Metric3D v2: A Versatile Monocular Geometric Foundation Model for Zero-shot Metric Depth and Surface Normal Estimation.

We introduce Metric3D v2, a geometric foundation model for zero-shot metric depth and surface normal estimation from a single image, which is crucial for metric 3D recovery. While depth and normal are geometrically related and highly complimentary, they present distinct challenges. State-of-the-art (SoTA) monocular depth methods achieve zero-shot generalization by learning affine-invariant depths, which cannot recover real-world metrics. Meanwhile, SoTA normal estimation methods have limited zero-shot performance due to the lack of large-scale labeled data. To tackle these issues, we propose solutions for both metric depth estimation and surface normal estimation. For metric depth estimation, we show that the key to a zero-shot single-view model lies in resolving the metric ambiguity from various camera models and large-scale data training. We propose a canonical camera space transformation module, which explicitly addresses the ambiguity problem and can be effortlessly plugged into existing monocular models. For surface normal estimation, we propose a joint depth-normal optimization module to distill diverse data knowledge from metric depth, enabling normal estimators to learn beyond normal labels. Equipped with these modules, our depth-normal models can be stably trained with over 16 million of images from thousands of camera models with different-type annotations, resulting in zero-shot generalization to in-the-wild images with unseen camera settings. Our method currently ranks the 1st on various zero-shot and non-zero-shot benchmarks for metric depth, affine-invariant-depth as well as surface-normal prediction, shown in Fig. 1. Notably, we surpassed the ultra-recent MarigoldDepth and DepthAnything on various depth benchmarks including NYUv2 and KITTI. Our method enables the accurate recovery of metric 3D structures on randomly collected internet images, paving the way for plausible single-image metrology. The potential benefits extend to downstream tasks, which can be significantly improved by simply plugging in our model. For example, our model relieves the scale drift issues of monocular-SLAM (Fig. 3), leading to high-quality metric scale dense mapping. These applications highlight the versatility of Metric3D v2 models as geometric foundation models. Our project page is at https://JUGGHM.github.io/Metric3Dv2.

Donor-Acceptor Type Supra-Carbon-Dots with Long Lifetime Photogenerated Radicals Boosting Tumor Photodynamic Therapy.

Carbon dots (CDs) have gained significant interest because of their potential in biomedical applications. Nevertheless, developing CDs with efficient photoinduced charge separation for tumor photodynamic therapy (PDT) remains a challenge. This study presents a novel class of supra-carbon-dots (supra-CDs) developed by fusing red emissive CDs with 2,3-dicyanohydroquinone (DCHQ) via post-solvothermal treatment. In supra-CDs, the core, acting as electron donors, is formed by assembled CDs with substantial sp2 domains, the fused interface originating from DCHQ with electron-withdrawing groups functions as the electron acceptor. This configuration creates the unique donor-acceptor nanostructure. Upon white light irradiation, the excited electrons from the assembled CDs were transferred to the electron-withdrawing interface, whereas the photogenerated holes were retained within the assembled CDs as radicals, leading to effective photoinduced charge separation. The separated photogenerated electrons then react with oxygen to generate superoxide radicals. Simultaneously, the photogenerated holes undergo oxidation of crucial cellular substrates. This dual action underscores the exceptional cell-killing efficacy of supra-CDs. Moreover, the increased particle sizes (~20 nm) ensure supra-CDs to exhibit a notable capacity for tumor accumulation via the improved permeability and retention effect, thereby achieving satisfactory anti-tumor PDT efficacy in a mouse subcutaneous tumor model.

Characterizing Short-Time Aging Precipitation Behavior of a Novel Nickel-Iron-Based Alloy via Electrical Performance.

In this paper, the precipitation behavior and its effect on resistivity in a new type of nickel-iron-based alloy during short-term aging were investigated. During the aging process, the γ' phase increases in average size and decreases in number, with its area fraction fluctuating over time. This fluctuation is caused by the mismatch in the redissolution and growth rates of the γ' phase. As the area fraction of the γ' phase increases, the content of solute atoms in the matrix that scatter electrons decreases, lowering the resistivity of the alloy. Additionally, the continuous precipitation of M23C6 at grain boundaries during aging causes the resistivity to gradually increase. This paper explains the fluctuation in the total amount of γ' phase during short-term aging and proposes a new method for characterizing the precipitation behavior of the γ' phase in the novel alloy using the relative trend of resistivity changes.

IdeNet: Making Neural Network Identify Camouflaged Objects Like Creatures.

Camouflaged objects often blend in with their surroundings, making the perception of a camouflaged object a more complex procedure. However, most neural-network-based methods that simulate the visual information processing pathway of creatures only roughly define the general process, which deficiently reproduces the process of identifying camouflaged objects. How to make modeled neural networks perceive camouflaged objects as effectively as creatures is a significant topic that deserves further consideration. After meticulous analysis of biological visual information processing, we propose an end-to-end prudent and comprehensive neural network, termed IdeNet, to model the critical information processing. Specifically, IdeNet divides the entire perception process into five stages: information collection, information augmentation, information filtering, information localization, and information correction and object identification. In addition, we design tailored visual information processing mechanisms for each stage, including the information augmentation module (IAM), the information filtering module (IFM), the information localization module (ILM), and the information correction module (ICM), to model the critical visual information processing and establish the inextricable association of biological behavior and visual information processing. The extensive experiments show that IdeNet outperforms state-of-the-art methods in all benchmarks, demonstrating the effectiveness of the five-stage partitioning of visual information processing pathway and the tailored visual information processing mechanisms for camouflaged object detection. Our code is publicly available at: https://github.com/whyandbecause/IdeNet.

Hydrogen sulfide enhances kiwifruit resistance to soft rot by regulating jasmonic acid signaling pathway.

As the third active gas signal molecule in plants, hydrogen sulfide (H2S) plays important roles in physiological metabolisms and biological process of fruits and vegetables during postharvest storage. In the present study, the effects of H2S on enhancing resistance against soft rot caused by Botryosphaeria dothidea and the involvement of jasmonic acid (JA) signaling pathway in kiwifruit during the storage were investigated. The results showed that 20 µL L-1 H2S fumigation restrained the disease incidence of B. dothidea-inoculated kiwifruit during storage, and delayed the decrease of firmness and the increase of soluble solids (SSC) content. H2S treatment increased the transcription levels of genes related to JA biosynthesis (AcLOX3, AcAOS, AcAOC2, and AcOPR) and signaling pathway (AcCOI1, AcJAZ5, AcMYC2, and AcERF1), as well as the JA accumulation. Meanwhile, H2S promoted the expression of defense-related genes (AcPPO, AcSOD, AcGLU, AcCHI, AcAPX, and AcCAT). Correlation analysis revealed that JA content was positively correlated with the expression levels of JA biosynthesis and defense-related genes. Overall, the results indicated that H2S could promote the increase of endogenous JA content and expression of defense-related genes by regulating the transcription levels of JA pathway-related genes, which contributed to the inhibition on the soft rot occurrence of kiwifruit.

Physiologic Network-Based Brain-Heart Interaction Quantification During Visual Emotional Elicitation.

In recent years, there has been a surge in interest regarding the intricate physiological interplay between the brain and the heart, particularly during emotional processing. This has led to the development of various signal processing techniques aimed at investigating Brain-Heart Interactions (BHI), reflecting a growing appreciation for their bidirectional communication and influence on each other. Our study contributes to this burgeoning field by adopting a network physiology approach, employing time-delay stability as a quantifiable metric to discern and measure the coupling strength between the brain and the heart, specifically during visual emotional elicitation. We extract and transform features from EEG and ECG signals into a 1 Hz format, facilitating the calculation of BHI coupling strength through stability analysis on their maximal cross-correlation. Notably, our investigation sheds light on the critical role played by low-frequency components in EEG, particularly in the δ , θ , and α bands, as essential mediators of information transmission during the complex processing of emotion-related stimuli by the brain. Furthermore, our analysis highlights the pivotal involvement of frontal pole regions, emphasizing the significance of δ - θ coupling in mediating emotional responses. Additionally, we observe significant arousal-dependent changes in the θ frequency band across different emotional states, particularly evident in the prefrontal cortex. By offering novel insights into the synchronized dynamics of cortical and heartbeat activities during emotional elicitation, our research enriches the expanding knowledge base in the field of neurophysiology and emotion research.

Engineering Magnetic Extracellular Vesicles Mimetics for Enhanced Targeting Chemodynamic Therapy to Overcome Ovary Cancer.

Chemodynamic therapy (CDT), employing metal ions to transform endogenous H2O2 into lethal hydroxyl radicals (•OH), has emerged as an effective approach for tumor treatment. Yet, its efficacy is diminished by glutathione (GSH), commonly overexpressed in tumors. Herein, a breakthrough strategy involving extracellular vesicle (EV) mimetic nanovesicles (NVs) encapsulating iron oxide nanoparticles (IONPs) and ß-Lapachone (Lapa) was developed to amplify intracellular oxidative stress. The combination, NV-IONP-Lapa, created through a serial extrusion from ovarian epithelial cells showed excellent biocompatibility and leveraged magnetic guidance to enhance endocytosis in ovarian cancer cells, resulting in selective H2O2 generation through Lapa catalysis by NADPH quinone oxidoreductase 1 (NQO1). Meanwhile, the iron released from IONPs ionization under acidic conditions triggered the conversion of H2O2 into •OH by the Fenton reaction. Additionally, the catalysis process of Lapa eliminated GSH in tumor, further amplifying oxidative stress. The designed NV-IONP-Lapa demonstrated exceptional tumor targeting, facilitating MR imaging, and enhanced tumor suppression without significant side effects. This study presents a promising NV-based drug delivery system for exploiting CDT against NQO1-overexpressing tumors by augmenting intratumoral oxidative stress.

Graph Convolutional Network With Connectivity Uncertainty for EEG-Based Emotion Recognition.

Automatic emotion recognition based on multichannel Electroencephalography (EEG) holds great potential in advancing human-computer interaction. However, several significant challenges persist in existing research on algorithmic emotion recognition. These challenges include the need for a robust model to effectively learn discriminative node attributes over long paths, the exploration of ambiguous topological information in EEG channels and effective frequency bands, and the mapping between intrinsic data qualities and provided labels. To address these challenges, this study introduces the distribution-based uncertainty method to represent spatial dependencies and temporal-spectral relativeness in EEG signals based on Graph Convolutional Network (GCN) architecture that adaptively assigns weights to functional aggregate node features, enabling effective long-path capturing while mitigating over-smoothing phenomena. Moreover, the graph mixup technique is employed to enhance latent connected edges and mitigate noisy label issues. Furthermore, we integrate the uncertainty learning method with deep GCN weights in a one-way learning fashion, termed Connectivity Uncertainty GCN (CU-GCN). We evaluate our approach on two widely used datasets, namely SEED and SEEDIV, for emotion recognition tasks. The experimental results demonstrate the superiority of our methodology over previous methods, yielding positive and significant improvements. Ablation studies confirm the substantial contributions of each component to the overall performance.

Structural and functional properties of Maillard-reacted casein phosphopeptides with different carbohydrates.

This study used glucose, fructose, maltose and dextran to explore the effects of different carbohydrates on the Maillard reaction of casein phosphopeptides (CPP). The color parameter results showed that heating time from 1 to 5 h led to brown color, which was consistent with the observed increased in browning intensity. Fourier transform infrared spectroscopy results verified that four carbohydrates reacted with CPP to produce Maillard conjugates. Fluorescence spectroscopy showed that the Maillard reaction changed the tertiary structure of CPP by decreasing the intrinsic fluorescence intensity and surface hydrophobicity compared with the CPP-carbohydrate mixture. At the same time, the Maillard reaction effectively improved the emulsifying properties, reducing power and DPPH radical scavenging activity of CPP. Furthermore, this study also found that glucose and fructose improved CPP more than maltose and dextran. Therefore, monosaccharides have good potential in modifying CPP via the Maillard reaction.

Active pullulan-based coatings incorporated with Auricularia auricular extracts for preserving potato fresh-cuts.

In the present study, Auricularia auricular polysaccharides (AAP) and Auricularia auricular proteins (AAPR) obtained from the waste products of Auricularia auricular were incorporated into pullulan (PUL) to obtain active packaging films/coatings. Results showed that incorporating AAP/AAPR into PUL-based films decreased their transparency, but increased the compactness, thermal stability, antioxidant, and antimicrobial properties. Adding 2% PUL films with 10%:10% of AAP/AAPR exhibiting good mechanical properties were applied to fresh-cut potatoes to avoid spoilage during eight days of storage, with significantly decreased in browning index, weight loss, microbial growth prevention and the total soluble solids was maintained. These results substantiated that pullulan containing AAP/AAPR as an active film/coating with antioxidant and antimicrobial properties has significant potential for maintaining safety and quality of fresh-cut potatoes and extending their shelf life.

A noval approach based on TCN-LSTM network for predicting waterlogging depth with waterlogging monitoring station.

As a result of climate change and rapid urbanization, urban waterlogging commonly caused by rainstorm, is becoming more frequent and more severe in developing countries. Urban waterlogging sometimes results in significant financial losses as well as human casualties. Accurate waterlogging depth prediction is critical for early warning system and emergency response. However, the existing hydrological models need to obtain more abundant hydrological data, and the model construction is complicated. The waterlogging depth prediction technology based on object detection model are highly dependent on image data. To solve the above problem, we propose a novel approach based on Temporal Convolutional Networks and Long Short-Term Memory networks to predicting urban waterlogging depth with Waterlogging Monitoring Station. The difficulty of data acquisition is small though Waterlogging Monitoring Station and TCN-LSTM model can be used to predict timely waterlogging depth. Waterlogging Monitoring Station is developed which integrates an automatic rain gauge and a water gauge. The rainfall and waterlogging depth can be obtained by periodic sampling at some areas with Waterlogging Monitoring Station. Precise hydrological data such as waterlogging depth and rainfall collected by Waterlogging Monitoring Station are used as training samples. Then training samples are used to train TCN-LSTM model, and finally a model with good prediction effect is obtained. The experimental results show that the difficulty of data acquisition is small, the complexity is low and the proposed TCN-LSTM hybrid model can properly predict the waterlogging depth of the current regional. There is no need for high dependence on image data. Meanwhile, compared with machine learning model and RNN model, TCN-LSTM model has higher prediction accuracy for time series data. Overall, the low-cost method proposed in this study can be used to obtain timely waterlogging warning information, and enhance the possibility of using existing social networks and traffic surveillance video systems to perform opportunistic waterlogging sensing.

Explaining nitrogen turnover in sediments and water through variations in microbial community composition and potential function.

Anthropogenic activities greatly impact nitrogen (N) biogeochemical cycling in aquatic ecosystems. High N concentrations in coastal aquaculture waters threaten fishery production and aquaculture ecosystems and have become an urgent problem to be solved. Existing microbial flora and metabolic potential significantly regulate N turnover in aquatic ecosystems. To clarify the contribution of microorganisms to N turnover in sediment and water, we investigated three types of aquaculture ecosystems in coastal areas of Guangdong, China. Nitrate nitrogen (NO3--N) was the dominant component of total nitrogen in the sediment (interstitial water, 90.4%) and water (61.6%). This finding indicates that NO3--N (1.67-2.86 mg/L and 2.98-7.89 mg/L in the sediment and water) is a major pollutant in aquaculture ecosystems. In water, the relative abundances of assimilation nitrogen reduction and aerobic denitrifying bacteria, as well as the metabolic potentials of nitrogen fixation and dissimilated nitrogen in fish monoculture, were only 61.0%, 31.5%, 47.5%, and 27.2% of fish and shrimp polyculture, respectively. In addition, fish-shrimp polyculture reduced NO3--N content (2.86 mg/L) compared to fish monoculture (7.89 mg/L), which was consistent with changes in aerobic denitrification and nitrate assimilation, suggesting that polyculture could reduce TN concentrations in water bodies and alleviate nitrogen pollution risks. Further analysis via structural equation modeling (SEM) revealed that functional pathways (36% and 31%) explained TN changes better than microbial groups in sediment and water (13% and 11%), suggesting that microbial functional capabilities explain TN better than microbial community composition and other factors (pH, O2, and aquaculture type). This study enhances our understanding of nitrogen pollution characteristics and microbial community and functional capabilities related to sediment-water nitrogen turnover in three types of aquaculture ecosystems, which can contribute to the preservation of healthy coastal ecosystems.

Poly(p-phenylenediamine)-Coated Metal-Organic Frameworks for High-Performance Sodium-Ion Batteries: The Balance of Capacity and Stability.

Metal-organic frameworks (MOFs) with well-defined porous structures and highly active frameworks are considered as promising electrode materials for sodium-ion batteries (SIBs). However, the structure pulverization upon sodiation/desodiation impacts on their practical application in SIBs. To address this issue, poly(p-phenylenediamine) (PPA) was uniformly coated onto the surface of MIL-88A, a typical Fe-based MOF through in situ polymerization initiated by the metal ions (Fe3+) of MIL-88A. Used as an anode material for SIBs, the PPA-coated MIL-88A, denoted as PPA@MIL-88A, showed significantly improved electrochemical performance. A reversible capacity as high as 230 mAh g-1 was achieved at 0.2 A g-1 even after 500 cycles. MIL-88A constructed with electrochemically active Fe3+ and fumaric acid ligands guarantees the high specific capacity, while the PPA polymer coating effectively inhibits the pulverization of MIL-88A. This work provides an efficient strategy for improving the structure and cycling stability of MOFs-based electrode materials.

Anion Receptor Enhanced Li Ion Transportation for High-Performance Lithium Metal Batteries.

High-potential lithium metal batteries (LMBs) are still facing many challenges, such as the growth of lithium (Li) dendrites and resultant safety hazards, low-rate capabilities, etc. To this end, electrolyte engineering is believed to be a feasible strategy and interests many researchers. In this work, a novel gel polymer electrolyte membrane, which is composed of polyethyleneimine (PEI)/poly(vinylidene fluoride-co-hexafluoropropylene) (PVDF-HFP) cross-linked membrane and electrolyte (PPCM GPE), is prepared successfully. Due to the fact that the amine groups on PEI molecular chains can provide the rich anion receptors and strongly pin the anions of electrolytes and thus confine the movement of anions, our designed PPCM GPE owns a high Li+ transference number (0.70) and finally contributes to the uniform Li+ deposition and inhibits the growth of Li dendrites. In addition, the cells with PPCM GPE as a separator behave the impressive electrochemical performances, i.e., a low overpotential and an ultralong and stable cycling performance in Li∥Li cells, a low overvoltage of about 34 mV after a stable cycling for 400 h even at a high current density of 5 mA/cm2, and, in Li∥LFP full batteries, a specific capacity of 78 mAh/g after 250 cycles at a 5 C rate. These excellent results suggest a potential application of our PPCM GPE in developing high-energy-density LMBs.

Inhibition of melanoma using a nanoceria-based prolonged oxygen-generating phototherapy hydrogel.

Tumor hypoxic environment is an inevitable obstacle for photodynamic therapy (PDT) of melanoma. Herein, a multifunctional oxygen-generating hydrogel loaded with hyaluronic acid-chlorin e6 modified nanoceria and calcium peroxide (Gel-HCeC-CaO2) was developed for the phototherapy of melanoma. The thermo-sensitive hydrogel could act as a sustained drug delivery system to accumulate photosensitizers (chlorin e6, Ce6) around the tumor, followed by cellular uptake mediated by nanocarrier and hyaluronic acid (HA) targeting. The moderate sustained oxygen generation in the hydrogel was produced by the reaction of calcium peroxide (CaO2) with infiltrated H2O in the presence of catalase mimetic nanoceria. The developed Gel-HCeC-CaO2 could efficiently alleviate the hypoxia microenvironment of tumors as indicated by the expression of hypoxia-inducible factor -1α (HIF-1α), meeting the "once injection, repeat irradiation" strategy and enhanced PDT efficacy. The prolonged oxygen-generating phototherapy hydrogel system provided a new strategy for tumor hypoxia alleviation and PDT.

A Novel ECG-Derived Respiration Method Combining Frequency-Domain Feature and Interacting Multiple Model Smoother.

OBJECTIVE: ECG-derived respiration (EDR) is a low-cost and productive means for capturing respiratory activity. In particular, as the primary procedure in some cardiorespiratory-related studies, the quality of EDR is decisive for the performance of subsequent analyses. APPROACH: In this paper, we proposed a novel EDR method based on the feature derived from the first moment (mean frequency) of the power spectrum (FMS). After obtaining the EDR signal from the feature, we introduced the Interacting Multiple Model (IMM) smoother to enhance the similarity of the EDR signal to the reference respiration. The assessment of the approach consisted of two steps: 1) the performance of extracted feature was verified against R-peak misalignment and noise. 2) the enhancement of IMM smoother to EDR waveforms was evaluated based on waveform correlation and respiratory rate estimation. All the assessments were conducted under the Fantasia database and Drivers database. RESULTS: The FMS improved robustness against R peak offsets compared to most established feature-based EDR algorithms, but a slight 5% improvement of waveform correlation against RR interval-based feature under accurate R peaks. The IMM smoother performed similarly with the Kalman filter in the static database but realized the enhancement of some extent of the EDR waveform in the ambulatory database. SIGNIFICANCE: The proposed method investigated frequency domain mapping of ECG morphological changes caused by respiratory modulation and explained the EDR signal as a non-stationary time series, which provided a direction of better fitting the natural respiration process and enhancing the EDR waveform.

High-Performance PEO-Based All-Solid-State Battery Achieved by Li-Conducting High Entropy Oxides.

A rock-salt-structured Li-conducting high entropy oxide was prepared and utilized as an active filler in a polyethylene oxide (PEO)-based solid-state composite electrolyte. X-ray diffraction and high-resolution transmission electron microscopy were adopted to analyze the crystal structure of the high entropy oxide containing 20% of Li ions (HL20). The HL20 was crystallized in the Fm3̅m space group with Li+ ions located at the center of the MO6 octahedra. The ionic conductivity of the composite membrane at 30 °C reaches 3.44 × 10-5 S cm-1. The inflection point of activation energy of the membrane with HL20 decreases by 5 °C compared with that of the pure PEO membrane. In the galvanostatic plating/stripping test, the Li||Li symmetric batteries could be cycled at a current density of 200 µA cm-2 for over 1200 h with an overpotential of 140 mV. The Li||LiFePO4 full battery could be charged/discharged at 0.5 C for 100 circles with a high capacity retention rate of 91%. Excellent rate performance is also achieved at lower temperatures and higher rates, showing the superiority of HL20 as an active filler. This work sheds light on the development of high entropy oxide as a new type of fast ionic conductor, promoting the practical application of all-solid-state batteries at a lower temperature.