Partitioning Model Research Articles

Efficient modelling of lateral discharge through a dike breach

Breaches in fluvial dikes can lead to major flooding in the hinterland with severe societal and economic consequences. The discharge partitioning at the location of a dike breach is a complex flow phenomenon with 2D and 3D flow features that needs to be predicted accurately for the estimation of flood hazard. Determining the exact location of a potential breach is highly uncertain and so are the circumstances in which it could appear. Therefore, many scenarios should be investigated. Fast and accurate modelling of the discharge partitioning with appropriate simplifications and parameterizations are required to allow for a large number of simulations within reasonable computational time. To achieve this, spatially lumped or one-dimensional flow models have been used in combination with side weir equations. For the first time, the present study systematically assesses the performance of eleven side weir equations for the determination of the lateral discharge through a breach in a dike that is parallel to the flow direction along a straight river reach. These side weir equations were implemented in a zero-dimensional spatially lumped flow model and in a one-dimensional spatially distributed flow model. Both models were evaluated against experimental data from laboratory tests with a side opening that was either fixed, or dynamically evolving. The performance of the side weir equations varied with the experimental data, highlighting the empirical nature of most of these equations. The coupling of the side weir equations with the spatially distributed flow model did not always generate better results than the coupling with the lumped model, which implies that increasing the model complexity does not systematically lead to better predictions of the dike breach discharge.

Mineralogy of the Martian mantle inferred from bulk chemical compositions

AbstractUnderstanding the mineralogy of the Martian mantle is essential for constructing geochemical and geophysical models of Mars. This study employs the pMELTS program to determine the mineralogy at the solidus from 11 published bulk silicate Mars (BSM) compositions, within a pressure range of 2–5 GPa. The pMELTS results align with experimental data and calculations from another thermodynamic program (Perple_X/stx11). Mineral modes from compositional models based on Martian meteorite geochemistry show relatively consistent abundances modes (olivine: 48–56 wt%, orthopyroxene: 20–25 wt%, clinopyroxene: 15–17 wt%, garnet: 6–9 wt%). In contrast, mineral modes from compositional models that are not based on Martian meteorite geochemistry exhibit a wider range of olivine and garnet abundances. Additionally, we constrained the mineral modes of the Martian mantle using trace element partitioning and partial melting models. Our calculations indicate that melts derived from mantle sources with a hypothesized garnet content of 5–10 wt% closely match the analyzed compositions of shergottites, validating the garnet mode (6–9 wt%) constrained in our pMELTS calculations. Extracting low‐degree (<4 wt%) melts from a BSM to form depleted Martian mantle (DMM) does not significantly alter the mineralogical modes of solid residues, but it does lead to substantial trace elemental depletion in the DMM. Therefore, enriched, intermediate, and depleted shergottite sources are likely characterized by similar mineral modes yet differ in incompatible element abundances.

Optimal control of rotavirus infection in breastfed and non-breastfed children

According to the World Health Organization’s guidelines, a two-year mandatory breastfeeding period is strongly recommended, supported by research demonstrating its significant benefits in reducing childhood illnesses and mortality rates. The present study is designed to address this recommendation by focusing on a specific infection, namely rotavirus. Acknowledging this evidence, researchers recognize the need for a deeper understanding of the dynamics of rotavirus disease. In response, our study focuses on constructing and analyzing an SIR epidemic model uniquely designed for transmitting rotavirus in children. The model partitions the children population into three compartments: susceptible, infected, and recovered. To enhance precision, we further categorize susceptible children based on breastfeeding status and infected individuals based on infectious or non-infectious nature. This detailed categorization enables a thorough examination of rotavirus transmission dynamics. The proposed model analyzes two equilibria, disease-free and endemic, revealing local and global stability conditions determined by the basic reproduction number (R0). Our exploration extends to local stability analysis for the endemic equilibrium when R0>1 and global stability assessment using the Lyapunov theory under specific conditions. Subsequently, we employ optimal control theory through Pontryagin’s maximum principle to minimize the cost burden associated with disease control in children. Then, our work provides a sensitivity analysis of different parameters for the basic reproduction number to enhance our understanding of model dynamics. Finally, numerical simulations conducted with MATLAB and Python software validate our analytical findings, offering a comprehensive and practical assessment of the proposed model’s implications for controlling rotavirus transmission in children.

Evaluation and modelling of polycyclic aromatic hydrocarbon (PAH) bioavailability in soils affected by coal tar asphalt

There are large masses of coal tar asphalt present in old roads, containing high concentrations of polycyclic aromatic hydrocarbons (PAHs). Uncertainty surrounding the risk they pose causes problems during road reconstruction and for the reuse of the asphalt present. To help elucidate potential risks, a parsimonious linear equilibrium partitioning model for the bioavailability of PAHs in soils contaminated by tar asphalt particles was developed. Furthermore, a set of partitioning coefficients for PAHs between sampled coal tar binders and water were determined experimentally, as well as measurements of freely dissolved concentrations using polyoxymethylene samplers in batch tests and column recirculation experiments with various mixtures of different soils (peat and sandy loam) and tar asphalts. The model predictions of freely dissolved concentrations were conservative and within an order of magnitude of measurements in both batch and column tests. The model presented here only relies on soil organic carbon content and the fraction coal tar binder in the soil to model PAH partitioning. This model could be used for more realistic. Low tier risk assessments towards rational prioritization of sensitive areas for risk reduction efforts.

Modeling compositional heterogeneity resolves deep phylogeny of flowering plants

Angiosperms experienced one of the most remarkable radiations of land plants and are now the dominant autotrophs in terrestrial ecosystems. Recent phylogenomic studies based on large-scale data from plastid, mitochondrial, or nuclear transcriptomes/genomes and increased taxon sampling have provided unprecedent resolution into the phylogeny of flowering plants. However, owing to ancient rapid radiations, the interrelationships among the five lineages of Mesangiospermae, the vast majority of angiosperms, remain contentious. Here we show that, although plastid and mitochondrial genomes lack sufficient phylogenetic signal for resolving deeper phylogeny, the relationships among five mesangiosperm lineages can be confidently resolved under better-fitting models using genome-scale data. According to our Bayesian cross-validation and model test in a maximum likelihood framework, site-heterogeneous models (e.g., CAT-GTR + G4, LG + C20 + F + G) outperform site-homogeneous or partition models often used in previous studies. Under site-heterogeneous models, the approximately unbiased test favored our preferred tree recovered from various datasets: Ceratophyllales (coontails) are robustly recovered as sister to monocots, and they together are sister to the clade comprising magnoliids, Chloranthales, and eudicots. Our phylogenomic analyses resolve the last enigma of the deeper phylogeny of angiosperms and emphasize the efficacy of modeling compositional heterogeneity in resolving rapid radiations of plants.

Volatile systematics in terrestrial igneous apatite: from microanalysis to decoding magmatic processes

Apatite has been recognized as a robust tool for the study of magmatic volatiles in terrestrial and extraterrestrial systems due to its ability to incorporate various volatile components and its common occurrence in igneous rocks. Most previous studies have utilized apatite to study individual magmatic systems or regions. However, volatile systematics in terrestrial magmatic apatite formed under different geological environments has been poorly understood. In this study, we filtered a large compilation of data for apatite in terrestrial igneous rocks (n > 20,000), categorized the data according to tectonic settings, rock types, and bulk-rock compositions, and conducted statistical analyses of the F–Cl–OH–S–CO2 contents (~ 11,000 data for halogen and less for other volatiles). We find that apatite from volcanic arcs preserves a high Cl signature in comparison to other tectonic settings and the median Cl contents differ between arcs. Apatite in various types and compositions of igneous rocks shows overlapping F–Cl–OH compositions and features in some rock groups. Specifically, apatite in kimberlite is characterized as Cl-poor, whereas apatite in plutonic rocks can contain higher F and lower Cl contents than the volcanic counterparts. Calculation using existing partitioning models indicates that apatite with a high OH (or F) content does not necessarily indicate a H2O-rich (or H2O-poor) liquid because it could be a result of high (or low) magma temperature. Our work may provide a new perspective on the use of apatite to investigate volatile behavior in magma genesis and evolution across tectonic settings, volatile recycling at subduction zones, and the volcanic-plutonic connection.

Prognostic value of tissue bridges in cervical spinal cord injury: a longitudinal, multicentre, retrospective cohort study

The accuracy of prognostication in patients with cervical spinal cord injury (SCI) needs to be improved. We aimed to explore the prognostic value of preserved spinal tissue bridges-injury-spared neural tissue adjacent to the lesion-for prediction of sensorimotor recovery in a large, multicentre cohort of people with SCI. For this longitudinal study, we included patients with acute cervical SCI (vertebrae C1-C7) admitted to one of three trauma or rehabilitation centres: Murnau, Germany (March 18, 2010-March 1, 2021); Zurich, Switzerland (May 12, 2002-March 2, 2019); and Denver, CO, USA (Jan 12, 2010-Feb 16, 2017). Patients were clinically assessed at admission (baseline), at discharge (3 months), and at 12 months post SCI. Midsagittal tissue bridges were quantified from T2-weighted images assessed at 3-4 weeks post SCI. Fractional regression and unbiased recursive partitioning models, adjusted for age, sex, centre, and neurological level of injury, were used to assess associations between tissue bridge width and baseline-adjusted total motor score, pinprick score, and light touch scores at 3 months and 12 months. Patients were stratified into subgroups according to whether they showed better or worse predicted recovery. The cohort included 227 patients: 93 patients from Murnau (22 [24%] female); 43 patients from Zurich (four [9%] female); and 91 patients from Denver (14 [15%] female). 136 of these participants (from Murnau and Zurich) were followed up for up to 12 months. At 3 months, per preserved 1 mm of tissue bridge at baseline, patients recovered a mean of 9·3% (SD 0·9) of maximal total motor score (95% CI 7·5-11.2), 8·6% (0·8) of maximal pinprick score (7·0-10·1), and 10·9% (0·8) of maximal light touch score (9·4-12·5). At 12 months post SCI, per preserved 1 mm of tissue bridge at baseline, patients recovered a mean of 10·9% (1·3) of maximal total motor score (8·4-13·4), 5·7% (1·3) of maximal pinprick score (3·3-8·2), and 6·9% (1·4) of maximal light touch score (4·1-9·7). Partitioning models identified a tissue bridge cutoff width of 2·0 mm to be indicative of higher or lower 3-month total motor, pinprick, and light touch scores, and a cutoff of 4·0 mm to be indicative of higher and lower 12-month scores. Compared with models that contained clinical predictors only, models additionally including tissue bridges had significantly improved prediction accuracy across all three centres. Tissue bridges, measured in the first few weeks after SCI, are associated with short-term and long-term clinical improvement. Thus, tissue bridges could potentially be used to guide rehabilitation decision making and to stratify patients into more homogeneous subgroups of recovery in regenerative and neuroprotective clinical trials. Wings for Life, International Foundation for Research in Paraplegia, EU project Horizon 2020 (NISCI grant), and ERA-NET NEURON.

Fast Algorithm of Passive Bistatic Radar Detection Based on Batches Processing of Sparse Representation and Recovery

In the passive bistatic radar (PBR) system,methods exist to address the issue of detecting weak targets without being influenced by non-ideal factors from adjacent strong targets. These methods utilize the sparsity in the delay-Doppler domain of the cross ambiguity function (CAF) to detect weak targets. However, the modeling and solving of this method involve substantial memory consumption and computational complexity. To address these challenges, this paper establishes a target detection model for PBR based on batch processing of sparse representation and recovery. This model partitions the CAF into blocks, identifies blocks requiring processing based on the presence of targets, and improves the construction and utilization of the measurement matrix. This results in a reduction in the computational complexity and memory resource requirements for sparse representation and recovery, and provides favorable conditions for parallel execution of the algorithm. Experimental results indicate that the proposed approach increases the number of blocks by a factor of four, and reduces the number of real multiplications by approximately an order of magnitude. Hence, compared with the traditional approach, the proposed approach enables fast and stable detection of weak targets.

Research on Product Advertising Design Combining Feature Extraction Technology and Web3D Technology

This work, built on the Unity3D development platform, presents a way for merging feature extraction technology and Web3D technology into advertising design to effectively address the issues of poor efficiency and distortion in the field. Using the candidate text layout generating technique of visual salience, we first build the vector function set based on the three main colors, then we produce the visual communication partition model of advertising design. Next, the number of feature parameters of the shape advertising design is obtained via the establishment of coding coefficient constraint features and the use of an upgraded neural network technique to extract local feature parameter information about the product. Finally, the product design model is brought to life using Web3D technology to boost advertising design's productivity and accuracy. The experiments show that this method not only results in a high rate of correct product identification but also offers a fresh viewpoint on the visual communication of product advertising design by merging the two disciplines.

Enhancing solid-phase extraction of tetracyclines with a hybrid biochar sorbent: A comparative study of chlorella and bamboo biochars

Biochar, a sustainable sorbent derived from pyrolyzed biomass, has garnered attention for its efficacy in solid-phase extraction (SPE) of antibiotics, with a particular focus on tetracyclines (TCs). Despite its recognized potential, the intricate separation mechanisms operative in biochar-based SPE systems have not been fully deciphered. This investigation contrasts chlorella biochar against commercial bamboo biochar, harnessing an array of analytical methodologies-microstructure characterization, adsorption thermodynamics, competitive adsorption kinetics, H+ back titration, and selectivity adsorption studies-complemented by a Box-Behnken design for the optimization of chlorella/bamboo-SPE and subsequent application in the analysis of animal-derived foodstuffs. The study unveils that a hybrid sorbent, integrating nitrogen-doped microporous chlorella biochar with mesoporous bamboo biochar in a 95/5 mass ratio, markedly diminishes irreversible adsorption while enhancing selectivity, surpassing the performance of single biochar SPE systems. The elucidated separation mechanisms implicate a partition model, propelled by oxygen-rich functional groups on chlorella biochar and the rapid adsorption kinetics of bamboo biochar, all orchestrated by electrostatic interactions within the mixed biochar framework. Moreover, the synergy of mixed biochar-SPE with high-performance liquid chromatography (HPLC) demonstrates exceptional proficiency in detecting TCs in animal viscera, evidenced by recovery rates spanning 80.80 % to 106.98 % and RSDs ranging from 0.24 % to 14.69 %. In essence, this research not only sheds light on the multifaceted factors influencing SPE efficiency but also propels the use of biochar towards new horizons in environmental monitoring and food safety assurance.

Profiling biomechanical abilities during sprint front-crawl swimming using IMU and functional clustering of variabilities

ABSTRACT This study aims to profile biomechanical abilities during sprint front crawl by identifying technical stroke characteristics, in light of performance level. Ninety-one recreational to world-class swimmers equipped with a sacrum-worn IMU performed 25 m all-out. Intra and inter-cyclic 3D kinematical variabilities were clustered using a functional double partition model. Clusters were analysed according to (1) swimming technique using continuous visualisation and discrete features (standard deviation and jerk cost) and (2) performance regarding speed and competition calibre using respectively one-way ANOVA and Chi-squared test as well as Gamma statistics. Swimmers displayed specific technical profiles of intra-cyclic (smoothy and jerky) and inter-cyclic stroke regulation (low, moderate and high repeatability) significantly discriminated by speed (p < 0.001, η2 = 0.62) and performance calibre (p < 0.001, V = 0.53). We showed that combining high levels of both kinds of variability (jerky + low repeatability) are associated with highest speed (1.86 ± 0.12 m/s) and competition calibre (ℽ = 0.75, p < 0.001). It highlights the crucial importance of variabilities combination. Technical skills might be driven by a specific alignment of stroke pattern and its associated dispersion according to the task constraints. This data-driven approach can assist eyes-based technical evaluation. Targeting the development of an explosive swimming style with a high level of body stability should be considered during training of sprinters.

A Projection Approach to Local Regression with Variable-Dimension Covariates

Incomplete covariate vectors are known to be problematic for estimation and inferences on model parameters, but their impact on prediction performance is less understood. We develop an imputation-free method that builds on a random partition model admitting variable-dimension covariates. Cluster-specific response models further incorporate covariates via linear predictors, facilitating estimation of smooth prediction surfaces with relatively few clusters. We exploit marginalization techniques of Gaussian kernels to analytically project response distributions according to any pattern of missing covariates, yielding a local regression with internally consistent uncertainty propagation that uses only one set of coefficients per cluster. Aggressive shrinkage of these coefficients regulates uncertainty due to missing covariates. The method allows in- and out-of-sample prediction for any missingness pattern, even if the pattern in a new subject’s incomplete covariate vector was not seen in the training data. We develop an MCMC algorithm for posterior sampling that improves a computationally expensive update for latent cluster allocation. Finally, we demonstrate the model’s effectiveness for nonlinear point and density prediction under various circumstances by comparing with other recent methods for regression of variable dimensions on synthetic and real data. Supplemental materials for this article are available online.

Hyperspectral Leaf Area Index and Chlorophyll Retrieval over Forest and Row-Structured Vineyard Canopies

As an unprecedented stream of decametric hyperspectral observations becomes available from recent and upcoming spaceborne missions, effective algorithms are required to retrieve vegetation biophysical and biochemical variables such as leaf area index (LAI) and canopy chlorophyll content (CCC). In the context of missions such as the Environmental Mapping and Analysis Program (EnMAP), Precursore Iperspettrale della Missione Applicativa (PRISMA), Copernicus Hyperspectral Imaging Mission for the Environment (CHIME), and Surface Biology Geology (SBG), several retrieval algorithms have been developed based upon the turbid medium Scattering by Arbitrarily Inclined Leaves (SAIL) radiative transfer model. Whilst well suited to cereal crops, SAIL is known to perform comparatively poorly over more heterogeneous canopies (including forests and row-structured crops). In this paper, we investigate the application of hybrid radiative transfer models, including a modified version of SAIL (rowSAIL) and the Invertible Forest Reflectance Model (INFORM), to such canopies. Unlike SAIL, which assumes a horizontally homogeneous canopy, such models partition the canopy into geometric objects, which are themselves treated as turbid media. By enabling crown transmittance, foliage clumping, and shadowing to be represented, they provide a more realistic representation of heterogeneous vegetation. Using airborne hyperspectral data to simulate EnMAP observations over vineyard and deciduous broadleaf forest sites, we demonstrate that SAIL-based algorithms provide moderate retrieval accuracy for LAI (RMSD = 0.92–2.15, NRMSD = 40–67%, bias = −0.64–0.96) and CCC (RMSD = 0.27–1.27 g m−2, NRMSD = 64–84%, bias = −0.17–0.89 g m−2). The use of hybrid radiative transfer models (rowSAIL and INFORM) reduces bias in LAI (RMSD = 0.88–1.64, NRMSD = 27–64%, bias = −0.78–−0.13) and CCC (RMSD = 0.30–0.87 g m−2, NRMSD = 52–73%, bias = 0.03–0.42 g m−2) retrievals. Based on our results, at the canopy level, we recommend that hybrid radiative transfer models such as rowSAIL and INFORM are further adopted for hyperspectral biophysical and biochemical variable retrieval over heterogeneous vegetation.

Fragment-based QSAR study to explore the structural requirements of DPP-4 inhibitors: a stepping stone towards better type 2 diabetes mellitus management

ABSTRACT Dipeptidyl peptidase-4 (DPP-4) inhibitors belong to a prominent group of pharmaceutical agents that are used in the governance of type 2 diabetes mellitus (T2DM). They exert their antidiabetic effects by inhibiting the incretin hormones like glucagon-like peptide-1 and glucose-dependent insulinotropic polypeptide which, play a pivotal role in the regulation of blood glucose homoeostasis in our body. DPP-4 inhibitors have emerged as an important class of oral antidiabetic drugs for the treatment of T2DM. Surprisingly, only a few 2D-QSAR studies have been reported on DPP-4 inhibitors. Here, fragment-based QSAR (Laplacian-modified Bayesian modelling and Recursive partitioning (RP) approaches have been utilized on a dataset of 108 DPP-4 inhibitors to achieve a deeper understanding of the association among their molecular structures. The Bayesian analysis demonstrated satisfactory ROC values for the training as well as the test sets. Meanwhile, the RP analysis resulted in decision tree 3 with 2 leaves (Tree 3: 2 leaves). This present study is an effort to get an insight into the pivotal fragments modulating DPP-4 inhibition.

Predicting therapeutic clinical trial enrollment for underrepresented patients in neuro-oncology using supervised machine learning.

1614 Background: Despite the unique ability of machine learning (ML) techniques to uncover complex interactive effects in datasets, studies exploring predictors of clinical trial enrollment have largely used standard statistical methods to discern associations. Furthermore, there have been limited applications of ML to close existing disparities in clinical trial enrollment of underrepresented patients in neuro-oncology. This study aims to use supervised machine learning to define and validate primary contributors of therapeutic clinical trial enrollment for a) all patients, b) women and c) NIH-designated minority patients with low- and high-grade glioma. Methods: Adult glioma patients who received care from the UCSF Brain Tumor Center between 1997- 2017 were identified in a prospective registry. Bootstrap forest (BF) and Recursive Partitioning (RP) models were created by randomly dividing patients in a 70/30 split into development (DEV) and validation (VAL) cohorts among all patients, women, and NIH-designated minority patients, separately. Model performance was assessed using the area under the curve (AUC) in the DEV and VAL cohorts. Results: Among 1042 patients, 350 patients (33.6%) enrolled in a therapeutic clinical trial. There were 445 women (42.7%), of which 144 (32.4%) enrolled, and 141 minority patients (13.5%), of which 39 (27.7%) enrolled. For all patients, in order of decreasing influence, the BF model selected median neighborhood household income, age, neighborhood poverty level, distance from hospital, tumor location, tumor volume, treatment with chemotherapy, occupation, KPS, insurance status, employment status, seizure at presentation, and volumetric extent of resection (EOR) to predict trial enrollment (DEV AUC: 0.984; VAL AUC: 0.746). For women, the BF model selected distance from hospital, age, household income, neighborhood poverty level, tumor location, treatment with chemotherapy, and KPS for trial prediction (DEV AUC: 0.972; VAL AUC: 0.746), whereas for minority patients, the BF model selected neighborhood poverty level, age, occupation, distance from hospital, tumor volume, insurance status, employment status, and EOR to predict trial enrollment (DEV AUC: 0.9830; VAL AUC: 0.769). The RP model for women designated those with non-White race as less likely to enroll (DEV AUC: 0.735; VAL AUC: 0.665) while the RP model for minority patients characterized those who preferred a non-English language or who were divorced or widowed as less likely to enroll (DEV AUC: 0.775; VAL AUC: 0.601). Conclusions: Supervised machine learning models achieved similar predictive performance among patient subgroups, while uncovering interactions between gender identity, minority status, and sociodemographic variables. These findings can guide targeted recruiting efforts to advance equitable trial enrollment for women and minorities.

Identifying uncertainty in physical–chemical property estimation with IFSQSAR

This study describes the development and evaluation of six new models for predicting physical–chemical (PC) properties that are highly relevant for chemical hazard, exposure, and risk estimation: solubility (in water SW and octanol SO), vapor pressure (VP), and the octanol–water (KOW), octanol–air (KOA), and air–water (KAW) partition ratios. The models are implemented in the Iterative Fragment Selection Quantitative Structure–Activity Relationship (IFSQSAR) python package, Version 1.1.0. These models are implemented as Poly-Parameter Linear Free Energy Relationship (PPLFER) equations which combine experimentally calibrated system parameters and solute descriptors predicted with QSPRs. Two other ancillary models have been developed and implemented, a QSPR for Molar Volume (MV) and a classifier for the physical state of chemicals at room temperature. The IFSQSAR methods for characterizing applicability domain (AD) and calculating uncertainty estimates expressed as 95% prediction intervals (PI) for predicted properties are described and tested on 9,000 measured partition ratios and 4,000 VP and SW values. The measured data are external to IFSQSAR training and validation datasets and are used to assess the predictivity of the models for “novel chemicals” in an unbiased manner. The 95% PI intervals calculated from validation datasets for partition ratios needed to be scaled by a factor of 1.25 to capture 95% of the external data. Predictions for VP and SW are more uncertain, primarily due to the challenges in differentiating their physical state (i.e., liquids or solids) at room temperature. The prediction accuracy of the models for log KOW, log KAW and log KOA of novel, data-poor chemicals is estimated to be in the range of 0.7 to 1.4 root mean squared error of prediction (RMSEP), with RMSEP in the range 1.7–1.8 for log VP and log SW. Scientific contributionNew partitioning models integrate empirical PPLFER equations and QSARs, allowing for seamless integration of experimental data and model predictions. This work tests the real predictivity of the models for novel chemicals which are not in the model training or external validation datasets.Graphical

Partitioning of water vapor and CO2 fluxes and underlying water use efficiency evaluation in a Brazilian seasonally dry tropical forest (Caatinga) using the Fluxpart model

The Fluxpart partitioning model was employed to assess transpiration, photosynthesis and evaporation, respiration based exclusively through high-frequency eddy covariance (EC) data. The model was implemented across three sites with two vegetation cover conditions (dense and sparse) during the drought episode between 2012 and 2015 in the Caatinga biome in Northeast Brazil. Fluxpart, an open-source Python software, partitions data by analyzing the flux variance similarity (FVS) relationship and conducting correlation analyses of EC data. The main contribution to the evapotranspiration (ET) process was transpiration (T), representing about 64 and 67% of the amount ET in areas with dense and sparse vegetation cover, respectively. When the Caatinga biome is in a good state of conservation, it behaves as a carbon sink; the net ecosystem exchange average was −6 g C m−2 day−1 (gram of carbon per square meter daily) for dense vegetation and 3 g C m−2 day−1 for sparse vegetation. Through ET, vapor pressure deficit (VPD), and gross primary production (GPP) via Fluxpart, an assessment was made concerning the underlying water use efficiency (uWUE) to understand the nonlinear effects of VPD on the carbon-water coupling at the ecosystem scale. The overvalues of 60 g C hPa0.5 kg−1 H2O−1 (grams of carbon times square root of hectopascals per kilogram of water) prevailing in the rainy season and below 20 g C hPa0.5 kg−1 H2O−1 were mostly in the dry season. uWUE was an important indicator of the ability of the Caatinga biome to optimize water loss and carbon gain according to the water available in the soil-plant-atmosphere system to withstand water stress, especially at dense vegetation covers.

Graph-Aligned Random Partition Model (GARP)

Bayesian nonparametric mixtures and random partition models are powerful tools for probabilistic clustering. However, standard independent mixture models can be restrictive in some applications such as inference on cell lineage due to the biological relations of the clusters. The increasing availability of large genomic data requires new statistical tools to perform model-based clustering and infer the relationship between homogeneous subgroups of units. Motivated by single-cell RNA data we develop a novel dependent mixture model to jointly perform cluster analysis and align the clusters on a graph. Our flexible graph-aligned random partition model (GARP) exploits Gibbs-type priors as building blocks, allowing us to derive analytical results for the probability mass function (pmf) on the graph-aligned random partition. We derive a generalization of the Chinese restaurant process from the pmf and a related efficient and neat MCMC algorithm to implement Bayesian inference. We illustrate posterior inference under the GARP using single-cell RNA-seq data from mice stem cells. We further investigate the performance of the model in recovering the underlying clustering structure as well as the underlying graph by means of simulation studies. Supplementary materials for this article are available online, including a standardized description of the materials available for reproducing the work.

The impact of gaseous degradation on the gas–particle partitioning of methylated polycyclic aromatic hydrocarbons

Abstract. The partitioning of semi-volatile organic compounds (SVOCs) between gas and particle phases plays a crucial role in their long-range transport and health risk assessment. However, the accurate prediction of the gas–particle (G–P) partitioning quotient (KP′) remains a challenge, especially for the light-molecular-weight (LMW) SVOCs due to their upward deviation from equilibrium state. In this study, the phenomenon with the influence of gaseous degradation on G–P partitioning was observed. Based on the diurnal study of concentrations and KP′ values for methylated polycyclic aromatic hydrocarbons (Me-PAHs), it was found that the KP′ values of methylated naphthalenes (Me-Naps; one type of LMW SVOC) during the daytime were higher than during the nighttime, and the regression lines of log KP′ versus log KOA (octanol–air partitioning coefficient) for daytime and nighttime were non-overlapping, which were different from other Me-PAHs. Compared with other diurnal influencing factors, the higher gaseous degradation of Me-Naps in the daytime than in the nighttime should partially explain their special diurnal variation in KP′, which provided a new explanation for the non-equilibrium behavior of KP′ for LMW SVOCs. Moreover, the influence of gaseous degradation on the deviation of KP′ from equilibrium state was deeply studied based on the steady-state G–P partitioning model considering particulate proportion in emission (ϕ0). The increasing times of KP′ influenced by the gaseous degradation deviated from equilibrium state can be calculated by 1 + 13.2ϕ0 × kdeg (kdeg, gaseous degradation rate). The increase in KP′ along with the increase in kdeg proved that higher gaseous degradation in the daytime could increase the KP′ value. Furthermore, an amplification in KP′ ranging from 1.11 to 5.58 times (90 % confidence interval: 1.01 to 14.4) under different ϕ0 values (0 to 1) in the temperature range of −50 to 50 °C was estimated by the Monte Carlo analysis. In summary, it can be concluded that the influence of gaseous degradation should also be considered in the G–P partitioning models of SVOCs, especially for the LMW SVOCs, which provided new insights into the related fields.

Model for partitioning the non-phytoplankton absorption coefficient of seawater in the ultraviolet and visible spectral range into the contributions of non-algal particulate and dissolved organic matter

Non-algal particles and chromophoric dissolved organic matter (CDOM) are two major classes of seawater constituents that contribute substantially to light absorption in the ocean within the ultraviolet (UV) and visible (VIS) spectral regions. The similarities in the spectral shape of these two constituent absorption coefficients, ad(λ) and ag(λ), respectively, have led to their common estimation as a single combined non-phytoplankton absorption coefficient, adg(λ), in optical remote-sensing applications. Given the different biogeochemical and ecological roles of non-algal particles and CDOM in the ocean, it is important to determine and characterize the absorption coefficient of each of these constituents separately. We describe an ADG model that partitions adg(λ) into ad(λ) and ag(λ). This model improves upon a recently published model [Appl. Opt. 58, 3790 (2019)APOPAI0003-693510.1364/AO.58.003790] through implementation of a newly assembled dataset of hyperspectral measurements of ad(λ) and ag(λ) from diverse oceanic environments to create the spectral shape function libraries of these coefficients, a better characterization of variability in spectral shape of ad(λ) and ag(λ), and a spectral extension of model output to include the near-UV (350–400 nm) in addition to the VIS (400–700 nm) part of the spectrum. We developed and tested two variants of the ADG model: the ADG_UV-VIS model, which determines solutions over the spectral range from 350 to 700 nm, and the ADG_VIS model, which determines solutions in the VIS but can also be coupled with an independent extrapolation model to extend output to the near-UV. This specific model variant is referred to as ADG_VIS-UVExt. Evaluation of the model with development and independent datasets demonstrates good performance of both ADG_UV-VIS and ADG_VIS-UVExt. Comparative analysis of model-derived and measured values of ad(λ) and ag(λ) indicates negligible or small median bias, generally within ±5% over the majority of the 350–700 nm spectral range but extending to or above 10% near the ends of the spectrum, and the median percent difference generally below 20% with a maximum reaching about 30%. The presented ADG models are suitable for implementation as a component of algorithms in support of satellite ocean color missions, especially the NASA PACE mission.