Thermodynamic analysis of ligand-induced changes in protein thermal unfolding applied to high-throughput determination of ligand affinities with extrinsic fluorescent dyes.

The quantification of protein-ligand interactions is essential for systems biology, drug discovery, and bioengineering. Ligand-induced changes in protein thermal stability provide a general, quantifiable signature of binding and may be monitored with dyes such as Sypro Orange (SO), which increase their fluorescence emission intensities upon interaction with the unfolded protein. This method is an experimentally straightforward, economical, and high-throughput approach for observing thermal melts using commonly available real-time polymerase chain reaction instrumentation. However, quantitative analysis requires careful consideration of the dye-mediated reporting mechanism and the underlying thermodynamic model. We determine affinity constants by analysis of ligand-mediated shifts in melting-temperature midpoint values. Ligand affinity is determined in a ligand titration series from shifts in free energies of stability at a common reference temperature. Thermodynamic parameters are obtained by fitting the inverse first derivative of the experimental signal reporting on thermal denaturation with equations that incorporate linear or nonlinear baseline models. We apply these methods to fit protein melts monitored with SO that exhibit prominent nonlinear post-transition baselines. SO can perturb the equilibria on which it is reporting. We analyze cases in which the ligand binds to both the native and denatured state or to the native state only and cases in which protein:ligand stoichiometry needs to treated explicitly.

WriteSim TCExam--an open source text simulation environment for training novice researchers in scientific writing.

BACKGROUND: The ability to write clearly and effectively is of central importance to the scientific enterprise. Encouraged by the success of simulation environments in other biomedical sciences, we developed WriteSim TCExam, an open-source, Web-based, textual simulation environment for teaching effective writing techniques to novice researchers. We shortlisted and modified an existing open source application - TCExam to serve as a textual simulation environment. After testing usability internally in our team, we conducted formal field usability studies with novice researchers. These were followed by formal surveys with researchers fitting the role of administrators and users (novice researchers) RESULTS: The development process was guided by feedback from usability tests within our research team. Online surveys and formal studies, involving members of the Research on Research group and selected novice researchers, show that the application is user-friendly. Additionally it has been used to train 25 novice researchers in scientific writing to date and has generated encouraging results. CONCLUSION: WriteSim TCExam is the first Web-based, open-source textual simulation environment designed to complement traditional scientific writing instruction. While initial reviews by students and educators have been positive, a formal study is needed to measure its benefits in comparison to standard instructional methods.

Understanding GPU programming for statistical computation: Studies in massively parallel massive mixtures

This article describes advances in statistical computation for large-scale data analysis in structured Bayesian mixture models via graphics processing unit (GPU) programming. The developments are partly motivated by computational challenges arising in fitting models of increasing heterogeneity to increasingly large datasets. An example context concerns common biological studies using high-throughput technologies generating many, very large datasets and requiring increasingly high-dimensional mixture models with large numbers of mixture components.We outline important strategies and processes for GPU computation in Bayesian simulation and optimization approaches, give examples of the benefits of GPU implementations in terms of processing speed and scale-up in ability to analyze large datasets, and provide a detailed, tutorial-style exposition that will benefit readers interested in developing GPU-based approaches in other statistical models. Novel, GPU-oriented approaches to modifying existing algorithms software design can lead to vast speed-up and, critically, enable statistical analyses that presently will not be performed due to compute time limitations in traditional computational environments. Supplementalmaterials are provided with all source code, example data, and details that will enable readers to implement and explore the GPU approach in this mixture modeling context. © 2010 American Statistical Association, Institute of Mathematical Statistics, and Interface Foundation of North America.

Comparison of extramedullary versus intramedullary referencing for tibial component alignment in total ankle arthroplasty.

BACKGROUND: The majority of total ankle arthroplasty (TAA) systems use extramedullary alignment guides for tibial component placement. However, at least 1 system offers intramedullary referencing. In total knee arthroplasty, studies suggest that tibial component placement is more accurate with intramedullary referencing. The purpose of this study was to compare the accuracy of extramedullary referencing with intramedullary referencing for tibial component placement in total ankle arthroplasty. METHODS: The coronal and sagittal tibial component alignment was evaluated on the postoperative weight-bearing anteroposterior (AP) and lateral radiographs of 236 consecutive fixed-bearing TAAs. Radiographs were measured blindly by 2 investigators. The postoperative alignment of the prosthesis was compared with the surgeon's intended alignment in both planes. The accuracy of tibial component alignment was compared between the extramedullary and intramedullary referencing techniques using unpaired t tests. Interrater and intrarater reliabilities were assessed with intraclass correlation coefficients (ICCs). RESULTS: Eighty-three tibial components placed with an extramedullary referencing technique were compared with 153 implants placed with an intramedullary referencing technique. The accuracy of the extramedullary referencing was within a mean of 1.5 ± 1.4 degrees and 4.1 ± 2.9 degrees in the coronal and sagittal planes, respectively. The accuracy of intramedullary referencing was within a mean of 1.4 ± 1.1 degrees and 2.5 ± 1.8 degrees in the coronal and sagittal planes, respectively. There was a significant difference (P < .001) between the 2 techniques with respect to the sagittal plane alignment. Interrater ICCs for coronal and sagittal alignment were high (0.81 and 0.94, respectively). Intrarater ICCs for coronal and sagittal alignment were high for both investigators. CONCLUSIONS: Initial sagittal plane tibial component alignment was notably more accurate when intramedullary referencing was used. Further studies are needed to determine the effect of this difference on clinical outcomes and long-term survivability of the implants. LEVEL OF EVIDENCE: Level III, retrospective comparative study.

Cross-Domain Multitask Learning with Latent Probit Models

Learning multiple tasks across heterogeneous domains is a challenging problem since the feature space may not be the same for different tasks. We assume the data in multiple tasks are generated from a latent common domain via sparse domain transforms and propose a latent probit model (LPM) to jointly learn the domain transforms, and the shared probit classifier in the common domain. To learn meaningful task relatedness and avoid over-fitting in classification, we introduce sparsity in the domain transforms matrices, as well as in the common classifier. We derive theoretical bounds for the estimation error of the classifier in terms of the sparsity of domain transforms. An expectation-maximization algorithm is derived for learning the LPM. The effectiveness of the approach is demonstrated on several real datasets.

Effect of ritonavir-induced cytochrome P450 3A4 inhibition on plasma fentanyl concentrations during patient-controlled epidural labor analgesia: a pharmacokinetic simulation.

BACKGROUND: Ritonavir inhibition of cytochrome P450 3A4 decreases the elimination clearance of fentanyl by 67%. We used a pharmacokinetic model developed from published data to simulate the effect of sample patient-controlled epidural labor analgesic regimens on plasma fentanyl concentrations in the absence and presence of ritonavir-induced cytochrome P450 3A4 inhibition. METHODS: Fentanyl absorption from the epidural space was modeled using tanks-in-series delay elements. Systemic fentanyl disposition was described using a three-compartment pharmacokinetic model. Parameters for epidural drug absorption were estimated by fitting the model to reported plasma fentanyl concentrations measured after epidural administration. The validity of the model was assessed by comparing predicted plasma concentrations after epidural administration to published data. The effect of ritonavir was modeled as a 67% decrease in fentanyl elimination clearance. Plasma fentanyl concentrations were simulated for six sample patient-controlled epidural labor analgesic regimens over 24 h using ritonavir and control models. Simulated data were analyzed to determine if plasma fentanyl concentrations producing a 50% decrease in minute ventilation (6.1 ng/mL) were achieved. RESULTS: Simulated plasma fentanyl concentrations in the ritonavir group were higher than those in the control group for all sample labor analgesic regimens. Maximum plasma fentanyl concentrations were 1.8 ng/mL and 3.4 ng/mL for the normal and ritonavir simulations, respectively, and did not reach concentrations associated with 50% decrease in minute ventilation. CONCLUSION: Our model predicts that even with maximal clinical dosing regimens of epidural fentanyl over 24 h, ritonavir-induced cytochrome P450 3A4 inhibition is unlikely to produce plasma fentanyl concentrations associated with a decrease in minute ventilation.

People who expect to enter psychotherapy are prone to believing that they have forgotten memories of childhood trauma and abuse.

We asked 1004 undergraduates to estimate both the probability that they would enter therapy and the probability that they experienced but could not remember incidents of potentially life-threatening childhood traumas or physical and sexual abuse. We found a linear relation between the expectation of entering therapy and the belief that one had, but cannot now remember, childhood trauma and abuse. Thus individuals who are prone to seek psychotherapy are also prone to accept a suggested memory of childhood trauma or abuse as fitting their expectations. In multiple regressions predicting the probability of forgotten memories of childhood traumas and abuse, the expectation of entering therapy remained as a substantial predictor when self-report measures of mood, anxiety, post-traumatic stress disorder symptom severity, and trauma exposure were included.

One hundred years of forgetting: A quantitative description of retention

A sample of 210 published data sets were assembled that (a) plotted amount remembered versus time, (b) had 5 or more points, and (c) were smooth enough to fit at least 1 of the functions tested with a correlation coefficient of .90 or greater. Each was fit to 105 different 2-parameter functions. The best fits were to the logarithmic function, the power function, the exponential in the square root of time, and the hyperbola in the square root of time. It is difficult to distinguish among these 4 functions with the available data, but the same set of 4 functions fit most data sets, with autobiographical memory being the exception. Theoretical motivations for the best fitting functions are offered. The methodological problems of evaluating functions and the advantages of searching existing data for regularities before formulating theories are considered.

Recognition of Planar Segments in Point Cloud Based on Wavelet Transform

© 2005-2012 IEEE.Within industrial automation systems, three-dimensional (3-D) vision provides very useful feedback information in autonomous operation of various manufacturing equipment (e.g., industrial robots, material handling devices, assembly systems, and machine tools). The hardware performance in contemporary 3-D scanning devices is suitable for online utilization. However, the bottleneck is the lack of real-time algorithms for recognition of geometric primitives (e.g., planes and natural quadrics) from a scanned point cloud. One of the most important and the most frequent geometric primitive in various engineering tasks is plane. In this paper, we propose a new fast one-pass algorithm for recognition (segmentation and fitting) of planar segments from a point cloud. To effectively segment planar regions, we exploit the orthonormality of certain wavelets to polynomial function, as well as their sensitivity to abrupt changes. After segmentation of planar regions, we estimate the parameters of corresponding planes using standard fitting procedures. For point cloud structuring, a z-buffer algorithm with mesh triangles representation in barycentric coordinates is employed. The proposed recognition method is tested and experimentally validated in several real-world case studies.

Electrostatic Energetics of Bacillus subtilis Ribonuclease P Protein Determined by Nuclear Magnetic Resonance-Based Histidine pKa Measurements.

The pKa values of ionizable groups in proteins report the free energy of site-specific proton binding and provide a direct means of studying pH-dependent stability. We measured histidine pKa values (H3, H22, and H105) in the unfolded (U), intermediate (I), and sulfate-bound folded (F) states of RNase P protein, using an efficient and accurate nuclear magnetic resonance-monitored titration approach that utilizes internal reference compounds and a parametric fitting method. The three histidines in the sulfate-bound folded protein have pKa values depressed by 0.21 ± 0.01, 0.49 ± 0.01, and 1.00 ± 0.01 units, respectively, relative to that of the model compound N-acetyl-l-histidine methylamide. In the unliganded and unfolded protein, the pKa values are depressed relative to that of the model compound by 0.73 ± 0.02, 0.45 ± 0.02, and 0.68 ± 0.02 units, respectively. Above pH 5.5, H22 displays a separate resonance, which we have assigned to I, whose apparent pKa value is depressed by 1.03 ± 0.25 units, which is ∼0.5 units more than in either U or F. The depressed pKa values we observe are consistent with repulsive interactions between protonated histidine side chains and the net positive charge of the protein. However, the pKa differences between F and U are small for all three histidines, and they have little ionic strength dependence in F. Taken together, these observations suggest that unfavorable electrostatics alone do not account for the fact that RNase P protein is intrinsically unfolded in the absence of ligand. Multiple factors encoded in the P protein sequence account for its IUP property, which may play an important role in its function.

Accurate localized resolution of identity approach for linear-scaling hybrid density functionals and for many-body perturbation theory

© 2015 IOP Publishing Ltd and Deutsche Physikalische Gesellschaft.A key component in calculations of exchange and correlation energies is the Coulomb operator, which requires the evaluation of two-electron integrals. For localized basis sets, these four-center integrals are most efficiently evaluated with the resolution of identity (RI) technique, which expands basis-function products in an auxiliary basis. In this work we show the practical applicability of a localized RI-variant ('RI-LVL'), which expands products of basis functions only in the subset of those auxiliary basis functions which are located at the same atoms as the basis functions. We demonstrate the accuracy of RI-LVL for Hartree-Fock calculations, for the PBE0 hybrid density functional, as well as for RPA and MP2 perturbation theory. Molecular test sets used include the S22 set of weakly interacting molecules, the G3 test set, as well as the G2-1 and BH76 test sets, and heavy elements including titanium dioxide, copper and gold clusters. Our RI-LVL implementation paves the way for linear-scaling RI-based hybrid functional calculations for large systems and for all-electron many-body perturbation theory with significantly reduced computational and memory cost.