26), viral 61·5% (16/26) and IFD 3·8% (1/26). With ibrutinib, the rate of bacterial infections was significantly higher compared to idelalisib (66·7% vs. 34·6%; P = 0·007), while viral infections were most frequent in idelalisib (61·5% vs. 19·6%; P < 0·001). Although a higher rate of IFD was observed in patients treated with ibrutinib, the difference was not statistically significant (13·7% vs. 3·8% respectively; P = 0·18). Bacteria are the most frequent infections with ibrutinib, while viruses are most frequently involved with idelalisib