A machine-learning approach to predict postprandial hypoglycemia

Background For an effective artificial pancreas (AP) system and an improved therapeutic intervention with continuous glucose monitoring (CGM), predicting the occurrence of hypoglycemia accurately is very important. While there have been many studies reporting successful algorithms for predicting nocturnal hypoglycemia, predicting postprandial hypoglycemia still remains a challenge due to extreme glucose fluctuations that occur around mealtimes. The goal of this study is to evaluate the feasibility of easy-to-use, computationally efficient machine-learning algorithm to predict postprandial hypoglycemia with a unique feature set. Methods We use retrospective CGM datasets of 104 people who had experienced at least one hypoglycemia alert value during a three-day CGM session. The algorithms were developed based on four machine learning models with a unique data-driven feature set: a random forest (RF), a support vector machine using a linear function or a radial basis function, a K-nearest neighbor, and a logistic regression. With 5-fold cross-subject validation, the average performance of each model was calculated to compare and contrast their individual performance. The area under a receiver operating characteristic curve (AUC) and the F1 score were used as the main criterion for evaluating the performance. Results In predicting a hypoglycemia alert value with a 30-min prediction horizon, the RF model showed the best performance with the average AUC of 0.966, the average sensitivity of 89.6%, the average specificity of 91.3%, and the average F1 score of 0.543. In addition, the RF showed the better predictive performance for postprandial hypoglycemic events than other models. Conclusion In conclusion, we showed that machine-learning algorithms have potential in predicting postprandial hypoglycemia, and the RF model could be a better candidate for the further development of postprandial hypoglycemia prediction algorithm to advance the CGM technology and the AP technology further.11Ysciescopu

The Development of Web3D-based Open-pit Mine Monitoring System

Large-scale open-pit mines are critical infrastructure for acquiring natural resources. However, this type of mine can experience environmental and safety problems during operations and thus requires continuous monitoring. In this study, a web three-dimensional(3D)-based monitoring system is constructed using geospatial information open platform and open-source geospatial information software which targets open-pit mines in Gangwon-do, Korea. The purpose is to develop a monitoring system of open-pit mines that enables any person to monitor the topographic and environmental changes caused by mine operations and to develop and restore the area’s ecology. Open-pit mines were classified into active or inactive mines and monitoring items and methodologies were established for each type of mine. Cesium which is a WebGL-based open-source platform was chosen as it supports dynamic data visualization and hardware-accelerated graphics related to elapsed time which is the essential factor in the monitoring. The open-pit mine monitoring system was developed based on the geospatial database which contains information required for mine monitoring as time elapses, and by developing the open-source-based system software. The geospatial information database for monitoring consists of digital imagery and terrain data and also includes vector data and the restoration plan datas. The basic geospatial information used in the monitoring includes high resolution ortho image(GSD 0.5 m or above) for all areas of the mines. This is acquired by periodically using an airborne laser scanning system and a LiDAR DEM (grid size 1m × 1 m). In addition, geospatial information data were acquired by using an UAV and terrestrial LiDAR for small-scale areas; these tools are frequently used for rapid and irregular data acquisition. The geospatial information acquired for the monitoring of the open-pit mines represents various spatial resolutions and different terrain data. The database was constructed by converging this geospatial information with the Cesium-based geospatial information open platform of the ESRI World Imagery map and with SDK World Terrain meshes. The problems that resulted from the process of fusing aerial imagery and terrain data were solved in the Cesium-based open source environment. The prototype menu for the monitoring system was designed according to the monitoring item which was determined by the type of mine. The scene of the mine and changes in terrain were controlled and analyzed using the raster function of PostGIS according to the elapsed time. Using the GeoServer, the aerial imagery, terrain and restoration information for each period were serviced using the web standard interface, and the monitoring system was completed by visualizing these elements in Cesium in 3D format according to the elapsed time. This study has established a monitoring methodology for open-pit mines according to the type of mine and proposes a method for upgrading the imagery and terrain data required for monitoring. The study also showed the possibility of developing a Web3D-based open-pit mine monitoring system that is applicable to a wide range of mashup service developments

Prediction of Daytime Hypoglycemic Events Using Continuous Glucose Monitoring Data and Classification Technique

Daytime hypoglycemia should be accurately predicted to achieve normoglycemia and to avoid disastrous situations. Hypoglycemia, an abnormally low blood glucose level, is divided into daytime hypoglycemia and nocturnal hypoglycemia. Many studies of hypoglycemia prevention deal with nocturnal hypoglycemia. In this paper, we propose new predictor variables to predict daytime hypoglycemia using continuous glucose monitoring (CGM) data. We apply classification and regression tree (CART) as a prediction method. The independent variables of our prediction model are the rate of decrease from a peak and absolute level of the BG at the decision point. The evaluation results showed that our model was able to detect almost 80% of hypoglycemic events 15 min in advance, which was higher than the existing methods with similar conditions. The proposed method might achieve a real-time prediction as well as can be embedded into BG monitoring device.1

Lasing on nonlinear localized waves in curved geometry

The use of geometrical constraints opens many new perspectives in photonics and in fundamental studies of nonlinear waves. By implementing surface structures in vertical cavity surface emitting lasers as manifolds for curved space, we experimentally study the impacts of geometrical constraints on nonlinear wave localization. We observe localized waves pinned to the maximal curvature in an elliptical-ring, and confirm the reduction in the localization length of waves by measuring near and far field patterns, as well as the corresponding dispersion relation. Theoretically, analyses based on a dissipative model with a parabola curve give good agreement remarkably to experimental measurement on the transition from delocalized to localized waves. The introduction of curved geometry allows to control and design lasing modes in the nonlinear regime.Comment: 6 pages, 6 figure

NIST 2007 Language Recognition Evaluation: From the Perspective of IIR

PACLIC / The University of the Philippines Visayas Cebu College Cebu City, Philippines / November 20-22, 200

Ferroelastic-switching-driven colossal shear strain and piezoelectricity in a hybrid ferroelectric

Materials that can produce large controllable strains are widely used in shape memory devices, actuators and sensors. Great efforts have been made to improve the strain outputs of various material systems. Among them, ferroelastic transitions underpin giant reversible strains in electrically-driven ferro/piezoelectrics and thermally- or magneticallydriven shape memory alloys. However, large-strain ferroelastic switching in conventional ferroelectrics is very challenging while magnetic and thermal controls are not desirable for applications. Here, we demonstrate an unprecedentedly large shear strain up to 21.5 % in a hybrid ferroelectric, C6H5N(CH3)3CdCl3. The strain response is about two orders of magnitude higher than those of top-performing conventional ferroelectric polymers and oxides. It is achieved via inorganic bond switching and facilitated by the structural confinement of the large organic moieties, which prevents the undesired 180-degree polarization switching. Furthermore, Br substitution can effectively soften the bonds and result in giant shear piezoelectric coefficient (d35 ~ 4800 pm/V) in Br-rich end of the solid solution, C6H5N(CH3)3CdBr3xCl3(1-x). The superior electromechanical properties of the compounds promise their potential in lightweight and high energy density devices, and the strategy described here should inspire the development of next-generation piezoelectrics and electroactive materials based on hybrid ferroelectrics.Comment: 32 pages, 14 figures, 5 table

Improvement of Biological Effects of Root-Filling Materials for Primary Teeth by Incorporating Sodium Iodide

Therapeutic iodoform (CHI3) is commonly used as a root-filling material for primary teeth; however, the side effects of iodoform-containing materials, including early root resorption, have been reported. To overcome this problem, a water-soluble iodide (NaI)-incorporated root-filling material was developed. Calcium hydroxide, silicone oil, and NaI were incorporated in different weight proportions (30:30:X), and the resulting material was denoted DX (D5~D30), indicating the NaI content. As a control, iodoform instead of NaI was incorporated at a ratio of 30:30:30, and the material was denoted I30. The physicochemical (flow, film thickness, radiopacity, viscosity, water absorption, solubility, and ion releases) and biological (cytotoxicity, TRAP, ARS, and analysis of osteoclastic markers) properties were determined. The amount of iodine, sodium, and calcium ion releases and the pH were higher in D30 than I30, and the highest level of unknown extracted molecules was detected in I30. In the cell viability test, all groups except 100% D30 showed no cytotoxicity. In the 50% nontoxic extract, D30 showed decreased osteoclast formation compared with I30. In summary, NaI-incorporated materials showed adequate physicochemical properties and low osteoclast formation compared to their iodoform-counterpart. Thus, NaI-incorporated materials may be used as a substitute for iodoform-counterparts in root-filling materials after further (pre)clinical investigation

Depression, antidepressant use, and the risk of type 2 diabetes: a nationally representative cohort study

BackgroundPrevious studies have reported that depression can increase the risk of type 2 diabetes. However, they did not sufficiently consider antidepressants or comorbidity.MethodsThe National Health Insurance Sharing Service database was used. Among the sample population, 276,048 subjects who had been diagnosed with depression and prescribed antidepressants (DEP with antidepressants group) and 79,119 subjects who had been diagnosed with depression but not prescribed antidepressants (DEP without antidepressants group) were found to be eligible for this study. Healthy controls (HCs) were 1:1 matched with the DEP with antidepressants group for age and sex. We followed up with them for the occurrence of type 2 diabetes.ResultsIn the group of DEP with antidepressants, although the risk of type 2 diabetes increased compared to HCs in a crude analysis, it decreased when comorbidity was adjusted for. In the group of DEP without antidepressants, the risk of type 2 diabetes decreased both in the crude model and the adjusted models. The risk varied by age group and classes or ingredients of antidepressants, with young adult patients showing an increased risk even in the fully adjusted model.ConclusionOverall, those with depression had a reduced risk of type 2 diabetes. However, the risk varied according to the age at onset, comorbidity, and type of antidepressants

Management for the Children with Otitis Media with Effusion in the Tertiary Hospital

ObjectivesRecently, new evidence-based recommendations have been introduced for diagnosing and managing otitis media with effusion (OME) in children. However, there are some difficulties to follow the general guidelines in the tertiary hospitals. The purpose is to evaluate the efficiency of antibiotics or antihistamines for treatment of children with OME in the tertiary hospital with a randomized prospective clinical study.MethodsEighty-four children with OME who had been diagnosed in the tertiary hospital were randomized to receive 5 different medications for 2 weeks. We prescribed antibiotics (amoxicillin-clavulanate syrup) in Group I (n=16), antibiotics/steroids (prednisolone) in Group II (n=18), antibiotics/antihistamines (ebastine) in Group III (n=15), antibiotics/steroids/antihistamines in Group IV (n=17), and mucolytics (ivy leaf extract) in Group V (n=17) for control. We followed-up children every 2 weeks and evaluated the state of OME at 3 months.ResultsThirty six (42.9%) of 84 children were resolved within average 6.9 weeks after the treatments. Thirty-six (42.9%) were treated with ventilation tube insertion and 12 patients (14.3%) were observed. There was no difference in the resolution rates of OME among the five different protocols (P>0.05). There was no difference in the resolution rates among groups who used steroids, antihistamines, steroids and antihistamines, or other medications to manage 42 children with allergies (P>0.05).ConclusionIn the tertiary hospital, the cure rate of children with OME was not as high as well-known, and antibiotics or anti-allergic medications were not more effective than control. We may, therefore, need any other guidelines which are different from the previous evidence-based recommendations, including early operation in the tertiary hospitals