Existence Of Prior Knowledge Research Articles

Notable Differences in Drug Lag Between Korea and Japan of New Drugs Between 2009 and 2017.

Although great efforts have been made to address delays in new-drug approvals between different regions, little is known regarding drug lag in Korea. We compared drug lag in Korea with that in Japan, which is well known through many previous studies. We extracted approval information from public data obtained from the regulatory authorities on all new drugs that were listed in both Korea and Japan between 2009 and 2017. Relative drug lag represented the difference between the date of approval in the country of interest and the date of the first global approval. Relative drug lag was measured as median approval lag. Multiple regression models were used to identify the factors associated with drug lag. The factors considered included origin of substance, nationality of manufacturer, original approval date, orphan drug status, and therapeutic class. In total, 394 new drugs were approved in both countries between 2009 and 2017. The median approval lag was 28.2 and 54.1 months in Korea and Japan, respectively. Domestic company drugs were associated with longer median approval lag in Korea ([Formula: see text]), sensory organ drugs were associated with shorter median approval lag in Japan ([Formula: see text]), and orphan drug status was associated with longer duration of drug approval in Japan ([Formula: see text]). Notable drug lags were observed in both Korea and Japan, but the lag was shorter in Korea. Inclusion of global clinical trials and existence of prior knowledge in new drug development were critical to reduce drug lag.

Notable Differences in Drug Lag Between Korea and Japan of New Drugs Between 2009 and 2017

Although great efforts have been made to address delays in new-drug approvals between different regions, little is known regarding drug lag in Korea. We compared drug lag in Korea with that in Japan, which is well known through many previous studies. We extracted approval information from public data obtained from the regulatory authorities on all new drugs that were listed in both Korea and Japan between 2009 and 2017. Relative drug lag represented the difference between the date of approval in the country of interest and the date of the first global approval. Relative drug lag was measured as median approval lag. Multiple regression models were used to identify the factors associated with drug lag. The factors considered included origin of substance, nationality of manufacturer, original approval date, orphan drug status, and therapeutic class. In total, 394 new drugs were approved in both countries between 2009 and 2017. The median approval lag was 28.2 and 54.1 months in Korea and Japan, respectively. Domestic company drugs were associated with longer median approval lag in Korea (β = 12.076, P = .015), sensory organ drugs were associated with shorter median approval lag in Japan (β = -46.144, P = .009), and orphan drug status was associated with longer duration of drug approval in Japan (β = 9.695, P = .024). Notable drug lags were observed in both Korea and Japan, but the lag was shorter in Korea. Inclusion of global clinical trials and existence of prior knowledge in new drug development were critical to reduce drug lag.

Multiresolution interval partial least squares: A framework for waveband selection and resolution optimization

Spectroscopic data generated by several PAT technologies is routinely used for the rapid assessment of quality properties in several industrial sectors, such as agrofood, beverages, pharmaceutics, chemicals, pulp & paper, etc. While spectra can easily provide hundreds of measurements across several wavelengths, only a fraction of the collected spectrum conveys relevant information to predict the property of interest. Therefore, the performance of current models is highly related with the ability to select key wavebands, for which the existence of prior knowledge cannot be always secured. Therefore, several feature selection procedures consisting of variants of interval partial least squares (iPLS) have been proposed. These methodologies are however solely focused on determining the most relevant wavebands and do not attempt to further enhance the prediction capabilities within each interval. On the other hand, standard full-spectrum models are often improved by reducing the spectral resolution, but this operation has not been yet synergistically integrated together with waveband selection. As spectral aggregation can effectively improve modelling performance, a multiresolution selection algorithm that simultaneously selects the most relevant wavebands and their optimal resolution is here proposed. By design, this methodology leads to prediction models that are at least as good as the full-spectrum models. The performance comparison made on simulated data and real NIR spectra of gasoline samples also shows that the proposed methodology outperforms iPLS and its variants based on forward and backward selection of intervals in a statistically significant way.

The Shifting Paradigm: Learning to Unlearn

The Shifting Paradigm: Learning to Unlearn

Erratum to: Evaluation of the EC8-3 confidence factors for the characterization of concrete strength in existing structures

A probabilistic framework is defined to evaluate the values of the Confidence Factors (CFs) proposed in Eurocode 8 Part 3 (EC8-3) for the characterization of material properties. This evaluation is presented for the concrete compressive strength but its validity for other material properties can also be inferred from the results obtained. The number of material tests and the existence of prior knowledge are the essential aspects for the CF quantification. The probabilistic framework proposed in the first part of the study does not consider the existence of prior knowledge and is based on the concept of confidence intervals. In the second part of the study, the effects of prior knowledge are considered using a Bayesian framework. The combination of testing data obtained from different types of tests is also addressed as an extension of the referred Bayesian approach. Results indicate that the EC8-3 proposed CFs for KL1 and KL2 are adequate, but for KL3 it is suggested that a larger value should be used.

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

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.

Facilitating unlearning during implementation of new technology

PurposeOne of the critical issues for change management, particularly in relation to the implementation of new technologies, is the existence of prior knowledge and established mental models which may hinder change efforts. Understanding unlearning and how it might assist during organizational change is a way to address this resistance. The purpose of this paper is to present research designed to identify specific factors that facilitate unlearning.Design/methodology/approachDrawing together issues identified as potential influencers of unlearning, a survey questionnaire was developed and administered in an Australian corporation undergoing large‐scale change due to the implementation of an enterprise information system. The results were analyzed to identify specific factors that impact on unlearning.FindingsFindings from this paper identify factors that hinder or help the unlearning process during times of change including understanding the need for change, the level of organizational support and training, assessment of the change, positive experience and informal support, the organization's history of change, individual's prior outlooks, and individuals' feelings and expectations.Research limitations/implicationsThe use of only one organization does not allow for comparisons between organizations of different sizes, cultures or industries and therefore extension of this research is recommended.Practical implicationsFor practitioners, this paper provides specific elements at both the level of individuals and the organization that need to be considered for optimal unlearning during times of change.Originality/valuePrevious literature on unlearning has been predominantly conceptual and theoretical. These empirical findings serve to further an earlier model based on qualitative research into potential influencers of unlearning.

Semiparametric Bayesian Analysis of Selection Models

Selection models are appropriate when a datum x enters the sample only with probability or weight w(x). It is typically assumed that the weight function w is monotone, but the precise functional form of the weight function is often unknown. In this article, the Dirichlet process prior, centered on a parametric form, is used as a prior distribution on the weight function. This allows for incorporation of knowledge about the weight function, without restricting it to be of some particular functional form. By introducing latent variables related to the selection mechanism, computation via Gibbs sampling can be implemented in the case where the total number of selected and unselected observations, N, is known. When N is unknown, a reversible jump Markov chain sampler is needed to carry out the computations. An important difficulty that can be thought of as “practical nonidentifiability” is revealed, even for selection models in which the weight functions are theoretically identifiable. The proposed solution to this problem depends on the existence of prior knowledge concerning the effective range of the weight function.

Spatial situation models and narrative understanding: Some generalizations and extensions

The generality of effects of situation models on narrative comprehension was investigated. In three experiments, a spatial gradient of accessibility in situation models was observed. The accessibility of objects contained in the situation model decreased with increasing spatial distance between the object and the focus of attention in the readers’ situation model. A variety of factors that might influence the spatial gradient effect were investigated: the way the relevant spatial information was acquired (studying a text vs. a layout), the spatial scenario (a research center vs. a day care center), the direction of spatial distance (backward vs. forward on the route), the language used (English vs. German), the manner in which the accessibility of objects was probed (object probe pairs vs. anaphoric sentences), the existence of prior knowledge about the objects (objects learned as part of the scenario vs. unknown objects), and the participants’ task (reading narratives vs. imagining their own movements). ...