Partial Behavioural Models for Requirements and Early Design

The talk will discuss the problem of creation, management, and specifically merging of partial behavioural models, expressed as model transition systems. We argue why this formalism is essential in the early stages of the software cycle and then discuss why and how to merge information coming from different sources using this formalism. The talk is based on papers presented in FSE\u2704 and FME\u2706 and will also include emerging results on synthesizing partial behavioural models from temporal properties and scenarios

Effect of natalizumab on disease progression in secondary progressive multiple sclerosis (ASCEND). a phase 3, randomised, double-blind, placebo-controlled trial with an open-label extension

Background: Although several disease-modifying treatments are available for relapsing multiple sclerosis, treatment effects have been more modest in progressive multiple sclerosis and have been observed particularly in actively relapsing subgroups or those with lesion activity on imaging. We sought to assess whether natalizumab slows disease progression in secondary progressive multiple sclerosis, independent of relapses. Methods: ASCEND was a phase 3, randomised, double-blind, placebo-controlled trial (part 1) with an optional 2 year open-label extension (part 2). Enrolled patients aged 18–58 years were natalizumab-naive and had secondary progressive multiple sclerosis for 2 years or more, disability progression unrelated to relapses in the previous year, and Expanded Disability Status Scale (EDSS) scores of 3·0–6·5. In part 1, patients from 163 sites in 17 countries were randomly assigned (1:1) to receive 300 mg intravenous natalizumab or placebo every 4 weeks for 2 years. Patients were stratified by site and by EDSS score (3·0–5·5 vs 6·0–6·5). Patients completing part 1 could enrol in part 2, in which all patients received natalizumab every 4 weeks until the end of the study. Throughout both parts, patients and staff were masked to the treatment received in part 1. The primary outcome in part 1 was the proportion of patients with sustained disability progression, assessed by one or more of three measures: the EDSS, Timed 25-Foot Walk (T25FW), and 9-Hole Peg Test (9HPT). The primary outcome in part 2 was the incidence of adverse events and serious adverse events. Efficacy and safety analyses were done in the intention-to-treat population. This trial is registered with ClinicalTrials.gov, number NCT01416181. Findings: Between Sept 13, 2011, and July 16, 2015, 889 patients were randomly assigned (n=440 to the natalizumab group, n=449 to the placebo group). In part 1, 195 (44%) of 439 natalizumab-treated patients and 214 (48%) of 448 placebo-treated patients had confirmed disability progression (odds ratio [OR] 0·86; 95% CI 0·66–1·13; p=0·287). No treatment effect was observed on the EDSS (OR 1·06, 95% CI 0·74–1·53; nominal p=0·753) or the T25FW (0·98, 0·74–1·30; nominal p=0·914) components of the primary outcome. However, natalizumab treatment reduced 9HPT progression (OR 0·56, 95% CI 0·40–0·80; nominal p=0·001). In part 1, 100 (22%) placebo-treated and 90 (20%) natalizumab-treated patients had serious adverse events. In part 2, 291 natalizumab-continuing patients and 274 natalizumab-naive patients received natalizumab (median follow-up 160 weeks [range 108–221]). Serious adverse events occurred in 39 (13%) patients continuing natalizumab and in 24 (9%) patients initiating natalizumab. Two deaths occurred in part 1, neither of which was considered related to study treatment. No progressive multifocal leukoencephalopathy occurred. Interpretation: Natalizumab treatment for secondary progressive multiple sclerosis did not reduce progression on the primary multicomponent disability endpoint in part 1, but it did reduce progression on its upper-limb component. Longer-term trials are needed to assess whether treatment of secondary progressive multiple sclerosis might produce benefits on additional disability components. Funding: Biogen

Advancing research for seamless Earth system prediction

Whether on an urban or planetary scale, covering time scales of a few minutes or a few decades, the societal need for more accurate weather, climate, water, and environmental information has led to a more seamless thinking across disciplines and communities. This challenge, at the intersection of scientific research and society’s need, is among the most important scientific and technological challenges of our time. The “Science Summit on Seamless Research for Weather, Climate, Water, and Environment” organized by the World Meteorological Organization (WMO) in 2017, has brought together researchers from a variety of institutions for a cross-disciplinary exchange of knowledge and ideas relating to seamless Earth system science. The outcomes of the Science Summit, and the interactions it sparked, highlight the benefit of a seamless Earth system science approach. Such an approach has the potential to break down artificial barriers that may exist due to different observing systems, models, time and space scales, and compartments of the Earth system. In this context, the main future challenges for research infrastructures have been identified. A value cycle approach has been proposed to guide innovation in seamless Earth system prediction. The engagement of researchers, users, and stakeholders will be crucial for the successful development of a seamless Earth system science that meets the needs of society

31st Annual Meeting and Associated Programs of the Society for Immunotherapy of Cancer (SITC 2016) : part two

Background The immunological escape of tumors represents one of the main ob- stacles to the treatment of malignancies. The blockade of PD-1 or CTLA-4 receptors represented a milestone in the history of immunotherapy. However, immune checkpoint inhibitors seem to be effective in specific cohorts of patients. It has been proposed that their efficacy relies on the presence of an immunological response. Thus, we hypothesized that disruption of the PD-L1/PD-1 axis would synergize with our oncolytic vaccine platform PeptiCRAd. Methods We used murine B16OVA in vivo tumor models and flow cytometry analysis to investigate the immunological background. Results First, we found that high-burden B16OVA tumors were refractory to combination immunotherapy. However, with a more aggressive schedule, tumors with a lower burden were more susceptible to the combination of PeptiCRAd and PD-L1 blockade. The therapy signifi- cantly increased the median survival of mice (Fig. 7). Interestingly, the reduced growth of contralaterally injected B16F10 cells sug- gested the presence of a long lasting immunological memory also against non-targeted antigens. Concerning the functional state of tumor infiltrating lymphocytes (TILs), we found that all the immune therapies would enhance the percentage of activated (PD-1pos TIM- 3neg) T lymphocytes and reduce the amount of exhausted (PD-1pos TIM-3pos) cells compared to placebo. As expected, we found that PeptiCRAd monotherapy could increase the number of antigen spe- cific CD8+ T cells compared to other treatments. However, only the combination with PD-L1 blockade could significantly increase the ra- tio between activated and exhausted pentamer positive cells (p= 0.0058), suggesting that by disrupting the PD-1/PD-L1 axis we could decrease the amount of dysfunctional antigen specific T cells. We ob- served that the anatomical location deeply influenced the state of CD4+ and CD8+ T lymphocytes. In fact, TIM-3 expression was in- creased by 2 fold on TILs compared to splenic and lymphoid T cells. In the CD8+ compartment, the expression of PD-1 on the surface seemed to be restricted to the tumor micro-environment, while CD4 + T cells had a high expression of PD-1 also in lymphoid organs. Interestingly, we found that the levels of PD-1 were significantly higher on CD8+ T cells than on CD4+ T cells into the tumor micro- environment (p < 0.0001). Conclusions In conclusion, we demonstrated that the efficacy of immune check- point inhibitors might be strongly enhanced by their combination with cancer vaccines. PeptiCRAd was able to increase the number of antigen-specific T cells and PD-L1 blockade prevented their exhaus- tion, resulting in long-lasting immunological memory and increased median survival

Abstract A Characterization of Merging Partial Behavioural Models

Constructing comprehensive operational models of intended system behaviour is a complex and costly task. Consequently, practitioners have adopted techniques that support partial be-haviour description and focus on elaborating these descriptions iteratively. Scenario-based specifications, for example, are incrementally elaborated to cover system behaviour that is of interest. However, how should partial behavioural models described by different stakeholders with different viewpoints be composed? How should partial models of component instances of the same type be put together? In this thesis, we use model merging based on observational refinement as a general solu-tion to these questions, where merging consistent models is a process that results in a minimal common refinement. We prove several mathematical characterizations of merging and consis-tency, study algebraic properties of the merge operator, and give new and improved algorithms related to constructing merge. Finally, we present a case study that illustrates the utility of our results. ii Acknowledgement

A Mixture Model for Learning Sparse Representations

In a latent variable model, an overcomplete representation is one in which the number of latent variables is at least as large as the dimension of the data observations. Overcomplete representations have been advocated due to robustness in the presence of noise, the ability to be sparse, and an inherent flexibility in modeling the structure of data [9]. In this report, we modify factor analysis to obtain a method for learning overcomplete sparse representations by replacing the Gaussian prior on the factors with a prior that encourages sparseness. This is achieved by using the factorable Laplacian, which implicitly adds a lasso-type penalty term on the latent variables. In order to approximate the intractable integrals introduced into this model, a variational technique is used to lower bound the posterior distributions. Using this lower bound, it is possible to develop an Expectation-Maximization (EM) learning algorithm for estimating the model parameters. We use this technique to extend the sparse factor analysis model to a mixture of sparse factor analyzers and develop an EM algorithm. The new EM algorithm for the mixture model is applied to a handwritten digit recognition problem and is compared to existing methods.

Behaviour Model Synthesis From Properties and Scenarios

Synthesis of behaviour models from software development artifacts such as scenario-based descriptions or requirements specifications not only helps significantly reduce the effort of model construction, but also provides a bridge between approaches geared toward requirements analysis and those geared towards reasoning about system design at the architectural level. However, the models favoured by existing synthesis approaches are not sufficiently expressive to describe both universal constraints provided by requirements and existential statements provided by scenarios. In this paper, we propose a novel synthesis technique that constructs behaviour models in the form of Modal Transition Systems (MTS) from a combination of safety properties and scenarios. MTSs distinguish required, possible and proscribed behaviour, and their elaboration not only guarantees the preservation of the properties and scenarios used for synthesis but also supports further elicitation of new requirements.