RESUMEN

Insights in understanding critical growth drivers of innovation are essential for industrial development and economic growth for competitive advantage. This study identifies indicators from the world's major indexes and industry-level concerns. The indicators are mapped to these concerns using expert opinions and then treated mathematically using technique of order preference by similarity to an ideal solution (TOPSIS). This mapping ranks and identifies the most favorable indicators for several concerns. It, thus, identifies the critical role the indicators play for the drivers to the most effective advantage using the TOPSIS method as a comprehensive ranking of indicators effectively facilitates decision-making for estimated levels. The method highlights are as follows: • The most prevailing indicators for innovation are considered from the major innovation indexes for industries for mapping with concerns within the industry. • Industry-specific concerns for the pharmaceutical industry are selected for the study. • The mapping of indicators to concerns using expert opinion and using the TOPSIS method generated a matrix of the ranked indicators, aids in prioritizing resources and existing knowledge to resolve the concerns.

RESUMEN

Identifying collective variables (CVs) for chemical reactions is essential to reduce the 3N-dimensional energy landscape into lower dimensional basins and barriers of interest. However, in condensed phase processes, the nonmeaningful motions of bulk solvent often overpower the ability of dimensionality reduction methods to identify correlated motions that underpin collective variables. Yet solvent can play important indirect or direct roles in reactivity, and much can be lost through treatments that remove or dampen solvent motion. This has been amply demonstrated within principal component analysis (PCA), although less is known about the behavior of nonlinear dimensionality reduction methods, e.g., uniform manifold approximation and projection (UMAP), that have become recently utilized. The latter presents an interesting alternative to linear methods though often at the expense of interpretability. This work presents distance-attenuated projection methods of atomic coordinates that facilitate the application of both PCA and UMAP to identify collective variables in the presence of explicit solvent and further the specific identity of solvent molecules that participate in chemical reactions. The performance of both methods is examined in detail for two reactions where the explicit solvent plays very different roles within the collective variables. When applied to raw molecular dynamics data in solution, both PCA and UMAP representations are dominated by bulk solvent motions. On the other hand, when applied to data preprocessed by our attenuated projection methods, both PCA and UMAP identify the appropriate collective variables (though varying sensitivity is observed due to the presence of explicit solvent that results from the projection method). Importantly, this approach allows identification of specific solvent molecules that are relevant to the CVs and their importance.

RESUMEN

Encoding the complex features of an energy landscape is a challenging task, and often, chemists pursue the most salient features (minima and barriers) along a highly reduced space, i.e., two- or three-dimensions. Even though disconnectivity graphs or merge trees summarize the connectivity of the local minima of an energy landscape via the lowest-barrier pathways, there is much information to be gained by also considering the topology of each connected component at different energy thresholds (or sublevelsets). We propose sublevelset persistent homology as an appropriate tool for this purpose. Our computations on the configuration phase space of n-alkanes from butane to octane allow us to conjecture, and then prove, a complete characterization of the sublevelset persistent homology of the alkane CmH2m+2 Potential Energy Landscapes (PELs), for all m, in all homological dimensions. We further compare both the analytical configurational PELs and sampled data from molecular dynamics simulation using the united and all-atom descriptions of the intramolecular interactions. In turn, this supports the application of distance metrics to quantify sampling fidelity and lays the foundation for future work regarding new metrics that quantify differences between the topological features of high-dimensional energy landscapes.

RESUMEN

Phenomics is an emerging branch of modern biology that uses high throughput phenotyping tools to capture multiple environmental and phenotypic traits, often at massive spatial and temporal scales. The resulting high dimensional data represent a treasure trove of information for providing an in-depth understanding of how multiple factors interact and contribute to the overall growth and behavior of different genotypes. However, computational tools that can parse through such complex data and aid in extracting plausible hypotheses are currently lacking. In this article, we present Hyppo-X, a new algorithmic approach to visually explore complex phenomics data and in the process characterize the role of environment on phenotypic traits. We model the problem as one of unsupervised structure discovery, and use emerging principles from algebraic topology and graph theory for discovering higher-order structures of complex phenomics data. We present an open source software which has interactive visualization capabilities to facilitate data navigation and hypothesis formulation. We test and evaluate Hyppo-X on two real-world plant (maize) data sets. Our results demonstrate the ability of our approach to delineate divergent subpopulation-level behavior. Notably, our approach shows how environmental factors could influence phenotypic behavior, and how that effect varies across different genotypes and different time scales. To the best of our knowledge, this effort provides one of the first approaches to systematically formalize the problem of hypothesis extraction for phenomics data. Considering the infancy of the phenomics field, tools that help users explore complex data and extract plausible hypotheses in a data-guided manner will be critical to future advancements in the use of such data.

Asunto(s)

RESUMEN

The structural features that protrude above or below a soft matter interface are well-known to be related to interfacially mediated chemical reactivity and transport processes. It is a challenge to develop a robust algorithm for identifying these organized surface structures, as the morphology can be highly varied and they may exist on top of an interface containing significant interfacial roughness. A new algorithm that employs concepts from geometric measure theory, algebraic topology, and optimization is developed to identify candidate structures at a soft matter surface, and then, using a probabilistic approach, to rank their likelihood of being a complex structural feature. The algorithm is tested for a surfactant laden water/oil interface, where it is robust to identifying protrusions responsible for water transport against a set identified by visual inspection. To our knowledge, this is the first example of applying geometric measure theory to analyze the properties of a chemical/materials science system.

RESUMEN

PURPOSE: Results of a small case series indicate an increased risk of dens fractures in patients with osteoarthritis. The purpose of this retrospective cohort study was to analyze the relative risks associated with degeneration of the cervical spine in the occurrence of dens fractures in older patients. METHODS: We performed a retrospective CT study of 1,794 patients > 55 years of age with and without dens fractures for signs of osteoarthritis (OA). RESULTS: OA of the atlanto-dens interval (AdI) was present in 75.9% of fracture patients, whereas 63.5% of non-fracture patients had OA of the AdI (p = 0.04). In cases of osteoarthritis of the facet joints, we did find a significant increase (p < 0.05) in the dens fracture risk in patients with OA. CONCLUSIONS: This study indicates an association between OA of the cervical spine and the risk of sustaining a dens fracture. OA can lead to a reduction in the range of motion of the cervical spine. As a consequence, a relatively low-energy trauma can induce a forced sagittal motion, which will produce a torque at the base of the odontoid process resulting in a fracture.

Asunto(s)

RESUMEN

This study examined the relationship of surgical management of humerus shaft fractures (HSFs) with race, gender, insurance status, and presence of lower extremity fracture in 19,818 patients from the National Trauma Data Bank years 2007-2012. Using a multivariate logistic regression model, black males (OR 0.73, 95% CI 0.66-0.81, p < 0.001) and white females (OR 0.85, 95% CI 0.80-0.91, p < 0.001) had reduced odds of surgery compared to white males. Insurance status was not significant. These disparities may reflect bias within the surgical treatment team.

RESUMEN

BACKGROUND CONTEXT: Although it is generally believed that the magnitude of dens fracture displacement is proportional to the amount of force applied to the cervical spine during injury, the factors responsible for displacement have not been studied. PURPOSE: Our aim was to determine factors that contribute to horizontal and angular displacement of dens fractures. STUDY DESIGN/SETTING: We conducted a retrospective review of adult patients who were admitted to our level 1 trauma center between January 1, 2008 and December 31, 2013. PATIENT SAMPLE: Angular and horizontal displacements of the fractured dens in 57 patients were measured. Subjects were grouped based on mechanism of fracture: motor vehicle accident, ground level fall, and higher falls. OUTCOME MEASURES: Cervical lordosis was measured between C2 and T1. C3-C4, C4-C5, C5-C6, and C6-C7 disc inclination angles were measured. Anteroposterior sagittal balance was assessed by comparing the sagittal position of the C2 body with the C7 body. METHODS: Data were analyzed using Pearson correlations, independent t tests, and support vector regression to construct predictive models that determine factors contributing to the angular and horizontal displacements. RESULTS: The mean horizontal displacement of the fractured dens was not significantly different among groups. However, the dens in those with ground level falls had a significantly greater mean fracture angle compared with the higher energy trauma groups (p=.01). There were positive correlations between angular displacement and C5-C6 disc space inclination angle (r=0.67, p<.01) and C6-C7 disc space inclination angle (r=0.61, p<.01). There were positive correlations between horizontal displacement and C6-C7 inclination angle (r=0.40, p<.01) and sagittal alignment (r=0.32, p<.01). The predictive model using all variables demonstrated that angular fracture displacement was only dependent on C5-C6 disc space inclination angle. Horizontal displacement was only dependent on C6-C7 inclination angle and anteroposterior sagittal balance. CONCLUSIONS: Disc space inclination angles of the lower cervical spine and the cervical sagittal balance most contribute to the magnitude of angular and horizontal displacement of the dens after fracture.

Asunto(s)

Asunto(s)

RESUMEN

While metal or plastic interbody spinal fusion devices are manufactured to appropriate mechanical standards, mechanical properties of commercially prepared structural allograft bone remain relatively unassessed. Robust models predicting compressive load to failure of structural allograft bone based on easily measured variables would be useful. Three hundred twenty seven femoral rings from 34 cadaver femora were tested to failure in axial compression. Predictive variables included age, gender, bone mineral density (BMD), position along femoral shaft, maximum/minimum wall thickness, outer/inner diameter, and area. We used support vector regression and 10-fold cross-validation to develop robust nonlinear predictive models for load to failure. Model performance was measured by the root-mean-squared-deviation (RMSD) and correlation coefficients (r). A polynomial model using all variables had RMSD = 7.92, r = 0.84, indicating excellent performance. A model using all variables except BMD was essentially unchanged (RMSD = 8.12, r = 0.83). Eliminating both age and BMD produced a model with RMSD = 8.41, r = 0.82, again essentially unchanged. Compressive strength of structural allograft bone can be estimated using easily measured geometric parameters, without including BMD or age. As DEXA is costly and cumbersome, and setting upper age-limits for potential donors reduces the supply, our results may prove helpful to increase the quality and availability of structural allograft.

Asunto(s)

RESUMEN

Eukaryotic cells maintain strict control over protein secretion, in part by using the pH gradient maintained within their secretory pathway. How eukaryotic proteins evolved from prokaryotic orthologs to exploit the pH gradient for biological functions remains a fundamental question in cell biology. Our laboratory previously demonstrated that protein domains located within precursor proteins, propeptides, encode histidine-driven pH sensors to regulate organelle-specific activation of the eukaryotic proteases furin and proprotein convertase-1/3. Similar findings have been reported in other unrelated protease families. By analyzing >10,000 unique proteases within evolutionarily unrelated families, we show that eukaryotic propeptides are enriched in histidines compared with prokaryotic orthologs. On this basis, we hypothesize that eukaryotic proteins evolved to enrich histidines within their propeptides to exploit the tightly controlled pH gradient of the secretory pathway, thereby regulating activation within specific organelles. Enrichment of histidines in propeptides may therefore be used to predict the presence of pH sensors in other proteases or even protease substrates.

Asunto(s)

RESUMEN

In this paper, we studied the effects of wrapping surfaces on muscle paths and moment arms of the neck muscle, semispinalis capitis. Sensitivities to wrapping surface size and the kinematic linkage to vertebral segments were evaluated. Kinematic linkage, but not radius, significantly affected the accuracy of model muscle paths compared to centroid paths from images. Both radius and linkage affected the moment arm significantly. Wrapping surfaces that provided the best match to centroid paths over a range of postures had consistent moment arms. For some wrapping surfaces with poor matches to the centroid path, a kinematic method (tendon excursion) predicted flexion moment arms in certain postures, whereas geometric method (distance to instant centre) predicted extension. This occurred because the muscle lengthened as it wrapped around the surface. This study highlights the sensitivity of moment arms to wrapping surface parameters and the importance of including multiple postures when evaluating muscle paths and moment arm.

Asunto(s)

RESUMEN

Temperature-sensitive (TS) mutants are powerful tools to study gene function in vivo. These mutants exhibit wild-type activity at permissive temperatures and reduced activity at restrictive temperatures. Although random mutagenesis can be used to generate TS mutants, the procedure is laborious and unfeasible in multicellular organisms. Further, the underlying molecular mechanisms of the TS phenotype are poorly understood. To elucidate TS mechanisms, we used a machine learning method-logistic regression-to investigate a large number of sequence and structure features. We developed and tested 133 features, describing properties of either the mutation site or the mutation site neighborhood. We defined three types of neighborhood using sequence distance, Euclidean distance, and topological distance. We discovered that neighborhood features outperformed mutation site features in predicting TS mutations. The most predictive features suggest that TS mutations tend to occur at buried and rigid residues, and are located at conserved protein domains. The environment of a buried residue often determines the overall structural stability of a protein, thus may lead to reversible activity change upon temperature switch. We developed TS prediction models based on logistic regression and the Lasso regularized procedure. Through a ten-fold cross-validation, we obtained the area under the curve of 0.91 for the model using both sequence and structure features. Testing on independent datasets suggested that the model predicted TS mutations with a 50% precision. In summary, our study elucidated the molecular basis of TS mutants and suggested the importance of neighborhood properties in determining TS mutations. We further developed models to predict TS mutations derived from single amino acid substitutions. In this way, TS mutants can be efficiently obtained through experimentally introducing the predicted mutations.

Asunto(s)

RESUMEN

BACKGROUND: Mutagenesis is commonly used to engineer proteins with desirable properties not present in the wild type (WT) protein, such as increased or decreased stability, reactivity, or solubility. Experimentalists often have to choose a small subset of mutations from a large number of candidates to obtain the desired change, and computational techniques are invaluable to make the choices. While several such methods have been proposed to predict stability and reactivity mutagenesis, solubility has not received much attention. RESULTS: We use concepts from computational geometry to define a three body scoring function that predicts the change in protein solubility due to mutations. The scoring function captures both sequence and structure information. By exploring the literature, we have assembled a substantial database of 137 single- and multiple-point solubility mutations. Our database is the largest such collection with structural information known so far. We optimize the scoring function using linear programming (LP) methods to derive its weights based on training. Starting with default values of 1, we find weights in the range [0,2] so that predictions of increase or decrease in solubility are optimized. We compare the LP method to the standard machine learning techniques of support vector machines (SVM) and the Lasso. Using statistics for leave-one-out (LOO), 10-fold, and 3-fold cross validations (CV) for training and prediction, we demonstrate that the LP method performs the best overall. For the LOOCV, the LP method has an overall accuracy of 81%. AVAILABILITY: Executables of programs, tables of weights, and datasets of mutants are available from the following web page: http://www.wsu.edu/~kbala/OptSolMut.html.

RESUMEN

MOTIVATION: There is a need for an efficient and accurate computational method to identify the effects of single- and multiple-residue mutations on the stability and reactivity of proteins. Such a method should ideally be consistent and yet applicable in a widespread manner, i.e. it should be applied to various proteins under the same parameter settings, and have good predictive power for all of them. RESULTS: We develop a Delaunay tessellation-based four-body scoring function to predict the effects of single- and multiple-residue mutations on the stability and reactivity of proteins. We test our scoring function on sets of single-point mutations used by several previous studies. We also assemble a new, diverse set of 237 single- and multiple-residue mutations, from over 24 different publications. The four-body scoring function correctly predicted the changes to the stability of 169 out of 210 mutants (80.5%), and the changes to the reactivity of 17 out of 27 mutants (63%). For the mutants that had the changes in stability/reactivity quantified (using reaction rates, temperatures, etc.), an average Spearman rank correlation coefficient of 0.67 was achieved with the four-body scores. We also develop an efficient method for screening huge numbers of mutants of a protein, called combinatorial mutagenesis. In one study, 64 million mutants of a cold-shock nucleus binding domain protein 1CSQ, with six of its residues being changed to all possible (20) amino acids, were screened within a few hours on a PC, and all five stabilizing mutants reported were correctly identified as stabilizing by combinatorial mutagenesis.

Asunto(s)

RESUMEN

MOTIVATION: Most scoring functions used in protein fold recognition employ two-body (pseudo) potential energies. The use of higher-order terms may improve the performance of current algorithms. METHODS: Proteins are represented by the side chain centroids of amino acids. Delaunay tessellation of this representation defines all sets of nearest neighbor quadruplets of amino acids. Four-body contact scoring function (log likelihoods of residue quadruplet compositions) is derived by the analysis of a diverse set of proteins with known structures. A test protein is characterized by the total score calculated as the sum of the individual log likelihoods of composing amino acid quadruplets. RESULTS: The scoring function distinguishes native from partially unfolded or deliberately misfolded structures. It also discriminates between pre- and post-transition state and native structures in the folding simulations trajectory of Chymotrypsin Inhibitor 2 (CI2).

Asunto(s)