Recent Submissions

  • A Study of Recurrent and Convolutional Neural Networks in the Native Language Identification Task

    Werfelmann, Robert (2018-05-24)
    Native Language Identification (NLI) is the task of predicting the native language of an author from their text written in a second language. The idea is to find writing habits that transfer from an author’s native language to their second language. Many approaches to this task have been studied, from simple word frequency analysis, to analyzing grammatical and spelling mistakes to find patterns and traits that are common between different authors of the same native language. This can be a very complex task, depending on the native language and the proficiency of the author’s second language. The most common approach that has seen very good results is based on the usage of n-gram features of words and characters. In this thesis, we attempt to extract lexical, grammatical, and semantic features from the sentences of non-native English essays using neural networks. The training and testing data was obtained from a large corpus of publicly available essays written by authors of several countries around the world. The neural network models consisted of Long Short-Term Memory and Convolutional networks using the sentences of each document as the input. Additional statistical features were generated from the text to complement the predictions of the neural networks, which were then used as feature inputs to a Support Vector Machine, making the final prediction. Results show that Long Short-Term Memory neural network can improve performance over a naive bag of words approach, but with a much smaller feature set. With more fine-tuning of neural network hyperparameters, these results will likely improve significantly.
  • Neural Inductive Matrix Completion for Predicting Disease-Gene Associations

    Hou, Siqing (2018-05-21)
    In silico prioritization of undiscovered associations can help find causal genes of newly discovered diseases. Some existing methods are based on known associations, and side information of diseases and genes. We exploit the possibility of using a neural network model, Neural inductive matrix completion (NIMC), in disease-gene prediction. Comparing to the state-of-the-art inductive matrix completion method, using neural networks allows us to learn latent features from non-linear functions of input features. Previous methods use disease features only from mining text. Comparing to text mining, disease ontology is a more informative way of discovering correlation of dis- eases, from which we can calculate the similarities between diseases and help increase the performance of predicting disease-gene associations. We compare the proposed method with other state-of-the-art methods for pre- dicting associated genes for diseases from the Online Mendelian Inheritance in Man (OMIM) database. Results show that both new features and the proposed NIMC model can improve the chance of recovering an unknown associated gene in the top 100 predicted genes. Best results are obtained by using both the new features and the new model. Results also show the proposed method does better in predicting associated genes for newly discovered diseases.
  • Large-scale Comparative Study of Hi-C-based Chromatin 3D Structure Modeling Methods

    Wang, Cheng (2018-05-17)
    Chromatin is a complex polymer molecule in eukaryotic cells, primarily consisting of DNA and histones. Many works have shown that the 3D folding of chromatin structure plays an important role in DNA expression. The recently proposed Chro- mosome Conformation Capture technologies, especially the Hi-C assays, provide us an opportunity to study how the 3D structures of the chromatin are organized. Based on the data from Hi-C experiments, many chromatin 3D structure modeling methods have been proposed. However, there is limited ground truth to validate these methods and no robust chromatin structure alignment algorithms to evaluate the performance of these methods. In our work, we first made a thorough literature review of 25 publicly available population Hi-C-based chromatin 3D structure modeling methods. Furthermore, to evaluate and to compare the performance of these methods, we proposed a novel data simulation method, which combined the population Hi-C data and single-cell Hi-C data without ad hoc parameters. Also, we designed a global and a local alignment algorithms to measure the similarity between the templates and the chromatin struc- tures predicted by different modeling methods. Finally, the results from large-scale comparative tests indicated that our alignment algorithms significantly outperform the algorithms in literature.
  • Efficiency-limiting processes in OPV bulk heterojunctions of GeNIDTBT and IDT-based acceptors

    Al-Saggaf, Sarah M. (2018-05-16)
    The successful realization of highly efficient bulk heterojunction OPV devices requires the development of organic donor and acceptor materials with tailored properties. Recently, non-fullerene acceptors (NFAs) have emerged as an alternative to the ubiquitously used fullerene derivatives. NFAs showed a rapid increase in efficiencies, now exceeding a PCE of 13%. In my thesis research, I used two small molecule IDT-based acceptors, namely O-IDTBR and O-IDTBCN, in combination with a wide bandgap donor polymer, GeNIDT-BT, as active material in BHJ solar cells and investigated their photophysical characteristics. The polymer combined with O-IDTBR as acceptor achieved a power conversion efficiency of only 2%, which is significantly lower than that obtained for the system of GeNIDT-BT: O-IDTBCN (5.3%). Using nano- to microsecond transient absorption spectroscopy, I investigated both systems and demonstrated that GeNIDT-BT:O-IDTBR exhibits more geminate recombination of interfacial charge-transfer states, leading to lower short circuit currents. Using time-delayed collection field experiments, I studied the field dependence of charge generation and its impact on the device fill factor. Overall, my results provide a qualitative understanding of the efficiency-limiting processes in both systems and their impact on device performance.
  • Enhancing Network Data Obliviousness in Trusted Execution Environment-based Stream Processing Systems

    Alsibyani, Hassan M. (2018-05-15)
    Cloud computing usage is increasing and a common concern is the privacy and security of the data and computation. Third party cloud environments are not considered fit for processing private information because the data will be revealed to the cloud provider. However, Trusted Execution Environments (TEEs), such as Intel SGX, provide a way for applications to run privately and securely on untrusted platforms. Nonetheless, using a TEE by itself for stream processing systems is not sufficient since network communication patterns may leak properties of the data under processing. This work addresses leaky topology structures and suggests mitigation techniques for each of these. We create specific metrics to evaluate leaks occurring from the network patterns; the metrics measure information leaked when the stream processing system is running. We consider routing techniques for inter-stage communication in a streaming application to mitigate this data leakage. We consider a dynamic policy to change the mitigation technique depending on how much information is currently leaking. Additionally, we consider techniques to hide irregularities resulting from a filtering stage in a topology. We also consider leakages resulting from applications containing cycles. For each of the techniques, we explore their effectiveness in terms of the advantage they provide in overcoming the network leakage. The techniques are tested partly using simulations and some were implemented in a prototype SGX-based stream processing system.
  • Local Likelihood Approach for High-Dimensional Peaks-Over-Threshold Inference

    Baki, Zhuldyzay (2018-05-14)
    Global warming is affecting the Earth climate year by year, the biggest difference being observable in increasing temperatures in the World Ocean. Following the long- term global ocean warming trend, average sea surface temperatures across the global tropics and subtropics have increased by 0.4–1◦C in the last 40 years. These rates become even higher in semi-enclosed southern seas, such as the Red Sea, threaten- ing the survival of thermal-sensitive species. As average sea surface temperatures are projected to continue to rise, careful study of future developments of extreme temper- atures is paramount for the sustainability of marine ecosystem and biodiversity. In this thesis, we use Extreme-Value Theory to study sea surface temperature extremes from a gridded dataset comprising 16703 locations over the Red Sea. The data were provided by Operational SST and Sea Ice Analysis (OSTIA), a satellite-based data system designed for numerical weather prediction. After pre-processing the data to account for seasonality and global trends, we analyze the marginal distribution of ex- tremes, defined as observations exceeding a high spatially varying threshold, using the Generalized Pareto distribution. This model allows us to extrapolate beyond the ob- served data to compute the 100-year return levels over the entire Red Sea, confirming the increasing trend of extreme temperatures. To understand the dynamics govern- ing the dependence of extreme temperatures in the Red Sea, we propose a flexible local approach based on R-Pareto processes, which extend the univariate Generalized Pareto distribution to the spatial setting. Assuming that the sea surface temperature varies smoothly over space, we perform inference based on the gradient score method over small regional neighborhoods, in which the data are assumed to be stationary in space. This approach allows us to capture spatial non-stationarity, and to reduce the overall computational cost by taking advantage of distributed computing resources. Our results reveal an interesting extremal spatial dependence structure: in particular, from our estimated model, we conclude that significant extremal dependence prevails for distances up to about 2500 km, which roughly corresponds to the Red Sea length.
  • A Priori Regularity of Parabolic Partial Differential Equations

    Berkemeier, Francisco (2018-05-13)
    In this thesis, we consider parabolic partial differential equations such as the heat equation, the Fokker-Planck equation, and the porous media equation. Our aim is to develop methods that provide a priori estimates for solutions with singular initial data. These estimates are obtained by understanding the time decay of norms of solutions. First, we derive regularity results for the heat equation by estimating the decay of Lebesgue norms. Then, we apply similar methods to the Fokker-Planck equation with suitable assumptions on the advection and diffusion. Finally, we conclude by extending our techniques to the porous media equation. The sharpness of our results is confirmed by examining known solutions of these equations. The main contribution of this thesis is the use of functional inequalities to express decay of norms as differential inequalities. These are then combined with ODE methods to deduce estimates for the norms of solutions and their derivatives.
  • Ontology Design Patterns for Combining Pathology and Anatomy: Application to Study Aging and Longevity in Inbred Mouse Strains

    Alghamdi, Sarah M. (2018-05-13)
    In biomedical research, ontologies are widely used to represent knowledge as well as to annotate datasets. Many of the existing ontologies cover a single type of phenomena, such as a process, cell type, gene, pathological entity or anatomical structure. Consequently, there is a requirement to use multiple ontologies to fully characterize the observations in the datasets. Although this allows precise annotation of different aspects of a given dataset, it limits our ability to use the ontologies in data analysis, as the ontologies are usually disconnected and their combinations cannot be exploited. Motivated by this, here we present novel ontology design methods for combining pathology and anatomy concepts. To this end, we use a dataset of mouse models which has been characterized through two ontologies: one of them is the mouse pathology ontology (MPATH) covering pathological lesions while the other is the mouse anatomy ontology (MA) covering the anatomical site of the lesions. We propose four novel ontology design patterns for combining these ontologies, and use these patterns to generate four ontologies in a data-driven way. To evaluate the generated ontologies, we utilize these in ontology-based data analysis, including ontology enrichment analysis and computation of semantic similarity. We demonstrate that there are significant differences between the four ontologies in different analysis approaches. In addition, when using semantic similarity to confirm the hypothesis that genetically identical mice should develop more similar diseases, the generated combined ontologies lead to significantly better analysis results compared to using each ontology individually. Our results reveal that using ontology design patterns to combine different facets characterizing a dataset can improve established analysis methods.
  • Convergent Difference Schemes for Hamilton-Jacobi equations

    Duisembay, Serikbolsyn (2018-05-07)
    In this thesis, we consider second-order fully nonlinear partial differential equations of elliptic type. Our aim is to develop computational methods using convergent difference schemes for stationary Hamilton-Jacobi equations with Dirichlet and Neumann type boundary conditions in arbitrary two-dimensional domains. First, we introduce the notion of viscosity solutions in both continuous and discontinuous frameworks. Next, we review Barles-Souganidis approach using monotone, consistent, and stable schemes. In particular, we show that these schemes converge locally uniformly to the unique viscosity solution of the first-order Hamilton-Jacobi equations under mild assumptions. To solve the scheme numerically, we use Euler map with some initial guess. This iterative method gives the viscosity solution as a limit. Moreover, we illustrate our numerical approach in several two-dimensional examples.
  • Collision Analysis at 60-GHz mmWave Mesh Networks: The Case With Blockage and Shadowing

    Lyu, Kangjia (2018-05)
    This thesis can be viewed as two parts. The first part focuses on performance analysis of millimeter wave (mmWave) communications. We investigate how the interference behaves in the outdoor mesh network operating at 60-GHz when block age and shadowing are present using the probability of collision as a metric, under both the protocol model and the physical model. In contrast with results reported in mmWave mesh networks at 60-GHz that advocates that interference has only a marginal effect, our results show that for a short-range link of 100 m, the collision probability gets considerably larger (beyond 0.1) at the signal-to-interference-plus-noise ratio (SINR) of interest (for example, the reference value is chosen as 15 dB for uncoded quadrature phase shift keying (QPSK)). Compensation or compromise should be made in order to maintain a low probability of collision, either by reducing transmitter node density which is to the detriment of the network connectivity, or by switching to a compact linear antenna array with more at-top elements, which places more stringent requirements in device integration techniques. The second part of this thesis focuses on finding the optimal unmanned aerial vehicle (UAV) deployment in the sense that it can maximize over specific network connectivity. We have introduced a connectivity measure based on the commonly used network connectivity metric, which is refered to as global soft connectivity. This measure can be easily extended to account for different propagation models, such as Rayleigh fading and Nakagami fading. It can also be modified to incorporate the link state probability and beam alignment errors in highly directional networks. As can be shown, under the line-of-sight (LOS) and Rayleigh fading assumptions, the optimization regarding the global soft connectivity can be expressed as a weighted sum of the square of link distances between the nodes within the network, namely the ground-to-ground links, the UAV-to-UAV links and the ground-to-UAV links. This can be shown to be a quadratically constrained quadratic program (QCQP) problem with non-convex constraints. We have also extended our global connectivity to other types of connectivity criteria: network k-section connectivity and k-connectivity. In all the three cases, we have proposed a heuristic and straightforward way of finding the suboptimal UAV locations. The simulation results have shown that all these methods can improve our network connectivity considerably, which can achieve a gain of up to 30% for a five UAV scenario.
  • Seismic Imaging and Velocity Analysis Using a Pseudo Inverse to the Extended Born Approximation

    Alali, Abdullah A. (2018-05)
    Prestack depth migration requires an accurate kinematic velocity model to image the subsurface correctly. Wave equation migration velocity analysis techniques aim to update the background velocity model by minimizing image residuals to achieve the correct model. The most commonly used technique is differential semblance optimization (DSO), which depends on applying an image extension and penalizing the energy in the non-physical extension. However, studies show that the conventional DSO gradient is contaminated with artifact noise and unwanted oscillations which might lead to local minima. To deal with this issue and improve the stability of DSO, recent studies proposed to use an inversion formula rather than migration to obtain the image. Migration is defined as the adjoint of Born modeling. Since the inversion is complicated and expensive, a pseudo inverse is used instead. A pseudo inverse formula has been developed recently for the horizontal space shift extended Born. This formula preserves the true amplitude and reduces the artifact noise even when an incorrect velocity is used. Although the theory for such an inverse is well developed, it has only been derived and tested on laterally homogeneous models. This is because the formula contains a derivative of the image with respect to a vertical extension evaluated at zero offset. Implementing the vertical extension is computationally expensive, which means this derivative needs to be computed without applying the additional extension. For laterally invariant models, the inverse is simplified and this derivative is eliminated. I implement the full asymptotic inverse to the extended Born to account for laterally heterogeneity. I compute the derivative of the image with respect to a vertical extension without performing any additional shift. This is accomplished by applying the derivative to the imaging condition and utilizing the chain rule. The fact that this derivative is evaluated at zero offset vertical extension, makes it possible to compute the derivative without applying the extension. I also verify the newly proposed inversion formula on a laterally variant velocity model. In addition, I test the effect of the computed derivative and compare its contribution with the full formula. This additional term has overall limited influence on conventional images. Its largest impact is on vertical reflectors such as salt flanks, granted the velocity is varying laterally in the background as often is in this case. Otherwise, for most applications, we can obtain good quality extended images without this additional term.
  • A Game-theoretical Approach for Distributed Cooperative Control of Autonomous Underwater Vehicles

    Lu, Yimeng (2018-05)
    This thesis explores a game-theoretical approach for underwater environmental monitoring applications. We first apply game-theoretical algorithm to multi-agent resource coverage problem in drifting environments. Furthermore, existing utility design and learning process of the algorithm are modified to fit specific constraints of underwater exploration/monitoring tasks. The revised approach can take the real scenario of underwater monitoring applications such as the effect of sea current, previous knowledge of the resource and occasional communications between agents into account, and adapt to them to reach better performance. As the motivation of this thesis is from real applications, in this work we emphasize highly on implementation phase. A ROS-Gazebo simulation environment was created for preparation of actual tests. The algorithms are implemented in simulating both the dynamics of vehicles and the environment. After that, a multi-agent underwater autonomous robotic system was developed for hardware test in real settings with local controllers to make their own decisions. These systems are used for testing above mentioned algorithms and future development of other underwater projects. After that, other works related to robotics during this thesis will be briefly mentioned, including contributions in MBZIRC robotics competition and distributed control of UAVs in an adversarial environment.
  • Microhabitat Association of Cryptobenthic Reef Fishes (Teleostei: Gobiidae) in the Central Red Sea

    Troyer, Emily (2018-05)
    Knowledge of biodiversity within an ecosystem is essential when trying to understand the function and importance of that ecosystem. A challenge when assessing biodiversity of reef habitats is cryptobenthic fishes, which encompass many groups that have close associations with the substrate. These fishes can be behaviorally cryptic, by seeking refuge within the reef matrix, or visually cryptic, using cryptic coloration to match the surrounding habitat. These factors make visual surveys inadequate for sampling these fishes. One such group of cryptobenthic fishes are the gobies, family Gobiidae, which currently represent over 1600 species, although new species are continually being discovered. Gobies are often small (less than 5 cm), and many species will be associated with a very specific microhabitat type. Due to the understudied nature of the Red Sea, little is known about habitat preferences of gobies within the region. In order to determine the differences in goby community structure within the central Red Sea, fishes were sampled at one reef using 1 m² enclosed rotenone stations from three distinct microhabitats: hard coral, rubble, and sand. Following collection, specimens were photographed and sequenced using COI, to aid in species identification. 232 individuals were collected representing 31 species of goby. Rubble microhabitats were found to host the majority of collected gobies (69%), followed by hard coral (20.6%), then sand (9.9%). Goby assemblages in the three microhabitats were significantly different from each other, and evidence of habitat-specialists was found. These results provide essential baseline information about the ecology of understudied cryptobenthic fishes that can be used in future large-scale studies in the Red Sea region.
  • Displacement Convexity for First-Order Mean-Field Games

    Seneci, Tommaso (2018-05-01)
    In this thesis, we consider the planning problem for first-order mean-field games (MFG). These games degenerate into optimal transport when there is no coupling between players. Our aim is to extend the concept of displacement convexity from optimal transport to MFGs. This extension gives new estimates for solutions of MFGs. First, we introduce the Monge-Kantorovich problem and examine related results on rearrangement maps. Next, we present the concept of displacement convexity. Then, we derive first-order MFGs, which are given by a system of a Hamilton-Jacobi equation coupled with a transport equation. Finally, we identify a large class of functions, that depend on solutions of MFGs, which are convex in time. Among these, we find several norms. This convexity gives bounds for the density of solutions of the planning problem.
  • Engineering Plant Immunity via CRISPR/Cas13a System

    Aljedaani, Fatimah R. (2018-05)
    Viral diseases constitute a major threat to the agricultural production and food security throughout the world. Plants cope with the invading viruses by triggering immune responses and small RNA interference (RNAi) systems. In prokaryotes, CRISPR/Cas systems function as an adaptive immune system to provide bacteria with resistance against invading phages and conjugative plasmids. Interestingly, CRISPR/Cas9 system was shown to interfere with eukaryotic DNA viruses and confer resistance against plant DNA viruses. The majority of the plant viruses have RNA genomes. The aim of this study is to test the ability of the newly discovered CRISPR/Cas13a immune system, that targets and cleaves single stranded RNA (ssRNA) in prokaryotes, to provide resistance against RNA viruses in plants. Here, I employ the CRISPR/Cas13a system for molecular interference against Turnip Mosaic Virus (TuMV), a plant RNA virus. The results of this study established the CRISPR/Cas13a as a molecular interference machinery against RNA viruses in plants. Specifically, my data show that the CRISPR/Cas13a machinery is able to interfere with and degrade the TuMV (TuMV-GFP) RNA genome. In conclusion, these data indicate that the CRISPR/Cas13 systems can be employed for engineering interference and durable resistance against RNA viruses in diverse plant species.
  • Ground Deformation Related to Caldera Collapse and Ring-Fault Activity

    Liu, Yuan-Kai (2018-05)
    Volcanic subsidence, caused by partial emptying of magma in the subsurface reservoir has long been observed by spaceborne radar interferometry. Monitoring long-term crustal deformation at the most notable type of volcanic subsidence, caldera, gives us insights of the spatial and hazard-related information of subsurface reservoir. Several subsiding calderas, such as volcanoes on the Galapagos islands have shown a complex ground deformation pattern, which is often composed of a broad deflation signal affecting the entire edifice and a localized subsidence signal focused within the caldera floor. Although numerical or analytical models with multiple reservoirs are proposed as the interpretation, geologically and geophysically evidenced ring structures in the subsurface are often ignored. Therefore, it is still debatable how deep mechanisms relate to the observed deformation patterns near the surface. We aim to understand what kind of activities can lead to the complex deformation. Using two complementary approaches, we study the three-dimensional geometry and kinematics of deflation processes evolving from initial subsidence to later collapse of calderas. Firstly, the analog experiments analyzed by structure-from-motion photogrammetry (SfM) and particle image velocimetry (PIV) helps us to relate the surface deformation to the in-depth structures. Secondly, the numerical modeling using boundary element method (BEM) simulates the characteristic deformation patterns caused by a sill-like source and a ring-fault. Our results show that the volcano-wide broad deflation is primarily caused by the emptying of the deep magma reservoir, whereas the localized deformation on the caldera floor is related to ring-faulting at a shallower depth. The architecture of the ring-fault to a large extent determines the deformation localization on the surface. Since series evidence for ring-faulting at several volcanoes are provided, we highlight that it is vital to include ring-fault activity in numerical or analytical deformation source formulation. Ignoring the process of ring-faulting in models by using multiple point sources for various magma reservoirs will result in erroneous, thus meaningless estimates of depth and volume change of the magmatic reservoir(s).
  • Conformational Regulation of the Essential Epigenetic Regulator UHRF1

    Pantoja Angles, Aaron (2018-05)
    UHRF1 is an essential epigenetic regulator implicated in the maintenance of DNA methylation. While its functional state has been suggested to be allosterically regulated by phosphatidylinositol 5-phosphate and dependent on purification conditions and tags coupled to the protein, the expression system might have a broader impact on UHRF1s interaction properties. We hypothesized that the translation kinetics defined by the expression host has an impact on the folding process of the protein, which ultimately affects its structure and function. To test this idea, the cDNA of UHRF1 was recoded in order to generate optimized and harmonized sequences that were expected to alter the overall translation speed. Both proteins were expressed in Escherichia coli BL21-DE3 and their interaction profiles with H3K9me3 and unmodified H3 peptides were determined by microscale thermophoresis assays. The dissociation constants were compared by ttests in order to evaluate a possible change in the interaction properties of the optimized and harmonized proteins, compared to non-optimized UHRF1 expressed in E. coli BL21-DE3. While no difference was found for the interaction of optimized UHRF1 with the H3K9me3 peptide, a significant difference was found for its interaction with the unmodified H3 peptide. Moreover, both the interactions of harmonized UHRF1 with H3K9me3 and unmodified H3 peptides were determined to change. For this reason, we concluded that translation kinetics dependent on the expression system impacts the functional state of UHRF1. To further study this phenomenon, we expressed the consensus sequence of UHRF1 in Escherichia coli BL21-Codon Plus-(DE3)-RIL, a bacterial strain that is enriched with arginine, isoleucine, and leucine tRNA isoacceptors. Differences in its interaction profile with histone peptides were found when compared with UHRF1 expressed in Escherichia coli BL21-DE3. Since the major difference between these strains is the abundance of tRNAs, we obtained further findings that support our initial hypothesis. Additionally, the interaction profiles from the consensus UHRF1 protein were determined in the presence of PI5P to get an insight into how this phosphoinositide might impact the final structure and function of UHRF1. MST measurements and limited proteolysis assays led us to the idea of a partially open conformation for the UHRF1 expressed in E. coli BL21-DE3 and E.coli Codon Plus-(DE3)-RIL.
  • Exploring Trianglamine Derivatives and Trianglamine Coordination Complexes as Porous Organic Materials

    Eziashi, Magdalene (2018-05)
    Trianglamines are triangular chiral macrocycles that were first synthesized by Gawronski’s group in Poland in the year 2000.1 Despite their unique properties; triangular pore shape, chirality, symmetric structure and tunable pore size, they are still a poorly researched class of macrocycles today. Trianglamines have yet a role to play as porous organic molecules for separation processes, as macrocyclic precursors to build increasingly complex supramolecular assemblies and as building blocks for caged porous organic structures. The aim of the Thesis work is to explore trianglamine, its derivatives, and assemblies as viable porous organic molecules for potential gas capture and separation.
  • Engineering of kinase-based protein interacting devices: active expression of tyrosine kinase domains

    Diaz Galicia, Miriam Escarlet (2018-05)
    Protein-protein interactions modulate cellular processes in health and disease. However, tracing weak or rare associations or dissociations of proteins is not a trivial task. Kinases are often regulated through interaction partners and, at the same time, themselves regulate cellular interaction networks. The use of kinase domains for creating a synthetic sensor device that reads low concentration protein-protein interactions and amplifies them to a higher concentration interaction which is then translated into a FRET (Fluorescence Resonance Energy Transfer) signal is here proposed. To this end, DNA constructs for interaction amplification (split kinases), positive controls (intact kinase domains), scaffolding proteins and phosphopeptide - SH2-domain modules for the reading of kinase activity were assembled and expression protocols for fusion proteins containing Lyn, Src, and Fak kinase domains in bacterial and in cell-free systems were optimized. Also, two non-overlapping methods for measuring the kinase activity of these proteins were stablished and, finally, a protein-fragment complementation assay with the split-kinase constructs was tested. In conclusion, it has been demonstrated that features such as codon optimization, vector design and expression conditions have an impact on the expression yield and activity of kinase-based proteins. Furthermore, it has been found that the defined PURE cell-free system is insufficient for the active expression of catalytic kinase domains. In contrast, the bacterial co-expression with phosphatases produced active kinase fusion proteins for two out of the three tested Tyrosine kinase domains.
  • All Organic Polymers Based Morphing Skin with Controllable Surface Texture

    Favero Bolson, Natanael (2018-05)
    Smart skins are integrating an increasing number of functionalities in order to improve the interaction between the systems they equip and their ambient environment. Here we have developed an electromechanical soft actuator with controlled surface texture due to applied thermal gradient via electrical voltage. The device was fabricated and integrated with optimized process parameters for a prepared heater element [doped PEDOT: PSS (poly-(3, 4 ethylenedioxythiophene): poly (styrene sulfonic acid))], a soft actuator (Ecoflex 00-50/ethanol) and overall packaging case [PDMS (polydimethylsiloxane)]. To study a potential application of the proposed smart skin, we analyze the fluid drag reduction in a texture controlled water flow unit. As a result, we obtained a reduction of approximately 14% in the skin drag friction coefficient during the actuation. We conclude that the proposed soft actuator device is a preferred option for a texture-controlled skin that reduces the skin drag friction coefficient.

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