Genotyping of the G1138A mutation of the FGFR3 gene in patients with achondroplasia using high-resolution melting analysis

[[abstract]]Objectives: The fibroblast growth factor receptor 3 gene (FGFR3) plays a critical role in cartilage growth-plate differentiation and bony development. It has been shown that 97% of patients with achondroplasia have a G to A transition mutation at position 1138 (c.1138 G>A) of codon 380 of the FGFR3 gene. Design and methods: Exon 8 of the FGFR3 gene was analyzed in 40 patients with achondroplasia, as well as in 50 control individuals for the presence of the c.1138G>A variant using melting curve analysis with a high-resolution melting instrument (HR-1). Results: The high-resolution melting curve analysis successfully genotyped the c.1138G>A mutation in exon 8 of the FGFR3 gene in all 40 patients with achondroplasia without the need of further assays. The technique had a sensitivity and specificity of 100%. Conclusion: High-resolution melting analysis is a simple, rapid, and sensitive one tube assay for genotyping the FGFR3 gene. The technique is a low cost high-throughput FGFR3 screening assay. (c) 2007 The Canadian Society of Clinical Chemists. Published by Elsevier Inc. All rights reserved

Assessing the Integrity of Spillway Foundations

The erosion under a spillway can be a long-term issue that threatens the structural integrity of a water reservoir. The spillway under investigation was suspected to be defective after it had been commissioned in 1987 (Figure 1). Potholes and subsurface cavities were confirmed in the safety assessment using various NDT techniques including ground penetrating radar and impact echo. The GPR inspection was able to differentiate the intact region from the cavities under concrete slabs (Figure 2). The impact echo results and associated analyses provided further evidence of inferior condition in the soil under the concrete slabs. The engineering team designed and executed the repair project based on the conclusion of the integrity assessment. Subsequent GPR inspection has been performed so as to monitor the integrity of the spillway in a period of 18 months following the repair

Traditional Chinese Medicine Diagnosis “ Yang-Xu Zheng

Pathogenesis of sepsis includes complex interaction between pathogen activities and host response, manifesting highly variable signs and symptoms, possibly delaying diagnosis and timely life-saving interventions. This study applies traditional Chinese medicine (TCM) Zheng diagnosis in patients with severe sepsis and septic shock to evaluate its adaptability and use as an early predictor of sepsis mortality. Three-year prospective observational study enrolled 126 septic patients. TCM Zheng diagnosis, Acute Physiology and Chronic Health Evaluation (APACHE) II score, and blood samples for host response cytokines measurement (tumor necrosis factor-α, Interleukin-6, Interleukin-8, Interleukin-10, Interleukin-18) were collected within 24 hours after admission to Intensive Care Unit. Main outcome was 28-day mortality; multivariate logistic regression analysis served to determine predictive variables of the sepsis mortality. APACHE II score, frequency of Nutrient-phase heat, and Qi-Xu and Yang-Xu Zhengs were significantly higher in nonsurvivors. The multivariate logistic regression analysis identified Yang-Xu Zheng as the outcome predictor. APACHE II score and levels of five host response cytokines between patients with and without Yang-Xu Zheng revealed significant differences. Furthermore, cool extremities and weak pulse, both diagnostic signs of Yang-Xu Zheng, were also proven independent predictors of sepsis mortality. TCM diagnosis “Yang-Xu Zheng” may provide a new mortality predictor for septic patients

The research of hidden Markov models for overall equipment effectiveness analysis in smart manufacturing system

In the manufacturing industry, excellent product quality and increased production flexibility can be achieved by eliminating waste and improving production efficiency. In the past, the manufacturing industry used manual records of production information, but this method is characterized by low efficiency and high error rates. Even if a programmable logic controller and radio-frequency identification are employed, problems still occur because of constraints such as different machine types and high costs. The use of a cyber–physical system and information visualization requires the collection of manufacturing information in order to facilitate the analysis of manufacturing data. Monitoring the machining status. This study proposes an approach for segmenting machine-processed signals. With plug-and-play noninvasive current-sensing equipment to collect machine production information, this approach can immediately determine the state of the manufacturing process and calculate the machine utilization, machine production cycle, and production quantity. The goal is to enable the use of this method with this equipment, improve machine utilization, instantly identify the production quantity, and reduce equipment idle time to reduce manufacturing waste, thus rendering production management more convenient and faster

Machine Learning-based Indoor Positioning Systems Using Multi-Channel Information

The received signal strength indicator (RSSI) is a metric of the power measured by a sensor in a receiver. Many indoor positioning technologies use RSSI to locate objects in indoor environments. Their positioning accuracy is significantly affected by reflection and absorption from walls, and by non-stationary objects such as doors and people. Therefore, it is necessary to increase transceivers in the environment to reduce positioning errors. This paper proposes an indoor positioning technology that uses the machine learning algorithm of channel state information (CSI) combined with fingerprinting. The experimental results showed that the proposed method outperformed traditional RSSI-based localization systems in terms of average positioning accuracy up to 6.13% and 54.79% for random forest (RF) and back propagation neural networks (BPNN), respectively

Machine Learning-based Indoor Positioning Systems Using Multi-Channel Information

The received signal strength indicator (RSSI) is a metric of the power measured by a sensor in a receiver. Many indoor positioning technologies use RSSI to locate objects in indoor environments. Their positioning accuracy is significantly affected by reflection and absorption from walls, and by non-stationary objects such as doors and people. Therefore, it is necessary to increase transceivers in the environment to reduce positioning errors. This paper proposes an indoor positioning technology that uses the machine learning algorithm of channel state information (CSI) combined with fingerprinting. The experimental results showed that the proposed method outperformed traditional RSSI-based localization systems in terms of average positioning accuracy up to 6.13% and 54.79% for random forest (RF) and back propagation neural networks (BPNN), respectively

A Shoulder Surfing Resistant Graphical Authentication System

Authentication based on passwords is used largely in applications for computer security and privacy. However, human actions such as choosing bad passwords and inputting passwords in an insecure way are regarded as ”the weakest link” in the authentication chain. Rather than arbitrary alphanumeric strings, users tend to choose passwords either short or meaningful for easy memorization. With web applications and mobile apps piling up, people can access these applications anytime and anywhere with various devices. This evolution brings great convenience but also increases the probability of exposing passwords to shoulder surfing attacks. Attackers can observe directly or use external recording devices to collect users’ credentials. To overcome this problem, we proposed a novel authentication system PassMatrix, based on graphical passwords to resist shoulder surfing attacks. With a one-time valid login indicator and circulative horizontal and vertical bars covering the entire scope of pass-images, PassMatrix offers no hint for attackers to figure out or narrow down the password even they conduct multiple camera-based attacks. We also implemented a PassMatrix prototype on Android and carried out real user experiments to evaluate its memorability and usability. From the experimental result, the proposed system achieves better resistance to shoulder surfing attacks while maintaining usability