Smart clothing has demonstrated potential applications in a wide range of wearable fields for human body monitoring and self-adaption. However, current wearable sensors often suffer from not seamlessly integrating with normal clothing, restricting sensing ability, and a negative wearing experience. Here, integrated smart clothing is fabricated by employing multiscale disordered porous elastic fibers as sensing units, which show the capability of inherently autonomous self-sensing (i.e., strain and temperature sensing) and self-cooling. The multiscale disordered porous structure of the fibers contributes to the high transparency of mid-infrared human body radiation and backscatter of visible light, which allows the microenvironment temperature between the skin and clothing to drop at least ∼2.5 °C compared with cotton fabrics. After the capillary-assisted adsorption of graphene inks, the modified porous fibers could also possess real-time strain and temperature-sensing capacities with a high gauge factor and thermal coefficient of resistance. As a proof of concept, the integrated smart sportswear achieved the measuring of body temperature, the tracking of large-scale limb movements, and the collection of subtle human physiological signals, along with the intrinsic self-cooling ability.

High-frequency disease-causing alleles exist, but their number is rather small. This study aimed to interpret and reclassify common pathogenic (P) and likely pathogenic (LP) variants in ClinVar and to identify indicators linked with reclassification. We analyzed P/LP variants without conflicting interpretations in ClinVar. Only variants with an allele frequency exceeding 0.5% in at least one ancestry in gnomAD were included. Variants were manually interpreted according to the guidelines of the American College of Medical Genetics and Genomics and the Association for Molecular Pathology. Of 326 variants retrieved, 217 variants in 173 genes were selected for curation. Overall, 87 (40%) variants were downgraded to benign, likely benign or variant of uncertain significance. Five variants (2%) were found to be more likely to be risk factors. Most of the reclassifications were of variants with a low rank, an older classification, a higher allele frequency, or which were collected through methods other than clinical testing. ClinVar provides a universal platform for users who intend to share the classification variants, resulting in the improved concordance of variant interpretation. P/LP variants with a high allele frequency should be used with caution. Ongoing improvements would further improve the practicability of ClinVar database.

The availability of viral entry factors is a prerequisite for the cross-species transmission of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Large-scale single-cell screening of animal cells could reveal the expression patterns of viral entry genes in different hosts. However, such exploration for SARS-CoV-2 remains limited. Here, we perform single-nucleus RNA sequencing for 11 non-model species, including pets (cat, dog, hamster, and lizard), livestock (goat and rabbit), poultry (duck and pigeon), and wildlife (pangolin, tiger, and deer), and investigated the co-expression of ACE2 and TMPRSS2. Furthermore, cross-species analysis of the lung cell atlas of the studied mammals, reptiles, and birds reveals core developmental programs, critical connectomes, and conserved regulatory circuits among these evolutionarily distant species. Overall, our work provides a compendium of gene expression profiles for non-model animals, which could be employed to identify potential SARS-CoV-2 target cells and putative zoonotic reservoirs.

A few animals have been suspected to be intermediate hosts of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). However, a large-scale