Examining the Validity of Fitbit Charge HR \u3csup\u3eTM\u3c/sup\u3e for Measuring Heart Rate in Free-Living Conditions

Optical blood flow sensors (i.e. photoplethysmographic techniques) have recently been utilized in wearable activity trackers. The Fitbit Charge HRTM (FBHR) is one of the widely recognized wearable activity trackers that utilizes Fitbit’s proprietary PurePulse optical heart rate (HR) technology to automatically measure wrist-based HR. Despite its increasing popularity, however, no study to date has addressed the validity of FBHR for measuring HR in free-living conditions. PURPOSE: The purpose of this study was to examine the validity of FBHR for measuring HR using a chest strap Polar HR monitor (PHR) as a reference measure in free-living conditions. METHODS: Ten healthy college students (8 males; mean age = 26.5 ±5.4 years; mean body mass index (BMI) = 24.5 ±3.23 kg·m2) participated in the study. The participants were asked to perform normal daily activities for 8 hours in a day while wearing the PHR (model RS400) on their chest and two FBHRs on their dominant and non-dominant wrists, respectively. HR was recorded every minute and the minute-by-minute HR data from each monitor were synchronized by time of day. Pearson correlation was used to examine the linearity of average beats-per-minute (bpm) estimated from FBHRs with respect to the PHR. Mean differences in average bpm between the monitors were examined by a general linear model for repeated measures. Lastly, mean absolute percentage error (MAPE) of minute-by-minute bpm estimated from the FBHRs were calculated against the PHR. RESULTS: Average HRs (mean ±SD) for PHR, FBHR non-dominant, and FBHR dominant were 75.6 ±18.5 bpm, 72.8 ±16.7 bpm, and 73.9 ±17.06 bpm, respectively. Pearson correlation coefficients (r) between the PHR and FBHR non-dominant and dominant were r=.805 and r=.793, respectively. MAPE were 9.17 ±10.9% for FBHR non-dominant and 9.71 ± 12.4% for FBHR HR dominant. ANOVA and post-hoc analyses with Bonferroni revealed significant differences in estimating HR from FBHR non-dominant wrist (p=.001) and FBHR dominant wrist (p=.001) compared to PHR monitor. CONCLUSION: The results indicated that the wrist-oriented Fitbit Charge HRTM device does not provide an accurate measurement of HR during free-living condition in this study. However, further research is needed to validate these monitors with a larger sample with different population groups. Optical blood flow sensors (i.e. photoplethysmographic techniques) have recently been utilized in wearable activitytrackers. The Fitbit Charge HRTM (FBHR) is one of the widely recognized wearable activity trackers that utilizesFitbit’sproprietary PurePulse optical heart rate (HR) technology to automatically measure wrist-based HR. Despiteits increasing popularity, however, no study to date has addressed the validity of FBHR for measuring HR in free-living conditions. PURPOSE: The purpose of this study was to examine the validity of FBHR for measuring HRusing a chest strap Polar HR monitor (PHR) as a reference measure in free-living conditions. METHODS: Tenhealthy college students (8 males; mean age = 26.5 ±5.4 years; mean body mass index (BMI) = 24.5 ±3.23kg·m2) participated in the study. The participants were asked to perform normal daily activities for 8 hours in a daywhile wearing the PHR (model RS400) on their chest and two FBHRs on their dominant and non-dominant wrists,respectively. HR was recorded every minute and the minute-by-minute HR data from each monitor weresynchronized by time of day. Pearson correlation was used to examine the linearity of average beats-per-minute(bpm) estimated from FBHRs with respect to the PHR. Mean differences in average bpm between the monitorswere examined by a general linear model for repeated measures. Lastly, mean absolute percentage error (MAPE)of minute-by-minute bpm estimated from the FBHRs were calculated against the PHR. RESULTS: Average HRs(mean ±SD) for PHR, FBHR non-dominant, and FBHR dominant were 75.6 ±18.5 bpm, 72.8 ±16.7 bpm, and73.9 ±17.06 bpm, respectively. Pearson correlation coefficients (r) between the PHR and FBHR non-dominantand dominant were r=.805 and r=.793, respectively. MAPE were 9.17 ±10.9% for FBHR non-dominant and 9.71 ±12.4% for FBHR HR dominant. ANOVA and post-hoc analyses with Bonferroni revealed significant differences inestimating HR from FBHR non-dominant wrist (p=.001) and FBHR dominant wrist (p=.001) compared to PHRmonitor. CONCLUSION: The results indicated that the wrist-oriented Fitbit Charge HRTM device does not providean accurate measurement of HR during free-living condition in this study. However, further research is needed tovalidate these monitors with a larger sample with different population groups

Reliability of DEXA on Body Composition in Korean Athletes

PURPOSE: The purpose of this study was to assess the reliability of DEXA for measuring body composition in Korean Athletes. METHODS: Twenty-nine athletes (n=29) registered for the college athlete program voluntarily participated in the study. Participants’ height and weight were measured, and BMI (Body Mass Index) was calculated before the participants’ body composition was measured. Muscle mass (kg), lean mass (kg), bone mineral density (BMC) (g·cm-2), and total fat mass (kg) of each participant was assessed by DEXA lunar DPX-L (GE Lunar, Madison, USA) for four times within a day to examine the difference by time frames. Four trials consist of ‘early in the morning × 2 with fasting’ with 30min break between two trials, ‘after lunch × 2’ with 30 min break between the two trials. Intra-class correlation (ICC) was conducted for overall reliability (p\u3c0.05) and a repeated measure ANOVA was performed to compare the difference of each trial (p\u3c0.05). RESULTS: The mean ± SD of muscle mass, lean mass, BMC, and fat mass was 56.4 ± 4.6kg, 59.4 ± 5.0kg, 2.3 ± 0.4g·cm-2, and 9.3 ± 4.8kg respectively. Each trail (mean ± SD) of muscle mass were 56.4 ± 4.7kg, 56.1 ± 4.8kg, 56.5 ± 4.6kg, and 56.4 ± 4.7kg, respectively, lean mass were 59.4 ± 5.1kg, 59.2 ± 5.1kg, 59.5 ± 5.0kg, and 59.4 ± 5.0kg, respectively, BMC were 3.0 ± 0.4g·cm-2, 3.0 ± 0.4g·cm-2, 3.0 ± 0.4g·cm- 2, and 3.0 ± 0.4g·cm-2, respectively, and fat mass were 9.3 ± 4.9kg, 9.2 ± 4.8kg, 9.3 ± 4.9kg, and 9.3 ± 4.9kg, respectively. Reliability of the ICC test showed strong agreement on muscle mass (r=0. 994 and p\u3c0.0001), lean mass (r=0. 995 and p\u3c0.0001), BMC (r=0. 995 and p\u3c0.0001), and fat mass (r=0. 998 and p\u3c0.0001). Cronbach’s alpha were 0.99 (muscle mass), 0.99 (Lean Mass), 0.99 (BMC), and 1.00 (Fat mass). No significant difference between each trial was observed in fat mass (p\u3e0.36). However, there was a significant difference in muscle mass (p\u3c0.001), lean mass (p\u3c0.001), and BMC (p\u3c0.04). CONCLUSION: Although all of the variables showed strong agreement on overall reliability from the ICC test, the reliability for the muscle mass, lean mass, and BMC showed significant differences in different time frame

A New Steroidal Saponin from the Tubers of Ophiopogon japonicus and Its Protective Effect Against Cisplatin-Induced Renal Cell Toxicity

A new furostanol saponin, ophiopogonin T, was isolated from the tubers of Ophiopogon japonicus. Its structure was established by extensive spectroscopic techniques including 1D ( 1 H and 13 C) and 2D nuclear magnetic resonance (NMR) experiments (correlation spectroscopy (COSY), heteronuclear single quantum coherence (HSQC), heteronuclear multiple bond correlation (HMBC) and nuclear Overhauser effect spectroscopy (NOESY)), high-resolution electrospray ionization mass spectrometry (ESIMS), and chemical methods. Using cell-based assays, this compound was evaluated for its cytotoxic effect on cancer cell lines and its protective effect against anticancer drug-induced nephrotoxicity. Cisplatin-induced cytotoxicity in porcine kidney (LLC-PK1) cells was significantly reduced upon treatment with ophiopogonin T, without affecting human hepatoma (HepG2) cancer cell proliferation or tube formation in human umbilical vein endothelial cells (HUVECs). These results collectively reflect the beneficial effect of ophiopogonin T on the side effects of cisplatin

Activation of Peroxisome Proliferator-Activated Receptor Gamma by Rosiglitazone Increases Sirt6 Expression and Ameliorates Hepatic Steatosis in Rats

Sirt6 has been implicated in the regulation of hepatic lipid metabolism and the development of hepatic steatosis. The aim of this study was to address the potential role of Sirt6 in the protective effects of rosiglitazone (RGZ) on hepatic steatosis.) by stomach gavage for 6 weeks. The involvement of Sirt6 in the RGZ's regulation was evaluated by Sirt6 knockdown in AML12 mouse hepatocytes.RGZ treatment ameliorated hepatic lipid accumulation and increased expression of Sirt6, peroxisome proliferator-activated receptor gamma coactivtor-1-α (Ppargc1a/PGC1-α) and Forkhead box O1 (Foxo1) in rat livers. AMP-activated protein kinase (AMPK) phosphorylation was also increased by RGZ, accompanied by alterations in phosphorylation of LKB1. Interestingly, in free fatty acid-treated cells, Sirt6 knockdown increased hepatocyte lipid accumulation measured as increased triglyceride contents (p = 0.035), suggesting that Sirt6 may be beneficial in reducing hepatic fat accumulation. In addition, Sirt6 knockdown abolished the effects of RGZ on hepatocyte fat accumulation, mRNA and protein expression of Ppargc1a/PGC1-α and Foxo1, and phosphorylation levels of LKB1 and AMPK, suggesting that Sirt6 is involved in RGZ-mediated metabolic effects.Our results demonstrate that RGZ significantly decreased hepatic lipid accumulation, and that this process appeared to be mediated by the activation of the Sirt6-AMPK pathway. We propose Sirt6 as a possible therapeutic target for hepatic steatosis

A community resource for paired genomic and metabolomic data mining

Genomics and metabolomics are widely used to explore specialized metabolite diversity. The Paired Omics Data Platform is a community initiative to systematically document links between metabolome and (meta)genome data, aiding identification of natural product biosynthetic origins and metabolite structures.Peer reviewe

One-Class Convolutional Neural Networks for Water-Level Anomaly Detection

Companies that own water systems to provide water storage and distribution services always strive to enhance and efficiently distribute water to different places for various purposes. However, these water systems are likely to face problems ranging from leakage to destruction of infrastructures, leading to economic and life losses. Thus, apprehending the nature of abnormalities that may interrupt or aggravate the service or cause the destruction is at the core of their business model. Normally, companies use sensor networks to monitor these systems and record operational data including any fluctuations in water levels considered abnormalities. Detecting abnormalities allows water companies to enhance the service’s sustainability, quality, and affordability. This study investigates a 2D-CNN-based method for detecting water-level abnormalities as time-series anomaly pattern detection in the One-Class Classification (OCC) problem. Moreover, since abnormal data are usually scarce or unavailable, we explored a cheap method to generate synthetic temporal data and use them as a target class in addition to the normal data to train the CNN model for feature extraction and classification. These settings allow us to train a model to learn relevant pattern representations of the given classes in a binary classification fashion using cross-entropy loss. The ultimate goal of these investigations is to determine if any 2D-CNN-based model can be trained from scratch or if transfer learning of any pre-trained CNN model can be partially trained and used as the base network for one-class classification. The evaluation of the proposed One-Class CNN and previous approaches have shown that our approach has outperformed several state-of-the-art approaches by a significant margin. Additionally, in this paper, we mention two interesting findings: using synthetic data as the pseudo-class is a promising direction, and transfer learning should be dealt with considering that underfitting can happen because the transferred model is too complicated for training data

Betulinic Acid Suppresses Ovarian Cancer Cell Proliferation through Induction of Apoptosis

Ovarian cancer is one of the leading causes of cancer deaths worldwide in women, and the most malignant cancer among the different gynecological cancers. In this study, we explored potentially anticancer compounds from Cornus walteri (Cornaceae), the MeOH extract of which has been reported to show considerable cytotoxicity against several cancer cell lines. Phytochemical investigations of the MeOH extract of the stem and stem bark of C. walteri by extensive application of chromatographic techniques resulted in the isolation of 14 compounds (1–14). The isolated compounds were evaluated for inhibitory effects on the viability of A2780 human ovarian carcinoma cells and the underlying molecular mechanisms were investigated. An 3-(4,5-dimethylthiazol-2-yl)-2,5-diphenyltetrazolium bromide (MTT) assay was employed to assess the anticancer effects of compounds 1–14 on A2780 cells, which showed that compound 11 (betulinic acid) reduced the viability of these cells in a concentration-dependent manner and had an half maximal (50%) inhibitory concentration (IC50) of 44.47 μM at 24 h. Nuclear staining and image-based cytometric assay were carried out to detect the induction of apoptosis by betulinic acid. Betulinic acid significantly increased the condensation of nuclei and the percentage of apoptotic cells in a concentration-dependent manner in A2780 cells. Western blot analysis was performed to investigate the underlying mechanism of apoptosis. The results indicated that the expression levels of cleaved caspase-8, -3, -9, and Bax were increased in A2780 cells treated with betulinic acid, whereas those of Bcl-2 were decreased. Thus, we provide the experimental evidence that betulinic acid can induce apoptosis in A2780 cells through both mitochondria-dependent and -independent pathways and suggest the potential use of betulinic acid in the development of novel chemotherapeutics for ovarian cancer therapy

Anomaly Detection of Water Level Using Deep Autoencoder

Anomaly detection is one of the crucial tasks in daily infrastructure operations as it can prevent massive damage to devices or resources, which may then lead to catastrophic outcomes. To address this challenge, we propose an automated solution to detect anomaly pattern(s) of the water levels and report the analysis and time/point(s) of abnormality. This research’s motivation is the level difficulty and time-consuming managing facilities responsible for controlling water levels due to the rare occurrence of abnormal patterns. Consequently, we employed deep autoencoder, one of the types of artificial neural network architectures, to learn different patterns from the given sequences of data points and reconstruct them. Then we use the reconstructed patterns from the deep autoencoder together with a threshold to report which patterns are abnormal from the normal ones. We used a stream of time-series data collected from sensors to train the model and then evaluate it, ready for deployment as the anomaly detection system framework. We run extensive experiments on sensor data from water tanks. Our analysis shows why we conclude vanilla deep autoencoder as the most effective solution in this scenario

Long-Term Prediction of Crack Growth Using Deep Recurrent Neural Networks and Nonlinear Regression: A Comparison Study

Cracks in a building can potentially result in financial and life losses. Thus, it is essential to predict when the crack growth is reaching a certain threshold, to prevent possible disaster. However, long-term prediction of the crack growth in newly built facilities or existing facilities with recently installed sensors is challenging because only the short-term crack sensor data are usually available in the aforementioned facilities. In contrast, we need to obtain equivalently long or longer crack sensor data to make an accurate long-term prediction. Against this background, this research aims to make a reasonable long-term estimation of crack growth within facilities that have crack sensor data with limited length. We show that deep recurrent neural networks such as LSTM suffer when the prediction’s interval is longer than the observed data points. We also observe a limitation of simple linear regression if there are abrupt changes in a dataset. We conclude that segmented nonlinear regression is suitable for this problem because of its advantage in splitting the data series into multiple segments, with the premise that there are sudden transitions in data

Long-Term Prediction of Crack Growth Using Deep Recurrent Neural Networks and Nonlinear Regression: A Comparison Study

Cracks in a building can potentially result in financial and life losses. Thus, it is essential to predict when the crack growth is reaching a certain threshold, to prevent possible disaster. However, long-term prediction of the crack growth in newly built facilities or existing facilities with recently installed sensors is challenging because only the short-term crack sensor data are usually available in the aforementioned facilities. In contrast, we need to obtain equivalently long or longer crack sensor data to make an accurate long-term prediction. Against this background, this research aims to make a reasonable long-term estimation of crack growth within facilities that have crack sensor data with limited length. We show that deep recurrent neural networks such as LSTM suffer when the prediction’s interval is longer than the observed data points. We also observe a limitation of simple linear regression if there are abrupt changes in a dataset. We conclude that segmented nonlinear regression is suitable for this problem because of its advantage in splitting the data series into multiple segments, with the premise that there are sudden transitions in data