Enhanced compound-protein binding affinity prediction by representing protein multimodal information via a coevolutionary strategy.

Due to the lack of a method to efficiently represent the multimodal information of a protein, including its structure and sequence information, predicting compound-protein binding affinity (CPA) still suffers from low accuracy when applying machine-learning methods. To overcome this limitation, in a novel end-to-end architecture (named FeatNN), we develop a coevolutionary strategy to jointly represent the structure and sequence features of proteins and ultimately optimize the mathematical models for predicting CPA. Furthermore, from the perspective of data-driven approach, we proposed a rational method that can utilize both high- and low-quality databases to optimize the accuracy and generalization ability of FeatNN in CPA prediction tasks. Notably, we visually interpret the feature interaction process between sequence and structure in the rationally designed architecture. As a result, FeatNN considerably outperforms the state-of-the-art (SOTA) baseline in virtual drug evaluation tasks, indicating the feasibility of this approach for practical use. FeatNN provides an outstanding method for higher CPA prediction accuracy and better generalization ability by efficiently representing multimodal information of proteins via a coevolutionary strategy.

Experimental Study on Damage Effect of Mid-Infrared Pulsed Laser on Charge Coupled Device (CCD) and HgCgTe Detectors.

As the weak link in electro-optical imaging systems, photodetectors have always faced the threat of laser damage. In this paper, we experimentally investigated the damage mechanism of the photodetector induced by the out-of-band laser. The damage thresholds of the mid-infrared pulsed laser for Charge Coupled Device (CCD) and HgCdTe detectors were determined through damage experiments. The analysis of the damage phenomena and data for both CCD and HgCdTe detectors clearly demonstrated that out-of-band mid-infrared pulsed lasers could entirely incapacitate CCD and HgCdTe detectors. Our analysis of the damage process and data revealed that the primary mechanism of damage to CCD and HgCdTe detectors by mid-infrared pulsed lasers was primarily thermal. This study serves as a reference for further research on the mid-infrared pulsed laser damage mechanisms of CCD and HgCdTe detectors, as well as for laser protection and performance optimization in imaging systems.

Dazzling Evaluation of the Impact of a High-Repetition-Rate CO2 Pulsed Laser on Infrared Imaging Systems.

This article utilizes the Canny edge extraction algorithm based on contour curvature and the cross-correlation template matching algorithm to extensively study the impact of a high-repetition-rate CO2 pulsed laser on the target extraction and tracking performance of an infrared imaging detector. It establishes a quantified dazzling pattern for lasers on infrared imaging systems. By conducting laser dazzling and damage experiments, a detailed analysis of the normalized correlation between the target and the dazzling images is performed to quantitatively describe the laser dazzling effects. Simultaneously, an evaluation system, including target distance and laser power evaluation factors, is established to determine the dazzling level and whether the target is recognizable. The research results reveal that the laser power and target position are crucial factors affecting the detection performance of infrared imaging detector systems under laser dazzling. Different laser powers are required to successfully interfere with the recognition algorithm of the infrared imaging detector at different distances. And laser dazzling produces a considerable quantity of false edge information, which seriously affects the performance of the pattern recognition algorithm. In laser damage experiments, the detector experienced functional damage, with a quarter of the image displaying as completely black. The energy density threshold required for the functional damage of the detector is approximately 3 J/cm2. The dazzling assessment conclusions also apply to the evaluation of the damage results. Finally, the proposed evaluation formula aligns with the experimental results, objectively reflecting the actual impact of laser dazzling on the target extraction and the tracking performance of infrared imaging systems. This study provides an in-depth and accurate analysis for understanding the influence of lasers on the performance of infrared imaging detectors.

Digital Modeling on Large Kernel Metamaterial Neural Network.

Deep neural networks (DNNs) utilized recently are physically deployed with computational units (e.g., CPUs and GPUs). Such a design might lead to a heavy computational burden, significant latency, and intensive power consumption, which are critical limitations in applications such as the Internet of Things (IoT), edge computing, and the usage of drones. Recent advances in optical computational units (e.g., metamaterial) have shed light on energy-free and light-speed neural networks. However, the digital design of the metamaterial neural network (MNN) is fundamentally limited by its physical limitations, such as precision, noise, and bandwidth during fabrication. Moreover, the unique advantages of MNN's (e.g., light-speed computation) are not fully explored via standard 3×3 convolution kernels. In this paper, we propose a novel large kernel metamaterial neural network (LMNN) that maximizes the digital capacity of the state-of-the-art (SOTA) MNN with model re-parametrization and network compression, while also considering the optical limitation explicitly. The new digital learning scheme can maximize the learning capacity of MNN while modeling the physical restrictions of meta-optic. With the proposed LMNN, the computation cost of the convolutional front-end can be offloaded into fabricated optical hardware. The experimental results on two publicly available datasets demonstrate that the optimized hybrid design improved classification accuracy while reducing computational latency. The development of the proposed LMNN is a promising step towards the ultimate goal of energy-free and light-speed AI.

Engineering a CRISPR interference system targeting AcrAB-TolC efflux pump to prevent multidrug resistance development in Escherichia coli.

OBJECTIVES: We engineered a CRISPR interference (CRISPRi) system targeting the AcrAB-TolC efflux pump to prevent MDR development in Escherichia coli. METHODS: Nine specific single-guide RNAs (sgRNAs) were designed to target the components of the AcrAB-TolC efflux pump, namely AcrA, AcrB and TolC. A total of thirteen CRISPRi recombinant plasmids were constructed with single or clustered sgRNAs. The transcriptional levels of the target genes, MICs of multiple antibiotics and biofilm formation in each CRISPRi strain were tested. RESULTS: The CRISPRi system expressing sgRNA clusters targeting acrB and tolC simultaneously exhibited the highest inhibitory effect on AcrAB-TolC efflux pump activity in E. coli HB101, with 78.3%, 90.0% and 65.4% inhibition rates on the transcriptional levels of acrA, acrB and tolC, respectively. The CRISPRi system resulted in ∼2-, ∼8- and 16-fold increased susceptibility to rifampicin, erythromycin and tetracycline, respectively. In addition, the constructed CRISPRi system reduced biofilm formation with inhibition rates in the range of 11.2% to 58.2%. CONCLUSIONS: To the best of our knowledge, this is the first report on the construction of an inducible CRISPRi system targeting the AcrAB-TolC efflux pump to prevent MDR development in E. coli. This study provides insights for future regulation and manipulation of AcrAB-TolC activity and bacterial MDR by a CRISPRi system.

Structural characterization and biological activities of a new polysaccharide isolated from Morchella Sextelata.

Morchella is the famous medicinal fungi in the ascomycetes. In this study, a new water-soluble polysaccharide (MSP-3-1) with an average molecular weight of 2.35 × 107 Da was extracted and purified from fruiting bodies of cultivated M. Sextelata. The structural characterization and biological activities of purified polysaccharide was further investigated. The results indicated that MSP-3-1 was mainly a α-glucan, mainly consisting of mannose (Man), glucose (Glc) and galactose (Gal) in a ratio of 5.10: 91.39: 3.51. Its surface morphology exhibited irregular lamellar structures with small voids. And the particle size analysis showed that MSP-3-1 was the homogeneous nanoparticle in water solution. Furthermore, the antioxidant activity analysis showed that MSP-3-1 possessed certain scavenging activity against hydroxyl radicals, DPPH radicals and ABTS radicals in a dose-dependent manner. Immunological tests suggested that MSP-3-1 could significantly promote the proliferation, phagocytosis and nitric oxide (NO) production of macrophage RAW264.7. Thus, our results will provide a theoretical basis for the development and utilization of Morchella Sextelata polysaccharides as an immunmodulatory component in functional foods.

Reconfigurable Metasurface for Image Processing.

Optical Fourier transform-based processing is an attractive technique due to the fast processing times and large-data rates. Furthermore, it has recently been demonstrated that certain Fourier-based processors can be realized in compact form factors using flat optics. The flat optics, however, have been demonstrated as static filters where the operator is fixed, limiting the applicability of the approach. Here, we demonstrate a reconfigurable metasurface that can be dynamically tuned to provide a range of processing modalities including bright-field imaging, low-pass and high-pass filtering, and second-order differentiation. The dynamically tunable metasurface can be directly combined with standard coherent imaging systems and operates with a numerical aperture up to 0.25 and over a 60 nm bandwidth. The ability to dynamically control light in the wave vector domain, while doing so in a compact form factor, may open new doors to applications in microscopy, machine vision, and sensing.

Broadband and large-aperture metasurface edge encoders for incoherent infrared radiation.

The prevalence of computer vision systems necessitates hardware-based approaches to relieve the high computational demand of deep neural networks in resource-limited applications. One solution would be to off-load low-level image feature extraction, such as edge detection, from the digital network to the analog imaging system. To that end, this work demonstrates incoherent, broadband, low-noise optical edge detection of real-world scenes by combining the wavefront shaping of a 24-mm aperture metasurface with a refractive lens. An inverse design approach is used to optimize the metasurface for Laplacian-based edge detection across the 7.5- to 13.5-µm LWIR imaging band, allowing for facile integration with uncooled microbolometer-based LWIR imagers to encode edge information. A polarization multiplexed approach leveraging a birefringent metasurface is also demonstrated as a single-aperture implementation. This work could be applied to improve computer vision capabilities of resource-constrained systems by leveraging optical preprocessing to alleviate the computational requirements for high-accuracy image segmentation and classification.

Exploring the nexus between green finance and energy efficiency: Unravelling the impact through green technology innovation and energy structure.

This study delves into the intricate relationship between green finance and energy efficiency, focusing on how green technology innovation and energy structure transformations contribute to this dynamic. Utilizing panel data from China's provinces over the period 2015-2022, the research aims to uncover the nuances of how green finance can serve as a catalyst for enhancing energy efficiency across different regions. The objective is to quantify the impact of green finance on energy efficiency, considering the mediating roles of green technology innovation and shifts in energy structure. The analysis employs a sophisticated panel entropy weighting technique to analyze the data, ensuring a robust examination of the relationships between these variables. The results reveal a significant positive impact of green finance on energy efficiency, mediated by advances in green technology and modifications in the energy structure towards more sustainable forms. Specifically, regions with higher engagement in green finance initiatives demonstrated marked improvements in energy efficiency, attributed to substantial investments in green technologies and a gradual shift away from traditional, inefficient energy sources. These findings underscore the pivotal role of green finance in driving the transition towards a more energy-efficient and sustainable economic model. Policy implications drawn from this study suggest that targeted financial policies promoting green investments can significantly bolster energy efficiency.

Cross-Modality Medical Image Segmentation via Enhanced Feature Alignment and Cross Pseudo Supervision Learning.

Given the diversity of medical images, traditional image segmentation models face the issue of domain shift. Unsupervised domain adaptation (UDA) methods have emerged as a pivotal strategy for cross modality analysis. These methods typically utilize generative adversarial networks (GANs) for both image-level and feature-level domain adaptation through the transformation and reconstruction of images, assuming the features between domains are well-aligned. However, this assumption falters with significant gaps between different medical image modalities, such as MRI and CT. These gaps hinder the effective training of segmentation networks with cross-modality images and can lead to misleading training guidance and instability. To address these challenges, this paper introduces a novel approach comprising a cross-modality feature alignment sub-network and a cross pseudo supervised dual-stream segmentation sub-network. These components work together to bridge domain discrepancies more effectively and ensure a stable training environment. The feature alignment sub-network is designed for the bidirectional alignment of features between the source and target domains, incorporating a self-attention module to aid in learning structurally consistent and relevant information. The segmentation sub-network leverages an enhanced cross-pseudo-supervised loss to harmonize the output of the two segmentation networks, assessing pseudo-distances between domains to improve the pseudo-label quality and thus enhancing the overall learning efficiency of the framework. This method's success is demonstrated by notable advancements in segmentation precision across target domains for abdomen and brain tasks.

Multichannel meta-imagers for accelerating machine vision.

Rapid developments in machine vision technology have impacted a variety of applications, such as medical devices and autonomous driving systems. These achievements, however, typically necessitate digital neural networks with the downside of heavy computational requirements and consequent high energy consumption. As a result, real-time decision-making is hindered when computational resources are not readily accessible. Here we report a meta-imager designed to work together with a digital back end to offload computationally expensive convolution operations into high-speed, low-power optics. In this architecture, metasurfaces enable both angle and polarization multiplexing to create multiple information channels that perform positively and negatively valued convolution operations in a single shot. We use our meta-imager for object classification, achieving 98.6% accuracy in handwritten digits and 88.8% accuracy in fashion images. Owing to its compactness, high speed and low power consumption, our approach could find a wide range of applications in artificial intelligence and machine vision applications.

Impact of digital empowerment on labor employment in manufacturing enterprises: Evidence from China.

Many studies have examined the influence of digital technologies, such as robots and artificial intelligence, on enterprise labor, but few have investigated the underlying mechanisms and impact paths of digital empowerment on labor employment. Therefore, this study uses data on manufacturing enterprises listed on China's Shanghai and Shenzhen A-share markets from 2011 to 2020, and applies a panel fixed effect model to test the relationship between digital empowerment and labor employment, and the mechanisms underlying this relationship. We find that digital empowerment increases labor employment. However, the effects are heterogeneous: firms with better corporate governance, more competitive industry, and less favorable regional business environments are more motivated to optimize the structure of their labor resources. Through robustness test and mediation effect model test, we find that digital empowerment can improve enterprise human capital by increasing economies scale and managerial efficiency, especially the employment of R&D and innovation personnel and management personnel; it can also affect the amount of human capital by improving total factor productivity.

Develop Targeted Protein Drug Carriers through a High-Throughput Screening Platform and Rational Design.

Protein-based drugs offer advantages, such as high specificity, low toxicity, and minimal side effects compared to small molecule drugs. However, delivery of proteins to target tissues or cells remains challenging due to the instability, diverse structures, charges, and molecular weights of proteins. Polymers have emerged as a leading choice for designing effective protein delivery systems, but identifying a suitable polymer for a given protein is complicated by the complexity of both proteins and polymers. To address this challenge, a fluorescence-based high-throughput screening platform called ProMatch to efficiently collect data on protein-polymer interactions, followed by in vivo and in vitro experiments with rational design is developed. Using this approach to streamline polymer selection for targeted protein delivery, candidate polymers from commercially available options are identified and a polyhexamethylene biguanide (PHMB)-based system for delivering proteins to white adipose tissue as a treatment for obesity is developed. A branched polyethylenimine (bPEI)-based system for neuron-specific protein delivery to stimulate optic nerve regeneration is also developed. The high-throughput screening methodology expedites identification of promising polymer candidates for tissue-specific protein delivery systems, thereby providing a platform to develop innovative protein-based therapeutics.

Synthesis, characterization, and cytotoxicity analysis of selenium nanoparticles stabilized by Morchella sextelata polysaccharide.

Natural bioactive molecules have been widely used as stabilizers in the functional improvement of selenium nanoparticles (SeNPs) in recent years. In this study, Morchella sextelata polysaccharide (MSP) was introduced as a novel stabilizer for the synthesis of SeNPs based on the redox system of sodium selenite and ascorbic acid. The size, morphology, stability, and anti-cancer cell activities were respectively analyzed by various methods. The results showed that the synthesized SeNPs with MSP were 72.07 ± 0.53 nm in size, red in color, spherical in shape, and amorphous in nature. MSP-SeNPs showed high scavenging activity against DPPH and ABTS radicals. And, these MSP-SeNPs exhibited a significant anti-proliferation effect on human liver (HepG2) and cervical cancer (Hela) cells in vitro, while no significant cytotoxicity against normal human kidney cells (HK-2) was observed. Moreover, the mitochondria-dependent apoptosis pathway triggered by MSP-SeNPs in HepG2 cell was identified. The expression levels of p53, Bax, cytochrome c, caspase-3 and caspase-9 were all up-regulated in HepG2 cells after MSP-SeNPs treatment, while Bcl-2 expression was down-regulated. These results suggest that MSP-SeNPs have strong potential as the food supplement for application in cancer chemoprevention.

Controlled delivery of a neurotransmitter-agonist conjugate for functional recovery after severe spinal cord injury.

Despite considerable unmet medical needs, effective pharmacological treatments that promote functional recovery after spinal cord injury remain limited. Although multiple pathological events are implicated in spinal cord injuries, the development of a microinvasive pharmacological approach that simultaneously targets the different mechanisms involved in spinal cord injury remains a formidable challenge. Here we report the development of a microinvasive nanodrug delivery system that consists of amphiphilic copolymers responsive to reactive oxygen species and an encapsulated neurotransmitter-conjugated KCC2 agonist. Upon intravenous administration, the nanodrugs enter the injured spinal cord due to a disruption in the blood-spinal cord barrier and disassembly due to damage-triggered reactive oxygen species. The nanodrugs exhibit dual functions in the injured spinal cord: scavenging accumulated reactive oxygen species in the lesion, thereby protecting spared tissues, and facilitating the integration of spared circuits into the host spinal cord through targeted modulation of inhibitory neurons. This microinvasive treatment leads to notable functional recovery in rats with contusive spinal cord injury.

Porous microneedle patch with sustained delivery of extracellular vesicles mitigates severe spinal cord injury.

The transplantation of mesenchymal stem cells-derived secretome, particularly extracellular vesicles is a promising therapy to suppress spinal cord injury-triggered neuroinflammation. However, efficient delivery of extracellular vesicles to the injured spinal cord, with minimal damage, remains a challenge. Here we present a device for the delivery of extracellular vesicles to treat spinal cord injury. We show that the device incorporating mesenchymal stem cells and porous microneedles enables the delivery of extracellular vesicles. We demonstrate that topical application to the spinal cord lesion beneath the spinal dura, does not damage the lesion. We evaluate the efficacy of our device in a contusive spinal cord injury model and find that it reduces the cavity and scar tissue formation, promotes angiogenesis, and improves survival of nearby tissues and axons. Importantly, the sustained delivery of extracellular vesicles for at least 7 days results in significant functional recovery. Thus, our device provides an efficient and sustained extracellular vesicles delivery platform for spinal cord injury treatment.

Downregulation of UBE4B promotes CNS axon regrowth and functional recovery after stroke.

The limited intrinsic regrowth capacity of corticospinal axons impedes functional recovery after cortical stroke. Although the mammalian target of rapamycin (mTOR) and p53 pathways have been identified as the key intrinsic pathways regulating CNS axon regrowth, little is known about the key upstream regulatory mechanism by which these two major pathways control CNS axon regrowth. By screening genes that regulate ubiquitin-mediated degradation of the p53 proteins in mice, we found that ubiquitination factor E4B (UBE4B) represses axonal regrowth in retinal ganglion cells and corticospinal neurons. We found that axonal regrowth induced by UBE4B depletion depended on the cooperative activation of p53 and mTOR. Importantly, overexpression of UbV.E4B, a competitive inhibitor of UBE4B, in corticospinal neurons promoted corticospinal axon sprouting and facilitated the recovery of corticospinal axon-dependent function in a cortical stroke model. Thus, our findings provide a translatable strategy for restoring corticospinal tract-dependent functions after cortical stroke.

Rapid and selective detection of Bacillus cereus in food using cDNA-based up-conversion fluorescence spectrum copy and aptamer modified magnetic separation.

A sensitive luminescent bioassay for the detection of Bacillus cereus (B. cereus), a common bacterium, harmful to human health, was established based on up-conversion fluorescence and magnetic separation technology. Herein, aptamers (Apt) are modified on the surface of magnetic nanoparticles (MNPs) to form Apt-MNPs capture probes. The aptamer complementary strands (cDNA) are connected to upconversion nanoparticles (UCNPs) to form UCNPs-cDNA signal probes. In the absence of analyte, the UCNPs-cDNA-Apt-MNPs complex will be formed due to the specific binding between the aptamer and the complementary strand. In the presence of B. cereus, the amount of free UCNPs-cDNA increased in the system, which ultimately increased the fluorescence intensity of the solution. Hence, when the UCNPs-cDNA-Apt-MNPs system was excited by a 980 nm near-infrared light, a decrease in the fluorescence of the complex was observed at 548 nm due to the detachment of UCNPs-cDNA. Therefore, based on this principle, the calibration curve is constructed between the concentration of the analyte (B. cereus) and the fluorescence intensity. The results show that the method has a good quantitative ability for B. cereus in the range of 49-49 × 106 cfu/mL under the optimal conditions with a detection limit of 22 cfu/mL. Moreover, the proposed detection method also exhibits a high degree of specificity. The spiked recovery rate of the actual sample was in the range of 90.54%-111.28%, with good relative standard deviation values (2.12%-3.13%), indicating that the method has good reproducibility and stability. This study demonstrates that the constructed method can be used successfully for the rapid detection of B. cereus in food.

Compound Meta-Optics for Complete and Loss-Less Field Control.

Optical metasurfaces offer a compact platform for manipulation of the amplitude, phase, and polarization state of light. Independent control over these properties, however, is hindered by the symmetric transmission matrix associated with single-layer metasurfaces. Here, we utilize multilayer birefringent meta-optics to realize high-efficiency, independent control over the amplitude, phase, and polarization state of light. High-efficiency control is enabled by redistributing the wavefront between cascaded metasurfaces, while end-to-end inverse design is used to realize independent complex-valued functions for orthogonal polarization states. Based on this platform, we demonstrate spatial mode division multiplexing, optical mode conversion, and universal vectorial holograms, all with diffraction efficiencies over 80%. This meta-optic platform expands the design space of flat optics and could lead to advances in optical communications, quantum entanglement, and information encryption.

Meta-optic accelerators for object classifiers.

Rapid advances in deep learning have led to paradigm shifts in a number of fields, from medical image analysis to autonomous systems. These advances, however, have resulted in digital neural networks with large computational requirements, resulting in high energy consumption and limitations in real-time decision-making when computation resources are limited. Here, we demonstrate a meta-optic-based neural network accelerator that can off-load computationally expensive convolution operations into high-speed and low-power optics. In this architecture, metasurfaces enable both spatial multiplexing and additional information channels, such as polarization, in object classification. End-to-end design is used to co-optimize the optical and digital systems, resulting in a robust classifier that achieves 93.1% accurate classification of handwriting digits and 93.8% accuracy in classifying both the digit and its polarization state. This approach could enable compact, high-speed, and low-power image and information processing systems for a wide range of applications in machine vision and artificial intelligence.