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- Garcia Cerdan, Jose Gines, 1977-
(författare)
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Functions of REP27 and the low molecular weight proteins PsbX and PsbW in repair and assembly of photosystem II
- 2009
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Doktorsavhandling (övrigt vetenskapligt/konstnärligt)abstract
- Oxygenic photosynthesis is the major producer of both oxygen and organic compounds on earth and takes place in plants, green algae and cyanobacteria. The thylakoid membranes are the site of the photosynthetic light reactions that involve the concerted action of four major protein complexes known as photosystem II (PSII), cytochrome b6f complex, ATP synthase and photosystem I (PSI). The function of PSII is of particular interest as it performs the light–driven water splitting reaction driving the photosynthetic electron transport. My thesis addressed different aspects of PSII assembly and the functions of its low molecular weight PSII subunits PsbX and PsbW. Photosynthesis in green algae and higher plants is controlled by the nucleus. Many proteins of nuclear origin participate in the regulation of the efficient assembly of the photosynthetic protein complexes. In this investigation we have identified one of these nuclear encoded auxiliary proteins of photosystem II, REP27, which participates in the assembly of the D1 reaction center protein and repair of photodamaged PSII in the green algae Chlamydomonas reinhardtii. Interestingly, PSII is specially enriched in Low Molecular Weight (LMW) subunits that have masses less than 10kDa. These proteins account for more than the half of the PSII subunits. Several questions remains poorly understood regarding the LMW: Which is their evolutionary origin? What function do they perform in the protein complex? Where are they located in the protein structure? In this investigation the functions of two of these LMW subunits (PsbX and PsbW) have been studied using antisense inhibition and T-DNA knockout mutant plants in Arabidopsis thaliana. Deficiency of the PsbX protein leads to impaired accumulation and functionality of PSII. Characterization of PsbW knock-out plants show that PsbW participates in stabilization of the macro-organization of PSII and the peripheral antenna (Light Harvesting Complex, LHCII) in the grana stacks of the chloroplast, also known as PSII-LHCII supercomplexes.
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| 2. |
- García Martín, Eva, 1989-
(författare)
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Energy Efficiency in Machine Learning : Approaches to Sustainable Data Stream Mining
- 2020
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Doktorsavhandling (övrigt vetenskapligt/konstnärligt)abstract
- Energy efficiency in machine learning explores how to build machine learning algorithms and models with low computational and power requirements. Although energy consumption is starting to gain interest in the field of machine learning, still the majority of solutions focus on obtaining the highest predictive accuracy, without a clear focus on sustainability.This thesis explores green machine learning, which builds on green computing and computer architecture to design sustainable and energy efficient machine learning algorithms. In particular, we investigate how to design machine learning algorithms that automatically learn from streaming data in an energy efficient manner.We first illustrate how energy can be measured in the context of machine learning, in the form of a literature review and a procedure to create theoretical energy models. We use this knowledge to analyze the energy footprint of Hoeffding trees, presenting an energy model that maps the number of computations and memory accesses to the main functionalities of the algorithm. We also analyze the hardware events correlated to the execution of the algorithm, their functions and their hyper parameters.The final contribution of the thesis is showcased by two novel extensions of Hoeffding tree algorithms, the Hoeffding tree with nmin adaptation and the Green Accelerated Hoeffding Tree. These solutions are able to reduce their energy consumption by twenty and thirty percent, with minimal effect on accuracy. This is achieved by setting an individual splitting criteria for each branch of the decision tree, spending more energy on the fast growing branches and saving energy on the rest.This thesis shows the importance of evaluating energy consumption when designing machine learning algorithms, proving that we can design more energy efficient algorithms and still achieve competitive accuracy results.
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