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Synapses are the fundamental structures that regulate the functionality of the neural circuit. The ability of the synapse to modulate its structure and function at a fast rate due to various sensory inputs provides the strength to the nervous system to incorporate new adaptations and behaviors in the animal. The synapses are very dynamic throughout the life of the animal starting from early development. Continuous events of formation and elimination of synapse, activation and inhibition of synaptic function are observed in almost all synapses. These processes occur at a high speed and require controlled cellular mechanisms. Imbalance in these processes results in defective nervous system and has been reported in many neurological disorders. Thus, it is important to understand the mechanisms that regulate process of synapse development maintenance and function.
Kinases and phosphatases are the key regulators of cellular mechanisms. Understanding the function of these molecules in the neuron will shed light on the molecular mechanisms of synaptic plasticity. Using Drosophila melanogaster larval neuromuscular junction as a model, Bulat et al. (2014) performed a large RNAi based screen targeting kinome and phosphatome of Drosophila to identify the essential kinases and phosphatases and found Myeloid leukemia factor-1 adaptor molecule (Madm) and Protein phosphatase 4 (PP4) as novel regulators of synapse development and maintenance. The function of these molecules in the nervous system has not been reported and hence I investigated on the role of Madm and PP4 in the regulation of synapse development, maintenance and function.
Myeloid leukemia factor-1 adaptor molecule (Madm), a ubiquitously expressing psuedokinase essentially functions to regulate synaptic growth, stability and function. Using a combination of genetic and high throughput imaging, I could demonstrate that Madm functions to regulate the synaptic growth and stability from the presynapse and synaptic organization form the postsynapse. Also, I could demonstrate that Madm functions in association with mTOR pathway to regulate synapse growth acting downstream of 4E-BP. In addition, using electrophysiology, we could demonstrate that Madm is essential for the basic synaptic transmission with an additive function of retrograde synaptic potentiation. In summary, I could demonstrate that Madm is a novel regulator of synaptic development, maintenance and function.
Protein phosphatase 4 (PP4), a ubiquitously expressing protein phosphatase is involved in the regulation of multiple aspects of the nervous system. I could demonstrate that PP4 is essential for the development of nervous system and the metamorphosis. Using genetics and imaging analysis, I could demonstrate that loss of PP4 results in the abnormal morphology of cell organelles. In addition, I could show that loss of PP4 results in defective brain development with poorly developed structures.
Altogether, in this study, I could demonstrate the importance of novel molecules, a pesudokinase Madm and protein phosphatases PP4 in the nervous system to regulate distinct aspects of the neuron.
The number of sensors used in modern devices is rapidly increasing, and the interaction with sensors demands analog-to-digital data conversion (ADC). A conventional ADC in leading-edge technologies faces
many issues due to signal swings, manufacturing deviations, noise, etc. Designers of ADCs are moving to the
time domain and digital designs techniques to deal with these issues. This work pursues a novel self-adaptive
spiking neural ADC (SN-ADC) design with promising features, e.g., technology scaling issues, low-voltage
operation, low power, and noise-robust conditioning. The SN-ADC uses spike time to carry the information.
Therefore, it can be effectively translated to aggressive new technologies to implement reliable advanced sensory electronic systems. The SN-ADC supports self-x (self-calibration, self-optimization, and self-healing) and
machine learning required for the internet of things (IoT) and Industry 4.0. We have designed the main part of
SN-ADC, which is an adaptive spike-to-digital converter (ASDC). The ASDC is based on a self-adaptive complementary metal–oxide–semiconductor (CMOS) memristor. It mimics the functionality of biological synapses,
long-term plasticity, and short-term plasticity. The key advantage of our design is the entirely local unsupervised
adaptation scheme. The adaptation scheme consists of two hierarchical layers; the first layer is self-adapted, and
the second layer is manually treated in this work. In our previous work, the adaptation process is based on 96 variables. Therefore, it requires considerable adaptation time to correct the synapses’ weight. This paper proposes a
novel self-adaptive scheme to reduce the number of variables to only four and has better adaptation capability
with less delay time than our previous implementation. The maximum adaptation times of our previous work
and this work are 15 h and 27 min vs. 1 min and 47.3 s. The current winner-take-all (WTA) circuits have issues, a
high-cost design, and no identifying the close spikes. Therefore, a novel WTA circuit with memory is proposed.
It used 352 transistors for 16 inputs and can process spikes with a minimum time difference of 3 ns. The ASDC
has been tested under static and dynamic variations. The nominal values of the SN-ADC parameters’ number
of missing codes (NOMCs), integral non-linearity (INL), and differential non-linearity (DNL) are no missing
code, 0.4 and 0.22 LSB, respectively, where LSB stands for the least significant bit. However, these values are
degraded due to the dynamic and static deviation with maximum simulated change equal to 0.88 and 4 LSB and
6 codes for DNL, INL, and NOMC, respectively. The adaptation resets the SN-ADC parameters to the nominal
values. The proposed ASDC is designed using X-FAB 0.35 µm CMOS technology and Cadence tools.
A potential fucoidan-based PEGylated PLGA nanoparticles (NPs) offering a proper delivery of N-methyl anthranilic acid (MA, a model of hydrophobic anti-inflammatory drug) have been
developed via the formation of fucoidan aqueous coating surrounding PEGylated PLGA NPs. The optimum formulation (FuP2) composed of fucoidan:m-PEG-PLGA (1:0.5 w/w) with particle size(365 ± 20.76 nm), zeta potential (-22.30 ± 2.56 mV), % entrapment efficiency (85.45 ± 7.41), drug loading (51.36 ± 4.75 µg/mg of NPs), % initial burst (47.91 ± 5.89), and % cumulative release
(102.79 ± 6.89) has been further investigated for the anti-inflammatory in vivo study. This effect of
FuP2 was assessed in rats’ carrageenan-induced acute inflammation model. The average weight of the
paw edema was significantly lowered (p ≤ 0.05) by treatment with FuP2. Moreover, cyclooxygenase-2 and tumor necrosis factor-alpha immunostaining were decreased in FuP2 treated group compared to the other groups. The levels of prostaglandin E2, nitric oxide, and malondialdehyde were significantly
reduced (p ≤ 0.05) in the FuP2-treated group. A significant reduction (p ≤ 0.05) in the expression
of interleukins (IL-1b and IL-6) with an improvement of the histological findings of the paw tissues was observed in the FuP2-treated group. Thus, fucoidan-based PEGylated PLGA–MA NPs are a promising anti-inflammatory delivery system that can be applied for other similar drugs potentiating their pharmacological and pharmacokinetic properties.
With the burgeoning computing power available, multiscale modelling and simulation has these days become increasingly capable of capturing the details of physical processes on different scales. The mechanical behavior of solids is oftentimes the result of interaction between multiple spatial and temporal scales at different levels and hence it is a typical phenomena of interest exhibiting multiscale characteristic. At the most basic level, properties of solids can be attributed to atomic interactions and crystal structure that can be described on nano scale. Mechanical properties at the macro scale are modeled using continuum mechanics for which we mention stresses and strains. Continuum models, however they offer an efficient way of studying material properties they are not accurate enough and lack microstructural information behind the microscopic mechanics that cause the material to behave in a way it does. Atomistic models are concerned with phenomenon at the level of lattice thereby allowing investigation of detailed crystalline and defect structures, and yet the length scales of interest are inevitably far beyond the reach of full atomistic computation and is rohibitively expensive. This makes it necessary the need for multiscale models. The bottom line and a possible avenue to this end is, coupling different length scales, the continuum and the atomistics in accordance with standard procedures. This is done by recourse to the Cauchy-Born rule and in so doing, we aim at a model that is efficient and reasonably accurate in mimicking physical behaviors observed in nature or laboratory. In this work, we focus on concurrent coupling based on energetic formulations that links the continuum to atomistics. At the atomic scale, we describe deformation of the solid by the displaced positions of atoms that make up the solid and at the continuum level deformation of the solid is described by the displacement field that minimize the total energy. In the coupled model, continuum-atomistic, a continuum formulation is retained as the overall framework of the problem and the atomistic feature is introduced by way of constitutive description, with the Cauchy-Born rule establishing the point of contact. The entire formulation is made in the framework of nonlinear elasticity and all the simulations are carried out within the confines of quasistatic settings. The model gives direct account to measurable features of microstructures developed by crystals through sequential lamination.
Understanding human crowd behaviour has been an intriguing topic of interdisciplinary research in recent decades. Modelling of crowd dynamics using differential equations is an indispensable approach to unraveling the various complex dynamics involved in such interacting particle systems. Numerical simulation of pedestrian crowd via these mathematical models allows us to study different realistic scenarios beyond the limitations of studies via controlled experiments.
In this thesis, the main objective is to understand and analyse the dynamics in a domain shared by both pedestrians and moving obstacles. We model pedestrian motion by combining the social force concept with the idea of optimal path computation. This leads to a system of ordinary differential equations governing the dynamics of individual pedestrians via the interaction forces (social forces) between them. Additionally, a non-local force term involving the optimal path and desired velocity governs the pedestrian trajectory. The optimal path computation involves solving a time-independent Eikonal equation, which is coupled to the system of ODEs. A hydrodynamic model is developed from this microscopic model via the mean-field limit.
To consider the interaction with moving obstacles in the domain, we model a set of kinematic equations for the obstacle motion. Two kinds of obstacles are considered - "passive", which move in their predefined trajectories and have only a one-way interaction with pedestrians, and "dynamic", which have a feedback interaction with pedestrians and have their trajectories changing dynamically. The coupled model of pedestrians and obstacles is used to discern pedestrian collision avoidance behaviour in different computational scenarios in a long rectangular domain. We observe that pedestrians avoid collisions through route choice strategies that involve changes in speed and path. We extend this model to consider the interaction between pedestrians and vehicular traffic. We appropriately model the interactions of vehicles, following lane traffic, based on the car-following approach. We observe how the deceleration and braking mechanism of vehicles is executed at pedestrian crossings depending on the right of way on the roads.
As a second objective, we study the disease contagion in moving crowds. We consider the influence of the crowd motion in a complex dynamical environment on the course of infection of pedestrians. A hydrodynamic model for multi-group pedestrian flow is derived from the kinetic equations based on a social force model. It is coupled along with an Eikonal equation to a non-local SEIS contagion model for disease spread. Here, apart from the description of local contacts, the influence of contact times has also been modelled. We observe that the nature of the flow and the geometry of the domain lead to changes in density which affect the contact time and, consequently, the rate of spread of infection.
Finally, the social force model is compared to a variable speed based rational behaviour pedestrian model. We derive a hierarchy of the heuristics-based model from microscopic to macroscopic scales and numerically investigate these models in different density scenarios. Various numerical test cases are considered, including uni- and bi-directional flows and scenarios with and without obstacles. We observe that in low-density scenarios, collision avoidance forces arising from the behavioural heuristics give valid results. Whereas in high-density scenarios, repulsive force terms are essential.
The numerical simulations of all the models are carried out using a mesh-free particle method based on least square approximations. The meshfree numerical framework provides an efficient and elegant way to handle complex geometric situations involving boundaries and stationary or moving obstacles.
This Dissertation tried to provide insights into the influences of individual and contextual factors on Technical and Vocational Education and Training (TVET) teachers’ learning and professional development in Ethiopia. Specifically, this research focused on identifying and determining the influences of teachers’ self perception as learners and professionals, and investigates the impact of the context, process and content of their learning and experiences on their professional development. The knowledge of these factors and their impacts help in improving the learning and professional development of the TVET teachers and their professionalization. This research tried to provide answers for the following five research questions. (1) How do TVET teachers perceive themselves as active learners and as professionals? And what are the implications of their perceptions on their learning and development? (2) How do TVET teachers engage themselves in learning and professional development activities? (3) What contextual factors facilitated or hindered the TVET Teachers’ learning and professional development? (4) Which competencies are found critical for the TVET teachers’ learning and professional development? (5) What actions need to be considered to enhance and sustain TVET teachers learning and professional development in their context? It is believed that the research results are significant not only to the TVET teachers, but also to schools leaders, TVET Teacher Training Institutions, education experts and policy makers, researchers and others stakeholders in the TVET sector. The theoretical perspectives adopted in this research are based on the systemic constructivist approach to professional development. An integrated approach to professional development requires that the teachers’ learning and development activities to be taken as an adult education based on the principles of constructivism. Professional development is considered as context - specific and long-term process in which teachers are trusted, respected and empowered as professionals. Teachers’ development activities are sought as more of collaborative activities portraying the social nature of learning. Schools that facilitate the learning and development of teachers exhibit characteristics of a learning organisation culture where, professional collaboration, collegiality and shared leadership are practiced. This research has drawn also relevant point of views from studies and reports on vocational education and TVET teacher education programs and practices at international, continental and national levels. The research objectives and the types of research questions in this study implied the use of a qualitative inductive research approach as a research strategy. Primary data were collected from TVET teachers in four schools using a one-on-one qualitative in-depth interview method. These data were analyzed using a Qualitative Content Analysis method based on the inductive category development procedure. ATLAS.ti software was used for supporting the coding and categorization process. The research findings showed that most of the TVET teachers neither perceive themselves as professionals nor as active learners. These perceptions are found to be one of the major barriers to their learning and development. Professional collaborations in the schools are minimal and teaching is sought as an isolated individual activity; a secluded task for the teacher. Self-directed learning initiatives and individual learning projects are not strongly evident. The predominantly teacher-centered approach used in TVET teacher education and professional development programs put emphasis mainly to the development of technical competences and has limited the development of a range of competences essential to teachers’ professional development. Moreover, factors such as the TVET school culture, the society’s perception of the teaching profession, economic conditions, and weak links with industries and business sectors are among the major contextual factors that hindered the TVET teachers’ learning and professional development. A number of recommendations are forwarded to improve the professional development of the TVET teachers. These include change in the TVET schools culture, a paradigm shift in TVET teacher education approach and practice, and development of educational policies that support the professionalization of TVET teachers. Areas for further theoretical research and empirical enquiry are also suggested to support the learning and professional development of the TVET teachers in Ethiopia.
Manipulating deformable linear objects - Vision-based recognition of contact state transitions -
(1999)
A new and systematic approach to machine vision-based robot manipulation of deformable (non-rigid) linear objects is introduced. This approach reduces the computational needs by using a simple state-oriented model of the objects. These states describe the relation of the object with respect to an obstacle and are derived from the object image and its features. Therefore, the object is segmented from a standard video frame using a fast segmentation algorithm. Several object features are presented which allow the state recognition of the object while being manipulated by the robot.
A new and systematic basic approach to force- and vision-based robot manipulation of deformable (non-rigid) linear objects is introduced. This approach reduces the computational needs by using a simple state-oriented model of the objects. These states describe the relation between the deformable and rigid obstacles, and are derived from the object image and its features. We give an enumeration of possible contact states and discuss the main characteristics of each state. We investigate the performance of robust transitions between the contact states and derive criteria and conditions for each of the states and for two sensor systems, i.e. a vision sensor and a force/torque sensor. This results in a new and task-independent approach in regarding the handling of deformable objects and in a sensor-based implementation of manipulation primitives for industrial robots. Thus, the usage of sensor processing is an appropriate solution for our problem. Finally, we apply the concept of contact states and state transitions to the description of a typical assembly task. Experimental results show the feasibility of our approach: A robot performs several contact state transitions which can be combined for solving a more complex task.
A geoscientifically relevant wavelet approach is established for the classical (inner) displacement problem corresponding to a regular surface (such as sphere, ellipsoid, actual earth's surface). Basic tools are the limit and jump relations of (linear) elastostatics. Scaling functions and wavelets are formulated within the framework of the vectorial Cauchy-Navier equation. Based on appropriate numerical integration rules a pyramid scheme is developed providing fast wavelet transform (FWT). Finally multiscale deformation analysis is investigated numerically for the case of a spherical boundary.
The focus of this work has been to develop two families of wavelet solvers for the inner displacement boundary-value problem of elastostatics. Our methods are particularly suitable for the deformation analysis corresponding to geoscientifically relevant (regular) boundaries like sphere, ellipsoid or the actual Earth's surface. The first method, a spatial approach to wavelets on a regular (boundary) surface, is established for the classical (inner) displacement problem. Starting from the limit and jump relations of elastostatics we formulate scaling functions and wavelets within the framework of the Cauchy-Navier equation. Based on numerical integration rules a tree algorithm is constructed for fast wavelet computation. This method can be viewed as a first attempt to "short-wavelength modelling", i.e. high resolution of the fine structure of displacement fields. The second technique aims at a suitable wavelet approximation associated to Green's integral representation for the displacement boundary-value problem of elastostatics. The starting points are tensor product kernels defined on Cauchy-Navier vector fields. We come to scaling functions and a spectral approach to wavelets for the boundary-value problems of elastostatics associated to spherical boundaries. Again a tree algorithm which uses a numerical integration rule on bandlimited functions is established to reduce the computational effort. For numerical realization for both methods, multiscale deformation analysis is investigated for the geoscientifically relevant case of a spherical boundary using test examples. Finally, the applicability of our wavelet concepts is shown by considering the deformation analysis of a particular region of the Earth, viz. Nevada, using surface displacements provided by satellite observations. This represents the first step towards practical applications.