Emerging Standards and Analytical Science for Nanoenabled Medical Products.
Ontology highlight
ABSTRACT:
SUBMITTER: Nelson BC
PROVIDER: S-EPMC8221451 | biostudies-literature |
REPOSITORIES: biostudies-literature
Similar Datasets
Project description:The purpose of this work was to describe a computational and analytical methodology for profiling small RNA by high-throughput sequencing. The datasets here were used to develop synthetic oligoribonucleotides as spike-in standards.
2009-06-03 | GSE14695 | GEO
Project description:The purpose of this work was to describe a computational and analytical methodology for profiling small RNA by high-throughput sequencing. The datasets here were used to develop synthetic oligoribonucleotides as spike-in standards. We assessed the use of synthetic oligoribonucleotide standards as spike-in controls. These standards can be used to set an objective standard against which to compare samples. Standards were added to the total RNA (100 ug) in the following amounts: Std2 (TATATGCAAGTCCGGCCATAC) 0.01 pmol, Std3 (TAGCTAACGCATATCCGCATC) 0.1 pmol, Std6 (TGAAGCTGACATCGGTCATCC) 1.0 pmol.
2009-06-02 | E-GEOD-14695 | biostudies-arrayexpress
Project description:Citizen science involves a range of practices involving public participation in scientific knowledge production, but outcomes evaluation is complicated by the diversity of the goals and forms of citizen science. Publications and citations are not adequate metrics to describe citizen-science productivity. We address this gap by contributing a science products inventory (SPI) tool, iteratively developed through an expert panel and case studies, intended to support general-purpose planning and evaluation of citizen-science projects with respect to science productivity. The SPI includes a collection of items for tracking the production of science outputs and data practices, which are described and illustrated with examples. Several opportunities for further development of the initial inventory are highlighted, as well as potential for using the inventory as a tool to guide project management, funding, and research on citizen science.
| S-EPMC5972590 | biostudies-literature
Project description:Citizen science involves a range of practices involving public participation in scientific knowledge production, but outcomes evaluation is complicated by the diversity of the goals and forms of citizen science. Publications and citations are not adequate metrics to describe citizen-science productivity. We address this gap by contributing a science products inventory (SPI) tool, iteratively developed through an expert panel and case studies, intended to support general-purpose planning and evaluation of citizen-science projects with respect to science productivity. The SPI includes a collection of items for tracking the production of science outputs and data practices, which are described and illustrated with examples. Several opportunities for further development of the initial inventory are highlighted, as well as potential for using the inventory as a tool to guide project management, funding, and research on citizen science.
| S-EPMC5972569 | biostudies-literature
Project description:With a realistic threat against biodiversity in rain forests and in the sea, a sustainable use of natural products is becoming more and more important. Basic research directed against different organisms in Nature could reveal unexpected insights into fundamental biological mechanisms but also new pharmaceutical or biotechnological possibilities of more immediate use. Many different strategies have been used prospecting the biodiversity of Earth in the search for novel structure-activity relationships, which has resulted in important discoveries in drug development. However, we believe that the development of multidisciplinary incentives will be necessary for a future successful exploration of Nature. With this aim, one way would be a modernization and renewal of a venerable proven interdisciplinary science, Pharmacognosy, which represents an integrated way of studying biological systems. This has been demonstrated based on an explanatory model where the different parts of the model are explained by our ongoing research. Anti-inflammatory natural products have been discovered based on ethnopharmacological observations, marine sponges in cold water have resulted in substances with ecological impact, combinatory strategy of ecology and chemistry has revealed new insights into the biodiversity of fungi, in depth studies of cyclic peptides (cyclotides) has created new possibilities for engineering of bioactive peptides, development of new strategies using phylogeny and chemography has resulted in new possibilities for navigating chemical and biological space, and using bioinformatic tools for understanding of lateral gene transfer could provide potential drug targets. A multidisciplinary subject like Pharmacognosy, one of several scientific disciplines bridging biology and chemistry with medicine, has a strategic position for studies of complex scientific questions based on observations in Nature. Furthermore, natural product research based on intriguing scientific questions in Nature can be of value to increase the attraction for young students in modern life science.
| S-EPMC2912726 | biostudies-literature
Project description:Science standards have a long history in the United States and currently form the backbone of efforts to improve primary and secondary education in science, technology, engineering, and math (STEM). Although there has been much political controversy over the influence of standards on teacher autonomy and student performance, little light has been shed on how well standards cover science content. We assessed the coverage of genetics content in the Next Generation Science Standards (NGSS) using a consensus list of American Society of Human Genetics (ASHG) core concepts. We also compared the NGSS against state science standards. Our goals were to assess the potential of the new standards to support genetic literacy and to determine if they improve the coverage of genetics concepts relative to state standards. We found that expert reviewers cannot identify ASHG core concepts within the new standards with high reliability, suggesting that the scope of content addressed by the standards may be inconsistently interpreted. Given results that indicate that the disciplinary core ideas (DCIs) included in the NGSS documents produced by Achieve, Inc. clarify the content covered by the standards statements themselves, we recommend that the NGSS standards statements always be viewed alongside their supporting disciplinary core ideas. In addition, gaps exist in the coverage of essential genetics concepts, most worryingly concepts dealing with patterns of inheritance, both Mendelian and complex. Finally, state standards vary widely in their coverage of genetics concepts when compared with the NGSS. On average, however, the NGSS support genetic literacy better than extant state standards.
| S-EPMC4519196 | biostudies-literature
Project description:We report on the development of an item test bank and associated instruments based on the National Research Council (NRC) K-8 life sciences content standards. Utilizing hundreds of studies in the science education research literature on student misconceptions, we constructed 476 unique multiple-choice items that measure the degree to which test takers hold either a misconception or an accepted scientific view. Tested nationally with 30,594 students, following their study of life science, and their 353 teachers, these items reveal a range of interesting results, particularly student difficulties in mastering the NRC standards. Teachers also answered test items and demonstrated a high level of subject matter knowledge reflecting the standards of the grade level at which they teach, but exhibiting few misconceptions of their own. In addition, teachers predicted the difficulty of each item for their students and which of the wrong answers would be the most popular. Teachers were found to generally overestimate their own students' performance and to have a high level of awareness of the particular misconceptions that their students hold on the K-4 standards, but a low level of awareness of misconceptions related to the 5-8 standards.
| S-EPMC3763021 | biostudies-literature
Project description: Not available
| S-EPMC7158987 | biostudies-literature
Project description:Non-targeted analysis provides a comprehensive approach to analyze environmental and biological samples for nearly all chemicals present. One of the main shortcomings of current analytical methods and workflows is that they are unable to provide any quantitative information constituting an important obstacle in understanding environmental fate and human exposure. Herein, we present an in silico quantification method using mahine-learning for chemicals analyzed using electrospray ionization (ESI). We considered three data sets from different instrumental setups: (i) capillary electrophoresis electrospray ionization-mass spectrometry (CE-MS) in positive ionization mode (ESI+), (ii) liquid chromatography quadrupole time-of-flight mass spectrometry (LC-QTOF/MS) in ESI+ and (iii) LC-QTOF/MS in negative ionization mode (ESI-). We developed and applied two different machine-learning algorithms: a random forest (RF) and an artificial neural network (ANN) to predict the relative response factors (RRFs) of different chemicals based on their physicochemical properties. Chemical concentrations can then be calculated by dividing the measured abundance of a chemical, as peak area or peak height, by its corresponding RRF. We evaluated our models and tested their predictive power using 5-fold cross-validation (CV) and y randomization. Both the RF and the ANN models showed great promise in predicting RRFs. However, the accuracy of the predictions was dependent on the data set composition and the experimental setup. For the CE-MS ESI+ data set, the best model predicted measured RRFs with a mean absolute error (MAE) of 0.19 log units and a cross-validation coefficient of determination (Q2) of 0.84 for the testing set. For the LC-QTOF/MS ESI+ data set, the best model predicted measured RRFs with an MAE of 0.32 and a Q2 of 0.40. For the LC-QTOF/MS ESI- data set, the best model predicted measured RRFs with a MAE of 0.50 and a Q2 of 0.20. Our findings suggest that machine-learning algorithms can be used for predicting concentrations of nontargeted chemicals with reasonable uncertainties, especially in ESI+, while the application on ESI- remains a more challenging problem.
| S-EPMC7328371 | biostudies-literature
Project description:The science surrounding vitamin D presents both challenges and opportunities. Although many uncertainties are associated with the understandings concerning vitamin D, including its physiological function, the effects of excessive intake, and its role in health, it is at the same time a major interest in the research and health communities. The approach to evaluating and interpreting the available evidence about vitamin D should be founded on the quality of the data and on the conclusions that take into account the totality of the evidence. In addition, these activities can be used to identify critical data gaps and to help structure future research. The Office of Dietary Supplements (ODS) at the National Institutes of Health has as part of its mission the goal of supporting research and dialogues for topics with uncertain data, including vitamin D. This review considers vitamin D in the context of systematically addressing the uncertainty and in identifying research needs through the filter of the work of ODS. The focus includes the role of systematic reviews, activities that encompass considerations of the totality of the evidence, and collaborative activities to clarify unknowns or to fix methodological problems, as well as a case study using the relationship between cancer and vitamin D.
| S-EPMC5748758 | biostudies-other