Hereditäre Kardiomyopathien sind durch klinische und genetische Heterogenität gekennzeichnet, welche die Kardiogenetik vor Herausforderungen stellt. In dieser Arbeit wurden manche dieser Herausforderungen angegangen, indem anhand einer Kohorte von 61 Patienten mit Kardiomyopathie bzw. primärer Arrhythmie eine Exom-Diagnostik mit anschließender stufenweiser Datenanalyse vorgenommen wurde. Ein Ziel der Arbeit war, die aktuellen diagnostischen Detektionsraten zu prüfen sowie zu bewerten, ob eine erweiterte Exom-Diagnostik im Vergleich zur üblichen Genpanel-Analyse einen diagnostischen Zugewinn bringt. Zudem sollten potenzielle Krankheitsgene sowie komplexe Genotypen identifiziert werden. Die Ergebnisse zeigten, dass bei insgesamt 64% der Patienten eine Variante von Interesse gefunden wurde. Hervorzuheben ist die hohe Detektionsrate in der größten Subkohorte, die aus Patienten mit dilatativer bzw. linksventrikulärer Non-Compaction Kardiomyopathie bestand: 69% und damit höher im Vergleich zur in der Literatur berichteten Detektionsrate von bis zu 50%. Im Rahmen der stufenweisen Daten-Auswertung zeigte sich zwar, dass die meisten kausalen Varianten in den phänotypspezifischen Panels zu finden waren, die Analyse eines erweiterten Panels mit 79 Genen sowie der Gesamtexom-Daten aber zu einer zusätzlichen Aufklärungsquote von 13% bzw. 5% führte. Durch die Erweiterung der Diagnostik konnten interessante, teilweise neue Assoziationen zwischen Genotyp und Phänotyp sowie neue Kandidatengene identifiziert werden. Das beste Beispiel dafür ist eine trunkierende Variante im STK38-Gen, das an der Phosphorylierung eines Regulators der Expression kardialer Gene beteiligt ist. Zusammenfassend konnte gezeigt werden, dass, obwohl die Detektionsrate von Genpanels für die Routine-Diagnostik akzeptabel ist, die Anwendung von Exom-Diagnostik einen diagnostischen Zugewinn, die Entdeckung von interessanten Genotyp-Phänotyp-Korrelationen sowie die Identifizierung von Kandidatengenen ermöglicht. / Hereditary cardiomyopathies are characterized by clinical and genetic heterogeneity, which poses challenges to genetic diagnostics in cardiogenetics. In this study, some of these challenges were addressed on the basis of the genetic analysis of 61 cardiomyopathy and arrhythmia patients using exome sequencing with subsequent stepwise analysis of the genetic data. One objective of the study was to examine the current diagnostic yield of genetic analysis as well as to assess the diagnostic benefit of an extended exome analysis vs. targeted gene panel analysis. Another aim was to identify novel candidate genes and describe new genotype-phenotype correlations. Regarding the results, a variant of interest could be detected in 64% of the patients. Of note is the high detection rate in the main subcohort of patients with dilated cardiomyopathy and/or left ventricular noncompaction cardiomyopathy: 69% vs. the reported detection rate of max. 50% in the literature. To evaluate the additional diagnostic benefit of extensive exome testing, a stepwise analysis of the exome data was performed. It was shown here that most of the variants of interest were detected in the phenotype-specific core gene panels; however, the analysis of an extended gene set with 79 genes and subsequently of the complete exome data led to an additional diagnostic yield of 13% and 5% respectively. Through the expansion of the genetic analysis, interesting or new genotype-phenotype correlations could be documented and candidate genes could be identified. The best candidate was a truncating variant in STK38, a gene coding for a kinase that phosphorylates a transcription regulator of genes encoding for cardiac sarcomere proteins. In conclusion, although the detection rate of gene panels is acceptable for the clinical routine, the use of exome analysis enables the highest possible diagnostic yield, the detection of interesting genotype-phenotype correlations as well as the identification of new candidate genes.

Communications over millimeter wave (mmWave) frequencies is a key component of the fifth generation (5G) cellular networks due to the large bandwidth available at mmWave bands. Thanks to the short wavelength of mmWave bands, large antenna arrays (32 to 256 elements are common) can be mounted at the transceivers. The array sizes are typical of a massive MIMO communication system, which makes fully digital beamforming difficult to implement due to high power consumption and hardware cost. This motivates the development of hybrid beamforming due to its versatile tradeoff between implementation cost (including hardware cost and power consumption) and system performance. However, due to the non-convex constraints on hardware (phase shifters), finding the global optima for hybrid beamforming design is often intractable. In this thesis, we focus on hybrid beamforming design for mmWave cellular communications both narrowband and wideband scenarios are considered. Starting from narrowband SU-MIMO mmWave communications, we propose a Gram-Schmidt orthogonalization (GSO) aided hybrid precoding algorithm to reduce computation complexity. GSO is a recursive process that depends on the order in which the matrix columns are selected. A heuristic solution to the order of column selection is suggested according to the array response vector along which the full digital precoder has the maximum projection. The proposed algorithm, not only constrained to uniform linear arrays (ULAs), can avoid the matrix inversion in designing the digital precoder compared to the orthogonal matching pursuit (OMP) algorithm. For the narrowband MU-MIMO mmWave communications, we propose an interference cancellation (IC) framework on hybrid beamforming design for downlink mmWave multi-user massive MIMO system. Based on the proposed framework, three successive interference cancellation (SIC) aided hybrid beamforming algorithms are proposed to deal with inter-user and intra-user interference. Furthermore, the optimal detection order of data streams is derived according to the post-detection signal-to-interference- plus-noise ratio (SINR). When considering wideband MU-MIMO mmWave communications, how to design a common RF beamformer across all subcarriers becomes the main challenge. Furthermore, the common RF beamformer in wideband channels leads to the need of more effective baseband schemes. By adopting a relaxation of the original mutual information and spectral efficiency maximization problems at the transceiver, we design the radio frequency (RF) precoder and combiner by leveraging the average of the covariance matrices of frequency domain channels, then a SIC aided baseband precoder and combiner are proposed to eliminate inter-user and intra-user interference / Graduate

By endowing the base station with hundreds of antennas and relying on spatial multiplexing, massive multiple-input-multiple-output (MIMO) allows impressive advantages in many fronts. To reduce this promising technology to reality, thorough performance analysis has to be conducted. Along this line, this work is focused on the convenient high-dimensionality of massive MIMO’s corresponding model. Indeed, the large number of antennas allows us to harness asymptotic results from Random Matrix Theory to provide accurate approximations of the main performance metrics. The derivations yield simple closed-form expressions that can be easily interpreted and manipulated in contrast to their alternative random equivalents. Accordingly, in this dissertation, we investigate and optimize the performance of massive MIMO in different contexts. First, we explore the spectral efficiency of massive MIMO in large-scale multi-tier heterogeneous networks that aim at network densification. This latter is epitomized by the joint implementation of massive MIMO and small cells to reap their benefits. Our interest is on the design of coordinated beamforming that mitigates cross-tier interference. Thus, we propose a regularized SLNR-based precoding in which the regularization factor is used to allow better resilience to channel estimation errors. Second, we move to studying massive MIMO under Line-of-Sight (LoS) propagation conditions. To this end, we carry out an analysis of the uplink (UL) of a massive MIMO system with per-user channel correlation and Rician factor. We start by analyzing conventional processing schemes such as LMMSE and MRC under training-based imperfect-channel-estimates, and then, propose a statistical combining technique that is more suitable in LoS-prevailing environments. Finally, we look into the interplay between LoS and the fundamental limitation of massive MIMO systems, namely, pilot contamination. We propose to analyze and compare the performance using single-cell and multi-cell detection methods. In this regard, the single-cell schemes are shown to produce higher SEs as the LoS strengthens, yet remain hindered by LoS-induced interference and pilot contamination. In contrast, for multi-cell combining, we analytically demonstrate that M-MMSE outperforms both single-cell detectors by generating a capacity that scales linearly with the number of antennas, and is further enhanced with LoS.

Indiana University-Purdue University Indianapolis (IUPUI) / In the forensic science community, there is an immense need for tools to help assist investigations where conventional DNA profiling methods have been non-informative. Forensic DNA Phenotyping (FDP) aims to bridge that gap and aid investigations by providing physical appearance information when other investigative methods have been exhausted. To create a “biological eye witness”, it becomes necessary to constantly improve these methods in order to develop a complete and accurate image of the individual who left the sample. To add to our previous prediction systems IrisPlex and HIrisPlex, we have developed the HIrisPlex-S system for the all-in-one combined prediction of eye, hair, and skin color from DNA. The skin color prediction model uses 36 variants that were recently proposed for the accurate prediction of categorical skin color on a global scale, and the system is completed by the developmental validation of a 17-plex capillary electrophoresis (CE) genotyping assay that is run in conjunction with the HIrisPlex assay to generate these genotypes. The predicted skin color output includes Very Pale, Pale, Intermediate, Dark and Dark-to-Black categories in addition to categorical eye (Blue, Intermediate, and Brown) and hair (Black, Brown, Blond, and Red) color predictions. We demonstrate that the HIrisPlex-S assay performs in full agreement with guidelines from the Scientific Working Group on DNA Analysis Methods (SWGDAM), achieving high sensitivity levels with a minimum 63pg DNA input. In addition to adding skin color to complete the pigmentation prediction system termed HIrisPlex-S, we successfully designed a Massively Parallel Sequencing (MPS) assay to complement the system and bring Next Generation Sequencing (NGS) to the forefront of forensic DNA analyses methods. Using Illumina’s MiSeq system enables the generation of HIrisPlex-S’s 41 variants using sequencing data that has the capacity to xiii better deconvolute mixtures and perform with even more sensitivity and accuracy. This transition opens the door for a plethora of new ways in which this physical appearance assay can grow as sequencing technology is not limited by variant number; therefore, in essence many more traits have the potential to be included in this one assay design. For now, the HIrisPlex-S design of 41 variants using MPS is being fully assessed according to SWGDAM validated guidelines; therefore, this design paves the way for Forensic DNA Phenotyping to be used in any forensic laboratory. This new and improved HIrisPlex-S system will have a profound impact on casework, missing persons cases, and anthropological cases, as it is relatively inexpensive to run, HIrisPlex-S is easy to use, developmentally validated and one of the largest systems freely available online for physical appearance prediction from DNA using the freely available online web tool found at https://hirisplex.erasmusmc.nl/. Lastly, moving forward in our aim to include additional traits for prediction from DNA, we contributed to a large-scale research collaboration to unearth variants associated with hair morphology. 1026 samples were successfully sequenced using an inhouse MPS design at 91 proposed hair morphological loci. From this reaction, we were able to contribute to the identification of significant correlations between the SNPs rs2219783, rs310642 and rs80293268 with categorical hair morphology: straight, wavy or curly.

Indiana University-Purdue University Indianapolis (IUPUI) / In the forensic science community, there is a vast need for tools to help assist investigations when standard DNA profiling methods are uninformative. Methods such as Forensic DNA Phenotyping (FDP) and proteomics aims to help this problem and provide aid in investigations when other methods have been exhausted. FDP is useful by providing physical appearance information, while proteomics allows for the examination of difficult samples, such as hair, to infer human identity and ancestry. To create a “biological eye witness” or develop informative probability of identity match statistics through proteomically inferred genetic profiles, it is necessary to constantly strive to improve these methods. Currently, two developmentally validated FDP prediction assays, ‘HIrisPlex’ and ‘HIrisplex-S’, are used on the capillary electrophoresis to develop a phenotypic prediction for eye, hair, and skin color based on 41 variants. Although highly useful, these assays are limited in their ability when used on the CE due to a 25 variant per assay cap. To overcome these limitations and expand the capacities of FDP, we successfully designed and validated a massive parallel sequencing (MPS) assay for use on both the ThermoFisher Scientific Ion Torrent and Illumina MiSeq systems that incorporates all HIrisPlex-S variants into one sensitive assay. With the migration of this assay to an MPS platform, we were able to create a semi-automated pipeline to extract SNP-specific sequencing data that can then be easily uploaded to the freely accessible online phenotypic prediction tool (found at https://hirisplex.erasmusmc.nl) and a mixture deconvolution tool with built-in read count thresholds. Based on sequencing reads counts, this tool can be used to assist in the separation of difficult two-person mixture samples and outline the confidence in each genotype call. In addition to FDP, proteomic methods, specifically in hair protein analysis, opens doors and possibilities for forensic investigations when standard DNA profiling methods come up short. Here, we analyzed 233 genetically variant peptides (GVPs) within hair-associated proteins and genes for 66 individuals. We assessed the proteomic methods ability to accurately infer and detect genotypes at each of the 233 SNPs and generated statistics for the probability of identity (PID). Of these markers, 32 passed all quality control and population genetics criteria and displayed an average PID of 3.58 x 10-4. A population genetics assessment was also conducted to identify any SNP that could be used to infer ancestry and/or identity. Providing this information is valuable for the future use of this set of markers for human identification in forensic science settings.

京都大学 / 0048 / 新制・課程博士 / 博士(理学) / 甲第18793号 / 理博第4051号 / 新制||理||1583(附属図書館) / 31744 / 京都大学大学院理学研究科物理学・宇宙物理学専攻 / (主査)教授 田中 貴浩, 教授 中村 卓史, 教授 高柳 匡 / 学位規則第4条第1項該当 / Doctor of Science / Kyoto University / DFAM

Our walking patterns must be adjusted continuously in everyday living, whether for maneuvering on slippery surfaces or stepping over cracks on the street. Walking becomes more challenging as it requires more energy to lift and accelerate the body due to additional loads on the body as we move through space. This dissertation investigates gait, stability, and adaptation in adults with range of adiposity. First, we studied how people with obesity adapt to spatial (obstacle crossing) and temporal (metronome walking) task constraints during walking over-ground. Results indicated that people prioritized a spatial over temporal constraint when attempting to meet both constraints at the same time. Second, we tested how massive weight loss affects gait and stability. We measured how bariatric surgery patients walked and crossed obstacles before and one year after surgery. Findings indicated that massive weight loss improved not only gait but also postural stability during gait. Third, we quantified whole-body rotational characteristics in adults with obesity through changes in angular momentum quantities during steady-state walking. I found that angular momentum (1) was greater in adults with higher BMI, (2) was highly regulated by foot placement, and (3) did not change with walking speed. Taken together these results suggest that gait and stability can be adapted. These findings may help to develop interventions to target specific walking deficits in patients with mobility limitations such as obesity.

The mechanism by which massive stars form is not nearly as well understood as it is for lower mass stars. For instance, at the onset of massive star formation, it is still not clear whether the mass for a given massive star comes from the turbulent collapse of a dense core (i.e McKee & Tan, 2003) or whether the star continues to accrete material from the cores environment as it grows (i.e. Bonnell et al., 1998). From this point, it is suggested that the cold, massive core (an Infrared Dar Cloud) begins to heat up and form a Hot Core. Later in its protostellar evolution, an HII region forms from the ionizing radiation being produced by the massive star. How, or even whether, accretion onto the massive protostar can continue in the presence of the large outward thermal and radiation pressures from the star is also quite uncertain. Can the star continue to accrete ionized gas (i.e. Keto & Wood, 2006)? Are the accretion rates high enough early on to account for the final observed masses (i.e. Klaassen et al., 2006)? Or, is there some way of minimizing the radiation pressure affecting the infalling gas (i.e. McKee & Ostriker, 2007, and references therein). Here, we present observations which suggest that there is a statistically significant, although short, period in which rotation and infall of molecular gas (which powers a bipolar outflow) continue after the formation of an HII region. This continued infall of material is seen on both large and small scales, and appears to be continuing to produce outflows in many of the sources observed in this study. That it is not seen in all sources suggests that this stage is short lived. / Thesis / Doctor of Philosophy (PhD)

<p> In the past decade, sites of hydrothermal activity along the Juan de Fuca Ridge have gained a growing amount of attention. Increased sampling has provided the materials for more detailed studies, including those collected from Axial Seamount, a large shield volcano on the central portion of the ridge. Axial Seamount is host to at least three active vent sites, one of which, along the northwest caldera wall, consists of recently active eruptive-fissures and nearby chimney-like spires. A sampled spire from this location and samples from other vent areas are described using reflected and transmitted light techniques. They have been found to consist of the sulphide phases sphalerite, wurtzite, pyrite, marcasite, chalcopyrite, isocubanite, and galena. Jordanite and tetrahedrite-tennanite are also known to precipitate at these vent sites, but were not observed here. The most dominant non-sulphide minerals are amorphous silica and barite. Native sulfur, Fe and Mn oxides and a variety of sulfosalts may also be present. The complex textures in these samples reflect precipitation and growth from higher temperature fluids that are mixing with local ambient seawater. Fluid inclusions in sphalerite have revealed salinities in the 5.6 to 7.0 weight % NaCl range, with homogenization temperatures ranging from 214.8°C to 269.4°C. Temperatures of homogenization require pressure-corrections between approximately 9.0°C and 13.0°C, to set-up a range of trapping temperatures that lie between 211.8°C and 279.4°C. Fluid inclusions are also found in wurtzite, barite, and amorphous silica however, these would yield no data. Middle Valley is a sedimented rift valley that lies at the extreme north end of the Juan de Fuca Ridge. A number of hydrothermal sulphide mounds that lie atop the sediment pile have been sampled. The mineralogy is similar to that at Axial Seamount; however, the dominant sulphide phase at Middle Valley is pyrrhotite. Textural differences between the two sites are significant, a result of the traversing of hydrothermal fluids through a thick package of hemepelagic sediment at Middle Valley. Samples from Middle Valley are found to contain no measurable fluid inclusions in the sections available for study. Mineralogical and fluid inclusion studies are important methods that can be used to help solve the complex growth history of sulfides that are and have been accumulating at mid-ocean ridge vent sites.</p> / Thesis / Bachelor of Science (BSc)

Internet of things (IoT) is envisioned as a promising paradigm to interconnect enormous wireless devices. However, the success of IoT is challenged by the difficulty of access management of the massive amount of sporadic and unpredictable user traffics. This thesis focuses on the contention-based random access in massive cellular IoT systems and introduces two novel frameworks to provide enhanced scalability, real-time quality of service management, and resource efficiency. First, a local communication based congestion control framework is introduced to distribute the random access attempts evenly over time under bursty traffic. Second, a multi-agent reinforcement learning based preamble selection framework is designed to increase the access capacity under a fixed number of preambles. Combining the two mechanisms provides superior performance under various 3GPP-specified machine type communication evaluation scenarios in terms of achieving much lower access latency and fewer access failures. / Master of Science / In the age of internet of things (IoT), massive amount of devices are expected to be connected to the wireless networks in a sporadic and unpredictable manner. The wireless connection is usually established by contention-based random access, a four-step handshaking process initiated by a device through sending a randomly selected preamble sequence to the base station. While different preambles are orthogonal, preamble collision happens when two or more devices send the same preamble to a base station simultaneously, and a device experiences access failure if the transmitted preamble cannot be successfully received and decoded. A failed device needs to wait for another random access opportunity to restart the aforementioned process and hence the access delay and resource consumption are increased. The random access control in massive IoT systems is challenged by the increased access intensity, which results in higher collision probability. In this work, we aim to provide better scalability, real-time quality of service management, and resource efficiency in random access control for such systems. Towards this end, we introduce 1) a local communication based congestion control framework by enabling a device to cooperate with neighboring devices and 2) a multi-agent reinforcement learning (MARL) based preamble selection framework by leveraging the ability of MARL in forming the decision-making policy through the collected experience. The introduced frameworks are evaluated under the 3GPP-specified scenarios and shown to outperform the existing standard solutions in terms of achieving lower access delays with fewer access failures.