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Register a new userA Cloud connected NO2 and Ozone Sensor System for Personalized Pediatric Asthma Research and Management
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This paper presents a cloud-connected indoor air quality sensor system that can be deployed to patients homes to study personal microenvironmental exposure for asthma research and management. The system consists of multiple compact sensor units that can measure residential NO2, ozone, humidity, and temperature at one minute resolution and a cloud based informatic system that acquires, stores, and visualizes the microenvironmental data in real time. The sensor hardware can measure NO2 as low as 10 ppb and ozone at 15 ppb. The cloud informatic system is implemented using open-source software on Amazon Web Service for easy deployment and scalability. This system was successfully deployed to pediatric asthma patients homes in a pilot study. In this study, we discovered that some families can have short term NO2 exposure higher than EPAs one hour exposure limit (100 ppb), and NO2 micropollution episodes often arise from natural gas appliance usage such as gas stove burning during cooking. By combining the personalized air pollutant exposure measurements with the physiological responses from a patient diary and medical record, this system can enable novel asthma research and personalized asthma management.
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We describe the current state and future plans for a set of tools for scientific data management (SDM) designed to support scientific transparency and reproducible research. SDM has been in active use at our MRI Center for more than two years. We designed the system to be used from the beginning of a research project, which contrasts with conventional end-state databases that accept data as a project concludes. A number of benefits accrue from using scientific data management tools early and throughout the project, including data integrity as well as reuse of the data and of computational methods.
We consider a detection problem where sensors experience noisy measurements and intermittent communication opportunities to a centralized fusion center (or cloud). The objective of the problem is to arrive at the correct estimate of event detection in the environment. The sensors may communicate locally with other sensors (local clusters) where they fuse their noisy sensor data to estimate the detection of an event locally. In addition, each sensor cluster can intermittently communicate to the cloud, where a centralized fusion center fuses estimates from all sensor clusters to make a final determination regarding the occurrence of the event across the deployment area. We refer to this hybrid communication scheme as a cloud-cluster architecture. Minimizing the expected loss function of networks where noisy sensors are intermittently connected to the cloud, as in our hybrid communication scheme, has not been investigated to our knowledge. We leverage recently improved concentration inequalities to arrive at an optimized decision rule for each cluster and we analyze the expected detection performance resulting from our hybrid scheme. Our analysis shows that clustering the sensors provides resilience to noise in the case of low communication probability with the cloud. For larger clusters, a steep improvement in detection performance is possible even for a low communication probability by using our cloud-cluster architecture.
Nosocomial infections place a substantial burden on health care systems and represent a major issue in current public health, requiring notable efforts for its prevention. Understanding the dynamics of infection transmission in a hospital setting is essential for tailoring interventions and predicting the spread among individuals. Mathematical models need to be informed with accurate data on contacts among individuals. We used wearable active Radio-Frequency Identification Devices to detect face-to-face contacts among individuals with a spatial resolution of about 1.5 meters, and a time resolution of 20 seconds. The study was conducted in a general pediatrics hospital ward, during a one-week period, and included 119 participants. Nearly 16,000 contacts were recorded during the study, with a median of approximately 20 contacts per participants per day. Overall, 25% of the contacts involved a ward assistant, 23% a nurse, 22% a patient, 22% a caregiver, and 8% a physician. The majority of contacts were of brief duration, but long and frequent contacts especially between patients and caregivers were also found. In the setting under study, caregivers do not represent a significant potential for infection spread to a large number of individuals, as their interactions mainly involve the corresponding patient. Nurses would deserve priority in prevention strategies due to their central role in the potential propagation paths of infections. Our study shows the feasibility of accurate and reproducible measures of the pattern of contacts in a hospital setting. The results are particularly useful for the study of the spread of respiratory infections, for monitoring critical patterns, and for setting up tailored prevention strategies. Proximity-sensing technology should be considered as a valuable tool for measuring such patterns and evaluating nosocomial prevention strategies in specific settings.
Plant breeding is fundamentally comprised of three cyclic activities: 1) intermating lines to generate novel allelic combinations, 2) evaluation of new plant cultivars in distinct environments, and 3) selection of superior individuals to be used as parents in the next breeding cycle. While digital technologies and tools are commonly utilized for the latter two stages, many plant research programs still rely on manual annotation and paper tags to track the crosses that constitute the basis of a plant breeding program. This presence of analog data is a crack in the foundation of a digital breeding ecosystem and a significant occasion for errors to be introduced that will propagate through the entire breeding program. However, implementing digital cross tracking into breeding programs is difficult due to the non-standardized workflows that different breeders have adopted. Intercross, an open-source Android app, aims to provide scientists with a robust and simple solution for planning, tracking, and managing the crosses being made each season and aims to serve as the primary tool to digitize crossing data for breeding programs. The simplicity and flexibility of Intercross allows rapid and broad adoption by diverse breeding programs and will solidify the concepts of a digital breeding ecosystem.
Background Little is known about the population pharmacokinetics (PPK) of tacrolimus (TAC) in pediatric primary nephrotic syndrome (PNS). This study aimed to compare the predictive performance between nonlinear and linear PK models and investigate the significant factors of TAC PK characteristics in pediatric PNS. Methods Data were obtained from 71 pediatric patients with PNS, along with 525 TAC trough concentrations at steady state. The demographic, medical, and treatment details were collected. Genetic polymorphisms were analyzed. The PPK models were developed using nonlinear mixed effects model software. Two modeling strategies, linear compartmental and nonlinear Michaelis Menten (MM) models, were evaluated and compared. Results Body weight, age, daily dose of TAC, co-therapy drugs (including azole antifungal agents and diltiazem), and CYP3A5*3 genotype were important factors in the final linear model (onecompartment model), whereas only body weight, codrugs, and CYP3A5*3 genotype were the important factors in the nonlinear MM model. Apparent clearance and volume of distribution in the final linear model were 7.13 L/h and 142 L, respectively. The maximal dose rate (Vmax) of the nonlinear MM model was 1.92 mg/day and the average concentration at steady state at half-Vmax (Km) was 1.98 ng/mL. The nonlinear model described the data better than the linear model. Dosing regimens were proposed based on the nonlinear PK model.Conclusion Our findings demonstrate that the nonlinear MM model showed better predictive performance than the linear compartmental model, providing reliable support for optimizing TAC dosing and adjustment in children with PNS.
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