Defining null expectations for animal site fidelity

Site fidelity—the tendency to return to previously visited locations—is widespread across taxa. Returns may be driven by several mechanisms, including memory, habitat selection, or chance; however, pattern-based definitions group different generating mechanisms under the same label of ‘site fidelity’, often assuming memory as the main driver. We propose an operational definition of site fidelity as patterns of return that deviate from a null expectation derived from a memory-free movement model. First, using agent-based simulations, we show that without memory, intrinsic movement characteristics and extrinsic landscape characteristics are key determinants of return patterns and that even random movements may generate substantial probabilities of return. Second, we illustrate how to implement our framework empirically to establish ecologically meaningful, system-specific null expectations for site fidelity. Our approach provides a conceptual and operational framework to test hypotheses on site fidelity across systems and scales.     

Framework for validation and implementation of in vitro toxicity tests

Summary The development and application of in vitro alternatives designed to reduce or replace the use of animals, or to lessen the distress and discomfort of laboratory animals, is a rapidly developing trend in toxicology. However, at present there is no formal administrative process to organize, coordinate, or evaluate validation activities. A framework capable of fostering the validation of new methods is essential for the effective transfer of new technologic developments from the research laboratory into practical use. This committee has identified four essential validation resources: chemical bank(s), cell and tissue banks, a data bank, and reference laboratories. The creation of a Scientific Advisory Board composed of experts in the various aspects and endpoints of toxicity testing, and representing the academic, industrial, and regulatory communities, is recommended. Test validation acceptance is contingent on broad buy-in by disparate groups in the scientific community—academics, industry, and government. This is best achieved by early and frequent communication among parties and agreement on common goals. It is hoped that the creation of a validation infrastructure composed of the elements described in this report will facilitate scientific acceptance and utilization of alternative methodologies and speed implementation of replacement, reduction, and refinement alternatives in toxicity testing.     

Design considerations for two-stage enrichment clinical trials

When there is a predictive biomarker, enrichment can focus the clinical trial on a benefiting subpopulation. We describe a two-stage enrichment design, in which the first stage is designed to efficiently estimate a threshold and the second stage is a “phase III-like” trial on the enriched population. The goal of this paper is to explore design issues: sample size in Stages 1 and 2, and re-estimation of the Stage 2 sample size following Stage 1. By treating these as separate trials, we can gain insight into how the predictive nature of the biomarker specifically impacts the sample size. We also show that failure to adequately estimate the threshold can have disastrous consequences in the second stage. While any bivariate model could be used, we assume a continuous outcome and continuous biomarker, described by a bivariate normal model. The correlation coefficient between the outcome and biomarker is the key to understanding the behavior of the design, both for predictive and prognostic biomarkers. Through a series of simulations we illustrate the impact of model misspecification, consequences of poor threshold estimation, and requisite sample sizes that depend on the predictive nature of the biomarker. Such insight should be helpful in understanding and designing enrichment trials.     

Model misspecification and bias for inverse probability weighting estimators of average causal effects

Commonly used semiparametric estimators of causal effects specify parametric models for the propensity score (PS) and the conditional outcome. An example is an augmented inverse probability weighting (IPW) estimator, frequently referred to as a doubly robust estimator, because it is consistent if at least one of the two models is correctly specified. However, in many observational studies, the role of the parametric models is often not to provide a representation of the data-generating process but rather to facilitate the adjustment for confounding, making the assumption of at least one true model unlikely to hold. In this paper, we propose a crude analytical approach to study the large-sample bias of estimators when the models are assumed to be approximations of the data-generating process, namely, when all models are misspecified. We apply our approach to three prototypical estimators of the average causal effect, two IPW estimators, using a misspecified PS model, and an augmented IPW (AIPW) estimator, using misspecified models for the outcome regression (OR) and the PS. For the two IPW estimators, we show that normalization, in addition to having a smaller variance, also offers some protection against bias due to model misspecification. To analyze the question of when the use of two misspecified models is better than one we derive necessary and sufficient conditions for when the AIPW estimator has a smaller bias than a simple IPW estimator and when it has a smaller bias than an IPW estimator with normalized weights. If the misspecification of the outcome model is moderate, the comparisons of the biases of the IPW and AIPW estimators show that the AIPW estimator has a smaller bias than the IPW estimators. However, all biases include a scaling with the PS-model error and we suggest caution in modeling the PS whenever such a model is involved. For numerical and finite sample illustrations, we include three simulation studies and corresponding approximations of the large-sample biases. In a dataset from the National Health and Nutrition Examination Survey, we estimate the effect of smoking on blood lead levels.     

Substance use disorders: a comprehensive update of classification,epidemiology, neurobiology,clinical aspects,treatment and prevention

Substance use disorders (SUDs) are highly prevalent and exact a large toll on individuals’ health, well-being, and social functioning. Long-lasting changes in brain networks involved in reward, executive function, stress reactivity, mood, and self-awareness underlie the intense drive to consume substances and the inability to control this urge in a person who suffers from addiction (moderate or severe SUD). Biological (including genetics and developmental life stages) and social (including adverse childhood experiences) determinants of health are recognized factors that contribute to vulnerability for or resilience against developing a SUD. Consequently, prevention strategies that target social risk factors can improve outcomes and, when deployed in childhood and adolescence, can decrease the risk for these disorders. SUDs are treatable, and evidence of clinically significant benefit exists for medications (in opioid, nicotine and alcohol use disorders), behavioral therapies (in all SUDs), and neuromodulation (in nicotine use disorder). Treatment of SUDs should be considered within the context of a Chronic Care Model, with the intensity of intervention adjusted to the severity of the disorder and with the concomitant treatment of comorbid psychiatric and physical conditions. Involvement of health care providers in detection and management of SUDs, including referral of severe cases to specialized care, offers sustainable models of care that can be further expanded with the use of telehealth. Despite advances in our understanding and management of SUDs, individuals with these conditions continue to be stigmatized and, in some countries, incarcerated, highlighting the need to dismantle policies that perpetuate their criminalization and instead develop policies to ensure support and access to prevention and treatment.     

ORGANIZATION OF PREDATOR-PREY COMMUNITIES AS AN EVOLUTIONARY GAME

We consider a simple predator-prey model of coevolution. By allowing coevolution both within and between trophic levels the model breaks the traditional dichotomy between coevolution among competitors and coevolution between a prey and its predator. By allowing the diversity of prey and predator species to emerge as a property of the evolutionarily stable strategies (ESS), the model breaks another constraint of most approaches to coevolution that consider as fixed the number of coevolving species. The number of species comprising the ESS is influenced by a parameter that determines the predator's niche breadth. Depending upon the parameter's value the ESS may contain: 1) one prey and one predator species, 2) two prey and one predator, 3) two prey and two predators, 4) three prey and two predators, 5) three prey and three predators, etc. Evolutionarily, these different ESSs all emerge from the same model. Ecologically, however, these ESSs result in very different patterns of community organization. In some communities the predator species are ecologically keystone in that their removal results in extinctions among the prey species. In others, the removal of a predator species has no significant impact on the prey community. These varied ecological roles for the predator species contrasts sharply with the essential evolutionary role of the predators in promoting prey species diversity. The ghost of predation past in which a predator's insignificant ecological role obscures its essential evolutionary role may be a frequent property of communities of predator and prey.     

Mercury translocation in and evaporation from soil. III. quantification of evaporation of mercury from podzolized soil profiles treated with Hg Cl

Mercury evaporation from undisturbed iron‐humus podzol lysimeters was measured over 3 months after treatment with HgCl₂ spiked with radioactive ²⁰³Hg. The relative evaporation rate from HgCl₂ treated soils followed the sum of two exponential functions. Because evaporation asymptotically approaches zero with time, the integral of the fit curve represents the evaporative loss in percent of atmospheric deposition. For the soil investigated, about 5% of atmospheric Hg deposition was reemitted into the atmosphere. It is hypothesized that mercury evaporation can decrease the leaching of mercury in and from soil significantly; this effect is probably increasing with decreasing rain acidity or soil acidity. Mercury deposited as soluble salt remains susceptible to reemission to air for 300 d after incorporation into the soil matrix. Indications are found that Hg evaporation from soils in geological background areas predominantly derives from recent atmospheric Hg deposition and not from geological sources.     

Exploiting the complete yeast genome sequence

The completion of the genome sequence of the budding yeast Saccharomyces cerevisiae marks the dawn of an exciting new era in eukaryotic biology that will bring with it a new understanding of yeast, other model organisms, and human beings. This body of sequence data benefits yeast researchers by obviating the need for piecemeal sequencing of genes, and allows researchers working with other organisms to tap into experimental advantages inherent in the yeast system and learn from functionally characterized yeast gene products which are their proteins of interest. In addition, the yeast post-genome sequence era is serving as a testing ground for powerful new technologies, and proven experimental approaches are being applied for the first time in a comprehensive fashion on a complete eukaryotic gene repertoire.     

Modeling of the H-reflex facilitation during ramp and hold contractions

Healthy subjects were asked to make a voluntary ramp and hold contraction. The duration of the ramp stage was 500 ms, and the torque increment in this period was set to 15 Nm. The contraction was made from a relaxed and from a 5 Nm background torque situation. Hoffmann (H-) reflexes were elicited during the voluntary contraction, mostly with 100 ms intervals. These experiments showed an increase (facilitation) in the H-reflex before the torque or the EMG started to increase. This facilitation of the H-reflex remained during all the stages of the voluntary movement and declined to normal levels again only at the very end of the hold phase, which lasted for one second. This specific pattern of facilitation during a voluntary contraction was modeled using a modeling language, that is specifically designed to calculate neuronal systems with a high degree of reality (Ekeberg et al., 1991). Our model consisted of a motoneuron pool with 200 neurons connected to an EMG-model of the human soleus muscle and an extra group of higher-level neurons for controlling the amount of decrease of presynaptic inhibition. The model was used to simulate the observed modulation of the H-reflex with both a presynaptic and a postsynaptic mechanism. Simulations showed that a continuous change in the descending control signals is needed to make the model based on postsynaptic mechanism fit with the experimental data, whereas no extra control from the CNS over the excitatory drive to the motoneuron pool is needed when the decrease of presynaptic inhibition mechanism is applied.