text
stringlengths 4
179k
| predicted_class
stringclasses 5
values | confidence
float64 0
100
|
|---|---|---|
In order to better understand the sources, formation pathways, and physicochemical properties of organic aerosols, we studied the molecular distributions of the diacids and related compounds at Chichijima Island between 2001 and 2013. Molecular distributions are shown in Figure S2. Throughout the observation period, we found the predominance of oxalic acid (C2) followed by malonic (C3) and/or succinic (C4) acids. This molecular distribution is consistent with our previous study for 1990–1993 at the same observation site44 and other East Asian sites such as Okinawa Island49, 50, the Gosan site, Jeju Island in South Korea51, Mt. Tai in North China52, urban sites in China53, 54, as well as different sites in the world including Tanzania, East Africa55, remote western European continental sites56, west-east transect in the European atmosphere57, and Los Angeles26.
|
other
| 36.06 |
Figure 1(a–d) presents daily 7-day isentropic air mass back trajectories at an altitude of 500 m above the ground level using the HYSPLIT model47 for different seasons during the year 2013 over the WNP as an example. Air mass transport from East Asia to the sampling site in the Pacific is stronger during winter (December to February) and spring (March to May) than during summer and autumn to deliver continental air masses via long-range atmospheric transport. The continental air mass transport is almost absent in summer (June to August). Air masses mostly come from the central Pacific carrying pristine air masses to the observation site in summer, whereas in autumn (September to November) the air mass pathway shifts from southeasterly to northwesterly towards winter. More detailed information of air mass transport over the WNP is described elsewhere48.Figure 1Daily 7-day HYSPLIT backward air mass trajectories for different seasons over the WNP for the year 2013. The star (*) indicates the sampling site, Chichijima Island. Trajectory data downloaded from the NOAA ARL website (http://www.arl.noaa.gov) and plotted using origin lab (v.8) software (http://www.originlab.com/).
|
other
| 33.2 |
Daily 7-day HYSPLIT backward air mass trajectories for different seasons over the WNP for the year 2013. The star (*) indicates the sampling site, Chichijima Island. Trajectory data downloaded from the NOAA ARL website (http://www.arl.noaa.gov) and plotted using origin lab (v.8) software (http://www.originlab.com/).
|
other
| 29.94 |
Glyoxylic acid (ωC2) was the dominant species among all ω-oxoacids and the fourth most abundant species detected, whereas methylglyoxal (MeGly) was more abundant than glyoxal (Gly) in the WNP aerosols. Throughout the observation period, we found the molecular distributions of the diacids and related compounds at Chichijima as C2 > C3 > C4 > ωC2 > MeGly > Ph > ωC7 > C5 > ωC8 > Gly > C6 > ωC9 > C9. However, seasonal molecular distributions provided a different picture depending on the source strengths and formation mechanisms.
|
other
| 33.9 |
Table 1 summarizes the regression statistics such as range, mean ± SD, and the trend (slope) for water-soluble diacids, ω-oxocarboxylic acids, pyruvic acid, α-dicarbonyls, and their diagnostic ratios during the period of 2001 to 2013. Figure 2 presents the temporal trends of the major diacids and related compounds and diagnostic mass ratios during the whole study period.Table 1Regression statistics (range, mean ± SD, and slope) of water-soluble dicarboxylic acids and related compounds (n = 607) in remote marine TSP aerosols collected at Chichijima Island during the period of 2001 to 2013.Organic compoundsRange (mean ± SD)Slope (m, diacid year−1)Uncertainty (σm)Trend (% year−1)Dicarboxylic acids (ng m−3)Normal chain saturated diacids Oxalic, C2 2.21–514 (73.9 ± 66.8)+0.00320.0020+0.004 Malonic, C3 0.28–55.6 (11.5 ± 9.4)+0.000050.0002+0.0004 Succinic, C4 0.05–52.4 (6.12 ± 6.41)+0.0002*0.0002 + 0.003 Glutaric, C5 0–7.48 (1.11 ± 1.19)−0.00003*0.00003−0.0027 Adipic, C6 0.01–5.08 (0.59 ± 0.60)−0.000020.0000017−0.0033 Pimelic, C7 0–1.34 (0.15 ± 0.16)−0.000014*0.0000047−0.009 Suberic, C8 0–1.24 (0.13 ± 0.14)−0.00003*0.0000038−0.023 Azelaic, C9 0.01–2.50 (0.53 ± 0.34)−0.00003*0.00001−0.005 Decanedioic, C10 0–0.95 (0.06 ± 0.08)−0.0000030.0000023−0.005 Undecanedioic, C11 0–8.03 (0.08 ± 0.34)−0.0000020.0000098−0.002 Dodecanedioic, C12 0–0.34 (0.01 ± 0.04)−0.000004*0.000001−0.04Branched chain saturated diacids Methylmalonic, iC4 0–1.45 (0.31 ± 0.25)−0.0000090.0000074−0.0029 Methylsuccinic, iC5 0–3.13 (0.52 ± 0.48)+0.0000120.000014+0.0019 Methylglutaric, iC6 0–0.96 (0.08 ± 0.09)−0.000006*0.000002−0.0075Multi functional saturated diacids Hydroxysuccinic, hC4 0–15.2 (0.26 ± 1.12)−0.000111*0.000032−0.038 Ketomalonic, kC3 0–5.40 (0.38 ± 0.54)+0.000020.000015+0.0052 Ketopimelic, kC7 0–3.67 (0.50 ± 0.57)−0.0000070.000017−0.0014Unsaturated aliphatic diacids Maleic, M0–2 (0.43 ± 0.40)−0.00005*0.00001−0.011 Fumaric, F0–2.27 (0.41 ± 0.32)−0.00002*0.0000092−0.005 Methylmaleic, mM0–7.66 (0.22 ± 0.44)−0.00007*0.000012−0.031Aromatic diacids Phthalic, Ph0.01–12.8 (1.31 ± 1.49)−0.00009*0.000043−0.0068 Isophthalic, iPh0–11.1 (0.14 ± 0.49)+0.000010.000014+0.0071 Terephthalic, tPh0–7.18 (0.39 ± 0.52)+0.000020.000015+0.0051 Total diacids 2.93–555 (99.2 ± 86.4)+0.0030.0025+0.003ω-Oxocarboxylic acids (ng m−3) Glyoxylic, ωC2 0.09–29 (4.91 ± 5.13)+0.000070.00015+0.001 3-Oxopropanoic, ωC3 0–22.4 (0.26 ± 0.94)+0.00006*0.000027+0.023 4-Oxobutanoic, ωC4 0–5.56 (0.39 ± 0.47)+0.00004*0.000013+0.010 5-Oxopentanoic, ωC5 0–0.65 (0.09 ± 0.08)+0.00001*0.0000024+0.01 7-Oxoheptanoic, ωC7 0.02–11.6 (1.13 ± 1.27)+0.0000060.000037+0.0005 8-Oxooctanoic, ωC8 0–6.32 (0.72 ± 0.89)+0.000020.000026+0.0027 9-Oxononanoic, ωC9 0–5.72 (0.59 ± 0.68)+0.00006*0.00002+0.010 Total ω-oxoacids 0.21–52.7 (8.10 ± 8.20)+0.00030.00024+0.0037Ketoacid (ng m−3) Pyruvic, Pyr0–6.16 (0.79 ± 0.90)+0.0002*0.000025+0.025α-Dicarbonyls (ng m−3) Glyoxal, Gly0–4.69 (0.80 ± 0.78)−0.000050.000036−0.006 Methylglyoxal, MeGly0–23.9 (1.73 ± 2.28)+0.00014*0.000067+0.008 Total α-dicarbonyls 0.04–26.4 (2.34 ± 2.74)+0.0003*0.00008+0.012Ratios F/M 0–20.8 (1.83 ± 2.47)+0.0003*0.000072+0.016 C 2/C 3 1.46–33.1 (6.55 ± 2.86)+0.00034*0.000083+0.0045 C 2/C 4 0.80–154 (17.0 ± 12.3)+0.001*0.00036+0.005 C 3/C 4 0.15–27.7 (2.76 ± 1.93)+0.000020.000057+0.0007 Ph/C 9 0.01–77.0 (3.47 ± 5.10)−0.0000060.00015−0.0002 C 2/∑(C 2 -C 12)0.38–0.94 (0.77 ± 0.06)+0.000008*0.0000017+0.0010 C 2/ωC 2 5.48–131 (22.2 ± 14.7) + 0.0013*0.0004+0.0058 C 2/Gly 20.3–4050 (135 ± 332)+0.00220.01+0.002 C 2/MeGly 0–758 (90.8 ± 86.3)+0.00670.0025+0.007 Gly/MeGly 0–6.71 (0.98 ± 0.81)−0.00008*0.000037−0.008The symbol, *, indicates the trends are significant at a 95% (p < 0.05) confidence level. Figure 2Trends in (a–f) temporal variations of water-soluble dicarboxylic acids (ng m−3) and related compounds (ng m−3) and (g,h) F/M and Gly/MeGly mass ratios in TSP aerosols collected at Chichijima Island during 2001 to 2013. Linear regression trends are given inset and applied over the whole observation period. The symbol, *, indicates the trends are significant at a 95% (p < 0.05) confidence level. The solid red line represents the trend line.
|
other
| 28.55 |
Trends in (a–f) temporal variations of water-soluble dicarboxylic acids (ng m−3) and related compounds (ng m−3) and (g,h) F/M and Gly/MeGly mass ratios in TSP aerosols collected at Chichijima Island during 2001 to 2013. Linear regression trends are given inset and applied over the whole observation period. The symbol, *, indicates the trends are significant at a 95% (p < 0.05) confidence level. The solid red line represents the trend line.
|
other
| 31.44 |
C2, C3, and C4 acids are the end or near-end products of photochemical reaction chains of hydrocarbons and biogenic unsaturated fatty acids, accounting for approximately 74%, 11%, and 6% of the total diacids, respectively, during the 13-year study period at Chichijima. From Table 1, it is obvious that C4 shows an increasing trend (+0.003% yr−1), whereas those of C5–C12 diacids (particularly, C5, C7, C8, C9, and C12) show significant (p < 0.05) decreasing trends during the study period. These results suggest a photochemical conversion of higher to lower molecular weight diacids during the long-range atmospheric transport over the WNP. This point is further supported by significant increasing trends of diagnostic mass ratios of diacids, as discussed below.
|
other
| 34.38 |
It has been suggested that maleic acid (cis configuration) (M), a photo-oxidation product of aromatic hydrocarbons such as benzene and toluene58, may be isomerized to fumaric acid (trans configuration) (F) under a high solar radiation in the atmosphere1. Therefore, the F/M ratio may be a good indicator of photochemical processing. In our study, we found a significant (p < 0.05) increasing trend (+0.016% yr−1) of F/M ratios during 13 years of the study period (Fig. 2g), indicating an intensive photochemical aging or increased oxidant levels over the WNP. Further, it has been suggested that C2 and C3 are likely produced in the marine atmosphere by the photooxidation of C4 through intermediates such as hydroxylsuccinic or malic acid (hC4) and ketomalonic acid (kC3)24, 59, 60. In this study, we found that C3/C4 ratios showed an increasing trend, although it is not significant (p > 0.05). However, C2/C3 and C2/C4 ratios show increases (+0.004% yr−1 and +0.005% yr−1) during the 13 year study period. This observation suggests that C2 could be largely produced by the photochemical degradation of C3 and C4 diacids. A significant (p < 0.05) increase (+0.002% yr−1) of C2/Σ (C2–C12) further supports the photochemical aging of diacids over the remote marine aerosols (Table 1). All these results suggest that the production of diacids over the WNP may be closely linked with an increased photochemical oxidation of biogenic and anthropogenic precursors that are delivered from the Asian continent by long-range atmospheric transport.
|
other
| 28.9 |
Previous studies suggested that oxidation of aromatic hydrocarbons such as naphthalene and o-xylene, which originate from an incomplete combustion of fossil fuel26, is one of the major sources of phthalic acid (Ph) in the atmosphere61, 62. High abundances of aromatic hydrocarbons are reported over China during winter63, 64. Similarly, adipic acid (C6) is probably produced through the oxidation of cyclohexene by ozone in the atmosphere and has been proposed as an anthropogenic tracer46. It is also well documented that glyoxal (Gly) is largely produced in the atmosphere by the oxidation of many anthropogenic aromatic hydrocarbons65–67, although it has small contribution from biogenic and marine origin20, 68, 69. In this context, we found a decreasing trend in the concentrations of Ph (p < 0.05; −0.007% yr−1), C6 (p > 0.05; −0.003% yr−1) and Gly (p > 0.05; −0.006% yr−1) (Fig. 2b,e, and Table 1). These results suggest that combustion (fossil fuel) derived aerosols have declined (or constant) over the WNP during 2001–2013. This point is further supported by the study of Boreddy, et al.70, who reported that declined concentrations of elemental carbon (EC) over WNP occurred during 2001–2012 over the WNP. However, it should be noted that although Gly concentrations are decreased, its processing to C2 71 is not decreased over the WNP, as evidenced by the increasing trend (+0.002% yr−1; p > 0.05) of C2/Gly ratios (Table 1).
|
study
| 29.34 |
On the other hand, a modeling study by Stavrakou, et al.36 observed a continuous increase of isoprene emissions over Asia during 1979–2012. They found a strong correlation (r > 0.90) between isoprene emissions and above-canopy solar radiation, suggesting that enhanced solar radiation intensifies isoprene emissions from terrestrial higher plants over Asia (particularly in China). Similarly, Zhang, et al.72 have recently reported an increase of biogenic isoprene emissions in northern China during 1982–2010 using the biogenic emission model. It has also been documented that 79% of MeGly may come from biogenic isoprene emissions globally, as inferred from modeling studies73. Further, pyruvic (Pyr) and ωC2 have been suggested as in-cloud oxidation products of isoprene, which are subsequently oxidized to C2 18. Therefore, it may be possible that biogenic isoprene derived volatile organic precursors (e.g., MeGly) over East Asia/China are taken up by aqueous-phase aerosol particles in the atmosphere and transported to the WNP.
|
study
| 29.47 |
In this study, we found a significant increasing trend (p < 0.05) in the concentrations of MeGly (+0.008% yr−1), Pyr (+0.025% yr−1) (Fig. 2d and f) and C2/MeGly ratios (+0.007% yr−1), suggesting the formation of C2 from the oxidation of MeGly and Pyr via an intermediate compound, i.e., ωC2 (MeGly → Pyr → acetic acid → ωC2 → C2) in the aqueous-phase18. These results suggest that enhanced concentrations of the diacids are probably caused by an increase of biogenic isoprene-derived precursors (i.e., MeGly and Pyr)74, followed by the subsequent photochemical oxidation during long-range atmospheric transport over the WNP. A concurrent negative trend in Gly/MeGly (−0.008% yr−1; p < 0.05) (Fig. 2h) also suggests an increase of biogenic precursors over the WNP. This point is further supported by our previous study of Boreddy and Kawamura48, which reported that a significant increase (p < 0.05) in the concentrations of methanesulfonic acid (MSA−; a tracer for biogenic sources) occurred during 2001–2012 over the WNP.
|
other
| 28.61 |
The observed trends of diacids may be not only reflected by changes in emissions/air masses, but also influenced by changes in oxidant levels. To better understand the variations in the trends of oxidant levels over the WNP, we downloaded the monthly mean levels of total columnar ozone (DU) and daily tropospheric columnar NO2 (cm−2) for the periods from 2002 to 2013 and from 2005 to 2013, respectively, from the NASA website (https://giovanni.gsfc.nasa.gov/) (Figure S3). From Figure S3, it is clear that both oxidant levels (O3 and NO2) showed significant (p < 0.05) increase in those trends (+0.0004% yr−1 and +0.008% yr−1, respectively) during the study period, indicating that increased oxidation processes over the WNP lead to increases in the formation of diacids during long-range atmospheric transport.
|
other
| 32.94 |
Based on the above results, we conclude that the enhanced concentrations of diacids over the WNP may be caused due to the increased oxidations of biogenic precursor compounds during long-range atmospheric transport. In contrast, anthropogenic precursors (e.g., Gly) have decreased (or constant) during the study period. It should be noted that all these trends are explained for the whole period (2001–2013); however, seasonal trends may give different results over the WNP because the formations of diacids are very sensitive to the sources of an air mass and the meteorological parameters as reported in Tables S4–S7.
|
other
| 32.66 |
As seen from Tables S4–S7, it is noteworthy that concentrations of total diacids in all seasons showed increases in their trends, except for summer, which showed a decreasing trend (−0.002% yr−1; p > 0.05) (Table S6). The declined concentrations of diacids and related compounds in summer are probably due to the pristine air masses, which suggest the negligible local anthropogenic emissions as well as long-range continental outflow over the WNP. It is also seen from Tables S4–S7 that the trend in concentrations of C9 showed a decrease in all seasons, except for summer. In summer, the trend of C9 showed an increase (+0.001% yr−1) during 2001–2013, indicating an importance of oxidation of biogenic unsaturated fatty acids over the WNP, particularly in summer.
|
other
| 29.6 |
Figure 3 presents box and whisker plots of monthly variations of diacids, ω-oxocarboxylic acids, pyruvic acid, α-dicarbonyls, and diagnostic mass ratios at Chichijima Island for the period of 2001 to 2013. Almost all the organic compounds showed clear monthly/seasonal variations with higher concentrations in winter/spring under the continental outflow from East Asia and lower concentrations in summer/autumn due to the pristine marine air mass, except for C9 (Fig. 3i). As shown in Fig. 3a, concentrations of total diacids are characterized by a gradual increase from autumn to winter, with a peak in early spring (March), and a decrease with a minimum in summer although a small peak was observed in August. Total ω-oxoacids showed a similar seasonal variation (Fig. 3b). The peak in spring is more significant than in summer. On the other hand, total concentrations of α-dicarbonyls gradually increased from late autumn to early spring and then decreased towards the summer months (Fig. 3c). These seasonal variations can be explained primarily by an enhanced Asian outflow in winter/spring with the heterogeneity of air masses and changes in emission strength and meteorology as discussed below.Figure 3Monthly variations in water-soluble dicarboxylic acids (ng m−3) and related compounds (ng m−3) and various mass ratios at Chichijima during 2001 to 2013. The horizontal line and dot inside the box indicate maiden and mean, respectively. The vertical hinges represent data points from the lower to the upper quartile (i.e., 25th and 75th percentiles). The whiskers represent data points from the 5th to 95th percentiles. Open circles indicate the outliers.
|
other
| 31.11 |
Monthly variations in water-soluble dicarboxylic acids (ng m−3) and related compounds (ng m−3) and various mass ratios at Chichijima during 2001 to 2013. The horizontal line and dot inside the box indicate maiden and mean, respectively. The vertical hinges represent data points from the lower to the upper quartile (i.e., 25th and 75th percentiles). The whiskers represent data points from the 5th to 95th percentiles. Open circles indicate the outliers.
|
other
| 28.86 |
As discussed above, there is a clear seasonal difference in the origin of air masses during the study period at Chichijima Island in the WNP. Such changes in air masses may lead to different effects on the formation of diacids and related compounds as well as the characteristics of chemical properties of aerosols. During winter/spring, the air mass transport is stronger; mostly coming from East Asia with westerly winds. The sampling site is influenced by long-range atmospheric transport of continental air masses, whereas the air masses are originating from the central Pacific via easterly winds during summer/autumn. This heterogeneity in air mass origins may clearly reflect the emission strength of organic aerosols and their precursors with higher concentrations during winter/spring and lower concentrations in summer/autumn over the WNP.
|
other
| 33.94 |
Gradual increases of total diacids, ω-oxoacids and α-dicarbonyls during late autumn to early spring are attributable to the combined effect of anthropogenic/biogenic volatile organic compounds (VOCs) emitted from East Asia followed by subsequent oxidation during long-range atmospheric transport. In our previous study, higher concentrations of non-sea salt sulfate (nss-SO4 2−), nitrate (NO3 −), non-sea salt calcium (nss-Ca2+) and MSA− were found in winter/spring over the same site under continental influence during the study period48. Being consistent with monthly variations of inorganic ion concentrations, C2–C4 diacids show similar trends with a gradual increase from late autumn to early spring and decrease towards summer months. The seasonal variations of C6, Ph, and Gly are characterized by winter maxima and summer minima (Fig. 3h,j and m). Similarly, concentrations of Pyr and MeGly maximized in winter/spring and minimized in summer (Fig. 3l,n). These results demonstrate that, during late autumn to early spring, East Asian emissions of organic acids and their precursors, followed by long-range atmospheric transport, are important factors in controlling the distributions of diacids and related compounds in the WNP. High speed westerly winds also play an important role in causing the highest concentrations of diacids in spring over the WNP (Figure S1b).
|
study
| 30.94 |
On the other hand, lower concentrations of diacids and related compounds in summer suggest a minor contribution either from local emissions over the sampling site or marine emission of diacids and their precursors in the WNP. Mochida, et al.44 documented that local anthropogenic emissions for diacids are insignificant at Chichijima, based on the lower concentration ratios of benzo[a]pyrene (BaP) to (C2–C11). Therefore, it is reasonable to believe that the observed concentrations of diacids and their precursors during summer may be associated with marine biological sources and are attributable to the oxidation of unsaturated fatty acids75. In this connect, the concentrations of C9 (aqueous phase photo-oxidation of biogenic unsaturated fatty acids) show a gradual increase from late spring to late autumn with a maximum in June and thereafter show a gradual decrease towards winter and spring months (Fig. 3i). These results suggest that oxidations of biogenic unsaturated fatty acids are important sources of diacids over the WNP in summer/autumn, although their contribution is relatively small compared to those in winter and spring.
|
other
| 31.64 |
Meteorological parameters such as wind speed, solar radiation and cloud cover are crucial for understanding the emission strengths of organic compounds and their oxidation processes in the atmosphere. As inferred from Figure S1, solar radiation was maximized in summer and minimized in winter/spring, indicating a strong photochemical oxidation during summer over the WNP. This result was further discussed in terms of seasonal variations in the mass concentration ratios of diacids. Being consistent with solar radiation, F/M, C2/C4, and C3/C4 ratios increased gradually from late spring to summer and stayed high until late autumn, and then decreased towards winter (Fig. 3o,q,r). These seasonal changes suggest an enhanced photochemical oxidation, superimposed with changing regional biology and meteorology in summer and important sources of the diacids over the WNP. On the other hand, wind speed and cloud cover were higher during spring and/or winter (Figure S1), suggesting the enhanced processing of precursor compounds associated with Asian outflows during atmospheric long-range transport that leads to higher concentrations of diacids during spring and winter over the WNP. Similar seasonal variations have been found in the mass concentration ratios of C6/C9 and Ph/C9 with higher values in winter and/or spring (Fig. 3s and t). C2/ωC2 and C2/MeGly ratios showed a gradual increase from early spring to autumn with a peak in early autumn, i.e., September (Fig. 3v and x). These ratios, then decreased towards winter. However, ratios of C2/Gly did not show any clear seasonal trend during the study period, although ratios are slightly higher in summer and autumn (Fig. 3w). Although precipitation occurs throughout the year over the WNP, it was maximized in summer (Figure S1d); therefore, it is also an important sink for the diacids in addition to photochemical decompositions of oxalate-iron complexes, particularly in summer.
|
study
| 30.55 |
To quantitatively estimate the contribution of different sources to C2 over the WNP, we performed a positive matrix factorization (PMF) analysis for the different seasons and the whole period (2001–2013) as shown in Fig. 4. PMF (version 5.1) is an effective source apportionment receptor model developed by the United States Environmental Protection Agency (U. S. EPA) and is often used in determining the sources of atmospheric aerosols76, 77. A complete description of PMF analysis is discussed elsewhere78–80. The concentrations of C2–C6, C9, Ph, ωC2, Gly, MeGly, and the tracers of water-soluble ions (MSA−, Cl−, nss-SO4 2−, Na+, NH4 +, nss-K+) were used as inputs in the PMF analysis. A total of 607 samples were used for this analysis. We identified 6 source profiles such as biogenic photochemical (indicated by blue color), mixed photochemical (green), anthropogenic 1 (purple), anthropogenic 2 (weak purple), marine biogenic (cyan), and biomass burning (red). The derived variations (%) of the species are shown in Fig. 4a–f. The contributions of all sources to the individual diacids for the different seasons as well as for the whole period are shown in Fig. 4g. The detailed descriptions of each PMF resolved-sources are described below for the different seasons over the WNP.Figure 4The PMF derived explained variance (%) for the source profiles for the different seasons as well as the whole period (2001–2013) during the study (a–f). Contributions of each source profile to individual acids (g) and oxalic acid (h) for the different seasons over the WNP. Each color indicates the different source as mentioned in plots a-f. Therefore, for interpretation of the colors in plots g and h, the reader is referred to the color version of this article.
|
other
| 32.22 |
The PMF derived explained variance (%) for the source profiles for the different seasons as well as the whole period (2001–2013) during the study (a–f). Contributions of each source profile to individual acids (g) and oxalic acid (h) for the different seasons over the WNP. Each color indicates the different source as mentioned in plots a-f. Therefore, for interpretation of the colors in plots g and h, the reader is referred to the color version of this article.
|
other
| 28.69 |
Based on the high variation (%) of loading of MSA−, C2, C3, ωC2, MeGly and weak or no loading of C6, Ph, and Gly, we identified source 1 as biogenic photochemical 1. Source 2 was identified as mixed photochemical, which was confirmed by a significant loading of all species, which are majorly associated with photochemical oxidation of longer to shorter chain diacids. Source 3 was identified as anthropogenic 1 as evidenced by high loading of nss-SO4 2− and NH4 +. Source 4 was attributed to anthropogenic 2, because it may be associated with biomass burning-derived VOCs as evidenced by high loading of Gly. Source 5 was considered as marine biogenic unsaturated fatty acids, as evidenced by high loading of Na+, Cl−, and C9. Source 6 was attributed to biomass burning, which was majorly associated with primary emissions, as indicated by high loading of nss-K+.
|
other
| 29.9 |
Figure 4h shows the contributions of all sources to C2 for the different seasons and the whole period. The biogenic photochemical process is a prominent source of C2, whose contribution to C2 is highest in summer (47%), followed by winter (40%) and lowest in autumn (27%). The next prominent source is a mixed photochemical process, whose contribution is more abundant during winter (36%), summer (34%) and autumn (29%) and lowest in spring (28%). The contribution of anthropogenic source 1 is higher in autumn (14%) and winter (11%), whereas those of anthropogenic source 2 are higher in spring (24%), autumn (20%) and lower in summer (less than 9%) and winter (7%). Contributions of biomass burning to C2 are highest in winter (3%) or autumn (2%) and lowest in summer (<1%), while the contributions of marine biogenic unsaturated fatty acids to C2 are highest in summer (10%) followed by autumn (8%) and lowest in spring (3%). Overall, for the whole study period (Fig. 4h5), the contribution of biogenic photochemical process (42%) was the dominant source of C2 followed by anthropogenic sources (1 plus 2 contribute ~32%) and mixed photochemical sources (20%). We found that marine biogenic unsaturated fatty acids are important sources for the formation of diacids over the WNP during winter and summer, respectively.
|
study
| 30.77 |
The molecular distributions of diacids showed a predominance of C2 followed by C3 and C4. Seasonal variations of diacids and their precursor compounds showed maxima in winter to spring and minima in summer, except for C9, which was maximized in summer. Annual concentrations of total diacids, ω-oxoacids, pyruvic acid and α-dicarbonyls showed continuous increases toward more recent years.
|
other
| 30.33 |
A decrease in anthropogenic emissions is inferred from a decrease in the trends of anthropogenic tracer compounds such as phthalic acid (Ph), adipic acid (C6) and glyoxal (Gly), while an increase in biogenic emissions is confirmed from an increase in the concentrations of biogenic tracers including pyruvic acid (Pyr) and methylglyoxal (MeGly) during 2001 to 2013. On the other hand, satellite-derived oxidation levels (total columnar O3 and tropospheric columnar NO2) showed significant increases during the study period. These results demonstrate that the increased concentrations of diacids over the WNP are probably due to not only the increased biogenic emissions from East Asia but also increased oxidation processes during atmospheric long-range transport, while anthropogenic precursors of the diacids are decreased or constant during the study period over the WNP. These results further support our previous study, which reported the declined and increased concentrations of nss-SO4 2− and MSA−, respectively, over the WNP during 2001–2012. We also found increased concentrations of C9 and ωC9 in summer, suggesting that marine biogenic unsaturated fatty acids are becoming important sources of diacids over the WNP, particularly, in summer.
|
other
| 30.39 |
These inferences are further supported by PMF analysis, which showed a biogenic photochemical contribution (42%) was a predominant source for C2. This is the first study to explain the impact of heterogeneity in air masses on long-term trends of organic acids over the WNP. Therefore, the assessment of future climate effects of East Asian aerosols over the WNP will need continued observations because of the rapid changes in the emission strength of aerosols and their precursors over East Asia. The results of this study should be important for climate modelers, who are interested in radiative forcing calculations over the WNP.
|
other
| 30.22 |
Aerosol samples were collected on a quartz filter (20 × 25 cm, Pallflex 2500QAT-UP) from 2001 to 2013 using a high volume sampler (HVS) with a flow rate of 1 m3 min−1 at Chichijima Island (27°04′N, 142°13′E) in the WNP (Fig. 1). Before sampling, filters were pre-combusted at 450 °C for three hours. The HVS was set up 5 m above ground level at the Satellite Tracking Centre of Japanese Aerospace Exploration Agency (JAXA, elevation 254 m a.s.l.) in Chichijima Island45. 4–6 day integrated samples were collected during the study period. After the sampling, filters were put in a pre-baked (450 °C for 6 hrs) glass bottle with a Teflon lined screw cap and stored at −20 °C prior to the analysis of diacids. A total of 607 aerosol and 61 field blank samples were used in this study. A field blank sample was collected every ten aerosol samples by placing a clean filter in the cartridge of the HVS for 10 sec without the pump running.
|
other
| 34.1 |
The collected filter samples were analyzed for diacids and related compounds using the improved method of Kawamura81 and Kawamura and Ikushima1. Briefly, a portion of each filter sample was extracted three times with 5 ml of organic-free ultrapure water (resistivity of >18 MΩ cm−1, Sartorius arium 611 UV) under ultrasonication. The extracts were filtered through a Pasteur pipette packed with quartz wool to remove the filter debris and insoluble materials and placed in a 50 ml pear-shaped flask. The water extracts were pH-adjusted to 8.5–9.0 using a 0.05 M potassium hydroxide (KOH) solution and then concentrated to almost dryness using a rotary evaporator under vacuum. A 14% borontrifluoride in n-butanol solution was added to the extracts and then heated at 100 °C for 1 hour to derivatize the carboxyl and aldehyde groups. The derived dibutyl esters and dibutoxy acetals were extracted with n-hexane and washed three times with ultrapure water (for the removal of polar compounds, including hydrogen fluoride (HF) and boric acid (H3BO4) derived from the borontrifluoride) and concentrated using a rotary evaporator in a vacuum and nitrogen (N2) blow down system. After being dried, a known amount of n-hexane was added to the ester fraction and derivatives were determined using a gas chromatography with a flame ionization detector (GC/FID; Hewlett-Packard, HP6890). Identification of the GC peaks was confirmed by comparing the GC retention times with those of authentic standards and confirmed by mass spectral examination using a GC/mass spectrometry (GC/MS; Thermoquest, Trace MS).
|
other
| 38.06 |
In order to check the recovery, 5 μL of authentic diacids were spiked on the pre-combusted (450 °C for 6 hrs) quartz filters and analyzed like a real sample using the above-mentioned procedure. The recoveries were 85% for oxalic acid, 90% for malonic acid and more than 90% for succinic, glutaric, and adipic acids. The analytical errors in the duplicate analyses are less than 10%. The concentrations of all diacids and related compounds reported in this study have been corrected using the field blanks. The blank levels were less than 5% for the major species measured in the real samples.
|
other
| 33.4 |
Several studies show that positive and negative artifacts can be significant during filter sampling and organic analysis of aerosols82, 83. They generally arise from adsorption of gaseous species to the substrate surface and from particle losses on the walls and volatilization of semi-volatile species, respectively. In order to evaluate the potential artifacts, including adsorption or evaporation of collected particles to the gas phase, we performed simultaneous measurements of the diacids in marine aerosols using a HVS and a denuder/filter/denuder system43. It is well established that the artifacts due to adsorption or evaporation are to be minor for the denuder system. We found good agreement between the two different techniques, suggesting that the HVS technique is valid for collection of diacids in marine aerosols43. This is true because, in general, evaporation loss of particulate organic species is minor compared to gases adsorbed on the filter surfaces84. On the other hand, adsorption of gases is limited because diacids are predominantly observed in particle phase85. However, it is noteworthy that organic compounds originating from continental regions are much more aged during atmospheric transport toward Chichijima Island; therefore the artifacts may be minor for the diacids. More details about the potential sampling artifacts are described in elsewhere44.
|
other
| 32.84 |
To explore the inter-annual difference in the concentrations of the diacids and related compounds, the statistical regression analysis of variations (ANOVA)86 was performed by comparing all the data points during the study period. Differences with p < 0.05 were considered to be statistically significant and indicated by a star (*) in the trend analyses as shown in Table 1. This statistical approach is simple, robust and easy to interpret87. For example, the sign of the diacid trend depends on the value of the slope of the regression analysis. In this kind of interpretation, when the slope is greater than zero, the trend is positive (increases) whereas the trend is negative (decreases) when the slope is less than zero. When the slope is equal to zero, there is no trend in the diacid concentrations. Uncertainties in the trends are reported as standard errors in the slope of regression lines and variations in the trends (% yr−1) are also reported for each organic compound in Table 1.
|
other
| 33.34 |
Breast cancer mortality rates in North American and Asian countries are comparable, with one study noting that approximately 50% to 75% of Asian women have hormone receptor (HR) –positive/human epidermal growth factor receptor 2 (HER2) –negative breast cancer.1,2 The median age of Asians at the time of breast cancer diagnosis (45 to 50 years) is lower than that of Western patients (55 to 60 years), including those in the United States.3,4 Thus, the rate of premenopausal women with breast cancer is higher in Asian populations compared with non-Asian populations.5,6 Cancer therapy effectiveness can also vary between Asians and non-Asians, and Asians may have a different adverse event (AE) experience versus women from other regions as a result of various reasons such as pharmacogenomics and differences in the metabolism of a specific drug.7
|
other
| 32.56 |
In patients with HR-positive/HER2-negative metastatic breast cancer (MBC), endocrine therapy is the mainstay of treatment8; however, major challenges exist when treating patients who have developed resistance to endocrine therapy with tamoxifen or aromatase inhibitors.9,10 Thus, treatments that can overcome endocrine therapy resistance and improve outcomes are essential.
|
other
| 31.12 |
Palbociclib, an oral small-molecule inhibitor of cyclin-dependent kinases 4 and 6 (CDK4/6), prevents DNA synthesis by blocking the progression of the cell cycle from the G1 to the S phase.11,12 The Palbociclib Ongoing Trials in the Management of Breast Cancer 3 (PALOMA-3) study included women with HR-positive/HER2-negative advanced breast cancer whose cancer had relapsed or progressed during or after prior endocrine therapy.13,14 In the endocrine-resistant setting, palbociclib plus fulvestrant demonstrated improved efficacy versus fulvestrant plus placebo (median progression-free survival [PFS], 9.5 v 4.6 months, respectively; hazard ratio [HR], 0.46; 95% CI, 0.36 to 0.59; P < .001).13 This subgroup analysis evaluates the efficacy and safety of palbociclib plus fulvestrant versus placebo plus fulvestrant in Asians and non-Asians enrolled onto PALOMA-3, a placebo-controlled clinical study.
|
other
| 32 |
PALOMA-3, an international, multicenter, randomized, double-blind, placebo-controlled, parallel-group, phase III clinical trial, included women with HR-positive/HER2-negative advanced breast cancer whose cancer had relapsed or progressed (on the basis of histologic or cytologic confirmation of recurrent local or distant disease progression) during or within 12 months of completing adjuvant endocrine therapy or while on or within 1 month from prior endocrine therapy for advanced breast cancer or MBC.13,14 One previous line of chemotherapy for advanced or metastatic disease was allowed. Asian patients in this analysis were defined as all patients who self-identified their race as Asian to investigators from the following options provided on the case report form: white, black, Asian, or other. Asian patients were included from eight study sites in Japan (n = 35), five sites in Korea (n = 43), and two sites in Taiwan (n = 4); 23 other Asian patients also were included in this analysis.
|
other
| 36.03 |
Patients were randomly assigned 2:1 to receive palbociclib plus fulvestrant or placebo plus fulvestrant. Patients received placebo or palbociclib 125 mg/d orally for 3 weeks followed by 1 week off; fulvestrant 500 mg was administered intramuscularly on days 1 and 15 of cycle 1 and then every 28 days (± 7 days) thereafter starting from day 1 of cycle 1.13,14 In premenopausal patients, any luteinizing hormone–releasing hormone (LHRH) agonist was administered starting at least 4 weeks before study therapy initiation. Patients who did not receive goserelin as their LHRH agonist before study entry were switched to goserelin from the time of random assignment through the entire study treatment period. The primary objective was investigator-assessed PFS; secondary objectives included clinical benefit response (CBR), objective response rate (ORR), survival probabilities, safety and tolerability, and patient-reported outcomes (PROs). In April 2015, the independent data monitoring committee reviewed the results of the study and concluded that its primary objective had been met as the study crossed the prespecified Haybittle-Peto efficacy stopping boundary (α = .00135).13 The updated results of the overall population have been previously published, and these data (cutoff date: March 16, 2015) were also used in this present analysis.13
|
other
| 31.81 |
An institutional review board/independent ethics committee approved the protocol; the study was conducted in accordance with the Declaration of Helsinki. All patients provided written informed consent before any study procedures were started. Additional patient eligibility criteria and study design details have been described previously.13,14
|
other
| 29.42 |
PFS was defined as the time from the date of random assignment to the date of first documentation of objective progression of disease or death as a result of any cause in the absence of documented progression of disease, whichever occurred first. CBR was defined as the overall rate of complete response, partial response, or stable disease ≥ 24 weeks according to the Response Evaluation Criteria in Solid Tumors (RECIST) version 1.1. Objective response was defined as the overall complete response or partial response according to RECIST version 1.1. Using x-ray, computed tomography, or magnetic resonance imaging, tumor assessments were performed at baseline and every 8 weeks for the first year and then every 12 weeks. The type, incidence, severity, and seriousness of AEs and the relationship of AEs to study medications were recorded. Severity of AEs was graded on the basis of the National Cancer Institute Common Terminology Criteria for Adverse Events version 4.0. A serious AE was defined as an AE that results in death, is life threatening, requires inpatient hospitalization or prolongation of existing hospitalization, results in persistent or significant disability or incapacity, or results in congenital anomaly or birth defect. An AE could additionally be considered serious by the investigator if it jeopardized the patient or required intervention to prevent one of the other AE outcomes.
|
other
| 32.8 |
In addition, pharmacokinetic (PK) data and PROs were assessed by race. Trough PK samples for determination of palbociclib plasma concentrations were collected from all randomly assigned patients on day 15 of cycles 1 and 2. PROs were assessed using the European Organisation for Research and Treatment of Cancer Quality of Life Questionnaire C30, a 30-item questionnaire that includes functional scales, symptom scales, and a global health status/quality-of-life (QOL) scale.15,16 For functional and global QOL scales, higher scores represent a better level of functioning. For symptom-oriented scales, a higher score represents more severe symptoms. PRO questionnaires were completed before dose on day 1 of cycles 1 to 4, then on day 1 of every other subsequent cycle starting with cycle 6, and finally, at the end of treatment. For PK assessments, a post hoc analysis was used for the comparison of racial subgroups.
|
other
| 35 |
Study assessments of efficacy, safety, and PROs were prespecified; efficacy subgroup analyses by various baseline variables, including race, were preplanned in the protocol and statistical analysis plan. Statistical analyses by race were conducted for exploratory purposes. Demographic and baseline disease characteristics were summarized by treatment arm in a frequency table for Asians and non-Asians. Quantitative baseline variables, including age, weight, and height, were summarized using descriptive statistics (ie, median and range). Quantitative baseline variables were compared between the two treatment arms using a Wilcoxon two-sample test without adjusting for multiplicity. Efficacy analyses were performed using the intent-to-treat principle. Kaplan-Meier estimates of median PFS and the respective 95% CIs were provided for both treatment groups. PFS data between the treatment groups were compared using a log-rank test. HR was estimated from the Cox proportional hazards regression model. The odds ratio estimator and the exact test were used to compare the rates of binary efficacy end points. AEs were summarized using descriptive statistics in Asians who took one or more doses of study treatment. The within-patient averages of the palbociclib steady-state trough PK samples were summarized and compared across subgroups. PRO analyses were based on the PRO-evaluable population (ie, patients in the intent-to-treat population with a baseline assessment and one or more postbaseline assessments before the end of study treatment). Completion rates were summarized by cycle. Repeated-measures mixed-effects analyses were performed to compare on-treatment overall scores and changes from baseline between treatment groups while controlling for baseline.
|
other
| 34.3 |
From October 7, 2013, to August 6, 2014, 105 Asians were enrolled onto the study (74 and 31 patients in the palbociclib and placebo arms, respectively; Fig 1). Demographic and baseline disease characteristics were generally similar between Asians and non-Asians except for age, weight, and percentage of premenopausal or perimenopausal patients. Asians, compared with non-Asians, were generally younger (mean age, 53.7 v 57.7 years, respectively; P = .0013) and weighed less (mean, 56.7 v 74.6 kg, respectively; P < .001; Table 1). The percentage of premenopausal or perimenopausal women at baseline was higher in Asians (42%) compared with non-Asians (15%). Among Asians, demographic and baseline disease characteristics were generally similar between the palbociclib and placebo arms.
|
other
| 34.53 |
The degree of PFS improvement in the palbociclib arm versus the placebo arm was similar in Asians and non-Asians (Fig 2). The median PFS in Asians was not reached in the palbociclib arm (95% CI, 9.2 months to not reached) but was 5.8 months (95% CI, 3.5 to 9.2 months) in the placebo arm (HR, 0.485; 95% CI, 0.27 to 0.87; P = .0065). In non-Asians, the median PFS was 9.5 months (95% CI, 7.6 to 11 months) in the palbociclib arm compared with 3.8 months (95% CI, 3.3 to 5.5 months) in the placebo arm (HR, 0.451; 95% CI, 0.34 to 0.59; P < .001). In Asians, the CBR was 70% (95% CI, 59% to 80%) with palbociclib plus fulvestrant and 52% (95% CI, 33% to 70%) with placebo plus fulvestrant (odds ratio, 2.216; 95% CI, 0.85 to 5.7; Table 2). In non-Asians, the CBR was 66% (95% CI, 60% to 71%) and 37% (95% CI, 29% to 46%) in the palbociclib and placebo arms, respectively (odds ratio, 3.234; 95% CI, 2.1 to 5.0; P < .001). The ORR in Asians was 19% in the palbociclib arm and 13% in the placebo arm. The sample size was underpowered to perform any statistical analysis. However, the degrees of improvement for CBR and ORR in Asians were similar to those seen in non-Asians.
|
other
| 28.9 |
The exposure to study treatments was comparable between Asians and non-Asians (Table 3). Among Asians, 100% of patients in the palbociclib arm and 94% in the placebo arm experienced treatment-emergent AEs of any grade (Table 4). The most common AEs among Asians were neutropenia and leukopenia. Febrile neutropenia occurred in three Asians (4%) in the palbociclib arm, with two of these cases reported as a serious AE. On the basis of results that were unadjusted for sample size differences between Asians and non-Asians, non-Asians in the palbociclib arm generally experienced similar treatment-emergent AEs at comparable incidences (< 10%); however, in Asians, compared with non-Asians, the incidence of fatigue (19% v 44%, respectively) was lower, and the rates of neutropenia (92% v 78%, respectively), stomatitis (41% v 24%, respectively), rash (32% v 11%, respectively), and nasopharyngitis (21% v 10%, respectively) were higher (Table 4).
|
other
| 29.16 |
The median number of treatment interruptions per patient was not different between Asians and non-Asians, regardless of treatment group. The number of cycle delays per patient was higher in Asians than non-Asians, regardless of treatment group. The median relative dose was lower in Asians than non-Asians in the palbociclib group and similar between Asians and non-Asians in the placebo group (Table 3). Fourteen non-Asian patients (5.1%) in the palbociclib arm and five non-Asian patients (3.5%) in the placebo arm discontinued palbociclib or placebo treatment because of an AE.
|
other
| 28.55 |
In Asians, the overall incidence of serious AEs was 14% (10 of 73 patients) in the palbociclib arm and 23% (seven of 31 patients) in the placebo arm (Appendix Table A1). In non-Asians, the incidence of serious AEs was 13% (34 of 272 patients) and 16% (23 of 141 patients) in the palbociclib and placebo arms, respectively. In the placebo plus fulvestrant group, the incidence of serious AEs in Asians (23%) was similar to the incidence in non-Asians (16%).
|
other
| 28.64 |
Comparison of the within-patient mean steady-state palbociclib trough concentrations in Asians and non-Asians demonstrated relative consistency in the central tendency and range of the observed values across subpopulations, indicating similar palbociclib exposure in these subpopulations (Fig 3). Geometric mean values of the within-patient mean steady-state palbociclib trough concentration values were similar for Asians and non-Asians (85.7 and 74.8 ng/mL, respectively). A population PK-pharmacodynamic (PD) analysis performed to assess the exposure-response relationship for neutropenia within PALOMA-3 showed that Asian race, baseline ALT level, and age were significant covariates on the baseline absolute neutrophil count (ANC) values. Asian race, lower baseline ALT level, and younger age were associated with lower baseline ANC values. Importantly, race was not found to be a covariate on any of the model PD response parameters. Generally, Asians in PALOMA-3 had a baseline ANC value that was 19% lower than non-Asians (Appendix Table A2).
|
other
| 29.33 |
Plasma palbociclib within-patient mean steady-state trough concentration in Asian and non-Asian patients. Diamonds represent the subpopulation geometric mean values, and open circles represent individual patient values. The dashed line represents the arithmetic mean value of all data from all patients. The box plot provides median and 25% and 75% quartiles with whiskers to the last point within 1.5 times the interquartile range.
|
other
| 30.78 |
Questionnaire completion rates were high at baseline and during treatment (from baseline to cycle 12, ≥ 90% of patients in each group completed all questions on the European Organisation for Research and Treatment of Cancer Quality of Life Questionnaire C30). In Asians, no significant deterioration from baseline in global QOL was observed within the palbociclib arm. Among the Asian subgroup in the study, no significant differences between treatment arms were observed for global QOL, functioning, pain, fatigue, or nausea and vomiting (Appendix Fig A1A). Significantly greater deterioration was observed in the placebo arm versus the palbociclib arm for dyspnea (score, 1.2 v 9.2, respectively; P < .05; Appendix Fig A1B).
|
other
| 28.47 |
CDK4/6 inhibitors are now an integral part of the management of HR-positive/HER2-negative MBC.17 Palbociclib, the first-in-class CDK4/6 inhibitor approved for the treatment of HR-positive MBC, has shown impressive PFS improvement when combined with either an aromatase inhibitor18 or selective estrogen receptor downregulator14 in both patients who are endocrine sensitive and endocrine resistant. In the PALOMA-1 phase II study and PALOMA-2 phase III study of patients who had not previously received endocrine therapy, longer PFS was reported with palbociclib plus letrozole versus letrozole alone.18,19 Similarly, in the PALOMA-3 study, in patients who had previously received endocrine therapy, palbociclib plus fulvestrant resulted in longer PFS than fulvestrant alone.14 Palbociclib has been approved in the United States and has been used in more than 48,000 patients since February 2015.20 Palbociclib is also approved by regulatory authorities for advanced breast cancer in the following countries in Asia: Singapore, Malaysia, Macau, Hong Kong, and Korea. In many of these countries, palbociclib will be reviewed by health technology agencies, payers, or both. The positive clinical value of palbociclib in Asian patients should be considered alongside the economic implications.
|
study
| 33.66 |
Substantial clinical experience has been accumulated in white patients. Although few Asians were enrolled onto the PALOMA-1 study,21 21% of patients in the palbociclib arm and 18% of patients in the fulvestrant arm in PALOMA-3 were Asian.14 This study adds to the limited body of literature assessing a CDK4/6 inhibitor in Asians and represents the largest patient experience with palbociclib in Asians. The present findings show that palbociclib plus fulvestrant improved PFS in Asians with HR-positive/HER2-negative MBC who experienced progression on prior endocrine therapy and that the safety profile of palbociclib plus fulvestrant in Asians was generally consistent with that observed in non-Asians. Together, these findings suggest that palbociclib is beneficial in patients who have not previously received endocrine therapy and in Asians and non-Asians who experienced relapse or progression during prior endocrine therapy.
|
study
| 28.69 |
MBC in premenopausal women is not well studied because clinical trials often exclude this patient population. One phase II study of 73 patients with HR-positive MBC showed that the efficacy of first-line therapy with letrozole plus goserelin in premenopausal patients was comparable with the efficacy of letrozole alone in postmenopausal patients25; these findings support additional research into assessing the efficacy of other treatments in combination with goserelin in premenopausal patients with breast cancer. MBC in premenopausal women is rare in the Western world; however, higher incidences are seen in Asian countries and in developing countries such as Mexico, Latin America, and Egypt, where breast cancer is more common in younger women and is frequently diagnosed at later stages as a result of suboptimal access to health care.3,4,26-29 Palbociclib plus fulvestrant improved PFS in both premenopausal and postmenopausal Asians in PALOMA-3. Because of the small number of patients in this cohort, no formal statistical analysis could be performed. Nevertheless, palbociclib plus fulvestrant in addition to an LHRH agonist could be a reasonable treatment option for younger patients with breast cancer who are premenopausal, including Asian patients.
|
study
| 31.17 |
Assessing PROs is important to comprehensively define the risk-benefit profile of treatments. In the current study, Asians in the palbociclib group maintained good QOL throughout the study, which is important in establishing the benefit-risk profile of combination therapy.
|
other
| 31.72 |
Differences in racial background can be associated with variable efficacy outcomes and safety profiles.22 As a result of genetic variations in an enzyme responsible for doxorubicin metabolism,23 Asians have been shown to be more susceptible to myelosuppression induced by doxorubicin compared with whites.22 In addition, a higher incidence of febrile neutropenia with docetaxel has been reported in Asians compared with whites.22 Genetic differences associated with race also can lead to differences in treatment response and efficacy. In Koreans with MBC, CYP2D6*10/*10 genetic polymorphisms have been associated with reduced plasma concentrations of the tamoxifen active metabolites endoxifen and 4-hydroxytamoxifen, as well as reduced clinical benefit (complete response, partial response, or stable disease ≥ 24 weeks) and significantly shorter median time to progression (P = .0032).24 These racial variations highlight the importance of evaluating the efficacy and safety of cancer medications within the Asian population.
|
other
| 36.2 |
Similar to findings from the present analysis, the most common AEs reported in the PALOMA-1 study with palbociclib were neutropenia and leukopenia.14,18 In the PALOMA-3 study, nonhematologic AEs were predominantly mild or moderate in severity. Moreover, an important difference of treatment exposure was observed between Asians and non-Asians in the palbociclib arm, with higher percentages of Asians experiencing dose interruptions, dose reductions, and cycle delays than non-Asians. Interestingly, the rates of grade 3 and grade 4 neutropenia were modestly higher in Asians than non-Asians. Because palbociclib exposure was similar in Asians and non-Asians, the increased rates of neutropenia cannot be explained by differential drug exposure across racial subgroups. Asian race, lower baseline ALT, and younger age were all predictors of a lower baseline ANC value. The Asians in PALOMA-3, on average, were younger and had a lower baseline ALT than the non-Asians, thus compounding effects of the covariates. Overall, in the PALOMA-3 patient population, a typical Asian patient (52 years old at enrollment with a baseline ALT of 17 U/L) had a baseline ANC value 19% lower than a typical non-Asian patient (58 years old at enrollment with a baseline ALT of 21 U/L), which may partially explain the higher rate of neutropenia observed in Asians. Importantly, race was not demonstrated to be a covariate on any of the PD response parameters, suggesting that there was no increased sensitivity to palbociclib-induced neutropenia in Asians.
|
other
| 30.98 |
In conclusion, as observed in the full study population, PFS was longer in Asians with HR-positive/HER2-negative MBC who received palbociclib plus fulvestrant versus those who received placebo plus fulvestrant. Furthermore, QOL was maintained in Asians who received palbociclib. The safety profile of palbociclib was consistent with that previously reported and was similar in Asians and non-Asians. The protocol-defined dosing modification instructions for palbociclib, including adjusting dose on the basis of individual tolerability, enabled Asians to avoid discontinuation from the study as a result of an AE, allowing them to stay on treatment as long as non-Asians and thus maintain the same efficacy benefit from combination therapy. Overall, palbociclib plus fulvestrant seems to be a reasonable treatment option in Asians with HR-positive/HER2-negative MBC that has progressed on prior endocrine therapy.
|
study
| 33.2 |
The study of historical demography is important for understanding the ecology and evolution of species. In particular, timing population size changes allows the discussion of past population patterns in the context of historical geological events such as island formation and climate change . One popular source of information to infer past population dynamics is the genealogical signal contained in linked polymorphic markers [3, 4], such as chloroplast microsatellites (cpSSRs). As highlighted in recent reviews [5, 6], cpSSRs are widely used in plant studies. cpSSRs remain popular despite the ascent of genome wide sequencing tools such as Restriction site-associated DNA sequencing (RADseq) , Genotyping-by-sequencing (GBS) and targeted sequencing due to two appealing properties: 1) their high mutation rate, ranging from 10−6 to 10−2 mutations per locus per generation , and 2) they can be applied in plant non-model species where few genomic resources have been developed .
|
other
| 28.72 |
High mutation rates combined with an approximately step-wise transition between allelic states make cpSSRs prone to homoplasious mutations. Homoplasy takes place in a cpSSR locus when different alleles at the locus are identical by state but are not identical by descent . Two cpSSRs copies of a locus are defined to be identical by state when they have the same size and are defined as identical by descent when there has not been a mutation since their divergence from a common ancestor. Previous studies have quantified the fraction of the homoplasy, called Molecularly accessible size homoplasy (MASH) [12–14] by measuring the differences in the DNA sequence of SSRs of identical size. Although that approach can reveal a fraction of the homoplasy in SSRs, it ignores the homoplastic events due to polymorphisms that lead to DNA sequences identical by state but not identical by descent. Therefore, MASH does not provide a direct estimate of homoplasy.
|
other
| 34.34 |
The occurrence of homoplasy is an important limitation of cpSSR based demographic inference in scenarios of population expansions, causing decreased ability to detect population growth and to systematic underestimation of the expansion time . Although some pseudo-likelihood and Bayesian methods of demographic inference [16, 17] successfully correct for homoplasy, they provide little insight into the relationship between homoplasy and the estimation of demographic parameters, nor do they provide estimates of homoplasy itself. In fact, to our knowledge, no formal analysis of the quantitative relation between homoplasy and the underestimation of the expansion time exists to date. Part of the reason is that the concept of homoplasy was developed to describe the proportion of haplotypes or markers that are identical by descent compared to those that are identical by state, while the problem of erroneous demographic inference is linked to an underestimation of the number of mutations between lineages. To illustrate this, the most common measure of homoplasy, the homoplasy index P , describes the probability that two cpSSR identical by state are not identical by descent. In the case of haplotypes composed of linked cpSSR, P has been defined as the probability that two haplotypes identical by state are not identical by descent and is dependent on the multi-locus heterozygosity . This is the definition of P we will employ here. While simulation studies show that higher values of P are associated with an underestimation of the expansion time , other studies have found that multi-locus heterozygosity is not particularly sensitive to homoplasy , suggesting that P may not be the most appropriate measure for describing effects on demographic inference. This motivates the necessity to propose alternative measures of homoplasy that are more directly relevant to demographic inference and that would allow for meaningful quantifications of the effects of homoplasious mutations on the estimation of the expansion time.
|
other
| 34.88 |
In this paper, we propose a new homoplasy metric. We analyze the relationship between three homoplasy metrics, including our proposed metric, and the underestimation of the expansion time. Second, we evaluate the extent to which these homoplasy metrics can be estimated from simulated cpSSR data using Approximate Bayesian Computation (ABC). Finally, we quantify the level of homoplasy in a real dataset from Pinus caribaea, providing an empirical estimate of homoplasy from population genetic data.
|
other
| 37.1 |
Throughout this study we assume a stepwise demographic expansion model . The model consists on three parameters: θ 0 = 2LN 0 u, θ 1 = 2LN 1 u and τ = 2Ltu, where u is the mutation rate per generation at each linked SSR, N 0 and N 1 are the effective population sizes before and after the expansion, L is the number of linked SSR loci and t is the time in generations since the expansion.
|
other
| 35.03 |
We generated two sets of haplotypes, hISM and hSMM, in the coalescent simulations under the stepwise demographic expansion model used in this study. We used the same genealogy along with the set of mutations falling in each branch of the genealogy to generate hISM and hSMM from each coalescent simulation. hISM represents a set of linked multi-locus SSR haplotypes evolving under the infinite sites model, ISM while hSMM are a set of linked multi-locus SSR haplotypes that evolved under the symmetrical stepwise mutation model, SMM . The haplotypes hISM are free of homoplasy while the haplotypes hSMM can contain homoplasious mutations. These coalescent simulations were performed using a modified a version of the coalescent simulator msHOT [21, 22]. The modified version of msHOT is available at https://github.com/dortegadelv/HomoplasyMetrics.
|
other
| 36.84 |
1\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$ P=1-\frac{1-{H}_{ISM}}{1-{H}_{SMM}}=1-\frac{F_{ISM}}{F_{SMM}} $$\end{document}P=1−1−HISM1−HSMM=1−FISMFSMM
|
other
| 26.44 |
Where H ISMand H SMM are the expected heterozygosities per haplotype estimated in a set of haplotypes containing L linked loci evolving under the infinite sites model (hISM) and the stepwise mutation model (hSMM), respectively. F ISM and F SMM are the expected homozygosities in the set of haplotypes hISM and hSMM. Note that F SMM is directly observable from the data, while F ISM is not, in real data of a set of haplotypes hSMM.
|
other
| 34.34 |
2\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$ MSH=1-\frac{\sum_{i=1}^L\frac{1-{H}_{ISM}^i}{1-{H}_{SMM}^i}}{L}=1-\frac{\sum_{i=1}^L\frac{F_{ISM}^i}{F_{SMM}^i}}{L} $$\end{document}MSH=1−∑i=1L1−HISMi1−HSMMiL=1−∑i=1LFISMiFSMMiL
|
other
| 27.73 |
Where L is the number of SSR in the haplotype. \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$ {H}_{ISM}^i $$\end{document}HISMi and \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$ {H}_{SMM}^i $$\end{document}HSMMi are the expected heterozygosities at the i locus in hISM and hSMM, respectively. \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$ {F}_{ISM}^i $$\end{document}FISMi and \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$ {F}_{SMM}^i $$\end{document}FSMMi are the expected homozygosities at the i locus in hISM and hSMM.
|
other
| 27.77 |
Inference of demographic growth using haplotypes with linked microsatellites is typically based on the distribution of pairwise differences between multi-locus haplotypes, also known as the mismatch distribution, as the shape of this distribution is determined by the time and magnitude of historical population expansions . Based on this, here we present a new metric, distance homoplasy (DH), which quantifies the proportion of mutations separating two multi-locus haplotypes that are not observed due to homoplasy. Our rationale for using this measure are studies that use the mode of the distribution of pairwise differences as the basis for estimating τ . Therefore, underestimation of the proportion of pairwise differences should impact the mismatch distribution which in turn should alter the inference of τ. DH is expressed as:
|
other
| 27.83 |
3\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$ DH=\frac{\pi_{ISM}-{\pi}_{SMM}}{\pi_{ISM}} $$\end{document}DH=πISM−πSMMπISM
|
other
| 29.34 |
We derived the expected values for the diversity statistics π, F i and F as a function of the mutation rate u of each linked SSR, the number of linked simulated SSR’s L and the coalescent time T ij, in number of generations, between a pair of haplotypes i and j present in the sample. We use the following equation \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$ E\left[\lambda \right]=E\left[E\left[\lambda |{T}_{ij}\right]\right]={\sum}_{x=1}^tE\left[\lambda |{T}_{ij}=x\right]P\left({T}_{ij}=x\right) $$\end{document}Eλ=EEλTij=∑x=1tEλTij=xPTij=x where λ stands for any diversity statistic and t is the time in generations since the expansion. T ij is scaled in units of N generations. We explain how to obtain the values of E[λ| T ij] for every diversity statistic under the ISM and SMM in the Appendix. The probability distribution of T ij under a stepwise demographic expansion model is equal to:
|
other
| 28.78 |
4\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$ P\left({T}_{ij}=x\right)=\left\{\begin{array}{cc}\raisebox{1ex}{$1$}\!\left/ \!\raisebox{-1ex}{$N$}\right.\left({e}^{-\raisebox{1ex}{$x$}\!\left/ \!\raisebox{-1ex}{$N$}\right.}\right)& \kern1.25em 0\le x<t-1\\ {}1-{\sum}_{x=1}^{t-1}P\left({T}_{ij}=x\right)& \kern1.5em x=t\end{array}\right. $$\end{document}PTij=x=1Ne−xN0≤x<t−11−∑x=1t−1PTij=xx=t
|
other
| 28.1 |
Where N is the effective population time in the present. The probability distribution of T ij is divided into two phases: 1) After the expansion, the population keeps a constant population size and, therefore, \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$ P\left({T}_{ij}=x\right)=\raisebox{1ex}{$1$}\!\left/ \!\raisebox{-1ex}{$N$}\right.{e}^{-\raisebox{1ex}{$x$}\!\left/ \!\raisebox{-1ex}{$N$}\right.} $$\end{document}PTij=x=1Ne−xN during that period of time. 2) Before the expansion, all individuals must coalesce quickly at a time very close to the expansion time T ij = t assuming that the population size is very small. To model that effect, we assume that all individuals coalesce exactly at time T ij = t if they have not already coalesced going forward in time.
|
other
| 30.08 |
The equations shown above to estimate the expected value of the diversity statistics are used to obtain estimates of the homoplasy parameters P, MSH and DH. As an example, following equation (1) the expected value of P can be calculated if we know the expected value of the diversity statistics F ISM and F SMM.
|
other
| 31.61 |
5\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$ E\left[P\right]=1-\frac{E\left[{F}_{ISM}\right]}{E\left[{F}_{SMM}\right]} $$\end{document}EP=1−EFISMEFSMM
|
other
| 27.47 |
6\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$ E\left[{F}_{SMM}\right]={\sum}_{x=1}^tE\left[{F}_{SMM}|{T}_{ij}=x\right]P\left({T}_{ij}=x\right) $$\end{document}EFSMM=∑x=1tEFSMMTij=xPTij=x 7\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$ E\left[{F}_{ISM}\right]={\sum}_{x=1}^tE\left[{F}_{ISM}|{T}_{ij}=x\right]P\left({T}_{ij}=x\right) $$\end{document}EFISM=∑x=1tEFISMTij=xPTij=x
|
clinical case
| 27.73 |
We used a simulation framework to test the accuracy of our estimates for the summary statistics π, F and F i under the ISM and the SMM along with our estimates for the homoplasy values P, MSH and DH. We used our modified version of the coalescent simulator msHOT [21, 22] to generate two different sets of haplotypes (hSMM and hISM) for each simulated genealogy. The modified version of msHOT is available at https://github.com/dortegadelv/HomoplasyMetrics. msHOT was used to make simulations under the stepwise demographic expansion model, where we used a value of θ 1 = 30, θ 0 = 0.03 and ten different values of τ {1.5, 3, 4.5, 6, 7.5, 9, 10.5, 12, 13.5, 15}. For each value of τ, we performed 100 simulations of 150 haplotypes with 6 linked SSRs. The ten command lines used for those simulations are shown in the Appendix (Command Line 1).
|
other
| 37.94 |
We also did 100 simulations for 9 different numbers of linked SSRs in the haplotype, going from L = 2 to L = 10 to examine how changes in the value of L affect P, MSH and DH. We simulated 150 haplotypes in each simulation, where the values of the demographic parameters were set to θ 1 = 5L, θ 0 = 0.005L, τ = L = 2tuL, where we kept the parameters t and u fixed to a certain value such that 2tu = 1. Notice that the divergence time t is kept fixed regardless of the number of linked SSRs L in these simulations. The nine command lines used for these simulations are shown in the Appendix (Command Line 2).
|
other
| 36.3 |
8\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$ TS=\frac{\widehat{\tau_{ISM}}-\widehat{\tau_{SMM}}}{\widehat{\tau_{ISM}}} $$\end{document}TS=τISM^−τSMM^τISM^
|
other
| 29.66 |
Values of the estimated expansion time τ for haplotypes hISM and hSMM, \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$ \widehat{\tau_{ISM}} $$\end{document}τISM^ and \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$ \widehat{\tau_{SMM}} $$\end{document}τSMM^ respectively, were obtained using the method by Schneider and Excoffier (1999), implemented in the software Arlequin . This method infers the parameters θ 0, θ 1 and τ based on the observed distribution of pairwise differences between haplotypes, also called mismatch distribution, and its expectation under a stepwise demographic expansion model . This approach assumes that there is no homoplasy in the sample of haplotypes, therefore any differences between \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$ \widehat{\tau_{ISM}} $$\end{document}τISM^ and \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$ \widehat{\tau_{SMM}} $$\end{document}τSMM^ are due to homoplasious mutations present in hSMM. Following , to use Arlequin for the hSMM analysis we coded the SSRs as binary data, where the number of repeats were coded with ‘1’ and shorter alleles were coded filling the difference in repeats with ‘0’ .
|
other
| 29.81 |
We simulated 100 replicates of 150 haplotypes with 6 linked SSRs for each of 10 different values of τ {1.5, 3, 4.5, 6, 7.5, 9, 10.5, 12, 13.5, 15}. We set a value of θ 1 equal to 30 and 60, which has the same order of magnitude of the value of θ 1 estimated for the Pinus caribaea dataset employed in this study (see Pinus caribaea dataset), and a value of θ 0 which was 1000 smaller than θ 1 for all simulations. The command lines used for the simulations are shown in the Appendix. Command Line 3 and 4 were used for the simulations done where θ 1 = 30 and θ 1 = 60, respectively.
|
other
| 35.44 |
The value of each homoplasy measure and TS was computed for each replicate of each simulation and the relationship of each homoplasy measure with TS was analyzed. In the simulations done with a value of θ 1 = 30, we removed 1 out of the 1000 simulations we performed where that simulation was the only that had a TS value smaller than -10 (see Additional file 1: Figure S1 for details on the removed simulation).
|
other
| 35.44 |
We used another modified version of the program msHOT [21, 22], also available at https://github.com/dortegadelv/HomoplasyMetrics, to implement an ABC algorithm that estimates the posterior distribution of demographic parameters θ 0, θ 1 and τ and the posterior predictive distribution of the three measures of homoplasy DH, MSH and P (see Appendix for details about the implementation of the ABC algorithm). We employed three summary statistics previously used to estimate demographic parameters in a model of population growth: The mean of the variance in the size of the SSRs across loci (V), the expected heterozygosity averaged across loci (H) and the number of distinct haplotypes (a). We used the mode of the posterior distribution and posterior predictive distribution as point estimates of the demographic parameters and homoplasy measures, respectively (see Appendix and Additional file 1: Figure S2 for a discussion on why we employed the mode as a point estimate). We also quantified the relative bias and estimated the 50%, 75% and 90% coverage of the demographic parameters and homoplasy measures to ascertain the quality of the point estimates and the inferred posterior distributions (see Appendix). The relative bias is the average difference between the estimated and true value of the parameter divided by its true value . The 50%, 75% and 90% coverage are the proportion of times that the true value is within the 50%, 75% and 90% credible interval.
|
other
| 35.7 |
We compared the real value of the homoplasy measures P, MSH and DH against the estimated values of the homoplasy measures using our ABC approach in 100 simulations of 150 haplotypes with 6 linked SSRs with parameters θ 1 = 30 and θ 0 = 0.03 and 10 different values of τ {1.5, 3, 4.5, 6, 7.5, 9, 10.5, 12, 13.5, 15}, where 10 simulations were done for each τ value. The ten command lines used for the simulations are shown in the Appendix (Command Line 5). We also estimated the three homoplasy measures in the simulations we explain in the next paragraph.
|
other
| 34.3 |
We compared the performance of three different methods to infer τ and θ 1 in three different sets of 100 simulations of 150 haplotypes with 6 linked SSRs done using three different τ values {3,6,9}, a θ 1 = 30 and a θ 0 = 0.03. The three command lines for these simulations are shown in the Appendix (Command Line 6). One of the three methods we used to estimate τ and θ 1 is our ABC approach, and the other two methods use the mismatch distribution to estimate those demographic parameters: 1) One of those methods is the approach taken by as implemented in the software Arlequin , which assumes that there is no homoplasy in the data (Least Squares approach without taking Homoplasy into account, LSWH). 2) The other method we used is a maximum-pseudolikelihood estimator that uses a model where it is assumed that homoplasy can occur in the data (Maximum Pseudolikelihood using a model with Homoplasy, MPH). Code for that method was kindly provided by Miguel Navascués.
|
other
| 34.66 |
We used a dataset of 7 SSR loci from 88 individuals of the species Pinus caribaea to estimate τ along with the homoplasy measures MSH and DH. This dataset is a subset of the data previously published in , where an analysis of population structure from four species of Pinus subsection Australes, including Pinus caribaea, identified four different groups (groups I-IV). We took the group containing the largest number of individuals distributed in Central America (group II), and retained only the individuals sampled from Central America in that group (88 out of 93 individuals) for our analysis. A hypothesis of population expansion could not be rejected using information from the mismatch distribution in group II , making this group suitable for analysis of expansion. We used ABC, LSWH and MPH to estimate τ in that dataset. The estimations of \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$ \widehat{\tau} $$\end{document}τ^ in the three methods used above were later transformed to years using a mutation rate of 5.5 X 10−5 per SSR per generation and a generation time of 42.5 years .
|
other
| 36.03 |
We also report the 95% confidence interval of the estimation of \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$ \widehat{\tau} $$\end{document}τ^ with LSWH, MPH and ABC using a parametric bootstrap approach as in Arlequin . We report the 95% confidence intervals for the ABC method instead of the 95% credible intervals to compare the 95% confidence intervals created with ABC with those obtained using LSWH and MPH. For each particular inference method (LSWH, MPH or ABC), the approach involves the simulation of 1000 datasets of 88 individuals with 7 SSR using the demographic parameters estimated for the Pinus caribaea data using a particular inference method. The value of the parameter τ · from each of the 1000 datasets was estimated using the inference method under study. Then, for a confidence level of α = 0.05, the approximate limits of the confidence interval were defined as the α/2 and 1 – α/2 percentile values of the 1000 values of τ ·. This parametric bootstrap approach was also used to estimate the 95% confidence interval of MSH and DH using the ABC method and 1000 simulations done using the demographic parameters inferred by ABC.
|
other
| 36.97 |
As can be seen in Fig. 1a-b, the accumulation of homoplasious mutations causes a monotonic increase in the difference between \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$ {F}_{ISM}^i $$\end{document}FISMi and \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$ {F}_{SMM}^i $$\end{document}FSMMi and between π SMM and π ISM with the expansion time, something that is not observed for the difference between the two measures of haplotype homozygosity F ISM and F SMM (Fig. 1c). This translates to both MSH and DH increasing steadily with expansion time, while P has a parabolic relationship (Fig. 1d) and stays at a constant value close to 0.09 when τ is equal or larger than 8 (Fig. 1d). Therefore, the values of P do not seem likely to relate to underestimation of population expansion time, in contrast with MSH and DH. Additionally, we found that the number of linked SSRs in the haplotype does not influence the values of MSH and DH, but it does change the value of P given a fixed divergence time t (Additional file 1: Figure S3).
|
other
| 30.89 |
Simulations of demographic expansion under different values of τ reveal that the standard homoplasy index P is not strongly correlated with TS, which measures the underestimation of τ due to homoplasy (Fig. 2a, Pearson’s ρ = −0.1282, p-value = 4.8*10−5). Contrarily, MSH and DH,have a stronger correlation with an underestimation of τ, where MSH has a slightly lower correlation with TS (Fig. 2b, ρ = 0.6903, p-value <2.2*10−16) than DH, which has the strongest correlation with TS of all the homoplasy measures inspected (Fig. 2c, ρ = 0.6989, p-value <2.2*10−16). Correlation between the latter two measures was strong (0.9208), whereas neither of them was strongly correlated with P (ρ between MSH and P = −0.2685; ρ between DH and P = −0.0977). Simulations with a higher value of θ 1 = 60 (Additional file 1: Figure S4), produced a nearly identical relationship between DH, MSH and TS (ρ between P and TS = −0.2617; ρ between MSH and TS = 0.8673; ρ between DH and TS = 0.8777), showing that DH and MSH are robust predictors of an underestimation of τ while P is not.Fig. 2Linear relationship between TS and three measures of homoplasy: a P (ρ = −0.1282, intercept = 0.3783, slope = −0.2509, p-value = 4.83e−5), b MSH (ρ =0.6903, intercept = 0.0893, slope = 0.9868, p-value <2.2e−16) and c DH (ρ =0.6989, intercept = −0.0541, slope = 1.1424, p-value <2.2e−16) in 999 simulations made with the demographic parameters θ 0 = 0.03, θ 1 = 30 and 10 different values of τ
|
study
| 27.08 |
First we evaluated the response in the three different measures of homoplasy and their components, π, F i and F, to changes in expansion time under the demographic stepwise expansion model in haplotypes containing completely linked SSRs. We thereby corroborate our theoretical expectations for the different metrics and found that our simulations validate their predictions (Fig. 1a-d).Fig. 1Homoplasy values in the stepwise demographic expansion model for different values of the expansion time parameter τ. The points in each plot are the average values for each statistic across 100 simulations for plots (a-c), those average values were used to calculate the mean values of the homoplasy index (P), mean size homoplasy (MSH) and distance homoplasy (DH) that are plotted as points in (d). The dashed lines are the approximated expected values estimated from our derivations. a π ISM and π SMM; b \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$ {F}_{ISM}^i $$\end{document}FISMi and \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$ {F}_{SMM}^i $$\end{document}FSMMi; c F ISM and F SMM d P, MSH and DH
|
other
| 29.52 |
Homoplasy values in the stepwise demographic expansion model for different values of the expansion time parameter τ. The points in each plot are the average values for each statistic across 100 simulations for plots (a-c), those average values were used to calculate the mean values of the homoplasy index (P), mean size homoplasy (MSH) and distance homoplasy (DH) that are plotted as points in (d). The dashed lines are the approximated expected values estimated from our derivations. a π ISM and π SMM; b \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$ {F}_{ISM}^i $$\end{document}FISMi and \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$ {F}_{SMM}^i $$\end{document}FSMMi; c F ISM and F SMM d P, MSH and DH
|
other
| 30.11 |
Linear relationship between TS and three measures of homoplasy: a P (ρ = −0.1282, intercept = 0.3783, slope = −0.2509, p-value = 4.83e−5), b MSH (ρ =0.6903, intercept = 0.0893, slope = 0.9868, p-value <2.2e−16) and c DH (ρ =0.6989, intercept = −0.0541, slope = 1.1424, p-value <2.2e−16) in 999 simulations made with the demographic parameters θ 0 = 0.03, θ 1 = 30 and 10 different values of τ
|
other
| 32.94 |
We used simulated data to evaluate the estimation of homoplasy metrics on an ABC framework. We performed linear regressions of the estimated values of the homoplasy measures obtained by our ABC approach on their true homoplasy values (Fig. 3) We also measured the relative bias and the correlation between the estimated and true values of the homoplasy measures. On simulations done over a range of τ values, we found that our estimates of MSH and DH were highly correlated with their real values (r = 0.881 and r = 0.740, respectively) and their relative bias was small (relative bias = −0.040 and 0.030, respectively), indicating that MSH and DH values are well estimated by our ABC approach. On the other hand, the estimates of P had a smaller correlation with their true values (r = 0.486) and their relative bias is −0.132, indicating that our ABC approach underestimates P values by approximately 13.2%. The underestimation can also be seen in Fig. 3. Despite differences in the quality of the point estimates of P, MSH and DH, we found that the 50%, 75% and 95% coverage of the homoplasy measures indicate that the inferred posterior distribution of those measures are well estimated (Additional file 1: Table S1 and Appendix).Fig. 3 ABC estimates of a P, b MSH and c DH compared with their true values in 100 simulations done with the demographic parameters θ 0 = 0.03, θ 1 = 30 and 10 different values of τ. A linear model was fitted to analyze the relationship between each homoplasy measure true value and their ABC estimate of P (ρ = 0.4864, intercept = 0.0385, slope = 0.3104, p-value = 2.88e−7), MSH (ρ = 0.8809, intercept = 0.0220, slope = 0.8667, p-value <2.2e−16) and DH (ρ = 0.7399, intercept = 0.0512, slope = 0.8771, p-value <2.2e−16)
|
other
| 31.12 |
ABC estimates of a P, b MSH and c DH compared with their true values in 100 simulations done with the demographic parameters θ 0 = 0.03, θ 1 = 30 and 10 different values of τ. A linear model was fitted to analyze the relationship between each homoplasy measure true value and their ABC estimate of P (ρ = 0.4864, intercept = 0.0385, slope = 0.3104, p-value = 2.88e−7), MSH (ρ = 0.8809, intercept = 0.0220, slope = 0.8667, p-value <2.2e−16) and DH (ρ = 0.7399, intercept = 0.0512, slope = 0.8771, p-value <2.2e−16)
|
other
| 37 |
We performed more simulations to analyze the performance of the ABC approach on simulations done using the same demographic parameters. We evaluated this by creating three sets of simulations done over a single value of τ (τ = 3, 6 or 9). We found that the 50%, 75% and 90% coverage indicate that the posterior distributions of the homoplasy measures are correctly inferred (Additional file 1: Table S2 and Appendix). Second, we found that the average relative bias was small for all homoplasy measures (relative bias = 0.053, 0.043 and 0.068 for P, MSH and DH, respectively; Additional file 1: Table S3). This indicates that, on average, the ABC method slightly overestimates the value of the homoplasy measures by approximately 5% on these sets of simulations.
|
other
| 31.94 |
Apart from estimating homoplasy measures, we also used ABC to estimate the value of τ. We found that ABC and the pseudo-likelihood estimator (MPH) perform equally well to obtain estimates of the value of τ, showing that both methods can correct for the effects of homoplasy. As expected, the expansion time is strongly underestimated by the method that does not take homoplasy into account (LSWH), particularly for older expansion events where there are higher values of MSH and DH (Fig. 4). Additionally, we found that ABC gave good estimations of the value of θ 1, compared to LSWH and MPH which gave overestimations of the actual value of θ 1 (Additional file 1: Figure S5), in line with previous studies done using LSWH and MPH . It must be pointed out that in ABC, as in any Bayesian method, the estimates of the parameters depend on the prior distributions used for the parameters. Prior distributions should contain all the possible demographic parameter values and should not be very wide to avoid low acceptance rates in the ABC algorithm (step 5 of the ABC algorithm in the Appendix).Fig. 4Estimation of τ using three methods (LSWH, MPH and ABC). The boxplots of the estimation of τ were done on simulations where θ 1 = 30, θ 0 = 0.03 and three different values of τ were used, a τ = 3, b τ = 6 and c τ = 9. 100 simulations were performed for each value of τ. The actual value of τ in each plot is displayed with the dashed line
|
other
| 31.6 |
Estimation of τ using three methods (LSWH, MPH and ABC). The boxplots of the estimation of τ were done on simulations where θ 1 = 30, θ 0 = 0.03 and three different values of τ were used, a τ = 3, b τ = 6 and c τ = 9. 100 simulations were performed for each value of τ. The actual value of τ in each plot is displayed with the dashed line
|
other
| 28.86 |
The usefulness of homoplasy metrics such as DH and MSH depends in part on how well they can be estimated from data. We have shown that we can obtain reasonable average estimates of MSH and DH using ABC . Compared to MASH, the ABC method we propose is not biased by the fraction of homoplasy unmeasurable by MASH. It thereby offers a natural solution to the quantification of homoplasy. Additionally, ABC can estimate the posterior distribution of demographic parameters of interest through explicit modeling of SSR evolution under different demographic scenarios. The approach proved successful in correcting for bias in the inference of expansion time, with similar performance to MPH which also explicitly accounts for homoplasy assuming the SSR’s evolve according to a SMM. One advantage of ABC, in addition to allowing for direct estimates of homoplasy, is that more complicated mutational models of SSR evolution can easily be incorporated. This could be important, as many SSR are known not to evolve in a strictly stepwise manner .
|
other
| 31.95 |
Given the potential for erroneous demographic inference when using linked SSR, it is important to obtain such homoplasy estimates from empirical data. With our ABC approach, we were able to estimate values of MSH and DH in a published dataset of Pinus caribaea. We found that the underestimation of the expansion time assuming a model that does not take homoplasy into account is of around 80,000 to 100,000 years, a reduction of around 28 to 32% compared to the value estimated with methods that use a more realistic model of SSR evolution where homoplasious events are possible. As with the ABC approach proposed here, other authors have suggested to use model-based approaches to infer past demographic events using linked cpSSR markers in spruces . Since ABC simulation based approaches provide an estimate of homoplasy, we believe that ABC approaches are useful to quantify the effect of homoplasy on demographic parameters and summary statistics of interest. To our knowledge, this is the first time that homoplasy parameters have been inferred using population genetic data.
|
other
| 33.1 |
The time of expansion for one population of Pinus caribaea in Central America was obtained using LSWH, MPH and ABC (Table 1). We found that the only method where homoplasy is not taken into account, LSWH, produces lower estimates of \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$ \widehat{\tau} $$\end{document}τ^ compared to ABC and MPH, suggesting that homoplasy may cause the expansion time to be underestimated by approximately 100,000 years in this case. The 95% confidence intervals of \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$ \widehat{\tau} $$\end{document}τ^ for all methods is large however (Table 1). ABC-based estimates of homoplasy are 0.11 and 0.246 for MSH and DH respectively, which agrees with the theoretical estimates of 0.106 and 0.269 obtained for those homoplasy measures using equations (24, Appendix) and (17, Appendix) given the demographic parameters estimated using ABC.Table 1Estimates of homoplasy and times of expansion in the population of Pinus caribaea analyzed \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$ \widehat{\tau_{LSWH}} $$\end{document}τLSWH^ \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$ \widehat{\tau_{MPH}} $$\end{document}τMPH^ \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$ \widehat{\tau_{ABC}} $$\end{document}τABC^ MSHDHTheoretical estimate of MSHTheoretical estimate of DH4.074 (1.359 – 6.086)5.994 (1.732 – 10.297)5.591 (2.645 – 11.005)0.115 (0.026 – 0.244)0.246 (0.106 – 0.415)0.1060.269Time of expansion in years (LSWH)Time of expansion in years (MPH)Time of expansion in years (ABC)224,900 (75,000 – 335,900)330,800 (95,600 – 568,400)308,600 (146,000 – 607,400)The estimated values of the time of expansion were obtained using three different methods (LSWH, MPH and ABC). The estimated values of MSH and DH were obtained using ABC. The numbers inside the parentheses denote the upper and lower limits of the 95% confidence interval for the parameter or homoplasy measure. The theoretical estimates of MSH and DH were estimated using the values of τ and θ 1 obtained using ABC and the Eqs. (24) and (17)
|
clinical case
| 26.3 |
Although conceptually and empirically DH most closely relates to the way that homoplasy causes underestimation of expansion time, the average decrease of per-locus heterozygosity, MSH, has a rather similar relation to population expansion time (Fig. 1) and also correlates strongly with the underestimation of the population expansion time (Fig. 2). It has been shown that in constant population sizes the value of MSH is determined by θ , the expected number of mutations between a pair of sequences, so the fact that it also increases with τ is not entirely surprising. Our theoretical estimates indeed confirm that MSH increases monotonically with expansion time under the stepwise demographic expansion model (Fig. 1) and also show that there is a relationship between the number of mutations, predicted by older coalescent times due to older population expansions, and MSH on the stepwise population expansion model. Additionally, MSH and DH are not affected by changes in the number of linked SSRs analyzed, while P does depend on the number of linked SSRs studied.
|
other
| 32.7 |
Noncoding RNAs (ncRNAs), though do not encode proteins, contain genetic information or have function in the biological process of cells. NcRNAs include structural RNAs such as rRNAs and tRNAs involved in mRNA translation, small nuclear RNAs (snRNAs) involved in splicing, and regulatory RNAs such as microRNAs (miRNAs) and long noncoding RNAs (lncRNAs) . All of them have important roles in regulating gene expression in development, physiology and pathology. Among these ncRNAs, the well-known miRNAs (~22nts), which are considered as central post-transcriptional gene regulators through their complementarity with the target mRNA sequences , and lncRNAs ( > 200nts), known as the “transcriptional noise”, which exhibit numerous functions in normal and abnormal tissues, are developing gradually [14, 15]. Recently, there is an interesting cross-regulation between lncRNAs and miRNAs, and the emerging evidence provides that this crosstalk has a great impact on the mechanisms of cancer metastasis . In this review, we summarized miRNAs’ and lncRNAs’ control of EMT/MET, emphasized the influence of lncRNA-miRNA crosstalk in this multi-step process of human tumor progression, and harnessed this knowledge for translational medicine.
|
other
| 31.12 |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.