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ABSTRACT Biomimetic designs are inspired by the complex and unique behavior of naturally occurring materials, and can be applied to many systems, including polymers. ZIPer polymers (Zwitter arene‐ion like polymer) are inspired by byssal threads found on mussels, and their physical state is highly sensitive to various environmental conditions. Specifically, the ZIPer polymer undergoes chemospecific phase transitions, exhibiting potential for its use as an ionic responsive technology. Though this phenomenon has been observed with Raman spectroscopy, little is known about how salt identity or concentration affect polymer inter‐ and intra‐chain interactions. Previous studies have used Raman spectroscopy to analyze ZIPer polymer behavior in the presence of salt; however, the effect is typically only observed with sodium chloride and often only compares spectra at two concentrations. Additionally, studies have mainly focused on the spectral evidence of cation–π interactions, significantly narrowing their spectral range. In order to develop a more predictive framework for ZIPer polymer behavior, a range of salt identities and concentrations need to be tested. This study uses Raman spectroscopy to investigate ZIPer polymer behavior in the presence of a series of salts, namely NaCl, NaOTFA, NaBr, NaBF₄, and NaPF₆, each at 0.1 M, 0.5 M, 1.0 M, and 1.5 M concentrations. Moreover, we observe spectral changes in a range from 550 to 2000 cm⁻¹. Spectral evidence suggests that the cation–π interactions previously hypothesized to be the driver of ZIPer polymer behavior are not the only mechanism determining the chemoresponsive phase transitions. We hypothesize that cation–π interactions and dispersion forces are competing mechanisms controlling ZIPer polymer behavior. Furthermore, we suggest that at certain concentrations the dominating mechanism transitions, and this inflection point is salt identity dependent. 

The unsupervised task of aligning two or more distributions in a shared latent space has many applications including fair representations, batch effect mitigation, and unsupervised domain adaptation. Existing flow-based approaches estimate multiple flows independently, which is equivalent to learning multiple full generative models. Other approaches require adversarial learning, which can be computationally expensive and challenging to optimize. Thus, we aim to jointly align multiple distributions while avoiding adversarial learning. Inspired by efficient alignment algorithms from optimal transport (OT) theory for univariate distributions, we develop a simple iterative method to build deep and expressive flows. Our method decouples each iteration into two subproblems: 1) form a variational approximation of a distribution divergence and 2) minimize this variational approximation via closed-form invertible alignment maps based on known OT results. Our empirical results give evidence that this iterative algorithm achieves competitive distribution alignment at low computational cost while being able to naturally handle more than two distributions. 

Eukaryotic filamentous plant pathogens with biotrophic growth stages like the devastating hemibiotrophic rice blast fungus Magnaporthe oryzae grow for extended periods in living host plant cells without eliciting defense responses. M. oryzae elaborates invasive hyphae (IH) that grow in and between living rice cells while separated from host cytoplasm by plant-derived membrane interfaces. However, although critical to the plant infection process, the molecular mechanisms and metabolic strategies underpinning this intracellular growth phase are poorly understood. Eukaryotic cell growth depends on activated target-of-rapamycin (TOR) kinase signaling, which inhibits autophagy. Here, using live-cell imaging coupled with plate growth tests and RNAseq, proteomic, quantitative phosphoproteomics and metabolic approaches, we show how cycles of autophagy in IH modulate TOR reactivation via α-ketoglutarate to sustain biotrophic growth and maintain biotrophic interfacial membrane integrity in host rice cells. Deleting the M. oryzae serine-threonine protein kinase Rim15-encoding gene attenuated biotrophic growth, disrupted interfacial membrane integrity and abolished the in planta autophagic cycling we observe here for the first time in wild type. Δrim15 was also impaired for glutaminolysis and depleted for α-ketoglutarate. α-ketoglutarate treatment of Δrim15-infected leaf sheaths remediated Δrim15 biotrophic growth. In WT, α-ketoglutarate treatment suppressed autophagy. α-ketoglutarate signaling is amino acid prototrophy- and GS-GOGAT cycle-dependent. We conclude that, following initial IH elaboration, cycles of Rim15- dependent autophagic flux liberate α-ketoglutarate – via the GS-GOGAT cycle – as an amino acid-sufficiency signal to trigger TOR reactivation and promote fungal biotrophic growth in nutrient-restricted host rice cells.