Hardware algorithm design and validation for nuclear radiation imaging based on Monte Carlo simulation.

The nuclear radiation imaging technology, aimed at illustrating the position and distribution of radioactive sources, has undergone extensive research. By relying on a simulated radiation imaging system for data acquisition, we can significantly expedite the development cycle of these imaging instruments. Establishing simulated experimental scenarios and radiation imaging systems is of paramount significance in obtaining output signals for algorithmic testing and validation. This study is divided into two parts: simulation and hardware algorithm. In the simulation part, precise simulation of scintillation light transport in a crystal was achieved using the GEANT4 Monte Carlo simulation toolkit. A LaBr3(Ce) detector system was simulated by digitizing photon interactions. In the hardware algorithm part, a positioning algorithm based on a fully connected neural network was implemented and optimized using a heterogeneous distributed storage approach. The system validated and assessed the FPGA-based neural network gamma camera positioning algorithm, demonstrating significant consistency with computer-generated images in capturing the shape and dispersion of radioactive sources (planar, multi-point, and ring-shaped).

Biofertilizer-induced response to cadmium accumulation in Oryza sativa L. grains involving exogenous organic matter and soil bacterial community structure.

The accumulation of cadmium (Cd) in Oryza sativa L., the world's most significant staple crop, is a health threat to millions of people. The objective of this study was to evaluate the effectiveness of commercially available biofertilizers (with high (BF2) and low organic matter (OM) content (BF1)) on Cd accumulation in two types of soils and to determine the bacterial community responses by high-throughput sequencing. The study was conducted in the form of pot experiment in greenhouse in 2018. Four treatments were set: BF1, BF2, organic fertilizer (OF), and control (CK) and the amendments were applied before the rice cultivation. The results showed that the addition of biofertilizers immobilized or mobilized Cd in soils, depending on the soil type and the OM content in biofertilizers. The exogenous OM in biofertilizers was the driving factor for the difference in pH and Cd accumulation in rice grains. The application of biofertilizers with high OM content was effective in reducing Cd accumulation in the rice grains (19.7% lower than CK) by significantly increasing soil pH (from 6.02 to 6.67) in acid silt loam soil (TZ). The consumption of acid fermentation products by soil chemoorganotrophs and the complexation of organic anions in the biofertilizer treatment tended to buffer the pH drop in the drainage and decrease the Cd availability. However, in the weak acid silty clay loam soil (SX), the addition of biofertilizer with high OM significantly increased Cd accumulation in rice grains (21.9% higher than CK), probably owing to the release of acid substances, resulting from the significant increase of the predominant bacteria Chloroflexi. The addition of biofertilizer with low OM content did not significantly change Cd accumulation in rice grains or affect the soil microbial structures in both soils. In conclusion, the effects of biofertilizer on rice Cd accumulation were related to the OM content and soil bacterial community. Biofertilizers with high organic matter may not be suitable for amendments in the paddy soils with high clay content to reduce Cd accumulation in rice grains.

The validity of synthetic clinical data: a validation study of a leading synthetic data generator (Synthea) using clinical quality measures.

BACKGROUND: Clinical data synthesis aims at generating realistic data for healthcare research, system implementation and training. It protects patient confidentiality, deepens our understanding of the complexity in healthcare, and is a promising tool for situations where real world data is difficult to obtain or unnecessary. However, its validity has not been fully examined, and no previous study has validated it from the perspective of healthcare quality, a critical aspect of a healthcare system. This study fills this gap by calculating clinical quality measures using synthetic data. METHODS: We examined an open-source well-documented synthetic data generator Synthea, which was composed of the key advancements in this emerging technique. We selected a representative 1.2-million Massachusetts patient cohort generated by Synthea. Four quality measures, Colorectal Cancer Screening, Chronic Obstructive Pulmonary Disease (COPD) 30-Day Mortality, Rate of Complications after Hip/Knee Replacement, and Controlling High Blood Pressure, were selected based on clinical significance. Calculated rates were then compared with publicly reported rates based on real-world data of Massachusetts and United States. RESULTS: Of the total Synthea Massachusetts population (n = 1,193,439), 394,476 were eligible for the "colorectal cancer screening" quality measure, and 248,433 (63%) were considered compliant, compared to the publicly reported Massachusetts and national rates being 77.3 and 69.8%, respectively. Of the 409 eligible patients, 0.7% of died within 30 days after COPD exacerbation, versus 7% reported in Massachusetts and 8% nationally. Using an expanded logic, this rate increased to 5.7%. No Synthea residents had complications after Hip/Knee Replacement (Massachusetts: 2.9%, national: 2.8%) or had their blood pressure controlled after being diagnosed with hypertension (Massachusetts: 74.52%, national: 69.7%). Results show that Synthea is quite reliable in modeling demographics and probabilities of services being offered in an average healthcare setting. However, its capabilities to model heterogeneous health outcomes post services are limited. CONCLUSIONS: Synthea and other synthetic patient generators do not currently model for deviations in care and the potential outcomes that may result from care deviations. To output a more realistic data set, we propose that synthetic data generators should consider important quality measures in their logic and model when clinicians may deviate from standard practice.