Combinatorial Design of the MAUT and PAMSSEM II Methods for Multiple Attributes Group Decision Making with Probabilistic Linguistic Information

This paper considers the application of probabilistic linguistic term sets (PLTS) in multiple-attribute group decision-making (MAGDM) when the weights can’t be determined. First, as an improvement of the PROMETHEE method, the PAMSSEM method can not only handle missing evaluations, but also proposes a rejection threshold to calculate the overall consistency of the plan, so as to rank the plan more reasonably. At the same time, the MAUT uses the marginal utility function to reallocate the attribute values of the alternatives in the interval , and then calculate the total utility to sort them. Because the utility function is beneficial in expressing consumer satisfaction, we combine the MAUT method and PAMSSEM II method and apply it to solve decision-making problems under probabilistic linguistic environment. Secondly the coefficient of variation method, entropy method and analytic hierarchy process are used to calculate the weights in a combination. In the process of data processing, we use the transfer function to convert the PLTS into the hesitant probabilistic fuzzy set (HPFS) for calculation. Finally, the PL-MAUT-PAMSSEM II method, PROMRTHE method, TOPSIS method and ARAS method are compared with each other.

Constructions and Necessities of Some Permutation Polynomials over Finite Fields

Let F q denote the finite field with q elements. Permutation polynomials over finite fields have important applications in many areas of science and engineering such as coding theory, cryptography, and combinatorial design. The study of permutation polynomials has a long history, and many results are obtained in recent years. In this paper, we obtain some further results about the permutation properties of permutation polynomials. Some new classes of permutation polynomials are constructed, and the necessities of some permutation polynomials are studied.

Butterfly Triple System Algorithm Based on Graph Theory

In combinatorial design theory, clustering elements into a set of three elements is the heart of classifying data. This article will provide insight into formulating algorithm for a new type of triple system, called a Butterfly triple system. Basically, in this algorithm development, a starter of cyclic near-resolvable ((v-1)/2)-cycle system of the 2-fold complete graph 2K_v is employed to construct the starter of cyclic ((v-1)/2)-star decomposition of 2K_v. These starters were then decomposed into triples and classified as a starter of a cyclic Butterfly triple system. The obtained starter set generated a triple system of order A special reference for case 𝑣𝑣 ≡ 9 (mod 12) was presented to demonstrate the development of the Butterfly triple system.

672 The effect of chemoradiotherapy and tumor histology on the immune contexture of tumor-draining lymph nodes in NSCLC

BackgroundRecently, the concept of locally delivered immune modulatory agents (re-)invigorating sub-optimally primed tumor-specific T cells and lifting suppression in the tumor microenvironment (TME) and tumor-draining lymph nodes (TDLN) has gained attention. TDLN play an important role in the induction of tumor-specific effector T cells. It is here that specialized dendritic cell (DC) subsets present tumor-derived antigens to naïve T cells and start effective adaptive immune responses to cancer. Unfortunately, TDLN are also rapidly targeted by tumors for immune suppression, which may impair the efficacy of immunotherapy. Currently, there is limited knowledge on the immune contexture of TDLN in non-small cell lung cancer (NSCLC), differences between types of tumor histology, and the influence of standard treatment.MethodsIn an exploratory study, we collected and analyzed viable cells from TDLN from patients with NSCLC, scheduled for surgical resection. To date, we have analyzed 43 TDLN from a total of 10 patients with multiparameter flowcytometry panels, either untreated or after neoadjuvant chemoradiotherapy (nCRT).ResultsOur analyses reveal differences between squamous cell carcinoma (SCC) and adenocarcinoma (AC), discernable even within this small cohort. In AC, higher levels of PD-L1 on CD11c+CD1c- LN-resident macrophages and CD1a+ migratory DC were accompanied by a lower activation state of CD8+ T cells by PD-1, CTLA-4 and CD69 expression levels. Furthermore, we found decreased activation of LN-resident DCs (by PD-L1 and CD83 levels) and a striking decrease in PD-1 and CD69 on CD8+ T cells, a decrease in effector and central memory CD8+ T cells, and an increase in naïve CD8+ T cells and Treg subsets after nCRT treatment, the current standard treatment of stage III NSCLC patients.ConclusionsThese AC/SCC –related differences and nCRT-induced alterations in the immune status of hold clues for future patient stratification and combinatorial design of CRT with immunotherapy.Ethics ApprovalThis study was approved by the Medical Ethics Committee; 2017.545

Novel drug design and bioinformatics: an introduction

In the current era of high-throughput technology, where enormous amounts of biological data are generated day by day via various sequencing projects, thereby the staggering volume of biological targets deciphered. The discovery of new chemical entities and bioisosteres of relatively low molecular weight has been gaining high momentum in the pharmacopoeia, and traditional combinatorial design wherein chemical structure is used as an initial template for enhancing efficacy pharmacokinetic selectivity properties. Once the compound is identified, it undergoes ADMET filtration to ensure whether it has toxic and mutagenic properties or not. If the compound has no toxicity and mutagenicity is either considered a potential lead molecule. Understanding the mechanism of lead molecules with various biological targets is imperative to advance related functions for drug discovery and development. Notwithstanding, a tedious and costly process, taking around 10–15 years and costing around $4 billion, cascaded approached of Bioinformatics and Computational biology viz., structure-based drug design (SBDD) and cognate ligand-based drug design (LBDD) respectively rely on the availability of 3D structure of target biomacromolecules and vice versa has made this process easy and approachable. SBDD encompasses homology modelling, ligand docking, fragment-based drug design and molecular dynamics, while LBDD deals with pharmacophore mapping, QSAR, and similarity search. All the computational methods discussed herein, whether for target identification or novel ligand discovery, continuously evolve and facilitate cost-effective and reliable outcomes in an era of overwhelming data.

A broad analysis of splicing regulation in yeast using a large library of synthetic introns

RNA splicing is a key process in eukaryotic gene expression, in which an intron is spliced out of a pre-mRNA molecule to eventually produce a mature mRNA. Most intron-containing genes are constitutively spliced, hence efficient splicing of an intron is crucial for efficient regulation of gene expression. Here we use a large synthetic oligo library of ~20,000 variants to explore how different intronic sequence features affect splicing efficiency and mRNA expression levels in S. cerevisiae. Introns are defined by three functional sites, the 5’ donor site, the branch site, and the 3’ acceptor site. Using a combinatorial design of synthetic introns, we demonstrate how non-consensus splice site sequences in each of these sites affect splicing efficiency. We then show that S. cerevisiae splicing machinery tends to select alternative 3’ splice sites downstream of the original site, and we suggest that this tendency created a selective pressure, leading to the avoidance of cryptic splice site motifs near introns’ 3’ ends. We further use natural intronic sequences from other yeast species, whose splicing machineries have diverged to various extents, to show how intron architectures in the various species have been adapted to the organism’s splicing machinery. We suggest that the observed tendency for cryptic splicing is a result of a loss of a specific splicing factor, U2AF1. Lastly, we show that synthetic sequences containing two introns give rise to alternative RNA isoforms in S. cerevisiae, demonstrating that merely a synthetic fusion of two introns might be suffice to facilitate alternative splicing in yeast. Our study reveals novel mechanisms by which introns are shaped in evolution to allow cells to regulate their transcriptome. In addition, it provides a valuable resource to study the regulation of constitutive and alternative splicing in a model organism.

QUANTUM COMBINATORIAL DESIGN

Combinatorial Design such as configuration design, design optioneering, component selection, and generative design, is common across engineering. Generating solutions for a combinatorial design task often involves the application of classical computing solvers that can either map or navigate design spaces. However, it has been observed that classical computing resource power-law scales with many design space models. This observation suggests classical computing may not be capable of modelling our future design space needs.To meet future design space modelling needs, this paper examines quantum computing and the characteristics that enables its resources to scale polynomially with design space size. The paper then continues to present a combinatorial design problem that is subsequently represented, constrained and solved by quantum computing. The results of which are the derivation of an initial set of circuits that represent design space constraints. The study shows the game-changing possibilities of quantum computing as an engineering design tool and is the start of an exciting new journey for design research.

A Combination Strategy of Solubility Enhancers for Effective Production of Soluble and Bioactive Human Enterokinase

Enterokinase is one of the hydrolases that catalyze hydrolysis to regulate biological processes in intestinal visceral mucosa. Enterokinase plays an essential role in accelerating the process of protein digestion as it converts trypsinogen into active trypsin by accurately recognizing and cleaving a specific peptide sequence, (Asp)4-Lys. Due to its exceptional substrate specificity, enterokinase is widely used as a versatile molecular tool in various bioprocessing, especially in removing fusion tags from recombinant proteins. Despite its biotechnological importance, mass production of soluble enterokinase in bacteria still remains an unsolved challenge. Here, we present an effective production strategy of human enterokinase using tandemly linked solubility enhancers consisting of thioredoxin, phosphoglycerate kinase or maltose-binding protein. The resulting enterokinases exhibited significantly enhanced solubility and bacterial expression level while retaining enzymatic activity, which demonstrates that combinatorial design of fusion proteins has the potential to provide an efficient way to produce recombinant proteins in bacteria.