Epilepsy in Leigh Syndrome With Mitochondrial DNA Mutations

Background: Leigh syndrome is a mitochondrial cytopathy that presents as a neurodegenerative disease with apparent manifestation in the central nervous system. The aim of the present study was to describe its dominant neurological clinical features and analyze data related to epilepsy in Leigh syndrome accompanied by a mitochondrial DNA mutation.Methods: Whole mitochondrial sequencing was performed on 125 patients clinically suspected of Leigh syndrome. Among them, 25 patients were identified to have mitochondrial DNA associated Leigh syndrome. Electroencephalography (EEG) findings, semiology, brain imaging findings, and biochemical results, were evaluated. We also compared brain magnetic resonance imaging findings and biochemical features in patients with Leigh syndrome based on the presence of epilepsy.Results: Clinical seizures were observed in 14 out of 25 enrolled patients (56%), with focal seizures being the most common type (6/14, 42.8%). All patients were found to have slow and disorganized background neural activity while eight exhibited epileptic discharges on EEG. Mutations at base pairs 10,191 and 8,993 were revealed in a relatively larger number of patients of Leigh syndrome with epilepsy. The presence of gastrointestinal symptoms was significantly more frequent in the epilepsy group (P = 0.042). Diffuse cerebral atrophy was significantly increased (P = 0.042) and cortex signal abnormalities were also increased (P = 0.033) in the epilepsy group.Conclusions: Patients with Leigh syndrome and mitochondrial DNA mutations had a high proportion of central nervous system comorbidities, though the prevalence of epilepsy in this population was not particularly high. Various types of seizure and EEG findings are common in those with Leigh syndrome. Future imaging studies involving more patients and proper mitochondrial DNA mutation analyses are needed to further evaluate the natural course of Leigh syndrome with epilepsy

Picture Quality and Sound Quality of OLED TVs

Unlike the past when cathode-ray tube (CRT) dominated display industry, many different types of flat panel displays (FPDs) are now leading the industry. Of these, organic light-emitting diode (OLED) display has recently become a next-generation display since this display is recognised as having advantages over other competing technologies in picture quality and form factor. With major attributes of picture quality considered, a series of evaluations based on objective measures was performed with an OLED TV compared to an LCD TV. OLED TV outperformed LCD TV 100 times in black, 20 times in colour contrast, 30% in dynamic range coverage, 50 times in local contrast and 20 times in viewing angle. In addition, sound quality of the OLED TV was assessed using both objective and subjective evaluation methods compared to conventional TV speakers since OLED panel speaker technology was recently commercialised. The OLED panel speaker showed better performance both in objective and subjective methods

Low-Profile Dual-Wideband MIMO Antenna with Low ECC for LTE and Wi-Fi Applications

This paper presents a low-profile dual-wideband multiple input multiple output (MIMO) antenna with low envelop correlation coefficient (ECC) for long-term evolution (LTE) and wireless fidelity (Wi-Fi) applications. The antenna covers LTE band 7 and Wi-Fi as well as wireless broadband (Wibro) and Worldwide Interoperability for Microwave Access (WiMax) (except for the 3.5-GHz band). To aid with integration of a practical mobile terminal, the MIMO antenna elements are placed at appropriate locations by analyzing the surface current distribution and without using any additional isolation techniques. The measured bandwidths with reflection coefficients of <−10 dB are 36.8% in the range 2.02–2.93 GHz and 23.4% in the range 5.10–6.45 GHz. Isolation is satisfied to be >20 dB in the operating frequency ranges of both LTE band 7 and Wi-Fi. Additionally, the calculated ECC is in the range 0.005<ρ<0.025, which is considerably lower than the ρ<0.5 required for MIMO applications. The measured radiation patterns are appropriate for mobile terminals, and omnidirectional radiation patterns are obtained

Extending Data Quality Management for Smart Connected Product Operations

Smart connected product (SCP) operation embodies the concept of the internet of things (IoT). To increase the probability of success of SCP operations for customers, the high quality of the IoT data across operations is imperative. IoT data go beyond sensor data, as integrate some other various type of data such as timestamps, device metadata, business data, and external data through SCP operation processes. Therefore, traditional data-centric approaches that analyze sensor data and correct their errors are not enough to preserve, in long-term basis, adequate levels of quality of IoT data. This research provides and alternative framework of data quality management as a process-centric approach to improve the quality of IoT data. The proposed framework extends the process reference model (PRM) for data quality management (DQM) defined in ISO 8000-61, and tailored to fully adapt to the special requirements of the IoT data management. These involve several adaptations: first, the scope of the SCP operations for data quality management is determined, and the processes required for SCP operations are defined following the process description format of ISO 8000-61. Second, the relationship between the processes and the structure of the processes in the technology stack of the SCP operations are described to cover the actual nature of the IoT data flows. Finally, a new IoT DQM-PRM is proposed by integrating the processes for the SCP operations with DQM-PRM. When these processes are executed in the organization, the quality of IoT data composed of data of various types can be continuously improved and the utilization rate of SCP operations is expected to increase.We would like to acknowledge the financial support provided by the Korea-Spain joint R&D program of MOTIE (Project No. N0002610), CDTI (DQIoT, Project No. INNO-20171086) and EUREKA (Project No. E!11737). Also, we would like to acknowledge the ECLIPSE (RTI2018-094283-B-C31) and GEMA (SBPLY/17/180501/000293) projects

DAQUA-MASS: An ISO 8000-61 Based Data Quality Management Methodology for Sensor Data

The Internet-of-Things (IoT) introduces several technical and managerial challenges when it comes to the use of data generated and exchanged by and between various Smart, Connected Products (SCPs) that are part of an IoT system (i.e., physical, intelligent devices with sensors and actuators). Added to the volume and the heterogeneous exchange and consumption of data, it is paramount to assure that data quality levels are maintained in every step of the data chain/lifecycle. Otherwise, the system may fail to meet its expected function. While Data Quality (DQ) is a mature field, existing solutions are highly heterogeneous. Therefore, we propose that companies, developers and vendors should align their data quality management mechanisms and artefacts with well-known best practices and standards, as for example, those provided by ISO 8000-61. This standard enables a process-approach to data quality management, overcoming the difficulties of isolated data quality activities. This paper introduces DAQUA-MASS, a methodology based on ISO 8000-61 for data quality management in sensor networks. The methodology consists of four steps according to the Plan-Do-Check-Act cycle by Deming.This work was primarily funded by DQIoT project (Eureka program, E!11737; and CDTI (Centro Para el Desarrollo Tecnológico Industrial), INNO-20171086). Additionally, this work was partially funded by SEQUOIA project (TIN2015-63502-C3-1-R and TIN2015-63502-C3-3-R) (MINECO/FEDER); GEMA SBPLY/17/180501/000293, Consejería de Educación, Cultura y Deporte de la Dirección General de Universidades, Investigación e Innovación de la JCCM); ECD project (Evaluación y Certificación de la Calidad de Datos) (PTQ-16-08504) (Torres Quevedo Program, MINECO). Finally, it was also supported through a grant to Ricardo Pérez-Castillo enjoys from JCCM within the initiatives for talent retention and return in line with RIS3 goals

HLAscan: genotyping of the HLA region using next-generation sequencing data

Background Several recent studies showed that next-generation sequencing (NGS)-based human leukocyte antigen (HLA) typing is a feasible and promising technique for variant calling of highly polymorphic regions. To date, however, no method with sufficient read depth has completely solved the allele phasing issue. In this study, we developed a new method (HLAscan) for HLA genotyping using NGS data. Results HLAscan performs alignment of reads to HLA sequences from the international ImMunoGeneTics project/human leukocyte antigen (IMGT/HLA) database. The distribution of aligned reads was used to calculate a score function to determine correctly phased alleles by progressively removing false-positive alleles. Comparative HLA typing tests using public datasets from the 1000 Genomes Project and the International HapMap Project demonstrated that HLAscan could perform HLA typing more accurately than previously reported NGS-based methods such as HLAreporter and PHLAT. In addition, the results of HLA-A, −B, and -DRB1 typing by HLAscan using data generated by NextGen were identical to those obtained using a Sanger sequencing–based method. We also applied HLAscan to a family dataset with various coverage depths generated on the Illumina HiSeq X-TEN platform. HLAscan identified allele types of HLA-A, −B, −C, −DQB1, and -DRB1 with 100% accuracy for sequences at ≥ 90× depth, and the overall accuracy was 96.9%. Conclusions HLAscan, an alignment-based program that takes read distribution into account to determine true allele types, outperformed previously developed HLA typing tools. Therefore, HLAscan can be reliably applied for determination of HLA type across the whole-genome, exome, and target sequences

A comparative study on gene-set analysis methods for assessing differential expression associated with the survival phenotype

Abstract Background Many gene-set analysis methods have been previously proposed and compared through simulation studies and analysis of real datasets for binary phenotypes. We focused on the survival phenotype and compared the performances of Gene Set Enrichment Analysis (GSEA), Global Test (GT), Wald-type Test (WT) and Global Boost Test (GBST) methods in a simulation study and on two ovarian cancer data sets. We considered two versions of GSEA by allowing different weights: GSEA1 uses equal weights, yielding results similar to the Kolmogorov-Smirnov test; while GSEA2's weights are based on the correlation between genes and the phenotype. Results We compared GSEA1, GSEA2, GT, WT and GBST in a simulation study with various settings for the correlation structure of the genes and the association parameter between the survival outcome and the genes. Simulation results indicated that GT, WT and GBST consistently have higher power than GSEA1 and GSEA2 across all scenarios. However, the power of the five tests depends on the combination of correlation structure and association parameter. For the ovarian cancer data set, using the FDR threshold of q Conclusion Simulation studies and a real data example indicate that GT, WT and GBST tend to have high power, whereas GSEA1 and GSEA2 have lower power. We also found that the power of the five tests is much higher when genes are correlated than when genes are independent, when survival is positively associated with genes. It seems that there is a synergistic effect in detecting significant gene sets when significant genes have within-class correlation and the association between survival and genes is positive or negative (i.e., one-direction correlation).</p