cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.6084/m9.figshare.1378881.v4
title: Genome-wide differentially methylated genes in prostate cancer tissues from African-American and Caucasian men

description: Increasing evidence suggests that aberrant DNA methylation changes may contribute to prostate cancer (PCa) ethnic disparity. To comprehensively identify DNA methylation alterations in PCa disparity, we used the Illumina 450K methylation platform to interrogate the methylation status of 485,577 CpG sites focusing on gene-associated regions of the human genome. Genomic DNA from African-American (AA; 7 normal and 3 cancers) and Caucasian (Cau; 8 normal and 3 cancers) was used in the analysis. Hierarchical clustering analysis identified probe-sets unique to AA and Cau samples, as well as common to both. We selected 25 promoter-associated novel CpG sites most differentially methylated by race (fold change &gt; 1.5-fold; adjusted <i>P</i> &lt; 0.05) and compared the β-value of these sites provided by the Illumina, Inc. array with quantitative methylation obtained by pyrosequencing in 7 prostate cell lines. We found very good concordance of the methylation levels between β-value and pyrosequencing. Gene expression analysis using qRT-PCR in a subset of 8 genes after treatment with 5-aza-2′-deoxycytidine and/or trichostatin showed up-regulation of gene expression in PCa cells. Quantitative analysis of 4 genes, <i>SNRPN, SHANK</i>2, <i>MST1R</i>, and <i>ABCG</i>5, in matched normal and PCa tissues derived from AA and Cau PCa patients demonstrated differential promoter methylation and concomitant differences in mRNA expression in prostate tissues from AA vs. Cau. Regression analysis in normal and PCa tissues as a function of race showed significantly higher methylation prevalence for <i>SNRPN</i> (<i>P</i> = 0.012), <i>MST1R</i> (<i>P</i> = 0.038), and <i>ABCG</i>5 (<i>P</i> &lt; 0.0002) for AA vs. Cau samples. We selected the <i>ABCG5</i> and <i>SNRPN</i> genes and verified their biological functions by Western blot analysis and siRNA gene knockout effects on cell proliferation and invasion in 4 PCa cell lines (2 AA and 2 Cau patients-derived lines). Knockdown of either ABCG5 or SNRPN resulted in a significant decrease in both invasion and proliferation in Cau PCa cell lines but we did not observe these remarkable loss-of-function effects in AA PCa cell lines. Our study demonstrates how differential genome-wide DNA methylation levels influence gene expression and biological functions in AA and Cau PCa.
DOI: 10.6084/m9.figshare.1378876.v2
title: Four-leaf clover qRT-PCR: A convenient method for selective quantification of mature transfer RNA

description: ABSTRACTTransfer RNAs (tRNAs) play a central role in translation and also recently appear to have a variety of other functions in biological processes beyond translation. Here we report the development of Four-Leaf clover qRT-PCR (FL-PCR), a convenient PCR-based method, which can specifically quantify individual mature tRNA species. In FL-PCR, T4 RNA ligase 2 specifically ligates a stem-loop adapter to mature tRNAs but not to precursor tRNAs or tRNA fragments. Subsequent TaqMan qRT-PCR amplifies only unmodified regions of the tRNA-adapter ligation products; therefore, FL-PCR quantification is not influenced by tRNA post-transcriptional modifications. FL-PCR has broad applicability for the quantification of various tRNAs in different cell types, and thus provides a much-needed simple method for analyzing tRNA abundance and heterogeneity.
DOI: 10.6084/m9.figshare.1378871
title: High-Dimensional Fused Lasso Regression Using Majorization–Minimization and Parallel Processing

description: In this article, we propose a majorization–minimization (MM) algorithm for high-dimensional fused lasso regression (FLR) suitable for parallelization using graphics processing units (GPUs). The MM algorithm is stable and flexible as it can solve the FLR problems with various types of design matrices and penalty structures within a few tens of iterations. We also show that the convergence of the proposed algorithm is guaranteed. We conduct numerical studies to compare our algorithm with other existing algorithms, demonstrating that the proposed MM algorithm is competitive in many settings including the two-dimensional FLR with arbitrary design matrices. The merit of GPU parallelization is also exhibited. Supplementary materials are available online.
DOI: 10.6084/m9.figshare.1378873
title: Image Denoising by a Local Clustering Framework

description: Images often contain noise due to imperfections in various image acquisition techniques. Noise should be removed from images so that the details of image objects (e.g., blood vessels, inner foldings, or tumors in the human brain) can be clearly seen, and the subsequent image analyses are reliable. With broad usage of images in many disciplines—for example, medical science—image denoising has become an important research area. In the literature, there are many different types of image denoising techniques, most of which aim to preserve image features, such as edges and edge structures, by estimating them explicitly or implicitly. Techniques based on explicit edge detection usually require certain assumptions on the smoothness of the image intensity surface and the edge curves which are often invalid especially when the image resolution is low. Methods that are based on implicit edge detection often use multiresolution smoothing, weighted local smoothing, and so forth. For such methods, the task of determining the correct image resolution or choosing a reasonable weight function is challenging. If the edge structure of an image is complicated or the image has many details, then these methods would blur such details. This article presents a novel image denoising framework based on local clustering of image intensities and adaptive smoothing. The new denoising method can preserve complicated edge structures well even if the image resolution is low. Theoretical properties and numerical studies show that it works well in various applications.
DOI: 10.6084/m9.figshare.1378872
title: Review of greenhouse gas emissions from the storage and land application of farm dairy effluent

description: The amounts of farm dairy effluent stored in ponds and irrigated to land have steadily increased with the steady growth of New Zealand's dairy industry. About 80% of dairy farms now operate with effluent storage ponds allowing deferred irrigation. These storage and irrigation practices cause emissions of greenhouse gases (GHG) and ammonia. The current knowledge of the processes causing these emissions and the amounts emitted is reviewed here. Methane emissions from ponds are the largest contributor to the total GHG emissions from effluent in managed manure systems in New Zealand. Nitrous oxide emissions from anaerobic ponds are negligible, while ammonia emissions vary widely between different studies, probably because they depend strongly on pH and manure composition. The second-largest contribution to GHG emissions from farm dairy effluent comes from nitrous oxide emissions from land application. Ammonia emissions from land application of effluent in New Zealand were found to be less than those reported elsewhere from the application of slurries. Recent studies have suggested that New Zealand's current GHG inventory method to estimate methane emissions from effluent ponds should be revised. The increasing importance of emissions from ponds, while being a challenge for the inventory, also provides an opportunity to achieve mitigation of emissions due to the confined location of where these emissions occur.
DOI: 10.6084/m9.figshare.1378876.v3
title: Four-leaf clover qRT-PCR: A convenient method for selective quantification of mature tRNA

description: Transfer RNAs (tRNAs) play a central role in translation and also recently appear to have a variety of other functions in biological processes beyond translation. Here we report the development of Four-Leaf clover qRT-PCR (FL-PCR), a convenient PCR-based method, which can specifically quantify individual mature tRNA species. In FL-PCR, T4 RNA ligase 2 specifically ligates a stem-loop adapter to mature tRNAs but not to precursor tRNAs or tRNA fragments. Subsequent TaqMan qRT-PCR amplifies only unmodified regions of the tRNA-adapter ligation products; therefore, FL-PCR quantification is not influenced by tRNA post-transcriptional modifications. FL-PCR has broad applicability for the quantification of various tRNAs in different cell types, and thus provides a much-needed simple method for analyzing tRNA abundance and heterogeneity.
DOI: 10.6084/m9.figshare.1378871.v2
title: High-Dimensional Fused Lasso Regression Using Majorization–Minimization and Parallel Processing

description: In this article, we propose a majorization–minimization (MM) algorithm for high-dimensional fused lasso regression (FLR) suitable for parallelization using graphics processing units (GPUs). The MM algorithm is stable and flexible as it can solve the FLR problems with various types of design matrices and penalty structures within a few tens of iterations. We also show that the convergence of the proposed algorithm is guaranteed. We conduct numerical studies to compare our algorithm with other existing algorithms, demonstrating that the proposed MM algorithm is competitive in many settings including the two-dimensional FLR with arbitrary design matrices. The merit of GPU parallelization is also exhibited. Supplementary materials are available online.
DOI: 10.6084/m9.figshare.1378871.v1
title: High-Dimensional Fused Lasso Regression Using Majorization–Minimization and Parallel Processing

description: In this article, we propose a majorization–minimization (MM) algorithm for high-dimensional fused lasso regression (FLR) suitable for parallelization using graphics processing units (GPUs). The MM algorithm is stable and flexible as it can solve the FLR problems with various types of design matrices and penalty structures within a few tens of iterations. We also show that the convergence of the proposed algorithm is guaranteed. We conduct numerical studies to compare our algorithm with other existing algorithms, demonstrating that the proposed MM algorithm is competitive in many settings including the two-dimensional FLR with arbitrary design matrices. The merit of GPU parallelization is also exhibited. Supplementary materials are available online.
DOI: 10.6084/m9.figshare.1378872.v2
title: Review of greenhouse gas emissions from the storage and land application of farm dairy effluent

description: The amounts of farm dairy effluent stored in ponds and irrigated to land have steadily increased with the steady growth of New Zealand's dairy industry. About 80% of dairy farms now operate with effluent storage ponds allowing deferred irrigation. These storage and irrigation practices cause emissions of greenhouse gases (GHG) and ammonia. The current knowledge of the processes causing these emissions and the amounts emitted is reviewed here. Methane emissions from ponds are the largest contributor to the total GHG emissions from effluent in managed manure systems in New Zealand. Nitrous oxide emissions from anaerobic ponds are negligible, while ammonia emissions vary widely between different studies, probably because they depend strongly on pH and manure composition. The second-largest contribution to GHG emissions from farm dairy effluent comes from nitrous oxide emissions from land application. Ammonia emissions from land application of effluent in New Zealand were found to be less than those reported elsewhere from the application of slurries. Recent studies have suggested that New Zealand's current GHG inventory method to estimate methane emissions from effluent ponds should be revised. The increasing importance of emissions from ponds, while being a challenge for the inventory, also provides an opportunity to achieve mitigation of emissions due to the confined location of where these emissions occur.
DOI: 10.6084/m9.figshare.1378873.v1
title: Image Denoising by a Local Clustering Framework

description: Images often contain noise due to imperfections in various image acquisition techniques. Noise should be removed from images so that the details of image objects (e.g., blood vessels, inner foldings, or tumors in the human brain) can be clearly seen, and the subsequent image analyses are reliable. With broad usage of images in many disciplines—for example, medical science—image denoising has become an important research area. In the literature, there are many different types of image denoising techniques, most of which aim to preserve image features, such as edges and edge structures, by estimating them explicitly or implicitly. Techniques based on explicit edge detection usually require certain assumptions on the smoothness of the image intensity surface and the edge curves which are often invalid especially when the image resolution is low. Methods that are based on implicit edge detection often use multiresolution smoothing, weighted local smoothing, and so forth. For such methods, the task of determining the correct image resolution or choosing a reasonable weight function is challenging. If the edge structure of an image is complicated or the image has many details, then these methods would blur such details. This article presents a novel image denoising framework based on local clustering of image intensities and adaptive smoothing. The new denoising method can preserve complicated edge structures well even if the image resolution is low. Theoretical properties and numerical studies show that it works well in various applications.
DOI: 10.6084/m9.figshare.1378870
title: LDRC-HUB mentorship slides

description: Slides on getting postdoc and faculty positions, and what life is like for both
DOI: 10.6084/m9.figshare.1378872.v1
title: Review of greenhouse gas emissions from the storage and land application of farm dairy effluent

description: The amounts of farm dairy effluent stored in ponds and irrigated to land have steadily increased with the steady growth of New Zealand's dairy industry. About 80% of dairy farms now operate with effluent storage ponds allowing deferred irrigation. These storage and irrigation practices cause emissions of greenhouse gases (GHG) and ammonia. The current knowledge of the processes causing these emissions and the amounts emitted is reviewed here. Methane emissions from ponds are the largest contributor to the total GHG emissions from effluent in managed manure systems in New Zealand. Nitrous oxide emissions from anaerobic ponds are negligible, while ammonia emissions vary widely between different studies, probably because they depend strongly on pH and manure composition. The second-largest contribution to GHG emissions from farm dairy effluent comes from nitrous oxide emissions from land application. Ammonia emissions from land application of effluent in New Zealand were found to be less than those reported elsewhere from the application of slurries. Recent studies have suggested that New Zealand's current GHG inventory method to estimate methane emissions from effluent ponds should be revised. The increasing importance of emissions from ponds, while being a challenge for the inventory, also provides an opportunity to achieve mitigation of emissions due to the confined location of where these emissions occur.
DOI: 10.6084/m9.figshare.1378876.v4
title: Four-leaf clover qRT-PCR: A convenient method for selective quantification of mature tRNA

description: Transfer RNAs (tRNAs) play a central role in translation and also recently appear to have a variety of other functions in biological processes beyond translation. Here we report the development of Four-Leaf clover qRT-PCR (FL-PCR), a convenient PCR-based method, which can specifically quantify individual mature tRNA species. In FL-PCR, T4 RNA ligase 2 specifically ligates a stem-loop adapter to mature tRNAs but not to precursor tRNAs or tRNA fragments. Subsequent TaqMan qRT-PCR amplifies only unmodified regions of the tRNA-adapter ligation products; therefore, FL-PCR quantification is not influenced by tRNA post-transcriptional modifications. FL-PCR has broad applicability for the quantification of various tRNAs in different cell types, and thus provides a much-needed simple method for analyzing tRNA abundance and heterogeneity.
DOI: 10.6084/m9.figshare.1378870.v1
title: LDRC-HUB mentorship slides

description: Slides on getting postdoc and faculty positions, and what life is like for both
DOI: 10.6084/m9.figshare.1378876.v6
title: Four-leaf clover qRT-PCR: A convenient method for selective quantification of mature tRNA

description: Transfer RNAs (tRNAs) play a central role in translation and also recently appear to have a variety of other functions in biological processes beyond translation. Here we report the development of Four-Leaf clover qRT-PCR (FL-PCR), a convenient PCR-based method, which can specifically quantify individual mature tRNA species. In FL-PCR, T4 RNA ligase 2 specifically ligates a stem-loop adapter to mature tRNAs but not to precursor tRNAs or tRNA fragments. Subsequent TaqMan qRT-PCR amplifies only unmodified regions of the tRNA-adapter ligation products; therefore, FL-PCR quantification is not influenced by tRNA post-transcriptional modifications. FL-PCR has broad applicability for the quantification of various tRNAs in different cell types, and thus provides a much-needed simple method for analyzing tRNA abundance and heterogeneity.
DOI: 10.6084/m9.figshare.1378868
title: Sparse Penalized Forward Selection for Support Vector Classification

description: We propose a new binary classification and variable selection technique especially designed for high-dimensional predictors. Among many predictors, typically, only a small fraction of them have significant impact on prediction. In such a situation, more interpretable models with better prediction accuracy can be obtained by variable selection along with classification. By adding an ℓ<sub>1</sub>-type penalty to the loss function, common classification methods such as logistic regression or support vector machines (SVM) can perform variable selection. Existing penalized SVM methods all attempt to jointly solve all the parameters involved in the penalization problem altogether. When data dimension is very high, the joint optimization problem is very complex and involves a lot of memory allocation. In this article, we propose a new penalized forward search technique that can reduce high-dimensional optimization problems to one-dimensional optimization by iterating the selection steps. The new algorithm can be regarded as a forward selection version of the penalized SVM and its variants. The advantage of optimizing in one dimension is that the location of the optimum solution can be obtained with intelligent search by exploiting convexity and a piecewise linear or quadratic structure of the criterion function. In each step, the predictor that is most able to predict the outcome is chosen in the model. The search is then repeatedly used in an iterative fashion until convergence occurs. Comparison of our new classification rule with ℓ<sub>1</sub>-SVM and other common methods show very promising performance, in that the proposed method leads to much leaner models without compromising misclassification rates, particularly for high-dimensional predictors.
DOI: 10.6084/m9.figshare.1378868.v1
title: Sparse Penalized Forward Selection for Support Vector Classification

description: We propose a new binary classification and variable selection technique especially designed for high-dimensional predictors. Among many predictors, typically, only a small fraction of them have significant impact on prediction. In such a situation, more interpretable models with better prediction accuracy can be obtained by variable selection along with classification. By adding an ℓ<sub>1</sub>-type penalty to the loss function, common classification methods such as logistic regression or support vector machines (SVM) can perform variable selection. Existing penalized SVM methods all attempt to jointly solve all the parameters involved in the penalization problem altogether. When data dimension is very high, the joint optimization problem is very complex and involves a lot of memory allocation. In this article, we propose a new penalized forward search technique that can reduce high-dimensional optimization problems to one-dimensional optimization by iterating the selection steps. The new algorithm can be regarded as a forward selection version of the penalized SVM and its variants. The advantage of optimizing in one dimension is that the location of the optimum solution can be obtained with intelligent search by exploiting convexity and a piecewise linear or quadratic structure of the criterion function. In each step, the predictor that is most able to predict the outcome is chosen in the model. The search is then repeatedly used in an iterative fashion until convergence occurs. Comparison of our new classification rule with ℓ<sub>1</sub>-SVM and other common methods show very promising performance, in that the proposed method leads to much leaner models without compromising misclassification rates, particularly for high-dimensional predictors.
DOI: 10.6084/m9.figshare.1378876.v5
title: Four-leaf clover qRT-PCR: A convenient method for selective quantification of mature tRNA

description: Transfer RNAs (tRNAs) play a central role in translation and also recently appear to have a variety of other functions in biological processes beyond translation. Here we report the development of Four-Leaf clover qRT-PCR (FL-PCR), a convenient PCR-based method, which can specifically quantify individual mature tRNA species. In FL-PCR, T4 RNA ligase 2 specifically ligates a stem-loop adapter to mature tRNAs but not to precursor tRNAs or tRNA fragments. Subsequent TaqMan qRT-PCR amplifies only unmodified regions of the tRNA-adapter ligation products; therefore, FL-PCR quantification is not influenced by tRNA post-transcriptional modifications. FL-PCR has broad applicability for the quantification of various tRNAs in different cell types, and thus provides a much-needed simple method for analyzing tRNA abundance and heterogeneity.
DOI: 10.6084/m9.figshare.1378856
title: Purification, cDNA cloning, and characterization of LysM-containing plant chitinase from horsetail (<i>Equisetum arvense</i>)

description: Chitinase-A (EaChiA), molecular mass 36 kDa, was purified from the vegetative stems of a horsetail (<i>Equisetum arvense</i>) using a series of column chromatography. The N-terminal amino acid sequence of EaChiA was similar to the lysin motif (LysM). A cDNA encoding EaChiA was cloned by rapid amplification of cDNA ends and polymerase chain reaction. It consisted of 1320 nucleotides and encoded an open reading frame of 361 amino acid residues. The deduced amino acid sequence indicated that EaChiA is composed of a N-terminal LysM domain and a C-terminal plant class IIIb chitinase catalytic domain, belonging to the glycoside hydrolase family 18, linked by proline-rich regions. EaChiA has strong chitin-binding activity, however, no antifungal activity. This is the first report of a chitinase from Equisetopsida, a class of fern plants, and the second report of a LysM-containing chitinase from a plant.