Convex Constraint Decomposition of Circular Dichroism Curves of Proteins

A new algorithm, called convex analysis, has been developed to deduce the chiral contribution of the common secondary structures directly from experimental circular dichroism (CD) curves of a large number of proteins. The analysis is based on CD data reported by Yang et aU Test runs were performed on sets of artificial protein spectra created by the Monte Carlo technique using poly-u-Iysine based component spectra. Application of the decomposition algorithm for the created sets of spectra resulted in component spectra [B (2, i)] and weights [C (i, k)] with excellent Pearson correlation coefficients (r).2 The algori thm, independent of X-ray data, revealed that the CD spectrum of a given protein is composed of at least four independent sources of chirality. Three of the computed component curves show remarkable resemblance to the CD spectra of known protein secondary structures. This approach yields a significant improvement compared to the eigenvector analysis of Hennessey and Johnson." The new method is a useful tool not only in analyzing CD spectra but also in treating other decomposition problems where an additivity constraint is valid

Conformations of proline‐containing cyclic peptides: 1H and 13C n.m.r. evidence for a solvent stabilized All‐Cis X‐Pro conformer of cyclo‐(Pro‐Gly‐Gly‐Pro)2

As inferred from 13C, 1H n.m.r. data, CD measurements and ion‐binding experiments, the title molecule can assume two major C2 symmetric conformations. One of these has an all‐trans X‐Pro peptide backbone with two 1 4 intramolecular H‐bonds and represents the predominant (≥ 95%) form in D2O and nonpolar (CD3CN) solvents. Stabilized by specific solvent‐solute interactions, the other conformer becomes competitive (45%) in DMSO solution. It is shown to possess a four‐cis X‐Pro skeleton and no intramolecular H‐bonds. The Mg++ complex of the cyclic peptide in CD3CN is again C2 symmetric and its formation proceeds via a slow trans → cis isomerization of two X‐Pro peptide bonds

Active learning for sound event classification using Bayesian neural networks with Gaussian variational posterior

Manual annotation of audio material is cumbersome. Active learning aims at minimizing the annotation effort by iteratively selecting an acquisition batch of unlabeled data, asking a human to annotate the selected data and re-training a classifier until an annotation budget is depleted. In this paper we propose the Gaussian-dense active learning (GDAL) algorithm to train a sound event classifier. The classifier is a Bayesian neural network where the weights are normally distributed. This is in contrast to conventional neural networks where weights are not distributed, but have assigned values. The Bayesian nature of the classifier empowers GDAL to select acquisition batches from a set of unlabeled audio clips based on their estimated informativeness. Evaluation results on the UrbanSound8k dataset show that GDAL outperforms a state-of-the-art algorithm based on medoid active learning for all considered annotation budgets and an algorithm based on dropout active learning for sufficiently large annotation budgets

The effect of ethanol on lactate and base deficit as predictors of morbidity and mortality in trauma

Objective—The objective of this study was to assess the predictive value of lactate and base deficit in determining outcomes in trauma patients who are positive for ethanol. Methods—Retrospective cohort study of patients admitted to a level 1 trauma center between 2005 and 2014. Adult patients who had a serum ethanol, lactate, base deficit, and negative urine drug screen obtained upon presentation were included. Results—Data for 2482 patients were analyzed with 1127 having an elevated lactate and 1092 an elevated base deficit. In these subgroups, patients with a positive serum ethanol had significantly lower 72-hour mortality, overall mortality, and hospital length of stay compared with the negative ethanol group. Abnormal lactate (odds ratio [OR], 2.607; 95% confidence interval [CI], 1.629– 4.173; P = .000) and base deficit (OR, 1.917; 95% CI, 1.183–3.105; P = .008) were determined to be the strongest predictors of mortality in the ethanol-negative patients. Injury Severity Score was found to be the lone predictor of mortality in patients positive for ethanol (OR, 1.104; 95% CI, 1.070–1.138; P=.000). Area under the curve and Youden index analyses supported a relationship between abnormal lactate, base deficit, and mortality in ethanol-positive patients when the serum lactate was greater than 4.45 mmol/L and base deficit was greater than −6.95 mmol/L. Conclusions—Previously established relationships between elevated lactate, base deficit, and outcome do not remain consistent in patients presenting with positive serum ethanol concentrations. Ethanol skews the relationship between lactate, base deficit, and mortality thus resetting the threshold in which lactate and base deficit are associated with increased mortality

Active learning for sound event classification using Monte-Carlo dropout and PANN embeddings

Labeling audio material to train classifiers comes with a large amount of human labor. In this paper, we propose an active learning method for sound event classification, where a human annotator is asked to manually label sound segments up to a certain labeling budget. The sound event classifier is incrementally re-trained on pseudo-labeled sound segments and manually labeled segments. The segments to be labeled during the active learning process are selected based on the model uncertainty of the classifier, which we propose to estimate using Monte Carlo dropout, a technique for Bayesian inference in neural networks. Evaluation results on the UrbanSound8K dataset show that the proposed active learning method, which uses pre-trained audio neural network (PANN) embeddings as input features, outperforms two baseline methods based on medoid clustering, especially for low labeling budgets