Background: Social intelligence refers to an important psychosocial skill set encompassing an array of abilities, including effective self-expression, understanding of social contexts, and acting wisely in social interactions. While there is ample evidence of its importance in various mental health outcomes, particularly social anxiety, little is known on the brain correlates underlying social intelligence and how it can mitigate social anxiety. Objective: This research aims to investigate the functional neural markers of social intelligence and their relations to social anxiety. Methods: Data of resting-state functional magnetic resonance imaging and behavioral measures were collected from 231 normal students aged 16 to 20 years (48% male). Whole-brain voxel-wise correlation analysis was conducted to detect the functional brain clusters related to social intelligence. Correlation and mediation analyses explored the potential role of social intelligence in the linkage of resting-state brain activities to social anxiety. Results: Social intelligence was correlated with neural activities (assessed as the fractional amplitude of low-frequency fluctuations, fALFF) among two key brain clusters in the social cognition networks: negatively correlated in left superior frontal gyrus (SFG) and positively correlated in right middle temporal gyrus. Further, the left SFG fALFF was positively correlated with social anxiety; brain-personality-symptom analysis revealed that this relationship was mediated by social intelligence. Conclusion: These results indicate that resting-state activities in the social cognition networks might influence a person's social anxiety via social intelligence: lower left SFG activity â higher social intelligence â lower social anxiety. These may have implication for developing neurobehavioral interventions to mitigate social anxiety.
Nanoresolved doping of polymeric semiconductors can overcome scaling limitations to create highly integrated flexible electronics, but remains a fundamental challenge due to isotropic diffusion of the dopants. Here we report a general methodology for achieving nanoscale ion-implantation-like electrochemical doping of polymeric semiconductors. This approach involves confining counterion electromigration within a glassy electrolyte composed of room-temperature ionic liquids and high-glass-transition-temperature insulating polymers. By precisely adjusting the electrolyte glass transition temperature (Tg) and the operating temperature (T), we create a highly localized electric field distribution and achieve anisotropic ion migration that is nearly vertical to the nanotip electrodes. The confined doping produces an excellent resolution of 56 nm with a lateral-extended doping length down to as little as 9.3 nm. We reveal a universal exponential dependence of the doping resolution on the temperature difference (Tg - T) that can be used to depict the doping resolution for almost infinite polymeric semiconductors. Moreover, we demonstrate its implications in a range of polymer electronic devices, including a 200% performance-enhanced organic transistor and a lateral p-n diode with seamless junction widths of <100 nm. Combined with a further demonstration in the scalability of the nanoscale doping, this concept may open up new opportunities for polymer-based nanoelectronics.
Two-dimensional (2D) magnet-superconductor hybrid systems are intensively studied due to their potential for the realization of 2D topological superconductors with Majorana edge modes. It is theoretically predicted that this quantum state is ubiquitous in spin-orbit-coupled ferromagnetic or skyrmionic 2D spin-lattices in proximity to an s-wave superconductor. However, recent examples suggest that the requirements for topological superconductivity are complicated by the multiorbital nature of the magnetic components and disorder effects. Here, we investigate Fe monolayer islands grown on a surface of the s-wave superconductor with the largest gap of all elemental superconductors, Nb, with respect to magnetism and superconductivity using spin-resolved scanning tunneling spectroscopy. We find three types of islands which differ by their reconstruction inducing disorder, the magnetism and the subgap electronic states. All three types are ferromagnetic with different coercive fields, indicating diverse exchange and anisotropy energies. On all three islands, there is finite spectral weight throughout the substrate's energy gap at the expense of the coherence peak intensity, indicating the formation of Shiba bands overlapping with the Fermi energy. A strong lateral variation of the spectral weight of the Shiba bands signifies substantial disorder on the order of the substrate's pairing energy with a length scale of the period of the three different reconstructions. There are neither signs of topological gaps within these bands nor of any kind of edge modes. Our work illustrates that a reconstructed growth mode of magnetic layers on superconducting surfaces is detrimental for the formation of 2D topological superconductivity.
Alzheimer's disease (AD) is the most common form of dementia. Currently, only symptomatic management is available, and early diagnosis and intervention are crucial for AD treatment. As a recent deep learning strategy, generative adversarial networks (GANs) are expected to benefit AD diagnosis, but their performance remains to be verified. This study provided a systematic review on the application of the GAN-based deep learning method in the diagnosis of AD and conducted a meta-analysis to evaluate its diagnostic performance. A search of the following electronic databases was performed by two researchers independently in August 2021: MEDLINE (PubMed), Cochrane Library, EMBASE, and Web of Science. The Quality Assessment of Diagnostic Accuracy Studies-2 (QUADAS-2) tool was applied to assess the quality of the included studies. The accuracy of the model applied in the diagnosis of AD was determined by calculating odds ratios (ORs) with 95% confidence intervals (CIs). A bivariate random-effects model was used to calculate the pooled sensitivity and specificity with their 95% CIs. Fourteen studies were included, 11 of which were included in the meta-analysis. The overall quality of the included studies was high according to the QUADAS-2 assessment. For the AD vs. cognitively normal (CN) classification, the GAN-based deep learning method exhibited better performance than the non-GAN method, with significantly higher accuracy (OR 1.425, 95% CI: 1.150-1.766, P = 0.001), pooled sensitivity (0.88 vs. 0.83), pooled specificity (0.93 vs. 0.89), and area under the curve (AUC) of the summary receiver operating characteristic curve (SROC) (0.96 vs. 0.93). For the progressing MCI (pMCI) vs. stable MCI (sMCI) classification, the GAN method exhibited no significant increase in the accuracy (OR 1.149, 95% CI: 0.878-1.505, P = 0.310) or the pooled sensitivity (0.66 vs. 0.66). The pooled specificity and AUC of the SROC in the GAN group were slightly higher than those in the non-GAN group (0.81 vs. 0.78 and 0.81 vs. 0.80, respectively). The present results suggested that the GAN-based deep learning method performed well in the task of AD vs. CN classification. However, the diagnostic performance of GAN in the task of pMCI vs. sMCI classification needs to be improved. Systematic Review Registration: [PROSPERO], Identifier: [CRD42021275294].
Background: The house-tree-person (HTP) drawing test has received growing attention from researchers as a common projective test. However, the methods used to select and interpret drawing indicators still lack uniformity. Objective: This study aims to integrate drawing indicators into the process of screening for or classifying mental disorders by conducting a systematic review and meta-analysis of the application of the HTP test. Methods: A search of the following electronic databases was performed in May 2022: PubMed, Web of Science, Embase, EBSCO, CNKI, VIP, and Wanfang. Screening and checking of the literature were performed independently by two researchers. The empirical studies published on the use of the HTP test in mental disorders and studies providing specific data on the occurrence frequency of drawing characteristics were analyzed. A total of 30 studies were included in the meta-analysis, including 665 independent effect sizes and 6,295 participants. The strength of the association between drawing characteristics of the HTP test and the prevalence of mental disorders was measured by the ratio (OR) with a 95% CI. Publication bias was assessed using a funnel plot, Rosenthal's fail-safe number (N fs), and the trim and fill method. Results: The results revealed 50 drawing characteristics that appeared at least three times in previous studies, of which 39 were able to significantly predict mental disorders. The HTP test can be divided into the following four dimensions: house, tree, person, and the whole. These dimensions reflect the structure, size, and other characteristics of the picture. The results showed that the greatest predictor of mental disorders was the whole (OR = 4.20, p < 0.001), followed by the house (OR = 3.95, p < 0.001), the tree (OR = 2.70, p < 0.001), and the person (OR = 2.16, p < 0.001). The valid predictors can be categorized into the following four types: item absence, bizarre or twisted, excessive details, and small or simplified. The subgroup analysis showed that the affective-specific indicators included no motion, leaning house, and decorated roof; thought-specific indicators included excessive separation among items, no window, loss of facial features, and inappropriate body proportions; and common indicators of mental disorders included no additional decoration, simplified drawing, very small house, two-dimensional house, and very small tree. Conclusion: These findings can promote the standardization of the HTP test and provide a theoretical reference for the screening and clinical diagnosis of mental disorders.
Alzheimer's disease (AD) is a neurodegenerative disease that severely affects the activities of daily living in aged individuals, which typically needs to be diagnosed at an early stage. Generative adversarial networks (GANs) provide a new deep learning method that show good performance in image processing, while it remains to be verified whether a GAN brings benefit in AD diagnosis. The purpose of this research is to systematically review psychoradiological studies on the application of a GAN in the diagnosis of AD from the aspects of classification of AD state and AD-related image processing compared with other methods. In addition, we evaluated the research methodology and provided suggestions from the perspective of clinical application. Compared with other methods, a GAN has higher accuracy in the classification of AD state and better performance in AD-related image processing (e.g. image denoising and segmentation). Most studies used data from public databases but lacked clinical validation, and the process of quantitative assessment and comparison in these studies lacked clinicians' participation, which may have an impact on the improvement of generation effect and generalization ability of the GAN model. The application value of GANs in the classification of AD state and AD-related image processing has been confirmed in reviewed studies. Improvement methods toward better GAN architecture were also discussed in this paper. In sum, the present study demonstrated advancing diagnostic performance and clinical applicability of GAN for AD, and suggested that the future researchers should consider recruiting clinicians to compare the algorithm with clinician manual methods and evaluate the clinical effect of the algorithm.
Practical applications of semiconductor spintronic devices necessitate ferromagnetic behavior at or above room temperature. In this paper, we demonstrate a two-dimensional manganese gallium nitride surface structure (MnGaN-2D) which is atomically thin and shows ferromagnetic domain structure at room temperature as measured by spin-resolved scanning tunneling microscopy and spectroscopy. Application of small magnetic fields proves that the observed magnetic domains follow a hysteretic behavior. Two initially oppositely oriented MnGaN-2D domains are rotated into alignment with only 120 mT and remain mostly in alignment at remanence. The measurements are further supported by first-principles theoretical calculations which reveal highly spin-polarized and spin-split surface states with spin polarization of up to 95% for manganese local density of states.
