The Impact and Utilization of Reading Interventionists

The purpose of this qualitative, semi-structured interview study was to explore the role of a reading interventionist within an elementary school setting to help bring increased clarity to the responsibilities of a reading interventionist. Research questions focused on the role and impact reading interventionists when working with developing readers at the elementary level. Twelve reading interventionists from around the United States were chosen to participate in the interview. Multiple data sources were used including audio recordings transcribed, follow-up questions, and memos. Elementary schools have implemented a Response to Intervention model where reading interventionists focused on Tier 2 and Tier 3 students. They had multiple duties and responsibilities within the school, including working with all school personnel and providing data sources to drive intervention. Limitations included all female interviewees and limited participants due to emotionally and physically demanding times in teaching. This study recommends that administrators acknowledge the heavy workload that is expected of reading interventionists and provide proper training needed to adequately support students and staff

2000 Philip C. Jessup

The State of Kuraca and the Republic of Senhava have submitted their differences concerning the vaccine trials to the International Court of Justice for resolution through a Special Agreement, in accordance with Article 40(1) of the Statute of the International Court of Justice

Towards meaningful research and engagement: Indigenous knowledge systems and Great Lakes governance

For thousands of years, Indigenous peoples governed their relations in the Great Lakes region, guided by distinct political, legal, governance, and knowledge systems. Despite historic and ongoing exclusion of Indigenous peoples from Great Lakes governance in the Canadian context and other assaults on Indigenous sovereignty, authority, jurisdiction and responsibilities, Indigenous peoples have maintained their relationships with the Great Lakes. In recent years, Indigenous knowledge systems (IKS) have made inroads in Great Lakes governance, thanks primarily to First Nation political advocacy. However, it remains a challenge to include Indigenous knowledge and implement approaches that bridge Indigenous and Western ways of knowing. Instead of asking, ‘‘What needs to be done to support research into Indigenous knowledge systems?”, more appropriate questions addressed in this paper are: ‘‘What needs to be done to support Indigenous peoples to uphold, strengthen, revitalize Indigenous knowledge systems so they are able to share knowledge if they wish?” and ‘‘How can external institutions, agencies, and people engaged in sustainable management of Great Lakes ecosystems better prepare to engage with IKS respectfully and in the manner required by First Nations?”. In this paper, we demonstrate a First Nations-led knowledge sharing approach to research. In addition to making important contributions to Great Lakes governance and to the scientific research landscape in Canada, this paper points to the requirement to support Indigenous research capacity by building the necessary infrastructure and funding to ensure Indigenous people can lead their own research

Genomic imbalance of HMMR/RHAMM regulates the sensitivity and response of malignant peripheral nerve sheath tumour cells to aurora kinase inhibition

Malignant peripheral nerve sheath tumours (MPNST) are rare, hereditary cancers associated with neurofibromatosis type I. MPNSTs lack effective treatment options as they often resist chemotherapies and have high rates of disease recurrence. Aurora kinase A (AURKA) is an emerging target in cancer and an aurora kinase inhibitor (AKI), termed MLN8237, shows promise against MPNST cell lines in vitro and in vivo. Here, we test MLN8237 against two primary human MPNST grown in vivo as xenotransplants and find that treatment results in tumour cells exiting the cell cycle and undergoing endoreduplication, which cumulates in stabilized disease. Targeted therapies can often fail in the clinic due to insufficient knowledge about factors that determine tumour susceptibilities, so we turned to three MPNST cell-lines to further study and modulate the cellular responses to AKI. We find that the sensitivity of cell-lines with amplification of AURKA depends upon the activity of the kinase, which correlates with the expression of the regulatory gene products TPX2 and HMMR/RHAMM. Silencing of HMMR/RHAMM, but not TPX2, augments AURKA activity and sensitizes MPNST cells to AKI. Furthermore, we find that AURKA activity is critical to the propagation and self-renewal of sphere-enriched MPNST cancer stem-like cells. AKI treatment significantly reduces the formation of spheroids, attenuates the self-renewal of spheroid forming cells, and promotes their differentiation. Moreover, silencing of HMMR/RHAMM is sufficient to endow MPNST cells with an ability to form and maintain sphere culture. Collectively, our data indicate that AURKA is a rationale therapeutic target for MPNST and tumour cell responses to AKI, which include differentiation, are modulated by the abundance of HMMR/RHAMM

Data Assimilation Enhancements to Air Force Weathers Land Information System

The United States Air Force (USAF) has a proud and storied tradition of enabling significant advancements in the area of characterizing and modeling land state information. 557th Weather Wing (557 WW; DoDs Executive Agent for Land Information) provides routine geospatial intelligence information to warfighters, planners, and decision makers at all echelons and services of the U.S. military, government and intelligence community. 557 WW and its predecessors have been home to the DoDs only operational regional and global land data analysis systems since January 1958. As a trusted partner since 2005, Air Force Weather (AFW) has relied on the Hydrological Sciences Laboratory at NASA/GSFC to lead the interagency scientific collaboration known as the Land Information System (LIS). LIS is an advanced software framework for high performance land surface modeling and data assimilation of geospatial intelligence (GEOINT) information

Analysis of neonatal clinical trials with twin births

<p>Abstract</p> <p>Background</p> <p>In neonatal trials of pre-term or low-birth-weight infants, twins may represent 10–20% of the study sample. Mixed-effects models and generalized estimating equations are common approaches for handling correlated continuous or binary data. However, the operating characteristics of these methods for mixes of correlated and independent data are not well established.</p> <p>Methods</p> <p>Simulation studies were conducted to compare mixed-effects models and generalized estimating equations to linear regression for continuous outcomes. Similarly, mixed-effects models and generalized estimating equations were compared to ordinary logistic regression for binary outcomes. The parameter of interest is the treatment effect in two-armed clinical trials. Data from the National Institute of Child Health & Human Development Neonatal Research Network are used for illustration.</p> <p>Results</p> <p>For continuous outcomes, while the coverage never fell below 0.93, and the type I error rate never exceeded 0.07 for any method, overall linear mixed-effects models performed well with respect to median bias, mean squared error, coverage, and median width. For binary outcomes, the coverage never fell below 0.90, and the type I error rate never exceeded 0.07 for any method. In these analyses, when randomization of twins was to the same treatment group or done independently, ordinary logistic regression performed best. When randomization of twins was to opposite treatment arms, a rare method of randomization in this setting, ordinary logistic regression still performed adequately. Overall, generalized linear mixed models showed the poorest coverage values.</p> <p>Conclusion</p> <p>For continuous outcomes, using linear mixed-effects models for analysis is preferred. For binary outcomes, in this setting where the amount of related data is small, but non-negligible, ordinary logistic regression is recommended.</p

Occam's Razor and Petascale Visual Data Analysis

One of the central challenges facing visualization research is how to effectively enable knowledge discovery. An effective approach will likely combine application architectures that are capable of running on today?s largest platforms to address the challenges posed by large data with visual data analysis techniques that help find, represent, and effectively convey scientifically interesting features and phenomena