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Chemicals and materialsAnalytical standard ( > 99% purity) of spiromesifen, BSN2060-enol, and chromafenozide were purchased from AB Solution Co., Ltd., Hwaseong-si, Gyeonggi-do, Republic of Korea. HPLC grade water and acetonitrile were supplied by Merck, Darmstadt, Germany. QuEChERS kit (4.0 g magnesium sulfate, 1.0 g sodium chloride, 1.0 g sodium citrate tribasic dihydrate, 0.5 g disodium citrate sesquihydrate) were obtained from Phenomenex, California, USA. Individual stock solutions of the target compounds were prepared in acetonitrile and stored at − 20 °C before use.Field experimentsThe trials were carried out in a greenhouse farm during the season 2018 at two different sites (with approximately 24 km distance between both sites) located in Chuncheon and Hongcheon-gun, Gangwon-do, Republic of Korea following the method described by the Organization for Economic Co-operation and Development (OECD)38. The field test of lettuce (Latuca sativa L.) crop was conducted in Chuncheon city, and perilla (Perilla frutescens (L.) Britton) crop in Hongcheon city. The area of each field was divided into two plots (treatment and control). The treatment plots were further divided into three replicates (subplots 33 m2). The control plot was separated by a buffer zone of 3 m2 from the treated site. To minimize spray overlap, buffer zones (1 m) were set up between subplots. The commercial products of spiromesifen 20% SC diluted 2000 times and chromafenozide 5% EC diluted 1000 times were sprayed twice at 7-days intervals using an automatic sprayer. After the second spray samples (lettuce and perilla leaves) were collected from each subplot at 0 (2 h after spraying), 1, 3, 5, and 7 days according to the Korean RDA23 method. Thirty samples 1.0 kg each from the collected crop were placed in polyethylene bag and labeled. After collection, the samples were transported to the laboratory, where they were chopped and homogenized. The homogenized samples were kept frozen at − 20 °C until analysis.We confirm all plant samples used in the current work comply with the IUCN Policy Statement on Research Involving Species at Risk of Extinction and the Convention on the Trade in Endangered Species of Wild Fauna and Flora.Samples pretreatmentA QuEChERS method was used for the extraction of the targeted compounds from lettuce and perilla leaves. A 10 g of previously homogenized samples were weighed into a 50 mL polypropylene centrifuge tube and mixed with 10 mL of water followed by 10 mL of acetonitrile. The samples were shaken at 1500 rpm in a shaker machine for 10 min. Then commercial QuEChERS kit was added, and the mixtures were shaken vigorously for 2 min in a shaker. Subsequently, the samples were centrifuged at 3584 g-force for 10 min. After centrifugation, the supernatant was filtered with a 0.22 μm membrane filter and transferred into the glass vial for instrumental analysis.LC-MS/MS analysisQuantitative determination of the tested compounds was carried out by using HPLC system Dionex Ultimate 3000 (Thermo Science, USA) coupled with tandem mass spectrometry (MS/MS) (TSQ Quantum Access Max (Thermo Science, USA). Water (solvent A) and acetonitrile (solvent B) containing 0.1% formic acid and 5 mM ammonium format were used as mobile phase at a flow rate of 0.4 mL/min and injection volume 1.0 µL. To obtain desirable chromatographic peaks, two different instrumental conditions were used. The chromatographic separation of spiromesifen was separated by Capcell core-C18 (2.1 mm I.D. × 150 mm × 2.7 μm, Shiseido Co., Ltd., Tokyo, Japan) and BSN2060-enol was performed by C18 column (Poroshell 120 SB-Ag, 2.1 mm I.D. × 100 mm × 2.7-μm, Agilent Technologies, Santa Clara, California, USA) with a gradient elution as follows (mobile phase B%): 0.0 min, 5.0%; 2.0 min 5%; 2.5 min, 95%; 6.0 min, 95%; 6.5 min, 5.0%; 10 min, 5.0%. Likewise, chromafenozide was separated by C18 column (Imtakt Unison UK-C18, 2.0 mm I.D. × 100 mm × 3.0-μm, Imtakt, Portland, USA) with a gradient elution as follows (mobile phase B%): 0.0 min, 5%; 1.0 min, 5.0%; 1.5 min, 90%; 5.0 min, 90%; 7.0 min, 5.0%; 10 min, 5.0%. An MS/MS system (TSQ quantum ultra, Thermo Science, USA) equipped with an electrospray ionization source operating in positive mode (ESI+) was used. The MS/MS parameters and selected product ions are shown in supplementary Tables S2 and S3.The calculation of spiromesifen total residuesThe total residues in lettuce and perilla samples were calculated using Eq. (1)23.Total residues of spiromesifen (mg/kg) = spiromesifen + (BSN2060 residue × 1.36). The conversion factor was calculated as follow;$${1}.{36},{text{(conversion}},{text{factor)}} = frac{{370.49left( {{text{spiromesifen}},{text{MW}}} right)}}{{272.34{ }left( {{text{BSN}}2060,{text{MW}}} right)}}$$
(1)
where MW molecular weight.Initial deposition calculationThe initial residues of spiromesifen and chromafenozide deposition in lettuce and perilla leaves were calculated from 0-day according to Eq. (2) described by Kang et al.12 as follow;$${text{A }},({text{mg}}/{text{kg}}) = {text{B(mg}}/{text{kg)}} times frac{100}{{{text{C}}({text{% }})}} times frac{1}{{text{E}}} times 1000$$
(2)
A: Initial residue (mg/kg), B: Residues (mg/kg) on 0 day, C: active ingredients, E: dilution factor.Method validationThe analytical method was validated in terms of different performance criteria such as linearity, accuracy, precision, and method limit of quantitation (MLOQ). Matrix-matched standards were used to construct the calibration curve by evaporating (0.01, 0.05, 0.1, 0.2, 0.5, 0.7 and 1.0 mg/kg) working solution (1 mL) and re-dissolved in the extract of control sample. The linearity of the matrix-matched calibration curve was evaluated by the values of the correlation coefficient (R2). The accuracy and precision were obtained in terms of recovery (70–120%) and repeatability (n = 3). The recoveries were determined by spiking the analytes at two concentrations levels (0.1 mg/L) and (0.5 mg/L) in 10 g control samples and were quantified by comparing the response of analytes in samples with response in calibration standard solutions prepared in matrix. The repeatability expressed as the relative standard deviation (RSD) of the analyzed samples was calculated from three repetitions. The MLOQ was calculated by Eq. (3) taking into consideration the following factors: the instrument limit of detection, volume of extraction solvent, injection volume, dilution factor, and sample amount39,40.$${text{MLOQ}},{text{(mg}}/{text{kg)}} = {text{A(ng)}} times frac{{text{B(mL)}}}{{{text{C(}}upmu {text{L)}}}} times frac{{text{D}}}{{text{E(g)}}}$$
(3)
where A: instrument limit of detection, B: volume of extraction solvent, C: injection volume, D: dilution factor, E: sample amount.Half-life calculationThe dissipation patterns of spiromesifen and chromafenozide in lettuce and perilla leaves over time were found following the first-order kinetics model28. The half-life was determined by the following equation:$${text{C}}_{{text{t}}} = {text{C}}_{0} times {text{e}}^{{ – {text{kt}}}} ,{text{DT}}_{{{5}0}} = {text{ln2}}/{text{k}}$$where Ct is the concentration of the insecticide, C0 represents the initial residue concentration of insecticide, t is the time (days) after insecticide application, and k is the constant rate.Safety assessmentIn this study, the safety assessments (percent acceptable daily intake; %ADI) of the target insecticides that are consumed with lettuce and perilla leaves were calculated by the ratio of estimated daily intake (EDI) to acceptable daily intake (ADI). The EDI was calculated using insecticide concentration and average consumption of food commodities per person per day. In addition, the theoretical maximum daily intakes (TMDIs) of both insecticides were calculated using the maximum residue limits (MRLs) and average body weight (60 kg) of adults in Republic of Korea. TMDIs were calculated following the equation described by Kim et al.41.$$begin{aligned} & {text{ADI (mg}}/{text{person}}/{text{day)}} = {text{ADI}},({text{mg}}/{text{kg}}/{text{body weight}}/{text{day}}),{text{of target insecticide}} times {text{6}}0,({text{average body weight}}) \ & {text{EDI (mg}}/{text{kg}}/{text{person)}} = {text{concentration of target insecticide (mg}}/{text{kg)}} times {text{ daily food intake (g)}} \ & % {text{ADI}} = {text{EDI}}/{text{ADI}} times {text{1}}00 \ & {text{TMDI}}% = sum % {text{ADI of all registered crops}} \ end{aligned}$$ More

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Study sites and samplingWe sampled gas and soil in 29 regions throughout the A (rainy tropical), C (temperate), and D (boreal) climate types of the Köppen classification from six continents during the vegetation period between August 2011 and June 2018, following a standard protocol26. According to the protocol, the gas and soil samples were collected from locations in public domain or in previous agreement with the local community and/or property owner. The samples were exported from the origin countries and imported to Estonia, EU in cooperation with customs officers of the respective states, following the legal provisions of soil export and import, specifically exemptions for scientific purposes. To capture the full range of environmental conditions in each region, we established 76 wetland soil sites under different vegetation (mosses, sedges, grasses, herbs, trees, and bare soil) and land-use types (natural—raised bog, fen, and forest; agricultural—arable, hay field and pasture; and a peat extraction area) (Fig. 1a; Supplementary Data 1). We used a four-grade land-use intensity index to quantify the effect of land conversion: 0, no agricultural land use (natural mire, swamp, or bog forest); 1, moderate grazing or mowing (once a year or less); 2, intensive grazing or mowing (more than once a year); and 3, arable land (directly fertilized or unfertilized). The vegetation and land-use intensity types and the land-use intensity index were estimated from observations and contacts with site managers and local researchers.Within the sites, we established 1–4 stations 15–500 m apart to maximize the captured environmental variation. Each of the 196 stations were equipped with 3–5 opaque PVC 65 L truncated conical chambers 1.5–5 m apart and an observation well (perforated, 50 mm diameter PP-HT pipe wrapped in geotextile; 1 m in length). From each of the 645 chambers, N2O fluxes were measured following the static chamber method37 using PVC collars (0.5 m diameter, installed to 0.1 m depth in soil). Stabilization of 3–12 h was allowed before gas sampling to reduce the disturbance effect of inserting the collars on fluxes. The chambers were placed into water-filled rings on top of the collars. Gases were sampled from the chamber headspace into a 50 mL glass vial every 20 min during a 1-h session. The vials had been evacuated in the laboratory 2–6 days before the sampling. At least three sampling sessions per location were run within 3 days. Water-table height was recorded from the observation wells during the gas sampling at least 8 h after placement. Soil temperature was measured at the 10 and 20 cm depth.Soil samples of 150–200 g were collected from the chambers at 0–10 cm depth after the final gas sampling, and transported to laboratories in Tartu, Estonia. The homogenized samples were divided into subsamples for physical–chemical analyses and DNA extraction. The samples for chemical analyses were stored at 4 °C and microbiological samples were stored at –20 °C. DNA extraction was provided at the Tartu University environmental microbiology laboratory (see details below). Using a PP-HT plastic cylinder, intact soil cores (diameter 6.8 cm, height 6 cm) for the N2 analysis with the He–O2 method38 were collected from the topsoil (0−10 cm) inside 252 chambers at 26 sites, starting from 2014. Samples from different climates were run at respective temperatures. During transport, the soil samples were kept below the ambient soil temperature from which they were collected.Gas flux analysesThe gas samples were analyzed for N2O concentration within 2 weeks using two Shimadzu GC-2014 gas chromatographs equipped with ECD, TCD, and a Loftfield-type autosampler. The N2O fluxes were determined on linear regressions obtained from consecutive N2O concentrations taken when the chamber was closed, using p  0.05 we removed one outlier. If the regression remained insignificant but the flux value fell below the gas-chromatography measuring accuracy (regression change of N2O concentration δv  More

In the work reported here, we have examined the interaction of symbiotic partners representative of the three major diversification centers. Although P. vulgaris could establish symbiosis with diverse rhizobial lineages, Rhizobium etli seemed to predominate in nature in the bean nodules collected from the Americas8,9, while the Americas is where the origin and diversification of the host have been experimentally supported19,20. Genotypes other than R. etli that also induce nodule formation in the bean have already been taxonomically defined as species, for instance Rhizobium tropici and Rhizobium ecuadorense, both of which were isolated from areas in northwestern South America, namely Ecuador, Brazil, and Colombia.American-bean rhizobia, from soil samples retrieved by the common bean as well as isolates from nodules found in nature have possessed polymorphism in the nodC gene, disclosing three nodC genotypes namely α, (upgamma), and (updelta)9. The different nodC alleles in American strains exhibit a varying predominance in their regional distributions in correlation with the centers of bean genetic diversification. The nodC types α and (upgamma) were detected both in bean nodules and in soils from Mexico, whereas the nodC type (updelta) was clearly predominant in soil and nodules from the Southern Andes (i. e., in Bolivia and northwest Argentina9). A quantitatively balanced representation of rhizobia with nodC type α and (upgamma) was detected in soils from Ecuador, but the nodC type (upgamma) had been found to be predominantly isolated from nodules formed in nature in that area5,9,10. It should be noted that we have reported finding of equal distribution of allele nodC type α and γ among the nine R etli isolates from bean in Mexico reported by Silva et al.7,9. The occurrence of this polymorphism proved to contribute to examining rhizobial populations inhabiting the Americas and characterizing the interaction with beans in BGD centers from Mexico to the northwest of Argentina. In performing our nodC analysis, we were aware that rhizobia genes for symbiosis are carried on plasmids which might mediate horizontal transfer, however in agreement with Silva et al.7 we assumed that although genetic exchange could be important, it is not so extensive to prevent epidemic clones from arising at significant frequency. Similar findings were found in R. leguminosarum bv trifolii associated with native Trifolium species growing in nature21.Investigations in the last decade have proposed an evolutionary pathway for the host bean that provided us with a framework for examining our results on rhizobia-bean interactions and facilitated an interpretation of the results. The current model proposes the occurrence of a Mesoamerican origin from where dispersion by independent migrations over time led to the Mesoamerican and Andean gene pools and to the Ecuador-Peru wild common-bean populations2,19,20. We found a balanced competition between α and (upgamma) nodC types in beans from Mesoamerica and the southern Andes, whereas the beans from Ecuador and Peru revealed a clear affinity for nodulation with strains of nodC type α rather than with the sympatric strains nodC type (upgamma) that we assayed (R. ecuadorense, CIAT894 and Bra-5). Nevertheless, we have previously reported that native strains and isolates with respectively both nodC types α and (upgamma) were found in soils and bean nodules from Mexico9, whereas lineages harboring nodC type (upgamma) were found to be predominant in beans from the northern and central regions of Ecuador-Peru8,9. The present results, however, indicated a clear affinity of the Ecuadorean-Peruvian—i. e., AHD—beans for strains nodC type α when assessed for competition against nodC type (upgamma) (Fig. 2A). We also found that nodC type (updelta) displayed a clear predominant occupancy of nodules of the AHD beans in contrast to the scarce occupancy of nodules of the Mesoamerican and Andean beans (Fig. 2B). Taken together, these results indicate no affinity of AHD beans for sympatric rhizobial strains containing nodC type (upgamma)—despite the finding that rhizobia of nodC type (upgamma) appear to predominate in isolates of nodules formed in Ecuador9,10.We conclude that although rhizobial type nodC (upgamma) was previously found to predominate in bean nodules from Ecuador, the competitiveness of that rhizobial strain for nodulation compared to other genotypes of bean rhizobia was relatively low. A possible explanation could be that seeds might be assumed to play a key role as carriers during the dissemination of the bean throughout the American regions. Thus, we can hypothesize that at the time of bean dissemination both R. etli nodC types α and (upgamma) (R. ecuadorense and other lineages) moved in conjunction with the host from Mesoamerica to northern Ecuador-Peru, but the strains bearing nodC type (upgamma) achieved an adaptation—probably due to edaphic characteristics, environmental stresses, and other features—in such a way that that strain predominated in soils and succeeded in nodulation.Alternatively, that prevalence might arise from a host selection for a rhizobium that is more effective in nitrogen fixation in a new and different environment. A poor relationship, however, between competitiveness and efficiency was found in the pea22. In line with the concept of adaptation, the bean had been found to be preferentially nodulated by species of R. tropici in acidic soils in regions of Brazil and Africa4,23. Since the environment could also be a major influence on the host and its symbiotic interactions, the Andean area represents a cooler climate for the growth of the bean than the Mesoamerican region24,25. Furthermore, since our assays were performed in laboratory environment parameters, we do not rule out the effect -if any- by the diverse and complex soil microbial community coexisting with bean rhizobia. Within this context, two contrasting soils from Argentina which differ in geolocation and edaphic properties and the perlite substrate were assayed side by side in nodule occupancy of Negro Xamapa after inoculation with a mixture of strains nodC type α and γ (Results not shown). Our results showed that the predominance of nodC type γ in the occupation of the nodules of this variety (about 80% occupation) is not affected by the type of substrate (p = 0.5566). Yet, we assume that the performance in diverse soil and ecosystems should be further evaluated in situ. In agreement, a good coevolution of rhizobia strains with nodC type (upgamma) was detected in nodules of bean varieties from the Mesoamerica and Andean genetic pools inoculated with soil samples from Mexico, Ecuador, and Northwest of Argentina, respectively (see Table 2 in Aguilar et al., 2004) [9].With respect to the interaction in the southern Andes, we propose another interpretation that takes into consideration the bottleneck that occurred before domestication in the Andes, as was indicated by Bitocchi et al.26, which scenario enables the assumption that the adaptation and concomitant diversification involved a coevolution of the symbioses. Therefore, similar profiles of competitiveness for nodulation in Mesoamerican and Andean beans were found between nodC type (upgamma) versus nodC types α and (updelta), but a significant occupancy by the nodC type (updelta) was recorded in the Andean beans.Our work suggests that the genetics of both the host and the bacteria determine the mutual affinity and additionally indicates that symbiotic interaction is another trait of legumes sensitive to the effects of evolution and ecological adaptation to the locale environment such as the characteristics of the soil and the climate.The analysis of the genetic sequences of the bean that encode genes associated with symbiosis, revealed variation of NFR1, NFR5 and NIN over the representative accessions of the Mesoamerican, the Andean, and the AHD gene pools. It is proposed that a receptor complex composed of NFR1 and NFR5 initiates signal transduction in response to Nod-factor synthesized and released by rhizobia27. Although the variation consisted mainly in neutral-amino-acid substitutions, thus suggesting only minimal changes in the functionality, if any at all; we could cite the convincing and relevant evidence reported by Radutoiu et al.27 that the amino-acid residue 118 of the second LysM module of NFR5 is essential for the recognition of rhizobia by species of Lotus japonicus and Lotus filicaulis. Our finding that the Mesoamerican-bean NFR5 has glutamine (Q) in position 151, whereas the Andean and the AHD both have proline (P)—neither of which amino acids is neutral—would merit further investigation to evaluate if such a mutation might play a role in nodulation preference. Although this result must be considered with caution, we found that the conserved polymorphism in the NFR1 and NFR5 proteins has caused the beans representative of the genetic pool Ecuador-Peru—i. e., the AHD—to be grouped in a cluster separate from those of Mesoamerica and the Andes. What we found to be interesting was that the phylogenetic and RMSD profiles of grouping the sequences are consistent with different evolutionary pathways in beans from the AHD and the Andean areas. This observation agrees with the proposal of Randón-Anaya et al.2 that those former beans from northern Peru-Ecuador originated from an ancestral form earlier than that of Mesoamerican- and Andean-bean genotypes. In addition, by applying a suppressive subtractive hybridization approach a set of bean genes were identified in our laboratory to be expressed in early step of infection by the cognate rhizobia28. Taken these results together, we conclude that genomic regions and patterns of expression in the host appear associated with an affinity for nodulation.Within a broader context, we believe that our results on the biogeography of bean-rhizobia interactions in the region where the origin and domestication of the host plants occurred provide novel useful issues to be considered in inoculation programs, for instance those involving selection of strains and cultivars, and invite to validate these findings in follow up field trials. More

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Stochastic age-specific transmission modelWe formulate a stochastic age-specific transmission model in the general Susceptible(S)-Exposed(E)-Reported(I)-Unreported(U)-Recovered(R) framework. For a particular age group (i) at time (t-1) ((i=1) corresponding to the 0–17 years, (i=2) to 18–44, (i=3) to 45–64 and (i=4) to 65+), we have$$begin{array}{l}{S}_{i}(t)= {S}_{i}(t-1)-{n}_{S{E}_{i}}(t)\ {E}_{i}(t)= {E}_{i}(t-1)+{n}_{S{E}_{i}}(t)-\ {n}_{E{I}_{i}}(t)-{n}_{E{U}_{i}}(t)\ {I}_{i}(t)= {I}_{i}(t-1)+{n}_{E{I}_{i}}(t)-{n}_{I{R}_{i}}(t)\ {U}_{i}(t)= {U}_{i}(t-1)+{n}_{E{U}_{i}}(t)-{n}_{U{R}_{i}}(t)\ {R}_{i}(t)= {R}_{i}(t-1)+{n}_{I{R}_{i}}(t)+{n}_{U{R}_{i}}(t),end{array}$$
(1)
where ({n}_{{XY}_{i}}(t)) represents number of transitions between a class X and class Y for age group (i) at time (t).The number of transitions from the susceptible to exposed class for group (i) at time (t) is modelled by$$begin{aligned}{n}_{S{E}_{i}}(t)&sim Poi({S}_{i}(t-1)times {gamma }_{i}(t)times \ & quad sum_{j=1}beta (t)times {c}_{j,i}(t)times {{I}_{j}(t-1)+{U}_{j}(t-1)}).end{aligned}$$
(2)
Here, (beta (t)) denotes the average infectiousness of an infectious individual and ({c}_{j,i}(t)) is the average number of contacts per day made by age group (j) to (i). Also note that the product (beta (t)times {c}_{j,i}(t)) may represent age-specific transmissibility (of age group (j)) accounting for contacts. We allow and infer two change points of (beta (t)) (one potentially correlates to changes due to the implementation of lockdown and another one to changes due to the lifting of lockdown), i.e.,$$beta left(tright)=left{begin{array}{ll}{beta }_{0},&quad if; tle {T}_{1}\ {beta }_{1}={omega }_{1}times {beta }_{0},&quad if ;{T}_{1}{T}_{2},end{array}right.$$
(3)
where ({T}_{1}) and ({T}_{2}) are the two change points to be inferred (({T}_{2}ge {T}_{1})). ({gamma }_{i}(t)) denotes the susceptibility of group (i) relative to the oldest age group (i.e., ({gamma }_{4}=1)), which is also allowed to change proportionally after lifting the lockdown. Note that ({gamma }_{i}(t)) implicitly incorporates any behavioral effects (e.g., potential reduction of risk of getting infection due to facemask wearing). Transitions between other classes are modelled as:$$begin{aligned}{n}_{E{U}_{i}}(t)sim & Bin({n}_{S{E}_{i}}(t-{D}_{EU}),{p}_{{U}_{i}}(t-{D}_{EU}))\ {n}_{E{I}_{i}}(t)=& {n}_{S{E}_{i}}(t-{D}_{EI})-{n}_{E{U}_{i}}(t)\ {n}_{I{R}_{i}}(t)=& {n}_{E{I}_{i}}(t-{D}_{IR})\ {n}_{U{R}_{i}}(t)=& {n}_{E{U}_{i}}(t-{D}_{UR}),end{aligned}$$
(4)
where ({D}_{EI}), ({D}_{EU}), ({D}_{IR}) and ({D}_{UR}) denote the mean waiting times between the indicated two classes. We assume that ({D}_{EI})= ({D}_{EU})=7 days and ({D}_{IR})= ({D}_{UR})=14 days. ({p}_{{U}_{i}}(t)) represents probability that an infection is unreported at times (t) for age group (i), we assume$${p}_{{U}_{i}}(t)=1-frac{{e}^{{f}_{i}(t)}}{1+{e}^{{f}_{i}(t)}}.$$
(5)
({f}_{i}(.)) is an increasing function with ({f}_{i}(t)={a}_{i}+{b}_{i}times t), where (-infty More

Analysing the relations between FTSE100 and self-reported measures of emotional well-being we confirmed that market ups (higher FTSE100 scores) were associated with higher scores of “happiness” and lower scores in self-reported “negative emotional facets”: irritability, hurt and nervous feelings, anxiety (Fig. 1; Table 1). The identified association also held true for the 5.5-years of the MRI subsample (Supplementary Table S2). We further explored non-imaging variables that are associated with mood changes, i.e. alcohol intake (overall intake frequency and a composite score reflecting weekly intake of all alcoholic beverages) and diastolic blood pressure (automatic readings in mmHg measured at rest), and showed that they were also highly correlated with the FTSE100 (Fig. 1A) in that both measures increased when the stock market decreased in value. Several of these effects (relation between stock market and negative emotions, blood pressure or alcohol-intake) were reproduced in the My Connectome data-set consisting of one single subject whose measurements were taken at 81 timepoints during a period or 1.5 years (Fig. 1B).Figure 1Non-MRI variables and stock market moves. The figure illustrates the identified associations between stock market moves and non-MRI indicators of well-being in the UK Biobank sample (top panel A) and My Connectome data, a single-subject study (bottom panel B); *p  More