Forward Approaches Research Articles

Numerical Simulation of SEIR Model for COVID-19 Infection

One of the most basic models in epidemiology, the SEIR (Susceptible-Exposed-Infectious-Recovered) model explains how viral infections spread through communities. This study analyzes the SEIR model's differential equations to learn more about its dynamic behaviors. This study discusses the 2019 corona virus disease (COVID-19) epidemic model using numerical methods of susceptible exposed infected recovered (SEIR). A numerical description of the notion is provided using two numerical approaches, including forward Euler and RK-4 approaches. The subplots of SEIR model are drawn by ode 45 commands. The stability of disease free equilibrium and Endemic equilibrium points are depicted. The significance of comprehending the interaction between epidemiological variables and population dynamics in developing efficient public health treatments is brought to light in this study, which investigates the consequences of these equations on the transmission and management of infectious diseases. Insights into the dynamics of illness transmission and potential measures for mitigating its effects are presented through the use of mathematical analysis and computational simulations. Jagannath University Journal of Science, Volume 11, Number 1, June 2024, pp. 172−185

Discovery of genes that positively affect biomass and stress associated traits in poplar

Woody biomass serves as a renewable resource for various industries, including pulp and paper production, construction, biofuels, and electricity generation. However, the molecular mechanisms behind biomass traits are poorly understood, which significantly curtails the speed and efficiency of their improvement. We used activation tagging to discover genes that can positively affect tree biomass-associated traits. We generated and screened under greenhouse conditions a population of 2,700 independent activation tagging lines. A total of 761 lines, which had significantly and positively affected at least one biomass-associated trait, were discovered. The tag was positioned in the genome for forty lines which were affected in multiple traits and activation of proximal genes validated for a subset. For two lines we fully recapitulated the phenotype of the original lines through overexpression. Moreover, the overexpression led to more pronounced and additional improvements, not observed in the original lines. Importantly, the overexpression of a Fasciclin-like gene (PtaFLA10) and a Patatin-like gene (PtaPAT) was found to substantially improve biomass, with a 40% increase in dry-stem weight, and enhance drought tolerance, respectively. Additionally, PtaPAT overexpression increased cellulose content, which is crucial for biofuel production. Our work shows that the activation tagging approach applied even on a non-genome saturation scale in a poplar tree can be successfully used for the discovery of genes positively modify biomass productivity. Such dominant forward genetics approaches can aid in biotechnological manipulation of woody biomass traits and help unravel the functions and mechanisms of individual genes, gene families, and regulatory modules.

LIN28-Targeting Chromenopyrazoles and Tetrahydroquinolines Induced Cellular Morphological Changes and Showed High Biosimilarity with BRD PROTACs.

The probing of small molecules with heterocyclic scaffolds covering unexplored chemical space and the evaluation of their biological relevance are essential parts of forward chemical genetics approaches and for the development of potential small-molecule therapeutics. In this study, we profiled sets of chromenopyrazoles (CMPs) and tetrahydroquinolines (THQs), originally developed to target the protein-RNA interaction of LIN28-let-7, in a cell painting assay (CPA) measuring cellular morphological changes. Selected LIN28-inactive CMPs and THQs induced cellular morphological changes to different extents. The most CPA-active CMPs 2 and 3 exhibited high bio-similarity with the LCH and BET clusters, while the most CPA-active THQs 13 and 20 indicated a mechanism of action beyond the currently established biosimilarity clusters. Overall, this work demonstrated that CPA is useful in revealing "hidden" biological targets and mechanisms of action for biologically inactive small molecules, which are CMPs and THQs targeting the RNA-binding protein LIN28 in this case, evaluated in target-based strategies. When compared with annotated reference compounds, CMP 3 exhibited a high biosimilarity with the dual BRD7/9 degrading PROTAC VZ185, suggesting that CPA could potentially function as a new phenotypic approach to identify degrader molecules.

PerturbAtlas: a comprehensive atlas of public genetic perturbation bulk RNA-seq datasets.

Manipulating gene expression is crucial for understanding gene function, with high-throughput sequencing techniques such as RNA-seq elucidating the downstream mechanisms involved. However, the lack of a standardized metadata format for small-scale perturbation expression datasets in public repositories hinders their reuse. To address this issue, we developed PerturbAtlas, an add-value resource that re-analyzes publicly archived RNA-seq libraries to provide quantitative data on gene expression, transcript profiles, and alternative splicing events following genetic perturbation. PerturbAtlas assists users in identifying trends at the gene and isoform levels in perturbation assays by re-analyzing a curated set of 122801 RNA-seq libraries across 13 species. This resource is freely available at https://perturbatlas.kratoss.site as both raw data tables and an interactive browser, allowing searches by species, tissueor genomic features. The results provide detailed information on alterations following perturbations, accessible through both forward and reverse approaches, thereby enabling the exploration of perturbation consequences and the identification of potential causal perturbations.

Data-driven inverse design of a multiband second-order phononic topological insulator

Second-order phononic topological insulators (SPTIs) have sparked vast interest in manipulating elastic waves, owing to their unique topological corner states with robustness against geometric perturbations. However, it remains a challenge to develop multiband SPTIs that yield multi-frequency corner states using prevailing forward design approaches via trial and error, and most inverse design approaches substantially rely on time-consuming numerical solvers to evaluate band structures of phononic crystals (PnCs), showing low efficiency particularly when applied to different optimization tasks. In this study, we develop and validate a new inverse design framework, to enable the multiband SPTI by integrating data-driven machine learning (ML) with genetic algorithm (GA). The relationship between shapes of scatterers and frequency bounds of multi-order bandgaps of PnCs is mapped via developing artificial neural networks (ANNs), and a multiband SPTI with multi-frequency topological corner states is cost-effectively designed using the proposed inverse optimization framework. Our results indicate that the data-driven approach can provide a high-efficiency solution for on-demand inverse designs of multiband second-order topological mechanical devices, enabling diverse application prospects including multi-frequency robust amplification and confinement of elastic waves.

PBPK model-based gender-specific risk assessment of N-nitrosodimethylamine (NDMA) using human biomonitoring data.

Despite several screening levels for NDMA reported in water, soil, air, and drugs, the human risk assessment using biomonitoring concentrations has not been performed. In this study, gender-specific exposure guidance values were determined in humans, then biomonitoring measurements in healthy Korean subjects (32 men and 40 women) were compared to the exposure guidance values to evaluate the current exposure level to NDMA. For the human risk assessment of NDMA, the gender-specific physiologically based pharmacokinetic (PBPK) model was developed in humans using proper physiological parameters, partition coefficients, and biochemical parameters. Using the PBPK model, a Monte Carlo simulation was performed to describe the magnitudes of inter-individual variability and uncertainty on the single model predictions. The PBPK modeling and Monte Carlo simulation allowed the estimation of the relationship between external dose and blood concentration for the risk assessment. The procedure for the human risk assessment was summarized as follows: (1) estimating a steady-state blood concentration (Cavg) corresponding to the daily no observed adverse effect level (NOAEL) administration in rats; (2) applying uncertainty factors (UFs) for deriving the human Cavg; (3) determining the exposure guidance values as screening criteria; (4) interpreting the human biomonitoring measurements by forward and reverse dosimetry approaches. Using the biomonitoring concentrations, current daily exposures to NDMA were estimated to be 3.95μg/day/kg for men and 10.60μg/day/kg for women, respectively. The result of the study could be used as a basis for implementing further risk management and regulatory decision-making for NDMA.

Plant-induced bacterial gene silencing: a novel control method for bacterial wilt disease.

Ralstonia pseudosolanacearum, a notorious phytopathogen, is responsible for causing bacterial wilt, leading to significant economic losses globally in many crops within the Solanaceae family. Despite various cultural and chemical control strategies, managing bacterial wilt remains a substantial challenge. This study demonstrates, for the first time, the effective use of plant-induced bacterial gene silencing against R. pseudosolanacearum, facilitated by Tobacco rattle virus-mediated gene silencing, to control bacterial wilt symptoms in Nicotiana benthamiana. The methodology described in this study could be utilized to identify novel phytobacterial virulence factors through both forward and reverse genetic approaches. To validate plant-induced gene silencing, small RNA fractions extracted from plant exudates were employed to silence bacterial gene expression, as indicated by the reduction in the expression of GFP and virulence genes in R. pseudosolanacearum. Furthermore, treatment of human and plant pathogenic Gram-negative and Gram-positive bacteria with plant-generated small RNAs resulted in the silencing of target genes within 48 hours. Taken together, the results suggest that this technology could be applied under field conditions, offering precise, gene-based control of target bacterial pathogens while preserving the indigenous microbiota.

A fully sequenced collection of homozygous EMS mutants for forward and reverse genetic screens in Arabidopsis thaliana.

Genetic screens are powerful tools for biological research and are one of the reasons for the success of the thale cress Arabidopsis thaliana as a research model. Here, we describe the whole-genome sequencing of 871 Arabidopsis lines from the Homozygous EMS Mutant (HEM) collection as a novel resource for forward and reverse genetics. With an average 576 high-confidence mutations per HEM line, over three independent mutations altering protein sequences are found on average per gene in the collection. Pilot reverse genetics experiments on reproductive, developmental, immune and physiological traits confirmed the efficacy of the tool for identifying both null, knockdown and gain-of-function alleles. The possibility of conducting subtle repeated phenotyping and the immediate availability of the mutations will empower forward genetic approaches. The sequence resource is searchable with the ATHEM web interface (https://lipm-browsers.toulouse.inra.fr/pub/ATHEM/), and the biological material is distributed by the Versailles Arabidopsis Stock Center.

Reduction of the model prediction error in vibration-based SHM applications

The accuracy of an SHM scheme is partly controlled by the accuracy of the predictive models involved within the design process of the detector that associates sensor data with a corresponding health state. On the other hand, the accuracy of the predictive model, which is quantified by the error between predictions and observations, is partly controlled by its parameter magnitudes. This work provides a unified framework that allows for reducing the model prediction error by updating the model parameters in the light of experimental observations. The problem is cast in a probabilistic setting and tackled through inferential statistics. Forward and inverse uncertainty quantification approaches are employed in a sequential manner. The framework is able to yield point estimates in accordance to a Maximum Likelihood Estimator (MLE) and is also able to construct credible intervals through computational Bayesian updating. The former is treated by genetic algorithms whereas the later through Markov Chain Monte Carlo computational statistics. The framework is showcased on a vibration problem of the simplified benchmark case named as “Jim Beam”. The first ten modal parameters are considered as the discriminant features of interest . A surrogate of the finite element model of the considered specimen serves as the predictive model and the eigenfrequencies are extracted from a linear modal analysis. The parameters of inferential interest are the Young’s modulus and an artificial stiffness related parameter that is introduced so as to model the flexural rigidity of the specimen’s bolted connections. Experimental Modal Analysis and Operational Modal Analysis are the two approaches that were employed for experimentally identifying the modes and their corresponding frequencies. Results are provided from both the MLE and the Bayesian updating process and the effectiveness of each approach is discussed.

Oil prices and developing stock markets: Evidence from East Asia

The COVID-19 pandemic has introduced unprecedented complexity in the relationship between oil prices and stock markets. This work employs a Threshold Auto-Regressive Distributed Lag (ARDL) framework to analyze this relationship, focusing on two distinct periods: pre-mid-June 2020 and post-mid-June 2020, marked by the onset of the second wave of the pandemic. By examining financial variables including stock and bond returns, exchange rates, and WTI crude oil prices, this research identifies significant differences in market dynamics across these periods. The results indicate that the statistical significance and behavior of the WTI index vary, which is particularly evident in China’s stock market. Impulse Response Functions (IRFs) reveal that Region 1 (pre-mid-June 2020) experienced more pronounced market fluctuations in response to shocks, while Region 2 (post-mid-June 2020) demonstrated increased resilience and adaptability. These findings show heightened stock market vulnerability to oil price shocks during periods of economic policy uncertainty. Consequently, policymakers are advised to put forward adaptive approaches to financial regulation and economic policy, emphasizing market flexibility in response to exogenous shocks and tailoring responses to the specific dynamics of each market.

Advancements in genetic techniques and functional genomics for enhancing crop traits and agricultural sustainability.

Genetic variability is essential for the development of new crop varieties with economically beneficial traits. The traits can be inherited from wild relatives or induced through mutagenesis. Novel genetic elements can then be identified and new gene functions can be predicted. In this study, forward and reverse genetics approaches were described, in addition to their applications in modern crop improvement programs and functional genomics. By using heritable phenotypes and linked genetic markers, forward genetics searches for genes by using traditional genetic mapping and allele frequency estimation. Despite recent advances in sequencing technology, omics and computation, genetic redundancy remains a major challenge in forward genetics. By analyzing close-related genes, we will be able to dissect their functional redundancy and predict possible traits and gene activity patterns. In addition to these predictions, sophisticated reverse gene editing tools can be used to verify them, including TILLING, targeted insertional mutagenesis, gene silencing, gene targeting and genome editing. By using gene knock-down, knock-up and knock-out strategies, these tools are able to detect genetic changes in cells. In addition, epigenome analysis and editing enable the development of novel traits in existing crop cultivars without affecting their genetic makeup by increasing epiallelic variants. Our understanding of gene functions and molecular dynamics of various biological phenomena has been revised by all of these findings. The study also identifies novel genetic targets in crop species to improve yields and stress tolerances through conventional and non-conventional methods. In this article, genetic techniques and functional genomics are specifically discussed and assessed for their potential in crop improvement.

The Molecular Landscape of Lung Metastasis in Primary Head and Neck Squamous Cell Carcinomas.

Background Lung metastasis in head and neck cancer (HNC) patients is a critical concern, often indicating an advanced disease stage and a poor prognosis. This study explores the molecular complexities of such metastases, identifying specific genes and pathways that may serve as valuable targets for diagnosis and treatment. The findings underscore the potential for significantly improved patient outcomes through targeted therapeutic strategies. Methodology In this research, we systematically collected raw gene expression data from head and neck squamous cell carcinoma (HNSCC) and lung squamous cell carcinoma (LSCC). By comparing tumorous and normal gene expression profiles from paired patient samples, we identified differentially expressed genes (DEGs). Network analysis helped visualize protein interactions and pinpoint crucial hub genes. Through validation and comparison across several datasets, we identified common DEGs. Additionally, we employed Kaplan-Meier analysis and log-rank testing to examine the relationship between gene expression patterns and patient survival. Result The study identified 145 overlapping DEGs in both HNSCC and LSCC, which are crucial for cancer progression and linked to lung metastasis, offering vital targets for personalized therapy by identifying key genes affecting disease development and patient survival. Pathway analyses linked these to lung metastasis, while protein-protein interaction network construction and hub gene identification highlighted genes crucial for development and patient survival, offering targets for personalized therapy. Conclusion Identifying key genes and pathways in lung metastasis from HNC, this study highlights potential targets for enhanced diagnosis and therapy. It underscores the crucial role of molecular insights in driving forward personalized treatment approaches and improving patient outcomes.

Abstract 543: Identifying drivers of chemoresistance in SQLC using forward and reverse genetic approaches

Abstract Non-small cell lung cancer, mainly lung adenocarcinoma (LUAD) and lung squamous cell carcinoma (SQLC), accounts for about 85 % of all lung cancers. While LUAD treatment options have greatly improved, SQLC remains challenging, as targetable mutations are rare. Immunotherapy provides benefits, nevertheless, chemotherapy remains a backbone of systemic treatment strategies for SQLC patients. Accordingly, a major challenge for SQLC treatment is the development of chemoresistant tumors, oftentimes leading to cross-resistance to multiple drugs and limiting patient survival. Currently, the causal pathways enabling tumor cells to survive therapeutic stress and drivers of chemotherapy resistance are insufficiently known, which impedes the development of novel therapeutic strategies. We therefore leverage forward and reverse genetic approaches including large-scale chemo-genetic perturbations to systematically map the landscape of molecular drivers involved in chemotherapy resistance in SQLC. By integrating the resulting data we derive specific knowledge of the underlying processes, in order to identify druggable targets in chemoresistant tumors and contribute to improved patient care. To assess genes causally mediating chemotherapy resistance upon overexpression, we performed genome-wide CRISPR activation screens utilizing clinically used cisplatin and taxol. Additionally, we performed CRISPRa screens against four further drugs currently in clinical trials in order to distinguish genes causing cross-resistance to multiple drugs versus drug-specific mechanisms. In parallel, we generated chemoresistant clones by prolonged low-dose treatment, to analyze transcriptomic adaptions to chemoresistance and validate the CRISPRa screening hits. Initial findings show a large selection of unique and shared gene sets that were significantly enriched or reduced after treatment in the CRISPRa screen, indicating their involvement in the development of chemoresistance. Of note, transcriptomic analysis of chemoresistant cells shows an overlap with the CRISPRa screen specifically for the unfolded protein response (UPR) that is transcriptionally enriched in chemoresistant cells and a positive hit in the CRISPRa screen. Chemoresistant clones show increased resistance to tunicamycine induced ER-stress, indicating an increased ability to overcome the accumulation of misfolded protein under cytotoxic treatment through the UPR. Specific inhibition of the UPR during cytotoxic treatment shows promising results in chemoresistant cells. By combining our CRISPR activation screen and systematic analysis of chemoresistant cells we were able to identify the UPR as a driver and a druggable synthetically lethal dependency of chemoresistance in SQLC. This opens the possibility of studying the UPR as a potential target in the treatment of patients with chemoresistant tumors, improving patient outcome. Citation Format: Mareike Haarmann, Dennis Said Gadalla, Johannes Brägelmann. Identifying drivers of chemoresistance in SQLC using forward and reverse genetic approaches [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 543.

An Efficient Kinematic Calibration Method for Parallel Robots With Compact Multi-Degrees-of-Freedom Joint Models

Abstract Forward kinematics-based modeling approaches are capable of constructing complete kinematic error models for parallel robots in a general way. The existing forward kinematics-based modeling methods replace multi-degrees-of-freedom (multi-DOF) joints with several 1DOF joints, allowing each limb of the parallel robot to be modeled like a serial robot. Nonetheless, this substitution complicates the kinematic model and results in additional computation. To overcome this limitation, an efficient kinematic calibration method adopting compact multi-DOF joint models is proposed. First, compact kinematic models for multi-DOF joints are established with the product of exponentials formula and adopted in the forward kinematic formulation of limbs. Error models of limbs are derived by simplifying the forward kinematic formulas' differentials, and the geometric error model for parallel robots is established by further concatenating and reformulating the limb error models. Next, the kinematic model is iteratively updated with the geometric parameter errors identified by the Levenberg–Marquardt algorithm. Error compensation is achieved through the inverse kinematics of the calibrated kinematic model. Finally, simulations and an experiment are implemented for validation. Compared with the existing forward kinematics-based modeling approaches, the error modeling procedures are simplified as the equivalent substitution of multi-DOF joints is avoided. The proposed approach also enhances the error compensation efficiency while maintaining high accuracy improvement.

Genome-wide identification and characterization of flowering genes in Citrus sinensis (L.) Osbeck: a comparison among C. Medica L., C. Reticulata Blanco, C. Grandis (L.) Osbeck and C. Clementina

BackgroundFlowering plays an important role in completing the reproductive cycle of plants and obtaining next generation of plants. In case of citrus, it may take more than a year to achieve progeny. Therefore, in order to fasten the breeding processes, the juvenility period needs to be reduced. The juvenility in plants is regulated by set of various flowering genes. The citrus fruit and leaves possess various medicinal properties and are subjected to intensive breeding programs to produce hybrids with improved quality traits. In order to break juvenility in Citrus, it is important to study the role of flowering genes. The present study involved identification of genes regulating flowering in Citrus sinensis L. Osbeck via homology based approach. The structural and functional characterization of these genes would help in targeting genome editing techniques to induce mutations in these genes for producing desirable results.ResultsA total of 43 genes were identified which were located on all the 9 chromosomes of citrus. The in-silico analysis was performed to determine the genetic structure, conserved motifs, cis-regulatory elements (CREs) and phylogenetic relationship of the genes. A total of 10 CREs responsible for flowering were detected in 33 genes and 8 conserved motifs were identified in all the genes. The protein structure, protein-protein interaction network and Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis was performed to study the functioning of these genes which revealed the involvement of flowering proteins in circadian rhythm pathways. The gene ontology (GO) and gene function analysis was performed to functionally annotate the genes. The structure of the genes and proteins were also compared among other Citrus species to study the evolutionary relationship among them. The expression study revealed the expression of flowering genes in floral buds and ovaries. The qRT-PCR analysis revealed that the flowering genes were highly expressed in bud stage, fully grown flower and early stage of fruit development.ConclusionsThe findings suggested that the flowering genes were highly conserved in citrus species. The qRT-PCR analysis revealed the tissue specific expression of flowering genes (CsFT, CsCO, CsSOC, CsAP, CsSEP and CsLFY) which would help in easy detection and targeting of genes through various forward and reverse genetic approaches.

Differentiable stochastic halo occupation distribution

ABSTRACT In this work, we demonstrate how differentiable stochastic sampling techniques developed in the context of deep reinforcement learning can be used to perform efficient parameter inference over stochastic, simulation-based, forward models. As a particular example, we focus on the problem of estimating parameters of halo occupation distribution (HOD) models that are used to connect galaxies with their dark matter haloes. Using a combination of continuous relaxation and gradient re-parametrization techniques, we can obtain well-defined gradients with respect to HOD parameters through discrete galaxy catalogue realizations. Having access to these gradients allows us to leverage efficient sampling schemes, such as Hamiltonian Monte Carlo, and greatly speed up parameter inference. We demonstrate our technique on a mock galaxy catalogue generated from the Bolshoi simulation using a standard HOD model and find near-identical posteriors as standard Markov chain Monte Carlo techniques with an increase of ∼8× in convergence efficiency. Our differentiable HOD model also has broad applications in full forward model approaches to cosmic structure and cosmological analysis.

Complex plant responses to drought and heat stress under climate change.

Global climate change is predicted to result in increased yield losses of agricultural crops caused by environmental conditions. In particular, heat and drought stress are major factors that negatively affect plant development and reproduction, and previous studies have revealed how these stresses induce plant responses at physiological and molecular levels. Here, we provide a comprehensive overview of current knowledge concerning how drought, heat, and combinations of these stress conditions affect the status of plants, including crops, by affecting factors such as stomatal conductance, photosynthetic activity, cellular oxidative conditions, metabolomic profiles, and molecular signaling mechanisms. We further discuss stress-responsive regulatory factors such as transcription factors and signaling factors, which play critical roles in adaptation to both drought and heat stress conditions and potentially function as 'hubs' in drought and/or heat stress responses. Additionally, we present recent findings based on forward genetic approaches that reveal natural variations in agricultural crops that play critical roles in agricultural traits under drought and/or heat conditions. Finally, we provide an overview of the application of decades of study results to actual agricultural fields as a strategy to increase drought and/or heat stress tolerance. This review summarizes our current understanding of plant responses to drought, heat, and combinations of these stress conditions.

Central Channelopathies in Obesity.

As obesity has raised heightening awareness, researchers have attempted to identify potential targets that can be treated for therapeutic intervention. Focusing on the central nervous system (CNS), the key organ in maintaining energy balance, a plethora of ion channels that are expressed in the CNS have been inspected and determined through manipulation in different hypothalamic neural subpopulations for their roles in fine-tuning neuronal activity on energy state alterations, possibly acting as metabolic sensors. However, a remaining gap persists between human clinical investigations and mouse studies. Despite having delineated the pathways and mechanisms of how the mouse study-identified ion channels modulate energy homeostasis, only a few targets overlap with the obesity-related risk genes extracted from human genome-wide association studies. Here, we present the most recently discovered CNS-specific metabolism-correlated ion channels using reverse and forward genetics approaches in mice and humans, respectively, in the hope of illuminating the prospects for future therapeutic development.

Erosional modulation of the balance between alkalinity and acid generation from rock weathering

The balance between alkalinity generation by carbonate and silicate weathering and sulfuric acid generation by sulfide weathering controls the effect of weathering on atmospheric pCO2 over geologic timescales. How this balance varies across environmental gradients remains poorly constrained. Here, we analyze this balance across an erosional gradient of two orders of magnitude in the Three Rivers (Yangtze, Mekong, and Salween Rivers) Headwater Region (TRHR), eastern Qinghai-Tibet Plateau (QTP). By employing major element chemistry and multiple isotopes (δ34SSO4, δ18OSO4, and δ18OH2O) coupled with forward and inverse approaches, we unmix contributions of silicates, carbonates, evaporites, and sulfides to the total weathering budget. Across the TRHR, riverine SO42– is derived mainly from a mixture of an evaporite source with uniform values of δ34SSO4 and δ18OSO4, and a sulfide source that contributes highly variable values of δ34S (−12.2 ‰ to +4.1 ‰) and δ18O (−17.7 ‰ to −1.6 ‰). Contributions of sulfide oxidation to riverine SO42– vary from 16 % to 94 %, and sulfuric acid consumes 6 % to 63 % of the alkalinity produced by weathering. The fractions of weathering alkalinity derived from carbonate weathering range from 36 % to 98 % relative to silicate weathering. The combination of silicate, carbonate, and sulfide weathering suggests that the instantaneous weathering fluxes of most sampled catchments in the TRHR act as a sink of atmospheric CO2 over timescales shorter than marine carbonate burial (∼104 years), but as a carbon source over timescales longer than carbonate burial and shorter than sulfide burial (∼107 years). The spatial variability of the balance between alkalinity and acid generation, and, thus, the relationship between chemical weathering and atmospheric pCO2, are largely dependent on lithology. However, within comparable lithologic settings, sulfide and carbonate weathering rates rise with increasing erosion, whereas silicate weathering rates remain constant. Consequently, plateau weathering shifts from a sink to a source of atmospheric CO2 with increasing erosion. These findings suggest that sulfide weathering is more sensitive to erosion than carbonate and silicate weathering, and that it could play an important role in the long-term carbon cycle during mountain building.

173 Awardee Talk: Spot Sample Protocol for Gas Quantification of Grazing Beef Cattle Using a Greenfeed

Abstract Through a natural digestive process called enteric fermentation, cattle produce greenhouse gases that are an environmental concern and an energetic loss. Gas fluxes can be quantified with several techniques; however, an open circuit gas quantification system (OCGQS) allows for unencumbered quantification of methane (CH4), carbon dioxide (CO2), and oxygen (O2) from grazing cattle. In the literature, there is wide variation in the minimum number of spot samples recommended to evaluate gas fluxes of an individual animal, while no recommendations have been made to evaluate metabolic heat production. The objective of this study was to characterize the minimum number of spot samples needed to accurately quantify metabolic heat production and CH4, CO2, and O2 gas fluxes in grazing beef cattle. A minimum of 100 spot samples each were collected from seventeen grazing animals using a OCGQS (GreenFeed, C-Lock, Inc). The mean gas fluxes were computed starting from the first 10 visits (forward) and increasing by increments of 10 until 100 visits was reached. Mean gas fluxes were also computed starting from visit 100 moving back in time in increments of 10 (reverse) using the same approach. Mean gas fluxes were used to calculate individual animal metabolic heat production for each interval with both the forward and reverse approaches. Pearson and Spearman correlations were computed between the full 100 visits and each shortened visit interval. The minimum number of spot samples was determined when correlations with the full 100 visits were greater than 0.95. The 0.95 correlation threshold was met between 30 and 40 visits for all gas fluxes and metabolic heat production. Therefore, mean forward and reverse gas fluxes and metabolic heat production were also computed starting at 30 visits and increasing by 2 until 40 visits and vice versa. The results indicated that the minimum number of spot samples needed for accurate quantification of CH4, CO2, and O2 gas fluxes are 38, 40, and 40, respectively. Using gas fluxes collected by the OCGQS, metabolic heat production can be calculated after 36 spot samples. The practical calculation of metabolic heat production will require 40 spot samples given the necessary number of spot samples for component gases in the calculation. Animals met the required number of spot samples for quantification of metabolic heat production, CH4, CO2, and O2 in 29.5 ± 8.7, 30.5 ± 9.1, 31.8 ± 9.2, and 31.8 ± 9.2 days, respectively. Large variation existed around the number of days needed to achieve the recommended number of spot samples. For this reason, protocols for the OCGQS should be based on the total number of spot samples, rather than a test duration.