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The construction of high‐efficiency self‐supported ceramic photoelectrode based on ideal semiconductor materials is essential for achieving effective degradation of pollutants by photoelectrocatalysis (PEC) technology. Herein, a Ti4O7/h‐BN composite ceramic photoelectrode with a unique microstructure was fabricated by a step‐by‐step calcination process and used in PEC water pollution remediation. The PEC activity of Ti4O7 ceramic photoelectrode could be enhanced by introducing hexagonal boron nitride (h‐BN) nanoparticles on the surface. The most optimized Ti4O7/h‐BN photoelectrode exhibited the decolorization rate of active brilliant blue KN‐R at about 97.79% in 30 min. The PEC activities could remain stable during five degradation cycles. The excellent photoelectrocatalytic performance of Ti4O7/h‐BN ceramic photoelectrode could be attributed to the low Tafel slope, low charge transfer resistance, large electrochemical active area, and excellent photo‐generated carrier separation efficiency. A type‐II heterojunction was formed between the Ti4O7 and h‐BN, which caused more effective carrier separation and enhanced the generation of dominant active species •O2− and h+. This work provided a mature synthesis strategy of Ti4O7/h‐BN self‐supported ceramic photoelectrodes with excellent practical application prospects to achieve superior PEC performance for water purification. [ABSTRACT FROM AUTHOR]

Background: Cell‐free long RNAs in human plasma and extracellular vesicles (EVs) have shown promise as biomarkers in liquid biopsy, despite their fragmented nature. Methods: To investigate these fragmented cell‐free RNAs (cfRNAs), we developed a cost‐effective cfRNA sequencing method called DETECTOR‐seq (depletion‐assisted multiplexed cell‐free total RNA sequencing). DETECTOR‐seq utilised a meticulously tailored set of customised guide RNAs to remove large amounts of unwanted RNAs (i.e., fragmented ribosomal and mitochondrial RNAs) in human plasma. Early barcoding strategy was implemented to reduce costs and minimise plasma requirements. Results: Using DETECTOR‐seq, we conducted a comprehensive analysis of cell‐free transcriptomes in both whole human plasma and EVs. Our analysis revealed discernible distributions of RNA types in plasma and EVs. Plasma exhibited pronounced enrichment in structured circular RNAs, tRNAs, Y RNAs and viral RNAs, while EVs showed enrichment in messenger RNAs (mRNAs) and signal recognition particle RNAs (srpRNAs). Functional pathway analysis highlighted RNA splicing‐related ribonucleoproteins (RNPs) and antimicrobial humoral response genes in plasma, while EVs demonstrated enrichment in transcriptional activity, cell migration and antigen receptor‐mediated immune signals. Our study indicates the comparable potential of cfRNAs from whole plasma and EVs in distinguishing cancer patients (i.e., colorectal and lung cancer) from healthy donors. And microbial cfRNAs in plasma showed potential in classifying specific cancer types. Conclusions: Our comprehensive analysis of total and EV cfRNAs in paired plasma samples provides valuable insights for determining the need for EV purification in cfRNA‐based studies. We envision the cost effectiveness and efficiency of DETECTOR‐seq will empower transcriptome‐wide investigations in the fields of cfRNAs and liquid biopsy. Keypoints: DETECTOR‐seq (depletion‐assisted multiplexed cell‐free total RNA sequencing) enabled efficient and specific depletion of sequences derived from fragmented ribosomal and mitochondrial RNAs in plasma.Distinct human and microbial cell‐free RNA (cfRNA) signatures in whole Plasma versus extracellular vesicles (EVs) were revealed.Both Plasma and EV cfRNAs were capable of distinguishing cancer patients from normal individuals, while microbial RNAs in Plasma cfRNAs enabled better classification of cancer types than EV cfRNAs. [ABSTRACT FROM AUTHOR]

Differentiable physics is an approach that effectively combines physical models with deep learning, providing valuable information about physical systems during the training process of neural networks. This integration enhances the generalization ability and ensures better consistency with physical principles. In this work, we propose a framework for estimating the temperature of a permanent magnet synchronous motor by combining neural networks with the differentiable physical thermal model, as well as utilizing the simulation results. In detail, we first implement a differentiable thermal model based on a lumped parameter thermal network within an automatic differentiation framework. Subsequently, we add a neural network to predict thermal resistances, capacitances, and losses in real time and utilize the thermal parameters' optimized empirical values as the initial output values of the network to improve the accuracy and robustness of the final temperature estimation. We validate the conceivable advantages of the proposed method through extensive experiments based on both synthetic data and real-world data and then provide some further potential applications. [ABSTRACT FROM AUTHOR]

Carbonyl sulfide (COS) measurements provide an important mechanism for quantifying the terrestrial carbon and water cycles. Terrestrial photosynthesis can be inferred from vegetative uptake of COS due to the shared diffusion pathway of COS and CO2 into the chloroplasts. However, the efficacy of different leaf‐to‐canopy upscaling approaches for estimating plant COS uptake and photosynthesis at the canopy scale remains uncertain. In this study, the daytime canopy COS uptake rate (Fcosc) and gross primary productivity (GPP) were simulated using big leaf (BL), two big leaf (TBL) and two leaf (TL) upscaling approaches in the Boreal Ecosystem Productivity Simulator terrestrial biosphere model for two forest sites. We found that TL and TBL performed better than BL in estimating GPP as evaluated by the eddy covariance derived GPP at the studied sites. BL overestimated GPP and canopy stomatal conductance to COS (Gs_cos), resulting from the overestimation of canopy intercepted solar radiation. The positive biases of GPP by TBL could be attributed to the positive biases in GPP of shaded leaves by TBL. The results in this study demonstrate the improved accuracy in using TL for estimating GPP at the canopy scale. Improved leaf‐to‐canopy upscaling approach in terrestrial biosphere models will improve the accuracy of canopy COS uptake and GPP estimation on regional to global scales. Plain Language Summary: Over the past two decades, measurements of carbonyl sulfide (COS) concentrations and fluxes have emerged as an important tool for investigating terrestrial carbon and water cycles. COS and CO2 diffuse from the atmosphere into chloroplasts in leaves through a shared pathway and terrestrial photosynthesis can be inferred from vegetative uptake of COS. However, the efficacy of different leaf‐to‐canopy upscaling approaches for estimating the vegetation COS uptake at the canopy scale remains uncertain. In this study, the daytime canopy COS uptake rate, gross primary productivity (GPP) and canopy stomatal conductance were simulated using big leaf (BL), two big leaf (TBL) and two leaf (TL) upscaling approaches in the Boreal Ecosystem Productivity Simulator terrestrial biosphere model for two forest sites. We found that TL and TBL performed better than BL in estimating GPP and canopy stomatal conductance to COS. Improved leaf‐to‐canopy upscaling approach in terrestrial biosphere models will improve the accuracy of canopy COS uptake and GPP estimation on regional to global scales. Key Points: Big leaf (BL) overestimated gross primary productivity (GPP) and canopy conductance to carbonyl sulfide (COS) due to its overestimation of the instantaneous canopy intercepted solar radiationTwo leaf and two big leaf performed better than BL in estimating GPP and canopy conductance to COS at the studied sitesThe biases in GPP estimates by BL decreased with increasing leaf area index [ABSTRACT FROM AUTHOR]