Iron deficiency up-regulates iron absorption from ferrous sulphate but not ferric pyrophosphate and consequently food fortification with ferrous sulphate has relatively greater efficacy in iron-deficient individuals

Fe absorption from water-soluble forms of Fe is inversely proportional to Fe status in humans. Whether this is true for poorly soluble Fe compounds is uncertain. Our objectives were therefore (1) to compare the up-regulation of Fe absorption at low Fe status from ferrous sulphate (FS) and ferric pyrophosphate (FPP) and (2) to compare the efficacy of FS with FPP in a fortification trial to increase body Fe stores in Fe-deficient children v. Fe-sufficient children. Using stable isotopes in test meals in young women (n 49) selected for low and high Fe status, we compared the absorption of FPP with FS. We analysed data from previous efficacy trials in children (n 258) to determine whether Fe status at baseline predicted response to FS v. FPP as salt fortificants. Plasma ferritin was a strong negative predictor of Fe bioavailability from FS (P <0·0001) but not from FPP. In the efficacy trials, body Fe at baseline was a negative predictor of the change in body Fe for both FPP and FS, but the effect was significantly greater with FS (P <0·01). Because Fe deficiency up-regulates Fe absorption from FS but not from FPP, food fortification with FS may have relatively greater impact in Fe-deficient children. Thus, more soluble Fe compounds not only demonstrate better overall absorption and can be used at lower fortification levels, but they also have the added advantage that, because their absorption is up-regulated in Fe deficiency, they innately ‘target’ Fe-deficient individuals in a populatio

Efficacy of iron-biofortified crops

Biofortification aims to increase the content of micronutrients in staple crops without sacrificing agronomic yield, making the new varieties attractive to farmers. Food staples that provide a major energy supply in low- and middle-income populations are the primary focus. The low genetic variability of iron in the germplasm of most cereal grains is a major obstacle on the path towards nutritional impact with these crops, which is solvable only by turning to transgenic approaches. However, biofortified varieties of common beans and pearl millet have been developed successfully and made available with iron contents as high as 100 mg/kg and 80 mg/kg, respectively, two to five times greater than the levels in the regular varieties. This brief review summarizes the research to date on the bioavailability and efficacy of iron-biofortified crops, highlights their potential and limitations, and discusses the way forward with multiple biofortified crop approaches suitable for diverse cultures and socio-economic milieu. Like post-harvest iron fortification, these biofortified combinations might provide enough iron to meet the additional iron needs of many iron deficient women and children that are not covered at present by their traditional diets.Keywords: Biofortification, Iron, Beans, Pearl millet, Rice, Polyphenols, Phytic acid, Anemia, Efficacy, Nutrition-Agriculture linkage

The ATLAS trigger system for LHC Run 3 and trigger performance in 2022

The ATLAS trigger system is a crucial component of the ATLAS experiment at the LHC. It is responsible for selecting events in line with the ATLAS physics programme. This paper presents an overview of the changes to the trigger and data acquisition system during the second long shutdown of the LHC, and shows the performance of the trigger system and its components in the proton-proton collisions during the 2022 commissioning period as well as its expected performance in proton-proton and heavy-ion collisions for the remainder of the third LHC data-taking period (2022–2025)

Search for lepton-favour violation in high-mass dilepton final states using 139 fb−1 of pp collisions at √s = 13 TeV with the ATLAS detector

A search is performed for a heavy particle decaying into different-flavour, dilepton final states, using 139 fb−1 of proton-proton collision data at √s = 13 TeV collected in 2015–2018 by the ATLAS detector at the Large Hadron Collider. Final states with electrons, muons and hadronically decaying tau leptons are considered (eμ, eτ or μτ). No significant excess over the Standard Model predictions is observed. Upper limits on the production cross-section are set as a function of the mass of a Z′ boson, a supersymmetric τ-sneutrino, and a quantum black-hole. The observed 95% CL lower mass limits obtained on a typical benchmark model Z′ boson are 5.0 TeV (eμ), 4.0 TeV (eτ), and 3.9 TeV (μτ), respectively

Search for the Zγ decay mode of new high-mass resonances in pp collisions at √s = 13 TeV with the ATLAS detector

This letter presents a search for narrow, high-mass resonances in the Zγ final state with the Z boson decaying into a pair of electrons or muons. The √s = 13 TeV pp collision data were recorded by the ATLAS detector at the CERN Large Hadron Collider and have an integrated luminosity of 140 fb−1. The data are found to be in agreement with the Standard Model background expectation. Upper limits are set on the resonance production cross section times the decay branching ratio into Zγ. For spin-0 resonances produced via gluon–gluon fusion, the observed limits at 95% confidence level vary between 65.5 fb and 0.6 fb, while for spin-2 resonances produced via gluon–gluon fusion (or quark–antiquark initial states) limits vary between 77.4 (76.1) fb and 0.6 (0.5) fb, for the mass range from 220 GeV to 3400 GeV

Search for excited τ-leptons and leptoquarks in the final state with τ-leptons and jets in pp collisions at s√ = 13 TeV with the ATLAS detector

A search is reported for excited τ-leptons and leptoquarks in events with two hadronically decaying τ-leptons and two or more jets. The search uses proton-proton (pp) collision data at s√ = 13 TeV recorded by the ATLAS experiment during the Run 2 of the Large Hadron Collider in 2015–2018. The total integrated luminosity is 139 fb−1. The excited τ-lepton is assumed to be produced and to decay via a four-fermion contact interaction into an ordinary τ-lepton and a quark-antiquark pair. The leptoquarks are assumed to be produced in pairs via the strong interaction, and each leptoquark is assumed to couple to a charm or lighter quark and a τ-lepton. No excess over the background prediction is observed. Excited τ-leptons with masses below 2.8 TeV are excluded at 95% CL in scenarios with the contact interaction scale Λ set to 10 TeV. At the extreme limit of model validity where Λ is set equal to the excited τ-lepton mass, excited τ-leptons with masses below 4.6 TeV are excluded. Leptoquarks with masses below 1.3 TeV are excluded at 95% CL if their branching ratio to a charm quark and a τ-lepton equals 1. The analysis does not exploit flavour-tagging in the signal region

Measurement of the cross-sections of the electroweak and total production of a Zγ pair in association with two jets in pp collisions at √s = 13 TeV with the ATLAS detector

This Letter presents the measurement of the fiducial and differential cross-sections of the electroweak production of a Zγ pair in association with two jets. The analysis uses 140 fb−1 of LHC proton–proton collision data taken at √s = 13 TeV recorded by the ATLAS detector during the years 2015–2018. Events with a Z boson candidate decaying into either an e+e− or μ+μ− pair, a photon and two jets are selected. The electroweak component is extracted by requiring a large dijet invariant mass and by using the information about the centrality of the system and is measured with an observed and expected significance well above five standard deviations. The fiducial pp → Zγ jj cross-section for the electroweak production is measured to be 3.6 ± 0.5 fb. The total fiducial cross-section that also includes contributions where the jets arise from strong interactions is measured to be 16.8+2.0 −1.8 fb. The results are consistent with the Standard Model predictions. Differential cross-sections are also measured using the same events and are compared with parton-shower Monte Carlo simulations. Good agreement is observed between data and predictions