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Precise control of gene expression is critical for optimizing cellular metabolism and improving the production of valuable biochemicals. However, hard-wired approaches to pathway engineering, such as optimizing promoters, can take time and effort. Moreover, limited tools exist for controlling gene regulation in non-conventional hosts. Here, we develop a two-channel chemically-regulated gene expression system for the multi-stress tolerant yeast Kluyveromyces marxianus and use it to tune ethyl acetate production, a native metabolite produced at high titers in this yeast. To achieve this, we repurposed the plant hormone sensing modules (PYR1ABA/HAB1 and PYR1*MANDI/HAB1*) for high dynamic-range gene activation and repression controlled by either abscisic acid (ABA) or mandipropamid (mandi). To redirect metabolic flux towards ethyl acetate biosynthesis, we simultaneously repress pyruvate dehydrogenase (PDA1) and activate pyruvate decarboxylase (PDC1) to enhance ethyl acetate titers. Thus, we have developed new tools for chemically tuning gene expression in K. marxianus and S. cerevisiae that should be deployable across many non-conventional eukaryotic hosts. 

A<sc>bstract</sc> In this paper, we present a method of embedding physics data manifolds with metric structure into lower dimensional spaces with simpler metrics, such as Euclidean and Hyperbolic spaces. We then demonstrate that it can be a powerful step in the data analysis pipeline for many applications. Using progressively more realistic simulated collisions at the Large Hadron Collider, we show that this embedding approach learns the underlying latent structure. With the notion of volume in Euclidean spaces, we provide for the first time a viable solution to quantifying the true search capability of model agnostic search algorithms in collider physics (i.e. anomaly detection). Finally, we discuss how the ideas presented in this paper can be employed to solve many practical challenges that require the extraction of physically meaningful representations from information in complex high dimensional datasets. 

Radiosonde observations over Antarctica and the surrounding oceans were enhanced during the Year of Polar Prediction in the Southern Hemisphere (YOPP‐SH) summer Special Observing Period (SOP). Observing System Experiments (OSEs) were conducted in a continuous cycling framework using the Weather Research and Forecasting (WRF) Model and its data assimilation system. Routinely available observations were assimilated in the CTL (control) experiment, and special radiosonde observations from the YOPP‐SH SOP were additionally assimilated in the YOPP experiment. The results were compared to investigate the effects of additional radiosonde observations on analyses and forecasts over and around Antarctica. Verifications against ERA5 re‐analysis, radiosonde observations, and Automatic Weather Station (AWS) observations show overall positive effects of additional radiosonde observations. These positive effects are most noticeable in temperature at lower levels at earlier forecast lead times; afterward, wind forecast improvements at upper levels are the most noticeable. Although routine and special radiosonde observations are concentrated over the eastern and coastal regions of Antarctica (compared to the western and inland regions), the effects of the extra data spread in longitudinal and latitudinal directions; therefore, the effects on the forecasts are not limited to only the areas near the radiosonde observations. A case study reveals how cyclone forecasts are improved through the assimilation of the additional YOPP‐SH SOP radiosonde observations. This study provides insights into future observation strategies in Antarctica, such as horizontal/vertical observation locations, observation variables, and so forth to maximize effects of new observations on forecasts over Antarctica. 

Homodimeric class 1 cytokine receptors include the erythropoietin (EPOR), thrombopoietin (TPOR), granulocyte colony-stimulating factor 3 (CSF3R), growth hormone (GHR), and prolactin receptors (PRLR). These cell-surface single-pass transmembrane (TM) glycoproteins regulate cell growth, proliferation, and differentiation and induce oncogenesis. An active TM signaling complex consists of a receptor homodimer, one or two ligands bound to the receptor extracellular domains and two molecules of Janus Kinase 2 (JAK2) constitutively associated with the receptor intracellular domains. Although crystal structures of soluble extracellular domains with ligands have been obtained for all the receptors except TPOR, little is known about the structure and dynamics of the complete TM complexes that activate the downstream JAK-STAT signaling pathway. Three-dimensional models of five human receptor complexes with cytokines and JAK2 were generated here using AlphaFold Multimer. Given the large size of the complexes (from 3220 to 4074 residues), the modeling required a stepwise assembly from smaller parts with selection and validation of the models through comparisons with published experimental data. The modeling of active and inactive complexes supports a general activation mechanism that involves ligand binding to a monomeric receptor followed by receptor dimerization and rotational movement of the receptor TM α-helices causing proximity, dimerization, and activation of associated JAK2 subunits. The binding mode of two eltrombopag molecules to TM α-helices of the active TPOR dimer was proposed. The models also help elucidating the molecular basis of oncogenic mutations that may involve a non-canonical activation route. Models equilibrated in explicit lipids of the plasma membrane are publicly available.