Coagulopatia in pazienti affetti da discrasia plasmacellulare: studio caso-controllo.

Disordini del fibrinogeno sono di solito conseguenza di mutazioni genetiche che esitano in ridotti livelli di proteina (ipofibrinogenemia) o in una molecola anomala (disfibrinogenemia). Tuttavia, fattori plasmatici o microambientali possono determinarne un difetto acquisito: ridotta concentrazione o alterata funzione. Ad esempio, anticorpi possono legare fibrinogeno e/o fibrina interferendo con la polimerizzazione ed inibendo la coagulazione. Il nostro obiettivo è quello di individuare la causa di disfibrinogenemia in un uomo di 65 anni, diagnosticata a seguito del rilievo di una discrepanza tra valori di Fg immunologico e valori di Fg coagulativo; nonostante una ridotta concentrazione di fibrinogeno funzionale ed un allungamento del tempo di Trombina (TT), del tempo di tromboplastina parziale attivata (aPTT) e del tempo di protrombina (PT) non potevamo porre diagnosi di deficit ereditario, visti i normali valori dei test emocoagulativi eseguiti dal paziente circa 12 mesi prima. L’individuazione di una banda, riferibile a catena leggera k, in prossimità della banda del fibrinogeno, ci ha indotto ad indagare per una possibile discrasia plasmacellulare. Le indagini hanno evidenziato una monoclonalità k a carico dell’80% delle plasmacellule midollari del paziente. La percentuale delle plasmacellule risultava nella norma sia con studio morfologico del mieloaspirato (4-5% di plasmacellule) che nelle sezioni istologiche di biopsia ossea (4% di plasmacellule). Indagini di biologia molecolare atte ad individuare riarrangiamenti a carico del JH risultavano positive per lo stesso. Veniva fatta diagnosi di FLC-MGUS (gammopatia monoclonale di incerto significato con presenza di CM costituita da sole catene leggere). Test che prevedevano l’utilizzo di pool di plasma normale (PPN) sono stati allestiti per valutare l’interferenza delle catene leggere libere con la molecola del fibrinogeno, sospettata in base al particolare profilo di migrazione all’IF su plasma. Questo caso è particolarmente inusuale perché l’effetto inibitorio era indotto da una singola catena leggera piuttosto che da una molecola anticorpale completa. Il trattamento farmacologico con solo desametasone ha comportato una quasi completa correzione dei parametri emocoagulativi (con la sola eccezione del persistere di un allungamento del TT); a distanza di circa due mesi dal termine della terapia il paziente mostra nuovamente un TT incoagulabile. A fronte di alterazioni laboratoristiche importanti non si sono mai avute manifestazioni emorragiche e/o trombotiche. Poiché le immunoglobuline patologiche di pazienti con Mieloma Multiplo (MM) sappiamo interferire con test emocoagulativi, sono state eseguite indagini anche in un gruppo di pazienti con diagnosi di MM conclamato, per individuare analogie con il quadro emocoagulativo del pz in esame. Nel gruppo di controllo 4 dei 20 pazienti, indipendentemente dal tipo di MM, presentavano un allungamento del TT con normali valori di fibrinogeno coagulabile; nessuno di loro ha avuto esperienza di manifestazioni emorragiche e/o trombotiche conclamate. Due di loro, nel decorso della malattia, hanno effettuato indagini nel sospetto di manifestazione trombotica( 1 - ECD arti inferiori; 2- Scintigrafia polmonare). L’immunofissazione su plasma non evidenziava in nessuno dei 4 pazienti una CM che migrasse alla stessa altezza della banda del Fg, come accadeva invece nel pz in esame. I test con PPN non hanno evidenziato interferenza delle catene leggere con la molecola del Fg

Fine-tuning and data augmentation techniques for semantic segmentation of heritage point clouds

This topic of this contribution falls within the broader debate on Digital Humanities. Experiencing and testing an approach that combines geomatics and its production of three-dimensional data of the built cultural heritage (CH) with information technology is the core point. In the digital CH domain, the ever-increasing availability of three-dimensional data, provides the opportunity to rapidly generate detailed 3D scenes to support restoration and conservation activities of built heritage. Concurrently, the recent research trends in geomatics are facing the issue of managing these heritage data to enrich the geometrical representation of the asset, creating a complete informative data collector. HBIM (Historic Building Information Modeling) constitutes a reference, and they typically rely on point clouds to perform the scan-to-BIM processes. These processes are still mostly manually carried out by domain experts, making the workflow very time-consuming, not fully exploiting the potential of point clouds and wasting an uncountable amount of data. In fact, parametric objects can be described through a few relevant points or contours. The use of Artificial Intelligence algorithms, in particular Deep Learning (DL) techniques, for the automatic recognition of architectural elements from point clouds can therefore provide valuable support through the semantic segmentation task. A proposal to tackle this framework was outlined in previous works, and the methodology here proposed constitutes a development of their results. Starting from those former tests obtained with the Dynamic Graph Convolutional Neural Network (DGCNN), close attention is paid to: i) transfer learning techniques, ii) the combination with external classifiers, such as Random Forest (RF), iii) the evaluation of data augmentation techniques on a domain-specific dataset (ArCH dataset). Besides, an investigation on how to make the whole workflow more functional and "friendly" for external users is carried out too. With regard to transfer learning techniques, the fine-tuning approach is proposed to understand if, also in the CH domain, it can lead to performances improvement, introducing a new scene in a pre-trained network. In fact, the peculiarities of each scene do not guarantee certain and definite results, as for other domains. This section is divided into two subsections: a classic fine-tuning and a fine-tuning with the addition of the RF in the final part of the prediction. In the latter case, the choice of adding the RF is due to the results obtained in some stateof-the-art works, where this classifier provides excellent results in a short time and even in the presence of relatively limited data. In this hybrid approach, the network weights are employed as well as in the classic fine-tuning technique. Then, the final part of the DGCNN performing the segmentation of the points is excluded, leading the network to be used as a feature extractor method; afterwards, a scene of the dataset never seen by the network is chosen and divided into one part for training and one for the test. Finally, the features of both parts are extracted, using the feature extractor, and exploited as input for training the RF classifier. Tests conducted on data augmentation show that it does not significantly affect overall performances, but still provide proper support for those categories with fewer points. On the other side, the tests on the fine-tuning have given rise to manifold considerations. Firstly, the standard fine-tuning can achieve performances almost equal to those where only the DGCNN is used, considerably improving some categories. Thus, they confirm that, once the DNN is pre-trained, data processing and prediction times can be significantly reduced (from ca. 48 to 0.5 h), in the case of heritage point clouds too. Then, performances similar to the reference tests are obtained also with the use of the DGCNN as a feature extractor and the RF as a classifier, demonstrating that the final classifier does not affect the prediction

Transferencia de técnicas de aprendizaje y mejora del rendimiento en la segmentación semántica profunda de nubes de puntos del patrimonio construido

[EN] The growing availability of three-dimensional (3D) data, such as point clouds, coming from Light Detection and Ranging (LiDAR), Mobile Mapping Systems (MMSs) or Unmanned Aerial Vehicles (UAVs), provides the opportunity to rapidly generate 3D models to support the restoration, conservation, and safeguarding activities of cultural heritage (CH). The so-called scan-to-BIM process can, in fact, benefit from such data, and they can themselves be a source for further analyses or activities on the archaeological and built heritage. There are several ways to exploit this type of data, such as Historic Building Information Modelling (HBIM), mesh creation, rasterisation, classification, and semantic segmentation. The latter, referring to point clouds, is a trending topic not only in the CH domain but also in other fields like autonomous navigation, medicine or retail. Precisely in these sectors, the task of semantic segmentation has been mainly exploited and developed with artificial intelligence techniques. In particular, machine learning (ML) algorithms, and their deep learning (DL) subset, are increasingly applied and have established a solid state-of-the-art in the last half-decade. However, applications of DL techniques on heritage point clouds are still scarce; therefore, we propose to tackle this framework within the built heritage field. Starting from some previous tests with the Dynamic Graph Convolutional Neural Network (DGCNN), in this contribution close attention is paid to: i) the investigation of fine-tuned models, used as a transfer learning technique, ii) the combination of external classifiers, such as Random Forest (RF), with the artificial neural network, and iii) the evaluation of the data augmentation results for the domain-specific ArCH dataset. Finally, after taking into account the main advantages and criticalities, considerations are made on the possibility to profit by this methodology also for non-programming or domain experts.[ES] La creciente disponibilidad de datos tridimensionales (3D), como nubes de puntos, provenientes de la detección de la luz y distancia (LiDAR), sistemas de mapeado móvil (MMS) o vehículos aéreos no tripulados (UAV), brinda la oportunidad de generar rápidamente modelos 3D para apoyar las actividades de restauración, conservación y salvaguardia del patrimonio cultural (CH). El llamado proceso de escaneado-a-BIM puede, de hecho, beneficiarse de dichos datos, y ellos mismos pueden ser una fuente para futuros análisis o actividades sobre el patrimonio arqueológico y el construido. Hay varias formas de explotar este tipo de datos, como el modelado de información de edificios históricos (HBIM), la creación de mallas, la rasterización, la clasificación y la segmentación semántica. Este último, referido a las nubes de puntos, es un tema de máxima actualidad no solo en el dominio del PC sino también en otros campos como la navegación autónoma, la medicina o el comercio minorista. Precisamente en estos sectores, la tarea de la segmentación semántica se ha explotado y desarrollado principalmente con técnicas de inteligencia artificial. En particular, los algoritmos de aprendizaje automático (AA) y su subconjunto de aprendizaje profundo (AP) se aplican cada vez más y han establecido un sólido estado de la técnica en la última media década. Sin embargo, las aplicaciones de las técnicas de AP en las nubes de puntos tradicionales son todavía escasas; por tanto, nos proponemos abordar este marco dentro del ámbito del patrimonio construido. Partiendo de algunas pruebas anteriores con la Red Neural Convolucional de Gráfico Dinámico (DGCNN), en esta contribución se presta atención a: i) la investigación de modelos afinados, utilizados como técnica de aprendizaje por transferencia, ii) la combinación de clasificadores externos, como Random Forest (RF), con la red neuronal artificial, y iii) la evaluación de los resultados de aumentación de datos para el conjunto de datos específico del dominio ArCH. Finalmente, después de tener en cuenta las principales ventajas y criticidades, se hace una consideración sobre la posibilidad de beneficiarse de esta metodología también a expertos no programadores o del campo.Matrone, F.; Martini, M. (2021). Transfer learning and performance enhancement techniques for deep semantic segmentation of built heritage point clouds. Virtual Archaeology Review. 12(25):73-84. https://doi.org/10.4995/var.2021.15318OJS73841225Armeni, I., Sener, O., Zamir, A. R., Jiang, H., Brilakis, I., Fischer, M., & Savarese, S. (2016). 3D semantic parsing of large-scale indoor spaces. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 1534-1543. https://doi.org/10.1109/CVPR.2016.170Baraldi, L., Cornia, M., Grana, C., & Cucchiara, R. (2018). Aligning text and document illustrations: towards visually explainable digital humanities. In 24th International Conference on Pattern Recognition (ICPR), 1097-1102. IEEE. https://doi.org/10.1109/ICPR.2018.8545064Bassier, M., Yousefzadeh, M., & Vergauwen, M. (2020). Comparison of 2D and 3D wall reconstruction algorithms from point cloud data for as-built BIM. Journal of Information Technology in Construction (ITcon), 25(11), 173-192. https://doi.org/10.36680/j.itcon.2020.011Boulch, A., Guerry, J., Le Saux, B., & Audebert, N. (2018). SnapNet: 3D point cloud semantic labeling with 2D deep segmentation networks. Computers & Graphics, 71, 189-198. https://doi.org/10.1016/j.cag.2017.11.010Chadwick, J., (2020). Google launches hieroglyphics translator that uses AI to decipher images of Ancient Egyptian script. Available at https://www.dailymail.co.uk/sciencetech/article-8540329/Google-launches-hieroglyphics-translator-uses-AI-decipher-Ancient-Egyptian-script.html Last access 24/11/2020Fiorucci, M., Khoroshiltseva, M., Pontil, M., Traviglia, A., Del Bue, A., & James, S. (2020). Machine learning for cultural heritage: a survey. Pattern Recognition Letters, 133, 102-108. https://doi.org/10.1016/j.patrec.2020.02.017Geiger, A., Lenz, P., Stiller, C., & Urtasun, R. (2013). Vision meets robotics: The KITTI dataset. The International Journal of Robotics Research, 32(11), 1231-1237. https://doi.org/10.1177/0278364913491297Grilli, E., & Remondino, F. (2019). Classification of 3D digital heritage. Remote Sensing, 11(7), 847. https://doi.org/10.3390/rs11070847Grilli, E., & Remondino, F. (2020). Machine learning generalisation across different 3D architectural heritage. ISPRS International Journal of Geo-Information, 9(6), 379. https://doi.org/10.3390/ijgi9060379Grilli, E., Özdemir, E., & Remondino, F. (2019a). Application Of Machine And Deep Learning Strategies For The Classification Of Heritage Point Clouds. International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, XLII-4/W18, 447-454, 2019. https://doi.org/10.5194/isprs-archives-XLII-4-W18-447-2019Grilli, E., Farella, E. M., Torresani, A., & Remondino, F. (2019b). Geometric features analysis for the classification of cultural heritage point clouds. International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, XLII-2/W15, 541-548, 2019 https://doi.org/10.5194/isprs-archives-XLII-2-W15-541-2019Hackel, T., Savinov, N., Ladicky, L., Wegner, J. D., Schindler, K., & Pollefeys, M. (2017). Semantic3d.net: A new large-scale point cloud classification benchmark. arXiv:1704.03847He, K., Zhang, X., Ren, S., & Sun, J. (2016). Deep residual learning for image recognition. In Proceedings of the IEEE conference on computer vision and pattern recognition, 770-778. arXiv:1512.03385Korc, F., & Förstner, W. (2009). eTRIMS Image Database for interpreting images of man-made scenes. Dept. of Photogrammetry, University of Bonn, Tech. Rep. TR-IGG-P-2009-01.Landrieu, L., & Simonovsky, M. (2018). Large-scale point cloud semantic segmentation with superpoint graphs. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 4558-4567. arXiv:1711.09869Llamas, J., M Lerones, P., Medina, R., Zalama, E., & Gómez-García-Bermejo, J. (2017). Classification of architectural heritage images using deep learning techniques. Applied Sciences, 7(10), 992. https://doi.org/10.3390/app7100992Mathias, M., Martinovic, A., Weissenberg, J., Haegler, S., & VanGool, L. (2011). Automatic architectural style recognition. International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, XXXVIII-5/W16, 171-176 3. https://doi.org/10.3390/app7100992Matrone, F., Grilli, E., Martini, M., Paolanti, M., Pierdicca, R., & Remondino, F. (2020a). Comparing machine and deep learning methods for large 3D heritage semantic segmentation. ISPRS International Journal of Geo-Information, 9(9), 535. https://doi.org/10.3390/ijgi9090535Matrone, F., Lingua, A., Pierdicca, R., Malinverni, E. S., Paolanti, M., Grilli, E., Remondino, F., Murtiyoso, A., & Landes, T. (2020b). A benchmark for large-scale heritage point cloud semantic segmentation. International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, XLIII-B2-2020, 1419-1426. https://doi.org/10.5194/isprs-archives-XLIII-B2-2020-1419-2020Murtiyoso, A., & Grussenmeyer, P. (2019a). Automatic heritage building point cloud segmentation and classification using geometrical rules. International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, XLII-2/W15, 821-827. https://doi.org/10.5194/isprs-archives-XLII-2-W15-821-2019Murtiyoso, A., & Grussenmeyer, P. (2019b). Point cloud segmentation and semantic annotation aided by GIS data for heritage complexes. International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, XLII-2/W9, 523-528, 2019. https://doi.org/10.5194/isprs-archives-XLII-2-W9-523-2019Oses, N., Dornaika, F., & Moujahid, A. (2014). Image-based delineation and classification of built heritage masonry. Remote Sensing, 6(3), 1863-1889. https://doi.org/10.3390/rs6031863Park, Y., & Guldmann, J. M. (2019). Creating 3D city models with building footprints and LIDAR point cloud classification: A machine learning approach. Computers, Environment and Urban Systems, 75, 76-89. https://doi.org/10.1016/j.compenvurbsys.2019.01.004Pierdicca, R., Paolanti, M., Matrone, F., Martini, M., Morbidoni, C., Malinverni, E. S. & Lingua, A. M. (2020). Point cloud semantic segmentation using a deep learning framework for cultural heritage. Remote Sensing, 12(6), 1005. https://doi.org/10.3390/rs12061005Qi, C. R., Su, H., Mo, K., & Guibas, L. J. (2017). Pointnet: Deep learning on point sets for 3D classification and segmentation. In Proceedings of the IEEE conference on computer vision and pattern recognition, 652-660. arXiv:1612.00593Sharafi, S., Fouladvand, S., Simpson, I., & Alvarez, J. A. B. (2016). Application of pattern recognition in detection of buried archaeological sites based on analysing environmental variables, Khorramabad Plain, West Iran. Journal of Archaeological Science: Reports, 8, 206-215. https://doi.org/10.1016/j.jasrep.2016.06.024Simonyan, K., & Zisserman, A. (2014). Very deep convolutional networks for large-scale image recognition. arXiv:1409.1556Stathopoulou, E. K., & Remondino, F. (2019). Semantic photogrammetry: boosting image-based 3D reconstruction with semantic labeling. International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 42(2), W9. https://doi.org/10.5194/isprs-archives-XLII-2-W9-685-2019Teboul, O., Kokkinos, I., Simon, L., Koutsourakis, P., & Paragios, N. (2012). Parsing facades with shape grammars and reinforcement learning. IEEE transactions on pattern analysis and machine intelligence, 35(7), 1744-1756. https://doi.org/10.1109/TPAMI.2012.252.Teruggi, S., Grilli, E., Russo, M., Fassi, F., & Remondino, F. (2020). A hierarchical machine learning approach for multi-level and multi-resolution 3D point cloud classification. Remote Sensing, 12(16), 2598. https://doi.org/10.3390/rs12162598Tyleček, R., & Šára, R. (2013). Spatial pattern templates for recognition of objects with regular structure. In German Conference on Pattern Recognition, Springer, Berlin, Heidelberg, 364-374. https://doi.org/10.1007/978-3-642-40602-7_39Verschoof-van der Vaart, W. B., & Lambers, K. (2019). Learning to Look at LiDAR: the use of R-CNN in the automated detection of archaeological objects in LiDAR data from the Netherlands. Journal of Computer Applications in Archaeology, 2(1). https://doi.org/10.5334/jcaa.32Wang, Y., Sun, Y., Liu, Z., Sarma, S. E., Bronstein, M. M., &Solomon, J. M. (2019). Dynamic graph CNN for learning on point clouds. ACM Transactions On Graphics, 38(5), 1-12. arXiv:1801.07829Weinmann, M., Jutzi, B., Hinz, S., & Mallet, C. (2015). Semantic point cloud interpretation based on optimal neighborhoods, relevant features and efficient classifiers. ISPRS Journal of Photogrammetry and Remote Sensing, 105, 286-304. https://doi.org/10.1016/j.isprsjprs.2015.01.016Xie, Y., Tian, J., & Zhu, X. X. (2019). Linking points with labels in 3D: a review of point cloud semantic segmentation. arXiv:1908.08854Yan, H., Ding, Y., Li, P., Wang, Q., Xu, Y., & Zuo, W. (2017). Mind the class weight bias: Weighted maximum mean discrepancy for unsupervised domain adaptation. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, (pp. 2272-2281). arXiv:1705.00609 https://doi.org/10.1109/CVPR.2017.10

Technical Debt Prioritization: State of the Art. A Systematic Literature Review

Background. Software companies need to manage and refactor Technical Debt issues. Therefore, it is necessary to understand if and when refactoring Technical Debt should be prioritized with respect to developing features or fixing bugs. Objective. The goal of this study is to investigate the existing body of knowledge in software engineering to understand what Technical Debt prioritization approaches have been proposed in research and industry. Method. We conducted a Systematic Literature Review among 384 unique papers published until 2018, following a consolidated methodology applied in Software Engineering. We included 38 primary studies. Results. Different approaches have been proposed for Technical Debt prioritization, all having different goals and optimizing on different criteria. The proposed measures capture only a small part of the plethora of factors used to prioritize Technical Debt qualitatively in practice. We report an impact map of such factors. However, there is a lack of empirical and validated set of tools. Conclusion. We observed that technical Debt prioritization research is preliminary and there is no consensus on what are the important factors and how to measure them. Consequently, we cannot consider current research conclusive and in this paper, we outline different directions for necessary future investigations

Morphometric analysis of fat globules in ewe's milk and correlation with qualitative parameters

In this study the morphometric characteristics of fat globules from ewe’s milk have been correlated with the chemicaland technological parameters of the milk.Milk samples were taken from twenty-five Massese ewes, subdivided according to the parity; the animals were homogeneousfor lactation phase and diet. The morphometric analysis of fat globules (n./ml, diameter, surface area/volume),the standard chemical analysis, SCC and determination of the rheological parameters were performed on milk samplesobtained during the morning milking.The mean number of fat globules/ml was 3.09 x 109, with a mean diameter of 3.93 μm, ranging from 1.20 μm to 12.30μm. For all parities, a fat globule diameter ranging from 3.21 to 4.20 μm was found most frequently. Animals in the firstlambing order showed a significantly lower percentage (5.26%) of large globules (>5.21μm), while animals in the fifthlambing order showed a higher percentage (20.75%). The number of globules/ml was negatively correlated to milk production(P≤0.01) and curd firmness at 45 min (P≤0.05); whereas it was positively correlated to protein content, non-fatdry matter, and curd firming time (P≤0.05). Fat globule dimensions varied according to the parity of the animals andinfluenced various qualitative parameters of the milk

Training verbal working memory in children with mild intellectual disabilities: effects on problem-solving

This multiple case study explores the effects of a cognitive training program in children with mild to borderline intellectual disability. Experimental training effects were evaluated comparing pre-post-test changes after (a) a baseline phase versus a training phase in the same participant, (b) an experimental training versus either a no intervention phase or a control training in two pairs of children matched for cognitive profile. Key elements of the training program included (1) exercises and card games targeting inhibition, switching, and verbal working memory, (2) guided practice emphasizing concrete strategies to engage in exercises, and (3) a variable amount of adult support. The results show that both verbal working memory analyzed with the listening span test and problem-solving tested with the Raven’s matrices were significantly enhanced after the experimental trainin

The production of relative clauses by Italian cochlear-implanted and hearing children

This study investigates the elicited production of subject (SRs) and object relatives (ORs) in Italian by 13 cochlear-implanted (CI) children (age:7;9-10;8) to determine whether and to what extent they differ from three groups of 13 normal hearing (NH) children matched on morphosyntactic abilities (age:5;0-7;9), chronological age (age:7;5-10;3), and auditory age (e.g. duration of CI use (age:4;11-9;4)) respectively. Results showed that for CI children, SRs are more accurate than ORs. The same asymmetry is observed in all NH groups, although NH children’s percentages of target responses are higher for both sentence typologies. The syntactic difficulty with ORs led CI and NH groups to adopt a considerable number of answering strategies: among them, production of passive relatives, causative constructions, and wh- elements replacing the complementizer che (‘that’). Individual performance variability within the CI group is observed. Some CI children showed good competence in Italian and age-peer performance by producing passive relatives, which are largely attested in older children’s production. For other CI children, however, the tendency to produce sentences attested in young children’s production is evidence of the linguistic delay associated to hearing impairment. In this case, the performance of these CI children was comparable to that of younger NH children

Nanometric and sub-nanometric structural properties of complex functional materials by means of solid-state NMR techniques

This thesis is about the application of SSNMR techniques to the study of different kind of functional materials with the aim of shedding light on the correlations between the structural and dynamic properties on a nanometric and sub-nanometric scale and the functional behaviour. This thesis is articulated in seven chapters. Chapter 1 introduces the SSNMR framework, presenting the theoretical background necessary to the comprehension of the SSNMR studies described in Chapters 3-7. Chapter 2 contains an introduction to functional materials, particularly focusing on the correlations between “microscopic” properties and functional behaviour, and on the role of SSNMR in the study of these systems. Then, in the remaining Chapters, the most important SSNMR studies that I carried out during my PhD on different classes of functional materials are presented. Each chapter starts with a presentation of the materials investigated, along with the results obtained by other experimental techniques, if present. The presentation of the SSNMR results is supported by descriptions of the employed SSNMR techniques and methodologies, which were different for the different studies. Chapter 3 presents an example of the application of SSNMR to the study of a novel anion exchange membrane based on polymeric materials. The combination of 13C CP/MAS experiments with 1H T1 and T1ρ measurements allowed information on the phase and structural properties of both the polymeric matrix and the conductive functional groups to be obtained, so contributing a complete and detailed picture of the properties of the material to be achieved. In the study reported in Chapter 4, the phase and dynamic behaviour of polymeric luminescent indicators were investigated, with the final aim of identifying the processes, occurring at a molecular and/or supramolecular level, responsible for the luminescent response of these materials under heating. In this case, the analyses of on resonance 1H FIDs acquired at increasing temperatures was found to be a very powerful tool for investigating the phase transformations occurring in polymeric domains, providing both structural and dynamic information. In Chapter 5 a detailed characterization of the dynamic properties in polymeric photoactive materials for solar cells is presented. In particular, the simultaneous analysis of 1H and 13C T1 curves as functions of temperature through suitable theoretical models was used to achieve a detailed characterization of the motional processes occurring in the MHz regime. Chapters 6 and 7 deal with the application of advanced high-resolution SSNMR techniques to the measurements of 19F and 1H chemical shift anisotropies in crystalline materials containing both organic and inorganic components. In Chapter 6, a methodological approach for the measurement of 19F chemical shift anisotropy based on both the analysis of spinning sideband profiles and the use of two-dimensional recoupling experiments is presented, along with the results obtained on two reference samples. Finally, Chapter 7 contains the applications of the methods described in Chapter 6 to the structural chararacterization of different zirconium phosphonates

Anisotropy and NMR spectroscopy

Abstract In this paper, different aspects concerning anisotropy in Nuclear Magnetic Resonance (NMR) spectroscopy have been reviewed. In particular, the relevant theory has been presented, showing how anisotropy stems from the dependence of internal nuclear spin interactions on the molecular orientation with respect to the external magnetic field direction. The consequences of anisotropy in the use of NMR spectroscopy have been critically discussed: on one side, the availability of very detailed structural and dynamic information, and on the other side, the loss of spectral resolution. The experiments used to measure the anisotropic properties in solid and soft materials, where, in contrast to liquids, such properties are not averaged out by the molecular tumbling, have been described. Such experiments can be based either on static low-resolution techniques or on one- and two-dimensional pulse sequences exploiting Magic Angle Spinning (MAS). Examples of applications of NMR spectroscopy have been shown, which exploit anisotropy to obtain important physico-chemical information on several categories of systems, including pharmaceuticals, inorganic materials, polymers, liquid crystals, and self-assembling amphiphiles in water. Solid-state NMR spectroscopy can be considered, nowadays, one of the most powerful characterization techniques for all kinds of solid, either amorphous or crystalline, and semi-solid systems for the obtainment of both structural and dynamic properties on a molecular and supra-molecular scale. Graphic abstrac