University of Kansas

About: University of Kansas is a education organization based out in Lawrence, Kansas, United States. It is known for research contribution in the topics: Population & Poison control. The organization has 38183 authors who have published 81381 publications receiving 2986312 citations. The organization is also known as: KU & Univ of Kansas.

Mediation Analysis in Social Psychology: Current Practices and New Recommendations

Abstract: A key aim of social psychology is to understand the psychological processes through which independent variables affect dependent variables in the social domain. This objective has given rise to statistical methods for mediation analysis. In mediation analysis, the significance of the relationship between the independent and dependent variables has been integral in theory testing, being used as a basis to determine (1) whether to proceed with analyses of mediation and (2) whether one or several proposed mediator(s) fully or partially accounts for an effect. Synthesizing past research and offering new arguments, we suggest that the collective evidence raises considerable concern that the focus on the significance between the independent and dependent variables, both before and after mediation tests, is unjustified and can impair theory development and testing. To expand theory involving social psychological processes, we argue that attention in mediation analysis should be shifted towards assessing the magnitude and significance of indirect effects.

A distributed, architecture-centric approach to computing accurate recommendations from very large and sparse datasets

Abstract: The use of recommender systems is an emerging trend today, when user behavior information is abundant. There are many large datasets available for analysis because many businesses are interested in future user opinions. Sophisticated algorithms that predict such opinions can simplify decision-making, improve customer satisfaction, and increase sales. However, modern datasets contain millions of records, which represent only a small fraction of all possible data. Furthermore, much of the information in such sparse datasets may be considered irrelevant for making individual recommendations. As a result, there is a demand for a way to make personalized suggestions from large amounts of noisy data. Current recommender systems are usually all-in-one applications that provide one type of recommendation. Their inflexible architectures prevent detailed examination of recommendation accuracy and its causes. We introduce a novel architecture model that supports scalable, distributed suggestions from multiple independent nodes. Our model consists of two components, the input matrix generation algorithm and multiple platform-independent combination algorithms. A dedicated input generation component provides the necessary data for combination algorithms, reduces their size, and eliminates redundant data processing. Likewise, simple combination algorithms can produce recommendations from the same input, so we can more easily distinguish between the benefits of a particular combination algorithm and the quality of the data it receives. Such flexible architecture is more conducive for a comprehensive examination of our system. We believe that a user's future opinion may be inferred from a small amount of data, provided that this data is most relevant. We propose a novel algorithm that generates a more optimal recommender input. Unlike existing approaches, our method sorts the relevant data twice. Doing this is slower, but the quality of the resulting input is considerably better. Furthermore, the modular nature of our approach may improve its performance, especially in the cloud computing context. We implement and validate our proposed model via mathematical modeling, by appealing to statistical theories, and through extensive experiments, data analysis, and empirical studies. Our empirical study examines the effectiveness of accuracy improvement techniques for collaborative filtering recommender systems. We evaluate our proposed architecture model on the Netflix dataset, a popular (over 130,000 solutions), large (over 100,000,000 records), and extremely sparse (1.1%) collection of movie ratings. The results show that combination algorithm tuning has little effect on recommendation accuracy. However, all algorithms produce better results when supplied with a more relevant input. Our input generation algorithm is the reason for a considerable accuracy improvement.

Human microRNAs are processed from capped, polyadenylated transcripts that can also function as mRNAs

Abstract: The factors regulating the expression of microRNAs (miRNAs), a ubiquitous family of approximately 22-nt noncoding regulatory RNAs, remain undefined. However, it is known that miRNAs are first transcribed as a largely unstructured precursor, termed a primary miRNA (pri-miRNA), which is sequentially processed in the nucleus, to give the approximately 65-nt pre-miRNA hairpin intermediate, and then in the cytoplasm, to give the mature miRNA. Here we have sought to identify the RNA polymerase responsible for miRNA transcription and to define the structure of a full-length human miRNA. We show that the pri-miRNA precursors for nine human miRNAs are both capped and polyadenylated and report the sequence of the full-length, approximately 3433-nt pri-miR-21 RNA. This pri-miR-21 gene sequence is flanked 5' by a promoter element able to transcribe heterologous mRNAs and 3' by a consensus polyadenylation sequence. Nuclear processing of pri-miRNAs was found to be efficient, thus largely preventing the nuclear export of full-length pri-miRNAs. Nevertheless, an intact miRNA stem-loop precursor located in the 3' UTR of a protein coding gene only moderately inhibited expression of the linked open reading frame, probably because the 3' truncated mRNA could still be exported and expressed. Together, these data show that human pri-miRNAs are not only structurally similar to mRNAs but can, in fact, function both as pri-miRNAs and mRNAs.

A Model for Radar Images and Its Application to Adaptive Digital Filtering of Multiplicative Noise

Abstract: Standard image processing techniques which are used to enhance noncoherent optically produced images are not applicable to radar images due to the coherent nature of the radar imaging process. A model for the radar imaging process is derived in this paper and a method for smoothing noisy radar images is also presented. The imaging model shows that the radar image is corrupted by multiplicative noise. The model leads to the functional form of an optimum (minimum MSE) filter for smoothing radar images. By using locally estimated parameter values the filter is made adaptive so that it provides minimum MSE estimates inside homogeneous areas of an image while preserving the edge structure. It is shown that the filter can be easily implemented in the spatial domain and is computationally efficient. The performance of the adaptive filter is compared (qualitatively and quantitatively) with several standard filters using real and simulated radar images.