High temperature superconducting magnet control actuators for the NGST

for the United States government is permitted

A Systems Biology Approach to Understanding the Pathophysiology of High-Grade Serous Ovarian Cancer: Focus on Iron and Fatty Acid Metabolism.

Ovarian cancer (OVC) is the most lethal of the gynecological malignancies, with diagnosis often occurring during advanced stages of the disease. Moreover, a majority of cases become refractory to chemotherapeutic approaches. Therefore, it is important to improve our understanding of the molecular dependencies underlying the disease to identify novel diagnostic and precision therapeutics for OVC. Cancer cells are known to sequester iron, which can potentiate cancer progression through mechanisms that have not yet been completely elucidated. We developed an algorithm to identify novel links between iron and pathways implicated in high-grade serous ovarian cancer (HGSOC), the most common and deadliest subtype of OVC, using microarray gene expression data from both clinical sources and an experimental model. Using our approach, we identified several links between fatty acid (FA) and iron metabolism, and subsequently developed a network for iron involvement in FA metabolism in HGSOC. FA import and synthesis pathways are upregulated in HGSOC and other cancers, but a link between these processes and iron-related genes has not yet been identified. We used the network to derive hypotheses of specific mechanisms by which iron and iron-related genes impact and interact with FA metabolic pathways to promote tumorigenesis. These results suggest a novel mechanism by which iron sequestration by cancer cells can potentiate cancer progression, and may provide novel targets for use in diagnosis and/or treatment of HGSOC

Systems biology of ferroptosis: A modeling approach.

Ferroptosis is a recently discovered form of iron-dependent regulated cell death (RCD) that occurs via peroxidation of phospholipids containing polyunsaturated fatty acid (PUFA) moieties. Activating this form of cell death is an emerging strategy in cancer treatment. Because multiple pathways and molecular species contribute to the ferroptotic process, predicting which tumors will be sensitive to ferroptosis is a challenge. We thus develop a mathematical model of several critical pathways to ferroptosis in order to perform a systems-level analysis of the process. We show that sensitivity to ferroptosis depends on the activity of multiple upstream cascades, including PUFA incorporation into the phospholipid membrane, and the balance between levels of pro-oxidant factors (reactive oxygen species, lipoxogynases) and antioxidant factors (GPX4). We perform a systems-level analysis of ferroptosis sensitivity as an outcome of five input variables (ACSL4, SCD1, ferroportin, transferrin receptor, and p53) and organize the resulting simulations into \u27high\u27 and \u27low\u27 ferroptosis sensitivity groups. We make a novel prediction corresponding to the combinatorial requirements of ferroptosis sensitivity to SCD1 and ACSL4 activity. To validate our prediction, we model the ferroptotic response of an ovarian cancer stem cell line following single- and double-knockdown of SCD1 and ACSL4. We find that the experimental outcomes are consistent with our simulated predictions. This work suggests that a systems-level approach is beneficial for understanding the complex combined effects of ferroptotic input, and in predicting cancer susceptibility to ferroptosis

Semiautomatic robust regression clustering of international trade data

The purpose of this paper is to show in regression clustering how to choose the most relevant solutions, analyze their stability, and provide information about best combinations of optimal number of groups, restriction factor among the error variance across groups and level of trimming. The procedure is based on two steps. First we generalize the information criteria of constrained robust multivariate clustering to the case of clustering weighted models. Differently from the traditional approaches which are based on the choice of the best solution found minimizing an information criterion (i.e. BIC), we concentrate our attention on the so called optimal stable solutions. In the second step, using the monitoring approach, we select the best value of the trimming factor. Finally, we validate the solution using a confirmatory forward search approach. A motivating example based on a novel dataset concerning the European Union trade of face masks shows the limitations of the current existing procedures. The suggested approach is initially applied to a set of well known datasets in the literature of robust regression clustering. Then, we focus our attention on a set of international trade datasets and we provide a novel informative way of updating the subset in the random start approach. The Supplementary material, in the spirit of the Special Issue, deepens the analysis of trade data and compares the suggested approach with the existing ones available in the literature

Superconducting rebalance acceleration and rate sensor

The goal of this program is the development of a high precision multisensor based on a high T(sub c) superconducting proof mass. The design of a prototype is currently underway. Key technical issues appear resolvable. High temperature superconductors have complicated, hysteretic flux dynamics but the forces on them can be linearly controlled for small displacements. Current data suggests that the forces on the superconductors decay over a short time frame and then stabilize, though very long term data is not available. The hysteretic force characteristics are substantial for large scale excursions, but do not appear to be an issue for the very small displacements required in this device. Sufficient forces can be exerted for non-contact suspension of a centimeter sized proof mass in a vacuum sealed nitrogen jacket cryostat. High frequency capacitive sensing using stripline technology will yield adequate position resolution for 0.1 micro-g measurements at 100 Hz. Overall, a reasonable cost, but very high accuracy, system is feasible with this technology

Analytic Considerations in Economic Evaluations of Multinational Cardiovascular Clinical Trials

OBJECTIVES: The growing number of economic evaluations that use data collected in multinational clinical trials raises numerous questions regarding their execution and interpretation. Although recommendations for conducting economic evaluations have been widely disseminated, relatively little guidance has been given for conducting economic evaluations alongside clinical trials, particularly multinational trials. METHODS: Building on a literature review that was conducted in preparation for an expert workshop, we evaluated a subset of methodological issues related to conducting economic evaluations alongside multinational clinical trials. RESULTS: We found wide variation in the types of costs included as part of the analyses and in the methods used to assign costs to hospitalization events. Furthermore, we found that the extrapolation of costs and survival outcomes beyond the trial period is an inconsistent practice and is often not dependent on whether a survival benefit was observed in the trial or on the epidemiology or practice patterns in the country to which the findings are directed. CONCLUSIONS: Although the limited sample size precluded a quantitative analysis of trial characteristics and their associations with the methodologies employed, our findings highlight the need for more guidance to analysts regarding the execution of economic evaluations using data from multinational clinical trials. As the research community grapples with the complexities of methodological and logistical issues involved in multinational economic evaluations, the development of a standardized format to report the basic methodological characteristics of such studies would help to improve transparency and comparability for other analysts and decision-makers

How to prioritize bridge maintenance using a functional priority index

The progressive aging of civil infrastructures makes it essential to develop managerial tools and instrument for planning maintenance activities. As public entities, typically in charge of the management of infrastructures, have limited resources, it is crucial to define clear prioritization criteria. Addressing this need, this work introduces the usage of a functional priority index for ranking infrastructures on the basis of the impact of their closure. The impact is expressed in terms of induced travel delay for people due to path detour. To estimate this delay an analytical strategy is introduced and applied to assess the priority index on a sample of 290 bridges in Lombardy. Relevant information are gathered integrating two data sources providing information on the transportation network and on the travel demand, i.e. road network data and Origin Destination matrices. The results of this application show that the method enables the identification of the most critical infrastructures and the detection, for each bridge closure, of the most impacted areas of the region and the most impacted hours of the day