The Sensor Network Workbench: Towards Functional Specification, Verification and Deployment of Constrained Distributed Systems

As the commoditization of sensing, actuation and communication hardware increases, so does the potential for dynamically tasked sense and respond networked systems (i.e., Sensor Networks or SNs) to replace existing disjoint and inflexible special-purpose deployments (closed-circuit security video, anti-theft sensors, etc.). While various solutions have emerged to many individual SN-centric challenges (e.g., power management, communication protocols, role assignment), perhaps the largest remaining obstacle to widespread SN deployment is that those who wish to deploy, utilize, and maintain a programmable Sensor Network lack the programming and systems expertise to do so. The contributions of this thesis centers on the design, development and deployment of the SN Workbench (snBench). snBench embodies an accessible, modular programming platform coupled with a flexible and extensible run-time system that, together, support the entire life-cycle of distributed sensory services. As it is impossible to find a one-size-fits-all programming interface, this work advocates the use of tiered layers of abstraction that enable a variety of high-level, domain specific languages to be compiled to a common (thin-waist) tasking language; this common tasking language is statically verified and can be subsequently re-translated, if needed, for execution on a wide variety of hardware platforms. snBench provides: (1) a common sensory tasking language (Instruction Set Architecture) powerful enough to express complex SN services, yet simple enough to be executed by highly constrained resources with soft, real-time constraints, (2) a prototype high-level language (and corresponding compiler) to illustrate the utility of the common tasking language and the tiered programming approach in this domain, (3) an execution environment and a run-time support infrastructure that abstract a collection of heterogeneous resources into a single virtual Sensor Network, tasked via this common tasking language, and (4) novel formal methods (i.e., static analysis techniques) that verify safety properties and infer implicit resource constraints to facilitate resource allocation for new services. This thesis presents these components in detail, as well as two specific case-studies: the use of snBench to integrate physical and wireless network security, and the use of snBench as the foundation for semester-long student projects in a graduate-level Software Engineering course

2 5/18/07 Donald A. Williams III Owls Head. ME

Subject: [Fwd: ublic comment on federal register of 4/30107 vol 72 #82 pg 21226]-------- Original Message------

Bio-optical Water Quality Dynamics Observed from MERIS in Pensacola Bay, Florida

Observed bio-optical water quality data collected from 2009 to 2011 in Pensacola Bay, Florida were used to develop empirical remote sensing retrieval algorithms for chlorophyll a (Chla), colored dissolved organic matter (CDOM), and suspended particulate matter (SPM). Time-series of the three bio-optical water quality variables were generated from MEdium Resolution Imaging Spectrometer (MERIS) observations from 2003 to 2011. Bio-optical water quality in this estuary exhibited spatial and temporal variations that were correlated to river discharge and wind. Both annual mean and monthly mean bio-optical water quality variables were positively correlated to river discharge. Monthly mean bio-optical water quality variables were also positively correlated to wind speed and wind density (defined by the number of days with daily mean wind speed \u3e 3 m s−1 in a month) over this estuary. These results indicate that bio-optical water quality dynamics in this estuary are vulnerable to changes in river discharge and river constituent loads and local weather conditions such as winter storms and hurricanes

Organoid profiling identifies common responders to chemotherapy in pancreatic cancer

Pancreatic cancer is the most lethal common solid malignancy. Systemic therapies are often ineffective, and predictive biomarkers to guide treatment are urgently needed. We generated a pancreatic cancer patient–derived organoid (PDO) library that recapitulates the mutational spectrum and transcriptional subtypes of primary pancreatic cancer. New driver oncogenes were nominated and transcriptomic analyses revealed unique clusters. PDOs exhibited heterogeneous responses to standard-of-care chemotherapeutics and investigational agents. In a case study manner, we found that PDO therapeutic profiles paralleled patient outcomes and that PDOs enabled longitudinal assessment of chemosensitivity and evaluation of synchronous metastases. We derived organoid-based gene expression signatures of chemosensitivity that predicted improved responses for many patients to chemotherapy in both the adjuvant and advanced disease settings. Finally, we nominated alternative treatment strategies for chemorefractory PDOs using targeted agent therapeutic profiling. We propose that combined molecular and therapeutic profiling of PDOs may predict clinical response and enable prospective therapeutic selection. SIGNIfICANCE: New approaches to prioritize treatment strategies are urgently needed to improve survival and quality of life for patients with pancreatic cancer. Combined genomic, transcriptomic, and therapeutic profiling of PDOs can identify molecular and functional subtypes of pancreatic cancer, predict therapeutic responses, and facilitate precision medicine for patients with pancreatic cancer

Organoid Profiling Identifies Common Responders to Chemotherapy in Pancreatic Cancer

Pancreatic cancer is the most lethal common solid malignancy. Systemic therapies are often ineffective, and predictive biomarkers to guide treatment are urgently needed. We generated a pancreatic cancer patient-derived organoid (PDO) library that recapitulates the mutational spectrum and transcriptional subtypes of primary pancreatic cancer. New driver oncogenes were nominated and transcriptomic analyses revealed unique clusters. PDOs exhibited heterogeneous responses to standard-of-care chemotherapeutics and investigational agents. In a case study manner, we found that PDO therapeutic profiles paralleled patient outcomes and that PDOs enabled longitudinal assessment of chemosensitivity and evaluation of synchronous metastases. We derived organoid-based gene expression signatures of chemosensitivity that predicted improved responses for many patients to chemotherapy in both the adjuvant and advanced disease settings. Finally, we nominated alternative treatment strategies for chemorefractory PDOs using targeted agent therapeutic profiling. We propose that combined molecular and therapeutic profiling of PDOs may predict clinical response and enable prospective therapeutic selection.Significance: New approaches to prioritize treatment strategies are urgently needed to improve survival and quality of life for patients with pancreatic cancer. Combined genomic, transcriptomic, and therapeutic profiling of PDOs can identify molecular and functional subtypes of pancreatic cancer, predict therapeutic responses, and facilitate precision medicine for patients with pancreatic cancer. Cancer Discov; 8(9); 1112-29. ©2018 AACR.See related commentary by Collisson, p. 1062This article is highlighted in the In This Issue feature, p. 1047