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Multiomics integration unveils photoperiodic plasticity in the molecular rhythms of marine phytoplankton
No full textEarth's tilted rotation and translation around the Sun produce pervasive rhythms on our planet, giving rise to photoperiodic changes in diel cycles. Although marine phytoplankton plays a key role in ecosystems, multiomics analysis of its responses to these periodic environmental signals remains largely unexplored. The marine picoalga Ostreococcus tauri was chosen as a model organism due to its cellular and genomic simplicity. Ostreococcus was subjected to different light regimes to investigate its responses to periodic environmental signals: long summer days, short winter days, constant light, and constant dark conditions. Although <5% of the transcriptome maintained oscillations under both constant conditions, 80% presented diel rhythmicity. A drastic reduction in diel rhythmicity was observed at the proteome level, with 39% of the detected proteins oscillating. Photoperiod-specific rhythms were identified for key physiological processes such as the cell cycle, photosynthesis, carotenoid biosynthesis, starch accumulation, and nitrate assimilation. In this study, a photoperiodic plastic global orchestration among transcriptome, proteome, and physiological dynamics was characterized to identify photoperiod-specific temporal offsets between the timing of transcripts, proteins, and physiological responses.</p
Building a synthesis-ready research ecosystem: fostering collaboration and open science to accelerate biomedical translation
No full textDensity regression via Dirichlet process mixtures of normal structured additive regression models
No full textWithin Bayesian nonparametrics, dependent Dirichlet process mixture models provide a flexible approach for conducting inference about the conditional density function. However, several formulations of this class make either restrictive modelling assumptions or involve intricate algorithms for posterior inference. We propose a flexible and computationally convenient approach for density regression based on a single-weights dependent Dirichlet process mixture of normal distributions model for univariate continuous responses. We assume an additive structure for the mean of each mixture component and incorporate the effects of continuous covariates through smooth functions. The key components of our modelling approach are penalised B-splines and their bivariate tensor product extension. Our method also seamlessly accommodates categorical covariates, linear effects of continuous covariates, varying coefficient terms, and random effects, which is why we refer our model as a Dirichlet process mixture of normal structured additive regression models. A notable feature of our method is the simplicity of posterior simulation using Gibbs sampling, as closed-form full conditional distributions for all model parameters are available. Results from a simulation study demonstrate that our approach successfully recovers the true conditional densities and other regression functionals in challenging scenarios. Applications to three real datasets further underpin the broad applicability of our method. An R package, DDPstar, implementing the proposed method is provided.</p
A pilot cluster-controlled trial of interventions to improve detection of depression in primary healthcare in Ethiopia
No full textControlled high-current-induced scanning tunnelling microscope modification of C60
No full textWe demonstrate controlled STM-induced modification/destruction of Ih-C60 supported on a Cu(111) surface, showing that the molecule is more resilient to high currents for bias voltages greater than ca. 3.5 V. This is due to the enhanced charge transport through the diffuse SAMO orbitals of the molecule with lower probability for electron-vibration coupling than found for resonant low bias transport through π-molecular orbitals. Experimental and theoretical DFT results demonstrate the destruction mechanism comes from C2 emission from the fullerene cage and the formation of smaller fullerenes via sequential emission of C2
Declining earnings inequality, rising income inequality:What explains discordant inequality trends in the United States?
No full textFrom 2010 to 2019, personal earnings inequality declined in the United States (U.S.) for the first time in decades, yet household income inequality continued to increase. Discordance between the inequality trends reached its highest rate in recent history. We introduce a framework to decompose differences in inequality trends. We find that 46% of post-2010 discordance in inequality trends is due to changing household composition, namely a larger share of young workers living with their parents and combining low (but increasing) personal earnings with high household incomes. The remaining discordance stems from increases in private income among higher-earning households and declining redistributive effects of government transfers. Declines in personal earnings inequality do not imply declines in household income inequality
The profits of personality:Advancing the fourth "I" in international political economy research
Get PDFTwo decades after Hay’s classification of political economy research, not much has changed. Interests, institutions, and ideas continue to dominate, especially in international political economy (IPE) scholarship. Despite calls for more actor-centred and psychological approaches, the individual remains a missing ‘I’ in most IPE research. This paper advances the fourth ‘I’ in IPE by drawing on psychological work in foreign policy analysis (FPA) and demonstrating the utility of personality analysis in accounting for ways that political leaders manage integrated national economies. We use corporate taxation and tax competition as our illustrative case study, focusing in particular on the different ways leaders in Ecuador and Peru responded to windfall profits during the 2003–2013 commodities boom. We argue that interests, institutions, and ideas do not completely account for variation in the taxation of windfall profits in these countries and we employ leadership trait analyses of Presidents Correa and Humala to provide the missing link. Our results suggest personality differences, when combined with contextualised understandings of the pressures that leaders face, best explain their different approaches. Overall, the paper points to the profits of including personality in IPE and forges stronger links between IPE and FPA research
The functional organisation of the centromere and kinetochore during meiosis
Get PDFMeiosis generates gametes through a specialised cell cycle that reduces the genome by half. Homologous chromosomes are segregated in meiosis I and sister chromatids are segregated in meiosis II. Centromeres and kinetochores play central roles in instructing this specialised chromosome segregation pattern. Accordingly, kinetochores acquire meiosis-specific modifications. Here we contextualise recent highlights in our understanding of how centromeres and kinetochores direct the sorting of chromosomes into gametes via meiosis
Visible Light Driven Heterogeneous Cu‐Ti‐gCN Photoredox Catalyzed Synthesis of Diverse N‐Heterocyclic Derivatives via ODH and CDC Reactions
Get PDFVisible light-mediated oxidative dehydrogenation (ODH) of partially saturated heterocycles and different cross dehydrogenative coupling (CDC) reactions using heterogeneous photoredox catalyst is described in this paper. A systematic study led to an ODH of partially saturated heterocycles and different CDC reactions in very good-to-excellent yields. Oxygen is used as a clean oxidant in both ODH and CDC reactions. The methodology is atom economic and exhibits excellent tolerance toward various functional groups, and broad substrate scopes. This methodology was found to be suitable for scale-up and reusability.</p
AInsectID version 1.1: an insect species identification software based on the transfer learning of deep convolutional neural networks
Get PDFAInsectID Version 1.1 is a Graphical User Interface (GUI)-operable open-source insect species identification, color processing, and image analysis software. The software has a current database of 150 insects and integrates artificial intelligence approaches to streamline the process of species identification, with a focus on addressing the prediction challenges posed by insect mimics. This paper presents the methods of algorithmic development, coupled to rigorous machine training used to enable high levels of validation accuracy. Our work integrates the transfer learning of prominent convolutional neural network (CNN) architectures, including VGG16, GoogLeNet, InceptionV3, MobileNetV2, ResNet50, and ResNet101. Here, we employ both fine tuning and hyperparameter optimization approaches to improve prediction performance. After extensive computational experimentation, ResNet101 is evidenced as being the most effective CNN model, achieving a validation accuracy of 99.65%. The dataset utilized for training AInsectID is sourced from the National Museum of Scotland, the Natural History Museum London, and open source insect species datasets from Zenodo (CERN's Data Center), ensuring a diverse and comprehensive collection of insect species.<br/
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