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"Orfeo out of Care"
(2022)
The paper focuses on an example of multiple-step reception: the contribution of the classical story of Orpheus and Eurydice and the mediaeval lay Sir Orfeo to Tolkien’s work.
In the first part, I compare the lay with Virgilian and Ovidian versions of Orpheus’ myth. This comparison shows the anonymous author’s deep knowledge of the ancient texts and complex way of rewriting them through stealing and hybridization.
The lay was highly esteemed by Tolkien, who translated it and took inspiration from it while describing the Elven kingdom in The Hobbit and building the storyline of Beren and Lúthien in The Silmarillion. Through this key tale, Orpheus/Orfeo’s romance has a deep influence also on Aragorn and Arwen’s story in The Lord of the Rings. The most important element that Tolkien takes from the Sir Orfeo figuration of the ancient story is undoubtedly the insertion of political theme: the link established between the recovery of the main character’s beloved and the return to royal responsability.
The second part of the paper is, thus, dedicated to the reception of Sir Orfeo and the classical myth in Tolkien. It shows how in his work the different steps of the tradition of Orpheus’ story are co-present, creating an inextricable substrate of inspiration that nourishes his imagination.
The paper investigates Tolkien’s narratives of decline through the lens of their classical ancestry. Narratives of decline are widespread in ancient culture, in both philosophical and literary discourses. They normally posit a gradual degradation (moral and ontological) from an idealized Golden Age, which went hand-in-hand with increasing detachment of gods from mortal affairs. Narratives of decline are also at the core of Tolkien’s mythology, constituting yet another underresearched aspect of classical influence on Tolkien. Such Classical narratives reverberate e.g. in Tolkien’s division of Arda’s history into ages, from an idealized First Age filled with Joy and Light to a Third Age, described as “Twilight Age (…) the first of the broken and changed world” (Letters 131). More generally, these narratives are related to Tolkien’s notorious perception of history as a “long defeat” (Letters 195) and to that “heart-racking sense of the vanished past” which pervades Tolkien’s works – the emotion which, in his words, moved him “supremely” and which he found “small difficulty in evoking” (Letters 91). The paper analyses the reception of narratives of decline in Tolkien’s legendarium, pointing out similarities but also contrasts and differences, with the aim to discuss some key patterns of (classical) reception in Tolkien’s theory and practice (‘renewal’, ‘accommodation’, ‘focalization’).
40Ar/39Ar dating of a hydrothermal pegmatitic buddingtonite–muscovite assemblage from Volyn, Ukraine
(2022)
We determined Ar-40/Ar-39 ages of buddingtonite, occurring together with muscovite, with the laser-ablation method. This is the first attempt to date the NH4-feldspar buddingtonite, which is typical for sedimentary-diagenetic environments of sediments, rich in organic matter, or in hydrothermal environments, associated with volcanic geyser systems. The sample is a hydrothermal breccia, coming from the Paleoproterozoic pegmatite field of the Korosten Plutonic Complex, Volyn, Ukraine. A detailed characterization by optical methods, electron microprobe analyses, backscattered electron imaging, and IR analyses showed that the buddingtonite consists of euhedral-appearing platy crystals of tens of micrometers wide, 100 or more micrometers in length, which consist of fine-grained fibers of <= 1 mu m thickness. The crystals are sector and growth zoned in terms of K-NH4-H3O content. The content of K allows for an age determination with the Ar-40/Ar-39 method, as well as in the accompanying muscovite, intimately intergrown with the buddingtonite. The determinations on muscovite yielded an age of 1491 +/- 9 Ma, interpreted as the hydrothermal event forming the breccia. However, buddingtonite apparent ages yielded a range of 563 +/- 14 Ma down to 383 +/- 12 Ma, which are interpreted as reset ages due to Ar loss of the fibrous buddingtonite crystals during later heating. We conclude that buddingtonite is suited for Ar-40/Ar-39 age determinations as a supplementary method, together with other methods and minerals; however, it requires a detailed mineralogical characterization, and the ages will likely represent minimum ages.
A Cell-free Expression Pipeline for the Generation and Functional Characterization of Nanobodies
(2022)
Cell-free systems are well-established platforms for the rapid synthesis, screening, engineering and modification of all kinds of recombinant proteins ranging from membrane proteins to soluble proteins, enzymes and even toxins. Also within the antibody field the cell-free technology has gained considerable attention with respect to the clinical research pipeline including antibody discovery and production. Besides the classical full-length monoclonal antibodies (mAbs), so-called "nanobodies" (Nbs) have come into focus. A Nb is the smallest naturally-derived functional antibody fragment known and represents the variable domain (VHH, similar to 15 kDa) of a camelid heavy-chain-only antibody (HCAb). Based on their nanoscale and their special structure, Nbs display striking advantages concerning their production, but also their characteristics as binders, such as high stability, diversity, improved tissue penetration and reaching of cavity-like epitopes. The classical way to produce Nbs depends on the use of living cells as production host. Though cell-based production is well-established, it is still time-consuming, laborious and hardly amenable for high-throughput applications. Here, we present for the first time to our knowledge the synthesis of functional Nbs in a standardized mammalian cell-free system based on Chinese hamster ovary (CHO) cell lysates. Cell-free reactions were shown to be time-efficient and easy-to-handle allowing for the "on demand" synthesis of Nbs. Taken together, we complement available methods and demonstrate a promising new system for Nb selection and validation.
Incorporation of noncanonical amino acids (ncAAs) with bioorthogonal reactive groups by amber suppression allows the generation of synthetic proteins with desired novel properties. Such modified molecules are in high demand for basic research and therapeutic applications such as cancer treatment and in vivo imaging. The positioning of the ncAA-responsive codon within the protein's coding sequence is critical in order to maintain protein function, achieve high yields of ncAA-containing protein, and allow effective conjugation. Cell-free ncAA incorporation is of particular interest due to the open nature of cell-free systems and their concurrent ease of manipulation. In this study, we report a straightforward workflow to inquire ncAA positions in regard to incorporation efficiency and protein functionality in a Chinese hamster ovary (CHO) cell-free system. As a model, the well-established orthogonal translation components Escherichia coli tyrosyl-tRNA synthetase (TyrRS) and tRNATyr(CUA) were used to site-specifically incorporate the ncAA p-azido-l-phenylalanine (AzF) in response to UAG codons. A total of seven ncAA sites within an anti-epidermal growth factor receptor (EGFR) single-chain variable fragment (scFv) N-terminally fused to the red fluorescent protein mRFP1 and C-terminally fused to the green fluorescent protein sfGFP were investigated for ncAA incorporation efficiency and impact on antigen binding. The characterized cell-free dual fluorescence reporter system allows screening for ncAA incorporation sites with high incorporation efficiency that maintain protein activity. It is parallelizable, scalable, and easy to operate. We propose that the established CHO-based cell-free dual fluorescence reporter system can be of particular interest for the development of antibody-drug conjugates (ADCs).
Genomic prediction has revolutionized crop breeding despite remaining issues of transferability of models to unseen environmental conditions and environments. Usage of endophenotypes rather than genomic markers leads to the possibility of building phenomic prediction models that can account, in part, for this challenge. Here, we compare and contrast genomic prediction and phenomic prediction models for 3 growth-related traits, namely, leaf count, tree height, and trunk diameter, from 2 coffee 3-way hybrid populations exposed to a series of treatment-inducing environmental conditions. The models are based on 7 different statistical methods built with genomic markers and ChlF data used as predictors. This comparative analysis demonstrates that the best-performing phenomic prediction models show higher predictability than the best genomic prediction models for the considered traits and environments in the vast majority of comparisons within 3-way hybrid populations. In addition, we show that phenomic prediction models are transferrable between conditions but to a lower extent between populations and we conclude that chlorophyll a fluorescence data can serve as alternative predictors in statistical models of coffee hybrid performance. Future directions will explore their combination with other endophenotypes to further improve the prediction of growth-related traits for crops.
A comparative whole-genome approach identifies bacterial traits for marine microbial interactions
(2022)
Luca Zoccarato, Daniel Sher et al. leverage publicly available bacterial genomes from marine and other environments to examine traits underlying microbial interactions.
Their results provide a valuable resource to investigate clusters of functional and linked traits to better understand marine bacteria community assembly and dynamics.
Microbial interactions shape the structure and function of microbial communities with profound consequences for biogeochemical cycles and ecosystem health. Yet, most interaction mechanisms are studied only in model systems and their prevalence is unknown. To systematically explore the functional and interaction potential of sequenced marine bacteria, we developed a trait-based approach, and applied it to 473 complete genomes (248 genera), representing a substantial fraction of marine microbial communities.
We identified genome functional clusters (GFCs) which group bacterial taxa with common ecology and life history. Most GFCs revealed unique combinations of interaction traits, including the production of siderophores (10% of genomes), phytohormones (3-8%) and different B vitamins (57-70%). Specific GFCs, comprising Alpha- and Gammaproteobacteria, displayed more interaction traits than expected by chance, and are thus predicted to preferentially interact synergistically and/or antagonistically with bacteria and phytoplankton. Linked trait clusters (LTCs) identify traits that may have evolved to act together (e.g., secretion systems, nitrogen metabolism regulation and B vitamin transporters), providing testable hypotheses for complex mechanisms of microbial interactions.
Our approach translates multidimensional genomic information into an atlas of marine bacteria and their putative functions, relevant for understanding the fundamental rules that govern community assembly and dynamics.
The past three decades of policy process studies have seen the emergence of a clear intellectual lineage with regard to complexity. Implicitly or explicitly, scholars have employed complexity theory to examine the intricate dynamics of collective action in political contexts. However, the methodological counterparts to complexity theory, such as computational methods, are rarely used and, even if they are, they are often detached from established policy process theory. Building on a critical review of the application of complexity theory to policy process studies, we present and implement a baseline model of policy processes using the logic of coevolving networks. Our model suggests that an actor's influence depends on their environment and on exogenous events facilitating dialogue and consensus-building. Our results validate previous opinion dynamics models and generate novel patterns. Our discussion provides ground for further research and outlines the path for the field to achieve a computational turn.
Model-informed precision dosing (MIPD) is a quantitative dosing framework that combines prior knowledge on the drug-disease-patient system with patient data from therapeutic drug/ biomarker monitoring (TDM) to support individualized dosing in ongoing treatment. Structural models and prior parameter distributions used in MIPD approaches typically build on prior clinical trials that involve only a limited number of patients selected according to some exclusion/inclusion criteria. Compared to the prior clinical trial population, the patient population in clinical practice can be expected to also include altered behavior and/or increased interindividual variability, the extent of which, however, is typically unknown. Here, we address the question of how to adapt and refine models on the level of the model parameters to better reflect this real-world diversity. We propose an approach for continued learning across patients during MIPD using a sequential hierarchical Bayesian framework. The approach builds on two stages to separate the update of the individual patient parameters from updating the population parameters. Consequently, it enables continued learning across hospitals or study centers, because only summary patient data (on the level of model parameters) need to be shared, but no individual TDM data. We illustrate this continued learning approach with neutrophil-guided dosing of paclitaxel. The present study constitutes an important step toward building confidence in MIPD and eventually establishing MIPD increasingly in everyday therapeutic use.
The investigation of metabolic fluxes and metabolite distributions within cells by means of tracer molecules is a valuable tool to unravel the complexity of biological systems. Technological advances in mass spectrometry (MS) technology such as atmospheric pressure chemical ionization (APCI) coupled with high resolution (HR), not only allows for highly sensitive analyses but also broadens the usefulness of tracer-based experiments, as interesting signals can be annotated de novo when not yet present in a compound library. However, several effects in the APCI ion source, i.e., fragmentation and rearrangement, lead to superimposed mass isotopologue distributions (MID) within the mass spectra, which need to be corrected during data evaluation as they will impair enrichment calculation otherwise. Here, we present and evaluate a novel software tool to automatically perform such corrections. We discuss the different effects, explain the implemented algorithm, and show its application on several experimental datasets. This adjustable tool is available as an R package from CRAN.