Space Shuttle Abort Evolution

This paper documents some of the evolutionary steps in developing a rigorous Space Shuttle launch abort capability. The paper addresses the abort strategy during the design and development and how it evolved during Shuttle flight operations. The Space Shuttle Program made numerous adjustments in both the flight hardware and software as the knowledge of the actual flight environment grew. When failures occurred, corrections and improvements were made to avoid a reoccurrence and to provide added capability for crew survival. Finally some lessons learned are summarized for future human launch vehicle designers to consider

Inhibition of nuclear factor-kappa B enhances the tumor growth of ovarian cancer cell line derived from a low-grade papillary serous carcinoma in p53-independent pathway

Background: NF-kB can function as an oncogene or tumor suppressor depending on cancer types. The role of NF-kB in low-grade serous ovarian cancer, however, has never been tested. We sought to elucidate the function of NF-kB in the low-grade serous ovarian cancer. Methods: The ovarian cancer cell line, HOC-7, derived from a low-grade papillary serous carcinoma. Introduction of a dominant negative mutant, IkBαM, which resulted in decrease of NF-kB function in ovarian cancer cell lines. The transcription ability, tumorigenesis, cell proliferation and apoptosis were observed in derivative cell lines in comparison with parental cells. Results: Western blot analysis indicated increased expression of the anti-apoptotic proteins Bcl-xL and reduced expression of the pro-apoptotic proteins Bax, Bad, and Bid in HOC-7/IĸBαM cell. Further investigations validate this conclusion in KRAS wildtype cell line SKOV3. Interesting, NF-kB can exert its pro-apoptotic effect by activating mitogen-activated protein kinase (MAPK) phosphorylation in SKOV3 ovarian cancer cell, whereas opposite changes detected in p-MEK in HOC-7 ovarian cancer cell, the same as some chemoresistant ovarian cancer cell lines. In vivo animal assay performed on BALB/athymic mice showed that injection of HOC-7 induced subcutaneous tumor growth, which was completely regressed within 7 weeks. In comparison, HOC-7/IĸBαM cells caused sustained tumor growth and abrogated tumor regression, suggesting that knock-down of NF-kB by IĸBαM promoted sustained tumor growth and delayed tumor regression in HOC-7 cells. Conclusion: Our results demonstrated that NF-kB may function as a tumor suppressor by facilitating regression of low grade ovarian serous carcinoma through activating pro-apoptotic pathways

Controlling Risk of Web Question Answering

Web question answering (QA) has become an indispensable component in modern search systems, which can significantly improve users' search experience by providing a direct answer to users' information need. This could be achieved by applying machine reading comprehension (MRC) models over the retrieved passages to extract answers with respect to the search query. With the development of deep learning techniques, state-of-the-art MRC performances have been achieved by recent deep methods. However, existing studies on MRC seldom address the predictive uncertainty issue, i.e., how likely the prediction of an MRC model is wrong, leading to uncontrollable risks in real-world Web QA applications. In this work, we first conduct an in-depth investigation over the risk of Web QA. We then introduce a novel risk control framework, which consists of a qualify model for uncertainty estimation using the probe idea, and a decision model for selectively output. For evaluation, we introduce risk-related metrics, rather than the traditional EM and F1 in MRC, for the evaluation of risk-aware Web QA. The empirical results over both the real-world Web QA dataset and the academic MRC benchmark collection demonstrate the effectiveness of our approach.Comment: 42nd International ACM SIGIR Conference on Research and Development in Information Retrieva

CCN2 reduction mediates protective effects of BMP7 treatment in obstructive nephropathy

Treatment with rhBMP7 exerts profound protective effects in a wide variety of experimental models of renal disease. However, little is known about how these protective effects are mediated, and which cells in the kidney are targeted by exogenous rhBMP7 treatment. To determine if rhBMP7 increases glomerular and tubulointerstitial canonical BMP signaling, we performed Unilateral Ureteral Obstruction w(UUO, a widely used obstructive nephropathy model) in mice reporting transcriptional activity downstream of canonical BMP signaling by the expression of GFP under the BMP Responsive Element of the Id1 promoter (BRE:gfp mice). We also analysed the impact of rhBMP7 treatment on severity of the UUO phenotype, on TGFβ signaling, and on expression of CCN2 (CTGF). Despite profound protective effects with respect to morphological damage, macrophage infiltration, and fibrosis, no significant difference in GFP-expression was observed upon rhBMP7 administration. Also TGFβ signalling was similar in rhBMP7 and vehicle treated mice, but CCN2 expression in obstructed kidneys was significantly reduced by rhBMP7 treatment. Of note, in heterozygous CCN2 mice (CCN2+/−) treatment with rhBMP7 did not (further) reduce the severity of kidney damage in the UUO-model. These data suggest that protection against obstructive nephropathy by exogenous rhBMP7 treatment relies primarily on non-canonical BMP signaling, and may be mediated in large part by downregulation of CCN2 expression

HybridMingler: Towards Mixed-Reality Support for Mingling at Hybrid Conferences

Mingling, the activity of ad-hoc, private, opportunistic conversations ahead of, during, or after breaks, is an important socializing activity for attendees at scheduled events, such as in-person conferences. The Covid-19 pandemic had a dramatic impact on the way conferences are organized, so that most of them now take place in a hybrid mode where people can either attend on-site or remotely. While on-site attendees can resume in-person mingling, hybrid modes make it challenging for remote attendees to mingle with on-site peers. In addressing this problem, we propose a collaborative mixed-reality (MR) concept, including a prototype, called HybridMingler. This is a distributed MR system supporting ambient awareness and allowing both on-site and remote conference attendees to virtually mingle. HybridMingler aims to provide both on-site and remote attendees with a spatial sense of co-location in the very same venue location, thus ultimately improving perceived presence

LVM-Med: Learning Large-Scale Self-Supervised Vision Models for Medical Imaging via Second-order Graph Matching

Obtaining large pre-trained models that can be fine-tuned to new tasks with limited annotated samples has remained an open challenge for medical imaging data. While pre-trained deep networks on ImageNet and vision-language foundation models trained on web-scale data are prevailing approaches, their effectiveness on medical tasks is limited due to the significant domain shift between natural and medical images. To bridge this gap, we introduce LVM-Med, the first family of deep networks trained on large-scale medical datasets. We have collected approximately 1.3 million medical images from 55 publicly available datasets, covering a large number of organs and modalities such as CT, MRI, X-ray, and Ultrasound. We benchmark several state-of-the-art self-supervised algorithms on this dataset and propose a novel self-supervised contrastive learning algorithm using a graph-matching formulation. The proposed approach makes three contributions: (i) it integrates prior pair-wise image similarity metrics based on local and global information; (ii) it captures the structural constraints of feature embeddings through a loss function constructed via a combinatorial graph-matching objective; and (iii) it can be trained efficiently end-to-end using modern gradient-estimation techniques for black-box solvers. We thoroughly evaluate the proposed LVM-Med on 15 downstream medical tasks ranging from segmentation and classification to object detection, and both for the in and out-of-distribution settings. LVM-Med empirically outperforms a number of state-of-the-art supervised, self-supervised, and foundation models. For challenging tasks such as Brain Tumor Classification or Diabetic Retinopathy Grading, LVM-Med improves previous vision-language models trained on 1 billion masks by 6-7% while using only a ResNet-50.Comment: Update Appendi

Developing a Model of Aged Decellularized Muscle Matrix with Advanced Glycation Cross-linking

Volumetric muscle loss (VML) has been found to overwhelm muscle regeneration, resulting in loss of long-term muscle functionality. Decellularized muscle matrices (DMMs) provide an effective environment for muscle regeneration; however, the age of their source has not been adequately explored for clinical translation. Advanced glycation end-products (AGEs) are chemical cross-links that contribute to the aging process by accumulating on collagen fibers, resulting in a stiffening of the collagenous matrix and an increase in inflammation via the receptor for advanced glycation end-products (RAGE). In previous experiments, we found increased levels of AGE-specific cross-links within DMMs in old mice compared to young as proven by ALT-711 treatment. In this study, we developed a model of aged rat DMMs using AGE cross-links and hypothesized that our AGE-DMM model will contain a higher number of collagen cross-links compared to the control. This AGE-DMM model aims to elucidate the effect of AGEs on muscle regeneration when used in vitro or implanted in a volumetric muscle loss model.https://scholarscompass.vcu.edu/uresposters/1424/thumbnail.jp

Mapping for engagement: setting up a community based participatory research project to reach underserved communities at risk for Hepatitis C in Ho Chi Minh City, Vietnam

Background: Approximately 1. 07 million people in Vietnam are infected with hepatitis C virus (HCV). To address this epidemic, the South East Asian Research Collaborative in Hepatitis (SEARCH) launched a 600-patient cohort study and two clinical trials, both investigating shortened treatment strategies for chronic HCV infection with direct-acting antiviral drugs. We conducted ethnographic research with a subset of trial participants and found that the majority were aware of HCV infection and its implications and were motivated to seek treatment. However, people who inject drugs (PWID), and other groups at risk for HCV were under-represented, although injecting drug use is associated with high rates of HCV. Material and Methods: We designed a community-based participatory research (CBPR) study to engage in dialogues surrounding HCV and other community-prioritized health issues with underserved groups at risk for HCV in Ho Chi Minh City. The project consists of three phases: situation analysis, CBPR implementation, and dissemination. In this paper, we describe the results of the first phase (i.e., the situation analysis) in which we conducted desk research and organized stakeholder mapping meetings with representatives from local non-government and community-based organizations where we used participatory research methods to identify and analyze key stakeholders working with underserved populations. Results: Twenty six institutions or groups working with the key underserved populations were identified. Insights about the challenges and dynamics of underserved communities were also gathered. Two working groups made up of representatives from the NGO and CBO level were formed. Discussion: Using the information provided by local key stakeholders to shape the project has helped us to build solid relationships, give the groups a sense of ownership from the early stages, and made the project more context specific. These steps are not only important preliminary steps for participatory studies but also for other research that takes place within the communities

GenQ: Automated Question Generation to Support Caregivers While Reading Stories with Children

When caregivers ask open--ended questions to motivate dialogue with children, it facilitates the child's reading comprehension skills.Although there is scope for use of technological tools, referred here as "intelligent tutoring systems", to scaffold this process, it is currently unclear whether existing intelligent systems that generate human--language like questions is beneficial. Additionally, training data used in the development of these automated question generation systems is typically sourced without attention to demographics, but people with different cultural backgrounds may ask different questions. As a part of a broader project to design an intelligent reading support app for Latinx children, we crowdsourced questions from Latinx caregivers and noncaregivers as well as caregivers and noncaregivers from other demographics. We examine variations in question--asking within this dataset mediated by individual, cultural, and contextual factors. We then design a system that automatically extracts templates from this data to generate open--ended questions that are representative of those asked by Latinx caregivers

The foundations of big data sharing: A CGIAR international research organization perspective

The potential of big data capabilities to transform and understand global agricultural and biological systems often relies on data from different sources that must be considered together or aggregated to provide insights. The value of data is however not only in its collection and storage, but largely in its re-use. Big data storage repositories are not enough when we consider a world brimming with escalating volumes of data, here we need to consider innovative systems and tools which address data harmonization and standardization and importantly, ones that can bridge the gap between science and end users. In this paper, we will demonstrate how CGIAR (including the Alliance of Bioversity International and CIAT) develops a culture of co-operation and collaboration among custodians of agrobiodiversity data, as well as new directions for big data. CGIAR first launched the Platform for Big Data in Agriculture to enhance the development and maintenance of its data. This helped establish workflows of cross-platform synthesis, annotate and apply the lessons learnt. The Platform then built GARDIAN (Global Agricultural Research Data Innovation and Acceleration Network)—a digital tool that harvests from ∼40 separate open data and publication repositories that 15 CGIAR centres have used for data synthesis. While there have been significant advances in big data management and storage, we also identify the gaps to improve use, and the re-use of data in order to reveal its added value in decision making