Poor Alignments Research Articles

Improving text collations by local text resegmentation

Abstract In almost all current approaches, the collation of large texts is applied to a fixed given segmentation of the two texts witnesses to be compared and consists of two consecutive steps. First, the segments of the two texts are aligned, and then the aligned segments are compared in detail. For larger manuscripts or books consisting of many pages, the segments are usually the paragraphs of the texts. When comparing two texts, where the second text is a revised version of the first, poor local alignments can arise. This occurs in places where paragraphs have been split into two smaller paragraphs to insert a new paragraph in between, or where several consecutive sentences have been moved from one paragraph to the previous or next paragraph. Most paragraph collation tools cannot handle these scenarios properly because they align each paragraph with at most one paragraph of the other text. In this paper, we discuss this problem in detail and present a heuristic for resegmenting the two texts to be compared in order to achieve a better collation.

Sacrificial-Rotating Rod-Based 3D Bioprinting Technique for the Development of an In Vitro Cardiovascular Model.

Several studies have attempted to develop complex cardiovascular models, but the use of multiple cell types and poor cell alignments after fabrication have limited the practical application of these models. Among various bioprinting methods, extrusion-based bioprinting is the most widely used in the bioengineering field. This method not only has the potential to construct complex 3D biological structures but it also enables the alignment of cells in the printing direction owing to the application of shear stress to the cells during the printing process. Therefore, this study developed an in vitro cardiovascular model using an extrusion-based bioprinting method that utilizes a rotating rod as a printing platform. The rotating rod was made of polyvinyl alcohol (PVA) and used as a sacrificial rod. This rotating platform approach enabled the printing of longer tubular-vascular structures of multiple shapes, including disease models, and the water-soluble properties of PVA facilitated the isolation of the printed vascular models. In addition, this method enabled the printing of the endothelial cells in the bloodstream direction and smooth muscle cells in the circumferential direction to better mimic the anatomy of real blood vessels. Consequently, a cardiovascular model was successfully printed using a gelatin methacryloyl bioink with cells. In conclusion, the proposed fabrication method can facilitate the fabrication of various cardiovascular models that mimic the alignment of real blood vessels.

Use of atmospheric concentrations and passive samplers to assess surface-atmosphere exchange of gaseous mercury in forests

Use of atmospheric concentrations and passive samplers to assess surface-atmosphere exchange of gaseous mercury in forests

Detecting and quantifying spatial misalignment between longitudinal kilovoltage computed tomography (kVCT) scans of the head and neck by using convolutional neural networks (CNNs).

Adaptive radiotherapy (ART) aims to address anatomical modifications appearing during the treatment of patients by modifying the planning treatment according to the daily positioning image. Clinical implementation of ART relies on the quality of the deformable image registration (DIR) algorithms included in the ART workflow. To translate ART into clinical practice, automatic DIR assessment is needed. This article aims to estimate spatial misalignment between two head and neck kilovoltage computed tomography (kVCT) images by using two convolutional neural networks (CNNs). The first CNN quantifies misalignments between 0 mm and 15 mm and the second CNN detects and classifies misalignments into two classes (poor alignment and good alignment). Both networks take pairs of patches of 33x33x33 mm3 as inputs and use only the image intensity information. The training dataset was built by deforming kVCT images with basis splines (B-splines) to simulate DIR error maps. The test dataset was built using 2500 landmarks, consisting of hard and soft landmark tissues annotated by 6 clinicians at 10 locations. The quantification CNN reaches a mean error of 1.26 mm (± 1.75 mm) on the landmark set which, depending on the location, has annotation errors between 1 mm and 2 mm. The errors obtained for the quantification network fit the computed interoperator error. The classification network achieves an overall accuracy of 79.32%, and although the classification network overdetects poor alignments, it performs well (i.e., it achieves a rate of 90.4%) in detecting poor alignments when given one. The performances of the networks indicate the feasibility of using CNNs for an agnostic and generic approach to misalignment quantification and detection.

Rational Design for High Bioorthogonal Fluorogenicity of Tetrazine-Encoded Green Fluorescent Proteins.

The development of bioorthogonal fluorogenic probes constitutes a vital force to advance life sciences. Tetrazine-encoded green fluorescent proteins (GFPs) show high bioorthogonal reaction rate and genetic encodability, but suffer from low fluorogenicity. Here, we unveil the real-time fluorescence mechanisms by investigating two site-specific tetrazine-modified superfolder GFPs via ultrafast spectroscopy and theoretical calculations. Förster resonance energy transfer (FRET) is quantitatively modeled and revealed to govern the fluorescence quenching; for GFP150-Tet with a fluorescence turn-on ratio of ~9, it contains trimodal subpopulations with good (P1), random (P2), and poor (P3) alignments between the transition dipole moments of protein chromophore (donor) and tetrazine tag (Tet-v2.0, acceptor). By rationally designing a more free/tight environment, we created new mutants Y200A/S202Y to introduce more P2/P1 populations and improve the turn-on ratios to ~14/31, making the fluorogenicity of GFP150-Tet-S202Y the highest among all up-to-date tetrazine-encoded GFPs. In live eukaryotic cells, the GFP150-Tet-v3.0-S202Y mutant demonstrates notably increased fluorogenicity, substantiating our generalizable design strategy.

A Skin-Conformal, Stretchable, and Breathable Fiducial Marker Patch for Surgical Navigation Systems

Augmented reality (AR) surgical navigation systems have attracted considerable attention as they assist medical professionals in visualizing the location of ailments within the human body that are not readily seen with the naked eye. Taking medical imaging with a parallel C-shaped arm (C-arm) as an example, surgical sites are typically targeted using an optical tracking device and a fiducial marker in real-time. These markers then guide operators who are using a multifunctional endoscope apparatus by signaling the direction or distance needed to reach the affected parts of the body. In this way, fiducial markers are used to accurately protect the vessels and nerves exposed during the surgical process. Although these systems have already shown potential for precision implantation, delamination of the fiducial marker, which is a critical component of the system, from human skin remains a challenge due to a mechanical mismatch between the marker and skin, causing registration problems that lead to poor position alignments and surgical degradation. To overcome this challenge, the mechanical modulus and stiffness of the marker patch should be lowered to approximately 150 kPa, which is comparable to that of the epidermis, while improving functionality. Herein, we present a skin-conformal, stretchable yet breathable fiducial marker for the application in AR-based surgical navigation systems. By adopting pore patterns, we were able to create a fiducial marker with a skin-like low modulus and breathability. When attached to the skin, the fiducial marker was easily identified using optical recognition equipment and showed skin-conformal adhesion when stretched and shrunk repeatedly. As such, we believe the marker would be a good fiducial marker candidate for patients under surgical navigation systems.

Effects of the erosion zone of magnetron sputtering targets on the spatial distribution of structural and electrical properties of transparent conductive Al-doped ZnO polycrystalline films

We investigated the effects of the erosion zone of magnetron sputtering (MS) targets on the deposition rates of magnetron-sputtered Al-doped ZnO (AZO) polycrystalline films and on the structural and electrical properties of the resulting AZO films. We deposited AZO films on glass substrates at a substrate temperature of 200 °C, which were placed parallel to the target surface, by radio frequency (RF), direct current (DC) or RF-superimposed DC (RF/DC) MS. Sintered AZO targets with an Al2O3 content of 2.0 wt. % were used. The structural and electrical properties were investigated by X-ray diffraction and Hall-effect measurements, respectively. For RF-magnetron-sputtered AZO films, the results of the electron probe microanalyzer and photo luminescence measurements show that the effects of the erosion zone of the targets on the properties were very small at any given substrate position, resulting in small differences in the structural and electrical properties among the AZO films grown at different substrate positions. In the case of DC-magnetron-sputtered AZO films, residual damage owing to recoiling Ar atoms was found, particularly for the films grown at substrate positions in the area opposite to the erosion zone of the targets. This gives rise to the formation of nonradiative recombination centers, low carrier concentration, and high contribution of grain boundary scattering to carrier transport owing to poor alignments between columnar grains, resulting in low Hall mobility. In this study, we prove that the use of the RF/DC MS technique is effective in reducing the amount of residual damage.

Prediction of MNS Blood Group Antigens Using Next Generation Sequencing

Prediction of MNS Blood Group Antigens Using Next Generation Sequencing

Unconventional exo selectivity in thermal normal-electron-demand Diels-Alder reactions.

The Diels–Alder reaction is a useful tool for generating functionalized chiral molecules through the concerted cycloaddition of dienes and dienophiles leading to six-membered rings. Traditionally, the selective predictions of the products rely heavily on consideration of the secondary orbital interactions that stabilize the endo pathway. However, there remain some basic examples defying this notion and produce the exo-isomer as major product. Here we systematically evaluated of the structural features driving exo selectivity in thermal normal-electron-demand Diels–Alder reactions. Substitution at the Cβ position and the size and electronegativity of the electron-withdrawing group of the dienophile are contributing factors. Experimental and computational studies both point toward the steric and electrostatic forces between the substituents in both the diene and the dienophile that increase the likelihood of the exo pathway. For these substrates, the dominance of the endo pathway is reduced by transition state distortions and poor structural alignments of the reacting partners. We also noted the tilt of the dienophile with respect to the diene causing steric strain on the functionalities at the more advanced bond forming carbon-carbon position of the endo transition state. Insights into such factors may benefit synthetic planning and asserting control over this important named reaction.

Development of pharmacophore similarity-based quantitative activity hypothesis and its applicability domain: applied on a diverse data-set of HIV-1 integrase inhibitors

Quantitative pharmacophore hypothesis combines the 3D spatial arrangement of pharmacophore features with biological activities of the ligand data-set and predicts the activities of geometrically and/or pharmacophoric similar ligands. Most pharmacophore discovery programs face difficulties in conformational flexibility, molecular alignment, pharmacophore features sampling, and feature selection to score models if the data-set constitutes diverse ligands. Towards this focus, we describe a ligand-based computational procedure to introduce flexibility in aligning the small molecules and generating a pharmacophore hypothesis without geometrical constraints to define pharmacophore space, enriched with chemical features necessary to elucidate common pharmacophore hypotheses (CPHs). Maximal common substructure (MCS)-based alignment method was adopted to guide the alignment of carbon molecules, deciphered the MCS atom connectivity to cluster molecules in bins and subsequently, calculated the pharmacophore similarity matrix with the bin-specific reference molecules. After alignment, the carbon molecules were enriched with original atoms in their respective positions and conventional pharmacophore features were perceived. Distance-based pharmacophoric descriptors were enumerated by computing the interdistance between perceived features and MCS-aligned ‘centroid’ position. The descriptor set and biological activities were used to develop support vector machine models to predict the activities of the external test set. Finally, fitness score was estimated based on pharmacophore similarity with its bin-specific reference molecules to recognize the best and poor alignments and, also with each reference molecule to predict outliers of the quantitative hypothesis model. We applied this procedure to a diverse data-set of 40 HIV-1 integrase inhibitors and discussed its effectiveness with the reported CPH model.

Tempering Response to Different Morphologies of Martensite in Tensile Deformation of Dual-Phase Steel

A low alloy steel containing 0.2%C was heat treated with three cycles of heat treatments with the aim to acquire different morphologies of martensite in dual phase microstructure. Microscopic examination revealed that the morphologies consisting of grain boundary growth, scattered laths and bulk form of martensite were obtained. These morphologies have their distinct patterns of distribution in the matrix (ferrite). In tensile properties observations the dual phase steel with bulk morphology of martensite showed minimum of ductility but high tensile strength as compared to other two morphologies. This may be due to poor alignments of bulk martensite particles along tensile axes during deformation. Tempering was employed with various holding times at 550°C to induce ductility in the heat treated material. The tempering progressively increased the ductility by increasing holding time. However, tempering response to strengths and ductilities was different to all three morphologies of martensite.

Treelength optimization for phylogeny estimation.

The standard approach to phylogeny estimation uses two phases, in which the first phase produces an alignment on a set of homologous sequences, and the second phase estimates a tree on the multiple sequence alignment. POY, a method which seeks a tree/alignment pair minimizing the total treelength, is the most widely used alternative to this two-phase approach. The topological accuracy of trees computed under treelength optimization is, however, controversial. In particular, one study showed that treelength optimization using simple gap penalties produced poor trees and alignments, and suggested the possibility that if POY were used with an affine gap penalty, it might be able to be competitive with the best two-phase methods. In this paper we report on a study addressing this possibility. We present a new heuristic for treelength, called BeeTLe (Better Treelength), that is guaranteed to produce trees at least as short as POY. We then use this heuristic to analyze a large number of simulated and biological datasets, and compare the resultant trees and alignments to those produced using POY and also maximum likelihood (ML) and maximum parsimony (MP) trees computed on a number of alignments. In general, we find that trees produced by BeeTLe are shorter and more topologically accurate than POY trees, but that neither POY nor BeeTLe produces trees as topologically accurate as ML trees produced on standard alignments. These findings, taken as a whole, suggest that treelength optimization is not as good an approach to phylogenetic tree estimation as maximum likelihood based upon good alignment methods.

SATé-II: Very Fast and Accurate Simultaneous Estimation of Multiple Sequence Alignments and Phylogenetic Trees

Highly accurate estimation of phylogenetic trees for large data sets is difficult, in part because multiple sequence alignments must be accurate for phylogeny estimation methods to be accurate. Coestimation of alignments and trees has been attempted but currently only SATé estimates reasonably accurate trees and alignments for large data sets in practical time frames (Liu K., Raghavan S., Nelesen S., Linder C.R., Warnow T. 2009b. Rapid and accurate large-scale coestimation of sequence alignments and phylogenetic trees. Science. 324:1561-1564). Here, we present a modification to the original SATé algorithm that improves upon SATé (which we now call SATé-I) in terms of speed and of phylogenetic and alignment accuracy. SATé-II uses a different divide-and-conquer strategy than SATé-I and so produces smaller more closely related subsets than SATé-I; as a result, SATé-II produces more accurate alignments and trees, can analyze larger data sets, and runs more efficiently than SATé-I. Generally, SATé is a metamethod that takes an existing multiple sequence alignment method as an input parameter and boosts the quality of that alignment method. SATé-II-boosted alignment methods are significantly more accurate than their unboosted versions, and trees based upon these improved alignments are more accurate than trees based upon the original alignments. Because SATé-I used maximum likelihood (ML) methods that treat gaps as missing data to estimate trees and because we found a correlation between the quality of tree/alignment pairs and ML scores, we explored the degree to which SATé's performance depends on using ML with gaps treated as missing data to determine the best tree/alignment pair. We present two lines of evidence that using ML with gaps treated as missing data to optimize the alignment and tree produces very poor results. First, we show that the optimization problem where a set of unaligned DNA sequences is given and the output is the tree and alignment of those sequences that maximize likelihood under the Jukes-Cantor model is uninformative in the worst possible sense. For all inputs, all trees optimize the likelihood score. Second, we show that a greedy heuristic that uses GTR+Gamma ML to optimize the alignment and the tree can produce very poor alignments and trees. Therefore, the excellent performance of SATé-II and SATé-I is not because ML is used as an optimization criterion for choosing the best tree/alignment pair but rather due to the particular divide-and-conquer realignment techniques employed.

A Monte Carlo Method for Assessing the Quality of Duplication-Aware Alignment Algorithms

The increasing availability of high throughput sequencing technologies poses several challenges concerning the analysis of genomic data. Within this context, duplication-aware sequence alignment taking into account complex mutation events is regarded as an important problem, particularly in light of recent evolutionary bioinformatics researches that highlighted the role of tandem duplications as one of the most important mutation events. Traditional sequence comparison algorithms do not take into account these events, resulting in poor alignments in terms of biological significance, mainly because of their assumption of statistical independence among contiguous residues. Several duplication-aware algorithms have been proposed in the last years which differ either for the type of duplications they consider or for the methods adopted to identify and compare them. However, there is no solution which clearly outperforms the others and no methods exist for assessing the reliability of the resulting alignments. This paper proposes a Monte Carlo method for assessing the quality of duplication-aware alignment algorithms and for driving the choice of the most appropriate alignment technique to be used in a specific context.The applicability and usefulness of the proposed approach are demonstrated on a case study, namely, the comparison of alignments based on edit distance with or without repeat masking.

TASSER_low‐zsc: An approach to improve structure prediction using lowz‐score–ranked templates

In a variety of threading methods, often poorly ranked (low z-score) templates have good alignments. Here, a new method, TASSER_low-zsc that identifies these low z-score-ranked templates to improve protein structure prediction accuracy, is described. The approach consists of clustering of threading templates by affinity propagation on the basis of structural similarity (thread_cluster) followed by TASSER modeling, with final models selected by using a TASSER_QA variant. To establish the generality of the approach, templates provided by two threading methods, SP(3) and SPARKS(2), are examined. The SP(3) and SPARKS(2) benchmark datasets consist of 351 and 357 medium/hard proteins (those with moderate to poor quality templates and/or alignments) of length < or =250 residues, respectively. For SP(3) medium and hard targets, using thread_cluster, the TM-scores of the best template improve by approximately 4 and 9% over the original set (without low z-score templates) respectively; after TASSER modeling/refinement and ranking, the best model improves by approximately 7 and 9% over the best model generated with the original template set. Moreover, TASSER_low-zsc generates 22% (43%) more foldable medium (hard) targets. Similar improvements are observed with low-ranked templates from SPARKS(2). The template clustering approach could be applied to other modeling methods that utilize multiple templates to improve structure prediction.

Accuracy of Protein-Protein Binding Sites in High-Throughput Template-Based Modeling

The accuracy of protein structures, particularly their binding sites, is essential for the success of modeling protein complexes. Computationally inexpensive methodology is required for genome-wide modeling of such structures. For systematic evaluation of potential accuracy in high-throughput modeling of binding sites, a statistical analysis of target-template sequence alignments was performed for a representative set of protein complexes. For most of the complexes, alignments containing all residues of the interface were found. The full interface alignments were obtained even in the case of poor alignments where a relatively small part of the target sequence (as low as 40%) aligned to the template sequence, with a low overall alignment identity (<30%). Although such poor overall alignments might be considered inadequate for modeling of whole proteins, the alignment of the interfaces was strong enough for docking. In the set of homology models built on these alignments, one third of those ranked 1 by a simple sequence identity criteria had RMSD<5 Å, the accuracy suitable for low-resolution template free docking. Such models corresponded to multi-domain target proteins, whereas for single-domain proteins the best models had 5 Å<RMSD<10 Å, the accuracy suitable for less sensitive structure-alignment methods. Overall, ∼50% of complexes with the interfaces modeled by high-throughput techniques had accuracy suitable for meaningful docking experiments. This percentage will grow with the increasing availability of co-crystallized protein-protein complexes.

An Extramedullary Femoral Alignment System in Total Knee Arthroplasty Using the Inter-Femoral Head Center Distance

Purpose: Total knee arthroplasty using the extramedullary technique for alignment has some difficulty for detecting the center of the femoral head intra-operatively. In this study we tried to evaluate the usefulness and accuracy of a newly developed Mechanical Axis Marker that synchronizes the center of the knee joint and femoral head with the mechanical axis for the distal femoral cutting and femoral prosthesis alignment. Materials and Methods: Between October 2008 and January 2009, 255 knees in 156 patients underwent total knee arthroplasty. We measured the distance between each centers of the femoral head using the PACS system and we applied the distance to the newly developed Mechanical Axis Marker. Subsequently, we applied the new marker to patients to align the centers of knee, the femoral head and the marker in line with the mechanical axis intra-operatively. The accuracy of the marker was validated with C-arm fluoroscopy pre-operatively in 20 patients. Post-operatively we measured and analyzed the frontal femoral component angle to evaluate the coronal alignment of the femoral implant. The accuracy was rated as excellent when the alignment was , as good when the alignment was , and as poor when the alignment was > . Results: The pre-operative validation study with the C-arm fluoroscopy showed that the distance between the femoral head center and the metal peg of the marker was within 5 mm in 95% of the patients, which implied acceptable accuracy. The average frontal femoral component angle against the mechanical axis was (range ). The proportion of excellent, good, and poor alignments was 90.6% (231 cases), 8.6% (22 cases), and 0.8% (2 cases), respectively. The intraclass correlation coefficient between the two observers for the frontal femoral component angle was 0.972 which showed high concordance. Conclusion: Our results indicate that the extramedullary technique assisted by our new Mechanical Axis Marker can easily identify the center of femoral head and improve the accuracy of frontal femoral component alignment with the proper mechanical axis.

Evolutionary analysis reveals low coverage as the major challenge for protein interaction network alignment

Local alignments of protein interaction networks have found little conservation among several species. While this could be a consequence of the incompleteness of interaction data-sets and presence of error, an intriguing prospect is that the process of network evolution is sufficient to erase any evidence of conservation. Here, we aim to test this hypothesis using models of network evolution and also investigate the role of error in the results of network alignment. We devised a distance metric based on summary statistics to assess the fit between experimental and simulated network alignments. Our results indicate that network evolution alone is unlikely to account for the poor quality alignments given by real data. Alignments of simulated networks undergoing evolution are considerably (4 to 5 times) larger than real alignments. We compare several error models in their ability to explain this discrepancy. Our estimates of false negative rates vary from 20 to 60% dependent on whether incomplete proteome sampling is taken into account or not. We also find that false positives appear to affect network alignments little compared to false negatives indicating that incompleteness, not spurious links, is the major challenge for interactome-level comparisons.

Protein Structure Prediction by Pro-Sp3-TASSER

Protein Structure Prediction by Pro-Sp3-TASSER

Template-Based Modeling of Protein-Protein Interfaces

Template-Based Modeling of Protein-Protein Interfaces