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In 1843, a hitherto unknown plant pathogen entered the US and spread to potato fields in the northeast. By 1845, the pathogen had reached Ireland leading to devastating famine. Questions arose immediately about the source of the outbreaks and how the disease should be managed. The pathogen, now known asPhytophthora infestans, still continues to threaten food security globally. A wealth of untapped knowledge exists in both archival and modern documents, but is not readily available because the details are hidden in descriptive text. In this work, we (1) used text analytics of unstructured historical reports (1843–1845) to map US late blight outbreaks; (2) characterized theories on the source of the pathogen and remedies for control; and (3) created modern late blight intensity maps using Twitter feeds. The disease spread from 5 to 17 states and provinces in the US and Canada between 1843 and 1845. Crop losses, Andean sources of the pathogen, possible causes and potential treatments were discussed. Modern disease discussion on Twitter included near-global coverage and local disease observations. Topic modeling revealed general disease information, published research, and outbreak locations. The tools described will help researchers explore and map unstructured text to track and visualize pandemics.

A fundamental goal in evolutionary biology and population genetics is to understand how selection shapes the fate of new mutations. Here, we test the null hypothesis that insertion–deletion (indel) events in protein-coding regions occur randomly with respect to secondary structures. We identified indels across 11,444 sequence alignments in mouse, rat, human, chimp, and dog genomes and then quantified their overlap with four different types of secondary structure—alpha helices, beta strands, protein bends, and protein turns—predicted by deep-learning methods of AlphaFold2. Indels overlapped secondary structures 54% as much as expected and were especially underrepresented over beta strands, which tend to form internal, stable regions of proteins. In contrast, indels were enriched by 155% over regions without any predicted secondary structures. These skews were stronger in the rodent lineages compared to the primate lineages, consistent with population genetic theory predicting that natural selection will be more efficient in species with larger effective population sizes. Nonsynonymous substitutions were also less common in regions of protein secondary structure, although not as strongly reduced as in indels. In a complementary analysis of thousands of human genomes, we showed that indels overlapping secondary structure segregated at significantly lower frequency than indels outside of secondary structure. Taken together, our study shows that indels are selected against if they overlap secondary structure, presumably because they disrupt the tertiary structure and function of a protein.

The hadron mass can be obtained through the calculation of the trace of the energy-momentum tensor in the hadron which includes the trace anomaly and sigma terms. The anomaly due to conformal symmetry breaking is believed to be an important ingredient for hadron mass generation and confinement. In this work, we will present the calculation of the glue part of the trace anomaly form factors of the pion up to$Q^2\sim 4.3{\mathrm{GeV}}^2$and the nucleon up to$Q^2\sim 1{\mathrm{GeV}}^2$. The calculations are performed on a domain wall fermion ensemble with overlap valence quarks at seven valence pion masses varying from$\sim 250$to$\sim 540\mathrm{MeV}$, including the unitary point$\sim 340\mathrm{MeV}$. We calculate the radius of the glue trace anomaly for the pion and the nucleon from the$z$expansion. By performing a two-dimensional Fourier transform on the glue trace anomaly form factors in the infinite momentum frame with no energy transfer, we also obtain their spatial distributions for several valence quark masses. The results are qualitatively extrapolated to the physical valence pion mass with systematic errors from the unphysical sea quark mass, discretization effects in the renormalization sum rule, and finite-volume effects to be addressed in the future. We find the pion’s form factor changes sign, as does its spatial distribution, for light quark masses. This explains how the trace anomaly contribution to the pion mass approaches zero toward the chiral limit.

We present six epochs of optical spectropolarimetry of the Type IIP supernova (SN) 2021yja ranging from ∼25 to 95 d after the explosion. An unusually high continuum linear polarization of $p \approx 0.9~{{\ \rm per\ cent}}$ is measured during the early photospheric phase, followed by a steady decrease well before the onset of the nebular phase. This behaviour has not been observed before in Type IIP supernovae (SNe IIP). The observed continuum polarization angle does not change significantly during the photospheric phase. We find a pronounced axis of symmetry in the global ejecta that is shared in common with the Hα and Ca ii near-infrared triplet lines. These observations are consistent with an ellipsoidal geometry. The temporal evolution of the continuum polarization is also compatible with the SN ejecta interacting with aspherical circumstellar matter (CSM), although no spectroscopic features that may be associated with strong interaction can be identified. Alternatively, we consider the source of the high polarization to be an extended hydrogen envelope that is indistinguishable from low-density CSM.

The rise in smart water technologies has introduced new cybersecurity vulnerabilities for water infrastructures. However, the implications of cyber‐physical attacks on the systems like urban drainage systems remain underexplored. This research delves into this gap, introducing a method to quantify flood risks in the face of cyber‐physical threats. We apply this approach to a smart stormwater system—a real‐time controlled network of pond‐conduit configurations, fitted with water level detectors and gate regulators. Our focus is on a specific cyber‐physical threat: false data injection (FDI). In FDI attacks, adversaries introduce deceptive data that mimics legitimate system noises, evading detection. Our risk assessment incorporates factors like sensor noises and weather prediction uncertainties. Findings reveal that FDIs can amplify flood risks by feeding the control system false data, leading to erroneous outflow directives. Notably, FDI attacks can reshape flood risk dynamics across different storm intensities, accentuating flood risks during less severe but more frequent storms. This study offers valuable insights for strategizing investments in smart stormwater systems, keeping cyber‐physical threats in perspective. Furthermore, our risk quantification method can be extended to other water system networks, such as irrigation channels and multi‐reservoir systems, aiding in cyber‐defense planning.