PDV Detector Monitor System

Author Institution: Los Alamos National LaboratoryAuthor Institution: Lawrence Livermore National LaboratoryAuthor Institution: National Security Technologies, LLCSlides presented at the 2nd Annual Photonic Doppler Velocimetry (PDV) Workshop held at Lawrence Livermore National Laboratory, Livermore, California, August 16-17, 2007

Lessons Learned Study Final Report for the Exploration Systems Mission Directorate

This report is the final product of a 90-day study performed for the Exploration Systems Mission Directorate. The study was to assemble lessons NASA has learned from previous programs that could help the Exploration Systems Mission Directorate pursue the Exploration vision. It focuses on those lessons that should have the greatest significance to the Directorate during the formulation of program and mission plans. The study team reviewed a large number of lessons learned reports and data bases, including the Columbia Accident Investigation Board and Rogers Commission reports on the Shuttle accidents, accident reports from robotic space flight systems, and a number of management reviews by the Defense Sciences Board, Government Accountability Office, and others. The consistency of the lessons, findings, and recommendations validate the adequacy of the data set. In addition to reviewing existing databases, a series of workshops was held at each of the NASA centers and headquarters that included senior managers from the current workforce as well as retirees. The full text of the workshop reports is included in Appendix A. A lessons learned website was opened up to permit current and retired NASA personnel and on-site contractors to input additional lessons as they arise. These new lessons, when of appropriate quality and relevance, will be brought to the attention of managers. The report consists of four parts: Part 1 provides a small set of lessons, called the Executive Lessons Learned, that represent critical lessons that the Exploration Systems Mission Directorate should act on immediately. This set of Executive Lessons and their supporting rationale have been reviewed at length and fully endorsed by a team of distinguished NASA alumni; Part 2 contains a larger set of lessons, called the Selected Lessons Learned, which have been chosen from the lessons database and center workshop reports on the basis of their specific significance and relevance to the near-term work of the Exploration Directorate. These lessons frequently support the Executive lessons but are more general in nature; Part 3 consists of the reports of the center workshops that were conducted as part of this activity. These reports are included in their entirety (approximately 200 pages) in Appendix G and have significance for specific managers; Part 4 consists of the remainder of the lessons that have been selected by this effort and assembled into a database for the use of the Explorations Directorate. The database is archived and hosted in the Lessons Learned Knowledge Network, which provides a flexible search capability using a wide variety of search terms. Finally, a spreadsheet lists databases searched and a bibliography identifies reports that have been reviewed as sources of lessons for this task. NASA has been presented with many learning opportunities. We have conducted numerous programs, some extremely successful and others total failures. Most have been documented with a formal lessons learned activity, but we have not always incorporated these learning opportunities into our normal modes of business. For example, the Robbins Report of 2001 clearly indicates that many project failures of the past two decades were the result of violating well documented best practices, often in direct violation of management instructions and directives. An overarching lesson emerges: that disciplined execution in accordance with proven best practices is the greatest single contributor to a successful program. The Lessons Learned task team offers a sincere hope that the lessons presented herein will be helpful to the Exploration Systems Directorate in charting and executing their course. The success of the Directorate and of NASA in general depends on our collective ability to move forward without having to relearn the lessons of those who have gone before

Excisional treatment in women with cervical adenocarcinoma in situ (AIS): a prospective randomised controlled noninferiority trial to compare AIS persistence/recurrence after loop electrosurgical excision procedure with cold knife cone biopsy: protocol for a pilot study

Introduction: Adenocarcinoma in situ (AIS) of the uterine cervix is the precursor to invasive endocervical adenocarcinoma. An excisional biopsy such as a cold knife cone biopsy (CKC) should be performed to exclude invasive adenocarcinoma. Loop electrosurgical excision procedure (LEEP) is an alternative modality to CKC but is controversial in AIS. There is a perception that there is a greater likelihood of incomplete excision of AIS with LEEP because the depth of excised tissue tends to be smaller and the tissue margins may show thermal artefact which can interfere with pathology assessment. In the USA, guidelines recommend that any treatment modality can be used to excise AIS, provided that the specimen remains intact with interpretable margins. However, there are no high-quality studies comparing LEEP with CKC and well-designed prospective studies are needed. If such a study were to show that LEEP was non-inferior to CKC for the outcomes of post-treatment persistence, recurrence and adenocarcinoma, LEEP could be recommended as an appropriate treatment option for AIS in selected patients. This would benefit women because, unlike CKC, LEEP does not require general anaesthesia and may be associated with reduced morbidity. Methods and analysis: The proposed exploratory study is a parallel group trial with an allocation ratio of 2:1 in favour of the intervention (LEEP: CKC). Participants are women aged ≥18 to ≤45 years diagnosed with AIS on cervical screening and/or colposcopically directed biopsy in Australia and New Zealand, who are to receive excisional treatment in a tertiary level centre. Ethics and dissemination: Ethical approval for the study has been granted by the St John of God Healthcare Human Research Ethics Committee (reference number #1137)

Ecohydrological separation in wet, low energy northern environments? A preliminary assessment using different soil water extraction techniques

Funded by European Research Council ERC. Grant Number: project GA 335910 VEWA ACKNOWLEDGEMENTS The constructive comments and suggestions from two anonymous reviewers greatly improved an earlier version of this manuscript. Jon Dick, Jason Lesselsand Jane Bang Poulsen are thanked for assistance with data collection; Audrey Innes for sample preparation and assistance with the cryogenic extraction of water samples; Paula Craib for glassware design; Colleagues in Prof. J. Anderson’s lab for day-to-day assistance incryogenic extraction; Todd Dawson and Nathalie Schultz for providing information on extraction techniques and the analysis of vegetation water; Hedda Weitz for help with the centrifugation of soil samples;and Iain Malcolm and colleagues at the Marine Scotland Freshwater Lab for providing meteorological data. We thank Jason Newton and the Scottish Universities Environmental Research Centre (SUERC) Mass Spectrometry Facility Laboratory in East Kilbride for theisotopic analyses of the xylem water samples. The European Research Council ERC (project GA 335910VEWA) is thanked for funding.Peer reviewedPostprin

Construction Division

REPORT NO

Oncostatin M Promotes Mammary Tumor Metastasis to Bone and Osteolytic Bone Degradation

Oncostatin M (OSM) is an interleukin-6 (IL-6) family cytokine that has been implicated in a number of biological processes including inflammation, hematopoiesis, immune responses, development, and bone homeostasis. Recent evidence suggests that OSM may promote breast tumor invasion and metastasis. We investigated the role of OSM in the formation of bone metastases in vivo using the 4T1.2 mouse mammary tumor model in which OSM expression was knocked down using shRNA (4T1.2-OSM). 4T1.2-OSM cells were injected orthotopically into Balb/c mice, resulting in a greater than 97% decrease in spontaneous metastasis to bone compared to control cells. Intratibial injection of these same 4T1.2-OSM cells also dramatically reduced the osteolytic destruction of trabecular bone volume compared to control cells. Furthermore, in a tumor resection model, mice bearing 4T1.2-OSM tumors showed an increase in survival by a median of 10 days. To investigate the specific cellular mechanisms important for OSM-induced osteolytic metastasis to bone, an in vitro model was developed using the RAW 264.7 preosteoclast cell line co-cultured with 4T1.2 mouse mammary tumor cells. Treatment of co-cultures with OSM resulted in a 3-fold induction of osteoclastogenesis using the TRAP assay. We identified several tumor cell–induced factors including vascular endothelial growth factor, IL-6, and a previously uncharacterized OSM-regulated bone metastasis factor, amphiregulin (AREG), which increased osteoclast differentiation by 4.5-fold. In addition, pretreatment of co-cultures with an anti-AREG neutralizing antibody completely reversed OSM-induced osteoclastogenesis. Our results suggest that one mechanism for OSM-induced osteoclast differentiation is via an AREG autocrine loop, resulting in decreased osteoprotegerin secretion by the 4T1.2 cells. These data provide evidence that OSM might be an important therapeutic target for the prevention of breast cancer metastasis to bone

Novel deep learning methods for track reconstruction

For the past year, the HEP.TrkX project has been investigating machine learning solutions to LHC particle track reconstruction problems. A variety of models were studied that drew inspiration from computer vision applications and operated on an image-like representation of tracking detector data. While these approaches have shown some promise, image-based methods face challenges in scaling up to realistic HL-LHC data due to high dimensionality and sparsity. In contrast, models that can operate on the spacepoint representation of track measurements ("hits") can exploit the structure of the data to solve tasks efficiently. In this paper we will show two sets of new deep learning models for reconstructing tracks using space-point data arranged as sequences or connected graphs. In the first set of models, Recurrent Neural Networks (RNNs) are used to extrapolate, build, and evaluate track candidates akin to Kalman Filter algorithms. Such models can express their own uncertainty when trained with an appropriate likelihood loss function. The second set of models use Graph Neural Networks (GNNs) for the tasks of hit classification and segment classification. These models read a graph of connected hits and compute features on the nodes and edges. They adaptively learn which hit connections are important and which are spurious. The models are scaleable with simple architecture and relatively few parameters. Results for all models will be presented on ACTS generic detector simulated data.Comment: CTD 2018 proceeding