Cutting-edge R&D activities of CIRTEN in support of the Technology Park annexed to the Italian National Repository of radioactive waste

R&D activities taking place at the institutions belonging to Consorzio Interuniversitario per la Ricerca TEcnologica Nucleare are here presented and discussed. A special focus is on Technology Park annexed to the Italian National Repository of radioactive waste

Cutting-edge R&D activities of CIRTEN in support of the Technology Park annexed to the Italian National Repository of radioactive waste

R&D activities taking place at the institutions belonging to Consorzio Interuniversitario per la Ricerca TEcnologica Nucleare are here presented and discussed. A special focus is on Technology Park annexed to the Italian National Repository of radioactive waste

2022 Review of Data-Driven Plasma Science

Data-driven science and technology offer transformative tools and methods to science. This review article highlights the latest development and progress in the interdisciplinary field of data-driven plasma science (DDPS), i.e., plasma science whose progress is driven strongly by data and data analyses. Plasma is considered to be the most ubiquitous form of observable matter in the universe. Data associated with plasmas can, therefore, cover extremely large spatial and temporal scales, and often provide essential information for other scientific disciplines. Thanks to the latest technological developments, plasma experiments, observations, and computation now produce a large amount of data that can no longer be analyzed or interpreted manually. This trend now necessitates a highly sophisticated use of high-performance computers for data analyses, making artificial intelligence and machine learning vital components of DDPS. This article contains seven primary sections, in addition to the introduction and summary. Following an overview of fundamental data-driven science, five other sections cover widely studied topics of plasma science and technologies, i.e., basic plasma physics and laboratory experiments, magnetic confinement fusion, inertial confinement fusion and high-energy-density physics, space and astronomical plasmas, and plasma technologies for industrial and other applications. The final section before the summary discusses plasma-related databases that could significantly contribute to DDPS. Each primary section starts with a brief introduction to the topic, discusses the state-of-the-art developments in the use of data and/or data-scientific approaches, and presents the summary and outlook. Despite the recent impressive signs of progress, the DDPS is still in its infancy. This article attempts to offer a broad perspective on the development of this field and identify where further innovations are required

Laser-Ionized Underdense Passive Plasma Lens for Focusing Electron Beams

Next generation accelerators and colliders using relativistic electron beams are continuously pushing the demand for higher luminosity beams with smaller and smaller spot sizes. To address this need, passive plasma lenses operating in the nonlinear blowout regime of beam-driven plasma wakefield acceleration (PWFA) are capable of providing focusing forces to electron beams orders of magnitude stronger than conventional quadrupole magnets. To realize these lenses in practice, high intensity lasers can be used to ionize a small volume of gas and producing a plasma lens with precisely determined density profile. The quality of an electron beam focused by a plasma lens is determined by the phase space evolution of the beam while it is within the plasma wakefield. The strong electric fields can also increase an electron beam's emittance through chromatic phase spreading, which deteriorates the transverse quality of the beam. This dissertation covers the formalism used to describe the focusing of an electron beam from a passive, underdense plasma lens and demonstrates use cases for these lenses in experiments using relativistic electron beams. To perform plasma lens experiments, we propose a setup to ionize a 100&nbsp;&micro;m&nbsp;scale plasma lens via laser ionization of a gas jet outflow at the FACET-II accelerator facility of SLAC National Accelerator Laboratory. We investigate possible focusing aberrations induced by nonuniform transverse density profiles. Finally, we report on experimental progress towards the demonstration of an underdense thin plasma lens, including the analysis of preliminary data from commissioning shifts carried out at FACET-II.</p

Computational models of gene expression regulation

Throughout the last several decades, many efforts have been put into elucidating the genetic or epigenetic defects that result in various diseases. Gene regulation, i.e., the process of how genes are turned on and off in the right place and at the right time, is a paramount and prevailing question for researchers. Thanks to the discoveries made by researchers in this field, our understanding of interactions between proteins and DNA or proteins with themselves, as well as the dynamics of chromatin structure under different conditions, have substantially advanced. Even though there has been a lot achieved through these discoveries, there are still many unknown aspects about gene regulation. For instance, proteins called transcription factors (TFs) recognize and bind to specific regions of DNA and recruit the transcriptional machinery, which is essential for gene regulation. As there have been more than 2000 TFs identified in the human genome, it is important to study where they bind to or which genes they target. Computational approaches are important, in particular, as the biological experiments are often very expensive and cannot be done for all TFs. In 2016, a competition named DREAM Challenge was held encouraging researchers to develop novel computational tools for predicting the binding sites of several TFs. The first chapter of this thesis describes our machine learning approach to address this challenge within the scope of the competition. Using ensembles of random forest classifiers, we formulated our framework such that it is able to benefit from the tissue specificity inherent in the data leading to better generalization. Also, our models were tailored for spotting cofactors involved in the binding of TFs of interest. Comparing the important TFs that our computational models suggested with protein-protein association networks revealed that the models preferentially select motifs of TFs that are potential interaction partners in those networks. Another important aspect beyond predicting TF binding is to link epigeneomics, such as histone modification (HM) data, with gene expression. We, particularly, concentrated on predicting expression in a subset of genes called bidirectional. Bidirectional genes are referred to as pairs of genes that are located on opposite strands of DNA close to each other. As the sequencing technologies advance, more such bidirectional configurations are being detected. This indicates that in order to understand the gene regulatory mechanisms, it would be beneficial to account for such promoter architectures. In the second and third chapters, we focused on genes having bidirectional promoter architectures utilizing high resolution epigenomic signatures and single cell RNA-seq data to dissect the complex epigenetic architecture at these promoters. Using single-cell RNA-seq data as the estimate of gene expression, we were able to generate a hypothetical model for gene regulation in bidirectional promoters. We showed that bidirectional promoters can be categorized into three architecture types with distinct characteristics. Each of these categories corresponds to a unique gene expression profile at single cell level. The single cell RNA-seq data proved to be a powerful means for studying gene regulation. Therefore, in the last chapter, we proposed a novel approach for predicting gene expression at the single cell level using cis-regulatory motifs as well as epigenetic features. To achieve this, we designed a tree-guided multi-task learning framework that considers each cell as a task. Through this framework we were able to explain the single cell gene expression values using either TF binding affinities or TF ChIP-seq data measured at specific genomic regions. This allowed us to identify distinct TFs that show cell-type specific regulation in induced pluripotent stem cells. Our approach does not only limit to TFs, rather it can take any type of data that can potentially be used in explaining gene expression at single cell level. We believe that our findings can be used in drug discovery and development that can regulate the presence of TFs or other regulatory factors, which lead the cell fate into abnormal states, to prevent or cure diseases.In den letzten Jahrzehnten wurden große Anstrengungen unternommen, um die genetischen oder epigenetischen Defekte aufzuklären, die zu verschiedenen Krankheiten führen. Die Genregulation, d.h. der Prozess der Ein- und Abschaltung der Gene am richtigen Ort und zur richtigen Zeit reguliert, ist für die Forscher eine Frage von zentraler Bedeutung. Dank der Entdeckungen von Forschern auf diesem Gebiet ist unser Verständnis der Wechselwirkungen zwischen zwischen den Proteinen und der DNA oder der Proteine untereinander sowie der Dynamik der Chromatinstruktur unter verschiedenen Bedingungen wesentlich fortgeschritten. Obwohl durch diese Entdeckungen viel erreicht wurde, gibt es noch viele unbekannte Aspekte der Genregulation. Beispielsweise erkennen Proteine, sogenannte Transkriptionsfaktoren (Transcription Factors, TFs), bestimmte Bereiche der DNA und binden an diese und rekrutieren die Transkriptionsmaschinerie, die für die Genregulation erforderlich ist. Da mehr als 2000 TFs im menschlichen Genom identifiziert wurden, ist es wichtig zu untersuchen, wo sie binden oder auf welche Gene sie abzielen. Rechnerische Ansätze sind insbesondere wichtig, da die biologischen Experimente oft sehr teuer sind und nicht für alle TFs durchgeführt werden können. Im Jahr 2016 fand ein Wettbewerb namens DREAM Challenge statt, bei dem Forscher aufgefordert wurden, neuartige Rechenwerkzeuge zur Vorhersage der Bindungsstellen mehrerer TFs zu entwickeln. Das erste Kapitel dieser Arbeit beschreibt unseren Ansatz des maschinellen Lernens, um diese Herausforderung im Rahmen des Wettbewerbs anzugehen. Unter Verwendung von Ensembles von Random Forest Klassifikatoren haben wir unser Framework so formuliert, dass es von der Gewebespezifität der Daten profitiert und damit zu einer besseren Generalisierung führt. Außerdem wurden unsere Modelle auf das Erkennen von Kofaktoren angepasst, die an der Bindung von TFs beteiligt sind, die für uns von Interesse sind. Der Vergleich der wichtigen TFs, die unsere Computermodelle mit Protein-Protein-Assoziationsnetzwerken vorschlugen, ergab, dass die Modelle bevorzugt Motive von TFs auswählen, die potenzielle Interaktionspartner in diesen Netzwerken sind. Ein weiterer wichtiger Aspekt, der über die Vorhersage der TF-Bindung hinausgeht, besteht darin, epigeneomische Faktoren wie Histonmodifikationsdaten (HM-Daten) mit der Genexpression zu verknüpfen. Wir konzentrierten uns insbesondere auf die Vorhersage der Expression in einer Untergruppe von Genen, die als bidirektional bezeichnet werden. Bidirektionale Gene werden als Paare von Genen bezeichnet, die sich auf gegenüberliegenden DNA-Strängen befinden und nahe beieinander liegen. Mit dem Fortschritt der Sequenzierungstechnologien werden immer mehr solche bidirektionalen Konfigurationen erkannt. Dies weist darauf hin, dass es zum Verständnis der Genregulationsmechanismen vorteilhaft wäre, solche Promotorarchitekturen zu berücksichtigen. Im zweiten und dritten Kapitel konzentrierten wir uns auf Gene mit bidirektionalen Promotorarchitekturen, um mit Hilfe von epigenomischen Signaturen und Einzelzell-RNA-Sequenzdaten die komplexe epigenetische Architektur an diesen Promotoren zu analysieren. Unter Verwendung von Einzelzell-RNA-Sequenzdaten als Schätzung der Genexpression konnten wir ein hypothetisches Modell für die Genregulation in bidirektionalen Promotoren aufstellen. Wir haben gezeigt, dass bidirektionale Promotoren in drei Architekturtypen mit unterschiedlichen Merkmalen eingeteilt werden können. Jede dieser Kategorien entspricht einem eindeutigen Genexpressionsprofil auf Einzelzellebene. Die Einzelzell-RNA-Sequenzdaten erwiesen sich als leistungsstarkes Mittel zur Untersuchung der Genregulation. Daher haben wir im letzten Kapitel einen neuen Ansatz zur Vorhersage der Genexpression auf Einzelzellebene unter Verwendung von cis-regulatorischen Motiven sowie epigenetischen Merkmalen vorgeschlagen. Um dies zu erreichen, haben wir ein baumgesteuertes Multitasking-Lernsystem entwickelt, das jede Zelle als eine Aufgabe betrachtet. Durch dieses Gerüst konnten wir die Einzelzellgenexpressionswerte entweder mit TF-Bindungsaffinitäten oder mit TF-ChIP-Sequenzdaten erklären, die in bestimmten Genomregionen gemessen wurden. Dies ermöglichte es uns, verschiedene TFs zu identifizieren, die eine zelltypspezifische Regulation in induzierten pluripotenten Stammzellen zeigen. Unser Ansatz beschränkt sich nicht nur auf TFs, sondern kann jede Art von Daten verwenden, die potentiell zur Erklärung der Genexpression auf Einzelzellebene verwendet werden können. Wir glauben, dass unsere Erkenntnisse für die Entdeckung und Entwicklung von Arzneimitteln verwendet werden können, die das Vorhandensein von TFs oder anderen regulatorischen Faktoren regulieren können, die die Zellen abnormal werden lassen, um Krankheiten zu verhindern oder zu heilen

Etude structurale et fonctionnelle de l'ARN-polymérase du virus de la grippe

Influenza A virus is a negative single stranded RNA virus that replicates in the nucleus of infected cells. Its genome contains eight single stranded negative-sense RNA segments (vRNA) covered by the viral nucleoprotein (NP). The highly conserved 3' and 5' ends of the vRNA are bound to the RNA-dependent RNA-polymerase (RdRp) which consists of three subunits, PA, PB1 and PB2. The complex between vRNA, NP and the RdRp forms a particle called ribonucleoprotein (RNP). The RNP acts as an independent molecular machine for transcription and replication in the nucleus. Within the context of the RNP, these two processes are mediated by the RdRp. The influenza A RdRp complex has been remarkably intractable to structural analysis and in the last eight years, crystal structures of independent domains covering roughly half of the heterotrimeric RdRp have been determined. In addition, electron microscopy reconstructions have described the RdRp. Nonetheless, a complete model characterizing the RdRp as a whole is still lacking. To overcome this issue, a new strategy has been developed to obtain the RdRp heterotrimeric complex using the baculovirus infected cells expression system. This method has produced a truncated form of the flu A RdRp which has been studied from a structural and functional point of view. Several three-dimensional reconstructions by electron microscopy have been obtained and a crystal structure at low resolution (7,7 Å) has been solved. Functional studies focused on the activities carried by the truncated RdRp and a particular emphasis was placed on the study of the interactions with RNA. In vitro functional data showed highly metal ion-dependent activities. To know more about the subcellular metal context, metallic ions distribution in influenza A infected cells has been studied by X-ray microscopy giving the opportunity to integrate biochemical and biophysical data in the context of the whole cell.Le virus influenza de type A est un virus à ARN simple brin de polarité négative qui se réplique dans le noyau des cellules infectées. Son génome se compose de huit segments d'ARN viral (ARNv). Chaque segment est recouvert de multiples copies de nucléoprotéines virales (NP). Chacune des extrémités strictement conservées 3' et 5' des huit segments d'ARNv interagit avec une ARN-polymérase ARN-dépendante. Le complexe entre l'ARNv, NP et l'ARN-polymérase ARN-dépendante forme une particule appelée ribonucléoprotéine (RNP) constituant l'entité fonctionnelle pour la réplication du génome viral et sa transcription en ARN messagers. Dans le contexte de la RNP, ces deux processus sont assurés par l'ARN-polymérase ARN-dépendante formée de trois protéines (PA, PB1 et PB2). L'ARN-polymérase du virus influenza A fait l'objet de nombreuses études. Son étude structurale se heurte à la difficulté d'obtenir ce complexe hétérotrimérique sous forme soluble et en grande quantité, deux conditions qu'impose la biologie structurale. Ainsi, durant les huit dernières années, les données structurales à l'échelle atomique n'ont été obtenues que sur des domaines isolés de l'ARN-polymérase laissant l'essentiel de la structure de PB1 inconnue. Pour contourner ce problème, une nouvelle stratégie s'appuyant sur le système d'expression en cellules d'insecte infectées par baculovirus a été développée. Cette stratégie a permis la production d'une forme tronquée de l'ARN-polymérase du virus influenza de type A qui a été étudiée d'un point de vue structural et fonctionnel. Plusieurs reconstructions en trois dimensions ont été obtenues par microscopie électronique et une structure cristalline à faible résolution (7,7 Å) a pu être résolue. Les études fonctionnelles se sont axées sur les activités portées par l'hétérotrimère tronqué et un accent particulier a été mis sur l'étude des interactions avec l'ARN. Les informations sur les activités catalytiques obtenues in vitro ont mis en évidence un rôle clef de certains ions métalliques. Afin de connaitre l'environnement en ions métalliques et cibler leur rôle à l'échelle cellulaire, leur distribution dans la cellule infectée par le virus influenza A a été étudiée par microscopie rayons-X. Cet aspect de l'infection étant très peu documenté, cette étude s'inscrit dans une démarche originale et a offert l'opportunité d'intégrer les données biochimiques et biophysiques à l'échelle de la cellule entière

Etude structurale et fonctionnelle de l'ARN-polymérase du virus de la grippe

Influenza A virus is a negative single stranded RNA virus that replicates in the nucleus of infected cells. Its genome contains eight single stranded negative-sense RNA segments (vRNA) covered by the viral nucleoprotein (NP). The highly conserved 3' and 5' ends of the vRNA are bound to the RNA-dependent RNA-polymerase (RdRp) which consists of three subunits, PA, PB1 and PB2. The complex between vRNA, NP and the RdRp forms a particle called ribonucleoprotein (RNP). The RNP acts as an independent molecular machine for transcription and replication in the nucleus. Within the context of the RNP, these two processes are mediated by the RdRp. The influenza A RdRp complex has been remarkably intractable to structural analysis and in the last eight years, crystal structures of independent domains covering roughly half of the heterotrimeric RdRp have been determined. In addition, electron microscopy reconstructions have described the RdRp. Nonetheless, a complete model characterizing the RdRp as a whole is still lacking. To overcome this issue, a new strategy has been developed to obtain the RdRp heterotrimeric complex using the baculovirus infected cells expression system. This method has produced a truncated form of the flu A RdRp which has been studied from a structural and functional point of view. Several three-dimensional reconstructions by electron microscopy have been obtained and a crystal structure at low resolution (7,7 Å) has been solved. Functional studies focused on the activities carried by the truncated RdRp and a particular emphasis was placed on the study of the interactions with RNA. In vitro functional data showed highly metal ion-dependent activities. To know more about the subcellular metal context, metallic ions distribution in influenza A infected cells has been studied by X-ray microscopy giving the opportunity to integrate biochemical and biophysical data in the context of the whole cell.Le virus influenza de type A est un virus à ARN simple brin de polarité négative qui se réplique dans le noyau des cellules infectées. Son génome se compose de huit segments d'ARN viral (ARNv). Chaque segment est recouvert de multiples copies de nucléoprotéines virales (NP). Chacune des extrémités strictement conservées 3' et 5' des huit segments d'ARNv interagit avec une ARN-polymérase ARN-dépendante. Le complexe entre l'ARNv, NP et l'ARN-polymérase ARN-dépendante forme une particule appelée ribonucléoprotéine (RNP) constituant l'entité fonctionnelle pour la réplication du génome viral et sa transcription en ARN messagers. Dans le contexte de la RNP, ces deux processus sont assurés par l'ARN-polymérase ARN-dépendante formée de trois protéines (PA, PB1 et PB2). L'ARN-polymérase du virus influenza A fait l'objet de nombreuses études. Son étude structurale se heurte à la difficulté d'obtenir ce complexe hétérotrimérique sous forme soluble et en grande quantité, deux conditions qu'impose la biologie structurale. Ainsi, durant les huit dernières années, les données structurales à l'échelle atomique n'ont été obtenues que sur des domaines isolés de l'ARN-polymérase laissant l'essentiel de la structure de PB1 inconnue. Pour contourner ce problème, une nouvelle stratégie s'appuyant sur le système d'expression en cellules d'insecte infectées par baculovirus a été développée. Cette stratégie a permis la production d'une forme tronquée de l'ARN-polymérase du virus influenza de type A qui a été étudiée d'un point de vue structural et fonctionnel. Plusieurs reconstructions en trois dimensions ont été obtenues par microscopie électronique et une structure cristalline à faible résolution (7,7 Å) a pu être résolue. Les études fonctionnelles se sont axées sur les activités portées par l'hétérotrimère tronqué et un accent particulier a été mis sur l'étude des interactions avec l'ARN. Les informations sur les activités catalytiques obtenues in vitro ont mis en évidence un rôle clef de certains ions métalliques. Afin de connaitre l'environnement en ions métalliques et cibler leur rôle à l'échelle cellulaire, leur distribution dans la cellule infectée par le virus influenza A a été étudiée par microscopie rayons-X. Cet aspect de l'infection étant très peu documenté, cette étude s'inscrit dans une démarche originale et a offert l'opportunité d'intégrer les données biochimiques et biophysiques à l'échelle de la cellule entière

Molecular Biological Studies of the Induction of Flowering

Potential changes in gene expression which occur in the leaves during photoperiodic floral induction were investigated in a variety of plant species including Nicotiana plumbaginifolia (day neutral plant), Amaranthus caudatus (short day plant), Silene coeli-rosa (long day plant), and Anagallis arvensis (long day plant) which exhibit a range of photoperiodic responses. The approach taken was to employ techniques which would demonstrate differential gene expression at the level of mRNA or protein including in vitro translation of leaf mRNA and analysis of products using one- or two-dimensional sodium dodecyl sulphate polyacrylamide gel electrophoresis (SDS-PAGE)1analysis of leaf total protein using one- or two-dimensional SDS-PAGE, differential immunoadsorbtion techniques for the identification of 'induced' specific proteins, and differential screening of an 'induced' cDNA library