Between Dec 19, 2024 and Jan 2, 2025, datasets can be submitted to DRUM but will not be processed until after the break. Staff will not be available to answer email during this period, and will not be able to provide DOIs until after Jan 2. If you are in need of a DOI during this period, consider Dryad or OpenICPSR. Submission responses to the UDC may also be delayed during this time.

Browsing by Subject "Respiration"

Now showing 1 - 5 of 5
  • Thumbnail Image
    Item
    Garment-Based Respiration And Pulse Oximetry Sensing Using A Stitched Sensor And Chest Mounted Pulse Oximetry Sensor
    (2023-08) Clarke, Megan
    Garment-based wearable devices have the potential to make on-body sensing of vital signs a more seamless part of everyday life. This research seeks to investigate a wearable chest-mounted stitched strain sensor and pulse oximeter for the purpose of developing a garment-based sensing device. A wearable or garment-based device could be used for long term or long distance monitoring a wearer’s respiratory health when regular access to healthcare is challenging due to distance, such as is the case in many rural communities. However, the effect of fit and sizing of a wearable device is a significant challenge when it comes to the balancing comfort and sensor accuracy needs in a wearable device. A stitched conductive thread sensor and an adapted pulse oximeter probe integrated into a chest-mounted mounted adjustable sensor belt were investigated to understand their performance relative to more typical sensing approaches. Two fit conditions were employed to measure effects on sensor performance and understand the challenges presented by garment-based sensing of respiratory signals. This research found that in general a tighter fit condition improved the performance of the stitched respiration sensor and chest mounted pulse oximeter, however sensor dropout greatly influenced both blood oxygen saturation (SpO₂) and beats-per-minute (BPM) data resulting in suboptimal readings. The stitched sensor was more accurate in measuring breath frequency than the comparison clinical device when fit was not optimized. As a result of this research, it is clear that the fit and sizing of a garment-based sensing device is a crucial factor in developing sensing garments suitable for everyday use.
  • Thumbnail Image
    Item
    High Root Temperatures: A Buried Threat to Plant Growth
    (2019-05) Guenthner, George
    Growing plants in containerized systems can result in high root temperatures (HRT) as containers, media, and roots above the ground are exposed to air and sunlight, commonly experiencing temperatures over 50C. Damage caused by HRT and associated consequences for growth are not well characterized amongst herbaceous plants. The research in this thesis evaluated how HRT impacted physiological and morphological responses of eight tomato (Solanum lycopersicum) varieties characterized as ‘heat-tolerant’ or ‘sensitive’ based upon aboveground traits. The first pair of experiments quantified respiration rates and electrolyte leakage of excised whole root masses in response to acute HRT exposure between 48 and 62C. Root respiration rates increased from 21.6 µmol hr-1 g-1 at 48C to 26.9 µmol hr-1 g-1 at 51C, and then decreased to approximately 0 µmol hr-1 g-1 at 57C. Varieties did not differ in responses to root temperature. Root temperature and variety interacted to impact proportional electrolyte leakage, which increased across varieties between 50 and 54C. Results of these experiments suggested that critical physical and metabolic damage occurs to tomato roots at >50C. For the second pair of experiments, morphological and photosynthetic responses of two tomato varieties previously characterized as heat-tolerant (‘Solar Fire’) or -sensitive (‘Amana Orange’) were assessed. Plants were grown at root temperatures ranging from 25 to 60C for 8 h-1 d-1 over 10 d, and differences in morphology were noted. Plant height and leaf size decreased as temperature increased. Shoot and root fresh and dry mass gain decreased when RT increased from 35 to 50C. ‘Solar Fire’ and ‘Amana Orange’ did not differ in fresh and dry mass gain responses or percent reduction in shoot and root mass gain. Root masses of ‘Solar Fire’ and ‘Amana Orange’ were also heated to 55C for 260 min in the afternoon of one day and plants were evaluated for changes in leaf photosynthetic rate and stomatal conductance the following four days. Photosynthetic rate and stomatal conductance decreased after one 55C RT exposure for 4 d compared to plants maintained at 25C. ‘Solar Fire’ and ‘Amana Orange’ differed in percent reduction in stomatal conductance. The results suggested diurnal, short-term HRT negatively impacted growth and photosynthesis regardless of reported above-ground heat tolerance, and that even one supraoptimal HRT event could reduce photosynthetic activity for days. Lastly, five root-associated fungi and bacteria (Azospirillum brasiliense, Bacillus amyloliquifaciens, Curvularia protuberata, Glomus intraradices, and Trichoderma harzianum), thought to confer increased resistance to biotic and abiotic stresses, were explored for their potential to alleviate HRT effects on tomato growth. ‘Amana Orange’ seedlings were inoculated with the before-mentioned microbes and exposed to root temperatures between 35 (control) and 55C (HRT) for 8 h-1 d-1 over a 10 d period. Plant height and shoot, root, and total plant fresh and dry mass decreased as root temperature increased from 35 to 50C. Dry mass gain of roots and shoots did not differ between un-inoculated and inoculated plants, but some differences were observed between inoculant species. The results suggested HRT have detrimental effects on above- and below-ground tomato growth and inoculation with the before-mentioned organisms did not alleviate those negative effects.
  • Thumbnail Image
    Item
    Life, Death, And Coexistence: Exploring And Manipulating The Respiratory Lifestyle Of Shewanella Oneidensis
    (2019-08) Kees, Eric
    In their natural environment, microbes often exist in stressful, suboptimal, ever-changing conditions and have evolved innumerable and varied successful strategies for managing stresses and thriving in flux. Microbial ecosystems are defined not only by specialized members occupying defined narrow niches, but also members that move between niches and exist in a mode of constant opportunism. One such opportunistic group of organisms are those belonging to the genus Shewanella which are largely defined by their respiratory versatility. A particularly well-studied member of this genus, S. oneidensis, is the most versatile respiratory organism described to date, and is a model organism for extracellular electron transfer. This thesis explores the respiratory lifestyle of S. oniedensis primarily through the lens of cell physiology and competitive fitness under optimal growth conditions and those that yield catastrophic death. The second chapter of this thesis is a study in cofactor acquisition by a key respiratory enzyme in S. oneidensis MR-1. The periplasmic protein FccA is both a fumarate reductase and an electron carrier protein for extracellular electron transfer, that requires a flavin cofactor for its function. Through genetic manipulation, growth experiments, and biochemical experiments, we found that for S. oneidensis, self-secretion of flavins comes at minimal metabolic cost and is required for periplasmic flavoprotein cofactor acquisition. The third chapter is a probing of natural and engineered factors that enable survival under respiratory stress. Shewanella is considered an obligate respiring organism, and when placed under conditions in which respiration cannot normally function, it experiences massive loss in viability accompanied by cell lysis. Despite, 99-99.99% of cells undergoing death in this condition, many persist. This study leveraged synthetic proton motive force supplementation, which affords enhanced survival of S. oneidensis, to profile the innate strategies used to survive under respiratory stress. The key finding of this study is that the sodium motive force plays a key role in survival of S. oneidensis under respiratory stress, even when survival is enhanced by proton motive force supplementation. The fourth chapter of this thesis is a series of competition experiments reframing a central paradigm of the competitive exclusion principle: that two organisms occupying the same niche cannot coexist. This principle states that in this scenario any organism with a reproductive advantage will ultimately overtake a population. This study demonstrated that two engineered strains of S. oneidensis utilizing the same medium with the same food and nutrient sources, but growing a vastly different rates, can remain at stable frequencies when grown attached to an electrode as the sole sink of electrons in respiration. The primary reason for this stability is that the original parent population remains actively growing on a surface to which daughtered cells are constantly removed. While the scenario we have engineered to “break” the competitive exclusion principle could be considered a form of niche differentiation, it demonstrates an effective strategy to combat strain degeneration and contamination in industrial fermentation, by allowing a selected population of less competitively fit individuals to act indefinitely as a progenitor population. The work in this thesis brings special attention to the adaptation towards an obligate respiratory lifestyle in S. oneidenisis. The systems it has evolved to secrete flavins as both respiratory cofactors and intermediates and the marked death it experiences under respiratory stress together emphasize adaptations in Shewanella to thrive in redox stratified environments, supported by the wide variety of respiratory nodes it can utilize. Finally, this work highlights the utility of S. oneidensis as a test platform for ideas, with potential benefits for biotechnological and industrial applications.
  • Thumbnail Image
    Item
    Predicting the effects of climate change on water yield and forest production in the northeastern United States
    (1995) Aber, John D; Ollinger, Scott V; Federer, C. Anthony; Reich, Peter B; Goulden, Michael L; Kicklighter, David W; Melillo, Jerry M; Lathrop, Richard G Jr
    Rapid and simultaneous changes in temperature, precipitation and the atmospheric concentration of CO2 are predicted to occur over the next century. Simple, well-validated models of ecosystem function are required to predict the effects of these changes. This paper describes an improved version of a forest carbon and water balance model (PnET-II) and the application of the model to predict stand- and regional-level effects of changes in temperature, precipitation and atmospheric CO2 concentration. PnET-II is a simple, generalized, monthly time-step model of water and carbon balances (gross and net) driven by nitrogen availability as expressed through foliar N concentration. Improvements from the original model include a complete carbon balance and improvements in the prediction of canopy phenology, as well as in the computation of canopy structure and photosynthesis. The model was parameterized and run for 4 forest/site combinations and validated against available data for water yield, gross and net carbon exchange and biomass production. The validation exercise suggests that the determination of actual water availability to stands and the occurrence or non-occurrence of soil-based water stress are critical to accurate modeling of forest net primary production (NPP) and net ecosystem production (NEP). The model was then run for the entire NewEngland/New York (USA) region using a 1 km resolution geographic information system. Predicted long-term NEP ranged from -85 to +275 g C m-2 yr-1 for the 4 forest/site combinations, and from -150 to 350 g C m-2 yr-1 for the region, with a regional average of 76 g C m-2 yr-1. A combination of increased temperature (+6*C), decreased precipitation (-15%) and increased water use efficiency (2x, due to doubling of CO2) resulted generally in increases in NPP and decreases in water yield over the region.
  • Thumbnail Image
    Item
    Wearable Inertial Sensors for Motion Analysis in Respiration, Diet Monitoring, and Vehicular Safety Applications
    (2021-07) Johnson, Gregory
    This thesis is concerned with the development and application of motion analysis algorithms based on signals from inertial measurement units (IMUs). In particular, the application areas discussed in the thesis are respiratory monitoring, dietary monitoring, and vehicular safety. Usage of IMUs for attitude and heading estimation has a rich legacy, but it is only in recent years that they have become low-cost commodity sensors found in nearly every smart phone and smart watch, making them particularly applicable sensors for everyday applications. Despite the existence of well-established orientation estimation techniques, motion analysis using inexpensive wearable sensor applications targeted to the general population requires special attention. All three application areas discussed in this thesis require a similar approach to the estimation of motion variables in that they depend on the partial or full orientation of the device relative to the human user and/or the user’s orientation relative to earth. However, the class of mobile-phone grade IMUs utilized here offer notoriously poor accuracy compared to much more expensive aerospace-grade IMUs. Inexpensive IMUs typically suffer from bias instability, which requires careful calibration or specialized algorithms. Further, full orientation estimation traditionally relies on the IMU’s magnetometer to sense the geomagnetic field. But, the geomagnetic field is relatively weak and can often be dwarfed by magnetic fields from ferromagnetic objects routinely encountered in indoor environments. Thus, applications targeted for use by the general population must utilize algorithms that can overcome these limitations in a robust manner. The first application area addressed is respiratory monitoring. The physical motions of the thoracoabdominal wall during respiration are important in many diseases, and differentiation of normal from abnormal respiratory kinematics can be used to monitor disease state. In this application, a novel wearable device is developed that allows for long-term, out-of-clinic monitoring and analysis of respiration under the assumption of static body position (non- ambulatory). In particular, the device measures respiratory accelerations at multiple points on the thoracoabdominal surface and estimates respiratory displacements along with a variety of clinically useful metrics. After careful removal of gravity from the acceleration measurement using a multiplicative Kalman Smoother, the algorithm double integrates and high pass filters the residual signal to obtain three-dimensional respiratory displacements. The accuracy is on the order of the accuracy of a reference optical motion tracking system, and this thesis presents an analysis of the factors contributing to displacement errors. From the displacements, a variety of additional temporal, phasic, and volumetric respiratory variables may be estimated. After developing methods and discussing experimental results from a single subject for estimating respiratory displacements and subsequently several respiratory variables from these displacements, we then present the results from an initial small cohort IRB-approved study using the device. In the study, subjects wore the respiratory monitor while faced with a variety of airway occlusions. Despite the ultra-low respiratory rates encountered, the system was able to detect thoracoabdominal asynchrony with limited accuracy. Real-life medical situations involving respiratory distress are likely to present higher respiratory rates and thus higher potential for more accurate estimates. The developed system offers a combination of capabilities unmatched by existing technology in terms of its portability and the suite of respiratory variables it is able to estimate. The second application area addressed in this thesis is the development of a novel Food Intake Monitoring (FIM) device. Typical methods for dietary tracking in obesity research such as the 24-hour food intake recall are well known to be inaccurate, and there is clear need for a device to automatically detect and capture eating events as an adjunct to these existing methods. In this thesis, a wrist-worn IMU and microcontroller are utilized to detect when a person is eating (under the assumption that the food is eaten primarily with the sensor-affixed arm), optimize the capture of the food being eaten using an on-board camera, and classify the obtained image as containing food or not. The detection, image capture, and classification modules are organized in a decision tree format, an approach which minimizes system power consumption while maximizing user privacy, as opposed to having a camera always on with constant wireless data being streamed. In the first iteration of the FIM, hand proximity to the mouth is decided based on two IMUs, one on the upper arm, and one on the lower arm. In the second iteration of the device, only a single IMU is utilized, and hand proximity is determined using the IMU’s magnetometer along with a magnet worn on the body near the collar bone. Once hand-mouth proximity has been detected, it is shown that a simple linear Support Vector Machine is able to accurately classify eating activities versus other hand-near-mouth activities, such as teeth brushing and shaving. After eating is detected, the system takes an image of the food in front of the user using an on- board camera. The timing of the image capture is based on estimation of the device orientation relative to gravity using a straightforward Kalman Filter, and a method is developed that predicts optimal image capture timing using the gyroscope. Finally, it is shown that images may be classified as containing food or not using a special Convolutional Neural Network (CNN) adapted to microcontroller deployment using integer quantization. The final health and safety application considered concerns vehicular safety and phone use while driving. Distracted driving due to phone or mobile device usage is one of the primary causes of vehicular accidents, and one approach to reducing such accidents is to automatically disable devices when the user is driving. In this thesis, IMU signals on a mobile phone or smart watch are utilized to determine whether or not the user is in the driver’s seat of a moving vehicle, under the assumption that the device is in a static position inside the vehicle and close to level road grade. First, the algorithm must estimate the orientation of the device relative to the vehicle. As in the other applications, fundamental limitations of mobile-phone grade IMUs prevent estimation of orientation using traditional methods. Instead, the algorithm uses motion signals obtained during braking to determine the forward direction of the vehicle, while estimation of the gravity direction fully constrains the phone orientation. Once the orientation is determined, the pitch and roll dynamics encountered during braking and turning the vehicle are used to determine which quadrant of the vehicle the device is in relative to the vehicle’s center of gravity. Successful identification of seat position is demonstrated first in simulation and then experimentally using data taken during real-world city driving conditions.