• Childhood Kidney Diseases
• /
• v.17 no.2
• /
• pp.137-142
• /
• 2013

Pseudohypoaldosteronism (PHA) is a condition characterized by renal salt wasting, hyperkalemia, and metabolic acidosis due to renal tubular resistance to aldosterone. Systemic PHA1 is a more severe condition caused by defective transepithelial sodium transport due to mutations in the genes encoding the ${\alpha}$ (SCNN1A), ${\beta}$ (SCNN1B), or ${\gamma}$ (SCNN1G) subunits of the epithelial sodium channel at the collecting duct, and involves the sweat glands, salivary glands, colon, and lung. Although systemic PHA1 is a rare disease, we believe that genetic studies should be performed in patients with normal renal function but with high plasma renin and aldosterone levels, without a history of potassium-sparing diuretic use or obstructive uropathy. In the present report, we describe a case of autosomal recessive PHA1 that was genetically diagnosed in a newborn after severe hyperkalemia was noted.

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• v.15 no.12
• /
• pp.4345-4363
• /
• 2021

Deep Learning as a Service (DLaaS), utilizing the cloud-based deep neural network models to provide customer prediction services, has been widely deployed on mobile cloud computing (MCC). Such services raise privacy concerns since customers need to send private data to untrusted service providers. In this paper, we devote ourselves to building an efficient protocol to classify users' images using the convolutional neural network (CNN) model trained and held by the server, while keeping both parties' data secure. Most previous solutions commonly employ homomorphic encryption schemes based on Ring Learning with Errors (RLWE) hardness or two-party secure computation protocols to achieve it. However, they have limitations on large communication overheads and costs in MCC. To address this issue, we present LeHE4SCNN, a scalable privacy-preserving and communication-efficient framework for CNN-based DLaaS. Firstly, we design a novel low-expansion rate homomorphic encryption scheme with packing and unpacking methods (LeHE). It supports fast homomorphic operations such as vector-matrix multiplication and addition. Then we propose a secure prediction framework for CNN. It employs the LeHE scheme to compute linear layers while exploiting the data shuffling technique to perform non-linear operations. Finally, we implement and evaluate LeHE4SCNN with various CNN models on a real-world dataset. Experimental results demonstrate the effectiveness and superiority of the LeHE4SCNN framework in terms of response time, usage cost, and communication overhead compared to the state-of-the-art methods in the mobile cloud computing environment.

• Korean Journal of Poultry Science
• /
• v.44 no.1
• /
• pp.19-28
• /
• 2017

The eggshell is an intricate and highly ordered structure composed of multiple layers and a calcified matrix. The eggshell is formed at the uterine segment of the chicken oviduct. In this study, histological changes in the uterine endometrium and the expression of the eggshell-related genes were investigated according to hen age. We analyzed the expression of eggshell protein-related genes, such as OCX-32, OCX-36, OC-17, OC-116, and eggshell-ion-related genes, such as CABL-1, SPP1, SCNN1G, ATP2A2, CA2, and CALM1. In chicken uterine endometrium, histological deformation, fibrosis, atrophy and elimination of micro-villi were found with increasing hen age. The concentration of blood-ion components did not significantly change with age. The amount of telomeric DNA in uterine endometrial cells decreased with increasing hen age. The expression of most of the eggshell-related genes changed significantly with increasing hen age. The expression of some ovo-proteins, which play a role in eggshell formation, increased with increasing hen age; however, there were no significant correlations among eggshell protein genes. Eggshell ion-related genes, such as ATP2A2, SCNN1G, CA2, and CALM1, were closely related to each other. The OCX-32 and OCX-36 genes were closely related to some of the eggshell ion genes. Eggshell protein-related genes, such as the OCX-32, OCX-36 genes and ion-related genes such as CALB-1, ATP2A2, SCNN1G, CA2, CALM1, affected eggshell formation, mutually or independently. This study shows that, uterine although endometrial cell damage occurs with increasing hen age, normal eggshells can be formed in old hens. This suggests that eggshell protein-and eggshell ion-related genes also control the homeostasis of eggshell formation.

• The Journal of the Institute of Internet, Broadcasting and Communication
• /
• v.9 no.5
• /
• pp.205-211
• /
• 2009

The Weightless Neural Network(WNN) has the advantage of the processing speed, less computability than weighted neural network which readjusts the weight. Especially, The behavior information such as sequential gesture has many serial correlation. So, It is required the high computability and processing time to recognize. To solve these problem, Many algorithms used that added preprocessing and hardware interface device to reduce the computability and speed. In this paper, we proposed the Ram based Sequential Cumulative Neural Network(SCNN) model which is sign language recognition system without preprocessing and hardware interface. We experimented with using compound words in continuous korean sign language which was input binary image with edge detection from camera. The recognition system of sign language without preprocessing got 93% recognition rate.

• Korean Journal of Remote Sensing
• /
• v.38 no.6_1
• /
• pp.1505-1514
• /
• 2022

Image matching is a crucial preprocessing step for effective utilization of multi-temporal and multi-sensor very high resolution (VHR) satellite images. Deep learning (DL) method which is attracting widespread interest has proven to be an efficient approach to measure the similarity between image pairs in quick and accurate manner by extracting complex and detailed features from satellite images. However, Image matching of VHR satellite images remains challenging due to limitations of DL models in which the results are depending on the quantity and quality of training dataset, as well as the difficulty of creating training dataset with VHR satellite images. Therefore, this study examines the feasibility of DL-based method in matching pair extraction which is the most time-consuming process during image registration. This paper also aims to analyze factors that affect the accuracy based on the configuration of training dataset, when developing training dataset from existing multi-sensor VHR image database with bias for DL-based image matching. For this purpose, the generated training dataset were composed of correct matching pairs and incorrect matching pairs by assigning true and false labels to image pairs extracted using a grid-based Scale Invariant Feature Transform (SIFT) algorithm for a total of 12 multi-temporal and multi-sensor VHR images. The Siamese convolutional neural network (SCNN), proposed for matching pair extraction on constructed training dataset, proceeds with model learning and measures similarities by passing two images in parallel to the two identical convolutional neural network structures. The results from this study confirm that data acquired from VHR satellite image database can be used as DL training dataset and indicate the potential to improve efficiency of the matching process by appropriate configuration of multi-sensor images. DL-based image matching techniques using multi-sensor VHR satellite images are expected to replace existing manual-based feature extraction methods based on its stable performance, thus further develop into an integrated DL-based image registration framework.