Sleep is defined as a period of inactivity regulated by a circadian process (process C) which regulates the timing of sleep and a homeostatic process (process S) regulating sleep intensity. Over the years, the molecular basis of process C has become more defined such as the BMAL1-CLOCK feedback loop, but the genetic background and regulation of sleep homeostasis have yet to be discovered. In humans, as well as mice, sleep is identified by physiological markers such as changes in brain activity which can be monitored using an electroencephalogram (EEG). Thus, REM and NREM EEG recordings were obtained from 26 inbred mice strains and computationally analysed with non sleep phenotypes such as age and bone mineral density using GeneNetwork. A Pearson correlation was taken for each type of sleep and both non sleep phenotypes. The data indicates that there is a negative correlation between the age of male mice and all three types of sleep. Contrastingly, a positive correlation was observed between the bone mineral density and all three types of sleep. This data will lead to identifying genetic markers that regulate sleep.

Sleep is defined as a period of inactivity regulated by a homeostatic process and a circadian process. It is essential because prolonged sleep deprivation can lead to physical impairments as well as cognitive loss. In recent years, there has been a push to understand the molecular mechanisms and genetic regulation of sleep circuitry. The circadian regulation of sleep has been well established, and a few genes regulating sleep length have been revealed in fruit flies, such as Dec2; however, genes involved in homeostatic mechanisms are not well known. To begin showing genetic factors of sleep homeostasis, Electroencephalogram (EEG) recordings were obtained from 26 strains of inbred mice. Then, GeneNetwork was used to correlate 14 sleep phenotypes during active phase baseline and recovery sleep. Using the Pearson correlation test, both recovery and baseline phenotypes revealed various amounts of association to one another. The data indicate that recovery phenotypes related to sleep deprivation, such as (Record ID:50452,50453,50449), have a more negative association with other phenotypes. Baseline phenotypes (Record ID 50578, 50586, 50538) have a more positive association with similar phenotypes. These correlations are crucial in understanding the genetic links to sleep. Future advancement of this project includes focusing on the specific phenotypes that are highly frequent in the data and linking genetic information together by QTL mapping The genes that could be found by using QTL analysis could reveal genetic variation in sleep.

The instrumentation of buildings with data acquisition systems, such as accelerometers, provide insight into building damage and functionality following an earthquake. A relational database provides a platform to house large inventories of collected seismic response data records, along with associated building and earthquake parameters. An efficient database schema was designed by organizing all tables and shared fields (keys) to eliminate data redundancy. Appropriate data types for attributes, particularly vectors of time series data, were researched and implemented. Queries were written to facilitate data extraction based on user-defined criteria. Optimally stored, easily accessible, and high quality data provides the foundation for advanced data analysis, which, in the context of this dataset, can help reduce the social, economic, and environmental impact of earthquakes.

The exponential growth in the Internet of Things (IoT) devices and wearable sensing technologies have created an unprecedented opportunity to enable personalized medicine, through real-time biomonitoring of individuals and enabling actionable feedback. Currently, commercialized IoT devices and wearable sensors are only capable of tracking physical activities and vital signs and fail to access molecular-level biomarker information to provide insight into the body’s dynamic chemistry. Sweat-based wearable biomonitoring has emerged as one of the most promising candidates to merge this gap. Sweat is a rich source of biomarkers that can be retrieved unobtrusively. Accordingly, by specifically designing and integrating compact and flexible electrochemical sensors into wearable electronic devices, we can efficiently, comfortably, and accurately track these biomarkers to the users and provide actionable feedback about their health status at molecular levels. Additionally, we develop a novel sensor fabrication/integration methodology, which allows for seamless and compact integration of disposable electrochemical sensors in highly complex biological media which can provide reliable signal readouts. Thus, we can track and monitor any existing health conditions or illnesses accurately seamlessly using our integrated electronic and sensing technology.

Exponential growth in Internet of Things (IoT) devices and wearable sensing technologies has created an unprecedented opportunity to enable personalized medicine through real-time biomonitoring of individuals, enabling actionable feedback. Although commercialized IoT devices and wearable sensors are capable of tracking physical activities and vital signs, they fail to access molecular-level biomarker information to provide insight into the body’s dynamic chemistry. Thus, sweat-based wearable biomonitoring has emerged as one of the most promising candidates to merge this gap due to sweat being a rich source of biomarkers that can be retrieved unobtrusively. By specifically designing and integrating compact and flexible electrochemical sensors into wearable electronic devices, we can non-invasively, and accurately track these biomarkers and provide actionable feedback about users’ health status at molecular level. Additionally, we develop a novel sensor fabrication/integration methodology, which allows seamless and compact integration of electrochemical sensors in highly complex biological media. Our design consists of a battery-free electrochemical skin adhesive and Ion-Selective Electrode (ISE)-based sensor arrays for biomarker monitoring. The electrochemical adhesive utilizes Near Field Communication (NFC) for wireless power delivery and signal processing. The electrochemical sensor array is fabricated on anisotropic adhesive tapes for vertical integration of the sensors into a flexible sensing adhesive. As a result, this design methodology improves signal readout sensitivity by about three times. Thus, we can continuously track and monitor subjects’ biomarkers and underlying health status to provide actionable feedback and personalized prescriptions.