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Background: Monitoring of measurable residual disease (MRD) requires highly sensitive flow cytometry protocols to provide an accurate prediction of shorter progression-free survival. High assay sensitivity generally requires rapid processing to avoid cell loss from small bone marrow sample volumes, but this requirement conflicts with the need in most clinical cytometry laboratories for long processing and acquisition times, especially when multiple MRD studies coincide on the same day. Methods: The proposed protocol was applied to 226 human bone marrow and 45 peripheral blood samples submitted for the study of MRD or the detection of rare cells. Samples were processed within 24 h of extraction and acquired with an eight-color flow cytometer. Results: The FACSLyse-Bulk protocol allows for the labelling of millions of cells in under 90 min in small sample volumes without affecting the FSC/SSC pattern or antigen expression, and it also allows antigens to be fixed to the membrane, thus avoiding the capping phenomenon. Conclusions: The proposed protocol would allow clinical flow cytometry laboratories to perform MRD studies in house and easily achieve a limit of detection and limit of quantification <0.001%, thus avoiding the need to outsource analysis to specialized cytometry laboratories. Full article

We open here many new tracks of research in anti-Ramsey Theory, considering edge-coloring problems inspired by rainbow coloring and further by odd colorings and conflict-free colorings. Let G be a graph and $\mathcal{F}$ any given family of graphs. For every integer $n\ge \left|G\right|$, let $f(n,G|\mathcal{F})$ denote the smallest integer k such that any edge coloring of $K_n$ with at least k colors forces a copy of G in which each color class induces a member of $\mathcal{F}$. Observe that in anti-Ramsey problems, each color class is a single edge, i.e., $\mathcal{F}=\left\{K_2\right\}$. Among the many results introduced in this paper, we mention the following. (1) For every graph G, there exists a constant $c=c\left(G\right)$ such that in any edge coloring of $K_n$ with at least $cn$ colors there is a copy of G in which every vertex v is incident with an edge whose color appears only once among all edges incident with v. (2) In sharp contrast to the above result we prove that if $\mathcal{F}$ is the class of all odd graphs (having vertices with odd degrees only) then $f(n,K_k|\mathcal{F})=(1+o\left(1\right))\mathrm{ex}(n,K_{\lceil k/2\rceil })$, which is quadratic for $k\ge 5$. (3) We exactly determine $f(n,G|\mathcal{F})$ for small graphs when $\mathcal{F}$ belongs to several families representing various odd/even coloring constraints. Full article

A smart contract is a special form of computer program that runs on a blockchain and provides a new way to implement financial and business transactions in a conflict-free and transparent environment. In blockchain systems such as Ethereum, smart contracts can handle and autonomously transfer assets of considerable value to other parties. Hence, it is particularly important to ensure that smart contracts function as intended since bugs or vulnerabilities may lead, and indeed have led, to substantial economic losses and erosion of trust for blockchain. While a number of approaches and tools have been developed to find vulnerabilities, formal methods present the highest level of confidence in the security of smart contracts. In this paper, we propose a formal solution to model a smart contract based on colored Petri nets (CPNs). Herein, we focus on the most common type of security bugs in smart contract, i.e., reentrancy bugs, which led to a serious financial loss of around USD 34 million for the Cream Finance project in 2021. We present a hierarchical CPN modelling method to analyze potential security vulnerabilities at the contract’s source code level. Then, modeling analysis methods such as correlation matrix, state space report and state space graph generated via CPN Tools simulation are exploited for formal analysis of smart contracts. The example shows the full state space and wrong path in accordance with our expected results. Finally, the conclusion was verified on the Ethereum network based on the Remix platform. Full article