High altitude acclimatization is a series of physiological responses taking places when subjects go to altitude. Many factors could influence these processes, such as altitude, ascending speed and individual characteristics. In this study, based on a repeated measurement design of three sequential measurements at baseline, acute phase and chronic phase, we evaluated the effect of BMI, smoking and drinking on a number of physiological responses in high altitude acclimatization by using mixed model and partial least square path model on a sample of 755 Han Chinese young males. We found that subjects with higher BMI responses were reluctant to hypoxia. The effect of smoking was not significant at acute phase. But at chronic phase, red blood cell volume increased less while respiratory function increased more for smoking subjects compared with nonsmokers. For drinking subjects, red blood cell volume increased less than nondrinkers at both acute and chronic phases, while blood pressures increased more than nondrinkers at acute phase and respiratory function, red blood cell volume and oxygen saturation increased more than nondrinkers at chronic phase. The heavy and long-term effect of smoking, drinking and other factors in high altitude acclimatization needed to be further studied.

Hypoxia inducible factors, including HIF1A and HIF2A, play central roles in response to high-altitude hypoxia and genetic variants of HIF1A or HIF2A were associated with high-altitude sickness or adaptation. However, it remains to determine whether they are associated with tolerance to different levels of high-altitude selection pressure among native Tibetans. We recruited 189 Tibetan subjects living at 2,700 meters (Low level of high altitude, LHA), 197 at 3,200 meters (Middle level of high altitude of high altitude, MHA), 249 at 3,700 meters (High level of high altitude, HHA) and 269 at 4,700 meters (Very high level of high altitude, VHA) and performed association analysis of twelve tSNPs (tagging SNPs) in HIF1A and HIF2A with high-altitude. We found (1) a increasing trend of HIF2A rs5621780-C(18.4%, 15.9%, 32.8% and 31.1%, respectively, in LHA, MHA, HHA and VHA)(P = 3.56E-9); (2) increasing trends of HIF2A rs6756667-A(68.7%, 73.4%, 79.9% and 89.6%), rs7589621- G(74.6%, 77.9%, 83.7%, and 92.1%) and rs1868092-A(64.1%, 67.3%, 75.1% and 84.4%) (P = 3.56E-9, 4.68E-16, 1.17E-13 and 7.09E-14, respectively); (3) a increasing trend of haplotype AG (68.7%, 73.1%, 79.9% and 89.6%) (P = 2.22E-7) which was constructed by rs6756667 and rs7589621; (4) a strong linear correlation between major alleles of rs6756667-A (R 2 = 0.997, P = 0.002), rs7589621-G (R 2 = 0.994, P = 0.003), rs1868092-A (R 2 = 0.985, P = 0.008) and altitude by linear correlation test. The associations between HIF2A variants and different level of high altitude support that extremely high-altitude hypoxia challenge imposes selective effects on HIF2A variants among native Tibetans.

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Lyme disease, recognized as one of the most important vector-borne diseases worldwide, has been increasing in incidence and spatial extend in United States. In the Northeast and Upper Midwest, Lyme disease is transmitted by Ixodes scapularis. Currently, many studies have been conducted to identify factors influencing Lyme disease risk in the Northeast, however, relatively few studies focused on the Upper Midwest. In this study, we explored and compared the climatic and landscape factors that shape the spatial patterns of human Lyme cases in these two regions, using the generalized linear mixed models. Our results showed that climatic variables generally had opposite correlations with Lyme disease risk, while landscape factors usually had similar effects in these two regions. High precipitation and low temperature were correlated with high Lyme disease risk in the Upper Midwest, while with low Lyme disease risk in the Northeast. In both regions, size and fragmentation related factors of residential area showed positive correlations with Lyme disease risk. Deciduous forests and evergreen forests had opposite effects on Lyme disease risk, but the effects were consistent between two regions. In general, this study provides new insight into understanding the differences of risk factors of human Lyme disease risk in these two regions.

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