H-1 Nuclear Magnetic Resonance Spin-Lattice Relaxation, C-13 Magic-Angle-Spinning Nuclear Magnetic Resonance Spectroscopy, Differential Scanning Calorimetry, and X-Ray Diffraction of Two Polymorphs of 2,6-Di-Tert-Butylnaphthalene

Polymorphism, the presence of structurally distinct solid phases of the same chemical species, affords a unique opportunity to evaluate the structural consequences of intermolecular forces. The study of two polymorphs of 2,6-di-tert-butylnaphthalene by single-crystal x-ray diffraction, differential scanning calorimetry (DSC), C-13 magic-angle-spinning (MAS) nuclear magnetic resonance (NMR) spectroscopy, and H-1 NMR spin-lattice relaxation provides a picture of the differences in structure and dynamics in these materials. The subtle differences in structure, observed with x-ray diffraction and chemical shifts, strikingly affect the dynamics, as reflected in the relaxation measurements. We analyze the dynamics in terms of both discrete sums and continuous distributions of Poisson processes

Framework Mobility in the Metal−Organic Framework Crystal IRMOF-3: Evidence for Aromatic Ring and Amine

a b s t r a c t The framework motions in IRMOF-3 (Zn 4 O(BDC-NH 2 ) 3 ), where BDC-NH 2 represents 2-amino-1,4-ben zenedicarboxylate, have been investigated with 1 H NMR relaxation measurements. Isotopic enrichment of the 2-amino group with 15 N was critical in elucidating the lattice dynamics and enhancing spectral resolution. These results indicate a low energy process associated with rotation of the amino group, with an activation energy of 1.8 ± 0.6 kcal/mol, and full 180°rotation of the phenylene group in the BDC-NH 2 moiety with an activation energy of 5.0 ± 0.2 kcal/mol. A relatively low pre-exponential factor for amine rotation (1.3 Â 10 7 s À1 ) is tentatively associated with the need to break a hydrogen bond as the rate-limiting step. Both amine rotation and the aromatic ring flip occur at frequencies that provide an effective relaxation mechanism for the 99.6% natural abundance quadrupola

Incorporating prior knowledge improves detection of differences in bacterial growth rate

BACKGROUND: Robust statistical detection of differences in the bacterial growth rate can be challenging, particularly when dealing with small differences or noisy data. The Bayesian approach provides a consistent framework for inferring model parameters and comparing hypotheses. The method captures the full uncertainty of parameter values, whilst making effective use of prior knowledge about a given system to improve estimation. RESULTS: We demonstrated the application of Bayesian analysis to bacterial growth curve comparison. Following extensive testing of the method, the analysis was applied to the large dataset of bacterial responses which are freely available at the web-resource, ComBase. Detection was found to be improved by using prior knowledge from clusters of previously analysed experimental results at similar environmental conditions. A comparison was also made to a more traditional statistical testing method, the F-test, and Bayesian analysis was found to perform more conclusively and to be capable of attributing significance to more subtle differences in growth rate. CONCLUSIONS: We have demonstrated that by making use of existing experimental knowledge, it is possible to significantly improve detection of differences in bacterial growth rate

Interpol: An R package for preprocessing of protein sequences

<p>Abstract</p> <p>Background</p> <p>Most machine learning techniques currently applied in the literature need a fixed dimensionality of input data. However, this requirement is frequently violated by real input data, such as DNA and protein sequences, that often differ in length due to insertions and deletions. It is also notable that performance in classification and regression is often improved by numerical encoding of amino acids, compared to the commonly used sparse encoding.</p> <p>Results</p> <p>The software "Interpol" encodes amino acid sequences as numerical descriptor vectors using a database of currently 532 descriptors (mainly from AAindex), and normalizes sequences to uniform length with one of five linear or non-linear interpolation algorithms. Interpol is distributed with open source as platform independent R-package. It is typically used for preprocessing of amino acid sequences for classification or regression.</p> <p>Conclusions</p> <p>The functionality of Interpol widens the spectrum of machine learning methods that can be applied to biological sequences, and it will in many cases improve their performance in classification and regression.</p

電極界面を利用したマイクロチップの高機能化

指導教員: 北森, 武

Improved Bevirimat resistance prediction by combination of structural and sequence-based classifiers

<p>Abstract</p> <p>Background</p> <p>Maturation inhibitors such as Bevirimat are a new class of antiretroviral drugs that hamper the cleavage of HIV-1 proteins into their functional active forms. They bind to these preproteins and inhibit their cleavage by the HIV-1 protease, resulting in non-functional virus particles. Nevertheless, there exist mutations in this region leading to resistance against Bevirimat. Highly specific and accurate tools to predict resistance to maturation inhibitors can help to identify patients, who might benefit from the usage of these new drugs.</p> <p>Results</p> <p>We tested several methods to improve Bevirimat resistance prediction in HIV-1. It turned out that combining structural and sequence-based information in classifier ensembles led to accurate and reliable predictions. Moreover, we were able to identify the most crucial regions for Bevirimat resistance computationally, which are in line with experimental results from other studies.</p> <p>Conclusions</p> <p>Our analysis demonstrated the use of machine learning techniques to predict HIV-1 resistance against maturation inhibitors such as Bevirimat. New maturation inhibitors are already under development and might enlarge the arsenal of antiretroviral drugs in the future. Thus, accurate prediction tools are very useful to enable a personalized therapy.</p

INTCare: a knowledge discovery based intelligent decision support system for intensive care medicine

This paper introduces the INTCare system, an intelligent information system based on a completely automated Knowledge Discovery process and on the Agents paradigm. The system was designed to work in Hospital Intensive Care Units, supporting the physicians’ decisions by means of prognostic Data Mining models. In particular, these techniques were used to predict organ failure and mortality assessment. The main intention is to change the current reactive behaviour to a pro-active one, enhancing the quality of service. Current applications and experimentations, the functional and structural aspects, and technological options are presented