Comparison between different subjects in their deformable registration, deformation fields, and quantified distances.

<p>The symmetric log-domain diffeomorphic demons registration well aligned the reference and source images, providing informative deformation fields that accurately reflect morphological difference between subjects.</p

Classification results of normal elderly controls vs AD.

<p>Classification results of normal elderly controls vs AD.</p

Demographic information of the studied population.

<p>Demographic information of the studied population.</p

Fine-Tuning LUMO Energy Levels of Conjugated Polymers Containing a B←N Unit

The LUMO and HOMO energy levels (<i>E</i>_LUMO/<i>E</i>_HOMO) are key parameters for conjugated polymers, which can greatly affect their applications in organic opto-electronic devices. In this manuscript, with donor–acceptor (D–A) type conjugated polymers based on double B←N bridged bipyridine (BNBP) unit, we report fine-tuning of <i>E</i>_LUMO of conjugated polymers in a wide range via substitutions on both D unit and A unit. We synthesize eight D–A type conjugated polymers with alternating electron-deficient BNBP unit and electron-rich bithiophene (BT) unit in the main chain. By changing the substitutes on BNBP or BT, the <i>E</i>_LUMO of these polymers can be finely tuned in a wide range from −3.3 eV to −3.7 eV. We comprehensively investigate the electronic structures, photophysical properties, charge-transporting properties and polymer solar cell (PSC) device applications of these polymers. In PSC devices, these BNBP-based polymers can be used either as electron donors (with high-lying <i>E</i>_LUMO/<i>E</i>_HOMO) or as electron acceptors (with low-lying <i>E</i>_LUMO/<i>E</i>_HOMO). The PSC device with the BNBP-based polymer donor exhibits a PCE of 2.92% and the PSC device with the BNBP-based polymer acceptor exhibits a PCE of 5.16%. These results indicate a new approach to modulate the LUMO energy levels of D–A type conjugated polymers by modifications on both D unit and A unit

Classification performance comparison for different ROIs.

<p>Classification performance comparison for different ROIs.</p

Classification results of sMCI vs pMCI.

<p>Classification results of sMCI vs pMCI.</p

Prediction and classification of Alzheimer disease based on quantification of MRI deformation - Fig 3

<p>(a) Classification sensitivity (green), specificity (blue), and accuracy (red) of normal elderly controls versus AD patients with different ROIs. The highest accuracy (96.5%) was achieved using the whole-brain gray matter as ROI with 93.85% sensitivity and 97.78% specificity. The algorithm obtained high sensitivity and specificity (>90%) with half of the ROIs. (b) The ROC curve of the prediction accuracy between normal controls versus AD. The AUCs were larger than 0.98 for the whole-brain gray matter and white matter (left), amygdala and hippocampus (middle), parietal and temporal lobes (right).</p

Diurnal variations of λ_⊥ map.

<p>Brain regions with significant diurnal variations of λ_⊥ between AM and PM (P<0.05, cluster size >168 voxels). The results are projected on the Ch2.nii template. The left side of the image is the right side of the brain (radiological representation). Color bar indicates t value (Red-yellow, AM >PM, blue-green, AM</p

Summary of neuroanatomical regions with significant diurnal variations in FA, ADC, λ_// and λ_⊥.

<p>The AM value and PM value are given as intersubject mean ± intersubject standard deviation.</p

Diurnal variations of λ_// map.

<p>Brain areas with significant increase of λ_// in AM than in PM (P<0.05, cluster size >168 voxels). The results are projected on the Ch2.nii template. The left side of the image is the right side of the brain (radiological representation). Color bar indicates t value (Red-yellow, AM >PM, blue-green, AM</p