Determination of Malignant and Invasive Predictors in Branch Duct Type Intraductal Papillary Mucinous Neoplasms of the Pancreas: A Suggested Scoring Formula

Prediction of malignancy or invasiveness of branch duct type intraductal papillary mucinous neoplasm (Br-IPMN) is difficult, and proper treatment strategy has not been well established. The authors investigated the characteristics of Br-IPMN and explored its malignancy or invasiveness predicting factors to suggest a scoring formula for predicting pathologic results. From 1994 to 2008, 237 patients who were diagnosed as Br-IPMN at 11 tertiary referral centers in Korea were retrospectively reviewed. The patients' mean age was 63.1 ± 9.2 yr. One hundred ninty-eight (83.5%) patients had nonmalignant IPMN (81 adenoma, 117 borderline atypia), and 39 (16.5%) had malignant IPMN (13 carcinoma in situ, 26 invasive carcinoma). Cyst size and mural nodule were malignancy determining factors by multivariate analysis. Elevated CEA, cyst size and mural nodule were factors determining invasiveness by multivariate analysis. Using the regression coefficient for significant predictors on multivariate analysis, we constructed a malignancy-predicting scoring formula: 22.4 (mural nodule [0 or 1]) + 0.5 (cyst size [mm]). In invasive IPMN, the formula was expressed as invasiveness-predicting score = 36.6 (mural nodule [0 or 1]) + 32.2 (elevated serum CEA [0 or 1]) + 0.6 (cyst size [mm]). Here we present a scoring formula for prediction of malignancy or invasiveness of Br-IPMN which can be used to determine a proper treatment strategy

Acoustic Sensor Based Recognition of Human Activity in Everyday Life for Smart Home Services

A novel activity recognition method is proposed based on acoustic information acquired from microphones in an unobtrusive and privacy-preserving manner. Behavior detection mechanisms may be useful in context-aware domains in everyday life, but they may be inaccurate, and privacy violation is a concern. For example, vision-based behavior detection using cameras is difficult to apply in a private space such as a home, and inaccuracies in identifying user behaviors reduce acceptance of the technology. In addition, activity recognition using wearable sensors is very uncomfortable and costly to apply for commercial purposes. In this study, an acoustic information-based behavior detection algorithm is proposed for use in private spaces. This system classifies human activities using acoustic information. It combines strategies of elimination and similarity and establishes new rules. The performance of the proposed algorithm was compared with that of commonly used classification algorithms such as case-based reasoning, k-nearest neighbors, support vector machine, and multiple regression

Bottleneck-Stationary Compact Model Accelerator With Reduced Requirement on Memory Bandwidth for Edge Applications

State-of-the-art compact models such as MobileNets and EfficientNets are structured using a linear bottleneck and inverted residuals. Hardware architecture using a single dataflow strategy fails to balance the required memory bandwidth with the given computational resources. This work presents a heterogeneous dual-core accelerator that performs a block-wise pipelined process as a unit using a bottleneck-stationary (BS) dataflow. The BS greatly relieves the requirement on DRAM bandwidth and on-chip SRAM capacity. A look-behind-only attention is also proposed as a co-optimized algorithm. Compared to the state-of-the-art hardware scheme, the proposed accelerator demonstrates a reduction of 1.8-2.9 in latency and 2.2-3 in energy consumption, respectively.For verification, the accelerator with a 16-bit integer precision was implemented using 28nm CMOS process. Measurements show energy efficiencies of 0.5-to-3.75 TOPS/W in a supply voltage range of 0.55-to-1.15V. IEEE11Nsciescopu

Analysis of sensory system aspects of postural stability during quiet standing in adolescent idiopathic scoliosis patients

Abstract Background The aim of this study was to quantitatively analyze quite standing postural stability of adolescent idiopathic scoliosis (AIS) patients in respect to three sensory systems (visual, vestibular, and somatosensory). Method In this study, we analyzed the anterior-posterior center of pressure (CoP) signal using discrete wavelet transform (DWT) between AIS patients (n = 32) and normal controls (n = 25) during quiet standing. Result The energy rate (∆E EYE %) of the CoP signal was significantly higher in the AIS group than that in the control group at levels corresponding to vestibular and somatosensory systems (p < 0.01). Conclusions This implies that AIS patients use strategies to compensate for possible head position changes and spinal asymmetry caused by morphological deformations of the spine through vestibular and somatosensory systems. This could be interpreted that such compensation could help them maintain postural stability during quiet standing. The interpretation of CoP signal during quiet standing in AIS patients will improve our understanding of changes in physical exercise ability due to morphological deformity of the spine. This result is useful for evaluating postural stability before and after treatments (spinal fusion, bracing, rehabilitation, and so on)

Development of Data Cleaning and Integration Algorithm for Asset Management of Power System

Asset management technology is rapidly growing in the electric power industry because utilities are paying attention to which of their aged assets should be replaced first. The global trend of asset management follows risk management that comprehensively considers the probability and consequences of failures. In the asset management system, the risk assessment algorithm operates by interfacing digital datasets from various legacy systems. In this study, among the various electric power assets, we consider transmission cable systems as a representative linear asset consisting of different segments. First, the configurations and characteristics of linear asset datasets are analyzed. Second, six types of data cleaning functions are proposed for extracting dirty data from the entire dataset. Third, three types of data integration functions are developed to simulate the risk assessment algorithm. This technique supports the integration of distributed asset data in various legacy systems into one dataset. Finally, an automatic data cleaning and integration system is developed and the algorithm could repeat the cleaning and integration process until data quality is satisfied. To evaluate the performance of the proposed system, an automatic cleaning process is demonstrated using actual legacy datasets

Development of Data Cleaning and Integration Algorithm for Asset Management of Power System

Asset management technology is rapidly growing in the electric power industry because utilities are paying attention to which of their aged assets should be replaced first. The global trend of asset management follows risk management that comprehensively considers the probability and consequences of failures. In the asset management system, the risk assessment algorithm operates by interfacing digital datasets from various legacy systems. In this study, among the various electric power assets, we consider transmission cable systems as a representative linear asset consisting of different segments. First, the configurations and characteristics of linear asset datasets are analyzed. Second, six types of data cleaning functions are proposed for extracting dirty data from the entire dataset. Third, three types of data integration functions are developed to simulate the risk assessment algorithm. This technique supports the integration of distributed asset data in various legacy systems into one dataset. Finally, an automatic data cleaning and integration system is developed and the algorithm could repeat the cleaning and integration process until data quality is satisfied. To evaluate the performance of the proposed system, an automatic cleaning process is demonstrated using actual legacy datasets

Wireless Smart Contact Lens for Diabetic Diagnosis and Therapy

A smart contact lens can be used as an excellent interface between the human body and an electronic device for wearable healthcare applications. Despite wide investigations of smart contact lenses for diagnostic applications, there has been no report on electrically controlled drug delivery in combination with real-time biometric analysis. Here, we developed smart contact lenses for both continuous glucose monitoring and treatment of diabetic retinopathy. The smart contact lens device, built on a biocompatible polymer, contains ultrathin, flexible electrical circuits and a microcontroller chip for real-time electrochemical biosensing, on-demand controlled drug delivery, wireless power management, and data communication. In diabetic rabbit models, we could measure tear glucose levels to be validated by the conventional invasive blood glucose tests and trigger drugs to be released from reservoirs for treating diabetic retinopathy. Together, we successfully demonstrated the feasibility of smart contact lenses for noninvasive and continuous diabetic diagnosis and diabetic retinopathy therapy.11Ysciescopu