Analysis of significant protein abundance from multiple reaction-monitoring data

Background Discovering reliable protein biomarkers is one of the most important issues in biomedical research. The ELISA is a traditional technique for accurate quantitation of well-known proteins. Recently, the multiple reaction-monitoring (MRM) mass spectrometry has been proposed for quantifying newly discovered protein and has become a popular alternative to ELISA. For the MRM data analysis, linear mixed modeling (LMM) has been used to analyze MRM data. MSstats is one of the most widely used tools for MRM data analysis that is based on the LMMs. However, LMMs often provide various significance results, depending on model specification. Sometimes it would be difficult to specify a correct LMM method for the analysis of MRM data. Here, we propose a new logistic regression-based method for Significance Analysis of Multiple Reaction Monitoring (LR-SAM). Results Through simulation studies, we demonstrate that LMM methods may not preserve type I error, thus yielding high false- positive errors, depending on how random effects are specified. Our simulation study also shows that the LR-SAM approach performs similarly well as LMM approaches, in most cases. However, LR-SAM performs better than the LMMs, particularly when the effects sizes of peptides from the same protein are heterogeneous. Our proposed method was applied to MRM data for identification of proteins associated with clinical responses of treatment of 115 hepatocellular carcinoma (HCC) patients with the tyrosine kinase inhibitor sorafenib. Of 124 candidate proteins, LMM approaches provided 6 results varying in significance, while LR-SAM, by contrast, yielded 18 significant results that were quite reproducibly consistent. Conclusion As exemplified by an application to HCC data set, LR-SAM more effectively identified proteins associated with clinical responses of treatment than LMM did.This research was supported by a grant of the Korea Health Technology R&D Project through the Korea Health Industry Development Institute (KHIDI), funded by the Ministry of Health & Welfare, Republic of Korea (grant number: HI16C2037, HI15C2165). Publication of this article was sponsored by HI16C2037 grant

Satellite Data-Based Phenological Evaluation of the Nationwide Reforestation of South Korea

Through the past 60 years, forests, now of various age classes, have been established in the southern part of the Korean Peninsula through nationwide efforts to reestablish forests since the Korean War (1950-53), during which more than 65% of the nation's forest was destroyed. Careful evaluation of long-term changes in vegetation growth after reforestation is one of the essential steps to ensuring sustainable forest management. This study investigated nationwide variations in vegetation phenology using satellite-based growing season estimates for 1982-2008. The start of the growing season calculated from the normalized difference vegetation index (NDVI) agrees reasonably with the ground-observed first flowering date both temporally (correlation coefficient, r = 0.54) and spatially (r = 0.64) at the 95% confidence level. Over the entire 27-year period, South Korea, on average, experienced a lengthening of the growing season of 4.5 days decade(-1), perhaps due to recent global warming. The lengthening of the growing season is attributed mostly to delays in the end of the growing season. The retrieved nationwide growing season data were used to compare the spatial variations in forest biomass carbon density with the time-averaged growing season length for 61 forests. Relatively higher forest biomass carbon density was observed over the regions having a longer growing season, especially for the regions dominated by young (<30 year) forests. These results imply that a lengthening of the growing season related to the ongoing global warming may have positive impacts on carbon sequestration, an important aspect of large-scale forest management for sustainable development.open2

Long-term trends of March temperature in the 25 selected cities.

<p>The long-term trend of (a) average temperature in March, (b) average daily minimum temperature in March, and (c) average daily maximum temperature in March are shown. Statistically significant differences (<i>p</i> < 0.05)—significant enough to reject a null hypothesis of no change—are denoted by bold blue cross.</p

Distributions of the first flowering and last frost date and the days between them.

<p>The box-and-whisker plot of the entire records of each variable is depicted with total averages (red vertical bar) and city-averages (crosses) for 1973–2015. The color of the crosses represents the long-term average March temperature. Small circles are the outliers which are larger or smaller than median value by 1.5 times of interquartile range (i.e., 1.5 times of difference between 75^th and 25^th percentiles).</p

Long-term trends of first flowering date (FFD), last frost date (LFD) and the days between them.

<p>(a) FFD_f, (b) FFD_a, (c) LFD₋₁, (d) LFD₋₂. Also shown are the days between (e) FFD_f and LFD₋₁, (f) FFD_a and LFD₋₁, (g) FFD_f and LFD₋₂, and (h) FFD_a and LFD₋₂. The red number and red line indicate the average value of each city group. Statistically significant (<i>p</i> < 0.05) trends—large enough to reject a null hypothesis of no change—are denoted by bold blue crosses.</p

Locations and population changes in the 25 selected cities.

<p>The changes in population for 1975–2015 are denoted by direction and triangle size.</p

Error statistics of the predicted first flowering date (FFD) of root mean square error (RMSE), correlation coefficient (<i>r</i>), and bias.

<p>Error statistics of the predicted first flowering date (FFD) of root mean square error (RMSE), correlation coefficient (<i>r</i>), and bias.</p

Long-term trends of March temperature in the 25 selected cities.

<p>The long-term trend of (a) average temperature in March, (b) average daily minimum temperature in March, and (c) average daily maximum temperature in March are shown. Statistically significant differences (<i>p</i> < 0.05)—significant enough to reject a null hypothesis of no change—are denoted by bold blue cross.</p

Phenology Shifts at Start vs. End of Growing Season in Temperate Vegetation Over the Northern Hemisphere for the Period 1982-2008

Changes in vegetative growing seasons are dominant indicators of the dynamic response of ecosystems to climate change. Therefore, knowledge of growing seasons over the past decades is essential to predict ecosystem changes. In this study, the long-term changes in the growing seasons of temperate vegetation over the Northern Hemisphere were examined by analyzing satellite-measured normalized difference vegetation index and reanalysis temperature during 1982 2008. Results showed that the length of the growing season (LOS) increased over the analysis period; however, the role of changes at the start of the growing season (SOS) and at the end of the growing season (EOS) differed depending on the time period. On a hemispheric scale, SOS advanced by 5.2 days in the early period (1982-1999) but advanced by only 0.2 days in the later period (2000-2008). EOS was delayed by 4.3 days in the early period, and it was further delayed by another 2.3 days in the later period. The difference between SOS and EOS in the later period was due to less warming during the preseason (January-April) before SOS compared with the magnitude of warming in the preseason (June September) before EOS. At a regional scale, delayed EOS in later periods was shown. In North America, EOS was delayed by 8.1 days in the early period and delayed by another 1.3 days in the later period. In Europe, the delayed EOS by 8.2 days was more significant than the advanced SOS by 3.2 days in the later period. However, in East Asia, the overall increase in LOS during the early period was weakened in the later period. Admitting regional heterogeneity, changes in hemispheric features suggest that the longer-lasting vegetation growth in recent decades can be attributed to extended leaf senescence in autumn rather than earlier spring leaf-out. Keywords: climate change, growing season, NDVI (normalized difference vegetation index), Northern Hemisphere, phenology

Urbanization may reduce the risk of frost damage to spring flowers: A case study of two shrub species in South Korea - Fig 6

<p><b>Long-term changes in the predicted FFD of (a), (d) alternating and (b), (e) modified alternating models and (c), (f) the difference between them (alternating minus modified alternating).</b> Statistically significant (<i>p</i> < 0.05) trends)—large enough to reject a null hypothesis of no change—are denoted by bold blue crosses.</p