Binary Profile Research Articles

Beam deflection by an aperiodic binary diffraction grating

In this article we investigate the beam deflection by a one-dimensional binary diffraction grating. We propose a simple theoretical expression that can be used to predict the beam deflection angle due to both periodic and aperiodic binary grating profiles. We show that the theoretically calculated beam deflection angles agree well with the experimentally obtained deflection angles for various grating patterns. Thus the expression can be used to precisely position the deflected beam at a predetermined location. Further, we show that the theoretical expression can be used to construct a map between the spatial frequency of the grating and the deflection angle which can be employed to deflect the beam at equal intervals by sequentially changing the spatial frequency in accordance with the map. We also demonstrate the superior beam repeatability of a binary grating based beam scanner.

IBBiG: iterative binary bi-clustering of gene sets

Motivation: Meta-analysis of genomics data seeks to identify genes associated with a biological phenotype across multiple datasets; however, merging data from different platforms by their features (genes) is challenging. Meta-analysis using functionally or biologically characterized gene sets simplifies data integration is biologically intuitive and is seen as having great potential, but is an emerging field with few established statistical methods.Results: We transform gene expression profiles into binary gene set profiles by discretizing results of gene set enrichment analyses and apply a new iterative bi-clustering algorithm (iBBiG) to identify groups of gene sets that are coordinately associated with groups of phenotypes across multiple studies. iBBiG is optimized for meta-analysis of large numbers of diverse genomics data that may have unmatched samples. It does not require prior knowledge of the number or size of clusters. When applied to simulated data, it outperforms commonly used clustering methods, discovers overlapping clusters of diverse sizes and is robust in the presence of noise. We apply it to meta-analysis of breast cancer studies, where iBBiG extracted novel gene set—phenotype association that predicted tumor metastases within tumor subtypes.Availability: Implemented in the Bioconductor package iBBiGContact: aedin@jimmy.harvard.edu

GlycoPP: A Webserver for Prediction of N- and O-Glycosites in Prokaryotic Protein Sequences

Glycosylation is one of the most abundant post-translational modifications (PTMs) required for various structure/function modulations of proteins in a living cell. Although elucidated recently in prokaryotes, this type of PTM is present across all three domains of life. In prokaryotes, two types of protein glycan linkages are more widespread namely, N- linked, where a glycan moiety is attached to the amide group of Asn, and O- linked, where a glycan moiety is attached to the hydroxyl group of Ser/Thr/Tyr. For their biologically ubiquitous nature, significance, and technology applications, the study of prokaryotic glycoproteins is a fast emerging area of research. Here we describe new Support Vector Machine (SVM) based algorithms (models) developed for predicting glycosylated-residues (glycosites) with high accuracy in prokaryotic protein sequences. The models are based on binary profile of patterns, composition profile of patterns, and position-specific scoring matrix profile of patterns as training features. The study employ an extensive dataset of 107 N-linked and 116 O-linked glycosites extracted from 59 experimentally characterized glycoproteins of prokaryotes. This dataset includes validated N-glycosites from phyla Crenarchaeota, Euryarchaeota (domain Archaea), Proteobacteria (domain Bacteria) and validated O-glycosites from phyla Actinobacteria, Bacteroidetes, Firmicutes and Proteobacteria (domain Bacteria). In view of the current understanding that glycosylation occurs on folded proteins in bacteria, hybrid models have been developed using information on predicted secondary structures and accessible surface area in various combinations with training features. Using these models, N-glycosites and O-glycosites could be predicted with an accuracy of 82.71% (MCC 0.65) and 73.71% (MCC 0.48), respectively. An evaluation of the best performing models with 28 independent prokaryotic glycoproteins confirms the suitability of these models in predicting N- and O-glycosites in potential glycoproteins from aforementioned organisms, with reasonably high confidence. A web server GlycoPP, implementing these models is available freely at http:/www.imtech.res.in/raghava/glycopp/.

Identifying the time of step change in binary profiles

Control charts are intended to aid quality practitioners in monitoring whether a change has occurred in a process. When a control chart indicates an out-of-control signal, it means that the process has changed. However, control chart signals do not indicate the real time of process changes; so estimators are applied to indicate the time when a change in the process takes place, which is referred to as the change point. This paper provides a maximum likelihood estimator to identify the real time of a step change in phase II monitoring of binary profiles, in which the quality of a process is characterized by a logistic regression between the response and predictor variables. Simulation studies are provided to evaluate the effectiveness of the change point estimator.

An EUV beamsplitter based on conical grazing incidence diffraction

We present an innovative grating design based on conical diffraction which acts as an almost perfect and low-loss beamsplitter for extreme ultraviolet radiation. The scheme is based on a binary profile operated in grazing incidence along the grating bars under total external reflection. It is shown that periods of a few 10(2) nm may permit an exclusive (±1)(st) order diffraction with efficiencies up to ~ 35% in each of them, whereas higher evanescent orders vanish. In contrast, destructive interference eliminates the 0(th) order. For a sample made of SiO(2) on silicon, measured data and simulated results from rigorous coupled wave analysis procedures are given.

Interventions to Enhance Adherence to Medications in Patients With Heart Failure

Prognosis remains poor for patients with chronic heart failure (CHF), despite improvements in the prevention and treatment of heart failure over the last 25 years. Recent estimates indicate that the median survival after a first episode of heart failure is 2.3 years for men and 1.8 years for women.1 It is suggested that the improvements in outcomes that have been achieved can be partly explained by increases in prescribing rates of medications such as angiotensin-converting enzyme inhibitors,2 β-blockers,3 and spironolactone4 over this period.1 Although the evidence on medication efficacy for certain subgroups of patients with CHF is clear, there are also compelling data showing that many of these patients do not take their medications as prescribed by health care providers.5,6 This “nonadherence” to medication therefore remains a significant barrier to enhancing the effectiveness of existing treatments. Estimates for nonadherence to medications in CHF have varied widely.5 One of the largest studies found that only 80% of patients with a prescription for angiotensin-converting enzyme inhibitors at hospital discharge completed the prescription form 30 days after discharge, and this rate subsequently fell to 60% over 1 year.7 Full adherence, defined as filing enough prescriptions to have daily medication available for 1 year, may be as low as 10% in CHF.8 Poor adherence to medication in CHF is associated with worse outcomes in observational studies, including shorter event-free survival.9 Therefore, strategies to enhance adherence provide a potentially valuable strategy for improving survival, reducing hospitalization and managing patient symptoms in CHF. In this report, we provide an up-to-date review and analysis of those studies that have developed and evaluated medication adherence interventions in CHF. Because CHF is typically symptomatic and includes medication with actions that are discernible to the patient within hours …

Optimal pupil apodizations of arbitrary apertures for high-contrast imaging

In the context of exoplanet direct detection and characterization, where high-contrast imaging is mandatory, we present fully optimized two-dimensional pupil apodizations for which no specific geometric constraints are put on the pupil plane apodization, apart from the shape of the aperture itself. Masks for circular and segmented apertures are displayed, with and without a central obstruction and spiders. We can now optimize apodizers for any aperture shape, and examples of optimal masks are shown for the Subaru telescope, the Space Infrared telescope for Cosmology and Astrophysics (SPICA) and the James Webb Space Telescope (JWST). Several high-contrast regions are considered with different sizes, positions, shapes and contrasts. It is interesting to note that all the masks that result from these optimizations tend to have a binary transmission profile.

Comparison of a multiplexed MassARRAY system with real-time allele-specific PCR technology for genotyping of methicillin-resistant Staphylococcus aureus

The Sequenom MassARRAY iPLEX single-nucleotide polymorphism (SNP) typing platform uses matrix-assisted laser desorption ionization time-of-flight mass spectrometry (MALDI-TOF MS) coupled with single-base extension PCR for high-throughput multiplex SNP detection. In this study, we investigated the use of iPLEX MassARRAY technology for methicillin-resistant Staphylococcus aureus (MRSA) genotyping. A 16-plex MassARRAY iPLEX GOLD assay (MRSA-iPLEX) was developed that targets a set of informative SNPs and binary genes for MRSA characterization. The method was evaluated with 147 MRSA isolates, and the results were compared with those of an established SYBR Green-based real-time PCR system utilizing the same SNP–binary markers. A total of 2352 markers belonging to 44 SNP–binary profiles were analysed by both real-time PCR and MRSA-iPLEX. With real-time PCR as the reference standard, MRSA-iPLEX correctly assigned 2298 of the 2352 (97.7%) markers. Sequence variation in the MRSA-iPLEX primer targets accounted for the majority of MRSA-iPLEX erroneous results, highlighting the importance of primer target selection. MRSA-iPLEX provided optimal throughput for MRSA genotyping, and was, on a reagent basis, more cost-effective than the real-time PCR methods. The 16-plex MRSA-iPLEX is a suitable alternative to SYBR Green-based real-time PCR typing of major sequence types and clonal complexes of MRSA.

Identification of Mannose Interacting Residues Using Local Composition

BackgroundMannose binding proteins (MBPs) play a vital role in several biological functions such as defense mechanisms. These proteins bind to mannose on the surface of a wide range of pathogens and help in eliminating these pathogens from our body. Thus, it is important to identify mannose interacting residues (MIRs) in order to understand mechanism of recognition of pathogens by MBPs.ResultsThis paper describes modules developed for predicting MIRs in a protein. Support vector machine (SVM) based models have been developed on 120 mannose binding protein chains, where no two chains have more than 25% sequence similarity. SVM models were developed on two types of datasets: 1) main dataset consists of 1029 mannose interacting and 1029 non-interacting residues, 2) realistic dataset consists of 1029 mannose interacting and 10320 non-interacting residues. In this study, firstly, we developed standard modules using binary and PSSM profile of patterns and got maximum MCC around 0.32. Secondly, we developed SVM modules using composition profile of patterns and achieved maximum MCC around 0.74 with accuracy 86.64% on main dataset. Thirdly, we developed a model on a realistic dataset and achieved maximum MCC of 0.62 with accuracy 93.08%. Based on this study, a standalone program and web server have been developed for predicting mannose interacting residues in proteins (http://www.imtech.res.in/raghava/premier/).ConclusionsCompositional analysis of mannose interacting and non-interacting residues shows that certain types of residues are preferred in mannose interaction. It was also observed that residues around mannose interacting residues have a preference for certain types of residues. Composition of patterns/peptide/segment has been used for predicting MIRs and achieved reasonable high accuracy. It is possible that this novel strategy may be effective to predict other types of interacting residues. This study will be useful in annotating the function of protein as well as in understanding the role of mannose in the immune system.

Simulations of nanograting-assisted light coupling in GaN planar waveguide

The numerical simulations of nanogratings integrated with gallium nitride (GaN) planar waveguides as well as the experimental in-coupling results are presented. A simulation tool based on the eigenmode expansion method and advanced boundary conditions provided a rigorous model of 400-nm-period grating couplers. A full-vectorial Maxwell solver allowed performing a number of simulations with varying grating parameters, where coupling efficiency, reflection and transmission characteristics of device were calculated. Gratings with different etch depths and arbitrary shapes were simulated using a staircase approximation, with an optimized number of steps per single slope. For the first time, an impact of dry etch processing on GaN coupler efficiency was evaluated, due to the inclusion of the sloped sidewalls, with regard to the technological constrains. Finally, the experimental results in the visible spectrum region (λ = 633 nm), for 400-nm-deep gratings etched in GaN waveguide, were presented together with theoretical data for binary and trapezoidal profiles of a grating, for different optical mode profiles (\({{\rm TE}_{0}\div {\rm TE}_{3}\,{\rm modes}}\)).

OptiFLP: software for automated optimization of amplified fragment length polymorphism scoring parameters

Amplified fragment length polymorphism (AFLP), a widely used method for DNA fingerprinting, has shifted from polyacrylamide gel to capillary electrophoresis over the last years. Currently, most AFLP data are generated in a computer-readable format, and several programs are available that automatically score raw data into binary profiles. Good scoring parameters are the key to good AFLP profiles. optiFLP is the first open source program for automatic optimization of AFLP scoring parameters. It searches parameter space to maximize the contrast among groups of AFLP profiles, with the allocation of profiles to groups in either a supervised or an unsupervised mode. The software produces output files ready for use in a range of downstream applications.

Protein Remote Homology Detection and Fold Recognition based on Features Extracted from Frequency Profiles

Protein remote homology detection and fold recognition are central problems in bioinformatics. Currently, discriminative methods based on support vector machine (SVM) are the most effective and accurate methods for solving these problems. The performance of SVM depends on the method of protein vectorization, so a suitable representation of the protein sequence is a key step for the SVM-based methods. In this paper, two kinds of profile-level building blocks of proteins, binary profiles and N-nary profiles, have been presented, which contain the evolutionary information of the protein sequence frequency profile. The protein sequence frequency profiles calculated from the multiple sequence alignments outputted by PSI-BLAST are converted into binary profiles or N-nary profiles. The protein sequences are transformed into fixed-dimension feature vectors by the occurrence times of each binary profile or N-nary profile and then the corresponding vectors are inputted to support vector machines. The latent semantic analysis (LSA) model, an efficient feature extraction algorithm, is adopted to further improve the performance of our methods. Experiments with protein remote homology detection and fold recognition show that the methods based on profile-level building blocks give better results compared to related methods.

Characterization of Microsatellite Loci and Reliable Genotyping in a Polyploid Plant, Mercurialis perennis (Euphorbiaceae)

For many applications in population genetics, codominant simple sequence repeats (SSRs) may have substantial advantages over dominant anonymous markers such as amplified fragment length polymorphisms (AFLPs). In high polyploids, however, allele dosage of SSRs cannot easily be determined and alleles are not easily attributable to potentially diploidized loci. Here, we argue that SSRs may nonetheless be better than AFLPs for polyploid taxa if they are analyzed as effectively dominant markers because they are more reliable and more precise. We describe the transfer of SSRs developed for diploid Mercurialis huetii to the clonal dioecious M. perennis. Primers were tested on a set of 54 male and female plants from natural decaploid populations. Eight of 65 tested loci produced polymorphic fragments. Binary profiles from 4 different scoring routines were used to define multilocus lineages (MLLs). Allowing for fragment differences within 1 MLL, all analyses revealed the same 14 MLLs without conflicting with merigenet, sex, or plot assignment. For semiautomatic scoring, a combination of as few as 2 of the 4 most polymorphic loci resulted in unambiguous discrimination of clones. Our study demonstrates that microsatellite fingerprinting of polyploid plants is a cost efficient and reliable alternative to AFLPs, not least because fewer loci are required than for diploids.

Coaxial electrospun nanofibers for treatment of diabetic ulcers with binary release of multiple growth factors

Two different growth factors were physically and chemically loaded into a single nanofibrous matrix to increase wound healing efficacy and to obtain bi-phasic release profiles of the loaded growth factors. Amine-functionalized block copolymers composed of PCL and PEG were synthesized and subsequently co-electrospun with a bFGF solution to prepare coaxial nanofibrous meshes. The nanofibrous meshes were chemically modified with an EGF by conjugating surface-exposed amine groups of nanofibers to carboxylate groups of EGF. The characterization of a core-encapsulated bFGF and a surface-immobilized EGF by X-ray photoelectron spectroscopy revealed distinctive peaks of nitrogen atoms, which confirm the presence of a surface-immobilized EGF on the nanofiber. The release profiles of the bFGF and the EGF clearly demonstrated binary release profiles of each protein: the bFGF showed a high initial burst in 24 h, whereas the EGF showed no negligible release in 7 days. Human primary keratinocyte and fibroblast cells cultivated on the nanofibrous meshes showed the highest cellular proliferation on mesh composed of the bFGF and the EGF. In an animal study, the wound closure rates of diabetic ulcers were significantly increased in 7 days when bFGF/EGF nanofibrous meshes were administered to dorsal wound sites. The expression levels of keratinocyte-specific markers were examined by RT-PCR, and keratin 14, 5, 1 have higher expression levels than the control groups. This outcome strongly suggests that bi-phasic release of bFGF and EGF greatly supported tissue recovery with the similar phenotypes as the original keratinized tissues. A histological examination of the recovered tissue also confirms that bFGF/EGF nanofibrous meshes increase the accumulation of both collagen and a cemented matrix of keratin. Thus, the nanofibrous matrix is a promising wound dressing material that can increase wound healing rates while reducing scar formation.

One-step fabrication of polymer components for microphotonics by gray scale electron beam lithography

We demonstrate an application of gray scale electron beam lithography (EBL) for the fabrication of polymer waveguides and grating output couplers with depth variable features, using the SU-8 resist. The technique is mainly applicable for multi-level binary profile, where groove depths of the structure are controlled by choosing a proper exposure dose. Unlike reactive ion etching which is limited by the lag effect, the gray scale EBL allows free combination of groove widths and depths. Shrinking effect which is critical in polymer couplers' writing is taken into account and can be compensated. For better fabrication feasibility, the grating couplers can be simultaneously produced with waveguides with no inter-step alignment required. Therefore, this is a promising technique in manufacturing grating output couplers for polymer based waveguides with high performance in terms of mode matching/confinement and coupling efficiency.

An Improved Profile-Level Domain Linker Propensity Index for Protein Domain Boundary Prediction.

Protein domain boundary prediction is critical for understanding protein structure and function. In this study, we present a novel method, an order profile domain linker propensity index (OPI), which uses the evolutionary information extracted from the protein sequence frequency profiles calculated from the multiple sequence alignments. A protein sequence is first converted into smooth and normalized numeric order profiles by OPI, from which the domain linkers can be predicted. By discriminating the different frequencies of the amino acids in the protein sequence frequency profiles, OPI clearly shows better performance than our previous method, a binary profile domain linker propensity index (PDLI). We tested our new method on two different datasets, SCOP-1 dataset and SCOP-2 dataset, and we were able to achieve a precision of 0.82 and 0.91 respectively. OPI also outperforms other residue-level, profile-level indexes as well as other state-of-the-art methods.

A novel method for assigning functional linkages to proteins using enhanced phylogenetic trees

Functional linkages implicate pairwise relationships between proteins that work together to implement biological tasks. During evolution, functionally linked proteins are likely to be preserved or eliminated across a range of genomes in a correlated fashion. Based on this hypothesis, phylogenetic profiling-based approaches try to detect pairs of protein families that show similar evolutionary patterns. Traditionally, the evolutionary pattern of a protein is encoded by either a binary profile of presence and absence of this protein across species or an occurrence profile that indicates the distribution of copies of this protein across species. In our study, we characterize each protein by its enhanced phylogenetic tree, a novel graphical model of the evolution of a protein family with explicitly marked by speciation and duplication events. By topological comparison between enhanced phylogenetic trees, we are able to detect the functionally associated protein pairs. Because the enhanced phylogenetic trees contain more evolutionary information of proteins, our method shows greater performance and discovers functional linkages among proteins more reliably compared with the conventional approaches.

The Impact of Gene Expression Regulation on Evolution of Extracellular Signaling Pathways

Extracellular protein interactions are crucial to the development of multicellular organisms because they initiate signaling pathways and enable cellular recognition cues. Despite their importance, extracellular protein interactions are often under-represented in large scale protein interaction data sets because most high throughput assays are not designed to detect low affinity extracellular interactions. Due to the lack of a comprehensive data set, the evolution of extracellular signaling pathways has remained largely a mystery. We investigated this question using a combined data set of physical pairwise interactions between zebrafish extracellular proteins, mainly from the immunoglobulin superfamily and leucine-rich repeat families, and their spatiotemporal expression profiles. We took advantage of known homology between proteins to estimate the relative rates of changes of four parameters after gene duplication, namely extracellular protein interaction, expression pattern, and the divergence of extracellular and intracellular protein sequences. We showed that change in expression profile is a major contributor to the evolution of signaling pathways followed by divergence in intracellular protein sequence, whereas extracellular sequence and interaction profiles were relatively more conserved. Rapidly evolving expression profiles will eventually drive other parameters to diverge more quickly because differentially expressed proteins get exposed to different environments and potential binding partners. This allows homologous extracellular receptors to attain specialized functions and become specific to tissues and/or developmental stages.

Binary IS Typing for Staphylococcus aureus

BackgroundWe present an easily applicable test for rapid binary typing of Staphylococcus aureus: binary interspace (IS) typing. This test is a further development of a previously described molecular typing technique that is based on length polymorphisms of the 16S-23S rDNA interspace region of S. aureus.Methodology/Principal FindingsA novel approach of IS-typing was performed in which binary profiles are created. 424 human and animal derived MRSA and MSSA isolates were tested and a subset of these isolates was compared with multi locus sequence typing (MLST) and Amplified Fragment Length Polymorphism (AFLP). Binary IS typing had a high discriminatory potential and a good correlation with MLST and AFLP.Conclusions/SignificanceBinary IS typing is easy to perform and binary profiles can be generated in a standardized fashion. These two features, combined with the high correlation with MLST clonal complexes, make the technique applicable for large-scale inter-laboratory molecular epidemiological comparisons.

Gene Ontology-Based Protein Function Prediction by Using Sequence Composition Information

The prediction of protein function is a difficult and important problem in computational biology. In this study, an efficient method is presented to predict protein function with sequence composition information. Four kinds of basic building blocks of protein sequences are investigated, including N-grams, binary profiles, PFAM domains and InterPro domains. The protein sequences are mapped into high-dimensional vectors by using the occurrence frequencies of each kind of building blocks. The resulting vectors are then taken as input to support vector machine to predict their function based on gene ontology. Experiments are conducted over the subset of GOA database. The experimental results show that the protein function can be predicted from primary sequence information. The method based on InterPro domains outperforms the other building blocks, and gets an overall accuracy of 0.87 and ROC score is 0.93. We also demonstrate that the use of feature extraction algorithms such as latent semantic analysis and nonnegative matrix factorization, can efficiently remove noise and improve the prediction efficiency without significantly degrading the performance. The results obtained here are helpful for the prediction of protein function by using only sequence information.