| 14421. |
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| 14422. |
- Lehtiö, Janne, 1970-
(författare)
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Functional studies and engineering of family 1 carbohydrate-binding modules
- 2001
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Doktorsavhandling (övrigt vetenskapligt/konstnärligt)abstract
- The family 1 cellulose-binding modules (CBM1) form a groupof small, stable carbohydrate-binding proteins. These modulesare essential for fungal cellulosedegradation. This thesisdescribes both functional studies of the CBM1s as well asprotein engineering of the modules for several objectives.The characteristics and specificity of CBM1s from theTrichoderma reeseiCel7A and Cel6A, along with severalother wild type and mutated CBMs, were studied using bindingexperiments and transmission electron microscopy (TEM). Datafrom the binding studies confirmed that the presence of onetryptophan residue on the CBM1 binding face enhances itsbinding to crystalline cellulose. The twoT. reeseiCBM1s as well as the CBM3 from theClostridium thermocellumCipA were investigated by TEMexperiments. All three CBMs were found to bind in lineararrangements along the sides of the fibrils. Further analysesof the bound CBMs indicated that the CBMs bind to the exposedhydrophobic surfaces, the so called (200) crystalline face ofValoniacellulose crystals.The function and specificity of CBM1s as a part of an intactenzyme were studied by replacing the CBM from the exo-actingCel7A by the CBM1 from the endoglucanase Cel7B. Apart fromslightly improved affinity of the hybrid enzyme, the moduleexchange did not significantly influence the function of theCel7A. This indicates that the two CBM1s are analogous in theirbinding properties and function during cellulosehydrolysis.The CBM1 was also used for immobilization studies. Toimprove heterologous expression of a CBM1-lipase fusionprotein, a linker stability study was carried out inPichia pastoris. A proline/threonine rich linker peptidewas found to be stable for protein production in this host. Forwhole bacterial cell immobilization, theT. reeseiCel6A CBM1 was expressed on the surface of thegram-positive bacteria,Staphylococcus carnosus. The engineeredS. carnosuscells were shown to bind cellulosefibers.To exploit the stable CBM1 fold as a starting point forgenerating novel binders, a phage display library wasconstructed. Binding proteins against an amylase as well asagainst a metal ion were selected from the library. Theamylase-binding proteins were found to bind and inhibit thetarget enzyme. The metal binding proteins selected from thelibrary were cloned on the surface of theS. carnosusand clearly enhanced the metal bindingability of the engineered bacteria.Keywords: cellulose-binding, family 1carbohydrate-binding module, phage display, bacterial surfacedisplay, combinatorial protein library, metal binding, proteinengineering,Trichoderma reesei, Staphyloccus carnosus.
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| 14423. |
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| 14424. |
- Lei, Wanlu
(författare)
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A study of wireless communications with reinforcement learning
- 2022
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Doktorsavhandling (övrigt vetenskapligt/konstnärligt)abstract
- The explosive proliferation of mobile users and wireless data traffic in recent years pose imminent challenges upon wireless system design. The trendfor wireless communications becoming more complicated, decentralized andintelligent is inevitable. Lots of key issues in this field are decision-makingrelated problems such as resource allocation, transmission control, intelligentbeam tracking in millimeter Wave (mmWave) systems and so on. Reinforcement learning (RL) was once a languishing field of AI for solving varioussequential decision-making problems. However, it got revived in the late 80sand early 90s when it was connected to dynamic programming (DP). Then,recently RL has progressed in many applications, especially when underliningmodels do not have explicit mathematical solutions and simulations must beused. For instance, the success of RL in AlphaGo and AlphaZero motivatedlots of recent research activities in RL from both academia and industries.Moreover, since computation power has dramatically increased within thelast decade, the methods of simulations and online learning (planning) become feasible for implementations and deployment of RL. Despite of its potentials, the applications of RL to wireless communications are still far frommature. Therefore, it is of great interest to investigate RL-based methodsand algorithms to adapt to different wireless communication scenarios. Morespecifically, this thesis with regards to RL in wireless communications can beroughly divided into the following parts:In the first part of the thesis, we develop a framework based on deepRL (DRL) to solve the spectrum allocation problem in the emerging integrated access and backhaul (IAB) architecture with large scale deploymentand dynamic environment. We propose to use the latest DRL method by integrating an actor-critic spectrum allocation (ACSA) scheme and a deep neuralnetwork (DNN) to achieve real-time spectrum allocation in different scenarios. The proposed methods are evaluated through numerical simulations andshow promising results compared with some baseline allocation policies.In the second part of the thesis, we investigate the decentralized RL algorithms using Alternating direction method of multipliers (ADMM) in applications of Edge IoT. For RL in a decentralized setup, edge nodes (agents)connected through a communication network aim to work collaboratively tofind a policy to optimize the global reward as the sum of local rewards. However, communication costs, scalability and adaptation in complex environments with heterogeneous agents may significantly limit the performance ofdecentralized RL. ADMM has a structure that allows for decentralized implementation and has shown faster convergence than gradient-descent-basedmethods. Therefore, we propose an adaptive stochastic incremental ADMM(asI-ADMM) algorithm and apply the asI-ADMM to decentralized RL withedge computing-empowered IoT networks. We provide convergence properties for proposed algorithms by designing a Lyapunov function and prove thatthe asI-ADMM has O(1=k) + O(1=M) convergence rate where k and M are thenumber of iterations and batch samples, respectively.The third part of the thesis considers the problem of joint beam training and data transmission control of delay-sensitive communications overvimmWave channels. We formulate the problem as a constrained Markov Decision Process (MDP), which aims to minimize the cumulative energy consumption over the whole considered period of time under delay constraints.By introducing a Lagrange multiplier, we reformulate the constrained MDPto an unconstrained one. Then, we solve it using the parallel-rollout-basedRL method in a data-driven manner. Our numerical results demonstrate thatthe optimized policy obtained from parallel rollout significantly outperformsother baseline policies in both energy consumption and delay performance.The final part of the thesis is a further study of the beam tracking problem using supervised learning approach. Due to computation and delay limitation in real deployment, a light-weight algorithm is desired in the beamtracking problem in mmWave networks. We formulate the beam tracking(beam sweeping) problem as a binary-classification problem, and investigatesupervised learning methods for the solution. The methods are tested in bothsimulation scenarios, i.e., ray-tracing model, and real testing data with Ericsson over-the-air (OTA) dataset. It showed that the proposed methods cansignificantly improve cell capacity and reduce overhead consumption whenthe number of UEs increases in the network.
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| 14425. |
- Lei, Wanlu, et al.
(författare)
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Adaptive Beam Tracking With Supervised Learning
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Annan publikation (övrigt vetenskapligt/konstnärligt)abstract
- Utilizing millimeter-wave (mmWave) frequencies forwireless communication in mobile systems is challenging sincecontinuous tracking of the beam direction is needed. For the purpose, beam sweeping is performed periodically. Such approachcan be sufficient in the initial deployment of the network whenthe number of users is small. However, a more efficient solutionis needed when lots of users are connected to the network due tohigher overhead consumption. We explore a supervised learningapproach to adaptively perform beam sweeping, which has lowimplementation complexity and can improve cell capacity byreducing beam sweeping overhead. By formulating the beamtracking problem as a binary classification problem, we appliedsupervised learning methods to solve the formulated problem.The methods were tested on two scenarios: ray-tracing outdoorscenario and over-the-air (OTA) testing dataset from Ericsson.Both experimental results show that the proposed methodssignificantly increase cell throughput comparing with existingexhaustive sweeping and periodical sweeping strategies.
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| 14426. |
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| 14427. |
- Leijon, Arne, 1947-
(författare)
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Bayesian learning of Gaussian mixtures: Variational "over-pruning" revisited
- 2013
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Rapport (övrigt vetenskapligt/konstnärligt)abstract
- This study reconsiders two simple toy data examples proposed by MacKay (2001) to illustrate what he called “symmetry-breaking” and inappropriate “over-pruning” by the variational inference (VI) approximation in Bayesian learning of probabilistic mixture models.The exact Bayesian solution is derived formally, including the effects of parameter values in the prior distribution of mixture weights. The exact solution is then compared to the results of VI approximation.In both toy examples both the exact solution and the VI approxi- mation normally assigned each data cluster entirely to its own mixture component. In both methods the number of active mixture components is normally the same as the number of data clusters. In this sense, the VI approach causes no “over-pruning”. In one extreme example with two clusters with only 1 and 3 samples, and very small parameter values in the prior Dirichlet distribution of mixture weights, the exact Bayesian solution assigned all samples to the same component, i.e., with “over-pruning”, whereas the VI approximation still converged to a solution using both mixture components, i.e., with no “over-pruning”. Thus, if inappropriate over-pruning occurs, it is probably caused by inappropriate selection of prior model parameters, and not by the VI approach.The VI approximation shows “symmetry-breaking” because it converges to one of the arbitrary and equivalent permutations of the indices of mixture components. The “symmetric” exact solution formally in- cludes all these permutations, but this is precisely what makes the exact Bayesian solution computationally impractical. Thus, in these toy examples, we must conclude that “symmetry-breaking” is not the same thing as “over-pruning”. The VI approximation shows “symmetry-breaking” but no “over-pruning”.
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| 14428. |
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| 14429. |
- Leijon, Felicia, 1983-
(författare)
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Understanding and manipulating primary cell walls in plant cell suspension cultures
- 2019
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Doktorsavhandling (övrigt vetenskapligt/konstnärligt)abstract
- The cell wall is required for many aspects of plant function and development. It is also an accessible and renewable resource utilized both in unrefined forms and as raw material for further development. Increased knowledge regarding cell wall structure and components will contribute to better utilization of plants and the resources they provide. In this thesis aspects of the primary cell wall of Populus trichocarpa and Nicotiana tabacum are explored.In Publication I a method for isolation and biochemical characterization of plant glycosyltransferases using a spectrophotometric or a radiometric assay was optimized. The radiometric assay was applied in Publication II where the proteome of the plasmodesmata isolated from P. trichocarpa was analyzed. Proteins identified belonged to functional classes such as “transport”, “signalling” and “stress responses”. Plasmodesmata-enriched fractions had high levels of callose synthase activity under ion depleted conditions as well as with calcium present.The second part of the thesis comprises the alteration of the cell wall of N. tabacum cells and A. thaliana plants through in vivo expression of a carbohydrate binding module (CBM) (Publication III). In tobacco this resulted in cell walls with loose ultrastructure containing an increased proportion of 1,4-β-glucans. The cell walls were more susceptible to saccharification, possibly due to changes in the structure of cellulose or xyloglucan. Arabidopsis plants showed increased saccharification after mild pretreatment, suggesting that heterologous expression of CBMs is a promising method for cell wall engineering. In Publication IV cellulose microfibrils (CMFs) and nanocrystals (CNCs) were extracted from the transgenic cells. CNC preparation resulted in higher yields and longer CNCs. Nanopapers prepared from the CMFs of the CBM line demonstrated enhanced strength and toughness. Thus, changes to the ordered regions of cellulose were suggested to take place due to CBM expression.
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| 14430. |
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