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Processing social information from faces is difficult for individuals with autism spectrum disorder (ASD). However, it remains unclear whether individuals with ASD make high-level social trait judgments from faces in the same way as neurotypical individuals. Here, we comprehensively addressed this question using naturalistic face images and representatively sampled traits. Despite similar underlying dimensional structures across traits, online adult participants with self-reported ASD showed different judgments and reduced specificity within each trait compared with neurotypical individuals. Deep neural networks revealed that these group differences were driven by specific types of faces and differential utilization of features within a face. Our results were replicated in well-characterized in-lab participants and partially generalized to more controlled face images (a preregistered study). By investigating social trait judgments in a broader population, including individuals with neurodevelopmental variations, we found important theoretical implications for the fundamental dimensions, variations, and potential behavioral consequences of social cognition. 

The study of high-entropy materials has attracted enormous interest since they could show new functional properties that are not observed in their related parent phases. Here, we report single crystal growth, structure, thermal transport, and magnetic property studies on a novel high-entropy oxide with the spinel structure (MgMnFeCoNi)Al2O4. We have successfully grown high-quality single crystals of this high-entropy oxide using the optical floating zone growth technique for the first time. The sample was confirmed to be a phase pure high-entropy oxide using x-ray diffraction and energy-dispersive spectroscopy. Through magnetization measurements, we found (MgMnFeCoNi)Al2O4 exhibits a cluster spin glass state, though the parent phases show either antiferromagnetic ordering or spin glass states. Furthermore, we also found that (MgMnFeCoNi)Al2O4 has much greater thermal expansion than its CoAl2O4 parent compound using high resolution neutron Larmor diffraction. We further investigated the structure of this high-entropy material via Raman spectroscopy and extended x-ray absorption fine structure spectroscopy (EXAFS) measurements. From Raman spectroscopy measurements, we observed (MgMnFeCoNi)Al2O4 to display a combination of the active Raman modes in its parent compounds with the modes shifted and significantly broadened. This result, together with the varying bond lengths probed by EXAFS, reveals severe local lattice distortions in this high-entropy phase. Additionally, we found a substantial decrease in thermal conductivity and suppression of the low temperature thermal conductivity peak in (MgMnFeCoNi)Al2O4, consistent with the increased lattice defects and strain. These findings advance the understanding of the dependence of thermal expansion and transport on the lattice distortions in high-entropy materials. 

Rapid identification of newly emerging or circulating viruses is an important first step toward managing the public health response to potential outbreaks. A portable virus capture device, coupled with label-free Raman spectroscopy, holds the promise of fast detection by rapidly obtaining the Raman signature of a virus followed by a machine learning (ML) approach applied to recognize the virus based on its Raman spectrum, which is used as a fingerprint. We present such an ML approach for analyzing Raman spectra of human and avian viruses. A convolutional neural network (CNN) classifier specifically designed for spectral data achieves very high accuracy for a variety of virus type or subtype identification tasks. In particular, it achieves 99% accuracy for classifying influenza virus type A versus type B, 96% accuracy for classifying four subtypes of influenza A, 95% accuracy for differentiating enveloped and nonenveloped viruses, and 99% accuracy for differentiating avian coronavirus (infectious bronchitis virus [IBV]) from other avian viruses. Furthermore, interpretation of neural net responses in the trained CNN model using a full-gradient algorithm highlights Raman spectral ranges that are most important to virus identification. By correlating ML-selected salient Raman ranges with the signature ranges of known biomolecules and chemical functional groups—for example, amide, amino acid, and carboxylic acid—we verify that our ML model effectively recognizes the Raman signatures of proteins, lipids, and other vital functional groups present in different viruses and uses a weighted combination of these signatures to identify viruses.