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Sepsis, whole‐body inflammation caused by the contamination of blood by bacteria and endotoxins, affects millions of patients annually with high mortality rates. A recent promising approach to treat sepsis involves the removal of bacteria and endotoxins using extracorporeal blood‐cleansing devices. However, poor specificity, slow recognition of pathogens, and high costs remain the main limitations. Here, the melanin, a biologically derived pigment, is reported for the rapid binding of bacteria and endotoxins from the contaminated blood . This novel approach utilizes the specific binding between Zn²⁺‐loaded melanin and bacteria/endotoxins with minimal nonspecific interactions with human blood components. Melanin contains various chemical functional groups that allow reversible chelation of metallic ions such as Zn²⁺via redox reactions. Zn²⁺enables rapid and specific binding with bacteria/endotoxins due to the strong electrostatic interactions between Zn²⁺and phosphate ions. The presence of various zinc‐binding proteins on the bacterial cell membrane further enhances the binding. The well‐known biocompatibility and low cost make melanin an ideal material to interface with human blood. Zn²⁺‐charged melanin can remove 90% ofE. coliand 100% of endotoxin in PBS and human blood. Zn²⁺‐melanin also demonstrated excellent hemocompatibility shown by protein adsorption, blood coagulation, and hemolysis tests.

 

The local explanation provides heatmaps on images to explain how Convolutional Neural Networks (CNNs) derive their output. Due to its visual straightforwardness, the method has been one of the most popular explainable AI (XAI) methods for diagnosing CNNs. Through our formative study (S1), however, we captured ML engineers' ambivalent perspective about the local explanation as a valuable and indispensable envision in building CNNs versus the process that exhausts them due to the heuristic nature of detecting vulnerability. Moreover, steering the CNNs based on the vulnerability learned from the diagnosis seemed highly challenging. To mitigate the gap, we designed DeepFuse, the first interactive design that realizes the direct feedback loop between a user and CNNs in diagnosing and revising CNN's vulnerability using local explanations. DeepFuse helps CNN engineers to systemically search unreasonable local explanations and annotate the new boundaries for those identified as unreasonable in a labor-efficient manner. Next, it steers the model based on the given annotation such that the model doesn't introduce similar mistakes. We conducted a two-day study (S2) with 12 experienced CNN engineers. Using DeepFuse, participants made a more accurate and reasonable model than the current state-of-the-art. Also, participants found the way DeepFuse guides case-based reasoning can practically improve their current practice. We provide implications for design that explain how future HCI-driven design can move our practice forward to make XAI-driven insights more actionable.

 

A unified approach to mono- and disubstituted N–H indoles is described by means of oxidative cyclization of 2-alkenyl anilines, which are prepared by cross-coupling of the corresponding o-bromoanilines. This procedure is operationally expedient and tolerant of common functional groups to allow regiospecific installation of the alkyl and aryl substituents.

 

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. 

Anisotropic planar polaritons - hybrid electromagnetic modes mediated by phonons, plasmons, or excitons - in biaxial two-dimensional (2D) van der Waals crystals have attracted significant attention due to their fundamental physics and potential nanophotonic applications. In this Perspective, we review the properties of planar hyperbolic polaritons and the variety of methods that can be used to experimentally tune them. We argue that such natural, planar hyperbolic media should be fairly common in biaxial and uniaxial 2D and 1D van der Waals crystals, and identify the untapped opportunities they could enable for functional (i.e. ferromagnetic, ferroelectric, and piezoelectric) polaritons. Lastly, we provide our perspectives on the technological applications of such planar hyperbolic polaritons.

 

Connecting pre-bent liquid crystal elastomer fibers into a loop generates a self-regulated synchronized motion with snap through. 

Abstract We present a chemodynamical analysis of 11,562 metal-rich, high-eccentricity halo-like main-sequence stars, which have been referred to as the Splash or Splashed Disk, selected from the Sloan Digital Sky Survey and Large Sky Area Multi-Object Fiber Spectroscopic Telescope. When divided into two groups, a low-[ α /Fe] population (LAP) and a high-[ α /Fe] population (HAP), based on kinematics and chemistry, we find that they exhibit very distinct properties, indicative of different origins. From a detailed analysis of their orbital inclinations, we suggest that the HAP arises from a large fraction (∼90%) of heated disk stars and a small fraction (∼10%) of in situ stars from a starburst population, likely induced by interaction of the Milky Way with the Gaia-Sausage/Enceladus (GSE) or another early merger. The LAP comprises about half accreted stars from the GSE and half formed by the GSE-induced starburst. Our findings further imply that the Splash stars in our sample originated from at least three different mechanisms: accretion, disk heating, and a merger-induced starburst.