BACKGROUND Lifestyle-related diseases can be controlled by improving individuals’ lifestyles; however, improving and maintaining a healthy lifestyle is difficult. Mobile health (mHealth) applications have recently attracted attention as tools for maintaining and improving health, and their use may also increase physical activity. OBJECTIVE This study aimed to verify the effect of registration in Asmile, a mHealth application provided by the Osaka Prefectural Government, on step counts using a causal Impact approach based on the step count data recorded in the Asmile application. METHODS This observational study included Osaka residents in their 20s–70s, newly registered to Asmile, between the fiscal years (FYs) 2020 and 2023. Of these, 80,689 participants with step count records for four weeks before and after the day they registered to Asmile were included in the analysis. We used daily step counts that were automatically transferred from a standard smartphone healthcare application into Asmile. We used a Causal Impact model to estimate the increase in step count after registration to Asmile. RESULTS Of the 80,689 participants analyzed, 38.5% were men, and the mean age was 51.6±13.2 years. The mean step count before registration was 5,923±4,860 steps/day, with the highest proportion of new users registered in spring (47.6%) and in FY2020 (42.7%). The analysis revealed that the effect of Asmile registration on step counts was 360 steps (95% confidence interval [CI]: 331–389) per day and 10,041 steps (95% CI: 9,632–10,450) over four weeks. Stratified analysis showed that the impact of increased step count was more pronounced in younger groups and groups with fewer step counts before registration. Conversely, the effect of registration on step count was relatively minor in the groups registered in summer or winter. CONCLUSIONS This study demonstrates increased physical activity among users registered with the Asmile app. These findings suggest that mHealth apps such as Asmile can effectively promote healthier lifestyles and potentially reduce the risk of lifestyle-related diseases.
A novel application of machine-learning (ML) based image processing algorithms is proposed to analyze an all-sky map (ASM) obtained using the Fermi Gamma-ray Space Telescope. An attempt was made to simulate a one-year ASM from a short-exposure ASM generated from one-week observation by applying three ML based image processing algorithms: dictionary learning, U-net, and Noise2Noise. Although the inference based on ML is less clear compared to standard likelihood analysis, the quality of the ASM was generally improved. In particular, the complicated diffuse emission associated with the galactic plane was successfully reproduced only from one-week observation data to mimic a ground truth (GT) generated from a one-year observation. Such ML algorithms can be implemented relatively easily to provide sharper images without various assumptions of emission models. In contrast, large deviations between simulated ML maps and GT map were found, which are attributed to the significant temporal variability of blazar-type active galactic nuclei (AGNs) over a year. Thus, the proposed ML methods are viable not only to improve the image quality of an ASM, but also to detect variable sources, such as AGNs, algorithmically, i.e., without human bias. Moreover, we argue that this approach is widely applicable to ASMs obtained by various other missions; thus, it has the potential to examine giant structures and transient events, both of which are rarely found in pointing observations.
Abstract Stem cell-based products have clinical and industrial applications. Thus, there is a need to develop quality control methods to standardize stem cell manufacturing. Here, we report a deep learning-based automated cell tracking (DeepACT) technology for noninvasive quality control and identification of cultured human stem cells. The combination of deep learning-based cascading cell detection and Kalman filter algorithm-based tracking successfully tracked the individual cells within the densely packed human epidermal keratinocyte colonies in the phase-contrast images of the culture. DeepACT rapidly analyzed the motion of individual keratinocytes, which enabled the quantitative evaluation of keratinocyte dynamics in response to changes in culture conditions. Furthermore, DeepACT can distinguish keratinocyte stem cell colonies from non-stem cell-derived colonies by analyzing the spatial and velocity information of cells. This system can be widely applied to stem cell cultures used in regenerative medicine and provides a platform for developing reliable and noninvasive quality control technology.
Two ASCA observations were made of two ultraluminous compact X-ray sources (ULXs) in the spiral galaxy IC 342. In the 1993 observation, source 2 showed a 0.5-10 keV luminosity of 6 × 1039 ergs s-1 (assuming a distance of 4.0 Mpc) and a hard power-law spectrum of photon index ~1.4. As already reported, source 1 was ~3 times brighter on that occasion and exhibited a soft spectrum represented by a multicolor disk model with an inner-disk temperature of ~1.8 keV. The second observation, made in 2000 February, revealed that source 1 had made a transition into a hard spectral state, while source 2 made a transition into a soft spectral state. The ULXs are therefore inferred to exhibit two distinct spectral states, and they sometimes make transitions between them. These results significantly reinforce the scenario that describes ULXs as mass-accreting black holes.
The anomalous X-ray pulsar 4U 0142+61 was observed with Suzaku on 2007 August 15 for a net exposure of -100 ks, and was detected in a 0.4 to ~70 keV energy band. The intrinsic pulse period was determined as 8.68878 \pm 0.00005 s, in agreement with an extrapolation from previous measurements. The broadband Suzaku spectra enabled a first simultaneous and accurate measurement of the soft and hard components of this object by a single satellite. The former can be reproduced by two blackbodies, or slightly better by a resonant cyclotron scattering model. The hard component can be approximated by a power-law of photon index \Gamma h ~0.9 when the soft component is represented by the resonant cyclotron scattering model, and its high-energy cutoff is constrained as >180 keV. Assuming an isotropic emission at a distance of 3.6 kpc, the unabsorbed 1-10 keV and 10-70 keV luminosities of the soft and hard components are calculated as 2.8e+35 erg s^{-1} and 6.8e+34 erg s^{-1}, respectively. Their sum becomes ~10^3 times as large as the estimated spin-down luminosity. On a time scale of 30 ks, the hard component exhibited evidence of variations either in its normalization or pulse shape.
Purpose: For dose calculation of kV Cone Beam CT (CBCT), Monte‐Carlo simulation method is the best for accuracy. However, Monte‐Carlo method is very time‐consuming. Therefore it is not practical to be used in daily clinical work. The CTDI has been a useful tool to estimate dose for human bodies, however, this index is not intended to calculate the dose distribution in our bodies. Clearly, we need a reasonably fast and accurate calculation method. We propose a new calculation method by use of super position algorithm in Pinnacle. We calculated low‐energy kernels in the range of 10– 100 keV and implemented them in Pinnacle3 and estimated the dose from CBCT. Methods: We used a user code of EGSnrc to make low energy kernels. To implement a cross section of low energy photons in the kernels, a cross section table on NIST XCOM was used. Then we implemented these kernels for 10–100 keV in Pinnacle3 and the modeling of the kV cone beam was performed. In order to validate the accuracy of them, we calculated a PDD and beam profiles in a water phantom and compared them to the measured values. To simulate the dose distribution in a human body, we used a RANDO phantom and measured the dose with glass dosimeters. Results: The comparison of the results shows good agreement of the calculated beam profiles and measured values. Estimated maximum dose in the body from the RANDO phantom measurement was about 2 cGy that is about 1 % of the clinical dose. The discrepancy between glass dose and calculated dose by Pinnacle is within 20–30 %. Conclusions: A fast and reasonably accurate dose calculation method by use of Pinnacle3 was proposed and validated. This is the first step toward a total treatment planning involving CBCT dose in clinical use.
