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The design of channel geometry plays a critical role in the performance of proton exchange membrane electrolytic cells (PEMECs), particularly in addressing challenges such as bubble accumulation and pressure drop, which hinder efficient hydrogen production. This study introduces an innovative uneven wave-like protrusion channel structure for PEMECs, designed to optimize mass transfer and bubble removal while minimizing energy losses. A combination of three-dimensional numerical simulations and the Taguchi design method is employed to systematically investigate the impact of protrusion height, width, and spacing on key performance metrics, including pressure drop, oxygen output, and volumetric gas content. The effects of different water supply flow rates and temperatures on the electrolytic cell were also investigated through visualization experiments. The results show that the channel with inhomogeneous waveform protrusions has superior PEMEC performance compared with the conventional single serpentine channel. In addition, the waveforms of the waveform protrusions were optimized using the Taguchi design method. The results obtained further optimized the PEMEC performance by increasing the outlet oxygen volume by 8.97%, reducing the average pressure drop by 4.4%, and decreasing the volumetric gas content by 20.26%.

Quantifying the stomatal responses of plants to global change factors is crucial for modeling terrestrial carbon and water cycles. Here we synthesize worldwide experimental data to show that stomatal conductance ( g s ) decreases with elevated carbon dioxide (CO 2 ), warming, decreased precipitation, and tropospheric ozone pollution, but increases with increased precipitation and nitrogen (N) deposition. These responses vary with treatment magnitude, plant attributes (ambient g s , vegetation biomes, and plant functional types), and climate. All two-factor combinations (except warming + N deposition) significantly reduce g s , and their individual effects are commonly additive but tend to be antagonistic as the effect sizes increased. We further show that rising CO 2 and warming would dominate the future change of plant g s across biomes. The results of our meta-analysis provide a foundation for understanding and predicting plant g s across biomes and guiding manipulative experiment designs in a real world where global change factors do not occur in isolation. Stomatal conductance is an important plant ecophysiological trait and a common parameter in earth system models. This global meta-analysis shows how CO 2 , warming and other global change factors affect stomatal conductance individually and interactively.

BackgroundSemaphorins (SEMAs), originally identified as axon guidance factors, have been found to play crucial roles in tumor growth, invasiveness, neoangiogenesis, and the modulation of immune responses. However, the prognostic value of SEMA-related genes in colorectal cancer (CRC) remains unclear.MethodsWe applied a novel machine learning framework that incorporated 10 machine learning algorithms and their 101 combinations to construct a SEMAs-related score (SRS). Multi-omics analysis was performed, including single-cell RNA sequencing (scRNA-seq), and spatial transcriptome (ST) to gain a more comprehensive understanding of the SRS. A series of cell experiments were conducted to prove the impact of key genes on CRC biological behavior.ResultA consensus SRS was finally constructed based on a 101-combination machine learning computational framework, demonstrating outstanding performance in predicting overall survival. Moreover, distinct biological functions, mutation burden, immune cell infiltration, and immunotherapy response were observed between the high- and low-SRS groups. scRNA-seq and ST demonstrated unique cellular heterogeneity in CRC. We observed that SRS-high and SRS-low malignant epithelial cells exhibit different biological characteristics. High SRS malignant epithelial cells interact with myeloid and endothelial cells via SPP1 and COL4A2-ITGAV-ITGB8 pathways, respectively. Low SRS cells engage with myeloid and endothelial cells through MIF and JAG1-NOTCH4 pathways. Additionally, knocking down SEMA4C significantly inhibits the proliferation and invasion of CRC cells, while promoting apoptosis in vitro .ConclusionSRS could serve as an effective tool to predict survival and identify potential patients benefiting from immunotherapy in CRC. It also reveals tumor heterogeneity and provides valuable biological insights in CRC.

Drought stress, particularly at the tasseling stage, is the most devastating abiotic factor and major contributor to yield reduction in maize ( Zea mays L.). Despite recent scientific advances in deciphering maize drought stress responses, the overall picture of key genes and proteins regulating maize tasseling drought tolerance remains less understood. In this study, we conducted comparative physiological, transcriptomic and proteomic analyses to monitor the changes in the leaf tissues of two contrasting maize hybrid cultivars exposed to drought stress at the tasseling stage. We identified 1701 differentially expressed genes (DEGs) in RNA-sequence runs and 424 differentially expressed proteins (DEPs) from an iTRAQ-based analysis. Mapman analysis revealed several regulatory processes influenced by drought conditions, including signal transduction, cell-wall remodeling, cellular redox homeostasis and hormone metabolism that were observed at both mRNA and protein levels. However, transcription factor regulation and secondary metabolism were specifically identified at the transcript level, whereas photosynthesis was uniquely identified to be affected by drought stress at the protein level. Meanwhile, a weak correlation between DEGs and DEPs was observed, indicating the drought response of maize at tasseling stage is largely regulated post-transcriptionally. Furthermore, comparative physiological analysis and qRT-PCR results substantiated the trancriptomic and proteomic findings. Additionally, we screened ZmPOD , ZmRAV1 , ZmTPP and performed phenotypical and physiological characterizations of transgenic Arabidopsis thaliana (Arabidopsis) lines and wild-type. Resultantly, the transgenic Arabidopsis lines exhibited stronger tolerance to drought than the WT. This functional verification reinforces the reliability of our omics-based candidate gene selection. Overall, our research provides insights on the drought-responsive genes and pathways mediating maize drought tolerance at the tasseling stage.

The robotic drilling of assembly holes is a crucial process in aerospace manufacturing, in which measuring the normal of the workpiece surface is a key step to guide the robot to the correct pose and guarantee the perpendicularity of the hole axis. Multiple laser displacement sensors can be used to satisfy the portable and in-site measurement requirements, but there is still a lack of accurate analysis and layout design. In this paper, a simplified parametric method is proposed for multi-sensor normal measurement devices with a symmetrical layout, using three parameters: the sensor number, the laser beam slant angle, and the laser spot distribution radius. A normal measurement error distribution simulation method considering the random sensor errors is proposed. The measurement error distribution laws at different sensor numbers, the laser beam slant angle, and the laser spot distribution radius are revealed as a pyramid-like region. The influential factors on normal measurement accuracy, such as sensor accuracy, quantity and installation position, are analyzed by a simulation and verified experimentally on a five-axis precision machine tool. The results show that increasing the laser beam slant angle and laser spot distribution radius significantly reduces the normal measurement errors. With the laser beam slant angle ≥15° and the laser spot distribution radius ≥19 mm, the normal measurement error falls below 0.05°, ensuring normal accuracy in robotic drilling.

The visual measurement of deep holes in composite material workpieces constitutes a critical step in the robotic assembly of aerospace components. The positioning accuracy of assembly holes significantly impacts the assembly quality of components. However, the complex texture of the composite material surface and mutual interference between the imaging of the inlet and outlet edges of deep holes significantly challenge hole detection. A visual measurement method for deep holes in composite materials based on the radial penalty Laplacian operator is proposed to address the issues by suppressing visual noise and enhancing the features of hole edges. Coupled with a novel inflection-point-removal algorithm, this approach enables the accurate detection of holes with a diameter of 10 mm and a depth of 50 mm in composite material components, achieving a measurement precision of 0.03 mm.

Plant roots play a critical role in regulating the uptake of soil water and nutrients. Shifts in root growth and biomass distribution resulting from climate change can impact ecosystem water and carbon cycling. Such interactions between root growth and ecosystem functioning have not been integrated into current terrestrial ecosystem models. Here a three‐dimensional dynamic root model (DyRoot) was developed and implemented into the Integrated Biosphere Simulator (IBIS) ecosystem model for simulating root growth, architecture (i.e., fine and coarse roots), and distribution driven by soil water and nitrogen availabilities. Field root biomass observations were used for model calibration and validation. Results showed that DyRoot was able to simulate the root biomass distribution across five forest ecosystem types with good performance (R2 = 0.79, P < 0.01). The validations of root distribution in three forest chronosequences suggested that DyRoot captured the changes in rooting depth with the increase in stand age. The sensitivity of simulated root distribution to soil water availability was compared against root biomass observations from two precipitation reduction experiments. Results indicated that DyRoot reasonably represented the decrease of root biomass in topsoil layers in response to the deficits of soil moisture simulated by (IBIS). The DyRoot model algorithm in this study has great implications for representing an optimized root distribution in ecosystem models, which can improve model performance by simulating the feedbacks of root evolution in response to climate change. Furthermore, allowing explicit root architecture in the DyRoot model also sheds light on depicting the responses of root traits to environmental changes. Key Points Current ecosystem models adopted a static root distribution profile, which results in the absent responses of roots to environmental change A dynamic root model (DyRoot) was developed for simulating root growth and distribution driven by soil water and nitrogen availabilities The DyRoot model has great implications for representing an optimized root distribution in terrestrial ecosystem models

The CO 2 efflux from soil (soil respiration (SR)) is one of the largest fluxes in the global carbon (C) cycle and its response to climate change could strongly influence future atmospheric CO 2 concentrations. Still, a large divergence of global SR estimates and its autotrophic (AR) and heterotrophic (HR) components exists among process based terrestrial ecosystem models. Therefore, alternatively derived global benchmark values are warranted for constraining the various ecosystem model output. In this study, we developed models based on the global soil respiration database (version 5.0), using the random forest (RF) method to generate the global benchmark distribution of total SR and its components. Benchmark values were then compared with the output of ten different global terrestrial ecosystem models. Our observationally derived global mean annual benchmark rates were 85.5 ± 40.4 (SD) Pg C yr −1 for SR, 50.3 ± 25.0 (SD) Pg C yr −1 for HR and 35.2 Pg C yr −1 for AR during 1982–2012, respectively. Evaluating against the observations, the RF models showed better performance in both of SR and HR simulations than all investigated terrestrial ecosystem models. Large divergences in simulating SR and its components were observed among the terrestrial ecosystem models. The estimated global SR and HR by the ecosystem models ranged from 61.4 to 91.7 Pg C yr −1 and 39.8 to 61.7 Pg C yr −1 , respectively. The most discrepancy lays in the estimation of AR, the difference (12.0–42.3 Pg C yr −1 ) of estimates among the ecosystem models was up to 3.5 times. The contribution of AR to SR highly varied among the ecosystem models ranging from 18% to 48%, which differed with the estimate by RF (41%). This study generated global SR and its components (HR and AR) fluxes, which are useful benchmarks to constrain the performance of terrestrial ecosystem models.

The occurrence and progression of tumors can be established through a complex interplay among tumor cells undergoing epithelial-mesenchymal transition (EMT), invasive factors and immune cells. In this study, we employed single-cell RNA sequencing (scRNA-seq) and spatially resolved transcriptomics (ST) to evaluate the pseudotime trajectory and spatial interactive relationship between EMT-invasive malignant tumors and immune cells in primary colorectal cancer (CRC) tissues at different stages (stage I/II and stage III with tumor deposit). Our research characterized the spatiotemporal relationship among different invasive tumor programs by constructing pseudotime endpoint-EMT-invasion tumor programs (EMTPs) located at the edge of ST, utilizing evolution trajectory analysis integrated with EMT-invasion genes. Strikingly, the invasive and expansive process of tumors undergoes remarkable spatial reprogramming of regulatory and immunosuppressive cells, such as myeloid-derived suppressor cells (MDSCs), tumor-associated macrophages (TAMs), regulatory T cells (Treg), and exhausted T cells (Tex). These EMTP-adjacent cell are linked to EMT-related invasion genes, especially the C-X-C motif ligand 1 (CXCL1) and CXCL8 genes that are important for CRC prognosis. Interestingly, the EMTPs in stage I mainly produce an inflammatory margin invasive niche, while the EMTPs in stage III tissues likely produce a hypoxic pre-invasive niche. Our data demonstrate the crucial role of regulatory and immunosuppressive cells in tumor formation and progression of CRC. This study provides a framework to delineate the spatiotemporal invasive niche in CRC samples. Graphical Abstract