Fine Mapping of qroot-yield-1.06, a QTL for Root, Plant Vigor and Yield in Maize

Root-yield-1.06 is a major QTL affecting root system architecture (RSA) and other agronomic traits in maize. The effect of this QTL has been evaluated with the development of near isogenic lines (NILs) differing at the QTL position. The objective of this study was to fine map qroot-yield-1.06 by marker-assisted searching for chromosome recombinants in the QTL interval and concurrent root phenotyping in both controlled and field conditions, through successive generations. Complementary approaches such as QTL meta-analysis and RNA-seq were deployed in order to help prioritizing candidate genes within the QTL target region. Using a selected group of genotypes, field based root analysis by ‘shovelomics’ enabled to accurately collect RSA information of adult maize plants. Shovelomics combined with software-assisted root imaging analysis proved to be an informative and relatively highly automated phenotyping protocol. A QTL interval mapping was conducted using a segregating population at the seedling stage grown in controlled environment. Results enabled to narrow down the QTL interval and to identify new polymorphic markers for MAS in field experiments. A collection of homozygous recombinant NILs was developed by screening segregating populations with markers flanking qroot-yield-1.06. A first set of lines from this collection was phenotyped based on the adapted shovelomics protocol. QTL analysis based on these data highlighted an interval of 1.3 Mb as completely linked with the target QTL but, a larger safer interval of 4.1 Mb was selected for further investigations. QTL meta-analysis allows to synthetize information on root QTLs and two mQTLs were identified in the qroot-yield-1.06 interval. Trascriptomics analysis based on RNA-seq data of the two contrasting QTL-NILs, confirmed alternative haplotypes at chromosome bin 1.06. qroot-yield-1.06 has now been delimited to a 4.1-Mb interval, and thanks to the availability of additional untested homozygous recombinant NILs, the potentially achievable mapping resolution at qroot-yield-1.06 is c. 50 kb.

Scene Mapping and Understanding by Robotic Vision

The first mechanical Automaton concept was found in a Chinese text written in the 3rd century BC, while Computer Vision was born in the late 1960s. Therefore, visual perception applied to machines (i.e. the Machine Vision) is a young and exciting alliance. When robots came in, the new field of Robotic Vision was born, and these terms began to be erroneously interchanged. In short, we can say that Machine Vision is an engineering domain, which concern the industrial use of Vision. The Robotic Vision, instead, is a research field that tries to incorporate robotics aspects in computer vision algorithms. Visual Servoing, for example, is one of the problems that cannot be solved by computer vision only. Accordingly, a large part of this work deals with boosting popular Computer Vision techniques by exploiting robotics: e.g. the use of kinematics to localize a vision sensor, mounted as the robot end-effector. The remainder of this work is dedicated to the counterparty, i.e. the use of computer vision to solve real robotic problems like grasping objects or navigate avoiding obstacles. Will be presented a brief survey about mapping data structures most widely used in robotics along with SkiMap, a novel sparse data structure created both for robotic mapping and as a general purpose 3D spatial index. Thus, several approaches to implement Object Detection and Manipulation, by exploiting the aforementioned mapping strategies, will be proposed, along with a completely new Machine Teaching facility in order to simply the training procedure of modern Deep Learning networks.

QTL mapping identifies novel major loci for ear fasciation, ear prolificacy and tillering in maize (ZEA MAYS L.)

Maize ear fasciation originates from excessive or abnormal proliferation of the ear meristem and usually manifests as multiple-tipped ear, ear flatness and/or disordered kernel arrangement. Ear prolificacy expresses as multiple ears per node. Both traits can affect grain yield. In this study, the genetic control of the two traits was analyzed using two recombinant inbred lines (RIL) populations (B73 × Lo1016 and Lo964 × Lo1016) with Lo1016 and Lo964 as donors of ear fasciation and prolificacy, respectively. Four ear fasciation-related traits (ear fasciation, kernel distribution and ear ovality indexes and ratio of ear diameters), number of kernel rows, ear prolificacy and number of tillers were phenotyped in multi-year field experiments. Ear fasciation traits and number of kernel rows showed relatively high heritability (h2 > 0.5) except ratio of ear diameters, and showed correlation. Prolificacy and tillering h2 ranged 0.41 - 0.78 and did not correlate. QTL mapping identified four QTL for ear fasciation, on chr. 1 (two QTLs), 5 and 7, the latter two overlapping with QTLs for number of kernel rows. However, the strongest effect QTL for number of kernel rows mapped on chr. 2 independently from ear fasciation. Four and five non-overlapping QTLs were mapped for ear prolificacy and tillering, respectively. Two ear fasciation QTLs from this study, qFas1.2 and qFas7, overlapped with formerly known fasciation QTLs and spanned candidate genes expressed in ear meristems namely compact plant2 and ramosa1. Our study identified novel ear fasciation, ear prolificacy and tillering loci which are unexpectedly still segregating in elite maize materials, and provides foundation for genomics-assisted breeding for yield components

Mapping socialist solidarities: SWAPO, the Namibian liberation struggle, and the Global Cold War

This thesis addresses the entanglements between the Namibian liberation struggle and the global Cold War, focusing on the socialist support provided to the South West African People Organization (SWAPO), the liberation movement that fought for the independence of the country from the South African regime. This thesis aims at analyzing three socialist models of solidarity with the SWAPO’s struggle that developed especially from the late 1970s. Combining archival sources and biographical accounts, it examines the politics of solidarity with SWAPO implemented by East Germany, Cuba, and the Italian Communist Party. The interest lies in understanding how solidarity was declined and received by internal promoters and external addressees. Thus, I explore how these three actors constructed their concept of solidarity with SWAPO according to their national and ideological contexts and how this was received by the SWAPO members who experienced it in various ways. Each socialist actor promoted solidarity with SWAPO by using varying narratives, pursuing their own objectives, and employing diverse instruments, thus carrying out different and sometimes competing visions of socialism and solidarity. On its side, SWAPO was able to take advantage from such visions, as each of them could serve its different needs in diverse ways. In providing a general overview of these three solidarity policies, this thesis has the objective of highlighting the internal pluralization of the “socialist solidarity regime” while at the same time contributing to the debate on the extent of SWAPO’s commitment to socialism during the Namibian liberation struggle. It argues that, while pragmatism has always guided SWAPO during the liberation struggle and the post-independence period, and non-alignment has always been its international stance, socialism has to some extent been a model for the revolution in Namibia, to the point that it is still influencing the SWAPO party today.

Quantitative susceptibility mapping as biomarker of neurodegeneration: methodological models and clinical applications

Quantitative Susceptibility Mapping (QSM) is an advanced magnetic resonance technique that can quantify in vivo biomarkers of pathology, such as alteration in iron and myelin concentration. It allows for the comparison of magnetic susceptibility properties within and between different subject groups. In this thesis, QSM acquisition and processing pipeline are discussed, together with clinical and methodological applications of QSM to neurodegeneration. In designing the studies, significant emphasis was placed on results reproducibility and interpretability. The first project focuses on the investigation of cortical regions in amyotrophic lateral sclerosis. By examining various histogram susceptibility properties, a pattern of increased iron content was revealed in patients with amyotrophic lateral sclerosis compared to controls and other neurodegenerative disorders. Moreover, there was a correlation between susceptibility and upper motor neuron impairment, particularly in patients experiencing rapid disease progression. Similarly, in the second application, QSM was used to examine cortical and sub-cortical areas in individuals with myotonic dystrophy type 1. The thalamus and brainstem were identified as structures of interest, with relevant correlations with clinical and laboratory data such as neurological evaluation and sleep records. In the third project, a robust pipeline for assessing radiomic susceptibility-based features reliability was implemented within a cohort of patients with multiple sclerosis and healthy controls. Lastly, a deep learning super-resolution model was applied to QSM images of healthy controls. The employed model demonstrated excellent generalization abilities and outperformed traditional up-sampling methods, without requiring a customized re-training. Across the three disorders investigated, it was evident that QSM is capable of distinguishing between patient groups and healthy controls while establishing correlations between imaging measurements and clinical data. These studies lay the foundation for future research, with the ultimate goal of achieving earlier and less invasive diagnoses of neurodegenerative disorders within the context of personalized medicine.