Genomic and phenotypic characterization of finger millet indicates a complex diversification history

Advances in sequencing technologies mean that insights into crop diversification can now be explored in crops beyond major staples. We use a genome assembly of finger millet, an allotetraploid orphan crop, to analyze DArTseq single nucleotide polymorphisms (SNPs) at the whole and sub‐genome level. A set of 8778 SNPs and 13 agronomic traits was used to characterize a diverse panel of 423 landraces from Africa and Asia. Through principal component analysis (PCA) and discriminant analysis of principal components, four distinct groups of accessions were identified that coincided with the primary geographic regions of finger millet cultivation. Notably, East Africa, presumed to be the crop's origin, exhibited the lowest genetic diversity. The PCA of phenotypic data also revealed geographic differentiation, albeit with differing relationships among geographic areas than indicated with genomic data. Further exploration of the sub‐genomes A and B using neighbor‐joining trees revealed distinct features that provide supporting evidence for the complex evolutionary history of finger millet. Although genome‐wide association study found only a limited number of significant marker‐trait associations, a clustering approach based on the distribution of marker effects obtained from a ridge regression genomic model was employed to investigate trait complexity. This analysis uncovered two distinct clusters. Overall, the findings suggest that finger millet has undergone complex and context‐specific diversification, indicative of a lengthy domestication history. These analyses provide insights for the future development of finger millet

The value of Sentinel-2 spectral bands for the assessment of winter wheat growth and development

Leaf Area Index (LAI) and chlorophyll content are strongly related to plant development and productivity. Spatial and temporal estimates of these variables are essential for efficient and precise crop management. The availability of open-access data from the European Space Agency’s (ESA) Sentinel-2 satellite—delivering global coverage with an average 5-day revisit frequency at a spatial resolution of up to 10 metres—could provide estimates of these variables at unprecedented (i.e., sub-field) resolution. Using synthetic data, past research has demonstrated the potential of Sentinel-2 for estimating crop variables. Nonetheless, research involving a robust analysis of the Sentinel-2 bands for supporting agricultural applications is limited. We evaluated the potential of Sentinel-2 data for retrieving winter wheat LAI, leaf chlorophyll content (LCC) and canopy chlorophyll content (CCC). In coordination with destructive and non-destructive ground measurements, we acquired multispectral data from an Unmanned Aerial Vehicle (UAV)-mounted sensor measuring key Sentinel-2 spectral bands (443 to 865 nm). We applied Gaussian processes regression (GPR) machine learning to determine the most informative Sentinel-2 bands for retrieving each of the variables. We further evaluated the GPR model performance when propagating observation uncertainty. When applying the best-performing GPR models without propagating uncertainty, the retrievals had a high agreement with ground measurements—the mean R2 and normalised root-mean-square error (NRMSE) were 0.89 and 8.8%, respectively. When propagating uncertainty, the mean R2 and NRMSE were 0.82 and 11.9%, respectively. When accounting for measurement uncertainty in the estimation of LAI and CCC, the number of most informative Sentinel-2 bands was reduced from four to only two—the red-edge (705 nm) and near-infrared (865 nm) bands. This research demonstrates the value of the Sentinel-2 spectral characteristics for retrieving critical variables that can support more sustainable crop management practices

Comparing magnetic resonance liver fat fraction measurements with histology in fibrosis: the difference between proton density fat fraction and tissue mass fat fraction

Magnetic resonance spectroscopy (MRS) provides a powerful method of measuring fat fraction. However, previous studies have shown that MRS results give lower values compared with visual estimates from biopsies in fibrotic livers. This study investigated these discrepancies and considered whether a tissue water content correction, as assessed by MRI relaxometry, could provide better agreement. 110 patients were scanned in a 1.5 T Philips scanner and biopsies were obtained. Multiple echo MRS (30 × 30 ×  30 mm volume) was used to determine Proton Density Fat Fraction (PDFF). Biopsies were assessed by visual assessment for fibrosis and steatosis grading. Digital image analysis (DIA) was also used to quantify fat fraction within tissue samples. T relaxation times were then used to estimate tissue water content to correct PDFF for confounding factors. PDFF values across the four visually assessed steatosis grades were significantly less in the higher fibrosis group (F3-F4) compared to the lower fibrosis group (F0-F2). The slope of the linear regression of PDFF vs DIA fat fraction was ~ 1 in the low fibrosis group and 0.77 in the high fibrosis group. Correcting for water content based on T increased the gradient but it did not reach unity. In fibrotic livers, PDFF underestimated fat fraction compared to DIA methods. Values were improved by applying a water content correction, but fat fractions were still underestimated

Measurement of inclusive jet and dijet cross-sections in proton-proton collisions at s √ =13 TeV with the ATLAS detector

Inclusive jet and dijet cross-sections are measured in proton-proton collisions at a centre-of-mass energy of 13 TeV. The measurement uses a dataset with an integrated luminosity of 3.2 fb−1 recorded in 2015 with the ATLAS detector at the Large Hadron Collider. Jets are identified using the anti-kt algorithm with a radius parameter value of R = 0.4. The inclusive jet cross-sections are measured double-differentially as a function of the jet transverse momentum, covering the range from 100 GeV to 3.5 TeV, and the absolute jet rapidity up to |y| = 3. The double-differential dijet production cross-sections are presented as a function of the dijet mass, covering the range from 300 GeV to 9 TeV, and the half absolute rapidity separation between the two leading jets within |y| < 3, y∗, up to y∗ = 3. Next-to-leading-order, and next-to-next-to-leading-order for the inclusive jet measurement, perturbative QCD calculations corrected for non-perturbative and electroweak effects are compared to the measured cross-sections

Gay Rights, the Devil and the End Times: Public Religion and the Enchantment of the Homosexuality Debate in Zambia

This article contributes to the understanding of the role of religion in the public and political controversies about homosexuality in Africa. As a case study it investigates the heated public debate in Zambia following a February 2012 visit by United Nations Secretary General Ban Ki-moon, who emphasised the need for the country to recognise the human rights of homosexuals. The focus is on a particular Christian discourse in this debate, in which the international pressure to recognise gay rights is considered a sign of the end times, and Ban Ki-moon, the UN and other international organisations are associated with the Antichrist and the Devil. Here, the debate about homosexuality becomes eschatologically enchanted through millennialist thought. Building on discussions about public religion and religion and politics in Africa, this article avoids popular explanations in terms of fundamentalist religion and African homophobia, but rather highlights the political significance of this discourse in a postcolonial African context

ATEC manuscript 3 - supporting data: "Combining Process Modelling and LAI Observations to Diagnose Winter Wheat Nitrogen Status and Forecast Yield"

Climate, nitrogen (N) and leaf area index (LAI) are key determinants of crop yield. N additions can enhance yield but must be managed efficiently to reduce pollution. Complex process models estimate N status by simulating soil-crop N interactions, but such models require extensive inputs that are seldom available. Through model-data fusion (MDF), we combine climate and LAI time-series with an intermediate-complexity model to infer leaf N and yield. The DALEC-Crop model was calibrated for wheat leaf N and yields across field experiments covering N applications ranging from 0 to 200 kg N ha-1 in Scotland, UK. Requiring daily meteorological inputs, this model simulates crop C cycle responses to LAI, N and climate. The model, which includes a leaf N-dilution function, was calibrated across N treatments based on LAI observations, and tested at validation plots. We showed that a single parameterization varying only in leaf N could simulate LAI development and yield across all treatments—the mean normalized root-mean-square-error (NRMSE) for yield was 10%. Leaf N was accurately retrieved by the model (NRMSE = 6%). Yield could also be reasonably estimated (NRMSE = 14%) if LAI data are available for assimilation during periods of typical N application (April and May). Our MDF approach generated robust leaf N content estimates and timely yield predictions that could complement existing agricultural technologies. Moreover, EO-derived LAI products at high spatial and temporal resolutions provides a means to apply our approach regionally. Testing yield predictions from this approach over agricultural fields is a critical next step to determine broader utility.Revill, Andrew; Florence, Anna; Hoad, Stephen; Rees, Robert; Williams, Mathew. (2021). ATEC manuscript 3 - supporting data: "Combining Process Modelling and LAI Observations to Diagnose Winter Wheat Nitrogen Status and Forecast Yield", 2017-2018 [dataset]. University of Edinburgh. School of GeoSciences. https://doi.org/10.7488/ds/2989