Measuring progress towards the application of freedom of association and collective bargaining rights: A tabular presentation of the findings of the ILO supervisory system

This document is part of a digital collection provided by the Martin P. Catherwood Library, ILR School, Cornell University, pertaining to the effects of globalization on the workplace worldwide. Special emphasis is placed on labor rights, working conditions, labor market changes, and union organizing.ILO_MeasuringProgressTowardApplicationofFreedomofAssociation_CollectiveBargainingRights.pdf: 636 downloads, before Oct. 1, 2020

Deindustrialization and changes in manufacturing trade: Factor content calculations for 1978-1995

Input-output analysis is used to estimate the labor content embodied in changes in manufacturing output resulting from changing patterns of manufacturing trade. For ten OECD countries from the late 1970s to the mid-1990s, changes in world trade of manufactures are estimated to have had a negative net effect on manufacturing employment of 3.5 million jobs, 2.0 million in the US alone, compared to a 6.2 million decline in actual manufacturing employment. The employment losses resulted mainly from North-South trade. At the industry level, there were large losses in labor-intensive industries and in industries that were strategically targeted by developing country industrial policies. There were employment losses in nearly all manufacturing industries, not a mixture of winners and losers. Such a pattern may result not from surging imports from the South but rather declining exports to the South in the aftermath of the 1980s debt crisis. JEL no. F14, F16, O2

Trade contraction in the global crisis: Employment and inequality effects in India and South Africa

The paper estimates the effects of the 2008-09 trade contraction on employment and incomes in India and South Africa, using social accounting matrices (SAMs) in a Leontief multiplier model. Employment results are presented at aggregate and industry levels and examine gender and skills biases. Income results examine inequality at the level of rural and urban household income quintiles. The most notable finding is that India and South Africa experienced substantial employment and income declines as a result of trade contraction with the EU and the US. A large share of these declines occurred in the non-tradeable sector and resulted from income-induced effects, illustrating how a shock originated in the tradeable goods sector had strong ripple effects throughout India and South Africa.trade / employment / household income / income distribution / economic recession / India / South Africa

Comparing Spatial Distributions of Solar Prominence Mass Derived from Coronal Absorption

In the present work we extend the use of this mass-inference technique to a sample of prominences observed in at least two coronal lines. This approach, in theory, allows a direct calculation of prominence mass and helium abundance and how these properties vary spatially and temporally. Our motivation is two-fold: to obtain a He(exp 0)/H(exp 0) abundance ratio, and to determine how the relative spatial distribution of the two species varies in prominences. The first of these relies on the theoretical expectation that the amount of absorption at each EUV wavelength is well-characterized. However, in this work we show that due to a saturation of the continuum absorption in the 625 A and 368 A lines (which have much higher opacity compared to 195 A-) the uncertainties in obtaining the relative abundances are too high to give meaningful estimates. This is an important finding because of its impact on future studies in this area. The comparison of the spatial distribution of helium and hydrogen presented here augments previous observational work indicating that cross-field diffusion of neutrals is an important mechanism for mass loss. Significantly different loss timescales for neutral He and H (helium drains much more rapidly than hydrogen) can impact prominence structure, and both the present and past studies suggest this mechanism is playing a role in structure and possibly dynamics. Section 2 of this paper contains a description of the observations and Section 3 summarizes the method used to infer mass along with the criteria imposed in choosing prominences appropriate for this study. Section 3 also contains a discussion of the problems due to limitations of the available data and the implications for determining relative abundances. We present our results in Section 4, including plots of radial-like scans of prominence mass in different lines to show the spatial distribution of the different species. The last section contains a discussion summarizing the importance of the qualitative results found in this work. The Appendix provides a detailed derivation of how to obtain prominence mass and helium abundance (A 1) and includes the data for all prominences studied (A2)

Epistasis between 5-HTTLPR and ADRA2B polymorphisms influences attentional bias for emotional information in healthy volunteers

Individual differences in emotional processing are likely to contribute to vulnerability and resilience to emotional disorders such as depression and anxiety. Genetic variation is known to contribute to these differences but they remain incompletely understood. The serotonin transporter (5-HTTLPR) and alpha(2B)-adrenergic autoreceptor (ADRA2B) insertion/deletion polymorphisms impact on two separate but interacting monaminergic signalling mechanisms that have been implicated in both emotional processing and emotional disorders. Recent studies suggest that the 5-HTTLPR s allele is associated with a negative attentional bias and an increased risk of emotional disorders. However, such complex behavioural traits are likely to exhibit polygenicity, including epistasis. This study examined the contribution of the 5-HTTLPR and ADRA2B insertion/deletion polymorphisms to attentional biases for aversive information in 94 healthy male volunteers and found evidence of a significant epistatic effect (p < 0.001). Specifically, in the presence of the 5-HTTLPR s allele, the attentional bias for aversive information was attenuated by possession of the ADRA2B deletion variant whereas in the absence of the s allele, the bias was enhanced. These data identify a cognitive mechanism linking genotype-dependent serotonergic and noradrenergic signalling that is likely to have implications for the development of cognitive markers for depression/anxiety as well as therapeutic drug effects and personalized approaches to treatment

Early life telomeres are influenced by environments acting at multiple temporal and spatial scales

An individual's telomere length early in life may reflect or contribute to key life history processes sensitive to environmental variation. Yet, the relative importance of genetic and environmental factors in shaping early life telomere length is not well understood as it requires samples collected from multiple generations with known developmental histories. We used a confirmed pedigree and conducted an animal model analysis of telomere lengths obtained from nestling house sparrows (Passer domesticus) sampled over a span of 22 years. We found significant additive genetic variation for early life telomere length, but it comprised a relatively small proportion (9%) of the total biological variation. Three sources of environmental variation were important: among cohorts, among breeding attempts within years and families, and among nestmates. The magnitude of variation among breeding attempts and among nestmates also differed by cohort, suggesting that interactive effects of environmental factors across time or spatial scales were important, yet we were unable to identify the specific causes of these interactions. The mean amount of precipitation during the breeding season positively predicted telomere length, but neither weather during a given breeding attempt nor date in the breeding season contributed to an offspring's telomere length. At the residual level, level of individual nestlings, offspring sex, size, and mass at 10 days of age also did not predict telomere length. Environmental effects appear especially important in shaping early life telomere length in some species, and an array of complex more focus on how environmental factors that interactions across scales may help to explain some of the variation observed among studies.Funding provided by: National Science FoundationCrossref Funder Registry ID: http://dx.doi.org/10.13039/100000001Award Number: IBN-9816989Funding provided by: National Science FoundationCrossref Funder Registry ID: http://dx.doi.org/10.13039/100000001Award Number: IBN-0542097Funding provided by: National Science FoundationCrossref Funder Registry ID: http://dx.doi.org/10.13039/100000001Award Number: IOS-1257718Funding provided by: National Science FoundationCrossref Funder Registry ID: http://dx.doi.org/10.13039/100000001Award Number: IOS-1656194Funding provided by: National Science FoundationCrossref Funder Registry ID: http://dx.doi.org/10.13039/100000001Award Number: IOS-1656212Study population: This study made use of a library of blood samples collected from 1993 to 2014 from a population of house sparrows located at the University of Kentucky's North Farm Agricultural Research Station. Westneat et al. (2002, 2009) and Heidinger et al. (2021) provide details about this population and the standard methods used in the field to mark individuals, monitor their reproduction and the collection of and storage of blood samples. Briefly, all nestlings were given uniquely numbered metal bands at 10 days of age, and both independent juveniles and adults were given unique combinations of colored-plastic bands during trapping sessions throughout the year. The present data consists of the families of breeding adults who had hatched in focal nest boxes, were banded as nestlings, and had a blood sample taken when they were 10 days old. While we had samples from any nesting attempt of these focal birds that reached 10 days of age in one of our boxes, we often targeted a subset of these. If the focal adult produced only one brood, we analyzed the DNA of all offspring. If they had multiple broods but only within a single season, we analyzed the brood with the most offspring. If they had broods across more than one season, we sampled the largest brood in each season. Some additional broods were analyzed for other reasons and these were also included in the analysis. We identified breeding partners of target individuals and if a blood sample was available, we measured the telomere length from that sample. Most such partners were captured as adults, so their exact age was unknown, but we knew relative age from the date of capture. This sampling regime resulted in 1,591 individuals with telomere lengths of which 1,500 had been sampled at 10 days of age. Telomere measures: Some of the telomere measures used in this analysis came from the dataset used in Heidinger et al. (2021). Subjects in that analysis had all been sampled at 10 days of age and resampled at least once, but many did not breed. The present analysis included breeders only and also added individuals sampled at 10 days of age that were not recaptured and resampled. The birds used in the Heidinger et al. (2021) dataset were randomly assigned to assays that were carried out by A. Kucera whereas the remaining samples were randomly assigned to assays and processed by R. Young. All assays used the same reference sample (described below). Telomeres were measured in whole blood samples, a highly replicative tissue that can be non-destructively sampled and is well suited for telomere analyses, especially in birds which have nucleated red blood cells. We extracted DNA using Qiagen DNeasy Blood and Tissue DNA Extraction Spin Column Kits and following the manufacturer' instructions. DNA concentration was quantified using a Nanodrop 8000 Spectrophotometer (Thermo Scientific, Waltham, MA, USA). DNA quality was assured using electrophoresis on a 2% agarose gel.We used real-time quantitative polymerase chain reaction (qPCR) on an Mx3000P (Stratagene, San Diego, CA, USA) to measure relative telomere length. The methods followed those of Criscuolo et al. (2009) adapted for house sparrows (Young et al. 2022). To measure relative telomere lengths (T/S ratios) for each sample, we ran separate qPCR reactions for telomeres and the single copy control gene, Glyceraldehyde 3-phosphate dehydrogenase (GAPDH), on different plates. The suitability of GAPDH as a control gene in this species has previously been verified using a melt curve analysis (Young et al. 2022). All the samples were run in duplicate and randomly distributed across plates.Each telomere and GAPDH reaction contained a total volume of 25 µl comprised of 20 ng of DNA and either telomere ((forward tel1b (5'-CGGTTTGTTTGGGTTTGGGTTTGGGTTTGGGTTTGGGTT-3') and reverse tel2b (5'- GGCTTGCCTTACCCTTACCCTTACCCTTACCCTTACCCT-3')) or GAPDH primers (GAPDH - forward (5'-AACCAGCCAAGTACGATGACAT-3') and reverse GAPDH (5'-CCATCAGCAGCAGCCTTCA-3')) at 200 nM concentration mixed with 12.5 ul of perfecta SYBRE green supermix Low Rox (Quantabio). The thermal profiles were 10 min at 95 °C, followed by 27 cycles of 15 s at 95 °C, 30 s at 58 °C, and 30 s at 72 °C, finishing with 1 min at 95 °C, 30 s at 58 °C, and 30 s at 95 °C for the telomere reactions and 10 min at 95 °C, followed by 40 cycles of 30 s at 95 °C and 30 s at 60 °C, finishing with 1 min at 95 °C, 30 s at 55 °C, and 30 s at 95 °C.We calculated relative telomere lengths (T/S ratio) according to the following formula: 2ΔΔCt, where ΔΔCt = (Ct telomere - Ct GAPDH) reference sample – (Ct telomere - Ct GAPDH) focal sample (Stratagene 2007), where the Ct value is the number of PCR cycles necessary to accumulate a sufficient fluorescent signal to cross a threshold. Individuals with longer telomeres cross this threshold more quickly than individuals with shorter telomeres, relative to the same house sparrow reference sample used on all plates. This same reference sample was also used to create a 5-point standard curve (40, 20, 10, 5, 2.5 ng) to ensure that all samples fell within the bounds of the standard curve and to calculate reaction efficiencies. In total we ran 53 plates and in all cases the efficiencies were between the recommended 85-115 % (mean ± SEM: telomere: 96.5 ± 0.85% and GAPDH 100 ± 0.99%). This assay produces highly repeatable T/S ratios (ICC: 0.86-0.88) when samples are run in random well locations across plates (Heidinger et al. 2021, Young et al. 2022).Parentage confirmation and pedigree construction: We confirmed maternity and paternity using genotypes obtained from a multiplex PCR of 5 highly polymorphic microsatellite loci adapted from well-established protocols (Stewart et al. 2006, Dawson et al. 2012). Generally, parents and offspring were organized by family, amplified on the same plate, and products analyzed in the same round on an ABI 3730. If adults had multiple broods across several years or switched mates, they may have been run on a different plate than some of their offspring. In some cases, subjects were analyzed 2-3 times on different plates and in all cases, their allele scores were within 2 base pairs of each other. Adults were excluded as parents if 2 or more loci diverged by more than 1 base pair. In some cases, adults were excluded from being the parent of a whole brood. This sometimes led to the identification of alternative adults that did match, and if these were not among our target individuals, that brood was excluded from further analysis. Only offspring that were genetically related to a target adult were included in the dataset. If the focal adult was a male and the offspring was extra-pair, it was excluded from our analysis. If the focal adult was a female, then any of her offspring sired by an extra-pair male were included in the analysis, but we did not identify the true sire in these cases. The parentage information allowed us to create a pedigree of individuals with known telomere lengths and their relatedness to other individuals.The resulting pedigree of 1,629 individuals had 1,591 telomere measures (both nestlings and adults), 1,500 of which were from individuals sampled at 10 days of age. This included 211 sires and 228 mothers, no cases of inbreeding, and 7 tiers (Table S1, Figure S1) with one lineage having 6 generations. The data set covered 428 nesting attempts with at least one nestling sampled. Most mothers had only one nesting attempt (N = 152) but two were represented by offspring from 10 attempts. Mothers were typically part of only one pair (N = 215), meaning that they only had one male partner, but 44 had 2 or more partners with one having 6. Since extra-pair offspring were not assigned to a sire, these numbers reflect social pairings that produced within-pair offspring. We determined the sex of all nestlings using PCR of extracted DNA, following procedures described in Westneat et al. (2002).Statistical Analysis Goal 1: Variance partitioning. Our initial goal was to partition the variance in telomere lengths among sources given the structure of the data. The initial animal model included all adults with a telomere measure, regardless of when they were sampled. We repeated the analysis restricting the set of adult telomere measures to those that were sampled at 10 days of age. These models were fitted with a set of random effects and no fixed effects. We included the relatedness matrix as a key term for assessing covariances among individuals by relatedness, the assay identity as a measure of lab artifacts, year (cohort) to assess effects of year-to-year environmental variation (note: prior analyses showed no effect of latency between sampling and analysis on telomere length, Heidinger et al. 2021), breeding attempt identity to assess all environmental effects common to a breeding attempt (e.g., date in the season, weather conditions during embryo and nestling development, size of the brood), nesting location (labeled as "barn" since nest boxes were clustered on the sides of farm buildings) to capture local environmental effects common to offspring reared in the same location, and pair identity to capture consistent joint influences of the male and female associated with breeding attempts. We did not include maternal identity alone, as males can provide extensive care during incubation and chick rearing, and most adult females in the dataset were members of only one pair and had only one breeding attempt, so the combination of pairD, motherID, and attemptID (or attemptID, motherID and sireID) would have over-specified the model.We reanalyzed the initial model to include only data from individuals sampled at 10 days of age. Variance partitioning models were fitted twice in the R computing environment (R Core Team 2019), first using brms (Burkner 2017) with 2 chains, default priors, 10,000 iterations with a burnin of 1000 and a sampling every 5 iterations. We used this to assess how well the model performed and get initial insight into the magnitude of variance components. The models took a long time to run in brms, so once we confirmed that models were fitting well (no divergent chains and rhats less than 1.1), we reran models in the program MCMCglmm (Hadfield 2010) using weakly informative priors (V = 1, nu = 0.002, Lemoine 2019), 1.1 x 105 iterations, a burnin of 10,000 and sampling every 50 iterations. We then analyzed the data in more detail to assess three possible influences on genetic variance in telomeres. First, we asked if parent age at the time of fertilization influenced telomere length (sex-specific parent age entered as two fixed effects). Second, we asked if the genetic variance in telomere length was a function of parent age via separate analysis of sire-specific (1-9) or mother-specific (1-6) age as a linear random slope in the "animal' random effect (Class et al. 2019. In these models we dropped any random effects that could not be distinguished from 0 and replaced pair identity with either mother or sire identity depending on which sex was in the random slope term. We allowed the model to estimate covariances between the animal intercept and slope. These models were fitted in brms with a linear slope, cauchy priors (0,2), 10000 iterations with a burnin of 1000, and a sample every 5 iterations. Finally, we assessed the role of offspring sex, both as a fixed effect to repeat a test for any sex differences in telomere length (Heidinger et al. 2021, Le Pepke 2021) and to assess sex-specific heritability. The former was tested by adding a fixed effect of offspring sex to the models described below that explored specific environmental factors. For sex-specific heritability, we fitted a bivariate model with the telomere length in male and female nestlings as two responses to investigate differences in heritability (Olsson et al. 2011, Chick et al. 2022) and estimate genetic correlations between the sexes. We also included assay identity, year, and attempt identity as random effects and modelled the covariance between male and female for each of these as well. Models were fitted in brms with each of three prior types for variance/covariances: default, V = diag(2), nu = 1.002, and V = diag(2)*(0.002/1.002), nu = 1.002. Results were affected only slightly. All models had 10,000 iterations, burnin of 1000, and a sampling of every 5. Goal 2: Influence of environmental effects. We examined types of environmental effects in more detail using a sequenced approach. Step one was to assess the magnitude of variance explained by the "environmental" variance components in the initial animal model described above. Our plan was to then explore specific environmental variables in more detail by adding fixed effects that varied most at the requisite level. For example, we expected from Le Pepke et al (2022b) that cohort (year) might explain some variation. If so, then yearly differences in weather might be relevant, so we gathered year-specific mean temperature and precipitation in either the "spring" months (February and March) preceding each breeding season or "summer" months through the period of breeding (April – August). Similarly, any among-attempt variation might be influenced by date in the season in which attempts were started, brood size at hatch, or the specific mean precipitation that occurred during the 25 days after the first egg was laid in that breeding attempt. Because temperature for a specific nesting attempt is highly correlated with date, and previous analyses of both variables revealed that date was a better variable to include for some traits (Westneat et al. 2009), we left attempt-specific temperature out of the model. Because these models focused on environmental sources of variance, we omitted the pedigree and included year and attempt identity as random effects. Each of these investigations was modelled in brms using default priors, 10,000 iterations, a burnin of 1000, and a sample every 5 iterations. We also explored the potential for interactive effects among environmental factors in two ways. To gather general evidence of interactions, we paired down the mixed model to just the informative and relevant random effects (omitting PairID, Barn, and the pedigree). We then split either the breeding attempt random term or the residual via grouping by a higher order random effect. For example, we calculated among-breeding attempt variance and among-nestling-within-attempt (residual) variance in telomere for each year. We also assessed if among- or within-attempt variance differed among parental ages by converting parental age to a random effect (lumping older ages into a 5+ category) and splitting among attempt and residual variance by age, with each parent tested in separate models. These models were fitted in a frequentist framework in SAS 9.4 (SAS 2015) Proc Mixed given the ease with which SAS coding allows this (see supplementary material). The magnitude of improved fit was assessed using a likelihood ratio test against the base model without group-specific variances. We also tested a set of two-way interactions as fixed effects that we reasoned were likely to explain variance at the appropriate level. For instance, if variation among attempts was important, then date by brood size seemed a likely influence given prior work showing that date affects clutch size (Westneat et al. 2009) and nestling growth (Mock et al. 2009) and brood size influences telomeres in a North Dakota population (Young et al. 2022). Similarly, at the residual level, brood size or parent age by the sex of the nestling might be important. We constructed these models after running the initial random effect models. Because these proposed fixed effects differed dramatically in measurement scale, we standardized ((x-mean)/SD) all values of all variables and analyzed them in a mixed model with a reduced set of random effects (omitting Barn, PairID, and the pedigree) in brms with default priors, 10,000 iterations, a burnin of 1000, and sampling every 5 iterations. Goal 3: Phenotypic and genetic covariance between telomeres and body size. Two opposing results already published encouraged us to investigate links between individual offspring telomere length and their condition at the time of measurement. Young et al. (2022) found that relative offspring size positively predicted day 10 telomere length but Le Pepke et al. (2022a) found that telomere length was negatively correlated with offspring size with age controlled, suggesting that higher growth led to shorter telomeres. We used two bivariate analyses to explore these relationships in our data set. One of the bivariate equations had telomere length as the response, included assay identity as a random effect and the important random effects from the analyses described above (year and attempt identity) along with the relatedness matrix. The other equation had either nestling tarsus length, a standard measure of size, or nestling mass as the response. These equations both included nestling age as a fixed effect since there was some variation in the age at which nestlings were measured, and year, attempt identity, and the relatedness matrix as random effects. We set the models to extract covariances for each random effect including the relatedness matrix to assess environmental and genetic correlations and the residual covariance. These models were fitted using brms using default priors, 10,000 iterations, a burnin of 1000 and sampling every 5. As a check, we reran these models with Cholesky priors (lkj(2) as recommended for correlations among random effects in the set prior notes for brms. We found no substantive differences in results

Journey 'Round the Sun: STEREO Science and Spacecraft Performance Results

The Solar TErrestrial RElations Observatory (STEREO) was originally designed as a two to five year heliocentric orbit mission to study coronal mass ejections (CMEs), solar energetic particles (SEPs), and the solar wind. After over ten years of continuous science data collection, the twin NASA STEREO observatories have significantly advanced the understanding of Heliophysics. This mission was the first to image CMEs all the way from the Sun to Earth and to observe the entire sphere of the Sun at one time. STEREO has demonstrated the importance of a point of view beyond the Sun-Earth line to significantly improve CME arrival time estimates and in understanding CME structure and trajectories and the longitudinal distribution of SEPs. STEREO was also the first to use one launch vehicle to insert two spacecraft into opposing heliocentric orbits, undergo a 3.5 month long superior solar conjunction, implement unattended daily science operations on two deep space observatories, maintain 7 arcsec continuous pointing without gyros, and detect and attempt to recover a spacecraft after a 22-month long communications anomaly at a range of 2 AU. This paper discusses the significant performance results after the first ten years of operations of the STEREO mission from its journey around the Sun

Journey 'Round the Sun: STEREO Science and Spacecraft Performance Results

The Solar TErrestrial RElations Observatory (STEREO) was originally designed as a two- to five-year heliocentric orbit mission to study coronal mass ejections (CMEs), solar energetic particles (SEPs), and the solar wind. After over ten years of continuous science data collection, the twin NASA STEREO observatories have significantly advanced the understanding of Heliophysics. This mission was the first to image CMEs all the way from the Sun to Earth and to observe the entire sphere of the Sun at one time. STEREO has demonstrated the importance of a point of view beyond the Sun-Earth line to significantly improve CME arrival time estimates and in understanding CME structure and trajectories and the longitudinal distribution of SEPs. STEREO was also the first to use one launch vehicle to insert two spacecraft into opposing heliocentric orbits, undergo a 3.5-month-long superior solar conjunction, implement unattended daily science operations on two deep space observatories, maintain 7 arcsec continuous pointing without gyros, and detect and attempt to recover a spacecraft after a 22-month long communications anomaly at a range of 2 AU (Astronomical Units). This paper discusses the significant performance results after the first ten years of operations of the STEREO mission from its journey around the Sun

Comparison of Upper Extremity Physical Characteristics Between Adolescent Competitive Swimmers and Nonoverhead Athletes

Alterations to upper extremity physical characteristics of competitive swimmers (posture, range of motion [ROM], and subacromial-space distance) are commonly attributed to cumulative training load during a swimmer's competitive career. However, this accepted clinical belief has not been established in the literature. It is important to understand whether alterations in posture and associated physical characteristics occur as a result of sport training or factors other than swimming participation to better understand injury risk and possible interventions

Evaluating two concepts for the modelling of intermediates accumulation during biological denitrification in wastewater treatment

The accumulation of the denitrification intermediates in wastewater treatment systems is highly undesirable, since both nitrite and nitric oxide (NO) are known to be toxic to bacteria, and nitrous oxide (N2O) is a potent greenhouse gas and an ozone depleting substance. To date, two distinct concepts for the modelling of denitrification have been proposed, which are represented by the Activated Sludge Model for Nitrogen (ASMN) and the Activated Sludge Model with Indirect Coupling of Electrons (ASM-ICE), respectively. The two models are fundamentally different in describing the electron allocation among different steps of denitrification. In this study, the two models were examined and compared in their ability to predict the accumulation of denitrification intermediates reported in four different experimental datasets in literature. The N-oxide accumulation predicted by the ASM-ICE model was in good agreement with values measured in all four cases, while the ASMN model was only able to reproduce one of the four cases. The better performance of the ASM-ICE model is due to that it adopts an “indirect coupling” modelling concept through electron carriers to link the carbon oxidation and the nitrogen reduction processes, which describes the electron competition well. The ASMN model, on the other hand, is inherently limited by its structural deficiency in assuming that carbon oxidation is always able to meet the electron demand by all denitrification steps, therefore discounting electron competition among these steps. ASM-ICE therefore offers a better tool for predicting and understanding intermediates accumulation in biological denitrification