bluuebunny/crossref_metadata_2025_split huggingface dataset:
Corrosion Reliability of Electronic Systems
Authors: Ambat Rajan , Jensen Stine G. , Mo̸ller Per 
Published: March 2008

Inherently two factors namely multi-material usage and potential bias makes electronic devices susceptible to corrosion if exposed to humid conditions. The problem is compounded today due to miniaturization and contamination effects. The reduction in size of the components and close spacing on a Printed Circuit Board (PCB) for high density packing has greatly increased the risk of corrosion under humid	conditions. An important issue is the failures due to	electrolytic metal migration. This paper describes an	investigation of the electrolytic migration of Sn-Pb solder lines on PCBs in humid environments under applied potential conditions. Studies were carried out using two electrode	potentiostatic polarization experiments and measuring the resulting current due to electrolytic migration. The surface morphology of the electrodes before and after migration testing was investigates using SEM and EDS. An in-service failure of a Ceramic Capacitor due to electrolytic migration in humid	environments is also presented.
Composite Passive Layers of Ni(OH)2/Poly-(Neutral Red) on Nickel in a Weakly Acid Sulphate Medium Grown under Potentiodynamic Conditions
Authors: Vicente Francisco , Agrisuelas Jerónimo , Gabrielli Claude , García-Jareño José Juan J. , Giménez-Romero David , Perrot Hubert 
Published: March 2008

Poly-(Neutral Red) has been deposited on the Ni(OH)2 passive layer in consonance with the semi-conducting character of this layer. EQCM result shows that polymer obtained under such	conditions hinders to a great extend nickel electrodissolution process. There exist evidences for the formation of a composite Ni(OH)2/Poly-(Neutral Red) material. The instantaneous mass/charge ratio Fdm/dQ analysis allows to obtain information about active/passive transition and nickel transpassive dissolution mechanisms.
BH3-only proteins Noxa, Bmf, and Bim are necessary for arsenic trioxide–induced cell death in myeloma
Authors: Morales Alejo A. , Gutman Delia , Lee Kelvin P. , Boise Lawrence H. 
Published: March 2008

AbstractThe use of arsenic trioxide (ATO) to treat multiple myeloma (MM) is supported by preclinical studies as well as several phase 2 studies, but the precise mechanism(s) of action of ATO has not been completely elucidated. We used gene expression profiling to determine the regulation of apoptosis-related genes by ATO in 4 MM cell lines and then focused on Bcl-2 family genes. ATO induced up-regulation of 3 proapoptotic BH3-only proteins (Noxa, Bmf, and Puma) and down-regulation of 2 antiapoptotic proteins Mcl-1 and Bcl-XL. Coimmunoprecipitation demonstrated that Noxa and Puma bind Mcl-1 to release Bak and Bim within 6 hours of ATO addition. Bak and Bim are also released from Bcl-XL. Silencing of Bmf, Noxa, and Bim significantly protected cells from ATO-induced apoptosis, while Puma silencing had no effect. Consistent with a role for Noxa inhibition of Mcl-1, the Bad-mimetic ABT-737 synergized with ATO in the killing of 2 MM lines. Finally, Noxa expression was enhanced by GSH depletion and inhibited by increasing GSH levels in the cells. Understanding the pattern of BH3-only protein response should aid in the rational design of arsenic-containing regimens.
Methods for simultaneously identifying coherent local clusters with smooth global patterns in gene expression profiles
Authors: Tien Yin-Jing , Lee Yun-Shien , Wu Han-Ming , Chen Chun-Houh 
Published: March 2008

Abstract

Background
The hierarchical clustering tree (HCT) with a dendrogram [1] and the singular value decomposition (SVD) with a dimension-reduced representative map [2] are popular methods for two-way sorting the gene-by-array matrix map employed in gene expression profiling. While HCT dendrograms tend to optimize local coherent clustering patterns, SVD leading eigenvectors usually identify better global grouping and transitional structures.

Results
This study proposes a flipping mechanism for a conventional agglomerative HCT using a rank-two ellipse (R2E, an improved SVD algorithm for sorting purpose) seriation by Chen [3] as an external reference. While HCTs always produce permutations with good local behaviour, the rank-two ellipse seriation gives the best global grouping patterns and smooth transitional trends. The resulting algorithm automatically integrates the desirable properties of each method so that users have access to a clustering and visualization environment for gene expression profiles that preserves coherent local clusters and identifies global grouping trends.

Conclusion
We demonstrate, through four examples, that the proposed method not only possesses better numerical and statistical properties, it also provides more meaningful biomedical insights than other sorting algorithms. We suggest that sorted proximity matrices for genes and arrays, in addition to the gene-by-array expression matrix, can greatly aid in the search for comprehensive understanding of gene expression structures. Software for the proposed methods can be obtained at http://gap.stat.sinica.edu.tw/Software/GAP.
Systematic sequencing of mRNA from the Antarctic krill (Euphausia superba) and first tissue specific transcriptional signature
Authors: De Pittà Cristiano , Bertolucci Cristiano , Mazzotta Gabriella M , Bernante Filippo , Rizzo Giorgia , De Nardi Barbara , Pallavicini Alberto , Lanfranchi Gerolamo , Costa Rodolfo 
Published: March 2008

Abstract

Background
Little is known about the genome sequences of Euphausiacea (krill) although these crustaceans are abundant components of the pelagic ecosystems in all oceans and used for aquaculture and pharmaceutical industry. This study reports the results of an expressed sequence tag (EST) sequencing project from different tissues of Euphausia superba (the Antarctic krill).

Results
We have constructed and sequenced five cDNA libraries from different Antarctic krill tissues: head, abdomen, thoracopods and photophores. We have identified 1.770 high-quality ESTs which were assembled into 216 overlapping clusters and 801 singletons resulting in a total of 1.017 non-redundant sequences. Quantitative RT-PCR analysis was performed to quantify and validate the expression levels of ten genes presenting different EST countings in krill tissues. In addition, bioinformatic screening of the non-redundant E. superba sequences identified 69 microsatellite containing ESTs. Clusters, consensuses and related similarity and gene ontology searches were organized in a dedicated E. superba database http://krill.cribi.unipd.it.

Conclusion
We defined the first tissue transcriptional signatures of E. superba based on functional categorization among the examined tissues. The analyses of annotated transcripts showed a higher similarity with genes from insects with respect to Malacostraca possibly as an effect of the limited number of Malacostraca sequences in the public databases. Our catalogue provides for the first time a genomic tool to investigate the biology of the Antarctic krill.
Flexible nets: disorder and induced fit in the associations of p53 and 14-3-3 with their partners
Authors: Oldfield Christopher J , Meng Jingwei , Yang Jack Y , Yang Mary Qu , Uversky Vladimir N , Dunker A Keith 
Published: March 2008

Abstract

Background
Proteins are involved in many interactions with other proteins leading to networks that regulate and control a wide variety of physiological processes. Some of these proteins, called hub proteins or hubs, bind to many different protein partners. Protein intrinsic disorder, via diversity arising from structural plasticity or flexibility, provide a means for hubs to associate with many partners (Dunker AK, Cortese MS, Romero P, Iakoucheva LM, Uversky VN: Flexible Nets: The roles of intrinsic disorder in protein interaction networks. FEBS J 2005, 272:5129-5148).

Results
Here we present a detailed examination of two divergent examples: 1) p53, which uses different disordered regions to bind to different partners and which also has several individual disordered regions that each bind to multiple partners, and 2) 14-3-3, which is a structured protein that associates with many different intrinsically disordered partners. For both examples, three-dimensional structures of multiple complexes reveal that the flexibility and plasticity of intrinsically disordered protein regions as well as induced-fit changes in the structured regions are both important for binding diversity.

Conclusions
These data support the conjecture that hub proteins often utilize intrinsic disorder to bind to multiple partners and provide detailed information about induced fit in structured regions.
Supervised learning method for the prediction of subcellular localization of proteins using amino acid and amino acid pair composition
Authors: Habib Tanwir , Zhang Chaoyang , Yang Jack Y , Yang Mary Qu , Deng Youping 
Published: March 2008

Abstract

Background
Occurrence of protein in the cell is an important step in understanding its function. It is highly desirable to predict a protein's subcellular locations automatically from its sequence. Most studied methods for prediction of subcellular localization of proteins are signal peptides, the location by sequence homology, and the correlation between the total amino acid compositions of proteins. Taking amino-acid composition and amino acid pair composition into consideration helps improving the prediction accuracy.

Results
We constructed a dataset of protein sequences from SWISS-PROT database and segmented them into 12 classes based on their subcellular locations. SVM modules were trained to predict the subcellular location based on amino acid composition and amino acid pair composition. Results were calculated after 10-fold cross validation. Radial Basis Function (RBF) outperformed polynomial and linear kernel functions. Total prediction accuracy reached to 71.8% for amino acid composition and 77.0% for amino acid pair composition. In order to observe the impact of number of subcellular locations we constructed two more datasets of nine and five subcellular locations. Total accuracy was further improved to 79.9% and 85.66%.

Conclusions
A new SVM based approach is presented based on amino acid and amino acid pair composition. Result shows that data simulation and taking more protein features into consideration improves the accuracy to a great extent. It was also noticed that the data set needs to be crafted to take account of the distribution of data in all the classes.
Identification of transcription factor and microRNA binding sites in responsible to fetal alcohol syndrome
Authors: Wang Guohua , Wang Xin , Wang Yadong , Yang Jack Y , Li Lang , Nephew Kenneth P , Edenberg Howard J , Zhou Feng C , Liu Yunlong 
Published: March 2008

Abstract
This is a first report, using our MotifModeler informatics program, to simultaneously identify transcription factor (TF) and microRNA (miRNA) binding sites from gene expression microarray data. Based on the assumption that gene expression is controlled by combinatorial effects of transcription factors binding in the 5'-upstream regulatory region and miRNAs binding in the 3'-untranslated region (3'-UTR), we developed a model for (1) predicting the most influential cis-acting elements under a given biological condition, and (2) estimating the effects of those elements on gene expression levels. The regulatory regions, TF and miRNA, which mediate the differential genes expression in fetal alcohol syndrome were unknown; microarray data from alcohol exposure paradigm was used. The model predicted strong inhibitory effects of 5' cis-acting elements and stimulatory effects of 3'-UTR under alcohol treatment. Current predictive model derived a key hypothesis for the first time a novel role of miRNAs in gene expression changes associated with abnormal mouse embryo development after alcohol exposure. This suggests that disturbance of miRNA functions may contribute to the alcohol-induced developmental deficiencies.
xMAN: extreme MApping of OligoNucleotides
Authors: Li Wei , Carroll Jason S , Brown Myles , Liu X Shirley 
Published: March 2008

Abstract

Background
The ability to rapidly map millions of oligonucleotide fragments to a reference genome is crucial to many high throughput genomic technologies.

Results
We propose an intuitive and efficient algorithm, titled ex treme MA pping of OligoN ucleotide (xMAN), to rapidly map millions of oligonucleotide fragments to a genome of any length. By converting oligonucleotides to integers hashed in RAM, xMAN can scan through genomes using bit shifting operation and achieve at least one order of magnitude speed increase over existing tools. xMAN can map the 42 million 25-mer probes on the Affymetrix whole human genome tiling arrays to the entire genome in less than 6 CPU hours.

Conclusions
In addition to the speed advantage, we found the probe mapping of xMAN to substantially improve the final analysis results in both a spike-in experiment on ENCODE tiling arrays and an estrogen receptor ChIP-chip experiment on whole human genome tiling arrays. Those improvements were confirmed by direct ChIP and real-time PCR assay. xMAN can be further extended for application to other high-throughput genomic technologies for oligonucleotide mapping.
A hybrid machine learning-based method for classifying the Cushing's Syndrome with comorbid adrenocortical lesions
Authors: Yang Jack Y , Yang Mary Qu , Luo Zuojie , Ma Yan , Li Jianling , Deng Youping , Huang Xudong 
Published: March 2008

Abstract

Background
The prognosis for many cancers could be improved dramatically if they could be detected while still at the microscopic disease stage. It follows from a comprehensive statistical analysis that a number of antigens such as hTERT, PCNA and Ki-67 can be considered as cancer markers, while another set of antigens such as P27KIP1 and FHIT are possible markers for normal tissue. Because more than one marker must be considered to obtain a classification of cancer or no cancer, and if cancer, to classify it as malignant, borderline, or benign, we must develop an intelligent decision system that can fullfill such an unmet medical need.

Results
We have developed an intelligent decision system using machine learning techniques and markers to characterize tissue as cancerous, non-cancerous or borderline. The system incorporates learning techniques such as variants of support vector machines, neural networks, decision trees, self-organizing feature maps (SOFM) and recursive maximum contrast trees (RMCT). These variants and algorithms we have developed, tend to detect microscopic pathological changes based on features derived from gene expression levels and metabolic profiles. We have also used immunohistochemistry techniques to measure the gene expression profiles from a number of antigens such as cyclin E, P27KIP1, FHIT, Ki-67, PCNA, Bax, Bcl-2, P53, Fas, FasL and hTERT in several particular types of neuroendocrine tumors such as pheochromocytomas, paragangliomas, and the adrenocortical carcinomas (ACC), adenomas (ACA), and hyperplasia (ACH) involved with Cushing's syndrome. We provided statistical evidence that higher expression levels of hTERT, PCNA and Ki-67 etc. are associated with a higher risk that the tumors are malignant or borderline as opposed to benign. We also investigated whether higher expression levels of P27KIP1 and FHIT, etc., are associated with a decreased risk of adrenomedullary tumors. While no significant difference was found between cell-arrest antigens such as P27KIP1 for malignant, borderline, and benign tumors, there was a significant difference between expression levels of such antigens in normal adrenal medulla samples and in adrenomedullary tumors.

Conclusions
Our frame work focused on not only different classification schemes and feature selection algorithms, but also ensemble methods such as boosting and bagging in an effort to improve upon the accuracy of the individual classifiers. It is evident that when all sorts of machine learning and statistically learning techniques are combined appropriately into one integrated intelligent medical decision system, the prediction power can be enhanced significantly. This research has many potential applications; it might provide an alternative diagnostic tool and a better understanding of the mechanisms involved in malignant transformation as well as information that is useful for treatment planning and cancer prevention.
Gene rearrangement analysis and ancestral order inference from chloroplast genomes with inverted repeat
Authors: Yue Feng , Cui Liying , dePamphilis Claude W , Moret Bernard ME , Tang Jijun 
Published: March 2008

Abstract

Background
Genome evolution is shaped not only by nucleotide substitutions, but also by structural changes including gene and genome duplications, insertions, deletions and gene order rearrangements. The most popular methods for reconstructing phylogeny from genome rearrangements include GRAPPA and MGR. However these methods are limited to cases where equal gene content or few deletions can be assumed. Since conserved duplicated regions are present in many chloroplast genomes, the inference of inverted repeats is needed in chloroplast phylogeny analysis and ancestral genome reconstruction.

Results
We extend GRAPPA and develop a new method GRAPPA-IR to handle chloroplast genomes. A test of GRAPPA-IR using divergent chloroplast genomes from land plants and green algae recovers the phylogeny congruent with prior studies, while analysis that do not consider IR structure fail to obtain the accepted topology. Our extensive simulation study also confirms that GRAPPA has better accuracy then the existing methods.

Conclusions
Tests on a biological and simulated dataset show GRAPPA-IR can accurately recover the genome phylogeny as well as ancestral gene orders. Close analysis of the ancestral genome structure suggests that genome rearrangement in chloroplasts is probably limited by inverted repeats with a conserved core region. In addition, the boundaries of inverted repeats are hot spots for gene duplications or deletions. The new GRAPPA-IR is available from http://phylo.cse.sc.edu.
Improving prediction accuracy of tumor classification by reusing genes discarded during gene selection
Authors: Yang Jack Y , Li Guo-Zheng , Meng Hao-Hua , Yang Mary Qu , Deng Youping 
Published: March 2008

Abstract

Background
Since the high dimensionality of gene expression microarray data sets degrades the generalization performance of classifiers, feature selection, which selects relevant features and discards irrelevant and redundant features, has been widely used in the bioinformatics field. Multi-task learning is a novel technique to improve prediction accuracy of tumor classification by using information contained in such discarded redundant features, but which features should be discarded or used as input or output remains an open issue.

Results
We demonstrate a framework for automatically selecting features to be input, output, and discarded by using a genetic algorithm, and propose two algorithms: GA-MTL (Genetic algorithm based multi-task learning) and e-GA-MTL (an enhanced version of GA-MTL). Experimental results demonstrate that this framework is effective at selecting features for multi-task learning, and that GA-MTL and e-GA-MTL perform better than other heuristic methods.

Conclusions
Genetic algorithms are a powerful technique to select features for multi-task learning automatically; GA-MTL and e-GA-MTL are shown to to improve generalization performance of classifiers on microarray data sets.
Investigation of transmembrane proteins using a computational approach
Authors: Yang Jack Y , Yang Mary Qu , Dunker A Keith , Deng Youping , Huang Xudong 
Published: March 2008

Abstract

Background
An important subfamily of membrane proteins are the transmembrane α-helical proteins, in which the membrane-spanning regions are made up of α-helices. Given the obvious biological and medical significance of these proteins, it is of tremendous practical importance to identify the location of transmembrane segments. The difficulty of inferring the secondary or tertiary structure of transmembrane proteins using experimental techniques has led to a surge of interest in applying techniques from machine learning and bioinformatics to infer secondary structure from primary structure in these proteins. We are therefore interested in determining which physicochemical properties are most useful for discriminating transmembrane segments from non-transmembrane segments in transmembrane proteins, and for discriminating intrinsically unstructured segments from intrinsically structured segments in transmembrane proteins, and in using the results of these investigations to develop classifiers to identify transmembrane segments in transmembrane proteins.

Results
We determined that the most useful properties for discriminating transmembrane segments from non-transmembrane segments and for discriminating intrinsically unstructured segments from intrinsically structured segments in transmembrane proteins were hydropathy, polarity, and flexibility, and used the results of this analysis to construct classifiers to discriminate transmembrane segments from non-transmembrane segments using four classification techniques: two variants of the Self-Organizing Global Ranking algorithm, a decision tree algorithm, and a support vector machine algorithm. All four techniques exhibited good performance, with out-of-sample accuracies of approximately 75%.

Conclusions
Several interesting observations emerged from our study: intrinsically unstructured segments and transmembrane segments tend to have opposite properties; transmembrane proteins appear to be much richer in intrinsically unstructured segments than other proteins; and, in approximately 70% of transmembrane proteins that contain intrinsically unstructured segments, the intrinsically unstructured segments are close to transmembrane segments.
Protein disorder prediction at multiple levels of sensitivity and specificity
Authors: Hecker Joshua , Yang Jack Y , Cheng Jianlin 
Published: March 2008

Abstract

Background
Many protein regions and some entire proteins have no definite tertiary structure, existing instead as dynamic, disorder ensembles under different physiochemical circumstances. Identification of these protein disorder regions is important for protein production, protein structure prediction and determination, and protein function annotation. A number of different disorder prediction software and web services have been developed since the first predictor was designed by Dunker's lab in 1997. However, most of the software packages use a pre-defined threshold to select ordered or disordered residues. In many situations, users need to choose ordered or disordered residues at different sensitivity and specificity levels.

Results
Here we benchmark a state of the art disorder predictor, DISpro, on a large protein disorder dataset created from Protein Data Bank and systematically evaluate the relationship of sensitivity and specificity. Also, we extend its functionality to allow users to trade off specificity and sensitivity by setting different decision thresholds. Moreover, we compare DISpro with seven other automated disorder predictors on the 95 protein targets used in the seventh edition of Critical Assessment of Techniques for Protein Structure Prediction (CASP7). DISpro is ranked as one of the best predictors.

Conclusion
The evaluation and extension of DISpro make it a more valuable and useful tool for structural and functional genomics.