PM2.5, vegetation density, and childhood cancer: a case-control registry-based study from Texas 1995-2011.

BACKGROUND: Air pollution is positively associated with some childhood cancers, whereas greenness is inversely associated with some adult cancers. The interplay between air pollution and greenness in childhood cancer etiology is unclear. We estimated the association between early-life air pollution and greenness exposure and childhood cancer in Texas (1995 to 2011). METHODS: We included 6101 cancer cases and 109 762 controls (aged 0 to 16 years). We linked residential birth address to census tract annual average fine particulate matter <2.5 µg/m³ (PM2.5) and Normalized Difference Vegetation Index (NDVI). We estimated odds ratios (ORs) and 95% confidence intervals (CIs) between PM2.5/NDVI interquartile range increases and cancer. We assessed statistical interaction between PM2.5 and NDVI (likelihood ratio tests). RESULTS: Increasing residential early-life PM2.5 exposure was associated with all childhood cancers (OR = 1.10, 95% CI = 1.06 to 1.15), lymphoid leukemias (OR = 1.15, 95% CI = 1.07 to 1.23), Hodgkin lymphomas (OR = 1.27, 95% CI = 1.02 to 1.58), non-Hodgkin lymphomas (OR = 1.24, 95% CI = 1.02 to 1.51), ependymoma (OR = 1.27, 95% CI = 1.01 to 1.60), and others. Increasing NDVI exposure was inversely associated with ependymoma (0- to 4-year-old OR = 0.75, 95% CI = 0.58 to 0.97) and medulloblastoma (OR = 0.75, 95% CI = 0.62 to 0.91) but positively associated with malignant melanoma (OR = 1.75, 95% CI = 1.23 to 2.47) and Langerhans cell histiocytosis (OR = 1.56, 95% CI = 1.07 to 2.28). There was evidence of statistical interaction between NDVI and PM2.5 (P < .04) for all cancers. CONCLUSION: Increasing early-life exposure to PM2.5 increased the risk of childhood cancers. NDVI decreased the risk of 2 cancers yet increased the risk of others. These findings highlight the complexity between PM2.5 and NDVI in cancer etiology.

A Functional Model for Unifying Brain Computer Interface Terminology.

Brain Computer Interface (BCI) technology is a critical area both for researchers and clinical practitioners. The IEEE P2731 working group is developing a comprehensive BCI lexicography and a functional model of BCI. The glossary and the functional model are inextricably intertwined. The functional model guides the development of the glossary. Terminology is developed from the basis of a BCI functional model. This paper provides the current status of the P2731 working group's progress towards developing a BCI terminology standard and functional model for the IEEE.

Immunoglobulin allotypes in Southwest Asia: populations at the crossroads.

Southwest Asia has a long history of contact with Central Asian and with Sub-Saharan African populations. Is the genetic structure of these populations reflective of these historical facts? To study this, data was generated on the immunoglobulin heavy chain (GM) and light chain (KM) allotypes from seven Arab and three non-Arab populations in SW Asia to examine the relationship of these populations to SE European, NW Indian, Sub-Saharan African, and Central Asian populations. Like mtDNA and Y chromosome markers, the GM haplotypes are largely continent specific making them an excellent tool for the detection of gene flow whereas the KM markers are less informative. Six of the nine GM haplotypes detected in SW Asians are Indo-European, Sub-Saharan African, or East Asian specific. The allotype results indicate variable but significantly higher Sub-Saharan African gene flow in Arab populations (average 26.9%; 15.0-61.6%) vs. the non-Arab populations (average 7.3%; 9.0-13.4%), but higher levels of Central Asian gene flow in the non-Arab populations (average 28.8%; 10.5-48.8%) vs. the Arab populations (average 9.0%; 0.0-26.4%). Principal components analysis and hierarchical cluster analysis based on the immunoglobulin allotypes are consistent with the historical population contacts of this part of the world and reflect the power of the GM haplotypes in dissecting population relationships. However, the KM*1 frequencies were only correlated with the degree of African gene flow (Pearson r = 0.69, P = 0.026) in SW Asian populations.