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Microphysics regimes due to haze–cloud interactions: cloud oscillation and cloud collapse
Get PDFIt is known that aqueous haze particles can be activated into cloud droplets in a supersaturated environment. However, haze–cloud interactions have not been fully explored, partly because haze particles are not represented in most cloud-resolving models. Here, we conduct a series of large-eddy simulations (LESs) of a cloud in a convection chamber using a haze-capable Eulerian-based bin microphysics scheme to explore haze–cloud interactions over a wide range of aerosol injection rates. Results show that the cloud is in a slow microphysics regime at low aerosol injection rates, where the cloud responds slowly to an environmental change and droplet deactivation is negligible. The cloud is in a fast microphysics regime at moderate aerosol injection rates, where the cloud responds quickly to an environmental change and haze–cloud interactions are important. More interestingly, two more microphysics regimes are observed at high aerosol injection rates due to haze–cloud interactions. Cloud oscillation is driven by the oscillation of the mean supersaturation around the critical supersaturation of aerosol due to haze–cloud interactions. Cloud collapse happens under weaker forcing of supersaturation where the chamber transfers cloud droplets to haze particles efficiently, leading to a significant decrease (collapse) in cloud droplet number concentration. One special case of cloud collapse is the haze-only regime. It occurs at extremely high aerosol injection rates, where droplet activation is inhibited, and the sedimentation of haze particles is balanced by the aerosol injection rate. Our results suggest that haze particles and their interactions with cloud droplets should be considered, especially in polluted conditions
Fracture Initiation Pressure as a Measure of Cemented Paste Backfill Strength
Get PDFThis laboratory-scale study presents the development and validation of a hydraulic fracturing technique to directly measure the tensile strength of cemented paste backfill (CPB), providing an alternative to traditional strength testing methods. Fracture initiation pressure (FIP) was used as the primary measure of CPB strength. Experimental results were compared with traditional benchmark measures such as uniaxial compressive strength (UCS), Brazilian tensile strength (BTS), and critical Mode-I fracture toughness (KIc). Regression analysis of experimental results revealed a strong linear relationship between FIP and these benchmark strength measures, indicating that FIP can be used as a reliable predictor of CPB strength. However, traditional linear elastic failure models did not adequately explain the observed FIP values, as they significantly over-predicted the CPB tensile strength. To address this, the Point Stress (PS) model was applied, which provided a more accurate prediction of tensile strength, especially in cases involving small boreholes. The PS model explained observed effects of borehole size on the material’s response to hydraulic pressurization. This study confirms that hydraulic fracturing, interpreted through the PS model, is an effective method for determining CPB strength and provides a practical alternative measure to conventional testing methods
Subsurface heatwaves in lakes
Get PDFLake heatwaves (extreme hot water events) can substantially disrupt aquatic ecosystems. Although surface heatwaves are well studied, their vertical structures within lakes remain largely unexplored. Here we analyse the characteristics of subsurface lake heatwaves (extreme hot events occurring below the surface) using a spatiotemporal modelling framework. Our findings reveal that subsurface heatwaves are frequent, often longer lasting but less intense than surface events. Deep-water heatwaves (bottom heatwaves) have increased in frequency (7.2 days decade−1), duration (2.1 days decade−1) and intensity (0.2 °C days decade−1) over the past 40 years. Moreover, vertically compounding heatwaves, where extreme heat occurs simultaneously at the surface and bottom, have risen by 3.3 days decade−1. By the end of the century, changes in heatwave patterns, particularly under high emissions, are projected to intensify. These findings highlight the need for subsurface monitoring to fully understand and predict the ecological impacts of lake heatwaves
Tropical Forest Soil Microbiome Modulates Leaf Heat Tolerance More Strongly Under Warming Than Ambient Conditions
Get PDFIt is unclear how plants respond to increasing temperatures. Leaf heat tolerance (LHT) is often at its upper limit in tropical forests, suggesting that climate change might negatively impact these forests. We hypothesized that intraspecific variation in LHT might be associated with changes in the soil microbiome, which might also respond to climate. We hypothesized that warming would increase LHT through changes in the soil microbiome: we combined an in situ tropical warming experiment with a shade house experiment in Puerto Rico. The shade house experiment consisted of growing seedlings of Guarea guidonia, a dominant forest species, under different soil microbiome treatments (reduced arbuscular mycorrhizal fungi, reduced plant pathogens, reduced microbes, and unaltered) and soil inoculum from the field experiment. Heat tolerance was determined using chlorophyll fluorescence (F /F ) on individual seedlings in the field and on groups of seedlings (per pot) in the shade house. We sequenced soil fungal DNA to analyze the impacts of the treatments on the soil microbiome. In the field, seedlings from ambient temperature plots showed higher F /F values under high temperatures (0.648 at 46°C and 0.067 at 52°C) than seedlings from the warming plots (0.535 at 46°C and 0.031 at 52°C). In the shade house, the soil microbiome treatments significantly influenced the fungal community composition and LHT (T and F /F ). Reduction in fungal pathogen abundance and diversity altered F /F before T for seedlings grown with soil inoculum from the warming plots but after T for seedlings grown with soil inoculum from the ambient plots. Our findings emphasize that the soil microbiome plays an important role in modulating the impacts of climate change on plants. Understanding and harnessing this relationship might be vital for mitigating the effects of warming on forests, emphasizing the need for further research on microbial responses to climate change
Synthesis of Sensitive Oligodeoxynucleotides Containing Acylated Cytosine, Adenine, and Guanine Nucleobases
Get PDFBackground/Objective: Oligodeoxynucleotides (ODNs) containing base-labile modifications such as N4-acetyldeoxycytidine (4acC), N6-acetyladenosine (6acA), N2- acetylguanosine (2acG), and N4-methyoxycarbonyldeoxycytidine (4mcC) are highly challenging to synthesize because standard ODN synthesis methods require deprotection and cleavage under strongly basic and nucleophilic conditions, and there is a lack of ideal alternative methods to solve the problem. The objective of this work is to explore the capability of the recently developed 1,3-dithian-2-yl-methoxycarbonyl (Dmoc) method for the incorporation of multiple 4acC modifications into a single ODN molecule and the feasibility of using the method for the incorporation of the 6acA, 2acG and 4mcC modifications into ODNs. Methods: The sensitive ODNs were synthesized on an automated solid phase synthesizer using the Dmoc group as the linker and the methyl Dmoc (meDmoc) group for the protection of the exo-amino groups of nucleobases. Deprotection and cleavage were achieved under non-nucleophilic and weakly basic conditions. Results: The 4acC, 6acA, 2acG, and 4mcC were all found to be stable under the mild ODN deprotection and cleavage conditions. Up to four 4acC modifications were able to be incorporated into a single 19-mer ODN molecule. ODNs containing the 6acA, 2acG, and 4mcC modifications were also successfully synthesized. The ODNs were characterized using RP HPLC, capillary electrophoresis, gel electrophoresis and MALDI MS. Conclusions: Among the modified nucleotides, 4acC has been found in nature and proven beneficial to DNA duplex stability. A method for the synthesis of ODNs containing multiple 4acC modifications is expected to find applications in biological studies involving 4acC. Although 6acA, 2acG, and 4mcC have not been found in nature, a synthetic route to ODNs containing them is expected to facilitate projects aimed at studying their biophysical properties as well as their potential for antisense, RNAi, CRISPR, and mRNA therapeutic applications
Minimum GHG emissions and energy consumption of U.S. PET and polyolefin packaging supply chains in a circular economy
No full textThere is a wide agreement on the urgency of transforming linear management of plastics towards a circular economy model. However, no clear pathways exist as to required recycling technologies involved and system-wide environmental impacts. This study explores such pathways in the U.S. for the most commonly used packaging plastics through a combination of mechanical and emerging advanced recycling technologies. A system optimization model aimed at minimizing environmental impacts was developed to determine optimal end-of-life (EOL) management and locations of existing and emerging U.S. recycling infrastructures. Our study includes material flows from virgin resin production through semi-manufacturing processes to existing EOL disposal and recycling processes. An optimized circular plastics packaging system achieved greenhouse gas (GHG) emission savings of up to 28% and cumulative energy demand (CED) savings of up to 46%, compared to the linear economy. Moreover, these savings of GHG emissions and CED impacts represent a reduction of 0.16% and 0.49% compared to annual U.S. GHG emissions and energy consumption in 2022, respectively. The optimal recycling rates and systems-level circularity ranged from 78-99% and 57-75%, respectively. Increased energy savings led to increased GHG emissions showing a potential trade-off between GHG emissions and energy. Analysis of 40 scenarios showed the importance of material collection distances, blend limit of mechanically recycled resins, process yields, and mandated recycling rates for achieving a sustainable circular economy of plastics
The Inflation Reduction Act: Implications for energy development, energy sovereignty, and self-determination for federally recognized Tribal Nations in the US
No full textThe sovereignty of federally recognized Tribal Nations in the United States is codified in the US Constitution, federal law, and myriad treaty agreements, but the realities of sovereignty are complex. Federal law has obfuscated Tribal sovereignty, complicating and restricting Tribally owned or managed energy development on Tribal land. This paper discusses the divergence of Western and Indigenous concepts of sovereignty and analyzes federal Tribal law and policy and its implications for Tribal energy development, making two contributions. The first is to explore whether past federal laws and policies have supported or impeded the ability of Tribal Nations to develop energy projects and utilize energy resources on Tribals lands in the US. The second is to explore how the Inflation Reduction Act (IRA) altered US federal energy policy in the context of Tribal energy sovereignty, assessing the impacts of US federal Tribal policy on Tribal sovereignty in terms of de recto (by right), de facto (in fact), or de jure (by law). This research reveals that the IRA encouraged de facto Tribal energy sovereignty, advancing Tribal self-determination by reducing dependence, increasing benefits, and empowering decision making for Tribal Nations in the US
Finite Volume Incompressible Lattice Boltzmann Framework for Non-Newtonian Flow Simulations in Complex Geometries
Get PDFArterial diseases are a leading cause of morbidity worldwide, necessitating the development of robust simulation tools to understand their progression mechanisms. In this study, we present a finite volume solver based on the incompressible lattice Boltzmann method (iLBM) to model complex cardiovascular flows. Standard LBM suffers from compressibility errors and is constrained to uniform Cartesian meshes, limiting its applicability to realistic vascular geometries. To address these issues, we developed an incompressible LBM scheme that recovers the incompressible Navier–Stokes equations (NSEs) and integrated it into a finite volume (FV) framework to handle unstructured meshes while retaining the simplicity of the LBM algorithm. The FV-iLBM model with linear reconstruction (LR) scheme was then validated against benchmark cases, including Taylor–Green vortex flow, shear wave attenuation, Womersley flow, and lid-driven cavity flow, demonstrating improved accuracy in reducing compressibility errors. In simulating flow over National Advisory Committee for Aeronautics (NACA) 0012 airfoil, the FV-iLBM model accurately captured vortex shedding and aerodynamic forces. After validating the FV-iLBM solver for simulating non-Newtonian flows, pulsatile blood flow through an artery afflicted with multiple stenoses was simulated, accurately predicting wall shear stress and flow separation. The results establish FV-iLBM as an efficient and accurate method for modeling cardiovascular flows
COMPUTATIONAL TOOLS FOR PROTEIN CLASSIFICATION IN METAGENOMES
Get PDFAdvances in genomic sequencing have dramatically increased the amount and the speed at which genomic data is being generated. These technological advances have enabled the ability to profile the genetic information of organisms and communities at unprecedented scales. Many methods have been developed to identify and classify genes within these datasets. However, many generic pipelines for gene annotation struggle to accurately predict specific protein classes that may not be represented in their databases. Two major challenges exist for classification of specific protein classes in metagenomic databases. The first is the fact that many metagenomic assemblies are highly fragmented with many genes being partial genes. Secondly, many protein families have very few representatives in databases, many of which have ambiguous annotations. The accurate detection of bacterial toxins in complex environments and the reliable prediction of ice-binding proteins (IBPs) remain unresolved issues for current bioinformatics pipelines. Fragmented or incomplete reads often obscure the identification of toxin-encoding genes within metagenomic datasets, while small, rigidly defined IBP datasets and outdated learning algorithms hamper predictive accuracy for antifreeze (AFPs) and ice-nucleation (INPs) proteins. Here, we introduce two computational strategies—one leveraging profile Hidden Markov Models (pHMMs) for toxin detection, the other employing a protein language model (ESM-2) for IBP classification—that tackle these methodological constraints. Our findings underscore a substantial improvement over existing tools. The pHMM-based toxin detection system, by focusing on core structural domains, achieved 99% sensitivity and specificity: 5,120 toxin-related sequences were identified from wastewater samples, yet no positives were detected in food samples. Simultaneously, the PLM-ICE framework yielded a Matthews correlation coefficient (MCC) of 0.984 for antifreeze proteins and 0.927 for ice-nucleation proteins—metrics that exceed those of previously described classifiers. Future studies can expand the approach to include other protein families or unify these strategies into broader annotation platforms and offer a path toward more nuanced analyses of large set genomic data
One-Dimensional Kalman Filter with Measurement Noise Replaced by Quantization
No full textSeveral communication systems applications rely on state estimators for dynamic monitoring and control. Kalman Filters have been widely used for state estimation of stochastic processes. Low communication bandwidth adds more complexity to state estimator models, as state quantization should be considered. This paper studies a one-dimensional Kalman Filter with a 1-bit quantization process replacing noisy measurements. 2-bin and 3-bin 1-bit quantizers are considered. State distributions are calculated using discrete convolutions on a fine grid, and with Gaussian approximations. For the 3-bin 1-bit quantizer, two variations of the Gaussian approximation are developed. MATLAB simulations for the five resulting methods indicate that discrete convolutions are not worth the high computational cost, and that the 3-bin 1-bit quantizer eliminates the granular noise of the conventional 2-bin 1-bit quantizer