The relation between aggression and theory of mind in children: A meta-analysis.

Previous findings on the association between theory of mind (ToM) and aggression in children are mixed. The social skills deficit view regarded ToM as a single-edged sword and proposed that a lack of ToM can lead to aggression, while the double-edged sword view proposed that children with advanced ToM can still show much aggression because children can also leverage ToM to harm others. To resolve the dispute between the two views, we conducted a meta-analysis combining cross-sectional and longitudinal findings from 53 studies including a total of 11,579 children aged between 2 and 15 years. The overall negative correlation between ToM and aggression was small but significant (r = -0.08). The negative correlation was robust, with the magnitude of the correlation being similar across physical versus relational aggression, proactive versus reactive aggression, cognitive versus affective ToM, preschoolers versus school-aged children, different aggression measurements, and different levels of societal individualism. Moreover, the negative correlation was found regardless of whether ToM and aggression were measured concurrently or at different time points, but the correlation was the largest when ToM was measured before aggression. Whether the aggression was bullying or not also moderated the association, with ToM only being negatively related to non-bullying aggression but not bullying. Together, these findings suggest that ToM is a single-edged sword to decrease general aggression and that aggression might also give rise to lower ToM capacity during development. RESEARCH HIGHLIGHTS: This study systemically reviewed the existing mixed findings on the association between aggression and ToM during childhood using a meta-analysis. There was a negative correlation between ToM and aggression in children aged between 2 and 15 years. The negative correlation between aggression and ToM was stronger when ToM was measured first, followed by aggression, than vice versa. There was a negative correlation between non-bullying aggression and ToM, whereas there was no correlation between bullying and ToM.

Enantiomeric Discrimination by Surface-Enhanced Raman Scattering-Chiral Anisotropy of Chiral Nanostructured Gold Films.

A surface-enhanced Raman scattering-chiral anisotropy (SERS-ChA) effect is reported that combines chiral discrimination and surface Raman scattering enhancement on chiral nanostructured Au films (CNAFs) equipped in the normal Raman scattering Spectrometer. The CNAFs provided remarkably higher enhancement factors of Raman scattering (EFs) for particular enantiomers, and the SERS intensity was proportional to the enantiomeric excesses (ee) values. Except for molecules with mesomeric species, all of the tested enantiomers exhibited high SERS-ChA asymmetry factors (g), ranging between 1.34 and 1.99 regardless of polarities, sizes, chromophores, concentrations and ee. The effect might be attributed to selective resonance coupling between the induced electric and magnetic dipoles associated with enantiomers and chiral plasmonic modes of CNAFs.

Detection and Monitoring of Oil Spills Using Moderate/High-Resolution Remote Sensing Images.

Current marine oil spill detection and monitoring methods using high-resolution remote sensing imagery are quite limited. This study presented a new bottom-up and top-down visual saliency model. We used Landsat 8, GF-1, MAMS, HJ-1 oil spill imagery as dataset. A simplified, graph-based visual saliency model was used to extract bottom-up saliency. It could identify the regions with high visual saliency object in the ocean. A spectral similarity match model was used to obtain top-down saliency. It could distinguish oil regions and exclude the other salient interference by spectrums. The regions of interest containing oil spills were integrated using these complementary saliency detection steps. Then, the genetic neural network was used to complete the image classification. These steps increased the speed of analysis. For the test dataset, the average running time of the entire process to detect regions of interest was 204.56 s. During image segmentation, the oil spill was extracted using a genetic neural network. The classification results showed that the method had a low false-alarm rate (high accuracy of 91.42%) and was able to increase the speed of the detection process (fast runtime of 19.88 s). The test image dataset was composed of different types of features over large areas in complicated imaging conditions. The proposed model was proved to be robust in complex sea conditions.

Chiral mesostructured hydroxide zinc carbonate for enantioseparation in high performance liquid chromatography.

Chiral mesostructured hydroxide zinc carbonate coated silica gel (CMHZC@S) was prepared by a hydrothermal route using amino acids as symmetry-breaking agents. CMHZC@S with three levels of hierarchical chiralities, as a chiral stationary phase (CSP), showed enantiomeric separation ability for high performance liquid chromatography (HPLC).

Intelligent COVID-19 screening platform based on breath analysis.

The spread of coronavirus disease 2019 (COVID-19) results in an increasing incidence and mortality. The typical diagnosis technique for severe acute respiratory syndrome coronavirus 2 infection is reverse transcription polymerase chain reaction, which is relatively expensive, time-consuming, professional, and suffered from false-negative results. A reliable, non-invasive diagnosis method is in urgent need for the rapid screening of COVID-19 patients and controlling the epidemic. Here we constructed an intelligent system based on the volatile organic compound (VOC) biomarkers in human breath combined with machine learning models. The VOC profiles of 122 breath samples (65 of COVID-19 infections and 57 of controls) were identified with a portable gas chromatograph-mass spectrometer. Among them, eight VOCs exhibited significant differences (p< 0.001) between the COVID-19 and the control groups. The cross-validation algorithm optimized support vector machine (SVM) model was employed for the prediction of COVID-19 infection. The proposed SVM model performed a powerful capability in discriminating COVID-19 patients from healthy controls, with an accuracy of 97.3%, a sensitivity of 100%, a specificity of 94.1%, and a precision of 95.2%, and anF1 score of 97.6%. The SVM model was also compared with other common machine models, including artificial neural network,k-nearest neighbor, and logistic regression, and demonstrated obvious superiority in the prediction of COVID-19 infection. Furthermore, user-friendly software was developed based on the optimized SVM model. The developed intelligent platform based on breath analysis provides a new strategy for the point-of-care screening of COVID and shows great potential in clinical application.

A visual physiological temperature sensor developed with gelatin-stabilized luminescent silver nanoclusters.

A visual physiological temperature sensor was successfully developed with newly hydrothermally prepared fluorescent silver nanoclusters (AgNCs) at room temperature using gelatin as the protective and reducing agent. The as-prepared gelatin-stabilized AgNCs was water-soluble, uniform and exhibited a narrow distribution with an average size of 1.16 nm, showing a maximum emission band at 552 nm (2.45 eV) when excited at 445 nm (2.79 eV). The large Stokes shift of 110 nm of the gelatin-stabilized AgNCs makes it actually applicable with very low background and light scattering interferences. It was found that the as-prepared gelatin-stabilized AgNCs is temperature-sensitive over the range from 5°C to 45°C, and thus a visual physiological temperature sensor could be developed with the gelatin-AgNCs as under the irradiation of visible light.