GPR56 C-terminal fragment mediates signal received by N-terminal fragment of another adhesion GPCR Latrophilin1 in neurons.

Adhesion GPCRs (aGPCRs) are a subfamily of GPCRs that are involved in cell adhesion, cell proliferation, and cell migration in various tissues. G protein-coupled receptor proteolytic site (GPS) of aGPCR is required to cleave the extracellular domain autocatalytically, generating two fragments; a N-terminal fragment (NTF) and a C-terminal fragment (CTF) containing seven transmembrane structure. NTF can interact with CTF non-covalently after cleavage, however the physiological significance of the cleavage of aGPCR at GPS, and also the interaction between NTF and CTF have not been fully clarified yet. In this study, we first investigated the expression profiles of two aGPCRs, GPR56/ADGRG1, and LPHN1/ADGRL1 in mouse brain, and found that the NTF and CTF of GPR56 independently expressed in different brain region at different developmental stages. Immunoprecipitation of GPR56CTF co-immunoprecipitated LPHN1NTF from mouse brain and HEK293T cells expressing both fragments. Stimulation with LPHN1 ligand, α-Latrotoxin N4C (αLTXN4C), to cells expressing LPHN1NTF and GPR56CTF increased intracellular Ca2+ concentration ([Ca2+ ]i). We also demonstrated that GPR56KO mouse neurons attenuated their Ca2+ response to αLTXN4C. These results suggest the possibility of functional and chimeric complex containing LPHN1NTF and GPR56CTF in neuronal signal transduction.

How to Specify a Reference Point in Hypervolume Calculation for Fair Performance Comparison.

The hypervolume indicator has frequently been used for comparing evolutionary multi-objective optimization (EMO) algorithms. A reference point is needed for hypervolume calculation. However, its specification has not been discussed in detail from a viewpoint of fair performance comparison. A slightly worse point than the nadir point is usually used for hypervolume calculation in the EMO community. In this paper, we propose a reference point specification method for fair performance comparison of EMO algorithms. First, we discuss the relation between the reference point specification and the optimal distribution of solutions for hypervolume maximization. It is demonstrated that the optimal distribution of solutions strongly depends on the location of the reference point when a multi-objective problem has an inverted triangular Pareto front. Next, we propose a reference point specification method based on theoretical discussions on the optimal distribution of solutions. The basic idea is to specify the reference point so that a set of well-distributed solutions over the entire linear Pareto front has a large hypervolume and all solutions in such a solution set have similar hypervolume contributions. Then, we examine whether the proposed method can appropriately specify the reference point through computational experiments on various test problems. Finally, we examine the usefulness of the proposed method in a hypervolume-based EMO algorithm. Our discussions and experimental results clearly show that a slightly worse point than the nadir point is not always appropriate for performance comparison of EMO algorithms.

Multi-Label Classification via Adaptive Resonance Theory-Based Clustering.

This article proposes a multi-label classification algorithm capable of continual learning by applying an Adaptive Resonance Theory (ART)-based clustering algorithm and the Bayesian approach for label probability computation. The ART-based clustering algorithm adaptively and continually generates prototype nodes corresponding to given data, and the generated nodes are used as classifiers. The label probability computation independently counts the number of label appearances for each class and calculates the Bayesian probabilities. Thus, the label probability computation can cope with an increase in the number of labels. Experimental results with synthetic and real-world multi-label datasets show that the proposed algorithm has competitive classification performance to other well-known algorithms while realizing continual learning.

A Multi-Population Multi-Objective Evolutionary Algorithm Based on the Contribution of Decision Variables to Objectives for Large-Scale Multi/Many-Objective Optimization.

Most existing multiobjective evolutionary algorithms treat all decision variables as a whole to perform genetic operations and optimize all objectives with one population at the same time. Considering different control attributes, different decision variables have different optimization effects on each objective, so decision variables can be divided into convergence- or diversity-related variables. In this article, we propose a new metric called the optimization degree of the convergence-related decision variable to each objective to calculate the contribution objective of each decision variable. All decision variables are grouped according to their contribution objectives. Then, a multiobjective evolutionary algorithm, namely, decision variable contributing to objectives evolutionary algorithm (DVCOEA), has been proposed. In order to balance the convergence and diversity of the population, the DVCOEA algorithm combines the multipopulation multiobjective framework, where two different optimization strategies are designed to optimize the subpopulation and individuals in the external archive, respectively. Finally, DVCOEA is compared with several state-of-the-art algorithms on a number of benchmark functions. Experimental results show that DVCOEA is a competitive approach for solving large-scale multi/many-objective problems.

P4HA1 Promotes Cell Migration and Colonization in Hypopharyngeal Squamous Cell Carcinoma.

BACKGROUND/AIM: This study aimed to identify key molecules associated with the survival of patients with hypopharyngeal squamous cell carcinoma (HpSCC) by combining in silico and in vitro analyses. MATERIALS AND METHODS: Differentially expressed genes (DEGs) were screened using the Gene Expression Omnibus database. For DEGs, we performed functional enrichment and protein-protein interaction network analyses to identify potential biological functions and hub genes. Functional analysis of HpSCC cell lines verified the critical roles of the hub genes. RESULTS: DEGs were associated with the extracellular matrix. Among the hub genes, high expression of prolyl 4-hydroxylase subunit alpha 1 (P4HA1) was significantly associated with shorter survival. In addition, P4HA1 knockdown inhibited cell migration and colonization. Suppression of cell proliferation was demonstrated using P4HA1-selective inhibitors. CONCLUSION: P4HA1 may be a useful therapeutic target for the treatment of HpSCC.

Effect of HER2-targeted therapy on PDX and PDX-derived organoids generated from HER2-positive salivary duct carcinoma.

BACKGROUND: We previously established a patient-derived xenograft (PDX) model, patient-derived organoids (PDOs), and PDX-derived organoids (PDXOs) for salivary duct carcinoma (SDC). Using these models, this study examined the therapeutic effect of human epidermal growth factor receptor 2 (HER2) blockade on HER2-positive SDC. METHODS: The therapeutic effect of lapatinib was assessed in SDC PDXOs with regards to cell growth, receptor/downstream signaling molecule expression, phosphorylation levels, and apoptosis. Effect of lapatinib treatment was evaluated in vivo in SDC PDX mice. RESULTS: The siRNA knockdown of HER2 and lapatinib suppressed cell proliferation in SDC PDXOs. Lapatinib inhibited the phosphorylation of HER2 and its downstream targets, and induced apoptosis in SDC PDXOs. Lapatinib also significantly reduced tumor volumes compared with that of the control in SDC PDX mice. CONCLUSION: For the first time, we demonstrated the efficacy of anti-HER2 therapy in HER2-positive SDC using preclinical models of SDC PDX and PDXO.

Establishment of experimental salivary gland cancer models using organoid culture and patient-derived xenografting.

PURPOSE: Depending on its histological subtype, salivary gland carcinoma (SGC) may have a poor prognosis. Due to the scarcity of preclinical experimental models, its molecular biology has so far remained largely unknown, hampering the development of new treatment modalities for patients with these malignancies. The aim of this study was to generate experimental human SGC models of multiple histological subtypes using patient-derived xenograft (PDX) and organoid culture techniques. METHODS: Tumor specimens from surgically resected SGCs were processed for the preparation of PDXs and patient-derived organoids (PDOs). Specimens from SGC PDXs were also processed for PDX-derived organoid (PDXO) generation. In vivo tumorigenicity was assessed using orthotopic transplantation of SGC organoids. The pathological characteristics of each model were compared to those of the original tumors using immunohistochemistry. RNA-seq was used to analyze the genetic traits of our models. RESULTS: Three series of PDOs, PDXs and PDXOs of salivary duct carcinomas, one series of PDOs, PDXs and PDXOs of mucoepidermoid carcinomas and PDXs of myoepithelial carcinomas were successfully generated. We found that PDXs and orthotopic transplants from PDOs/PDXOs showed similar histological features as the original tumors. Our models also retained their genetic traits, i.e., transcription profiles, genomic variants and fusion genes of the corresponding histological subtypes. CONCLUSION: We report the generation of SGC PDOs, PDXs and PDXOs of multiple histological subtypes, recapitulating the histological and genetical characteristics of the original tumors. These experimental SGC models may serve as a useful resource for the development of novel therapeutic strategies and for investigating the molecular mechanisms underlying the development of these malignancies.

Interactive evolutionary computation with minimum fitness evaluation requirement and offline algorithm design.

In interactive evolutionary computation (IEC), each solution is evaluated by a human user. Usually the total number of examined solutions is very small. In some applications such as hearing aid design and music composition, only a single solution can be evaluated at a time by a human user. Moreover, accurate and precise numerical evaluation is difficult. Based on these considerations, we formulated an IEC model with the minimum requirement for fitness evaluation ability of human users under the following assumptions: They can evaluate only a single solution at a time, they can memorize only a single previous solution they have just evaluated, their evaluation result on the current solution is whether it is better than the previous one or not, and the best solution among the evaluated ones should be identified after a pre-specified number of evaluations. In this paper, we first explain our IEC model in detail. Next we propose a ([Formula: see text])ES-style algorithm for our IEC model. Then we propose an offline meta-level approach to automated algorithm design for our IEC model. The main feature of our approach is the use of a different mechanism (e.g., mutation, crossover, random initialization) to generate each solution to be evaluated. Through computational experiments on test problems, our approach is compared with the ([Formula: see text])ES-style algorithm where a solution generation mechanism is pre-specified and fixed throughout the execution of the algorithm.