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Abstract Electron microscopy (EM) revolutionized the way to visualize cellular ultrastructure. Volume EM (vEM) has further broadened its three-dimensional nanoscale imaging capacity. However, intrinsic trade-offs between imaging speed and quality of EM restrict the attainable imaging area and volume. Isotropic imaging with vEM for large biological volumes remains unachievable. Here, we developed EMDiffuse, a suite of algorithms designed to enhance EM and vEM capabilities, leveraging the cutting-edge image generation diffusion model. EMDiffuse generates realistic predictions with high resolution ultrastructural details and exhibits robust transferability by taking only one pair of images of 3 megapixels to fine-tune in denoising and super-resolution tasks. EMDiffuse also demonstrated proficiency in the isotropic vEM reconstruction task, generating isotropic volume even in the absence of isotropic training data. We demonstrated the robustness of EMDiffuse by generating isotropic volumes from seven public datasets obtained from different vEM techniques and instruments. The generated isotropic volume enables accurate three-dimensional nanoscale ultrastructure analysis. EMDiffuse also features self-assessment functionalities on predictions’ reliability. We envision EMDiffuse to pave the way for investigations of the intricate subcellular nanoscale ultrastructure within large volumes of biological systems.

Effective modeling and management are critical in wastewater treatment facilities since the aeration process accounts for 65–70% of the overall energy consumption. This study assesses control strategies specifically designed for different sizes of WWTP, analyzing their economic, environmental, and energy-related effects. Small WWTPs see advantages from the utilization of on/off and proportional–integral–derivative (PID) control methods, resulting in 10–25% energy savings and the reduction in dissolved oxygen (DO) levels by 5–30%. Cascade control and model predictive control (MPC) improve energy efficiency by 15–30% and stabilize DO levels by 15–35% in medium-sized WWTPs. Advanced WWTPs that utilize technologies such as MPC integrated with artificial intelligence (AI) and machine learning (ML) can decrease energy usage by 30–40% and enhance DO levels by 35–40%. Life cycle assessment (LCA) demonstrates substantial decreases in greenhouse gas (GHG) emissions: 5–20% for small, 10–25% for medium, and 30–35% for large WWTPs. These findings illustrate the feasibility and expandability of these tactics in both controlled laboratory environments and real-world situations, emphasizing the significance of customized methods for improving energy efficiency and sustainability in wastewater treatment. Subsequent investigations should prioritize integrating renewable energy sources and resolving obstacles in developing nations to enhance wastewater treatment plants’ energy efficiency and sustainability.

This paper presents the verification results of an experimental site that employed a particle tracking algorithm to assess the transport of tracers through the composite formation of gravel and Cholan in northwest Taiwan. A suitable hydrogeological conceptual model that describes the flow characteristics of the gravel formation and Cholan formation is essential to evaluate groundwater flow and management at this site. Continuous porous medium (CPM) can be easily applied in the gravel formation, while the Cholan formation, characterized by argillaceous sandstone, is commonly treated as a porous medium. However, this study obtains its fracture distribution through geological surveys, and the key fracture parameters are also collected, analyzed, and incorporated into the model. Four hydrogeological conceptual models, including CPM, discrete fracture network (DFN), equivalent continuous porous medium (ECPM), and hybrid DFN/ECPM, are generated for this complex formation. This study combines the conceptual models of the gravel and Cholan formation into four cases to describe the characteristics of the composite formation. The groundwater flow field of four cases is simulated, and the particle tracking method is employed to model the tracer transport. Simulation results from the four hybrid models all yielded a breakthrough curve (BTC) for the first 15 h, indicating that the tracer arrived at the designated outlet within this timeframe and primarily flowed through the gravel formation, while long-time particle tracking revealed a possible flow path through the Cholan formation after 15 h. The breakthrough curve of the four cases shows that the ECPM model is more suitable for representing the heterogeneity of the Cholan formation than the common CPM model. This study provides a suitable numerical algorithm of the conceptual model of the Cholan formation based on strong evidence by considering different models and comparing them with in situ tracer tests.

IntroductionOsteosarcoma is a common bone malignant tumor in adolescents with high mortality and poor prognosis. At present, the progress of osteosarcoma and effective treatment strategies are not clear. This study provides a new potential target for the progression and treatment of osteosarcoma.MethodsThe relationship between lncRNA PRR7-AS1 and osteosarcoma was analyzed using the osteosarcoma databases and clinical sample testing. Cell function assays and tumor lung metastasis were employed to study the effects of PRR7-AS1 on tumorigenesis in vivo and in vitro. Potential downstream RNF2 of PRR7-AS1 was identified and explored using RNA pulldown and RIP. The GTRD and KnockTF database were used to predict the downstream target gene, MTUS1, and ChIP-qPCR experiments were used to verify the working mechanismy. Rescue experiments were utilized to confirm the role of MTUS1 in the pathway.ResultsDeep mining of osteosarcoma databases combined with clinical sample testing revealed a positive correlation between lncRNA PRR7-AS1 and osteosarcoma progression. Knockdown of PRR7-AS1 inhibited osteosarcoma cell proliferation and metastasis in vitro and in vivo. Mechanistically, RNA pulldown and RIP revealed that PRR7-AS1 may bind RNF2 to play a cancer-promoting role. ChIP-qPCR experiments were utilized to validate the working mechanism of the downstream target gene MTUS1. RNF2 inhibited the transcription of MTUS1 through histone H2A lysine 119 monoubiquitin. Rescue experiments confirmed MTUS1 as a downstream direct target of PRR7-AS1 and RNF2.DiscussionWe identified lncRNA PRR7-AS1 as an important oncogene in osteosarcoma progression, indicating that it may be a potential target for diagnosis and prognosis of osteosarcoma.