PLAGL1 protein, human

We have previously described in detail the approach used at the Regeneron Genetics Center to perform exome sequencing in DNA samples from the UK Biobank study^{3 (link)}. In brief, genomic DNA samples were transferred to the Regeneron Genetics Center from the UK Biobank and stored in an automated sample biobank at −80 °C before sample preparation. DNA libraries were then created by enzymatically shearing DNA to a mean fragment size of 200 base pairs, and a common Y-shaped adapter was ligated to all DNA libraries. Unique, asymmetric 10-base-pair barcodes were added to the DNA fragment during library amplification to facilitate multiplexed exome capture and sequencing. Equal amounts of sample were pooled before overnight exome capture, with a slightly modified version of IDT’s xGen probe library. The initial 50,000 samples were processed with IDT ‘lot 1’ and all other samples with ‘lot 2’. The captured DNA was PCR-amplified and quantified by quantitative PCR. The multiplexed samples were pooled and then sequenced using 75-base-pair paired-end reads with two 10-base-pair index reads on the Illumina NovaSeq 6000 platform using S2 (first 50,000 samples) or S4 (all other samples) flow cells. We sequenced all samples delivered to us by the UK Biobank. A portion of samples (about 30,000) could not be delivered because of the COVID-19 pandemic.

The subjective measure of the perceived neighborhood environment used an adapted version of the Neighborhood Environment Walkability Scale (NEWS) [9 (link)]. It comprises 67 items which are grouped into 8 factors:a

Residential density—types of residences in the neighborhood

b

Land use mix—diversity (“Stores, facilities, and other things in your neighbourhood”)

was assessed by reports of facilities and amenities in the neighborhood, such as “About how long would it take to get from your home to the nearest businesses or facilities listed below if you walked to them?” and “How much it influences your participation in doing activities”c

Street connectivity—includes streets in the neighborhood, places for walking and cycling and neighborhood surroundings

d

Land use mix—access (Access to services) such as “I can do most of my shopping at local stores”, “It is easy to walk to a transit stop (bus, train) from my home” (1 = strongly disagree, 2 = somewhat disagree, 3 = somewhat agree, 4 = strongly agree)

e

Infrastructure—places for walking and cycling

f

Aesthetics (Neighborhood surroundings)

g

Traffic safety

h

Safety from crime [10 (link), 11 (link)].

Objective measures of the neighborhood environment was based on Geographical Information System (GIS) variables measured in Euclidean or straight-line distances buffer within 500 m of the centroid of a neighborhood using the software ArcGIS® version 10 [12 ]:a

Street connectivity—based on the number of true intersections within a given area, was defined by the number of street links divided by the number of street nodes within the buffer area.

b

Residential density—the number of dwelling units was divided by the land area in residential use within the area

c

Land use mix—the distribution of development across five uses (residential, commercial, industrial, recreation and other) is assessed to measure the land use mix.

d

Public park density—obtained by dividing the total area of public parks by the total area of the buffer and multiplying by 100

The Walkability Index was in turn derived as a composite value of combinations and weights of individual built environment characteristics, including residential density, street connectivity, land-use mix [13 (link)–16 (link)]. Since urban Singapore is characterized by a mixed land-used and compact urban environment, the net retail area was omitted in this study. Because of the unavailability of GIS layer of walkable paths within the study area, the impact of street networks is considered to be homogenous. “Residential lot coverage” and “street density” were used as proxy variables respectively for residential density and street connectivity. Therefore, the modified walkability index included residential lot coverage, street density and land-use mix. The built environment characteristics were measured in numerical values by ArcGIS® and Z-scores were calculated after arbitrarily dividing the whole study area into 18 zones (study area units). The constituent variables of walkability index, i.e. residential density, street density and land-use mix, were calculated based on the formulas provided in the Neighborhood Environment for Active Transport-Geographic Information Systems(NEAT-GIS) protocol [17 ]. Specifically,
Walkability index = Residential lot coverage + 1.5 × street density + land use mix
The Accessibility Index was assessed by measuring the walking access to 30 types of community service and amenity destinations to which proximity could plausibly encourage residents to walk more for leisure or transport [18 (link)]. Accessibility index is calculated for the 18 individual zones based on the multiplication of the sum of building weight within each zone and the residential density:
Accessibility index = building weights of the zone × residential density.
Both the Walkability and Accessibility Indexes were categorized into 3 levels, namely low, medium and high.
Physical activity (walking for transportation purpose); The study participants were also asked “How often do you walk from your house to (nearest) various types of businesses or facilities” with response ranging from never to daily. The scores of all items were summed and a higher scores denoting more frequently walking for transport. Transportation physical activity was the primary outcome of the analysis.
Covariates The study participant’s self-rated health was reported as ‘excellent’ , ‘very good’ , ‘good’ , ‘poor’ , or ‘very poor’. Physical Performance: gait and balance were measured with the performance oriented mobility assessment (POMA) tool [19 (link)]. Static sitting balance (rising from the sitting position without using hands) was assessed using graded scores by the need for assistance and the number of attempts; standing balance was assessed within the first 5 s after the subject’s sternum was gently pushed by the examiner, and when stance was stabilized. Staggering or excessive sway of the subject was examined with the subject standing and eyes closed. Steadiness and continuity of steps were observed with the subject turning in a complete 360° circle. The gait assessment was performed with the subject walking 6 m and returning quickly to the starting point, noting the ability to initiate walking and any hesitancy, step height and length, the lack of symmetry or inability to clear the floor, step continuity, deviation in the path and walking stance. The POMA scores for balance and gait were tallied separately using standard scoring criteria. Socio-demographic data included age, gender, ethnicity, educational attainment and housing type.

The alfalfa seed (Alfalfa cultivar ‘Zhongmu No. 3’) was provided by a specialized seed producer (Xintai Zhouquan Agricultural Technology Co., LTD, China). The similar size of healthy and plump seeds was screened and then sorted into three groups (brown, yellow and green) according to the seed coat color by visual inspection. The seed proportion was calculated by counting the number of seeds with different colors. Yellow seeds comprised the major part (59%) of the seed lot used for this study, while the relative amount of brown seeds was 33%. Green seeds comprised only 8% of the seed lot (Table 1). GA3, D-IAA, D-ABA, D-GA4 were purchased from OlChemIm Ltd. (Olomouc, Czech Republic). IAA was purchased from LGC Standards Ltd. (Tetdington, UK). ABA was purchased from Sigma-Aldrich (Merck, Germany).