Collaborative sensor network algorithm for predicting the spatiotemporal evolution of hazardous phenomena

We present a novel decentralized Wireless Sensor Network (WSN) algorithm which can estimate both the speed and direction of an evolving diffusive hazardous phenomenon (e.g. a wildfire, oil spill, etc.). In the proposed scheme we approximate a progressing hazard’s front as a set of line segments. The spatiotemporal evolution of each line segment is modeled by a modified 2D Gaussian function. As the phenomenon evolves, the parameters of this model are updated based on the analytical solution of a Kullback – Leibler (KL) divergence minimization problem. This leads to an efficient WSN distributed parameters estimation algorithm that can be implemented by dynamically formed clusters (triplets) of collaborating sensor nodes. Computer simulations show that our approach is able to track the evolving phenomenon with reasonable accuracy even if a percentage of sensors fails due to the hazard and/or the phenomenon has a time varying speed

Predictive modeling of the spatiotemporal evolution of an environmental hazard and its sensor network implementation

Predicting accurately the spatiotemporal evolution of a diffusive environmental hazard is of paramount importance for its effective containment. We approximate the front line of a hazard with a set of line segments (local front models). We model the progression characteristics of these front segments by appropriately modified 2D Gaussian functions. The modified Gaussian model parameters are adjusted based on the solution of a Kullback-Leibler (KL) divergence minimization problem. The whole scheme can be realized by a wireless sensor network by forming dynamically triplets of cooperating sensor nodes along the path of the hazard. It is shown that the algorithm can track effectively the front characteristics (in terms of direction and speed) even in the presence of faulty sensor nodes

Estimating the spatiotemporal evolution characteristics of diffusive hazards using wireless sensor networks

There is a fast growing interest in exploiting Wireless Sensor Networks (WSNs) for tracking the boundaries and predicting the evolution properties of diffusive hazardous phenomena (e.g. wildfires, oil slicks etc.) often modeled as “continuous objects”. We present a novel distributed algorithm for estimating and tracking the local evolution characteristics of continuous objects. The hazard’s front line is approximated as a set of line segments, and the spatiotemporal evolution of each segment is modeled by a small number of parameters (orientation, direction and speed of motion). As the hazard approaches, these parameters are re-estimated using adhoc clusters (triplets) of collaborating sensor nodes. Parameters updating is based on algebraic closed-form expressions resulting from the analytical solution of a Bayesian estimation problem. Therefore, it can be implemented by microprocessors of the WSN nodes, while respecting their limited processing capabilities and strict energy constraints. Extensive computer simulations demonstrate the ability of the proposed distributed algorithm to estimate accurately the evolution characteristics of complex hazard fronts under different conditions by using reasonably dense WSNs. The proposed in-network processing scheme does not require sensor node clocks synchronization and is shown to be robust to sensor node failures and communication link failures, which are expected in harsh environments

Simulation-driven emulation of collaborative algorithms to assess their requirements for a large-scale WSN implementation

Assessing how the performance of a decentralized wireless sensor network (WSN) algorithm's implementation scales, in terms of communication and energy costs, as the network size increases is an essential requirement before its field deployment. Simulations are commonly used for this purpose, especially for large-scale environmental monitoring applications. However, it is difficult to evaluate energy consumption, processing and memory requirements before the algorithm is really ported to a real WSN platform. We propose a method for emulating the operation of collaborative algorithms in large-scale WSNs by re-using a small number of available real sensor nodes. We demonstrate the potential of the proposed simulation-driven WSN emulation approach by using it to estimate how communication and energy costs scale with the network’s size when implementing a collaborative algorithm we developed in for tracking the spatiotemporal evolution of a progressing environmental hazard

Modeling alcohol-associated liver disease in a human Liver-Chip.

Alcohol-associated liver disease (ALD) is a global health issue and leads to progressive liver injury, comorbidities, and increased mortality. Human-relevant preclinical models of ALD are urgently needed. Here, we leverage a triculture human Liver-Chip with biomimetic hepatic sinusoids and bile canaliculi to model ALD employing human-relevant blood alcohol concentrations (BACs) and multimodal profiling of clinically relevant endpoints. Our Liver-Chip recapitulates established ALD markers in response to 48 h of exposure to ethanol, including lipid accumulation and oxidative stress, in a concentration-dependent manner and supports the study of secondary insults, such as high blood endotoxin levels. We show that remodeling of the bile canalicular network can provide an in vitro quantitative readout of alcoholic liver toxicity. In summary, we report the development of a human ALD Liver-Chip as a powerful platform for modeling alcohol-induced liver injury with the potential for direct translation to clinical research and evaluation of patient-specific responses

A Novel Microphysiological Colon Platform to Decipher Mechanisms Driving Human Intestinal Permeability

BACKGROUND & AIMS: The limited availability of organoid systems that mimic the molecular signatures and architecture of human intestinal epithelium has been an impediment to allowing them to be harnessed for the development of therapeutics as well as physiological insights. We developed a microphysiological Organ-on-Chip (Emulate, Inc, Boston, MA) platform designed to mimic properties of human intestinal epithelium leading to insights into barrier integrity. METHODS: We combined the human biopsy-derived leucine-rich repeat-containing G-protein-coupled receptor 5-positive organoids and Organ-on-Chip technologies to establish a micro-engineered human Colon Intestine-Chip (Emulate, Inc, Boston, MA). We characterized the proximity of the model to human tissue and organoids maintained in suspension by RNA sequencing analysis, and their differentiation to intestinal epithelial cells on the Colon Intestine-Chip under variable conditions. Furthermore, organoids from different donors were evaluated to understand variability in the system. Our system was applied to understanding the epithelial barrier and characterizing mechanisms driving the cytokine-induced barrier disruption. RESULTS: Our data highlight the importance of the endothelium and the in vivo tissue-relevant dynamic microenvironment in the Colon Intestine-Chip in the establishment of a tight monolayer of differentiated, polarized, organoid-derived intestinal epithelial cells. We confirmed the effect of interferon-gamma on the colonic barrier and identified reorganization of apical junctional complexes, and induction of apoptosis in the intestinal epithelial cells as mediating mechanisms. We show that in the human Colon Intestine-Chip exposure to interleukin 22 induces disruption of the barrier, unlike its described protective role in experimental colitis in mice. CONCLUSIONS: We developed a human Colon Intestine-Chip platform and showed its value in the characterization of the mechanism of action of interleukin 22 in the human epithelial barrier. This system can be used to elucidate, in a time- and challenge-dependent manner, the mechanism driving the development of leaky gut in human beings and to identify associated biomarkers

Data_Sheet_3_Respiratory Microbiome Profiling for Etiologic Diagnosis of Pneumonia in Mechanically Ventilated Patients.XLSX

<p>Etiologic diagnosis of bacterial pneumonia relies on identification of causative pathogens by cultures, which require extended incubation periods and have limited sensitivity. Next-generation sequencing of microbial DNA directly from patient samples may improve diagnostic accuracy for guiding antibiotic prescriptions. In this study, we hypothesized that enhanced pathogen detection using sequencing can improve upon culture-based diagnosis and that certain sequencing profiles correlate with host response. We prospectively collected endotracheal aspirates and plasma within 72 h of intubation from patients with acute respiratory failure. We performed 16S rRNA gene sequencing to determine pathogen abundance in lung samples and measured plasma biomarkers to assess host responses to detected pathogens. Among 56 patients, 12 patients (21%) had positive respiratory cultures. Sequencing revealed lung communities with low diversity (p < 0.02) dominated by taxa (>50% relative abundance) corresponding to clinically isolated pathogens (concordance p = 0.009). Importantly, sequencing detected dominant pathogens in 20% of the culture-negative patients exposed to broad-spectrum empiric antibiotics. Regardless of culture results, pathogen dominance correlated with increased plasma markers of host injury (receptor of advanced glycation end-products-RAGE) and inflammation (interleukin-6, tumor necrosis factor receptor 1-TNFR1) (p < 0.05), compared to subjects without dominant pathogens in their lung communities. Machine-learning algorithms identified pathogen abundance by sequencing as the most informative predictor of culture positivity. Thus, enhanced detection of pathogenic bacteria by sequencing improves etiologic diagnosis of pneumonia, correlates with host responses, and offers substantial opportunity for individualized therapeutic targeting and antimicrobial stewardship. Clinical translation will require validation with rapid whole meta-genome sequencing approaches to guide real-time antibiotic prescriptions.</p

Data_Sheet_2_Respiratory Microbiome Profiling for Etiologic Diagnosis of Pneumonia in Mechanically Ventilated Patients.XLSX

<p>Etiologic diagnosis of bacterial pneumonia relies on identification of causative pathogens by cultures, which require extended incubation periods and have limited sensitivity. Next-generation sequencing of microbial DNA directly from patient samples may improve diagnostic accuracy for guiding antibiotic prescriptions. In this study, we hypothesized that enhanced pathogen detection using sequencing can improve upon culture-based diagnosis and that certain sequencing profiles correlate with host response. We prospectively collected endotracheal aspirates and plasma within 72 h of intubation from patients with acute respiratory failure. We performed 16S rRNA gene sequencing to determine pathogen abundance in lung samples and measured plasma biomarkers to assess host responses to detected pathogens. Among 56 patients, 12 patients (21%) had positive respiratory cultures. Sequencing revealed lung communities with low diversity (p < 0.02) dominated by taxa (>50% relative abundance) corresponding to clinically isolated pathogens (concordance p = 0.009). Importantly, sequencing detected dominant pathogens in 20% of the culture-negative patients exposed to broad-spectrum empiric antibiotics. Regardless of culture results, pathogen dominance correlated with increased plasma markers of host injury (receptor of advanced glycation end-products-RAGE) and inflammation (interleukin-6, tumor necrosis factor receptor 1-TNFR1) (p < 0.05), compared to subjects without dominant pathogens in their lung communities. Machine-learning algorithms identified pathogen abundance by sequencing as the most informative predictor of culture positivity. Thus, enhanced detection of pathogenic bacteria by sequencing improves etiologic diagnosis of pneumonia, correlates with host responses, and offers substantial opportunity for individualized therapeutic targeting and antimicrobial stewardship. Clinical translation will require validation with rapid whole meta-genome sequencing approaches to guide real-time antibiotic prescriptions.</p