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The large population movement during the Spring Festival travel in China can considerably accelerate the spread of epidemics, especially after the relaxation of strict control measures against COVID-19. This study aims to assess the impact of population migration in Spring Festival holiday on epidemic spread under different scenarios. Using inter-city population movement data, we construct the population flow network during the non-holiday time as well as the Spring Festival holiday. We build a large-scale metapopulation model to simulate the epidemic spread among 371 Chinese cities. We analyze the impact of Spring Festival travel on the peak timing and peak magnitude nationally and in each city. Assuming an R0 (basic reproduction number) of 15 and the initial conditions as the reported COVID-19 infections on 17 December 2022, model simulations indicate that the Spring Festival travel can substantially increase the national peak magnitude of infection. The infection peaks arrive at most cities 1–4 days earlier as compared to those of the non-holiday time. While peak infections in certain large cities, such as Beijing and Shanghai, are decreased due to the massive migration of people to smaller cities during the pre-Spring Festival period, peak infections increase significantly in small- or medium-sized cities. For a less transmissible disease (R0 = 5), infection peaks in large cities are delayed until after the Spring Festival. Small- or medium-sized cities may experience a larger infection due to the large-scale population migration from metropolitan areas. The increased disease burden may impose considerable strain on the healthcare systems in these resource-limited areas. For a less transmissible disease, particular attention needs to be paid to outbreaks in large cities when people resume work after holidays. 

Non-line-of-sight (NLOS) detection and ranging aim to identify hidden objects by sensing indirect light reflections. Although numerous computational methods have been proposed for NLOS detection and imaging, the post-signal processing required by peripheral circuits remains complex. One possible solution for simplifying NLOS detection and ranging involves the use of neuromorphic devices, such as memristors, which have intrinsic resistive-switching capabilities and can store spatiotemporal information. In this study, we employed the memristive spike-timing-dependent plasticity learning rule to program the time-of-flight (ToF) depth information directly into a memristor medium. By coupling the transmitted signal from the source with the photocurrent from the target object into a single memristor unit, we were able to induce a tunable programming pulse based on the time interval between the two signals that were superimposed. Here, this neuromorphic ToF principle is employed to detect and range NLOS objects without requiring complex peripheral circuitry to process raw signals. We experimentally demonstrated the effectiveness of the neuromorphic ToF principle by integrating a HfO2 memristor and an avalanche photodiode to detect NLOS objects in multiple directions. This technology has potential applications in various fields, such as automotive navigation, machine learning, and biomedical engineering. 

There are currently no effective biomarkers for diagnosing Parkinson’s disease (PD) or tracking its progression. Here, we developed an artificial intelligence (AI) model to detect PD and track its progression from nocturnal breathing signals. The model was evaluated on a large dataset comprising 7,671 individuals, using data from several hospitals in the United States, as well as multiple public datasets. The AI model can detect PD with an area-under-the-curve of 0.90 and 0.85 on held-out and external test sets, respectively. The AI model can also estimate PD severity and progression in accordance with the Movement Disorder Society Unified Parkinson’s Disease Rating Scale (R = 0.94, P = 3.6 × 10–25). The AI model uses an attention layer that allows for interpreting its predictions with respect to sleep and electroencephalogram. Moreover, the model can assess PD in the home setting in a touchless manner, by extracting breathing from radio waves that bounce off a person’s body during sleep. Our study demonstrates the feasibility of objective, noninvasive, at-home assessment of PD, and also provides initial evidence that this AI model may be useful for risk assessment before clinical diagnosis. 

Recently, chiral metal‐organic coordination materials have emerged as promising candidates for a wide range of applications in chiroptoelectronics, chiral catalysis, and information encryption, etc. Notably, the chiroptical effect of coordination chromophores makes them appealing for applications such as photodetectors, OLEDs, 3D displays, and bioimaging. The direct synthesis of chiral coordination materials using chiral organic ligands or complexes with metal‐centered chirality is very often tedious and costly. In the case of ionic coordination materials, the combination of chiral anions with cationic, achiral coordination compounds through noncovalent interactions may endow molecular materials with desirable chiroptical properties. The use of such a simple chiral strategy has been proven effective in inducing promising circular dichroism and/or circularly polarized luminescence signals. This concept article mainly delves into the latest advances in exploring the efficacy of such a chiral anion strategy for transforming achiral coordination materials into chromophores with superb photo‐ or electro‐chiroptical properties. In particular, ionic small‐molecular metal complexes, metal clusters, coordination supramolecular assemblies, and metal‐organic frameworks containing chiral anions are discussed. A perspective on the future opportunities on the preparation of chiroptical materials with the chiral anion strategy is also presented.

 

Abstract One of the most common approaches for quenching single-photon avalanche diodes is to use a passive resistor in series with it. A drawback of this approach has been the limited recovery speed of the single-photon avalanche diodes. High resistance is needed to quench the avalanche, leading to slower recharging of the single-photon avalanche diodes depletion capacitor. We address this issue by replacing a fixed quenching resistor with a bias-dependent adaptive resistive switch. Reversible generation of metallic conduction enables switching between low and high resistance states under unipolar bias. As an example, using a Pt/Al 2 O 3 /Ag resistor with a commercial silicon single-photon avalanche diodes, we demonstrate avalanche pulse widths as small as ~30 ns, 10× smaller than a passively quenched approach, thus significantly improving the single-photon avalanche diodes frequency response. The experimental results are consistent with a model where the adaptive resistor dynamically changes its resistance during discharging and recharging the single-photon avalanche diodes. 

A glut of dinitrogen‐derived ammonia (NH₃) over the past century has resulted in a heavily imbalanced nitrogen cycle and consequently, the large‐scale accumulation of reactive nitrogen such as nitrates in our ecosystems has led to detrimental environmental issues. Electrocatalytic upcycling of waste nitrogen back into NH₃holds promise in mitigating these environmental impacts and reducing reliance on the energy‐intensive Haber–Bosch process. Herein, we report a high‐performance electrolyzer using an ultrahigh alkalinity electrolyte, NaOH−KOH−H₂O, for low‐cost NH₃electrosynthesis. At 3,000 mA/cm², the device with a Fe−Cu−Ni ternary catalyst achieves an unprecedented faradaic efficiency (FE) of 92.5±1.5 % under a low cell voltage of 3.83 V; whereas at 1,000 mA/cm², an FE of 96.5±4.8 % under a cell voltage of only 2.40 V was achieved. Techno‐economic analysis revealed that our device cuts the levelized cost of ammonia electrosynthesis by ~40 % ($30.68 for Fe−Cu−Ni vs. $48.53 for Ni foam per kmol‐NH₃). The NaOH−KOH−H₂O electrolyte together with the Fe−Cu−Ni ternary catalyst can enable the high‐throughput nitrate‐to‐ammonia applications for affordable and scalable real‐world wastewater treatments.

 

Abstract There are currently no effective biomarkers for diagnosing Parkinson’s disease (PD) or tracking its progression. Here, we developed an artificial intelligence (AI) model to detect PD and track its progression from nocturnal breathing signals. The model was evaluated on a large dataset comprising 7,671 individuals, using data from several hospitals in the United States, as well as multiple public datasets. The AI model can detect PD with an area-under-the-curve of 0.90 and 0.85 on held-out and external test sets, respectively. The AI model can also estimate PD severity and progression in accordance with the Movement Disorder Society Unified Parkinson’s Disease Rating Scale ( R  = 0.94, P  = 3.6 × 10 –25 ). The AI model uses an attention layer that allows for interpreting its predictions with respect to sleep and electroencephalogram. Moreover, the model can assess PD in the home setting in a touchless manner, by extracting breathing from radio waves that bounce off a person’s body during sleep. Our study demonstrates the feasibility of objective, noninvasive, at-home assessment of PD, and also provides initial evidence that this AI model may be useful for risk assessment before clinical diagnosis. 

Hydrogels are widely used as substrates to investigate interactions between cells and their microenvironment as they mimic many attributes of the extracellular matrix. The stiffness of hydrogels is an important property that is known to regulate cell behavior. Beside stiffness, cells also respond to structural cues such as mesh size. However, since the mesh size of hydrogel is intrinsically coupled to its stiffness, its role in regulating cell behavior has never been independently investigated. Here, we report a hydrogel system whose mesh size and stiffness can be independently controlled. Cell behavior, including spreading, migration, and formation of focal adhesions is significantly altered on hydrogels with different mesh sizes but with the same stiffness. At the transcriptional level, hydrogel mesh size affects cellular mechanotransduction by regulating nuclear translocation of yes-associated protein. These findings demonstrate that the mesh size of a hydrogel plays an important role in cell-substrate interactions.