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The Polycistronic Transcription Landscape of the Populus Genome
No full textPolycistronic transcripts, which span multiple gene loci, are common in prokaryotes but have rarely been observed in eukaryotic genes. This long-standing view, however, has been challenged by recent advances in long-read transcriptome sequencing technologies. In this study, we employed nanopore direct RNA sequencing (DRS) to investigate polycistronic transcription in the nuclear genome of Populus under drought. We detected widespread polycistronic-like RNAs associated with approximately 60% of transcribed genes in two Populus species: black cottonwood and the hybrid 717. Thousands of incomplete dicistronic gene loci were identified, many of which encode modified or fusion open reading frames (ORFs), and are drought-responsive and functionally linked to organelles and cellular membrane systems. These polycistronic RNAs are frequently alternatively spliced and exhibit longer poly(A) tails and distinct m6A base modifications compared to their monocistronic counterparts. Additionally, we observed a positive expression correlation between monocistronic gene pairs within dicistronic loci, with a particularly strong correlation between polycistronic RNAs and the monocistronic gene at the 5′ end of the polycistronic locus. This suggests a complex regulatory mechanism governing transcription from incomplete polycistronic gene loci in Populus. Together, these findings highlight the unique structural and regulatory features of polycistronic RNAs and their potential roles in plant adaptation to environmental stress
Feasibility of helium and nitrogen as surrogate gases for hydrogen jet using planar laser-induced fluorescence in a constant volume chamber
No full textThis study investigates the feasibility of using helium and nitrogen as surrogate gases for visualizing the non-reactive behavior of hydrogen jets in a constant volume chamber. Hydrogen, helium, and nitrogen were individually mixed with acetone vapor and injected using a hollow-cone injector. The fuel distribution was visualized using Planar Laser-Induced Fluorescence (PLIF), while simultaneous Schlieren imaging was employed to capture overall jet structures. Quantitative comparisons of jet areas extracted from both imaging techniques demonstrated that acetone effectively followed the gas-phase flow of each species, validating its use as a flow tracer. Furthermore, helium and nitrogen exhibited jet dispersion patterns comparable to those of hydrogen across varying ambient pressure conditions, confirming their potential as optical surrogates. The findings suggest that non-reactive surrogate gases can be reliably used for safer experimental investigations of hydrogen injection phenomena, offering a valuable methodology for future optical diagnostics in hydrogen research
Bioinspired acoustic metamaterials: mimicking the cuttlebone for advanced noise control applications
No full textCuttlebones—the internal shells of cuttlefish—are lightweight and buoyant while exhibiting sufficient stiffness and strength to protect the animal from predators and withstand deep-sea pressures up to 2 MPa. Studies indicate that these multifunctional properties stem from their unique porous architecture. Here, we draw inspiration from cuttlebones to design cuttlebone-mimicking porous acoustic metamaterials for passive noise control applications. Using a MATLAB routine, we systematically control pore architecture parameters, including the length, height, and curvature of labyrinthine internal walls, the depth of individual cavities, and the thickness and porosity of the septa. The designed geometries are fabricated via fused filament fabrication, and their sound absorption properties are evaluated using the normal incidence impedance tube test method. Preliminary results suggest that the acoustic performance of these cuttlebone-mimicking materials can be tuned to achieve broadband noise reduction while maintaining their multifunctional characteristics
Predicting potential postfire debris-flow hazards across California prior to wildfire
No full textBackground: Wildfires and consequent postfire hazards, specifically runoff-generated debris flows, are a major threat to California communities. Aim: To help prefire planning efforts across California, we identified areas that are most susceptible to postfire debris flows before fire occurs. Methods: We developed a calibration method for an established model that relates existing vegetation type to fire severity, a critical input to the US Geological Survey’s postfire debris-flow likelihood model. We calibrated the model for eight regions with data from 81 wildfires that occurred in 2020 and 2021 in California. Key results: We predicted debris-flow likelihood, volume, and combined hazard classification, and created statewide maps that use simulated fire frequency and rainfall data to predict the probability that a basin will experience a wildfire and subsequent debris flow. Conclusions: We suggest that the model predictions are useful for identifying areas that pose the greatest risk of postfire debris-flow hazard for a simplified wildfire scenario. Implications: Although actual patterns of wildfire severity may vary from our simulated products, we show that applying a consistent methodology for all of California is useful for identifying areas that are likely to pose the greatest postfire hazards, which should help focus prefire mitigation efforts
Snow loss estimation for photovoltaic single-axis tracker systems
No full textSingle-axis tracker (SAT) photovoltaic (PV) systems dominate the utility-scale solar market in the United States, yet research to quantify and optimize their performance under snow conditions remains limited. To address this gap, we developed a novel snow-sensing system for monitoring snow on SAT systems to calculate snow losses by comparing measured DC power output in winter with modeled power under no-snow conditions. By isolating and mapping the generation losses based on observations, we are now positioned to develop models to predict snow-induced energy deficits for SAT configurations. Overall, Our work 1) demonstrates the need for, and provides a technical basis for, SAT-specific performance models; 2) offers crucial insights for optimizing tilt angles to reduce snow accumulation and 3) details a methodology for quantifying snow losses across different designs, providing a technical rationale for snow-shedding strategies. Moreover, we demonstrate the potential for refining SAT snow-loss estimation by incorporating factors such as panel temperature, and real-time snow depth. Extending these investigations to multi-year datasets will further improve the design of reliable and cost-effective PV plants deployed in snow-prone regions and guide the future development of robust, SAT-specific snow-loss models. Performance data from January to March 2025 are still being collected, and the final analysis will include these data as well as snow less estimates over the monitored period
Progressive Insights into 3D Bioprinting for Corneal Tissue Restoration
No full textThe complex architecture of the cornea, characterized by specifically organized collagen fibrils and distinct cellular layers, poses significant challenges for traditional tissue engineering strategies to replicate its native function. 3D Bioprinting offers a promising solution by enabling the precise, layer-by-layer fabrication of corneal tissues, closely mimicking the essential characteristics needed for vision restoration and long-term graft success. This Review critically examines the key biomechanical, optical, and structural attributes of the cornea necessary for its effective engineering and accurate 3D bioprinting. It provides a comprehensive overview of different 3D bioprinting modalities utilized for corneal tissue engineering and offers insights into potential improvements. Additionally, it details the requirements for a corneal bioink suitable for 3D bioprinting, ensuring it meets the necessary corneal functions. The Review also delves into the current challenges in 3D bioprinting of corneal tissue and proposes potential solutions to successfully replicate the complex architecture and function of the cornea. Furthermore, it explores innovative approaches such as the use of induced pluripotent stem cells, gene therapy, and cornea-on-a-chip technologies, which hold promise for advancing corneal regeneration. The Review aims to visualize the future of corneal 3D bioprinting and the potential of integrating it with other techniques. Lastly, the review discusses clinical implications, emphasizing the potential of bioprinted corneal implants to address the global donor cornea shortage and significantly improve patient outcomes
Machine learning classification of EEG responses to pain-related vs non-pain-related stimulus in preterm infants
No full textINTRODUCTION: Unmanaged pain in preterm infants can lead to long-term developmental consequences. Current pain assessment methods lack specificity, resulting in possible pain mismanagement in Neonatal Intensive Care Units (NICUs). This study explores the application of machine learning (ML) to differentiate between pain-related and non-pain-related cortical activity in preterm infants. OBJECTIVE: To evaluate the performance of ML models in distinguishing cortical EEG activity during a painful procedure in preterm infants across different postmenstrual ages (PMAs). METHODS: This observational study was conducted from June 2015 to May 2024 at Mount Sinai Hospital in Toronto, Canada, and University College London Hospital, United Kingdom. EEG data were collected from 72 preterm infants (27 females) during routine heel lance procedures while held in skin-to-skin contact. Infants\u27 gestational ages ranged from 24 to 36 weeks with a mean PMA of 32.87 weeks. Five ML models-XGBoost, support vector machines, Random Forest, Logistic Regression (LR), and convolutional neural networks-distinguished EEG activity pre-heel and post-heel lance. RESULTS: Model performance was assessed using accuracy and area under the receiver operating characteristic curve (AUC). In the oldest PMA group (≥34 weeks), LR achieved the highest mean accuracy (82%) and AUC (0.90). Similarly, LR achieved the highest mean accuracy (70%) and AUC (0.94) in the middle PMA group (32-33 weeks, 6 days). In the youngest group (\u3c 32 weeks), all models except XGBoost performed relatively the same with a mean accuracy of 76% or 77% and a mean AUC of 0.82 or 0.80. CONCLUSION: Machine learning models demonstrate potential in distinguishing pain-related cortical activity, offering a pathway for improved neonatal pain assessment in NICUs
Minable coal reserve estimation by incorporating tonnage and calorific value uncertainties by successive multiple-point and two-point geostatistical simulation algorithms
No full textEstimating reserves and quantifying resources stand as pivotal and intricate endeavours within the realm of coal mining operations. Intricate geological formations compound the challenges in resource estimation, thereby complicating reserve calculations. The uncertainties tied to geological attributes of coal, encompassing parameters like tonnage and coal quality, wield significant sway over resource and reserve computations within coal mines. This research delves into the domain of geological uncertainties, with a specific focus on calorific value, aiming to numerically characterise resources and reserves within an open-pit coal mine situated in Indonesia. To quantify resources, the coal seam geometry underwent simulation via a multi-point geostatistical technique known as single normal equation simulation. A geologically established coal seam served as the training image for generating 20 equiprobable coal models. To simulate CV, 50 realisations were generated for each simulated coal seam, utilising sequential Gaussian simulation. Deviations of the simulated coal seams ranged from-0.07% to 5.48% in comparison to the training image. The CV simulation yielded an average value of 5,920.29 kcal/ kg, accompanied by a standard deviation of 586.54 kcal/kg. However, the average CV spanned from 5,305.26 kcal/kg to 6,526.55 kcal/kg across diverse simulations. For reserve calculation within the context of geological uncertainties, an algorithm rooted in maximum flow graph theory was employed to construct the ultimate pit for the coal mine. Within this final pit, the average stripping ratio was 1.62, coupled with a CV value of 6,019.66 kcal/kg. When juxtaposed with the deterministic model, the findings underscore that the stochastic ultimate pit delineates a more expansive excavation, accompanied by a heightened undiscounted cash flow