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Accurate classification, morphological analysis and translation of compound words is a problem that has not been satisfactorily solved in many of its aspects. For example, as of the date of this paper, Google translates “Trittbrettunsterblichkeit’, a GCW meaning, in the English idom, the act of “riding on someone’s coattails to achieve immortality” as “footboard immortality.” This is a literal translation that does not capture the meaning. Conversely, when one tries to describe this idiom in an effort to get “Trittbrettunsterblichkeit”, there is no way to get this word unless one inputs “footboard immortality”, which makes no sense in English. Inputting “immortality achieved by riding on someone’s coattails”, which is a fairly accurate definition of “Trittbrettunterblichkeit” translates as the awkward phrase: “Unsterblichkeit, die durch das Reiten auf den Fellschwänzen eines Menschen erreicht wird.” Clearly, constructing a GCW to match a concept in English, even when the word exists as a succinct native German word, is a problem. The goal of this thesis is to explore generation of GCWs, existing or non-existing, based on inputs of component root words. Although the methods explored may be adaptable to generation of various words in various languages, the focus here is German compound words (GCWs), known also called Komposita. In particular, this thesis discusses the problem of predicting the correct linking element of the GCW. To accomplish this a recurrent neural network (hereinafter ‘GCW RNN’) with Attention is used, trained upon the characters of the constituent words of the GCW in the training set. From this, a prediction is made as to the linking element. This report contains a description of the problem, the dataset, the model, and the results.

High-ionization iron coronal lines (CLs) are a rare phenomenon observed in galaxy and quasi-stellar object spectra that are thought to be created by high-energy emission from active galactic nuclei and certain types of transients. In cases known as extreme coronal line emitting galaxies (ECLEs), these CLs are strong and fade away on a timescale of years. The most likely progenitors of these variable CLs are tidal disruption events (TDEs), which produce sufficient high-energy emission to create and sustain the CLs over these timescales. To test the possible connection between ECLEs and TDEs, we present the most complete variable ECLE rate calculation to date and compare the results to TDE rates from the literature. To achieve this, we search for ECLEs in the Sloan Digital Sky Survey (SDSS). We detect sufficiently strong CLs in 16 galaxies, more than doubling the number previously found in SDSS. We find that none of the nine new ECLEs evolve in a manner consistent with that of the five previously discovered variable ECLEs. Using this sample of five variable ECLEs, we calculate the galaxy-normalized rate of variable ECLEs in SDSS to be \\(R_\\mathrm{G}=3.6~^{+2.6}_{-1.8}~(\\mathrm{statistical})~^{+5.1}_{-0.0} (\\mathrm{systematic})\\times10^{-6}~\\mathrm{galaxy}^{-1}~\\mathrm{yr}^{-1}\\). The mass-normalised rate is \\(R_\\mathrm{M}=3.1~^{+2.3}_{-1.5}~(\\mathrm{statistical})~^{+4.4}_{-0.0}~(\\mathrm{systematic})\\times10^{-17}~\\mathrm{M_\\odot^{-1}}~\\mathrm{yr}^{-1}\\) and the volumetric rate is \\(R_\\mathrm{V}=7~^{+20}_{-5}~(\\mathrm{statistical})~^{+10}_{-0.0}~(\\mathrm{systematic})\\times10^{-9}~\\mathrm{Mpc}^{-3}~\\mathrm{yr}^{-1}\\). Our rates are one to two orders of magnitude lower than TDE rates from the literature, which suggests that only 10 to 40 per cent of all TDEs produce variable ECLEs. Additional uncertainties in the rates arising from the structure of the interstellar medium have yet to be included.

We present new spectroscopic and photometric follow-up observations of the known sample of extreme coronal line emitting galaxies (ECLEs) identified in the Sloan Digital Sky Survey (SDSS). With these new data, observations of the ECLE sample now span a period of two decades following their initial SDSS detections. We confirm the nonrecurrence of the iron coronal line signatures in five of the seven objects, further supporting their identification as the transient light echoes of tidal disruption events (TDEs). Photometric observations of these objects in optical bands show little overall evolution. In contrast, mid-infrared (MIR) observations show ongoing long-term declines. The remaining two objects had been classified as active galactic nuclei (AGN) with unusually strong coronal lines rather than being TDE related, given the persistence of the coronal lines in earlier follow-up spectra. We confirm this classification, with our spectra continuing to show the presence of strong, unchanged coronal-line features and AGN-like MIR colours and behaviour. We have constructed spectral templates of both subtypes of ECLE to aid in distinguishing the likely origin of newly discovered ECLEs. We highlight the need for higher cadence, and more rapid, follow-up observations of such objects to better constrain their properties and evolution. We also discuss the relationships between ECLEs, TDEs, and other identified transients having significant MIR variability.

Here we present the results of our search through the Early Data Release (EDR) of the Dark Energy Spectroscopic Instrument (DESI) for extreme coronal line emitters (ECLEs) - a rare classification of galaxies displaying strong, high-ionization iron coronal emission lines within their spectra. With the requirement of a strong X-ray continuum to generate the coronal emission, ECLEs have been linked to both active galactic nuclei (AGNs) and tidal disruption events (TDEs). We focus our search on identifying TDE-linked ECLEs. We identify three such objects within the EDR sample, highlighting DESI's effectiveness for discovering new nuclear transients, and determine a galaxy-normalized TDE-linked ECLE rate of \\(R_\\mathrm{G}=5~^{+5}_{-3}\\times10^{-6}~\\mathrm{galaxy}^{-1}~\\mathrm{yr}^{-1}\\) at a median redshift of z = 0.2 - broadly consistent with previous works. Additionally, we also identify more than 200 AGNs displaying coronal emission lines, which serve as the primary astrophysical contaminants in searches for TDE-related events. We also include an outline of the custom python code developed for this search.

We present fresh insights into the nature of the tidal disruption event (TDE) candidate AT 2018dyk. AT 2018dyk has sparked a debate in the literature around its classification as either a bona-fide TDE or as an active galactic nucleus (AGN) turn-on state change. A new follow-up spectrum taken with the Dark Energy Spectroscopic Instrument, in combination with host-galaxy analysis using archival SDSS-MaNGA data, supports the identification of AT 2018dyk as a TDE. Specifically, we classify this object as a TDE that occurred within a gas-rich environment, which was responsible for both its mid-infrared (MIR) outburst and development of Fe coronal emission lines. Comparison with the known sample of TDE-linked extreme coronal line emitters (TDE-ECLEs) and other TDEs displaying coronal emission lines (CrL-TDEs) reveals similar characteristics and shared properties. For example, the MIR properties of both groups appear to form a continuum with links to the content and density of the material in their local environments. This includes evidence for a MIR colour-luminosity relationship in TDEs occurring within such gas-rich environments, with those with larger MIR outbursts also exhibiting redder peaks.

Extreme coronal line emitters (ECLEs) are a rare class of galaxy that exhibit strong, high-ionization iron coronal emission lines in their spectra. In some cases, these lines are transient and may be the result of tidal disruption event (TDEs). To test this connection, we calculate the rate of variable ECLEs (vECLEs) at redshift \\(\\sim0.3\\). We search for ECLEs in the Baryon Oscillation Spectroscopic Survey (BOSS) LOWZ sample and discover two candidate ECLEs. Using follow-up spectra from the Dark Energy Spectroscopic Instrument and Gemini Multi-Object Spectrograph, and mid-infrared observations from the Wide-field Infrared Survey Explorer, we determine that one of these galaxies is a vECLE. Using this galaxy, we calculate the galaxy-normalized vECLE rate at redshift \\(\\sim0.3\\) to be \\(R_\\mathrm{G}=1.6~^{+3.8}_{-1.4}\\times10^{-6}~\\mathrm{galaxy}^{-1}~\\mathrm{yr}^{-1}\\) and the mass-normalized rate to be \\(R_\\mathrm{M}=7~^{+16}_{-6}\\times10^{-18}~\\mathrm{M_\\odot^{-1}}~\\mathrm{yr}^{-1}\\). This is then converted to a volumetric rate of \\(R_\\mathrm{V}=1.8~^{+4.5}_{-1.5}\\times10^{-9}~\\mathrm{Mpc}^{-3}~\\mathrm{yr}^{-1}\\). Formally, the LOWZ vECLE rates are \\(2-4\\) times lower than the rates calculated from the Sloan Digital Sky Survey Legacy sample at redshift \\(\\sim0.1\\). However, given the large uncertainties on both measurements, they are consistent with each other at \\(1\\sigma\\). Both the galaxy-normalized and volumetric rates are one to two orders of magnitude lower than TDE rates from the literature, consistent with vECLEs being caused by \\(5-20\\) per cent of all TDEs.

The resilience literature is often criticised for lacking clarity in the conceptualisation and measurement of resilience, with the literature yet to consider within-person profiles of resilience and how such profiles might influence reactions to different adverse contexts. To significantly enhance the research area, the current set of studies propose and test a four-stage process model of resilience including proactive (anticipation & minimizing) and reactive (managing & mending) components. We suggest the four processes can function independently within five separate domains (general, physical, social, cognitive, and emotional). Specifically, in Studies 1 ( n = 181) and 2 ( n = 284) we develop a measure of resilience reflecting our four-stage process model and demonstrated validity of a 13-item measure for each of the five proposed domains via a Bayesian structural equation modelling approach. Focusing on the general domain and based on the four resilience processes (anticipate, minimize, manage, & mend), Study 3 ( n = 400) explored resilience profiles in a pilot study, and then confirmed these profiles and their relationship with psychological and behavioral outcomes related to the COVID-19 pandemic in a main study. Using latent profile and latent transition analysis, results revealed four distinct profiles, predicting a range of psychological outcomes. For example, those with lower resilience (particularly profiles with high anticipation but low levels of the other processes), showed higher anxiety (especially with high anticipation), depression, impulsiveness, and lower coping effectiveness. Those with higher resilience (Profile 3 and 4) across the four processes exhibited lower depression, anxiety, and impulsiveness, as well as higher well-being, better perceived coping effectiveness, and preventative behaviors. Taken together the results from the studies presented, support the process model of resilience and underscore the benefits of considering resilience profiles in relation to understanding how people deal with adverse contexts.

Genome sequencing of large numbers of individuals promises to advance the understanding, treatment, and prevention of human diseases, among other applications. We describe a genome sequencing platform that achieves efficient imaging and low reagent consumption with combinatorial probe anchor ligation chemistry to independently assay each base from patterned nanoarrays of self-assembling DNA nanoballs. We sequenced three human genomes with this platform, generating an average of 45-to 87-fold coverage per genome and identifying 3.2 to 4.5 million sequence variants per genome. Validation of one genome data set demonstrates a sequence accuracy of about 1 false variant per 100 kilobases. The high accuracy, affordable cost of $4400 for sequencing consumables, and scalability of this platform enable complete human genome sequencing for the detection of rare variants in large-scale genetic studies.

Rapid advances in DNA sequencing promise to enable new diagnostics and individualized therapies. Achieving personalized medicine, however, will require extensive research on highly reidentifiable, integrated datasets of genomic and health information. To assist with this, participants in the Personal Genome Project choose to forgo privacy via our institutional review board- approved “open consent” process. The contribution of public data and samples facilitates both scientific discovery and standardization of methods. We present our findings after enrollment of more than 1,800 participants, including whole-genome sequencing of 10 pilot participant genomes (the PGP-10). We introduce the Genome-Environment-Trait Evidence (GET-Evidence) system. This tool automatically processes genomes and prioritizes both published and novel variants for interpretation. In the process of reviewing the presumed healthy PGP-10 genomes, we find numerous literature references implying serious disease. Although it is sometimes impossible to rule out a late-onset effect, stringent evidence requirements can address the high rate of incidental findings. To that end we develop a peer production system for recording and organizing variant evaluations according to standard evidence guidelines, creating a public forum for reaching consensus on interpretation of clinically relevant variants. Genome analysis becomes a two-step process: using a prioritized list to record variant evaluations, then automatically sorting reviewed variants using these annotations. Genome data, health and trait information, participant samples, and variant interpretations are all shared in the public domain—we invite others to review our results using our participant samples and contribute to our interpretations. We offer our public resource and methods to further personalized medical research.