Predicting Responses to Mechanical Ventilation for Preterm Infants with Acute Respiratory Illness using Artificial Neural Networks

Infants born prematurely are particularly susceptible to respiratory illness due to underdeveloped lungs, which can often result in fatality. Preterm infants in acute stages of respiratory illness typically require mechanical ventilation assistance, and the efficacy of the type of mechanical ventilation and its delivery has been the subject of a number clinical studies. With recent advances in machine learning approaches, particularly deep learning, it may be possible to estimate future responses to mechanical ventilation in real‐time, based on ventilation monitoring up to the point of analysis. In this work, recurrent neural networks are proposed for predicting future ventilation parameters due to the highly nonlinear behavior of the ventilation measures of interest and the ability of recurrent neural networks to model complex nonlinear functions. The resulting application of this particular class of neural networks shows promise in its ability to predict future responses for different ventilation modes. Towards improving care and treatment of preterm newborns, further development of this prediction process for ventilation could potentially aid in important clinical decisions or studies to improve preterm infant health

On State Fusers Over Long-Haul Sensor Networks

Abstract-We consider a network of sensors wherein the state estimates are sent from sensors to a fusion center to generate a global state estimate. The underlying fusion algorithm affects the performance measure QCC (τ ) (with subscripts CC indicating the effects of the communications and computing quality) of the global state estimate computed within the allocated time τ . We present a probabilistic performance bound on QCC (τ ) as a function of the distributions of state estimates, communications parameters as well as the fusion algorithm. We present simulations of simplified scenarios to illustrate the qualitative effects of different fusers, and system-level simulations to complement the analytical results

Biodiversity in changing environments: An external‐driver internal‐topology framework to guide intervention

Accompanying the climate crisis is the more enigmatic biodiversity crisis. Rapid reorganization of biodiversity due to global environmental change has defied prediction and tested the basic tenets of conservation and restoration. Conceptual and practical innovation is needed to support decision making in the face of these unprecedented shifts. Critical questions include: How can we generalize biodiversity change at the community level? When are systems able to reorganize and maintain integrity, and when does abiotic change result in collapse or restructuring? How does this understanding provide a template to guide when and how to intervene in conservation and restoration? To this end, we frame changes in community organization as the modulation of external abiotic drivers on the internal topology of species interactions, using plant-plant interactions in terrestrial communities as a starting point. We then explore how this framing can help translate available data on species abundance and trait distributions to corresponding decisions in management. Given the expectation that community response and reorganization are highly complex, the external-driver internal-topology (EDIT) framework offers a way to capture general patterns of biodiversity that can help guide resilience and adaptation in changing environments

Rise of the Colorado Plateau: A Synthesis of Paleoelevation Constraints From the Region and a Path Forward Using Temperature-Based Elevation Proxies

The Colorado Plateau’s complex landscape has motivated over a century of debate, key to which is understanding the timing and processes of surface uplift of the greater Colorado Plateau region, and its interactions with erosion, drainage reorganization, and landscape evolution. Here, we evaluate what is known about the surface uplift history from prior paleoelevation estimates from the region by synthesizing and evaluating estimates 1) in context inferred from geologic, geomorphic, and thermochronologic constraints, and 2) in light of recent isotopic and paleobotanical proxy method advancements. Altogether, existing data and estimates suggest that half-modern surface elevations were attained by the end of the Laramide orogeny (∼40 Ma), and near-modern surface elevations by the mid-Miocene (∼16 Ma). However, our analysis of paleoelevation proxy methods highlights the need to improve proxy estimates from carbonate and floral archives including the ∼6–16 Ma Bidahochi and ∼34 Ma Florissant Formations and explore understudied (with respect to paleoelevation) Laramide basin deposits to fill knowledge gaps. We argue that there are opportunities to leverage recent advancements in temperature-based paleoaltimetry to refine the surface uplift history; for instance, via systematic comparison of clumped isotope and paleobotanical thermometry methods applied to lacustrine carbonates that span the region in both space and time, and by use of paleoclimate model mediated lapse rates in paleoelevation reconstruction

The Abundance of Pink-Pigmented Facultative Methylotrophs in the Root Zone of Plant Species in Invaded Coastal Sage Scrub Habitat

Pink-pigmented facultative methylotrophic bacteria (PPFMs) are associated with the roots, leaves and seeds of most terrestrial plants and utilize volatile C1 compounds such as methanol generated by growing plants during cell division. PPFMs have been well studied in agricultural systems due to their importance in crop seed germination, yield, pathogen resistance and drought stress tolerance. In contrast, little is known about the PPFM abundance and diversity in natural ecosystems, let alone their interactions with non-crop species. Here we surveyed PPFM abundance in the root zone soil of 5 native and 5 invasive plant species along ten invasion gradients in Southern California coastal sage scrub habitat. PPFMs were present in every soil sample and ranged in abundance from 102 to 105 CFU/g dry soil. This abundance varied significantly among plant species. PPFM abundance was 50% higher in the root zones of annual or biennial species (many invasives) than perennial species (all natives). Further, PPFM abundance appears to be influenced by the plant community beyond the root zone; pure stands of either native or invasive species had 50% more PPFMs than mixed species stands. In sum, PPFM abundance in the root zone of coastal sage scrub plants is influenced by both the immediate and surrounding plant communities. The results also suggest that PPFMs are a good target for future work on plant-microorganism feedbacks in natural ecosystems

Robust State Fusers Over Long-Haul Sensor Networks With Applications to Target Tracking

<p>In general, sensor networks consist of sensing, data processing, and communication components, and these sensors may communicate with each other or with a central processing center, which then performs some form of data aggregation or data fusion. The terms aggregation and fusion are often used for the same general purpose: how to simultaneously use pieces of information provided by several sources in order to come to a conclusion or a decision. A number of data fusion methods have been developed for sensor networks for a variety of applications, with a primary function of taking in the data from multiple sensors and combining this data to produce a condensed set of meaningful information with the highest possible degree of accuracy and certainty. In this work we primarily explore the use of state fusers for target tracking applications that utilize long-haul communication networks where the underlying target dynamics are nonlinear (as is the case, for example, for a maneuvering target or a ballistic target). However, it is noted that the most popular approaches linearly combine the data. Therefore, the goal of the work is two-fold: 1) investigate/improve nonlinear fusion algorithms for target tracking and 2) develop methods to ensure that these nonlinear fusion algorithms are also robust against packet losses and delays that result from long-haul communications. In particular, we investigate the use of artificial neural networks (ANNs) for multisensor fusion. ANNs possess the capability of modeling arbitrary mappings, as long as a sufficient number of training samples are available from the same distribution. This also provides us with the ability to use nonlinear functions for fusing the data, which may yield better results than with linear fusion given proper training. More specifically, this thesis investigates several aspects of using ANN fusers for multisensor fusion in target tracking. Simulation experiments show that a significant amount of training data is required in close proximity to the test target in order to obtain good performance. Alternate methods in ANN training are then introduced which reduce the amount of training data required to obtain good performance, and widens the allowable training space as well. Then, the use of multiple fusers, different input features, and varied ANN architectures are investigated with the intent to further improve fuser performance. The effects of imperfect communications are then explored for the ANN fuser, and another training enhancement is suggested to generate ANN fusers that are more robust against packet losses and delays. Overall, this thesis intends to provide suggestions as to what parameters or aspects of the ANN may be explored to help improve fuser performance for use in target tracking.</p

Estimation and Fusion for Tracking Over Long-Haul Links Using Artificial Neural Networks

In a long-haul sensor network, sensors are remotely deployed over a large geographical area to perform certain tasks, such as tracking and/or monitoring of one or more dynamic targets. A remote fusion center fuses the information provided by these sensors so that a final estimate of certain target characteristics – such as the position – is expected to possess much improved quality. In this work, we pursue learning-based approaches for estimation and fusion of target states in longhaul sensor networks. In particular, we consider learning based on various implementations of artificial neural networks (ANNs). The joint effect of (i) imperfect communication condition, namely, link-level loss and delay, and (ii) computation constraints, in the form of low-quality sensor estimates, on ANN-based estimation and fusion, is investigated by means of analytical and simulation studies

A Framework for End-to-End Latency Measurements in a Satellite Network Environment

In this work, a precise method for measuring end-to-end (E2E) latency in satellite Internet Protocol (IP) networks is proposed. Latency (i.e., time delay) is considered a key parameter that affects the quality of service (QoS) and the performance of communication systems. This is more pronounced in the IP over Satellite. Metrics such as throughput and bandwidth performance of communication systems are dependent on latency, which also has a direct impact on other QoS metrics, such as Internet packet transfer delay and delay variation (or jitter). The upper limits of QoS objective performance metrics are defined by E2E latency for different QoS traffic classes in this environment. Therefore, there is a need to develop efficient methods for the accurate measurement of E2E latency in a satellite IP environment. Two case study scenarios were developed for satellite heterogeneous networks to measure the latency in a satellite IP network. Two geostationary satellite network services were used to compare the performance of the different scenarios and networks. The results demonstrate that at least 50% of the E2E latency is due to the processing and transmitting of IP packets over the satellite in both scenarios. Inconsistent latency behaviour was also observed from daily results at different times of the day, which may degrade performance of jitter sensitive applications

Telemedicine via Satellite: Improving Access to Healthcare for Remote Rural Communities in Africa

In this paper, realistic telemedicine implementation scenarios with architecture are proposed to help in extending quality healthcare using satellite and integrated satellite-terrestrial networks (ISTNs). Telemedicine is the use of telecommunications and information technology to extend healthcare service delivery to underserved, remotely isolated communities. Global coverage, broadcast/multicast capability and the high capacity of satellites in Geostationary Earth Orbit (GEO) could potentially serve as a tool to extend quality healthcare to underserved remote rural areas. However, Long End-to-End latency or Round-Trip-Time (RTT) attributed to the GEO satellites could degrade the performance of data communications leading underutilisation of the high available capacity due to high link errors and the long latency, particularly when using Transmission Control Protocol (TCP) over the internet, which accounts for about 90% of the internet traffic today. The actual latency (RTT) of GEO satellites is about 1700ms to 3000ms, which could lead to capacity utilisation as low as 39% of maximum 464kbps available capacity of our testbed service provider. However, TCP Performance could be improved by adopting other transmission protocols which we are currently testing and investigating possible modifications for even more enhance performance over satellite and hybrid (ISTN) channels network environment