Radioactivity estimation of radioactive hotspots using a Compton camera and derivation of dose rates in the surrounding environment.

At the Fukushima Daiichi Nuclear Power Station, radiation sources released in the accident were deposited on various equipment and building structures. During decommissioning, it is crucial to understand the distribution of radiation sources and ambient dose equivalent rates to reduce worker exposure and implement detailed work planning. In this study, the author introduces a method for visualizing radiation sources, estimates their radioactivity using a Compton camera, and derives the dose rate around the radiation sources. In the demonstration test, the Compton camera was used to visualize radioactive hotspots caused by 137Cs radiation sources deposited in the outdoor environment and estimated the radioactivity. Furthermore, the dose rate around the hotspots was calculated from the estimated radioactivity, which confirmed that the calculated dose rate correlated with the dose rate measured using a survey meter. This approach is novel, where a series of analyses were conducted using the Compton camera to visualize radioactive hotspots, estimate the radioactivity, and derive the dose rate in the surrounding environment.

Dual Wavelength Photoplethysmography Framework for Heart Rate Calculation.

The quality of heart rate (HR) measurements extracted from human photoplethysmography (PPG) signals are known to deteriorate under appreciable human motion. Auxiliary signals, such as accelerometer readings, are usually employed to detect and suppress motion artifacts. A 2019 study by Yifan Zhang and his coinvestigatorsused the noise components extracted from an infrared PPG signal to denoise a green PPG signal from which HR was extracted. Until now, this approach was only tested on "micro-motion" such as finger tapping. In this study, we extend this technique to allow accurate calculation of HR under high-intensity full-body repetitive "macro-motion". Our Dual Wavelength (DWL) framework was tested on PPG data collected from 14 human participants while running on a treadmill. The DWL method showed the following attributes: (1) it performed well under high-intensity full-body repetitive "macro-motion", exhibiting high accuracy in the presence of motion artifacts (as compared to the leading accelerometer-dependent HR calculation techniques TROIKA and JOSS); (2) it used only PPG signals; auxiliary signals such as accelerometer signals were not needed; and (3) it was computationally efficient, hence implementable in wearable devices. DWL yielded a Mean Absolute Error (MAE) of 1.22|0.57 BPM, Mean Absolute Error Percentage (MAEP) of 0.95|0.38%, and performance index (PI) (which is the frequency, in percent, of obtaining an HR estimate that is within ±5 BPM of the HR ground truth) of 95.88|4.9%. Moreover, DWL yielded a short computation period of 3.0|0.3 s to process a 360-second-long run.

Monte Carlo-Based Error Propagation for a More Reliable Regression Analysis across Specific Rates in Bioprocesses.

During process development, bioprocess data need to be converted into applicable knowledge. Therefore, it is crucial to evaluate the obtained data under the usage of transparent and reliable data reduction and correlation techniques. Within this contribution, we show a generic Monte Carlo error propagation and regression approach applied to two different, industrially relevant cultivation processes. Based on measurement uncertainties, errors for cell-specific growth, uptake, and production rates were determined across an evaluation chain, with interlinked inputs and outputs. These uncertainties were subsequently included in regression analysis to derive the covariance of the regression coefficients and the confidence bounds for prediction. The usefulness of the approach is shown within two case studies, based on the relations across biomass-specific rate control limits to guarantee high productivities in E. coli, and low lactate formation in a CHO cell fed-batch could be established. Besides the possibility to determine realistic errors on the evaluated process data, the presented approach helps to differentiate between reliable and unreliable correlations and prevents the wrong interpretations of relations based on uncertain data.

The impact of data selection and fitting on SAR estimation for magnetic nanoparticle heating.

BACKGROUND: Magnetic fluid heating has great potential in the fields of thermal medicine and cryopreservation. However, variations among experimental parameters, analysis methods and experimental uncertainty make quantitative comparisons of results among laboratories difficult. Herein, we focus on the impact of calculating the specific absorption rate (SAR) using Time-Rise and Box-Lucas fitting. Time-Rise assumes adiabatic conditions, which is experimentally unachievable, but can be reasonably assumed (quasi-adiabatic) only for specific and limited evaluation times when heat loss is negligible compared to measured heating rate. Box-Lucas, on the other hand, accounts for heat losses but requires longer heating. METHODS: Through retrospective analysis of data obtained from two laboratories, we demonstrate measurement time is a critical parameter to consider when calculating SAR. Volumetric SAR were calculated using the two methods and compared across multiple iron-oxide nanoparticles. RESULTS: We observed the lowest volumetric SAR variation from both fitting methods between 1-10 W/mL, indicating an ideal SAR range for heating measurements. Furthermore, our analysis demonstrates that poorly chosen fitting method can generate reproducible but inaccurate SAR. CONCLUSION: We provide recommendations to select measurement time for data analysis with either Modified Time-Rise or Box-Lucas method, and suggestions to enhance experimental precision and accuracy when conducting heating experiments.

Assessing tritium internalisation in zebrafish early life stages: Importance of rapid isotopic exchange.

Tritium (3H) is mainly released into the environment in the form of tritiated water (HTO) by nuclear power plants and nuclear fuel reprocessing plants. To better understand how organisms may be affected by contamination to 3H it is essential to link observed effects to a correct estimation of absorbed dose rates. Due to quick isotopic exchanges between 3H and hydrogen, 3H measurement is difficult in small organisms such as zebrafish embryo, a model in ecotoxicological assay. This work aimed to optimise tritium measurement protocol to better characterise internalisation by early life stages of zebrafish. Zebrafish eggs were exposed at one HTO activity concentration of 1.22 × 105 Bq/mL. This activity was calculated to correspond to theoretical dose rates of 0.4 mGy/h, where some deleterious effects are expected on young fish. A protocol for the preparation of biological samples was adapted from the method classically used to segregate the different forms of tritium in organisms. To deal with very quick isotopic exchanges of 3H with hydrogen, the impacts of washing by non-tritiated water as well as the bias induced by absorbed tritium around organisms on the measured activity concentration were studied. We managed to develop protocols to perform total tritium and total organically bound tritium (OBT) activity concentrations measurements in zebrafish eggs and larvae. The measurement of these both forms allowed the calculation of tissue-free-water-tritium (TFWT). To better understand total tritium internalisation, a study of total tritium kinetics from 4 hpf (hour post-fertilization) to 168 hpf was performed. OBT and TFWT were also assessed to complete the total tritium internalisation kinetics. The internalisation is a rapid phenomenon reaching a steady-state within 24 h after the beginning of contamination for total tritium and TFWT, with concentration factors and TFWT/HTO close to unity. OBT formation seemed to be slower. It appeared that OBT content in organisms was low with an OBT/TFWT ratio of about 8% for both stages (24 hpf and 96 hpf). To verify absorbed dose rates at key developmental stages (24 hpf eggs and 96 hpf larvae), they were calculated from total tritium activity concentrations after exposure at 1.22 × 105 and 1.22 × 106 Bq/mL, as these two activity concentrations were used to assess effects of tritium in another part of the study. Dose rates calculated from total tritium activity concentrations measured in 24 hpf eggs and 96 hpf larvae were consistent with the nominal ones, which validates the robustness of the protocol developed in the present study.

[Design and Implementation of the Wireless ECG Monitor Based on AD8232 and CC2530].

Based on the heart rate monitor front end AD8232 chips and Zigbee system solutions CC2530 single chip, the wireless ECG monitor has been realized in order to implement the acquisition, processing and wireless transmission of electrocardiogram signal. Meanwhile, the real-time heart rate has been calculated and wirelessly transmitted. The wireless ECG monitor is characterized by low power consumption, small size and simple operation. It can be seen from the experimental results that the wireless ECG monitor designed in this paper can successfully obtain favorable ECG data and real-time heart rate data and display it.

Accounting for Non-Gaussian Sources of Spatial Correlation in Parametric Functional Magnetic Resonance Imaging Paradigms II: A Method to Obtain First-Level Analysis Residuals with Uniform and Gaussian Spatial Autocorrelation Function and Independent and Identically Distributed Time-Series.

In a recent study Eklund et al. have shown that cluster-wise family-wise error (FWE) rate-corrected inferences made in parametric statistical method-based functional magnetic resonance imaging (fMRI) studies over the past couple of decades may have been invalid, particularly for cluster defining thresholds less stringent than p < 0.001; principally because the spatial autocorrelation functions (sACFs) of fMRI data had been modeled incorrectly to follow a Gaussian form, whereas empirical data suggest otherwise. Hence, the residuals from general linear model (GLM)-based fMRI activation estimates in these studies may not have possessed a homogenously Gaussian sACF. Here we propose a method based on the assumption that heterogeneity and non-Gaussianity of the sACF of the first-level GLM analysis residuals, as well as temporal autocorrelations in the first-level voxel residual time-series, are caused by unmodeled MRI signal from neuronal and physiological processes as well as motion and other artifacts, which can be approximated by appropriate decompositions of the first-level residuals with principal component analysis (PCA), and removed. We show that application of this method yields GLM residuals with significantly reduced spatial correlation, nearly Gaussian sACF and uniform spatial smoothness across the brain, thereby allowing valid cluster-based FWE-corrected inferences based on assumption of Gaussian spatial noise. We further show that application of this method renders the voxel time-series of first-level GLM residuals independent, and identically distributed across time (which is a necessary condition for appropriate voxel-level GLM inference), without having to fit ad hoc stochastic colored noise models. Furthermore, the detection power of individual subject brain activation analysis is enhanced. This method will be especially useful for case studies, which rely on first-level GLM analysis inferences.

Accounting for Non-Gaussian Sources of Spatial Correlation in Parametric Functional Magnetic Resonance Imaging Paradigms I: Revisiting Cluster-Based Inferences.

In a recent study, Eklund et al. employed resting-state functional magnetic resonance imaging data as a surrogate for null functional magnetic resonance imaging (fMRI) datasets and posited that cluster-wise family-wise error (FWE) rate-corrected inferences made by using parametric statistical methods in fMRI studies over the past two decades may have been invalid, particularly for cluster defining thresholds less stringent than p < 0.001; this was principally because the spatial autocorrelation functions (sACF) of fMRI data had been modeled incorrectly to follow a Gaussian form, whereas empirical data suggested otherwise. Here, we show that accounting for non-Gaussian signal components such as those arising from resting-state neural activity as well as physiological responses and motion artifacts in the null fMRI datasets yields first- and second-level general linear model analysis residuals with nearly uniform and Gaussian sACF. Further comparison with nonparametric permutation tests indicates that cluster-based FWE corrected inferences made with Gaussian spatial noise approximations are valid.

Design and Implementation of the Wireless ECG Monitor Based on AD8232 and CC2530 / 中国医疗器械杂志

Based on the heart rate monitor front end AD8232 chips and Zigbee system solutions CC2530 single chip, the wireless ECG monitor has been realized in order to implement the acquisition, processing and wireless transmission of electrocardiogram signal. Meanwhile, the real-time heart rate has been calculated and wirelessly transmitted. The wireless ECG monitor is characterized by low power consumption, small size and simple operation. It can be seen from the experimental results that the wireless ECG monitor designed in this paper can successfully obtain favorable ECG data and real-time heart rate data and display it.