Quantum Diffusive Dynamics of Macromolecular Transitions

We study the role of quantum fluctuations of atomic nuclei in the real-time dynamics of non-equilibrium macro-molecular transitions. To this goal we introduce an extension of the Dominant Reaction Pathways (DRP) formalism, in which the quantum corrections to the classical overdamped Langevin dynamics are rigorously taken into account to order h^2 . We first illustrate our approach in simple cases, and compare with the results of the instanton theory. Then we apply our method to study the C7_eq to C7_ax transition of alanine dipeptide. We find that the inclusion of quantum fluctuations can significantly modify the reaction mechanism for peptides. For example, the energy difference which is overcome along the most probable pathway is reduced by as much as 50%.Comment: Final version, to appear in the Journal of Chemical Physic

Ab-initio Dynamics of Rare Thermally Activated Reactions

We introduce a framework to investigate ab-initio the dynamics of rare thermally activated reactions. The electronic degrees of freedom are described at the quantum-mechanical level in the Born-Oppenheimer approximation, while the nuclear degrees of freedom are coupled to a thermal bath, through a Langevin equation. This method is based on the path integral representation for the stochastic dynamics and yields the time evolution of both nuclear and electronic degrees of freedom, along the most probable reaction pathways, without spending computational time to explore metastable states. This approach is very efficient and allows to study thermally activated reactions which cannot be simulated using ab-initio molecular dynamics techniques. As a first illustrative application, we characterize the dominant pathway in the cyclobutene to butadiene reaction.Comment: 4 pages, 4 figure

Gas adsorption and dynamics in Pillared Graphene Frameworks

We thank prof. Marco Frasconi for advice on the kind of moieties to be used as pillars. N.M.P. is supported by the European Research Council PoC 2015 “Silkene” No. 693670, by the European Commission H2020 under the Graphene Flagship Core 1 No. 696656 (WP14 “Polymer Nanocomposites”) and under the Fet Proactive “Neurofibres” No. 732344. S.T and G.G. acknowledge funding from previous WP14 “Polymer Nanocomposites” grant. Access to computing and storage facilities owned by parties and projects contributing to the Czech National Grid Infrastructure MetaCentrum provided under the programme “Projects of Large Research, Development, and Innovations Infrastructures” (CESNET LM2015042), is greatly appreciated (https://www.metacentrum.cz/en/)

Predictor Analysis in Radiofrequency Ablation of Benign Thyroid Nodules: A Single Center Experience

PURPOSE: To confirm the efficacy of ultrasound (US) guided radiofrequency ablation (RFA) in the treatment of benign thyroid nodules, we evaluated as primary outcome the technical efficacy and clinical success in a single center dataset. The secondary outcome was to find a correlation between nodules’ pre-treatment features and volume reduction rate (VRR) ≥75% at 12 months after RFA and during follow-up period. METHODS: This retrospective study included 119 consecutive patients (99 females, 20 males, 51.5 ± 14.4 years) with benign thyroid nodules treated in our hospital between October 2014 and December 2018 with a mean follow-up of 26.8 months (range 3–48). Clinical and US features before and after RFA were evaluated by a US examination at 1, 3, 6, 12 months and annually thereafter up to 48 months. RESULTS: The median pre-treatment volume was 22.4 ml; after RFA we observed a statistically significant volume reduction from the first month (11.7 ml) to the last follow-up (p 22.4 ml (HR 0.54, p 0.036) were found to be independent positive and negative predictors of VRR ≥75% respectively. One-month post RFA VRR ≥50% represented the best positive predictor of technical success. CONCLUSIONS: This study confirmed the efficacy of RFA in the treatment of benign thyroid nodules. In particular we show that by selecting macrocystic nodules smaller than 22.4 ml better long-term response can be achieved, which is predicted by an early shrinkage of the nodule

Thyroid ultrasonography reporting: consensus of Italian Thyroid Association (AIT), Italian Society of Endocrinology (SIE), Italian Society of Ultrasonography in Medicine and Biology (SIUMB) and Ultrasound Chapter of Italian Society of Medical Radiology (SIRM)

Thyroid ultrasonography (US) is the gold standard for thyroid imaging and its widespread use is due to an optimal spatial resolution for superficial anatomic structures, a low cost and the lack of health risks. Thyroid US is a pivotal tool for the diagnosis and follow-up of autoimmune thyroid diseases, for assessing nodule size and echostructure and defining the risk of malignancy in thyroid nodules. The main limitation of US is the poor reproducibility, due to the variable experience of the operators and the different performance and settings of the equipments. Aim of this consensus statement is to standardize the report of thyroid US through the definition of common minimum requirements and a correct terminology. US patterns of autoimmune thyroid diseases are defined. US signs of malignancy in thyroid nodules are classified and scored in each nodule. We also propose a simplified nodule risk stratification, based on the predictive value of each US sign, classified and scored according to the strength of association with malignancy, but also to the estimated reproducibility among different operators

Cost-Effective and Non-Invasive Automated Benign & Malignant Thyroid Lesion Classification in 3D Contrast-Enhanced Ultrasound Using Combination of Wavelets and Textures: A Class of ThyroScan™ Algorithms

Ultrasound has great potential to aid in the differential diagnosis of malignant and benign thyroid lesions, but interpretative pitfalls exist and the accuracy is still poor. To overcome these difficulties, we developed and analyzed a range of knowledge representation techniques, which are a class of ThyroScan™ algorithms from Global Biomedical Technologies Inc., California, USA, for automatic classification of benign and malignant thyroid lesions. The analysis is based on data obtained from twenty nodules (ten benign and ten malignant) taken from 3D contrast-enhanced ultrasound images. Fine needle aspiration biopsy and histology confirmed malignancy. Discrete Wavelet Transform (DWT) and texture algorithms are used to extract relevant features from the thyroid images. The resulting feature vectors are fed to three different classifiers: K-Nearest Neighbor (K-NN), Probabilistic Neural Network (PNN), and Decision Tree (DeTr). The performance of these classifiers is compared using Receiver Operating Characteristic (ROC) curves. Our results show that combination of DWT and texture features coupled with K-NN resulted in good performance measures with the area of under the ROC curve of 0.987, a classification accuracy of 98.9%, a sensitivity of 98%, and a specificity of 99.8%. Finally, we have proposed a novel integrated index called Thyroid Malignancy Index (TMI), which is made up of texture features, to diagnose benign or malignant nodules using just one index. We hope that this TMI will help clinicians in a more objective detection of benign and malignant thyroid lesions

Instantaneous Normal Mode analysis of liquid HF

We present an Instantaneous Normal Modes analysis of liquid HF aimed to clarify the origin of peculiar dynamical properties which are supposed to stem from the arrangement of molecules in linear hydrogen-bonded network. The present study shows that this approach is an unique tool for the understanding of the spectral features revealed in the analysis of both single molecule and collective quantities. For the system under investigation we demonstrate the relevance of hydrogen-bonding ``stretching'' and fast librational motion in the interpretation of these features.Comment: REVTeX, 7 pages, 5 eps figures included. Minor changes in the text and in a figure. Accepted for publication in Phys. Rev. Let

Effect of complex wavelet transform filter on thyroid tumor classification in three-dimensional ultrasound

Ultrasonography has great potential in differentiating malignant thyroid nodules from the benign ones. However, visual interpretation is limited by interobserver variability, and further, the speckle distribution poses a challenge during the classification process. This article thus presents an automated system for tumor classification in three-dimensional contrast-enhanced ultrasonography data sets. The system first processes the contrast-enhanced ultrasonography images using complex wavelet transform-based filter to mitigate the effect of speckle noise. The higher order spectra features are then extracted and used as input for training and testing a fuzzy classifier. In the off-line training system, higher order spectra features are extracted from a set of images known as the training images. These higher order spectra features along with the clinically assigned ground truth are used to train the classifier and obtain an estimate of the classifier or training parameters. The ground truth tells the class label of the image (i.e. whether the image belongs to a benign or malignant nodule). During the online testing phase, the estimated classifier parameters are applied on the higher order spectra features that are extracted from the testing images to predict their class labels. The predicted class labels are compared with their corresponding original ground truth to evaluate the performance of the classifier. Without utilizing the complex wavelet transform filter, the fuzzy classifier demonstrated an accuracy of 91.6%, while utilizing the complex wavelet transform filter, the accuracy significantly boosted to 99.1%