2021S47 Avviso ulteriore posticipo comunicazione calendario e sedi prove d'esame

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2020N66 Esito colloquio

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Rapid diagnosis and predicting the progression rate of ALS are now possible

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Thanks to the artificial intelligence of machine learning, the analysis of a specific set of lipid particles present in the blood (extracellular vesicles) makes it possible to diagnose the onset of Amyotrophic Lateral Sclerosis (ALS) accurately and quickly. With machine learning, predicting the speed and future outcome of ALS progression is also possible, as is reported in a study published in the scientific journal Molecular Neurodegeneration, which includes research conduct at the University of Padua Department of Mathematics.      

Coordinated by the Head of the Laboratory of Translational Biomarkers Valentina Bonetto of the Mario Negri Institute of Pharmacological Research IRCCS, and Manuela Basso of the Department of Cellular, Computational and Integrative Biology (CIBIO) of the University of Trento, the research outlines the collection and analysis of extracellular vesicles through a simple blood sample. Francesco Rinaldi of the Department of Mathematics of the University of Padua analysed and processed data from the isolated and characterized vesicles collected from each blood sample.  Rinaldi used a trained learning machine that can distinguish the extracellular vesicles of a healthy individual from that of someone affected by the degenerative disease.    

The published research represents an important perspective for a disease diagnosed only after several medical visits by experienced neurologists. Like a message in a bottle, extracellular vesicles release a set of proteins and nucleic acids into the bloodstream that highlights the health of cells.  

Amyotrophic Lateral Sclerosis (ALS) is the most common form of motor neuron disease. The progressiveness of the disease affects the nerve cells in the brain and spinal cord, causing loss of muscle control with poor prognosis over time. Despite numerous advances in basic and clinical research, there is no cure. Unfortunately, its diagnosis occurs most often only after the first motor symptoms appear during the onset of its advanced stage of damage. For years, the Laboratory of Translational Biomarkers of the Mario Negri Institute has been studying new markers capable of anticipating the diagnosis of the disease over time and defining its prognosis more accurately.

The study was made possible due to the availability and collaboration from the many neurological experts at the CRESLA Centre, the AOU City of Health and Science of Turin, the University Hospital of Padua, the NeMO Clinical Center of Milan, ICS Maugeri of Milan, and the Casa Cura Policlinico of Milan.

The study was funded thanks to the Italian Ministry of Health’s "Young Researchers" Project, of which Dr. Basso and Dr. Pasetto are responsible and co-responsible of respectively(GR-2016-02361552), and by the European Marie-Sklodowska -Curie Individual Fellowships Funding Program awarded to Dr. Basso (752470).

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Thanks to the artificial intelligence of machine learning, the analysis of a specific set of lipid particles present in the blood (extracellular vesicles) makes it possible to diagnose the onset of Amyotrophic Lateral Sclerosis (ALS) accurately and quickly. With machine learning, predicting the speed and future outcome of ALS progression is also possible, as is reported in a study published in the scientific journal Molecular Neurodegeneration, which includes research conduct at the University of Padua Department of Mathematics.      

Coordinated by the Head of the Laboratory of Translational Biomarkers Valentina Bonetto of the Mario Negri Institute of Pharmacological Research IRCCS, and Manuela Basso of the Department of Cellular, Computational and Integrative Biology (CIBIO) of the University of Trento, the research outlines the collection and analysis of extracellular vesicles through a simple blood sample. Francesco Rinaldi of the Department of Mathematics of the University of Padua analysed and processed data from the isolated and characterized vesicles collected from each blood sample.  Rinaldi used a trained learning machine that can distinguish the extracellular vesicles of a healthy individual from that of someone affected by the degenerative disease.    

The published research represents an important perspective for a disease diagnosed only after several medical visits by experienced neurologists. Like a message in a bottle, extracellular vesicles release a set of proteins and nucleic acids into the bloodstream that highlights the health of cells.  

Amyotrophic Lateral Sclerosis (ALS) is the most common form of motor neuron disease. The progressiveness of the disease affects the nerve cells in the brain and spinal cord, causing loss of muscle control with poor prognosis over time. Despite numerous advances in basic and clinical research, there is no cure. Unfortunately, its diagnosis occurs most often only after the first motor symptoms appear during the onset of its advanced stage of damage. For years, the Laboratory of Translational Biomarkers of the Mario Negri Institute has been studying new markers capable of anticipating the diagnosis of the disease over time and defining its prognosis more accurately.

The study was made possible due to the availability and collaboration from the many neurological experts at the CRESLA Centre, the AOU City of Health and Science of Turin, the University Hospital of Padua, the NeMO Clinical Center of Milan, ICS Maugeri of Milan, and the Casa Cura Policlinico of Milan.

The study was funded thanks to the Italian Ministry of Health’s "Young Researchers" Project, of which Dr. Basso and Dr. Pasetto are responsible and co-responsible of respectively(GR-2016-02361552), and by the European Marie-Sklodowska -Curie Individual Fellowships Funding Program awarded to Dr. Basso (752470).

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Thanks to the artificial intelligence of machine learning, the analysis of a specific set of lipid particles present in the blood (extracellular vesicles) makes it possible to diagnose the onset of Amyotrophic Lateral Sclerosis (ALS) accurately and quickly. With machine learning, predicting the speed and future outcome of ALS progression is also possible, as is reported in a study published in the scientific journal Molecular Neurodegeneration, which includes research conduct at the University of Padua Department of Mathematics.      

Coordinated by the Head of the Laboratory of Translational Biomarkers Valentina Bonetto of the Mario Negri Institute of Pharmacological Research IRCCS, and Manuela Basso of the Department of Cellular, Computational and Integrative Biology (CIBIO) of the University of Trento, the research outlines the collection and analysis of extracellular vesicles through a simple blood sample. Francesco Rinaldi of the Department of Mathematics of the University of Padua analysed and processed data from the isolated and characterized vesicles collected from each blood sample.  Rinaldi used a trained learning machine that can distinguish the extracellular vesicles of a healthy individual from that of someone affected by the degenerative disease.    

The published research represents an important perspective for a disease diagnosed only after several medical visits by experienced neurologists. Like a message in a bottle, extracellular vesicles release a set of proteins and nucleic acids into the bloodstream that highlights the health of cells.  

Amyotrophic Lateral Sclerosis (ALS) is the most common form of motor neuron disease. The progressiveness of the disease affects the nerve cells in the brain and spinal cord, causing loss of muscle control with poor prognosis over time. Despite numerous advances in basic and clinical research, there is no cure. Unfortunately, its diagnosis occurs most often only after the first motor symptoms appear during the onset of its advanced stage of damage. For years, the Laboratory of Translational Biomarkers of the Mario Negri Institute has been studying new markers capable of anticipating the diagnosis of the disease over time and defining its prognosis more accurately.

The study was made possible due to the availability and collaboration from the many neurological experts at the CRESLA Centre, the AOU City of Health and Science of Turin, the University Hospital of Padua, the NeMO Clinical Center of Milan, ICS Maugeri of Milan, and the Casa Cura Policlinico of Milan.

The study was funded thanks to the Italian Ministry of Health’s "Young Researchers" Project, of which Dr. Basso and Dr. Pasetto are responsible and co-responsible of respectively(GR-2016-02361552), and by the European Marie-Sklodowska -Curie Individual Fellowships Funding Program awarded to Dr. Basso (752470).

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Thanks to the artificial intelligence of machine learning, the analysis of a specific set of lipid particles present in the blood (extracellular vesicles) makes it possible to diagnose the onset of Amyotrophic Lateral Sclerosis (ALS) accurately and quickly. With machine learning, predicting the speed and future outcome of ALS progression is also possible, as is reported in a study published in the scientific journal Molecular Neurodegeneration, which includes research conduct at the University of Padua Department of Mathematics.      

Coordinated by the Head of the Laboratory of Translational Biomarkers Valentina Bonetto of the Mario Negri Institute of Pharmacological Research IRCCS, and Manuela Basso of the Department of Cellular, Computational and Integrative Biology (CIBIO) of the University of Trento, the research outlines the collection and analysis of extracellular vesicles through a simple blood sample. Francesco Rinaldi of the Department of Mathematics of the University of Padua analysed and processed data from the isolated and characterized vesicles collected from each blood sample.  Rinaldi used a trained learning machine that can distinguish the extracellular vesicles of a healthy individual from that of someone affected by the degenerative disease.    

The published research represents an important perspective for a disease diagnosed only after several medical visits by experienced neurologists. Like a message in a bottle, extracellular vesicles release a set of proteins and nucleic acids into the bloodstream that highlights the health of cells.  

Amyotrophic Lateral Sclerosis (ALS) is the most common form of motor neuron disease. The progressiveness of the disease affects the nerve cells in the brain and spinal cord, causing loss of muscle control with poor prognosis over time. Despite numerous advances in basic and clinical research, there is no cure. Unfortunately, its diagnosis occurs most often only after the first motor symptoms appear during the onset of its advanced stage of damage. For years, the Laboratory of Translational Biomarkers of the Mario Negri Institute has been studying new markers capable of anticipating the diagnosis of the disease over time and defining its prognosis more accurately.

The study was made possible due to the availability and collaboration from the many neurological experts at the CRESLA Centre, the AOU City of Health and Science of Turin, the University Hospital of Padua, the NeMO Clinical Center of Milan, ICS Maugeri of Milan, and the Casa Cura Policlinico of Milan.

The study was funded thanks to the Italian Ministry of Health’s "Young Researchers" Project, of which Dr. Basso and Dr. Pasetto are responsible and co-responsible of respectively(GR-2016-02361552), and by the European Marie-Sklodowska -Curie Individual Fellowships Funding Program awarded to Dr. Basso (752470).

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Thanks to the artificial intelligence of machine learning, the analysis of a specific set of lipid particles present in the blood (extracellular vesicles) makes it possible to diagnose the onset of Amyotrophic Lateral Sclerosis (ALS) accurately and quickly. With machine learning, predicting the speed and future outcome of ALS progression is also possible, as is reported in a study published in the scientific journal Molecular Neurodegeneration, which includes research conduct at the University of Padua Department of Mathematics.      

Coordinated by the Head of the Laboratory of Translational Biomarkers Valentina Bonetto of the Mario Negri Institute of Pharmacological Research IRCCS, and Manuela Basso of the Department of Cellular, Computational and Integrative Biology (CIBIO) of the University of Trento, the research outlines the collection and analysis of extracellular vesicles through a simple blood sample. Francesco Rinaldi of the Department of Mathematics of the University of Padua analysed and processed data from the isolated and characterized vesicles collected from each blood sample.  Rinaldi used a trained learning machine that can distinguish the extracellular vesicles of a healthy individual from that of someone affected by the degenerative disease.    

The published research represents an important perspective for a disease diagnosed only after several medical visits by experienced neurologists. Like a message in a bottle, extracellular vesicles release a set of proteins and nucleic acids into the bloodstream that highlights the health of cells.  

Amyotrophic Lateral Sclerosis (ALS) is the most common form of motor neuron disease. The progressiveness of the disease affects the nerve cells in the brain and spinal cord, causing loss of muscle control with poor prognosis over time. Despite numerous advances in basic and clinical research, there is no cure. Unfortunately, its diagnosis occurs most often only after the first motor symptoms appear during the onset of its advanced stage of damage. For years, the Laboratory of Translational Biomarkers of the Mario Negri Institute has been studying new markers capable of anticipating the diagnosis of the disease over time and defining its prognosis more accurately.

The study was made possible due to the availability and collaboration from the many neurological experts at the CRESLA Centre, the AOU City of Health and Science of Turin, the University Hospital of Padua, the NeMO Clinical Center of Milan, ICS Maugeri of Milan, and the Casa Cura Policlinico of Milan.

The study was funded thanks to the Italian Ministry of Health’s "Young Researchers" Project, of which Dr. Basso and Dr. Pasetto are responsible and co-responsible of respectively(GR-2016-02361552), and by the European Marie-Sklodowska -Curie Individual Fellowships Funding Program awarded to Dr. Basso (752470).

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Thanks to the artificial intelligence of machine learning, the analysis of a specific set of lipid particles present in the blood (extracellular vesicles) makes it possible to diagnose the onset of Amyotrophic Lateral Sclerosis (ALS) accurately and quickly. With machine learning, predicting the speed and future outcome of ALS progression is also possible, as is reported in a study published in the scientific journal Molecular Neurodegeneration, which includes research conduct at the University of Padua Department of Mathematics.      

Coordinated by the Head of the Laboratory of Translational Biomarkers Valentina Bonetto of the Mario Negri Institute of Pharmacological Research IRCCS, and Manuela Basso of the Department of Cellular, Computational and Integrative Biology (CIBIO) of the University of Trento, the research outlines the collection and analysis of extracellular vesicles through a simple blood sample. Francesco Rinaldi of the Department of Mathematics of the University of Padua analysed and processed data from the isolated and characterized vesicles collected from each blood sample.  Rinaldi used a trained learning machine that can distinguish the extracellular vesicles of a healthy individual from that of someone affected by the degenerative disease.    

The published research represents an important perspective for a disease diagnosed only after several medical visits by experienced neurologists. Like a message in a bottle, extracellular vesicles release a set of proteins and nucleic acids into the bloodstream that highlights the health of cells.  

Amyotrophic Lateral Sclerosis (ALS) is the most common form of motor neuron disease. The progressiveness of the disease affects the nerve cells in the brain and spinal cord, causing loss of muscle control with poor prognosis over time. Despite numerous advances in basic and clinical research, there is no cure. Unfortunately, its diagnosis occurs most often only after the first motor symptoms appear during the onset of its advanced stage of damage. For years, the Laboratory of Translational Biomarkers of the Mario Negri Institute has been studying new markers capable of anticipating the diagnosis of the disease over time and defining its prognosis more accurately.

The study was made possible due to the availability and collaboration from the many neurological experts at the CRESLA Centre, the AOU City of Health and Science of Turin, the University Hospital of Padua, the NeMO Clinical Center of Milan, ICS Maugeri of Milan, and the Casa Cura Policlinico of Milan.

The study was funded thanks to the Italian Ministry of Health’s "Young Researchers" Project, of which Dr. Basso and Dr. Pasetto are responsible and co-responsible of respectively(GR-2016-02361552), and by the European Marie-Sklodowska -Curie Individual Fellowships Funding Program awarded to Dr. Basso (752470).

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Thanks to the artificial intelligence of machine learning, the analysis of a specific set of lipid particles present in the blood (extracellular vesicles) makes it possible to diagnose the onset of Amyotrophic Lateral Sclerosis (ALS) accurately and quickly.

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Thanks to the artificial intelligence of machine learning, the analysis of a specific set of lipid particles present in the blood (extracellular vesicles) makes it possible to diagnose the onset of Amyotrophic Lateral Sclerosis (ALS) accurately and quickly. With machine learning, predicting the speed and future outcome of ALS progression is also possible, as is reported in a study published in the scientific journal Molecular Neurodegeneration, which includes research conduct at the University of Padua Department of Mathematics.      

Coordinated by the Head of the Laboratory of Translational Biomarkers Valentina Bonetto of the Mario Negri Institute of Pharmacological Research IRCCS, and Manuela Basso of the Department of Cellular, Computational and Integrative Biology (CIBIO) of the University of Trento, the research outlines the collection and analysis of extracellular vesicles through a simple blood sample. Francesco Rinaldi of the Department of Mathematics of the University of Padua analysed and processed data from the isolated and characterized vesicles collected from each blood sample.  Rinaldi used a trained learning machine that can distinguish the extracellular vesicles of a healthy individual from that of someone affected by the degenerative disease.    

The published research represents an important perspective for a disease diagnosed only after several medical visits by experienced neurologists. Like a message in a bottle, extracellular vesicles release a set of proteins and nucleic acids into the bloodstream that highlights the health of cells.  

Amyotrophic Lateral Sclerosis (ALS) is the most common form of motor neuron disease. The progressiveness of the disease affects the nerve cells in the brain and spinal cord, causing loss of muscle control with poor prognosis over time. Despite numerous advances in basic and clinical research, there is no cure. Unfortunately, its diagnosis occurs most often only after the first motor symptoms appear during the onset of its advanced stage of damage. For years, the Laboratory of Translational Biomarkers of the Mario Negri Institute has been studying new markers capable of anticipating the diagnosis of the disease over time and defining its prognosis more accurately.

The study was made possible due to the availability and collaboration from the many neurological experts at the CRESLA Centre, the AOU City of Health and Science of Turin, the University Hospital of Padua, the NeMO Clinical Center of Milan, ICS Maugeri of Milan, and the Casa Cura Policlinico of Milan.

The study was funded thanks to the Italian Ministry of Health’s "Young Researchers" Project, of which Dr. Basso and Dr. Pasetto are responsible and co-responsible of respectively(GR-2016-02361552), and by the European Marie-Sklodowska -Curie Individual Fellowships Funding Program awarded to Dr. Basso (752470).

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Thanks to the artificial intelligence of machine learning, the analysis of a specific set of lipid particles present in the blood (extracellular vesicles) makes it possible to diagnose the onset of Amyotrophic Lateral Sclerosis (ALS) accurately and quickly. With machine learning, predicting the speed and future outcome of ALS progression is also possible, as is reported in a study published in the scientific journal Molecular Neurodegeneration, which includes research conduct at the University of Padua Department of Mathematics.      

Coordinated by the Head of the Laboratory of Translational Biomarkers Valentina Bonetto of the Mario Negri Institute of Pharmacological Research IRCCS, and Manuela Basso of the Department of Cellular, Computational and Integrative Biology (CIBIO) of the University of Trento, the research outlines the collection and analysis of extracellular vesicles through a simple blood sample. Francesco Rinaldi of the Department of Mathematics of the University of Padua analysed and processed data from the isolated and characterized vesicles collected from each blood sample.  Rinaldi used a trained learning machine that can distinguish the extracellular vesicles of a healthy individual from that of someone affected by the degenerative disease.    

The published research represents an important perspective for a disease diagnosed only after several medical visits by experienced neurologists. Like a message in a bottle, extracellular vesicles release a set of proteins and nucleic acids into the bloodstream that highlights the health of cells.  

Amyotrophic Lateral Sclerosis (ALS) is the most common form of motor neuron disease. The progressiveness of the disease affects the nerve cells in the brain and spinal cord, causing loss of muscle control with poor prognosis over time. Despite numerous advances in basic and clinical research, there is no cure. Unfortunately, its diagnosis occurs most often only after the first motor symptoms appear during the onset of its advanced stage of damage. For years, the Laboratory of Translational Biomarkers of the Mario Negri Institute has been studying new markers capable of anticipating the diagnosis of the disease over time and defining its prognosis more accurately.

The study was made possible due to the availability and collaboration from the many neurological experts at the CRESLA Centre, the AOU City of Health and Science of Turin, the University Hospital of Padua, the NeMO Clinical Center of Milan, ICS Maugeri of Milan, and the Casa Cura Policlinico of Milan.

The study was funded thanks to the Italian Ministry of Health’s "Young Researchers" Project, of which Dr. Basso and Dr. Pasetto are responsible and co-responsible of respectively(GR-2016-02361552), and by the European Marie-Sklodowska -Curie Individual Fellowships Funding Program awarded to Dr. Basso (752470).

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Thanks to the artificial intelligence of machine learning, the analysis of a specific set of lipid particles present in the blood (extracellular vesicles) makes it possible to diagnose the onset of Amyotrophic Lateral Sclerosis (ALS) accurately and quickly. With machine learning, predicting the speed and future outcome of ALS progression is also possible, as is reported in a study published in the scientific journal Molecular Neurodegeneration, which includes research conduct at the University of Padua Department of Mathematics.      

Coordinated by the Head of the Laboratory of Translational Biomarkers Valentina Bonetto of the Mario Negri Institute of Pharmacological Research IRCCS, and Manuela Basso of the Department of Cellular, Computational and Integrative Biology (CIBIO) of the University of Trento, the research outlines the collection and analysis of extracellular vesicles through a simple blood sample. Francesco Rinaldi of the Department of Mathematics of the University of Padua analysed and processed data from the isolated and characterized vesicles collected from each blood sample.  Rinaldi used a trained learning machine that can distinguish the extracellular vesicles of a healthy individual from that of someone affected by the degenerative disease.    

The published research represents an important perspective for a disease diagnosed only after several medical visits by experienced neurologists. Like a message in a bottle, extracellular vesicles release a set of proteins and nucleic acids into the bloodstream that highlights the health of cells.  

Amyotrophic Lateral Sclerosis (ALS) is the most common form of motor neuron disease. The progressiveness of the disease affects the nerve cells in the brain and spinal cord, causing loss of muscle control with poor prognosis over time. Despite numerous advances in basic and clinical research, there is no cure. Unfortunately, its diagnosis occurs most often only after the first motor symptoms appear during the onset of its advanced stage of damage. For years, the Laboratory of Translational Biomarkers of the Mario Negri Institute has been studying new markers capable of anticipating the diagnosis of the disease over time and defining its prognosis more accurately.

The study was made possible due to the availability and collaboration from the many neurological experts at the CRESLA Centre, the AOU City of Health and Science of Turin, the University Hospital of Padua, the NeMO Clinical Center of Milan, ICS Maugeri of Milan, and the Casa Cura Policlinico of Milan.

The study was funded thanks to the Italian Ministry of Health’s "Young Researchers" Project, of which Dr. Basso and Dr. Pasetto are responsible and co-responsible of respectively(GR-2016-02361552), and by the European Marie-Sklodowska -Curie Individual Fellowships Funding Program awarded to Dr. Basso (752470).

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Thanks to the artificial intelligence of machine learning, the analysis of a specific set of lipid particles present in the blood (extracellular vesicles) makes it possible to diagnose the onset of Amyotrophic Lateral Sclerosis (ALS) accurately and quickly. With machine learning, predicting the speed and future outcome of ALS progression is also possible, as is reported in a study published in the scientific journal Molecular Neurodegeneration, which includes research conduct at the University of Padua Department of Mathematics.      

Coordinated by the Head of the Laboratory of Translational Biomarkers Valentina Bonetto of the Mario Negri Institute of Pharmacological Research IRCCS, and Manuela Basso of the Department of Cellular, Computational and Integrative Biology (CIBIO) of the University of Trento, the research outlines the collection and analysis of extracellular vesicles through a simple blood sample. Francesco Rinaldi of the Department of Mathematics of the University of Padua analysed and processed data from the isolated and characterized vesicles collected from each blood sample.  Rinaldi used a trained learning machine that can distinguish the extracellular vesicles of a healthy individual from that of someone affected by the degenerative disease.    

The published research represents an important perspective for a disease diagnosed only after several medical visits by experienced neurologists. Like a message in a bottle, extracellular vesicles release a set of proteins and nucleic acids into the bloodstream that highlights the health of cells.  

Amyotrophic Lateral Sclerosis (ALS) is the most common form of motor neuron disease. The progressiveness of the disease affects the nerve cells in the brain and spinal cord, causing loss of muscle control with poor prognosis over time. Despite numerous advances in basic and clinical research, there is no cure. Unfortunately, its diagnosis occurs most often only after the first motor symptoms appear during the onset of its advanced stage of damage. For years, the Laboratory of Translational Biomarkers of the Mario Negri Institute has been studying new markers capable of anticipating the diagnosis of the disease over time and defining its prognosis more accurately.

The study was made possible due to the availability and collaboration from the many neurological experts at the CRESLA Centre, the AOU City of Health and Science of Turin, the University Hospital of Padua, the NeMO Clinical Center of Milan, ICS Maugeri of Milan, and the Casa Cura Policlinico of Milan.

The study was funded thanks to the Italian Ministry of Health’s "Young Researchers" Project, of which Dr. Basso and Dr. Pasetto are responsible and co-responsible of respectively(GR-2016-02361552), and by the European Marie-Sklodowska -Curie Individual Fellowships Funding Program awarded to Dr. Basso (752470).

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Thanks to the artificial intelligence of machine learning, the analysis of a specific set of lipid particles present in the blood (extracellular vesicles) makes it possible to diagnose the onset of Amyotrophic Lateral Sclerosis (ALS) accurately and quickly. With machine learning, predicting the speed and future outcome of ALS progression is also possible, as is reported in a study published in the scientific journal Molecular Neurodegeneration, which includes research conduct at the University of Padua Department of Mathematics.      

Coordinated by the Head of the Laboratory of Translational Biomarkers Valentina Bonetto of the Mario Negri Institute of Pharmacological Research IRCCS, and Manuela Basso of the Department of Cellular, Computational and Integrative Biology (CIBIO) of the University of Trento, the research outlines the collection and analysis of extracellular vesicles through a simple blood sample. Francesco Rinaldi of the Department of Mathematics of the University of Padua analysed and processed data from the isolated and characterized vesicles collected from each blood sample.  Rinaldi used a trained learning machine that can distinguish the extracellular vesicles of a healthy individual from that of someone affected by the degenerative disease.    

The published research represents an important perspective for a disease diagnosed only after several medical visits by experienced neurologists. Like a message in a bottle, extracellular vesicles release a set of proteins and nucleic acids into the bloodstream that highlights the health of cells.  

Amyotrophic Lateral Sclerosis (ALS) is the most common form of motor neuron disease. The progressiveness of the disease affects the nerve cells in the brain and spinal cord, causing loss of muscle control with poor prognosis over time. Despite numerous advances in basic and clinical research, there is no cure. Unfortunately, its diagnosis occurs most often only after the first motor symptoms appear during the onset of its advanced stage of damage. For years, the Laboratory of Translational Biomarkers of the Mario Negri Institute has been studying new markers capable of anticipating the diagnosis of the disease over time and defining its prognosis more accurately.

The study was made possible due to the availability and collaboration from the many neurological experts at the CRESLA Centre, the AOU City of Health and Science of Turin, the University Hospital of Padua, the NeMO Clinical Center of Milan, ICS Maugeri of Milan, and the Casa Cura Policlinico of Milan.

The study was funded thanks to the Italian Ministry of Health’s "Young Researchers" Project, of which Dr. Basso and Dr. Pasetto are responsible and co-responsible of respectively(GR-2016-02361552), and by the European Marie-Sklodowska -Curie Individual Fellowships Funding Program awarded to Dr. Basso (752470).

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Thanks to the artificial intelligence of machine learning, the analysis of a specific set of lipid particles present in the blood (extracellular vesicles) makes it possible to diagnose the onset of Amyotrophic Lateral Sclerosis (ALS) accurately and quickly. With machine learning, predicting the speed and future outcome of ALS progression is also possible, as is reported in a study published in the scientific journal Molecular Neurodegeneration, which includes research conduct at the University of Padua Department of Mathematics.      

Coordinated by the Head of the Laboratory of Translational Biomarkers Valentina Bonetto of the Mario Negri Institute of Pharmacological Research IRCCS, and Manuela Basso of the Department of Cellular, Computational and Integrative Biology (CIBIO) of the University of Trento, the research outlines the collection and analysis of extracellular vesicles through a simple blood sample. Francesco Rinaldi of the Department of Mathematics of the University of Padua analysed and processed data from the isolated and characterized vesicles collected from each blood sample.  Rinaldi used a trained learning machine that can distinguish the extracellular vesicles of a healthy individual from that of someone affected by the degenerative disease.    

The published research represents an important perspective for a disease diagnosed only after several medical visits by experienced neurologists. Like a message in a bottle, extracellular vesicles release a set of proteins and nucleic acids into the bloodstream that highlights the health of cells.  

Amyotrophic Lateral Sclerosis (ALS) is the most common form of motor neuron disease. The progressiveness of the disease affects the nerve cells in the brain and spinal cord, causing loss of muscle control with poor prognosis over time. Despite numerous advances in basic and clinical research, there is no cure. Unfortunately, its diagnosis occurs most often only after the first motor symptoms appear during the onset of its advanced stage of damage. For years, the Laboratory of Translational Biomarkers of the Mario Negri Institute has been studying new markers capable of anticipating the diagnosis of the disease over time and defining its prognosis more accurately.

The study was made possible due to the availability and collaboration from the many neurological experts at the CRESLA Centre, the AOU City of Health and Science of Turin, the University Hospital of Padua, the NeMO Clinical Center of Milan, ICS Maugeri of Milan, and the Casa Cura Policlinico of Milan.

The study was funded thanks to the Italian Ministry of Health’s "Young Researchers" Project, of which Dr. Basso and Dr. Pasetto are responsible and co-responsible of respectively(GR-2016-02361552), and by the European Marie-Sklodowska -Curie Individual Fellowships Funding Program awarded to Dr. Basso (752470).

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ALL’ORTO BOTANICO APRE LA MOSTRA HOW NATURE WORKS. La realtà post-naturale di Krištof Kintera

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II grado - suddivisione, orario convocazione e informazioni (12 ottobre 2021) - sostegno 2021

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I grado - suddivisione, orario convocazione e informazioni (11 ottobre 2021) - sostegno 2021

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Merging Galaxies in the Early Universe

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An international team of astronomers, coordinated by the University of Padua PhD student Michael Romano and colleagues from the National Institute of Astrophysics (INAF) found that about 40% of galaxies in the early Universe are found in merging systems. This discovery confirms that in the early stages of their evolution, galaxies significantly increased their mass by merging with each other.   

Collisions between galaxies (galactic mergers) occur when two or more galaxies get close enough and begin spiralling with each other due to gravity, until they merge into a single larger galaxy. If the two galaxies have more or less the same number of stars (therefore the same stellar mass), the resulting galaxy will have about twice the mass of the individual ones, which is understood as the fastest mechanism by which galaxies grow. However, only 1% of the galaxies in the local Universe are observed merging: today, galaxies mainly increase because they grow cold gas, transforming them into stars (the so-called "star formation" mechanism).  

Today, the identification of this phenomenon in the distant Universe is made more complicated by the presence of interstellar dust, which prevents the light produced by young stars from reaching classic optical telescopes, and by the difficulty of these telescopes to detect the motion of the galaxies themselves. 

the ALPINE team (ALMA Large Program to INvestigate C + at Early times), which also includes the UniPD researcher, reports the discovery of dozens of galactic mergers in the early Universe thanks to the powerful antennas of the ALMA interferometer (Atacama Large Millimeter / submillimeter Array), in Chile. The ALMA radio telescope is able to observe the light obscured by the dust and identifying galaxies that would otherwise be invisible, by revealing their three-dimensional structure.       

The ALPINE program, coordinated among others by Paolo Cassata, a professor at the University of Padua, has systematically studied a sample of a hundred galaxies dating back to when the Universe was "only" one billion years old. Thanks to ALMA, it was possible to detect the light emanating from these distant galaxies by a particular carbon ion, called C +.     

"Thanks to the ALPINE project - says Michael Romano - we discovered that, 12 billion years ago, mergers were about 40 times more frequent than today, making a significant contribution to the mass growth of galaxies in the distant Universe " .    

"The results of our work show that the conversion of gas into stars is the primary mechanism that allows galaxies to increase their mass - concludes Michael Romano" .   

 

 

 

[summary] => [format] => 2 [safe_value] =>

An international team of astronomers, coordinated by the University of Padua PhD student Michael Romano and colleagues from the National Institute of Astrophysics (INAF) found that about 40% of galaxies in the early Universe are found in merging systems. This discovery confirms that in the early stages of their evolution, galaxies significantly increased their mass by merging with each other.   

Collisions between galaxies (galactic mergers) occur when two or more galaxies get close enough and begin spiralling with each other due to gravity, until they merge into a single larger galaxy. If the two galaxies have more or less the same number of stars (therefore the same stellar mass), the resulting galaxy will have about twice the mass of the individual ones, which is understood as the fastest mechanism by which galaxies grow. However, only 1% of the galaxies in the local Universe are observed merging: today, galaxies mainly increase because they grow cold gas, transforming them into stars (the so-called "star formation" mechanism).  

Today, the identification of this phenomenon in the distant Universe is made more complicated by the presence of interstellar dust, which prevents the light produced by young stars from reaching classic optical telescopes, and by the difficulty of these telescopes to detect the motion of the galaxies themselves. 

the ALPINE team (ALMA Large Program to INvestigate C + at Early times), which also includes the UniPD researcher, reports the discovery of dozens of galactic mergers in the early Universe thanks to the powerful antennas of the ALMA interferometer (Atacama Large Millimeter / submillimeter Array), in Chile. The ALMA radio telescope is able to observe the light obscured by the dust and identifying galaxies that would otherwise be invisible, by revealing their three-dimensional structure.       

The ALPINE program, coordinated among others by Paolo Cassata, a professor at the University of Padua, has systematically studied a sample of a hundred galaxies dating back to when the Universe was "only" one billion years old. Thanks to ALMA, it was possible to detect the light emanating from these distant galaxies by a particular carbon ion, called C +.     

"Thanks to the ALPINE project - says Michael Romano - we discovered that, 12 billion years ago, mergers were about 40 times more frequent than today, making a significant contribution to the mass growth of galaxies in the distant Universe " .    

"The results of our work show that the conversion of gas into stars is the primary mechanism that allows galaxies to increase their mass - concludes Michael Romano" .   

 

 

 

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An international team of astronomers, coordinated by the University of Padua PhD student Michael Romano and colleagues from the National Institute of Astrophysics (INAF) found that about 40% of galaxies in the early Universe are found in merging systems. This discovery confirms that in the early stages of their evolution, galaxies significantly increased their mass by merging with each other.   

Collisions between galaxies (galactic mergers) occur when two or more galaxies get close enough and begin spiralling with each other due to gravity, until they merge into a single larger galaxy. If the two galaxies have more or less the same number of stars (therefore the same stellar mass), the resulting galaxy will have about twice the mass of the individual ones, which is understood as the fastest mechanism by which galaxies grow. However, only 1% of the galaxies in the local Universe are observed merging: today, galaxies mainly increase because they grow cold gas, transforming them into stars (the so-called "star formation" mechanism).  

Today, the identification of this phenomenon in the distant Universe is made more complicated by the presence of interstellar dust, which prevents the light produced by young stars from reaching classic optical telescopes, and by the difficulty of these telescopes to detect the motion of the galaxies themselves. 

the ALPINE team (ALMA Large Program to INvestigate C + at Early times), which also includes the UniPD researcher, reports the discovery of dozens of galactic mergers in the early Universe thanks to the powerful antennas of the ALMA interferometer (Atacama Large Millimeter / submillimeter Array), in Chile. The ALMA radio telescope is able to observe the light obscured by the dust and identifying galaxies that would otherwise be invisible, by revealing their three-dimensional structure.       

The ALPINE program, coordinated among others by Paolo Cassata, a professor at the University of Padua, has systematically studied a sample of a hundred galaxies dating back to when the Universe was "only" one billion years old. Thanks to ALMA, it was possible to detect the light emanating from these distant galaxies by a particular carbon ion, called C +.     

"Thanks to the ALPINE project - says Michael Romano - we discovered that, 12 billion years ago, mergers were about 40 times more frequent than today, making a significant contribution to the mass growth of galaxies in the distant Universe " .    

"The results of our work show that the conversion of gas into stars is the primary mechanism that allows galaxies to increase their mass - concludes Michael Romano" .   

 

 

 

[summary] => [format] => 2 [safe_value] =>

An international team of astronomers, coordinated by the University of Padua PhD student Michael Romano and colleagues from the National Institute of Astrophysics (INAF) found that about 40% of galaxies in the early Universe are found in merging systems. This discovery confirms that in the early stages of their evolution, galaxies significantly increased their mass by merging with each other.   

Collisions between galaxies (galactic mergers) occur when two or more galaxies get close enough and begin spiralling with each other due to gravity, until they merge into a single larger galaxy. If the two galaxies have more or less the same number of stars (therefore the same stellar mass), the resulting galaxy will have about twice the mass of the individual ones, which is understood as the fastest mechanism by which galaxies grow. However, only 1% of the galaxies in the local Universe are observed merging: today, galaxies mainly increase because they grow cold gas, transforming them into stars (the so-called "star formation" mechanism).  

Today, the identification of this phenomenon in the distant Universe is made more complicated by the presence of interstellar dust, which prevents the light produced by young stars from reaching classic optical telescopes, and by the difficulty of these telescopes to detect the motion of the galaxies themselves. 

the ALPINE team (ALMA Large Program to INvestigate C + at Early times), which also includes the UniPD researcher, reports the discovery of dozens of galactic mergers in the early Universe thanks to the powerful antennas of the ALMA interferometer (Atacama Large Millimeter / submillimeter Array), in Chile. The ALMA radio telescope is able to observe the light obscured by the dust and identifying galaxies that would otherwise be invisible, by revealing their three-dimensional structure.       

The ALPINE program, coordinated among others by Paolo Cassata, a professor at the University of Padua, has systematically studied a sample of a hundred galaxies dating back to when the Universe was "only" one billion years old. Thanks to ALMA, it was possible to detect the light emanating from these distant galaxies by a particular carbon ion, called C +.     

"Thanks to the ALPINE project - says Michael Romano - we discovered that, 12 billion years ago, mergers were about 40 times more frequent than today, making a significant contribution to the mass growth of galaxies in the distant Universe " .    

"The results of our work show that the conversion of gas into stars is the primary mechanism that allows galaxies to increase their mass - concludes Michael Romano" .   

 

 

 

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An international team of astronomers, coordinated by the University of Padua PhD student Michael Romano and colleagues from the National Institute of Astrophysics (INAF) found that about 40% of galaxies in the early Universe are found in merging systems. This discovery confirms that in the early stages of their evolution, galaxies significantly increased their mass by merging with each other.   

Collisions between galaxies (galactic mergers) occur when two or more galaxies get close enough and begin spiralling with each other due to gravity, until they merge into a single larger galaxy. If the two galaxies have more or less the same number of stars (therefore the same stellar mass), the resulting galaxy will have about twice the mass of the individual ones, which is understood as the fastest mechanism by which galaxies grow. However, only 1% of the galaxies in the local Universe are observed merging: today, galaxies mainly increase because they grow cold gas, transforming them into stars (the so-called "star formation" mechanism).  

Today, the identification of this phenomenon in the distant Universe is made more complicated by the presence of interstellar dust, which prevents the light produced by young stars from reaching classic optical telescopes, and by the difficulty of these telescopes to detect the motion of the galaxies themselves. 

the ALPINE team (ALMA Large Program to INvestigate C + at Early times), which also includes the UniPD researcher, reports the discovery of dozens of galactic mergers in the early Universe thanks to the powerful antennas of the ALMA interferometer (Atacama Large Millimeter / submillimeter Array), in Chile. The ALMA radio telescope is able to observe the light obscured by the dust and identifying galaxies that would otherwise be invisible, by revealing their three-dimensional structure.       

The ALPINE program, coordinated among others by Paolo Cassata, a professor at the University of Padua, has systematically studied a sample of a hundred galaxies dating back to when the Universe was "only" one billion years old. Thanks to ALMA, it was possible to detect the light emanating from these distant galaxies by a particular carbon ion, called C +.     

"Thanks to the ALPINE project - says Michael Romano - we discovered that, 12 billion years ago, mergers were about 40 times more frequent than today, making a significant contribution to the mass growth of galaxies in the distant Universe " .    

"The results of our work show that the conversion of gas into stars is the primary mechanism that allows galaxies to increase their mass - concludes Michael Romano" .   

 

 

 

[summary] => [format] => 2 [safe_value] =>

An international team of astronomers, coordinated by the University of Padua PhD student Michael Romano and colleagues from the National Institute of Astrophysics (INAF) found that about 40% of galaxies in the early Universe are found in merging systems. This discovery confirms that in the early stages of their evolution, galaxies significantly increased their mass by merging with each other.   

Collisions between galaxies (galactic mergers) occur when two or more galaxies get close enough and begin spiralling with each other due to gravity, until they merge into a single larger galaxy. If the two galaxies have more or less the same number of stars (therefore the same stellar mass), the resulting galaxy will have about twice the mass of the individual ones, which is understood as the fastest mechanism by which galaxies grow. However, only 1% of the galaxies in the local Universe are observed merging: today, galaxies mainly increase because they grow cold gas, transforming them into stars (the so-called "star formation" mechanism).  

Today, the identification of this phenomenon in the distant Universe is made more complicated by the presence of interstellar dust, which prevents the light produced by young stars from reaching classic optical telescopes, and by the difficulty of these telescopes to detect the motion of the galaxies themselves. 

the ALPINE team (ALMA Large Program to INvestigate C + at Early times), which also includes the UniPD researcher, reports the discovery of dozens of galactic mergers in the early Universe thanks to the powerful antennas of the ALMA interferometer (Atacama Large Millimeter / submillimeter Array), in Chile. The ALMA radio telescope is able to observe the light obscured by the dust and identifying galaxies that would otherwise be invisible, by revealing their three-dimensional structure.       

The ALPINE program, coordinated among others by Paolo Cassata, a professor at the University of Padua, has systematically studied a sample of a hundred galaxies dating back to when the Universe was "only" one billion years old. Thanks to ALMA, it was possible to detect the light emanating from these distant galaxies by a particular carbon ion, called C +.     

"Thanks to the ALPINE project - says Michael Romano - we discovered that, 12 billion years ago, mergers were about 40 times more frequent than today, making a significant contribution to the mass growth of galaxies in the distant Universe " .    

"The results of our work show that the conversion of gas into stars is the primary mechanism that allows galaxies to increase their mass - concludes Michael Romano" .   

 

 

 

[safe_summary] => ) ) [#formatter] => text_summary_or_trimmed [0] => Array ( [#markup] =>

An international team of astronomers, coordinated by the University of Padua PhD student Michael Romano and colleagues from the National Institute of Astrophysics (INAF) found that about 40% of galaxies in the early Universe are found in merging systems. This discovery confirms that in the early stages of their evolution, galaxies significantly increased their mass by merging with each other.   

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An international team of astronomers, coordinated by the University of Padua PhD student Michael Romano and colleagues from the National Institute of Astrophysics (INAF) found that about 40% of galaxies in the early Universe are found in merging systems. This discovery confirms that in the early stages of their evolution, galaxies significantly increased their mass by merging with each other.   

Collisions between galaxies (galactic mergers) occur when two or more galaxies get close enough and begin spiralling with each other due to gravity, until they merge into a single larger galaxy. If the two galaxies have more or less the same number of stars (therefore the same stellar mass), the resulting galaxy will have about twice the mass of the individual ones, which is understood as the fastest mechanism by which galaxies grow. However, only 1% of the galaxies in the local Universe are observed merging: today, galaxies mainly increase because they grow cold gas, transforming them into stars (the so-called "star formation" mechanism).  

Today, the identification of this phenomenon in the distant Universe is made more complicated by the presence of interstellar dust, which prevents the light produced by young stars from reaching classic optical telescopes, and by the difficulty of these telescopes to detect the motion of the galaxies themselves. 

the ALPINE team (ALMA Large Program to INvestigate C + at Early times), which also includes the UniPD researcher, reports the discovery of dozens of galactic mergers in the early Universe thanks to the powerful antennas of the ALMA interferometer (Atacama Large Millimeter / submillimeter Array), in Chile. The ALMA radio telescope is able to observe the light obscured by the dust and identifying galaxies that would otherwise be invisible, by revealing their three-dimensional structure.       

The ALPINE program, coordinated among others by Paolo Cassata, a professor at the University of Padua, has systematically studied a sample of a hundred galaxies dating back to when the Universe was "only" one billion years old. Thanks to ALMA, it was possible to detect the light emanating from these distant galaxies by a particular carbon ion, called C +.     

"Thanks to the ALPINE project - says Michael Romano - we discovered that, 12 billion years ago, mergers were about 40 times more frequent than today, making a significant contribution to the mass growth of galaxies in the distant Universe " .    

"The results of our work show that the conversion of gas into stars is the primary mechanism that allows galaxies to increase their mass - concludes Michael Romano" .   

 

 

 

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An international team of astronomers, coordinated by the University of Padua PhD student Michael Romano and colleagues from the National Institute of Astrophysics (INAF) found that about 40% of galaxies in the early Universe are found in merging systems. This discovery confirms that in the early stages of their evolution, galaxies significantly increased their mass by merging with each other.   

Collisions between galaxies (galactic mergers) occur when two or more galaxies get close enough and begin spiralling with each other due to gravity, until they merge into a single larger galaxy. If the two galaxies have more or less the same number of stars (therefore the same stellar mass), the resulting galaxy will have about twice the mass of the individual ones, which is understood as the fastest mechanism by which galaxies grow. However, only 1% of the galaxies in the local Universe are observed merging: today, galaxies mainly increase because they grow cold gas, transforming them into stars (the so-called "star formation" mechanism).  

Today, the identification of this phenomenon in the distant Universe is made more complicated by the presence of interstellar dust, which prevents the light produced by young stars from reaching classic optical telescopes, and by the difficulty of these telescopes to detect the motion of the galaxies themselves. 

the ALPINE team (ALMA Large Program to INvestigate C + at Early times), which also includes the UniPD researcher, reports the discovery of dozens of galactic mergers in the early Universe thanks to the powerful antennas of the ALMA interferometer (Atacama Large Millimeter / submillimeter Array), in Chile. The ALMA radio telescope is able to observe the light obscured by the dust and identifying galaxies that would otherwise be invisible, by revealing their three-dimensional structure.       

The ALPINE program, coordinated among others by Paolo Cassata, a professor at the University of Padua, has systematically studied a sample of a hundred galaxies dating back to when the Universe was "only" one billion years old. Thanks to ALMA, it was possible to detect the light emanating from these distant galaxies by a particular carbon ion, called C +.     

"Thanks to the ALPINE project - says Michael Romano - we discovered that, 12 billion years ago, mergers were about 40 times more frequent than today, making a significant contribution to the mass growth of galaxies in the distant Universe " .    

"The results of our work show that the conversion of gas into stars is the primary mechanism that allows galaxies to increase their mass - concludes Michael Romano" .   

 

 

 

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An international team of astronomers, coordinated by the University of Padua PhD student Michael Romano and colleagues from the National Institute of Astrophysics (INAF) found that about 40% of galaxies in the early Universe are found in merging systems. This discovery confirms that in the early stages of their evolution, galaxies significantly increased their mass by merging with each other.   

Collisions between galaxies (galactic mergers) occur when two or more galaxies get close enough and begin spiralling with each other due to gravity, until they merge into a single larger galaxy. If the two galaxies have more or less the same number of stars (therefore the same stellar mass), the resulting galaxy will have about twice the mass of the individual ones, which is understood as the fastest mechanism by which galaxies grow. However, only 1% of the galaxies in the local Universe are observed merging: today, galaxies mainly increase because they grow cold gas, transforming them into stars (the so-called "star formation" mechanism).  

Today, the identification of this phenomenon in the distant Universe is made more complicated by the presence of interstellar dust, which prevents the light produced by young stars from reaching classic optical telescopes, and by the difficulty of these telescopes to detect the motion of the galaxies themselves. 

the ALPINE team (ALMA Large Program to INvestigate C + at Early times), which also includes the UniPD researcher, reports the discovery of dozens of galactic mergers in the early Universe thanks to the powerful antennas of the ALMA interferometer (Atacama Large Millimeter / submillimeter Array), in Chile. The ALMA radio telescope is able to observe the light obscured by the dust and identifying galaxies that would otherwise be invisible, by revealing their three-dimensional structure.       

The ALPINE program, coordinated among others by Paolo Cassata, a professor at the University of Padua, has systematically studied a sample of a hundred galaxies dating back to when the Universe was "only" one billion years old. Thanks to ALMA, it was possible to detect the light emanating from these distant galaxies by a particular carbon ion, called C +.     

"Thanks to the ALPINE project - says Michael Romano - we discovered that, 12 billion years ago, mergers were about 40 times more frequent than today, making a significant contribution to the mass growth of galaxies in the distant Universe " .    

"The results of our work show that the conversion of gas into stars is the primary mechanism that allows galaxies to increase their mass - concludes Michael Romano" .   

 

 

 

[summary] => [format] => 2 [safe_value] =>

An international team of astronomers, coordinated by the University of Padua PhD student Michael Romano and colleagues from the National Institute of Astrophysics (INAF) found that about 40% of galaxies in the early Universe are found in merging systems. This discovery confirms that in the early stages of their evolution, galaxies significantly increased their mass by merging with each other.   

Collisions between galaxies (galactic mergers) occur when two or more galaxies get close enough and begin spiralling with each other due to gravity, until they merge into a single larger galaxy. If the two galaxies have more or less the same number of stars (therefore the same stellar mass), the resulting galaxy will have about twice the mass of the individual ones, which is understood as the fastest mechanism by which galaxies grow. However, only 1% of the galaxies in the local Universe are observed merging: today, galaxies mainly increase because they grow cold gas, transforming them into stars (the so-called "star formation" mechanism).  

Today, the identification of this phenomenon in the distant Universe is made more complicated by the presence of interstellar dust, which prevents the light produced by young stars from reaching classic optical telescopes, and by the difficulty of these telescopes to detect the motion of the galaxies themselves. 

the ALPINE team (ALMA Large Program to INvestigate C + at Early times), which also includes the UniPD researcher, reports the discovery of dozens of galactic mergers in the early Universe thanks to the powerful antennas of the ALMA interferometer (Atacama Large Millimeter / submillimeter Array), in Chile. The ALMA radio telescope is able to observe the light obscured by the dust and identifying galaxies that would otherwise be invisible, by revealing their three-dimensional structure.       

The ALPINE program, coordinated among others by Paolo Cassata, a professor at the University of Padua, has systematically studied a sample of a hundred galaxies dating back to when the Universe was "only" one billion years old. Thanks to ALMA, it was possible to detect the light emanating from these distant galaxies by a particular carbon ion, called C +.     

"Thanks to the ALPINE project - says Michael Romano - we discovered that, 12 billion years ago, mergers were about 40 times more frequent than today, making a significant contribution to the mass growth of galaxies in the distant Universe " .    

"The results of our work show that the conversion of gas into stars is the primary mechanism that allows galaxies to increase their mass - concludes Michael Romano" .   

 

 

 

[safe_summary] => ) ) ) [field_date_box_lancio_news] => Array ( [und] => Array ( [0] => Array ( [value] => 2021-10-08T00:00:00 [timezone] => Europe/Paris [timezone_db] => Europe/Paris [date_type] => date ) ) ) [field_etichetta_box_lancio_news] => Array ( ) [field_img_box_lancio_news] => Array ( [und] => Array ( [0] => Array ( [fid] => 98730 [uid] => 2032 [filename] => potw1821a.jpeg [uri] => public://potw1821a_0.jpeg [filemime] => image/jpeg [filesize] => 16770 [status] => 1 [timestamp] => 1633686760 [type] => image [field_file_image_alt_text] => Array ( ) [field_file_image_title_text] => Array ( ) [field_folder] => Array ( [und] => Array ( [0] => Array ( [tid] => 2048 ) ) ) [metadata] => Array ( [height] => 227 [width] => 677 ) [height] => 227 [width] => 677 [alt] => galaxies [title] => ) ) ) [field_link_alla_news] => Array ( ) [field_link_esterno_news] => Array ( [und] => Array ( [0] => Array ( [value] => [format] => [safe_value] => ) ) ) [field_pagina_associata] => Array ( ) [field_link_etichetta] => Array ( ) [field_abstract_news] => Array ( [und] => Array ( [0] => Array ( [value] => The University of Padua and an international research team publishes its findings in the "Astronomy & Astrophysics" scientific journal. The findings are concerned with the early evolutionary stages of galaxies and the significance in the increased of their mass. The work reveals the presence of a significant merging activity of galaxies during the early University, with a merger fraction of almost 40% [format] => [safe_value] => The University of Padua and an international research team publishes its findings in the "Astronomy & Astrophysics" scientific journal. The findings are concerned with the early evolutionary stages of galaxies and the significance in the increased of their mass. 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An international team of astronomers, coordinated by the University of Padua PhD student Michael Romano and colleagues from the National Institute of Astrophysics (INAF) found that about 40% of galaxies in the early Universe are found in merging systems. This discovery confirms that in the early stages of their evolution, galaxies significantly increased their mass by merging with each other.   

Collisions between galaxies (galactic mergers) occur when two or more galaxies get close enough and begin spiralling with each other due to gravity, until they merge into a single larger galaxy. If the two galaxies have more or less the same number of stars (therefore the same stellar mass), the resulting galaxy will have about twice the mass of the individual ones, which is understood as the fastest mechanism by which galaxies grow. However, only 1% of the galaxies in the local Universe are observed merging: today, galaxies mainly increase because they grow cold gas, transforming them into stars (the so-called "star formation" mechanism).  

Today, the identification of this phenomenon in the distant Universe is made more complicated by the presence of interstellar dust, which prevents the light produced by young stars from reaching classic optical telescopes, and by the difficulty of these telescopes to detect the motion of the galaxies themselves. 

the ALPINE team (ALMA Large Program to INvestigate C + at Early times), which also includes the UniPD researcher, reports the discovery of dozens of galactic mergers in the early Universe thanks to the powerful antennas of the ALMA interferometer (Atacama Large Millimeter / submillimeter Array), in Chile. The ALMA radio telescope is able to observe the light obscured by the dust and identifying galaxies that would otherwise be invisible, by revealing their three-dimensional structure.       

The ALPINE program, coordinated among others by Paolo Cassata, a professor at the University of Padua, has systematically studied a sample of a hundred galaxies dating back to when the Universe was "only" one billion years old. Thanks to ALMA, it was possible to detect the light emanating from these distant galaxies by a particular carbon ion, called C +.     

"Thanks to the ALPINE project - says Michael Romano - we discovered that, 12 billion years ago, mergers were about 40 times more frequent than today, making a significant contribution to the mass growth of galaxies in the distant Universe " .    

"The results of our work show that the conversion of gas into stars is the primary mechanism that allows galaxies to increase their mass - concludes Michael Romano" .   

 

 

 

[summary] => [format] => 2 [safe_value] =>

An international team of astronomers, coordinated by the University of Padua PhD student Michael Romano and colleagues from the National Institute of Astrophysics (INAF) found that about 40% of galaxies in the early Universe are found in merging systems. This discovery confirms that in the early stages of their evolution, galaxies significantly increased their mass by merging with each other.   

Collisions between galaxies (galactic mergers) occur when two or more galaxies get close enough and begin spiralling with each other due to gravity, until they merge into a single larger galaxy. If the two galaxies have more or less the same number of stars (therefore the same stellar mass), the resulting galaxy will have about twice the mass of the individual ones, which is understood as the fastest mechanism by which galaxies grow. However, only 1% of the galaxies in the local Universe are observed merging: today, galaxies mainly increase because they grow cold gas, transforming them into stars (the so-called "star formation" mechanism).  

Today, the identification of this phenomenon in the distant Universe is made more complicated by the presence of interstellar dust, which prevents the light produced by young stars from reaching classic optical telescopes, and by the difficulty of these telescopes to detect the motion of the galaxies themselves. 

the ALPINE team (ALMA Large Program to INvestigate C + at Early times), which also includes the UniPD researcher, reports the discovery of dozens of galactic mergers in the early Universe thanks to the powerful antennas of the ALMA interferometer (Atacama Large Millimeter / submillimeter Array), in Chile. The ALMA radio telescope is able to observe the light obscured by the dust and identifying galaxies that would otherwise be invisible, by revealing their three-dimensional structure.       

The ALPINE program, coordinated among others by Paolo Cassata, a professor at the University of Padua, has systematically studied a sample of a hundred galaxies dating back to when the Universe was "only" one billion years old. Thanks to ALMA, it was possible to detect the light emanating from these distant galaxies by a particular carbon ion, called C +.     

"Thanks to the ALPINE project - says Michael Romano - we discovered that, 12 billion years ago, mergers were about 40 times more frequent than today, making a significant contribution to the mass growth of galaxies in the distant Universe " .    

"The results of our work show that the conversion of gas into stars is the primary mechanism that allows galaxies to increase their mass - concludes Michael Romano" .   

 

 

 

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T4L_19 novembre, 26 novembre, 3 dicembre 2021– ciclo di incontri

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Workshop “Le giornate della didattica”

15:30-18.30 - Sala meeting della Biblioteca Beato Pellegrino, Via Beato Pellegrino 28

Comunicazione e Improvvisazione

CambiScena Improvvisazione Teatrale

(Claudia Gafà, Marianna Cerbone, Antonio Contartese)

Incontro per tutti i docenti dell'Ateneo
Per partecipare
è necessario prenotare entro il 16.11.2021

[summary] => [format] => 2 [safe_value] =>

Workshop “Le giornate della didattica”

15:30-18.30 - Sala meeting della Biblioteca Beato Pellegrino, Via Beato Pellegrino 28

Comunicazione e Improvvisazione

CambiScena Improvvisazione Teatrale

(Claudia Gafà, Marianna Cerbone, Antonio Contartese)

Incontro per tutti i docenti dell'Ateneo
Per partecipare
è necessario prenotare entro il 16.11.2021

[safe_summary] => ) ) ) [field_accordion_state] => Array ( [und] => Array ( [0] => Array ( [value] => chiuso ) ) ) [field_allegato_element] => Array ( ) [field_outline_level] => Array ( [und] => Array ( [0] => Array ( [value] => h3 ) ) ) [field_titolo_frontend] => Array ( [und] => Array ( [0] => Array ( [value] => 19 novembre, 26 novembre, 3 dicembre 2021– ciclo di incontri [format] => [safe_value] => 19 novembre, 26 novembre, 3 dicembre 2021– ciclo di incontri ) ) ) [name] => teaching4learning [picture] => 0 [data] => b:0; [num_revisions] => 1 [current_revision_id] => 369262 [is_current] => 1 [is_pending] => [revision_moderation] => [entity_view_prepared] => 1 ) [#items] => Array ( [0] => Array ( [value] => 19 novembre, 26 novembre, 3 dicembre 2021– ciclo di incontri [format] => [safe_value] => 19 novembre, 26 novembre, 3 dicembre 2021– ciclo di incontri ) ) [#formatter] => text_default [0] => Array ( [#markup] => 19 novembre, 26 novembre, 3 dicembre 2021– ciclo di incontri ) ) [body] => Array ( [#theme] => field [#weight] => -3 [#title] => Body [#access] => 1 [#label_display] => hidden [#view_mode] => teaser [#language] => und [#field_name] => body [#field_type] => text_with_summary [#field_translatable] => 0 [#entity_type] => node [#bundle] => elemento_accordion [#object] => stdClass Object ( [vid] => 369262 [uid] => 10762 [title] => T4L_19 novembre, 26 novembre, 3 dicembre 2021– ciclo di incontri [log] => [status] => 1 [comment] => 0 [promote] => 1 [sticky] => 0 [nid] => 82022 [type] => elemento_accordion [language] => it [created] => 1633686530 [changed] => 1694523302 [tnid] => 0 [translate] => 0 [revision_timestamp] => 1694523302 [revision_uid] => 4 [taxonomy_vocabulary_8] => Array ( ) [body] => Array ( [und] => Array ( [0] => Array ( [value] =>

Workshop “Le giornate della didattica”

15:30-18.30 - Sala meeting della Biblioteca Beato Pellegrino, Via Beato Pellegrino 28

Comunicazione e Improvvisazione

CambiScena Improvvisazione Teatrale

(Claudia Gafà, Marianna Cerbone, Antonio Contartese)

Incontro per tutti i docenti dell'Ateneo
Per partecipare
è necessario prenotare entro il 16.11.2021

[summary] => [format] => 2 [safe_value] =>

Workshop “Le giornate della didattica”

15:30-18.30 - Sala meeting della Biblioteca Beato Pellegrino, Via Beato Pellegrino 28

Comunicazione e Improvvisazione

CambiScena Improvvisazione Teatrale

(Claudia Gafà, Marianna Cerbone, Antonio Contartese)

Incontro per tutti i docenti dell'Ateneo
Per partecipare
è necessario prenotare entro il 16.11.2021

[safe_summary] => ) ) ) [field_accordion_state] => Array ( [und] => Array ( [0] => Array ( [value] => chiuso ) ) ) [field_allegato_element] => Array ( ) [field_outline_level] => Array ( [und] => Array ( [0] => Array ( [value] => h3 ) ) ) [field_titolo_frontend] => Array ( [und] => Array ( [0] => Array ( [value] => 19 novembre, 26 novembre, 3 dicembre 2021– ciclo di incontri [format] => [safe_value] => 19 novembre, 26 novembre, 3 dicembre 2021– ciclo di incontri ) ) ) [name] => teaching4learning [picture] => 0 [data] => b:0; [num_revisions] => 1 [current_revision_id] => 369262 [is_current] => 1 [is_pending] => [revision_moderation] => [entity_view_prepared] => 1 ) [#items] => Array ( [0] => Array ( [value] =>

Workshop “Le giornate della didattica”

15:30-18.30 - Sala meeting della Biblioteca Beato Pellegrino, Via Beato Pellegrino 28

Comunicazione e Improvvisazione

CambiScena Improvvisazione Teatrale

(Claudia Gafà, Marianna Cerbone, Antonio Contartese)

Incontro per tutti i docenti dell'Ateneo
Per partecipare
è necessario prenotare entro il 16.11.2021

[summary] => [format] => 2 [safe_value] =>

Workshop “Le giornate della didattica”

15:30-18.30 - Sala meeting della Biblioteca Beato Pellegrino, Via Beato Pellegrino 28

Comunicazione e Improvvisazione

CambiScena Improvvisazione Teatrale

(Claudia Gafà, Marianna Cerbone, Antonio Contartese)

Incontro per tutti i docenti dell'Ateneo
Per partecipare
è necessario prenotare entro il 16.11.2021

[safe_summary] => ) ) [#formatter] => text_summary_or_trimmed [0] => Array ( [#markup] =>

Workshop “Le giornate della didattica”

15:30-18.30 - Sala meeting della Biblioteca Beato Pellegrino, Via Beato Pellegrino 28

Comunicazione e Improvvisazione

CambiScena Improvvisazione Teatrale

(Claudia Gafà, Marianna Cerbone, Antonio Contartese)

) ) [field_accordion_state] => Array ( [#theme] => field [#weight] => -1 [#title] => Aperto/Chiuso [#access] => 1 [#label_display] => above [#view_mode] => teaser [#language] => und [#field_name] => field_accordion_state [#field_type] => list_text [#field_translatable] => 0 [#entity_type] => node [#bundle] => elemento_accordion [#object] => stdClass Object ( [vid] => 369262 [uid] => 10762 [title] => T4L_19 novembre, 26 novembre, 3 dicembre 2021– ciclo di incontri [log] => [status] => 1 [comment] => 0 [promote] => 1 [sticky] => 0 [nid] => 82022 [type] => elemento_accordion [language] => it [created] => 1633686530 [changed] => 1694523302 [tnid] => 0 [translate] => 0 [revision_timestamp] => 1694523302 [revision_uid] => 4 [taxonomy_vocabulary_8] => Array ( ) [body] => Array ( [und] => Array ( [0] => Array ( [value] =>

Workshop “Le giornate della didattica”

15:30-18.30 - Sala meeting della Biblioteca Beato Pellegrino, Via Beato Pellegrino 28

Comunicazione e Improvvisazione

CambiScena Improvvisazione Teatrale

(Claudia Gafà, Marianna Cerbone, Antonio Contartese)

Incontro per tutti i docenti dell'Ateneo
Per partecipare
è necessario prenotare entro il 16.11.2021

[summary] => [format] => 2 [safe_value] =>

Workshop “Le giornate della didattica”

15:30-18.30 - Sala meeting della Biblioteca Beato Pellegrino, Via Beato Pellegrino 28

Comunicazione e Improvvisazione

CambiScena Improvvisazione Teatrale

(Claudia Gafà, Marianna Cerbone, Antonio Contartese)

Incontro per tutti i docenti dell'Ateneo
Per partecipare
è necessario prenotare entro il 16.11.2021

[safe_summary] => ) ) ) [field_accordion_state] => Array ( [und] => Array ( [0] => Array ( [value] => chiuso ) ) ) [field_allegato_element] => Array ( ) [field_outline_level] => Array ( [und] => Array ( [0] => Array ( [value] => h3 ) ) ) [field_titolo_frontend] => Array ( [und] => Array ( [0] => Array ( [value] => 19 novembre, 26 novembre, 3 dicembre 2021– ciclo di incontri [format] => [safe_value] => 19 novembre, 26 novembre, 3 dicembre 2021– ciclo di incontri ) ) ) [name] => teaching4learning [picture] => 0 [data] => b:0; [num_revisions] => 1 [current_revision_id] => 369262 [is_current] => 1 [is_pending] => [revision_moderation] => [entity_view_prepared] => 1 ) [#items] => Array ( [0] => Array ( [value] => chiuso ) ) [#formatter] => text_default [0] => Array ( [#markup] => chiuso ) ) [links] => Array ( [#theme] => links__node [#pre_render] => Array ( [0] => drupal_pre_render_links ) [#attributes] => Array ( [class] => Array ( [0] => links [1] => inline ) ) [node] => Array ( [#theme] => links__node__node [#links] => Array ( [node-readmore] => Array ( [title] => Read more about T4L_19 novembre, 26 novembre, 3 dicembre 2021– ciclo di incontri [href] => node/82022 [html] => 1 [attributes] => Array ( [rel] => tag [title] => T4L_19 novembre, 26 novembre, 3 dicembre 2021– ciclo di incontri ) ) ) [#attributes] => Array ( [class] => Array ( [0] => links [1] => inline ) ) ) ) [field_outline_level] => Array ( [#theme] => field [#weight] => 31 [#title] => Livello outline [#access] => 1 [#label_display] => above [#view_mode] => teaser [#language] => und [#field_name] => field_outline_level [#field_type] => list_text [#field_translatable] => 0 [#entity_type] => node [#bundle] => elemento_accordion [#object] => stdClass Object ( [vid] => 369262 [uid] => 10762 [title] => T4L_19 novembre, 26 novembre, 3 dicembre 2021– ciclo di incontri [log] => [status] => 1 [comment] => 0 [promote] => 1 [sticky] => 0 [nid] => 82022 [type] => elemento_accordion [language] => it [created] => 1633686530 [changed] => 1694523302 [tnid] => 0 [translate] => 0 [revision_timestamp] => 1694523302 [revision_uid] => 4 [taxonomy_vocabulary_8] => Array ( ) [body] => Array ( [und] => Array ( [0] => Array ( [value] =>

Workshop “Le giornate della didattica”

15:30-18.30 - Sala meeting della Biblioteca Beato Pellegrino, Via Beato Pellegrino 28

Comunicazione e Improvvisazione

CambiScena Improvvisazione Teatrale

(Claudia Gafà, Marianna Cerbone, Antonio Contartese)

Incontro per tutti i docenti dell'Ateneo
Per partecipare
è necessario prenotare entro il 16.11.2021

[summary] => [format] => 2 [safe_value] =>

Workshop “Le giornate della didattica”

15:30-18.30 - Sala meeting della Biblioteca Beato Pellegrino, Via Beato Pellegrino 28

Comunicazione e Improvvisazione

CambiScena Improvvisazione Teatrale

(Claudia Gafà, Marianna Cerbone, Antonio Contartese)

Incontro per tutti i docenti dell'Ateneo
Per partecipare
è necessario prenotare entro il 16.11.2021

[safe_summary] => ) ) ) [field_accordion_state] => Array ( [und] => Array ( [0] => Array ( [value] => chiuso ) ) ) [field_allegato_element] => Array ( ) [field_outline_level] => Array ( [und] => Array ( [0] => Array ( [value] => h3 ) ) ) [field_titolo_frontend] => Array ( [und] => Array ( [0] => Array ( [value] => 19 novembre, 26 novembre, 3 dicembre 2021– ciclo di incontri [format] => [safe_value] => 19 novembre, 26 novembre, 3 dicembre 2021– ciclo di incontri ) ) ) [name] => teaching4learning [picture] => 0 [data] => b:0; [num_revisions] => 1 [current_revision_id] => 369262 [is_current] => 1 [is_pending] => [revision_moderation] => [entity_view_prepared] => 1 ) [#items] => Array ( [0] => Array ( [value] => h3 ) ) [#formatter] => text_default [0] => Array ( [#markup] => h3 ) ) )

T4L_12 novembre 2021

Array ( [field_titolo_frontend] => Array ( [#theme] => field [#weight] => -4 [#title] => Titolo frontend [#access] => 1 [#label_display] => above [#view_mode] => teaser [#language] => und [#field_name] => field_titolo_frontend [#field_type] => text_long [#field_translatable] => 0 [#entity_type] => node [#bundle] => elemento_accordion [#object] => stdClass Object ( [vid] => 369259 [uid] => 10762 [title] => T4L_12 novembre 2021 [log] => [status] => 1 [comment] => 0 [promote] => 1 [sticky] => 0 [nid] => 82021 [type] => elemento_accordion [language] => it [created] => 1633686448 [changed] => 1633686448 [tnid] => 0 [translate] => 0 [revision_timestamp] => 1633686448 [revision_uid] => 10762 [taxonomy_vocabulary_8] => Array ( ) [body] => Array ( [und] => Array ( [0] => Array ( [value] =>

Workshop “Le giornate della didattica”

14:00-17:00 - Aula T3, edificio CLA, Via Venezia 16

Smart Learning Design 25 - Insegnare è importante, ma è più importante imparare

Relatrice Susanna Sancassani

Incontro per tutti i docenti dell'Ateneo
Per partecipare è necessario prenotare entro il 10.11.2021

[summary] => [format] => 2 [safe_value] =>

Workshop “Le giornate della didattica”

14:00-17:00 - Aula T3, edificio CLA, Via Venezia 16

Smart Learning Design 25 - Insegnare è importante, ma è più importante imparare

Relatrice Susanna Sancassani

Incontro per tutti i docenti dell'Ateneo
Per partecipare è necessario prenotare entro il 10.11.2021

[safe_summary] => ) ) ) [field_accordion_state] => Array ( [und] => Array ( [0] => Array ( [value] => chiuso ) ) ) [field_allegato_element] => Array ( ) [field_outline_level] => Array ( [und] => Array ( [0] => Array ( [value] => h3 ) ) ) [field_titolo_frontend] => Array ( [und] => Array ( [0] => Array ( [value] => 12 novembre 2021 [format] => [safe_value] => 12 novembre 2021 ) ) ) [name] => teaching4learning [picture] => 0 [data] => b:0; [num_revisions] => 1 [current_revision_id] => 369259 [is_current] => 1 [is_pending] => [revision_moderation] => [entity_view_prepared] => 1 ) [#items] => Array ( [0] => Array ( [value] => 12 novembre 2021 [format] => [safe_value] => 12 novembre 2021 ) ) [#formatter] => text_default [0] => Array ( [#markup] => 12 novembre 2021 ) ) [body] => Array ( [#theme] => field [#weight] => -3 [#title] => Body [#access] => 1 [#label_display] => hidden [#view_mode] => teaser [#language] => und [#field_name] => body [#field_type] => text_with_summary [#field_translatable] => 0 [#entity_type] => node [#bundle] => elemento_accordion [#object] => stdClass Object ( [vid] => 369259 [uid] => 10762 [title] => T4L_12 novembre 2021 [log] => [status] => 1 [comment] => 0 [promote] => 1 [sticky] => 0 [nid] => 82021 [type] => elemento_accordion [language] => it [created] => 1633686448 [changed] => 1633686448 [tnid] => 0 [translate] => 0 [revision_timestamp] => 1633686448 [revision_uid] => 10762 [taxonomy_vocabulary_8] => Array ( ) [body] => Array ( [und] => Array ( [0] => Array ( [value] =>

Workshop “Le giornate della didattica”

14:00-17:00 - Aula T3, edificio CLA, Via Venezia 16

Smart Learning Design 25 - Insegnare è importante, ma è più importante imparare

Relatrice Susanna Sancassani

Incontro per tutti i docenti dell'Ateneo
Per partecipare è necessario prenotare entro il 10.11.2021

[summary] => [format] => 2 [safe_value] =>

Workshop “Le giornate della didattica”

14:00-17:00 - Aula T3, edificio CLA, Via Venezia 16

Smart Learning Design 25 - Insegnare è importante, ma è più importante imparare

Relatrice Susanna Sancassani

Incontro per tutti i docenti dell'Ateneo
Per partecipare è necessario prenotare entro il 10.11.2021

[safe_summary] => ) ) ) [field_accordion_state] => Array ( [und] => Array ( [0] => Array ( [value] => chiuso ) ) ) [field_allegato_element] => Array ( ) [field_outline_level] => Array ( [und] => Array ( [0] => Array ( [value] => h3 ) ) ) [field_titolo_frontend] => Array ( [und] => Array ( [0] => Array ( [value] => 12 novembre 2021 [format] => [safe_value] => 12 novembre 2021 ) ) ) [name] => teaching4learning [picture] => 0 [data] => b:0; [num_revisions] => 1 [current_revision_id] => 369259 [is_current] => 1 [is_pending] => [revision_moderation] => [entity_view_prepared] => 1 ) [#items] => Array ( [0] => Array ( [value] =>

Workshop “Le giornate della didattica”

14:00-17:00 - Aula T3, edificio CLA, Via Venezia 16

Smart Learning Design 25 - Insegnare è importante, ma è più importante imparare

Relatrice Susanna Sancassani

Incontro per tutti i docenti dell'Ateneo
Per partecipare è necessario prenotare entro il 10.11.2021

[summary] => [format] => 2 [safe_value] =>

Workshop “Le giornate della didattica”

14:00-17:00 - Aula T3, edificio CLA, Via Venezia 16

Smart Learning Design 25 - Insegnare è importante, ma è più importante imparare

Relatrice Susanna Sancassani

Incontro per tutti i docenti dell'Ateneo
Per partecipare è necessario prenotare entro il 10.11.2021

[safe_summary] => ) ) [#formatter] => text_summary_or_trimmed [0] => Array ( [#markup] =>

Workshop “Le giornate della didattica”

14:00-17:00 - Aula T3, edificio CLA, Via Venezia 16

Smart Learning Design 25 - Insegnare è importante, ma è più importante imparare

Relatrice Susanna Sancassani

) ) [field_accordion_state] => Array ( [#theme] => field [#weight] => -1 [#title] => Aperto/Chiuso [#access] => 1 [#label_display] => above [#view_mode] => teaser [#language] => und [#field_name] => field_accordion_state [#field_type] => list_text [#field_translatable] => 0 [#entity_type] => node [#bundle] => elemento_accordion [#object] => stdClass Object ( [vid] => 369259 [uid] => 10762 [title] => T4L_12 novembre 2021 [log] => [status] => 1 [comment] => 0 [promote] => 1 [sticky] => 0 [nid] => 82021 [type] => elemento_accordion [language] => it [created] => 1633686448 [changed] => 1633686448 [tnid] => 0 [translate] => 0 [revision_timestamp] => 1633686448 [revision_uid] => 10762 [taxonomy_vocabulary_8] => Array ( ) [body] => Array ( [und] => Array ( [0] => Array ( [value] =>

Workshop “Le giornate della didattica”

14:00-17:00 - Aula T3, edificio CLA, Via Venezia 16

Smart Learning Design 25 - Insegnare è importante, ma è più importante imparare

Relatrice Susanna Sancassani

Incontro per tutti i docenti dell'Ateneo
Per partecipare è necessario prenotare entro il 10.11.2021

[summary] => [format] => 2 [safe_value] =>

Workshop “Le giornate della didattica”

14:00-17:00 - Aula T3, edificio CLA, Via Venezia 16

Smart Learning Design 25 - Insegnare è importante, ma è più importante imparare

Relatrice Susanna Sancassani

Incontro per tutti i docenti dell'Ateneo
Per partecipare è necessario prenotare entro il 10.11.2021

[safe_summary] => ) ) ) [field_accordion_state] => Array ( [und] => Array ( [0] => Array ( [value] => chiuso ) ) ) [field_allegato_element] => Array ( ) [field_outline_level] => Array ( [und] => Array ( [0] => Array ( [value] => h3 ) ) ) [field_titolo_frontend] => Array ( [und] => Array ( [0] => Array ( [value] => 12 novembre 2021 [format] => [safe_value] => 12 novembre 2021 ) ) ) [name] => teaching4learning [picture] => 0 [data] => b:0; [num_revisions] => 1 [current_revision_id] => 369259 [is_current] => 1 [is_pending] => [revision_moderation] => [entity_view_prepared] => 1 ) [#items] => Array ( [0] => Array ( [value] => chiuso ) ) [#formatter] => text_default [0] => Array ( [#markup] => chiuso ) ) [links] => Array ( [#theme] => links__node [#pre_render] => Array ( [0] => drupal_pre_render_links ) [#attributes] => Array ( [class] => Array ( [0] => links [1] => inline ) ) [node] => Array ( [#theme] => links__node__node [#links] => Array ( [node-readmore] => Array ( [title] => Read more about T4L_12 novembre 2021 [href] => node/82021 [html] => 1 [attributes] => Array ( [rel] => tag [title] => T4L_12 novembre 2021 ) ) ) [#attributes] => Array ( [class] => Array ( [0] => links [1] => inline ) ) ) ) [field_outline_level] => Array ( [#theme] => field [#weight] => 31 [#title] => Livello outline [#access] => 1 [#label_display] => above [#view_mode] => teaser [#language] => und [#field_name] => field_outline_level [#field_type] => list_text [#field_translatable] => 0 [#entity_type] => node [#bundle] => elemento_accordion [#object] => stdClass Object ( [vid] => 369259 [uid] => 10762 [title] => T4L_12 novembre 2021 [log] => [status] => 1 [comment] => 0 [promote] => 1 [sticky] => 0 [nid] => 82021 [type] => elemento_accordion [language] => it [created] => 1633686448 [changed] => 1633686448 [tnid] => 0 [translate] => 0 [revision_timestamp] => 1633686448 [revision_uid] => 10762 [taxonomy_vocabulary_8] => Array ( ) [body] => Array ( [und] => Array ( [0] => Array ( [value] =>

Workshop “Le giornate della didattica”

14:00-17:00 - Aula T3, edificio CLA, Via Venezia 16

Smart Learning Design 25 - Insegnare è importante, ma è più importante imparare

Relatrice Susanna Sancassani

Incontro per tutti i docenti dell'Ateneo
Per partecipare è necessario prenotare entro il 10.11.2021

[summary] => [format] => 2 [safe_value] =>

Workshop “Le giornate della didattica”

14:00-17:00 - Aula T3, edificio CLA, Via Venezia 16

Smart Learning Design 25 - Insegnare è importante, ma è più importante imparare

Relatrice Susanna Sancassani

Incontro per tutti i docenti dell'Ateneo
Per partecipare è necessario prenotare entro il 10.11.2021

[safe_summary] => ) ) ) [field_accordion_state] => Array ( [und] => Array ( [0] => Array ( [value] => chiuso ) ) ) [field_allegato_element] => Array ( ) [field_outline_level] => Array ( [und] => Array ( [0] => Array ( [value] => h3 ) ) ) [field_titolo_frontend] => Array ( [und] => Array ( [0] => Array ( [value] => 12 novembre 2021 [format] => [safe_value] => 12 novembre 2021 ) ) ) [name] => teaching4learning [picture] => 0 [data] => b:0; [num_revisions] => 1 [current_revision_id] => 369259 [is_current] => 1 [is_pending] => [revision_moderation] => [entity_view_prepared] => 1 ) [#items] => Array ( [0] => Array ( [value] => h3 ) ) [#formatter] => text_default [0] => Array ( [#markup] => h3 ) ) )

Trasparenza SA Chiara Maurizio - Dichiarazione circa l'assunzione di altre cariche/incarichi 2021

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