Cardiac Dysrhythmias, Cardiomyopathy and Muscular Dystrophy in Patients with Emery-Dreifuss Muscular Dystrophy and Limb-Girdle Muscular Dystrophy Type 1B

Emery-Dreifuss muscular dystrophy (EDMD) and limb-girdle muscular dystrophy type 1B (LGMD1B) are characterized by cardiac dysrhythmias, late-onset cardiomyopathy, slowly progressive skeletal myopathy and contractures of the neck, elbows and ankles. The causative mutation is either in the emerin gene (X-linked recessive EDMD) or lamin A/C gene (autosomal dominant EDMD2 or LGMD1B). We report three cases of EDMD, EDMD2 and LGMD1B. A 14-yr-old boy showed limitation of cervical flexion and contractures of both elbows and ankles. Sinus arrest with junctional escape beats was noted. He was diagnosed as X-linked recessive EDMD (MIM 310300). A 28-yr-old female showed severe wasting and weakness of humeroperoneal muscles. Marked limitation of cervical flexion and contractures of both elbows and ankles were noted. Varying degrees of AV block were noted. She was diagnosed as autosomal dominant EDMD2 (MIM 181350). A 41-yr-old female had contractures of both ankles and limb-girdle type muscular dystrophy. ECG revealed atrial tachycardia with high grade AV block. She was diagnosed as autosomal dominant LGMD1B (MIM 159001). Cardiac dysrhythmias in EDMD and LGMD1B include AV block, bradycardia, atrial tachycardia, atrial fibrillation, and atrial standstill, causing sudden death necessitating pacemaker implantation. Cardiologists should know about these unusual genetic diseases with conduction defects, especially in young adults

Spontaneous Oscillatory Rhythm in Retinal Activities of Two Retinal Degeneration (rd1 and rd10) Mice

Previously, we reported that besides retinal ganglion cell (RGC) spike, there is ~ 10 Hz oscillatory rhythmic activity in local field potential (LFP) in retinal degeneration model, rd1 mice. The more recently identified rd10 mice have a later onset and slower rate of photoreceptor degeneration than the rd1 mice, providing more therapeutic potential. In this study, before adapting rd10 mice as a new animal model for our electrical stimulation study, we investigated electrical characteristics of rd10 mice. From the raw waveform of recording using 8×8 microelectrode array (MEA) from in vitro-whole mount retina, RGC spikes and LFP were isolated by using different filter setting. Fourier transform was performed for detection of frequency of bursting RGC spikes and oscillatory field potential (OFP). In rd1 mice, ~10 Hz rhythmic burst of spontaneous RGC spikes is always phase-locked with the OFP and this phase-locking property is preserved regardless of postnatal ages. However, in rd10 mice, there is a strong phase-locking tendency between the spectral peak of bursting RGC spikes (~5 Hz) and the first peak of OFP (~5 Hz) across different age groups. But this phase-locking property is not robust as in rd1 retina, but maintains for a few seconds. Since rd1 and rd10 retina show phase-locking property at different frequency (~10 Hz vs. ~5 Hz), we expect different response patterns to electrical stimulus between rd1 and rd10 retina. Therefore, to extract optimal stimulation parameters in rd10 retina, first we might define selection criteria for responding rd10 ganglion cells to electrical stimulus

Characteristics of pediatric rhabdomyolysis and the associated risk factors for acute kidney injury: a retrospective multicenter study in Korea

Background The clinical features of pediatric rhabdomyolysis differ from those of the adults with rhabdomyolysis; however, multicenter studies are lacking. This study aimed to investigate the characteristics of pediatric rhabdomyolysis and reveal the risk factors for acute kidney injury (AKI) in such cases. Methods This retrospective study analyzed the medical records of children and adolescents diagnosed with rhabdomyolysis at 23 hospitals in South Korea between January 2007 and December 2016. Results Among 880 patients, those aged 3 to 5 years old composed the largest subgroup (19.4%), and all age subgroups were predominantly male. The incidence of AKI was 11.3%. Neurological disorders (53.6%) and infection (39.0%) were the most common underlying disorder and cause of rhabdomyolysis, respectively. The median age at diagnosis in the AKI subgroup was older than that in the non-AKI subgroup (12.2 years vs. 8.0 years). There were no significant differences in body mass index, myalgia, dark-colored urine, or the number of causal factors between the two AKI-status subgroups. The multivariate logistic regression model indicated that the following factors were independently associated with AKI: multiorgan failure, presence of an underlying disorder, strong positive urine occult blood, increased aspartate aminotransferase and uric acid levels, and reduced calcium levels. Conclusions Our study revealed characteristic clinical and laboratory features of rhabdomyolysis in a Korean pediatric population and highlighted the risk factors for AKI in these cases. Our findings will contribute to a greater understanding of pediatric rhabdomyolysis and may enable early intervention against rhabdomyolysis-induced AKI

Prediction of cognitive impairment via deep learning trained with multi-center neuropsychological test data

Background Neuropsychological tests (NPTs) are important tools for informing diagnoses of cognitive impairment (CI). However, interpreting NPTs requires specialists and is thus time-consuming. To streamline the application of NPTs in clinical settings, we developed and evaluated the accuracy of a machine learning algorithm using multi-center NPT data. Methods Multi-center data were obtained from 14,926 formal neuropsychological assessments (Seoul Neuropsychological Screening Battery), which were classified into normal cognition (NC), mild cognitive impairment (MCI) and Alzheimers disease dementia (ADD). We trained a machine learning model with artificial neural network algorithm using TensorFlow (https://www.tensorflow.org) to distinguish cognitive state with the 46-variable data and measured prediction accuracies from 10 randomly selected datasets. The features of the NPT were listed in order of their contribution to the outcome using Recursive Feature Elimination. Results The ten times mean accuracies of identifying CI (MCI and ADD) achieved by 96.66 ± 0.52% of the balanced dataset and 97.23 ± 0.32% of the clinic-based dataset, and the accuracies for predicting cognitive states (NC, MCI or ADD) were 95.49 ± 0.53 and 96.34 ± 1.03%. The sensitivity to the detection CI and MCI in the balanced dataset were 96.0 and 96.0%, and the specificity were 96.8 and 97.4%, respectively. The time orientation and 3-word recall score of MMSE were highly ranked features in predicting CI and cognitive state. The twelve features reduced from 46 variable of NPTs with age and education had contributed to more than 90% accuracy in predicting cognitive impairment. Conclusions The machine learning algorithm for NPTs has suggested potential use as a reference in differentiating cognitive impairment in the clinical setting.The publication costs, design of the study, data management and writing the manuscript for this article were supported by the Ministry of Education of the Republic of Korea and the National Research Foundation of Korea (NRF-2017S1A6A3A01078538), Korea Ministry of Health & Welfare, and from the Original Technology Research Program for Brain Science through the National Research Foundation of Korea funded by the Korean Government (MSIP; No. 2014M3C7A1064752)

New Era of Air Quality Monitoring from Space: Geostationary Environment Monitoring Spectrometer (GEMS)

GEMS will monitor air quality over Asia at unprecedented spatial and temporal resolution from GEO for the first time, providing column measurements of aerosol, ozone and their precursors (nitrogen dioxide, sulfur dioxide and formaldehyde). Geostationary Environment Monitoring Spectrometer (GEMS) is scheduled for launch in late 2019 - early 2020 to monitor Air Quality (AQ) at an unprecedented spatial and temporal resolution from a Geostationary Earth Orbit (GEO) for the first time. With the development of UV-visible spectrometers at sub-nm spectral resolution and sophisticated retrieval algorithms, estimates of the column amounts of atmospheric pollutants (O3, NO2, SO2, HCHO, CHOCHO and aerosols) can be obtained. To date, all the UV-visible satellite missions monitoring air quality have been in Low Earth orbit (LEO), allowing one to two observations per day. With UV-visible instruments on GEO platforms, the diurnal variations of these pollutants can now be determined. Details of the GEMS mission are presented, including instrumentation, scientific algorithms, predicted performance, and applications for air quality forecasts through data assimilation. GEMS will be onboard the GEO-KOMPSAT-2 satellite series, which also hosts the Advanced Meteorological Imager (AMI) and Geostationary Ocean Color Imager (GOCI)-2. These three instruments will provide synergistic science products to better understand air quality, meteorology, the long-range transport of air pollutants, emission source distributions, and chemical processes. Faster sampling rates at higher spatial resolution will increase the probability of finding cloud-free pixels, leading to more observations of aerosols and trace gases than is possible from LEO. GEMS will be joined by NASA's TEMPO and ESA's Sentinel-4 to form a GEO AQ satellite constellation in early 2020s, coordinated by the Committee on Earth Observation Satellites (CEOS)

QoS-guaranteed scheduling for small cell networks

This paper proposes a QoS-guaranteed scheduling algorithm that considers the available resources and traffic load of small cell. Even though the available resources are scarce or traffics to be scheduled are overloaded, Guaranteed Bit Rate (GBR) traffic such as VoIP and video should be supported. The proposed algorithm provides a flexible weight ratio between GBR and Non-GBR traffic according to resources and traffic load. Furthermore, since QoS is considered by simple QoS Class Identifier (QCI), low complexity is achieved. Simulation results show that the proposed algorithm improves QoS including delay and Packet Loss Rate (PLR) performance effectively. Keywords: QoS, Scheduling, Traffic load, Guaranteed Bit Rate, Small cel

Experimental and Numerical Study of the Aerodynamic Characteristics of an Archimedes Spiral Wind Turbine Blade

A new type of horizontal axis wind turbine adopting the Archimedes spiral blade is introduced for urban-use. Based on the angular momentum conservation law, the design formula for the blade was derived using a variety of shape factors. The aerodynamic characteristics and performance of the designed Archimedes wind turbine were examined using computational fluid dynamics (CFD) simulations. The CFD simulations showed that the new type of wind turbine produced a power coefficient (Cp) of approximately 0.25, which is relatively high compared to other types of urban-usage wind turbines. To validate the CFD results, experimental studies were carried out using a scaled-down model. The instantaneous velocity fields were measured using the two-dimensional particle image velocimetry (PIV) method in the near field of the blade. The PIV measurements revealed the presence of dominant vortical structures downstream the hub and near the blade tip. The interaction between the wake flow at the rotor downstream and the induced velocity due to the tip vortices were strongly affected by the wind speed and resulting rotational speed of the blade. The mean velocity profiles were compared with those predicted by the steady state and unsteady state CFD simulations. The unsteady CFD simulation agreed better with those of the PIV experiments than the steady state CFD simulations