Protein backbone and sidechain torsion angles predicted from NMR chemical shifts using artificial neural networks

A new program, TALOS-N, is introduced for predicting protein backbone torsion angles from NMR chemical shifts. The program relies far more extensively on the use of trained artificial neural networks than its predecessor, TALOS+. Validation on an independent set of proteins indicates that backbone torsion angles can be predicted for a larger, ≥90 % fraction of the residues, with an error rate smaller than ca 3.5 %, using an acceptance criterion that is nearly two-fold tighter than that used previously, and a root mean square difference between predicted and crystallographically observed (?, ψ) torsion angles of ca 12º. TALOS-N also reports sidechain χ¹ rotameric states for about 50 % of the residues, and a consistency with reference structures of 89 %. The program includes a neural network trained to identify secondary structure from residue sequence and chemical shifts.     

IGERS: Inferring Gibbs Energy Changes of Biochemical Reactions from Reaction Similarities

Mathematical analysis and modeling of biochemical reaction networks requires knowledge of the permitted directionality of reactions and membrane transport processes. This information can be gathered from the standard Gibbs energy changes (ΔG⁰) of reactions and the concentration ranges of their reactants. Currently, experimental ΔG⁰ values are not available for the vast majority of cellular biochemical processes. We propose what we believe to be a novel computational method to infer the unknown ΔG⁰ value of a reaction from the known ΔG⁰ value of the chemically most similar reaction. The chemical similarity of two arbitrary reactions is measured by the relative number (T) of co-occurring changes in the chemical attributes of their reactants. Testing our method across a validated reference set of 173 biochemical reactions with experimentally determined ΔG⁰ values, we found that a minimum reaction similarity of T = 0.6 is required to infer ΔG⁰ values with an error of <10 kJ/mol. Applying this criterion, our method allows us to assign ΔG⁰ values to 458 additional reactions of the BioPath database. We believe our approach permits us to minimize the number of ΔG⁰ measurements required for a full coverage of a given reaction network with reliable ΔG⁰ values.     

Species distribution models backing taxa delimitation: the case of the lichen Squamarina cartilaginea in Italy

Species distribution models (SDMs) have been extensively used for a variety of purposes, including investigation of taxonomic problems, together with molecular, chemical and morphological analysis. The two varieties of the lichen Squamarina cartilaginea known to occur in Italy (var. cartilaginea and var. pseudocrassa), which are morphologically indistinguishable, can be identified only by a medullar chemical spot test. In this paper, SDMs are used to support the separation of these two varieties, to determine whether they are also characterized by a differential spatial distribution. Occurrence data were obtained by geo-referencing a posteriori 114 herbarium specimens identified to variety level by a medullar spot test. The spatial distribution was modeled by using Random Forest (RF) and Generalized Linear Models. Suitability areas were obtained by applying the 0% omission error criterion in the probability map produced by RF, which proved to be the more statistically reliable of the two methods. Kendall's tau statistic test applied to RF suitability maps indicates that the two varieties tend to segregate ecologically in the Italian peninsula. Var. pseudocrassa appears to be more widespread in the Mediterranean region, as well as in coastal and hilly areas, while var. cartilaginea is more abundant in the temperate region and mountainous areas. For both varieties the spatial distribution is determined by similar climatic variables (mean yearly temperature, mean temperature of the coldest month and summer precipitation). These findings lead to a new hypothesis on the role of these environmental factors on the evolutionary history and geographical distribution of the two varieties. This study also corroborates the usefulness of SDMs in delimiting taxonomical entities.     

Measurement of peak impact loads differ between accelerometers – Effects of system operating range and sampling rate

A wide variety of accelerometer systems, with differing sensor characteristics, are used to detect impact loading during physical activities. The study examined the effects of system characteristics on measured peak impact loading during a variety of activities by comparing outputs from three separate accelerometer systems, and by assessing the influence of simulated reductions in operating range and sampling rate. Twelve healthy young adults performed seven tasks (vertical jump, box drop, heel drop, and bilateral single leg and lateral jumps) while simultaneously wearing three tri-axial accelerometers including a criterion standard laboratory-grade unit (Endevco 7267A) and two systems primarily used for activity-monitoring (ActiGraph GT3X+, GCDC X6-2mini). Peak acceleration (gmax) was compared across accelerometers, and errors resulting from down-sampling (from 640 to 100 Hz) and range-limiting (to ±6 g) the criterion standard output were characterized. The Actigraph activity-monitoring accelerometer underestimated gmax by an average of 30.2%; underestimation by the X6-2mini was not significant. Underestimation error was greater for tasks with greater impact magnitudes. gmax was underestimated when the criterion standard signal was down-sampled (by an average of 11%), range limited (by 11%), and by combined down-sampling and range-limiting (by 18%). These effects explained 89% of the variance in gmax error for the Actigraph system. This study illustrates that both the type and intensity of activity should be considered when selecting an accelerometer for characterizing impact events. In addition, caution may be warranted when comparing impact magnitudes from studies that use different accelerometers, and when comparing accelerometer outputs to osteogenic impact thresholds proposed in literature.     

On the optimality of the neighbor-joining algorithm

The popular neighbor-joining (NJ) algorithm used in phylogenetics is a greedy algorithm for finding the balanced minimum evolution (BME) tree associated to a dissimilarity map. From this point of view, NJ is "optimal" when the algorithm outputs the tree which minimizes the balanced minimum evolution criterion. We use the fact that the NJ tree topology and the BME tree topology are determined by polyhedral subdivisions of the spaces of dissimilarity maps to study the optimality of the neighbor-joining algorithm. In particular, we investigate and compare the polyhedral subdivisions for n ≤ 8. This requires the measurement of volumes of spherical polytopes in high dimension, which we obtain using a combination of Monte Carlo methods and polyhedral algorithms. Our results include a demonstration that highly unrelated trees can be co-optimal in BME reconstruction, and that NJ regions are not convex. We obtain the l ₂ radius for neighbor-joining for n = 5 and we conjecture that the ability of the neighbor-joining algorithm to recover the BME tree depends on the diameter of the BME tree.     

Thermally induced chemical artifacts in lichens

Unsuspected thermal degradation of secondary products in lichens prior to chemical study, although probably uncommon, can lead to mistaken conclusions and taxonomic error. Thermally induced artifacts were found in the chemistries of Hypotrachyna partita from a site of volcanic activity in Costa Rica and in the type of H. prolongata from Haiti that had been dried for the herbarium with heat. In the Southern Appalachian Mountains normal material of H. prolongata has been considered a chemically different species. Thermal stability of secondary products may be an important factor in the chemical evolution of lichens inhabiting extreme environments.     

A computational technique to measure fracture callus in radiographs

Callus formation occurs in the presence of secondary bone healing and has relevance to the fracture's mechanical environment. An objective image processing algorithm was developed to standardize the quantitative measurement of periosteal callus area in plain radiographs of long bone fractures. Algorithm accuracy and sensitivity were evaluated using surrogate models. For algorithm validation, callus formation on clinical radiographs was measured manually by orthopaedic surgeons and compared to non-clinicians using the algorithm. The algorithm measured the projected area of surrogate calluses with less than 5% error. However, error will increase when analyzing very small areas of callus and when using radiographs with low image resolution (i.e. 100 pixels per inch). The callus size extracted by the algorithm correlated well to the callus size outlined by the surgeons (R²=0.94, p<0.001). Furthermore, compared to clinician results, the algorithm yielded results with five times less inter-observer variance. This computational technique provides a reliable and efficient method to quantify secondary bone healing response.     

Nonlinear Mixed-Effects (NLME) Diameter Growth Models for Individual China-Fir (Cunninghamia lanceolata) Trees in Southeast China

An individual-tree diameter growth model was developed for Cunninghamia lanceolata in Fujian province, southeast China. Data were obtained from 72 plantation-grown China-fir trees in 24 single-species plots. Ordinary non-linear least squares regression was used to choose the best base model from among 5 theoretical growth equations; selection criteria were the smallest absolute mean residual and root mean square error and the largest adjusted coefficient of determination. To account for autocorrelation in the repeated-measures data, we developed one-level and nested two-level nonlinear mixed-effects (NLME) models, constructed on the selected base model; the NLME models incorporated random effects of the tree and plot. The best random-effects combinations for the NLME models were identified by Akaike''s information criterion, Bayesian information criterion and −2 logarithm likelihood. Heteroscedasticity was reduced with two residual variance functions, a power function and an exponential function. The autocorrelation was addressed with three residual autocorrelation structures: a first-order autoregressive structure [AR(1)], a combination of first-order autoregressive and moving average structures [ARMA(1,1)] and a compound symmetry structure (CS). The one-level (tree) NLME model performed best. Independent validation data were used to test the performance of the models and to demonstrate the advantage of calibrating the NLME models.     

Full conjugate analysis of normal multiple traits with missing records using a generalized inverted Wishart distribution

A Markov chain Monte Carlo (MCMC) algorithm to sample an exchangeable covariance matrix, such as the one of the error terms (R₀) in a multiple trait animal model with missing records under normal-inverted Wishart priors is presented. The algorithm (FCG) is based on a conjugate form of the inverted Wishart density that avoids sampling the missing error terms. Normal prior densities are assumed for the ''fixed'' effects and breeding values, whereas the covariance matrices are assumed to follow inverted Wishart distributions. The inverted Wishart prior for the environmental covariance matrix is a product density of all patterns of missing data. The resulting MCMC scheme eliminates the correlation between the sampled missing residuals and the sampled R₀, which in turn has the effect of decreasing the total amount of samples needed to reach convergence. The use of the FCG algorithm in a multiple trait data set with an extreme pattern of missing records produced a dramatic reduction in the size of the autocorrelations among samples for all lags from 1 to 50, and this increased the effective sample size from 2.5 to 7 times and reduced the number of samples needed to attain convergence, when compared with the ''data augmentation'' algorithm.     

Linear Mixed-Effects Models to Describe Individual Tree Crown Width for China-Fir in Fujian Province,Southeast China

A multiple linear model was developed for individual tree crown width of Cunninghamia lanceolata (Lamb.) Hook in Fujian province, southeast China. Data were obtained from 55 sample plots of pure China-fir plantation stands. An Ordinary Linear Least Squares (OLS) regression was used to establish the crown width model. To adjust for correlations between observations from the same sample plots, we developed one level linear mixed-effects (LME) models based on the multiple linear model, which take into account the random effects of plots. The best random effects combinations for the LME models were determined by the Akaike’s information criterion, the Bayesian information criterion and the -2logarithm likelihood. Heteroscedasticity was reduced by three residual variance functions: the power function, the exponential function and the constant plus power function. The spatial correlation was modeled by three correlation structures: the first-order autoregressive structure [AR(1)], a combination of first-order autoregressive and moving average structures [ARMA(1,1)], and the compound symmetry structure (CS). Then, the LME model was compared to the multiple linear model using the absolute mean residual (AMR), the root mean square error (RMSE), and the adjusted coefficient of determination (adj-R ²). For individual tree crown width models, the one level LME model showed the best performance. An independent dataset was used to test the performance of the models and to demonstrate the advantage of calibrating LME models.     

QT interval measurement using RMED curve; a novel approach based on wavelet techniques

Hypothesis/objective: Prolonged QT interval is an index of propensity for dangerous ventricular tachyarrhythmias. The aim of this article is to establish an automatic algorithm for QT interval measurement.

Method: The proposed method is based on the continuous wavelet transform. In this method, the concepts of the rescaled wavelet coefficients and dominant scales of the electrocardiogram (ECG) components are used to perform detection of ECG characteristic points. A new concept of rescaled maximum energy density is introduced so as to perform the estimation of the QT interval.

Results and conclusion: We have applied the algorithm to the PTB database of the Physiobank?Physionet in lead II. Then, the results were evaluated using pertinent reference QT. The criterion used for evaluation of the method's performance is the root mean square (RMS) error. The method approached the RMS error of 27.89 ms for 549 subjects. The proposed method is fast, simple and is applicable to a wide range of ECG cardio cycle morphologies.     

Implicit Value Updating Explains Transitive Inference Performance: The Betasort Model

Transitive inference (the ability to infer that B > D given that B > C and C > D) is a widespread characteristic of serial learning, observed in dozens of species. Despite these robust behavioral effects, reinforcement learning models reliant on reward prediction error or associative strength routinely fail to perform these inferences. We propose an algorithm called betasort, inspired by cognitive processes, which performs transitive inference at low computational cost. This is accomplished by (1) representing stimulus positions along a unit span using beta distributions, (2) treating positive and negative feedback asymmetrically, and (3) updating the position of every stimulus during every trial, whether that stimulus was visible or not. Performance was compared for rhesus macaques, humans, and the betasort algorithm, as well as Q-learning, an established reward-prediction error (RPE) model. Of these, only Q-learning failed to respond above chance during critical test trials. Betasort’s success (when compared to RPE models) and its computational efficiency (when compared to full Markov decision process implementations) suggests that the study of reinforcement learning in organisms will be best served by a feature-driven approach to comparing formal models.     

Observation effects on learning in horses

Sixteen horses, divided into 2 groups of 8, were used to study observational learning in horses. One group served as controls while the other group served as the treated group (observers). Observers were allowed to watch a correctly performed discrimination task for 5 days prior to testing their learning response using the same task. Discrimination testing was conducted on all horses daily for 14 days, with criterion set at 7 out of 8 responses correct with the last 5 consecutively correct. The maximum number of trials performed without reaching criterion was limited to 20 per day. Mean trials to criteria (MT) by group were: control, 11.25; observer, 10.70. Mean error (ME) scores were: control, 2.37; observer, 2.02. Average initial discrimination error scores were 11.13 for control and 10.38 for observers (P < 0.10). Asymptote was reached by Day 8 for both control and observer groups. Analysis of variance with repeated measures showed an extreme-day effect indicative of learning (P < 0.01), with non-significant differences in learning rate between experimental groups. Whether the initial ability of the horses to perform a discrimination learning task was enhanced by observation of other horses' performance of that task was not obvious from these data.     

Peakmatch: a simple and robust method for peak list matching

Peak lists are commonly used in NMR as input data for various software tools such as automatic assignment and structure calculation programs. Inconsistencies of chemical shift referencing among different peak lists or between peak and chemical shift lists can cause severe problems during peak assignment. Here we present a simple and robust tool to achieve self-consistency of the chemical shift referencing among a set of peak lists. The Peakmatch algorithm matches a set of peak lists to a specified reference peak list, neither of which have to be assigned. The chemical shift referencing offset between two peak lists is determined by optimizing an assignment-free match score function using either a complete grid search or downhill simplex optimization. It is shown that peak lists from many different types of spectra can be matched reliably as long as they contain at least two corresponding dimensions. Using a simulated peak list, the Peakmatch algorithm can also be used to obtain the optimal agreement between a chemical shift list and experimental peak lists. Combining these features makes Peakmatch a useful tool that can be applied routinely before automatic assignment or structure calculation in order to obtain an optimized input data set.     

Heuristic quadratic approximation for the universality theorem

Voronin’s Universality Theorem states grosso modo, that any non-vanishing analytic function can be uniformly approximated by certain shifts of the Riemann zeta-function ζ(s). However, the problem of obtaining a concrete approximants for a given function is computationally highly challenging. The present note deals with this problem, using a finite number n of factors taken from the Euler product definition of ζ(s). The main result of the present work is the design and implementation of a sequential and a parallel heuristic method for the computation of those approximants. The main properties of this method are: (i) the computation time grows quadratically as a function of the quotient n/m, where m is the number of coefficients calculated in one iteration of the heuristic; (ii) the error does not vary significantly as m changes and is similar to the error of the exact algorithm.     

Retrieval of phycocyanin concentration from remote-sensing reflectance using a semi-analytic model in eutrophic lakes

With the rapid development of the economy in recent years, massive algal (blue-green algae in particular) blooms have often observed in Chinese eutrophic lakes. The concentration of the cyanobacterial pigment phycocyanin (PC), an accessory pigment unique to freshwater blue-green algae, is often used as a quantitative indicator of blue-green algae in eutrophic inland waters. The purpose of this study was to evaluate the semi-analytic PC retrieval algorithm proposed by Simis et al. and to explore the potential to improve this PC algorithm so that it is more suitable for eutrophic lakes, such as Taihu Lake. In this paper, we recalculated the correction coefficients γ and δ to calculate the absorptions of chlorophyll-a at 665 nm and the absorptions of phycocyanin at 620 nm in terms of in situ measurements and observed that the values of these coefficients differed from the values used by Simis et al. and Randolph et al. The two coefficients are site dependent due to the different bio-optical properties of lakes. We also observed that the specific PC absorption at 620 nm a_pc*(620) decreases exponentially with an increase in PC concentrations. Therefore, a non-linear power–function of a_pc*(620), instead of a constant value of a_pc*(620) as used by Simis et al., was proposed for our improved PC retrieval algorithm in Taihu Lake, yielding a squared correlation coefficient (R²) of 0.55 and a root mean square error (RMSE) of 58.89 μg/L. Compared with the original PC retrieval algorithm by Simis et al., the improved retrieval algorithm has generally superior performance. In evaluating the limitation of the PC retrieval algorithms, we observed that the ratio of the total suspended solids to phycocyanin can be used as a primary measure for retrieval performance. Validation in Dianchi Lake and an error analysis proved that the improved PC algorithm has a better universality and is more suitable for eutrophic lakes with higher PC concentrations.     

Adjust quality scores from alignment and improve sequencing accuracy

In shotgun sequencing, statistical reconstruction of a consensus from alignment requires a model of measurement error. Churchill and Waterman proposed one such model and an expectation–maximization (EM) algorithm to estimate sequencing error rates for each assembly matrix. Ewing and Green defined Phred quality scores for base-calling from sequencing traces by training a model on a large amount of data. However, sample preparations and sequencing machines may work under different conditions in practice and therefore quality scores need to be adjusted. Moreover, the information given by quality scores is incomplete in the sense that they do not describe error patterns. We observe that each nucleotide base has its specific error pattern that varies across the range of quality values. We develop models of measurement error for shotgun sequencing by combining the two perspectives above. We propose a logistic model taking quality scores as covariates. The model is trained by a procedure combining an EM algorithm and model selection techniques. The training results in calibration of quality values and leads to a more accurate construction of consensus. Besides Phred scores obtained from ABI sequencers, we apply the same technique to calibrate quality values that come along with Beckman sequencers.     

Generating a "Humanized" Drosophila S2 Cell Line Sensitive to Pharmacological Inhibition of Kinesin-5

Kinetochores are large protein-based structures that assemble on centromeres during cell division and link chromosomes to spindle microtubules. Proper distribution of the genetic material requires that sister kinetochores on every chromosome become bioriented by attaching to microtubules from opposite spindle poles before progressing into anaphase. However, erroneous, non-bioriented attachment states are common and cellular pathways exist to both detect and correct such attachments during cell division. The process by which improper kinetochore-microtubule interactions are destabilized is referred to as error correction. To study error correction in living cells, incorrect attachments are purposely generated via chemical inhibition of kinesin-5 motor, which leads to monopolar spindle assembly, and the transition from mal-orientation to biorientation is observed following drug washout. The large number of chromosomes in many model tissue culture cell types poses a challenge in observing individual error correction events. Drosophila S2 cells are better subjects for such studies as they possess as few as 4 pairs of chromosomes. However, small molecule kinesin-5 inhibitors are ineffective against Drosophila kinesin-5 (Klp61F). Here we describe how to build a Drosophila cell line that effectively replaces Klp61F with human kinesin-5, which renders the cells sensitive to pharmacological inhibition of the motor and suitable for use in the cell-based error correction assay.     

An improved algorithm for fast resonant Mie scatter correction of infrared spectra of cells and tissues

Mie scattering effects create serious problems for the interpretation of Fourier‐transform infrared spectroscopy spectra of single cells and tissues. During recent years, different techniques were proposed to retrieve pure absorbance spectra from spectra with Mie distortions. Recently, we published an iterative algorithm for correcting Mie scattering in spectra of single cells and tissues, which we called “the fast resonant Mie scatter correction algorithm.” The algorithm is based on extended multiplicative signal correction (EMSC) and employs a meta‐model for a parameter range of refractive index and size parameters. In the present study, we suggest several improvements of the algorithm. We demonstrate that the improved algorithm reestablishes chemical features of the measured spectra, and show that it tends away from the reference spectrum employed in the EMSC. We suggest strategies for choosing parameter ranges and other model parameters such as the number of principal components of the meta‐model and the number of iterations. We demonstrate that the suggested algorithm optimizes an error function of the refractive index in a forward Mie model. We suggest a stop criterion for the iterative algorithm based on the error function of the forward model.      

Accurate Construction of Photoactivated Localization Microscopy (PALM) Images for Quantitative Measurements

Localization-based superresolution microscopy techniques such as Photoactivated Localization Microscopy (PALM) and Stochastic Optical Reconstruction Microscopy (STORM) have allowed investigations of cellular structures with unprecedented optical resolutions. One major obstacle to interpreting superresolution images, however, is the overcounting of molecule numbers caused by fluorophore photoblinking. Using both experimental and simulated images, we determined the effects of photoblinking on the accurate reconstruction of superresolution images and on quantitative measurements of structural dimension and molecule density made from those images. We found that structural dimension and relative density measurements can be made reliably from images that contain photoblinking-related overcounting, but accurate absolute density measurements, and consequently faithful representations of molecule counts and positions in cellular structures, require the application of a clustering algorithm to group localizations that originate from the same molecule. We analyzed how applying a simple algorithm with different clustering thresholds (t_Thresh and d_Thresh) affects the accuracy of reconstructed images, and developed an easy method to select optimal thresholds. We also identified an empirical criterion to evaluate whether an imaging condition is appropriate for accurate superresolution image reconstruction with the clustering algorithm. Both the threshold selection method and imaging condition criterion are easy to implement within existing PALM clustering algorithms and experimental conditions. The main advantage of our method is that it generates a superresolution image and molecule position list that faithfully represents molecule counts and positions within a cellular structure, rather than only summarizing structural properties into ensemble parameters. This feature makes it particularly useful for cellular structures of heterogeneous densities and irregular geometries, and allows a variety of quantitative measurements tailored to specific needs of different biological systems.