Psychological processes mediating the association between developmental trauma and specific psychotic symptoms in adults: a systematic review and meta‐analysis

Experiencing psychological trauma during childhood and/or adolescence is associated with an increased risk of psychosis in adulthood. However, we lack a clear knowledge of how developmental trauma induces vulnerability to psychotic symptoms. Understanding the psychological processes involved in this association is crucial to the development of preventive interventions and improved treatments. We sought to systematically review the literature and combine findings using meta‐analytic techniques to establish the potential roles of psychological processes in the associations between developmental trauma and specific psychotic experiences (i.e., hallucinations, delusions and paranoia). Twenty‐two studies met our inclusion criteria. We found mediating roles of dissociation, emotional dysregulation and post‐traumatic stress disorder (PTSD) symptoms (avoidance, numbing and hyperarousal) between developmental trauma and hallucinations. There was also evidence of a mediating role of negative schemata, i.e. mental constructs of meanings, between developmental trauma and delusions as well as paranoia. Many studies to date have been of poor quality, and the field is limited by mostly cross‐sectional research. Our findings suggest that there may be distinct psy­chological pathways from developmental trauma to psychotic phenomena in adulthood. Clinicians should carefully ask people with psychosis about their history of developmental trauma, and screen patients with such a history for dissociation, emotional dysregulation and PTSD symptoms. Well conducted research with prospective designs, including neurocognitive assessment, is required in order to fully understand the biopsychosocial mechanisms underlying the association between developmental trauma and psychosis.     

Genomic and signalling pathway characterization of the NZM panel of melanoma cell lines: A valuable model for studying the impact of genetic diversity in melanoma

Melanoma is a disease associated with a very high mutation burden and thus the possibility of a diverse range of oncogenic mechanisms that allow it to evade therapeutic interventions and the immune system. Here, we describe the characterization of a panel of 102 cell lines from metastatic melanomas (the NZM lines), including using whole‐exome and RNA sequencing to analyse genetic variants and gene expression changes in a subset of this panel. Lines possessing all major melanoma genotypes were identified, and hierarchical clustering of gene expression profiles revealed four broad subgroups of cell lines. Immunogenotyping identified a range of HLA haplotypes as well as expression of neoantigens and cancer–testis antigens in the lines. Together, these characteristics make the NZM panel a valuable resource for cell‐based, immunological and xenograft studies to better understand the diversity of melanoma biology and the responses of melanoma to therapeutic interventions.     

Molecular dating for phylogenies containing a mix of populations and species by using Bayesian and RelTime approaches

Simultaneous molecular dating of population and species divergences is essential in many biological investigations, including phylogeography, phylodynamics and species delimitation studies. In these investigations, multiple sequence alignments consist of both intra‐ and interspecies samples (mixed samples). As a result, the phylogenetic trees contain interspecies, interpopulation and within‐population divergences. Bayesian relaxed clock methods are often employed in these analyses, but they assume the same tree prior for both inter‐ and intraspecies branching processes and require specification of a clock model for branch rates (independent vs. autocorrelated rates models). We evaluated the impact of a single tree prior on Bayesian divergence time estimates by analysing computer‐simulated data sets. We also examined the effect of the assumption of independence of evolutionary rate variation among branches when the branch rates are autocorrelated. Bayesian approach with coalescent tree priors generally produced excellent molecular dates and highest posterior densities with high coverage probabilities. We also evaluated the performance of a non‐Bayesian method, RelTime, which does not require the specification of a tree prior or a clock model. RelTime's performance was similar to that of the Bayesian approach, suggesting that it is also suitable to analyse data sets containing both populations and species variation when its computational efficiency is needed.     

Climatic Factors Influencing the Anthrax Outbreak of 2016 in Siberia,Russia

In 2016, an outbreak of anthrax killing thousands of reindeer and affecting dozens of humans occurred on the Yamal peninsula, Northwest Siberia, after 70 years of epidemiological situation without outbreaks. The trigger of the outbreak has been ascribed to the activation of spores due to permafrost thaw that was accelerated during the summer heat wave. The focus of our study is on the dynamics of local environmental factors in connection with the observed anthrax revival. We show that permafrost was thawing rapidly for already 6 years before the outbreak. During 2011–2016, relatively warm years were followed by cold years with a thick snow cover, preventing freezing of the soil. Furthermore, the spread of anthrax was likely intensified by an extremely dry summer of 2016. Concurrent with the long-term decreasing trend in the regional annual precipitation, the rainfall in July 2016 was less than 10% of its 30-year mean value. We conclude that epidemiological situation of anthrax in the previously contaminated Arctic regions requires monitoring of climatic factors such as warming and precipitation extremes.

     

Direct response of tree growth to soil water and its implications for terrestrial carbon cycle modelling

Wood growth constitutes the main process for long‐term atmospheric carbon sequestration in vegetation. However, our understanding of the process of wood growth and its response to environmental drivers is limited. Current dynamic global vegetation models (DGVMs) are mainly photosynthesis‐driven and thus do not explicitly include a direct environmental effect on tree growth. However, physiological evidence suggests that, to realistically model vegetation carbon allocation under increased climatic stressors, it is crucial to treat growth responses independently from photosynthesis. A plausible growth response function suitable for global simulations in DGVMs has been lacking. Here, we present the first soil water‐growth response function and parameter range for deciduous and evergreen conifers. The response curve was calibrated against European larch and Norway spruce in a dry temperate forest in the Swiss Alps. We present a new data‐driven approach based on a combination of tree ring width (TRW) records, growing season length and simulated subdaily soil hydrology to parameterize ring width increment simulations. We found that a simple linear response function, with an intercept at zero moisture stress, used in growth simulations reproduced 62.3% and 59.4% of observed TRW variability for larch and spruce respectively and, importantly, the response function slope was much steeper than literature values for soil moisture effects on photosynthesis and stomatal conductance. Specifically, we found stem growth stops at soil moisture potentials of ?0.47 MPa for larch and ?0.66 MPa for spruce, whereas photosynthesis in trees continues down to ?1.2 MPa or lower, depending on species and measurement method. These results are strong evidence that the response functions of source and sink processes are indeed very different in trees, and need to be considered separately to correctly assess vegetation responses to environmental change. The results provide a parameterization for the explicit representation of growth responses to soil water in vegetation models.     

Sixteen hundred years of increasing tree cover prior to modern deforestation in Southern Amazon and Central Brazilian savannas

Tropical ecosystems are under increasing pressure from land‐use change and deforestation. Changes in tropical forest cover are expected to affect carbon and water cycling with important implications for climatic stability at global scales. A major roadblock for predicting how tropical deforestation affects climate is the lack of baseline conditions (i.e., prior to human disturbance) of forest–savanna dynamics. To address this limitation, we developed a long‐term analysis of forest and savanna distribution across the Amazon–Cerrado transition of central Brazil. We used soil organic carbon isotope ratios as a proxy for changes in woody vegetation cover over time in response to fluctuations in precipitation inferred from speleothem oxygen and strontium stable isotope records. Based on stable isotope signatures and radiocarbon activity of organic matter in soil profiles, we quantified the magnitude and direction of changes in forest and savanna ecosystem cover. Using changes in tree cover measured in 83 different locations for forests and savannas, we developed interpolation maps to assess the coherence of regional changes in vegetation. Our analysis reveals a broad pattern of woody vegetation expansion into savannas and densification within forests and savannas for at least the past ~1,600 years. The rates of vegetation change varied significantly among sampling locations possibly due to variation in local environmental factors that constrain primary productivity. The few instances in which tree cover declined (7.7% of all sampled profiles) were associated with savannas under dry conditions. Our results suggest a regional increase in moisture and expansion of woody vegetation prior to modern deforestation, which could help inform conservation and management efforts for climate change mitigation. We discuss the possible mechanisms driving forest expansion and densification of savannas directly (i.e., increasing precipitation) and indirectly (e.g., decreasing disturbance) and suggest future research directions that have the potential to improve climate and ecosystem models.