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Mixed-product production is common practice in semiconductor manufacturing and has attracted considerable academic attention recently. The main purpose of the mixed-product mode is to manufacture a wide variety of products in the same time period, to meet customer demand. The complex and changeable group of products is called “mixed-product production mode.” A specific tool used and a specific product manufactured are together called a “thread.” There is a need for “threaded” Run-to-Run (RtR) control in semiconductor manufacturing. This article develops a new product-based, disturbance-accumulating strategy in threaded RtR control by means of the double exponentially weighted moving average (dEWMA) technique. The main idea is to develop two disturbance-accumulating strategies; one for the intercept update of the same product between cycles and the other for the disturbance update in terms of the previous product between cycles, cope with the transient and transition effects, respectively. A comprehensive simulation study is conducted to compare the performance of the proposed product-based RtR controller with the existing controls specifically designed for the mixed-product mode. The proposed control has great promise as an extremely competitive threaded RtR controller in practice. Note to Practitioners —Run-to-run (RtR) control has been widely applied in batch manufacturing processes to reduce variation and has been identified as a turn-key technology for maintaining quality outputs in semiconductor manufacturing. The mixed-product production mode poses a great challenge of how to adjust process recipes between different products to alleviate initial recipe bias, process shifts, and patterned disturbances from run to run, product to product, and cycle to cycle. The proposed mixed-product RtR controller is developed based on a novel disturbance-accumulating, product-based strategy. This new controller is easy to implement in practice and has been shown to be effective in producing high-quality control outputs.

Recent developments in network technologies have led to the application of cloud computing and big data analysis to industrial automation. However, the automation of process monitoring still has numerous issues that need to be addressed. Traditionally, offline statistical processes are generally used for process monitoring, thus, problems are often detected too late. This study focused on the construction of an automated process monitoring system based on sound and vibration frequency signals. First, empirical mode decomposition was combined with intrinsic mode functions to construct different sound frequency combinations and differentiate sound frequencies according to anomalies. Then, linear discriminant analysis (LDA) was adopted to classify abnormal and normal sound frequency signals, and a control line was constructed to monitor the sound frequency. In a case study, the proposed method was applied to detect abnormal sounds at high and low frequencies, and a detection accuracy of over 90% was realized. In another case study, the proposed method was applied to analyze electrocardiography signals and was similarly able to identify abnormal situations. Thus, the proposed method can be applied to real-time process monitoring and the detection of abnormalities with high accuracy in various situations.

The semiconductor industry has become fully automated during the manufacturing process and abundant process parameters are collected online by sensors for fault detection and classification purposes. Analyzing process parameters and identifying a smaller set of key parameters that have crucial influence on wafer quality will bring great benefits in stabilizing the manufacturing process and enhancing productive yield. Typically, this type of the parameter set is called the “raw trace data.” This paper considers image processing techniques as a novel approach for analyzing and visualizing the raw trace data. First, the 1-D time series data of a wafer batch was transformed into a 2-D image. Fisher’s criterion ratios of the labeled good and defective wafer image maps are computed to identify the key parameters. The key parameters identified by the proposed image processing technique are consistent with the technical experience of the process engineers. Furthermore, the texture analysis technique with 2-D Fourier transform is utilized to analyze the images of the key parameters to detect defective wafers. The proposed key parameter identification and wafer classification method proves to be a viable solution under the paradigm of advanced process control practice for semiconductor manufacturing.

Summary Gene coexpression analysis refers to the discovery of sets of genes which exhibit similar expression patterns across multiple transcriptomic data sets, such as microarray experiment data of public repositories. Arabidopsis Coexpression Tool (ACT), a gene coexpression analysis web tool for Arabidopsis thaliana, identifies genes which are correlated to a driver gene. Primary microarray data from ATH1 Affymetrix platform were processed with Single-Channel Array Normalization algorithm and combined to produce a coexpression tree which contains ∼21,000 A. thaliana genes. ACT was developed to present subclades of coexpressed genes, as well as to perform gene set enrichment analysis, being unique in revealing enriched transcription factors targeting coexpressed genes. ACT offers a simple and user-friendly interface producing working hypotheses which can be experimentally verified for the discovery of gene partnership, pathway membership, and transcriptional regulation. ACT analyses have been successful in identifying not only genes with coordinated ubiquitous expressions but also genes with tissue-specific expressions., Graphical abstract, Highlights • 3500 primary microarray data from ATH1 Affymetrix chip were normalized using SCAN • A tree with 21,287 Arabidopsis thaliana gene coexpression was constructed using UPGMA • ACT is a web tool that identifies genes which are coexpressed to a driver gene • ACT performs biological term enrichment analysis in coexpressed gene tree clades, Plant genetics; Bioinformatics; Plant bioinformatics

In this work, titanium-free and titanium carbide-strengthened dual-phase steels were studied. While both had the same base composition of Fe–0.1C–1.5Mn–0.5Si–0.6Cr (wt%), the latter included a 0.1 wt% Ti addition for interphase precipitation. Utilizing the Gleeble thermal simulations, both two dual-phase steels were produced with the same ferrite grain size and the same ferrite/martensite volume fractions of 0.7 and 0.3, respectively. Their corresponding microstructures (including deformed structures) and mechanical properties were examined. The results indicate that the titanium carbide-strengthened dual-phase steel possesses an excellent combination of the strengthened ferrite and the weakened martensite, which brings about increased yield and tensile strengths without sacrificing total elongation.

The methodology of profile monitoring combines both the model fitting and statistical process control (SPC) techniques. Over the past ten years, a variety of profile monitoring methods have been proposed and extensively investigated in terms of different process profiles. However, monitoring tasks still exhibit a primary problem in that the errors surrounding the functional relationship are frequently assumed to be independent within every single profile. However, the assumption of independence is an unrealistic assumption in many practical instances. In particular, within-profile autocorrelation often occurs in the profile data. To mitigate the within-profile autocorrelation, a monitoring method incorporating an autoregressive (AR)(1) model to cope with autocorrelation is proposed. In this paper, the reflow process with small samples in surface mount technology (SMT) is investigated. In Phase I, three different process models are compared in combination with the first-order autoregressive model, while an appropriate profile model is sought. The Hotelling T2 and exponentially weighted moving average (EWMA) control charts are used together to monitor the parameter estimates (i.e., profile shape) and residuals (i.e., profile variability), respectively.

In this article, a new approach is proposed which uses the hyperbolic tangent function to model and monitor vacuum heat treatment process data. The proposed hyperbolic tangent function approach is compared to the smoothing spline approach. The latter se..

Defective wafer detection is essential to avoid loss of yield due to process abnormalities in semiconductor manufacturing. For most complex processes in semiconductor manufacturing, various sensors are installed on equipment to capture process information and equipment conditions, including pressure, gas flow, temperature, and power. Because defective wafers are rare in current practice, supervised learning methods usually perform poorly as there are not enough defective wafers for fault detection (FD). The existing methods of anomaly detection often rely on linear excursion detection, such as principal component analysis (PCA), k-nearest neighbor (kNN) classifier, or manual inspection of equipment sensor data. However, conventional methods of observing equipment sensor readings directly often cannot identify the critical features or statistics for detection of defective wafers. To bridge the gap between research-based knowledge and semiconductor practice, this paper proposes an anomaly detection method that uses a denoise autoencoder (DAE) to learn a main representation of normal wafers from equipment sensor readings and serve as the one-class classification model. Typically, the maximum reconstruction error (MaxRE) is used as a threshold to differentiate between normal and defective wafers. However, the threshold by MaxRE usually yields a high false positive rate of normal wafers due to the outliers in an imbalanced data set. To resolve this difficulty, the Hampel identifier, a robust method of outlier detection, is adopted to determine a new threshold for detecting defective wafers, called MaxRE without outlier (MaxREwoo). The proposed method is illustrated using an empirical study based on the real data of a wafer fabrication. Based on the experimental results, the proposed DAE shows great promise as a viable solution for on-line FD in semiconductor manufacturing.

Lately, profile monitoring has received considerable attention in the statistical process control research field. This paper proposes a novel monitoring framework for the reflow process data, which uses two goodness-of-fit criteria to select the change points in the mixture polynomial model. Among change points, the mixture second-order polynomials are utilized to piecewisely approximate the nonlinear profile data of the reflow process. The well-known Hotelling T2 and proposed EWMA4 control charts are then employed to monitor the parameter estimates. The experimental results demonstrate that the proposed monitoring framework presents better performances in detecting outlying profiles than the conventional methods in phase I. In phase II, the performance of the proposed framework is assessed in terms of the out-of-control average run length. Copyright © 2014 John Wiley & Sons, Ltd.

Fuzzy segmentation methods, especially fuzzy $$c$$ c -means algorithms, have been widely used in medical imaging in past decades. This paper proposes a novel neighborhood intuitionistic fuzzy $$c$$ c -means clustering algorithm with a genetic algorithm (NIFCMGA). This new clustering algorithm technology can retain the advantages of an intuitionistic fuzzy $$c$$ c -means clustering algorithm to maximize benefits and reduce noise/outlier influences through neighborhood membership. Furthermore, the genetic algorithms were used simultaneously to select the optimal parameters of the proposed clustering algorithm. This proposed technology has been successfully applied to the clustering of different regions of magnetic resonance imaging and computerized tomography scanning, which may be extended to the diagnosis of abnormalities. Comparisons with other approaches demonstrate the superior performance of the proposed NIFCMGA.

Recently, the dEWMA control system has been applied extensively for the semiconductor manufacturing. However, this control scheme exhibits a problem is that the existing R2R control system is scarcely discussed and evaluated about the operation of fault detection. In view of this, the research would combine double exponentially weighted moving average (dEWMA) control, weighted sum squared residual (WSSR), and joint angle analysis (JAA) methods to execute the process control and evaluated the performance of fault detection. Using various process simulations, a better parameter condition in WSSR and JAA algorithm can also be found. At last, a critical step for superconducting wire was used to illustrate the detection procedure for verifying the feasibility in practical application.

Highlights? The paper used the health examinations to uncover risk factors. ? The risk factors used to construct the early warning criteria by data mining. ? The early-warning criteria will lead to detect chronic illnesses earlier. ? The patients can reduce the risk of suffering from the relate complications. Chronic disease has gradually become a major fatality cause in Taiwan. Being afflicted with such illnesses elevate vulnerability to other complications as well. Therefore, this paper adopts a preventative perspective and ascertains the impacts of important physiological indicators and clinical test values for various chronic illnesses. The paper investigates five chronic diseases: hypertension, diabetes, cardiovascular disease, disease of the liver, and renal disease. Utilizing chronic diseases risk factors to establish early-warning criteria may reduce the complication occurrence rate. K-nearest neighbor, linear discriminant analysis, and sequential forward selection are utilized, which is divided into two parts. The first part classifies and screens both healthy persons and those affiliated with the abovementioned chronic illnesses for characteristic value determination. The second part determines the critical value of the important risk factors of each chronic illness and builds early-warning criteria to recognition the chronic illnesses. This paper uses data from a medical center in Taiwan to verify the proposed methodology. The results reveal that classifying materials and screening important factors are both positively efficient with a corrected rate of 80%. Additionally, through the important factors of early-warning criteria, not only can help patients understand the risks of suffering diseases, but also effectively offer diagnosis reference criteria for medical personnel.

In general, the fuzzy Graphical Evaluation and Review Technique (GERT) usually evaluates/analyzes variables with interval arithmetic (α-cut arithmetic) operations, especially those with complicated fuzzy systems. Thus the interval arithmetic operations may occur accumulating phenomenon of fuzziness in complicated systems, and the accumulating phenomenon of fuzziness may make decision-maker that cannot effectively evaluate problems/systems under vague environment. In order to overcome the accumulating phenomenon of fuzziness or credibly reduce fuzzy spreads, this study adopts approximate fuzzy arithmetic operations under the weakest t-norm arithmetic operations (Tω) to evaluate fuzzy reliability models based on fuzzy GERT simulation technology. The approximate fuzzy arithmetic operations employ principle of interval arithmetic under the weakest t-norm arithmetic operations. Therefore, the novel fuzzy arithmetic operations may obtain fitter decision values, which have smaller fuzziness accumulating, under vague environment. In numerical examples the approximate fuzzy arithmetic operations has evidenced that it can successfully calculate results of fuzzy operations as interval arithmetic, and can more effectively reduce fuzzy spreads. In the real fuzzy repairable reliability model the performance also shows that the approximate fuzzy arithmetic operations successfully analyze the reliability problem and obtain more confident fuzzy results.

Recently, detailed investigations of various ‘run-to-run’ (R2R) control schemes for semiconductor manufacturing have been conducted. However, a pure R2R control scheme cannot ensure sufficient control quality when the process suddenly undergoes a larger change (herein, a larger change is defined when the output exceeds 2σe ). In many semiconductor processes, sudden changes in the situation are often generated through the operation of different devices during the same process or changes in the control environment (e.g. process ageing or the influence of chemical concentrations). We propose an integrated R2R control system (IRCS) to alleviate this problem; the system includes an on-line experiment, R2R double exponentially weighted moving average (dEWMA) control, R2R triple EWMA (triEWMA) control and R2R self-tuning control. The IRCS initially employs a warning threshold to evaluate the process changes. When the process is changed, the system then uses the 2k centre points design to amend the process model ...

In many practical situations, the quality of a process, or product, is better characterized and summarized by the relationship between a response variable and one or more explanatory variables. Such a relationship between the response variable and explanatory variables is called a profile. Recently, profile monitoring has become a fertile research field in statistical process control (SPC). To handle the nonlinear profile data, the proposal considered in this paper is that the entire curve is broken into several segments of data points that exhibit a statistical fit to the linear model, and therefore each of them can be monitored separately by using existing linear profile SPC methods. A new method that determines the locations of change points based on the slop change is proposed. Two goodness-of-fit criteria are utilized for determining the best number of change points to avoid over-fitting. Two nonlinear profile examples taken from the literature are used to illustrate the proposed change-point model. Monitoring performances using the existing T 2 and EWMA-based approaches are presented when the nonlinear profile data is fitted by using the proposed change-point model.

Many semiconductor processes have the major problem that die-level signature profiles often look completely different on different devices. During 'in-parallel' and 'in-series' manufacturing control procedures, equipment frequently suffer ageing and tool-wearing effects that induce control model changes and generate unstable control. Therefore, if a control system does not compensate for this variation, the next device will not carry out the required follow-up process control. To solve this problem, we propose a novel control framework that integrates the concepts of self-tuning control, on-line experiments, and linear discriminant analysis (LDA). In this paper, LDA with self-tuning control is used to evaluate process changes and adjust the process recipe. When the system determines that the process has generated a large change, the system applies the design-of-experiment concept continuously to collect process data over an expanded experimental range and uses these data with the least squares error (LSE) method to estimate new model parameters. The proposed control strategy then uses the minimized-total-cost principle and applies a trust-region algorithm to obtain a recipe for the next period. Simulation studies show that the proposed system is able to satisfy the control requirement precisely. Finally, a relevant semiconductor multiple-input multiple-output (MIMO) model is used to illustrate the necessary control procedure for verifying the practicability of the proposed system in a MIMO dynamic process.

During the past decade, a variety of run-to-run (R2R) control techniques have been proposed and extensively used to control various semiconductor manufacturing processes. The R2R control methodology combines response surface modeling, engineering process control, and statistical process control, with the main objective of fine-tuning the recipe so that the process output of each run can be maintained as close to the nominal target as possible. In this paper, the single-input single-output (SISO) model is addressed. To overcome the shortcomings in the traditional R2R EWMA controller, a fuzzy neural network (FNN) control strategy is proposed. When a process has large autoregressive parameters, traditional EWMA control methods cannot establish stable SISO process control. To solve this problem, an SISO process control model based on an FNN was used to build an SISO process control procedure. The analysis results from a numerical simulation indicated that when the coefficient of autocorrelation @f>0.6, the MSE ratio when using the FNN controller was 97.11% lower than when using the EWMA controller and 61.12% lower than when using an adaptive EWMA controller. This showed that the FNN control method established better SISO process control than the EWMA and adaptive EWMA control methods.

Most industrial problems involve multiple-quality characteristics. The methods used for optimising such a multiple-response problem are those normally used for dealing with a single response problem, such as the experimental design, response surface methods (RSM) and regression techniques. There is a major difficulty with this traditional approach on the point estimation of the optimal solution, thus the obtained solution may not be practically feasible. In this research, a simulated regression approach via Monte Carlo simulations and the nonlinear optimisation via the trust-region methods were used to determine an optimal operational region (OOR). In practice, an engineer can refer to this region for adjusting manufacturing parameters in the production environment to minimise the response variations. An example with three controllable variables is used to demonstrate the application of the proposed methodology.

During the past decade, a variety of ‘run-to-run’ (R2R) control schemes have been proposed and investigated extensively using various semiconductor manufacturing methods. However, such control has a problem when it is suddenly faced with a larger process change that does not satisfy the control requirements. In view of this situation, a new process control framework is proposed, which integrates response surface modelling, evolutionary operation (EVOP) and R2R control principles. The primary objective of this study is to improve dynamic model parameter prediction, enabling more effective optimized recipe calculations. The recursive least squares (RLS) algorithm is used to evaluate changes in the process parameters. If the evaluated parameter values exceed a joint parameter threshold, the recipe moves to a new optimum point. This movement, which continuously applies the design of experiment (DOE) concepts to collect process data in the experimental range, uses this data with the least squares error (LSE) m...

Motivation: For any time-course microarray data in which the gene interactions and the associated paired patterns are dependent, the proposed pattern recognition (PARE) approach can infer time-lagged genetic interactions, a challenging task due to the small number of time points and large number of genes. PARE utilizes a non-linear score to identify subclasses of gene pairs with different time lags. In each subclass, PARE extracts non-linear characteristics of paired gene-expression curves and learns weights of the decision score applying an optimization algorithm to microarray gene-expression data (MGED) of some known interactions, from biological experiments or published literature. Namely, PARE integrates both MGED and existing knowledge via machine learning, and subsequently predicts the other genetic interactions in the subclass. Results: PARE, a time-lagged correlation approach and the latest advance in graphical Gaussian models were applied to predict 112 (132) pairs of TC/TD (transcriptional regulatory) interactions. Checked against qRT-PCR results (published literature), their true positive rates are 73% (77%), 46% (51%), and 52% (59%), respectively. The false positive rates of predicting TC and TD (AT and RT) interactions in the yeast genome are bounded by 13 and 10% (10 and 14%), respectively. Several predicted TC/TD interactions are shown to coincide with existing pathways involving Sgs1, Srs2 and Mus81. This reinforces the possibility of applying genetic interactions to predict pathways of protein complexes. Moreover, some experimentally testable gene interactions involving DNA repair are predicted. Availability: Supplementary data and PARE software are available at http://www.stat.sinica.edu.tw/~gshieh/pare.htm. Contact: gshieh@stat.sinica.edu.tw

Somatic stem cell transplantation holds great promise in regenerative medicine. The best-characterized adult stem cells are mesenchymal stem cells (MSCs), neural stem cells (NSCs), and CD133+ hematopoietic stem cells (HSCs). The applications of HSCs are hampered since these cells are difficult to maintain in an undifferentiated state in vitro. Understanding genes responsible for stem cell properties and their interactions will help on this issue. The construction of stem cell genetic networks will also help to develop rational strategies to revert somatic cells back to a stem-like state. We performed a systemic study on human CD133+ HSCs, NSCs, MSCs, and embryonic stem cells and two different progenies of CD133+ HSCs, microvascular endothelial cells (MVECs) and peripheral blood mononuclear cells. Genes abundant in each or in all three somatic stem cells were identified. We also observed complex genetic networks functioning in postnatal stem cells, in which several genes, such as PTPN11 and DHFR, acted as hubs to maintain the stability and connectivity of the whole genetic network. Eighty-seven HSC genes, including ANGPT1 and GATA2, were independently identified by comparing CD34+CD33−CD38− hematopoietic stem cells with CD34+ precursors and various matured progenies. Introducing GATA2 into MVECs resulted in dedifferentiation-like transcriptome reprogramming, with HSC genes (such as ANGPT1) being up and endothelial genes (such as EPHB2) being down. This study provides a foundation for a more detailed understanding of human somatic stem cells. Expressing the newly discovered stem cell genes in matured cells might lead to a global reversion of somatic transcriptome to a stem-like status. Disclosure of potential conflicts of interest is found at the end of this article.

In vitro analyses of plant GATA transcription factors have implicated some proteins in light-mediated and circadian-regulated gene expression, and, more recently, the analysis of mutants has uncovered further diverse roles for plant GATA factors. To facilitate function discovery for the 29 GATA genes in Arabidopsis (Arabidopsis thaliana), we have experimentally verified gene structures and determined expression patterns of all family members across adult tissues and suspension cell cultures, as well as in response to light and signals from the circadian clock. These analyses have identified two genes that are strongly developmentally light regulated, expressed predominantly in photosynthetic tissue, and with transcript abundance peaking before dawn. In contrast, several GATA factor genes are light down-regulated. The products of these light-regulated genes are candidates for those proteins previously implicated in light-regulated transcription. Coexpression of these genes with well-characterized light-responsive transcripts across a large microarray data set supports these predictions. Other genes show additional tissue-specific expression patterns suggesting novel and unpredicted roles. Genome-wide analysis using coexpression scatter plots for paralogous gene pairs reveals unexpected differences in cocorrelated gene expression profiles. Clustering the Arabidopsis GATA factor gene family by similarity of expression patterns reveals that genes of recent descent do not uniformly show conserved current expression profiles, yet some genes showing more distant evolutionary origins have acquired common expression patterns. In addition to defining developmental and environmental dynamics of GATA transcript abundance, these analyses offer new insights into the evolution of gene expression profiles following gene duplication events.

We present a new WWW-based tool for plant gene analysis, the Arabidopsis Co-Expression Tool (ACT), based on a large Arabidopsis thaliana microarray data set obtained from the Nottingham Arabidopsis Stock Centre. The co-expression analysis tool allows users to identify genes whose expression patterns are correlated across selected experiments or the complete data set. Results are accompanied by estimates of the statistical significance of the correlation relationships, expressed as probability (P) and expectation (E) values. Additionally, highly ranked genes on a correlation list can be examined using the novel CLIQUE FINDER tool to determine the sets of genes most likely to be regulated in a similar manner. In combination, these tools offer three levels of analysis: creation of correlation lists of co-expressed genes, refinement of these lists using two-dimensional scatter plots, and dissection into cliques of co-regulated genes. We illustrate the applications of the software by analysing genes encoding functionally related proteins, as well as pathways involved in plant responses to environmental stimuli. These analyses demonstrate novel biological relationships underlying the observed gene co-expression patterns. To demonstrate the ability of the software to develop testable hypotheses on gene function within a defined biological process we have used the example of cell wall biosynthesis genes. The resource is freely available at http://www.arabidopsis.leeds.ac.uk/ACT/.

During recent years, run-to-run (R2R) control techniques have been developed and used to control various semiconductor manufacturing processes. The R2R control methodology combines response surface modelling, engineering process control, and statistical process control. The main objective of such control is to manipulate the recipe to maintain the process output of each run as close to the nominal target as possible. The primary focus of this research is on the multiple-input multiple-output self-tuning control of R2R processes. A general control scheme is presented that can compensate for a variety of noise disturbances frequently encountered in semiconductor manufacturing. The controller can also compensate for various system dynamics, including autocorrelated responses, deterministic drifts, and varying process gains and offsets. Self-tuning controllers are developed to provide on-line parameter estimation and control. A recursive least squares algorithm is normally used to provide on-line parameter es...

As a continuous effort to develop automated methods for predicting membrane protein types that was initiated by Chou and Elrod (PROTEINS: Structure, Function, and Genetics, 1999, 34, 137-153), the support vector machine (SVM) is introduced. Results obtained through re-substitution, jackknife, and independent data set tests, respectively, have indicated that the SVM approach is quite a promising one, suggesting that the covariant discriminant algorithm (Chou and Elrod, Protein Eng. 12 (1999) 107) and SVM, if effectively complemented with each other, will become a powerful tool for predicting membrane protein types and the other protein attributes as well.

During the past decade, a variety of 'run-to-run' (R2R) control schemes have been proposed and extensively investigated under various assumed models of process dynamics and disturbances whilst applied to the setpoints (recipe) adjustment between batches for the production of semiconductor manufacturing. The R2R control mechanism of this kind represents an interdisciplinary methodology combining response surface modelling, engineering process control (EPC) and statistical process control (SPC), of which the main objective is to fine-tune the recipe so as to maintain the process output of each run as close to the nominal target as possible. In this paper, the single-input single-output (SISO) case is addressed. To overcome the shortcomings in published R2R controllers, an EWMA feedback controller through a strategy of triple smoothing is proposed (in line with the construction of EWMA statistics) which, in the standard R2R practice, compensates process shifts, a deterministic process drift, disturbance of c...

Given the significant racial and ethnic diversity in genetic variation, we are intrigued to find out whether the single nucleotide polymorphisms (SNPs) identified in genome-wide association studies of colorectal cancer (CRC) susceptibility in East Asian populations are also relevant to the population of Taiwan. Moreover, loss of heterozygosity (LOH) may provide insight into how variants alter CRC risk and how regulatory elements control gene expression. To investigate the racial and ethnic diversity of CRC-susceptibility genetic variants and their relevance to the Taiwanese population, we genotyped 705 CRC cases and 1,802 healthy controls (Taiwan Biobank) for fifteen previously reported East Asian CRC-susceptibility SNPs and four novel genetic variants identified by whole-exome sequencing. We found that rs10795668 in FLJ3802842 and rs4631962 in CCND2 were significantly associated with CRC risk in the Taiwanese population. The previously unreported rs1338565 was associated with a significant increased risk of CRC. In addition, we also genotyped tumor tissue and paired adjacent normal tissues of these 705 CRC cases to search for LOH, as well as risk-associated and protective alleles. LOH analysis revealed preferential retention of three SNPs, rs12657484, rs3802842, and rs4444235, in tumor tissues. rs4444235 has been recently reported to be a cis-acting regulator of BMP4 gene; in this study, the C allele was preferentially retained in tumor tissues (p = 0.0023). rs4631962 and rs10795668 contribute to CRC risk in the Taiwanese and East Asian populations, and the newly identified rs1338565 was specifically associated with CRC, supporting the ethnic diversity of CRC-susceptibility SNPs. LOH analysis suggested that the three CRC risk variants, rs12657484, rs3802842, and rs4444235, exhibited somatic allele-specific imbalance and might be critical during neoplastic progression.

Telomeric heterochromatin assembly in budding yeast propagates through the association of Silent Information Regulator (SIR) proteins with nucleosomes, and the nucleosome array has been assumed to fold into a compacted structure. It is believed that the level of compaction and gene repression within heterochromatic regions can be modulated by histone modifications, such as acetylation of H3 lysine 56 and H4 lysine 16, and monoubiquitylation of H2B lysine 123. However, it remains unclear as to whether or not gene silencing is a direct consequence of the compaction of chromatin. Here, by investigating the role of the carboxy-terminus of histone H2B in heterochromatin formation, we identify that the disorderly compaction of chromatin induced by a mutation at H2B T122 specifically hinders telomeric heterochromatin formation. H2B T122 is positioned within the highly conserved AVTKY motif of the aC helix of H2B. Heterochromatin containing the T122E substitution in H2B remains inaccessible to ectopic dam methylase with dramatically increased mobility in sucrose gradients, indicating a compacted chromatin structure. Genetic studies indicate that this unique phenotype is independent of H2B K123 ubiquitylation and Sir4. In addition, using ChIP analysis, we demonstrate that telomere structure in the mutant is further disrupted by a defect in Sir2/Sir3 binding and the resulting invasion of euchromatic histone marks. Thus, we have revealed that the compaction of chromatin per se is not sufficient for heterochromatin formation. Instead, these results suggest that an appropriately arrayed chromatin mediated by H2B C-terminus is required for SIR binding and the subsequent formation of telomeric chromatin in yeast, thereby identifying an intrinsic property of the nucleosome that is required for the establishment of telomeric heterochromatin. This requirement is also likely to exist in higher eukaryotes, as the AVTKY motif of H2B is evolutionarily conserved.

Background Alternative RNA splicing greatly increases proteome diversity and thereby contribute to species- or tissue-specific functions. The possibility to study alternative splicing (AS) events on a genomic scale using splicing-sensitive microarrays, including the Affymetrix GeneChip Exon 1.0 ST microarray (exon array), has appeared very recently. However, the application of this new technology is hindered by the lack of free and user-friendly software devoted to these novel platforms. Results In this study we present a Java-based freeware, easyExon http://microarray.ym.edu.tw/easyexon, to process, filtrate and visualize exon array data with an analysis pipeline. This tool implements the most commonly used probeset summarization methods as well as AS-orientated filtration algorithms, e.g. MIDAS and PAC, for the detection of alternative splicing events. We include a biological filtration function according to GO terms, and provide a module to visualize and interpret the selected exons and transcripts. Furthermore, easyExon can integrate with other related programs, such as Integrate Genome Browser (IGB) and Affymetrix Power Tools (APT), to make the whole analysis more comprehensive. We applied easyExon on a public accessible colon cancer dataset as an example to illustrate the analysis pipeline of this tool. Conclusion EasyExon can efficiently process and analyze the Affymetrix exon array data. The simplicity, flexibility and brevity of easyExon make it a valuable tool for AS event identification in genomic research.

Background The identification of specific gene expression signature for distinguishing sample groups is a dominant field in cancer research. Although a number of tools have been developed to identify optimal gene expression signatures, the number of signature genes obtained is often overly large to be applied clinically. Furthermore, experimental verification is sometimes limited by the availability of wet-lab materials such as antibodies and reagents. A tool to evaluate the discrimination power of candidate genes is therefore in high demand by clinical researchers. Results Signature Evaluation Tool (SET) is a Java-based tool adopting the Golub's weighted voting algorithm as well as incorporating the visual presentation of prediction strength for each array sample. SET provides a flexible and easy-to-follow platform to evaluate the discrimination power of a gene signature. Here, we demonstrated the application of SET for several purposes: (1) for signatures consisting of a large number of genes, SET offers the ability to rapidly narrow down the number of genes; (2) for a given signature (from third party analyses or user-defined), SET can re-evaluate and re-adjust its discrimination power by selecting/de-selecting genes repeatedly; (3) for multiple microarray datasets, SET can evaluate the classification capability of a signature among datasets; and (4) by providing a module to visualize the prediction strength for each sample, SET allows users to re-evaluate the discrimination power on mis-grouped or less-certain samples. Information obtained from the above applications could be useful in prognostic analyses or clinical management decisions. Conclusion Here we present SET to evaluate and visualize the sample-discrimination ability of a given gene expression signature. This tool provides a filtration function for signature identification and lies between clinical analyses and class prediction (or feature selection) tools. The simplicity, flexibility and brevity of SET could make it an invaluable tool for marker identification in clinical research.

The Arabidopsis Co-expression Tool, ACT, ranks the genes across a large microarray dataset according to how closely their expression follows the expression of a query gene. A database stores pre-calculated co-expression results for approximately 21,800 genes based on data from over 300 arrays. These results can be corroborated by calculation of co-expression results for user-defined sub-sets of arrays or experiments from the NASC/GARNet array dataset. Clique Finder (CF) identifies groups of genes which are consistently co-expressed with each other across a user-defined co-expression list. The parameters can be altered easily to adjust cluster size and the output examined for optimal inclusion of genes with known biological roles. Alternatively, a Scatter Plot tool displays the correlation coefficients for all genes against two user-selected queries on a scatter plot which can be useful for visual identification of clusters of genes with similar r-values. User-input groups of genes can be highlighted on the scatter plots. Inclusion of genes with known biology in sets of genes identified using CF and Scatter Plot tools allows inferences to be made about the roles of the other genes in the set and both tools can therefore be used to generate short lists of genes for further characterization. ACT is freely available at www.Arabidopsis.leeds.ac.uk/ACT.

Overlapping transcripts in antisense orientation have the potential to form double-stranded RNA (dsRNA), a substrate for a number of different RNA-modification pathways. One prominent route for dsRNA is its breakdown by Dicer enzyme complexes into small RNAs, a pathway that is widely exploited by RNA interference technology to inactivate defined genes in transgenic lines. The significance of this pathway for endogenous gene regulation remains unclear. We have examined transcription data for overlapping gene pairs in Arabidopsis thaliana. On the basis of an analysis of transcripts with coding regions, we find the majority of overlapping gene pairs to be convergently overlapping pairs (COPs), with the potential for dsRNA formation. In all tissues, COP transcripts are present at a higher frequency compared to the overall gene pool. The probability that both the sense and antisense copy of a COP are co-transcribed matches the theoretical value for coexpression under the assumption that the expression of one partner does not affect the expression of the other. Among COPs, we observe an over-representation of spliced (intron-containing) genes (90%) and of genes with alternatively spliced transcripts. For loci where antisense transcripts overlap with sense transcript introns, we also find a significant bias in favor of alternative splicing and variation of polyadenylation. The results argue against a predominant RNA degradation effect induced by dsRNA formation. Instead, our data support alternative roles for dsRNAs. They suggest that at least for a subgroup of COPs, antisense expression may induce alternative splicing or polyadenylation.

Recently, detailed investigations of various ‘run-to-run’ (R2R) control schemes for semiconductor manufacturing have been conducted. However, the R2R control scheme has a major problem that needs to be solved, namely, how to detect and differentiate the faults in the control system. In view of this consideration, the objective of this research was to construct a novel module for fault diagnosis so that the R2R control system can operate smoothly. In this study, a fault diagnostic module is added to the multiple-input multiple-output (MIMO) R2R self-tuning control system. The proposed system integrates the R2R selftuning control, weighted sum of squared residuals (WSSR), and joint angle analysis (JAA) methods to detect the system faults, and executes the process control. First, the WSSR is applied to detect whether the system has generated the fault characteristics. If the process is judged to exhibit an obvious fault, the system then uses JAA to define the faulty controllable variable. Finally, a critical step, the chemical mechanical planarization (CMP) in semiconductor manufacturing, is used to illustrate the procedure of fault diagnosis in order to verify the feasibility in practical application.