Real-time Breath Research Articles

Real-Time Breath Analysis Reveals Specific Metabolic Signatures of COPD Exacerbations

Exacerbations of COPD are defined by acute worsening of respiratory symptoms leading to a change in therapy. Identifying altered metabolic processes in patients at risk for future exacerbations is desirable for treatment optimization, the development of new therapeutic strategies, and perhaps diagnostic value. We aimed to identify affected pathways using the profiles of volatile organic compounds in exhaled breath from patients with COPD with and without frequent exacerbations (≥ 2 exacerbations within the past 12months). In this matched cohort study, exhaled breath profiles from patients with COPD and frequent exacerbations ("frequent exacerbators") and without frequent exacerbations ("nonfrequent exacerbators") were analyzed during an exacerbation-free interval using real-time secondary electrospray ionization high-resolution mass spectrometry. We analyzed exhaled breath from 26 frequent exacerbators and 26 nonfrequent exacerbators that were matched in terms of age, sex, and smoking history. To obtain new pathophysiological insights, we investigated significantly altered metabolites, which can be assigned to specific pathways. Metabolites were identified by using a Wilcoxon rank-sum test. Metabolite levels from the ω-oxidation pathway, namely ω-hydroxy, ω-oxo, and dicarboxylic acids, were consistently decreased in frequent exacerbators. Additionally, several new nitro-aromatic metabolites, which were significantly increased in frequent exacerbators, were identified. Real-time breath analysis by secondary electrospray high-resolution mass spectrometry allows molecular profiling of exhaled breath, providing insights about ongoing biochemical processes in patients with COPD at risk for exacerbations. ClinicalTrials.gov; No.: NCT02186639; URL: www.clinicaltrials.gov.

Unsupervised Detection of Apnea Using Commodity RFID Tags With a Recurrent Variational Autoencoder

With the rapid development of intelligent health sensing in the Internet of Things (IoT), vital sign monitoring (e.g., respiration) and abnormal respiration detection have attracted increasing attention. Considering the challenging and the cost of collecting labeled training data from patients with breathing related diseases, we develop the AutoTag system, an unsupervised recurrent variational autoencoder-based method for respiration rate estimation and abnormal breathing detection with off-the-shelf RFID tags. Moreover, for real-time breath monitoring, a novel method is proposed to cancel the distortion on measured phase values caused by channel hopping for FCC-complaint RFID systems. The efficacy of the proposed system is demonstrated by the extensive experiments conducted in two indoor environments, while the impact of various design and environmental factors is also evaluated.

The anthropocosmic vision of mixed reality: Taking HCI artwork Flow of Qi (2007) as a case study

This article critically investigates the potential confrontation that exists between mixed reality in Human-Computer Interaction (HCI) artworks that are, respectively, based on the concepts of phenomenology and Chinese Qi. My proposal is exemplified by the Industrial Technology Research Institute of Taiwan’s Flow of Qi (2007), which uses ultra wide band technology that instantly transforms the data of a pair of participants’ real-time breath into the replication of calligraphy masterworks projected onto the floor in front of the participants. To explore how efficiency of breath constitutes mixed reality in Flow of Qi, I draw a parallel between Qi, which is embodied as breath, and agency whereby, as discussed by Murray and Hansen, the notion of the perception-action relationship is emphasized. However, compared to agency, Qi is characterized as evoking ‘the unity of the cosmos with humanity’ (t’ien-jen ho-yi) because the participants are involved in a process wherein the masters used their brushstrokes to grasp the respiration of the cosmos. Therefore, employing the concepts of Chinese Qi draws out an anthropocosmic vision of mixed reality that links participants with the cosmos and manifests a Qi world-view through digital media.

New Generation Wearable Antenna Based on Multimaterial Fiber for Wireless Communication and Real-Time Breath Detection

Smart textiles and wearable antennas along with broadband mobile technologies have empowered the wearable sensors for significant impact on the future of digital health care. Despite the recent development in this field, challenges related to lack of accuracy, reliability, user’s comfort, rigid form and challenges in data analysis and interpretation have limited their wide-scale application. Therefore, the necessity of developing a new reliable and user friendly approach to face these problems is more than urgent. In this paper, a new generation of wearable antenna is presented, and its potential use as a contactless and non-invasive sensor for human breath detection is demonstrated. The antenna is made from multimaterial fiber designed for short-range wireless network applications at 2.4 GHz frequency. The used composite metal-glass-polymer fibers permits their integration into a textile without compromising comfort or restricting movement of the user due to their high flexibility, and shield efficiently the antenna from the environmental perturbation. The multimaterial fiber approach provided a good radio-frequency emissive properties, while preserving the mechanical and cosmetic properties of the garments. With a smart textile featuring a spiral shape fiber antenna placed on a human chest, a significant shift of the operating frequency of the antenna was observed during the breathing process. The frequency shift is caused by the deformation of the antenna geometry due to the chest expansion, and to the modification of the dielectric properties of the chest during the breath. We demonstrate experimentally that the standard wireless networks, which measure the received signal strength indicator (RSSI) via standard Bluetooth protocol, can be used to reliably detect human breathing and estimate the breathing rate in real time. The mobile platform takes the form of a wearable stretching T-shirt featuring a sensor and a detection base station. The sensor is formed by a spiral-shaped antenna connected to a compact Bluetooth transmitter. Breathing patterns were recorded in the case of female and male volunteers. Although the chest anatomy of females and males is different compared, the sensor’s flexibility allowed recording successfully a breathing rate of 0.3 Hz for the female and 0.5 Hz for the male, which corresponds to a breathing rate of 21 breaths per minutes (bpm) and 30 bpm, respectively.

Positive matrix factorization: A data preprocessing strategy for direct mass spectrometry-based breath analysis

Interest in exhaled breath has grown considerably in recent years, as breath biosampling has shown promise for non-invasive disease diagnosis, therapeutic drug monitoring, and environmental exposure. Real time breath analysis can be accomplished via direct online mass spectrometry (MS)-based methods, which can provide more accurate and detailed data and an enhanced understanding of the temporal evolution of exhaled VOCs in the breath; however, the complicated chemical composition and large raw datasets involved in breath analysis have hindered the discovery of sources contributing to the exhaled VOCs. The positive matrix factorization (PMF) receptor model has been widely used for source apportionment in atmospheric studies. Since the exhaled VOCs contain compounds from various sources, such as alveolar air, mouth air and respiratory dead-space air, PMF may be also helpful for source apportionment of exhaled VOCs in the breath. Thus, this study explores the application of PMF in the pretreatment of direct breath measurement data. The results indicate that (i) endogenous compounds and background contaminants sources can be readily distinguished by PMF in data obtained from replicate measurements of human exhaled breath at single time points (~30 s/measurement), which may benefit both exhalome investigations and the identification of exposure biomarkers; (ii) sources resolved from online measurement data collected over longer periods (1.5 h) can be used to isolate the evolution of exhaled VOCs and investigate processes such as the pharmacokinetics of ketamine and its major metabolites. Therefore, PMF has shown promise for both data processing and subsequent data mining for the ambient MS-based breath analysis.

Novel Self-Assembly Route Assisted Ultra-Fast Trace Volatile Organic Compounds Gas Sensing Based on Three-Dimensional Opal Microspheres Composites for Diabetes Diagnosis.

The development of ultra-fast response semiconductor gas sensors for high-accuracy detection of trace volatile organic compounds in human exhaled breath still remains a challenge. Herein, we propose a novel self-assembly synthesis concept for preparing intricate three-dimensional (3D) opal porous (OP) SnO2-ZnO hollow microspheres (HM), by employing sulfonated polystyrene (S-PS) spheres template-assisted ultrasonic spray pyrolysis. The high gas accessibility of the unique opal hollow structures resulted in the existence of 3D interconnection and bimodal (mesoscale and macroscale) pores, and the n-n heterojunction-induced change in oxygen adsorption. The 3D OP SnO2-ZnO HM sensor exhibited high response and ultra-fast dynamic process (response time ∼4 s and recovery time ∼17 s) to 1.8 ppm acetone under highly humid ambient condition (98% relative humidity), and it could rapidly identify the states of the exhaled breath of healthy people and simulated diabetics. In addition, the rational structure design of the 3D OP SnO2 HM enables the ultra-fast detection (within 1 s) of ethanol in simulation drunk driving testing. Our results obtained in this work provided not only a facile self-assembly approach to fabricate metal oxides with 3D OP HM structures but also a new methodology for achieving noninvasive real-time exhaled breath detection.

Modeling Pulmonary Gas Exchange and Single-Exhalation Profiles of Carbon Monoxide.

Exhaled breath carbon monoxide (eCO) is a candidate biomarker for non-invasive assessment of oxidative stress and respiratory diseases. Standard end-tidal CO analysis, however, cannot distinguish, whether eCO reflects endogenous CO production, lung diffusion properties or exogenous sources, and is unable to resolve a potential airway contribution. Coupling real-time breath gas analysis to pulmonary gas exchange modeling holds promise to improve the diagnostic value of eCO. A trumpet model with axial diffusion (TMAD) is used to simulate the dynamics of CO gas exchange in the respiratory system and corresponding eCO concentrations for the first time. The mass balance equation is numerically solved employing a computationally inexpensive routine implementing the method of lines, which provides the distribution of CO in the respiratory tract during inhalation, breath-holding, and exhalation with 1 mm spatial and 0.01 s temporal resolution. Initial estimates of the main TMAD parameters, the maximum CO fluxes and diffusing capacities in alveoli and airways, are obtained using healthy population tissue, blood and anatomical data. To verify the model, mouth-exhaled expirograms from two healthy subjects, measured with a novel, home-built laser-based CO sensor, are compared to single-exhalation profiles simulated using actual breath sampling data, such as exhalation flow rate (EFR) and volume. A very good agreement is obtained in exhalation phases I and III for EFRs between 55 and 220 ml/s and after 10 and 20 s of breath-holding, yielding a unique set of TMAD parameters. The results confirm the recently observed EFR dependence of CO expirograms and suggest that measured end-tidal eCO is always lower than alveolar and capillary CO. Breath-holding allows the observation of close-to-alveolar CO concentrations and increases the sensitivity to the airway TMAD parameters in exhalation phase I. A parametric simulation study shows that a small increase in airway flux can be distinguished from an increase in alveolar flux, and that slight changes in alveolar flux and diffusing capacity have a significantly different effect on phase III of the eCO profiles.

An in silico performance characterization of respiratory motion guided 4DCT for high-quality low-dose lung cancer imaging

This work aims to characterize the performance of an improved 4DCT technique aiming to overcome irregular breathing-related image artifacts. To address this, we have developed respiratory motion guided (RMG) 4DCT, which uses real-time breathing motion analysis to prospectively gate scans based on detection of irregular breathing. This is the first investigation of RMG-4DCT using a real-time software prototype, testing the hypothesis that it can reduce breathing irregularities during imaging, reduce image oversampling and improve image quality compared to a ‘conventional’ 4DCT protocol without breathing guidance. RMG-4DCT scans were simulated based on 100+ hours of breathing motion acquired for 20 lung cancer patients. Scan performance was quantified in terms of the beam on time (a surrogate for imaging dose), total scan time and the breathing irregularity during imaging (via RMSE of the breathing motion during acquisition). A conventional 4DCT protocol was also implemented using the same software prototype for a direct comparator to the RMG-4DCT results. We investigated the impact of key RMG-4DCT parameters such as gating tolerance, gantry rotation time and the use of baseline drift correction. Using a representative set of algorithm parameters, RMG-4DCT achieved significant mean reductions in estimated imaging dose (−17.8%, p < 0.001) and breathing RMSE during imaging (−12.6%, p < 0.001) compared to conventional 4DCT. These improvements came with increased scan times, roughly doubled on average (104%, p < 0.001). Image quality simulations were performed using the deformable digital XCAT phantom, with image quality quantified based on the normalized cross correlation (NCC) between axial slices. RMG-4DCT demonstrated qualitative image quality improvements for three out of 10 phase bins, however the improvement was not significant across all 10 phases (p = 0.08) at a population level. In choosing RMG-4DCT scan parameters, the trade-off between gating sensitivity and scan time may be optimized, demonstrating potential for RMG-4DCT as a viable pathway to improve clinical 4DCT imaging.

Hepatobiliary MRI: Signal intensity based assessment of liver function correlated to 13C-Methacetin breath test

Gadoxetic acid (Gd-EOB-DTPA) is a paramagnetic MRI contrast agent with raising popularity and has been used for evaluation of imaging-based liver function in recent years. In order to verify whether liver function as determined by real-time breath analysis using the intravenous administration of 13C-methacetin can be estimated quantitatively from Gd-EOB-DTPA-enhanced MRI using signal intensity (SI) values. 110 patients underwent Gd-EOB-DTPA-enhanced 3-T MRI and, for the evaluation of liver function, a 13C-methacetin breath test (13C-MBT). SI values from before (SIpre) and 20 min after (SIpost) contrast media injection were acquired by T1-weighted volume-interpolated breath-hold examination (VIBE) sequences with fat suppression. The relative enhancement (RE) between the plain and contrast-enhanced SI values was calculated and evaluated in a correlation analysis of 13C-MBT values to SIpost and RE to obtain a SI-based estimation of 13C-MBT values. The simple regression model showed a log-linear correlation of 13C-MBT values with SIpost and RE (p < 0.001). Stratified by 3 different categories of 13C-MBT readouts, there was a constant significant decrease in both SIpost (p ≤ 0.002) and RE (p ≤ 0.033) with increasing liver disease progression as assessed by the 13C-MBT. Liver function as determined using real-time 13C-methacetin breath analysis can be estimated quantitatively from Gd-EOB-DTPA-enhanced MRI using SI-based indices.

Humidity-Independent Oxide Semiconductor Chemiresistors Using Terbium-Doped SnO2 Yolk-Shell Spheres for Real-Time Breath Analysis.

The chemiresistive sensing characteristics of metal oxide gas sensors depend closely on ambient humidity. Herein, we report that gas sensors using Tb-doped SnO2 yolk-shell spheres can be used for reliable acetone detection, regardless of the variations in humidity. Pure SnO2 and Tb-doped SnO2 yolk-shell spheres were prepared via ultrasonic spray pyrolysis and their chemiresistive sensing characteristics were studied. The sensor resistance and gas response of the pure SnO2 yolk-shell spheres significantly changed and deteriorated upon exposure to moisture. In stark contrast, the Tb-doped SnO2 yolk-shell spheres exhibited similar gas responses and sensor resistances in both dry and humid [relative humidity (RH) 80%] atmospheres. In addition, the Tb-doped SnO2 yolk-shell sensors showed a high gas response (resistance ratio) of 1.21 to the sub-ppm-levels (50 ppb) of acetone with low responses to the other interference gases. The effects of Tb oxide and the chemical interactions among the Tb oxide, SnO2, and water vapor on this humidity-independent gas sensing behavior of the Tb-doped SnO2 yolk-shell sensors were investigated. This strategy can provide a new road to achieve highly sensitive, selective, and humidity-independent sensing of acetone, which will facilitate miniaturized and real-time exhaled breath analysis for diagnosing diabetes.

Continuous real-time breath analysis in ruminants: effect of eructation on exhaled VOC profiles

Background. The analysis of volatile organic compounds (VOCs) in breath allows non-invasive investigations of diseases. Animal studies are conducted as a model to perform research of VOCs and their relation to diseases. In large animal models ruminants were often used as experimental targets. The effect of their physiological eructation on VOC exhalation has not been examined yet and is the objective of this study. Methods. Continuous breath profiles of two young cattle, four adult goats and four adult sheep were measured through a mask, covering mouth and nose, in real-time (200 ms) by means of proton transfer reaction time of flight mass spectrometry. Each animal was analysed twelve times for 3 consecutive minutes. Results. Real-time monitoring yielded a distinction of different episodes in the breath profiles of ruminants. An algorithm to separate eructation episodes and alveolar breath was established. In the first exhalation after eructation at least 19 VOC concentrations increased (up to 36-fold) and went back to initial levels in subsequent exhalations in all investigated ruminants. Decay of concentrations was substance specific. In goats, less VOCs were affected by the eructation compared to cattle and sheep. Breath profiles without exclusion of eructation episodes showed higher variations and median values than profiles where eructation episodes were excluded. Conclusion. Real-time breath analysis of ruminants enables the discrimination and characterisation of alveolar breath and eructation episodes. This leads to a better understanding of variation in breath data and possible origins of VOCs: breath or digestion related. To avoid impairment of breath gas results and to gain further information on bacterial products from the rumen, eructation and alveolar breath data should be analysed separately.

Real-time exhaled breath analysis in patients with cystic fibrosis and controls

We aimed at defining profiles of volatile organic compounds in exhaled breath from patients with cystic fibrosis (CF) using a novel real-time mass spectrometry technique. In this prospective matched case-control study, 30 patients with CF, and 30 healthy control subjects were matched one-to-one according to age, gender, and smoking state. We performed exhaled breath analysis by untargeted secondary electrospray ionization-high resolution mass spectrometry (SESI-HRMS). Patients with CF (mean age 26.0 ± 13.0 years) and controls (mean age 27.9 ± 14.0 years) were analyzed using SESI-HRMS. 49 exhaled breath features were found to be altered (p-value < 0.05/q-value < 0.1) in CF patients, in comparison to healthy controls. The two most discriminating features showed a prediction AUROC of 77.1% (95% CI 62.2%–87.8%) with a specificity of 80.0% and a sensitivity of 63.3%. Levels of oxidative stress metabolites such as fatty acids were found to differ significantly between patients with CF and healthy controls. Furthermore, in patients with CF, 11 features correlated with the mucus concentration of Stenotrophomonas maltophilia bacteria. Exhaled breath analysis with SESI-HRMS allows the identification of CF specific compounds in real-time and may trace bacterial strains in affected patients with CF.

Gd-EOB-DTPA-enhanced T1 relaxometry for assessment of liver function determined by real-time 13C-methacetin breath test.

To determine whether liver function as determined by intravenous administration of 13C-methacetin and continuous real-time breath analysis can be estimated quantitatively from gadoxetic acid (Gd-EOB-DTPA)-enhanced magnetic resonance (MR) relaxometry. Sixty-six patients underwent a 13C-methacetin breath test (13C-MBT) for evaluation of liver function and Gd-EOB-DTPA-enhanced T1-relaxometry at 3 T. A transverse 3D VIBE sequence with an inline T1 calculation based on variable flip angles was acquired prior to (T1 pre) and 20 min post-Gd-EOB-DTPA (T1 post) administration. The reduction rate of T1 relaxation time (rrT1) and T1 relaxation velocity index (∆R1) between pre- and post-contrast images was evaluated. 13C-MBT values were correlated with T1post, ∆R1 and rrT1, providing an MRI-based estimated 13C-MBT value. The interobserver reliability was assessed by determining the intraclass correlation coefficient (ICC). Stratified by three different categories of 13C-MBT readouts, there was a constant increase of T1 post with increasing progression of diminished liver function (p ≤ 0.030) and a constant significant decrease of ∆R1 (p ≤ 0.025) and rrT1 (p < 0.018) with progression of liver damage as assessed by 13C-methacetin breath analysis. ICC for all T1 relaxation values and indices was excellent (> 0.88). A simple regression model showed a log-linear correlation of 13C-MBT values with T1post (r = 0.57; p < 0.001), ∆R1 (r = 0.59; p < 0.001) and rrT1 (r = 0.70; p < 0.001). Liver function as determined using real-time 13C-methacetin breath analysis can be estimated quantitatively from Gd-EOB-DTPA-enhanced MR relaxometry. • Gd-EOB-DTPA-enhanced T1 relaxometry quantifies liver function • Gd-EOB-DTPA-enhanced MR relaxometry may provide parameters for assessing liver function before surgery • Gd-EOB-DTPA-enhanced MR relaxometry may be useful for monitoring liver disease progression • Gd-EOB-DTPA-enhanced MR relaxometry has the potential to become a novel liver function index.

Real-time Breath Analysis by Using Secondary Nanoelectrospray Ionization Coupled to High Resolution Mass Spectrometry

Real-time Breath Analysis by Using Secondary Nanoelectrospray Ionization Coupled to High Resolution Mass Spectrometry

Real-time Breath Analysis by Using Secondary Nanoelectrospray Ionization Coupled to High Resolution Mass Spectrometry.

Exhaled volatile organic compounds (VOCs) have aroused considerable interest, since they can serve as biomarkers for disease diagnosis and environmental exposure in a non-invasive manner. In this work, we present a protocol to characterize the exhaled VOCs in real time by using secondary nanoelectrospray ionization coupled to high resolution mass spectrometry (Sec-nanoESI-HRMS). The homemade Sec-nanoESI source was readily set up based on a commercial nanoESI source. Hundreds of peaks were observed in the background-subtracted mass spectra of exhaled breath, and the mass accuracy values are -4.0-13.5 ppm and -20.3-1.3 ppm in the positive and negative ion detection modes, respectively. The peaks were assigned with accurate elemental composition according to the accurate mass and isotopic pattern. Less than 30 s is used for one exhalation measurement, and it takes approximately 7 min for six replicated measurements.

A Novel 1-D Convolution Neural Network With SVM Architecture for Real-Time Detection Applications

To enhance the performance and sensitivity of continuous monitoring systems for detection of chronic diseases, selection of optimal machine learning algorithms is pivotal. Presently, the commonly used algorithms face constraints, such as high computational cost and lack of optimal feature selection on application to real time signals thereby reducing the efficiency of such analysis. Deep learning approaches, such as the convolution neural network, overcome these drawbacks by calculating automated features from raw signal and classifying the derived features. This architecture shows good merits. However, the use of fully connected multi-layer perceptron algorithms have shown low classification performance. This paper proposes to develop a modified deep learning convolution neural network algorithm integrated with support vector machines to address the drawbacks present in multi-layer perceptron and thereby improving the overall performance of real-time detection applications. The system is validated on real-time breath signals for non-invasive detection of diabetes. The performance of this proposed algorithm is evaluated and compared with the existing technique.

Change of Safe Needling Depth at Acupoint GB21 according to Posture and Breathing.

Acupoint GB21 (Jianjing) is used for treating back and shoulder pain but is associated with a risk of pneumothorax. We aimed to determine the SND (safe needling depth) at GB21 according to posture and breathing in real time. Ultrasonographic images of GB21 during normal breathing, inspiration, and expiration in a SP (sitting position) were acquired for 52 healthy volunteers. Images were also acquired during normal respiration in the PP (prone position) with arms raised and lowered. The average SND was greater for men than for women (p < 0.05). Analysis of variance revealed that the SND was greater for the PP than for the SP (p = 0.01 and p < 0.05, resp.). Although the SND tended to change according to posture, the average depth tended to deviate widely in some subjects. During breathing, the differences between inspiration and expiration were less than 1 mm in most subjects, but some showed differences more than 4.5 mm. The SND at GB21 was greater in overweight subjects and significantly greater in the PP and during maximal expiration. However, intragroup differences were greater than the intergroup differences. Therefore, it is dangerous to simply apply needling depth on a gender or BMI basis. The practitioner would adjust the SND by examining the individual anatomical structures.

Highly precise and real-time measurements of 13CO2/12CO2 isotopic ratio in breath using a 2 μm diode laser

Real-time breath gas analysis with high accuracy, precision and time resolution, as a promising, non-invasive, fast and reliable tool, is important in medical diagnostics. Especially stable isotopologues of carbon dioxide is applied to multiple research areas including the diagnosis of gastrointestinal diseases. Helicobacter pylori (H. pylori) is one of the most frequent bacterial infectious diseases in human beings and is now recognized as one of the key risk factors for chronic gastritis, peptic ulcers, stomach cancer and lymphoma. In contrast to traditional invasive tests, the most reliable non-invasive method in the diagnosis of the H. pylori infection is considered to be 13C-urea breath test which is implemented by measuring the 13CO2/12CO2 isotope ratio in human breath. Tunable diode laser absorption spectroscopy (TDLAS) has the advantages of fast response, low drift, good gas selectivity and high detection sensitivity, and it is very convenient to develop a high precision, real-time and online measurement system. A precision laser spectrometer for the measurement of CO2 isotope abundance in human breath (with CO2 concentration of 4%-5%) or high concentration gas is designed and evaluated based on TDLAS technology. The spectrometer contains a novel compact dense-pattern multipass cell with a small volume of 280 cm3 and an effective optical path length of 26. 4 m. The cell is in conjunction with a fiber-coupled distributed feedback diode laser operating at 2.008 μm. Wavelength modulation spectroscopy approach is used. The mass flow, pressure and temperature of the cell are actively controlled, and able to keep long-term stability. The influence of laser power fluctuation is eliminated by fitting the baseline with cubic polynomial to normalize the raw spectrum. Moving window regression is used to remove the influence of frequency drift on measuring isotope abundance. The system measurement precision is improved by wavelet denosing and Kalman filtering. The experimental results demonstrate that moving window regression method not only extends the stability time of the system but also improves the measurement precision of isotope abundance well, the wavelet denoising improves the signal-to-noise ratio by 2 times that by the method of multi-spectral average, the stability time of the system is 100 s given by Allan variance, and the measurement precision of CO2 isotope ratio is 0. 067‰ after Kalman filtering. The use of small multi-pass cell and the default of denoising devices make the system more portable and improve the real-time and online measurement performance of the system. In addition to the measurement of 13CO2/12CO2 isotope ratio in human breath, by replacing different lasers, the spectrometer can also be used to measure trace gas concentration and the stable isotope abundance of many gas molecules in atmosphere. Therefore, the spectrometer will have broad applications in the areas of medical diagnosis, carbon cycle study and environmental monitoring.

The MindfulBreather: Motion Guided Mindfulness.

For millennia, humans have focused their attention on the breath to develop mindfulness, but finding a scientific way to harness mindful breathing has proven elusive. Existing attempts to objectively measure and feedback on mindfulness have relied on specialist external hardware including electroencephalograms or respirometers that have been impractical for the majority of people learning to meditate. Consequently, training in the key skill of breath-awareness has lacked practical objective measures and guidance to enhance training. Here, we provide a brief technology report on an invention, The MindfulBreather® that addresses these issues. The technology is available to download embedded in a smartphone app that targets, measures and feedbacks on mindfulness of breathing in realtime to enhance training. The current article outlines only the technological concept with future studies quantifying efficacy, validity and reliability to be reported elsewhere. The MindfulBreather works by generating Motion Guided Mindfulness through interacting gyroscopic and touchscreen sensors in a three phase process: Mindfulness Induction (Phase I) gives standardized instruction to users to place their smartphone on their abdomen, breathe mindfully and to tap only at the peak of their inhalation. The smartphone’s gyroscope detects periodic tilts during breathing to generate sinusoidal waveforms. Waveform-tap patterns are analyzed to determine whether the user is mindfully tapping only at the correct phase of the breathing cycle, indicating psychobiological synchronization. Mindfulness Maintenance (Phase II) provides reinforcing pleasant feedback sounds each time a breath is mindfully tapped at the right time, and the App records a mindful breath. Lastly, data-driven Insights are fed back to the user (Phase III), including the number of mindful breaths tapped and breathing rate reductions associated with parasympathetic engagement during meditation. The new MGM technology is then evaluated and contrasted with traditional mindfulness approaches and a novel Psychobiological Synchronization Model is proposed. In summary, unlike existing technology, the MindfulBreather requires no external hardware and repurposes regular smartphones to deliver app-embedded Motion-Guided Mindfulness. Technological applications include reducing mindwandering and down-regulation of the brain’s default mode through enhanced mindful awareness. By objectively harnessing breath awareness, The MindfulBreather aims to realize the ancient human endeavor of mindfulness for the 21st century.

Principal component reconstruction (PCR) for cine CBCT with motion learning from 2D fluoroscopy.

This work aims to generate cine CT images (i.e., 4D images with high-temporal resolution) based on a novel principal component reconstruction (PCR) technique with motion learning from 2D fluoroscopic training images. In the proposed PCR method, the matrix factorization is utilized as an explicit low-rank regularization of 4D images that are represented as a product of spatial principal components and temporal motion coefficients. The key hypothesis of PCR is that temporal coefficients from 4D images can be reasonably approximated by temporal coefficients learned from 2D fluoroscopic training projections. For this purpose, we can acquire fluoroscopic training projections for a few breathing periods at fixed gantry angles that are free from geometric distortion due to gantry rotation, that is, fluoroscopy-based motion learning. Such training projections can provide an effective characterization of the breathing motion. The temporal coefficients can be extracted from these training projections and used as priors for PCR, even though principal components from training projections are certainly not the same for these 4D images to be reconstructed. For this purpose, training data are synchronized with reconstruction data using identical real-time breathing position intervals for projection binning. In terms of image reconstruction, with a priori temporal coefficients, the data fidelity for PCR changes from nonlinear to linear, and consequently, the PCR method is robust and can be solved efficiently. PCR is formulated as a convex optimization problem with the sum of linear data fidelity with respect to spatial principal components and spatiotemporal total variation regularization imposed on 4D image phases. The solution algorithm of PCR is developed based on alternating direction method of multipliers. The implementation is fully parallelized on GPU with NVIDIA CUDA toolbox and each reconstruction takes about a few minutes. The proposed PCR method is validated and compared with a state-of-art method, that is, PICCS, using both simulation and experimental data with the on-board cone-beam CT setting. The results demonstrated the feasibility of PCR for cine CBCT and significantly improved reconstruction quality of PCR from PICCS for cine CBCT. With a priori estimated temporal motion coefficients using fluoroscopic training projections, the PCR method can accurately reconstruct spatial principal components, and then generate cine CT images as a product of temporal motion coefficients and spatial principal components.