Materials and resources for teaching Italian pragmatics

Màster de Lingüística Aplicada i Adquisició de Llengües en Contextos Multilingües, Departament de Filologia Anglesa i Alemanya, Universitat de Barcelona, Curs: 2019-2020, Tutora: Júlia BarónThis study aims to describe the materials and the resources employed for the instruction of Italian pragmatics, in both foreign and second language contexts. 139 teachers of Italian answered an online questionnaire which elicited information about the materials and resources they used in the classroom as well as information regarding their teaching techniques. Their answers were clustered into five main categories. The results revealed that the most commonly used materials were printed, audiovisual, self-produced, students’ oral production and digital materials. Within these categories, textbooks (printed materials) and videos (audiovisual material) were considered as the most preferred materials by the teachers. Regarding teaching techniques, the most frequently used by teachers were role plays, watching videos and listening exercises. The findings of the present study suggest that textbooks, the main resource for teaching pragmatics, should be implemented with specific activities on this topic. By receiving guidelines teachers could appropriately teach pragmatics in their classes, without the need to create their own materials

Integration of bioinformatic predictions and experimental data to identify circRNA-miRNA associations

Circular RNAs (circRNAs) have recently emerged as a novel class of transcripts, characterized by covalently linked 3′–5′ ends that result in the so-called backsplice junction. During the last few years, thousands of circRNAs have been identified in different organisms. Yet, despite their role as disease biomarker started to emerge, depicting their function remains challenging. Different studies have shown that certain circRNAs act as miRNA sponges, but any attempt to generalize from the single case to the “circ-ome” has failed so far. In this review, we explore the potential to define miRNA “sponging” as a more general function of circRNAs and describe the different approaches to predict miRNA response elements (MREs) in known or novel circRNA sequences. Moreover, we discuss how experiments based on Ago2-IP and experimentally validated miRNA:target duplexes can be used to either prioritize or validate putative miRNA-circRNA associations

A locally adaptive statistical procedure (LAP) to identify differentially expressed chromosomal regions

Abstract Motivation: The systematic integration of expression profiles and other types of gene information, such as chromosomal localization, ontological annotations and sequence characteristics, still represents a challenge in the gene expression arena. In particular, the analysis of transcriptional data in context of the physical location of genes in a genome appears promising in detecting chromosomal regions with transcriptional imbalances often characterizing cancer. Results: A computational tool named locally adaptive statistical procedure (LAP), which incorporates transcriptional data and structural information for the identification of differentially expressed chromosomal regions, is described. LAP accounts for variations in the distance between genes and in gene density by smoothing standard statistics on gene position before testing the significance of their differential levels of gene expression. The procedure smoothes parameters and computes p-values locally to account for the complex structure of the genome and to more precisely estimate the differential expression of chromosomal regions. The application of LAP to three independent sets of raw expression data allowed identifying differentially expressed regions that are directly involved in known chromosomal aberrations characteristic of tumors. Availability: Functions in R for implementing the LAP method are available at Contact: [email protected] Supplementary Information

Computational Methods for the Integrative Analysis of Genomics and Pharmacological Data

Since the pioneering NCI-60 panel of the late'80's, several major screenings of genetic profiling and drug testing in cancer cell lines have been conducted to investigate how genetic backgrounds and transcriptional patterns shape cancer's response to therapy and to identify disease-specific genes associated with drug response. Historically, pharmacogenomics screenings have been largely heterogeneous in terms of investigated cell lines, assay technologies, number of compounds, type and quality of genomic data, and methods for their computational analysis. The analysis of this enormous and heterogeneous amount of data required the development of computational methods for the integration of genomic profiles with drug responses across multiple screenings. Here, we will review the computational tools that have been developed to integrate cancer cell lines' genomic profiles and sensitivity to small molecule perturbations obtained from different screenings

COVID-19 health policy evaluation: integrating health and economic perspectives with a data envelopment analysis approach

The COVID-19 pandemic is a global challenge to humankind. To improve the knowledge regarding relevant, efficient and effective COVID-19 measures in health policy, this paper applies a multi-criteria evaluation approach with population, health care, and economic datasets from 19 countries within the OECD. The comparative investigation was based on a Data Envelopment Analysis approach as an efficiency measurement method. Results indicate that on the one hand, factors like population size, population density, and country development stage, did not play a major role in successful pandemic management. On the other hand, pre-pandemic healthcare system policies were decisive. Healthcare systems with a primary care orientation and a high proportion of primary care doctors compared to specialists were found to be more efficient than systems with a medium level of resources that were partly financed through public funding and characterized by a high level of access regulation. Roughly two weeks after the introduction of ad hoc measures, e.g., lockdowns and quarantine policies, we did not observe a direct impact on country-level healthcare efficiency, while delayed lockdowns led to significantly lower efficiency levels during the first COVID-19 wave in 2020. From an economic perspective, strategies without general lockdowns were identified as a more efficient strategy than the full lockdown strategy. Additionally, governmental support of short-term work is promising. Improving the efficiency of COVID-19 countermeasures is crucial in saving as many lives as possible with limited resources

Marker identification and classification of cancer types using gene expression data and SIMCA

Objectives. High-throughput technologies are radically boosting the understanding of living systems, thus creating enormous opportunities to elucidate the biological processes of cells in different physiological states. In particular, the application of DNA microarrays to monitor expression profiles from tumor cells is improving cancer analysis to levels that classical methods have been unable to reach. However, molecular diagnostics based on expression profiling requires addressing computational issues as the overwhelming number of variables and the complex, multi-class nature of tumor samples. Thus, the objective of the present research has been the development of a computational procedure for feature extraction and classification of gene expression data.Methods. The Soft Independent Modeling of Class Analogy (SIMCA) approach has been implemented in a data mining scheme, which allows the identification of those genes that are most likely to confer robust and accurate classification of samples from multiple tumor types.Results: The proposed method has been tested on two different microarray data sets, namely Golub's analysis of acute human leukemia [1] and the small round blue cell tumors study presented by Khan et al. [2]. The identified features represent a rational and dimensionally reduced base for understanding the biology of diseases, defining targets of therapeutic intervention, and developing diagnostic tools for classification of pathological states.Conclusions: The analysis of the SIMCA model residuals allows the identification of specific phenotype markers. At the some time, the class analogy approach provides the assignment to multiple classes, such as different pathological conditions or tissue samples, for previously unseen instances

Acute Leukemia Subclassification: A Marker Protein Expression Perspective

Improved leukemia classification and tailoring of therapy have greatly improved patient outcome particularly for children with acute leukemia (AL). Using immunophenotyping, molecular genetics and cytogenetics the low hanging fruits of biomedical research have been successfully incorporated in routine diagnosis of leukemia subclasses. Future improvements in the classification and understanding of leukemia biology will very likely be more slow and laborious. Recently, gene expression profiling has provided a framework for the global molecular analysis of hematological cancers, and high throughput proteomic analysis of leukemia samples is on the way. Here we consider classification of acute leukemia samples by flow cytometry using the marker proteins of immunophenotyping as a component of the proteome. Marker protein expressions are converted into quantitative expression values and subjected to computational analysis. Quantitative multivariate analysis from panels of marker proteins has demonstrated that marker protein expression profiles can distinguish MLLre from non-MLLre ALL cases and also allow to specifically distinguish MLL/AF4 cases. Potentially, these quantitative expression analyses can be used in clinical diagnosis. Immunophenotypic data collection using flow cytometry is a fast and relatively easily accessible technology that has already been implemented in most centers for leukemia diagnosis and the translation into quantitative expression data sets is a matter of flow cytometer settings and output calibration. However, before application in clinical diagnostics can occur it is crucial that quantitative immunophenotypic data set analysis is validated in independent experiments and in large data sets

MATERIALS AND RESOURCES FOR TEACHING ITALIAN PRAGMATICS

This study aims to describe the materials and the resources utilized for teaching Italian pragmatics, both in foreign and second language contexts. 139 teachers of Italian answered an online questionnaire which elicited information about the materials and resources they used in the classroom as well as information regarding their teaching techniques. Their answers were clustered into five main categories. The results revealed that the most commonly used materials were printed, audiovisual, self-produced, students’ oral production and digital materials. Within these categories, textbooks (printed materials) and videos (audiovisual material) were considered as the most preferred materials by the teachers. Regarding teaching techniques, the most frequently used by teachers were role plays, watching videos and listening exercises. The findings of the present study suggest that textbooks, the main resource for teaching pragmatics, should be implemented with specific activities on this topic. By receiving guidelines, teachers could appropriately teach pragmatics in their classes without the need to create their own materials.   Materiali e risorse per l’insegnamento della pragmatica italiana Questo studio mira a descrivere i materiali e le risorse utilizzate per l’insegnamento della pragmatica italiana, sia in contesti di lingua straniera che di lingua seconda. 139 insegnanti di italiano hanno risposto a un questionario online predisposto allo scopo di raccogliere informazioni sui materiali e le risorse che usavano in classe e sulle loro tecniche di insegnamento. Le risposte degli insegnanti sono state raggruppate in cinque principali categorie. I risultati hanno rivelato che i materiali più comunemente usati sono a stampa, audiovisivi, autoprodotti, produzione orale degli studenti e digitali. All’interno di queste categorie, i libri di testo (materiali stampati) e i video (materiale audiovisivo) sono i materiali preferiti dagli insegnanti. Per quanto riguarda le tecniche di insegnamento, le più utilizzate dagli insegnanti risultano essere i giochi di ruolo, la visione di video e gli esercizi di ascolto. I risultati del presente studio suggeriscono che i libri di testo, la risorsa principale per l’insegnamento della pragmatica, dovrebbero essere implementati con attività specifiche a questo dedicate. Disponendo di linee guida, gli insegnanti potrebbero insegnare in modo appropriato la pragmatica nelle loro classi senza avere la necessità di creare materiali propri

Impact of probe annotation on the integration of miRNA-mRNA expression profiles for miRNA target detection

MicroRNAs (miRNAs) are small non-coding RNAs that mediate gene expression at the post-transcriptional and translational levels by an imperfect binding to target mRNA 3'UTR regions. While the ab-initio computational prediction of miRNA-mRNA interactions still poses significant challenges, it is possible to overcome some of its limitations by carefully integrating into the analysis the paired expression profiles of miRNAs and mRNAs. In this work, we show how the choice of a proper probe annotation for microarray platforms is an essential requirement to achieve good sensitivity in the identification of miRNA-mRNA interactions. We compare the results obtained from the analysis of the same expression profiles using both gene and transcript based custom CDFs that we have developed for a number of different annotations (ENSEMBL, RefSeq, AceView). In all cases, transcript-based annotations clearly improve the effectiveness of data integration and thus provide a more reliable confirmation of computationally predicted miRNA-mRNA interaction

GNN-LoFI: a Novel Graph Neural Network through Localized Feature-based Histogram Intersection

Graph neural networks are increasingly becoming the framework of choice for graph-based machine learning. In this paper, we propose a new graph neural network architecture that substitutes classical message passing with an analysis of the local distribution of node features. To this end, we extract the distribution of features in the egonet for each local neighbourhood and compare them against a set of learned label distributions by taking the histogram intersection kernel. The similarity information is then propagated to other nodes in the network, effectively creating a message passing-like mechanism where the message is determined by the ensemble of the features. We perform an ablation study to evaluate the network's performance under different choices of its hyper-parameters. Finally, we test our model on standard graph classification and regression benchmarks, and we find that it outperforms widely used alternative approaches, including both graph kernels and graph neural networks