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Introduction: Coronavirus disease 2019 (COVID-19) has had a profound impact globally, causing the death of millions of people and deeply affecting socio-psychological, human health, and economic systems, with some nations bearing a disproportionate burden. Despite obesity having been established as one of the major risk factors of COVID-19 severity and other degenerative diseases, the effects that dietary pattern plays in COVID-19 outcomes remain poorly understood. The goal of this study is to look into the connection between eating habits, the number of non-obese and obese people, and COVID-19 outcomes in countries with populations exhibiting normal Body Mass Index (BMI), which is an indicator of obesity. Methods: The analysis includes data from 170 countries. From the 170 countries, we focused on 53 nations where the average, BMI falls within the normal range (18.5 to 24.9). A subset of 20 nations was selected for a more detailed examination, comprising 10 nations with the lowest BMI values within the normal range (18.5-19.8) and 10 nations with the highest BMI values within the normal range (23.5-24.9). We used Artificial Intelligence (AI) and Machine Learning (ML) applications to evaluate key metrics, including dietary patterns (sugar and vegetable intake), obesity prevalence, incidence rate, mortality rate, and Case Fatality Rate (CFR). Results: The results demonstrate a significant correlation between higher obesity prevalence and increased COVID-19 severity, evidenced by elevated incidence, mortality, and CFRs in countries like North Macedonia and Italy. In contrast, nations such as Iceland and New Zealand with well-established healthcare systems revealed low mortality rates and case fatality rates despite variations in dietary habits. The study also revealed that vegetable consumption appears to provide a slight to significant protective effect, suggesting that dietary patterns alone do not consistently predict COVID-19 Outcomes. Conclusion: Data generated from this study showed the crucial role of healthcare infrastructure along with the testing capacity and data reporting in influencing the success of pandemic responses. It also highlights the need to integrate public health strategies, which focus on obesity management and improvement of healthcare preparedness. In addition, AI-driven predictive modeling offers valuable insights that may guide pandemic response efforts in the future, thereby enhancing global health crisis management and mitigating the impact of future health emergencies. 

One of the tenets of introductory biology courses is learning how the form or structure of any unit determines its function, and that form and function has long been used to understand patterns throughout the natural world (Grew, 1682; Wainwright, 1988; D􏰀ıaz et al., 2016). Form predicts function at the cellular and organ level in plants; for instance, xylem cell diameter and pit pore size are great indicators of stem xylem hydraulic conductance and vulner- ability to embolism (Pittermann & Sperry, 2005). Additionally, both theoretical treatments and experimental evidence indicate that root hydraulic function is coupled to leaf function (Cruiziat et al., 2002; Domec et al., 2009) but few studies have investigated how the anatomy and morphology of the roots may influence leaf function. In this issue of New Phytologist, Zhou et al. (2021; pp. 1481–1491) present results from a long-term precipitation experiment, showing that root form predicted leaf physiology. This study highlights that the form of one organ can have effects on function across the entire organism, providing a unique perspective in the study of form–function relationships. 

Abstract The accurate simulation of additional interactions at the ATLAS experiment for the analysis of proton–proton collisions delivered by the Large Hadron Collider presents a significant challenge to the computing resources. During the LHC Run 2 (2015–2018), there were up to 70 inelastic interactions per bunch crossing, which need to be accounted for in Monte Carlo (MC) production. In this document, a new method to account for these additional interactions in the simulation chain is described. Instead of sampling the inelastic interactions and adding their energy deposits to a hard-scatter interaction one-by-one, the inelastic interactions are presampled, independent of the hard scatter, and stored as combined events. Consequently, for each hard-scatter interaction, only one such presampled event needs to be added as part of the simulation chain. For the Run 2 simulation chain, with an average of 35 interactions per bunch crossing, this new method provides a substantial reduction in MC production CPU needs of around 20%, while reproducing the properties of the reconstructed quantities relevant for physics analyses with good accuracy. 

Abstract A search for long-lived charginos produced either directly or in the cascade decay of heavy prompt gluino states is presented. The search is based on proton–proton collision data collected at a centre-of-mass energy of $$\sqrt{s}$$ s  = 13 T $$\text {eV}$$ eV between 2015 and 2018 with the ATLAS detector at the LHC, corresponding to an integrated luminosity of 136 fb $$^{-1}$$ - 1 . Long-lived charginos are characterised by a distinct signature of a short and then disappearing track, and are reconstructed using at least four measurements in the ATLAS pixel detector, with no subsequent measurements in the silicon-microstrip tracking volume nor any associated energy deposits in the calorimeter. The final state is complemented by a large missing transverse-momentum requirement for triggering purposes and at least one high-transverse-momentum jet. No excess above the expected backgrounds is observed. Exclusion limits are set at 95% confidence level on the masses of the chargino and gluino for different chargino lifetimes. Chargino masses up to 660 (210) G $$\text {eV}$$ eV are excluded in scenarios where the chargino is a pure wino (higgsino). For charginos produced during the cascade decay of a heavy gluino, gluinos with masses below 2.1 T $$\text {eV}$$ eV are excluded for a chargino mass of 300 G $$\text {eV}$$ eV and a lifetime of 0.2 ns. 

Abstract During LHC Run 2 (2015–2018) the ATLAS Level-1 topological trigger allowed efficient data-taking by the ATLAS experiment at luminosities up to 2.1 $$\times $$ × 10 $$^{34}$$ 34  cm $$^{-2}$$ - 2 s $$^{-1}$$ - 1 , which exceeds the design value by a factor of two. The system was installed in 2016 and operated in 2017 and 2018. It uses Field Programmable Gate Array processors to select interesting events by placing kinematic and angular requirements on electromagnetic clusters, jets, $$\tau $$ τ -leptons, muons and the missing transverse energy. It allowed to significantly improve the background event rejection and signal event acceptance, in particular for Higgs and B -physics processes. 

A bstract A search for the exotic decay of the Higgs boson ( H ) into a b $$ \overline{b} $$ b ¯ resonance plus missing transverse momentum is described. The search is performed with the ATLAS detector at the Large Hadron Collider using 139 fb − 1 of pp collisions at $$ \sqrt{s} $$ s = 13 TeV. The search targets events from ZH production in an NMSSM scenario where H → $$ {\overset{\sim }{\chi}}_2^0{\overset{\sim }{\chi}}_1^0 $$ χ ~ 2 0 χ ~ 1 0 , with $$ {\overset{\sim }{\chi}}_2^0 $$ χ ~ 2 0 → $$ a{\overset{\sim }{\chi}}_1^0 $$ a χ ~ 1 0 , where a is a light pseudoscalar Higgs boson and $$ {\overset{\sim }{\chi}}_{1,2}^0 $$ χ ~ 1 , 2 0 are the two lightest neutralinos. The decay of the a boson into a pair of b -quarks results in a peak in the dijet invariant mass distribution. The final-state signature consists of two leptons, two or more jets, at least one of which is identified as originating from a b -quark, and missing transverse momentum. Observations are consistent with Standard Model expectations and upper limits are set on the product of cross section times branching ratio for a three-dimensional scan of the masses of the $$ {\overset{\sim }{\chi}}_2^0 $$ χ ~ 2 0 , $$ {\overset{\sim }{\chi}}_1^0 $$ χ ~ 1 0 and a boson. 

Abstract A search for chargino–neutralino pair production in three-lepton final states with missing transverse momentum is presented. The study is based on a dataset of $$\sqrt{s} = 13$$ s = 13  TeV pp collisions recorded with the ATLAS detector at the LHC, corresponding to an integrated luminosity of 139  $$\hbox {fb}^{-1}$$ fb - 1 . No significant excess relative to the Standard Model predictions is found in data. The results are interpreted in simplified models of supersymmetry, and statistically combined with results from a previous ATLAS search for compressed spectra in two-lepton final states. Various scenarios for the production and decay of charginos ( $${\tilde{\chi }}^\pm _1$$ χ ~ 1 ± ) and neutralinos ( $${\tilde{\chi }}^0_2$$ χ ~ 2 0 ) are considered. For pure higgsino $${\tilde{\chi }}^\pm _1{\tilde{\chi }}^0_2$$ χ ~ 1 ± χ ~ 2 0 pair-production scenarios, exclusion limits at 95% confidence level are set on $${\tilde{\chi }}^0_2$$ χ ~ 2 0 masses up to 210 GeV. Limits are also set for pure wino $${\tilde{\chi }}^\pm _1{\tilde{\chi }}^0_2$$ χ ~ 1 ± χ ~ 2 0 pair production, on $${\tilde{\chi }}^0_2$$ χ ~ 2 0 masses up to 640 GeV for decays via on-shell W and Z bosons, up to 300 GeV for decays via off-shell W and Z bosons, and up to 190 GeV for decays via W and Standard Model Higgs bosons. 

Abstract Several improvements to the ATLAS triggers used to identify jets containing b -hadrons ( b -jets) were implemented for data-taking during Run 2 of the Large Hadron Collider from 2016 to 2018. These changes include reconfiguring the b -jet trigger software to improve primary-vertex finding and allow more stable running in conditions with high pile-up, and the implementation of the functionality needed to run sophisticated taggers used by the offline reconstruction in an online environment. These improvements yielded an order of magnitude better light-flavour jet rejection for the same b -jet identification efficiency compared to the performance in Run 1 (2011–2012). The efficiency to identify b -jets in the trigger, and the conditional efficiency for b -jets that satisfy offline b -tagging requirements to pass the trigger are also measured. Correction factors are derived to calibrate the b -tagging efficiency in simulation to match that observed in data. The associated systematic uncertainties are substantially smaller than in previous measurements. In addition, b -jet triggers were operated for the first time during heavy-ion data-taking, using dedicated triggers that were developed to identify semileptonic b -hadron decays by selecting events with geometrically overlapping muons and jets.