Dissipation Characteristics of Tornadic Vortex Signatures Associated with Long-Duration Tornadoes

Weather Surveillance Radar–1988 Doppler (WSR-88D) data from 36 tornadic supercell cases from 2012 to 2016 are investigated to identify common tornadic vortex signature (TVS) behaviors prior to tornado dissipation. Based on the results of past case studies, four characteristics of TVSs associated with tornado dissipation were identified: weak or decreasing TVS intensity, rearward storm-relative motion of the TVS, large or increasing TVS vertical tilt, and large or increasing TVS horizontal displacement from the main storm updraft. Only cases in which a TVS was within 60 km of a WSR-88D site in at least four consecutive volumes at the end of the tornado life cycle were examined. The space and time restrictions on case selection ensured that the aforementioned quantities could be determined within ~500 m of the surface at several time periods despite the relatively coarse spatiotemporal resolution of WSR-88D systems. It is found that prior to dissipation, TVSs become increasingly less intense, tend to move rearward in a storm-relative framework, and become increasingly more separated from the approximate location of the main storm updraft. There is no clear signal in the relationship between tornado tilt, as measured in inclination angle, and TVS dissipation. The frequency of combinations of TVS dissipation behaviors, the impact of increased low-level WSR-88D scanning on dissipation detection, and prospects for future nowcasting of tornado life cycles also are discussed

Fibronectin and Cyclic Strain Improve Cardiac Progenitor Cell Regenerative Potential In Vitro.

Cardiac progenitor cells (CPCs) have rapidly advanced to clinical trials, yet little is known regarding their interaction with the microenvironment. Signaling cues present in the microenvironment change with development and disease. This work aims to assess the influence of two distinct signaling moieties on CPCs: cyclic biaxial strain and extracellular matrix. We evaluate four endpoints for improving CPC therapy: paracrine signaling, proliferation, connexin43 expression, and alignment. Vascular endothelial growth factor A (about 900 pg/mL) was secreted by CPCs cultured on fibronectin and collagen I. The application of mechanical strain increased vascular endothelial growth factor A secretion 2-4-fold for CPCs cultured on poly-L-lysine, laminin, or a naturally derived cardiac extracellular matrix. CPC proliferation was at least 25% higher on fibronectin than that on other matrices, especially for lower strain magnitudes. At 5% strain, connexin43 expression was highest on fibronectin. With increasing strain magnitude, connexin43 expression decreased by as much as 60% in CPCs cultured on collagen I and a naturally derived cardiac extracellular matrix. Cyclic mechanical strain induced the strongest CPC alignment when cultured on fibronectin or collagen I. This study demonstrates that culturing CPCs on fibronectin with 5% strain magnitude is optimal for their vascular endothelial growth factor A secretion, proliferation, connexin43 expression, and alignment

Generation of orthotopic patient-derived xenografts from gastrointestinal stromal tumor.

BackgroundGastrointestinal stromal tumor (GIST) is the most common sarcoma and its treatment with imatinib has served as the paradigm for developing targeted anti-cancer therapies. Despite this success, imatinib-resistance has emerged as a major problem and therefore, the clinical efficacy of other drugs has been investigated. Unfortunately, most clinical trials have failed to identify efficacious drugs despite promising in vitro data and pathological responses in subcutaneous xenografts. We hypothesized that it was feasible to develop orthotopic patient-derived xenografts (PDXs) from resected GIST that could recapitulate the genetic heterogeneity and biology of the human disease.MethodsFresh tumor tissue from three patients with pathologically confirmed GISTs was obtained immediately following tumor resection. Tumor fragments (4.2-mm3) were surgically xenografted into the liver, gastric wall, renal capsule, and pancreas of immunodeficient mice. Tumor growth was serially assessed with ultrasonography (US) every 3-4 weeks. Tumors were also evaluated with positron emission tomography (PET). Animals were sacrificed when they became moribund or their tumors reached a threshold size of 2500-mm3. Tumors were subsequently passaged, as well as immunohistochemically and histologically analyzed.ResultsHerein, we describe the first model for generating orthotopic GIST PDXs. We have successfully xenografted three unique KIT-mutated tumors into a total of 25 mice with an overall success rate of 84% (21/25). We serially followed tumor growth with US to describe the natural history of PDX growth. Successful PDXs resulted in 12 primary xenografts in NOD-scid gamma or NOD-scid mice while subsequent successful passages resulted in 9 tumors. At a median of 7.9 weeks (range 2.9-33.1 weeks), tumor size averaged 473 ± 695-mm³ (median 199-mm3, range 12.6-2682.5-mm³) by US. Furthermore, tumor size on US within 14 days of death correlated with gross tumor size on necropsy. We also demonstrated that these tumors are FDG-avid on PET imaging, while immunohistochemically and histologically the PDXs resembled the primary tumors.ConclusionsWe report the first orthotopic model of human GIST using patient-derived tumor tissue. This novel, reproducible in vivo model of human GIST may enhance the study of GIST biology, biomarkers, personalized cancer treatments, and provide a preclinical platform to evaluate new therapeutic agents for GIST

Approaches to three-dimensional reconstruction of plant shoot topology and geometry

There are currently 805 million people classified as chronically undernourished, and yet the World’s population is still increasing. At the same time, global warming is causing more frequent and severe flooding and drought, thus destroying crops and reducing the amount of land available for agriculture. Recent studies show that without crop climate adaption, crop productivity will deteriorate. With access to 3D models of real plants it is possible to acquire detailed morphological and gross developmental data that can be used to study their ecophysiology, leading to an increase in crop yield and stability across hostile and changing environments. Here we review approaches to the reconstruction of 3D models of plant shoots from image data, consider current applications in plant and crop science, and identify remaining challenges. We conclude that although phenotyping is receiving an increasing amount of attention – particularly from computer vision researchers – and numerous vision approaches have been proposed, it still remains a highly interactive process. An automated system capable of producing 3D models of plants would significantly aid phenotyping practice, increasing accuracy and repeatability of measurements

Hurricane Impact on Emergency Services and Use of Telehealth to Support Prehospital Care

The impact of hurricanes on emergency services is well-known. Recent history demonstrates the need for prehospital and emergency department coordination to serve communities during evacuation, storm duration, and cleanup. The use of telehealth applications may enhance this coordination while lessening the impact on health-care systems. These applications can address triage, stabilization, and diversion and may be provided in collaboration with state and local emergency management operations through various shelters, as well as during other emergency medical responses

The NASA/IPAC Teacher Archive Research Program (NITARP)

NITARP, the NASA/IPAC Teacher Archive Research Program, partners small groups of predominantly high school educators with research astronomers for a year-long research project. This paper presents a summary of how NITARP works and the lessons learned over the last 13 years. The program lasts a calendar year, January to January, and involves three ~week-long trips: to the American Astronomical Society (AAS) winter meeting, to Caltech in the summer (with students), and back to a winter AAS meeting (with students) to present their results. Because NITARP has been running since 2009, and its predecessor ran from 2005-2008, there have been many lessons learned over the last 13 years that have informed the development of the program. The most critical is that scientists must see their work with the educators on their team as a partnership of equals who have specialized in different professions. NITARP teams appear to function most efficiently with approximately 5 people: a mentor astronomer, a mentor teacher (who has been through the program before), and 3 new educators. Educators are asked to step into the role of learner and develop their question-asking skills as they work to develop an understanding of a subject in which they will not have command of all the information and processes needed. Critical to the success of each team is the development of communication skills and fluid plan of action to keep the lines of communication open. This program has allowed more than 100 educators to present more than 60 total science posters at the AAS

Three-dimensional reconstruction of plant shoots from multiple images using an active vision system

The reconstruction of 3D models of plant shoots is a challenging problem central to the emerging discipline of plant phenomics – the quantitative measurement of plant structure and function. Current approaches are, however, often limited by the use of static cameras. We propose an automated active phenotyping cell to reconstruct plant shoots from multiple images using a turntable capable of rotating 360 degrees and camera mounted robot arm. To overcome the problem of static camera positions we develop an algorithm capable of analysing the environment and determining viewpoints from which to capture initial images suitable for use by a structure from motion technique

RootNav 2.0: Deep learning for automatic navigation of complex plant root architectures

© The Author(s) 2019. Published by Oxford University Press. BACKGROUND: In recent years quantitative analysis of root growth has become increasingly important as a way to explore the influence of abiotic stress such as high temperature and drought on a plant's ability to take up water and nutrients. Segmentation and feature extraction of plant roots from images presents a significant computer vision challenge. Root images contain complicated structures, variations in size, background, occlusion, clutter and variation in lighting conditions. We present a new image analysis approach that provides fully automatic extraction of complex root system architectures from a range of plant species in varied imaging set-ups. Driven by modern deep-learning approaches, RootNav 2.0 replaces previously manual and semi-automatic feature extraction with an extremely deep multi-task convolutional neural network architecture. The network also locates seeds, first order and second order root tips to drive a search algorithm seeking optimal paths throughout the image, extracting accurate architectures without user interaction. RESULTS: We develop and train a novel deep network architecture to explicitly combine local pixel information with global scene information in order to accurately segment small root features across high-resolution images. The proposed method was evaluated on images of wheat (Triticum aestivum L.) from a seedling assay. Compared with semi-automatic analysis via the original RootNav tool, the proposed method demonstrated comparable accuracy, with a 10-fold increase in speed. The network was able to adapt to different plant species via transfer learning, offering similar accuracy when transferred to an Arabidopsis thaliana plate assay. A final instance of transfer learning, to images of Brassica napus from a hydroponic assay, still demonstrated good accuracy despite many fewer training images. CONCLUSIONS: We present RootNav 2.0, a new approach to root image analysis driven by a deep neural network. The tool can be adapted to new image domains with a reduced number of images, and offers substantial speed improvements over semi-automatic and manual approaches. The tool outputs root architectures in the widely accepted RSML standard, for which numerous analysis packages exist (http://rootsystemml.github.io/), as well as segmentation masks compatible with other automated measurement tools. The tool will provide researchers with the ability to analyse root systems at larget scales than ever before, at a time when large scale genomic studies have made this more important than ever