Monitoring Fatigue Damage of Orthotropic Steel Decks Using Nonlinear Ultrasonic Waves.

Orthotropic steel decks (OSDs) are commonly used in the construction of bridges due to their load-bearing capabilities. However, they are prone to fatigue damage over time due to the cyclic loads from vehicles. Therefore, the early structural health monitoring of fatigue damage in OSDs is crucial for ensuring bridge safety. Moreover, Lamb waves, as elastic waves propagating in OSD plate-like structures, are characterized by their long propagation distances and minimal attenuation. This paper introduces a method of emitting high-energy ultrasonic waves onto the OSD surface to capture the nonlinear Lamb waves formed, thereby calculating the nonlinear parameters. These parameters are then correlated with the fatigue damage endured, forming a damage index (DI) for monitoring the fatigue life of OSDs. Experimental results indicate that as fatigue damage increases, the nonlinear parameters exhibit a significant initial increase followed by a decrease. The behavior is distinct from the characteristic parameters of linear ultrasound (velocity and energy), which also exhibit changes but to a relatively smaller extent. The proposed DI and fatigue life based on nonlinear parameters can be fitted with a Gaussian curve, with the R-squared value of the fitting curve being close to 1. Additionally, this paper discusses the influence of rib welds within the OSDs on the DI, whereby as fatigue damage increases, it enlarges the value of the nonlinear parameters without altering their trend. The proposed method provides a more effective approach for monitoring early fatigue damage in OSDs.

Effect of Solvent Composition on Non-DLVO Forces and Oriented Attachment of Zinc Oxide Nanoparticles.

Oriented attachment (OA) occurs when nanoparticles in solution align their crystallographic axes prior to colliding and subsequently fuse into single crystals. Traditional colloidal theories such as DLVO provide a framework for evaluating OA but fail to capture key particle interactions due to the atomistic details of both the crystal structure and the interfacial solution structure. Using zinc oxide as a model system, we investigated the effect of the solvent on short-ranged and long-ranged particle interactions and the resulting OA mechanism. In situ TEM imaging showed that ZnO nanocrystals in toluene undergo long-range attraction comparable to 1kT at separations of 10 nm and 3kT near particle contact. These observations were rationalized by considering non-DLVO interactions, namely, dipole-dipole forces and torques between the polar ZnO nanocrystals. Langevin dynamics simulations showed stronger interactions in toluene compared to methanol solvents, consistent with the experimental results. Concurrently, we performed atomic force microscopy measurements using ZnO-coated probes for the short-ranged interaction. Our data are relevant to another type of non-DLVO interaction, namely, the repulsive solvation force. Specifically, the solvation force was stronger in water compared to ethanol and methanol, due to the stronger hydrogen bonding and denser packing of water molecules at the interface. Our results highlight the importance of non-DLVO forces in a general framework for understanding and predicting particle aggregation and attachment.

A biomass phosphonamide enables biodegradable polylactide with favorable flame retardancy and rapid crystallization.

PLA is widely known as biodegradable plastics whose further application in fields such as automotive and architectural is still constrained by its flammability and unsatisfactory crystallization properties. To address the aforementioned concerns, a novel biomass phosphonamide PDPA was synthesized with chemical structure confirmed by FTIR, NMR and elemental analysis tests. Immediately thereafter, PLA/PDPA composites were prepared by melting blending, with a focus on flame retardancy, crystallization properties and flame-retardant mechanism. As expected, PDPA efficiently enhanced both the flame retardancy and crystallization properties of PLA. Specifically, the PLA/4.0PDPA obtained UL-94 V-0 grade and the LOI value increased to 28.6 % with only 4 wt% PDPA added, which comes down to the superior free radical capture and dilution effect of PDPA in the vapor phase and the melting droplet effect. More appealingly, the crystallinity of PLA/4.0PDPA was significantly enhanced to 43.4 % from 2.5 % of PLA, and the shortest t1/2 was 4 mins in the isothermal crystallization process due to the excellent heterogeneous nucleation of PDPA. Moreover, PLA/PDPA composites maintain almost the same mechanical performance as pure PLA. In brief, this work provides a green strategy for the preparation of PLA composites with excellent comprehensive performance and shows great potential in engineering materials.

Discovery of novel natural-product-derived mutant isocitrate dehydrogenases 1 inhibitors: Structure-based virtual screening, biological evaluation and structure-activity relationship study.

Mutations in IDH1 are commonly observed across various cancers, causing the conversion of α-KG to 2-HG. Elevated levels of 2-HG disrupt histone and DNA demethylation processes, promoting tumor development. Consequently, there is substantial interest in developing small molecule inhibitors targeting the mutant enzymes. Herein, we report a structure-based high-throughput virtual screening strategy using a natural products library, followed by hit-to-lead optimization. Through this process, we discover a potent compound, named 11s, which exhibited significant inhibition to IDH1 R132H and IDH1 R132C with IC50 values of 124.4 and 95.7 nM, respectively. Furthermore, 11s effectively reduced 2-HG formation, with EC50 values of 182 nM in U87 R132H cell, and 84 nM in HT-1080 cell. In addition, 11s significantly reduced U87 R132H and HT-1080 cell proliferation with GC50 values of 3.48 and 1.38 µM, respectively. PK-PD experiments further confirmed that compound 11s significantly decreased 2-HG formation in an HT-1080 xenograft mouse model, resulting in notable suppression of tumor growth without apparent loss in body weight.

Unlocking the potential: T1-weighed MRI as a powerful predictor of levodopa response in Parkinson's disease.

BACKGROUND: The efficacy of levodopa, the most crucial metric for Parkinson's disease diagnosis and treatment, is traditionally gauged through the levodopa challenge test, which lacks a predictive model. This study aims to probe the predictive power of T1-weighted MRI, the most accessible modality for levodopa response. METHODS: This retrospective study used two datasets: from the Parkinson's Progression Markers Initiative (219 records) and the external clinical dataset from Ruijin Hospital (217 records). A novel feature extraction method using MedicalNet, a pre-trained deep learning network, along with three previous approaches was applied. Three machine learning models were trained and tested on the PPMI dataset and included clinical features, imaging features, and their union set, using the area under the curve (AUC) as the metric. The most significant brain regions were visualized. The external clinical dataset was further evaluated using trained models. A paired one-tailed t-test was performed between the two sets; statistical significance was set at p < 0.001. RESULTS: For 46 test set records (mean age, 62 ± 9 years, 28 men), MedicalNet-extracted features demonstrated a consistent improvement in all three machine learning models (SVM 0.83 ± 0.01 versus 0.73 ± 0.01, XgBoost 0.80 ± 0.04 versus 0.74 ± 0.02, MLP 0.80 ± 0.03 versus 0.70 ± 0.07, p < 0.001). Both feature sets were validated on the clinical dataset using SVM, where MedicalNet features alone achieved an AUC of 0.64 ± 0.03. Key responsible brain regions were visualized. CONCLUSION: The T1-weighed MRI features were more robust and generalizable than the clinical features in prediction; their combination provided the best results. T1-weighed MRI provided insights on specific regions responsible for levodopa response prediction. CRITICAL RELEVANCE STATEMENT: This study demonstrated that T1w MRI features extracted by a deep learning model have the potential to predict the levodopa response of PD patients and are more robust than widely used clinical information, which might help in determining treatment strategy. KEY POINTS: This study investigated the predictive value of T1w features for levodopa response. MedicalNet extractor outperformed all other previously published methods with key region visualization. T1w features are more effective than clinical information in levodopa response prediction.

Effect of Cyclic Ice Plug Deformation on Microstructure and Mechanical Behaviors of Nuclear-Grade Low-Carbon Tubular Steel.

This study subjected nuclear-grade 20# pipeline steel to cyclic freeze-thaw ice plugging tests, simulating the plastic deformation experienced by pipes during ice plug removal procedures. Subsequently, the dislocation morphology and mechanical properties of the specimens post cyclic ice plugging were examined. The cyclic ice plugging process led to an increase in the dislocation density within the specimens. After 20 and 40 cycles of ice plugging, the internal dislocation structures evolved from individual dislocation lines and dislocation tangles to high-density dislocation walls and dislocation cells. These high-density dislocation walls and cells hindered dislocation motion, giving rise to strain hardening phenomena, thereby resulting in increased strength and hardness of the specimens with an increasing number of ice plugging cycles. In addition, a large stress field was generated around the dislocation buildup, which reduced the pipe material's plastic toughness. The findings elucidate the effects of cyclic ice plugging on the microstructure and properties of nuclear-grade 20# pipeline steel, aiming to provide a theoretical basis for the safe and stable application of ice plugging technology in nuclear piping systems.

Lead Identification of Novel Naphthyridine Derivatives as Potent SOS1 Inhibitors.

SOS1, a guanine nucleotide exchange factor (GEF), plays a critical role in catalyzing the conversion of KRAS from its GDP- to GTP-bound form, regardless of KRAS mutation status, and represents a promising new drug target to treat all KRAS-driven tumors. Herein, we employed a scaffold hopping strategy to design, synthesize, and optimize a series of novel binary ring derivatives as SOS1 inhibitors. Among them, compound 10f (HH0043) displayed potent activities in both biochemical and cellular assays and favorable pharmacokinetic profiles. Oral administration of HH0043 resulted in a significant tumor inhibitory effect in a subcutaneous KRAS G12C-mutated NCI-H358 (human lung cancer cell line) xenograft mouse model, and the tumor inhibitory effect of HH0043 was superior to that of BI-3406 at the same dose (total growth inhibition, TGI: 76% vs 49%). On the basis of these results, HH0043, with a novel 1,7-naphthyridine scaffold that is distinct from currently reported SOS1 inhibitors, is nominated as the lead compound for this discovery project.

[Application of LA-UNet network model in remote sensing classification of urban green space]. / LA-UNet网络模型在城市绿地遥感分类中的应用.

The accurate identification and monitoring of urban green space is of great significance in urban planning and ecological management. In view of the complex background of urban green space, the traditional remote sensing classification technology is prone to the problem of misalignment and adhesion. Taking Yuhua District of Changsha City as the research area and Gaofen-2 (GF-2) remote sensing image as the data source, we proposed a remote sensing classification method for urban green space based on the LA-UNet model, which was based on the UNet model. We introduced the DWTCA channel attention mechanism module to improve the attention of the network to green space information, and used the CARAFE module to up sample the extracted features to achieve accurate classification of trees, shrubs and other land types in the complex background of the city. The results showed that the LA-UNet model had the best classification effect of urban green space when using standard false color remote sensing images. The overall accuracy and mean intersection over union were 96.3% and 90.9%, which were 2.8% and 6.1% higher than the UNet model, respectively. In the Potsdam public dataset, the overall accuracy and mean intersection over union of the LA-UNet model were also better than those of the UNet model, which increased by 0.9% and 1.8%, respectively, indicating that the LA-UNet model had good robustness and versatility. In summary, the proposed LA-UNet model could effectively alleviate the problems of misalignment and adhesion of urban green space, with advantages in the remote sensing classification of urban green space. The improved LA-UNet model had a smaller parameter volume than the UNet model, which could effectively improve the classification accuracy of urban green space. This study would provide a methodological reference for the accurate classification and understanding the spatial distribution of urban green space.

EXPRESS: Astragaloside IV ameliorated neuroinflammation and improved neurological functions in mice exposed to traumatic brain injury by modulating the PERK-eIF2α-ATF4 signaling pathway.

Increasing evidence suggests that endoplasmic reticulum stress (ER stress) and neuroinflammation are involved in the complex pathological process of traumatic brain injury (TBI). However, the pathological mechanisms of their interactions in TBI remain incompletely elucidated. Therefore, investigating and ameliorating neuroinflammation and ER stress post-TBI may represent effective strategies for treating secondary brain injury. Astragaloside IV (AS-IV) has been reported as a potential neuroprotective and anti-inflammatory agent in neurological diseases. This study utilized a mouse TBI model to investigate the pathological mechanisms and crosstalk of ER stress, neuroinflammation, and microglial cell morphology in TBI, as well as the mechanisms and potential of AS-IV in improving TBI. The research revealed that post-TBI, inflammatory factors IL-6, IL-1ß, and TNF-α increased, microglial cells were activated, and the specific inhibitor of PERK phosphorylation, GSK2656157, intervened to alleviate neuroinflammation and inhibit microglial cell activation. Post-TBI, levels of ER stress-related proteins (p-PERK, p-eIF2a, ATF4, ATF6, and p-IRE1a) increased. Following AS-IV treatment, neurological dysfunction in TBI mice improved. Levels of p-PERK, p-eIF2a, and ATF4 decreased, along with reductions in inflammatory factors IL-6, IL-1ß, and TNF-α. Changes in microglial/macrophage M1/M2 polarization were observed. Additionally, the PERK activator CCT020312 intervention eliminated the impact of AS-IV on post-TBI inflammation and ER stress-related proteins p-PERK, p-eIF2a, and ATF4. These results indicate that AS-IV alleviates neuroinflammation and brain damage post-TBI through the PERK pathway, offering new directions and theoretical insights for TBI treatment.

Triply Interlocked [2]catenanes: Rational Synthesis, Reversible Conversion Studies and Unprecedented Application in Photothermal Responsive Elastomer.

Triply interlocked [2]catenane complexes featuring two identical, mechanically interlocked units are extraordinarily rare chemical compounds, whose properties and applications remain open to detailed studies. Herein, we introduce the rational design of a new ligand precursor, L1, suitable for the synthesis of six triply interlocked [2]catenanes by coordination-driven self-assembly. The interlocked compounds can be reversibly converted into the corresponding simple triangular prism metallacage by addition of H2O or DMF solvents to their CH3OH solutions, thereby demonstrating the importance of π⋅⋅⋅π stacking and hydrogen bonding interactions in the formation of triply interlocked [2]catenanes. Moreover, extensive studies have been conducted to assess the remarkable photothermal conversion performance. Complex 6 a, exhibiting outstanding photothermal conversion performance (conversion efficiency in solution : 31.82 %), is used to prepare novel photoresponsive elastomer in combination with thermally activated liquid crystal elastomer. The resultant material displays robust response to near-infrared (NIR) laser and the capability of completely reforming the shape and reversible actuation, paving the way for the application of half-sandwich organometallic units in photo-responsive smart materials.

Synergizing habits and goals with variational Bayes.

Behaving efficiently and flexibly is crucial for biological and artificial embodied agents. Behavior is generally classified into two types: habitual (fast but inflexible), and goal-directed (flexible but slow). While these two types of behaviors are typically considered to be managed by two distinct systems in the brain, recent studies have revealed a more sophisticated interplay between them. We introduce a theoretical framework using variational Bayesian theory, incorporating a Bayesian intention variable. Habitual behavior depends on the prior distribution of intention, computed from sensory context without goal-specification. In contrast, goal-directed behavior relies on the goal-conditioned posterior distribution of intention, inferred through variational free energy minimization. Assuming that an agent behaves using a synergized intention, our simulations in vision-based sensorimotor tasks explain the key properties of their interaction as observed in experiments. Our work suggests a fresh perspective on the neural mechanisms of habits and goals, shedding light on future research in decision making.

Electromagnetohydrodynamic (EMHD) flow of Jeffrey fluid through a rough circular microchannel with surface charge-dependent slip.

This research examines the electromagnetohydrodynamic (EMHD) flow of Jeffrey fluid in a rough circular microchannel while considering the effect of surface charge on slip. The channel wall corrugations are described as periodic sinusoidal waves with small amplitudes. The perturbation method is employed to derive solutions for velocity and volumetric flow rate, and a combination of three-dimensional (3D) and two-dimensional (2D) graphical representations is utilized to effectively illustrate the impacts of relevant parameters on them. The significance of the Reynolds number R e $Re$ in investigations of EMHD flow is particularly emphasized. Furthermore, the effect of wall roughness ε $\varepsilon $ and wave number k $k$ on velocity and the influence of wall roughness ε $\varepsilon $ and surface charge density σ s ${\sigma }_s$ on volumetric flow rate are primarily focused on, respectively, at various Reynolds numbers. The results suggest that increasing the wall roughness leads to a reduction in velocity at low Reynolds numbers ( R e = 1 $Re = 1$ ) and an increment at high Reynolds numbers ( R e = 10 $Re = 10$ ). For any Reynolds number, a roughness with an odd multiple of wave number ( k = 6 , 10 $k = 6,10$ ) will result in a more stable velocity profile compared to one with an even multiple of wave number ( k = 4 , 8 $k = 4,8$ ). Decreasing the relaxation time λ ¯ 1 ${\bar{\lambda }}_1$ while increasing the retardation time λ ¯ 2 ${\bar{\lambda }}_2$ and Hartmann number H a $Ha$ can diminish the impact of wall roughness ε $\varepsilon $ and surface charge density σ s ${\sigma }_s$ on volumetric flow rate, independent of the Reynolds number. Interestingly, in the existence of wall roughness, further consideration of the effect of surface charge on slip leads to a 15% drop in volumetric flow rate at R e = 1 $Re = 1$ and a 32% slippage at R e = 10 $Re = 10$ . However, in the condition where the effect of surface charge on slip is considered, further examination of the presence of wall roughness only results in a 1.4% decline in volumetric flow rate at R e = 1 $Re = 1$ and a 1.6% rise at R e = 10 $Re = 10$ . These findings are crucial for optimizing the EMHD flow models in microchannels.

Constructing an Asymmetric Covalent Triazine Framework to Boost the Efficiency and Selectivity of Visible-Light-Driven CO2 Photoreduction.

The photocatalytic reduction of CO2 represents an environmentally friendly and sustainable approach for generating valuable chemicals. In this study, a thiophene-modified highly conjugated asymmetric covalent triazine framework (As-CTF-S) is developed for this purpose. Significantly, single-component intramolecular energy transfer can enhance the photogenerated charge separation, leading to the efficient conversion of CO2 to CO during photocatalysis. As a result, without the need for additional photosensitizers or organic sacrificial agents, As-CTF-S demonstrates the highest photocatalytic ability of 353.2 µmol g-1 and achieves a selectivity of ≈99.95% within a 4 h period under visible light irradiation. This study provides molecular insights into the rational control of charge transfer pathways for high-efficiency CO2 photoreduction using single-component organic semiconductor catalysts.

Clinical features and prognosis of idiopathic inflammatory myopathies with coexistent multiple myositis-specific antibodies.

OBJECTIVES: This study aimed to evaluate the clinical significance of the coexistence of 2 or more myositis-specific antibodies (multiple MSAs) in adult patients with idiopathic inflammatory myopathies (IIM). METHODS: We assessed a cohort of 202 consecutive patients with IIM. Clinical features and survival rates were compared between patients with and without multiple MSAs. RESULTS: Of those 202 patients, 44 (21.8%) were found to have multiple MSAs. 63.6% of the 44 patients tested positive for anti-aminoacyl-tRNA synthetase antibodies (anti-ARS+) and 52.3% positive for anti-melanoma differentiation-associated protein-5 antibody (anti-MDA5+). The presence of multiple MSAs was associated with less rapidly progressive interstitial lung disease (RP-ILD), fever, rash, periungual erythema, more muscle involvement and dysphagia, higher albumin level, and higher positive rate of ANA antibody in anti-MDA5+ population. In anti-ARS+ population with multiple MSAs, there were more V-neck sign, skin ulcers, dysphagia and peripheral edema. No differences in survival rates were observed between patients with or without multiple MSAs in the overall and anti-ARS+ populations. However, the survival rate in anti-MDA5+ population with multiple MSAs was significantly higher than those without multiple MSAs (p = 0.003). Moreover, multiple MSAs remained an independent protective factor against mortality in multivariable Cox regression analysis of anti-MDA5+ population [HR 0.108 (95% CI 0.013, 0.908), p=0.041]. CONCLUSIONS: Multiple MSAs coexist in some IIM patients and their existence indicates mixed features from concomitant MSAs in anti-MDA5+ population and anti-ARS+ population. Identifying multiple MSAs could help to discover a more favourable disease phenotype with decreased mortality in anti-MDA5+ population.

Reversing the Interfacial Electric Field in Metal Phosphide Heterojunction by Fe-Doping for Large-Current Oxygen Evolution Reaction.

Developing non-precious-metal electrocatalysts that can operate with a low overpotential at a high current density for industrial application is challenging. Heterogeneous bimetallic phosphides have attracted much interest. Despite high hydrogen evolution reaction (HER) performance, the ordinary oxygen evolution reaction (OER) performance hinders their practical use. Herein, it is shown that Fe-doping reverses and enlarges the interfacial electrical field at the heterojunction, turning the H intermediate favorable binding sites for HER into O intermediate favorable sites for OER. Specifically, the self-supported heterojunction catalysts on nickel foam (CoP@Ni2P/NF and Fe-CoP@Fe-Ni2P/NF) are readily synthesized. They only require the overpotentials of 266 and 274 mV to drive a large current density of 1000 mA cm-2 (j1000) for HER and OER, respectively. Furthermore, a water splitting cell equipped with these electrodes only requires a voltage of 1.724 V to drive j1000 with excellent durability, demonstrating the potential of industrial application. This work offers new insights on interfacial engineering for heterojunction catalysts.

Decentralized stochastic sharpness-aware minimization algorithm.

In recent years, distributed stochastic algorithms have become increasingly useful in the field of machine learning. However, similar to traditional stochastic algorithms, they face a challenge where achieving high fitness on the training set does not necessarily result in good performance on the test set. To address this issue, we propose to use of a distributed network topology to improve the generalization ability of the algorithms. We specifically focus on the Sharpness-Aware Minimization (SAM) algorithm, which relies on perturbation weights to find the maximum point with better generalization ability. In this paper, we present the decentralized stochastic sharpness-aware minimization (D-SSAM) algorithm, which incorporates the distributed network topology. We also provide sublinear convergence results for non-convex targets, which is comparable to consequence of Decentralized Stochastic Gradient Descent (DSGD). Finally, we empirically demonstrate the effectiveness of these results in deep networks and discuss their relationship to the generalization behavior of SAM.

A novel NIR-II FL/ PA imaging-guided synergistic photothermal-immune therapy: Biomineralizing nanosystems integrated with anti-tumor and bone repair.

Advanced stages of breast cancer are frequently complicated by bone metastases, which cause significant cancer-related bone destruction and mortality. However, the early precise theranostics of bone metastasis remains a formidable challenge in clinical practice. Herein,a novel all-in-one nanotheranostic system (ABI NYs) combining NIR-II FL/PA dual-modal imaging with photothermal-immunity therapeutic functionalities in one component was designed to precisely localize bone metastasis microscopic lesions and achieve complete tumor ablation at an early stage. The surface modification of the nanosystem with ibandronate (IBN) facilitates both passive and active targeting, significantly improving the detection rate of bone metastasis and suppressing the bone resorption. Superior photothermal performance produces sufficient heat to kill tumor cells while stimulating the upregulation of heat shock proteins 70 (HSP70), which triggers the immunogenic cell death (ICD) effect and the anti-tumor immune response. These all-in-one nanosystems precisely demonstrated early lesion localization in bone metastases and total tumor ablation with a single integration via "one-component, multi-functions" technique. To sum up, ABI NYs, as novel biomineralizing nanosystems integrated with anti-tumor and bone repair, present a synergistic therapy strategy, providing insight into the theranostics of bone metastases and clinical research.

Dual regulation of osteosarcoma hypoxia microenvironment by a bioinspired oxygen nanogenerator for precise single-laser synergistic photodynamic/photothermal/induced antitumor immunity therapy.

The hypoxic tumor microenvironment (TME) of osteosarcoma (OS) is the Achilles' heel of oxygen-dependent photodynamic therapy (PDT), and tremendous challenges are confronted to reverse the hypoxia. Herein, we proposed a "reducing expenditure of O2 and broadening sources" dual-strategy and constructed ultrasmall IrO2@BSA-ATO nanogenerators (NGs) for decreasing the O2-consumption and elevating the O2-supply simultaneously. As O2 NGs, the intrinsic catalase (CAT) activity could precisely decompose the overexpressed H2O2 to produce O2 in situ, enabling exogenous O2 infusion. Moreover, the cell respiration inhibitor atovaquone (ATO) would be at the tumor sites, effectively inhibiting cell respiration and elevating oxygen content for endogenous O2 conservation. As a result, IrO2@BSA-ATO NGs systematically increase tumor oxygenation in dual ways and significantly enhance the antitumor efficacy of PDT. Moreover, the extraordinary photothermal conversion efficiency allows the implementation of precise photothermal therapy (PTT) under photoacoustic guidance. Upon a single laser irradiation, this synergistic PDT, PTT, and the following immunosuppression regulation performance of IrO2@BSA-ATO NGs achieved a superior tumor cooperative eradicating capability both in vitro and in vivo. Taken together, this study proposes an innovative dual-strategy to address the serious hypoxia problem, and this microenvironment-regulable IrO2@BSA-ATO NGs as a multifunctional theranostics platform shows great potential for OS therapy.

Inhibition and Mechanism of Protein Nonenzymatic Glycation by Lactobacillus fermentum.

Lactobacillus fermentum (L. fermentum) was first evaluated as a potential advanced glycation end-product (AGE) formation inhibitor by establishing a bovine serum albumin (BSA) + glucose (glu) glycation model in the present study. The results showed that the highest inhibition rates of pentosidine and total fluorescent AGEs by L. fermentum were approximately 51.67% and 77.22%, respectively, which were higher than that of aminoguanidine (AG). Mechanistic analysis showed that L. fermentum could capture methylglyoxal and glyoxal, inhibit carbonyl and sulfhydryl oxidation, reduce the binding of glucose and amino groups, increase total phenolic content and antioxidant activity, and release intracellular substances to scavenge free radicals; these abilities were the basis of the antiglycation mechanism of L. fermentum. In addition, L. fermentum significantly prevented conformational changes in proteins during glycation, reduced protein cross-linking by 35.67%, and protected the intrinsic fluorophore. Therefore, the inhibition of L. fermentum on glycation mainly occurs through antioxidation, the capture of dicarbonyl compounds, and the protection of the BSA structure. These findings collectively suggest that Lactobacillus is an inhibitor of protein glycation and AGE formation and has the potential for nutraceutical applications.

Hierarchical Recognizing Vector Graphics and A New Chart-based Vector Graphics Dataset.

The conventional approach to image recognition has been based on raster graphics, which can suffer from aliasing and information loss when scaled up or down. In this paper, we propose a novel approach that leverages the benefits of vector graphics for object localization and classification. Our method, called YOLaT (You Only Look at Text), takes the textual document of vector graphics as input, rather than rendering it into pixels. YOLaT builds multi-graphs to model the structural and spatial information in vector graphics and utilizes a dual-stream graph neural network (GNN) to detect objects from the graph. However, for real-world vector graphics, YOLaT only models in flat GNN with vertexes as nodes ignore higher-level information of vector data. Therefore, we propose YOLaT++ to learn Multi-level Abstraction Feature Learning from a new perspective: Primitive Shapes to Curves and Points. On the other hand, given few public datasets focus on vector graphics, data-driven learning cannot exert its full power on this format. We provide a large-scale and challenging dataset for Chart-based Vector Graphics Detection and Chart Understanding, termed VG-DCU, with vector graphics, raster graphics, annotations, and raw data drawn for creating these vector charts. Experiments show that the YOLaT series outperforms both vector graphics and raster graphics-based object detection methods on both subsets of VG-DCU in terms of both accuracy and efficiency, showcasing the potential of vector graphics for image recognition tasks. Our codes, models, and the VG-DCU dataset are available at: https://github.com/microsoft/YOLaT-VectorGraphicsRecognition.