Diogene-CT: tools and methodologies for teaching and learning coding

AbstractComputational thinking is the capacity of undertaking a problem-solving process in various disciplines (including STEM, i.e. science, technology, engineering and mathematics) using distinctive techniques that are typical of computer science. It is nowadays considered a fundamental skill for students and citizens, that has the potential to affect future generations. At the roots of computational-thinking abilities stands the knowledge of computer programming, i.e. coding. With the goal of fostering computational thinking in young students, we address the challenging and open problem of using methods, tools and techniques to support teaching and learning of computer-programming skills in school curricula of the secondary grade and university courses. This problem is made complex by several factors. In fact, coding requires abstraction capabilities and complex cognitive skills such as procedural and conditional reasoning, planning, and analogical reasoning. In this paper, we introduce a new paradigm called ACME ("Code Animation by Evolved Metaphors") that stands at the foundation of the Diogene-CT code visualization environment and methodology. We develop consistent visual metaphors for both procedural and object-oriented programming. Based on the metaphors, we introduce a playground architecture to support teaching and learning of the principles of coding. To the best of our knowledge, this is the first scalable code visualization tool using consistent metaphors in the field of the Computing Education Research (CER). It might be considered as a new kind of tools named as code visualization environments

On federated single sign-on in e-government interoperability frameworks

We consider the problem of handling digital identities within serviceoriented architecture (SOA) architectures. We explore federated, single signon (SSO) solutions based on identity managers and service providers. After an overview of the different standards and protocols, we introduce a middlewarebased architecture to simplify the integration of legacy systems within such platforms. Our solution is based on a middleware module that decouples the legacy system from the identity-management modules.We consider both standard point-to-point service architectures, and complex government interoperability frameworks, and report experiments to show that our solution provides clear advantages both in terms of effectiveness and performance

Greg, ML – Machine Learning for Healthcare at a Scale

This paper introduces the Greg, ML platform, a machine-learning engine and toolset conceived to generate automatic diagnostic suggestions based on patient profiles. Greg, ML departs from many other experiences in machine learning for healthcare in the fact that it was designed to handle a large number of different diagnoses, in the order of the hundreds. We discuss the architecture that stands at the core of Greg, ML, designed to handle the complex challenges posed by this ambitious goal, and confirm its effectiveness with experimental results based on the working prototype we have developed. Finally, we discuss challenges and opportunities related to the use of this kind of tools in medicine, and some important lessons learned while developing the tool. In this respect, we underline that Greg, ML should be conceived primarily as a support for expert doctors in their diagnostic decisions, and can hardly replace humans in their judgment

Pythia: Unsupervised generation of ambiguous textual claims from relational data

Applications such as computational fact checking and data-to-text generation exploit the relationship between relational data and natural language text. Despite promising results in these areas, state of the art solutions simply fail in managing “data-ambiguity”, i.e., the case when there are multiple interpretations of the relationship between the textual sentence and the relational data. To tackle this problem, we introduce Pythia, a system that, given a relational table D, generates textual sentences that contain factual ambiguities w.r.t. the data in D. Such sentences can then be used to train target applications in handling data-ambiguity. In this demonstration, we first show how our system generates data ambiguous sentences for a given table in an unsupervised fashion by data profiling and query generation. We then demonstrate how two existing applications benefit from Pythia’s generated sentences, improving the state-of-the-art results. The audience will interact with Pythia by changing input parameters in an interactive fashion, including the upload of their own dataset to see what data ambiguous sentences are generated for it

Concomitant mutations G12D and G13D on the exon 2 of the KRAS gene. Two cases of women with colon adenocarcinoma

Colorectal cancer (CRC) is rapidly increasing representing the second most frequent cause of cancer-related deaths. From a clinical-molecular standpoint the therapeutically management of CRC focuses on main alterations found in the RAS family protein, where single mutations of KRAS are considered both the hallmark and the target of this tumor. Double and concomitant alterations of KRAS are still far to be interpreted as molecular characteristics which could potentially address different and more personalized treatments for patients. Here, we firstly describe the case of two patients at different stages (pT2N0M0 and pT4cN1cM1) but similarly showing a double concurrent mutations G12D and G13D in the exon 2 of the KRAS gene, normally mutually exclusive. We also evaluated genetic testing of dihydropyrimidine dehydrogenase (DPYD) and microsatellite instability (MSI) by real-time PCR and additional molecular mutations by next generation sequencing (NGS) which resulted coherently to the progression of the disease. Accordingly, we reinterpreted and discuss the clinical history of both cases treated as single mutations of KRAS but similarly progressing towards a metastatic asset. We concluded that double mutations of KRAS cannot be interpreted as univocal genomic alterations and that they could severely impact the clinical outcome in CRC, requiring a tighter monitoring of patients throughout the time.Abstract: Colorectal cancer (CRC) is rapidly increasing representing the second most frequent cause of cancer-related deaths. From a clinical-molecular standpoint the therapeutically management of CRC focuses on main alterations found in the RAS family protein, where single mutations of KRAS are considered both the hallmark and the target of this tumor. Double and concomitant alterations of KRAS are still far to be interpreted as molecular characteristics which could potentially address different and more personalized treatments for patients. Here, we firstly describe the case of two patients at different stages (pT2N0M0 and pT4cN1cM1) but similarly showing a double concurrent mutations G12D and G13D in the exon 2 of the KRAS gene, normally mutually exclusive. We also evaluated genetic testing of dihydropyrimidine dehydrogenase (DPYD) and microsatellite instability (MSI) by real-time PCR and additional molecular mutations by next generation sequencing (NGS) which resulted coherently to the progression of the disease. Accordingly, we reinterpreted and discuss the clinical history of both cases treated as single mutations of KRAS but similarly progressing towards a metastatic asset. We concluded that double mutations of KRAS cannot be interpreted as univocal genomic alterations and that they could severely impact the clinical outcome in CRC, requiring a tighter monitoring of patients throughout the time

Science communication and concept of risk in bio-tech-sciences: Is it a part of neo-liberalism, or foucaultian bio-politics?

In this work a Raman flow cytometer is presented. It consists of a microfluidic device that takes advantages of the basic principles of Raman spectroscopy and flow cytometry. The microfluidic device integrates calibrated microfluidic channels- where the cells can flow one-by-one -, allowing single cell Raman analysis. The microfluidic channel integrates plasmonic nanodimers in a fluidic trapping region. In this way it is possible to perform Enhanced Raman Spectroscopy on single cell. These allow a label-free analysis, providing information about the biochemical content of membrane and cytoplasm of the each cell. Experiments are performed on red blood cells (RBCs), peripheral blood lymphocytes (PBLs) and myelogenous leukemia tumor cells (K562)