Insulin Attenuates Beta-Amyloid-Associated Insulin/Akt/EAAT Signaling Perturbations in Human Astrocytes

The excitatory amino acid transporters 1 and 2 (EAAT1 and EAAT2), mostly located on astrocytes, are the main mediators for glutamate clearance in humans. Malfunctions of these transporters may lead to excessive glutamate accumulation and subsequent excitotoxicity to neurons, which has been implicated in many kinds of neurodegenerative disorders including Alzheimer’s disease (AD). Yet, the specific mechanism of the glutamate system dysregulation remains vague. To explore whether the insulin/protein kinase B (Akt)/EAAT signaling in human astrocytes could be disturbed by beta-amyloid protein (Aβ) and be protected by insulin, we incubated HA-1800 cells with varying concentrations of Aβ1–42 oligomers and insulin. Then the alterations of several key substrates in this signal transduction pathway were determined. Our results showed that expressions of insulin receptor, phospho-insulin receptor, phospho-protein kinase B, phospho-mammalian target of rapamycin, and EAAT1 and EAAT2 were decreased by the Aβ1–42 oligomers in a dose-dependent manner (p 0.05), and the mRNA levels of EAAT1 and EAAT2 were also unchanged (p > 0.05). Taken together, this study indicates that Aβ1–42 oligomers could cause disturbances in insulin/Akt/EAAT signaling in astrocytes, which might be responsible for AD onset and progression. Additionally, insulin can exert protective functions to the brain by modulating protein modifications or expressions

Community Detection Based on Modularity and k-Plexes

Abstract(#br)Community identification is of great worth for analyzing the structure or characteristics of a complex network. Many community detection methods have been developed, such as modularity-based optimization models, which are widely used but significantly restricted in “resolution limit”. In this paper, we propose a novel algorithm, called modularity optimization with k -plexes (MOKP), to solve this problem, and this algorithm can identify communities smaller than a scale. The proposed algorithm uses k -plexes to generate community seeds from the whole network and assigns the remaining nodes by modularity optimization. To save computational time, we further propose the improved MOKP algorithm (IMOKP) by reducing the scale of the network before community seeds generation and adjusting rules of nodes assignment. Extensive experimental results demonstrate our proposed algorithms perform better than several state-of-the-art algorithms in terms of accuracy of detected communities on various networks, and can effectively detect small communities in terms of a newly defined index, namely small community level, on multiple networks as well

Single-cell sequencing of genomic DNA resolves sub-clonal heterogeneity in a melanoma cell line

Abstract: We performed shallow single-cell sequencing of genomic DNA across 1475 cells from a cell-line, COLO829, to resolve overall complexity and clonality. This melanoma tumor-line has been previously characterized by multiple technologies and is a benchmark for evaluating somatic alterations. In some of these studies, COLO829 has shown conflicting and/or indeterminate copy number and, thus, single-cell sequencing provides a tool for gaining insight. Following shallow single-cell sequencing, we first identified at least four major sub-clones by discriminant analysis of principal components of single-cell copy number data. Based on clustering, break-point and loss of heterozygosity analysis of aggregated data from sub-clones, we identified distinct hallmark events that were validated within bulk sequencing and spectral karyotyping. In summary, COLO829 exhibits a classical Dutrillaux’s monosomic/trisomic pattern of karyotype evolution with endoreduplication, where consistent sub-clones emerge from the loss/gain of abnormal chromosomes. Overall, our results demonstrate how shallow copy number profiling can uncover hidden biological insights

Joint analysis of three genome-wide association studies of esophageal squamous cell carcinoma in Chinese populations

We conducted a joint (pooled) analysis of three genome-wide association studies (GWAS) 1-3 of esophageal squamous cell carcinoma (ESCC) in ethnic Chinese (5,337 ESCC cases and 5,787 controls) with 9,654 ESCC cases and 10,058 controls for follow-up. In a logistic regression model adjusted for age, sex, study, and two eigenvectors, two new loci achieved genome-wide significance, marked by rs7447927 at 5q31.2 (per-allele odds ratio (OR) = 0.85, 95% CI 0.82-0.88; P=7.72x10−20) and rs1642764 at 17p13.1 (per-allele OR= 0.88, 95% CI 0.85-0.91; P=3.10x10−13). rs7447927 is a synonymous single nucleotide polymorphism (SNP) in TMEM173 and rs1642764 is an intronic SNP in ATP1B2, near TP53. Furthermore, a locus in the HLA class II region at 6p21.32 (rs35597309) achieved genome-wide significance in the two populations at highest risk for ESSC (OR=1.33, 95% CI 1.22-1.46; P=1.99x10−10). Our joint analysis identified new ESCC susceptibility loci overall as well as a new locus unique to the ESCC high risk Taihang Mountain region

Static and Discrete Berth Allocation for Large-Scale Marine-Loading Problem by Using Iterative Variable Grouping Genetic Algorithm

In this paper, we study the static discrete berth allocation problems (BAPs) for large-scale time-critical marine-loading scenarios. The objective is to allocate the vessels to different types of berths so that all the vessels can be loaded within the minimum time under the tidal condition. The BAP is formalized as a min–max problem. This problem is rather complex as the vessels and berths are quite numerous in the large-scale marine-loading problem. We analyze this problem from a novel perspective, and find out that this problem has the characteristic of partially separable. Therefore, the iterative variable grouping genetic algorithm (IVGGA) is designed to search the near-optimal berth allocation plans. The vessels and berths are divided into subgroups, and the genetic algorithm (GA) is applied to generate the near-optimal berth allocation plans in each subgroup. To achieve the balance of loading tasks among subgroups, we propose reallocating some vessels among subgroups according to the berth allocation plans in subgroups. To guarantee the convergency of the algorithm, an iterative vessel reallocation policy is devised considering the loading tasks of different types of berths. We demonstrate the proposed algorithm in dealing with large-scale BAPs through numerical experiments. According to the results, we find that the proposed algorithm would have good performance when the number of vessels in each subgroup are kept in medium scale. Compared with the original GA, our algorithm shows the effectiveness of the iterative variable grouping strategy. The performance of our algorithm is almost not changed as the number of vessels and berths increases. The proposed algorithm could obtain efficient berth allocation plans for the large-scale marine-loading problem