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The production of recombinant proteins for health applications accounts for a large share of the biopharmaceutical market. While many drugs are produced in microbial and mammalian systems, plants gain more attention as expression platforms to produce eukaryotic proteins. In particular the GMP-compliant moss Physcomitrella (Physcomitrium patens) has outstanding features such as genetic amenability, reproducible bioreactor cultivation, and humanized glycosylation patterns. In this study, we selected and characterized new terminators for their effects on heterologous gene expression. The Physcomitrella genome contains 53,346 unique 3’UTRs (untranslated regions) of which 7,964 transcripts contain at least one intron. Over 91% of 3’UTRs exhibit more than one polyadenylation site, indicating the prevalence of alternative polyadenylation in Physcomitrella. Out of all 3’UTRs, 14 terminator candidates were selected and characterized via transient Dual Luciferase assays, yielding a collection of endogenous terminators performing equally high as established heterologous terminators CaMV35S, AtHSP90, and NOS. High performing candidates were selected for testing as double terminators which impact reporter levels, dependent on terminator identity and positioning. Testing of 3’UTRs among the different promoters NOS, CaMV35S, and PpActin5 showed an increase of more than 1,000-fold between promoters PpActin5 and NOS, whereas terminators increased reporter levels by less than 10-fold, demonstrating the stronger effect promoters play as compared to terminators. The number of polyadenylation sites as well as polyadenylation signals were found to be major determinants of terminator performance. Our results improve the biotechnology platform Physcomitrella and further our understanding of how terminators influence gene expression in plants in general. The supplemental files describe the selection process of terminator candidates using different datasets and selection criteria. The process is described in detail in the respective publication., {"references":["Perroud, P.-F., Haas, F. B., Hiss, M., Ullrich, K. K., Alboresi, A., Amirebrahimi, M., Barry, K., Bassi, R., Bonhomme, S., Chen, H., Coates, J. C., Fujita, T., Guyon-Debast, A., Lang, D., Lin, J., Lipzen, A., Nogué, F., Oliver, M. J., Ponce de León, I., … Rensing, S. A. (2018): The Physcomitrella patens gene atlas project: large-scale RNA-seq based expression data. Plant Journal 95, 168–182. https://doi.org/10.1111/tpj.13940","Fernandez-Pozo, N., Haas, F. B., Meyberg, R., Ullrich, K. K., Hiss, M., Perroud, P.-F., Hanke, S., Kratz, V., Powell, A. F., Vesty, E. F., Daum, C. G., Zane, M., Lipzen, A., Sreedasyam, A., Grimwood, J., Coates, J. C., Barry, K., Schmutz, J., Mueller, L. A., & Rensing, S. A. (2020): PEATmoss (Physcomitrella Expression Atlas Tool): a unified gene expression atlas for the model plant Physcomitrella patens. Plant Journal 102, 165–177. https://doi.org/10.1111/tpj.14607","Hoernstein, S.N.W., Fode, B., Wiedemann, G., Lang, D., Niederkrueger, H., Berg, B., Schaaf, A., Frischmuth, T., Schlosser, A., Decker, E.L., Reski, R. (2018): Host Cell Proteome of Physcomitrella patens Harbors Proteases and Protease Inhibitors under Bioproduction Conditions. Journal of proteome research 17, 3749-3760. http://dx.doi.org/10.1021/acs.jproteome.8b00423"]}

In this paper we study the problem of reconstructing the poset structure of a scheme using coverings. For a finite, dominant and separable morphism $X'\to{X}$ of normal, connected Noetherian schemes, we show that the poset structure of $X'$ can be recovered from the poset structure of $X$ and a set of Galois gluing data over $X$. This data consists of a set of local Galois orbits that are glued using relative Galois groups and transfer maps. To find this gluing data in practice, we introduce symbolic multivariate Newton-Puiseux algorithms that calculate adic approximations of the roots of a univariate polynomial over chains of prime ideals. By matching power series over intersecting chains of prime ideals, we are then able to glue the local Galois orbits of the approximations to obtain the global poset structure of the scheme. We show how to apply these algorithms to two types of coverings: coverings of semistable models of curves and sch\"{o}n coverings of very affine varieties. The first gives an algorithm to calculate the Berkovich skeleton of a curve, and the second an algorithm to calculate boundary complexes., Comment: 43 pages, 12 figures. Versions of Proposition 2.7, and Sections 3.1 and 4.3.2 in the current paper can also be found in arXiv:2007.09449, which will not be submitted for publication. That manuscript is left as a separate entry, as it contains expository sections and proofs that might be useful to the reader

25. International Conference on Material Forming, ESAFORM, Braga, Portugal, 27 Apr 2022 - 29 Apr 2022; Key engineering materials 926, 2371-2381 (2022). doi:10.4028/p-hachrx special issue: "Achievements and Trends in Material Forming : Peer reviewed extended papers selected from the 25th International Conference on Material Forming (ESAFORM 2022) / Edited by Gabriela Vincze, Frédéric Barlat", Published by Trans Tech Publications Ltd., Baech

Towards the growing trends in lightweight, flexible, and optical advantages, thin glasses become key components in numerous applications such as consumer electronics like foldable smartphones, or automotive interiors. Nonisothermal glass molding promises a viable technology for the cost-efficient production of precision glass components. In the existing production, the quality of the glass products can only be accessed at the end of the hot forming process. Due to high rates of product failures often appeared in the precision glass molding processes, the current quality control of the produced optical products suffers low process efficiency. This work introduces an enabling approach for monitoring the product quality in real-time using thermography and machine learning. Specifically, the acquisition of the temperature fields of the glass components during the hot forming stage enabled by an infrared thermographic camera allows machine learning to predict the final shape of the molded components at the end of the forming process. Several transfer learning models have been investigated to demonstrate the proposed method. To further enhance the prediction performance, self-built convolutional neural network models were developed using different types of image data. By incorporating the time-series image data as an input to the learning models, the prediction performance was achieved. The model built in the present work demonstrates an excellent prediction accuracy where the difference between the measured and predicted shapes of the glass products can be kept at low double-digit micrometers. Such accuracy achieved by our self-developed machine learning model promisingly satisfy the quality control in serial productions of numerous precision optical glass components in automotive and consumer electronics sectors.

Music-evoked autobiographical memories (MEAMs) are typically elicited by music that listeners have heard before. However, recent evidence indicates that even music that is unfamiliar to the listener can still cue autobiographical memory. Here we examined how perceived familiarity, music-evoked affect, and developmental timing of music release (childhood, adolescence, young adulthood) were associated with evoked memories in older adults (N=75, ages 65-80) who listened to familiar and unfamiliar music in a pre-registered study. More positive music-evoked affect was related to higher MEAM likelihood for both familiar and unfamiliar music. Higher perceived familiarity was associated with the occurrence of MEAMs in response to familiar, but not unfamiliar, music. We also replicated “reminiscence bump” effects for familiar music such that participants reported more MEAMs in response to music released during their adolescence (14-18) than young adulthood (20-25); however, our results indicate that this bump may begin earlier (i.e., middle childhood). Together, our results suggest different mechanisms underlying MEAMs for familiar and unfamiliar music: music-evoked affect may facilitate MEAMs regardless of previous exposure, but perceived familiarity supports MEAMs only for familiar music.

We present a Bayesian jackknife test for assessing the probability that a data set contains biased subsets, and, if so, which of the subsets are likely to be biased. The test can be used to assess the presence and likely source of statistical tension between different measurements of the same quantities in an automated manner. Under certain broadly applicable assumptions, the test is analytically tractable. We also provide an open-source code, chiborg, that performs both analytic and numerical computations of the test on general Gaussian-distributed data. After exploring the information theoretical aspects of the test and its performance with an array of simulations, we apply it to data from the Hydrogen Epoch of Reionization Array (HERA) to assess whether different sub-seasons of observing can justifiably be combined to produce a deeper 21 cm power spectrum upper limit. We find that, with a handful of exceptions, the HERA data in question are statistically consistent and this decision is justified. We conclude by pointing out the wide applicability of this test, including to CMB experiments and the H0 tension.

Feedbacks between the land surface and the atmosphere are expected to play a role in the response of the Earth system to climate change. Earth system models (ESMs) are designed to simulate these processes, but their implementation is only as good as scientific knowledge and technological capabilities allow. This puts ESMs in a dual role with respect to land-atmosphere feedbacks: On the one hand, they represent the most important tools researchers have for understanding land-atmosphere feedbacks and how they will affect the Earth system. On the other hand, they acquire substantial uncertainty as a result of limitations in their representation of feedback processes.In this dissertation, I used ESMs within the context of both roles. I used them as tools for understanding how land-atmosphere feedbacks affect the Earth system and our understanding of it. I also used observational data to constrain land-atmosphere feedbacks in ESMs with the goal of evaluating and improving their ability to project future responses to climatic forcing.The goal of my first science chapter was to evaluate how well ESMs represent seasonal-scale feedbacks between terrestrial water availability and atmospheric conditions. I developed a metric that was designed to use terrestrial water storage data from the Gravity Recovery and Climate Experiment (GRACE) satellites in conjunction with other remote sensing data sources for atmospheric temperature and humidity, precipitation, and downwelling shortwave radiation. First, I used GRACE to identify the months of the year when the land surface loses more water to evapotranspiration and runoff than it gains from precipitation. I then related the interannual water storage anomaly at the onset of this interval with subsequent atmospheric conditions as a measure of the land surface forcing on the atmosphere, as well as relating the atmospheric conditions with the subsequent water storage anomaly as a measure of the land surface response to atmospheric forcing. By calculating the equivalent quantities in an ensemble of ESMs, I demonstrated that the models tended to overestimate the strength of these relationships. These results are consistent with complementary efforts using other data sources and spatiotemporal scales, and suggest that overly simplistic representations of the heterogeneity within vegetation cover and bare soil could cause models to overestimate soil moisture feedbacks with the atmosphere.My next chapter was partially motivated by a remaining question from the previous research: How much of the measured relationships was driven directly by interactions between soil moisture and the atmosphere, and how much is due to co-variability due to external forcing such as sea surface temperature (SST) anomalies. An additional motivation stemmed from the well-known relationship between the El Niño-Southern Oscillation (ENSO) and interannual variability in the growth rate of atmospheric CO2 concentrations. This relationship is attributed primarily to the response of tropical terrestrial ecosystems, and temperature has been widely implicated as the primary driver. However, there is an ongoing debate in the literature suggesting that hydrology is also an important driver, which, as I demonstrated in the previous chapter, is not independent of temperature.To address these questions, I performed a set of experiments using the Energy Exascale Earth System Model (E3SM). I modified E3SM in order to decouple the interannual variability of soil moisture and SST, which allowed me to isolate the influence of each of these factors in the response of the Amazon rainforest to ENSO. I found that in E3SM, soil moisture served to amplify and extend the land surface response to ENSO in the Amazon. SST anomalies coupled with atmospheric circulation drove an immediate response, which coincides with the Amazon wet season. As the ecosystem was generally not water limited at this time, temperature played the dominant role in carbon cycle variability. However, soil moisture anomalies persisted into the dry season, intensifying and extending the response of both temperature and the terrestrial CO2 flux. This highlights the importance of considering the feedback between soil moisture and temperature when considering their relative importance as drivers of CO2 variability in the Amazon.For my final science chapter, I used a global dataset of vertically resolved soil radiocarbon observations to evaluate the representation of soil carbon processes in the Community Land Model (CLM) and the E3SM land model (ELM). I found that while ELM slightly overestimated radiocarbon ages in temperate latitudes, it underestimated them in boreal and tropical latitudes, particularly at depth, enough to lead to a young bias globally. CLM, on the other hand, underestimated radiocarbon ages at all depths, latitudes, and vegetation types. This suggests that carbon was cycling through the soil too quickly in the models, leading them to overestimate the rate at which soils could sequester carbon in response to increasing atmospheric CO2 concentrations.I used the observed radiocarbon profiles to constrain kinetic rate constants and transfer coefficients in ELM, which improved the young age bias and improved a low bias in the tropical soil carbon stock. Constraining the model increased the global soil carbon stock as a result of the improvements in the tropics, and reduced the contribution of soil to the terrestrial carbon sink. This suggests that soil is likely to respond very slowly to increasing atmospheric CO2 concentrations, and is unlikely to serve as a short-term carbon sink.These three chapters illustrate the range applications for ESMs in the context of land-atmosphere feedbacks. ESMs can be evaluated for how well they reproduce observations of relationships between multiple climatic variables, as in the first chapter. ESMs can be used to understand land-atmosphere feedbacks in the Earth system, by isolating factors that are difficult or impossible to disentangle in nature, as in the second chapter. ESMs can be constrained by observations in order to reduce uncertainty in their simulation of land-atmosphere feedbacks, as in the final chapter. This dissertation represents steps toward understanding how energy, water, and carbon flow through terrestrial ecosystems, and improving the representation of these processes in ESMs.