Nowe metody przetwarzania losowo próbkowanych wielowymiarowych eksperymentów NMR

The topic of this dissertation is a new algorithm for processing of sparsely sampled data sets from multidimensional nuclear magnetic resonance (NMR) experiments. NMR remains one of the major experimental technique for studying biological macromolecules. However, increasing size of investigated objects poses a challenge for NMR due to rapidly decreasing sensitivity and increasing signal crowding. The first chapter focuses on recent advances in sensitivity enhancements and summarises a few solutions for resolution of spectral overlap. Subsequently, one describes the crucial and limiting problem of signal sampling in multidimensional NMR, which, up to recently, has impeded the widespread use of high-dimensional NMR methods. Major fast acquisition and non-uniform sampling (NUS) approaches are presented. The particular emphasis was put on detailed discussion of competetive approaches to processing of data from NUS experiments. In chapter 3 the new iterative algorithm is proposed for artefact suppression in high-resolution NMR spectra. The detailed description of its design and implementation is given, and followed by comparison with selected processing methods. The efficacy of the algorithm is demonstrated on model synthetic and experimental data. The last chapter of the thesis shows various applications of the proposed method to existing and new four- and five-dimensional NMR experiments. The algorithm is proven most beneficial in challenging applications including spectra for assignment of sidechain resonances in protein and nucleic acids, NOESY spectra for structural analysis, and cross-correlated relaxation measurements for proteins. // Niniejsza praca jest poświecona nowej metodzie przetwarzania danych pochodzących z oszczędnie próbkowanych wielowymiarowych eksperymentów jądrowego rezonansu magnetycznego (ang. Nuclear Magnetic Resonance, NMR). Technika ta jest, obok krystalografii rentgenowskiej, główną eksperymentalną metodą badawczą pozwalającą na określenie struktury i dynamiki makromolekuł o znaczeniu biologicznym. Jednakże NMR napotyka dwie istotne przeszkody w odniesieniu do dużych biomolekuł, a mianowicie gwałtownie pogarszającą się czułość oraz krytyczne zatłoczenie sygnałów w widmach. W rozdziale pierwszym przedstawiono ostatnie osiagnięcia w poprawie czułości technik NMR oraz rozwiązania służące podniesieniu rozdzielczości widm. Następnie opisano kluczowy problem próbkowania wielowymiarowych sygnałów NMR, który do niedawna uniemożliwiał wykorzystanie pełnego potencjału tych technik do rozdzielenia sygnałów. Omówiono pokrótce współczesne podejścia do szybkiej akwizycji i oszczędnego próbkowania sygnałów NMR (ang. non-uniform sampling, NUS). Szczególny nacisk położono na porównanie i dyskusje wad i zalet stosowanych obecnie metod przetwarzania sygnałów niejednorodnie próbkowanych. W rozdziale 3-cim opisano nowy iteracyjny algorytm oparty o transformacje Fouriera, usuwający artefakty oszczędnego próbkowania w wysokorozdzielczych widmach NMR. Szczegółowo omówiono schemat algorytmu oraz jego programową implementację. Rozdział uzupełnia porównanie wyników algorytmu oraz wybranych metod przetwarzania na wysymulowanych oraz modelowych danych eksperymentalnych. W ostatnim rozdziale pracy zademonstrowano użyteczność nowej metody do literaturowych oraz nowych cztero- i pieciowymiarowych eksperymentów NMR. Wśród proponowanych zastosowań wymienić można widma do przypisania sygnałów w łańcuchach bocznych aminokwasów (w białkach) i pierścieniach rybozy (w kwasach rybonukleinowych), widma NOESY służące określeniu struktury trójwymiarowej biomolekuł, oraz pomiary szybkości relaksacji skorelowanej w łańcuchach głównych białek

Development of radiofrequency pulses for fast and motion-robust brain MRI

This thesis is based on three projects and the three scientific articles that were the result of each project. Each project deals with various kinds of technical software development in the field of magnetic resonance imaging (MRI). The projects are in many ways very different, encompassing several acquisition and reconstruction strategies. However, there are at least two common denominators. The first is the projects shared the same goal of producing fast and motion robust methods. The second common denominator is that all the projects were carried out with a particular focus on the radiofrequency (RF) pulses used. The first project combined the acceleration method simultaneous multi-slice (SMS) with the acquisition method called PROPELLER. This combination was utilized to acquire motion-corrected thin-sliced reformattable T2-weighted and T1-FLAIR image volumes, thereby producing a motion robust alternative to 3D sequences. The second project analyzed the effect of the excitation RF pulse on T1-weighted images acquired with 3D echo planar imaging (EPI). It turned out that an RF pulse that reduced magnetization transfer (MT) effects significantly increased the gray/white matter contrast. The 3D EPI sequence was then used to rapidly image tumor patients after gadolinium enhancement. The third project combined PROPELLER’s retrospective motion correction with the prospective motion correction of an intelligent marker (the WRAD). With this combination, sharp T1-FLAIR images were acquired during large continuous head movements

Quantum optimal control in quantum technologies. Strategic report on current status, visions and goals for research in Europe

Quantum optimal control, a toolbox for devising and implementing the shapes of external fields that accomplish given tasks in the operation of a quantum device in the best way possible, has evolved into one of the cornerstones for enabling quantum technologies. The last few years have seen a rapid evolution and expansion of the field. We review here recent progress in our understanding of the controllability of open quantum systems and in the development and application of quantum control techniques to quantum technologies. We also address key challenges and sketch a roadmap for future developments.Comment: this is a living document - we welcome feedback and discussio

Improved 3D MR Image Acquisition and Processing in Congenital Heart Disease

Congenital heart disease (CHD) is the most common type of birth defect, affecting about 1% of the population. MRI is an essential tool in the assessment of CHD, including diagnosis, intervention planning and follow-up. Three-dimensional MRI can provide particularly rich visualization and information. However, it is often complicated by long scan times, cardiorespiratory motion, injection of contrast agents, and complex and time-consuming postprocessing. This thesis comprises four pieces of work that attempt to respond to some of these challenges. The first piece of work aims to enable fast acquisition of 3D time-resolved cardiac imaging during free breathing. Rapid imaging was achieved using an efficient spiral sequence and a sparse parallel imaging reconstruction. The feasibility of this approach was demonstrated on a population of 10 patients with CHD, and areas of improvement were identified. The second piece of work is an integrated software tool designed to simplify and accelerate the development of machine learning (ML) applications in MRI research. It also exploits the strengths of recently developed ML libraries for efficient MR image reconstruction and processing. The third piece of work aims to reduce contrast dose in contrast-enhanced MR angiography (MRA). This would reduce risks and costs associated with contrast agents. A deep learning-based contrast enhancement technique was developed and shown to improve image quality in real low-dose MRA in a population of 40 children and adults with CHD. The fourth and final piece of work aims to simplify the creation of computational models for hemodynamic assessment of the great arteries. A deep learning technique for 3D segmentation of the aorta and the pulmonary arteries was developed and shown to enable accurate calculation of clinically relevant biomarkers in a population of 10 patients with CHD

Optics for AI and AI for Optics

Artificial intelligence is deeply involved in our daily lives via reinforcing the digital transformation of modern economies and infrastructure. It relies on powerful computing clusters, which face bottlenecks of power consumption for both data transmission and intensive computing. Meanwhile, optics (especially optical communications, which underpin today’s telecommunications) is penetrating short-reach connections down to the chip level, thus meeting with AI technology and creating numerous opportunities. This book is about the marriage of optics and AI and how each part can benefit from the other. Optics facilitates on-chip neural networks based on fast optical computing and energy-efficient interconnects and communications. On the other hand, AI enables efficient tools to address the challenges of today’s optical communication networks, which behave in an increasingly complex manner. The book collects contributions from pioneering researchers from both academy and industry to discuss the challenges and solutions in each of the respective fields

Quantum optimal control in quantum technologies. Strategic report on current status, visions and goals for research in Europe

Quantum optimal control, a toolbox for devising and implementing the shapes of external fields that accomplish given tasks in the operation of a quantum device in the best way possible, has evolved into one of the cornerstones for enabling quantum technologies. The last few years have seen a rapid evolution and expansion of the field. We review here recent progress in our understanding of the controllability of open quantum systems and in the development and application of quantum control techniques to quantum technologies. We also address key challenges and sketch a roadmap for future developments

Robust and Fast Quantitative MRI for Clinical Deployment

Within this thesis, my work carried out in order to prepare an existing quantitative imaging method, multi-parameter mapping, for clinical use, is summarized. My tasks were to improve the motion-robustness of the acquisitions used in this protocol, and to reduce the scan time of the protocol to a clinically viable level. In order to reduce acquisition times, I investigated the use of higher parallel imaging acceleration factors, compared to those used in the protocol to date. I found that increasing the acceleration factor from 2 to 2-by-2 is a viable approach to decrease scan time, as is elliptical k-space coverage. In order to improve the robustness versus inter-scan motion, I investigated the effect of inter-scan motion on the quantitative maps derived from the protocol. I found that, while rigid-body motion correction is not sufficient in cases where a map is calculated from more than one scan, as the changes in the receive field are unaccounted for. I introduced a correction method, based on measuring the receive field for each structural scan, and showed that it improves image quality in the presence of inter-scan motion. Motion robustness was also improved by selecting a relatively motioninsensitive acquisition trajectory, from a set of clinically available trajectories. To further address the issue of intra-scan motion, I developed a novel navigator technique, based on acquiring data concurrent with gradient spoiling. Crucially, the acquisition of this navigator did not require additional scan time. I found that this navigator is sufficiently sensitive to motion, such that outlier rejection can be used to identify motion-corrupted data points. I implemented a data re-acquisition approach, based on the outlier rejection, and showed that image quality can be improved by this method

Revisiting Allostery In Lac Repressor

Lac repressor (LacI) is an allosterically regulated transcription factor which controls expression of the lac operon in bacteria. LacI consists of a DNA-binding domain (DBD) and regulatory domain (RD), connected by a linker called the “hinge”. Binding of a small molecule inducer to the RD relieves repression through what is presumed to be a series of conformational changes mediated through the hinge. Despite decades of study, our understanding of this allosteric transition remains incomplete—mostly inferred from partial crystal structures and low-resolution scattering studies. In principle, solution-NMR could provide structural and dynamical information unobtainable by X-ray methods. However, due to LacI’s high molecular weight, low solubility, and transient stability, such studies have been limited to the non-allosteric, isolated DBD. Here, we present a solution-NMR study of the changes in structure and dynamics that underlie the allosteric transition of intact LacI. First, an optimized expression system is presented which enables characterization of LacI using NMR methodologies for high molecular weight proteins. Next, alternative NMR data sampling methods are implemented and further extended to overcome the low-solubility and transient stability limitations. Finally, these developments are combined to characterize LacI in each of its functional states. It is shown that the RD but not the DBD of apo LacI exists in an equilibrium between induced and repressed states with exchange occurring on the �s-ms timescale. Inducer binding in the absence of operator mostly quenches exchange but does not result in structural changes in the hinge or DBD. Conformational dynamics detected in the induced state are shown to be localized to a “network” of RD residues previously characterized to be critical for allostery. These dynamics are shown to be quenched in non-allosteric mutants which suggests functional relevance. Operator binding results in globally quenched dynamics and dramatic changes to the structure of the hinge. Inducer binding in the presence of operator results in only minor structural perturbation in the hinge and DBD. However, dynamics are shown to be activated in the RD. These results suggest that conformational dynamics may be critical to the allosteric transition of LacI

Neuromorphic Engineering Editors' Pick 2021

This collection showcases well-received spontaneous articles from the past couple of years, which have been specially handpicked by our Chief Editors, Profs. André van Schaik and Bernabé Linares-Barranco. The work presented here highlights the broad diversity of research performed across the section and aims to put a spotlight on the main areas of interest. All research presented here displays strong advances in theory, experiment, and methodology with applications to compelling problems. This collection aims to further support Frontiers’ strong community by recognizing highly deserving authors