IN MIGRANTS’ SHOES. A GAME TO RAISE AWARENESS AND SUPPORT LONG-LASTING LEARNING

Questo contributo guarda al gioco come tecnologia per la comunicazione e l’apprendimento, analizzandolo in particolare come volto all’integrazione di migranti, tramite l’analisi del gioco urbano persuasivo A Hostile World e dei risultati di ricerca conseguiti in occasione della sua applicazione su due gruppi di adolescenti individuati per i loro comportamenti ostili nei confronti degli immigrati. Lo scopo del gioco è far immergere i partecipanti in situazioni inconsuete, per problematizzare e modi care attitudini mentali e preconcetti esistenti, promuovendo acquisizioni di saperi capaci di modi care comportamenti e aumentare l’empatia. Lo studio è una ricerca-azione condotta tramite questionari qualitativi somministrati pre- e post-esperienza, brevi interviste e focus group. L’analisi dei risultati rivela che i giocatori sono stati coinvolti in toccanti, scomodi processi di identi cazione che hanno ridotto pregiudizi esistenti, incrementando la comprensione delle fatiche e fragilità altrui, con risultati rilevanti in termini di apprendimento trasformativo, che ancora persiste.This contribution looks at the game as a technology for communicating, sharing and learning. It poses a specific focus on the play activity as a means to address cultural integration, presenting the analysis and research outcomes gleaned enquiring the persuasive urban game AHW (full name removed for blind peer review) and its application to a group of adolescents who manifested hostile feelings towards foreigners. The game intends to immerse players into awkward situations to problematise and modify their former mindset, prejudices and biases towards migrants, fostering effective learning outcomes able to affect behaviours and increase empathy. The enquiry is an action research conducted via pre- and post-experience qualitative questionnaires, short interviews and focus groups. The analysis reveals that players were involved in processes of moving, uncomfortable identification that lessened existing prejudices, increasing the comprehension of certain immigrants’ conditions and fragility, with relevant outcomes in terms of persisting transformative learning

Discovery of large genomic inversions using long range information.

BackgroundAlthough many algorithms are now available that aim to characterize different classes of structural variation, discovery of balanced rearrangements such as inversions remains an open problem. This is mainly due to the fact that breakpoints of such events typically lie within segmental duplications or common repeats, which reduces the mappability of short reads. The algorithms developed within the 1000 Genomes Project to identify inversions are limited to relatively short inversions, and there are currently no available algorithms to discover large inversions using high throughput sequencing technologies.ResultsHere we propose a novel algorithm, VALOR, to discover large inversions using new sequencing methods that provide long range information such as 10X Genomics linked-read sequencing, pooled clone sequencing, or other similar technologies that we commonly refer to as long range sequencing. We demonstrate the utility of VALOR using both pooled clone sequencing and 10X Genomics linked-read sequencing generated from the genome of an individual from the HapMap project (NA12878). We also provide a comprehensive comparison of VALOR against several state-of-the-art structural variation discovery algorithms that use whole genome shotgun sequencing data.ConclusionsIn this paper, we show that VALOR is able to accurately discover all previously identified and experimentally validated large inversions in the same genome with a low false discovery rate. Using VALOR, we also predicted a novel inversion, which we validated using fluorescent in situ hybridization. VALOR is available at https://github.com/BilkentCompGen/VALOR

Synthetic Training Set Generation using Text-To-Audio Models for Environmental Sound Classification

In recent years, text-to-audio models have revolutionized the field of automatic audio generation. This paper investigates their application in generating synthetic datasets for training data-driven models. Specifically, this study analyzes the performance of two environmental sound classification systems trained with data generated from text-to-audio models. We considered three scenarios: a) augmenting the training dataset with data generated by text-to-audio models; b) using a mixed training dataset combining real and synthetic text-driven generated data; and c) using a training dataset composed entirely of synthetic audio. In all cases, the performance of the classification models was tested on real data. Results indicate that text-to-audio models are effective for dataset augmentation, with consistent performance when replacing a subset of the recorded dataset. However, the performance of the audio recognition models drops when relying entirely on generated audio

Reconstructing complex regions of genomes using long-read sequencing technology

Cataloged from PDF version of article.Obtaining high-quality sequence continuity of complex regions of recent segmental duplication remains one of the major challenges of finishing genome assemblies. In the human and mouse genomes, this was achieved by targeting large-insert clones using costly and laborious capillary-based sequencing approaches. Sanger shotgun sequencing of clone inserts, however, has now been largely abandoned, leaving most of these regions unresolved in newer genome assemblies generated primarily by next-generation sequencing hybrid approaches. Here we show that it is possible to resolve regions that are complex in a genome-wide context but simple in isolation for a fraction of the time and cost of traditional methods using long-read single molecule, real-time (SMRT) sequencing and assembly technology from Pacific Biosciences (PacBio). We sequenced and assembled BAC clones corresponding to a 1.3-Mbp complex region of chromosome 17q21.31, demonstrating 99.994% identity to Sanger assemblies of the same clones. We targeted 44 differences using Illumina sequencing and find that PacBio and Sanger assemblies share a comparable number of validated variants, albeit with different sequence context biases. Finally, we targeted a poorly assembled 766-kbp duplicated region of the chimpanzee genome and resolved the structure and organization for a fraction of the cost and time of traditional finishing approaches. Our data suggest a straightforward path for upgrading genomes to a higher quality finished state

Evaluation of Anti-Candida Activity of Vitis vinifera L. Seed Extracts Obtained from Wine and Table Cultivars

For the first time, grape seed extracts (GSEs), obtained from wine and table cultivars of Vitis vinifera L., cultured in experimental fields of Lazio and Puglia regions of Italy and grown in different agronomic conditions, have been tested on 43 Candida species strains.We demonstrated a significant correlation between the content of the flavan-3-ols in GSEs extracts, with a polymerization degree 654, and anti-Candida activity.Moreover, we demonstrated thatGSEs, obtained from plants cultured with reduced irrigation, showed a content of polymeric flavan-3-ols >250mg/gwith geometric mean MICvalues between 5.7 and 20.2 mg/L against Candida albicans reference strains. GSE, showing 573mg/g of polymeric flavan-3-ols, has been tested in an experimental murine model of vaginal candidiasis by using noninvasive in vivo imaging technique. The results pointed out a significant inhibition of Candida albicans load 5 days after challenge.These findings indicate that GSEs with high content of polymeric flavan-3-ols can be used in mucosal infection as vaginal candidiasis

Room Transfer Function Reconstruction Using Complex-valued Neural Networks and Irregularly Distributed Microphones

Reconstructing the room transfer functions needed to calculate the complex sound field in a room has several important real-world applications. However, an unpractical number of microphones is often required. Recently, in addition to classical signal processing methods, deep learning techniques have been applied to reconstruct the room transfer function starting from a very limited set of measurements at scattered points in the room. In this paper, we employ complex-valued neural networks to estimate room transfer functions in the frequency range of the first room resonances, using a few irregularly distributed microphones. To the best of our knowledge, this is the first time that complex-valued neural networks are used to estimate room transfer functions. To analyze the benefits of applying complex-valued optimization to the considered task, we compare the proposed technique with a state-of-the-art kernel-based signal processing approach for sound field reconstruction, showing that the proposed technique exhibits relevant advantages in terms of phase accuracy and overall quality of the reconstructed sound field. For informative purposes, we also compare the model with a similarly-structured data-driven approach that, however, applies a real-valued neural network to reconstruct only the magnitude of the sound field.Comment: Accepted at EUSIPCO 202

Gioco e disabilità, un’oscillazione tra limite e piacere

The article intends to start a critical and problematizing reflection on play and on disability, both of which are dimensions kept to the sidelines of our society which is dedicated to growth and productivity by a disciplining and normalizing pedagogical thought. A renewed view is proposed on play that can recognize its constitutive ambivalence, its transformative and subversive power andan expert and respectful view on disability which can approach the fragile and recondite dimensions of existence. Only through this view can play be accepted and reinstated into educational contexts and also into the life projects of people with disabilities as a fundamental and vital experience in view of their well-being and social inclusion.The experience of the Play Area of the non-profit association L’abilità is presented as a paradigmatic and innovative example of the implementation and realization of a radical and transformative thought which has established and returned a precious space and time for play to children with disabilities. It is a project which has been evolving continuously for fifteen years in search of newpossibilities, uncertainties and elation, swinging between the border and the pleasure of daring

Synthetic training set generation using text-to-audio models for environmental sound classification

In recent years, text-to-audio models have revolutionized the field of automatic audio generation. This paper investigates their application in generating synthetic datasets for training data-driven models. Specifically, this study analyzes the performance of two environmental sound classification systems trained with data generated from text-to-audio models. We considered three scenarios: a) augmenting the training dataset with data generated by text-to-audio models; b) using a mixed training dataset combining real and synthetic text-driven generated data; and c) using a training dataset composed entirely of synthetic audio. In all cases, the performance of the classification models was tested on real data. Results indicate that text-to-audio models are effective for dataset augmentation, with consistent performance when replacing a subset of the recorded dataset. However, the performance of the audio recognition models drops when relying entirely on generated audio