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ObjectiveTo analyze the whole-genome characteristics of four coxsackievirus B5 (CVB5) strains isolated in Yunnan province, China in 2022. MethodsPrimers were designed to amplify and sequence the whole genome of CVB5 isolates. The whole genome was amplified in segments, and sequenced using the "primer walking" method. Sequence splicing and assembly were performed. Genetic characteristics of the CVB5 strains were analyzed using software such as MEGA 7.0, Simplot 3.5.1, and DNAStar 7.1. ResultsBased on VP1 and whole-genome phylogenetic analysis, all four CVB5 isolates belonged to genotype C. Compared with the prototype strain, multiple amino acid mutations occurred in the VP1 region of the four strains. Phylogenetic analysis of P1, P2, and P3 regions and Simplot recombination analysis showed that the CVB5 isolates in this study may have undergone recombination with multiple enterovirus B strains. ConclusionThe four CVB5 strains isolated from clinical samples of hand, foot and mouth disease (HFMD) patients in Kunming, Yunnan province in 2022 all belonged to genotype C.

Abstract MoS2 as a typical layered transition metal dichalcogenide (LTMD) has attracted considerable attention to work as sodium host materials for sodium‐ion batteries (SIBs). However, it suffers from low semiconducting behavior and high Na+ diffusion barriers. Herein, intercalation of N‐doped amorphous carbon (NAC) into each interlayer of the tiny MoS2 nanosheets embedded on rGO conductive network is achieved, resulting in formation of rGO@MoS2/NAC hierarchy with interoverlapped MoS2/NAC superlattices for high‐performance SIBs. Attributed to intercalation of NAC, the resulting MoS2/NAC superlattices with wide MoS2 interlayer of 1.02 nm facilitates rapid Na+ insertion/extraction and accelerates reaction kinetics. Theoretical calculations uncover that the MoS2/NAC superlattices are beneficial for enhanced electron transport, decreased Na+ diffusion barrier and improved Na+ adsorption energy. The rGO@MoS2/NAC anode presents significantly improved high‐rate capabilities of 228, 207, and 166 mAh g−1 at 20, 30, and 50 A g−1, respectively, compared with two control samples of pristine MoS2 and MoS2/NAC counterparts. Excellent long‐term cyclability over 10 000 cycles with extremely low capacity decay is demonstrated at high current densities of 20 and 50 A g−1. Sodium‐ion full cells based on the rGO@MoS2/NAC anode are also demonstrated, yielding decent cycling stability of 200 cycles at 5C. Our work provides a novel interlayer strategy to regulate electron/Na+ transport for fast‐charging SIBs.

Abstract Background In recent years, natural bone extracellular matrix (ECM)-inspired materials have found widespread application as scaffolds for bone tissue engineering. However, the challenge of creating scaffolds that mimic natural bone ECM’s mechanical strength and hierarchical nano-micro-macro structures remains. The purposes of this study were to introduce an innovative bone ECM-inspired scaffold that integrates a 3D-printed framework with hydroxyapatite (HAp) mineralized graphene oxide-collagen (GO-Col) microscaffolds and find its application in the repair of mandibular bone defects. Methods Initially, a 3D-printed polycaprolactone (PCL) scaffold was designed with cubic disks and square pores to mimic the macrostructure of bone ECM. Subsequently, we developed multi-layer mineralized GO-Col-HAp microscaffolds (MLM GCH) to simulate natural bone ECM's nano- and microstructural features. Systematic in vitro and in vivo experiments were introduced to evaluate the ECM-inspired structure of the scaffold and to explore its effect on cell proliferation and its ability to repair rat bone defects. Results The resultant MLM GCH/PCL composite scaffolds exhibited robust mechanical strength and ample assembly space. Moreover, the ECM-inspired MLM GCH microscaffolds displayed favorable attributes such as water absorption and retention and demonstrated promising cell adsorption, proliferation, and osteogenic differentiation in vitro. The MLM GCH/PCL composite scaffolds exhibited successful bone regeneration within mandibular bone defects in vivo. Conclusions This study presents a well-conceived strategy for fabricating ECM-inspired scaffolds by integrating 3D-printed PCL frameworks with multilayer mineralized porous microscaffolds, enhancing cell proliferation, osteogenic differentiation, and bone regeneration. This construction approach holds the potential for extension to various other biomaterial types. Graphical Abstract

Loquat (Eriobotrya japonica) is a subtropical evergreen fruit tree with high economic benefits and nutritional value. AP2/ERF (APETALA2/ethylene responsive factor) are a wide range of transcription factors found in plants, which play an important role in regulating plant development and biological processes. However, the systematic identification and characterization of AP2/ERF genes in loquat remains unclear. In the present research, a total of 189 loquat AP2/ERF members were identified. According to the phylogenetic tree, these members were divided into three subfamilies, including AP2 (42), ERF (96), and DREB (51). Except for three genes located on the skeleton, most of the loquat AP2/ERF genes were unevenly distributed across all chromosomes. The collinearity results showed that 175 duplicate gene pairs were found in the AP2/ERF gene family of loquat. Ka/Ks results indicated that these genes underwent purification selection pressure. The syntenic analysis of AP2/ERF genes between Arabidopsis, pomegranate, peach, and loquat provided valuable clues for the potential evolution of AP2/ERF family in loquat. Cis-element analysis showed that most of the AP2/ERF genes in loquat had multiple cis-elements related to stress response, plant hormone signal transduction, and plant growth and development. Transcriptomic data indicated that the expression patterns of AP2/ERF genes exhibited diversity in different loquat cultivars during fruit development and ripening. The results of this study displayed the first comprehensive analysis of AP2/ERF genes in loquat, which provides a solid foundation for future research on the function of AP2/ERF genes in loquat.

BackgroundEGFR kinase domain duplication (EGFR-KDD) is an infrequent oncogenic driver mutation in lung adenocarcinoma. It may be a potential target benefit from EGFR-tyrosine kinase inhibitors (TKIs) treatment.Case presentationA 66-year-old Chinese male was diagnosed with lung adenocarcinoma in stage IVb with brain metastases. Next-generation sequencing revealed EGFR-KDD mutation. The patient received furmonertinib 160mg daily for anti-cancer treatment and obtained therapeutic efficacy with partial response (PR). Progression-free survival (PFS) duration from monotherapy was 16 months. With slow progressions, combined radiotherapy and anti-vascular targeted therapy also brought a continuous decrease in the tumors. The patient has an overall survival (OS) duration of more than 22 months and still benefits from double-dose furmonertinib.ConclusionsThis report provided direct evidence for the treatment of EGFR-KDD to use furmonertinib. A Large-scale study is needed to confirm this preliminary finding.

Polystyrene microplastics (PS-MPs) are new types of environmental pollutant that have garnered significant attention in recent years since they were found to cause damage to the human respiratory system when they are inhaled. The pulmonary fibrosis is one of the serious consequences of PS-MPs inhalation. However, the impact and underlying mechanisms of PS-MPs on pulmonary fibrosis are not clear. In this study, we studied the potential lung toxicity and PS-MPs-developed pulmonary fibrosis by long-term intranasal inhalation of PS-MPs. The results showed that after exposing to the PS-MPs, the lungs of model mouse had different levels of damage and fibrosis. Meanwhile, exposing to the PS-MPs resulted in a markedly decrease in glutathione (GSH), an increase in malondialdehyde (MDA), and iron overload in the lung tissue of mice and alveolar epithelial cells (AECs). These findings suggested the occurrence of PS-MP-induced ferroptosis. Inhibitor of ferroptosis (Fer-1) had alleviated the PS-MPs-induced ferroptosis. Mechanically, PS-MPs triggered cell ferroptosis and promoted the development of pulmonary fibrosis via activating the cGAS/STING signaling pathway. Inhibition of cGAS/STING with G150/H151 attenuated pulmonary fibrosis after PS-MPs exposure. Together, these data provided novel mechanistic insights of PS-MPs-induced pulmonary fibrosis and a potential therapeutic paradigm.

Layered transition metal dichalcogenides are promising candidates for sodium storage but suffering from low intrinsic electronic conductivity and limited interlayer spacing for fast electron/ion transport, which restricts their high-rate capability and cycling stability. In this work, rGO@MoSe2/NAC hierarchical architectures, consisting of conductive reduced graphene oxide (rGO) supported by hollow nanospheres that are rolled from superlattices of alternatively overlapped MoSe2 and N-doped amorphous carbon (NAC) monolayers, are synthesized as a high-performance sodium storage anode. Theoretical calculations reveal the intercalation of NAC monolayer between two adjacent MoSe2 monolayers improving electronic conductivity of MoSe2 in both surface and internal bulk to fully accelerate electron transport and enhance Na+ ​adsorption. The interoverlapped MoSe2/NAC superlattice featuring a wide interlayer expansion (72.3 ​%) of MoSe2 dramatically decreases Na+ ​diffusion barriers for fast insertion/extraction. Moreover, the hollow nanospheres and the rGO conductive network contribute to a robust hiberarchy that can well release internal stress and buffer the volume expansion, thereby enabling outstanding structural stability. Consequently, the rGO@MoSe2/NAC anode exhibits excellent high-rate capability of 194 mAh g−1 and ultralong cyclability of 12 ​000 cycles with a low capacity fading rate of 0.0038 ​% per cycle at an ultra-high current of 50 ​A ​g−1, delivering the best high-rate cycling performance to date. Remarkably, the Na3V2(PO4)3‖rGO@MoSe2/NAC full cells also present outstanding cycling stability (600 cycles) at 10C rate, which proves the great potential in fast-charging applications.

The stability of scaffolding structures is crucial for quality management in construction. Currently, scaffolding assembly quality monitoring relies on visual inspections performed by designated on-site personnel, which are highly subjective, inaccurate, and inefficient, hindering the advancement of intelligent construction practices. This study proposes an automated method for scaffolding assembly quality inspection using point cloud data and feature selection algorithms. High-precision point cloud data of the scaffolding are captured by a Trimble X7 3D laser scanner. After registration with the forward design model, a 2D slicing comparison method is developed to measure geometric dimensions with an accuracy controlled within 0.1 mm. The collected data are used to build an SVM model for automated assembly quality inspection. To combat the curse of dimensionality associated with high-dimensional data, an optimized genetic algorithm is employed for the dimensionality reduction in the raw sample data, effectively eliminating data redundancy and significantly enhancing convergence speed and classification accuracy of the detection model. Case studies indicate that the proposed method can reduce feature dimensionality by 70% while simultaneously improving classification accuracy by 13.9%. The proposed method enables high-precision automated inspection of scaffolding assembly quality. By identifying the optimal feature subset, the method differentiates the priority of various structural parameters during inspection, providing insights for optimizing the quality inspection process.

Abstract Purpose To investigate the clinical efficacy and safety of combination therapy of hepatic arterial infusion chemotherapy (HAIC) and anti‐programmed cell death protein‐1 (PD‐1) therapy in the treatment of advanced hepatocellular carcinoma (HCC). Methods In this retrospective clinical research, from March 2018 to December 2019, 1158 HCC patients categorized as BCLC stage C were reviewed for eligibility. We utilized propensity score matching (PSM) to mitigate initial disparities between the groups. The evaluation of the best tumor response was conducted in accordance with mRECIST 1.1 criteria. The difference in survival outcomes including overall survival (OS), progression‐free survival (PFS), and objective response rate (ORR) between groups were compared. Results Following the eligibility review, 453 patients underwent a combined treatment of HAIC with PD1 inhibitors (HAIC‐PD1 group), while 221 patients received HAIC monotherapy (HAIC group) met the inclusion criteria and were finally enrolled in this study. In the entire cohort, the HAIC‐PD1 group exhibited significantly prolonged overall survival (median overall survival: 40.4 months vs. 9.7 months, p

Silicon carbide (SiC) power devices are attracting significant attention due to their outstanding stability in high-voltage, high-temperature, and highfrequency environments. To replace toxic Pb-based glass, there is an urgent need to develop Pb-free glass for SiC power device encapsulation, considering its superior electrical insulation properties and processing capabilities. Here, we developed a Bi-based glass for effective encapsulation practical of SiC power devices. By increasing the content of glass modifier BaO, the structure of bismuth borate glass can be tuned to reduce glass network density, leading to the transition of structural units from [BO4] to [BO3]. The softened temperature is reduced to 363.4°C with the BaO content increasing to 15 mol%. After encapsulating the SiC power devices using Bi-based glass, the glass demonstrated a reverse breakdown voltage of 650 V and an extremely low leakage current. Therefore, ourwork provided a route for adapting Pb-free-based low-melting glass for encapsulating SiC devices and offered potential for advanced semiconductor packaging. [ABSTRACT FROM AUTHOR]

Layered transition metal dichalcogenides are promising candidates for sodium storage but suffering from low intrinsic electronic conductivity and limited interlayer spacing for fast electron/ion transport, which restricts their high-rate capability and cycling stability. In this work, rGO@MoSe2/NAC hierarchical architectures, consisting of conductive reduced graphene oxide (rGO) supported by hollow nanospheres that are rolled from superlattices of alternatively overlapped MoSe2 and N-doped amorphous carbon (NAC) monolayers, are synthesized as a highperformance sodium storage anode. Theoretical calculations reveal the intercalation of NAC monolayer between two adjacent MoSe2 monolayers improving electronic conductivity of MoSe2 in both surface and internal bulk to fully accelerate electron transport and enhance Na+ adsorption. The interoverlapped MoSe2/NAC superlattice featuring a wide interlayer expansion (72.3 %) of MoSe2 dramatically decreases Na+ diffusion barriers for fast insertion/extraction. Moreover, the hollow nanospheres and the rGO conductive network contribute to a robust hiberarchy that can well release internal stress and buffer the volume expansion, thereby enabling outstanding structural stability. Consequently, the rGO@MoSe2/NAC anode exhibits excellent high-rate capability of 194 mAh g-1 and ultralong cyclability of 12 000 cycles with a low capacity fading rate of 0.0038 % per cycle at an ultra-high current of 50 A g-1, delivering the best high-rate cycling performance to date. Remarkably, the Na3V2 (PO4)3krGO@MoSe2/NAC full cells also present outstanding cycling stability (600 cycles) at 10C rate, which proves the great potential in fast-charging applications. [ABSTRACT FROM AUTHOR]

In this study, the discrete element software EDEM was applied to establish a simulation model of non-uniform-sized particle units for Broussonetia papyrifera stalks, which aimed to address the low utilization rate of existing Broussonetia papyrifera harvesting machinery, the significant variation between the simulated model of Broussonetia papyrifera stalks and their actual appearance, as well as the absence of contact parameter calibration. Through a combination of the free-fall collision method, inclined plane sliding method, and inclined plane rolling method, numerical simulation was conducted to analyze the pattern of variations in contact parameters between Broussonetia papyrifera stalks and the steel material of the machinery. Accordingly, these parameters were calibrated. The results showed that the coefficient of restitution between Broussonetia papyrifera stalks and steel materials was 0.321, the static friction factor was 0.589, and the rolling friction factor was 0.078. With the parameters of contact between Broussonetia papyrifera stalks as variables and the experimentally measured pile angle as the objective of optimization, the steepest ascent experiment and the three-factor five-level rotation combination experiment were conducted. In this way, a second-order response model was constructed to analyze the relationship between the contact parameters and the pile angle. Through the optimization analysis of experimental data, it was determined that the coefficient of restitution between Broussonetia papyrifera stalks was 0.21, the static friction factor was 0.24, and the rolling friction factor was 0.03. Furthermore, the calibration results were validated through experimentation to show that the relative error between the obtained pile angle under the context of optimal parameter combination and the actual one was 4.11%. In addition, the relative error of mass flow rate in spiral transport was within a reasonable range, this study lays a foundation both theoretically and statistically for the simulation of contact parameters for Broussonetia papyrifera stalk harvesting processing, mechanical harvesting, and so on. [ABSTRACT FROM AUTHOR]

Abstract Multimodal deep learning plays a pivotal role in supporting the processing and learning of diverse data types within the realm of artificial intelligence generated content (AIGC). However, most photonic neuromorphic processors for deep learning can only handle a single data modality (either vision or audio) due to the lack of abundant parameter training in optical domain. Here, we propose and demonstrate a trainable diffractive optical neural network (TDONN) chip based on on-chip diffractive optics with massive tunable elements to address these constraints. The TDONN chip includes one input layer, five hidden layers, and one output layer, and only one forward propagation is required to obtain the inference results without frequent optical-electrical conversion. The customized stochastic gradient descent algorithm and the drop-out mechanism are developed for photonic neurons to realize in situ training and fast convergence in the optical domain. The TDONN chip achieves a potential throughput of 217.6 tera-operations per second (TOPS) with high computing density (447.7 TOPS/mm2), high system-level energy efficiency (7.28 TOPS/W), and low optical latency (30.2 ps). The TDONN chip has successfully implemented four-class classification in different modalities (vision, audio, and touch) and achieve 85.7% accuracy on multimodal test sets. Our work opens up a new avenue for multimodal deep learning with integrated photonic processors, providing a potential solution for low-power AI large models using photonic technology.

With the advancement of technology in recent years, the application of artificial intelligence in real life has become more extensive. Graphic recognition is a hot spot in the current research of related technologies. It involves machines extracting key information from pictures and combining it with natural language processing for in-depth understanding. Existing methods still have obvious deficiencies in fine-grained recognition and deep understanding of contextual context. Addressing these issues to achieve high-quality image-text recognition is crucial for various application scenarios, such as accessibility technologies, content creation, and virtual assistants. To tackle this challenge, a novel approach is proposed that combines the Mask R-CNN, DCGAN, and ALBERT models. Specifically, the Mask R-CNN specializes in high-precision image recognition and segmentation, the DCGAN captures and generates nuanced features from images, and the ALBERT model is responsible for deep natural language processing and semantic understanding of this visual information. Experimental results clearly validate the superiority of this method. Compared to traditional image-text recognition techniques, the recognition accuracy is improved from 85.3% to 92.5%, and performance in contextual and situational understanding is enhanced. The advancement of this technology has far-reaching implications for research in machine vision and natural language processing and open new possibilities for practical applications.

Matrix inversion is a fundamental and widely utilized linear algebraic operation but computationally expensive in digital-clock-based platforms. Optical computing is a new computing paradigm with high speed and energy efficiency, and the computation can be realized through light propagation. However, there is a scarcity of experimentally implemented matrix inverters that exhibit both high integration density and the capability to perform complex-valued operations in existing optical systems. For the first time, we experimentally demonstrated an iterative all-optical chip-scale processor to perform the computation of complex-valued matrix inversion using the Richardson method. Our chip-scale processor achieves an iteration speed of 10 GHz, which can facilitate ultra-fast matrix inversion with the assistance of high-speed Mach–Zehnder interferometer modulators. The convergence can be attained within 20 iterations, yielding an accuracy of 90%. The proposed chip-scale all-optical complex-valued matrix inverter represents a distinctive innovation in the field of all-optical recursive systems, offering significant potential for solving computationally intensive mathematical problems.

The physicochemical properties, chemical fractions of six metals (Cu, Zn, Pb, Cd, Cr, and Mn), and microbial communities of soil around a typical sanitary landfill were analyzed. The results indicate that soils around the landfill were from neutral to weak alkalinity. The contents of organic matter (OM), total nitrogen (TN), total phosphorous (TP), and activities of catalase, cellulase, and urease were significantly higher in landfill soils than those in background soils. Negative correlations were found between pH and metals. Cr was the dominant metal. Cu, Pb, Cr, and Mn were accumulated in the nearby farmland soils. Cd had the highest percentage of exchangeable fraction (33.7%−51.8%) in landfill and farmland soils, suggesting a high bioavailability to the soil environment affected by the landfill. Pb, Cr, and Mn existed mostly in oxidable fraction, and Cu and Zn were dominant in residual fraction. There was a low risk of soil metals around the landfill based on the RI values, while according to RAC classification, Cd had high to very high environmental risk. The MisSeq sequencing results showed that Actinobacteria, Proteobacteria, Chloroflexi, and Acidobacteria were the dominant phyla of bacteria, and the most abundant phylum of fungi was Ascomycota. The NMDS analysis revealed that the landfill could influence soil fungal communities more intensely than bacterial communities. TN, cellulase, and bioavailable metals (Pb-Bio and Cr-Bio) were identified to have main influences on microbial communities. Pb-Bio was the most dominant driving factor for bacterial community structures. For fungi, Pb-Bio was significantly negatively related to Olpidiomycota and Cr-Bio had a significantly negative correlation with Ascomycota. It manifests that bioavailable metals play important roles in assessing environmental risks and microbial community structures of soil around landfill.

In the paper, we describe a method for deriving generalized symmetries for a generic discrete quadrilateral equation that allows a Lax pair. Its symmetry can be interpreted as a flow along the tangent direction of its solution evolving with a Lie group parameter t. Starting from the spectral problem of the quadrilateral equation and assuming the eigenfunction evolves with the parameter t, one can obtain a differential-difference equation hierarchy, of which the flows are proved to be commuting symmetries of the quadrilateral equation. We prove this result by using the zero-curvature representations of these flows. As an example, we apply this method to derive symmetries for the lattice potential Korteweg–de Vries equation.