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Byline: Xun Wang Large language models (LLMs) can understand and generate human language. These generative AI tools are powerful and popular, with over 90% of retail and ecommerce leaders reporting [...]

Subnanometer materials (SNMs) refer to nanomaterials with a feature size close to 1 nm, similar to the diameter of a single polymer, DNA strand, and a single cluster/unit cell. The growth and assembly of subnanometer building blocks can be controlled by interactions at atomic levels, representing the limit for the precise manipulation of materials. The size, geometry, and flexibility of 1D SNMs inorganic backbones are similar to the polymer chains, bringing excellent gelability, adhesiveness, and processability different from inorganic nanocrystals. The ultrahigh surface atom ratio of SNMs results in significantly increased surface energy, leading to significant rearrangement of surface atoms. Unconventional phases, immiscible metal alloys, and high entropy materials with few atomic layers can be stabilized, and the spontaneous twisting of SNMs may induce the intrinsic structural chirality. Electron delocalization may also emerge at the subnanoscale, giving rise to the significantly enhanced catalytic activity. In this perspective, we summarized recent progress on SNMs, including their synthesis, polymer-like properties, metastable phases, structural chirality, and catalytic properties, toward energy conversion. As a critical size region in nanoscience, the development of functional SNMs may fuse the boundary of inorganic materials and polymers and conduce to the precise manufacturing of materials at atomic levels.

In this paper, Bi-doped hydroquinone (HDQ) molecularly imprinted TiO2(Bi-HDQ-TiO2) with the synthesis of Bi2Ti2O7–TiO2as the main ingredient was developed. Using HDQ as an imprinted molecule, Bi(NO3)3-5H2O was introduced into the synthesis of Bi-HDQ-TiO2, which revealed that bismuth oxide was protected by HDQ in the temperature range of 0–450 °C, and during the gradual increase of the temperature up to 550 °C, the HDQ eluted completely, then the surrounding TiO2was nucleated by bismuth oxide and bonded with the bismuth oxide, causing all of them to be transformed into Bi2Ti2O7. The excited electrons of the catalyst could be transported efficiently in various surface interfaces, meanwhile inhibiting the complexation of photogenerated carriers, thereby improving the efficiency of photocatalysis. A degradation efficiency of 96.35% of HDQ was achieved under 30 min UV irradiation, indicating that the candidate material has a promising future application in environmental purification or human health.

Bark beetles, major pests that bore into forest stems, cause significant economic damage to forests globally. (+)-α-Pinene is the precursor to (+)-cis-verbenol, a crucial component of the aggregation pheromones produced by bark beetles. This paper describes the de novosynthesis of (+)-cis-verbenol in Escherichia coli. Initially, the truncation position of (+)-α-pinene synthase (PtPS30 from Pinus taeda) and monoterpene precursor (geranyl diphosphate/neryl diphosphate) synthases were evaluated. Neryl diphosphate synthase from Solanum lycopersicum(SlNPPS1) and truncated (+)-α-pinene synthase (PtPS30–39) were selected as promising candidates. Subsequently, the titer of (+)-α-pinene was significantly increased 8.9-fold by using the fusion tag CM29, which enhanced the solubility of PtPS30–39. In addition, by optimizing expression elements (ribosomal binding sites, linkers, and up elements) and overexpressing CM29*PtPS30–39, a yield of 134.12 mg/L (+)-α-pinene was achieved. Finally, the first de novosynthesis of enantiopure (+)-cis-verbenol was achieved by introducing a cytochrome P450 mutant from Pseudomonas putida(P450camF89W,Y98F,L246A), resulting in a yield of 11.13 mg/L. This study lays the groundwork for developing verbenol-based trapping technology for controlling bark beetles.

Cross-lingual cross-modal retrieval aims at leveraging human-labeled annotations in a source language to construct cross-modal retrieval models for a new target language, due to the lack of manually-annotated dataset in low-resource languages (target languages). Contrary to the growing developments in the field of monolingual cross-modal retrieval, there has been less research focusing on cross-modal retrieval in the cross-lingual scenario. A straightforward method to obtain target-language labeled data is translating source-language datasets utilizing Machine Translations (MT). However, as MT is not perfect, it tends to introduce noise during translation, rendering textual embeddings corrupted and thereby compromising the retrieval performance. To alleviate this, we propose Noise-Robust Fine-tuning (NRF) which tries to extract clean textual information from a possibly noisy target-language input with the guidance of its source-language counterpart. Besides, contrastive learning involving different modalities are performed to strengthen the noise-robustness of our model. Different from traditional cross-modal retrieval methods which only employ image/video-text paired data for fine-tuning, in NRF, selected parallel data plays a key role in improving the noise-filtering ability of our model. Extensive experiments are conducted on three video-text and image-text retrieval benchmarks across different target languages, and the results demonstrate that our method significantly improves the overall performance without using any image/video-text paired data on target languages.

Protein–protein interactions are the basis of many protein functions, and understanding the contact and conformational changes of protein–protein interactions is crucial for linking the protein structure to biological function. Although difficult to detect experimentally, molecular dynamics (MD) simulations are widely used to study the conformational ensembles and dynamics of protein–protein complexes, but there are significant limitations in sampling efficiency and computational costs. In this study, a generative neural network was trained on protein–protein complex conformations obtained from molecular simulations to directly generate novel conformations with physical realism. We demonstrated the use of a deep learning model based on the transformer architecture to explore the conformational ensembles of protein–protein complexes through MD simulations. The results showed that the learned latent space can be used to generate unsampled conformations of protein–protein complexes for obtaining new conformations complementing pre-existing ones, which can be used as an exploratory tool for the analysis and enhancement of molecular simulations of protein–protein complexes.

Formic acid (FA) is a sustainable liquid organic hydrogen carrier and the catalyst for hydrogen production from FA has received significant attention. However, the development of efficient non-noble metal catalysts still remains challenges. In this work, we provide a technologically rather simple and environmental-friendly strategy to synthesize Co2P catalyst for dehydrogenation of FA by pyrolyzing soybean powder and cobalt salt. The K-containing solid bases in catalyst could act as Lewis acid sites for the HCOO−intermediate adsorption while the self-doped N could act as Lewis base sites to enhance the H+adsorption. The P contained in soybean could combine with Co to form Co2P for H−C bond cleavage of HCOO−. At a Co(NO3)2·6H2O/soybean mass ratio of 1:15, the as prepared Co2P catalyst demonstrated a gas production rate of 237.47 mL/(g·h) and a good stability. This study provides a novel strategy to develop non-noble metal heterogeneous catalysts for FA dehydrogenation.

High‐power ablation has been widely used in atrial fibrillation (AF). However, there were many studies observed the outcomes of the short‐term follow‐up. This study aims to the long‐term results of high‐power ablation guided by ablation index (AI) in patients with AF. Analysis of patients with AF, who first received high‐power (40–50 W) ablation, to pulmonary vein isolation (PVI) in the Second Hospital of Shanxi Medical University from May 2020 to March 2022. All patients were managed perioperatively according to the routine treatment procedures. High‐power ablation was conducted under the guidance of our conventional power AIand baseline data, first‐pass PVIrate, ablation time, operative time, and long‐term surgical success rate were analyzed. A total of 83 patients with atrial fibrillation were enrolled in the study, with an average age of 61.62 ± 9.04 years, 47 male patients, and 49 paroxysmal atrial fibrillation. All patients achieved PVI, and the rate of first pass was 82%. The ablation time of the left atrial was 28.54 ± 9.11 min. There were no serious complications related to ablation, and only a small amount of pericardial effusion was found in 4 patients. During the follow‐up period of 26.36 ± 6.11 months, 8 patients were lost to follow‐up and the overall success rate was 84%, including 91% for paroxysmal AFand 71% for persistent AF. High‐power ablation long‐term results appear a high freedom atrial arrhythmia, but further expanded samples are needed for controlled studies. This study demonstrates that the long‐term outcome and safety after high‐power ablation with target AI in AF patients. After the long‐term follow‐up, the high‐power ablation with target AI has a high efficacy and long‐term results to appear a high freedom atrial arrhythmia.

Chirality evolution from molecule levels to the nanoscale in an achiral system is a fundamental issue that remains undiscovered. Here, we report the assembly of polyoxometalate (POM) clusters into chiral subnanostructures in achiral systems by programmable single-molecule interactions. Driven by the competing binding of Ca2+and surface ligands, POM assemblies would twist into helical nanobelts, nanorings, and nanotubes with tunable helicity. Chiral molecules can be used to differentiate the formation energies of chiral isomers and immobilize the homochiral isomer, where strong circular dichroism (CD) signals are obtained in both solutions and films. Chiral helical nanobelts can be used as circularly polarized light (CPL) photodetectors due to their distinct chiroptic responsivity for right and left CPL. By the fine-tuning of interactions at single-molecule levels, the morphology and CD spectra of helical assemblies can be precisely controlled, providing an atomic precision model for investigation of the structure–chirality relationship and chirality manipulation at the nanoscale.

The diagnosis of solid pseudopapillary neoplasm of the pancreas (SPN) can be challenging due to potential confusion with other pancreatic neoplasms, particularly pancreatic neuroendocrine tumors (NETs), using current pathological diagnostic markers. We conducted a comprehensive analysis of bulk RNA sequencing data from SPNs, NETs, and normal pancreas, followed by experimental validation. This analysis revealed an increased accumulation of peroxisomes in SPNs. Moreover, we observed significant upregulation of the peroxisome marker ABCD1 in both primary and metastatic SPN samples compared with normal pancreas and NETs. To further investigate the potential utility of ABCD1 as a diagnostic marker for SPN via immunohistochemistry staining, we conducted verification in a large-scale patient cohort with pancreatic tumors, including 127 SPN (111 primary, 16 metastatic samples), 108 NET (98 nonfunctional pancreatic neuroendocrine tumor, NF-NET, and 10 functional pancreatic neuroendocrine tumor, F-NET), 9 acinar cell carcinoma (ACC), 3 pancreatoblastoma (PB), 54 pancreatic ductal adenocarcinoma (PDAC), 20 pancreatic serous cystadenoma (SCA), 19 pancreatic mucinous cystadenoma (MCA), 12 pancreatic ductal intraepithelial neoplasia (PanIN) and 5 intraductal papillary mucinous neoplasm (IPMN) samples. Our results indicate that ABCD1 holds promise as an easily applicable diagnostic marker with exceptional efficacy (AUC=0.999, sensitivity=99.10%, specificity=100%) for differentiating SPN from NET and other pancreatic neoplasms through immunohistochemical staining.

We present a visible light-assisted template method to construct multimetallic nanosheets. The Pt1Ag0.1Bi0.16Te0.29nanosheets as effective anode electrocatalysts are applied for formic acid-/alcohol-air fuel cells.

The lack of highly efficient and inexpensive catalysts severely hinders the large-scale application of Zn–air batteries (ZABs). High-entropy oxides (HEOs) exhibit unique structures and attractive properties; thus, they are promising to be used in ZABs. However, conventional high-temperature synthesis methods tend to obtain microscale HEOs with a lower exposure rate of active sites. Here, we report a facile solvothermal strategy for preparing two-dimensional (2D) HEO sub-1 nm nanosheets (SNSs) induced by polyoxometalate (POM) clusters. Taking advantage of the special 2D sub-1 nm structure and precise element regulation, these 2D HEOs-POM SNSs exhibit enhanced bifunctional oxygen evolution and oxygen reduction reaction activity under light irradiation. Further applying these 2D HEOs-POM SNSs to ZABs as cathode catalysts, the CoFeNiMnCuZnOx-phosphomolybdic acid SNSs-based ZABs deliver a low charge/discharge voltage gap of 0.25 V at 2 mA cm–2under light irradiation. Meanwhile, it could maintain an ultralong-term stability for 1600 h at 2 mA cm–2and 930 h at 10 mA cm–2. The 2D sub-1 nm structure and fine element control in HEOs provide opportunities to solve the problems of low intrinsic activity, limited active sites, and instability of air cathodes in ZABs.

Fusariumwilt is a worldwide soil-borne fungal disease caused by Fusarium oxysporumthat causes serious damage to agricultural products. Therefore, preventing and treating fusarium wilt is of great significance. In this study, we purified ten single lipopeptide fengycin components from Bacillus subtilisFAJT-4 and found that C17fengycin B inhibited the growth of F. oxysporumFJAT-31362. We observed early apoptosis hallmarks, including reactive oxygen species accumulation, mitochondrial dysfunction, and phosphatidylserine externalization in C17fengycin B-treated F. oxysporumcells. Further data showed that C17fengycin B induces cell apoptosis in a metacaspase-dependent manner. Importantly, we found that the expression of autophagy-related genes in the TOR signaling pathway was significantly upregulated; simultaneously, the accumulation of acidic autophagy vacuoles in F. oxysporumcell indicated that the autophagy pathway was activated during apoptosis induced by C17fengycin B. Therefore, this study provides new insights into the antifungal mechanism of fengycin.

The comprehensive management of a patient with chronic graft-versus-host disease skin ulcers after hematopoietic stem cell transplantation is challenging. This report describes the case of a 53-year-old woman who presented with ulcers on her right leg 140 weeks after a bone marrow transplant. The patient received wound assessment and management based on the Triangle of Wound Assessment and Wound Bed Preparation 2021, respectively. Hydrogel and antibacterial protease dressings were applied along with systemic oral administration of moxifloxacin hydrochloride (two capsules, two times daily) and JiXueGanPian tablets (classic Chinese herbal formula; two capsules, two times daily), hospital-community-home continuous care, and patient-centered education. Finally, after 133 days of nursing, the patient’s wound was completely healed without complications or other skin issues. The use of hydrogel combined with the antibacterial protease dressing was a promising technique for handling this type of wound, enhanced by multidisciplinary collaboration. Of course, providing patients with education that focuses on prevention is necessary.

Developing new electrochemiluminescence (ECL) luminators with high stability, wide applicability, and strong designability is of great strategic significance to promote the ECL field to the frontier. Here, driven by the I···N bond, 1,3,5-trifluoro-2,4,6-triiodobenzene (TFTI) and 2,4,6-trimethyl-1,3,5-triazine (TMT) self-assembled into a novel halogen cocrystal (TFTI-TMT) through slow solution volatilization. Significant difference of charge density existed between the N atoms on TMT and the σ-hole of the I atoms on TFTI. Upon the induction of σ-hole effect, high-speed and spontaneous charge transferring from TMT to the σ-hole of TFTI occurred, stimulating exciting ECL signals. Besides, the σ-hole of the I atoms could capture iodine ions specifically, which blocked the original charge transfer from the N atoms to the σ-hole, causing the ECL signal of TFTI-TMT to undergo a quenching rate as high as 92.9%. Excitingly, the ECL sensing of TFTI-TMT toward I–possessed a wide linear range (10–5000 nM) and ultralow detection limit (3 nM) in a real water sample. The halogen cocrystal strategy makes σ-hole a remarkable new viewpoint of ECL luminator design and enables ECL analysis technology to contribute to addressing the environmental and health threats posed by iodide pollution.

• The liquid-liquid phase transition occurs in a Fe-based metallic glass with the evolution of edge-sharing connections at medium-range order. • Magnetic nanodomains with topological order are tunable by the liquid-liquid phase transition. • The nanodomains have stronger exchange interactions, enhancing saturation magnetization and stress-impedance performance. Fe-based metallic glasses are promising functional materials for advanced magnetism and sensor fields. Tailoring magnetic performance in amorphous materials requires a thorough knowledge of the correlation between structural disorder and magnetic order, which remains ambiguous. Two practical difficulties remain: the first is directly observing subtle magnetic structural changes on multiple scales, and the second is precisely regulating the various amorphous states. Here we propose a novel approach to tailor the amorphous structure through the liquid-liquid phase transition. In-situ synchrotron diffraction has unraveled a medium-range ordering process dominated by edge-sharing cluster connectivity during the liquid-liquid phase transition. Moreover, nanodomains with topological order have been found to exist in composition with liquid-liquid phase transition, manifesting as hexagonal patterns in small-angle neutron scattering profiles. The liquid-liquid phase transition can induce the nanodomains to be more locally ordered, generating stronger exchange interactions due to the reduced Fe–Fe bond length and the enhanced structural order, leading to the increment of saturation magnetization. Furthermore, the increased local heterogeneity at the medium-range scale enhances the magnetic anisotropy, promoting the permeability response under applied stress and leading to a better stress-impedance effect. These experimental results pave the way to tailor the magnetic structure and performance through the liquid-liquid phase transition. [Display omitted] [ABSTRACT FROM AUTHOR]

Fluorite and calcite were separated with nitrilotriacetic acid (NTA) as a depressant. The single mineral flotation experiment confirmed that with 40 mg/L NaOL and 80 mg/L NTA, the fluorite recovery and calcite recovery were 24.37 and 94.13%, respectively, at pH 9. Meanwhile, in the fluorite-calcite binary mixed ore flotation experiment, the calcite recovery and fluorite recovery were 75.50 and 26.84%, respectively, and the CaCO3and CaF2grade in concentrate was 74.32 and 25.61%, respectively. The results confirmed that NTA could be used as a depressant to selectively inhibit fluorite flotation. The mechanism study illustrated that NTA was selectively reacted with fluorite by chemical interaction between O of NTA and Ca of fluorite. The adsorption of NTA on fluorite will impede the interaction between fluorite and NaOL. NTA could adsorb on fluorite in three ways, while the dominant two ways were the complex between double O of NTA and Ca of fluorite in a vertical model and the complex between double O of NTA and Ca of fluorite in a horizontal model.

IMPORTANCE: China has experienced both rapid urbanization and major increases in myopia prevalence. Previous studies suggest that green space exposure reduces the risk of myopia, but the association between myopia risk and specific geometry and distribution characteristics of green space has yet to be explored. These must be understood to craft effective interventions to reduce myopia. OBJECTIVE: To evaluate the associations between myopia and specific green space morphology using novel quantitative data from high-resolution satellite imaging. DESIGN, SETTING, AND PARTICIPANTS: This prospective cohort study included students grades 1 to 4 (aged 6 to 9 years) in Shenzhen, China. Baseline data were collected in 2016-2017, and students were followed up in 2018-2019. Data were analyzed from September 2020 to January 2022. EXPOSURES: Eight landscape metrics were calculated using land cover data from high-resolution Gaofen-2 satellite images to measure area, aggregation, and shape of green space. MAIN OUTCOME AND MEASURES: The 2-year cumulative change in myopia prevalence at each school and incidence of myopia at the student level after 2 years were calculated as main outcomes. The associations between landscape metrics and school myopia were assessed, controlling for geographical, demographic, and socioeconomic factors. Principal component analyses were performed to further assess the joint effect of landscape metrics at the school and individual level. RESULTS: A total of 138 735 students were assessed at baseline. Higher proportion, aggregation, and better connectivity of green space were correlated with slower increases in myopia prevalence. In the principal component regression, a 1-unit increase in the myopia-related green space morphology index (the first principal component) was negatively associated with a 1.7% (95% CI, −2.7 to −0.6) decrease in myopia prevalence change at the school level (P = .002). At the individual level, a 1-unit increase in myopia-related green space morphology index was associated with a 9.8% (95% CI, 4.1 to 15.1) reduction in the risk of incident myopia (P < .001), and the association remained after further adjustment for outdoor time, screen time, reading time, and parental myopia (adjusted odds ratio, 0.88; 95% CI, 0.80 to 0.97; P = .009). CONCLUSIONS AND RELEVANCE: Structure of green space was associated with a decreased relative risk of myopia, which may provide guidance for construction and renovation of schools. Since risk estimates only indicate correlations rather than causation, further interventional studies are needed to assess the effect on school myopia of urban planning and environmental designs, especially size and aggregation metrics of green space, on school myopia.

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ABSTRACT As promising structural materials in seismic isolation and energy dissipation systems, low yield point (LYP) steels have been developed and have begun to be adopted in seismic design. This [...]

Dynamic 3D covalent organic frameworks (dynaCOFs) have shown concerted structural transformation and responses upon adaptive guest adsorption. The multivariate (MTV) strategy incorporating multiple functionalities within a backbone is attractive for tuning the framework flexibility and dynamic responses. However, a major synthetic challenge arises from the different chemical reactivities of linkers usually resulting in phase separation. Here, we report a general synthetic protocol for making 3D MTV-COFs by balancing the linker reactivity and solvent polarity. Specifically, 15 crystalline and phase pure MTV-COF-300 isostructures are constructed by linking a tetrahedral unit with eight ditopic struts carrying various functional groups. We find that the electron-donating groups make the linker reactivity too low to allow the reaction to proceed fully, while the electron-withdrawing groups afford increased reactivity and hardly yield crystalline materials. To overcome the crystallization dilemma, the combination of polar aprotic with nonpolar solvents was used to improve the solubility of oligomers and slow the reaction kinetics in MTV-COF synthesis. We demonstrate the abilities of these MTV-COFs to tune gas dynamic behaviors and the separation of benzene and cyclohexane. These findings reveal the integration of multivariate functionalities into dynaCOFs with on-demand flexibility to achieve dynamic synergism in particular applications, outperforming their pure, monofunctional counterparts.

Spatially confining isolated atomic sites in low-dimensional nanostructures is a promising strategy for preparing high-performance single-atom catalysts (SACs). Herein, fascinating polyoxometalate cluster-based single-walled nanotubes (POM-SWNTs) with atomically precise structures, uniform diameter, and single-cluster wall thickness are constructed by lacunary POM clusters (PW11and P2W17clusters). Isolated metal centers are accurately incorporated into the PW11-SWNTs and P2W17-SWNTs supports. The structures of the resulting MPW11-SWNTs and MP2W17-SWNTs are well established (M = Cu, Pt). Molecular dynamics simulations demonstrate the stability of POM-SWNTs. Furthermore, the turnover frequency of PtP2W17-SWNTs is 20 times higher than that of PtP2W17cluster units and 140 times higher than that of Pt nanoparticles in the alcoholysis of dimethylphenylsilane. Theoretical studies indicate that incorporating a Pt atom into the P2W17support induces straightforward electron transfer between them, combining the nanoconfined environment to enhance the catalytic activity of PtP2W17-SWNTs. This work shows the feasibility of using subnanometric POM clusters to assemble single-walled cluster nanotubes, highlighting their potential to prepare superior SACs with precise structures.

Adaptive waveform design for carrier-free ultrawideband (UWB) signals can effectively improve the performance and anti-interference capability of sensor systems. To meet the real-time and low-complexity requirements for near detection of carrier-free UWB cognitive sensors, this article proposes a fast power spectrum fitting method based on the orthogonal matching pursuit (OMP) algorithm to meet the design requirements of combined Gaussian pulse waveforms for carrier-free UWB cognitive radio sensors. The method utilizes the formation properties of Gaussian pulses to construct a Gaussian pulse redundancy dictionary. Based on designing the optimal power spectrum, the combination of pulses that best matches the power spectrum template is quickly determined by the OMP algorithm, and its weighting coefficient is calculated. It essentially utilizes the orthogonality of the least squares method to achieve the best approximate representation of the power spectrum template using the atomic column space of the selected dictionary. The residual vector is updated during each iteration to maintain orthogonality with existing atoms, thereby accelerating the convergence speed of the algorithm. The simulation results demonstrate that the waveforms designed by the proposed algorithm exhibit superior anti-interference performance compared with other waveform design methods. In addition, the algorithm significantly reduces computational complexity and dramatically shortens the design time. These findings have significant implications for practical applications in waveform design.

Multidimensional multiplexing (MM) is a crucial technology for achieving high-capacity terahertz (THz) wireless communication. However, the non-ideal characteristics of polarization-related THz devices, such as the low cross-polarization isolation (XPI) of the ortho-mode transducer (OMT) and antenna mismatch, result in reduced power of co-polarization and increased power of cross-polarization signal in the receiver. This leads to high polarization crosstalk (PC), such as reduced signal-to-interference-plus-noise ratio (SINR) and degraded error vector magnitude (EVM) in the polarization division multiplexing (PDM) system. This article proposes a THz line-of-sight (LoS) dual linear polarized model to analyze the PC phenomenon, with consideration of polarization-related devices and transmitted power for the first time involving OMT antennas and channel (OACAO model). We explore influencing factors, including the imbalance of polarized transmitted power, XPI of OMT, antenna polarization mismatch, channel attenuation, and depolarization effect in 3D space. To deal with the PC problem, we adopt an XPI optimization strategy based on RF hardware debugging and a cross-polarization interference cancellation (XPIC) algorithm, which enhances XPI to 22–25 dB, and increases SINR by 1–2 dB. Finally, we establish our THz wireless system using PDM and frequency division multiplexing (FDM) in the 220-GHz band, demonstrating real-time transmission of uncompressed 8K video. Using 16-QAM and I/Q modulation, the wireless link succeeded in a total data rate of 83.2 Gb/s over 100 m with SINR exceeding 19.8 dB, and a block error rate (BLER) below 1e-9. This demonstration provides insight for future research on THz ultra-high-speed communication systems.

Background: Transplantation of stem cells/scaffold is an efficient approach for treating tissue injury including full-thickness skin defects. However, the application of stem cells is limited by preservation issues, ethical restriction, low viability, and immune rejection in vivo. The mesenchymal stem cell conditioned medium is abundant in bioactive functional factors, making it a viable alternative to living cells in regeneration medicine.Methods: Nasal mucosa-derived ecto-mesenchymal stem cells (EMSCs) of rats were identified and grown in suspension sphere-forming 3D culture. The EMSCs-conditioned medium (EMSCs-CM) was collected, lyophilized, and analyzed for its bioactive components. Next, fibrinogen and chitosan were further mixed and cross-linked with the lyophilized powder to obtain functional skin patches. Their capacity to gradually release bioactive substances and biocompatibility with epidermal cells were assessed in vitro. Finally, a full-thickness skin defect model was established to evaluate the therapeutic efficacy of the skin patch.Results: The EMSCs-CM contains abundant bioactive proteins including VEGF, KGF, EGF, bFGF, SHH, IL-10, and fibronectin. The bioactive functional composite skin patch containing EMSCs-CM lyophilized powder showed the network-like microstructure could continuously release the bioactive proteins, and possessed ideal biocompatibility with rat epidermal cells in vitro. Transplantation of the composite skin patch could expedite the healing of the full-thickness skin defect by promoting endogenous epidermal stem cell proliferation and skin appendage regeneration in rats.Conclusion: In summary, the bioactive functional composite skin patch containing EMSCs-CM lyophilized powder can effectively accelerate skin repair, which has promising application prospects in the treatment of skin defects.

Ground penetrating radar (GPR) full-waveform inversion (FWI) can make full use of kinematics information and dynamics information to achieve the highest theoretical resolution, serving as a promising tool for reconstructing subsurface structures and the physical properties of the medium. However, conventional FWI is constrained by strong nonlinearity, easily falls into the local minimum, and requires multiple forward simulations coupled with intensive adjoint wavefield calculations, which cannot satisfy the requirements of engineering exploration. To mitigate the nonlinearity of the inversion and improve computational efficiency, this article designs an FWI framework based on deep learning, featuring a multifrequency and multiscale fusion strategy. Utilizing a multioutput convolutional neural network (CNN) constructed by the hybrid dilated convolution (HDC), the receptive field is expanded without incurring additional computational complexity and memory consumption. The dilated CNN predicts multiple sets of available low-frequency data from its respective higher frequency components of GPR data and integrates the multifrequency strategy to guide FWI to converge the global minimum. The sizes of computational models are selected according to distinct electromagnetic wave frequencies, and the very deep super-resolution (VDSR) model facilitates the automatic mapping of grids at different scales, which reduces unnecessary calculation and boosts inversion efficiency. The synthetic and field cases prove that the proposed framework significantly enhances the spatial resolution, robustness, and efficiency of FWI. The dilated CNN and VDSR constructed have demonstrated robust generalization and noise tolerance abilities, which are suitable for geophysical tasks.

Single event transients (SETs) sensitivity in two 28 nm bulk CMOS linear voltage regulators are studied by experiments and simulations. The differences between the two regulators are the structure of error amplifiers (EAs), load capacitance of EA, and size of power MOSFETs. The heavy ion experiments show that the SET responses of the two regulators have different amplitudes and widths. The SPICE simulation results reveal that the load capacitance of EAs can affect the amplitude of SETs. The sizes of power MOSFETs have little influence on the amplitude or width. The SETs in the regulator with the simply folded cascode amplifier have lower amplitudes and shorter widths than that of the regulator with the complementary folded cascode (CFC) amplifier. It is the size of MOSFETs that the capacitor on the gate node of power MOSFETs is charged through that determines the variation of transient amplitude. Moreover, the size of the transient width is determined by the size of the MOSFETs through which the capacitor on the gate node of power MOSFETs is discharged. The result of this article offers a reference for SET hardening.

Conditional generative adversarial networks (cGANs) aim to synthesize diverse images given the input conditions and the latent codes, but they are prone to map an input to a single output regardless of the variations in latent code, which is also well known as the mode collapse problem of cGANs. To alleviate the problem, in this paper, we investigate explicitly enhancing the statistical dependency between the latent code and the synthesized image in cGANs by utilizing mutual information neural estimators to estimate and maximize the conditional mutual information (CMI) between them given the input condition. The method provides a new perspective from information theory to improve diversity for cGANs and can facilitate many existing conditional image synthesis frameworks with a simple neural estimator extension. Moreover, our studies show that several key designs, including the neural estimator choice, the neural estimator’s network design, and the sampling strategy, are crucial to the success of the method. Extensive experiments on four popular conditional image synthesis tasks, including class-conditioned image generation, paired and unpaired image-to-image translation, and text-to-image generation, demonstrate the effectiveness and superiority of the proposed method.

In audio-magnetotellurics (AMT) inversion, the resistivity model derived from data is crucial for understanding geological properties. Current AMT inversion methods such as Gaussian–Newton (GN) and nonlinear conjugate gradient (NLCG) have limitations, including sensitivity to data errors and reliance on initial models, leading to nonuniqueness and slow convergence. To address these issues, we propose an AMT inversion framework incorporating borehole data and geological constraints. By leveraging borehole information and considering geological patterns, we develop three machine learning data construction methods that enhance the stability and speed of the inversion process. However, borehole data acquisition is costly and limited, and it represents geological properties discretely within a narrow range. Relying solely on it or unconstrained inversion can compromise accuracy. Our approach integrates borehole data into the supervised descent method (SDM) inversion, resolving data gaps and model variations. SDM results are then used as initial models for GN inversion. Synthetic and field data examples demonstrate the efficiency and feasibility of our framework, showing rapid convergence and high-quality results. This approach accelerates AMT inversion by effectively using borehole information, providing a practical solution for improving the process.

This article is concerned with the security problems for networked Takagi–Sugeno (T–S) fuzzy systems with asynchronous premise constraints. The primary objective of this article is twofold. First, a novel important-data-based (IDB) denial-of-service (DoS) attack mechanism is proposed from the perspective of the adversary for the first time to reinforce the destructive effect of the DoS attacks. Different from most existing DoS attack models, the proposed attack mechanism can utilize the information of packets, evaluate the importance degree of packets, and only attack the most “important” ones. As such, a larger system performance degradation can be expected. Second, corresponding to the proposed IDB DoS mechanism, a resilient $H_{\infty }$ fuzzy filter is designed from the defender’s point of view to alleviate the negative effect of the attack. Furthermore, since the defender does not know the attack parameter, an algorithm is designed to estimate it. In a word, a unified attack-defense framework is developed in this article for networked T–S fuzzy systems with asynchronous premise constraints. With the help of the Lyapunov functional method, sufficient conditions are successfully established to compute the desired filtering gains and ensure the $H_{\infty }$ performance of the filtering error system. Finally, two examples are exploited to demonstrate the destructiveness of the proposed IDB DoS attack and the usefulness of the developed resilient $H_{\infty }$ filter.

Designing and constructing bifunctional electrocatalysts with high efficiency, high stability and low cost for overall water splitting to produce clean hydrogen fuel is attractive but highly challenging. Here we constructed puffed quaternary FexCoyNi1-x-yP nanoarrays as bifunctional electrodes for robust overall water splitting. The iron was used as the modulator to manipulate the electron density of NiCoP nanoarray, which could increase the positive charges of metal (Ni and Co) and P sites. The resultant electronic structure of FexCoyNi1-x-yP was supposed to balance the adsorption and desorption of H and accelerate the oxygen evolution reaction (OER) kinetics. Moreover, the morphological structure of FexCoyNi1-x-yP was modulated through the kinetically controlled alkaline etching by using the amphoteric features of initial FeCoNi hydroxide nanowires. The resultant puffed structure has rich porosity, cavity and defects, which benefit the exposure of more active sites and the transport of mass/ charge. As a result, the cell integrated with the puffed quaternary FexCoyNi1-x-yP nanoarrays as both the cathode and anode only requires the overpotentials of 25 and 230 mV for hydrogen evolution reaction (HER) and OER at the current density of 10 mA cm-2in alkaline media and a cell voltage of 1.48 V to drive the overall water splitting. Moreover, the puffed FexCoyNi1-x-yP demonstrates remarkable durability for continuous electrolysis even at a large current density of 240 mA cm-2.

In comparison to convolutional neural networks (CNN), the newly created vision transformer (ViT) has demonstrated impressive outcomes in human pose estimation (HPE). However, (1) there is a quadratic rise in complexity with respect to image size, which causes the traditional ViT to be unsuitable for scaling, and (2) the attention process at the transformer encoder as well as decoder also adds substantial computational costs to the detector’s overall processing time. Motivated by this, we propose a novel Going shallow and deeper with vIsion Transformers for human Pose estimation (GITPose) without CNN backbones for feature extraction. In particular, we introduce a hierarchical transformer in which we utilize multilayer perceptrons to encode the richest local feature tokens in the initial phases (i.e., shallow), whereas self-attention modules are employed to encode long-term relationships in the deeper layers (i.e., deeper), and a decoder for keypoint detection. In addition, we offer a learnable deformable token association module (DTA) to non-uniformly and dynamically combine informative keypoint tokens. Comprehensive evaluation and testing on the COCO and MPII benchmark datasets reveal that GITPose achieves a competitive average precision (AP) on pose estimation compared to its state-of-the-art approaches.

In this letter, a terahertz ultra wideband high-speed on-chip modulator based on coupling resonance was proposed. By nesting multiple diodes in the coplanar waveguide to form multiple composite modulation units, the equivalent physical state of meta-units is controlled by external voltage signal, and then control the electromagnetic resonance characteristics of the modulation units to achieve high-speed broadband modulation of terahertz waves. This modulation mechanism simplifies the resonant structure, suppresses the parasitic parameters and improves the modulation rate of the existing terahertz external modulator. The bandwidth is expanded through cascade coupling resonance, thus realizing ultra-wideband amplitude modulation of terahertz waves transmitted in coplanar waveguides. The experimental results show that the high-speed ultra-broadband modulation of terahertz waves is achieved at 110 GHz-160 GHz with a maximum modulation depth of 15dB (97%) and a maximum modulation rate of 30 Gbps, providing a promising prospect for the development and application of the integrated terahertz direct modulation technology.

In diploid mammals, allele-specific three-dimensional (3D) genome architecture may lead to imbalanced gene expression. Through ultradeep in situ Hi-C sequencing of three representative somatic tissues (liver, skeletal muscle, and brain) from hybrid pigs generated by reciprocal crosses of phenotypically and physiologically divergent Berkshire and Tibetan pigs, we uncover extensive chromatin reorganization between homologous chromosomes across multiple scales. Haplotype-based interrogation of multi-omic data revealed the tissue dependence of 3D chromatin conformation, suggesting that parent-of-origin-specific conformation may drive gene imprinting. We quantify the effects of genetic variations and histone modifications on allelic differences of long-range promoter–enhancer contacts, which likely contribute to the phenotypic differences between the parental pig breeds. We also observe the fine structure of somatically paired homologous chromosomes in the pig genome, which has a functional implication genome-wide. This work illustrates how allele-specific chromatin architecture facilitates concomitant shifts in allele-biased gene expression, as well as the possible consequential phenotypic changes in mammals.

AimsTo characterise retinal microvascular alterations in the eyes of pregnant patients with anaemia (PA) and to compare the alterations with those in healthy controls (HC) using optical coherence tomography angiography (OCTA).MethodsThis nested case‐control study included singleton PA and HC from the Eye Health in Pregnancy Study. Fovea avascular zone (FAZ) metrics, perfusion density (PD) in the superficial capillary plexus, deep capillary plexus and flow deficit (FD) density in the choriocapillaris (CC) were quantified using FIJI software. Linear regressions were conducted to evaluate the differences in OCTA metrics between PA and HC. Subgroup analyses were performed based on comparisons between PA diagnosed in the early or late trimester and HC.ResultsIn total, 99 eyes of 99 PA and 184 eyes of 184 HC were analysed. PA had a significantly reduced FAZ perimeter (βcoefficient=−0.310, p<0.001), area (βcoefficient=−0.121, p=0.001) and increased circularity (βcoefficient=0.037, p<0.001) compared with HC. Furthermore, higher PD in the central (βcoefficient=0.327, p=0.001) and outer (βcoefficient=0.349, p=0.007) regions were observed in PA. PA diagnosed in the first trimester had more extensive central FD (βcoefficient=4.199, p=0.003) in the CC, indicating impaired perfusion in the CC.ConclusionIt was found that anaemia during pregnancy was associated with macular microvascular abnormalities, which differed in PA as pregnancy progressed. The results suggest that quantitative OCTA metrics may be useful for risk evaluation before clinical diagnosis.Trial registration numbers2021KYPJ098 and ChiCTR2100049850.

Cover crop cultivation is a soil management practice to increase orchard sustainability. This study aimed to clarify the effects of eight cover crop treatments on weed control and soil qualities in the citrus orchard. The present study used coverage, soil physical properties, nutrient contents, enzyme activities, and soil bacterial community of eight cover crop species (February orchid, Chinese milk vetch, white clover, common vetch, perennial ryegrass, rattail fescue, white clover + perennial ryegrass, perennial ryegrass + tall fescue + orchardgrass + annual meadow grass) and a control (clean tillage) to characterize the impact of treatment on soil for 1 year. White clover, common vetch, rattail fescue, and the white clover + perennial grass mixture showed the high percentage coverage to control weeds. The rattail fescue treatment allowed significantly increased soil porosity. Fabaceae showed a distinct effect to increased soil organic matter content, which was reduced in soil planted with cereal species. In all cover crop treatments, bacterial and fungal diversity generally increased compared to the control treatment. Redundancy analysis indicated that soil organic matter content and available nitrogen, phosphorus, and potassium contents were the most important environmental factors that influence microbial communities. Among the different cover crop species, white clover, common vetch, rattail fescue, and mixed-species treatment comprising white clover plus perennial ryegrass showed promise for weed control. Compared with the clean tillage applied herbicide, soil qualities in cover crop soil generally showed increasing trends especially soil physical properties and microbial diversity.

The karst forest is one of the extremely sensitive and fragile ecosystems in southwest China, where the biogeochemical cycling of mercury (Hg) is largely unknown. In this study, we investigated the litterfall deposition, accumulation, and soil migration of Hg in an evergreen-deciduous broadleaf karst forest using high-resolution sampling and stable isotope techniques. Results show that elevated litterfall Hg concentrations and fluxes in spring are due to the longer lifespan of evergreen tree foliage exposed to atmospheric Hg0. The hillslope has 1–2 times higher litterfall Hg concentration compared to the low-lying land due to the elevated atmospheric Hg levels induced by topographical and physiological factors. The Hg isotopic model suggests that litterfall Hg depositions account for ∼80% of the Hg source contribution in surface soil. The spatial trend of litterfall Hg deposition cannot solely explain the trend of Hg accumulation in the surface soil. Indeed, soil erosion enhances Hg accumulation in soil of low-lying land, with soil Hg concentration up to 5-times greater than the concentration on the hillslope. The high level of soil Hg migration in the karst forest poses significant ecological risks to groundwater and downstream aquatic ecosystems.

Ultrathin two-dimensional (2D) cluster-assembled materials (CAMs) have emerged as a new class of 2D materials with fascinating structures and exceptional properties that differ from their individualities and bulks. Due to the covalent/non-covalent and cluster orbital interactions, 2D CAMs with molecular-level thickness and atomically precise structures exhibit unique electronic properties and exceptional applications. Moreover, the topological and electronic structures of CAMs can be regulated by the type of clusters and interactions between them. In this perspective, we summarize ultrathin 2D CAMs with well-defined structures constructed by noble metal clusters, polyhedral oligomeric silsesquioxane clusters, polyoxometalate clusters, and C60clusters based on the interaction between clusters. The relationships between their electronic structures and catalytic properties are discussed from experimental and theoretical perspectives. Finally, we list the limitations and opportunities for developing ultrathin 2D CAMs. This viewpoint offers a fresh perspective on the in-depth understanding and design synthesis of ultrathin 2D cluster-based materials.

The oxidative coupling of methane to C2 oxygenates merits great scientific and technological potential yet remains a challenge due to its inferior selectivity. Subnanomaterials (SNMs) with “p–n–p–n”-type heteroconstructions feature enhanced external field coupling properties and tunable electronic structures, serving as promising catalysts for the selective partial oxidation of methane. Here we develop NiO–polyoxometalate (POM) subnanocoils with a thickness of 1.8 nm, showing excellent catalytic activity toward photoelectrochemical coupling of methane into a C2 product under mild conditions (1 bar, 25 °C) with a notable productivity (up to 4.48 mmol gcat–1h–1) and a high selectivity (>99%). Under photoelectrochemical coupling, C–H bonds can be activated by NiO, and the resulted *COOH intermediates are stabilized by the delocalized electrons in POM clusters. The contiguous active sites of NiO and POM at the molecular level allow the in situcoupling of *COOH into oxalate. This work points out an economic way for the oxidation of methane under mild conditions and may enlighten the design of functional SNMs from fundamental standpoints.

ABSTRACT Irregular and complicated tunnel lining defects cannot be finely depicted with the standard finite difference simulation method based on regular mesh due to its irregular geometrical characteristics and complex [...]

ABSTRACT As new advanced high performance structural steels, low yield point (LYP) steels have been widely accepted and used in the technology of energy dissipation for seismic design. This paper [...]

ABSTRACT For seismic control and isolation techniques, low yield point steels are among the most reliable and ideal energy-dissipating materials. Structures under earthquake conditions are usually subjected to cyclic loads [...]

Understanding mercury (Hg) complexation with soil organic matter is important in assessing atmospheric Hg accumulation and sequestration processes in forest ecosystems. Separating soil organic matter into particulate organic matter (POM) and mineral-associated organic matter (MAOM) can help in the understanding of Hg dynamics and cycling due to their very different chemical constituents and associated formation and functioning mechanisms. The concentration of Hg, carbon, and nitrogen contents and isotopic signatures of POM and MAOM in a deglaciated forest chronosequence were determined to construct the processes of Hg accumulation and sequestration. The results show that Hg in POM and MAOM are mainly derived from atmospheric Hg0deposition. Hg concentration in MAOM is up to 76% higher than that in POM of broadleaf forests and up to 60% higher than that in POM of coniferous forests. Hg accumulation and sequestration in organic soil vary with the vegetation succession. Variations of δ202Hg and Δ199Hg are controlled by source mixing in the broadleaf forest and by Hg sequestration processes in the coniferous forest. Accumulation of atmospheric Hg and subsequent microbial reduction enrich heavier Hg isotopes in MAOM compared to POM due to the specific chemical constituents and nutritional role of MAOM.

In the data space, time-series analysis has emerged in many fields, including biology, healthcare, and numerous large-scale scientific facilities like astronomy, climate science, particle physics, and genomics. Clustering is one of the most critical methods in time-series analysis. So far, the state-of-art time series clustering algorithm k-Shape has been widely used not only because of its high accuracy, but also because of its relatively low computation cost. However, due to the high heterogeneity of time series data, it can not be simply regarded as a high-dimensional vector. Two time series often need some alignment method in similarity comparison. The alignment between sequences is often a time-consuming process. For example, when using dynamic time warping as a sequence alignment algorithm and if the length of time series is greater than 1,000, a single iteration in the clustering process may take hundreds to tens of thousands of seconds, while the entire clustering cycle often requires dozens of iterations. In this article, we propose a set of novel parallel strategies suitable for GPU's computation model, called Times-C, which is an abbreviation for Time Series Clustering. We define three stages in the analysis process: aggregation, centroid, and class assignment. Times-C includes efficient parallel algorithms and corresponding implementations for these three stages. Overall, the experimental results show that the Times-C algorithm exhibits a performance improvement of one to two orders of magnitude compared to the multi-core CPU version of k-Shape. Furthermore, compared to the GPU version of the k-Shape algorithm, the Times-C algorithm achieves a maximum acceleration of up to 345 times.

• A specialized canning-compression technique has improved the tensile plasticity and strength of a Zr-based bulk metallic glass. • State-of-art in-situ characterization techniques have probed the multiple-scale structural response to tension. • The structural origin of the medium-range order evolution for canning-compressed bulk metallic glasses under tension has been revealed. Developing ductile bulk metallic glasses (BMGs) can benefit from an in-depth understanding of the structure-property relation during plastic deformation. However, endowing BMGs with tensile ductility in BMGs needs to reveal the response of critical structure units during deformation. Here, we report the experimental results of an in-situ synchrotron high-energy X-ray study of a Zr-based BMG under uniaxial tension after preprocessing by canning compression of the three-dimensional compressive stress state. It is revealed that the canning-compressed BMG (CC-BMG) sample has better tensile ductility and higher ultimate strength than the as-cast sample, which possesses heterogeneous and loosely packed local structures on medium-range scales. The experimental results revealed two stages of plastic deformation in the CC-BMGs compared with one stage of plastic deformation in the as-cast BMG. Moreover, the shift in the first sharp diffraction peak along the tension direction for the canning-compressed sample is substantially more pronounced than that of the as-cast sample. Furthermore, the real-space analysis illustrates a competition mechanism between the 2-atom and 3-atom connection modes on medium-range order during the plastic deformation of the CC-BMG. Additionally, the ordering on the medium-range scale decreases in the first plastic deformation stage but increases in the second plastic deformation stage. Therefore, a structural crossover phenomenon occurs in the CC-BMG during plastic deformation. Our results demonstrate a structure-property correlation for the CC-BMGs of heterogeneous medium-range ordered structures, which may be beneficial for endowing BMGs with ductility based on medium-range order engineering techniques. [Display omitted] [ABSTRACT FROM AUTHOR]

Supramolecular organogel coatings that can disinfect the deposited microbial pathogens are emerging as an effective vehicle to prevent pathogen transmission. However, the development of anti-pathogen supramolecular adhesives with mechanical robustness and controlled oil inclusion is technically challenging. Here, we report supramolecular adhesives with mechanical integrity and robust interfacial adhesion over a wide range of biogenic antimicrobial oil. Bifunctional monomers are synthesized and assembled into linear polymers with semicrystalline stackings through hierarchical hydrogen bonds, where incorporated bioactive oil could regulate the semicrystalline stackings into nanosized crystalline domains through intermolecular hydrogen bonds. The abundant bonding motifs provided by the supramolecular cross-linked networks could accommodate oil molecules with high inclusion capability and provide more interfacial binding sites with high adhesion strength, and the nanosized crystalline domains could stabilize the organogel network and compensate for the interactions with oil molecules to enhance structural and mechanical stability. In addition, rapid healing, robust adhesion, and antimicrobial and antiviral properties of the resultant organogel coatings are demonstrated. This study paves the way for the development of high-performance antimicrobial supramolecular adhesives with controlled oil inclusion, showing potential applications in soft robotics, tissue engineering, and biomedical devices.

In this paper, we propose a Dynamic Speed Warping (DSW) algorithm to enable one-shot learning for device-free acoustic gesture signals performed by different users. The design of DSW is based on the observation that the gesture type is determined by the trajectory of hand components rather than the movement speed. By dynamically scaling the speed distribution and tracking the movement distance along the trajectory, DSW can effectively match gesture signals from different domains with a ten-fold difference in speeds. Our experimental results show that DSW can achieve a recognition accuracy of 97% for gestures performed by unknown users while only using one training sample of each gesture type from four training users.

Infection diseases such as AIDS and COVID-19 remain challenging in regard to protective vaccine design, while adjuvants are critical for subunit vaccines to induce strong, broad, and durable immune responses against variable pathogens. Here, we demonstrate that periodic mesoporous organosilica (PMO) acts as a multifunctional nanoadjuvant by adsorbing recombinant protein antigens. It can effectively deliver antigens to lymph nodes (LNs), prolong antigen exposure, and rapidly elicit germinal center (GC) responses by directly activating naive B cells via the C-type lectin receptor signaling pathway. In mice, both the gp120 trimer (HIV-1 antigen) and the receptor-binding domain (SARS-CoV-2 antigen) with the PMO nanoadjuvant elicit potent and durable antibodies that neutralize heterologous virus strains. LN immune cells analysis shows that PMO helps to effectively activate the T-follicular helper cells, GC B cells, and memory B cells and eventually develop broad and durable humoral responses. Moreover, the PMO nanoadjuvant elicits a strong cellular immune response and shapes this immune response by eliciting high levels of effector T helper cell cytokines. This study identifies a promising nanoadjuvant for subunit vaccines against multiple pathogens.