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Originating from the desire to improve sustainability, producing fuels and chemicals from the conversion of biomass and waste plastic has become an important research topic in the twenty-first century. Although biomass is natural and plastic synthetic, the chemical nature of the two are not as distinct as they first appear. They share substantial structural similarities in terms of their polymeric nature and the types of bonds linking their monomeric units, resulting in close relationships between the two materials and their conversions. Previously, their transformations were mostly studied and reviewed separately in the literature. Here, we summarize the catalytic conversion of biomass and waste plastics, with a focus on bond activation chemistry and catalyst design. By tracking the historical and more recent developments, it becomes clear that biomass and plastic have not only evolved their unique conversion pathways but have also started to cross paths with each other, with each influencing the landscape of the other. As a result, this Review on the catalytic conversion of biomass and waste plastic in a unified angle offers improved insights into existing technologies, and more importantly, may enable new opportunities for future advances. Biomass and plastic share structural similarities in their composition and types of bond linkage between their monomeric units. Reviewing their catalytic conversion technologies in a unified angle provides new insights and opportunities for future advances.

Surface deformation monitoring is essential for controlling instability processes such as urban infrastructure deformation, mining-induced subsidence, and landslide deformation. However, missing data often disrupt the continuity of the various deformation time series and compromise the reliability of monitoring results. This issue is particularly critical in long-term landslide studies, where conventional missing data imputation methods often neglect the nonlinear characteristics of slope deformation and fail to account for external influences under complex environmental conditions. To address these limitations, this study proposes a deep learning-based imputation method that integrates multi-source monitoring data. A Seq2Seq LSTM (sequence-to-sequence long short-term memory) model is constructed to reconstruct missing deformation values, and a posterior correction module is integrated to optimize the preliminary outputs, thereby enhancing imputation accuracy. The proposed approach is validated using a case study of the southern dump slope landslide at the Hesigewula South Open-Pit Coal Mine in Inner Mongolia, China. Experimental results on the test set demonstrate that the Seq2Seq LSTM–Posterior Correction model significantly outperforms traditional methods such as linear regression and baseline LSTM models. This method offers an effective solution to data gaps in landslide deformation monitoring, demonstrating strong potential for accurate nonlinear imputation in complex environments and providing a practical approach for long-term InSAR-based landslide studies in areas affected by missing SAR data.

The rapid growth of the integrated circuit (IC) industry has led to the generation of highly complex and challenging-to-treat wastewater streams. This work presents a sustainable paradigm for transforming metal-containing IC wastewater into efficient heterogeneous catalysts. As a proof of concept, Cu-rich IC wastewater is converted into a multifunctional Cu/SiO 2 catalyst via a simple ammonia-evaporation process, achieving up to 99.9% Cu recovery. The catalyst exhibits outstanding activity in the upcycling of various real-world polyethylene terephthalate (PET) wastes to p-xylene (PX) with near-quantitative yield (>99.9%), surpassing commercial Cu/SiO 2 . The superior catalytic performance is attributed to the modulation of the catalyst structure by trace coexisting metals in the wastewater, which promote the formation of abundant Cu/CuO x interfacial sites, facilitating H 2 dissociation and C–O bond activation. Furthermore, this strategy also enables the complete transformation of Cu species onto other supports to construct various Cu-based catalysts. Overall, this work establishes a new paradigm for valorizing IC wastewater, which can be extended to other metal-containing wastewaters (e.g., Ni, Co, Pt), in alignment with the principles of green chemistry and the circular economy. Managing hazardous industrial wastewater remains a critical hurdle for sustainable manufacturing and environmental protection. This study converts metal-rich integrated circuit waste into high-performance catalysts that efficiently upcycle plastic pollution.

Polyethylene terephthalate (PET) plastic upcycling typically features two-step processes involving cascade depolymerization and functionalization to mixed products. Here, we realize the direct hydrogenolysis of PET over a Co@CoO heterogeneous catalyst, with high yield of either p -xylene (PX, >97 %) or 1,4-dimethylcyclohexane (1,4-DMC, >90 %). Pyrolysis of Co-MOF-71 yields carbon-supported Co nanoparticles partially encapsulated by CoO, whose metal/oxide interface facilitates hydrogen spillover. Experimental and computational investigations reveal Co δ+ atoms proximate to CoO particles selectively catalyze carboxylate C–O bond cleavage at 250 °C and 1 MPa H 2 to yield PX. In contrast, hydrogen spillover from Co metal to CoO sites at 280 °C and 3 MPa H 2 promotes selective ring hydrogenation to 1,4-DMC. Co/CoO-800 has excellent stability and efficacy for depolymerizing commercial PET plastics. Life cycle assessment indicates the PET-to-PX process offers negative CO 2 emissions and outperforms fossil-fuel PX production. Direct hydrogenolysis over Earth-abundant catalysts offers a simple strategy for polyester waste upcycling. Turning PET plastic waste into useful chemicals usually takes several steps and makes mixtures. This study uses a cobalt catalyst to directly convert PET into high yields of two valuable products, with good stability and lower emissions.

The valorization of plastic waste represents a major challenge of the 21st century due to its severe environmental impact. Here, we report a stepwise hydrogen spillover-constructed CuCo/CoO x catalyst that enables near-quantitative conversion of waste polyethylene terephthalate to p -xylene (>99.9%), significantly outperforming the performance of various Cu- and Co-based catalysts as well as previously reported noble metal catalysts. The stepwise hydrogen spillover induces the formation of partially phase-transformed Co 0 species and abundant oxygen-vacancy-rich Co 0 /CoO x interfaces. The former enhances H 2 dissociation efficiency, while the latter facilitates C-O bond activation in polyethylene terephthalate and regulates substrate-product adsorption equilibria, synergistically contributing to the exceptional catalytic performance. The catalyst demonstrates broad applicability to more than 30 real-world polyester plastics. Furthermore, techno-economic analysis reveals significant reductions in CO 2 emissions and competitive processing costs. This breakthrough in near-quantitative polyethylene terephthalate conversion and stepwise hydrogen spillover-enabled active site construction offers valuable insights into plastic waste upcycling and the design of advanced heterogeneous catalysts. Valorizing plastic waste remains a major challenge due to its severe environmental impact. Here, the authors report a CuCo/CoOₓ catalyst constructed via stepwise hydrogen spillover, enabling near-quantitative conversion of waste polyethylene terephthalate to p-xylene.

Traditional landslide early-warning systems usually focus on displacement, rainfall, and stress, neglecting temperature effects. Rock deformation and failure processes are fundamentally characterized by energy dissipation, which can manifest through thermal changes. It is essential to explore whether temperature response inside landslides could help predict instability. This study investigates a representative landslide in southeastern Jilin Province, which has repeatedly experienced intense disturbances due to consecutive typhoon events. By employing a comprehensive multi-source monitoring strategy, including digital elevation model differencing (DoD), underground instrumentation, and detailed field surveys, we tracked temperature fluctuations, internal deformation, and surface displacement of the landslide. Our findings demonstrate that exceptionally intense rainfall events, occurring approximately once every sixty years, nearly reactivated landslides previously considered stable. Reactivated landslides exhibited accelerated movement, presenting severe hazards to infrastructure and nearby communities. Significantly, temperature within the landslide mass displayed distinctive fluctuation patterns at the onset of instability: Sharp Peak & Gentle Slope (SPGS) and Roller-Coaster (RC). These thermal signatures correlated strongly with rainfall intensity and deformation rates. To explain these observations, we propose two theoretical frameworks: the ambient heat-driven SPGS fluctuation patterns and the spontaneous heat-driven RC fluctuation patterns. The coupled temperature-accumulated rainfall-rainfall intensity analyses showed that: Temperature data can establish joint thresholds for cumulative rainfall and rainfall intensity. Further validation of the SPGS fluctuation patterns is essential for practical application in rainfall-induced landslide early-warning systems. Further research and validation efforts are essential to conclusively determine the predictive value and reliability of temperature changes induced by rainfall in forecasting landslide initiation. Highlights Exploring the impact of extreme rainfall events brought by continuous typhoons on FS landslide evolution. The correlation between temperature-deformation response within the FS landslide mass and rainfall was established. Introduced a novel differential display method to reveal more details.

Originating from the desire to improve sustainability, producing fuels and chemicals from the conversion of biomass and waste plastic has become an important research topic in the twenty-first century. Although biomass is natural and plastic synthetic, the chemical nature of the two are not as distinct as they first appear. They share substantial structural similarities in terms of their polymeric nature and the types of bonds linking their monomeric units, resulting in close relationships between the two materials and their conversions. Previously, their transformations were mostly studied and reviewed separately in the literature. Here, we summarize the catalytic conversion of biomass and waste plastics, with a focus on bond activation chemistry and catalyst design. By tracking the historical and more recent developments, it becomes clear that biomass and plastic have not only evolved their unique conversion pathways but have also started to cross paths with each other, with each influencing the landscape of the other. As a result, this Review on the catalytic conversion of biomass and waste plastic in a unified angle offers improved insights into existing technologies, and more importantly, may enable new opportunities for future advances. Biomass and plastic share structural similarities in their composition and types of bond linkage between their monomeric units. Reviewing their catalytic conversion technologies in a unified angle provides new insights and opportunities for future advances.

Benign prostatic hyperplasia (BPH) is a common issue among older men. Diagnosis of BPH currently relies on imaging tests and assessment of urinary flow rate due to the absence of definitive diagnostic markers. Developing more accurate markers is crucial to improve BPH diagnosis. The BPH dataset utilized in this study was sourced from the Gene Expression Omnibus (GEO). Initially, differential expression and functional analyses were conducted, followed by the application of multiple machine learning techniques to identify key diagnostic markers. Subsequent investigations have focused on elucidating the functions and mechanisms associated with these markers. The ssGSEA method was employed to evaluate immune cell scores in BPH samples, facilitating the exploration of the relationship between key diagnostic markers and immune cells. Additionally, molecular docking was performed to assess the binding affinity of these key markers to therapeutic drugs for BPH. Tissue samples from BPH patients were collected for experimental validation of the expression differences of the aforementioned genes. A total of 185 differential genes were identified, comprising 67 up-regulated and 118 down-regulated genes. These genes are implicated in pathways that regulate extracellular matrix tissue composition and cellular responses to transforming growth factor beta stimulation, as well as critical signaling pathways such as AMPK and mTOR. Through the application of various machine learning techniques, DACH1, CACNA1D, STARD13, and RUNDC3B were identified as key diagnostic markers. The ssGSEA algorithm further corroborated the association of these diagnostic genes with diverse immune cells. Moreover, molecular docking analysis revealed strong binding affinities of these markers to tamsulosin and finasteride, suggesting their potential as drug targets. Finally, experimental validation confirmed the expression differences of DACH1, CACNA1D, STARD13, and RUNDC3B in BPH tissues. This study introduces novel immune-related diagnostic markers for BPH and highlights their promise as new drug targets, providing a valuable approach for predictive diagnosis and targeted therapy of BPH.

Hypertension is increasingly recognized as a contributor to intestinal barrier disruption and gut microbiota dysbiosis, thereby promoting systemic inflammation and end-organ damage. Sirtuin 7 (SIRT7), a nicotinamide adenine dinucleotide (NAD + )-dependent deacetylase, has been identified as a crucial regulator in the progression of cardiovascular diseases through multiple mechanistic pathways. However, the roles and underlying mechanisms of SIRT7 in the development of hypertensive intestinal injury remains unclear, and further investigation is required to determine whether SIRT7 can alleviate intestinal damage through modulation of the gut microbiota. In this study, SIRT7 expression and intestinal pathology were assessed in spontaneously hypertensive rats (SHRs). An intestinal SIRT7 overexpression model was subsequently established in SHRs to evaluate its effects on intestinal dysfunction and microbial composition. Histological and immunofluorescence staining were performed to examine the small intestine, and 16S rRNA amplicon sequencing was conducted to analyze the gut microbiota. There was a marked deficiency of SIRT7 in the intestinal tract of hypertensive animals, which was closely associated with reduced expression of tight junction proteins, including Occludin and zonula occludens-1, as well as intestinal pathological damage in SHRs. SIRT7 overexpression strikingly alleviated intestinal fibrosis, structural damage, and increased intestinal permeability. More importantly, restoration of SIRT7 partially reversed hypertension-associated gut microbiota dysbiosis. In summary, our findings provide novel mechanistic insights into the role of SIRT7 as a critical protector of intestinal barrier integrity and microenvironmental homeostasis under hypertensive stress, and highlight the intricate interplay between SIRT7 and the gut microbiota during hypertension.

The current study explores the fully distributed containment control problem of fractional-order time-delay multi-agent systems by introducing a novel pull-based dynamic event-triggered approach. Firstly, to reduce communication overhead and mitigate time delays in controller updates, a pull-based dynamic event-triggered strategy is proposed. Secondly, in virtue of a Lyapunov candidate function, the proposed pull-based dynamic event-triggered control protocol exhibits inherent distributed properties enabling agents to operate independently and cooperatively without global information. Thirdly, we design adaptive parameters to ensure containment control convergence and provide a rigorous proof to preclude Zeno behavior. Eventually, numerical simulations are performed to verify the validity of the theoretical analysis.