Glyceraldehyde-3-phosphate dehydrogenase (GAPDH) moonlights as an adhesin in Mycoplasma hyorhinis adhesion to epithelial cells as well as a plasminogen receptor mediating extracellular matrix degradation

Mycoplasma hyorhinis infects pigs causing polyserositis and polyarthritis, and has also been reported in a variety of human tumor tissues. The occurrence of disease is often linked with the systemic invasion of the pathogen. Glyceraldehyde-3-Phosphate Dehydrogenase (GAPDH), one of the key enzymes of glycolysis, was reported as a surface multifunctional molecule in several bacteria. Here, we investigated whether GAPDH could manifest binary functions; as an adhesin to promote colonization as well as a plasminogen receptor functioning in extracellular matrix (ECM) degradation to promote systemic invasion. The surface localization of GAPDH was observed in M. hyorhinis with flow cytometry and colony blot analysis. Recombinant GAPDH (rGAPDH) was found to be able to bind porcine-derived PK-15 and human-derived NCI-H292 cells. The incubation with anti-GAPDH antibody significantly decreased the adherence of M. hyorhinis to both cell lines. To investigate its function in recruiting plasminogen, firstly, the interaction between rGAPDH and plasminogen was demonstrated by ELISA and Far-Western blot assay. The activation of the rGAPDH-bound plasminogen into plasmin was proved by using a chromogenic substrate, and furtherly confirmed to degrade extracellular matrix by using a reconstituted ECM. Finally, the ability of rGAPDH to bind different ECM components was demonstrated, including fibronectin, laminin, collagen type IV and vitronectin. Collectively, our data imply GAPDH as an important adhesion factor of M. hyrohinis and a receptor for hijacking host plasminogen to degrade ECM. The multifunction of GAPDH to bind both plasminogen and ECM components is believed to increase the targeting of proteolysis and facilitate the dissemination of M. hyorhinis.

On n-dependent groups and fields II

We continue the study of n-dependent groups, fields and related structures, largely motivated by the conjecture that every n-dependent field is dependent. We provide evidence toward this conjecture by showing that every infinite n-dependent valued field of positive characteristic is henselian, obtaining a variant of Shelah’s Henselianity Conjecture in this case and generalizing a recent result of Johnson for dependent fields. Additionally, we prove a result on intersections of type-definable connected components over generic sets of parameters in n-dependent groups, generalizing Shelah’s absoluteness of $G^{00}$ in dependent theories and relative absoluteness of $G^{00}$ in $2$ -dependent theories. In an effort to clarify the scope of this conjecture, we provide new examples of strictly $2$ -dependent fields with additional structure, showing that Granger’s examples of non-degenerate bilinear forms over dependent fields are $2$ -dependent. Along the way, we obtain some purely model-theoretic results of independent interest: we show that n-dependence is witnessed by formulas with all but one variable singletons; provide a type-counting criterion for $2$ -dependence and use it to deduce $2$ -dependence for compositions of dependent relations with arbitrary binary functions (the Composition Lemma); and show that an expansion of a geometric theory T by a generic predicate is dependent if and only if it is n-dependent for some n, if and only if the algebraic closure in T is disintegrated. An appendix by Martin Bays provides an explicit isomorphism in the Kaplan-Scanlon-Wagner theorem.

“Pee in Peace” or “Make Everyone Uncomfortable”: Public Perceptions of Transgender Rights

We analyze a survey of Nebraskans as a case study to examine public opinion of transgender rights. Using a mixed-methods design, we find an even divide among mostly cisgender survey respondents on whether transgender people should be able to use the restroom that aligns with their gender identity. Our findings mirror national data and show that identifying as female, being more liberal politically, and being less religious are associated with supporting this belief. Qualitative analysis of open-ended responses reveals that both supporters and opponents of transgender rights employ logics that implicate (1) the nature of transgender identities, (2) the experiences of transgender people, and (3) the regulation of transgender bodies in public spaces. Despite drawing on similar themes, supporters and opponents construct divergent gendered realities that either validate or preclude the recognition of transgender people. Our findings shed light on how the cisgender/transgender binary functions as a facet of inequality.

Quantum Speedup Based on Classical Decision Trees

Lin and Lin \cite{LL16} have recently shown how starting with a classical query algorithm (decision tree) for a function, we may find upper bounds on its quantum query complexity. More precisely, they have shown that given a decision tree for a function f:{0,1}n→[m] whose input can be accessed via queries to its bits, and a guessing algorithm that predicts answers to the queries, there is a quantum query algorithm for f which makes at most O(GT) quantum queries where T is the depth of the decision tree and G is the maximum number of mistakes of the guessing algorithm. In this paper we give a simple proof of and generalize this result for functions f:[ℓ]n→[m] with non-binary input as well as output alphabets. Our main tool for this generalization is non-binary span program which has recently been developed for non-binary functions, and the dual adversary bound. As applications of our main result we present several quantum query upper bounds, some of which are new. In particular, we show that topological sorting of vertices of a directed graph G can be done with O(n3/2) quantum queries in the adjacency matrix model. Also, we show that the quantum query complexity of the maximum bipartite matching is upper bounded by O(n3/4m+n) in the adjacency list model.

Notes on Monotone Recognition in Multi-Valued Grids

A novel method of monotone recognition based on the partitioning of the grid into discrete structures isomorphic to binary cubes (called “cube-split” technique) was proposed in our recent work, and a theoretical level description of two algorithms /algorithmic schemes/ solving this problem was also introduced. This paper provides implementation details of those algorithms, as well as focuses on the recognition of monotone binary functions with a small number of units

Span program for non-binary functions

Span programs characterize the quantum query complexity of binary functions f:\{0,\ldots,\ell\}^n \to \{0,1\} up to a constant factor. In this paper we generalize the notion of span programs for functions with non-binary input/output alphabets f: [\ell]^n \to [m]. We show that non-binary span program characterizes the quantum query complexity of any such function up to a constant factor. We argue that this non-binary span program is indeed the generalization of its binary counterpart. We also generalize the notion of span programs for a special class of relations. Learning graphs provide another tool for designing quantum query algorithms for binary functions. In this paper, we also generalize this tool for non-binary functions, and as an application of our non-binary span program show that any non-binary learning graph gives an upper bound on the quantum query complexity.

Flexibility of Boolean Network Reservoir Computers in Approximating Arbitrary Recursive and Non-Recursive Binary Filters

Reservoir computers (RCs) are biology-inspired computational frameworks for signal processing that are typically implemented using recurrent neural networks. Recent work has shown that Boolean networks (BN) can also be used as reservoirs. We analyze the performance of BN RCs, measuring their flexibility and identifying the factors that determine the effective approximation of Boolean functions applied in a sliding-window fashion over a binary signal, both non-recursively and recursively. We train and test BN RCs of different sizes, signal connectivity, and in-degree to approximate three-bit, five-bit, and three-bit recursive binary functions, respectively. We analyze how BN RC parameters and function average sensitivity, which is a measure of function smoothness, affect approximation accuracy as well as the spread of accuracies for a single reservoir. We found that approximation accuracy and reservoir flexibility are highly dependent on RC parameters. Overall, our results indicate that not all reservoirs are equally flexible, and RC instantiation and training can be more efficient if this is taken into account. The optimum range of RC parameters opens up an angle of exploration for understanding how biological systems might be tuned to balance system restraints with processing capacity.

Flexibility of Boolean Network Reservoir Computers in Approximating Arbitrary Recursive and Non-recursive Binary Filters

Reservoir computers (RCs) are a biology inspired computational framework for signal processing typically implemented using recurrent neural networks. Recent work has shown that Boolean networks (BN) can also be used as reservoirs. We analyze the performance of BN RCs, measuring their flexibility and identifying factors that determine effective approximation of Boolean functions that are applied in a sliding-window fashion over a binary signal, either non-recursively or recursively. We train and test BN RCs of different sizes, signal connectivity, and in-degree to approximate 3-bit, 5-bit and 3-bit recursive binary functions. We analyze how BN RC parameters and function average sensitivity, a measure of function smoothness, affect approximation accuracy as well as the spread of accuracies for a single reservoir. We found that approximation accuracy and reservoir flexibility are highly dependent on RC parameters. Overall, our results indicate that not all reservoirs are equally flexible and RC instantiation and training can be more efficient if this is taken into account. The optimum range of RC parameters opens up an angle of exploration for understanding how biological systems might be tuned to balance system restraints with processing capacity.