Antiferroelectric materials, characterized by an antiparallel array of adjacent dipoles, are holding a bright future for solid-state refrigeration based on their electrocaloric (EC) effects. Despite great advances of inorganic oxides and some organic soft polymers, their EC effects are achieved under quite high electric fields that result in too low EC strengths for practical application. Currently, it is a challenge to exploit soft antiferroelectric with strong EC strengths. Here, by the mixed-cation alloying, we present a new perovskite-type soft antiferroelectric, (isopentylammonium)2CsPb2Br7 (1), which incorporates both an organic spacing cation and an inorganic perovskitizer Cs+ moiety. Remarkably, the synergic cooperativity between the reorientation of the organic spacer and atomic displacement of Cs+ cation triggers its multiple ferroelectric-antiferroelectric-paraelectric phase transitions at 321 and 350 K. Their natural polarization vs electric field hysteresis loops are characterized to confirm ferroelectric and antiferroelectric orders of 1, respectively. It is emphasized that, under a low electric field of 13 kV/cm, the antipolar dipole realignment in 1 endows a giant near-room-temperature EC strength (ΔTEC/ΔE) of 15.4 K m MV-1 at antiferroelectric phase. This merit is on par with the record-high value of BaTiO3 (∼16 K m/MV) but far beyond the state-of-the-art soft polymers. The underlying EC mechanism for 1 is ascribed to the extremely low critical field to switch dipoles, involving the reorientation of the organic spacer and the shift of the Cs+ cation. Besides, notable EC entropy change (∼4.1 J K-1 kg-1) and temperature change (∼2 K) reveal potentials of 1 for solid-state refrigeration. As far as we know, this discovery of near-room-temperature EC strengths is unprecedented in the hybrid perovskite family, which sheds light on the exploration of new soft antiferroelectrics toward high-efficiency refrigeration devices.Targeting mitochondria has always been a challenging goal for therapeutic nanoparticle agents due to their heterotypic features and size, which usually lead to a lysosome/endosome endocytosis pathway. To overcome this limitation, in this work, a portfolio targeting strategy combining a small targeting molecule with a biomembrane was developed. Modification of small targeting molecule H2N-TPP on gold nanoparticles (GNPs) could not only facilitate the mitochondrial targeting but could also induce gold nanoparticle assembly. https://www.selleckchem.com/products/skf96365.html Therefore, the GNPs were endowed with good absorption and photothermal conversion abilities in the near-infrared (NIR) region. Meanwhile, a biomimetic strategy was adopted by wrapping the gold nanoparticle assembly (GNA) with cancer cell membranes (CCMs), which helped the GNA enter the prostatic cancer cell via a homotypic membrane-fusion process to avoid being trapped in endosomes/lysosomes. Thereafter, the GNA remaining in the cytoplasm could reach mitochondria more efficiently via guidance from H2N-TPP molecules. This "biomembrane-small molecule" combination targeting process was evidenced by fluorescence microscopy, and the highly efficient photothermal ablation of prostatic tumors in vivo was demonstrated. This portfolio targeting strategy could be extended to various nanodrugs/agents to realize an accurate subcellular targeting efficiency for cancer treatments or cell detections.Deep-learning (DL)-based image processing has potential to revolutionize the use of smartphones in mobile health (mHealth) diagnostics of infectious diseases. However, the high variability in cellphone image data acquisition and the common need for large amounts of specialist-annotated images for traditional DL model training may preclude generalizability of smartphone-based diagnostics. Here, we employed adversarial neural networks with conditioning to develop an easily reconfigurable virus diagnostic platform that leverages a dataset of smartphone-taken microfluidic chip photos to rapidly generate image classifiers for different target pathogens on-demand. Adversarial learning was also used to augment this real image dataset by generating 16,000 realistic synthetic microchip images, through style generative adversarial networks (StyleGAN). We used this platform, termed smartphone-based pathogen detection resource multiplier using adversarial networks (SPyDERMAN), to accurately detect different intact viruses in clinical samples and to detect viral nucleic acids through integration with CRISPR diagnostics. We evaluated the performance of the system in detecting five different virus targets using 179 patient samples. The generalizability of the system was confirmed by rapid reconfiguration to detect SARS-CoV-2 antigens in nasal swab samples (n = 62) with 100% accuracy. Overall, the SPyDERMAN system may contribute to epidemic preparedness strategies by providing a platform for smartphone-based diagnostics that can be adapted to a given emerging viral agent within days of work.This work provides a globally regionalized approach for quantifying particulate matter (PM2.5) health impacts. Atmospheric transport and pollutant chemistry of primary particulate matter, sulfur dioxide (SO2), nitrogen oxide (NO x ), and ammonia (NH3) from stack emissions were modeled and used to calculate monthly high-resolution maps of global characterization factors that can be used for life cycle impact assessment (LCIA) and risk assessment. These characterization factors are applied to a global data set of coal power emissions. The results show large regional and temporal differences in health impacts per kg of emission and per amount of coal power generation (5-1300 DALY TWh-1). While small emission reductions of PM2.5 and SO2 from coal power lead to similar health benefits across densely populated areas of Asia and Europe, we find that larger emission reductions result in up to three times higher health benefits in parts of Asia because of the nonlinear health responses to pollution exposure changes. Hence, many regions in Asia benefit disproportionately much from large coal power PM2.5 and SO2 emission reductions. NO x emission reductions can lead to equally high health benefits, where unfavorable atmospheric conditions coincide with elevated NH3 background pollution and large population (e.g., in Central Europe, Indonesia, or Japan but also numerous other places).