Per una mappatura storica delle scuole Montessori : percorso di ricerca sulle fonti

Il contributo si propone di rendere visibile il percorso di ricerca archivistica retrostante una delle azioni centrali in cui sono state coinvolte tutte le Unità di ricerca del PRIN Maria Montessori: tra storia e attualità. Ricezione e diffusione della sua pedagogia in Italia a 150 anni dalla nascita: la Mappatura storica delle scuole montessoriane in Italia dal 1907 ad oggi. L'articolo mira quindi ad esplicitare i passaggi salienti del percorso intrapreso, le scelte metodologiche, l’individuazione e selezione delle possibili fonti e le difficoltà riscontrate nelle fasi di raccolta. A seguire viene presentata ed approfondita la fase di classificazione ed elaborazione dei dati, nonché la definizione di uno strumento di catalogazione. I vari passaggi di ricerca e analisi delle fonti danno così la possibilità di comprendere più nitidamente il disegno d'insieme e le azioni diversificate confluenti e concorrenti alla realizzazione della mappa storica interattiva, elaborata dall'Unità dell'Università Bicocca di Milano

Implementation of guidelines about women with previous cesarean section through educational/motivational interventions in providers

Objective: The study reports the effect of a quality improvement project with an educational/motivational intervention, in northern Italy on the implementation of the trial of labor after Caesarean Section (CS). Method: A pre-post study design was used. Every birth center (23) of the Emilia-Romagna region was included. Gynecologists' opinion leaders were first trained about CS Italian recommendations. Barriers to implementation were discussed and shared. Educational/motivational interventions were implemented. Data of multipara with previous CS, with a single, cephalic pregnancy at term, were collected in 2 periods, before (2012-2014) and after (2017-2019) the intervention (2015-2016). The primary outcome was the rate of vaginal birth after CS (VBAC) and perinatal outcomes. Results: A total of 20,496 women were included. The VBAC rate increased from 18.1% to 23.1% after intervention (p<0.001). The likelihood of VBAC, adjusted for age ≥40, Caucasian, BMI ≥30, previous vaginal delivery, and labor induction, was increased by the intervention of 42% (OR=1.42, 95% CI 1.31-1.54). Neonatal well-being was improved by intervention, indeed neonates requiring resuscitation decreased from 2.1% to 1.6% (p=0.001). Conclusion: Educating and motivating gynecologists toward the trial of labor after CS is worth pursuing. Health quality improvement is demonstrated by increased VBAC even improving neonatal well-being

A functional genetic toolbox for human tissue-derived organoids.

Funder: Alzheimers Research UK Stem Cell Research CentreHuman organoid systems recapitulate key features of organs offering platforms for modelling developmental biology and disease. Tissue-derived organoids have been widely used to study the impact of extrinsic niche factors on stem cells. However, they are rarely used to study endogenous gene function due to the lack of efficient gene manipulation tools. Previously, we established a human foetal lung organoid system (Nikolić et al., 2017). Here, using this organoid system as an example we have systematically developed and optimised a complete genetic toolbox for use in tissue-derived organoids. This includes 'Organoid Easytag' our efficient workflow for targeting all types of gene loci through CRISPR-mediated homologous recombination followed by flow cytometry for enriching correctly-targeted cells. Our toolbox also incorporates conditional gene knock-down or overexpression using tightly-inducible CRISPR interference and CRISPR activation which is the first efficient application of these techniques to tissue-derived organoids. These tools will facilitate gene perturbation studies in tissue-derived organoids facilitating human disease modelling and providing a functional counterpart to many on-going descriptive studies, such as the Human Cell Atlas Project

miRNA levels are associated with body mass index in endometrial cancer and may have implications for therapy

Endometrial cancer (EC) is the most prevalent gynecological cancer in high-income countries. Its incidence is skyrocketing due to the increase in risk factors such as obesity, which represents a true pandemic. This study aimed to evaluate microRNA (miRNA) expression in obesity-related EC to identify potential associations between this specific cancer type and obesity. miRNA levels were analyzed in 84 EC patients stratified based on body mass index (BMI; >= 30 or <30) and nine noncancer women with obesity. The data were further tested in The Cancer Genome Atlas (TCGA) cohort, including 384 EC patients, 235 with BMI >= 30 and 149 with BMI <30. Prediction of miRNA targets and analysis of their expression were also performed to identify the potential epigenetic networks involved in obesity modulation. In the EC cohort, BMI >= 30 was significantly associated with 11 deregulated miRNAs. The topmost deregulated miRNAs were first analyzed in 84 EC samples by single miRNA assay and then tested in the TCGA dataset. This independent validation provided further confirmation about the significant difference of three miRNAs (miR-199a-5p, miR-449a, miR-449b-5p) in normal-weight EC patients versus EC patients with obesity, resulting significantly higher expressed in the latter. Moreover, the three miRNAs were significantly correlated with grade, histological type, and overall survival. Analysis of their target genes revealed that these miRNAs may regulate obesity-related pathways. In conclusion, we identified specific miRNAs associated with BMI that are potentially involved in modulating obesity-related pathways and that may provide novel implications for the clinical management of obese EC patients

International consensus statement on the diagnosis and management of autosomal dominant polycystic kidney disease in children and young people

These recommendations were systematically developed on behalf of the Network for Early Onset Cystic Kidney Disease (NEOCYST) by an international group of experts in autosomal dominant polycystic kidney disease (ADPKD) from paediatric and adult nephrology, human genetics, paediatric radiology and ethics specialties together with patient representatives. They have been endorsed by the International Pediatric Nephrology Association (IPNA) and the European Society of Paediatric Nephrology (ESPN). For asymptomatic minors at risk of ADPKD, ongoing surveillance (repeated screening for treatable disease manifestations without diagnostic testing) or immediate diagnostic screening are equally valid clinical approaches. Ultrasonography is the current radiological method of choice for screening. Sonographic detection of one or more cysts in an at-risk child is highly suggestive of ADPKD, but a negative scan cannot rule out ADPKD in childhood. Genetic testing is recommended for infants with very-early-onset symptomatic disease and for children with a negative family history and progressive disease. Children with a positive family history and either confirmed or unknown disease status should be monitored for hypertension (preferably by ambulatory blood pressure monitoring) and albuminuria. Currently, vasopressin antagonists should not be offered routinely but off-label use can be considered in selected children. No consensus was reached on the use of statins, but mTOR inhibitors and somatostatin analogues are not recommended. Children with ADPKD should be strongly encouraged to achieve the low dietary salt intake that is recommended for all children

Computational approaches to semantic change

Semantic change — how the meanings of words change over time — has preoccupied scholars since well before modern linguistics emerged in the late 19th and early 20th century, ushering in a new methodological turn in the study of language change. Compared to changes in sound and grammar, semantic change is the least  understood. Ever since, the study of semantic change has progressed steadily, accumulating a vast store of knowledge for over a century, encompassing many languages and language families. Historical linguists also early on realized the potential of computers as research tools, with papers at the very first international conferences in computational linguistics in the 1960s. Such computational studies still tended to be small-scale, method-oriented, and qualitative. However, recent years have witnessed a sea-change in this regard. Big-data empirical quantitative investigations are now coming to the forefront, enabled by enormous advances in storage capability and processing power. Diachronic corpora have grown beyond imagination, defying exploration by traditional manual qualitative methods, and language technology has become increasingly data-driven and semantics-oriented. These developments present a golden opportunity for the empirical study of semantic change over both long and short time spans. A major challenge presently is to integrate the hard-earned  knowledge and expertise of traditional historical linguistics with  cutting-edge methodology explored primarily in computational linguistics. The idea for the present volume came out of a concrete response to this challenge.  The 1st International Workshop on Computational Approaches to Historical Language Change (LChange'19), at ACL 2019, brought together scholars from both fields. This volume offers a survey of this exciting new direction in the study of semantic change, a discussion of the many remaining challenges that we face in pursuing it, and considerably updated and extended versions of a selection of the contributions to the LChange'19 workshop, addressing both more theoretical problems —  e.g., discovery of "laws of semantic change" — and practical applications, such as information retrieval in longitudinal text archives

Computational approaches to semantic change

Semantic change — how the meanings of words change over time — has preoccupied scholars since well before modern linguistics emerged in the late 19th and early 20th century, ushering in a new methodological turn in the study of language change. Compared to changes in sound and grammar, semantic change is the least  understood. Ever since, the study of semantic change has progressed steadily, accumulating a vast store of knowledge for over a century, encompassing many languages and language families. Historical linguists also early on realized the potential of computers as research tools, with papers at the very first international conferences in computational linguistics in the 1960s. Such computational studies still tended to be small-scale, method-oriented, and qualitative. However, recent years have witnessed a sea-change in this regard. Big-data empirical quantitative investigations are now coming to the forefront, enabled by enormous advances in storage capability and processing power. Diachronic corpora have grown beyond imagination, defying exploration by traditional manual qualitative methods, and language technology has become increasingly data-driven and semantics-oriented. These developments present a golden opportunity for the empirical study of semantic change over both long and short time spans. A major challenge presently is to integrate the hard-earned  knowledge and expertise of traditional historical linguistics with  cutting-edge methodology explored primarily in computational linguistics. The idea for the present volume came out of a concrete response to this challenge.  The 1st International Workshop on Computational Approaches to Historical Language Change (LChange'19), at ACL 2019, brought together scholars from both fields. This volume offers a survey of this exciting new direction in the study of semantic change, a discussion of the many remaining challenges that we face in pursuing it, and considerably updated and extended versions of a selection of the contributions to the LChange'19 workshop, addressing both more theoretical problems —  e.g., discovery of "laws of semantic change" — and practical applications, such as information retrieval in longitudinal text archives

Computational approaches to semantic change

Semantic change — how the meanings of words change over time — has preoccupied scholars since well before modern linguistics emerged in the late 19th and early 20th century, ushering in a new methodological turn in the study of language change. Compared to changes in sound and grammar, semantic change is the least  understood. Ever since, the study of semantic change has progressed steadily, accumulating a vast store of knowledge for over a century, encompassing many languages and language families. Historical linguists also early on realized the potential of computers as research tools, with papers at the very first international conferences in computational linguistics in the 1960s. Such computational studies still tended to be small-scale, method-oriented, and qualitative. However, recent years have witnessed a sea-change in this regard. Big-data empirical quantitative investigations are now coming to the forefront, enabled by enormous advances in storage capability and processing power. Diachronic corpora have grown beyond imagination, defying exploration by traditional manual qualitative methods, and language technology has become increasingly data-driven and semantics-oriented. These developments present a golden opportunity for the empirical study of semantic change over both long and short time spans. A major challenge presently is to integrate the hard-earned  knowledge and expertise of traditional historical linguistics with  cutting-edge methodology explored primarily in computational linguistics. The idea for the present volume came out of a concrete response to this challenge.  The 1st International Workshop on Computational Approaches to Historical Language Change (LChange'19), at ACL 2019, brought together scholars from both fields. This volume offers a survey of this exciting new direction in the study of semantic change, a discussion of the many remaining challenges that we face in pursuing it, and considerably updated and extended versions of a selection of the contributions to the LChange'19 workshop, addressing both more theoretical problems —  e.g., discovery of "laws of semantic change" — and practical applications, such as information retrieval in longitudinal text archives

Computational approaches to semantic change

Semantic change — how the meanings of words change over time — has preoccupied scholars since well before modern linguistics emerged in the late 19th and early 20th century, ushering in a new methodological turn in the study of language change. Compared to changes in sound and grammar, semantic change is the least  understood. Ever since, the study of semantic change has progressed steadily, accumulating a vast store of knowledge for over a century, encompassing many languages and language families. Historical linguists also early on realized the potential of computers as research tools, with papers at the very first international conferences in computational linguistics in the 1960s. Such computational studies still tended to be small-scale, method-oriented, and qualitative. However, recent years have witnessed a sea-change in this regard. Big-data empirical quantitative investigations are now coming to the forefront, enabled by enormous advances in storage capability and processing power. Diachronic corpora have grown beyond imagination, defying exploration by traditional manual qualitative methods, and language technology has become increasingly data-driven and semantics-oriented. These developments present a golden opportunity for the empirical study of semantic change over both long and short time spans. A major challenge presently is to integrate the hard-earned  knowledge and expertise of traditional historical linguistics with  cutting-edge methodology explored primarily in computational linguistics. The idea for the present volume came out of a concrete response to this challenge.  The 1st International Workshop on Computational Approaches to Historical Language Change (LChange'19), at ACL 2019, brought together scholars from both fields. This volume offers a survey of this exciting new direction in the study of semantic change, a discussion of the many remaining challenges that we face in pursuing it, and considerably updated and extended versions of a selection of the contributions to the LChange'19 workshop, addressing both more theoretical problems —  e.g., discovery of "laws of semantic change" — and practical applications, such as information retrieval in longitudinal text archives

Computational approaches to semantic change

Semantic change — how the meanings of words change over time — has preoccupied scholars since well before modern linguistics emerged in the late 19th and early 20th century, ushering in a new methodological turn in the study of language change. Compared to changes in sound and grammar, semantic change is the least  understood. Ever since, the study of semantic change has progressed steadily, accumulating a vast store of knowledge for over a century, encompassing many languages and language families. Historical linguists also early on realized the potential of computers as research tools, with papers at the very first international conferences in computational linguistics in the 1960s. Such computational studies still tended to be small-scale, method-oriented, and qualitative. However, recent years have witnessed a sea-change in this regard. Big-data empirical quantitative investigations are now coming to the forefront, enabled by enormous advances in storage capability and processing power. Diachronic corpora have grown beyond imagination, defying exploration by traditional manual qualitative methods, and language technology has become increasingly data-driven and semantics-oriented. These developments present a golden opportunity for the empirical study of semantic change over both long and short time spans. A major challenge presently is to integrate the hard-earned  knowledge and expertise of traditional historical linguistics with  cutting-edge methodology explored primarily in computational linguistics. The idea for the present volume came out of a concrete response to this challenge.  The 1st International Workshop on Computational Approaches to Historical Language Change (LChange'19), at ACL 2019, brought together scholars from both fields. This volume offers a survey of this exciting new direction in the study of semantic change, a discussion of the many remaining challenges that we face in pursuing it, and considerably updated and extended versions of a selection of the contributions to the LChange'19 workshop, addressing both more theoretical problems —  e.g., discovery of "laws of semantic change" — and practical applications, such as information retrieval in longitudinal text archives