DNA fragmentation simulation method (FSM) and fragment size matching improve aCGH performance of FFPE tissues

Whole-genome copy number analysis platforms, such as array comparative genomic hybridization (aCGH) and single nucleotide polymorphism (SNP) arrays, are transformative research discovery tools. In cancer, the identification of genomic aberrations with these approaches has generated important diagnostic and prognostic markers, and critical therapeutic targets. While robust for basic research studies, reliable whole-genome copy number analysis has been unsuccessful in routine clinical practice due to a number of technical limitations. Most important, aCGH results have been suboptimal because of the poor integrity of DNA derived from formalin-fixed paraffin-embedded (FFPE) tissues. Using self-hybridizations of a single DNA sample we observed that aCGH performance is significantly improved by accurate DNA size determination and the matching of test and reference DNA samples so that both possess similar fragment sizes. Based on this observation, we developed a novel DNA fragmentation simulation method (FSM) that allows customized tailoring of the fragment sizes of test and reference samples, thereby lowering array failure rates. To validate our methods, we combined FSM with Universal Linkage System (ULS) labeling to study a cohort of 200 tumor samples using Agilent 1 M feature arrays. Results from FFPE samples were equivalent to results from fresh samples and those available through the glioblastoma Cancer Genome Atlas (TCGA). This study demonstrates that rigorous control of DNA fragment size improves aCGH performance. This methodological advance will permit the routine analysis of FFPE tumor samples for clinical trials and in daily clinical practice.     

Random DNA fragmentation allows detection of single-copy,single-exon alterations of copy number by oligonucleotide array CGH in clinical FFPE samples

Genomic technologies, such as array comparative genomic hybridization (aCGH), increasingly offer definitive gene dosage profiles in clinical samples. Historically, copy number profiling was limited to large fresh-frozen tumors where intact DNA could be readily extracted. Genomic analyses of pre-neoplastic tumors and diagnostic biopsies are often limited to DNA processed by formalin-fixation and paraffin-embedding (FFPE). We present specialized protocols for DNA extraction and processing from FFPE tissues utilizing DNase processing to generate randomly fragmented DNA. The protocols are applied to FFPE clinical samples of varied tumor types, from multiple institutions and of varied block age. Direct comparative analyses with regression coefficient were calculated on split-sample (portion fresh/portion FFPE) of colorectal tumor samples. We show equal detection of a homozygous loss of SMAD4 at the exon-level in the SW480 cell line and gene-specific alterations in the split tumor samples. aCGH application to a set of archival FFPE samples of skin squamous cell carcinomas detected a novel hemizygous deletion in INPP5A on 10q26.3. Finally we present data on derivative of log ratio, a particular sensitive detector of measurement variance, for 216 sequential hybridizations to assess protocol reliability over a wide range of FFPE samples.     

Genomic DNA extraction methods using formalin-fixed paraffin-embedded tissue

As new technologies come within reach for the average cytogenetic laboratory, the study of chromosome structure has become increasingly more sophisticated. Resolution has improved from karyotyping (in which whole chromosomes are discernible) to fluorescence in situ hybridization and comparative genomic hybridization (CGH, with which specific megabase regions are visualized), array-based CGH (aCGH, examining hundreds of base pairs), and next-generation sequencing (providing single base pair resolution). Whole genome next-generation sequencing remains a cost-prohibitive method for many investigators. Meanwhile, the cost of aCGH has been reduced during recent years, even as resolution has increased and protocols have simplified. However, aCGH presents its own set of unique challenges. DNA of sufficient quantity and quality to hybridize to arrays and provide meaningful results is required. This is especially difficult for DNA from formalin-fixed paraffin-embedded (FFPE) tissues. Here, we compare three different methods for acquiring DNA of sufficient length, purity, and “amplifiability” for aCGH and other downstream applications. Phenol–chloroform extraction and column-based commercial kits were compared with adaptive focused acoustics (AFA). Of the three extraction methods, AFA samples showed increased amplicon length and decreased polymerase chain reaction (PCR) failure rate. These findings support AFA as an improvement over previous DNA extraction methods for FFPE tissues.     

Modeling recurrent DNA copy number alterations in array CGH data

MOTIVATION: Recurrent DNA copy number alterations (CNA) measured with array comparative genomic hybridization (aCGH) reveal important molecular features of human genetics and disease. Studying aCGH profiles from a phenotypic group of individuals can determine important recurrent CNA patterns that suggest a strong correlation to the phenotype. Computational approaches to detecting recurrent CNAs from a set of aCGH experiments have typically relied on discretizing the noisy log ratios and subsequently inferring patterns. We demonstrate that this can have the effect of filtering out important signals present in the raw data. In this article we develop statistical models that jointly infer CNA patterns and the discrete labels by borrowing statistical strength across samples. RESULTS: We propose extending single sample aCGH HMMs to the multiple sample case in order to infer shared CNAs. We model recurrent CNAs as a profile encoded by a master sequence of states that generates the samples. We show how to improve on two basic models by performing joint inference of the discrete labels and providing sparsity in the output. We demonstrate on synthetic ground truth data and real data from lung cancer cell lines how these two important features of our model improve results over baseline models. We include standard quantitative metrics and a qualitative assessment on which to base our conclusions. AVAILABILITY: http://www.cs.ubc.ca/~sshah/acgh.     

Comparative genomics and experimental promoter analysis reveal functional liver-specific elements in mammalian hepatic lipase genes

Background

Array-based comparative genomic hybridization (aCGH) is a high-throughput method for measuring genome-wide DNA copy number changes. Current aCGH methods have limited resolution, sensitivity and reproducibility. Microarrays for aCGH are available only for a few organisms and combination of aCGH data with expression data is cumbersome.

Results

We present a novel method of using commercial oligonucleotide expression microarrays for aCGH, enabling DNA copy number measurements and expression profiles to be combined using the same platform. This method yields aCGH data from genomic DNA without complexity reduction at a median resolution of approximately 17,500 base pairs. Due to the well-defined nature of oligonucleotide probes, DNA amplification and deletion can be defined at the level of individual genes and can easily be combined with gene expression data.

Conclusion

A novel method of gene resolution analysis of copy number variation (graCNV) yields high-resolution maps of DNA copy number changes and is applicable to a broad range of organisms for which commercial oligonucleotide expression microarrays are available. Due to the standardization of oligonucleotide microarrays, graCNV results can reliably be compared between laboratories and can easily be combined with gene expression data using the same platform.     

Using next-generation sequencing for high resolution multiplex analysis of copy number variation from nanogram quantities of DNA from formalin-fixed paraffin-embedded specimens

The use of next-generation sequencing technologies to produce genomic copy number data has recently been described. Most approaches, however, reply on optimal starting DNA, and are therefore unsuitable for the analysis of formalin-fixed paraffin-embedded (FFPE) samples, which largely precludes the analysis of many tumour series. We have sought to challenge the limits of this technique with regards to quality and quantity of starting material and the depth of sequencing required. We confirm that the technique can be used to interrogate DNA from cell lines, fresh frozen material and FFPE samples to assess copy number variation. We show that as little as 5 ng of DNA is needed to generate a copy number karyogram, and follow this up with data from a series of FFPE biopsies and surgical samples. We have used various levels of sample multiplexing to demonstrate the adjustable resolution of the methodology, depending on the number of samples and available resources. We also demonstrate reproducibility by use of replicate samples and comparison with microarray-based comparative genomic hybridization (aCGH) and digital PCR. This technique can be valuable in both the analysis of routine diagnostic samples and in examining large repositories of fixed archival material.     

A Statistical Change Point Model Approach for the Detection of DNA Copy Number Variations in Array CGH Data

Array comparative genomic hybridization (aCGH) provides a high-resolution and high-throughput technique for screening of copy number variations (CNVs) within the entire genome. This technique, compared to the conventional CGH, significantly improves the identification of chromosomal abnormalities. However, due to the random noise inherited in the imaging and hybridization process, identifying statistically significant DNA copy number changes in aCGH data is challenging. We propose a novel approach that uses the mean and variance change point model (MVCM) to detect CNVs or breakpoints in aCGH data sets. We derive an approximate p-value for the test statistic and also give the estimate of the locus of the DNA copy number change. We carry out simulation studies to evaluate the accuracy of the estimate and the p-value formulation. These simulation results show that the approach is effective in identifying copy number changes. The approach is also tested on fibroblast cancer cell line data, breast tumor cell line data, and breast cancer cell line aCGH data sets that are publicly available. Changes that have not been identified by the circular binary segmentation (CBS) method but are biologically verified are detected by our approach on these cell lines with higher sensitivity and specificity than CBS.     

A fused lasso latent feature model for analyzing multi-sample aCGH data

Array-based comparative genomic hybridization (aCGH) enables the measurement of DNA copy number across thousands of locations in a genome. The main goals of analyzing aCGH data are to identify the regions of copy number variation (CNV) and to quantify the amount of CNV. Although there are many methods for analyzing single-sample aCGH data, the analysis of multi-sample aCGH data is a relatively new area of research. Further, many of the current approaches for analyzing multi-sample aCGH data do not appropriately utilize the additional information present in the multiple samples. We propose a procedure called the Fused Lasso Latent Feature Model (FLLat) that provides a statistical framework for modeling multi-sample aCGH data and identifying regions of CNV. The procedure involves modeling each sample of aCGH data as a weighted sum of a fixed number of features. Regions of CNV are then identified through an application of the fused lasso penalty to each feature. Some simulation analyses show that FLLat outperforms single-sample methods when the simulated samples share common information. We also propose a method for estimating the false discovery rate. An analysis of an aCGH data set obtained from human breast tumors, focusing on chromosomes 8 and 17, shows that FLLat and Significance Testing of Aberrant Copy number (an alternative, existing approach) identify similar regions of CNV that are consistent with previous findings. However, through the estimated features and their corresponding weights, FLLat is further able to discern specific relationships between the samples, for example, identifying 3 distinct groups of samples based on their patterns of CNV for chromosome 17.     

Genomic DNA Copy Number Aberrations,Histological Diagnosis,Oral Subsite and Aneuploidy in OPMDs/OSCCs

Oral potentially malignant disorders (OPMDs) characterized by the presence of dysplasia and DNA copy number aberrations (CNAs), may reflect chromosomal instability (CIN) and predispose to oral squamous cell carcinoma (OSCC). Early detection of OPMDs with such characteristics may play a crucial role in OSCC prevention. The aim of this study was to explore the relationship between CNAs, histological diagnosis, oral subsite and aneuploidy in OPMDs/OSCCs. Samples from OPMDs and OSCCs were processed by high-resolution DNA flow cytometry (hr DNA-FCM) to determine the relative nuclear DNA content. Additionally, CNAs were obtained for a subset of these samples by genome-wide array comparative genomic hybridization (aCGH) using DNA extracted from either diploid or aneuploid nuclei suspension sorted by FCM. Our study shows that: i) aneuploidy, global genomic imbalance (measured as the total number of CNAs) and specific focal CNAs occur early in the development of oral cancer and become more frequent at later stages; ii) OPMDs limited to tongue (TNG) mucosa display a higher frequency of aneuploidy compared to OPMDs confined to buccal mucosa (BM) as measured by DNA-FCM; iii) TNG OPMDs/OSCCs show peculiar features of CIN compared to BM OPMDs/OSCCs given the preferential association with total broad and specific focal CNA gains. Follow-up studies are warranted to establish whether the presence of DNA aneuploidy and specific focal or broad CNAs may predict cancer development in non-dysplastic OPMDs.     

Use of BAC array CGH for evaluation of chromosomal stability of clinically used human mesenchymal stem cells and of cancer cell lines

Array-based comparative genomic hybridization (aCGH) using bacterial artificial chromosomes (BAC) is a powerful method to analyze DNA copy number aberrations of the entire human genome. In fact, CGH and aCGH have revealed various DNA copy number aberrations in numerous cancer cells and cancer cell lines examined so far. In this report, BAC aCGH was applied to evaluate the stability or instability of cell lines. Established cell lines have greatly contributed to advancements in not only biology but also medical science. However, cell lines have serious problems, such as alteration of biological properties during long-term cultivation. Firstly, we investigated two cancer cell lines, HeLa and Caco-2. HeLa cells, established from a cervical cancer, showed significantly increased DNA copy number alterations with passage time. Caco-2 cells, established from a colon cancer, showed no remarkable differences under various culture conditions. These results indicate that BAC aCGH can be used for the evaluation and validation of genomic stability of cultured cells. Secondly, BAC aCGH was applied to evaluate and validate the genomic stabilities of three patient's mesenchymal stem cells (MSCs), which were already used for their treatments. These three MSCs showed no significant differences in DNA copy number aberrations over their entire chromosomal regions. Therefore, BAC aCGH is highly recommended for use for a quality check of various cells before using them for any kind of biological investigation or clinical application.     

High fidelity of whole-genome amplified DNA on high-density single nucleotide polymorphism arrays

Current microarray technology allows researchers to genotype a large number of SNPs with relatively small amounts of DNA. Nevertheless, researchers and clinicians still frequently face the problem of acquiring enough high-quality DNA for analysis. Whole-genome amplification (WGA) methods offer a solution for this problem, and earlier studies have shown that WGA samples perform reasonably well in small-scale genetic analyses (e.g. Affymetrix 10K array). To determine the performance of WGA products on a large-scale genotyping array, we compared the Affymetrix 250K array genotyping results of genomic DNA and their WGA products from four individuals. Our results indicate that WGA product performs well on the 250K array compared to genomic DNA, especially when using the BRLMM calling algorithm. WGA samples have high call rates (97.5% on average, compared to 99.4% for genomic DNA) and excellent concordance rates with their corresponding genomic DNA samples (98.7% on average). In addition, no apparent systematic genomic amplification bias can be detected. This study demonstrates that, although there is a slight decrease in the total call rates, WGA methods provide a reliable approach for increasing the amount of DNA samples for use with a common SNP genotyping array.     

DNA甲基化分析中重亚硫酸盐处理DNA转化效率的评估方法

为建立一种评估重亚硫酸盐处理DNA样本后胞嘧啶转化效率的有效方法,以两组不同的TaqMan qPCR检测梯度稀释的重亚硫酸盐处理和未处理的DNA标准品,建立转化与未转化的DNA Ct值以及对应的DNA拷贝数的标准曲线。使用相同的探针定量检测重亚硫酸盐处理后的DNA样本评估转化效率。结果显示该方法应用两组探针,根据相应的标准曲线,精确评估样本经重亚硫酸盐处理的转化效率。使用已知转化和未转化拷贝数的混合DNA作为模板,证实了该方法的可靠性。同时也对不同重亚硫酸盐试剂盒处理DNA的转化效率进行了评估,结果显示,该方法能够有效地评估DNA样品重亚硫酸盐的转化效率,为DNA甲基化准确分析提供了可靠快捷的方法。     

Identification of genes with correlated patterns of variations in DNA copy number and gene expression level in gastric cancer

To identify DNA copy number changes that had a direct influence on mRNA expression in gastric cancer, cDNA microarray-based comparative genomic hybridization (aCGH) and gene expression profiling were performed using 17 K cDNA microarrays. A set of 158 genes showing Pearson correlation coefficients over 0.6 between DNA copy number changes and mRNA expression level variations was selected. In an independent gene expression profiling of 60 tissue samples, the 158 genes were able to distinguish most of the normal and tumor tissues in an unsupervised hierarchical clustering, suggesting that the differential expression patterns displayed by this specific group of genes are most likely based on the gene copy number changes. Furthermore, 43 statistically significant (P<0.01) genes were selected that correctly distinguished all of the tissue samples. The copy number changes detected by aCGH can be verified by fluorescence in situ hybridization and real-time polymerase chain reaction. The selected genes include those that were previously identified as being tumor suppressors or deleted in various tumors, including GATA binding protein 4 (GATA4), monoamine oxidase A (MAOA), cyclin C (CCNC), and oncogenes including malignant fibrous histiocytoma amplified sequence 1 (MFHAS1/MASL1), high mobility group AT-hook 2 (HMGA2), PPAR binding protein (PPARBP), growth factor receptor-bound protein 7 (GRB7), and TBC1 (tre-2, BUB2, cdc16) domain family, member 1 (TBC1D1).     

Array-based comparative genomic hybridization and copy number variation in cancer research

Array-based comparative genomic hybridization (aCGH) is a molecular cytogenetic technique used in detecting and mapping DNA copy number alterations. aCGH is able to interrogate the entire genome at a previously unattainable, high resolution and has directly led to the recent appreciation of a novel class of genomic variation: copy number variation (CNV) in mammalian genomes. All forms of DNA variation/polymorphism are important for studying the basis of phenotypic diversity among individuals. CNV research is still at its infancy, requiring careful collation and annotation of accumulating CNV data that will undoubtedly be useful for accurate interpretation of genomic imbalances identified during cancer research.     

Bisulfite-modified target DNA array for aberrant methylation analysis

Aberrant DNA methylation of CpG islands is among the earliest and most frequent alterations in cancer. It is of great importance to develop simple and high-throughput methods of methylation analysis for earlier cancer diagnosis or the detection of recurrence. In this study, bisulfite-modified target DNA arrays were prepared on positively charged nylon membrane with two different procedures: fixing PCR products and fixing genomic DNA. First, a bisulfite PCR product array was prepared through fixing PCR products amplified in bisulfite sequencing primers from the bisulfite-modified genomic DNA of different clinical samples on membrane. Furthermore, bisulfite-modified genomic DNA of the different samples was directly fixed on membrane to fabricate bisulfite genomic DNA arrays. The two kinds of arrays were hybridized by probes labeled with digoxigenin, and the hybridization signals were obtained through chemiluminescent detection. The methylation statuses of the IGFBP7 gene for breast tumor and normal tissue samples and for normal human blood cell samples were detected successfully by the two procedures. It was shown that the methods are reliable and sensitive and that they have high potential in screening molecular methylation markers from a large number of clinical samples.     

Progress in the detection of human genome structural variations

The emerging of high-throughput and high-resolution genomic technologies led to the detection of submicroscopic variants ranging from 1 kb to 3 Mb in the human genome. These variants include copy number variations (CNVs), inversions, insertions, deletions and other complex rearrangements of DNA sequences. This paper briefly reviews the commonly used technologies to discover both genomic structural variants and their potential influences. Particularly, we highlight the array-based, PCR-based and sequencing-based assays, including array-based comparative genomic hybridization (aCGH), representational oligonucleotide microarray analysis (ROMA), multiplex amplifiable probe hybridization (MAPH), multiplex ligation-dependent probe amplification (MLPA), paired-end mapping (PEM), and next-generation DNA sequencing technologies. Furthermore, we discuss the limitations and challenges of current assays and give advices on how to make the database of genomic variations more reliable. Supported by the National High Technology Research and Development Program of China (Grant No. 2006AA020704).     

Determining Frequent Patterns of Copy Number Alterations in Cancer

Cancer progression is often driven by an accumulation of genetic changes but also accompanied by increasing genomic instability. These processes lead to a complicated landscape of copy number alterations (CNAs) within individual tumors and great diversity across tumor samples. High resolution array-based comparative genomic hybridization (aCGH) is being used to profile CNAs of ever larger tumor collections, and better computational methods for processing these data sets and identifying potential driver CNAs are needed. Typical studies of aCGH data sets take a pipeline approach, starting with segmentation of profiles, calls of gains and losses, and finally determination of frequent CNAs across samples. A drawback of pipelines is that choices at each step may produce different results, and biases are propagated forward. We present a mathematically robust new method that exploits probe-level correlations in aCGH data to discover subsets of samples that display common CNAs. Our algorithm is related to recent work on maximum-margin clustering. It does not require pre-segmentation of the data and also provides grouping of recurrent CNAs into clusters. We tested our approach on a large cohort of glioblastoma aCGH samples from The Cancer Genome Atlas and recovered almost all CNAs reported in the initial study. We also found additional significant CNAs missed by the original analysis but supported by earlier studies, and we identified significant correlations between CNAs.     

Integration of DNA copy number alterations and transcriptional expression analysis in human gastric cancer

Background

Genomic instability with frequent DNA copy number alterations is one of the key hallmarks of carcinogenesis. The chromosomal regions with frequent DNA copy number gain and loss in human gastric cancer are still poorly defined. It remains unknown how the DNA copy number variations contributes to the changes of gene expression profiles, especially on the global level.

Principal Findings

We analyzed DNA copy number alterations in 64 human gastric cancer samples and 8 gastric cancer cell lines using bacterial artificial chromosome (BAC) arrays based comparative genomic hybridization (aCGH). Statistical analysis was applied to correlate previously published gene expression data obtained from cDNA microarrays with corresponding DNA copy number variation data to identify candidate oncogenes and tumor suppressor genes. We found that gastric cancer samples showed recurrent DNA copy number variations, including gains at 5p, 8q, 20p, 20q, and losses at 4q, 9p, 18q, 21q. The most frequent regions of amplification were 20q12 (7/72), 20q12–20q13.1 (12/72), 20q13.1–20q13.2 (11/72) and 20q13.2–20q13.3 (6/72). The most frequent deleted region was 9p21 (8/72). Correlating gene expression array data with aCGH identified 321 candidate oncogenes, which were overexpressed and showed frequent DNA copy number gains; and 12 candidate tumor suppressor genes which were down-regulated and showed frequent DNA copy number losses in human gastric cancers. Three networks of significantly expressed genes in gastric cancer samples were identified by ingenuity pathway analysis.

Conclusions

This study provides insight into DNA copy number variations and their contribution to altered gene expression profiles during human gastric cancer development. It provides novel candidate driver oncogenes or tumor suppressor genes for human gastric cancer, useful pathway maps for the future understanding of the molecular pathogenesis of this malignancy, and the construction of new therapeutic targets.     

Micro-Scale Genomic DNA Copy Number Aberrations as Another Means of Mutagenesis in Breast Cancer

Introduction

In breast cancer, the basal-like subtype has high levels of genomic instability relative to other breast cancer subtypes with many basal-like-specific regions of aberration. There is evidence that this genomic instability extends to smaller scale genomic aberrations, as shown by a previously described micro-deletion event in the PTEN gene in the Basal-like SUM149 breast cancer cell line.

Methods

We sought to identify if small regions of genomic DNA copy number changes exist by using a high density, gene-centric Comparative Genomic Hybridizations (CGH) array on cell lines and primary tumors. A custom tiling array for CGH (244,000 probes, 200 bp tiling resolution) was created to identify small regions of genomic change, which was focused on previously identified basal-like-specific, and general cancer genes. Tumor genomic DNA from 94 patients and 2 breast cancer cell lines was labeled and hybridized to these arrays. Aberrations were called using SWITCHdna and the smallest 25% of SWITCHdna-defined genomic segments were called micro-aberrations (<64 contiguous probes, ∼ 15 kb).

Results

Our data showed that primary tumor breast cancer genomes frequently contained many small-scale copy number gains and losses, termed micro-aberrations, most of which are undetectable using typical-density genome-wide aCGH arrays. The basal-like subtype exhibited the highest incidence of these events. These micro-aberrations sometimes altered expression of the involved gene. We confirmed the presence of the PTEN micro-amplification in SUM149 and by mRNA-seq showed that this resulted in loss of expression of all exons downstream of this event. Micro-aberrations disproportionately affected the 5′ regions of the affected genes, including the promoter region, and high frequency of micro-aberrations was associated with poor survival.

Conclusion

Using a high-probe-density, gene-centric aCGH microarray, we present evidence of small-scale genomic aberrations that can contribute to gene inactivation. These events may contribute to tumor formation through mechanisms not detected using conventional DNA copy number analyses.     

Robust smooth segmentation approach for array CGH data analysis

MOTIVATION: Array comparative genomic hybridization (aCGH) provides a genome-wide technique to screen for copy number alteration. The existing segmentation approaches for analyzing aCGH data are based on modeling data as a series of discrete segments with unknown boundaries and unknown heights. Although the biological process of copy number alteration is discrete, in reality a variety of biological and experimental factors can cause the signal to deviate from a stepwise function. To take this into account, we propose a smooth segmentation (smoothseg) approach. METHODS: To achieve a robust segmentation, we use a doubly heavy-tailed random-effect model. The first heavy-tailed structure on the errors deals with outliers in the observations, and the second deals with possible jumps in the underlying pattern associated with different segments. We develop a fast and reliable computational procedure based on the iterative weighted least-squares algorithm with band-limited matrix inversion. RESULTS: Using simulated and real data sets, we demonstrate how smoothseg can aid in identification of regions with genomic alteration and in classification of samples. For the real data sets, smoothseg leads to smaller false discovery rate and classification error rate than the circular binary segmentation (CBS) algorithm. In a realistic simulation setting, smoothseg is better than wavelet smoothing and CBS in identification of regions with genomic alterations and better than CBS in classification of samples. For comparative analyses, we demonstrate that segmenting the t-statistics performs better than segmenting the data. AVAILABILITY: The R package smoothseg to perform smooth segmentation is available from http://www.meb.ki.se/~yudpaw.