Tissue type and location within forest together regulate decay trajectories of Abies faxoniana logs at early and mid-decay stage

Deadwood decomposition plays a crucial role in global carbon and nutrient cycles. Factors controlling deadwood decomposition at local scales could also have strong effects at broader scales. We tested how trait variation within stems (i.e. tissue types) and forest habitat heterogeneity (i.e. location within forest) together influence the deadwood decay trajectory and decay rate. We conducted an in situ decomposition experiment of Abies faxoniana logs in an alpine forest on the eastern Qinghai-Tibetan Plateau, decomposing logs from a series of decay classes I-III (on a 5-class scale) for five years on the forest floor in canopy gap, gap edge and under closed canopy (each sized 25 ± 3 × 25 ± 3 m). We found strong differences in density and chemical composition between tissue types at least across decay classes I-III, which revealed the distinct contribution of each tissue type to carbon and nutrient cycling. There were remarkable interactions of tissue types and locations within forest. We found bark always decomposed faster than wood, while heartwood can decompose faster than sapwood in canopy edge and canopy gap. Locations within forest influenced the best fit decay model and decay rate of bark and sapwood in the same way, while it had no corresponding effects for heartwood decay dynamics. The largest difference in T0.25 and T0.4 (time to 25% and 40% mass loss) between locations were 1.52 and 3.21 (bark), 19.41 and 37.61 (wood overall), 31.82 and 60.15 (sapwood), and 12.86 and 22.84 (heartwood), respectively. We also found that pH was significantly negatively related with sapwood and heartwood mass loss, demonstrating that pH can potentially be applied to evaluate sapwood and heartwood mass loss when density correction is difficult to achieve at least at early to mid-decay stages. However, whether pH is a powerful predictor of decomposition trajectory across more species and biomes remains to be tested. We strongly recommend that further model predictions of coarse log decay include radial positions within stem and locations within forest as factors to increase the reliability of carbon budget estimates

Filtration artefacts in bacterial community composition can affect the outcome of dissolved organic matter biolability assays

Inland waters are large contributors to global carbon dioxide (CO2) emissions, in part due to the vulnerability of dissolved organic matter (DOM) to microbial decomposition and respiration to CO2 during transport through aquatic systems. To assess the degree of this vulnerability, aquatic DOM is often incubated in standardized biolability assays. These assays isolate the dissolved fraction of aquatic OM by size filtration prior to incubation. We test whether this size selection has an impact on the bacterial community composition and the consequent dynamics of DOM degradation using three different filtration strategies: 0.2 μm (filtered and inoculated), 0.7 μm (generally the most common DOM filter size) and 106 μm (unfiltered). We found that bacterial community composition, based on 16S rRNA amplicon sequencing, was significantly affected by the different filter sizes. At the same time, the filtration strategy also affected the DOM degradation dynamics, including the δ13C signature. However, the dynamics of these two responses were decoupled, suggesting that filtration primarily influences biolability assays through bacterial abundance and the presence of their associated predators. By the end of the 41-day incubations all treatments tended to converge on a common total DOM biolability level, with the 0.7 μm filtered incubations reaching this point the quickest. These results suggest that assays used to assess the total biolability of aquatic DOM should last long enough to remove filtration artefacts in the microbial population. Filtration strategy should also be taken into account when comparing results across biolability assays

Soil carbon loss in warmed subarctic grasslands is rapid and restricted to topsoil

Global warming may lead to carbon transfers from soils to the atmosphere, yet this positive feedback to the climate system remains highly uncertain, especially in subsoils (Ilyina and Friedlingstein, 2016; Shi et al., 2018). Using natural geothermal soil warming gradients of up to +6.4 degrees C in subarctic grasslands (Sigurdsson et al., 2016), we show that soil organic carbon (SOC) stocks decline strongly and linearly with warming (-2.8 t ha(-1) degrees C-1). Comparison of SOC stock changes following medium-term (5 and 10 years) and long-term (> 50 years) warming revealed that all SOC stock reduction occurred within the first 5 years of warming, after which continued warming no longer reduced SOC stocks. This rapid equilibration of SOC observed in Andosol suggests a critical role for ecosystem adaptations to warming and could imply short-lived soil carbon-climate feedbacks. Our data further revealed that the soil C loss occurred in all aggregate size fractions and that SOC stock reduction was only visible in topsoil (0-10 cm). SOC stocks in subsoil (10-30 cm), where plant roots were absent, showed apparent conservation after > 50 years of warming. The observed depth-dependent warming responses indicate that explicit vertical resolution is a prerequisite for global models to accurately project future SOC stocks for this soil type and should be investigated for soils with other mineralogies

New genetic loci implicated in fasting glucose homeostasis and their impact on type 2 diabetes risk.

Levels of circulating glucose are tightly regulated. To identify new loci influencing glycemic traits, we performed meta-analyses of 21 genome-wide association studies informative for fasting glucose, fasting insulin and indices of beta-cell function (HOMA-B) and insulin resistance (HOMA-IR) in up to 46,186 nondiabetic participants. Follow-up of 25 loci in up to 76,558 additional subjects identified 16 loci associated with fasting glucose and HOMA-B and two loci associated with fasting insulin and HOMA-IR. These include nine loci newly associated with fasting glucose (in or near ADCY5, MADD, ADRA2A, CRY2, FADS1, GLIS3, SLC2A2, PROX1 and C2CD4B) and one influencing fasting insulin and HOMA-IR (near IGF1). We also demonstrated association of ADCY5, PROX1, GCK, GCKR and DGKB-TMEM195 with type 2 diabetes. Within these loci, likely biological candidate genes influence signal transduction, cell proliferation, development, glucose-sensing and circadian regulation. Our results demonstrate that genetic studies of glycemic traits can identify type 2 diabetes risk loci, as well as loci containing gene variants that are associated with a modest elevation in glucose levels but are not associated with overt diabetes

Hundreds of variants clustered in genomic loci and biological pathways affect human height

Most common human traits and diseases have a polygenic pattern of inheritance: DNA sequence variants at many genetic loci influence the phenotype. Genome-wide association (GWA) studies have identified more than 600 variants associated with human traits, but these typically explain small fractions of phenotypic variation, raising questions about the use of further studies. Here, using 183,727 individuals, we show that hundreds of genetic variants, in at least 180 loci, influence adult height, a highly heritable and classic polygenic trait. The large number of loci reveals patterns with important implications for genetic studies of common human diseases and traits. First, the 180 loci are not random, but instead are enriched for genes that are connected in biological pathways (P = 0.016) and that underlie skeletal growth defects (P < 0.001). Second, the likely causal gene is often located near the most strongly associated variant: in 13 of 21 loci containing a known skeletal growth gene, that gene was closest to the associated variant. Third, at least 19 loci have multiple independently associated variants, suggesting that allelic heterogeneity is a frequent feature of polygenic traits, that comprehensive explorations of already-discovered loci should discover additional variants and that an appreciable fraction of associated loci may have been identified. Fourth, associated variants are enriched for likely functional effects on genes, being over-represented among variants that alter amino-acid structure of proteins and expression levels of nearby genes. Our data explain approximately 10% of the phenotypic variation in height, and we estimate that unidentified common variants of similar effect sizes would increase this figure to approximately 16% of phenotypic variation (approximately 20% of heritable variation). Although additional approaches are needed to dissect the genetic architecture of polygenic human traits fully, our findings indicate that GWA studies can identify large numbers of loci that implicate biologically relevant genes and pathways.

Contesting the Dominant Discourse of Child Sexual Abuse: Sexual Subjects, Agency, and Ethics

Responding to previous scholars’ call to explore the complexities of child sexual abuse (CSA), this article presents narratives of CSA and scrutinizes a binary construction underpinning this discourse of CSA, namely, the positioning of children as powerless and adults as powerful. The narratives belong to three Indonesian young people who have had sexual interactions with adults when they were children. The findings demonstrate how this binary positioning has been both drawn upon and resisted in the ways participants understand their sexual experiences. This article contributes to the existing literature by providing analyses of some vignettes of everyday experiences of how children might be constituted as sexual subjects, including their capability to exercise agency, perform resistance, and negotiate ethics. The implications of the findings are discussed in relation to how the recognition of children as sexual subjects and their sexual agency might be beneficial for parents, educators, and counselors

Genome-Wide Association Identifies Nine Common Variants Associated With Fasting Proinsulin Levels and Provides New Insights Into the Pathophysiology of Type 2 Diabetes

OBJECTIVE: Proinsulin is a precursor of mature insulin and C-peptide. Higher circulating proinsulin levels are associated with impaired β-cell function, raised glucose levels, insulin resistance, and type 2 diabetes (T2D). Studies of the insulin processing pathway could provide new insights about T2D pathophysiology. RESEARCH DESIGN AND METHODS: We have conducted a meta-analysis of genome-wide association tests of ∼2.5 million genotyped or imputed single nucleotide polymorphisms (SNPs) and fasting proinsulin levels in 10,701 nondiabetic adults of European ancestry, with follow-up of 23 loci in up to 16,378 individuals, using additive genetic models adjusted for age, sex, fasting insulin, and study-specific covariates. RESULTS: Nine SNPs at eight loci were associated with proinsulin levels (P < 5 × 10−8). Two loci (LARP6 and SGSM2) have not been previously related to metabolic traits, one (MADD) has been associated with fasting glucose, one (PCSK1) has been implicated in obesity, and four (TCF7L2, SLC30A8, VPS13C/C2CD4A/B, and ARAP1, formerly CENTD2) increase T2D risk. The proinsulin-raising allele of ARAP1 was associated with a lower fasting glucose (P = 1.7 × 10−4), improved β-cell function (P = 1.1 × 10−5), and lower risk of T2D (odds ratio 0.88; P = 7.8 × 10−6). Notably, PCSK1 encodes the protein prohormone convertase 1/3, the first enzyme in the insulin processing pathway. A genotype score composed of the nine proinsulin-raising alleles was not associated with coronary disease in two large case-control datasets. CONCLUSIONS: We have identified nine genetic variants associated with fasting proinsulin. Our findings illuminate the biology underlying glucose homeostasis and T2D development in humans and argue against a direct role of proinsulin in coronary artery disease pathogenesis

Maternal and fetal genetic effects on birth weight and their relevance to cardio-metabolic risk factors.

Birth weight variation is influenced by fetal and maternal genetic and non-genetic factors, and has been reproducibly associated with future cardio-metabolic health outcomes. In expanded genome-wide association analyses of own birth weight (n = 321,223) and offspring birth weight (n = 230,069 mothers), we identified 190 independent association signals (129 of which are novel). We used structural equation modeling to decompose the contributions of direct fetal and indirect maternal genetic effects, then applied Mendelian randomization to illuminate causal pathways. For example, both indirect maternal and direct fetal genetic effects drive the observational relationship between lower birth weight and higher later blood pressure: maternal blood pressure-raising alleles reduce offspring birth weight, but only direct fetal effects of these alleles, once inherited, increase later offspring blood pressure. Using maternal birth weight-lowering genotypes to proxy for an adverse intrauterine environment provided no evidence that it causally raises offspring blood pressure, indicating that the inverse birth weight-blood pressure association is attributable to genetic effects, and not to intrauterine programming.The Fenland Study is funded by the Medical Research Council (MC_U106179471) and Wellcome Trust

Localization of type 1 diabetes susceptibility to the MHC class I genes HLA-B and HLA-A

The major histocompatibility complex (MHC) on chromosome 6 is associated with susceptibility to more common diseases than any other region of the human genome, including almost all disorders classified as autoimmune. In type 1 diabetes the major genetic susceptibility determinants have been mapped to the MHC class II genes HLA-DQB1 and HLA-DRB1 (refs 1-3), but these genes cannot completely explain the association between type 1 diabetes and the MHC region. Owing to the region's extreme gene density, the multiplicity of disease-associated alleles, strong associations between alleles, limited genotyping capability, and inadequate statistical approaches and sample sizes, which, and how many, loci within the MHC determine susceptibility remains unclear. Here, in several large type 1 diabetes data sets, we analyse a combined total of 1,729 polymorphisms, and apply statistical methods - recursive partitioning and regression - to pinpoint disease susceptibility to the MHC class I genes HLA-B and HLA-A (risk ratios >1.5; Pcombined = 2.01 × 10-19 and 2.35 × 10-13, respectively) in addition to the established associations of the MHC class II genes. Other loci with smaller and/or rarer effects might also be involved, but to find these, future searches must take into account both the HLA class II and class I genes and use even larger samples. Taken together with previous studies, we conclude that MHC-class-I-mediated events, principally involving HLA-B*39, contribute to the aetiology of type 1 diabetes. ©2007 Nature Publishing Group