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The magic angle of Twistronics has attracted a lot of attention because of its peculiar electrical characteristics. Moiré patterns formed by the superlattice of a twisted bilayer change overall physical properties. Circular dichroism can also be manipulated through the generated moiré pattern. Here, we report a polymer-based twisted bilayer meta-device fabricated by multilayer nanoimprint technology and study the magic angle of chirality. The superlattice of the bilayer meta-device creates moiré patterns and brings unique chiral optical responses. The bilayer nanoimprint technology is developed for metasurfaces with relative twist angles. Via the twist angle control, polymer materials with a low refractive index can manipulate the electric field of the light and reveal the chiral magic angle. Moreover, the shape of the meta-atoms plays a key role in chiral magic angle tuning. The chirality engineering by the reported nanoimprint technology and chiral meta-devices may contribute to applications in chiral imaging, biomedical sensing, lasing, and tunable optical devices.

In this paper, the fabrication, characterization, and investigation of the optical properties of high aspect ratio shape memory polymer (SMP) nanopillars are presented. The SMP nanopillars are fabricated using nanoimprint lithography and double‐replication techniques, resulting in well‐defined structures with a height of 1.3 µm and a diameter of 270 nm that can be uniformly bent across a large area. The optical transmission spectra of both straight and bent SMP nanopillars are studied. Experimental measurements, along with finite‐difference time‐domain simulations, reveal significant alterations in the transmission spectra due to the presence of the nanopillars. The SMP nanopillars provide a smart optical power splitting effect, enabling wavelength‐dependent splitting of incident light by controlling the SMP nanopillar bending angle and direction dynamically with temperature. These findings highlight the potential of SMP nanopillars as versatile components for manipulating light in wavelength‐dependent optical devices. [ABSTRACT FROM AUTHOR]

The formation of floods, as a complex physical process, exhibits dynamic changes in its driving factors over time and space under climate change. Due to the black‐box nature of deep learning, its use alone does not enhance understanding of hydrological processes. The challenge lies in employing deep learning to uncover new knowledge on flood formation mechanism. This study proposes an interpretable framework for deep learning flood modeling that employs interpretability techniques to elucidate the inner workings of a peak‐sensitive Informer, revealing the dynamic response of floods to driving factors in 482 watersheds across the United States. Accurate simulation is a prerequisite for interpretability techniques to provide reliable information. The study reveals that comparing the Informer with Transformer and LSTM, the former showed superior performance in peak flood simulation (Nash‐Sutcliffe Efficiency over 0.6 in 70% of watersheds). By interpreting Informer's decision‐making process, three primary flood‐inducing patterns were identified: Precipitation, excess soil water, and snowmelt. The controlling effect of dominant factors is regional, and their impact on floods in time steps shows significant differences, challenging the traditional understanding that variables closer to the timing of flood event occurrence have a greater impact. Over 40% of watersheds exhibited shifts in dominant driving factors between 1981 and 2020, with precipitation‐dominated watersheds undergoing more significant changes, corroborating climate change responses. Additionally, the study unveils the interplay and dynamic shifts among variables. These findings suggest that interpretable deep learning, through reverse deduction, transforms data‐driven models from merely fitting nonlinear relationships to effective tools for enhancing understanding of hydrological characteristics. Plain Language Summary: The formation of floods is often dynamically influenced by multiple driving factors. Traditional methods based on statistics or hydrological models struggle to clearly understand flood mechanisms due to their limitations. Although deep learning has become an effective tool for flood modeling, its black‐box nature makes it difficult to enrich understanding of flood processes through it. Our proposed interpretable deep learning offers a perspective to unveil the dynamic drivers of floods by extracting patterns from deep learning in a reverse deduction approach. We started with models that perform best on flood extremes and identified the dominant factors and variations of floods across 482 watersheds in the United States. These variations exhibit regional characteristics, with precipitation playing a more significant role and showing more pronounced trends in watersheds where floods are primarily driven by precipitation. We found that variables from 2 days before a flood could have a greater impact than those from the previous days. Furthermore, we revealed the combined impact of variables on flooding through deep learning, showcasing their dynamic changes. Interpretable deep learning explores a new avenue to derive new hydrological insights from inverse data, helping hydrologists better understand natural physical processes. Key Points: Proposed a framework for interpretable time series deep learning based on optimal performance to reveal the dynamic drivers of floodsUnveiled the changes in the dominant driving factors of floods and the roles of driving factors at different moments before a floodElucidated the dynamic responses of floods to the interactions among driving factors [ABSTRACT FROM AUTHOR]

Distant metastasis is the primary cause of unsuccessful treatment in nasopharyngeal carcinoma (NPC), suggesting the crucial need to comprehend this process. A tumor related to NPC does not have flat surfaces, but consists of confined microenvironments, proteins, and surface topography. To mimic the complex microenvironment, three-dimensional platforms with microwells and connecting channels were designed and developed with a fibronectin (FN) coating or nanohole topography. The potential of the transforming growth factor-β (TGF-β) inhibitor (galunisertib) for treating NPC was also investigated using the proposed platform. Our results demonstrated an increased traversing probability of NPC43 cells through channels with an FN coating, which correlated with enhanced cell motility and dispersion. Conversely, the presence of nanohole topography patterned on the platform bottom and the TGF-β inhibitor led to a reduced cell traversing probability and decreased cell motility, likely due to the decrease in the F-actin concentration in NPC43 cells. This study highlights the significant impact of confinement levels, surface proteins, nanotopography, and the TGF-β inhibitor on the metastatic probability of cancer cells, providing valuable insights for the development of novel treatment therapies for NPC. The developed platforms proved to be useful tools for evaluating the metastatic potential of cells and are applicable for drug screening. [ABSTRACT FROM AUTHOR]

Shape memory polymer (SMP) is a smart material that can memorize a permanent shape and, when exposed to a certain stimulus, recovers its original shape from a programmed temporary shape. High aspect‐ratio microposts are fabricated using deep reactive‐ion etching and thermal imprint, and are mechanically deformed to temporary bent morphologies. By applying a temperature gradient across this array of bent SMP microposts, they gradually recover at different speed to their original vertical position, starting from the heated end, resulting in a spatial dependence of bending recovery. The gradual recovery phenomenon is employed to illustrate the spatially dependent optical transmissivity of the micropost array, which exhibit different micropost angles. These findings provide evidence for the manipulation of optical transmission characteristics, encompassing diffraction, waveguiding, and depolarization, across various orientations. This high aspect ratio SMP micropost array is potentially useful for various optical devices and applications. [ABSTRACT FROM AUTHOR]

Retinal prostheses are biomedical devices that directly utilize electrical stimulation to create an artificial vision to help patients with retinal diseases such as retinitis pigmentosa. A major challenge in the microelectrode array (MEA) design for retinal prosthesis is to have a close topographical fit on the retinal surface. The local retinal topography can cause the electrodes in certain areas to have gaps up to several hundred micrometers from the retinal surface, resulting in impaired, or totally lost electrode functions in specific areas of the MEA. In this manuscript, an MEA with dynamically controlled electrode positions was proposed to reduce the electrode-retina distance and eliminate areas with poor contact after implantation. The MEA prototype had a polydimethylsiloxane and polyimide hybrid flexible substrate with gold interconnect lines and poly(3,4-ethylenedioxythiophene) polystyrene sulfonate electrodes. Ring shaped counter electrodes were placed around the main electrodes to measure the distance between the electrode and the model retinal surface in real time. The results showed that this MEA design could reduce electrode-retina distance up to $100~\mu \text{m}$ with 200 kPa pressure. Meanwhile, the impedance between the main and counter electrodes increased with smaller electrode-model retinal surface distance. Thus, the change of electrode-counter electrode impedance could be used to measure the separation gap and to confirm successful electrode contact without the need of optical coherence tomography scan. The amplitude of the stimulation signal on the model retinal surface with originally poor contact could be significantly improved after pressure was applied to reduce the gap. [ABSTRACT FROM AUTHOR]

Objective: To study the clinicopathological features, the histogenesis, and the correlation between the expressions of estrogen receptor(ER) and epidermal growth factor receptor(EGFR) in leiomyoma of the vagina., Methods: The data of 26 patients with leiomyoma of the vagina were analyzed retrospectively, and S-P immunohistochemistry was used to detect smooth muscle actin(SMA), S-100 protein, CD34, ER and EGFR in the tumor tissues., Results: All patients with leiomyoma of the vagina in this group were married women, with age ranging from 30 to 52 years (mean, 44.5 years). Leiomyoma of the vagina was slow in growth and occasionally recurred with borderline and malignant transformation. Immunohistochemical staining demonstrated to be strongly positive for SMA, and negative for S-100 protein and CD34 in all cases. Positive expression rates for ER and EGFR were 38.5 (10/26) and 34.6 (9/26) respectively. There was a significant positive correlation between the expressions of ER and EGFR (Fisher exact, P = 0.0023, r = 0.75). Resection of the tumor mass was an effective method to cure the disease., Conclusion: Leiomyoma of the vagina is a rare condition, which may be derived from stem cells of mesenchyma of the vagina differentiating towards smooth muscle. The estrogen hormone and EGF may play an important role in the development and growth of leiomyoma of the vagina. Follow-up is needed after operation for the patient with borderline tumor.