On the need to consider wood formation processes in global vegetation models and a suggested approach

Dynamic global vegetation models are key tools for interpreting and forecasting the responses of terrestrial ecosystems to climatic variation and other drivers. They estimate plant growth as the outcome of the supply of carbon through photosynthesis. However, growth is itself under direct control, and not simply controlled by the amount of available carbon. Therefore predictions by current photosynthesis driven models of large increases in future vegetation biomass due to increasing concentrations of atmospheric CO2 may be significant over-estimations. We describe how current understanding of wood formation can be used to reformulate global vegetation models, with potentially major implications for their behaviour

The association between clinical parameters recorded at vet gates during Fédération Equestre International endurance rides and the imminent risk of elimination

Background: Endurance competitions over distances of 80 to 160 km are required by Fédération Equestre Internationale (FEI) rules to be divided into between three and six stages, known as “loops”. Veterinary inspections, designed to ensure horse welfare, are conducted at the end of each loop, with details recorded on a separate “vet card” for each horse. Objectives: To identify risk factors recorded on vet cards that were associated with elimination at subsequent loops. Study design: Retrospective cohort study. Methods: Data relating to 3,213 horse starts worldwide in international (CEI) events during 2014 were analysed. Descriptive statistics and univariable logistic regression to identify risk factors for potential inclusion in the final multivariable logistic regression models. Models were constructed stepwise using backwards‐removal and assessed using the Bayesian information criterion. Results: Risk factors were identified, which would allow an “in‐ride” risk profile to be constructed for each horse which evolves as the horse progresses through the ride. Some risk factors such as abnormal gait and high heart rate were found to be repeatedly associated with imminent failure to qualify. Main limitations: This is a relatively small study in terms of cohort size, based on the data that were available at the time of the study. Although comprehensive ride history data were also available for each horse via the main FEI database, training data was not. Conclusions: By identifying risk factors observed during the veterinary inspections at the end of a loop that are strongly associated with elimination at the end of the next or subsequent loops, these results provide an evidence‐base for educational initiatives and regulatory changes that will inform the way veterinary delegates use veterinary inspections to help identify horses at risk of imminent FTQ

Impact of Orientational Glass Formation and Local Strain on Photo-Induced Halide Segregation in Hybrid Metal-Halide Perovskites.

Band gap tuning of hybrid metal-halide perovskites by halide substitution holds promise for tailored light absorption in tandem solar cells and emission in light-emitting diodes. However, the impact of halide substitution on the crystal structure and the fundamental mechanism of photo-induced halide segregation remain open questions. Here, using a combination of temperature-dependent X-ray diffraction and calorimetry measurements, we report the emergence of a disorder- and frustration-driven orientational glass for a wide range of compositions in CH3NH3Pb(Cl x Br1-x )3. Using temperature-dependent photoluminescence measurements, we find a correlation between halide segregation under illumination and local strains from the orientational glass. We observe no glassy behavior in CsPb(Cl x Br1-x )3, highlighting the importance of the A-site cation for the structure and optoelectronic properties. Using first-principles calculations, we identify the local preferential alignment of the organic cations as the glass formation mechanism. Our findings rationalize the superior photostability of mixed-cation metal-halide perovskites and provide guidelines for further stabilization strategies

A machine learning algorithm to differentiate bipolar disorder from major depressive disorder using an online mental health questionnaire and blood biomarker data.

The vast personal and economic burden of mood disorders is largely caused by their under- and misdiagnosis, which is associated with ineffective treatment and worsening of outcomes. Here, we aimed to develop a diagnostic algorithm, based on an online questionnaire and blood biomarker data, to reduce the misdiagnosis of bipolar disorder (BD) as major depressive disorder (MDD). Individuals with depressive symptoms (Patient Health Questionnaire-9 score ≥5) aged 18-45 years were recruited online. After completing a purpose-built online mental health questionnaire, eligible participants provided dried blood spot samples for biomarker analysis and underwent the World Health Organization World Mental Health Composite International Diagnostic Interview via telephone, to establish their mental health diagnosis. Extreme Gradient Boosting and nested cross-validation were used to train and validate diagnostic models differentiating BD from MDD in participants who self-reported a current MDD diagnosis. Mean test area under the receiver operating characteristic curve (AUROC) for separating participants with BD diagnosed as MDD (N = 126) from those with correct MDD diagnosis (N = 187) was 0.92 (95% CI: 0.86-0.97). Core predictors included elevated mood, grandiosity, talkativeness, recklessness and risky behaviour. Additional validation in participants with no previous mood disorder diagnosis showed AUROCs of 0.89 (0.86-0.91) and 0.90 (0.87-0.91) for separating newly diagnosed BD (N = 98) from MDD (N = 112) and subclinical low mood (N = 120), respectively. Validation in participants with a previous diagnosis of BD (N = 45) demonstrated sensitivity of 0.86 (0.57-0.96). The diagnostic algorithm accurately identified patients with BD in various clinical scenarios, and could help expedite accurate clinical diagnosis and treatment of BD

Carbon residence time dominates uncertainty in terrestrial vegetation responses to future climate and atmospheric CO2.

Future climate change and increasing atmospheric CO2 are expected to cause major changes in vegetation structure and function over large fractions of the global land surface. Seven global vegetation models are used to analyze possible responses to future climate simulated by a range of general circulation models run under all four representative concentration pathway scenarios of changing concentrations of greenhouse gases. All 110 simulations predict an increase in global vegetation carbon to 2100, but with substantial variation between vegetation models. For example, at 4 °C of global land surface warming (510-758 ppm of CO2), vegetation carbon increases by 52-477 Pg C (224 Pg C mean), mainly due to CO2 fertilization of photosynthesis. Simulations agree on large regional increases across much of the boreal forest, western Amazonia, central Africa, western China, and southeast Asia, with reductions across southwestern North America, central South America, southern Mediterranean areas, southwestern Africa, and southwestern Australia. Four vegetation models display discontinuities across 4 °C of warming, indicating global thresholds in the balance of positive and negative influences on productivity and biomass. In contrast to previous global vegetation model studies, we emphasize the importance of uncertainties in projected changes in carbon residence times. We find, when all seven models are considered for one representative concentration pathway × general circulation model combination, such uncertainties explain 30% more variation in modeled vegetation carbon change than responses of net primary productivity alone, increasing to 151% for non-HYBRID4 models. A change in research priorities away from production and toward structural dynamics and demographic processes is recommended.The research leading to these results has received funding from the European Community’s Seventh Framework Programme (FP7 2007-2013) under Grant 238366. R.B., R.K., R.D., A.W., and P.D.F. were supported by the Joint Department of Energy and Climate Change/Department for Environment, Food and Rural Affairs Met Office Hadley Centre Climate Programme (GA01101). A.I. and K.N. were supported by the Environment Research and Technology Development Fund (S-10) of the Ministry of the Environment, Japan. We acknowledge the World Climate Research Programme’s Working Group on Coupled Modelling, which is responsible for the Coupled Model Intercomparison Project (CMIP), and we thank the climate modeling groups responsible for the GFDL-ESM2M, HadGEM2-ES, IPSL-CM5A-LR, MIROC-ESM-CHEM, and NorESM1-M models for producing and making available their model output. For CMIP, the US Department of Energy’s Program for Climate Model Diagnosis and Intercomparison provides coordinating support and led development of software infrastructure in partnership with the Global Organization for Earth System Science Portals. This work has been conducted under the framework of the Inter-Sectoral Impact Model Intercomparison Project (ISI-MIP). The ISI-MIP Fast Track project was funded by the German Federal Ministry of Education and Research (BMBF) with project funding Reference 01LS1201A.This is the author accepted manuscript. The final version is available from PNAS via http://dx.doi.org/10.1073/pnas.122247711

A machine learning algorithm to differentiate bipolar disorder from major depressive disorder using an online mental health questionnaire and blood biomarker data.

The vast personal and economic burden of mood disorders is largely caused by their under- and misdiagnosis, which is associated with ineffective treatment and worsening of outcomes. Here, we aimed to develop a diagnostic algorithm, based on an online questionnaire and blood biomarker data, to reduce the misdiagnosis of bipolar disorder (BD) as major depressive disorder (MDD). Individuals with depressive symptoms (Patient Health Questionnaire-9 score ≥5) aged 18-45 years were recruited online. After completing a purpose-built online mental health questionnaire, eligible participants provided dried blood spot samples for biomarker analysis and underwent the World Health Organization World Mental Health Composite International Diagnostic Interview via telephone, to establish their mental health diagnosis. Extreme Gradient Boosting and nested cross-validation were used to train and validate diagnostic models differentiating BD from MDD in participants who self-reported a current MDD diagnosis. Mean test area under the receiver operating characteristic curve (AUROC) for separating participants with BD diagnosed as MDD (N = 126) from those with correct MDD diagnosis (N = 187) was 0.92 (95% CI: 0.86-0.97). Core predictors included elevated mood, grandiosity, talkativeness, recklessness and risky behaviour. Additional validation in participants with no previous mood disorder diagnosis showed AUROCs of 0.89 (0.86-0.91) and 0.90 (0.87-0.91) for separating newly diagnosed BD (N = 98) from MDD (N = 112) and subclinical low mood (N = 120), respectively. Validation in participants with a previous diagnosis of BD (N = 45) demonstrated sensitivity of 0.86 (0.57-0.96). The diagnostic algorithm accurately identified patients with BD in various clinical scenarios, and could help expedite accurate clinical diagnosis and treatment of BD

A machine learning algorithm to differentiate bipolar disorder from major depressive disorder using an online mental health questionnaire and blood biomarker data

The vast personal and economic burden of mood disorders is largely caused by their under- and misdiagnosis, which is associated with ineffective treatment and worsening of outcomes. Here, we aimed to develop a diagnostic algorithm, based on an online questionnaire and blood biomarker data, to reduce the misdiagnosis of bipolar disorder (BD) as major depressive disorder (MDD). Individuals with depressive symptoms (Patient Health Questionnaire-9 score >= 5) aged 18-45 years were recruited online. After completing a purpose-built online mental health questionnaire, eligible participants provided dried blood spot samples for biomarker analysis and underwent the World Health Organization World Mental Health Composite International Diagnostic Interview via telephone, to establish their mental health diagnosis. Extreme Gradient Boosting and nested cross-validation were used to train and validate diagnostic models differentiating BD from MDD in participants who self-reported a current MDD diagnosis. Mean test area under the receiver operating characteristic curve (AUROC) for separating participants with BD diagnosed as MDD (N = 126) from those with correct MDD diagnosis (N = 187) was 0.92 (95% CI: 0.86-0.97). Core predictors included elevated mood, grandiosity, talkativeness, recklessness and risky behaviour. Additional validation in participants with no previous mood disorder diagnosis showed AUROCs of 0.89 (0.86-0.91) and 0.90 (0.87-0.91) for separating newly diagnosed BD (N = 98) from MDD (N = 112) and subclinical low mood (N = 120), respectively. Validation in participants with a previous diagnosis of BD (N = 45) demonstrated sensitivity of 0.86 (0.57-0.96). The diagnostic algorithm accurately identified patients with BD in various clinical scenarios, and could help expedite accurate clinical diagnosis and treatment of BD

Subduction or sagduction? Ambiguity in constraining the origin of ultramafic–mafic bodies in the Archean crust of NW Scotland

The Lewisian Complex of NW Scotland is a fragment of the North Atlantic Craton. It comprises mostly Archean tonalite–trondhjemite–granodiorite (TTG) orthogneisses that were variably metamorphosed and reworked in the late Neoarchean to Paleoproterozoic. Within the granulite facies central region of the mainland Lewisian Complex, discontinuous belts composed of ultramafic–mafic rocks and structurally overlying garnet–biotite gneiss (brown gneiss) are spatially associated with steeply-inclined amphibolite facies shear zones that have been interpreted as terrane boundaries. Interpretation of the primary chemical composition of these rocks is complicated by partial melting and melt loss during granulite facies metamorphism, and contamination with melts derived from the adjacent migmatitic TTG host rocks. Notwithstanding, the composition of the layered ultramafic–mafic rocks is suggestive of a protolith formed by differentiation of tholeiitic magma, where the ultramafic portions of these bodies represent the metamorphosed cumulates and the mafic portions the metamorphosed fractionated liquids. Although the composition of the brown gneiss does not clearly discriminate the protolith, it most likely represents a metamorphosed sedimentary or volcano-sedimentary sequence. For Archean rocks, particularly those metamorphosed to granulite facies, the geochemical characteristics typically used for discrimination of paleotectonic environments are neither strictly appropriate nor clearly diagnostic. Many of the rocks in the Lewisian Complex have ‘arc-like’ trace element signatures. These signatures are interpreted to reflect derivation from hydrated enriched mantle and, in the case of the TTG gneisses, partial melting of amphibolite source rocks containing garnet and a Ti-rich phase, probably rutile. However, it is becoming increasingly recognised that in Archean rocks such signatures may not be unique to a subduction environment but may relate to processes such as delamination and dripping. Consequently, it is unclear whether the Lewisian ultramafic–mafic rocks and brown gneisses represent products of plate margin or intraplate magmatism. Although a subduction-related origin is possible, we propose that an intraplate origin is equally plausible. If the second alternative is correct, the ultramafic–mafic rocks and brown gneisses may represent the remnants of intracratonic greenstone belts that sank into the deep crust due to their density contrast with the underlying partially molten low viscosity TTG orthogneisses

Innovating Pedagogy 2024

In this series of annual reports, we continue to explore new forms of teaching, learning, and assessment for an interactive world, to guide teachers and policy makers in productive innovation. This twelfth report proposes another ten innovations which are already in currency but have the potential to exert a greater influence on education. To produce the report, a group of academics at the Institute of Educational Technology at The Open University (UK) collaborated with researchers and practitioners from the LIVE Initiative at Vanderbilt University in the US. A wide range of pedagogical innovations were proposed by the authors and then, in a process of collective discussion of major themes and associated research, ten ideas were developed through multiple drafts and peer review, with reference to published studies and other sources from research and practice. This twelfth report covers: 1. Speculative worlds 2. Pedagogies of peace 3. Climate action pedagogy 4. Learning in conversation with generative AI  5. Talking AI ethics with young people 6. AI enhanced multimodal writing 7. Intelligent textbooks 8. Assessments through extended reality 9. Immersive language and culture 10. Exploring scientific models from the insid

Women and ARVâ based prevention: opportunities and challenges

Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/138349/1/jia29419.pd