Slow-Onset Inhibition of Mycobacterium tuberculosis InhA: Revealing Molecular Determinants of Residence Time by MD Simulations

An important kinetic parameter for drug efficacy is the residence time of a compound at a drug target, which is related to the dissociation rate constant k_off. For the essential antimycobacterial target InhA, this parameter is most likely governed by the ordering of the flexible substrate binding loop (SBL). Whereas the diphenyl ether inhibitors 6PP and triclosan (TCL) do not show loop ordering and thus, no slow-binding inhibition and high k_off values, the slightly modified PT70 leads to an ordered loop and a residence time of 24 minutes. To assess the structural differences of the complexes from a dynamic point of view, molecular dynamics (MD) simulations with a total sampling time of 3.0 µs were performed for three ligand-bound and two ligand-free (perturbed) InhA systems. The individual simulations show comparable conformational features with respect to both the binding pocket and the SBL, allowing to define five recurring conformational families. Based on their different occurrence frequencies in the simulated systems, the conformational preferences could be linked to structural differences of the respective ligands to reveal important determinants of residence time. The most abundant conformation besides the stable EI* state is characterized by a shift of Ile202 and Val203 toward the hydrophobic pocket of InhA. The analyses revealed potential directions for avoiding this conformational change and, thus, hindering rapid dissociation: (1) an anchor group in 2''-position of the B-ring for scaffold stabilization, (2) proper occupation of the hydrophobic pocket, and (3) the introduction of a barricade substituent in 5''-position of the diphenyl ether B-ring.     

Irrational decision-making in an amoeboid organism: transitivity and context-dependent preferences

Most models of animal foraging and consumer choice assume that individuals make choices based on the absolute value of items and are therefore ‘economically rational’. However, frequent violations of rationality by animals, including humans, suggest that animals use comparative valuation rules. Are comparative valuation strategies a consequence of the way brains process information, or are they an intrinsic feature of biological decision-making? Here, we examine the principles of rationality in an organism with radically different information-processing mechanisms: the brainless, unicellular, slime mould Physarum polycephalum. We offered P. polycephalum amoebas a choice between food options that varied in food quality and light exposure (P. polycephalum is photophobic). The use of an absolute valuation rule will lead to two properties: transitivity and independence of irrelevant alternatives (IIA). Transitivity is satisfied if preferences have a consistent, linear ordering, while IIA states that a decision maker''s preference for an item should not change if the choice set is expanded. A violation of either of these principles suggests the use of comparative rather than absolute valuation rules. Physarum polycephalum satisfied transitivity by having linear preference rankings. However, P. polycephalum''s preference for a focal alternative increased when a third, inferior quality option was added to the choice set, thus violating IIA and suggesting the use of a comparative valuation process. The discovery of comparative valuation rules in a unicellular organism suggests that comparative valuation rules are ubiquitous, if not universal, among biological decision makers.     

Human-nature connection and soundscape perception: Insights from Tierra del Fuego,Argentina

Human disconnection from nature is thought to have contributed to the environmental crises we currently face, and increasing connection with nature has been proposed as one way of promoting pro-environmental behavior, nature conservation, and social-ecological sustainability. Some efforts to increase connection with nature (“nature relatedness”) have centered on exploring the social-ecological importance of soundscapes, but there is a paucity of empirical evidence supporting the theoretical linkage between soundscape perception and nature relatedness. Using prerecorded and in situ soundscape prompts, we conducted a street intercept survey in Ushuaia, Tierra del Fuego, Argentina to assess: 1) the relative importance of senses in experiences of nature, 2) the relationship between nature relatedness and soundscape perception, 3) differences in soundscape perception between various soundscapes, and 4) possible sociodemographic influences on sense importance, nature relatedness, and soundscape perception. Participants reported that hearing was of secondary importance to vision in experiences of nature. We also found that nature relatedness was positively correlated with the valuation of soundscapes—particularly more natural ones—but not with the discernment of soundscapes or identification of where soundscapes were recorded. Valuation of more natural soundscapes was higher than valuation of more technophonically dominated soundscapes, while soundscape discernment and location identification were higher for soundscapes that were likely more familiar to listeners. Sociodemographic influences on these variables were minor, but women reported higher sense importance, and having a nature-based occupation was associated with higher nature relatedness and valuation of a soundscape from a penguin colony. Our study highlighted a number of potential research areas concerning soundscape perception, including differences between prerecorded and in situ soundscape prompts, defining various aspects of soundscape perception, and the relative influences of sound sources and quantitative acoustic parameters on soundscape perception. Further research is certainly needed to account for global diversity in cultures and soundscapes, but we found some promising empirical support for the use of natural-soundscape-focused educational programs in efforts to promote nature relatedness.     

Estimating Litter Decomposition Rate in Single-Pool Models Using Nonlinear Beta Regression

Litter decomposition rate (k) is typically estimated from proportional litter mass loss data using models that assume constant, normally distributed errors. However, such data often show non-normal errors with reduced variance near bounds (0 or 1), potentially leading to biased k estimates. We compared the performance of nonlinear regression using the beta distribution, which is well-suited to bounded data and this type of heteroscedasticity, to standard nonlinear regression (normal errors) on simulated and real litter decomposition data. Although the beta model often provided better fits to the simulated data (based on the corrected Akaike Information Criterion, AIC_c), standard nonlinear regression was robust to violation of homoscedasticity and gave equally or more accurate k estimates as nonlinear beta regression. Our simulation results also suggest that k estimates will be most accurate when study length captures mid to late stage decomposition (50–80% mass loss) and the number of measurements through time is ≥5. Regression method and data transformation choices had the smallest impact on k estimates during mid and late stage decomposition. Estimates of k were more variable among methods and generally less accurate during early and end stage decomposition. With real data, neither model was predominately best; in most cases the models were indistinguishable based on AIC_c, and gave similar k estimates. However, when decomposition rates were high, normal and beta model k estimates often diverged substantially. Therefore, we recommend a pragmatic approach where both models are compared and the best is selected for a given data set. Alternatively, both models may be used via model averaging to develop weighted parameter estimates. We provide code to perform nonlinear beta regression with freely available software.     

Estimating leaf area index and light extinction coefficient using Random Forest regression algorithm in a tropical moist deciduous forest,India

Leaf area index (LAI) and light extinction coefficient (k) are the key structural parameters controlling many canopy functions like radiation and water interception, radiation extinction, water and gas exchange. The present study aims at developing predictive models for generating spatial distribution of LAI and k by integrating remote sensing imagery and field data. The study was carried out in a tropical moist deciduous forest of Uttarakhand, India. Various spectral variables were derived from Landsat 8 Operational Land Imager (OLI) data of 8 May 2017 to predict LAI and k. In-situ measurements of LAI, incident Photosynthetically Active Radiation (PAR) above canopy (I_o) and below canopy (I) were taken using CI-110 Plant Canopy Imager. Canopy gap fraction and k (using Beer-Lambert's equation) were calculated. Random Forest (RF) algorithm was used to predict the spatial distribution of LAI and k using the best predictor variables. The best predictor variables for LAI included band 6 (Short wave infra-red (SWIR) -1) and band 7 (SWIR-2), tasseled cap wetness, Moisture Stress Index (MSI), and Normalized Difference Moisture Index (NDMI). For prediction of k, the best predictor variables were band 6 (SWIR-1) and band 7 (SWIR-2), NDMI, tasseled cap wetness, MSI and Normalized Difference Vegetation Index (NDVI). These variables were selected to generate RF-based models to predict LAI and k. On validation, the models were able to predict LAI with R² = 0.79 and % RMSE = 14.25% and k with R² = 0.77 and % RMSE = 11.98%. The predicted LAI and k followed an inverse relation in accordance with the Beer Lambert's Law. The results showed that RF can be effectively applied to predict the spatial distribution of LAI and k.     

Motor response vigour and visual fixation patterns reflect subjective valuation during intertemporal choice

Value-based decision-making is of central interest in cognitive neuroscience and psychology, as well as in the context of neuropsychiatric disorders characterised by decision-making impairments. Studies examining (neuro-)computational mechanisms underlying choice behaviour typically focus on participants’ decisions. However, there is increasing evidence that option valuation might also be reflected in motor response vigour and eye movements, implicit measures of subjective utility. To examine motor response vigour and visual fixation correlates of option valuation in intertemporal choice, we set up a task where the participants selected an option by pressing a grip force transducer, simultaneously tracking fixation shifts between options. As outlined in our preregistration (https://osf.io/k6jct), we used hierarchical Bayesian parameter estimation to model the choices assuming hyperbolic discounting, compared variants of the softmax and drift diffusion model, and assessed the relationship between response vigour and the estimated model parameters. The behavioural data were best explained by a drift diffusion model specifying a non-linear scaling of the drift rate by the subjective value differences. Replicating previous findings, we found a magnitude effect for temporal discounting, such that higher rewards were discounted less. This magnitude effect was further reflected in motor response vigour, such that stronger forces were exerted in the high vs. the low magnitude condition. Bayesian hierarchical linear regression further revealed higher grip forces, faster response times and a lower number of fixation shifts for trials with higher subjective value differences. An exploratory analysis revealed that subjective value sums across options showed an even more pronounced association with trial-wise grip force amplitudes. Our data suggest that subjective utility or implicit valuation is reflected in motor response vigour and visual fixation patterns during intertemporal choice. Taking into account response vigour might thus provide deeper insight into decision-making, reward valuation and maladaptive changes in these processes, e.g. in the context of neuropsychiatric disorders.     

The dynamics of a discrete population model with threshold

The dynamics of the discrete, scalar population model x_{k + 1} = ax²_k(1 – x_k) are investigated. In addition to density dependence, which has been studied previously by many, this equation models the threshold phenomenon. Some similarities to and differences from previous models are observed. In particular, for large a values this model exhibits chaos which is restricted to a nowhere dense Cantor set of measure 0. In order to explain this, a piecewise linear simplification of the model is considered. Other models exhibiting similar dynamics are also mentioned.     

Female Adolescents with Severe Substance and Conduct Problems Have Substantially Less Brain Gray Matter Volume

ObjectiveStructural neuroimaging studies have demonstrated lower regional gray matter volume in adolescents with severe substance and conduct problems. These research studies, including ours, have generally focused on male-only or mixed-sex samples of adolescents with conduct and/or substance problems. Here we compare gray matter volume between female adolescents with severe substance and conduct problems and female healthy controls of similar ages. Hypotheses: Female adolescents with severe substance and conduct problems will show significantly less gray matter volume in frontal regions critical to inhibition (i.e. dorsolateral prefrontal cortex and ventrolateral prefrontal cortex), conflict processing (i.e., anterior cingulate), valuation of expected outcomes (i.e., medial orbitofrontal cortex) and the dopamine reward system (i.e. striatum).MethodsWe conducted whole-brain voxel-based morphometric comparison of structural MR images of 22 patients (14-18 years) with severe substance and conduct problems and 21 controls of similar age using statistical parametric mapping (SPM) and voxel-based morphometric (VBM8) toolbox. We tested group differences in regional gray matter volume with analyses of covariance, adjusting for age and IQ at p<0.05, corrected for multiple comparisons at whole-brain cluster-level threshold.ResultsFemale adolescents with severe substance and conduct problems compared to controls showed significantly less gray matter volume in right dorsolateral prefrontal cortex, left ventrolateral prefrontal cortex, medial orbitofrontal cortex, anterior cingulate, bilateral somatosensory cortex, left supramarginal gyrus, and bilateral angular gyrus. Considering the entire brain, patients had 9.5% less overall gray matter volume compared to controls.ConclusionsFemale adolescents with severe substance and conduct problems in comparison to similarly aged female healthy controls showed substantially lower gray matter volume in brain regions involved in inhibition, conflict processing, valuation of outcomes, decision-making, reward, risk-taking, and rule-breaking antisocial behavior.     

Exact Multiple Comparisons of Three or More Regression Lines: Pairwise Comparisons and Comparisons with a Control

The problem of finding exact simultaneous confidence bounds for differences in regression models for k groups via the union‐intersection method is considered. The error terms are taken to be iid normal random variables. Under an assumption slightly more general than having identical design matrices for each of the k groups, it is shown that an existing probability point for the multivariate studentized range can be used to find the necessary probability point for pairwise comparisons of regression models. The resulting methods can be used with simple or multiple regression. Under a weaker assumption on the k design matrices that allows more observations to be taken from the control group than from the k‐1 treatment groups, a method is developed for computing exact probability points for comparing the simple linear regression models of the k‐1 groups to that of the control. Within a class of designs, the optimal design for comparisons with a control takes the square root of (k‐1) times as many observations from the control than from each treatment group. The simultaneous confidence bounds for all pairwise differences and for comparisons with a control are much narrower than Spurrier's intervals for all contrasts of k regression lines.     

Dissonance-Based Eating Disorder Prevention Program Reduces Reward Region Response to Thin Models; How Actions Shape Valuation

Research supports the effectiveness of a dissonance-based eating disorder prevention program wherein high-risk young women with body dissatisfaction critique the thin ideal, which reduces pursuit of this ideal, and the theory that dissonance induction contributes to these effects. Based on evidence that dissonance produces attitudinal change by altering neural representation of valuation, we tested whether completing the Body Project would reduce response of brain regions implicated in reward valuation to thin models. Young women with body dissatisfaction were randomized to this intervention or an educational control condition, completing assessments and fMRI scans while viewing images of thin versus average-weight female models at pre and post. Whole brain analyses indicated that, compared to controls, Body Project participants showed greater reductions in caudate response to images of thin versus average-weight models, though participants in the two conditions showed pretest differences in responsivity of other brain regions that might have contributed to this effect. Greater pre-post reductions in caudate and putamen response to thin models correlated with greater reductions in body dissatisfaction. The finding that the Body Project reduces caudate response to thin models provides novel preliminary evidence that this intervention reduces valuation of media images thought to contribute to body dissatisfaction and eating disorders, providing support for the intervention theory by documenting that this intervention alters an objective biological outcome.     

Deconvolution of complex differential scanning calorimetry profiles for protein transitions under kinetic control

A frequent outcome in differential scanning calorimetry (DSC) experiments carried out with large proteins is the irreversibility of the observed endothermic effects. In these cases, DSC profiles are analyzed according to methods developed for temperature-induced denaturation transitions occurring under kinetic control. In the one-step irreversible model (native → denatured) the characteristics of the observed single-peaked endotherm depend on the denaturation enthalpy and the temperature dependence of the reaction rate constant, k. Several procedures have been devised to obtain the parameters that determine the variation of k with temperature. Here, we have elaborated on one of these procedures in order to analyze more complex DSC profiles. Synthetic data for a heat capacity curve were generated according to a model with two sequential reactions; the temperature dependence of each of the two rate constants involved was determined, according to the Eyring's equation, by two fixed parameters. It was then shown that our deconvolution procedure, by making use of heat capacity data alone, permits to extract the parameter values that were initially used. Finally, experimental DSC traces showing two and three maxima were analyzed and reproduced with relative success according to two- and four-step sequential models.     

Sustained fitness gains and variability in fitness trajectories in the long-term evolution experiment with Escherichia coli

Many populations live in environments subject to frequent biotic and abiotic changes. Nonetheless, it is interesting to ask whether an evolving population''s mean fitness can increase indefinitely, and potentially without any limit, even in a constant environment. A recent study showed that fitness trajectories of Escherichia coli populations over 50 000 generations were better described by a power-law model than by a hyperbolic model. According to the power-law model, the rate of fitness gain declines over time but fitness has no upper limit, whereas the hyperbolic model implies a hard limit. Here, we examine whether the previously estimated power-law model predicts the fitness trajectory for an additional 10 000 generations. To that end, we conducted more than 1100 new competitive fitness assays. Consistent with the previous study, the power-law model fits the new data better than the hyperbolic model. We also analysed the variability in fitness among populations, finding subtle, but significant, heterogeneity in mean fitness. Some, but not all, of this variation reflects differences in mutation rate that evolved over time. Taken together, our results imply that both adaptation and divergence can continue indefinitely—or at least for a long time—even in a constant environment.     

Forage selection by Royle's pika (Ochotona roylei) in the western Himalaya,India

Forage selection decisions of herbivores are often complex and dynamic; they are modulated by multiple cues, such as quality, accessibility and abundance of forage plants. To advance the understanding of plant–herbivore interactions, we explored foraging behavior of the alpine lagomorph Royle's pika (Ochotona roylei) in Kedarnath Wildlife Sanctuary, India. Pika bite counts on food plants were recorded through focal sampling in three permanently marked plots. Food plant abundance was recorded by traditional quadrat procedures; forage selection was estimated with Jacob's selection index. Multiple food-choice experiments were conducted to determine whether forage selection criteria would change with variation in food plant composition. We also analyzed leaf morphology and nutrient content in both major food plants and abundantly available non-food plants. Linear regression models were used to test competing hypotheses in order to identify factors governing forage selection. Royle's pika fed primarily on 17 plant species and each forage selection decision was positively modulated by leaf area and negatively modulated by contents of avoided substances (neutral detergent fiber, acid detergent fiber, acid detergent lignin and tannin) in food plants. Furthermore, significance of the interaction term “leaf size × avoided substance” indicates that plants with large leaves were selected only when they had low avoided substance content. The forage selection criteria did not differ between field and laboratory experiments. The parameter estimates of best fit models indicate that the influence of leaf size or amount of avoided substance on pika forage selection was modulated by the magnitude of predation risk.     

Biochemical characterization of tirabrutinib and other irreversible inhibitors of Bruton's tyrosine kinase reveals differences in on - and off - target inhibition

BackgroundBruton's tyrosine kinase (BTK) is a key component of the B-cell receptor (BCR) pathway and a clinically validated target for small molecule inhibitors such as ibrutinib in the treatment of B-cell malignancies. Tirabrutinib (GS-4059/ONO-4059) is a selective, once daily, oral BTK inhibitor with clinical activity against many relapsed/refractory B-cell malignancies.MethodsCovalent binding of tirabrutinib to BTK Cys-481 was assessed by LC-MSMS analysis of BTK using compound as a variable modification search parameter. Inhibition potency of tirabrutinib, ibrutinib, acalabrutinib, and spebrutinib against BTK and related kinases was studied in a dose-dependent manner either after a fixed incubation time (as used in conventional IC₅₀ studies) or following a time course where inactivation kinetics were measured.ResultsTirabrutinib irreversibly and covalently binds to BTK Cys-481. The inactivation efficiency k_inact/K_i was measured and used to calculate selectivity among different kinases for each of the four inhibitors studied. Tirabrutinib showed a k_inact/K_i value of 2.4 ± 0.6 × 10⁴ M⁻¹ s⁻¹ for BTK with selectivity against important off-targets.ConclusionsFor the BTK inhibitors tested in this study, analysis of the inactivation kinetics yielded a more accurate measurement of potency and selectivity than conventional single-time point inhibition measurements. Subtle but clear differences were identified between clinically tested BTK inhibitors which may translate into differentiated clinical efficacy and safety.General significanceThis is the first study that offers a detailed side-by-side comparison of four clinically-relevant BTK inhibitors with respect to their inactivation of BTK and related kinases.     

Protein affinities for small molecules: conceptions and misconceptions.

There is much confusion and error in published treatments of data for multiple binding of ligands (e.g., substrates) by proteins (e.g., enzymes). There is a widespread impression that if the equilibrium binding, r, of ligand, A, by a protein with n sites can be fitted to an equation with two hyperbolic terms, i.e., $\text{r=}\text{n}{}_{\mathrm{\alpha }}\text{k}{}_{\mathrm{\alpha }}\text{(A)}\text{1+k}{}_{\mathrm{\alpha }}\text{(A)}\text{+}\text{n}{}_{\mathrm{\beta }}\text{k}{}_{\mathrm{\beta }}\text{(A)}\text{1+k}{}_{\mathrm{\beta }}\text{(A)}\text{(n}{}_{\mathrm{\alpha }}\text{+n}{}_{\mathrm{\beta }}\text{=n)}$ then k_β and k_β are always the intrinsic binding constants for two sets of sites. Such a conclusion is often incorrect. For example, in many cases, the protein is constituted of identical protomers with initially identical sites for binding ligands, and yet graphical representations of the binding data appear to behave as if the sites are partitioned between two classes. Although the use of a linear combination of hyperbolic terms to represent binding of ligands by macromolecules always yields empirical parameters k_α, k_β … k_λ, they cannot correspond to site binding constants when there are interactions between sites. In some circumstances their values may even be imaginary, complex numbers. On the other hand, stoichiometric binding constants can be assigned unambiguously without making any assumption regarding the nature of the interactions among binding sites. These principles are illustrated concretely by analyses of binding measurements for several different proteins containing two to six sites.     

An algebraic model to determine substrate kinetic parameters by global nonlinear fit of progress curves

We propose that the time course of an enzyme reaction following the Michaelis-Menten reaction mechanism can be conveniently described by a newly derived algebraic equation, which includes the Lambert Omega function. Following Northrop's ideas [Anal. Biochem.321, 457–461, 1983], the integrated rate equation contains the Michaelis constant (K_M) and the specificity number (k_S≡k_cat/K_M$k_{\text{S}}\equiv k_{\text{cat}}/K_{\text{M}}$) as adjustable parameters, but not the turnover number k_cat. A modification of the usual global-fit approach involves a combinatorial treatment of nominal substrate concentrations being treated as fixed or alternately optimized model parameters. The newly proposed method is compared with the standard approach based on the “initial linear region” of the reaction progress curves, followed by nonlinear fit of initial rates to the hyperbolic Michaelis-Menten equation. A representative set of three chelation-enhanced fluorescence EGFR kinase substrates is used for experimental illustration. In one case, both data analysis methods (linear and nonlinear) produced identical results. However, in another test case, the standard method incorrectly reported a finite (50–70 μM) K_M value, whereas the more rigorous global nonlinear fit shows that the K_M is immeasurably high.     

Bayes factor between Student t and Gaussian mixed models within an animal breeding context

The implementation of Student t mixed models in animal breeding has been suggested as a useful statistical tool to effectively mute the impact of preferential treatment or other sources of outliers in field data. Nevertheless, these additional sources of variation are undeclared and we do not know whether a Student t mixed model is required or if a standard, and less parameterized, Gaussian mixed model would be sufficient to serve the intended purpose. Within this context, our aim was to develop the Bayes factor between two nested models that only differed in a bounded variable in order to easily compare a Student t and a Gaussian mixed model. It is important to highlight that the Student t density converges to a Gaussian process when degrees of freedom tend to infinity. The twomodels can then be viewed as nested models that differ in terms of degrees of freedom. The Bayes factor can be easily calculated from the output of a Markov chain Monte Carlo sampling of the complex model (Student t mixed model). The performance of this Bayes factor was tested under simulation and on a real dataset, using the deviation information criterion (DIC) as the standard reference criterion. The two statistical tools showed similar trends along the parameter space, although the Bayes factor appeared to be the more conservative. There was considerable evidence favoring the Student t mixed model for data sets simulated under Student t processes with limited degrees of freedom, and moderate advantages associated with using the Gaussian mixed model when working with datasets simulated with 50 or more degrees of freedom. For the analysis of real data (weight of Pietrain pigs at six months), both the Bayes factor and DIC slightly favored the Student t mixed model, with there being a reduced incidence of outlier individuals in this population.     

Gender differences in sexual jealousy: Adaptionist or social learning explanation?

It has often been speculated, and some evidence suggests, that men and women differ in the elicitation of jealousy: Men appear to be more likely than women to become upset over threats to sexual exclusivity; whereas women are more likely than men to react negatively to potential loss of partner time and attention. Both adaptionist and traditional social learning theories have been used to explain these apparent gender differences. In the present article we outline both explanations and review the relevant psychological literature on gender differences in the elicitation of jealousy. We propose that the difference in men's and women's psychological mechanisms for elicitation of jealousy is best characterized (at least in this culture) as a greater sensitivity among men to cues indicative of possible sexual infidelity rather than greater emotional upset in response to the occurence of extradyadic sex on the part of one's mate. We also provide data testing a traditional social learning explanation for the elicitation of jealousy. Results of a survey administered to college students (N = 223) demonstrate the subtle nature of gender differences in the elicitation of jealousy within this culture. Men and women were most likely to differ (in the hypothesized directions) when items pertained to concern over a partner's potential extradyadic sex rather than to reactions to sexual infidelity that is suspected to have already occurred. Although men reported placing more value on sexual activity within dating relationships and women reported placing more value on emotional intimacy, these ratings of relationship rewards did not explain the gender differences in reported jealousy. Results failed to support a traditional social learning explanation of jealousy and are discussed with regard to evolutionary theory and directions for future research.     

Residence Times and Decay Rates of Downed Woody Debris Biomass/Carbon in Eastern US Forests

A key component in describing forest carbon (C) dynamics is the change in downed dead wood biomass through time. Specifically, there is a dearth of information regarding the residence time of downed woody debris (DWD), which may be reflected in the diversity of wood (for example, species, size, and stage of decay) and site attributes (for example, climate) across the study region of eastern US forests. The empirical assessment of DWD rate of decay and residence time is complicated by the decay process itself, as decomposing logs undergo not only a reduction in wood density over time but also reductions in biomass, shape, and size. Using DWD repeated measurements coupled with models to estimate durations in various stages of decay, estimates of DWD half-life (T _HALF), residence time (T _RES), and decay rate (k constants) were developed for 36 tree species common to eastern US forests. Results indicate that estimates for T _HALF averaged 18 and 10 years for conifers and hardwoods, respectively. Species that exhibited shorter T _HALF tended to display a shorter T _RES and larger k constants. Averages of T _RES ranged from 57 to 124 years for conifers and from 46 to 71 years for hardwoods, depending on the species and methodology for estimating DWD decomposition considered. Decay rate constants (k) increased with increasing temperature of climate zones and ranged from 0.024 to 0.040 for conifers and from 0.043 to 0.064 for hardwoods. These estimates could be incorporated into dynamic global vegetation models to elucidate the role of DWD in forest C dynamics.