Visuocortical tuning to a threat-related feature persists after extinction and consolidation of conditioned fear

Neurons in the visual cortex sharpen their orientation tuning as humans learn aversive contingencies. A stimulus orientation (CS+) that reliably predicts an aversive noise (unconditioned stimulus: US) is selectively enhanced in lower-tier visual cortex, while similar unpaired orientations (CS−) are inhibited. Here, we examine in male volunteers how sharpened visual processing is affected by fear extinction learning (where no US is presented), and how fear and extinction memory undergo consolidation one day after the original learning episode. Using steady-state visually evoked potentials from electroencephalography in a fear generalization task, we found that extinction learning prompted rapid changes in orientation tuning: Both conditioned visuocortical and skin conductance responses to the CS+ were strongly reduced. Next-day re-testing (delayed recall) revealed a brief but precise return-of-tuning to the CS+ in visual cortex accompanied by a brief, more generalized return-of-fear in skin conductance. Explorative analyses also showed persistent tuning to the threat cue in higher visual areas, 24 h after successful extinction, outlasting peripheral responding. Together, experience-based changes in the sensitivity of visual neurons show response patterns consistent with memory consolidation and spontaneous recovery, the hallmarks of long-term neural plasticity

Multi-responsive cellulose nanocrystal–rhodamine conjugates: an advanced structure study by solid-state dynamic nuclear polarization (DNP) NMR

Multi-stimuli responsive materials based on cellulose nanocrystals (CNCs), especially using non-conventional stimuli including light, still need more explorations, to fulfill the requirements of complicated application environments. The structure determination of functional groups on the CNC surface constitutes a significant challenge, partially due to their low amounts. In this study, rhodamine spiroamide groups are immobilized onto the surface of CNCs leading to a hybrid compound being responsive to pH-values, heat and UV light. After the treatment with external stimuli, the fluorescent and correlated optical color change can be induced, which refers to a ring opening and closing process. Amine and amide groups in rhodamine spiroamide play the critical role in this switching process. Solid-state NMR spectroscopy coupled with sensitivity-enhanced dynamic nuclear polarization (DNP) was used to measure 13C and 15N in natural abundance, allowing the determination of structural changes during the switching process. It is shown that a temporary bond through an electrostatic interaction could be formed within the confined environment on the CNC surface during the heat treatment. The carboxyl groups on the CNC surface play a pivotal role in stabilizing the open status of rhodamine spiroamide groups

Computer-assisted mitotic count using a deep learning-based algorithm improves interobserver reproducibility and accuracy

The mitotic count (MC) is an important histological parameter for prognostication of malignant neoplasms. However, it has inter- and intraobserver discrepancies due to difficulties in selecting the region of interest (MC-ROI) and in identifying or classifying mitotic figures (MFs). Recent progress in the field of artificial intelligence has allowed the development of high-performance algorithms that may improve standardization of the MC. As algorithmic predictions are not flawless, computer-assisted review by pathologists may ensure reliability. In the present study, we compared partial (MC-ROI preselection) and full (additional visualization of MF candidates and display of algorithmic confidence values) computer-assisted MC analysis to the routine (unaided) MC analysis by 23 pathologists for whole-slide images of 50 canine cutaneous mast cell tumors (ccMCTs). Algorithmic predictions aimed to assist pathologists in detecting mitotic hotspot locations, reducing omission of MFs, and improving classification against imposters. The interobserver consistency for the MC significantly increased with computer assistance (interobserver correlation coefficient, ICC = 0.92) compared to the unaided approach (ICC = 0.70). Classification into prognostic stratifications had a higher accuracy with computer assistance. The algorithmically preselected hotspot MC-ROIs had a consistently higher MCs than the manually selected MC-ROIs. Compared to a ground truth (developed with immunohistochemistry for phosphohistone H3), pathologist performance in detecting individual MF was augmented when using computer assistance (F1-score of 0.68 increased to 0.79) with a reduction in false negatives by 38%. The results of this study demonstrate that computer assistance may lead to more reproducible and accurate MCs in ccMCTs