Dataset Information

Decoding the ECD Spectra of Poly(phenylacetylene)s: Structural Significance.

ABSTRACT: The role of the main dihedral angles in the electronic circular dichroism (ECD) spectra of poly(phenylacetylene)s (PPAs) was estimated by using time-dependent density functional theory (TD-DFT) for oligo(phenylacetylene)s (n = 12). These studies reveal that in cis-transoidal arrangements, the first Cotton effect is dominated by excitations involving molecular orbitals (MOs) mainly related to the polyene backbone. Hence, for this scaffold, the ± sign of the first Cotton effect reflects the P/M helical sense of the internal helix of the polymer. However, in cis-cisoidal arrangements, contribution of MOs in the polyene and the aryl rings of the PPA backbone produce the first Cotton effect band. As a result, two different ECD signatures with three or four alternating Cotton effects can be produced depending on the sign of the ?₁ and ?₃ dihedral angles which determine the helical sense of the polyene (?₁) and the relative orientation of the aryl ring toward the polyene (?₃), respectively. Thus, on the one hand, if ?₁ and ?₃ rotate in opposite directions, a CD with three alternating Cotton effects is observed, where the sign of first Cotton correlates with the P/M helical sense of the polyene. On the other hand, if ?₁ and ?₃ rotate in the same direction, a CD signature with four alternating Cotton effects is produced where the information relative to the P/M helical sense of the polyene is contained in the second Cotton effect.

SUBMITTER: Fernandez B

PROVIDER: S-EPMC6648364 | biostudies-literature | 2019 Mar

REPOSITORIES: biostudies-literature

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Decoding the ECD Spectra of Poly(phenylacetylene)s: Structural Significance.

Fernández Berta B Rodríguez Rafael R Quiñoá Emilio E Riguera Ricardo R Freire Félix F

ACS omega 20190313 3

The role of the main dihedral angles in the electronic circular dichroism (ECD) spectra of poly(phenylacetylene)s (PPAs) was estimated by using time-dependent density functional theory (TD-DFT) for oligo(phenylacetylene)s (<i>n</i> = 12). These studies reveal that in cis-transoidal arrangements, the first Cotton effect is dominated by excitations involving molecular orbitals (MOs) mainly related to the polyene backbone. Hence, for this scaffold, the ± sign of the first Cotton effect reflects the ...[more]

PMID: 31459695

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Project description:Clinical research based on neuroimaging data has benefited from machine learning methods, which have the ability to provide individualized predictions and to account for the interaction among units of information in the brain. Application of machine learning in structural imaging to investigate diseases that involve brain injury presents an additional challenge, especially in conditions like stroke, due to the high variability across patients regarding characteristics of the lesions. Extracting data from anatomical images in a way that translates brain damage information into features to be used as input to learning algorithms is still an open question. One of the most common approaches to capture regional information from brain injury is to obtain the lesion load per region (i.e. the proportion of voxels in anatomical structures that are considered to be damaged). However, no systematic evaluation has yet been performed to compare this approach with using patterns of voxels (i.e. considering each voxel as a single feature). In this paper we compared both approaches applying Gaussian Process Regression to decode motor scores in 50 chronic stroke patients based solely on data derived from structural MRI. For both approaches we compared different ways to delimit anatomical areas: regions of interest from an anatomical atlas, the corticospinal tract, a mask obtained from fMRI analysis with a motor task in healthy controls and regions selected using lesion-symptom mapping. Our analysis showed that extracting features through patterns of voxels that represent lesion probability produced better results than quantifying the lesion load per region. In particular, from the different ways to delimit anatomical areas compared, the best performance was obtained with a combination of a range of cortical and subcortical motor areas as well as the corticospinal tract. These results will inform the appropriate methodology for predicting long term motor outcomes from early post-stroke structural brain imaging.

Dataset Information

Decoding the ECD Spectra of Poly(phenylacetylene)s: Structural Significance.

Publications

Decoding the ECD Spectra of Poly(phenylacetylene)s: Structural Significance.

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