Lightweight and static verification of UML executable models

Executable models play a key role in many development methods (such as MDD and MDA) by facilitating the immediate simulation/implementation of the software system under development. This is possible because executable models include a fine-grained specification of the system behaviour using an action language. Executable models are not a new concept but are now experiencing a comeback. As a relevant example, the OMG has recently published the first version of the “Foundational Subset for Executable UML Models” (fUML) standard, an executable subset of the UML that can be used to define, in an operational style, the structural and behavioural semantics of systems. The OMG has also published a beta version of the “Action Language for fUML” (Alf) standard, a concrete syntax conforming to the fUML abstract syntax, that provides the constructs and textual notation to specify the fine-grained behaviour of systems. The OMG support to executable models is substantially raising the interest of software companies for this topic. Given the increasing importance of executable models and the impact of their correctness on the final quality of software systems derived from them, the existence of methods to verify the correctness of executable models is becoming crucial. Otherwise, the quality of the executable models (and in turn the quality of the final system generated from them) will be compromised. Despite the number of research works targetting the verification of software models, their computational cost and poor feedback makes them difficult to integrate in current software development processes. Therefore, there is the need for efficient and useful methods to check the correctness of executable models and tools integrated to the modelling tools used by designers. In this thesis we propose a verification framework to help the designers to improve the quality of their executable models. Our framework is composed of a set of lightweight static methods, i.e. methods that do not require to execute the model in order to check the desired property. These methods are able to check several properties over the behavioural part of an executable model (for instance, over the set of operations that compose a behavioural executable model) such as syntactic correctness (i.e. all the operations in the behavioural model conform to the syntax of the language in which it is described), non-redundancy (i.e. there is no another operation with exactly the same behaviour), executability (i.e. after the execution of an operation, the reached system state is -in case of strong executability- or may be -in case of weak executability- consistent with the structural model and its integrity constraints) and completeness (i.e. all possible changes on the system state can be performed through the execution of the operations defined in the executable model). For incorrect models, the methods that compose our verification framework return a meaningful feedback that helps repairing the detected inconsistencies

Systems Analytics and Integration of Big Omics Data

A “genotype"" is essentially an organism's full hereditary information which is obtained from its parents. A ""phenotype"" is an organism's actual observed physical and behavioral properties. These may include traits such as morphology, size, height, eye color, metabolism, etc. One of the pressing challenges in computational and systems biology is genotype-to-phenotype prediction. This is challenging given the amount of data generated by modern Omics technologies. This “Big Data” is so large and complex that traditional data processing applications are not up to the task. Challenges arise in collection, analysis, mining, sharing, transfer, visualization, archiving, and integration of these data. In this Special Issue, there is a focus on the systems-level analysis of Omics data, recent developments in gene ontology annotation, and advances in biological pathways and network biology. The integration of Omics data with clinical and biomedical data using machine learning is explored. This Special Issue covers new methodologies in the context of gene–environment interactions, tissue-specific gene expression, and how external factors or host genetics impact the microbiome

Transitions in teacher education and professional identities: proceedings

The University of Minho, Braga, Portugal, was the host for the 2014 Annual Conference of the Association for Teacher Education in Europe (ATEE), which took place in August, from the 25th to the 27th. The Conference focused on Transitions in Teacher Education and Professional Identities looked at the transitions in teacher education and analysed different experiences in professional identity of (student) teachers from an international perspective. Three keywords may be identified: challenges in teaching, dilemmas in teacher education and in teacher educators’ role and current trends that are shaping teacher education in different contexts. Similar dilemmas and even contradictions have been identified in different settings with different modes of government intervention in teacher education in which content, structure and duration are also diverse but with similar features. Another key theme discussed at the Conference was the complexity of the concept of identity and also the contested nature of the transitions: transitions for what? How? Why? These transitions and shifts in teacher education and professional identities need to be examined within the context of current policies but also in the light of the complexities and contradictions of teaching as a profession. Teacher educators are also facing transitions in teacher education curricula but also regarding their own identities. These are complex processes that may include resistance and turbulence because transitions may be troublesome for many reasons. In this regard context and language matter but also the kinds of policies and practices that exist within teacher education. There are questions that remain unanswered. However, despite the differences, the dilemmas, and even the contradictions, teacher education can make a difference in professional identity development as was the case of successful experiences that have been described in the Conference

Extracellular vesicles from induced neurons trigger epigenetic silencing of a brain neurotransmitter.

Introduction: Antithrombin (AT) is a glycoprotein involved in the regulation of blood coagulation. It belongs to the family of serine-protease inhibitors and acts as the most important antagonist of different clot- ting factors. Two types of inherited AT deficiency can be distinguished: Type I (quantitative deficit), and Type II (qualitative deficit). The latter is characterized by an impaired inhibitory activity related to dysfunc- tional domains of the protein. Three Type II subtypes can be defined: Type IIa (reactive site defect), Type IIb (heparin binding site defect) and Type IIc (pleiotropic defect). This classification has clinical importance since these subtypes have a different thrombotic risk. No functional routine diagnostic assay, however, can be assumed to detect all forms of Type II deficiencies since false-negative results may hamper the diagnosis. Methods: We analysed the biochemical/biophysical association of ATT to EVs. We separated EVs from plasma of healthy or Type II affected patients or from cultured hepatocytes through differential ultracentrifu- gation followed by sucrose density gradient and/or immunoprecipitation. We next combined dot blot ana- lysis, WB, 2D electrophoresis and enzymatic assays to reveal the nature of ATT association to EVs. Results: We evidenced that ATT is associated to the external leaflet of EVs. We also found that specific ATT isoforms are enriched in EV preparations in respect to total plasma and that those isoforms are selectively associated to EVs when comparing healthy or ATT type II deficient patients. Summary/Conclusion: ATT selective association pat- tern to EVs might be related either to mutations in the primary sequence of the protein or alterations in the glycosylation process, hence experiments are ongoing to reveal the nature of this phenomenon. Our findings suggest that analysis of ATT enriched in EV prepara- tions might be useful to gain insights into the patho- genesis and be of support in the diagnostic algorithm of ATT deficiency. Funding: This work acknowledges FFABR (Fondo finanziamento attività Base di ricerca from MIUR, Ministry of Education, Universities and Research, Italy) for financial support

Animal Modeling in Cancer

Dear Readers, Understanding the pathological mechanisms involved in human diseases and their possible treatment has been historically based on comparative analysis of diverse animal species that share a similar genetic, physiological and behavioural composition. The ancient Greeks were the first to use animals as models for anatomy and physiology, and this was consequently adopted by other cultures and led to important discoveries. In recent years, there have been many efforts to understand and fight cancer through new revolutionary personalized treatments and wider screenings that help diagnose and treat cancer. A fundamental part of this effort is to develop suitable cancer animal models that simulate the different disease variants and their progression. Ranging from tumor-derived xenografts to genetically engineered models, a wide variety of systems are applied for this purpose, and many technological breakthroughs are changing the way cancer is studied and analyzed. In this Special Issue, we collected a set of research articles and reviews that focus on the generation of cancer animal models that are used for understanding the disease and contribute to designing and testing new drugs for cancer prevention or treatment. Vladimir Korinek Collection Edito