Jasminum

We used single cell data sets from 10 published studies (Bi et al., 2021 (link); Chung et al., 2017 (link); Darmanis et al., 2017 (link); He et al., 2021 (link); Lee et al., 2020 (link); Ma et al., 2019 (link); Neftel et al., 2019 (link); Puram et al., 2017 (link); Tirosh et al., 2016 (link); Venteicher et al., 2017 (link)) for the evaluation of number of expressed genes in tumor versus normal cells to identify significant heterogenous patterns among the two phenotypes. Annotations of cell identity were also downloaded from each publication. We filtered all the data sets by removing non-expressed genes and then applied regularized negative binomial regression implemented in Seurat for normalization. We used C2 (n = 6226), C3 (n = 3556) and Hallmarks (n = 50) modules from MSigDB (Subramanian et al., 2005 (link)) v.7.2, to calculate the ratio of signature genes across all data sets and further signature scoring. We tested five tools for signature score calculations, including SCSE (Pont et al., 2019 (link)), AUCell (Aibar et al., 2017 (link)), ssGSEA, GSVA (Hänzelmann et al., 2013 (link)), and JASMINE. GSVA was included in tumor-normal comparisons but was dropped in gold standard tests and down sampling experiments due to slow running speed and highly correlated outputs with ssGSEA. We used GSVA and ssGSEA methods implemented in the GSVA Bioconductor (Hänzelmann et al., 2013 (link)) and AUCell method from AUCell Bioconductor packages (Aibar et al., 2017 (link)) with default parameters. We implemented SCSE in the R environment (v4.0) according to the equation reported in their paper (Pont et al., 2019 (link)). The output scores were used as is in tumor/normal cell comparisons and simulation analyses.

The age-standardized rate (ASR) is a critical and representative parameter when considering differences in the age structure of multiple populations. The ASR is a weighted mean of age-specific rates. The ASR is calculated on basis of the age structure of standard populations using the following formula: $$\text{ASR}=\frac{{\sum }_{i=1}^A{a}_i{w}_i}{{\sum }_{i=1}^A{w}_i}\times 100,000$$
where a_i is the age-specific rate in the i^th age group, w_i is the number of persons (or the weight) in the corresponding i^th age subgroup of the selected reference standard population, and A is the number of age groups.
The ASR trend is a widely accepted measure that estimates the changing patterns of disease burden. The estimated annual percentage change (EAPC) is a common index reflecting the temporal trend of ASR, which has been described in detail elsewhere [18 (link)]. A regression line was fitted to the natural logarithm of the ASR. EAPC and its 95% confidence interval (CI) were estimated with the linear regression model. The formula is as following: $$\text{y}=\alpha +\beta \text{x}+\epsilon$$ $$\text{EAPC}=100\times \left(\text{exp}\left(\beta \right)-1\right)$$
where y = ln (ASR), and x is the calendar year. The trends were judged: if the EAPC and its 95% CI were > 0, the ASR was in an increasing trend; if EAPC and its 95% CI were < 0, the ASR was in a decreasing trend; other values meant that the ASR was stable over time. For the aim to analyze the impact factors of EAPC, the respective associations between EAPCs and ASR in 2000, and between EAPCs and HDI in 2017, were calculated by using the Pearson correlation analysis in the Statistical Package for Social Sciences (SPSS; version 25.0; SPSS Inc., Chicago, IL, USA). The choropleth maps were drawn using the statistical software R 3.6.2 (Lucent Technologies, Jasmine Mountain, USA). A P value of less than 0.05 was deemed to be statistically significant.

We conducted several sensitivity analyses to check the robustness of our results. Firstly, we repeated the analyses using alternative df values (from 3 df to 4, 5, and 6 df) for mean temperature, relative humidity, and precipitation. Secondly, we used alternative moving average lag structures for all meteorological factors: lag 0–1 (current month and preceding 1 month) and lag 0–2 (current month and preceding 2 months). Thirdly, we refitted the CITS models using data from 2017 to 2020 to avoid inconsistencies in trends between different periods. Finally, for diseases with adequate sample sizes with the elderly, we restricted analyses to people aged between 20 and 54 and people aged above 55, respectively. We used the statistical software R 4.0.1 (Lucent Technologies, Jasmine Mountain, USA) to perform all analyses. A two-sided P-value < 0.05 was considered statistically significant.

Thai jasmine rice cultivar ‘Khao Dowk Mali 105’ (KDML 105), which is susceptible to blast disease, was used in the present experiment. Rice seeds were sterilized with 1% sodium hypochlorite solution for 1 min and then rinsed with sterile water three times. The seeds were soaked in water 24 h before planting. Ten seedlings were planted in 10–inch plant pots containing approximately 5 kg of clay soil. The pots were incubated in a greenhouse under 12 h of natural light at 28°C ± 4°C with 60%–75% humidity.

The reference beverage, an oral glucose solution containing 50 g available carbohydrate, was prepared as 54.9 g Glucodin™ powder (iNova Pharmaceuticals Aust Pty Ltd., NSW, Australia) dissolved in 250 ml water (Table 1). The reference beverage was consumed by each participant on three separate occasions (sessions 1, 4, and 6). In addition, participants also tested three different beverage treatments which were consumed with a standardised, high GI meal. A computer-generated research randomiser program determined the randomised consumption order for each of the three meal-with-beverage treatments. Each meal and beverage treatment was consumed on one occasion, with at least 1 day in between consecutive test sessions.
The three beverage treatments were; 330 ml of soda water (Schweppes™, Asahi Beverages, VIC, Australia) that served as a placebo control, diet lemonade soft drink (Schweppes™ Zero Sugar, Asahi Beverages, VIC, Australia), and organic kombucha (The Good Brew Company Pty Ltd., VIC, Australia). The kombucha, which was made from spring water, organic oolong and green tea along with organic sugar, contained a highly complex mix of 200 probiotic species and a high concentration of polyphenols that have been previously characterised (19 (link)). The 330 ml of kombucha beverage contributed an additional 3 g of available carbohydrate (1.7 g of which was sugar) to the test meal, while the soda water and diet lemonade did not contain any sugar. The nutritional compositions of the three meal-with-beverage treatments are shown in Table 1. The standardised meal provided 50 g available carbohydrate from microwave Jasmine rice (147.2 g, SunRice™, Ricegrowers Ltd., NSW, Australia), with an additional 2.9 g available carbohydrate provided by green peas (20 g, McCain’s™, McCain Foods Aust. Pty Ltd., VIC, Australia) and soy sauce (10 g, Kikkoman Corporation).
The test portion of microwave Jasmine rice and frozen green peas were combined together in a bowl and cooked in the microwave for 1 min on high. The soy sauce was then added to the prepared meal and immediately served to a participant with the appropriate refrigerated test beverage (soda water, diet soft drink or kombucha). The participants were required to consume all food and fluid served and were instructed to consume the test beverage with the meal (ie. alternate mouthfuls of meal and beverage).
Participants were required to consume a carbohydrate-based evening meal, excluding legumes and alcohol, on the evening prior to each test session. On the morning of each session, participants arrived following a 10–12 h overnight fast. Two capillary blood samples (≥0.5 ml blood) were collected from a warmed hand into heparin-coated tubes in the fasted state (−5 and 0 min). Participants then consumed either the reference glucose solution or one of the test meal-with-beverage treatments within 12 min. Additional capillary blood samples were collected at regular intervals (15, 30, 45, 60, 90, and 120 min) after commencement of the reference solution or test meal. Participants were required to remain seated with minimal movement throughout each 120 min test session.
Each capillary blood sample was centrifuged at 10,000xg for 45 s immediately after collection. The plasma layer was then transferred into an uncoated tube and stored at −30°C for later glucose and insulin analysis. Plasma glucose concentration was measured in duplicate using a glucose hexokinase assay (Beckman Coulter Inc.) on an automatic centrifugal spectrophotometric clinical chemistry analyser (Beckman Coulter AU480^®, Beckman Instruments Inc., United States). Plasma insulin concentration was measured using an insulin sandwich type enzyme-linked immunoassay (Insulin ELISA kit, ALPCO^®, Salem, NH, United States). All samples for a given participant were analysed within the same assay.