Semi-supervised and Active-learning Scenarios: Efficient Acoustic Model Refinement for a Low Resource Indian Language

We address the problem of efficient acoustic-model refinement (continuous retraining) using semi-supervised and active learning for a low resource Indian language, wherein the low resource constraints are having i) a small labeled corpus from which to train a baseline `seed' acoustic model and ii) a large training corpus without orthographic labeling or from which to perform a data selection for manual labeling at low costs. The proposed semi-supervised learning decodes the unlabeled large training corpus using the seed model and through various protocols, selects the decoded utterances with high reliability using confidence levels (that correlate to the WER of the decoded utterances) and iterative bootstrapping. The proposed active learning protocol uses confidence level based metric to select the decoded utterances from the large unlabeled corpus for further labeling. The semi-supervised learning protocols can offer a WER reduction, from a poorly trained seed model, by as much as 50% of the best WER-reduction realizable from the seed model's WER, if the large corpus were labeled and used for acoustic-model training. The active learning protocols allow that only 60% of the entire training corpus be manually labeled, to reach the same performance as the entire data

Identification of Distinct Characteristics of Antibiofilm Peptides and Prospection of Diverse Sources for Efficacious Sequences

A majority of microbial infections are associated with biofilms. Targeting biofilms is considered an effective strategy to limit microbial virulence while minimizing the development of antibiotic resistance. Toward this need, antibiofilm peptides are an attractive arsenal since they are bestowed with properties orthogonal to small molecule drugs. In this work, we developed machine learning models to identify the distinguishing characteristics of known antibiofilm peptides, and to mine peptide databases from diverse habitats to classify new peptides with potential antibiofilm activities. Additionally, we used the reported minimum inhibitory/eradication concentration (MBIC/MBEC) of the antibiofilm peptides to create a regression model on top of the classification model to predict the effectiveness of new antibiofilm peptides. We used a positive dataset containing 242 antibiofilm peptides, and a negative dataset which, unlike previous datasets, contains peptides that are likely to promote biofilm formation. Our model achieved a classification accuracy greater than 98% and harmonic mean of precision-recall (F1) and Matthews correlation coefficient (MCC) scores greater than 0.90; the regression model achieved an MCC score greater than 0.81. We utilized our classification-regression pipeline to evaluate 135,015 peptides from diverse sources for potential antibiofilm activity, and we identified 185 candidates that are likely to be effective against preformed biofilms at micromolar concentrations. Structural analysis of the top 37 hits revealed a larger distribution of helices and coils than sheets, and common functional motifs. Sequence alignment of these hits with known antibiofilm peptides revealed that, while some of the hits showed relatively high sequence similarity with known peptides, some others did not indicate the presence of antibiofilm activity in novel sources or sequences. Further, some of the hits had previously recognized therapeutic properties or host defense traits suggestive of drug repurposing applications. Taken together, this work demonstrates a new in silico approach to predicting antibiofilm efficacy, and identifies promising new candidates for biofilm eradication

Electrochemical Behavior of Biomedical Titanium Alloys Coated with Diamond Carbon in Hanks’ Solution

Biomedical implants in the knee and hip are frequent failures because of corrosion and stress on the joints. To solve this important problem, metal implants can be coated with diamond carbon, and this coating plays a critical role in providing an increased resistance to implants toward corrosion. In this study, we have employed diamond carbon coating over Ti-6Al-4V and Ti-13Nb-13Zr alloys using hot filament chemical vapor deposition method which is well-established coating process that significantly improves the resistance toward corrosion, wears and hardness. The diamond carbon-coated Ti-13Nb-13Zr alloy showed an increased microhardness in the range of 850 HV. Electrochemical impedance spectroscopy and polarization studies in SBF solution (simulated body fluid solution) were carried out to understand the in vitro behavior of uncoated as well as coated titanium alloys. The experimental results showed that the corrosion resistance of Ti-13Nb-13Zr alloy is relatively higher when compared with diamond carbon-coated Ti-6Al-4V alloys due to the presence of β phase in the Ti-13Nb-13Zr alloy. Electrochemical impedance results showed that the diamond carbon-coated alloys behave as an ideal capacitor in the body fluid solution. Moreover, the stability in mechanical properties during the corrosion process was maintained for diamond carbon-coated titanium alloys

Alternative algorithm for the computation of Lyapunov spectra of dynamical systems

Recently a new method for the computation of Lyapunov exponents that does not require rescaling and reorthogonalization was proposed ͓Rangarajan, Habib, and Ryne, Phys. Rev. Lett. 80, 3747 ͑1998͔͒. In this paper we make a detailed numerical comparison of the new method and a standard algorithm, as regards accuracy and efficiency, by applying them to some typical two-, three-, and four-dimensional systems. We find that in most cases there is reasonable agreement between the Lyapunov spectra obtained using the two algorithms. The CPU times required for computation are also comparable. However, in certain strongly chaotic cases, the new method was found to be either inefficient ͑taking a lot of CPU time for computation͒ or inaccurate. ͓S1063-651X͑99͒50907-0

Fibrin prestress due to platelet aggregation and contraction increases clot stiffness

Efficient hemorrhagic control is attained through the formation of strong and stable blood clots at the site of injury. Although it is known that platelet-driven contraction can dramatically influence clot stiffness, the underlying mechanisms by which platelets assist fibrin in resisting external loads are not understood. In this study, we delineate the contribution of platelet-fibrin interactions to clot tensile mechanics using a combination of new mechanical measurements, image analysis, and structural mechanics simulation. Based on uniaxial tensile test data using custom-made microtensometer and fluorescence microscopy of platelet aggregation and platelet-fibrin interactions, we show that integrin-mediated platelet aggregation and actomyosin-driven platelet contraction synergistically increase the elastic modulus of the clots. We demonstrate that the mechanical and geometric response of an active contraction model of platelet aggregates compacting vicinal fibrin is consistent with the experimental data. The model suggests that platelet contraction induces prestress in fibrin fibers and increases the effective stiffness in both cross-linked and noncross-linked clots. Our results provide evidence for fibrin compaction at discrete nodes as a major determinant of mechanical response to applied loads