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Alzheimer’s disease (AD) is a progressive neurodegenerative disease associated with a complex genetic etiology. Besides the apolipoprotein E ε4 (APOE ε4) allele, a few dozen other genetic loci associated with AD have been identified through genome-wide association studies (GWAS) conducted mainly in individuals of European ancestry. Recently, several GWAS performed in other ethnic groups have shown the importance of replicating studies that identify previously established risk loci and searching for novel risk loci. APOE-stratified GWAS have yielded novel AD risk loci that might be masked by, or be dependent on, APOE alleles. We performed whole-genome sequencing (WGS) on DNA from blood samples of 331 AD patients and 169 elderly controls of Korean ethnicity who were APOE ε4 carriers. Based on WGS data, we designed a customized AD chip (cAD chip) for further analysis on an independent set of 543 AD patients and 894 elderly controls of the same ethnicity, regardless of their APOE ε4 allele status. Combined analysis of WGS and cAD chip data revealed that SNPs rs1890078 (P = 6.64E−07) and rs12594991 (P = 2.03E−07) in SORCS1 and CHD2 genes, respectively, are novel genetic variants among APOE ε4 carriers in the Korean population. In addition, nine possible novel variants that were rare in individuals of European ancestry but common in East Asia were identified. This study demonstrates that APOE-stratified analysis is important for understanding the genetic background of AD in different populations.

Background The incidence and etiology of hepatocellular carcinoma (HCC) vary widely according to race and geographic regions. The insertional mutagenesis of adeno-associated virus 2 (AAV2) has recently been considered a new viral etiology of HCC. The aim of this study was to investigate the frequency and clinical characteristics of AAV2 in Korean patients with HCC. Methods A total of 289 unrelated Korean patients with HCC, including 159 Hepatitis-B-related cases, 16 Hepatitis-C-related cases, and 114 viral serology-negative cases, who underwent surgery at the Samsung Medical Center in Korea from 2009 to 2014 were enrolled in this study. The presence of AAV2 in fresh-frozen tumor tissues was investigated by DNA PCR and Sanger sequencing. The clinical and pathological characteristics of AAV2-associated HCC in these patients were compared with previous findings in French patients. Results The AAV2 detection rate in Korean patients (2/289) was very low compared with that in French patients (11/193). Similar to the French patients, the Korean patients with AAV2-related HCC showed no signs of liver cirrhosis. The Korean patients were younger than the French patients with the same AAV2-associated HCC; the ages at diagnosis of the two Korean patients were 47 and 39 yr, while the median age of the 11 French patients was 55 yr (range 43-90 yr). Conclusions AAV2-associated HCC was very rare in Korean patients with HCC. Despite a limited number of cases, this study is the first to report the clinical characteristics of Korean patients with AAV2-associated HCC. These findings suggest epidemiologic differences in viral hepatocarcinogenesis between Korean and European patients.

This is a report of a human proteome project (HPP) related to chromosome 9 (Chr 9). To reveal missing proteins and undiscovered features in proteogenomes, both LC-MS/MS analysis and next-generation RNA sequencing (RNA-seq)-based identification and characterization were conducted on five pairs of lung adenocarcinoma tumors and adjacent nontumor tissues. Before our previous Chromosome-Centric Human Proteome Project (C-HPP) special issue, there were 170 remaining missing proteins on Chr 9 (neXtProt 2013.09.26 rel.); 133 remain at present (neXtProt 2015.04.28 rel.). In the proteomics study, we found two missing protein candidates that require follow-up work and one unrevealed protein across all chromosomes. RNA-seq analysis detected RNA expression for four nonsynonymous (NS) single nucleotide polymorphisms (SNPs) (in CDH17, HIST1H1T, SAPCD2, and ZNF695) and three synonymous SNPs (in CDH17, CST1, and HNF1A) in all five tumor tissues but not in any of the adjacent normal tissues. By constructing a cancer patient sample-specific protein database based on individual RNA-seq data and by searching the proteomics data from the same sample, we identified four missense mutations in four genes (LTF, HDLBP, TF, and HBD). Two of these mutations were found in tumor samples but not in paired normal tissues. In summary, our proteogenomic study of human primary lung tumor tissues detected additional and revealed novel missense mutations and synonymous SNP signatures, some of which are specific to lung cancers. Data from mass spectrometry have been deposited in the ProteomeXchange with the identifier PXD002523.

The advent of resting-state (RS) functional magnetic resonance imaging (fMRI) technology has made it possible to classify Alzheimer's disease (AD) states based on the quantitative activity indices of brain regions. Current connectivity-based classification techniques suffer from limited reproducibility due to the need for prior knowledge on discriminative brain regions and intrinsic heterogeneity in the course of AD progression. Actually, similar challenges have been already addressed in molecular bioinformatics communities. They have achieved higher and reproducible classification accuracy and have identified interpretable markers by incorporating molecular pathway information in their classification. We have adopted a similar strategy to the RS-fMRI-based AD classification problem. After collecting various functional brain pathways from literature, we have quantified which pathways show significantly different activity levels between AD patients and healthy subjects. Moreover, discriminatory pathways between AD patients and healthy subjects may facilitate the interpretation of functional alterations in the course of AD progression.

Aquaculture systems need to provide artificial monitoring and control functions to maintain essential environmental factors within the high productivity ranges. This paper suggests that autonomous underwater vehicles (AUV) equipped with various sensors, communication devices, and navigational intelligence can perform effective and broad-range monitoring missions of cage-based aquaculture system surroundings. After explaining the target aquaculture system, we describe functional and performance requirements of AUVs for surveillance of coastal cage aquafarms. It also describes several design options considered for this ongoing project as well as artificial immune-based intelligence features.

Autonomous underwater vehicles(AUVs) have been drawing increasing interests in various marine applications such as coastal structure inspection, sea floor exploration, and oceanographic monitoring. Due to the complexity of underwater stream dynamics and the prevalence of unexpected underwater obstacles, it is imperative to develop self-adjustable, intelligent navigation control functions for AUVs. Among various control techniques, we focus on the proportional-integral-derivative(PID) controller since it is still one of the dominant techniques in actual underwater vehicle control systems. We propose to apply the Clonal Selection Algorithm to determine optimal combination of three gain coefficients, KP, KD, KI of the PID controller. Our simulation shows that the proposed technique provides better responses than the existing Ziegler-Nichols technique with respect to the settling time, overshoot and an affinity in submerging under water and turning the yaw angle through simulation. We expect that AUVs could autonomously regulate three coefficients of six degree-of-freedom(DOF) PID controllers through real-time onboard processing.

Cage aquafarm systems need to provide artificial monitoring and control functions to maintain essential environmental factors for the high productivity of marine products. Autonomous underwater vehicles (AUVs), which have been utilized for various ocean applications such as coastal structure inspection and undersea exploration, are emerging as effective candidate tools for aquafarm surveillance due to their capability of broad range navigation. This paper proposes a technique for intelligent navigation of AUVs around cage aquafarms based on the artificial immune technology. We adopt a clonal selection algorithm(CSA) to determine optimal navigation paths surrounding the aquafarms while reducing energy consumption.

Background Alzheimer's disease (AD) is a neurodegenerative and progressive disorder that results in brain malfunctions. Resting-state (RS) functional magnetic resonance imaging (fMRI) techniques have been successfully applied for quantifying brain activities of both Alzheimer's disease (AD) and amnestic mild cognitive impairment (aMCI) patients. Region-based approaches are widely utilized to classify patients from cognitively normal subjects (CN). Nevertheless, region-based approaches have a few limitations, reproducibility owing to selection of disease-specific brain regions, and heterogeneity of brain activities during disease progression. For coping with these issues, network-based approaches have been suggested in the field of molecular bioinformatics. In comparison with individual gene-based approaches, they acquired more accurate results in diverse disease classification, and reproducibility was confirmed by replication studies. In our work, we applied a similar methodology integrating brain pathway information into pathway activity inference, and permitting classification of both aMCI and AD patients based on pathway activities rather than single region activities. Results After aggregating the 59 brain pathways from literature, we estimated brain pathway activities by using exhaustive search algorithms between patients and cognitively normal subjects, and identified discriminatory pathways according to disease progression. We used three different data sets and each data set consists of two different groups. Our results show that the pathway-based approach (AUC = 0.89, 0.9, 0.75) outperformed the region-based approach (AUC = 0.69, 0.8, 0.68). Also, our approach provided enhanced diagnostic power achieving higher accuracy, sensitivity, and specificity (pathway-based approach: accuracy = 83%; sensitivity = 86%; specificity = 78%, region-based approach: accuracy = 74%; sensitivity = 78%; specificity = 76%). Conclusions We proposed a novel method inferring brain pathway activities for disease classification. Our approach shows better classification performance than region-based approach in four classification models. We expect that brain pathway-based approach would be helpful for precise classification of brain disorders, and provide new opportunities for uncovering disrupted brain pathways caused by disease. Moreover, discriminatory pathways between patients and cognitively normal subjects may facilitate the interpretation of functional alterations during disease progression.