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A foal is born at midnight, on the homestead side of the river. Coal black. Star ablaze. Moonlight in her eyes. On October 31, 1917, the 4th and 12th Regiments of the Australian Light Horse took part in one of the last great cavalry charges in history. Among the first to leap the enemy trenches was Lieutenant Guy Haydon riding his beloved mare, Midnight. This is their story.

While autopsy studies identify many abnormalities in the central nervous system (CNS) of subjects dying with neurological diseases, without their quantification in living subjects across the lifespan, pathogenic processes cannot be differentiated from epiphenomena. Using machine learning (ML), we searched for likely pathogenic mechanisms of multiple sclerosis (MS). We aggregated cerebrospinal fluid (CSF) biomarkers from 1305 proteins, measured blindly in the training dataset of untreated MS patients (N = 129), into models that predict past and future speed of disability accumulation across all MS phenotypes. Healthy volunteers (N = 24) data differentiated natural aging and sex effects from MS-related mechanisms. Resulting models, validated (Rho 0.40-0.51, p < 0.0001) in an independent longitudinal cohort (N = 98), uncovered intra-individual molecular heterogeneity. While candidate pathogenic processes must be validated in successful clinical trials, measuring them in living people will enable screening drugs for desired pharmacodynamic effects. This will facilitate drug development making, it hopefully more efficient and successful. Multiple sclerosis (MS) changes the composition of the CSF. Here the authors use patient samples and aggregate CSF biomarkers into models that predict disability across all MS phenotypes, and identify potentially causal mechanisms and molecular disease heterogeneity.

The 2019 UK general election took place against a background of rising online hostility levels toward politicians, and concerns about the impact of this on democracy, as a record number of politicians cited the abuse they had been receiving as a reason for not standing for re-election. We present a four-factor framework in understanding who receives online abuse and why. The four factors are prominence, events, online engagement and personal characteristics. We collected 4.2 million tweets sent to or from election candidates in the six week period spanning from the start of November until shortly after the December 12th election. We found abuse in 4.46% of replies received by candidates, up from 3.27% in the matching period for the 2017 UK general election. Abuse levels have also been climbing month on month throughout 2019. Abuse also escalated throughout the campaign period. Abuse focused mainly on a small number of high profile politicians, with the most prominent individuals receiving not only more abuse by volume, but also as a percentage of replies. Abuse is “spiky”, triggered by external events such as debates, or certain tweets. Some tweets may become viral targets for personal abuse. On average, men received more general and political abuse; women received more sexist abuse. Conservative candidates received more political and general abuse. We find that individuals choosing not to stand for re-election had received more abuse across the preceding year.

While progress has been made in identifying common genetic variants associated with human diseases, for most of common complex diseases, the identified genetic variants only account for a small proportion of heritability. Challenges remain in finding additional unknown genetic variants predisposing to complex diseases. With the advance in next-generation sequencing technologies, sequencing studies have become commonplace in genetic research. The ongoing exome-sequencing and whole-genome-sequencing studies generate a massive amount of sequencing variants and allow researchers to comprehensively investigate their role in human diseases. The discovery of new disease-associated variants can be enhanced by utilizing powerful and computationally efficient statistical methods. In this paper, we propose a functional analysis of variance (FANOVA) method for testing an association of sequence variants in a genomic region with a qualitative trait. The FANOVA has a number of advantages: (1) it tests for a joint effect of gene variants, including both common and rare; (2) it fully utilizes linkage disequilibrium and genetic position information; and (3) allows for either protective or risk-increasing causal variants. Through simulations, we show that FANOVA outperform two popularly used methods - SKAT and a previously proposed method based on functional linear models (FLM), - especially if a sample size of a study is small and/or sequence variants have low to moderate effects. We conduct an empirical study by applying three methods (FANOVA, SKAT and FLM) to sequencing data from Dallas Heart Study. While SKAT and FLM respectively detected ANGPTL 4 and ANGPTL 3 associated with obesity, FANOVA was able to identify both genes associated with obesity.

Individual plant cells possess a genetic network, the circadian clock, that times internal processes to the day‐night cycle. Mathematical models of the clock are typically either “whole‐plant” that ignore tissue or cell type‐specific clock behavior, or “phase‐only” that do not include molecular components. To address the complex spatial coordination observed in experiments, here we implemented a clock network model on a template of a seedling. In our model, the sensitivity to light varies across the plant, and cells communicate their timing via local or long‐distance sharing of clock components, causing their rhythms to couple. We found that both varied light sensitivity and long‐distance coupling could generate period differences between organs, while local coupling was required to generate the spatial waves of clock gene expression observed experimentally. We then examined our model under noisy light‐dark cycles and found that local coupling minimized timing errors caused by the noise while allowing each plant region to maintain a different clock phase. Thus, local sensitivity to environmental inputs combined with local coupling enables flexible yet robust circadian timing. Synopsis A spatial molecular model is developed to understand the design principles of plant clock coordination. The model shows that local cell‐to‐cell coupling combined with varied environmental signaling allows robust, yet flexible, timing. A spatial molecular model of the plant circadian clock provides a framework for understanding timing across cellular, organ, and whole‐plant scales. Varied sensing of the environment by cells can explain the period differences observed within plants in experiments. Local cell‐to‐cell communication drives spatial waves of gene expression whereas long‐distance communication can create period differences between organs. Under noisy light‐dark conditions, local cell‐to‐cell communication improves timing accuracy yet allows regional phase differences to persist. Graphical Abstract A spatial molecular model is developed to understand the design principles of plant clock coordination. The model shows that local cell‐to‐cell coupling combined with varied environmental signaling allows robust, yet flexible, timing.

Bacterial strains isolated from Antarctic environments were used to assess the role of carotenoid pigments as cryo- and solar radiation protectants. Isolates were subjected to one hundred 12-hr freeze-thaw cycles and exposed to ambient simulated solar radiation (300 Wm−2) with growth recovery evaluated after pre-set time intervals. Differences in survival were observed between carotenoid pigmented and non-pigmented strains in response to the different stresses based upon the enumeration of colony forming units. On average carotenoid pigmented strains were more resistant to freeze-thaw cycles as compared to the non-pigmented strains. Survival for non-pigmented strains decreased precipitously from 2 × 107 to 1.5 × 104 cells mL−1, on average, within the first 20 cycles. Similar results were found in the solar radiation experiments. After 2 hrs of solar radiation exposure, 61% of the pigmented organisms survived versus 0.01% for the non-pigmented isolates. We applied an additive mixed model to estimate differences between the carotenoid pigmented and non-pigmented bacterial groups. Modeled results confirmed a positive effect of pigmentation on survivability and provide evidence that carotenoid pigmentation in heterotrophic bacteria isolated from Antarctic environments increases resistance to environmental stressors.

The Arabidopsis circadian clock orchestrates gene regulation across the day/night cycle. Although a multiple feedback loop circuit has been shown to generate the 24-hr rhythm, it remains unclear how robust the clock is in individual cells, or how clock timing is coordinated across the plant. Here we examine clock activity at the single cell level across Arabidopsis seedlings over several days under constant environmental conditions. Our data reveal robust single cell oscillations, albeit desynchronised. In particular, we observe two waves of clock activity; one going down, and one up the root. We also find evidence of cell-to-cell coupling of the clock, especially in the root tip. A simple model shows that cell-to-cell coupling and our measured period differences between cells can generate the observed waves. Our results reveal the spatial structure of the plant clock and suggest that unlike the centralised mammalian clock, the Arabidopsis clock has multiple coordination points. The cycle of day and night sets the pace for the existence of most life on Earth. In response, many organisms have evolved internal biological clocks which are synchronized with the rhythm of light and dark. Thanks to this ‘circadian clock’, plants and animals can predict the onset of dawn and dusk, and schedule biological processes to specific times of the day. Each cell possesses its own circadian clock, which is formed of a complex network of genes and proteins that get activated along a 24-hour cycle. This raises the question: how do these cells coordinate to create a body clock at the level of the entire organism? In mammals, this synchronisation happens thanks to a structure in the brain, but much less is known about how this occurs in plants. This is partly because few studies exist that focus on measuring the clocks of individual plant cells. Here, Gould, Domijan et al. monitor the circadian clock of single cells in seedlings of a plant called Arabidopsis thaliana across several days and under constant environmental conditions. To do so, they use time-lapse microscopy and genetic methods to see when and where one of the clock’s core genes is switched on. The results show that, at the level of the plant, Arabidopsis has two waves of clock gene expression, one that goes up and one that goes down the root. In addition, the various parts of the plant have slightly different circadian rhythms – for instance, the tip of the root has a faster clock. Robust clock rhythms are also detected in individual cells across the plant. Clocks in neighbouring cells are found to communicate with each other to keep track of time, which might be contributing to this robustness. Mathematical simulations show that, when the individual clocks interact, they generate patterns of clock activity across the plant, which explains the two waves of gene expression in the root. Plant circadian rhythms control traits that are crucial for agriculture, such as growth, yield, disease resistance and flowering time. Understanding, and ultimately controlling, the intricate cogs of these clocks may one day allow scientists to create better performing crops.

Individual plant cells have a genetic circuit, the circadian clock, that times key processes to the day-night cycle. These clocks are aligned to the day-night cycle by multiple environmental signals that vary across the plant. How does the plant integrate clock rhythms, both within and between organs, to ensure coordinated timing? To address this question, we examined the clock at the sub-tissue level across Arabidopsis thaliana seedlings under multiple environmental conditions and genetic backgrounds. Our results show that the clock runs at different speeds (periods) in each organ, which causes the clock to peak at different times across the plant in both constant environmental conditions and light-dark (LD) cycles. Closer examination reveals that spatial waves of clock gene expression propagate both within and between organs. Using a combination of modeling and experiment, we reveal that these spatial waves are the result of the period differences between organs and local coupling, rather than long-distance signaling. With further experiments we show that the endogenous period differences, and thus the spatial waves, can be generated by the organ specificity of inputs into the clock. We demonstrate this by modulating periods using light and metabolic signals, as well as with genetic perturbations. Our results reveal that plant clocks can be set locally by organ-specific inputs but coordinated globally via spatial waves of clock gene expression.

Background Antipsychotic prescription information is commonly derived from structured fields in clinical health records. However, utilising diverse and comprehensive sources of information is especially important when investigating less frequent patterns of medication prescribing such as antipsychotic polypharmacy (APP). This study describes and evaluates a novel method of extracting APP data from both structured and free-text fields in electronic health records (EHRs), and its use for research purposes. Methods Using anonymised EHRs, we identified a cohort of patients with serious mental illness (SMI) who were treated in South London and Maudsley NHS Foundation Trust mental health care services between 1 January and 30 June 2012. Information about antipsychotic co-prescribing was extracted using a combination of natural language processing and a bespoke algorithm. The validity of the data derived through this process was assessed against a manually coded gold standard to establish precision and recall. Lastly, we estimated the prevalence and patterns of antipsychotic polypharmacy. Results Individual instances of antipsychotic prescribing were detected with high precision (0.94 to 0.97) and moderate recall (0.57-0.77). We detected baseline APP (two or more antipsychotics prescribed in any 6-week window) with 0.92 precision and 0.74 recall and long-term APP (antipsychotic co-prescribing for 6 months) with 0.94 precision and 0.60 recall. Of the 7,201 SMI patients receiving active care during the observation period, 338 (4.7 %; 95 % CI 4.2-5.2) were identified as receiving long-term APP. Two second generation antipsychotics (64.8 %); and first -second generation antipsychotics were most commonly co-prescribed (32.5 %). Conclusions These results suggest that this is a potentially practical tool for identifying polypharmacy from mental health EHRs on a large scale. Furthermore, extracted data can be used to allow researchers to characterize patterns of polypharmacy over time including different drug combinations, trends in polypharmacy prescribing, predictors of polypharmacy prescribing and the impact of polypharmacy on patient outcomes.

Achieving food security is a critical challenge of the Anthropocene that may conflict with environmental and societal goals such as increased energy access. The “fuel versus food” debate coupled with climate mitigation efforts has given rise to next-generation biofuels. Findings of this systematic review indicate just over half of the studies (56% of 224 publications) reported a negative impact of bioenergy production on food security. However, no relationship was found between bioenergy feedstocks that are edible versus inedible and food security ( P value = 0.15). A strong relationship was found between bioenergy and type of food security parameter ( P value < 0.001), sociodemographic index of study location ( P value = 0.001), spatial scale ( P value < 0.001), and temporal scale ( P value = 0.017). Programs and policies focused on bioenergy and climate mitigation should monitor multiple food security parameters at various scales over the long term toward achieving diverse sustainability goals.