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Ecosystem modeling, a pillar of the systems ecology paradigm (SEP), addresses questions such as, how much carbon and nitrogen are cycled within ecological sites, landscapes, or indeed the earth system? Or how are human activities modifying these flows? Modeling, when coupled with field and laboratory studies, represents the essence of the SEP in that they embody accumulated knowledge and generate hypotheses to test understanding of ecosystem processes and behavior. Initially, ecosystem models were primarily used to improve our understanding about how biophysical aspects of ecosystems operate. However, current ecosystem models are widely used to make accurate predictions about how large-scale phenomena such as climate change and management practices impact ecosystem dynamics and assess potential effects of these changes on economic activity and policy making. In sum, ecosystem models embedded in the SEP remain our best mechanism to integrate diverse types of knowledge regarding how the earth system functions and to make quantitative predictions that can be confronted with observations of reality. Modeling efforts discussed are the Century ecosystem model, DayCent ecosystem model, Grassland Ecosystem Model ELM, food web models, Savanna model, agent-based and coupled systems modeling, and Bayesian modeling.
There is mounting evidence for the potential for the natural dietary antioxidant and anti-inflammatory amino acid l-Ergothioneine (ERGO) to prevent or mitigate chronic diseases of aging. This has led to the suggestion that it could be considered a ‘longevity vitamin.’ ERGO is produced in nature only by certain fungi and a few other microbes. Mushrooms are, by far, the leading dietary source of ERGO, but it is found in small amounts throughout the food chain, most likely due to soil-borne fungi passing it on to plants. Because some common agricultural practices can disrupt beneficial fungus–plant root relationships, ERGO levels in foods grown under those conditions could be compromised. Thus, research is needed to further analyse the role agricultural practices play in the availability of ERGO in the human diet and its potential to improve our long-term health.
Mechanistic endophenotypes can inform process models of psychopathology and aid interpretation of genetic risk factors. Smaller total brain and subcortical volumes are associated with attention-deficit hyperactivity disorder (ADHD) and provide clues to its development. This study evaluates whether common genetic risk for ADHD is associated with total brain volume (TBV) and hypothesized subcortical structures in children.
Children 7–15 years old were recruited for a case–control study (N = 312, N = 199 ADHD). Children were assessed with a multi-informant, best-estimate diagnostic procedure and motion-corrected MRI measured brain volumes. Polygenic scores were computed based on discovery data from the Psychiatric Genomics Consortium (N = 19 099 ADHD, N = 34 194 controls) and the ENIGMA + CHARGE consortium (N = 26 577).
ADHD was associated with smaller TBV, and altered volumes of caudate, cerebellum, putamen, and thalamus after adjustment for TBV; however, effects were larger and statistically reliable only in boys. TBV was associated with an ADHD polygenic score [β = −0.147 (−0.27 to −0.03)], and mediated a small proportion of the effect of polygenic risk on ADHD diagnosis (average ACME = 0.0087, p = 0.012). This finding was stronger in boys (average ACME = 0.019, p = 0.008). In addition, we confirm genetic variation associated with whole brain volume, via an intracranial volume polygenic score.
Common genetic risk for ADHD is not expressed primarily as developmental alterations in subcortical brain volumes, but appears to alter brain development in other ways, as evidenced by TBV differences. This is among the first demonstrations of this effect using molecular genetic data. Potential sex differences in these effects warrant further examination.
Kochia is one of the most problematic weeds in the United States. Field studies were conducted in five states (Wyoming, Colorado, Kansas, Nebraska, and South Dakota) over 2 yr (2010 and 2011) to evaluate kochia control with selected herbicides registered in five common crop scenarios: winter wheat, fallow, corn, soybean, and sugar beet to provide insight for diversifying kochia management in crop rotations. Kochia control varied by experimental site such that more variation in kochia control and biomass production was explained by experimental site than herbicide choice within a crop. Kochia control with herbicides currently labeled for use in sugar beet averaged 32% across locations. Kochia control was greatest and most consistent from corn herbicide programs (99%), followed by soybean (96%) and fallow (97%) herbicide programs. Kochia control from wheat herbicide programs was 93%. With respect to the availability of effective herbicide options, glyphosate-resistant kochia control was easiest in corn, soybean, and fallow, followed by wheat; and difficult to manage with herbicides in sugar beet.
Wildlife conservation in the Anthropocene means there is a pressing need to find ways for wildlife and humans to share landscapes. However, this is challenging due to the complex interactions that occur within social-ecological systems (SES). This challenge is exemplified by grey wolf management in the American West, where human governance systems influence where and at what densities carnivores persist, thereby regulating and limiting the impacts of carnivores on both human and ecological communities. Here, we build a SES conceptual framework to disentangle the interdependencies between wolves and humans, including the ecological impacts of wolves and people in anthropogenic landscapes and the socio-economic forces shaping human–wolf interactions now and in the future. A key lesson is that coexistence rests not only on the biophysical capacity of a landscape to be shared by humans and wolves, but also on the capacity for human societies to adjust to and accept some level of conflict with wolves. As such, a holistic view that recognizes humans, our social systems and institutions as key actors and attributes of ecological systems can advance the theory and practice of coexistence.
Drawing on a landscape analysis of existing data-sharing initiatives, in-depth interviews with expert stakeholders, and public deliberations with community advisory panels across the U.S., we describe features of the evolving medical information commons (MIC). We identify participant-centricity and trustworthiness as the most important features of an MIC and discuss the implications for those seeking to create a sustainable, useful, and widely available collection of linked resources for research and other purposes.
Prenatal adversity shapes child neurodevelopment and risk for later mental health problems. The quality of the early care environment can buffer some of the negative effects of prenatal adversity on child development. Retrospective studies, in adult samples, highlight epigenetic modifications as sentinel markers of the quality of the early care environment; however, comparable data from pediatric cohorts are lacking. Participants were drawn from the Maternal Adversity Vulnerability and Neurodevelopment (MAVAN) study, a longitudinal cohort with measures of infant attachment, infant development, and child mental health. Children provided buccal epithelial samples (mean age = 6.99, SD = 1.33 years, n = 226), which were used for analyses of genome-wide DNA methylation and genetic variation. We used a series of linear models to describe the association between infant attachment and (a) measures of child outcome and (b) DNA methylation across the genome. Paired genetic data was used to determine the genetic contribution to DNA methylation at attachment-associated sites. Infant attachment style was associated with infant cognitive development (Mental Development Index) and behavior (Behavior Rating Scale) assessed with the Bayley Scales of Infant Development at 36 months. Infant attachment style moderated the effects of prenatal adversity on Behavior Rating Scale scores at 36 months. Infant attachment was also significantly associated with a principal component that accounted for 11.9% of the variation in genome-wide DNA methylation. These effects were most apparent when comparing children with a secure versus a disorganized attachment style and most pronounced in females. The availability of paired genetic data revealed that DNA methylation at approximately half of all infant attachment-associated sites was best explained by considering both infant attachment and child genetic variation. This study provides further evidence that infant attachment can buffer some of the negative effects of early adversity on measures of infant behavior. We also highlight the interplay between infant attachment and child genotype in shaping variation in DNA methylation. Such findings provide preliminary evidence for a molecular signature of infant attachment and may help inform attachment-focused early intervention programs.
Hill (Twin Research and Human Genetics, Vol. 21, 2018, 84–88) presented a critique of our recently published paper in Cell Reports entitled ‘Large-Scale Cognitive GWAS Meta-Analysis Reveals Tissue-Specific Neural Expression and Potential Nootropic Drug Targets’ (Lam et al., Cell Reports, Vol. 21, 2017, 2597–2613). Specifically, Hill offered several interrelated comments suggesting potential problems with our use of a new analytic method called Multi-Trait Analysis of GWAS (MTAG) (Turley et al., Nature Genetics, Vol. 50, 2018, 229–237). In this brief article, we respond to each of these concerns. Using empirical data, we conclude that our MTAG results do not suffer from ‘inflation in the FDR [false discovery rate]’, as suggested by Hill (Twin Research and Human Genetics, Vol. 21, 2018, 84–88), and are not ‘more relevant to the genetic contributions to education than they are to the genetic contributions to intelligence’.
OBJECTIVES/SPECIFIC AIMS: Negative symptoms of schizophrenia, including motivational deficits, social withdrawal, poverty of speech, decreased emotional reactivity, and psychomotor retardation, have been shown to be most predictive of functional impairment and poor outcome in patients with schizophrenia. Furthermore, these symptoms tend not to be responsive to antipsychotic medications. Inflammation could be one mechanism underlying these difficult to treat symptoms. METHODS/STUDY POPULATION: Three cohorts of patients, reflecting different phases of disease, were studied. One cohort was comprised of a sample of patients with deficit schizophrenia (characterized by primary and enduring negative symptoms; n=17), nondeficit patients (n=39), and healthy controls (n=28). ANOVA and multivariate general linear models were used to compare groups, and linear regression models were used to examine relationships between inflammatory cytokines and negative symptoms. The second cohort was comprised of 80 individuals at clinical high risk for psychosis from the North American Prodromal Longitudinal Study. Linear regression models examined the relationship between baseline inflammatory markers and subsequent negative symptoms at follow-up visits up to 2 years. The third cohort consisted of patients with treatment-resistant schizophrenia (TRS) on clozapine (n=10). Correlations were performed to examine relationships between inflammatory markers and negative symptoms. In a subgroup of patients from this third sample, resting state functional connectivity analyses were performed on fMRI data to explore relationships between inflammatory markers and connectivity in brain reward circuitry. RESULTS/ANTICIPATED RESULTS: In a sample of patients with the deficit syndrome of schizophrenia (n=17), a subtype of the disorder characterized by primary and enduring negative Symptoms, tumor necrosis factor (TNF) was significantly increased relative to nondeficit patients (n=39) and healthy controls (n=28; F2,57=3.51, p=0.036), and predicted total negative symptoms (β=0.31, p=0.012), alogia (β=0.30, p=0.024), and blunted affect (β=0.31, p=0.018) items of the Positive and Negative Symptom Scale in linear regression models while controlling for antipsychotics. In another sample of individuals at clinical-high risk for psychosis (n=80), baseline concentrations of TNF significantly predicted negative symptoms, including anhedonia, apathy, and loss of interest in linear regression models, at the 6-month (β=0.25, p=0.011) and 12-month follow-up (β=0.39, p=0.001). Interleukin (IL)-1 receptor antagonist also predicted these symptoms at the 6-month follow-up (β=0.21, p=0.037). In a third sample (n=10) of patients with TRS treated with clozapine, IL-1β was correlated with passive/apathetic social withdrawal (r=0.657, p=0.039) and disturbance of volition (r=0.686, p=0.029) items of the Positive and Negative Symptom Scale and the global avolition-apathy scores of the Scale for the Assessment of Negative Symptoms (r=0.751, p=0.012). Finally, in a small subsample (n=5) of patients from this TRS cohort for whom we collected fMRI data, we found resting-state functional connectivity from a right nucleus accumbens seed to a cluster in medial prefrontal cortex. We found relationships between higher inflammation and decreased connectivity for TNF (r=−0.64) and CRP (r=−0.89). DISCUSSION/SIGNIFICANCE OF IMPACT: Taken together, these preliminary data show the predicted relationship between inflammatory markers and negative symptoms and demonstrate the reproducibility of TNF and other monocytic-derived cytokines as reliably elevated in schizophrenia and associated with negative symptoms across samples of patients with schizophrenia and individuals at high risk for psychosis. Cytokines may exert their effects via their impact on brain reward circuitry, and could represent novel treatment targets for motivational deficits and negative symptoms of schizophrenia.
Depression can impair the immunogenicity of vaccine administration in adults. Whereas many vaccinations are administered in childhood, it is not known whether adolescent or adult onset depression is associated with impairments in the maintenance of protection of childhood vaccines. This study tested the hypothesis that individuals with adolescent or adult onset mood disorders would display compromised immunity to measles, a target of childhood vaccination.
IgG antibodies to measles were quantified using a solid phase immunoassay in volunteers with bipolar disorder (BD, n = 64, mean age of onset = 16.6 ± 5.6), currently depressed individuals with major depressive disorder (cMDD, n = 85, mean age of onset = 17.9 ± 7.0), remitted individuals with a history of MDD (rMDD, n = 82, mean age of onset = 19.2 ± 8.6), and non-depressed comparison controls (HC, n = 202), all born after the introduction of the measles vaccine in the USA in 1963.
Relative to HC, both the cMDD group (p = 0.021, adjusted odds ratios (OR) = 0.47, confidence interval (CI) = 0.24–0.90), and the rMDD group (p = 0.038, adjusted OR = 0.50, CI = 0.26–0.97) were less likely to test seropositive for measles. Compared with unmedicated MDD participants, currently medicated MDD participants had a longer lifetime duration of illness and were less likely to test seropositive for measles.
Individuals with adolescent or adult onset MDD are less likely to test seropositive for measles. Because lower IgG titers are associated with increased risk of measles infection, MDD may increase the risk and severity of infection possibly because of impaired maintenance of vaccine-related protection from measles.
The Neotoma Paleoecology Database is a community-curated data resource that supports interdisciplinary global change research by enabling broad-scale studies of taxon and community diversity, distributions, and dynamics during the large environmental changes of the past. By consolidating many kinds of data into a common repository, Neotoma lowers costs of paleodata management, makes paleoecological data openly available, and offers a high-quality, curated resource. Neotoma’s distributed scientific governance model is flexible and scalable, with many open pathways for participation by new members, data contributors, stewards, and research communities. The Neotoma data model supports, or can be extended to support, any kind of paleoecological or paleoenvironmental data from sedimentary archives. Data additions to Neotoma are growing and now include >3.8 million observations, >17,000 datasets, and >9200 sites. Dataset types currently include fossil pollen, vertebrates, diatoms, ostracodes, macroinvertebrates, plant macrofossils, insects, testate amoebae, geochronological data, and the recently added organic biomarkers, stable isotopes, and specimen-level data. Multiple avenues exist to obtain Neotoma data, including the Explorer map-based interface, an application programming interface, the neotoma R package, and digital object identifiers. As the volume and variety of scientific data grow, community-curated data resources such as Neotoma have become foundational infrastructure for big data science.
Late Medieval Castles is a companion to Anglo-Norman Castles (2003), a volume that brought together a series of historiographically significant articles on castles and castle-building in the period from the Norman Conquest to the early thirteenth century. The format and themes of the present collection are broadly comparable with the earlier book, but with the focus on those castles dating to the period c.1250–1500.
In the course of bringing Anglo-Norman Castles to publication the somewhat arbitrary cut-off date of c.1225 seemed unsatisfactory for a number of reasons. On a practical level, there were highly relevant articles that could not be included because the subject matter fell outside the chronological range of the volume. A more scholarly concern was the fact that a number of issues pertinent to castle-building in the eleventh and twelfth centuries could not be satisfactorily addressed without reference to subsequent developments in the thirteenth and fourteenth. Allied to this, a focus on Anglo-Norman building (no matter how justifiable in historical terms) does perhaps contribute, albeit unwittingly, to the erroneous idea that the eleventh and twelfth centuries are the most important centuries for castle-building, a time when the ‘true’ castle is to be found, and that the period that follows, particularly after 1300, is something of an anti-climax. The present volume should therefore be seen as a continuation of the broad themes discussed in the introduction to Anglo-Norman Castles, with the aim of pursuing them in a late medieval context.
In the years since 2003 there have been a number of important publications in the field of castle studies, and castles continue to be a source of controversy and to provoke debate. Despite the fact that the availability of some secondary material has been made easier through electronic access, I have been consistently reminded by academic colleagues that a compilation such as this is worthwhile, both for the student reader and those seeking a path into the specialist secondary literature. This author at least also believes that there is value in bringing together in one place a series of important contributions that have defined the subject and which also illustrate a diversity of approaches.