We contribute the largest publicly available dataset of naturally occurring factual claims for the purpose of automatic claim verification. It is collected from 26 fact checking websites in English, paired with textual sources and rich metadata, and labelled for veracity by human expert journalists. We present an in-depth analysis of the dataset, highlighting characteristics and challenges. Further, we present results for automatic veracity prediction, both with established baselines and with a novel method for joint ranking of evidence pages and predicting veracity that outperforms all baselines. Significant performance increases are achieved by encoding evidence, and by modelling metadata. Our best-performing model achieves a Macro F1 of 49.2%, showing that this is a challenging testbed for claim veracity prediction.

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Summary A series of hydroxamates, which are not metalloprotease inhibitors, have been found to be selectively toxic to a range of transformed and human tumour cells without killing normal cells (fibroblasts, melanocytes) at the same concentrations. Within 24 h of treatment, drug action is characterized by morphological reversion of tumour cells to a more normal phenotype (dendritic morphology), and rapid and reversible acetylation of histone H4 in both tumour and normal cells. Two hydroxamates inhibited growth of xenografts of human melanoma cells in nude mice; resistance did not develop in vivo or in vitro. A third hydroxamate, trichostatin A, was active in vitro but became inactivated and had no anti-tumour activity in vivo. Development of dendritic morphology was found to be dependent upon phosphatase activity, RNA and protein synthesis. Proliferating hybrid clones of sensitive and resistant cells remained sensitive to ABHA, indicating a dominant-negative mechanism of sensitivity. Histone H4 hyperacetylation suggests that these agents act at the chromatin level. This work may lead to new drugs that are potent, and selective anti-tumour agents with low toxicity to normal cells.

We examine the impact of the Green Revolution, defined as the diffusion of high-yielding crop varieties (HYVs), on aggregate economic outcomes in developing countries during the second half of the 20th century. We use time variation in the development and diffusion of HYVs of 10 major crops, and the spatial variation in agro-climatically suitability for growing them, to identify the causal effects of adoption. In a sample of 84 counties, we estimate that a 10 percentage points increase in HYV adoption increases GDP per capita by about 15 percent. This effect is fully accounted for by a combination of the direct effect on crop yields, factor adjustment in agriculture, and structural transformation. Our analysis also reveals that the Green Revolution reduced fertility and that the reduction was only partly offset by decreasing mortality rates. The net effect on population growth was therefore negative.

Patients with severe COVID-19 have overwhelmed healthcare systems worldwide. We hypothesized that machine learning (ML) models could be used to predict risks at different stages of management and thereby provide insights into drivers and prognostic markers of disease progression and death. From a cohort of approx. 2.6 million citizens in Denmark, SARS-CoV-2 PCR tests were performed on subjects suspected for COVID-19 disease; 3944 cases had at least one positive test and were subjected to further analysis. SARS-CoV-2 positive cases from the United Kingdom Biobank was used for external validation. The ML models predicted the risk of death (Receiver Operation Characteristics—Area Under the Curve, ROC-AUC) of 0.906 at diagnosis, 0.818, at hospital admission and 0.721 at Intensive Care Unit (ICU) admission. Similar metrics were achieved for predicted risks of hospital and ICU admission and use of mechanical ventilation. Common risk factors, included age, body mass index and hypertension, although the top risk features shifted towards markers of shock and organ dysfunction in ICU patients. The external validation indicated fair predictive performance for mortality prediction, but suboptimal performance for predicting ICU admission. ML may be used to identify drivers of progression to more severe disease and for prognostication patients in patients with COVID-19. We provide access to an online risk calculator based on these findings.