Knowledge Modeling in Prior Art Search

NASA Astrophysics Data System (ADS)

Graf, Erik; Frommholz, Ingo; Lalmas, Mounia; van Rijsbergen, Keith

This study explores the benefits of integrating knowledge representations in prior art patent retrieval. Key to the introduced approach is the utilization of human judgment available in the form of classifications assigned to patent documents. The paper first outlines in detail how a methodology for the extraction of knowledge from such an hierarchical classification system can be established. Further potential ways of integrating this knowledge with existing Information Retrieval paradigms in a scalable and flexible manner are investigated. Finally based on these integration strategies the effectiveness in terms of recall and precision is evaluated in the context of a prior art search task for European patents. As a result of this evaluation it can be established that in general the proposed knowledge expansion techniques are particularly beneficial to recall and, with respect to optimizing field retrieval settings, further result in significant precision gains.

Network reconstruction based on proteomic data and prior knowledge of protein connectivity using graph theory.

PubMed

Stavrakas, Vassilis; Melas, Ioannis N; Sakellaropoulos, Theodore; Alexopoulos, Leonidas G

2015-01-01

Modeling of signal transduction pathways is instrumental for understanding cells' function. People have been tackling modeling of signaling pathways in order to accurately represent the signaling events inside cells' biochemical microenvironment in a way meaningful for scientists in a biological field. In this article, we propose a method to interrogate such pathways in order to produce cell-specific signaling models. We integrate available prior knowledge of protein connectivity, in a form of a Prior Knowledge Network (PKN) with phosphoproteomic data to construct predictive models of the protein connectivity of the interrogated cell type. Several computational methodologies focusing on pathways' logic modeling using optimization formulations or machine learning algorithms have been published on this front over the past few years. Here, we introduce a light and fast approach that uses a breadth-first traversal of the graph to identify the shortest pathways and score proteins in the PKN, fitting the dependencies extracted from the experimental design. The pathways are then combined through a heuristic formulation to produce a final topology handling inconsistencies between the PKN and the experimental scenarios. Our results show that the algorithm we developed is efficient and accurate for the construction of medium and large scale signaling networks. We demonstrate the applicability of the proposed approach by interrogating a manually curated interaction graph model of EGF/TNFA stimulation against made up experimental data. To avoid the possibility of erroneous predictions, we performed a cross-validation analysis. Finally, we validate that the introduced approach generates predictive topologies, comparable to the ILP formulation. Overall, an efficient approach based on graph theory is presented herein to interrogate protein-protein interaction networks and to provide meaningful biological insights.

The Influence of Prior Knowledge on the Retrieval-Directed Function of Note Taking in Prior Knowledge Activation

ERIC Educational Resources Information Center

Wetzels, Sandra A. J.; Kester, Liesbeth; van Merrienboer, Jeroen J. G.; Broers, Nick J.

2011-01-01

Background: Prior knowledge activation facilitates learning. Note taking during prior knowledge activation (i.e., note taking directed at retrieving information from memory) might facilitate the activation process by enabling learners to build an external representation of their prior knowledge. However, taking notes might be less effective in…

A Knowledge-Based Arrangement of Prototypical Neural Representation Prior to Experience Contributes to Selectivity in Upcoming Knowledge Acquisition

PubMed Central

Kurashige, Hiroki; Yamashita, Yuichi; Hanakawa, Takashi; Honda, Manabu

2018-01-01

Knowledge acquisition is a process in which one actively selects a piece of information from the environment and assimilates it with prior knowledge. However, little is known about the neural mechanism underlying selectivity in knowledge acquisition. Here we executed a 2-day human experiment to investigate the involvement of characteristic spontaneous activity resembling a so-called “preplay” in selectivity in sentence comprehension, an instance of knowledge acquisition. On day 1, we presented 10 sentences (prior sentences) that were difficult to understand on their own. On the following day, we first measured the resting-state functional magnetic resonance imaging (fMRI). Then, we administered a sentence comprehension task using 20 new sentences (posterior sentences). The posterior sentences were also difficult to understand on their own, but some could be associated with prior sentences to facilitate their understanding. Next, we measured the posterior sentence-induced fMRI to identify the neural representation. From the resting-state fMRI, we extracted the appearances of activity patterns similar to the neural representations for posterior sentences. Importantly, the resting-state fMRI was measured before giving the posterior sentences, and thus such appearances could be considered as preplay-like or prototypical neural representations. We compared the intensities of such appearances with the understanding of posterior sentences. This gave a positive correlation between these two variables, but only if posterior sentences were associated with prior sentences. Additional analysis showed the contribution of the entorhinal cortex, rather than the hippocampus, to the correlation. The present study suggests that prior knowledge-based arrangement of neural activity before an experience contributes to the active selection of information to be learned. Such arrangement prior to an experience resembles preplay activity observed in the rodent brain. In terms of

Fuzzy-based propagation of prior knowledge to improve large-scale image analysis pipelines

PubMed Central

Mikut, Ralf

2017-01-01

Many automatically analyzable scientific questions are well-posed and a variety of information about expected outcomes is available a priori. Although often neglected, this prior knowledge can be systematically exploited to make automated analysis operations sensitive to a desired phenomenon or to evaluate extracted content with respect to this prior knowledge. For instance, the performance of processing operators can be greatly enhanced by a more focused detection strategy and by direct information about the ambiguity inherent in the extracted data. We present a new concept that increases the result quality awareness of image analysis operators by estimating and distributing the degree of uncertainty involved in their output based on prior knowledge. This allows the use of simple processing operators that are suitable for analyzing large-scale spatiotemporal (3D+t) microscopy images without compromising result quality. On the foundation of fuzzy set theory, we transform available prior knowledge into a mathematical representation and extensively use it to enhance the result quality of various processing operators. These concepts are illustrated on a typical bioimage analysis pipeline comprised of seed point detection, segmentation, multiview fusion and tracking. The functionality of the proposed approach is further validated on a comprehensive simulated 3D+t benchmark data set that mimics embryonic development and on large-scale light-sheet microscopy data of a zebrafish embryo. The general concept introduced in this contribution represents a new approach to efficiently exploit prior knowledge to improve the result quality of image analysis pipelines. The generality of the concept makes it applicable to practically any field with processing strategies that are arranged as linear pipelines. The automated analysis of terabyte-scale microscopy data will especially benefit from sophisticated and efficient algorithms that enable a quantitative and fast readout. PMID

Prior Knowledge and Online Inquiry-Based Science Reading: Evidence from Eye Tracking

ERIC Educational Resources Information Center

Ho, Hsin Ning Jessie; Tsai, Meng-Jung; Wang, Ching-Yeh; Tsai, Chin-Chung

2014-01-01

This study employed eye-tracking technology to examine how students with different levels of prior knowledge process text and data diagrams when reading a web-based scientific report. Students' visual behaviors were tracked and recorded when they read a report demonstrating the relationship between the greenhouse effect and global climate…

Creating illusions of knowledge: learning errors that contradict prior knowledge.

PubMed

Fazio, Lisa K; Barber, Sarah J; Rajaram, Suparna; Ornstein, Peter A; Marsh, Elizabeth J

2013-02-01

Most people know that the Pacific is the largest ocean on Earth and that Edison invented the light bulb. Our question is whether this knowledge is stable, or if people will incorporate errors into their knowledge bases, even if they have the correct knowledge stored in memory. To test this, we asked participants general-knowledge questions 2 weeks before they read stories that contained errors (e.g., "Franklin invented the light bulb"). On a later general-knowledge test, participants reproduced story errors despite previously answering the questions correctly. This misinformation effect was found even for questions that were answered correctly on the initial test with the highest level of confidence. Furthermore, prior knowledge offered no protection against errors entering the knowledge base; the misinformation effect was equivalent for previously known and unknown facts. Errors can enter the knowledge base even when learners have the knowledge necessary to catch the errors. 2013 APA, all rights reserved

The Importance of Prior Knowledge.

ERIC Educational Resources Information Center

Cleary, Linda Miller

1989-01-01

Recounts a college English teacher's experience of reading and rereading Noam Chomsky, building up a greater store of prior knowledge. Argues that Frank Smith provides a theory for the importance of prior knowledge and Chomsky's work provided a personal example with which to interpret and integrate that theory. (RS)

Knowledge base and functionality of concepts of some Filipino biology teachers in five biology topics

NASA Astrophysics Data System (ADS)

Barquilla, Manuel B.

2018-01-01

This mixed research, is a snapshot of some Filipino Biology teachers' knowledge structure and how their concepts of the five topics in Biology (Photosynthesis, Cellular Respiration, human reproductive system, Mendelian genetics and NonMendelian genetics) functions and develops inside a biology classroom. The study focuses on the six biology teachers and a total of 222 students in their respective classes. Of the Six (6) teachers, three (3) are under the Science curriculum and the other three (3) are under regular curriculum in both public and private schools in Iligan city and Lanao del Norte, Philippines. The study utilized classroom discourses, concept maps, interpretative case-study method, bracketing method, and concept analysis for qualitative part; the quantitative part uses a nonparametric statistical tool, Kendall's tau Coefficient for determining relationship and congruency while measures of central tendencies and dispersion (mean, and standard deviation) for concept maps scores interpretation. Knowledge Base of Biology teachers were evaluated by experts in field of specialization having a doctorate program (e.g. PhD in Genetics) and PhD Biology candidates. The data collection entailed seven (7) months immersion: one (1) month for preliminary phase for the researcher to gain teachers' and students' confidence and the succeeding six (6) months for main observation and data collection. The evaluation of teachers' knowledge base by experts indicated that teachers' knowledge of (65%) is lower than the minimum (75%) recommended by ABD-el-Khalick and Boujaoude (1997). Thus, the experts believe that content knowledge of the teachers is hardly adequate for their teaching assignment. Moreover, the teachers in this study do not systematically use reallife situation to apply the concepts they teach. They can identify concepts too abstract for their student; however, they seldom use innovative ways to bring the discussion to their students' level of readiness and

Menarche: Prior Knowledge and Experience.

ERIC Educational Resources Information Center

Skandhan, K. P.; And Others

1988-01-01

Recorded menstruation information among 305 young women in India, assessing the differences between those who did and did not have knowledge of menstruation prior to menarche. Those with prior knowledge considered menarche to be a normal physiological function and had a higher rate of regularity, lower rate of dysmenorrhea, and earlier onset of…

Neural Mechanisms for Integrating Prior Knowledge and Likelihood in Value-Based Probabilistic Inference

PubMed Central

Ting, Chih-Chung; Yu, Chia-Chen; Maloney, Laurence T.

2015-01-01

In Bayesian decision theory, knowledge about the probabilities of possible outcomes is captured by a prior distribution and a likelihood function. The prior reflects past knowledge and the likelihood summarizes current sensory information. The two combined (integrated) form a posterior distribution that allows estimation of the probability of different possible outcomes. In this study, we investigated the neural mechanisms underlying Bayesian integration using a novel lottery decision task in which both prior knowledge and likelihood information about reward probability were systematically manipulated on a trial-by-trial basis. Consistent with Bayesian integration, as sample size increased, subjects tended to weigh likelihood information more compared with prior information. Using fMRI in humans, we found that the medial prefrontal cortex (mPFC) correlated with the mean of the posterior distribution, a statistic that reflects the integration of prior knowledge and likelihood of reward probability. Subsequent analysis revealed that both prior and likelihood information were represented in mPFC and that the neural representations of prior and likelihood in mPFC reflected changes in the behaviorally estimated weights assigned to these different sources of information in response to changes in the environment. Together, these results establish the role of mPFC in prior-likelihood integration and highlight its involvement in representing and integrating these distinct sources of information. PMID:25632152

Investigating the Effectiveness of Inquiry-Based Instruction on Students with Different Prior Knowledge and Reading Abilities

ERIC Educational Resources Information Center

Wang, Jing-Ru; Wang, Yuh-Chao; Tai, Hsin-Jung; Chen, Wen-Ju

2010-01-01

This study examined the differential impacts of an inquiry-based instruction on conceptual changes across levels of prior knowledge and reading ability. The instrument emphasized four simultaneously important components: conceptual knowledge, reading ability, attitude toward science, and learning environment. Although the learning patterns and…

Prior Knowledge Guides Speech Segregation in Human Auditory Cortex.

PubMed

Wang, Yuanye; Zhang, Jianfeng; Zou, Jiajie; Luo, Huan; Ding, Nai

2018-05-18

Segregating concurrent sound streams is a computationally challenging task that requires integrating bottom-up acoustic cues (e.g. pitch) and top-down prior knowledge about sound streams. In a multi-talker environment, the brain can segregate different speakers in about 100 ms in auditory cortex. Here, we used magnetoencephalographic (MEG) recordings to investigate the temporal and spatial signature of how the brain utilizes prior knowledge to segregate 2 speech streams from the same speaker, which can hardly be separated based on bottom-up acoustic cues. In a primed condition, the participants know the target speech stream in advance while in an unprimed condition no such prior knowledge is available. Neural encoding of each speech stream is characterized by the MEG responses tracking the speech envelope. We demonstrate that an effect in bilateral superior temporal gyrus and superior temporal sulcus is much stronger in the primed condition than in the unprimed condition. Priming effects are observed at about 100 ms latency and last more than 600 ms. Interestingly, prior knowledge about the target stream facilitates speech segregation by mainly suppressing the neural tracking of the non-target speech stream. In sum, prior knowledge leads to reliable speech segregation in auditory cortex, even in the absence of reliable bottom-up speech segregation cue.

The Interpretation of Cellular Transport Graphics by Students with Low and High Prior Knowledge

ERIC Educational Resources Information Center

Cook, Michelle; Carter, Glenda; Wiebe, Eric N.

2008-01-01

The purpose of this study was to examine how prior knowledge of cellular transport influenced how high school students in the USA viewed and interpreted graphic representations of this topic. The participants were Advanced Placement Biology students (n = 65); each participant had previously taken a biology course in high school. After assessing…

Microarray missing data imputation based on a set theoretic framework and biological knowledge.

PubMed

Gan, Xiangchao; Liew, Alan Wee-Chung; Yan, Hong

2006-01-01

Gene expressions measured using microarrays usually suffer from the missing value problem. However, in many data analysis methods, a complete data matrix is required. Although existing missing value imputation algorithms have shown good performance to deal with missing values, they also have their limitations. For example, some algorithms have good performance only when strong local correlation exists in data while some provide the best estimate when data is dominated by global structure. In addition, these algorithms do not take into account any biological constraint in their imputation. In this paper, we propose a set theoretic framework based on projection onto convex sets (POCS) for missing data imputation. POCS allows us to incorporate different types of a priori knowledge about missing values into the estimation process. The main idea of POCS is to formulate every piece of prior knowledge into a corresponding convex set and then use a convergence-guaranteed iterative procedure to obtain a solution in the intersection of all these sets. In this work, we design several convex sets, taking into consideration the biological characteristic of the data: the first set mainly exploit the local correlation structure among genes in microarray data, while the second set captures the global correlation structure among arrays. The third set (actually a series of sets) exploits the biological phenomenon of synchronization loss in microarray experiments. In cyclic systems, synchronization loss is a common phenomenon and we construct a series of sets based on this phenomenon for our POCS imputation algorithm. Experiments show that our algorithm can achieve a significant reduction of error compared to the KNNimpute, SVDimpute and LSimpute methods.

Exploring the Impact of Prior Knowledge and Appropriate Feedback on Students' Perceived Cognitive Load and Learning Outcomes: Animation-based earthquakes instruction

NASA Astrophysics Data System (ADS)

Yeh, Ting-Kuang; Tseng, Kuan-Yun; Cho, Chung-Wen; Barufaldi, James P.; Lin, Mei-Shin; Chang, Chun-Yen

2012-07-01

The aim of this study was to develop an animation-based curriculum and to evaluate the effectiveness of animation-based instruction; the report involved the assessment of prior knowledge and the appropriate feedback approach, for the purpose of reducing perceived cognitive load and improving learning. The curriculum was comprised of five subunits designed to teach the 'Principles of Earthquakes.' Each subunit consisted of three modules: evaluation of prior knowledge with/without in-time feedback; animation-based instruction; and evaluation of learning outcomes with feedback. The 153 participants consisted of 10th grade high-school students. Seventy-eight students participated in the animation-based instruction, involving assessment of prior knowledge and appropriate feedback mechanism (APA group). A total of 75 students participated in animation-based learning that did not take into account their prior knowledge (ANPA group). The effectiveness of the instruction was then evaluated by using a Science Conception Test (SCT), a self-rating cognitive load questionnaire (CLQ), as well as a structured interview. The results indicated that: (1) Students' perceived cognitive load was reduced effectively through improving their prior knowledge by providing appropriate feedback. (2) When students perceived lower levels of cognitive load, they showed better learning outcome. The result of this study revealed that students of the APA group showed better performance than those of the ANPA group in an open-ended question. Furthermore, students' perceived cognitive load was negatively associated with their learning outcomes.

XML-based data model and architecture for a knowledge-based grid-enabled problem-solving environment for high-throughput biological imaging.

PubMed

Ahmed, Wamiq M; Lenz, Dominik; Liu, Jia; Paul Robinson, J; Ghafoor, Arif

2008-03-01

High-throughput biological imaging uses automated imaging devices to collect a large number of microscopic images for analysis of biological systems and validation of scientific hypotheses. Efficient manipulation of these datasets for knowledge discovery requires high-performance computational resources, efficient storage, and automated tools for extracting and sharing such knowledge among different research sites. Newly emerging grid technologies provide powerful means for exploiting the full potential of these imaging techniques. Efficient utilization of grid resources requires the development of knowledge-based tools and services that combine domain knowledge with analysis algorithms. In this paper, we first investigate how grid infrastructure can facilitate high-throughput biological imaging research, and present an architecture for providing knowledge-based grid services for this field. We identify two levels of knowledge-based services. The first level provides tools for extracting spatiotemporal knowledge from image sets and the second level provides high-level knowledge management and reasoning services. We then present cellular imaging markup language, an extensible markup language-based language for modeling of biological images and representation of spatiotemporal knowledge. This scheme can be used for spatiotemporal event composition, matching, and automated knowledge extraction and representation for large biological imaging datasets. We demonstrate the expressive power of this formalism by means of different examples and extensive experimental results.

Calculus Instructors' Responses to Prior Knowledge Errors

ERIC Educational Resources Information Center

Talley, Jana Renee

2009-01-01

This study investigates the responses to prior knowledge errors that Calculus I instructors make when assessing students. Prior knowledge is operationalized as any skill or understanding that a student needs to successfully navigate through a Calculus I course. A two part qualitative study consisting of student exams and instructor interviews was…

Use of Elaborative Interrogation to Help Students Acquire Information Consistent with Prior Knowledge and Information Inconsistent with Prior Knowledge.

ERIC Educational Resources Information Center

Woloshyn, Vera E.; And Others

1994-01-01

Thirty-two factual statements, half consistent and half not consistent with subjects' prior knowledge, were processed by 140 sixth and seventh graders. Half were directed to use elaborative interrogation (using prior knowledge) to answer why each statement was true. Across all memory measures, elaborative interrogation subjects performed better…

Relations among conceptual knowledge, procedural knowledge, and procedural flexibility in two samples differing in prior knowledge.

PubMed

Schneider, Michael; Rittle-Johnson, Bethany; Star, Jon R

2011-11-01

Competence in many domains rests on children developing conceptual and procedural knowledge, as well as procedural flexibility. However, research on the developmental relations between these different types of knowledge has yielded unclear results, in part because little attention has been paid to the validity of the measures or to the effects of prior knowledge on the relations. To overcome these problems, we modeled the three constructs in the domain of equation solving as latent factors and tested (a) whether the predictive relations between conceptual and procedural knowledge were bidirectional, (b) whether these interrelations were moderated by prior knowledge, and (c) how both constructs contributed to procedural flexibility. We analyzed data from 2 measurement points each from two samples (Ns = 228 and 304) of middle school students who differed in prior knowledge. Conceptual and procedural knowledge had stable bidirectional relations that were not moderated by prior knowledge. Both kinds of knowledge contributed independently to procedural flexibility. The results demonstrate how changes in complex knowledge structures contribute to competence development.

Does Teaching Experience Matter? Examining Biology Teachers' Prior Knowledge for Teaching in an Alternative Certification Program

ERIC Educational Resources Information Center

Friedrichsen, Patricia J.; Abell, Sandra K.; Pareja, Enrique M.; Brown, Patrick L.; Lankford, Deanna M.; Volkmann, Mark J.

2009-01-01

Alternative certification programs (ACPs) have been proposed as a viable way to address teacher shortages, yet we know little about how teacher knowledge develops within such programs. The purpose of this study was to investigate prior knowledge for teaching among students entering an ACP, comparing individuals with teaching experience to those…

The relation between prior knowledge and students' collaborative discovery learning processes

NASA Astrophysics Data System (ADS)

Gijlers, Hannie; de Jong, Ton

2005-03-01

In this study we investigate how prior knowledge influences knowledge development during collaborative discovery learning. Fifteen dyads of students (pre-university education, 15-16 years old) worked on a discovery learning task in the physics field of kinematics. The (face-to-face) communication between students was recorded and the interaction with the environment was logged. Based on students' individual judgments of the truth-value and testability of a series of domain-specific propositions, a detailed description of the knowledge configuration for each dyad was created before they entered the learning environment. Qualitative analyses of two dialogues illustrated that prior knowledge influences the discovery learning processes, and knowledge development in a pair of students. Assessments of student and dyad definitional (domain-specific) knowledge, generic (mathematical and graph) knowledge, and generic (discovery) skills were related to the students' dialogue in different discovery learning processes. Results show that a high level of definitional prior knowledge is positively related to the proportion of communication regarding the interpretation of results. Heterogeneity with respect to generic prior knowledge was positively related to the number of utterances made in the discovery process categories hypotheses generation and experimentation. Results of the qualitative analyses indicated that collaboration between extremely heterogeneous dyads is difficult when the high achiever is not willing to scaffold information and work in the low achiever's zone of proximal development.

Effects of Prior Knowledge on Memory: Implications for Education

ERIC Educational Resources Information Center

Shing, Yee Lee; Brod, Garvin

2016-01-01

The encoding, consolidation, and retrieval of events and facts form the basis for acquiring new skills and knowledge. Prior knowledge can enhance those memory processes considerably and thus foster knowledge acquisition. But prior knowledge can also hinder knowledge acquisition, in particular when the to-be-learned information is inconsistent with…

Explanation and Prior Knowledge Interact to Guide Learning

ERIC Educational Resources Information Center

Williams, Joseph J.; Lombrozo, Tania

2013-01-01

How do explaining and prior knowledge contribute to learning? Four experiments explored the relationship between explanation and prior knowledge in category learning. The experiments independently manipulated whether participants were prompted to explain the category membership of study observations and whether category labels were informative in…

Neuro-symbolic representation learning on biological knowledge graphs.

PubMed

Alshahrani, Mona; Khan, Mohammad Asif; Maddouri, Omar; Kinjo, Akira R; Queralt-Rosinach, Núria; Hoehndorf, Robert

2017-09-01

Biological data and knowledge bases increasingly rely on Semantic Web technologies and the use of knowledge graphs for data integration, retrieval and federated queries. In the past years, feature learning methods that are applicable to graph-structured data are becoming available, but have not yet widely been applied and evaluated on structured biological knowledge. Results: We develop a novel method for feature learning on biological knowledge graphs. Our method combines symbolic methods, in particular knowledge representation using symbolic logic and automated reasoning, with neural networks to generate embeddings of nodes that encode for related information within knowledge graphs. Through the use of symbolic logic, these embeddings contain both explicit and implicit information. We apply these embeddings to the prediction of edges in the knowledge graph representing problems of function prediction, finding candidate genes of diseases, protein-protein interactions, or drug target relations, and demonstrate performance that matches and sometimes outperforms traditional approaches based on manually crafted features. Our method can be applied to any biological knowledge graph, and will thereby open up the increasing amount of Semantic Web based knowledge bases in biology to use in machine learning and data analytics. https://github.com/bio-ontology-research-group/walking-rdf-and-owl. robert.hoehndorf@kaust.edu.sa. Supplementary data are available at Bioinformatics online. © The Author(s) 2017. Published by Oxford University Press.

Relationship of Students' Prior Knowledge and Order of Questions on Tests to Students' Test Scores.

ERIC Educational Resources Information Center

Papp, Klara K.; And Others

1987-01-01

A study examined whether students beginning a cell biology course with prior knowledge of its three areas (genetics, histology, and biochemistry) would retain that advantage throughout the course and whether achievement was influenced by the order of questions in a test. (MSE)

Predictive top-down integration of prior knowledge during speech perception.

PubMed

Sohoglu, Ediz; Peelle, Jonathan E; Carlyon, Robert P; Davis, Matthew H

2012-06-20

A striking feature of human perception is that our subjective experience depends not only on sensory information from the environment but also on our prior knowledge or expectations. The precise mechanisms by which sensory information and prior knowledge are integrated remain unclear, with longstanding disagreement concerning whether integration is strictly feedforward or whether higher-level knowledge influences sensory processing through feedback connections. Here we used concurrent EEG and MEG recordings to determine how sensory information and prior knowledge are integrated in the brain during speech perception. We manipulated listeners' prior knowledge of speech content by presenting matching, mismatching, or neutral written text before a degraded (noise-vocoded) spoken word. When speech conformed to prior knowledge, subjective perceptual clarity was enhanced. This enhancement in clarity was associated with a spatiotemporal profile of brain activity uniquely consistent with a feedback process: activity in the inferior frontal gyrus was modulated by prior knowledge before activity in lower-level sensory regions of the superior temporal gyrus. In parallel, we parametrically varied the level of speech degradation, and therefore the amount of sensory detail, so that changes in neural responses attributable to sensory information and prior knowledge could be directly compared. Although sensory detail and prior knowledge both enhanced speech clarity, they had an opposite influence on the evoked response in the superior temporal gyrus. We argue that these data are best explained within the framework of predictive coding in which sensory activity is compared with top-down predictions and only unexplained activity propagated through the cortical hierarchy.

Nudging toward Inquiry: Awakening and Building upon Prior Knowledge

ERIC Educational Resources Information Center

Fontichiaro, Kristin, Comp.

2010-01-01

"Prior knowledge" (sometimes called schema or background knowledge) is information one already knows that helps him/her make sense of new information. New learning builds on existing prior knowledge. In traditional reporting-style research projects, students bypass this crucial step and plow right into answer-finding. It's no wonder that many…

Counting-On, Trading and Partitioning: Effects of Training and Prior Knowledge on Performance on Base-10 Tasks

ERIC Educational Resources Information Center

Saxton, Matthew; Cakir, Kadir

2006-01-01

Factors affecting performance on base-10 tasks were investigated in a series of four studies with a total of 453 children aged 5-7 years. Training in counting-on was found to enhance child performance on base-10 tasks (Studies 2, 3, and 4), while prior knowledge of counting-on (Study 1), trading (Studies 1 and 3), and partitioning (Studies 1 and…

When does prior knowledge disproportionately benefit older adults’ memory?

PubMed Central

Badham, Stephen P.; Hay, Mhairi; Foxon, Natasha; Kaur, Kiran; Maylor, Elizabeth A.

2016-01-01

ABSTRACT Material consistent with knowledge/experience is generally more memorable than material inconsistent with knowledge/experience – an effect that can be more extreme in older adults. Four experiments investigated knowledge effects on memory with young and older adults. Memory for familiar and unfamiliar proverbs (Experiment 1) and for common and uncommon scenes (Experiment 2) showed similar knowledge effects across age groups. Memory for person-consistent and person-neutral actions (Experiment 3) showed a greater benefit of prior knowledge in older adults. For cued recall of related and unrelated word pairs (Experiment 4), older adults benefited more from prior knowledge only when it provided uniquely useful additional information beyond the episodic association itself. The current data and literature suggest that prior knowledge has the age-dissociable mnemonic properties of (1) improving memory for the episodes themselves (age invariant), and (2) providing conceptual information about the tasks/stimuli extrinsically to the actual episodic memory (particularly aiding older adults). PMID:26473767

The value of prior knowledge in machine learning of complex network systems.

PubMed

Ferranti, Dana; Krane, David; Craft, David

2017-11-15

Our overall goal is to develop machine-learning approaches based on genomics and other relevant accessible information for use in predicting how a patient will respond to a given proposed drug or treatment. Given the complexity of this problem, we begin by developing, testing and analyzing learning methods using data from simulated systems, which allows us access to a known ground truth. We examine the benefits of using prior system knowledge and investigate how learning accuracy depends on various system parameters as well as the amount of training data available. The simulations are based on Boolean networks-directed graphs with 0/1 node states and logical node update rules-which are the simplest computational systems that can mimic the dynamic behavior of cellular systems. Boolean networks can be generated and simulated at scale, have complex yet cyclical dynamics and as such provide a useful framework for developing machine-learning algorithms for modular and hierarchical networks such as biological systems in general and cancer in particular. We demonstrate that utilizing prior knowledge (in the form of network connectivity information), without detailed state equations, greatly increases the power of machine-learning algorithms to predict network steady-state node values ('phenotypes') and perturbation responses ('drug effects'). Links to codes and datasets here: https://gray.mgh.harvard.edu/people-directory/71-david-craft-phd. dcraft@broadinstitute.org. Supplementary data are available at Bioinformatics online. © The Author 2017. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com

A Knowledge Base for Teaching Biology Situated in the Context of Genetic Testing

NASA Astrophysics Data System (ADS)

van der Zande, Paul; Waarlo, Arend Jan; Brekelmans, Mieke; Akkerman, Sanne F.; Vermunt, Jan D.

2011-10-01

Recent developments in the field of genomics will impact the daily practice of biology teachers who teach genetics in secondary education. This study reports on the first results of a research project aimed at enhancing biology teacher knowledge for teaching genetics in the context of genetic testing. The increasing body of scientific knowledge concerning genetic testing and the related consequences for decision-making indicate the societal relevance of such a situated learning approach. What content knowledge do biology teachers need for teaching genetics in the personal health context of genetic testing? This study describes the required content knowledge by exploring the educational practice and clinical genetic practices. Nine experienced teachers and 12 respondents representing the clinical genetic practices (clients, medical professionals, and medical ethicists) were interviewed about the biological concepts and ethical, legal, and social aspects (ELSA) of testing they considered relevant to empowering students as future health care clients. The ELSA suggested by the respondents were complemented by suggestions found in the literature on genetic counselling. The findings revealed that the required teacher knowledge consists of multiple layers that are embedded in specific genetic test situations: on the one hand, the knowledge of concepts represented by the curricular framework and some additional concepts (e.g. multifactorial and polygenic disorder) and, on the other hand, more knowledge of ELSA and generic characteristics of genetic test practice (uncertainty, complexity, probability, and morality). Suggestions regarding how to translate these characteristics, concepts, and ELSA into context-based genetics education are discussed.

Using Hypermedia: Effects of Prior Knowledge and Goal Strength.

ERIC Educational Resources Information Center

Last, David A.; O'Donnell, Angela M.; Kelly, Anthony E.

The influences of a student's prior knowledge and desired goal on the difficulties and benefits associated with using hypertext were examined in this study. Participants, 12 students from an undergraduate course in educational psychology, were assigned to either the low or high prior knowledge category. Within these two groups, subjects were…

Boosting probabilistic graphical model inference by incorporating prior knowledge from multiple sources.

PubMed

Praveen, Paurush; Fröhlich, Holger

2013-01-01

Inferring regulatory networks from experimental data via probabilistic graphical models is a popular framework to gain insights into biological systems. However, the inherent noise in experimental data coupled with a limited sample size reduces the performance of network reverse engineering. Prior knowledge from existing sources of biological information can address this low signal to noise problem by biasing the network inference towards biologically plausible network structures. Although integrating various sources of information is desirable, their heterogeneous nature makes this task challenging. We propose two computational methods to incorporate various information sources into a probabilistic consensus structure prior to be used in graphical model inference. Our first model, called Latent Factor Model (LFM), assumes a high degree of correlation among external information sources and reconstructs a hidden variable as a common source in a Bayesian manner. The second model, a Noisy-OR, picks up the strongest support for an interaction among information sources in a probabilistic fashion. Our extensive computational studies on KEGG signaling pathways as well as on gene expression data from breast cancer and yeast heat shock response reveal that both approaches can significantly enhance the reconstruction accuracy of Bayesian Networks compared to other competing methods as well as to the situation without any prior. Our framework allows for using diverse information sources, like pathway databases, GO terms and protein domain data, etc. and is flexible enough to integrate new sources, if available.

Boosting Probabilistic Graphical Model Inference by Incorporating Prior Knowledge from Multiple Sources

PubMed Central

Praveen, Paurush; Fröhlich, Holger

2013-01-01

Inferring regulatory networks from experimental data via probabilistic graphical models is a popular framework to gain insights into biological systems. However, the inherent noise in experimental data coupled with a limited sample size reduces the performance of network reverse engineering. Prior knowledge from existing sources of biological information can address this low signal to noise problem by biasing the network inference towards biologically plausible network structures. Although integrating various sources of information is desirable, their heterogeneous nature makes this task challenging. We propose two computational methods to incorporate various information sources into a probabilistic consensus structure prior to be used in graphical model inference. Our first model, called Latent Factor Model (LFM), assumes a high degree of correlation among external information sources and reconstructs a hidden variable as a common source in a Bayesian manner. The second model, a Noisy-OR, picks up the strongest support for an interaction among information sources in a probabilistic fashion. Our extensive computational studies on KEGG signaling pathways as well as on gene expression data from breast cancer and yeast heat shock response reveal that both approaches can significantly enhance the reconstruction accuracy of Bayesian Networks compared to other competing methods as well as to the situation without any prior. Our framework allows for using diverse information sources, like pathway databases, GO terms and protein domain data, etc. and is flexible enough to integrate new sources, if available. PMID:23826291

Structuring and extracting knowledge for the support of hypothesis generation in molecular biology

PubMed Central

Roos, Marco; Marshall, M Scott; Gibson, Andrew P; Schuemie, Martijn; Meij, Edgar; Katrenko, Sophia; van Hage, Willem Robert; Krommydas, Konstantinos; Adriaans, Pieter W

2009-01-01

Background Hypothesis generation in molecular and cellular biology is an empirical process in which knowledge derived from prior experiments is distilled into a comprehensible model. The requirement of automated support is exemplified by the difficulty of considering all relevant facts that are contained in the millions of documents available from PubMed. Semantic Web provides tools for sharing prior knowledge, while information retrieval and information extraction techniques enable its extraction from literature. Their combination makes prior knowledge available for computational analysis and inference. While some tools provide complete solutions that limit the control over the modeling and extraction processes, we seek a methodology that supports control by the experimenter over these critical processes. Results We describe progress towards automated support for the generation of biomolecular hypotheses. Semantic Web technologies are used to structure and store knowledge, while a workflow extracts knowledge from text. We designed minimal proto-ontologies in OWL for capturing different aspects of a text mining experiment: the biological hypothesis, text and documents, text mining, and workflow provenance. The models fit a methodology that allows focus on the requirements of a single experiment while supporting reuse and posterior analysis of extracted knowledge from multiple experiments. Our workflow is composed of services from the 'Adaptive Information Disclosure Application' (AIDA) toolkit as well as a few others. The output is a semantic model with putative biological relations, with each relation linked to the corresponding evidence. Conclusion We demonstrated a 'do-it-yourself' approach for structuring and extracting knowledge in the context of experimental research on biomolecular mechanisms. The methodology can be used to bootstrap the construction of semantically rich biological models using the results of knowledge extraction processes. Models specific

The effects of activating prior topic and metacognitive knowledge on text comprehension scores.

PubMed

Kostons, Danny; van der Werf, Greetje

2015-09-01

Research on prior knowledge activation has consistently shown that activating learners' prior knowledge has beneficial effects on learning. If learners activate their prior knowledge, this activated knowledge serves as a framework for establishing relationships between the knowledge they already possess and new information provided to them. Thus far, prior knowledge activation has dealt primarily with topic knowledge in specific domains. Students, however, likely also possess at least some metacognitive knowledge useful in those domains, which, when activated, should aid in the deployment of helpful strategies during reading. In this study, we investigated the effects of both prior topic knowledge activation (PTKA) and prior metacognitive knowledge activation (PMKA) on text comprehension scores. Eighty-eight students in primary education were randomly distributed amongst the conditions of the 2 × 2 (PTKA yes/no × PMKA yes/no) designed experiment. Results show that activating prior metacognitive knowledge had a beneficial effect on text comprehension, whereas activating prior topic knowledge, after correcting for the amount of prior knowledge, did not. Most studies deal with explicit instruction of metacognitive knowledge, but our results show that this may not be necessary, specifically in the case of students who already have some metacognitive knowledge. However, existing metacognitive knowledge needs to be activated in order for students to make better use of this knowledge. © 2015 The British Psychological Society.

Formative Assessment Pre-Test to Identify College Students' Prior Knowledge, Misconceptions and Learning Difficulties in Biology

ERIC Educational Resources Information Center

Lazarowitz, Reuven; Lieb, Carl

2006-01-01

A formative assessment pretest was administered to undergraduate students at the beginning of a science course in order to find out their prior knowledge, misconceptions and learning difficulties on the topic of the human respiratory system and energy issues. Those findings could provide their instructors with the valuable information required in…

The Effects of Prior-knowledge and Online Learning Approaches on Students' Inquiry and Argumentation Abilities

NASA Astrophysics Data System (ADS)

Yang, Wen-Tsung; Lin, Yu-Ren; She, Hsiao-Ching; Huang, Kai-Yi

2015-07-01

This study investigated the effects of students' prior science knowledge and online learning approaches (social and individual) on their learning with regard to three topics: science concepts, inquiry, and argumentation. Two science teachers and 118 students from 4 eighth-grade science classes were invited to participate in this research. Students in each class were divided into three groups according to their level of prior science knowledge; they then took either our social- or individual-based online science learning program. The results show that students in the social online argumentation group performed better in argumentation and online argumentation learning. Qualitative analysis indicated that the students' social interactions benefited the co-construction of sound arguments and the accurate understanding of science concepts. In constructing arguments, students in the individual online argumentation group were limited to knowledge recall and self-reflection. High prior-knowledge students significantly outperformed low prior-knowledge students in all three aspects of science learning. However, the difference in inquiry and argumentation performance between low and high prior-knowledge students decreased with the progression of online learning topics.

Preparing learners with partly incorrect intuitive prior knowledge for learning

PubMed Central

Ohst, Andrea; Fondu, Béatrice M. E.; Glogger, Inga; Nückles, Matthias; Renkl, Alexander

2014-01-01

Learners sometimes have incoherent and fragmented intuitive prior knowledge that is (partly) “incompatible” with the to-be-learned contents. Such knowledge in pieces can cause conceptual disorientation and cognitive overload while learning. We hypothesized that a pre-training intervention providing a generalized schema as a structuring framework for such knowledge in pieces would support (re)organizing-processes of prior knowledge and thus reduce unnecessary cognitive load during subsequent learning. Fifty-six student teachers participated in the experiment. A framework group underwent a pre-training intervention providing a generalized, categorical schema for categorizing primary learning strategies and related but different strategies as a cognitive framework for (re-)organizing their prior knowledge. Our control group received comparable factual information but no framework. Afterwards, all participants learned about primary learning strategies. The framework group claimed to possess higher levels of interest and self-efficacy, achieved higher learning outcomes, and learned more efficiently. Hence, providing a categorical framework can help overcome the barrier of incorrect prior knowledge in pieces. PMID:25071638

When generating answers benefits arithmetic skill: the importance of prior knowledge.

PubMed

Rittle-Johnson, Bethany; Kmicikewycz, Alexander Oleksij

2008-09-01

People remember information better if they generate the information while studying rather than read the information. However, prior research has not investigated whether this generation effect extends to related but unstudied items and has not been conducted in classroom settings. We compared third graders' success on studied and unstudied multiplication problems after they spent a class period generating answers to problems or reading the answers from a calculator. The effect of condition interacted with prior knowledge. Students with low prior knowledge had higher accuracy in the generate condition, but as prior knowledge increased, the advantage of generating answers decreased. The benefits of generating answers may extend to unstudied items and to classroom settings, but only for learners with low prior knowledge.

Discrete Logic Modelling Optimization to Contextualize Prior Knowledge Networks Using PRUNET

PubMed Central

Androsova, Ganna; del Sol, Antonio

2015-01-01

High-throughput technologies have led to the generation of an increasing amount of data in different areas of biology. Datasets capturing the cell’s response to its intra- and extra-cellular microenvironment allows such data to be incorporated as signed and directed graphs or influence networks. These prior knowledge networks (PKNs) represent our current knowledge of the causality of cellular signal transduction. New signalling data is often examined and interpreted in conjunction with PKNs. However, different biological contexts, such as cell type or disease states, may have distinct variants of signalling pathways, resulting in the misinterpretation of new data. The identification of inconsistencies between measured data and signalling topologies, as well as the training of PKNs using context specific datasets (PKN contextualization), are necessary conditions to construct reliable, predictive models, which are current challenges in the systems biology of cell signalling. Here we present PRUNET, a user-friendly software tool designed to address the contextualization of a PKNs to specific experimental conditions. As the input, the algorithm takes a PKN and the expression profile of two given stable steady states or cellular phenotypes. The PKN is iteratively pruned using an evolutionary algorithm to perform an optimization process. This optimization rests in a match between predicted attractors in a discrete logic model (Boolean) and a Booleanized representation of the phenotypes, within a population of alternative subnetworks that evolves iteratively. We validated the algorithm applying PRUNET to four biological examples and using the resulting contextualized networks to predict missing expression values and to simulate well-characterized perturbations. PRUNET constitutes a tool for the automatic curation of a PKN to make it suitable for describing biological processes under particular experimental conditions. The general applicability of the implemented

The Influence of Prior Knowledge on Memory: A Developmental Cognitive Neuroscience Perspective

PubMed Central

Brod, Garvin; Werkle-Bergner, Markus; Shing, Yee Lee

2013-01-01

Across ontogenetic development, individuals gather manifold experiences during which they detect regularities in their environment and thereby accumulate knowledge. This knowledge is used to guide behavior, make predictions, and acquire further new knowledge. In this review, we discuss the influence of prior knowledge on memory from both the psychology and the emerging cognitive neuroscience literature and provide a developmental perspective on this topic. Recent neuroscience findings point to a prominent role of the medial prefrontal cortex (mPFC) and of the hippocampus (HC) in the emergence of prior knowledge and in its application during the processes of successful memory encoding, consolidation, and retrieval. We take the lateral PFC into consideration as well and discuss changes in both medial and lateral PFC and HC across development and postulate how these may be related to the development of the use of prior knowledge for remembering. For future direction, we argue that, to measure age differential effects of prior knowledge on memory, it is necessary to distinguish the availability of prior knowledge from its accessibility and use. PMID:24115923

Finding gene regulatory network candidates using the gene expression knowledge base.

PubMed

Venkatesan, Aravind; Tripathi, Sushil; Sanz de Galdeano, Alejandro; Blondé, Ward; Lægreid, Astrid; Mironov, Vladimir; Kuiper, Martin

2014-12-10

Network-based approaches for the analysis of large-scale genomics data have become well established. Biological networks provide a knowledge scaffold against which the patterns and dynamics of 'omics' data can be interpreted. The background information required for the construction of such networks is often dispersed across a multitude of knowledge bases in a variety of formats. The seamless integration of this information is one of the main challenges in bioinformatics. The Semantic Web offers powerful technologies for the assembly of integrated knowledge bases that are computationally comprehensible, thereby providing a potentially powerful resource for constructing biological networks and network-based analysis. We have developed the Gene eXpression Knowledge Base (GeXKB), a semantic web technology based resource that contains integrated knowledge about gene expression regulation. To affirm the utility of GeXKB we demonstrate how this resource can be exploited for the identification of candidate regulatory network proteins. We present four use cases that were designed from a biological perspective in order to find candidate members relevant for the gastrin hormone signaling network model. We show how a combination of specific query definitions and additional selection criteria derived from gene expression data and prior knowledge concerning candidate proteins can be used to retrieve a set of proteins that constitute valid candidates for regulatory network extensions. Semantic web technologies provide the means for processing and integrating various heterogeneous information sources. The GeXKB offers biologists such an integrated knowledge resource, allowing them to address complex biological questions pertaining to gene expression. This work illustrates how GeXKB can be used in combination with gene expression results and literature information to identify new potential candidates that may be considered for extending a gene regulatory network.

CONCEPTUAL FRAMEWORK FOR THE CHEMICAL EFFECTS IN BIOLOGICAL SYSTEMS (CEBS) TOXICOGENOMICS KNOWLEDGE BASE

EPA Science Inventory

Conceptual Framework for the Chemical Effects in Biological Systems (CEBS) T oxicogenomics Knowledge Base

Abstract
Toxicogenomics studies how the genome is involved in responses to environmental stressors or toxicants. It combines genetics, genome-scale mRNA expressio...

Active Prior Tactile Knowledge Transfer for Learning Tactual Properties of New Objects

PubMed Central

Feng, Di

2018-01-01

Reusing the tactile knowledge of some previously-explored objects (prior objects) helps us to easily recognize the tactual properties of new objects. In this paper, we enable a robotic arm equipped with multi-modal artificial skin, like humans, to actively transfer the prior tactile exploratory action experiences when it learns the detailed physical properties of new objects. These experiences, or prior tactile knowledge, are built by the feature observations that the robot perceives from multiple sensory modalities, when it applies the pressing, sliding, and static contact movements on objects with different action parameters. We call our method Active Prior Tactile Knowledge Transfer (APTKT), and systematically evaluated its performance by several experiments. Results show that the robot improved the discrimination accuracy by around 10% when it used only one training sample with the feature observations of prior objects. By further incorporating the predictions from the observation models of prior objects as auxiliary features, our method improved the discrimination accuracy by over 20%. The results also show that the proposed method is robust against transferring irrelevant prior tactile knowledge (negative knowledge transfer). PMID:29466300

Does prior domain-specific content knowledge influence students' recall of arguments surrounding interdisciplinary topics?

PubMed

Schmidt, Hiemke K; Rothgangel, Martin; Grube, Dietmar

2017-12-01

Awareness of various arguments can help interactants present opinions, stress points, and build counterarguments during discussions. At school, some topics are taught in a way that students learn to accumulate knowledge and gather arguments, and later employ them during debates. Prior knowledge may facilitate recalling information on well structured, fact-based topics, but does it facilitate recalling arguments during discussions on complex, interdisciplinary topics? We assessed the prior knowledge in domains related to a bioethical topic of 277 students from Germany (approximately 15 years old), their interest in the topic, and their general knowledge. The students read a text with arguments for and against prenatal diagnostics and tried to recall the arguments one week later and again six weeks later. Prior knowledge in various domains related to the topic individually and separately helped students recall the arguments. These relationships were independent of students' interest in the topic and their general knowledge. Copyright © 2017 The Foundation for Professionals in Services for Adolescents. Published by Elsevier Ltd. All rights reserved.

Creative design inspired by biological knowledge: Technologies and methods

NASA Astrophysics Data System (ADS)

Tan, Runhua; Liu, Wei; Cao, Guozhong; Shi, Yuan

2018-05-01

Biological knowledge is becoming an important source of inspiration for developing creative solutions to engineering design problems and even has a huge potential in formulating ideas that can help firms compete successfully in a dynamic market. To identify the technologies and methods that can facilitate the development of biologically inspired creative designs, this research briefly reviews the existing biological-knowledge-based theories and methods and examines the application of biological-knowledge-inspired designs in various fields. Afterward, this research thoroughly examines the four dimensions of key technologies that underlie the biologically inspired design (BID) process. This research then discusses the future development trends of the BID process before presenting the conclusions.

Prior-knowledge-based spectral mixture analysis for impervious surface mapping

DOE Office of Scientific and Technical Information (OSTI.GOV)

Zhang, Jinshui; He, Chunyang; Zhou, Yuyu

2014-01-03

In this study, we developed a prior-knowledge-based spectral mixture analysis (PKSMA) to map impervious surfaces by using endmembers derived separately for high- and low-density urban regions. First, an urban area was categorized into high- and low-density urban areas, using a multi-step classification method. Next, in high-density urban areas that were assumed to have only vegetation and impervious surfaces (ISs), the Vegetation-Impervious model (V-I) was used in a spectral mixture analysis (SMA) with three endmembers: vegetation, high albedo, and low albedo. In low-density urban areas, the Vegetation-Impervious-Soil model (V-I-S) was used in an SMA analysis with four endmembers: high albedo, lowmore » albedo, soil, and vegetation. The fraction of IS with high and low albedo in each pixel was combined to produce the final IS map. The root mean-square error (RMSE) of the IS map produced using PKSMA was about 11.0%, compared to 14.52% using four-endmember SMA. Particularly in high-density urban areas, PKSMA (RMSE = 6.47%) showed better performance than four-endmember (15.91%). The results indicate that PKSMA can improve IS mapping compared to traditional SMA by using appropriately selected endmembers and is particularly strong in high-density urban areas.« less

The critical success factors and impact of prior knowledge to nursing students when transferring nursing knowledge during nursing clinical practise.

PubMed

Tsai, Ming-Tien; Tsai, Ling-Long

2005-11-01

Nursing practise plays an important role in transferring nursing knowledge to nursing students. From the related literature review, prior knowledge will affect how learners gain new knowledge. There has been no direct examination of the prior knowledge interaction effect on students' performance and its influence on nursing students when evaluating the knowledge transfer success factors. This study explores (1) the critical success factors in transferring nursing knowledge, (2) the impact of prior knowledge when evaluating the success factors for transferring nursing knowledge. This research utilizes in-depth interviews to probe the initial success factor phase. A total of 422 valid questionnaires were conducted by the authors. The data were analysed by comparing the mean score and t-test between two groups. Seventeen critical success factors were identified by the two groups of students. Twelve items were selected to examine the diversity in the two groups. Students with prior knowledge were more independent than the other group. They also preferred self-directed learning over students without prior knowledge. Students who did not have prior knowledge were eager to take every opportunity to gain experience and more readily adopted new knowledge.

OWL reasoning framework over big biological knowledge network.

PubMed

Chen, Huajun; Chen, Xi; Gu, Peiqin; Wu, Zhaohui; Yu, Tong

2014-01-01

Recently, huge amounts of data are generated in the domain of biology. Embedded with domain knowledge from different disciplines, the isolated biological resources are implicitly connected. Thus it has shaped a big network of versatile biological knowledge. Faced with such massive, disparate, and interlinked biological data, providing an efficient way to model, integrate, and analyze the big biological network becomes a challenge. In this paper, we present a general OWL (web ontology language) reasoning framework to study the implicit relationships among biological entities. A comprehensive biological ontology across traditional Chinese medicine (TCM) and western medicine (WM) is used to create a conceptual model for the biological network. Then corresponding biological data is integrated into a biological knowledge network as the data model. Based on the conceptual model and data model, a scalable OWL reasoning method is utilized to infer the potential associations between biological entities from the biological network. In our experiment, we focus on the association discovery between TCM and WM. The derived associations are quite useful for biologists to promote the development of novel drugs and TCM modernization. The experimental results show that the system achieves high efficiency, accuracy, scalability, and effectivity.

OWL Reasoning Framework over Big Biological Knowledge Network

PubMed Central

Chen, Huajun; Chen, Xi; Gu, Peiqin; Wu, Zhaohui; Yu, Tong

2014-01-01

Recently, huge amounts of data are generated in the domain of biology. Embedded with domain knowledge from different disciplines, the isolated biological resources are implicitly connected. Thus it has shaped a big network of versatile biological knowledge. Faced with such massive, disparate, and interlinked biological data, providing an efficient way to model, integrate, and analyze the big biological network becomes a challenge. In this paper, we present a general OWL (web ontology language) reasoning framework to study the implicit relationships among biological entities. A comprehensive biological ontology across traditional Chinese medicine (TCM) and western medicine (WM) is used to create a conceptual model for the biological network. Then corresponding biological data is integrated into a biological knowledge network as the data model. Based on the conceptual model and data model, a scalable OWL reasoning method is utilized to infer the potential associations between biological entities from the biological network. In our experiment, we focus on the association discovery between TCM and WM. The derived associations are quite useful for biologists to promote the development of novel drugs and TCM modernization. The experimental results show that the system achieves high efficiency, accuracy, scalability, and effectivity. PMID:24877076

Understanding the Complexities of Prior Knowledge

ERIC Educational Resources Information Center

Soiferman, L. Karen

2014-01-01

The purpose of the study was to gain an understanding of the kinds of prior knowledge students bring with them from high school as it relates to the conventions of writing that they are expected to follow in ARTS 1110 Introduction to University. The research questions were "Can first-year students taking the Arts 1110 Introduction to…

Prior Knowledge Facilitates Mutual Gaze Convergence and Head Nodding Synchrony in Face-to-face Communication

PubMed Central

Thepsoonthorn, C.; Yokozuka, T.; Miura, S.; Ogawa, K.; Miyake, Y.

2016-01-01

As prior knowledge is claimed to be an essential key to achieve effective education, we are interested in exploring whether prior knowledge enhances communication effectiveness. To demonstrate the effects of prior knowledge, mutual gaze convergence and head nodding synchrony are observed as indicators of communication effectiveness. We conducted an experiment on lecture task between lecturer and student under 2 conditions: prior knowledge and non-prior knowledge. The students in prior knowledge condition were provided the basic information about the lecture content and were assessed their understanding by the experimenter before starting the lecture while the students in non-prior knowledge had none. The result shows that the interaction in prior knowledge condition establishes significantly higher mutual gaze convergence (t(15.03) = 6.72, p < 0.0001; α = 0.05, n = 20) and head nodding synchrony (t(16.67) = 1.83, p = 0.04; α = 0.05, n = 19) compared to non-prior knowledge condition. This study reveals that prior knowledge facilitates mutual gaze convergence and head nodding synchrony. Furthermore, the interaction with and without prior knowledge can be evaluated by measuring or observing mutual gaze convergence and head nodding synchrony. PMID:27910902

Prior Knowledge Facilitates Mutual Gaze Convergence and Head Nodding Synchrony in Face-to-face Communication.

PubMed

Thepsoonthorn, C; Yokozuka, T; Miura, S; Ogawa, K; Miyake, Y

2016-12-02

As prior knowledge is claimed to be an essential key to achieve effective education, we are interested in exploring whether prior knowledge enhances communication effectiveness. To demonstrate the effects of prior knowledge, mutual gaze convergence and head nodding synchrony are observed as indicators of communication effectiveness. We conducted an experiment on lecture task between lecturer and student under 2 conditions: prior knowledge and non-prior knowledge. The students in prior knowledge condition were provided the basic information about the lecture content and were assessed their understanding by the experimenter before starting the lecture while the students in non-prior knowledge had none. The result shows that the interaction in prior knowledge condition establishes significantly higher mutual gaze convergence (t(15.03) = 6.72, p < 0.0001; α = 0.05, n = 20) and head nodding synchrony (t(16.67) = 1.83, p = 0.04; α = 0.05, n = 19) compared to non-prior knowledge condition. This study reveals that prior knowledge facilitates mutual gaze convergence and head nodding synchrony. Furthermore, the interaction with and without prior knowledge can be evaluated by measuring or observing mutual gaze convergence and head nodding synchrony.

The Effects of Prior Knowledge Activation on Free Recall and Study Time Allocation.

ERIC Educational Resources Information Center

Machiels-Bongaerts, Maureen; And Others

The effects of mobilizing prior knowledge on information processing were studied. Two hypotheses, the cognitive set-point hypothesis and the selective attention hypothesis, try to account for the facilitation effects of prior knowledge activation. These hypotheses predict different recall patterns as a result of mobilizing prior knowledge. In…

Gene network biological validity based on gene-gene interaction relevance.

PubMed

Gómez-Vela, Francisco; Díaz-Díaz, Norberto

2014-01-01

In recent years, gene networks have become one of the most useful tools for modeling biological processes. Many inference gene network algorithms have been developed as techniques for extracting knowledge from gene expression data. Ensuring the reliability of the inferred gene relationships is a crucial task in any study in order to prove that the algorithms used are precise. Usually, this validation process can be carried out using prior biological knowledge. The metabolic pathways stored in KEGG are one of the most widely used knowledgeable sources for analyzing relationships between genes. This paper introduces a new methodology, GeneNetVal, to assess the biological validity of gene networks based on the relevance of the gene-gene interactions stored in KEGG metabolic pathways. Hence, a complete KEGG pathway conversion into a gene association network and a new matching distance based on gene-gene interaction relevance are proposed. The performance of GeneNetVal was established with three different experiments. Firstly, our proposal is tested in a comparative ROC analysis. Secondly, a randomness study is presented to show the behavior of GeneNetVal when the noise is increased in the input network. Finally, the ability of GeneNetVal to detect biological functionality of the network is shown.

Exploring the Impact of Prior Knowledge and Appropriate Feedback on Students' Perceived Cognitive Load and Learning Outcomes: Animation-Based Earthquakes Instruction

ERIC Educational Resources Information Center

Yeh, Ting-Kuang; Tseng, Kuan-Yun; Cho, Chung-Wen; Barufaldi, James P.; Lin, Mei-Shin; Chang, Chun-Yen

2012-01-01

The aim of this study was to develop an animation-based curriculum and to evaluate the effectiveness of animation-based instruction; the report involved the assessment of prior knowledge and the appropriate feedback approach, for the purpose of reducing perceived cognitive load and improving learning. The curriculum was comprised of five subunits…

Brief Report: Teachers' Awareness of the Relationship between Prior Knowledge and New Learning

ERIC Educational Resources Information Center

Journal for Research in Mathematics Education, 2016

2016-01-01

The author examined the degree to which experienced teachers are aware of the relationship between prior knowledge and new learning. Interviews with teachers revealed that they were explicitly aware of when students made connections between prior knowledge and new learning, when they applied their prior knowledge to new contexts, and when they…

"Dare I Ask?": Eliciting Prior Knowledge and Its Implications for Teaching and Learning

ERIC Educational Resources Information Center

Dávila, Liv Thorstensson

2015-01-01

This article examines high school teachers' engagement of newcomer English learner students' prior knowledge. Three central research questions guided this study: 1) To what extent do teachers function as mediators of their students' prior knowledge? 2) What goes into teachers' thinking about how and when to elicit prior knowledge? and 3) How do…

Incorporating Biological Knowledge into Evaluation of Casual Regulatory Hypothesis

NASA Technical Reports Server (NTRS)

Chrisman, Lonnie; Langley, Pat; Bay, Stephen; Pohorille, Andrew; DeVincenzi, D. (Technical Monitor)

2002-01-01

Biological data can be scarce and costly to obtain. The small number of samples available typically limits statistical power and makes reliable inference of causal relations extremely difficult. However, we argue that statistical power can be increased substantially by incorporating prior knowledge and data from diverse sources. We present a Bayesian framework that combines information from different sources and we show empirically that this lets one make correct causal inferences with small sample sizes that otherwise would be impossible.

Effect of Altered Prior Knowledge on Passage Recall.

ERIC Educational Resources Information Center

Langer, Judith A.; Nicolich, Mark

A study was conducted to determine: (1) the relationships between prior knowledge and passage recall; (2) the effect of a prereading activity (PReP) on available knowledge; and (3) the effect of the PReP activity on total comprehension scores. The subjects were 161 sixth grade students from a middle class suburban Long Island, New York, public…

Mind wandering during film comprehension: The role of prior knowledge and situational interest.

PubMed

Kopp, Kristopher; Mills, Caitlin; D'Mello, Sidney

2016-06-01

This study assessed the occurrence and factors that influence mind wandering (MW) in the domain of film comprehension. The cascading model of inattention assumes that a stronger mental representation (i.e., a situation model) during comprehension results in less MW. Accordingly, a suppression hypothesis suggests that MW would decrease as a function of having the knowledge of the plot of a film prior to viewing, because the prior-knowledge would help to strengthen the situation model during comprehension. Furthermore, an interest-moderation hypothesis would predict that the suppression effect of prior-knowledge would only emerge when there was interest in viewing the film. In the current experiment, 108 participants either read a short story that depicted the plot (i.e., prior-knowledge condition) or read an unrelated story of equal length (control condition) prior to viewing the short film (32.5 minutes) entitled The Red Balloon. Participants self-reported their interest in viewing the film immediately before the film was presented. MW was tracked using a self-report method targeting instances of MW with metacognitive awareness. Participants in the prior-knowledge condition reported less MW compared with the control condition, thereby supporting the suppression hypothesis. MW also decreased over the duration of the film, but only for those with prior-knowledge of the film. Finally, prior-knowledge effects on MW were only observed when interest was average or high, but not when interest was low.

Contribution of prior semantic knowledge to new episodic learning in amnesia.

PubMed

Kan, Irene P; Alexander, Michael P; Verfaellie, Mieke

2009-05-01

We evaluated whether prior semantic knowledge would enhance episodic learning in amnesia. Subjects studied prices that are either congruent or incongruent with prior price knowledge for grocery and household items and then performed a forced-choice recognition test for the studied prices. Consistent with a previous report, healthy controls' performance was enhanced by price knowledge congruency; however, only a subset of amnesic patients experienced the same benefit. Whereas patients with relatively intact semantic systems, as measured by an anatomical measure (i.e., lesion involvement of anterior and lateral temporal lobes), experienced a significant congruency benefit, patients with compromised semantic systems did not experience a congruency benefit. Our findings suggest that when prior knowledge structures are intact, they can support acquisition of new episodic information by providing frameworks into which such information can be incorporated.

Biological standards for the Knowledge-Based BioEconomy: What is at stake.

PubMed

de Lorenzo, Víctor; Schmidt, Markus

2018-01-25

The contribution of life sciences to the Knowledge-Based Bioeconomy (KBBE) asks for the transition of contemporary, gene-based biotechnology from being a trial-and-error endeavour to becoming an authentic branch of engineering. One requisite to this end is the need for standards to measure and represent accurately biological functions, along with languages for data description and exchange. However, the inherent complexity of biological systems and the lack of quantitative tradition in the field have largely curbed this enterprise. Fortunately, the onset of systems and synthetic biology has emphasized the need for standards not only to manage omics data, but also to increase reproducibility and provide the means of engineering living systems in earnest. Some domains of biotechnology can be easily standardized (e.g. physical composition of DNA sequences, tools for genome editing, languages to encode workflows), while others might be standardized with some dedicated research (e.g. biological metrology, operative systems for bio-programming cells) and finally others will require a considerable effort, e.g. defining the rules that allow functional composition of biological activities. Despite difficulties, these are worthy attempts, as the history of technology shows that those who set/adopt standards gain a competitive advantage over those who do not. Copyright © 2017 Elsevier B.V. All rights reserved.

Learners' strategies for reconstructing cognitive frameworks and navigating conceptual change from prior conception to consensual genetics knowledge

NASA Astrophysics Data System (ADS)

Parrott, Annette M.

Problem. Science teachers are charged with preparing students to become scientifically literate individuals. Teachers are given curriculum that specifies the knowledge that students should come away with; however, they are not necessarily aware of the knowledge with which the student arrives or how best to help them navigate between the two knowledge states. Educators must be aware, not only of where their students are conceptually, but how their students move from their prior knowledge and naive theories, to scientifically acceptable theories. The understanding of how students navigate this course has the potential to revolutionize educational practices. Methods. This study explored how five 9th grade biology students reconstructed their cognitive frameworks and navigated conceptual change from prior conception to consensual genetics knowledge. The research questions investigated were: (1) how do students in the process of changing their naive science theories to accepted science theories describe their journey from prior knowledge to current conception, and (2) what are the methods that students utilize to bridge the gap between alternate and consensual science conceptions to effect conceptual change. Qualitative and quantitative methods were employed to gather and analyze the data. In depth, semi-structured interviews formed the primary data for probing the context and details of students' conceptual change experience. Primary interview data was coded by thematic analysis. Results and discussion. This study revealed information about students' perceived roles in learning, the role of articulation in the conceptual change process, and ways in which a community of learners aids conceptual change. It was ascertained that students see their role in learning primarily as repeating information until they could add that information to their knowledge. Students are more likely to consider challenges to their conceptual frameworks and be more motivated to become active

Can Prior Knowledge Hurt Text Comprehension? An Answer Borrowed from Plato, Aristotle, and Descartes.

ERIC Educational Resources Information Center

Friedman, Lawrence B.

Taking a philosophical approach based on what Plato, Aristotle, and Descartes said about knowledge, this paper addresses some of the murkiness in the conceptual space surrounding the issue of whether prior knowledge does or does not facilitate text comprehension. Specifically, the paper first develops a non-exhaustive typology of cases in which…

Understanding the Role of Prior Knowledge in a Multimedia Learning Application

ERIC Educational Resources Information Center

Rias, Riaza Mohd; Zaman, Halimah Badioze

2013-01-01

This study looked at the effects that individual differences in prior knowledge have on student understanding in learning with multimedia in a computer science subject. Students were identified as having either low or high prior knowledge from a series of questions asked in a survey conducted at the Faculty of Computer and Mathematical Sciences at…

The Effect of the States of Prior Knowledge on Question Answering.

ERIC Educational Resources Information Center

Holmes, Betty C.

A study was conducted to gain insight into the question answering abilities of good and poor readers by comparing how well they answered questions when their prior knowledge was at two different levels (high, low) and in four different states. These states of prior knowledge consisted of the ways in which answers to the questions were stored in…

In silico model-based inference: a contemporary approach for hypothesis testing in network biology

PubMed Central

Klinke, David J.

2014-01-01

Inductive inference plays a central role in the study of biological systems where one aims to increase their understanding of the system by reasoning backwards from uncertain observations to identify causal relationships among components of the system. These causal relationships are postulated from prior knowledge as a hypothesis or simply a model. Experiments are designed to test the model. Inferential statistics are used to establish a level of confidence in how well our postulated model explains the acquired data. This iterative process, commonly referred to as the scientific method, either improves our confidence in a model or suggests that we revisit our prior knowledge to develop a new model. Advances in technology impact how we use prior knowledge and data to formulate models of biological networks and how we observe cellular behavior. However, the approach for model-based inference has remained largely unchanged since Fisher, Neyman and Pearson developed the ideas in the early 1900’s that gave rise to what is now known as classical statistical hypothesis (model) testing. Here, I will summarize conventional methods for model-based inference and suggest a contemporary approach to aid in our quest to discover how cells dynamically interpret and transmit information for therapeutic aims that integrates ideas drawn from high performance computing, Bayesian statistics, and chemical kinetics. PMID:25139179

In silico model-based inference: a contemporary approach for hypothesis testing in network biology.

PubMed

Klinke, David J

2014-01-01

Inductive inference plays a central role in the study of biological systems where one aims to increase their understanding of the system by reasoning backwards from uncertain observations to identify causal relationships among components of the system. These causal relationships are postulated from prior knowledge as a hypothesis or simply a model. Experiments are designed to test the model. Inferential statistics are used to establish a level of confidence in how well our postulated model explains the acquired data. This iterative process, commonly referred to as the scientific method, either improves our confidence in a model or suggests that we revisit our prior knowledge to develop a new model. Advances in technology impact how we use prior knowledge and data to formulate models of biological networks and how we observe cellular behavior. However, the approach for model-based inference has remained largely unchanged since Fisher, Neyman and Pearson developed the ideas in the early 1900s that gave rise to what is now known as classical statistical hypothesis (model) testing. Here, I will summarize conventional methods for model-based inference and suggest a contemporary approach to aid in our quest to discover how cells dynamically interpret and transmit information for therapeutic aims that integrates ideas drawn from high performance computing, Bayesian statistics, and chemical kinetics. © 2014 American Institute of Chemical Engineers.

Using Genetic Programming with Prior Formula Knowledge to Solve Symbolic Regression Problem.

PubMed

Lu, Qiang; Ren, Jun; Wang, Zhiguang

2016-01-01

A researcher can infer mathematical expressions of functions quickly by using his professional knowledge (called Prior Knowledge). But the results he finds may be biased and restricted to his research field due to limitation of his knowledge. In contrast, Genetic Programming method can discover fitted mathematical expressions from the huge search space through running evolutionary algorithms. And its results can be generalized to accommodate different fields of knowledge. However, since GP has to search a huge space, its speed of finding the results is rather slow. Therefore, in this paper, a framework of connection between Prior Formula Knowledge and GP (PFK-GP) is proposed to reduce the space of GP searching. The PFK is built based on the Deep Belief Network (DBN) which can identify candidate formulas that are consistent with the features of experimental data. By using these candidate formulas as the seed of a randomly generated population, PFK-GP finds the right formulas quickly by exploring the search space of data features. We have compared PFK-GP with Pareto GP on regression of eight benchmark problems. The experimental results confirm that the PFK-GP can reduce the search space and obtain the significant improvement in the quality of SR.

Signaling Text-Picture Relations in Multimedia Learning: The Influence of Prior Knowledge

ERIC Educational Resources Information Center

Richter, Juliane; Scheiter, Katharina; Eitel, Alexander

2018-01-01

Multimedia integration signals highlight correspondences between text and pictures with the aim of supporting learning from multimedia. A recent meta-analysis revealed that only learners with low domain-specific prior knowledge benefit from multimedia integration signals. To more thoroughly investigate the influence of prior knowledge on the…

Knowledge-making distinctions in synthetic biology.

PubMed

O'Malley, Maureen A; Powell, Alexander; Davies, Jonathan F; Calvert, Jane

2008-01-01

Synthetic biology is an increasingly high-profile area of research that can be understood as encompassing three broad approaches towards the synthesis of living systems: DNA-based device construction, genome-driven cell engineering and protocell creation. Each approach is characterized by different aims, methods and constructs, in addition to a range of positions on intellectual property and regulatory regimes. We identify subtle but important differences between the schools in relation to their treatments of genetic determinism, cellular context and complexity. These distinctions tie into two broader issues that define synthetic biology: the relationships between biology and engineering, and between synthesis and analysis. These themes also illuminate synthetic biology's connections to genetic and other forms of biological engineering, as well as to systems biology. We suggest that all these knowledge-making distinctions in synthetic biology raise fundamental questions about the nature of biological investigation and its relationship to the construction of biological components and systems. (c) 2007 Wiley Periodicals, Inc.

Feedback Both Helps and Hinders Learning: The Causal Role of Prior Knowledge

ERIC Educational Resources Information Center

Fyfe, Emily R.; Rittle-Johnson, Bethany

2016-01-01

Feedback can be a powerful learning tool, but its effects vary widely. Research has suggested that learners' prior knowledge may moderate the effects of feedback; however, no causal link has been established. In Experiment 1, we randomly assigned elementary school children (N = 108) to a condition based on a crossing of 2 factors: induced strategy…

The Role of Prior Knowledge in Learning from Analogies in Science Texts

ERIC Educational Resources Information Center

Braasch, Jason L. G.; Goldman, Susan R.

2010-01-01

Two experiments examined whether inconsistent effects of analogies in promoting new content learning from text are related to prior knowledge of the analogy "per se." In Experiment 1, college students who demonstrated little understanding of weather systems and different levels of prior knowledge (more vs. less) of an analogous everyday…

Main Road Extraction from ZY-3 Grayscale Imagery Based on Directional Mathematical Morphology and VGI Prior Knowledge in Urban Areas

PubMed Central

Liu, Bo; Wu, Huayi; Wang, Yandong; Liu, Wenming

2015-01-01

Main road features extracted from remotely sensed imagery play an important role in many civilian and military applications, such as updating Geographic Information System (GIS) databases, urban structure analysis, spatial data matching and road navigation. Current methods for road feature extraction from high-resolution imagery are typically based on threshold value segmentation. It is difficult however, to completely separate road features from the background. We present a new method for extracting main roads from high-resolution grayscale imagery based on directional mathematical morphology and prior knowledge obtained from the Volunteered Geographic Information found in the OpenStreetMap. The two salient steps in this strategy are: (1) using directional mathematical morphology to enhance the contrast between roads and non-roads; (2) using OpenStreetMap roads as prior knowledge to segment the remotely sensed imagery. Experiments were conducted on two ZiYuan-3 images and one QuickBird high-resolution grayscale image to compare our proposed method to other commonly used techniques for road feature extraction. The results demonstrated the validity and better performance of the proposed method for urban main road feature extraction. PMID:26397832

Synthetic biology between technoscience and thing knowledge.

PubMed

Gelfert, Axel

2013-06-01

Synthetic biology presents a challenge to traditional accounts of biology: Whereas traditional biology emphasizes the evolvability, variability, and heterogeneity of living organisms, synthetic biology envisions a future of homogeneous, humanly engineered biological systems that may be combined in modular fashion. The present paper approaches this challenge from the perspective of the epistemology of technoscience. In particular, it is argued that synthetic-biological artifacts lend themselves to an analysis in terms of what has been called 'thing knowledge'. As such, they should neither be regarded as the simple outcome of applying theoretical knowledge and engineering principles to specific technological problems, nor should they be treated as mere sources of new evidence in the general pursuit of scientific understanding. Instead, synthetic-biological artifacts should be viewed as partly autonomous research objects which, qua their material-biological constitution, embody knowledge about the natural world-knowledge that, in turn, can be accessed via continuous experimental interrogation. Copyright © 2013 Elsevier Ltd. All rights reserved.

Prior-based artifact correction (PBAC) in computed tomography

DOE Office of Scientific and Technical Information (OSTI.GOV)

Heußer, Thorsten, E-mail: thorsten.heusser@dkfz-heidelberg.de; Brehm, Marcus; Ritschl, Ludwig

2014-02-15

Purpose: Image quality in computed tomography (CT) often suffers from artifacts which may reduce the diagnostic value of the image. In many cases, these artifacts result from missing or corrupt regions in the projection data, e.g., in the case of metal, truncation, and limited angle artifacts. The authors propose a generalized correction method for different kinds of artifacts resulting from missing or corrupt data by making use of available prior knowledge to perform data completion. Methods: The proposed prior-based artifact correction (PBAC) method requires prior knowledge in form of a planning CT of the same patient or in form ofmore » a CT scan of a different patient showing the same body region. In both cases, the prior image is registered to the patient image using a deformable transformation. The registered prior is forward projected and data completion of the patient projections is performed using smooth sinogram inpainting. The obtained projection data are used to reconstruct the corrected image. Results: The authors investigate metal and truncation artifacts in patient data sets acquired with a clinical CT and limited angle artifacts in an anthropomorphic head phantom data set acquired with a gantry-based flat detector CT device. In all cases, the corrected images obtained by PBAC are nearly artifact-free. Compared to conventional correction methods, PBAC achieves better artifact suppression while preserving the patient-specific anatomy at the same time. Further, the authors show that prominent anatomical details in the prior image seem to have only minor impact on the correction result. Conclusions: The results show that PBAC has the potential to effectively correct for metal, truncation, and limited angle artifacts if adequate prior data are available. Since the proposed method makes use of a generalized algorithm, PBAC may also be applicable to other artifacts resulting from missing or corrupt data.« less

Prior knowledge-based approach for associating contaminants with biological effects: A case study in the St. Croix River basin, MN, WI, USA

USGS Publications Warehouse

Schroeder, Anthony L.; Martinovic-Weigelt, Dalma; Ankley, Gerald T.; Lee, Kathy E.; Garcia-Reyero, Natalia; Perkins, Edward J.; Schoenfuss, Heiko L.; Villeneuve, Daniel L.

2017-01-01

Evaluating potential adverse effects of complex chemical mixtures in the environment is challenging. One way to address that challenge is through more integrated analysis of chemical monitoring and biological effects data. In the present study, water samples from five locations near two municipal wastewater treatment plants in the St. Croix River basin, on the border of MN and WI, USA, were analyzed for 127 organic contaminants. Known chemical-gene interactions were used to develop site-specific knowledge assembly models (KAMs) and formulate hypotheses concerning possible biological effects associated with chemicals detected in water samples from each location. Additionally, hepatic gene expression data were collected for fathead minnows (Pimephales promelas) exposed in situ, for 12 d, at each location. Expression data from oligonucleotide microarrays were analyzed to identify functional annotation terms enriched among the differentially-expressed probes. The general nature of many of the terms made hypothesis formulation on the basis of the transcriptome-level response alone difficult. However, integrated analysis of the transcriptome data in the context of the site-specific KAMs allowed for evaluation of the likelihood of specific chemicals contributing to observed biological responses. Thirteen chemicals (atrazine, carbamazepine, metformin, thiabendazole, diazepam, cholesterol, p-cresol, phenytoin, omeprazole, ethyromycin, 17β-estradiol, cimetidine, and estrone), for which there was statistically significant concordance between occurrence at a site and expected biological response as represented in the KAM, were identified. While not definitive, the approach provides a line of evidence for evaluating potential cause-effect relationships between components of a complex mixture of contaminants and biological effects data, which can inform subsequent monitoring and investigation.

Prior knowledge-based approach for associating contaminants with biological effects: A case study in the St. Croix River basin, MN, WI, USA.

PubMed

Schroeder, Anthony L; Martinović-Weigelt, Dalma; Ankley, Gerald T; Lee, Kathy E; Garcia-Reyero, Natalia; Perkins, Edward J; Schoenfuss, Heiko L; Villeneuve, Daniel L

2017-02-01

Evaluating potential adverse effects of complex chemical mixtures in the environment is challenging. One way to address that challenge is through more integrated analysis of chemical monitoring and biological effects data. In the present study, water samples from five locations near two municipal wastewater treatment plants in the St. Croix River basin, on the border of MN and WI, USA, were analyzed for 127 organic contaminants. Known chemical-gene interactions were used to develop site-specific knowledge assembly models (KAMs) and formulate hypotheses concerning possible biological effects associated with chemicals detected in water samples from each location. Additionally, hepatic gene expression data were collected for fathead minnows (Pimephales promelas) exposed in situ, for 12 d, at each location. Expression data from oligonucleotide microarrays were analyzed to identify functional annotation terms enriched among the differentially-expressed probes. The general nature of many of the terms made hypothesis formulation on the basis of the transcriptome-level response alone difficult. However, integrated analysis of the transcriptome data in the context of the site-specific KAMs allowed for evaluation of the likelihood of specific chemicals contributing to observed biological responses. Thirteen chemicals (atrazine, carbamazepine, metformin, thiabendazole, diazepam, cholesterol, p-cresol, phenytoin, omeprazole, ethyromycin, 17β-estradiol, cimetidine, and estrone), for which there was statistically significant concordance between occurrence at a site and expected biological response as represented in the KAM, were identified. While not definitive, the approach provides a line of evidence for evaluating potential cause-effect relationships between components of a complex mixture of contaminants and biological effects data, which can inform subsequent monitoring and investigation. Published by Elsevier Ltd.

Network analysis reveals stage-specific changes in zebrafish embryo development using time course whole transcriptome profiling and prior biological knowledge.

PubMed

Zhang, Yuji

2015-01-01

Molecular networks act as the backbone of molecular activities within cells, offering a unique opportunity to better understand the mechanism of diseases. While network data usually constitute only static network maps, integrating them with time course gene expression information can provide clues to the dynamic features of these networks and unravel the mechanistic driver genes characterizing cellular responses. Time course gene expression data allow us to broadly "watch" the dynamics of the system. However, one challenge in the analysis of such data is to establish and characterize the interplay among genes that are altered at different time points in the context of a biological process or functional category. Integrative analysis of these data sources will lead us a more complete understanding of how biological entities (e.g., genes and proteins) coordinately perform their biological functions in biological systems. In this paper, we introduced a novel network-based approach to extract functional knowledge from time-dependent biological processes at a system level using time course mRNA sequencing data in zebrafish embryo development. The proposed method was applied to investigate 1α, 25(OH)2D3-altered mechanisms in zebrafish embryo development. We applied the proposed method to a public zebrafish time course mRNA-Seq dataset, containing two different treatments along four time points. We constructed networks between gene ontology biological process categories, which were enriched in differential expressed genes between consecutive time points and different conditions. The temporal propagation of 1α, 25-Dihydroxyvitamin D3-altered transcriptional changes started from a few genes that were altered initially at earlier stage, to large groups of biological coherent genes at later stages. The most notable biological processes included neuronal and retinal development and generalized stress response. In addition, we also investigated the relationship among

Students' inductive reasoning skills and the relevance of prior knowledge: an exploratory study with a computer-based training course on the topic of acne vulgaris.

PubMed

Horn-Ritzinger, Sabine; Bernhardt, Johannes; Horn, Michael; Smolle, Josef

2011-04-01

The importance of inductive instruction in medical education is increasingly growing. Little is known about the relevance of prior knowledge regarding students' inductive reasoning abilities. The purpose is to evaluate this inductive teaching method as a means of fostering higher levels of learning and to explore how individual differences in prior knowledge (high [HPK] vs. low [LPK]) contribute to students' inductive reasoning skills. Twenty-six LPK and 18 HPK students could train twice with an interactive computer-based training object to discover the underlying concept before doing the final comprehension check. Students had a median of 76.9% of correct answers in the first, 90.9% in the second training, and answered 92% of the final assessment questions correctly. More important, 86% of all students succeeded with inductive learning, among them 83% of the HPK students and 89% of the LPK students. Prior knowledge did not predict performance on overall comprehension. This inductive instructional strategy fostered students' deep approaches to learning in a time-effective way.

Science Literacy and Prior Knowledge of Astronomy MOOC Students

NASA Astrophysics Data System (ADS)

Impey, Chris David; Buxner, Sanlyn; Wenger, Matthew; Formanek, Martin

2018-01-01

Many of science classes offered on Coursera fall into fall into the category of general education or general interest classes for lifelong learners, including our own, Astronomy: Exploring Time and Space. Very little is known about the backgrounds and prior knowledge of these students. In this talk we present the results of a survey of our Astronomy MOOC students. We also compare these results to our previous work on undergraduate students in introductory astronomy courses. Survey questions examined student demographics and motivations as well as their science and information literacy (including basic science knowledge, interest, attitudes and beliefs, and where they get their information about science). We found that our MOOC students are different than the undergraduate students in more ways than demographics. Many MOOC students demonstrated high levels of science and information literacy. With a more comprehensive understanding of our students’ motivations and prior knowledge about science and how they get their information about science, we will be able to develop more tailored learning experiences for these lifelong learners.

SVS: data and knowledge integration in computational biology.

PubMed

Zycinski, Grzegorz; Barla, Annalisa; Verri, Alessandro

2011-01-01

In this paper we present a framework for structured variable selection (SVS). The main concept of the proposed schema is to take a step towards the integration of two different aspects of data mining: database and machine learning perspective. The framework is flexible enough to use not only microarray data, but other high-throughput data of choice (e.g. from mass spectrometry, microarray, next generation sequencing). Moreover, the feature selection phase incorporates prior biological knowledge in a modular way from various repositories and is ready to host different statistical learning techniques. We present a proof of concept of SVS, illustrating some implementation details and describing current results on high-throughput microarray data.

Incorporating prior knowledge induced from stochastic differential equations in the classification of stochastic observations.

PubMed

Zollanvari, Amin; Dougherty, Edward R

2016-12-01

In classification, prior knowledge is incorporated in a Bayesian framework by assuming that the feature-label distribution belongs to an uncertainty class of feature-label distributions governed by a prior distribution. A posterior distribution is then derived from the prior and the sample data. An optimal Bayesian classifier (OBC) minimizes the expected misclassification error relative to the posterior distribution. From an application perspective, prior construction is critical. The prior distribution is formed by mapping a set of mathematical relations among the features and labels, the prior knowledge, into a distribution governing the probability mass across the uncertainty class. In this paper, we consider prior knowledge in the form of stochastic differential equations (SDEs). We consider a vector SDE in integral form involving a drift vector and dispersion matrix. Having constructed the prior, we develop the optimal Bayesian classifier between two models and examine, via synthetic experiments, the effects of uncertainty in the drift vector and dispersion matrix. We apply the theory to a set of SDEs for the purpose of differentiating the evolutionary history between two species.

Semantically-based priors and nuanced knowledge core for Big Data, Social AI, and language understanding.

PubMed

Olsher, Daniel

2014-10-01

Noise-resistant and nuanced, COGBASE makes 10 million pieces of commonsense data and a host of novel reasoning algorithms available via a family of semantically-driven prior probability distributions. Machine learning, Big Data, natural language understanding/processing, and social AI can draw on COGBASE to determine lexical semantics, infer goals and interests, simulate emotion and affect, calculate document gists and topic models, and link commonsense knowledge to domain models and social, spatial, cultural, and psychological data. COGBASE is especially ideal for social Big Data, which tends to involve highly implicit contexts, cognitive artifacts, difficult-to-parse texts, and deep domain knowledge dependencies. Copyright © 2014 Elsevier Ltd. All rights reserved.

Interactive web-based learning modules prior to general medicine advanced pharmacy practice experiences.

PubMed

Isaacs, Alex N; Walton, Alison M; Nisly, Sarah A

2015-04-25

To implement and evaluate interactive web-based learning modules prior to advanced pharmacy practice experiences (APPEs) on inpatient general medicine. Three clinical web-based learning modules were developed for use prior to APPEs in 4 health care systems. The aim of the interactive modules was to strengthen baseline clinical knowledge before the APPE to enable the application of learned material through the delivery of patient care. For the primary endpoint, postassessment scores increased overall and for each individual module compared to preassessment scores. Postassessment scores were similar among the health care systems. The survey demonstrated positive student perceptions of this learning experience. Prior to inpatient general medicine APPEs, web-based learning enabled the standardization and assessment of baseline student knowledge across 4 health care systems.

Acquired prior knowledge modulates audiovisual integration.

PubMed

Van Wanrooij, Marc M; Bremen, Peter; John Van Opstal, A

2010-05-01

Orienting responses to audiovisual events in the environment can benefit markedly by the integration of visual and auditory spatial information. However, logically, audiovisual integration would only be considered successful for stimuli that are spatially and temporally aligned, as these would be emitted by a single object in space-time. As humans do not have prior knowledge about whether novel auditory and visual events do indeed emanate from the same object, such information needs to be extracted from a variety of sources. For example, expectation about alignment or misalignment could modulate the strength of multisensory integration. If evidence from previous trials would repeatedly favour aligned audiovisual inputs, the internal state might also assume alignment for the next trial, and hence react to a new audiovisual event as if it were aligned. To test for such a strategy, subjects oriented a head-fixed pointer as fast as possible to a visual flash that was consistently paired, though not always spatially aligned, with a co-occurring broadband sound. We varied the probability of audiovisual alignment between experiments. Reaction times were consistently lower in blocks containing only aligned audiovisual stimuli than in blocks also containing pseudorandomly presented spatially disparate stimuli. Results demonstrate dynamic updating of the subject's prior expectation of audiovisual congruency. We discuss a model of prior probability estimation to explain the results.

Learning gait of quadruped robot without prior knowledge of the environment

NASA Astrophysics Data System (ADS)

Xu, Tao; Chen, Qijun

2012-09-01

Walking is the basic skill of a legged robot, and one of the promising ways to improve the walking performance and its adaptation to environment changes is to let the robot learn its walking by itself. Currently, most of the walking learning methods are based on robot vision system or some external sensing equipment to estimate the walking performance of certain walking parameters, and therefore are usually only applicable under laboratory condition, where environment can be pre-defined. Inspired by the rhythmic swing movement during walking of legged animals and the behavior of their adjusting their walking gait on different walking surfaces, a concept of walking rhythmic pattern(WRP) is proposed to evaluate the walking specialty of legged robot, which is just based on the walking dynamics of the robot. Based on the onboard acceleration sensor data, a method to calculate WRP using power spectrum in frequency domain and diverse smooth filters is also presented. Since the evaluation of WRP is only based on the walking dynamics data of the robot's body, the proposed method doesn't require prior knowledge of environment and thus can be applied in unknown environment. A gait learning approach of legged robots based on WRP and evolution algorithm(EA) is introduced. By using the proposed approach, a quadruped robot can learn its locomotion by its onboard sensing in an unknown environment, where the robot has no prior knowledge about this place. The experimental result proves proportional relationship exits between WRP match score and walking performance of legged robot, which can be used to evaluate the walking performance in walking optimization under unknown environment.

Age differences in suggestibility to contradictions of demonstrated knowledge: the influence of prior knowledge.

PubMed

Umanath, Sharda

2016-11-01

People maintain intact general knowledge into very old age and use it to support remembering. Interestingly, when older and younger adults encounter errors that contradict general knowledge, older adults suffer fewer memorial consequences: Older adults use fewer recently-encountered errors as answers for later knowledge questions. Why do older adults show this reduced suggestibility, and what role does their intact knowledge play? In three experiments, I examined suggestibility following exposure to errors in fictional stories that contradict general knowledge. Older adults consistently demonstrated more prior knowledge than younger adults but also gained access to even more across time. Additionally, they did not show a reduction in new learning from the stories, indicating lesser involvement of episodic memory failures. Critically, when knowledge was stably accessible, older adults relied more heavily on that knowledge compared to younger adults, resulting in reduced suggestibility. Implications for the broader role of knowledge in aging are discussed.

MRAC Control with Prior Model Knowledge for Asymmetric Damaged Aircraft

PubMed Central

Zhang, Jing

2015-01-01

This paper develops a novel state-tracking multivariable model reference adaptive control (MRAC) technique utilizing prior knowledge of plant models to recover control performance of an asymmetric structural damaged aircraft. A modification of linear model representation is given. With prior knowledge on structural damage, a polytope linear parameter varying (LPV) model is derived to cover all concerned damage conditions. An MRAC method is developed for the polytope model, of which the stability and asymptotic error convergence are theoretically proved. The proposed technique reduces the number of parameters to be adapted and thus decreases computational cost and requires less input information. The method is validated by simulations on NASA generic transport model (GTM) with damage. PMID:26180839

An Optimization-Based Framework for the Transformation of Incomplete Biological Knowledge into a Probabilistic Structure and Its Application to the Utilization of Gene/Protein Signaling Pathways in Discrete Phenotype Classification.

PubMed

Esfahani, Mohammad Shahrokh; Dougherty, Edward R

2015-01-01

Phenotype classification via genomic data is hampered by small sample sizes that negatively impact classifier design. Utilization of prior biological knowledge in conjunction with training data can improve both classifier design and error estimation via the construction of the optimal Bayesian classifier. In the genomic setting, gene/protein signaling pathways provide a key source of biological knowledge. Although these pathways are neither complete, nor regulatory, with no timing associated with them, they are capable of constraining the set of possible models representing the underlying interaction between molecules. The aim of this paper is to provide a framework and the mathematical tools to transform signaling pathways to prior probabilities governing uncertainty classes of feature-label distributions used in classifier design. Structural motifs extracted from the signaling pathways are mapped to a set of constraints on a prior probability on a Multinomial distribution. Being the conjugate prior for the Multinomial distribution, we propose optimization paradigms to estimate the parameters of a Dirichlet distribution in the Bayesian setting. The performance of the proposed methods is tested on two widely studied pathways: mammalian cell cycle and a p53 pathway model.

Mathematical learning models that depend on prior knowledge and instructional strategies

NASA Astrophysics Data System (ADS)

Pritchard, David E.; Lee, Young-Jin; Bao, Lei

2008-06-01

We present mathematical learning models—predictions of student’s knowledge vs amount of instruction—that are based on assumptions motivated by various theories of learning: tabula rasa, constructivist, and tutoring. These models predict the improvement (on the post-test) as a function of the pretest score due to intervening instruction and also depend on the type of instruction. We introduce a connectedness model whose connectedness parameter measures the degree to which the rate of learning is proportional to prior knowledge. Over a wide range of pretest scores on standard tests of introductory physics concepts, it fits high-quality data nearly within error. We suggest that data from MIT have low connectedness (indicating memory-based learning) because the test used the same context and representation as the instruction and that more connected data from the University of Minnesota resulted from instruction in a different representation from the test.

Features of Knowledge Building in Biology: Understanding Undergraduate Students’ Ideas about Molecular Mechanisms

PubMed Central

Southard, Katelyn; Wince, Tyler; Meddleton, Shanice; Bolger, Molly S.

2016-01-01

Research has suggested that teaching and learning in molecular and cellular biology (MCB) is difficult. We used a new lens to understand undergraduate reasoning about molecular mechanisms: the knowledge-integration approach to conceptual change. Knowledge integration is the dynamic process by which learners acquire new ideas, develop connections between ideas, and reorganize and restructure prior knowledge. Semistructured, clinical think-aloud interviews were conducted with introductory and upper-division MCB students. Interviews included a written conceptual assessment, a concept-mapping activity, and an opportunity to explain the biomechanisms of DNA replication, transcription, and translation. Student reasoning patterns were explored through mixed-method analyses. Results suggested that students must sort mechanistic entities into appropriate mental categories that reflect the nature of MCB mechanisms and that conflation between these categories is common. We also showed how connections between molecular mechanisms and their biological roles are part of building an integrated knowledge network as students develop expertise. We observed differences in the nature of connections between ideas related to different forms of reasoning. Finally, we provide a tentative model for MCB knowledge integration and suggest its implications for undergraduate learning. PMID:26931398

Features of Knowledge Building in Biology: Understanding Undergraduate Students' Ideas about Molecular Mechanisms.

PubMed

Southard, Katelyn; Wince, Tyler; Meddleton, Shanice; Bolger, Molly S

2016-01-01

Research has suggested that teaching and learning in molecular and cellular biology (MCB) is difficult. We used a new lens to understand undergraduate reasoning about molecular mechanisms: the knowledge-integration approach to conceptual change. Knowledge integration is the dynamic process by which learners acquire new ideas, develop connections between ideas, and reorganize and restructure prior knowledge. Semistructured, clinical think-aloud interviews were conducted with introductory and upper-division MCB students. Interviews included a written conceptual assessment, a concept-mapping activity, and an opportunity to explain the biomechanisms of DNA replication, transcription, and translation. Student reasoning patterns were explored through mixed-method analyses. Results suggested that students must sort mechanistic entities into appropriate mental categories that reflect the nature of MCB mechanisms and that conflation between these categories is common. We also showed how connections between molecular mechanisms and their biological roles are part of building an integrated knowledge network as students develop expertise. We observed differences in the nature of connections between ideas related to different forms of reasoning. Finally, we provide a tentative model for MCB knowledge integration and suggest its implications for undergraduate learning. © 2016 K. Southard et al. CBE—Life Sciences Education © 2016 The American Society for Cell Biology. This article is distributed by The American Society for Cell Biology under license from the author(s). It is available to the public under an Attribution–Noncommercial–Share Alike 3.0 Unported Creative Commons License (http://creativecommons.org/licenses/by-nc-sa/3.0).

Translational systems biology using an agent-based approach for dynamic knowledge representation: An evolutionary paradigm for biomedical research.

PubMed

An, Gary C

2010-01-01

The greatest challenge facing the biomedical research community is the effective translation of basic mechanistic knowledge into clinically effective therapeutics. This challenge is most evident in attempts to understand and modulate "systems" processes/disorders, such as sepsis, cancer, and wound healing. Formulating an investigatory strategy for these issues requires the recognition that these are dynamic processes. Representation of the dynamic behavior of biological systems can aid in the investigation of complex pathophysiological processes by augmenting existing discovery procedures by integrating disparate information sources and knowledge. This approach is termed Translational Systems Biology. Focusing on the development of computational models capturing the behavior of mechanistic hypotheses provides a tool that bridges gaps in the understanding of a disease process by visualizing "thought experiments" to fill those gaps. Agent-based modeling is a computational method particularly well suited to the translation of mechanistic knowledge into a computational framework. Utilizing agent-based models as a means of dynamic hypothesis representation will be a vital means of describing, communicating, and integrating community-wide knowledge. The transparent representation of hypotheses in this dynamic fashion can form the basis of "knowledge ecologies," where selection between competing hypotheses will apply an evolutionary paradigm to the development of community knowledge.

Automated quantitative assessment of proteins' biological function in protein knowledge bases.

PubMed

Mayr, Gabriele; Lepperdinger, Günter; Lackner, Peter

2008-01-01

Primary protein sequence data are archived in databases together with information regarding corresponding biological functions. In this respect, UniProt/Swiss-Prot is currently the most comprehensive collection and it is routinely cross-examined when trying to unravel the biological role of hypothetical proteins. Bioscientists frequently extract single entries and further evaluate those on a subjective basis. In lieu of a standardized procedure for scoring the existing knowledge regarding individual proteins, we here report about a computer-assisted method, which we applied to score the present knowledge about any given Swiss-Prot entry. Applying this quantitative score allows the comparison of proteins with respect to their sequence yet highlights the comprehension of functional data. pfs analysis may be also applied for quality control of individual entries or for database management in order to rank entry listings.

Learning Using Dynamic and Static Visualizations: Students' Comprehension, Prior Knowledge and Conceptual Status of a Biotechnological Method

NASA Astrophysics Data System (ADS)

Yarden, Hagit; Yarden, Anat

2010-05-01

The importance of biotechnology education at the high-school level has been recognized in a number of international curriculum frameworks around the world. One of the most problematic issues in learning biotechnology has been found to be the biotechnological methods involved. Here, we examine the unique contribution of an animation of the polymerase chain reaction (PCR) in promoting conceptual learning of the biotechnological method among 12th-grade biology majors. All of the students learned about the PCR using still images ( n = 83) or the animation ( n = 90). A significant advantage to the animation treatment was identified following learning. Students’ prior content knowledge was found to be an important factor for students who learned PCR using still images, serving as an obstacle to learning the PCR method in the case of low prior knowledge. Through analysing students’ discourse, using the framework of the conceptual status analysis, we found that students who learned about PCR using still images faced difficulties in understanding some mechanistic aspects of the method. On the other hand, using the animation gave the students an advantage in understanding those aspects.

Reputation-based collaborative network biology.

PubMed

Binder, Jean; Boue, Stephanie; Di Fabio, Anselmo; Fields, R Brett; Hayes, William; Hoeng, Julia; Park, Jennifer S; Peitsch, Manuel C

2015-01-01

A pilot reputation-based collaborative network biology platform, Bionet, was developed for use in the sbv IMPROVER Network Verification Challenge to verify and enhance previously developed networks describing key aspects of lung biology. Bionet was successful in capturing a more comprehensive view of the biology associated with each network using the collective intelligence and knowledge of the crowd. One key learning point from the pilot was that using a standardized biological knowledge representation language such as BEL is critical to the success of a collaborative network biology platform. Overall, Bionet demonstrated that this approach to collaborative network biology is highly viable. Improving this platform for de novo creation of biological networks and network curation with the suggested enhancements for scalability will serve both academic and industry systems biology communities.

The Effectiveness of Worked Examples Associated with Presentation Format and Prior Knowledge: A Web-Based Experiment

ERIC Educational Resources Information Center

Hsiao, E-Ling

2010-01-01

The aim of this study is to explore whether presentation format and prior knowledge affect the effectiveness of worked examples. The experiment was conducted through a specially designed online instrument. A 2X2X3 factorial before-and-after design was conducted. Three-way ANOVA was employed for data analysis. The result showed first, that prior…

Indications of Knowledge Retention in the Transition to Higher Education

ERIC Educational Resources Information Center

Jones, Harriet; Black, Beth; Green, Jon; Langton, Phil; Rutherford, Stephen; Scott, Jon; Brown, Sally

2015-01-01

First year undergraduate courses in higher education tend to be designed based on assumptions of students' prior knowledge. Almost 600 undergraduates at five UK universities, studying biological sciences, were given an MCQ test in their first week at university, based on biology A-level (pre-university examination) core criteria. Results…

Conceptual Model-Based Systems Biology: Mapping Knowledge and Discovering Gaps in the mRNA Transcription Cycle

PubMed Central

Somekh, Judith; Choder, Mordechai; Dori, Dov

2012-01-01

We propose a Conceptual Model-based Systems Biology framework for qualitative modeling, executing, and eliciting knowledge gaps in molecular biology systems. The framework is an adaptation of Object-Process Methodology (OPM), a graphical and textual executable modeling language. OPM enables concurrent representation of the system's structure—the objects that comprise the system, and behavior—how processes transform objects over time. Applying a top-down approach of recursively zooming into processes, we model a case in point—the mRNA transcription cycle. Starting with this high level cell function, we model increasingly detailed processes along with participating objects. Our modeling approach is capable of modeling molecular processes such as complex formation, localization and trafficking, molecular binding, enzymatic stimulation, and environmental intervention. At the lowest level, similar to the Gene Ontology, all biological processes boil down to three basic molecular functions: catalysis, binding/dissociation, and transporting. During modeling and execution of the mRNA transcription model, we discovered knowledge gaps, which we present and classify into various types. We also show how model execution enhances a coherent model construction. Identification and pinpointing knowledge gaps is an important feature of the framework, as it suggests where research should focus and whether conjectures about uncertain mechanisms fit into the already verified model. PMID:23308089

Knowledge-based nonuniform sampling in multidimensional NMR.

PubMed

Schuyler, Adam D; Maciejewski, Mark W; Arthanari, Haribabu; Hoch, Jeffrey C

2011-07-01

The full resolution afforded by high-field magnets is rarely realized in the indirect dimensions of multidimensional NMR experiments because of the time cost of uniformly sampling to long evolution times. Emerging methods utilizing nonuniform sampling (NUS) enable high resolution along indirect dimensions by sampling long evolution times without sampling at every multiple of the Nyquist sampling interval. While the earliest NUS approaches matched the decay of sampling density to the decay of the signal envelope, recent approaches based on coupled evolution times attempt to optimize sampling by choosing projection angles that increase the likelihood of resolving closely-spaced resonances. These approaches employ knowledge about chemical shifts to predict optimal projection angles, whereas prior applications of tailored sampling employed only knowledge of the decay rate. In this work we adapt the matched filter approach as a general strategy for knowledge-based nonuniform sampling that can exploit prior knowledge about chemical shifts and is not restricted to sampling projections. Based on several measures of performance, we find that exponentially weighted random sampling (envelope matched sampling) performs better than shift-based sampling (beat matched sampling). While shift-based sampling can yield small advantages in sensitivity, the gains are generally outweighed by diminished robustness. Our observation that more robust sampling schemes are only slightly less sensitive than schemes highly optimized using prior knowledge about chemical shifts has broad implications for any multidimensional NMR study employing NUS. The results derived from simulated data are demonstrated with a sample application to PfPMT, the phosphoethanolamine methyltransferase of the human malaria parasite Plasmodium falciparum.

The Relation between Prior Knowledge and Students' Collaborative Discovery Learning Processes

ERIC Educational Resources Information Center

Gijlers, Hannie; de Jong, Ton

2005-01-01

In this study we investigate how prior knowledge influences knowledge development during collaborative discovery learning. Fifteen dyads of students (pre-university education, 15-16 years old) worked on a discovery learning task in the physics field of kinematics. The (face-to-face) communication between students was recorded and the interaction…

Inference of Gene Regulatory Networks Incorporating Multi-Source Biological Knowledge via a State Space Model with L1 Regularization

PubMed Central

Hasegawa, Takanori; Yamaguchi, Rui; Nagasaki, Masao; Miyano, Satoru; Imoto, Seiya

2014-01-01

Comprehensive understanding of gene regulatory networks (GRNs) is a major challenge in the field of systems biology. Currently, there are two main approaches in GRN analysis using time-course observation data, namely an ordinary differential equation (ODE)-based approach and a statistical model-based approach. The ODE-based approach can generate complex dynamics of GRNs according to biologically validated nonlinear models. However, it cannot be applied to ten or more genes to simultaneously estimate system dynamics and regulatory relationships due to the computational difficulties. The statistical model-based approach uses highly abstract models to simply describe biological systems and to infer relationships among several hundreds of genes from the data. However, the high abstraction generates false regulations that are not permitted biologically. Thus, when dealing with several tens of genes of which the relationships are partially known, a method that can infer regulatory relationships based on a model with low abstraction and that can emulate the dynamics of ODE-based models while incorporating prior knowledge is urgently required. To accomplish this, we propose a method for inference of GRNs using a state space representation of a vector auto-regressive (VAR) model with L1 regularization. This method can estimate the dynamic behavior of genes based on linear time-series modeling constructed from an ODE-based model and can infer the regulatory structure among several tens of genes maximizing prediction ability for the observational data. Furthermore, the method is capable of incorporating various types of existing biological knowledge, e.g., drug kinetics and literature-recorded pathways. The effectiveness of the proposed method is shown through a comparison of simulation studies with several previous methods. For an application example, we evaluated mRNA expression profiles over time upon corticosteroid stimulation in rats, thus incorporating corticosteroid

Prior knowledge of category size impacts visual search.

PubMed

Wu, Rachel; McGee, Brianna; Echiverri, Chelsea; Zinszer, Benjamin D

2018-03-30

Prior research has shown that category search can be similar to one-item search (as measured by the N2pc ERP marker of attentional selection) for highly familiar, smaller categories (e.g., letters and numbers) because the finite set of items in a category can be grouped into one unit to guide search. Other studies have shown that larger, more broadly defined categories (e.g., healthy food) also can elicit N2pc components during category search, but the amplitude of these components is typically attenuated. Two experiments investigated whether the perceived size of a familiar category impacts category and exemplar search. We presented participants with 16 familiar company logos: 8 from a smaller category (social media companies) and 8 from a larger category (entertainment/recreation manufacturing companies). The ERP results from Experiment 1 revealed that, in a two-item search array, search was more efficient for the smaller category of logos compared to the larger category. In a four-item search array (Experiment 2), where two of the four items were placeholders, search was largely similar between the category types, but there was more attentional capture by nontarget members from the same category as the target for smaller rather than larger categories. These results support a growing literature on how prior knowledge of categories affects attentional selection and capture during visual search. We discuss the implications of these findings in relation to assessing cognitive abilities across the lifespan, given that prior knowledge typically increases with age. © 2018 Society for Psychophysiological Research.

Biological knowledge bases using Wikis: combining the flexibility of Wikis with the structure of databases.

PubMed

Brohée, Sylvain; Barriot, Roland; Moreau, Yves

2010-09-01

In recent years, the number of knowledge bases developed using Wiki technology has exploded. Unfortunately, next to their numerous advantages, classical Wikis present a critical limitation: the invaluable knowledge they gather is represented as free text, which hinders their computational exploitation. This is in sharp contrast with the current practice for biological databases where the data is made available in a structured way. Here, we present WikiOpener an extension for the classical MediaWiki engine that augments Wiki pages by allowing on-the-fly querying and formatting resources external to the Wiki. Those resources may provide data extracted from databases or DAS tracks, or even results returned by local or remote bioinformatics analysis tools. This also implies that structured data can be edited via dedicated forms. Hence, this generic resource combines the structure of biological databases with the flexibility of collaborative Wikis. The source code and its documentation are freely available on the MediaWiki website: http://www.mediawiki.org/wiki/Extension:WikiOpener.

The Effect of Knowledge Linking Levels in Biology Lessons upon Students' Knowledge Structure

ERIC Educational Resources Information Center

Wadouh, Julia; Liu, Ning; Sandmann, Angela; Neuhaus, Birgit J.

2014-01-01

Knowledge structure is an important aspect for defining students' competency in biology learning, but how knowledge structure is influenced by the teaching process in naturalistic biology classroom settings has scarcely been empirically investigated. In this study, 49 biology lessons in the teaching unit "blood and circulatory system" in…

fMRI evidence of equivalent neural suppression by repetition and prior knowledge.

PubMed

Poppenk, J; McIntosh, A R; Moscovitch, M

2016-09-01

Stimulus repetition speeds behavioral responding (behavioral priming) and is accompanied by suppressed neural responses (repetition suppression; RS) that have been observed up to three days after initial exposure. While some proposals have suggested the two phenomena are linked, behavioral priming has been observed many years after initial exposure, whereas RS is widely considered a transitory phenomenon. This raises the question: what is the true upper limit of RS persistence? To answer this question, we scanned healthy, English-native adults with fMRI as they viewed novel (Asian) proverbs, recently repeated (Asian) proverbs, and previously known (English) proverbs that were matched on various dimensions. We then estimated RS by comparing repeated or previously known proverbs against novel ones. Multivariate analyses linked previously known and repeated proverbs with statistically indistinguishable RS in a broad visual-linguistic network. In each suppressed region, prior knowledge and repetition also induced a common shift in functional connectivity, further underscoring the similarity of the RS phenomenon induced by these conditions. By contrast, activated regions readily distinguished prior knowledge and repetition conditions in a manner consistent with engagement of semantic and episodic memory systems, respectively. Our results illustrate that regardless of whether RS is understood in terms of its magnitude, spatial extent or functional connectivity profile, typical RS effects can be elicited even under conditions where recently triggered biological processes or episodic memory are unlikely to play a prominent role. These results provide important new evidence that RS (of the kind observed after an interval of at least several minutes) reflects the facilitation of perceptual and comprehension processes by any type of information retrieved from long-term memory. Copyright © 2016 Elsevier Ltd. All rights reserved.

Stochastic formulation of patient positioning using linac-mounted cone beam imaging with prior knowledge

DOE Office of Scientific and Technical Information (OSTI.GOV)

Hoegele, W.; Loeschel, R.; Dobler, B.

2011-02-15

Purpose: In this work, a novel stochastic framework for patient positioning based on linac-mounted CB projections is introduced. Based on this formulation, the most probable shifts and rotations of the patient are estimated, incorporating interfractional deformations of patient anatomy and other uncertainties associated with patient setup. Methods: The target position is assumed to be defined by and is stochastically determined from positions of various features such as anatomical landmarks or markers in CB projections, i.e., radiographs acquired with a CB-CT system. The patient positioning problem of finding the target location from CB projections is posed as an inverse problem withmore » prior knowledge and is solved using a Bayesian maximum a posteriori (MAP) approach. The prior knowledge is three-fold and includes the accuracy of an initial patient setup (such as in-room laser and skin marks), the plasticity of the body (relative shifts between target and features), and the feature detection error in CB projections (which may vary depending on specific detection algorithm and feature type). For this purpose, MAP estimators are derived and a procedure of using them in clinical practice is outlined. Furthermore, a rule of thumb is theoretically derived, relating basic parameters of the prior knowledge (initial setup accuracy, plasticity of the body, and number of features) and the parameters of CB data acquisition (number of projections and accuracy of feature detection) to the expected estimation accuracy. Results: MAP estimation can be applied to arbitrary features and detection algorithms. However, to experimentally demonstrate its applicability and to perform the validation of the algorithm, a water-equivalent, deformable phantom with features represented by six 1 mm chrome balls were utilized. These features were detected in the cone beam projections (XVI, Elekta Synergy) by a local threshold method for demonstration purposes only. The accuracy of

A Knowledge Base for Teaching Biology Situated in the Context of Genetic Testing

ERIC Educational Resources Information Center

van der Zande, Paul; Waarlo, Arend Jan; Brekelmans, Mieke; Akkerman, Sanne F.; Vermunt, Jan D.

2011-01-01

Recent developments in the field of genomics will impact the daily practice of biology teachers who teach genetics in secondary education. This study reports on the first results of a research project aimed at enhancing biology teacher knowledge for teaching genetics in the context of genetic testing. The increasing body of scientific knowledge…

Potentiation in young infants: The origin of the prior knowledge effect?

PubMed Central

Barr, Rachel; Rovee-Collier, Carolyn; Learmonth, Amy

2011-01-01

In two experiments with 6-month-old infants, we found that prior learning of an operant task (remembered for 2 weeks) mediated new learning of a modeling event (remembered for only 1 day) and increased its recall. Infants first learned to associate lever pressing with moving a toy train housed in a large box. One or 2 weeks later, three target actions were modeled on a hand puppet while the train box (a retrieval cue) was in view. Merely retrieving the train memory strengthened it, and simultaneously pairing its retrieved memory with the modeled actions potentiated their learning and recall. When paired 1 week later, deferred imitation increased from 1 day to 4 weeks; when paired 2 weeks later, it increased from 1 day to 6 weeks. The striking parallels between potentiated learning in infants and the prior knowledge effect in adults suggests that the prior knowledge effect originates in early infancy. PMID:21264602

Learning from Instructional Animations: How Does Prior Knowledge Mediate the Effect of Visual Cues?

ERIC Educational Resources Information Center

Arslan-Ari, I.

2018-01-01

The purpose of this study was to investigate the effects of cueing and prior knowledge on learning and mental effort of students studying an animation with narration. This study employed a 2 (no cueing vs. visual cueing) × 2 (low vs. high prior knowledge) between-subjects factorial design. The results revealed a significant interaction effect…

Synthetic biology and the ethics of knowledge

PubMed Central

Douglas, Thomas; Savulescu, Julian

2011-01-01

Synthetic biologists aim to generate biological organisms according to rational design principles. Their work may have many beneficial applications, but it also raises potentially serious ethical concerns. In this article, we consider what attention the discipline demands from bioethicists. We argue that the most important issue for ethicists to examine is the risk that knowledge from synthetic biology will be misused, for example, in biological terrorism or warfare. To adequately address this concern, bioethics will need to broaden its scope, contemplating not just the means by which scientific knowledge is produced, but also what kinds of knowledge should be sought and disseminated. PMID:20935316

Using Student Self-Assessment of Biological Concepts in an Introductory Biology Course.

ERIC Educational Resources Information Center

Heinze-Fry, Jane Ann

1992-01-01

Describes the author's methods to establish what students enrolled in an introductory biology course for nonmajors know about biology prior to instruction. The project also compared preinstructional knowledge to postinstructional knowledge. Beginning students knew the least about plant transport/chemical control and cellular metabolism. Students…

Creating Illusions of Knowledge: Learning Errors that Contradict Prior Knowledge

ERIC Educational Resources Information Center

Fazio, Lisa K.; Barber, Sarah J.; Rajaram, Suparna; Ornstein, Peter A.; Marsh, Elizabeth J.

2013-01-01

Most people know that the Pacific is the largest ocean on Earth and that Edison invented the light bulb. Our question is whether this knowledge is stable, or if people will incorporate errors into their knowledge bases, even if they have the correct knowledge stored in memory. To test this, we asked participants general-knowledge questions 2 weeks…

Building dynamical models from data and prior knowledge: the case of the first period-doubling bifurcation.

PubMed

Aguirre, Luis Antonio; Furtado, Edgar Campos

2007-10-01

This paper reviews some aspects of nonlinear model building from data with (gray box) and without (black box) prior knowledge. The model class is very important because it determines two aspects of the final model, namely (i) the type of nonlinearity that can be accurately approximated and (ii) the type of prior knowledge that can be taken into account. Such features are usually in conflict when it comes to choosing the model class. The problem of model structure selection is also reviewed. It is argued that such a problem is philosophically different depending on the model class and it is suggested that the choice of model class should be performed based on the type of a priori available. A procedure is proposed to build polynomial models from data on a Poincaré section and prior knowledge about the first period-doubling bifurcation, for which the normal form is also polynomial. The final models approximate dynamical data in a least-squares sense and, by design, present the first period-doubling bifurcation at a specified value of parameters. The procedure is illustrated by means of simulated examples.

Deepening Understanding of Prior Knowledge: What Diverse First-Generation College Students in the U.S. Can Teach Us

ERIC Educational Resources Information Center

Castillo-Montoya, Milagros

2017-01-01

Educational research indicates that teachers revealing and utilizing students' prior knowledge supports students' academic learning. Yet, the variation in students' prior knowledge is not fully known. To better understand students' prior knowledge, I drew on sociocultural learning theories to examine racially and ethnically diverse college…

Building on prior knowledge without building it in.

PubMed

Hansen, Steven S; Lampinen, Andrew K; Suri, Gaurav; McClelland, James L

2017-01-01

Lake et al. propose that people rely on "start-up software," "causal models," and "intuitive theories" built using compositional representations to learn new tasks more efficiently than some deep neural network models. We highlight the many drawbacks of a commitment to compositional representations and describe our continuing effort to explore how the ability to build on prior knowledge and to learn new tasks efficiently could arise through learning in deep neural networks.

Integration of prior knowledge into dense image matching for video surveillance

NASA Astrophysics Data System (ADS)

Menze, M.; Heipke, C.

2014-08-01

Three-dimensional information from dense image matching is a valuable input for a broad range of vision applications. While reliable approaches exist for dedicated stereo setups they do not easily generalize to more challenging camera configurations. In the context of video surveillance the typically large spatial extent of the region of interest and repetitive structures in the scene render the application of dense image matching a challenging task. In this paper we present an approach that derives strong prior knowledge from a planar approximation of the scene. This information is integrated into a graph-cut based image matching framework that treats the assignment of optimal disparity values as a labelling task. Introducing the planar prior heavily reduces ambiguities together with the search space and increases computational efficiency. The results provide a proof of concept of the proposed approach. It allows the reconstruction of dense point clouds in more general surveillance camera setups with wider stereo baselines.

The Effect of Prior Knowledge Activation on Text Recall: An Investigation of Two Conflicting Hypotheses.

ERIC Educational Resources Information Center

Machiels-Bongaerts, Maureen; And Others

Two hypotheses, the cognitive capacity hypothesis and the selective attention hypothesis, try to account for the facilitation effects of prior knowledge activation. They appear to be mutually exclusive since they predict different recall patterns as a result of prior knowledge activation. This study was designed to determine whether the two…

Utilizing knowledge from prior plans in the evaluation of quality assurance

NASA Astrophysics Data System (ADS)

Stanhope, Carl; Wu, Q. Jackie; Yuan, Lulin; Liu, Jianfei; Hood, Rodney; Yin, Fang-Fang; Adamson, Justus

2015-06-01

Increased interest regarding sensitivity of pre-treatment intensity modulated radiotherapy and volumetric modulated arc radiotherapy (VMAT) quality assurance (QA) to delivery errors has led to the development of dose-volume histogram (DVH) based analysis. This paradigm shift necessitates a change in the acceptance criteria and action tolerance for QA. Here we present a knowledge based technique to objectively quantify degradations in DVH for prostate radiotherapy. Using machine learning, organ-at-risk (OAR) DVHs from a population of 198 prior patients’ plans were adapted to a test patient’s anatomy to establish patient-specific DVH ranges. This technique was applied to single arc prostate VMAT plans to evaluate various simulated delivery errors: systematic single leaf offsets, systematic leaf bank offsets, random normally distributed leaf fluctuations, systematic lag in gantry angle of the mutli-leaf collimators (MLCs), fluctuations in dose rate, and delivery of each VMAT arc with a constant rather than variable dose rate. Quantitative Analyses of Normal Tissue Effects in the Clinic suggests V75Gy dose limits of 15% for the rectum and 25% for the bladder, however the knowledge based constraints were more stringent: 8.48   ±   2.65% for the rectum and 4.90   ±   1.98% for the bladder. 19   ±   10 mm single leaf and 1.9   ±   0.7 mm single bank offsets resulted in rectum DVHs worse than 97.7% (2σ) of clinically accepted plans. PTV degradations fell outside of the acceptable range for 0.6   ±   0.3 mm leaf offsets, 0.11   ±   0.06 mm bank offsets, 0.6   ±   1.3 mm of random noise, and 1.0   ±   0.7° of gantry-MLC lag. Utilizing a training set comprised of prior treatment plans, machine learning is used to predict a range of achievable DVHs for the test patient’s anatomy. Consequently, degradations leading to statistical outliers may be identified

Selective influence of prior allocentric knowledge on the kinesthetic learning of a path.

PubMed

Lafon, Matthieu; Vidal, Manuel; Berthoz, Alain

2009-04-01

Spatial cognition studies have described two main cognitive strategies involved in the memorization of traveled paths in human navigation. One of these strategies uses the action-based memory (egocentric) of the traveled route or paths, which involves kinesthetic memory, optic flow, and episodic memory, whereas the other strategy privileges a survey memory of cartographic type (allocentric). Most studies have dealt with these two strategies separately, but none has tried to show the interaction between them in spite of the fact that we commonly use a map to imagine our journey and then proceed using egocentric navigation. An interesting question is therefore: how does prior allocentric knowledge of the environment affect the egocentric, purely kinesthetic navigation processes involved in human navigation? We designed an experiment in which blindfolded subjects had first to walk and memorize a path with kinesthetic cues only. They had previously been shown a map of the path, which was either correct or distorted (consistent shrinking or growing). The latter transformations were studied in order to observe what influence a distorted prior knowledge could have on spatial mechanisms. After having completed the first learning travel along the path, they had to perform several spatial tasks during the testing phase: (1) pointing towards the origin and (2) to specific points encountered along the path, (3) a free locomotor reproduction, and (4) a drawing of the memorized path. The results showed that prior cartographic knowledge influences the paths drawn and the spatial inference capacity, whereas neither locomotor reproduction nor spatial updating was disturbed. Our results strongly support the notion that (1) there are two independent neural bases underlying these mechanisms: a map-like representation allowing allocentric spatial inferences, and a kinesthetic memory of self-motion in space; and (2) a common use of, or a switching between, these two strategies is

Relationship of prior knowledge and working engineers' learning preferences: implications for designing effective instruction

NASA Astrophysics Data System (ADS)

Baukal, Charles E.; Ausburn, Lynna J.

2017-05-01

Continuing engineering education (CEE) is important to ensure engineers maintain proficiency over the life of their careers. However, relatively few studies have examined designing effective training for working engineers. Research has indicated that both learner instructional preferences and prior knowledge can impact the learning process, but it has not established if these factors are interrelated. The study reported here considered relationships of prior knowledge and three aspects of learning preferences of working engineers at a manufacturing company: learning strategy choices, verbal-visual cognitive styles, and multimedia preferences. Prior knowledge was not found to be significantly related to engineers' learning preferences, indicating independence of effects of these variables on learning. The study also examined relationships of this finding to the Multimedia Cone of Abstraction and implications for its use as an instructional design tool for CEE.

The Effects of the Timing of Isolated FFI on the Explicit Knowledge and Written Accuracy of Learners with Different Prior Knowledge of the Linguistic Target

ERIC Educational Resources Information Center

Shintani, Natsuko

2017-01-01

This study examines the effects of the timing of explicit instruction (EI) on grammatical accuracy. A total of 123 learners were divided into two groups: those with some productive knowledge of past-counterfactual conditionals (+Prior Knowledge) and those without such knowledge (-Prior Knowledge). Each group was divided into four conditions. Two…

A knowledge base of the chemical compounds of intermediary metabolism.

PubMed

Karp, P D

1992-08-01

This paper describes a publicly available knowledge base of the chemical compounds involved in intermediary metabolism. We consider the motivations for constructing a knowledge base of metabolic compounds, the methodology by which it was constructed, and the information that it currently contains. Currently the knowledge base describes 981 compounds, listing for each: synonyms for its name, a systematic name, CAS registry number, chemical formula, molecular weight, chemical structure and two-dimensional display coordinates for the structure. The Compound Knowledge Base (CompoundKB) illustrates several methodological principles that should guide the development of biological knowledge bases. I argue that biological datasets should be made available in multiple representations to increase their accessibility to end users, and I present multiple representations of the CompoundKB (knowledge base, relational data base and ASN. 1 representations). I also analyze the general characteristics of these representations to provide an understanding of their relative advantages and disadvantages. Another principle is that the error rate of biological data bases should be estimated and documented-this analysis is performed for the CompoundKB.

Relevant Prior Knowledge Moderates the Effect of Elaboration during Small Group Discussion on Academic Achievement

ERIC Educational Resources Information Center

Van Blankenstein, Floris M.; Dolmans, Diana H. J. M.; Van der Vleuten, Cees P. M.; Schmidt, Henk G.

2013-01-01

This study set out to test whether relevant prior knowledge would moderate a positive effect on academic achievement of elaboration during small-group discussion. In a 2 × 2 experimental design, 66 undergraduate students observed a video showing a small-group problem-based discussion about thunder and lightning. In the video, a teacher asked…

Influence of using challenging tasks in biology classrooms on students' cognitive knowledge structure: an empirical video study

NASA Astrophysics Data System (ADS)

Nawani, Jigna; Rixius, Julia; Neuhaus, Birgit J.

2016-08-01

Empirical analysis of secondary biology classrooms revealed that, on average, 68% of teaching time in Germany revolved around processing tasks. Quality of instruction can thus be assessed by analyzing the quality of tasks used in classroom discourse. This quasi-experimental study analyzed how teachers used tasks in 38 videotaped biology lessons pertaining to the topic 'blood and circulatory system'. Two fundamental characteristics used to analyze tasks include: (1) required cognitive level of processing (e.g. low level information processing: repetiition, summary, define, classify and high level information processing: interpret-analyze data, formulate hypothesis, etc.) and (2) complexity of task content (e.g. if tasks require use of factual, linking or concept level content). Additionally, students' cognitive knowledge structure about the topic 'blood and circulatory system' was measured using student-drawn concept maps (N = 970 students). Finally, linear multilevel models were created with high-level cognitive processing tasks and higher content complexity tasks as class-level predictors and students' prior knowledge, students' interest in biology, and students' interest in biology activities as control covariates. Results showed a positive influence of high-level cognitive processing tasks (β = 0.07; p < .01) on students' cognitive knowledge structure. However, there was no observed effect of higher content complexity tasks on students' cognitive knowledge structure. Presented findings encourage the use of high-level cognitive processing tasks in biology instruction.

Leveraging prior quantitative knowledge in guiding pediatric drug development: a case study.

PubMed

Jadhav, Pravin R; Zhang, Jialu; Gobburu, Jogarao V S

2009-01-01

The manuscript presents the FDA's focus on leveraging prior knowledge in designing informative pediatric trial through this case study. In developing written request for Drug X, an anti-hypertensive for immediate blood pressure (BP) control, the sponsor and FDA conducted clinical trial simulations (CTS) to design trial with proper sample size and support the choice of dose range. The objective was to effectively use prior knowledge from adult patients for drug X, pediatric data from Corlopam (approved for a similar indication) trial and general experience in developing anti-hypertensive agents. Different scenarios governing the exposure response relationship in the pediatric population were simulated to perturb model assumptions. The choice of scenarios was based on the past observation that pediatric population is less responsive and sensitive compared with adults. The conceptual framework presented here should serve as an example on how the industry and FDA scientists can collaborate in designing the pediatric exclusivity trial. Using CTS, inter-disciplinary scientists with the sponsor and FDA can objectively discuss the choice of dose range, sample size, endpoints and other design elements. These efforts are believed to yield plausible trial design, qrational dosing recommendations and useful labeling information in pediatrics. Published in 2009 by John Wiley & Sons, Ltd.

Teaching Practice: A Perspective on Inter-Text and Prior Knowledge

ERIC Educational Resources Information Center

Costley, Kevin C.; West, Howard G.

2012-01-01

The use of teaching practices that involve intertextual relationship discovery in today's elementary classrooms is increasingly essential to the success of young learners of reading. Teachers must constantly strive to expand their perspective of how to incorporate the dialogue included in prior knowledge assessment. Teachers must also consider how…

Elaborative-Interrogation and Prior-Knowledge Effects on Learning of Facts.

ERIC Educational Resources Information Center

Woloshyn, Vera E.; And Others

1992-01-01

The differences among elaborative-interrogation, reading-to-understand, and no-exposure control conditions with familiar domain material in contrast to unfamiliar domain material were studied for 50 Canadian and 50 west German undergraduates. Results provide evidence of effects of both elaborative interrogation and prior knowledge on learning.…

Composing Knowledge: Writing, Rhetoric, and Reflection in Prior Learning Assessment

ERIC Educational Resources Information Center

Leaker, Cathy; Ostman, Heather

2010-01-01

In this article, we argue that prior learning assessment (PLA) essays manifest a series of issues central to composition research and practice: they foreground the "contact zone" between the unauthorized writer, institutional power, and the articulation of knowledge claims; they reinforce the central role of a multifaceted approach to…

Breastfeeding knowledge, attitudes, prior exposure, and intent among undergraduate students.

PubMed

Kavanagh, Katherine F; Lou, Zixin; Nicklas, Jennifer C; Habibi, Mona F; Murphy, Lee T

2012-11-01

Understanding breastfeeding knowledge, attitudes, and exposures among nonpregnant youth who are likely to be future parents may provide significant pathways to successfully increasing breastfeeding as the normal, accepted way of feeding infants. However, based on a recent review of the literature, only 3 studies have assessed these factors in nonpregnant, young adults in the United States in the past 10 years. The objective of this study was to gather more recent data regarding breastfeeding knowledge, attitudes, and prior exposure among undergraduate university students. This was a cross-sectional survey, conducted in November 2010. A convenience sample, consisting of undergraduates in attendance in 2 sections of an introductory nutrition class at a large research university, was used for this project (N = 248). Breastfeeding knowledge was relatively good. However, overall breastfeeding attitudes were more neutral, which appeared to be explained by the belief that breastfeeding is painful, restrictive, and inconvenient, both in general and specifically for the working mother. Though support for breastfeeding in public was low, men were significantly less likely than women to believe it to be embarrassing or unacceptable. In addition, breastfeeding attitudes were more positive among older students and those who were breastfed as infants. Those who were breastfed as infants were also significantly more likely to intend to breastfeed future children. Though this sample indicates good breastfeeding knowledge, attitudes were more neutral, and support for breastfeeding in public appears low. This finding is contradictory and warrants further exploration.

GO-PCA: An Unsupervised Method to Explore Gene Expression Data Using Prior Knowledge.

PubMed

Wagner, Florian

2015-01-01

Genome-wide expression profiling is a widely used approach for characterizing heterogeneous populations of cells, tissues, biopsies, or other biological specimen. The exploratory analysis of such data typically relies on generic unsupervised methods, e.g. principal component analysis (PCA) or hierarchical clustering. However, generic methods fail to exploit prior knowledge about the molecular functions of genes. Here, I introduce GO-PCA, an unsupervised method that combines PCA with nonparametric GO enrichment analysis, in order to systematically search for sets of genes that are both strongly correlated and closely functionally related. These gene sets are then used to automatically generate expression signatures with functional labels, which collectively aim to provide a readily interpretable representation of biologically relevant similarities and differences. The robustness of the results obtained can be assessed by bootstrapping. I first applied GO-PCA to datasets containing diverse hematopoietic cell types from human and mouse, respectively. In both cases, GO-PCA generated a small number of signatures that represented the majority of lineages present, and whose labels reflected their respective biological characteristics. I then applied GO-PCA to human glioblastoma (GBM) data, and recovered signatures associated with four out of five previously defined GBM subtypes. My results demonstrate that GO-PCA is a powerful and versatile exploratory method that reduces an expression matrix containing thousands of genes to a much smaller set of interpretable signatures. In this way, GO-PCA aims to facilitate hypothesis generation, design of further analyses, and functional comparisons across datasets.

Influence of prior knowledge of exercise duration on pacing strategies during game-based activities.

PubMed

Gabbett, Tim J; Walker, Ben; Walker, Shane

2015-04-01

To investigate the influence of prior knowledge of exercise duration on the pacing strategies employed during game-based activities. Twelve semiprofessional team-sport athletes (mean ± SD age 22.8 ± 2.1 y) participated in this study. Players performed 3 small-sided games in random order. In one condition (Control), players were informed that they would play the small-sided game for 12 min and then completed the 12-min game. In a 2nd condition (Deception), players were told that they would play the small-sided game for 6 minutes, but after completing the 6-min game, they were asked to complete another 6 min. In a 3rd condition (Unknown), players were not told how long they would be required to play the small-sided game, but the activity was terminated after 12 min. Movement was recorded using a GPS unit sampling at 10 Hz. Post hoc inspection of video footage was undertaken to count the number of possessions and the number and quality of disposals. Higher initial intensities were observed in the Deception (130.6 ± 3.3 m/min) and Unknown (129.3 ± 2.4 m/min) conditions than the Control condition (123.3 ± 3.4 m/min). Greater amounts of high-speed running occurred during the initial phases of the Deception condition, and more low-speed activity occurred during the Unknown condition. A moderately greater number of total skill involvements occurred in the Unknown condition than the Control condition. These findings suggest that during game-based activities, players alter their pacing strategy based on the anticipated endpoint of the exercise bout.

Effects of Students' Prior Knowledge on Scientific Reasoning in Density.

ERIC Educational Resources Information Center

Yang, Il-Ho; Kwon, Yong-Ju; Kim, Young-Shin; Jang, Myoung-Duk; Jeong, Jin-Woo; Park, Kuk-Tae

2002-01-01

Investigates the effects of students' prior knowledge on the scientific reasoning processes of performing the task of controlling variables with computer simulation and identifies a number of problems that students encounter in scientific discovery. Involves (n=27) 5th grade students and (n=33) 7th grade students. Indicates that students' prior…

Biology Factual Knowledge at Eleventh Grade of Senior High School Students in Pacitan based on Favorite Schools

NASA Astrophysics Data System (ADS)

Yustiana, I. A.; Paidi; Mercuriani, I. S.

2018-03-01

This study aimed to determine the Biology factual knowledge at eleventh grade of senior high school students in Pacitan based on favorite schools. This research was a descriptive research by using survey method. The population in this study was all of senior high school students in Pacitan. The sampling technique used purposive sampling technique and obtained 3 favorite schools and 3 non-favorite schools. The technique of collecting data used test form which was as the instrument of the research. Data analysis technique used Mann-Whitney U test. Based on the test, it was obtained p = 0,000 (p <0,05) so there was a significant difference between the factual knowledge of the students in the favorite schools and non-favorite schools in Pacitan. The factual knowledge of students in favorite schools was higher with an average of 5.32 while non-favorite schools were obtained an average of 4.36.

Sleep Spindle Density Predicts the Effect of Prior Knowledge on Memory Consolidation

PubMed Central

Lambon Ralph, Matthew A.; Kempkes, Marleen; Cousins, James N.; Lewis, Penelope A.

2016-01-01

Information that relates to a prior knowledge schema is remembered better and consolidates more rapidly than information that does not. Another factor that influences memory consolidation is sleep and growing evidence suggests that sleep-related processing is important for integration with existing knowledge. Here, we perform an examination of how sleep-related mechanisms interact with schema-dependent memory advantage. Participants first established a schema over 2 weeks. Next, they encoded new facts, which were either related to the schema or completely unrelated. After a 24 h retention interval, including a night of sleep, which we monitored with polysomnography, participants encoded a second set of facts. Finally, memory for all facts was tested in a functional magnetic resonance imaging scanner. Behaviorally, sleep spindle density predicted an increase of the schema benefit to memory across the retention interval. Higher spindle densities were associated with reduced decay of schema-related memories. Functionally, spindle density predicted increased disengagement of the hippocampus across 24 h for schema-related memories only. Together, these results suggest that sleep spindle activity is associated with the effect of prior knowledge on memory consolidation. SIGNIFICANCE STATEMENT Episodic memories are gradually assimilated into long-term memory and this process is strongly influenced by sleep. The consolidation of new information is also influenced by its relationship to existing knowledge structures, or schemas, but the role of sleep in such schema-related consolidation is unknown. We show that sleep spindle density predicts the extent to which schemas influence the consolidation of related facts. This is the first evidence that sleep is associated with the interaction between prior knowledge and long-term memory formation. PMID:27030764

Biological knowledge is more tentative than physics knowledge: Taiwan high school adolescents' views about the nature of biology and physics.

PubMed

Tsai, Chin-Chung

2006-01-01

Many educational psychologists believe that students' beliefs about the nature of knowledge, called epistemological beliefs, play an essential role in their learning process. Educators also stress the importance of helping students develop a better understanding of the nature of knowledge. The tentative and creative nature of science is often highlighted by contemporary science educators. However, few previous studies have investigated students' views of more specific knowledge domains, such as biology and physics. Consequently, this study developed a questionnaire to assess students' views specifically about the tentative and creative nature of biology and physics. From a survey of 428 Taiwanese high school adolescents, this study found that although students showed an understanding of the tentative and creative nature of biology and physics, they expressed stronger agreement as to the tentativeness of biology than that of physics. In addition, male students tended to agree more than did females that physics had tentative and creative features and that biology had tentative features. Also, students with more years of science education tended to show more agreement regarding the creative nature of physics and biology than those with fewer years.

An investigation of prior knowledge in Automatic Music Transcription systems.

PubMed

Cazau, Dorian; Revillon, Guillaume; Krywyk, Julien; Adam, Olivier

2015-10-01

Automatic transcription of music is a long-studied research field with many operational systems available commercially. In this paper, a generic transcription system able to host various prior knowledge parameters has been developed, followed by an in-depth investigation of their impact on music transcription. Explicit links between musical knowledge and algorithmic formalism have been made. Musical knowledge covers classes of timbre, musicology, and playing style of an instrument repertoire. An evaluation sound corpus gathering musical pieces played by human performers from three different instrument repertoires, namely, classical piano, steel-string acoustic guitar, and the marovany zither from Madagascar, has been developed. The different components of musical knowledge have been successively incorporated in a complete transcription system, consisting mainly of a Probabilistic Latent Component Analysis algorithm post-processed with a Hidden Markov Model, and their impact on transcription results have been comparatively evaluated.

A biological network-based regularized artificial neural network model for robust phenotype prediction from gene expression data.

PubMed

Kang, Tianyu; Ding, Wei; Zhang, Luoyan; Ziemek, Daniel; Zarringhalam, Kourosh

2017-12-19

Stratification of patient subpopulations that respond favorably to treatment or experience and adverse reaction is an essential step toward development of new personalized therapies and diagnostics. It is currently feasible to generate omic-scale biological measurements for all patients in a study, providing an opportunity for machine learning models to identify molecular markers for disease diagnosis and progression. However, the high variability of genetic background in human populations hampers the reproducibility of omic-scale markers. In this paper, we develop a biological network-based regularized artificial neural network model for prediction of phenotype from transcriptomic measurements in clinical trials. To improve model sparsity and the overall reproducibility of the model, we incorporate regularization for simultaneous shrinkage of gene sets based on active upstream regulatory mechanisms into the model. We benchmark our method against various regression, support vector machines and artificial neural network models and demonstrate the ability of our method in predicting the clinical outcomes using clinical trial data on acute rejection in kidney transplantation and response to Infliximab in ulcerative colitis. We show that integration of prior biological knowledge into the classification as developed in this paper, significantly improves the robustness and generalizability of predictions to independent datasets. We provide a Java code of our algorithm along with a parsed version of the STRING DB database. In summary, we present a method for prediction of clinical phenotypes using baseline genome-wide expression data that makes use of prior biological knowledge on gene-regulatory interactions in order to increase robustness and reproducibility of omic-scale markers. The integrated group-wise regularization methods increases the interpretability of biological signatures and gives stable performance estimates across independent test sets.

Stepwise group sparse regression (SGSR): gene-set-based pharmacogenomic predictive models with stepwise selection of functional priors.

PubMed

Jang, In Sock; Dienstmann, Rodrigo; Margolin, Adam A; Guinney, Justin

2015-01-01

Complex mechanisms involving genomic aberrations in numerous proteins and pathways are believed to be a key cause of many diseases such as cancer. With recent advances in genomics, elucidating the molecular basis of cancer at a patient level is now feasible, and has led to personalized treatment strategies whereby a patient is treated according to his or her genomic profile. However, there is growing recognition that existing treatment modalities are overly simplistic, and do not fully account for the deep genomic complexity associated with sensitivity or resistance to cancer therapies. To overcome these limitations, large-scale pharmacogenomic screens of cancer cell lines--in conjunction with modern statistical learning approaches--have been used to explore the genetic underpinnings of drug response. While these analyses have demonstrated the ability to infer genetic predictors of compound sensitivity, to date most modeling approaches have been data-driven, i.e. they do not explicitly incorporate domain-specific knowledge (priors) in the process of learning a model. While a purely data-driven approach offers an unbiased perspective of the data--and may yield unexpected or novel insights--this strategy introduces challenges for both model interpretability and accuracy. In this study, we propose a novel prior-incorporated sparse regression model in which the choice of informative predictor sets is carried out by knowledge-driven priors (gene sets) in a stepwise fashion. Under regularization in a linear regression model, our algorithm is able to incorporate prior biological knowledge across the predictive variables thereby improving the interpretability of the final model with no loss--and often an improvement--in predictive performance. We evaluate the performance of our algorithm compared to well-known regularization methods such as LASSO, Ridge and Elastic net regression in the Cancer Cell Line Encyclopedia (CCLE) and Genomics of Drug Sensitivity in Cancer (Sanger

Prospective regularization design in prior-image-based reconstruction

NASA Astrophysics Data System (ADS)

Dang, Hao; Siewerdsen, Jeffrey H.; Webster Stayman, J.

2015-12-01

Prior-image-based reconstruction (PIBR) methods leveraging patient-specific anatomical information from previous imaging studies and/or sequences have demonstrated dramatic improvements in dose utilization and image quality for low-fidelity data. However, a proper balance of information from the prior images and information from the measurements is required (e.g. through careful tuning of regularization parameters). Inappropriate selection of reconstruction parameters can lead to detrimental effects including false structures and failure to improve image quality. Traditional methods based on heuristics are subject to error and sub-optimal solutions, while exhaustive searches require a large number of computationally intensive image reconstructions. In this work, we propose a novel method that prospectively estimates the optimal amount of prior image information for accurate admission of specific anatomical changes in PIBR without performing full image reconstructions. This method leverages an analytical approximation to the implicitly defined PIBR estimator, and introduces a predictive performance metric leveraging this analytical form and knowledge of a particular presumed anatomical change whose accurate reconstruction is sought. Additionally, since model-based PIBR approaches tend to be space-variant, a spatially varying prior image strength map is proposed to optimally admit changes everywhere in the image (eliminating the need to know change locations a priori). Studies were conducted in both an ellipse phantom and a realistic thorax phantom emulating a lung nodule surveillance scenario. The proposed method demonstrated accurate estimation of the optimal prior image strength while achieving a substantial computational speedup (about a factor of 20) compared to traditional exhaustive search. Moreover, the use of the proposed prior strength map in PIBR demonstrated accurate reconstruction of anatomical changes without foreknowledge of change locations in

The Effect of Prior Knowledge and Feedback Type Design on Student Achievement and Satisfaction in Introductory Accounting

ERIC Educational Resources Information Center

Campbell, Donald P.

2013-01-01

This study investigated the effect of student prior knowledge and feedback type on student achievement and satisfaction in an introductory managerial accounting course using computer-based formative assessment tools. The study involved a redesign of the existing Job Order Costing unit using the ADDIE model of instructional design. The…

PRIOR-WK&E: Social Software for Policy Making in the Knowledge Society

NASA Astrophysics Data System (ADS)

Turón, Alberto; Aguarón, Juan; Escobar, María Teresa; Gallardo, Carolina; Moreno-Jiménez, José María; Salazar, José Luis

This paper presents a social software application denominated as PRIOR-WK&E. It has been developed by the Zaragoza Multicriteria Decision Making Group (GDMZ) with the aim of responding to the challenges of policy making in the Knowledge Society. Three specific modules have been added to PRIOR, the collaborative tool used by the research group (GDMZ) for considering the multicriteria selection of a discrete set of alternatives. The first module (W), that deals with multiactor decision making through the Web, and the second (K), that concerns the extraction and diffusion of knowledge related to the scientific resolution of the problem, were explained in [1]. The new application strengthens securitization and includes a third module (E) that evaluates the effectiveness of public administrations policy making.

Exploring expectation effects in EMDR: does prior treatment knowledge affect the degrading effects of eye movements on memories?

PubMed Central

Littel, Marianne; van Schie, Kevin; van den Hout, Marcel A.

2017-01-01

ABSTRACT Background: Eye movement desensitization and reprocessing (EMDR) is an effective psychological treatment for posttraumatic stress disorder. Recalling a memory while simultaneously making eye movements (EM) decreases a memory’s vividness and/or emotionality. It has been argued that non-specific factors, such as treatment expectancy and experimental demand, may contribute to the EMDR’s effectiveness. Objective: The present study was designed to test whether expectations about the working mechanism of EMDR would alter the memory attenuating effects of EM. Two experiments were conducted. In Experiment 1, we examined the effects of pre-existing (non-manipulated) knowledge of EMDR in participants with and without prior knowledge. In Experiment 2, we experimentally manipulated prior knowledge by providing participants without prior knowledge with correct or incorrect information about EMDR’s working mechanism. Method: Participants in both experiments recalled two aversive, autobiographical memories during brief sets of EM (Recall+EM) or keeping eyes stationary (Recall Only). Before and after the intervention, participants scored their memories on vividness and emotionality. A Bayesian approach was used to compare two competing hypotheses on the effects of (existing/given) prior knowledge: (1) Prior (correct) knowledge increases the effects of Recall+EM vs. Recall Only, vs. (2) prior knowledge does not affect the effects of Recall+EM. Results: Recall+EM caused greater reductions in memory vividness and emotionality than Recall Only in all groups, including the incorrect information group. In Experiment 1, both hypotheses were supported by the data: prior knowledge boosted the effects of EM, but only modestly. In Experiment 2, the second hypothesis was clearly supported over the first: providing knowledge of the underlying mechanism of EMDR did not alter the effects of EM. Conclusions: Recall+EM appears to be quite robust against the effects of prior

Prior Knowledge and Story Processing: Integration, Selection, and Variation. Technical Report No. 138.

ERIC Educational Resources Information Center

Spiro, Rand J.

Psychological research concerning several aspects of the relationship between existing knowledge schemata and the processing of text is summarized in this report. The first section is concerned with dynamic processes of story understanding, with emphasis on the integration of information. The role of prior knowledge in accommodating parts of…

Relationship of resident characteristics, attitudes, prior training and clinical knowledge to communication skills performance.

PubMed

Laidlaw, Toni Suzuki; Kaufman, David M; MacLeod, Heather; van Zanten, Sander; Simpson, David; Wrixon, William

2006-01-01

A substantial body of literature demonstrates that communication skills in medicine can be taught and retained through teaching and practice. Considerable evidence also reveals that characteristics such as gender, age, language and attitudes affect communication skills performance. Our study examined the characteristics, attitudes and prior communication skills training of residents to determine the relationship of each to patient-doctor communication. The relationship between communication skills proficiency and clinical knowledge application (biomedical and ethical) was also examined through the use of doctor-developed clinical content checklists, as very little research has been conducted in this area. A total of 78 first- and second-year residents across all departments at Dalhousie Medical School participated in a videotaped 4-station objective structured clinical examination presenting a range of communication and clinical knowledge challenges. A variety of instruments were used to gather information and assess performance. Two expert raters evaluated the videotapes. Significant relationships were observed between resident characteristics, prior communication skills training, clinical knowledge and communication skills performance. Females, younger residents and residents with English as first language scored significantly higher, as did residents with prior communication skills training. A significant positive relationship was found between the clinical content checklist and communication performance. Gender was the only characteristic related significantly to attitudes. Gender, age, language and prior communication skills training are related to communication skills performance and have implications for resident education. The positive relationship between communication skills proficiency and clinical knowledge application is important and should be explored further.

Integrative Systems Biology for Data Driven Knowledge Discovery

PubMed Central

Greene, Casey S.; Troyanskaya, Olga G.

2015-01-01

Integrative systems biology is an approach that brings together diverse high throughput experiments and databases to gain new insights into biological processes or systems at molecular through physiological levels. These approaches rely on diverse high-throughput experimental techniques that generate heterogeneous data by assaying varying aspects of complex biological processes. Computational approaches are necessary to provide an integrative view of these experimental results and enable data-driven knowledge discovery. Hypotheses generated from these approaches can direct definitive molecular experiments in a cost effective manner. Using integrative systems biology approaches, we can leverage existing biological knowledge and large-scale data to improve our understanding of yet unknown components of a system of interest and how its malfunction leads to disease. PMID:21044756

Genetic Pedagogical Content Knowledge (PCK) Ability Profile of Prospective Biology Teacher

NASA Astrophysics Data System (ADS)

Purwianingsih, W.; Muthmainnah, E.; Hidayat, T.

2017-02-01

Genetics is one of the topics or subject matter in biology that are considered difficult. Student difficulties of understanding genetics, can be caused by lack of understanding this concept and the way of teachers teach. Pedagogical Content Knowledge (PCK) is a way to understand the complex relationships between teaching and content taught through the use of specific teaching approaches. The aims of study was to analyze genetic PCK ability profile of prospective biology teacher.13 student of sixth semester Biology education department who learned Kapita Selekta Biologi SMA course, participated in this study. PCK development was measured by CoRes (Content Representation). Before students fill CoRes, students are tested mastery genetic concepts through a multiple-choice test with three tier-test. Data was obtained from the prior CoRes and its revisions, as well as the mastery concept in pre and post test. Results showed that pre-test of genetic mastery concepts average on 55.4% (low category) and beginning of the writing CoRes, student get 43.2% (Pra PCK). After students get lecture and simulating learning, the post-test increased to 63.8% (sufficient category) and PCK revision is also increase 58.1% (growing PCK). It can be concluded that mastery of subject matter could affects the ability of genetic PCK.

GO-PCA: An Unsupervised Method to Explore Gene Expression Data Using Prior Knowledge

PubMed Central

Wagner, Florian

2015-01-01

Method Genome-wide expression profiling is a widely used approach for characterizing heterogeneous populations of cells, tissues, biopsies, or other biological specimen. The exploratory analysis of such data typically relies on generic unsupervised methods, e.g. principal component analysis (PCA) or hierarchical clustering. However, generic methods fail to exploit prior knowledge about the molecular functions of genes. Here, I introduce GO-PCA, an unsupervised method that combines PCA with nonparametric GO enrichment analysis, in order to systematically search for sets of genes that are both strongly correlated and closely functionally related. These gene sets are then used to automatically generate expression signatures with functional labels, which collectively aim to provide a readily interpretable representation of biologically relevant similarities and differences. The robustness of the results obtained can be assessed by bootstrapping. Results I first applied GO-PCA to datasets containing diverse hematopoietic cell types from human and mouse, respectively. In both cases, GO-PCA generated a small number of signatures that represented the majority of lineages present, and whose labels reflected their respective biological characteristics. I then applied GO-PCA to human glioblastoma (GBM) data, and recovered signatures associated with four out of five previously defined GBM subtypes. My results demonstrate that GO-PCA is a powerful and versatile exploratory method that reduces an expression matrix containing thousands of genes to a much smaller set of interpretable signatures. In this way, GO-PCA aims to facilitate hypothesis generation, design of further analyses, and functional comparisons across datasets. PMID:26575370

Effects of Activation of Prior Knowledge on the Recall of a Clinical Case.

ERIC Educational Resources Information Center

Schmidt, Henk G.; Boshuizen, Henny P. A.

A study investigated the known phenomenon of "intermediate effect" in which medical students with an intermediate amount of knowledge and experience demonstrate higher amounts of recall of the text of a medical case than either experienced clinicians or novices. In this study the amount of activation of prior knowledge was controlled by…

Use of knowledge-sharing web-based portal in gross and microscopic anatomy.

PubMed

Durosaro, Olayemi; Lachman, Nirusha; Pawlina, Wojciech

2008-12-01

Changes in worldwide healthcare delivery require review of current medical school curricula structure to develop learning outcomes that ensures mastery of knowledge and clinical competency. In the last 3 years, Mayo Medical School implemented outcomes-based curriculum to encompass new graduate outcomes. Standard courses were replaced by 6-week clinically-integrated didactic blocks separated by student-self selected academic enrichment activities. Gross and microscopic anatomy was integrated with radiology and genetics respectively. Laboratory components include virtual microscopy and anatomical dissection. Students assigned to teams utilise computer portals to share learning experiences. High-resolution computed tomographic (CT) scans of cadavers prior to dissection were made available for correlative learning between the cadaveric material and radiologic images. Students work in teams on assigned presentations that include histology, cell and molecular biology, genetics and genomic using the Nexus Portal, based on DrupalEd, to share their observations, reflections and dissection findings. New generation of medical students are clearly comfortable utilising web-based programmes that maximise their learning potential of conceptually difficult and labor intensive courses. Team-based learning approach emphasising the use of knowledge-sharing computer portals maximises opportunities for students to master their knowledge and improve cognitive skills to ensure clinical competency.

The Androgen Receptor and Its Use in Biological Assays: Looking Toward Effect-Based Testing and Its Applications

PubMed Central

Cadwallader, Amy B.; Lim, Carol S.; Rollins, Douglas E.; Botrè, Francesco

2015-01-01

Steroid abuse is a growing problem among amateur and professional athletes. Because of an inundation of newly and illegally synthesized steroids with minor structural modifications and other designer steroid receptor modulators, there is a need to develop new methods of detection which do not require prior knowledge of the abused steroid structure. The number of designer steroids currently being abused is unknown because detection methods in general are only identifying substances with a known structure. The detection of doping is moving away from merely checking for exposure to prohibited substance toward detecting an effect of prohibited substances, as biological assays can do. Cell-based biological assays are the next generation of assays which should be utilized by antidoping laboratories; they can detect androgenic anabolic steroid and other human androgen receptor (hAR) ligand presence without knowledge of their structure and assess the relative biological activity of these compounds. This review summarizes the hAR and its action and discusses its relevance to sports doping and its use in biological assays. PMID:22080898

Fostering Students' Conceptual Knowledge in Biology in the Context of German National Education Standards

NASA Astrophysics Data System (ADS)

Förtsch, Christian; Dorfner, Tobias; Baumgartner, Julia; Werner, Sonja; von Kotzebue, Lena; Neuhaus, Birgit J.

2018-04-01

The German National Education Standards (NES) for biology were introduced in 2005. The content part of the NES emphasizes fostering conceptual knowledge. However, there are hardly any indications of what such an instructional implementation could look like. We introduce a theoretical framework of an instructional approach to foster students' conceptual knowledge as demanded in the NES (Fostering Conceptual Knowledge) including instructional practices derived from research on single core ideas, general psychological theories, and biology-specific features of instructional quality. First, we aimed to develop a rating manual, which is based on this theoretical framework. Second, we wanted to describe current German biology instruction according to this approach and to quantitatively analyze its effectiveness. And third, we aimed to provide qualitative examples of this approach to triangulate our findings. In a first step, we developed a theoretically devised rating manual to measure Fostering Conceptual Knowledge in videotaped lessons. Data for quantitative analysis included 81 videotaped biology lessons of 28 biology teachers from different German secondary schools. Six hundred forty students completed a questionnaire on their situational interest after each lesson and an achievement test. Results from multilevel modeling showed significant positive effects of Fostering Conceptual Knowledge on students' achievement and situational interest. For qualitative analysis, we contrasted instruction of four teachers, two with high and two with low student achievement and situational interest using the qualitative method of thematic analysis. Qualitative analysis revealed five main characteristics describing Fostering Conceptual Knowledge. Therefore, implementing Fostering Conceptual Knowledge in biology instruction seems promising. Examples of how to implement Fostering Conceptual Knowledge in instruction are shown and discussed.

Building Knowledge through Portfolio Learning in Prior Learning Assessment and Recognition

ERIC Educational Resources Information Center

Conrad, Dianne

2008-01-01

It is important for academic credibility that the process of prior learning assessment and recognition (PLAR) keeps learning and knowledge as its foundational tenets. Doing so ensures PLAR's recognition as a fertile ground for learners' cognitive and personal growth. In many postsecondary venues, PLAR is often misunderstood and confused with…

Students' Achievement in Relation to Reasoning Ability, Prior Knowledge and Gender

ERIC Educational Resources Information Center

Yenilmez, Ayse; Sungur, Semra; Tekkaya, Ceren

2006-01-01

This study investigated students' achievement regarding photosynthesis and respiration in plants in relation to reasoning ability, prior knowledge and gender. A total of 117 eighth-grade students participated in the study. Test of logical thinking and the two-tier multiple choice tests were administered to determine students' reasoning ability and…

How to achieve synergy between medical education and cognitive neuroscience? An exercise on prior knowledge in understanding.

PubMed

Ruiter, Dirk J; van Kesteren, Marlieke T R; Fernandez, Guillen

2012-05-01

A major challenge in contemporary research is how to connect medical education and cognitive neuroscience and achieve synergy between these domains. Based on this starting point we discuss how this may result in a common language about learning, more educationally focused scientific inquiry, and multidisciplinary research projects. As the topic of prior knowledge in understanding plays a strategic role in both medical education and cognitive neuroscience it is used as a central element in our discussion. A critical condition for the acquisition of new knowledge is the existence of prior knowledge, which can be built in a mental model or schema. Formation of schemas is a central event in student-centered active learning, by which mental models are constructed and reconstructed. These theoretical considerations from cognitive psychology foster scientific discussions that may lead to salient issues and questions for research with cognitive neuroscience. Cognitive neuroscience attempts to understand how knowledge, insight and experience are established in the brain and to clarify their neural correlates. Recently, evidence has been obtained that new information processed by the hippocampus can be consolidated into a stable, neocortical network more rapidly if this new information fits readily into a schema. Opportunities for medical education and medical education research can be created in a fruitful dialogue within an educational multidisciplinary platform. In this synergetic setting many questions can be raised by educational scholars interested in evidence-based education that may be highly relevant for integrative research and the further development of medical education.

Computer-Based Semantic Network in Molecular Biology: A Demonstration.

ERIC Educational Resources Information Center

Callman, Joshua L.; And Others

This paper analyzes the hardware and software features that would be desirable in a computer-based semantic network system for representing biology knowledge. It then describes in detail a prototype network of molecular biology knowledge that has been developed using Filevision software and a Macintosh computer. The prototype contains about 100…

The Importance of Prior Knowledge when Comparing Examples: Influences on Conceptual and Procedural Knowledge of Equation Solving

ERIC Educational Resources Information Center

Rittle-Johnson, Bethany; Star, Jon R.; Durkin, Kelley

2009-01-01

Comparing multiple examples typically supports learning and transfer in laboratory studies and is considered a key feature of high-quality mathematics instruction. This experimental study investigated the importance of prior knowledge in learning from comparison. Seventh- and 8th-grade students (N = 236) learned to solve equations by comparing…

Biology Procedural Knowledge at Eleventh Grade of Senior High School in West Lampung Based on Curriculum

NASA Astrophysics Data System (ADS)

Sari, T. M.; Paidi; Mercuriani, I. S.

2018-03-01

This study was aim to determine Biology procedural knowledge of senior high school in West Lampung based on curriculum at 11th grade in even semester. This research was descriptive research. The population was all students of senior high school in West Lampung. The sampling technique in this research used purposive sampling technique, so the researcher obtained 3 schools using K13 and 3 schools using KTSP. Data collecting technique used instrument test. Data analysis technique used U-Mann Whitney test. The result showed that p=0.028 (p<0.05), so there was significant differences between school using K13 and KTSP. The procedural knowledge of schools which using K13 is higher than school which using KTSP, with the mean score K13=4.35 and KTSP=4.00

Participation in introductory biology laboratories: An integrated assessment based on surveys, behavioral observations, and qualitative interviews

NASA Astrophysics Data System (ADS)

Russell, Connie Adelle

Scope and method of study. The purpose of this study was to evaluate the effect of gender, major, and prior knowledge of and attitude toward biology on participation in introductory biology laboratories. Subjects for this study were 3,527 students enrolled in college-level introductory biology courses. During the study, three introductory courses were replaced with one mixed-majors course. The new course adopted a different pedagological approach from the previous courses in that an inquiry-based approach was used in lectures and laboratories. All subjects completed a survey that measured content knowledge using the NABT/NSTA High School Biology Examination Version 1990 and attitude using Russell and Hollander's Biology Attitude Scale. I used and discuss the merits of using ethological methods and data collection software, EthoScribeTM (Tima Scientific) to collect behavioral data from 145 students. I also evaluated participation using qualitative interviews of 30 students. I analyzed content knowledge and attitude data using ANOVA and Pearson correlation, and behavioral data using Contingency Table Analysis. I analyzed interviews following methods outlined by Rubin and Rubin. Findings. Course style and gender were the most useful variables in distinguishing differences among groups of students with regard to attitude, content knowledge, and participation in laboratories. Attitude toward biology and achievement measured by the surveys were found to be positively correlated; however, gender, major, class standing, course style and interactions between these variables also had effects on these variables. I found a positive association among attitude, achievement and participation in hands-on activities in laboratories. Differences in participation also were associated group type. In a traditional introductory biology course, females in single-gender groups, gender-equal, or groups in which females were the majority spent more time performing hands-on science

The Effect of Prior Knowledge and Gender on Physics Achievement

NASA Astrophysics Data System (ADS)

Stewart, John; Henderson, Rachel

2017-01-01

Gender differences on the Conceptual Survey in Electricity and Magnetism (CSEM) have been extensively studied. Ten semesters (N=1621) of CSEM data is presented showing male students outperform female students on the CSEM posttest by 5 % (p < . 001). Male students also outperform female students on qualitative in-semester test questions by 3 % (p = . 004), but no significant difference between male and female students was found on quantitative test questions. Male students enter the class with superior prior preparation in the subject and score 4 % higher on the CSEM pretest (p < . 001). If the sample is restricted to students with little prior knowledge who answer no more than 8 of the 32 questions correctly (N=822), male and female differences on the CSEM and qualitative test questions cease to be significant. This suggests no intrinsic gender bias exists in the CSEM itself and that gender differences are the result of prior preparation measured by CSEM pretest score. Gender differences between male and female students increase with pretest score. Regression analyses are presented to further explore interactions between preparation, gender, and achievement.

Wet Lab Accelerator: A Web-Based Application Democratizing Laboratory Automation for Synthetic Biology.

PubMed

Bates, Maxwell; Berliner, Aaron J; Lachoff, Joe; Jaschke, Paul R; Groban, Eli S

2017-01-20

Wet Lab Accelerator (WLA) is a cloud-based tool that allows a scientist to conduct biology via robotic control without the need for any programming knowledge. A drag and drop interface provides a convenient and user-friendly method of generating biological protocols. Graphically developed protocols are turned into programmatic instruction lists required to conduct experiments at the cloud laboratory Transcriptic. Prior to the development of WLA, biologists were required to write in a programming language called "Autoprotocol" in order to work with Transcriptic. WLA relies on a new abstraction layer we call "Omniprotocol" to convert the graphical experimental description into lower level Autoprotocol language, which then directs robots at Transcriptic. While WLA has only been tested at Transcriptic, the conversion of graphically laid out experimental steps into Autoprotocol is generic, allowing extension of WLA into other cloud laboratories in the future. WLA hopes to democratize biology by bringing automation to general biologists.

On the Limitations of Biological Knowledge

PubMed Central

Dougherty, Edward R; Shmulevich, Ilya

2012-01-01

Scientific knowledge is grounded in a particular epistemology and, owing to the requirements of that epistemology, possesses limitations. Some limitations are intrinsic, in the sense that they depend inherently on the nature of scientific knowledge; others are contingent, depending on the present state of knowledge, including technology. Understanding limitations facilitates scientific research because one can then recognize when one is confronted by a limitation, as opposed to simply being unable to solve a problem within the existing bounds of possibility. In the hope that the role of limiting factors can be brought more clearly into focus and discussed, we consider several sources of limitation as they apply to biological knowledge: mathematical complexity, experimental constraints, validation, knowledge discovery, and human intellectual capacity. PMID:23633917

Estimating kinetic mechanisms with prior knowledge I: Linear parameter constraints.

PubMed

Salari, Autoosa; Navarro, Marco A; Milescu, Mirela; Milescu, Lorin S

2018-02-05

To understand how ion channels and other proteins function at the molecular and cellular levels, one must decrypt their kinetic mechanisms. Sophisticated algorithms have been developed that can be used to extract kinetic parameters from a variety of experimental data types. However, formulating models that not only explain new data, but are also consistent with existing knowledge, remains a challenge. Here, we present a two-part study describing a mathematical and computational formalism that can be used to enforce prior knowledge into the model using constraints. In this first part, we focus on constraints that enforce explicit linear relationships involving rate constants or other model parameters. We develop a simple, linear algebra-based transformation that can be applied to enforce many types of model properties and assumptions, such as microscopic reversibility, allosteric gating, and equality and inequality parameter relationships. This transformation converts the set of linearly interdependent model parameters into a reduced set of independent parameters, which can be passed to an automated search engine for model optimization. In the companion article, we introduce a complementary method that can be used to enforce arbitrary parameter relationships and any constraints that quantify the behavior of the model under certain conditions. The procedures described in this study can, in principle, be coupled to any of the existing methods for solving molecular kinetics for ion channels or other proteins. These concepts can be used not only to enforce existing knowledge but also to formulate and test new hypotheses. © 2018 Salari et al.

Novel nonlinear knowledge-based mean force potentials based on machine learning.

PubMed

Dong, Qiwen; Zhou, Shuigeng

2011-01-01

The prediction of 3D structures of proteins from amino acid sequences is one of the most challenging problems in molecular biology. An essential task for solving this problem with coarse-grained models is to deduce effective interaction potentials. The development and evaluation of new energy functions is critical to accurately modeling the properties of biological macromolecules. Knowledge-based mean force potentials are derived from statistical analysis of proteins of known structures. Current knowledge-based potentials are almost in the form of weighted linear sum of interaction pairs. In this study, a class of novel nonlinear knowledge-based mean force potentials is presented. The potential parameters are obtained by nonlinear classifiers, instead of relative frequencies of interaction pairs against a reference state or linear classifiers. The support vector machine is used to derive the potential parameters on data sets that contain both native structures and decoy structures. Five knowledge-based mean force Boltzmann-based or linear potentials are introduced and their corresponding nonlinear potentials are implemented. They are the DIH potential (single-body residue-level Boltzmann-based potential), the DFIRE-SCM potential (two-body residue-level Boltzmann-based potential), the FS potential (two-body atom-level Boltzmann-based potential), the HR potential (two-body residue-level linear potential), and the T32S3 potential (two-body atom-level linear potential). Experiments are performed on well-established decoy sets, including the LKF data set, the CASP7 data set, and the Decoys “R”Us data set. The evaluation metrics include the energy Z score and the ability of each potential to discriminate native structures from a set of decoy structures. Experimental results show that all nonlinear potentials significantly outperform the corresponding Boltzmann-based or linear potentials, and the proposed discriminative framework is effective in developing knowledge-based

Student Perceived and Determined Knowledge of Biology Concepts in an Upper-Level Biology Course.

PubMed

Ziegler, Brittany; Montplaisir, Lisa

2014-01-01

Students who lack metacognitive skills can struggle with the learning process. To be effective learners, students should recognize what they know and what they do not know. This study examines the relationship between students' perception of their knowledge and determined knowledge in an upper-level biology course utilizing a pre/posttest approach. Significant differences in students' perception of their knowledge and their determined knowledge exist at the beginning (pretest) and end (posttest) of the course. Alignment between student perception and determined knowledge was significantly more accurate on the posttest compared with the pretest. Students whose determined knowledge was in the upper quartile had significantly better alignment between their perception and determined knowledge on the pre- and posttest than students in the lower quartile. No difference exists between how students perceived their knowledge between upper- and lower-quartile students. There was a significant difference in alignment of perception and determined knowledge between males and females on the posttest, with females being more accurate in their perception of knowledge. This study provides evidence of discrepancies that exist between what students perceive they know and what they actually know. © 2014 B. Ziegler and L. Montplaisir. CBE—Life Sciences Education © 2014 The American Society for Cell Biology. This article is distributed by The American Society for Cell Biology under license from the author(s). It is available to the public under an Attribution–Noncommercial–Share Alike 3.0 Unported Creative Commons License (http://creativecommons.org/licenses/by-nc-sa/3.0).

Survey of rheumatologists on the use of the Philippine Guidelines on the Screening for Tuberculosis prior to use of Biologic Agents.

PubMed

Aquino-Villamin, Melissa; Tankeh-Torres, Sandra; Lichauco, Juan Javier

2016-11-01

The use of biologic agents has become an important option in treating patients with rheumatoid arthritis. However, these drugs have been associated with an increased risk of tuberculosis (TB) reactivation. Local guidelines for TB screening prior to the use of biologic agents were developed to address this issue. This study is a survey describing the compliance of Filipino rheumatologists to these guidelines. Eighty-seven rheumatologists in the Philippines were given the questionnaire and responses from 61 rheumatologists were included in the analysis. All respondents agree that patients should be screened prior to giving the biologic agents. Local guidelines recommend screening with tuberculin skin test (TST) and chest radiograph. However, cut-off values considered for a positive TST and timing of initiation of biologic agents after starting TB prophylaxis and treatment varied among respondents. In addition, screening of close household contacts were only performed by 41 (69.5%) respondents. There were 11 respondents who reported 16 patients developing TB during or after receiving biologic agents, despite adherence to the guidelines. This survey describes the compliance rate of Filipino rheumatologists in applying current local recommendations for TB screening prior to initiating biologic agents. The incidence of new TB cases despite the current guidelines emphasizes the importance of compliance and the need to revise the guidelines based on updated existing literature. © 2015 Asia Pacific League of Associations for Rheumatology and Wiley Publishing Asia Pty Ltd.

The SwissLipids knowledgebase for lipid biology

PubMed Central

Liechti, Robin; Hyka-Nouspikel, Nevila; Niknejad, Anne; Gleizes, Anne; Götz, Lou; Kuznetsov, Dmitry; David, Fabrice P.A.; van der Goot, F. Gisou; Riezman, Howard; Bougueleret, Lydie; Xenarios, Ioannis; Bridge, Alan

2015-01-01

Motivation: Lipids are a large and diverse group of biological molecules with roles in membrane formation, energy storage and signaling. Cellular lipidomes may contain tens of thousands of structures, a staggering degree of complexity whose significance is not yet fully understood. High-throughput mass spectrometry-based platforms provide a means to study this complexity, but the interpretation of lipidomic data and its integration with prior knowledge of lipid biology suffers from a lack of appropriate tools to manage the data and extract knowledge from it. Results: To facilitate the description and exploration of lipidomic data and its integration with prior biological knowledge, we have developed a knowledge resource for lipids and their biology—SwissLipids. SwissLipids provides curated knowledge of lipid structures and metabolism which is used to generate an in silico library of feasible lipid structures. These are arranged in a hierarchical classification that links mass spectrometry analytical outputs to all possible lipid structures, metabolic reactions and enzymes. SwissLipids provides a reference namespace for lipidomic data publication, data exploration and hypothesis generation. The current version of SwissLipids includes over 244 000 known and theoretically possible lipid structures, over 800 proteins, and curated links to published knowledge from over 620 peer-reviewed publications. We are continually updating the SwissLipids hierarchy with new lipid categories and new expert curated knowledge. Availability: SwissLipids is freely available at http://www.swisslipids.org/. Contact: alan.bridge@isb-sib.ch Supplementary information: Supplementary data are available at Bioinformatics online. PMID:25943471

The Impact of Prior Programming Knowledge on Lecture Attendance and Final Exam

ERIC Educational Resources Information Center

Veerasamy, Ashok Kumar; D'Souza, Daryl; Lindén, Rolf; Laakso, Mikko-Jussi

2018-01-01

In this article, we report the results of the impact of prior programming knowledge (PPK) on lecture attendance (LA) and on subsequent final programming exam performance in a university level introductory programming course. This study used Spearman's rank correlation coefficient, multiple regression, Kruskal-Wallis, and Bonferroni correction…

The Integration of Javanese Indigenous Knowledge in Biology Learning Resources Development

NASA Astrophysics Data System (ADS)

Anazifa, D.; Hadi, R. F.

2017-02-01

The student’s difficulties in learning and understanding Biology concepts are caused by the adoption of scientific phenomenon that not suitable with the environment they live in. Students who comes from the Javanese background sometimes find the Biology concepts hard to understand. Science content that comes from the West sometimes is not suitable with the student’s background, because the cultural and geographical background that underlining the science development are different. It can potentially cause the clash in constructing knowledge of students. The proportion of western knowledge and indigenous knowledge has to be balanced, in order to give the scientific rationale of the natural phenomenon that faced by students in everyday life. The ethnoscience experienced by student is still in the form of concrete experience as a result of the interaction with the nature. As one of the largest tribe in Indonesia, Javanese has many unique cultures that can be adopted in science classroom especially in Biology class. The role on ethnoscience in the context of developing Biology learning resources is to connect the science concept with the real world situation. By considering indigenous knowledge as one of learning resources, teachers can start to adjust the Javanese indigenous knowledge into the curriculum. This paper is literature review which will present the background, rationale, and procedure in integrating Javanese indigenous knowledge into Biology classroom as learning resources. The integration of Javanese indigenous knowledge in Biology learning resources development is necessary in order to connect the Biology concept into real situation.

Effects of Prior Knowledge in Mathematics on Learner-Interface Interactions in a Learning-by-Teaching Intelligent Tutoring System

ERIC Educational Resources Information Center

Bringula, Rex P.; Basa, Roselle S.; Dela Cruz, Cecilio; Rodrigo, Ma. Mercedes T.

2016-01-01

This study attempted to determine the influence of prior knowledge in mathematics of students on learner-interface interactions in a learning-by-teaching intelligent tutoring system. One hundred thirty-nine high school students answered a pretest (i.e., the prior knowledge in mathematics) and a posttest. In between the pretest and posttest, they…

Prior Knowledge Assessment Guide

DTIC Science & Technology

2014-12-01

marksmanship, advanced rifle marksmanship, and even specialized shooting courses. A comparison of the means on the test for the two groups showed that the...hands- on evaluations of student knowledge and/or skills. Pretests however, determine how much knowledge a student currently possesses of the course...content; thus, questions on pretests assess knowledge about what is to be taught in the course. Also, most pretests will include test items

The Influence of Prior Knowledge and Viewing Repertoire on Learning from Video

ERIC Educational Resources Information Center

de Boer, Jelle; Kommers, Piet A. M.; de Brock, Bert; Tolboom, Jos

2016-01-01

Video is increasingly used as an instructional tool. It is therefore becoming more important to improve learning of students from video. We investigated whether student learning effects are influenced through an instruction about other viewing behaviours, and whether these learning effects depend on their prior knowledge. In a controlled…

Prior-knowledge-based feedforward network simulation of true boiling point curve of crude oil.

PubMed

Chen, C W; Chen, D Z

2001-11-01

Theoretical results and practical experience indicate that feedforward networks can approximate a wide class of functional relationships very well. This property is exploited in modeling chemical processes. Given finite and noisy training data, it is important to encode the prior knowledge in neural networks to improve the fit precision and the prediction ability of the model. In this paper, as to the three-layer feedforward networks and the monotonic constraint, the unconstrained method, Joerding's penalty function method, the interpolation method, and the constrained optimization method are analyzed first. Then two novel methods, the exponential weight method and the adaptive method, are proposed. These methods are applied in simulating the true boiling point curve of a crude oil with the condition of increasing monotonicity. The simulation experimental results show that the network models trained by the novel methods are good at approximating the actual process. Finally, all these methods are discussed and compared with each other.

Examining the pedagogical content knowledge and practice of experienced secondary biology teachers for teaching diffusion and osmosis

NASA Astrophysics Data System (ADS)

Lankford, Deanna

Teachers are the most important factor in student learning (National Research Council, 1996); yet little is known about the specialized knowledge held by experienced teachers. The purpose of this study was twofold: first, to make explicit the pedagogical content knowledge (PCK) for teaching diffusion and osmosis held by experienced biology teachers and, second, to reveal how topic-specific PCK informs teacher practice. The Magnusson et al. (1999) PCK model served as the theoretical framework for the study. The overarching research question was: When teaching lessons on osmosis and diffusion, how do experienced biology teachers draw upon their topic-specific pedagogical content knowledge? Data sources included observations of two consecutive lessons, three semi-structured interviews, lesson plans, and student handouts. Data analysis indicated five of the six teachers held a constructivist orientation to science teaching and engaged students in explorations of diffusion and osmosis prior to introducing the concepts to students. Explanations for diffusion and osmosis were based upon students' observations and experiences during explorations. All six teachers used representations at the molecular, cellular, and plant organ levels to serve as foci for explorations of diffusion and osmosis. Three potential learning difficulties identified by the teachers included: (a) understanding vocabulary terms, (b) predicting the direction of osmosis, and (c) identifying random molecular motion as the driving force for diffusion and osmosis. Participants used student predictions as formative assessments to reveal misconceptions before instruction and evaluate conceptual understanding during instruction. This study includes implications for teacher preparation, research, and policy.

Making Connections in Math: Activating a Prior Knowledge Analogue Matters for Learning

ERIC Educational Resources Information Center

Sidney, Pooja G.; Alibali, Martha W.

2015-01-01

This study investigated analogical transfer of conceptual structure from a prior-knowledge domain to support learning in a new domain of mathematics: division by fractions. Before a procedural lesson on division by fractions, fifth and sixth graders practiced with a surface analogue (other operations on fractions) or a structural analogue (whole…

Knowledge-based IMRT treatment planning for prostate cancer.

PubMed

Chanyavanich, Vorakarn; Das, Shiva K; Lee, William R; Lo, Joseph Y

2011-05-01

To demonstrate the feasibility of using a knowledge base of prior treatment plans to generate new prostate intensity modulated radiation therapy (IMRT) plans. Each new case would be matched against others in the knowledge base. Once the best match is identified, that clinically approved plan is used to generate the new plan. A database of 100 prostate IMRT treatment plans was assembled into an information-theoretic system. An algorithm based on mutual information was implemented to identify similar patient cases by matching 2D beam's eye view projections of contours. Ten randomly selected query cases were each matched with the most similar case from the database of prior clinically approved plans. Treatment parameters from the matched case were used to develop new treatment plans. A comparison of the differences in the dose-volume histograms between the new and the original treatment plans were analyzed. On average, the new knowledge-based plan is capable of achieving very comparable planning target volume coverage as the original plan, to within 2% as evaluated for D98, D95, and D1. Similarly, the dose to the rectum and dose to the bladder are also comparable to the original plan. For the rectum, the mean and standard deviation of the dose percentage differences for D20, D30, and D50 are 1.8% +/- 8.5%, -2.5% +/- 13.9%, and -13.9% +/- 23.6%, respectively. For the bladder, the mean and standard deviation of the dose percentage differences for D20, D30, and D50 are -5.9% +/- 10.8%, -12.2% +/- 14.6%, and -24.9% +/- 21.2%, respectively. A negative percentage difference indicates that the new plan has greater dose sparing as compared to the original plan. The authors demonstrate a knowledge-based approach of using prior clinically approved treatment plans to generate clinically acceptable treatment plans of high quality. This semiautomated approach has the potential to improve the efficiency of the treatment planning process while ensuring that high quality plans are

A linear programming computational framework integrates phosphor-proteomics and prior knowledge to predict drug efficacy.

PubMed

Ji, Zhiwei; Wang, Bing; Yan, Ke; Dong, Ligang; Meng, Guanmin; Shi, Lei

2017-12-21

In recent years, the integration of 'omics' technologies, high performance computation, and mathematical modeling of biological processes marks that the systems biology has started to fundamentally impact the way of approaching drug discovery. The LINCS public data warehouse provides detailed information about cell responses with various genetic and environmental stressors. It can be greatly helpful in developing new drugs and therapeutics, as well as improving the situations of lacking effective drugs, drug resistance and relapse in cancer therapies, etc. In this study, we developed a Ternary status based Integer Linear Programming (TILP) method to infer cell-specific signaling pathway network and predict compounds' treatment efficacy. The novelty of our study is that phosphor-proteomic data and prior knowledge are combined for modeling and optimizing the signaling network. To test the power of our approach, a generic pathway network was constructed for a human breast cancer cell line MCF7; and the TILP model was used to infer MCF7-specific pathways with a set of phosphor-proteomic data collected from ten representative small molecule chemical compounds (most of them were studied in breast cancer treatment). Cross-validation indicated that the MCF7-specific pathway network inferred by TILP were reliable predicting a compound's efficacy. Finally, we applied TILP to re-optimize the inferred cell-specific pathways and predict the outcomes of five small compounds (carmustine, doxorubicin, GW-8510, daunorubicin, and verapamil), which were rarely used in clinic for breast cancer. In the simulation, the proposed approach facilitates us to identify a compound's treatment efficacy qualitatively and quantitatively, and the cross validation analysis indicated good accuracy in predicting effects of five compounds. In summary, the TILP model is useful for discovering new drugs for clinic use, and also elucidating the potential mechanisms of a compound to targets.

AIDS Knowledge: The Media and the Biology Teacher.

ERIC Educational Resources Information Center

Vener, Arthur M.; Krupka, Lawrence R.

1988-01-01

Reports on a study to determine the level of knowledge college students possessed about Acquired Immune Deficiency Syndrome. Concluded that overall enhancement of knowledge occurred among young adults and that mass media was partially responsible. Lists biological terms necessary for understanding the disease. (RT)

Exploring Biology Teachers' Pedagogical Content Knowledge in the Teaching of Genetics in Swaziland Science Classrooms

NASA Astrophysics Data System (ADS)

Mthethwa-Kunene, Eunice; Oke Onwu, Gilbert; de Villiers, Rian

2015-05-01

This study explored the pedagogical content knowledge (PCK) and its development of four experienced biology teachers in the context of teaching school genetics. PCK was defined in terms of teacher content knowledge, pedagogical knowledge and knowledge of students' preconceptions and learning difficulties. Data sources of teacher knowledge base included teacher-constructed concept maps, pre- and post-lesson teacher interviews, video-recorded genetics lessons, post-lesson teacher questionnaire and document analysis of teacher's reflective journals and students' work samples. The results showed that the teachers' individual PCK profiles consisted predominantly of declarative and procedural content knowledge in teaching basic genetics concepts. Conditional knowledge, which is a type of meta-knowledge for blending together declarative and procedural knowledge, was also demonstrated by some teachers. Furthermore, the teachers used topic-specific instructional strategies such as context-based teaching, illustrations, peer teaching, and analogies in diverse forms but failed to use physical models and individual or group student experimental activities to assist students' internalization of the concepts. The finding that all four teachers lacked knowledge of students' genetics-related preconceptions was equally significant. Formal university education, school context, journal reflection and professional development programmes were considered as contributing to the teachers' continuing PCK development. Implications of the findings for biology teacher education are briefly discussed.

Correlation set analysis: detecting active regulators in disease populations using prior causal knowledge

PubMed Central

2012-01-01

Background Identification of active causal regulators is a crucial problem in understanding mechanism of diseases or finding drug targets. Methods that infer causal regulators directly from primary data have been proposed and successfully validated in some cases. These methods necessarily require very large sample sizes or a mix of different data types. Recent studies have shown that prior biological knowledge can successfully boost a method's ability to find regulators. Results We present a simple data-driven method, Correlation Set Analysis (CSA), for comprehensively detecting active regulators in disease populations by integrating co-expression analysis and a specific type of literature-derived causal relationships. Instead of investigating the co-expression level between regulators and their regulatees, we focus on coherence of regulatees of a regulator. Using simulated datasets we show that our method performs very well at recovering even weak regulatory relationships with a low false discovery rate. Using three separate real biological datasets we were able to recover well known and as yet undescribed, active regulators for each disease population. The results are represented as a rank-ordered list of regulators, and reveals both single and higher-order regulatory relationships. Conclusions CSA is an intuitive data-driven way of selecting directed perturbation experiments that are relevant to a disease population of interest and represent a starting point for further investigation. Our findings demonstrate that combining co-expression analysis on regulatee sets with a literature-derived network can successfully identify causal regulators and help develop possible hypothesis to explain disease progression. PMID:22443377

An integrative framework for Bayesian variable selection with informative priors for identifying genes and pathways.

PubMed

Peng, Bin; Zhu, Dianwen; Ander, Bradley P; Zhang, Xiaoshuai; Xue, Fuzhong; Sharp, Frank R; Yang, Xiaowei

2013-01-01

The discovery of genetic or genomic markers plays a central role in the development of personalized medicine. A notable challenge exists when dealing with the high dimensionality of the data sets, as thousands of genes or millions of genetic variants are collected on a relatively small number of subjects. Traditional gene-wise selection methods using univariate analyses face difficulty to incorporate correlational, structural, or functional structures amongst the molecular measures. For microarray gene expression data, we first summarize solutions in dealing with 'large p, small n' problems, and then propose an integrative Bayesian variable selection (iBVS) framework for simultaneously identifying causal or marker genes and regulatory pathways. A novel partial least squares (PLS) g-prior for iBVS is developed to allow the incorporation of prior knowledge on gene-gene interactions or functional relationships. From the point view of systems biology, iBVS enables user to directly target the joint effects of multiple genes and pathways in a hierarchical modeling diagram to predict disease status or phenotype. The estimated posterior selection probabilities offer probabilitic and biological interpretations. Both simulated data and a set of microarray data in predicting stroke status are used in validating the performance of iBVS in a Probit model with binary outcomes. iBVS offers a general framework for effective discovery of various molecular biomarkers by combining data-based statistics and knowledge-based priors. Guidelines on making posterior inferences, determining Bayesian significance levels, and improving computational efficiencies are also discussed.

Nursing and the new biology: towards a realist, anti-reductionist approach to nursing knowledge.

PubMed

Nairn, Stuart

2014-10-01

As a system of knowledge, nursing has utilized a range of subjects and reconstituted them to reflect the thinking and practice of health care. Often drawn to a holistic model, nursing finds it difficult to resist the reductionist tendencies in biological and medical thinking. In this paper I will propose a relational approach to knowledge that is able to address this issue. The paper argues that biology is not characterized by one stable theory but is often a contentious topic and employs philosophically diverse models in its scientific research. Biology need not be seen as a reductionist science, but reductionism is nonetheless an important current within biological thinking. These reductionist currents can undermine nursing knowledge in four main ways. Firstly, that the conclusions drawn from reductionism go far beyond their data based on an approach that prioritizes biological explanations and eliminates others. Secondly, that the methods employed by biologists are sometimes weak, and the limitations are insufficiently acknowledged. Thirdly, that the assumptions that drive the research agenda are problematic, and finally that uncritical application of these ideas can be potentially disastrous for nursing practice. These issues are explored through an examination of the problems reductionism poses for the issue of gender, mental health, and altruism. I then propose an approach based on critical realism that adopts an anti-reductionist philosophy that utilizes the conceptual tools of emergence and a relational ontology. © 2014 John Wiley & Sons Ltd.

A Bayesian framework for extracting human gait using strong prior knowledge.

PubMed

Zhou, Ziheng; Prügel-Bennett, Adam; Damper, Robert I

2006-11-01

Extracting full-body motion of walking people from monocular video sequences in complex, real-world environments is an important and difficult problem, going beyond simple tracking, whose satisfactory solution demands an appropriate balance between use of prior knowledge and learning from data. We propose a consistent Bayesian framework for introducing strong prior knowledge into a system for extracting human gait. In this work, the strong prior is built from a simple articulated model having both time-invariant (static) and time-variant (dynamic) parameters. The model is easily modified to cater to situations such as walkers wearing clothing that obscures the limbs. The statistics of the parameters are learned from high-quality (indoor laboratory) data and the Bayesian framework then allows us to "bootstrap" to accurate gait extraction on the noisy images typical of cluttered, outdoor scenes. To achieve automatic fitting, we use a hidden Markov model to detect the phases of images in a walking cycle. We demonstrate our approach on silhouettes extracted from fronto-parallel ("sideways on") sequences of walkers under both high-quality indoor and noisy outdoor conditions. As well as high-quality data with synthetic noise and occlusions added, we also test walkers with rucksacks, skirts, and trench coats. Results are quantified in terms of chamfer distance and average pixel error between automatically extracted body points and corresponding hand-labeled points. No one part of the system is novel in itself, but the overall framework makes it feasible to extract gait from very much poorer quality image sequences than hitherto. This is confirmed by comparing person identification by gait using our method and a well-established baseline recognition algorithm.

Effects of Biology Teachers' Professional Knowledge and Cognitive Activation on Students' Achievement

ERIC Educational Resources Information Center

Förtsch, Christian; Werner, Sonja; von Kotzebue, Lena; Neuhaus, Birgit J.

2016-01-01

This study examined the effects of teachers' biology-specific dimensions of professional knowledge--pedagogical content knowledge (PCK) and content knowledge (CK)--and cognitively activating biology instruction, as a feature of instructional quality, on students' learning. The sample comprised 39 German secondary school teachers whose lessons on…

Interplay of Prior Knowledge, Self-Regulation and Motivation in Complex Multimedia Learning Environments

ERIC Educational Resources Information Center

Song, H. S.; Kalet, A. L.; Plass, J. L.

2016-01-01

This study examined the direct and indirect effects of medical clerkship students' prior knowledge, self-regulation and motivation on learning performance in complex multimedia learning environments. The data from 386 medical clerkship students from six medical schools were analysed using structural equation modeling. The structural model revealed…

The Social Construction of Biological Knowledge.

ERIC Educational Resources Information Center

Bain, Linda L.

The influence of biological science research on the development of physical education curriculum is examined in this paper. The social construction of scientific knowledge is described as occurring in the selection of problems to be studied, the collection and interpretation of data, the dissemination of results, and the educational uses of…

Preservice Biology Teachers' Professional Knowledge: Structure and Learning Opportunities

ERIC Educational Resources Information Center

Großschedl, Jörg; Harms, Ute; Kleickmann, Thilo; Glowinski, Ingrid

2015-01-01

What learning opportunities in higher education promote the development of content knowledge (CK), pedagogical content knowledge (PCK), and pedagogical knowledge (PK)? In order to investigate this question, a cross-sectional study with a total of 274 German preservice biology teachers (21.5% male, average age 22.8 years) was conducted in German…

Predicting Mycobacterium tuberculosis Complex Clades Using Knowledge-Based Bayesian Networks

PubMed Central

Bennett, Kristin P.

2014-01-01

We develop a novel approach for incorporating expert rules into Bayesian networks for classification of Mycobacterium tuberculosis complex (MTBC) clades. The proposed knowledge-based Bayesian network (KBBN) treats sets of expert rules as prior distributions on the classes. Unlike prior knowledge-based support vector machine approaches which require rules expressed as polyhedral sets, KBBN directly incorporates the rules without any modification. KBBN uses data to refine rule-based classifiers when the rule set is incomplete or ambiguous. We develop a predictive KBBN model for 69 MTBC clades found in the SITVIT international collection. We validate the approach using two testbeds that model knowledge of the MTBC obtained from two different experts and large DNA fingerprint databases to predict MTBC genetic clades and sublineages. These models represent strains of MTBC using high-throughput biomarkers called spacer oligonucleotide types (spoligotypes), since these are routinely gathered from MTBC isolates of tuberculosis (TB) patients. Results show that incorporating rules into problems can drastically increase classification accuracy if data alone are insufficient. The SITVIT KBBN is publicly available for use on the World Wide Web. PMID:24864238

Integrating clinicians, knowledge and data: expert-based cooperative analysis in healthcare decision support

PubMed Central

2010-01-01

Background Decision support in health systems is a highly difficult task, due to the inherent complexity of the process and structures involved. Method This paper introduces a new hybrid methodology Expert-based Cooperative Analysis (EbCA), which incorporates explicit prior expert knowledge in data analysis methods, and elicits implicit or tacit expert knowledge (IK) to improve decision support in healthcare systems. EbCA has been applied to two different case studies, showing its usability and versatility: 1) Bench-marking of small mental health areas based on technical efficiency estimated by EbCA-Data Envelopment Analysis (EbCA-DEA), and 2) Case-mix of schizophrenia based on functional dependency using Clustering Based on Rules (ClBR). In both cases comparisons towards classical procedures using qualitative explicit prior knowledge were made. Bayesian predictive validity measures were used for comparison with expert panels results. Overall agreement was tested by Intraclass Correlation Coefficient in case "1" and kappa in both cases. Results EbCA is a new methodology composed by 6 steps:. 1) Data collection and data preparation; 2) acquisition of "Prior Expert Knowledge" (PEK) and design of the "Prior Knowledge Base" (PKB); 3) PKB-guided analysis; 4) support-interpretation tools to evaluate results and detect inconsistencies (here Implicit Knowledg -IK- might be elicited); 5) incorporation of elicited IK in PKB and repeat till a satisfactory solution; 6) post-processing results for decision support. EbCA has been useful for incorporating PEK in two different analysis methods (DEA and Clustering), applied respectively to assess technical efficiency of small mental health areas and for case-mix of schizophrenia based on functional dependency. Differences in results obtained with classical approaches were mainly related to the IK which could be elicited by using EbCA and had major implications for the decision making in both cases. Discussion This paper presents Eb

Integrating clinicians, knowledge and data: expert-based cooperative analysis in healthcare decision support.

PubMed

Gibert, Karina; García-Alonso, Carlos; Salvador-Carulla, Luis

2010-09-30

Decision support in health systems is a highly difficult task, due to the inherent complexity of the process and structures involved. This paper introduces a new hybrid methodology Expert-based Cooperative Analysis (EbCA), which incorporates explicit prior expert knowledge in data analysis methods, and elicits implicit or tacit expert knowledge (IK) to improve decision support in healthcare systems. EbCA has been applied to two different case studies, showing its usability and versatility: 1) Bench-marking of small mental health areas based on technical efficiency estimated by EbCA-Data Envelopment Analysis (EbCA-DEA), and 2) Case-mix of schizophrenia based on functional dependency using Clustering Based on Rules (ClBR). In both cases comparisons towards classical procedures using qualitative explicit prior knowledge were made. Bayesian predictive validity measures were used for comparison with expert panels results. Overall agreement was tested by Intraclass Correlation Coefficient in case "1" and kappa in both cases. EbCA is a new methodology composed by 6 steps:. 1) Data collection and data preparation; 2) acquisition of "Prior Expert Knowledge" (PEK) and design of the "Prior Knowledge Base" (PKB); 3) PKB-guided analysis; 4) support-interpretation tools to evaluate results and detect inconsistencies (here Implicit Knowledg -IK- might be elicited); 5) incorporation of elicited IK in PKB and repeat till a satisfactory solution; 6) post-processing results for decision support. EbCA has been useful for incorporating PEK in two different analysis methods (DEA and Clustering), applied respectively to assess technical efficiency of small mental health areas and for case-mix of schizophrenia based on functional dependency. Differences in results obtained with classical approaches were mainly related to the IK which could be elicited by using EbCA and had major implications for the decision making in both cases. This paper presents EbCA and shows the convenience of

`She Has to Drink Blood of the Snake': Culture and prior knowledge in science|health education

NASA Astrophysics Data System (ADS)

Bricker, Leah A.; Reeve, Suzanne; Bell, Philip

2014-06-01

In this analysis, we argue that science education should attend more deeply to youths' cultural resources and practices (e.g. material, social, and intellectual). Inherent in our argument is a call for revisiting conceptions of 'prior knowledge' to theorize how people make sense of the complex ecologies of experience, ideas, and cultural practices that undergird any learning moment. We illustrate our argument using examples from the domain of personal health, chosen because of its tremendous societal impact and its significant areas of overlap with biology, chemistry, physics, and other scientific disciplines taught as core subjects in schools. Using data from a team ethnography of young people's science and technology learning across settings and over developmental timescales, we highlight two youths' experiences and understandings related to personal health, and how those experiences and understandings influenced the youths' sense-making about the natural world. We then discuss the implications of our argument for science education.

Effects of Subject-Matter Knowledge in the Teaching of Biology and Physics.

ERIC Educational Resources Information Center

Hashweh, Maher Z.

An analysis of science teacher's knowledge of specific biology and physics topics and the effects of this knowledge on their planning for instruction and on simulated teaching are discussed in this report. Six experienced secondary school teachers participated in the study. Each teacher's knowledge of a biology topic and a physics topic was…

Is an Illustration Always Worth Ten Thousand Words? Effects of Prior Knowledge, Learning Style and Multimedia Illustrations on Text Comprehension.

ERIC Educational Resources Information Center

Ollerenshaw, Alison; Aidman, Eugene; Kidd, Garry

1997-01-01

This study examined comprehension in four groups of undergraduates under text only, multimedia, and two diagram conditions of text supplementation. Results indicated that effects of text supplementation are mediated by prior knowledge and learning style: multimedia appears more beneficial to surface learners with little prior knowledge and makes…

The Influence of Prior Knowledge on Perception and Action: Relationships to Autistic Traits

ERIC Educational Resources Information Center

Buckingham, Gavin; Michelakakis, Elizabeth Evgenia; Rajendran, Gnanathusharan

2016-01-01

Autism is characterised by a range of perceptual and sensorimotor deficits, which might be related to abnormalities in how autistic individuals use prior knowledge. We investigated this proposition in a large non-clinical population in the context of the size-weight illusion, where individual's expectations about object weight influence their…

Noun and knowledge retrieval for biological and non-biological entities following right occipitotemporal lesions.

PubMed

Bruffaerts, Rose; De Weer, An-Sofie; De Grauwe, Sophie; Thys, Miek; Dries, Eva; Thijs, Vincent; Sunaert, Stefan; Vandenbulcke, Mathieu; De Deyne, Simon; Storms, Gerrit; Vandenberghe, Rik

2014-09-01

We investigated the critical contribution of right ventral occipitotemporal cortex to knowledge of visual and functional-associative attributes of biological and non-biological entities and how this relates to category-specificity during confrontation naming. In a consecutive series of 7 patients with lesions confined to right ventral occipitotemporal cortex, we conducted an extensive assessment of oral generation of visual-sensory and functional-associative features in response to the names of biological and nonbiological entities. Subjects also performed a confrontation naming task for these categories. Our main novel finding related to a unique case with a small lesion confined to right medial fusiform gyrus who showed disproportionate naming impairment for nonbiological versus biological entities, specifically for tools. Generation of visual and functional-associative features was preserved for biological and non-biological entities. In two other cases, who had a relatively small posterior lesion restricted to primary visual and posterior fusiform cortex, retrieval of visual attributes was disproportionately impaired compared to functional-associative attributes, in particular for biological entities. However, these cases did not show a category-specific naming deficit. Two final cases with the largest lesions showed a classical dissociation between biological versus nonbiological entities during naming, with normal feature generation performance. This is the first lesion-based evidence of a critical contribution of the right medial fusiform cortex to tool naming. Second, dissociations along the dimension of attribute type during feature generation do not co-occur with category-specificity during naming in the current patient sample. Copyright © 2014 Elsevier Ltd. All rights reserved.

Thai University Students' Prior Knowledge about P-Waves Generated during Particle Motion

ERIC Educational Resources Information Center

Rakkapao, Suttida; Arayathanikul, Kwan; Pananont, Passakorn

2009-01-01

The goal of this study is to identify Thai students' prior knowledge about particle motion when P-waves arrive. This existing idea significantly influences what and how students learn in the classroom. The data were collected via conceptual open-ended questions designed by the researchers and through explanatory follow-up interviews. Participants…

An Integrative Framework for Bayesian Variable Selection with Informative Priors for Identifying Genes and Pathways

PubMed Central

Ander, Bradley P.; Zhang, Xiaoshuai; Xue, Fuzhong; Sharp, Frank R.; Yang, Xiaowei

2013-01-01

The discovery of genetic or genomic markers plays a central role in the development of personalized medicine. A notable challenge exists when dealing with the high dimensionality of the data sets, as thousands of genes or millions of genetic variants are collected on a relatively small number of subjects. Traditional gene-wise selection methods using univariate analyses face difficulty to incorporate correlational, structural, or functional structures amongst the molecular measures. For microarray gene expression data, we first summarize solutions in dealing with ‘large p, small n’ problems, and then propose an integrative Bayesian variable selection (iBVS) framework for simultaneously identifying causal or marker genes and regulatory pathways. A novel partial least squares (PLS) g-prior for iBVS is developed to allow the incorporation of prior knowledge on gene-gene interactions or functional relationships. From the point view of systems biology, iBVS enables user to directly target the joint effects of multiple genes and pathways in a hierarchical modeling diagram to predict disease status or phenotype. The estimated posterior selection probabilities offer probabilitic and biological interpretations. Both simulated data and a set of microarray data in predicting stroke status are used in validating the performance of iBVS in a Probit model with binary outcomes. iBVS offers a general framework for effective discovery of various molecular biomarkers by combining data-based statistics and knowledge-based priors. Guidelines on making posterior inferences, determining Bayesian significance levels, and improving computational efficiencies are also discussed. PMID:23844055

Experimentally superposing two pure states with partial prior knowledge

NASA Astrophysics Data System (ADS)

Li, Keren; Long, Guofei; Katiyar, Hemant; Xin, Tao; Feng, Guanru; Lu, Dawei; Laflamme, Raymond

2017-02-01

Superposition, arguably the most fundamental property of quantum mechanics, lies at the heart of quantum information science. However, how to create the superposition of any two unknown pure states remains as a daunting challenge. Recently, it was proved that such a quantum protocol does not exist if the two input states are completely unknown, whereas a probabilistic protocol is still available with some prior knowledge about the input states [M. Oszmaniec et al., Phys. Rev. Lett. 116, 110403 (2016), 10.1103/PhysRevLett.116.110403]. The knowledge is that both of the two input states have nonzero overlaps with some given referential state. In this work, we experimentally realize the probabilistic protocol of superposing two pure states in a three-qubit nuclear magnetic resonance system. We demonstrate the feasibility of the protocol by preparing a families of input states, and the average fidelity between the prepared state and expected superposition state is over 99%. Moreover, we experimentally illustrate the limitation of the protocol that it is likely to fail or yields very low fidelity, if the nonzero overlaps are approaching zero. Our experimental implementation can be extended to more complex situations and other quantum systems.

Report of the matrix of biological knowledge workshop

DOE Office of Scientific and Technical Information (OSTI.GOV)

Morowitz, H.J.; Smith, T.

1987-10-30

Current understanding of biology involves complex relationships rooted in enormous amounts of data. These data include entries from biochemistry, ecology, genetics, human and veterinary medicine, molecular structure studies, agriculture, embryology, systematics, and many other disciplines. The present wealth of biological data goes beyond past accumulations now include new understandings from molecular biology. Several important biological databases are currently being supported, and more are planned; however, major problems of interdatabase communication and management efficiency abound. Few scientists are currently capable of keeping up with this ever-increasing wealth of knowledge, let alone searching it efficiently for new or unsuspected links and importantmore » analogies. Yet this is what is required if the continued rapid generation of such data is to lead most effectively to the major conceptual, medical, and agricultural advances anticipated over the coming decades in the United States. The opportunity exists to combine the potential of modern computer science, database management, and artificial intelligence in a major effort to organize the vast wealth of biological and clinical data. The time is right because the amount of data is still manageable even in its current highly-fragmented form; important hardware and computer science tools have been greatly improved; and there have been recent fundamental advances in our comprehension of biology. This latter is particularly true at the molecular level where the information for nearly all higher structure and function is encoded. The organization of all biological experimental data coordinately within a structure incorporating our current understanding - the Matrix of Biological Knowledge - will provide the data and structure for the major advances foreseen in the years ahead.« less

Student Perceived and Determined Knowledge of Biology Concepts in an Upper-Level Biology Course

ERIC Educational Resources Information Center

Ziegler, Brittany; Montplaisir, Lisa

2014-01-01

Students who lack metacognitive skills can struggle with the learning process. To be effective learners, students should recognize what they know and what they do not know. This study examines the relationship between students' perception of their knowledge and determined knowledge in an upper-level biology course utilizing a pre/posttest…

What You Know Can Hurt You: Effects of Age and Prior Knowledge on the Accuracy of Judgments of Learning

PubMed Central

Toth, Jeffrey P.; Daniels, Karen A.; Solinger, Lisa A.

2011-01-01

How do aging and prior knowledge affect memory and metamemory? We explored this question in the context of a dual-process approach to Judgments of Learning (JOLs) which require people to predict their ability to remember information at a later time. Young and older adults (n's = 36, mean ages = 20.2 & 73.1) studied the names of actors that were famous in the 1950s or 1990s, providing a JOL for each. Recognition memory for studied and unstudied actors was then assessed using a Recollect/Know/No-Memory (R/K/N) judgment task. Results showed that prior knowledge increased recollection in both age groups such that older adults recollected significantly more 1950s actors than younger adults. Also, for both age groups and both decades, actors judged R at test garnered significantly higher JOLs at study than actors judged K or N. However, while the young showed benefits of prior knowledge on relative JOL accuracy, older adults did not, showing lower levels of JOL accuracy for 1950s actors despite having higher recollection for, and knowledge about, those actors. Overall, the data suggest that prior knowledge can be a double-edged sword, increasing the availability of details that can support later recollection, but also increasing non-diagnostic feelings of familiarity that can reduce the accuracy of memory predictions. PMID:21480715

Memory integration in amnesia: Prior knowledge supports verbal short-term memory

PubMed Central

Race, Elizabeth; Palombo, Daniela J.; Cadden, Margaret; Burke, Keely; Verfaellie, Mieke

2015-01-01

Short-term memory (STM) and long-term memory (LTM) have traditionally been considered cognitively distinct. However, it is known that STM can improve when to-be-remembered information appears in contexts that make contact with prior knowledge, suggesting a more interactive relationship between STM and LTM. The current study investigated whether the ability to leverage LTM in support of STM critically depends on the integrity of the hippocampus. Specifically, we investigated whether the hippocampus differentially supports between-domain versus within-domain STM–LTM integration given prior evidence that the representational domain of the elements being integrated in memory is a critical determinant of whether memory performance depends on the hippocampus. In Experiment 1, we investigated hippocampal contributions to within-domain STM–LTM integration by testing whether immediate verbal recall of words improves in MTL amnesic patients when words are presented in familiar verbal contexts (meaningful sentences) compared to unfamiliar verbal contexts (random word lists). Patients demonstrated a robust sentence superiority effect, whereby verbal STM performance improved in familiar compared to unfamiliar verbal contexts, and the magnitude of this effect did not differ from that in controls. In Experiment 2, we investigated hippocampal contributions to between-domain STM–LTM integration by testing whether immediate verbal recall of digits improves in MTL amnesic patients when digits are presented in a familiar visuospatial context (a typical keypad layout) compared to an unfamiliar visuospatial context (a random keypad layout). Immediate verbal recall improved in both patients and controls when digits were presented in the familiar compared to the unfamiliar keypad array, indicating a preserved ability to integrate activated verbal information with stored visuospatial knowledge. Together, these results demonstrate that immediate verbal recall in amnesia can benefit

Effects of Example Variability and Prior Knowledge in How Students Learn to Solve Equations

ERIC Educational Resources Information Center

Guo, Jian-Peng; Yang, Ling-Yan; Ding, Yi

2014-01-01

Researchers have consistently demonstrated that multiple examples are better than one example in facilitating learning because the comparison evoked by multiple examples supports learning and transfer. However, research outcomes are unclear regarding the effects of example variability and prior knowledge on learning from comparing multiple…

Three forms of assessment of prior knowledge, and improved performance following an enrichment programme, of English second language biology students within the context of a marine theme

NASA Astrophysics Data System (ADS)

Feltham, Nicola F.; Downs, Colleen T.

2002-02-01

The Science Foundation Programme (SFP) was launched in 1991 at the University of Natal, Pietermaritzburg, South Africa in an attempt to equip a selected number of matriculants from historically disadvantaged schools with the skills, resources and self-confidence needed to embark on their tertiary studies. Previous research within the SFP biology component suggests that a major contributor to poor achievement and low retention rates among English second language (ESL) students in the Life Sciences is the inadequate background knowledge in natural history. In this study, SFP student background knowledge was assessed along a continuum of language dependency using a set of three probes. Improved student performance in each of the respective assessments examined the extent to which a sound natural history background facilitated meaningful learning relative to ESL proficiency. Student profiles and attitudes to biology were also examined. Results indicated that students did not perceive language to be a problem in biology. However, analysis of the student performance in the assessment probes indicated that, although the marine course provided the students with the background knowledge that they were initially lacking, they continued to perform better in the drawing and MCQ tools in the post-tests, suggesting that it is their inability to express themselves in the written form that hampers their development. These results have implications for curriculum development within the constructivist framework of the SFP.

Connecting Learning: Brain-Based Strategies for Linking Prior Knowledge in the Library Media Center

ERIC Educational Resources Information Center

Vanderbilt, Kathi L.

2005-01-01

The brain is a complex organ and learning is a complex process. While there is not complete agreement among researchers about brain-based learning and its direct connection to neuroscience, knowledge about the brain as well as the examination of cognitive psychology, anthropology, professional experience, and educational research can provide…

Negotiating the dynamics of uncomfortable knowledge: The case of dual use and synthetic biology

PubMed Central

Marris, Claire; Jefferson, Catherine; Lentzos, Filippa

2014-01-01

Institutions need to ignore some knowledge in order to function. This is “uncomfortable knowledge” because it undermines the ability of those institutions to pursue their goals (Rayner, 2012). We identify three bodies of knowledge that are relevant to understandings of the dual use threat posed by synthetic biology but are excluded from related policy discussions. We demonstrate how these “unknown knowns” constitute uncomfortable knowledge because they disrupt the simplified worldview that underpins contemporary discourse on the potential misuse of synthetic biology by malign actors. We describe how these inconvenient truths have been systematically ignored and argue that this is because they are perceived as a threat by organisations involved in the promotion of synthetic biology as well as by those involved in managing biosecurity risks. This has led to a situation where concerns about the biosecurity threat posed by synthetic biology are not only exaggerated, but are, more importantly, misplaced. This, in turn, means that related policies are misdirected and unlikely to have much impact. We focus on the dynamics of discussions about synthetic biology and dual use to demonstrate how the same “knowns” that are denied or dismissed as “unknown knowns” in certain circumstances are sometimes mobilised as “known knowns” by the same category of actors in a different context, when this serves to sustain the goals of the individuals and institutions involved. Based on our own experience, we argue that negotiating the dynamics of uncomfortable knowledge is a difficult, but necessary, component of meaningful transdisciplinary collaborations. PMID:25484910

Relationship of Prior Knowledge and Working Engineers' Learning Preferences: Implications for Designing Effective Instruction

ERIC Educational Resources Information Center

Baukal, Charles E.; Ausburn, Lynna J.

2017-01-01

Continuing engineering education (CEE) is important to ensure engineers maintain proficiency over the life of their careers. However, relatively few studies have examined designing effective training for working engineers. Research has indicated that both learner instructional preferences and prior knowledge can impact the learning process, but it…

Agent-based models in translational systems biology

PubMed Central

An, Gary; Mi, Qi; Dutta-Moscato, Joyeeta; Vodovotz, Yoram

2013-01-01

Effective translational methodologies for knowledge representation are needed in order to make strides against the constellation of diseases that affect the world today. These diseases are defined by their mechanistic complexity, redundancy, and nonlinearity. Translational systems biology aims to harness the power of computational simulation to streamline drug/device design, simulate clinical trials, and eventually to predict the effects of drugs on individuals. The ability of agent-based modeling to encompass multiple scales of biological process as well as spatial considerations, coupled with an intuitive modeling paradigm, suggests that this modeling framework is well suited for translational systems biology. This review describes agent-based modeling and gives examples of its translational applications in the context of acute inflammation and wound healing. PMID:20835989

How Prior Knowledge and Colour Contrast Interfere Visual Search Processes in Novice Learners: An Eye Tracking Study

ERIC Educational Resources Information Center

Sonmez, Duygu; Altun, Arif; Mazman, Sacide Guzin

2012-01-01

This study investigates how prior content knowledge and prior exposure to microscope slides on the phases of mitosis effect students' visual search strategies and their ability to differentiate cells that are going through any phases of mitosis. Two different sets of microscope slide views were used for this purpose; with high and low colour…

The acquisition of biological knowledge during childhood: Cognitive conflict or tabula rasa?

NASA Astrophysics Data System (ADS)

Lawson, Anton E.

Clinical interviews were conducted with three elementary school children, who varied in age but not in family or school environment, to determine the extent to which they held naive misconceptions about important biological topics and to determine agewise trends in the development of biological knowledge. Does early biological knowledge acquisition follow a pattern of spontaneous naive theory construction and cognitive conflict or does it follow a pattern of gradual accretion to an initially blank slate? Contrary to findings in the physical sciences, little evidence was found for biological misconceptions as knowledge acquisition appeared to more directly follow the gradual accretion hypothesis with the primary source of that knowledge adult authority rather than personal experience. However, conceptual change teaching is still advocated due to its ability to provoke students to consider and test alternative conceptions (even if they are not their own) as a means of encouraging the development of important general reasoning patterns utilized in the testing of causal hypotheses.

Knowledge management for systems biology a general and visually driven framework applied to translational medicine.

PubMed

Maier, Dieter; Kalus, Wenzel; Wolff, Martin; Kalko, Susana G; Roca, Josep; Marin de Mas, Igor; Turan, Nil; Cascante, Marta; Falciani, Francesco; Hernandez, Miguel; Villà-Freixa, Jordi; Losko, Sascha

2011-03-05

To enhance our understanding of complex biological systems like diseases we need to put all of the available data into context and use this to detect relations, pattern and rules which allow predictive hypotheses to be defined. Life science has become a data rich science with information about the behaviour of millions of entities like genes, chemical compounds, diseases, cell types and organs, which are organised in many different databases and/or spread throughout the literature. Existing knowledge such as genotype-phenotype relations or signal transduction pathways must be semantically integrated and dynamically organised into structured networks that are connected with clinical and experimental data. Different approaches to this challenge exist but so far none has proven entirely satisfactory. To address this challenge we previously developed a generic knowledge management framework, BioXM™, which allows the dynamic, graphic generation of domain specific knowledge representation models based on specific objects and their relations supporting annotations and ontologies. Here we demonstrate the utility of BioXM for knowledge management in systems biology as part of the EU FP6 BioBridge project on translational approaches to chronic diseases. From clinical and experimental data, text-mining results and public databases we generate a chronic obstructive pulmonary disease (COPD) knowledge base and demonstrate its use by mining specific molecular networks together with integrated clinical and experimental data. We generate the first semantically integrated COPD specific public knowledge base and find that for the integration of clinical and experimental data with pre-existing knowledge the configuration based set-up enabled by BioXM reduced implementation time and effort for the knowledge base compared to similar systems implemented as classical software development projects. The knowledgebase enables the retrieval of sub-networks including protein

Knowledge management for systems biology a general and visually driven framework applied to translational medicine

PubMed Central

2011-01-01

Background To enhance our understanding of complex biological systems like diseases we need to put all of the available data into context and use this to detect relations, pattern and rules which allow predictive hypotheses to be defined. Life science has become a data rich science with information about the behaviour of millions of entities like genes, chemical compounds, diseases, cell types and organs, which are organised in many different databases and/or spread throughout the literature. Existing knowledge such as genotype - phenotype relations or signal transduction pathways must be semantically integrated and dynamically organised into structured networks that are connected with clinical and experimental data. Different approaches to this challenge exist but so far none has proven entirely satisfactory. Results To address this challenge we previously developed a generic knowledge management framework, BioXM™, which allows the dynamic, graphic generation of domain specific knowledge representation models based on specific objects and their relations supporting annotations and ontologies. Here we demonstrate the utility of BioXM for knowledge management in systems biology as part of the EU FP6 BioBridge project on translational approaches to chronic diseases. From clinical and experimental data, text-mining results and public databases we generate a chronic obstructive pulmonary disease (COPD) knowledge base and demonstrate its use by mining specific molecular networks together with integrated clinical and experimental data. Conclusions We generate the first semantically integrated COPD specific public knowledge base and find that for the integration of clinical and experimental data with pre-existing knowledge the configuration based set-up enabled by BioXM reduced implementation time and effort for the knowledge base compared to similar systems implemented as classical software development projects. The knowledgebase enables the retrieval of sub

Developing a kidney and urinary pathway knowledge base

PubMed Central

2011-01-01

Background Chronic renal disease is a global health problem. The identification of suitable biomarkers could facilitate early detection and diagnosis and allow better understanding of the underlying pathology. One of the challenges in meeting this goal is the necessary integration of experimental results from multiple biological levels for further analysis by data mining. Data integration in the life science is still a struggle, and many groups are looking to the benefits promised by the Semantic Web for data integration. Results We present a Semantic Web approach to developing a knowledge base that integrates data from high-throughput experiments on kidney and urine. A specialised KUP ontology is used to tie the various layers together, whilst background knowledge from external databases is incorporated by conversion into RDF. Using SPARQL as a query mechanism, we are able to query for proteins expressed in urine and place these back into the context of genes expressed in regions of the kidney. Conclusions The KUPKB gives KUP biologists the means to ask queries across many resources in order to aggregate knowledge that is necessary for answering biological questions. The Semantic Web technologies we use, together with the background knowledge from the domain’s ontologies, allows both rapid conversion and integration of this knowledge base. The KUPKB is still relatively small, but questions remain about scalability, maintenance and availability of the knowledge itself. Availability The KUPKB may be accessed via http://www.e-lico.eu/kupkb. PMID:21624162

[Physicians' knowledge in Israel on the biology and control of head lice].

PubMed

Mumcuoglu, Kosta Y; Mumcuoglu, Michael; Danilevich, Maria; Gilead, Leon

2008-10-01

Health providers such as physicians, nurses and pharmacists should be knowledgeable about the biology of head lice and the ways to control them effectively, in order to reduce the proportion of children infested with head lice. To evaluate the knowledge of physicians in Israel on the biology and epidemiology of lice, as well as their experience with infested individuals and their preferences for diagnosis, prophylaxis and control. An anonymous questionnaire with 37 questions was used. The first 20 questions addressed the general knowledge of physicians on lice biology and control, while the remaining 17 questions were related to their personal experience with lice and louse treatment. Out of 273 physicians interviewed 66.8% had good knowledge of lice, while the remaining 33.2% had some knowledge on lice. The difference between the groups of physicians with medium and good knowledge on lice was borderline significant (P=0.0722), with the dermatologists borderline significantly less knowledgeable than the rest (P=0.0765). Significant differences were found between those physicians with 4-6 or 11-20 years of professional experience and the remaining groups (twice P<0.001). Although the percentage of female physicians who had a good knowledge on louse biology and control was higher than male physicians (39.4% and 29.4%, respectively), the differences were borderline significant (P=0.09). Pediatricians and dermatologists examined significantly more children than family physicians and general practitioners (P <0.001). The results of this study suggest that healthcare professionals' knowledge is of paramount importance for the correct diagnosis and control of head louse infestations.

The Influence of Prior Knowledge, Peer Review, Age, and Gender in Online Philosophy Discussions

ERIC Educational Resources Information Center

Cuddy, Lucas Stebbins

2016-01-01

Using a primarily experimental design, this study investigated whether discussion boards in online community college philosophy classes can be designed in the Blackboard course management system to lead to higher order thinking. Discussions were designed using one of two teaching techniques: the activation of prior knowledge or the use of peer…

Prior Knowledge about Objects Determines Neural Color Representation in Human Visual Cortex.

PubMed

Vandenbroucke, A R E; Fahrenfort, J J; Meuwese, J D I; Scholte, H S; Lamme, V A F

2016-04-01

To create subjective experience, our brain must translate physical stimulus input by incorporating prior knowledge and expectations. For example, we perceive color and not wavelength information, and this in part depends on our past experience with colored objects ( Hansen et al. 2006; Mitterer and de Ruiter 2008). Here, we investigated the influence of object knowledge on the neural substrates underlying subjective color vision. In a functional magnetic resonance imaging experiment, human subjects viewed a color that lay midway between red and green (ambiguous with respect to its distance from red and green) presented on either typical red (e.g., tomato), typical green (e.g., clover), or semantically meaningless (nonsense) objects. Using decoding techniques, we could predict whether subjects viewed the ambiguous color on typical red or typical green objects based on the neural response of veridical red and green. This shift of neural response for the ambiguous color did not occur for nonsense objects. The modulation of neural responses was observed in visual areas (V3, V4, VO1, lateral occipital complex) involved in color and object processing, as well as frontal areas. This demonstrates that object memory influences wavelength information relatively early in the human visual system to produce subjective color vision. © The Author 2014. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com.

Figure-ground segmentation based on class-independent shape priors

NASA Astrophysics Data System (ADS)

Li, Yang; Liu, Yang; Liu, Guojun; Guo, Maozu

2018-01-01

We propose a method to generate figure-ground segmentation by incorporating shape priors into the graph-cuts algorithm. Given an image, we first obtain a linear representation of an image and then apply directional chamfer matching to generate class-independent, nonparametric shape priors, which provide shape clues for the graph-cuts algorithm. We then enforce shape priors in a graph-cuts energy function to produce object segmentation. In contrast to previous segmentation methods, the proposed method shares shape knowledge for different semantic classes and does not require class-specific model training. Therefore, the approach obtains high-quality segmentation for objects. We experimentally validate that the proposed method outperforms previous approaches using the challenging PASCAL VOC 2010/2012 and Berkeley (BSD300) segmentation datasets.

The Influence of the Knowledge Base on the Development of Mnemonic Strategies.

ERIC Educational Resources Information Center

Ornstein, Peter A.; Naus, Mary J.

A dominant theme in cognitive psychology is that prior knowledge in long-term memory has a strong influence on an individual's cognitive processing. Citing numerous memory studies with children, knowledge base effects are presented as part of a broader picture of memory development. Using the sort/recall procedure (asking subjects to group sets of…

The Instructional Effectiveness of Animated Signaling among Learners with High and Low Prior Knowledge

ERIC Educational Resources Information Center

Li, Shanshan

2012-01-01

The purpose of this study was to investigate the instructional effectiveness of animated signals among learners with high and low prior knowledge. Each of the two treatments was presented with animated instruction either with signals or without signals on the content of how an airplane achieves lift. Subjects were eighty-seven undergraduate…

A Natural Language Interface Concordant with a Knowledge Base.

PubMed

Han, Yong-Jin; Park, Seong-Bae; Park, Se-Young

2016-01-01

The discordance between expressions interpretable by a natural language interface (NLI) system and those answerable by a knowledge base is a critical problem in the field of NLIs. In order to solve this discordance problem, this paper proposes a method to translate natural language questions into formal queries that can be generated from a graph-based knowledge base. The proposed method considers a subgraph of a knowledge base as a formal query. Thus, all formal queries corresponding to a concept or a predicate in the knowledge base can be generated prior to query time and all possible natural language expressions corresponding to each formal query can also be collected in advance. A natural language expression has a one-to-one mapping with a formal query. Hence, a natural language question is translated into a formal query by matching the question with the most appropriate natural language expression. If the confidence of this matching is not sufficiently high the proposed method rejects the question and does not answer it. Multipredicate queries are processed by regarding them as a set of collected expressions. The experimental results show that the proposed method thoroughly handles answerable questions from the knowledge base and rejects unanswerable ones effectively.

Promoting knowledge integration of scientific principles and environmental stewardship: Assessing an issue-based approach to teaching evolution and marine conservation

NASA Astrophysics Data System (ADS)

Zimmerman, Timothy David

2005-11-01

Students and citizens need to apply science to important issues every day. Yet the design of science curricula that foster integration of science and everyday decisions is not well understood. For example, can curricula be designed that help learners apply scientific reasons for choosing only environmentally sustainable seafood for dinner? Learners must develop integrated understandings of scientific principles, prior experiences, and current decisions in order to comprehend how everyday decisions impact environmental resources. In order to investigate how such integrated understandings can be promoted within school science classes, research was conducted with an inquiry-oriented curriculum that utilizes technology and a visit to an informal learning environment (aquarium) to promote the integration of scientific principles (adaptation) with environmental stewardship. This research used a knowledge integration approach to teaching and learning that provided a framework for promoting the application of science to environmental issues. Marine biology, often forsaken in classrooms for terrestrial biology, served as the scientific context for the curriculum. The curriculum design incorporated a three-phase pedagogical strategy and new technology tools to help students integrate knowledge and experiences across the classroom and aquarium learning environments. The research design and assessment protocols included comparisons among and within student populations using two versions of the curriculum: an issue-based version and a principle-based version. These inquiry curricula were tested with sophomore biology students attending a marine-focused academy within a coastal California high school. Pretest-posttest outcomes were compared between and within the curricular treatments. Additionally, comparisons were made between the inquiry groups and seniors in an Advanced Placement biology course who attend the same high school. Results indicate that the inquiry curricula

Latent tuberculosis infection screening prior to biological treatment in Tunisian patients.

PubMed

Slouma, Marwa; Mahmoud, Ines; Saidane, Olfa; Bouden, Selma; Abdelmoula, Leila

2017-10-01

The screening of latent tuberculosis infection (LTBI) is necessary to prevent infection in patients with chronic inflammatory disease (CID) undergoing biological treatment. We aimed to assess the efficacy of LTBI screening prior to biological treatment in Tunisia, considered as a high-incidence area of active TB disease. We conducted a retrospective study over a period of 8 years [2007-2014] including patients with chronic inflammatory rheumatism receiving biologic agents since at least 6 months. The screening of LTBI was performed according to national Tunisian guidelines. There were 35 men and 78 women. The mean age was 47.67±13.50 years. Rheumatoid arthritis (70.8%) was the most common cause of CID. The diagnosis of LTBI was established in 23 cases. Among these 23 patients, 12 patients had negative tuberculin skin test (TST) associated with positive QuantiFERON-TB Gold (QFT-G), 10 had TST more than 10mm, one patient had a TST between 5 and 10mm associated with positive QFT-G and one patient had a history of tuberculosis inadequately treated. Preventive anti-tuberculous therapy was prescribed before biological therapy initiation in cases of LTBI. During the follow-up period (3.91 years), no case of tuberculosis reactivation has been reported among patients diagnosed with LTBI. However, 2 cases of active pulmonary tuberculosis were reported in patients with initially negative TST and QFT-G. Our study showed that the Tunisian recommendations allowed detecting a LTBI in 20% of biologic therapy candidates. Preventive measures including screening of LTBI and eventually a prophylactic treatment improve the safety of biological treatments. Copyright © 2017 Société française de pharmacologie et de thérapeutique. Published by Elsevier Masson SAS. All rights reserved.

Memory integration in amnesia: prior knowledge supports verbal short-term memory.

PubMed

Race, Elizabeth; Palombo, Daniela J; Cadden, Margaret; Burke, Keely; Verfaellie, Mieke

2015-04-01

Short-term memory (STM) and long-term memory (LTM) have traditionally been considered cognitively distinct. However, it is known that STM can improve when to-be-remembered information appears in contexts that make contact with prior knowledge, suggesting a more interactive relationship between STM and LTM. The current study investigated whether the ability to leverage LTM in support of STM critically depends on the integrity of the hippocampus. Specifically, we investigated whether the hippocampus differentially supports between-domain versus within-domain STM-LTM integration given prior evidence that the representational domain of the elements being integrated in memory is a critical determinant of whether memory performance depends on the hippocampus. In Experiment 1, we investigated hippocampal contributions to within-domain STM-LTM integration by testing whether immediate verbal recall of words improves in MTL amnesic patients when words are presented in familiar verbal contexts (meaningful sentences) compared to unfamiliar verbal contexts (random word lists). Patients demonstrated a robust sentence superiority effect, whereby verbal STM performance improved in familiar compared to unfamiliar verbal contexts, and the magnitude of this effect did not differ from that in controls. In Experiment 2, we investigated hippocampal contributions to between-domain STM-LTM integration by testing whether immediate verbal recall of digits improves in MTL amnesic patients when digits are presented in a familiar visuospatial context (a typical keypad layout) compared to an unfamiliar visuospatial context (a random keypad layout). Immediate verbal recall improved in both patients and controls when digits were presented in the familiar compared to the unfamiliar keypad array, indicating a preserved ability to integrate activated verbal information with stored visuospatial knowledge. Together, these results demonstrate that immediate verbal recall in amnesia can benefit from two

Teachers' Beliefs about the Role of Prior Language Knowledge in Learning and How These Influence Teaching Practices

ERIC Educational Resources Information Center

De Angelis, Gessica

2011-01-01

The present study was developed to assess teachers' beliefs on (1) the role of prior language knowledge in language learning; (2) the perceived usefulness of language knowledge in modern society; and (3) the teaching practices to be used with multilingual students. Subjects were 176 secondary schoolteachers working in Italy (N = 103), Austria (N =…

Effects of Prior Knowledge and Concept-Map Structure on Disorientation, Cognitive Load, and Learning

ERIC Educational Resources Information Center

Amadieu, Franck; van Gog, Tamara; Paas, Fred; Tricot, Andre; Marine, Claudette

2009-01-01

This study explored the effects of prior knowledge (high vs. low; HPK and LPK) and concept-map structure (hierarchical vs. network; HS and NS) on disorientation, cognitive load, and learning from non-linear documents on "the infection process of a retrograde virus (HIV)". Participants in the study were 24 adults. Overall subjective ratings of…

Assessment of knowledge of participants on basic molecular biology techniques after 5-day intensive molecular biology training workshops in Nigeria.

PubMed

Yisau, J I; Adagbada, A O; Bamidele, T; Fowora, M; Brai, B I C; Adebesin, O; Bamidele, M; Fesobi, T; Nwaokorie, F O; Ajayi, A; Smith, S I

2017-07-08

The deployment of molecular biology techniques for diagnosis and research in Nigeria is faced with a number of challenges, including the cost of equipment and reagents coupled with the dearth of personnel skilled in the procedures and handling of equipment. Short molecular biology training workshops were conducted at the Nigerian Institute of Medical Research (NIMR), to improve the knowledge and skills of laboratory personnel and academics in health, research, and educational facilities. Five-day molecular biology workshops were conducted annually between 2011 and 2014, with participants drawn from health, research facilities, and the academia. The courses consisted of theoretical and practical sessions. The impact of the workshops on knowledge and skill acquisition was evaluated by pre- and post-tests which consisted of 25 multiple choice and other questions. Sixty-five participants took part in the workshops. The mean knowledge of molecular biology as evaluated by the pre- and post-test assessments were 8.4 (95% CI 7.6-9.1) and 13.0 (95 CI 11.9-14.1), respectively. The mean post-test score was significantly greater than the mean pre-test score (p < 0.0001). The five-day molecular biology workshop significantly increased the knowledge and skills of participants in molecular biology techniques. © 2017 by The International Union of Biochemistry and Molecular Biology, 45(4):313-317, 2017. © 2017 The International Union of Biochemistry and Molecular Biology.

A convenient dichotomy: critical eyes on the limits to biological knowledge

NASA Astrophysics Data System (ADS)

Milne, Catherine

2011-06-01

In The Secret Identity of a Biology Textbook: straight and naturally sexed, Jesse Bazzul and Heather Sykes conduct a case study of a biology textbook as an oppressive instructional material. Using queer theory they explore how the text of the biology textbook produces "truths" about sex, gender, and sexuality. Their analysis is complemented by the Forum papers by Jay Lemke and Francis Broadway who broaden the analysis examining the way that what counts as knowledge in science is a political decision while also encouraging authors, including Bazzul and Sykes, to also look critically at their own theoretical lenses. In this paper I pull together their ideas while exploring cultural contexts for a more nuanced representation of biological knowledge and the politics of what it means to know science.

Primary School Student Teachers' Perceived and Actual Knowledge in Biology

ERIC Educational Resources Information Center

Eija, Yli-Panula; Eila, Jeronen; Pongsakdi, Nonmanut

2017-01-01

Individuals' perceptions of their knowledge can have an important role in shaping their cognition and influencing their behaviour. However, there has been a scarcity of studies in biology on how perceived knowledge relates to actual knowledge. The focus of this article is on quantitative results analysing and interpreting student teachers'…

Direct Experience with Nature and the Development of Biological Knowledge

ERIC Educational Resources Information Center

Longbottom, Sarah E.; Slaughter, Virginia

2016-01-01

Research Findings: An emerging consensus is that casual, direct contact with nature influences the development of children's biological knowledge. Here we review the existing literature on this topic, focusing on the effects of (a) rural versus urban rearing environments and (b) pet ownership and care on children's biological concepts and…

An Investigation of the Relationships between Prior Knowledge and Vocabulary Development with Culturally Diverse Students.

ERIC Educational Resources Information Center

Karbon, Jacqueline C.

Using a semantic mapping technique for vocabulary instruction, a study explored how children of diverse groups bring different cultural backgrounds and prior knowledge to tasks involved in learning new words. The study was conducted in three sixth-grade classrooms--one containing rural Native American (especially Menominee) children, another…

What are they up to? The role of sensory evidence and prior knowledge in action understanding.

PubMed

Chambon, Valerian; Domenech, Philippe; Pacherie, Elisabeth; Koechlin, Etienne; Baraduc, Pierre; Farrer, Chlöé

2011-02-18

Explaining or predicting the behaviour of our conspecifics requires the ability to infer the intentions that motivate it. Such inferences are assumed to rely on two types of information: (1) the sensory information conveyed by movement kinematics and (2) the observer's prior expectations--acquired from past experience or derived from prior knowledge. However, the respective contribution of these two sources of information is still controversial. This controversy stems in part from the fact that "intention" is an umbrella term that may embrace various sub-types each being assigned different scopes and targets. We hypothesized that variations in the scope and target of intentions may account for variations in the contribution of visual kinematics and prior knowledge to the intention inference process. To test this hypothesis, we conducted four behavioural experiments in which participants were instructed to identify different types of intention: basic intentions (i.e. simple goal of a motor act), superordinate intentions (i.e. general goal of a sequence of motor acts), or social intentions (i.e. intentions accomplished in a context of reciprocal interaction). For each of the above-mentioned intentions, we varied (1) the amount of visual information available from the action scene and (2) participant's prior expectations concerning the intention that was more likely to be accomplished. First, we showed that intentional judgments depend on a consistent interaction between visual information and participant's prior expectations. Moreover, we demonstrated that this interaction varied according to the type of intention to be inferred, with participant's priors rather than perceptual evidence exerting a greater effect on the inference of social and superordinate intentions. The results are discussed by appealing to the specific properties of each type of intention considered and further interpreted in the light of a hierarchical model of action representation.

What Are They Up To? The Role of Sensory Evidence and Prior Knowledge in Action Understanding

PubMed Central

Chambon, Valerian; Domenech, Philippe; Pacherie, Elisabeth; Koechlin, Etienne; Baraduc, Pierre; Farrer, Chlöé

2011-01-01

Explaining or predicting the behaviour of our conspecifics requires the ability to infer the intentions that motivate it. Such inferences are assumed to rely on two types of information: (1) the sensory information conveyed by movement kinematics and (2) the observer's prior expectations – acquired from past experience or derived from prior knowledge. However, the respective contribution of these two sources of information is still controversial. This controversy stems in part from the fact that “intention” is an umbrella term that may embrace various sub-types each being assigned different scopes and targets. We hypothesized that variations in the scope and target of intentions may account for variations in the contribution of visual kinematics and prior knowledge to the intention inference process. To test this hypothesis, we conducted four behavioural experiments in which participants were instructed to identify different types of intention: basic intentions (i.e. simple goal of a motor act), superordinate intentions (i.e. general goal of a sequence of motor acts), or social intentions (i.e. intentions accomplished in a context of reciprocal interaction). For each of the above-mentioned intentions, we varied (1) the amount of visual information available from the action scene and (2) participant's prior expectations concerning the intention that was more likely to be accomplished. First, we showed that intentional judgments depend on a consistent interaction between visual information and participant's prior expectations. Moreover, we demonstrated that this interaction varied according to the type of intention to be inferred, with participant's priors rather than perceptual evidence exerting a greater effect on the inference of social and superordinate intentions. The results are discussed by appealing to the specific properties of each type of intention considered and further interpreted in the light of a hierarchical model of action representation. PMID

Stress affects the neural ensemble for integrating new information and prior knowledge.

PubMed

Vogel, Susanne; Kluen, Lisa Marieke; Fernández, Guillén; Schwabe, Lars

2018-06-01

Prior knowledge, represented as a schema, facilitates memory encoding. This schema-related learning is assumed to rely on the medial prefrontal cortex (mPFC) that rapidly integrates new information into the schema, whereas schema-incongruent or novel information is encoded by the hippocampus. Stress is a powerful modulator of prefrontal and hippocampal functioning and first studies suggest a stress-induced deficit of schema-related learning. However, the underlying neural mechanism is currently unknown. To investigate the neural basis of a stress-induced schema-related learning impairment, participants first acquired a schema. One day later, they underwent a stress induction or a control procedure before learning schema-related and novel information in the MRI scanner. In line with previous studies, learning schema-related compared to novel information activated the mPFC, angular gyrus, and precuneus. Stress, however, affected the neural ensemble activated during learning. Whereas the control group distinguished between sets of brain regions for related and novel information, stressed individuals engaged the hippocampus even when a relevant schema was present. Additionally, stressed participants displayed aberrant functional connectivity between brain regions involved in schema processing when encoding novel information. The failure to segregate functional connectivity patterns depending on the presence of prior knowledge was linked to impaired performance after stress. Our results show that stress affects the neural ensemble underlying the efficient use of schemas during learning. These findings may have relevant implications for clinical and educational settings. Copyright © 2018 Elsevier Inc. All rights reserved.

Development of Korean Rare Disease Knowledge Base

PubMed Central

Seo, Heewon; Kim, Dokyoon; Chae, Jong-Hee; Kang, Hee Gyung; Lim, Byung Chan; Cheong, Hae Il

2012-01-01

Objectives Rare disease research requires a broad range of disease-related information for the discovery of causes of genetic disorders that are maladies caused by abnormalities in genes or chromosomes. A rarity in cases makes it difficult for researchers to elucidate definite inception. This knowledge base will be a major resource not only for clinicians, but also for the general public, who are unable to find consistent information on rare diseases in a single location. Methods We design a compact database schema for faster querying; its structure is optimized to store heterogeneous data sources. Then, clinicians at Seoul National University Hospital (SNUH) review and revise those resources. Additionally, we integrated other sources to capture genomic resources and clinical trials in detail on the Korean Rare Disease Knowledge base (KRDK). Results As a result, we have developed a Web-based knowledge base, KRDK, suitable for study of Mendelian diseases that commonly occur among Koreans. This knowledge base is comprised of disease summary and review, causal gene list, laboratory and clinic directory, patient registry, and so on. Furthermore, database for analyzing and giving access to human biological information and the clinical trial management system are integrated on KRDK. Conclusions We expect that KRDK, the first rare disease knowledge base in Korea, may contribute to collaborative research and be a reliable reference for application to clinical trials. Additionally, this knowledge base is ready for querying of drug information so that visitors can search a list of rare diseases that is relative to specific drugs. Visitors can have access to KRDK via http://www.snubi.org/software/raredisease/. PMID:23346478

Current knowledge and attitudes: Russian olive biology, ecology and management

Treesearch

Sharlene E. Sing; Kevin J. Delaney

2016-01-01

The primary goals of a two-day Russian olive symposium held in February 2014 were to disseminate current knowledge and identify data gaps regarding Russian olive biology and ecology, distributions, integrated management, and to ascertain the feasibility and acceptance of a proposed program for classical biological control of Russian olive. The symposium was...

Measuring Knowledge Elaboration Based on a Computer-Assisted Knowledge Map Analytical Approach to Collaborative Learning

ERIC Educational Resources Information Center

Zheng, Lanqin; Huang, Ronghuai; Hwang, Gwo-Jen; Yang, Kaicheng

2015-01-01

The purpose of this study is to quantitatively measure the level of knowledge elaboration and explore the relationships between prior knowledge of a group, group performance, and knowledge elaboration in collaborative learning. Two experiments were conducted to investigate the level of knowledge elaboration. The collaborative learning objective in…

Overweight and obesity knowledge prior to pregnancy: a survey study.

PubMed

Nitert, Marloes Dekker; Foxcroft, Katie F; Lust, Karin; Fagermo, Narelle; Lawlor, Debbie A; O'Callaghan, Michael; McIntyre, H David; Callaway, Leonie K

2011-11-21

Overweight and obesity are associated with increased risk for pregnancy complications. Knowledge about increased risks in overweight and obese women could contribute to successful prevention strategies and the aim of this study is to assess current levels of knowledge in a pregnant population. Cross sectional survey of 412 consecutive unselected women in early pregnancy in Brisbane, Australia: 255 public women attending their first antenatal clinic visit and 157 women at private maternal fetal medicine clinics undergoing a routine ultrasound evaluation prior to 20 weeks gestation. The cohort was stratified according to pre pregnancy BMI (< 25.0 or ≥ 25.0). The main outcome measure was knowledge regarding the risks of overweight and obesity in pregnancy. Over 75% of respondents identified that obese women have an increased risk of overall complications, including gestational diabetes and hypertensive disorders of pregnancy compared to women of normal weight. More than 60% of women asserted that obesity would increase the risk of caesarean section and less than half identified an increased risk of adverse neonatal outcomes. Women were less likely to know about neonatal complications (19.7% did not know about the effect of obesity on these) than maternal complications (7.4%). Knowledge was similar amongst women recruited at the public hospital and those recruited whilst attending for an ultrasound scan at a private clinic. For most areas they were also similar between women of lower and higher BMI, but women with BMI < 25.0 were less likely to know that obesity was associated with increased rate of Caesarean section than those with higher BMI (16.8% versus 4.5%, P < 0.001). Higher educational status was associated with more knowledge of the risks of overweight and obesity in pregnancy. Many women correctly identify that overweight and obesity increases the overall risk of complications of pregnancy and childbirth. The increased risks of maternal complications

The Effects of Prior-Knowledge and Online Learning Approaches on Students' Inquiry and Argumentation Abilities

ERIC Educational Resources Information Center

Yang, Wen-Tsung; Lin, Yu-Ren; She, Hsiao-Ching; Huang, Kai-Yi

2015-01-01

This study investigated the effects of students' prior science knowledge and online learning approaches (social and individual) on their learning with regard to three topics: science concepts, inquiry, and argumentation. Two science teachers and 118 students from 4 eighth-grade science classes were invited to participate in this research. Students…

Developing Year 2 Students' Theory of Biology with Concepts of the Gene and DNA

ERIC Educational Resources Information Center

Venville, Grady; Donovan, Jenny

2007-01-01

This paper presents a case study of a teaching intervention designed to enrich Year 2 students' theory of biology through the introduction of causal mechanisms of inheritance such as the gene and DNA. The researchers worked collaboratively with the classroom teacher to design the intervention based on the students' prior knowledge of living things…

Mapping biological ideas: Concept maps as knowledge integration tools for evolution education

NASA Astrophysics Data System (ADS)

Schwendimann, Beat Adrian

-specific form of concept map, called Knowledge Integration Map (KIM), which aims to help learners connect ideas across levels (for example, genotype and phenotype levels) towards an integrated understanding of evolution. Using a design-based research approach (Brown, 1992; Cobb et al., 2003), three iterative studies were implemented in ethically and economically diverse public high schools classrooms using the web-based inquiry science environment (WISE) (Linn et al., 2003; Linn et al., 2004). Study 1 investigates concept maps as generative assessment tools. Study 1A compares the concept map generation and critique process of biology novices and experts. Findings suggest that concept maps are sensitive to different levels of knowledge integration but require scaffolding and revision. Study 1B investigates the implementation of concept maps as summative assessment tools in a WISE evolution module. Results indicate that concept maps can reveal connections between students' alternative ideas of evolution. Study 2 introduces KIMs as embedded collaborative learning tools. After generating KIMs, student dyads revise KIMs through two different critique activities (comparison against an expert or peer generated KIM). Findings indicate that different critique activities can promote the use of different criteria for critique. Results suggest that the combination of generating and critiquing KIMs can support integrating evolution ideas but can be time-consuming. As time in biology classrooms is limited, study 3 distinguishes the learning effects from either generating or critiquing KIMs as more time efficient embedded learning tools. Findings suggest that critiquing KIMs can be more time efficient than generating KIMs. Using KIMs that include common alternative ideas for critique activities can create genuine opportunities for students to critically reflect on new and existing ideas. Critiquing KIMs can encourage knowledge integration by fostering self-monitoring of students' learning

Light microscopy applications in systems biology: opportunities and challenges

PubMed Central

2013-01-01

Biological systems present multiple scales of complexity, ranging from molecules to entire populations. Light microscopy is one of the least invasive techniques used to access information from various biological scales in living cells. The combination of molecular biology and imaging provides a bottom-up tool for direct insight into how molecular processes work on a cellular scale. However, imaging can also be used as a top-down approach to study the behavior of a system without detailed prior knowledge about its underlying molecular mechanisms. In this review, we highlight the recent developments on microscopy-based systems analyses and discuss the complementary opportunities and different challenges with high-content screening and high-throughput imaging. Furthermore, we provide a comprehensive overview of the available platforms that can be used for image analysis, which enable community-driven efforts in the development of image-based systems biology. PMID:23578051

Knowledge-Based Topic Model for Unsupervised Object Discovery and Localization.

PubMed

Niu, Zhenxing; Hua, Gang; Wang, Le; Gao, Xinbo

Unsupervised object discovery and localization is to discover some dominant object classes and localize all of object instances from a given image collection without any supervision. Previous work has attempted to tackle this problem with vanilla topic models, such as latent Dirichlet allocation (LDA). However, in those methods no prior knowledge for the given image collection is exploited to facilitate object discovery. On the other hand, the topic models used in those methods suffer from the topic coherence issue-some inferred topics do not have clear meaning, which limits the final performance of object discovery. In this paper, prior knowledge in terms of the so-called must-links are exploited from Web images on the Internet. Furthermore, a novel knowledge-based topic model, called LDA with mixture of Dirichlet trees, is proposed to incorporate the must-links into topic modeling for object discovery. In particular, to better deal with the polysemy phenomenon of visual words, the must-link is re-defined as that one must-link only constrains one or some topic(s) instead of all topics, which leads to significantly improved topic coherence. Moreover, the must-links are built and grouped with respect to specific object classes, thus the must-links in our approach are semantic-specific , which allows to more efficiently exploit discriminative prior knowledge from Web images. Extensive experiments validated the efficiency of our proposed approach on several data sets. It is shown that our method significantly improves topic coherence and outperforms the unsupervised methods for object discovery and localization. In addition, compared with discriminative methods, the naturally existing object classes in the given image collection can be subtly discovered, which makes our approach well suited for realistic applications of unsupervised object discovery.Unsupervised object discovery and localization is to discover some dominant object classes and localize all of object

Knowledge Transfer in Biology and Translation across External Representations: Experts' Views and Challenges for Learning

ERIC Educational Resources Information Center

Schonborn, Konrad J.; Bogeholz, Susanne

2009-01-01

Recent curriculum reform promotes core competencies such as desired "content knowledge" and "communication" for meaningful learning in biology. Understanding in biology is demonstrated when pupils can apply acquired knowledge to new tasks. This process requires the transfer of knowledge and the subordinate process of translation across external…

Student Perceived and Determined Knowledge of Biology Concepts in an Upper-Level Biology Course

PubMed Central

Montplaisir, Lisa

2014-01-01

Students who lack metacognitive skills can struggle with the learning process. To be effective learners, students should recognize what they know and what they do not know. This study examines the relationship between students’ perception of their knowledge and determined knowledge in an upper-level biology course utilizing a pre/posttest approach. Significant differences in students’ perception of their knowledge and their determined knowledge exist at the beginning (pretest) and end (posttest) of the course. Alignment between student perception and determined knowledge was significantly more accurate on the posttest compared with the pretest. Students whose determined knowledge was in the upper quartile had significantly better alignment between their perception and determined knowledge on the pre- and posttest than students in the lower quartile. No difference exists between how students perceived their knowledge between upper- and lower-quartile students. There was a significant difference in alignment of perception and determined knowledge between males and females on the posttest, with females being more accurate in their perception of knowledge. This study provides evidence of discrepancies that exist between what students perceive they know and what they actually know. PMID:26086662

Effects of biology teachers' professional knowledge and cognitive activation on students' achievement

NASA Astrophysics Data System (ADS)

Förtsch, Christian; Werner, Sonja; von Kotzebue, Lena; Neuhaus, Birgit J.

2016-11-01

This study examined the effects of teachers' biology-specific dimensions of professional knowledge - pedagogical content knowledge (PCK) and content knowledge (CK) - and cognitively activating biology instruction, as a feature of instructional quality, on students' learning. The sample comprised 39 German secondary school teachers whose lessons on the topic neurobiology were videotaped twice. Teachers' instruction was coded with regard to cognitive activation using a rating manual. Multilevel path analysis results showed a positive significant effect of cognitive activation on students' learning and an indirect effect of teachers' PCK on students' learning mediated through cognitive activation. These findings highlight the importance of PCK in preservice biology teachers' education. Items of the rating manual may be used to provide exemplars of concrete teaching situations during university seminars for preservice teacher education or professional development initiatives for in-service teachers.

Ontological knowledge structure of intuitive biology

NASA Astrophysics Data System (ADS)

Martin, Suzanne Michele

It has become increasingly important for individuals to understand infections disease, as there has been a tremendous rise in viral and bacterial disease. This research examines systematic misconceptions regarding the characteristics of viruses and bacteria present in individuals previously educated in biological sciences at a college level. 90 pre-nursing students were administered the Knowledge Acquisition Device (KAD) which consists of 100 True/False items that included statements about the possible attributes of four entities: bacteria, virus, amoeba, and protein. Thirty pre-nursing students, who incorrectly stated that viruses were alive, were randomly assigned to three conditions. (1) exposed to information about the ontological nature of viruses, (2) Information about viruses, (3) control. In the condition that addressed the ontological nature of a virus, all of those participants were able to classify viruses correctly as not alive; however any items that required inferences, such as viruses come in male and female forms or viruses breed with each other to make baby viruses were still incorrectly answered by all conditions in the posttest. It appears that functional knowledge, ex. If a virus is alive or dead, or how it is structured, is not enough for an individual to have a full and accurate understanding of viruses. Ontological knowledge information may alter the functional knowledge but underlying inferences remain systematically incorrect.

Mechanisms underlying comprehension of health information in adulthood: the roles of prior knowledge and working memory capacity.

PubMed

Soederberg Miller, Lisa M; Gibson, Tanja N; Applegate, Elizabeth A; de Dios, Jeannette

2011-07-01

Prior knowledge, working memory capacity (WMC), and conceptual integration (attention allocated to integrating concepts in text) are critical within many contexts; however, their impact on the acquisition of health information (i.e. learning) is relatively unexplored.We examined how these factors impact learning about nutrition within a cross-sectional study of adults ages 18 to 81. Results showed that conceptual integration mediated the effects of knowledge and WMC on learning, confirming that attention to concepts while reading is important for learning about health. We also found that when knowledge was controlled, age declines in learning increased, suggesting that knowledge mitigates the effects of age on learning about nutrition.

Trust from the past: Bayesian Personalized Ranking based Link Prediction in Knowledge Graphs

DOE Office of Scientific and Technical Information (OSTI.GOV)

Zhang, Baichuan; Choudhury, Sutanay; Al-Hasan, Mohammad

2016-02-01

Estimating the confidence for a link is a critical task for Knowledge Graph construction. Link prediction, or predicting the likelihood of a link in a knowledge graph based on prior state is a key research direction within this area. We propose a Latent Feature Embedding based link recommendation model for prediction task and utilize Bayesian Personalized Ranking based optimization technique for learning models for each predicate. Experimental results on large-scale knowledge bases such as YAGO2 show that our approach achieves substantially higher performance than several state-of-art approaches. Furthermore, we also study the performance of the link prediction algorithm in termsmore » of topological properties of the Knowledge Graph and present a linear regression model to reason about its expected level of accuracy.« less

Image segmentation with a novel regularized composite shape prior based on surrogate study

DOE Office of Scientific and Technical Information (OSTI.GOV)

Zhao, Tingting, E-mail: tingtingzhao@mednet.ucla.edu; Ruan, Dan, E-mail: druan@mednet.ucla.edu

Purpose: Incorporating training into image segmentation is a good approach to achieve additional robustness. This work aims to develop an effective strategy to utilize shape prior knowledge, so that the segmentation label evolution can be driven toward the desired global optimum. Methods: In the variational image segmentation framework, a regularization for the composite shape prior is designed to incorporate the geometric relevance of individual training data to the target, which is inferred by an image-based surrogate relevance metric. Specifically, this regularization is imposed on the linear weights of composite shapes and serves as a hyperprior. The overall problem is formulatedmore » in a unified optimization setting and a variational block-descent algorithm is derived. Results: The performance of the proposed scheme is assessed in both corpus callosum segmentation from an MR image set and clavicle segmentation based on CT images. The resulted shape composition provides a proper preference for the geometrically relevant training data. A paired Wilcoxon signed rank test demonstrates statistically significant improvement of image segmentation accuracy, when compared to multiatlas label fusion method and three other benchmark active contour schemes. Conclusions: This work has developed a novel composite shape prior regularization, which achieves superior segmentation performance than typical benchmark schemes.« less

Using Multiple Lenses to Examine the Development of Beginning Biology Teachers' Pedagogical Content Knowledge for Teaching Natural Selection Simulations

NASA Astrophysics Data System (ADS)

Sickel, Aaron J.; Friedrichsen, Patricia

2018-02-01

Pedagogical content knowledge (PCK) has become a useful construct to examine science teacher learning. Yet, researchers conceptualize PCK development in different ways. The purpose of this longitudinal study was to use three analytic lenses to understand the development of three beginning biology teachers' PCK for teaching natural selection simulations. We observed three early-career biology teachers as they taught natural selection in their respective school contexts over two consecutive years. Data consisted of six interviews with each participant. Using the PCK model developed by Magnusson et al. (1999), we examined topic-specific PCK development utilizing three different lenses: (1) expansion of knowledge within an individual knowledge base, (2) integration of knowledge across knowledge bases, and (3) knowledge that explicitly addressed core concepts of natural selection. We found commonalities across the participants, yet each lens was also useful to understand the influence of different factors (e.g., orientation, subject matter preparation, and the idiosyncratic nature of teacher knowledge) on PCK development. This multi-angle approach provides implications for considering the quality of beginning science teachers' knowledge and future research on PCK development. We conclude with an argument that explicitly communicating lenses used to understand PCK development will help the research community compare analytic approaches and better understand the nature of science teacher learning.

High School Biology Students' Knowledge and Certainty about Diffusion and Osmosis Concepts

ERIC Educational Resources Information Center

Odom, Arthur L.; Barrow, Lloyd H.

2007-01-01

The purpose of this study was to investigate students' understanding about scientifically acceptable content knowledge by exploring the relationship between knowledge of diffusion and osmosis and the students' certainty in their content knowledge. Data was collected from a high school biology class with the Diffusion and Osmosis Diagnostic Test…

Developing Guided Inquiry-Based Student Lab Worksheet for Laboratory Knowledge Course

NASA Astrophysics Data System (ADS)

Rahmi, Y. L.; Novriyanti, E.; Ardi, A.; Rifandi, R.

2018-04-01

The course of laboratory knowledge is an introductory course for biology students to follow various lectures practicing in the biology laboratory. Learning activities of laboratory knowledge course at this time in the Biology Department, Universitas Negeri Padang has not been completed by supporting learning media such as student lab worksheet. Guided inquiry learning model is one of the learning models that can be integrated into laboratory activity. The study aimed to produce student lab worksheet based on guided inquiry for laboratory knowledge course and to determine the validity of lab worksheet. The research was conducted using research and developmet (R&D) model. The instruments used in data collection in this research were questionnaire for student needed analysis and questionnaire to measure the student lab worksheet validity. The data obtained was quantitative from several validators. The validators consist of three lecturers. The percentage of a student lab worksheet validity was 94.18 which can be categorized was very good.

Developmental "roots" in mature biological knowledge.

PubMed

Goldberg, Robert F; Thompson-Schill, Sharon L

2009-04-01

Young children tend to claim that moving artifacts and nonliving natural kinds are alive, but neglect to ascribe life to plants. This research tested whether adults exhibit similar confusions when verifying life status in a speeded classification task. Experiment 1 showed that undergraduates encounter greater difficulty (reduced accuracy and increased response times) in determining life status for plants, relative to animals, and for natural and moving nonliving things, relative to artifacts and non-moving things. Experiment 2 replicated these effects in university biology professors. The professors showed a significantly reduced effect size for living things, as compared with the students, but still showed greater difficulty for plants than animals, even as no differences from the students were apparent in their responses to nonliving things. These results suggest that mature biological knowledge relies on a developmental foundation that is not radically overwritten or erased with the profound conceptual changes that accompany mastery of the domain.

Needle Steering in Biological Tissue using Ultrasound-based Online Curvature Estimation

PubMed Central

Moreira, Pedro; Patil, Sachin; Alterovitz, Ron; Misra, Sarthak

2014-01-01

Percutaneous needle insertions are commonly performed for diagnostic and therapeutic purposes. Accurate placement of the needle tip is important to the success of many needle procedures. The current needle steering systems depend on needle-tissue-specific data, such as maximum curvature, that is unavailable prior to an interventional procedure. In this paper, we present a novel three-dimensional adaptive steering method for flexible bevel-tipped needles that is capable of performing accurate tip placement without previous knowledge about needle curvature. The method steers the needle by integrating duty-cycled needle steering, online curvature estimation, ultrasound-based needle tracking, and sampling-based motion planning. The needle curvature estimation is performed online and used to adapt the path and duty cycling. We evaluated the method using experiments in a homogenous gelatin phantom, a two-layer gelatin phantom, and a biological tissue phantom composed of a gelatin layer and in vitro chicken tissue. In all experiments, virtual obstacles and targets move in order to represent the disturbances that might occur due to tissue deformation and physiological processes. The average targeting error using our new adaptive method is 40% lower than using the conventional non-adaptive duty-cycled needle steering method. PMID:26229729

A Theme-Based Approach to Teaching Nonmajors Biology: Helping Students Connect Biology to Their Lives

ERIC Educational Resources Information Center

Chaplin, Susan B.; Manske, Jill M.

2005-01-01

This article describes the curriculum for a highly student-centered human biology course constructed around a series of themes that enables the integration of the same basic paradigms found in a traditional survey lecture course without sacrificing essential content. The theme-based model enhances student interest, ability to integrate knowledge,…

Ontologies, Knowledge Bases and Knowledge Management

DTIC Science & Technology

2002-07-01

AFRL-IF-RS-TR-2002-163 Final Technical Report July 2002 ONTOLOGIES, KNOWLEDGE BASES AND KNOWLEDGE MANAGEMENT USC Information ...and layer additional information necessary to make specific uses of the knowledge in this core. Finally, while we were able to find adequate solutions... knowledge base and inference engine. Figure 3.2: SDA Editor Interface 46 Although the SDA has access to information about the situation, we wanted the user

Top-down (Prior Knowledge) and Bottom-up (Perceptual Modality) Influences on Spontaneous Interpersonal Synchronization.

PubMed

Gipson, Christina L; Gorman, Jamie C; Hessler, Eric E

2016-04-01

Coordination with others is such a fundamental part of human activity that it can happen unintentionally. This unintentional coordination can manifest as synchronization and is observed in physical and human systems alike. We investigated the role of top-down influences (prior knowledge of the perceptual modality their partner is using) and bottom-up factors (perceptual modality combination) on spontaneous interpersonal synchronization. We examine this phenomena with respect to two different theoretical perspectives that differently emphasize top-down and bottom-up factors in interpersonal synchronization: joint-action/shared cognition theories and ecological-interactive theories. In an empirical study twelve dyads performed a finger oscillation task while attending to each other's movements through either visual, auditory, or visual and auditory perceptual modalities. Half of the participants were given prior knowledge of their partner's perceptual capabilities for coordinating across these different perceptual modality combinations. We found that the effect of top-down influence depends on the perceptual modality combination between two individuals. When people used the same perceptual modalities, top-down influence resulted in less synchronization and when people used different perceptual modalities, top-down influence resulted in more synchronization. Furthermore, persistence in the change in behavior as a result of having perceptual information about each other ('social memory') was stronger when this top-down influence was present.

Integrated Bio-Entity Network: A System for Biological Knowledge Discovery

PubMed Central

Bell, Lindsey; Chowdhary, Rajesh; Liu, Jun S.; Niu, Xufeng; Zhang, Jinfeng

2011-01-01

A significant part of our biological knowledge is centered on relationships between biological entities (bio-entities) such as proteins, genes, small molecules, pathways, gene ontology (GO) terms and diseases. Accumulated at an increasing speed, the information on bio-entity relationships is archived in different forms at scattered places. Most of such information is buried in scientific literature as unstructured text. Organizing heterogeneous information in a structured form not only facilitates study of biological systems using integrative approaches, but also allows discovery of new knowledge in an automatic and systematic way. In this study, we performed a large scale integration of bio-entity relationship information from both databases containing manually annotated, structured information and automatic information extraction of unstructured text in scientific literature. The relationship information we integrated in this study includes protein–protein interactions, protein/gene regulations, protein–small molecule interactions, protein–GO relationships, protein–pathway relationships, and pathway–disease relationships. The relationship information is organized in a graph data structure, named integrated bio-entity network (IBN), where the vertices are the bio-entities and edges represent their relationships. Under this framework, graph theoretic algorithms can be designed to perform various knowledge discovery tasks. We designed breadth-first search with pruning (BFSP) and most probable path (MPP) algorithms to automatically generate hypotheses—the indirect relationships with high probabilities in the network. We show that IBN can be used to generate plausible hypotheses, which not only help to better understand the complex interactions in biological systems, but also provide guidance for experimental designs. PMID:21738677

Prior Knowledge Influence on Self-Explanation Effectiveness when Solving Problems: An Exploratory Study in Science Learning

ERIC Educational Resources Information Center

Ionas, Ioan Gelu; Cernusca, Dan; Collier, Harvest L.

2012-01-01

This exploratory study presents the outcomes of using self-explanation to improve learners' performance in solving basic chemistry problems. The results of the randomized experiment show the existence of a moderation effect between prior knowledge and the level of support self-explanation provides to learners, suggestive of a synergistic effect…

The Impact of Learner's Prior Knowledge on Their Use of Chemistry Computer Simulations: A Case Study

ERIC Educational Resources Information Center

Liu, Han-Chin; Andre, Thomas; Greenbowe, Thomas

2008-01-01

It is complicated to design a computer simulation that adapts to students with different characteristics. This study documented cases that show how college students' prior chemistry knowledge level affected their interaction with peers and their approach to solving problems with the use of computer simulations that were designed to learn…

[Knowledge and power at a molecular level; biological psychiatry in a social context].

PubMed

Verhoeff, B

2009-01-01

How do we acquire our knowledge about psychiatric disorders and how did the current biologically way of thinking in psychiatry originate? With the help of the philosophy of Michel Foucault and Nikolas Rose this essay describes the conditions that made possible today's biological approach in psychiatry. It will become clear that research in the life sciences and the psychiatric knowledge arising from this research are shaped and formed in a complex network of social, economic, political and scientific forces. The biological approach to psychiatric disorders is the product of present-day relationships between scientific developments and commercial corporations.

Representations of the Nature of Scientific Knowledge in Turkish Biology Textbooks

ERIC Educational Resources Information Center

Irez, Serhat

2016-01-01

Considering the impact of textbooks on learning, this study set out to assess representations of the nature of scientific knowledge in Turkish 9th grade biology textbooks. To this end, the ten most commonly used 9th grade biology textbooks were analyzed. A qualitative research approach was utilized and the textbooks were analyzed using…

Prior Conceptual Knowledge and Textbook Search.

ERIC Educational Resources Information Center

Byrnes, James P.; Guthrie, John T.

1992-01-01

The role of a subject's conceptual knowledge in the procedural task of searching a text for information was studied for 51 college undergraduates in 2 experiments involving knowledge of anatomy. Students with more anatomical information were able to search a text more quickly. Educational implications are discussed. (SLD)

The Role of Specificity, Targeted Learning Activities, and Prior Knowledge for the Effects of Relevance Instructions

ERIC Educational Resources Information Center

Roelle, Julian; Lehmkuhl, Nina; Beyer, Martin-Uwe; Berthold, Kirsten

2015-01-01

In 2 experiments we examined the role of (a) specificity, (b) the type of targeted learning activities, and (c) learners' prior knowledge for the effects of relevance instructions on learning from instructional explanations. In Experiment 1, we recruited novices regarding the topic of atomic structure (N = 80) and found that "specific"…

"She Has to Drink Blood of the Snake": Culture and Prior Knowledge in Science|Health Education

ERIC Educational Resources Information Center

Bricker, Leah A.; Reeve, Suzanne; Bell, Philip

2014-01-01

In this analysis, we argue that science education should attend more deeply to youths' cultural resources and practices (e.g. material, social, and intellectual). Inherent in our argument is a call for revisiting conceptions of "prior knowledge" to theorize how people make sense of the complex ecologies of experience, ideas, and cultural…

The Mediation Effect of In-Game Performance between Prior Knowledge and Posttest Score. CRESST Report 819

ERIC Educational Resources Information Center

Kerr, Deirdre; Chung, Gregory K. W. K.

2012-01-01

Though video games are commonly considered to hold great potential as learning environments, their effectiveness as a teaching tool has yet to be determined. One reason for this is that researchers often run into the problem of multicollinearity between prior knowledge, in-game performance, and posttest scores, thereby making the determination of…

A knowledge-based system for patient image pre-fetching in heterogeneous database environments--modeling, design, and evaluation.

PubMed

Wei, C P; Hu, P J; Sheng, O R

2001-03-01

When performing primary reading on a newly taken radiological examination, a radiologist often needs to reference relevant prior images of the same patient for confirmation or comparison purposes. Support of such image references is of clinical importance and may have significant effects on radiologists' examination reading efficiency, service quality, and work satisfaction. To effectively support such image reference needs, we proposed and developed a knowledge-based patient image pre-fetching system, addressing several challenging requirements of the application that include representation and learning of image reference heuristics and management of data-intensive knowledge inferencing. Moreover, the system demands an extensible and maintainable architecture design capable of effectively adapting to a dynamic environment characterized by heterogeneous and autonomous data source systems. In this paper, we developed a synthesized object-oriented entity- relationship model, a conceptual model appropriate for representing radiologists' prior image reference heuristics that are heuristic oriented and data intensive. We detailed the system architecture and design of the knowledge-based patient image pre-fetching system. Our architecture design is based on a client-mediator-server framework, capable of coping with a dynamic environment characterized by distributed, heterogeneous, and highly autonomous data source systems. To adapt to changes in radiologists' patient prior image reference heuristics, ID3-based multidecision-tree induction and CN2-based multidecision induction learning techniques were developed and evaluated. Experimentally, we examined effects of the pre-fetching system we created on radiologists' examination readings. Preliminary results show that the knowledge-based patient image pre-fetching system more accurately supports radiologists' patient prior image reference needs than the current practice adopted at the study site and that radiologists may

When Relationships Depicted Diagrammatically Conflict with Prior Knowledge: An Investigation of Students' Interpretations of Evolutionary Trees

ERIC Educational Resources Information Center

Novick, Laura R.; Catley, Kefyn M.

2014-01-01

Science is an important domain for investigating students' responses to information that contradicts their prior knowledge. In previous studies of this topic, this information was communicated verbally. The present research used diagrams, specifically trees (cladograms) depicting evolutionary relationships among taxa. Effects of college…

Temporal Learning in 4 1/2- and 6-Year-Old Children: Role of Instructions and Prior Knowledge.

ERIC Educational Resources Information Center

Droit, Sylvie; And Others

1990-01-01

Examined the role of prior temporal knowledge of 4 1/2- and 6-year-olds through the use of high-rate, interval, and minimal instructions in a fixed-interval training schedule. Determined that the subjects' learning depended on their verbal self-control skills. (BC)

Pathway-based analyses.

PubMed

Kent, Jack W

2016-02-03

New technologies for acquisition of genomic data, while offering unprecedented opportunities for genetic discovery, also impose severe burdens of interpretation and penalties for multiple testing. The Pathway-based Analyses Group of the Genetic Analysis Workshop 19 (GAW19) sought reduction of multiple-testing burden through various approaches to aggregation of highdimensional data in pathways informed by prior biological knowledge. Experimental methods testedincluded the use of "synthetic pathways" (random sets of genes) to estimate power and false-positive error rate of methods applied to simulated data; data reduction via independent components analysis, single-nucleotide polymorphism (SNP)-SNP interaction, and use of gene sets to estimate genetic similarity; and general assessment of the efficacy of prior biological knowledge to reduce the dimensionality of complex genomic data. The work of this group explored several promising approaches to managing high-dimensional data, with the caveat that these methods are necessarily constrained by the quality of external bioinformatic annotation.

The Impacts of Virtual Manipulatives and Prior Knowledge on Geometry Learning Performance in Junior High School

ERIC Educational Resources Information Center

Lee, Chun-Yi; Chen, Ming-Jang

2014-01-01

Previous studies on the effects of virtual and physical manipulatives have failed to consider the impact of prior knowledge on the efficacy of manipulatives. This study focuses on the learning of plane geometry in junior high schools, including the sum of interior angles in polygons, the sum of exterior angles in polygons, and the properties of…

Visual representations in science education: The influence of prior knowledge and cognitive load theory on instructional design principles

NASA Astrophysics Data System (ADS)

Cook, Michelle Patrick

2006-11-01

Visual representations are essential for communicating ideas in the science classroom; however, the design of such representations is not always beneficial for learners. This paper presents instructional design considerations providing empirical evidence and integrating theoretical concepts related to cognitive load. Learners have a limited working memory, and instructional representations should be designed with the goal of reducing unnecessary cognitive load. However, cognitive architecture alone is not the only factor to be considered; individual differences, especially prior knowledge, are critical in determining what impact a visual representation will have on learners' cognitive structures and processes. Prior knowledge can determine the ease with which learners can perceive and interpret visual representations in working memory. Although a long tradition of research has compared experts and novices, more research is necessary to fully explore the expert-novice continuum and maximize the potential of visual representations.

The Effects of Prior Knowledge and Instruction on Understanding Image Formation.

ERIC Educational Resources Information Center

Galili, Igal; And Others

1993-01-01

Reports a study (n=27) concerning the knowledge about image formation exhibited by students following instruction in geometrical optics in an activity-based college physics course for prospective elementary teachers. Student diagrams and verbal comments indicate their knowledge can be described as an intermediate state: a hybridization of…

Logarithmic Laplacian Prior Based Bayesian Inverse Synthetic Aperture Radar Imaging.

PubMed

Zhang, Shuanghui; Liu, Yongxiang; Li, Xiang; Bi, Guoan

2016-04-28

This paper presents a novel Inverse Synthetic Aperture Radar Imaging (ISAR) algorithm based on a new sparse prior, known as the logarithmic Laplacian prior. The newly proposed logarithmic Laplacian prior has a narrower main lobe with higher tail values than the Laplacian prior, which helps to achieve performance improvement on sparse representation. The logarithmic Laplacian prior is used for ISAR imaging within the Bayesian framework to achieve better focused radar image. In the proposed method of ISAR imaging, the phase errors are jointly estimated based on the minimum entropy criterion to accomplish autofocusing. The maximum a posterior (MAP) estimation and the maximum likelihood estimation (MLE) are utilized to estimate the model parameters to avoid manually tuning process. Additionally, the fast Fourier Transform (FFT) and Hadamard product are used to minimize the required computational efficiency. Experimental results based on both simulated and measured data validate that the proposed algorithm outperforms the traditional sparse ISAR imaging algorithms in terms of resolution improvement and noise suppression.

WE-F-BRB-00: New Developments in Knowledge-Based Treatment Planning and Automation

DOE Office of Scientific and Technical Information (OSTI.GOV)

NONE

2015-06-15

Advancements in informatics in radiotherapy are opening up opportunities to improve our ability to assess treatment plans. Models on individualizing patient dose constraints from prior patient data and shape relationships have been extensively researched and are now making their way into commercial products. New developments in knowledge based treatment planning involve understanding the impact of the radiation dosimetry on the patient. Akin to radiobiology models that have driven intensity modulated radiotherapy optimization, toxicity and outcome predictions based on treatment plans and prior patient experiences may be the next step in knowledge based planning. In order to realize these predictions, itmore » is necessary to understand how the clinical information can be captured, structured and organized with ontologies and databases designed for recall. Large databases containing radiation dosimetry and outcomes present the opportunity to evaluate treatment plans against predictions of toxicity and disease response. Such evaluations can be based on dose volume histogram or even the full 3-dimensional dose distribution and its relation to the critical anatomy. This session will provide an understanding of ontologies and standard terminologies used to capture clinical knowledge into structured databases; How data can be organized and accessed to utilize the knowledge in planning; and examples of research and clinical efforts to incorporate that clinical knowledge into planning for improved care for our patients. Learning Objectives: Understand the role of standard terminologies, ontologies and data organization in oncology Understand methods to capture clinical toxicity and outcomes in a clinical setting Understand opportunities to learn from clinical data and its application to treatment planning Todd McNutt receives funding from Philips, Elekta and Toshiba for some of the work presented.« less

Does the transition to an active-learning environment for the introductory course reduce students' overall knowledge of the various disciplines in biology?

PubMed

Simurda, Maryanne C

2012-01-01

As biology education is being redesigned toward an interdisciplinary focus and as pedagogical trends move toward active-learning strategies and investigative experiences, a restructuring of the course content for the Introductory Biology course is necessary. The introductory course in biology has typically been a survey of all the biosciences. If the total number of topics covered is reduced, is the students' overall knowledge of biology also reduced? Our introductory course has been substantially modified away from surveying the biological sciences and toward providing a deep understanding of a particular biological topic, as well as focusing on developing students' analytical and communication skills. Because of this shift to a topic-driven approach for the introductory course, we were interested in assessing our graduating students' overall knowledge of the various biological disciplines. Using the Major Field Test - Biology (Educational Testing Service (ETS), Princeton, NJ), we compared the test performance of graduating students who had a traditional lecture-based introductory course to those who had a topic-driven active-learning introductory course. Our results suggest that eliminating the traditional survey of biology and, instead, focusing on quantitative and writing skills at the introductory level do not affect our graduating students' overall breadth of knowledge of the various biosciences.

Prototype Biology-Based Radiation Risk Module Project

NASA Technical Reports Server (NTRS)

Terrier, Douglas; Clayton, Ronald G.; Patel, Zarana; Hu, Shaowen; Huff, Janice

2015-01-01

Biological effects of space radiation and risk mitigation are strategic knowledge gaps for the Evolvable Mars Campaign. The current epidemiology-based NASA Space Cancer Risk (NSCR) model contains large uncertainties (HAT #6.5a) due to lack of information on the radiobiology of galactic cosmic rays (GCR) and lack of human data. The use of experimental models that most accurately replicate the response of human tissues is critical for precision in risk projections. Our proposed study will compare DNA damage, histological, and cell kinetic parameters after irradiation in normal 2D human cells versus 3D tissue models, and it will use a multi-scale computational model (CHASTE) to investigate various biological processes that may contribute to carcinogenesis, including radiation-induced cellular signaling pathways. This cross-disciplinary work, with biological validation of an evolvable mathematical computational model, will help reduce uncertainties within NSCR and aid risk mitigation for radiation-induced carcinogenesis.

A schema theory analysis of students' think aloud protocols in an STS biology context

NASA Astrophysics Data System (ADS)

Quinlan, Catherine Louise

This dissertation study is a conglomerate of the fields of Science Education and Applied Cognitive Psychology. The goal of this study is to determine what organizational features and knowledge representation patterns high school students exhibit over time for issues pertinent to science and society. Participants are thirteen tenth grade students in a diverse suburban-urban classroom in a northeastern state. Students' think alouds are recorded, pre-, post-, and late-post treatment. Treatment consists of instruction in three Science, Technology, and Society (STS) biology issues, namely the human genome project, nutrition and health, and stem cell research. Coding and analyses are performed using Marshall's knowledge representations---identification knowledge, elaboration knowledge, planning knowledge, and execution knowledge, as well as qualitative research analysis methods. Schema theory, information processing theory, and other applied cognitive theory provide a framework in which to understand and explain students' schema descriptions and progressions over time. The results show that students display five organizational features in their identification and elaboration knowledge. Students also fall into one of four categories according to if they display prior schema or no prior schema, and their orientation "for" or "against," some of the issues. Students with prior schema and orientation "against" display the most robust schema descriptions and schema progressions. Those with no prior schemas and orientation "against" show very modest schema progressions best characterized by their keyword searches. This study shows the importance in considering not only students' integrated schemas but also their individual schemes. A role for the use of a more schema-based instruction that scaffolds student learning is implicated.

Improving Students' Critical Thinking Skills through Remap NHT in Biology Classroom

ERIC Educational Resources Information Center

Mahanal, Susriyati; Zubaidah, Siti; Bahri, Arsad; Syahadatud Dinnurriya, Maratusy

2016-01-01

Previous studies in Malang, Indonesia, showed that there were the failure biology learning caused by not only the low students' prior knowledge, but also biology learning model has not improved the students' critical thinking skills yet, which affected the low of cognitive learning outcomes. The learning model is required to improve students'…

Local knowledge and perception of biological soil crusts by land users in the Sahel (Niger)

NASA Astrophysics Data System (ADS)

J-M Ambouta, K.; Hassan Souley, B.; Malam Issa, O.; Rajot, J. L.; Mohamadou, A.

2012-04-01

Local knowledge, i.e. knowledge based on accumulation of observations is of great interest for many scientific fields as it can help for identification, evaluation and selection of relevant indicators and furthermore for progress through conservation goals. This study aimed at gathering and understanding the local knowledge and perception of biological soil crusts (BSC) by users of land, pastoralists that cross the Sahel and sedentary farmers. The methodological approach is based on a semi-direct surveys conducted on a north-south rainfall gradient (350 to 650 mm/year) including agricultural- and pastoral-dominated areas in western Niger. Denomination, formation processes, occurrence, distribution and role of biological soil crusts are among the major issues of the inquiry. The results of the surveys showed that BSC are mainly identified by the names of "Bankwado" and "Korobanda", respectively in hausa and zarma langages, what means "toad back". Other denominations varying according to region, ethnic groups and users are used. They are all related to the aspects, colors and behaviour of BSC with regard wetting and drying cycle. From the point of view of users depressed areas and land lied fallow are favourable places for the occurrence of BSC, while cultivation and observed changes in rainfall regimes represent negative factors. The formation processes of BSC are mainly related to the occurrence and the impact of rain and wind on soil surface. Their roles in protecting soil against degradation or as an indicator of soil fertility were recognised by at least 83% of farmers and breeders. This study reveals significant aspects of BSC already validated by scientific knowledge. Integrating the two forms of knowledge will help to define relevant indicators of soil surface dynamics and to perform practices to minimize farming and grazing impacts on BSCs.

Incorporating linguistic knowledge for learning distributed word representations.

PubMed

Wang, Yan; Liu, Zhiyuan; Sun, Maosong

2015-01-01

Combined with neural language models, distributed word representations achieve significant advantages in computational linguistics and text mining. Most existing models estimate distributed word vectors from large-scale data in an unsupervised fashion, which, however, do not take rich linguistic knowledge into consideration. Linguistic knowledge can be represented as either link-based knowledge or preference-based knowledge, and we propose knowledge regularized word representation models (KRWR) to incorporate these prior knowledge for learning distributed word representations. Experiment results demonstrate that our estimated word representation achieves better performance in task of semantic relatedness ranking. This indicates that our methods can efficiently encode both prior knowledge from knowledge bases and statistical knowledge from large-scale text corpora into a unified word representation model, which will benefit many tasks in text mining.

Incorporating Linguistic Knowledge for Learning Distributed Word Representations

PubMed Central

Wang, Yan; Liu, Zhiyuan; Sun, Maosong

2015-01-01

Combined with neural language models, distributed word representations achieve significant advantages in computational linguistics and text mining. Most existing models estimate distributed word vectors from large-scale data in an unsupervised fashion, which, however, do not take rich linguistic knowledge into consideration. Linguistic knowledge can be represented as either link-based knowledge or preference-based knowledge, and we propose knowledge regularized word representation models (KRWR) to incorporate these prior knowledge for learning distributed word representations. Experiment results demonstrate that our estimated word representation achieves better performance in task of semantic relatedness ranking. This indicates that our methods can efficiently encode both prior knowledge from knowledge bases and statistical knowledge from large-scale text corpora into a unified word representation model, which will benefit many tasks in text mining. PMID:25874581

Spatially adapted augmentation of age-specific atlas-based segmentation using patch-based priors

NASA Astrophysics Data System (ADS)

Liu, Mengyuan; Seshamani, Sharmishtaa; Harrylock, Lisa; Kitsch, Averi; Miller, Steven; Chau, Van; Poskitt, Kenneth; Rousseau, Francois; Studholme, Colin

2014-03-01

One of the most common approaches to MRI brain tissue segmentation is to employ an atlas prior to initialize an Expectation- Maximization (EM) image labeling scheme using a statistical model of MRI intensities. This prior is commonly derived from a set of manually segmented training data from the population of interest. However, in cases where subject anatomy varies significantly from the prior anatomical average model (for example in the case where extreme developmental abnormalities or brain injuries occur), the prior tissue map does not provide adequate information about the observed MRI intensities to ensure the EM algorithm converges to an anatomically accurate labeling of the MRI. In this paper, we present a novel approach for automatic segmentation of such cases. This approach augments the atlas-based EM segmentation by exploring methods to build a hybrid tissue segmentation scheme that seeks to learn where an atlas prior fails (due to inadequate representation of anatomical variation in the statistical atlas) and utilize an alternative prior derived from a patch driven search of the atlas data. We describe a framework for incorporating this patch-based augmentation of EM (PBAEM) into a 4D age-specific atlas-based segmentation of developing brain anatomy. The proposed approach was evaluated on a set of MRI brain scans of premature neonates with ages ranging from 27.29 to 46.43 gestational weeks (GWs). Results indicated superior performance compared to the conventional atlas-based segmentation method, providing improved segmentation accuracy for gray matter, white matter, ventricles and sulcal CSF regions.

Cooperative Knowledge Bases.

DTIC Science & Technology

1988-02-01

intellegent knowledge bases. The present state of our system for concurrent evaluation of a knowledge base of logic clauses using static allocation...de Kleer, J., An assumption-based TMS, Artificial Intelligence, Vol. 28, No. 2, 1986. [Doyle 79) Doyle, J. A truth maintenance system, Artificial

Developmental “Roots” in Mature Biological Knowledge

PubMed Central

Goldberg, Robert F.; Thompson-Schill, Sharon L.

2009-01-01

Young children tend to claim that moving artifacts and nonliving natural kinds are alive, but neglect to ascribe life to plants. This research tested whether adults exhibit similar confusions when verifying life status in a speeded classification task. Experiment 1 showed that undergraduates encounter greater difficulty (reduced accuracy and increased response times) in determining life status for plants, relative to animals, and for natural and moving nonliving things, relative to artifacts and non-moving things. Experiment 2 replicated these effects in university biology professors. The professors showed a significantly reduced effect size for living things, as compared with the students, but still showed greater difficulty for plants than animals, even as no differences from the students were apparent in their responses to nonliving things. These results suggest that mature biological knowledge relies on a developmental foundation that is not radically overwritten or erased with the profound conceptual changes that accompany mastery of the domain. PMID:19399979

Ligand placement based on prior structures: the guided ligand-replacement method

DOE Office of Scientific and Technical Information (OSTI.GOV)

Klei, Herbert E.; Bristol-Myers Squibb, Princeton, NJ 08543-4000; Moriarty, Nigel W., E-mail: nwmoriarty@lbl.gov

2014-01-01

A new module, Guided Ligand Replacement (GLR), has been developed in Phenix to increase the ease and success rate of ligand placement when prior protein-ligand complexes are available. The process of iterative structure-based drug design involves the X-ray crystal structure determination of upwards of 100 ligands with the same general scaffold (i.e. chemotype) complexed with very similar, if not identical, protein targets. In conjunction with insights from computational models and assays, this collection of crystal structures is analyzed to improve potency, to achieve better selectivity and to reduce liabilities such as absorption, distribution, metabolism, excretion and toxicology. Current methods formore » modeling ligands into electron-density maps typically do not utilize information on how similar ligands bound in related structures. Even if the electron density is of sufficient quality and resolution to allow de novo placement, the process can take considerable time as the size, complexity and torsional degrees of freedom of the ligands increase. A new module, Guided Ligand Replacement (GLR), was developed in Phenix to increase the ease and success rate of ligand placement when prior protein–ligand complexes are available. At the heart of GLR is an algorithm based on graph theory that associates atoms in the target ligand with analogous atoms in the reference ligand. Based on this correspondence, a set of coordinates is generated for the target ligand. GLR is especially useful in two situations: (i) modeling a series of large, flexible, complicated or macrocyclic ligands in successive structures and (ii) modeling ligands as part of a refinement pipeline that can automatically select a reference structure. Even in those cases for which no reference structure is available, if there are multiple copies of the bound ligand per asymmetric unit GLR offers an efficient way to complete the model after the first ligand has been placed. In all of these applications

Improved compressed sensing-based cone-beam CT reconstruction using adaptive prior image constraints

NASA Astrophysics Data System (ADS)

Lee, Ho; Xing, Lei; Davidi, Ran; Li, Ruijiang; Qian, Jianguo; Lee, Rena

2012-04-01

Volumetric cone-beam CT (CBCT) images are acquired repeatedly during a course of radiation therapy and a natural question to ask is whether CBCT images obtained earlier in the process can be utilized as prior knowledge to reduce patient imaging dose in subsequent scans. The purpose of this work is to develop an adaptive prior image constrained compressed sensing (APICCS) method to solve this problem. Reconstructed images using full projections are taken on the first day of radiation therapy treatment and are used as prior images. The subsequent scans are acquired using a protocol of sparse projections. In the proposed APICCS algorithm, the prior images are utilized as an initial guess and are incorporated into the objective function in the compressed sensing (CS)-based iterative reconstruction process. Furthermore, the prior information is employed to detect any possible mismatched regions between the prior and current images for improved reconstruction. For this purpose, the prior images and the reconstructed images are classified into three anatomical regions: air, soft tissue and bone. Mismatched regions are identified by local differences of the corresponding groups in the two classified sets of images. A distance transformation is then introduced to convert the information into an adaptive voxel-dependent relaxation map. In constructing the relaxation map, the matched regions (unchanged anatomy) between the prior and current images are assigned with smaller weight values, which are translated into less influence on the CS iterative reconstruction process. On the other hand, the mismatched regions (changed anatomy) are associated with larger values and the regions are updated more by the new projection data, thus avoiding any possible adverse effects of prior images. The APICCS approach was systematically assessed by using patient data acquired under standard and low-dose protocols for qualitative and quantitative comparisons. The APICCS method provides an

Exploring Biology Teachers' Pedagogical Content Knowledge in the Teaching of Genetics in Swaziland Science Classrooms

ERIC Educational Resources Information Center

Mthethwa-Kunene, Eunice; Onwu, Gilbert Oke; de Villiers, Rian

2015-01-01

This study explored the pedagogical content knowledge (PCK) and its development of four experienced biology teachers in the context of teaching school genetics. PCK was defined in terms of teacher content knowledge, pedagogical knowledge and knowledge of students' preconceptions and learning difficulties. Data sources of teacher knowledge base…

Effects of Prior Knowledge of Topics and the Instructional Objectives on Students' Achievement in Literature-in-English

ERIC Educational Resources Information Center

Mbah, Blessing Akaraka

2015-01-01

This study investigated the effects of prior knowledge of topics with their instructional objectives on senior secondary school class two (SS II) students. The study was carried out in Abakaliki Education Zone of Ebonyi State, Nigeria. The design of the study is quasi experimental of pretest-posttest of non-equivalent control group. Two research…

Knowledge-based processing for aircraft flight control

NASA Technical Reports Server (NTRS)

Painter, John H.

1991-01-01

The purpose is to develop algorithms and architectures for embedding artificial intelligence in aircraft guidance and control systems. With the approach adopted, AI-computing is used to create an outer guidance loop for driving the usual aircraft autopilot. That is, a symbolic processor monitors the operation and performance of the aircraft. Then, based on rules and other stored knowledge, commands are automatically formulated for driving the autopilot so as to accomplish desired flight operations. The focus is on developing a software system which can respond to linguistic instructions, input in a standard format, so as to formulate a sequence of simple commands to the autopilot. The instructions might be a fairly complex flight clearance, input either manually or by data-link. Emphasis is on a software system which responds much like a pilot would, employing not only precise computations, but, also, knowledge which is less precise, but more like common-sense. The approach is based on prior work to develop a generic 'shell' architecture for an AI-processor, which may be tailored to many applications by describing the application in appropriate processor data bases (libraries). Such descriptions include numerical models of the aircraft and flight control system, as well as symbolic (linguistic) descriptions of flight operations, rules, and tactics.

Subject-specific pedagogical content knowledge: Implications for alternatively and traditionally trained biology teachers

NASA Astrophysics Data System (ADS)

Ravgiala, Rebekah Rae

Theories regarding the development of expertise hold implications for alternative and traditional certification programs and the teachers they train. The literature suggests that when compared to experts in the field of teaching, the behaviors of novices differ in ways that are directly attributed to their pedagogical content knowledge. However, few studies have examined how first and second year biology teachers entering the profession from traditional and alternative training differ in their demonstration of subject-specific pedagogical content knowledge. The research problem in this multicase, naturalistic inquiry investigated how subject-specific pedagogical content knowledge was manifested among first and second year biology teachers in the task of transforming subject matter into forms that are potentially meaningful to students when explicit formal training has been and has not been imparted to them as preservice teachers. Two first year and two second year biology teachers were the subjects of this investigation. Allen and Amber obtained their certification through an alternative summer training institute in consecutive years. Tiffany and Tricia obtained their certification through a traditional, graduate level training program in consecutive years. Both programs were offered at the same northeastern state university. Participants contributed to six data gathering techniques including an initial semi-structured interview, responses to the Conceptions of Teaching Science questionnaire (Hewson & Hewson, 1989), three videotaped biology lessons, evaluation of three corresponding lesson plans, and a final semi-structured interview conducted at the end of the investigation. An informal, end-of-study survey intended to offer participants an opportunity to disclose their thoughts and needs as first year teachers was also employed. Results indicate that while conceptions of teaching science may vary slightly among participants, there is no evidence to suggest that

The effects of prior knowledge on study-time allocation and free recall: investigating the discrepancy reduction model.

PubMed

Verkoeijen, Peter P J L; Rikers, Remy M J P; Schmidt, Henk G

2005-01-01

In this study, the authors examined the influence of prior knowledge activation on information processing by means of a prior knowledge activation procedure adopted from the read-generate paradigm. On the basis of cue-target pairs, participants in the experimental groups generated two different sets of items before studying a relevant list. Subsequently, participants were informed that they had to study the items in the list and that they should try to remember as many items as possible. The authors assessed the processing time allocated to the items in the list and free recall of those items. The results revealed that the experimental groups spent less time on items that had already been activated. In addition, the experimental groups outperformed the control group in overall free recall and in free recall of the activated items. Between-group comparisons did not demonstrate significant effects with respect to the processing time and free recall of nonactivated items. The authors interpreted these results in terms of the discrepancy reduction model of regulating the amount of processing time allocated to different parts of the list.

Students' Ability to Solve Process-Diagram Problems in Secondary Biology Education

ERIC Educational Resources Information Center

Kragten, Marco; Admiraal, Wilfried; Rijlaarsdam, Gert

2015-01-01

Process diagrams are important tools in biology for explaining processes such as protein synthesis, compound cycles and the like. The aim of the present study was to measure the ability to solve process-diagram problems in biology and its relationship with prior knowledge, spatial ability and working memory. For this purpose, we developed a test…

Structural and Network-based Methods for Knowledge-Based Systems

DTIC Science & Technology

2011-12-01

depth) provide important information about knowledge gaps in the KB. For example, if SuccessEstimate (causes-EventEvent, Typhoid - Fever , 1, 3) is...equal to 0, it points toward lack of biological knowledge about Typhoid - Fever in our KB. Similar information can also be obtained from the...position of the consequent. ⋃ ( ( ) ) Therefore, if Q does not contain Typhoid - Fever , then obtaining

Using diagrams versus text for spaced restudy: Effects on learning in 10th grade biology classes.

PubMed

Bergey, Bradley W; Cromley, Jennifer G; Kirchgessner, Mandy L; Newcombe, Nora S

2015-03-01

Spaced restudy has been typically tested with written learning materials, but restudy with visual representations in actual classrooms is under-researched. We compared the effects of two spaced restudy interventions: A Diagram-Based Restudy (DBR) warm-up condition and a business-as-usual Text-Based Restudy (TBR) warm-up condition. One hundred and twenty-eight consented high school students in 15 classes. Students completed daily warm-ups over a 4-week period. Students were randomly assigned to conditions within classrooms. Warm-ups were independently completed at the start of class meetings and consisted of questions about content covered 1-10 days prior to each warm-up. Students received feedback on their answers each week. A series of ANOVAs and ANCOVAs was conducted. Results showed equal and significant growth from pre- to post-test for both conditions (d = .31-.67) on three outcomes: Biology knowledge, biology diagram comprehension (near transfer), and geology diagram comprehension (far transfer). ANCOVA results suggested that the magnitude of this increase was linked to the number of questions attempted during the intervention. For the DBR condition only, there were interactions with content knowledge on diagram comprehension gain scores and interactions with spatial scores on biology knowledge gain scores. Students with lower biology knowledge and lower Paper Folding Test scores were disadvantaged in the DBR condition, whereas the TBR condition was equitable across all levels of knowledge and spatial ability. © 2014 The British Psychological Society.

Integration of genome-wide association studies with biological knowledge identifies six novel genes related to kidney function.

PubMed

Chasman, Daniel I; Fuchsberger, Christian; Pattaro, Cristian; Teumer, Alexander; Böger, Carsten A; Endlich, Karlhans; Olden, Matthias; Chen, Ming-Huei; Tin, Adrienne; Taliun, Daniel; Li, Man; Gao, Xiaoyi; Gorski, Mathias; Yang, Qiong; Hundertmark, Claudia; Foster, Meredith C; O'Seaghdha, Conall M; Glazer, Nicole; Isaacs, Aaron; Liu, Ching-Ti; Smith, Albert V; O'Connell, Jeffrey R; Struchalin, Maksim; Tanaka, Toshiko; Li, Guo; Johnson, Andrew D; Gierman, Hinco J; Feitosa, Mary F; Hwang, Shih-Jen; Atkinson, Elizabeth J; Lohman, Kurt; Cornelis, Marilyn C; Johansson, Asa; Tönjes, Anke; Dehghan, Abbas; Lambert, Jean-Charles; Holliday, Elizabeth G; Sorice, Rossella; Kutalik, Zoltan; Lehtimäki, Terho; Esko, Tõnu; Deshmukh, Harshal; Ulivi, Sheila; Chu, Audrey Y; Murgia, Federico; Trompet, Stella; Imboden, Medea; Coassin, Stefan; Pistis, Giorgio; Harris, Tamara B; Launer, Lenore J; Aspelund, Thor; Eiriksdottir, Gudny; Mitchell, Braxton D; Boerwinkle, Eric; Schmidt, Helena; Cavalieri, Margherita; Rao, Madhumathi; Hu, Frank; Demirkan, Ayse; Oostra, Ben A; de Andrade, Mariza; Turner, Stephen T; Ding, Jingzhong; Andrews, Jeanette S; Freedman, Barry I; Giulianini, Franco; Koenig, Wolfgang; Illig, Thomas; Meisinger, Christa; Gieger, Christian; Zgaga, Lina; Zemunik, Tatijana; Boban, Mladen; Minelli, Cosetta; Wheeler, Heather E; Igl, Wilmar; Zaboli, Ghazal; Wild, Sarah H; Wright, Alan F; Campbell, Harry; Ellinghaus, David; Nöthlings, Ute; Jacobs, Gunnar; Biffar, Reiner; Ernst, Florian; Homuth, Georg; Kroemer, Heyo K; Nauck, Matthias; Stracke, Sylvia; Völker, Uwe; Völzke, Henry; Kovacs, Peter; Stumvoll, Michael; Mägi, Reedik; Hofman, Albert; Uitterlinden, Andre G; Rivadeneira, Fernando; Aulchenko, Yurii S; Polasek, Ozren; Hastie, Nick; Vitart, Veronique; Helmer, Catherine; Wang, Jie Jin; Stengel, Bénédicte; Ruggiero, Daniela; Bergmann, Sven; Kähönen, Mika; Viikari, Jorma; Nikopensius, Tiit; Province, Michael; Ketkar, Shamika; Colhoun, Helen; Doney, Alex; Robino, Antonietta; Krämer, Bernhard K; Portas, Laura; Ford, Ian; Buckley, Brendan M; Adam, Martin; Thun, Gian-Andri; Paulweber, Bernhard; Haun, Margot; Sala, Cinzia; Mitchell, Paul; Ciullo, Marina; Kim, Stuart K; Vollenweider, Peter; Raitakari, Olli; Metspalu, Andres; Palmer, Colin; Gasparini, Paolo; Pirastu, Mario; Jukema, J Wouter; Probst-Hensch, Nicole M; Kronenberg, Florian; Toniolo, Daniela; Gudnason, Vilmundur; Shuldiner, Alan R; Coresh, Josef; Schmidt, Reinhold; Ferrucci, Luigi; Siscovick, David S; van Duijn, Cornelia M; Borecki, Ingrid B; Kardia, Sharon L R; Liu, Yongmei; Curhan, Gary C; Rudan, Igor; Gyllensten, Ulf; Wilson, James F; Franke, Andre; Pramstaller, Peter P; Rettig, Rainer; Prokopenko, Inga; Witteman, Jacqueline; Hayward, Caroline; Ridker, Paul M; Parsa, Afshin; Bochud, Murielle; Heid, Iris M; Kao, W H Linda; Fox, Caroline S; Köttgen, Anna

2012-12-15

In conducting genome-wide association studies (GWAS), analytical approaches leveraging biological information may further understanding of the pathophysiology of clinical traits. To discover novel associations with estimated glomerular filtration rate (eGFR), a measure of kidney function, we developed a strategy for integrating prior biological knowledge into the existing GWAS data for eGFR from the CKDGen Consortium. Our strategy focuses on single nucleotide polymorphism (SNPs) in genes that are connected by functional evidence, determined by literature mining and gene ontology (GO) hierarchies, to genes near previously validated eGFR associations. It then requires association thresholds consistent with multiple testing, and finally evaluates novel candidates by independent replication. Among the samples of European ancestry, we identified a genome-wide significant SNP in FBXL20 (P = 5.6 × 10(-9)) in meta-analysis of all available data, and additional SNPs at the INHBC, LRP2, PLEKHA1, SLC3A2 and SLC7A6 genes meeting multiple-testing corrected significance for replication and overall P-values of 4.5 × 10(-4)-2.2 × 10(-7). Neither the novel PLEKHA1 nor FBXL20 associations, both further supported by association with eGFR among African Americans and with transcript abundance, would have been implicated by eGFR candidate gene approaches. LRP2, encoding the megalin receptor, was identified through connection with the previously known eGFR gene DAB2 and extends understanding of the megalin system in kidney function. These findings highlight integration of existing genome-wide association data with independent biological knowledge to uncover novel candidate eGFR associations, including candidates lacking known connections to kidney-specific pathways. The strategy may also be applicable to other clinical phenotypes, although more testing will be needed to assess its potential for discovery in general.

Integration of genome-wide association studies with biological knowledge identifies six novel genes related to kidney function

PubMed Central

Chasman, Daniel I.; Fuchsberger, Christian; Pattaro, Cristian; Teumer, Alexander; Böger, Carsten A.; Endlich, Karlhans; Olden, Matthias; Chen, Ming-Huei; Tin, Adrienne; Taliun, Daniel; Li, Man; Gao, Xiaoyi; Gorski, Mathias; Yang, Qiong; Hundertmark, Claudia; Foster, Meredith C.; O'Seaghdha, Conall M.; Glazer, Nicole; Isaacs, Aaron; Liu, Ching-Ti; Smith, Albert V.; O'Connell, Jeffrey R.; Struchalin, Maksim; Tanaka, Toshiko; Li, Guo; Johnson, Andrew D.; Gierman, Hinco J.; Feitosa, Mary F.; Hwang, Shih-Jen; Atkinson, Elizabeth J.; Lohman, Kurt; Cornelis, Marilyn C.; Johansson, Åsa; Tönjes, Anke; Dehghan, Abbas; Lambert, Jean-Charles; Holliday, Elizabeth G.; Sorice, Rossella; Kutalik, Zoltan; Lehtimäki, Terho; Esko, Tõnu; Deshmukh, Harshal; Ulivi, Sheila; Chu, Audrey Y.; Murgia, Federico; Trompet, Stella; Imboden, Medea; Coassin, Stefan; Pistis, Giorgio; Harris, Tamara B.; Launer, Lenore J.; Aspelund, Thor; Eiriksdottir, Gudny; Mitchell, Braxton D.; Boerwinkle, Eric; Schmidt, Helena; Cavalieri, Margherita; Rao, Madhumathi; Hu, Frank; Demirkan, Ayse; Oostra, Ben A.; de Andrade, Mariza; Turner, Stephen T.; Ding, Jingzhong; Andrews, Jeanette S.; Freedman, Barry I.; Giulianini, Franco; Koenig, Wolfgang; Illig, Thomas; Meisinger, Christa; Gieger, Christian; Zgaga, Lina; Zemunik, Tatijana; Boban, Mladen; Minelli, Cosetta; Wheeler, Heather E.; Igl, Wilmar; Zaboli, Ghazal; Wild, Sarah H.; Wright, Alan F.; Campbell, Harry; Ellinghaus, David; Nöthlings, Ute; Jacobs, Gunnar; Biffar, Reiner; Ernst, Florian; Homuth, Georg; Kroemer, Heyo K.; Nauck, Matthias; Stracke, Sylvia; Völker, Uwe; Völzke, Henry; Kovacs, Peter; Stumvoll, Michael; Mägi, Reedik; Hofman, Albert; Uitterlinden, Andre G.; Rivadeneira, Fernando; Aulchenko, Yurii S.; Polasek, Ozren; Hastie, Nick; Vitart, Veronique; Helmer, Catherine; Wang, Jie Jin; Stengel, Bénédicte; Ruggiero, Daniela; Bergmann, Sven; Kähönen, Mika; Viikari, Jorma; Nikopensius, Tiit; Province, Michael; Ketkar, Shamika; Colhoun, Helen; Doney, Alex; Robino, Antonietta; Krämer, Bernhard K.; Portas, Laura; Ford, Ian; Buckley, Brendan M.; Adam, Martin; Thun, Gian-Andri; Paulweber, Bernhard; Haun, Margot; Sala, Cinzia; Mitchell, Paul; Ciullo, Marina; Kim, Stuart K.; Vollenweider, Peter; Raitakari, Olli; Metspalu, Andres; Palmer, Colin; Gasparini, Paolo; Pirastu, Mario; Jukema, J. Wouter; Probst-Hensch, Nicole M.; Kronenberg, Florian; Toniolo, Daniela; Gudnason, Vilmundur; Shuldiner, Alan R.; Coresh, Josef; Schmidt, Reinhold; Ferrucci, Luigi; Siscovick, David S.; van Duijn, Cornelia M.; Borecki, Ingrid B.; Kardia, Sharon L.R.; Liu, Yongmei; Curhan, Gary C.; Rudan, Igor; Gyllensten, Ulf; Wilson, James F.; Franke, Andre; Pramstaller, Peter P.; Rettig, Rainer; Prokopenko, Inga; Witteman, Jacqueline; Hayward, Caroline; Ridker, Paul M; Parsa, Afshin; Bochud, Murielle; Heid, Iris M.; Kao, W.H. Linda; Fox, Caroline S.; Köttgen, Anna

2012-01-01

In conducting genome-wide association studies (GWAS), analytical approaches leveraging biological information may further understanding of the pathophysiology of clinical traits. To discover novel associations with estimated glomerular filtration rate (eGFR), a measure of kidney function, we developed a strategy for integrating prior biological knowledge into the existing GWAS data for eGFR from the CKDGen Consortium. Our strategy focuses on single nucleotide polymorphism (SNPs) in genes that are connected by functional evidence, determined by literature mining and gene ontology (GO) hierarchies, to genes near previously validated eGFR associations. It then requires association thresholds consistent with multiple testing, and finally evaluates novel candidates by independent replication. Among the samples of European ancestry, we identified a genome-wide significant SNP in FBXL20 (P = 5.6 × 10−9) in meta-analysis of all available data, and additional SNPs at the INHBC, LRP2, PLEKHA1, SLC3A2 and SLC7A6 genes meeting multiple-testing corrected significance for replication and overall P-values of 4.5 × 10−4–2.2 × 10−7. Neither the novel PLEKHA1 nor FBXL20 associations, both further supported by association with eGFR among African Americans and with transcript abundance, would have been implicated by eGFR candidate gene approaches. LRP2, encoding the megalin receptor, was identified through connection with the previously known eGFR gene DAB2 and extends understanding of the megalin system in kidney function. These findings highlight integration of existing genome-wide association data with independent biological knowledge to uncover novel candidate eGFR associations, including candidates lacking known connections to kidney-specific pathways. The strategy may also be applicable to other clinical phenotypes, although more testing will be needed to assess its potential for discovery in general. PMID:22962313

Weaving Traditional Ecological Knowledge into Biological Education: A Call to Action.

ERIC Educational Resources Information Center

Kimmerer, Robin Wall

2002-01-01

Traditional ecological knowledge has value not only for the wealth of biological information it contains but also for the cultural framework of respect, reciprocity, and responsibility in which it is embedded. (Contains 48 references.) (DDR)

An information-based approach to change-point analysis with applications to biophysics and cell biology.

PubMed

Wiggins, Paul A

2015-07-21

This article describes the application of a change-point algorithm to the analysis of stochastic signals in biological systems whose underlying state dynamics consist of transitions between discrete states. Applications of this analysis include molecular-motor stepping, fluorophore bleaching, electrophysiology, particle and cell tracking, detection of copy number variation by sequencing, tethered-particle motion, etc. We present a unified approach to the analysis of processes whose noise can be modeled by Gaussian, Wiener, or Ornstein-Uhlenbeck processes. To fit the model, we exploit explicit, closed-form algebraic expressions for maximum-likelihood estimators of model parameters and estimated information loss of the generalized noise model, which can be computed extremely efficiently. We implement change-point detection using the frequentist information criterion (which, to our knowledge, is a new information criterion). The frequentist information criterion specifies a single, information-based statistical test that is free from ad hoc parameters and requires no prior probability distribution. We demonstrate this information-based approach in the analysis of simulated and experimental tethered-particle-motion data. Copyright © 2015 Biophysical Society. Published by Elsevier Inc. All rights reserved.

The Extension-Reduction Strategy: Activating Prior Knowledge

ERIC Educational Resources Information Center

Sloyer, Cliff W.

2004-01-01

A mathematical problem is solved using the extension-reduction or build it up-tear it down tactic. This technique is implemented in reviving students' earlier knowledge to enable them to apply this knowledge to solving new problems.

Investigating Lebanese Grade Seven Biology Teachers Mathematical Knowledge and Skills: A Case Study

ERIC Educational Resources Information Center

Raad, Nawal Abou; Chatila, Hanadi

2016-01-01

This paper investigates Lebanese grade 7 biology teachers' mathematical knowledge and skills, by exploring how they explain a visual representation in an activity depending on the mathematical concept "Function". Twenty Lebanese in-service biology teachers participated in the study, and were interviewed about their explanation for the…

Hippocampus segmentation using locally weighted prior based level set

NASA Astrophysics Data System (ADS)

Achuthan, Anusha; Rajeswari, Mandava

2015-12-01

Segmentation of hippocampus in the brain is one of a major challenge in medical image segmentation due to its' imaging characteristics, with almost similar intensity between another adjacent gray matter structure, such as amygdala. The intensity similarity has causes the hippocampus to have weak or fuzzy boundaries. With this main challenge being demonstrated by hippocampus, a segmentation method that relies on image information alone may not produce accurate segmentation results. Therefore, it is needed an assimilation of prior information such as shape and spatial information into existing segmentation method to produce the expected segmentation. Previous studies has widely integrated prior information into segmentation methods. However, the prior information has been utilized through a global manner integration, and this does not reflect the real scenario during clinical delineation. Therefore, in this paper, a locally integrated prior information into a level set model is presented. This work utilizes a mean shape model to provide automatic initialization for level set evolution, and has been integrated as prior information into the level set model. The local integration of edge based information and prior information has been implemented through an edge weighting map that decides at voxel level which information need to be observed during a level set evolution. The edge weighting map shows which corresponding voxels having sufficient edge information. Experiments shows that the proposed integration of prior information locally into a conventional edge-based level set model, known as geodesic active contour has shown improvement of 9% in averaged Dice coefficient.

Activating Junior Secondary School Students' Prior Knowledge for the Development of Vocabulary, Concepts and Mathematics through Instructional Strategies

ERIC Educational Resources Information Center

Oyinloye, Olu; Popoola, Abiodun A.

2013-01-01

This paper investigates the activation of students' prior knowledge for the development of vocabulary, concepts and mathematics. It has been observed that many secondary school students are not performing well in the examination conducted by the West African Examinations Council and National Examinations Council of Nigeria. The situation became…

Biological Bases for Radiation Adaptive Responses in the Lung

DOE Office of Scientific and Technical Information (OSTI.GOV)

Scott, Bobby R.; Lin, Yong; Wilder, Julie

2015-03-01

Our main research objective was to determine the biological bases for low-dose, radiation-induced adaptive responses in the lung, and use the knowledge gained to produce an improved risk model for radiation-induced lung cancer that accounts for activated natural protection, genetic influences, and the role of epigenetic regulation (epiregulation). Currently, low-dose radiation risk assessment is based on the linear-no-threshold hypothesis, which now is known to be unsupported by a large volume of data.

Knowledge Discovery in Biological Databases for Revealing Candidate Genes Linked to Complex Phenotypes.

PubMed

Hassani-Pak, Keywan; Rawlings, Christopher

2017-06-13

Genetics and "omics" studies designed to uncover genotype to phenotype relationships often identify large numbers of potential candidate genes, among which the causal genes are hidden. Scientists generally lack the time and technical expertise to review all relevant information available from the literature, from key model species and from a potentially wide range of related biological databases in a variety of data formats with variable quality and coverage. Computational tools are needed for the integration and evaluation of heterogeneous information in order to prioritise candidate genes and components of interaction networks that, if perturbed through potential interventions, have a positive impact on the biological outcome in the whole organism without producing negative side effects. Here we review several bioinformatics tools and databases that play an important role in biological knowledge discovery and candidate gene prioritization. We conclude with several key challenges that need to be addressed in order to facilitate biological knowledge discovery in the future.

Directed evolution and synthetic biology applications to microbial systems.

PubMed

Bassalo, Marcelo C; Liu, Rongming; Gill, Ryan T

2016-06-01

Biotechnology applications require engineering complex multi-genic traits. The lack of knowledge on the genetic basis of complex phenotypes restricts our ability to rationally engineer them. However, complex phenotypes can be engineered at the systems level, utilizing directed evolution strategies that drive whole biological systems toward desired phenotypes without requiring prior knowledge of the genetic basis of the targeted trait. Recent developments in the synthetic biology field accelerates the directed evolution cycle, facilitating engineering of increasingly complex traits in biological systems. In this review, we summarize some of the most recent advances in directed evolution and synthetic biology that allows engineering of complex traits in microbial systems. Then, we discuss applications that can be achieved through engineering at the systems level. Copyright © 2016 Elsevier Ltd. All rights reserved.

Variable Selection with Prior Information for Generalized Linear Models via the Prior LASSO Method.

PubMed

Jiang, Yuan; He, Yunxiao; Zhang, Heping

LASSO is a popular statistical tool often used in conjunction with generalized linear models that can simultaneously select variables and estimate parameters. When there are many variables of interest, as in current biological and biomedical studies, the power of LASSO can be limited. Fortunately, so much biological and biomedical data have been collected and they may contain useful information about the importance of certain variables. This paper proposes an extension of LASSO, namely, prior LASSO (pLASSO), to incorporate that prior information into penalized generalized linear models. The goal is achieved by adding in the LASSO criterion function an additional measure of the discrepancy between the prior information and the model. For linear regression, the whole solution path of the pLASSO estimator can be found with a procedure similar to the Least Angle Regression (LARS). Asymptotic theories and simulation results show that pLASSO provides significant improvement over LASSO when the prior information is relatively accurate. When the prior information is less reliable, pLASSO shows great robustness to the misspecification. We illustrate the application of pLASSO using a real data set from a genome-wide association study.

Noticing relevant problem features: activating prior knowledge affects problem solving by guiding encoding

PubMed Central

Crooks, Noelle M.; Alibali, Martha W.

2013-01-01

This study investigated whether activating elements of prior knowledge can influence how problem solvers encode and solve simple mathematical equivalence problems (e.g., 3 + 4 + 5 = 3 + __). Past work has shown that such problems are difficult for elementary school students (McNeil and Alibali, 2000). One possible reason is that children's experiences in math classes may encourage them to think about equations in ways that are ultimately detrimental. Specifically, children learn a set of patterns that are potentially problematic (McNeil and Alibali, 2005a): the perceptual pattern that all equations follow an “operations = answer” format, the conceptual pattern that the equal sign means “calculate the total”, and the procedural pattern that the correct way to solve an equation is to perform all of the given operations on all of the given numbers. Upon viewing an equivalence problem, knowledge of these patterns may be reactivated, leading to incorrect problem solving. We hypothesized that these patterns may negatively affect problem solving by influencing what people encode about a problem. To test this hypothesis in children would require strengthening their misconceptions, and this could be detrimental to their mathematical development. Therefore, we tested this hypothesis in undergraduate participants. Participants completed either control tasks or tasks that activated their knowledge of the three patterns, and were then asked to reconstruct and solve a set of equivalence problems. Participants in the knowledge activation condition encoded the problems less well than control participants. They also made more errors in solving the problems, and their errors resembled the errors children make when solving equivalence problems. Moreover, encoding performance mediated the effect of knowledge activation on equivalence problem solving. Thus, one way in which experience may affect equivalence problem solving is by influencing what students encode about the

The Interpretation of a Knowledge Claim in the Recognition of Prior Learning (RPL) and the Impact of This on RPL Practice

ERIC Educational Resources Information Center

de Graaff, Frederika

2014-01-01

The question addressed in this paper is: what does a knowledge claim consist of in the context of the Recognition of Prior Learning (RPL)? The research comprises a case study of RPL applicants' entry into a postgraduate diploma (a fourth-year programme) in project management. The focus is on the knowledge claims made as part of the RPL application…

Knowledge Acquisition Using Linguistic-Based Knowledge Analysis

Treesearch

Daniel L. Schmoldt

1998-01-01

Most knowledge-based system developmentefforts include acquiring knowledge from one or more sources. difficulties associated with this knowledge acquisition task are readily acknowledged by most researchers. While a variety of knowledge acquisition methods have been reported, little has been done to organize those different methods and to suggest how to apply them...

Use of Assessments in College Chemistry Courses: Examining Students' Prior Conceptual Knowledge, Chemistry Self-efficacy, and Attitude

NASA Astrophysics Data System (ADS)

Villafane-Garcia, Sachel M.

Students' retention in STEM-related careers is of great concern for educators and researchers, especially the retention of underrepresented groups such as females, Hispanics, and Blacks in these careers. Therefore it is important to study factors that could potentially influence students' decision to stay in STEM. The work described in this dissertation involved three research studies where assessments have been used in college chemistry courses to assess students' prior content knowledge, chemistry-self-efficacy, and attitude toward science. These three factors have been suggested to have an influence on students' performance in a course and could eventually be a retention factor. The first research study involved the development and use of an instrument to measure biochemistry prior knowledge of foundational concepts from chemistry and biology that are considered important for biochemistry learning. This instrument was developed with a parallel structure where three items were used to measure a concept and common incorrect ideas were used as distractors. The specific structure of this instrument allows the identification of common incorrect ideas that students have when entering biochemistry and that can hinder students' learning of biochemistry concepts. This instrument was given as pre/posttest to students enrolled in introductory biochemistry courses. The findings indicated that some incorrect ideas are persistent even after instruction, as is the case for bond energy and the structure of the alpha helix concepts. This study highlights the importance of measuring prior conceptual knowledge; so that instructors can plan interventions to help students overcome their incorrect ideas. For the second research study, students' chemistry self-efficacy was measured five times during a semester of preparatory college chemistry. Chemistry self-efficacy beliefs have been linked to students' achievement, and students with stronger self-efficacy are more likely to try

Foundation: Transforming data bases into knowledge bases

NASA Technical Reports Server (NTRS)

Purves, R. B.; Carnes, James R.; Cutts, Dannie E.

1987-01-01

One approach to transforming information stored in relational data bases into knowledge based representations and back again is described. This system, called Foundation, allows knowledge bases to take advantage of vast amounts of pre-existing data. A benefit of this approach is inspection, and even population, of data bases through an intelligent knowledge-based front-end.

Estimating the effect of lay knowledge and prior contact with pulmonary TB patients, on health-belief model in a high-risk pulmonary TB transmission population.

PubMed

Zein, Rizqy Amelia; Suhariadi, Fendy; Hendriani, Wiwin

2017-01-01

The research aimed to investigate the effect of lay knowledge of pulmonary tuberculosis (TB) and prior contact with pulmonary TB patients on a health-belief model (HBM) as well as to identify the social determinants that affect lay knowledge. Survey research design was conducted, where participants were required to fill in a questionnaire, which measured HBM and lay knowledge of pulmonary TB. Research participants were 500 residents of Semampir, Asemrowo, Bubutan, Pabean Cantian, and Simokerto districts, where the risk of pulmonary TB transmission is higher than other districts in Surabaya. Being a female, older in age, and having prior contact with pulmonary TB patients significantly increase the likelihood of having a higher level of lay knowledge. Lay knowledge is a substantial determinant to estimate belief in the effectiveness of health behavior and personal health threat. Prior contact with pulmonary TB patients is able to explain the belief in the effectiveness of a health behavior, yet fails to estimate participants' belief in the personal health threat. Health authorities should prioritize males and young people as their main target groups in a pulmonary TB awareness campaign. The campaign should be able to reconstruct people's misconception about pulmonary TB, thereby bringing around the health-risk perception so that it is not solely focused on improving lay knowledge.

Features of Knowledge Building in Biology: Understanding Undergraduate Students' Ideas about Molecular Mechanisms

ERIC Educational Resources Information Center

Southard, Katelyn; Wince, Tyler; Meddleton, Shanice; Bolger, Molly S.

2016-01-01

Research has suggested that teaching and learning in molecular and cellular biology (MCB) is difficult. We used a new lens to understand undergraduate reasoning about molecular mechanisms: the knowledge-integration approach to conceptual change. Knowledge integration is the dynamic process by which learners acquire new ideas, develop connections…

The Use of Pre-Lectures in a University Biology Course--Eliminating the Need for Prerequisites

ERIC Educational Resources Information Center

da Silva, Karen Burke; Hunter, Narelle

2009-01-01

First year biology students at Flinders University with no prior biology background knowledge fail at almost twice the rate as those with a background. To remedy this discrepancy we enabled students to attend a weekly series of pre-lectures aimed at providing basic biological concepts, thereby removing the need for students to complete a…

Using student motivation to design groups in a non-majors biology course for team-based collaborative learning: Impacts on knowledge, views, attitudes, and perceptions

NASA Astrophysics Data System (ADS)

Walters, Kristi L.

The importance of student motivation and its connection to other learning variables (i.e., attitudes, knowledge, persistence, attendance) is well established. Collaborative work at the undergraduate level has been recognized as a valuable tool in large courses. However, motivation and collaborative group work have rarely been combined. This project utilized student motivation to learn biology to place non-major biology undergraduates in collaborative learning groups at East Carolina University, a mid-sized southeastern American university, to determine the effects of this construct on student learning. A pre-test measuring motivation to learn biology, attitudes toward biology, perceptions of biology and biologists, views of science, and content knowledge was administered. A similar post-test followed as part of the final exam. Two sections of the same introductory biology course (n = 312) were used and students were divided into homogeneous and heterogeneous groups (based on their motivation score). The heterogeneous groups (n = 32) consisted of a mixture of different motivation levels, while the homogeneous groups (n = 32) were organized into teams with similar motivation scores using tiers of high-, middle-, and low-level participants. Data analysis determined mixed perceptions of biology and biologists. These include the perceptions biology was less intriguing, less relevant, less practical, less ethical, and less understandable. Biologists were perceived as being neat and slightly intelligent, but not very altruistic, humane, ethical, logical, honest, or moral. Content knowledge scores more than doubled from pre- to post-test. Half of the items measuring views of science were not statistically significantly different from pre- to post-test. Many of the factors for attitudes toward biology became more agreeable from pre- to post-test. Correlations between motivation scores, participation levels, attendance rates, and final course grades were examined at both the

Reproductive biology knowledge, and behaviour of teenagers in East, Central and Southern Africa: the Zimbabwe case study.

PubMed

Mbizvo, M T; Kasule, J; Gupta, V; Rusakaniko, S; Gumbo, J; Kinoti, S N; Mpanju-Shumbusho, W; Sebina-Zziwa; Mwateba, R; Padayachy, J

1995-11-01

Sexuality in the teenager is often complicated by unplanned/unwanted pregnancy, abortion and the risks of STDs including AIDS. There is therefore a need for improved understanding of factors affecting adolescent sexuality and the implementation of programmes designed to improve their knowledge, risk awareness and subsequent behavioural outcomes. A multicentre study of reproductive health knowledge and behaviour followed by a health education intervention was undertaken amongst teenagers in selected countries of East, Central and Southern Africa. Reported here are findings at baseline derived from the Zimbabwe component on reproductive biology knowledge and behavior. A self-administered questionnaire was used among 1 689 adolescent pupils drawn from rural, urban, co-education, single sex, boarding and day secondary schools in Zimbabwe. Correct knowledge on reproductive biology as measured by the meaning and interpretation of menstruation and wet dreams varied by school from 68 pc to 86 pc, with a significant trend (p < 0,01) based on level of education at baseline. The reported mean age at which menarche took place was 13,5 years +/- 1,3 (mean +/- SD). First coitus was reported to have taken place at the mean age of 12 years for boys and 13,6 years for girls. Seventeen pc of the adolescent pupils reported that they were sexually experienced and 33,2 had relationships. There were misconceptions reported on menstruation with 23 pc reporting that it was an illness. Peers, followed by magazines were the first sources of information on various aspects of reproductive biology, both of which might not provide the correct first information. Among pupils reporting that they were sexually experienced, the largest proportion (56 pc) had unprotected sex. The findings point to the need for targeting the adolescent pupils for information on reproductive biology and increased awareness on the risks of pregnancy, STDs and HIV.

The impact of ecolabel knowledge to purchase decision of green producton biology students

NASA Astrophysics Data System (ADS)

Sigit, Diana Vivanti; Fauziah, Rizky; Heryanti, Erna

2017-08-01

The world needs real solutions to reduce the impact of environmental damages. Students as agents of changes have a role to overcome these problems. One of the important solution is to be a critical consumer who has purchase decisions in a green product. To show the quality of an environmental friendly product, it is then required an ecolabel on the green product which indicates that the product has been through the production processed and come from environmental friendly substances. The research aimed at finding out whether there was an impact of ecolabel knowledge with purchase decision of green product on biology students. This research was conducted in Biology Department. This research used a survey descriptive method. The population used was biology students of Universitas Negeri Jakarta while the sampling technique was done through simple random sampling technique with 147 respondents. Instrument used were ecolabel knowledge test and a questionnaire of green product purchase decision. The result of prerequisite test showed that the data was normally distributed and homogenous variance. The regression model obtained was Ŷ=77.083+ 0.370X. Meanwhile, the determinant coefficient (r2) obtained was 0.047 or 4.7% that mean ecolabel knowledge just contributed 4,71% to the green product purchase decision. These implied that many factors contributed in the purchase decision of green product instead of ecolabel knowledge.

Marginally specified priors for non-parametric Bayesian estimation

PubMed Central

Kessler, David C.; Hoff, Peter D.; Dunson, David B.

2014-01-01

Summary Prior specification for non-parametric Bayesian inference involves the difficult task of quantifying prior knowledge about a parameter of high, often infinite, dimension. A statistician is unlikely to have informed opinions about all aspects of such a parameter but will have real information about functionals of the parameter, such as the population mean or variance. The paper proposes a new framework for non-parametric Bayes inference in which the prior distribution for a possibly infinite dimensional parameter is decomposed into two parts: an informative prior on a finite set of functionals, and a non-parametric conditional prior for the parameter given the functionals. Such priors can be easily constructed from standard non-parametric prior distributions in common use and inherit the large support of the standard priors on which they are based. Additionally, posterior approximations under these informative priors can generally be made via minor adjustments to existing Markov chain approximation algorithms for standard non-parametric prior distributions. We illustrate the use of such priors in the context of multivariate density estimation using Dirichlet process mixture models, and in the modelling of high dimensional sparse contingency tables. PMID:25663813

Event-related potentials reveal the effect of prior knowledge on competition for representation and attentional capture.

PubMed

Hilimire, Matthew R; Corballis, Paul M

2014-01-01

Objects compete for representation in our limited capacity visual system. We examined how this competition is influenced by top-down knowledge using event-related potentials. Competition was manipulated by presenting visual search arrays in which the target or distractor was the only color singleton compared to displays in which both singletons were presented. Experiments 1 and 2 manipulated whether the observer knew the color of the target in advance. Experiment 3 ruled out low-level sensory explanations. Results show that, under conditions of competition, the distractor does not elicit an N2pc when the target color is known. However, the N2pc elicited by the target is reduced in the presence of a distractor. These findings suggest that top-down knowledge can prevent the capture of attention by distracting information, but this prior knowledge does not eliminate the competitive influence of the distractor on the target. Copyright © 2013 Society for Psychophysiological Research.

Margin based ontology sparse vector learning algorithm and applied in biology science.

PubMed

Gao, Wei; Qudair Baig, Abdul; Ali, Haidar; Sajjad, Wasim; Reza Farahani, Mohammad

2017-01-01

In biology field, the ontology application relates to a large amount of genetic information and chemical information of molecular structure, which makes knowledge of ontology concepts convey much information. Therefore, in mathematical notation, the dimension of vector which corresponds to the ontology concept is often very large, and thus improves the higher requirements of ontology algorithm. Under this background, we consider the designing of ontology sparse vector algorithm and application in biology. In this paper, using knowledge of marginal likelihood and marginal distribution, the optimized strategy of marginal based ontology sparse vector learning algorithm is presented. Finally, the new algorithm is applied to gene ontology and plant ontology to verify its efficiency.

Elaboration of Cognitive Knowledge of Biology from Childhood to Adulthood.

ERIC Educational Resources Information Center

Fisher, Kathleen M.

Word association techniques were used to examine the growth of biological knowledge over a period of years, from fourth-grade to college students. Results were analyzed by classifying stimulus-response word pairs according to the nature of the relationship between the words in each pair. Three hypotheses were tested: (1) the proportion of enactive…

Assessment of knowledge transfer in the context of biomechanics

NASA Astrophysics Data System (ADS)

Hutchison, Randolph E.

The dynamic act of knowledge transfer, or the connection of a student's prior knowledge to features of a new problem, could be considered one of the primary goals of education. Yet studies highlight more instances of failure than success. This dissertation focuses on how knowledge transfer takes place during individual problem solving, in classroom settings and during group work. Through the lens of dynamic transfer, or how students connect prior knowledge to problem features, this qualitative study focuses on a methodology to assess transfer in the context of biomechanics. The first phase of this work investigates how a pedagogical technique based on situated cognition theory affects students' ability to transfer knowledge gained in a biomechanics class to later experiences both in and out of the classroom. A post-class focus group examined events the students remembered from the class, what they learned from them, and how they connected them to later relevant experiences inside and outside the classroom. These results were triangulated with conceptual gains evaluated through concept inventories and pre- and post- content tests. Based on these results, the next two phases of the project take a more in-depth look at dynamic knowledge transfer during independent problem-solving and group project interactions, respectively. By categorizing prior knowledge (Source Tools), problem features (Target Tools) and the connections between them, results from the second phase of this study showed that within individual problem solving, source tools were almost exclusively derived from "propagated sources," i.e. those based on an authoritative source. This differs from findings in the third phase of the project, in which a mixture of "propagated" sources and "fabricated" sources, i.e. those based on student experiences, were identified within the group project work. This methodology is effective at assessing knowledge transfer in the context of biomechanics through evidence of

Thinking about Digestive System in Early Childhood: A Comparative Study about Biological Knowledge

ERIC Educational Resources Information Center

AHI, Berat

2017-01-01

The current study aims to explore how children explain the concepts of biology and how biological knowledge develops across ages by focusing on the structure and functions of the digestive system. The study was conducted with 60 children. The data were collected through the interviews conducted within a think-aloud protocol. The interview data…

Case-based tutoring from a medical knowledge base.

PubMed

Chin, H L; Cooper, G F

1989-01-01

The past decade has seen the emergence of programs that make use of large knowledge bases to assist physicians in diagnosis within the general field of internal medicine. One such program, Internist-I, contains knowledge about over 600 diseases, covering a significant proportion of internal medicine. This paper describes the process of converting a subset of this knowledge base--in the area of cardiovascular diseases--into a probabilistic format, and the use of this resulting knowledge base to teach medical diagnostic knowledge. The system (called KBSimulator--for Knowledge-Based patient Simulator) generates simulated patient cases and uses these cases as a focal point from which to teach medical knowledge. This project demonstrates the feasibility of building an intelligent, flexible instructional system that uses a knowledge base constructed primarily for medical diagnosis.

Knowledge-based machine indexing from natural language text: Knowledge base design, development, and maintenance

NASA Technical Reports Server (NTRS)

Genuardi, Michael T.

1993-01-01

One strategy for machine-aided indexing (MAI) is to provide a concept-level analysis of the textual elements of documents or document abstracts. In such systems, natural-language phrases are analyzed in order to identify and classify concepts related to a particular subject domain. The overall performance of these MAI systems is largely dependent on the quality and comprehensiveness of their knowledge bases. These knowledge bases function to (1) define the relations between a controlled indexing vocabulary and natural language expressions; (2) provide a simple mechanism for disambiguation and the determination of relevancy; and (3) allow the extension of concept-hierarchical structure to all elements of the knowledge file. After a brief description of the NASA Machine-Aided Indexing system, concerns related to the development and maintenance of MAI knowledge bases are discussed. Particular emphasis is given to statistically-based text analysis tools designed to aid the knowledge base developer. One such tool, the Knowledge Base Building (KBB) program, presents the domain expert with a well-filtered list of synonyms and conceptually-related phrases for each thesaurus concept. Another tool, the Knowledge Base Maintenance (KBM) program, functions to identify areas of the knowledge base affected by changes in the conceptual domain (for example, the addition of a new thesaurus term). An alternate use of the KBM as an aid in thesaurus construction is also discussed.

Systems Biology and Ratio-Based, Real-Time Disease Surveillance.

PubMed

Fair, J M; Rivas, A L

2015-08-01

Most infectious disease surveillance methods are not well fit for early detection. To address such limitation, here we evaluated a ratio- and Systems Biology-based method that does not require prior knowledge on the identity of an infective agent. Using a reference group of birds experimentally infected with West Nile virus (WNV) and a problem group of unknown health status (except that they were WNV-negative and displayed inflammation), both groups were followed over 22 days and tested with a system that analyses blood leucocyte ratios. To test the ability of the method to discriminate small data sets, both the reference group (n = 5) and the problem group (n = 4) were small. The questions of interest were as follows: (i) whether individuals presenting inflammation (disease-positive or D+) can be distinguished from non-inflamed (disease-negative or D-) birds, (ii) whether two or more D+ stages can be detected and (iii) whether sample size influences detection. Within the problem group, the ratio-based method distinguished the following: (i) three (one D- and two D+) data classes; (ii) two (early and late) inflammatory stages; (iii) fast versus regular or slow responders; and (iv) individuals that recovered from those that remained inflamed. Because ratios differed in larger magnitudes (up to 48 times larger) than percentages, it is suggested that data patterns are likely to be recognized when disease surveillance methods are designed to measure inflammation and utilize ratios. Published 2013. This article is a U.S. Government work and is in the public domain in the USA.

New Knowledge Derived from Learned Knowledge: Functional-Anatomic Correlates of Stimulus Equivalence

ERIC Educational Resources Information Center

Schlund, Michael W.; Hoehn-Saric, Rudolf; Cataldo, Michael F.

2007-01-01

Forming new knowledge based on knowledge established through prior learning is a central feature of higher cognition that is captured in research on stimulus equivalence (SE). Numerous SE investigations show that reinforcing behavior under control of distinct sets of arbitrary conditional relations gives rise to stimulus control by new, "derived"…

Teaching Statistics in Biology: Using Inquiry-based Learning to Strengthen Understanding of Statistical Analysis in Biology Laboratory Courses

PubMed Central

2008-01-01

There is an increasing need for students in the biological sciences to build a strong foundation in quantitative approaches to data analyses. Although most science, engineering, and math field majors are required to take at least one statistics course, statistical analysis is poorly integrated into undergraduate biology course work, particularly at the lower-division level. Elements of statistics were incorporated into an introductory biology course, including a review of statistics concepts and opportunity for students to perform statistical analysis in a biological context. Learning gains were measured with an 11-item statistics learning survey instrument developed for the course. Students showed a statistically significant 25% (p < 0.005) increase in statistics knowledge after completing introductory biology. Students improved their scores on the survey after completing introductory biology, even if they had previously completed an introductory statistics course (9%, improvement p < 0.005). Students retested 1 yr after completing introductory biology showed no loss of their statistics knowledge as measured by this instrument, suggesting that the use of statistics in biology course work may aid long-term retention of statistics knowledge. No statistically significant differences in learning were detected between male and female students in the study. PMID:18765754

Teaching statistics in biology: using inquiry-based learning to strengthen understanding of statistical analysis in biology laboratory courses.

PubMed

Metz, Anneke M

2008-01-01

There is an increasing need for students in the biological sciences to build a strong foundation in quantitative approaches to data analyses. Although most science, engineering, and math field majors are required to take at least one statistics course, statistical analysis is poorly integrated into undergraduate biology course work, particularly at the lower-division level. Elements of statistics were incorporated into an introductory biology course, including a review of statistics concepts and opportunity for students to perform statistical analysis in a biological context. Learning gains were measured with an 11-item statistics learning survey instrument developed for the course. Students showed a statistically significant 25% (p < 0.005) increase in statistics knowledge after completing introductory biology. Students improved their scores on the survey after completing introductory biology, even if they had previously completed an introductory statistics course (9%, improvement p < 0.005). Students retested 1 yr after completing introductory biology showed no loss of their statistics knowledge as measured by this instrument, suggesting that the use of statistics in biology course work may aid long-term retention of statistics knowledge. No statistically significant differences in learning were detected between male and female students in the study.

A novel knowledge-based potential for RNA 3D structure evaluation

NASA Astrophysics Data System (ADS)

Yang, Yi; Gu, Qi; Zhang, Ben-Gong; Shi, Ya-Zhou; Shao, Zhi-Gang

2018-03-01

Ribonucleic acids (RNAs) play a vital role in biology, and knowledge of their three-dimensional (3D) structure is required to understand their biological functions. Recently structural prediction methods have been developed to address this issue, but a series of RNA 3D structures are generally predicted by most existing methods. Therefore, the evaluation of the predicted structures is generally indispensable. Although several methods have been proposed to assess RNA 3D structures, the existing methods are not precise enough. In this work, a new all-atom knowledge-based potential is developed for more accurately evaluating RNA 3D structures. The potential not only includes local and nonlocal interactions but also fully considers the specificity of each RNA by introducing a retraining mechanism. Based on extensive test sets generated from independent methods, the proposed potential correctly distinguished the native state and ranked near-native conformations to effectively select the best. Furthermore, the proposed potential precisely captured RNA structural features such as base-stacking and base-pairing. Comparisons with existing potential methods show that the proposed potential is very reliable and accurate in RNA 3D structure evaluation. Project supported by the National Science Foundation of China (Grants Nos. 11605125, 11105054, 11274124, and 11401448).

Accessing and integrating data and knowledge for biomedical research.

PubMed

Burgun, A; Bodenreider, O

2008-01-01

To review the issues that have arisen with the advent of translational research in terms of integration of data and knowledge, and survey current efforts to address these issues. Using examples form the biomedical literature, we identified new trends in biomedical research and their impact on bioinformatics. We analyzed the requirements for effective knowledge repositories and studied issues in the integration of biomedical knowledge. New diagnostic and therapeutic approaches based on gene expression patterns have brought about new issues in the statistical analysis of data, and new workflows are needed are needed to support translational research. Interoperable data repositories based on standard annotations, infrastructures and services are needed to support the pooling and meta-analysis of data, as well as their comparison to earlier experiments. High-quality, integrated ontologies and knowledge bases serve as a source of prior knowledge used in combination with traditional data mining techniques and contribute to the development of more effective data analysis strategies. As biomedical research evolves from traditional clinical and biological investigations towards omics sciences and translational research, specific needs have emerged, including integrating data collected in research studies with patient clinical data, linking omics knowledge with medical knowledge, modeling the molecular basis of diseases, and developing tools that support in-depth analysis of research data. As such, translational research illustrates the need to bridge the gap between bioinformatics and medical informatics, and opens new avenues for biomedical informatics research.

Accessing and Integrating Data and Knowledge for Biomedical Research

PubMed Central

Burgun, A.; Bodenreider, O.

2008-01-01

Summary Objectives To review the issues that have arisen with the advent of translational research in terms of integration of data and knowledge, and survey current efforts to address these issues. Methods Using examples form the biomedical literature, we identified new trends in biomedical research and their impact on bioinformatics. We analyzed the requirements for effective knowledge repositories and studied issues in the integration of biomedical knowledge. Results New diagnostic and therapeutic approaches based on gene expression patterns have brought about new issues in the statistical analysis of data, and new workflows are needed are needed to support translational research. Interoperable data repositories based on standard annotations, infrastructures and services are needed to support the pooling and meta-analysis of data, as well as their comparison to earlier experiments. High-quality, integrated ontologies and knowledge bases serve as a source of prior knowledge used in combination with traditional data mining techniques and contribute to the development of more effective data analysis strategies. Conclusion As biomedical research evolves from traditional clinical and biological investigations towards omics sciences and translational research, specific needs have emerged, including integrating data collected in research studies with patient clinical data, linking omics knowledge with medical knowledge, modeling the molecular basis of diseases, and developing tools that support in-depth analysis of research data. As such, translational research illustrates the need to bridge the gap between bioinformatics and medical informatics, and opens new avenues for biomedical informatics research. PMID:18660883

WE-DE-BRA-01: SCIENCE COUNCIL JUNIOR INVESTIGATOR COMPETITION WINNER: Acceleration of a Limited-Angle Intrafraction Verification (LIVE) System Using Adaptive Prior Knowledge Based Image Estimation

DOE Office of Scientific and Technical Information (OSTI.GOV)

Zhang, Y; Yin, F; Ren, L

Purpose: To develop an adaptive prior knowledge based image estimation method to reduce the scan angle needed in the LIVE system to reconstruct 4D-CBCT for intrafraction verification. Methods: The LIVE system has been previously proposed to reconstructs 4D volumetric images on-the-fly during arc treatment for intrafraction target verification and dose calculation. This system uses limited-angle beam’s eye view (BEV) MV cine images acquired from the treatment beam together with the orthogonally acquired limited-angle kV projections to reconstruct 4D-CBCT images for target verification during treatment. In this study, we developed an adaptive constrained free-form deformation reconstruction technique in LIVE to furthermore » reduce the scanning angle needed to reconstruct the CBCT images. This technique uses free form deformation with energy minimization to deform prior images to estimate 4D-CBCT based on projections acquired in limited angle (orthogonal 6°) during the treatment. Note that the prior images are adaptively updated using the latest CBCT images reconstructed by LIVE during treatment to utilize the continuity of patient motion.The 4D digital extended-cardiac-torso (XCAT) phantom was used to evaluate the efficacy of this technique with LIVE system. A lung patient was simulated with different scenario, including baseline drifts, amplitude change and phase shift. Limited-angle orthogonal kV and beam’s eye view (BEV) MV projections were generated for each scenario. The CBCT reconstructed by these projections were compared with the ground-truth generated in XCAT.Volume-percentage-difference (VPD) and center-of-mass-shift (COMS) were calculated between the reconstructed and the ground-truth tumors to evaluate the reconstruction accuracy. Results: Using orthogonal-view of 6° kV and BEV- MV projections, the VPD/COMS values were 12.7±4.0%/0.7±0.5 mm, 13.0±5.1%/0.8±0.5 mm, and 11.4±5.4%/0.5±0.3 mm for the three scenarios, respectively. Conclusion: The

Knowledge-based analysis of microarrays for the discovery of transcriptional regulation relationships.

PubMed

Seok, Junhee; Kaushal, Amit; Davis, Ronald W; Xiao, Wenzhong

2010-01-18

The large amount of high-throughput genomic data has facilitated the discovery of the regulatory relationships between transcription factors and their target genes. While early methods for discovery of transcriptional regulation relationships from microarray data often focused on the high-throughput experimental data alone, more recent approaches have explored the integration of external knowledge bases of gene interactions. In this work, we develop an algorithm that provides improved performance in the prediction of transcriptional regulatory relationships by supplementing the analysis of microarray data with a new method of integrating information from an existing knowledge base. Using a well-known dataset of yeast microarrays and the Yeast Proteome Database, a comprehensive collection of known information of yeast genes, we show that knowledge-based predictions demonstrate better sensitivity and specificity in inferring new transcriptional interactions than predictions from microarray data alone. We also show that comprehensive, direct and high-quality knowledge bases provide better prediction performance. Comparison of our results with ChIP-chip data and growth fitness data suggests that our predicted genome-wide regulatory pairs in yeast are reasonable candidates for follow-up biological verification. High quality, comprehensive, and direct knowledge bases, when combined with appropriate bioinformatic algorithms, can significantly improve the discovery of gene regulatory relationships from high throughput gene expression data.

Pre-service teacher professional development on climate change: Assessment of workshop success and influence of prior knowledge

NASA Astrophysics Data System (ADS)

Veron, D. E.; Ad-Marbach, G.; Fox-Lykens, R.; Ozbay, G.; Sezen-Barrie, A.; Wolfson, J.

2017-12-01

As states move to adopt the next generation science standards, in-service teachers are being provided with professional development that introduces climate change content and best practices for teaching climate change in the classroom. However, research has shown that it is challenging to bring this information into the higher education curriculum in education courses for pre-service teachers due to curricular and programming constraints. Over two years, the Maryland and Delaware Climate Change Assessment and Research (MADE-CLEAR) project explored a professional development approach for pre-service teachers which employed paired workshops that resulted in participant-developed lesson plans based on climate change content. The workshops were designed to provide pre-service teachers with climate change content related to the carbon cycle and to model a variety of techniques and activities for presenting this information in the classroom. Lesson plans were developed between the first and second workshop, presented at the second workshop and discussed with peers and in-service teachers, and then revised in response to feedback from the second workshop. Participant climate change content knowledge was assessed before the first workshop, and after the final revision of the lesson plan was submitted to the MADE-CLEAR team. Climate content knowledge was also assessed using the same survey for additional pre-service teacher groups who did not participate in the professional development. Results show that while the paired workshop approach increased climate content knowledge, the amount of improvement varied depending on the participants' prior knowledge in climate change content. In addition, some alternate conceptions of climate change were not altered by participant involvement in the professional development approach. Revised lesson plans showed understanding of underlying climate change impacts and demonstrated awareness of appropriate techniques for introducing this

A shape prior-based MRF model for 3D masseter muscle segmentation

NASA Astrophysics Data System (ADS)

Majeed, Tahir; Fundana, Ketut; Lüthi, Marcel; Beinemann, Jörg; Cattin, Philippe

2012-02-01

Medical image segmentation is generally an ill-posed problem that can only be solved by incorporating prior knowledge. The ambiguities arise due to the presence of noise, weak edges, imaging artifacts, inhomogeneous interior and adjacent anatomical structures having similar intensity profile as the target structure. In this paper we propose a novel approach to segment the masseter muscle using the graph-cut incorporating additional 3D shape priors in CT datasets, which is robust to noise; artifacts; and shape deformations. The main contribution of this paper is in translating the 3D shape knowledge into both unary and pairwise potentials of the Markov Random Field (MRF). The segmentation task is casted as a Maximum-A-Posteriori (MAP) estimation of the MRF. Graph-cut is then used to obtain the global minimum which results in the segmentation of the masseter muscle. The method is tested on 21 CT datasets of the masseter muscle, which are noisy with almost all possessing mild to severe imaging artifacts such as high-density artifacts caused by e.g. the very common dental fillings and dental implants. We show that the proposed technique produces clinically acceptable results to the challenging problem of muscle segmentation, and further provide a quantitative and qualitative comparison with other methods. We statistically show that adding additional shape prior into both unary and pairwise potentials can increase the robustness of the proposed method in noisy datasets.

Computing biological functions using BioΨ, a formal description of biological processes based on elementary bricks of actions

PubMed Central

Pérès, Sabine; Felicori, Liza; Rialle, Stéphanie; Jobard, Elodie; Molina, Franck

2010-01-01

Motivation: In the available databases, biological processes are described from molecular and cellular points of view, but these descriptions are represented with text annotations that make it difficult to handle them for computation. Consequently, there is an obvious need for formal descriptions of biological processes. Results: We present a formalism that uses the BioΨ concepts to model biological processes from molecular details to networks. This computational approach, based on elementary bricks of actions, allows us to calculate on biological functions (e.g. process comparison, mapping structure–function relationships, etc.). We illustrate its application with two examples: the functional comparison of proteases and the functional description of the glycolysis network. This computational approach is compatible with detailed biological knowledge and can be applied to different kinds of systems of simulation. Availability: www.sysdiag.cnrs.fr/publications/supplementary-materials/BioPsi_Manager/ Contact: sabine.peres@sysdiag.cnrs.fr; franck.molina@sysdiag.cnrs.fr Supplementary information: Supplementary data are available at Bioinformatics online. PMID:20448138

A knowledge base architecture for distributed knowledge agents

NASA Technical Reports Server (NTRS)

Riedesel, Joel; Walls, Bryan

1990-01-01

A tuple space based object oriented model for knowledge base representation and interpretation is presented. An architecture for managing distributed knowledge agents is then implemented within the model. The general model is based upon a database implementation of a tuple space. Objects are then defined as an additional layer upon the database. The tuple space may or may not be distributed depending upon the database implementation. A language for representing knowledge and inference strategy is defined whose implementation takes advantage of the tuple space. The general model may then be instantiated in many different forms, each of which may be a distinct knowledge agent. Knowledge agents may communicate using tuple space mechanisms as in the LINDA model as well as using more well known message passing mechanisms. An implementation of the model is presented describing strategies used to keep inference tractable without giving up expressivity. An example applied to a power management and distribution network for Space Station Freedom is given.

Markov prior-based block-matching algorithm for superdimension reconstruction of porous media

NASA Astrophysics Data System (ADS)

Li, Yang; He, Xiaohai; Teng, Qizhi; Feng, Junxi; Wu, Xiaohong

2018-04-01

A superdimension reconstruction algorithm is used for the reconstruction of three-dimensional (3D) structures of a porous medium based on a single two-dimensional image. The algorithm borrows the concepts of "blocks," "learning," and "dictionary" from learning-based superresolution reconstruction and applies them to the 3D reconstruction of a porous medium. In the neighborhood-matching process of the conventional superdimension reconstruction algorithm, the Euclidean distance is used as a criterion, although it may not really reflect the structural correlation between adjacent blocks in an actual situation. Hence, in this study, regular items are adopted as prior knowledge in the reconstruction process, and a Markov prior-based block-matching algorithm for superdimension reconstruction is developed for more accurate reconstruction. The algorithm simultaneously takes into consideration the probabilistic relationship between the already reconstructed blocks in three different perpendicular directions (x , y , and z ) and the block to be reconstructed, and the maximum value of the probability product of the blocks to be reconstructed (as found in the dictionary for the three directions) is adopted as the basis for the final block selection. Using this approach, the problem of an imprecise spatial structure caused by a point simulation can be overcome. The problem of artifacts in the reconstructed structure is also addressed through the addition of hard data and by neighborhood matching. To verify the improved reconstruction accuracy of the proposed method, the statistical and morphological features of the results from the proposed method and traditional superdimension reconstruction method are compared with those of the target system. The proposed superdimension reconstruction algorithm is confirmed to enable a more accurate reconstruction of the target system while also eliminating artifacts.

A Discussion of Knowledge Based Design

NASA Technical Reports Server (NTRS)

Wood, Richard M.; Bauer, Steven X. S.

1999-01-01

A discussion of knowledge and Knowledge- Based design as related to the design of aircraft is presented. The paper discusses the perceived problem with existing design studies and introduces the concepts of design and knowledge for a Knowledge- Based design system. A review of several Knowledge-Based design activities is provided. A Virtual Reality, Knowledge-Based system is proposed and reviewed. The feasibility of Virtual Reality to improve the efficiency and effectiveness of aerodynamic and multidisciplinary design, evaluation, and analysis of aircraft through the coupling of virtual reality technology and a Knowledge-Based design system is also reviewed. The final section of the paper discusses future directions for design and the role of Knowledge-Based design.

Dealing with difficult deformations: construction of a knowledge-based deformation atlas

NASA Astrophysics Data System (ADS)

Thorup, S. S.; Darvann, T. A.; Hermann, N. V.; Larsen, P.; Ólafsdóttir, H.; Paulsen, R. R.; Kane, A. A.; Govier, D.; Lo, L.-J.; Kreiborg, S.; Larsen, R.

2010-03-01

Twenty-three Taiwanese infants with unilateral cleft lip and palate (UCLP) were CT-scanned before lip repair at the age of 3 months, and again after lip repair at the age of 12 months. In order to evaluate the surgical result, detailed point correspondence between pre- and post-surgical images was needed. We have previously demonstrated that non-rigid registration using B-splines is able to provide automated determination of point correspondences in populations of infants without cleft lip. However, this type of registration fails when applied to the task of determining the complex deformation from before to after lip closure in infants with UCLP. The purpose of the present work was to show that use of prior information about typical deformations due to lip closure, through the construction of a knowledge-based atlas of deformations, could overcome the problem. Initially, mean volumes (atlases) for the pre- and post-surgical populations, respectively, were automatically constructed by non-rigid registration. An expert placed corresponding landmarks in the cleft area in the two atlases; this provided prior information used to build a knowledge-based deformation atlas. We model the change from pre- to post-surgery using thin-plate spline warping. The registration results are convincing and represent a first move towards an automatic registration method for dealing with difficult deformations due to this type of surgery.

Knowledge Driven Variable Selection (KDVS) – a new approach to enrichment analysis of gene signatures obtained from high–throughput data

PubMed Central

2013-01-01

Background High–throughput (HT) technologies provide huge amount of gene expression data that can be used to identify biomarkers useful in the clinical practice. The most frequently used approaches first select a set of genes (i.e. gene signature) able to characterize differences between two or more phenotypical conditions, and then provide a functional assessment of the selected genes with an a posteriori enrichment analysis, based on biological knowledge. However, this approach comes with some drawbacks. First, gene selection procedure often requires tunable parameters that affect the outcome, typically producing many false hits. Second, a posteriori enrichment analysis is based on mapping between biological concepts and gene expression measurements, which is hard to compute because of constant changes in biological knowledge and genome analysis. Third, such mapping is typically used in the assessment of the coverage of gene signature by biological concepts, that is either score–based or requires tunable parameters as well, limiting its power. Results We present Knowledge Driven Variable Selection (KDVS), a framework that uses a priori biological knowledge in HT data analysis. The expression data matrix is transformed, according to prior knowledge, into smaller matrices, easier to analyze and to interpret from both computational and biological viewpoints. Therefore KDVS, unlike most approaches, does not exclude a priori any function or process potentially relevant for the biological question under investigation. Differently from the standard approach where gene selection and functional assessment are applied independently, KDVS embeds these two steps into a unified statistical framework, decreasing the variability derived from the threshold–dependent selection, the mapping to the biological concepts, and the signature coverage. We present three case studies to assess the usefulness of the method. Conclusions We showed that KDVS not only enables the

An investigation of the relationships between junior high school students' (8th and 9th grades) background variables and structure of knowledge recall of biological content

NASA Astrophysics Data System (ADS)

Demetrius, Olive Joyce

The purpose of this study was to examine the relationships between Junior High School students' (8th and 9th grades) background variables (e.g. cognitive factors, prior knowledge, preference for science versus non-science activities, formal and informal activities) and structure of information recall of biological content. In addition, this study will illustrate how flow maps, a graphic display, designed to represent the sequential flow and cross linkage of ideas in information recalled by the learner can be used as a tool for analyzing science learning data. The participants (46 junior high school students) were taught a lesson on the human digestive system during which they were shown a model of the human torso. Their pattern of information recall was determined by using an interview technique to elicit their understanding of the functional anatomy of the human digestive system. The taped responses were later transcribed for construction of the flow map. The interview was also used to assess knowledge recall of biological content. The flow map, science interest questionnaire and the cognitive operations (based on content analysis of student's narrative) were used to analyze data from each respondent. This is a case study using individual subjects and interview techniques. The findings of this study are: (1) Based on flow map data higher academic ability students have more networking of ideas than low ability students. (2) A large percentage of 9th grade low ability students intend to pursue science/applied science course work after leaving school but they lack well organized ways of representing science knowledge in memory. (3) Content analysis of the narratives shows that students with more complex ideational networks use higher order cognitive thought processes compared to those with less networking of ideas. If students are to make a successful transition from low academic performance to high academic performance it seems that more emphasis should be placed on

Automatic Detection of Student Mental Models during Prior Knowledge Activation in MetaTutor

ERIC Educational Resources Information Center

Rus, Vasile; Lintean, Mihai; Azevedo, Roger

2009-01-01

This paper presents several methods to automatically detecting students' mental models in MetaTutor, an intelligent tutoring system that teaches students self-regulatory processes during learning of complex science topics. In particular, we focus on detecting students' mental models based on student-generated paragraphs during prior knowledge…

Prediction of Slot Shape and Slot Size for Improving the Performance of Microstrip Antennas Using Knowledge-Based Neural Networks.

PubMed

Khan, Taimoor; De, Asok

2014-01-01

In the last decade, artificial neural networks have become very popular techniques for computing different performance parameters of microstrip antennas. The proposed work illustrates a knowledge-based neural networks model for predicting the appropriate shape and accurate size of the slot introduced on the radiating patch for achieving desired level of resonance, gain, directivity, antenna efficiency, and radiation efficiency for dual-frequency operation. By incorporating prior knowledge in neural model, the number of required training patterns is drastically reduced. Further, the neural model incorporated with prior knowledge can be used for predicting response in extrapolation region beyond the training patterns region. For validation, a prototype is also fabricated and its performance parameters are measured. A very good agreement is attained between measured, simulated, and predicted results.

Formalizing Knowledge in Multi-Scale Agent-Based Simulations

PubMed Central

Somogyi, Endre; Sluka, James P.; Glazier, James A.

2017-01-01

Multi-scale, agent-based simulations of cellular and tissue biology are increasingly common. These simulations combine and integrate a range of components from different domains. Simulations continuously create, destroy and reorganize constituent elements causing their interactions to dynamically change. For example, the multi-cellular tissue development process coordinates molecular, cellular and tissue scale objects with biochemical, biomechanical, spatial and behavioral processes to form a dynamic network. Different domain specific languages can describe these components in isolation, but cannot describe their interactions. No current programming language is designed to represent in human readable and reusable form the domain specific knowledge contained in these components and interactions. We present a new hybrid programming language paradigm that naturally expresses the complex multi-scale objects and dynamic interactions in a unified way and allows domain knowledge to be captured, searched, formalized, extracted and reused. PMID:29338063

Formalizing Knowledge in Multi-Scale Agent-Based Simulations.

PubMed

Somogyi, Endre; Sluka, James P; Glazier, James A

2016-10-01

Multi-scale, agent-based simulations of cellular and tissue biology are increasingly common. These simulations combine and integrate a range of components from different domains. Simulations continuously create, destroy and reorganize constituent elements causing their interactions to dynamically change. For example, the multi-cellular tissue development process coordinates molecular, cellular and tissue scale objects with biochemical, biomechanical, spatial and behavioral processes to form a dynamic network. Different domain specific languages can describe these components in isolation, but cannot describe their interactions. No current programming language is designed to represent in human readable and reusable form the domain specific knowledge contained in these components and interactions. We present a new hybrid programming language paradigm that naturally expresses the complex multi-scale objects and dynamic interactions in a unified way and allows domain knowledge to be captured, searched, formalized, extracted and reused.

Bayesian hierarchical functional data analysis via contaminated informative priors.

PubMed

Scarpa, Bruno; Dunson, David B

2009-09-01

A variety of flexible approaches have been proposed for functional data analysis, allowing both the mean curve and the distribution about the mean to be unknown. Such methods are most useful when there is limited prior information. Motivated by applications to modeling of temperature curves in the menstrual cycle, this article proposes a flexible approach for incorporating prior information in semiparametric Bayesian analyses of hierarchical functional data. The proposed approach is based on specifying the distribution of functions as a mixture of a parametric hierarchical model and a nonparametric contamination. The parametric component is chosen based on prior knowledge, while the contamination is characterized as a functional Dirichlet process. In the motivating application, the contamination component allows unanticipated curve shapes in unhealthy menstrual cycles. Methods are developed for posterior computation, and the approach is applied to data from a European fecundability study.

The effects of explicit visual cues in reading biological diagrams

NASA Astrophysics Data System (ADS)

Ge, Yun-Ping; Unsworth, Len; Wang, Kuo-Hua

2017-03-01

Drawing on cognitive theories, this study intends to investigate the effects of explicit visual cues which have been proposed as a critical factor in facilitating understanding of biological images. Three diagrams from Taiwanese textbooks with implicit visual cues, involving the concepts of biological classification systems, fish taxonomy, and energy pyramid, were selected as the reading materials for the control group and reformatted in tree structure or with additional arrows as the diagrams for the treatment group. A quasi-experiment with an online reading test was conducted to examine the effect of the different image conditions on reading comprehension of the two groups. In total, 192 Taiwanese participants from year 7 were assigned randomly into either control group or treatment group according to the pre-test of relevant prior knowledge. The results indicated that not all explicit visual cues were significantly efficient. Only the explicit tree-structured diagrams cued significantly the key concepts of qualitative class-inclusion, parallel relations, and fish taxonomy. Meanwhile the effect of indexical arrows was not significant. The inconsistent effect of tree structure and arrows might be related to the extent of image reformation in which the tree-structured diagrams had undergone radical change of knowledge representation; meanwhile, the arrows had not changed the diagram structure of energy pyramid. The factor of prior knowledge was essential in considering the influence of image design as the effect of diagrams was very different for low and high prior knowledge students. Implications are drawn for the importance of visual design in textbooks.

Protecting Traditional Knowledge Related to Biological Resources: Is Scientific Research Going to Become More Bureaucratized?

PubMed Central

Reddy, Prashant; Lakshmikumaran, Malathi

2015-01-01

For the past several decades, there has been a world debate on the need for protecting traditional knowledge. A global treaty appears to be a distant reality. Of more immediate concern are the steps taken by the global community to protect access to biological resources in the name of protecting traditional knowledge. The Indian experience with implementing the Convention on Biological Diversity has created substantial legal uncertainty in collaborative scientific research between Indians and foreigners apart from bureaucratizing the entire process of scientific research, especially with regard to filing of applications for intellectual property rights. The issue therefore is whether the world needs to better balance the needs of the scientific community with the rights of those who have access to traditional knowledge. PMID:26101205

Interactions among Children in Scholastic Contexts and Knowledge Acquisition in Biology

ERIC Educational Resources Information Center

Ponce, Corinne; Schneeberger, Patricia

2002-01-01

This article presents the first results of an investigation in a scholastic context aimed a determining the conditions that favour the acquisition of knowledge in biology within interactions in groups of 4 pupils. There were three work sessions in small groups, and some sessions in class groups. The pupils' conceptions were assessed at the…

What Do Beginner Biology Teacher Candidates Know of Genetics and Genes?

ERIC Educational Resources Information Center

Oztas, Fulya; Oztas, Haydar

2016-01-01

Misconceptions are a barrier to understanding biology hence, to promote meaningful learning, it is necessary to overcome these difficulties with the help of different instructional methods rather than traditional instructional methods. Therefore it could be very interesting to find out "how students' prior knowledge of genetics affects…

Knowledge-based analysis of microarrays for the discovery of transcriptional regulation relationships

PubMed Central

2010-01-01

Background The large amount of high-throughput genomic data has facilitated the discovery of the regulatory relationships between transcription factors and their target genes. While early methods for discovery of transcriptional regulation relationships from microarray data often focused on the high-throughput experimental data alone, more recent approaches have explored the integration of external knowledge bases of gene interactions. Results In this work, we develop an algorithm that provides improved performance in the prediction of transcriptional regulatory relationships by supplementing the analysis of microarray data with a new method of integrating information from an existing knowledge base. Using a well-known dataset of yeast microarrays and the Yeast Proteome Database, a comprehensive collection of known information of yeast genes, we show that knowledge-based predictions demonstrate better sensitivity and specificity in inferring new transcriptional interactions than predictions from microarray data alone. We also show that comprehensive, direct and high-quality knowledge bases provide better prediction performance. Comparison of our results with ChIP-chip data and growth fitness data suggests that our predicted genome-wide regulatory pairs in yeast are reasonable candidates for follow-up biological verification. Conclusion High quality, comprehensive, and direct knowledge bases, when combined with appropriate bioinformatic algorithms, can significantly improve the discovery of gene regulatory relationships from high throughput gene expression data. PMID:20122245

Multi-energy CT based on a prior rank, intensity and sparsity model (PRISM).

PubMed

Gao, Hao; Yu, Hengyong; Osher, Stanley; Wang, Ge

2011-11-01

We propose a compressive sensing approach for multi-energy computed tomography (CT), namely the prior rank, intensity and sparsity model (PRISM). To further compress the multi-energy image for allowing the reconstruction with fewer CT data and less radiation dose, the PRISM models a multi-energy image as the superposition of a low-rank matrix and a sparse matrix (with row dimension in space and column dimension in energy), where the low-rank matrix corresponds to the stationary background over energy that has a low matrix rank, and the sparse matrix represents the rest of distinct spectral features that are often sparse. Distinct from previous methods, the PRISM utilizes the generalized rank, e.g., the matrix rank of tight-frame transform of a multi-energy image, which offers a way to characterize the multi-level and multi-filtered image coherence across the energy spectrum. Besides, the energy-dependent intensity information can be incorporated into the PRISM in terms of the spectral curves for base materials, with which the restoration of the multi-energy image becomes the reconstruction of the energy-independent material composition matrix. In other words, the PRISM utilizes prior knowledge on the generalized rank and sparsity of a multi-energy image, and intensity/spectral characteristics of base materials. Furthermore, we develop an accurate and fast split Bregman method for the PRISM and demonstrate the superior performance of the PRISM relative to several competing methods in simulations.

Biological interactions of carbon-based nanomaterials: From coronation to degradation.

PubMed

Bhattacharya, Kunal; Mukherjee, Sourav P; Gallud, Audrey; Burkert, Seth C; Bistarelli, Silvia; Bellucci, Stefano; Bottini, Massimo; Star, Alexander; Fadeel, Bengt

2016-02-01

Carbon-based nanomaterials including carbon nanotubes, graphene oxide, fullerenes and nanodiamonds are potential candidates for various applications in medicine such as drug delivery and imaging. However, the successful translation of nanomaterials for biomedical applications is predicated on a detailed understanding of the biological interactions of these materials. Indeed, the potential impact of the so-called bio-corona of proteins, lipids, and other biomolecules on the fate of nanomaterials in the body should not be ignored. Enzymatic degradation of carbon-based nanomaterials by immune-competent cells serves as a special case of bio-corona interactions with important implications for the medical use of such nanomaterials. In the present review, we highlight emerging biomedical applications of carbon-based nanomaterials. We also discuss recent studies on nanomaterial 'coronation' and how this impacts on biodistribution and targeting along with studies on the enzymatic degradation of carbon-based nanomaterials, and the role of surface modification of nanomaterials for these biological interactions. Advances in technology have produced many carbon-based nanomaterials. These are increasingly being investigated for the use in diagnostics and therapeutics. Nonetheless, there remains a knowledge gap in terms of the understanding of the biological interactions of these materials. In this paper, the authors provided a comprehensive review on the recent biomedical applications and the interactions of various carbon-based nanomaterials. Copyright © 2015 The Authors. Published by Elsevier Inc. All rights reserved.

Distributed, cooperating knowledge-based systems

NASA Technical Reports Server (NTRS)

Truszkowski, Walt

1991-01-01

Some current research in the development and application of distributed, cooperating knowledge-based systems technology is addressed. The focus of the current research is the spacecraft ground operations environment. The underlying hypothesis is that, because of the increasing size, complexity, and cost of planned systems, conventional procedural approaches to the architecture of automated systems will give way to a more comprehensive knowledge-based approach. A hallmark of these future systems will be the integration of multiple knowledge-based agents which understand the operational goals of the system and cooperate with each other and the humans in the loop to attain the goals. The current work includes the development of a reference model for knowledge-base management, the development of a formal model of cooperating knowledge-based agents, the use of testbed for prototyping and evaluating various knowledge-based concepts, and beginning work on the establishment of an object-oriented model of an intelligent end-to-end (spacecraft to user) system. An introductory discussion of these activities is presented, the major concepts and principles being investigated are highlighted, and their potential use in other application domains is indicated.

Prediction of Slot Shape and Slot Size for Improving the Performance of Microstrip Antennas Using Knowledge-Based Neural Networks

PubMed Central

De, Asok

2014-01-01

In the last decade, artificial neural networks have become very popular techniques for computing different performance parameters of microstrip antennas. The proposed work illustrates a knowledge-based neural networks model for predicting the appropriate shape and accurate size of the slot introduced on the radiating patch for achieving desired level of resonance, gain, directivity, antenna efficiency, and radiation efficiency for dual-frequency operation. By incorporating prior knowledge in neural model, the number of required training patterns is drastically reduced. Further, the neural model incorporated with prior knowledge can be used for predicting response in extrapolation region beyond the training patterns region. For validation, a prototype is also fabricated and its performance parameters are measured. A very good agreement is attained between measured, simulated, and predicted results. PMID:27382616

Influence of Prior Knowledge and Interest on Fourth- and Fifth-Grade Passage Comprehension on the Qualitative Reading Inventory-4

ERIC Educational Resources Information Center

Clark, Mary Kristen; Kamhi, Alan G.

2014-01-01

Purpose: In 2 experiments, we examined the influence of prior knowledge and interest on 4th- and 5th-grade students' passage comprehension scores on the Qualitative Reading Inventory-4 (QRI-4) and 2 experimenter constructed passages. Method: In Experiment 1, 4th- and 5th-grade students were administered 4 Level 4 passages or 4 Level 5…

Protecting Traditional Knowledge Related to Biological Resources: Is Scientific Research Going to Become More Bureaucratized?

PubMed

Reddy, Prashant; Lakshmikumaran, Malathi

2015-06-22

For the past several decades, there has been a world debate on the need for protecting traditional knowledge. A global treaty appears to be a distant reality. Of more immediate concern are the steps taken by the global community to protect access to biological resources in the name of protecting traditional knowledge. The Indian experience with implementing the Convention on Biological Diversity has created substantial legal uncertainty in collaborative scientific research between Indians and foreigners apart from bureaucratizing the entire process of scientific research, especially with regard to filing of applications for intellectual property rights. The issue therefore is whether the world needs to better balance the needs of the scientific community with the rights of those who have access to traditional knowledge. Copyright © 2015 Cold Spring Harbor Laboratory Press; all rights reserved.

Prior Knowledge Activation: How Different Concept Mapping Tasks Lead to Substantial Differences in Cognitive Processes, Learning Outcomes, and Perceived Self-Efficacy

ERIC Educational Resources Information Center

Gurlitt, Johannes; Renkl, Alexander

2010-01-01

Two experiments investigated the effects of characteristic features of concept mapping used for prior knowledge activation. Characteristic demands of concept mapping include connecting lines representing the relationships between concepts and labeling these lines, specifying the type of the semantic relationships. In the first experiment,…

Military Base Closures: Updated Status of Prior Base Realignments and Closures

EPA Pesticide Factsheets

As the Department of Defense (DOD) prepares for the 2005 base realignment and closure (BRAC) round, questions continue to be raised about the transfer and environmental cleanup of unneeded property arising from the prior four BRAC rounds and their impact on cost and savings and on local economies.

A blind deconvolution method based on L1/L2 regularization prior in the gradient space

NASA Astrophysics Data System (ADS)

Cai, Ying; Shi, Yu; Hua, Xia

2018-02-01

In the process of image restoration, the result of image restoration is very different from the real image because of the existence of noise, in order to solve the ill posed problem in image restoration, a blind deconvolution method based on L1/L2 regularization prior to gradient domain is proposed. The method presented in this paper first adds a function to the prior knowledge, which is the ratio of the L1 norm to the L2 norm, and takes the function as the penalty term in the high frequency domain of the image. Then, the function is iteratively updated, and the iterative shrinkage threshold algorithm is applied to solve the high frequency image. In this paper, it is considered that the information in the gradient domain is better for the estimation of blur kernel, so the blur kernel is estimated in the gradient domain. This problem can be quickly implemented in the frequency domain by fast Fast Fourier Transform. In addition, in order to improve the effectiveness of the algorithm, we have added a multi-scale iterative optimization method. This paper proposes the blind deconvolution method based on L1/L2 regularization priors in the gradient space can obtain the unique and stable solution in the process of image restoration, which not only keeps the edges and details of the image, but also ensures the accuracy of the results.

Development of the biology card sorting task to measure conceptual expertise in biology.

PubMed

Smith, Julia I; Combs, Elijah D; Nagami, Paul H; Alto, Valerie M; Goh, Henry G; Gourdet, Muryam A A; Hough, Christina M; Nickell, Ashley E; Peer, Adrian G; Coley, John D; Tanner, Kimberly D

2013-01-01

There are widespread aspirations to focus undergraduate biology education on teaching students to think conceptually like biologists; however, there is a dearth of assessment tools designed to measure progress from novice to expert biological conceptual thinking. We present the development of a novel assessment tool, the Biology Card Sorting Task, designed to probe how individuals organize their conceptual knowledge of biology. While modeled on tasks from cognitive psychology, this task is unique in its design to test two hypothesized conceptual frameworks for the organization of biological knowledge: 1) a surface feature organization focused on organism type and 2) a deep feature organization focused on fundamental biological concepts. In this initial investigation of the Biology Card Sorting Task, each of six analytical measures showed statistically significant differences when used to compare the card sorting results of putative biological experts (biology faculty) and novices (non-biology major undergraduates). Consistently, biology faculty appeared to sort based on hypothesized deep features, while non-biology majors appeared to sort based on either surface features or nonhypothesized organizational frameworks. Results suggest that this novel task is robust in distinguishing populations of biology experts and biology novices and may be an adaptable tool for tracking emerging biology conceptual expertise.

Development of the Biology Card Sorting Task to Measure Conceptual Expertise in Biology

PubMed Central

Smith, Julia I.; Combs, Elijah D.; Nagami, Paul H.; Alto, Valerie M.; Goh, Henry G.; Gourdet, Muryam A. A.; Hough, Christina M.; Nickell, Ashley E.; Peer, Adrian G.; Coley, John D.; Tanner, Kimberly D.

2013-01-01

There are widespread aspirations to focus undergraduate biology education on teaching students to think conceptually like biologists; however, there is a dearth of assessment tools designed to measure progress from novice to expert biological conceptual thinking. We present the development of a novel assessment tool, the Biology Card Sorting Task, designed to probe how individuals organize their conceptual knowledge of biology. While modeled on tasks from cognitive psychology, this task is unique in its design to test two hypothesized conceptual frameworks for the organization of biological knowledge: 1) a surface feature organization focused on organism type and 2) a deep feature organization focused on fundamental biological concepts. In this initial investigation of the Biology Card Sorting Task, each of six analytical measures showed statistically significant differences when used to compare the card sorting results of putative biological experts (biology faculty) and novices (non–biology major undergraduates). Consistently, biology faculty appeared to sort based on hypothesized deep features, while non–biology majors appeared to sort based on either surface features or nonhypothesized organizational frameworks. Results suggest that this novel task is robust in distinguishing populations of biology experts and biology novices and may be an adaptable tool for tracking emerging biology conceptual expertise. PMID:24297290

Mathematical Learning Models that Depend on Prior Knowledge and Instructional Strategies

ERIC Educational Resources Information Center

Pritchard, David E.; Lee, Young-Jin; Bao, Lei

2008-01-01

We present mathematical learning models--predictions of student's knowledge vs amount of instruction--that are based on assumptions motivated by various theories of learning: tabula rasa, constructivist, and tutoring. These models predict the improvement (on the post-test) as a function of the pretest score due to intervening instruction and also…

Verbal Final Exam in Introductory Biology Yields Gains in Student Content Knowledge and Longitudinal Performance

ERIC Educational Resources Information Center

Luckie, Douglas B.; Rivkin, Aaron M.; Aubry, Jacob R.; Marengo, Benjamin J.; Creech, Leah R.; Sweeder, Ryan D.

2013-01-01

We studied gains in student learning over eight semesters in which an introductory biology course curriculum was changed to include optional verbal final exams (VFs). Students could opt to demonstrate their mastery of course material via structured oral exams with the professor. In a quantitative assessment of cell biology content knowledge,…

Content-Related Knowledge of Biology Teachers from Secondary Schools: Structure and learning opportunities

NASA Astrophysics Data System (ADS)

Großschedl, Jörg; Mahler, Daniela; Kleickmann, Thilo; Harms, Ute

2014-09-01

Teachers' content-related knowledge is a key factor influencing the learning progress of students. Different models of content-related knowledge have been proposed by educational researchers; most of them take into account three categories: content knowledge, pedagogical content knowledge, and curricular knowledge. As there is no consensus about the empirical separability (i.e. empirical structure) of content-related knowledge yet, a total of 134 biology teachers from secondary schools completed three tests which were to capture each of the three categories of content-related knowledge. The empirical structure of content-related knowledge was analyzed by Rasch analysis, which suggests content-related knowledge to be composed of (1) content knowledge, (2) pedagogical content knowledge, and (3) curricular knowledge. Pedagogical content knowledge and curricular knowledge are highly related (rlatent = .70). The latent correlations between content knowledge and pedagogical content knowledge (rlatent = .48)-and curricular knowledge, respectively (rlatent = .35)-are moderate to low (all ps < .001). Beyond the empirical structure of content-related knowledge, different learning opportunities for teachers were investigated with regard to their relationship to content knowledge, pedagogical content knowledge, and curricular knowledge acquisition. Our results show that an in-depth training in teacher education, professional development, and teacher self-study are positively related to particular categories of content-related knowledge. Furthermore, our results indicate that teaching experience is negatively related to curricular knowledge, compared to no significant relationship with content knowledge and pedagogical content knowledge.

Patterns of thinking about phylogenetic trees: A study of student learning and the potential of tree thinking to improve comprehension of biological concepts

NASA Astrophysics Data System (ADS)

Naegle, Erin

Evolution education is a critical yet challenging component of teaching and learning biology. There is frequently an emphasis on natural selection when teaching about evolution and conducting educational research. A full understanding of evolution, however, integrates evolutionary processes, such as natural selection, with the resulting evolutionary patterns, such as species divergence. Phylogenetic trees are models of evolutionary patterns. The perspective gained from understanding biology through phylogenetic analyses is referred to as tree thinking. Due to the increasing prevalence of tree thinking in biology, understanding how to read phylogenetic trees is an important skill for students to learn. Interpreting graphics is not an intuitive process, as graphical representations are semiotic objects. This is certainly true concerning phylogenetic tree interpretation. Previous research and anecdotal evidence report that students struggle to correctly interpret trees. The objective of this research was to describe and investigate the rationale underpinning the prior knowledge of introductory biology students' tree thinking Understanding prior knowledge is valuable as prior knowledge influences future learning. In Chapter 1, qualitative methods such as semi-structured interviews were used to explore patterns of student rationale in regard to tree thinking. Seven common tree thinking misconceptions are described: (1) Equating the degree of trait similarity with the extent of relatedness, (2) Environmental change is a necessary prerequisite to evolution, (3) Essentialism of species, (4) Evolution is inherently progressive, (5) Evolution is a linear process, (6) Not all species are related, and (7) Trees portray evolution through the hybridization of species. These misconceptions are based in students' incomplete or incorrect understanding of evolution. These misconceptions are often reinforced by the misapplication of cultural conventions to make sense of trees

A Short Note on Haroutunian's View of Piaget's Biological Conception of Knowledge.

ERIC Educational Resources Information Center

Doll, William E., Jr.

1981-01-01

The author discusses major premises of a paper, by Sophie Haroutunian (Educational Theory, v30 n3), that relates Jean Piaget's conception of knowledge to his biological theory of equilibrium. Doll argues that Piaget's theory of equilibration (striving for control over the environment) is not sufficiently appreciated by Haroutunian. (PP)

Hybrid forecasting of chaotic processes: Using machine learning in conjunction with a knowledge-based model

NASA Astrophysics Data System (ADS)

Pathak, Jaideep; Wikner, Alexander; Fussell, Rebeckah; Chandra, Sarthak; Hunt, Brian R.; Girvan, Michelle; Ott, Edward

2018-04-01

A model-based approach to forecasting chaotic dynamical systems utilizes knowledge of the mechanistic processes governing the dynamics to build an approximate mathematical model of the system. In contrast, machine learning techniques have demonstrated promising results for forecasting chaotic systems purely from past time series measurements of system state variables (training data), without prior knowledge of the system dynamics. The motivation for this paper is the potential of machine learning for filling in the gaps in our underlying mechanistic knowledge that cause widely-used knowledge-based models to be inaccurate. Thus, we here propose a general method that leverages the advantages of these two approaches by combining a knowledge-based model and a machine learning technique to build a hybrid forecasting scheme. Potential applications for such an approach are numerous (e.g., improving weather forecasting). We demonstrate and test the utility of this approach using a particular illustrative version of a machine learning known as reservoir computing, and we apply the resulting hybrid forecaster to a low-dimensional chaotic system, as well as to a high-dimensional spatiotemporal chaotic system. These tests yield extremely promising results in that our hybrid technique is able to accurately predict for a much longer period of time than either its machine-learning component or its model-based component alone.

Balancing the Role of Priors in Multi-Observer Segmentation Evaluation

PubMed Central

Huang, Xiaolei; Wang, Wei; Lopresti, Daniel; Long, Rodney; Antani, Sameer; Xue, Zhiyun; Thoma, George

2009-01-01

Comparison of a group of multiple observer segmentations is known to be a challenging problem. A good segmentation evaluation method would allow different segmentations not only to be compared, but to be combined to generate a “true” segmentation with higher consensus. Numerous multi-observer segmentation evaluation approaches have been proposed in the literature, and STAPLE in particular probabilistically estimates the true segmentation by optimal combination of observed segmentations and a prior model of the truth. An Expectation–Maximization (EM) algorithm, STAPLE’S convergence to the desired local minima depends on good initializations for the truth prior and the observer-performance prior. However, accurate modeling of the initial truth prior is nontrivial. Moreover, among the two priors, the truth prior always dominates so that in certain scenarios when meaningful observer-performance priors are available, STAPLE can not take advantage of that information. In this paper, we propose a Bayesian decision formulation of the problem that permits the two types of prior knowledge to be integrated in a complementary manner in four cases with differing application purposes: (1) with known truth prior; (2) with observer prior; (3) with neither truth prior nor observer prior; and (4) with both truth prior and observer prior. The third and fourth cases are not discussed (or effectively ignored) by STAPLE, and in our research we propose a new method to combine multiple-observer segmentations based on the maximum a posterior (MAP) principle, which respects the observer prior regardless of the availability of the truth prior. Based on the four scenarios, we have developed a web-based software application that implements the flexible segmentation evaluation framework for digitized uterine cervix images. Experiment results show that our framework has flexibility in effectively integrating different priors for multi-observer segmentation evaluation and it also