Towards Early Warning Signals for Desertification

Dryland ecosystems cover a large share of the world’s terrestrial surface. Deficiency and spatio-temporal variability of precipitation as well as low vegetation growth rates make dry rangelands prone to degradation, especially under changing climate and intensified land use. Degradation often occurs gradually but sometimes, a sudden and surprising shift from a healthy to a degraded rangeland can be observed, where perennial grasses are lost, and bare soil is exposed. If such changes are sudden and irreversible, they are coined a tipping point. Due to their abrupt appearance, it is a great challenge to discover early warning signals that precede the regime shifts. Theory predicts that variance and autocorrelation in state conditions could be used as early warning signals. However, these theoretical assumptions have rarely been tested in real ecosystems. Here, we use a data-based approach to contribute to filling this research gap using desertification processes in a semi-arid rangeland as a case study. In order to test the applicability of theoretical early warning signals for tipping points, we looked at a dataset from Widou, Senegal, that includes annual observations of rainfall, grazing intensity and primary production from 1981 – 2007. We analysed productivity-based metrics, such as rain use efficiency, in order to detect patterns that may precede a shift between alternate stable states. Strong signals of a regime shift were detected that were expressed in a sudden alteration of species composition and general decline of productivity after a drought. However, we did not find any changes in the theoretically proposed parameters that may reflect early warning signals for a critical transition, i.e. the regime shift was essentially unpredictable. We suggest that while the theory around tipping points and early recognition thereof may be robust, the applicability of theoretical concepts to the real world may be challenging

Functional Responses of South African Rangelands in Contrasting Tenure Systems

Land degradation in South African rangelands has frequently been studied in the context of tenure systems, because both governmental policy and range management practices were historically implemented in contrasting forms. We compared the functional response of vegetation along grazing gradients between a communal (CU) and commercial (CO) farming areas. One transect was established per farm from the waterpoint to a mid-field position. Six equally spaced plots (5 m × 5 m) were set up along each transect. Using a taxon-free sampling procedure, we recorded the response of 15 community-aggregated plant functional traits (CPFT) in: (1) mature standing biomass; and (2) after four weeks’ regrowth following clipping. Additionally, species identity was recorded. Grazing on CU was continuous and stocking rate not controlled, while CO applied rotational grazing with recommended stocking rates. From the results, CPFT differences were not significant (Student’s t-test, P \u3c 0.01) between tenure systems. A principal component analysis of CPFT showed largely overlapping functional responses in the two tenure systems in the case of mature standing biomass, while the functional response of regrowing vegetation was clearly separated in the ordination space. Communal rangelands had twice the species richness of commercial farms.We concluded that, from a functional perspective, communities under different tenure systems were similar. However, the functional response of vegetation regrowth might be different as well as the ecological services provided (biodiversity)

APC/CCdh1 is required for the termination of chromosomal passenger complex activity upon mitotic exit

During mitosis, the chromosomal passenger complex (CPC) ensures the faithful transmission of the genome. The CPC is composed of the enzymatic component Aurora B (AURKB) and the three regulatory and targeting components borealin, INCENP, and survivin (also known as BIRC5). Although the CPC is known to be involved in diverse mitotic events, it is still unclear how CPC function terminates after mitosis. Here we show that borealin is ubiquitylated by the anaphase promoting complex/cyclosome (APC/C) and its cofactor Cdh1 (also known as FZR1) and is subsequently degraded in G1 phase. Cdh1 binds to regions within the N terminus of borealin that act as a non-canonical degron. Aurora B has also been shown previously to be degraded by the APC/CCdh1 from late mitosis to G1. Indeed, Cdh1 depletion sustains an Aurora B activity with stable levels of borealin and Aurora B throughout the cell cycle, and causes reduced efficiency of DNA replication after release from serum starvation. Notably, inhibition of Aurora B kinase activity improves the efficiency of DNA replication in Cdh1-depleted cells. We thus propose that APC/CCdh1 terminates CPC activity upon mitotic exit and thereby contributes to proper control of DNA replication

Two interlinked bistable switches govern mitotic control in mammalian cells

Distinct protein phosphorylation levels in interphase and M phase require tight regulation of Cdk1 activity [1, 2]. A bistable switch, based on positive feedback in the Cdk1 activation loop, has been proposed to generate different thresholds for transitions between these cell-cycle states [3, 4, 5]. Recently, the activity of the major Cdk1-counteracting phosphatase, PP2A:B55, has also been found to be bistable due to Greatwall kinase-dependent regulation [6]. However, the interplay of the regulation of Cdk1 and PP2A:B55 in vivo remains unexplored. Here, we combine quantitative cell biology assays with mathematical modeling to explore the interplay of mitotic kinase activation and phosphatase inactivation in human cells. By measuring mitotic entry and exit thresholds using ATP-analog-sensitive Cdk1 mutants, we find evidence that the mitotic switch displays hysteresis and bistability, responding differentially to Cdk1 inhibition in the mitotic and interphase states. Cdk1 activation by Wee1/Cdc25 feedback loops and PP2A:B55 inactivation by Greatwall independently contributes to this hysteretic switch system. However, elimination of both Cdk1 and PP2A:B55 inactivation fully abrogates bistability, suggesting that hysteresis is an emergent property of mutual inhibition between the Cdk1 and PP2A:B55 feedback loops. Our model of the two interlinked feedback systems predicts an intermediate but hidden steady state between interphase and M phase. This could be verified experimentally by Cdk1 inhibition during mitotic entry, supporting the predictive value of our model. Furthermore, we demonstrate that dual inhibition of Wee1 and Gwl kinases causes loss of cell-cycle memory and synthetic lethality, which could be further exploited therapeutically

Observing many body effects on lepton pair production from low mass enhancement and flow at RHIC and LHC energies

The $\rho$ spectral function at finite temperature calculated using the real-time formalism of thermal field theory is used to evaluate the low mass dilepton spectra. The analytic structure of the $\rho$ propagator is studied and contributions to the dilepton yield in the region below the bare $\rho$ peak from the different cuts in the spectral function are discussed. The space-time integrated yield shows significant enhancement in the region below the bare $\rho$ peak in the invariant mass spectra. It is argued that the variation of the inverse slope of the transverse mass ($M_T$) distribution can be used as an efficient tool to predict the presence of two different phases of the matter during the evolution of the system. Sensitivity of the effective temperature obtained from the slopes of the $M_T$ spectra to the medium effects are studied

Estimation of Activity Related Energy Expenditure and Resting Metabolic Rate in Freely Moving Mice from Indirect Calorimetry Data

Physical activity (PA) is a main determinant of total energy expenditure (TEE) and has been suggested to play a key role in body weight regulation. However, thus far it has been challenging to determine what part of the expended energy is due to activity in freely moving subjects. We developed a computational method to estimate activity related energy expenditure (AEE) and resting metabolic rate (RMR) in mice from activity and indirect calorimetry data. The method is based on penalised spline regression and takes the time dependency of the RMR into account. In addition, estimates of AEE and RMR are corrected for the regression dilution bias that results from inaccurate PA measurements. We evaluated the performance of our method based on 500 simulated metabolic chamber datasets and compared it to that of conventional methods. It was found that for a sample time of 10 minutes the penalised spline model estimated the time-dependent RMR with 1.7 times higher accuracy than the Kalman filter and with 2.7 times higher accuracy than linear regression. We assessed the applicability of our method on experimental data in a case study involving high fat diet fed male and female C57Bl/6J mice. We found that TEE in male mice was higher due to a difference in RMR while AEE levels were similar in both groups, even though female mice were more active. Interestingly, the higher activity did not result in a difference in AEE because female mice had a lower caloric cost of activity, which was likely due to their lower body weight. In conclusion, TEE decomposition by means of penalised spline regression provides robust estimates of the time-dependent AEE and RMR and can be applied to data generated with generic metabolic chamber and indirect calorimetry set-ups

Analysis of shared heritability in common disorders of the brain

ience, this issue p. eaap8757 Structured Abstract INTRODUCTION Brain disorders may exhibit shared symptoms and substantial epidemiological comorbidity, inciting debate about their etiologic overlap. However, detailed study of phenotypes with different ages of onset, severity, and presentation poses a considerable challenge. Recently developed heritability methods allow us to accurately measure correlation of genome-wide common variant risk between two phenotypes from pools of different individuals and assess how connected they, or at least their genetic risks, are on the genomic level. We used genome-wide association data for 265,218 patients and 784,643 control participants, as well as 17 phenotypes from a total of 1,191,588 individuals, to quantify the degree of overlap for genetic risk factors of 25 common brain disorders. RATIONALE Over the past century, the classification of brain disorders has evolved to reflect the medical and scientific communities' assessments of the presumed root causes of clinical phenomena such as behavioral change, loss of motor function, or alterations of consciousness. Directly observable phenomena (such as the presence of emboli, protein tangles, or unusual electrical activity patterns) generally define and separate neurological disorders from psychiatric disorders. Understanding the genetic underpinnings and categorical distinctions for brain disorders and related phenotypes may inform the search for their biological mechanisms. RESULTS Common variant risk for psychiatric disorders was shown to correlate significantly, especially among attention deficit hyperactivity disorder (ADHD), bipolar disorder, major depressive disorder (MDD), and schizophrenia. By contrast, neurological disorders appear more distinct from one another and from the psychiatric disorders, except for migraine, which was significantly correlated to ADHD, MDD, and Tourette syndrome. We demonstrate that, in the general population, the personality trait neuroticism is significantly correlated with almost every psychiatric disorder and migraine. We also identify significant genetic sharing between disorders and early life cognitive measures (e.g., years of education and college attainment) in the general population, demonstrating positive correlation with several psychiatric disorders (e.g., anorexia nervosa and bipolar disorder) and negative correlation with several neurological phenotypes (e.g., Alzheimer's disease and ischemic stroke), even though the latter are considered to result from specific processes that occur later in life. Extensive simulations were also performed to inform how statistical power, diagnostic misclassification, and phenotypic heterogeneity influence genetic correlations. CONCLUSION The high degree of genetic correlation among many of the psychiatric disorders adds further evidence that their current clinical boundaries do not reflect distinct underlying pathogenic processes, at least on the genetic level. This suggests a deeply interconnected nature for psychiatric disorders, in contrast to neurological disorders, and underscores the need to refine psychiatric diagnostics. Genetically informed analyses may provide important "scaffolding" to support such restructuring of psychiatric nosology, which likely requires incorporating many levels of information. By contrast, we find limited evidence for widespread common genetic risk sharing among neurological disorders or across neurological and psychiatric disorders. We show that both psychiatric and neurological disorders have robust correlations with cognitive and personality measures. Further study is needed to evaluate whether overlapping genetic contributions to psychiatric pathology may influence treatment choices. Ultimately, such developments may pave the way toward reduced heterogeneity and improved diagnosis and treatment of psychiatric disorders

The handbook for standardized field and laboratory measurements in terrestrial climate change experiments and observational studies (ClimEx)

1. Climate change is a world‐wide threat to biodiversity and ecosystem structure, functioning and services. To understand the underlying drivers and mechanisms, and to predict the consequences for nature and people, we urgently need better understanding of the direction and magnitude of climate change impacts across the soil–plant–atmosphere continuum. An increasing number of climate change studies are creating new opportunities for meaningful and high‐quality generalizations and improved process understanding. However, significant challenges exist related to data availability and/or compatibility across studies, compromising opportunities for data re‐use, synthesis and upscaling. Many of these challenges relate to a lack of an established ‘best practice’ for measuring key impacts and responses. This restrains our current understanding of complex processes and mechanisms in terrestrial ecosystems related to climate change. 2. To overcome these challenges, we collected best‐practice methods emerging from major ecological research networks and experiments, as synthesized by 115 experts from across a wide range of scientific disciplines. Our handbook contains guidance on the selection of response variables for different purposes, protocols for standardized measurements of 66 such response variables and advice on data management. Specifically, we recommend a minimum subset of variables that should be collected in all climate change studies to allow data re‐use and synthesis, and give guidance on additional variables critical for different types of synthesis and upscaling. The goal of this community effort is to facilitate awareness of the importance and broader application of standardized methods to promote data re‐use, availability, compatibility and transparency. We envision improved research practices that will increase returns on investments in individual research projects, facilitate second‐order research outputs and create opportunities for collaboration across scientific communities. Ultimately, this should significantly improve the quality and impact of the science, which is required to fulfil society's needs in a changing world