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53. Detecting Arguments in CJEU Decisions on Fiscal State Aid

Author

Grundler Giulia, Santin Piera, Galassi Andrea, Galli Federico, Godano Francesco, Lagioia Francesca, Palmieri Elena, Ruggeri Federico, Sartor Giovanni, Torroni Paolo, and Grundler Giulia , Santin Piera , Galassi Andrea , Galli Federico , Godano Francesco , Lagioia Francesca , Palmieri Elena , Ruggeri Federico , Sartor Giovanni , Torroni Paolo

Subjects
argument mining legal documents fiscal state aid legal analytics
Abstract

The successful application of argument mining in the legal domain can dramatically impact many disciplines related to law. For this purpose, we present Demosthenes, a novel corpus for argument mining in legal documents, composed of 40 decisions of the Court of Justice of the European Union on matters of fiscal state aid. The annotation specifies three hierarchical levels of information: the argumentative elements, their types, and their argument schemes. In our experimental evaluation, we address 4 different classification tasks, combining advanced language models and traditional classifiers.

Published
2022

54. A Sentiment and Emotion Annotated Dataset for Bitcoin Price Forecasting Based on Reddit Posts

Author

Seroyizhko Pavlo, Zhexenova Zhanel, Shafiq Muhammad Zohaib, Merizzi Fabio, Galassi Andrea, Ruggeri Federico, and Seroyizhko Pavlo, Zhexenova, Zhanel , Shafiq Muhammad Zohaib, Merizzi Fabio, Galassi Andrea, Ruggeri Federico

Subjects
sentiment bitcoin cryptocurrencies
Abstract

Cryptocurrencies have gained enormous momentum in finance and are nowadays commonly adopted as a medium of exchange for online payments. After recent events during which GameStop’s stocks were believed to be influenced by WallStreetBets subReddit, Reddit has become a very hot topic on the cryptocurrency market. The influence of public opinions on cryptocurrency price trends has inspired researchers on exploring solutions that integrate such information in crypto price change forecasting. A popular integration technique regards representing social media opinions via sentiment features. However, this research direction is still in its infancy, where a limited number of publicly available datasets with sentiment annotations exists. We propose a novel Bitcoin Reddit Sentiment Dataset, a ready-to-use dataset annotated with state-of-the-art sentiment and emotion recognition. The dataset contains pre-processed Reddit posts and comments about Bitcoin from several domain-related subReddits along with Bitcoin’s financial data. We evaluate several widely adopted neural architectures for crypto price change forecasting. Our results show controversial benefits of sentiment and emotion features advocating for more sophisticated social media integration techniques. We make our dataset publicly available for research.

Published
2022

55. Multimodal Argument Mining: A Case Study in Political Debates

Author

Mancini Eleonora, Ruggeri Federico, Galassi Andrea, Torroni Paolo, and Mancini Eleonora, Ruggeri Federico, Galassi Andrea , Torroni Paolo

Subjects
argument mining political debates multimodal speech
Abstract

We propose a study on multimodal argument mining in the domain of political debates. We collate and extend existing corpora and provide an initial empirical study on multimodal architectures, with a special emphasis on input encoding methods. Our results provide interesting indications about future directions in this important domain.

Published
2022

63. AMICA: An Argumentative Search Engine for COVID-19 Literature

Author

Lippi, Marco, primary, Antici, Francesco, additional, Brambilla, Gianfranco, additional, Cisbani, Evaristo, additional, Galassi, Andrea, additional, Giansanti, Daniele, additional, Magurano, Fabio, additional, Rosi, Antonella, additional, Ruggeri, Federico, additional, and Torroni, Paolo, additional

Published
2022
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64. Erratum to: A roadmap to parathyroidectomy for kidney transplant candidates

Author

Cianciolo, Giuseppe, primary, Tondolo, Francesco, additional, Barbuto, Simona, additional, Angelini, Andrea, additional, Ferrara, Francesca, additional, Iacovella, Francesca, additional, Raimondi, Concettina, additional, La Manna, Gaetano, additional, Serra, Carla, additional, De Molo, Chiara, additional, Cavicchi, Ottavio, additional, Piccin, Ottavio, additional, D'Alessio, Pasquale, additional, De Pasquale, Loredana, additional, Felisati, Giovanni, additional, Ciceri, Paola, additional, Galassi, Andrea, additional, and Cozzolino, Mario, additional

Published
2022
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68. MO409: GDF-15 is a Predictor of Mortality in Chronic Kidney Disease Patients With Covid-19 Infection

Author

Ciceri, Paola, primary, Bono, Valeria, additional, Magagnoli, Lorenza, additional, Sala, Matteo, additional, Artioli, Luisa, additional, Rovito, Roberta, additional, Adla, Mohamad, additional, Yellenki, Vaibhav, additional, D'arminio Monforte, Antonella, additional, Galassi, Andrea, additional, Tincati, Camilla, additional, Marchetti, Giulia, additional, and Gennaro Cozzolino, Mario, additional

Published
2022
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69. 1,25-dihydroxyvitamin D as Predictor of Renal Worsening Function in Chronic Kidney Disease. Results From the PASCaL-1,25D Study

Author

Galassi, Andrea, primary, Fasulo, Eliana Maria, additional, Ciceri, Paola, additional, Casazza, Roberta, additional, Bonelli, Fabrizio, additional, Zierold, Claudia, additional, Calleri, Mariella, additional, Blocki, Frank A., additional, Palmieri, Maria Assunta, additional, Mastronardo, Claudio, additional, and Cozzolino, Mario G., additional

Published
2022
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72. A roadmap to parathyroidectomy for kidney transplant candidates

Author

Cianciolo, Giuseppe, primary, Tondolo, Francesco, additional, Barbuto, Simona, additional, Angelini, Andrea, additional, Ferrara, Francesca, additional, Iacovella, Francesca, additional, Raimondi, Concettina, additional, La Manna, Gaetano, additional, Serra, Carla, additional, De Molo, Chiara, additional, Cavicchi, Ottavio, additional, Piccin, Ottavio, additional, D'Alessio, Pasquale, additional, De Pasquale, Loredana, additional, Felisati, Giovanni, additional, Ciceri, Paola, additional, Galassi, Andrea, additional, and Cozzolino, Mario, additional

Published
2022
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76. The Role of Uremic Retention Solutes in the MIA Syndrome in Hemodialysis Subjects.

Author

Ciceri, Paola, Artioli, Luisa, Magagnoli, Lorenza, Barassi, Alessandra, Alvarez, Jean-Claude, Massy, Ziad A., Galassi, Andrea, and Cozzolino, Mario

Subjects
BLOOD cholesterol, CARDIOVASCULAR diseases, VASCULAR smooth muscle, KIDNEY calcification, SYNDROMES, CARDIOVASCULAR disease related mortality, CHRONIC kidney failure
Abstract

Introduction: In chronic kidney disease (CKD), the high morbidity and mortality risk for cardiovascular disease (CVD) are not easily explained only on the basis of traditional factors. Among nontraditional ones involved in CKD, malnutrition, inflammation, and atherosclerosis/calcification have been described as the "MIA syndrome." Methods: In this pilot study, we evaluated the association between the variation in serum levels of 27 uremic retention solutes plus 6 indexes related to the MIA syndrome processes in a population of dialysis patients. Results: As expected, we found a direct correlation between serum albumin and both phosphate and total cholesterol (r = 0.54 and 0.37, respectively; p < 0.05). Moreover, total cholesterol and phosphate directly correlate (r = 0.40, p < 0.05). The relationship between malnutrition and inflammation is highlighted by the correlation of serum cholesterol levels with serum alpha-1 acid glycoprotein and IL-6 levels (r = −0.56, r = −0.39, respectively; p < 0.05). Moreover, the relation between inflammation and atherosclerosis/calcification is supported by the correlation of IL-6 with VEGF levels and vascular smooth muscle cell high-Pi in vitro calcification (r = 0.81, r = 0.66, respectively; p < 0.01). Conclusion: We found significant correlations between several uremic retention solutes and malnutrition, inflammation, and atherosclerosis/calcification. Our findings support the hypothesis of a central role of the uremic milieu in the MIA syndrome and ultimately in the pathogenesis of CKD-specific CVD risk factors. [ABSTRACT FROM AUTHOR]

Published
2023
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78. Renal microsporidiosis due to Encephalitozoon cuniculi in an HIV/AIDS patient with persistent fever and kidney injury

Author

Augello, Matteo, primary, Bai, Francesca, additional, Galassi, Andrea, additional, Bono, Valeria, additional, Moro, Alessia, additional, Carsana, Luca, additional, Tonello, Cristina, additional, Tosoni, Antonella, additional, Nebuloni, Manuela, additional, Monforte, Antonella d’Arminio, additional, and Marchetti, Giulia, additional

Published
2021
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80. Deep Networks and Knowledge: from Rule Learning to Neural-Symbolic Argument Mining

Author

Galassi, Andrea and Torroni, Paolo

Subjects
ING-INF/05 Sistemi di elaborazione delle informazioni
Abstract

Deep Learning has revolutionized the whole discipline of machine learning, heavily impacting fields such as Computer Vision, Natural Language Processing, and other domains concerned with the processing of raw inputs. Nonetheless, Deep Networks are still difficult to interpret, and their inference process is all but transparent. Moreover, there are still challenging tasks for Deep Networks: contexts where the success depends on structured knowledge that can not be easily provided to the networks in a standardized way. We aim to investigate the behavior of Deep Networks, assessing whether they are capable of learning complex concepts such as rules and constraints without explicit information, and then how to improve them by providing such symbolic knowledge in a general and modular way. We start by addressing two tasks: learning the rule of a game and learning to construct the solution to Constraint Satisfaction Problems. We provide the networks only with examples, without encoding any information regarding the task. We observe that the networks are capable of learning to play by the rules and to make feasible assignments in the CSPs. Then, we move to Argument Mining, a complex NLP task which consists of finding the argumentative elements in a document and identifying their relationships. We analyze Neural Attention, a mechanism widely used in NLP to improve networks' performance and interpretability, providing a taxonomy of its implementations. We exploit such a method to train an ensemble of deep residual networks and test them on four different corpora for Argument Mining, reaching or advancing the state of the art in most of the datasets we considered for this study. Finally, we realize the first implementation of neural-symbolic argument mining. We use the Logic Tensor Networks framework to introduce logic rules during the training process and establish that they give a positive contribution under multiple dimensions.

Published
2021

81. Current treatment options for secondary hyperparathyroidism in patients with stage 3 to 4 chronic kidney disease and vitamin D deficiency

Author

Galassi, Andrea, primary, Ciceri, Paola, additional, Porata, Giulia, additional, Iatrino, Rossella, additional, Boni Brivio, Giulia, additional, Fasulo, Eliana, additional, Magagnoli, Lorenza, additional, Stucchi, Andrea, additional, Frittoli, Michela, additional, Cara, Anila, additional, and Cozzolino, Mario, additional

Published
2021
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82. A corpus for multilingual analysis of online terms of service

Author

Drawzeski, K., Galassi, Andrea, Jablonowska, A., Lagioia, F., Lippi, M., Micklitz, H.W., Sartor, G., Tagiuri, G., Torroni, P., Aletras, N., Androutsopoulos, I., Barrett, L., Goanta, C., Preotiuc-Pietro, D., ACELG (FdR), and Kasper Drawzeski, Andrea Galassi, Agnieszka Jablonowska, Francesca Lagioia, Marco Lippi, Hans Wolfgang Micklitz, Giovanni Sartor, Giacomo Tagiuri, Paolo Torroni

Subjects
Terms of services, Potentially unfair clauses, Natural language processing, Consumers empowerment, Multilingualism
Abstract

We present the first annotated corpus for multilingual analysis of potentially unfair clauses in online Terms of Service. The data set comprises a total of 100 contracts, obtained from 25 documents annotated in four different languages: English, German, Italian, and Polish. For each contract, potentially unfair clauses for the consumer are annotated, for nine different unfairness categories. We show how a simple yet efficient annotation projection technique based on sentence embeddings could be used to automatically transfer annotations across languages.

Published
2021

83. Deep Networks and Knowledge: from Rule Learning to Neural-Symbolic Argument Mining

Author

Torroni, Paolo, Galassi, Andrea <1992>, Torroni, Paolo, and Galassi, Andrea <1992>

Abstract

Deep Learning has revolutionized the whole discipline of machine learning, heavily impacting fields such as Computer Vision, Natural Language Processing, and other domains concerned with the processing of raw inputs. Nonetheless, Deep Networks are still difficult to interpret, and their inference process is all but transparent. Moreover, there are still challenging tasks for Deep Networks: contexts where the success depends on structured knowledge that can not be easily provided to the networks in a standardized way. We aim to investigate the behavior of Deep Networks, assessing whether they are capable of learning complex concepts such as rules and constraints without explicit information, and then how to improve them by providing such symbolic knowledge in a general and modular way. We start by addressing two tasks: learning the rule of a game and learning to construct the solution to Constraint Satisfaction Problems. We provide the networks only with examples, without encoding any information regarding the task. We observe that the networks are capable of learning to play by the rules and to make feasible assignments in the CSPs. Then, we move to Argument Mining, a complex NLP task which consists of finding the argumentative elements in a document and identifying their relationships. We analyze Neural Attention, a mechanism widely used in NLP to improve networks' performance and interpretability, providing a taxonomy of its implementations. We exploit such a method to train an ensemble of deep residual networks and test them on four different corpora for Argument Mining, reaching or advancing the state of the art in most of the datasets we considered for this study. Finally, we realize the first implementation of neural-symbolic argument mining. We use the Logic Tensor Networks framework to introduce logic rules during the training process and establish that they give a positive contribution under multiple dimensions.

Published
2021

85. roadmap to parathyroidectomy for kidney transplant candidates.

Author

Cianciolo, Giuseppe, Tondolo, Francesco, Barbuto, Simona, Angelini, Andrea, Ferrara, Francesca, Iacovella, Francesca, Raimondi, Concettina, Manna, Gaetano La, Serra, Carla, Molo, Chiara De, Cavicchi, Ottavio, Piccin, Ottavio, D'Alessio, Pasquale, Pasquale, Loredana De, Felisati, Giovanni, Ciceri, Paola, Galassi, Andrea, and Cozzolino, Mario

Subjects
RENAL osteodystrophy, KIDNEY transplantation, PARATHYROIDECTOMY
Abstract

Chronic kidney disease mineral and bone disorder may persist after successful kidney transplantation. Persistent hyperparathyroidism has been identified in up to 80% of patients throughout the first year after kidney transplantation. International guidelines lack strict recommendations about the management of persistent hyperparathyroidism. However, it is associated with adverse graft and patient outcomes, including higher fracture risk and an increased risk of all-cause mortality and allograft loss. Secondary hyperparathyroidism may be treated medically (vitamin D, phosphate binders and calcimimetics) or surgically (parathyroidectomy). Guideline recommendations suggest medical therapy first but do not clarify optimal parathyroid hormone targets or indications and timing of parathyroidectomy. There are no clear guidelines or long-term studies about the impact of hyperparathyroidism therapy. Parathyroidectomy is more effective than medical treatment, although it is associated with increased short-term risks. Ideally parathyroidectomy should be performed before kidney transplantation to prevent persistent hyperparathyroidism and improve graft outcomes. We now propose a roadmap for the management of secondary hyperparathyroidism in patients eligible for kidney transplantation that includes the indications and timing (pre- or post-kidney transplantation) of parathyroidectomy, the evaluation of parathyroid gland size and the integration of parathyroid gland size in the decision-making process by a multidisciplinary team of nephrologists, radiologists and surgeons. [ABSTRACT FROM AUTHOR]

Published
2022
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87. Data Science to understand Green House Emmissions on EUA industries

Author

Galassi, Andrea Nicole, Gibert, Karina, and Universitat Politècnica de Catalunya. Institut Universitari de Recerca en Ciència i Tecnologies de la Sostenibilitat

Subjects
Principal Component Analysis, Enginyeria civil [Àrees temàtiques de la UPC], Greenhouse gases, Sustainable development, Data Science, Desenvolupament sostenible, Mineria de dades, Carbon Dioxide, Data mining, Hierarchical clustering
Abstract

La ciència de dades o "Data Science" és una disciplina emergent on l'aplicació de les eines de mineria de dades és una poderosa tècnica utilitzada per descobrir tendències i relacions subjacents en volums de dades complexos que poden no haver estat evidents a l’observació de les dades en conjunt (Gibert, et al., 2018). La mineria de dades i la ciència de dades tenen el poder de revelar les complexitats de les dades on sorgeixen relacions entre variables i ajuden a comprendre fenòmens complexos. D'altra banda, el canvi climàtic s'està convertint en una amenaça existencial per als sistemes de vida de la Terra i és causat principalment per l'alliberament de gasos amb efecte d’hivernacle (GEH) a l'atmosfera. Comprendre les complexitats de les emissions de GEH i les relacions i tendències a les emissions industrials de GEH pot conduir a la utilització de pràctiques i polítiques més ecològiques. Estats Units és responsable de la majoria de les emissions de GEH del planeta des de la revolució industrial; per tant, conèixer les tendències i relacions entre indústries i sectors en termes d'emissions de GEH és fonamental per al desenvolupament i impuls de polítiques efectives orientades a la reducció d'emissions. Aquest informe presenta una conceptualització del poder de la ciència de dades a la qual són analitzats els inventaris d'emissions de GEH del Programa d'Informes de GEH (GHGRP) de l'Agència de Protecció Ambiental dels Estats Units (USEPA) a la recerca de tendències i relacions. Els inventaris d'emissions de GEH dels informes dels anys (RY) 2012, RY2015 i RY2018 van ser pre-processats, es va fer una extracció de dades i posteriorment es van processar amb la finalitat d’obtenir 10 perfils de classe únics i estadísticament significatius mitjançant tècniques de agrupació jeràrquica d’on emergeixen tendències i relacions a les dades La ciencia de datos es una disciplina emergente en la que la aplicación de las herramientas de minería de datos es una poderosa técnica utilizada para descubrir tendencias y relaciones subyacentes en conjuntos de datos complejos que pueden no haber sido evidentes al observar los datos en su conjunto (Gibert, et al., 2018). La minería de datos y la ciencia de datos tienen el poder de revelar las complejidades de los datos donde cuyas relaciones entre variables conducen y ayudan a comprender fenómenos complejos. Por otro lado, el cambio climático se está convirtiendo en una amenaza existencial para los sistemas de vida de la Tierra y es causado principalmente por la liberación de gases de efecto invernadero (GEI) a la atmósfera. Comprender las complejidades de las emisiones de GEI y las relaciones y tendencias en las emisiones industriales de GEI puede conducir a la utilización de prácticas y políticas más ecológicas. Estados Unidos es responsable de la mayoría de las emisiones de GEI del planeta desde la revolución industrial; por lo tanto, conocer las tendencias y relaciones entre industrias y sectores en términos de emisiones de GEI es fundamental para el desarrollo e impulso de políticas efectivas orientadas a la reducción de emisiones. Este informe presenta una conceptualización del poder de la ciencia de datos en la cual son analizados los inventarios de emisiones de GEI del Programa de Informes de GEI (GHGRP) de la Agencia de Protección Ambiental de los Estados Unidos (USEPA) en busca de tendencias. Los inventarios de emisiones de GEI de los informes de los años (RY) 2012, RY2015 y RY2018 fueron pre-procesados, se hizo una extracción de datos y posteriormente se procesaron para obtener 10 perfiles de clase únicos y estadísticamente significativos a través de técnicas de agrupación jerárquica de las que emergen tendencias en los datos. Data Science is an emergent discipline in which the application of Data Mining tools is a powerful technique used to uncover underlying trends and relationships in complex datasets which may not have been apparent when observing the dataset as a whole (Gibert, et al., 2018). Data mining and data science have the power to disclose the complexities in data in which relationships between and among variables emerge which can lead to insight and understanding when dealing with complex phenomenon. Climate change is becoming in existential threat to Earth’s life systems and is caused by releasing greenhouse gases (GHGs) to the atmosphere. Understanding the complexities of GHG emissions and the relationships and trends industries and sectors have with GHG emissions can lead to greener practices and policies. The United States is responsible for the majority of the planet’s GHG emissions since the industrial revolution; therefore, understanding the trends and relationships between industries and sectors in terms of GHG emissions is pivotal for creating and influencing effective emission reduction policies (Ritchie & Roser, 2017). This report shows the power of data science in a conceptualization where GHG emission inventories from the United States Environmental Protection Agency’s (USEPA) GHG Reporting Program (GHGRP) are analyzed for trends and relationships (USEPA, 2020).. GHG emission inventories from reporting years (RY) 2012, RY2015 and RY2018 are preprocessed, data mined, and post processed in order to obtain 10 unique and statistically significant profiles of industries through hierarchical clustering and post-processing techniques in which trends and relationships in the data emerge.

Published
2020

88. Data Mining of United States Greenhouse Gas Emission Inventory for Visualization and Analysis of Emission Trends

Author

Universitat Politècnica de Catalunya. Institut Universitari de Recerca en Ciència i Tecnologies de la Sostenibilitat, Gibert, Karina, Galassi, Andrea Nicole, Universitat Politècnica de Catalunya. Institut Universitari de Recerca en Ciència i Tecnologies de la Sostenibilitat, Gibert, Karina, and Galassi, Andrea Nicole

Abstract

La ciència de dades o "Data Science" és una disciplina emergent on l'aplicació de les eines de mineria de dades és una poderosa tècnica utilitzada per descobrir tendències i relacions subjacents en volums de dades complexos que poden no haver estat evidents a l’observació de les dades en conjunt (Gibert, et al., 2018). La mineria de dades i la ciència de dades tenen el poder de revelar les complexitats de les dades on sorgeixen relacions entre variables i ajuden a comprendre fenòmens complexos. D'altra banda, el canvi climàtic s'està convertint en una amenaça existencial per als sistemes de vida de la Terra i és causat principalment per l'alliberament de gasos amb efecte d’hivernacle (GEH) a l'atmosfera. Comprendre les complexitats de les emissions de GEH i les relacions i tendències a les emissions industrials de GEH pot conduir a la utilització de pràctiques i polítiques més ecològiques. Estats Units és responsable de la majoria de les emissions de GEH del planeta des de la revolució industrial; per tant, conèixer les tendències i relacions entre indústries i sectors en termes d'emissions de GEH és fonamental per al desenvolupament i impuls de polítiques efectives orientades a la reducció d'emissions. Aquest informe presenta una conceptualització del poder de la ciència de dades a la qual són analitzats els inventaris d'emissions de GEH del Programa d'Informes de GEH (GHGRP) de l'Agència de Protecció Ambiental dels Estats Units (USEPA) a la recerca de tendències i relacions. Els inventaris d'emissions de GEH dels informes dels anys (RY) 2012, RY2015 i RY2018 van ser pre-processats, es va fer una extracció de dades i posteriorment es van processar amb la finalitat d’obtenir 10 perfils de classe únics i estadísticament significatius mitjançant tècniques de agrupació jeràrquica d’on emergeixen tendències i relacions a les dades, La ciencia de datos es una disciplina emergente en la que la aplicación de las herramientas de minería de datos es una poderosa técnica utilizada para descubrir tendencias y relaciones subyacentes en conjuntos de datos complejos que pueden no haber sido evidentes al observar los datos en su conjunto (Gibert, et al., 2018). La minería de datos y la ciencia de datos tienen el poder de revelar las complejidades de los datos donde cuyas relaciones entre variables conducen y ayudan a comprender fenómenos complejos. Por otro lado, el cambio climático se está convirtiendo en una amenaza existencial para los sistemas de vida de la Tierra y es causado principalmente por la liberación de gases de efecto invernadero (GEI) a la atmósfera. Comprender las complejidades de las emisiones de GEI y las relaciones y tendencias en las emisiones industriales de GEI puede conducir a la utilización de prácticas y políticas más ecológicas. Estados Unidos es responsable de la mayoría de las emisiones de GEI del planeta desde la revolución industrial; por lo tanto, conocer las tendencias y relaciones entre industrias y sectores en términos de emisiones de GEI es fundamental para el desarrollo e impulso de políticas efectivas orientadas a la reducción de emisiones. Este informe presenta una conceptualización del poder de la ciencia de datos en la cual son analizados los inventarios de emisiones de GEI del Programa de Informes de GEI (GHGRP) de la Agencia de Protección Ambiental de los Estados Unidos (USEPA) en busca de tendencias. Los inventarios de emisiones de GEI de los informes de los años (RY) 2012, RY2015 y RY2018 fueron pre-procesados, se hizo una extracción de datos y posteriormente se procesaron para obtener 10 perfiles de clase únicos y estadísticamente significativos a través de técnicas de agrupación jerárquica de las que emergen tendencias en los datos., Data Science is an emergent discipline in which the application of Data Mining tools is a powerful technique used to uncover underlying trends and relationships in complex datasets which may not have been apparent when observing the dataset as a whole (Gibert, et al., 2018). Data mining and data science have the power to disclose the complexities in data in which relationships between and among variables emerge which can lead to insight and understanding when dealing with complex phenomenon. Climate change is becoming in existential threat to Earth’s life systems and is caused by releasing greenhouse gases (GHGs) to the atmosphere. Understanding the complexities of GHG emissions and the relationships and trends industries and sectors have with GHG emissions can lead to greener practices and policies. The United States is responsible for the majority of the planet’s GHG emissions since the industrial revolution; therefore, understanding the trends and relationships between industries and sectors in terms of GHG emissions is pivotal for creating and influencing effective emission reduction policies (Ritchie & Roser, 2017). This report shows the power of data science in a conceptualization where GHG emission inventories from the United States Environmental Protection Agency’s (USEPA) GHG Reporting Program (GHGRP) are analyzed for trends and relationships (USEPA, 2020).. GHG emission inventories from reporting years (RY) 2012, RY2015 and RY2018 are preprocessed, data mined, and post processed in order to obtain 10 unique and statistically significant profiles of industries through hierarchical clustering and post-processing techniques in which trends and relationships in the data emerge.

Published
2020

90. A Corpus for Multilingual Analysis of Online Terms of Service

Author

Drawzeski, Kasper, primary, Galassi, Andrea, additional, Jablonowska, Agnieszka, additional, Lagioia, Francesca, additional, Lippi, Marco, additional, Micklitz, Hans Wolfgang, additional, Sartor, Giovanni, additional, Tagiuri, Giacomo, additional, and Torroni, Paolo, additional

Published
2021
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93. SARS‐CoV‐2–related ARDS in a maintenance hemodialysis patient: case report on tailored approach by daily hemodialysis, noninvasive ventilation, tocilizumab, anxiolytics, and point‐of‐care ultrasound

Author

Galassi, Andrea, primary, Casanova, Francesca, additional, Gazzola, Lidia, additional, Rinaldo, Rocco, additional, Ceresa, Marco, additional, Restelli, Elena, additional, Giorgini, Alessia, additional, Birocchi, Simone, additional, Giovenzana, Marco, additional, Zoni, Ulisse, additional, Valli, Federica, additional, Massironi, Laura, additional, Belletti, Sebastiano, additional, Magagnoli, Lorenza, additional, Stucchi, Andrea, additional, Ippolito, Michela, additional, Carugo, Stefano, additional, Parazzini, Elena, additional, and Cozzolino, Mario, additional

Published
2020
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95. Calciphylaxis after kidney transplantation: a rare but life-threatening disorder.

Author

Podestà, Manuel Alfredo, Ciceri, Paola, Galassi, Andrea, and Cozzolino, Mario

Subjects
KIDNEY transplantation, ARTERIAL calcification, CALCIPHYLAXIS, PAROXYSMAL hemoglobinuria, KIDNEY failure, KIDNEY physiology
Abstract

Calciphylaxis is a rare disorder characterized by vascular calcification and thrombosis of the subcutaneous microcirculation, leading to painful necrotic skin lesions and bearing a dreadfully high mortality rate. This syndrome is frequently also termed uraemic calcific arteriolopathy, since most cases are observed in patients with kidney failure. However, it is increasingly clear that calciphylaxis may also affect patients with normal or only slightly impaired renal function, including kidney transplant recipients. A precise definition of the characteristics and risk factors of calciphylaxis developing after kidney transplantation has been hindered by the extreme rarity of this condition, which also hampered the development of effective therapeutic strategies. In the present issue of CKJ , Guillén and colleagues report the largest case series of calciphylaxis in kidney transplant recipients to date, outlining several features that are apparently specific to this population. In this editorial, we briefly present the epidemiology and pathogenesis of calciphylaxis in different patient populations and discuss recent findings for its therapeutic management. [ABSTRACT FROM AUTHOR]

Published
2022
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96. Contributors

Author

Abaterusso, Cataldo, primary, Ahern, Stéphane P., additional, Aisa, Maria Cristina, additional, Albright, Robert C., additional, Alfaro, Vicente, additional, Al-Khafaji, Ali, additional, Allo, Jean-Christophe, additional, Amerling, Richard, additional, Amore, Alessandro, additional, Anderson, Robert J., additional, Andreucci, Michele, additional, Andreucci, Vittorio E., additional, Andrikos, Emilios, additional, Arroyo, Vicente, additional, Arthur, John M., additional, Ash, Stephen R., additional, Assanelli, Emilio, additional, Aucella, Filippo, additional, Bagshaw, Sean M., additional, Balafa, Olga, additional, Balbi, André Luís, additional, Baldwin, Ian, additional, Ballestri, Marco, additional, Bargman, Joanne M., additional, Barletta, Gina-Marie, additional, Barletta, Jeffrey F., additional, Barozzi, Libero, additional, Barsoum, Rashad S., additional, Beaulieu, Monica, additional, Beer-Stolz, Donna, additional, Bellomo, Rinaldo, additional, Bernardo, Jose, additional, Bertolotto, Michele, additional, Bestoso, John T., additional, Betro, Gerard A., additional, Bhattacharya, Mallar, additional, Bihl, Geoffrey R., additional, Blot, Stijn I., additional, Boer, Willem, additional, Boim, Mirian A., additional, Bonello, Monica, additional, Bonventre, Joseph V., additional, Booth, A.D., additional, Bourke, Edmund, additional, Braitberg, George, additional, Brancaccio, Diego, additional, Brendolan, Alessandra, additional, Brezzi, Brigida, additional, Brodie, James C., additional, Brophy, Patrick D., additional, Brown, Ryan, additional, Bucala, Richard, additional, Buckmaster, Jonathan, additional, Budisavljevic, Milos N., additional, Bunchman, Timothy E., additional, Burdmann, Emmanuel A., additional, Bussolati, Benedetta, additional, Butkus, Matthew A., additional, Buzzelli, Daniela, additional, Calzavacca, Paolo, additional, Camussi, Giovanni, additional, Canaud, Bernard, additional, Cano, Noël J.M., additional, Cantaluppi, Vincenzo, additional, Capasso, Giovambattista, additional, Cappelli, Gianni, additional, Carlesso, Eleonora, additional, Casino, Francesco G., additional, Castillo, Leticia, additional, Cerutti, Roberta, additional, Chawla, Lakhmir S., additional, Chionh, Chang Yin, additional, Chiu, Alexander, additional, Chow, May T., additional, Chugh, Kirpal S., additional, Cianciaruso, Bruno, additional, Cignarelli, Mauro, additional, Claessens, Yann-Erick, additional, Clark, John A., additional, Clark, William R., additional, Cohen, David J., additional, Cohen, Scott D., additional, Constable, Peter, additional, Coppo, Rosanna, additional, Corey, Howard E., additional, Cozzolino, Mario, additional, Craig, Maureen, additional, Cramer, Carl H., additional, Crandall, Mark, additional, Cravedi, Paolo, additional, Crepaldi, Carlo, additional, Crew, R. John, additional, Cronin, Donald F., additional, Cruz, Dinna N., additional, Canton, Antonio Dal, additional, Dan, Maurizio, additional, D'Angelo, Angela, additional, Davenport, Andrew, additional, Davies, Andrew R., additional, Dear, James W., additional, Gasperi, Andrea De, additional, Dell'Aquila, Roberto, additional, Rocca, Giorgio Della, additional, Prete, Dorella Del, additional, Delude, Russell L., additional, Depner, Thomas, additional, Derchi, Lorenzo E., additional, Devarajan, Prasad, additional, Waele, Jan J. De, additional, Dhainaut, Jean-François, additional, Diaz-Buxo, José A., additional, Micco, Lucia Di, additional, Divino-Filho, José Carolina, additional, Doernberg, Sarah, additional, Doig, Gordon S., additional, Dries, David J., additional, Drudi, Francesco Maria, additional, Druml, Wilfred, additional, Duke, Graeme, additional, Durward, Andrew, additional, Egi, Moritoki, additional, Esposito, Ciro, additional, Evenepoel, Pieter, additional, Faga, Teresa, additional, Fan, Sheung Tat, additional, Feinfeld, Donald A., additional, Féraille, Eric, additional, Fernández, Javier, additional, Finfer, Simon, additional, Fink, Mitchell P., additional, Finkel, Kevin W., additional, Flessner, Michael F., additional, Formica, Marco, additional, Forni, Lui G., additional, Fortenberry, James D., additional, French, Craig, additional, Fumagalli, Roberto, additional, Furlanut, Mario, additional, Fute, Micheline Djouguela, additional, Gabriel, Daniela Ponce, additional, Galassi, Andrea, additional, Galbusera, Miriam, additional, Galli, Francesco, additional, Galli, Giovanni, additional, Gallieni, Maurizio, additional, Gallo, Giampiero, additional, Gambaro, Giovanni, additional, Gammill, Hilary S., additional, Gangemi, Ezio Nicola, additional, Gao, Dayong, additional, Garwood, Susan, additional, Garzotto, Francesco, additional, Gatti, Antonietta M., additional, Gattinoni, Luciano, additional, Geibel, John P., additional, George, Stephen, additional, Gesualdo, Loreto, additional, Gibney, R.T. Noel, additional, Gipson, Debbie S., additional, Glezerman, Ilya G., additional, Glorieux, Griet, additional, Goldstein, Stuart L., additional, Golper, Thomas A., additional, Gowrishankar, Manjula, additional, Grandi, Fabio, additional, Gregoretti, Cesare, additional, Grinnell, Brian W., additional, Groeneveld, A.B. Johan, additional, Gruber, Steven J., additional, Guadagni, Gualtiero, additional, Gunnerson, Kyle J., additional, Gupta, Akanksha, additional, Gura, Victor, additional, Guzzo, Isabella, additional, Hackbarth, Richard, additional, Halperin, Mitchell L., additional, Harbord, Nikolas, additional, Haymann, Jean-Philippe, additional, Heffner, Alan C., additional, Hennessy, Anthony J., additional, Heyman, Samuel N., additional, Hill, Graham L., additional, Hilton, Philip J., additional, Himmelfarb, Jonathan, additional, Hirasawa, Hiroyuki, additional, Hoenich, Nicholas A., additional, Holdsworth, Stephen R., additional, Holley, Anthony, additional, Honoré, Patrick M., additional, Hoste, Eric A.J., additional, House, Andrew A., additional, Huang, David T., additional, Huang, Zhongping, additional, Hubmayr, Rolf D., additional, Hughes, Alun D., additional, Humes, H. David, additional, Ikizler, T. Alp, additional, Imberti, Barbara, additional, Ing, Todd S., additional, Jaber, Bertrand L., additional, Janvier, Gérard, additional, Jeyabalan, Arundhathi, additional, Jha, Vivekanand, additional, Joannes-Boyau, Olivier, additional, Joannidis, Michael, additional, Jones, Daryl A., additional, Jörres, Achim, additional, Kamel, Kamel S., additional, Kamp, Ryan C., additional, Kannan, Neeta, additional, Kaplan, Lewis J., additional, Karacan, Özgür, additional, Karajala-Subramanyam, Vijay, additional, Kaushal, Gur P., additional, Kellum, John A., additional, Kemper, Markus J., additional, Khan, Asjad, additional, Khanna, Ramesh, additional, Kher, Vijay, additional, Kimmel, Paul L., additional, Kindgen-Milles, Detlef, additional, Kitching, A. Richard, additional, Kjellstrand, Carl M., additional, Kohn, Orly F., additional, Kooienga, Laura A., additional, Kooman, Jeroen P., additional, Kotanko, Peter, additional, Krediet, Raymond T., additional, Kroon, A.A., additional, Kuang, Dingwei, additional, Kuhlmann, Martin K., additional, Kuiper, Jan Willem, additional, Lam, Man-Fai, additional, Lamacchia, Olga, additional, Lameire, Norbert, additional, Langenberg, Christoph, additional, Lauri, Gianfranco, additional, Leblanc, Martine, additional, Ledebo, Ingrid, additional, Lentini, Paolo, additional, Leonard, Edward F., additional, Letteri, Jeffrey J., additional, Leunissen, Karel M., additional, Leverve, Xavier M., additional, Levin, Adeera, additional, Leypoldt, John K., additional, Liangos, Orfeas, additional, Licari, Elisa, additional, Lieberthal, Wilfred, additional, Linden, Peter K., additional, Lipman, Jeffrey, additional, Liu, Kathleen D., additional, Liu, Shiguang, additional, Livigni, Sergio, additional, Lo, Wai-Kei, additional, Lugano, Manuela, additional, Lui, Sing-Leung, additional, Lupo, Antonio, additional, Luyckx, Valerie A., additional, Macias, William L., additional, Madden, Nicholas, additional, Madore, François, additional, Majors, Daniel S., additional, Mancini, Elena, additional, Mangione, Filippo, additional, Mankad, Sunil, additional, Manzini, Pier Paolo, additional, Marangella, Martino, additional, Marenzi, Giancarlo, additional, Mariano, Filippo, additional, Marik, Paul E., additional, Marini, John J., additional, Marquis, François, additional, Marshall, John C., additional, Marshall, Mark R., additional, Mathew, Roy, additional, Matsuda, Kenichi, additional, Matthay, Michael A., additional, Maxvold, Norma J., additional, May, Clive N., additional, McCauley, Jerry, additional, McCunn, Maureen, additional, McKenna, Joseph, additional, Mehta, Ravindra L., additional, Mele, Caterina, additional, Merouani, Aicha, additional, Mesnard, Laurent, additional, Messa, Piergiorgio, additional, Metnitz, Philipp G.H., additional, Misra, Madhukar, additional, Mitzner, Steffen R., additional, Mizock, Barry A., additional, Mokhlesi, Babak, additional, Molitoris, Bruce A., additional, Morelli, Andrea, additional, Morgan, Thomas John, additional, Morigi, Marina, additional, Mount, Peter, additional, Mucelli, Roberto Pozzi, additional, Mueller, Bruce A., additional, Murray, Patrick, additional, Murugan, Raghavan, additional, Nakamura, Masataka, additional, Nalesso, Federico, additional, Nester, Carla M., additional, Nissenson, Allen, additional, Nolph, Karl, additional, Ocampo, Catalina, additional, Oda, Shigeto, additional, Okusa, Mark D., additional, Opal, Steven M., additional, Opdam, Helen, additional, Osswald, Hartmut, additional, Straaten, Heleen M. Oudemans–van, additional, Padalino, Massimo A., additional, Paden, Matthew L., additional, Paganini, Emil P., additional, Palevsky, Paul M., additional, Panat, Mani John, additional, Paolini, Francesco, additional, Parikh, Dipen, additional, Patroniti, Nicolò, additional, Pavlica, Pietro, additional, de La Garanderie, Didier Payen, additional, Pea, Federico, additional, Peng, Zhiyong, additional, Perazella, Mark A., additional, Perego, Angelo F., additional, Pereira, Estela Regina, additional, Pérez, Evans R. Fernández, additional, Perico, Norberto, additional, Pertica, Nicoletta, additional, Pertosa, Giovanni, additional, Peruzzi, Licia, additional, Petras, Dimitris, additional, Pham, Phuong-Chi, additional, Pham, Phuong-Thu, additional, Phoon, Richard K.S., additional, Picca, Stefano, additional, Piccinni, Pasquale, additional, Pinsk, Maury N., additional, Pinsky, Michael R., additional, Piroddi, Marta, additional, Plamondon, Isabelle, additional, Plank, Lindsay D., additional, Plötz, Frans B., additional, Poghosyan, Lusine, additional, Polanco, Natalia, additional, Polanco, Patricio M., additional, Polaschegg, Hans Dietrich, additional, Ponikvar, Rafael, additional, Porecca, Silvia, additional, Portilla, Didier, additional, Powell, T. Brian, additional, Quigley, Raymond, additional, Rabb, Hamid, additional, Ragaller, Maximilian, additional, Rampino, Teresa, additional, Ranpuria, Reena, additional, Rastogi, Anjay, additional, Ratanarat, Ranistha, additional, Raza, Naem, additional, Reade, Michael C., additional, Reeves, John H., additional, Reiter, Karl, additional, Remuzzi, Giuseppe, additional, Ricci, Zaccaria, additional, Ricksten, Sven-Erik, additional, Ridel, Christophe, additional, Rifai, Kinan, additional, Ring, Troels, additional, Riopel, Julie, additional, Rocha, Eduardo, additional, Roessler, Eric, additional, Rona, Roberto, additional, Ronco, Claudio, additional, Rondeau, Eric, additional, Rosen, Seymour, additional, Rosenberger, Christian, additional, Rowan, Shane, additional, Roy, Thomas, additional, Saab, Georges, additional, Sadahiro, Tomohito, additional, Sala, Carla, additional, Sala, Chiara, additional, Salama, Alan D., additional, Salmon, Adrian, additional, Salvatori, Gabriela, additional, Sam, Ramin, additional, Sandoval, Ruben M., additional, Santoro, Antonio, additional, Saotome, Takao, additional, Sappington, Penny L., additional, Sarma, J. Vidya, additional, Savige, Judy, additional, Schena, Francesco Paolo, additional, Schepers, Eva, additional, Schetz, Miet, additional, Schmidt, Gregory A., additional, Schor, Nestor, additional, Schusterschitz, Nicola, additional, Segoloni, Giuseppe, additional, Shah, Nirva, additional, Shah, Shamik H., additional, Shah, Sudhir V., additional, Sharfuddin, Asif A., additional, Shaw, Andrew, additional, Shiga, Hidetoshi, additional, Shoji, Hisataka, additional, Shukla, Ashutosh, additional, Simpson, Fiona, additional, Singbartl, Kai, additional, Singer, Mervyn, additional, Solez, Kim, additional, Sowinski, Kevin M., additional, Stafford-Smith, Mark, additional, Stange, Jan, additional, Stefanelli, Luca, additional, Stein, Deborah M., additional, Stella, Maurizio, additional, Stellin, Giovanni, additional, Story, David A., additional, Subramanian, Sanjay, additional, Swärd, Kristina, additional, Symons, Jordan M., additional, Tai, Kian Bun, additional, Tattersall, James, additional, Tedeschi, Luisa, additional, Teitelbaum, Isaac, additional, Teixeira, Vicente P. Castro, additional, Tetta, Ciro, additional, Thakar, Charuhas V., additional, Theilen, Hermann, additional, Thomas, Karl W., additional, Tolwani, Ashita, additional, Trepiccione, Francesco, additional, Triolo, Giorgio, additional, Tsang, Jennifer L.Y., additional, Tutal, Emre, additional, Uchino, Shigehiko, additional, Unruh, Mark, additional, Vail, G. Matthew, additional, Valentino, Massimo, additional, Vallon, Volker, additional, Van Biesen, Wim, additional, van der Sande, Frank M., additional, Vandijck, Dominique M., additional, Vanholder, Raymond, additional, Varghese, Sanju A., additional, Venkataraman, Ramesh, additional, Venkatesh, Bala, additional, Verbine, Anton, additional, Vincent, Jean-Louis, additional, Vinsonneau, Christophe, additional, Visvanathan, Ravindran, additional, Voinescu, Alexandra, additional, Walters, Scott, additional, Wan, Li, additional, Ward, Peter A., additional, Ward, Richard A., additional, Warrillow, Stephen, additional, Webb, Steve, additional, Whitlow, Kenneth Scott, additional, Wieslander, Anders, additional, Wilkinson, Alan H., additional, Wille, Keith, additional, Winchester, James Frank, additional, Wu, Christine, additional, Yassin, James, additional, Yeun, Jane Y., additional, Yip, Terence Pok-Siu, additional, Yu, Alex W., additional, Zacchia, Miriam, additional, Zaida, Najam, additional, Zamperetti, Nereo, additional, Zappitelli, Michael, additional, and Zarbock, Alexander, additional

Published
2009
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