Analysis of the Zebrafish Segmentation Clock

In vertebrates, the repeating vertebrae in the vertebral column are the clearest indicators of the segmented body plan. The embryonic precursors of the vertebral column and skeletal musculature are bilaterally symmetric blocks of tissue flanking the notochord called somites. Somites are generated sequentially and periodically from an unsegmented tissue called the presomitic mesoderm (PSM) by a process called somitogenesis. Underlying the periodicity of somitogenesis are transcriptional oscillations of cyclic genes in the cells of the PSM. On a tissue level, these oscillations manifest as travelling waves, departing from the posterior and arresting in the anterior. The position of arrest prefigures the position of the new somite boundary. The molecular network that comprises the cyclic genes and their regulation in the PSM is termed the segmentation clock. Retinoic acid (RA) has been previously proposed to be a differentiation signal that acts to arrest the oscillations at the anterior of the PSM. This thesis shows the zebrafish RA catabolism mutant giraffe has an altered cyclic gene wave pattern, an observation that suggests that rather than stop their oscillations, cells tune their frequencies in response to RA signalling, introducing a novel function for RA in the zebrafish segmentation clock. In amniotes, the segmentation clock instructs the metamery of the vertebral column, but in zebrafish, the relationship is not established. This thesis demonstrates that the segmentation clock is not required in zebrafish for the development of a periodic vertebral column by using a novel segmentation clock mutant, thereby supporting a role for the notochord in the development of vertebral column metamery. Therefore, two periodic patterning processes establish zebrafish body pattern – one segments the somites and musculature, and the second segments the vertebral column. This thesis advances the understanding of the mechanisms of body pattern establishment by way of these novel insights

Machine learning in orthopedics: a literature review

In this paper we present the findings of a systematic literature review covering the articles published in the last two decades in which the authors described the application of a machine learning technique and method to an orthopedic problem or purpose. By searching both in the Scopus and Medline databases, we retrieved, screened and analyzed the content of 70 journal articles, and coded these resources following an iterative method within a Grounded Theory approach. We report the survey findings by outlining the articles\u2019 content in terms of the main machine learning techniques mentioned therein, the orthopedic application domains, the source data and the quality of their predictive performance

U-net and its variants for medical image segmentation: A review of theory and applications

U-net is an image segmentation technique developed primarily for image segmentation tasks. These traits provide U-net with a high utility within the medical imaging community and have resulted in extensive adoption of U-net as the primary tool for segmentation tasks in medical imaging. The success of U-net is evident in its widespread use in nearly all major image modalities, from CT scans and MRI to Xrays and microscopy. Furthermore, while U-net is largely a segmentation tool, there have been instances of the use of U-net in other applications. Given that U-net’s potential is still increasing, this narrative literature review examines the numerous developments and breakthroughs in the U-net architecture and provides observations on recent trends. We also discuss the many innovations that have advanced in deep learning and discuss how these tools facilitate U-net. In addition, we review the different image modalities and application areas that have been enhanced by U-net

U-Net and its variants for medical image segmentation: theory and applications

U-net is an image segmentation technique developed primarily for medical image analysis that can precisely segment images using a scarce amount of training data. These traits provide U-net with a very high utility within the medical imaging community and have resulted in extensive adoption of U-net as the primary tool for segmentation tasks in medical imaging. The success of U-net is evident in its widespread use in all major image modalities from CT scans and MRI to X-rays and microscopy. Furthermore, while U-net is largely a segmentation tool, there have been instances of the use of U-net in other applications. As the potential of U-net is still increasing, in this review we look at the various developments that have been made in the U-net architecture and provide observations on recent trends. We examine the various innovations that have been made in deep learning and discuss how these tools facilitate U-net. Furthermore, we look at image modalities and application areas where U-net has been applied.Comment: 42 pages, in IEEE Acces

Sequential and additive expression of miR-9 precursors control timing of neurogenesis

This work was supported by a Wellcome Trust Senior Research Fellowship (090868/Z/09/Z) and a Wellcome Trust Investigator Award (224394/Z/21/Z) to N.P. and a Medical Research Council Career Development Award to C.S.M. (MR/V032534/1). J.B. was supported by a Wellcome Trust Four-Year PhD Studentship in Basic Science (219992/Z/19/Z). Open Access funding provided by The University of Manchester.MicroRNAs (miRs) have an important role in tuning dynamic gene expression. However, the mechanism by which they are quantitatively controlled is unknown. We show that the amount of mature miR-9, a key regulator of neuronal development, increases during zebrafish neurogenesis in a sharp stepwise manner. We characterize the spatiotemporal profile of seven distinct microRNA primary transcripts (pri-mir)-9s that produce the same mature miR-9 and show that they are sequentially expressed during hindbrain neurogenesis. Expression of late-onset pri-mir-9-1 is added on to, rather than replacing, the expression of early onset pri-mir-9-4 and -9-5 in single cells. CRISPR/Cas9 mutation of the late-onset pri-mir-9-1 prevents the developmental increase of mature miR-9, reduces late neuronal differentiation and fails to downregulate Her6 at late stages. Mathematical modelling shows that an adaptive network containing Her6 is insensitive to linear increases in miR-9 but responds to stepwise increases of miR-9. We suggest that a sharp stepwise increase of mature miR-9 is created by sequential and additive temporal activation of distinct loci. This may be a strategy to overcome adaptation and facilitate a transition of Her6 to a new dynamic regime or steady state.Publisher PDFPeer reviewe

Machine Learning in Orthopedics: A Literature Review

In this paper we present the findings of a systematic literature review covering the articles published in the last two decades in which the authors described the application of a machine learning technique and method to an orthopedic problem or purpose. By searching both in the Scopus and Medline databases, we retrieved, screened and analyzed the content of 70 journal articles, and coded these resources following an iterative method within a Grounded Theory approach. We report the survey findings by outlining the articles' content in terms of the main machine learning techniques mentioned therein, the orthopedic application domains, the source data and the quality of their predictive performance

Investigating the role of extracellular matrix in regulating signaling pathways and embryonic development

During my PhD I was involved in different projects exploiting the zebrafish animal model in order to analyse the role of extracellular matrix proteins in regulating signaling pathways. In particular, in the first period of my PhD, I focused my attention on the in vivo function of Emilin3 in zebrafish animal model, with the aim of fully understanding the biological role of this extracellular matrix glycoprotein, both in embryonic development and in tissue homeostasis. In particular, I have contributed to in vivo studies, using several genetic approaches. These results demonstrated that Emilin3, a component of the peri-notochordal basement membrane, is required not only for the proper maturation of the notochord, but also for the regulation of Hedgehog signals derived from the notochord itself. Moreover, I also contributed to in vitro experiments that allowed us to understand how Emilin3 limits Hedgehog signals secreted by the notochord. We found that Emilin3 is able to interact with Scube2, a secreted factor that acts in a permissive way in the generation of Hedgehog ligand gradients. In this context, I generated several deletion constructs of Emilin3 and Scube2, which allowed us to determine the specific action of Emilin3, revealing that this extracellular matrix protein is able to interact with the EGF motifs of Scube2. Overall, this study revealed that the interaction between Emilin3 and Scube2 within the peri-notochordal basement membrane is essential for the proper notochord patterning activity. Such results were published in a full article in Development (Corallo et al., 2013). Alongside this work, my increasing interest on notochord patterning and functions led me to contribute to two review manuscripts on this subject (Corallo et al., Cell Mol Life Sci 2015; Corallo et al., J Cell Sci, in preparation). Since the second year of my PhD, I independently started new functional studies in zebrafish focused on the role of Collagen VI during embryonic development. Mutations of Collagen VI genes in humans are causative for different forms of inherited muscle diseases, including Bethlem myopathy and Ullrich dystrophy. Previous work carried out in Collagen VI knockout (Col6a1–/–) mice revealed a crucial role for this extracellular matrix component in tissues homeostasis, and in particular in skeletal muscles, and demonstrated that ablation of Collagen VI has a remarkable impact on cell survival and organelle turnover. Indeed, these studies showed that collagen VI deficiency is causative for the myopatic syndrome of the mouse model and patients, characterized by spontaneous apoptosis, organelles alterations and deficient autophagy in muscle fibers. Although past work demonstrated that Collagen VI is broadly and dynamically expressed in a large number of tissues during embryonic development and postnatal life, no study until now ever assessed which roles Collagen VI plays during development and whether and how this major extracellular matrix component is regulating signaling pathways. Therefore, the aim of this part of my PhD work was to investigate which signaling pathways and tissues are affected by ablation of Collagen VI, in particular during embryonic development and using zebrafish as a model organism. Towards this aim, I first carried out a characterization of the structure, organization and gene expression of Collagen VI chains in zebrafish. These information were the basis for further functional studies. By means of morpholino-mediated Collagen VI knockdown in different transgenic reporter zebrafish lines, I identified not only an alteration of muscle fibers development, but also an axonal growth defect of motor neurons in Collagen VI morphant embryos. In addition, knockdown of Collagen VI led to variations in Wnt and BMP signals during embryogenesis, thus suggesting a possible correlation between the developmental defects and the signaling pathway alterations caused by Collagen VI knockdown. Finally, I successfully applied the CRISPR/Cas9 technology for in vivo site-specific mutagenesis, generating a zebrafish col6a1 VI null line that represents a valuable tool for the thorough understanding of the functions of Collagen VI during development and in regulating signaling pathways

DEFEG: deep ensemble with weighted feature generation.

With the significant breakthrough of Deep Neural Networks in recent years, multi-layer architecture has influenced other sub-fields of machine learning including ensemble learning. In 2017, Zhou and Feng introduced a deep random forest called gcForest that involves several layers of Random Forest-based classifiers. Although gcForest has outperformed several benchmark algorithms on specific datasets in terms of classification accuracy and model complexity, its input features do not ensure better performance when going deeply through layer-by-layer architecture. We address this limitation by introducing a deep ensemble model with a novel feature generation module. Unlike gcForest where the original features are concatenated to the outputs of classifiers to generate the input features for the subsequent layer, we integrate weights on the classifiers’ outputs as augmented features to grow the deep model. The usage of weights in the feature generation process can adjust the input data of each layer, leading the better results for the deep model. We encode the weights using variable-length encoding and develop a variable-length Particle Swarm Optimisation method to search for the optimal values of the weights by maximizing the classification accuracy on the validation data. Experiments on a number of UCI datasets confirm the benefit of the proposed method compared to some well-known benchmark algorithms