Endogenous Information and Inter-state War Expansion

Scholars have long debated the causes of late third party state joining in ongoing inter-state wars. This research has generally concluding that peace-time conditions, measured in terms of: third party capabilities; proximity to warring states; and inter-state alliances, are determining factors in the decision to join. However, these studies utilize theories derived from static pre-war measures of capabilities and motivation to explain late joining; indeed, the same measures that fail to predict participation at war's outset. Further, extant research has no explanation for why weak and non-proximate states every participate. Existing theory thus fails to provide a comprehensive explanation of joining behavior. This project contends that a resolution lies the interaction between pre-war conditions and intra-war events. Intra-war events that are allowed to vary on a per battle basis, including change in combat location and alliance entry and exit from combat, reveal new information about the war and its progress, thereby forcing third party states to recalculate their initial decision to abstain in relation to their pre-existing situation. Incorporation of intra-war processes helps to better explain decisions by third party states to join ongoing inter-state wars late in their development, and why states that frequently choose to abstain (e.g., weak states) ever choose to participate. This project is executed using a combination of ex post facto historical case studies, a theory of joining based on pre and intra-war environments, and large-N empirical analysis on all inter-state wars 1823-1988, conducted utilizing a novel collection of event-level data based on inter-state war battles

Political activism across the life course

The study of political activism has neglected people’s personal and social relationships to time. Age, life course and generation have become increasing important experiences for understanding political participation and political outcomes (e.g. Brexit), and current policies of austerity across the world are affecting people of all ages. At a time when social science is struggling to understand the rapid and unexpected changes to the current political landscape, the essay argues that the study of political activism can be enriched by engaging with the temporal dimensions of people’s everyday social experiences because it enables the discovery of political activism in mundane activities as well as in banal spaces. The authors suggest that a values-based approach that focuses on people’s relationships of concern would be a suitable way to surface contemporary political sites and experiences of activism across the life course and for different generations

Look Who’s Talking:Using creative, playful arts-based methods in research with young children

Young children are often ignored or marginalised in the drive to address children’s participation and their wider set of rights. This is the case generally in social research, as well as within the field of Arts-Based Education Research. This article contributes to the growing literature on young children’s involvement in arts-based research, by providing a reflective account of our learning and playful engagement with children using creative methods. This small pilot project forms part of a larger international project titled Look Who’s Talking: Eliciting the Voices of Children from Birth to Seven, led by Professor Kate Wall at the University of Strathclyde. Visiting one nursery in Scotland, we worked with approximately 30 children from 3 to 5 years old. Seeking to connect with their play-based nursery experiences, we invited children to participate in a range of arts-based activities including drawing, craft-making, sculpting, a themed ‘play basket’ with various props, puppetry and videography. In this article, we develop reflective, analytical stories of our successes and dilemmas in the project. We were keen to establish ways of working with children that centred their own creativity and play, shaped by the materials we provided but not directed by us. However, we struggled to balance our own agenda with the more open-ended methods we had used. We argue that an intergenerational approach to eliciting voice with young children – in which adults are not afraid to shape the agenda, but do so in responsive, gradual and sensitive ways – creates the potential for a more inclusive experience for children that also meets researcher needs

The nature of singlet exciton fission in carotenoid aggregates.

Singlet exciton fission allows the fast and efficient generation of two spin triplet states from one photoexcited singlet. It has the potential to improve organic photovoltaics, enabling efficient coupling to the blue to ultraviolet region of the solar spectrum to capture the energy generally lost as waste heat. However, many questions remain about the underlying fission mechanism. The relation between intermolecular geometry and singlet fission rate and yield is poorly understood and remains one of the most significant barriers to the design of new singlet fission sensitizers. Here we explore the structure-property relationship and examine the mechanism of singlet fission in aggregates of astaxanthin, a small polyene. We isolate five distinct supramolecular structures of astaxanthin generated through self-assembly in solution. Each is capable of undergoing intermolecular singlet fission, with rates of triplet generation and annihilation that can be correlated with intermolecular coupling strength. In contrast with the conventional model of singlet fission in linear molecules, we demonstrate that no intermediate states are involved in the triplet formation: instead, singlet fission occurs directly from the initial 1B(u) photoexcited state on ultrafast time scales. This result demands a re-evaluation of current theories of polyene photophysics and highlights the robustness of carotenoid singlet fission.This work was supported by the EPSRC (UK) (EP/G060738/ 1), the European Community (LASERLAB-EUROPE, grant agreement no. 284464, EC’s Seventh Framework Programme; and Marie-Curie ITN-SUPERIOR, PITN-GA-2009-238177), and the Winton Programme for the Physics of Sustainability. G.C. acknowledges support by the European Research Council Advanced Grant STRATUS (ERC-2011-AdG No. 291198). J.C. acknowledges support by the Royal Society Dorothy Hodgkin Fellowship and The University of Sheffield’s Vice- Chancellor’s Fellowship scheme.This is the final published version. It was first made available by ACS at http://pubs.acs.org/doi/abs/10.1021/jacs.5b01130

Automatic Robust Neurite Detection and Morphological Analysis of Neuronal Cell Cultures in High-content Screening

Cell-based high content screening (HCS) is becoming an important and increasingly favored approach in therapeutic drug discovery and functional genomics. In HCS, changes in cellular morphology and biomarker distributions provide an information-rich profile of cellular responses to experimental treatments such as small molecules or gene knockdown probes. One obstacle that currently exists with such cell-based assays is the availability of image processing algorithms that are capable of reliably and automatically analyzing large HCS image sets. HCS images of primary neuronal cell cultures are particularly challenging to analyze due to complex cellular morphology. Here we present a robust method for quantifying and statistically analyzing the morphology of neuronal cells in HCS images. The major advantages of our method over existing software lie in its capability to correct non-uniform illumination using the contrast-limited adaptive histogram equalization method; segment neuromeres using Gabor-wavelet texture analysis; and detect faint neurites by a novel phase-based neurite extraction algorithm that is invariant to changes in illumination and contrast and can accurately localize neurites. Our method was successfully applied to analyze a large HCS image set generated in a morphology screen for polyglutaminemediated neuronal toxicity using primary neuronal cell cultures derived from embryos of a Drosophila Huntington’s Disease (HD) model.National Institutes of Health (U.S.) (Grant

Linking Symptom Inventories using Semantic Textual Similarity

An extensive library of symptom inventories has been developed over time to measure clinical symptoms, but this variety has led to several long standing issues. Most notably, results drawn from different settings and studies are not comparable, which limits reproducibility. Here, we present an artificial intelligence (AI) approach using semantic textual similarity (STS) to link symptoms and scores across previously incongruous symptom inventories. We tested the ability of four pre-trained STS models to screen thousands of symptom description pairs for related content - a challenging task typically requiring expert panels. Models were tasked to predict symptom severity across four different inventories for 6,607 participants drawn from 16 international data sources. The STS approach achieved 74.8% accuracy across five tasks, outperforming other models tested. This work suggests that incorporating contextual, semantic information can assist expert decision-making processes, yielding gains for both general and disease-specific clinical assessment

Verbal Learning and Memory Deficits across Neurological and Neuropsychiatric Disorders: Insights from an ENIGMA Mega Analysis.

Deficits in memory performance have been linked to a wide range of neurological and neuropsychiatric conditions. While many studies have assessed the memory impacts of individual conditions, this study considers a broader perspective by evaluating how memory recall is differentially associated with nine common neuropsychiatric conditions using data drawn from 55 international studies, aggregating 15,883 unique participants aged 15–90. The effects of dementia, mild cognitive impairment, Parkinson’s disease, traumatic brain injury, stroke, depression, attention-deficit/hyperactivity disorder (ADHD), schizophrenia, and bipolar disorder on immediate, short-, and long-delay verbal learning and memory (VLM) scores were estimated relative to matched healthy individuals. Random forest models identified age, years of education, and site as important VLM covariates. A Bayesian harmonization approach was used to isolate and remove site effects. Regression estimated the adjusted association of each clinical group with VLM scores. Memory deficits were strongly associated with dementia and schizophrenia (p \u3c 0.001), while neither depression nor ADHD showed consistent associations with VLM scores (p \u3e 0.05). Differences associated with clinical conditions were larger for longer delayed recall duration items. By comparing VLM across clinical conditions, this study provides a foundation for enhanced diagnostic precision and offers new insights into disease management of comorbid disorders