Practice Report

The University of Massachusetts (UMass) STEM Education Institute and the UMass School of Education hosted a National Science Foundation funded conference entitled “Science, Technology, Engineering and Math—Alternative Certification for Teachers” (STEM-ACT) in Arlington, Virginia on May 5-7, 2006. This “practice” white paper summarizes issues presented at the conference that are of importance for providers of alternative certification (AC) for science teachers, highlights what we know so far about effective alternative certification programs, and discusses what we still need to know through future rigorous research on AC programs for science teachers. This paper also provides guidelines for assessment of alternative certification programs for science teachers. Two similar papers have been prepared for academic researchers and policy makers

Policy Report

The University of Massachusetts (UMass) STEM Education Institute and the UMass School of Education hosted a National Science Foundation funded conference entitled “Science, Technology, Engineering and Math—Alternative Certification for Teachers” (STEM-ACT) in Arlington, Virginia on May 5-7, 2006. This white paper summarizes issues presented at the conference that are of importance to policy makers on alternative certification (AC). It focuses on issues concerning science teachers, analyzing the nature and scope of the policy endeavor as a solution to current and projected teacher shortages, and discussing the implications of AC policies on teacher supply and demand and on teacher turnover. Two similar papers have been prepared for academic researchers and AC program provider

Executive Summary

A National Science Foundation funded conference entitled, “Science, Technology, Engineering and Math – Alternative Certification for Teachers” (STEM-ACT) was held in May, 2006 in Arlington, VA. The conference was designed to facilitate a significant exchange of information, which was then synthesized to produce white papers on the three threads of the conference, i.e., policy, practice, and research. This summary presents the highlights of the three white papers

Antitumor effect of a pyrazolone-based complex [Cu(PMPP-SAL)(EtOH)] against murine melanoma B16 cell in vitro and in vivo

Pyrazolone-based derivative metal complexes were reported to have cytotoxicity in some tumor cells. In this study, the antitumor effect of [Cu(PMPP-SAL)(EtOH)] (PMPP-SAL = N-(1-phenyl-3-methyl-4-propenylidene-5-pyrazolone)-salicylidene hydrazide anion) in murine melanoma B16 cells in vitro and in vivo was investigated. The result showed that [Cu(PMPP-SAL)(EtOH)] inhibited the survival of B16 cells in vitro, and the IC50 value was superior to cisplatin (DDP) (p < 0.001). B16 cell apoptosis was significantly higher in comparison to the control group (DMSO) (p < 0.01), and cell cycle arrest occurred at the G0/G1 phase. When challenged C57 BL/6J mice were treated with [Cu(PMPP-SAL)(EtOH)], a smaller volume of B16 solid tumors were reported than the control group (p < 0.01), with lower positive expression indices of CD 34, vascular endothelial growth factor (VEGF) and basic fibroblast growth factor (bFGF) (p < 0.01). Moreover, the tumor growth was suppressed in mice due to the induction of apoptosis, as detected by the TUNEL assay (p < 0.001). In summary, [Cu(PMPP-SAL)(EtOH)] effectively inhibited the growth of B16 cells in vitro and in vivo due to the induction of apoptosis and the inhibition of intra-tumoral angiogenesis, demonstrating its therapeutic potential in melanoma treatment

ODSum: New Benchmarks for Open Domain Multi-Document Summarization

Open-domain Multi-Document Summarization (ODMDS) is a critical tool for condensing vast arrays of documents into coherent, concise summaries. With a more inter-related document set, there does not necessarily exist a correct answer for the retrieval, making it hard to measure the retrieving performance. We propose a rule-based method to process query-based document summarization datasets into ODMDS datasets. Based on this method, we introduce a novel dataset, ODSum, a sophisticated case with its document index interdependent and often interrelated. We tackle ODMDS with the \textit{retrieve-then-summarize} method, and the performance of a list of retrievers and summarizers is investigated. Through extensive experiments, we identify variances in evaluation metrics and provide insights into their reliability. We also found that LLMs suffer great performance loss from retrieving errors. We further experimented methods to improve the performance as well as investigate their robustness against imperfect retrieval. We will release our data and code at https://github.com/yale-nlp/ODSum