Models of Mechanics and Growth in Developmental Biology: A Computational Morphodinamics approach

Recent evidence has revealed the role of mechanical cues in the development of shapes in organisms. This thesis is an effort to test some of the fundamental hypotheses about the relation between mechanics and patterning in plants. To do this, we develop mechanical models designed to include specific features of plant cell walls. These are heterogeneous stiffness and material anisotropy as well as rates and directions of growth, which we then relate to different domains of the plant tissue.In plant cell walls, anisotropic fiber deposition is the main controller of longitudinal growth. In our model, this is achieved spontaneously, by applying feedback from the maximal stress direction to the fiber orientation. We show that a stress feedback model is in fact an energy minimization process. This can be considered as an evolutionary motivation for the emergence of a stress feedback mechanism. Then we add continuous growth and cell division to the model and employ the strain signal directing large growth deformations. We show the advantages of strain-based growth model for emergence of plant-like organ shapes as well as for reproducing microtubular dynamics in hypocotyls and roots. We also investigate possibilities for describing microtubular patterns, at root hair outgrowth sites according to stress patterns. Altogether, the work described in this thesis, provides a new improved growth model for plant tissue, where mechanical properties are handled with appropriate care in the event of growth driven by either molecular or mechanical signals. The model unifies the patterning process for several different plant tissues, from shoot to single root hair cells, where it correctly predict microtubular dynamics and growth patterns. In a long-term perspective, this understanding can propagate to novel technologies for improvement of yield in agriculture and the forest industry

Exploring the similarity of medical imaging classification problems

Supervised learning is ubiquitous in medical image analysis. In this paper we consider the problem of meta-learning -- predicting which methods will perform well in an unseen classification problem, given previous experience with other classification problems. We investigate the first step of such an approach: how to quantify the similarity of different classification problems. We characterize datasets sampled from six classification problems by performance ranks of simple classifiers, and define the similarity by the inverse of Euclidean distance in this meta-feature space. We visualize the similarities in a 2D space, where meaningful clusters start to emerge, and show that the proposed representation can be used to classify datasets according to their origin with 89.3\% accuracy. These findings, together with the observations of recent trends in machine learning, suggest that meta-learning could be a valuable tool for the medical imaging community

Tiny Homes in the American City

This article explores the idea of tiny homes in urban settings, and questions the ways in which tiny homes are both a subversive gesture that challenges existing paradigms around urban development, the home, and the family, as well as a projection of the American Dream in the urban arena. We also consider the opportunity that tiny homes present in helping to address certain challenges faced by cities, but also argue that addressing some of these challenges will require local governments to be inclusive of populations that have previously been marginalized for their attempts to live in settings that do not fit neatly into the social and physical fabric of the city. Because several local governments have begun to consider tiny homes as a potential solution to several pressing urban issues -- including affordability and homelessness -- we also explore how the history of government interventions in housing and the home have shaped urban and suburban communities in the past. We end by arguing that the current movement and recent explorations of local government show creativity but that local governments must take proactive steps to fold tiny homes into the everyday fabric of the city

Bottom up approach toward prediction of effective thermophysical properties of carbon-based nanofluids

Carbon-based nanofluids, mainly suspensions of carbon nanotubes or graphene sheets in water, are typically characterized by superior thermal and optical properties. However, their multiscale nature is slowing down the investigation of optimal geometrical, chemical, and physical nanoscale parameters for enhancing the thermal conductivity while limiting the viscosity increase at the same time. In this work, a bottom up approach is developed to systematically explore the thermophysical properties of carbon-based nanofluids with different characteristics. Prandtl number is suggested as the most adequate parameter for evaluating the best compromise between thermal conductivity and viscosity increases. By comparing the Prandtl number of nanofluids with different characteristics, promising overall performances (that is, nanofluid/base fluid Prandtl number ratios equal to 0.7) are observed for semidilute (volume fraction  ⩽ 0.004) aqueous suspensions of carbon nanoparticles with extreme aspect ratios (larger than 100 for nanotubes, smaller than 0.01 for nanoplatelets) and limited defects concentrations (<5%). The bottom up approach discussed in this work may ease a more systematic exploration of carbon-based nanofluids for thermal applications, especially solar ones

Relationship between PI3K Mutation and Sodium-Iodide Symporter in Anaplastic Thyroid Carcinoma

The sodium-iodide symporter is a transmembrane protein that has important role in radio-iodide therapy in various cancers such as anaplastic thyroid carcinoma. Anaplastic thyroid carcinoma is a rare undifferentiated thyroid tumor, but highly aggressive and lethal malignancy. Usually it is resistant to radio-iodide therapy and a cause of this appearance knows through dysfunction of the sodium-iodide symporter. Some genomic mutations, like PI3K gene mutations, can affect on sodium-iodide symporter functions. This review article explains briefly about PI3K signaling pathway and survey its gene mutations in carcinomas especially in anaplastic thyroid carcinoma and its influence on sodium-iodide symporter and iodide uptake

Thermal transmittance in graphene based networks for polymer matrix composites

Graphene nanoribbons (GNRs) can be added as fillers in polymer matrix composites for enhancing their thermo-mechanical properties. In the present study, we focus on the effect of chemical and geometrical characteristics of GNRs on the thermal conduction properties of composite materials. Configurations consisting of single and triple GNRs are here considered as representative building blocks of larger filler networks. In particular, GNRs with different length, relative orientation and number of cross-linkers are investigated. Based on results obtained by Reverse Non-equilibrium Molecular Dynamics simulations, we report correlations relating thermal conductivity and thermal boundary resistance of GNRs with their geometrical and chemical characteristics. These effects in turn affect the overall thermal transmittance of graphene based networks. In the broader context of effective medium theory, such results could be beneficial to predict the thermal transport properties of devices made of polymer matrix composites, which currently find application in energy, automotive, aerospace, electronics, sporting goods, and infrastructure industries

BRAF Mutation and its effects on Radioiodine Uptake in Patients with Anaplastic Thyroid Cancer

Context: Anaplastic thyroid carcinoma (ATC) is poorly differentiated subtype of thyroid cancer which either resistant to radioactive iodine (RAI) therapy or conventional chemotherapy. Each process of the biological characteristics in normal thyroid cells, including iodide uptake by sodium-iodide symporter (NIS), synthesis of thyroglobulin (Tg), expression of thyroid peroxidase (TPO) and receptor for thyrotropin (TSHR), can be an onset stage for emerging thyroid carcinoma. Decrease or absence of NIS mRNA in thyroid carcinomas has well described for resistant to RAI therapy in these patients. Evidence Acquisition: The original articles related to the role of the BRAF mutations on the sodium-iodide symporter functions and radioiodine uptake in patients with anaplastic thyroid carcinoma were found by a search in Scopus, PubMed, Science direct, Springer and some else with an emphasis on literature published in the recent years.Results: The related studies disclosed that mutations in the mitogen-activated protein kinase (MAPK) pathway happen in more than 90% of thyroid cancer. Also serine/threonine-protein kinase BRAF is an important component of the MAPK pathway. Its mutations cause reduction of NIS mRNA compared to tumors with other mutations

Articulatory-WaveNet: Deep Autoregressive Model for Acoustic-to-Articulatory Inversion

Acoustic-to-Articulatory Inversion, the estimation of articulatory kinematics from speech, is an important problem which has received significant attention in recent years. Estimated articulatory movements from such models can be used for many applications, including speech synthesis, automatic speech recognition, and facial kinematics for talking-head animation devices. Knowledge about the position of the articulators can also be extremely useful in speech therapy systems and Computer-Aided Language Learning (CALL) and Computer-Aided Pronunciation Training (CAPT) systems for second language learners. Acoustic-to-Articulatory Inversion is a challenging problem due to the complexity of articulation patterns and significant inter-speaker differences. This is even more challenging when applied to non-native speakers without any kinematic training data. This dissertation attempts to address these problems through the development of up-graded architectures for Articulatory Inversion. The proposed Articulatory-WaveNet architecture is based on a dilated causal convolutional layer structure that improves the Acoustic-to-Articulatory Inversion estimated results for both speaker-dependent and speaker-independent scenarios. The system has been evaluated on the ElectroMagnetic Articulography corpus of Mandarin Accented English (EMA-MAE) corpus, consisting of 39 speakers including both native English speakers and Mandarin accented English speakers. Results show that Articulatory-WaveNet improves the performance of the speaker-dependent and speaker-independent Acoustic-to-Articulatory Inversion systems significantly compared to the previously reported results