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The inherent shortcomings of a zinc anode in aqueous zinc‐ion batteries (ZIBs) such as zinc dendrites and side reactions severely limit their practical application. Herein, to address these issues, an ion‐oriented transport channel constructed by graphdiyne (GDY) nanowalls is designed and grown in situ on the surface of a zinc electrode. The vertically stacked GDY nanowalls with a unique hierarchical porous structure and mechanical properties form a nanomesh‐like interface on the zinc electrode, acting as an ion‐oriented channel, which can efficiently confine the segmented growth of zinc metal in microscopic regions of hundreds of nanometers. In those microscopic regions, the uniform domain current density is effortlessly maintained compared with a large surface area, thereby inhibiting zinc dendrites effectively. Besides, due to the presence of the ion‐oriented channel, the modified zinc anode demonstrates long‐term stable zinc plating/stripping performance for more than 600 h at 1 mAh cm−2 in an aqueous electrolyte. In addition, full‐cells coupled with MnO2 show high specific capacity and power density, as well as excellent cycling stability with a capacity retention of 82% after 5000 cycles at 1 A g−1. This work provides a feasible and accessible surface engineering approach to modify the electrode interface for confined and dendrite‐free zinc deposition in aqueous ZIBs. A graphdiyne nanowall with a vertically stacked structure is in situ prepared as a functional nanomesh interface on a zinc electrode for aqueous zinc‐ion batteries. Intriguing properties of graphdiyne nanowalls enable reversible interactions with Zn2+ ions and confined zinc deposition. The resultant ion‐oriented channel on the zinc anode demonstrates a dendrite‐free zinc anode and excellent cycling stability.

In the quest for computational efficiency, binary neural networks (BNNs) have emerged as a promising paradigm, offering significant reductions in memory footprint and computational latency. In traditional BNN implementation, the first and last layers are typically full-precision, which causes higher logic usage in field-programmable gate array (FPGA) implementation. To solve these issues, we introduce a novel approach named Ponte (Represent Totally Binary Neural Network Toward Efficiency) that extends the binarization process to the first and last layers of BNNs. We challenge the convention by proposing a fully binary layer replacement that mitigates the computational overhead without compromising accuracy. Our method leverages a unique encoding technique, Ponte::encoding, and a channel duplication strategy, Ponte::dispatch, and Ponte::sharing, to address the non-linearity and capacity constraints posed by binary layers. Surprisingly, all of them are back-propagation-supported, which allows our work to be implemented in the last layer through extensive experimentation on benchmark datasets, including CIFAR-10 and ImageNet. We demonstrate that Ponte not only preserves the integrity of input data but also enhances the representational capacity of BNNs. The proposed architecture achieves comparable, if not superior, performance metrics while significantly reducing the computational demands, thereby marking a step forward in the practical deployment of BNNs in resource-constrained environments.

Suberin is a hydrophobic biopolymer that can be deposited at the periphery of cells, forming protective barriers against biotic and abiotic stress. In roots, suberin forms lamellae at the periphery of endodermal cells where it plays crucial roles in the control of water and mineral transport. Suberin formation is highly regulated by developmental and environmental cues. However, the mechanisms controlling its spatiotemporal regulation are poorly understood. Here, we show that endodermal suberin is regulated independently by developmental and exogenous signals to fine-tune suberin deposition in roots. We found a set of four MYB transcription factors (MYB41, MYB53, MYB92, and MYB93), each of which is individually regulated by these two signals and is sufficient to promote endodermal suberin. Mutation of these four transcription factors simultaneously through genome editing leads to a dramatic reduction in suberin formation in response to both developmental and environmental signals. Most suberin mutants analyzed at physiological levels are also affected in another endodermal barrier made of lignin (Casparian strips) through a compensatory mechanism. Through the functional analysis of these four MYBs, we generated plants allowing unbiased investigation of endodermal suberin function, without accounting for confounding effects due to Casparian strip defects, and were able to unravel specific roles of suberin in nutrient homeostasis.

Developing stable electrodes for seawater splitting remains a great challenge due to the detachment of catalysts at a large operating current and severe anode corrosion caused by chlorine. Herein, divalent anion intercalation and etching-hydrolysis strategies are deployed to synthesize the ultra-stable anode, dendritic Fe(OH) 3 grown on Ni(SO 4 ) 0.3 (OH) 1.4 -Ni(OH) 2 . Experimental results reveal that the anode exhibits good activity and excellent stability in alkaline simulated seawater. After 500 h, the current density operated at 1.72 V remains 99.5%, about 210 mA cm −2 . The outstanding stability originates from the etching-hydrolysis strategy, which strengthens the interaction between the catalyst and the carrier and retards thus the detachment of catalysts at a large current density. Besides, theoretical simulations confirm that the intercalated divalent anions, such as SO 4 2− and CO 3 2− , can weaken the adsorption strength of chlorine on the surface of catalysts and hinder the coupling and hybridization between chlorine and nickel, which slows down the anode corrosion and improves catalytic stability. Furthermore, the two-electrode system shows the remarkable 95.1% energy efficiency at 2,000 A m −2 and outstanding stability in 6 mol L −1 KOH + seawater at 80 °C.

• RBOHF from Arabidopsis thaliana represents a multifunctional NADPH oxidase regulating biotic and abiotic stress tolerance, developmental processes and guard cell aperture. The molecular components and mechanisms determining RBOHF activity remain to be elucidated. • Here we combined protein interaction studies, biochemical and genetic approaches, and pathway reconstitution analyses to identify and characterize proteins that confer RBOHF regulation and elucidated mechanisms that adjust RBOHF activity. • While the Ca2+ sensor-activated kinases CIPK11 and CIPK26 constitute alternative paths for RBOHF activation, the combined activity of CIPKs and the kinase open stomata 1 (OST1) triggers complementary activation of this NADPH oxidase, which is efficiently counteracted through dephosphorylation by the phosphatase ABI1. Within RBOHF, several distinct phosphorylation sites (p-sites) in the N-terminus of RBOHF appear to contribute individually to activity regulation. • These findings identify RBOHF as a convergence point targeted by a complex regulatory network of kinases and phosphatases. We propose that this allows for fine-tuning of plant reactive oxygen species (ROS) production by RBOHF in response to different stimuli and in diverse physiological processes.

Omicron SARS-CoV-2 is rapidly spreading worldwide. To delineate the impact of emerging mutations on spike’s properties, we performed systematic structural analyses on apo Omicron spike and its complexes with human ACE2 or S309 neutralizing antibody (NAb) by cryo-EM. The Omicron spike preferentially adopts the one-RBD-up conformation both before and after ACE2 binding, which is in sharp contrast to the orchestrated conformational changes to create more up-RBDs upon ACE2 binding as observed in the prototype and other four variants of concern (VOCs). Furthermore, we found that S371L, S373P and S375F substitutions enhance the stability of the one-RBD-up conformation to prevent exposing more up-RBDs triggered by ACE2 binding. The increased stability of the one-RBD-up conformation restricts the accessibility of S304 NAb, which targets a cryptic epitope in the closed conformation, thus facilitating the immune evasion by Omicron. These results expand our understanding of Omicron spike’s conformation, receptor binding and antibody evasion mechanism. The SARS-CoV-2 Omicron variant spreads rapidly. Here the authors show that Omicron S preferentially adopts the one-RBD-up conformation, which leads to a non-RBM-binding monoclonal antibody escape. Mutagenesis reveals that S371L, S373P and S375F substitutions enhance the conformational stability.

Reconfigurable processor-based acceleration of deep convolutional neural network (DCNN) algorithms has emerged as a widely adopted technique, with particular attention on sparse neural network acceleration as an active research area. However, many computing devices that claim high computational power still struggle to execute neural network algorithms with optimal efficiency, low latency, and minimal power consumption. Consequently, there remains significant potential for further exploration into improving the efficiency, latency, and power consumption of neural network accelerators across diverse computational scenarios. This paper investigates three key techniques for hardware acceleration of sparse neural networks. The main contributions are as follows: (1) Most neural network inference tasks are typically executed on general-purpose computing devices, which often fail to deliver high energy efficiency and are not well-suited for accelerating sparse convolutional models. In this work, we propose a specialized computational circuit for the convolutional operations of sparse neural networks. This circuit is designed to detect and eliminate the computational effort associated with zero values in the sparse convolutional kernels, thereby enhancing energy efficiency. (2) The data access patterns in convolutional neural networks introduce significant pressure on the high-latency off-chip memory access process. Due to issues such as data discontinuity, the data reading unit often fails to fully exploit the available bandwidth during off-chip read and write operations. In this paper, we analyze bandwidth utilization in the context of convolutional accelerator data handling and propose a strategy to improve off-chip access efficiency. Specifically, we leverage a compiler optimization plugin developed for Vitis HLS, which automatically identifies and optimizes on-chip bandwidth utilization. (3) In coefficient-based accelerators, the synchronous operation of individual computational units can significantly hinder efficiency. Previous approaches have achieved asynchronous convolution by designing separate memory units for each computational unit; however, this method consumes a substantial amount of on-chip memory resources. To address this issue, we propose a shared feature map cache design for asynchronous convolution in the accelerators presented in this paper. This design resolves address access conflicts when multiple computational units concurrently access a set of caches by utilizing a hash-based address indexing algorithm. Moreover, the shared cache architecture reduces data redundancy and conserves on-chip resources. Using the optimized accelerator, we successfully executed ResNet50 inference on an Intel Arria 10 1150GX FPGA, achieving a throughput of 497 GOPS, or an equivalent computational power of 1579 GOPS, with a power consumption of only 22 watts.

Uncovering human mobility patterns is of fundamental importance to the understanding of epidemic spreading, urban transportation and other socioeconomic dynamics embodying spatiality and human travel. According to the direct travel diaries of volunteers, we show the absence of scaling properties in the displacement distribution at the individual level,while the aggregated displacement distribution follows a power law with an exponential cutoff. Given the constraint on total travelling cost, this aggregated scaling law can be analytically predicted by the mixture nature of human travel under the principle of maximum entropy. A direct corollary of such theory is that the displacement distribution of a single mode of transportation should follow an exponential law, which also gets supportive evidences in known data. We thus conclude that the travelling cost shapes the displacement distribution at the aggregated level.

Digital video forensics plays a vital role in judicial forensics, media reports, e-commerce, finance, and public security. Although many methods have been developed, there is currently no efficient solution to real-life videos with illumination noises and jitter noises. To solve this issue, we propose a detection method that adapts to brightness and jitter for video inter-frame forgery. For videos with severe brightness changes, we relax the brightness constancy constraint and adopt intensity normalization to propose a new optical flow algorithm. For videos with large jitter noises, we introduce motion entropy to detect the jitter and extract the stable feature of texture changes fraction for double-checking. Experimental results show that, compared with previous algorithms, the proposed method is more accurate and robust for videos with significant brightness variance or videos with heavy jitter on public benchmark datasets.

The recent outbreak of mpox epidemic, caused by monkeypox virus (MPXV), poses a new threat to global public health. Here, we initially assessed the preexisting antibody level to the MPXV B6 protein in vaccinia vaccinees born before the end of the immunization program and then identified two monoclonal antibodies (MAbs), hMB621 and hMB668, targeting distinct epitopes on B6, from one vaccinee. Binding assays demonstrate that both MAbs exhibit broad binding abilities to B6 and its orthologs in vaccinia (VACV), variola (VARV) and cowpox viruses (CPXV). Neutralizing assays reveal that the two MAbs showed potent neutralization against VACV. Animal experiments using a BALB/c female mouse model indicate that the two MAbs showed effective protection against VACV via intraperitoneal injection. Additionally, we determined the complex structure of B6 and hMB668, revealing the structural feature of B6 and the epitope of hMB668. Collectively, our study provides two promising antibody candidates for the treatment of orthopoxvirus infections, including mpox. There are limited therapeutics available for treatment of mpox. In this study, the authors identify two non-competing human neutralizing monoclonal antibodies with protective effects against orthopoxvirus infection in a mouse model and structurally resolve the targeted epitope within the MPXV B6 protein.