Nicotiana tabacum

For each risk factor, we systematically searched for published studies, household surveys, censuses, administrative data, ground monitor data, or remote sensing data that could inform estimates of risk exposure. To estimate mean levels of exposure by age-sex-location-year, specific methods varied across risk factors (appendix 1 sections 2.1, 4). For many risk factors, exposure data were modelled using either spatiotemporal Gaussian process regression or DisMod-MR 2.1,17 (link), 18 (link) which are Bayesian statistical models developed over the past 12 years for GBD analyses. For most risk factors, the distribution of exposure across individuals was estimated by modelling a measure of dispersion, usually the SD, and fitting an ensemble of parametric distributions to the predicted mean and SD. Ensemble distributions for each risk were estimated based on individual-level data. Details for each risk factor modelling for mean, SD, and ensemble distribution are available in appendix 1 (section 4). Because of the strong dependency between birthweight and gestational age, exposure for these risks was modelled as a joint distribution using the copula method.¹⁹
In many cases, exposure data were available for the reference method of ascertainment and for alternative methods, such as tobacco surveys reporting daily smoking versus total smoking; in these cases, we estimated the statistical relationship between the reference and alternative methods of ascertainment using network meta-regression and corrected the alternative data using this relationship.

A core set of key performance indicators are included in each STS survey (see Table 2 for assessments routinely included each month). Specific questions are added to the survey to address particular issues (e.g. to assess the impact of Smokefree legislation and public support for a levy on tobacco products to fund tobacco control initiatives). The postal follow-up questionnaire is much shorter. Questions include current smoking status, number of cigarettes smoked, attempts to stop and characteristics of those attempts, attitudes towards smoking, cutting down smoking behaviour and tobacco dependence.
Smoking status and cigarettes smoked per day are analysed in the current paper. Smoking status was assessed with the following question: 'Which of the following best applies to you? I smoke cigarettes (including hand-rolled) every day, I smoke cigarettes (including hand-rolled), but not every day; I do not smoke cigarettes at all, but I do smoke tobacco of some kind (e.g. pipe or cigar); I have stopped smoking completely in the last year; I stopped smoking completely more than a year ago; I have never been a smoker (i.e. smoked for a year or more); Don't Know'. Those who responded that they smoked cigarettes every day or that they smoked cigarettes but not every day are coded as current cigarette smokers. Cigarette consumption is measured using the following question 'How many cigarettes per day do/did you usually smoke'. Those who do not smoke every day can give a figure per week or per month.
Socio-demographic information includes: gender, age, and social grade based on information about the occupation of the chief income earner, as used in the British National Readership Survey [19 ]. The social grade categories are: AB = higher and intermediate professional/managerial, C1 = supervisory, clerical, junior managerial/administrative/professional, C2 = skilled manual workers, D = semi-skilled and unskilled manual workers, and E = on state benefit, unemployed, lowest grade workers. These are dichotomised into ABC1 and C2DE in the current analyses.

Pregnant CD1 mice were exposed (via direct inhalation) to e-cig vapor containing 2.4% nicotine (Blu^™, 24 mg/ml nicotine) mixed with oxygenated air or oxygenated air alone, 6 times/day; 1 cartridge/day from gestational day 5 (E5) until delivery, and it was continued after delivery until the pups were 7 days old. After birth, the pups would be exposed to nicotine via lactation [40 (link), 41 (link)]. This exposure model was adopted following a study by Sifat et al. who investigated the effects of prenatal electronic cigarette exposure on offspring in mice model [6 (link), 8 (link)]. In our study, Blu^™ was used as this is one of the most popular e-cig brands still on the market, the cig-a-like structure fits well in our smoking apparatus, and there have been previously reported studies using Blu^™ [6 (link), 34 (link), 37 (link)]. A modified CORESTA (Cooperation Centre for Scientific Research Relative to Tobacco) standard smoking protocol adapted to study e-cig exposure (27.5 ml puff depth volume, 3 s puff duration, 2 puffs per 60 s, 32 puffs/session) was followed in the laboratory. E-cig vapor was generated using a Single Cigarette Smoking Machines (SCSM, CH Technologies Inc.) following a previously published method used by our laboratory [6 (link), 34 (link), 37 (link)]. This method was developed to mimic the smoking behavior of a human chronic/heavy smoker/vaper and yields plasma levels of cotinine (111 ng/ml) which is in the range of blood cotinine level (30–250 ng/ml) found in other preclinical rodent models of chronic e-cig exposure [42 (link), 43 (link)]. The smoking exposure was done between 9 a.m. and 2 p.m.

The sequences of the GmHXKs proteins obtained from the G. max genome were aligned using DNAMAN 7.0 software to search for conserved domains by inspection using sites present in AtHXK1 as a reference. To compare evolutionary relationships, the putative HXKs from G. max, A. thaliana, Solanum lycopersicum, O. sativa and Nicotiana tabacum were used to construct the phylogenetic tree using MEGA-X with the neighbor-joining (NJ) method and 1,000 bootstrap replicates (Kumar et al., 2018 (link)). Expression data on GmHXK gene family members at different developmental stages and in different tissues under normal conditions were downloaded from the Soybase (https://www.soybase.org/). Data on differential expression for only 14 members were eventually obtained and used for subsequent analysis.
To analyze expression pattern of soybean seedlings under salt stress, soybean seedlings were grown in a growth chamber under greenhouse conditions of 28°C under a16-h light/8-h dark cycle. Three-week-old seedlings were treated with 0.5% NaCl (salt stress) or drought treatment (10%PEG 6000). The root samples of the seedlings were collected after treatment for 2-h, 8-h, 24-h, and 72-h. Then, different samples were frozen quickly in liquid nitrogen, and stored at −80°C for RNA extraction and analysis. Total RNA was isolated using the Plant RNA Kit (CWBIO, Beijing, China), and its concentration and purity were determined by Nanodrop2000 nucleic acid analyzer (Thermo, America). First-strand cDNA was synthesized from 0.5 µg of total RNA using the HiFi-MMLV cDNA Kit (CWBIO, Beijing, China), and then used as a template for qRT-PCR analysis using gene-specific primers (Supplementary Table S1). Data analysis of RT-qPCR was performed using 2^−ΔΔCT method (Livak and Schmittgen, 2001 (link)).