Quantification of the relative contribution of estrogen to bone mineral density in men and women

Background: The study quantified the relative contributions of estrogen (E2) and total testosterone (TT) to variation in bone mineral density in men and women. Methods. This was a cross-sectional study which involved 200 men and 415 women aged 18 to 89 years. BMD at the lumbar spine (LS) and femoral neck (FN) was measured by DXA. Serum levels of E2 and TT were measured by electrochemiluminescence immunoassays. The association between E2, TT, and BMD was analyzed by the multiple linear regression model, adjusting for age and BMI. The contribution of each hormone to the variation in BMD was quantified by the bootstrap method. Results: In women, higher serum levels of E2, but not TT, were significantly associated with greater BMD at the FN (P = 0.001) and LS (P < 0.0001). In men, higher serum levels of E2 were independently associated with greater FNBMD (P = 0.008) and LSBMD (P = 0.086). In the multiple linear regression model, age, body weight and E2 accounted for 50-55% variance in FNBMD, and 25% (in men) and 48% (in women) variance in LSBMD. Variation in E2 accounted for 2.5% (95% CI 0.4 - 7.8%) and 11.3% (95% CI 8.1 - 15.3%) variation in FNBMD in men and women, respectively. Moreover, E2 contributed 1.2% (95% CI 0.1 - 5.8%) and 11.7% (95% CI 8.5 - 15.9%) variation in LSBMD in men and women, respectively. Conclusions: Estrogen is more important than testosterone in the determination of age-related bone mineral density men and women of Vietnamese background. However, the relative contributions of estrogen to bone mineral density in men are likely modest. © 2013 Ho-Pham et al.; licensee BioMed Central Ltd

Development and validation of a prognostic model for predicting 30-day mortality risk in medical patients in emergency department (ED)

© 2017 The Author(s). The primary aim of this prospective study is to develop and validate a new prognostic model for predicting the risk of mortality in Emergency Department (ED) patients. The study involved 1765 patients in the development cohort and 1728 in the validation cohort. The main outcome was mortality up to 30 days after admission. Potential risk factors included clinical characteristics, vital signs, and routine haematological and biochemistry tests. The Bayesian Model Averaging method within the Cox's regression model was used to identify independent risk factors for mortality. In the development cohort, the incidence of 30-day mortality was 9.8%, and the following factors were associated with a greater risk of mortality: male gender, increased respiratory rate and serum urea, decreased peripheral oxygen saturation and serum albumin, lower Glasgow Coma Score, and admission to intensive care unit. The area under the receiver operating characteristic curve for the model with the listed factors was 0.871 (95% CI, 0.844-0.898) in the development cohort and 0.783 (95% CI, 0.743-0.823) in the validation cohort. Calibration analysis found a close agreement between predicted and observed mortality risk. We conclude that the risk of mortality among ED patients could be accurately predicted by using common clinical signs and biochemical tests

A novel hybrid swarm optimized multilayer neural network for spatial prediction of flash floods in tropical areas using sentinel-1 SAR imagery and geospatial data

© 2018 by the authors. Licensee MDPI, Basel, Switzerland. Flash floods are widely recognized as one of the most devastating natural hazards in the world, therefore prediction of flash flood-prone areas is crucial for public safety and emergency management. This research proposes a new methodology for spatial prediction of flash floods based on Sentinel-1 SAR imagery and a new hybrid machine learning technique. The SAR imagery is used to detect flash flood inundation areas, whereas the new machine learning technique, which is a hybrid of the firefly algorithm (FA), Levenberg–Marquardt (LM) backpropagation, and an artificial neural network (named as FA-LM-ANN), was used to construct the prediction model. The Bac Ha Bao Yen (BHBY) area in the northwestern region of Vietnam was used as a case study. Accordingly, a Geographical Information System (GIS) database was constructed using 12 input variables (elevation, slope, aspect, curvature, topographic wetness index, stream power index, toposhade, stream density, rainfall, normalized difference vegetation index, soil type, and lithology) and subsequently the output of flood inundation areas was mapped. Using the database and FA-LM-ANN, the flash flood model was trained and verified. The model performance was validated via various performance metrics including the classification accuracy rate, the area under the curve, precision, and recall. Then, the flash flood model that produced the highest performance was compared with benchmarks, indicating that the combination of FA and LM backpropagation is proven to be very effective and the proposed FA-LM-ANN is a new and useful tool for predicting flash flood susceptibility

Enhanced efficiency for better wastewater sludge hydrolysis conversion through ultrasonic hydrolytic pretreatment

© 2016 Taiwan Institute of Chemical Engineers The major requirements for accelerating the process of anaerobic digestion and energy production are breaking the structure of waste activated sludge (WAS), and transforming it into a soluble form suitable for biodegradation. This work investigated and analysed a novel bench-scale ultrasonic system for WAS disruption and hydrolysis using ultrasonic homogenization. Different commercial sonoreactors were used at low frequencies under a variety of operating conditions (intensity, density, power, sonication time, and total suspended solids) to evaluate the effects of the equipment on sludge hydrolysis and to generate new insights into the empirical models and mechanisms applicable to the real-world processing of wastewater sludge. A relationship was established between the operating parameters and the experimental data. Results indicated an increase in sonication time or ultrasonic intensity correlated with improved sludge hydrolysis rates, sludge temperature, and reduction rate of volatile solids (33.51%). It also emerged that ultrasonication could effectively accelerate WAS hydrolysis to achieve disintegration within 5–10 min, depending on the ultrasonic intensity. This study also determined multiple alternative parameters to increase the efficiency of sludge treatment and organic matter reduction, and establish the practicality of applying ultrasonics to wastewater sludge pretreatment

White hard clam (Meretrix lyrata) shells media to improve phosphorus removal in lab-scale horizontal sub-surface flow constructed wetlands: Performance, removal pathways, and lifespan.

This work examined the phosphorus (P) removal from the synthetic pretreated swine wastewater using lab-scale horizontal sub-surface flow constructed wetlands (HSSF-CWs). White hard clam (Meretrix lyrata) shells (WHC) and Paspalum atratum were utilized as substrate and plant, respectively. The focus was placed on treatment performance, removal mechanisms and lifespan of the HSSF-CWs. Results indicated that WHC-based HSSF-CW with P. atratum exhibited a high P removal (89.9%). The mean P efluent concentration and P removal rate were 1.34 ± 0.95 mg/L and 0.32 ± 0.03 g/m2/d, respectively. The mass balance study showed that media sorption was the dominant P removal pathway (77.5%), followed by microbial assimilation (14.5%), plant uptake (5.4%), and other processes (2.6%). It was estimated the WHC-based bed could work effectively for approximately 2.84 years. This WHC-based HSSF-CWs technology will therefore pave the way for recycling Ca-rich waste materials as media in HSSF-CWs to enhance P-rich wastewater purification

Reference Ranges for Bone Mineral Density and Prevalence of Osteoporosis in Vietnamese Men and Women

<p>Abstract</p> <p>Background</p> <p>The aim of this study was to examine the effect of different reference ranges in bone mineral density on the diagnosis of osteoporosis.</p> <p>Methods</p> <p>This cross-sectional study involved 357 men and 870 women aged between 18 and 89 years, who were randomly sampled from various districts within Ho Chi Minh City, Vietnam. BMD at the femoral neck, lumbar spine and whole body was measured by DXA (Hologic QDR4500). Polynomial regression models and bootstraps method were used to determine peak BMD and standard deviation (<it>SD</it>). Based on the two parameters, we computed T-scores (denoted by <it>T</it>_VN) for each individual in the study. A similar diagnosis was also done based on T-scores provided by the densitometer (<it>T</it>_DXA), which is based on the US White population (NHANES III). We then compared the concordance between <it>T</it>_VNand <it>T</it>_DXAin the classification of osteoporosis. Osteoporosis was defined according to the World Health Organization criteria.</p> <p>Results</p> <p>In post-menopausal women, the prevalence of osteoporosis based on femoral neck <it>T</it>_VNwas 29%, but when the diagnosis was based on <it>T</it>_DXA, the prevalence was 44%. In men aged 50+ years, the <it>T</it>_VN-based prevalence of osteoporosis was 10%, which was lower than <it>T</it>_DXA-based prevalence (30%). Among 177 women who were diagnosed with osteoporosis by <it>T</it>_DXA, 35% were actually osteopenia by <it>T</it>_VN. The kappa-statistic was 0.54 for women and 0.41 for men.</p> <p>Conclusion</p> <p>These data suggest that the <it>T-</it>scores provided by the Hologic QDR4500 over-diagnosed osteoporosis in Vietnamese men and women. This over-diagnosis could lead to over-treatment and influence the decision of recruitment of participants in clinical trials.</p

Highly accurate step counting at variouswalking states using low-cost inertial measurement unit support indoor positioning system

© 2018 by the authors. Licensee MDPI, Basel, Switzerland. Accurate step counting is essential for indoor positioning, health monitoring systems, and other indoor positioning services. There are several publications and commercial applications in step counting. Nevertheless, over-counting, under-counting, and false walking problems are still encountered in these methods. In this paper, we propose to develop a highly accurate step counting method to solve these limitations by proposing four features: Minimal peak distance, minimal peak prominence, dynamic thresholding, and vibration elimination, and these features are adaptive with the user’s states. Our proposed features are combined with periodicity and similarity features to solve false walking problem. The proposed method shows a significant improvement of 99.42% and 96.47% of the average of accuracy in free walking and false walking problems, respectively, on our datasets. Furthermore, our proposed method also achieves the average accuracy of 97.04% on public datasets and better accuracy in comparison with three commercial step counting applications: Pedometer and Weight Loss Coach installed on Lenovo P780, Health apps in iPhone 5s (iOS 10.3.3), and S-health in Samsung Galaxy S5 (Android 6.01)

Prototype edge-grown nanowire sensor array for the real-time monitoring and classification of multiple gases

The monitoring and classification of different gases using a single resistive semiconductor sensor are challenging because of the similar response characteristics. An array of separated sensors can be used as an electronic nose, but such arrays have a bulky structure and complex fabrication processes. Herein, we easily fabricated a gas-sensor array based on edge-grown SnO2 nanowires for the real-time monitoring and classification of multiple gases. The array comprised four sensors and was designed on a glass substrate. SnO2 nanowires were grown on-chip from the edge of electrodes, made contact together, and acted as sensing elements. This method was advantageous over the post-synthesis technique because the SnO2 nanowires were directly grown from the edge of the electrodes rather than on the surface. Accordingly, damage to the electrode was avoided by alloying Sn with Pt at a high growth temperature. The sensing characteristics of the sensor array were further examined for different gases, including methanol, isopropanol, ethanol, ammonia, hydrogen sulphide and hydrogen. Radar plots were used to improve the selective detection of different gases and enable effective classification

Design and fabrication of effective gradient temperature sensor array based on bilayer SnO2/Pt for gas classification

Classification of different gases is important, and it is possible to use different gas sensors for this purpose. Electronic noses, for example, combine separated gas sensors into an array for detecting different gases. However, the use of separated sensors in an array suffers from being bulky, high-energy consumption and complex fabrication processes. Generally, gas sensing properties, including gas selectivity, of semiconductor gas sensors are strongly dependent on their working temperature. It is therefore feasible to use a single device composed of identical sensors arranged in a temperature gradient for classification of multiple gases. Herein, we introduce a design for simple fabrication of gas sensor array based on bilayer Pt/SnO2 for real-time monitoring and classification of multiple gases. The study includes design simulation of the sensor array to find an effective gradient temperature, fabrication of the sensors and test of their performance. The array, composed of five sensors, was fabricated on a glass substrate without the need of backside etching to reduce heat loss. A SnO2 thin film sensitized with Pt on top deposited by sputtering was used as sensing material. The sensor array was tested against different gases including ethanol, methanol, isopropanol, acetone, ammonia, and hydrogen. Radar plots and principal component analysis were used to visualize the distinction of the tested gases and to enable effective classification

3D Geometric Analysis of Tubular Objects based on Surface Normal Accumulation

This paper proposes a simple and efficient method for the reconstruction and extraction of geometric parameters from 3D tubular objects. Our method constructs an image that accumulates surface normal information, then peaks within this image are located by tracking. Finally, the positions of these are optimized to lie precisely on the tubular shape centerline. This method is very versatile, and is able to process various input data types like full or partial mesh acquired from 3D laser scans, 3D height map or discrete volumetric images. The proposed algorithm is simple to implement, contains few parameters and can be computed in linear time with respect to the number of surface faces. Since the extracted tube centerline is accurate, we are able to decompose the tube into rectilinear parts and torus-like parts. This is done with a new linear time 3D torus detection algorithm, which follows the same principle of a previous work on 2D arc circle recognition. Detailed experiments show the versatility, accuracy and robustness of our new method.Comment: in 18th International Conference on Image Analysis and Processing, Sep 2015, Genova, Italy. 201