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Tea (Camellia sinensis) has a long history of medicinal use in the world. The chemical components of tea mainly consist of polyphenols (TPP), proteins, polysaccharides (TPS), chlorophyll, alkaloids, and so on. Great advances have been made in chemical and bioactive studies of catechins and TPP from tea in recent decades. However, the TPS from tea materials have received much less consideration than that of TPP. The number of relevant publications on the TPS from tea leaves and flowers has increased rapidly in recent years. This mini-review summarizes the structure-function relationship of TPS from tea leaves and flowers. The application of purified TPS from tea material as functional or nutritional foods was still little. It will help to develop the function foods with tea TPS and better understand the structure-bioactivity relationship of tea TPS.

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The mitogen-activated protein kinase (MAPK) cassette of the cell wall integrity (CWI) pathway is primarily responsible for orchestrating changes of cell wall. However, functions of this cassette in other cellular processes are not well understood. Here, we found that the Botrytis cinerea mutant of MAPK kinase (BcMkk1) displays more serious defects in mycelial growth, conidiation, responses to cell wall and oxidative stresses, but possesses less reduced virulence than the mutants of its upstream (BcBck1) and downstream (BcBmp3) kinases. Interestingly, BcMkk1, but not BcBck1 and BcBmp3, negatively regulates production of oxalic acid (OA) and activity of extracellular hydrolases (EHs) that are proposed to be virulence factors of B. cinerea. Moreover, we obtained evidence that BcMkk1 negatively controls OA production via impeding phosphorylation of the Per-Arnt-Sim (PAS) kinase BcRim15 by the Ser/Thr kinase BcSch9. In addition, the fungal Pro40 homolog BcPro40 was found to interact simultaneously with three MAPKs, implying that BcPro40 is a scaffold protein of the CWI pathway in B. cinerea. Taken together, results of this study reveal that BcMkk1 negatively modulates virulence via suppressing OA biosynthesis in B. cinerea, which provides novel insight into conserved and species-specific functions of the MAPK kinase in fungi.

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The distributed computation of Nash equilibria is assuming growing relevance in engineering where such problems emerge in the context of distributed control. Accordingly, we present schemes for computing equilibria of two classes of static stochastic convex games complicated by a parametric misspecification, a natural concern in the control of large-scale networked engineered system. In both schemes, players learn the equilibrium strategy while resolving the misspecification: (1) Monotone stochastic Nash games: We present a set of coupled stochastic approximation schemes distributed across agents in which the first scheme updates each agent's strategy via a projected (stochastic) gradient step while the second scheme updates every agent's belief regarding its misspecified parameter using an independently specified learning problem. We proceed to show that the produced sequences converge in an almost-sure sense to the true equilibrium strategy and the true parameter , respectively. Surprisingly, convergence in the equilibrium strategy achieves the optimal rate of convergence in a mean-squared sense with a quantifiable degradation in the rate constant; (2) Stochastic Nash-Cournot games with unobservable aggregate output: We refine (1) to a Cournot setting where we assume that the tuple of strategies is unobservable while payoff functions and strategy sets are public knowledge through a common knowledge assumption. By utilizing observations of noise-corrupted prices, iterative fixedpoint schemes are developed, allowing for simultaneously learning the equilibrium strategies and the misspecified parameter in an almost-sure sense.

Abstract. We consider the solution of a stochastic convex optimization problem E[f (x; θ * , ξ)] over a closed and convex set X in a regime where θ * is unavailable and ξ is a suitably defined random variable. Instead, θ * may be obtained through the solution of a learning problem that requires minimizing a metric E[g(θ; η)] in θ over a closed and convex set Θ. Traditional approaches have been either sequential or direct variational approaches. In the case of the former, this entails the following steps: (i) a solution to the learning problem, namely θ * , is obtained; and (ii) a solution is obtained to the associated computational problem which is parametrized by θ * . Such avenues prove difficult to adopt particularly since the learning process has to be terminated finitely and consequently, in large-scale instances, sequential approaches may often be corrupted by error. On the other hand, a variational approach requires that the problem may be recast as a possibly non-monotone stochastic variational inequality problem in the (x, θ) space; but there are no known first-order stochastic approximation schemes are currently available for the solution of this problem. To resolve the absence of convergent efficient schemes, we present a coupled stochastic approximation scheme which simultaneously solves both the computational and the learning problems. The obtained schemes are shown to be equipped with almost sure convergence properties in regimes when the function f is either strongly convex as well as merely convex. Importantly, the scheme displays the optimal rate for strongly convex problems while in merely convex regimes, through an averaging approach, we quantify the degradation associated with learning by noting that the error in function value after K steps is O ln(K)/K , rather than O 1/K when θ * is available. Notably, when the averaging window is modified suitably, it can be see that the originakl rate of O 1/K is recovered. Additionally, we consider an online counterpart of the misspecified optimization problem and provide a non-asymptotic bound on the average regret with respect to an offline counterpart. In the second part of the paper, we extend these statements to a class of stochastic variational inequality problems, an object that unifies stochastic convex optimization problems and a range of stochastic equilibrium problems. Analogous almost-sure convergence statements are provided in strongly monotone and merely monotone regimes, the latter facilitated by using an iterative Tikhonov regularization. In the merely monotone regime, under a weak-sharpness requirement, we quantify the degradation associated with learning and show that expected error associated with dist(x k , X * ) is O ln(K)/K . Preliminary numerics demonstrate the performance of the prescribed schemes.

The type 2A protein phosphatases (PP2As) are holoenzymes in all eukaryotes but their activators remain unknown in filamentous fungi. Fusarium graminearum contains three PP2As (FgPp2A, FgSit4, and FgPpg1), which play critical roles in fungal growth, development, and virulence. Here, we identified two PP2A activators (PTPAs), FgRrd1 and FgRrd2, and found that they control PP2A activity in a PP2A-specific manner. FgRrd1 interacts with FgPpg1, but FgRrd2 interacts with FgPp2A and very weakly with FgSit4. Furthermore, FgRrd2 activates FgPp2A via regulating FgPp2A methylation. Phenotypic assays showed that FgRrd1 and FgRrd2 regulate mycelial growth, conidiation, sexual development, and lipid droplet biogenesis. More importantly, both FgRrd1 and FgRrd2 interact with RNA polymerase II, subsequently modulating its enrichments at the promoters of mycotoxin biosynthesis genes, which is independent on PP2A. In addition, FgRrd2 modulates response to phenylpyrrole fungicide, via regulating the phosphorylation of kinase FgHog1 in the high-osmolarity glycerol pathway, and to caffeine, via modulating FgPp2A methylation. Taken together, results of this study indicate that FgRrd1 and FgRrd2 regulate multiple physiological processes via different regulatory mechanisms in F. graminearum, which provides a novel insight into understanding the biological functions of PTPAs in fungi.