LOFT fuel rod thermal/mechanical behavior predictions

Best-estimate predictions of LOFT fuel rod behavior during the first LOFT nuclear test series have been made with both RELAP/FLOOD4/FRAP-T and RELAP/SCORE/MOXY computer code combinations. The purpose of the predictions are to test the best-estimate code accuracy by comparison of predictions to subsequent test data and to develop plans for post-test fuel replacements and examinations. The FRAP-T3 computer code was utilized which couples the thermal, mechanical, internal gas, and material property behavior of a nuclear rod during various postulated accident sequences. The cladding deformation models account for elastic-plastic behavior. FRAP-T3 requires coolant conditions to be specified as input boundary conditions. The coolant conditions for the blowdown and refill phases of a loss-of-coolant experiment (LOCE) were calculated with RELAP4/MOD6 and for the reflood portion with FLOOD4

Neurophysiology

Contains research objectives and summary of research on seventeen research projects and reports on four research projects.National Institutes of Health (Grant 5 TOl EY00090-02)Bell Telephone Laboratories, Inc. (Grant)National Institutes of Health (Grant 5 ROI EY01149-03)National Institutes of Health (Grant NS 12307-01)National Institutes of Health (Grant 1 K04 NS00010

The development of common data elements for a multi-institute prostate cancer tissue bank: The Cooperative Prostate Cancer Tissue Resource (CPCTR) experience

BACKGROUND: The Cooperative Prostate Cancer Tissue Resource (CPCTR) is a consortium of four geographically dispersed institutions that are funded by the U.S. National Cancer Institute (NCI) to provide clinically annotated prostate cancer tissue samples to researchers. To facilitate this effort, it was critical to arrive at agreed upon common data elements (CDEs) that could be used to collect demographic, pathologic, treatment and clinical outcome data. METHODS: The CPCTR investigators convened a CDE curation subcommittee to develop and implement CDEs for the annotation of collected prostate tissues. The draft CDEs were refined and progressively annotated to make them ISO 11179 compliant. The CDEs were implemented in the CPCTR database and tested using software query tools developed by the investigators. RESULTS: By collaborative consensus the CPCTR CDE subcommittee developed 145 data elements to annotate the tissue samples collected. These included for each case: 1) demographic data, 2) clinical history, 3) pathology specimen level elements to describe the staging, grading and other characteristics of individual surgical pathology cases, 4) tissue block level annotation critical to managing a virtual inventory of cases and facilitating case selection, and 5) clinical outcome data including treatment, recurrence and vital status. These elements have been used successfully to respond to over 60 requests by end-users for tissue, including paraffin blocks from cases with 5 to 10 years of follow up, tissue microarrays (TMAs), as well as frozen tissue collected prospectively for genomic profiling and genetic studies. The CPCTR CDEs have been fully implemented in two major tissue banks and have been shared with dozens of other tissue banking efforts. CONCLUSION: The freely available CDEs developed by the CPCTR are robust, based on "best practices" for tissue resources, and are ISO 11179 compliant. The process for CDE development described in this manuscript provides a framework model for other organ sites and has been used as a model for breast and melanoma tissue banking efforts

Structural Change and the Fall of Income Inequality in Latin America : Agricultural Development, Inter-sectoral Duality, and the Kuznets Curve

In this study we approach the recent decline in income inequality in Latin America from the perspective of structural change with a focus on the relative performance of the agricultural sector. Our focus is on the underlying forces implied by Kuznets (1965). We zoom in on the relative performance of agriculture in the development process and the rural-urban duality and pay particular attention to the last couple of decades in relation to the entire post-1950 period. We attempt to estimate empirically possible theoretical relations with regard to these patterns by posing the following basic questions: how does the resurgence of agriculture relate to the reduction of income inequality and to what extent is this an expression of Latin America moving downward on the Kuznets curve? The literature on agriculture’s relation to the recent changes of income distribution in Latin America is quite limited. For instance, in a recent ECLAC report titled “Structural change for equality” (2012), the role of agriculture is not even mentioned. By agriculture we mean both farming and agro-business that processes and transports that output. To our knowledge, this paper is the first attempt to investigate this relationship for the recent decades in the perspective of structural change in Latin America. There are strong theoretical reasons to connect agricultural development to income distribution. The closing of the rural-urban income gap reflects what Reynolds (1975) called a “dynamic” transformation of agriculture and relates to the contribution agriculture provides for overall growth of the economy. In addition, the elasticity of poverty reduction with respect to growth is estimated to be stronger when growth emanates in the agricultural sector (Ravallion and Chen 2007, de Janvry and Sadoulet 2009). Productivity growth in the lagging sector should also contribute to sectoral labor productivity to convergence and thus helps to reduce inequality (Timmer 1988). For these reasons, the resurgence of agriculture driven partly by improving commodity prices should be given due attention when assessing the decline in income inequality in Latin America. According to the logic of the Kuznets curve, the hypothesized “turning point” of the inverted U-curve is generated by a reduction of income inequality in one or both of the sectors and/or a reduction of the rural-urban income gap as the weight of the agricultural sector diminishes, and the income per capita gap between them declines. We find that the recent decline in income inequality is related to the recent resurgence of Latin American agriculture, and, by inference, its lack of decline across most of the 20th century must be related to a lack of productivity change in agriculture. We provide estimates showing that during the recent decades inter-sectoral duality has been reduced by agricultural productivity growth. The duality expressed as an inter-sectoral Gini shows the shape of an inverted U-curve and as such the closing of the rural-urban income gap corroborates with the theoretical expectations postulated by Kuznets. The wider implication of the study is, however, that with slower growth in agricultural labor productivity, continuing improvement in the income distribution becomes more difficult. In the absence of strong manufacturing growth, agriculture might be able to reduce income inequality further if agro-industries remain unskilled labor intensive, thus raising the opportunity cost of unskilled workers. On the other hand, the traditional service sector has perhaps become the “new agricultural sector” in terms of productivity and labor surplus. In other words, the source of the remaining dualism does not come only from rural areas, but also from urban areas

The National COVID Cohort Collaborative (N3C): Rationale, design, infrastructure, and deployment.

OBJECTIVE: Coronavirus disease 2019 (COVID-19) poses societal challenges that require expeditious data and knowledge sharing. Though organizational clinical data are abundant, these are largely inaccessible to outside researchers. Statistical, machine learning, and causal analyses are most successful with large-scale data beyond what is available in any given organization. Here, we introduce the National COVID Cohort Collaborative (N3C), an open science community focused on analyzing patient-level data from many centers. MATERIALS AND METHODS: The Clinical and Translational Science Award Program and scientific community created N3C to overcome technical, regulatory, policy, and governance barriers to sharing and harmonizing individual-level clinical data. We developed solutions to extract, aggregate, and harmonize data across organizations and data models, and created a secure data enclave to enable efficient, transparent, and reproducible collaborative analytics. RESULTS: Organized in inclusive workstreams, we created legal agreements and governance for organizations and researchers; data extraction scripts to identify and ingest positive, negative, and possible COVID-19 cases; a data quality assurance and harmonization pipeline to create a single harmonized dataset; population of the secure data enclave with data, machine learning, and statistical analytics tools; dissemination mechanisms; and a synthetic data pilot to democratize data access. CONCLUSIONS: The N3C has demonstrated that a multisite collaborative learning health network can overcome barriers to rapidly build a scalable infrastructure incorporating multiorganizational clinical data for COVID-19 analytics. We expect this effort to save lives by enabling rapid collaboration among clinicians, researchers, and data scientists to identify treatments and specialized care and thereby reduce the immediate and long-term impacts of COVID-19

Synthetic Nanoparticles for Vaccines and Immunotherapy

The immune system plays a critical role in our health. No other component of human physiology plays a decisive role in as diverse an array of maladies, from deadly diseases with which we are all familiar to equally terrible esoteric conditions: HIV, malaria, pneumococcal and influenza infections; cancer; atherosclerosis; autoimmune diseases such as lupus, diabetes, and multiple sclerosis. The importance of understanding the function of the immune system and learning how to modulate immunity to protect against or treat disease thus cannot be overstated. Fortunately, we are entering an exciting era where the science of immunology is defining pathways for the rational manipulation of the immune system at the cellular and molecular level, and this understanding is leading to dramatic advances in the clinic that are transforming the future of medicine.1,2 These initial advances are being made primarily through biologic drugs– recombinant proteins (especially antibodies) or patient-derived cell therapies– but exciting data from preclinical studies suggest that a marriage of approaches based in biotechnology with the materials science and chemistry of nanomaterials, especially nanoparticles, could enable more effective and safer immune engineering strategies. This review will examine these nanoparticle-based strategies to immune modulation in detail, and discuss the promise and outstanding challenges facing the field of immune engineering from a chemical biology/materials engineering perspectiveNational Institutes of Health (U.S.) (Grants AI111860, CA174795, CA172164, AI091693, and AI095109)United States. Department of Defense (W911NF-13-D-0001 and Awards W911NF-07-D-0004