Cross-Modal Object Tracking via Modality-Aware Fusion Network and a Large-Scale Dataset.

Visual object tracking often faces challenges such as invalid targets and decreased performance in low-light conditions when relying solely on RGB image sequences. While incorporating additional modalities like depth and infrared data has proven effective, existing multimodal imaging platforms are complex and lack real-world applicability. In contrast, near-infrared (NIR) imaging, commonly used in surveillance cameras, can switch between RGB and NIR based on light intensity. However, tracking objects across these heterogeneous modalities poses significant challenges, particularly due to the absence of modality switch signals during tracking. To address these challenges, we propose an adaptive cross-modal object tracking algorithm called modality-aware fusion network (MAFNet). MAFNet efficiently integrates information from both RGB and NIR modalities using an adaptive weighting mechanism, effectively bridging the appearance gap and enabling a modality-aware target representation. It consists of two key components: an adaptive weighting module and a modality-specific representation module. The adaptive weighting module predicts fusion weights to dynamically adjust the contribution of each modality, while the modality-specific representation module captures discriminative features specific to RGB and NIR modalities. MAFNet offers great flexibility as it can effortlessly integrate into diverse tracking frameworks. With its simplicity, effectiveness, and efficiency, MAFNet outperforms state-of-the-art methods in cross-modal object tracking. To validate the effectiveness of our algorithm and overcome the scarcity of data in this field, we introduce CMOTB, a comprehensive and extensive benchmark dataset for cross-modal object tracking. CMOTB consists of 61 categories and 1000 video sequences, comprising a total of over 799K frames. We believe that our proposed method and dataset offer a strong foundation for advancing cross-modal object-tracking research. The dataset, toolkit, experimental data, and source code will be publicly available at: https://github.com/mmic-lcl/ Datasets-and-benchmark-code.

Bridging the Intention-Behavior Gap in Organic Food Consumption: Empirical Evidence from China.

The transition to a low-carbon economy emphasizes the importance of green and low-carbon consumption; yet, there is often a discrepancy between consumers' intentions and their actual behavior regarding organic foods. This study aims to identify strategies to bridge this gap. The research model of organic food consumption intention and behavior is constructed, and a structural equation model is used to test the research hypotheses based on a valid sample of 480 residents of Guangdong Province through an online questionnaire survey. Further, the intention‒behavior gap is defined and its determinants are investigated through multiclass logistic regression. Finally, we categorize and forecast the alignment between consumption intentions and behaviors using machine learning algorithms. The results reveal that attitudes, social interactions, and cognitive information play crucial roles in aligning intentions with behaviors. By enhancing social information exchange or improving cognitive understanding, consumers can reduce their intention‒behavior discrepancy. This research offers valuable policy recommendations for fostering green consumption among residents from various perspectives.

Metal nanoparticle hybrid hydrogels: the state-of-the-art of combining hard and soft materials to promote wound healing.

Wounds represent a grave affliction that profoundly impacts human well-being. Establishing barriers, preventing infections, and providing a conducive microenvironment constitute the crux of wound therapy. Hydrogel, a polymer with an intricate three-dimensional lattice, serves as a potent tool in erecting physical barriers and nurturing an environment conducive to wound healing. This enables effective control over exudation, hemostasis, accelerated wound closure, and diminished scar formation. As a result, hydrogels have gained extensive traction in the realm of wound treatment. Metallic nanoparticle carriers, characterized by their multifaceted responses encompassing acoustics, optics, and electronics, have demonstrated efficacy in wound management. Nevertheless, these carriers encounter challenges associated with swift clearance and nonuniform effectiveness. The hybridization of metallic nanoparticle carriers with hydrogels overcomes the shortcomings inherent in metallic nanoparticle-based wound therapy. This amalgamation not only addresses the limitations but also augments the mechanical robustness of hydrogels. It confers upon them attributes such as environmental responsiveness and multifunctionality, thereby synergizing strengths and compensating for weaknesses. This integration culminates in the precise and intelligent management of wounds. This review encapsulates the structural classifications, design strategies, therapeutic applications, and underlying mechanisms of metal nanoparticle hybrid hydrogels in the context of acute and chronic wound treatment. The discourse delves into the generation of novel or enhanced attributes arising from hybridization and how the current paradigm of wound therapy leverages these attributes. Amidst this continually evolving frontier, the potential of metal nanoparticle hybrid hydrogels to revolutionize wound treatment is underscored.

Autophagy-modulating biomembrane nanostructures: A robust anticancer weapon by modulating the inner and outer cancer environment.

Recently, biomembrane nanostructures, such as liposomes, cell membrane-coated nanostructures, and exosomes, have demonstrated promising anticancer therapeutic effects. These nanostructures possess remarkable biocompatibility, multifunctionality, and low toxicity. However, their therapeutic efficacy is impeded by chemoresistance and radiotherapy resistance, which are closely associated with autophagy. Modulating autophagy could enhance the therapeutic sensitivity and effectiveness of these biomembrane nanostructures by influencing the immune system and the cancer microenvironment. For instance, autophagy can regulate the immunogenic cell death of cancer cells, antigen presentation of dendritic cells, and macrophage polarization, thereby activating the inflammatory response in the cancer microenvironment. Furthermore, combining autophagy-regulating drugs or genes with biomembrane nanostructures can exploit the targeting and long-term circulation properties of these nanostructures, leading to increased drug accumulation in cancer cells. This review explores the role of autophagy in carcinogenesis, cancer progression, metastasis, cancer immune responses, and resistance to treatment. Additionally, it highlights recent research advancements in the synergistic anticancer effects achieved through autophagy regulation by biomembrane nanostructures. The review also discusses the prospects and challenges associated with the future clinical translation of these innovative treatment strategies. In summary, these findings provide valuable insights into autophagy, autophagy-modulating biomembrane-based nanostructures, and the underlying molecular mechanisms, thereby facilitating the development of promising cancer therapeutics.

Exposure to hexafluoropropylene oxide trimer acid (HFPO-TA) impairs 5-HT metabolism by impacting the brain-gut axis in mice.

Hexafluoropropylene oxide trimer acid (HFPO-TA) has been found to cause hepatotoxicity, lipotoxicity, and cytotoxicity. However, the effects of HFPO-TA exposure on nervous system toxicity are still unclear. Here, six-week-old male C57BL/6J mice were treated with 2, 20, and 200 µg/L HFPO-TA for six weeks. The untargeted transcriptome analysis was employed to identify differentially expressed mRNAs in the tissue of mouse hippocampi. Then, the levels of neurotransmitters were detected by ELISA analysis in hippocampal and colonic tissues. Real-time quantitative PCR and western blotting analysis were performed to detect the expression of genes associated with modulation of serotonin (5-HT) metabolism and blood-brain barrier. HFPO-TA exposure reduced the mRNA and protein expression of several tight junction protein-coded genes, including Occludin, Claudin-1, and ZO-1, in mice hippocampi, indicating that the blood-brain barrier was disrupted. Moreover, HFPO-TA exposure elevated the expression of neuroinflammatory factors, including TNF-α, IL-6, IL-1ß, TGF-α, and TGF-ß. Analysis of hippocampal transcriptomics suggested that HFPO-TA exposure would impair 5-HT generation and metabolic pathways. In keeping with this prediction, our findings confirmed that the levels of several neurotransmitters, including tryptophan (TRP), 5-HT, 5-HTP, and 5-HIAA, were all impaired by HFPO-TA exposure in the serum, colon, and hippocampus, as was the colonic and hippocampal expression of TRP and 5-HT metabolism-related genes such as SERT, MAO-A, and IDO. These results suggest that HFPO-TA nervous system toxicity in mice may be partly modulated by the brain-gut axis and that HFPO-TA exposure may negatively impact human mental health.

Novel Insights into the Effects of Different Cooking Methods on Salted Egg Yolks: Physicochemical and Sensory Analysis.

In this study, the flavor characteristics and physicochemical properties of salted egg yolk (SEY) under different cooking methods (steaming/baking/microwaving) were investigated. The microwave-treated SEY exhibited the highest levels of salt content, cooking loss, lightness, and b* value, as well as the highest content of flavor amino acids. A total of 31, 27, and 29 volatile compounds were detected after steaming, baking, and microwave treatments, respectively, covering 10 chemical families. The partial least squares discriminant analysis confirmed that 21 compounds, including octanol, pyrazine, 2-pentyl-furan, and 1-octen-3-ol, were the key volatile compounds affecting the classification of SEY aroma. The electronic nose revealed a sharp distinction in the overall flavor profile of SEY with varying heat treatments. However, no dramatic differences were observed in terms of fatty acid composition. Microwave treatment was identified as presenting a promising approach for enhancing the aroma profile of SEY. These findings contribute novel insights into flavor evaluation and the development of egg products as ingredients for thermal processing.

Involvement of METTL3-mediated m6A methylation in the early development of porcine cloned embryos.

METTL3-mediated N6-methyladenosine (m6A) modification is critical for gametogenesis and early embryonic development. However, the function of METTL3-mediated m6A modification in the early development of somatic nuclear transfer embryos (SCNT) remains unclear. Here, we found that METTL3 mRNA and protein levels exhibit dynamic changes during the early development of porcine SCNT embryos. The levels of METTL3 mRNA and protein in SCNT embryos at specific developmental stages differ from those in parthenogenetic activation (PA) counterparts. SiRNA injection effectively reduced the levels of METTL3 mRNA and protein in 4-cell embryos and blastocysts. METTL3 knockdown significantly reduced the cleavage and blastocyst rates of SCNT embryos. METTL3 knockdown significantly reduced the number of total cells and trophectoderm (TE) cells in the resulting blastocysts and perturbed cell lineage allocation. In addition, METTL3 knockdown reduced the levels of m6A modification in 4-cell embryos and blastocysts. Importantly, METTL3 knockdown decreased the expression levels of CDX2, GATA3, NANOG and YAP, and increased the expression levels of SOX2 and OCT4. Taken together, these results demonstrate that METTL3-mediated m6A modification regulates early development and lineage differentiation of porcine SCNT embryos.

Growing-season carbon budget of alpine meadow ecosystem in the Qinghai Lake Basin: a continued carbon sink through this century according to the Biome-BGC model.

BACKGROUND: The alpine meadow is one of the most important ecosystems in the Qinghai-Tibet Plateau (QTP), and critically sensitive to climate change and human activities. Thus, it is crucial to precisely reveal the current state and predict future trends in the carbon budget of the alpine meadow ecosystem. The objective of this study was to explore the applicability of the Biome-BGC model (BBGC) in the Qinghai Lake Basin (QLB), identify the key parameters affecting the variation of net ecosystem exchange (NEE), and further predict the future trends in carbon budget in the QLB. RESULTS: The alpine meadow mainly acted as carbon sink during the growing season. For the eco-physiological factors, the YEL (Yearday to end litterfall), YSNG (Yearday to start new growth), CLEC (Canopy light extinction coefficient), FRC:LC (New fine root C: new leaf C), SLA (Canopy average specific leaf area), C:Nleaf (C:N of leaves), and FLNR (Fraction of leaf N in Rubisco) were confirmed to be the top seven parameters affecting carbon budget of the alpine meadow. For the meteorological factors, the sensitivity of NEE to precipitation was greater than that to vapor pressure deficit (VPD), and it was greater to radiation than to air temperature. Moreover, the combined effect of two different meteorological factors on NEE was higher than the individual effect of each one. In the future, warming and wetting would enhance the carbon sink capacity of the alpine meadow during the growing season, but extreme warming (over 3.84 ℃) would reduce NEE (about 2.9%) in the SSP5-8.5 scenario. CONCLUSION: Overall, the alpine meadow ecosystem in the QLB generally performs as a carbon sink at present and in the future. It is of great significance for the achievement of the goal of carbon neutrality and the management of alpine ecosystems.