Spatial distribution, source identification, and transportation paths of plutonium in the Beibu Gulf, South China Sea.

To investigate the spatial distribution and source of plutonium isotopes in the Beibu Gulf, surface sediments were collected and analyzed using sector field inductively coupled plasma mass spectrometry (SF-ICP-MS). The activities of 239+240Pu in surface sediments ranged from 0.012 to 0.451 mBq/g (mean: 0.171 ± 0.138 mBq/g, n = 36), indicating a decreasing trend in a counterclockwise direction from the southern bay mouth. The counterclockwise decreasing trend in the south of the bay mouth is similar to the current in the Beibu Gulf. The 240Pu/239Pu atom ratios in surface sediments ranged from 0.156 to 0.283 (mean: 0.236 ± 0.031, n = 36), slightly higher than that of the global fallout value of 0.18. This suggests that the Pu in the Beibu Gulf was a combination of global fallout and Pacific Proving Ground (PPG). The average contribution of the plutonium (Pu) derived from the PPG in the sediment was estimated to be 52 % ± 24 %.

coPLINK: A complementary tool to PLINK.

In genome-wide association studies (GWAS), a wide variety of analysis tools have been designed, leading to various formats of GWAS data. How to convert a dataset in non-PLINK format into PLINK format to use its powerful analysis performance, or to convert a dataset in PLINK format into the format of other analysis tools, is a problem that needs to be faced and solved. To address this issue, we developed a tool called coPLINK, a complementary tool to PLINK, to cooperate with PLINK to implement the conversions of GWAS data formats and to provide some additional functions, such as data files comparison. The tool can implement mutual conversions not only between an existing data format and PLINK PED/BED, but also between a user-defined data format and PLINK PED. The usage and performance of the tool are similar to PLINK. The characteristics of the conversions of existing data formats and user-defined formats make it be a good assistant to PLINK or other tools and, have good potential for GWAS studies or other works.

Density distribution of gene expression profiles and evaluation of using maximal information coefficient to identify differentially expressed genes.

The hypothesis of data probability density distributions has many effects on the design of a new statistical method. Based on the analysis of a group of real gene expression profiles, this study reveal that the primary density distributions of the real profiles are normal/log-normal and t distributions, accounting for 80% and 19% respectively. According to these distributions, we generated a series of simulation data to make a more comprehensive assessment for a novel statistical method, maximal information coefficient (MIC). The results show that MIC is not only in the top tier in the overall performance of identifying differentially expressed genes, but also exhibits a better adaptability and an excellent noise immunity in comparison with the existing methods.