ArPNO-Catalyzed Acylative Dynamic Kinetic Resolution of 3‑Hydroxyphthalides: Access to Enantioenriched Phthalidyl Esters

A chiral 4-aryl-pyridine-N-oxide nucleophilic organocatalyst was used to synthesize chiral phthalidyl ester prodrugs by the acylative dynamic kinetic resolution process. By using the 3,5-dimethylphenyl-derived ArPNO catalyst, the phthalidyl esters were obtained in up to 97% yield with 97% ee at room temperature. Two phthalidyl esters of prodrugs, talosalate and talmetacin, were generated. By control experiments and density functional theory calculations, an acyl transfer mechanism was proposed

Causal interactions in resting-state networks predict perceived loneliness

<div><p>Loneliness is broadly described as a negative emotional response resulting from the differences between the actual and desired social relations of an individual, which is related to the neural responses in connection with social and emotional stimuli. Prior research has discovered that some neural regions play a role in loneliness. However, little is known about the differences among individuals in loneliness and the relationship of those differences to differences in neural networks. The current study aimed to investigate individual differences in perceived loneliness related to the causal interactions between resting-state networks (RSNs), including the dorsal attentional network (DAN), the ventral attentional network (VAN), the affective network (AfN) and the visual network (VN). Using conditional granger causal analysis of resting-state fMRI data, we revealed that the weaker causal flow from DAN to VAN is related to higher loneliness scores, and the decreased causal flow from AfN to VN is also related to higher loneliness scores. Our results clearly support the hypothesis that there is a connection between loneliness and neural networks. It is envisaged that neural network features could play a key role in characterizing the loneliness of an individual.</p></div

Relationships between the predicting and the original loneliness scores in lonely individual.

<p>(A) AfN→VN, and (B) DAN→VAN. The estimated loneliness scores were obtained by SVR predicting model and LOOCV.</p

The main steps of data analysis.

<p>(A) group ICA analysis, (B) extract the four RSNs, and (C) conditional granger causal analysis</p

Granger causality networks between the four resting-state networks (RSNs) and correlations between loneliness scores and GC value for the CGCA.

<p>(A) lonely individuals (high loneliness scores > 45), (B) non-lonely individuals (low loneliness scores < 28), (C) The normalized scores of connectivity, and (D) granger causal influence between RSNs as function of loneliness scores. RSNs included the dorsal attentional network (DAN), the ventral attentional network (VAN), the affective network (AfN) and the visual network (VN). All negative Pearson’s correlations were observed (p<0.05, FDR correction).</p

Cortical representation of the four RSNs of resting state fMRI data from a group of results across all subjects.

<p>Top view: RSNs including: affective network (AfN); Visual network (VN); Dorsal attentional network (DAN), and ventral attentional network (VAN). Bottom view: The spatial correlation coefficients of 40 ICs from all subjects with respect to the four RSN templates. The largest correlations with the templates were chosen.</p

The 4 nodes clustered by using <i>G</i>^<i>2</i>, <i>P</i>^<i>2</i> and ((<i>G+P</i>)/2)² respectively if we suppose that all landmarks will be clustered into 4 groups.

<p>The colors of nodes were randomly assigned. If we check (A) resulted from using geodesic distance (<i>G</i>^<i>2</i>) only, those landmarks close in spatial space are divided into one group. While in (B), all landmarks were divided by using fiber connection information (<i>P</i>^<i>2</i>) only, and those landmarks with strong fiber connection were clustered into one group. The result of these two feature’s concurrent effect is exhibited in (C), which looks like a trade-off between (A) and (B).</p

DataSheet1_The Scalp Time-Varying Networks of N170: Reference, Latency, and Information Flow.docx

<p>Using the scalp time-varying network method, the present study is the first to investigate the temporal influence of the reference on N170, a negative event-related potential component (ERP) appeared about 170 ms that is elicited by facial recognition, in the network levels. Two kinds of scalp electroencephalogram (EEG) references, namely, AR (average of all recording channels) and reference electrode standardization technique (REST), were comparatively investigated via the time-varying processing of N170. Results showed that the latency and amplitude of N170 were significantly different between REST and AR, with the former being earlier and smaller. In particular, the information flow from right temporal-parietal P8 to left P7 in the time-varying network was earlier in REST than that in AR, and this phenomenon was reproduced by simulation, in which the performance of REST was closer to the true case at source level. These findings indicate that reference plays a crucial role in ERP data interpretation, and importantly, the newly developed approximate zero-reference REST would be a superior choice for precise evaluation of the scalp spatio-temporal changes relating to various cognitive events.</p

The illustration for basic random walk spectral clustering.

<p>The illustration for basic random walk spectral clustering.</p

The predicted fiber bundles for SZ and control.

<p>The predicted fiber bundles for SZ and control.</p